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Random forest model based fine scale spatiotemporal O3 trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017

Ambient ozone (O3) concentrations have shown an upward trend in China and its health hazards have also been recognized in recent years. High-resolution exposure data based on statistical models are needed. Our study aimed to build high-performance random forest (RF) models based on training data fro...

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Published in:Environmental pollution (1987) 2021-05, Vol.276, p.116635, Article 116635
Main Authors: Ma, Runmei, Ban, Jie, Wang, Qing, Zhang, Yayi, Yang, Yang, He, Mike Z., Li, Shenshen, Shi, Wenjiao, Li, Tiantian
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cited_by cdi_FETCH-LOGICAL-c484t-832084bcdb50789d4cb835467acd2dcc8bba5625655c9386f7a3fdb50e01c3b23
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container_start_page 116635
container_title Environmental pollution (1987)
container_volume 276
creator Ma, Runmei
Ban, Jie
Wang, Qing
Zhang, Yayi
Yang, Yang
He, Mike Z.
Li, Shenshen
Shi, Wenjiao
Li, Tiantian
description Ambient ozone (O3) concentrations have shown an upward trend in China and its health hazards have also been recognized in recent years. High-resolution exposure data based on statistical models are needed. Our study aimed to build high-performance random forest (RF) models based on training data from 2013 to 2017 in the Beijing-Tianjin-Hebei (BTH) region in China at a 0.01 ° × 0.01 ° resolution, and estimated daily maximum 8h average O3 (O3-8hmax) concentration, daily average O3 (O3-mean) concentration, and daily maximum 1h O3 (O3-1hmax) concentration from 2010 to 2017. Model features included meteorological variables, chemical transport model output variables, geographic variables, and population data. The test-R2 of sample-based O3-8hmax, O3-mean and O3-1hmax models were all greater than 0.80, while the R2 of site-based and date-based model were 0.68–0.87. From 2010 to 2017, O3-8hmax, O3-mean, and O3-1hmax concentrations in the BTH region increased by 4.18 μg/m3, 0.11 μg/m3, and 4.71 μg/m3, especially in more developed regions. Due to the influence of weather conditions, which showed high contribution to the model, the long-term spatial distribution of O3 concentrations indicated a similar pattern as altitude, where high concentration levels were distributed in regions with higher altitude. [Display omitted] •Random forest models of O3 with 0.01° resolution in BTH region were built.•Three metrics of daily O3 concentration during 2010–2017 were simulated.•Reliable model performance was indicated by test-R2 over 0.8.•Long-term temporal and spatial O3 concentration trend were provided. Main finding: Multi-variable RF models of O3 with 0.01° resolution in Beijing-Tianjin-Hebei in China were built, with test-R2 over 0.80.
doi_str_mv 10.1016/j.envpol.2021.116635
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Due to the influence of weather conditions, which showed high contribution to the model, the long-term spatial distribution of O3 concentrations indicated a similar pattern as altitude, where high concentration levels were distributed in regions with higher altitude. [Display omitted] •Random forest models of O3 with 0.01° resolution in BTH region were built.•Three metrics of daily O3 concentration during 2010–2017 were simulated.•Reliable model performance was indicated by test-R2 over 0.8.•Long-term temporal and spatial O3 concentration trend were provided. 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Due to the influence of weather conditions, which showed high contribution to the model, the long-term spatial distribution of O3 concentrations indicated a similar pattern as altitude, where high concentration levels were distributed in regions with higher altitude. [Display omitted] •Random forest models of O3 with 0.01° resolution in BTH region were built.•Three metrics of daily O3 concentration during 2010–2017 were simulated.•Reliable model performance was indicated by test-R2 over 0.8.•Long-term temporal and spatial O3 concentration trend were provided. 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Due to the influence of weather conditions, which showed high contribution to the model, the long-term spatial distribution of O3 concentrations indicated a similar pattern as altitude, where high concentration levels were distributed in regions with higher altitude. [Display omitted] •Random forest models of O3 with 0.01° resolution in BTH region were built.•Three metrics of daily O3 concentration during 2010–2017 were simulated.•Reliable model performance was indicated by test-R2 over 0.8.•Long-term temporal and spatial O3 concentration trend were provided. Main finding: Multi-variable RF models of O3 with 0.01° resolution in Beijing-Tianjin-Hebei in China were built, with test-R2 over 0.80.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.envpol.2021.116635</doi><orcidid>https://orcid.org/0000-0003-2938-3917</orcidid><orcidid>https://orcid.org/0000-0003-2357-3883</orcidid><oa>free_for_read</oa></addata></record>
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1873-6424
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source Elsevier
subjects algorithms
altitude
Ambient ozone
China
ozone
pollution
Random forest model
Simulation
title Random forest model based fine scale spatiotemporal O3 trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017
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