Quantitative assessment of upstream source influences on total gaseous mercury observations in Ontario, Canada
Hourly total gaseous mercury (TGM) concentrations at three monitoring sites (receptors) in Ontario were predicted for four selected periods at different seasons in 2002 using the Stochastic Time-Inverted Lagrangian Transport (STILT) model, which transports Lagrangian air parcels backward in time fro...
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Copernicus Publications
2011-02-01
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Series: | Atmospheric Chemistry and Physics |
Online Access: | http://www.atmos-chem-phys.net/11/1405/2011/acp-11-1405-2011.pdf |
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author | D. Wen J. C. Lin F. Meng P. K. Gbor Z. He J. J. Sloan |
author_facet | D. Wen J. C. Lin F. Meng P. K. Gbor Z. He J. J. Sloan |
author_sort | D. Wen |
collection | DOAJ |
description | Hourly total gaseous mercury (TGM) concentrations at three monitoring sites (receptors) in Ontario were predicted for four selected periods at different seasons in 2002 using the Stochastic Time-Inverted Lagrangian Transport (STILT) model, which transports Lagrangian air parcels backward in time from the receptors to provide linkages to the source region in the upwind area. The STILT model was modified to deal with Hg deposition and high stack Hg emissions. The model-predicted Hg concentrations were compared with observations at three monitoring sites. Estimates of transport errors (uncertainties in simulated concentrations due to errors in wind fields) are also provided that suggest such errors can reach approximately 10% of simulated concentrations. Results from a CMAQ chemical transport model (CTM) simulation in which the same emission and meteorology inputs were used are also reported. The comparisons show that STILT-predicted Hg concentrations usually agree better with observations than CMAQ except for a subset of cases that are subject to biases in the coarsely resolved boundary conditions. In these comparisons STILT captures high frequency concentration variations better than the Eulerian CTM, likely due to its ability to account for the sub-grid scale position of the receptor site and to minimize numerical diffusion. Thus it is particularly valuable for the interpretation of plumes (short-term concentration variations) that require the use of finer mesh sizes or controls on numerical diffusion in Eulerian models. We report quantitative assessments of the relative importance of different upstream sources for the selected episodes, based on emission fluxes and STILT footprints. The STILT simulations indicate that natural sources (which include re-emission from historical anthropogenic activities) contribute much more than current-day anthropogenic emissions to the Hg concentrations observed at the three sites. |
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spelling | doaj.art-b3c44600aca2499098ccd107e6bf98e42022-12-22T00:38:47ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242011-02-011141405141510.5194/acp-11-1405-2011Quantitative assessment of upstream source influences on total gaseous mercury observations in Ontario, CanadaD. WenJ. C. LinF. MengP. K. GborZ. HeJ. J. SloanHourly total gaseous mercury (TGM) concentrations at three monitoring sites (receptors) in Ontario were predicted for four selected periods at different seasons in 2002 using the Stochastic Time-Inverted Lagrangian Transport (STILT) model, which transports Lagrangian air parcels backward in time from the receptors to provide linkages to the source region in the upwind area. The STILT model was modified to deal with Hg deposition and high stack Hg emissions. The model-predicted Hg concentrations were compared with observations at three monitoring sites. Estimates of transport errors (uncertainties in simulated concentrations due to errors in wind fields) are also provided that suggest such errors can reach approximately 10% of simulated concentrations. Results from a CMAQ chemical transport model (CTM) simulation in which the same emission and meteorology inputs were used are also reported. The comparisons show that STILT-predicted Hg concentrations usually agree better with observations than CMAQ except for a subset of cases that are subject to biases in the coarsely resolved boundary conditions. In these comparisons STILT captures high frequency concentration variations better than the Eulerian CTM, likely due to its ability to account for the sub-grid scale position of the receptor site and to minimize numerical diffusion. Thus it is particularly valuable for the interpretation of plumes (short-term concentration variations) that require the use of finer mesh sizes or controls on numerical diffusion in Eulerian models. We report quantitative assessments of the relative importance of different upstream sources for the selected episodes, based on emission fluxes and STILT footprints. The STILT simulations indicate that natural sources (which include re-emission from historical anthropogenic activities) contribute much more than current-day anthropogenic emissions to the Hg concentrations observed at the three sites.http://www.atmos-chem-phys.net/11/1405/2011/acp-11-1405-2011.pdf |
spellingShingle | D. Wen J. C. Lin F. Meng P. K. Gbor Z. He J. J. Sloan Quantitative assessment of upstream source influences on total gaseous mercury observations in Ontario, Canada Atmospheric Chemistry and Physics |
title | Quantitative assessment of upstream source influences on total gaseous mercury observations in Ontario, Canada |
title_full | Quantitative assessment of upstream source influences on total gaseous mercury observations in Ontario, Canada |
title_fullStr | Quantitative assessment of upstream source influences on total gaseous mercury observations in Ontario, Canada |
title_full_unstemmed | Quantitative assessment of upstream source influences on total gaseous mercury observations in Ontario, Canada |
title_short | Quantitative assessment of upstream source influences on total gaseous mercury observations in Ontario, Canada |
title_sort | quantitative assessment of upstream source influences on total gaseous mercury observations in ontario canada |
url | http://www.atmos-chem-phys.net/11/1405/2011/acp-11-1405-2011.pdf |
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