Overview of receptor-based source apportionment studies for speciated atmospheric mercury
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 ap...
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doaj-40937f5e82974c82b8d9c29dec281f882020-11-25T00:50:53ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242015-07-0115147877789510.5194/acp-15-7877-2015Overview of receptor-based source apportionment studies for speciated atmospheric mercuryI. Cheng0X. Xu1L. Zhang2Air Quality Research Division, Science and Technology Branch, Environment Canada, 4905 Dufferin Street, Toronto, Ontario, M3H 5T4, CanadaDepartment of Civil and Environmental Engineering, University of Windsor, 401 Sunset Avenue, Windsor, Ontario, N9B 3P4, CanadaAir Quality Research Division, Science and Technology Branch, Environment Canada, 4905 Dufferin Street, Toronto, Ontario, M3H 5T4, CanadaReceptor-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. <br><br> 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.http://www.atmos-chem-phys.net/15/7877/2015/acp-15-7877-2015.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
I. Cheng X. Xu L. Zhang |
spellingShingle |
I. Cheng X. Xu L. Zhang Overview of receptor-based source apportionment studies for speciated atmospheric mercury Atmospheric Chemistry and Physics |
author_facet |
I. Cheng X. Xu L. Zhang |
author_sort |
I. Cheng |
title |
Overview of receptor-based source apportionment studies for speciated atmospheric mercury |
title_short |
Overview of receptor-based source apportionment studies for speciated atmospheric mercury |
title_full |
Overview of receptor-based source apportionment studies for speciated atmospheric mercury |
title_fullStr |
Overview of receptor-based source apportionment studies for speciated atmospheric mercury |
title_full_unstemmed |
Overview of receptor-based source apportionment studies for speciated atmospheric mercury |
title_sort |
overview of receptor-based source apportionment studies for speciated atmospheric mercury |
publisher |
Copernicus Publications |
series |
Atmospheric Chemistry and Physics |
issn |
1680-7316 1680-7324 |
publishDate |
2015-07-01 |
description |
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.
<br><br>
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. |
url |
http://www.atmos-chem-phys.net/15/7877/2015/acp-15-7877-2015.pdf |
work_keys_str_mv |
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