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https://doi.org/10.5194/acp-2020-950
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-950
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Oct 2020

27 Oct 2020

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This preprint is currently under review for the journal ACP.

Development and intercity transferability of land-use regression models for predicting ambient PM10, PM2.5, NO2 and O3 concentrations in northern Taiwan

Zhiyuan Li1, Kin-Fai Ho2,1, and Steve Hung Lam Yim3,4,1 Zhiyuan Li et al.
  • 1Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
  • 2The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
  • 3Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
  • 4Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China

Abstract. To provide long-term air pollutant exposure estimates for epidemiological studies, it is essential to test the feasibility of developing land-use regression (LUR) models using only routine air quality measurement data and to evaluate the transferability of LUR models between nearby cities. In this study, we develop and evaluate the intercity transferability of annual average LUR models for ambient respirable suspended particulates (PM10), fine suspended particulates (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in the Taipei–Keelung metropolitan area of northern Taiwan in 2019. Ambient PM10, PM2.5, NO2, and O3 measurements at 30 fixed-site stations were used as the dependent variables, and a total of 156 potential predictor variables in six categories (i.e., population density, road network, land-use type, normalized difference vegetation index, meteorology, and elevation) were extracted using buffer spatial analysis. The LUR models were developed using the supervised forward linear regression approach. The LUR models for ambient PM10, PM2.5, NO2, and O3 achieved relatively high prediction performance, with R2 and leave-one-out cross-validation (LOOCV) R2 values of > 0.72 and > 0.53, respectively. The intercity transferability of LUR models varied among the air pollutants, with transfer-predictive R2 values of > 0.62 for NO2 and < 0.56 for the other three pollutants. The LUR-model-based 500 m×500 m spatial distribution maps of these air pollutants illustrated pollution hotspots and the heterogeneity of population exposure, which provide valuable information for policymakers in designing effective air pollution control strategies. The LUR-model-based air pollution exposure estimates captured the spatial variability of exposure for participants in a cohort study. This study highlights that LUR models can be reasonably established upon a routine monitoring network but there exist uncertainties when transferring LUR models between nearby cities. To the best of our knowledge, our study is the first to evaluate the intercity transferability of LUR models in Asia.

Zhiyuan Li et al.

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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 of 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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