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 

机构地区：[1]Big Data Research Center for Ecology and Environment,Environment Research Institute,Shandong University,Qingdao 266237,China [2]Shandong Provincial Eco-environment Monitoring Center,Jinan 250101,China

出　　处：《Journal of Environmental Sciences》2024年第3期30-40,共11页环境科学学报（英文版）

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

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.

关 键 词：Air quality prediction Deep learning Ensemble method Nitrogen dioxide Spatiotemporal covariates 

分 类 号：X51[环境科学与工程—环境工程]