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Applicability of CMAQ-DDM to source apportionment and control strategy development

This paper discusses the applicability of the CMAQ-DDM model in source apportionment and air quality control strategy development, examining how various emissions sources impact pollutant concentrations and assessing the effectiveness of control measures. It explores the complexities of nonlinearity and offers insights into utilizing the HDDM-3D model for sensitivity analysis and strategy evaluation. The study provides recommendations for optimizing modeling methods in source apportionment and strategy assessment, emphasizing the importance of considering aggregate emissions effects when formulating air quality strategies.

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Applicability of CMAQ-DDM to source apportionment and control strategy development

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  1. Applicability of CMAQ-DDM to source apportionment and control strategy development Daniel Cohan Georgia Institute of Technology 2004 Models-3 Users’ Workshop October 19, 2004

  2. Policy Applications of AQ Models • Source apportionment • How much of ambient pollutant concentrations can be attributed to each emission source? • Control strategy assessment • How would concentrations change under a control measure? • Forward projections • How will concentrations change under future trends (regulation, technology, growth)?

  3. Emissions, Initial Conditions, Boundary Conditions, etc. ∆ (e.g., Atlanta Emissions) Air Quality Model Air Quality Model Concentrations Sensitivities ∆ Check scientific understanding Extend beyond observations Forecasting and prediction Atmospheric response Control strategies Source apportionment

  4. Complication: Nonlinearity ENOx • Often, only a handful of sensitivities are modeled (e.g., 30% NOx reduction, 30% VOC reduction) • Linear scaling and additivity assumption may be inaccurate • But it may be impractical to model all combinations of emission sources or control measures High O3 Low O3 EVOC

  5. Brute ForceandHDDM-3D Ozone A + CA ∆C a1 B + CB EVOC -DeEA EB EA

  6. Applications of HDDM-3D Incremental sensitivity Control strategy Source apportionment Taylor expansion(=-1) S.C. ≈S(1) - 0.5∙S(2) First-order sensitivity S(1) = ∂C/∂ Taylor expansion(<0) C ≈C0+ S(1) + 0.52S(2)

  7. Consistency of local sensitivities Brute Force HDDM-3D R2 > 0.99 Low bias & error

  8. HDDM performance & nonlinearity 1st+2nd order: Well captures response DDM – Brute Force 1st order only: Extent of nonlinearity % emission reduction For 8-hr ozone, averaged over 12-km domain, Aug. 13-19, 2000 (2007 emissions)

  9. Interactions of emission impacts Impact of single perturbation: E(x,t)=E0(x,t)+εjpj(x,t) 1st-order 2nd-order Impact of dual perturbation: E(x,t)=E0(x,t)+εjpj(x,t)+εkpk(x,t) 1st-order 2nd-order Cross term

  10. Isopleths of atmospheric response Atlanta Macon Ozone (ppmV) August 17, 2000 peak-hour ozone (from the method of Hakami et al., 2004)

  11. Atlanta apportionment by NOx category Cross-sens. Contribution to Atlanta ozone (ppm) Sum of parts Atlanta NOx: Aggregate Atlanta NOx: By Category Atlanta MSA, 8-hour ozone, Aug. 13-19, 2000 (Year 2007 emissions)

  12. Macon apportionment by NOx source region A B S M Macon MSA, Aug. 13-19, 2000 (2007 emissions)

  13. Recommendations • HDDM-3D is a powerful scoping tool for examining numerous source contributions or control measures, and the interactions among pairs • Caution: Only 1st order DDM-3D is being implemented for PM • Due to nonlinearity and non-additivity, an aggregate brute force assessment should be used to evaluate the cumulative effect of the entire strategy • Iterative DDM-3D / brute force approach may be considered • Important to match modeling methods to objectives in source apportionment and strategy assessment • Contribution of aggregate emissions may differ from sum of parts

  14. Acknowledgments • Funding: Fall-Line Air Quality Study (Georgia Environmental Protection Division and the Fall-Line cities) • Amir Hakami, Yongtao Hu, Ted Russell

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