Evaluation of WRF-CMAQ simulated climatological mean and extremes of fine particulate matter of the United States and its correlation with climate extremes

Abstract

Fine particulate matter (PM2.5, with aerodynamic diameters < 2.5 μm) pollution is one of the most pervasive air quality problems facing the world. Reliable air quality modeling of PM2.5 is essential to future air quality projection, which serves as a critical source of information for policy-making. Although various evaluation methods have been suggested to assess the capability of air quality models in reproducing PM2.5, most of studies were focused on the mean behaviors of air quality models, with little emphasis on extreme conditions, which may be more crucial for human health and climate change. To address this need, we proposed an evaluation framework in this study to characterize both mean and extreme conditions of PM2.5 and applied it to the WRF-CMAQ simulations over contiguous United States for the period of 2001–2010. Results from statistical, spatiotemporal, and extreme quantile evaluation methods show consistent good performance of the model in the Eastern U.S. However, PM2.5 mean variations and extreme trends in the western U.S. are not well represented by the model attributable to the existence of complex terrains and active fire activities. In addition, the magnitude of decreasing trends for extreme events is smaller than that for the mean PM2.5. Strong correspondence is found between PM2.5 extremes and meteorological extremes that are associated with a stagnant condition. More extreme PM2.5 pollution episodes are expected in a warming climate, with rural regions and the western U.S. suffering the most. Our results highlight the urgency for proper forest management and joint-control of air quality and carbon emissions in order to combat extreme air pollution events in the future.