Atmospheric optical and radiative effects of anthropogenic aerosol constituents from India

A box model has been used to compare the burdens, optical depths and direct radiative forcing from anthropogenic PM2.5 aerosol constituents over the I ndian subcontinent. A PM2.5 emission inventory from India for 1990, compile d for the first time, placed anthropogenic aerosol emissions at 12.6 Tg yr( -1). The contribution from various aerosol constituents was 28% sulphate, 2 5% mineral (clay), 23% fly-ash, 20% organic matter and 4% black carbon. Fos sil fuel combustion and biomass burning accounted for 68% and 32%, respecti vely, of the combustion aerosol emissions. The monthly mean aerosol burdens ranged from 4.9 to 54.4 mg m(-2) with an annual average of 18.4 +/- 22.1 m g m(-2). The largest contribution was from fly-ash from burning of coal (40 %), which has a high average ash content of 30%. This was followed by contr ibutions of organic matter (23 %) and sulphate (22%). Alkaline constituents of fly-ash could neutralise rainfall acidity and contribute to the observe d high rainfall alkalinity in this region. The estimated annual average opt ical depth was 0.08 +/- 0.06, with sulphate accounting For 36%, organic mat ter for 32% and black carbon for 13%, in general agreement with those of Sa theesh et al. (1999). The mineral aerosol contribution (5%) was lower than that from the previous study because of wet deposition from high rainfall i n the months of high emissions and the complete mixing assumption in the bo x model. The annual average radiative forcing was - 1.73 +/- 1.93 W m-2 wit h contributions of 49% from sulphate aerosols, followed by organic matter ( 26%), black carbon (11%) and fly-ash (11%). These results indicate the impo rtance of organic matter and fly-ash to atmospheric optical and radiative e ffects. The uncertainties in estimated parameters range 80-120% and result largely from uncertainties in emission and wet deposition rates. Therefore, improvement is required in the emissions estimates and scavenging ratios, to increase confidence in these predictions. (C) 2000 Elsevier Science Ltd. All rights reserved.