艾文強(qiáng),肖紅偉*,孫啟斌,張永運(yùn),張澤雨,李靜雯

氮氧同位素示蹤南昌秋冬季降水中NO3-來(lái)源及其貢獻(xiàn)

艾文強(qiáng)1,2,肖紅偉1,2*,孫啟斌3,張永運(yùn)3,張澤雨2,李靜雯2

(1.東華理工大學(xué),江西省大氣污染成因與控制重點(diǎn)實(shí)驗(yàn)室,江西 南昌 330013；2.東華理工大學(xué)水資源與環(huán)境工程學(xué)院,江西 南昌 330013；3.中山大學(xué)大氣科學(xué)學(xué)院,廣東 珠海 519082)

為確定南昌市秋冬季降水中硝酸鹽來(lái)源及貢獻(xiàn),于2016年9月1日至2017年2月28日對(duì)南昌地區(qū)雨水進(jìn)行采集,分析了其化學(xué)組成及NO3-同位素組成并利用貝葉斯混合模型對(duì)NO3-四種潛在來(lái)源貢獻(xiàn)進(jìn)行計(jì)算.結(jié)果顯示NO3-濃度范圍為7.3～99.5μmol/L,平均值為36.1μmol/L;δ15N-NO3-變化范圍為-6.0‰～+8.3‰,平均值為-0.8‰,兩者均呈現(xiàn)冬季高秋季低的變化趨勢(shì).NO3-濃度季節(jié)性變化可能是受到降雨量等因素的影響,而δ15N-NO3-變化可能是冬季降水中機(jī)動(dòng)車(chē)尾氣排放偏高和秋季降水中煤燃燒來(lái)源偏高雙重因素作用的結(jié)果.同位素及貝葉斯混合模型源解析結(jié)果表明,南昌市降水中NO3-主要來(lái)源于生物質(zhì)燃燒(32.5%)、機(jī)動(dòng)車(chē)尾氣排放(30.8%)和煤燃燒(23.1%),三者貢獻(xiàn)超過(guò)86%;而機(jī)動(dòng)車(chē)尾氣排放和生物質(zhì)燃燒釋放均超過(guò)30%,這可能與近年來(lái)機(jī)動(dòng)車(chē)快速增加和秋冬季野外生物質(zhì)大量燃燒有關(guān).煤燃燒雖然也是重要來(lái)源,但相對(duì)生物質(zhì)燃燒和機(jī)動(dòng)車(chē)尾氣排放較小,這可能與近年我國(guó)減排措施有關(guān).

南昌；NO3-；降雨；氮同位素；貝葉斯混合模型(MixSIA)

硝酸鹽(NO3-)主要由NO(NO+NO2)轉(zhuǎn)化形成,是大氣氣溶膠和酸雨的重要組分[1-4].近幾十年來(lái),隨著工業(yè)化和城鎮(zhèn)化的快速推進(jìn),人類活動(dòng)產(chǎn)生的NO被大量釋放至大氣[4-5].研究表明,NO釋放至大氣后主要通過(guò)兩種途徑形成HNO3[6].白天,在光照條件下,NO發(fā)生Leighton循環(huán)(R1～R3),隨后同光解產(chǎn)生的×OH反應(yīng)生成氣態(tài)HNO3;夜間,由于光照停止,R5成為夜間NO2消耗的主要反應(yīng),生成的NO3可與NO2結(jié)合形成N2O5(R6)并在氣溶膠表面非均相水解形成液態(tài)HNO3(R7)[5,7-9].其中均相反應(yīng)生成的HNO3,可改變大氣的酸堿環(huán)境,也可在氣溶膠表面經(jīng)一系列反應(yīng)生成顆粒態(tài)硝酸鹽或與堿性物質(zhì)反應(yīng)形成顆粒物或吸附在堿性顆粒物上,改變其理化特性并使其粒徑增長(zhǎng)[6],造成灰霾現(xiàn)象.2013年以來(lái),為控制酸雨和灰霾等大氣污染現(xiàn)象,我國(guó)實(shí)施了一系列減排措施.研究發(fā)現(xiàn),我國(guó)的減排措施雖有效控制了大氣中SO2濃度及SO42-的形成,但大氣中NO濃度下降并不明顯,仍在高位波動(dòng),并表現(xiàn)為硝酸鹽在細(xì)顆粒物和大氣降水組成中占比不斷增重要.因此,準(zhǔn)確評(píng)估大氣中硝酸鹽來(lái)源及其貢獻(xiàn),實(shí)高[10-16],這使得硝酸鹽對(duì)酸雨和氣溶膠的貢獻(xiàn)更為現(xiàn)NO精準(zhǔn)控制,對(duì)緩解我國(guó)酸雨和灰霾等污染現(xiàn)象具有重要意義.

在大氣中,硝酸鹽主要來(lái)源于生物質(zhì)燃燒、煤燃燒、機(jī)動(dòng)車(chē)尾氣排放和土壤中微生物作用產(chǎn)生的NO的二次轉(zhuǎn)化,且不同NO來(lái)源N同位素特征值具有明顯差異,如:煤燃燒源δ15N偏正( +13.7± 4.6‰)[17-19],機(jī)動(dòng)車(chē)尾氣排放源δ15N偏負(fù)(-7.3± 7.8‰)[19-22],生物質(zhì)燃燒源δ15N偏正(+1.0± 4.1‰)[23-25],土壤排放源δ15N偏負(fù)( -33.8± 12.2‰)[20,26],因此,可根據(jù)硝酸鹽δ15N組成范圍判斷其可能來(lái)源[1,3-5,9,27-28].近年來(lái),國(guó)內(nèi)外學(xué)者通過(guò)同位素示蹤法對(duì)城市降水中硝酸鹽來(lái)源解析已有越來(lái)越多的報(bào)道,如Fang等[1]的研究發(fā)現(xiàn),廣州降水中硝酸鹽主要來(lái)源于煤燃燒; Li等[9]的研究發(fā)現(xiàn),沈陽(yáng)秋冬季降水中硝酸鹽除了受供暖期煤燃燒影響,還受生物質(zhì)燃燒影響;Liu等[29]發(fā)現(xiàn)機(jī)動(dòng)車(chē)尾氣也越來(lái)越成為城市降水中硝酸鹽的重要來(lái)源.這些研究報(bào)道為揭示我國(guó)城市降水中硝酸鹽來(lái)源提供了重要依據(jù),但其主要集中在經(jīng)濟(jì)發(fā)達(dá)地區(qū)或北方城市.相比經(jīng)濟(jì)發(fā)達(dá)地區(qū)和北方城市,中部等經(jīng)濟(jì)正高速發(fā)展地區(qū)關(guān)于降水中硝酸鹽來(lái)源解析的相關(guān)研究卻鮮有報(bào)道.

江西地處長(zhǎng)江經(jīng)濟(jì)帶,是中部地區(qū)的重要省份,經(jīng)濟(jì)增速穩(wěn)居全國(guó)前5.目前,對(duì)江西省大氣降水的研究多集中于降水的化學(xué)特征分析,利用同位素示蹤法和貝葉斯混合模型解析大氣降水中硝酸鹽來(lái)源及其貢獻(xiàn)的研究幾乎未見(jiàn)報(bào)道.因此,本研究以江西省會(huì)南昌市為例,采集了南昌市秋冬季降水,利用NO3-氮氧同位素和貝葉斯混合模型對(duì)降水中硝酸鹽來(lái)源及其貢獻(xiàn)進(jìn)行解析,以期為中部地區(qū)治理大氣硝酸鹽污染提供科學(xué)的理論基礎(chǔ).

O+O2?O3(R3)

1 材料與方法

1.1 樣品采集及處理

本研究于2016年9月1日至2017年2月28日期間對(duì)南昌市雨水進(jìn)行采集,共采集33個(gè)降雨事件.采樣點(diǎn)位于東華理工大學(xué)南昌校區(qū)校園內(nèi)(28.72￠N,115.83￠E),周?chē)鸁o(wú)明顯污染源.采樣器采用90cm×40cm(長(zhǎng)×寬)專用聚乙烯箱,在每次收集雨水前,用milli-Q(18.2MΩ)超純水對(duì)采樣箱內(nèi)部進(jìn)行反復(fù)沖洗;在不下雨期間,采樣箱用專用箱蓋封口避免大氣干沉降及其他可能污染物的影響.雨水樣品采集后,用孔徑為0.45μm微孔濾膜進(jìn)行過(guò)濾,過(guò)濾后取部分樣品分裝于兩支15mL離心管用于測(cè)定雨水中陰、陽(yáng)離子濃度,剩余樣品分裝于洗凈聚乙烯瓶中用于測(cè)定同位素.所有樣品在測(cè)定前均置于-20℃冰箱保存,僅在測(cè)定前取出.

1.2 分析方法

1.2.1 雨水樣品中化學(xué)組成分析 降水中主要陰離子(F-、Cl-、NO3-、SO42-)濃度,采用ICS-90型離子色譜儀(美國(guó)Dionex)進(jìn)行測(cè)定,檢出限分別為1.58、0.845、1.29、0.781μmol·L-1,相對(duì)標(biāo)準(zhǔn)偏差為0.57%、2.55%、1.16%、1.36%;陽(yáng)離子(Ca2+、K+、Mg2+、Na+)濃度,采用MPX型電感耦合等離子體-發(fā)射光譜儀(美國(guó)VISTA,ICP-OES)進(jìn)行測(cè)定,檢出限分別為0.0750、1.54、0.0206、0.870μmol/L,儀器相對(duì)標(biāo)準(zhǔn)偏差￡1.5%.NH4+濃度采用紫外線分光光度計(jì)測(cè)定,儀器檢出限為5.56μmol/L,相對(duì)標(biāo)準(zhǔn)偏差￡5.0%.

1.2.2 NO3-氮氧同位素分析 降水中硝酸鹽15N和18O值通過(guò)微生物反硝化法測(cè)定.反硝化法是廣泛用于測(cè)定雨水、海水和氣溶膠中硝酸鹽15N和18O前處理方法[30-34].本方法主要步驟為:將過(guò)濾后,NO3-濃度為20nmol/L的雨水加入含有反硝化細(xì)菌(Pseudomonas aureofaciens,ATCC13985)培養(yǎng)基,隨后反硝化細(xì)菌將NO3-還原為N2O,然后用GasBench II連續(xù)流質(zhì)譜儀(IRMS, Thermo Delta V Advantage)測(cè)定N2O的氮氧同位素[35],所測(cè)得的N2O的15N和18O值即為硝酸鹽的15N和18O.測(cè)定過(guò)程中采用兩個(gè)國(guó)際標(biāo)準(zhǔn)USGS34、IAEA-N3和兩個(gè)實(shí)驗(yàn)室工作標(biāo)準(zhǔn)對(duì)儀器進(jìn)行校正,15N測(cè)試精度為±0.2‰,18O測(cè)試精度為±0.5‰分析方法具體流程參見(jiàn)Luo等[35],Xiao等[28].

1.3 后向軌跡模型及衛(wèi)星火點(diǎn)地圖

HYSPLIT模型是由美國(guó)國(guó)家海洋和大氣管理局(NOAA)的空氣資源實(shí)驗(yàn)室和澳大利亞氣象局聯(lián)合研發(fā)的一種用于計(jì)算和分析大氣污染物輸送、擴(kuò)散軌跡的專業(yè)模型.本文利用TrajStat軟件,通過(guò)美國(guó)空氣資源實(shí)驗(yàn)室(NOAA ARL)提供的GDAS數(shù)據(jù)對(duì)每次降雨過(guò)程的氣團(tuán)后向軌跡進(jìn)行模擬計(jì)算(http: //ready.arl.noaa.gov/HYSPLIT.php). 軌跡的起始高度為500m,起始時(shí)間為降水事件的0點(diǎn),并向后推2d(-48h).衛(wèi)星火點(diǎn)地圖基于美國(guó)國(guó)家航空航天局(NASA)火災(zāi)信息資源管理系統(tǒng)(https://firms. modaps.eosdis.nasa.gov/map/)全球在線衛(wèi)星監(jiān)測(cè)火點(diǎn)數(shù)據(jù)制作.衛(wèi)星火點(diǎn)數(shù)據(jù)取每個(gè)下雨事件前48小時(shí)及下雨當(dāng)天采樣點(diǎn)及氣團(tuán)軌跡途徑區(qū)域數(shù)據(jù).

1.4 貝葉斯混合模型(MixSIA)

1.4.1 模型簡(jiǎn)介 貝葉斯混合模型可以通過(guò)穩(wěn)定同位素來(lái)評(píng)估源對(duì)混合物貢獻(xiàn)的概率分布,同時(shí)明確地考慮到多個(gè)源、分餾和同位素特征間的不確定性[8,36].基于貝葉斯混合模型,可通過(guò)NO排放源15N特征計(jì)算NO來(lái)源貢獻(xiàn).本文通過(guò)內(nèi)置同位素分餾計(jì)算模塊的貝葉斯模型,計(jì)算煤燃燒、機(jī)動(dòng)車(chē)尾氣排放、生物質(zhì)燃燒、土壤排放四種潛在來(lái)源貢獻(xiàn) .此模型基本計(jì)算原理可表達(dá)為:

(q∣數(shù)據(jù))=(數(shù)據(jù)∣)×(q)/∑β(數(shù)據(jù)∣q) ×(q)

式中:β(數(shù)據(jù)∣q)為基于先驗(yàn)信息的混合數(shù)據(jù)的概率;(q)為基于先驗(yàn)信息的先驗(yàn)概率;∑(數(shù)據(jù)∣q)×(q)為數(shù)據(jù)的邊緣概率的近似值,其具體原理參見(jiàn)文獻(xiàn)[5,8].

1.4.2 數(shù)據(jù)選擇 MixSIA模型計(jì)算硝酸鹽不同來(lái)源貢獻(xiàn)需要輸入3類同位素?cái)?shù)據(jù),包括樣品的15N、18O和源譜的15N信息.源譜信息為國(guó)內(nèi)外學(xué)者實(shí)驗(yàn)測(cè)定的硝酸鹽四種潛在來(lái)源15N的平均值和標(biāo)準(zhǔn)差,其中煤燃燒源、機(jī)動(dòng)車(chē)尾氣排放源、生物質(zhì)燃燒源和土壤排放源的15N-NO3-分別為(+13.7‰± 4.6‰)[17-19]、(-7.3‰±7.8‰)[19-22]、(+1.0‰±4.1‰)[23-25]和(-33.8‰±12.2‰)[20,26].樣品15N、18O信息為實(shí)驗(yàn)室測(cè)定的固定數(shù)值,其中樣品18O主要用于計(jì)算硝酸鹽形成途徑,并基于硝酸鹽不同形成途徑貢獻(xiàn),計(jì)算硝酸鹽氮同位素分餾,從而對(duì)硝酸鹽來(lái)源進(jìn)行準(zhǔn)確的定量分析.

1.4.3 同位素分餾計(jì)算 大氣中,硝酸鹽形成過(guò)程(R1～R7)通常伴隨著同位素分餾現(xiàn)象,為更精確計(jì)算硝酸鹽不同來(lái)源貢獻(xiàn),對(duì)硝酸鹽主要形成過(guò)程的同位素分餾進(jìn)行計(jì)算,計(jì)算公式為E1～E7,其中E1～E3用于計(jì)算氮同位素分餾,E4～E6用于計(jì)算硝酸鹽不同形成途徑貢獻(xiàn), E7用于計(jì)算分餾系數(shù),具體計(jì)算過(guò)程可參見(jiàn)文獻(xiàn)[5,7-8].

式中:為同位素分餾貢獻(xiàn)占比;/z為物質(zhì)和的分餾系數(shù),=15或18;NO2取值范圍為0.2～0.95;為開(kāi)爾文溫度,、、、為開(kāi)爾文溫度150～450K條件下常數(shù),數(shù)據(jù)來(lái)源于文獻(xiàn)[37-38].

表1 開(kāi)爾文溫度150～450K條件下常數(shù)A、B、C、D取值

2 結(jié)果與討論

2.1 基于化學(xué)組成分析降水中NO3-來(lái)源

圖1 采樣期間降雨量和NO3-離子濃度變化

秋季:1～22號(hào),冬季:23～33號(hào)

如圖1和表2所示,南昌市秋冬季降水中NO3-濃度變化范圍為7.3～99.5μmol/L,平均值為36.1μmol/L,變化范圍較大.研究表明降雨量對(duì)硝酸鹽濃度有較大影響[39],如圖1所示,在本研究中,秋季降水量波動(dòng)較大, NO3-濃度變化范圍更大,而冬季降水較平均,NO3-濃度變化范圍更小,且NO3-濃度與降雨量呈顯著負(fù)相關(guān)(=-0.52,<0.01),說(shuō)明硝酸鹽濃度變化受降雨量影響.與其他城市相比,南昌市降水中硝酸鹽濃度明顯低于長(zhǎng)江三角洲、珠江三角洲[1,40-41]等經(jīng)濟(jì)發(fā)達(dá)區(qū)域以及沈陽(yáng)、北京、天津等[9,42-43]北方燃煤城市降水中硝酸鹽濃度,但遠(yuǎn)高于百慕大、永興島、拉薩等[32,44-45]受人為活動(dòng)影響較小區(qū)域降水中硝酸鹽濃度(表2),這表明南昌地區(qū)降水中硝酸鹽受人為活動(dòng)影響仍較為嚴(yán)重.

通過(guò)對(duì)降水的化學(xué)組成進(jìn)一步分析發(fā)現(xiàn), NO3-與SO42-和K+呈顯著正相關(guān)(=0.39,<0.05;=0.63,<0.01),這表明NO3-與SO42-和K+可能存在相似來(lái)源[3,8,35,46].在大氣中,SO2是形成SO42-前體物質(zhì),主要來(lái)源于煤燃燒[1];而K+主要來(lái)源于生物質(zhì)燃燒,是生物質(zhì)燃燒的指示性離子[8,46],這表明煤燃燒和生物質(zhì)燃燒產(chǎn)生的NO可能是南昌市秋冬季降水中硝酸鹽的重要來(lái)源.

2.2 基于同位素分析降水中硝酸鹽來(lái)源

圖2 南昌秋、冬季大氣降水中δ15N-NO3-組成

秋季:1～22號(hào),冬季:23～33號(hào)

如圖2所示,南昌市秋季降水15N-NO3-變化范圍為-6.0～+8.3‰,平均值為0.0‰,主要分布在煤燃燒、機(jī)動(dòng)車(chē)尾氣和生物質(zhì)燃燒范圍內(nèi);冬季15N- NO3-變化范圍為-4.3～+0.4‰,平均值為-2.2‰,均分布在生物質(zhì)燃燒和機(jī)動(dòng)車(chē)尾氣15N值范圍內(nèi),說(shuō)明秋季降水中硝酸鹽可能主要來(lái)源于煤燃燒、生物質(zhì)燃燒和機(jī)動(dòng)車(chē)尾氣排放,而冬季主要來(lái)源于生物質(zhì)燃燒和機(jī)動(dòng)車(chē)尾氣排放.與其他城市相比,南昌市秋、冬季降水中硝酸鹽15N值較為偏負(fù),均遠(yuǎn)低于沈陽(yáng)、西安、北京等秋冬季存在燃煤供暖的北方城市和廣州、湛江等東南沿海燃煤量較高城市降水中硝酸鹽15N值(表2)[1-2,9,47],說(shuō)明煤燃燒源可能對(duì)南昌市秋冬季降水中硝酸鹽貢獻(xiàn)占比較小.就秋季而言,降水中硝酸鹽15N值與杭州和湖州等[3]城市降水中硝酸鹽15N值相接近,表明南昌可能與杭州、湖州存在著相似來(lái)源構(gòu)成.在杭州和湖州降水中,硝酸鹽受機(jī)動(dòng)車(chē)尾氣排放和北方煤燃燒傳輸影響[3].在冬季降水中硝酸鹽15N值與貴陽(yáng)等[29]地區(qū)降水中15N-NO3-值相接近.在貴陽(yáng)降水中,機(jī)動(dòng)車(chē)尾氣排放和生物質(zhì)燃燒是硝酸鹽最主要來(lái)源[29],說(shuō)明機(jī)動(dòng)車(chē)尾氣排放和生物質(zhì)燃燒源可能也是南昌市冬季降水中硝酸鹽主要來(lái)源.

表2 不同地區(qū)大氣降水中δ15N-NO3-值

注:冷季為10月至次年3月;—表示無(wú)數(shù)據(jù);*表示該采樣點(diǎn)為秋冬季數(shù)據(jù).

2.3 利用貝葉斯混合模型評(píng)估不同來(lái)源硝酸鹽貢獻(xiàn)

圖3 南昌秋、冬季大氣降水中NO3?來(lái)源貢獻(xiàn)占比

近年來(lái),貝葉斯混合模型被廣泛用于計(jì)算大氣中硝酸鹽不同來(lái)源的貢獻(xiàn)[2,4-5,8-9,29,35].為量化南昌市降水中硝酸鹽各來(lái)源貢獻(xiàn),本文利用貝葉斯混合模型對(duì)南昌市秋季、冬季及整個(gè)采樣期間硝酸鹽來(lái)源貢獻(xiàn)進(jìn)行計(jì)算.如圖3所示,在四種潛在來(lái)源中,生物質(zhì)燃燒占比最高,約占32.5%,說(shuō)明生物質(zhì)燃燒對(duì)秋冬季降水中硝酸鹽來(lái)源具有重要貢獻(xiàn),這可能與秋冬季南昌市周邊野外生物質(zhì)燃燒有關(guān)[50].美國(guó)航空航天局MODIS衛(wèi)星火點(diǎn)地圖顯示,在采樣期間,采樣點(diǎn)周?chē)巴饣瘘c(diǎn)分布密集(圖4).機(jī)動(dòng)車(chē)尾氣排放是僅次于生物質(zhì)燃燒的第二大來(lái)源,貢獻(xiàn)占比超過(guò)30%(圖3),說(shuō)明機(jī)動(dòng)車(chē)尾氣排放已成為秋冬季降水中硝酸鹽的主要來(lái)源,這可能與南昌市機(jī)動(dòng)車(chē)保有量快速增加有關(guān),且《江西省環(huán)境統(tǒng)計(jì)年報(bào)》數(shù)據(jù)顯示南昌市NO排放組成中機(jī)動(dòng)車(chē)尾氣排放約占75%[51].煤炭是江西省的主要能源,在能源結(jié)構(gòu)中占比超過(guò)60%[52-53],但在南昌市秋冬季降水中煤燃燒貢獻(xiàn)占比相對(duì)較小,約占23.1%,這可能與近年來(lái)我國(guó)實(shí)施的減排措施有關(guān).在南昌市NO排放組成中,煤燃燒排放的NO僅占22%[51].在四種潛在來(lái)源中,土壤排放源占比最小,約占13.5%±8.4%,這可能與秋冬季溫度較低,土壤中微生物活動(dòng)減少有關(guān)[4-5,9].

就季節(jié)變化而言,機(jī)動(dòng)車(chē)尾氣排放對(duì)秋季降水貢獻(xiàn)占比為27.6%±18.9%,對(duì)冬季貢獻(xiàn)占比為34.7%±20.4%(圖3),呈現(xiàn)明顯冬季高秋季低趨勢(shì),說(shuō)明冬季機(jī)動(dòng)車(chē)尾氣污染更為嚴(yán)重.與機(jī)動(dòng)車(chē)尾氣排放不同,生物質(zhì)燃燒在秋季貢獻(xiàn)占比為33.5%± 21.9%,冬季貢獻(xiàn)占比為30.7%±21.8%,表現(xiàn)為秋季高冬季低的趨勢(shì),說(shuō)明秋季生物質(zhì)燃燒現(xiàn)象更為嚴(yán)重.美國(guó)航空航天局MODIS衛(wèi)星火點(diǎn)地圖顯示,秋季采樣點(diǎn)周?chē)巴饣瘘c(diǎn)分布更為密集(圖4),也進(jìn)一步表明南昌市秋季降水中NO3-可能受生物質(zhì)燃燒影響更大.此外,煤燃燒對(duì)南昌市降水貢獻(xiàn)也存在季節(jié)性差異,表現(xiàn)為秋季高冬季低的趨勢(shì).在秋季,其貢獻(xiàn)占比為25.0%±14.8%,冬季貢獻(xiàn)占比為22.0%±14.0%.結(jié)合采樣期間氣團(tuán)后向軌跡分析推測(cè),這能與外源輸入有關(guān).在秋季,氣團(tuán)主要起源于北方和東部沿海發(fā)達(dá)城市,而冬季氣團(tuán)主要起源于省內(nèi)西南區(qū)域及中部區(qū)域(圖4).在東部城市及長(zhǎng)三角區(qū)域,如:武漢、上海、南京等城市,其大氣降水或氣溶膠中NO3-來(lái)源中煤燃燒源占比較高[3,5,54-55],而北方存在燃煤供暖影響.

a:秋季,b:冬季

3 結(jié)論

3.1 采樣期間,降水的NO3-濃度變化范圍為7.3～ 99.5μmol/L,平均值為36.1μmol/L,低于經(jīng)濟(jì)發(fā)達(dá)城市和北方同時(shí)期降水中硝酸鹽濃度,但遠(yuǎn)高于邊遠(yuǎn)地區(qū)降水中硝酸鹽濃度,表明南昌受人為活動(dòng)影響仍較為嚴(yán)重.

3.2 南昌市秋季δ15N-NO3-平均值為0.0‰,冬季δ15N-NO3-平均值為-2.2‰,呈秋季高冬季低的趨勢(shì),存在明顯季節(jié)性變化.引起這一變化的原因可能是冬季降水中機(jī)動(dòng)車(chē)尾氣排放偏高和秋季降水中煤燃燒來(lái)源偏高雙重因素作用的結(jié)果.

3.3 同位素及貝葉斯模型源解析結(jié)果表明南昌秋冬季降水中硝酸鹽主要來(lái)源于生物質(zhì)燃燒、機(jī)動(dòng)車(chē)尾氣排放和煤燃燒,三者貢獻(xiàn)超過(guò)86%;而機(jī)動(dòng)車(chē)尾氣排放和生物質(zhì)燃燒是最主要來(lái)源,二者貢獻(xiàn)均超過(guò)30%.煤燃燒雖然也是重要來(lái)源,但相對(duì)生物質(zhì)燃燒和機(jī)動(dòng)車(chē)尾氣排放較小,約占23%,這可能與近年我國(guó)減排措施有關(guān).因此,政府應(yīng)在對(duì)煤燃燒管控的同時(shí),進(jìn)一步加強(qiáng)生物質(zhì)燃燒和機(jī)動(dòng)車(chē)尾氣管控,從而減少NO排放.

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Tracking the source and contribution of NO3-in precipitation in Nanchang in autumn and winter by dual isotope.

AI Wen-qiang1,2, XIAO Hong-wei1,2*, SUN Qi-bin3, ZHANG Yong-yun3, ZHANG Zei-yu2, LI Jing-wen2

(1.Jiangxi Province Key Laboratory of the Causes and Control of Atmospheric Pollution, Nanchang 330013, China；2.School of Water Resources and Environmental Engineering, East China University of Technology, Nanchang 330013, China；3.School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China)., 2021,41(9)：4043～4050

Rainwater was collected from September 1, 2016 to February 28, 2017 in Nanchang, as well as stable isotope of nitrate (δ15N) and chemical compositions of rainwater were analyzed. The results showed that the concentration of NO3-ranged from 7.3 to 99.5μmol/L, and the mean is 36.1μmol/L; the δ15N of NO3-ranged from -6.0‰ to +8.3‰, and the mean is -0.8‰. Combined with chemical composition and isotope analysis, it is shown that NO3-was mainly affected by the region and mainly comes from biomass burning, traffic, coal combustion while biological soil is the secondary source in the sampling period. Bayesian mixing model (MixSIA) which took account of the isotope fractionation wes used to make precise estimations of the contribution of different sources. The results showed that the contribution of biomass burning emissions, traffic and coal combustion was more than 86%. However, in this case, the contribution of biomass burning emissions and traffic was more than 63%, and the coal combustion was only 23.1%, which indicated that a necessity to control NOfrom traffic and biomass burning strictly to reduce air pollution.

Nanchang；nitrate；precipitation；nitrogen isotope；Bayesian mixing model (MixSIA)

X571

A

1000-6923(2021)09-4043-08

艾文強(qiáng)(1994-),男,江西南昌人,碩士研究生,主要研究方向?yàn)橥凰氐厍颦h(huán)境化學(xué).發(fā)表論文1篇.

2021-02-03

國(guó)家自然科學(xué)基金(42063001,41663003)

*責(zé)任作者, 副研究員, xiaohw@ecut.edu.cn