Final Report: Optimization of Multipollutant Air Quality Management Strategies

EPA Grant Number: R835218
Title: Optimization of Multipollutant Air Quality Management Strategies
Investigators: Liao, Kuo-Jen
Institution: Texas A & M University - Kingsville
EPA Project Officer: Chung, Serena
Project Period: June 1, 2012 through May 31, 2015 (Extended to February 29, 2016)
Project Amount: $249,115
RFA: Dynamic Air Quality Management (2011) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Air

Objective:

This project has four primary objectives:

Objective 1: Assessment of the effectiveness of reductions in emissions from various U.S. regions and local primary fine particulate matter for improving multipollutant air quality in urban areas.

Objective 2: Development of a least-cost decision-making model that allows the identification of optimal control strategies for attaining prescribed multipollutant air quality targets at multiple locations simultaneously.

Objective 3: Development of a resource allocation model that achieves the largest health benefits with limited resources (i.e., budgets) for improving regional air quality.

Objective 4: Demonstration of the capability of the proposed least-cost and resource allocation models for developing multipollutant air quality management strategies for urban areas in the United States.

Summary/Accomplishments (Outputs/Outcomes):

To accomplish the goal and objectives of this EPA-funded project, we developed two optimization models, OPtimal integrated Emission Reduction Alternatives (OPERA-I and OPERA-II), which can be used to develop multipollutant air quality management strategies. We have three main findings for this project. First, it is challenging for decision-makers developing multipollutant air quality management strategies. Second, it is important to have tools (i.e., OPERA-I and II) that can be directly used by decision-makers for preparing air pollution mitigation strategies. Third, case studies should be conducted to demonstrate how the tools can be used to develop multipollutant air quality management strategies for multiple locations simultaneously. OPERA-I, formulated as a nonlinear programming model, can help identify least-cost control strategies for attaining prescribed multipollutant air quality targets at multiple locations simultaneously. OPERA-I involves four steps. First, relationships between ambient pollutants (e.g., ozone and PM2.5) levels and precursor emission controls are quantified through regional air quality simulations and sensitivity analyses. Second, cost functions of emission reductions are developed using a cost analysis tool, which includes a pool of available control measures for different emission sectors and regions. Third, determine required reductions in concentrations of air pollutants for areas of interest. The last step is to identify the optimal control strategies from a wide variety of combinations of control measures: This is done by minimizing costs of emission controls using the cost functions, sensitivities of air pollutants to emission changes, and constraints for feasible emission reduction ratios. To demonstrate how to develop air quality management strategies using OPERA-I, we conducted a case study focusing on ozone and PM2.5 air quality in summer 2007 in five major cities (i.e., Atlanta; Chicago; Washington, DC; New York; and Philadelphia) in the eastern United States. From the results of the case study, we have the following findings. First, emission reductions from distant regions could be cost-effective for achieving prescribed ozone and PM2.5 levels in the cities examined. Second, reducing regional NOx and volatile organic compounds, as well as local primary PM2.5 emissions, was more cost-effective than controlling SO2 emissions for decreasing ozone and PM2.5 concentrations simultaneously. It was because reductions in SO2 emissions would only decrease PM2.5 concentrations, and reductions in primary PM2.5 emissions were more cost-effective than SO2 emission controls. A major strength of OPERA-I is its flexibility that allows for changes in regulations, involving agencies, study regions, etc., to be readily incorporated. Overall, OPERA-I has been demonstrated that it is efficient to develop least-cost emission control strategies for achieving multipollutant air quality targets at multiple locations simultaneously.

Our second finding for this project is the importance of effective tools that can be directly used by decision-makers for preparing air pollution mitigation strategies. Specifically, in addition to OPERA-I, we developed an innovative resource allocation model (i.e., OPERA-II), which can help identify the best resource allocation strategies that maximize human health benefits of air quality protection when limited resources (i.e., budgets) are considered. OPERA-II takes into account (1) air quality sensitivities to emission controls, (2) responses of human health to air quality, (3) costs of air pollutant emission controls, and (4) limitations of resources (i.e., budgets) for air quality protection. We applied the OPERA-II model to develop resource allocation strategies for top five Metropolitan Statistical Areas (MSAs) (i.e., New York, Los Angeles, Chicago, Dallas-Fort Worth, and Philadelphia) in the continental United States. The study episode is from August 8 to August 21, 2010, because several of the MSAs had high ozone levels during the period. Given constraints of air quality mitigation budgets in the United States, the results suggest that controls of primary PM2.5 emissions from EPA Regions 2, 3, 5, and 9, and SO2 emission from EPA Region 2, would achieve the most significant health benefits for the five selected MSAs collectively. Around 30,800 air pollution-related mortalities could be avoided during the selected period for the five selected MSAs if the budgets could be allocated following the results of the resource allocation modeling.

Conclusions:

Overall, we have demonstrated how OPERA-I and II could help decision-makers develop integrated air quality management strategies that achieve multipollutant air quality targets and maximize human health benefits when limited resources (i.e., budgets) are considered. Although only five U.S. cities were considered in the case study of OPERA-I and II, the models can be used to develop multipollutant air quality management strategies for more cities over a region and the U.S. Matlab scripts for using the OPERA-I and II models have been uploaded to the principal investigator's website (Kou-Jen Liao | Texas A&M University Kingsville Exit) and can be downloaded and used by decision-makers at no cost.


Journal Articles on this Report : 5 Displayed | Download in RIS Format

Other project views: All 14 publications 5 publications in selected types All 5 journal articles
Type Citation Project Document Sources
Journal Article Chang C-Y, Faust E, Hou X, Lee P, Kim HC, Hedquist BC, Liao K-J. Investigating ambient ozone formation regimes in neighboring cities of shale plays in the Northeast United States using photochemical modeling and satellite retrievals. Atmospheric Environment 2016;142:152-170. R835218 (Final)
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  • Journal Article Hou X, Strickland MJ, Liao K-J. Contributions of regional air pollutant emissions to ozone and fine particulate matter-related mortalities in eastern U.S. urban areas. Environmental Research 2015;137:475-484. R835218 (2014)
    R835218 (Final)
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  • Journal Article Liao K-J, Hou X, Strickland MJ. Resource allocation for mitigating regional air pollution-related mortality: a summertime case study for five cities in the United States. Journal of the Air & Waste Management Association 2016;66(8):748-757. R835218 (Final)
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  • Journal Article Liao K-J, Hou X, Baker DR. Impacts of interstate transport of pollutants on high ozone events over the Mid-Atlantic United States. Atmospheric Environment 2014;84:100-112. R835218 (2012)
    R835218 (Final)
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  • Journal Article Liao K-J, Hou X. Optimization of multipollutant air quality management strategies:a case study for five cities in the United States. Journal of the Air & Waste Management Association 2015;65(6):732-742. R835218 (2014)
    R835218 (Final)
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  • Supplemental Keywords:

    Air quality, multipollutant, optimization;

    Relevant Websites:

    Kou-Jen Liao | Texas A&M University Kingsville Exit

    Progress and Final Reports:

    Original Abstract
    2012 Progress Report
    2014 Progress Report