An Exploratory Approach Using Regression and Machine Learning in the Analysis of Mass Absorption Cross Section of Black Carbon Aerosols: Model Development and Evaluation
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An Exploratory Approach Using Regression and Machine Learning in the Analysis of Mass Absorption Cross Section of Black Carbon Aerosols: Model Development and Evaluation

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  • Journal Title:
    Atmosphere
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    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.
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  • Source:
    Atmosphere 2020, 11(11), 1185
  • DOI:
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA16OAR4310109
  • Rights Information:
    CC BY
  • Compliance:
    Submitted
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