Projected changes in particulate matter concentrations in the South Coast Air Basin due to basin-wide reductions in nitrogen oxides, volatile organic compounds, and ammonia emissions.

An ozone abatement strategy for the South Coast Air Basin (SoCAB) has been proposed by the South Coast Air Quality Management District (SCAQMD) and the California Air Resources Board (ARB). The proposed emissions reduction strategy is focused on the reduction of nitrogen oxide (NO) emissions by the year 2030. Two high PM concentration episodes with high ammonium nitrate compositions occurring during September and November 2008 were simulated with the Community Multi-scale Air Quality model (CMAQ). All simulations were made with same meteorological files provided by the SCAQMD to allow them to be more directly compared with their previous modeling studies. Although there was an overall under-prediction bias, the CMAQ simulations were within an overall normalized mean error of 50%; a range that is considered acceptable performance for PM modeling. A range of simulations of these episodes were made to evaluate sensitivity to NO and ammonia emissions inputs for the future year 2030. It was found that the current ozone control strategy will reduce daily average PM concentrations. However, the targeted NO reductions for ozone were not found to be optimal for reducing PM concentrations. Ammonia emission reductions reduced PM and this might be considered as part of a PM control strategy. Implications: The SCAQMD and the ARB have proposed an ozone abatement strategy for the SoCAB that focuses on NO emission reductions. Their strategy will affect both ozone and PM. Two episodes that occurred during September and November 2008 with high PM concentrations and high ammonium nitrate composition were selected for simulation with different levels of nitrogen oxide and ammonia emissions for the future year 2030. It was found that the ozone control strategy will reduce maximum daily average PM concentrations but its effect on PM concentrations is not optimal.