Emission inventory of non-methane volatile organic compounds from anthropogenic sources in India

Sharma, Sumit; Goel, Anuj Kumar; Gupta, Divya; Kumar, Atul; Mishra, Arabinda; Kundu, Seema; Chatani, Satoru; Klimont, Zbigniew

doi:10.1016/j.atmosenv.2014.11.070

Cited by 51 publications

(37 citation statements)

References 26 publications

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“…However, statistics on activity data and information of emission factors for these sources are often less available than those for fuel combustion and industrial processes. In this study, default activity data and emission factors were obtained from REASv2.1 and were updated if information was available in recently published papers (Wei et al (2011) for China and Sharma et al (2015) for India). In general, activity data of the past years are not available, and, in such cases, proxy data are prepared for trend factors.…”

Section: Other Sourcesmentioning

confidence: 99%

Long-term historical trends in air pollutant emissions in Asia: Regional Emission inventory in ASia (REAS) version 3.1

Kurokawa¹,

Ohara²

2019

Preprint

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Abstract. A long-term historical emission inventory of air and climate pollutants in East, Southeast, and South Asia from 1950&#8211;2015 was developed as the Regional Emission inventory in ASia version 3.1 (REASv3.1). REASv3.1 provides details of emissions from major anthropogenic sources for each country and its sub-regions and also provides monthly gridded data with 0.25&#176;&#8201;&#215;&#8201;0.25&#176; resolution. The average total emissions in Asia during 1950&#8211;1955 and from 2010&#8211;2015 (growth rates in these 60 years) are as follows: SO2: 3.15&#8201;Tg, 42.4&#8201;Tg (13.5); NOx: 1.83&#8201;Tg, 47.6&#8201;Tg (26.0); CO: 62.2&#8201;Tg, 319&#8201;Tg (5.13); non-methane volatile organic compounds: 9.14&#8201;Tg, 61.8&#8201;Tg (6.77); NH3: 7.99&#8201;Tg, 31.3&#8201;Tg (3.92); CO2: 1.12&#8201;Pg, 18.3&#8201;Pg (16.3); PM10: 5.76&#8201;Tg, 28.4&#8201;Tg (4.92); PM2.5: 4.52&#8201;Tg, 20.3&#8201;Tg (4.50); black carbon: 0.751&#8201;Tg, 3.38&#8201;Tg (4.51); and organic carbon: 2.62&#8201;Tg, 6.92&#8201;Tg (2.64). Clearly, all the air pollutant emissions in Asia increased significantly during these six decades, but situations were different among countries and regions. Due to China's rapid economic growth in recent years, its relative contribution to emissions in Asia has been the largest. However, most pollutant species reached their peaks by 2015 and the growth rates of other species was found to be reduced or almost zero. On the other hand, air pollutant emissions from India showed an almost continuous increasing trend. As a result, the relative ratio of emissions of India to that of Asia have increased recently. The trend observed in Japan was different from the rest of Asia. In Japan, emissions increased rapidly during 1950s&#8211;1970s, which reflected the economic situation of the period; however, most emissions decreased from their peak values, which were approximately 40 years ago, due to the introduction of regulations and laws for air pollution. Similar features were found in the Republic of Korea and Taiwan. In the case of other Asian countries, air pollutant emissions generally showed an increase along with economic growth and motorization. Trends and spatial distribution of air pollutants in Asia are becoming complicated. Datasets of REASv3.1, including table of emissions by countries and sub-regions for major sectors and fuel types, and monthly gridded data with 0.25&#176;&#8201;&#215;&#8201;0.25&#176; resolution for major source categories are available through the following URL: <a href=" http://www.nies.go.jp/REAS/"target="_blank">http://www.nies.go.jp/REAS/</a>.

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Section: Other Sourcesmentioning

confidence: 99%

Long-term historical trends in air pollutant emissions in Asia: Regional Emission inventory in ASia (REAS) version 3.1

Kurokawa¹,

Ohara²

2019

Preprint

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“…The GAINS (Greenhouse Gas -Air Pollution Interactions and Synergies) model (Amann et al, 2011) has been used to produce regional and global emission estimates for several recent years (1990-2010; in 5-year intervals) together with projections to 2020 and beyond (Amann et al, 2013;Cofala et al, 2007;Klimont et al, 2009). These have been developed with substantial consultation with national experts, especially for Europe and Asia (Amann et al, 2008(Amann et al, , 2015Purohit et al, 2010;Sharma et al, 2015;Wang et al, 2014;Zhang et al, 2007;Zhao et al, 2013a). The newly developed ECLIPSE emission sets include several extensions and updates in the GAINS model and are also available in a gridded form (Klimont et al, 2017a) and have been used in a number of recent modeling exercises (Eckhardt et al, 2015;IEA, 2016b;Rao et al, 2016;Stohl et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS)

et al. 2018

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Abstract. We present a new data set of annual historical anthropogenic chemically reactive gases (CO, CH 4 , NH 3 , NO x , SO 2 , NMVOCs), carbonaceous aerosols (black carbon -BC, and organic carbon -OC), and CO 2 developed with the Community Emissions Data System (CEDS). We improve upon existing inventories with a more consistent and reproducible methodology applied to all emission species, updated emission factors, and recent estimates through 2014. The data system relies on existing energy consumption data sets and regional and country-specific inventories to produce trends over recent decades. All emission species are consistently estimated using the same activity data over all time periods. Emissions are provided on an annual basis at the level of country and sector and gridded with monthly seasonality. These estimates are comparable to, but generally slightly higher than, existing global inventories. Emissions over the most recent years are more uncertain, particularly in low-and middle-income regions where country-specific emission inventories are less available. Future work will involve refining and updating these emission estimates, estimating emissions' uncertainty, and publication of the system as open-source software.

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“…The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al, 2015(Sharma et al, , 2016, an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al (2018b), and PM 2.5 speciation from cooking fires from Jayarathne et al (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain.…”

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confidence: 99%

“…Residential emissions inventory sources by species. Bond (University of Illinois) NO x usingSharma et al (2015), NO : NO 2 = 10 : 1…”

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confidence: 99%

Rooney et al 2019, author responses to reviewer comments

Seinfeld¹

2019

Preprint

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Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM 2.5 and O 3 ) using state-of-thescience emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015(Sharma et al., , 2016, an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM 2.5 speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O 3 and PM 2.5 levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions.

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Emission inventory of non-methane volatile organic compounds from anthropogenic sources in India

Cited by 51 publications

References 26 publications

Long-term historical trends in air pollutant emissions in Asia: Regional Emission inventory in ASia (REAS) version 3.1

Long-term historical trends in air pollutant emissions in Asia: Regional Emission inventory in ASia (REAS) version 3.1

Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS)

Rooney et al 2019, author responses to reviewer comments

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