overview of receptor-based source apportionment studies for speciated atmospheric mercury
Clicks: 132
ID: 217534
2015
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
132 views
32 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #511 of 850 articles by views in Journal of agricultural and food chemistry
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 850 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
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.
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.
| Reference Key |
cheng2015atmosphericoverview
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;I. Cheng;X. Xu;L. Zhang |
| Journal | Journal of agricultural and food chemistry |
| Year | 2015 |
| DOI |
10.5194/acp-15-7877-2015
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.