Mapping microscale PM2.5 distribution on walkable roads in a high-density city
(2021) In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14. p.6855-6870- Abstract
Monitoring pollution of PM2.5 on walkable roads is important for resident health in high-density cities. Due to the spatiotemporal resolution limitations of Aerosol Optical Depth (AOD) observation, fixed-point monitoring, or traditional mobile measurement instruments, the microscale PM2.5 distribution in the walking environment cannot be fully estimated at the fine scale. In this study, by the integration of mobile measurement data, OpenStreetMap (OSM) data, Landsat images, and other multi-source data in land-use regression (LUR) models, a novel framework is proposed to estimate and map PM2.5 distribution in a typical microscale walkable environment of the high-density city Hong Kong. First, the PM2.5 data on the typical walking paths... (More)
Monitoring pollution of PM2.5 on walkable roads is important for resident health in high-density cities. Due to the spatiotemporal resolution limitations of Aerosol Optical Depth (AOD) observation, fixed-point monitoring, or traditional mobile measurement instruments, the microscale PM2.5 distribution in the walking environment cannot be fully estimated at the fine scale. In this study, by the integration of mobile measurement data, OpenStreetMap (OSM) data, Landsat images, and other multi-source data in land-use regression (LUR) models, a novel framework is proposed to estimate and map PM2.5 distribution in a typical microscale walkable environment of the high-density city Hong Kong. First, the PM2.5 data on the typical walking paths were collected by the handheld mobile measuring instruments, to be selected as the dependent variables. Second, Geographic prediction factors calculated by Google Street View, OpenStreetMap (OSM) data, Landsat images, and other multi-source data were further selected as independent variables. Then, these dependent and independent variables were put into the LUR models to estimate the PM2.5 concentration on sidewalks, footbridges, and footpaths in the microscale walkable environment. The proposed models showed high performance relative to those in similar studies (adj R2, 0.593 to 0.615 [sidewalks]; 0.641 to 0.682 [footpaths]; 0.783 to 0.797 [footbridges]). This study is beneficial for mapping PM2.5 concentration in the microscale walking environment and the identification of hot spots of air pollution, thereby helping people avoid the PM2.5 hotspots and indicating a healthier walking path.
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- author
- Tong, Chengzhuo ; Shi, Zhicheng ; Shi, Wenzhong ; Zhao, Pengxiang LU and Zhang, Anshu
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Air pollution, Atmospheric modeling, Instruments, Legged locomotion, Monitoring, Pollution measurement, Roads, Urban areas
- in
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- volume
- 14
- pages
- 16 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85105111994
- ISSN
- 1939-1404
- DOI
- 10.1109/JSTARS.2021.3075442
- language
- English
- LU publication?
- yes
- id
- b33c9f8a-a60c-424c-b302-5eb64dfe67be
- date added to LUP
- 2021-05-31 16:00:51
- date last changed
- 2022-04-27 02:08:59
@article{b33c9f8a-a60c-424c-b302-5eb64dfe67be,
abstract = {{<p>Monitoring pollution of PM2.5 on walkable roads is important for resident health in high-density cities. Due to the spatiotemporal resolution limitations of Aerosol Optical Depth (AOD) observation, fixed-point monitoring, or traditional mobile measurement instruments, the microscale PM2.5 distribution in the walking environment cannot be fully estimated at the fine scale. In this study, by the integration of mobile measurement data, OpenStreetMap (OSM) data, Landsat images, and other multi-source data in land-use regression (LUR) models, a novel framework is proposed to estimate and map PM2.5 distribution in a typical microscale walkable environment of the high-density city Hong Kong. First, the PM2.5 data on the typical walking paths were collected by the handheld mobile measuring instruments, to be selected as the dependent variables. Second, Geographic prediction factors calculated by Google Street View, OpenStreetMap (OSM) data, Landsat images, and other multi-source data were further selected as independent variables. Then, these dependent and independent variables were put into the LUR models to estimate the PM2.5 concentration on sidewalks, footbridges, and footpaths in the microscale walkable environment. The proposed models showed high performance relative to those in similar studies (adj R2, 0.593 to 0.615 [sidewalks]; 0.641 to 0.682 [footpaths]; 0.783 to 0.797 [footbridges]). This study is beneficial for mapping PM2.5 concentration in the microscale walking environment and the identification of hot spots of air pollution, thereby helping people avoid the PM2.5 hotspots and indicating a healthier walking path.</p>}},
author = {{Tong, Chengzhuo and Shi, Zhicheng and Shi, Wenzhong and Zhao, Pengxiang and Zhang, Anshu}},
issn = {{1939-1404}},
keywords = {{Air pollution; Atmospheric modeling; Instruments; Legged locomotion; Monitoring; Pollution measurement; Roads; Urban areas}},
language = {{eng}},
pages = {{6855--6870}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}},
title = {{Mapping microscale PM2.5 distribution on walkable roads in a high-density city}},
url = {{http://dx.doi.org/10.1109/JSTARS.2021.3075442}},
doi = {{10.1109/JSTARS.2021.3075442}},
volume = {{14}},
year = {{2021}},
}