Studies of potential sources that contributed to atmospheric mercury in Toronto, Canada

Cheng, Irene; Lu, Junjie; Song, Xinjie

doi:10.1016/j.atmosenv.2009.09.008

Cited by 26 publications

(32 citation statements)

References 46 publications

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“…There are several statistics that have been determined to assess whether the data set is suitable for PCA, such as the Kaiser-MeyerOlkin measure of sampling adequacy (> 0.6 criterion) and Bartlett's test of sphericity (p < 0.05 criterion). The number of components to retain is determined by other statistics such as Kaiser's criterion (eigenvalues > 1), scree plot, analysis of variance, and/or parallel analysis, as well as by achieving some minimal value of percent variance of the data set explained by all the components (e.g., 70-80 %) and how easily the components can be interpreted (Blanchard et al, 2002;Lynam and Keeler, 2006;Temme et al, 2007;Cheng et al, 2009). The number of components in a suitable solution to Eq.…”

Section: Principal Components Analysis (Pca) Descriptionmentioning

confidence: 99%

“…Back trajectories are often included in source apportionment studies to supplement the multivariate models previously described because the simulated airflows incorporate meteorological data (Hopke and Cohen, 2011). The HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model (Draxler and Rolph, 2014; has often been used in atmospheric mercury source-receptor studies (Han et al, 2004(Han et al, , 2005Lynam and Keeler, 2005;Liu et al, 2007;Rutter et al, 2007;Abbott et al, 2008;Choi et al, 2008;Li et al, 2008;Lyman and Gustin, 2008;Sprovieri and Pirrone, 2008;Cheng et al, 2009;Peterson et al, 2009;Sigler et al, 2009;Kolker et al, 2010). The HYSPLIT model simulates the transport of an air parcel by wind and estimates the position of the parcel using velocity vectors that have been spatially and temporally interpolated onto a grid (Han et al, 2005).…”

Section: Back Trajectory Receptor Modelsmentioning

confidence: 99%

“…The Canadian sites are located in Point Petre and Egbert, Ontario (Blanchard et al, 2002); CAMNet stations (Temme et al, 2007), Toronto (Cheng et al, 2009), northwestern Ontario (Cheng et al, 2012); Kejimkujik National Park (Cheng et al, 2013a); Flin Flon, Manitoba (Eckley et al, 2013); Fort McMurray, Alberta (Parsons et al, 2013); and Windsor, Ontario (Xu et al, 2014). The US sites included south Florida (Graney et al, 2004); Detroit, Michigan (Lynam and Keeler, 2006;Liu et al, 2007); Mount Bachelor, Oregon (Swartzendruber et al, 2006); Athens, Ohio (Gao, 2007); Rochester, New York (Huang et al, 2010); and Grand Bay, Mississippi (Ren et al, 2014).…”

Section: Source Apportionmentmentioning

confidence: 99%

“…PMF is ideal for a data set with a large number of samples (e.g., > 100, Watson et al, 2008). For atmospheric Hg source apportionment, the input variables have included speciated atmospheric Hg (GEM, GOM, PBM), trace gases (CO, NO x , O 3 , SO 2 ), trace metals, PM 2.5 , particle number concentrations, and/or carbon (black carbon, Delta-C) measured at the receptor site (Liu et al, 2003;Cheng et al, 2009;Wang et al, 2013). Delta-C is the difference in black carbon measured at two wavelengths, 370 and 880 nm, which is indicative of wood combustion (Wang et al, 2013).…”

Section: Positive Matrix Factorization (Pmf) Model Descriptionmentioning

confidence: 99%

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Overview of receptor-based source apportionment studies for speciated atmospheric mercury

Cheng

Zhang

2015

Atmos. Chem. Phys.

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Abstract. Receptor-based source apportionment studies of speciated atmospheric mercury are not only concerned with source contributions but also with the influence of transport, transformation, and deposition processes on speciated atmospheric mercury concentrations at receptor locations. Previous studies applied multivariate receptor models including principal components analysis and positive matrix factorization, and back trajectory receptor models including potential source contribution function, gridded frequency distributions, and concentration-back trajectory models. Combustion sources (e.g., coal combustion, biomass burning, and vehicular, industrial and waste incineration emissions), crustal/soil dust, and chemical and physical processes, such as gaseous elemental mercury (GEM) oxidation reactions, boundary layer mixing, and GEM flux from surfaces were inferred from the multivariate studies, which were predominantly conducted at receptor sites in Canada and the US. Back trajectory receptor models revealed potential impacts of large industrial areas such as the Ohio River valley in the US and throughout China, metal smelters, mercury evasion from the ocean and the Great Lakes, and free troposphere transport on receptor measurements. Input data and model parameters specific to atmospheric mercury receptor models are summarized and model strengths and weaknesses are also discussed. Multivariate models are suitable for receptor locations with intensive air monitoring because they require long-term collocated and simultaneous measurements of speciated atmospheric Hg and ancillary pollutants. The multivariate models provide more insight about the types of Hg emission sources and Hg processes that could affect speciated atmospheric Hg at a receptor location, whereas back trajectory receptor models are mainly ideal for identifying potential regional Hg source locations impacting elevated Hg concentrations. Interpretation of the multivariate model output to sources can be subjective and challenging when speciated atmospheric Hg is not correlated with ancillary pollutants and when source emissions profiles and knowledge of Hg chemistry are incomplete. The majority of back trajectory receptor models have not accounted for Hg transformation and deposition processes and could not distinguish between upwind and downwind sources effectively. Ensemble trajectories should be generated to take into account the trajectory uncertainties where possible. One area of improvement that applies to all the receptor models reviewed in this study is the greater focus on evaluating the accuracy of the models at identifying potential speciated atmospheric mercury sources, source locations, and chemical and physical processes in the atmosphere. In addition to receptor model improvements, the data quality of speciated atmospheric Hg plays an equally important part in producing accurate receptor model results.

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Section: Principal Components Analysis (Pca) Descriptionmentioning

confidence: 99%

Section: Back Trajectory Receptor Modelsmentioning

confidence: 99%

Section: Source Apportionmentmentioning

confidence: 99%

Section: Positive Matrix Factorization (Pmf) Model Descriptionmentioning

confidence: 99%

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Overview of receptor-based source apportionment studies for speciated atmospheric mercury

Cheng

Zhang

2015

Atmos. Chem. Phys.

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“…These observations were typically attributed to major sources of Hg surrounding the urban sites, such as coal-fired power plants and industrial facilities. However, not all urban sites were located near combustion and industrial sources; therefore in these cases, the sources were attributed to regional and long-range transport of Hg emissions and local vehicular emissions and surface emissions (Lynam and Keeler, 2006;Cheng et al, 2009;Xu and Akhtar, 2010).…”

Section: Introductionmentioning

confidence: 99%

Concentration-weighted trajectory approach to identifying potential sources of speciated atmospheric mercury at an urban coastal site in Nova Scotia, Canada

Cheng¹,

et al. 2013

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Regional and local sources contributing to gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particle-bound mercury (PBM) at an urban coastal site in Dartmouth, Nova Scotia, Canada were investigated using the Concentration-Weighted Trajectory model (CWT) and Conditional Probability Function. From 2010–2011, GEM, GOM, and PBM concentrations were 1.67 ± 1.01 ng m⁻³, 2.07 ± 3.35 pg m⁻³, and 2.32 ± 3.09 pg m⁻³, respectively. Seasonal variability was observed, with statistically higher GEM and PBM concentrations in winter and spring and higher GOM in spring. In the CWT, concentrations are the weighting factors for the trajectory residence time in modeled grid cells, which results in the identification of source areas based on the CWT values in the grid cells. Potential source areas were identified in regions with known industrial Hg sources particularly in the fall season, but also in regions without these sources (e.g. Atlantic Ocean, northern Ontario and Quebec). CWTs for GOM and PBM that were associated with ≥ 5 kg industrial Hg emissions from 2010–2011 were statistically larger than those with zero Hg emissions, despite a lack of strong correlations. A large proportion of elevated CWTs (85–97%) was in regions with zero industrial Hg sources indicating the potential role of non-point sources, natural emissions, and residential-scale combustion. Analysis of wind data suggests that a commercial harbor and vehicular traffic were potential local sources. Evaluating modeled source areas against Hg emissions inventories was not an ideal method for assessing the CWT model accuracy because of insufficient data on Hg emissions at more precise locations

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Chemometric Analysis for Pollution Source Assessment of Harbour Sediments in Arctic Locations

Pedersen

Lejon

Jensen

et al. 2015

Water Air Soil Pollut

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Pollution levels, pollutant distribution and potential source assessments based on multivariate analysis (chemometrics) were made for harbour sediments from two Arctic locations; Hammerfest in Norway and Sisimiut in Greenland. High levels of heavy metals were detected in addition to organic pollutants. Preliminary assessments based on Principal Component Analysis (PCA) revealed different sources and pollutant distribution in the sediments of the two harbours. Tributyltin (TBT) was, however, found to originate from point source(s) and the highest concentrations of TBT in both harbours were found adjacent to the former shipyards. Polyaromatic hydrocarbons (PAH) ratios and PCA plots revealed that the predominant source in both harbours was pyrogenic related to coal/biomass combustion. Comparison of commercial polychlorinated biphenyls (PCB) mixtures with PCB compositions in the sediments indicated relation primarily to German, Russian and American mixtures in Hammerfest; and American, Russian and Japanese mixtures in Sisimiut. PCA was shown to be an important tool for identifying pollutant sources and differences in pollutant composition in relation to sediment characteristics.

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Studies of potential sources that contributed to atmospheric mercury in Toronto, Canada

Cited by 26 publications

References 46 publications

Overview of receptor-based source apportionment studies for speciated atmospheric mercury

Overview of receptor-based source apportionment studies for speciated atmospheric mercury

Concentration-weighted trajectory approach to identifying potential sources of speciated atmospheric mercury at an urban coastal site in Nova Scotia, Canada

Chemometric Analysis for Pollution Source Assessment of Harbour Sediments in Arctic Locations

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