Source apportionment of exposures to volatile organic compounds: II. Application of receptor models to TEAM study data

Anderson, Melissa J.; Daly, Eileen P.; Miller, Shelly L.; Milford, Jana B.

doi:10.1016/s1352-2310(02)00280-7

Cited by 57 publications

(25 citation statements)

References 16 publications

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“…The sixth factor had high loads on 1,4-dichlorobenzene associated with deodorizers. Similarly, the TEAM studies source apportionment analysis (Anderson et al, 2001(Anderson et al, , 2002) also found that 1,4-dichlorobenzene came up as a separate factor representing mothballs and deodorizers.…”

Section: Factor Analysismentioning

confidence: 84%

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Ambient, indoor and personal exposure relationships of volatile organic compounds in Mexico City Metropolitan Area

Serrano-Trespalacios

Ryan

Spengler

2004

J Expo Sci Environ Epidemiol

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Air pollution standards and control strategies are based on ambient measurements. For many outdoor air pollutants, individuals are closer to their sources (especially traffic) and there are important indoor sources influencing the relationship between ambient and personal exposures. This paper examines the relationship between volatile organic compounds (VOCs) measured at central site monitoring stations and personal exposures in the Mexico City Metropolitan Area. Over a 1-year period, personal exposures to 34 VOCs were measured for 90 volunteers from 30 families living close to one of five central monitoring stations. Simultaneous 24-h indoor, outdoor and central site measurements were also taken. Dual packed thermal desorption tubes and C 18 DNPH-coated cartridges were used for sampling VOCs and these were analyzed by GC/MS and HPLC, respectively. A factor analysis of the personal exposure data aided in grouping compounds by the most likely source type: vehicular (BTEX, styrene and 1,3-butadiene), secondary formed or photochemical (most aldehydes), building materials and consumer products (formaldehyde and benzaldehyde), cleaning solvents (tetrachloroethene and 1,1,1-trichloroethane), volatilization from water (chloroform and trichloroethene) and deodorizers (1,4-dichlorobenzene). Mean ambient, indoor and personal concentrations were 7/7/14 mg/m 3 for benzene, 1/3/3 for 1,3-butadiene, 6/20/20 for formaldehyde and 3/9/50 for 1,4-dichlorobenzene. Geometric mean (GM) ambient concentrations of trichloroethene and carbon tetrachloride were similar to GM personal exposures. While outdoor and indoor home GM concentrations for most vehicular related compounds (benzene, MTBE, xylenes and styrene) were comparable, the GM personal exposures were twice as high. Indoor concentrations of 1,3-butadiene, 1,1,1-trichloroethane, tetrachloroethane, chloroform, formaldehyde, valeraldehyde, propionaldehyde and n-butyraldehyde were comparable to personal exposures. For certain compounds, such as chloroform, aldehydes, toluene, 1,3-butadiene and 1,4-dichlorobenzene, GM personal exposures were more than two times greater than GM ambient measurements.

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Section: Factor Analysismentioning

confidence: 84%

“…Participants with total exposure concentrations above 2000 mg/m 3 were excluded (just one participant). VOCs were measured actively in Tenax-GC (Anderson et al, 2002). c German Environmental Survey carried out in the Western Part of Germany in 1990-1991.…”

Section: Discussionmentioning

confidence: 99%

Ambient, indoor and personal exposure relationships of volatile organic compounds in Mexico City Metropolitan Area

Serrano-Trespalacios

Ryan

Spengler

2004

J Expo Sci Environ Epidemiol

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“…Initially, a program called PMF2 utilizes a unique algorithm (Paatero, 1997) for solving the factor analytic task. PMF2 has been used in a number of recent factor analytic studies (Anttila et al, 1995;Huang et al, 1999;Polissar et al, 1996Polissar et al, , 1998Polissar et al, , 1999Polissar et al, , 2001Prendes et al, 1999;Xie et al, 1999a;Lee et al, 1999Lee et al, , 2002Lee et al, , 2003Paterson et al, 1999;Chueinta et al, 2000;Ramadan et al, 2000;Anderson et al, 2001Anderson et al, , 2002Claiborn et al, 2002;Miller et al, 2002;Qin et al, 2002;Begum et al, 2004;Buzcu et al, 2003;Gao et al, 2003;Kim et al, 2003aKim et al, , b, 2004Larsen and Baker, 2003;Maykut et al, 2003;Qin and Oduyemi, 2003;Hien et al, 2004;Kim and Hopke, 2004a Subsequently, an alternative approach that provides a flexible modeling system, the multilinear engine (ME), has been developed for solving the various PMF factor analysis least-squares problems (Paatero, 1999). ME has already been applied to a number of environmental problems (Xie et al, 1999b;Paatero and Hopke, 2002;Hopke et al, 2003;Kim et al, 2003c;Paatero et al, 2003;Ramadan et al, 2003;Chueinta et al, 2004).…”

Section: Positive Matrix Factorizationmentioning

confidence: 99%

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

Hopke

Ito

Mar

et al. 2005

J Expo Sci Environ Epidemiol

204

129

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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 ¼ 7% and 11% for traffic; in Phoenix, secondary sulfate SE ¼ 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, r40.9 for traffic and secondary sulfates in Phoenix and for traffic and secondary nitrates in Washington. The sulfate mean r value is 40.75 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 PM 2.5 mass source apportionment results are consistent across users and methods, and that today's source apportionment methods are robust enough for application to PM 2.5 health effects assessments.

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“…Therefore PMF, a new receptor model technique limiting all the elements in the source profiles and the source contributions matrix to be positive, is an alternative choice to explore sources of AHs. It has been applied extensively in identifying VOC contributing sources at different locations in the world, such as Los Angeles [40], New Jersey and California [41], Boston [42], Ontario [43], Brisbane [44], Shanghai [45], and Beijing [46,47].…”

Section: Introductionmentioning

confidence: 99%

Source attributions of hazardous aromatic hydrocarbons in urban, suburban and rural areas in the Pearl River Delta (PRD) region

Zhang

Wang

Barletta

et al. 2013

Journal of Hazardous Materials

132

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h i g h l i g h t sWe measured aromatic hydrocarbons at four contrasting sites in the PRD region. Diagnostic ratios were used to imply sources of aromatic hydrocarbons. Sources of aromatic hydrocarbons were apportioned by PMF receptor model. Solvent use, vehicle exhaust and biomass burning contributed over 89% AHs. Biomass burning contributed to AHs, particularly for Benzene in the rural. t r a c tAromatic hydrocarbons (AHs) are both hazardous air pollutants and important precursors to ozone and secondary organic aerosols. Here we investigated 14 C 6 -C 9 AHs at one urban, one suburban and two rural sites in the Pearl River Delta region during November-December 2009. The ratios of individual aromatics to acetylene were compared among these contrasting sites to indicate their difference in source contributions from solvent use and vehicle emissions. Ratios of toluene to benzene (T/B) in urban (1.8) and suburban (1.6) were near that of vehicle emissions. Higher T/B of 2.5 at the rural site downwind the industry zones reflected substantial contribution of solvent use while T/B of 0.8 at the upwind rural site reflected the impact of biomass burning. Source apportionment by positive matrix factorization (PMF) revealed that solvent use, vehicle exhaust and biomass burning altogether accounted for 89-94% of observed AHs. Vehicle exhaust was the major source for benzene with a share of 43-70% and biomass burning in particular contributed 30% to benzene in the upwind rural site; toluene, C 8 -aromatics and C 9 -aromatics, however, were mainly from solvent use, with contribution percentages of 47-59%, 52-59% and 41-64%, respectively.

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Source apportionment of exposures to volatile organic compounds: II. Application of receptor models to TEAM study data

Cited by 57 publications

References 16 publications

Ambient, indoor and personal exposure relationships of volatile organic compounds in Mexico City Metropolitan Area

Ambient, indoor and personal exposure relationships of volatile organic compounds in Mexico City Metropolitan Area

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

Source attributions of hazardous aromatic hydrocarbons in urban, suburban and rural areas in the Pearl River Delta (PRD) region

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