Application of AERMOD for short-term air quality prediction with forecasted meteorology using WRF model
Abstract Many methods are available for air quality forecasting based on statistical and back trajectory models which require past time series data. Future air quality prediction through models is the best tool to make rational decisions by policy maker. Limited work has been done on air quality for...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kumar, Awkash [verfasserIn] Patil, Rashmi S. [verfasserIn] Dikshit, Anil Kumar [verfasserIn] Kumar, Rakesh [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Clean Products and Processes - Springer-Verlag, 2001, 19(2017), 7 vom: 23. Juni, Seite 1955-1965 |
|---|---|
| Übergeordnetes Werk: |
volume:19 ; year:2017 ; number:7 ; day:23 ; month:06 ; pages:1955-1965 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10098-017-1379-0 |
|---|
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SPR008729077 |
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| 520 | |a Abstract Many methods are available for air quality forecasting based on statistical and back trajectory models which require past time series data. Future air quality prediction through models is the best tool to make rational decisions by policy maker. Limited work has been done on air quality forecasting using dispersion models which require better meteorological boundary conditions. The Weather Research and Forecasting (WRF) and American Meteorological Society/Environmental Policy Agency Regulatory Model (AERMOD) models have not yet been combined for air quality forecasting. Here, a case study has been carried out to forecast air quality using onsite meteorological data from WRF model and a dispersion model named AERMOD. Prior to the use of AERMOD, a comprehensive emission inventory has been prepared for all the sources in the study region Chembur of Mumbai city. Chembur has been notified as the “air pollution control region” by local authority due to high levels of air pollution caused by the presence of four major industries, six major roads in addition to a crematorium and a biomedical waste incineration facility. The WRF–AERMOD system was applied for prediction of concentration levels of pollutants $ SO_{2} $, $ NO_{x} $ and $ PM_{10} $. A reasonable agreement was obtained when predicted values were compared with observed data. Results of the study indicated that forecasting of air quality can be carried out using AERMOD with forecasted meteorological parameters derived from WRF without any requirement of past time series air quality data. Such kind of forecasting method can be used for air quality management of any region by policy makers. | ||
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| 700 | 1 | |a Patil, Rashmi S. |e verfasserin |4 aut | |
| 700 | 1 | |a Dikshit, Anil Kumar |e verfasserin |4 aut | |
| 700 | 1 | |a Kumar, Rakesh |e verfasserin |4 aut | |
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10.1007/s10098-017-1379-0 doi (DE-627)SPR008729077 (SPR)s10098-017-1379-0-e DE-627 ger DE-627 rakwb eng Kumar, Awkash verfasserin aut Application of AERMOD for short-term air quality prediction with forecasted meteorology using WRF model 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Many methods are available for air quality forecasting based on statistical and back trajectory models which require past time series data. Future air quality prediction through models is the best tool to make rational decisions by policy maker. Limited work has been done on air quality forecasting using dispersion models which require better meteorological boundary conditions. The Weather Research and Forecasting (WRF) and American Meteorological Society/Environmental Policy Agency Regulatory Model (AERMOD) models have not yet been combined for air quality forecasting. Here, a case study has been carried out to forecast air quality using onsite meteorological data from WRF model and a dispersion model named AERMOD. Prior to the use of AERMOD, a comprehensive emission inventory has been prepared for all the sources in the study region Chembur of Mumbai city. Chembur has been notified as the “air pollution control region” by local authority due to high levels of air pollution caused by the presence of four major industries, six major roads in addition to a crematorium and a biomedical waste incineration facility. The WRF–AERMOD system was applied for prediction of concentration levels of pollutants $ SO_{2} $, $ NO_{x} $ and $ PM_{10} $. A reasonable agreement was obtained when predicted values were compared with observed data. Results of the study indicated that forecasting of air quality can be carried out using AERMOD with forecasted meteorological parameters derived from WRF without any requirement of past time series air quality data. Such kind of forecasting method can be used for air quality management of any region by policy makers. Air quality model (dpeaa)DE-He213 AERMOD (dpeaa)DE-He213 Urban area (dpeaa)DE-He213 Weather Research and Forecasting (WRF) (dpeaa)DE-He213 Meteorological model (dpeaa)DE-He213 Patil, Rashmi S. verfasserin aut Dikshit, Anil Kumar verfasserin aut Kumar, Rakesh verfasserin aut Enthalten in Clean Products and Processes Springer-Verlag, 2001 19(2017), 7 vom: 23. Juni, Seite 1955-1965 (DE-627)SPR008711836 nnns volume:19 year:2017 number:7 day:23 month:06 pages:1955-1965 https://dx.doi.org/10.1007/s10098-017-1379-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 19 2017 7 23 06 1955-1965 |
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10.1007/s10098-017-1379-0 doi (DE-627)SPR008729077 (SPR)s10098-017-1379-0-e DE-627 ger DE-627 rakwb eng Kumar, Awkash verfasserin aut Application of AERMOD for short-term air quality prediction with forecasted meteorology using WRF model 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Many methods are available for air quality forecasting based on statistical and back trajectory models which require past time series data. Future air quality prediction through models is the best tool to make rational decisions by policy maker. Limited work has been done on air quality forecasting using dispersion models which require better meteorological boundary conditions. The Weather Research and Forecasting (WRF) and American Meteorological Society/Environmental Policy Agency Regulatory Model (AERMOD) models have not yet been combined for air quality forecasting. Here, a case study has been carried out to forecast air quality using onsite meteorological data from WRF model and a dispersion model named AERMOD. Prior to the use of AERMOD, a comprehensive emission inventory has been prepared for all the sources in the study region Chembur of Mumbai city. Chembur has been notified as the “air pollution control region” by local authority due to high levels of air pollution caused by the presence of four major industries, six major roads in addition to a crematorium and a biomedical waste incineration facility. The WRF–AERMOD system was applied for prediction of concentration levels of pollutants $ SO_{2} $, $ NO_{x} $ and $ PM_{10} $. A reasonable agreement was obtained when predicted values were compared with observed data. Results of the study indicated that forecasting of air quality can be carried out using AERMOD with forecasted meteorological parameters derived from WRF without any requirement of past time series air quality data. Such kind of forecasting method can be used for air quality management of any region by policy makers. Air quality model (dpeaa)DE-He213 AERMOD (dpeaa)DE-He213 Urban area (dpeaa)DE-He213 Weather Research and Forecasting (WRF) (dpeaa)DE-He213 Meteorological model (dpeaa)DE-He213 Patil, Rashmi S. verfasserin aut Dikshit, Anil Kumar verfasserin aut Kumar, Rakesh verfasserin aut Enthalten in Clean Products and Processes Springer-Verlag, 2001 19(2017), 7 vom: 23. Juni, Seite 1955-1965 (DE-627)SPR008711836 nnns volume:19 year:2017 number:7 day:23 month:06 pages:1955-1965 https://dx.doi.org/10.1007/s10098-017-1379-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 19 2017 7 23 06 1955-1965 |
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10.1007/s10098-017-1379-0 doi (DE-627)SPR008729077 (SPR)s10098-017-1379-0-e DE-627 ger DE-627 rakwb eng Kumar, Awkash verfasserin aut Application of AERMOD for short-term air quality prediction with forecasted meteorology using WRF model 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Many methods are available for air quality forecasting based on statistical and back trajectory models which require past time series data. Future air quality prediction through models is the best tool to make rational decisions by policy maker. Limited work has been done on air quality forecasting using dispersion models which require better meteorological boundary conditions. The Weather Research and Forecasting (WRF) and American Meteorological Society/Environmental Policy Agency Regulatory Model (AERMOD) models have not yet been combined for air quality forecasting. Here, a case study has been carried out to forecast air quality using onsite meteorological data from WRF model and a dispersion model named AERMOD. Prior to the use of AERMOD, a comprehensive emission inventory has been prepared for all the sources in the study region Chembur of Mumbai city. Chembur has been notified as the “air pollution control region” by local authority due to high levels of air pollution caused by the presence of four major industries, six major roads in addition to a crematorium and a biomedical waste incineration facility. The WRF–AERMOD system was applied for prediction of concentration levels of pollutants $ SO_{2} $, $ NO_{x} $ and $ PM_{10} $. A reasonable agreement was obtained when predicted values were compared with observed data. Results of the study indicated that forecasting of air quality can be carried out using AERMOD with forecasted meteorological parameters derived from WRF without any requirement of past time series air quality data. Such kind of forecasting method can be used for air quality management of any region by policy makers. Air quality model (dpeaa)DE-He213 AERMOD (dpeaa)DE-He213 Urban area (dpeaa)DE-He213 Weather Research and Forecasting (WRF) (dpeaa)DE-He213 Meteorological model (dpeaa)DE-He213 Patil, Rashmi S. verfasserin aut Dikshit, Anil Kumar verfasserin aut Kumar, Rakesh verfasserin aut Enthalten in Clean Products and Processes Springer-Verlag, 2001 19(2017), 7 vom: 23. Juni, Seite 1955-1965 (DE-627)SPR008711836 nnns volume:19 year:2017 number:7 day:23 month:06 pages:1955-1965 https://dx.doi.org/10.1007/s10098-017-1379-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 19 2017 7 23 06 1955-1965 |
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10.1007/s10098-017-1379-0 doi (DE-627)SPR008729077 (SPR)s10098-017-1379-0-e DE-627 ger DE-627 rakwb eng Kumar, Awkash verfasserin aut Application of AERMOD for short-term air quality prediction with forecasted meteorology using WRF model 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Many methods are available for air quality forecasting based on statistical and back trajectory models which require past time series data. Future air quality prediction through models is the best tool to make rational decisions by policy maker. Limited work has been done on air quality forecasting using dispersion models which require better meteorological boundary conditions. The Weather Research and Forecasting (WRF) and American Meteorological Society/Environmental Policy Agency Regulatory Model (AERMOD) models have not yet been combined for air quality forecasting. Here, a case study has been carried out to forecast air quality using onsite meteorological data from WRF model and a dispersion model named AERMOD. Prior to the use of AERMOD, a comprehensive emission inventory has been prepared for all the sources in the study region Chembur of Mumbai city. Chembur has been notified as the “air pollution control region” by local authority due to high levels of air pollution caused by the presence of four major industries, six major roads in addition to a crematorium and a biomedical waste incineration facility. The WRF–AERMOD system was applied for prediction of concentration levels of pollutants $ SO_{2} $, $ NO_{x} $ and $ PM_{10} $. A reasonable agreement was obtained when predicted values were compared with observed data. Results of the study indicated that forecasting of air quality can be carried out using AERMOD with forecasted meteorological parameters derived from WRF without any requirement of past time series air quality data. Such kind of forecasting method can be used for air quality management of any region by policy makers. Air quality model (dpeaa)DE-He213 AERMOD (dpeaa)DE-He213 Urban area (dpeaa)DE-He213 Weather Research and Forecasting (WRF) (dpeaa)DE-He213 Meteorological model (dpeaa)DE-He213 Patil, Rashmi S. verfasserin aut Dikshit, Anil Kumar verfasserin aut Kumar, Rakesh verfasserin aut Enthalten in Clean Products and Processes Springer-Verlag, 2001 19(2017), 7 vom: 23. Juni, Seite 1955-1965 (DE-627)SPR008711836 nnns volume:19 year:2017 number:7 day:23 month:06 pages:1955-1965 https://dx.doi.org/10.1007/s10098-017-1379-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 19 2017 7 23 06 1955-1965 |
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| doi_str_mv |
10.1007/s10098-017-1379-0 |
| author2-role |
verfasserin |
| title_sort |
application of aermod for short-term air quality prediction with forecasted meteorology using wrf model |
| title_auth |
Application of AERMOD for short-term air quality prediction with forecasted meteorology using WRF model |
| abstract |
Abstract Many methods are available for air quality forecasting based on statistical and back trajectory models which require past time series data. Future air quality prediction through models is the best tool to make rational decisions by policy maker. Limited work has been done on air quality forecasting using dispersion models which require better meteorological boundary conditions. The Weather Research and Forecasting (WRF) and American Meteorological Society/Environmental Policy Agency Regulatory Model (AERMOD) models have not yet been combined for air quality forecasting. Here, a case study has been carried out to forecast air quality using onsite meteorological data from WRF model and a dispersion model named AERMOD. Prior to the use of AERMOD, a comprehensive emission inventory has been prepared for all the sources in the study region Chembur of Mumbai city. Chembur has been notified as the “air pollution control region” by local authority due to high levels of air pollution caused by the presence of four major industries, six major roads in addition to a crematorium and a biomedical waste incineration facility. The WRF–AERMOD system was applied for prediction of concentration levels of pollutants $ SO_{2} $, $ NO_{x} $ and $ PM_{10} $. A reasonable agreement was obtained when predicted values were compared with observed data. Results of the study indicated that forecasting of air quality can be carried out using AERMOD with forecasted meteorological parameters derived from WRF without any requirement of past time series air quality data. Such kind of forecasting method can be used for air quality management of any region by policy makers. |
| abstractGer |
Abstract Many methods are available for air quality forecasting based on statistical and back trajectory models which require past time series data. Future air quality prediction through models is the best tool to make rational decisions by policy maker. Limited work has been done on air quality forecasting using dispersion models which require better meteorological boundary conditions. The Weather Research and Forecasting (WRF) and American Meteorological Society/Environmental Policy Agency Regulatory Model (AERMOD) models have not yet been combined for air quality forecasting. Here, a case study has been carried out to forecast air quality using onsite meteorological data from WRF model and a dispersion model named AERMOD. Prior to the use of AERMOD, a comprehensive emission inventory has been prepared for all the sources in the study region Chembur of Mumbai city. Chembur has been notified as the “air pollution control region” by local authority due to high levels of air pollution caused by the presence of four major industries, six major roads in addition to a crematorium and a biomedical waste incineration facility. The WRF–AERMOD system was applied for prediction of concentration levels of pollutants $ SO_{2} $, $ NO_{x} $ and $ PM_{10} $. A reasonable agreement was obtained when predicted values were compared with observed data. Results of the study indicated that forecasting of air quality can be carried out using AERMOD with forecasted meteorological parameters derived from WRF without any requirement of past time series air quality data. Such kind of forecasting method can be used for air quality management of any region by policy makers. |
| abstract_unstemmed |
Abstract Many methods are available for air quality forecasting based on statistical and back trajectory models which require past time series data. Future air quality prediction through models is the best tool to make rational decisions by policy maker. Limited work has been done on air quality forecasting using dispersion models which require better meteorological boundary conditions. The Weather Research and Forecasting (WRF) and American Meteorological Society/Environmental Policy Agency Regulatory Model (AERMOD) models have not yet been combined for air quality forecasting. Here, a case study has been carried out to forecast air quality using onsite meteorological data from WRF model and a dispersion model named AERMOD. Prior to the use of AERMOD, a comprehensive emission inventory has been prepared for all the sources in the study region Chembur of Mumbai city. Chembur has been notified as the “air pollution control region” by local authority due to high levels of air pollution caused by the presence of four major industries, six major roads in addition to a crematorium and a biomedical waste incineration facility. The WRF–AERMOD system was applied for prediction of concentration levels of pollutants $ SO_{2} $, $ NO_{x} $ and $ PM_{10} $. A reasonable agreement was obtained when predicted values were compared with observed data. Results of the study indicated that forecasting of air quality can be carried out using AERMOD with forecasted meteorological parameters derived from WRF without any requirement of past time series air quality data. Such kind of forecasting method can be used for air quality management of any region by policy makers. |
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| title_short |
Application of AERMOD for short-term air quality prediction with forecasted meteorology using WRF model |
| url |
https://dx.doi.org/10.1007/s10098-017-1379-0 |
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| author2 |
Patil, Rashmi S. Dikshit, Anil Kumar Kumar, Rakesh |
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Patil, Rashmi S. Dikshit, Anil Kumar Kumar, Rakesh |
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| doi_str |
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2024-07-03T22:49:45.826Z |
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