OPNet: A deep-learning approach for estimating particulate matter’s oxidative potential from satellite imagery

The oxidative potential (OP) of particulate matter (PM) reflects its ability to trigger oxidative stress in the respiratory system and is increasingly recognised as a key metric for assessing PM toxicity. Concurrently, PM has gained importance as a health indicator, leading to its inclusion in European regulations. As OP is not routinely monitored at many sites, understanding exposure and related risks remains challenging. While satellite imagery is commonly used to estimate PM mass concentration, its application to OP has not yet been explored. We present a novel deep-learning-based approach employing satellite-based surface features for OP estimation, using both OPAA and OPDTT assays on 24-hour PM10 samples collected over five years in Grenoble (France). We propose OPNet, which consists of two parts: a deep backbone that extracts surface features from one satellite image, and a predictor estimating OPAA and OPDTT using the extracted features combined with contextual variables. The architecture is trained in two stages: in the domain-adaptive task, both are jointly trained to predict daily PM10 concentration, with the backbone initialised from weights from a general classification problem. In the domain-specific task, they are jointly updated to predict either OPAA or OPDTT, with the backbone initialised from the best weights obtained in the first stage. This approach explains up to 75% of the variance in OPAA and 58% in OPDTT when using both satellite imagery and auxiliary data. It offers a cost-effective solution to improve the estimation of OP, with implications for large-scale air quality monitoring and health impact assessments.