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[1] Throughout South Asia biomass is commonly used as a fuel source for cooking and heating homes. The smoke from domestic use of these fuels is expected to be a major source of atmospheric particulate matter in the region and needs to be characterized for input in regional source apportionment models and global climate models. Biomass fuel samples including coconut leaves, rice straw, jackfruit branches, dried cowdung patties, and biomass briquettes manufactured from compressed biomass material were obtained from Bangladesh. The fuel samples were burned in a wood stove to collect and characterize the particulate matter emissions. The bulk chemical composition including total organic and elemental carbon, sulfate, nitrate, ammonium and chloride ions, and bulk elements such as potassium and sodium did not show conclusive differences among the biomass samples tested. Unique features, however, exist in the detailed organic characterization of the combustion smoke from the different sources. The organic compound fingerprints of the particulate matter are shown to be distinct from one another and distinct from North American wood fuels. Fecal stanols including 5b-stigmastanol, coprostanol, and cholestanol are found to be good molecular markers for the combustion of cowdung. Additionally, the patterns of methoxyphenols and plant sterols provide a unique signature for each biomass sample and are conducive as source apportionment tracers.

[1] Fine particle organic carbon in Delhi, Mumbai, Kolkata, and Chandigarh is speciated to quantify sources contributing to fine particle pollution. Gas chromatography/mass spectrometry of 29 particle-phase organic compounds, including n-alkanes, polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes, and levoglucosan along with quantification of silicon, aluminum, and elemental carbon are used in a molecular-marker based source apportionment model to quantify the primary source contributions to the PM 2.5 mass concentrations for four seasons in three sites and for the summer in Chandigarh. Five primary sources are identified and quantified: diesel engine exhaust, gasoline engine exhaust, road dust, coal combustion, and biomass combustion. Important trends in the seasonal and spatial patterns of the impact of these five sources are observed. On average, primary emissions from fossil fuel combustion (coal, diesel, and gasoline) are responsible for about 25-33% of PM 2.5 mass in Delhi, 21-36% in Mumbai, 37-57% in Kolkata, and 28% in Chandigarh. These figures can be compared to the biomass combustion contributions to ambient PM 2.5 of 7-20% for Delhi, 7-20% for Mumbai, 13-18% for Kolkata, and 8% for Chandigarh. These measurements provide important information about the seasonal and spatial distribution of fine particle phase organic compounds in Indian cities as well as quantifying source contributions leading to the fine particle air pollution in those cities.

As a part of a longitudinal study in the highlands of Guatemala to elicit the chronic health effects of wood smoke from cooking, mean area and personal 48 h concentrations of 2.5 microm particulate matter (PM2.5) and carbon monoxide (CO) were measured every 3 months over 19 months. Monitoring was conducted in 63 households, 28 using traditional open wood fires and 35 using wood cookstoves with chimneys. The goal of this paper is to estimate personal exposure concentrations to PM2.5 using the measurements from CO diffusion tubes as a proxy. CO tubes are cheaper and easier to use than PM-monitoring devices, and can be worn by all family members, even infants. The relationship of PM2.5 and CO was determined by comparing measurements from both co-located instruments. CO measurements in ppm were corrected for temperature and pressure to mass concentrations. PM2.5 exposure was modeled with the following linear regression created using measured concentrations: PM2.5 (mg m(-3)) = 0.10 (0.093, 0.12) x CO (mg m(-3)) + 0.067 (0.0069, 0.13), R(2) = 0.76. No significant difference was found between the separate regressions for open fires and cookstoves. No significant improvement was obtained by applying a mixed statistical model. The equation was used to estimate personal exposures of PM2.5 using personal CO measurements from CO tubes worn by women, infants under 18 months, and children 48-72 months. Estimated 48 h mean personal PM2.5 concentrations for mother, infants, and children in open-fire homes were 0.27 +/- 0.02, 0.20 +/- 0.02, and 0.16 +/- 0.02 mg m(-3) respectively. In chimney-stove homes, mothers and children experienced PM2.5 personal concentrations of 0.22 +/- 0.03 and 0.14 +/- 0.03 mg m(-3), respectively.

Abstract. Predicting the cloud condensation nuclei (CCN) supersaturation spectrum from aerosol properties is a fairly straightforward matter, as long as those properties are simple. During the Indian Ocean Experiment we measured CCN spectra, size-resolved aerosol chemical composition, and aerosol number distributions and attempted to reconcile them using a modified form of K6hler theory. We obtained general agreement between our measured and modeled CCN spectra. However, the agreement was not as good during a time period when organic carbon comprised a quarter of the total mass of the aerosol in the submicron size range. The modeled concentrations overpredict those actually measured during that time period. This suggests that some component, presumably organic material, can inhibit the uptake of water by the electrolytic fraction of the mass.