Optimization of Multipollutant Air Quality Management StrategiesEPA 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
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 which 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 U.S.
Two types of integrated decision-making models for regional air quality management will be developed using the technique of mathematical programming. Cost functions of emission reductions, sensitivities of air pollutants to emission reductions as well as constraints of resources and emission reduction efficiencies will be required for developing the proposed integrated decision-making models. A U.S. EPA’s emission control analysis model, AirControlNET, will be used to estimate costs of emission reductions for different emission control measures (by species, state, cost per ton, etc.). The EPA’s Models-3 regional air quality modeling system, which consists of WRF, SMOKE and CMAQ, along with the decoupled direct method (DDM), will be used to simulate concentrations of air pollutants and their sensitivities to emission reductions for an episode of 2007-2009. Finally, integrated multipollutant air quality management strategies will be developed using the proposed decision-making tools for multiple urban areas in the U.S.
Through this STAR project, three-dimensional time-dependent concentrations of air pollutants over the U.S. will be simulated. Sensitivities of ozone and PM2.5 concentrations in urban areas to precursor emissions from various U.S. regions will be quantified. The science regarding how long-transported precursors affect regional air quality will be better understood. The effectiveness of local primary PM2.5 emission emissions for improving air quality will also be assessed. The cost analysis tool will provide annual costs of emission reduction for various precursors. Dominant uncertainties in air quality modeling results due to emissions processing will be quantified. Finally, two types of integrated decision-making models for air quality management will be developed. Application of the proposed decision-making tools for developing air quality strategies in the continental and southeastern U.S. will be demonstrated. The proposed decision-making models can be directly used by air quality and public health managers when developing air quality management strategies.