Using multiple type composition data and wind data in PMF analysis to apportion and locate sources of air pollutants

Chan, Y.C.; Hawas, Olga; Hawker, Darryl William; Vowles, P.D.; Cohen, David D.; Stelcer, Eduard; Simpson, R.W.; Golding, Gary; Christensen, Erik R.

doi:10.1016/j.atmosenv.2010.09.060

Cited by 89 publications

(33 citation statements)

References 16 publications

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“…The hourly contribution matrix G (i.e., g ip in Eq. (1)) was generated by using average contributions (Table S1 in the Supporting Information) that were available in the literature (Zheng et al, 2002;Lee et al, 2003;Kim et al, 2005;Marmur et al, 2005;Alastuey et al, 2006;Hopke et al, 2006;Kulkarni et al, 2007;Song et al, 2008;Yuan et al, 2009;Chan et al, 2011;Guo et al, 2011). Next, hourly fluctuations were added by assuming random variability and a lognormal distribution (Lingwall and Christensen, 2007) for each source, except for vehicle exhaust.…”

Section: Data Simulationmentioning

confidence: 99%

Evaluation of a Modified Receptor Model for Solving Multiple Time Resolution Equations: A Simulation Study

Liao

Kuo

Hopke

et al. 2013

Aerosol Air Qual. Res.

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This study was conducted to evaluate the performance of an improved source apportionment model that is suitable for incorporating data with multiple time resolutions. This evaluation was achieved by using synthetic data sets that simulated environmental concentrations of volatile organic compounds (VOCs) and fine particulate matter (PM 2.5 ) from the five following sources: petroleum refinery, vehicle exhaust, industrial coating, coal combustion, and natural gas. Hourly VOCs and speciated PM 2.5 data were simulated for a one-week period. The PM 2.5 data were further averaged every twelve hours to generate data sets with mixed temporal resolutions. The Multilinear Engine program was applied to resolve the source profiles and contributions. A series of sensitivity analyses was conducted to examine how uncertainties in the profile variation, measurement error, and source collinearity affected the model performance. The resolved factor profiles closely matched the input profiles, and the measurement error had a larger impact on the modeling results than the profile variation. In the most comprehensive data set that contained all three types of uncertainty, the R 2 values between the input and retrieved source contributions were between 0.87 and 0.95. The estimated percentage contributions were also comparable with the input ones, demonstrating the applicability and validity of this improved model.

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Section: Data Simulationmentioning

confidence: 99%

Evaluation of a Modified Receptor Model for Solving Multiple Time Resolution Equations: A Simulation Study

Liao

Kuo

Hopke

et al. 2013

Aerosol Air Qual. Res.

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“…PMF analysis yielded five factors for PM 2.5 and six factors for PM 10 and the relative contributions of measured parameters for each factor are depicted in Figs samples whose concentrations were below detection limits. Two data points from Pb and one from Mn were replaced with the species median and assigned error of 4 times of measurement uncertainty (Chan et al, 2011) because they were unrealistically high compared to other values (beyond 5 × standard deviation from average concentration). Number of factors from 3 to 7 was examined on the basis of linearity of fit as indicated by the residual distributions as well as interpretability of the resulting profiles.…”

Section: Source Identification Using Pmf Analysismentioning

confidence: 99%

Atmospheric Mineral Dust and Trace Metals over Urban Environment in Western India during Winter

Sudheer

Rengarajan

2012

Aerosol Air Qual. Res.

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Trace metal concentrations in PM 2.5 and PM 10 are studied from Ahmedabad, an urban location in western India. During winter, concentrations of Zn, Cd and Pb are in the range of 16.5-290, 0.1-5.4, 28-1023 ng/m 3 in PM 2.5 and 38-459, 0.21-8.4, 48-1223 ng/m 3 in PM 10 , respectively. Enrichment Factor (EF) analysis with respect to Al showed significant enrichment of elements like Cd, Pb, Zn and Cu in fine mode particles, suggesting their strong anthropogenic contribution. About 43% of PM 10 mass is constituted by mineral dust, and the dominant fraction (~88%) of the same exists in coarse fraction with a characteristic Fe/Al ratio of 0.53. The Ca/Al ratio is ~1.1 in PM 10 and ~0.8 in PM 2.5 indicating that aerosol over this region is rich in Ca minerals compared to the average upper continental crust. Positive Matrix Factorization (PMF) analysis using trace metal and major components reveals five sources for PM 2.5 and six for PM 10 . The PMF results suggest anthropogenic sources contribute ~80% and 40-50% of the PM 2.5 and PM 10 mass, respectively. Incineration/industrial emission, biomass burning, vehicular emissions and re-suspended/long range transported dust are the other prominent sources identified in this work. These source contributions exhibit large temporal variations during winter, as the sampling location is influenced by air masses from different source regions.

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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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Using multiple type composition data and wind data in PMF analysis to apportion and locate sources of air pollutants

Cited by 89 publications

References 16 publications

Evaluation of a Modified Receptor Model for Solving Multiple Time Resolution Equations: A Simulation Study

Evaluation of a Modified Receptor Model for Solving Multiple Time Resolution Equations: A Simulation Study

Atmospheric Mineral Dust and Trace Metals over Urban Environment in Western India during Winter

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

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