Use of inverse modeling in air quality management

Inverse modeling has been used in the past to constrain atmospheric model parameters, particularly emission estimates, based upon ambient measurements. Here, inverse modeling is applied to air quality planning by calculating how emissions should change to achieve desired reduction in air pollutants....

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Main Author: Akhtar, Farhan Hussain
Published: Georgia Institute of Technology 2011
Subjects:
Online Access:http://hdl.handle.net/1853/37213
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spelling ndltd-GATECH-oai-smartech.gatech.edu-1853-372132013-01-07T20:36:56ZUse of inverse modeling in air quality managementAkhtar, Farhan HussainInverse methodsAir pollution policyUrban and city planningAir quality managementAir qualityInverse modeling has been used in the past to constrain atmospheric model parameters, particularly emission estimates, based upon ambient measurements. Here, inverse modeling is applied to air quality planning by calculating how emissions should change to achieve desired reduction in air pollutants. Specifically, emissions of nitrogen oxides (NOx = NO + NO2) are adjusted to achieve reductions in tropospheric ozone, a respiratory irritant, during an historic episode of elevated concentrations in urban Atlanta, GA. Understanding how emissions should change in aggregate without specifying discrete abatement options is particularly applicable to long-term and regional air pollution management. Using a cost/benefit approach, desired reductions in ozone concentrations are found for a future population in Atlanta, GA. The inverse method is applied to find NOx emission adjustments to reach this desired reduction in air pollution. An example of how emissions adjustments may aid the planning process in two neighborhoods is demonstrated using urban form indicators from a land use and transportation database. Implications of this method on establishing regional and market-based air quality management systems in light of recent legal decisions are also discussed. Both ozone and secondary particulate matter with diameters of less than 2.5μm (PM2.5) are formed in the atmosphere from common precursor species. Recent assessments of air quality management policies have stressed the need for pollutant abatement strategies addressing these mutual sources. The relative contribution of several important precursor species (NOx, sulfur dioxide, ammonia, and anthropogenic volatile organic compounds) to the formation of ozone and secondary PM2.5 in Atlanta during May 2007 - April 2008 is simulated using CMAQ/DDM-3D. This sensitivity analysis is then used to find adjustments in emissions of precursor species to achieve goal reductions for both ozone and secondary PM2.5 during a summertime episode of elevated concentrations. A discussion of the implications of these controls on air pollutant concentrations during the remaining year follows.Georgia Institute of Technology2011-03-04T20:54:57Z2011-03-04T20:54:57Z2009-08-21Dissertationhttp://hdl.handle.net/1853/37213
collection NDLTD
sources NDLTD
topic Inverse methods
Air pollution policy
Urban and city planning
Air quality management
Air quality
spellingShingle Inverse methods
Air pollution policy
Urban and city planning
Air quality management
Air quality
Akhtar, Farhan Hussain
Use of inverse modeling in air quality management
description Inverse modeling has been used in the past to constrain atmospheric model parameters, particularly emission estimates, based upon ambient measurements. Here, inverse modeling is applied to air quality planning by calculating how emissions should change to achieve desired reduction in air pollutants. Specifically, emissions of nitrogen oxides (NOx = NO + NO2) are adjusted to achieve reductions in tropospheric ozone, a respiratory irritant, during an historic episode of elevated concentrations in urban Atlanta, GA. Understanding how emissions should change in aggregate without specifying discrete abatement options is particularly applicable to long-term and regional air pollution management. Using a cost/benefit approach, desired reductions in ozone concentrations are found for a future population in Atlanta, GA. The inverse method is applied to find NOx emission adjustments to reach this desired reduction in air pollution. An example of how emissions adjustments may aid the planning process in two neighborhoods is demonstrated using urban form indicators from a land use and transportation database. Implications of this method on establishing regional and market-based air quality management systems in light of recent legal decisions are also discussed. Both ozone and secondary particulate matter with diameters of less than 2.5μm (PM2.5) are formed in the atmosphere from common precursor species. Recent assessments of air quality management policies have stressed the need for pollutant abatement strategies addressing these mutual sources. The relative contribution of several important precursor species (NOx, sulfur dioxide, ammonia, and anthropogenic volatile organic compounds) to the formation of ozone and secondary PM2.5 in Atlanta during May 2007 - April 2008 is simulated using CMAQ/DDM-3D. This sensitivity analysis is then used to find adjustments in emissions of precursor species to achieve goal reductions for both ozone and secondary PM2.5 during a summertime episode of elevated concentrations. A discussion of the implications of these controls on air pollutant concentrations during the remaining year follows.
author Akhtar, Farhan Hussain
author_facet Akhtar, Farhan Hussain
author_sort Akhtar, Farhan Hussain
title Use of inverse modeling in air quality management
title_short Use of inverse modeling in air quality management
title_full Use of inverse modeling in air quality management
title_fullStr Use of inverse modeling in air quality management
title_full_unstemmed Use of inverse modeling in air quality management
title_sort use of inverse modeling in air quality management
publisher Georgia Institute of Technology
publishDate 2011
url http://hdl.handle.net/1853/37213
work_keys_str_mv AT akhtarfarhanhussain useofinversemodelinginairqualitymanagement
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