基于机器学习算法的中国近地面臭氧浓度模拟  

Simulation of ground-level ozone concentration in China based on a machine learning algorithm

作　　者：张顺顺 陆开来 马润美 刘欣 班婕[2,3] 费鲜芸 李湉湉[2,3,5,6] ZHANG Shun-shun;LU Kai-lai;MA Run-mei;LIU Xin;BAN Jie;FEI Xian-yun;LI Tian-tian(School of Geomatics and Marine Information,Jiangsu Ocean University,Lianyungang 222005,China;China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health,Chinese Center for Disease Control and Prevention;National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases/National Institute of Environmental Health,Chinese Center for Disease Control and Prevention;Energy Foundation;School of Public Health,Nanjing Medical University;School of Public Health,Shandong University)

机构地区：[1]江苏海洋大学海洋技术与测绘学院,连云港222005 [2]中国疾病预防控制中心环境与人群健康重点实验室中国疾病预防控制中心环境与健康相关产品安全所 [3]传染病溯源预警与智能决策全国重点实验室中国疾病预防控制中心环境与健康相关产品安全所 [4]能源基金会 [5]南京医科大学公共卫生学院 [6]山东大学公共卫生学院

出　　处：《环境卫生学杂志》2024年第2期121-128,共8页JOURNAL OF ENVIRONMENTAL HYGIENE

基　　金：能源基金会(高温热浪与臭氧污染复合暴露事件的健康影响:G2210-34198);国家自然科学基金面上项目(面向街区尺度交通排放污染的出行人群暴露测度及健康风险评估:52272340);国家自然科学基金项目(基于高精度暴露、人群队列、脂质标志物的大气臭氧长期暴露对中老年人高甘油三酯血症的影响研究:82204001)。

摘　　要：目的探索基于多种机器学习模型的我国近地面臭氧浓度高精度模拟方法。方法基于2013—2017年的多源数据,建立基于多种机器学习算法的全国近地面臭氧浓度模拟模型。结果随机森林(random forest,RF)模型的性能最佳,R^(2)为0.752,RMSE和MAE分别为23.264和16.094μg/m^(3)。地面下沉短波辐射为近地面臭氧浓度模拟的最关键因素。结论基于气象、地理、排放等多元变量的RF模型可实现近地面臭氧高精度模拟。未来可进一步引入空气污染物的自然源排放量数据以提高模型精度。Objective To explore a high-precision simulation method for ground-level ozone concentration in China based on mul-tiple machine learning models.Methods Based on multi-source data from 2013 to 2017,a national ground-level ozone concentration simulation model was established using multiple machine learning algorithms.Results The random forest(RF)model had the best performance with an R^(2) of 0.752,and RMSE and MAE of 23.264μg/m^(3)and 16.094μg/m^(3),respectively.The surface downwelling shortwave radiation was the most critical factor for ground-level ozone concentration simulation.Conclusion The RF model based on multivariate variables such as meteorology,geography,and emission can realize high-precision simulation of ground-level ozone.In the future,the natural source emission data of air pollutants can be further introduced to improve the accuracy of the model.

关 键 词：近地面臭氧 模拟 机器学习算法 多源数据 

分 类 号：R122[医药卫生—环境卫生学]