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Abstract: A prerequisite to an air quality management plan for a city is some idea of the main sources of pollution and their contributions for a city. This paper presents the results of an application of the SIM-air modeling tool in six Indian cities – Pune, Chennai, Indore, Ahmedabad, Surat, and Rajkot. Using existing and publicly available data, we put together a baseline of multi-pollutant emissions for each of the cities and then calculate concentrations, health impacts, and model alternative scenarios for 2020. The measured annual PM10 (particulate matter with aerodynamic diameter less than 10 micron meter) concentrations in μg m−3 averaged 94.7 ± 45.4 in Pune, 73.1 ± 33.7 in Chennai, 118.8 ± 44.3 in Indore, 94.0 ± 20.4 in Ahmedabad, 89.4 ± 12.1 in Surat, and 105.0 ± 25.6 in Rajkot, all exceeding the annual standard of 60 μg m−3. The PM10 inventory in tons/year for the year 2010 of 38,400 in Pune, 50,200 in Chennai, 18,600 in Indore, 31,900 in Ahmedabad, 20,000 in Surat, and 14,000 in Rajkot, is further spatially segregated into 1 km grids and includes all known sources such as transport, road dust, residential, power plants, industries (including the brick kilns), waste burning, and diesel generator sets. We use the ATMoS chemical transport model to validate the emissions inventory and estimate an annual premature mortality due to particulate pollution of 15,200 for the year 2010 for the six cities. Of the estimated 21,400 premature deaths in the six cities in 2020, we estimate that implementation of the six interventions in the transport and brick kiln sectors, can potentially save 5870 lives (27%) annually and result in an annual reduction of 16.8 million tons of carbon dioxide emissions in the six cities. [Copyright &y& Elsevier]

Abstract: This paper discusses the extent of Black Carbon (BC) radiative forcing in the total aerosol atmospheric radiative forcing over Pune, an urban site in India. Collocated measurements of aerosol optical properties, chemical composition and BC were carried out for a period of six months (during October 2004 to May 2005) over the site. Observed aerosol chemical composition in terms of water soluble, insoluble and BC components were used in Optical Properties of Aerosols and Clouds (OPAC) to derive aerosol optical properties of composite aerosols. The BC fraction alone was used in OPAC to derive optical properties of BC aerosols. The aerosol optical properties for composite and BC aerosols were separately used in SBDART model to derive direct aerosol radiative forcing due to composite and BC aerosols. The atmospheric radiative forcing for composite aerosols were found to be +35.5, +32.9 and +47.6Wm−2 during post-monsoon, winter and pre-monsoon seasons, respectively. The average BC mass fraction found to be 4.83, 6.33 and 4μgm−3 during the above seasons contributing around 2.2 to 5.8% to the total aerosol load. The atmospheric radiative forcing estimated due to BC aerosols was +18.8, +23.4 and +17.2Wm−2, respectively during the above seasons. The study suggests that even though BC contributes only 2.2–6% to the total aerosol load; it is contributing an average of around 55% to the total lower atmospheric aerosol forcing due to strong radiative absorption, and thus enhancing greenhouse warming. [Copyright &y& Elsevier]

This paper examines the role of meteorology in linking near surface emissions of particulate matter and associated ambient concentrations in urban areas. The examination is conducted with two models: a steady state model based on urban dispersion models developed earlier, and an unsteady state model that accounts for time varying meteorological and emission inputs. After conducting sensitivity studies with the models, they are applied in Pune, India to (a) check consistency between estimates of surface emissions of particulate matter (with aerodynamic diameters of less than m, referred to as PM and observed ambient concentrations and (b) identify the variables that govern air quality. Results from the modeling exercise indicate that (1) nighttime meteorology governs both hourly as well as 24h averaged concentrations and (2) because the wind speeds in urban areas are typically low, concentration estimates from the steady state model differ substantially from those of the unsteady state model during the nighttime hours both in magnitude and in timing of the peaks; however, the difference between the 24h averaged concentrations from the two models is less than 5% for the cases considered here. Because our understanding of nighttime meteorology in urban areas is limited, there is a need for experimental programs to relate the diurnal variation of concentrations with associated meteorology, especially during the night. [Copyright &y& Elsevier]