Abstract
Mass absorption cross-section of black carbon (MACBC) describes the absorptive cross-section per unit mass of black carbon, and is, thus, an essential parameter to estimate the radiative forcing of black carbon. Many studies have sought to estimate MACBC from a theoretical perspective, but these studies require the knowledge of a set of aerosol properties, which are difficult and/or labor-intensive to measure. We therefore investigate the ability of seven data analytical approaches (including different multivariate regressions, support vector machine, and neural networks) in predicting MACBC for both ambient and biomass burning measurements. Our model utilizes multi-wavelength light absorption and scattering as well as the aerosol size distributions as input variables to predict MACBC across different wavelengths. We assessed the applicability of the proposed approaches in estimating MACBC using different statistical metrics (such as coefficient of determination (R2), mean square error (MSE), fractional error, and fractional bias). Overall, the approaches used in this study can estimate MACBC appropriately, but the prediction performance varies across approaches and atmospheric environments. Based on an uncertainty evaluation of our models and the empirical and theoretical approaches to predict MACBC, we preliminarily put forth support vector machine (SVM) as a recommended data analytical technique for use. We provide an operational tool built with the approaches presented in this paper to facilitate this procedure for future users.
Highlights
Black carbon (BC) aerosols are emitted from incomplete combustion processes as fine particles [1,2]
We include parameterizations of both empirical particle number distributions and particle volume distributions as candidates for our models. Even though these are inherently related, we argue that including representations of both is somewhat analogous to the TwO-Moment Aerosol Sectional (TOMAS) model [57], which tracks both number and mass distributions, and has been implemented in chemistry-climate models (e.g., [58,59]) to predict aerosol microphysics
We only present the results of ordinary least squares (OLS), least absolute shrinkage and selection operation (LASSO), and support vector machine (SVM) in this figure; results for the other models are provided in Figures S7 and S8
Summary
Black carbon (BC) aerosols are emitted from incomplete combustion processes (e.g., fossil fuel and biomass burning) as fine particles [1,2]. BC has a major role in the climate system due to its ability to absorb solar radiation and interactions with clouds [3,4,5]. Understanding the properties of BC and quantifying its mass concentration in the atmosphere are essential to estimate its impacts on climate change. Babs can be measured using various instruments (such as in situ and filter-based optical instruments [7,9]), the value of MAC needs to be estimated or known in advance when performing the numerical calculation of BC mass. The derived BC mass is sensitive to the adopted MAC value, so improved estimation of MACBC will improve estimates of BC mass concentration from ground-based networks providing Babs , Atmosphere 2020, 11, 1185; doi:10.3390/atmos11111185 www.mdpi.com/journal/atmosphere
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