Loading…
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...
Saved in:
Published in: | Environmental pollution (1987) 2021-05, Vol.276, p.116635, Article 116635 |
---|---|
Main Authors: | , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c484t-832084bcdb50789d4cb835467acd2dcc8bba5625655c9386f7a3fdb50e01c3b23 |
---|---|
cites | cdi_FETCH-LOGICAL-c484t-832084bcdb50789d4cb835467acd2dcc8bba5625655c9386f7a3fdb50e01c3b23 |
container_end_page | |
container_issue | |
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 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2551966664</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S026974912100213X</els_id><sourcerecordid>2551966664</sourcerecordid><originalsourceid>FETCH-LOGICAL-c484t-832084bcdb50789d4cb835467acd2dcc8bba5625655c9386f7a3fdb50e01c3b23</originalsourceid><addsrcrecordid>eNp9kEtLxTAQhYMoeH38AxdZurDXvNtuBL34gguC6DqkyVRzaZOaVNF_b0tdO4uZWZxz4HwInVGypoSqy90awtcQuzUjjK4pVYrLPbSiVckLJZjYRyvCVF2UoqaH6CjnHSFEcM5X6PvZBBd73MYEecR9dNDhxmRwuPUBcLamm_ZgRh9H6IeYTIefOB4TBJexD3h8B3wDfufDW_HiTZie4gEa8DjBm49h1mzefTAXmBFK8BjnW56gg9Z0GU7_7jF6vbt92TwU26f7x831trCiEmNRcUYq0VjXSFJWtRO2qbgUqjTWMWdt1TRGKiaVlLbmlWpLw9tZDIRa3jB-jM6X3CHFj8-po-59ttB1JkD8zJpJSWs1jZikYpHaFHNO0Ooh-d6kH02JnkHrnV5A6xm0XkBPtqvFBlONLw9JZ-shWHA-gR21i_7_gF8aa4eT</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2551966664</pqid></control><display><type>article</type><title>Random forest model based fine scale spatiotemporal O3 trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017</title><source>Elsevier</source><creator>Ma, Runmei ; Ban, Jie ; Wang, Qing ; Zhang, Yayi ; Yang, Yang ; He, Mike Z. ; Li, Shenshen ; Shi, Wenjiao ; Li, Tiantian</creator><creatorcontrib>Ma, Runmei ; Ban, Jie ; Wang, Qing ; Zhang, Yayi ; Yang, Yang ; He, Mike Z. ; Li, Shenshen ; Shi, Wenjiao ; Li, Tiantian</creatorcontrib><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.</description><identifier>ISSN: 0269-7491</identifier><identifier>EISSN: 1873-6424</identifier><identifier>DOI: 10.1016/j.envpol.2021.116635</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>algorithms ; altitude ; Ambient ozone ; China ; ozone ; pollution ; Random forest model ; Simulation</subject><ispartof>Environmental pollution (1987), 2021-05, Vol.276, p.116635, Article 116635</ispartof><rights>2021 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c484t-832084bcdb50789d4cb835467acd2dcc8bba5625655c9386f7a3fdb50e01c3b23</citedby><cites>FETCH-LOGICAL-c484t-832084bcdb50789d4cb835467acd2dcc8bba5625655c9386f7a3fdb50e01c3b23</cites><orcidid>0000-0003-2938-3917 ; 0000-0003-2357-3883</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Ma, Runmei</creatorcontrib><creatorcontrib>Ban, Jie</creatorcontrib><creatorcontrib>Wang, Qing</creatorcontrib><creatorcontrib>Zhang, Yayi</creatorcontrib><creatorcontrib>Yang, Yang</creatorcontrib><creatorcontrib>He, Mike Z.</creatorcontrib><creatorcontrib>Li, Shenshen</creatorcontrib><creatorcontrib>Shi, Wenjiao</creatorcontrib><creatorcontrib>Li, Tiantian</creatorcontrib><title>Random forest model based fine scale spatiotemporal O3 trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017</title><title>Environmental pollution (1987)</title><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.</description><subject>algorithms</subject><subject>altitude</subject><subject>Ambient ozone</subject><subject>China</subject><subject>ozone</subject><subject>pollution</subject><subject>Random forest model</subject><subject>Simulation</subject><issn>0269-7491</issn><issn>1873-6424</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLxTAQhYMoeH38AxdZurDXvNtuBL34gguC6DqkyVRzaZOaVNF_b0tdO4uZWZxz4HwInVGypoSqy90awtcQuzUjjK4pVYrLPbSiVckLJZjYRyvCVF2UoqaH6CjnHSFEcM5X6PvZBBd73MYEecR9dNDhxmRwuPUBcLamm_ZgRh9H6IeYTIefOB4TBJexD3h8B3wDfufDW_HiTZie4gEa8DjBm49h1mzefTAXmBFK8BjnW56gg9Z0GU7_7jF6vbt92TwU26f7x831trCiEmNRcUYq0VjXSFJWtRO2qbgUqjTWMWdt1TRGKiaVlLbmlWpLw9tZDIRa3jB-jM6X3CHFj8-po-59ttB1JkD8zJpJSWs1jZikYpHaFHNO0Ooh-d6kH02JnkHrnV5A6xm0XkBPtqvFBlONLw9JZ-shWHA-gR21i_7_gF8aa4eT</recordid><startdate>20210501</startdate><enddate>20210501</enddate><creator>Ma, Runmei</creator><creator>Ban, Jie</creator><creator>Wang, Qing</creator><creator>Zhang, Yayi</creator><creator>Yang, Yang</creator><creator>He, Mike Z.</creator><creator>Li, Shenshen</creator><creator>Shi, Wenjiao</creator><creator>Li, Tiantian</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0003-2938-3917</orcidid><orcidid>https://orcid.org/0000-0003-2357-3883</orcidid></search><sort><creationdate>20210501</creationdate><title>Random forest model based fine scale spatiotemporal O3 trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017</title><author>Ma, Runmei ; Ban, Jie ; Wang, Qing ; Zhang, Yayi ; Yang, Yang ; He, Mike Z. ; Li, Shenshen ; Shi, Wenjiao ; Li, Tiantian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c484t-832084bcdb50789d4cb835467acd2dcc8bba5625655c9386f7a3fdb50e01c3b23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>algorithms</topic><topic>altitude</topic><topic>Ambient ozone</topic><topic>China</topic><topic>ozone</topic><topic>pollution</topic><topic>Random forest model</topic><topic>Simulation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ma, Runmei</creatorcontrib><creatorcontrib>Ban, Jie</creatorcontrib><creatorcontrib>Wang, Qing</creatorcontrib><creatorcontrib>Zhang, Yayi</creatorcontrib><creatorcontrib>Yang, Yang</creatorcontrib><creatorcontrib>He, Mike Z.</creatorcontrib><creatorcontrib>Li, Shenshen</creatorcontrib><creatorcontrib>Shi, Wenjiao</creatorcontrib><creatorcontrib>Li, Tiantian</creatorcontrib><collection>CrossRef</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Environmental pollution (1987)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ma, Runmei</au><au>Ban, Jie</au><au>Wang, Qing</au><au>Zhang, Yayi</au><au>Yang, Yang</au><au>He, Mike Z.</au><au>Li, Shenshen</au><au>Shi, Wenjiao</au><au>Li, Tiantian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Random forest model based fine scale spatiotemporal O3 trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017</atitle><jtitle>Environmental pollution (1987)</jtitle><date>2021-05-01</date><risdate>2021</risdate><volume>276</volume><spage>116635</spage><pages>116635-</pages><artnum>116635</artnum><issn>0269-7491</issn><eissn>1873-6424</eissn><abstract>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.</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> |
fulltext | fulltext |
identifier | ISSN: 0269-7491 |
ispartof | Environmental pollution (1987), 2021-05, Vol.276, p.116635, Article 116635 |
issn | 0269-7491 1873-6424 |
language | eng |
recordid | cdi_proquest_miscellaneous_2551966664 |
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 |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T20%3A54%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Random%20forest%20model%20based%20fine%20scale%20spatiotemporal%20O3%20trends%20in%20the%20Beijing-Tianjin-Hebei%20region%20in%20China,%202010%20to%202017&rft.jtitle=Environmental%20pollution%20(1987)&rft.au=Ma,%20Runmei&rft.date=2021-05-01&rft.volume=276&rft.spage=116635&rft.pages=116635-&rft.artnum=116635&rft.issn=0269-7491&rft.eissn=1873-6424&rft_id=info:doi/10.1016/j.envpol.2021.116635&rft_dat=%3Cproquest_cross%3E2551966664%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c484t-832084bcdb50789d4cb835467acd2dcc8bba5625655c9386f7a3fdb50e01c3b23%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2551966664&rft_id=info:pmid/&rfr_iscdi=true |