Long term variation of microphysical properties of black carbon in Beijing derived from observation and machine learning

Abstract

The microphysical attributes of black carbon (BC) can determine its absorption and hygroscopic properties. However, long-term information is difficult to obtain from the field. In this study, the BC properties including mass concentration, the coating volume ratio (VR) relative to the refractory BC (rBC), the rBC diameter and the fraction of cloud condensation nuclei (CCN), are derived from a number of field experiments using a random forest model. This model effectively derives the long-term BC microphysical properties in the Beijing region from 2013 to 2020 using continuous measurements of particulate matter, gas, BC mass concentration and meteorological parameters. The results reveal notably higher BC coatings (mean VR = 7.2) and a greater fraction of CCN-like BC (51%) in the winter compared to other seasons. Following the implementation of national air pollution control measures in 2017, BC mass exhibited a substantial reduction of 60% (29%) in the winter (summer), and VR decreased by 45% (24%). Apart from the influence of meteorological variations, these can be attributed to the declined primary emissions and the gas precursors which are associated with secondary formation of BC coatings. The reduction of both BC mass loading and coatings leads to its solar absorption decreasing by 50%, and the fraction of CCN-like BC (likely in clouds) decreasing by 23%. Environmental regulation will therefore continue to reduce both direct and indirect radiative impacts of BC in this region.

Keywords

• black carbon
• long-term
• microphysical properties
• machine learning