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@article{Song:2021:10.1109/jiot.2020.3041047,
author = {Song, J and Han, K and Stettler, M},
doi = {10.1109/jiot.2020.3041047},
journal = {IEEE Internet of Things Journal},
pages = {7649--7660},
title = {Deep-MAPS: machine learning based mobile air pollution sensing},
url = {http://dx.doi.org/10.1109/jiot.2020.3041047},
volume = {8},
year = {2021}
}
TY - JOUR
AB - 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, coined Deep-MAPS, and demonstrates its scientific and financial values in the following aspects. (1) Based on a combination of fixed and mobile air quality sensors, we perform spatial inference of PM2.5 concentrations in Beijing (3,025 km, 19 Jun -16 Jul 2018) for a spatial-temporal resolution of 1 km ×1 km and 1 hour, 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. (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.
AU - Song,J
AU - Han,K
AU - Stettler,M
DO - 10.1109/jiot.2020.3041047
EP - 7660
PY - 2021///
SN - 2327-4662
SP - 7649
TI - Deep-MAPS: machine learning based mobile air pollution sensing
T2 - IEEE Internet of Things Journal
UR - http://dx.doi.org/10.1109/jiot.2020.3041047
UR - https://ieeexplore.ieee.org/document/9272979
VL - 8
ER -
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