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

Yuxi Sun1, Peter Brimblecombe2, Peng Wei1

  • 1Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong SAR, China.

Sensors (Basel, Switzerland)
|August 26, 2022
PubMed
Summary

Mobile taxis equipped with sensors monitored traffic-related air pollution (TRAP) in Shanghai, revealing distinct spatial patterns for CO, NO2, and PM2.5. Pollution levels significantly decreased during the COVID-19 lockdown.

Keywords:
COCOVID-19NO2PM2.5Shanghaimobile networkmotorwaysroads

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Urban Planning

Background:

  • Traffic-related air pollution (TRAP) poses significant health risks in urban environments.
  • Understanding the spatial and temporal distribution of pollutants is crucial for effective mitigation strategies.
  • Mobile sensing offers a high-resolution approach to characterizing on-road air quality.

Purpose of the Study:

  • To deploy and evaluate a mobile sensor network for real-time monitoring of TRAP in Shanghai.
  • To investigate the spatial and temporal patterns of carbon monoxide (CO), nitrogen dioxide (NO2), and PM2.5 concentrations.
  • To assess the impact of the COVID-19 lockdown on urban air pollution levels.

Main Methods:

  • Utilized a network of 125 urban taxis equipped with sensors to collect real-time data on CO, NO2, and PM2.5 concentrations.
  • Achieved approximately 80% road coverage in Shanghai, providing high spatial resolution data (~200 m).
  • Analyzed pollutant concentrations in relation to road types, urban center proximity, and temporal variations, including the COVID-19 lockdown period.

Main Results:

  • Distinct spatial patterns were observed: higher CO in the urban center, higher NO2 on motorways, and lower PM2.5 in western areas.
  • During the COVID-19 lockdown (November 2019/December 2020), concentrations of CO, NO2, and PM2.5 decreased by 32%, 31%, and 41%, respectively.
  • Local traffic emission contributions showed minor changes, while background contributions varied seasonally.

Conclusions:

  • Mobile sensor networks provide a robust, high-resolution tool for real-time air quality monitoring in urban areas.
  • The study highlights the significant impact of traffic and lockdown measures on urban air pollution dynamics.
  • Findings support the use of mobile sensing for informing targeted air quality management interventions.