Air-quality Modelling in the Lake Baikal Region

Environ Monit Assess (2010) 165:665674DOI 10.1007/s10661-009-0977-7

Air-quality modelling in the Lake Baikal region

Karen Van de Vel Clemens Mensink Koen De Ridder Felix Deutsch Joachim Maes Jo Vliegen Artash Aloyan Alexander Yermakov Vardan Arutyunyan Tamara Khodzher Bas Mijling

Received: 15 December 2008 / Accepted: 13 May 2009 / Published online: 3 June 2009 Springer Science + Business Media B.V. 2009

Abstract In this paper, we assess the status ofthe air quality in the Lake Baikal region whichis strongly influenced by the presence of anthro-pogenic pollution sources. We combined the localdata, with global databases, remote sensing im-agery and modelling tools. This approach allowsto inventorise the air-polluting sources and toquantify the air-quality concentration levels in theLake Baikal region to a reasonable level, despitethe fact that local data are scarcely available. Inthe simulations, we focus on the month of July

K. Van de Vel (B) C. Mensink K. De Ridder F. Deutsch J. Maes J. VliegenFlemish Institute for Technological Research (VITO),Boeretang 200, 2400 Mol, Belgiume-mail: [email protected]

A. Aloyan V. ArutyunyanRussian Academy of Sciences,Institute for Numerical Mathematics,Moscow, Russia

A. YermakovRussian Academy of Sciences, Institute of EnergyProblems of Chemical Physics, Moscow, Russia

T. KhodzherRussian Academy of Sciences Siberian Branch,Limnological Institute, Irkutsk, Russia

B. MijlingRoyal Netherlands Meteorological Institute,De Bilt, The Netherlands

2003, as for this period, validation data are avail-able for a number of ground-based measurementstations within the Lake Baikal region.

Keywords Air quality Lake Baikal Pollution Air pollution

Introduction

Lake Baikal is the largest fresh water lake in theworld, shaped as a half moon in a mountain hol-low. The region around the lake is characterisedby complex terrain, consisting of steep hill slopescovered with thick taiga and forests. In the mid-1990s, one of the major industrial zones of Siberiaemerged along the Angara River located to thewest of the lake. Today, there are several largecities and industrial centres such as Irkutsk, An-garsk, Cheremkovo and Usolye-Sibirskoye. Themain industries are metal industry, energy, log-ging, oil and fuels, machine-building, chemicals,food industry and hydro-electricity. The total pop-ulation of the metropolitan area amounts to overone million. Despite the fact that Lake Baikalhouses the highest level of biodiversity in theworld, it is under serious threat due to anthro-pogenic pollution. Besides water pollution, airpollution also forms a threat to the fauna andflora in and around the lake, as well as for theinhabitants of the Lake Baikal area. The Lake

666 Environ Monit Assess (2010) 165:665674

Baikal region is one of the areas in Siberia expe-riencing high level of air pollution (Cofala et al.2005). However, local air quality data, both emis-sion levels for pollutants as well as air qualityconcentrations measurements, are sparse.

We combined the available local data withglobal databases, remote sensing imagery andmodelling tools in order to assess the air qualityin the Lake Baikal region. The emission map-ping (E-MAP) GIS tool was applied in orderto set up an emission inventory. E-MAP com-bines local emission data (bottom-up approach)with the top-down emission methodology, in or-der to represent the actual situation as closelyas possible. The regional-scale air quality modelAURORA has been used in order to analyse theair quality situation and local air flow patterns.The AURORA model is driven by the advancedregional prediction system (ARPS) meteorolog-ical model and the E-MAP emission data. Wedetermined the pollution levels for the month ofJuly 2003.

The remainder of this paper is organised asfollows. In Study domain and spatial data pro-cessing, a description is given of the studyarea together with the spatial data employed inorder to characterise the study domain. Meteoro-logical calculations describes the ARPS me-teorological calculations that provide input forthe AURORA air quality model. Emissionmodelling gives a description of the E-MAPmethodology to calculate pollutant emissions. Themeteorological fields and the emissions are sub-sequently used in the 3-D regional-scale air qual-ity model AURORA which is presented in TheAURORA air quality model. Meteorologicaland air quality simulations presents the val-idation results, and conclusions are drawn inConclusions.

Study domain and spatial data processing

The study area is located in Southeastern part ofSiberia, Russia, as shown in Fig. 1; the domain is150 150 km2 at a resolution of 3 km. As men-tioned in the Introduction it contains the majorpart of the Lake Baikal together with several largecities and industrial centres, including Irkutsk,

Angarsk, Cheremkovo and Usolye-Sibirskoye, tothe west of the lake.

In order to drive the atmospheric mod-els (for meteorology (ARPS) and air quality(AURORA)) as well as the E-MAP emissionmodel, we need to characterise the land surface.Below, we describe the different types of spatialdata used.

The land use map (second panel of Fig. 1) wasderived from the Global Land Cover (GLC2000)database of Joint Research Center (ref). TheGLC2000 database is a global gridded 1-km res-olution land cover map. It contains over 18 differ-ent land use categories. For the sake of the modelrequirements, these land use categories were ag-gregated to a smaller number of classes. As shownin Fig. 1, this yields the following categories:water bodies, continuous urban, discontinuousurban, industrial, pasture, crops, forest, snow/iceand shrubs.

From Fig. 1, it can be seen that the built-up landuse types are mainly located in the Angara valley.The major part of the study domain is covered bythe natural-agricultural land use types.

For the meteorological and air quality mod-eling, one needs to specify the fractional greenvegetation cover of each model grid cell at thesurface. This value was derived from the normal-ized difference vegetation index (NDVI) takenfrom SPOT-VEGETATION imagery. The leftpanel in Fig. 1 shows a monthly mean fractionalgreen vegetation cover for the month of July2003, the inhabited areas (sparse vegetation) con-trast to the more rural areas (vegetation moreabundant).

The third panel in Fig. 1 shows the land andwater surface temperatures for July 2003, withthe warmer lake water temperature during sum-mer time. The land surface temperature is amonthly average of the final analysis (FNL) me-teorological data from the US National Centrefor Environmental Prediction (NCEP). The watersurface temperature has been derived fromMODIS imagery.

Terrain height was interpolated from theGlobal 30 arcsec elevation dataset distributed bythe US Geological Survey (right panel in Fig. 1).The southern part of the Lake Baikal is especiallysurrounded by steep hills.

Environ Monit Assess (2010) 165:665674 667

Fig. 1 Simulation domainfor the atmosphericmodels, with centre atlongitude 104.25 andlatitude 52.3, at 3 kmresolution, covering150 150 km2, includingthe lake Baikal and thecity of Irkutsk. Theupper-left panel gives thesatellite-derivedpercentage vegetationcover. The upper-rightpanel shows the land usetypes, as follows: water(1), continuous urban (2),discontinuous urban (3),industrial (4), pasture (5),crops (6), forest (7),snow/ice (8), shrubs (9).The lower-left panelspecifies the land andwater surfacetemperature. Thelower-right panel givesthe terrain height

Other types of geographical information arerequired for the emission modelling, amongst oth-ers, the spatial distributions of population density(total, urban and rural) and the position of roads.The population density and road distribution wasobtained from global databases. (LANDSCAN-ORNL, global gridded 1-km population density).

In order to account for pollution sources andair pollution outside the actual study domain, boththe ARPS and AURORA models have nestingcapabilities, such that these effects can enter thedomain through the lateral boundaries. In thisstudy, we used three nesting steps in order togo from a large domain at coarse resolution tothe domain of interest at high resolution. The

outermost model domain was taken 1,800 km wideat 30-km resolution. The lateral boundary condi-tions used for this outer domain are mentioned inMeteorological calculations (meteorology) andThe AURORA air quality model (air quality),respectively. Within this domain, a smaller do-main was nested with a spatial extension of 500 kmand a resolution of 10 km. Within the latter, a stillsmaller domain covering an area of 150 150 km2and a resolution of 3 km. This is the final domainas shown in Fig. 1, as it contains the Southern LakeBaikal together with the major cities in the areawest of the lake.

Figure 2 gives an overview of the interplay be-tween the different resolution simulations as well


Fig. 2 Schematic overview of the AURORA-ARPS-E-MAP coupling as well as the interplay between the differ-ent nesting levels

as of the interplay between the ARPS meteoro-logical and AURORA air quality models; they aredefined on exactly the same model domain. TheE-MAP emission tool calculates air emissions atevery resolution level within the nesting chain.

Meteorological calculations

Meteorological fields, required for the AURORAmodel, were simulated using the ARPS, a non-hydrostatic mesoscale atmospheric model devel-oped at the University of Oklahoma (Xue et al.2000, 2001). The finite-difference equations of themodel are discretised on an Arakawa C-grid, em-ploying terrain-following coordinates. Advectionis solved with a fourth order central-differencingscheme and leapfrog time stepping. Turbulenceis represented by the 1.5 order turbulence modeland the Sun and Chang (1986) parameterisa-tion for the convective boundary layer. ARPScontains detailed parameterisations for cloud mi-crophysics, cumulus convection and radiationtransfer. As mentioned in Study domain andspatial data processing, ARPS has nesting capa-bilities, allowing large-scale atmospheric featuresto enter the domain through the lateral bound-aries. In order to specify the lateral boundaryconditions for the meteorological model (run at30 km resolution), we make use of archived outputdata from the FNL data from the US NCEP asillustrated in Fig. 2. These data are provided ata six-hourly time step, and at a resolution of 1(approx 100 km), they were interpolated to themodel grid. A detailed land surface scheme (DeRidder and Schayes 1997) was incorporated in

ARPS to calculate the energy and water fluxesbetween the land surface and the atmosphere,including the effects of vegetation and soils on thepartitioning of incident radiant energy betweenthe turbulent fluxes of sensible and latent heat andthe storage heat flux. In order to better representurban surfaces, the land surface model was modi-fied by including the Brutsaert (1975) temperatureroughness parameterisation (De Ridder 2006).

Emission modelling

The AURORA air quality model needs a correctspecification of the position and the strength ofthe emission sources. AURORA distinguishes be-tween ten anthropogenic emission classes and alsotakes biogenic emissions into account. In order torepresent the actual situation as closely as pos-sible, emission data from different sources werecombined: local data supplemented with global ornational emission inventories.

For the Irkutsk area, local emission data wereobtained for the sectors of industry and for com-munal heating systems and for the pollutants NOx,SO2 and CO. We note that emissions related tobuilding heating are of little importance for thesimulation period (July 2003) as high tempera-tures were recorded (see Meteorological and airquality simulations).

For the areas within the three study domainswhere no local emission data were available, aswell as for the other emission sectors, emissiondata were obtained with the E-MAP GIS tool(Maes et al. 2009). E-MAP performs a spatial dis-aggregation of the Emission Database for GlobalAtmospheric Research (EDGAR 3.2; Olivier andBerdowski 2001) emission inventory by using spa-tial surrogate data. The spatial variables includethe data mentioned in Study domain and spa-tial data processing. The EDGAR 3.2 emissioninventory uses the top-down approach (Samaraset al. 1995) in order to estimate emissions. An-thropogenic emissions of NOx, SO2, CO, and non-methane VOC are available for the sectors ofpower generation, industry, traffic and the domes-tic sector, as well as for biomass burning.

The annual emissions are distributed tempo-rally according to monthly (JanuaryDecember),


Fig. 3 Emission maps for the 3-km resolution domain showing NO2 (left panel) and SO2 (right panel) emissions

daily (MondaySunday) and hourly (023 h) fac-tors. These factors are specific to each pollutantand emission sector and, hence, reflect the differ-ent energy-use patterns as a function of time.

Apart from the above-mentioned emissions,biogenic emissions from forests were calculatedfollowing the methodology described in Simpsonet al. (1999), using the land cover data mentionedpreviously as well as temperature and shortwaveradiation values simulated by the ARPS meteoro-logical model.

Figure 3 shows a 2-D plot of NOx emissions(summed over all emission sectors) for the 3-kmresolution domain. One can clearly distinguish thecity of Irkutsk as well as the industrialised areas inthe Angara valley.

The AURORA air quality model

Regional-scale air quality was calculated withthe atmospheric transport-chemistry modelAURORA (Air quality modelling in urbanregions using an optimal resolution approach,see De Ridder et al. 2008).

In AURORA, advection is treated using theWalcek (2000) scheme, which is monotonic, ex-hibits a relatively limited numerical diffusion

and comes at a reasonable computational cost.Vertical diffusion is calculated with the CrankNicholson method (De Ridder and Mensink2002). For dry deposition, AURORA uses theWesely and Hicks (2000) formalism based on aresistance network. While the aerodynamic resis-tance and the laminar resistance can be readilycalculated from the frictional velocity, the canopyresistance depends on the land use type as well ason the degree of vegetation in a grid cell.

The gaseous-phase chemistry is treated bymeans of the CarbonBond IV scheme (Geryet al. 1989), which was enhanced to include theeffect of biogenic isoprene and terpene emissions.It covers the chemical reactions in the gas phaseand accounts for the formation of oxidants inthe atmosphere, e.g. ozone, sulphuric acid andnitric acid. Particulate matter concentrations werecalculated although only for secondary aerosolsas there are no emission data available for pri-mary particulate matter. As we do not disposeof emission data for all pollutants and given thefact that no boundary conditions are availablefor particulate species (see below), we used em-pirical relations for the gas-to-particle conversionto sulfate, ammonium and nitrate aerosols (VanEgmond and Kesseboom 1985; Calvert et al. 1978;De Leeuw et al. 1990).


Large-scale pollutant concentrations, which arerequired to account for remote emission sources,were interpolated from output generated by thechemistry-transport model TM4 (van Noije et al.2006) as shown in Fig. 2. TM4 is an offline3-D model which provides concentration levelsfor gaseous species; TM4 is driven by assimilatedmeteorological fields from the European Centrefor Medium-Range Weather Forecasts. It has ahorizontal resolution of 3 2 with 25 layersin the vertical. For the particulate species, zero-boundary conditions were applied for the outerdomain.

Meteorological and air quality simulations

The ARPS/AURORA simulations were per-formed for the month of July 2003. Only resultsfrom the smallest model domain will be presented,unless stated otherwise. In the next subsections,the validation of the individual model resultsfor emissions, meteorology and air quality arediscussed.

Emission validation

In order to assess the uncertainty involved in theemission estimates (which provide crucial inputfor the air quality modelling), it is of interest tocompare the two datasets that are available: onone hand, the local emission data, and the top-down EDGAR 3.2 emission database on the otherhand. As mentioned in Meteorological calcula-tions, this comparison is only possible for thesectors of industry and communal heating for thepollutants of NOx, SO2 and CO.

Table 1 displays the totals from the Irkutskbottom-up emission inventory and the EDGARemission inventory (Russia total), for the sectorsof industry and residential area, and this for thepollutants CO, NOx and SO2.

It can be seen that for CO, the industrial emis-sion total for Irkutsk is higher than the totalRussian emissions. The NOx and SO2 industrialemission numbers for the Irkutsk area constitute5.8%, 1.6%, respectively, of the national total.This indicates that there is a high uncertainty in-

Table 1 Comparison between the bottom-up emission in-ventory for the Irkutsk area, and the EDGAR data forRussia

Emission [Gg] CO NOx SO2Irkutskindustry 175.53 20.10 14.00Irkutskresidential 119.89 10.38 8.27Russia (EDGAR-F10) 137.93 348.96 861.72Russia (EDGAR-F40) 5436.99 412.09 981.70

Data are shown for the pollutants NO2, SO2 and CO, forthe sector of industry (EDGAR F10) and the residentialsector (EDGAR F40)

volved in the emission estimates, probably as wellas in the local data and in the EDGAR data.

ARPS validation

ARPS simulation results were validated by com-paring model output with observed meteorolog-ical parameters, from a measurement station inthe city of Irkutsk. Figure 4 shows validation re-sults for surface air temperature, wind speed andwind direction for the period 131 July 2003. Thesimulated values are in fair agreement with theobservations, and in particular, the temperaturedrop on the 27th of July is well captured.

We already mentioned that the Lake Baikal is ahuge water body surrounded by steep mountains(especially the Southern part) as also can be seenfrom the right panel of Fig. 1. During daytime,often a strong uphill lake breeze is observed; thisis a combined effect of (1) the land surface beinghotter than the water surface and (2) the mountainslopes heating up more intensely than the valleyareas. An example is shown in the right part ofFig. 5. At night, the situation reverses, producinga down-sloping land breeze, as exemplified in theleft panel of Fig. 5.

AURORA validation

Figure 6 shows the monthly average (July 2003)O3 field for the 3-km resolution domain. The sim-ulated formations of O3 plumes are mainly locatedin northwestern directions downwind of the ur-ban and industrialised areas. Relatively low ozoneconcentrations are found in the densely populatedand industrialised areas where the NOx emissionsare high as can be seen from Fig. 3. This indicates


Fig. 4 Validation ofsimulated surface airtemperature (upperpanel), wind direction(middle panel) and windspeed (lower panel) at thestation of Irkutsk, for theperiod the month of July2003. The solid linescorresponding with thesimulation result andthe symbols with theobserved values


Fig. 5 Two-dimensional wind fields showing the winddirection (direction of arrows) and wind speed (length ofarrows, with the maximum length corresponding with awind speed of 7 m/s). The area covered is the same asin Fig. 1, measurement stations are shown with stars: the

station of Listvyanka (close to Lake Baikal) is lying in theEast, the station of Irkutsk in the North and the station ofMondy in the West. The left panel shows a night breeze on2nd July 2003 at 4 a.m. local time, the right panel shows aday breeze on 3rd July 2003 at 3 p.m. local time

a VOC-limited regime over the urban areas. How-ever, as discussed out below, it is possible that theNOx emissions are too high for the Irkutsk urbanarea.

The concentration levels simulated with theAURORA model were compared to ground-based measurements obtained from three stationsin the study area, at Irkutsk (lat. 52.23, lon.

Fig. 6 Monthly averaged O3 concentrations in g m3,over the 3-km resolution domain

104.25), Listvyanka (lat. 51.85, lon. 104.9) andMondy (lat. 51.67, lon. 101.0). The position ofthe measurement stations are indicated with starsin Fig. 5. The station of Irkutsk is located inan urban area with industrial activities, too; theListvyanka station (close to the Lake Baikal) isa more rural site; the station of Mondy (mostwesterly station) can be considered as a ruralbackground station. We note that the stationof Mondy does not lie within the 3-km resolu-tion domain; for this station, we, hence, use the10-km resolution concentration fields to performthe comparison.

The monthly average O3 concentration forMondy (derived from the 10-km resolution do-main) amounts to 86 g/m3; this compares wellwith a measured O3 concentration of approx-imately 80 g/m3 (averaged over the years19971999; Holloway et al. 2008).

All three stations measure concentrations forHNO3, HCl, NH3, SO2 and for several ions. Aswe do not dispose of NH3 emissions, we will focusin the validation on the following species: HNO3,SO2, SO42 and NO3.

Before comparing the observed and modelledconcentrations of air pollutants, a general remarkhas to be made: The fact that observed concen-


Table 2 Simulated as compared to observed ground-level SO2, SO42, HNO3 and NO3 concentrations for the stations ofIrkutsk, Listvyanka and Mondy

SO2 SO42 HNO3 NO3 HNO3+ NO3

Irkutskmodel 5.89 2.19 1.58 1.79 3.37Irkutskmeasurement 2.50 2.92 0.68 1.67 2.35Listvyankamodel 1.84 1.74 0.19 1.31 1.50Listvyankameasurement 1.58 4.95 1.74 0.70 2.44Mondymodel 1.26 1.04 0.04 0.85 0.89Mondymeasurement 0.47 1.44 0.42 0.14 0.56

The simulated results are taken from the 3-km resolution AURORA calculations for the stations of Irkutsk and Listvyanka,whilst from the 10-km resolution calculations for the station of Mondy

trations of several key pollutants are unavail-able, especially the ones of NO2 (as precursor ofHNO3 and nitrates), O3 (as most important pho-tochemical pollutant governing all relevant oxida-tion processes) and NH3 (as probably the mainneutralizing agent of acids and important counter-ion of the nitrates and sulfates), makes a sounddiscussion of the results rather difficult.

Table 2 summarises the results for the pollu-tants HNO3, SO2, SO42 and NO3.

The overestimation of the SO2 concentrationsin Irkutsk is probably connected with an overesti-mation of the SO2 emissions in the Irkutsk area.SO2 concentrations in Listvyanka (with much lessemission sources) are modelled well, in contrast.The (slight) overestimation of SO2 in Mondycould be due to the lower resolution of the modeldomain at this location. We note that the sameSO2 concentration trends (high levels for Irkutskand low levels for Listvyanka and Mondy) areobserved for other summer months (Hollowayet al. 2008; Hayami et al. 2008); in these studies,the modelled SO2 concentration levels are of thesame order of magnitude as the current results.

The conversion of SO2 to sulphate is cur-rently treated as a constant conversion rate inAURORA, derived from typically observed con-version rates in Northwestern Europe. Becausethis oxidation process does not proceed very fast,significant amounts of sulphate are usually founddistant from the areas with strong sources. Themodelled and measured SO4 concentrations arein relatively good agreement for the three sites.Comparing the different sites, the measurementsgive a difference of a factor of 3, whilst the modelresults are different with a factor of 2.

The underestimation of HNO3 and the over-estimation of nitrate in Listvyanka and Mondyare connected due to an overestimation of theconversion of nitric acid to nitrate-aerosol in themodel. Indeed the agreement between measure-ment and model is much better when summingup the two species. The HNO3 concentration forIrkutsk is a factor of two too high probably due toan overestimation of the NOx emissions.

Conclusions

A description was given of a numerical set upwhich combines spatial datasets and atmosphericmodels, aiming at an evaluation of the air qualityin the Lake Baikal region for the month of July2003.

In a first step, an overview was given of thegeographical data processing needed in order tocharacterise the Lake Baikal region. These datawere subsequently coupled to the E-MAP emis-sion mapping GIS tool in order to generate de-tailed emissions for the study area. In a next step,the emission data and other spatial data wereused as input into the transport-chemistry modelAURORA. Together with meteorological fieldssimulated by the ARPS model, concentrations ofpollutants were simulated.

In a validation exercise, it was shown that sim-ulated temperature and wind speed showed a fairagreement with measured values. The ARPS me-teorological model is also capable of simulatingthe lake breezes. The AURORA pollutant con-centration levels compare relatively well with theavailable measurements. However, due to the lack


of a local data, it is difficult to obtain a completeair quality picture of the Lake Baikal region.

Acknowledgements The work described here was car-ried out with support of the Bilateral Scientific Coopera-tion Programme of the Flemish Government. The ARPSmeteorological model was made available by the Centerfor Analysis and Prediction of Storms at the Universityof Oklahoma. We would also like to acknowledge theUS Geological Survey for the GTOPO30 dataset, the JetPropulsion Laboratory for the SST imagery, VITO for theSPOT-VEGETATION NDVI imagery and JRC for theGlobal Land Cover 2000 database.

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Air-quality modelling in the Lake Baikal regionAbstractIntroductionStudy domain and spatial data processingMeteorological calculationsEmission modellingThe AURORA air quality modelMeteorological and air quality simulationsEmission validationARPS validationAURORA validation

ConclusionsReferences

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Air-quality Modelling in the Lake Baikal Region

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