Articles | Volume 12, issue 5
https://doi.org/10.5194/amt-12-2933-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-2933-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 May 2019
Research article |
| 28 May 2019
| 28 May 2019
Strategies of method selection for fine-scale PM2.5 mapping in an intra-urban area using crowdsourced monitoring
Shan Xu
School of Geosciences and Info-Physics, Central South University,
Changsha, Hunan, 410083, China
Bin Zou
CORRESPONDING AUTHOR
School of Geosciences and Info-Physics, Central South University,
Changsha, Hunan, 410083, China
Yan Lin
Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, New Mexico 87131, USA
Xiuge Zhao
Chinese Research Academy of Environmental Sciences, Beijing, 100012,
China
Shenxin Li
School of Geosciences and Info-Physics, Central South University,
Changsha, Hunan, 410083, China
Chenxia Hu
School of Geosciences and Info-Physics, Central South University,
Changsha, Hunan, 410083, China
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Cited
22 citations as recorded by crossref.
- A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023 X. Ma et al. https://doi.org/10.1016/j.envint.2024.108430
- Efforts in reducing air pollution exposure risk in China: State versus individuals B. Zou et al. https://doi.org/10.1016/j.envint.2020.105504
- Examining the Amount of Particulate Matter (PM) Emissions in Urban Areas B. Šarkan et al. https://doi.org/10.3390/app13031845
- A New Wearable System for Sensing Outdoor Environmental Conditions for Monitoring Hyper-Microclimate R. Cureau et al. https://doi.org/10.3390/s22020502
- Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey F. Salim et al. https://doi.org/10.1016/j.buildenv.2020.106964
- Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk B. Zou et al. https://doi.org/10.1016/j.compenvurbsys.2019.101456
- Local PM2.5 Hotspot Detector at 300 m Resolution: A Random Forest–Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology T. Zheng et al. https://doi.org/10.3390/rs13071356
- Identification of places with deteriorated air quality in city of Žilina in relation to road transport B. Šarkan et al. https://doi.org/10.14669/AM/176958
- Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval X. Yan et al. https://doi.org/10.1021/acs.est.4c02701
- A methodology for the design of an effective air quality monitoring network in port areas L. Mocerino et al. https://doi.org/10.1038/s41598-019-57244-7
- Fine simulation of PM2.5 combined with NPP-VIIRS night light remote sensing and mobile monitoring data D. Li et al. https://doi.org/10.1016/j.scitotenv.2024.169955
- PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants using GDAL/OGR library in Python X. Ma et al. https://doi.org/10.1007/s11783-020-1221-5
- Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping H. Shen et al. https://doi.org/10.3390/ijerph16214102
- Transformational IoT sensing for air pollution and thermal exposures J. Pantelic et al. https://doi.org/10.3389/fbuil.2022.971523
- Performance evaluation of MeteoTracker mobile sensor for outdoor applications F. Barbano et al. https://doi.org/10.5194/amt-17-3255-2024
- Assessing schoolchildren's exposure to air pollution during the daily commute - A systematic review X. Ma et al. https://doi.org/10.1016/j.scitotenv.2020.140389
- Exploring Global Aerosol Size Dynamics from 2001 to 2024 Using the Pretrained Remote sensing pIxel-based Spatial-teMporal Deep Neural Network X. Yan et al. https://doi.org/10.1021/acs.est.5c13469
- A temporally-calibrated method for crowdsourcing based mapping of intra-urban PM2.5 concentrations Z. Zheng et al. https://doi.org/10.1016/j.jclepro.2020.122347
- Investigation of CO2 Variation and Mapping Through Wearable Sensing Techniques for Measuring Pedestrians’ Exposure in Urban Areas I. Pigliautile et al. https://doi.org/10.3390/su12093936
- High spatiotemporal resolution mapping of PM2.5 concentrations under a pollution scene assumption S. Xu et al. https://doi.org/10.1016/j.jclepro.2021.129409
- Insights from application of a hierarchical spatio-temporal model to an intensive urban black carbon monitoring dataset T. Wai et al. https://doi.org/10.1016/j.atmosenv.2022.119069
- PM2.5 Pollutant in Asia—A Comparison of Metropolis Cities in Indonesia and Taiwan W. Kusuma et al. https://doi.org/10.3390/ijerph16244924
22 citations as recorded by crossref.
- A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023 X. Ma et al. https://doi.org/10.1016/j.envint.2024.108430
- Efforts in reducing air pollution exposure risk in China: State versus individuals B. Zou et al. https://doi.org/10.1016/j.envint.2020.105504
- Examining the Amount of Particulate Matter (PM) Emissions in Urban Areas B. Šarkan et al. https://doi.org/10.3390/app13031845
- A New Wearable System for Sensing Outdoor Environmental Conditions for Monitoring Hyper-Microclimate R. Cureau et al. https://doi.org/10.3390/s22020502
- Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey F. Salim et al. https://doi.org/10.1016/j.buildenv.2020.106964
- Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk B. Zou et al. https://doi.org/10.1016/j.compenvurbsys.2019.101456
- Local PM2.5 Hotspot Detector at 300 m Resolution: A Random Forest–Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology T. Zheng et al. https://doi.org/10.3390/rs13071356
- Identification of places with deteriorated air quality in city of Žilina in relation to road transport B. Šarkan et al. https://doi.org/10.14669/AM/176958
- Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval X. Yan et al. https://doi.org/10.1021/acs.est.4c02701
- A methodology for the design of an effective air quality monitoring network in port areas L. Mocerino et al. https://doi.org/10.1038/s41598-019-57244-7
- Fine simulation of PM2.5 combined with NPP-VIIRS night light remote sensing and mobile monitoring data D. Li et al. https://doi.org/10.1016/j.scitotenv.2024.169955
- PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants using GDAL/OGR library in Python X. Ma et al. https://doi.org/10.1007/s11783-020-1221-5
- Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping H. Shen et al. https://doi.org/10.3390/ijerph16214102
- Transformational IoT sensing for air pollution and thermal exposures J. Pantelic et al. https://doi.org/10.3389/fbuil.2022.971523
- Performance evaluation of MeteoTracker mobile sensor for outdoor applications F. Barbano et al. https://doi.org/10.5194/amt-17-3255-2024
- Assessing schoolchildren's exposure to air pollution during the daily commute - A systematic review X. Ma et al. https://doi.org/10.1016/j.scitotenv.2020.140389
- Exploring Global Aerosol Size Dynamics from 2001 to 2024 Using the Pretrained Remote sensing pIxel-based Spatial-teMporal Deep Neural Network X. Yan et al. https://doi.org/10.1021/acs.est.5c13469
- A temporally-calibrated method for crowdsourcing based mapping of intra-urban PM2.5 concentrations Z. Zheng et al. https://doi.org/10.1016/j.jclepro.2020.122347
- Investigation of CO2 Variation and Mapping Through Wearable Sensing Techniques for Measuring Pedestrians’ Exposure in Urban Areas I. Pigliautile et al. https://doi.org/10.3390/su12093936
- High spatiotemporal resolution mapping of PM2.5 concentrations under a pollution scene assumption S. Xu et al. https://doi.org/10.1016/j.jclepro.2021.129409
- Insights from application of a hierarchical spatio-temporal model to an intensive urban black carbon monitoring dataset T. Wai et al. https://doi.org/10.1016/j.atmosenv.2022.119069
- PM2.5 Pollutant in Asia—A Comparison of Metropolis Cities in Indonesia and Taiwan W. Kusuma et al. https://doi.org/10.3390/ijerph16244924
Saved (final revised paper)
Latest update: 11 Jun 2026
Short summary
This study presents strategies of method selection for 100 m scale PM2.5 mapping using a crowdsourced sampling campaign. Interestingly, PM2.5 concentrations in micro-environments varied significantly in intra-urban areas. These local PM2.5 variations can be effectively revealed by crowdsourcing sampling rather than national air quality monitoring sites. The selection of models for fine-scale PM2.5 mapping should be adjusted with the changing sampling and pollution circumstances.
This study presents strategies of method selection for 100 m scale PM2.5 mapping using a...
