[Daily NO2 Simulation Research Based on Automatic Machine Learning Ensemble Models].

Q2 Environmental Science
Kai-Kai Lu, Jing Li, De-Ren Liu, Fa-Zhao Xu, Yu-Na Zhang, Shi-Xing Zhu
{"title":"[Daily NO<sub>2</sub> Simulation Research Based on Automatic Machine Learning Ensemble Models].","authors":"Kai-Kai Lu, Jing Li, De-Ren Liu, Fa-Zhao Xu, Yu-Na Zhang, Shi-Xing Zhu","doi":"10.13227/j.hjkx.202311087","DOIUrl":null,"url":null,"abstract":"<p><p>To understand the spatial distribution of NO<sub>2</sub> near the surface, we utilized measured data from NO<sub>2</sub> monitoring stations and combined it with column concentration data from the Tropospheric Monitoring Instrument (TROPOMI), taking the Yangtze River Delta region as the study area. We considered the impact of factors such as population, elevation, and meteorological conditions on NO<sub>2</sub> levels. We used automated machine learning to select five machine-learning algorithms with high simulation accuracy, namely ET, RF, XGBoost, LightGBM, and Catboost, and then integrated these five algorithms using the Stacking model to simulate the daily NO<sub>2</sub> concentration in the Yangtze River Delta region from March 2020 to February 2021. The results indicated that the RMAE and MAE values of the Stacking ensemble model were 7.078 and 5.270, respectively, which outperformed the single algorithms of ET, RF, XGBoost, LightGBM, and Catboost. The spatial distribution of high NO<sub>2</sub> concentrations in the Yangtze River Delta region, consisting of three provinces and one municipality, exhibited a U-shaped pattern with the convergence point located at the intersection of the three provinces, extending towards the southwest. Notably, urban pollution was particularly significant in the urban agglomerations centered around Shanghai, Hangzhou, Nanjing, and Hefei. There were 27 cities that exceeded the national standard daily limit. Changzhou was the city with the most serious NO<sub>2</sub> pollution, with the NO<sub>2</sub> concentration exceeding the standard for 14 d, followed by Shanghai, with 13 d. In terms of seasonal variation, the order of severity was as follows: winter, autumn, spring, and summer, with the least NO<sub>2</sub> pollution occurring on July 9th during the summer, and the most severe NO<sub>2</sub> pollution was observed on December 23rd during the winter.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"45 10","pages":"5740-5747"},"PeriodicalIF":0.0000,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"环境科学","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.13227/j.hjkx.202311087","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0

Abstract

To understand the spatial distribution of NO2 near the surface, we utilized measured data from NO2 monitoring stations and combined it with column concentration data from the Tropospheric Monitoring Instrument (TROPOMI), taking the Yangtze River Delta region as the study area. We considered the impact of factors such as population, elevation, and meteorological conditions on NO2 levels. We used automated machine learning to select five machine-learning algorithms with high simulation accuracy, namely ET, RF, XGBoost, LightGBM, and Catboost, and then integrated these five algorithms using the Stacking model to simulate the daily NO2 concentration in the Yangtze River Delta region from March 2020 to February 2021. The results indicated that the RMAE and MAE values of the Stacking ensemble model were 7.078 and 5.270, respectively, which outperformed the single algorithms of ET, RF, XGBoost, LightGBM, and Catboost. The spatial distribution of high NO2 concentrations in the Yangtze River Delta region, consisting of three provinces and one municipality, exhibited a U-shaped pattern with the convergence point located at the intersection of the three provinces, extending towards the southwest. Notably, urban pollution was particularly significant in the urban agglomerations centered around Shanghai, Hangzhou, Nanjing, and Hefei. There were 27 cities that exceeded the national standard daily limit. Changzhou was the city with the most serious NO2 pollution, with the NO2 concentration exceeding the standard for 14 d, followed by Shanghai, with 13 d. In terms of seasonal variation, the order of severity was as follows: winter, autumn, spring, and summer, with the least NO2 pollution occurring on July 9th during the summer, and the most severe NO2 pollution was observed on December 23rd during the winter.

[基于自动机器学习集合模型的每日二氧化氮模拟研究]。
为了解近地面二氧化氮的空间分布,我们利用二氧化氮监测站的实测数据,并结合对流层监测仪(TROPOMI)的柱浓度数据,以长江三角洲地区为研究区域。我们考虑了人口、海拔和气象条件等因素对二氧化氮水平的影响。我们采用自动机器学习方法,选择了五种模拟精度较高的机器学习算法,即 ET、RF、XGBoost、LightGBM 和 Catboost,然后将这五种算法集成到 Stacking 模型中,模拟了 2020 年 3 月至 2021 年 2 月长三角地区的 NO2 日浓度。结果表明,Stacking集合模型的RMAE和MAE值分别为7.078和5.270,优于ET、RF、XGBoost、LightGBM和Catboost等单一算法。由三省一市组成的长三角地区二氧化氮高浓度的空间分布呈现出 U 型模式,汇聚点位于三省交汇处,并向西南方向延伸。值得注意的是,以上海、杭州、南京和合肥为中心的城市群的城市污染尤为严重。有 27 个城市的日均空气质量超过国家标准限值。二氧化氮污染最严重的城市是常州,二氧化氮浓度超标 14 天,其次是上海,超标 13 天。从季节变化来看,严重程度依次为冬季、秋季、春季和夏季,夏季二氧化氮污染最轻的时间为 7 月 9 日,冬季二氧化氮污染最严重的时间为 12 月 23 日。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
环境科学
环境科学 Environmental Science-Environmental Science (all)
CiteScore
4.40
自引率
0.00%
发文量
15329
期刊介绍:
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信