PM source apportionment and health effects:: 1.: Intercomparison of source apportionment results

被引:178
作者
Hopke, PK [1 ]
Ito, K
Mar, T
Christensen, WF
Eatough, DJ
Henry, RC
Kim, E
Laden, F
Lall, R
Larson, TV
Liu, H
Neas, L
Pinto, J
Stölzel, M
Suh, H
Paatero, P
Thurston, GD
机构
[1] Clarkson Univ, Ctr Air Resources Engn & Sci, Potsdam, NY 13699 USA
[2] NYU, Inst Environm Med, Tuxedo Pk, NY 10987 USA
[3] Univ Washington, Dept Environm Hlth, Seattle, WA 98195 USA
[4] Brigham Young Univ, Dept Stat, Provo, UT 84602 USA
[5] Brigham Young Univ, Dept Chem & Biochem, Provo, UT 84602 USA
[6] Univ So Calif, Dept Civil & Environm Engn, Los Angeles, CA USA
[7] Harvard Univ, Sch Publ Hlth, Dept Environm Hlth, Boston, MA 02115 USA
[8] Univ Washington, Dept Civil & Environm Engn, Seattle, WA 98195 USA
[9] Univ Washington, Dept Biostat, Seattle, WA 98195 USA
[10] US EPA, Natl Hlth & Environm Effects Res Lab, Chapel Hill, NC USA
[11] US EPA, Natl Ctr Environm Assessment, Res Triangle Pk, NC 27711 USA
[12] GSF, Natl Res Ctr Environm & Hlth, GSF Focus Network Aerosols & Hlth, Inst Epidemiol, Neuherberg, Germany
[13] Univ Helsinki, Dept Phys Sci, Helsinki, Finland
关键词
receptor modelling; source apportionment; PM2.5; positive matrix factorization; PMF; Unmix;
D O I
10.1038/sj.jea.7500458
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
During the past three decades, receptor models have been used to identify and apportion ambient concentrations to sources. A number of groups are employing these methods to provide input into air quality management planning. A workshop has explored the use of resolved source contributions in health effects models. Multiple groups have analyzed particulate composition data sets from Washington, DC and Phoenix, AZ. Similar source profiles were extracted from these data sets by the investigators using different factor analysis methods. There was good agreement among the major resolved source types. Crustal ( soil), sulfate, oil, and salt were the sources that were most unambiguously identified ( generally highest correlation across the sites). Traffic and vegetative burning showed considerable variability among the results with variability in the ability of the methods to partition the motor vehicle contributions between gasoline and diesel vehicles. However, if the total motor vehicle contributions are estimated, good correspondence was obtained among the results. The source impacts were especially similar across various analyses for the larger mass contributors ( e. g., in Washington, secondary sulfate SE = 7% and 11% for traffic; in Phoenix, secondary sulfate SE 17% and 7% for traffic). Especially important for time-series health effects assessment, the source-specific impacts were found to be highly correlated across analysis methods/researchers for the major components ( e. g., mean analysis to analysis correlation, r>0.9 for traffic and secondary sulfates in Phoenix and for traffic and secondary nitrates in Washington. The sulfate mean r value is >0.75 in Washington.). Overall, although these intercomparisons suggest areas where further research is needed ( e. g., better division of traffic emissions between diesel and gasoline vehicles), they provide support the contention that PM2.5 mass source apportionment results are consistent across users and methods, and that today's source apportionment methods are robust enough for application to PM2.5 health effects assessments.
引用
收藏
页码:275 / 286
页数:12
相关论文
共 84 条
[1]   Source apportionment of exposures to volatile organic compounds: II. Application of receptor models to TEAM study data [J].
Anderson, MJ ;
Daly, EP ;
Miller, SL ;
Milford, JB .
ATMOSPHERIC ENVIRONMENT, 2002, 36 (22) :3643-3658
[2]   Source apportionment of exposure to toxic volatile organic compounds using positive matrix factorization [J].
Anderson, MJ ;
Miller, SL ;
Milford, JB .
JOURNAL OF EXPOSURE ANALYSIS AND ENVIRONMENTAL EPIDEMIOLOGY, 2001, 11 (04) :295-307
[3]   SOURCE IDENTIFICATION OF BULK WET DEPOSITION IN FINLAND BY POSITIVE MATRIX FACTORIZATION [J].
ANTTILA, P ;
PAATERO, P ;
TAPPER, U ;
JARVINEN, O .
ATMOSPHERIC ENVIRONMENT, 1995, 29 (14) :1705-1718
[4]   Investigation of sources of atmospheric aerosol at urban and semi-urban areas in Bangladesh [J].
Begum, BA ;
Kim, E ;
Biswas, SK ;
Hopke, PK .
ATMOSPHERIC ENVIRONMENT, 2004, 38 (19) :3025-3038
[5]   A FACTOR ANALYSIS MODEL OF LARGE SCALE POLLUTION [J].
BLIFFORD, IH ;
MEEKER, GO .
ATMOSPHERIC ENVIRONMENT, 1967, 1 (02) :147-&
[6]   Source identification and apportionment of fine particulate matter in Houston, TX, using positive matrix factorization [J].
Buzcu, B ;
Fraser, MP ;
Kulkarni, P ;
Chellam, S .
ENVIRONMENTAL ENGINEERING SCIENCE, 2003, 20 (06) :533-545
[7]   THE DRI THERMAL OPTICAL REFLECTANCE CARBON ANALYSIS SYSTEM - DESCRIPTION, EVALUATION AND APPLICATIONS IN UNITED-STATES AIR-QUALITY STUDIES [J].
CHOW, JC ;
WATSON, JG ;
PRITCHETT, LC ;
PIERSON, WR ;
FRAZIER, CA ;
PURCELL, RG .
ATMOSPHERIC ENVIRONMENT PART A-GENERAL TOPICS, 1993, 27 (08) :1185-1201
[8]   Review of PM2.5 and PM10 apportionment for fossil fuel combustion and other sources by the chemical mass balance receptor model [J].
Chow, JC ;
Watson, JG .
ENERGY & FUELS, 2002, 16 (02) :222-260
[9]   Accounting for dependence in a flexible multivariate receptor model [J].
Christensen, WF ;
Sain, SR .
TECHNOMETRICS, 2002, 44 (04) :328-337
[10]  
CHRISTENSEN WF, 2004, UNPUB ENVIRONMETRICS