Spatial decomposition analysis of NO2 and PM2.5 air pollution in the United States

Abstract

Abstract Length scales for spatial variability of air pollution concentrations depend on the pollutant and the location. In this paper, we develop a readily scalable algorithm based on “spatial-increment”, to decompose the air pollution concentration into four spatial components: long-range, mid-range, neighborhood, and near-source. We apply the algorithm to annual-average concentrations of outdoor nitrogen dioxide (NO2) and fine particulate matter (PM2.5) for all census blocks in the contiguous US. For NO2, “neighborhood” and “mid-range” components dominate both within-city and between-city concentration differences (both components are ~5-fold larger in large urbanized areas than rural areas). For PM2.5, the “long-range” component dominates; this component varies by region (e.g., is three times greater in the Midwest [7 μg/m3] than in the West [2.3 μg/m3]), whereas variation by urban area size is relatively minor. Our study provides the first nation-level fine-scale decomposed pollution surfaces to date; this dataset is publicly available. Results can be used to estimate, at least to a zeroth order, the contribution of sources at different distances from the receptor to the annual average pollution in a location of interest.