深圳市大氣中PM2.5載帶金屬污染特征及健康風(fēng)險(xiǎn)

古添發(fā),閆潤華,姚沛廷,林曉玉,羅 遙,曹禮明,張明棣,黃曉鋒*

深圳市大氣中PM2.5載帶金屬污染特征及健康風(fēng)險(xiǎn)

古添發(fā)1,閆潤華2,姚沛廷2,林曉玉2,羅 遙2,曹禮明2,張明棣1,黃曉鋒2*

(1.廣東省深圳生態(tài)環(huán)境監(jiān)測中心站,廣東 深圳 518049；2.北京大學(xué)深圳研究生院環(huán)境與能源學(xué)院大氣觀測超級站實(shí)驗(yàn)室,廣東 深圳 518055)

于2020年3月～2021年2月對深圳市道路環(huán)境空氣中PM2.5載帶的15種金屬元素的質(zhì)量濃度進(jìn)行時(shí)間分辨率為1h的全年在線觀測.結(jié)果顯示,深圳市道路環(huán)境空氣中PM2.5載帶金屬元素的總平均濃度為(1062.3±434.6) ng/m3,其中Fe、Al、K、Ca和Zn為主要貢獻(xiàn)元素,在金屬元素中總貢獻(xiàn)達(dá)到95.5%.Fe的濃度較高,受到與道路揚(yáng)塵和機(jī)動車排放的強(qiáng)烈影響.金屬濃度存在著顯著的季節(jié)性差異,冬季濃度最高(1709.3ng/m3),夏季最低(644.1ng/m3).Mn、Fe、Cr、Zn和Ca元素呈現(xiàn)明顯的雙峰日變化分布,與機(jī)動車流量高峰一致.晝夜?jié)舛确植冀Y(jié)果顯示,夜間船舶排放V和Ni的濃度高值得關(guān)注,而Mn、Zn和Ca的濃度白天較夜晚高,與白天機(jī)動車流量較高有關(guān).高污染日總金屬日間濃度和夜間濃度均為全年日間和夜間平均濃度的1.9倍.成年人與兒童暴露于深圳市道路環(huán)境空氣的非致癌風(fēng)險(xiǎn)均低于閾值1,但是總致癌風(fēng)險(xiǎn)(6.5×10-6)超過閾值10-6,且As和Cr(主要來自機(jī)動車排放)的致癌風(fēng)險(xiǎn)之和在總致癌風(fēng)險(xiǎn)中占比88.9%,說明交通排放PM2.5載帶金屬的致癌風(fēng)險(xiǎn)仍值得重視并需持續(xù)控制.

道路環(huán)境空氣；PM2.5載帶金屬；日變化；健康風(fēng)險(xiǎn)

大氣顆粒物表面具有附著各種有毒物質(zhì)的能力,例如有毒金屬和細(xì)菌等[1].金屬元素是顆粒物中至關(guān)重要的毒性成分,人體吸入以后會對呼吸、免疫、神經(jīng)等多個(gè)系統(tǒng)造成直接或者間接的危害.此外,金屬元素不僅是大氣顆粒物中總質(zhì)量的重要組成部分,而且具備復(fù)雜的環(huán)境氣候效應(yīng).PM2.5具有體積小、比表面積大和活性強(qiáng)的特征,載帶的金屬(例如Pd、Ni、Cd、Cr、Co等)因其高毒、持久、隱蔽和生物積累性等特點(diǎn),引起了公眾的廣泛關(guān)注[2].

近年來,國內(nèi)外對于城市環(huán)境空氣中PM2.5的金屬污染特征和健康風(fēng)險(xiǎn)研究較多.道路環(huán)境空氣是城市大氣的重要組成且來源相對豐富[3-4].以道路環(huán)境空氣進(jìn)行大氣金屬的相關(guān)研究相對較少.He等(2008)對中國珠江三角洲地區(qū)珠江隧道中PM2.5的排放綜合特征研究發(fā)現(xiàn),Fe、Ca、Mg等微量元素豐度較大[5].國外也報(bào)道了公路交通排放會影響PM2.5載帶微量元素的濃度[6].有研究表明公路環(huán)境中,金屬元素易富集在細(xì)顆粒物中,會引發(fā)不利的非致癌和致癌風(fēng)險(xiǎn)[7-8].

2020年深圳市機(jī)動車保有量達(dá)到359萬輛,每公里機(jī)動車密度超過510輛,居全國首位(至2020年1月).之前的研究結(jié)果表明,機(jī)動車排放是深圳市PM2.5的重要來源,約占PM2.5質(zhì)量濃度的20%[9].深圳市是中國首個(gè)達(dá)到世界衛(wèi)生組織空氣質(zhì)量PM2.5中期目標(biāo)(IT-2,25μg/m3)的特大城市,也是中國發(fā)展最快的特大城市之一.因此對深圳市道路環(huán)境空氣中金屬污染特性及其健康風(fēng)險(xiǎn)研究有利于深圳市空氣質(zhì)量的精細(xì)化管理,同時(shí)也為其他城市污染管控提供了借鑒參考.

1 儀器和方法

1.1 采樣點(diǎn)位

本研究采樣點(diǎn)位于深圳市東南部福田區(qū)深南中路(22.54°N,114.10°E),采樣點(diǎn)距離路邊約3m,該觀測點(diǎn)位為深圳市藍(lán)天工程路邊站,以監(jiān)測道路交通空氣質(zhì)量為目標(biāo)[10].道路雙向8車道,為I類干線,代表了典型的道路環(huán)境,且周邊無顯著工業(yè)等其他排放來源.

1.2 金屬元素分析

大量的金屬元素分析方法基于濾膜離線采樣法,盡管離線分析提供了較低的痕量金屬檢測限,但濾膜采樣通常會受到不連續(xù)的時(shí)間覆蓋的影響.特別是對于快速變化的道路環(huán)境空氣中[11],難以捕捉金屬的快速變化.在線測量技術(shù)在單個(gè)季節(jié)或少數(shù)嚴(yán)重霧霾事件應(yīng)用較多[12-13].本研究利用高時(shí)間分辨率Xact-625型多金屬在線分析儀(Cooper Environmental Services LLC, USA)于2020年3月～ 2021年2月對深圳市道路環(huán)境空氣PM2.5中的15種元素(Al、K、Ca、V、Cr、Mn、Fe、Co、Ni、Cu、Zn、As、Cd、Ba和Pb)進(jìn)行在線連續(xù)采集與質(zhì)量濃度分析,時(shí)間分辨率為1h,采樣流量為16.7L/ min, 元素的檢出限為0.06～100(ng/m3).Xact-625型多金屬在線分析儀通過PM2.5切割器采集空氣,顆粒物富集在濾帶,由轉(zhuǎn)軸帶入分析區(qū).儀器采用非破壞性的X射線熒光法分析顆粒物,輸出采集時(shí)段金屬濃度值,同時(shí)空白濾帶部分進(jìn)行下一樣品的采集[14].更多儀器原理詳見文獻(xiàn)[15].

1.3 健康風(fēng)險(xiǎn)評價(jià)

根據(jù)美國環(huán)境保護(hù)署、國際癌癥研究機(jī)構(gòu)和綜合風(fēng)險(xiǎn)信息數(shù)據(jù)庫,污染物可分為致癌物質(zhì)和非致癌物質(zhì).本研究中Pb、Zn、Cr、Cu、Ni、Mn、Al、Cd、Co、V和Ba為非致癌物質(zhì)而Co、Pb、Cd、Cr和Ni為致癌物質(zhì)[16-17].通過計(jì)算3個(gè)主要暴露途徑的平均每日劑量(ADD)來評估PM2.5結(jié)合金屬的非致癌風(fēng)險(xiǎn)(NCR):手口攝食途徑(ing)、口鼻呼吸途徑(inh)和皮膚吸收途徑(derm).

式中:代表金屬濃度(呼吸途徑計(jì)算單位為mg/m3,另外兩個(gè)途徑為mg/kg).通過將大氣濃度(ng/m3)和PM2.5樣品的質(zhì)量濃度(μg/m3)換算得到皮膚和攝食途徑暴露的濃度.不同價(jià)態(tài)的Cr的健康差異不同,測得的Cr濃度需和致癌物Cr(VI)濃度進(jìn)行替換.本文參考大氣中Cr(VI)的濃度是Cr的1/7進(jìn)行替換[18]. IngR是攝入量,mg/d;ED是指暴露持續(xù)時(shí)間(兒童6a,成人24a);EF是暴露頻率,d;BW是平均體重,kg;非致癌風(fēng)險(xiǎn)AT=ED×365d,致癌風(fēng)險(xiǎn)AT=75.8×365d;InhR為吸入量,m3/d;SA為暴露的皮膚表面積,cm2;CF為換算系數(shù),kg/mg;SL為皮膚粘附因子,mg/cm2× d; ABS為皮膚吸收系數(shù).具體各個(gè)參數(shù)數(shù)值見表1.本文中,兒童和成人的非致癌風(fēng)險(xiǎn)通過式(1～3)計(jì)算.

危險(xiǎn)商(HQ)計(jì)算公式為式4:

HQ=ADD/RfD(4)

式中:RfD是指特定金屬的可接受風(fēng)險(xiǎn)水平(表2),然后將危害指數(shù)(HI)計(jì)算為所有具有非致癌作用的物種的組合.HI或HQ>1表明在成人和兒童的一生中,不良非致癌影響的發(fā)生概率很高[19].對于致癌風(fēng)險(xiǎn)(CR),使用來自呼吸暴露途徑的終生平均每日劑量(LADD)并計(jì)算如式5[20-21]:

特定金屬(即Cd、Co、Cr(VI)、Ni、Pb)的潛在致癌風(fēng)險(xiǎn)計(jì)算為式6:

CR=LADD′SF(6)

式中:SF表示對應(yīng)于特定物種的斜率因子,mg/ (kg×d)(表2).由于大多數(shù)物種無法獲得皮膚吸收或攝食途徑的SF,呼吸是先前研究的3種途徑中PM2.5的主要暴露途徑[22-23],因此根據(jù)呼吸途徑估算CR.CR可以表示為一個(gè)人因終生接觸致癌危害而患上癌癥的概率.CR的閾值風(fēng)險(xiǎn)定義為1× 10-6～1×10-4[19].1×10-6的目標(biāo)參考風(fēng)險(xiǎn)是最小風(fēng)險(xiǎn)值,這意味著百萬分之一的人可能因此患癌的終身暴露.1×10-4是可接受范圍內(nèi)的最大風(fēng)險(xiǎn)值,這意味著可能會在一萬人中發(fā)生一個(gè)額外的癌癥病例.另外,在健康風(fēng)險(xiǎn)評估上,雖然本研究在健康風(fēng)險(xiǎn)評估中已盡可能選擇最新的暴露參數(shù)和較為全面的金屬元素,但依然較難獲得特定區(qū)域的暴露參數(shù),對于精細(xì)化評價(jià)深圳市人群整體暴露水平存在局限.

表1 健康風(fēng)險(xiǎn)評估的暴露參數(shù)

2 結(jié)果與討論

2.1 深圳市道路環(huán)境空氣中PM2.5載帶金屬元素的全年及四季污染特征

研究期間的深圳市道路環(huán)境空氣PM2.5載帶金屬全年污染特征分布如圖1所示,Fe(306.7ng/m3)> Al(255.5ng/m3)>K(252.4ng/m3)>Ca(131.8ng/m3)>Zn(67.9ng/m3)>Mn(12.5ng/m3)>Cu(9.8ng/m3)>Ba(9.2ng/m3)>Pb(7.9ng/m3)>As(3.2ng/m3)>Ni(2.0ng/m3)>Cr(1.7ng/m3)>V(1.2ng/m3)>Cd(0.7ng/m3)>Co(0.1ng/m3).其中Fe、Al、K、Ca和Zn在金屬中總貢獻(xiàn)達(dá)到95.5%.與之前深圳市環(huán)境空氣中PM2.5載帶金屬研究結(jié)果K含量最高結(jié)論不同[33],道路環(huán)境空氣中Fe含量較高,這可能與Fe主要來自地殼元素[34],并且受到道路揚(yáng)塵與機(jī)動車排放的影響有關(guān)[35].Fe在道路環(huán)境空氣中含量較高與珠三角地區(qū)隧道實(shí)驗(yàn)結(jié)論一致[5].我國環(huán)境空氣質(zhì)量標(biāo)準(zhǔn)(GB 3095-2012)對Pb、Cd和As的限值大氣濃度(ng/m3)分別為500、5和6,本研究期間深圳市的金屬濃度未超過限值.即使深圳市PM2.5濃度較低,但是其載帶金屬元素濃度相比很多歐美國家處于痕量水平仍然存在差距[33].

金屬元素濃度存在著顯著的季節(jié)性差異(圖1).15種元素總體呈現(xiàn)冬季(1709.3ng/m3) >秋季(1071.8ng/m3)>春季(824.8ng/m3)>夏季(644.1ng/ m3)的特征.這與深圳市PM2.5的季節(jié)性分布一致[36].季節(jié)性分布特征主要受深圳市的氣象條件驅(qū)動,秋季和冬季常受東北地區(qū)的污染氣團(tuán)傳輸,另外較低的風(fēng)速與邊界層等不利條件的綜合,導(dǎo)致秋冬季節(jié)污染濃度的加重.夏季的氣團(tuán)則主要來自于清潔海面,另外夏季降水頻次較多,有利于污染物稀釋擴(kuò)散.春季和秋季的主要貢獻(xiàn)元素為Fe,分別占比為32.4%和32.0%.夏季的主要貢獻(xiàn)元素為Al,占比為36.2%.Al是典型的地殼元素[37],之前的深圳市PM2.5來源解析表明Al的主要來源為揚(yáng)塵[36].冬季貢獻(xiàn)較高的為K(30.9%),K通常作為生物質(zhì)燃燒示蹤物[36],這可能與冬季取暖等活動增加有關(guān).

圖1 PM2.5載帶金屬元素的年均濃度及四季變化特征

2.2 金屬元素的日變化特征

圖2為觀測期間主要的金屬濃度日變化趨勢.Al、As和Pb不存在明顯的日變化趨勢,之前的研究表明Pb與氣象參數(shù)風(fēng)速之間無相關(guān)性[14],這說明日變化趨勢較小的元素在大氣中混合較為均勻.Mn、Fe、Cr、Zn和Ca的日變化呈現(xiàn)明顯的雙峰分布.Mn常被用作汽油或柴油排放的示蹤物[38-39], Cr、Fe、Ba和高Zn含量可能與制動器和輪胎的磨損以及機(jī)動車的銹粒有關(guān)[40-41],Ca主要來自建筑揚(yáng)塵[42]、土壤及道路揚(yáng)塵[43-44].之前深圳市PM2.5載帶金屬的來源解析研究表明這幾種元素為機(jī)動車排放來源[33].本研究中這幾種元素的雙峰出現(xiàn)在8:00～ 11:00與17:00～20:00區(qū)間段,這與機(jī)動車通勤的早晚高峰時(shí)間相吻合.Ni和V的日變化特征較為一致,均在下午15:00左右出現(xiàn)最低點(diǎn),觀測期間Ni和V的相關(guān)性較好,R2達(dá)到0.75以上.Ni和V這兩個(gè)元素是典型的燃油示蹤物[45-46],并且之前的研究表示這些元素的排放與珠三角的船舶運(yùn)輸排放密切相關(guān)[47]. Ba存在明顯的單峰(8:00～12:00),另外從2020年2月11日20:00起,Ba的濃度急升,到2月17日,最高濃度達(dá)到752.9ng/m3,是Ba年均濃度的82倍左右.這可能與春節(jié)期間煙花爆竹燃放有關(guān)[48].煙花為達(dá)到閃光著色效果需要加入Al、Fe、Cu、K等,燃放高峰期Ba與Al、Fe、Cu、K呈現(xiàn)高度相關(guān)(R2大于0.88),與Cu的相關(guān)性最高達(dá)到0.99.

2.3 特定情景下金屬元素的晝夜變化特征

由于氣象條件、污染源排放及人類活動不盡相同,使得元素日間(7:00～19:00)與夜間(20:00～次日6:00)濃度分布也存在差異.總金屬日間濃度約為1111.0ng/m3,夜間濃度約為995.5ng/m3(圖3a).這可能與金屬中主要貢獻(xiàn)元素(例如Fe、Al、Ca等)夜間人為活動減少導(dǎo)致進(jìn)入大氣環(huán)境中金屬的量降低, 同時(shí)夜間近地面大氣較為穩(wěn)定,有利于富集因子較低的元素沉降[49].晝夜?jié)舛炔町愖畲蟮氖荲,夜間濃度是白天濃度的1.9倍,另外Ni的夜間濃度是白天濃度的1.3倍,說明船舶來源的夜間排放值得關(guān)注. 而Mn、Zn和Ca,白天的濃度分別是夜間濃度的1.6、1.6和1.3倍.這3種元素在機(jī)動車來源中分布較為豐富,可能與白天機(jī)動車流量較高有關(guān).其他元素的晝夜變化相對較小.

由于深圳市PM2.5質(zhì)量濃度相對較低,為精細(xì)分析深圳市的高污染日金屬變化特征,本文中將PM2.5濃度高于35μg/m3定義為高污染日.高污染日總金屬日間濃度約為2139.0ng/m3,夜間濃度約為1932.4ng/m3(圖3b).均為全年日/夜間濃度的1.9倍.主要的貢獻(xiàn)元素在高污染日與全年分布一致,均為Fe、Al、K、Ca.但是高污染日Pb的日間濃度是全年日間濃度的3.3倍,夜間濃度是全年夜間濃度的3.5倍.之前研究表明,深圳市的Pb主要來自于機(jī)動車與工業(yè)排放[50].As在高污染日的日間濃度和夜間濃度均是全年日間和夜間濃度的2.6倍.As和Pb的相關(guān)性(2=0.86)較好,說明可能同源.高污染日Mn的日間濃度是全年日間濃度的2.4倍,夜間濃度是全年夜間濃度的3.0倍.而Zn的日間濃度是全年日間濃度的2.2倍,夜間濃度是全年夜間濃度的2.5倍.高污染日夜間濃度上升幅度較大說明Mn和Zn除了機(jī)動車排放來源,高污染日可能存在其他來源排放共同影響.高污染日K的日間濃度和夜間濃度均是全年日間和夜間濃度的2.1倍.

2.4 金屬元素的健康風(fēng)險(xiǎn)

經(jīng)手口攝食、呼吸與皮膚接觸的非致癌風(fēng)險(xiǎn)如表3所示.成年人和兒童暴露于金屬的非致癌風(fēng)險(xiǎn)趨勢一致.成年人暴露于道路環(huán)境空氣的非致癌風(fēng)險(xiǎn)的危害指數(shù)為0.29,兒童的非致癌風(fēng)險(xiǎn)的危害指數(shù)是成年人的1.4倍,均低于1的閾值.對于所有討論的元素,呼吸是成年人和兒童的主要暴露途徑,超過96%.在金屬非致癌風(fēng)險(xiǎn)的等級排序上,Mn> Al>Cd>Ba>Ni>Co>Cu>As>Cr>Pb>Zn>V,其中Mn貢獻(xiàn)了69.4%,Al貢獻(xiàn)了14.2%,Cd貢獻(xiàn)了5.6%,Ba貢獻(xiàn)了5.1%,其他元素貢獻(xiàn)了0.1%～1.8%.這一貢獻(xiàn)比例對于成人與兒童均一致.在以往研究報(bào)道中,Mn在珠三角城市顆粒物中的非致癌風(fēng)險(xiǎn)貢獻(xiàn)最高[51-52].

元素總的致癌風(fēng)險(xiǎn)是6.5×10-6.金屬風(fēng)險(xiǎn)等級分布為:As(4.8×10-6)>Cr(9.8×10-7)>Cd(4.4×10-7)>Ni (1.7×10-7)>Co(7.9×10-8)>Pb(3.3×10-8).其中元素總致癌風(fēng)險(xiǎn)與As的致癌風(fēng)險(xiǎn)已經(jīng)超過致癌風(fēng)險(xiǎn)閾值10-6,As和Cr的致癌風(fēng)險(xiǎn)在總致癌風(fēng)險(xiǎn)中占比88.9%.有研究表明,自2009年以來,As和Cr一直是深圳PM2.5載帶金屬的主要致癌風(fēng)險(xiǎn)貢獻(xiàn)元素[53].此外也有研究結(jié)果表明,由于地區(qū)間主要致癌金屬的差異,需要采取不同的控制政策,中國的南方地區(qū)需要更嚴(yán)格的控制As源和Cr源[54],本研究結(jié)果也證實(shí)了這一研究結(jié)論.我國西北方地區(qū)研究表明Cr和As主要來自燃煤[55],這可能與中國約80%的煤炭資源分布在中國西部和北部有關(guān).我國南方地區(qū)也有研究表明As在工業(yè)來源(冶金、皮革、電鍍行業(yè))與機(jī)動車來源中均有排放[51].本研究中As與機(jī)動車排放相關(guān)的Mn、Cr、Fe、Zn、Ca等元素相關(guān)性較其他元素顯著,說明道路環(huán)境中PM2.5載帶的As與機(jī)動車排放來源更相關(guān).之前的研究表明,Cr在機(jī)動車排放中較為突出,尤其是機(jī)動車的非尾氣排放[33].因此深圳市道路環(huán)境空氣中交通排放的PM2.5載帶金屬的致癌風(fēng)險(xiǎn)需引起關(guān)注.

表3 暴露于深圳市道路環(huán)境空氣中PM2.5載帶金屬的非致癌與致癌風(fēng)險(xiǎn)

3 結(jié)論

3.1 深圳市道路環(huán)境空氣PM2.5載帶金屬的濃度范圍為0.1～306.7ng/m3,主要貢獻(xiàn)元素為Fe、Al、K、Ca和Zn,在金屬中總貢獻(xiàn)達(dá)到95.5%.金屬元素濃度存在著顯著的季節(jié)性差異,冬季濃度最高,夏季最低.季節(jié)性分布特征不僅受到深圳市的氣象條件驅(qū)動,而且也可能受到人為排放污染源影響.

3.2 典型汽油示蹤元素Mn,剎車磨損元素Fe、Cr、Zn和道路塵示蹤元素Ca均呈現(xiàn)明顯的雙峰(通勤早晚高峰)日變化分布,主要與機(jī)動車排放密切相關(guān);燃油示蹤物V和Ni日變化特征較為一致,推測具有共同的船舶排放來源;春節(jié)期間Ba濃度急升,并與煙花為達(dá)到閃光著色效果加入的Al、Fe、Cu、K元素呈現(xiàn)高度相關(guān),主要是煙花爆竹排放影響. Al、As和Pb不存在明顯的日變化趨勢.

3.3 金屬元素存在晝夜?jié)舛炔町?V和Ni的夜間濃度是白天濃度的1.3倍以上,說明船舶來源的夜間排放值得關(guān)注.Mn、Zn和Ca白天的濃度較夜晚高,可能與白天機(jī)動車流量較高有關(guān).高污染日總金屬日間濃度和夜間濃度均為全年日/夜間濃度的1.9倍. Pb在高污染日晝夜?jié)舛容^全年變化較大,可能與機(jī)動車和工業(yè)排放等有關(guān).

3.4 健康風(fēng)險(xiǎn)評估結(jié)果表明,成年人與兒童經(jīng)手口攝食、呼吸與皮膚接觸暴露于深圳市道路環(huán)境空氣的非致癌風(fēng)險(xiǎn)總和均低于1的閾值.其中兒童的非致癌風(fēng)險(xiǎn)的危害指數(shù)是成年人的1.4倍.總致癌風(fēng)險(xiǎn)(6.5×10-6)與As的致癌風(fēng)險(xiǎn)(4.8×10-6)已經(jīng)超過致癌風(fēng)險(xiǎn)閾值10-6.

[1] Coleman N C, Burnett R T, Ezzati M, et al. Fine Particulate Matter Exposure and Cancer Incidence: Analysis of SEER Cancer Registry Data from 1992-2016 [J]. Environmental Health Perspectives,2020, 128(10):107004.

[2] Ahmad H R, Sipra K M, Sardar M F, et al. Integrated risk assessment of potentially toxic elements and particle pollution in urban road dust of megacity of Pakistan [J]. Human and Ecological Risk Assessment,2019,26:1810-1831.

[3] Sun Y, Zhou Q, Xie X, et al. Spatial, sources and risk assessment of heavy metal contamination of urban soils in typical regions of Shenyang, China [J]. Journal of Hazardous Materials,2010,174(1-3): 455-462.

[4] Na Z, Liu J, Wang Q, et al. Heavy metals exposure of children from stairway and sidewalk dust in the smelting district, northeast of China [J]. Atmospheric Environment,2010,44(27):3239-3245.

[5] He L Y, Hu M, Zhang Y H, et al. Fine particle emissions from on-road vehicles in the Zhujiang Tunnel, China [J]. Environmental Science & Technology,2008,42(12):4461.

[6] Xia L, Gao Y. Characterization of trace elements in PM2.5aerosols in the vicinity of highways in Northeast New Jersey in the US East Coast [J]. Atmospheric Pollution Research,2011,2(1):34-44.

[7] 趙興敏,楊 揚(yáng),郭欣欣,等.長春市典型高架公路大氣環(huán)境顆粒物中重金屬污染特征[J]. 環(huán)境科學(xué)學(xué)報(bào),2017,37(9):9.

Zhao X M, Yang Y, Guo X X, et al. Pollution characteristics of heavy metals in atmospheric particulates from typical elevated highway in Changchun City [J]. Acta Scientiae Circumstantia,2017,37(9):9.

[8] Li P H, Yu J, Bi C L, et al. Health risk assessment for highway toll station workers exposed to PM2.5-bound heavy metals [J]. Atmospheric Pollution Research,2019,10(4):1024-1030.

[9] Su C P, Peng X, Huang X F, et al. Development and application of a mass closure PM2.5composition online monitoring system [J]. Atmospheric Measurement Techniques,2020,13(10):5407-5422.

[10] 古添發(fā),鄭錦怡,張明棣,等.深圳市藍(lán)天工程路邊站建設(shè)與發(fā)展[J]. 環(huán)境與發(fā)展,2021,33(3):9.

Gu T F, Zheng J Y, Zhang M D, et al. Analysis on the construction and development of roadside station of Shenzhen Lantian Project [J]. Environment and Development,2021,33(3):9.

[11] Yang X, Zheng M, Liu Y, et al. Exploring sources and health risks of metals in Beijing PM2.5: Insights from long-term online measurements [J]. 2022,814:151954.

[12] Cai J, Wang J, Zhang Y, et al. Source apportionment of Pb-containing particles in Beijing during January 2013 [J]. Environmental Pollution,2017,226(Jul.):30-40.

[13] Liu Y, Zheng M, Yu M, et al. High-time-resolution source apportionment of PM2.5in Beijing with multiple models [J]. Atmospheric Chemistry and Physics,2019,19(9):6595-6609.

[14] 雷建容,云 龍,蘇翠平,等.深圳城市大氣PM2.5中金屬元素的在線測量與來源特征[J]. 環(huán)境科學(xué)學(xué)報(bào),2019,39(1):6.

Lei J R, Yun L, Su C P, et al. On-line measurement and source characteristics of metals in PM2.5urban Shenzhen. [J]. Acta Scientiae Circumstantia, 2019,39(1):6.

[15] Furger M, Minguillón M C, Yadav V, et al. Elemental composition of ambient aerosols measured with high temporal resolution using an online XRF spectrometer [J]. Atmospheric Measurement Techniques,2017,10(6):1-26.

[16] Integrated Risk Information System. IRIS Assessments. [DB/OL]. https://www.epa.gov/iris. 2021.

[17] International Agency for Research on Cancer. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans. [EB/OL]. https://monographs.iarc.who.int/list-of-classifications. 2004.

[18] Huang R J, Cheng R, Jing M, et al. Source-specific health risk analysis on participate trace elements: coal combustion and traffic emission as major contributors in wintertime Beijing [J]. Environmental Science & Technology,2018,52(19):10967-10974.

[19] USEPA. Risk Characterization Handbook. [EB/OL]. https://www.epa. gov/risk/risk-characterization-handbook. 2001-02-26.

[20] Ferreira-Baptista L, De Miguel E. Geochemistry and risk assessment of street dust in Luanda, Angola: A tropical urban environment [J]. Atmospheric Environment,2005,39(25):4501-4512.

[21] Wang S S, Hu G G, Yu R L, et al. Bioaccessibility and source-specific health risk of heavy metals in PM2.5in a coastal city in China [J]. Environmental Advances,2021,4:100047.

[22] Dahmardeh Behrooz R, Kaskaoutis D G, Grivas G, et al. Human health risk assessment for toxic elements in the extreme ambient dust conditions observed in Sistan, Iran [J]. Chemosphere,2021,262: 127835.

[23] Roy D, Singh G, Seo Y C. Carcinogenic and non-carcinogenic risks from PM10-and PM2.5-Bound metals in a critically polluted coal mining area [J]. Atmospheric Pollution Research,2019,10(6):1964- 1975.

[24] United States Environmental Protection Agency. Highlights of the Exposure Factors Handbook (Final Report) [R]. https://cfpub.epa.gov/ ncea/risk/recordisplay.cfm?deid=221023. 2011.

[25] Cai Y, Zhou M G, Li X H, et al. Life expectancy and influence on disease in China, 2013 [J]. Chinese Journal of Epidemiology,2017, 38(8):4.

[26] Chen H, Teng Y, Lu S, et al. Contamination features and health risk of soil heavy metals in China [J]. Science of the Total Environment,2015,512:143-153.

[27] Lv Y, Li X, Xu T T, et al. Size distributions of polycyclic aromatic hydrocarbons in urban atmosphere: sorption mechanism and source contributions to respiratory deposition [J]. Atmospheric Chemistry and Physics,2016,16(5):2971-2983.

[28] 侯 捷,曲艷慧,寧大亮,等.我國居民暴露參數(shù)特征及其對風(fēng)險(xiǎn)評估的影響 [J]. 環(huán)境科學(xué)與技術(shù), 2014,37(8):179-187.

Hou J, Yanhui Q U, Ning D, et al. Characteristic of human exposure factors in china and their uncertainty analysis in health risk assessment [J]. Environmental Science and Technology,2014,37(8):179-187.

[29] Pena-Fernandez A, Gonzalez-Munoz M J, Lobo-Bedmar M C Establishing the importance of human health risk assessment for metals and metalloids in urban environments [J]. Environment International,2014,72:176-185.

[30] Zhao L, Xu Y, Hou H, et al. Source identification and health risk assessment of metals in urban soils around the Tanggu chemical industrial district, Tianjin, China [J]. Science of the Total Environment,2014,468:654-662.

[31] Bello S, Muhammad B G, Bature B Total Excess Lifetime Cancer Risk Estimation from Enhanced HeavyMetals Concentrations Resulting from Tailings in Katsina Steel RollingMill, Nigeria [J]. Journal of Material Sciences & Engineering,2017,6(3):2169-0022.

[32] Chen H, Lu X, Li L Y. Spatial distribution and risk assessment of metals in dust based on samples from nursery and primary schools of Xi'an, China [J]. Atmospheric Environment,2014,88:172-182.

[33] Yan R H, Peng X, Lin W W, et al. Trends and Challenges Regarding the Source-Specific Health Risk of PM2.5-Bound Metals in a Chinese Megacity from 2014 to 2020 [J]. Environmental Science & Technology,2022,56(11):6996-7005.

[34] Nguyen Q T, Skov H, S?Rensen L L, et al. Source apportionment of particles at Station Nord, North East Greenland during 2008～2010 using COPREM and PMF analysis [J]. Atmospheric Chemistry and Physics,2013,13:35-49.

[35] Ying X, Zhou J, Schauer J J, et al. Seasonal and spatial differences in source contributions to PM2.5in Wuhan, China [J]. Science of the Total Environment, 2017,577:155-165.

[36] 云 慧,何凌燕,黃曉鋒,等.深圳市PM2.5化學(xué)組成與時(shí)空分布特征[J]. 環(huán)境科學(xué),2013,34(4):1245-1251.

Yun H, He L Y, Huang X F, et al. Characterising seasonal variation and spatial distribution of PM2.5species in Shenzhen [J]. Environmental Science,2013,34(4):1245-1251.

[37] Nicolas J, Chiari M, Crespo J, et al. Quantification of Saharan and local dust impact in an arid Mediterranean area by the positive matrix factorization (PMF) technique [J]. Atmospheric Environment,2008, 42(39):8872-8882.

[38] Dall'ostoX M, Querol X, Amato F, et al. Hourly elemental concentrations in PM2.5aerosols sampled simultaneously at urban background and road site during SAPUSS – diurnal variations and PMF receptor modeling [J]. Atmospheric Chemistry and Physics,2013,13(8):4375-4392.

[39] Wang Y F, Huang K L, Li C T, et al. Emissions of fuel metals content from a diesel vehicle engine [J]. Atmospheric Environment,2003, 37(33):4637-4643.

[40] Dong S, Gonzalez R O, Harrison R M, et al. Isotopic signatures in atmospheric particulate matter suggest important contributions from recycled gasoline for lead and non-exhaust traffic sources for copper and zinc in aerosols in London, United Kingdom [J]. Atmospheric Environment,2017,165:88-98.

[41] Johansson, C, Norman, M, Burman, L. Road traffic emission factors for heavy metals [J]. Atmospheric Environment,2009,43(31):4681- 4688.

[42] Liu B, Li T, Yang J, et al. Source apportionment and a novel approach of estimating regional contributions to ambient PM2.5in Haikou, China [J]. Environmental Pollution,2017,223:334-345.

[43] Moreno T, Karanasiou A, Amato F, et al. Daily and hourly sourcing of metallic and mineral dust in urban air contaminated by traffic and coal-burning emissions [J]. Atmospheric Environment,2013,68:33- 44.

[44] Qi J, Liu X, Yao X, et al. The concentration, source and deposition flux of ammonium and nitrate in atmospheric particles during dust events at a coastal site in northern China [J]. Atmospheric Chemistry and Physics,2018,18(2):571-586.

[45] María C M, Marta C, Gerard H, et al. Spatial variability of trace elements and sources for improved exposure assessment in Barcelona [J]. Atmospheric Environment,2014,89(1):268-281.

[46] Sofowote U M, Su Y, Dabek-Zlotorzynska E, et al. Sources and temporal variations of constrained PMF factors obtained from multiple-year receptor modeling of ambient PM2.5data from five speciation sites in Ontario, Canada [J]. Atmospheric Environment,2015,108:140-150.

[47] Huang X F, Yun H, Gong Z H, et al. Source apportionment and secondary organic aerosol estimation of PM2.5in an urban atmosphere in China [J]. Science China Earth Sciences,2014,57(6):1352-1362.

[48] Kong S F, Li L, Li X X, et al. The impacts of firework burning at the Chinese Spring Festival on air quality: insights of tracers, source evolution and aging processes [J]. Atmos. Chem. Phys.,2015,15(4): 2167-2184.

[49] Chen X C, Cao J J, Ward T J, et al. Characteristics and toxicological effects of commuter exposure to black carbon and metal components of fine particles (PM2.5) in Hong Kong [J]. Science of the Total Environment,2020,742:140501.

[50] Zou B B, Huang X F, Zhang B, et al. Source apportionment of PM2.5pollution in an industrial city in southern China [J]. Atmospheric Pollution Research, 2017,8(6):1193-1202.

[51] Fu S J, Yue D L, Lin W W, et al. Insights into the source-specific health risk of ambient particle-bound metals in the Pearl River Delta region, China [J]. Ecotoxicology and Environmental Safety,2021,224: 112642.

[52] Xie J W, Jin L, Cui J L, et al. Health risk-oriented source apportionment of PM2.5-associated trace metals [J]. Environmental Pollution,2020,262:114655.

[53] 杜金花,張宜升,何凌燕,等.深圳某地區(qū)大氣PM2.5中重金屬的污染特征及健康風(fēng)險(xiǎn)評價(jià)[J]. 環(huán)境與健康雜志,2012,29(9):3.

Du J H, Zhang Y S, He L Y, et al. Pollution characteristics and health risk assessment of heavy metals in atmospheric PM2.5in a certain area of Shenzhen [J]. Journal of Environment and Health,2012,29(9):3.

[54] Hao Y F, Luo B, Simayi M, et al. Spatiotemporal patterns of PM2.5elemental composition over China and associated health risks [J]. Environmental Pollution,2020,265 (Pt B):114910.

[55] Yang X, Zheng M, Liu Y, et al. Exploring sources and health risks of metals in Beijing PM2.5: Insights from long-term online measurements [J]. Science of the Total Environment,2022,814:151954.

Characteristics and health risks of ambient PM2.5-bound metals in Shenzhen.

GU Tian-fa1, YAN Run-hua2, YAO Pei-ting2, LIN Xiao-yu2, LUO Yao2, CAO Li-ming2, ZHANG Ming-di1, HUANG Xiao-feng2*

(1.Shenzhen Environmental Monitoring Center of Guangdong Province, Shenzhen 518049, China；2.Laboratory of Atmospheric Observation Supersite, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China)., 2023,43(1)：88～95

In this study, mass concentrations of 15PM2.5-bound metals in Shenzhen's road ambient air were online observed with a resolution of 1h from March 2020 to February 2021. Results showed that: total average concentration of PM2.5-bound metals in the road ambient air in Shenzhen was 1062.3±434.6ng/m3, and Fe, Al, K, Ca and Zn were the main contributor, which contribute 95.5% of total metals. Higher concentration of Fe was strongly affected by road dust and vehicle emissions. Significant seasonal differences happened in of metals. The concentration was the highest in winter (1709.3ng/m3) and lowest in summer (644.1ng/m3). Mn, Fe, Cr, Zn and Ca obtained obvious diurnal variation, which were consistent with traffic peaks for vehicles. Diurnal variation analysis revealed that: high concentrations of V and Ni from ship emissions at night deserve attention, while the concentrations of Mn, Zn and Ca were higher during the day than at night, which was related to the higher traffic flow of vehicles during the day. The daytime and nighttime concentrations of total metals on high pollution days were both 1.9times the average daytime and nighttime concentrations throughout the year. Non-carcinogenic risks of adults and children exposed to the road ambient air in Shenzhen were lower than the threshold of 1. However, total carcinogenic risks (6.5×10-6) exceeded the carcinogenic risk threshold of 10-6, and the sum of the carcinogenic risks of As and Cr (mainly from vehicle emissions) accounted for 88.9% of the total carcinogenic risk, indicating that the carcinogenic risk in PM2.5-bound metals of traffic deserves attention and needs to be controlled continuously.

road ambient air；PM2.5-bound metals；diurnal variation；health risks

X513

A

1000-6923(2023)01-0088-08

古添發(fā)(1972-),男,廣東梅州人,高級工程師,學(xué)士,主要從事大氣污染監(jiān)測工作.發(fā)表論文5篇.

2022-05-13

國家自然科學(xué)基金資助項(xiàng)目(91744202);深圳市科技計(jì)劃項(xiàng)目(JCYJ20200109120401943)

* 責(zé)任作者, 教授, huangxf@pku.edu.cn