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Abstract: An emission pre-processing tool is generally needed to transform a bulk annual emission inventory into gridded, temporal (monthly, weekly, and hourly), and speciated emissions in order to use complex air quality models such as the Community Multi-scale Air Quality (CMAQ) model to assess control strategies, forecast air quality, and investigate pollution formation and transport processes. To support the regional air quality modeling in the Pearl River Delta (PRD), we developed an emission pre-processing system, Sparse Matrix Operator Kernel Emissions-PRD (SMOKE-PRD), based on the U.S. Environmental Protection Agency (USEPA)/SMOKE model. This paper introduces the methods and procedures for adapting the SMOKE model to the PRD regional bulk emissions. These include the compilation of the PRD local source-class clarification codes, the incorporation of the PRD local emission inventory, and the updating of spatial, temporal, and chemical speciation information. The SMOKE-PRD system was evaluated, and a case study on ozone simulation was conducted to demonstrate the applicability of the SMOKE-PRD system. Results show that the model can properly simulate temporal variations, spatial patterns, and peak values of O3 concentrations in the PRD region, suggesting the SMOKE-PRD system was successfully localized and can be used to provide model-ready emissions for regional air quality modeling in the PRD region. This work can be extended for adapting the SMOKE model to process emissions for modeling use in other regions of China. [Copyright &y& Elsevier]

Abstract: This paper provides a performance evaluation of the real-time, CONUS-scale National Air Quality Forecast Capability (NAQFC) that supported, in part, its transition into operational status. This evaluation focuses primarily on discrete forecasts for the maximum 8-h O3 concentrations covering the 4-month period, June through September, 2007, using measurements obtained from EPA''s AIRNow network. Results indicate that the 2007 NAQFC performed as well or better than previous configurations, despite the expansion of the forecast domain into the western half of the nation that is dominated by complex terrain. The mean, domain-wide, season-long correlation was 0.70. When examined over time, the domain-wide correlations exhibit a fairly consistent nature, with values exceeding 0.60 (0.70) over 90% (55%) of the days. The NAQFC systematically over-predicted the 8-h O3 concentrations, continuing a trend established by earlier NAQFC configurations, though to a lesser degree. The summer-long mean forecast value of 53.2ppb was 4.2ppb higher than the observed value, resulting in a domain-wide Normalized Mean Bias (NMB) of 8.7%. Most of the over-prediction is associated with observed concentrations less than 50ppb. In fact the model tends to under-predict when concentrations exceed 70ppb. As with the bias, the error associated with the latest configuration was also lower. The summer-long Root Mean Square Error of 13.0ppb (Normalized Mean Error (NME)=20.4%) represented marked improvements over earlier forecasts. Examination of the spatial distribution of both the NMB and NME reveals that the NAQFC was generally within 25% for the NME and 25% for the NMB over a majority of the domain. Several areas of poorer performance, where the NMB and NME often exceed 25% and in some cases 50%, were noted. These areas include southern California, where the NAQFC tended to under-predict concentrations (especially on weekends) and the southeast Atlantic and Gulf coasts regions, where the model over-predicted. Subsequent analysis revealed that the incorrect temporal allocation of precursor emissions was likely the source of the under-prediction in southern California, while inaccurate simulation of PBL heights likely contributed to the over-prediction in the coastal regions. [Copyright &y& Elsevier]

The United States Environmental Protection Agency issues periodic reports that describe air quality trends in the US. For some pollutants, such as ozone, both observed and meteorologically adjusted trends are displayed. This paper describes an improved statistical methodology for meteorologically adjusting ozone trends as well as characterizes the relationships between individual meteorological parameters and ozone. A generalized linear model that accommodates the nonlinear effects of the meteorological variables was fit to data collected for 39 major eastern US urban areas. Overall, the model performs very well, yielding R 2 statistics as high as 0.80. The analysis confirms that ozone is generally increasing with increasing temperature and decreasing with increasing relative humidity. Examination of the spatial gradients of these responses show that the effect of temperature on ozone is most pronounced in the north while the opposite is true of relative humidity. By including HYSPLIT-derived transport wind direction and distance in the model, it is shown that the largest incremental impact of wind direction on ozone occurs along the periphery of the study domain, which encompasses major NO x emission sources. [Copyright &y& Elsevier]