Estimation of real-time brown carbon absorption: An observationally constrained Mie theory-based optimization method

Abstract

Methods to estimate absorption of brown carbon (brc), a significant fraction of atmospheric absorption, rely on estimating the difference between total measured absorption at near-uv, and that of black carbon (bc). Extrapolation of absorption measured at near-ir wavelengths (assumed exerted by bc alone) use different assumptions of the wavelength dependence of absorption ångström exponent (aaebc). Here, we develop an improved method exploiting real-time multi-wavelength absorption and particle count measurements in a mie based optimization framework, incorporating spectral observational constraints (measured absorption at 880 nm and aae880-660). An optimization approach, using a mie model with core-shell and core-gray shell mixing schemes, is used to derive bc size distribution parameters (absorbing core diameter and scattering shell thickness). Goodness of fit (mie optimization model vs. Measurement) was r = 0.77–0.94 (near-ir absorption) and within 4%–30% for brc estimation. A sensitivity analysis of input parameters (bc geometric standard deviation and refractive index) bounded estimated brc of 32%. Application to a polluted urban site (delhi) and a regional background site (darjeeling) estimated brc absorption (% contribution) at 370 nm as 18–117 mm−1 (15%–29%) and 2–12 mm−1 (5%–21%), respectively. Estimated brc absorption was larger at the regional background site (darjeeling) but smaller at the polluted site (delhi) when compared to constant aae and two-component approaches. Method efficacy is reinforced through larger estimated brc absorption at delhi coinciding with agricultural stubble burning periods in north india. The developed method uses multi-wavelength absorption observational constraints to improve the robustness of brc estimation. © 2022