Sensors (Aug 2022)

High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network

  • Yuxi Sun,
  • Peter Brimblecombe,
  • Peng Wei,
  • Yusen Duan,
  • Jun Pan,
  • Qizhen Liu,
  • Qingyan Fu,
  • Zhiguang Peng,
  • Shuhong Xu,
  • Ying Wang,
  • Zhi Ning

DOI
https://doi.org/10.3390/s22166005
Journal volume & issue
Vol. 22, no. 16
p. 6005

Abstract

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Traffic-related air pollution (TRAP) was monitored using a mobile sensor network on 125 urban taxis in Shanghai (November 2019/December 2020), which provide real-time patterns of air pollution at high spatial resolution. Each device determined concentrations of carbon monoxide (CO), nitrogen dioxide (NO2), and PM2.5, which characterised spatial and temporal patterns of on-road pollutants. A total of 80% road coverage (motorways, trunk, primary, and secondary roads) required 80–100 taxis, but only 25 on trunk roads. Higher CO concentrations were observed in the urban centre, NO2 higher in motorway concentrations, and PM2.5 lower in the west away from the city centre. During the COVID-19 lockdown, concentrations of CO, NO2, and PM2.5 in Shanghai decreased by 32, 31 and 41%, compared with the previous period. Local contribution related to traffic emissions changed slightly before and after COVID-19 restrictions, while changing background contributions relate to seasonal variation. Mobile networks are a real-time tool for air quality monitoring, with high spatial resolution (~200 m) and robust against the loss of individual devices.

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