Predicting the Mass Concentration of Black Carbon in the Atmosphere

  • 17 November 2020
Predicting the Mass Concentration of Black Carbon in the Atmosphere

Black carbon is a type of particle emitted from fossil fuels and burning biomass. Due to its ability to absorb solar radiation and interact with clouds, black carbon plays a major role in climate systems. Quantifying the mass concentration of black carbon in the atmosphere is essential to estimate its impacts on climate change. One widely used method to do so is known as the mass absorption cross-section (MAC) technique. However, using MAC to determine black carbon mass is complex, differs across wavelengths, and is often labor-intensive to measure. The goal of researchers, funded in part by CPO’s Atmospheric Chemistry, Carbon Cycle, & Climate (AC4) program, was to explore and develop a straightforward yet accurate model for predicting changes in the MAC of black carbon over time. Hanyang Li of The Ohio State University and Andrew May from the University of California, Davis assessed the performance of seven different analytical approaches ranging from classical statistical regression to machine learning techniques in predicting AC for black carbon. Their study, published in Atmosphere, presents the framework of their model and evaluates it by testing aerosols from three different environments. Li and May find that any of their approaches can predict MAC for black carbon using data common to ground-based monitoring sites, but the support vector machine (SVM) approach appears to be the most useful over the widest range of aerosol types. Their study also includes an operational tool built with the discussed approaches for use by other researchers. 
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