宁夏大学学报（自然科学版）2024，Vol.45Issue(2)：196-202,7.
基于机器学习算法的高海拔地区臭氧影响因素重要性分析
Importance Analysis of Ozone Influencing Factors in High-altitude Regions Based on Machine Learning Algorithms
摘要
Abstract
Ozone(O3)is a crucial indicator of atmospheric oxidizing capability and photochemical pollution,pos-ing severe risks to organisms due to prolonged exposure to elevated O3 concentrations.Yinchuan City,located in the high-altitude region of Northwest China,experiences persistent high temperatures and intense ultraviolet radiation in summer,facilitating photochemical reactions that lead to frequent O3 production.Therefore,it is imperative to study O3 pollution and identify the key factors influencing O3 concentration changes.This study relies on data from the National Positioning Station of Yinchuan Urban Ecosystem in Ningxia,and focuses on Yinchuan Phoenix Park for field synchronous positioning observation experiments.Data on O3 concentration,meteorological factors and air pollutants were collected and analyzed using the random forest model,a machine learning algorithm,to identify the key meteorological factors and air pollutants affecting O3 concentration changes.The results indicate that:(1)The variance interpretation rate of the random forest model exceeds 88%,with a determination coefficient(R2)of 0.974 between observed and fitted values,and a root mean square error(RMSE)of 85.8,indicating a good fit.(2)The importance ranking of key factors influencing O3 concentration,as identified by the model,shows that the four variables with significant contributions are relative humidity(27.8),NO(20.1),NO2(16.1),and PM2.5(12.7).(3)There is a significant nonlinear relation-ship between each variable and O3 concentration,with nitrogen oxides(NO,NO2)having the largest threshold effect on O3 concentration,followed by relative humidity and temperature.Thus,the application of the random forest model provides a nuanced understanding of the nonlinear relationships between O3 concentration and its influencing factors,clarifying the critical factors and their threshold effects.These findings offer scientific basis and technical support for the prevention and control of O3 pollution in high-altitude regions.关键词
机器学习算法/臭氧/随机森林模型/气象因子/大气污染物Key words
machine learning algorithms/ozone/random forest model/meteorological factors/atmospheric pollutants分类
天文与地球科学引用本文复制引用
施光耀,杨思琪,张劲松,杜慧慧,庞丹波..基于机器学习算法的高海拔地区臭氧影响因素重要性分析[J].宁夏大学学报（自然科学版）,2024,45(2):196-202,7.基金项目
国家自然科学基金资助项目(32201631) （32201631）
宁夏自然科学基金资助项目(2023AAC03142) （2023AAC03142）
宁夏高层次人才科研启动项目(2023BSB03073) （2023BSB03073）
银川市科技支撑项目(2023SFZD04) （2023SFZD04）
宁夏高等学校自然科学优秀青年项目(NY2022006) （NY2022006）
