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Natural emissions for regional modeling of background ozone and particulate matter and impacts on emissions control strategies
Authors:Bonyoung Koo  Chao-Jung Chien  Gail Tonnesen  Ralph Morris  Jeremiah Johnson  Tanarit Sakulyanontvittaya  Piti Piyachaturawat  Greg Yarwood
Institution:1. Department of Atmospheric Chemistry and Climate, Institute of Physical Chemistry Rocasolano, CSIC, Madrid 28006, Spain;2. Environmental Modelling Laboratory, Department of Chemical & Environmental Engineering, Technical University of Madrid (UPM), Madrid, Spain;3. National Exposure Research Laboratory, Environmental Protection Agency, Research Triangle Park, NC 27711, United States
Abstract:Natural emissions adopted in current regional air quality modeling are updated to better describe natural background ozone and PM concentrations for North America. The revised natural emissions include organosulfur from the ocean, NO from lightning, sea salt, biogenic secondary organic aerosol (SOA) precursors, and pre-industrial levels of background methane. The model algorithm for SOA formation was also revised. Natural background ozone concentrations increase by up to 4 ppb in annual average over the southeastern US and Gulf of Mexico due to added NO from lightning while the revised biogenic emissions produced less ozone in the central and western US. Natural PM2.5 concentrations generally increased with the revised natural emissions. Future year (2018) simulations were conducted for several anthropogenic emission reduction scenarios to assess the impact of the revised natural emissions on anthropogenic emission control strategies. Overall, the revised natural emissions did not significantly alter the ozone responses to the emissions reductions in 2018. With revised natural emissions, ozone concentrations were slightly less sensitive to reducing NOx in the southeastern US than with the current natural emissions due to higher NO from lightning. The revised natural emissions have little impact on modeled PM2.5 responses to anthropogenic emission reductions. However, there are substantial uncertainties in current representations of natural sources in air quality models and we recommend that further study is needed to refine these representations.
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