Estimating mass-absorption cross-section of ambient black carbon aerosols: Theoretical, empirical, and machine learning models

Abstract

The mass-absorption cross-section of black carbon (MAC_BC) is an essential parameter to link the atmospheric concentration of black carbon (BC) with its radiative forcing. When a direct calculation of MAC_BC based on observations of aerosol light absorption and BC mass concentration is impossible, we rely on modeling and simulations to estimate MAC_BC, but currently, there is no consensus model that can be relied on for accurate predictions across all atmospheric environments when BC particles have different coating thicknesses. Here, we applied five MAC_BC prediction models (including three light scattering theories, an empirical model based on observations of particle mass concentrations, and a machine learning model developed in our previous work) to aerosols from three Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) field campaigns. While many studies have found that increasing the complexity of the models helps to constrain biases of the estimated MAC_BC, our effort is to evaluate the models based on the criteria of simplicity and accuracy. We find that our machine learning model (support vector machine for regression, SVM) generally performs well across all DOE ARM field campaign data, while the accuracy of core-shell Mie theory depends on the bias correction algorithm applied to filter-based light absorption data. Generally, the empirical model for internally mixed particles that we considered tends to over-predict MAC_BC, while Mie theory for externally mixed particles tends to under-predict MAC_BC. An examination of the influence of coating material on BC cores suggests that the performance of our current SVM model is degraded when the BC is thickly coated (e.g., it has undergone aging and mixing with other materials in the atmosphere).

EDITOR:

• Hans Moosmüller

This article is related to:

Application of machine learning approaches in the analysis of mass absorption cross-section of black carbon aerosols: Aerosol composition dependencies and sensitivity analyses

Data availability statement

The DOE ARM field campaign data are available through https://www.archive.arm.gov/discovery/. The FIREX data are available from the project website https://www.esrl.noaa.gov/csl/projects/firex/firelab/.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was funded by the Atmospheric Chemistry, Carbon Cycle, & Climate program within the National Oceanic and Atmospheric Administration’s Climate Program Office through award NA16OAR4310109. The ambient data at the TCAP, CACTI, LASIC, and GOAMAZON sites were obtained from the Atmospheric Radiation Measurement (ARM) user facility, a U.S. Department of Energy (DOE) Office of Science user facility managed by the Office of Biological and Environmental Research.