Time-sensitive prediction of NO_(2) concentration in China using an ensemble machine learning model from multi-source data

作     者：Chenliang Tao Man Jia Guoqiang Wang Yuqiang Zhang Qingzhu Zhang Xianfeng Wang Qiao Wang Wenxing Wang Chenliang Tao;Man Jia;Guoqiang Wang;Yuqiang Zhang;Qingzhu Zhang;Xianfeng Wang;Qiao Wang;Wenxing Wang

作者机构：Big Data Research Center for Ecology and EnvironmentEnvironment Research InstituteShandong UniversityQingdao 266237China Shandong Provincial Eco-environment Monitoring CenterJinan 250101China 

出 版 物：《Journal of Environmental Sciences》 (环境科学学报（英文版）)

年 卷 期：2024年第137卷第3期

页      面：30-40页

核心收录：

学科分类：0710[理学-生物学] 1002[医学-临床医学] 07[理学] 1001[医学-基础医学(可授医学、理学学位)] 070602[理学-大气物理学与大气环境] 0706[理学-大气科学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Taishan Scholars (No.ts201712003)。 

主　　题：Air quality prediction Deep learning Ensemble method Nitrogen dioxide Spatiotemporal covariates 

摘      要：Nitrogen dioxide(NO_(2))poses a critical potential risk to environmental quality and public health.A reliable machine learning(ML)forecasting framework will be useful to provide valuable information to support government decision-making.Based on the data from1609 air quality monitors across China from 2014-2020,this study designed an ensemble ML model by integrating multiple types of spatial-temporal variables and three sub-models for time-sensitive prediction over a wide range.The ensemble ML model incorporates a residual connection to the gated recurrent unit(GRU)network and adopts the advantage of Transformer,extreme gradient boosting(XGBoost)and GRU with residual connection network,resulting in a 4.1%±1.0%lower root mean square error over XGBoost for the test results.The ensemble model shows great prediction performance,with coefficient of determination of 0.91,0.86,and 0.77 for 1-hr,3-hr,and 24-hr averages for the test results,respectively.In particular,this model has achieved excellent performance with low spatial uncertainty in Central,East,and North China,the major site-dense zones.Through the interpretability analysis based on the Shapley value for different temporal resolutions,we found that the contribution of atmospheric chemical processes is more important for hourly predictions compared with the daily scale predictions,while the impact of meteorological conditions would be ever-prominent for the latter.Compared with existing models for different spatiotemporal scales,the present model can be implemented at any air quality monitoring station across China to facilitate achieving rapid and dependable forecast of NO_(2),which will help developing effective control policies.