Air-quality modeling and source-apportionment of fine particulate matter: implications and applications in time-series health studies

Abstract

Fine particulate matter (PM2.5) has been associated with adverse effects on human health, but whether specific components of PM2.5 are responsible for specific health effects is still under investigation. The complex chemical composition of PM2.5 and issues such as multi-component interactions, spatial variability and sampling/instrument error further complicates this analysis. A complementary approach to examining species-specific associations is to assess associations between health outcomes and sources contributing to PM2.5, which can provide critical information to regulators to tighten controls on sources that contribute most to adverse health effects and allows for better multi-pollutant epidemiologic analyses, as the number of source-categories is typically far less than the number of PM2.5 species. This study develops and evaluates various air quality modeling approaches for determining daily source contributions to ambient PM2.5. Results from long-term air quality simulations using an emissions-based model (Models-3/CMAQ - Community Multiscale Air-Quality model) were evaluated in terms of the model's ability to simulate short-term (e.g., daily) variability in concentrations of PM2.5 components. To examine source-specific health outcomes, an extended PM2.5 source-apportionment model, CMB-LGO (Chemical Mass Balance incorporating the Lipschitz Global Optimizer) was developed and compared with results based on other approaches such as CMB, PMF (Positive Matrix Factorization), and Models-3/CMAQ in terms of simulating the daily variability of source impacts. Based on findings from spatial and temporal analyses of tracer concentrations and source impacts, PM2.5 source-apportionment results from CMB-LGO and PMF were applied in a health-study for the Atlanta area. Despite methodological differences and uncertainties in the apportionment process, good agreement was observed between the CMB-LGO and PMF based risk ratios, indicating to the usefulness of applying apportionment methods in health studies.