Estimating mass-absorption cross-section of ambient black carbon aerosols: Theoretical, empirical, and machine learning models
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,...
Saved in:
Published in: | Aerosol science and technology 2022-11, Vol.56 (11), p.980-997 |
---|---|
Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | 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). |
---|---|
ISSN: | 0278-6826 1521-7388 |
DOI: | 10.1080/02786826.2022.2114311 |