A Review of Air Quality Modeling Studies in India: Local and Regional Scale
Abstract Developing countries like India require proper control strategies for reducing the enormous premature mortality associated with air pollution. Air quality models, in addition to helping to understand the severity of air pollution by providing the pollutant concentrations, also give knowledg...
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| Autor*in: |
Garaga, Rajyalakshmi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer International Publishing AG, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Current pollution reports - New York, NY [u.a.] : Springer, 2015, 4(2018), 2 vom: 24. Feb., Seite 59-73 |
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| Übergeordnetes Werk: |
volume:4 ; year:2018 ; number:2 ; day:24 ; month:02 ; pages:59-73 |
| Links: |
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| DOI / URN: |
10.1007/s40726-018-0081-0 |
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| Katalog-ID: |
SPR038071207 |
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| 520 | |a Abstract Developing countries like India require proper control strategies for reducing the enormous premature mortality associated with air pollution. Air quality models, in addition to helping to understand the severity of air pollution by providing the pollutant concentrations, also give knowledge of the sources. Previous local and regional air quality modeling studies carried out in India are reviewed in this current study with a goal of understanding the current gaps and exploring future directions. Studies carried out in different parts of India during past decade were precisely documented in this study using methodical Scopus, Web of Science, and Google searches. Majority of the air quality studies are concentrated in megacities leaving behind the small cities which require greater attention in future. While most of the modeling studies were carried out in northern India, very few studies concentrated on central region of the country. Review of both local and regional numerical models showed the need for better emission inputs, while the statistical models inferred the need for proper selection of key tracers for source allocation. Irrespective of emission inventory and models used, particulate matter concentrations are under predicted in Delhi, which faces huge air pollution-related issues. Dust and traffic emissions are the major sources of particulate matter in India. | ||
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10.1007/s40726-018-0081-0 doi (DE-627)SPR038071207 (SPR)s40726-018-0081-0-e DE-627 ger DE-627 rakwb eng Garaga, Rajyalakshmi verfasserin aut A Review of Air Quality Modeling Studies in India: Local and Regional Scale 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract Developing countries like India require proper control strategies for reducing the enormous premature mortality associated with air pollution. Air quality models, in addition to helping to understand the severity of air pollution by providing the pollutant concentrations, also give knowledge of the sources. Previous local and regional air quality modeling studies carried out in India are reviewed in this current study with a goal of understanding the current gaps and exploring future directions. Studies carried out in different parts of India during past decade were precisely documented in this study using methodical Scopus, Web of Science, and Google searches. Majority of the air quality studies are concentrated in megacities leaving behind the small cities which require greater attention in future. While most of the modeling studies were carried out in northern India, very few studies concentrated on central region of the country. Review of both local and regional numerical models showed the need for better emission inputs, while the statistical models inferred the need for proper selection of key tracers for source allocation. Irrespective of emission inventory and models used, particulate matter concentrations are under predicted in Delhi, which faces huge air pollution-related issues. Dust and traffic emissions are the major sources of particulate matter in India. India (dpeaa)DE-He213 PMF (dpeaa)DE-He213 CMAQ (dpeaa)DE-He213 AERMOD (dpeaa)DE-He213 PM2.5 (dpeaa)DE-He213 Toxic pollutants (dpeaa)DE-He213 VOCs (dpeaa)DE-He213 Sahu, Shovan Kumar aut Kota, Sri Harsha (orcid)0000-0002-1977-2954 aut Enthalten in Current pollution reports New York, NY [u.a.] : Springer, 2015 4(2018), 2 vom: 24. Feb., Seite 59-73 (DE-627)820057037 (DE-600)2813185-X 2198-6592 nnns volume:4 year:2018 number:2 day:24 month:02 pages:59-73 https://dx.doi.org/10.1007/s40726-018-0081-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2018 2 24 02 59-73 |
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10.1007/s40726-018-0081-0 doi (DE-627)SPR038071207 (SPR)s40726-018-0081-0-e DE-627 ger DE-627 rakwb eng Garaga, Rajyalakshmi verfasserin aut A Review of Air Quality Modeling Studies in India: Local and Regional Scale 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract Developing countries like India require proper control strategies for reducing the enormous premature mortality associated with air pollution. Air quality models, in addition to helping to understand the severity of air pollution by providing the pollutant concentrations, also give knowledge of the sources. Previous local and regional air quality modeling studies carried out in India are reviewed in this current study with a goal of understanding the current gaps and exploring future directions. Studies carried out in different parts of India during past decade were precisely documented in this study using methodical Scopus, Web of Science, and Google searches. Majority of the air quality studies are concentrated in megacities leaving behind the small cities which require greater attention in future. While most of the modeling studies were carried out in northern India, very few studies concentrated on central region of the country. Review of both local and regional numerical models showed the need for better emission inputs, while the statistical models inferred the need for proper selection of key tracers for source allocation. Irrespective of emission inventory and models used, particulate matter concentrations are under predicted in Delhi, which faces huge air pollution-related issues. Dust and traffic emissions are the major sources of particulate matter in India. India (dpeaa)DE-He213 PMF (dpeaa)DE-He213 CMAQ (dpeaa)DE-He213 AERMOD (dpeaa)DE-He213 PM2.5 (dpeaa)DE-He213 Toxic pollutants (dpeaa)DE-He213 VOCs (dpeaa)DE-He213 Sahu, Shovan Kumar aut Kota, Sri Harsha (orcid)0000-0002-1977-2954 aut Enthalten in Current pollution reports New York, NY [u.a.] : Springer, 2015 4(2018), 2 vom: 24. Feb., Seite 59-73 (DE-627)820057037 (DE-600)2813185-X 2198-6592 nnns volume:4 year:2018 number:2 day:24 month:02 pages:59-73 https://dx.doi.org/10.1007/s40726-018-0081-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2018 2 24 02 59-73 |
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10.1007/s40726-018-0081-0 doi (DE-627)SPR038071207 (SPR)s40726-018-0081-0-e DE-627 ger DE-627 rakwb eng Garaga, Rajyalakshmi verfasserin aut A Review of Air Quality Modeling Studies in India: Local and Regional Scale 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract Developing countries like India require proper control strategies for reducing the enormous premature mortality associated with air pollution. Air quality models, in addition to helping to understand the severity of air pollution by providing the pollutant concentrations, also give knowledge of the sources. Previous local and regional air quality modeling studies carried out in India are reviewed in this current study with a goal of understanding the current gaps and exploring future directions. Studies carried out in different parts of India during past decade were precisely documented in this study using methodical Scopus, Web of Science, and Google searches. Majority of the air quality studies are concentrated in megacities leaving behind the small cities which require greater attention in future. While most of the modeling studies were carried out in northern India, very few studies concentrated on central region of the country. Review of both local and regional numerical models showed the need for better emission inputs, while the statistical models inferred the need for proper selection of key tracers for source allocation. Irrespective of emission inventory and models used, particulate matter concentrations are under predicted in Delhi, which faces huge air pollution-related issues. Dust and traffic emissions are the major sources of particulate matter in India. India (dpeaa)DE-He213 PMF (dpeaa)DE-He213 CMAQ (dpeaa)DE-He213 AERMOD (dpeaa)DE-He213 PM2.5 (dpeaa)DE-He213 Toxic pollutants (dpeaa)DE-He213 VOCs (dpeaa)DE-He213 Sahu, Shovan Kumar aut Kota, Sri Harsha (orcid)0000-0002-1977-2954 aut Enthalten in Current pollution reports New York, NY [u.a.] : Springer, 2015 4(2018), 2 vom: 24. Feb., Seite 59-73 (DE-627)820057037 (DE-600)2813185-X 2198-6592 nnns volume:4 year:2018 number:2 day:24 month:02 pages:59-73 https://dx.doi.org/10.1007/s40726-018-0081-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2018 2 24 02 59-73 |
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10.1007/s40726-018-0081-0 doi (DE-627)SPR038071207 (SPR)s40726-018-0081-0-e DE-627 ger DE-627 rakwb eng Garaga, Rajyalakshmi verfasserin aut A Review of Air Quality Modeling Studies in India: Local and Regional Scale 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract Developing countries like India require proper control strategies for reducing the enormous premature mortality associated with air pollution. Air quality models, in addition to helping to understand the severity of air pollution by providing the pollutant concentrations, also give knowledge of the sources. Previous local and regional air quality modeling studies carried out in India are reviewed in this current study with a goal of understanding the current gaps and exploring future directions. Studies carried out in different parts of India during past decade were precisely documented in this study using methodical Scopus, Web of Science, and Google searches. Majority of the air quality studies are concentrated in megacities leaving behind the small cities which require greater attention in future. While most of the modeling studies were carried out in northern India, very few studies concentrated on central region of the country. Review of both local and regional numerical models showed the need for better emission inputs, while the statistical models inferred the need for proper selection of key tracers for source allocation. Irrespective of emission inventory and models used, particulate matter concentrations are under predicted in Delhi, which faces huge air pollution-related issues. Dust and traffic emissions are the major sources of particulate matter in India. India (dpeaa)DE-He213 PMF (dpeaa)DE-He213 CMAQ (dpeaa)DE-He213 AERMOD (dpeaa)DE-He213 PM2.5 (dpeaa)DE-He213 Toxic pollutants (dpeaa)DE-He213 VOCs (dpeaa)DE-He213 Sahu, Shovan Kumar aut Kota, Sri Harsha (orcid)0000-0002-1977-2954 aut Enthalten in Current pollution reports New York, NY [u.a.] : Springer, 2015 4(2018), 2 vom: 24. Feb., Seite 59-73 (DE-627)820057037 (DE-600)2813185-X 2198-6592 nnns volume:4 year:2018 number:2 day:24 month:02 pages:59-73 https://dx.doi.org/10.1007/s40726-018-0081-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2018 2 24 02 59-73 |
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10.1007/s40726-018-0081-0 doi (DE-627)SPR038071207 (SPR)s40726-018-0081-0-e DE-627 ger DE-627 rakwb eng Garaga, Rajyalakshmi verfasserin aut A Review of Air Quality Modeling Studies in India: Local and Regional Scale 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer International Publishing AG, part of Springer Nature 2018 Abstract Developing countries like India require proper control strategies for reducing the enormous premature mortality associated with air pollution. Air quality models, in addition to helping to understand the severity of air pollution by providing the pollutant concentrations, also give knowledge of the sources. Previous local and regional air quality modeling studies carried out in India are reviewed in this current study with a goal of understanding the current gaps and exploring future directions. Studies carried out in different parts of India during past decade were precisely documented in this study using methodical Scopus, Web of Science, and Google searches. Majority of the air quality studies are concentrated in megacities leaving behind the small cities which require greater attention in future. While most of the modeling studies were carried out in northern India, very few studies concentrated on central region of the country. Review of both local and regional numerical models showed the need for better emission inputs, while the statistical models inferred the need for proper selection of key tracers for source allocation. Irrespective of emission inventory and models used, particulate matter concentrations are under predicted in Delhi, which faces huge air pollution-related issues. Dust and traffic emissions are the major sources of particulate matter in India. India (dpeaa)DE-He213 PMF (dpeaa)DE-He213 CMAQ (dpeaa)DE-He213 AERMOD (dpeaa)DE-He213 PM2.5 (dpeaa)DE-He213 Toxic pollutants (dpeaa)DE-He213 VOCs (dpeaa)DE-He213 Sahu, Shovan Kumar aut Kota, Sri Harsha (orcid)0000-0002-1977-2954 aut Enthalten in Current pollution reports New York, NY [u.a.] : Springer, 2015 4(2018), 2 vom: 24. Feb., Seite 59-73 (DE-627)820057037 (DE-600)2813185-X 2198-6592 nnns volume:4 year:2018 number:2 day:24 month:02 pages:59-73 https://dx.doi.org/10.1007/s40726-018-0081-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2018 2 24 02 59-73 |
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Garaga, Rajyalakshmi |
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review of air quality modeling studies in india: local and regional scale |
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A Review of Air Quality Modeling Studies in India: Local and Regional Scale |
| abstract |
Abstract Developing countries like India require proper control strategies for reducing the enormous premature mortality associated with air pollution. Air quality models, in addition to helping to understand the severity of air pollution by providing the pollutant concentrations, also give knowledge of the sources. Previous local and regional air quality modeling studies carried out in India are reviewed in this current study with a goal of understanding the current gaps and exploring future directions. Studies carried out in different parts of India during past decade were precisely documented in this study using methodical Scopus, Web of Science, and Google searches. Majority of the air quality studies are concentrated in megacities leaving behind the small cities which require greater attention in future. While most of the modeling studies were carried out in northern India, very few studies concentrated on central region of the country. Review of both local and regional numerical models showed the need for better emission inputs, while the statistical models inferred the need for proper selection of key tracers for source allocation. Irrespective of emission inventory and models used, particulate matter concentrations are under predicted in Delhi, which faces huge air pollution-related issues. Dust and traffic emissions are the major sources of particulate matter in India. © Springer International Publishing AG, part of Springer Nature 2018 |
| abstractGer |
Abstract Developing countries like India require proper control strategies for reducing the enormous premature mortality associated with air pollution. Air quality models, in addition to helping to understand the severity of air pollution by providing the pollutant concentrations, also give knowledge of the sources. Previous local and regional air quality modeling studies carried out in India are reviewed in this current study with a goal of understanding the current gaps and exploring future directions. Studies carried out in different parts of India during past decade were precisely documented in this study using methodical Scopus, Web of Science, and Google searches. Majority of the air quality studies are concentrated in megacities leaving behind the small cities which require greater attention in future. While most of the modeling studies were carried out in northern India, very few studies concentrated on central region of the country. Review of both local and regional numerical models showed the need for better emission inputs, while the statistical models inferred the need for proper selection of key tracers for source allocation. Irrespective of emission inventory and models used, particulate matter concentrations are under predicted in Delhi, which faces huge air pollution-related issues. Dust and traffic emissions are the major sources of particulate matter in India. © Springer International Publishing AG, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Developing countries like India require proper control strategies for reducing the enormous premature mortality associated with air pollution. Air quality models, in addition to helping to understand the severity of air pollution by providing the pollutant concentrations, also give knowledge of the sources. Previous local and regional air quality modeling studies carried out in India are reviewed in this current study with a goal of understanding the current gaps and exploring future directions. Studies carried out in different parts of India during past decade were precisely documented in this study using methodical Scopus, Web of Science, and Google searches. Majority of the air quality studies are concentrated in megacities leaving behind the small cities which require greater attention in future. While most of the modeling studies were carried out in northern India, very few studies concentrated on central region of the country. Review of both local and regional numerical models showed the need for better emission inputs, while the statistical models inferred the need for proper selection of key tracers for source allocation. Irrespective of emission inventory and models used, particulate matter concentrations are under predicted in Delhi, which faces huge air pollution-related issues. Dust and traffic emissions are the major sources of particulate matter in India. © Springer International Publishing AG, part of Springer Nature 2018 |
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A Review of Air Quality Modeling Studies in India: Local and Regional Scale |
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Sahu, Shovan Kumar Kota, Sri Harsha |
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10.1007/s40726-018-0081-0 |
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| score |
7.401037 |