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High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network
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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9416088/ https://www.ncbi.nlm.nih.gov/pubmed/36015765 http://dx.doi.org/10.3390/s22166005 |
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author | Sun, Yuxi Brimblecombe, Peter Wei, Peng Duan, Yusen Pan, Jun Liu, Qizhen Fu, Qingyan Peng, Zhiguang Xu, Shuhong Wang, Ying Ning, Zhi |
author_facet | Sun, Yuxi Brimblecombe, Peter Wei, Peng Duan, Yusen Pan, Jun Liu, Qizhen Fu, Qingyan Peng, Zhiguang Xu, Shuhong Wang, Ying Ning, Zhi |
author_sort | Sun, Yuxi |
collection | PubMed |
description | 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 (NO(2)), and PM(2.5), 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, NO(2) higher in motorway concentrations, and PM(2.5) lower in the west away from the city centre. During the COVID-19 lockdown, concentrations of CO, NO(2), and PM(2.5) 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. |
format | Online Article Text |
id | pubmed-9416088 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94160882022-08-27 High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network Sun, Yuxi Brimblecombe, Peter Wei, Peng Duan, Yusen Pan, Jun Liu, Qizhen Fu, Qingyan Peng, Zhiguang Xu, Shuhong Wang, Ying Ning, Zhi Sensors (Basel) Article 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 (NO(2)), and PM(2.5), 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, NO(2) higher in motorway concentrations, and PM(2.5) lower in the west away from the city centre. During the COVID-19 lockdown, concentrations of CO, NO(2), and PM(2.5) 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. MDPI 2022-08-11 /pmc/articles/PMC9416088/ /pubmed/36015765 http://dx.doi.org/10.3390/s22166005 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sun, Yuxi Brimblecombe, Peter Wei, Peng Duan, Yusen Pan, Jun Liu, Qizhen Fu, Qingyan Peng, Zhiguang Xu, Shuhong Wang, Ying Ning, Zhi High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network |
title | High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network |
title_full | High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network |
title_fullStr | High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network |
title_full_unstemmed | High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network |
title_short | High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network |
title_sort | high resolution on-road air pollution using a large taxi-based mobile sensor network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9416088/ https://www.ncbi.nlm.nih.gov/pubmed/36015765 http://dx.doi.org/10.3390/s22166005 |
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