Characterizing spatial distribution and temporal variation of PM10 and PM2.5 mass concentrations in an urban area of Southwest China



AtmosphericPollutionResearch6(2015)842Ͳ848

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spheric Pollution

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Characterizing spatial distribution and temporal variation of PM10 and PM2.5 mass concentrations in an urban area of Southwest China WeiHuang,EnshenLong,JunWang,RuyiHuang,LiMa CollegeofArchitectureandEnvironment,SichuanUniversity,Chengdu610065,China

ABSTRACT



To investigate the temporal and spatial behavior of particulate matter (PM10 and PM2.5), daily data of PM10 and PM2.5 massconcentrationswerecollectedfromfiveair–qualitymonitoringstationsinChengdufromMarch2013toFebruary 2014.Inthisperiod,thedailyaverageconcentrationsofPM10andPM2.5were156.6and99.5ʅg/m³,respectively,which exceededboththeChineseambientair–qualitystandardsforPMandtheguidelinesoftheWorldHealthOrganization (WHO). Higher mass concentrations of both PM10 and PM2.5 were observed in winter and spring, indicating that meteorological parameters play an important role. Although PM mass concentrations were evidently lower than reportedinpreviousstudies,theaveragePM2.5/PM10ratiointhisstudywashigher,indicatingthatfineparticulate(PM2.5) pollution has become more serious. Weekly variations of PM concentrations were analyzed to estimate the impact of trafficrestrictionpolicies.TheresultsshowthatthehighestconcentrationsofparticleswereobservedonMondaysand thelowestonThursdays.Weekendeffectswerealsoobvious,whichweremainlyattributedtohumanactivities.  Keywords:Particulatematter,temporalvariation,spatialdistribution,air–qualitymonitoringstation

CorrespondingAuthor:

Enshen Long

:+86Ͳ28Ͳ85401015 :+86Ͳ28Ͳ85401015

:[email protected] 

ArticleHistory: Received:29October2014 Revised:06March2015 Accepted:07March2015

doi:10.5094/APR.2015.093 

1.Introduction 

Air pollution caused by human activities has attracted considerable concern in recent years. This is especially the case regardingambientlevelsofparticulatematter(PM),becauseofits adverse effects on human health (Pope III and Dockery, 2006), visibility (Wang et al., 2013a), and climate change (Yin and Chen, 2007). Thus, many atmospheric studies have been conducted in many regions to analyze the chemical composition (Souza et al., 2014) and spatial and temporal variation of PM (Elbayoumi et al., 2013),andthelevelsofhumanexposure(Anetal.,2013).  The rapid economic development, urbanization, and industrialization thathave occurredin China overthe past several decades have led to a deterioration of air quality (Tie and Cao, 2009).Manystudiesofairqualityhavebeenperformedinseveral developed regions of China (Li et al., 2013). However, although ambient PM10 mass concentrations are widely monitored, measurements of PM2.5 were not performed until late 2012 in manyChinesemegacities(Lietal.,2014a).  With increasing numbers of vehicles and rising energy consumption, ambient PM pollution has become increasingly seriousinChengdu(Shietal.,2011).Asthehometownofthegiant panda, Chengdu (30.67°N, 104.06°E) is the largest city in Southwestern China, situated in the western edge of the Sichuan basinandsurroundedbymountains.ThepopulationofChengduis about14millionwithinanareaof12390km2.Thecityexperiences a typical humid subtropical monsoon climate, characterized by 



sultrysummers,warmwinters,andhighlevelsofrelativehumidity (RH) throughout the year. The characteristics of the geographical environment mean that the annual frequency of ground temperature(Temp)inversionsisabout 15.6%,butcanbeashigh as 62.9% in winter, and the annual frequency of calm wind conditions canbe as high as 42% (Zhou, 2006). According to data released from the Chengdu Traffic Management Bureau (CTMB, 2014), the number of motor vehicles in Chengdu was almost 2.68million in 2013, ofwhich 1.14 millionwere inthe downtown area. All of these factors can contribute to serious air pollution. Moreover, various types of air–pollution events such as dust storms, biomass combustion, and firework displays, which occur frequently in certain periods in Chengdu, also have a serious impact on air quality. According to monitoring data regarding malignant tumors published by the Chengdu Center for Disease Control & Prevention (CDCDC, 2014), the morbidity from lung cancerwasatitshighestlevelin2013.  Knowledgeaboutspatialdistributionaswellastheimpactsof the traffic restriction policy on particulate mass in Chengdu is extremelylimited.Inthisstudy,weinvestigatedtheconcentrations ofPM10andPM2.5atfiveair–qualitymonitoringsitesinChengdu, fromMarch 2013to February 2014. Theaimsof this study are to establish the spatial distribution and temporal variation of PM concentrations within the city. Furthermore, patterns of weekly variations of PM concentrations are studied to investigate the effectsoftheimplementedtrafficrestrictionpoliciesandprovidea basisfortheformulationoffurtherrelevantstrategies.Theresults willbehelpfulintheeffectivemanagementofairquality.



©Author(s)2015.ThisworkisdistributedundertheCreativeCommonsAttribution3.0License.

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 2.Methods  2.1.Datacollection  PM concentration data were collected from five official air– quality monitoring stations in Chengdu from March 2013 to February2014.AsshowninFigure1,thelocationswereselectedto representdifferentaspectsofthecity.TableS1(seetheSupporting Material, SM) lists relevant information about the locations and exposures of the five official air–quality monitoring stations. The samplingbytheofficialair–qualitymonitoringstationswasdoneat heights of 15–25m above ground level. The TEOM 1405–F instrument(ThermoFisherScientificInc.,USA)wasusedtodetect the mass concentration of PM10 using filters and an oscillating microbalance with a flow rate of 16.7 L/min. The 5030i SHARP instrument(ThermoFisherScientificInc.,USA)wasusedtodetect themassconcentrationofPM2.5usingɴ–raydetectionwithaflow rateof16.7L/min.Excludingdayswithinstrumentfailure,353daily sampleswereobtained:92,83,88,and90inspring,summer,fall, andwinter,respectively.  2.2.Meteorologicaldata  Meteorological data including temperature (Temp), wind speed (WS), precipitation (Prec), atmospheric pressure (AP), and RH were obtained from the China Meteorological Administration (CMA,2014).  2.3.Trafficstatistics  Todate,thereareno available statisticson the magnitudeof traffic flow on the main roads next to the monitoring sites. Therefore,toanalyzetheimpactofdifferentmagnitudesoftraffic flow on PM concentrations, a week–long traffic flow survey was performedon4separateweeks(1weekselectedrandomlyineach of the four seasons) at selected sections of the two main roads next to the SH and RN monitoring sites (Renmin Road and Shudu Road, respectively) (Figure 1). The measured periods were from 

07:00–09:00and22:00–24:00(Beijingtime)onweekdaysandfrom 09:00–11:00 and 14:00–16:00 (Beijing time) on weekends, repͲ resenting the peaks and troughs of traffic flow, respectively. The numbers of vehicles on two–way roads were recorded by two automatic counters (Midwest, whh–CLJ–301, China) using induction coil with the area of 2 m2 per coil and the numbers of turns of the coil were 3. The average values of the sums of the peaks and troughs can be adopted as representative of the daily trafficflowinthiscity.  2.4.Statisticalanalysis  The collected data were analyzed by using Microsoft Excel 2000 (Microsoft Corp., USA) and SPSS 19.0.0 (SPSS Inc., USA) software. The coefficient of variation (CV) was used to quantitatively characterize the temporal variation of PM2.5. The single factor analysis of variance (ANOVA) was performed to determinethesignificanceofthespatialvariationsofallsampling sites simultaneously. Spearman’s rank correlation coefficient was used to assess the relationships between meteorological factors andmassconcentrations(Martuzeviciusetal.,2004)andbetween samplers from different sites (Li et al., 2014b). The day of week effects were analyzed by univariate method of the generalized linear model (GLM). Considering the particulate level of each site mayhavecertaindifference,thedailyaveragemassconcentration of all sampling sites was investigated using centering method supportedbyPollutionandHealth:aEuropeanApproach(APHEA– 2)project:  σହ௜ୀଵሾሺܺ௜௧ െ ܺ௜ ሻ ൅ ܺ ሿ (1) ܺ௧ ൌ  ͷ  where,Xtistheaveragemassconcentrationofallsamplingsiteson theTthday,XitisthemassconcentrationoftheisiteontheTthday, Xi is the average mass concentration of the i site during the sampling period, X is the average mass concentration of all sites duringthesamplingperiod.

 Figure1.LocationsofmonitoringstationsandtrafficstatisticslocationsinChengdu.



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3.ResultsandDiscussion  3.1.OverviewofPMmassconcentration  AsshowninTable1,duringthestudyperiodinChengdu,the average concentrations of PM10 and PM2.5 were 156.6 and 99.5ʅg/m³,respectively. These concentrationsexceededboth the Chinese ambient air–quality standards (GB3095–2012) for PM (150ʅg/m³forPM10and75ʅg/m³forPM2.5)andtheguidelinesof theWHO,setat50ʅg/m³forPM10and25ʅg/m³forPM2.5(WHO, 2006). Daily average PM10 concentrations ranged from 27.3 to 943.8ʅg/m³, whereas daily average PM2.5 concentrations ranged from18.8to435.4ʅg/m³.ThehighestmassconcentrationofPM10 was observed on March 11, 2013 and the highest mass concenͲ trationofPM2.5wasdetectedonJanuary31,2014.  Table S2 (see the SM) gives an overview of the average PM concentrations measured in this study and the results from a selection of other studies. It can be seen that the mass concentrationsofPMdetectedinChengduinthisstudywerelower than reported by Tian et al. (2013) for 2009–2010. In comparison with other cities in China, the PM concentrations detected in Chengdu in this study were relatively higher than in Tainan (Fang and Chang, 2010), but lower than in Tianjing (Kong et al., 2010). This may be due to the different geographical environment and industrial structure.Tainanisacoastalcitywithstrongseabreeze which is favorable for the dilution of particles, while Chengdu is located in a basin, lower wind speed is favorable for the aggregation of particles. As a heavy industrial city, industrial emissions and domestic heating in winter, resulting in serious air pollution in Tianjing. Compared with foreign cities, the PM concentrationsinthisstudyweremuchhigherthaninBern(Gehrig andBuchmann,2003),Athens(Paterakietal.,2013),andBarcelona (Rodriguezetal.,2004),butclosetothevaluesmeasuredinAgra (Kulshresthaetal.,2009).Differentwithdevelopedcountries,large numberofconstructionactivities,lowervehicleemissionstandards and different industrial structure in developing countries have led to serious particulate pollution. These findings suggest that ChengduisaffectedconsiderablybyPMandthatrelatedmeasures shouldbeadoptedtocontrolthelevelofpollution.  3.2.Spatialdistribution  The increased number of monitoring stations established at theendof2012providesimprovedinformationforunderstanding the spatial distribution of PM in Chengdu. The PM10 and PM2.5 concentrationsmeasuredatthefiveair–qualitymonitoringstations inChengduareshowninTable1.  TheANOVAtestwasusedtodeterminethesignificanceofthe spatialvariationsbymeasuringthesamplescollectedonthesame dayatallsites.Thepvaluewas0.85whichmeansthatthespatial distribution was not significant (p>0.05). The mass concentrations of PM at the five sites appear significantly correlated to one 

another,withSpearman’srankcorrelationcoefficientsrangingfrom 0.94 to 0.97 (p<0.01) of PM10 and 0.93 to 0.97 (p<0.01) of PM2.5. TheaverageconcentrationofPM10atthefivesitescanberankedin the order: RN>SH>LJX>JP>DS and that for PM2.5 ranked as: RN>DS>LJX>JP>SH.  The concentrations of both PM2.5 and PM10 were highest at theRNsite.Thissiteislocatedabout200mfromanurbanarterial street with high traffic flow, and there were many construction activitiesinprogressaroundthissiteduringthestudyperiod.Thus, itwasaffectedconsiderablybyvehicularemissions,roaddust,and cement dust. The high density of the surrounding residential and commercialemissionssuchascookingandsmokingalsocontribute totheconcentrationoffineparticles.InChengdu,thedirectionof the prevailing wind is from the NE and the RN site is located downwindofthecity.  Thelowest PM2.5concentration, observed atthe SH site, was because there was relatively lower traffic flow and fewer surrounding buildings, so conditions were more favorable for the dispersionofPM.Inaddition,commercialandresidentialemissions might have little effect on this site. Furthermore, the SH site lies withinthetrafficrestrictionareabetweenthe2ndand3rdringroads and thus the contribution from vehicular exhausts is reduced in comparison with other sites because of the policy that will be discussedlater.  The lowest value of PM10 concentration was observed at the DSsite,whichmighthavebeenbecauseitislocatedinadeveloped partofChengduwithlittleconstructionactivity.Incontrasttothe other sites, the DS site is close to a large artificial lake and thus coarseparticlessuchasre–suspendeddustscanbedepositedand quickly absorbed by the water. Furthermore, the area around the DS site has many trees, which means the ground surface is very roughandthereforethedrydepositionvelocityofcoarseparticles tendstobehighercomparedwithsiteswithsurfacesthatareless rough (Zhang and Vet, 2006). The relatively high PM2.5 concentrationsmeasuredattheDSsitewerelikelyduetothehigh RHcausedbytheevaporationofwater.HighRHisfavorableforthe processofconversionofgaseousmaterialintoPM.  The lowest PM2.5 concentrations, observed at the SH site, decreasedtoabout95.2%ofthemaximumvalueobservedatthe RN site. The lowest PM10concentrations, observed at the DS site, decreasedtoabout89.4%ofthemaximumvalueobservedatthe RNsite.Ingeneral,thespatialheterogeneityofPM2.5concentration waslowerthanPM10.ThephysicalpropertiesofPM2.5meanthatit resemblesgas;hence,ithaslongerresidencetimeinairandlonger transmissiondistancecomparedwithPM10(Jaffeetal.,2003).The minor differences in both PM10 and PM2.5 observed between the five monitoring sites were caused by contributions from different local sources and different microclimates caused by the local spatialmorphology.

Table1.SummaryofPMconcentrationsandCVsatthefivesitesduringthestudyperiod  Spring(n=92)a Summer(n=83) Fall(n=88) Winter(n=90) Year(n=353) S.D.b Minimum Maximum C.V.c a

PM10(ʅg/m³)

PM2.5(ʅg/m³)

JP

DS

LJX

SH

RN

Average

JP

DS

LJX

SH

RN

Average

193.3 83.9 115.2 212.7 153.1 103.9 22.0 963.0 67.9%

188.8 89.6 112.9 203.1 150.2 100.2 21.0 1026.0 66.7%

195.3 95.5 120.6 202.5 155.0 106.5 20.0 1068.0 68.7%

195.9 96.2 116.2 219.9 158.7 111.1 27.0 1047.0 70.0%

180.8 98.1 151.0 227.1 165.8 101.8 23.0 821.0 61.4%

190.8 92.7 123.2 213.1 156.6 101.7 27.3 943.8 65.0%

105.3 56.3 77.0 152.0 98.6 61.5 16.0 457.0 62.3%

107.7 59.9 79.5 152.4 100.8 61.5 17.0 419.0 61.0%

102.7 63.1 78.8 148.7 99.2 60.6 15.0 414.0 61.1%

103.2 53.5 78.6 148.4 96.9 62.8 13.0 466.0 64.8%

94.6 65.5 93.7 150.6 101.8 59.5 15.0 421.0 58.5%

102.7 59.6 81.5 150.4 99.5 59.6 18.8 435.4 59.9%

Samplesnumber,bStandarddeviation,cCoefficientofvariation

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3.3.Seasonalvariation  Based on Temp, the seasonal division was as follows: spring (March–May),summer(June–August),fall(September–November), and winter (December–February). Figure2 shows the seasonal variation of PM concentrations. The average concentrations of PM10 in spring, summer, fall, and winter were 190.8, 92.7, 123.2, and213.1ʅg/m³,respectively.TheaverageconcentrationsofPM2.5 in spring, summer, fall, and winter were 102.7, 59.6, 81.5, and 150.4ʅg/m³, respectively. The pattern of seasonal variation of PM10massconcentrationwassimilartoPM2.5.Theseresultsshow that there are clear seasonal variations in PM concentrations, whichmightbeinfluencedbythemeteorologicalconditionsofthe differentseasons.ThelowestseasonalvariationofPM10andPM2.5 were all observed during summer when the CV were 37.2% and 40.4%, favorable weather conditions and few air pollution events during summer likely the main reason; the highest variation of PM10 andPM2.5 wereobservedinspringandfallwhentheCVwere 73.2%and57.6%.Massofcoarseparticlesincreasedrapidlyduring duststormeventsinspring,whilemassoffineparticlesincreased rapidly during biomass combustion events in fall were likely the mainreason. 

Figure2.SeasonalvariationofPM10(a)andPM2.5(b)concentrations: mean±standarddeviation.

 Table 2 shows the seasonal statistical data of meteorological parameterscomprisingTemp,RH,WS,Prec,andAP.Inspring,the meteorologicalconditionsofChengducanbecharacterizedbythe lowest RH and highest WS of the year, which cause the resuspension of road dust in the atmosphere and result in relatively higher PM concentrations. Short–term and intensive construction activities were undertaken in spring 2013 in anticipationofaninternationalforumthatwastobeheldinJune. Therefore, crustal dust and cement dust provided greater contributionstoPMconcentrationsduringthisperiod.  However,thecontributingsourcesofPMcouldbeeitherlocal orinremoteregions.Ithasbeenreportedinmanystudiesthatin spring, Chengdu is frequently influenced by the long–range transportofdustfromnorthernregionssuchasGansuandXinjiang (Wang et al., 2013b). During dust storm episodes, the PM concentrationsincreasesignificantly(Lietal.,2014a).

Thebackgroundconcentrationsandanthropogenicsources in summer were relatively stable in this city. The height of the atmospheric mixing layer is deeper in summer and an unstable atmosphereisfavorableforthedilutionanddispersionofambient particles. Furthermore, increased Prec in summer results in lower PMmassconcentrationbecausescavengingbyPrecisanefficient means for the removal of particles from the atmosphere. The favorable effect of rainfall on atmospheric pollutants has been demonstratedinotherstudies(Liuetal.,2008;Wangetal.,2011). Therefore, it can be stated that stable contributing sources and favorablemeteorologicalconditionsweretheprincipalreasonsfor thelowestPMconcentrationsinsummerandearlyautumn.  ThehighestvaluesforbothPM10andPM2.5duringwintertime may be caused by unfavorable meteorological conditions. Low ambientTempandhighAPcancausefrequentthermalinversions in winter that could lead to the accumulation of ambient air pollutantsintheloweratmosphericlayer(Marcazzanetal.,2001; Malek et al., 2006). Furthermore, low ventilation capability is not conducive to the dispersion and dilution of PM and less Prec in winterreducesthelikelihoodofwetdepositionofPM.Ithasbeen proventhatatmosphericconditionshaveanimportanteffectonair quality (Chan and Kwok, 2000; Janssen et al., 2001), especially in basinareas(Paterakietal.,2008).  Relation between mass concentration and meteorological parameters.TheSpearman’srankcorrelationcoefficientsbetween mass concentrations and meteorological conditions are shown in Table3. In this study and notably different from other studies (Chan, 2002; Zhao et al., 2013), PM mass concentrations were significantly positively correlated with Temp with relatively higher coefficients, especially in summer. This result might be because higher Temp and higher RH in summer can cause stronger atmosphericoxidizingcapacityandreactivity,whichcouldresultin greater numbers of local secondary particles. For instance, higher TempinsummercouldresultinahigherconversionrateofSO2to SO2– 4 (Robargeetal.,2002).  The relationship between RH and PM concentrations varied from significant negative correlations in spring, to negative (not significant) relationships in summer and fall, and a significant positive correlation in winter. In spring, lower RH facilitates the suspension of particles in the air, resulting in higher PM mass concentrations, whereas higher RH in spring is always caused by rainfall, which can wash out the pollutants, lowering the PM concentrations. Because of relatively higher RH in winter, the hygroscopic growth of aerosol particles can cause an increase in PMconcentrations.  Significant negative correlations between PM concentrations and WS were observed during the study period. As previously discussed, WS plays an important role in PM concentrations. The strongest correlations between these two factors with relatively highcoefficientswerefoundinwinter,suggestingthatventilationis an effective means of reducing PM concentrations in this season comparedwithothermeteorologicalparameters.Inthisstudy,the correlationcoefficientsbetweenWSandPM2.5werelowerthanthe correlationcoefficientsbetweenWSandPM10,indicating thatWS has a greater impact on concentrations of coarse particles. The correlationbetweenAPandPMconcentrationswasnotsignificant.  Another important factor is Prec because it can scavenge particles from the atmosphere via wet deposition. Figure 3 illustrates that as Prec increased from spring to summer, the PM concentrations gradually reduced. In Chengdu, the bulk of the rainfall is received in summer, which is when the lowest PM concentrationsareobserved.ThehighestPMconcentrationswere observedinwinter,coincidingwiththeminimumamountofPrec.  

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  Table2.Variationsofmeteorologicalparametersduringthestudyperiod:mean±standarddeviation Season

Temp(°C)

RH(%)

WS(m/s)

Prec(mm)

AP(hPa)

Spring

20.2±3.6

68.8±9.0

1.68±0.66

4.52±2.53

948.44±5.45

Summer

27.3±2.2

79.1±6.4

1.57±0.64

16.23±5.68

941.28±3.25

Fall

18.7±4.5

81.3±6.5

1.28±0.55

5.06±3.24

955.04±4.68

Winter

8.3±2.5

80.6±6.4

1.08±0.63

0.58±0.41

957.44±4.94



PM2.5/PM10ratio.Toextendthestudy,thePM2.5/PM10ratioatthe five sites during the study period was investigated, as shown in Figure 4. In this study, the average ratio of PM2.5/PM10 was 0.64. Variations in the PM2.5/PM10 ratios were caused mainly by the different dominant sources of each season and different contributionratesofthesourcestotheparticleswithdifferentsize. 

Figure3.VariationsofmonthlyPMmassandPrec.

 The PM2.5/PM10 ratios were not constant during the observationperiod.Theaverageratiosinspring,summer,fall,and winter were 0.54, 0.64, 0.66, and 0.71, respectively. According to the WHO guidelines, the PM2.5/PM10 ratios in different seasons in Chengdu were all within the range found in urban areas of developed countries (0.5–0.8). The lowest ratio was observed in spring, indicating that particles with sizes of 2.5–10ʅm have greaterinfluenceinthisseasonthanotherseasons.FigureS1(see the SM) depicts satellite images of dust storm events during the period from March 10–15, 2013. The lowest ratio (0.31) in spring was found on March 13, 2013 must due to the dust storm event. Differentsourcesofcontributiontoparticlesofdifferentsizescan be analyzed by comparing the PM concentrations during the few days prior to air–pollution events with the PM concentrations during the air–pollution episode. During the 7 days prior to the duststormevent,theaveragePM10andPM2.5massconcentrations were 301.6 and 143.4 ʅg/m³, respectively, with an average PM2.5/PM10ratioof0.48.However,duringtheduststormepisode, the average PM10 and PM2.5 mass concentrations were 553.2 and 192.7ʅg/m³, respectively, with an average PM2.5/PM10 ratio of 0.35.Thus,theaveragePM10andPM2.5concentrationsincreasedby 83.4% and 34.1% during the dust storm episode. This result suggeststhatcrustaldustprovidedagreatercontributiontoPM10 thanPM2.5duringtheduststormepisode.Furthermore,thelowest PM2.5/PM10 ratio during spring coincided with high WS and dry

weatherconditions,whichfacilitatedtheresuspensionofroaddust intheatmosphere.  ThehighestvalueofPM2.5/PM10ratiowasobservedinwinter, indicating that particles <2.5ʅm have greater influence in this seasonthanotherseasons.Stableatmosphericconditionsinwinter are suitable for the dry deposition of coarse particles, but also favortheaccumulationoffineparticlesintheair.Thehighestratio inwinterwasfoundonJanuary31,2014,thefirstdayoftheLunar New Year in China (spring festival). On this day, the average concentrations of PM10 and PM2.5 were 524.6 and 435.4ʅg/m³, respectively,witharatioof0.83.ComparingthePMconcentrations duringthe7dayspriortothespringfestivalwiththePMconcenͲ trations on the spring festival shows that the average PM10 and PM2.5concentrationsincreasedby74.2%and104.3%,respectively. This means that during the spring festival episode, firework displays played an important role in the PM concentrations, especiallythefineparticles(Liuetal.,2014).  It should be noted that the PM2.5/PM10 ratio observed on October 13, 2013 was also relatively higher. On this day, the average concentrations of PM10 and PM2.5 were 204.4 and 167ʅg/m³, respectively, with a ratio of 0.82. Biomass burning activities were observed during the period from October 8–13, 2013.Duringthisperiod,ricestrawwasburnedaswasteaswellas to fertilize the soil. Comparing the PM concentrations during the 7days prior to the biomass–burning event with the PM concenͲ trations during the biomass–burning episode, the average PM10 and PM2.5 concentrations increased by 43.3% and 62.2%. The relatively higher ratios indicate that fine PM accounted for the majority of PM10 during the biomass combustion, which means that smoke from biomass burning is an important contributor to finePM.  3.4.Weeklyvariations  Anewpolicyofbanningselectedprivatevehiclesfromdriving in designated areas on one weekday based on their license plate numbers has been implemented in Chengdu since October 2012. Thedesignatedareaislocatedbetweenthe2ndand3rdringroads andtherestrictiontimeisfrom07:30–20:00(Beijingtime).  The generalized linear model (GLM) was used to assess the dayofweekeffectsbydividingthestudyperiodinto53weeks.The resultsshowthateverydayintheweekhadasignificanteffecton fine particles (p<0.05) and that for coarse particles was not significant(p>0.05).

 Table3.Spearman’srankcorrelationcoefficientsbetweenmassconcentrationsandmeteorologicalparameters 

a

Spring

Summer

Winter

PM2.5

PM10

PM2.5

PM10

PM2.5

PM10

PM2.5

Temp

–0.11

0.01

0.56a

0.52b

0.08

0.28a

0.30a

0.27a

RH

–0.51a

–0.37a

–0.36

–0.27

–0.21

–0.07

0.15

0.28a

a

b

a

a

a

a

WS

–0.42 

–0.41 

–0.31 

–0.26 

–0.51 

–0.49 

–0.76 

–0.72a

AP

–0.08

–0.13

–0.06

–0.12

–0.01

–0.18

–0.15

–0.20

p–valueissignificantatthe0.01level b p–valueissignificantatthe0.05level



Fall

PM10

a

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  As shown in Figure 5, the mass concentrations of particles wereshowninthreeways:estimatedmarginalmeanswhichwere plotted by GLM model, mathematical average and 5% trimmed mean which excluded the 5% extreme value, the results showed thatthevariationtrends were almost the same. Thehighestdaily PM10 mass concentrations were observed on Monday with an averagevalueof168.9ʅg/m³,whereasthelowestdailyPM10mass concentrations were observed on Thursday with an average value of150.2ʅg/m³.SmallvariationsofdailyPM10massconcentrations were found during the period from Tuesday–Friday, and the concentrations increased sharply on the weekends. The daily average mass concentrations of PM10 on weekdays and weekends were 155.6 and 159.2ʅg/m³, respectively. As part of PM10, the weekly variations of PM2.5 induced by vehicular emissions also contributed to the weekly variation of PM10. Furthermore, contributing sources other than vehicular emissions, such as soil dust induced by road activities, also add to the pattern of weekly variation of PM10. The daily average concentrations of PM2.5 decreased from Monday–Thursday and then rose from Friday– Sunday. The pattern of weekly variation of PM2.5 mass concenͲ trations was strongly associated with the traffic restriction policy and might also reflect the traveling habits and lifestyle of the Chengdupopulation.  ComparedwiththeresultsofstudiesinShenyang(Zhaoetal., 2013) and Agra (Kulshrestha et al., 2009), the pattern of weekly variation of PM concentration found in this study is almost the opposite.Accordingtotheresultsofasimplequestionnaire,many people hold meetings on Mondays and thus everyone regards Mondayasthemostimportantdayoftheweek,whichmeansthat the largest traffic flow is observed on Mondays. Relatively higher PM concentrations on the weekends could be associated with holiday travel. Furthermore, the traffic restriction policy does not extend to the weekends. The lowest PM concentrations observed on Thursdays might be due to the traffic restriction policy. Accordingtothepolicy,vehicleswithlicenseplatenumbers4and9 cannotbedrivenindesignatedareasonThursdays.  Table S3 (see the SM) depicts the magnitude of traffic flow withintheselectedsectionoftwomainroads.Significantpositive correlationswereobservedbetweenthemagnitudeoftheaverage traffic flow and daily average PM concentrations with high spearman’s rank correlation coefficients of 0.81 (p<0.05) for PM10 and 0.92 (p<0.05) for PM2.5, indicating that the patterns of daily variation of traffic flow have an important influence on PM mass concentrations, and that the influence was greater for PM2.5 than PM10. 

4.Conclusions  Data from long–term monitoring of PM10 and PM2.5 were collected from March 2013 to February 2014 to analyze the PM levels in Chengdu. The average concentrations of PM10 and PM2.5 all exceeded the Chinese National Ambient Air Quality Standards and the WHO guidelines, indicating that this city is affected considerably by PM. The relatively higher concentrations for both PM10andPM2.5observedattheRNsitemightbedueto stronger local contributing sources and special microclimates caused by localspatialmorphology. 

Figure5.VariationsofweeklyPM10(a)andPM2.5(b)mass concentrationsatthefivesitesduringthestudyperiod.

 Relatively higher PM mass concentrations were observed in winter, followed by spring. Air–pollution events such as dust storms, biomass burning, and firework displays provide greater contributions to particles of different size that can be demonstrated by the extreme values of the PM2.5/PM10 ratios observed during air–pollution episodes. A traffic restriction policy has been implemented in Chengdu since October 2012 and therefore patterns of weekly variation of PM concentrations provide better understanding of the levels of traffic–related exposureondifferentdaysoftheweek.HighvaluesforbothPM10 and PM2.5 concentrations were observed on Mondays and weekends. Significant positive correlations between the average magnitude of traffic flow and daily average PM concentrations indicate that patterns of daily variation of traffic flow have an importantinfluenceondailyvariationsofPMinChengdu. 

Acknowledgments 

Figure4.RatiosofPM2.5/PM10atthefivesitesduringthestudyperiod.

 In general, the traffic restriction policy has led to patterns of weeklyvariationinPMconcentrationsinChengdu.Comparedwith PM10, the pattern of variation of PM2.5 was even more marked. Therefore, levels of traffic–related exposure on different days of theweekshouldbeconsideredwhenthegovernmentispreparing toadjustthetrafficrestrictionpolicy.  

Theauthorsgratefullyacknowledgementthefinancialsupport from China Postdoctoral Science Foundation under Grant No. 2012M511930, China Postdoctoral Science Foundation Special Funded Project under Grant No. 2013T60853 and the National NaturalScienceFoundationofChinaunderGrantNo.51308361.

 SupportingMaterialAvailable  Satellite images of dust storm events in March 2013 (FigureS1),Informationonmonitoringstationsandsamplingsitein Chengdu (Table S1), Average concentrations of PM10 and PM2.5 in this work and other studies (Table S2), Statistical data on magnitudeoftrafficflowwithinselectedsectionoftwomainroads (Table S3). This information is available free of charge via the internetathttp://www.atmospolres.com.

Huang et al. – Atmospheric Pollution Research (APR)

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