Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data

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

doi:10.1016/s1352-2310(02)00279-0

Cited by 158 publications

(89 citation statements)

References 33 publications

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“…PMF has been widely used for receptor modeling, and it is most often applied when source profiles are unknown (such as Hwang and Hopke 2007; Lee and Hopke 2006;Lestari et al 2009;Miller et al 2002;Shrivastava et al 2007;Song et al 2007;Uchimiya et al 2008). Here, PMF was applied as a way to identify and evaluate the origins of the PM 1 .…”

Section: Source Assessmentmentioning

confidence: 99%

Chemical Characteristics of Fine Particles (PM₁) from Xi'an, China

Shen

Cao

Arimoto

et al. 2010

Aerosol Science and Technology

100

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Daily mass concentrations of water-soluble inorganic (WS-i) ions, organic carbon (OC), and elemental carbon (EC) were determined for fine particulate matter (PM 1 , particles < 1.0 µm in diameter) collected at Xi'an, China. The annual mean PM 1 mass concentration was 127.3 ± 62.1 µg m -3 : WS-i ions accounted for ∼38% of the PM 1 mass; carbonaceous aerosol was ∼30%; and an unidentified fraction, probably mostly mineral dust, was ∼32%. WS-i ions and carbonaceous aerosol were the dominant species in winter and autumn, whereas the unidentified fraction had stronger influences in spring and summer. Ion balance calculations indicate that PM 1 was more acidic than PM 2.5 from the same site. PM 1 mass, sulfate and nitrate concentrations followed the order winter > spring > autumn > summer, but OC and EC levels were higher in autumn than spring. Annual mean OC and EC concentrations were 21.0 ± 12.0 µg m −3 and 5.1 ± 2.7 µg m -3 with high OC/EC ratios, presumably reflecting emissions from coal combustion and biomass burning. Secondary organic carbon, estimated from the minimum OC/EC ratios, comprised 28.9% of the OC. Positive matrix factorization (PMF) analysis indicates that secondary aerosol and combustion emissions were the major sources for PM 1 .

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Section: Source Assessmentmentioning

confidence: 99%

Chemical Characteristics of Fine Particles (PM₁) from Xi'an, China

Shen

Cao

Arimoto

et al. 2010

Aerosol Science and Technology

100

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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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“…Thus, in recent years, there has been an increasing interest by researchers in analyzing such complex data using robust mathematics and statistical techniques, such as fuzzy comprehensive evaluation method (FCA), cluster analysis (CA), discriminant analysis (DA), and principal component analysis/factor analysis (PCA/FA), and absolute principal component score-multiple linear regression (APCS-MLR) [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. A literature review of these methods is described below.…”

Section: Introductionmentioning

confidence: 99%

“…However, a receptor-based model, such as APCS-MLR can solve the problem [18]. The method was firstly used for pollution source identification and apportionment in an atmospheric environment [24]. In recent years, there have been many researchers using this method to apportion the pollution sources in water environment research, as it is less dependent on the number of sources or their compositions.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Variations and Source Apportionment of Water Pollution in Danjiangkou Reservoir Basin, Central China

Chen

Lan

Zhang

2015

Water

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Understanding the spatio-temporal variation and the potential source of water pollution could greatly improve our knowledge of human impacts on the environment. In this work, data of 11 water quality indices were collected during 2012-2014 at 10 monitoring sites in the mainstream and major tributaries of the Danjiangkou Reservoir Basin, Central China. The fuzzy comprehensive assessment (FCA), the cluster analysis (CA) and the discriminant analysis (DA) were used to assess the water pollution status and analyze its spatio-temporal variation. Ten sites were classified by the high pollution (HP) region and the low pollution (LP) region, while 12 months were divided into the wet season and the dry season. It was found that the HP region was mainly in the small tributaries with small drainage areas and low average annual discharges, and it was also found that most of these rivers went through urban areas with industrial and domestic sewages input into the water body. Principal component analysis/factor analysis (PCA/FA) was applied to reveal potential pollution sources, whereas absolute principal component score-multiple linear regression (APCS-MLR) was used to identify their contributions to each water quality variable. The study area was found as being generally affected by OPEN ACCESSWater 2015, 7 2592 industrial and domestic sewage. Furthermore, the HP region was polluted by chemical industries, and the LP region was influenced by agricultural and livestock sewage.

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Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data

Cited by 158 publications

References 33 publications

Chemical Characteristics of Fine Particles (PM₁) from Xi'an, China

Chemical Characteristics of Fine Particles (PM₁) from Xi'an, China

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

Spatio-Temporal Variations and Source Apportionment of Water Pollution in Danjiangkou Reservoir Basin, Central China

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Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data

Cited by 158 publications

References 33 publications

Chemical Characteristics of Fine Particles (PM1) from Xi'an, China

Chemical Characteristics of Fine Particles (PM1) from Xi'an, China

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

Spatio-Temporal Variations and Source Apportionment of Water Pollution in Danjiangkou Reservoir Basin, Central China

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Product

Resources

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Chemical Characteristics of Fine Particles (PM₁) from Xi'an, China

Chemical Characteristics of Fine Particles (PM₁) from Xi'an, China