Seasonal variability and sources of brown carbon in Wuhan: Insights from Bayesian modeling and organic tracers-based source apportionment

Abstract

Brown carbon (BrC) is an important constituent of atmospheric carbonaceous aerosols, and accurately measuring its mass concentration and optical property is of great importance to reduce its uncertainties in model parametrization. This study deployed the novel Bayesian inference (BI) model to determine the BrC mass concentration and absorption on an hour-by-hour basis, using online measurements of multi-wavelength light absorption and total carbon (TC) data over June and December 2023 in Wuhan, central China. BrC mass contributed to 42 % of the TC aerosol and showed comparable level in two months, while its absorption showed distinct seasonal contrast with much higher values in December. Important BrC compositions including various organic tracers were measured, taking advantage of the concurrently collected daily 12-h offline filter samples. BrC mass and absorption showed good correlations with important components such as oxygenated-polycyclic aromatic hydrocarbons (PAHs), light PAHs and biomass burning tracer-saccharides. Organic-tracer based source apportionment was conducted for both BrC mass and absorption, and nine distinct source factors were resolved. Secondary sources especially secondary organic aerosol (SOA) factor dominated BrC mass contributions in June, while biomass burning emerged as the primary source in December. The primary sources contributing to BrC absorption were SOA and biomass burning in June, while biomass burning dominated in December. Coal combustion, which was a minor source to BrC mass, was a non-negligible contributor to BrC absorption in both months. Different control measures should be implemented to effectively control BrC mass for improved air quality and to mitigate BrC absorption for its climate effect. The results from this study broaden our understandings of the relationship among chemical, optical and sources of BrC, offering significant implications for environmental management.