深圳工業(yè)區(qū)夏秋季大氣揮發(fā)性有機(jī)物來(lái)源研究

張 月,夏士勇,魏成波,劉侍奇,曹禮明,*,于廣河,黃曉鋒

深圳工業(yè)區(qū)夏秋季大氣揮發(fā)性有機(jī)物來(lái)源研究

張 月1,夏士勇1,魏成波1,劉侍奇1,曹禮明1,2*,于廣河2,黃曉鋒1

(1.北京大學(xué)深圳研究生院環(huán)境與能源學(xué)院,大氣觀測(cè)超級(jí)站實(shí)驗(yàn)室,廣東 深圳 518055；2.深港產(chǎn)學(xué)研基地(北京大學(xué)香港科技大學(xué)深圳研修院),廣東 深圳 518057)

于2021年夏秋季(7～10月)在深圳北部工業(yè)區(qū)開(kāi)展大氣揮發(fā)性有機(jī)物(VOCs)長(zhǎng)期在線觀測(cè),以分析O3污染日和非污染日VOCs污染特征,并利用特征物種比值法和正交矩陣因子模型(PMF)對(duì)VOCs來(lái)源進(jìn)行精細(xì)化解析,以探究各類排放源的濃度貢獻(xiàn)、臭氧生成潛勢(shì)(OFP)貢獻(xiàn)以及氣象條件對(duì)其產(chǎn)生的影響.結(jié)果表明,深圳工業(yè)區(qū)夏秋季總揮發(fā)性有機(jī)物(TVOCs)平均濃度為50.48×10-9,其中濃度最高的為烷烴,其次為含氧有機(jī)物(OVOCs)和芳香烴,分別占比41.3%、22.2%和17.0%.與非污染日相比,OVOCs濃度在污染日上升幅度最高(63.1%).特征物種比值則表明該地區(qū)主要受到機(jī)動(dòng)車排放和有機(jī)溶劑使用的影響.利用PMF源解析出5個(gè)主要排放源,其中機(jī)動(dòng)車排放及汽油揮發(fā)的濃度貢獻(xiàn)最大(28.2%),其次為工藝過(guò)程排放(25.0%)、溶劑使用(22.9%)和生物質(zhì)燃燒(20.4%).在污染日,工藝過(guò)程排放和溶劑使用這兩類源的OFP貢獻(xiàn)超過(guò)70%.各類排放源風(fēng)場(chǎng)分布特征結(jié)果表明,機(jī)動(dòng)車排放及汽油揮發(fā)、工藝過(guò)程排放和溶劑使用主要來(lái)自于本地排放,而生物質(zhì)燃燒則更多地受到東北方向的傳輸貢獻(xiàn).建議加大本地工業(yè)源和交通源管控,同時(shí)關(guān)注生物質(zhì)燃燒,加強(qiáng)與周邊區(qū)域的聯(lián)防聯(lián)控.

工業(yè)區(qū)；揮發(fā)性有機(jī)物；臭氧生成潛勢(shì)；特征物種比值法；正交矩陣因子分析

自2013年“國(guó)十條”頒布以來(lái),我國(guó)PM2.5濃度持續(xù)下降,但O3污染卻呈現(xiàn)加重趨勢(shì),長(zhǎng)時(shí)間大范圍的O3污染事件頻繁發(fā)生[1-2].O3對(duì)于人體健康[3-4]、生態(tài)系統(tǒng)[5-6]、氣候變化[7-8]等方面會(huì)造成不利影響,而作為其前體物的揮發(fā)性有機(jī)物(VOCs)在其中扮演著重要角色[9-10].

大氣中VOCs種類繁多,不同地區(qū)來(lái)源復(fù)雜,主要包括天然源、生物質(zhì)燃燒、機(jī)動(dòng)車排放、工業(yè)排放等.目前國(guó)內(nèi)外關(guān)于VOCs的監(jiān)測(cè)方法分為離線技術(shù)和在線技術(shù),離線采樣方法有吸附劑采樣、氣袋采樣、不銹鋼罐采樣等,樣品采集后送入實(shí)驗(yàn)室進(jìn)行儀器分析.在線技術(shù)包括在線-氣相色譜-質(zhì)譜/氫火焰離子化檢測(cè)器(GC-MS/FID)、質(zhì)子轉(zhuǎn)移反應(yīng)質(zhì)譜儀(PTR-MS)等[11-13]. VOCs的分析方法包括利用OH消耗速率(LOH)和臭氧生成潛勢(shì)(OFP)分析VOCs的化學(xué)活性,利用特征物種比值法、排放清單法、主成分分析法、化學(xué)質(zhì)量平衡(CMB)、正交矩陣因子分析(PMF)等進(jìn)行VOCs的來(lái)源解析,利用基于觀測(cè)的模型(OBM)進(jìn)行O3敏感性分析等[14-17].

在相關(guān)VOCs排放清單的研究中,工業(yè)排放源在我國(guó)珠江三角洲地區(qū)VOCs總排放量中占據(jù)了主導(dǎo)地位,并且呈現(xiàn)持續(xù)增長(zhǎng)趨勢(shì)[18].研究表明,烷烴、芳香烴和OVOCs是珠三角地區(qū)VOCs的重要組分,機(jī)動(dòng)車排放和工業(yè)排放是主要的排放源[19-20].宋鑫等人[21]于2021年夏初對(duì)東莞某工業(yè)區(qū)VOCs組成及來(lái)源進(jìn)行研究,結(jié)果表明含氧VOCs的濃度占比最大(51.5%),且其具有較高的大氣化學(xué)活性.李佳佳等人[22]于2020年夏季對(duì)廣州某工業(yè)區(qū)的VOCs濃度及來(lái)源進(jìn)行研究,結(jié)果表明烷烴和芳香烴濃度占比較大,機(jī)動(dòng)車尾氣排放,工業(yè)溶劑蒸發(fā)、印刷等行業(yè)排放則是VOCs的可能排放來(lái)源.鄧思欣等人[23]于2019年在佛山產(chǎn)業(yè)重鎮(zhèn)開(kāi)展VOCs污染特征等研究,結(jié)果顯示環(huán)境VOCs主要組成為烷烴(56.5%)和芳香烴(30.1%),溶劑使用源(42.4%)和機(jī)動(dòng)車排放源(25.8%)是主要排放源.

深圳位于珠江三角洲東岸,是珠三角城市群中經(jīng)濟(jì)和工業(yè)活力較高的主要城市,研究深圳工業(yè)區(qū)的VOCs污染特征及來(lái)源對(duì)于珠三角地區(qū)VOCs減排具有重要意義.近年來(lái)研究表明,芳香烴和OVOCs對(duì)深圳市大氣VOCs具有重要貢獻(xiàn)[9].朱波等人[24]和陳雪等人[14]分別于2017年秋季和2019年秋季在深圳市開(kāi)展了多點(diǎn)位離線采樣,結(jié)果發(fā)現(xiàn)深圳市西部和北部工業(yè)區(qū)VOCs濃度較高,其中OVOCs和芳香烴濃度占比較大,可能與工藝過(guò)程排放和溶劑使用有關(guān).然而此前研究多為離線采樣,觀測(cè)時(shí)間短且時(shí)間分辨率低,有限的樣品數(shù)量難以有效揭示關(guān)鍵VOCs組分長(zhǎng)期變化趨勢(shì).此外,此前有關(guān)深圳市工業(yè)區(qū)的O3生成敏感性分析表明該地區(qū)處于VOCs控制區(qū)[25],對(duì)于另一種光化學(xué)特征指示物PAN的研究也發(fā)現(xiàn)前體物濃度是影響其生成的關(guān)鍵因子[26],因此有必要在該地區(qū)開(kāi)展光化學(xué)前體物VOCs的來(lái)源解析.本研究選取深圳市污染較嚴(yán)重的北部工業(yè)區(qū),采取在線監(jiān)測(cè)的方法,于2021年光化學(xué)污染較嚴(yán)重的夏秋季(7～10月)開(kāi)展VOCs長(zhǎng)期在線觀測(cè),從而快速捕捉VOCs物種變化情況,分析O3污染日和非污染日VOCs污染特征,并利用特征物種比值法和PMF模型對(duì)VOCs來(lái)源進(jìn)行精細(xì)化解析,以探究各類排放源的濃度貢獻(xiàn)、OFP貢獻(xiàn)以及氣象條件對(duì)其產(chǎn)生的影響,以期為珠三角主要工業(yè)區(qū)VOCs排放源的重點(diǎn)管控提供理論依據(jù).

1 材料與方法

1.1 觀測(cè)點(diǎn)位與觀測(cè)時(shí)間

深圳市龍華區(qū)是深圳重要的產(chǎn)業(yè)大區(qū),包含印刷行業(yè)、制鞋企業(yè)、電子制造、塑膠制品、家具制造和自行車制造等規(guī)模以上工業(yè)企業(yè)1865家[27].觀測(cè)點(diǎn)位于龍華區(qū)桂花小學(xué)(113°59′E,22°36′N)五樓樓頂,距離地面大約25m.觀測(cè)點(diǎn)位南面約150m處為交通主干道,四面被工業(yè)園區(qū)包圍,能夠在一定程度上表征深圳市工業(yè)區(qū)的大氣化學(xué)組分特征.觀測(cè)時(shí)間為2021年7月18日～10月31日.

1.2 儀器與分析方法

本次VOCs觀測(cè)使用鵬宇昌亞ZF-PKU- VOC1007型VOCs在線監(jiān)測(cè)系統(tǒng),搭載超低溫捕集系統(tǒng)和分析系統(tǒng)(島津GCMS-QP2010SE).該系統(tǒng)時(shí)間分辨率為1h,設(shè)備于整點(diǎn)時(shí)刻開(kāi)始采樣,采樣流量為60mL/min,采樣時(shí)長(zhǎng)5min.大氣樣品首先分為FID與MS兩路,分別進(jìn)入除水阱去除水分,然后進(jìn)入超低溫捕集阱濃縮富集.隨后捕集阱快速升溫加熱解析出VOCs, VOCs由載氣送入FID/MS檢測(cè)器.FID分析C2-C5的低碳數(shù)烴類物種,MS分析C5-C12烴類、OVOCs、鹵代烴以及乙腈等.分析結(jié)束后系統(tǒng)進(jìn)行加熱反吹去除殘留VOCs.本研究共觀測(cè)到99種VOCs物種,包括29種烷烴、10種烯烴、16種芳香烴、30種鹵代烴、12種OVOCs、乙炔以及乙腈.儀器的質(zhì)控校準(zhǔn)參照《環(huán)境空氣揮發(fā)性有機(jī)物氣相色譜連續(xù)監(jiān)測(cè)系統(tǒng)技術(shù)要求及檢測(cè)方法》(HJ1010- 2018)執(zhí)行[28],以確保監(jiān)測(cè)數(shù)據(jù)的準(zhǔn)確性、有效性. 采用美國(guó)Linde公司的標(biāo)準(zhǔn)氣體每月至少進(jìn)行一次多點(diǎn)標(biāo)定,確保90%以上物種工作曲線的決定系數(shù)(R2)大于0.99,各物種體積分?jǐn)?shù)檢出限在0.01×10-9～ 0.1×10-9范圍內(nèi).此外,每周還使用標(biāo)準(zhǔn)氣體進(jìn)行單點(diǎn)校準(zhǔn)核查,獲得大多數(shù)物種的體積分?jǐn)?shù)偏差低于20%,確保了觀測(cè)期間儀器的穩(wěn)定性.

光解常數(shù)數(shù)據(jù)來(lái)自同站點(diǎn)UF-CCD光解光譜儀,常規(guī)污染物(O3、NO、CO)以及氣象參數(shù)(風(fēng)速、風(fēng)向、氣溫、相對(duì)濕度、氣壓)數(shù)據(jù)來(lái)自同位于桂花小學(xué)校園內(nèi)的深圳市觀瀾國(guó)控點(diǎn)子站.

1.3 臭氧生成潛勢(shì)(OFP)

OFP常用于評(píng)估VOCs物種對(duì)O3生成的貢獻(xiàn), VOCs物種濃度和該物種MIR常數(shù)決定了OFP大小,計(jì)算公式如下:

OFP= MIR[VOCs](1)

式中:OFP為物種的臭氧生成潛勢(shì),μg/m3;[VOCs]為物種的平均濃度,μg/m3;MIR為物種的最大增量反應(yīng)活性,g O3/ g VOC,代表增加單位質(zhì)量的物種時(shí)O3的最大生成量.本文中MIR參考Carter[29]的研究.

1.4 正交矩陣因子分析(PMF)

PMF作為受體模型已被廣泛用于源解析,其不需要具體的源排放清單或源排放譜,只需要大量實(shí)測(cè)數(shù)據(jù)即可實(shí)現(xiàn)污染物的來(lái)源解析.該模型需輸入受體濃度和不確定度,通過(guò)非負(fù)約束限制進(jìn)行計(jì)算,得到多個(gè)不同的解析因子,最后根據(jù)因子結(jié)果的源譜特征,識(shí)別所對(duì)應(yīng)的排放源.模型計(jì)算公式如下[30-31]:

式中:X是樣品中物種的濃度;為排放源的數(shù)目;g表示第個(gè)源對(duì)樣品的貢獻(xiàn);f為第個(gè)源中物種的濃度;e表示模型殘差.PMF模型主要是將目標(biāo)函數(shù)最小化,目標(biāo)函數(shù)定義為:

式中:為樣本個(gè)數(shù);為物種個(gè)數(shù);m表示物種的不確定性,根據(jù)PMF 5.0指導(dǎo)方法要求計(jì)算.

PMF模型的基本原理是質(zhì)量守恒,假設(shè)污染源排放的污染物濃度在傳輸過(guò)程中保持不變.但實(shí)際上,VOCs在傳輸過(guò)程中會(huì)發(fā)生光化學(xué)反應(yīng),因此解析結(jié)果不能真實(shí)反映實(shí)際污染源貢獻(xiàn).為解決這一問(wèn)題, Wadden等[32]提出輸入該模型的物種應(yīng)保證成分相對(duì)穩(wěn)定, Liu等[33]去除了高反應(yīng)活性物種,朱玉凡等[34]按氣團(tuán)老化程度分類后進(jìn)行源解析,都在一定程度上改善了模型.本研究結(jié)合前人的研究成果,綜合考慮了物種濃度、活性和示蹤性,去除化學(xué)反應(yīng)活性過(guò)高和濃度低于檢出限的物種,從而盡量減少氣團(tuán)老化可能帶來(lái)的影響.對(duì)于源解析結(jié)果,一般認(rèn)為robust/true接近1,說(shuō)明解析結(jié)果具有較好的可靠性[35].

2 結(jié)果與分析

2.1 VOCs污染特征

深圳北部工業(yè)區(qū)觀測(cè)期間氣象參數(shù)及污染物時(shí)間序列如圖1所示.根據(jù)AQI評(píng)價(jià),當(dāng)日最大8h均值超過(guò)160μg/m3時(shí)視為O3污染[36],本研究共觀測(cè)到O3污染日17d.O3污染通常出現(xiàn)在光解速率較高、溫度較高、相對(duì)濕度較低、風(fēng)速較低的氣象條件下(表1),O3前體物(TVOCs和NO)峰值通常先于O3峰值出現(xiàn).觀測(cè)期間TVOCs濃度波動(dòng)較大,平均濃度為50.48×10-9,其中濃度最高的為烷烴(20.83×10-9),濃度占比41.3%,其次為OVOCs(11.21×10-9)、芳香烴(8.57×10-9)和鹵代烴(6.82×10-9),分別占比22.2%、17.0%和13.5%.與其他城市夏秋季相比,深圳北部工業(yè)區(qū)VOCs濃度與南京工業(yè)區(qū)(34.69×10-9± 34.08 ×10-9)和廣州工業(yè)區(qū)(41×10-9± 24×10-9)濃度相近,顯著低于東莞工業(yè)區(qū)(81.9×10-9± 45.4×10-9),略高于重慶主城區(qū)(41.35×10-9)和成都市區(qū)(31.85× 10-9)[21-22,37-39].

圖2給出了污染日和非污染日各類VOCs的濃度變化情況.與非污染日相比,各類VOCs在污染日濃度均升高,其中OVOCs上升幅度最高(63.1%),可能是因?yàn)榇髿庋趸栽鰪?qiáng),增加了OVOCs的二次生成[40-41].烷烴、芳香烴、鹵代烴、乙炔和乙腈濃度上升20%左右,烯烴濃度僅上升約1%,可能是由于烯烴反應(yīng)活性最強(qiáng),在污染日大量消耗.污染日VOCs濃度排名前10的物種依次為丙酮、二氯甲烷、乙醛、正丁烷、甲苯、丙烷、異丁烷、甲基乙基酮、間/對(duì)二甲苯和2,3-二甲基丁烷,共占TVOCs總濃度的70%.其中丙酮濃度上升幅度最大,其次為乙醛和甲基乙基酮,分別上升75.6%、61.3%和46.0%.

圖1 氣象參數(shù)及污染物時(shí)間序列

表1 觀測(cè)期間氣象參數(shù)及污染物濃度

圖2 各類VOCs的濃度

表2列出了各類VOCs組分在不同空氣質(zhì)量狀況下的OFP變化情況.觀測(cè)期間OFP為346.7μg/m3,其中芳香烴對(duì)臭氧生成的貢獻(xiàn)最大,貢獻(xiàn)率為52.3%,其次為OVOCs(21.3%)和烷烴(16.9%).在污染日OVOCs對(duì)臭氧生成的貢獻(xiàn)明顯增加,這與OVOCs濃度的大幅提升有關(guān).因此,加強(qiáng)對(duì)芳香烴和OVOCs的管控更有利于控制深圳北部工業(yè)區(qū)的O3污染.

表2 各類VOCs的OFP貢獻(xiàn)

2.2 特征物種比值分析

某些VOCs具有相似的OH自由基反應(yīng)速率,它們?cè)诖髿庵械臐舛缺戎悼梢苑从称鋪?lái)源特征,表3給出了本研究中一些特征物種的比值.本研究中,丙烷與正丁烷比值在污染日和非污染日分別為0.80和0.64,更接近汽油車的比值(0.49),且遠(yuǎn)低于LPG車中的比值(6.12),說(shuō)明丙烷與正丁烷主要來(lái)源于汽油車的排放[42-44].異戊烷和正戊烷的比值在0.56～0.58時(shí)表征燃煤排放,比值在0.82～0.89時(shí)主要來(lái)自天然氣排放,比值在1.5～3.0時(shí)代表機(jī)動(dòng)車排放[45-46].本研究中異戊烷和正戊烷比值為1.69,說(shuō)明該地區(qū)受到機(jī)動(dòng)車排放的影響.

甲苯和苯來(lái)源較為復(fù)雜,通常認(rèn)為甲苯與苯的比值遠(yuǎn)大于2時(shí)說(shuō)明有機(jī)溶劑使用的貢獻(xiàn)更高,比值遠(yuǎn)小于2時(shí)主要來(lái)自機(jī)動(dòng)車排放[47-50].本研究中甲苯和苯比值遠(yuǎn)大于2,說(shuō)明該地區(qū)甲苯主要來(lái)自于有機(jī)溶劑使用.間/對(duì)-二甲苯與乙苯的比值常用于評(píng)估本地排放和氣團(tuán)傳輸?shù)挠绊?比值在2.5～2.9范圍時(shí)認(rèn)為受本地排放貢獻(xiàn)較多,比值遠(yuǎn)小于3時(shí)說(shuō)明該地區(qū)氣團(tuán)經(jīng)歷較長(zhǎng)的光化學(xué)反應(yīng),較多受到區(qū)域傳輸?shù)挠绊慬51-52].本研究間/對(duì)-二甲苯與乙苯在污染日和非污染日比值分別為2.03和2.31,表明受本地排放貢獻(xiàn)較多.相比于非污染日,污染日比值較小,說(shuō)明污染日大氣氧化性更強(qiáng),氣團(tuán)老化程度更高,可能污染日較多受到區(qū)域傳輸?shù)挠绊?

表3 特征物種比值

2.3 PMF來(lái)源解析

圖3 PMF源解析結(jié)果

選取環(huán)境空氣中濃度高、活性和示蹤性強(qiáng)的29種VOCs物種,輸入PMF 5.0模型進(jìn)行來(lái)源解析.經(jīng)過(guò)多次模型運(yùn)算得到較穩(wěn)定的5個(gè)因子,此時(shí)robust/true為0.98, VOCs模擬濃度與樣品實(shí)測(cè)濃度相關(guān)性2=0.96,表明模擬效果較好,解析結(jié)果具有可靠性[35].圖3是PMF源解析結(jié)果,解析出5個(gè)主要排放源.第一個(gè)排放源中,C2-C5的低碳烷烴貢獻(xiàn)較高,其中丙烷、丁烷主要來(lái)自于汽油車排放,戊烷是汽油揮發(fā)的示蹤劑,MTBE是汽油添加劑,正癸烷和正十一烷貢獻(xiàn)也較大,是柴油車排放的示蹤劑[53],因此判斷第一個(gè)源為機(jī)動(dòng)車排放及汽油揮發(fā).第二個(gè)排放源中,作為植物排放的特征物[54],異戊二烯貢獻(xiàn)最大,因此可識(shí)別為天然源.第三個(gè)排放源中,乙烷、乙炔、一氯甲烷、乙腈和苯的貢獻(xiàn)較大,乙腈和一氯甲烷是生物質(zhì)燃燒的示蹤物,乙烷、乙炔和苯常作為不完全燃燒產(chǎn)物[55],因此可判別為生物質(zhì)燃燒.第四個(gè)排放源中,芳香烴的貢獻(xiàn)較大,甲苯、乙苯、二甲苯是溶劑使用的主要排放物,苯乙烯也是涂料和稀釋劑的典型組分[56],因此可認(rèn)為是溶劑使用.第五個(gè)排放源中, C5-C8的異構(gòu)化烷烴貢獻(xiàn)較大,苯系物也具有一定的貢獻(xiàn).其中2-甲基己烷、3-甲基己烷、甲基環(huán)己烷是印刷行業(yè)的主要排放物,環(huán)己烷和甲苯是電子制造和塑膠制品的主要排放物, 2-甲基戊烷和3-甲基戊烷是制鞋企業(yè)的主要排放物,二氯甲烷是膠粘工藝的粘合劑,這與深圳市典型工業(yè)行業(yè)VOCs排放清單相一致[57-58],因此可識(shí)別為工藝過(guò)程排放.綜上所述,該地區(qū)VOCs的排放源主要為機(jī)動(dòng)車排放及汽油揮發(fā)、天然源、生物質(zhì)燃燒、溶劑使用和工藝過(guò)程排放.

如圖4所示,從濃度貢獻(xiàn)來(lái)看,機(jī)動(dòng)車排放及汽油揮發(fā)貢獻(xiàn)最大,貢獻(xiàn)率為28.2%,其次為工藝過(guò)程排放(25.0%)和溶劑使用(22.9%).這與2.2節(jié)中特征物種比值法得到的結(jié)論相一致,反映出該地區(qū)受交通源和工業(yè)源影響非常顯著.此外,生物質(zhì)燃燒也具有一定的貢獻(xiàn)(20.4%).在污染日,工藝過(guò)程排放和溶劑使用的貢獻(xiàn)均有所增加,該地區(qū)工業(yè)生產(chǎn)會(huì)排放大量芳香烴和OVOCs,這可能是污染日OVOCs濃度大幅提升的原因.從OFP貢獻(xiàn)來(lái)看,溶劑使用的貢獻(xiàn)最大(42.1%),其次是工藝過(guò)程排放(27.2%)、機(jī)動(dòng)車排放及汽油揮發(fā)(17.0%)和生物質(zhì)燃燒(10.2%).在污染日,同樣是工藝過(guò)程排放和溶劑使用的貢獻(xiàn)增加,這兩類源的OFP貢獻(xiàn)超過(guò)70%;生物質(zhì)燃燒、機(jī)動(dòng)車排放及汽油揮發(fā)貢獻(xiàn)略微減少.

圖4 各類排放源濃度貢獻(xiàn)及OFP貢獻(xiàn)

2.4 氣象條件對(duì)VOCs來(lái)源的影響

為了進(jìn)一步探討不同排放源對(duì)該地區(qū)VOCs的影響,本研究結(jié)合氣象數(shù)據(jù)分析各類排放源隨溫度、光解速率JNO2的變化趨勢(shì)以及在不同風(fēng)速和風(fēng)向下的相對(duì)貢獻(xiàn).

圖5 觀測(cè)期間各類排放源隨溫度, JNO2變化趨勢(shì)及風(fēng)場(chǎng)分布特征

如圖5所示,機(jī)動(dòng)車排放及汽油揮發(fā)、工藝過(guò)程排放和溶劑使用隨溫度的變化趨勢(shì)相似,呈現(xiàn)出先隨溫度上升,當(dāng)溫度超過(guò)25℃時(shí)快速下降的趨勢(shì),這是因?yàn)樵谌臻g高溫時(shí)段VOCs作為光化學(xué)反應(yīng)的前體物會(huì)被快速消耗.此外,高溫時(shí)段大氣邊界層也較高,污染物垂直擴(kuò)散能力較強(qiáng).生物質(zhì)燃燒在15～ 20℃范圍時(shí)濃度較穩(wěn)定,當(dāng)溫度超過(guò)20℃時(shí)快速下降,同樣是因?yàn)榍绑w物VOCs發(fā)生光化學(xué)反應(yīng)被大量消耗.天然源則完全相反,呈現(xiàn)出隨溫度上升濃度快速升高的趨勢(shì),說(shuō)明溫度越高植物排放越強(qiáng),這與以往研究相一致[19].機(jī)動(dòng)車排放及汽油揮發(fā)、工藝過(guò)程排放、溶劑使用和生物質(zhì)燃燒隨JNO2變化呈現(xiàn)負(fù)相關(guān)關(guān)系,主要是因?yàn)殡S著光照強(qiáng)度增強(qiáng),前體物VOCs被大量消耗.而天然源卻隨JNO2呈現(xiàn)快速升高的趨勢(shì),說(shuō)明光照強(qiáng)度的增強(qiáng)有利于植物的一次排放.

在不同風(fēng)速和風(fēng)向下VOCs 排放源貢獻(xiàn)率也存在差異.機(jī)動(dòng)車排放及汽油揮發(fā)、工藝過(guò)程排放和溶劑使用在風(fēng)場(chǎng)中分布特征相似,主要為本地排放,這是因?yàn)樵擖c(diǎn)位處于深莞交界,往來(lái)車輛較多尾氣排放較大,周邊的加油站也存在汽油揮發(fā)現(xiàn)象.同時(shí),深圳西部和北部存在較多的工業(yè)園區(qū),對(duì)本地貢獻(xiàn)較大.而生物質(zhì)燃燒除了本地貢獻(xiàn)外,更多地受到東北方向的傳輸貢獻(xiàn),這是由于觀測(cè)期間主導(dǎo)風(fēng)向?yàn)闁|北風(fēng),觀測(cè)點(diǎn)會(huì)受到惠州和東莞方向污染物傳輸?shù)挠绊?這也與于廣河等[25]關(guān)于深圳市大氣VOCs潛在源貢獻(xiàn) (PSCF) 分析結(jié)果相一致.天然源在風(fēng)場(chǎng)中分布較分散,周邊各方向均存在明顯的植物一次排放.

3 結(jié)論

3.1 深圳北部工業(yè)區(qū)夏秋季TVOCs平均濃度為50.48×10-9,其中濃度占比:烷烴(41.3%)>OVOCs (22.2%)>芳香烴(17.0%)>鹵代烴(13.5%)>烯烴(3.5%)>乙炔(2.2%)>乙腈(0.4%).在污染日,OVOCs濃度上升幅度最高(63.1%),芳香烴對(duì)OFP貢獻(xiàn)最大(49.5%),因此控制芳香烴和OVOCs更有利于控制該地區(qū)O3污染.

3.2 特征物種比值結(jié)果表明,該地區(qū)主要受機(jī)動(dòng)車排放和有機(jī)溶劑使用的影響,且VOCs主要來(lái)自本地排放.

3.3 PMF源解析出5個(gè)主要排放源,其中機(jī)動(dòng)車排放及汽油揮發(fā)濃度貢獻(xiàn)最大(28.2%),其次為工藝過(guò)程排放(25.0%)、溶劑使用(22.9%)和生物質(zhì)燃燒(20.4%).在污染日工藝過(guò)程排放和溶劑使用這兩類源的OFP貢獻(xiàn)超過(guò)70%.

3.4 排放源風(fēng)場(chǎng)分布特征結(jié)果表明,機(jī)動(dòng)車排放及汽油揮發(fā)、工藝過(guò)程排放和溶劑使用主要來(lái)自本地排放,而生物質(zhì)燃燒則更多受到東北方向的傳輸貢獻(xiàn).因此建議加大本地工業(yè)源和交通源管控,同時(shí)關(guān)注生物質(zhì)燃燒,加強(qiáng)與周邊區(qū)域的聯(lián)防聯(lián)控.

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Source apportionment of atmospheric volatile organic compounds in summer and autumn in Shenzhen industrial area.

ZHANG Yue1, XIA Shi-yong1, WEI Cheng-bo1, LIU Shi-qi1, CAO Li-ming1,2*, YU Guang-he2, HUANG Xiao-feng1

(1.Laboratory of Atmospheric Observation Supersite, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China；2.PKU-HKUST Shenzhen-Hong Kong Institution, Shenzhen 518057, China)., 2023,43(8)：3857～3866

The long-term online observation of atmospheric volatile organic compounds (VOCs) was carried out in the northern industrial area of Shenzhen from July to October 2021 to analyze pollution characteristics of VOCs during ozone polluted days and non-polluted days. The refined source apportionment of VOCs was carried out by using ratio method of characteristics species and positive matrix factorization (PMF) model to analyze the concentration and OFP contribution of each emission source and the influence of meteorological conditions. The results showed that the average mixing ratio of TVOCs was 50.48×10-9, of which alkane contributed the most, followed by OVOCs and aromatic, accounting for 41.3%, 22.2% and 17.0% respectively. Comparing with non-polluted days, the mixing ratio of OVOCs increased the most during polluted days (63.1%). The ratio of characteristic species indicated that the area was mainly affected by vehicle emission and industrial solvent use. Five VOCs emission sources were determined by the PMF model, of which vehicle emissions and gasoline volatilization contributed the most to the concentration (28.2%), followed by solvent use (22.9%), process emissions (25.0%) and biomass combustion (20.4%). OFP contribution of process emissions and solvent use exceeded 70% during polluted days. The distribution characteristics with wind speed and direction indicated that vehicle emissions and gasoline volatilization, process emissions and solvent use mainly came from local emissions, while biomass combustion was more influenced by transmission from northeast. It is suggested to intensify local industrial and traffic source controls, while also paying attention to biomass combustion and strengthening joint prevention and control with neighboring areas.

industrial area；volatile organic compounds (VOCs)；ozone formation potential (OFP)；ratio method of characteristics species；positive matrix factorization (PMF)

X511

A

1000-6923(2023)08-3857-10

張 月(1998-),女,安徽人,北京大學(xué)碩士研究生,主要從事大氣揮發(fā)性有機(jī)物的測(cè)量及污染研究.發(fā)表論文2篇.zhang- yue@stu.pku.edu.cn.

張 月,夏士勇,魏成波,等.深圳工業(yè)區(qū)夏秋季大氣揮發(fā)性有機(jī)物來(lái)源研究 [J]. 中國(guó)環(huán)境科學(xué), 2023,43(8):3857-3866.

Zhang Y, Xia S Y, Wei C B, et al. Source apportionment of atmospheric volatile organic compounds in summer and autumn in Shenzhen industrial area [J]. China Environmental Science, 2023,43(8):3857-3866.

2023-01-05

深圳市科技計(jì)劃項(xiàng)目(KCXFZ202002011006340)

* 責(zé)任作者, 高級(jí)工程師, climing@pku.edu.cn