Articles | Volume 21, issue 6
https://doi.org/10.5194/acp-21-5063-2021
© 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-21-5063-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2021
Research article |
| 31 Mar 2021
| 31 Mar 2021
Development and intercity transferability of land-use regression models for predicting ambient PM10, PM2.5, NO2 and O3 concentrations in northern Taiwan
Zhiyuan Li
Institute of Environment, Energy and Sustainability, The Chinese
University of Hong Kong, Shatin, N.T., Hong Kong Special Administrative
Region
Kin-Fai Ho
The Jockey Club School of Public Health and Primary Care, The Chinese
University of Hong Kong, Shatin, N.T., Hong Kong Special Administrative
Region
Institute of Environment, Energy and Sustainability, The Chinese
University of Hong Kong, Shatin, N.T., Hong Kong Special Administrative
Region
Hsiao-Chi Chuang
School of Respiratory Therapy, College of Medicine, Taipei Medical
University, Taipei, Taiwan
Department of Geography and Resource Management, The Chinese
University of Hong Kong, Shatin, N.T., Hong Kong Special Administrative
Region
Stanley Ho Big Data Decision Analytics Research Centre, The Chinese
University of Hong Kong, Shatin, N.T., Hong Kong Special Administrative
Region
Institute of Environment, Energy and Sustainability, The Chinese
University of Hong Kong, Shatin, N.T., Hong Kong Special Administrative
Region
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- Air pollution exacerbates mild obstructive sleep apnea by disrupting nocturnal changes in lower-limb body composition: a cross-sectional study conducted in urban northern Taiwan Y. He et al. 10.1016/j.scitotenv.2023.163969
- Winter and Summer PM2.5 Land Use Regression Models for the City of Novi Sad, Serbia S. Dmitrašinović et al. 10.3390/su16135314
- A novel approach for assessing the spatiotemporal trend of health risk from ambient particulate matter components: Case of Hong Kong Z. Li et al. 10.1016/j.envres.2021.111866
- Correlation Between mRNA Expression of Activated Eosinophils and Air Pollutant Exposure in Patients With Asthma T. Lin et al. 10.1002/iid3.70065
- Association of traffic air pollution with severity of obstructive sleep apnea in urban areas of Northern Taiwan: A cross-sectional study Y. He et al. 10.1016/j.scitotenv.2022.154347
- The joint association of ambient air pollution and different sleep posture with mild obstructive sleep apnea: A study conducted at Taipei Sleep Center Y. He et al. 10.1016/j.scitotenv.2023.166531
- Land use regression model to predict nitrogen dioxide in the greater Philadelphia area B. Terry et al. 10.1016/j.apr.2024.102339
- Assessing the Spatiotemporal Characteristics, Factor Importance, and Health Impacts of Air Pollution in Seoul by Integrating Machine Learning into Land-Use Regression Modeling at High Spatiotemporal Resolutions Y. Li et al. 10.1021/acs.est.2c03027
- Pixel-Level Projection of PM2.5 Using Landsat Images and Cellular Automata Models in the Yangtze River Delta, China P. Tang et al. 10.1109/JSTARS.2023.3294614
- Development of Phase and Seasonally Dependent Land-Use Regression Models to Predict Atmospheric PAH Levels A. Tuerxunbieke et al. 10.3390/toxics11040316
- An Evaluation of Ground-Level Concentrations of Aerosols and Criteria Pollutants Using the CAMS Reanalysis Dataset over the Himawari-8 Observational Area, Including China, Indonesia, and Australia (2016–2023) M. Sowden 10.3390/air2040024
- Adopting urban morphological indicators to land use regression modeling of seasonal mean PM2.5 concentrations for a high-density city Y. Wan et al. 10.1007/s11869-021-01134-3
- Development of an integrated model framework for multi-air-pollutant exposure assessments in high-density cities Z. Li et al. 10.5194/acp-24-649-2024
- Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches Q. Lu et al. 10.1016/j.envpol.2024.125093
- Association of Traffic Air Pollution with Severity Of Obstructive Sleep Apnea in Urban Areas of Northern Taiwan: A Cross-Sectional Study Y. He et al. 10.2139/ssrn.3996005
- High spatial resolution estimates of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment approach Z. Li et al. 10.1016/j.scitotenv.2023.167932
- Mapping the Spatiotemporal Variability of Particulate Matter Pollution in Delhi: Insights from Land Use Regression Modelling D. Sharma et al. 10.1007/s12524-024-01879-1
- Integrating Doppler LiDAR and machine learning into land-use regression model for assessing contribution of vertical atmospheric processes to urban PM2.5 pollution Y. Li et al. 10.1016/j.scitotenv.2024.175632
- Effect of transit-oriented development on air quality in neighbourhoods of Delhi S. Bhatnagar et al. 10.1016/j.wds.2022.100015
19 citations as recorded by crossref.
- Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places Y. Shi et al. 10.3389/fpubh.2023.1213453
- Air pollution exacerbates mild obstructive sleep apnea by disrupting nocturnal changes in lower-limb body composition: a cross-sectional study conducted in urban northern Taiwan Y. He et al. 10.1016/j.scitotenv.2023.163969
- Winter and Summer PM2.5 Land Use Regression Models for the City of Novi Sad, Serbia S. Dmitrašinović et al. 10.3390/su16135314
- A novel approach for assessing the spatiotemporal trend of health risk from ambient particulate matter components: Case of Hong Kong Z. Li et al. 10.1016/j.envres.2021.111866
- Correlation Between mRNA Expression of Activated Eosinophils and Air Pollutant Exposure in Patients With Asthma T. Lin et al. 10.1002/iid3.70065
- Association of traffic air pollution with severity of obstructive sleep apnea in urban areas of Northern Taiwan: A cross-sectional study Y. He et al. 10.1016/j.scitotenv.2022.154347
- The joint association of ambient air pollution and different sleep posture with mild obstructive sleep apnea: A study conducted at Taipei Sleep Center Y. He et al. 10.1016/j.scitotenv.2023.166531
- Land use regression model to predict nitrogen dioxide in the greater Philadelphia area B. Terry et al. 10.1016/j.apr.2024.102339
- Assessing the Spatiotemporal Characteristics, Factor Importance, and Health Impacts of Air Pollution in Seoul by Integrating Machine Learning into Land-Use Regression Modeling at High Spatiotemporal Resolutions Y. Li et al. 10.1021/acs.est.2c03027
- Pixel-Level Projection of PM2.5 Using Landsat Images and Cellular Automata Models in the Yangtze River Delta, China P. Tang et al. 10.1109/JSTARS.2023.3294614
- Development of Phase and Seasonally Dependent Land-Use Regression Models to Predict Atmospheric PAH Levels A. Tuerxunbieke et al. 10.3390/toxics11040316
- An Evaluation of Ground-Level Concentrations of Aerosols and Criteria Pollutants Using the CAMS Reanalysis Dataset over the Himawari-8 Observational Area, Including China, Indonesia, and Australia (2016–2023) M. Sowden 10.3390/air2040024
- Adopting urban morphological indicators to land use regression modeling of seasonal mean PM2.5 concentrations for a high-density city Y. Wan et al. 10.1007/s11869-021-01134-3
- Development of an integrated model framework for multi-air-pollutant exposure assessments in high-density cities Z. Li et al. 10.5194/acp-24-649-2024
- Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches Q. Lu et al. 10.1016/j.envpol.2024.125093
- Association of Traffic Air Pollution with Severity Of Obstructive Sleep Apnea in Urban Areas of Northern Taiwan: A Cross-Sectional Study Y. He et al. 10.2139/ssrn.3996005
- High spatial resolution estimates of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment approach Z. Li et al. 10.1016/j.scitotenv.2023.167932
- Mapping the Spatiotemporal Variability of Particulate Matter Pollution in Delhi: Insights from Land Use Regression Modelling D. Sharma et al. 10.1007/s12524-024-01879-1
- Integrating Doppler LiDAR and machine learning into land-use regression model for assessing contribution of vertical atmospheric processes to urban PM2.5 pollution Y. Li et al. 10.1016/j.scitotenv.2024.175632
1 citations as recorded by crossref.
Latest update: 13 Dec 2024
Short summary
This study established land-use regression (LUR) models using only routine air quality measurement data to support long-term health studies in an Asian metropolitan area. The established LUR models captured the spatial variability in exposure to air pollution with remarkable predictive accuracy. This is the first Asian study to evaluate intercity transferability of LUR models, and it highlights that there exist uncertainties when transferring LUR models between nearby cities.
This study established land-use regression (LUR) models using only routine air quality...
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