An optimized tracer-based approach for estimating organic carbon emissions from biomass burning in Ulaanbaatar, Mongolia

Abstract

The impact of biomass burning (BB) on atmospheric particulate matter of <2.5 µm diameter (PM2.5) at Ulaanbaatar, Mongolia, was investigated using an optimized tracer-based approach during winter and spring 2017. Integrated 24 h PM2.5 samples were collected on quartz-fiber filters using a 30 L min−1 air sampler at an urban site in Ulaanbaatar. The aerosol samples were analyzed for organic carbon (OC) and elemental carbon (EC), anhydrosugars (levoglucosan, mannosan, and galactosan), and water-soluble ions. OC was found to be the predominant species, contributing 64 % and 56 % to the quantified aerosol components in PM2.5 in winter and spring, respectively. BB was identified as a major source of PM2.5, followed by dust and secondary aerosols. Levoglucosan ∕ mannosan and levoglucosan ∕ K+ ratios indicate that BB in Ulaanbaatar mainly originated from the burning of softwood. Because of the large uncertainty associated with the quantitative estimation of OC emitted from BB (OCBB), a novel approach was developed to optimize the OC ∕ levoglucosan ratio for estimating OCBB. The optimum OC ∕ levoglucosan ratio in Ulaanbaatar was obtained by regression analysis between OCnon-BB (OCtotal–OCBB) and levoglucosan concentrations that gives the lowest coefficient of determination (R2) and slope. The optimum OC ∕ levoglucosan ratio was found to be 27.6 and 18.0 for winter and spring, respectively, and these values were applied in quantifying OCBB. It was found that 68 % and 63 % of the OC were emitted from BB during winter and spring, respectively. This novel approach can also be applied by other researchers to quantify OCBB using their own chemical measurements. In addition to OCBB, sources of OCnon-BB were also investigated through multivariate correlation analysis. It was found that OCnon-BB originated mainly from coal burning, vehicles, and vegetative emissions.