DBPIA-NURIMEDIA

Transcription

1 Journal of Korean Society for Atmospheric Environment Vol. 31, No. 5, October 2015, pp DOI: p-issn , e-issn 최근중국의초미세먼지오염연구동향총설411 최근중국의초미세먼지오염연구동향 Review on the Recent PM 2.5 Studies in China 김유미 김진영 이승복 문길주 배귀남 * 한국과학기술연구원환경복지연구단 (2015 년 7 월 31 일접수, 2015 년 9 월 22 일수정, 2015 년 10 월 7 일채택 ) Yumi Kim, Jin Young Kim, Seung-Bok Lee, Kil-Choo Moon and Gwi-Nam Bae* Center for Environment, Health and Welfare Research, Korea Institute of Science and Technology, Seoul, Korea (Received 31 July 2015, revised 22 September 2015, accepted 7 October 2015) Abstract The Korea Ministry of Environment has established an air quality standard for PM 2.5 in 2012 and it is effective from January In this study, we review various aspects of PM 2.5 in China, including its measurement, modeling, source apportionment, and health effect, and suggest future research directions for PM 2.5 studies in Korea. Measurements studies for PM 2.5 have examined organic marker compounds and 14 C as well as inorganic aerosols for distinguishing sources. Modeling results supported that the control of PM 2.5 pollution in big city needs effective cooperation between city and its surrounding regions. The major PM 2.5 sources in China have been identified to be secondary sulfur, motor vehicle emissions, coal combustion, dust, biomass burning, and industrial sources, however, they have seasonal dependency. Especially, the severe haze pollution event during January 2013 over eastern and northern China was driven to a large extent by secondary aerosol formation. Short-term exposure to PM 2.5 is strongly associated with the increased risk of morbidity and mortality from cardiovascular and respiratory diseases, as well as total non-accidental mortality. Considered previous PM 2.5 studies in China, analysis of specific organic species using online measurement, chamber experiment for secondary aerosol formation mechanism, and development of parameterizing this process in the model are needed to elucidate factors governing the abundance and composition of PM 2.5 in Korea. Key words : PM 2.5, China, Haze, Observation, Modeling, Source apportionment, Health impact 1. 서론 최근중국에서발생된극심한초미세먼지 (PM 2.5 ) 오 *Corresponding author. Tel : +82-(0) , gnbae@kist.re.kr 염이사회적이슈로크게부각되면서오염현상과원인, 대책등다양한측면에서연구가수행되고있다 (Jiang et al., 2014; Wang et al., 2014b). 베이징 (Beijing) 을중심으로발생된 PM 2.5 오염이한반도까지영향을미치고있어한중일간국제협력도한층강화되고있는추세이다. 베이징대학교 (Peking University), 중국과학

2 412 김유미 김진영 이승복 문길주 배귀남 원 (Chinese Academy of Sciences, CAS) 등을중심으로 PM 2.5 오염현상을규명하려는연구가다양하게수행되고 (Wang et al., 2013a; Zhang et al., 2013b), 스위스 Paul Scherrer Institute (PSI) 등과국제공동연구도수행되어과학적자료를생산해내고있다 (Huang et al., 2014b). 독일은행의홍콩지점에서는 PM 2.5 오염현상을경제적관점에서분석하여저감대책을제안하는특별보고서를 2013년 2월발간하는등다양한이해당사자들이 PM 2.5 오염에적극적으로관심을보이고있다 (Deutsche Bank Market Research, 2013). 우리나라의경우 PM 2.5 에대한대기환경규제 ( 기준 : 연평균 25 μg/m 3 이하, 24시간평균 50 μg/m 3 이하 ) 가 2015년부터시행되고있으며, PM 2.5 에대한예보가본격적으로이루어지면서, PM 2.5 의배출원을규명하는것은매우중요한과제가되었다. 따라서과학적방법을기초로 PM 2.5 오염에대응하기위한국가적장기전략을수립할필요가있다. 중국의대기오염관리정책을포함한동아시아지역의국제협력에대해서는한국공학한림원연구보고서에상세하게기술되어있다 (The National Academy of Engineering of Korea, 2014). 이연구에서는중국에서발생된 PM 2.5 오염에대한최근연구결과를분석하여발생원인의규명과대책수립에대한시사점을찾아내고, 향후한국에서중점적으로추진해야할연구방향을모색하고자하였다. 2. 연구동향 2. 1 연구주제본연구에서는여섯가지의주제에따라중국지역의 PM 2.5 에대한최근연구동향을살펴보았다 ( 표 1). 먼저중국내 PM 2.5 에관한연구의리뷰 (review) 또는장기간의자료를활용한추세분석을살펴보았다. Pui et al. (2014) 은 PM 2.5 의관측, 발생원, 시정과건강영향및저감에대한전반적인연구를리뷰하였고, Liu et al. (2014b) 은중국베이징에서 2004년부터 2012년까지 9 년간의 PM 10 과 PM 2.5 의질량농도관측을통하여일변화와계절적변화를분석하였다. PM 2.5 의특성을파악하기위해관측과모델링을통한연구가중국내 외의연구기관에서활발히발표되고있다. 중국내도시와배경대기지역에서 PM 2.5 질량 농도의변동성 (Wang et al., 2013a; Zhao et al., 2009) 과무기물질및유기물질의화학적특성에관한관측및분석연구 (Zhang et al., 2014; Zhao et al., 2013b; Shen et al., 2009; Xu et al., 2005) 가수행되었으며, 더불어인공위성자료를활용하여 PM 2.5 농도를산정하는연구도수행되었다 (Ma et al., 2014). 모델링을활용한연구는특정지역의오염사례뿐만아니라 (Gao et al., 2014a; Lang et al., 2013), 중국전체에대한오염물질의배출원지역 (source region) 간의이동및기후변화에미치는영향을산정하는연구도수행되었다 (Gao et al., 2014b; Ying et al., 2014). 관측과모델링연구와더불어베이징, 상하이 (Shanghai) 및광저우 (Guangzhou) 등지에서 PM 2.5 의배출원기여도를산정하는연구 (Liu et al., 2014a; Gao et al., 2013; Wang et al., 2013b; Yu et al., 2013; Zhang et al., 2013b) 를통해그원인을규명하려고노력하고있다. 중국에서발생하는연무 (haze) 사례에관해서는과거부터베이징, 상하이, 항저우 (Hangzhou), 샤먼 (Xiamen), 광저우등지에서연무의유형, 발생에미치는기상장, 생성메커니즘, 화학적특성등의연구가수행되었다 (Zhang et al., 2013a; Huang et al., 2012a; Tan et al., 2011; Xiao et al., 2011; Sun et al., 2006). 특히 2013년 1월중국의넓은지역에걸쳐극심한연무가발생하면서이에관해중국전역에서활발하게연구가이루어졌다. Jiang et al. (2014) 은 74개도시의 PM 2.5 와 PM 10 의시간별질량농도를분석하여극심한연무의공간적분포를파악하였으며, Wang et al. (2014b) 은이러한연무가발생하게된기상학적원인을규명하였다. Huang et al. (2014a, b) 은연무의화학적특성과배출원기여도를, Wang et al. (2014a) 은물리적특성을, 그리고 Che et al. (2014) 은광학적특성을살펴보았다. 이러한지상관측과더불어 Wang et al. (2014c) 의경우관측결과를활용하여모델을평가하였다. 이러한 PM 2.5 가사람의건강에어떠한영향을미치는지에대한연구도진행되고있다 (Cheng et al., 2013) 연구기관중국에서초미세먼지에관한연구는공공기관, 대학및외국기관과의협력으로이루어지고있다. 대표적인중국내기관으로는중국과학원과중국기상국 (China Meteorological Administration) 을, 대학으로는베이징 한국대기환경학회지제 31 권제 5 호

3 Table 1. Classification of PM 2.5 studies in China. Classification Target area Main interest Institution 1) Reference Review & longterm analysis China Measurements, sources, visibility and health effect University of Minnesota Pui et al. (2014) Beijing Seasonal and diurnal variation in PM 10 and PM 2.5 (2004 ~ 2012) CAS Liu et al. (2014b) Beijing Citywide air pollution CAS, SDSU, CSU Wang et al. (2013a) Beijing, Tianjin, and Hebei Chemical composition CMA Zhao et al. (2013a, b) Observation CAWNET background site in Central China Chemical compositions, seasonal variations and regional pollution events Wuhan University, CAS, HUSTWB Zhang et al. (2014) China Estimating ground-level PM 2.5 using satellite remote sensing Nanjing University, Emory University Ma et al. (2014) Beijing Regional transport and characteristics of PM 2.5 pollution BUT, University of Northern British Columbia Lang et al. (2013) Modeling Yangtze River Delta Region PM 2.5 pollution episode CAS, CMA, Nanjing University, Environmental Monitoring Center Station of Hangzhou City Gao et al. (2014a) China Local and inter-regional contributions to PM 2.5 nitrate and sulfate Texas A&M University, University of California, Davis Ying et al. (2014) Source apportionment Severe haze cases (January 2013) Beijing Beijing Seasonal perspective Characterization and source apportionment of PM 2.5 elemental composition CAS, CMA, Ministry of Environmental Protection Academia Sinica, Peking University, Xi an Jiaotong University BNU, CAS, Beijing Radiation Center, Environment Canada, Peking University Zhang et al. (2013b) Yu et al. (2013) Shanghai Possible sources of PM 10 and PM 2.5 ECNU Wang et al. (2013b) Guangzhou Guangzhou Source apportionment of polycyclic aromatic hydrocarbons in PM 2.5 CAS, The Hong Kong Polytechnic University Gao et al. (2013) Radiocarbon and organic tracers for PM 2.5 carbonaceous aerosols CAS, University of Bern, CAS Liu et al. (2014a) 74 cities Hourly PM 2.5 and PM 10 concentrations Tsinghua University Jiang et al. (2014) China Meteorological causes CAMS, NUIST, Tsinghua University, BMB, CMA Wang et al. (2014b) Beijing Chemical characteristics, formation mechanism and role of fog processing Fudan University, The University of Tennessee, Beijing University of Technology Huang et al. (2014) 최근중국의초미세먼지오염연구동향 413

4 한국대기환경학회지제 31 권제 5 호 Table 1. Classification Continued. of PM 2.5 studies in China. Classification Target area Main interest Institution 1) Reference Severe haze cases (January 2013) Health effects Beijing, Shanghai, Guangzhou, Xi an Yangtze River Delta China Beijing, Shanghai, Xi an, Chongquing, Guangzhou, Shenyang Shanghai, Shenyang, Xi an, Guangzhou Secondary aerosol contribution Mechanism for the formation and microphysical characteristics Enhanced sulfate missing from current models Total, cardiovascular, and respiratory mortality Mortality classified by various effect modifiers PSI, CAS, University of Bern, The CUHK, University of Milano Bicocca, GmbH - HICE, University of Rostock Nanjing University, Jiaxing Environmental Monitoring Station, CAS Tsinghua University, Texas A&M University at Galveston, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Dalhousie University University of Minnesota, Fudan University Fudan University Huang et al. (2014b) Wang et al. (2014a) Wang et al. (2014c) Chen et al. (2011) Geng et al. (2013) Huang et al. (2009) Yang et al. (2012) Dai et al. (2004) Ma et al. (2011) Huang et al. (2012b) Yang et al. (2012) 1) Abbreviation of each institution is as follows: BARI: Beijing Automobile Research Institute Co. Ltd., BIT: Beijing Institute of Technology, BMB: Beijing Meteorological Bureau, BNU: Beijing Normal University, BUT: Beijing University of Technology, CAS: Chinese Academy of Sciences, CATRC: China Automobile Technology and Research Center, CIT: California Institute of Technology, USA, CAMS: Chinese Academy of Meteorological Sciences, CMA: China Meteorological Administration, CNEMC: China National Environmental Monitoring Center, CUIT: Chengdu University of Information Technology, CUHK: The Chinese University of Hong Kong, ECNU: East China Normal University, GIT: Georgia Institute of Technology, USA, HUSTWB: Huazhong University of Science and Technology Wuchang Branch, NASA/GSFC: The National Aeronautics and Space Administration/Goddard Space Flight Center, NUIST: Nanjing University of Information Science & Technology, SEPKLSCAPC: State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, SDSU: San Diego State University, USA, USTL: Université des Sciences et Technologies de Lille, UW-M: University of Wisconsin-Madison, USA, XJU: Xi an Jiaotong Univeristy, GmbH - HICE : Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Joint Mass Spectrometry Centre, Cooperation Group Comprehensive Molecular Analytics and Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health Aerosol and Health (HICE), PNL: Pacific Northwest National Laboratory, PSI: Paul Scherrer Institute 414 김유미 김진영 이승복 문길주 배귀남

5 최근중국의초미세먼지오염연구동향 415 CAS Organizational structure Fig. 1. CAS Organization structure ( 대학교, 칭화대학교 (Tsinghua University), 난징대학교 (Nanjing University) 및푸단대학교 (Fudan University) 를들수있다. 중국과학원은 1949년 11월베이징에설립된기초과학연구의중심기관으로베이징에본부가있으나부속기관들은각처에산재되어있다. 2012년말까지 11개의분소들 (management organizations) 은선양 (Shenyang), 창춘 (Changchun), 상하이, 난징 (Nanjing), 우한 (Wuhan), 광저우, 청두 (Chengdu), 쿤밍 (Kunming), 시안 (Xi an), 란저우 (Lanzhou) 와신장 (Xinjiang) 에위치해있으며, 104개연구기관 (research institutes), 2개부속대학교 (University of Science and Technology of China, University of Chinese Academy of Sciences), 기타정보기관, 기술지원센터및출판보도국으로구성되어있다 ( 중국과학원에는 6개의학술부문 (academic divisions) 이있는데, 이것은수학과물리, 화학, 생명과학과의료과학, 지구과학, 정보기술과학및기술과학이다 ( 그림 1). 이중초미세먼지와관련하여주로 Institute of Atmospheric Physics에서연구를수행중이며, 이외에도 Institute of Geographic Sciences and Natural Resources Research, Guangzhou Institute of Geochemistry, Institute of Earth Environment에서연구를수행하고있다. 중국기상국은 1949년에설립되어기상관련연구와정책수립등을관장하는국무원직속사업단위의하나로본사는베이징에위치하고있다 ( cn). 기상서비스뿐만아니라기상 / 기후와관련된연구를수행하며, 타기관및대학등과협력하고있다. 대학의경우베이징대학교의 College of Environmental Sciences and Engineering ( edu.cn/index.php), 칭화대학교의 School of Environment ( 난징대학교의 School of the Environment ( edu.cn/en_wbe/), 푸단대학교의 School of Public Health ( 에서초미세먼지의특성및건강영향에대한연구를수행하고있다 연구기법중국에서 PM 2.5 질량농도측정은주로 tapered element oscillating microbalance (TEOM) 방법또는 beta-

6 416 김유미 김진영 이승복 문길주 배귀남 75 E 90 E 105 E 120 E 135 E Kilometers N 40 N 30 N 20 N Elevation (m) < N > N South China Sea Ground PM 2.5 Monitoring Sites 50 km 50 km Grid 90 E 105 E 120 E Fig. 2. Spatial distribution of PM 2.5 monitoring sites. There are 835 monitoring sites in 113 cities. It should be noted that the monitoring sites are clustering in the urban areas of major cities whereas rural areas have little coverage. Many monitoring sites are overlaid in this map (Ma et al., 2014). attenuation 방법으로이루어지고있다. 그림 2는중국 113개도시의 835개 PM 2.5 모니터링사이트의분포를나타낸것이다 (Ma et al., 2014). Ma et al. (2014) 은관측지역이주요도시에몰려있어시골지역은상대적으로적은것을지적하면서위성의에어로졸광학두께 (aerosol optical depth) 와통계모델 (national-scale geographically weighted regression model) 을활용한지상 PM 2.5 질량농도추정방법을제시하고있다. 표 2는 PM 2.5 의화학적특성의측정및분석방법을정리한것이다. 양이온과음이온측정은샘플링된필터를추출하여 ion chromatography (IC) 로분석하며, 유기탄소 (organic carbon, OC) 와원소탄소 (element carbon, EC) 는 thermal optical carbon analyzer를사용하여 thermal-optical reflectance (TOR) 또는 thermal-optical transmittance (TOT) 방법으로분석하였다. 또한, 미량원소들 (trace elements) 은 inductively coupled plasmaatomic emission spectroscopy (ICP-AES), inductively coupled plasma-mass spectrometry (ICP-MS), energy dispersive X-ray fluorescence (ED-XRF) 및 particle induced X-ray emission (PIXE) 분석방법등을활용하 여개별화학조성들의질량농도를분석하고있다. 더불어유기성분의화학조성을정량분석하기위해필터샘플링결과를 thermal desorption gas chromatography time of flight mass spectrometry (TD-GC-MS) 방법이나 online 또는 offline의 aerosol mass spectrometer (AMS) 를운영하여, 세부유기물특성을분석하고발생원을알수있는지표 (marker) 를찾아내는연구에활용하고있다. 방사성탄소 (radiocarbon, 14 C) 는 mini radiocarbon dating system (MICADAS) 으로분석되어발생원의추정에활용되고있다. PM 2.5 의수치모의에관한연구는주로지역적규모를모의하는 U.S. Environmental Protection Agency (EPA) 의 community multi-scale air quality (CMAQ) 모델을활용하여진행되고있으며 (Ying et al., 2014), 전지구모델인 GEOS-Chem 역시활용되고있다. 모델링과더불어 PM 2.5 의발생원을추정하기위해화학조성자료의 PCA (principal component analysis) 분석이나기상장을활용한군집분석방법및 PSCF (potential source contribution function) 가수행되고있다. 더불어 complementary bilinear receptor models인 CMB (chemical 한국대기환경학회지제 31 권제 5 호

7 최근중국의초미세먼지오염연구동향 417 Table 2. Summary of chemical observation and analysis methods. Ions Measurement Method Note Reference OC&EC Trace elements Organic markers Ion chromatography Thermal optical carbon analyzer ICP-AES ICP-MS ICP-OES ED-XRF PIXE analytical method Thermal desorption gas chromatography time of flight mass spectrometry (TD-GC-MS) method Anions: SO 4 2-, NO 3 -, Cl -, F -, Cl -, oxalate, and methanesulfonate Cations: K +, Na +, Mg 2+, Ca 2+, and NH 4 + TOR TOT Al, Si, P, S, Cl, K, Fe, Na, Mg, K, Ca, Ba, Ti, Mn, Co, Ni, Cu, Zn, As, Pb, Mo, Cd, Sn, Sr, Sb, Pb, Tl, Ge, Cs, Ga, V, Cr, As, Se, Ba, and Rb Sugars, hopanes, PAHs Zhao et al. (2013a) Zhang et al. (2014) Huang et al. (2014a, b) Zhang et al. (2013b) Zhao et al. (2013a) Zhang et al. (2013b) Zhang et al. (2014) Huang et al. (2014b) Zhao et al. (2013a) Zhang et al. (2013b) Huang et al. (2014a, b) Yu et al. (2013) Huang et al. (2014b) Radiocarbon ( 14 C) MICADAS Huang et al. (2014b) Chemical components of single particles HR-ToF-AMS (Offline AMS) SPAMS manufactured by Hexin Analytical Instrument Co., Ltd. Organic aerosol high resolution mass spectra Huang et al. (2014b) 0.2 ~ 2.0 μm Wang et al. (2014a) mass balance) 모델과 PMF (positive matrix factorization) 모델이활용되고있다 (Liu et al., 2014a; Gao et al., 2013; Wang et al., 2013b; Yu et al., 2013; Zhang et al., 2013b). 건강영향 (health effect) 의경우 PM 2.5, SO 2, NO 2 자료와사망률관련자료및기상장자료 ( 일평균기온과상대습도 ) 를통계모델의입력자료로활용하여결과를산출한다. 통계모델은연구에따라 partial autocorrelation function (PACF) 을활용한선형 (linear) 모델이나 (Chen et al., 2011), log-linear 모델 (Huang et al., 2012b; Huang et al., 2009), lag non-linear 모델 (Geng et al., 2013), generalized additive model (GAM) (Dai et al., 2004) 을이용하며시간-층화된 (time-stratified) 환자교차 (case-crossover) 연구방법도제안되었다 (Yang et al., 2012; Ma et al., 2011) 연구결과 관측연구베이징의경우 2004년부터 2012년까지 9년간의평 균 PM 2.5 농도는 72.3±54.4 μg/m 3 이며, 연평균농도는다소감소하는경향을보였다 (Liu et al., 2014b). 반면 Jiang et al. (2014) 은베이징에서 2012년부터 2013년까지 PM 2.5 질량농도가증가하는경향이었다고보고하였다. PM 2.5 의일변동은오전 7시부터 8시사이와저녁 7 시부터 11시사이에피크를보이는이산형분포를보였다 ( 그림 3) (Liu et al., 2014b). 2009년부터 2010년의경우베이징, 텐진 (Tianjin), 스자좡 (Shijiazhuang) 지역의 PM 2.5 질량농도를비교한경우샘플링날들의 90% 이상의날짜에서 50 μg/m 3 을넘었으며, 스자좡의연평균농도가가장높게나타났다 (Zhao et al., 2013a). 지역적배경대기지역인 China Atmosphere Watch Network (CAWNET) 의한사이트인진사 (Jinsha) 의경우, 2012년 3월부터 2013년 3월까지연평균 PM 2.5 질량농도는 48.7±26.9 μg/m 3 으로중국대기환경기준 (35 μg/m 3 ) 을초과하였다 (Zhang et al., 2014). 계절적으로는겨울, 가을, 봄, 여름순으로질량농도가높게나타났다. 네도시지역들 (Beijing, Tianjin, Shijiazhuang과 Chengde) 과지역적배경대기측정소인샹단지 (Shang-

8 418 김유미 김진영 이승복 문길주 배귀남 Dec 110 Nov 105 Oct Sep PM 2.5 (μg/m 3 ) 90.0 Aug 85.0 Jul 80.0 Jun May 65.0 Apr 60.0 Mar 55.0 Feb 50.0 Jan PM 2.5 (μg/m 3 ) -1 PM 2.5 trend (μg/m 3 /yr) -2-4 Dec Jan Local time (hour) 0 Nov Oct Sep Aug Jul Jun May Apr Mar Feb PM 2.5 (μg/m 3 ) Fig. 3. PM 2.5 concentration in relation to month and local time. The red bars at top denote mean PM 2.5 over the entire period 2004 to 2012 for each hour (month). The blue curves denote PM concentration trend in Beijing from 2004 to 2012 (Liu et al., 2014b). Seasonal concentrations (μg/m 3 ) Secondary inorganic ions TC Crustal elements Spring Summer Autumn Winter Spring Summer Autumn Winter Spring Summer Autumn Winter Spring Summer Autumn Winter Spring Summer Autumn Winter SDZ BJ TJ SJZ CD Sampling sites Fig. 4. Seasonal concentrations of secondary inorganic ions, total carbon (TC), and crustal elements at Beijing (BJ), Tianjin (TJ), Shijiazhuang (SJZ), Chengde (CD), and Shangdianzi (SDZ) (Zhao et al., 2013a). dianzi) 의 2009 년부터 2010 년까지측정결과에따르면, 이차무기이온들 (secondary inorganic ions; NH 4 +, NO 3 -, SO 4 2- 의합 ) 이가장주요한성분으로 PM 2.5 의 24 ~ 43% 정도를차지하였으며, 네계절중여름에강 한광화학산화로인해높게나타났다 ( 그림 4) (Zhao et al., 2013a). 다섯지역모두 OC 와 EC 가봄과여름 한국대기환경학회지제 31 권제 5 호

9 최근중국의초미세먼지오염연구동향 419 에가을과겨울보다낮았으며, 이것은난방이나연료연소사용증가에기인한다. OC/EC 비율은여름에가장낮고겨울에가장높게나타났다. 더불어 SOC (secondary organic carbon) 또한가을과겨울에높고여름에가장낮았다. 이것은안정적대기상태와낮은온도가겨울과가을에더빈번히나타나므로오염물질이적체되며휘발성유기화합물 (volatile organic compounds) 의흡수나응결이가속되기때문이다. 베이징의 EC 농도의경우난방을위한석탄연소의배출감소로인해지난십년간 (1999 ~ 2009년 ) 감소경향이관측되었다 (Zhao et al., 2013b). 지각원소 (Al, Ca, Fe, Mg, Ti, Ba와 Sr) 는봄과가을에떠있거나날리는먼지 (dust) 로인해높았으며, 중금속 (heavy metals) 은샹단지에비해베이징, 텐진및허베이 (Hebei) 지역이더높았다 모델링연구모델링을통한연구는초미세먼지의이동및발생과정을규명하는데유용한결과를내고있다. Lang et al. (2013) 에따르면베이징지역에서연평균전체월경성영향이 PM 2.5, 황산염 (sulfate), 질산염 (nitrate) 과암모늄 (ammonium) 이각각 42.2%, 46.3%, 77.4%, 그리고 61.6% 로나타났으며, 고농도오염사례의경우그영향이더증가하여베이징지역의 PM 2.5 를제어하기위해서는인근주변지역의협조가필요함을밝히고있다. 항저우의 2011년 12월 8일 ~ 16일에나타난 PM 2.5 오염사례의경우에어로졸진행과정 ( 생성, 이류, 침적등 ) 의분석을통해살펴볼때, 일차와이차 PM 2.5 농도에배출량과에어로졸과정모두기여함을알수있었다. 이류과정 (the process of advection) 은질량을증가시켰으며, 확산과정이가장두드러진제거과정이었다. 건조와습윤침적과비균질과정도 PM 2.5 제거에영향을미쳤다. PM 2.5 농도가가장높게나타난날은확산을통한제거과정이비효율적이었기때문인것으로밝혀졌으며, 항저우외부지역에서발생한오염물질역시항저우의 PM 2.5 농도를증가시켰을뿐만아니라기간도연장시켰다 (Gao et al., 2014a). Ying et al. (2014) 은 CMAQ 모델의배출원지역을북중국, 북동중국, 동중국, 중앙중국, 남중국, 남서중국, 북서중국과나머지지역등총 8개영역으로나누어 North Northeast East Central South Southwest Northwest Fig. 5. Emission source region designation and abbreviation of 34 provincial level divisions (Ying et al., 2014). 각각의배출원에서배출된물질이타지역으로이동하여 PM 2.5 의황산염과질산염농도에어떠한영향을미치는지살펴보았다 ( 그림 5). 2009년 1월의모의결과에서쓰촨분지 (Sichuan Basin) 뿐만아니라화베이평원 (North China Plain, NCP) 과중하양쯔평원 (Middle and Lower Yangtze Plain, MLYP) 에서고농도질산염이나타났다 ( 그림 6). 북, 중앙그리고동중국으로부터배출된 NO x 는장거리를이동하여 NCP와 MLYP 지역에서다소균일한질산염농도를형성하였으며, 남중국의주강삼각주 (Pearl River Delta, PRD) 지역까지질산염농도에중요한영향을미쳤다. 겨울에황산염농도는질산염보다더넓은공간적분포를보였다. 이것은 SO 2 의체류시간 (life time) 이더길기때문이다. 2009년 8월에는느린북풍과북동풍이배출원지역간이동을감소시켰다. 질산염과황산염농도는북중국에서최고치를나타냈는데, 이것은더북쪽에위치한산들에의해확산이심각하게제한되었기때문이다. 질산염과황산염농도가상위 5위에드는지방들 (Chongqing, Sichuan, Guizhou, Anhui와 Hunan) 은모두북, 중앙과동중국의배출량에영향을받았으며, 더불어그들의국지적배출량에영향을받았다. 상위다섯곳중세지방 (Tianjin, Hebei와 Beijing) 은북중국에위치하며국지적기여가 55% 이상으로나타났다. 북중국배출이다른두지방 (Shandong in East China, Henan in Central China) 의오염도에미치는기여도는 20% 정도로계산되었다. 북중국의배출량과더불어상위다섯곳중네지방 (Tianjin, Henan, Hebei, Shan-

10 420 김유미 김진영 이승복 문길주 배귀남 Primary Nitrate Primary Sulfate Secondary Nitrate Secondary Sulfate 8 Source regions North Northeast East Central South Southwest Northwest Other Fig. 6. Regional contributions to average primary and secondary PM2.5 nitrate and sulfate in January, Units are μg/m3. The scales of the panels are different to better illustrate spatial distribution (Ying et al., 2014). dong)은 30% 또는 그 이상 동중국 배출량의 영향을 받 았다. 세 지역의 대규모 도시들 (Beijing, Shanghai와 농도의 80% 이상이 북, 동 그리고 중앙 중국의 배출량 에 영향을 받았다. Chongqing)과 큰 도시 집단 (PRD)도 여름보다 겨울에 더 강하고 빈번하게 외부 지역의 영향을 받았다. 비록 도시들이 대개 국지적 배출량에 주로 영향을 받지만 배출원 기여도 산정 연구 (source apportionment study) 배출원 지역별 이동에 의한 영향은 특히 겨울에 PRD 베이징 지역의 경우 PM2.5 질량농도의 계절 변동성 지역의 황산염 농도에 대해 뚜렷하게 나타나며, 전체 과 일변동성은 대기경계층과 배출원의 배출량에 영향 한국대기환경학회지 제 31 권 제 5 호

11 최근중국의초미세먼지오염연구동향 421 Source contributions (μg/m 3 ) Spring Summer Fall Winter Annual Secondary sulphur Vehicle exhaust Soil dust Road dust Biomass burning Fossil fuel combustion Metal processing Secondary sulphur, 26.5% Vehicle exhaust, 17.1% Annual percentages of sources Soil dust, 10.4% Metal processing, 6.0% Fossil fuel combustion, 16.0% Road dust, 12.7% Biomass burning, 11.2% Fig. 7. Seasonal and annual average source contributions to PM 2.5 mass in Beijing from January 1 to December 31, 2010 (Yu et al., 2013). 을받으며, 교통량과연소배출의영향이가장주요한영향으로나타났다. 기상학적으로는베이징남쪽에위치한이차에어로졸의발원지에서기원하는남풍계열의바람과관련성이높았다 (Liu et al., 2014b). Yu et al. (2013) 은베이징지역 PM 2.5 의배출원기여도를산정하기위해 PMF 모델내주요배출원을이차생성황 (secondary sulfur), 차량배출 (vehicle exhaust), 화석연료연소 (fossil fuel combustion), 도로먼지 (road dust), 바이오매스소각 (biomass burning), 토양먼지 (soil dust), 그리고금속가공업 (metal processing) 등총일곱가지로나누었다 ( 그림 7). 연평균기여도는 26.5 %, 17.1%, 16.0%, 12.7%, 11.2%, 10.4%, 그리고 6.0% 로나타났으며, 계절별로차이를보였다. 봄은토양먼지와도로먼지가 20.7% 로가장높은기여도를보였으며, 다른계절에비해 2배이상높았다. 연소와관련하여가을과겨울이다른두계절에비해기여도가높았으며, 이차생성황은여름에주된기여도를보였다. 차량과금속가공은뚜렷한계절적차이를보이지않았다. Zhang et al. (2013b) 역시 PMF 모델을활용하여베이징지역의배출원을토양먼지, 석탄연소 (coal combustion), 바이오매스소각, 차량 (traffic) 과폐기물소각장배출 (waste incineration emission), 산업오염 (industrial pollution), 그리고이차무기에어로졸 (secondary inorganic aerosol) 등총여섯가지로나누어각각의연평균기여도를 16%, 14%, 13%, 3%, 28%, 그리고 26% 로산정하였다. 계절별기여도차이를살펴보았을때, 봄에는토양먼지 (23%) 와차량과폐기물소각장배출 (5%) 이, 겨울에는석탄연소 (57%), 가을에는산업오염 (42%), 그리고여름에는이차무기에어로졸 (54%) 이높게나타났다. 더불어 PSCF 분석으로베이징남쪽지역이모든물질의주요한배출원지역으로나타났으며, 이차무기에어로졸과유기에어로졸과같이인위적물질은동쪽지역이중요하게나타났다. 북서쪽은자연기원먼지 (dust) 나인위적유기에어로졸의배출원지역으로분석되었다. 베이징과근처다른지역의석탄연소와차량배출기여도를살펴보면, 스자좡과청더 (Chengde) 에서는주요배출원이석탄연소이며, 텐진에서는석탄연소와차량배출이모두중요한배출원으로나타났다. 하지만베이징의경우최근석탄사용의감소와차량의급격한증가로인해차량배출이더주요한요인으로나타났다. 샹단지는남쪽도시로부터이동해오는오염물질이주요원인으로나타났다 (Zhao et al., 2013a). 상하이의경우주로산업활동 (industrial activities), 석탄연소 (coal combustion) 와차량배출원이주로기여하였다 (Wang et al., 2013b). 이와더불어세부유기물질분석이가능해지면서이

12 422 김유미 김진영 이승복 문길주 배귀남 (a) Total PAHs (b) Pyrene BB 31%±4% VE 11%±2% BB 75%±3% BB: Biomass burning VE: Vehicular emissions CC: Coal combustion CC 58%±4% CC 4%±1% VE 21%±3% (c) Chrysene (d) Benzo[g,h,i]perylene BB 48%±4% BB 30%±4% VE 14%±2% CC 32%±3% VE 20%±2% CC 57%±4% Fig. 8. Average source contributions to (a) the total PAHs, (b) Pyrene, (c) Chrysene, and (d) Benzo[g,h,i]perylene in Guangzhou (mean±standard error) (Gao et al., 2013). 에대한배출원기여도를산정하는연구가최근이루어지고있다. Gao et al. (2013) 이광저우에서관측한다환방향족탄화수소 (polycyclic aromatic hydrocarbons, PAHs) 의배출원을 PMF로추정한결과에의하면, 전체 PAHs와세종류의 PAHs의배출원기여도에차이를보였다 ( 그림 8). Pyrene의경우바이오매스소각이가장크게기여하였으나, 분자량이증가할수록 ( 예, benzo[g,h,i]perylene) 바이오매스소각의영향은덜중요한반면, 석탄연소가더중요한영향을끼쳤다. Liu et al. (2014a) 는 14 C 분석과세가지당무수물이성질체 (three anhydrosugar isomers: levoglucosan, galactosan 과 man nosan) 를활용하여바이오매스소각과화석연료의기여도를나누었고, 광저우에서이차유기탄소 (secondary organic carbon) 의 33% 가화석 (fossil) 기원임을밝혔다 년 1월에발생한극심한연무사례 2013년 1월중국에서발생한극심한연무에대한특성분석및원인규명과관련하여다양한연구가수행되었다. 먼저농도수준을살펴보면 74개도시에서 1월 한달의 PM 2.5 평균농도는중국대기환경기준치를훌쩍넘는 μg/m 3 이었다. 가장오염이심했던 2013 년 1월 12일은 13개도시에서일평균 300 μg/m 3 을넘는극심한오염상태가관측되었고, 다른 18개도시에서도 200 ~ 300 μg/m 3 수준을보였다. 특히지역적으로보았을때, 징진지 (Jing-Jin-Ji) 지역 (Beijing, Tianjin과 Hebei province) 이포함된 NCP에서가장오염이심하게나타났다 ( 그림 9) (Jiang et al., 2014). Wang et al. (2014b) 은 PM 2.5 관측자료와기상관측및모델자료를활용하여베이징의남쪽허베이성으로부터이동한오염물질이베이징에영향을미쳤음을시사하였다. 이것에영향을준기상학적원인은 (1) 위도에따른대규모대기순환이연무형성에이로웠으며, (2) 지역적으로안정하게성층화된대기경계층과약한난류가연무형성에좋은조건이었고, (3) 850~925 hpa 공기의이동흐름이남쪽허베이에서베이징으로오염물질이이송되기용이한상태였음을밝히고있다. Huang et al. (2014b) 은이기간동안베이징, 상하이, 광저우, 그리고시안 4개의도시에서 PM 2.5 의화학적조성과배출원기여도에대한연구를수행하였다 ( 그림 한국대기환경학회지제 31 권제 5 호

13 최근중국의초미세먼지오염연구동향 N 40 N 30 N 20 N 50 N 40 N 30 N 20 N (a) (b) 80 E 90 E 100 E 110 E 120 E 130 E 80 E 90 E 100 E 110 E 120 E 130 E 80 E 90 E 100 E 110 E 120 E 130 E Fig. 9. PM 2.5 concentrations in 74 Chinese cities: (a) daily average on 12th January 2013 and (b) monthly average in January 2013 (Jiang et al., 2014). 10). 네지역의 PM 2.5 농도는미국과유럽의도시지역 의관측값보다약 1~2 차수높은수준이었다. 유기물 (organic matter, OM) 이 PM 2.5 의가장주요한성분으로 분석되었으며, 시안을제외한지역에서이차무기에 어로졸 (secondary inorganic-rich aerosol) 과이차유기 에어로졸 (secondary organic-rich aerosol) 이주된배출 원기여도로밝혀졌다. 시안은토양먼지가가장주된 기여도로나타났다. 같은기간양쯔강삼각주 (Yangtze River Delta, YRD) 의경우바이오매스 / 바이오연료소각에기인한입자 들, 유기탄소, 원소탄소, 질산염및황산염의농도가중 요하게상승하여중금속, 토양먼지와해염 (sea salt) 과 같은물질의비중은청정했을때에비해감소하였다 (Wang et al., 2014a). PM 2.5 (μg/m 3 ) daily average 0~50 50~75 75~ ~ ~ ~ ~ ~ ~ N 40 N 30 N 20 N 50 N 40 N PM 2.5 (μg/m 3 ) monthly average 0~50 50~75 75~ ~ ~ ~ ~ N 20 N 또한, 이기간을 GEOS-Chem 으로모의하였을때 PM 2.5 농도가실제관측치보다낮게모의되었으며, 연 무기간이발생할정도로황산염농도의큰증가를모 의하지못했다. 기상장의보정과 SO 2 배출량을 2배로증가시켰을때관측치와유사하게나타났으나, 모델내황산염발생메커니즘의불확실도에기인한것으로판단하여추가로황산염발생과정을모델에매개변수화하여모델을개선하였다. 이것은황산염증가율의 70% 를증가시켰고 PM 2.5 에서황산염비중을 120% 증가시켰다. 따라서현재모델의모의수준에서배출량, 기상장및황산염화학의조정이모두필요함을시사한다 PM 2.5 의인체영향 PM 2.5 의단기노출 (short-term exposure) 이중국인구의건강에미치는영향에대한역학연구는많이진행되지않았으며, 그중유병률보다는사망률 (mortality) 에관한연구가더많다 (Pui et al., 2014). 일반적으로 PM 2.5 의단기노출은유병률 ( 예, 병원입원비율과응급실내원 ) 에대한과도한위험과사고로인한사망을제외한전체사망률뿐만아니라심혈관 (cardiovascular) 과호흡기 (respiratory) 와관련된사망률이양의상관관계및통계적으로중요한관련이있다고보고되고있다. 사망률과관련된연구에서 PM 2.5 의 10 μg/m 3 증가에따라전체사망률과심혈관및호흡기관련사망률에대한변화범위는각각 0.00~0.90%, 0.26~1.22%, 그리고 0.07~0.97% 의영향으로산정되었다 ( 그림 11). PM 2.5 와심폐 (cardio-respiratory) 의사망률은일반적으로전체사망률에비해더연관성이높게나타났으며, 호흡기질병의사망률위험도가상대적으로심혈관계질병의사망률의위험도보다높았다. 그림 12는잠재적영향한정자 (modifier) 들인나이, 성별, 교육정도, 그리고계절을포함한분석결과이다. 장년층, 여성그리고초등학교이하학력을가진사람들이그렇지않은그룹에비해일반적으로더높은영향을받고있었다. 3. 향후연구방향에대한제언중국에서수행되고있는초미세먼지에관한다양한연구의범위를참조하여우리나라연구의부족한부분을찾아보완하는것이필요하다. 먼저초미세먼지의

14 424 김유미 김진영 이승복 문길주 배귀남 Beijing: μg/m 3 Xi an: μg/m 3 Shanghai: 90.7 μg/m 3 AOD 55 N N N 0.4 Composition (%) OM Nitrate Sulphate Ammonium Chloride Measured trace elements EC Unidentified 25 N 15 N 70 E 90 E 110 E 130 E Guangzhou: 69.1 μg/m Sources/factors (%) Traffic Coal burning Biomass burning Cooking Dust related Secondary organic-rich Secondary inorganic-rich Fig. 10. Chemical composition and source apportionment of PM 2.5 collected during the high pollution events of 5 ~ 25 January 2013 at the urban sites of Beijing, Shanghai, Guangzhou and Xi an. Centre map showing locations of the four sites, indicated by arrows. Pie charts around the map show PM 2.5 composition and sources for each site. The measured PM 2.5 concentrations are shown next to the site name. The measured trace elements include K, Na, Ca, Mg, Fe, Ti, Pb, As, Cu, Zn and Ni, while the major crustal elements, Si and Al, could not be measured due to interference from the quartz fibre substrate of the sample. The central map presents aerosol optical depth (AOD, colour-coded, see key at right), retrieved from satellite (Terra/Modis) observations over the whole month of January 2013 ( and shows the large coverage of severe particle pollution in China (Huang et al., 2014b). 장거리수송및배출원을판단하는데도움이되는세부적화학적특성을살펴보는연구가필요하다. 예를들어, Huang et al. (2014b) 의경우 14 C 측정을활용하여탄소성에어로졸 (carbonaceous aerosol) 의화석과비화석기원을구분하였으며, 유기성탄소에어로졸의세부성분들 (e.g., HULIS, PAHs, dicarboxylic acids) 을분석하여바이오매스소각, 화석연료및이차생성등의기원을구분하는데활용하는연구가더필요하다. 더불어 AMS를측정및분석에활용한다면, 초미세먼지의배출원추정의정확도와범위가확대될수있을것이다. 이차생성물질의경우배출원-수용처관계 (sourcereceptor relationship) 에서화학반응식의비선형성에영향을받게되므로, 기존의기본모의 ( 모든배출량포함 ) 와민감도모의 ( 분석대상이되는지역의배출량을제거 ) 의비교와같은방식에는문제가있을수있다고지적되고있다 (Li et al., 2014). 초미세먼지의경우이차생성에어로졸의구성비가미세먼지보다더크므로이에대한구체적메커니즘개발이필요하며, 이러한연구에챔버 (chamber) 실험을통한모델내추가적인매개변수화개발을고려해야한다. 더불어 CMB 모델, PMF 모델의배출원프로파일을초미세먼지에맞추어개발하는것이필요하며, 배출원의기여도를 100% 이하로산정하는 SMP (Solver for Mixture Problem) 모델 한국대기환경학회지제 31 권제 5 호

15 최근중국의초미세먼지오염연구동향 %Increase Fig. 11. Estimated percentage increase and 95% confidence interval (CI) in total (nonaccidental) and cause-specific mortality associated with each 10 μg/m 3 increase in PM 2.5 (Pui et al., 2014). (Kim, 2013) 을활용하면배출원추정의정확도를향상시킬수있을것이다. 초미세먼지의경우국지적인배출량의영향을받는것뿐만아니라장거리수송된가스및입자들의영향을받으므로, 국가간의공동연구가필요하다 (He et al., 2003; Lee et al., 2001). 우리나라의경우중국의풍하측에위치하므로중국과공동연구를통해초미세먼지의원인을규명하고, 이에적합한저감정책을펼 치는것이중요하다. 초미세먼지의경우입자형태로국가간에이동할뿐만아니라가스상물질이이동과정에서또는특정지역을지나가면서이차생성될수있는부분이있으므로, 이러한구체적인원인을규명하기위해서는국가간에공동사례연구를실시할필요가있다. 특히겨울에빈번한연무사례가중국과우리나라에서발생하고있으므로, 이러한현상을규명하기위하여국가간의배출원, 기상장의영향, 이동기류

16 426 김유미 김진영 이승복 문길주 배귀남 %Increase Fig. 12. Estimated percentage increase and 95% confidence interval (CI) in total (nonaccidental) mortality per 10 μg/m 3 increase in PM 2.5 by various effect modifiers (Pui et al., 2014). 상의반응, 수용처지역의특성등을고려한관측, 모델 링, 배출원기여도산정등의통합적연구가필요하다. 마지막으로초미세먼지가인체에미치는영향에대한 연구는매우미흡하므로, 환경보건과환경오염분야가 협력하여장기계획을수립하여연구하여야한다. 한국대기환경학회지제 31 권제 5 호

17 최근중국의초미세먼지오염연구동향 427 감사의글본연구는한국공학한림원의 2014년정책연구사업과한국과학기술연구원의기관고유사업의지원을받아수행하였습니다. References Che, H., X. Xia, J. Zhu, Z. Li, O. Dubovik, B. Holben, P. Goloub, H. Chen, V. Estelles, and E. Cuevas-Agulló (2014) Column aerosol optical properties and aerosol radiative forcing during a serious haze-fog month over North China Plain in 2013 based on groundbased sunphotometer measurements, Atmos. Chem. Phys., 14(4), Chen, R., Y. Li, Y. Ma, G. Pan, G. Zeng, X. Xu, B. Chen, and H. Kan (2011) Coarse particles and mortality in three Chinese cities: the China Air Pollution and Health Effects Study (CAPES), Sci. Total Environ., 409(23), Cheng, Z., J. Jiang, O. Fajardo, S. Wang, and J. Hao (2013) Characteristics and health impacts of particulate matter pollution in China (2001~2011), Atmos. Environ., 65, Dai, H., W. Song, X. Gao, L. Chen, and M. Hu (2004) Study on relationship between ambient PM 10, PM 2.5 pollution and daily mortality in a district in Shanghai, J. Hyg. Res., 33(3), Deutsche Bank Market Research (2013) Big Bang Measures to Fight Air Pollution. 1 March fullermoney.com/content/ /deutsche_ ChinaStrategy28Feb2013.pdf Gao, B., H. Guo, X.-M. Wang, X.-Y. Zhao, Z.-H. Ling, Z. Zhang, and T.-Y. Liu (2013) Tracer-based source apportionment of polycyclic aromatic hydrocarbons in PM 2.5 in Guangzhou, southern China, using positive matrix factorization (PMF), Environ. Sci. Pollut. Res., 20(4), Gao, L., R. Zhang, Z. Han, C. Fu, P. Yan, T. Wang, S. Hong, and L. Jiao (2014a) A modeling study of a typical winter PM 2.5 pollution episode in a city in eastern China, Aerosol Air Qual. Res., 14(1), Gao, Y., C. Zhao, X. Liu, M. Zhang, and L.R. Leung (2014b) WRF-Chem simulations of aerosols and anthropogenic aerosol radiative forcing in East Asia, Atmos. Environ., 92, Geng, F., J. Hua, Z. Mu, L. Peng, X. Xu, R. Chen, and H. Kan (2013) Differentiating the associations of black carbon and fine particle with daily mortality in a Chinese city, Environ. Res., 120, He, Z., Y. Kim, K. Ogunjobi, and C. Hong (2003) Characteristics of PM 2.5 species and long-range transport of air masses at Taean background station, South Korea, Atmos. Environ., 37, Huang, K., G. Zhuang, J.S. Fu, Q. Wang, T. Liu, R. Zhang, Y. Jiang, C. Deng, Q. Fu, and N. Hsu (2012a) Typical types and formation mechanisms of haze in an Eastern Asia megacity, Shanghai, Atmos. Chem. Phys., 12, Huang, K., G. Zhuang, Q. Wang, J. Fu, Y. Lin, T. Liu, L. Han, and C. Deng (2014a) Extreme haze pollution in Beijing during January 2013: chemical characteristics, formation mechanism and role of fog processing, Atmos. Chem. Phys. Discuss., 14(6), Huang, R.-J., Y. Zhang, C. Bozzetti, K.-F. Ho, J.-J. Cao, Y. Han, K.R. Daellenbach, J.G. Slowik, S.M. Platt, and F. Canonaco (2014b) High secondary aerosol contribution to particulate pollution during haze events in China, Nat., 514(7521), Huang, W., J. Cao, Y. Tao, L. Dai, S.-E. Lu, B. Hou, Z. Wang, and T. Zhu (2012b) Seasonal variation of chemical species associated with short-term mortality effects of PM 2.5 in Xi an, a central city in China, Am. J. Epidemiol., 175, Huang, W., J. Tan, H. Kan, N. Zhao, W. Song, G. Song, G. Chen, L. Jiang, C. Jiang, and R. Chen (2009) Visibility, air quality and daily mortality in Shanghai, China, Sci. Total Environ., 407(10), Jiang, J., W. Zhou, Z. Cheng, S. Wang, K. He, and J. Hao (2014) Particulate matter distributions in China during a winter period with frequent pollution episodes (January 2013), Aerosol Air Qual. Res., DOI /aaqr Kim, B.M. (2013) Development of a new SMP model satisfying all known physical constraints in environmental application, Chemom. Intell. Lab. Syst., 121, Lang, J., S. Cheng, J. Li, D. Chen, Y. Zhou, X. Wei, L. Han, and H. Wang (2013) A monitoring and modeling study to investigate regional transport and characteristics of PM 2.5 pollution, Aerosol Air Qual. Res., 13(3), Lee, J.H., Y.P. Kim, K.-C. Moon, H.-K. Kim, and C.B. Lee (2001) Fine particle measurements at two back-

18 428 김유미 김진영 이승복 문길주 배귀남 ground sites in Korea between 1996 and 1997, Atmos. Environ., 35, Li, J., W. Yang, Z. Wang, H. Chen, B. Hu, J. Li, Y. Sun, and Y. Huang (2014) A modeling study of source-receptor relationships in atmospheric particulate matter over Northeast Asia, Atmos. Environ., 91, Liu, J., J. Li, Y. Zhang, D. Liu, P. Ding, C. Shen, K. Shen, Q. He, X. Ding, and X. Wang (2014a) Source apportionment using radiocarbon and organic tracers for PM 2.5 carbonaceous aerosols in Guangzhou, South China: Contrasting local- and regional-scale haze events, Environ. Sci. Technol., 48(20), Liu, Z., B. Hu, L. Wang, F. Wu, W. Gao, and Y. Wang (2014b) Seasonal and diurnal variation in particulate matter (PM 10 and PM 2.5 ) at an urban site of Beijing: analyses from a 9-year study, Environ. Sci. Pollut. Res., DOI /s Ma, Y., R. Chen, G. Pan, X. Xu, W. Song, B. Chen, and H. Kan (2011) Fine particulate air pollution and daily mortality in Shenyang, China, Sci. Total Environ., 409(13), Ma, Z., X. Hu, L. Huang, J. Bi, and Y. Liu (2014) Estimating ground-level PM 2.5 in China using satellite remote sensing, Environ. Sci. Technol., 48, Pui, D.Y., S.-C. Chen, and Z. Zuo (2014) PM 2.5 in China: Measurements, sources, visibility and health effects, and mitigation, Partic., 13, Shen, Z., J. Cao, R. Arimoto, Z. Han, R. Zhang, Y. Han, S. Liu, T. Okuda, S. Nakao, and S. Tanaka (2009) Ionic composition of TSP and PM 2.5 during dust storms and air pollution episodes at Xi an, China, Atmos. Environ., 43(18), Sun, Y., G. Zhuang, A. Tang, Y. Wang, and Z. An (2006) Chemical characteristics of PM 2.5 and PM 10 in haze-fog episodes in Beijing, Environ. Sci. Technol., 40(10), Tan, J., S. Guo, Y. Ma, J. Duan, Y. Cheng, K. He, and F. Yang (2011) Characteristics of particulate PAHs during a typical haze episode in Guangzhou, China, Atmos. Res., 102(1), The National Academy of Engineering of Korea (2014) Construction of International Collaboration Network against PM 2.5 Pollution in East Asia, Report No Wang, H., J. An, L. Shen, B. Zhu, C. Pan, Z. Liu, X. Liu, Q. Duan, X. Liu, and Y. Wang (2014a) Mechanism for the formation and microphysical characteristics of submicron aerosol during heavy haze pollution episode in the Yangtze River Delta, China, Sci. Total Environ., 490, Wang, H., J. Xu, M. Zhang, Y. Yang, X. Shen, Y. Wang, D. Chen, and J. Guo (2014b) A study of the meteorological causes of a prolonged and severe haze episode in January 2013 over central-eastern China, Atmos. Environ., 98, Wang, J.-F., M.-G. Hu, C.-D. Xu, G. Christakos, and Y. Zhao (2013a) Estimation of citywide air pollution in Beijing, PLoS ONE, 8(1), e DOI / journal.pone Wang, J., Z. Hu, Y. Chen, Z. Chen, and S. Xu (2013b) Contamination characteristics and possible sources of PM 10 and PM 2.5 in different functional areas of Shanghai, China, Atmos. Environ., 68, Wang, Y., Q. Zhang, J. Jiang, W. Zhou, B. Wang, K. He, F. Duan, Q. Zhang, S. Philip, and Y. Xie (2014c) Enhanced sulfate formation during China s severe winter haze episode in January 2013 missing from current models, J. Geophys. Res.: Atmos., 119(17), Xiao, Z.-M., Y.-F. Zhang, S.-M. Hong, X.-H. Bi, L. Jiao, Y.-C. Feng, and Y.-Q. Wang (2011) Estimation of the main factors influencing haze, based on a long-term monitoring campaign in Hangzhou, China, Aerosol Air Qual. Res., 11(7), Xu, D., M. Dan, Y. Song, Z. Chai, and G. Zhuang (2005) Concentration characteristics of extractable organohalogens in PM 2.5 and PM 10 in Beijing, China, Atmos. Environ., 39(22), Yang, C., X. Peng, W. Huang, R. Chen, Z. Xu, B. Chen, and H. Kan (2012) A time-stratified case-crossover study of fine particulate matter air pollution and mortality in Guangzhou, China, Int. Arch. Occup. Environ. Health, 85(5), Ying, Q., L. Wu, and H. Zhang (2014) Local and inter-regional contributions to PM 2.5 nitrate and sulfate in China, Atmos. Environ., 94, Yu, L., G. Wang, R. Zhang, L. Zhang, Y. Song, B. Wu, X. Li, K. An, and J. Chu (2013) Characterization and source apportionment of PM 2.5 in an urban environment in Beijing, Aerosol Air Qual. Res., 13(2), Zhang, F., H.-R. Cheng, Z.-W. Wang, X.-P. Lv, Z.-M. Zhu, G. Zhang, and X.-M. Wang (2014) Fine particles (PM 2.5 ) at a CAWNET background site in Central China: Chemical compositions, seasonal variations and regional pollution events, Atmos. Environ., 86, 한국대기환경학회지제 31 권제 5 호

19 최근중국의초미세먼지오염연구동향 429 Zhang, F., J. Chen, T. Qiu, L. Yin, X. Chen, and J. Yu (2013a) Pollution characteristics of PM 2.5 during a typical haze episode in Xiamen, China, Atmos. Clim. Sci., 3, Zhang, R., J. Jing, J. Tao, S.-C. Hsu, G. Wang, J. Cao, C. Lee, L. Zhu, Z. Chen, and Y. Zhao (2013b) Chemical characterization and source apportionment of PM 2.5 in Beijing: seasonal perspective, Atmos. Chem. Phys., 13(14), Zhao, P., F. Dong, D. He, X. Zhao, X. Zhang, W. Zhang, Q. Yao, and H. Liu (2013a) Characteristics of concentrations and chemical compositions for PM 2.5 in the region of Beijing, Tianjin, and Hebei, China, Atmos. Chem. Phys., 13(9), Zhao, P., F. Dong, Y. Yang, D. He, X. Zhao, W. Zhang, Q. Yao, and H. Liu (2013b) Characteristics of carbonaceous aerosol in the region of Beijing, Tianjin, and Hebei, China, Atmos. Environ., 71, Zhao, X., X. Zhang, X. Xu, J. Xu, W. Meng, and W. Pu (2009) Seasonal and diurnal variations of ambient PM 2.5 concentration in urban and rural environments in Beijing, Atmos. Environ., 43(18),