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Deep-MAPS: Machine-Learning-Based Mobile Air Pollution Sensing
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DOI：10.1109/JIOT.2020.3041047.png)
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Abstract
En 中文
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 (3 025 km(2), June 19-July 16, 2018) for a spatial-temporal resolution of 1 km x 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.
Keywords:
Air quality (AQ)
big data
machine learning
ubiquitous sensing
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