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K.J. Liao 1 , Praveen Amar 2 and A.G. Russell 3

Development of Cost-Minimized Integrated Control Strategies for Regional Ozone and PM 2.5 Reductions. K.J. Liao 1 , Praveen Amar 2 and A.G. Russell 3 1 Texas A&M University-Kingsville 2 NESCAUM 3 Georgia Institute of Technology. Emission Sources of Ozone and PM 2.5. Ozone. PM 2.5. SO 2.

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K.J. Liao 1 , Praveen Amar 2 and A.G. Russell 3

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  1. Development of Cost-Minimized Integrated Control Strategies for Regional Ozone and PM2.5 Reductions K.J. Liao1, Praveen Amar2 and A.G. Russell3 1Texas A&M University-Kingsville2NESCAUM 3Georgia Institute of Technology

  2. Emission Sources of Ozone and PM2.5 Ozone PM2.5 SO2 NOx VOC PM NH3

  3. Traditional Framework for Developing State Implementation Plan (SIP) Air Quality Modeling Cohan et al., 2007

  4. Objective • Development of optimal (cost-minimized) control strategies for: • achieving ozone and PM2.5 targets • at multiple locations simultaneously.

  5. Ozone Isopleths Unit: ppb VOC-sensitive Current Target Multiple choices for control strategies. NOx-sensitive http://www-personal.umich.edu/~sillman/ozone.htm

  6. Control Strategy 1 Air Quality Targets (e.g. NAAQS) Current Levels Control Strategy 2 Air Pollutant 1 Air Pollutant 2 Air Pollutant n Air Pollutant 1 Air Pollutant 2 Air Pollutant n Control Strategy n Optimal Air Pollution Control Strategy for Multi-Pollutants and Multi-Locations Cost-minimized? Challenge: Air pollutants at different locations have different responses to changes in precursor emissions from common sources

  7. What We Need to Optimize Air Quality Control Strategies? Emissions Responses of air pollutants to emission controls Air Quality • Cost function • Limit of control efficiency Optimal control strategy: Least-cost measures for achieving air quality target Emission Control Costs

  8. Northeast Northeast Northeast Southeast Southeast Southeast Mid Mid Mid - - - Atlantic Atlantic Atlantic Great Lake Great Lake Great Lake West West West Central Central Central Air Quality Modeling - EPA Models-3: - MM5 - SMOKE - CMAQ-DDM • Two pollutants: • ozone • PM2.5 • Regional precursors: • SO2 • NOx • VOC • Local primary PM2.5 Six regions

  9. Assumptions • First-order sensitivities: • Ignore co-benefits of emission reductions for multiple precursors • Primary PM2.5 emissions only have local effects on air quality (Napelenok et al., 2006, Kim et al., 2002): Metropolitan Statistical Area (MSA)

  10. Ozone Sensitivity (CMAQ-DDM) (July 2001)

  11. Sensitivity of PM2.5 and Primary PM2.5 Concentrations (CMAQ-DDM) (July 2001) Obs. 23.5 18.2 9.7 21.0 13.1

  12. Per-ton Cost of Emission Reduction(EPA AirControlNET)

  13. Per-ton Cost of Primary PM2.5 Emission Reductions(EPA AirControlNET)

  14. OPtimal Integrated Emission Reduction Alternatives (OPERA) Minimize Subject to: Ozone target PM2.5 target Constraints for emission control efficiency

  15. Solving Method • the objective function in OPERA: nonlinear and non-convex • using the Matlab “fmincon” function: Quasi-Newton method and multiple initial points [Mathworks, 2009]

  16. Conclusions • OPERA needs responses of air pollutants to emission controls, cost functions of emission reductions and emission control efficiencies. • Responses of air pollutants to emission controls are quantified using CMAQ-DDM. • Cost functions and emission control efficiencies are developed using AirControlNET. • OPERA is efficient in developing cost-minimized control strategies for achieving prescribed multi-pollutant targets at multiple locations and could help policy-makers improve their decision-making processes.

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