A system for developing and projecting PM2.5 spatial fields to correspond to just meeting national ambient air quality standards

Abstract PM 2.5 concentration fields that correspond to just meeting national ambient air quality standards (NAAQS) are useful for characterizing exposure in regulatory assessments. Computationally efficient methods that incorporate predictions from photochemical grid models (PGMs) are needed to realistically project baseline concentration fields for these assessments. Thorough cross validation (CV) of hybrid spatial prediction models is also needed to better assess their predictive capability in sparsely monitored areas. In this study, a system for generating, evaluating, and projecting PM 2.5 spatial fields to correspond with just meeting the PM 2.5 NAAQS is developed and demonstrated. Results of ten-fold CV based on standard and spatial cluster withholding approaches indicate that performance of three spatial prediction models improves with decreasing distance to the nearest neighboring monitor, improved PGM performance, and increasing distance from sources of PM 2.5 heterogeneity (e.g., complex terrain and fire). An air quality projection tool developed here is demonstrated to be effective for quickly projecting PM 2.5 spatial fields to just meet NAAQS using realistic spatial response patterns based on air quality modeling. PM 2.5 tends to be most responsive to primary PM 2.5 emissions in urban areas, whereas response patterns are relatively smooth for NOx and SO 2 emission changes. On average, PM 2.5 is more responsive to changes in anthropogenic primary PM 2.5 emissions than NOx and SO 2 emissions in the contiguous U.S.