Surface Flux Modeling for Air Quality Applications
Open Access
- 7 August 2011
- journal article
- review article
- Published by MDPI AG in Atmosphere
- Vol. 2 (3), 271-302
- https://doi.org/10.3390/atmos2030271
Abstract
For many gasses and aerosols, dry deposition is an important sink of atmospheric mass. Dry deposition fluxes are also important sources of pollutants to terrestrial and aquatic ecosystems. The surface fluxes of some gases, such as ammonia, mercury, and certain volatile organic compounds, can be upward into the air as well as downward to the surface and therefore should be modeled as bi-directional fluxes. Model parameterizations of dry deposition in air quality models have been represented by simple electrical resistance analogs for almost 30 years. Uncertainties in surface flux modeling in global to mesoscale models are being slowly reduced as more field measurements provide constraints on parameterizations. However, at the same time, more chemical species are being added to surface flux models as air quality models are expanded to include more complex chemistry and are being applied to a wider array of environmental issues. Since surface flux measurements of many of these chemicals are still lacking, resistances are usually parameterized using simple scaling by water or lipid solubility and reactivity. Advances in recent years have included bi-directional flux algorithms that require a shift from pre-computation of deposition velocities to fully integrated surface flux calculations within air quality models. Improved modeling of the stomatal component of chemical surface fluxes has resulted from improved evapotranspiration modeling in land surface models and closer integration between meteorology and air quality models. Satellite-derived land use characterization and vegetation products and indices are improving model representation of spatial and temporal variations in surface flux processes. This review describes the current state of chemical dry deposition modeling, recent progress in bi-directional flux modeling, synergistic model development research with field measurements, and coupling with meteorological land surface models.Keywords
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