Spatial and Temporal Extension of a Novel Hybrid Source Apportionment Model

Abstract

Exposure assessment and development of control strategies are limited by the air pollutants measured and the spatial and temporal resolution of the observations. Air quality modeling can provide more comprehensive estimates of the temporal and spatial variation of pollutant concentrations, however with significant uncertainties. Source apportionment, which can be conducted as part of the air quality modeling, provides estimates of the impacts of sources on the mixtures of pollutants and contains surrogate estimates for pollutants that are not measured. This study details results using a novel spatiotemporal hybrid source apportionment method employed with interpolation techniques to quantify the impact of 33 PM_2.5 source categories. The hybrid model, which aims to reduce estimating uncertainties, adjusts original source impact estimates from a chemical transport model at monitoring sites to closely reflect observed ambient concentrations of measured PM_2.5 species. Daily source impacts are calculated for the contiguous U.S. Two interpolation methods are used to generate the data needed for spatiotemporal hybrid source apportionment. Hybrid adjustment factors are spatially interpolated using kriging, and daily observations are calculated by temporally interpolating available monitoring data. Methods are evaluated by comparing daily simulated concentrations—generated by reconstruction of source impact results—to observed species concentrations from monitors independent of model development. Results also elucidate U.S. regions with relatively higher impacts from specific sources. Monitoring data in this study originated from the Chemical Speciation Network (CSN), EPA-funded supersites, and the Southeastern Aerosol Research Characterization (SEARCH) Network. Results are to be used in health impact assessments.