🤖 AI Summary
Environmental exposure assessment is often hindered by substantial uncertainty arising from spatiotemporal misalignment and high missingness across heterogeneous data sources (e.g., satellite and ground-based observations). To address this, we propose a Bayesian weighted regression framework with an embedded spatiotemporal kernel that directly models irregularly spaced spatiotemporal observations—bypassing preprocessing steps such as imputation or forced alignment. Our method innovatively integrates Voronoi spatial integration with irregular temporal increment approximation, thereby avoiding bias inherent in conventional two-stage modeling while principledly capturing spatial and temporal lag effects. Evaluated on PM₂.₅ estimation across California, the framework achieves over 50% higher R² than state-of-the-art models and markedly reduces prediction uncertainty, yielding robust, high-resolution spatiotemporal exposure surfaces. The framework is naturally extensible to multivariate settings and supports general kernel specifications, establishing a novel paradigm for environmental exposure modeling under complex observational constraints.
📝 Abstract
Estimating environmental exposures from multi-source data is central to public health research and policy. Integrating data from satellite products and ground monitors are increasingly used to produce exposure surfaces. However, spatio-temporal misalignment often induced from missing data introduces substantial uncertainty and reduces predictive accuracy. We propose a Bayesian weighted predictor regression framework that models spatio-temporal relationships when predictors are observed on irregular supports or have substantial missing data, and are not concurrent with the outcome. The key feature of our model is a spatio-temporal kernel that aggregates the predictor over local space-time neighborhoods, built directly into the likelihood, eliminating any separate gap-filling or forced data alignment stage. We introduce a numerical approximation using a Voronoi-based spatial quadrature combined with irregular temporal increments for estimation under data missingness and misalignment. We showed that misspecification of the spatial and temporal lags induced bias in the mean and parameter estimates, indicating the need for principled parameter selection. Simulation studies confirmed these theoretical findings, where careful tuning was critical to control bias and achieve accurate prediction, while the proposed quadrature performed well under severe missingness. As an illustrative application, we estimated fine particulate matter (PM$_{2.5}$) in northern California using satellite-derived aerosol optical depth (AOD) and wildfire smoke plume indicators. Relative to a traditional collocated linear model, our approach improved out-of-sample predictive performance (over 50% increase in R$^2$), reduced uncertainty, and yielded robust temporal predictions and spatial surface estimation. Our framework is extensible to additional spatio-temporally varying covariates and other kernel families.