Measurement of Black Carbon in Delhi: Evidences of Regional Transport, Meteorology and Local Sources for Pollution Episodes

Abstract

Measurement of particulate matter (PM) constituent such as black carbon (BC) over urban sites is critically important owing to its adverse health and climate impacts. However, the impacts associated with BC are poorly understood primarily because of the scarcity and uncertainties of measurements of BC. Here, we present BC measurement at an urban site of Delhi using a characterized continuous soot monitoring system (COSMOS) for a year-long period, i.e., from September, 2019 to August, 2020. This measurement period covers events, i.e., period of crop residue burnings from nearby states, festive events, e.g., Diwali and New Year, and first COVID-19 lockdown period. Effects of these events combining with local emissions and meteorological conditions on BC mass concentration (MBC) are investigated to find the possible cause of severe pollution levels in Delhi. Mean MBC for the complete observation period was found to be 5.02 ± 4.40 µg m–3. MBC showed significant seasonal as well diurnal variations. Winter season (December to February) is observed to be the most polluted season owing to increased local emissions and non-favorable meteorological conditions. Regional emission from crop burning in nearby states during October and November is the main contributing factor for increased pollution in this post-monsoon season. Furthermore, analysis reveals that cracker burning during festivals can also be considered as contributing factor to high MBC for a short period in post-monsoon and winter seasons. Significant decrease in MBC due to COVID-19 lockdown is also observed. MBC in summer and monsoon are lower as compared to other seasons but are still higher than mean MBC levels in several other urban cities of different countries. Also, the BC data obtained from nearby sites and Modern-Era Retrospective analysis for Research and Applications-version 2 (MERRA-2)’s surface black carbon (SBC) are compared against the MBC to evaluate coherency among the different datasets, and discussed in detail.