Spatio-Temporal Prediction of Personal Exposure to \text Using a Network of Stationary and Wearable Air Quality Monitors - Arvind, D K; Rao, R; Bates, C A (2024 IEEE Conference on Pervasive and Intelligent Computing (PICom), 2024)
Machine learning methods were exercised to predict personal exposure to airborne particulates of aerodynamic sizes less than 2.5\boldsymbol{μ} m(PM_{2.5}) . A labelled dataset of PM_{2.5} values, tagged with time-stamp and GPS location at 1-minute resolution, was collected by subjects going about their normal lives in Delhi, India and in London, England, using the wearable Airspeck-P(ersonal) sensor to monitor personal exposure to airborne particulates. Contemporaneous datasets of PM_{2.5} values were also gathered from a network of municipal stationary Air Quality Monitoring Stations (AQMS) at 1-hour resolution, and a network of solar-powered, fixed Airspeck-S(tationary) monitors at 5-minute resolution in Delhi and up to 30-minute resolution in London. Five machine learning models were trained on the labelled stationary network datasets to predict the personal exposure to PM2.5 at a given time (at minute-level resolution) and a GPS location. The PM_{2.5} dataset derived from a network of stationary Airspeck-S monitors were trained to predict personal PM_{2.5} exposure of subjects in Delhi, and the PM_{2.5} dataset from a network of AQMS in London were trained to predict personal PM_{2.5} exposure in London. Extra Trees model followed by Random Forest demonstrated the best accuracy in both cases with a Mean Absolute Percentage Error (MAPE) of less than 20%. These methods can be used to predict personal exposure to airborne particulates in cities endowed with a rich network of public AQMS, and a network of low-cost stationary Airspeck air quality monitors would suffice in places without such an infrastructure.