{"title":"Simulation of Long Time Series Spatial Distribution of PM2.5 in Beijing-Tianjin-Hebei Region Based on an Improved Machine Learning Method","authors":"Zheyuan Zhang;Huayang Song;Guang Tian;Hongyu Zhang;Jia Wang;Nina Xiong","doi":"10.1109/JSTARS.2025.3602240","DOIUrl":null,"url":null,"abstract":"The Beijing-Tianjin-Hebei (BTH) region has long been facing serious fine particulate matter (PM2.5) pollution issues due to its geographical characteristics and industrial structure. In this study, we innovatively integrated STL-derived seasonal-trend parameters to replace the conventional time variables as inputs to XGBoost, combined with Bayesian optimization and hyperband (BOHB) for hyperparameter tuning. This integrated STL-XGBoost-BOHB framework significantly addressed the bottleneck of missing early monitoring data in long-term PM2.5 inversion. Through the STL time series decomposition method, seasonal trend parameters reflecting the variation of PM2.5 in the BTH region were obtained. These parameters were used as substitutes for time data, addressed the limitations of ground-based PM2.5 monitoring and overcoming the limitation of the lack of early PM2.5 monitoring data in China. The BOHB algorithm was chosen to comparison. The STL-XGBoost-BOHB model has a coefficient of determination (<italic>R</i><sup>2</sup>) reaching 0.78 and root mean square error of 15.8 <italic>μ</i>g/m<sup>3</sup>, demonstrating outstanding performance in PM2.5 retrieval. Model results revealed a distinct spatial distribution of PM2.5, with concentrations decreasing from southeast to northwest. In terms of the temporal variation of PM2.5 concentration, there was a significant decrease in PM2.5 concentration in the BTH region from 2011 to 2020. However, combined with the PM2.5 pollution exposure study based on population data, it was found that the majority of the population in the region mainly concentrated in areas with higher PM2.5 concentrations, and the population-weighted PM2.5 concentration was significantly higher than the original PM2.5 concentration values without population weighting. This highlights the need for more targeted pollution control in densely populated areas.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"21985-21996"},"PeriodicalIF":5.3000,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11141525","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11141525/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The Beijing-Tianjin-Hebei (BTH) region has long been facing serious fine particulate matter (PM2.5) pollution issues due to its geographical characteristics and industrial structure. In this study, we innovatively integrated STL-derived seasonal-trend parameters to replace the conventional time variables as inputs to XGBoost, combined with Bayesian optimization and hyperband (BOHB) for hyperparameter tuning. This integrated STL-XGBoost-BOHB framework significantly addressed the bottleneck of missing early monitoring data in long-term PM2.5 inversion. Through the STL time series decomposition method, seasonal trend parameters reflecting the variation of PM2.5 in the BTH region were obtained. These parameters were used as substitutes for time data, addressed the limitations of ground-based PM2.5 monitoring and overcoming the limitation of the lack of early PM2.5 monitoring data in China. The BOHB algorithm was chosen to comparison. The STL-XGBoost-BOHB model has a coefficient of determination (R2) reaching 0.78 and root mean square error of 15.8 μg/m3, demonstrating outstanding performance in PM2.5 retrieval. Model results revealed a distinct spatial distribution of PM2.5, with concentrations decreasing from southeast to northwest. In terms of the temporal variation of PM2.5 concentration, there was a significant decrease in PM2.5 concentration in the BTH region from 2011 to 2020. However, combined with the PM2.5 pollution exposure study based on population data, it was found that the majority of the population in the region mainly concentrated in areas with higher PM2.5 concentrations, and the population-weighted PM2.5 concentration was significantly higher than the original PM2.5 concentration values without population weighting. This highlights the need for more targeted pollution control in densely populated areas.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.