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Abstract: In this paper, we present the experimentally determined emission factors and emission estimates of particulate matter (PM), SO2, NO and NO2 emitted from biomass fuels used as energy in rural area of Indo-Gangetic Plain (IGP), India. Biomass fuel samples were collected at district level from this region. The burning of the collected biomass fuels is performed by using the modified dilution sampler based on studies done by . In this study, the emission factor represents the total period of burning including pyrolysis, flaming and smoldering. The average emission factor of PM from dung cake, fuel-wood and crop residue over Delhi, Uttar Pradesh, Punjab, Haryana, Uttarakhand and Bihar are estimated as 16.26 ± 2.29 g kg−1, 4.34 ± 1.06 g kg−1 and 7.54 ± 4.17 g kg−1 respectively. Similarly, the average emission factor of SO2, NO and NO2 from dung cake, fuel-wood and crop residue over this region are also determined (SO2: 0.28 ± 0.09 g kg−1, 0.26 ± 0.10 g kg−1 and 0.27 ± 0.11 g kg−1, NO: 0.27 ± 0.21 g kg−1, 0.41 ± 0.25 g kg−1 and 0.54 ± 0.50 g kg−1 and NO2: 0.31 ± 0.23 g kg−1, 0.35 ± 0.28 g kg−1 and 0.54 ± 0.47 g kg−1 respectively). The emission of PM, SO2, NO and NO2 from biomass fuels used as energy in rural household over, IGP are also estimated in this paper. The result shows the regional emission inventory from Indian scenario with spatial variability. [Copyright &y& Elsevier]

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: 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]

This paper uses spectral methods to analyze changes in air quality at a single monitoring site in Delhi since 2000. Power spectral density calculations of daily concentration data for particulate matter (PM10), carbon monoxide (CO), oxides of nitrogen (NO x ) and oxides of sulfur (SO x ) reveal the presence of trends and periodic oscillations for all the pollutants. Singular Spectrum Analysis (SSA) is used to decompose daily data into statistically significant non-linear trends, seasonal cycles and other oscillations. Periods of sharp reductions were observed for both SO x and CO concentrations in 2001 and 2002, respectively. NO x concentration trends show a sustained rise from 2000 to 2004, followed by small decline thereafter. PM10 concentration trends remain essentially unchanged over the time period. All pollutants also show strong annual and biannual cycles. The observed trends in CO and NO x likely relate changes in Delhi''s vehicular traffic emissions. The sharp drop in both the trend and amplitude of the seasonal cycle of CO coincides with the switch to Compressed Natural Gas (CNG) as a fuel for Delhi''s public transport fleet. Observed changes in SO x and PM10 concentrations were most likely caused by sources unrelated to vehicular traffic. [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]