Unveiling hidden air pollution exposure and impacts with low-cost sensor network-based frameworks

Abstract

India has made some progress in mitigating severe air pollution; however, many regions continue to experience fine particulate matter (PM2.5) levels exceeding World Health Organization and national standards. Despite this, local-scale exposure information beyond urban centers, intra-regional inequities, and short-term mortality impacts remain poorly understood. This study integrates dense, low-cost sensor-based PM2.5 measurements with machine learning models, recent family health survey data, and India-specific relative risk information to assess sub-regional exposure inequity among socioeconomic groups and short-term mortality impacts in Bihar, one of the most polluted and understudied states of India. Here we show a clear north-south divide in PM2.5 exposure, with northern districts having mean exposure levels 1.2 times higher. District-level mortality (per 100,000) varied up to 1.7-fold, with daily mortality showing 3.2-fold variability. We observe limited exposure inequity at the state level but substantial inter- and intra-regional inequities within Bihar. This study integrates dense, low-cost sensor-based PM2.5 measurements with machine learning models, recent family health survey data, and India-specific relative risk information to assess sub-regional exposure inequity among socioeconomic groups and short-term mortality impacts in Bihar, one of the most polluted and understudied states of India.