Emissions inventory of anthropogenic PM2.5 and PM10 in Delhi during Commonwealth Games 2010

Sahu, Saroj Kumar; Beig, G.; Parkhi, Neha

doi:10.1016/j.atmosenv.2011.08.014

Cited by 141 publications

(100 citation statements)

References 7 publications

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“…Predominant sources for these regulated pollutants differ. For example in New Delhi, residential, transport and industrial sectors are major sources for PM 2.5 (Sahu et al, 2011); transport sector is the major source of For example, Sharma et al (2003) used Indian NAAQS and health standards by the US, for suspended particulate matter (SPM), CO, NO 2 , O 3 , SO 2 , and PM 10 to estimate AQI in an Indian city. Bhaskar and Mehta (2010) used the US method to estimate AQI at thirteen different sites in Ahmadabad city in India, and observed strong seasonal effects on AQI.…”

Section: Introductionmentioning

confidence: 99%

Significance of PM2.5 Air Quality at the Indian Capital

Sahu

Kota

2017

Aerosol Air Qual. Res.

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In New Delhi, the capital city of India, concentrations of regulated air pollutants often exceed the Indian national ambient air quality standards (INAAQS). As the sources of these pollutants differ, it is of utmost priority to understand the most dangerous air pollutant to formulate better control strategies in the city. In this study, regulated air pollutant concentrations in New Delhi during 2011 to 2014 were collected. Compared to other pollutants, PM 2.5 concentrations exceeded the INAAQS quite often. While PM 2.5 exceeded INAAQS during 85% of the days, NO 2 , O 3 , CO and SO 2 exceeded only on 37, 14, 11 and 0% of the days, respectively. Using air quality index approach, the most dominant pollutant was identified as PM 2.5 , for 75 to 90% of the days. However, a seasonal variation in the percentage dominance of PM 2.5 was observed. For example, PM 2.5 was dominant during 95% of the winter and 68% of monsoon days. In addition to absolute concentrations, pollutants can also be ranked by studying their associated short term mortality impacts. However, such studies are rare in India. For the first time, the short term impact of PM 2.5 concentrations on non-disease specific mortality in New Delhi was assessed using Poisson regression models. Results indicated that the excessive risk associated with PM 2.5 estimated was 0.57, which was higher than the other regulated pollutants. This indicates a projected 6.2 and 6.5% decrease in mortality by meeting the PM 2.5 Indian standards and WHO set limits, respectively.

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Section: Introductionmentioning

confidence: 99%

Significance of PM2.5 Air Quality at the Indian Capital

Sahu

Kota

2017

Aerosol Air Qual. Res.

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“…The same method has been used for developing emission inventories in different scales such as urban (Gurjar et al, 2004;Goyal et al, 2013), regional scale (Sahu et al, 2011;Guttikunda and Calori, 2013), national (Reddy and Venkataraman, 2002;Ramachandra et al, 2009). The various emission-producing sources have been discussed briefly below.…”

Section: Data Sourcesmentioning

confidence: 99%

“…Six vehicle categories have been considered in the present study which includes two-wheelers (2W), three wheelers (or auto-rickshaws) (3W), passenger cars (PCs), Buses, Light commercial vehicles (LCV) and Heavy commercial Vehicles (HCV). Annual average vehiclekilometer travelled is estimated as 10,000 for passenger cars; 36,000 for taxis and auto-rickshaws; 50,000 for buses and 30,000 for HCV and LCV (Guttikunda and Calori, 2013) and 27,000 for 2-wheelers (2W) (Sahu et al 2011). Buses and 3W have been assumed to use only compressed natural gas (CNG) as fuel.…”

Section: Emissions From Vehiclesmentioning

confidence: 99%

“…Further, Delhi registered the highest annual mean PM 2.5 (148.4 μg/m 3 ) concentration exceeding among 46 urban centers of India considered by Dey et al (2012). Delhi and its surrounding areas, as an important part of IGP, is one of the fastest growing economic centers of South Asia (Sahu et al, 2011). Delhi, with a human population of ~16 million (Singh and Dey, 2012), is ranked among the top 20 most polluted cities in the world according to WHO Ambient Air Pollution Database Report, (2014).…”

Section: Introductionmentioning

confidence: 99%

“…Delhi, with a human population of ~16 million (Singh and Dey, 2012), is ranked among the top 20 most polluted cities in the world according to WHO Ambient Air Pollution Database Report, (2014). The number of vehicles registered in Delhi has already reached 6 million in 2010 (Delhi Statistical Handbook (DSH), 2012) and another 2.2 million vehicles mostly from the surrounding areas namely-Gurgaon, Faridabad, Ghaziabad, Noida, Greater Noida, Bahadurgarh and Sonipat contribute to total vehicular population (Sahu et al, 2011), which are responsible for degrading the air quality (Sindhwani and Goyal, 2014). Vehicular pollution, contributed ~72% towards total air pollution load in (Goyal et al, 2006, which was only 23% in 1970-1971(Central Pollution Control Board (CPCB), 2008a over an area of 1483 km 2 of Delhi.…”

Section: Introductionmentioning

confidence: 99%

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Anthropogenic Emission Inventory of Criteria Air Pollutants of an Urban Agglomeration - National Capital Region (NCR), Delhi

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This study aims to develop a spatial high-resolution emission inventory (2 km × 2 km) of criteria air pollutants (CO, NO x , SO 2 and PM 10 ) for National Capital Region (NCR), Delhi. The inventory is centered at the metropolitan area of Delhi, and includes adjoining parts of the neighboring states of Haryana and Uttar Pradesh within an area of 70 km by 70 km for the year 2010. The bottom-up gridded emission inventory has been prepared taking into account land use pattern, population density as well as industrial areas which includes major emission sources of the region, namely vehicular exhaust, road-dust re-suspension, domestic, industrial, power plants, brick kilns, aircrafts and waste sectors. Data corresponding to various sectors along with related emission factors have been acquired from literature and various regulatory bodies for the study domain. The results reveal that total estimated emissions from vehicular exhaust, road dust and power plants contribute nearly 52%, 83%, 74% and 54% of PM 10 , SO 2 , NO x and CO emission respectively. Transport sector has been found as the bulk contributor towards CO and NO x emissions. Coal-fired power plants corresponds to the most polluting sector with regard to SO 2 contributing ~67%. Power plants Badarpur, Rajghat, Indraprastha and Faridabad power plant emerged as the primary hotspots for SO 2 and PM 10 emissions. Further, Primary and secondary emission hotspots for each criteria pollutant has been identified and discussed in detail for the year 2010. In addition to it, forward trajectory analysis has been performed to assess the impact of emissions over the regional scale. Finally, a qualitative approach has been employed to assess the uncertainty in the emission estimates.

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Delhi Model with Chemistry and aerosol framework (DM‐Chem) for high‐resolution fog forecasting

Jayakumar

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et al. 2021

Quart J Royal Meteoro Soc

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We introduce here the Delhi Model with Chemistry and aerosol framework (DM-Chem), a high-resolution (330 m) visibility and fog forecasting model with the UK Chemistry and Aerosol scheme. DM-Chem is developed at the National Centre for Medium-Range Weather Forecasting (NCMRWF) in collaboration with UK Met Office and Carnegie Mellon University. We present a revised visibility scheme here which uses prognostic aerosols (mass and number concentration) from the two-moment prognostic Global Model of Aerosol Processes (GLOMAP) -mode aerosol scheme. This scheme represents aerosol microphysical processes and the internal mixing of chemical components within five log-normal size modes. The cloud microphysics parameterization also makes use of the GLOMAP scheme to predict cloud droplet number concentration (N d ).Therefore we are able to evaluate the performance of the scheme indirectly by comparing the predicted N d with that derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) retrievals over the Indo-Gangetic Plain. Aerosol emissions for the inner (330 m) nest are prescribed with a high-resolution inventory for Delhi, from the System of Air Quality and Weather Forecasting project (SAFAR). Winter-time stubble burning is represented using the MODIS-based Fire Energetics and Emissions Research inventory, updated daily. In this article we consider case studies spanning both foggy and haze days of winter 2019-2020. The model compares well with the observed visibility and its spatial and diurnal patterns. The SAFAR emissions help improve the near-surface model fields. For the "haze" case, the single scattering albedo (SSA) is high along the regions of high AOD, and the surface radiative cooling is enhanced. During the "foggy" case, the SSA is not clearly related to AOD, but aerosol absorption does lead to a minor surface warming via the semi-direct effect at the time of onset of fog. The DM-Chem entered operational mode at NCMRWF from winter 2020 and provides high-resolution visibility forecast for Delhi and the adjoining region.

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Emissions inventory of anthropogenic PM2.5 and PM10 in Delhi during Commonwealth Games 2010

Cited by 141 publications

References 7 publications

Significance of PM2.5 Air Quality at the Indian Capital

Significance of PM2.5 Air Quality at the Indian Capital

Anthropogenic Emission Inventory of Criteria Air Pollutants of an Urban Agglomeration - National Capital Region (NCR), Delhi

Delhi Model with Chemistry and aerosol framework (DM‐Chem) for high‐resolution fog forecasting

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