Tracking changes in the emission strengths of source-specific aerosols by coupling a receptor model with machine learning

Reducing aerosol mass loading requires targeted control of emissions from anthropogenic sources. Accurately tracking the changes in emission strengths of specific aerosol sources is vital for assessing the effectiveness of regulatory policies. However, this task is challenging due to meteorological influences and the presence of multiple co-existing emissions. Using multi-year data on ambient black carbon (BC) and PM2.5 from Tianjin, China, as a case study, we employed a data-driven approach that integrates a dispersion-normalized factor analysis receptor model with a machine learning technique for meteorological normalization. This approach enabled us to differentiate between the emission sources of BC and PM2.5 and their meteorological impacts. The source-specific aerosol exhibited abrupt changes in response to human-made interventions, such as those during COVID-19 and holiday periods, after accounting for weather-related variables. Notably, significant reductions were observed in emissions from coal combustion, vehicles, dust, and biomass burning over years, affirming the effectiveness of policies such as clean winter heating initiatives and the support for the Clean Air Actions. This coupled approach holds significant promise for advancing air quality accountability studies.