Research Article Application of MM5-CAMx-PSAT Modeling...

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2013 Article ID 136453 12 pageshttpdxdoiorg1011552013136453

Research ArticleApplication of MM5-CAMx-PSAT Modeling Approach forInvestigating Emission Source Contribution to Atmospheric SO2Pollution in Tangshan Northern China

Li Li12 Shuiyuan Cheng1 Jianbing Li3 Jianlei Lang1 and Dongsheng Chen1

1 College of Environmental amp Energy Engineering Beijing University of Technology Beijing 100124 China2 Beijing General Research Institute of Mining amp Metallurgy Beijing 100070 China3 Environmental Engineering Program University of Northern British Columbia Prince George Canada V2N 4Z9

Correspondence should be addressed to Shuiyuan Cheng bjutpapergmailcom

Received 19 February 2013 Accepted 13 March 2013

Academic Editor Guohe Huang

Copyright copy 2013 Li Li et al This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The MM5-CMAx-PSAT modeling approach was presented to identify the variation of emission contribution from each modelinggrid to regional and urban air quality per unit emission rate change The method was applied to a case study in TangshanMunicipality a typical industrial region in northern China The variation of emission contribution to the monthly atmosphericSO2concentrations in Tangshan from each modeling grid of 9 times 9 km per 1000 tyr of emission rate change was simulated for four

representativemonths in 2006 It was found that the northwestern part of Tangshan region had themaximumcontribution variationratio (ie greater than 036) to regional air quality while the lowest contribution variation ratio (ie less than 03) occurredin the coastal areas Principal component analysis (PCA) canonical correlation analysis (CCA) and Pearson correlation analysisindicated that there was an obvious negative correlation between the grid-based variation of emission contribution to regional airquality and planetary boundary layer height (PBLH) as well as wind speed while terrain data presented insignificant impacts onemission contribution variation The proposed method was also applied to analyze the variation of emission contribution to theurban air quality of Tangshan (ie a smaller scale)

1 Introduction

Air pollution is a serious environmental problem faced bymany industrial cities in China as a consequence of manyyearsrsquo rapid economic expansion and insufficient environ-mental protection measures It not only poses threats tohuman health but also directly affects local economic devel-opment [1] A variety of factors such as emission sourcesland surface characteristics and meteorological conditionscould affect air pollution simulation Thus effective airquality management is usually a challenging task To tacklesuch difficulties it is of crucial importance to quantify theimpacts of pollutant emission sources on the air quality of a

planning region and understand the corresponding responseof atmospheric pollutant concentration to perturbations inpollutant emission rate [2]

Previously the method of wind rose based on wind speedand direction has been used for qualitatively investigatingthe impacts of emission sources on regional air quality [3]Nowadays computer modeling tools have been recognizedas useful means to investigate such impacts [4] Particularlythere has been a growing interest of applying advanced 3-D chemistry-transport models coupled with meteorolog-ical models for air quality studies such as the Model-3 Community Multiscale Air Quality (Model-3CMAQ)[5 6] the Comprehensive Air quality Model with extensions

2 Mathematical Problems in Engineering

(CAMx) [7 8] the PSUNCAR mesoscale meteorologicalmodel MM5 [9] and the Weather Research and Forecast(WRF) model coupled with Chemistry (WRF-Chem) [10]For example Cheng et al [11] used the coupled MM5-ARPS-CMAQ to examine contributions of various emissionsources to ambient PM

10concentrations in Beijing China

Titov et al [12] applied a MM5-CAMx for predicting PM10

concentrations over the city of Christchurch in New Zealandduring critical pollution episodes Lee et al [13] employedMM5-CAMx to simulate atmospheric pollutant transportand recirculation in the Santa Claria valley USA Shimaderaet al [14] applied MM5-CMAQ to estimate the contributionof transboundary transport of air pollutants from otherAsian countries to Japan Borrego et al [15] applied MM5-CAMx to simulate surface concentrations of ozone and itsprecursors over the metropolitan area of Porto Alegre Braziland identified the main emission sources of photochemicalpollution

In terms of examining the response of atmospheric pol-lutant concentration to perturbations in pollutant emissionrate a number of approaches have been proposed in thepast years by using various models [4 11 16 17] Particu-larly a technique named particulate matter source appor-tionment technology (PSAT) [18] has been implementedin CAMx to provide source apportionment for primaryand secondary particulate matter (PM) species according toemission source categories and their geographic locations[19]This technique is useful for identifying emission sourcesthat significantly contribute to gaseous or PM pollution Forexample Wagstrom et al [20] used PSAT to investigate thecontribution of power plant SO

2emissions to particulate

sulfate concentrations in the Eastern United States and theresults illustrated that PSAT could provide a computation-ally efficient particulate matter apportionment algorithm toinvestigate pollutant transport and emission source contri-butions on regional scales Koo et al [21] compared twodifferent methods of investigating relationships between PMconcentrations and emission sources and found that PSATwas best at apportioning sulfate nitrate and ammoniumto sources emitting SO

2 NO119909 and NH

3 respectively In

addition there are some other methods to examine emissionsource apportionment [22ndash24] which used principal com-ponent analysis (PCA) and multilinear regression analysis(MLRA) to identify possible sources of particulate matter(PM) and to determine their contribution to air pollution

In general many of the previous source apportionmentand emission contribution analysis works focused on exam-ining relationship between the total emission amount of asource from a large-scale planning region and its air pollutantconcentration [25] In fact for air pollution control strategydevelopment the more practical question is how pollutantconcentrations would respond to emission changes withindifferent small-scale areas of a large planning region [2] Thecontribution of emission sources within different small-scaleareas to regional and urban air quality could be quite differentdue to different land surface and meteorological conditionsThus it is of critical importance to identify the variationof atmospheric concentration to perturbations in emissionrates of small-scale areas within a large planning region

The priority regulation of emissions with high contributionvariation could result in significantly environmental and costeffectiveness As an extension of our previous efforts thisstudy was focused on the establishment and application ofthe MM5-CAMx-PAST modeling approach for examiningair quality variation due to perturbation in emission ratesfrom small-scale areas within a large planning region and itanalyzed the possible affected factors More accurate resultscan be obtained with the development of the advancedmodelsimulation The approach and results can provide sounddecisionmaking basis for effective air qualitymanagement Acase study for Tangshan a typical industrial region in Chinawas presented to illustrate the proposed methodology TheMM5-CAMx was used to provide meteorological inputs andto simulate atmospheric SO

2concentrations and PSAT was

applied to investigate the emission contribution variationsAn air quality modeling domain with a spatial resolutionof 9 by 9 km was adopted and the regional and urban airquality variations due to SO

2emission rate perturbation of

1000 tyrwithin eachmodeling gridwere simulated Principalcomponent analysis (PCA) canonical correlation analysis(CCA) and Pearson correlation analysis methods were thenused to analyze the impacts of meteorological variables andterrain data on the emission source contribution variations

2 Overview of the Study Area

Tangshan Municipality located at about 300 km east ofBeijing is the biggest industrial center within Hebei provincein northern China It has a total population of 69 millionin 2000 and a total area of 13472 km2 including 12 districtsas shown in Figure 1 The municipality is situated on thealluvial plain formed by the diluvial sediments from the YanMountains in the north Its mean sea level tends to decreasegradually from its northwest to southeast towards the BohaiBay It has a temperate continental climate influenced bywet monsoon and there is an apparent distinction amongfour seasons that is windy and dry spring hot and wetsummer mild and clear autumn and cold and dry winterThe annual average temperature is 10ndash1120∘C and annualaverage precipitation is about 600mm As one of the biggestindustrial centers in northern China Tangshan Municipalityhas experienced considerable changes through rapid indus-trialization and urbanization processes in the past decadesHowever its growth has also been associated with a numberof environmental concerns Among them the deterioratedair quality due to a combination of circumstances (ieincreased energy consumption population growth increasedindustrial emissions infrastructure constructionexpansiongrowth of passenger vehicles and ineffective pollution con-trol measures) posed significant challenges to the publicgovernments and industries Particularly SO

2pollution has

been recognized as an important environmental issue

3 Methodology

31 MM5-CAMx-PSAT Modeling The fifth-generationNCARPenn State mesoscale meteorological model (MM5)

Mathematical Problems in Engineering 3

lt1

gt1500

ZunhuaQianxi

Leting

Qianan

Yutian

Fengnan

Fengrun

luannan

Luanxian

Urban

Tanghai

Guye

0 10 20 30 405(km)

Terrain elevationunits (m)


1ndash2020ndash4040ndash6060ndash120


Figure 1 Tangshan Municipality and its surrounding cities

is a limited-area nonhydrostatic terrain-following sigma-coordinate model designed to predict meso- and regional-scale atmospheric circulations [9] It has been frequentlyused to provide meteorological inputs for many air qualitymodeling systems [26] In this study MM5 (version 37)model was applied and configured using two-level nestedmodeling domains (112ndash120∘E 37ndash43∘N) as shown inFigure 2 where domain 1 has a spatial resolution of 27 kmby 27 km and has been established with a dimension of 60times 60 grid cells and domain 2 has a spatial resolution of 9by 9 km and has been established with a dimension of 94 times82 grid cells Twenty-four full 120590 levels extending from theground surface to the top of modeling domain (ie 200 hpa)were applied The 3-D first-guess meteorological fields formodeling were obtained from the Global TroposphericAnalyses datasets provided by the US National Centerfor Environmental Prediction (NCEP FNL data) andwere available with six-hour resolution on a grid of 1∘times1∘ The four-dimensional data assimilation (FDDA) wasimplemented using the meteorological observations fromsurface (eight times a day) and upper air (two times aday) monitoring stations of the Chinese MeteorologicalInformation Comprehensive Analysis and Process System(MICAPS) The following physical parameters schemesin MM5 were selected including (a) land-use schemeusing five-layer LSM (b) PBL scheme using medium-rangeforecasts (MRF) (c) cloud microphysics selecting mixed-phase (d) cumulus parameterization schemes selecting

0 300 600150(km)

Figure 2 Two-level nested modeling domain for MM5

Grell and (e) radiation schemes selecting the highly accurateand efficient method (RRTM) The terrain and land-use datawere obtained from USGS with a spatial resolution of 30 s


The comprehensive air quality model CAMx version 51was used in this study It is an Eulerian photochemicaldispersion model that allows for an integrated assessmentof gaseous and particulate air pollutants over many scalesranging from suburban to continental This model simulatesemission dispersion chemical reaction and removal of pol-lutants in the troposphere by solving the pollutant continuityequation for each chemical species Itsmodeling input file for-mats are compatible with MM5 model To study the regionalemission contributions the PSAT has been implementedin CAMx to provide SO

2source apportionment among

specific geographic regions and source categories [19] For thesimulation of air quality in Tangshan Municipality CAMxwas configured using one modeling domain which was thesame as domain 2 of MM5 (Figure 2) Its physical parametersschemes were selected as follows (a) two-way interactive gridnesting (b) 12 vertical layers (c) gas-phase chemistry usingCB05 mechanism which includes 156 reactions formulationsand (d) aerosol chemistry using M4ISORROPIA In termsof air pollutant emission inventory it was provided byTangshan Environmental Protection Agency The emissioninventories of Tangshanrsquos surrounding regions includingHebei province Shanxi province Beijing Tianjin and InnerMongolia were obtained from the respective environmentalprotection administrations The emission inventory of otherregions was obtained from Zhang and Streets [27]

The MM5-CAMx was then used to simulate SO2con-

centrations in Tangshan for four representative monthsin 2006 including January April July and October Twoscenarios were selected including simulating the variationof emission contribution from emission rate perturbationin each modeling grid (9 times 9 km scale) to both regionaland urban air quality represented by the monthly averageSO2concentration of the entire Tangshan region (ie large-

scale receptor 1) and only its urban area (ie small-scalereceptor 2) (Figure 3) respectivelyThemodeling proceduresinclude (1) using MM5-CMAx to predict the temporal andspatial distributions of SO

2concentrations within Tangshan

Municipality based on its actual emissions in 2006 (ie baseemission inventory) and the monthly average SO

2concen-

trations within receptors 1 and 2 were then calculated basedon the simulated hourly concentrations respectively (2)identifying the contribution variation of each modeling gridto the monthly average SO

2concentrations through adding

1000 tyr of SO2emission (ie an arbitrarily selected number)

to each grid in addition to the base emission inventoryand the MM5-CMAx was used to predict the temporal andspatial distributions of SO


Municipality based on the new emission inventory (baseemission inventory plus 1000 tyr in a certain grid) and thenthe monthly average SO

2concentrations within receptors 1

and 2 were calculated respectively The difference betweenthe monthly average SO

2concentrations calculated using

base inventory and new inventory is regarded as the emissionsource contribution variation of that grid

32 Multivariate Analysis The multivariate analysis meth-ods including PCA and CCA were used to analyze the

impacts of meteorological variables and terrain data on thesimulated variation of emission contribution to regional andurban air quality PCA maximizes the correlation betweenthe original total variance to form new variables that aremutually orthogonal or uncorrelated The CCA applicationwas run to investigate possible relationship between thesetwo data sets especially to establish themaximumcorrelationamong sets of variables The objective of PCA was to obtaina small number of components that would explain most(ie typically above 60) of the total variation [28] In thisstudy the hourly data of six meteorological variables withinMM5 including PBL height (PBLH) temperature at 2mabove ground (T2) wind speed at 10m above ground (WS10)wind direction at 10m above ground (WD) sea level pressure(PSLV) and relative humidity (RH) were selected to analyzethe principal components of meteorological variables withinfour representative months in 2006 The objective of CCAwas then to investigate possible relationship between the sixselected meteorological variables as well as terrain data andthe contribution variation of emission within each modelinggrid [29]

4 Results and Discussions

41 Modeling Performance The performance of the MM5-CAMx was evaluated using scatter plots [30] The ground-based SO

2observation results from three air quality mon-

itoring stations located within Tangshan urban (ldquoUrbanrdquo isshowed in Figure 1) were averaged and were then comparedwith the predicted daily SO

2concentration of the Tangshan

urban area in the four selected months in 2006 Figure 4displays the comparison results The 119910 = 119909 line on the scatterplots represents perfect agreement between the two data setsA pair value above the 119910 = 119909 line indicates a situation ofoverprediction while the pair value below the line indicatesunderprediction In general Figure 4 shows that most ofthe scatter plots are adjacently distributed on both sides of119910 = 119909 line which does highlight a consistent over- andunderprediction for SO

2concentration using the modeling

system Considering the inherent uncertain nature associatedwith meteorological parameters and air quality predictionthis fluctuation still indicates that the accuracy of modelprediction is reasonable In fact the correlation coefficientsbetween simulated and observed data were calculated as0781 0621 0690 and 0801 for January April July andOctober respectively Thus the performance of the coupledmodeling system is satisfactory and acceptable [4]

42 Simulated SO2Concentration Distribution Using Base

Emission Inventory In the year of 2006 Tangshan Munic-ipality had a total of 598 industrial establishments includ-ing electrical metallurgical mining chemical constructionmaterials and textile industries Spatial distributions andemission rates of SO

2from these sources were investigated

and shown in Figure 5(a) The hourly SO2concentrations in

January 2006 in the entire Tangshan region were simulatedusing MM5-CAMx and their corresponding monthly aver-ages were then calculated Figure 5(b) displays the simulated


Receptor 1

Receptor 2

Figure 3 Schematic of modeling scenarios showing receptors 1 and2

0

01

02

03

0 01 02 03

SO2

simul

atio

n da

ta (m

gm

3)

SO2 observation data (mgm3)

Figure 4 Comparison between observed and predicted SO2con-

centrations (daily average value of January April July and October2006)

SO2concentration in Tangshan region in January 2006 and

it illustrates that SO2pollutions occurred in most areas of

Tangshan region due to pollutant emissions and unfavorablemeteorological conditions In general the air quality inTangshan region was not satisfactory and the municipalgovernment and industries need to take actions to improvesuch situation For cost-effective air quality management

in Tangshan the identification of the variation of emissionsource contribution to the regional and urban air quality dueto emission perturbation in each small-scale emission area isof fundamental importance Such information could providesound basis for identifying emission areas requiring priorityregulation

43 Variation of Seasonal Emission Source Contribution toRegional Air Quality The variation of emission contributionto the receptor 1 (shown in Figure 3) air quality due to SO

2

emission perturbation of 1000 tyr of each modeling gridwas calculated at first for the four representative months in2006 using the MM5-CAMx-PSATThen the correspondingmonthly arithmetic averages were plotted using GeographicInformation System (GIS) interpolation method Figure 6presents the spatial distribution of the variation of emissioncontribution to regional monthly SO

2concentration in Tang-

shan It is observed from Figure 6(a) that emissions in thejunction of Yutian and Zunhua Counties had the maximumvariation of emission contribution to air pollution in receptor1 with more than 52120583gm3 of variation per 1000 tyr ofSO2emission per 9 times 9 km modeling grid in January while

emissions from the coastal areas of the southeast of Tangshanhad the minimum variation of emission contribution (ieless than 35 120583gm3 of variation per 1000 tyr of SO

2emission

per modeling grid of 9 times 9 km) It can also be found thatthe grid-based variation of emission contribution to SO

2

pollution tended to gradually decrease from the northwest tosoutheast of Tangshan Municipality in January In April asshown in Figure 6(b) emissions from the southeast ofQiananCounty and the southern coastal areas of Tangshan displayedthe maximum variations of contribution to SO

2pollution

in receptor 1 with more than 139 120583gm3 of contributionvariation per 1000 tyr of SO

2emission per grid Meanwhile

emissions from the eastern area of Leting and northern partof Qianxi County showed minimum contribution variationsIn terms of July as shown in Figure 6(c) emissions from thecoastal areas of Tangshan made the largest contribution vari-ations (ie greater than 146 120583gm3 of variation per 1000 tyrof SO

2emission per grid) while the spatial distribution

of emission contribution variations showed several localhigh-value points and the minimum contribution variationoccurred in Qianxi County It is shown in Figure 6(d) thatthe variation of emission contributions to the average SO

2

concentration in receptor 1 in October displayed a relativelyeven distribution tending to gradually decrease from thehigh-value area of Yutian County (ie with contributionvariation of greater than 260120583gm3) to the east and southeastof Tangshan Consequently the simulation results indicatethat the largest variations of emission contribution to airpollution occurred in January and the contribution variationdistribution displayed an apparent seasonal differenceThis isdue to the fact that Tangshan has the temperate continentalclimate and different meteorological conditions among fourseasons would cause such seasonal differences

44 Impacts ofMeteorological Factors on Emission Source Con-tribution Variation PCA was used to identify the principal


50ndash200200ndash300


(tonyear) (tonyear)lt50

gt5000

(a)

(mgm3) (mgm3)


018ndash021021ndash024024ndash027

lt006

gt027

(b)

Figure 5 Annual emission rate of SO2and simulated monthly SO

2concentration in January 2006 (a) emission rate (b) concentration

distribution

components from six meteorological variables in TangshanTable 1 lists the PCA results for January April July andOctober 2006 respectively and the eigenvalues of PCAfor the meteorological variables are also presented It isfound from Table 1 that examination of 30-day data for eachmodeling grid in January led to three principal componentsaccounting for 818 of the total variance Using the valuesof the respective principal component loadings presentedin Table 1 there is a reasonable interpretation for thesecomponents Only loadings with absolute values greater than50were selected for PC interpretation [31]The first PC wasPBL height (with component loadings of minus0503) and thesecond PCs showed that a main source of variation was windspeed (with component loadings of 0529) and temperature(with component loadings of 0598) while the third PC waswind direction (with component loadings of 0801) Thusthe PCA results for January indicated low PBL height andprevalent northwest winds as well as inversion weatherThesemeteorological conditions could result in higher atmosphericstability in surface layer in Tangshan which then facilitatedthe accumulation of pollutants near the ground leading tothe highest variation of emission contributions to regionalair quality from the modeling grids as compared to othermonths (Figure 6(a)) In terms of meteorological conditionsin April Table 1 illustrates that the first PCs were PBL height(with component loadings of 0574) and relative humidity(with component loadings of minus0515) The second PCs weretemperature (with component loadings of 0715) and sealevel pressure (with component loadings of minus0603) whilethe third PC was wind speed (with component loadingsof 0723) The PCA results for April indicate a dry springwith high PBLH high temperature low sea level pressureand strong wind and such meteorological conditions were

conducive for dispersion of pollutants leading to relativelylow variation of emission contribution to regional air qual-ity from modeling grids (Figure 6(b)) For meteorologicalconditions in July the PCA results illustrate that the firstPCs were PBL height (with component loadings of 0603)and relative humidity (with component loadings of 0549)and the second PC was wind direction (with componentloadings of minus0698) while the third PC was wind speed (withcomponent loadings of minus0832) The PCA results indicate awet and rainy summer with high PBL height and prevalentsoutheast winds influenced by the maritime climate Suchmeteorological conditions would help disperse and reducepollutant concentrations leading to minimum variation ofemission contribution to regional air quality in July ascompared to other months (Figure 6(c)) For October thePCA results showed a mild and clear autumn with first PCsbeing the temperature (with component loading of 052) andsea level pressure (with component loading of minus0576) thesecond PCs being PBLH (with component loading of 0684)and wind speed (with component loading of 0649) and thethird PC being relative humidity (with component loadingof minus0696) These values illustrate that the temperature inautumn was slightly higher than that in spring wind was notstronger than that in spring and the prevalently northwestwind was influenced by the invasion of cold air Due to theimpact of such meteorological conditions the variation ofemission contribution to regional air quality in October frommodeling grids was between the minimum and maximum(Figure 6(d))

Results of CCA between grid-based variation of emissioncontribution to regional air quality and meteorology-terraindata in Tangshan are presented in Table 2 In this study therewas only one canonical variable (CV) The correlations of


January(120583gm3)

January(120583gm3)

350ndash430

430ndash460

460ndash490

490ndash520

lt350

gt520

(a)

April(120583gm3)

April(120583gm3)

125ndash130

130ndash133

133ndash136

136ndash139

lt125

gt139

(b)

July(120583gm3)

July(120583gm3)

125ndash130

130ndash135

135ndash140

140ndash145

lt125

gt145

(c)

October(120583gm3)

October(120583gm3)

180ndash200

200ndash220

220ndash240

240ndash260

lt180

gt260

(d)

Figure 6 Simulated monthly average emission contribution response to regional average SO2concentration (ie receptor 1) due to SO

2

emission perturbation of 1000 tyr

CV1 were 0781 0748 0725 and 0807 for the four selectedmonths respectively and all CCAs passed the statisticaltest of significance According to the variable loading valuesshown in Table 2 the main meteorological variables werePBL height and wind speed in January which showed anegative correlation with grid-based variation of emissioncontribution to air quality in receptor 1 Pearson correlationanalysis also gave the same results as CCA Figure 7 presents

the monthly average PBLH and WS10 in January and thecontours exhibit negative correlation with Figure 6(a) Thisindicates that high variation of emission contribution wasrelated to low PBL height and lowwind speed conditions It isfound from Table 2 that the variable loading values for Apriland October gave similar results for January However CCAand Pearson correlation analysis gave different results for JulyTheCCA results showed that relative humiditywas associated


Table 1 PCA results for meteorological variables in four selected months in 2006

PC Eigenvalue Proportion variance Cumulative proportion Variable Principal component loadingsPC1 PC2 PC3 PC4

(a) JanuaryPC1 1492 0371 0371 PBLH minus0503 0421 minus0209 0000

PC2 1279 0273 0644 T2m 0326 0598 0000 minus026

PC3 1022 0174 0818 WS10 minus0411 0529 minus0132 028

PC4 0772 0099 0918 PSLV minus0391 minus0405 minus0488 021

PC5 0554 0051 0969 RH 0479 0147 minus0244 0799

PC6 0433 0031 1000 WD minus0297 0000 0801 0412

(b) AprilPC1 1516 0383 0383 PBLH 0574 0207 minus0143 0000

PC2 1280 0273 0656 T2m minus0106 0715 minus0215 0000

PC3 0982 0161 0817 WS10 0318 0171 0723 minus056

PC4 0813 0110 0927 PSLV 0294 minus0603 minus0305 minus023

PC5 0523 0046 0973 RH minus0515 minus022 0453 0227

PC6 0405 0027 1000 WD 0455 0000 0336 0758

(c) JulyPC1 1435 0343 0343 PBLH 0603 minus0242 0000 minus0141

PC2 1182 0233 0576 T2m 0484 0337 minus0207 minus0465

PC3 1056 0186 0762 WS10 minus0137 minus0171 minus0832 minus033

PC4 0785 0103 0865 PSLV minus0279 minus0497 0404 minus071

PC5 0729 0089 0953 RH 0549 0255 minus0188 0000

PC6 0529 0047 1000 WD 0000 minus0698 minus0257 0381

(d) OctoberPC1 1535 0392 0392 PBLH minus0134 0684 minus0127 0000PC2 1324 0292 0685 T2m 052 0301 0397 0000PC3 0951 0151 0835 WS10 minus0226 0649 minus0132 0000PC4 0785 0103 0938 PSLV minus0576 minus0138 minus0352 minus013

PC5 0498 0041 0979 RH 0376 0000 minus0696 0607PC6 0351 0021 1000 WD minus0433 0000 0448 0781

with the second highest absolute loading value (ie minus0715)which indicated an obvious negative correlation betweenhumidity and grid-based variation of emission contributionHowever the Pearson correlation value for RH was justminus0066 Since it is widely recognized that wet depositionhas the function of removing pollutant the results of CCAseemed more reasonable to find the relationship betweenmore than two variables The terrain data did not showobvious correlation with grid-based emission contributionvariation throughCCAandPearson correlation analysisThiscan be explained by the fact that most areas of Tangshan areflat although it is located in the alluvial plains of the YanshanMountains with higher elevation in the northwestern partand lower elevation in the southeastern region

45 Variation of Annual Emission Contribution to RegionalAir Quality The modeling results (Figure 6) indicated sig-nificant seasonal change of emission contribution variationfor each modeling grid due to the impacts of many meteo-rological factors such as PBL height and wind speed Thus aparameter of emission contribution variation ratio was intro-duced in this study for investigating the variation of annual

average emission contribution to regional air quality for theconvenience of air quality management The calculation ofemission contribution variation ratio is as follows

119877119894=

1

4

1003816100381610038161003816100381610038161003816100381610038161003816

sum

119862119894119895

sum119899

119894=1119862119894119895

1003816100381610038161003816100381610038161003816100381610038161003816

(1)

where 119862119894119895

is the variation of emission contribution to themonthly average SO

2concentration of the receptor area in

month j (ie January April July and October) per 1000 tyrof emission rate change in grid i (120583gm3) 119877

119894is the annual

average emission contribution variation ratio of grid i dueto 1000 tyr of emission rate change n is the total numberof modeling grids Figure 8(a) presents the annual emissioncontribution variation ratio of each grid to average SO

2

concentration in receptor 1 in 2006 It is found that thenorthwestern part of Tangshan such as the junction area ofYutian and Fengrun Counties had the maximum emissioncontribution variation ratio (ie greater than 036) to theair quality of receptor 1 indicating that the regional air qualitywas more sensitive to the emissions from the northwesternpart of Tangshan The contribution variation ratio tended todecrease towards the north and southeast of Tangshan while


January JanuaryWS10 (ms) WS10 (ms)

18ndash25

25ndash28

28ndash30

30ndash32

lt18

gt32

(a)

January January

290ndash315

315ndash330

330ndash345

345ndash355

PBLH (m) PBLH (m)

lt290

gt355

(b)

Figure 7 Monthly average wind speed at 10m above ground (WS10) (a) and planetary boundary layer height (PBLH) (b) in January 2006

Table 2 Results of CCA between grid-based variation of emission contribution to air quality in receptor 1 and meteorologicalterrain datain Tangshan 2006

(a) January (b) AprilCV Correlation Pearson CV Correlation PearsonCV1 0781 Correlation CV1 0748 CorrelationVariable Loadings Variable LoadingsContribution 0987 1000 Contribution 0987 1000

PBLH minus0439 minus0677 PBLH minus0639 minus0483

T2m minus0062 minus0163 T2m minus0387 minus0188

WS10 minus0626 minus0746 WS10 minus0672 minus0588

TERRAIN 0009 0041 TERRAIN minus0039 0242

PSLV 0061 minus0002 PSLV minus0038 0205

RH minus0160 0094 RH minus0479 0221

WD minus0069 minus0150 WD minus0011 minus0243

(c) July (d) OctoberCV Correlation Pearson CV Correlation PearsonCV1 0725 Correlation CV1 0807 CorrelationVariable Loadings Variable LoadingsContribution 0987 1000 Contribution 0987 1000




TERRAIN minus0005 0346 TERRAIN minus0003 0045

PSLV minus0050 0029 PSLV 0010 minus0001

RH minus0715 minus0066 RH minus0120 0029

WD 0091 minus0086 WD 0010 minus0035


some local higher values occurred within Qianan CountyThe lowest emission contribution variation ratio (ie lessthan 03) occurred in the coastal areas of Tangshan and thenorthern part of Qianxi County implying that the regionalair quality was less sensitive to the emissions from theseareas As a result in order to improve regional air qualitythe industries (Figure 5(a)) located within the more sensitiveareas (ie northwestern part of Tangshan) should reducetheir emissions or be relocated to the less sensitive areas suchas the coastal area of Tangshan Municipality

As described earlier CCA indicated that a negative corre-lation existed between wind speed and grid-based emissioncontribution variation This can be proved from anotherperspective The data from three state-controlled weatherstations located in Zunhua (northwestern area) Tangshanurban (center area) and Leting (southeastern area) (Figure 1)were used for meteorological factor analysis The monthlyaverage wind speed and calm frequency were previouslyidentified as the main meteorological factors affecting airpollution [25] and thus were used for analysis in this studyTable 3 lists the average wind speed and calm frequency ofthe four representative months in 2006 Previous studiessuggested that the greater the wind speed and the smaller thecalm frequency the more beneficial for pollutant dispersionIt can be observed from Table 3 that the ranking of monthlyand yearly average wind speed from large to small is LetingTangshan urban and Zunhua This would indicate that thedispersion capability of pollutants gradually decreases fromthe coast (ie Leting) to inland area (ie Zunhua) leading togradually increased emission contribution variation from thecoastal area to inland area as shown in Figure 8(a) Howeverthe order of calm frequency for the three selected areas doesnot hold the same as that of average wind speed Althoughthe calm frequency in Zunhua area was higher than that inother two areas in all seasons which was less conducive tothe dispersion of pollutants the calm frequencies in Tanghanurban area in April and July were significantly lower thanthose in Leting which could give a good explanation for thelocal low emission contribution variation values shown in thecenter area of Tangshan in Figures 6(b) and 6(c) In additionFigure 8(a) not only displays the annual average emissioncontribution variation ratios of the modeling grids to airquality in the entire Tangshan region but also gives a visualrepresentation of the dominant wind direction It is foundfrom Figure 8(a) that the east-west direction modeling gridshad higher contribution variation ratios than north-southdirection girds implying that east-west was the dominantwind direction in Tangshan Municipality

46 Variation of Annual Emission Contribution to UrbanAir Quality Air quality control within a smaller area thanregional scale is usually important and more practical inurban environmental management In this study the urbanarea of Tangshan was selected as a control area (ie receptor2) and the grid-based variation of emission contributionto the average air quality of receptor 2 was then simulatedusingMM5-CAMx-PSAT Figure 8(b) shows the distributionof grid-based annual emission contribution variation ratio

Table 3Wind speed and calmwind frequency in Tangshan in 2006

Area Month Wind speed (ms) Calm frequency ()January 149 571April 261 300

Zunhua July 166 412October 143 1107Annual 180 601

Tangshanurban

January 191 122April 263 043July 191 082

October 177 779Annual 206 259January 209 367April 303 214

Leting July 208 123October 186 451Annual 227 290

It is found that the grids with largest contribution variationratios were receptor 2 itself (with contribution variation ratioof greater than 100) and the second were the grids mainlysurrounding receptor 2 Figure 8(b) also reveals that emissioncontribution variation ratio had correlation with the distancebetween emission grids and the receptor area The east-west modeling grids around receptor 2 had slightly higheremission contribution variation ratio than the north-southgrids This could be explained by the fact that east and westwinds were the main wind directions in the study area asobserved from themonitoring data in 2006The contributionvariation ratios of the remaining parts of Tangshan Munici-pality were very small with minimum contribution variationratios occurring in the coastal areas and northern parts ofTangshan (ie less than 02) The results indicated that thereceptor itself as emission grids had significant contributionto the urban air quality The obtained emission contributionvariation analysis results are of practical importance for airquality management For example to improve the urbanair quality in Tangshan the industries (Figure 5(a)) withinthe more sensitive areas (ie Tangshan urban Fengrun andFengnan) should be relocated to the less sensitive areas (iecoastal area of Tangshan) and the new industrial projectswith SO

2emissions such as power plants should also be

located within the less sensitive coastal areas

5 Conclusions

A modeling grid-based emission contribution analysisapproach was proposed to identify emission areas withhigher response of regional and urban air quality changedue to emission rate perturbation This approach relied ona coupled MM5-CAMx where MM5 was used to providemeteorological inputs for the air quality model CAMxwhile CAMx was used to predict air pollutant concentrationdistributions The particulate matter source apportioning


Year()

Year()

030ndash032

032ndash034

lt030 034-035

035-036

gt036

(a)

Year()

Year()

02ndash05

05ndash10

10ndash70

70ndash100

lt02

gt100

(b)

Figure 8 Distribution of annual average emission contribution variation ratio of modeling grids to air quality in (a) receptor 1 and (b)receptor 2

technology (PSAT) within CAMx was used to calculatethe variation of emission contribution to air quality fromemission rate perturbation within each modeling gridThe method was applied to a case study in TangshanMunicipality in northern China The MM5-CMAx wasimplemented to predict hourly SO

2concentrations based

on the base emission inventory of SO2

in 2006 withmodeling grid scale of 9 times 9 km and the impact of emissionperturbation in each modeling grid to atmospheric SO

2

concentrations was calculated by using PSAT technologythrough adding 1000 tyr of SO

2emission to the grid in

addition to the base emission inventory The variation ofemission contribution to regional air quality from eachmodeling grid per 1000 tyr of emission rate change wasobtained for four representative months (January AprilJuly and October) in 2006 PCA and CCA were conductedto examine the impacts of meteorological factors on thevariation of emission source contribution and the resultsindicated that there was an obvious negative correlationbetween emission contribution variation and planetaryboundary layer height (PBLH) as well as wind speed Theanalysis of the variation of emission contribution to annualregional SO

2concentration (ie larger scale) indicated that

the northwestern part of Tangshan was the most sensitivearea with emission contribution variation ratio of morethan 036 while the southern coastal area had the lowestcontribution variation ratio of less than 030The proposedmethod was also applied to analyze the variation of emissioncontribution to the SO

2pollution in the urban area in

Tangshan (ie at a smaller scale) and it was found that thelargest contribution grids were the urban area itself (withcontribution variation ratio of greater than 100) and the

minimum contribution variation ratios (ie less than 02)occurred in the coastal areas and northern parts of TangshanBased on the modeling results the emission sources withinthe areas with higher contribution variation ratios shouldbe regulated with priority or relocated to other areas withlower contribution variation ratios such as the coastal areasin Tangshan In summary the proposed methodology canbe applied to address many other regional and urban airpollution problems and the results would provide soundscientific basis for effective air quality management

Acknowledgments

This research was supported by the Natural Sciences Foun-dation of China (no 51038001) and the Ministry of Envi-ronmental Protection Special Funds for Scientific Researchon Public Causes (no 201209003) The authors would like tothank Natural Science Foundation of Beijing (no 8092004)Beijing NOVA Program of China (no 2009B07) InnovationTeam Project of Beijing Municipal Education Commission(PHR201007105) and the Cultivation Fund of the Key Scien-tific and Technical Innovation Project Ministry of Educationof China (708017) for supporting this work

References

[1] D S Chen S Y Cheng L Liu T Chen and X R GuoldquoAn integratedMM5mdashCMAQmodeling approach for assessingtrans-boundary PM

10contribution to the host city of 2008

Olympic summer gamesmdashBeijing Chinardquo Atmospheric Envi-ronment vol 41 no 6 pp 1237ndash1250 2007


[2] D S Cohan A Hakami Y Hu and A G Russell ldquoNonlinearresponse of ozone to emissions source apportionment andsensitivity analysisrdquo Environmental Science and Technology vol39 no 17 pp 6739ndash6748 2005

[3] F Wang D S Chen S Y Cheng and M J Li ldquoImpacts of airpollutant transport based on air trajectory clusteringrdquo Researchof Environmental Sciences vol 22 no 6 pp 637ndash642 2009(Chinese)

[4] S Cheng D Chen J Li X Guo and H Wang ldquoAn ARPS-CMAQ modeling approach for assessing the atmosphericassimilative capacity of the Beijing metropolitan regionrdquoWaterAir and Soil Pollution vol 181 no 1ndash4 pp 211ndash224 2007

[5] S M Lee H J S Fernando and S Grossman-Clarke ldquoMM5-SMOKE-CMAQ as a modeling tool for 8-h ozone regulatoryenforcement application to the state ofArizonardquoEnvironmentalModeling and Assessment vol 12 no 1 pp 63ndash74 2007

[6] Y Zhou S Y Cheng L Liu and D S Chen ldquoA Coupled MM5-CMAQ modeling system for assessing effects of restrictionmeasures on PM

10pollution in Olympic city of Beijing Chinardquo

Journal of Environmental Informatics vol 19 no 2 pp 120ndash1272012

[7] E Angelino M Bedogni C Carnevale et al ldquoPM10

chemicalmodel simulations over Northern Italy in the framework of thecitydelta exerciserdquoEnvironmentalModeling andAssessment vol13 no 3 pp 401ndash413 2008

[8] U Nopmongcol B Koo E Tai et al ldquoModeling Europewith CAMx for the air quality model evaluation internationalinitiative (AQMEII)rdquo Atmospheric Environment vol 53 no 7pp 177ndash185 2012

[9] J Dudhia D Gill K Manning W Wang and C BruyerePSUNCARMesoscaleModeling System Tutorial Class Notes andUserrsquos Guide MM5 Modeling System Version 3 National Centerfor Atmospheric Research 2004

[10] X Tie SMadronich G Li et al ldquoCharacterizations of chemicaloxidants in Mexico City a regional chemical dynamical model(WRF-Chem) studyrdquo Atmospheric Environment vol 41 no 9pp 1989ndash2008 2007

[11] S Cheng D Chen J Li HWang and X Guo ldquoThe assessmentof emission-source contributions to air quality by using acoupled MM5-ARPS-CMAQ modeling system a case studyin the Beijing metropolitan region Chinardquo EnvironmentalModelling and Software vol 22 no 11 pp 1601ndash1616 2007

[12] M Titov A P Sturman and P Zawar-Reza ldquoApplication ofMM5 and CAMx4 to local scale dispersion of particulatematter for the city of Christchurch New Zealandrdquo AtmosphericEnvironment vol 41 no 2 pp 327ndash338 2007

[13] S M Lee M Princevac S Mitsutomi and J Cassmassi ldquoMM5simulations for air quality modeling an application to a coastalarea with complex terrainrdquo Atmospheric Environment vol 43no 2 pp 447ndash457 2009

[14] H Shimadera A Kondo A Kaga K L Shrestha and YInoue ldquoContribution of transboundary air pollution to ionicconcentrations in fog in theKinki Region of JapanrdquoAtmosphericEnvironment vol 43 no 37 pp 5894ndash5907 2009

[15] C Borrego A Monteiro J Ferreira et al ldquoModelling thephotochemical pollution over the metropolitan area of PortoAlegre Brazilrdquo Atmospheric Environment vol 44 no 3 pp370ndash380 2010

[16] D G Streets J S Fu C J Jang et al ldquoAir quality during the 2008Beijing Olympic Gamesrdquo Atmospheric Environment vol 41 no3 pp 480ndash492 2007

[17] Y Zhou S Y Cheng J B Li J L Lang L Li and D SChen ldquoA new statistical modeling and optimization framework

for establishing high-resolution PM10

emission inventorymdashIIIntegrated air quality simulation and optimization for perfor-mance improvementrdquo Atmospheric Environment vol 60 pp623ndash631 2012

[18] Q Huang S Y Cheng J B Li D S Chen H Y Wang andX R Guo ldquoAssessment of PM

10emission sources for priority

regulation in urban air quality management using a new cou-pled MM5-CAMx-PSAT modeling approachrdquo EnvironmentalEngineering Science vol 29 no 5 pp 343ndash349 2012

[19] A M Dunker G Yarwood J P Ortmann and G M WilsonldquoComparison of source apportionment and source sensitivity ofozone in a three-dimensional air quality modelrdquo EnvironmentalScience and Technology vol 36 no 13 pp 2953ndash2964 2002

[20] K M Wagstrom S N Pandis G Yarwood G M Wilsonand R E Morris ldquoDevelopment and application of a compu-tationally efficient particulate matter apportionment algorithmin a three-dimensional chemical transport modelrdquoAtmosphericEnvironment vol 42 no 22 pp 5650ndash5659 2008

[21] B Koo G M Wilson R E Morris A M Dunker andG Yarwood ldquoComparison of source apportionment and sen-sitivity analysis in a particulate matter air quality modelrdquoEnvironmental Science and Technology vol 43 no 17 pp 6669ndash6675 2009

[22] S M Almeida C A Pio M C Freitas M A Reis and M ATrancoso ldquoSource apportionment of fine and coarse particulatematter in a sub-urban area at the Western European CoastrdquoAtmospheric Environment vol 39 no 17 pp 3127ndash3138 2005

[23] S S Park and Y J Kim ldquoSource contributions to fine particulatematter in an urban atmosphererdquoChemosphere vol 59 no 2 pp217ndash226 2005

[24] A Srivastava SGupta andVK Jain ldquoSource apportionment oftotal suspended particulatematter in coarse and fine size rangesover Delhirdquo Aerosol and Air Quality Research vol 8 no 2 pp188ndash200 2008

[25] ZHChen S Y Cheng J B Li X RGuoWHWang andD SChen ldquoRelationship between atmospheric pollution processesand synoptic pressure patterns in northern ChinardquoAtmosphericEnvironment vol 42 no 24 pp 6078ndash6087 2008

[26] F Wang D S Chen S Y Cheng J B Li M J Li and ZH Ren ldquoIdentification of regional atmospheric PM

10transport

pathways using HYSPLIT MM5-CMAQ and synoptic pressurepattern analysisrdquoEnvironmentalModelling and Software vol 25no 8 pp 927ndash934 2010

[27] Q Zhang and D G Streets ldquo2006 Asia Emissions forINTEX-Brdquo December 2009 httpwwwcgreruiowaeduEMI-SSION DATA newindex 16html

[28] M Viana X Querol A Alastuey J I Gil and M MenendezldquoIdentification of PM sources by principal component analysis(PCA) coupled with wind direction datardquoChemosphere vol 65no 11 pp 2411ndash2418 2006

[29] M Statheropoulos N Vassiliadis and A Pappa ldquoPrincipalcomponent and canonical correlation analysis for examining airpollution and meteorological datardquo Atmospheric Environmentvol 32 no 6 pp 1087ndash1095 1998

[30] V Isakov A Venkatram J S Touma D Koracin and TL Otte ldquoEvaluating the use of outputs from comprehensivemeteorological models in air quality modeling applicationsrdquoAtmospheric Environment vol 41 no 8 pp 1689ndash1705 2007

[31] S A Abdul-Wahab C S Bakheit and SMAl-Alawi ldquoPrincipalcomponent and multiple regression analysis in modelling ofground-level ozone and factors affecting its concentrationsrdquoEnvironmental Modelling and Software vol 20 no 10 pp 1263ndash1271 2005

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of


Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of


Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of


Mathematical PhysicsAdvances in

Complex AnalysisJournal of


OptimizationJournal of


CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of


Operations ResearchAdvances in

Journal of


Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Algebra

Discrete Dynamics in Nature and Society



Decision SciencesAdvances in

Discrete MathematicsJournal of


Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


(CAMx) [7 8] the PSUNCAR mesoscale meteorologicalmodel MM5 [9] and the Weather Research and Forecast(WRF) model coupled with Chemistry (WRF-Chem) [10]For example Cheng et al [11] used the coupled MM5-ARPS-CMAQ to examine contributions of various emissionsources to ambient PM

10concentrations in Beijing China

Titov et al [12] applied a MM5-CAMx for predicting PM10

concentrations over the city of Christchurch in New Zealandduring critical pollution episodes Lee et al [13] employedMM5-CAMx to simulate atmospheric pollutant transportand recirculation in the Santa Claria valley USA Shimaderaet al [14] applied MM5-CMAQ to estimate the contributionof transboundary transport of air pollutants from otherAsian countries to Japan Borrego et al [15] applied MM5-CAMx to simulate surface concentrations of ozone and itsprecursors over the metropolitan area of Porto Alegre Braziland identified the main emission sources of photochemicalpollution

In terms of examining the response of atmospheric pol-lutant concentration to perturbations in pollutant emissionrate a number of approaches have been proposed in thepast years by using various models [4 11 16 17] Particu-larly a technique named particulate matter source appor-tionment technology (PSAT) [18] has been implementedin CAMx to provide source apportionment for primaryand secondary particulate matter (PM) species according toemission source categories and their geographic locations[19]This technique is useful for identifying emission sourcesthat significantly contribute to gaseous or PM pollution Forexample Wagstrom et al [20] used PSAT to investigate thecontribution of power plant SO

2emissions to particulate

sulfate concentrations in the Eastern United States and theresults illustrated that PSAT could provide a computation-ally efficient particulate matter apportionment algorithm toinvestigate pollutant transport and emission source contri-butions on regional scales Koo et al [21] compared twodifferent methods of investigating relationships between PMconcentrations and emission sources and found that PSATwas best at apportioning sulfate nitrate and ammoniumto sources emitting SO

2 NO119909 and NH

3 respectively In

addition there are some other methods to examine emissionsource apportionment [22ndash24] which used principal com-ponent analysis (PCA) and multilinear regression analysis(MLRA) to identify possible sources of particulate matter(PM) and to determine their contribution to air pollution

In general many of the previous source apportionmentand emission contribution analysis works focused on exam-ining relationship between the total emission amount of asource from a large-scale planning region and its air pollutantconcentration [25] In fact for air pollution control strategydevelopment the more practical question is how pollutantconcentrations would respond to emission changes withindifferent small-scale areas of a large planning region [2] Thecontribution of emission sources within different small-scaleareas to regional and urban air quality could be quite differentdue to different land surface and meteorological conditionsThus it is of critical importance to identify the variationof atmospheric concentration to perturbations in emissionrates of small-scale areas within a large planning region

The priority regulation of emissions with high contributionvariation could result in significantly environmental and costeffectiveness As an extension of our previous efforts thisstudy was focused on the establishment and application ofthe MM5-CAMx-PAST modeling approach for examiningair quality variation due to perturbation in emission ratesfrom small-scale areas within a large planning region and itanalyzed the possible affected factors More accurate resultscan be obtained with the development of the advancedmodelsimulation The approach and results can provide sounddecisionmaking basis for effective air qualitymanagement Acase study for Tangshan a typical industrial region in Chinawas presented to illustrate the proposed methodology TheMM5-CAMx was used to provide meteorological inputs andto simulate atmospheric SO

2concentrations and PSAT was

applied to investigate the emission contribution variationsAn air quality modeling domain with a spatial resolutionof 9 by 9 km was adopted and the regional and urban airquality variations due to SO

2emission rate perturbation of

1000 tyrwithin eachmodeling gridwere simulated Principalcomponent analysis (PCA) canonical correlation analysis(CCA) and Pearson correlation analysis methods were thenused to analyze the impacts of meteorological variables andterrain data on the emission source contribution variations

2 Overview of the Study Area

Tangshan Municipality located at about 300 km east ofBeijing is the biggest industrial center within Hebei provincein northern China It has a total population of 69 millionin 2000 and a total area of 13472 km2 including 12 districtsas shown in Figure 1 The municipality is situated on thealluvial plain formed by the diluvial sediments from the YanMountains in the north Its mean sea level tends to decreasegradually from its northwest to southeast towards the BohaiBay It has a temperate continental climate influenced bywet monsoon and there is an apparent distinction amongfour seasons that is windy and dry spring hot and wetsummer mild and clear autumn and cold and dry winterThe annual average temperature is 10ndash1120∘C and annualaverage precipitation is about 600mm As one of the biggestindustrial centers in northern China Tangshan Municipalityhas experienced considerable changes through rapid indus-trialization and urbanization processes in the past decadesHowever its growth has also been associated with a numberof environmental concerns Among them the deterioratedair quality due to a combination of circumstances (ieincreased energy consumption population growth increasedindustrial emissions infrastructure constructionexpansiongrowth of passenger vehicles and ineffective pollution con-trol measures) posed significant challenges to the publicgovernments and industries Particularly SO

2pollution has

been recognized as an important environmental issue

3 Methodology

31 MM5-CAMx-PSAT Modeling The fifth-generationNCARPenn State mesoscale meteorological model (MM5)


lt1

gt1500

ZunhuaQianxi

Leting

Qianan

Yutian

Fengnan

Fengrun

luannan

Luanxian

Urban

Tanghai

Guye

0 10 20 30 405(km)







0 300 600150(km)












2concen-



























Receptor 1

Receptor 2


0

01

02

03

0 01 02 03

SO2

simul

atio

n da

ta (m

gm

3)









2





2emission


2


2pollution






2







gt5000

(a)

(mgm3) (mgm3)



lt006

gt027

(b)



distribution





January(120583gm3)

January(120583gm3)

350ndash430

430ndash460

460ndash490

490ndash520

lt350

gt520

(a)

April(120583gm3)

April(120583gm3)

125ndash130

130ndash133

133ndash136

136ndash139

lt125

gt139

(b)

July(120583gm3)

July(120583gm3)

125ndash130

130ndash135

135ndash140

140ndash145

lt125

gt145

(c)

October(120583gm3)

October(120583gm3)

180ndash200

200ndash220

220ndash240

240ndash260

lt180

gt260

(d)


2








PC2 1279 0273 0644 T2m 0326 0598 0000 minus026

PC3 1022 0174 0818 WS10 minus0411 0529 minus0132 028


PC5 0554 0051 0969 RH 0479 0147 minus0244 0799

PC6 0433 0031 1000 WD minus0297 0000 0801 0412


PC2 1280 0273 0656 T2m minus0106 0715 minus0215 0000

PC3 0982 0161 0817 WS10 0318 0171 0723 minus056


PC5 0523 0046 0973 RH minus0515 minus022 0453 0227

PC6 0405 0027 1000 WD 0455 0000 0336 0758


PC2 1182 0233 0576 T2m 0484 0337 minus0207 minus0465



PC5 0729 0089 0953 RH 0549 0255 minus0188 0000

PC6 0529 0047 1000 WD 0000 minus0698 minus0257 0381






119877119894=

1

4

1003816100381610038161003816100381610038161003816100381610038161003816

sum

119862119894119895

sum119899

119894=1119862119894119895

1003816100381610038161003816100381610038161003816100381610038161003816

(1)

where 119862119894119895




119894is the annual


2




18ndash25

25ndash28

28ndash30

30ndash32

lt18

gt32

(a)

January January

290ndash315

315ndash330

330ndash345

345ndash355

PBLH (m) PBLH (m)

lt290

gt355

(b)


























Tangshanurban







5 Conclusions



Year()

Year()

030ndash032

032ndash034

lt030 034-035

035-036

gt036

(a)

Year()

Year()

02ndash05

05ndash10

10ndash70

70ndash100

lt02

gt100

(b)






2









Acknowledgments


References





































10transport














Volume 2014




Journal of











Journal of


Function Spaces






Algebra










lt1

gt1500

ZunhuaQianxi

Leting

Qianan

Yutian

Fengnan

Fengrun

luannan

Luanxian

Urban

Tanghai

Guye

0 10 20 30 405(km)







0 300 600150(km)












2concen-



























Receptor 1

Receptor 2


0

01

02

03

0 01 02 03

SO2

simul

atio

n da

ta (m

gm

3)









2





2emission


2


2pollution






2







gt5000

(a)

(mgm3) (mgm3)



lt006

gt027

(b)



distribution





January(120583gm3)

January(120583gm3)

350ndash430

430ndash460

460ndash490

490ndash520

lt350

gt520

(a)

April(120583gm3)

April(120583gm3)

125ndash130

130ndash133

133ndash136

136ndash139

lt125

gt139

(b)

July(120583gm3)

July(120583gm3)

125ndash130

130ndash135

135ndash140

140ndash145

lt125

gt145

(c)

October(120583gm3)

October(120583gm3)

180ndash200

200ndash220

220ndash240

240ndash260

lt180

gt260

(d)


2








PC2 1279 0273 0644 T2m 0326 0598 0000 minus026

PC3 1022 0174 0818 WS10 minus0411 0529 minus0132 028


PC5 0554 0051 0969 RH 0479 0147 minus0244 0799

PC6 0433 0031 1000 WD minus0297 0000 0801 0412


PC2 1280 0273 0656 T2m minus0106 0715 minus0215 0000

PC3 0982 0161 0817 WS10 0318 0171 0723 minus056


PC5 0523 0046 0973 RH minus0515 minus022 0453 0227

PC6 0405 0027 1000 WD 0455 0000 0336 0758


PC2 1182 0233 0576 T2m 0484 0337 minus0207 minus0465



PC5 0729 0089 0953 RH 0549 0255 minus0188 0000

PC6 0529 0047 1000 WD 0000 minus0698 minus0257 0381






119877119894=

1

4

1003816100381610038161003816100381610038161003816100381610038161003816

sum

119862119894119895

sum119899

119894=1119862119894119895

1003816100381610038161003816100381610038161003816100381610038161003816

(1)

where 119862119894119895




119894is the annual


2




18ndash25

25ndash28

28ndash30

30ndash32

lt18

gt32

(a)

January January

290ndash315

315ndash330

330ndash345

345ndash355

PBLH (m) PBLH (m)

lt290

gt355

(b)


























Tangshanurban







5 Conclusions



Year()

Year()

030ndash032

032ndash034

lt030 034-035

035-036

gt036

(a)

Year()

Year()

02ndash05

05ndash10

10ndash70

70ndash100

lt02

gt100

(b)






2









Acknowledgments


References





































10transport














Volume 2014




Journal of











Journal of


Function Spaces






Algebra


















2concen-



























Receptor 1

Receptor 2


0

01

02

03

0 01 02 03

SO2

simul

atio

n da

ta (m

gm

3)









2





2emission


2


2pollution






2







gt5000

(a)

(mgm3) (mgm3)



lt006

gt027

(b)



distribution





January(120583gm3)

January(120583gm3)

350ndash430

430ndash460

460ndash490

490ndash520

lt350

gt520

(a)

April(120583gm3)

April(120583gm3)

125ndash130

130ndash133

133ndash136

136ndash139

lt125

gt139

(b)

July(120583gm3)

July(120583gm3)

125ndash130

130ndash135

135ndash140

140ndash145

lt125

gt145

(c)

October(120583gm3)

October(120583gm3)

180ndash200

200ndash220

220ndash240

240ndash260

lt180

gt260

(d)


2








PC2 1279 0273 0644 T2m 0326 0598 0000 minus026

PC3 1022 0174 0818 WS10 minus0411 0529 minus0132 028


PC5 0554 0051 0969 RH 0479 0147 minus0244 0799

PC6 0433 0031 1000 WD minus0297 0000 0801 0412


PC2 1280 0273 0656 T2m minus0106 0715 minus0215 0000

PC3 0982 0161 0817 WS10 0318 0171 0723 minus056


PC5 0523 0046 0973 RH minus0515 minus022 0453 0227

PC6 0405 0027 1000 WD 0455 0000 0336 0758


PC2 1182 0233 0576 T2m 0484 0337 minus0207 minus0465



PC5 0729 0089 0953 RH 0549 0255 minus0188 0000

PC6 0529 0047 1000 WD 0000 minus0698 minus0257 0381






119877119894=

1

4

1003816100381610038161003816100381610038161003816100381610038161003816

sum

119862119894119895

sum119899

119894=1119862119894119895

1003816100381610038161003816100381610038161003816100381610038161003816

(1)

where 119862119894119895




119894is the annual


2




18ndash25

25ndash28

28ndash30

30ndash32

lt18

gt32

(a)

January January

290ndash315

315ndash330

330ndash345

345ndash355

PBLH (m) PBLH (m)

lt290

gt355

(b)


























Tangshanurban







5 Conclusions



Year()

Year()

030ndash032

032ndash034

lt030 034-035

035-036

gt036

(a)

Year()

Year()

02ndash05

05ndash10

10ndash70

70ndash100

lt02

gt100

(b)






2









Acknowledgments


References





































10transport














Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Receptor 1

Receptor 2


0

01

02

03

0 01 02 03

SO2

simul

atio

n da

ta (m

gm

3)









2





2emission


2


2pollution






2







gt5000

(a)

(mgm3) (mgm3)



lt006

gt027

(b)



distribution





January(120583gm3)

January(120583gm3)

350ndash430

430ndash460

460ndash490

490ndash520

lt350

gt520

(a)

April(120583gm3)

April(120583gm3)

125ndash130

130ndash133

133ndash136

136ndash139

lt125

gt139

(b)

July(120583gm3)

July(120583gm3)

125ndash130

130ndash135

135ndash140

140ndash145

lt125

gt145

(c)

October(120583gm3)

October(120583gm3)

180ndash200

200ndash220

220ndash240

240ndash260

lt180

gt260

(d)


2








PC2 1279 0273 0644 T2m 0326 0598 0000 minus026

PC3 1022 0174 0818 WS10 minus0411 0529 minus0132 028


PC5 0554 0051 0969 RH 0479 0147 minus0244 0799

PC6 0433 0031 1000 WD minus0297 0000 0801 0412


PC2 1280 0273 0656 T2m minus0106 0715 minus0215 0000

PC3 0982 0161 0817 WS10 0318 0171 0723 minus056


PC5 0523 0046 0973 RH minus0515 minus022 0453 0227

PC6 0405 0027 1000 WD 0455 0000 0336 0758


PC2 1182 0233 0576 T2m 0484 0337 minus0207 minus0465



PC5 0729 0089 0953 RH 0549 0255 minus0188 0000

PC6 0529 0047 1000 WD 0000 minus0698 minus0257 0381






119877119894=

1

4

1003816100381610038161003816100381610038161003816100381610038161003816

sum

119862119894119895

sum119899

119894=1119862119894119895

1003816100381610038161003816100381610038161003816100381610038161003816

(1)

where 119862119894119895




119894is the annual


2




18ndash25

25ndash28

28ndash30

30ndash32

lt18

gt32

(a)

January January

290ndash315

315ndash330

330ndash345

345ndash355

PBLH (m) PBLH (m)

lt290

gt355

(b)


























Tangshanurban







5 Conclusions



Year()

Year()

030ndash032

032ndash034

lt030 034-035

035-036

gt036

(a)

Year()

Year()

02ndash05

05ndash10

10ndash70

70ndash100

lt02

gt100

(b)






2









Acknowledgments


References





































10transport














Volume 2014




Journal of











Journal of


Function Spaces






Algebra













gt5000

(a)

(mgm3) (mgm3)



lt006

gt027

(b)



distribution





January(120583gm3)

January(120583gm3)

350ndash430

430ndash460

460ndash490

490ndash520

lt350

gt520

(a)

April(120583gm3)

April(120583gm3)

125ndash130

130ndash133

133ndash136

136ndash139

lt125

gt139

(b)

July(120583gm3)

July(120583gm3)

125ndash130

130ndash135

135ndash140

140ndash145

lt125

gt145

(c)

October(120583gm3)

October(120583gm3)

180ndash200

200ndash220

220ndash240

240ndash260

lt180

gt260

(d)


2








PC2 1279 0273 0644 T2m 0326 0598 0000 minus026

PC3 1022 0174 0818 WS10 minus0411 0529 minus0132 028


PC5 0554 0051 0969 RH 0479 0147 minus0244 0799

PC6 0433 0031 1000 WD minus0297 0000 0801 0412


PC2 1280 0273 0656 T2m minus0106 0715 minus0215 0000

PC3 0982 0161 0817 WS10 0318 0171 0723 minus056


PC5 0523 0046 0973 RH minus0515 minus022 0453 0227

PC6 0405 0027 1000 WD 0455 0000 0336 0758


PC2 1182 0233 0576 T2m 0484 0337 minus0207 minus0465



PC5 0729 0089 0953 RH 0549 0255 minus0188 0000

PC6 0529 0047 1000 WD 0000 minus0698 minus0257 0381






119877119894=

1

4

1003816100381610038161003816100381610038161003816100381610038161003816

sum

119862119894119895

sum119899

119894=1119862119894119895

1003816100381610038161003816100381610038161003816100381610038161003816

(1)

where 119862119894119895




119894is the annual


2




18ndash25

25ndash28

28ndash30

30ndash32

lt18

gt32

(a)

January January

290ndash315

315ndash330

330ndash345

345ndash355

PBLH (m) PBLH (m)

lt290

gt355

(b)


























Tangshanurban







5 Conclusions



Year()

Year()

030ndash032

032ndash034

lt030 034-035

035-036

gt036

(a)

Year()

Year()

02ndash05

05ndash10

10ndash70

70ndash100

lt02

gt100

(b)






2









Acknowledgments


References





































10transport














Volume 2014




Journal of











Journal of


Function Spaces






Algebra










January(120583gm3)

January(120583gm3)

350ndash430

430ndash460

460ndash490

490ndash520

lt350

gt520

(a)

April(120583gm3)

April(120583gm3)

125ndash130

130ndash133

133ndash136

136ndash139

lt125

gt139

(b)

July(120583gm3)

July(120583gm3)

125ndash130

130ndash135

135ndash140

140ndash145

lt125

gt145

(c)

October(120583gm3)

October(120583gm3)

180ndash200

200ndash220

220ndash240

240ndash260

lt180

gt260

(d)


2








PC2 1279 0273 0644 T2m 0326 0598 0000 minus026

PC3 1022 0174 0818 WS10 minus0411 0529 minus0132 028


PC5 0554 0051 0969 RH 0479 0147 minus0244 0799

PC6 0433 0031 1000 WD minus0297 0000 0801 0412


PC2 1280 0273 0656 T2m minus0106 0715 minus0215 0000

PC3 0982 0161 0817 WS10 0318 0171 0723 minus056


PC5 0523 0046 0973 RH minus0515 minus022 0453 0227

PC6 0405 0027 1000 WD 0455 0000 0336 0758


PC2 1182 0233 0576 T2m 0484 0337 minus0207 minus0465



PC5 0729 0089 0953 RH 0549 0255 minus0188 0000

PC6 0529 0047 1000 WD 0000 minus0698 minus0257 0381






119877119894=

1

4

1003816100381610038161003816100381610038161003816100381610038161003816

sum

119862119894119895

sum119899

119894=1119862119894119895

1003816100381610038161003816100381610038161003816100381610038161003816

(1)

where 119862119894119895




119894is the annual


2




18ndash25

25ndash28

28ndash30

30ndash32

lt18

gt32

(a)

January January

290ndash315

315ndash330

330ndash345

345ndash355

PBLH (m) PBLH (m)

lt290

gt355

(b)


























Tangshanurban







5 Conclusions



Year()

Year()

030ndash032

032ndash034

lt030 034-035

035-036

gt036

(a)

Year()

Year()

02ndash05

05ndash10

10ndash70

70ndash100

lt02

gt100

(b)






2









Acknowledgments


References





































10transport














Volume 2014




Journal of











Journal of


Function Spaces






Algebra













PC2 1279 0273 0644 T2m 0326 0598 0000 minus026

PC3 1022 0174 0818 WS10 minus0411 0529 minus0132 028


PC5 0554 0051 0969 RH 0479 0147 minus0244 0799

PC6 0433 0031 1000 WD minus0297 0000 0801 0412


PC2 1280 0273 0656 T2m minus0106 0715 minus0215 0000

PC3 0982 0161 0817 WS10 0318 0171 0723 minus056


PC5 0523 0046 0973 RH minus0515 minus022 0453 0227

PC6 0405 0027 1000 WD 0455 0000 0336 0758


PC2 1182 0233 0576 T2m 0484 0337 minus0207 minus0465



PC5 0729 0089 0953 RH 0549 0255 minus0188 0000

PC6 0529 0047 1000 WD 0000 minus0698 minus0257 0381






119877119894=

1

4

1003816100381610038161003816100381610038161003816100381610038161003816

sum

119862119894119895

sum119899

119894=1119862119894119895

1003816100381610038161003816100381610038161003816100381610038161003816

(1)

where 119862119894119895




119894is the annual


2




18ndash25

25ndash28

28ndash30

30ndash32

lt18

gt32

(a)

January January

290ndash315

315ndash330

330ndash345

345ndash355

PBLH (m) PBLH (m)

lt290

gt355

(b)


























Tangshanurban







5 Conclusions



Year()

Year()

030ndash032

032ndash034

lt030 034-035

035-036

gt036

(a)

Year()

Year()

02ndash05

05ndash10

10ndash70

70ndash100

lt02

gt100

(b)






2









Acknowledgments


References





































10transport














Volume 2014




Journal of











Journal of


Function Spaces






Algebra











18ndash25

25ndash28

28ndash30

30ndash32

lt18

gt32

(a)

January January

290ndash315

315ndash330

330ndash345

345ndash355

PBLH (m) PBLH (m)

lt290

gt355

(b)


























Tangshanurban







5 Conclusions



Year()

Year()

030ndash032

032ndash034

lt030 034-035

035-036

gt036

(a)

Year()

Year()

02ndash05

05ndash10

10ndash70

70ndash100

lt02

gt100

(b)






2









Acknowledgments


References





































10transport














Volume 2014




Journal of











Journal of


Function Spaces






Algebra
















Tangshanurban







5 Conclusions



Year()

Year()

030ndash032

032ndash034

lt030 034-035

035-036

gt036

(a)

Year()

Year()

02ndash05

05ndash10

10ndash70

70ndash100

lt02

gt100

(b)






2









Acknowledgments


References





































10transport














Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Year()

Year()

030ndash032

032ndash034

lt030 034-035

035-036

gt036

(a)

Year()

Year()

02ndash05

05ndash10

10ndash70

70ndash100

lt02

gt100

(b)






2









Acknowledgments


References





































10transport














Volume 2014




Journal of











Journal of


Function Spaces






Algebra










































10transport














Volume 2014




Journal of











Journal of


Function Spaces






Algebra
















Volume 2014




Journal of











Journal of


Function Spaces






Algebra









Research Article Application of MM5-CAMx-PSAT Modeling...

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