Characteristics and source apportionment of black carbon aerosols over an urban site

Aethalometer based source apportionment model using the measured aerosol absorption coefficients at different wavelengths is used to apportion the contribution of fossil fuel and wood burning sources to the total black carbon (BC) mass concentration. Temporal and seasonal variabilities in BC mass concentrations, equivalent BC from fossil fuel (BC (f f) ), and wood burning (BC (w b) ) are investigated over an urban location in western India during January 2014 to December 2015. BC, BC (f f) , and BC (w b) mass concentrations exhibit strong diurnal variation and are mainly influenced by atmospheric dynamics. BC (f f) was higher by a factor of 2-4 than BC (w b) and contributes maximum to BC mass throughout the day, confirming consistent anthropogenic activities. Diurnal contribution of BC (f f) and BC (w b) exhibits opposite variation due to differences in emission sources over Ahmedabad. Night time BC values are about a factor of 1.4 higher than day time BC values. The annual mean percentage contributions of day time and night time are 42 and 58 %, respectively. BC, BC (f f) , and BC (w b) mass concentrations exhibit large and significant variations during morning, afternoon, evening, and night time. During afternoon, mass concentration values are minimum throughout the year because of the fully evolved boundary layer and reduced anthropogenic activities. BC exhibits a strong seasonal variability with postmonsoon high (8.3 mu g m(-3)) and monsoon low (1.9 mu g m(-3)). Annual mean BC (f f) and BC (w b) contributions are 80 and 20 %, respectively, to total BC, which suggests that major contribution of BC in Ahmedabad comes from fossil fuel emissions. The results show that the study location is dominated by fossil fuel combustion as compared to the emissions from wood burning. The results obtained represent a regional value over an urban regime which can be used as inputs on source apportionment to model BC emissions in regional and global climate models.