大气与环境光学学报2025，Vol.20Issue(2)：176-187,12.DOI:10.3969/j.issn.1673-6141.2025.02.006
基于机器学习和多源数据的大气颗粒物质量浓度估算方法
Estimation method of atmospheric particulate matter mass concentration based on machine learning and multi-source data
摘要
Abstract
High concentration of atmospheric particulate matter can cause the reduction of atmospheric visibility and has a negative impact on human health.Therefore,continuous monitoring,estimation and prediction of the temporal and spatial variation characteristics of particulate matter concentration are of great significance for environmental pollution assessment and treatment.In this work,by using the meteorological data of Rencheng District,Jining City,the air quality data from the Electrochemical Plant Station of Jining national environmental air quality monitoring point and the surface aerosol extinction coefficients inverted from lidar signal over Rencheng District from December 2021 to February 2022,a model of particle concentration estimation is established based on machine learning.First,the sliding average of 100 of the air quality index(AQI)in the previous 24-hour is used as the threshold value to divide the samples into two background conditions:clean and polluted.Then,the importance of input factors is ranked using random forest(RF)algorithm,and according to this importance rank,the factors are put into RF,mind evolution algorithm-back propagation neural network(MEA-BPNN),generalized regression neural network(GRNN)and wavelet neural network(WNN)models,respecitively.And then,by comparing the root mean square errors(ERMS)of different models,the optimal model and the optimal number of input factors for particulate matter concentration inversion under different atmospheric background are established.Finally,the particle estimation model developed is applied to the actual atmospheric observation data to evaluate the horizontal distribution of particle concentration under different atmospheric backgrounds based on the optimal model and input factors.关键词
激光雷达/气溶胶消光系数/颗粒物质量浓度/机器学习Key words
lidar/aerosol extinction coefficient/particle mass concentration/machine learning分类
大气科学引用本文复制引用
杨蕙荣,何南腾,卜令兵,莫祖斯,樊增昌,周晓梦,肃欣..基于机器学习和多源数据的大气颗粒物质量浓度估算方法[J].大气与环境光学学报,2025,20(2):176-187,12.基金项目
国家自然科学基金(42175145),南京信息工程大学大创项目(XJDCZX202110300001) （42175145）
