武漢軍運會前后臭氧及其前體物的特征和來源

馬 靜,燕瑩瑩*,孔少飛,2,宋藹莉,陳 楠,祝 波,全繼宏,祁士華,2

武漢軍運會前后臭氧及其前體物的特征和來源

馬 靜1,燕瑩瑩1*,孔少飛1,2,宋藹莉1,陳 楠2,3,祝 波2,3,全繼宏2,3,祁士華1,2

(1.中國地質(zhì)大學(武漢)環(huán)境學院,湖北 武漢 430074；2.湖北省大氣復合污染研究中心,湖北 武漢 430074；3.湖北省生態(tài)環(huán)境監(jiān)測中心站,湖北 武漢 430072)

利用湖北省超級站2019年10～11月的臭氧、NO(= NO + NO2)和102種VOCs物質(zhì)的小時數(shù)據(jù)分析了軍運會期間臭氧污染變化;基于DSMACC箱型模式模擬不同VOCs和NO濃度下臭氧的光化學生成敏感性;采用PMF模型對前體物VOCs進行源解析,并估算不同源類的臭氧生成潛勢.結(jié)果顯示,軍運會保障前臭氧日最大8小時濃度(最大MDA8:219.51mg/m3)超過國家二級標準,保障期臭氧MDA8濃度(135.11mg/m3)明顯下降,保障后濃度回升(140.98mg/m3).軍運會保障前中期臭氧濃度的差異受氣象條件影響更明顯,而保障后臭氧濃度的上升主要是因為前體物濃度的大幅增加.根據(jù)DSMACC模擬的EKMA曲線,武漢市軍運會期間臭氧的光化學生成主要受VOCs濃度變化的影響.進一步對VOCs進行源解析,結(jié)果顯示,保障前VOCs對臭氧生成貢獻較大的源類是燃燒源、石油化工和機動車,分別占23.0%、22.8%和22.5%;保障期間VOCs的主要來源是機動車(38.4%)和燃燒源(25.5%);保障后則主要是石油化工(32.6%)和燃料揮發(fā)(25.7%).三個階段對比發(fā)現(xiàn),軍運會的保障方案對石油化工源減排效果明顯,但對機動車和燃燒源排放的限制效果并不顯著.武漢市應該更注重對燃燒、燃料揮發(fā)和機動車排放的治理.

臭氧污染；軍運會；源解析；EKMA曲線

當前我國重點區(qū)域面臨臭氧和細顆粒物復合污染問題,其中臭氧污染日益突出[1].近年來臭氧濃度逐年上升[2-7],以臭氧為首要污染物的污染天氣占比逐漸增加[8],臭氧繼PM2.5之后備受關(guān)注[9].近地層臭氧濃度過高不僅會對農(nóng)作物的生長[10-12]和全球氣候變化[13]造成一定的影響,還會對人類身體健康產(chǎn)生威脅[14-15].我國城市近地層臭氧主要是由氮氧化物(NO)與揮發(fā)性有機化合物(VOCs)以及一氧化碳(CO)等多種前體物在光照條件下發(fā)生一系列光化學氧化反應產(chǎn)生[16],并通過化學消耗和干沉降過程從大氣中去除.

局地臭氧光化學過程與主要前體物(VOCs和NO)的關(guān)系受到廣泛關(guān)注[17-24].Tang等[25]發(fā)現(xiàn),華北地區(qū)夏季近地面大氣中的臭氧光化學生成由1997年的受NO控制轉(zhuǎn)變成2010年的受VOCs控制.Ding等[26]研究表明,長三角地區(qū)臭氧光化學生成受VOCs控制.對珠江三角洲地區(qū)臭氧污染做敏感性分析,Wang等[27]研究表明珠三角中部內(nèi)陸地區(qū)和周邊沿海地區(qū)的臭氧生成受VOCs控制,而珠三角西南部農(nóng)村地區(qū)的臭氧生成受NO控制.這些研究表明,我國城市地區(qū)臭氧的光化學生成對VOCs高度敏感.因而,VOCs來源解析以及對臭氧生成的貢獻成為近年來研究的熱點問題.已有研究表明,我國東部地區(qū)汽車尾氣排放和生物燃料燃燒占71%,汽油泄漏和溶劑使用占7%,生物質(zhì)燃燒和工業(yè)分別占11%[28];上海地區(qū)汽車尾氣是VOCs最大的排放源[29].

過去,我國臭氧污染的研究主要集中在四個區(qū)域:京津冀[30]、長三角[31]、珠三角[32]和成渝地區(qū)[33-34].然而,我國中部地區(qū)夏季臭氧污染頻發(fā),形成一個新的區(qū)域性大氣污染核心區(qū)[35-37].作為長江流域經(jīng)濟發(fā)展的中心,武漢及周邊城市群船舶、交通、工業(yè)和農(nóng)業(yè)活動等排放密集使得臭氧前體物污染源復雜,且具有高溫高濕的氣象條件,易于臭氧污染事件形成.近年來武漢地區(qū)臭氧污染呈明顯增加趨勢[38],該地區(qū)臭氧污染及其前體物的特征和來源值得關(guān)注.

2019年10月18日至27日第七屆世界軍人運動會(簡稱軍運會)在武漢舉行,這次軍運會是世界軍人運動會歷史上規(guī)模最大、參賽人員最多的一次運動會.根據(jù)軍運會環(huán)境空氣質(zhì)量保障工作要求,在生態(tài)環(huán)境部的統(tǒng)一部署下,湖北、河南、安徽等省份確立了保障方案,對武漢市各企業(yè)、各類工地施工和船舶、飛機、燃油非道路移動機械等大氣污染物排放源,嚴格執(zhí)行大氣環(huán)境質(zhì)量管理臨時性措施.保障方案明確會期保障時段為10月8日至10月28日,保障目標為確保會期武漢市環(huán)境空氣質(zhì)量達到優(yōu)良水平.

類似武漢軍運會這樣大型活動的環(huán)境狀況也有許多學者做過研究.例如,孫天樂等[39]對2008年殘奧會期間黑炭氣溶膠的研究表明,殘奧會期間本地黑炭排放有所降低.王羽琴等[40]研究表明深圳大運會期間PM2.5得到很好的控制,但污染控制措施取消后PM2.5濃度突增.在大型活動期間,采取相應的措施能有效地改善大氣環(huán)境.然而,這些研究大多關(guān)注細顆粒物,少有對臭氧的研究.

本研究分析對比了軍運會保障期間與保障前后湖北超級站臭氧及其前體物(NO和VOCs)濃度的變化.利用DSMACC箱型模式模擬了不同VOCs和NO濃度下臭氧的生成情況,確定了臭氧光化學生成的敏感性.進一步采用PMF模型對VOCs進行源解析,計算臭氧生成潛勢.本研究的主要創(chuàng)新點為揭示了中部地區(qū)特大城市武漢臭氧污染的主要來源,評估了軍運會保障期間武漢臭氧污染的改善情況及污染減排貢獻.研究結(jié)果可為武漢及周邊地區(qū)臭氧污染的預報預警、臭氧污染成因識別和污染防控政策完善提供科學依據(jù).

1 數(shù)據(jù)與方法

1.1 數(shù)據(jù)來源

本文所使用的觀測數(shù)據(jù)來自武漢市大氣復合污染實驗室(“超級站”),超級站位于湖北省武漢市江漢區(qū)香江路1號,具體地理坐標為(114.2777°E, 30.6042°N).該站點地處漢口中心城區(qū),為二類功能區(qū),觀測點距地面約25m,周圍為居民區(qū),無建筑物遮擋,視線較為開闊,無明顯工業(yè)污染源,監(jiān)測數(shù)據(jù)一定程度上可以代表武漢城市中心城區(qū)大氣污染狀況水平.分析的污染物有臭氧、NO、NO2和102種VOCs物質(zhì),時間段為2019年10月1日～11月7日.本文中,10月8～28日是軍運會保障期,10月1～8日為保障前,10月29日～11月7日為保障后.10月1～7日國慶期間,城市人口、車輛減少,大氣環(huán)境質(zhì)量較好[41].

1.2 DSMACC模擬

DSMACC(The Dynamical Simple Model of Atmospheric Chemical Complexity)是一種常用的箱型模式(https://github.com/barronh/DSMACC),可用于研究臭氧及其前體物之間的聯(lián)系.

采用DSMACC模型模擬武漢地區(qū)不同VOCs和NO濃度下臭氧的光化學生成情況,以表征O3-VOC-NO反應系統(tǒng)的非線性特征.時間設(shè)置為中午(12:00～13:00,臭氧生成效率和臭氧濃度在一天中都是最高的時段[42]),天氣條件設(shè)定為晴空.氣象輸入和污染物初始濃度根據(jù)觀測的平均值設(shè)置.

1.3 PMF模型

正矩陣分解(PMF)模型是美國環(huán)保署(USEPA)官方推薦的源解析技術(shù)方法,它被廣泛應用于VOCs源解析[43-44].它可以對數(shù)據(jù)產(chǎn)生更好的擬合,更好的描述污染物的來源[45-46].PMF模型將受體樣本矩陣(×)分解為貢獻矩陣(×)和因子矩陣(×),并使用多次迭代算法使目標函數(shù)Q最小化,進而確定最優(yōu)解析結(jié)果,計算公式如下:

式中:x表示第個樣本中第個物質(zhì)的質(zhì)量濃度,單位mg/m3;g表示第個來源對第個樣本的貢獻率;f表示第個排放源中第個物質(zhì)的質(zhì)量分數(shù);為排放源的種類數(shù);為需要解析的因子數(shù);e表示第個樣本中第個物質(zhì)的殘差;和分別表示樣本數(shù)和被測物質(zhì)數(shù);u表示第個樣本中第個物質(zhì)的不確定度;Q是基于樣品監(jiān)測誤差的目標函數(shù).

PMF通過最小二乘法來確定污染源類型及其貢獻,并且對污染源類型及其貢獻具有非負的限制.一般由誤差比例和方法檢出限來確定物種的不確定度.通常物質(zhì)的濃度低于或等于檢出限時,用公式(3)計算;物種的濃度高于檢出限時,用公式(4)計算:

式中:U為物種的不確定性;MDL為物種的檢出限;為物種的質(zhì)量濃度,mg/m3;EF為誤差分數(shù),本研究中取20%.

本文中,并非所有被測量的物種都輸入到PMF模型中,而是按照以下方法來選擇適當?shù)腣OCs物種:(1)保留高相關(guān)性物質(zhì);(2)保留能表征揮發(fā)性有機化合物來源的物質(zhì);(3)保留高于檢出限50%的物質(zhì)[47-48].

1.4 臭氧生成潛勢

臭氧生成潛勢(OFP)是衡量VOCs的反應活性及對臭氧影響的重要指標,能分析形成臭氧的優(yōu)勢成分.其計算公式為:

OFP= VOC× MIR(5)

式中:OFP是某個VOCs物質(zhì)的臭氧生成潛勢; VOC是該VOCs物質(zhì)的濃度;MIR是該VOCs物質(zhì)的反應性,數(shù)值選擇參考前人研究[49].

本文計算源解析后各因子的臭氧生成潛勢.其計算公式[43]為:

式中:g為第個源在第個樣本中的貢獻率;f為第個源中第種的濃度.

2 結(jié)果與討論

2.1 軍運會期間臭氧及其前體物的變化特征

2.1.1 武漢市臭氧變化情況武漢地區(qū)臭氧濃度具有明顯的時間變化規(guī)律,年際變化呈現(xiàn)上升的趨勢;在年內(nèi)變化上呈現(xiàn)春夏季高于秋冬的特點[4].由圖1(a)和圖2(a)可知,軍運會保障前臭氧MDA8濃度超標,最大濃度為219.51mg/m3;保障期臭氧MDA8濃度明顯下降,最大濃度為135.11mg/m3;保障后濃度反彈,最大濃度為140.98mg/m3.保障期較之前的臭氧平均濃度下降34.3%,保障后較保障期臭氧平均濃度上升17.0%.

圖1 (a)軍運會期間臭氧MDA8濃度及其對應時間NOx濃度和TVOC濃度日變化;(b)研究時段溫度、相對濕度、風速和輻射日變化

2.1.2 武漢市氮氧化物變化情況由圖1(a)和圖2(a)可知,保障期NO和NO2濃度相比于保障前變化不大,NO濃度不超過5.48mg/m3,NO2濃度不超過48.32mg/m3.保障后二者濃度有明顯的升高趨勢,最大濃度分別達到14.14mg/m3和89.45mg/m3,保障后二者總濃度較保障前上升43.2%,保障后NO和NO2出現(xiàn)嚴重反彈,這可能與飛機等非道路排放源和工業(yè)生產(chǎn)有關(guān).軍運會后解除了對非道路排放源和工廠的臨時措施(http://hbj.wuhan.gov.cn/fbjd_19/zc/ gfxwj/202001/t20200106_560629.html).

圖2 (a)軍運會保障前中后臭氧、NO、NO2和TVOC濃度的平均值,綠色線段表示標準偏差;(b)軍運會保障前中后的溫度、相對濕度、風速和輻射的平均值

2.1.3 武漢市TVOC變化情況由圖1(a)和圖2(a)可知,保障前和保障期VOCs日均濃度變化波動較小,平均值分別為79.70mg/m3和84.49mg/m3.保障后TVOC濃度明顯升高,最大濃度達到了155.19mg/m3,平均濃度較保障前上升了47.1%,達到117.23mg/m3.

表1列舉了14種標識性的VOCs物質(zhì),計算了不同保障時期每種物質(zhì)的最大和平均濃度.結(jié)果顯示,除異戊二烯總體濃度變化不大外,其余各VOCs物種(烷烴、烯烴和芳香烴類)在保障前中期濃度差異較小,保障后濃度均有不同程度的升高.

2.1.4 武漢市天氣條件變化情況由圖1(b)和圖2(b)可知,保障前溫度明顯偏高,最高為34.0℃,平均溫度為24.7℃.保障期除21號溫度較高達到27.6℃外,其余天溫度都較低,平均值為19.4℃.保障后溫度較保障期差異較小,最高為25.6℃,平均值為20.0℃.保障前中后相對濕度略有差異,平均值分別為68.4%、72.2%和58.6%;同時,風速都較小(0.70～2.75m/s).輻射的變化波動較大,1號和19號出現(xiàn)最大值,分別為28.21W/m2和25.81W/m2,保障期輻射平均值為8.55W/m2,較保障前(9.52W/m2)和保障后(9.88W/m2)略小.

表1 保障前中后部分VOCs的濃度

2.1.5 臭氧與其前體物和天氣條件的相關(guān)分析由表1可知,保障前臭氧及其前體物相關(guān)性不高,相關(guān)系數(shù)均小于0.10,且都未通過90%的顯著性檢驗,而溫度、相對濕度和輻射與臭氧的相關(guān)性高,相關(guān)系數(shù)都大于0.50,且都通過99%的顯著性檢驗.

表2 臭氧與其前體物和氣象條件的相關(guān)性及顯著水平

注:為統(tǒng)計量,99%、95%和90%的T臨界值分別為2.3,1.7,1.3.

保障期臭氧與前體物的相關(guān)性雖明顯大于保障前,但相關(guān)系數(shù)都較小(<0.4),而臭氧與溫度和相對濕度的相關(guān)性明顯減小,相關(guān)系數(shù)都減小0.20以上.保障后與保障期比,臭氧與其前體物的相關(guān)性更大,相關(guān)系數(shù)都在0.65以上,且都通過99%的顯著性檢驗,而臭氧與溫度、相對濕度和輻射相關(guān)性比保障前小,相關(guān)系數(shù)都減小0.2以上.相較于保障后期,保障前中期臭氧濃度變化受天氣條件影響更強烈,保障前溫度高,輻射強,相對濕度小,有利于臭氧生成.而保障后臭氧增加主要受氮氧化物和VOCs濃度回升的影響,天氣條件與保障前差異不大.

2.2 武漢市臭氧光化學生成的敏感性

圖3顯示了DSMACC模擬的臭氧光化學生成敏感性,其中等值線表示不同前體物(TVOC和NO)濃度下臭氧的生成情況,黑色脊線為最大臭氧形成值的連線,TVOC/NO比率為7.1.黑色圓圈為各階段白天(8:00～20:00)觀測的NO和TVOC小時平均濃度.

圖3 (a)～(c)DSMACC模擬的軍運會保障前中后的臭氧等值線圖;(d)～(f)軍運會保障前中后不同時期的TVOC/NOx比值箱型圖

由圖3可知,保障前TVOC/NO比率在0.6～4.7范圍內(nèi),保障期TVOC/NO比率在0.2～4.9范圍內(nèi),保障后TVOC/NO比率在0.4～5.8范圍內(nèi).保障前中后臭氧生成都處于VOCs控制區(qū),武漢市的臭氧形成主要受VOCs控制.三個時期臭氧的化學生成區(qū)間差異不大,說明軍運會的管控措施對NO和VOCs的管控力度相差較小,達到了協(xié)同減排的效果.

2.3 VOCs源解析及對臭氧生成的貢獻

2.3.1 VOCs來源根據(jù)EPA PMF5.0對VOCs源解析的計算結(jié)果,結(jié)合文獻中典型VOCs源類的標識成分,最終把污染源歸為六類(圖4).

因子1主要貢獻成分是異戊二烯,異戊二烯是植物光合作用排放的物質(zhì)[50],因此因子1歸為生物源.日變化顯示中午2點出現(xiàn)最大峰值,符合植物源排放的日內(nèi)變化.

因子2主要貢獻成分是丙烷,其次是鄰二甲苯、間/對二甲苯和乙苯等苯類物質(zhì),還有部分高碳烷烴,其中丙烷是天然氣的工業(yè)加工產(chǎn)物[51],鄰二甲苯、間/對二甲苯來自汽車尾氣排放[43],乙苯主要用于生產(chǎn)其它化學產(chǎn)品,而高碳烷烴主要來自工業(yè)生產(chǎn)[48],這里把因子2歸為石油化工.日變化顯示夜間污染物濃度高于白天,符合工廠夜間排放高于白天.

因子3主要貢獻成分是乙炔、1,2-二氯乙烷、1,2-二氯丙烷等,1,2-二氯乙烷、1,2-二氯丙烷為機動車尾氣排放[52],乙炔受機動車尾氣、油氣揮發(fā)、溶劑使用和LPG燃料等影響明顯[53],因此因子3歸為機動車尾氣.日變化中白天的值高于夜間值對應日間機動車使用高于夜間.

因子4主要貢獻成分是1,2,4-三甲基苯、1,3,5-三甲基苯、1,2,3-三甲基苯、1,3-二乙基苯、1,4-二乙基苯,這些物質(zhì)都與溶劑的使用有關(guān)[48][54],因此把因子4歸為溶劑使用.溶劑的排放與工業(yè)密切相關(guān),在夜間出現(xiàn)峰值.

因子5主要貢獻成分是乙烯、丙烯、1-丁烯,烯烴的主要來源為機動車排放、煤與生物質(zhì)燃燒、石化工業(yè)排放[55],其中丙烯是燃燒過程中排放的主要物質(zhì)[56],因此把因子5歸為燃燒源.日變化對應早上8點和晚上22點有峰值,最低值出現(xiàn)在下午16點.

因子6主要貢獻成分主要是甲基環(huán)戊烷和大量的C4～C6烷烴,甲基戊烷既是工業(yè)合成的原料,也來自于汽油車尾氣[57-58],C5烷烴是汽油揮發(fā)的重要組成物質(zhì)[58],本文把因子6歸為燃料揮發(fā).日變化顯示夜間高于白天,這符合11:00后由于光化學反應的消耗濃度降低,到18:00光化學反應減弱,濃度升高.

圖4 PMF源解析的結(jié)果,(a)～(f)為40種VOCs物質(zhì)對不同因子的貢獻率;(g)～(l)為各因子濃度的日內(nèi)變化

如圖5(a)～(c)所示,保障前對武漢市VOCs貢獻最大的源是機動車排放,貢獻率為29.7% (11.26mg/ m3),其次是石油化工22.1%(8.37mg/m3),燃料揮發(fā)、燃燒源、溶劑和生物源分別為13.7%、11.7%、11.6%和11.1%(5.18、4.44、4.39和4.21mg/m3).保障期較保障前機動車排放(40.0%)和燃燒源(17.1%)貢獻率有所增加,其中機動車貢獻率增加最為顯著,增加10.3%,燃燒源增加5.4%,而石油化工(17.6%)和燃料揮發(fā)(7.3%)分別減少4.5%和6.4%,溶劑使用和生物源減少不明顯,其貢獻率分別為8.4%和9.6%(3.72和4.24mg/m3).保障后石油化工的貢獻率最大(30.4%),其次是燃料揮發(fā)(25.3%)和機動車排放(25.2%),溶劑、燃燒源和生物源貢獻率分別為7.1%、6.5%和5.6%(4.94、4.57和3.89mg/m3),較保障期石油化工和燃料揮發(fā)貢獻率顯著增加,機動車貢獻率雖有明顯下降,但總體占比依然較大.

圖5 (a)～(c)軍運會保障前中后,各類源對VOCs濃度的貢獻率;(d)～(f)各類源的臭氧生成潛勢

2.3.2 臭氧生成潛勢如圖5(d)～(f)所示,保障前燃燒源對OFP貢獻最大,貢獻率為23.0%(1.02mg/m3),其次是石油化工22.8%(1.01mg/m3)和機動車排放22.5%(1.00mg/m3),三者的貢獻率相差不大,燃料揮發(fā)、溶劑和生物源貢獻率分別為11.5%、10.7%和9.5%(0.51、0.48和0.42mg/m3).保障期機動車的貢獻率最大,為38.4%(2.16mg/m3),較保障前增加了15.9%,而石油化工為12.7%(0.72mg/m3),較保障前貢獻率降低了10.1%,溶劑和燃料揮發(fā)的貢獻率略有減小,燃燒源和生物源的貢獻率變化不大.保障后石油化工和燃料揮發(fā)的貢獻率顯著增大,分別達32.6%和25.7%(3.81和3.00mg/m3),燃燒源的貢獻率顯著下降,下降到14.1%.總的來說,機動車對臭氧生成的貢獻沒有明顯的減少,保障前中后溶劑使用對OPF的貢獻率也沒有明顯減小的趨勢.武漢軍運會對燃料揮發(fā)和石油化工管控效果明顯,對燃燒源和機動車排放的管控效果較弱,但保障后石油化工和燃料揮發(fā)反彈嚴重.

根據(jù)前人對2010年上海世博會前后污染物濃度變化和來源解析研究表明[59-61],世博會期間氮氧化物得到了有效的控制,但后期由于天氣和治理力度不足等原因出現(xiàn)反彈,且治理方案對臭氧的減排效果不明顯.對來源的解析表明,汽車尾氣是重點需要關(guān)注的.這與本研究的結(jié)果類似,需要制定長期的管控方案,加強對機動車的管控.

3 結(jié)論

3.1 軍運會保障前臭氧日最大8h濃度(最大MDA8: 219.51mg/m3)超過國家二級標準,保障期臭氧MDA8濃度(135.11mg/m3)明顯下降,保障后濃度回升(140.98mg/m3).軍運會保障前和保障期臭氧濃度的差異受氣象條件影響更明顯,而保障后臭氧濃度的上升主要是因為前體物濃度的大幅增加.

3.2 根據(jù)DSMACC模擬的EKMA曲線,武漢市軍運會期間TVOC/NO比值都在7.1以下,屬于VOCs控制區(qū),臭氧的光化學生成主要受VOCs濃度變化的影響.

3.3 VOCs源解析結(jié)果顯示,保障前VOCs對臭氧生成貢獻較大的源類是燃燒源、石油化工和機動車,分別占23.0%、22.8%和22.5%;保障期間VOCs的主要來源是機動車(38.4%)和燃燒源(25.5%);保障后則主要是石油化工(32.6%)和燃料揮發(fā)(25.7%).三個階段對比發(fā)現(xiàn),軍運會的保障方案對石油化工源減排效果明顯,但對機動車和燃燒源排放的限制效果并不明顯.

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Characteristics and sources of ozone and its precursors aroundthe Wuhan Military Games.

MA Jing1, YAN Ying-ying1*, KONG Shao-fei1,2, SONG Ai-li1, CHEN Nan2,3, ZHU Bo2,3, QUAN Ji-hong2,3, QI Shi-hua1,2

(1.School of Environmental Studies, China University ofGeosciences, Wuhan 430074, China；2.Atmospheric Combined Pollution Research Center of Hubei, Wuhan 430074, China；3.Environmental Monitoring Central Station in Hubei Province, Wuhan 430072, China)., 2022,42(7)：3023～3032

The hourly data of ozone, NO(= NO + NO2) and VOCs (102species) during October～November 2019 provided by Hubei Superstationwere used to analyze the variation characteristics of ozone pollutionduring the Wuhan Military Games (WMG). The photochemical regime of ozone was simulated based on DSMACC box model. The source apportionment of VOCs was conducted by PMF model, and the ozone formation potential of different VOCs sourcewas estimated. The results showed that the maximum daily 8-hour average ozone concentration (maximum MDA8:219.51mg/m3) exceeded the national level II standard before the military games. The MDA8 values (135.11mg/m3on average) decreased significantly during the military games, and the concentrations (140.98mg/m3) rise after the WMG. The difference of ozone before and during the military games was mainly affected by meteorological conditions, and the increase of ozone after the WMG was mainly due to the significant increase of precursor emissions. The EKMA curve indicates that the photochemical formation of ozone was controlled by a VOC-limited regime during the study period. The VOCssources that contribute greatly to ozone formation before the military games were combustion, petrochemical industry and motor vehicles, accounting for 23.0%, 22.8% and 22.5%, respectively. The main sources of VOCs during the military games were motor vehicles (38.4%) and combustion (25.5%). While petrochemical industry (32.6%) and fuel volatilization (25.7%)were the main sources after the WMG. The results showed that the prevention strategies of the military games had a significant effect on the emission reduction of petrochemical industry, but the emission mitigation of motor vehicles and combustion was not significant. Wuhan should pay more attention to the control of combustion, fuel volatilization and motor vehicle emissions.

ozone pollution；Wuhan Military Games；VOCs source apportionment；EKMA curve

X511

A

1000-6923(2022)07-3023-10

馬 靜(1998-),男,四川南充人,中國地質(zhì)大學(武漢),碩士研究生,主要從事對流層臭氧污染研究.

2021-12-01

國家重點研發(fā)計劃(2017YFC0212602);國家自然科學基金資助項目(41905112)

* 責任作者, 副教授, yanyingying@cug.edu.cn