Characterization of the seasonal cycle of south Asian aerosols: A regional-scale modeling analysis

The sulfur transport and deposition model (STEM) is used to study the aerosol seasonality, distribution, and composition over south Asia from September 2004 to August 2005. Model predictions of sulfate, black carbon, primary organic carbon, other anthropogenic particulate matter, windblown mineral dusts, and sea salt are compared at two sites in south Asia where yearlong experimental observations are available from the Atmospheric Brown Cloud (ABC) project. The model predictions are able to capture both the magnitude and seasonality of aerosols over Hanimaadhoo Observatory, Maldives. However, the model is not able to explain the seasonality at the Kathmandu Observatory; but the model does capture Kathmandu's observed annual mean concentration. The absence of seasonal brick kiln emissions within Kathmandu valley in the current inventory is a probable reason for this problem. This model study reveals high‐anthropogenic aerosol loading over the Ganges valley even in the monsoonal months, which needs to be corroborated by experimental observations. Modeling results also show a high dust loading over south Asia with a distinct seasonality. Model results of aerosol monthly composition are also presented at five cities in south Asia. Total and fine‐mode monthly aerosol optical depth along with contribution from each aerosol species is presented; the results show that the anthropogenic fraction dominates in the postmonsoon and the early dry season with major contributions from sulfate and absorbing aerosols. Model sensitivity studies of dry deposition velocity and wet scavenging efficiency show that model improvements are needed in the treatment of carbonaceous aerosol dry and wet removal processes. Modeled SO2 conversion rate constrained with sulfate observations at Hanimaadhoo suggests the need to increase model sulfate production rate during the dry season to account for probable sulfate production via heterogeneous pathways.