Development of land use regression model and health risk assessment for NO2 in different functional areas: A case study of Xi'an, China

Liu, Zeyu; Guan, Qingyu; Luo, Haiping; Wang, Ning; Pan, Ninghui; Yang, Libin; Xiao, Shuyu; Lin, Junqi

doi:10.1016/j.atmosenv.2019.06.044

“…Air pollution (outdoor) causes 4.2 million deaths each year (WHO, 2020), principally in large cities, where the anthropogenic activities of industry and the energy sector along with vehicular traffic, are the greatest contributors of polluting gases ( Cai et al, 2020 ; Liu et al, 2019 ; Rovira et al, 2020 ). Of the most common atmospheric pollutants are particulate matter (PM), nitrogen dioxide (NO 2 ), tropospheric ozone (O 3 ), carbon monoxide (CO) and sulphur dioxide (SO 2 ) ( Burns et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

NO2 levels after the COVID-19 lockdown in Ecuador: A trade-off between environment and human health

Pacheco

¹

,

Díaz-López

²

,

Castro

³

et al. 2020

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The negative effects on human health, along with the fatalities caused by the new coronavirus, have led governments worldwide to take strict measures. However, a reduction in air pollution has been found in many regions on a global scale. This study is focused on how the COVID-19 pandemic is impacting on the air quality in Ecuador, one of the most alarming cases of COVID-19 contagion in Latin America, occupying the first place as regards deaths per capita. The spatio-temporal variations in NO 2 concentrations in 12 highly populated cities were evaluated by comparing the NO 2 tropospheric concentrations before (2019) and after (2020) the COVID-19 lockdown. The atmospheric data was collected from the TROPOMI on the Sentinel-5P satellite of the European Space Agency. A reduction in NO 2 concentrations (−13%) was observed as a consequence of the COVID-19 lockdown in Ecuador. However, this reduction occurred to the greatest extent in the cases of Guayaquil (−23.4%) and Quito (−22.4%), the two most highly populated cities. Linking NO 2 levels to confirmed cases/deaths of COVID-19, a strong correlation between air NO 2 concentrations and the cases/mortality caused by coronavirus ( r = 0.91; p<0.001) was observed. This work highlights the crucial role played by air quality as regards human health.

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“…The R 2 and root mean square error (RMSE) were estimated to evaluate the performance of the models (Li et al, 2021). Furthermore, leave-one-out cross-validation (LOOCV) was employed to evaluate the predictive capacity of the LUR models (Liu et al, 2019;Shi et al, 2020b;Yang et al, 2020). Spatial analysis and calculations were performed using ArcGIS software, version 10.6 (ESRI Inc., Redlands, CA, USA).…”

Section: Model Development and Validationmentioning

confidence: 99%

“…In recent years, a large number of air pollution LUR studies have been conducted in different areas around the world (Jones et al, 2020;Lee et al, 2017;Liu et al, 2016Liu et al, , 2019Lu et al, 2020;Miri et al, 2019;Ross et al, 2007;Wu et al, 2017). However, the development and application of LUR models in the Taiwan region were limited (Hsu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Li

¹

,

Ho

²

,

Chuang

³

et al. 2021

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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 developed and evaluated 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 values of > 0.72 and leave-one-out cross-validation (LOOCV) R2 values of > 0.53. 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 hot spots 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 in 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, this study is the first to evaluate the intercity transferability of LUR models in Asia.

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“…The R 2 and root mean square error (RMSE) were estimated to evaluate the performance of the models. Furthermore, leave-one-out cross validation (LOOCV) was employed to evaluate the predictive capacity of the LUR models (Liu et al, 2019;Yang et al, 2020).…”

Section: Model Development and Validationmentioning

confidence: 99%

“…In recent years, a large number of air pollution LUR studies have been conducted in different areas around the world (Jones et al, 2020;Lee et al, 2017;Liu et al, 2016;Liu et al, 2019;Lu et al, 2020;Miri et al, 2019;Ross et al, 2007;Wu et al, 2017). However, the development and application of LUR models in the Taiwan region have been limited (Hsu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Ho²,

Yim³

2020

Preprint

1

0

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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

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Development of land use regression model and health risk assessment for NO2 in different functional areas: A case study of Xi'an, China

Cited by 30 publications

References 94 publications

NO2 levels after the COVID-19 lockdown in Ecuador: A trade-off between environment and human health

NO2 levels after the COVID-19 lockdown in Ecuador: A trade-off between environment and human health

Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan

Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan

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Development of land use regression model and health risk assessment for NO2 in different functional areas: A case study of Xi'an, China

Cited by 30 publications

References 94 publications

NO2 levels after the COVID-19 lockdown in Ecuador: A trade-off between environment and human health

NO2 levels after the COVID-19 lockdown in Ecuador: A trade-off between environment and human health

Development and intercity transferability of land-use regression models for predicting ambient PM&lt;sub&gt;10&lt;/sub&gt;, PM&lt;sub&gt;2.5&lt;/sub&gt;, NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; concentrations in northern Taiwan

Development and intercity transferability of land-use regression models for predicting ambient PM&lt;sub&gt;10&lt;/sub&gt;, PM&lt;sub&gt;2.5&lt;/sub&gt;, NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; concentrations in northern Taiwan

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Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan

Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan