Fine particulate matter source apportionment using a hybrid chemical transport and receptor model approach
- 1School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- *now at: Program of Environmental Engineering, Universidad de La Salle, Bogota, Colombia
- **now at: Center for Global and Regional Environmental Research, University of Iowa, Iowa City, Iowa, USA
Abstract. A hybrid fine particulate matter (PM2.5) source apportionment approach based on a receptor model (RM) species balance and species specific source impacts from a chemical transport model (CTM) equipped with a sensitivity analysis tool is developed to provide physically and chemically consistent relationships between source emissions and receptor impacts. This hybrid approach enhances RM results by providing initial estimates of source impacts from a much larger number of sources than are typically used in RMs, and provides source–receptor relationships for secondary species. Further, the method addresses issues of source collinearities and accounts for emissions uncertainties. We apply this hybrid approach to conduct PM2.5 source apportionment at Chemical Speciation Network (CSN) sites across the US. Ambient PM2.5 concentrations at these receptor sites were apportioned to 33 separate sources. Hybrid method results led to large changes of impacts from CTM estimates for sources such as dust, woodstoves, and other biomass-burning sources, but limited changes to others. The refinements reduced the differences between CTM-simulated and observed concentrations of individual PM2.5 species by over 98% when using a weighted least-squares error minimization. The rankings of source impacts changed from the initial estimates, further demonstrating that CTM-only results should be evaluated with observations. Assessment with RM results at six US locations showed that the hybrid results differ somewhat from commonly resolved sources. The hybrid method also resolved sources that typical RM methods do not capture without extra measurement information for unique tracers. The method can be readily applied to large domains and long (such as multi-annual) time periods to provide source impact estimates for management- and health-related studies.