Int. J. Environ. Res., 7(1):205-218, Winter 2013ISSN: 1735-6865

Received 2 May 2012; Revised 17 July 2012; Accepted 27 July 2012

*Corresponding author E-mail: elsaid.zahran@eng.asu.edu.eg

205

A Novel Approach to Improve the Air Quality Predictions of Air PollutionDispersion Modelling systems

Zahran, E. M. M.

Department of Public Works, Faculty of Engineering, Ain Shams University, Cairo, Egypt

ABSTRACT: The aim of this research paper is the introduction of a novel mathematical approach to improvethe accuracy of the results of air pollution dispersion models based on the calibration of input backgroundconcentrations. Using the Dunkirk area of the City of Nottingham in the UK as a case study, an air pollutionmodel in ADMS-Roads was created for developing the mathematical approach. The iterative application ofthis approach to the input background concentrations effectively reduced the error between not only theannual mean, but also the hourly, calculated and monitored air pollution concentrations. The traffic flowprofiles of the modelled road network were included in the air pollution model and their impact on the modelresults, after the application of the calibration approach, was investigated. The inclusion of the traffic flowprofiles reduced further the error between the hourly, but not the annual means of, calculated and monitoredconcentrations.

Key words: Calibration,Validation, Background concentrations,Modelling and air pollution

INTRODUCTIONModelling the air quality is a powerful technique

that can be used to assess the ambient air quality againstthe mandatory air quality standards. In addition, it canbe used to assess the effectiveness of the proposedAir Quality Action Plans (AQAPs) in improving the airquality within areas in which air pollution exceeds thenational air quality standards. This technique can alsobe used as a tool to undertake a strategic air qualityassessment for a wide range of plans and programmes,including local transport plans (NCC and NCC, 2006).As the majority of national air quality standards are inthe form of annual mean and hourly objectives (AEA,2010), this requires accurate annual mean and hourlyair quality predictions.

The results of air pollution dispersion modellingshould be accurate enough to provide reliable airquality predictions. Recent air pollution dispersionmodelling research assesses the validation of airpollution models by the determination of the errorbetween calculated and monitored air pollutionconcentrations. However, this recent research has notinvestigated potential sources of this error so that itcan be minimised (Majumdar et al., 2009; Cai and Xie,2010; Ginnebaugh et al., 2010; Jain and Khare, 2010;Parra et al., 2010).

Nottingham City Council compared the monitoredannual mean NO2 concentrations at three continuousmonitoring stations to the calculated concentrationsby ADMS-Urban. The model overestimated the annualmean of monitored concentrations at the three sites(PCS, 2008). Therefore, the model results weremultiplied by an adjustment factor, the average ratioof monitored to calculated annual mean concentrationsat the three monitoring sites, to correct the annualmean results of the model. This might help to improvethe annual mean results; however it did not improvethe hourly calculated results of the model.

Namdeo et al. (2002) used the hourly predictionsof ADMS-Urban and the hourly observations for thefirst half of 1993 to derive a multiplicative adjustmentfactor. The factor was applied to the air qualitypredictions for the second half of 1993 and theadjusted predictions were compared to thecorresponding observations. This approach improvedthe long-term results over the second half of 1993;however it did not show how much improvement wasachieved on the short-term level. In addition,Cambridge Environmental Research Consultants(CERC), the developers of ADMS software, haverecommended that modellers should avoid theapplication of such an adjustment factor to the model

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results (CERC, 2009). Instead, CERC advised thatvarious details of the model set-up, such as input dataand modelling options, should be adjusted until thecalculated results fit the monitored concentrations.

DEFRA (2009) stated that the NOX (not NO2)concentrations should be verified and adjusted if NO2results of the model disagree with the monitoredconcentrations. It also commented that “Theadjustment of NOX is often carr ied out on thecomponent derived from local Road Traffic Emissions– the Road Contribution”. This is because the sourcecontribution is often small compared with thebackground contribution. Therefore, Nottingham CityCouncil used this approach to verify the annual meanNO2 results of ADMS-Urban (PCS, 2010).

ADMS-Urban was used to predict the annual meanroad contribution NOX concentrations. For eachmonitoring site, the annual mean background NOX wasestimated from the national background maps andsubtracted from the monitored total NOX. This resultedin the monitored annual mean road contribution NOXwhich was compared to the results of ADMS-Urbanfor each monitoring site to derive an averageadjustment factor. The results of ADMS-Urban weremultiplied by this factor, and the adjusted results ofNOX were used, along with the background NO2concentrations, to derive the adjusted calculated totalannual mean NO2 concentrations by using the LAQMTools – NOX to NO2 spreadsheet (DEFRA, 2010).

This approach did not eliminate the error betweenthe calculated and monitored annual mean NO2concentrations. This is probably due to inaccuracy inthe monitored annual mean road contribution NOX,caused by inaccuracy in the estimation of the annualmean background NOX from the national backgroundmaps. In addition, the simple NOX to NO2 spreadsheetis usually imprecise, and using a chemistry scheme tomodel the atmospheric chemical reactions of NOX, andderive the oxidised NO2 proportion, is recommended(CERC, 2009). Moreover, this verification approach isonly suitable for the calculated annual meanconcentrations and is not applicable to the short-term,e.g. hourly, concentrations (CERC, 2009).

Li et al. (2010) adjusted the air pollution model set-up by the calibration of emission rate inputs to themodel through the application of a genetic algorithm.This was helpful to reduce the uncertainties existingin air pollution emission inventories such as thoserelevant to traffic emission factors (Belalcazar et al.,2010). The calibration of input emission rates slightlyreduced the error (by 6.46%) between daily calculatedand monitored PM10 concentrations over eight days.This implies a non-significant reduction in the error

between hourly calculated and monitoredconcentrations over a large time period such as a fullmeteorological year. Furthermore, no validation wasundertaken for the output results of the model,calculated using the calibrated emission rates, againstmonitored concentrations at monitoring sitesindependent of the calibration process. This processalso required a very expensive computing time, due tothe use of a genetic algorithm, which may extend toseveral weeks on a single PC before the actual runningof the air pollution model, which may extend to severaldays to model the air pollution dispersion in a studyarea (Barrett and Britter, 2008; Barrett and Britter, 2009).Therefore, this research paper introduces amathematical approach for adjusting the model set-upby the calibration of input background concentrations,in order to improve significantly the accuracy of themodel results and reduce the computing time. Thisincludes the introduction of four new concepts to thescience of air pollution dispersion modelling; namelymacro-calibration, macro-validation, micro-calibrationand micro-validation. The background concentrationsare some of the most important input data to the broadvariety of air pollution dispersion models (Venegas andMazzeo, 2006). They account for all emission sourcesthat may affect the air quality in a model applicationarea, and are not defined explicitly in the air pollutionmodel. Therefore, a great uncertainty exists in inputbackground concentrations which may vary for thesame model according to the number of explicitlydefined air pollution sources. Consequently, thecalibration of input background concentrations isnecessary to provide the appropriate backgroundconcentrations for a certain model set-up. It may alsoaccount for the uncertainties existing in input airpollution emission rates.

In the following sections of this paper, the set-upof the air pollution model of the Dunkirk area inNottingham is described and the error betweencalculated and monitored air pollution concentrationsis illustrated. Then, the different development stagesof the calibration process are discussed, along withthe reduction in the error after each stage. The impactof including the traffic profiles of the modelled roadnetwork on the error between calculated and monitoredconcentrations is explained. Finally, the calibration ofbackground concentrations in ADMS-Roads iscompared to the use of grid air pollution sources inADMS-Urban.

MATERIALS & METHODSAs a study area, Dunkirk Air Quality Management

Area (AQMA) was used to set-up an air pollution modelin ADMS-Roads version 2.3 for the initial development

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of the calibration approach. ADMS-Roads wasdeveloped by CERC (CERC, 2006). Dunkirk AQMA isan urban study area in the city of Nottingham, asshown in Fig. 1, with NO2 levels exceeding thepermissible levels (PCS, 2001). Therefore, NO2 wasselected as the modelled air pollutant as the majorityof the available air pollution monitoring data, requiredto calibrate and validate the air pollution model, in andaround the Dunkirk AQMA was NO2 data.

2006 was selected as the modelling year of the airpollution model due to data availability for this year.The significant industrial air pollution sources relevantto the Dunkirk AQMA were identified and their emissionrates were obtained from Nottingham City Council,which also provided the traffic speed data of the mainroads in the Dunkirk AQMA. The emission sourcesdefined explicitly in the air pollution model were thetraffic on the main roads within, and close to, theDunkirk AQMA, as shown in Fig 1, and the relevantsignificant industrial air pollution sources. TheNottingham Watnall Weather Station (MO, 2010)provided the 2006 hourly sequential meteorological datawhich included surface temperature, wind speed at 10-metre height above the ground surface, wind direction,precipitation, cloud cover and degree of humidity. The2006 annual mean and hourly monitored NOX, NO2 andO3 concentrations by the Air Quality MonitoringStation (AQMS), located in the Dunkirk AQMA as

Fig. 1. The Dunkirk AQMA

shown in Fig. 1, were provided by Nottingham CityCouncil.

The traffic flow data of the main roads in theDunkirk AQMA were obtained from Nottingham CityCouncil in the form of the traffic count every fiveminutes collected automatically using detector loopsembedded in the main roads. A Visual Basic forApplications (VBA) computer program was written inMS Excel in order to calculate automatically the 2006Annual Average Daily Traffic (AADT) flow and the2006 hourly and monthly traffic flow profiles from thefive-minute traffic counts. The traffic flow profiles werecompiled to a special text file, a FAC file, which wasused in ADMS-Roads to reflect the hourly and monthlyvariations in the AADT flow on traffic air pollutionemissions, so that for each hour, the traffic flow, usedin the model to derive the traffic emissions, was theAADT flow × monthly factor × hourly factor. The 2003DMRB traffic emission factors (DMRB, 2007), built-inin ADMS-Roads, were used to derive the trafficemission rates from the traffic flow and speed data.

The Chemical Reaction Scheme (CRS) was usedto model the atmospheric conversion of NOX to NO2due to a number of chemical reactions with backgroundO3 (CERC, 2006). Modelling these atmospheric reactionswas necessary to get accurate NO2 results, so NOXand O3 were modelled in addition to NO2. However,using this chemical scheme requires inputs for NO2,

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Calibration of Background Concentrations

NOX and O3 background concentrations. Therefore,Nottingham City Council provided the 2006 hourlysequential NO2, NOX and O3 concentrations monitoredby the Rochester air quality monitoring station. This isa rural monitoring station remote from the DunkirkAQMA and far from urban air pollution, and hence itwas recommended to use its monitoring data as theinput background concentrations to avoid doublecounting (CERC, 2009).Calibration and validation of the backgroundconcentrations

An output receptor was defined in the air pollutionmodel at the geographical location of the AQMS. Withreference to Run 1 in Table 1, the calculated 2006 annualmean NOX and NO2 concentrations underestimated the

monitored ones by 37.6% and 25.6% respectively atthe AQMS. In addition, the calculated 2006 annualmean of O3 concentrations overestimated themonitored one by 42.7% at the AQMS. Thisnecessitated developing the set-up of the air pollutionmodel by performing two operations. The firstoperation was the iterative calibration of the ruralbackground concentrations so as to account for theurban background emissions, e.g. residual, poorly-defined or diffused emissions, from domestic heatingsources and minor roads, in the Dunkirk AQMA. Thesecond operation was the validation of the calculatedair pollution concentrations after each iteration of thecalibration process, in order to decide the finalacceptable iteration of this process.

run 1 ∆ background calculated concentrations target concentrations NO2 0 26.25 35.29 NOX 0 42.19 67.60 O3 0 44.23 31.00 run 9 ∆ background calculated concentrations Target concentrations NO2 +7.70 37.27 35.29 NOX +25.42 67.61 67.60 O3 -12.60 28.99 31.00 run 23 ∆ background calculated concentrations Target concentrations NO2 +1.48 35.45 35.29 NOX +25.42 67.60 67.60 O3 -5.40 31.01 31.00 run A ∆ background calculated concentrations Target concentrations NO2 +7.02 36.89 35.29 NOX +25.42 67.61 67.60 O3 -12.40 28.86 31.00 run B ∆ background calculated concentrations Target concentrations NO2 +10.12 38.73 35.29 NOX +25.42 67.61 67.60 O3 -13.20 29.46 31.00 run C ∆ background calculated concentrations Target concentrations NO2 +14.55 41.64 35.29 NOX +25.42 67.61 67.60 O3 -15.30 29.18 31.00 run D ∆ background calculated concentrations Target concentrations NO2 +17.18 43.56 35.29 NOX +25.42 67.61 67.60 O3 -16.71 28.69 31.00

Table 1. Macro-calibration development stages of the rural background concentrations

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RESULTS & DISCUSSIONThe term macro-calibration in this research paper

refers to the adjustment of input backgroundconcentrations, so that the error between the annualmeans of calculated and monitored air pollutionconcentrations can be effectively reduced. The macro-validation was undertaken by the direct comparisonbetween the calculated and monitored annual meansof NOX, NO2 and O3 concentrations at the AQMS. Ascalculated NO2 concentrations were linked to calculatedNOX and O3 concentrations through the atmosphericchemical reactions discussed above, it was decided tocalibrate NOX and O3, in addition to NO2, backgroundconcentrations which were subsequently entered tothe air pollution model to calculate NO2, NOX and O3concentrations. A trial and error approach was adoptedto macro-calibrate the hourly sequential ruralbackground concentrations until the above mentionedmacro-calibration criterion was achieved. Thisapproach comprised 23 runs of the model, and involvedchanging the background concentrations manuallyevery time. In Table 1, the results of an intermediaterun (run 9), and the final macro-calibration run (run23), are shown in order to illustrate the progress of thisapproach. For each macro-calibration iteration, thevalues in the ∆background’ field of Table 1 were addedto every hour of the 2006 NO2, NOX, and O3 ruralbackground concentrations. However, adding thesevalues to the original background concentrations fileresulted in having many consecutive hours with anegative O3 background concentration which raisedan error and interrupted the model run. This technicalproblem was overcome by replacing the negative,invalid, O3 background concentrations with zero in themacro-calibrated background concentrations file.Another computer logic was applied to this file in orderto preserve the fact that NOX is NO + NO2. Hence, forevery hour in the macro-calibrated backgroundconcentrations file, if NO2 > NOX, then re-adjust themacro-calibrated background NO2 concentration sothat NO2 = NOX. This assumed a zero NO background

concentration for violating hours in the macro-calibrated background concentrations file. After eachiteration of the macro-calibration, the macro-validationwas undertaken by comparing the calculatedconcentrations and the target concentrations in Table1. The calculated concentrations were the 2006 annualmeans of calculated NO2, NOX, and O3 concentrationsand the target concentrations were the 2006 annualmeans of monitored NO2, NOX, and O3 concentrationsat the AQMS. Run 23 in Table 1 gave the least errorbetween the calculated and target concentrations.Therefore, the background concentrationscorresponding to this run were considered the finalmacro-calibrated background concentrations. Thevalues corresponding to the final macro-calibration runin Table 1 were used to derive Equations 8, 9 and 10,which could be used to evaluate directly thebackground concentration adjustment values, requiredto macro-calibrate the rural background concentrationsof the Dunkirk AQMA air pollution model, without thetrial and error approach:

where is the annual mean of monitored

NO2 concentrations, is the annual

mean of calculated NO2 concentrations using the ruralbackground concentrations and 9.2 is the differencebetween the values of calculated annual mean NO2concentrations of run 23 (the final macro-calibrationrun) and run 1 (performed using the uncalibrated ruralbackground concentrations) in Table 1.

where is the annual mean of monitored

NOX concentrations and is the

annual mean of calculated NOX concentrations usingthe rural background concentrations.

(8)

(9)

(10)

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where is the annual mean of monitored

O3 concentrations, is the annual

mean of calculated O3 concentrations using the ruralbackground concentrations and -13.22 is the differencebetween the values of calculated annual mean O3concentrations of run 23 (the final macro-calibrationrun) and run 1 (performed using the uncalibrated ruralbackground concentrations) in Table 1.

The term micro-calibration in this research paperrefers to the adjustment of input backgroundconcentrations so that the error between not only theannual means of, but also the hourly, calculated andmonitored air pollution concentrations can be effectivelyreduced. The micro-calibration extends the macro-calibration as shown in Fig. 2. The micro-validation wasundertaken by comparing statistically two one-dimensional arrays of the 2006 calculated and monitoredhourly sequential NO2 concentrations at the AQMS.Pearson Correlation Coefficient (r) and the Root MeanSquare Error (RMSE) were used to compare the two arrays.Further details about these two descriptive statistics aregiven in Hanna et al. (1991; 1993) and Jain and Khare(2010). The slope of the regression line through the originwas also used to compare the two arrays of hourlycalculated and monitored concentrations.

The Dunkirk AQMA air pollution model was runwith the uncalibrated rural background concentrationsfile to output the 2006 calculated hourly NO2concentrations at the AQMS. This was carried out forthe identification of the initial discrepancy, before anycalibration, between the 2006 calculated and monitoredhourly NO2 concentrations at the AQMS, as shown inFig. 3. Then, the model was run with the macro-calibrated background concentrations file,corresponding to run 23 in Table 1, to output the 2006calculated hourly NO2 concentrations at the AQMS.This was for the micro-validation after the macro-calibration of the rural background concentrations asshown in Fig. 4. Pearson’s correlation coefficients werecalculated as 0.541 before any calibration, and then as0.412 after the macro-calibration, as shown in Fig.s 3and 4. The slight decline in Pearson’s correlationcoefficient after the macro-calibration implied that themacro-calibration slightly decreased the degree oflinearity of the actual relationship between the

calculated and monitored hourly NO2 concentrationsat the AQMS. Hence, the macro-calibration slightlyincreased the drift of the shape of this actualrelationship away from the perfect straight-linerelationship. On the other hand, the values of theRMSE were calculated as 18.45 µg/m3 before thecalibration, and then as 17.39 µg/m3 after the macro-calibration, as shown in Figs 3 and 4. The slight declinein the RMSE after the macro-calibration implied thatthe macro-calibration slightly lowered the differencebetween the calculated and monitored hourly NO2concentrations. Therefore, the macro-calibration notonly improved the NO2 predictions of the model on themacro, annual mean, level but also slightly improvedthe NO2 predictions on the micro, hourly, level. Theslope of the best fit line through the origin of the actualrelationship between the calculated and monitoredhourly NO2 concentrations at the AQMS wascalculated as 0.631 before any calibration, and then as0.755 after the macro-calibration, as shown in Figs 3and 4. Although the results of the macro-calibration,corresponding to run 23 in Table 1, very slightlyoverestimated the 2006 annual mean of monitored NO2concentrations at the AQMS, the slope of the best fitline through the origin after the macro-calibration wasless than 1.0. This indicated that, after the macro-calibration, the model generally underestimated themonitored NO2 concentrations at the AQMS on themicro, hourly, level. However, the slight increase in theslope of the best fit line after the macro-calibrationimplied that the macro-calibration slightly reduced thetendency of the model to underestimate the monitoredhourly NO2 concentrations at the AQMS. This,together with the reduction in the RMSE after themacro-calibration, confirmed the slight improvementof the NO2 predictions of the model, after the macro-calibration, on the micro, hourly, level.

To improve further the NO2 predictions of themodel on the micro level, the idea of micro-calibrationwas developed. This idea depended on themodification of Equations (8), (9) and (10) in order togenerate three one-dimensional arrays for ∆ NO2background, ∆ NOX background and ∆ O3 backgroundas follows:

where is the adjustment value for

the rural NO2 background concentration for the hour i.

is the monitored hourly NO2

(11)

(12)

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Fig. 2. Calibration and validation process for rural background concentrations

Fig. 3. Scatter diagram of hourly NO2 concentrations at the AQMS before any calibration

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Calibration of Background Concentrations

concentration for the hour i. is the

calculated hourly NO2 concentration for the hour i usingthe uncalibrated rural background concentrations.

is the calculated hourly NO2

concentration for the hour i using the macro-calibratedbackground concentrations. The value of i ranged from1 to 8760, which was the total number of hours in theyear 2006. 1.48 is the macro-calibration adjustment valuefor the rural NO2 background concentrations, as givenin the column headed ‘D background’ in Table 1 forrun 23 (the final macro-calibration run).

where is the adjustment value

for the rural NOX background concentration for the

hour i. is the monitored hourly NOX

concentration for the hour i. is the

calculated hourly NOX concentration for the hour iusing the uncalibrated rural backgroundconcentrations. The value of i ranged from 1 to 8760,which was the total number of hours in the year 2006.

where is the adjustment value for

the rural O3 background concentration for the hour i.

is the monitored hourly O3

concentration for the hour i. is the

calculated hourly O3 concentration for the hour i usingthe uncalibrated rural background concentrations.

is the calculated hourly O3 concentration

(13)

Fig. 4. Scatter diagram of hourly NO2 concentrations at the AQMS after macro-calibration

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for the hour i using the macro-calibrated backgroundconcentrations. The value of i ranged from 1 to 8760,which was the total number of hours in the year 2006.-5.4 is the macro-calibration adjustment value for therural O3 background concentrations, as given in thecolumn headed ∆ background’ in Table 1 for run 23(the final macro-calibration run). The three one-dimensional arrays of ∆ NO2 background, ∆ NOX backgroundand ∆ O3 background, calculated by Equations (11), (12) and(13), were added to the arrays of the un-calibratedhourly sequential rural background concentrations ofNO2, NOX and O3, respectively. Hence the micro-calibrated background concentrations file was createdbased on the above three equations. However, runningthe model with these micro-calibrated backgroundconcentrations resulted in the overestimation of theannual means of the monitored NO2, NOX and O3concentrations at the AQMS as shown in Table 2. Inaddition, using these micro-calibrated backgroundconcentrations increased the difference between thecalculated and monitored hourly NO2 concentrationson the micro, hourly, level. This was indicated by thelarge increase in the RMSE as shown in Table 2.

A possible reason for the large increase in theRMSE after the micro-calibration based on Equations(11), (12) and (13) was the use of the hourlyconcentrations calculated using the macro-calibratedbackground concentrations in these equations. Asdiscussed before with regard to Fig. 4, the hourlycalculated concentrations of the macro-calibratedmodel were not precise enough. The macro-calibratedmodel of the Dunkirk AQMA was validated only onthe macro, annual mean, level. Therefore, instead of

using and , the hourly NO2 and O3

concentrations calculated by the macro-calibratedmodel, it was decided to alter two of the three equationsfor the micro-calibration of the rural backgroundconcentrations, using the annual mean NO2 and O3concentrations calculated by the macro-calibratedmodel, so that:

where is the adjustment value for

the rural NO2 background concentration for the hour i.

(14)

is the monitored hourly NO2

concentration for the hour i. is the

calculated hourly NO2 concentration for the hour i usingthe uncalibrated rural background concentrations. Thevalue of i ranged from 1 to 8760, which was the total

number of hours in the year 2006. is the

annual mean NO2 concentration calculated using themacro-calibrated background concentrations, as givenin the column headed ‘calculated concentrations’ inTable 1 for a particular macro-calibration run.

is the annual mean NO2

concentration calculated using the uncalibrated ruralbackground concentrations, as given in the columnheaded ‘calculated concentrations’ in Table 1 for run

1. is the macro-calibration

adjustment value for the rural NO2 backgroundconcentrations, as given in the column headed∆background’ in Table 1 for a particular macro-calibration run.

where is the adjustment value for

the rural O3 background concentration for the hour i.

is the monitored hourly O3

concentration for the hour i. is the

calculated hourly O3 concentration for the hour i usingthe uncalibrated rural background concentrations. Thevalue of i ranged from 1 to 8760, which was the total

number of hours in the year 2006. is the

annual mean O3 concentration calculated using the

macro-calibrated background concentrations, as given

(15)

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Zahran, E. M. M.

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Int. J. Environ. Res., 7(1):205-218, Winter 2013

in the column headed ‘calculated concentrations’ inTable 1 for a particular macro-calibration run.

is the annual mean O3 concentration

calculated using the uncalibrated rural backgroundconcentrations, as given in the column headed‘calculated concentrations’ in Table 1 for run 1.

is the macro-calibration

adjustment value for the rural O3 backgroundconcentrations, as given in the column headed∆ background’ in Table 1 for a particular macro-calibration run.

A VBA computer program was written in MS Excelin order to automate the generation of the three hourlysequential one-dimensional arrays for ∆ NO2 background, ∆NOX background and ∆ O3 background using Equations (14), (12)and (15). For any hour in the year 2006, if either thecalculated or monitored hourly concentration wasmissing, then the equation relevant to the type ofmissing concentration would not be usable. This washandled in the VBA computer program as follows:

for the

hours of missing hourly NO2 concentrations, ∆

NOX background i = ∆ NOX macro background for the hours ofmissing hourly NOX concentrations, and ∆ O3 background i= ∆ O3 macro background for the hours of missing hourly O3concentrations. The VBA computer program appliedEquations (14), (12) and (15) using the valuescorresponding to run 23 in Table 1 to generate themicro-calibrated background concentrations file.Running the Dunkirk AQMA air pollution model withthis background concentrations file significantlyimproved the RMSE, r and the slope of the best fit linethrough the origin as shown in Table 2 and Fig. 5. Thisindicated a significant improvement for NO2 hourlypredictions by the model when using this backgroundconcentrations file. However, the model with thisbackground concentrations file underestimated theannual mean of monitored NO2 concentrations, andoverestimated the annual mean of monitored O3concentrations, at the AQMS as shown in Table 2.Hence, using the trial and error macro-calibrationapproach, it was necessary to undertake additionalruns of ADMS-Roads, beyond run 23, as shown inTable 1.

The background concentrations of theseadditional macro-calibration runs were modified so thatthe annual mean of monitored NO2 concentrations wasdeliberately overestimated, and the annual mean ofmonitored O3 concentrations was deliberately

Fig. 5. Scatter diagram of hourly NO2 concentrations at the AQMS after the micro-calibration based on Run 23

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underestimated, by these runs, named A-D in Table 1.Consequently, after the ‘normal’ micro-calibrationunderestimation of the annual mean of monitored NO2concentrations and the ‘normal’ micro-calibrationoverestimation of the annual mean of monitored O3concentrations, the micro-calibration runs based onthe results of these additional macro-calibration runsgave a good estimate of the annual means of both themonitored NO2 and O3 concentrations at the AQMS.This not only improved the results of the micro-calibrated model on the macro level, but also furtherimproved the results on the micro level as shown inTable 2 and Fig. 6. Therefore, the micro-calibratedbackground concentrations obtained by Equations (14),(12) and (15), based on the values corresponding torun D in Table 1, were considered the final micro-calibrated background concentrations.

The micro-calibration development, from run 23 torun D, increased the error between the calculated andmonitored NO2 concentrations at a few hours, asimplied by the comparison between the scatter in theoverestimated points on the lower left side of Figs 5and 6. A thorough investigation was undertaken inorder to identify the reason for such unexpectedbehaviour of the micro-calibration process at thesehours. A potential reason was the very high ratio ofthe monitored NOX concentration to the monitored NO2concentrations, e.g. 7, which was accompanied by a

high monitored O3 concentration at these hours.However, a high calculated NOX concentration by theair pollution model was accompanied by highcalculated NO2 concentration and low calculated O3concentration at these hours. This suggested eitherimprecise model simulation of the actual atmosphericchemical reactions between NOX and O3 due toinaccurate input meteorological data or imprecisemonitoring data at these hours. The high monitoredNOX concentration resulted in a high increase in theNOX background concentration due to the micro-calibration at these hours. Such a high increase in theNOX background concentration substantiallyincreased the calculated NO2 concentration, resultingin a big difference between the calculated and lowmonitored NO2 concentrations at these hours. At someof these hours, for which the NO2 concentration wasunderestimated before any calibration, the micro-calibration iterations increased the background NO2concentration in order to increase the calculated NO2concentration, which changed the NO2 underestimationinto an increasingly greater NO2 overestimation. At therest of these hours, for which the NO2 concentrationwas overestimated before any calibration, the reductionin calculated NO2 concentration due to the micro-calibration iterations was masked by the increase incalculated NO2 concentration due to the high NOXbackground concentration.

Fig. 6. Scatter diagram of hourly NO2 concentrations at the AQMS after the micro-calibration based on Run D

Calibration of Background Concentrations

CONCLUSIONThe macro-calibration of background

concentrations reduced effectively the error betweenthe calculated and monitored annual means of NOX,NO2, and O3 concentrations. The iterative applicationof the micro-calibration equations (14), (12) and (15) tobackground concentrations reduced effectively theerror between the calculated and monitored annualmeans of NOX, NO2, and O3 concentrations, and alsothe error between the hourly calculated and monitoredNO2 concentrations. Further research is required toadapt the macro-calibration and micro-calibrationequations for modelling the air pollution dispersion ofinert pollutants, e.g. CO and PM. As chemical reactionswill not be considered, the calibration equations mayreduce to one equation for the macro-calibration, andone equation for the micro-calibration, of the inputbackground concentrations. For the hours with missingmonitored air pollution concentrations, the micro-calibration equations were unusable. This wasaddressed by using the macro-calibrated backgroundconcentrations for these hours. As the macro-calibratedbackground concentrations give less precise calculatedconcentrations on the hourly level, such a strategymay reduce the reliability of the number of exceedancesand percentiles predicted by the air pollution model.Therefore, for the hours with missing monitored airpollution concentrations, further research is neededto investigate the impact of using the macro-calibratedbackground concentrations on the reliability of thepredicted number of exceedances and percentiles bythe air pollution model. In case of a significant adverseimpact, further research is recommended into the micro-calibration of the rural background concentrations ofthese hours, based on the meteorological data and themicro-calibrated background concentrations of otherhours with monitored concentrations. The inclusionof the hourly and monthly traffic profiles in the DunkirkAQMA air pollution model did not have a significantimpact on the error between the annual means ofcalculated and monitored concentrations. On the otherhand, the inclusion of these traffic profiles did reducethe RMSE between the hourly calculated andmonitored NO2 concentrations by 28.4%. As theDunkirk AQMA air pollution model did not include alarge number of road sources, further research isrecommended to investigate the impact of includingthe monthly and hourly traffic profiles on the micro-validation of an air pollution model that has a largenumber of road sources. This is to correlate betweenthe number of road sources with traffic profiles in theair pollution model and the possible reduction in theRMSE between the hourly calculated and monitoredNO2 concentrations.

ACKNOWLEDGEMENTThe authors wish to thank Nottingham City

Council for providing the air pollution and traffic data.The authors also wish to acknowledge the support ofCERC helpdesk regarding the use of ADMS-Roads andADMS-Urban.

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