Clarifying the trends in quantity, location, and causes of PM2.5 (particulate matter with an aerodynamic diameter <2.5 μm) emission changes is critical for evaluating and improving emission control strategies and reduce the risk posed to human health. According to the National Emissions Inventory (NEI) released by the U.S. Environmental Protection Agency (EPA), a general downward trend in PM2.5 emissions has been observed in the United States over the past decade. Although this trend is representative at the national level, it lacks the precision to locate emission hotspots at a finer scale. Moreover, the changes reported in the NEI are likely confounded by periodic modification of inventory methods, and imprecision for area sources. In this regard, it is imperative to acquire emission inventories with as much spatial and temporal details as possible to further our knowledge of particle emissions, exposure levels, and associated health risks. In this study, we employed the PEIRS (Particle Emission Inventory using Remote Sensing) approach (Tang et al., 2016) predict triennial-averaged emissions at 1 km × 1 km resolution across the Northeast United States from 2002 to 2013. Notably, the PEIRS approach is able to capture both primary emission and secondary formation of PM2.5. Regional emission trends were evaluated using quantile regression, and source-oriented trends were modeled with land use regression. The analysis found a regional decrease in PM2.5 emissions of 3.3 tons/yr/km2 (18%) over the 12-yr period. Furthermore, the rate of emission change at the extremes of the emission distribution was significantly different than that of the mean. Both quantile regression and spatial trends imply that the majority of the reduction in PM2.5 emissions was attributable to highly developed spaces such as metropolitan areas and important traffic corridors. This urban-rural disparity was particularly apparent during the cold season. Indirect evidence suggested that the emission decline during the warm season is primarily attributed to less secondary particle formation. These findings warrant closer investigation of the impact of seasonality on PM2.5 emissions.
Implications: Emission trend analysis provides crucial information for evaluating and enhancing the efficacies of emission control strategies as well as studying air pollution associated health risks. In this study, the patterns and trends of year-round and seasonal PM2.5 emission over the Northeast United States are presented at a spatial resolution of 1 km × 1 km for the period of 2002-2012.