资源环境科技发展态势分析平台

Air quality models provide spatial fields of wet deposition (WD) and dry deposition that explicitly account for the transport and transformation of emissions from thousands of sources. However, many sources of uncertainty in the air quality model including errors in emissions and meteorological inputs (particularly precipitation) and incomplete descriptions of the chemical and physical processes governing deposition can lead to bias and error in the simulation of WD. We present an approach to bias correct Community Multiscale Air Quality model output over the contiguous United States using observation-based gridded precipitation data generated by the Parameter-elevation Regressions on Independent Slopes Model and WD observations at the National Atmospheric Deposition Program National Trends Network sites. A cross-validation analysis shows that the adjusted annual accumulated WD for NO3-, NH4+, and SO42- from 2002 to 2012 has less bias and higher correlation with observed values than the base model output without adjustment. Temporal trends in observed WD are captured well by the adjusted model simulations across the entire contiguous United States. Consistent with previous trend analyses, WD NO3- and SO42- are shown to decrease during this period in the eastern half of the United States, particularly in the Northeast, while remaining nearly constant in the West. Trends in WD of NH4+ are more spatially and temporally heterogeneous, with some positive trends in the Great Plains and Central Valley of CA and slightly negative trends in the south.

Plain Language Summary We use observed wet deposition data and observation-based interpolated precipitation data to improve the Community Multiscale Air Quality model's performance in simulating wet deposition in the United States from 2002 to 2012 for nitrate, ammonium, and sulfate. Performance of the bias-corrected model output is improved, with lower bias and higher correlation with observations compared to base model output without adjustment. Community Multiscale Air Quality bias-corrected deposition fields can be used to more accurately estimate deposition for comparison to critical loads (i.e., the level of a pollutant below which no harmful ecological effect occurs) at locations lacking observations. Model-estimated trends during the 11-year time series after the bias correction are better able to capture observed trends compared to the raw model output. Model-based methods for estimating trends in wet deposition are important for accountability studies used to quantify the impact of emission control measures on ambient concentrations and wet deposition.