High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning

Rong Guo, Ying Qi, Bu Zhao, Ziyu Pei, Fei Wen, Shun Wu and Qiang Zhang
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Rong Guo: Department of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
Ying Qi: Department of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
Bu Zhao: School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA
Ziyu Pei: Department of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
Fei Wen: Gansu Academy of Eco-Environmental Sciences, Lanzhou 730070, China
Shun Wu: Sichuan Meteorological Service Centre, Chengdu 610072, China
Qiang Zhang: Department of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China

IJERPH, 2022, vol. 19, issue 13, 1-15

Abstract: Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution, with a coefficient of determination (R 2 ) value of 0.740 for PM 2.5 , 0.754 for CO and 0.716 for SO 2 . Meanwhile, we also show that the deployment density of the monitoring stations can have a significant impact on the air quality inference results. Our method is able to show both short-term and long-term distribution of multiple important pollutants in the city, which demonstrates the potential and feasibility of dense monitoring data combined with advanced data science methods to support urban atmospheric environment fine-management, policy making, and public health studies.

Keywords: air quality mapping; high-resolution; micro monitoring stations; LCS network; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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