陜南農(nóng)村冬季PM2.5主要化學(xué)組分特征

朱崇抒，曹軍驥，劉隨心，屈 垚，張 婷

（1.中國(guó)科學(xué)院地球環(huán)境研究所 中國(guó)科學(xué)院氣溶膠化學(xué)與物理重點(diǎn)實(shí)驗(yàn)室，西安710061；2.中國(guó)科學(xué)院地球環(huán)境研究所 黃土與第四紀(jì)地質(zhì)國(guó)家重點(diǎn)實(shí)驗(yàn)室，西安710061）

陜南農(nóng)村冬季PM2.5主要化學(xué)組分特征

朱崇抒1,2，曹軍驥1,2，劉隨心1,2，屈 垚1,2，張 婷1,2

（1.中國(guó)科學(xué)院地球環(huán)境研究所 中國(guó)科學(xué)院氣溶膠化學(xué)與物理重點(diǎn)實(shí)驗(yàn)室，西安710061；2.中國(guó)科學(xué)院地球環(huán)境研究所 黃土與第四紀(jì)地質(zhì)國(guó)家重點(diǎn)實(shí)驗(yàn)室，西安710061）

通過對(duì)陜南農(nóng)村冬季PM2.5采樣分析，獲得PM2.5質(zhì)量濃度及主要化學(xué)組分特征。PM2.5平均質(zhì)量濃度為89.5 ± 42.0 μg · m-3，超過國(guó)家二級(jí)標(biāo)準(zhǔn)。觀測(cè)期間PM2.5中OC、EC濃度平均值分別為16.0 ± 6.9 μg · m-3和5.7 ± 3.2 μg · m-3，OC/EC平均比值為3.0 ± 0.4。主要水溶性離子組分為、和。粒子數(shù)濃度與表面積濃度峰值主要集中在0.5 μm以下粒徑段。PAHs、BeP和BaP平均質(zhì)量濃度分別為48.9 ± 10.9 ng · m-3、3.0 ± 0.9 ng · m-3和1.2 ± 0.7 ng · m-3，PAHs污染較嚴(yán)重，強(qiáng)致癌物BaP濃度超過國(guó)家環(huán)境空氣質(zhì)量標(biāo)準(zhǔn)年平均濃度限值。當(dāng)?shù)剞r(nóng)村以石煤為主的能源結(jié)構(gòu)及采用的燃燒方式是導(dǎo)致污染的重要因素。

PM2.5；化學(xué)組分；農(nóng)村；陜南

PM2.5是指懸浮在大氣中空氣動(dòng)力學(xué)等效直徑小于或等于2.5 μm的顆粒物（曹軍驥，2014)。PM2.5自然源主要為火山噴發(fā)、海浪泡沫、沙塵暴、地面揚(yáng)塵、生物質(zhì)燃燒和植物排放等，人為源主要是工業(yè)及人類生產(chǎn)生活排放，包括化石燃料使用、生物質(zhì)燃燒和工業(yè)生產(chǎn)過程排放等。PM2.5的主要化學(xué)組分包括含碳物質(zhì)（有機(jī)碳、元素碳）、硫酸鹽、硝酸鹽、銨鹽、地質(zhì)塵等（曹軍驥，2014）。PM2.5組分對(duì)環(huán)境質(zhì)量、人體健康和氣候變化均有重要的影響（Ye et al，2003；Zhang etal，2007；Zhang et al，2009；Cao et al，2012；高偉和毛曉琴，2016；李國(guó)輝和馮添，2016）。

陜南地處秦嶺南部,屬亞熱帶溫濕氣候, 年平均氣溫13 — 15℃，年平均降水量為1000 —1500 mm，年平均蒸發(fā)量與降水量基本相同。石煤在中國(guó)分布廣泛，陜南則是石煤在陜西的唯一“聚集區(qū)”。該區(qū)境內(nèi)石煤具有埋藏淺、蘊(yùn)藏豐富、價(jià)格廉、易開采等特點(diǎn)，隨著當(dāng)?shù)胤馍接终叩膶?shí)施，石煤是當(dāng)?shù)鼐用穸救∨芭腼兊闹饕剂?。但石煤是一種含碳少、發(fā)熱值低的劣質(zhì)煤，其在燃燒過程中的排放易導(dǎo)致當(dāng)?shù)乜諝馕廴?，危害居民健康。以前有關(guān)大氣污染研究主要集中在城市區(qū)域及華北部分農(nóng)村區(qū)域（Jacobson et al，2000；Cao et al，2004，2005；Shen et al，2007，2009），在該石煤使用區(qū)未見相關(guān)研究報(bào)道，故該工作對(duì)了解陜南農(nóng)村大氣PM2.5主要化學(xué)組分的污染特征及有針對(duì)性的開展區(qū)域大氣污染治理有一定意義。

1 方法

1.1 采樣地點(diǎn)與時(shí)間

采樣點(diǎn)位于陜西省紫陽(yáng)縣東北部蒿坪鎮(zhèn)，距縣城20 km，周邊無大的工業(yè)企業(yè)排放。采樣點(diǎn)設(shè)在蒿坪鎮(zhèn)農(nóng)村一棟房頂，距地面大約10 m，冬季周圍農(nóng)戶多以當(dāng)?shù)爻霎a(chǎn)的石煤作為烹飪和取暖能源，能很好地代表該區(qū)域大氣環(huán)境狀況。采樣時(shí)間為2015年1月18日至2015年2月3日。

1.2 樣品采集與分析

采用微流量顆粒物采樣儀（Mini-volume samplers，Airmetrics，USA）收集大氣PM2.5樣品，設(shè)定流速為5 L · min-1，每個(gè)樣品連續(xù)采集24 h，共收集到15個(gè)PM2.5石英濾膜樣品。通過微電子天平（MettleM3，Switzerland，靈敏度為1 μg）稱重計(jì)算獲得PM2.5質(zhì)量濃度。采樣期間采用一臺(tái)OPS（Optical Particle Sizer，TSI公司3330型光學(xué)粒徑譜儀，流速為1 L · min-1）觀測(cè)不同粒徑顆粒物數(shù)濃度值。采用DRI Model 2001熱光碳分析儀（熱光反射法）對(duì)PM2.5樣品進(jìn)行碳組分分析，通過IMPROVE-A（Interagency Monitoring of Protected Visual Environment-A）分析協(xié)議獲得各碳組分含量（Chow et al，2007）。同時(shí)計(jì)算出OC和EC的8個(gè)組分含量（OC1、OC2、OC3、OC4、EC1、EC2、EC3、OP），IMPROVE-A協(xié)議將OC定義為OC1+OC2+OC3+OC4+OP，將EC定義為ECl+EC2+EC3 - OP，已有研究對(duì)碳分析過程質(zhì)控進(jìn)行報(bào)道（Chow et al，2011）。采用Dionex-600型離子色譜儀進(jìn)行無機(jī)水溶性離子組分檢測(cè)，用Chromeleon軟件進(jìn)行譜圖分析，得到水溶性離子組分的質(zhì)量濃度。采用進(jìn)樣口直接熱解析-氣相色譜/質(zhì)譜法（（Injection port thermal desorption）TDGC/MS）分析顆粒物中的多環(huán)芳烴濃度，該方法不需要任何外置的熱解析裝置，通過氣相色譜自帶的升溫程序，在GC進(jìn)樣口將樣品中的待測(cè)有機(jī)物熱解析出來，使待測(cè)有機(jī)物濃縮于色譜柱固定相的柱頭，目前TD-GC/MS方法可定量超過上百種有機(jī)物。

2 結(jié)果與討論

2.1 PM2.5質(zhì)量濃度、碳組分特征

研究期間陜南農(nóng)村大氣PM2.5平均質(zhì)量濃度為89.5 ± 42.0 μg · m-3，變化范圍是46.0 —188.0 μg · m-3，PM2.5日均濃度超過國(guó)家二級(jí)標(biāo)準(zhǔn)，表明研究區(qū)域冬季PM2.5污染較嚴(yán)重。在冬季因采暖燃煤量明顯增高，導(dǎo)致空氣中顆粒物濃度上升，加上冬季大氣比較穩(wěn)定容易形成逆溫層，導(dǎo)致顆粒物不易擴(kuò)散，使得顆粒累積量大，污染較嚴(yán)重（Cao et al，2005；Zhu et al，2012）。

觀測(cè)期間OC濃度的平均值為16.0 ± 6.9 μg · m-3，其變化范圍為8.1—33.3 μg · m-3，最高值是最低值的4倍。EC濃度平均值為5.7 ± 3.2 μg · m-3，其變化范圍為2.4 — 13.7 μg · m-3，最高值是最低值的6倍。從圖1中可以看出，PM2.5、OC和EC濃度變化序列表現(xiàn)出較好的同步趨勢(shì)。

觀測(cè)期間OC/EC比值的平均值為3.0 ± 0.4，其變化范圍為2.4 — 3.5，OC/EC比值變化比較平緩（圖1）。與前人的研究結(jié)果相比，OC/EC比值較低，說明觀測(cè)點(diǎn)一次排放貢獻(xiàn)較大。實(shí)地調(diào)查表明，與燃煤源相比，當(dāng)?shù)厣镔|(zhì)燃燒和機(jī)動(dòng)車尾氣排放貢獻(xiàn)較小。濃度較低的樣品9、10和12為雨雪天氣所采，表明降水對(duì)PM2.5、OC和EC的影響顯著，樣品10達(dá)到觀測(cè)期內(nèi)濃度最低值。由于EC比較穩(wěn)定，在常溫條件下，一般不發(fā)生大氣化學(xué)反應(yīng)，所以常被用作污染源一次排放的示蹤物。而OC包括了直接排放的一次有機(jī)碳和由前體物在大氣中經(jīng)過復(fù)雜的化學(xué)反應(yīng)（氣-粒轉(zhuǎn)化）而形成的二次有機(jī)碳。通過研究OC和EC濃度比值可一定程度反映出碳?xì)馊苣z的排放和轉(zhuǎn)化特征（Turpin and Lim，2001）。

圖1 陜南農(nóng)村PM2.5、OC和EC質(zhì)量濃度及OC/EC比值Fig.1 The concentrations of PM2.5, OC, EC, and the ratios of OC/EC in the rural of Shaannan

利用OC、EC的相關(guān)性可在一定程度上對(duì)大氣碳?xì)馊苣z的來源進(jìn)行定性分析。圖2中PM2.5的OC與EC相關(guān)關(guān)系很好，R= 0.98，這表明OC、EC的來源相對(duì)單一，因當(dāng)?shù)胤馍接?，生物質(zhì)燃燒很少，碳?xì)馊苣z可能主要為當(dāng)?shù)囟臼喝紵暙I(xiàn)。

圖2 PM2.5的OC和EC相關(guān)關(guān)系Fig.2 Correlations of OC and EC for PM2.5

2.2 PAHs污染特征

在本次研究期間PAHs、BeP和BaP平均質(zhì)量濃度分別為48.9 ± 10.9 ng · m-3、3.0 ± 0.9 ng · m-3和1.2 ± 0.7 ng · m-3。BeP和BaP濃度水平都超過上海市研究結(jié)果，其濃度值分別為1.26 ng · m-3和0.45 ng · m-3（Cao et al，2013）。PAHs變化范圍是33.1—72.1 ng · m-3，BeP變化范圍為1.9 — 5.0 ng · m-3，BaP變化范圍為0 — 2.4 ng · m-3，觀測(cè)期間BeP/(BeP+BaP）比值較高，達(dá)到0.74 ± 0.11（圖3）。

BaP是PAHs各單體中最具毒性和最常用以評(píng)價(jià)PAHs毒性總量的單體，BaP和二苯[ah]蒽（DahA）都是強(qiáng)致癌物。觀測(cè)期間非降雪天BaP平均質(zhì)量濃度為1.6 ng · m-3，BaP的環(huán)境空氣質(zhì)量標(biāo)準(zhǔn)（GB 3095—2012）為1.0 ng · m-3（年平均值），結(jié)果表明該農(nóng)村站點(diǎn)BaP污染較嚴(yán)重，BaP濃度遠(yuǎn)超過環(huán)境空氣質(zhì)量標(biāo)準(zhǔn)，此外，該農(nóng)村觀測(cè)點(diǎn)BeP的含量占PAHs比重較大。

2.3 質(zhì)量濃度、數(shù)濃度和表面積濃度粒徑分布

各粒徑段質(zhì)量濃度百分比大體呈現(xiàn)中間低兩邊高的情況，其中0.72 — 0.89 μm、1.12 — 1.39 μm這兩個(gè)粒徑段質(zhì)量濃度百分比最小。從圖4可以看出粒子數(shù)濃度與表面積濃度主要集中在0.3 —0.58 μm粒徑段。表面積濃度、質(zhì)量濃度、數(shù)量濃度在粒徑段0.9 — 1.1 μm上有一個(gè)峰，質(zhì)量濃度最明顯，表面積濃度次之，數(shù)量濃度不明顯。質(zhì)量濃度在粒徑0.4 — 0.5 μm上有個(gè)峰，且為0.3—10 μm中最高點(diǎn)，而該粒徑段正是硫酸鹽和硝酸鹽等成分聚集的粒徑范圍，與冬季顆粒物受到燃煤排放影響較大的結(jié)果一致（Seinfeld et al，1998）。同時(shí)質(zhì)量濃度和表面積濃度在粒徑段1.7 — 2.1 μm上也都有峰。數(shù)濃度和表面積濃度趨勢(shì)較統(tǒng)一，由高至低到平緩，而質(zhì)量濃度變化較為顯著。

圖3 PAHs、BaP和BeP質(zhì)量濃度及比值Fig.3 The concentrations of PAHs, BaP, BeP and the ratio of BeP/(BeP+BaP）

圖4 安康農(nóng)村各粒徑顆粒物質(zhì)量濃度（dM）、個(gè)數(shù)濃度（dN）和表面積濃度（dS）百分比Fig.4 Mass concentrations (dM), particle numbers (dN), and surface concentrations (dS) during the sampling period in the rural of Ankang

2.4 水溶性無機(jī)離子濃度變化

在PM2.5中，總水溶性離子組分占PM2.5的份額平均為48%，范圍是29% — 70%。陰離子中各個(gè)離子占PM2.5的份額依次為；陽(yáng)離子為（圖5）。研究期間PM2.5中水溶性離子組分、和分別占20.1%、10.4%和7.3%，對(duì)PM2.5中水溶性組分的貢獻(xiàn)相對(duì)較大。、和的平均質(zhì)量濃度分別為17.8 ± 9.1 μg · m-3、9.8 ± 7.0 μg · m-3和6.7 ± 4.0 μg · m-3。濃度與北京、青島和蘭州相近，高于香港和上海等國(guó)內(nèi)城市（Wang et al，2005，2006；Hu et a1，2002）的觀測(cè)值；濃度與北京、上海接近，高于香港、蘭州和重慶等城市的觀測(cè)值；濃度與上海、青島接近，低于北京濃度水平（Wang et a1，2002；Ho et a1，2003）。表明該農(nóng)村區(qū)域水溶性離子濃度并不比城市區(qū)域低。

水溶性離子組分中，Cl-、K+和Ca2+的平均質(zhì)量濃度分別為1.5 ± 0.7 μg · m-3、1.2 ± 0.6 μg · m-3和1.8 ± 0.3 μg · m-3，而F-、和Mg2+在PM2.5中含量很少，平均質(zhì)量濃度都小于1 μg · m-3。K+通常作為生物質(zhì)燃燒來源的一個(gè)標(biāo)志物，本研究獲得的K+濃度遠(yuǎn)低于關(guān)中平原農(nóng)村K+濃度水平（Zhu et al，2012），表明當(dāng)?shù)厣镔|(zhì)燃燒源對(duì)PM2.5貢獻(xiàn)較關(guān)中平原低。圖中可以看出，降水對(duì)PM2.5中水溶性離子影響較大，降水期間PM2.5中水溶性離子平均濃度與非降水期間的比值為0.77。

一般認(rèn)為，Ca2+主要來自于地表?yè)P(yáng)塵源（Nesbitt et al，1980），在觀測(cè)點(diǎn)Ca2+的質(zhì)量濃度與其他主要水溶性離子變化趨勢(shì)類似，可能是受采樣點(diǎn)附近地表?yè)P(yáng)塵和石煤燃燒排放影響。一般意義上，Na+和Cl-代表海洋源的氣溶膠，但是采樣點(diǎn)位居內(nèi)陸，遠(yuǎn)離海洋，所以Na+和Cl-可能主要來源于局地土壤鹽類及人為活動(dòng)影響。本次研究發(fā)現(xiàn)陜南部分農(nóng)村的大氣污染狀況不容樂觀，故不能只把大氣污染治理工作重心放在城市區(qū)域，農(nóng)村的大氣污染也應(yīng)根據(jù)當(dāng)?shù)匚廴驹辞闆r采取有效治理措施，給予更多關(guān)注。

3 結(jié)論與展望

本文通過對(duì)陜南農(nóng)村PM2.5污染特征進(jìn)行分析，初步獲得該區(qū)域冬季PM2.5質(zhì)量濃度及其主要化學(xué)組分濃度特征；該農(nóng)村站點(diǎn)PM2.5及BaP污染較重，濃度遠(yuǎn)超過環(huán)境空氣質(zhì)量標(biāo)準(zhǔn)，且BeP的含量占PAHs比重較大；鑒于該區(qū)域PM2.5及PAHs組分污染狀況,應(yīng)采取措施改善當(dāng)?shù)啬茉唇Y(jié)構(gòu)，尤其是對(duì)當(dāng)?shù)鼐用袷菏褂梅绞竭M(jìn)行優(yōu)化。

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The characteristics of chemical components for rural PM2.5in winter over Shaannan

ZHU Chongshu1,2, CAO Junji1,2, LIU Suixin1,2, QU Yao1,2, ZHANG Ting1,2
(1. Key Laboratory of Aerosol Chemistry & Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China; 2. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China)

Background, aim, and scopeThe usage of biomass and coal for cooking and heating is common in rural area in China. In reality, the emission largely contributes to the chemical components of particulates. The study here presented the levels of rural carbonaceous fractions, ions and PAHs over Shaannan.Materials and methodsThe observation campaign was conducted in a rural site at Shaannan. Samples were collected by using mini-volume samplers (Airmetrics, USA) operating with a fl ow rate of 5 L · min-1for 24 hours. All samples were collected on 47 mm Whatman quartz micro fi bre fi lters (QM/A). The filters were pre-heated before sampling at 800℃ for 3 hours. After collection, the filters were stored in a refrigerator before chemical analysis. Before and after field sampling, quartz filters were equilibrated for 24 hours in the box at a constant temperature (20℃ to 23℃) and relative humidity (35% to 45%). The PM2.5mass was determined by weighing the fi lters before and after sampling on an electronic microbalance (1 μg sensitivity) (Sartorius, MC5, Germany). All the fi lters were analyzed for carbon fractions using a DRI Model 2001 Thermal/Optical Carbon Analyzer (Atmoslytic Inc., Calabasas, CA, USA). Carbon fractions were analyzed following the Interagency Monitoring of Protected Visual Environments (IMPROVE-A) thermal/optical reflectance (TOR) protocol. The method produced data for four OC fractions (OC1, OC2, OC3, and OC4 in a helium atmosphere at 140℃, 280℃, 480℃, and580°C, respectively), a pyrolyzed carbon fraction (OP, determined when re fl ected laser light attained its original intensity after oxygen was added to the combustion atmosphere), and three EC fractions (EC1, EC2, and EC3 in a 2% oxygen/98% helium atmosphere at 580℃, 740℃, and 840℃, respectively). The IMPROVE protocol defined OC as OC1+OC2+OC3+OC4+OP and EC as EC1+EC2+EC3-OP. The analyzer was calibrated with known quantities of CH4each day. Replicate analyses were performed once every ten samples. The blank fi lters were also analyzed for quality control and the sample results were corrected by the average of the blank concentrations, which were 0.96 μg · m-3and 0.23 μg · m-3for OC and EC, respectively. The concentrations of three anions (Cl-,and) and fi ve cations (Na+,, K+, Mg2+and Ca2+) were determined in aqueous extracts of the sample fi lters by using a Dionex-600 Ion Chromatograph (Dionex Inc., Sunnyvale, CA, USA). Standard solution and blank test were performed before sample analysis and the result of correlation coef fi cient of standard samples was more than 0.999. One in 10 extracts was reanalyzed and none of the differences between these replicates exceeded precision intervals. All the reported data of water solvable ions were corrected by the filter blanks. Minimum detection limits were as follows: 0.001 μg · mL-1for Na+,, K+, Mg2+and Ca2+; 0.008 μg · mL-1for Cl-; 0.025 μg · mL-1for; and 0.027 μg · mL-1for. Traditional method for determining PAHs involve solvent extraction (SE) followed by gas chromatography/mass spectrometry (GC/MS). For our study, we used an in-injection port thermal desorption GC/MS method because it involves a short sample preparation time (＜1 min), the procedure minimizes contamination from solvent impurities, and detection limits as low as a few nanograms of the target analytes can be achieved.ResultsThe concentrations of rural PM2.5were measured in winter over Shaannan. Levels of carbon species as well as OC/EC ratios are also obtained. The average concentrations of PM2.5, OC and EC were 89.5 ± 42.0 μg · m-3, 16.0 ± 6.9 μg · m-3, and 5.7 ± 3.2 μg · m-3, respectively. The average OC/EC ratio of PM2.5was 3.0 ± 0.4. The result shows a high correlation between OC and EC for the rural environment in winter (R= 0.98).The major water soluble inorganic ions were,, and.contributions were the highest of the ionic species of PM2.5, followed by. The concentrations of PAHs, BeP, and BaP were 48.9 ± 10.9 ng · m-3, 3.0 ± 0.9 ng · m-3, and 1.2 ± 0.7 ng · m-3, respectively.DiscussionThe knowledge of the compositions of PM2.5is critical for understanding and then for ameliorating the atmospheric environments. According to our observations, the site experienced heavy smoke from coal burning for cooking and heating, which were the major contributors to fine particulate emissions in winter. Considering the patterns of local energy consumption, effective control measures were proposed to reduce the emissions of local coal combustion for residential heating and cooking.ConclusionsThe discussion presented in this work could give implications for the future strategies and implementation of rural air quality improvement. Owing to the large population living in rural areas, the coal and agricultural fuel burning-activity in rural areas could signi fi cantly contribute to emissions inventories.Recommendations and perspectivesClean energy resources, such as wind and solar energy, are currently underutilized. Strategies and technology for improving energy ef fi ciency and structure will be very important in reducing emissions in rural areas.

PM2.5; chemical components; rural area; Shaannan

ZHU Chongshu, E-mail: chongshu@ieecas.cn

10.7515/JEE201605006

2016-05-01；錄用日期：2016-08-07

Received Date:2016-05-01;Accepted Date:2016-08-07

國(guó)家自然科學(xué)基金項(xiàng)目(41271481)

Foundation Item:National Natural Science Foundation of China (41271481)

朱崇抒，E-mail: chongshu@ieecas.cn