Research ArticleA Least Cumulative Ventilation Cost Method for UrbanVentilation Environment Analysis

Peng Xie Dianfeng Liu Yanfang Liu and Yaolin Liu

School of Resource and Environmental Science Wuhan University 129 Luoyu Road Wuhan 430079 Hubei Province China

Correspondence should be addressed to Yanfang Liu yfliu610163com and Yaolin Liu yaolin610163com

Received 31 January 2020 Accepted 15 April 2020 Published 12 May 2020

Guest Editor Jianhong (Cecilia) Xia

Copyright copy 2020 Peng Xie et al -is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

-e correct urban building layout is an important influencing factor in urban ventilation and the heat island effect has become animportant factor affecting the quality of urban life Optimization of the urban building layout can play a role in mitigating the heatisland effect-e traditional ventilation corridor analysis method based on a least-cost path analysis can only generate a fewmainventilation corridors It is difficult to obtain global ventilation results covering the whole study area using this method of analysisOn the basis of urban morphology and a least-cost path analysis this study proposes a ldquoleast cumulative ventilation costrdquo methodfor analyzing urban ventilation Taking Wuhan downtown as a research area the urban ventilation environment under differentwind directions and seasons was analyzed -is method can effectively express the ventilation conditions throughout the wholestudy area and can simultaneously express the quality of the generated corridors effectively-e results show thatWuhan has threelevels of ventilation corridor Moreover the ventilation conditions in Wuchang (Wuchang Qingshan and Hongshan) are betterthan those in Hankou (Qiaokou Jianghan and Jiangrsquoan)

1 Introduction

-e urban heat island (UHI) effect is a phenomenon inwhich heat accumulates in an urban area due to peoplersquosactivities and the buildings It is one of the most prominentcharacteristics of the urban climate With rapid urbanizationand a continuous increase in population density the UHIeffect has become an important adverse factor affecting thequality of urban life [1ndash5] UHI effects caused by high-density urban buildings have been reported around theworld it has changed the urban thermal environment[6ndash10] As a result of its effects on regional climates urbanhydrology air quality urban biological distribution andbehavior and many urban ecological processes the UHIeffect has raised a number of ecological and environmentalissues [11ndash15] Numerous studies have found that the effectis closely related to urban anthropogenic heat release un-derlying surface properties and structure vegetation cov-erage population density and weather conditions Amongmany factors urban ventilation conditions caused by theunderlying surface properties and structure have a

significant impact on the UHI effect [16ndash20] In order forgovernments and city planners to make planning decisionsintuitively quantitative methods are needed to determineventilation paths in urban planning [21ndash23]

-e most common simulation methods of urban canopyventilation corridors use wind tunnels computational fluiddynamics (CFD) and morphological analysis based on GIStechnologyWind tunnels can be used for wind environmentsimulations at various scales [24ndash26] Although wind tunnelsimulations can objectively and realistically observe thedetails of changes of air flowing through the observationobject their high experimental costs limited simulationrange and physical model accuracy requirements limit thepopularity of this method -e CFD method is suitable forsmall-scale high-resolution fluid simulations [27ndash29] CFDis often used for industrial component design buildingstructure design urban planning environment predictionpollutant diffusion simulation and the like It has highreliability However due to the huge number of calculationsinvolved CFD is often used to simulate the wind envi-ronment of single multibody or small-scale building

HindawiComplexityVolume 2020 Article ID 9015923 13 pageshttpsdoiorg10115520209015923

groups -is method is not suitable for large-scale urban-level wind environment simulations

-e numerical simulation method uses GIS technologyto analyze urban ventilation environments by calculating thesurface roughness of the underlying surface [30ndash34] Manystudies have shown that this method can quickly and ef-fectively analyze urban ventilation environments and issuitable for large-scale analysis of ventilation environmentsIts implementation can be divided into two main ap-proaches one based on rules and the other based on a least-cost path analysis (hereafter the LCP-based method)[35ndash37]

-e rule-based method is based on the ventilationanalysis results of small-scale building groups in wind tunnelexperiments or CFD experiments -is method can obtainthe relationship between urban morphological parameters(such as the frontal area index or FAI) and ventilationconditions establish a rule recognition model and then usethe model to analyze the ventilation environment of theentire city -e FAI is one of many surface roughness in-dicators It represents the ratio of the projected area of thebuilding on the normal plane of the wind direction to theprojected area of the analysis region -e larger the value isthe greater the wind resistance is Yuan [38] used the FAI toanalyze the ventilation environment ofWuhan and obtainedan intuitive result of potential air paths -e rule-basedmethod is simple and easy to understand which is conduciveto implementation in planning but under this method it isparticularly difficult to analyze the dynamic differences ofurban ventilation environments under different backgroundwind conditions

Based on the roughness index the LCP-based methoduses a least-cost path analysis to obtain a more accurateventilation corridor distribution -is method can dynam-ically analyze the ventilation corridors of the urban canopyfollowing changes in wind direction-e LCP-based methodassumes that the airflow always flows in the direction withthe lowest wind resistance and the path with the least ac-cumulated wind resistance is the ventilation corridor [30]Many constructive results have been obtained using theLCP-based method confirming the credibility and cor-rectness of this approach [30ndash32] However this methodcannot effectively identify corridor width and it is difficult toevaluate the ventilation conditions of places without least-cost paths (LCPs)

Based on the above analysis a new ventilation corridoranalysis method is urgently needed which needs to meet thefollowing requirements First of all this method should havea lower operating cost to implement the analysis of the urbanventilation environment in large areas Secondly thismethod should have the ability of ventilation environmentanalysis of dynamic wind direction-en the analysis resultsneed to reflect the ventilation status of the whole study areaso as to analyze the impact of urban internal structure onventilation environment In this study a ventilation envi-ronment analysis method based on the least cumulativeventilation cost (LCVC) is proposed Taking Wuhan as acase study area the feasibility and accuracy of the methodare verified -e results show that the method can meet the

above three requirements and can be used as a new tool forurban ventilation environment analysis

2 Study Area

Wuhan is the capital city of Hubei Province with an area of8589 km2 located at 29deg58primendash31deg22prime N and 113deg41primendash115deg05prime E(Figure 1) Wuhan is located in the east of the Jianghan Plainand the middle reaches of the Yangtze River -e worldrsquosthird-largest river the Yangtze and its largest tributary theHan River meet in the city dividing Wuhanrsquos central urbanarea into three parts -ese are Hankou (including JianghanDistrict Jiangrsquoan District and Qiaokou District) Wuchang(including Wuchang District Qingshan District and Hon-gshan District) and Hanyang -e rivers and lakes (such asthe East Lake Tangxun Lake South Lake and Sand Lake) areintricately woven into the fabric of the city -e water areaoccupies a quarter of the cityrsquos total area providing the citywith a lot of open spaces along the lakes and rivers

Wuhan has a long history In recent years because it is astrategic development city in central China the scale of itsbuilt-up areas has expanded rapidly -e mix of old and newurban areas has brought great difficulties to urban planningand environmental protection however Wuhanrsquos UHI ef-fect is gradually increasing with the highest surface tem-perature (heat source center) reaching 587degC and themaximum surface temperature difference between cities andsuburbs reaching 20degC -e problem of UHIs is an in-creasingly important issue that plagues urban development-erefore it is very important to research and solve theproblems of urban ventilation in Wuhan

3 Data

-e fundamental geographic information database (in-cluding land use data road network data and 3D buildingdata) and point of interest (POI) database were provided bythe Wuhan Natural Resources and Planning Bureau (Ta-ble 1) -e building coordinates ground graphics andbuilding height information are contained in a 3D databasethat is used to calculate the ventilation resistance coefficientof buildings Daily wind data for 2018 were provided by theChina Meteorological Administration Landsat 8 imageryfor land surface temperature (LST) inversion was obtainedfrom the United States Geological Survey (USGS)

4 Method

41 Methods for the Determination of Roughness ParametersIn air quality and meteorological models surface roughnessis used to express the drag effect of rough surfaces Use of themorphometric method which is based on surface rough-ness for calculating wind profiles in urban boundary layers(UBLs) has been extensively studied [39 40] Using acurrent modeling method based on a 3D building databaseGal and Unger [41] studied the roughness (Z0) of urbanareas to analyze the urban wind environment In order tomake the assessment of urban air permeability morepractical and to provide urban planners with computable

2 Complexity

geometric parameters of the city many indices have beendeveloped to assess urban permeability such as the frontalarea index λf and the site coverage ratio λp [30 38 42] Inthis study the frontal area index λf was selected as theroughness measurement index

411 Calculation of the Frontal Area Index Windwardbuilding walls obstruct airflow -e frontal area index is ameasurement of the building frontal area facing into the wind-e frontal area index was first approximated by the productof the average height width and density of the buildings [39]

λf LyHρd (1)

where Ly is the average width of the buildings perpendicularto the wind direction H is the average height of buildingsand ρd is the density of buildings per unit area

Burian [40] calculated the frontal area index in any winddirection directly using ArcView Avenus script and animproved formula

λf(θ) Aproj

AT

(2)

where λf(θ) represents the frontal area index of buildingsfacing into the wind θ Aproj represents the total projectedarea of buildings on a plane perpendicular to the winddirection and AT represents the plan area

-ere is some ambiguity about the minimum distancebetween two adjacent buildings that divides them into twoseparate buildings -e problem is that as the two buildingsget closer the upwind building may begin to obscure thefacade of the leeward building Analyzing the exposed frontarea (shown in Figure 2) may be more important thananalyzing the total front area [40] Wong [30] eliminated thearea blocked by buildings upwind of the blocked area of theleeward buildings by setting incremental projection lines(with an interval of 5meters) In this study a program wasdeveloped that was based on the ArcGIS 102 in the NET 40environment -e projected areas of the buildings weredirectly integrated through a graphic method to calculate thefrontal area index -e average frontal area index λf isobtained by weighting λf(θ) with annual frequency Pθ ofwind direction θ n is the collection of wind direction In thispaper there are 16 wind directions so n 16-e equation isas follows

λf 1113944n

θ1λf(θ) middot Pθ (3)

412 Resolution Recent studies have mainly used twoaggregation methods One is to aggregate buildings based onbuilding blocks which treats buildings as a unit -e other isto use a regular grid to process the building data which is

0 10 Km

TrainRoad

Building upVegetationWater body

Jianghan

Hongshan

Hanyang

Wuhan centre area

Qiaokou

Jiangan

Qingshan

Wuchang

China

Hankou

Wuchang

N

Hanjiang river

East lake

Yang

tze ri

ver

5

Figure 1 Map of the study area

Table 1 -e data sources

Data Type SourcesFundamental geographic information database Vector Wuhan Natural Resources and Planning BureauDaily wind data Table China Meteorological AdministrationLandsat 8 Image USGSPOI Vector Wuhan Natural Resources and Planning Bureau

Complexity 3

often used for spatial data analysis Open space is a key factorin assessing urban ventilation but it cannot be expressedwell using the building block aggregate method -ereforein this study a regular grid was selected to divide the urbanspace [31]

Due to the scale effect analyzing the same data at dif-ferent scales will lead to different results -e selection of thegrid scale should consider the correlation between thecalculated value of the frontal area index and the actualventilation capacity of the study area as well as the ability ofthe frontal area index to express the details of ventilation[42] Hsieh [31] conducted an urban ventilation study inTainan using 100m grids -e Hong Kong Planning De-partment found 100m grids to be compatible with variousvariables for the thermal environment in urban climateresearch [30] -e correlation between the frontal area indexand UHI intensity was analyzed using a grid scale from400m to 40m in Hong Kong and it was found that the twoshowed the highest correlation on the 100m grid scale[30 43] A 100m grid has been used to study the ventilationenvironment of Wuhan central urban area [38] which hasthe same research area as this study In light of these researchresults a 100m regular grid was used to divide the researcharea in this study

413 Zero Displacement Height -emorphometric methodbased on surface roughness for calculating wind profiles inUBLs has been studied extensively Current morphologymodels such as those of MacDonald [44] and Bottema [45]assume that wake interference between surface obstacles isnegligible and that the average wind speed approaching eachobstacle is logarithmic Given these assumptions these

models are only valid when the frontal area index λf is lessthan 03ndash05 In other words as the roughness increases theaverage wind profile approaching each building becomesnonlogarithmic because the interference between buildingspromotes air turbulence near the ground -is is why it isnecessary to introduce zero displacement height (Zd) intothe logarithmic velocity profile

MacDonald [44] proposed that the frontal area indexabove the displacement height (λlowastf ) can estimate Z0 betterthan λf and proposed that the average wind speed below thedisplacement height be assumed to be zero Ng [42]sug-gested that wind speed below Zd might depend on buildinggeometries such as λf

prime (frontal area density below Zd) andused 15m as the value of Zd Wong [30] further simplifiedthe frontal area index under the displacement height Zd wasset as 20m indirectly by setting the lowest level interval of λf

as (0 02) within 100m grids -e 3D building data used inthis study do not include information on overpasses largetraffic billboards and large green vegetation and these areimportant details for calculating λf

prime -erefore this studyextends Wangrsquos method to set different Zd values for dif-ferent land types (Zb

d 10m for built-up areas andZwd 1m

for water bodies)

42 LCVC Analysis -e LCP-based method has beenconsidered feasible for identifying urban ventilation corri-dors -e LCP-based method assumes that the air alwaysflows in the direction of least resistance thus determiningthe path from the starting point to the end point on theventilation resistance grid [30 46] In the LCP-basedmethod the Zd value is defined as the resistance value -ehigher the Zd value is the higher the resistance value is In

Blocked area

Ventilated area

Wind direction

Plane area

Width

Windward building

Leedward building

h

Projected frontal area

Figure 2 Diagram of the frontal area index

4 Complexity

addition n starting points and m ending points are deter-mined in order to represent the air inlets and air outletsrespectively-e LCP-based method is then used to generatenlowastm LCPs between the start and end points Finally theLCP frequency of each grid is calculated -e higher thefrequency is the better the ventilation conditions are andvice versa -is method can effectively identify the mainventilation corridors but the disadvantages are also obviousnamely the lack of estimation of corridor width and theinestimability of the grids with zero LPC frequency

-is study proposes a new approach namely the LCVCFor any ventilation resistance surface let s be the air inlet d

be the air outlet and m be a point on an LCP between s andd -en the LCVC from s to d through m can be written asthe following formula

Cs⟶d|m Cs⟶m + Cm⟶d (4)

According to the calculation formula of λf(θ) the cal-culated values are equal when the difference of θ is π

λf(θ) λf(πminusθ) (5)

-erefore the values of LCVC from m to d and from d tom are equal which can be expressed by the followingformula

Cm⟶d Cd⟶m (6)

Combining equation (4) and equation (6) gives

Cs⟶d|m Cs⟶m + Cd⟶m (7)

-e smaller the value of Cs⟶d|m is the lower the cost ofairflow passing through the m point is and the better theventilation condition is Using equation (7) the cumulativeventilation cost values at any point to the air outlet and airinlet can easily be calculated and the LCVC at that point canbe obtained by superposition of the two -e average ven-tilation cost can be obtained by weighting the LCVC valueswith wind direction frequencies

43 Ventilation Potential and Urban Functional UnitsDifferent types of urban functional units are usually ac-companied by different building structures and buildingheights For example residential areas are usually accom-panied by middle- or high-rise buildings and public areasare usually accompanied by low- or middle-level buildingsIt is predicted that different urban functional units can becharacterized by λf and ventilation cost In this study theurban functional unit information was obtained from theurban POI database

44 Validation of Ventilation Cost -e LST is considered tobe related to the urban ventilation environment [33] InQiaorsquos research LST was used to verify the results of thecorridor analysis -is study uses LST inversed from Landsat8 data to verify the results Retrieve processing includes threemain steps from digital numbers to spectral radiation fromspectral radiation to sensor brightness temperature andfrom sensor brightness temperature to LST In this study the

retrieve processing was implemented by using the Landsat 8Surface Temperature Inversion System (V20) developed byRen Huazhong from the Institute of Remote Sensing andGIS Peking University

5 Results

51 Spatial Distribution of the Wind Resistance Coefficient-ere are 101328 buildings in the study area (Figure 3 andTable 2) -e building height range is between 0m and204m with 57720 buildings less than 10m accounting for4891 of the total building area As the height increases thenumber and area of the buildings gradually decreaseBuildings with a height between 10m and 24m account for3338 of the total number of buildings and 3846 of thetotal building area -ere are 9663 buildings with a heightbetween 24m and 100m accounting for 1248 of the totalbuilding area -ere are 120 buildings higher than 100maccounting for only 012 of the total number of buildings

-e wind resistance coefficient obtained according to themethod described in Section 4 is shown in Figure 4 -espatial distribution of the wind resistance coefficient ispositively related to building height -e average wind re-sistance coefficient of all grids is 0153 -e number of gridscontaining high-rise buildings (height greater than 24m)accounts for 3026 and the average wind resistance co-efficient is 0299 Qiaokou Jianghan Jiangrsquoan andWuchangare the old urban areas of Wuhan -e average wind re-sistance coefficient of these districts is 0177 and 0298 forthe grid containing high-rise buildings Qingshan is animportant heavy industry base -ere are many single-storyfactories and middle-rise residential buildings in this area soits average wind resistance coefficient is only 0113 Hanyangand Hongshan are development zones with an average windresistance coefficient of 0138

In order to determine the wind resistance formed bydifferent urban functional units and to prepare for theventilation cost analysis the resistance coefficients of 12types of urban functional elements were calculated as shownin Table 3 -e urban functional units were obtained fromthe Wuhan POI database which contains 155421 POIpoints-e ventilation resistance coefficient of outdoor openspaces (parks green spaces and squares) was 0111 which issignificantly lower than that of other urban functional unitsNext came public management and social organizationunits with an average ventilation resistance coefficient of0199 Public services and companies had the highest ven-tilation resistance coefficients with 0251 and 0243 re-spectively -ese two types of units are usually accompaniedby a strong financial correlation which results in a higherspatial distribution consistency

52 Statistics on Wind Direction and Frequency When an-alyzing the urban ventilation conditions the results for eachstandard wind direction need to be linearly weightedaccording to the actual wind direction frequency -e winddirection and frequency were thus plotted on a radar chartaccording to the daily meteorological statistics of Wuhan in

Complexity 5

0 105 km

Height (m)gt10024 ndash10010 ndash 24lt10

N

Figure 3 3D spatial distribution of buildings

Table 2 Height of buildings in the study area

Height (m) Number Percentage of all buildings () Building area (km2) Percentage of total building area ()Hle 10 57720 5696 3214 489110ltHle 24 33825 3338 2528 384624ltHle 100 9663 954 820 1248100ltH 120 012 010 015

0 105 Km

High 1

Low 0

ResistanceCoefficients

N

Figure 4 Resistance coefficients map

6 Complexity

2018 (Figure 5) -e main wind direction is northerly (in-cluding N NE NW NNW and WNW) followed bysoutheasterly (including E SE SSE and ESE) Affected bythe monsoon climate the dominant wind direction variesfrom season to season -e prevailing winds affected by thenorthern cold in the autumn and winter are mainly northwinds (including N NE NW and NNE) followed by a feweast winds (including E NE ENE and SE) -e meteoro-logical changes in spring are complicated and the prevailingwinds are still mainly northerly followed by southeasterly(including ESE and SSE) In summer the northerly andsouthwesterly winds (SW WSW and SSW) are similar infrequency and accompanied by a few southeasterly winds

53 Results of LCVC Analysis -e difference in wind di-rection frequency indicates the variation in the influence ofthe LCVC in each wind direction Given a time interval theaverage LCVC is obtained by weighting the LCVC of eachwind direction with the corresponding frequency

Figure 6 shows the results for the LCVC analysis of 16wind directions in the urban center of Wuhan In order tomaintain comparability the calculation results of differentwind directions use the same classifications and map colorsRed indicates high ventilation costs and blue indicates lowventilation costs -e coherent narrow passages formed bythe low-cost areas are the ventilation corridors Generallyspeaking the ventilation costs around the city and in openareas are less than those in densely populated areas Affectedby different wind directions the spatial distribution ofventilation corridors in the city takes on different shapesand the directionality of the corridors is consistent with thewind direction For the wind directions of NNE NE SSEand SW the Yangtze River is the most important urbanventilation corridor For the wind directions of E ESE Wand WNW the interconnected open space formed by theHan River and the East Lake forms the main ventilationcorridor

-e spatial distribution of the LCVC in the differentseasons in Wuhan is shown in Figure 7 Seasonal LCVCs arenormalized and divided into 10 levels Colors from blue to

red indicate higher ventilation costs Overall the distribu-tion patterns of the LCVCs are very similar in the fourseasons-e dominant wind direction ofWuhan throughoutthe year is NNE (N NNE and NE) -e Yangtze River runsthrough the city and forms the main NE-SW ventilationcorridor with its open waters and north-south connectivityTwo islands with high ventilation costs centered on Hankouand Wuchang are formed along the east and west sides ofthe Yangtze River -e areas of the two are equivalent butthe Hankou side presents a single-core model and the area ofhigh-value areas is large By contrast the Wuchang sidepresents a multicore model and the area of high-value areasis small and scattered -e East Lake has become anotherimportant urban ventilation corridor due to its large waterarea and its distribution deep into the cityrsquos hinterland-ere are also some differences in the distributions ofLCVCs in different seasons In summer due to the highwind frequency in an N-S direction the ventilation corridorformed by the Yangtze River is more obvious than in otherseasons In winter the width of the ventilation corridor inthe north of the East Lake is slightly narrower than in otherseasons At the same time the width of the ventilationcorridors formed by the middle-level LCVC area (from 5 to7) was also narrower than in other seasons or even dis-appeared In the spring and autumn when the frequency ofthe south wind is slightly higher than in winter and summerthe ventilation corridors running in an S-N is more obvious

Summarized below are the details of the spatial distri-bution pattern of ventilation costs and the three levels ofventilation corridors identified namely the main second-ary and capillary levels

-e main ventilation corridors lay the foundation forurban ventilation -e distribution of the main ventilationcorridors has a high spatial correlation with natural geo-graphical features (such as rivers and lakes)-e results showthat there are two main ventilation corridors in centralWuhan One is the NE-SW corridor formed by the YangtzeRiver and the other is the S-N and NW-SE corridor formedby the East Lake -e secondary ventilation corridors aremostly connected to the main ventilation corridors and

Table 3 Classification of urban functional elements and average resistance coefficients in Wuhan

Reclassification Description ResistancePublic management and socialorganizations

Government welfare agency revenue financial bureau jurisdiction bureauadministration for industry and village fire control police station court etc 0199

Residential districts Residential district villa dormitory etc 0215Commerce Commercial building supermarket shopping center market small store grocery etc 0214Factories Various types of manufacturing factory and technology enterprise 0184

Companies Company law office accountant office audit office tourist agency insurance companyconsulting corporation etc 0243

Public services Bank post office telecom bureau postal bank etc 0251Everyday services Hairdressing public bathroom laundry tailor car repair or wash shop etc 0224Hotels Hotel restaurant snack cafe teahouse etc 0212

Educational institutions University college middle school primary school kindergarten training center privateschool etc 0216

Medical institutions Hospital childrenrsquos hospital clinic emergency center psychiatric hospital eye hospitalbone hospital dental clinic pharmacy etc 0217

Outdoor open areas Park square botanic garden amusement park resort agricultural center etc 0111Transportation facilities Railway station bus station coach station parking lot etc 0209

Complexity 7

their distribution is affected by the main ventilation corri-dors-e secondary ventilation corridors spatially divide thehigh ventilation cost area into several subareas which playan important part in promoting urban ventilationDepending on the main ventilation corridors of the EastLake many N-S or SE-NW secondary ventilation corridorsare formed in Wuchang District Qingshan District andHongshan District -ese ventilation corridors promoteventilation in these three areas -e secondary ventilationcorridor formed by the Han River connects to the mainventilation corridor formed by the Yangtze River whichpromotes the ventilation conditions in Hanyang

Capillary ventilation corridors are distributed in areaswith high ventilation costs and the areas with maximumventilation costs are divided into several subregions Cap-illary ventilation corridors cannot determine the overallconditions of regional ventilation but can effectively reducethe distribution of the maximum value of ventilation costsand can be used as a supplementary means for secondaryventilation corridors Due to the large amount of capillaryventilation corridors their locations are not marked on themap It is easy to determine that the number of capillaryventilation corridors in Hankou (Wuchang District Qing-shan District and Hongshan District) is significantly less

than in Wuchang (Qiaokou District Jianghan District andJiangrsquoan District)

-e ventilation cost distribution map enables the visual-ization of urban ventilation conditions For example blueindicates a low ventilation cost area and red indicates a highventilation cost area Relatively low-cost areas between highcosts are potential ventilation corridors-is facilitates a betterunderstanding of urban ventilation conditions on both localand global scales-e frontal area index λf was performed on alocal scale When considering the entire urban area due to thecomplexity of the buildings and the road network it will bedifficult to achieve the identification of ventilation corridors byvisual inspection of the buildings and the windward indexmap[30]-e results of the LCVCmethod cover the entire researcharea thus solving the problem of no-path passages in someareas in the traditional LCP method

54 Validation of the LCVC Method LST is related toventilation conditions When the ventilation conditions aregood the average surface is usually lower -e areas withpoor ventilation conditions have a higher average surfacetemperature and less heterogeneity than other areas do Toverify the accuracy of the urban ventilation analysis LST

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(a)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(b)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(c)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(d)

Figure 5 A wind-rose diagram for each season in Wuhan (a) Spring (b) Summer (c) Autumn (d) Winter

8 Complexity

inversion was performed on the Landsat 8 data (Figure 8) Bycombining the LSTmap with the summer urban ventilationanalysis results (Figure 7(b)) the LST values for differentlevels of ventilation costs were calculated (Table 4)

-e results show that the average LSTof the area with thebest ventilation conditions is 3419degC significantly lowerthan that of other areas -e average LST in the area with theworst ventilation conditions is 4053degC In addition the areawith the worst ventilation conditions has less heterogeneitythan other areas do and the LST standard deviation is 171As ventilation conditions improve the LST standard devi-ation continues to increase

55 Urban Ventilation Conditions and POIs Table 5 showsthe average ventilation costs for functional urban units -eaverage ventilation costs of outdoor open areas are obviouslylower than the others at only 0469 -e ventilation costs are

highest for public service units with a value of 0568 -is ismainly because these units are distributed in the main coreareas of the city-e ventilation costs for the residential unitsare 0559 in the second order which is different from theventilation resistance coefficient ranking Residential unitsare usually larger and have a higher building densityresulting in higher ventilation costs -e ventilation costs ofeducational institutions are 0554 similar to those for res-idential units -e majority of educational institutions areprimary secondary high school and training institutionsMost of these institutions are spatially associated withdensely populated areas and therefore have similar venti-lation costs to residential areas Residential units are themain places where people live and usually have a highpopulation density Higher ventilation costs easily form heatislands and are not conducive to the spread of pollutionwhich affects peoplersquos health and quality of life -erefore inurban planning the construction of outdoor open spaces in

0 20 40 km

N NNE NE ENE

E ESE SE SSE

S SSW SW WSW

W WNW NW NNW

Min Max

NVentilation cost (Minimax normalized)

Figure 6 Spatial distribution of LCVC in different wind directions

Complexity 9

Ventilation cost level0 10 20 km

(a) (b)

(c) (d)

Main ventilation pathSecondary ventilation path

1 2 3 4 5 6 7 8 9 10 N

Figure 7 Spatial distribution of ventilation during different seasons (a) Spring (b) Summer (c) Autumn (d) Winter

LST (degC)High 50

Low 270 10 km

N

5

Figure 8 Spatial distribution of LST in summer

10 Complexity

residential areas should be strengthened to improve theventilation environment

6 Discussion and Conclusion

Using the urban morphological characteristics identified byGIS technology this study established an LCVC method todescribe quantitatively the urban ventilation environment atthe city scale First the frontal area index of three-dimen-sional urban buildings representing the ventilation resis-tance coefficient was calculated using a morphologicalmethod Second the minimum cumulative ventilation costalgorithm was applied to draw the urban ventilation costmaps under different wind directions -en the wind fre-quency weighting was used to explore the urban ventilationenvironment in different seasons and the urban ventilationcorridors of different levels were analyzed Finally the re-sults of the summer ventilation analysis were verified usingthe LST retrieved from Landsat 8 data

-e analysis of the urban ventilation environmentbased on the morphology and GIS technology has theadvantage of high calculation efficiency In addition theresults of the analysis are highly compatible with otherspatial analysis and mapping technologies -is studyproposes that the cumulative costs of ventilation canbetter reflect the differences in urban ventilation

conditions than the ventilation resistance coefficient canIn contrast to the corridor extraction method based onempirical rules the proposed method can directly observethe dynamic characteristics of urban ventilation underdifferent wind directions and frequencies In contrast tothe least-cost path analysis method the results of theLCVC method cover the whole study area and the cor-ridor width and area can also be measured Howeverseveral issues require further study -e urban wind en-vironment is affected not only by ground roughness butalso by atmospheric environment and local temperaturefactors -erefore it is difficult to achieve an accurateanalysis of local wind speeds and directions from ananalysis of building form alone In addition it is difficultto obtain internal high-density real-time wind speed dataespecially wind speed measurements in a vertical direc-tion -is makes it difficult to improve the model pa-rameters -ese issues will be further studied

Data Availability

-e data used to support the findings of the study areavailable from the corresponding author upon request

Conflicts of Interest

-e authors declare that they have no conflicts of interest

Acknowledgments

-is study was supported by the National Key Research andDevelopment Program of China (Grant no2017YFB0503601) and China Postdoctoral Science Foun-dation (Grant no 2019M662726)

References

[1] H Wang G Wang J Qi et al ldquoScarcity-weighted fossil fuelfootprint of China at the provincial levelrdquo Applied Energyvol 258 2020

[2] L Jiang B Xue Z Ma L Yu B Huang and X Chen ldquoA life-cycle based co-benefits analysis of biomass pellet productionin Chinardquo Renewable Energy vol 154 pp 445ndash452 2020

[3] HWang H Schandl GWang and L Ma ldquoRegional materialflow accounts for China examining Chinarsquos natural resourceuse at the provincial and national levelrdquo Research andAnalysis vol 6 2019

[4] B-J He Z-Q Zhao L-D Shen H-B Wang and L-G LildquoAn approach to examining performances of coolhot sourcesin mitigatingenhancing land surface temperature underdifferent temperature backgrounds based on landsat 8 imagerdquoSustainable Cities and Society vol 44 pp 416ndash427 2019

[5] B-J He L Yang and M Ye ldquoStrategies for creating goodwind environment around Chinese residencesrdquo SustainableCities and Society vol 10 pp 174ndash183 2014

[6] H Akbari C Cartalis D Kolokotsa et al ldquoLocal climatechange and urban heat island mitigation techniquesndashthe stateof the artrdquo Journal of Civil Engineering and Managementvol 22 no 1 pp 1ndash16 2016

[7] D Zhou L Zhang L Hao G Sun Y Liu and C ZhuldquoSpatiotemporal trends of urban heat island effect along the

Table 5 Urban functional units and average ventilation costs

Reclassification Ventilation costPublic management and social organizations 0535Residential districts 0559Commerce 0519Factories 0483Companies 0545Public services 0568Everyday services 0545Hotels 0538Educational institutions 0554Medical institutions 0544Outdoor open areas 0469Transportation facilities 0541

Table 4 Statistical characteristics of LST at different levels ofventilation cost

Ventilationlevels

Temperature (degC)

Lowest Highest Range Average Standarddeviation

2 2928 5046 2118 3419 3643 2817 5259 2442 3496 3744 2967 5599 2632 3769 4055 2916 4947 2031 3882 3246 3189 5617 2428 3807 3247 3210 5024 1814 3795 3358 3285 5555 2270 4008 3029 3250 5470 2220 4012 26010 3434 5073 1639 4042 20511 3466 4683 1217 4053 171

Complexity 11

urban development intensity gradient in Chinardquo Science of9e Total Environment vol 544 pp 617ndash626 2016

[8] D Zhou S Zhao L Zhang G Sun and Y Liu ldquo-e footprintof urban heat island effect in Chinardquo Scientific Reports vol 5p 11160 2015

[9] K Zhang Y Chen and L Wu ldquoGrey spectrum analysis of airquality index and housing price in Handanrdquo Complexityvol 2019 2019

[10] Y Zhao Y Li and Q Chen ldquoAnalysis of a stochastic sus-ceptible-infective epidemic model in a polluted atmosphericenvironmentrdquo Complexity vol 2019 2019

[11] W Zhang C M Mak Z T Ai andWM Siu ldquoA study of theventilation and thermal comfort of the environment sur-rounding a new university building under constructionrdquoIndoor and Built Environment vol 21 no 4 pp 568ndash5822011

[12] R A W Albers P R Bosch B Blocken et al ldquoOverview ofchallenges and achievements in the climate adaptation ofcities and in the Climate Proof Cities programrdquo Building andEnvironment vol 83 pp 1ndash10 2015

[13] L W A van Hove C M J Jacobs B G HeusinkveldJ A Elbers B L van Driel and A A M Holtslag ldquoTemporaland spatial variability of urban heat island and thermalcomfort within the Rotterdam agglomerationrdquo Building andEnvironment vol 83 pp 91ndash103 2015

[14] A Guo J Yang X Xiao J Xia C Jin and X Li ldquoInfluences ofurban spatial form on urban heat island effects at the com-munity level in Chinardquo Sustainable Cities and Society vol 532020

[15] Z-Q Zhao B-J He L-G Li H-B Wang and A DarkoldquoProfile and concentric zonal analysis of relationships be-tween land useland cover and land surface temperature casestudy of Shenyang Chinardquo Energy and Buildings vol 155pp 282ndash295 2017

[16] E Ng ldquoPolicies and technical guidelines for urban planning ofhigh-density citiesmdashair ventilation assessment (AVA) ofHong Kongrdquo Building and Environment vol 44 no 7pp 1478ndash1488 2009

[17] B Chun and J-M Guldmann ldquoSpatial statistical analysis andsimulation of the urban heat island in high-density centralcitiesrdquo Landscape and Urban Planning vol 125 pp 76ndash882014

[18] Y He A Tablada and N H Wong ldquoA parametric study ofangular road patterns on pedestrian ventilation in high-density urban areasrdquo Building and Environment vol 151pp 251ndash267 2019

[19] J Yang Y Wang X Xiao C Jin J Xia and X Li ldquoSpatialdifferentiation of urban wind and thermal environment indifferent grid sizesrdquo Urban Climate vol 28 2019

[20] J Yang S Jin X Xiao et al ldquoLocal climate zone ventilationand urban land surface temperatures towards a performance-based and wind-sensitive planning proposal in megacitiesrdquoSustainable Cities and Society vol 47 2019

[21] Z Qiao L Liu Y Qin et al ldquo-e impact of urban renewal onland surface temperature changes a case study in the maincity of guangzhou Chinardquo Remote Sensing vol 12 no 5

[22] Z Qiao C Wu D Zhao et al ldquoDetermining the boundaryand probability of surface urban heat island footprint basedon a logistic modelrdquo Remote Sensing vol 11 no 11 2019

[23] Z Qiao X Xu M Zhao FWang and L Liu ldquo-e applicationof a binary division procedure to the classification of forestsubcategories using MODIS time-series data during 2000-2010 in Chinardquo International Journal of Remote Sensingvol 37 no 10 pp 2433ndash2450 2016

[24] M Bady S Kato T Takahashi and H Huang ldquoAn experi-mental investigation of the wind environment and air qualitywithin a densely populated urban street canyonrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 99 no 8pp 857ndash867 2011

[25] M Carpentieri P Salizzoni A Robins and L SoulhacldquoEvaluation of a neighbourhood scale street network dis-persion model through comparison with wind tunnel datardquoEnvironmental Modelling amp Software vol 37 pp 110ndash1242012

[26] K T Tse A U Weerasuriya X Zhang S Li andK C S Kwok ldquoPedestrian-level wind environment aroundisolated buildings under the influence of twisted wind flowsrdquoJournal of Wind Engineering and Industrial Aerodynamicsvol 162 pp 12ndash23 2017

[27] H Montazeri B Blocken and J L M Hensen ldquoEvaporativecooling by water spray systems CFD simulation experi-mental validation and sensitivity analysisrdquo Building andEnvironment vol 83 pp 129ndash141 2015

[28] X Wang and Y Li ldquoPredicting urban heat island circulationusing CFDrdquo Building and Environment vol 99 pp 82ndash972016

[29] K R Gautam L Rong G Zhang and M Abkar ldquoCom-parison of analysis methods for wind-driven cross ventilationthrough large openingsrdquo Building and Environment vol 154pp 375ndash388 2019

[30] M S Wong J E Nichol P H To and J Wang ldquoA simplemethod for designation of urban ventilation corridors and itsapplication to urban heat island analysisrdquo Building and En-vironment vol 45 no 8 pp 1880ndash1889 2010

[31] C-M Hsieh andH-C Huang ldquoMitigating urban heat islandsa method to identify potential wind corridor for cooling andventilationrdquo Computers Environment and Urban Systemsvol 57 pp 130ndash143 2016

[32] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor andBuilt Environment vol 26 no 3 pp 422ndash437 2017

[33] Z Qiao X Xu F Wu et al ldquoUrban ventilation networkmodel a case study of the core zone of capital function inBeijing metropolitan areardquo Journal of Cleaner Productionvol 168 pp 526ndash535 2017

[34] J Yang J Sun Q Ge and X Li ldquoAssessing the impacts ofurbanization-associated green space on urban land surfacetemperature a case study of Dalian Chinardquo Urban Forestry ampUrban Greening vol 22 pp 1ndash10 2017

[35] J Srebric M Heidarinejad and J Liu ldquoBuilding neighbor-hood emerging properties and their impacts on multi-scalemodeling of building energy and airflowsrdquo Building andEnvironment vol 91 pp 246ndash262 2015

[36] X Yang and Y Li ldquo-e impact of building density andbuilding height heterogeneity on average urban albedo andstreet surface temperaturerdquo Building and Environmentvol 90 pp 146ndash156 2015

[37] F Peng M S Wong Y Wan and J E Nichol ldquoModeling ofurban wind ventilation using high resolution airborne LiDARdatardquo Computers Environment and Urban Systems vol 64pp 81ndash90 2017

[38] C Yuan C Ren and E Ng ldquoGIS-based surface roughnessevaluation in the urban planning system to improve the windenvironmentmdasha study in Wuhan Chinardquo Urban Climatevol 10 pp 585ndash593 2014

12 Complexity

[39] C S B Grimmond and T R Oke ldquoAerodynamic propertiesof urban areas derived from analysis of surface formrdquo Journalof Applied Meteorology vol 38 no 9 pp 1262ndash1292 1999

[40] S J Burian M J Brown and S P Linger ldquoMorphologicalanalyses using 3D building databases los Angeles CaliforniardquoLos Alamos National Laboratory vol 74 2002

[41] T Gal and J Unger ldquoDetection of ventilation paths usinghigh-resolution roughness parameter mapping in a largeurban areardquo Building and Environment vol 44 no 1pp 198ndash206 2009

[42] E Ng C Yuan L Chen C Ren and J C H Fung ldquoIm-proving the wind environment in high-density cities by un-derstanding urban morphology and surface roughness astudy in Hong Kongrdquo Landscape and Urban Planningvol 101 no 1 pp 59ndash74 2011

[43] M S Wong and J E Nichol ldquoSpatial variability of frontal areaindex and its relationship with urban heat island intensityrdquoInternational Journal of Remote Sensing vol 34 no 3pp 885ndash896 2013

[44] RWMacdonald R F Griffiths and D J Hall ldquoAn improvedmethod for the estimation of surface roughness of obstaclearraysrdquo Atmospheric Environment vol 32 no 11 pp 1857ndash1864 1998

[45] M Bottema and P G Mestayer ldquoUrban roughness map-pingmdashvalidation techniques and some first resultsrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 74ndash76pp 163ndash173 1998

[46] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor BuiltEnvironment vol 26 no 3 pp 422ndash437 2017

Complexity 13

geometric parameters of the city many indices have beendeveloped to assess urban permeability such as the frontalarea index λf and the site coverage ratio λp [30 38 42] Inthis study the frontal area index λf was selected as theroughness measurement index

411 Calculation of the Frontal Area Index Windwardbuilding walls obstruct airflow -e frontal area index is ameasurement of the building frontal area facing into the wind-e frontal area index was first approximated by the productof the average height width and density of the buildings [39]

λf LyHρd (1)

where Ly is the average width of the buildings perpendicularto the wind direction H is the average height of buildingsand ρd is the density of buildings per unit area

Burian [40] calculated the frontal area index in any winddirection directly using ArcView Avenus script and animproved formula

λf(θ) Aproj

AT

(2)

where λf(θ) represents the frontal area index of buildingsfacing into the wind θ Aproj represents the total projectedarea of buildings on a plane perpendicular to the winddirection and AT represents the plan area

-ere is some ambiguity about the minimum distancebetween two adjacent buildings that divides them into twoseparate buildings -e problem is that as the two buildingsget closer the upwind building may begin to obscure thefacade of the leeward building Analyzing the exposed frontarea (shown in Figure 2) may be more important thananalyzing the total front area [40] Wong [30] eliminated thearea blocked by buildings upwind of the blocked area of theleeward buildings by setting incremental projection lines(with an interval of 5meters) In this study a program wasdeveloped that was based on the ArcGIS 102 in the NET 40environment -e projected areas of the buildings weredirectly integrated through a graphic method to calculate thefrontal area index -e average frontal area index λf isobtained by weighting λf(θ) with annual frequency Pθ ofwind direction θ n is the collection of wind direction In thispaper there are 16 wind directions so n 16-e equation isas follows

λf 1113944n

θ1λf(θ) middot Pθ (3)

412 Resolution Recent studies have mainly used twoaggregation methods One is to aggregate buildings based onbuilding blocks which treats buildings as a unit -e other isto use a regular grid to process the building data which is

0 10 Km

TrainRoad

Building upVegetationWater body

Jianghan

Hongshan

Hanyang

Wuhan centre area

Qiaokou

Jiangan

Qingshan

Wuchang

China

Hankou

Wuchang

N

Hanjiang river

East lake

Yang

tze ri

ver

5

Figure 1 Map of the study area

Table 1 -e data sources

Data Type SourcesFundamental geographic information database Vector Wuhan Natural Resources and Planning BureauDaily wind data Table China Meteorological AdministrationLandsat 8 Image USGSPOI Vector Wuhan Natural Resources and Planning Bureau

Complexity 3

often used for spatial data analysis Open space is a key factorin assessing urban ventilation but it cannot be expressedwell using the building block aggregate method -ereforein this study a regular grid was selected to divide the urbanspace [31]

Due to the scale effect analyzing the same data at dif-ferent scales will lead to different results -e selection of thegrid scale should consider the correlation between thecalculated value of the frontal area index and the actualventilation capacity of the study area as well as the ability ofthe frontal area index to express the details of ventilation[42] Hsieh [31] conducted an urban ventilation study inTainan using 100m grids -e Hong Kong Planning De-partment found 100m grids to be compatible with variousvariables for the thermal environment in urban climateresearch [30] -e correlation between the frontal area indexand UHI intensity was analyzed using a grid scale from400m to 40m in Hong Kong and it was found that the twoshowed the highest correlation on the 100m grid scale[30 43] A 100m grid has been used to study the ventilationenvironment of Wuhan central urban area [38] which hasthe same research area as this study In light of these researchresults a 100m regular grid was used to divide the researcharea in this study

413 Zero Displacement Height -emorphometric methodbased on surface roughness for calculating wind profiles inUBLs has been studied extensively Current morphologymodels such as those of MacDonald [44] and Bottema [45]assume that wake interference between surface obstacles isnegligible and that the average wind speed approaching eachobstacle is logarithmic Given these assumptions these

models are only valid when the frontal area index λf is lessthan 03ndash05 In other words as the roughness increases theaverage wind profile approaching each building becomesnonlogarithmic because the interference between buildingspromotes air turbulence near the ground -is is why it isnecessary to introduce zero displacement height (Zd) intothe logarithmic velocity profile

MacDonald [44] proposed that the frontal area indexabove the displacement height (λlowastf ) can estimate Z0 betterthan λf and proposed that the average wind speed below thedisplacement height be assumed to be zero Ng [42]sug-gested that wind speed below Zd might depend on buildinggeometries such as λf

prime (frontal area density below Zd) andused 15m as the value of Zd Wong [30] further simplifiedthe frontal area index under the displacement height Zd wasset as 20m indirectly by setting the lowest level interval of λf

as (0 02) within 100m grids -e 3D building data used inthis study do not include information on overpasses largetraffic billboards and large green vegetation and these areimportant details for calculating λf

prime -erefore this studyextends Wangrsquos method to set different Zd values for dif-ferent land types (Zb

d 10m for built-up areas andZwd 1m

for water bodies)

42 LCVC Analysis -e LCP-based method has beenconsidered feasible for identifying urban ventilation corri-dors -e LCP-based method assumes that the air alwaysflows in the direction of least resistance thus determiningthe path from the starting point to the end point on theventilation resistance grid [30 46] In the LCP-basedmethod the Zd value is defined as the resistance value -ehigher the Zd value is the higher the resistance value is In

Blocked area

Ventilated area

Wind direction

Plane area

Width

Windward building

Leedward building

h

Projected frontal area

Figure 2 Diagram of the frontal area index

4 Complexity

addition n starting points and m ending points are deter-mined in order to represent the air inlets and air outletsrespectively-e LCP-based method is then used to generatenlowastm LCPs between the start and end points Finally theLCP frequency of each grid is calculated -e higher thefrequency is the better the ventilation conditions are andvice versa -is method can effectively identify the mainventilation corridors but the disadvantages are also obviousnamely the lack of estimation of corridor width and theinestimability of the grids with zero LPC frequency

-is study proposes a new approach namely the LCVCFor any ventilation resistance surface let s be the air inlet d

be the air outlet and m be a point on an LCP between s andd -en the LCVC from s to d through m can be written asthe following formula

Cs⟶d|m Cs⟶m + Cm⟶d (4)

According to the calculation formula of λf(θ) the cal-culated values are equal when the difference of θ is π

λf(θ) λf(πminusθ) (5)

-erefore the values of LCVC from m to d and from d tom are equal which can be expressed by the followingformula

Cm⟶d Cd⟶m (6)

Combining equation (4) and equation (6) gives

Cs⟶d|m Cs⟶m + Cd⟶m (7)

-e smaller the value of Cs⟶d|m is the lower the cost ofairflow passing through the m point is and the better theventilation condition is Using equation (7) the cumulativeventilation cost values at any point to the air outlet and airinlet can easily be calculated and the LCVC at that point canbe obtained by superposition of the two -e average ven-tilation cost can be obtained by weighting the LCVC valueswith wind direction frequencies

43 Ventilation Potential and Urban Functional UnitsDifferent types of urban functional units are usually ac-companied by different building structures and buildingheights For example residential areas are usually accom-panied by middle- or high-rise buildings and public areasare usually accompanied by low- or middle-level buildingsIt is predicted that different urban functional units can becharacterized by λf and ventilation cost In this study theurban functional unit information was obtained from theurban POI database

44 Validation of Ventilation Cost -e LST is considered tobe related to the urban ventilation environment [33] InQiaorsquos research LST was used to verify the results of thecorridor analysis -is study uses LST inversed from Landsat8 data to verify the results Retrieve processing includes threemain steps from digital numbers to spectral radiation fromspectral radiation to sensor brightness temperature andfrom sensor brightness temperature to LST In this study the

retrieve processing was implemented by using the Landsat 8Surface Temperature Inversion System (V20) developed byRen Huazhong from the Institute of Remote Sensing andGIS Peking University

5 Results

51 Spatial Distribution of the Wind Resistance Coefficient-ere are 101328 buildings in the study area (Figure 3 andTable 2) -e building height range is between 0m and204m with 57720 buildings less than 10m accounting for4891 of the total building area As the height increases thenumber and area of the buildings gradually decreaseBuildings with a height between 10m and 24m account for3338 of the total number of buildings and 3846 of thetotal building area -ere are 9663 buildings with a heightbetween 24m and 100m accounting for 1248 of the totalbuilding area -ere are 120 buildings higher than 100maccounting for only 012 of the total number of buildings

-e wind resistance coefficient obtained according to themethod described in Section 4 is shown in Figure 4 -espatial distribution of the wind resistance coefficient ispositively related to building height -e average wind re-sistance coefficient of all grids is 0153 -e number of gridscontaining high-rise buildings (height greater than 24m)accounts for 3026 and the average wind resistance co-efficient is 0299 Qiaokou Jianghan Jiangrsquoan andWuchangare the old urban areas of Wuhan -e average wind re-sistance coefficient of these districts is 0177 and 0298 forthe grid containing high-rise buildings Qingshan is animportant heavy industry base -ere are many single-storyfactories and middle-rise residential buildings in this area soits average wind resistance coefficient is only 0113 Hanyangand Hongshan are development zones with an average windresistance coefficient of 0138

In order to determine the wind resistance formed bydifferent urban functional units and to prepare for theventilation cost analysis the resistance coefficients of 12types of urban functional elements were calculated as shownin Table 3 -e urban functional units were obtained fromthe Wuhan POI database which contains 155421 POIpoints-e ventilation resistance coefficient of outdoor openspaces (parks green spaces and squares) was 0111 which issignificantly lower than that of other urban functional unitsNext came public management and social organizationunits with an average ventilation resistance coefficient of0199 Public services and companies had the highest ven-tilation resistance coefficients with 0251 and 0243 re-spectively -ese two types of units are usually accompaniedby a strong financial correlation which results in a higherspatial distribution consistency

52 Statistics on Wind Direction and Frequency When an-alyzing the urban ventilation conditions the results for eachstandard wind direction need to be linearly weightedaccording to the actual wind direction frequency -e winddirection and frequency were thus plotted on a radar chartaccording to the daily meteorological statistics of Wuhan in

Complexity 5

0 105 km

Height (m)gt10024 ndash10010 ndash 24lt10

N

Figure 3 3D spatial distribution of buildings

Table 2 Height of buildings in the study area

Height (m) Number Percentage of all buildings () Building area (km2) Percentage of total building area ()Hle 10 57720 5696 3214 489110ltHle 24 33825 3338 2528 384624ltHle 100 9663 954 820 1248100ltH 120 012 010 015

0 105 Km

High 1

Low 0

ResistanceCoefficients

N

Figure 4 Resistance coefficients map

6 Complexity

2018 (Figure 5) -e main wind direction is northerly (in-cluding N NE NW NNW and WNW) followed bysoutheasterly (including E SE SSE and ESE) Affected bythe monsoon climate the dominant wind direction variesfrom season to season -e prevailing winds affected by thenorthern cold in the autumn and winter are mainly northwinds (including N NE NW and NNE) followed by a feweast winds (including E NE ENE and SE) -e meteoro-logical changes in spring are complicated and the prevailingwinds are still mainly northerly followed by southeasterly(including ESE and SSE) In summer the northerly andsouthwesterly winds (SW WSW and SSW) are similar infrequency and accompanied by a few southeasterly winds

53 Results of LCVC Analysis -e difference in wind di-rection frequency indicates the variation in the influence ofthe LCVC in each wind direction Given a time interval theaverage LCVC is obtained by weighting the LCVC of eachwind direction with the corresponding frequency

Figure 6 shows the results for the LCVC analysis of 16wind directions in the urban center of Wuhan In order tomaintain comparability the calculation results of differentwind directions use the same classifications and map colorsRed indicates high ventilation costs and blue indicates lowventilation costs -e coherent narrow passages formed bythe low-cost areas are the ventilation corridors Generallyspeaking the ventilation costs around the city and in openareas are less than those in densely populated areas Affectedby different wind directions the spatial distribution ofventilation corridors in the city takes on different shapesand the directionality of the corridors is consistent with thewind direction For the wind directions of NNE NE SSEand SW the Yangtze River is the most important urbanventilation corridor For the wind directions of E ESE Wand WNW the interconnected open space formed by theHan River and the East Lake forms the main ventilationcorridor

-e spatial distribution of the LCVC in the differentseasons in Wuhan is shown in Figure 7 Seasonal LCVCs arenormalized and divided into 10 levels Colors from blue to

red indicate higher ventilation costs Overall the distribu-tion patterns of the LCVCs are very similar in the fourseasons-e dominant wind direction ofWuhan throughoutthe year is NNE (N NNE and NE) -e Yangtze River runsthrough the city and forms the main NE-SW ventilationcorridor with its open waters and north-south connectivityTwo islands with high ventilation costs centered on Hankouand Wuchang are formed along the east and west sides ofthe Yangtze River -e areas of the two are equivalent butthe Hankou side presents a single-core model and the area ofhigh-value areas is large By contrast the Wuchang sidepresents a multicore model and the area of high-value areasis small and scattered -e East Lake has become anotherimportant urban ventilation corridor due to its large waterarea and its distribution deep into the cityrsquos hinterland-ere are also some differences in the distributions ofLCVCs in different seasons In summer due to the highwind frequency in an N-S direction the ventilation corridorformed by the Yangtze River is more obvious than in otherseasons In winter the width of the ventilation corridor inthe north of the East Lake is slightly narrower than in otherseasons At the same time the width of the ventilationcorridors formed by the middle-level LCVC area (from 5 to7) was also narrower than in other seasons or even dis-appeared In the spring and autumn when the frequency ofthe south wind is slightly higher than in winter and summerthe ventilation corridors running in an S-N is more obvious

Summarized below are the details of the spatial distri-bution pattern of ventilation costs and the three levels ofventilation corridors identified namely the main second-ary and capillary levels

-e main ventilation corridors lay the foundation forurban ventilation -e distribution of the main ventilationcorridors has a high spatial correlation with natural geo-graphical features (such as rivers and lakes)-e results showthat there are two main ventilation corridors in centralWuhan One is the NE-SW corridor formed by the YangtzeRiver and the other is the S-N and NW-SE corridor formedby the East Lake -e secondary ventilation corridors aremostly connected to the main ventilation corridors and

Table 3 Classification of urban functional elements and average resistance coefficients in Wuhan

Reclassification Description ResistancePublic management and socialorganizations

Government welfare agency revenue financial bureau jurisdiction bureauadministration for industry and village fire control police station court etc 0199

Residential districts Residential district villa dormitory etc 0215Commerce Commercial building supermarket shopping center market small store grocery etc 0214Factories Various types of manufacturing factory and technology enterprise 0184

Companies Company law office accountant office audit office tourist agency insurance companyconsulting corporation etc 0243

Public services Bank post office telecom bureau postal bank etc 0251Everyday services Hairdressing public bathroom laundry tailor car repair or wash shop etc 0224Hotels Hotel restaurant snack cafe teahouse etc 0212

Educational institutions University college middle school primary school kindergarten training center privateschool etc 0216

Medical institutions Hospital childrenrsquos hospital clinic emergency center psychiatric hospital eye hospitalbone hospital dental clinic pharmacy etc 0217

Outdoor open areas Park square botanic garden amusement park resort agricultural center etc 0111Transportation facilities Railway station bus station coach station parking lot etc 0209

Complexity 7

their distribution is affected by the main ventilation corri-dors-e secondary ventilation corridors spatially divide thehigh ventilation cost area into several subareas which playan important part in promoting urban ventilationDepending on the main ventilation corridors of the EastLake many N-S or SE-NW secondary ventilation corridorsare formed in Wuchang District Qingshan District andHongshan District -ese ventilation corridors promoteventilation in these three areas -e secondary ventilationcorridor formed by the Han River connects to the mainventilation corridor formed by the Yangtze River whichpromotes the ventilation conditions in Hanyang

Capillary ventilation corridors are distributed in areaswith high ventilation costs and the areas with maximumventilation costs are divided into several subregions Cap-illary ventilation corridors cannot determine the overallconditions of regional ventilation but can effectively reducethe distribution of the maximum value of ventilation costsand can be used as a supplementary means for secondaryventilation corridors Due to the large amount of capillaryventilation corridors their locations are not marked on themap It is easy to determine that the number of capillaryventilation corridors in Hankou (Wuchang District Qing-shan District and Hongshan District) is significantly less

than in Wuchang (Qiaokou District Jianghan District andJiangrsquoan District)

-e ventilation cost distribution map enables the visual-ization of urban ventilation conditions For example blueindicates a low ventilation cost area and red indicates a highventilation cost area Relatively low-cost areas between highcosts are potential ventilation corridors-is facilitates a betterunderstanding of urban ventilation conditions on both localand global scales-e frontal area index λf was performed on alocal scale When considering the entire urban area due to thecomplexity of the buildings and the road network it will bedifficult to achieve the identification of ventilation corridors byvisual inspection of the buildings and the windward indexmap[30]-e results of the LCVCmethod cover the entire researcharea thus solving the problem of no-path passages in someareas in the traditional LCP method

54 Validation of the LCVC Method LST is related toventilation conditions When the ventilation conditions aregood the average surface is usually lower -e areas withpoor ventilation conditions have a higher average surfacetemperature and less heterogeneity than other areas do Toverify the accuracy of the urban ventilation analysis LST

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(a)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(b)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(c)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(d)

Figure 5 A wind-rose diagram for each season in Wuhan (a) Spring (b) Summer (c) Autumn (d) Winter

8 Complexity

inversion was performed on the Landsat 8 data (Figure 8) Bycombining the LSTmap with the summer urban ventilationanalysis results (Figure 7(b)) the LST values for differentlevels of ventilation costs were calculated (Table 4)

-e results show that the average LSTof the area with thebest ventilation conditions is 3419degC significantly lowerthan that of other areas -e average LST in the area with theworst ventilation conditions is 4053degC In addition the areawith the worst ventilation conditions has less heterogeneitythan other areas do and the LST standard deviation is 171As ventilation conditions improve the LST standard devi-ation continues to increase

55 Urban Ventilation Conditions and POIs Table 5 showsthe average ventilation costs for functional urban units -eaverage ventilation costs of outdoor open areas are obviouslylower than the others at only 0469 -e ventilation costs are

highest for public service units with a value of 0568 -is ismainly because these units are distributed in the main coreareas of the city-e ventilation costs for the residential unitsare 0559 in the second order which is different from theventilation resistance coefficient ranking Residential unitsare usually larger and have a higher building densityresulting in higher ventilation costs -e ventilation costs ofeducational institutions are 0554 similar to those for res-idential units -e majority of educational institutions areprimary secondary high school and training institutionsMost of these institutions are spatially associated withdensely populated areas and therefore have similar venti-lation costs to residential areas Residential units are themain places where people live and usually have a highpopulation density Higher ventilation costs easily form heatislands and are not conducive to the spread of pollutionwhich affects peoplersquos health and quality of life -erefore inurban planning the construction of outdoor open spaces in

0 20 40 km

N NNE NE ENE

E ESE SE SSE

S SSW SW WSW

W WNW NW NNW

Min Max

NVentilation cost (Minimax normalized)

Figure 6 Spatial distribution of LCVC in different wind directions

Complexity 9

Ventilation cost level0 10 20 km

(a) (b)

(c) (d)

Main ventilation pathSecondary ventilation path

1 2 3 4 5 6 7 8 9 10 N

Figure 7 Spatial distribution of ventilation during different seasons (a) Spring (b) Summer (c) Autumn (d) Winter

LST (degC)High 50

Low 270 10 km

N

5

Figure 8 Spatial distribution of LST in summer

10 Complexity

residential areas should be strengthened to improve theventilation environment

6 Discussion and Conclusion

Using the urban morphological characteristics identified byGIS technology this study established an LCVC method todescribe quantitatively the urban ventilation environment atthe city scale First the frontal area index of three-dimen-sional urban buildings representing the ventilation resis-tance coefficient was calculated using a morphologicalmethod Second the minimum cumulative ventilation costalgorithm was applied to draw the urban ventilation costmaps under different wind directions -en the wind fre-quency weighting was used to explore the urban ventilationenvironment in different seasons and the urban ventilationcorridors of different levels were analyzed Finally the re-sults of the summer ventilation analysis were verified usingthe LST retrieved from Landsat 8 data

-e analysis of the urban ventilation environmentbased on the morphology and GIS technology has theadvantage of high calculation efficiency In addition theresults of the analysis are highly compatible with otherspatial analysis and mapping technologies -is studyproposes that the cumulative costs of ventilation canbetter reflect the differences in urban ventilation

conditions than the ventilation resistance coefficient canIn contrast to the corridor extraction method based onempirical rules the proposed method can directly observethe dynamic characteristics of urban ventilation underdifferent wind directions and frequencies In contrast tothe least-cost path analysis method the results of theLCVC method cover the whole study area and the cor-ridor width and area can also be measured Howeverseveral issues require further study -e urban wind en-vironment is affected not only by ground roughness butalso by atmospheric environment and local temperaturefactors -erefore it is difficult to achieve an accurateanalysis of local wind speeds and directions from ananalysis of building form alone In addition it is difficultto obtain internal high-density real-time wind speed dataespecially wind speed measurements in a vertical direc-tion -is makes it difficult to improve the model pa-rameters -ese issues will be further studied

Data Availability

-e data used to support the findings of the study areavailable from the corresponding author upon request

Conflicts of Interest

-e authors declare that they have no conflicts of interest

Acknowledgments

-is study was supported by the National Key Research andDevelopment Program of China (Grant no2017YFB0503601) and China Postdoctoral Science Foun-dation (Grant no 2019M662726)

References

[1] H Wang G Wang J Qi et al ldquoScarcity-weighted fossil fuelfootprint of China at the provincial levelrdquo Applied Energyvol 258 2020

[2] L Jiang B Xue Z Ma L Yu B Huang and X Chen ldquoA life-cycle based co-benefits analysis of biomass pellet productionin Chinardquo Renewable Energy vol 154 pp 445ndash452 2020

[3] HWang H Schandl GWang and L Ma ldquoRegional materialflow accounts for China examining Chinarsquos natural resourceuse at the provincial and national levelrdquo Research andAnalysis vol 6 2019

[4] B-J He Z-Q Zhao L-D Shen H-B Wang and L-G LildquoAn approach to examining performances of coolhot sourcesin mitigatingenhancing land surface temperature underdifferent temperature backgrounds based on landsat 8 imagerdquoSustainable Cities and Society vol 44 pp 416ndash427 2019

[5] B-J He L Yang and M Ye ldquoStrategies for creating goodwind environment around Chinese residencesrdquo SustainableCities and Society vol 10 pp 174ndash183 2014

[6] H Akbari C Cartalis D Kolokotsa et al ldquoLocal climatechange and urban heat island mitigation techniquesndashthe stateof the artrdquo Journal of Civil Engineering and Managementvol 22 no 1 pp 1ndash16 2016

[7] D Zhou L Zhang L Hao G Sun Y Liu and C ZhuldquoSpatiotemporal trends of urban heat island effect along the

Table 5 Urban functional units and average ventilation costs

Reclassification Ventilation costPublic management and social organizations 0535Residential districts 0559Commerce 0519Factories 0483Companies 0545Public services 0568Everyday services 0545Hotels 0538Educational institutions 0554Medical institutions 0544Outdoor open areas 0469Transportation facilities 0541

Table 4 Statistical characteristics of LST at different levels ofventilation cost

Ventilationlevels

Temperature (degC)

Lowest Highest Range Average Standarddeviation

2 2928 5046 2118 3419 3643 2817 5259 2442 3496 3744 2967 5599 2632 3769 4055 2916 4947 2031 3882 3246 3189 5617 2428 3807 3247 3210 5024 1814 3795 3358 3285 5555 2270 4008 3029 3250 5470 2220 4012 26010 3434 5073 1639 4042 20511 3466 4683 1217 4053 171

Complexity 11

urban development intensity gradient in Chinardquo Science of9e Total Environment vol 544 pp 617ndash626 2016

[8] D Zhou S Zhao L Zhang G Sun and Y Liu ldquo-e footprintof urban heat island effect in Chinardquo Scientific Reports vol 5p 11160 2015

[9] K Zhang Y Chen and L Wu ldquoGrey spectrum analysis of airquality index and housing price in Handanrdquo Complexityvol 2019 2019

[10] Y Zhao Y Li and Q Chen ldquoAnalysis of a stochastic sus-ceptible-infective epidemic model in a polluted atmosphericenvironmentrdquo Complexity vol 2019 2019

[11] W Zhang C M Mak Z T Ai andWM Siu ldquoA study of theventilation and thermal comfort of the environment sur-rounding a new university building under constructionrdquoIndoor and Built Environment vol 21 no 4 pp 568ndash5822011

[12] R A W Albers P R Bosch B Blocken et al ldquoOverview ofchallenges and achievements in the climate adaptation ofcities and in the Climate Proof Cities programrdquo Building andEnvironment vol 83 pp 1ndash10 2015

[13] L W A van Hove C M J Jacobs B G HeusinkveldJ A Elbers B L van Driel and A A M Holtslag ldquoTemporaland spatial variability of urban heat island and thermalcomfort within the Rotterdam agglomerationrdquo Building andEnvironment vol 83 pp 91ndash103 2015

[14] A Guo J Yang X Xiao J Xia C Jin and X Li ldquoInfluences ofurban spatial form on urban heat island effects at the com-munity level in Chinardquo Sustainable Cities and Society vol 532020

[15] Z-Q Zhao B-J He L-G Li H-B Wang and A DarkoldquoProfile and concentric zonal analysis of relationships be-tween land useland cover and land surface temperature casestudy of Shenyang Chinardquo Energy and Buildings vol 155pp 282ndash295 2017

[16] E Ng ldquoPolicies and technical guidelines for urban planning ofhigh-density citiesmdashair ventilation assessment (AVA) ofHong Kongrdquo Building and Environment vol 44 no 7pp 1478ndash1488 2009

[17] B Chun and J-M Guldmann ldquoSpatial statistical analysis andsimulation of the urban heat island in high-density centralcitiesrdquo Landscape and Urban Planning vol 125 pp 76ndash882014

[18] Y He A Tablada and N H Wong ldquoA parametric study ofangular road patterns on pedestrian ventilation in high-density urban areasrdquo Building and Environment vol 151pp 251ndash267 2019

[19] J Yang Y Wang X Xiao C Jin J Xia and X Li ldquoSpatialdifferentiation of urban wind and thermal environment indifferent grid sizesrdquo Urban Climate vol 28 2019

[20] J Yang S Jin X Xiao et al ldquoLocal climate zone ventilationand urban land surface temperatures towards a performance-based and wind-sensitive planning proposal in megacitiesrdquoSustainable Cities and Society vol 47 2019

[21] Z Qiao L Liu Y Qin et al ldquo-e impact of urban renewal onland surface temperature changes a case study in the maincity of guangzhou Chinardquo Remote Sensing vol 12 no 5

[22] Z Qiao C Wu D Zhao et al ldquoDetermining the boundaryand probability of surface urban heat island footprint basedon a logistic modelrdquo Remote Sensing vol 11 no 11 2019

[23] Z Qiao X Xu M Zhao FWang and L Liu ldquo-e applicationof a binary division procedure to the classification of forestsubcategories using MODIS time-series data during 2000-2010 in Chinardquo International Journal of Remote Sensingvol 37 no 10 pp 2433ndash2450 2016

[24] M Bady S Kato T Takahashi and H Huang ldquoAn experi-mental investigation of the wind environment and air qualitywithin a densely populated urban street canyonrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 99 no 8pp 857ndash867 2011

[25] M Carpentieri P Salizzoni A Robins and L SoulhacldquoEvaluation of a neighbourhood scale street network dis-persion model through comparison with wind tunnel datardquoEnvironmental Modelling amp Software vol 37 pp 110ndash1242012

[26] K T Tse A U Weerasuriya X Zhang S Li andK C S Kwok ldquoPedestrian-level wind environment aroundisolated buildings under the influence of twisted wind flowsrdquoJournal of Wind Engineering and Industrial Aerodynamicsvol 162 pp 12ndash23 2017

[27] H Montazeri B Blocken and J L M Hensen ldquoEvaporativecooling by water spray systems CFD simulation experi-mental validation and sensitivity analysisrdquo Building andEnvironment vol 83 pp 129ndash141 2015

[28] X Wang and Y Li ldquoPredicting urban heat island circulationusing CFDrdquo Building and Environment vol 99 pp 82ndash972016

[29] K R Gautam L Rong G Zhang and M Abkar ldquoCom-parison of analysis methods for wind-driven cross ventilationthrough large openingsrdquo Building and Environment vol 154pp 375ndash388 2019

[30] M S Wong J E Nichol P H To and J Wang ldquoA simplemethod for designation of urban ventilation corridors and itsapplication to urban heat island analysisrdquo Building and En-vironment vol 45 no 8 pp 1880ndash1889 2010

[31] C-M Hsieh andH-C Huang ldquoMitigating urban heat islandsa method to identify potential wind corridor for cooling andventilationrdquo Computers Environment and Urban Systemsvol 57 pp 130ndash143 2016

[32] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor andBuilt Environment vol 26 no 3 pp 422ndash437 2017

[33] Z Qiao X Xu F Wu et al ldquoUrban ventilation networkmodel a case study of the core zone of capital function inBeijing metropolitan areardquo Journal of Cleaner Productionvol 168 pp 526ndash535 2017

[34] J Yang J Sun Q Ge and X Li ldquoAssessing the impacts ofurbanization-associated green space on urban land surfacetemperature a case study of Dalian Chinardquo Urban Forestry ampUrban Greening vol 22 pp 1ndash10 2017

[35] J Srebric M Heidarinejad and J Liu ldquoBuilding neighbor-hood emerging properties and their impacts on multi-scalemodeling of building energy and airflowsrdquo Building andEnvironment vol 91 pp 246ndash262 2015

[36] X Yang and Y Li ldquo-e impact of building density andbuilding height heterogeneity on average urban albedo andstreet surface temperaturerdquo Building and Environmentvol 90 pp 146ndash156 2015

[37] F Peng M S Wong Y Wan and J E Nichol ldquoModeling ofurban wind ventilation using high resolution airborne LiDARdatardquo Computers Environment and Urban Systems vol 64pp 81ndash90 2017

[38] C Yuan C Ren and E Ng ldquoGIS-based surface roughnessevaluation in the urban planning system to improve the windenvironmentmdasha study in Wuhan Chinardquo Urban Climatevol 10 pp 585ndash593 2014

12 Complexity

[39] C S B Grimmond and T R Oke ldquoAerodynamic propertiesof urban areas derived from analysis of surface formrdquo Journalof Applied Meteorology vol 38 no 9 pp 1262ndash1292 1999

[40] S J Burian M J Brown and S P Linger ldquoMorphologicalanalyses using 3D building databases los Angeles CaliforniardquoLos Alamos National Laboratory vol 74 2002

[41] T Gal and J Unger ldquoDetection of ventilation paths usinghigh-resolution roughness parameter mapping in a largeurban areardquo Building and Environment vol 44 no 1pp 198ndash206 2009

[42] E Ng C Yuan L Chen C Ren and J C H Fung ldquoIm-proving the wind environment in high-density cities by un-derstanding urban morphology and surface roughness astudy in Hong Kongrdquo Landscape and Urban Planningvol 101 no 1 pp 59ndash74 2011

[43] M S Wong and J E Nichol ldquoSpatial variability of frontal areaindex and its relationship with urban heat island intensityrdquoInternational Journal of Remote Sensing vol 34 no 3pp 885ndash896 2013

[44] RWMacdonald R F Griffiths and D J Hall ldquoAn improvedmethod for the estimation of surface roughness of obstaclearraysrdquo Atmospheric Environment vol 32 no 11 pp 1857ndash1864 1998

[45] M Bottema and P G Mestayer ldquoUrban roughness map-pingmdashvalidation techniques and some first resultsrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 74ndash76pp 163ndash173 1998

[46] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor BuiltEnvironment vol 26 no 3 pp 422ndash437 2017

Complexity 13

often used for spatial data analysis Open space is a key factorin assessing urban ventilation but it cannot be expressedwell using the building block aggregate method -ereforein this study a regular grid was selected to divide the urbanspace [31]

Due to the scale effect analyzing the same data at dif-ferent scales will lead to different results -e selection of thegrid scale should consider the correlation between thecalculated value of the frontal area index and the actualventilation capacity of the study area as well as the ability ofthe frontal area index to express the details of ventilation[42] Hsieh [31] conducted an urban ventilation study inTainan using 100m grids -e Hong Kong Planning De-partment found 100m grids to be compatible with variousvariables for the thermal environment in urban climateresearch [30] -e correlation between the frontal area indexand UHI intensity was analyzed using a grid scale from400m to 40m in Hong Kong and it was found that the twoshowed the highest correlation on the 100m grid scale[30 43] A 100m grid has been used to study the ventilationenvironment of Wuhan central urban area [38] which hasthe same research area as this study In light of these researchresults a 100m regular grid was used to divide the researcharea in this study

413 Zero Displacement Height -emorphometric methodbased on surface roughness for calculating wind profiles inUBLs has been studied extensively Current morphologymodels such as those of MacDonald [44] and Bottema [45]assume that wake interference between surface obstacles isnegligible and that the average wind speed approaching eachobstacle is logarithmic Given these assumptions these

models are only valid when the frontal area index λf is lessthan 03ndash05 In other words as the roughness increases theaverage wind profile approaching each building becomesnonlogarithmic because the interference between buildingspromotes air turbulence near the ground -is is why it isnecessary to introduce zero displacement height (Zd) intothe logarithmic velocity profile

MacDonald [44] proposed that the frontal area indexabove the displacement height (λlowastf ) can estimate Z0 betterthan λf and proposed that the average wind speed below thedisplacement height be assumed to be zero Ng [42]sug-gested that wind speed below Zd might depend on buildinggeometries such as λf

prime (frontal area density below Zd) andused 15m as the value of Zd Wong [30] further simplifiedthe frontal area index under the displacement height Zd wasset as 20m indirectly by setting the lowest level interval of λf

as (0 02) within 100m grids -e 3D building data used inthis study do not include information on overpasses largetraffic billboards and large green vegetation and these areimportant details for calculating λf

prime -erefore this studyextends Wangrsquos method to set different Zd values for dif-ferent land types (Zb

d 10m for built-up areas andZwd 1m

for water bodies)

42 LCVC Analysis -e LCP-based method has beenconsidered feasible for identifying urban ventilation corri-dors -e LCP-based method assumes that the air alwaysflows in the direction of least resistance thus determiningthe path from the starting point to the end point on theventilation resistance grid [30 46] In the LCP-basedmethod the Zd value is defined as the resistance value -ehigher the Zd value is the higher the resistance value is In

Blocked area

Ventilated area

Wind direction

Plane area

Width

Windward building

Leedward building

h

Projected frontal area

Figure 2 Diagram of the frontal area index

4 Complexity

addition n starting points and m ending points are deter-mined in order to represent the air inlets and air outletsrespectively-e LCP-based method is then used to generatenlowastm LCPs between the start and end points Finally theLCP frequency of each grid is calculated -e higher thefrequency is the better the ventilation conditions are andvice versa -is method can effectively identify the mainventilation corridors but the disadvantages are also obviousnamely the lack of estimation of corridor width and theinestimability of the grids with zero LPC frequency

-is study proposes a new approach namely the LCVCFor any ventilation resistance surface let s be the air inlet d

be the air outlet and m be a point on an LCP between s andd -en the LCVC from s to d through m can be written asthe following formula

Cs⟶d|m Cs⟶m + Cm⟶d (4)

According to the calculation formula of λf(θ) the cal-culated values are equal when the difference of θ is π

λf(θ) λf(πminusθ) (5)

-erefore the values of LCVC from m to d and from d tom are equal which can be expressed by the followingformula

Cm⟶d Cd⟶m (6)

Combining equation (4) and equation (6) gives

Cs⟶d|m Cs⟶m + Cd⟶m (7)

-e smaller the value of Cs⟶d|m is the lower the cost ofairflow passing through the m point is and the better theventilation condition is Using equation (7) the cumulativeventilation cost values at any point to the air outlet and airinlet can easily be calculated and the LCVC at that point canbe obtained by superposition of the two -e average ven-tilation cost can be obtained by weighting the LCVC valueswith wind direction frequencies

43 Ventilation Potential and Urban Functional UnitsDifferent types of urban functional units are usually ac-companied by different building structures and buildingheights For example residential areas are usually accom-panied by middle- or high-rise buildings and public areasare usually accompanied by low- or middle-level buildingsIt is predicted that different urban functional units can becharacterized by λf and ventilation cost In this study theurban functional unit information was obtained from theurban POI database

44 Validation of Ventilation Cost -e LST is considered tobe related to the urban ventilation environment [33] InQiaorsquos research LST was used to verify the results of thecorridor analysis -is study uses LST inversed from Landsat8 data to verify the results Retrieve processing includes threemain steps from digital numbers to spectral radiation fromspectral radiation to sensor brightness temperature andfrom sensor brightness temperature to LST In this study the

retrieve processing was implemented by using the Landsat 8Surface Temperature Inversion System (V20) developed byRen Huazhong from the Institute of Remote Sensing andGIS Peking University

5 Results

51 Spatial Distribution of the Wind Resistance Coefficient-ere are 101328 buildings in the study area (Figure 3 andTable 2) -e building height range is between 0m and204m with 57720 buildings less than 10m accounting for4891 of the total building area As the height increases thenumber and area of the buildings gradually decreaseBuildings with a height between 10m and 24m account for3338 of the total number of buildings and 3846 of thetotal building area -ere are 9663 buildings with a heightbetween 24m and 100m accounting for 1248 of the totalbuilding area -ere are 120 buildings higher than 100maccounting for only 012 of the total number of buildings

-e wind resistance coefficient obtained according to themethod described in Section 4 is shown in Figure 4 -espatial distribution of the wind resistance coefficient ispositively related to building height -e average wind re-sistance coefficient of all grids is 0153 -e number of gridscontaining high-rise buildings (height greater than 24m)accounts for 3026 and the average wind resistance co-efficient is 0299 Qiaokou Jianghan Jiangrsquoan andWuchangare the old urban areas of Wuhan -e average wind re-sistance coefficient of these districts is 0177 and 0298 forthe grid containing high-rise buildings Qingshan is animportant heavy industry base -ere are many single-storyfactories and middle-rise residential buildings in this area soits average wind resistance coefficient is only 0113 Hanyangand Hongshan are development zones with an average windresistance coefficient of 0138

In order to determine the wind resistance formed bydifferent urban functional units and to prepare for theventilation cost analysis the resistance coefficients of 12types of urban functional elements were calculated as shownin Table 3 -e urban functional units were obtained fromthe Wuhan POI database which contains 155421 POIpoints-e ventilation resistance coefficient of outdoor openspaces (parks green spaces and squares) was 0111 which issignificantly lower than that of other urban functional unitsNext came public management and social organizationunits with an average ventilation resistance coefficient of0199 Public services and companies had the highest ven-tilation resistance coefficients with 0251 and 0243 re-spectively -ese two types of units are usually accompaniedby a strong financial correlation which results in a higherspatial distribution consistency

52 Statistics on Wind Direction and Frequency When an-alyzing the urban ventilation conditions the results for eachstandard wind direction need to be linearly weightedaccording to the actual wind direction frequency -e winddirection and frequency were thus plotted on a radar chartaccording to the daily meteorological statistics of Wuhan in

Complexity 5

0 105 km

Height (m)gt10024 ndash10010 ndash 24lt10

N

Figure 3 3D spatial distribution of buildings

Table 2 Height of buildings in the study area

Height (m) Number Percentage of all buildings () Building area (km2) Percentage of total building area ()Hle 10 57720 5696 3214 489110ltHle 24 33825 3338 2528 384624ltHle 100 9663 954 820 1248100ltH 120 012 010 015

0 105 Km

High 1

Low 0

ResistanceCoefficients

N

Figure 4 Resistance coefficients map

6 Complexity

2018 (Figure 5) -e main wind direction is northerly (in-cluding N NE NW NNW and WNW) followed bysoutheasterly (including E SE SSE and ESE) Affected bythe monsoon climate the dominant wind direction variesfrom season to season -e prevailing winds affected by thenorthern cold in the autumn and winter are mainly northwinds (including N NE NW and NNE) followed by a feweast winds (including E NE ENE and SE) -e meteoro-logical changes in spring are complicated and the prevailingwinds are still mainly northerly followed by southeasterly(including ESE and SSE) In summer the northerly andsouthwesterly winds (SW WSW and SSW) are similar infrequency and accompanied by a few southeasterly winds

53 Results of LCVC Analysis -e difference in wind di-rection frequency indicates the variation in the influence ofthe LCVC in each wind direction Given a time interval theaverage LCVC is obtained by weighting the LCVC of eachwind direction with the corresponding frequency

Figure 6 shows the results for the LCVC analysis of 16wind directions in the urban center of Wuhan In order tomaintain comparability the calculation results of differentwind directions use the same classifications and map colorsRed indicates high ventilation costs and blue indicates lowventilation costs -e coherent narrow passages formed bythe low-cost areas are the ventilation corridors Generallyspeaking the ventilation costs around the city and in openareas are less than those in densely populated areas Affectedby different wind directions the spatial distribution ofventilation corridors in the city takes on different shapesand the directionality of the corridors is consistent with thewind direction For the wind directions of NNE NE SSEand SW the Yangtze River is the most important urbanventilation corridor For the wind directions of E ESE Wand WNW the interconnected open space formed by theHan River and the East Lake forms the main ventilationcorridor

-e spatial distribution of the LCVC in the differentseasons in Wuhan is shown in Figure 7 Seasonal LCVCs arenormalized and divided into 10 levels Colors from blue to

red indicate higher ventilation costs Overall the distribu-tion patterns of the LCVCs are very similar in the fourseasons-e dominant wind direction ofWuhan throughoutthe year is NNE (N NNE and NE) -e Yangtze River runsthrough the city and forms the main NE-SW ventilationcorridor with its open waters and north-south connectivityTwo islands with high ventilation costs centered on Hankouand Wuchang are formed along the east and west sides ofthe Yangtze River -e areas of the two are equivalent butthe Hankou side presents a single-core model and the area ofhigh-value areas is large By contrast the Wuchang sidepresents a multicore model and the area of high-value areasis small and scattered -e East Lake has become anotherimportant urban ventilation corridor due to its large waterarea and its distribution deep into the cityrsquos hinterland-ere are also some differences in the distributions ofLCVCs in different seasons In summer due to the highwind frequency in an N-S direction the ventilation corridorformed by the Yangtze River is more obvious than in otherseasons In winter the width of the ventilation corridor inthe north of the East Lake is slightly narrower than in otherseasons At the same time the width of the ventilationcorridors formed by the middle-level LCVC area (from 5 to7) was also narrower than in other seasons or even dis-appeared In the spring and autumn when the frequency ofthe south wind is slightly higher than in winter and summerthe ventilation corridors running in an S-N is more obvious

Summarized below are the details of the spatial distri-bution pattern of ventilation costs and the three levels ofventilation corridors identified namely the main second-ary and capillary levels

-e main ventilation corridors lay the foundation forurban ventilation -e distribution of the main ventilationcorridors has a high spatial correlation with natural geo-graphical features (such as rivers and lakes)-e results showthat there are two main ventilation corridors in centralWuhan One is the NE-SW corridor formed by the YangtzeRiver and the other is the S-N and NW-SE corridor formedby the East Lake -e secondary ventilation corridors aremostly connected to the main ventilation corridors and

Table 3 Classification of urban functional elements and average resistance coefficients in Wuhan

Reclassification Description ResistancePublic management and socialorganizations

Government welfare agency revenue financial bureau jurisdiction bureauadministration for industry and village fire control police station court etc 0199

Residential districts Residential district villa dormitory etc 0215Commerce Commercial building supermarket shopping center market small store grocery etc 0214Factories Various types of manufacturing factory and technology enterprise 0184

Companies Company law office accountant office audit office tourist agency insurance companyconsulting corporation etc 0243

Public services Bank post office telecom bureau postal bank etc 0251Everyday services Hairdressing public bathroom laundry tailor car repair or wash shop etc 0224Hotels Hotel restaurant snack cafe teahouse etc 0212

Educational institutions University college middle school primary school kindergarten training center privateschool etc 0216

Medical institutions Hospital childrenrsquos hospital clinic emergency center psychiatric hospital eye hospitalbone hospital dental clinic pharmacy etc 0217

Outdoor open areas Park square botanic garden amusement park resort agricultural center etc 0111Transportation facilities Railway station bus station coach station parking lot etc 0209

Complexity 7

their distribution is affected by the main ventilation corri-dors-e secondary ventilation corridors spatially divide thehigh ventilation cost area into several subareas which playan important part in promoting urban ventilationDepending on the main ventilation corridors of the EastLake many N-S or SE-NW secondary ventilation corridorsare formed in Wuchang District Qingshan District andHongshan District -ese ventilation corridors promoteventilation in these three areas -e secondary ventilationcorridor formed by the Han River connects to the mainventilation corridor formed by the Yangtze River whichpromotes the ventilation conditions in Hanyang

Capillary ventilation corridors are distributed in areaswith high ventilation costs and the areas with maximumventilation costs are divided into several subregions Cap-illary ventilation corridors cannot determine the overallconditions of regional ventilation but can effectively reducethe distribution of the maximum value of ventilation costsand can be used as a supplementary means for secondaryventilation corridors Due to the large amount of capillaryventilation corridors their locations are not marked on themap It is easy to determine that the number of capillaryventilation corridors in Hankou (Wuchang District Qing-shan District and Hongshan District) is significantly less

than in Wuchang (Qiaokou District Jianghan District andJiangrsquoan District)

-e ventilation cost distribution map enables the visual-ization of urban ventilation conditions For example blueindicates a low ventilation cost area and red indicates a highventilation cost area Relatively low-cost areas between highcosts are potential ventilation corridors-is facilitates a betterunderstanding of urban ventilation conditions on both localand global scales-e frontal area index λf was performed on alocal scale When considering the entire urban area due to thecomplexity of the buildings and the road network it will bedifficult to achieve the identification of ventilation corridors byvisual inspection of the buildings and the windward indexmap[30]-e results of the LCVCmethod cover the entire researcharea thus solving the problem of no-path passages in someareas in the traditional LCP method

54 Validation of the LCVC Method LST is related toventilation conditions When the ventilation conditions aregood the average surface is usually lower -e areas withpoor ventilation conditions have a higher average surfacetemperature and less heterogeneity than other areas do Toverify the accuracy of the urban ventilation analysis LST

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(a)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(b)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(c)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(d)

Figure 5 A wind-rose diagram for each season in Wuhan (a) Spring (b) Summer (c) Autumn (d) Winter

8 Complexity

inversion was performed on the Landsat 8 data (Figure 8) Bycombining the LSTmap with the summer urban ventilationanalysis results (Figure 7(b)) the LST values for differentlevels of ventilation costs were calculated (Table 4)

-e results show that the average LSTof the area with thebest ventilation conditions is 3419degC significantly lowerthan that of other areas -e average LST in the area with theworst ventilation conditions is 4053degC In addition the areawith the worst ventilation conditions has less heterogeneitythan other areas do and the LST standard deviation is 171As ventilation conditions improve the LST standard devi-ation continues to increase

55 Urban Ventilation Conditions and POIs Table 5 showsthe average ventilation costs for functional urban units -eaverage ventilation costs of outdoor open areas are obviouslylower than the others at only 0469 -e ventilation costs are

highest for public service units with a value of 0568 -is ismainly because these units are distributed in the main coreareas of the city-e ventilation costs for the residential unitsare 0559 in the second order which is different from theventilation resistance coefficient ranking Residential unitsare usually larger and have a higher building densityresulting in higher ventilation costs -e ventilation costs ofeducational institutions are 0554 similar to those for res-idential units -e majority of educational institutions areprimary secondary high school and training institutionsMost of these institutions are spatially associated withdensely populated areas and therefore have similar venti-lation costs to residential areas Residential units are themain places where people live and usually have a highpopulation density Higher ventilation costs easily form heatislands and are not conducive to the spread of pollutionwhich affects peoplersquos health and quality of life -erefore inurban planning the construction of outdoor open spaces in

0 20 40 km

N NNE NE ENE

E ESE SE SSE

S SSW SW WSW

W WNW NW NNW

Min Max

NVentilation cost (Minimax normalized)

Figure 6 Spatial distribution of LCVC in different wind directions

Complexity 9

Ventilation cost level0 10 20 km

(a) (b)

(c) (d)

Main ventilation pathSecondary ventilation path

1 2 3 4 5 6 7 8 9 10 N

Figure 7 Spatial distribution of ventilation during different seasons (a) Spring (b) Summer (c) Autumn (d) Winter

LST (degC)High 50

Low 270 10 km

N

5

Figure 8 Spatial distribution of LST in summer

10 Complexity

residential areas should be strengthened to improve theventilation environment

6 Discussion and Conclusion

Using the urban morphological characteristics identified byGIS technology this study established an LCVC method todescribe quantitatively the urban ventilation environment atthe city scale First the frontal area index of three-dimen-sional urban buildings representing the ventilation resis-tance coefficient was calculated using a morphologicalmethod Second the minimum cumulative ventilation costalgorithm was applied to draw the urban ventilation costmaps under different wind directions -en the wind fre-quency weighting was used to explore the urban ventilationenvironment in different seasons and the urban ventilationcorridors of different levels were analyzed Finally the re-sults of the summer ventilation analysis were verified usingthe LST retrieved from Landsat 8 data

-e analysis of the urban ventilation environmentbased on the morphology and GIS technology has theadvantage of high calculation efficiency In addition theresults of the analysis are highly compatible with otherspatial analysis and mapping technologies -is studyproposes that the cumulative costs of ventilation canbetter reflect the differences in urban ventilation

conditions than the ventilation resistance coefficient canIn contrast to the corridor extraction method based onempirical rules the proposed method can directly observethe dynamic characteristics of urban ventilation underdifferent wind directions and frequencies In contrast tothe least-cost path analysis method the results of theLCVC method cover the whole study area and the cor-ridor width and area can also be measured Howeverseveral issues require further study -e urban wind en-vironment is affected not only by ground roughness butalso by atmospheric environment and local temperaturefactors -erefore it is difficult to achieve an accurateanalysis of local wind speeds and directions from ananalysis of building form alone In addition it is difficultto obtain internal high-density real-time wind speed dataespecially wind speed measurements in a vertical direc-tion -is makes it difficult to improve the model pa-rameters -ese issues will be further studied

Data Availability

-e data used to support the findings of the study areavailable from the corresponding author upon request

Conflicts of Interest

-e authors declare that they have no conflicts of interest

Acknowledgments

-is study was supported by the National Key Research andDevelopment Program of China (Grant no2017YFB0503601) and China Postdoctoral Science Foun-dation (Grant no 2019M662726)

References

[1] H Wang G Wang J Qi et al ldquoScarcity-weighted fossil fuelfootprint of China at the provincial levelrdquo Applied Energyvol 258 2020

[2] L Jiang B Xue Z Ma L Yu B Huang and X Chen ldquoA life-cycle based co-benefits analysis of biomass pellet productionin Chinardquo Renewable Energy vol 154 pp 445ndash452 2020

[3] HWang H Schandl GWang and L Ma ldquoRegional materialflow accounts for China examining Chinarsquos natural resourceuse at the provincial and national levelrdquo Research andAnalysis vol 6 2019

[4] B-J He Z-Q Zhao L-D Shen H-B Wang and L-G LildquoAn approach to examining performances of coolhot sourcesin mitigatingenhancing land surface temperature underdifferent temperature backgrounds based on landsat 8 imagerdquoSustainable Cities and Society vol 44 pp 416ndash427 2019

[5] B-J He L Yang and M Ye ldquoStrategies for creating goodwind environment around Chinese residencesrdquo SustainableCities and Society vol 10 pp 174ndash183 2014

[6] H Akbari C Cartalis D Kolokotsa et al ldquoLocal climatechange and urban heat island mitigation techniquesndashthe stateof the artrdquo Journal of Civil Engineering and Managementvol 22 no 1 pp 1ndash16 2016

[7] D Zhou L Zhang L Hao G Sun Y Liu and C ZhuldquoSpatiotemporal trends of urban heat island effect along the

Table 5 Urban functional units and average ventilation costs

Reclassification Ventilation costPublic management and social organizations 0535Residential districts 0559Commerce 0519Factories 0483Companies 0545Public services 0568Everyday services 0545Hotels 0538Educational institutions 0554Medical institutions 0544Outdoor open areas 0469Transportation facilities 0541

Table 4 Statistical characteristics of LST at different levels ofventilation cost

Ventilationlevels

Temperature (degC)

Lowest Highest Range Average Standarddeviation

2 2928 5046 2118 3419 3643 2817 5259 2442 3496 3744 2967 5599 2632 3769 4055 2916 4947 2031 3882 3246 3189 5617 2428 3807 3247 3210 5024 1814 3795 3358 3285 5555 2270 4008 3029 3250 5470 2220 4012 26010 3434 5073 1639 4042 20511 3466 4683 1217 4053 171

Complexity 11

urban development intensity gradient in Chinardquo Science of9e Total Environment vol 544 pp 617ndash626 2016

[8] D Zhou S Zhao L Zhang G Sun and Y Liu ldquo-e footprintof urban heat island effect in Chinardquo Scientific Reports vol 5p 11160 2015

[9] K Zhang Y Chen and L Wu ldquoGrey spectrum analysis of airquality index and housing price in Handanrdquo Complexityvol 2019 2019

[10] Y Zhao Y Li and Q Chen ldquoAnalysis of a stochastic sus-ceptible-infective epidemic model in a polluted atmosphericenvironmentrdquo Complexity vol 2019 2019

[11] W Zhang C M Mak Z T Ai andWM Siu ldquoA study of theventilation and thermal comfort of the environment sur-rounding a new university building under constructionrdquoIndoor and Built Environment vol 21 no 4 pp 568ndash5822011

[12] R A W Albers P R Bosch B Blocken et al ldquoOverview ofchallenges and achievements in the climate adaptation ofcities and in the Climate Proof Cities programrdquo Building andEnvironment vol 83 pp 1ndash10 2015

[13] L W A van Hove C M J Jacobs B G HeusinkveldJ A Elbers B L van Driel and A A M Holtslag ldquoTemporaland spatial variability of urban heat island and thermalcomfort within the Rotterdam agglomerationrdquo Building andEnvironment vol 83 pp 91ndash103 2015

[14] A Guo J Yang X Xiao J Xia C Jin and X Li ldquoInfluences ofurban spatial form on urban heat island effects at the com-munity level in Chinardquo Sustainable Cities and Society vol 532020

[15] Z-Q Zhao B-J He L-G Li H-B Wang and A DarkoldquoProfile and concentric zonal analysis of relationships be-tween land useland cover and land surface temperature casestudy of Shenyang Chinardquo Energy and Buildings vol 155pp 282ndash295 2017

[16] E Ng ldquoPolicies and technical guidelines for urban planning ofhigh-density citiesmdashair ventilation assessment (AVA) ofHong Kongrdquo Building and Environment vol 44 no 7pp 1478ndash1488 2009

[17] B Chun and J-M Guldmann ldquoSpatial statistical analysis andsimulation of the urban heat island in high-density centralcitiesrdquo Landscape and Urban Planning vol 125 pp 76ndash882014

[18] Y He A Tablada and N H Wong ldquoA parametric study ofangular road patterns on pedestrian ventilation in high-density urban areasrdquo Building and Environment vol 151pp 251ndash267 2019

[19] J Yang Y Wang X Xiao C Jin J Xia and X Li ldquoSpatialdifferentiation of urban wind and thermal environment indifferent grid sizesrdquo Urban Climate vol 28 2019

[20] J Yang S Jin X Xiao et al ldquoLocal climate zone ventilationand urban land surface temperatures towards a performance-based and wind-sensitive planning proposal in megacitiesrdquoSustainable Cities and Society vol 47 2019

[21] Z Qiao L Liu Y Qin et al ldquo-e impact of urban renewal onland surface temperature changes a case study in the maincity of guangzhou Chinardquo Remote Sensing vol 12 no 5

[22] Z Qiao C Wu D Zhao et al ldquoDetermining the boundaryand probability of surface urban heat island footprint basedon a logistic modelrdquo Remote Sensing vol 11 no 11 2019

[23] Z Qiao X Xu M Zhao FWang and L Liu ldquo-e applicationof a binary division procedure to the classification of forestsubcategories using MODIS time-series data during 2000-2010 in Chinardquo International Journal of Remote Sensingvol 37 no 10 pp 2433ndash2450 2016

[24] M Bady S Kato T Takahashi and H Huang ldquoAn experi-mental investigation of the wind environment and air qualitywithin a densely populated urban street canyonrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 99 no 8pp 857ndash867 2011

[25] M Carpentieri P Salizzoni A Robins and L SoulhacldquoEvaluation of a neighbourhood scale street network dis-persion model through comparison with wind tunnel datardquoEnvironmental Modelling amp Software vol 37 pp 110ndash1242012

[26] K T Tse A U Weerasuriya X Zhang S Li andK C S Kwok ldquoPedestrian-level wind environment aroundisolated buildings under the influence of twisted wind flowsrdquoJournal of Wind Engineering and Industrial Aerodynamicsvol 162 pp 12ndash23 2017

[27] H Montazeri B Blocken and J L M Hensen ldquoEvaporativecooling by water spray systems CFD simulation experi-mental validation and sensitivity analysisrdquo Building andEnvironment vol 83 pp 129ndash141 2015

[28] X Wang and Y Li ldquoPredicting urban heat island circulationusing CFDrdquo Building and Environment vol 99 pp 82ndash972016

[29] K R Gautam L Rong G Zhang and M Abkar ldquoCom-parison of analysis methods for wind-driven cross ventilationthrough large openingsrdquo Building and Environment vol 154pp 375ndash388 2019

[30] M S Wong J E Nichol P H To and J Wang ldquoA simplemethod for designation of urban ventilation corridors and itsapplication to urban heat island analysisrdquo Building and En-vironment vol 45 no 8 pp 1880ndash1889 2010

[31] C-M Hsieh andH-C Huang ldquoMitigating urban heat islandsa method to identify potential wind corridor for cooling andventilationrdquo Computers Environment and Urban Systemsvol 57 pp 130ndash143 2016

[32] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor andBuilt Environment vol 26 no 3 pp 422ndash437 2017

[33] Z Qiao X Xu F Wu et al ldquoUrban ventilation networkmodel a case study of the core zone of capital function inBeijing metropolitan areardquo Journal of Cleaner Productionvol 168 pp 526ndash535 2017

[34] J Yang J Sun Q Ge and X Li ldquoAssessing the impacts ofurbanization-associated green space on urban land surfacetemperature a case study of Dalian Chinardquo Urban Forestry ampUrban Greening vol 22 pp 1ndash10 2017

[35] J Srebric M Heidarinejad and J Liu ldquoBuilding neighbor-hood emerging properties and their impacts on multi-scalemodeling of building energy and airflowsrdquo Building andEnvironment vol 91 pp 246ndash262 2015

[36] X Yang and Y Li ldquo-e impact of building density andbuilding height heterogeneity on average urban albedo andstreet surface temperaturerdquo Building and Environmentvol 90 pp 146ndash156 2015

[37] F Peng M S Wong Y Wan and J E Nichol ldquoModeling ofurban wind ventilation using high resolution airborne LiDARdatardquo Computers Environment and Urban Systems vol 64pp 81ndash90 2017

[38] C Yuan C Ren and E Ng ldquoGIS-based surface roughnessevaluation in the urban planning system to improve the windenvironmentmdasha study in Wuhan Chinardquo Urban Climatevol 10 pp 585ndash593 2014

12 Complexity

[39] C S B Grimmond and T R Oke ldquoAerodynamic propertiesof urban areas derived from analysis of surface formrdquo Journalof Applied Meteorology vol 38 no 9 pp 1262ndash1292 1999

[40] S J Burian M J Brown and S P Linger ldquoMorphologicalanalyses using 3D building databases los Angeles CaliforniardquoLos Alamos National Laboratory vol 74 2002

[41] T Gal and J Unger ldquoDetection of ventilation paths usinghigh-resolution roughness parameter mapping in a largeurban areardquo Building and Environment vol 44 no 1pp 198ndash206 2009

[42] E Ng C Yuan L Chen C Ren and J C H Fung ldquoIm-proving the wind environment in high-density cities by un-derstanding urban morphology and surface roughness astudy in Hong Kongrdquo Landscape and Urban Planningvol 101 no 1 pp 59ndash74 2011

[43] M S Wong and J E Nichol ldquoSpatial variability of frontal areaindex and its relationship with urban heat island intensityrdquoInternational Journal of Remote Sensing vol 34 no 3pp 885ndash896 2013

[44] RWMacdonald R F Griffiths and D J Hall ldquoAn improvedmethod for the estimation of surface roughness of obstaclearraysrdquo Atmospheric Environment vol 32 no 11 pp 1857ndash1864 1998

[45] M Bottema and P G Mestayer ldquoUrban roughness map-pingmdashvalidation techniques and some first resultsrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 74ndash76pp 163ndash173 1998

[46] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor BuiltEnvironment vol 26 no 3 pp 422ndash437 2017

Complexity 13

addition n starting points and m ending points are deter-mined in order to represent the air inlets and air outletsrespectively-e LCP-based method is then used to generatenlowastm LCPs between the start and end points Finally theLCP frequency of each grid is calculated -e higher thefrequency is the better the ventilation conditions are andvice versa -is method can effectively identify the mainventilation corridors but the disadvantages are also obviousnamely the lack of estimation of corridor width and theinestimability of the grids with zero LPC frequency

-is study proposes a new approach namely the LCVCFor any ventilation resistance surface let s be the air inlet d

be the air outlet and m be a point on an LCP between s andd -en the LCVC from s to d through m can be written asthe following formula

Cs⟶d|m Cs⟶m + Cm⟶d (4)

According to the calculation formula of λf(θ) the cal-culated values are equal when the difference of θ is π

λf(θ) λf(πminusθ) (5)

-erefore the values of LCVC from m to d and from d tom are equal which can be expressed by the followingformula

Cm⟶d Cd⟶m (6)

Combining equation (4) and equation (6) gives

Cs⟶d|m Cs⟶m + Cd⟶m (7)

-e smaller the value of Cs⟶d|m is the lower the cost ofairflow passing through the m point is and the better theventilation condition is Using equation (7) the cumulativeventilation cost values at any point to the air outlet and airinlet can easily be calculated and the LCVC at that point canbe obtained by superposition of the two -e average ven-tilation cost can be obtained by weighting the LCVC valueswith wind direction frequencies

43 Ventilation Potential and Urban Functional UnitsDifferent types of urban functional units are usually ac-companied by different building structures and buildingheights For example residential areas are usually accom-panied by middle- or high-rise buildings and public areasare usually accompanied by low- or middle-level buildingsIt is predicted that different urban functional units can becharacterized by λf and ventilation cost In this study theurban functional unit information was obtained from theurban POI database

44 Validation of Ventilation Cost -e LST is considered tobe related to the urban ventilation environment [33] InQiaorsquos research LST was used to verify the results of thecorridor analysis -is study uses LST inversed from Landsat8 data to verify the results Retrieve processing includes threemain steps from digital numbers to spectral radiation fromspectral radiation to sensor brightness temperature andfrom sensor brightness temperature to LST In this study the

retrieve processing was implemented by using the Landsat 8Surface Temperature Inversion System (V20) developed byRen Huazhong from the Institute of Remote Sensing andGIS Peking University

5 Results

51 Spatial Distribution of the Wind Resistance Coefficient-ere are 101328 buildings in the study area (Figure 3 andTable 2) -e building height range is between 0m and204m with 57720 buildings less than 10m accounting for4891 of the total building area As the height increases thenumber and area of the buildings gradually decreaseBuildings with a height between 10m and 24m account for3338 of the total number of buildings and 3846 of thetotal building area -ere are 9663 buildings with a heightbetween 24m and 100m accounting for 1248 of the totalbuilding area -ere are 120 buildings higher than 100maccounting for only 012 of the total number of buildings

-e wind resistance coefficient obtained according to themethod described in Section 4 is shown in Figure 4 -espatial distribution of the wind resistance coefficient ispositively related to building height -e average wind re-sistance coefficient of all grids is 0153 -e number of gridscontaining high-rise buildings (height greater than 24m)accounts for 3026 and the average wind resistance co-efficient is 0299 Qiaokou Jianghan Jiangrsquoan andWuchangare the old urban areas of Wuhan -e average wind re-sistance coefficient of these districts is 0177 and 0298 forthe grid containing high-rise buildings Qingshan is animportant heavy industry base -ere are many single-storyfactories and middle-rise residential buildings in this area soits average wind resistance coefficient is only 0113 Hanyangand Hongshan are development zones with an average windresistance coefficient of 0138

In order to determine the wind resistance formed bydifferent urban functional units and to prepare for theventilation cost analysis the resistance coefficients of 12types of urban functional elements were calculated as shownin Table 3 -e urban functional units were obtained fromthe Wuhan POI database which contains 155421 POIpoints-e ventilation resistance coefficient of outdoor openspaces (parks green spaces and squares) was 0111 which issignificantly lower than that of other urban functional unitsNext came public management and social organizationunits with an average ventilation resistance coefficient of0199 Public services and companies had the highest ven-tilation resistance coefficients with 0251 and 0243 re-spectively -ese two types of units are usually accompaniedby a strong financial correlation which results in a higherspatial distribution consistency

52 Statistics on Wind Direction and Frequency When an-alyzing the urban ventilation conditions the results for eachstandard wind direction need to be linearly weightedaccording to the actual wind direction frequency -e winddirection and frequency were thus plotted on a radar chartaccording to the daily meteorological statistics of Wuhan in

Complexity 5

0 105 km

Height (m)gt10024 ndash10010 ndash 24lt10

N

Figure 3 3D spatial distribution of buildings

Table 2 Height of buildings in the study area

Height (m) Number Percentage of all buildings () Building area (km2) Percentage of total building area ()Hle 10 57720 5696 3214 489110ltHle 24 33825 3338 2528 384624ltHle 100 9663 954 820 1248100ltH 120 012 010 015

0 105 Km

High 1

Low 0

ResistanceCoefficients

N

Figure 4 Resistance coefficients map

6 Complexity

2018 (Figure 5) -e main wind direction is northerly (in-cluding N NE NW NNW and WNW) followed bysoutheasterly (including E SE SSE and ESE) Affected bythe monsoon climate the dominant wind direction variesfrom season to season -e prevailing winds affected by thenorthern cold in the autumn and winter are mainly northwinds (including N NE NW and NNE) followed by a feweast winds (including E NE ENE and SE) -e meteoro-logical changes in spring are complicated and the prevailingwinds are still mainly northerly followed by southeasterly(including ESE and SSE) In summer the northerly andsouthwesterly winds (SW WSW and SSW) are similar infrequency and accompanied by a few southeasterly winds

53 Results of LCVC Analysis -e difference in wind di-rection frequency indicates the variation in the influence ofthe LCVC in each wind direction Given a time interval theaverage LCVC is obtained by weighting the LCVC of eachwind direction with the corresponding frequency

Figure 6 shows the results for the LCVC analysis of 16wind directions in the urban center of Wuhan In order tomaintain comparability the calculation results of differentwind directions use the same classifications and map colorsRed indicates high ventilation costs and blue indicates lowventilation costs -e coherent narrow passages formed bythe low-cost areas are the ventilation corridors Generallyspeaking the ventilation costs around the city and in openareas are less than those in densely populated areas Affectedby different wind directions the spatial distribution ofventilation corridors in the city takes on different shapesand the directionality of the corridors is consistent with thewind direction For the wind directions of NNE NE SSEand SW the Yangtze River is the most important urbanventilation corridor For the wind directions of E ESE Wand WNW the interconnected open space formed by theHan River and the East Lake forms the main ventilationcorridor

-e spatial distribution of the LCVC in the differentseasons in Wuhan is shown in Figure 7 Seasonal LCVCs arenormalized and divided into 10 levels Colors from blue to

red indicate higher ventilation costs Overall the distribu-tion patterns of the LCVCs are very similar in the fourseasons-e dominant wind direction ofWuhan throughoutthe year is NNE (N NNE and NE) -e Yangtze River runsthrough the city and forms the main NE-SW ventilationcorridor with its open waters and north-south connectivityTwo islands with high ventilation costs centered on Hankouand Wuchang are formed along the east and west sides ofthe Yangtze River -e areas of the two are equivalent butthe Hankou side presents a single-core model and the area ofhigh-value areas is large By contrast the Wuchang sidepresents a multicore model and the area of high-value areasis small and scattered -e East Lake has become anotherimportant urban ventilation corridor due to its large waterarea and its distribution deep into the cityrsquos hinterland-ere are also some differences in the distributions ofLCVCs in different seasons In summer due to the highwind frequency in an N-S direction the ventilation corridorformed by the Yangtze River is more obvious than in otherseasons In winter the width of the ventilation corridor inthe north of the East Lake is slightly narrower than in otherseasons At the same time the width of the ventilationcorridors formed by the middle-level LCVC area (from 5 to7) was also narrower than in other seasons or even dis-appeared In the spring and autumn when the frequency ofthe south wind is slightly higher than in winter and summerthe ventilation corridors running in an S-N is more obvious

Summarized below are the details of the spatial distri-bution pattern of ventilation costs and the three levels ofventilation corridors identified namely the main second-ary and capillary levels

-e main ventilation corridors lay the foundation forurban ventilation -e distribution of the main ventilationcorridors has a high spatial correlation with natural geo-graphical features (such as rivers and lakes)-e results showthat there are two main ventilation corridors in centralWuhan One is the NE-SW corridor formed by the YangtzeRiver and the other is the S-N and NW-SE corridor formedby the East Lake -e secondary ventilation corridors aremostly connected to the main ventilation corridors and

Table 3 Classification of urban functional elements and average resistance coefficients in Wuhan

Reclassification Description ResistancePublic management and socialorganizations

Government welfare agency revenue financial bureau jurisdiction bureauadministration for industry and village fire control police station court etc 0199

Residential districts Residential district villa dormitory etc 0215Commerce Commercial building supermarket shopping center market small store grocery etc 0214Factories Various types of manufacturing factory and technology enterprise 0184

Companies Company law office accountant office audit office tourist agency insurance companyconsulting corporation etc 0243

Public services Bank post office telecom bureau postal bank etc 0251Everyday services Hairdressing public bathroom laundry tailor car repair or wash shop etc 0224Hotels Hotel restaurant snack cafe teahouse etc 0212

Educational institutions University college middle school primary school kindergarten training center privateschool etc 0216

Medical institutions Hospital childrenrsquos hospital clinic emergency center psychiatric hospital eye hospitalbone hospital dental clinic pharmacy etc 0217

Outdoor open areas Park square botanic garden amusement park resort agricultural center etc 0111Transportation facilities Railway station bus station coach station parking lot etc 0209

Complexity 7

their distribution is affected by the main ventilation corri-dors-e secondary ventilation corridors spatially divide thehigh ventilation cost area into several subareas which playan important part in promoting urban ventilationDepending on the main ventilation corridors of the EastLake many N-S or SE-NW secondary ventilation corridorsare formed in Wuchang District Qingshan District andHongshan District -ese ventilation corridors promoteventilation in these three areas -e secondary ventilationcorridor formed by the Han River connects to the mainventilation corridor formed by the Yangtze River whichpromotes the ventilation conditions in Hanyang

Capillary ventilation corridors are distributed in areaswith high ventilation costs and the areas with maximumventilation costs are divided into several subregions Cap-illary ventilation corridors cannot determine the overallconditions of regional ventilation but can effectively reducethe distribution of the maximum value of ventilation costsand can be used as a supplementary means for secondaryventilation corridors Due to the large amount of capillaryventilation corridors their locations are not marked on themap It is easy to determine that the number of capillaryventilation corridors in Hankou (Wuchang District Qing-shan District and Hongshan District) is significantly less

than in Wuchang (Qiaokou District Jianghan District andJiangrsquoan District)

-e ventilation cost distribution map enables the visual-ization of urban ventilation conditions For example blueindicates a low ventilation cost area and red indicates a highventilation cost area Relatively low-cost areas between highcosts are potential ventilation corridors-is facilitates a betterunderstanding of urban ventilation conditions on both localand global scales-e frontal area index λf was performed on alocal scale When considering the entire urban area due to thecomplexity of the buildings and the road network it will bedifficult to achieve the identification of ventilation corridors byvisual inspection of the buildings and the windward indexmap[30]-e results of the LCVCmethod cover the entire researcharea thus solving the problem of no-path passages in someareas in the traditional LCP method

54 Validation of the LCVC Method LST is related toventilation conditions When the ventilation conditions aregood the average surface is usually lower -e areas withpoor ventilation conditions have a higher average surfacetemperature and less heterogeneity than other areas do Toverify the accuracy of the urban ventilation analysis LST

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(a)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(b)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(c)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(d)

Figure 5 A wind-rose diagram for each season in Wuhan (a) Spring (b) Summer (c) Autumn (d) Winter

8 Complexity

inversion was performed on the Landsat 8 data (Figure 8) Bycombining the LSTmap with the summer urban ventilationanalysis results (Figure 7(b)) the LST values for differentlevels of ventilation costs were calculated (Table 4)

-e results show that the average LSTof the area with thebest ventilation conditions is 3419degC significantly lowerthan that of other areas -e average LST in the area with theworst ventilation conditions is 4053degC In addition the areawith the worst ventilation conditions has less heterogeneitythan other areas do and the LST standard deviation is 171As ventilation conditions improve the LST standard devi-ation continues to increase

55 Urban Ventilation Conditions and POIs Table 5 showsthe average ventilation costs for functional urban units -eaverage ventilation costs of outdoor open areas are obviouslylower than the others at only 0469 -e ventilation costs are

highest for public service units with a value of 0568 -is ismainly because these units are distributed in the main coreareas of the city-e ventilation costs for the residential unitsare 0559 in the second order which is different from theventilation resistance coefficient ranking Residential unitsare usually larger and have a higher building densityresulting in higher ventilation costs -e ventilation costs ofeducational institutions are 0554 similar to those for res-idential units -e majority of educational institutions areprimary secondary high school and training institutionsMost of these institutions are spatially associated withdensely populated areas and therefore have similar venti-lation costs to residential areas Residential units are themain places where people live and usually have a highpopulation density Higher ventilation costs easily form heatislands and are not conducive to the spread of pollutionwhich affects peoplersquos health and quality of life -erefore inurban planning the construction of outdoor open spaces in

0 20 40 km

N NNE NE ENE

E ESE SE SSE

S SSW SW WSW

W WNW NW NNW

Min Max

NVentilation cost (Minimax normalized)

Figure 6 Spatial distribution of LCVC in different wind directions

Complexity 9

Ventilation cost level0 10 20 km

(a) (b)

(c) (d)

Main ventilation pathSecondary ventilation path

1 2 3 4 5 6 7 8 9 10 N

Figure 7 Spatial distribution of ventilation during different seasons (a) Spring (b) Summer (c) Autumn (d) Winter

LST (degC)High 50

Low 270 10 km

N

5

Figure 8 Spatial distribution of LST in summer

10 Complexity

residential areas should be strengthened to improve theventilation environment

6 Discussion and Conclusion

Using the urban morphological characteristics identified byGIS technology this study established an LCVC method todescribe quantitatively the urban ventilation environment atthe city scale First the frontal area index of three-dimen-sional urban buildings representing the ventilation resis-tance coefficient was calculated using a morphologicalmethod Second the minimum cumulative ventilation costalgorithm was applied to draw the urban ventilation costmaps under different wind directions -en the wind fre-quency weighting was used to explore the urban ventilationenvironment in different seasons and the urban ventilationcorridors of different levels were analyzed Finally the re-sults of the summer ventilation analysis were verified usingthe LST retrieved from Landsat 8 data

-e analysis of the urban ventilation environmentbased on the morphology and GIS technology has theadvantage of high calculation efficiency In addition theresults of the analysis are highly compatible with otherspatial analysis and mapping technologies -is studyproposes that the cumulative costs of ventilation canbetter reflect the differences in urban ventilation

conditions than the ventilation resistance coefficient canIn contrast to the corridor extraction method based onempirical rules the proposed method can directly observethe dynamic characteristics of urban ventilation underdifferent wind directions and frequencies In contrast tothe least-cost path analysis method the results of theLCVC method cover the whole study area and the cor-ridor width and area can also be measured Howeverseveral issues require further study -e urban wind en-vironment is affected not only by ground roughness butalso by atmospheric environment and local temperaturefactors -erefore it is difficult to achieve an accurateanalysis of local wind speeds and directions from ananalysis of building form alone In addition it is difficultto obtain internal high-density real-time wind speed dataespecially wind speed measurements in a vertical direc-tion -is makes it difficult to improve the model pa-rameters -ese issues will be further studied

Data Availability

-e data used to support the findings of the study areavailable from the corresponding author upon request

Conflicts of Interest

-e authors declare that they have no conflicts of interest

Acknowledgments

-is study was supported by the National Key Research andDevelopment Program of China (Grant no2017YFB0503601) and China Postdoctoral Science Foun-dation (Grant no 2019M662726)

References

[1] H Wang G Wang J Qi et al ldquoScarcity-weighted fossil fuelfootprint of China at the provincial levelrdquo Applied Energyvol 258 2020

[2] L Jiang B Xue Z Ma L Yu B Huang and X Chen ldquoA life-cycle based co-benefits analysis of biomass pellet productionin Chinardquo Renewable Energy vol 154 pp 445ndash452 2020

[3] HWang H Schandl GWang and L Ma ldquoRegional materialflow accounts for China examining Chinarsquos natural resourceuse at the provincial and national levelrdquo Research andAnalysis vol 6 2019

[4] B-J He Z-Q Zhao L-D Shen H-B Wang and L-G LildquoAn approach to examining performances of coolhot sourcesin mitigatingenhancing land surface temperature underdifferent temperature backgrounds based on landsat 8 imagerdquoSustainable Cities and Society vol 44 pp 416ndash427 2019

[5] B-J He L Yang and M Ye ldquoStrategies for creating goodwind environment around Chinese residencesrdquo SustainableCities and Society vol 10 pp 174ndash183 2014

[6] H Akbari C Cartalis D Kolokotsa et al ldquoLocal climatechange and urban heat island mitigation techniquesndashthe stateof the artrdquo Journal of Civil Engineering and Managementvol 22 no 1 pp 1ndash16 2016

[7] D Zhou L Zhang L Hao G Sun Y Liu and C ZhuldquoSpatiotemporal trends of urban heat island effect along the

Table 5 Urban functional units and average ventilation costs

Reclassification Ventilation costPublic management and social organizations 0535Residential districts 0559Commerce 0519Factories 0483Companies 0545Public services 0568Everyday services 0545Hotels 0538Educational institutions 0554Medical institutions 0544Outdoor open areas 0469Transportation facilities 0541

Table 4 Statistical characteristics of LST at different levels ofventilation cost

Ventilationlevels

Temperature (degC)

Lowest Highest Range Average Standarddeviation

2 2928 5046 2118 3419 3643 2817 5259 2442 3496 3744 2967 5599 2632 3769 4055 2916 4947 2031 3882 3246 3189 5617 2428 3807 3247 3210 5024 1814 3795 3358 3285 5555 2270 4008 3029 3250 5470 2220 4012 26010 3434 5073 1639 4042 20511 3466 4683 1217 4053 171

Complexity 11

urban development intensity gradient in Chinardquo Science of9e Total Environment vol 544 pp 617ndash626 2016

[8] D Zhou S Zhao L Zhang G Sun and Y Liu ldquo-e footprintof urban heat island effect in Chinardquo Scientific Reports vol 5p 11160 2015

[9] K Zhang Y Chen and L Wu ldquoGrey spectrum analysis of airquality index and housing price in Handanrdquo Complexityvol 2019 2019

[10] Y Zhao Y Li and Q Chen ldquoAnalysis of a stochastic sus-ceptible-infective epidemic model in a polluted atmosphericenvironmentrdquo Complexity vol 2019 2019

[11] W Zhang C M Mak Z T Ai andWM Siu ldquoA study of theventilation and thermal comfort of the environment sur-rounding a new university building under constructionrdquoIndoor and Built Environment vol 21 no 4 pp 568ndash5822011

[12] R A W Albers P R Bosch B Blocken et al ldquoOverview ofchallenges and achievements in the climate adaptation ofcities and in the Climate Proof Cities programrdquo Building andEnvironment vol 83 pp 1ndash10 2015

[13] L W A van Hove C M J Jacobs B G HeusinkveldJ A Elbers B L van Driel and A A M Holtslag ldquoTemporaland spatial variability of urban heat island and thermalcomfort within the Rotterdam agglomerationrdquo Building andEnvironment vol 83 pp 91ndash103 2015

[14] A Guo J Yang X Xiao J Xia C Jin and X Li ldquoInfluences ofurban spatial form on urban heat island effects at the com-munity level in Chinardquo Sustainable Cities and Society vol 532020

[15] Z-Q Zhao B-J He L-G Li H-B Wang and A DarkoldquoProfile and concentric zonal analysis of relationships be-tween land useland cover and land surface temperature casestudy of Shenyang Chinardquo Energy and Buildings vol 155pp 282ndash295 2017

[16] E Ng ldquoPolicies and technical guidelines for urban planning ofhigh-density citiesmdashair ventilation assessment (AVA) ofHong Kongrdquo Building and Environment vol 44 no 7pp 1478ndash1488 2009

[17] B Chun and J-M Guldmann ldquoSpatial statistical analysis andsimulation of the urban heat island in high-density centralcitiesrdquo Landscape and Urban Planning vol 125 pp 76ndash882014

[18] Y He A Tablada and N H Wong ldquoA parametric study ofangular road patterns on pedestrian ventilation in high-density urban areasrdquo Building and Environment vol 151pp 251ndash267 2019

[19] J Yang Y Wang X Xiao C Jin J Xia and X Li ldquoSpatialdifferentiation of urban wind and thermal environment indifferent grid sizesrdquo Urban Climate vol 28 2019

[20] J Yang S Jin X Xiao et al ldquoLocal climate zone ventilationand urban land surface temperatures towards a performance-based and wind-sensitive planning proposal in megacitiesrdquoSustainable Cities and Society vol 47 2019

[21] Z Qiao L Liu Y Qin et al ldquo-e impact of urban renewal onland surface temperature changes a case study in the maincity of guangzhou Chinardquo Remote Sensing vol 12 no 5

[22] Z Qiao C Wu D Zhao et al ldquoDetermining the boundaryand probability of surface urban heat island footprint basedon a logistic modelrdquo Remote Sensing vol 11 no 11 2019

[23] Z Qiao X Xu M Zhao FWang and L Liu ldquo-e applicationof a binary division procedure to the classification of forestsubcategories using MODIS time-series data during 2000-2010 in Chinardquo International Journal of Remote Sensingvol 37 no 10 pp 2433ndash2450 2016

[24] M Bady S Kato T Takahashi and H Huang ldquoAn experi-mental investigation of the wind environment and air qualitywithin a densely populated urban street canyonrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 99 no 8pp 857ndash867 2011

[25] M Carpentieri P Salizzoni A Robins and L SoulhacldquoEvaluation of a neighbourhood scale street network dis-persion model through comparison with wind tunnel datardquoEnvironmental Modelling amp Software vol 37 pp 110ndash1242012

[26] K T Tse A U Weerasuriya X Zhang S Li andK C S Kwok ldquoPedestrian-level wind environment aroundisolated buildings under the influence of twisted wind flowsrdquoJournal of Wind Engineering and Industrial Aerodynamicsvol 162 pp 12ndash23 2017

[27] H Montazeri B Blocken and J L M Hensen ldquoEvaporativecooling by water spray systems CFD simulation experi-mental validation and sensitivity analysisrdquo Building andEnvironment vol 83 pp 129ndash141 2015

[28] X Wang and Y Li ldquoPredicting urban heat island circulationusing CFDrdquo Building and Environment vol 99 pp 82ndash972016

[29] K R Gautam L Rong G Zhang and M Abkar ldquoCom-parison of analysis methods for wind-driven cross ventilationthrough large openingsrdquo Building and Environment vol 154pp 375ndash388 2019

[30] M S Wong J E Nichol P H To and J Wang ldquoA simplemethod for designation of urban ventilation corridors and itsapplication to urban heat island analysisrdquo Building and En-vironment vol 45 no 8 pp 1880ndash1889 2010

[31] C-M Hsieh andH-C Huang ldquoMitigating urban heat islandsa method to identify potential wind corridor for cooling andventilationrdquo Computers Environment and Urban Systemsvol 57 pp 130ndash143 2016

[32] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor andBuilt Environment vol 26 no 3 pp 422ndash437 2017

[33] Z Qiao X Xu F Wu et al ldquoUrban ventilation networkmodel a case study of the core zone of capital function inBeijing metropolitan areardquo Journal of Cleaner Productionvol 168 pp 526ndash535 2017

[34] J Yang J Sun Q Ge and X Li ldquoAssessing the impacts ofurbanization-associated green space on urban land surfacetemperature a case study of Dalian Chinardquo Urban Forestry ampUrban Greening vol 22 pp 1ndash10 2017

[35] J Srebric M Heidarinejad and J Liu ldquoBuilding neighbor-hood emerging properties and their impacts on multi-scalemodeling of building energy and airflowsrdquo Building andEnvironment vol 91 pp 246ndash262 2015

[36] X Yang and Y Li ldquo-e impact of building density andbuilding height heterogeneity on average urban albedo andstreet surface temperaturerdquo Building and Environmentvol 90 pp 146ndash156 2015

[37] F Peng M S Wong Y Wan and J E Nichol ldquoModeling ofurban wind ventilation using high resolution airborne LiDARdatardquo Computers Environment and Urban Systems vol 64pp 81ndash90 2017

[38] C Yuan C Ren and E Ng ldquoGIS-based surface roughnessevaluation in the urban planning system to improve the windenvironmentmdasha study in Wuhan Chinardquo Urban Climatevol 10 pp 585ndash593 2014

12 Complexity

[39] C S B Grimmond and T R Oke ldquoAerodynamic propertiesof urban areas derived from analysis of surface formrdquo Journalof Applied Meteorology vol 38 no 9 pp 1262ndash1292 1999

[40] S J Burian M J Brown and S P Linger ldquoMorphologicalanalyses using 3D building databases los Angeles CaliforniardquoLos Alamos National Laboratory vol 74 2002

[41] T Gal and J Unger ldquoDetection of ventilation paths usinghigh-resolution roughness parameter mapping in a largeurban areardquo Building and Environment vol 44 no 1pp 198ndash206 2009

[42] E Ng C Yuan L Chen C Ren and J C H Fung ldquoIm-proving the wind environment in high-density cities by un-derstanding urban morphology and surface roughness astudy in Hong Kongrdquo Landscape and Urban Planningvol 101 no 1 pp 59ndash74 2011

[43] M S Wong and J E Nichol ldquoSpatial variability of frontal areaindex and its relationship with urban heat island intensityrdquoInternational Journal of Remote Sensing vol 34 no 3pp 885ndash896 2013

[44] RWMacdonald R F Griffiths and D J Hall ldquoAn improvedmethod for the estimation of surface roughness of obstaclearraysrdquo Atmospheric Environment vol 32 no 11 pp 1857ndash1864 1998

[45] M Bottema and P G Mestayer ldquoUrban roughness map-pingmdashvalidation techniques and some first resultsrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 74ndash76pp 163ndash173 1998

[46] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor BuiltEnvironment vol 26 no 3 pp 422ndash437 2017

Complexity 13

0 105 km

Height (m)gt10024 ndash10010 ndash 24lt10

N

Figure 3 3D spatial distribution of buildings

Table 2 Height of buildings in the study area

Height (m) Number Percentage of all buildings () Building area (km2) Percentage of total building area ()Hle 10 57720 5696 3214 489110ltHle 24 33825 3338 2528 384624ltHle 100 9663 954 820 1248100ltH 120 012 010 015

0 105 Km

High 1

Low 0

ResistanceCoefficients

N

Figure 4 Resistance coefficients map

6 Complexity

2018 (Figure 5) -e main wind direction is northerly (in-cluding N NE NW NNW and WNW) followed bysoutheasterly (including E SE SSE and ESE) Affected bythe monsoon climate the dominant wind direction variesfrom season to season -e prevailing winds affected by thenorthern cold in the autumn and winter are mainly northwinds (including N NE NW and NNE) followed by a feweast winds (including E NE ENE and SE) -e meteoro-logical changes in spring are complicated and the prevailingwinds are still mainly northerly followed by southeasterly(including ESE and SSE) In summer the northerly andsouthwesterly winds (SW WSW and SSW) are similar infrequency and accompanied by a few southeasterly winds

53 Results of LCVC Analysis -e difference in wind di-rection frequency indicates the variation in the influence ofthe LCVC in each wind direction Given a time interval theaverage LCVC is obtained by weighting the LCVC of eachwind direction with the corresponding frequency

Figure 6 shows the results for the LCVC analysis of 16wind directions in the urban center of Wuhan In order tomaintain comparability the calculation results of differentwind directions use the same classifications and map colorsRed indicates high ventilation costs and blue indicates lowventilation costs -e coherent narrow passages formed bythe low-cost areas are the ventilation corridors Generallyspeaking the ventilation costs around the city and in openareas are less than those in densely populated areas Affectedby different wind directions the spatial distribution ofventilation corridors in the city takes on different shapesand the directionality of the corridors is consistent with thewind direction For the wind directions of NNE NE SSEand SW the Yangtze River is the most important urbanventilation corridor For the wind directions of E ESE Wand WNW the interconnected open space formed by theHan River and the East Lake forms the main ventilationcorridor

-e spatial distribution of the LCVC in the differentseasons in Wuhan is shown in Figure 7 Seasonal LCVCs arenormalized and divided into 10 levels Colors from blue to

red indicate higher ventilation costs Overall the distribu-tion patterns of the LCVCs are very similar in the fourseasons-e dominant wind direction ofWuhan throughoutthe year is NNE (N NNE and NE) -e Yangtze River runsthrough the city and forms the main NE-SW ventilationcorridor with its open waters and north-south connectivityTwo islands with high ventilation costs centered on Hankouand Wuchang are formed along the east and west sides ofthe Yangtze River -e areas of the two are equivalent butthe Hankou side presents a single-core model and the area ofhigh-value areas is large By contrast the Wuchang sidepresents a multicore model and the area of high-value areasis small and scattered -e East Lake has become anotherimportant urban ventilation corridor due to its large waterarea and its distribution deep into the cityrsquos hinterland-ere are also some differences in the distributions ofLCVCs in different seasons In summer due to the highwind frequency in an N-S direction the ventilation corridorformed by the Yangtze River is more obvious than in otherseasons In winter the width of the ventilation corridor inthe north of the East Lake is slightly narrower than in otherseasons At the same time the width of the ventilationcorridors formed by the middle-level LCVC area (from 5 to7) was also narrower than in other seasons or even dis-appeared In the spring and autumn when the frequency ofthe south wind is slightly higher than in winter and summerthe ventilation corridors running in an S-N is more obvious

Summarized below are the details of the spatial distri-bution pattern of ventilation costs and the three levels ofventilation corridors identified namely the main second-ary and capillary levels

-e main ventilation corridors lay the foundation forurban ventilation -e distribution of the main ventilationcorridors has a high spatial correlation with natural geo-graphical features (such as rivers and lakes)-e results showthat there are two main ventilation corridors in centralWuhan One is the NE-SW corridor formed by the YangtzeRiver and the other is the S-N and NW-SE corridor formedby the East Lake -e secondary ventilation corridors aremostly connected to the main ventilation corridors and

Table 3 Classification of urban functional elements and average resistance coefficients in Wuhan

Reclassification Description ResistancePublic management and socialorganizations

Government welfare agency revenue financial bureau jurisdiction bureauadministration for industry and village fire control police station court etc 0199

Residential districts Residential district villa dormitory etc 0215Commerce Commercial building supermarket shopping center market small store grocery etc 0214Factories Various types of manufacturing factory and technology enterprise 0184

Companies Company law office accountant office audit office tourist agency insurance companyconsulting corporation etc 0243

Public services Bank post office telecom bureau postal bank etc 0251Everyday services Hairdressing public bathroom laundry tailor car repair or wash shop etc 0224Hotels Hotel restaurant snack cafe teahouse etc 0212

Educational institutions University college middle school primary school kindergarten training center privateschool etc 0216

Medical institutions Hospital childrenrsquos hospital clinic emergency center psychiatric hospital eye hospitalbone hospital dental clinic pharmacy etc 0217

Outdoor open areas Park square botanic garden amusement park resort agricultural center etc 0111Transportation facilities Railway station bus station coach station parking lot etc 0209

Complexity 7

their distribution is affected by the main ventilation corri-dors-e secondary ventilation corridors spatially divide thehigh ventilation cost area into several subareas which playan important part in promoting urban ventilationDepending on the main ventilation corridors of the EastLake many N-S or SE-NW secondary ventilation corridorsare formed in Wuchang District Qingshan District andHongshan District -ese ventilation corridors promoteventilation in these three areas -e secondary ventilationcorridor formed by the Han River connects to the mainventilation corridor formed by the Yangtze River whichpromotes the ventilation conditions in Hanyang

Capillary ventilation corridors are distributed in areaswith high ventilation costs and the areas with maximumventilation costs are divided into several subregions Cap-illary ventilation corridors cannot determine the overallconditions of regional ventilation but can effectively reducethe distribution of the maximum value of ventilation costsand can be used as a supplementary means for secondaryventilation corridors Due to the large amount of capillaryventilation corridors their locations are not marked on themap It is easy to determine that the number of capillaryventilation corridors in Hankou (Wuchang District Qing-shan District and Hongshan District) is significantly less

than in Wuchang (Qiaokou District Jianghan District andJiangrsquoan District)

-e ventilation cost distribution map enables the visual-ization of urban ventilation conditions For example blueindicates a low ventilation cost area and red indicates a highventilation cost area Relatively low-cost areas between highcosts are potential ventilation corridors-is facilitates a betterunderstanding of urban ventilation conditions on both localand global scales-e frontal area index λf was performed on alocal scale When considering the entire urban area due to thecomplexity of the buildings and the road network it will bedifficult to achieve the identification of ventilation corridors byvisual inspection of the buildings and the windward indexmap[30]-e results of the LCVCmethod cover the entire researcharea thus solving the problem of no-path passages in someareas in the traditional LCP method

54 Validation of the LCVC Method LST is related toventilation conditions When the ventilation conditions aregood the average surface is usually lower -e areas withpoor ventilation conditions have a higher average surfacetemperature and less heterogeneity than other areas do Toverify the accuracy of the urban ventilation analysis LST

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(a)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(b)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(c)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(d)

Figure 5 A wind-rose diagram for each season in Wuhan (a) Spring (b) Summer (c) Autumn (d) Winter

8 Complexity

inversion was performed on the Landsat 8 data (Figure 8) Bycombining the LSTmap with the summer urban ventilationanalysis results (Figure 7(b)) the LST values for differentlevels of ventilation costs were calculated (Table 4)

-e results show that the average LSTof the area with thebest ventilation conditions is 3419degC significantly lowerthan that of other areas -e average LST in the area with theworst ventilation conditions is 4053degC In addition the areawith the worst ventilation conditions has less heterogeneitythan other areas do and the LST standard deviation is 171As ventilation conditions improve the LST standard devi-ation continues to increase

55 Urban Ventilation Conditions and POIs Table 5 showsthe average ventilation costs for functional urban units -eaverage ventilation costs of outdoor open areas are obviouslylower than the others at only 0469 -e ventilation costs are

highest for public service units with a value of 0568 -is ismainly because these units are distributed in the main coreareas of the city-e ventilation costs for the residential unitsare 0559 in the second order which is different from theventilation resistance coefficient ranking Residential unitsare usually larger and have a higher building densityresulting in higher ventilation costs -e ventilation costs ofeducational institutions are 0554 similar to those for res-idential units -e majority of educational institutions areprimary secondary high school and training institutionsMost of these institutions are spatially associated withdensely populated areas and therefore have similar venti-lation costs to residential areas Residential units are themain places where people live and usually have a highpopulation density Higher ventilation costs easily form heatislands and are not conducive to the spread of pollutionwhich affects peoplersquos health and quality of life -erefore inurban planning the construction of outdoor open spaces in

0 20 40 km

N NNE NE ENE

E ESE SE SSE

S SSW SW WSW

W WNW NW NNW

Min Max

NVentilation cost (Minimax normalized)

Figure 6 Spatial distribution of LCVC in different wind directions

Complexity 9

Ventilation cost level0 10 20 km

(a) (b)

(c) (d)

Main ventilation pathSecondary ventilation path

1 2 3 4 5 6 7 8 9 10 N

Figure 7 Spatial distribution of ventilation during different seasons (a) Spring (b) Summer (c) Autumn (d) Winter

LST (degC)High 50

Low 270 10 km

N

5

Figure 8 Spatial distribution of LST in summer

10 Complexity

residential areas should be strengthened to improve theventilation environment

6 Discussion and Conclusion

Using the urban morphological characteristics identified byGIS technology this study established an LCVC method todescribe quantitatively the urban ventilation environment atthe city scale First the frontal area index of three-dimen-sional urban buildings representing the ventilation resis-tance coefficient was calculated using a morphologicalmethod Second the minimum cumulative ventilation costalgorithm was applied to draw the urban ventilation costmaps under different wind directions -en the wind fre-quency weighting was used to explore the urban ventilationenvironment in different seasons and the urban ventilationcorridors of different levels were analyzed Finally the re-sults of the summer ventilation analysis were verified usingthe LST retrieved from Landsat 8 data

-e analysis of the urban ventilation environmentbased on the morphology and GIS technology has theadvantage of high calculation efficiency In addition theresults of the analysis are highly compatible with otherspatial analysis and mapping technologies -is studyproposes that the cumulative costs of ventilation canbetter reflect the differences in urban ventilation

conditions than the ventilation resistance coefficient canIn contrast to the corridor extraction method based onempirical rules the proposed method can directly observethe dynamic characteristics of urban ventilation underdifferent wind directions and frequencies In contrast tothe least-cost path analysis method the results of theLCVC method cover the whole study area and the cor-ridor width and area can also be measured Howeverseveral issues require further study -e urban wind en-vironment is affected not only by ground roughness butalso by atmospheric environment and local temperaturefactors -erefore it is difficult to achieve an accurateanalysis of local wind speeds and directions from ananalysis of building form alone In addition it is difficultto obtain internal high-density real-time wind speed dataespecially wind speed measurements in a vertical direc-tion -is makes it difficult to improve the model pa-rameters -ese issues will be further studied

Data Availability

-e data used to support the findings of the study areavailable from the corresponding author upon request

Conflicts of Interest

-e authors declare that they have no conflicts of interest

Acknowledgments

-is study was supported by the National Key Research andDevelopment Program of China (Grant no2017YFB0503601) and China Postdoctoral Science Foun-dation (Grant no 2019M662726)

References

[1] H Wang G Wang J Qi et al ldquoScarcity-weighted fossil fuelfootprint of China at the provincial levelrdquo Applied Energyvol 258 2020

[2] L Jiang B Xue Z Ma L Yu B Huang and X Chen ldquoA life-cycle based co-benefits analysis of biomass pellet productionin Chinardquo Renewable Energy vol 154 pp 445ndash452 2020

[3] HWang H Schandl GWang and L Ma ldquoRegional materialflow accounts for China examining Chinarsquos natural resourceuse at the provincial and national levelrdquo Research andAnalysis vol 6 2019

[4] B-J He Z-Q Zhao L-D Shen H-B Wang and L-G LildquoAn approach to examining performances of coolhot sourcesin mitigatingenhancing land surface temperature underdifferent temperature backgrounds based on landsat 8 imagerdquoSustainable Cities and Society vol 44 pp 416ndash427 2019

[5] B-J He L Yang and M Ye ldquoStrategies for creating goodwind environment around Chinese residencesrdquo SustainableCities and Society vol 10 pp 174ndash183 2014

[6] H Akbari C Cartalis D Kolokotsa et al ldquoLocal climatechange and urban heat island mitigation techniquesndashthe stateof the artrdquo Journal of Civil Engineering and Managementvol 22 no 1 pp 1ndash16 2016

[7] D Zhou L Zhang L Hao G Sun Y Liu and C ZhuldquoSpatiotemporal trends of urban heat island effect along the

Table 5 Urban functional units and average ventilation costs

Reclassification Ventilation costPublic management and social organizations 0535Residential districts 0559Commerce 0519Factories 0483Companies 0545Public services 0568Everyday services 0545Hotels 0538Educational institutions 0554Medical institutions 0544Outdoor open areas 0469Transportation facilities 0541

Table 4 Statistical characteristics of LST at different levels ofventilation cost

Ventilationlevels

Temperature (degC)

Lowest Highest Range Average Standarddeviation

2 2928 5046 2118 3419 3643 2817 5259 2442 3496 3744 2967 5599 2632 3769 4055 2916 4947 2031 3882 3246 3189 5617 2428 3807 3247 3210 5024 1814 3795 3358 3285 5555 2270 4008 3029 3250 5470 2220 4012 26010 3434 5073 1639 4042 20511 3466 4683 1217 4053 171

Complexity 11

urban development intensity gradient in Chinardquo Science of9e Total Environment vol 544 pp 617ndash626 2016

[8] D Zhou S Zhao L Zhang G Sun and Y Liu ldquo-e footprintof urban heat island effect in Chinardquo Scientific Reports vol 5p 11160 2015

[9] K Zhang Y Chen and L Wu ldquoGrey spectrum analysis of airquality index and housing price in Handanrdquo Complexityvol 2019 2019

[10] Y Zhao Y Li and Q Chen ldquoAnalysis of a stochastic sus-ceptible-infective epidemic model in a polluted atmosphericenvironmentrdquo Complexity vol 2019 2019

[11] W Zhang C M Mak Z T Ai andWM Siu ldquoA study of theventilation and thermal comfort of the environment sur-rounding a new university building under constructionrdquoIndoor and Built Environment vol 21 no 4 pp 568ndash5822011

[12] R A W Albers P R Bosch B Blocken et al ldquoOverview ofchallenges and achievements in the climate adaptation ofcities and in the Climate Proof Cities programrdquo Building andEnvironment vol 83 pp 1ndash10 2015

[13] L W A van Hove C M J Jacobs B G HeusinkveldJ A Elbers B L van Driel and A A M Holtslag ldquoTemporaland spatial variability of urban heat island and thermalcomfort within the Rotterdam agglomerationrdquo Building andEnvironment vol 83 pp 91ndash103 2015

[14] A Guo J Yang X Xiao J Xia C Jin and X Li ldquoInfluences ofurban spatial form on urban heat island effects at the com-munity level in Chinardquo Sustainable Cities and Society vol 532020

[15] Z-Q Zhao B-J He L-G Li H-B Wang and A DarkoldquoProfile and concentric zonal analysis of relationships be-tween land useland cover and land surface temperature casestudy of Shenyang Chinardquo Energy and Buildings vol 155pp 282ndash295 2017

[16] E Ng ldquoPolicies and technical guidelines for urban planning ofhigh-density citiesmdashair ventilation assessment (AVA) ofHong Kongrdquo Building and Environment vol 44 no 7pp 1478ndash1488 2009

[17] B Chun and J-M Guldmann ldquoSpatial statistical analysis andsimulation of the urban heat island in high-density centralcitiesrdquo Landscape and Urban Planning vol 125 pp 76ndash882014

[18] Y He A Tablada and N H Wong ldquoA parametric study ofangular road patterns on pedestrian ventilation in high-density urban areasrdquo Building and Environment vol 151pp 251ndash267 2019

[19] J Yang Y Wang X Xiao C Jin J Xia and X Li ldquoSpatialdifferentiation of urban wind and thermal environment indifferent grid sizesrdquo Urban Climate vol 28 2019

[20] J Yang S Jin X Xiao et al ldquoLocal climate zone ventilationand urban land surface temperatures towards a performance-based and wind-sensitive planning proposal in megacitiesrdquoSustainable Cities and Society vol 47 2019

[21] Z Qiao L Liu Y Qin et al ldquo-e impact of urban renewal onland surface temperature changes a case study in the maincity of guangzhou Chinardquo Remote Sensing vol 12 no 5

[22] Z Qiao C Wu D Zhao et al ldquoDetermining the boundaryand probability of surface urban heat island footprint basedon a logistic modelrdquo Remote Sensing vol 11 no 11 2019

[23] Z Qiao X Xu M Zhao FWang and L Liu ldquo-e applicationof a binary division procedure to the classification of forestsubcategories using MODIS time-series data during 2000-2010 in Chinardquo International Journal of Remote Sensingvol 37 no 10 pp 2433ndash2450 2016

[24] M Bady S Kato T Takahashi and H Huang ldquoAn experi-mental investigation of the wind environment and air qualitywithin a densely populated urban street canyonrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 99 no 8pp 857ndash867 2011

[25] M Carpentieri P Salizzoni A Robins and L SoulhacldquoEvaluation of a neighbourhood scale street network dis-persion model through comparison with wind tunnel datardquoEnvironmental Modelling amp Software vol 37 pp 110ndash1242012

[26] K T Tse A U Weerasuriya X Zhang S Li andK C S Kwok ldquoPedestrian-level wind environment aroundisolated buildings under the influence of twisted wind flowsrdquoJournal of Wind Engineering and Industrial Aerodynamicsvol 162 pp 12ndash23 2017

[27] H Montazeri B Blocken and J L M Hensen ldquoEvaporativecooling by water spray systems CFD simulation experi-mental validation and sensitivity analysisrdquo Building andEnvironment vol 83 pp 129ndash141 2015

[28] X Wang and Y Li ldquoPredicting urban heat island circulationusing CFDrdquo Building and Environment vol 99 pp 82ndash972016

[29] K R Gautam L Rong G Zhang and M Abkar ldquoCom-parison of analysis methods for wind-driven cross ventilationthrough large openingsrdquo Building and Environment vol 154pp 375ndash388 2019

[30] M S Wong J E Nichol P H To and J Wang ldquoA simplemethod for designation of urban ventilation corridors and itsapplication to urban heat island analysisrdquo Building and En-vironment vol 45 no 8 pp 1880ndash1889 2010

[31] C-M Hsieh andH-C Huang ldquoMitigating urban heat islandsa method to identify potential wind corridor for cooling andventilationrdquo Computers Environment and Urban Systemsvol 57 pp 130ndash143 2016

[32] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor andBuilt Environment vol 26 no 3 pp 422ndash437 2017

[33] Z Qiao X Xu F Wu et al ldquoUrban ventilation networkmodel a case study of the core zone of capital function inBeijing metropolitan areardquo Journal of Cleaner Productionvol 168 pp 526ndash535 2017

[34] J Yang J Sun Q Ge and X Li ldquoAssessing the impacts ofurbanization-associated green space on urban land surfacetemperature a case study of Dalian Chinardquo Urban Forestry ampUrban Greening vol 22 pp 1ndash10 2017

[35] J Srebric M Heidarinejad and J Liu ldquoBuilding neighbor-hood emerging properties and their impacts on multi-scalemodeling of building energy and airflowsrdquo Building andEnvironment vol 91 pp 246ndash262 2015

[36] X Yang and Y Li ldquo-e impact of building density andbuilding height heterogeneity on average urban albedo andstreet surface temperaturerdquo Building and Environmentvol 90 pp 146ndash156 2015

[37] F Peng M S Wong Y Wan and J E Nichol ldquoModeling ofurban wind ventilation using high resolution airborne LiDARdatardquo Computers Environment and Urban Systems vol 64pp 81ndash90 2017

[38] C Yuan C Ren and E Ng ldquoGIS-based surface roughnessevaluation in the urban planning system to improve the windenvironmentmdasha study in Wuhan Chinardquo Urban Climatevol 10 pp 585ndash593 2014

12 Complexity

[39] C S B Grimmond and T R Oke ldquoAerodynamic propertiesof urban areas derived from analysis of surface formrdquo Journalof Applied Meteorology vol 38 no 9 pp 1262ndash1292 1999

[40] S J Burian M J Brown and S P Linger ldquoMorphologicalanalyses using 3D building databases los Angeles CaliforniardquoLos Alamos National Laboratory vol 74 2002

[41] T Gal and J Unger ldquoDetection of ventilation paths usinghigh-resolution roughness parameter mapping in a largeurban areardquo Building and Environment vol 44 no 1pp 198ndash206 2009

[42] E Ng C Yuan L Chen C Ren and J C H Fung ldquoIm-proving the wind environment in high-density cities by un-derstanding urban morphology and surface roughness astudy in Hong Kongrdquo Landscape and Urban Planningvol 101 no 1 pp 59ndash74 2011

[43] M S Wong and J E Nichol ldquoSpatial variability of frontal areaindex and its relationship with urban heat island intensityrdquoInternational Journal of Remote Sensing vol 34 no 3pp 885ndash896 2013

[44] RWMacdonald R F Griffiths and D J Hall ldquoAn improvedmethod for the estimation of surface roughness of obstaclearraysrdquo Atmospheric Environment vol 32 no 11 pp 1857ndash1864 1998

[45] M Bottema and P G Mestayer ldquoUrban roughness map-pingmdashvalidation techniques and some first resultsrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 74ndash76pp 163ndash173 1998

[46] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor BuiltEnvironment vol 26 no 3 pp 422ndash437 2017

Complexity 13

2018 (Figure 5) -e main wind direction is northerly (in-cluding N NE NW NNW and WNW) followed bysoutheasterly (including E SE SSE and ESE) Affected bythe monsoon climate the dominant wind direction variesfrom season to season -e prevailing winds affected by thenorthern cold in the autumn and winter are mainly northwinds (including N NE NW and NNE) followed by a feweast winds (including E NE ENE and SE) -e meteoro-logical changes in spring are complicated and the prevailingwinds are still mainly northerly followed by southeasterly(including ESE and SSE) In summer the northerly andsouthwesterly winds (SW WSW and SSW) are similar infrequency and accompanied by a few southeasterly winds

53 Results of LCVC Analysis -e difference in wind di-rection frequency indicates the variation in the influence ofthe LCVC in each wind direction Given a time interval theaverage LCVC is obtained by weighting the LCVC of eachwind direction with the corresponding frequency

Figure 6 shows the results for the LCVC analysis of 16wind directions in the urban center of Wuhan In order tomaintain comparability the calculation results of differentwind directions use the same classifications and map colorsRed indicates high ventilation costs and blue indicates lowventilation costs -e coherent narrow passages formed bythe low-cost areas are the ventilation corridors Generallyspeaking the ventilation costs around the city and in openareas are less than those in densely populated areas Affectedby different wind directions the spatial distribution ofventilation corridors in the city takes on different shapesand the directionality of the corridors is consistent with thewind direction For the wind directions of NNE NE SSEand SW the Yangtze River is the most important urbanventilation corridor For the wind directions of E ESE Wand WNW the interconnected open space formed by theHan River and the East Lake forms the main ventilationcorridor

-e spatial distribution of the LCVC in the differentseasons in Wuhan is shown in Figure 7 Seasonal LCVCs arenormalized and divided into 10 levels Colors from blue to

red indicate higher ventilation costs Overall the distribu-tion patterns of the LCVCs are very similar in the fourseasons-e dominant wind direction ofWuhan throughoutthe year is NNE (N NNE and NE) -e Yangtze River runsthrough the city and forms the main NE-SW ventilationcorridor with its open waters and north-south connectivityTwo islands with high ventilation costs centered on Hankouand Wuchang are formed along the east and west sides ofthe Yangtze River -e areas of the two are equivalent butthe Hankou side presents a single-core model and the area ofhigh-value areas is large By contrast the Wuchang sidepresents a multicore model and the area of high-value areasis small and scattered -e East Lake has become anotherimportant urban ventilation corridor due to its large waterarea and its distribution deep into the cityrsquos hinterland-ere are also some differences in the distributions ofLCVCs in different seasons In summer due to the highwind frequency in an N-S direction the ventilation corridorformed by the Yangtze River is more obvious than in otherseasons In winter the width of the ventilation corridor inthe north of the East Lake is slightly narrower than in otherseasons At the same time the width of the ventilationcorridors formed by the middle-level LCVC area (from 5 to7) was also narrower than in other seasons or even dis-appeared In the spring and autumn when the frequency ofthe south wind is slightly higher than in winter and summerthe ventilation corridors running in an S-N is more obvious

Summarized below are the details of the spatial distri-bution pattern of ventilation costs and the three levels ofventilation corridors identified namely the main second-ary and capillary levels

-e main ventilation corridors lay the foundation forurban ventilation -e distribution of the main ventilationcorridors has a high spatial correlation with natural geo-graphical features (such as rivers and lakes)-e results showthat there are two main ventilation corridors in centralWuhan One is the NE-SW corridor formed by the YangtzeRiver and the other is the S-N and NW-SE corridor formedby the East Lake -e secondary ventilation corridors aremostly connected to the main ventilation corridors and

Table 3 Classification of urban functional elements and average resistance coefficients in Wuhan

Reclassification Description ResistancePublic management and socialorganizations

Government welfare agency revenue financial bureau jurisdiction bureauadministration for industry and village fire control police station court etc 0199

Residential districts Residential district villa dormitory etc 0215Commerce Commercial building supermarket shopping center market small store grocery etc 0214Factories Various types of manufacturing factory and technology enterprise 0184

Companies Company law office accountant office audit office tourist agency insurance companyconsulting corporation etc 0243

Public services Bank post office telecom bureau postal bank etc 0251Everyday services Hairdressing public bathroom laundry tailor car repair or wash shop etc 0224Hotels Hotel restaurant snack cafe teahouse etc 0212

Educational institutions University college middle school primary school kindergarten training center privateschool etc 0216

Medical institutions Hospital childrenrsquos hospital clinic emergency center psychiatric hospital eye hospitalbone hospital dental clinic pharmacy etc 0217

Outdoor open areas Park square botanic garden amusement park resort agricultural center etc 0111Transportation facilities Railway station bus station coach station parking lot etc 0209

Complexity 7

their distribution is affected by the main ventilation corri-dors-e secondary ventilation corridors spatially divide thehigh ventilation cost area into several subareas which playan important part in promoting urban ventilationDepending on the main ventilation corridors of the EastLake many N-S or SE-NW secondary ventilation corridorsare formed in Wuchang District Qingshan District andHongshan District -ese ventilation corridors promoteventilation in these three areas -e secondary ventilationcorridor formed by the Han River connects to the mainventilation corridor formed by the Yangtze River whichpromotes the ventilation conditions in Hanyang

Capillary ventilation corridors are distributed in areaswith high ventilation costs and the areas with maximumventilation costs are divided into several subregions Cap-illary ventilation corridors cannot determine the overallconditions of regional ventilation but can effectively reducethe distribution of the maximum value of ventilation costsand can be used as a supplementary means for secondaryventilation corridors Due to the large amount of capillaryventilation corridors their locations are not marked on themap It is easy to determine that the number of capillaryventilation corridors in Hankou (Wuchang District Qing-shan District and Hongshan District) is significantly less

than in Wuchang (Qiaokou District Jianghan District andJiangrsquoan District)

-e ventilation cost distribution map enables the visual-ization of urban ventilation conditions For example blueindicates a low ventilation cost area and red indicates a highventilation cost area Relatively low-cost areas between highcosts are potential ventilation corridors-is facilitates a betterunderstanding of urban ventilation conditions on both localand global scales-e frontal area index λf was performed on alocal scale When considering the entire urban area due to thecomplexity of the buildings and the road network it will bedifficult to achieve the identification of ventilation corridors byvisual inspection of the buildings and the windward indexmap[30]-e results of the LCVCmethod cover the entire researcharea thus solving the problem of no-path passages in someareas in the traditional LCP method

54 Validation of the LCVC Method LST is related toventilation conditions When the ventilation conditions aregood the average surface is usually lower -e areas withpoor ventilation conditions have a higher average surfacetemperature and less heterogeneity than other areas do Toverify the accuracy of the urban ventilation analysis LST

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(a)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(b)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(c)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(d)

Figure 5 A wind-rose diagram for each season in Wuhan (a) Spring (b) Summer (c) Autumn (d) Winter

8 Complexity

inversion was performed on the Landsat 8 data (Figure 8) Bycombining the LSTmap with the summer urban ventilationanalysis results (Figure 7(b)) the LST values for differentlevels of ventilation costs were calculated (Table 4)

-e results show that the average LSTof the area with thebest ventilation conditions is 3419degC significantly lowerthan that of other areas -e average LST in the area with theworst ventilation conditions is 4053degC In addition the areawith the worst ventilation conditions has less heterogeneitythan other areas do and the LST standard deviation is 171As ventilation conditions improve the LST standard devi-ation continues to increase

55 Urban Ventilation Conditions and POIs Table 5 showsthe average ventilation costs for functional urban units -eaverage ventilation costs of outdoor open areas are obviouslylower than the others at only 0469 -e ventilation costs are

highest for public service units with a value of 0568 -is ismainly because these units are distributed in the main coreareas of the city-e ventilation costs for the residential unitsare 0559 in the second order which is different from theventilation resistance coefficient ranking Residential unitsare usually larger and have a higher building densityresulting in higher ventilation costs -e ventilation costs ofeducational institutions are 0554 similar to those for res-idential units -e majority of educational institutions areprimary secondary high school and training institutionsMost of these institutions are spatially associated withdensely populated areas and therefore have similar venti-lation costs to residential areas Residential units are themain places where people live and usually have a highpopulation density Higher ventilation costs easily form heatislands and are not conducive to the spread of pollutionwhich affects peoplersquos health and quality of life -erefore inurban planning the construction of outdoor open spaces in

0 20 40 km

N NNE NE ENE

E ESE SE SSE

S SSW SW WSW

W WNW NW NNW

Min Max

NVentilation cost (Minimax normalized)

Figure 6 Spatial distribution of LCVC in different wind directions

Complexity 9

Ventilation cost level0 10 20 km

(a) (b)

(c) (d)

Main ventilation pathSecondary ventilation path

1 2 3 4 5 6 7 8 9 10 N

Figure 7 Spatial distribution of ventilation during different seasons (a) Spring (b) Summer (c) Autumn (d) Winter

LST (degC)High 50

Low 270 10 km

N

5

Figure 8 Spatial distribution of LST in summer

10 Complexity

residential areas should be strengthened to improve theventilation environment

6 Discussion and Conclusion

Using the urban morphological characteristics identified byGIS technology this study established an LCVC method todescribe quantitatively the urban ventilation environment atthe city scale First the frontal area index of three-dimen-sional urban buildings representing the ventilation resis-tance coefficient was calculated using a morphologicalmethod Second the minimum cumulative ventilation costalgorithm was applied to draw the urban ventilation costmaps under different wind directions -en the wind fre-quency weighting was used to explore the urban ventilationenvironment in different seasons and the urban ventilationcorridors of different levels were analyzed Finally the re-sults of the summer ventilation analysis were verified usingthe LST retrieved from Landsat 8 data

-e analysis of the urban ventilation environmentbased on the morphology and GIS technology has theadvantage of high calculation efficiency In addition theresults of the analysis are highly compatible with otherspatial analysis and mapping technologies -is studyproposes that the cumulative costs of ventilation canbetter reflect the differences in urban ventilation

conditions than the ventilation resistance coefficient canIn contrast to the corridor extraction method based onempirical rules the proposed method can directly observethe dynamic characteristics of urban ventilation underdifferent wind directions and frequencies In contrast tothe least-cost path analysis method the results of theLCVC method cover the whole study area and the cor-ridor width and area can also be measured Howeverseveral issues require further study -e urban wind en-vironment is affected not only by ground roughness butalso by atmospheric environment and local temperaturefactors -erefore it is difficult to achieve an accurateanalysis of local wind speeds and directions from ananalysis of building form alone In addition it is difficultto obtain internal high-density real-time wind speed dataespecially wind speed measurements in a vertical direc-tion -is makes it difficult to improve the model pa-rameters -ese issues will be further studied

Data Availability

-e data used to support the findings of the study areavailable from the corresponding author upon request

Conflicts of Interest

-e authors declare that they have no conflicts of interest

Acknowledgments

-is study was supported by the National Key Research andDevelopment Program of China (Grant no2017YFB0503601) and China Postdoctoral Science Foun-dation (Grant no 2019M662726)

References

[1] H Wang G Wang J Qi et al ldquoScarcity-weighted fossil fuelfootprint of China at the provincial levelrdquo Applied Energyvol 258 2020

[2] L Jiang B Xue Z Ma L Yu B Huang and X Chen ldquoA life-cycle based co-benefits analysis of biomass pellet productionin Chinardquo Renewable Energy vol 154 pp 445ndash452 2020

[3] HWang H Schandl GWang and L Ma ldquoRegional materialflow accounts for China examining Chinarsquos natural resourceuse at the provincial and national levelrdquo Research andAnalysis vol 6 2019

[4] B-J He Z-Q Zhao L-D Shen H-B Wang and L-G LildquoAn approach to examining performances of coolhot sourcesin mitigatingenhancing land surface temperature underdifferent temperature backgrounds based on landsat 8 imagerdquoSustainable Cities and Society vol 44 pp 416ndash427 2019

[5] B-J He L Yang and M Ye ldquoStrategies for creating goodwind environment around Chinese residencesrdquo SustainableCities and Society vol 10 pp 174ndash183 2014

[6] H Akbari C Cartalis D Kolokotsa et al ldquoLocal climatechange and urban heat island mitigation techniquesndashthe stateof the artrdquo Journal of Civil Engineering and Managementvol 22 no 1 pp 1ndash16 2016

[7] D Zhou L Zhang L Hao G Sun Y Liu and C ZhuldquoSpatiotemporal trends of urban heat island effect along the

Table 5 Urban functional units and average ventilation costs

Reclassification Ventilation costPublic management and social organizations 0535Residential districts 0559Commerce 0519Factories 0483Companies 0545Public services 0568Everyday services 0545Hotels 0538Educational institutions 0554Medical institutions 0544Outdoor open areas 0469Transportation facilities 0541

Table 4 Statistical characteristics of LST at different levels ofventilation cost

Ventilationlevels

Temperature (degC)

Lowest Highest Range Average Standarddeviation

2 2928 5046 2118 3419 3643 2817 5259 2442 3496 3744 2967 5599 2632 3769 4055 2916 4947 2031 3882 3246 3189 5617 2428 3807 3247 3210 5024 1814 3795 3358 3285 5555 2270 4008 3029 3250 5470 2220 4012 26010 3434 5073 1639 4042 20511 3466 4683 1217 4053 171

Complexity 11

urban development intensity gradient in Chinardquo Science of9e Total Environment vol 544 pp 617ndash626 2016

[8] D Zhou S Zhao L Zhang G Sun and Y Liu ldquo-e footprintof urban heat island effect in Chinardquo Scientific Reports vol 5p 11160 2015

[9] K Zhang Y Chen and L Wu ldquoGrey spectrum analysis of airquality index and housing price in Handanrdquo Complexityvol 2019 2019

[10] Y Zhao Y Li and Q Chen ldquoAnalysis of a stochastic sus-ceptible-infective epidemic model in a polluted atmosphericenvironmentrdquo Complexity vol 2019 2019

[11] W Zhang C M Mak Z T Ai andWM Siu ldquoA study of theventilation and thermal comfort of the environment sur-rounding a new university building under constructionrdquoIndoor and Built Environment vol 21 no 4 pp 568ndash5822011

[12] R A W Albers P R Bosch B Blocken et al ldquoOverview ofchallenges and achievements in the climate adaptation ofcities and in the Climate Proof Cities programrdquo Building andEnvironment vol 83 pp 1ndash10 2015

[13] L W A van Hove C M J Jacobs B G HeusinkveldJ A Elbers B L van Driel and A A M Holtslag ldquoTemporaland spatial variability of urban heat island and thermalcomfort within the Rotterdam agglomerationrdquo Building andEnvironment vol 83 pp 91ndash103 2015

[14] A Guo J Yang X Xiao J Xia C Jin and X Li ldquoInfluences ofurban spatial form on urban heat island effects at the com-munity level in Chinardquo Sustainable Cities and Society vol 532020

[15] Z-Q Zhao B-J He L-G Li H-B Wang and A DarkoldquoProfile and concentric zonal analysis of relationships be-tween land useland cover and land surface temperature casestudy of Shenyang Chinardquo Energy and Buildings vol 155pp 282ndash295 2017

[16] E Ng ldquoPolicies and technical guidelines for urban planning ofhigh-density citiesmdashair ventilation assessment (AVA) ofHong Kongrdquo Building and Environment vol 44 no 7pp 1478ndash1488 2009

[17] B Chun and J-M Guldmann ldquoSpatial statistical analysis andsimulation of the urban heat island in high-density centralcitiesrdquo Landscape and Urban Planning vol 125 pp 76ndash882014

[18] Y He A Tablada and N H Wong ldquoA parametric study ofangular road patterns on pedestrian ventilation in high-density urban areasrdquo Building and Environment vol 151pp 251ndash267 2019

[19] J Yang Y Wang X Xiao C Jin J Xia and X Li ldquoSpatialdifferentiation of urban wind and thermal environment indifferent grid sizesrdquo Urban Climate vol 28 2019

[20] J Yang S Jin X Xiao et al ldquoLocal climate zone ventilationand urban land surface temperatures towards a performance-based and wind-sensitive planning proposal in megacitiesrdquoSustainable Cities and Society vol 47 2019

[21] Z Qiao L Liu Y Qin et al ldquo-e impact of urban renewal onland surface temperature changes a case study in the maincity of guangzhou Chinardquo Remote Sensing vol 12 no 5

[22] Z Qiao C Wu D Zhao et al ldquoDetermining the boundaryand probability of surface urban heat island footprint basedon a logistic modelrdquo Remote Sensing vol 11 no 11 2019

[23] Z Qiao X Xu M Zhao FWang and L Liu ldquo-e applicationof a binary division procedure to the classification of forestsubcategories using MODIS time-series data during 2000-2010 in Chinardquo International Journal of Remote Sensingvol 37 no 10 pp 2433ndash2450 2016

[24] M Bady S Kato T Takahashi and H Huang ldquoAn experi-mental investigation of the wind environment and air qualitywithin a densely populated urban street canyonrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 99 no 8pp 857ndash867 2011

[25] M Carpentieri P Salizzoni A Robins and L SoulhacldquoEvaluation of a neighbourhood scale street network dis-persion model through comparison with wind tunnel datardquoEnvironmental Modelling amp Software vol 37 pp 110ndash1242012

[26] K T Tse A U Weerasuriya X Zhang S Li andK C S Kwok ldquoPedestrian-level wind environment aroundisolated buildings under the influence of twisted wind flowsrdquoJournal of Wind Engineering and Industrial Aerodynamicsvol 162 pp 12ndash23 2017

[27] H Montazeri B Blocken and J L M Hensen ldquoEvaporativecooling by water spray systems CFD simulation experi-mental validation and sensitivity analysisrdquo Building andEnvironment vol 83 pp 129ndash141 2015

[28] X Wang and Y Li ldquoPredicting urban heat island circulationusing CFDrdquo Building and Environment vol 99 pp 82ndash972016

[29] K R Gautam L Rong G Zhang and M Abkar ldquoCom-parison of analysis methods for wind-driven cross ventilationthrough large openingsrdquo Building and Environment vol 154pp 375ndash388 2019

[30] M S Wong J E Nichol P H To and J Wang ldquoA simplemethod for designation of urban ventilation corridors and itsapplication to urban heat island analysisrdquo Building and En-vironment vol 45 no 8 pp 1880ndash1889 2010

[31] C-M Hsieh andH-C Huang ldquoMitigating urban heat islandsa method to identify potential wind corridor for cooling andventilationrdquo Computers Environment and Urban Systemsvol 57 pp 130ndash143 2016

[32] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor andBuilt Environment vol 26 no 3 pp 422ndash437 2017

[33] Z Qiao X Xu F Wu et al ldquoUrban ventilation networkmodel a case study of the core zone of capital function inBeijing metropolitan areardquo Journal of Cleaner Productionvol 168 pp 526ndash535 2017

[34] J Yang J Sun Q Ge and X Li ldquoAssessing the impacts ofurbanization-associated green space on urban land surfacetemperature a case study of Dalian Chinardquo Urban Forestry ampUrban Greening vol 22 pp 1ndash10 2017

[35] J Srebric M Heidarinejad and J Liu ldquoBuilding neighbor-hood emerging properties and their impacts on multi-scalemodeling of building energy and airflowsrdquo Building andEnvironment vol 91 pp 246ndash262 2015

[36] X Yang and Y Li ldquo-e impact of building density andbuilding height heterogeneity on average urban albedo andstreet surface temperaturerdquo Building and Environmentvol 90 pp 146ndash156 2015

[37] F Peng M S Wong Y Wan and J E Nichol ldquoModeling ofurban wind ventilation using high resolution airborne LiDARdatardquo Computers Environment and Urban Systems vol 64pp 81ndash90 2017

[38] C Yuan C Ren and E Ng ldquoGIS-based surface roughnessevaluation in the urban planning system to improve the windenvironmentmdasha study in Wuhan Chinardquo Urban Climatevol 10 pp 585ndash593 2014

12 Complexity

[39] C S B Grimmond and T R Oke ldquoAerodynamic propertiesof urban areas derived from analysis of surface formrdquo Journalof Applied Meteorology vol 38 no 9 pp 1262ndash1292 1999

[40] S J Burian M J Brown and S P Linger ldquoMorphologicalanalyses using 3D building databases los Angeles CaliforniardquoLos Alamos National Laboratory vol 74 2002

[41] T Gal and J Unger ldquoDetection of ventilation paths usinghigh-resolution roughness parameter mapping in a largeurban areardquo Building and Environment vol 44 no 1pp 198ndash206 2009

[42] E Ng C Yuan L Chen C Ren and J C H Fung ldquoIm-proving the wind environment in high-density cities by un-derstanding urban morphology and surface roughness astudy in Hong Kongrdquo Landscape and Urban Planningvol 101 no 1 pp 59ndash74 2011

[43] M S Wong and J E Nichol ldquoSpatial variability of frontal areaindex and its relationship with urban heat island intensityrdquoInternational Journal of Remote Sensing vol 34 no 3pp 885ndash896 2013

[44] RWMacdonald R F Griffiths and D J Hall ldquoAn improvedmethod for the estimation of surface roughness of obstaclearraysrdquo Atmospheric Environment vol 32 no 11 pp 1857ndash1864 1998

[45] M Bottema and P G Mestayer ldquoUrban roughness map-pingmdashvalidation techniques and some first resultsrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 74ndash76pp 163ndash173 1998

[46] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor BuiltEnvironment vol 26 no 3 pp 422ndash437 2017

Complexity 13

their distribution is affected by the main ventilation corri-dors-e secondary ventilation corridors spatially divide thehigh ventilation cost area into several subareas which playan important part in promoting urban ventilationDepending on the main ventilation corridors of the EastLake many N-S or SE-NW secondary ventilation corridorsare formed in Wuchang District Qingshan District andHongshan District -ese ventilation corridors promoteventilation in these three areas -e secondary ventilationcorridor formed by the Han River connects to the mainventilation corridor formed by the Yangtze River whichpromotes the ventilation conditions in Hanyang

Capillary ventilation corridors are distributed in areaswith high ventilation costs and the areas with maximumventilation costs are divided into several subregions Cap-illary ventilation corridors cannot determine the overallconditions of regional ventilation but can effectively reducethe distribution of the maximum value of ventilation costsand can be used as a supplementary means for secondaryventilation corridors Due to the large amount of capillaryventilation corridors their locations are not marked on themap It is easy to determine that the number of capillaryventilation corridors in Hankou (Wuchang District Qing-shan District and Hongshan District) is significantly less

than in Wuchang (Qiaokou District Jianghan District andJiangrsquoan District)

-e ventilation cost distribution map enables the visual-ization of urban ventilation conditions For example blueindicates a low ventilation cost area and red indicates a highventilation cost area Relatively low-cost areas between highcosts are potential ventilation corridors-is facilitates a betterunderstanding of urban ventilation conditions on both localand global scales-e frontal area index λf was performed on alocal scale When considering the entire urban area due to thecomplexity of the buildings and the road network it will bedifficult to achieve the identification of ventilation corridors byvisual inspection of the buildings and the windward indexmap[30]-e results of the LCVCmethod cover the entire researcharea thus solving the problem of no-path passages in someareas in the traditional LCP method

54 Validation of the LCVC Method LST is related toventilation conditions When the ventilation conditions aregood the average surface is usually lower -e areas withpoor ventilation conditions have a higher average surfacetemperature and less heterogeneity than other areas do Toverify the accuracy of the urban ventilation analysis LST

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(a)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(b)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(c)

N NNE

NE

ENE

E

ESE

SE

SSESSW

SW

WSW

W

WNW

NW

NNW

S

(d)

Figure 5 A wind-rose diagram for each season in Wuhan (a) Spring (b) Summer (c) Autumn (d) Winter

8 Complexity

inversion was performed on the Landsat 8 data (Figure 8) Bycombining the LSTmap with the summer urban ventilationanalysis results (Figure 7(b)) the LST values for differentlevels of ventilation costs were calculated (Table 4)

-e results show that the average LSTof the area with thebest ventilation conditions is 3419degC significantly lowerthan that of other areas -e average LST in the area with theworst ventilation conditions is 4053degC In addition the areawith the worst ventilation conditions has less heterogeneitythan other areas do and the LST standard deviation is 171As ventilation conditions improve the LST standard devi-ation continues to increase

55 Urban Ventilation Conditions and POIs Table 5 showsthe average ventilation costs for functional urban units -eaverage ventilation costs of outdoor open areas are obviouslylower than the others at only 0469 -e ventilation costs are

highest for public service units with a value of 0568 -is ismainly because these units are distributed in the main coreareas of the city-e ventilation costs for the residential unitsare 0559 in the second order which is different from theventilation resistance coefficient ranking Residential unitsare usually larger and have a higher building densityresulting in higher ventilation costs -e ventilation costs ofeducational institutions are 0554 similar to those for res-idential units -e majority of educational institutions areprimary secondary high school and training institutionsMost of these institutions are spatially associated withdensely populated areas and therefore have similar venti-lation costs to residential areas Residential units are themain places where people live and usually have a highpopulation density Higher ventilation costs easily form heatislands and are not conducive to the spread of pollutionwhich affects peoplersquos health and quality of life -erefore inurban planning the construction of outdoor open spaces in

0 20 40 km

N NNE NE ENE

E ESE SE SSE

S SSW SW WSW

W WNW NW NNW

Min Max

NVentilation cost (Minimax normalized)

Figure 6 Spatial distribution of LCVC in different wind directions

Complexity 9

Ventilation cost level0 10 20 km

(a) (b)

(c) (d)

Main ventilation pathSecondary ventilation path

1 2 3 4 5 6 7 8 9 10 N

Figure 7 Spatial distribution of ventilation during different seasons (a) Spring (b) Summer (c) Autumn (d) Winter

LST (degC)High 50

Low 270 10 km

N

5

Figure 8 Spatial distribution of LST in summer

10 Complexity

residential areas should be strengthened to improve theventilation environment

6 Discussion and Conclusion

Using the urban morphological characteristics identified byGIS technology this study established an LCVC method todescribe quantitatively the urban ventilation environment atthe city scale First the frontal area index of three-dimen-sional urban buildings representing the ventilation resis-tance coefficient was calculated using a morphologicalmethod Second the minimum cumulative ventilation costalgorithm was applied to draw the urban ventilation costmaps under different wind directions -en the wind fre-quency weighting was used to explore the urban ventilationenvironment in different seasons and the urban ventilationcorridors of different levels were analyzed Finally the re-sults of the summer ventilation analysis were verified usingthe LST retrieved from Landsat 8 data

-e analysis of the urban ventilation environmentbased on the morphology and GIS technology has theadvantage of high calculation efficiency In addition theresults of the analysis are highly compatible with otherspatial analysis and mapping technologies -is studyproposes that the cumulative costs of ventilation canbetter reflect the differences in urban ventilation

conditions than the ventilation resistance coefficient canIn contrast to the corridor extraction method based onempirical rules the proposed method can directly observethe dynamic characteristics of urban ventilation underdifferent wind directions and frequencies In contrast tothe least-cost path analysis method the results of theLCVC method cover the whole study area and the cor-ridor width and area can also be measured Howeverseveral issues require further study -e urban wind en-vironment is affected not only by ground roughness butalso by atmospheric environment and local temperaturefactors -erefore it is difficult to achieve an accurateanalysis of local wind speeds and directions from ananalysis of building form alone In addition it is difficultto obtain internal high-density real-time wind speed dataespecially wind speed measurements in a vertical direc-tion -is makes it difficult to improve the model pa-rameters -ese issues will be further studied

Data Availability

-e data used to support the findings of the study areavailable from the corresponding author upon request

Conflicts of Interest

-e authors declare that they have no conflicts of interest

Acknowledgments

-is study was supported by the National Key Research andDevelopment Program of China (Grant no2017YFB0503601) and China Postdoctoral Science Foun-dation (Grant no 2019M662726)

References

[1] H Wang G Wang J Qi et al ldquoScarcity-weighted fossil fuelfootprint of China at the provincial levelrdquo Applied Energyvol 258 2020

[2] L Jiang B Xue Z Ma L Yu B Huang and X Chen ldquoA life-cycle based co-benefits analysis of biomass pellet productionin Chinardquo Renewable Energy vol 154 pp 445ndash452 2020

[3] HWang H Schandl GWang and L Ma ldquoRegional materialflow accounts for China examining Chinarsquos natural resourceuse at the provincial and national levelrdquo Research andAnalysis vol 6 2019

[4] B-J He Z-Q Zhao L-D Shen H-B Wang and L-G LildquoAn approach to examining performances of coolhot sourcesin mitigatingenhancing land surface temperature underdifferent temperature backgrounds based on landsat 8 imagerdquoSustainable Cities and Society vol 44 pp 416ndash427 2019

[5] B-J He L Yang and M Ye ldquoStrategies for creating goodwind environment around Chinese residencesrdquo SustainableCities and Society vol 10 pp 174ndash183 2014

[6] H Akbari C Cartalis D Kolokotsa et al ldquoLocal climatechange and urban heat island mitigation techniquesndashthe stateof the artrdquo Journal of Civil Engineering and Managementvol 22 no 1 pp 1ndash16 2016

[7] D Zhou L Zhang L Hao G Sun Y Liu and C ZhuldquoSpatiotemporal trends of urban heat island effect along the

Table 5 Urban functional units and average ventilation costs

Reclassification Ventilation costPublic management and social organizations 0535Residential districts 0559Commerce 0519Factories 0483Companies 0545Public services 0568Everyday services 0545Hotels 0538Educational institutions 0554Medical institutions 0544Outdoor open areas 0469Transportation facilities 0541

Table 4 Statistical characteristics of LST at different levels ofventilation cost

Ventilationlevels

Temperature (degC)

Lowest Highest Range Average Standarddeviation

2 2928 5046 2118 3419 3643 2817 5259 2442 3496 3744 2967 5599 2632 3769 4055 2916 4947 2031 3882 3246 3189 5617 2428 3807 3247 3210 5024 1814 3795 3358 3285 5555 2270 4008 3029 3250 5470 2220 4012 26010 3434 5073 1639 4042 20511 3466 4683 1217 4053 171

Complexity 11

urban development intensity gradient in Chinardquo Science of9e Total Environment vol 544 pp 617ndash626 2016

[8] D Zhou S Zhao L Zhang G Sun and Y Liu ldquo-e footprintof urban heat island effect in Chinardquo Scientific Reports vol 5p 11160 2015

[9] K Zhang Y Chen and L Wu ldquoGrey spectrum analysis of airquality index and housing price in Handanrdquo Complexityvol 2019 2019

[10] Y Zhao Y Li and Q Chen ldquoAnalysis of a stochastic sus-ceptible-infective epidemic model in a polluted atmosphericenvironmentrdquo Complexity vol 2019 2019

[11] W Zhang C M Mak Z T Ai andWM Siu ldquoA study of theventilation and thermal comfort of the environment sur-rounding a new university building under constructionrdquoIndoor and Built Environment vol 21 no 4 pp 568ndash5822011

[12] R A W Albers P R Bosch B Blocken et al ldquoOverview ofchallenges and achievements in the climate adaptation ofcities and in the Climate Proof Cities programrdquo Building andEnvironment vol 83 pp 1ndash10 2015

[13] L W A van Hove C M J Jacobs B G HeusinkveldJ A Elbers B L van Driel and A A M Holtslag ldquoTemporaland spatial variability of urban heat island and thermalcomfort within the Rotterdam agglomerationrdquo Building andEnvironment vol 83 pp 91ndash103 2015

[14] A Guo J Yang X Xiao J Xia C Jin and X Li ldquoInfluences ofurban spatial form on urban heat island effects at the com-munity level in Chinardquo Sustainable Cities and Society vol 532020

[15] Z-Q Zhao B-J He L-G Li H-B Wang and A DarkoldquoProfile and concentric zonal analysis of relationships be-tween land useland cover and land surface temperature casestudy of Shenyang Chinardquo Energy and Buildings vol 155pp 282ndash295 2017

[16] E Ng ldquoPolicies and technical guidelines for urban planning ofhigh-density citiesmdashair ventilation assessment (AVA) ofHong Kongrdquo Building and Environment vol 44 no 7pp 1478ndash1488 2009

[17] B Chun and J-M Guldmann ldquoSpatial statistical analysis andsimulation of the urban heat island in high-density centralcitiesrdquo Landscape and Urban Planning vol 125 pp 76ndash882014

[18] Y He A Tablada and N H Wong ldquoA parametric study ofangular road patterns on pedestrian ventilation in high-density urban areasrdquo Building and Environment vol 151pp 251ndash267 2019

[19] J Yang Y Wang X Xiao C Jin J Xia and X Li ldquoSpatialdifferentiation of urban wind and thermal environment indifferent grid sizesrdquo Urban Climate vol 28 2019

[20] J Yang S Jin X Xiao et al ldquoLocal climate zone ventilationand urban land surface temperatures towards a performance-based and wind-sensitive planning proposal in megacitiesrdquoSustainable Cities and Society vol 47 2019

[21] Z Qiao L Liu Y Qin et al ldquo-e impact of urban renewal onland surface temperature changes a case study in the maincity of guangzhou Chinardquo Remote Sensing vol 12 no 5

[22] Z Qiao C Wu D Zhao et al ldquoDetermining the boundaryand probability of surface urban heat island footprint basedon a logistic modelrdquo Remote Sensing vol 11 no 11 2019

[23] Z Qiao X Xu M Zhao FWang and L Liu ldquo-e applicationof a binary division procedure to the classification of forestsubcategories using MODIS time-series data during 2000-2010 in Chinardquo International Journal of Remote Sensingvol 37 no 10 pp 2433ndash2450 2016

[24] M Bady S Kato T Takahashi and H Huang ldquoAn experi-mental investigation of the wind environment and air qualitywithin a densely populated urban street canyonrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 99 no 8pp 857ndash867 2011

[25] M Carpentieri P Salizzoni A Robins and L SoulhacldquoEvaluation of a neighbourhood scale street network dis-persion model through comparison with wind tunnel datardquoEnvironmental Modelling amp Software vol 37 pp 110ndash1242012

[26] K T Tse A U Weerasuriya X Zhang S Li andK C S Kwok ldquoPedestrian-level wind environment aroundisolated buildings under the influence of twisted wind flowsrdquoJournal of Wind Engineering and Industrial Aerodynamicsvol 162 pp 12ndash23 2017

[27] H Montazeri B Blocken and J L M Hensen ldquoEvaporativecooling by water spray systems CFD simulation experi-mental validation and sensitivity analysisrdquo Building andEnvironment vol 83 pp 129ndash141 2015

[28] X Wang and Y Li ldquoPredicting urban heat island circulationusing CFDrdquo Building and Environment vol 99 pp 82ndash972016

[29] K R Gautam L Rong G Zhang and M Abkar ldquoCom-parison of analysis methods for wind-driven cross ventilationthrough large openingsrdquo Building and Environment vol 154pp 375ndash388 2019

[30] M S Wong J E Nichol P H To and J Wang ldquoA simplemethod for designation of urban ventilation corridors and itsapplication to urban heat island analysisrdquo Building and En-vironment vol 45 no 8 pp 1880ndash1889 2010

[31] C-M Hsieh andH-C Huang ldquoMitigating urban heat islandsa method to identify potential wind corridor for cooling andventilationrdquo Computers Environment and Urban Systemsvol 57 pp 130ndash143 2016

[32] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor andBuilt Environment vol 26 no 3 pp 422ndash437 2017

[33] Z Qiao X Xu F Wu et al ldquoUrban ventilation networkmodel a case study of the core zone of capital function inBeijing metropolitan areardquo Journal of Cleaner Productionvol 168 pp 526ndash535 2017

[34] J Yang J Sun Q Ge and X Li ldquoAssessing the impacts ofurbanization-associated green space on urban land surfacetemperature a case study of Dalian Chinardquo Urban Forestry ampUrban Greening vol 22 pp 1ndash10 2017

[35] J Srebric M Heidarinejad and J Liu ldquoBuilding neighbor-hood emerging properties and their impacts on multi-scalemodeling of building energy and airflowsrdquo Building andEnvironment vol 91 pp 246ndash262 2015

[36] X Yang and Y Li ldquo-e impact of building density andbuilding height heterogeneity on average urban albedo andstreet surface temperaturerdquo Building and Environmentvol 90 pp 146ndash156 2015

[37] F Peng M S Wong Y Wan and J E Nichol ldquoModeling ofurban wind ventilation using high resolution airborne LiDARdatardquo Computers Environment and Urban Systems vol 64pp 81ndash90 2017

[38] C Yuan C Ren and E Ng ldquoGIS-based surface roughnessevaluation in the urban planning system to improve the windenvironmentmdasha study in Wuhan Chinardquo Urban Climatevol 10 pp 585ndash593 2014

12 Complexity

[39] C S B Grimmond and T R Oke ldquoAerodynamic propertiesof urban areas derived from analysis of surface formrdquo Journalof Applied Meteorology vol 38 no 9 pp 1262ndash1292 1999

[40] S J Burian M J Brown and S P Linger ldquoMorphologicalanalyses using 3D building databases los Angeles CaliforniardquoLos Alamos National Laboratory vol 74 2002

[41] T Gal and J Unger ldquoDetection of ventilation paths usinghigh-resolution roughness parameter mapping in a largeurban areardquo Building and Environment vol 44 no 1pp 198ndash206 2009

[42] E Ng C Yuan L Chen C Ren and J C H Fung ldquoIm-proving the wind environment in high-density cities by un-derstanding urban morphology and surface roughness astudy in Hong Kongrdquo Landscape and Urban Planningvol 101 no 1 pp 59ndash74 2011

[43] M S Wong and J E Nichol ldquoSpatial variability of frontal areaindex and its relationship with urban heat island intensityrdquoInternational Journal of Remote Sensing vol 34 no 3pp 885ndash896 2013

[44] RWMacdonald R F Griffiths and D J Hall ldquoAn improvedmethod for the estimation of surface roughness of obstaclearraysrdquo Atmospheric Environment vol 32 no 11 pp 1857ndash1864 1998

[45] M Bottema and P G Mestayer ldquoUrban roughness map-pingmdashvalidation techniques and some first resultsrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 74ndash76pp 163ndash173 1998

[46] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor BuiltEnvironment vol 26 no 3 pp 422ndash437 2017

Complexity 13

inversion was performed on the Landsat 8 data (Figure 8) Bycombining the LSTmap with the summer urban ventilationanalysis results (Figure 7(b)) the LST values for differentlevels of ventilation costs were calculated (Table 4)

-e results show that the average LSTof the area with thebest ventilation conditions is 3419degC significantly lowerthan that of other areas -e average LST in the area with theworst ventilation conditions is 4053degC In addition the areawith the worst ventilation conditions has less heterogeneitythan other areas do and the LST standard deviation is 171As ventilation conditions improve the LST standard devi-ation continues to increase

55 Urban Ventilation Conditions and POIs Table 5 showsthe average ventilation costs for functional urban units -eaverage ventilation costs of outdoor open areas are obviouslylower than the others at only 0469 -e ventilation costs are

highest for public service units with a value of 0568 -is ismainly because these units are distributed in the main coreareas of the city-e ventilation costs for the residential unitsare 0559 in the second order which is different from theventilation resistance coefficient ranking Residential unitsare usually larger and have a higher building densityresulting in higher ventilation costs -e ventilation costs ofeducational institutions are 0554 similar to those for res-idential units -e majority of educational institutions areprimary secondary high school and training institutionsMost of these institutions are spatially associated withdensely populated areas and therefore have similar venti-lation costs to residential areas Residential units are themain places where people live and usually have a highpopulation density Higher ventilation costs easily form heatislands and are not conducive to the spread of pollutionwhich affects peoplersquos health and quality of life -erefore inurban planning the construction of outdoor open spaces in

0 20 40 km

N NNE NE ENE

E ESE SE SSE

S SSW SW WSW

W WNW NW NNW

Min Max

NVentilation cost (Minimax normalized)

Figure 6 Spatial distribution of LCVC in different wind directions

Complexity 9

Ventilation cost level0 10 20 km

(a) (b)

(c) (d)

Main ventilation pathSecondary ventilation path

1 2 3 4 5 6 7 8 9 10 N

Figure 7 Spatial distribution of ventilation during different seasons (a) Spring (b) Summer (c) Autumn (d) Winter

LST (degC)High 50

Low 270 10 km

N

5

Figure 8 Spatial distribution of LST in summer

10 Complexity

residential areas should be strengthened to improve theventilation environment

6 Discussion and Conclusion

Using the urban morphological characteristics identified byGIS technology this study established an LCVC method todescribe quantitatively the urban ventilation environment atthe city scale First the frontal area index of three-dimen-sional urban buildings representing the ventilation resis-tance coefficient was calculated using a morphologicalmethod Second the minimum cumulative ventilation costalgorithm was applied to draw the urban ventilation costmaps under different wind directions -en the wind fre-quency weighting was used to explore the urban ventilationenvironment in different seasons and the urban ventilationcorridors of different levels were analyzed Finally the re-sults of the summer ventilation analysis were verified usingthe LST retrieved from Landsat 8 data

-e analysis of the urban ventilation environmentbased on the morphology and GIS technology has theadvantage of high calculation efficiency In addition theresults of the analysis are highly compatible with otherspatial analysis and mapping technologies -is studyproposes that the cumulative costs of ventilation canbetter reflect the differences in urban ventilation

conditions than the ventilation resistance coefficient canIn contrast to the corridor extraction method based onempirical rules the proposed method can directly observethe dynamic characteristics of urban ventilation underdifferent wind directions and frequencies In contrast tothe least-cost path analysis method the results of theLCVC method cover the whole study area and the cor-ridor width and area can also be measured Howeverseveral issues require further study -e urban wind en-vironment is affected not only by ground roughness butalso by atmospheric environment and local temperaturefactors -erefore it is difficult to achieve an accurateanalysis of local wind speeds and directions from ananalysis of building form alone In addition it is difficultto obtain internal high-density real-time wind speed dataespecially wind speed measurements in a vertical direc-tion -is makes it difficult to improve the model pa-rameters -ese issues will be further studied

Data Availability

-e data used to support the findings of the study areavailable from the corresponding author upon request

Conflicts of Interest

-e authors declare that they have no conflicts of interest

Acknowledgments

-is study was supported by the National Key Research andDevelopment Program of China (Grant no2017YFB0503601) and China Postdoctoral Science Foun-dation (Grant no 2019M662726)

References

[1] H Wang G Wang J Qi et al ldquoScarcity-weighted fossil fuelfootprint of China at the provincial levelrdquo Applied Energyvol 258 2020

[2] L Jiang B Xue Z Ma L Yu B Huang and X Chen ldquoA life-cycle based co-benefits analysis of biomass pellet productionin Chinardquo Renewable Energy vol 154 pp 445ndash452 2020

[3] HWang H Schandl GWang and L Ma ldquoRegional materialflow accounts for China examining Chinarsquos natural resourceuse at the provincial and national levelrdquo Research andAnalysis vol 6 2019

[4] B-J He Z-Q Zhao L-D Shen H-B Wang and L-G LildquoAn approach to examining performances of coolhot sourcesin mitigatingenhancing land surface temperature underdifferent temperature backgrounds based on landsat 8 imagerdquoSustainable Cities and Society vol 44 pp 416ndash427 2019

[5] B-J He L Yang and M Ye ldquoStrategies for creating goodwind environment around Chinese residencesrdquo SustainableCities and Society vol 10 pp 174ndash183 2014

[6] H Akbari C Cartalis D Kolokotsa et al ldquoLocal climatechange and urban heat island mitigation techniquesndashthe stateof the artrdquo Journal of Civil Engineering and Managementvol 22 no 1 pp 1ndash16 2016

[7] D Zhou L Zhang L Hao G Sun Y Liu and C ZhuldquoSpatiotemporal trends of urban heat island effect along the

Table 5 Urban functional units and average ventilation costs

Reclassification Ventilation costPublic management and social organizations 0535Residential districts 0559Commerce 0519Factories 0483Companies 0545Public services 0568Everyday services 0545Hotels 0538Educational institutions 0554Medical institutions 0544Outdoor open areas 0469Transportation facilities 0541

Table 4 Statistical characteristics of LST at different levels ofventilation cost

Ventilationlevels

Temperature (degC)

Lowest Highest Range Average Standarddeviation

2 2928 5046 2118 3419 3643 2817 5259 2442 3496 3744 2967 5599 2632 3769 4055 2916 4947 2031 3882 3246 3189 5617 2428 3807 3247 3210 5024 1814 3795 3358 3285 5555 2270 4008 3029 3250 5470 2220 4012 26010 3434 5073 1639 4042 20511 3466 4683 1217 4053 171

Complexity 11

urban development intensity gradient in Chinardquo Science of9e Total Environment vol 544 pp 617ndash626 2016

[8] D Zhou S Zhao L Zhang G Sun and Y Liu ldquo-e footprintof urban heat island effect in Chinardquo Scientific Reports vol 5p 11160 2015

[9] K Zhang Y Chen and L Wu ldquoGrey spectrum analysis of airquality index and housing price in Handanrdquo Complexityvol 2019 2019

[10] Y Zhao Y Li and Q Chen ldquoAnalysis of a stochastic sus-ceptible-infective epidemic model in a polluted atmosphericenvironmentrdquo Complexity vol 2019 2019

[11] W Zhang C M Mak Z T Ai andWM Siu ldquoA study of theventilation and thermal comfort of the environment sur-rounding a new university building under constructionrdquoIndoor and Built Environment vol 21 no 4 pp 568ndash5822011

[12] R A W Albers P R Bosch B Blocken et al ldquoOverview ofchallenges and achievements in the climate adaptation ofcities and in the Climate Proof Cities programrdquo Building andEnvironment vol 83 pp 1ndash10 2015

[13] L W A van Hove C M J Jacobs B G HeusinkveldJ A Elbers B L van Driel and A A M Holtslag ldquoTemporaland spatial variability of urban heat island and thermalcomfort within the Rotterdam agglomerationrdquo Building andEnvironment vol 83 pp 91ndash103 2015

[14] A Guo J Yang X Xiao J Xia C Jin and X Li ldquoInfluences ofurban spatial form on urban heat island effects at the com-munity level in Chinardquo Sustainable Cities and Society vol 532020

[15] Z-Q Zhao B-J He L-G Li H-B Wang and A DarkoldquoProfile and concentric zonal analysis of relationships be-tween land useland cover and land surface temperature casestudy of Shenyang Chinardquo Energy and Buildings vol 155pp 282ndash295 2017

[16] E Ng ldquoPolicies and technical guidelines for urban planning ofhigh-density citiesmdashair ventilation assessment (AVA) ofHong Kongrdquo Building and Environment vol 44 no 7pp 1478ndash1488 2009

[17] B Chun and J-M Guldmann ldquoSpatial statistical analysis andsimulation of the urban heat island in high-density centralcitiesrdquo Landscape and Urban Planning vol 125 pp 76ndash882014

[18] Y He A Tablada and N H Wong ldquoA parametric study ofangular road patterns on pedestrian ventilation in high-density urban areasrdquo Building and Environment vol 151pp 251ndash267 2019

[19] J Yang Y Wang X Xiao C Jin J Xia and X Li ldquoSpatialdifferentiation of urban wind and thermal environment indifferent grid sizesrdquo Urban Climate vol 28 2019

[20] J Yang S Jin X Xiao et al ldquoLocal climate zone ventilationand urban land surface temperatures towards a performance-based and wind-sensitive planning proposal in megacitiesrdquoSustainable Cities and Society vol 47 2019

[21] Z Qiao L Liu Y Qin et al ldquo-e impact of urban renewal onland surface temperature changes a case study in the maincity of guangzhou Chinardquo Remote Sensing vol 12 no 5

[22] Z Qiao C Wu D Zhao et al ldquoDetermining the boundaryand probability of surface urban heat island footprint basedon a logistic modelrdquo Remote Sensing vol 11 no 11 2019

[23] Z Qiao X Xu M Zhao FWang and L Liu ldquo-e applicationof a binary division procedure to the classification of forestsubcategories using MODIS time-series data during 2000-2010 in Chinardquo International Journal of Remote Sensingvol 37 no 10 pp 2433ndash2450 2016

[24] M Bady S Kato T Takahashi and H Huang ldquoAn experi-mental investigation of the wind environment and air qualitywithin a densely populated urban street canyonrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 99 no 8pp 857ndash867 2011

[25] M Carpentieri P Salizzoni A Robins and L SoulhacldquoEvaluation of a neighbourhood scale street network dis-persion model through comparison with wind tunnel datardquoEnvironmental Modelling amp Software vol 37 pp 110ndash1242012

[26] K T Tse A U Weerasuriya X Zhang S Li andK C S Kwok ldquoPedestrian-level wind environment aroundisolated buildings under the influence of twisted wind flowsrdquoJournal of Wind Engineering and Industrial Aerodynamicsvol 162 pp 12ndash23 2017

[27] H Montazeri B Blocken and J L M Hensen ldquoEvaporativecooling by water spray systems CFD simulation experi-mental validation and sensitivity analysisrdquo Building andEnvironment vol 83 pp 129ndash141 2015

[28] X Wang and Y Li ldquoPredicting urban heat island circulationusing CFDrdquo Building and Environment vol 99 pp 82ndash972016

[29] K R Gautam L Rong G Zhang and M Abkar ldquoCom-parison of analysis methods for wind-driven cross ventilationthrough large openingsrdquo Building and Environment vol 154pp 375ndash388 2019

[30] M S Wong J E Nichol P H To and J Wang ldquoA simplemethod for designation of urban ventilation corridors and itsapplication to urban heat island analysisrdquo Building and En-vironment vol 45 no 8 pp 1880ndash1889 2010

[31] C-M Hsieh andH-C Huang ldquoMitigating urban heat islandsa method to identify potential wind corridor for cooling andventilationrdquo Computers Environment and Urban Systemsvol 57 pp 130ndash143 2016

[32] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor andBuilt Environment vol 26 no 3 pp 422ndash437 2017

[33] Z Qiao X Xu F Wu et al ldquoUrban ventilation networkmodel a case study of the core zone of capital function inBeijing metropolitan areardquo Journal of Cleaner Productionvol 168 pp 526ndash535 2017

[34] J Yang J Sun Q Ge and X Li ldquoAssessing the impacts ofurbanization-associated green space on urban land surfacetemperature a case study of Dalian Chinardquo Urban Forestry ampUrban Greening vol 22 pp 1ndash10 2017

[35] J Srebric M Heidarinejad and J Liu ldquoBuilding neighbor-hood emerging properties and their impacts on multi-scalemodeling of building energy and airflowsrdquo Building andEnvironment vol 91 pp 246ndash262 2015

[36] X Yang and Y Li ldquo-e impact of building density andbuilding height heterogeneity on average urban albedo andstreet surface temperaturerdquo Building and Environmentvol 90 pp 146ndash156 2015

[37] F Peng M S Wong Y Wan and J E Nichol ldquoModeling ofurban wind ventilation using high resolution airborne LiDARdatardquo Computers Environment and Urban Systems vol 64pp 81ndash90 2017

[38] C Yuan C Ren and E Ng ldquoGIS-based surface roughnessevaluation in the urban planning system to improve the windenvironmentmdasha study in Wuhan Chinardquo Urban Climatevol 10 pp 585ndash593 2014

12 Complexity

[39] C S B Grimmond and T R Oke ldquoAerodynamic propertiesof urban areas derived from analysis of surface formrdquo Journalof Applied Meteorology vol 38 no 9 pp 1262ndash1292 1999

[40] S J Burian M J Brown and S P Linger ldquoMorphologicalanalyses using 3D building databases los Angeles CaliforniardquoLos Alamos National Laboratory vol 74 2002

[41] T Gal and J Unger ldquoDetection of ventilation paths usinghigh-resolution roughness parameter mapping in a largeurban areardquo Building and Environment vol 44 no 1pp 198ndash206 2009

[42] E Ng C Yuan L Chen C Ren and J C H Fung ldquoIm-proving the wind environment in high-density cities by un-derstanding urban morphology and surface roughness astudy in Hong Kongrdquo Landscape and Urban Planningvol 101 no 1 pp 59ndash74 2011

[43] M S Wong and J E Nichol ldquoSpatial variability of frontal areaindex and its relationship with urban heat island intensityrdquoInternational Journal of Remote Sensing vol 34 no 3pp 885ndash896 2013

[44] RWMacdonald R F Griffiths and D J Hall ldquoAn improvedmethod for the estimation of surface roughness of obstaclearraysrdquo Atmospheric Environment vol 32 no 11 pp 1857ndash1864 1998

[45] M Bottema and P G Mestayer ldquoUrban roughness map-pingmdashvalidation techniques and some first resultsrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 74ndash76pp 163ndash173 1998

[46] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor BuiltEnvironment vol 26 no 3 pp 422ndash437 2017

Complexity 13

Ventilation cost level0 10 20 km

(a) (b)

(c) (d)

Main ventilation pathSecondary ventilation path

1 2 3 4 5 6 7 8 9 10 N

Figure 7 Spatial distribution of ventilation during different seasons (a) Spring (b) Summer (c) Autumn (d) Winter

LST (degC)High 50

Low 270 10 km

N

5

Figure 8 Spatial distribution of LST in summer

10 Complexity

residential areas should be strengthened to improve theventilation environment

6 Discussion and Conclusion

Using the urban morphological characteristics identified byGIS technology this study established an LCVC method todescribe quantitatively the urban ventilation environment atthe city scale First the frontal area index of three-dimen-sional urban buildings representing the ventilation resis-tance coefficient was calculated using a morphologicalmethod Second the minimum cumulative ventilation costalgorithm was applied to draw the urban ventilation costmaps under different wind directions -en the wind fre-quency weighting was used to explore the urban ventilationenvironment in different seasons and the urban ventilationcorridors of different levels were analyzed Finally the re-sults of the summer ventilation analysis were verified usingthe LST retrieved from Landsat 8 data

-e analysis of the urban ventilation environmentbased on the morphology and GIS technology has theadvantage of high calculation efficiency In addition theresults of the analysis are highly compatible with otherspatial analysis and mapping technologies -is studyproposes that the cumulative costs of ventilation canbetter reflect the differences in urban ventilation

conditions than the ventilation resistance coefficient canIn contrast to the corridor extraction method based onempirical rules the proposed method can directly observethe dynamic characteristics of urban ventilation underdifferent wind directions and frequencies In contrast tothe least-cost path analysis method the results of theLCVC method cover the whole study area and the cor-ridor width and area can also be measured Howeverseveral issues require further study -e urban wind en-vironment is affected not only by ground roughness butalso by atmospheric environment and local temperaturefactors -erefore it is difficult to achieve an accurateanalysis of local wind speeds and directions from ananalysis of building form alone In addition it is difficultto obtain internal high-density real-time wind speed dataespecially wind speed measurements in a vertical direc-tion -is makes it difficult to improve the model pa-rameters -ese issues will be further studied

Data Availability

-e data used to support the findings of the study areavailable from the corresponding author upon request

Conflicts of Interest

-e authors declare that they have no conflicts of interest

Acknowledgments

-is study was supported by the National Key Research andDevelopment Program of China (Grant no2017YFB0503601) and China Postdoctoral Science Foun-dation (Grant no 2019M662726)

References

[1] H Wang G Wang J Qi et al ldquoScarcity-weighted fossil fuelfootprint of China at the provincial levelrdquo Applied Energyvol 258 2020

[2] L Jiang B Xue Z Ma L Yu B Huang and X Chen ldquoA life-cycle based co-benefits analysis of biomass pellet productionin Chinardquo Renewable Energy vol 154 pp 445ndash452 2020

[3] HWang H Schandl GWang and L Ma ldquoRegional materialflow accounts for China examining Chinarsquos natural resourceuse at the provincial and national levelrdquo Research andAnalysis vol 6 2019

[4] B-J He Z-Q Zhao L-D Shen H-B Wang and L-G LildquoAn approach to examining performances of coolhot sourcesin mitigatingenhancing land surface temperature underdifferent temperature backgrounds based on landsat 8 imagerdquoSustainable Cities and Society vol 44 pp 416ndash427 2019

[5] B-J He L Yang and M Ye ldquoStrategies for creating goodwind environment around Chinese residencesrdquo SustainableCities and Society vol 10 pp 174ndash183 2014

[6] H Akbari C Cartalis D Kolokotsa et al ldquoLocal climatechange and urban heat island mitigation techniquesndashthe stateof the artrdquo Journal of Civil Engineering and Managementvol 22 no 1 pp 1ndash16 2016

[7] D Zhou L Zhang L Hao G Sun Y Liu and C ZhuldquoSpatiotemporal trends of urban heat island effect along the

Table 5 Urban functional units and average ventilation costs

Reclassification Ventilation costPublic management and social organizations 0535Residential districts 0559Commerce 0519Factories 0483Companies 0545Public services 0568Everyday services 0545Hotels 0538Educational institutions 0554Medical institutions 0544Outdoor open areas 0469Transportation facilities 0541

Table 4 Statistical characteristics of LST at different levels ofventilation cost

Ventilationlevels

Temperature (degC)

Lowest Highest Range Average Standarddeviation

2 2928 5046 2118 3419 3643 2817 5259 2442 3496 3744 2967 5599 2632 3769 4055 2916 4947 2031 3882 3246 3189 5617 2428 3807 3247 3210 5024 1814 3795 3358 3285 5555 2270 4008 3029 3250 5470 2220 4012 26010 3434 5073 1639 4042 20511 3466 4683 1217 4053 171

Complexity 11

urban development intensity gradient in Chinardquo Science of9e Total Environment vol 544 pp 617ndash626 2016

[8] D Zhou S Zhao L Zhang G Sun and Y Liu ldquo-e footprintof urban heat island effect in Chinardquo Scientific Reports vol 5p 11160 2015

[9] K Zhang Y Chen and L Wu ldquoGrey spectrum analysis of airquality index and housing price in Handanrdquo Complexityvol 2019 2019

[10] Y Zhao Y Li and Q Chen ldquoAnalysis of a stochastic sus-ceptible-infective epidemic model in a polluted atmosphericenvironmentrdquo Complexity vol 2019 2019

[11] W Zhang C M Mak Z T Ai andWM Siu ldquoA study of theventilation and thermal comfort of the environment sur-rounding a new university building under constructionrdquoIndoor and Built Environment vol 21 no 4 pp 568ndash5822011

[12] R A W Albers P R Bosch B Blocken et al ldquoOverview ofchallenges and achievements in the climate adaptation ofcities and in the Climate Proof Cities programrdquo Building andEnvironment vol 83 pp 1ndash10 2015

[13] L W A van Hove C M J Jacobs B G HeusinkveldJ A Elbers B L van Driel and A A M Holtslag ldquoTemporaland spatial variability of urban heat island and thermalcomfort within the Rotterdam agglomerationrdquo Building andEnvironment vol 83 pp 91ndash103 2015

[14] A Guo J Yang X Xiao J Xia C Jin and X Li ldquoInfluences ofurban spatial form on urban heat island effects at the com-munity level in Chinardquo Sustainable Cities and Society vol 532020

[15] Z-Q Zhao B-J He L-G Li H-B Wang and A DarkoldquoProfile and concentric zonal analysis of relationships be-tween land useland cover and land surface temperature casestudy of Shenyang Chinardquo Energy and Buildings vol 155pp 282ndash295 2017

[16] E Ng ldquoPolicies and technical guidelines for urban planning ofhigh-density citiesmdashair ventilation assessment (AVA) ofHong Kongrdquo Building and Environment vol 44 no 7pp 1478ndash1488 2009

[17] B Chun and J-M Guldmann ldquoSpatial statistical analysis andsimulation of the urban heat island in high-density centralcitiesrdquo Landscape and Urban Planning vol 125 pp 76ndash882014

[18] Y He A Tablada and N H Wong ldquoA parametric study ofangular road patterns on pedestrian ventilation in high-density urban areasrdquo Building and Environment vol 151pp 251ndash267 2019

[19] J Yang Y Wang X Xiao C Jin J Xia and X Li ldquoSpatialdifferentiation of urban wind and thermal environment indifferent grid sizesrdquo Urban Climate vol 28 2019

[20] J Yang S Jin X Xiao et al ldquoLocal climate zone ventilationand urban land surface temperatures towards a performance-based and wind-sensitive planning proposal in megacitiesrdquoSustainable Cities and Society vol 47 2019

[21] Z Qiao L Liu Y Qin et al ldquo-e impact of urban renewal onland surface temperature changes a case study in the maincity of guangzhou Chinardquo Remote Sensing vol 12 no 5

[22] Z Qiao C Wu D Zhao et al ldquoDetermining the boundaryand probability of surface urban heat island footprint basedon a logistic modelrdquo Remote Sensing vol 11 no 11 2019

[23] Z Qiao X Xu M Zhao FWang and L Liu ldquo-e applicationof a binary division procedure to the classification of forestsubcategories using MODIS time-series data during 2000-2010 in Chinardquo International Journal of Remote Sensingvol 37 no 10 pp 2433ndash2450 2016

[24] M Bady S Kato T Takahashi and H Huang ldquoAn experi-mental investigation of the wind environment and air qualitywithin a densely populated urban street canyonrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 99 no 8pp 857ndash867 2011

[25] M Carpentieri P Salizzoni A Robins and L SoulhacldquoEvaluation of a neighbourhood scale street network dis-persion model through comparison with wind tunnel datardquoEnvironmental Modelling amp Software vol 37 pp 110ndash1242012

[26] K T Tse A U Weerasuriya X Zhang S Li andK C S Kwok ldquoPedestrian-level wind environment aroundisolated buildings under the influence of twisted wind flowsrdquoJournal of Wind Engineering and Industrial Aerodynamicsvol 162 pp 12ndash23 2017

[27] H Montazeri B Blocken and J L M Hensen ldquoEvaporativecooling by water spray systems CFD simulation experi-mental validation and sensitivity analysisrdquo Building andEnvironment vol 83 pp 129ndash141 2015

[28] X Wang and Y Li ldquoPredicting urban heat island circulationusing CFDrdquo Building and Environment vol 99 pp 82ndash972016

[29] K R Gautam L Rong G Zhang and M Abkar ldquoCom-parison of analysis methods for wind-driven cross ventilationthrough large openingsrdquo Building and Environment vol 154pp 375ndash388 2019

[30] M S Wong J E Nichol P H To and J Wang ldquoA simplemethod for designation of urban ventilation corridors and itsapplication to urban heat island analysisrdquo Building and En-vironment vol 45 no 8 pp 1880ndash1889 2010

[31] C-M Hsieh andH-C Huang ldquoMitigating urban heat islandsa method to identify potential wind corridor for cooling andventilationrdquo Computers Environment and Urban Systemsvol 57 pp 130ndash143 2016

[32] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor andBuilt Environment vol 26 no 3 pp 422ndash437 2017

[33] Z Qiao X Xu F Wu et al ldquoUrban ventilation networkmodel a case study of the core zone of capital function inBeijing metropolitan areardquo Journal of Cleaner Productionvol 168 pp 526ndash535 2017

[34] J Yang J Sun Q Ge and X Li ldquoAssessing the impacts ofurbanization-associated green space on urban land surfacetemperature a case study of Dalian Chinardquo Urban Forestry ampUrban Greening vol 22 pp 1ndash10 2017

[35] J Srebric M Heidarinejad and J Liu ldquoBuilding neighbor-hood emerging properties and their impacts on multi-scalemodeling of building energy and airflowsrdquo Building andEnvironment vol 91 pp 246ndash262 2015

[36] X Yang and Y Li ldquo-e impact of building density andbuilding height heterogeneity on average urban albedo andstreet surface temperaturerdquo Building and Environmentvol 90 pp 146ndash156 2015

[37] F Peng M S Wong Y Wan and J E Nichol ldquoModeling ofurban wind ventilation using high resolution airborne LiDARdatardquo Computers Environment and Urban Systems vol 64pp 81ndash90 2017

[38] C Yuan C Ren and E Ng ldquoGIS-based surface roughnessevaluation in the urban planning system to improve the windenvironmentmdasha study in Wuhan Chinardquo Urban Climatevol 10 pp 585ndash593 2014

12 Complexity

[39] C S B Grimmond and T R Oke ldquoAerodynamic propertiesof urban areas derived from analysis of surface formrdquo Journalof Applied Meteorology vol 38 no 9 pp 1262ndash1292 1999

[40] S J Burian M J Brown and S P Linger ldquoMorphologicalanalyses using 3D building databases los Angeles CaliforniardquoLos Alamos National Laboratory vol 74 2002

[41] T Gal and J Unger ldquoDetection of ventilation paths usinghigh-resolution roughness parameter mapping in a largeurban areardquo Building and Environment vol 44 no 1pp 198ndash206 2009

[42] E Ng C Yuan L Chen C Ren and J C H Fung ldquoIm-proving the wind environment in high-density cities by un-derstanding urban morphology and surface roughness astudy in Hong Kongrdquo Landscape and Urban Planningvol 101 no 1 pp 59ndash74 2011

[43] M S Wong and J E Nichol ldquoSpatial variability of frontal areaindex and its relationship with urban heat island intensityrdquoInternational Journal of Remote Sensing vol 34 no 3pp 885ndash896 2013

[44] RWMacdonald R F Griffiths and D J Hall ldquoAn improvedmethod for the estimation of surface roughness of obstaclearraysrdquo Atmospheric Environment vol 32 no 11 pp 1857ndash1864 1998

[45] M Bottema and P G Mestayer ldquoUrban roughness map-pingmdashvalidation techniques and some first resultsrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 74ndash76pp 163ndash173 1998

[46] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor BuiltEnvironment vol 26 no 3 pp 422ndash437 2017

Complexity 13

residential areas should be strengthened to improve theventilation environment

6 Discussion and Conclusion

Using the urban morphological characteristics identified byGIS technology this study established an LCVC method todescribe quantitatively the urban ventilation environment atthe city scale First the frontal area index of three-dimen-sional urban buildings representing the ventilation resis-tance coefficient was calculated using a morphologicalmethod Second the minimum cumulative ventilation costalgorithm was applied to draw the urban ventilation costmaps under different wind directions -en the wind fre-quency weighting was used to explore the urban ventilationenvironment in different seasons and the urban ventilationcorridors of different levels were analyzed Finally the re-sults of the summer ventilation analysis were verified usingthe LST retrieved from Landsat 8 data

-e analysis of the urban ventilation environmentbased on the morphology and GIS technology has theadvantage of high calculation efficiency In addition theresults of the analysis are highly compatible with otherspatial analysis and mapping technologies -is studyproposes that the cumulative costs of ventilation canbetter reflect the differences in urban ventilation

conditions than the ventilation resistance coefficient canIn contrast to the corridor extraction method based onempirical rules the proposed method can directly observethe dynamic characteristics of urban ventilation underdifferent wind directions and frequencies In contrast tothe least-cost path analysis method the results of theLCVC method cover the whole study area and the cor-ridor width and area can also be measured Howeverseveral issues require further study -e urban wind en-vironment is affected not only by ground roughness butalso by atmospheric environment and local temperaturefactors -erefore it is difficult to achieve an accurateanalysis of local wind speeds and directions from ananalysis of building form alone In addition it is difficultto obtain internal high-density real-time wind speed dataespecially wind speed measurements in a vertical direc-tion -is makes it difficult to improve the model pa-rameters -ese issues will be further studied

Data Availability

-e data used to support the findings of the study areavailable from the corresponding author upon request

Conflicts of Interest

-e authors declare that they have no conflicts of interest

Acknowledgments

-is study was supported by the National Key Research andDevelopment Program of China (Grant no2017YFB0503601) and China Postdoctoral Science Foun-dation (Grant no 2019M662726)

References

[1] H Wang G Wang J Qi et al ldquoScarcity-weighted fossil fuelfootprint of China at the provincial levelrdquo Applied Energyvol 258 2020

[2] L Jiang B Xue Z Ma L Yu B Huang and X Chen ldquoA life-cycle based co-benefits analysis of biomass pellet productionin Chinardquo Renewable Energy vol 154 pp 445ndash452 2020

[3] HWang H Schandl GWang and L Ma ldquoRegional materialflow accounts for China examining Chinarsquos natural resourceuse at the provincial and national levelrdquo Research andAnalysis vol 6 2019

[4] B-J He Z-Q Zhao L-D Shen H-B Wang and L-G LildquoAn approach to examining performances of coolhot sourcesin mitigatingenhancing land surface temperature underdifferent temperature backgrounds based on landsat 8 imagerdquoSustainable Cities and Society vol 44 pp 416ndash427 2019

[5] B-J He L Yang and M Ye ldquoStrategies for creating goodwind environment around Chinese residencesrdquo SustainableCities and Society vol 10 pp 174ndash183 2014

[6] H Akbari C Cartalis D Kolokotsa et al ldquoLocal climatechange and urban heat island mitigation techniquesndashthe stateof the artrdquo Journal of Civil Engineering and Managementvol 22 no 1 pp 1ndash16 2016

[7] D Zhou L Zhang L Hao G Sun Y Liu and C ZhuldquoSpatiotemporal trends of urban heat island effect along the

Table 5 Urban functional units and average ventilation costs

Reclassification Ventilation costPublic management and social organizations 0535Residential districts 0559Commerce 0519Factories 0483Companies 0545Public services 0568Everyday services 0545Hotels 0538Educational institutions 0554Medical institutions 0544Outdoor open areas 0469Transportation facilities 0541

Table 4 Statistical characteristics of LST at different levels ofventilation cost

Ventilationlevels

Temperature (degC)

Lowest Highest Range Average Standarddeviation

2 2928 5046 2118 3419 3643 2817 5259 2442 3496 3744 2967 5599 2632 3769 4055 2916 4947 2031 3882 3246 3189 5617 2428 3807 3247 3210 5024 1814 3795 3358 3285 5555 2270 4008 3029 3250 5470 2220 4012 26010 3434 5073 1639 4042 20511 3466 4683 1217 4053 171

Complexity 11

urban development intensity gradient in Chinardquo Science of9e Total Environment vol 544 pp 617ndash626 2016

[8] D Zhou S Zhao L Zhang G Sun and Y Liu ldquo-e footprintof urban heat island effect in Chinardquo Scientific Reports vol 5p 11160 2015

[9] K Zhang Y Chen and L Wu ldquoGrey spectrum analysis of airquality index and housing price in Handanrdquo Complexityvol 2019 2019

[10] Y Zhao Y Li and Q Chen ldquoAnalysis of a stochastic sus-ceptible-infective epidemic model in a polluted atmosphericenvironmentrdquo Complexity vol 2019 2019

[11] W Zhang C M Mak Z T Ai andWM Siu ldquoA study of theventilation and thermal comfort of the environment sur-rounding a new university building under constructionrdquoIndoor and Built Environment vol 21 no 4 pp 568ndash5822011

[12] R A W Albers P R Bosch B Blocken et al ldquoOverview ofchallenges and achievements in the climate adaptation ofcities and in the Climate Proof Cities programrdquo Building andEnvironment vol 83 pp 1ndash10 2015

[13] L W A van Hove C M J Jacobs B G HeusinkveldJ A Elbers B L van Driel and A A M Holtslag ldquoTemporaland spatial variability of urban heat island and thermalcomfort within the Rotterdam agglomerationrdquo Building andEnvironment vol 83 pp 91ndash103 2015

[14] A Guo J Yang X Xiao J Xia C Jin and X Li ldquoInfluences ofurban spatial form on urban heat island effects at the com-munity level in Chinardquo Sustainable Cities and Society vol 532020

[15] Z-Q Zhao B-J He L-G Li H-B Wang and A DarkoldquoProfile and concentric zonal analysis of relationships be-tween land useland cover and land surface temperature casestudy of Shenyang Chinardquo Energy and Buildings vol 155pp 282ndash295 2017

[16] E Ng ldquoPolicies and technical guidelines for urban planning ofhigh-density citiesmdashair ventilation assessment (AVA) ofHong Kongrdquo Building and Environment vol 44 no 7pp 1478ndash1488 2009

[17] B Chun and J-M Guldmann ldquoSpatial statistical analysis andsimulation of the urban heat island in high-density centralcitiesrdquo Landscape and Urban Planning vol 125 pp 76ndash882014

[18] Y He A Tablada and N H Wong ldquoA parametric study ofangular road patterns on pedestrian ventilation in high-density urban areasrdquo Building and Environment vol 151pp 251ndash267 2019

[19] J Yang Y Wang X Xiao C Jin J Xia and X Li ldquoSpatialdifferentiation of urban wind and thermal environment indifferent grid sizesrdquo Urban Climate vol 28 2019

[20] J Yang S Jin X Xiao et al ldquoLocal climate zone ventilationand urban land surface temperatures towards a performance-based and wind-sensitive planning proposal in megacitiesrdquoSustainable Cities and Society vol 47 2019

[21] Z Qiao L Liu Y Qin et al ldquo-e impact of urban renewal onland surface temperature changes a case study in the maincity of guangzhou Chinardquo Remote Sensing vol 12 no 5

[22] Z Qiao C Wu D Zhao et al ldquoDetermining the boundaryand probability of surface urban heat island footprint basedon a logistic modelrdquo Remote Sensing vol 11 no 11 2019

[23] Z Qiao X Xu M Zhao FWang and L Liu ldquo-e applicationof a binary division procedure to the classification of forestsubcategories using MODIS time-series data during 2000-2010 in Chinardquo International Journal of Remote Sensingvol 37 no 10 pp 2433ndash2450 2016

[24] M Bady S Kato T Takahashi and H Huang ldquoAn experi-mental investigation of the wind environment and air qualitywithin a densely populated urban street canyonrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 99 no 8pp 857ndash867 2011

[25] M Carpentieri P Salizzoni A Robins and L SoulhacldquoEvaluation of a neighbourhood scale street network dis-persion model through comparison with wind tunnel datardquoEnvironmental Modelling amp Software vol 37 pp 110ndash1242012

[26] K T Tse A U Weerasuriya X Zhang S Li andK C S Kwok ldquoPedestrian-level wind environment aroundisolated buildings under the influence of twisted wind flowsrdquoJournal of Wind Engineering and Industrial Aerodynamicsvol 162 pp 12ndash23 2017

[27] H Montazeri B Blocken and J L M Hensen ldquoEvaporativecooling by water spray systems CFD simulation experi-mental validation and sensitivity analysisrdquo Building andEnvironment vol 83 pp 129ndash141 2015

[28] X Wang and Y Li ldquoPredicting urban heat island circulationusing CFDrdquo Building and Environment vol 99 pp 82ndash972016

[29] K R Gautam L Rong G Zhang and M Abkar ldquoCom-parison of analysis methods for wind-driven cross ventilationthrough large openingsrdquo Building and Environment vol 154pp 375ndash388 2019

[30] M S Wong J E Nichol P H To and J Wang ldquoA simplemethod for designation of urban ventilation corridors and itsapplication to urban heat island analysisrdquo Building and En-vironment vol 45 no 8 pp 1880ndash1889 2010

[31] C-M Hsieh andH-C Huang ldquoMitigating urban heat islandsa method to identify potential wind corridor for cooling andventilationrdquo Computers Environment and Urban Systemsvol 57 pp 130ndash143 2016

[32] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor andBuilt Environment vol 26 no 3 pp 422ndash437 2017

[33] Z Qiao X Xu F Wu et al ldquoUrban ventilation networkmodel a case study of the core zone of capital function inBeijing metropolitan areardquo Journal of Cleaner Productionvol 168 pp 526ndash535 2017

[34] J Yang J Sun Q Ge and X Li ldquoAssessing the impacts ofurbanization-associated green space on urban land surfacetemperature a case study of Dalian Chinardquo Urban Forestry ampUrban Greening vol 22 pp 1ndash10 2017

[35] J Srebric M Heidarinejad and J Liu ldquoBuilding neighbor-hood emerging properties and their impacts on multi-scalemodeling of building energy and airflowsrdquo Building andEnvironment vol 91 pp 246ndash262 2015

[36] X Yang and Y Li ldquo-e impact of building density andbuilding height heterogeneity on average urban albedo andstreet surface temperaturerdquo Building and Environmentvol 90 pp 146ndash156 2015

[37] F Peng M S Wong Y Wan and J E Nichol ldquoModeling ofurban wind ventilation using high resolution airborne LiDARdatardquo Computers Environment and Urban Systems vol 64pp 81ndash90 2017

[38] C Yuan C Ren and E Ng ldquoGIS-based surface roughnessevaluation in the urban planning system to improve the windenvironmentmdasha study in Wuhan Chinardquo Urban Climatevol 10 pp 585ndash593 2014

12 Complexity

[39] C S B Grimmond and T R Oke ldquoAerodynamic propertiesof urban areas derived from analysis of surface formrdquo Journalof Applied Meteorology vol 38 no 9 pp 1262ndash1292 1999

[40] S J Burian M J Brown and S P Linger ldquoMorphologicalanalyses using 3D building databases los Angeles CaliforniardquoLos Alamos National Laboratory vol 74 2002

[41] T Gal and J Unger ldquoDetection of ventilation paths usinghigh-resolution roughness parameter mapping in a largeurban areardquo Building and Environment vol 44 no 1pp 198ndash206 2009

[42] E Ng C Yuan L Chen C Ren and J C H Fung ldquoIm-proving the wind environment in high-density cities by un-derstanding urban morphology and surface roughness astudy in Hong Kongrdquo Landscape and Urban Planningvol 101 no 1 pp 59ndash74 2011

[43] M S Wong and J E Nichol ldquoSpatial variability of frontal areaindex and its relationship with urban heat island intensityrdquoInternational Journal of Remote Sensing vol 34 no 3pp 885ndash896 2013

[44] RWMacdonald R F Griffiths and D J Hall ldquoAn improvedmethod for the estimation of surface roughness of obstaclearraysrdquo Atmospheric Environment vol 32 no 11 pp 1857ndash1864 1998

[45] M Bottema and P G Mestayer ldquoUrban roughness map-pingmdashvalidation techniques and some first resultsrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 74ndash76pp 163ndash173 1998

[46] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor BuiltEnvironment vol 26 no 3 pp 422ndash437 2017

Complexity 13

urban development intensity gradient in Chinardquo Science of9e Total Environment vol 544 pp 617ndash626 2016

[8] D Zhou S Zhao L Zhang G Sun and Y Liu ldquo-e footprintof urban heat island effect in Chinardquo Scientific Reports vol 5p 11160 2015

[9] K Zhang Y Chen and L Wu ldquoGrey spectrum analysis of airquality index and housing price in Handanrdquo Complexityvol 2019 2019

[10] Y Zhao Y Li and Q Chen ldquoAnalysis of a stochastic sus-ceptible-infective epidemic model in a polluted atmosphericenvironmentrdquo Complexity vol 2019 2019

[11] W Zhang C M Mak Z T Ai andWM Siu ldquoA study of theventilation and thermal comfort of the environment sur-rounding a new university building under constructionrdquoIndoor and Built Environment vol 21 no 4 pp 568ndash5822011

[12] R A W Albers P R Bosch B Blocken et al ldquoOverview ofchallenges and achievements in the climate adaptation ofcities and in the Climate Proof Cities programrdquo Building andEnvironment vol 83 pp 1ndash10 2015

[13] L W A van Hove C M J Jacobs B G HeusinkveldJ A Elbers B L van Driel and A A M Holtslag ldquoTemporaland spatial variability of urban heat island and thermalcomfort within the Rotterdam agglomerationrdquo Building andEnvironment vol 83 pp 91ndash103 2015

[14] A Guo J Yang X Xiao J Xia C Jin and X Li ldquoInfluences ofurban spatial form on urban heat island effects at the com-munity level in Chinardquo Sustainable Cities and Society vol 532020

[15] Z-Q Zhao B-J He L-G Li H-B Wang and A DarkoldquoProfile and concentric zonal analysis of relationships be-tween land useland cover and land surface temperature casestudy of Shenyang Chinardquo Energy and Buildings vol 155pp 282ndash295 2017

[16] E Ng ldquoPolicies and technical guidelines for urban planning ofhigh-density citiesmdashair ventilation assessment (AVA) ofHong Kongrdquo Building and Environment vol 44 no 7pp 1478ndash1488 2009

[17] B Chun and J-M Guldmann ldquoSpatial statistical analysis andsimulation of the urban heat island in high-density centralcitiesrdquo Landscape and Urban Planning vol 125 pp 76ndash882014

[18] Y He A Tablada and N H Wong ldquoA parametric study ofangular road patterns on pedestrian ventilation in high-density urban areasrdquo Building and Environment vol 151pp 251ndash267 2019

[19] J Yang Y Wang X Xiao C Jin J Xia and X Li ldquoSpatialdifferentiation of urban wind and thermal environment indifferent grid sizesrdquo Urban Climate vol 28 2019

[20] J Yang S Jin X Xiao et al ldquoLocal climate zone ventilationand urban land surface temperatures towards a performance-based and wind-sensitive planning proposal in megacitiesrdquoSustainable Cities and Society vol 47 2019

[21] Z Qiao L Liu Y Qin et al ldquo-e impact of urban renewal onland surface temperature changes a case study in the maincity of guangzhou Chinardquo Remote Sensing vol 12 no 5

[22] Z Qiao C Wu D Zhao et al ldquoDetermining the boundaryand probability of surface urban heat island footprint basedon a logistic modelrdquo Remote Sensing vol 11 no 11 2019

[23] Z Qiao X Xu M Zhao FWang and L Liu ldquo-e applicationof a binary division procedure to the classification of forestsubcategories using MODIS time-series data during 2000-2010 in Chinardquo International Journal of Remote Sensingvol 37 no 10 pp 2433ndash2450 2016

[24] M Bady S Kato T Takahashi and H Huang ldquoAn experi-mental investigation of the wind environment and air qualitywithin a densely populated urban street canyonrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 99 no 8pp 857ndash867 2011

[25] M Carpentieri P Salizzoni A Robins and L SoulhacldquoEvaluation of a neighbourhood scale street network dis-persion model through comparison with wind tunnel datardquoEnvironmental Modelling amp Software vol 37 pp 110ndash1242012

[26] K T Tse A U Weerasuriya X Zhang S Li andK C S Kwok ldquoPedestrian-level wind environment aroundisolated buildings under the influence of twisted wind flowsrdquoJournal of Wind Engineering and Industrial Aerodynamicsvol 162 pp 12ndash23 2017

[27] H Montazeri B Blocken and J L M Hensen ldquoEvaporativecooling by water spray systems CFD simulation experi-mental validation and sensitivity analysisrdquo Building andEnvironment vol 83 pp 129ndash141 2015

[28] X Wang and Y Li ldquoPredicting urban heat island circulationusing CFDrdquo Building and Environment vol 99 pp 82ndash972016

[29] K R Gautam L Rong G Zhang and M Abkar ldquoCom-parison of analysis methods for wind-driven cross ventilationthrough large openingsrdquo Building and Environment vol 154pp 375ndash388 2019

[30] M S Wong J E Nichol P H To and J Wang ldquoA simplemethod for designation of urban ventilation corridors and itsapplication to urban heat island analysisrdquo Building and En-vironment vol 45 no 8 pp 1880ndash1889 2010

[31] C-M Hsieh andH-C Huang ldquoMitigating urban heat islandsa method to identify potential wind corridor for cooling andventilationrdquo Computers Environment and Urban Systemsvol 57 pp 130ndash143 2016

[32] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor andBuilt Environment vol 26 no 3 pp 422ndash437 2017

[33] Z Qiao X Xu F Wu et al ldquoUrban ventilation networkmodel a case study of the core zone of capital function inBeijing metropolitan areardquo Journal of Cleaner Productionvol 168 pp 526ndash535 2017

[34] J Yang J Sun Q Ge and X Li ldquoAssessing the impacts ofurbanization-associated green space on urban land surfacetemperature a case study of Dalian Chinardquo Urban Forestry ampUrban Greening vol 22 pp 1ndash10 2017

[35] J Srebric M Heidarinejad and J Liu ldquoBuilding neighbor-hood emerging properties and their impacts on multi-scalemodeling of building energy and airflowsrdquo Building andEnvironment vol 91 pp 246ndash262 2015

[36] X Yang and Y Li ldquo-e impact of building density andbuilding height heterogeneity on average urban albedo andstreet surface temperaturerdquo Building and Environmentvol 90 pp 146ndash156 2015

[37] F Peng M S Wong Y Wan and J E Nichol ldquoModeling ofurban wind ventilation using high resolution airborne LiDARdatardquo Computers Environment and Urban Systems vol 64pp 81ndash90 2017

[38] C Yuan C Ren and E Ng ldquoGIS-based surface roughnessevaluation in the urban planning system to improve the windenvironmentmdasha study in Wuhan Chinardquo Urban Climatevol 10 pp 585ndash593 2014

12 Complexity

[39] C S B Grimmond and T R Oke ldquoAerodynamic propertiesof urban areas derived from analysis of surface formrdquo Journalof Applied Meteorology vol 38 no 9 pp 1262ndash1292 1999

[40] S J Burian M J Brown and S P Linger ldquoMorphologicalanalyses using 3D building databases los Angeles CaliforniardquoLos Alamos National Laboratory vol 74 2002

[41] T Gal and J Unger ldquoDetection of ventilation paths usinghigh-resolution roughness parameter mapping in a largeurban areardquo Building and Environment vol 44 no 1pp 198ndash206 2009

[42] E Ng C Yuan L Chen C Ren and J C H Fung ldquoIm-proving the wind environment in high-density cities by un-derstanding urban morphology and surface roughness astudy in Hong Kongrdquo Landscape and Urban Planningvol 101 no 1 pp 59ndash74 2011

[43] M S Wong and J E Nichol ldquoSpatial variability of frontal areaindex and its relationship with urban heat island intensityrdquoInternational Journal of Remote Sensing vol 34 no 3pp 885ndash896 2013

[44] RWMacdonald R F Griffiths and D J Hall ldquoAn improvedmethod for the estimation of surface roughness of obstaclearraysrdquo Atmospheric Environment vol 32 no 11 pp 1857ndash1864 1998

[45] M Bottema and P G Mestayer ldquoUrban roughness map-pingmdashvalidation techniques and some first resultsrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 74ndash76pp 163ndash173 1998

[46] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor BuiltEnvironment vol 26 no 3 pp 422ndash437 2017

Complexity 13

[39] C S B Grimmond and T R Oke ldquoAerodynamic propertiesof urban areas derived from analysis of surface formrdquo Journalof Applied Meteorology vol 38 no 9 pp 1262ndash1292 1999

[40] S J Burian M J Brown and S P Linger ldquoMorphologicalanalyses using 3D building databases los Angeles CaliforniardquoLos Alamos National Laboratory vol 74 2002

[41] T Gal and J Unger ldquoDetection of ventilation paths usinghigh-resolution roughness parameter mapping in a largeurban areardquo Building and Environment vol 44 no 1pp 198ndash206 2009

[42] E Ng C Yuan L Chen C Ren and J C H Fung ldquoIm-proving the wind environment in high-density cities by un-derstanding urban morphology and surface roughness astudy in Hong Kongrdquo Landscape and Urban Planningvol 101 no 1 pp 59ndash74 2011

[43] M S Wong and J E Nichol ldquoSpatial variability of frontal areaindex and its relationship with urban heat island intensityrdquoInternational Journal of Remote Sensing vol 34 no 3pp 885ndash896 2013

[44] RWMacdonald R F Griffiths and D J Hall ldquoAn improvedmethod for the estimation of surface roughness of obstaclearraysrdquo Atmospheric Environment vol 32 no 11 pp 1857ndash1864 1998

[45] M Bottema and P G Mestayer ldquoUrban roughness map-pingmdashvalidation techniques and some first resultsrdquo Journal ofWind Engineering and Industrial Aerodynamics vol 74ndash76pp 163ndash173 1998

[46] S Ling Chen J Lu and W W Yu ldquoA quantitative method todetect the ventilation paths in a mountainous urban city forurban planning a case study in Guizhou Chinardquo Indoor BuiltEnvironment vol 26 no 3 pp 422ndash437 2017

Complexity 13