Deep-MAPS: Machine Learning based Mobile Air Pollution Sensing
This addresses urban air quality monitoring for environmental management, offering a cost-effective solution with incremental improvements in sensor network efficiency.
The paper tackles mobile air pollution sensing by proposing Deep-MAPS, a machine learning framework that achieves over 85% accuracy in inferring PM2.5 concentrations at high spatial-temporal resolution in Beijing, while saving up to 90% in hardware costs compared to fixed-sensor approaches.
Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, called Deep-MAPS, and demonstrates its scientific and financial values in the following aspects. (1) Based on a network of fixed and mobile air quality sensors, we perform spatial inference of PM2.5 concentrations in Beijing (3,025 km2, 19 Jun-16 Jul 2018) for a spatial-temporal resolution of 1km-by-1km and 1 hour, with over 85% accuracy. (2) We leverage urban big data to generate insights regarding the potential cause of pollution, which facilitates evidence-based sustainable urban management. (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.