朱崇抒，曹軍驥，劉隨心，屈 垚，張 婷

（1.中國科學院地球環(huán)境研究所 中國科學院氣溶膠化學與物理重點實驗室，西安710061；2.中國科學院地球環(huán)境研究所 黃土與第四紀地質國家重點實驗室，西安710061）

陜南農村冬季PM2.5主要化學組分特征

朱崇抒1,2，曹軍驥1,2，劉隨心1,2，屈 垚1,2，張 婷1,2

（1.中國科學院地球環(huán)境研究所 中國科學院氣溶膠化學與物理重點實驗室，西安710061；2.中國科學院地球環(huán)境研究所 黃土與第四紀地質國家重點實驗室，西安710061）

通過對陜南農村冬季PM2.5采樣分析，獲得PM2.5質量濃度及主要化學組分特征。PM2.5平均質量濃度為89.5 ± 42.0 μg · m-3，超過國家二級標準。觀測期間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污染較嚴重，強致癌物BaP濃度超過國家環(huán)境空氣質量標準年平均濃度限值。當?shù)剞r村以石煤為主的能源結構及采用的燃燒方式是導致污染的重要因素。

PM2.5；化學組分；農村；陜南

PM2.5是指懸浮在大氣中空氣動力學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、地面揚塵、生物質燃燒和植物排放等，人為源主要是工業(yè)及人類生產生活排放，包括化石燃料使用、生物質燃燒和工業(yè)生產過程排放等。PM2.5的主要化學組分包括含碳物質（有機碳、元素碳）、硫酸鹽、硝酸鹽、銨鹽、地質塵等（曹軍驥，2014）。PM2.5組分對環(huán)境質量、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)境內石煤具有埋藏淺、蘊藏豐富、價格廉、易開采等特點，隨著當?shù)胤馍接终叩膶嵤?，石煤是當?shù)鼐用穸救∨芭腼兊闹饕剂稀５菏且环N含碳少、發(fā)熱值低的劣質煤，其在燃燒過程中的排放易導致當?shù)乜諝馕廴荆：用窠】?。以前有關大氣污染研究主要集中在城市區(qū)域及華北部分農村區(qū)域（Jacobson et al，2000；Cao et al，2004，2005；Shen et al，2007，2009），在該石煤使用區(qū)未見相關研究報道，故該工作對了解陜南農村大氣PM2.5主要化學組分的污染特征及有針對性的開展區(qū)域大氣污染治理有一定意義。

1 方法

1.1 采樣地點與時間

采樣點位于陜西省紫陽縣東北部蒿坪鎮(zhèn)，距縣城20 km，周邊無大的工業(yè)企業(yè)排放。采樣點設在蒿坪鎮(zhèn)農村一棟房頂，距地面大約10 m，冬季周圍農戶多以當?shù)爻霎a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，設定流速為5 L · min-1，每個樣品連續(xù)采集24 h，共收集到15個PM2.5石英濾膜樣品。通過微電子天平（MettleM3，Switzerland，靈敏度為1 μg）稱重計算獲得PM2.5質量濃度。采樣期間采用一臺OPS（Optical Particle Sizer，TSI公司3330型光學粒徑譜儀，流速為1 L · min-1）觀測不同粒徑顆粒物數(shù)濃度值。采用DRI Model 2001熱光碳分析儀（熱光反射法）對PM2.5樣品進行碳組分分析，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）。同時計算出OC和EC的8個組分含量（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，已有研究對碳分析過程質控進行報道（Chow et al，2011）。采用Dionex-600型離子色譜儀進行無機水溶性離子組分檢測，用Chromeleon軟件進行譜圖分析，得到水溶性離子組分的質量濃度。采用進樣口直接熱解析-氣相色譜/質譜法（（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進樣口將樣品中的待測有機物熱解析出來，使待測有機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物。

2 結果與討論

2.1 PM2.5質量濃度、碳組分特征

研究期間陜南農村大氣PM2.5平均質量濃度為89.5 ± 42.0 μg · m-3，變化范圍是46.0 —188.0 μg · m-3，PM2.5日均濃度超過國家二級標準，表明研究區(qū)域冬季PM2.5污染較嚴重。在冬季因采暖燃煤量明顯增高，導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，導致顆粒物不易擴散，使得顆粒累積量大，污染較嚴重（Cao et al，2005；Zhu et al，2012）。

觀測期間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。

觀測期間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）。與前人的研究結果相比，OC/EC比值較低，說明觀測點一次排放貢獻較大。實地調查表明，與燃煤源相比，當?shù)厣镔|燃燒和機動車尾氣排放貢獻較小。濃度較低的樣品9、10和12為雨雪天氣所采，表明降水對PM2.5、OC和EC的影響顯著，樣品10達到觀測期內濃度最低值。由于EC比較穩(wěn)定，在常溫條件下，一般不發(fā)生大氣化學反應，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碳和由前體物在大氣中經(jīng)過復雜的化學反應（氣-粒轉化）而形成的二次有機碳。通過研究OC和EC濃度比值可一定程度反映出碳氣溶膠的排放和轉化特征（Turpin and Lim，2001）。

圖1 陜南農村PM2.5、OC和EC質量濃度及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的相關性可在一定程度上對大氣碳氣溶膠的來源進行定性分析。圖2中PM2.5的OC與EC相關關系很好，R= 0.98，這表明OC、EC的來源相對單一，因當?shù)胤馍接郑镔|燃燒很少，碳氣溶膠可能主要為當?shù)囟臼喝紵暙I。

圖2 PM2.5的OC和EC相關關系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，觀測期間BeP/(BeP+BaP）比值較高，達到0.74 ± 0.11（圖3）。

BaP是PAHs各單體中最具毒性和最常用以評價PAHs毒性總量的單體，BaP和二苯[ah]蒽（DahA）都是強致癌物。觀測期間非降雪天BaP平均質量濃度為1.6 ng · m-3，BaP的環(huán)境空氣質量標準（GB 3095—2012）為1.0 ng · m-3（年平均值），結果表明該農村站點BaP污染較嚴重，BaP濃度遠超過環(huán)境空氣質量標準，此外，該農村觀測點BeP的含量占PAHs比重較大。

2.3 質量濃度、數(shù)濃度和表面積濃度粒徑分布

各粒徑段質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這兩個粒徑段質量濃度百分比最小。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、質量濃度、數(shù)量濃度在粒徑段0.9 — 1.1 μm上有一個峰，質量濃度最明顯，表面積濃度次之，數(shù)量濃度不明顯。質量濃度在粒徑0.4 — 0.5 μm上有個峰，且為0.3—10 μm中最高點，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果一致（Seinfeld et al，1998）。同時質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較統(tǒng)一，由高至低到平緩，而質量濃度變化較為顯著。

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

圖4 安康農村各粒徑顆粒物質量濃度（dM）、個數(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%。陰離子中各個離子占PM2.5的份額依次為；陽離子為（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PM2.5中水溶性組分的貢獻相對較大。、和的平均質量濃度分別為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內城市（Wang et al，2005，2006；Hu et a1，2002）的觀測值；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，平均質量濃度都小于1 μg · m-3。K+通常作為生物質燃燒來源的一個標志物，本研究獲得的K+濃度遠低于關中平原農村K+濃度水平（Zhu et al，2012），表明當?shù)厣镔|燃燒源對PM2.5貢獻較關中平原低。圖中可以看出，降水對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。

一般認為，Ca2+主要來自于地表揚塵源（Nesbitt et al，1980），在觀測點Ca2+的質量濃度與其他主要水溶性離子變化趨勢類似，可能是受采樣點附近地表揚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，但是采樣點位居內陸，遠離海洋，所以Na+和Cl-可能主要來源于局地土壤鹽類及人為活動影響。本次研究發(fā)現(xiàn)陜南部分農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，農村的大氣污染也應根據(jù)當?shù)匚廴驹辞闆r采取有效治理措施，給予更多關注。

3 結論與展望

本文通過對陜南農村PM2.5污染特征進行分析，初步獲得該區(qū)域冬季PM2.5質量濃度及其主要化學組分濃度特征；該農村站點PM2.5及BaP污染較重，濃度遠超過環(huán)境空氣質量標準，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,應采取措施改善當?shù)啬茉唇Y構，尤其是對當?shù)鼐用袷菏褂梅绞竭M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

國家自然科學基金項目(41271481)

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

朱崇抒，E-mail: chongshu@ieecas.cn