高级搜索

PM2.5化学组成观测设计对PMF源解析结果影响综述

,  ,  , 

downloadPDF

上一篇

下一篇

缑亚峰, 余欢, 王成, 谢鸣捷. PM2.5化学组成观测设计对PMF源解析结果影响综述[J]. 环境化学, 2020, (7): 1744-1753. doi: 10.7524/j.issn.0254-6108.2020020301
引用本文: 缑亚峰, 余欢, 王成, 谢鸣捷. PM2.5化学组成观测设计对PMF源解析结果影响综述[J]. 环境化学, 2020, (7): 1744-1753. doi: 10.7524/j.issn.0254-6108.2020020301
shu
GOU Yafeng, YU Huan, WANG Cheng, XIE Mingjie. Review: Influence of PM2.5 composition measurement design on source apportionment using positive matrix factorization (PMF)[J]. Environmental Chemistry, 2020, (7): 1744-1753. doi: 10.7524/j.issn.0254-6108.2020020301
Citation: GOU Yafeng, YU Huan, WANG Cheng, XIE Mingjie. Review: Influence of PM2.5 composition measurement design on source apportionment using positive matrix factorization (PMF)[J]. Environmental Chemistry, 2020, (7): 1744-1753. doi: 10.7524/j.issn.0254-6108.2020020301
shu

PM2.5化学组成观测设计对PMF源解析结果影响综述

  • 1. 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制高技术研究重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京, 210044;

  • 2. 中国地质大学环境学院大气系, 武汉, 430074

    • 通讯作者: 王成, E-mail: wangcheng118@163.com ;  谢鸣捷, E-mail: mingjie.xie@nuist.edu.cn
  • 基金项目:

    国家自然科学基金青年项目（41701551）资助.

Review: Influence of PM2.5 composition measurement design on source apportionment using positive matrix factorization (PMF)

  • 1. School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing, 210044, China;

  • 2. Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China

    • Corresponding authors: WANG Cheng, wangcheng118@163.com ;  XIE Mingjie, mingjie.xie@nuist.edu.cn
  • Fund Project: Supported by the National Natural Science Foundation of China (41701551).

  • 摘要: 近年来我国城市地区灰霾污染频发，严重影响生态环境以及人体健康.了解PM2.5的化学组成、来源、大气传输过程和环境效应对灰霾污染有效控制对策的制定有重要意义，已成为国际大气环境领域的研究热点.本文通过总结国内外正定矩阵因子分析模型（positive matrix factorization，PMF）在PM2.5源解析方面的研究，阐释了PM2.5化学组成空间差异、待测化学组分选择、有机示踪物气固相分配、观测结果时间分辨率对PMF源解析结果的影响.评述结果表明，同一城市或地区基于不同采样点样品数据的源解析结果存在较大差异；对同组PM2.5样品，解析出的排放源类型和待观测化学组分的选择密切相关；因有机示踪物气固相分配作用的影响，低分子量有机物的源解析结果往往存在较大偏差；高时间分辨率观测可更好地反映不同示踪物间浓度的时间变化差异，有利于排放源的准确识别.
    Abstract: Chinese cities suffered from frequent haze pollution in recent years, and this brought a series of negative impacts on air quality and public health. In this case, studying the composition and sources of PM2.5 and its atmospheric processes and environmental effects are of great importance for drawing up effective regulatory strategies to reduce haze pollution, and become hotspots of the research on atmospheric environment. In this work, a number of studies on PM2.5 source apportionment using positive matrix factorization (PMF) were summarized, so as to elucidate the influence of spatial variability of PM2.5 composition, target components selection for analysis, gas/particle partitioning of organic tracers, and time resolution of speciation data on PMF results. The review showed that, even at the same city/area, PM2.5 source information retrieved from the compositional data at different sampling sites had substantial variance. For the same batch of aerosol samples, the output source types from PMF modeling were closely associated with the species selected for characterization. The source attribution of low molecular organic components was subject to large uncertainty due to the influences from gas/particle partitioning. High time-resolution measurements were more capable in capturing the difference in concentration time series between source tracers, improving the accuracy in source identification.
  • 加载中
  • [1] ZHOU W, WANG Q Q, ZHAO X J, et al. Characterization and source apportionment of organic aerosol at 260 m on a meteorological tower in Beijing, China[J]. Atmospheric Chemistry and Physics, 2018, 18:3951-3968.
    [2] 谷阳阳, 苏贵金, 柴涛, 等. 北京地区PM2.5浓度影响因素及估算模型[J]. 环境化学, 2018, 37(3):397-409.

    GU Y Y, SU G J, CHAI T, et al. Influencing factors and prediction model of PM2.5 concentration in Beijing area[J]. Environmental Chemistry, 2018, 37(3):397-409(in Chinese).

    [3] 刘兴瑞, 马嫣, 崔芬萍, 等. 南京北郊一次重污染事件期间PM2.5理化特性及其对大气消光的影响[J]. 环境化学, 2016, 35(6):1164-1171.

    LIU X R, MA Y, CUI F P, et al. Physicochemical characteristics of PM2.5 and impacts on light extinction during the heavy pollution period at North Suburban Nanjing[J]. Environmental Chemistry, 2016, 35(6):1164-1171(in Chinese).

    [4] 李倩, 吴琳, 张进生, 等. 廊坊市夏季大气气溶胶消光特性及其来源[J]. 中国环境科学, 2019, 39(6):2249-2257.

    LI Q, WU L, ZHANG J S, et al. Extinction characteristics of aerosol and the contribution of pollution source to light extinction in Langfang[J]. China Environmental Science, 2019, 39(6):2249-2257(in Chinese).

    [5] SCHLAG P, KIENDLER-SCHARR A, BLOM M J, et al. Aerosol source apportionment from 1-year measurements at the CESAR tower in Cabauw, the Netherlands[J]. Atmospheric Chemistry and Physics, 2016, 16:8831-8847.
    [6] MAYNARD D, COULL B A, GRYPARIS A, et al. Mortality risk associated with short-term exposure to traffic particles and sulfates[J]. Environmental Health Perspectives, 2007, 115:751-755.
    [7] OSTRO B, FENG W Y, BROADWIN R, et al. The effects of components of fine particulate air pollution on mortality in California:Results from CALFINE[J]. Environmental Health Perspectives, 2007, 115:13-19.
    [8] CAO J, XU H, XU Q, et al. Fine particulate matter constituents and cardiopulmonary mortality in a heavily polluted Chinese City[J]. Environmental Health Perspectives, 2012, 120:373-378.
    [9] XIAO H W, XIAO H Y, LUO L, et al. Atmospheric aerosol compositions over the South China Sea:Temporal variability and source apportionment[J]. Atmospheric Chemistry and Physics, 2017, 17:3199-3214.
    [10] 张延君, 郑玫, 蔡靖, 等. PM2.5源解析方法的比较与评述[J]. 科学通报, 2015, 60(2):109-121.

    ZHANG Y J, ZHENG M, CAI J, et al. Comparison and overview of PM2.5 source apportionment methods[J]. Chinese Science Bulletin, 2015, 60(2):109-121(in Chinese).

    [11] WATSON J G, ROBINSON N F, CHOW J C, et al. The USEPA/DRI chemical mass balance receptor model, CMB 7.0[J]. Environmental Software, 1990, 5(1):38-49.
    [12] JAECKELS J M, BAE M S, SCHAUER J J. Positive matrix factorization (PMF) analysis of molecular marker measurements to quantify the sources of organic aerosols[J]. Environmental Science and Technology, 2007, 41:5763-5769.
    [13] SHRIVASTAVA M K, SUBRAMANIAN R, ROGGE W F, et al. Sources of organic aerosol:Positive matrix factorization of molecular marker data and comparison of results from different source apportionment models[J]. Atmospheric Environment, 2007, 41:9353-9369.
    [14] PAATERO P, TAPPER U. Positive matrix factorization:A non-negative factor model with optimal utilization of error estimates of data values[J]. Environmetrics, 1994, 5:111-126.
    [15] ZHANG Y X, SHEESLEY R J, BAE M S, et al. Sensitivity of a molecular marker based positive matrix factorization model to the number of receptor observations[J]. Atmospheric Environment, 2009, 43:4951-4958.
    [16] KIM E, HOPKE P K, PINTO J P, et al. Spatial variability of fine particle mass, components, and source contributions during the regional air pollution study in St.Louis[J]. Environmental Science and Technology, 2005, 39:4172-4179.
    [17] WANG P, CAO J J, SHEN Z X, HAN Y M, et al. Spatial and seasonal variations of PM2.5 mass and species during 2010 in Xi'an, China[J]. Science of the Total Environment, 2015, 508:477-487.
    [18] XU H, XIAO Z, CHEN K, et al. Spatial and temporal distribution, chemical characteristics, and sources of ambient particulate matter in the Beijing-Tianjin-Hebei region[J]. Science of the Total Environment, 2019, 658:280-293.
    [19] LI M, HU M, DU B, et al. Temporal and spatial distribution of PM2.5 chemical composition in a coastal city of Southeast China[J]. Science of the Total Environment, 2017, 605/606:337-346.
    [20] MOOIBROEK D, SCHAAP M, WEIJERS E P, et al. Source apportionment and spatial variability of PM2.5 using measurements at five sites in the Netherlands[J]. Atmospheric Environment, 2011, 45:4180-4191.
    [21] HUEGLIN C, GEHRIG R, BALTENSPERGER U, et al. Chemical characterization of PM2.5, PM10 and coarse particles at urban, near-city and rural sites in Switzerland[J]. Atmospheric Environment, 2005, 39:637-651.
    [22] KIM E, HOPKE P K. Source characterization of ambient fine particles at multiple sites in the Seattle area[J]. Atmospheric Environment, 2008, 42:6047-6056.
    [23] XIE M J, COONS T L, DUTTON S J, et al. Intra-urban spatial variability of PM2.5-bound carbonaceous components[J]. Atmospheric Environment, 2012, 60:486-494.
    [24] XIE M J, COONS T L, HEMANN J G, et al. Intra-urban spatial variability and uncertainty assessment of PM2.5 sources based on carbonaceous species[J]. Atmospheric Environment, 2012, 60:305-315.
    [25] 李杏茹, 白羽, 陈曦, 等. 北京冬季重污染过程大气细颗粒物化学组成特征及来源分析[J]. 环境化学, 2018, 37(11):2397-2409.

    LI X R, BAI Y, CHEN X, et al. Chemical composition and source apportionment of PM2.5 during winter in Beijing[J]. Environmental Chemistry, 2018, 37(11):2397-2409(in Chinese).

    [26] TIAN H, WANG Y, XUE Z, et al. Atmospheric emissions estimation of Hg, As, and Se from coal-fired power plants in China, 2007[J]. Science of the Total Environment, 2011, 409:3078-3081.
    [27] CAO S, DUAN X, ZHAO X, et al. Health risks from the exposure of children to As, Se, Pb and other heavy metals near the largest coking plant in China[J]. Science of The Total Environment, 2014, 472:1001-1009.
    [28] JANG H N, SEO Y C, LEE J H, et al. Formation of fine particles enriched by V and Ni from heavy oil combustion:Anthropogenic sources and drop-tube furnace experiments[J]. Atmospheric Environment, 2007, 41:1053-1063.
    [29] BECAGLI S, SFERLAZZO D M, PACE G, et al. Evidence for heavy fuel oil combustion aerosols from chemical analyses at the island of Lampedusa:A possible large role of ships emissions in the Mediterranean[J]. Atmospheric Chemistry and Physics, 2012, 12:3479-3492.
    [30] MCKENZIE E R, MONEY J E, GREEN P G, et al. Metals associated with stormwater-relevant brake and tire samples[J]. Science of the Total Environment, 2009, 407:5855-5860.
    [31] SIMONEIT B R T, SCHAUER J J, NOLTE C G, et al. Levoglucosan, a tracer for cellulose in biomass burning and atmospheric particles[J]. Atmospheric Environment, 1999, 33:173-182.
    [32] ROGGE W F, HILDEMANN L M, MAZUREK M A, et al. Sources of fine organic aerosol. 1. Charbroilers and meat cooking operations[J]. Environmental Science & Technology, 1991, 25:1112-1125.
    [33] ROBINSON A L, SUBRAMANIAN R, DONAHUE N M, et al. Source apportionment of molecular markers and organic aerosol. 3. food cooking emissions[J]. Environmental Science & Technology, 2006, 40:7820-7827.
    [34] SIMONEIT B R T, MAZUREK M A. Organic matter of the troposphere-II. Natural background of biogenic lipid matter in aerosols over the rural western united states[J]. Atmospheric Environment, 1982, 16:2139-2159.
    [35] OROS D R, SIMONEIT B R T. Identification of molecular tracers in organic aerosols from temperate climate vegetation subjected to biomass burning[J]. Aerosol Science and Technology, 1999, 31:433-445.
    [36] SURRATT J D, MURPHY S M, KROLL J H, et al. Chemical composition of secondary organic aerosol formed from the photooxidation of isoprene[J]. The Journal of Physical Chemistry A, 2006, 110:9665-9690.
    [37] OROS D R, SIMONEIT B R T. Identification and emission rates of molecular tracers in coal smoke particulate matter[J]. Fuel, 2000, 79:515-536.
    [38] SIMONEIT B R T, BI X, OROS D R, et al. Phenols and Hydroxy-PAHs (Arylphenols) as tracers for coal smoke particulate matter:Source tests and ambient aerosol assessments[J]. Environmental Science & Technology, 2007, 41:7294-7302.
    [39] ROGGE W F, HILDEMANN L M, MAZUREK M A, et al. Sources of fine organic aerosol. 9. pine, oak, and synthetic log combustion in residential fireplaces[J]. Environmental Science & Technology, 1998, 32:13-22.
    [40] LI J, PÓSFAI M, HOBBS P V, et al. Individual aerosol particles from biomass burning in southern Africa:2, Compositions and aging of inorganic particles[J]. Journal of Geophysical Research, 2003, 108(D13):8484.
    [41] ROGGE W F, HILDEMANN L M, MAZUREK M A, et al. Sources of fine organic aerosol:2. Noncatalyst and catalyst-equipped automobiles and heavy-duty diesel trucks[J]. Environmental Science & Technology, 1993, 27:636-651.
    [42] SCHAUER J J, KLEEMAN M J, CASS G R, et al. Measurement of emissions from air pollution sources. 5. C-1-C-32 organic compounds from gasoline-powered motor vehicles[J]. Environmental Science & Technology, 2002, 36:1169-1180.
    [43] RIDDLE S G, JAKOBER C A, ROBERT M A, et al. Large PAHs detected in fine particulate matter emitted from light-duty gasoline vehicles[J]. Atmospheric Environment, 2007, 41:8658-8668.
    [44] SCHAUER J J, KLEEMAN M J, CASS G R, et al. Measurement of emissions from air pollution sources. 4. C-1-C-27 organic compounds from cooking with seed oils[J]. Environmental Science & Technology, 2002, 36:567-575.
    [45] KIM Y P, SEINFELD J H. Atmospheric gas-aerosol equilibrium:III. Thermodynamics of crustal elements Ca2+, K+, and Mg2+[J]. Aerosol Science and Technology, 1995, 22:93-110.
    [46] TAYLOR S R, MCLENNAN S M. The geochemical evolution of the continental crust[J]. Reviews of Geophysics, 1995, 33:241-265.
    [47] SIMONEIT B R T, ELIAS V O, KOBAYASHI M, et al. Sugars-dominant water-soluble organic compounds in soils and characterization as tracers in atmospheric particulate matter[J]. Environmental Science & Technology, 2004, 38:5939-5949.
    [48] GABRIEL R, VON GLASOW R, SANDER R, et al. Bromide content of sea-salt aerosol particles collected over the Indian Ocean during INDOEX 1999[J]. Journal of Geophysical Research, 2002, 107(D19):8032.
    [49] KEENE W C, PSZENNY A A P, GALLOWAY J N, et al. Sea-salt corrections and interpretation of constituent ratios in marine precipitation[J]. Journal of Geophysical Research, 1986, 91:6647-6658.
    [50] PYE H O T, LIAO H, WU S, et al. Effect of changes in climate and emissions on future sulfate-nitrate-ammonium aerosol levels in the United States[J]. Journal of Geophysical Research:Atmospheres, 2009, 114(D1):1-18.
    [51] WANG G, ZHANG R, GOMEZ M E, et al. Persistent sulfate formation from London Fog to Chinese haze[J]. Proceedings of the National Academy of Sciences, 2016, 113:13630.
    [52] KLEINDIENST T E, EDNEY E O, LEWANDOWSKI M, et al. Secondary organic carbon and aerosol yields from the irradiations of isoprene and α-pinene in the presence of NOx and SO2[J]. Environmental Science and Technology, 2006, 40(12):3807-3812.
    [53] XIE M J, HANNIGAN M P, DUTTON S J, et al. Positive matrix factorization of PM2.5:Comparison and implications of using different speciation data sets[J]. Environmental Science and Technology, 2012, 46:11962-11970.
    [54] WANG Y G, HOPKE P K, XIA X Y, et al. Source apportionment of airborne particulate matter using inorganic and organic species as tracers[J]. Atmospheric Environment, 2012, 55:525-532.
    [55] WANG Q Q, HUANG X H H, TAM F C V, et al. Source apportionment of fine particulate matter in Macao, China with and without organic tracers:A comparative study using positive matrix factorization[J]. Atmospheric Environment, 2019, 198:183-193.
    [56] WANG Q, HE X, HUANG X H H, GRIFFITH S M, et al. Impact of secondary organic aerosol tracers on tracer-based source apportionment of organic carbon and PM2.5:a case study in the Pearl River Delta, China[J]. ACS Earth and Space Chemistry, 2017, 1:562-571.
    [57] PANKOW J F. An absorption model of gas/particle partitioning of organic compounds in the atmosphere[J]. Atmospheric Environment, 1994, 28(2):185-188.
    [58] PANKOW J F. An absorption model of gas/particle partitioning involved in the formation of secondary organic aerosol[J]. Atmospheric Environment, 1994, 28(2):189-193.
    [59] XIE M J, HANNIGAN M P, BARSANTI K C. Gas/particle partitioning of n-alkanes, PAHs and oxygenated PAHs in urban Denver[J]. Atmospheric Environment, 2014, 95:355-362.
    [60] XIE M, BARSANTI K C, HANNIGAN M P, et al. Positive matrix factorization of PM2.5-eliminating the effects of gas/particle partitioning of semivolatile organic compounds[J]. Atmospheric Chemistry and Physics, 2013, 13:7381-7393.
    [61] XIE M J, PIEDRAHITA R, DUTTON S J, et al. Positive matrix factorization of a 32-month series of daily PM2.5 speciation data with incorporation of temperature stratification[J]. Atmospheric Environment, 2013, 65:11-20.
    [62] XIE M J, HANNIGAN M P, BARSANTI K C. Impact of gas/particle partitioning of semivolatile organic compounds on source apportionment with positive matrix factorization[J]. Environmental Science and Technology, 2014, 48:9053-9060.
    [63] XIE M J, HANNIGAN M P, BARSANTI K C. Gas/particle partitioning of 2-methyltetrols and levoglucosan at an urban site in Denver[J]. Environmental Science and Technology, 2014, 48:2835-2842.
    [64] GAO B, WANG X M, ZHAO X Y, et al. Source apportionment of atmospheric PAHs and their toxicity using PMF:Impact of gas/particle partitioning[J]. Atmospheric Environment, 2015, 103:114-120.
    [65] WANG Q Q, FENG Y M, HUANG X H H, et al. Nonpolar organic compounds as PM2.5 source tracers:Investigation of their sources and degradation in the Pearl River Delta, China[J]. Journal of Geophysical Research:Atmospheres, 2016, 121(19):11862-11879.
    [66] DALL'OSTO M, QUEROL X, AMATO F, et al. Hourly elemental concentrations in PM2.5 aerosols sampled simultaneously at urban background and road site during SAPUSS-diurnal variations and PMF receptor modeling[J]. Atmospheric Chemistry and Physics, 2013, 13:4375-4392.
    [67] GAO J, PENG X, CHEN G, et al. Insights into the chemical characterization and sources of PM2.5 in Beijing at a 1-h time resolution[J]. Science of the Total Environment, 2016, 542:162-171.
    [68] PENG X, SHI G L, GAO J, et al. Characteristics and sensitivity analysis of multiple-time -resolved source patterns of PM2.5 with real time data using Multilinear Engine 2[J]. Atmospheric Environment, 2016, 139:113-121.
    [69] WANG Q Q, QIAO L P, ZHOU M, et al. Source apportionment of PM2.5 using hourly measurements of elemental tracers and major constituents in an urban environment:Investigation of time-resolution influence[J]. Journal of Geophysical Research:Atmospheres, 2018, 123:5284-5300.
    [70] LUCARELLI F, BARRERA V, BECAGLI S, et al. Combined use of daily and hourly data sets for the source apportionment of particulate matter near a waste incinerator plant[J]. Environmental Pollution, 2019, 247:802-811.
    [71] YU Y Y, HE S Y, WU X L, et al.PM2.5 elements at an urban site in Yangtze River Delta,China:High time-resolved measurement and the application in source apportionment[J]. Environmental Pollution, 2019, 253:1089-1099.
    [72] WANG Q Q, QIAO L P, ZHOU M, et al. Source apportionment of PM2.5 using hourly measurements of elemental tracers and major constituents in an urban environment:Investigation of time-resolution influence[J]. Journal of Geophysical Research:Atmospheres, 2018, 123:5284-5300.
    [73] XIE M J, MLADENOV N, WILLIAMS M W, et al. Water soluble organic aerosols in the Colorado Rocky Mountains, USA:Composition, sources and optical properties[J]. Scientific Reports, 2016, 6(1):39339.
  • 加载中
WeChat 点击查看大图
计量
  • 文章访问数:  3742
  • HTML全文浏览数:  3742
  • PDF下载数:  195
  • 施引文献:  0
出版历程
  • 收稿日期:  2020-02-03
缑亚峰, 余欢, 王成, 谢鸣捷. PM2.5化学组成观测设计对PMF源解析结果影响综述[J]. 环境化学, 2020, (7): 1744-1753. doi: 10.7524/j.issn.0254-6108.2020020301
引用本文: 缑亚峰, 余欢, 王成, 谢鸣捷. PM2.5化学组成观测设计对PMF源解析结果影响综述[J]. 环境化学, 2020, (7): 1744-1753. doi: 10.7524/j.issn.0254-6108.2020020301
shu
GOU Yafeng, YU Huan, WANG Cheng, XIE Mingjie. Review: Influence of PM2.5 composition measurement design on source apportionment using positive matrix factorization (PMF)[J]. Environmental Chemistry, 2020, (7): 1744-1753. doi: 10.7524/j.issn.0254-6108.2020020301
Citation: GOU Yafeng, YU Huan, WANG Cheng, XIE Mingjie. Review: Influence of PM2.5 composition measurement design on source apportionment using positive matrix factorization (PMF)[J]. Environmental Chemistry, 2020, (7): 1744-1753. doi: 10.7524/j.issn.0254-6108.2020020301
shu

PM2.5化学组成观测设计对PMF源解析结果影响综述

    通讯作者: 王成, E-mail: wangcheng118@163.com ;  谢鸣捷, E-mail: mingjie.xie@nuist.edu.cn
  • 1. 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制高技术研究重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京, 210044;
  • 2. 中国地质大学环境学院大气系, 武汉, 430074
  • 收稿日期:  2020-02-03
基金项目:

国家自然科学基金青年项目（41701551）资助.

摘要: 近年来我国城市地区灰霾污染频发，严重影响生态环境以及人体健康.了解PM2.5的化学组成、来源、大气传输过程和环境效应对灰霾污染有效控制对策的制定有重要意义，已成为国际大气环境领域的研究热点.本文通过总结国内外正定矩阵因子分析模型（positive matrix factorization，PMF）在PM2.5源解析方面的研究，阐释了PM2.5化学组成空间差异、待测化学组分选择、有机示踪物气固相分配、观测结果时间分辨率对PMF源解析结果的影响.评述结果表明，同一城市或地区基于不同采样点样品数据的源解析结果存在较大差异；对同组PM2.5样品，解析出的排放源类型和待观测化学组分的选择密切相关；因有机示踪物气固相分配作用的影响，低分子量有机物的源解析结果往往存在较大偏差；高时间分辨率观测可更好地反映不同示踪物间浓度的时间变化差异，有利于排放源的准确识别.

English Abstract

    全文HTML

参考文献 (73)

返回顶部

目录

/

返回文章
返回

Copyright © 2019 中国科学院生态环境研究中心