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Abstract: This paper uses the 2006 IPCC Guidelines and latest country specific emission factors to estimate Indian methane emissions at sectoral and district level for the years 1990, 1995, 2005 and 2008. The estimates show that while methane emissions have increased steadily over past two decades, their share in India’s aggregate GHG emissions has declined from 31% in 1985 to 27% in 2008 mainly due to relatively higher growth CO2 emissions from the fossil fuels. The estimates for the year 2008 show that: i) agriculture sector, which employed two-thirds of India’s population and contributed 17% of GDP, accounted for 23% of India’s GHG emissions ii) 83% of country’s methane emissions are contributed by enteric fermentation, manure use and rice production, and iii) methane emissions from urban solid waste are steadily rising over the past two decades; their share in aggregate methane emissions has reached 8%. Resting on the disaggregated emissions estimates, the paper argues for using geographical and sectoral flexibilities to develop a roadmap for mitigation of methane emissions for India. [Copyright &y& Elsevier]

India has more than 100,000 brick kilns producing around 250 billion bricks annually. Indian brick industry is often a small scale industry and third largest consumer of coal in the country. With the growing demand for building materials and characterised by lack of pollution control measures the brick industry has a potential to cause adverse effects on the environment. This paper presents assessment of five brick making technologies based on the measurements carried out at seventeen individual brick kilns. Emissions of PM, SO 2 , CO and CO 2 were measured and these emissions were used to estimate the emission factors for comparing the emissions across different fuel or operating conditions. Estimated emission from brick kilns in South Asia are about 0.94 million tonnes of PM; 3.9 million tonnes of CO and 127 million tonnes of CO 2 per year. Among various technologies that are widely used in India, Zig zag and vertical shaft brick kilns showed better performance in terms of emissions over the traditional fixed chimney Bull's trench kilns. This suggests that the replacement of traditional technologies with Zig zag, vertical shaft brick kilns or other cleaner kiln technologies will contribute towards improvements in the environmental performance of brick kiln industry in the country. Zig zag kilns appear to be the logical replacement because of low capital investment, easy integration with the existing production process, and the possibility of retrofitting fixed chimney Bull's trench kilns into Zig zag firing. [ABSTRACT FROM AUTHOR]

The global burden of disease study estimated 695,000 premature deaths in 2010 due to continued exposure to outdoor particulate matter and ozone pollution for India. By 2030, the expected growth in many of the sectors (industries, residential, transportation, power generation, and construction) will result in an increase in pollution related health impacts for most cities. The available information on urban air pollution, their sources, and the potential of various interventions to control pollution, should help us propose a cleaner path to 2030. In this paper, we present an overview of the emission sources and control options for better air quality in Indian cities, with a particular focus on interventions like urban public transportation facilities; travel demand management; emission regulations for power plants; clean technology for brick kilns; management of road dust; and waste management to control open waste burning. Also included is a broader discussion on key institutional measures, like public awareness and scientific studies, necessary for building an effective air quality management plan in Indian cities. [ABSTRACT FROM AUTHOR]

Abstract: In the present paper, we have determined emission factor of chemical composition of the emission from the burning of biomass (e.g. Dung cake, Acacia, Neem, Mulberry, Indian Rosewood, Pigeon pea etc.) commonly used as a residential fuel in the rural sector of Indo-Gangetic Plain (IGP) (Delhi, Punjab, Haryana, Uttar Pradesh, Bihar and West Bengal), India. For comparison, we have selected only those biomass fuels, which are used in at least three of the above mentioned states. Dung cake from all the states reports highest emission of particulate matter (PM) (15.68 g kg−1), Organic Carbon (OC) (4.32 g kg−1) and Elemental Carbon (EC) (0.51 g kg−1). Among all biomass fuels studied, agricultural residue reports substantial amount of emission of Na+ (104 mg kg−1), K+ (331 mg kg−1) and Cl− (447 mg kg−1) particularly in Pigeon pea and Mustard stem. Eucalyptus (fuel wood) emits large amounts of Ca2+ (21.47 mg kg−1) and NO3 − (614 mg kg−1). The emission of PM from dung cake is higher in Delhi (19.31 g kg−1) and followed by Uttar Pradesh (17.58 g kg−1) > Haryana (15.46 g kg−1) > Bihar (14.99 g kg−1) > Punjab (12.06 g kg−1) > West Bengal (5.90 g kg−1). Carbonaceous aerosols (OC and EC) and dominant Ionic species (Cl−, K+, SO4 2−, NO3 − and PO4 3−) are altogether contributing 40–70% of total emissions. Characteristics and ratios of chemical species of emissions may help to develop a methodology of discriminating the sources of ambient particulate matter. Using a laboratory determined emission factor of chemical species, we have determined the emission budget over IGP, India. [Copyright &y& Elsevier]

Abstract: In Delhi, between 2008 and 2011, at seven monitoring stations, the daily average of particulates with diameter <2.5 μm (PM2.5) was 123 ± 87 μg m− 3 and particulates with diameter <10 μm (PM10) was 208 ± 137 μg m− 3. The bulk of the pollution is due to motorization, power generation, and construction activities. In this paper, we present a multi-pollutant emissions inventory for the National Capital Territory of Delhi, covering the main district and its satellite cities – Gurgaon, Noida, Faridabad, and Ghaziabad. For the base year 2010, we estimate emissions (to the nearest 000''s) of 63,000 tons of PM2.5, 114,000 tons of PM10, 37,000 tons of sulfur dioxide, 376,000 tons of nitrogen oxides, 1.42 million tons of carbon monoxide, and 261,000 tons of volatile organic compounds. The inventory is further spatially disaggregated into 80 × 80 grids at 0.01° resolution for each of the contributing sectors, which include vehicle exhaust, road dust re-suspension, domestic cooking and heating, power plants, industries (including brick kilns), diesel generator sets and waste burning. The GIS based spatial inventory coupled with temporal resolution of 1 h, was utilized for chemical transport modeling using the ATMoS dispersion model. The modeled annual average PM2.5 concentrations were 122 ± 10 μg m− 3 for South Delhi; 90 ± 20 μg m− 3 for Gurgaon and Dwarka; 93 ± 26 μg m− 3 for North-West Delhi; 93 ± 23 μg m− 3 for North-East Delhi; 42 ± 10 μg m− 3 for Greater Noida; 77 ± 11 μg m− 3 for Faridabad industrial area. The results have been compared to measured ambient PM pollution to validate the emissions inventory. [Copyright &y& Elsevier]

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: Landfills are important anthropogenic sources of methane (CH4) emission especially in fast urbanizing countries. This paper presents the CH4 emission estimations carried out using the in-situ CH4 measurements, IPCC 1996 Default methodology (DM), Modified Triangular Method (MTM) and First Order Decay (FOD) method for the three landfills currently operational in the capital city Delhi of India. The in-situ methodology has yielded the landfills specific methane emission factors (EFs). The annual average methane emission rates from three landfills namely, Ghazipur (GL), Bhalswa (BL) and Okhla (OL) are 14.6, 23.6 & 7.5 Gg y−1 by DM; 13.3, 10.6 & 7.2 Gg y−1 by the FOD; 17.0, 13.7 and 10.7 Gg y−1 by the MTM; and 4.6, 4.2 and 1.4 Gg y−1 by the in-situ measurement method respectively. The CH4 EFs have been found to be 9.7 ± 2.6, 5.5 ± 1.6 and 5.5 ± 1.7 g kg−1 of waste respectively for the GL, BL and OL landfills in Delhi. The study reveals that in-situ methodology seems to provide better representative emission estimation compared to other methods. The FOD method also yields comparable results with that of in-situ methodology in cases where good waste composition data is available. [Copyright &y& Elsevier]

Abstract: This paper presents the characteristics of particulate matter (PM) mass concentrations (PM10, PM2.5 and PM1) emitted from heterogeneous traffic in Chennai city during monsoon, winter and summer seasons of the year 2007–2009. The heterogeneous traffic characteristics at the study region indicated dominance of 2-wheelers (58%) followed by the 4-wheelers (29%), 3-wheelers (7%), light commercial vehicle (LCV = 2%) and heavy commercial vehicle (HCV = 4%). The vintage analysis of vehicles showed that 11, 24, 35 and 30% of the total vehicles were 15, 10, 5 and <5 years old vehicles, respectively (75% of LCV and 70% of HCV were older than 10 years and 82% of 2W, 85% of 3W, 79% of 4W were less than 10 years old). The travel demand on weekdays at the study site revealed that 60% of the traffic in the morning peak hours was due to school and office trips and 40% was due to the business trips. During the weekends the peak rush hours traffic was dominated by travel demand (90%) due to tourists and pleasure trips. At study region, the PM10 comprised a large fraction of PM2.5 (56% of PM2.5 and 44% of PM1), while PM2.5 comprised a large proportion of PM1 (81%). The estimated PM2.5/PM10 ratios for monsoon, winter and summer seasons were ranged between 0.44–0.62, 0.66–0.76 and 0.62–0.73, respectively. The high PM2.5/PM10 ratios (R 2 = 0.92–0.98) during peak hour indicated significant contribution from the vehicular emissions. Further, it was found that the PM10, PM2.5 and PM1 concentrations at the study site followed similar trend of 2W, 3W, 4W and HCV during morning peak hour traffic. The analysis of 24-hr average PM10, PM2.5 and PM1 concentrations showed maximum during monsoon (188.75 ± 71.75, 83.91 ± 33.18, 65.81 ± 28.47 μg/m3) and winter (134.58 ± 64.55, 72.95 ± 39.27, 59.00 ± 31.15 μg/m3) and minimum during summer (75.96 ± 43.15, 42.16 ± 19.76, 31.40 ± 16.05 μg/m3) seasons. The 24-hr average PM10 and PM2.5 indicated maximum violations of Indian national ambient air quality standards (NAAQS for PM10 = 100 μg/m3 and PM2.5 = 60 μg/m3) during winter and monsoon seasons and minimum during summer season. Further, PM10 and PM2.5 concentrations were well above the prescribed 24-hr average world health organization (WHO) standards (PM10 = 50 μg/m3; PM2.5 = 25 μg/m3). The PM chemical characterization indicated the dominance of soil based crustal elements (74%) – Al, Ca, Fe and Mg, salt based components (16%)-Na and K, and the other trace elements (10%) such as B, Ba, Cd, Co, Cr, Cu, Ga, Mn, Mo, Ni, Pb, Rb, Se, Sr, Te, V and Zn. The percentage composition of different group of elements indicates traffic as the main source of PM emission at the study site. The ion analysis showed presence of more anions (36–82%) than the cations (18–64%). SO4 2− and Mg2+ were major constituents. [Copyright &y& Elsevier]

Abstract: A decentralized emission inventories are prepared for road transport sector of India in order to design and implement suitable technologies and policies for appropriate mitigation measures. Globalization and liberalization policies of the government in 90''s have increased the number of road vehicles nearly 92.6% from 1980–1981 to 2003–2004. These vehicles mainly consume non-renewable fossil fuels, and are a major contributor of green house gases, particularly CO2 emission. This paper focuses on the statewise road transport emissions (CO2, CH4, CO, NOx, N2O, SO2, PM and HC), using region specific mass emission factors for each type of vehicles. The country level emissions (CO2, CH4, CO, NOx, N2O, SO2 and NMVOC) are calculated for railways, shipping and airway, based on fuel types. In India, transport sector emits an estimated 258.10 Tg of CO2, of which 94.5% was contributed by road transport (2003–2004). Among all the states and Union Territories, Maharashtra''s contribution is the largest, 28.85 Tg (11.8%) of CO2, followed by Tamil Nadu 26.41 Tg (10.8%), Gujarat 23.31 Tg (9.6%), Uttar Pradesh 17.42 Tg (7.1%), Rajasthan 15.17 Tg (6.22%) and, Karnataka 15.09 Tg (6.19%). These six states account for 51.8% of the CO2 emissions from road transport. [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]

Abstract: This paper provides the trends of greenhouse gas (GHG) and local air pollutant emissions of India for 1985–2005. The GHGs covered are six Kyoto gases, namely carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), perfluorocarbons (PFCs), hydrofluorocarbons (HFCs) and sulfur hexafluoride (SF6). The local air pollutants are sulfur dioxide (SO2), nitrogen oxides (NO X ), carbon monoxide (CO) and total suspended particulates (TSP). These estimates incorporate some of the most recent scientific assessments for India. The multigas emissions have varied sectoral and fuel-based dominance, as well as regional distribution patterns. Coal consumption in power sector dominates CO2 and SO2 emissions, while power and road transport equally contribute to NO X emissions. Rice cultivation and livestock-related emissions from the agriculture sector dominate CH4 emissions, while synthetic fertilizer use in the same sector is the major source of N2O emissions. PFC emissions are dominated by C2F6 and CF4 emissions from aluminum production. The majority of HFC emissions are contributed by HFC-23, a by-product during the production of HCFC-22 that is widely used in refrigeration industry. CO emissions have dominance from biomass burning. Particulate emissions are dominated by biomass burning (residential sector), road transport and coal combustion in large plants. These varied emission patterns provide interesting policy links and disjoints, such as—which and where mitigation flexibility for the Kyoto gases, exploring co-benefits of CO2 and SO2 mitigation, and technology and development pathway dependence of emissions. The present inventory assessment is a pointer to the future emission pathways for India wherein local air pollutant and GHG emissions, although connected, may not move in synchronization and therefore would require alignment through well crafted development and environment strategies. [Copyright &y& Elsevier]

Abstract: Chemical simulations in most of the atmospheric chemistry models require surface emissions in a specific form (gridded), which are often not available. Simple interpolation of broader level emissions to obtain gridded data may lead to erroneous results. An attempt has been made in this paper to develop a geographical information system (GIS) based methodology for distributing the emissions from a broader level inventory to finely gridded emission values, considering local micro-level details and activity data. Given the importance of Carbon Monoxide emissions in ozone pollution chemistry over India, an emission inventory for CO from various sources for India has been used to demonstrate the GIS-based gridding methodology. The total CO emissions over India for 2001, which are estimated to be around 69.0Tgyear−1, have been downscaled source-wise (distinguishing between rural and urban bio-fuel, vehicular traffic, coal and biomass burning) from state-level (28 points) to district level (∼500 points) before mapping through a GIS utility and finally gridded to a 1°×1° resolution with a data loss of only about 13%. The final results provide detailed information with emission “hot spots” and the relative contribution of various sources. This article focuses on usage of the GIS based statistical methodology for gridding the inventory and the results obtained thereof are discussed. [Copyright &y& Elsevier]