Preprints
https://doi.org/10.5194/acp-2020-1169
https://doi.org/10.5194/acp-2020-1169

  24 Nov 2020

24 Nov 2020

Review status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: Impacts of COVID-19 pandemic lockdown

Shibao Wang1, Yun Ma1, Zhongrui Wang1, Lei Wang1, Xuguang Chi1, Aijun Ding1, Mingzhi Yao2, Yunpeng Li2, Qilin Li2, Mengxian Wu3, Ling Zhang3, Yongle Xiao3, and Yanxu Zhang1 Shibao Wang et al.
  • 1School of Atmospheric Sciences, Nanjing University, Nanjing, China
  • 2Beijing SPC Environment Protection Tech Company Ltd., Beijing, China
  • 3Hebei Saihero Environmental Protection Hi-tech. Company Ltd., Shijiazhuang, China

Abstract. The development of low-cost sensors and novel calibration algorithms provides new hints to complement conventional ground-based observation sites to evaluate the spatial and temporal distribution of pollutants on hyper-local scales (tens of meters). Here we use sensors deployed on a taxi fleet to explore the air quality in the road network of Nanjing over the course of a year (Oct. 2019–Sep. 2020). Based on GIS technology, we develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3). Through hotspots identification analysis, we find three main sources of air pollutants including traffic, industrial emissions, and cooking fumes. We find that CO and NO2 concentrations show a pattern: highways > arterial roads > secondary roads > branch roads > residential streets, reflecting traffic volume. While the O3 concentrations in these five road types are in opposite order due to the titration effect of NOx. Combined the mobile measurements and the stationary station data, we diagnose that the contribution of traffic-related emissions to CO and NO2 are 42.6 % and 26.3 %, respectively. Compared to the pre-COVID period, the concentrations of CO and NO2 during COVID-lockdown period decreased for 44.9 % and 47.1 %, respectively, and the contribution of traffic-related emissions to them both decreased by more than 50 %. With the end of the COVID-lockdown period, traffic emissions and air pollutant concentrations rebounded substantially, indicating that traffic emissions have a crucial impact on the variation of air pollutants levels in urban regions. This research demonstrates the sense power of mobile monitoring for urban air pollution, which provides detailed information for source attribution, accurate traceability, and potential mitigation strategies at urban micro-scale.

Shibao Wang et al.

 
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Shibao Wang et al.

Shibao Wang et al.

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Short summary
Based on GIS technology, we develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3). Through hotspots identification analysis, we find three main sources of air pollutants including traffic, industrial emissions, and cooking fumes. Combined the mobile measurements and the stationary stations data, we diagnose the contribution of traffic-related emissions to different air pollutants.
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