趙倩彪,胡 鳴,伏晴艷

2016～2020年上海市PM2.5化學(xué)組成特征和來(lái)源解析

趙倩彪1,2,胡 鳴1,3,伏晴艷1*

(1.上海市環(huán)境監(jiān)測(cè)中心,上海 200235；2.南京大學(xué),環(huán)境規(guī)劃設(shè)計(jì)研究院集團(tuán)股份公司,江蘇 南京 210093；3.上海大學(xué),上海 200444)

2016～2020年在上海市區(qū)和郊區(qū)的6個(gè)點(diǎn)位開(kāi)展了顆粒物系統(tǒng)性觀測(cè)研究,分析了PM2.5的質(zhì)量濃度以及水溶性離子、有機(jī)碳/元素碳、無(wú)機(jī)元素等化學(xué)組分,并利用正矩陣因子分解模型對(duì)PM2.5的來(lái)源進(jìn)行了解析.結(jié)果表明,上海PM2.5濃度水平呈現(xiàn)下降趨勢(shì),年均質(zhì)量濃度依次為46,43,37,40, 39μg/m3,表現(xiàn)為冬高夏低,西高東低的時(shí)空分布特征.有機(jī)物在PM2.5中占比最高(30%～32%),不同年份和季節(jié)間的差異較小.二次無(wú)機(jī)離子(硫酸鹽、硝酸鹽和銨鹽)的區(qū)域性特征明顯,其中硝酸鹽的占比在5a間升高最多,且在冬季污染過(guò)程中起到了關(guān)鍵作用.解析得到PM2.5的來(lái)源有9類,分別為二次硝酸鹽(30.6%)、二次硫酸鹽(20.7%)、機(jī)動(dòng)車(12.6%)、工業(yè)(8.0%)、生物質(zhì)燃燒(7.7%)、揚(yáng)塵(6.5%)、燃煤(5.8%)、海鹽(4.8%)和船舶(3.2%).機(jī)動(dòng)車和船舶等移動(dòng)源、秸稈焚燒和煙花爆竹燃放等生物質(zhì)燃燒源的貢獻(xiàn)濃度在研究期間呈現(xiàn)下降趨勢(shì),體現(xiàn)了相關(guān)治理措施的管控效果.

細(xì)顆粒物(PM2.5)；上海；化學(xué)組分；來(lái)源解析

PM2.5由水溶性離子、有機(jī)物、黑炭、地殼物質(zhì)等多種復(fù)雜的成分組成, 且來(lái)源廣泛,包括工業(yè)燃煤、機(jī)動(dòng)車排放、揚(yáng)塵、生物質(zhì)燃燒、以及氣態(tài)污染物的二次轉(zhuǎn)化等[1].

近年來(lái),在上海開(kāi)展的顆粒物觀測(cè)大多以研究特定季節(jié)污染過(guò)程的理化特征和污染成因?yàn)槟繕?biāo),持續(xù)時(shí)間在1個(gè)月左右[2-4].部分觀測(cè)為了研究顆粒物的季節(jié)變化規(guī)律,采樣時(shí)間覆蓋全年[5-7],而如果要獲取污染物更長(zhǎng)時(shí)間跨度的年際變化特征,通常需要結(jié)合其他研究的結(jié)果綜合判定.但是,由于不同觀測(cè)之間采樣點(diǎn)位、季節(jié)、時(shí)長(zhǎng)、分析方法等差異,得到的結(jié)論可能存在系統(tǒng)性偏差.利用環(huán)境空氣自動(dòng)監(jiān)測(cè)網(wǎng)絡(luò)數(shù)據(jù)[8],或者通過(guò)衛(wèi)星遙感反演氣溶膠光學(xué)厚度[9-10],可以在同一觀測(cè)體系下獲取顆粒物年際變化趨勢(shì),但研究結(jié)果僅限于顆粒物的整體濃度,無(wú)法通過(guò)顆粒物化學(xué)組分來(lái)識(shí)別污染來(lái)源,對(duì)于污染防控的指示作用不夠具體.

本研究在2016～2020年期間,在上海市區(qū)和郊區(qū)的6個(gè)站點(diǎn)開(kāi)展顆粒物采樣和分析,為上海及周邊長(zhǎng)三角區(qū)域內(nèi)首次長(zhǎng)時(shí)間、多點(diǎn)位的系統(tǒng)性觀測(cè)研究,分析了上海市PM2.5質(zhì)量濃度和主要化學(xué)組分的時(shí)空分布規(guī)律,并利用正矩陣因子分解(PMF)模型解析PM2.5主要污染來(lái)源,評(píng)估研究期間污染治理措施的實(shí)施效果,為下一步污染防控對(duì)策的制定提供技術(shù)支持.

1 實(shí)驗(yàn)與方法

1.1 采樣點(diǎn)位

在上海市區(qū)的浦東和浦西,以及郊區(qū)的東南西北4個(gè)方向,分別選取了浦東監(jiān)測(cè)站(浦東)、普陀監(jiān)測(cè)站(普陀)、浦東惠南(惠南)、奉賢監(jiān)測(cè)站(奉賢)、青浦淀山湖(青浦)、崇明監(jiān)測(cè)站(崇明)6個(gè)點(diǎn)位,點(diǎn)位周邊環(huán)境基本以住宅區(qū)和農(nóng)田為主,沒(méi)有明顯的局地污染源.市區(qū)點(diǎn)位周邊路網(wǎng)相對(duì)密集,郊區(qū)點(diǎn)位周邊有1～2條交通主干道.站點(diǎn)的具體信息如表1.

表1 采樣點(diǎn)位信息

1.2 采樣方法

本研究的觀測(cè)時(shí)間為2016～2020年,時(shí)間跨度為5a.其中2016年1月～2019年8月,采樣時(shí)間為每月5日、10日、15日、20日、25日和30日;2019年9月～2020年12月,采樣頻次為1組樣品/3d.采樣時(shí)間均為10:00～次日10:00,采樣時(shí)長(zhǎng)為24h.

使用武漢天虹TH-16A型四通道大氣顆粒物采樣器采集大氣中的PM2.5,采樣流量為16.7L/min,并折算成標(biāo)準(zhǔn)狀態(tài)體積(273K,101.325Pa)用于質(zhì)量濃度計(jì)算.濾膜直徑為46.2mm,其中,第一和第二通道使用聚四氟乙烯(特氟龍)濾膜(#7592-104, Whatman,USA),稱重后分別用于水溶性離子和無(wú)機(jī)元素的分析;第三通道使用石英纖維濾膜(#1851-047,Whatman,USA),用于有機(jī)碳(OC)和元素碳(EC)的分析;第四通道與前三通道輪流全程平行比對(duì).

特氟龍濾膜在采樣前后均置于溫度(20 ± 1)℃、濕度(50% ± 1%)的恒溫恒濕稱重實(shí)驗(yàn)室內(nèi)平衡24h,并用1μg精度的電子天平(XP-2U,Mettler Toledo, Switzerland)進(jìn)行稱量.石英纖維濾膜存放于用鋁箔包裹內(nèi)壁的濾膜盒中,且濾膜和鋁箔在采樣前均在450℃的馬弗爐內(nèi)灼燒4h以去除有機(jī)物本底.

1.3 化學(xué)組分分析方法

水溶性離子分析采用離子色譜法,先將濾膜剪開(kāi)并置于超純水中超聲40min提取其中的水溶性組分,浸提液經(jīng)0.22μm的水相濾膜過(guò)濾后進(jìn)入離子色譜儀(ICS-6000,Thermo Fisher,USA)同時(shí)檢測(cè)陰陽(yáng)離子,所測(cè)組分包括Cl-、NO3-、SO42-、Na+、K+、Mg2+、Ca2+、NH4+.

含碳組分采用美國(guó)沙漠研究所開(kāi)發(fā)的OC/EC分析儀(DRI-2001A,Desert Research Institute,USA),選用IMPROVE-A升溫程序[11],通過(guò)熱光透射法[12]測(cè)定顆粒物中的OC和EC.

無(wú)機(jī)元素先用X射線熒光分析(XRF)儀器(AXIOS, PANalytical B.V., Netherland)在氦氣介質(zhì)下無(wú)損測(cè)定其中的Na、Mg、Al、Si、K、Ca、Ti、V、Cr、Mn、Fe、Co、Ni、Cu、Zn、As、Br、Rb、Sr、Cd、Sb、Ba、Pb、Se、Mo和Sn元素,隨后將濾膜剪開(kāi)置于1:3的純硝酸和純鹽酸混合溶液中在 100℃ 加熱回流 2h,浸提液經(jīng)過(guò)濾后進(jìn)入ICP-MS(iCAP TQ,Thermo Fisher,USA)測(cè)定其中的Sb、Al、As、Ba、Be、Cd、Cr、Co、Cu、Pb、Mn、Mo、Ni、Se、Ag、Tl、Th、U、V、Zn、Bi、Sr和Sn元素.

分析方法的檢出限如表2.所有實(shí)驗(yàn)室分析對(duì)每批次樣品單獨(dú)繪制了標(biāo)準(zhǔn)曲線,設(shè)置儀器空白、實(shí)驗(yàn)室空白、實(shí)驗(yàn)室平行、加標(biāo)回收等手段,結(jié)合全程空白和全程平行樣品,對(duì)全流程進(jìn)行質(zhì)量控制.

表2 實(shí)驗(yàn)室化學(xué)組分分析檢出限

續(xù)表2

1.4 數(shù)據(jù)分析

為了確保分析結(jié)果的代表性,統(tǒng)計(jì)分析和模型計(jì)算僅選取所有組分?jǐn)?shù)據(jù)齊全的2336組樣本,并以質(zhì)量濃度重建算法將顆粒物主要化學(xué)組分分為7類,包括硫酸鹽(SO42-)、硝酸鹽(NO3-)、銨鹽(NH4+)、氯鹽(Cl-)、元素碳(EC)、有機(jī)物(OM)和地殼物質(zhì)(CM)[13-14],如式(1):

PM2.5重建濃度=SO42-+ NO3-+ NH4++

Cl-+ EC + OM + CM (1)

式中:地殼物質(zhì)為所測(cè)地殼元素最高氧化態(tài)的加和[15],如式(2):

CM = 2.14 [Si] + 1.67 [Ti] + 1.89 [Al]+

1.95 [Ca] + 1.43 [Fe] (2)

有機(jī)物的估算先通過(guò)EC示蹤物的方法將OC分為一次有機(jī)碳(POC)和二次有機(jī)碳(SOC)[16-17],如式(3)和式(4):

POC = EC × (OC/EC)pri(3)

SOC = OC –POC (4)

式中:(OC/EC)pri常取OC/EC的最小值[18],本研究為了避免長(zhǎng)時(shí)間觀測(cè)過(guò)程中的特殊排放源造成OC/EC比值出現(xiàn)極端低值而帶來(lái)計(jì)算偏差, 如圖1,以O(shè)C/EC最低的5%數(shù)據(jù)(圖1中黑色點(diǎn))做線性擬合[19],得到(OC/EC)pri為1.92.該比值略高于Cao等[20]和Liu等[21]在上海地區(qū)的觀測(cè)結(jié)果,與研究中(OC/EC)pri逐年升高的趨勢(shì)相符.同時(shí),該差異也可能是由這兩次觀測(cè)中所用在線OC/EC觀測(cè)儀器的前置溶蝕器吸收了半揮發(fā)性有機(jī)物(SVOC)造成觀測(cè)整體的OC/EC偏低造成的[22].

圖1 OC/EC濃度散點(diǎn)圖

使用上海地區(qū)觀測(cè)中使用的經(jīng)驗(yàn)系數(shù)[3],分別將POC和SOC乘以系數(shù)得到一次有機(jī)物(POA)和二次有機(jī)物(SOA),如式(5)和式(6):

POA = POC × 1.2 (5)

SOA = SOC × 2.2 (6)

并相加得到總有機(jī)物(OM),如式7:

OM = POA + SOA (7)

根據(jù)PM2.5重建濃度與實(shí)測(cè)濃度的擬合結(jié)果,相關(guān)系數(shù)R為0.93,表明PM2.5質(zhì)量濃度和化學(xué)組分監(jiān)測(cè)之間的結(jié)果基本可比;可檢出組分約占PM2.5總質(zhì)量的87%,與長(zhǎng)三角及全球各地研究得到的比值(77%～100%)相當(dāng)[14,23-25].

1.5 正矩陣因子分解模型

PMF模型是一種基于最小二乘法的用于顆粒物來(lái)源解析的受體模型,可以通過(guò)分析實(shí)際環(huán)境樣品化學(xué)組分之間的關(guān)聯(lián)來(lái)獲取污染源的譜圖和時(shí)間貢獻(xiàn)序列[26-27].在所有的化學(xué)組分中,剔除了低于檢出限數(shù)據(jù)比例較高且污染源指示作用不明確的元素,并在相似的分析因子(如K+離子和K元素)中選取信噪比更高的組分,保留了SO42-、NO3-、NH4+、Cl-、K+、Na+、Mg2+、Ca2+8個(gè)水溶性離子,OC和EC兩個(gè)含碳組分,通過(guò)XRF方法分析的Al、Si、Ti、Zn 4個(gè)元素以及通過(guò)ICP-MS方法分析的V、Ni、Cd、Mn、Cu、Se、Pb 7個(gè)元素作為模型的輸入因子,并使用美國(guó)環(huán)保署開(kāi)發(fā)的USEPA PMF5.0版本[28]進(jìn)行計(jì)算.

模型的不確定度通過(guò)每批次樣品中全程平行樣品,即TH-16A采樣器第四通道獲得的樣品與前3個(gè)通道樣品之間的相對(duì)標(biāo)準(zhǔn)偏差計(jì)算得到,相比于以往研究中根據(jù)組分?jǐn)?shù)據(jù)的幾何平均值賦予每個(gè)組分不確定度[25],或根據(jù)化學(xué)分析儀器偏差賦予所有組分統(tǒng)一的不確定度[29-30],本研究使用的方法基于實(shí)際觀測(cè),更為客觀.同時(shí),研究還將Ca2+、Al、Si、Ti 4種組分的不確定度放大2倍,以減少其同源性[31]對(duì)PMF計(jì)算結(jié)果產(chǎn)生的影響.

PMF模型假設(shè)所有樣品共用同一套源譜,但是在實(shí)際環(huán)境中由于地域差異、產(chǎn)業(yè)結(jié)構(gòu)調(diào)整、化學(xué)過(guò)程變化等原因,不同時(shí)間和地點(diǎn)的源譜可能會(huì)有一些差異.為了減少該差異對(duì)模型計(jì)算帶來(lái)的影響,本研究除了將6個(gè)站點(diǎn)連續(xù)5a的觀測(cè)結(jié)果同時(shí)輸入模型(A-PMF)運(yùn)算以外,還將不同站點(diǎn)(S-PMF)和不同年份(Y-PMF)的數(shù)據(jù)分別輸入模型運(yùn)行,以評(píng)估源譜在時(shí)間和空間上的差異對(duì)結(jié)果的影響[32-33].

2 結(jié)果與討論

2.1 上海市PM2.5化學(xué)組分時(shí)空分布特征

2016～2020年上海PM2.5年均質(zhì)量濃度依次為46, 43,37,40,39μg/m3,與上海市生態(tài)環(huán)境狀況公報(bào)[34-38]中PM2.5濃度(45,39,36,35,32μg/m3)的趨勢(shì)基本一致,兩者之間絕對(duì)濃度的差異可能是由于監(jiān)測(cè)點(diǎn)位和監(jiān)測(cè)方法之間的區(qū)別,后2a的變化趨勢(shì)不同可能與環(huán)境公報(bào)中數(shù)據(jù)選用的狀態(tài)參數(shù)轉(zhuǎn)換有關(guān)[39-40].

該年均濃度水平低于2011～2015年期間的多個(gè)基于全年觀測(cè)的研究結(jié)果[41-42],表明上海顆粒物水平在近10a間呈現(xiàn)下降的趨勢(shì).

如圖2,有機(jī)物是顆粒物中最主要的組分,占比高達(dá)30%～32%.硫酸鹽、硝酸鹽和銨鹽(SNA)的加和為二次無(wú)機(jī)離子,占到了顆粒物所有組分質(zhì)量濃度的一半以上(56%～59%),其中銨鹽的占比相對(duì)穩(wěn)定,變化幅度在1%以內(nèi).硫酸鹽的占比在5a間下降了4%,年均濃度由2016年的8.4μg/m3下降至2020年的5.6μg/m3,下降幅度接近1/3,這主要得益于上海及整個(gè)我國(guó)中東部區(qū)域的二氧化硫總量減排和產(chǎn)業(yè)結(jié)構(gòu)優(yōu)化[43-44];與此相反,硝酸鹽的年均濃度在5a間變化不大,2016年和2020年均為7.7μg/m3,但占比升高了 5%,是占比升高最大的組分.地殼物質(zhì)的占比為 6%～8%,遠(yuǎn)小于以往在上海及長(zhǎng)三角區(qū)域的研究結(jié)果[14-15,45],體現(xiàn)了揚(yáng)塵管控的效果[46].氯鹽和元素碳的占比較低,分別為1%～2%和2%～5%,與近年來(lái)上海的觀測(cè)結(jié)果接近[5,7,47].

圖2 PM2.5化學(xué)組分月均濃度和年均濃度占比

餅圖面積與年均濃度成正比

根據(jù)全部時(shí)段顆粒物質(zhì)量月均濃度,如圖3(a),顆粒物呈現(xiàn)冬高夏低的變化趨勢(shì),與上海及長(zhǎng)三角大部分地區(qū)的觀測(cè)結(jié)果一致[48-49].總體而言,這是因?yàn)樯虾５靥帠|海之濱,受亞熱帶季風(fēng)氣候影響,夏季主導(dǎo)的東南風(fēng)來(lái)自海上,相對(duì)清潔;而冬季主導(dǎo)的西北風(fēng)常伴有污染氣團(tuán)的輸送,不利的擴(kuò)散條件也易于污染物累積.各年份的顆粒物濃度變化規(guī)律基本一致,但2020年2～5月趨勢(shì)略有不同,月均濃度不降反升(圖2),這是受到了新冠疫情對(duì)工業(yè)生產(chǎn)排放的影響[50-52].

圖3 PM2.5組分月均濃度

各組分的月均濃度變化如圖3(b),硝酸鹽和顆粒物總濃度一樣,呈現(xiàn)冬高夏低的變化趨勢(shì).冬季大陸性氣團(tuán)中氮氧化物濃度較高,且冬季夜間低溫高濕靜風(fēng)環(huán)境有利于硝酸鹽的生成.此外,硝酸銨作為硝酸鹽最主要的成分,其氣-固分配增強(qiáng)了硝酸鹽冬高夏低的特征,使其成為了季節(jié)變化幅度最大的組分.夏季硝酸銨在高溫條件下易于分解并存在于氣相中[53],使得硝酸鹽濃度最低,8月的平均濃度僅1.9μg/m3;冬季則完全相反,硝酸鹽濃度最高,1月和12月的平均濃度分別為13.2,13.9μg/m3.氯鹽同樣受到了海陸氣團(tuán)污染程度差異和氯化銨氣-固分配的影響[54],季節(jié)變化趨勢(shì)與硝酸鹽相似.

元素碳發(fā)生的化學(xué)變化較少[55],其冬高夏低的季節(jié)變化主要受到一次排放和氣象條件的影響[5,56].地殼物質(zhì)在春季和秋季較高,是受到了沙塵在這兩個(gè)季節(jié)的輸送貢獻(xiàn).

在以往的研究中,有機(jī)物的季節(jié)變化并不相同,多數(shù)研究觀測(cè)到了冬高夏低的特征[57-58],有些研究卻發(fā)現(xiàn)了夏高冬低的特征[6].本研究中有機(jī)物的變化特征與Zhao等[7]在2015～2016年期間用ACSM直測(cè)得到的結(jié)果相似,季節(jié)之間差異較小,可能是同時(shí)受到了一次排放量、光化學(xué)反應(yīng)活性、外來(lái)輸送等多個(gè)因素綜合影響的結(jié)果.其中,一次有機(jī)物和元素碳相似,主要受排放源和擴(kuò)散條件影響,呈現(xiàn)冬高夏低的季節(jié)變化特征;二次有機(jī)物在夏季氣溫高、光照充足的條件下,受光化學(xué)反應(yīng)活性影響濃度更高[59],如圖3(c).其中,二次有機(jī)物在6月出現(xiàn)了濃度低值,是因?yàn)榇藭r(shí)長(zhǎng)三角正處于梅雨季節(jié),光照條件不足降低了二次有機(jī)物的生成速率[60].硫酸鹽的月變化特征與二次有機(jī)物相似,表明光化學(xué)反應(yīng)同樣是二次硫酸鹽最重要的生成途徑.

PM2.5質(zhì)量濃度頻率呈現(xiàn)正偏態(tài)分布的特征(圖4),濃度范圍在15～35μg/m3的頻率均高于10%,其中20～25μg/m3對(duì)應(yīng)的頻率最高(13%).超過(guò)91%樣品的PM2.5濃度低于75μg/m3(環(huán)境空氣質(zhì)量標(biāo)準(zhǔn)[39]中PM2.5日均濃度限值),表明上海的PM2.5在2016～ 2020年期間已經(jīng)有了較高的達(dá)標(biāo)率.但與此同時(shí),日均濃度超過(guò)150μg/m3的重污染過(guò)程仍時(shí)有發(fā)生(0.6%).

伴隨著PM2.5濃度升高,硝酸鹽的占比顯著上升,從7.7%升高至38.3%,表明硝酸鹽在污染過(guò)程中起到了非常重要的作用.在重污染季節(jié)基于應(yīng)急減排清單針對(duì)機(jī)動(dòng)車、電廠、工業(yè)鍋爐或爐窯等主要氮氧化物排放源加以控制,降低硝酸鹽的濃度,會(huì)對(duì)PM2.5“削峰”起到一定的效果.

與此相反,有機(jī)物和地殼物質(zhì)的占比隨PM2.5濃度的升高而降低,是清潔天顆粒物中的主要組分.硫酸鹽的占比隨濃度變化較小,但是在PM2.5濃度頻率最高的15～35μg/m3范圍內(nèi)占比最高,表明硫酸鹽在大部分時(shí)間是主要的組分,對(duì)于PM2.5的全年整體濃度有重要貢獻(xiàn).

在所有的采樣點(diǎn)位中,位于西部郊區(qū)的青浦站點(diǎn)的PM2.5濃度最高(42.6±26.5)μg/m3,其次分別是崇明(42.0±29.0)μg/m3、奉賢(39.3±25.4)μg/m3、普陀(39.0±25.9)μg/m3和浦東(37.5±25.4)μg/m3,位于東部郊區(qū)的惠南站點(diǎn)濃度最低(35.1±25.4)μg/m3.站點(diǎn)之間濃度差異與此前上海的多站點(diǎn)研究結(jié)果相似[2,13,61],由于上海毗鄰東海,海面上的污染源分布少于陸地,因此污染物的空間分布也呈現(xiàn)西高東低的特征.

上海各站點(diǎn)PM2.5組分構(gòu)成相似(圖5),站點(diǎn)之間SNA的差異在3%以內(nèi),氯鹽、元素碳和地殼物質(zhì)的差異在1%以內(nèi),有機(jī)物的差異最大,為5%.惠南站點(diǎn)的硫酸鹽占比最高(22%),這與Zhao等[7]觀測(cè)到的海洋氣流中硫酸鹽占比較高的結(jié)果一致,可能是受到了海面上船舶排放硫氧化物的影響.青浦站點(diǎn)的硝酸鹽占比最高(24%),周邊長(zhǎng)三角區(qū)域的多個(gè)觀測(cè)同樣發(fā)現(xiàn)硝酸鹽在顆粒物中的重要貢獻(xiàn)[62-63],表明青浦作為上海西部郊區(qū)的站點(diǎn),受到了區(qū)域性污染的影響.奉賢站點(diǎn)的有機(jī)物占比最高(32%),站點(diǎn)距離上海南部沿杭州灣區(qū)域的化工園區(qū)距離較近,受到了工業(yè)生產(chǎn)排放的影響.

如圖6,幾乎所有站點(diǎn)之間PM2.5質(zhì)量濃度的相關(guān)系數(shù)()都在0.9以上,表明上海的細(xì)顆粒物在空間上具有較好的一致性.空間分布上看,無(wú)論P(yáng)M2.5質(zhì)量濃度還是其化學(xué)組分,都得到了惠南和青浦站點(diǎn)之間相關(guān)性最低的結(jié)果,表明東西郊區(qū)之間存在更大的空間差異.化學(xué)組分上看,SNA的相關(guān)性最高(所有擬合得到的均值>0.93),區(qū)域性特征顯著,表明二次無(wú)機(jī)離子受到局地排放源的影響較小,更多的是在污染氣團(tuán)輸送或相似氣象條件下的化學(xué)過(guò)程中造成濃度共同的升高和降低,因此在時(shí)間序列上具有高度的一致性.受到機(jī)動(dòng)車、工業(yè)、揚(yáng)塵等局地排放源的影響,氯鹽、有機(jī)物和元素碳的相關(guān)性低于SNA(的均值為0.82～0.84),地殼物質(zhì)的相關(guān)性最低(的均值<0.8).

圖5 站點(diǎn)PM2.5質(zhì)量濃度和化學(xué)組分占比

圖6 PM2.5化學(xué)組分質(zhì)量濃度在站點(diǎn)之間的相關(guān)系數(shù)

2.2 上海市PM2.5來(lái)源解析

如圖7,利用PMF模型解析所有PM2.5樣品的來(lái)源(A-PMF)得到9個(gè)因子.

第1個(gè)因子中SO42-和NH4+的貢獻(xiàn)比例較高,定義為二次硫酸鹽;第2個(gè)因子中NO3-和NH4+的比例較高,定義為二次硝酸鹽;第3個(gè)因子中OC和EC的比例較高,Zn和Cd也有一定貢獻(xiàn),這些組分和機(jī)動(dòng)車的排放有密切的聯(lián)系[64-66],定義為機(jī)動(dòng)車源;第4個(gè)因子中V和Ni的比例較高,是重油燃燒的示蹤元素,在上海主要指征的是船舶源[67-68];第5個(gè)因子中Zn、Mn、Cu、Se、Pb等元素的比例較高,OC和EC也有貢獻(xiàn),與上海的金屬冶煉及化工行業(yè)的工業(yè)排放密切相關(guān)[69-70],定義為工業(yè)源.第6個(gè)因子中Cl-的比例最高,指征燃煤源[71];第7個(gè)因子中K+的比例最高,是生物質(zhì)燃燒的示蹤元素[72];第8個(gè)因子中Mg2+、Ca2+、Al、Ti、Si等地殼物質(zhì)的比例較高,來(lái)源于工地?fù)P塵、道路揚(yáng)塵以及沙塵輸送等來(lái)源[73],定義為揚(yáng)塵源;第9個(gè)因子中Na+和Mg2+比例較高,考慮到上海毗鄰東海,主導(dǎo)偏東風(fēng),是海鹽的影響[74].

圖7 PMF模型解析PM2.5源廓線

柱子和誤差線為S-PMF和Y-PMF的范圍

S-PMF和Y-PMF的解析結(jié)果中,二次硫酸鹽、二次硝酸鹽、船舶、燃煤、揚(yáng)塵和海鹽的源廓線與A-PMF相似,表明這幾類來(lái)源的源譜在時(shí)間和空間尺度上都比較穩(wěn)定.生物質(zhì)燃燒源廓線中Pb的比例在不同站點(diǎn)之間的差異較大,可能是由于某些農(nóng)村地區(qū)在秸稈焚燒中還夾雜有垃圾焚燒,飛灰中含有Pb元素,相似的現(xiàn)象在深圳的觀測(cè)中也發(fā)現(xiàn)過(guò)[29].機(jī)動(dòng)車的源廓線在不同年份中有所差異,但都是以O(shè)C、EC、Zn和Cd為主要的示蹤組分.Y-PMF和S-PMF解析得到工業(yè)源的廓線均最不穩(wěn)定,體現(xiàn)出上海在不同區(qū)域產(chǎn)業(yè)結(jié)構(gòu)的差別,以及近些年產(chǎn)業(yè)結(jié)構(gòu)調(diào)整[70,75]造成源譜的變化.綜合不同尺度的PMF解析結(jié)果,源闊線在不同站點(diǎn)之間差異較小,不同年份之間差異更大,因此選用Y-PMF結(jié)果作為研究期間PMF解析的最終結(jié)果.

如圖8,在9個(gè)因子中,二次硝酸鹽的占比最高(30.6%),其次是二次硫酸鹽(20.7%),兩者加和超過(guò)50%,與近年來(lái)上海地區(qū)的多個(gè)源解析結(jié)果相似[4,76],表明通過(guò)二次化學(xué)反應(yīng)是顆粒物最重要的形成途徑.在一次來(lái)源當(dāng)中,移動(dòng)源貢獻(xiàn)比例最高,其中機(jī)動(dòng)車排放對(duì)PM2.5的貢獻(xiàn)為12.6%,船舶排放貢獻(xiàn)為3.2%.其他一次排放源的貢獻(xiàn)比例接近,分別為工業(yè)源8.0%、生物質(zhì)燃燒源7.7%、揚(yáng)塵源6.5%、燃煤源5.8%和海鹽源4.8%.

圖8 PM2.5來(lái)源貢獻(xiàn)比例

如圖9,2016～2020年二次硝酸鹽對(duì)PM2.5的貢獻(xiàn)濃度總體持平,二次硫酸鹽呈現(xiàn)下降趨勢(shì).移動(dòng)源總體呈下降趨勢(shì),其中機(jī)動(dòng)車排放在2016～2018年期間下降趨勢(shì)明顯,這可能得益于《上海清潔空氣行動(dòng)計(jì)劃(2013～2017年)》[46]淘汰黃標(biāo)車和老舊車輛、推廣新能源汽車等措施;同樣,隨著《上海市清潔空氣行動(dòng)計(jì)劃(2018～2022年)》[77]中岸電和清潔能源替代、內(nèi)河船舶污染控制等船舶污染防治措施效果落地,船舶源的貢獻(xiàn)在2018～2020年期間出現(xiàn)了下降的趨勢(shì).

海鹽的年均貢獻(xiàn)濃度先降后升,作為一種天然源,海鹽排放受人為活動(dòng)變化的影響小,年際變化與主導(dǎo)風(fēng)向關(guān)系密切.上海的鋼鐵、化工等工業(yè)排放源多分布在海邊,工業(yè)源的年際變化趨勢(shì)與海鹽源相似,同時(shí),工業(yè)源的排放量可能還受到產(chǎn)業(yè)結(jié)構(gòu)轉(zhuǎn)型、疫情停工復(fù)產(chǎn)等因素的影響,波動(dòng)幅度比海鹽源更大.研究期間生物質(zhì)燃燒源的貢獻(xiàn)濃度總體上低于前些年的觀測(cè)結(jié)果[72,78],表明近年來(lái)上海及周邊長(zhǎng)三角區(qū)域?qū)τ诼短旖斩挿贌蜔熁ū袢挤诺墓芸仄鸬搅艘欢ǖ男Ч?但在2017年仍觀測(cè)到了幾次零星的以生物質(zhì)燃燒為主的污染過(guò)程,當(dāng)年的年均貢獻(xiàn)濃度即高于其他年份.燃煤源和揚(yáng)塵源的貢獻(xiàn)濃度年際變化幅度較小,污染源排放強(qiáng)度穩(wěn)定,其中在2018年受到數(shù)次沙塵輸送污染過(guò)程,揚(yáng)塵源的年均濃度略高.

圖9 PM2.5來(lái)源年均貢獻(xiàn)濃度

圖10 站點(diǎn)PM2.5來(lái)源貢獻(xiàn)比例

不同站點(diǎn)PM2.5貢獻(xiàn)比例差異不超過(guò)4%(圖10),表明上海的PM2.5總體呈現(xiàn)區(qū)域性特征.惠南和浦東站點(diǎn)二次硫酸鹽和海鹽源、青浦的二次硝酸鹽比例較高,進(jìn)一步驗(yàn)證了硝酸鹽在陸地性氣團(tuán)以及硫酸鹽在海洋性氣團(tuán)中的重要貢獻(xiàn).其中,在我國(guó)中東部的陸地上已多次觀測(cè)到硝酸鹽的重要貢獻(xiàn)[79],而硫酸鹽對(duì)海洋性氣溶膠的貢獻(xiàn)可能與海面上船舶排放的二氧化硫的轉(zhuǎn)化[80]以及二甲基硫等生物硫?qū)Ψ呛｛}硫酸鹽[81]的貢獻(xiàn)有關(guān).惠南站點(diǎn)受到機(jī)動(dòng)車和工業(yè)等主要一次排放源的貢獻(xiàn)比例都較低,具有上風(fēng)向背景區(qū)域特征.青浦和崇明周邊農(nóng)田覆蓋面積較大,生物質(zhì)燃燒源的貢獻(xiàn)比例高于其他站點(diǎn).

3 結(jié)論

3.1 2016～2020年上海PM2.5年均質(zhì)量濃度依次為46,43,37,40,39μg/m3,呈現(xiàn)下降的趨勢(shì).SNA和有機(jī)物是顆粒物中最主要的化學(xué)組分,其中硝酸鹽的占比在5a間升高幅度最大,并且在污染過(guò)程中起到了非常重要的作用.

3.2 PM2.5總質(zhì)量濃度呈現(xiàn)冬高夏低的季節(jié)規(guī)律.受氣-固分配的影響,硝酸鹽和氯鹽冬高夏低的季節(jié)變化特征最顯著.二次有機(jī)物和硫酸鹽在夏季通過(guò)光化學(xué)反應(yīng)生成通量較高,一次有機(jī)物和元素碳受排放源和擴(kuò)散條件控制在冬季濃度更高,一次和二次有機(jī)物加和得到有機(jī)物的總濃度在四個(gè)季節(jié)差異較小.受沙塵輸送影響,地殼物質(zhì)的濃度在春秋季更高.

3.3 上海的PM2.5濃度呈現(xiàn)西高東低的空間分布規(guī)律,站點(diǎn)平均濃度從高到低依次為青浦>崇明>奉賢>普陀>浦東>惠南.站點(diǎn)之間的組分構(gòu)成相似,尤其二次組分的時(shí)間序列在站點(diǎn)之間高度一致(>0.9),呈現(xiàn)區(qū)域性特征.奉賢受化工園區(qū)的影響,有機(jī)物占比更高.

3.4 基于PMF模型解析得到上海的PM2.5來(lái)源分為9類,貢獻(xiàn)比例從大到小依次為二次硝酸鹽(30.6%)、二次硫酸鹽(20.7%)、機(jī)動(dòng)車(12.6%)、工業(yè)(8.0%)、生物質(zhì)燃燒(7.7%)、揚(yáng)塵(6.5%)、燃煤(5.8%)、海鹽(4.8%)和船舶(3.2%).受益于相關(guān)政策和治理措施,移動(dòng)源的貢獻(xiàn)濃度在研究期間呈現(xiàn)下降趨勢(shì),生物質(zhì)燃燒和煙花爆竹燃放管控也起到了一定的效果.

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致謝：本研究的顆粒物現(xiàn)場(chǎng)采樣和稱重工作由上海市環(huán)境監(jiān)測(cè)中心大氣環(huán)境監(jiān)測(cè)室張懿華、陳佳、陳沁晨等協(xié)助完成,化學(xué)組分分析由上海大學(xué)、中國(guó)科學(xué)院上海硅酸鹽研究所、生態(tài)環(huán)境部華南環(huán)境科學(xué)研究所等分析團(tuán)隊(duì)協(xié)助完成,在此表示感謝.

Chemical characterization and source apportionment of fine particulate matter in Shanghai during 2016～2020.

ZHAO Qian-biao1,2, HU Ming1,3, FU Qing-yan1*

(1.Shanghai Environmental Monitoring Center, Shanghai 200235, China；2.Academy of Environmental Planning & Design, Co., Ltd., Nanjing University, Nanjing 210093, China；3.Shanghai University, Shanghai 200444, China)., 2022,42(11)：5036～5046

A systematic observation of particulate matter was carried out from 2016 to 2020 at six urban and suburban sites in Shanghai. Chemical species including water-soluble ions, organic carbon/elemental carbon, and inorganic elements of fine particulate matter (PM2.5) were analyzed. Source apportionment of PM2.5was conducted using the Positive Matrix Factorization model. Results show that the annual PM2.5mass concentrations of Shanghai from 2016 to 2020 were 46, 43, 37, 40, and 39μg/m-3successively, exhibiting a decreasing annual trend and spatiotemporal features of higher in the west and in winter while lower in the east and in summer. Organic aerosol was the most abundant species in PM2.5(30%～32%), with small annual and seasonal variations. Secondary inorganic ions including sulfate, nitrate and ammonium showed an evidently regional characterization. Among all the inorganic species, nitrate played an important role in pollution episode in winter, and its proportion increased the most from 2016 to 2020. Nine PM2.5sources were identified, including secondary nitrate (30.6%), secondary sulfate (20.7%), vehicle emission (12.6%), industry (8.0%), biomass burning (7.7%), dust (6.5%), coal combustion (5.8%), sea salt (4.8%) and ship emission (3.2%). Owing to the atmospheric emission control strategies, the contributions of mobile sources including vehicle and ship emissions as well as biomass burning sources including straw burning and fireworks emission showed a decreasing trend during the focused period.

fine particulate matter (PM2.5)；Shanghai；chemical species；source apportionment

X513

A

1000-6923(2022)11-5036-11

趙倩彪(1986-),男,江西南昌人,工程師,博士,主要從事大氣環(huán)境科學(xué)研究.發(fā)表論文20余篇.

2022-04-05

上海市科技計(jì)劃項(xiàng)目(20dz1204000)

* 責(zé)任作者, 教授級(jí)高工, qingyanf@sheemc.cn