Year‐round radiocarbon‐based source apportionment of carbonaceous aerosols at two background sites in South Asia

Abstract

[1] Atmospheric Brown Clouds (ABC), regional-scale haze events, are a significant concern for both human cardiopulmonary health and regional climate impacts. In order to effectively mitigate this pollution-based phenomenon, it is imperative to understand the magnitude, scope and source of ABC in regions such as South Asia. Two sites in S. Asia were chosen for a 15-month field campaign focused on isotope-based source apportionment of carbonaceous aerosols in [2008][2009]. Both the Maldives Climate Observatory in Hanimaadhoo (MCOH) and a mountaintop site in Sinhagad, India (SINH) act as regionally mixed receptor sites. Annual radiocarbon-based source apportionment for soot elemental carbon (SEC) at MCOH and SINH revealed 73 AE 6% and 59 AE 5% contribution from biomass combustion, respectively (remainder from fossil fuel). The contributions from biogenic/biomass combustion to total organic carbon were similar between MCOH and SINH (69 AE 5% and 64 AE 5, respectively). The biomass combustion contribution for SEC in the current study, especially the results from MCOH, shows good agreement with published black carbon emissions inventories for India. Geographic source assessment, including clustered back trajectory analysis and carbon contribution by source region, indicated that the highest SEC/TOC loads originated from the W. Indian coastal margin, including the coastal city of Mumbai, India. The winter dry season 14 C-based source apportionment of the BC-tracing SEC fraction for 2006, 2008, 2009 were not statistically different (p = 0.7) and point to a near-constant two-thirds contribution from biomass combustion practices, including wood and other biofuels as well as burning of agricultural crop residues.

The impact of residential combustion emissions on atmospheric aerosol, human health, and climate

“…To estimate non-fossil values from the model, we assume that 90 % of residential BC transported to Hanimaadhoo originates from residential biofuel sources (consistent with ≥ 90 % estimates from the GAINS model), while the remaining non-fossil BC originates from open biomass burning (including agricultural waste and open waste/rubbish burning). We find a small contribution (< 10 % for all simulations) of open biomass burning to simulated BC at Hanimaadhoo, confirming that the non-fossil contribution at this location is likely dominated by residential biomass/biofuel sources, which is supported by the observed consistent con- tribution from a non-fossil source (Sheesley et al, 2012). The simulated contribution of non-fossil sources to total BC at this location is ∼ 57-79 %, depending on the time of year and model simulation.…”

“…Bosch et al (2014) estimate that 59 ± 8 % of EC is from non-fossil biomass (February-March). Sheesley et al (2012) estimated that 73 ± 6 % of EC originated from non-fossil biomass during the dry season (November-February). The observed contribution of non-fossil BC (EC) therefore spans a range of 46-73 %.…”

“…We further evaluate the simulated contribution of residential emissions to BC concentrations using 14 C source apportionment studies on the island of Hanimaadhoo (Gustafsson et al, 2009;Sheesley et al, 2012;Bosch et al, 2014), which is influenced by pollution transported from the Indian subcontinent. The model simulates well both BC and OC concentrations observed at this location (Sect.…”

“…We also use information on BC fossil and non-fossil fractions as obtained from three separate source apportionment studies (Gustafsson et al, 2009;Sheesley et al, 2012;Bosch et al, 2014) that use 14 C analysis of carbonaceous aerosol taken at Hanimaadhoo in the Indian Ocean. This technique determines the fossil and non-fossil fractions of carbonaceous aerosol, since 14 C is depleted in fossil fuel aerosol (half-life 5730 years), whereas non-fossil aerosol (e.g.…”

Chemical composition of the inorganic fraction of cloud-water at a high altitude station in West India

“…The vehicles that enter the mountain stop at a distance of about 1 km away from the sampling site. In other directions the station is influenced by regional as well as long distance sources including those in west to central India with additional possible influence from the Indo-Gangetic Plains situated to the northeast (Momin et al, 2005;Gustafsson et al, 2009;Budhavant et al, 2009Budhavant et al, , 2012Sheesley et al, 2012).…”