Abstract
Environment and Climate Change Canada’s ADAGIO project (Atmospheric Deposition Analysis Generated by Optimal Interpolation using Observations) produces maps of wet, dry and total annual deposition of oxidized and reduced nitrogen (N) and sulphur (S) and ozone in Canada and the United States by combining in an optimal way observed and modeled data. Optimal interpolation methods are used to provide the best objective analyses of seasonally-averaged surface concentrations of gaseous, particulate, and precipitation species predicted by Environment and Climate Change Canada’s in-line regional air quality model GEM-MACH (Global Environmental Multiscale model—Modelling Air quality and Chemistry) based on the difference between the modeled and measured values at network observation sites. The resulting objective analyses (OA) for gas and particulate species concentration fields are then combined with effective deposition velocities from GEM-MACH to calculate dry deposition. Concentrations of precipitation ions are combined with precipitation amounts from the Canadian Precipitation Analysis (CaPA), in which all available precipitation data sets are used to adjust precipitation amounts predicted by GEM, to calculate wet deposition. Results from the 2010 development year are compared with previously-generated wet deposition kriging maps, results from the USEPA’s Total Deposition (TDEP) method, and surface measurements not used in the analysis where available. It was found that the biggest sources of uncertainties are the dry deposition velocities and error statistics (weight matrix used to produce OA). Therefore, more work is needed to reduce these uncertainties.
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Acknowledgements
The first author wishes to acknowledge helpful discussions with Martin Deshaies and Richard Ménard concerning some aspects of error statistics and cross-validation.
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Robichaud, A. et al. (2020). Total Deposition Maps Evaluated from Measurement-Model Fusion in North America (ADAGIO Project). In: Mensink, C., Gong, W., Hakami, A. (eds) Air Pollution Modeling and its Application XXVI. ITM 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-22055-6_40
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DOI: https://doi.org/10.1007/978-3-030-22055-6_40
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