A Hybrid Transfer Learning Approach for Ambient PM_2.5 Exposure Modeling in LMIC Contexts: A Case Study from Lima,...

Exposure to fine particulate matter (PM_2.5) contributes to millions of premature deaths annually worldwide. While epidemiological studies increasingly rely on geospatial technologies, such as satellite remote sensing, low-cost sensors, and chemical transport models, to quantify air pollution exposure, sparse monitoring in many low- and middle-income countries often introduces sampling bias, potentially skewing exposure estimates. We developed a hybrid and transfer learning approach in Lima, Peru, that integrates multiple geospatial technologies and machine learning to mitigate sampling bias and improve PM_2.5 predictions. Our approach incorporated ground PM_2.5 observations from reference-grade and low-cost monitors, satellite-retrieved aerosol optical depth, simulations from Weather Research and Forecasting model coupled with Chemistry, and additional pollution-related variables. We accounted for the sampling bias using a geo-envrionmental similarity-based transfer learning, leveraging training data from data-rich regions (the United States) to inform the data-poor target region (Lima). We then built an instance-weighted random forest model to predict daily PM_2.5 concentrations across Lima from 2010 to 2023. The model achieved a cross-validated R ² of 0.76 and reduced potential overestimation in mountainous regions (average PM_2.5 reduced from 34.19 to 29.23 μg/m³). This approach provides a scalable solution to support robust epidemiological research and public health planning across data-scarce settings.