Deep-MAPS: Machine Learning based Mobile Air Pollution Sensing

Mobile and ubiquitous sensing of urban air quality (AQ) has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial–temporal resolution. This article proposes a machine-learning-based mobile air pollution sensing framework, coined Deep-MAPS, and demonstrates its scientific and financial values in the following aspects: 1) based on a combination of fixed and mobile AQ sensors, we perform spatial inference of PM2.5 concentrations in Beijing (3025 km2, June 19–July 16, 2018) for a spatial–temporal resolution of 1 km $\times $ 1 km and 1 h, with under 15% SMAPE; 2) we leverage urban big data to generate insights regarding the potential cause of pollution, which facilitates evidence-based sustainable urban management; and 3) to achieve such spatial–temporal coverage and accuracy, Deep-MAPS can save up to 90% hardware investment, compared with ubiquitous sensing that relies primarily on fixed sensors.