Estimating Regional Spatial and Temporal Variability of PM _2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information

TLDR

PM2.5 health studies are limited by sparse ground measurements. The study evaluates using GOES AOD combined with land use and meteorology in a two‑stage GAM to estimate ground‑level PM2.5 across Massachusetts. A two‑stage generalized additive model was built, with an AOD‑based model for days when AOD is available and a non‑AOD model for missing AOD, each incorporating land‑use and meteorological predictors. The AOD model outperformed the non‑AOD model (adjusted R² 0.79 vs 0.48), predicting 0.8–0.9 µg/m³ higher PM2.5, yielding smoother spatial patterns and higher rural concentrations, and demonstrating that meteorology largely drives the improved performance.

Abstract

BackgroundStudies of chronic health effects due to exposures to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM2.5 ground networks to cover a much larger area.ObjectivesIn this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM2.5 concentrations.MethodsWe developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM2.5 concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain.ResultsThe AOD model has a higher predicting power judged by adjusted R2 (0.79) than does the non-AOD model (0.48). The predicted PM2.5 concentrations by the AOD model are, on average, 0.8–0.9 μg/m3 higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM2.5, meteorologic parameters are major contributors to the better performance of the AOD model.ConclusionsGOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM2.5 concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM2.5 spatial patterns related to AOD availability.