Articles | Volume 21, issue 9
https://doi.org/10.5194/acp-21-7199-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-7199-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 May 2021
Research article |
| 11 May 2021
| 11 May 2021
Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: impacts of COVID-19 pandemic lockdown
Shibao Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Yun Ma
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Zhongrui Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Lei Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Xuguang Chi
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Aijun Ding
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Mingzhi Yao
Beijing SPC Environment Protection Tech Company Ltd., Beijing, China
Yunpeng Li
Beijing SPC Environment Protection Tech Company Ltd., Beijing, China
Qilin Li
Beijing SPC Environment Protection Tech Company Ltd., Beijing, China
Mengxian Wu
Hebei Sailhero Environmental Protection Hi-tech. Ltd.,
Shijiazhuang, Hebei, China
Ling Zhang
Hebei Sailhero Environmental Protection Hi-tech. Ltd.,
Shijiazhuang, Hebei, China
Yongle Xiao
Hebei Sailhero Environmental Protection Hi-tech. Ltd.,
Shijiazhuang, Hebei, China
Yanxu Zhang
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Nanjing University, Nanjing, China
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Cited
32 citations as recorded by crossref.
- The effect of urban morphological characteristics on the spatial variation of PM2.5 air quality in downtown Nanjing T. Kokkonen et al. 10.1039/D1EA00035G
- Role of Vehicular Emissions in Urban Air Quality: The COVID-19 Lockdown Experiment M. Llaguno-Munitxa & E. Bou-Zeid 10.2139/ssrn.4160364
- The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK T. Bush et al. 10.1016/j.buildenv.2023.110330
- Leveraging machine learning algorithms to advance low-cost air sensor calibration in stationary and mobile settings A. Wang et al. 10.1016/j.atmosenv.2023.119692
- Drive-by Air Pollution Sensing Systems: Challenges and Future Directions H. Zarrar & V. Dyo 10.1109/JSEN.2023.3305779
- A review of Space-Air-Ground integrated remote sensing techniques for atmospheric monitoring B. Zhou et al. 10.1016/j.jes.2021.12.008
- Quantifying and predicting air quality on different road types in urban environments using mobile monitoring and automated machine learning C. Miao et al. 10.1016/j.apr.2023.102015
- AQ-Bench: a benchmark dataset for machine learning on global air quality metrics C. Betancourt et al. 10.5194/essd-13-3013-2021
- Machine learning arbitrated prediction of disease prevalence due to air pollution over United Arab Emirates J. Mogaraju 10.1007/s11869-023-01366-5
- Suitability of Low-Cost Sensors for Submicron Aerosol Particle Measurement D. Stoll et al. 10.3390/asi6040069
- Vertical evaluation of air quality improvement by urban forest using unmanned aerial vehicles C. Miao et al. 10.3389/fevo.2022.1045937
- Urban crowdsensing by personal mobility vehicles to manage air pollution P. Jiménez et al. 10.1016/j.trpro.2023.11.071
- Role of Vehicular Emissions in Urban Air Quality: The Covid-19 Lockdown Experiment M. Llaguno-Munitxa & E. Bou-Zeid 10.2139/ssrn.3997569
- Atmospheric environment monitoring technology and equipment in China: A review and outlook Y. Sun et al. 10.1016/j.jes.2022.01.014
- Impacts of pollution heterogeneity on population exposure in dense urban areas using ultra-fine resolution air quality data W. Che et al. 10.1016/j.jes.2022.02.041
- Analysis of Air Pollutants for a Small Paintshop by Means of a Mobile Platform and Geostatistical Methods I. Sówka et al. 10.3390/en16237716
- Evaluating cost and benefit of air pollution control policies in China: A systematic review X. Liu et al. 10.1016/j.jes.2022.02.043
- Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona: a case study with CALIOPE-Urban v1.0 A. Criado et al. 10.5194/gmd-16-2193-2023
- Machine learning techniques to improve the field performance of low-cost air quality sensors T. Bush et al. 10.5194/amt-15-3261-2022
- Air Quality Monitoring and Analysis for Sustainable Development of Solid Waste Dump Yards Using Smart Drones and Geospatial Technology R. Ranganathan et al. 10.3390/su151813347
- Impact of the COVID-19 on the vertical distributions of major pollutants from a tower in the Pearl River Delta L. Li et al. 10.1016/j.atmosenv.2022.119068
- Development and Performance Evaluation of a Low-Cost Portable PM2.5 Monitor for Mobile Deployment M. Chen et al. 10.3390/s22072767
- Role of vehicular emissions in urban air quality: The COVID-19 lockdown experiment M. Llaguno-Munitxa & E. Bou-Zeid 10.1016/j.trd.2022.103580
- Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties C. Betancourt et al. 10.5194/gmd-15-4331-2022
- Monitoring the Impact of COVID-19 Lockdown on the Production of Nitrogen Dioxide (NO2) Pollutants Using Satellite Imagery: A Case Study of South Asia F. Hassan et al. 10.3390/su13137184
- Characterizing spatiotemporal patterns of elevated PM2.5 exposures in a megacity of China using combined mobile and stationary measurements G. Huang et al. 10.1016/j.atmosenv.2023.119821
- Employing an Eigenfunction Eigendecomposition algorithm to cartographically and statistically delineate traffic-related carbon monoxide pollution in Hillsborough County, Florida L. Jing et al. 10.5897/JPHE2023.1461
- Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model V. Costa et al. 10.1016/j.apenergy.2022.118848
- Low-cost urban carbon monitoring network and implications for china: a comprehensive review H. Jiang et al. 10.1007/s11356-023-29836-4
- Predicting high-resolution air quality using machine learning: Integration of large eddy simulation and urban morphology data S. Wang et al. 10.1016/j.envpol.2024.123371
- Modeling of Various Spatial Patterns of SARS-CoV-2: The Case of Germany A. Mościcka et al. 10.3390/jcm10071409
- Impact of traffic on air pollution in a mid-sized urban city during COVID-19 lockdowns N. Hay et al. 10.1007/s11869-023-01330-3
30 citations as recorded by crossref.
- The effect of urban morphological characteristics on the spatial variation of PM2.5 air quality in downtown Nanjing T. Kokkonen et al. 10.1039/D1EA00035G
- Role of Vehicular Emissions in Urban Air Quality: The COVID-19 Lockdown Experiment M. Llaguno-Munitxa & E. Bou-Zeid 10.2139/ssrn.4160364
- The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK T. Bush et al. 10.1016/j.buildenv.2023.110330
- Leveraging machine learning algorithms to advance low-cost air sensor calibration in stationary and mobile settings A. Wang et al. 10.1016/j.atmosenv.2023.119692
- Drive-by Air Pollution Sensing Systems: Challenges and Future Directions H. Zarrar & V. Dyo 10.1109/JSEN.2023.3305779
- A review of Space-Air-Ground integrated remote sensing techniques for atmospheric monitoring B. Zhou et al. 10.1016/j.jes.2021.12.008
- Quantifying and predicting air quality on different road types in urban environments using mobile monitoring and automated machine learning C. Miao et al. 10.1016/j.apr.2023.102015
- AQ-Bench: a benchmark dataset for machine learning on global air quality metrics C. Betancourt et al. 10.5194/essd-13-3013-2021
- Machine learning arbitrated prediction of disease prevalence due to air pollution over United Arab Emirates J. Mogaraju 10.1007/s11869-023-01366-5
- Suitability of Low-Cost Sensors for Submicron Aerosol Particle Measurement D. Stoll et al. 10.3390/asi6040069
- Vertical evaluation of air quality improvement by urban forest using unmanned aerial vehicles C. Miao et al. 10.3389/fevo.2022.1045937
- Urban crowdsensing by personal mobility vehicles to manage air pollution P. Jiménez et al. 10.1016/j.trpro.2023.11.071
- Role of Vehicular Emissions in Urban Air Quality: The Covid-19 Lockdown Experiment M. Llaguno-Munitxa & E. Bou-Zeid 10.2139/ssrn.3997569
- Atmospheric environment monitoring technology and equipment in China: A review and outlook Y. Sun et al. 10.1016/j.jes.2022.01.014
- Impacts of pollution heterogeneity on population exposure in dense urban areas using ultra-fine resolution air quality data W. Che et al. 10.1016/j.jes.2022.02.041
- Analysis of Air Pollutants for a Small Paintshop by Means of a Mobile Platform and Geostatistical Methods I. Sówka et al. 10.3390/en16237716
- Evaluating cost and benefit of air pollution control policies in China: A systematic review X. Liu et al. 10.1016/j.jes.2022.02.043
- Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona: a case study with CALIOPE-Urban v1.0 A. Criado et al. 10.5194/gmd-16-2193-2023
- Machine learning techniques to improve the field performance of low-cost air quality sensors T. Bush et al. 10.5194/amt-15-3261-2022
- Air Quality Monitoring and Analysis for Sustainable Development of Solid Waste Dump Yards Using Smart Drones and Geospatial Technology R. Ranganathan et al. 10.3390/su151813347
- Impact of the COVID-19 on the vertical distributions of major pollutants from a tower in the Pearl River Delta L. Li et al. 10.1016/j.atmosenv.2022.119068
- Development and Performance Evaluation of a Low-Cost Portable PM2.5 Monitor for Mobile Deployment M. Chen et al. 10.3390/s22072767
- Role of vehicular emissions in urban air quality: The COVID-19 lockdown experiment M. Llaguno-Munitxa & E. Bou-Zeid 10.1016/j.trd.2022.103580
- Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties C. Betancourt et al. 10.5194/gmd-15-4331-2022
- Monitoring the Impact of COVID-19 Lockdown on the Production of Nitrogen Dioxide (NO2) Pollutants Using Satellite Imagery: A Case Study of South Asia F. Hassan et al. 10.3390/su13137184
- Characterizing spatiotemporal patterns of elevated PM2.5 exposures in a megacity of China using combined mobile and stationary measurements G. Huang et al. 10.1016/j.atmosenv.2023.119821
- Employing an Eigenfunction Eigendecomposition algorithm to cartographically and statistically delineate traffic-related carbon monoxide pollution in Hillsborough County, Florida L. Jing et al. 10.5897/JPHE2023.1461
- Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model V. Costa et al. 10.1016/j.apenergy.2022.118848
- Low-cost urban carbon monitoring network and implications for china: a comprehensive review H. Jiang et al. 10.1007/s11356-023-29836-4
- Predicting high-resolution air quality using machine learning: Integration of large eddy simulation and urban morphology data S. Wang et al. 10.1016/j.envpol.2024.123371
Latest update: 26 Apr 2024
Short summary
Mobile monitoring with low-cost sensors is a promising approach to garner high-spatial-resolution observations representative of the community scale. We develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3) based on GIS technology. Our results demonstrate the sensing power of mobile monitoring for urban air pollution, which provides detailed information for source attribution and accurate traceability at the urban micro-scale.
Mobile monitoring with low-cost sensors is a promising approach to garner...
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