Source apportionment of extinction of ambient particulate matter in Shenzhen using machine learning
High time resolution observations of scattering coefficient of particulate matter(PM)were conducted using turbidimeter,while PM2.5 and its chemical compositions were analysed using others corresponding online instruments from November 2021 to December 2021 in Shenzhen,China.Receptor model and interpretable machine learning models were employed to identify the major sources of PM scattering coefficient.The average PM scattering coefficient at 525nm was 119.6 1/Mm during the observation period in Shenzhen.Correlation analysis revealed that PM scattering coefficient had low correlations with relative humidity(RH)and high correlations with PM2.5 and its components,such as NH4+,NO3-,m/z 44,OM,SO42-,and black carbon,indicating the important influence of secondary components and black carbon in PM2.5 on PM scattering coefficient in Shenzhen.The XGBoost model was employed to predict the PM scattering coefficient based on RH and PM2.5 sources identified by the receptor model,and then the SHAP model was used to identify the major factors of PM scattering coefficient.The results indicated that secondary nitrate,secondary sulfate,vehicle emissions,and secondary organic aerosols were the major sources of PM scattering coeffiicient in Shenzhen during fall and winter,followed by coal combustion and RH.Biomass burning,ship emissions,industrial emissions,fugitive dust and construction dust had small contributions.
万方数据
维普
