Integrating Fragmented Mobile Monitoring Data into a High-Resolution Spatiotemporal Exposure Model for Black Carbon

This study developed a mobile monitoring-based spatiotemporal exposure modeling framework for estimating ambient BC exposure in a pregnancy cohort in Beijing, China. Equivalent black carbon (eBC) was measured using a vehicle platform during three seasonal campaigns from 2023 to 2024 in urban residential areas, generating a spatially rich but temporally fragmented real-time data set that was aggregated to the hourly level.

To fill temporal gaps at locations with mobile BC measurements, we integrated mobile measurements with external temporal predictors using tree-based machine learning models and interpreted feature contributions using SHapley Additive exPlanations (SHAP) values. The resulting site-level daily BC estimates were subsequently used to develop a hierarchical geostatistical model based on geographic covariates, which was applied directly to estimate daily BC exposure at unmonitored cohort locations.