2014 Progress 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 Period Covered by this Report: June 1, 2014 through May 31,2015
Project Amount: $249,115
RFA: Dynamic Air Quality Management (2011) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Air

Objective:

The objectives of this project are to: 1) assess 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; 2) develop a least-cost decision-making model that allows identification of optimal control strategies for attaining prescribed multipollutant air quality targets at multiple locations simultaneously; 3) develop a resource allocation model that achieves the largest health benefits with limited resources (i.e., budgets) for improving regional air quality; and 4) demonstrate 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.

Progress Summary:

In the first 2 years of the project, we achieved Objectives 1 and 2 in the Project Research Plan. Matlab scripts for using the optimization model (i.e., OPERA I) developed during Year 2 had been uploaded to the PI’s website (Kuo-Jen Liao, Ph.D.| Texas A&M University-Kingsville Exit), and can be downloaded and used by the public at no cost. During the third year of this STAR project (i.e., June 1, 2014–May 31, 2015), we developed a resource allocation model (i.e., Objective 3), which allows identifications of multipollutant air quality management that maximize health benefits when limited resources (i.e., budgets) are considered. We also applied the model to develop resource allocation strategies for five megacities (i.e., New York, Los Angeles, Chicago, Dallas-Fort Worth, and Philadelphia) in the continental United States assuming 20% of the EPA’s air quality budget in year 2010 was used for the five cities (i.e., Objective 4 in Project Research Plan). The resource allocation approach is formulated as a mathematical programming model, and four pieces of information needed for the resource allocation model are: (1) air quality sensitivities to emission controls, (2) cost functions of emission reductions, (3) responses of human health to changes in air quality, and (4) budget constraints of air quality management. Specifically, we leverage health effect functions as well as the CMAQ air quality model and AirControlNET cost analysis tool, developed by the EPA, to develop the resource allocation model. The results of the five megacity study suggest that that controls of primary particulate matter emissions from EPA Regions 2, 3, 5 and 9 as well as the SO2 emissions from EPA Region 2 would achieve the most significant health benefits for the five U.S. megacities as a whole. Although only five U.S. cities are considered in the case study, we believe that the resource allocation model can be used by decision makers to allocate funds of air pollution controls to achieve the most significant health benefits for more cities over a region or the continental United States.

In Year 3 of the study, air quality modeling results were compared against observed air quality data to investigate the capability of EPA’s CMAQ model estimating air pollutant concentrations during simulation episodes. Observations of ozone concentrations were retrieved from EPA's Air Quality System (AQS) database, which contains a multitude of hourly aerometric data, including ozone and PM2.5 concentrations, collected by federal, state, and local agencies at thousands of locations nationwide. The results of the air quality evaluation show that, for the 2010 summer episode, ambient ozone and PM2.5 concentrations modeled by CMAQ meet the performance recommended by EPA for most of the areas in the case study. The evaluation results also show that modeled ozone concentrations in the Los Angeles and Chicago MSAs as well as PM2.5 concentration in the Dallas-Fort Worth MSA are slightly underestimated when compared to the AQS data. Because the objective of the Year 3 study is to develop the resource allocation model, we have not specifically examined the details of the underestimations of ozone and PM2.5 concentrations. However, the issue of underestimation of modeled ozone and PM2.5 air quality will be addressed during the no-cost extension period (i.e., 6/1/2015-2/28/2016).

Future Activities:

Planned activities for the subsequent reporting periods (9 months no-cost extension 6/1/2015-2/28/2016):


Journal Articles on this Report : 2 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 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. 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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