Model Representation of Local Air Quality Characteristics

Daily (24 h) and hourly air quality data at several sites are used to examine the performance of the fifthgeneration Pennsylvania State University–NCAR Mesoscale Model (MM5)–Community Multiscale Air Quality Model (CMAQ) system over a 3-month period in 2003. A coarse (36 km) model grid was expected to provide relatively poor performance for ozone and comparatively better performance for fine particles, especially the more regional sulfate and carbonaceous aerosols. However, results were different from this expectation. Modeling showed significant skill for ozone at several locations but very little skill for particulate species. Modeling did poorly identifying surface wind directions associated with the highest and lowest pollutant exposures at most sites, although results varied widely by location. Model skill appeared to be better for ozone when spatial–temporal (S–T) patterns were examined, due in part to the ability of the model to reproduce much of the temporal variance associated with the diurnal photochemical cycle. At some sites the modeling even performed well in replicating the directional variability of hourly ozone despite relatively low spatial resolution. MM5–CMAQ spatial (directional) representation of 24-h-average particulate data was not good in most cases, but model skill improved somewhat when hourly data were examined. Modeling exhibited skill for sulfate at only one of nine sites using 24-h data averaged by daily resultant wind direction, at two of six sites when hourly data were averaged by direction, and at four of six sites when the combined spatial and temporal variance of sulfate was examined. Results were generally poorer for total carbon aerosol mass and total mass of particulate matter with diameter of less than 2.5 μm(PM2.5). The primary result of this study is that an S–T analysis of pollutant patterns reveals model performance insights that cannot be realized by only examining model error statistics as is typically done for regulatory applications. Use of this S–T analysis technique is recommended for better understanding model performance during longer simulation periods, especially when using grids of finer spatial resolution for applications supporting local air quality management studies. Of course, using this approach will require measuring semicontinuous fine particle data at more sites and for longer periods.