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Conference on the Environment- GUERRA presentation Nov 19, 2014

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Case study of EMVAP and airing of background and modeled concentrations

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Conference on the Environment- GUERRA presentation Nov 19, 2014

  1. 1. INNOVATIVE DISPERSION MODELING PRACTICES TO ACHIEVE A REASONABLE LEVEL OF CONSERVATISM IN AERMOD MODELING DEMONSTRATIONS CASE STUDY TO EVALUATE EMVAP, AND BACKGROUND CONCENTRATIONS 29th Annual Conference on the Environment-St. Paul, MN November 19, 2014 Sergio A. Guerra - Wenck Associates, Inc.
  2. 2. 2 All truth passes through three stages. First, it is ridiculed. Second, it is violently opposed. Third, it is accepted as being self-evident. Arthur Schopenhauer
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  4. 4. 4 Challenge of new short-term NAAQS
  5. 5. AERMOD Model Accuracy Appendix W: 9.1.2 Studies of Model Accuracy a. A number of studies have been conducted to examine model accuracy, particularly with respect to the reliability of short-term concentrations required for ambient standard and increment evaluations. The results of these studies are not surprising. Basically, they confirm what expert atmospheric scientists have said for some time: (1) Models are more reliable for estimating longer time-averaged concentrations than for estimating short-term concentrations at specific locations; and (2) the models are reasonably reliable in estimating the magnitude of highest concentrations occurring sometime, somewhere within an area. For example, errors in highest estimated concentrations of ± 10 to 40 percent are found to be typical, i.e., certainly well within the often quoted factor-of-two accuracy that has long been recognized for these models. However, estimates of concentrations that occur at a specific time and site, are poorly correlated with actually observed concentrations and are much less reliable. • Bowne, N.E. and R.J. Londergan, 1983. Overview, Results, and Conclusions for the EPRI Plume Model Validation and Development Project: Plains Site. EPRI EA–3074. Electric Power Research Institute, Palo Alto, CA. • Moore, G.E., T.E. Stoeckenius and D.A. Stewart, 1982. A Survey of Statistical Measures of Model Performance and Accuracy for Several Air Quality Models. Publication No. EPA–450/4–83–001. Office of Air Quality Planning & Standards, Research Triangle Park, NC. 5
  6. 6. Perfect Model 6 MONITORED CONCENTRATIONS AERMOD CONCENTRATIONS
  7. 7. Monitored vs Modeled Data: Paired in time and space AERMOD performance evaluation of three coal-fired electrical generating units in Southwest Indiana Kali D. Frost Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014 7
  8. 8. SO2 Concentrations Paired in Time & Space Probability analyses of combining background concentrations with model-predicted concentrations Douglas R. Murray, Michael B. Newman Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014 8
  9. 9. SO2 Concentrations Paired in Time Only Probability analyses of combining background concentrations with model-predicted concentrations Douglas R. Murray, Michael B. Newman Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014 9
  10. 10. 10 EMVAP • Problem: Currently assume continuous emissions from proposed project or modification • Current modeling practices prescribe that an emission source (e.g., power plant) be modeled as if in continuous operation at maximum capacity. • EMVAP assigns emission rates at random over numerous iterations. • The resulting distribution from EMVAP yields a more representative approximation of actual impacts
  11. 11. Background Concentrations 11
  12. 12. Siting of Ambient Monitors According to the Ambient Monitoring Guidelines for Prevention of Significant Deterioration (PSD): The existing monitoring data should be representative of three types of area: 1) The location(s) of maximum concentration increase from the proposed source or modification; 2) The location(s) of the maximum air pollutant concentration from existing sources; and 3) The location(s) of the maximum impact area, i.e., where the maximum pollutant concentration would hypothetically occur based on the combined effect of existing sources and the proposed source or modification. (EPA, 1987) U.S. EPA. (1987). “Ambient Monitoring Guidelines for Prevention of Significant Deterioration (PSD).”EPA‐450/4‐87‐007, Research Triangle Park, NC. 12
  13. 13. Exceptional Events http://blogs.mprnews.org/updraft/2012/06/co_smoke_plume_now_visible_abo/ 13
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  15. 15. Exceptional Events 15
  16. 16. 24-hr PM2.5 Santa Fe, NM Airport Background Concentration and Methods to Establish Background Concentrations in Modeling. Presented at the Guideline on Air Quality Models: The Path Forward. Raleigh, NC, 2013. Bruce Nicholson 16
  17. 17. Probability of two unusual events 17
  18. 18. Combining 99th percentile Pre and Bkg (1-hr SO2) P(Pre ∩ Bkg) = P(Pre) * P(Bkg) = (1-0.99) * (1-0.99) = (0.01) * (0.01) = 0.0001 = 1 / 10,000 Equivalent to one exceedance every 27 years! = 99.99th percentile of the combined distribution 18
  19. 19. Proposed Approach to Combine Modeled and Monitored Concentrations • Combining the 99th (for 1-hr SO2) % monitored concentration with the 99th % predicted concentration is too conservative. • A more reasonable approach is to use a monitored value closer to the main distribution (i.e., the median). Evaluation of the SO2 and NOX offset ratio method to account for secondary PM2.5 formation Sergio A. Guerra, Shannon R. Olsen, Jared J. Anderson Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014 19
  20. 20. Combining 99th Pre and 50th Bkg P(Pre ∩ Bkg) = P(Pre) * P(Bkg) = (1-0.99) * (1-0.50) = (0.01) * (0.50) = 0.005 = 1 / 200 = 99.5th percentile of the combined distribution Evaluation of the SO2 and NOX offset ratio method to account for secondary PM2.5 formation Sergio A. Guerra, Shannon R. Olsen, Jared J. Anderson Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014 20
  21. 21. Positively Skewed Distribution http://www.agilegeoscience.com 21
  22. 22. 22 Case Study: Three cases evaluated 1. Using AERMOD by assuming a constant maximum emission rate (current modeling practice) 2. Using AERMOD by assuming a variable emission rate 3. Using EMVAP to account for emission variability
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  24. 24. 24 Three cases used to model the power plant Input parameter Case 1 Case 2 Case 3 Description of Dispersion Modeling Current Modeling Practices AERMOD with hourly emission EMVAP (500 iterations) SO2 Emission rate (g/s) 478.7 Actual emission rates from CEMS data Bin1: 478.7 (5.0% time) Bin 2: 228.7 (95% time) Stack height (m) 122 Exit temperature 416 (degrees K) Diameter (m) 5.2 Exit velocity (m/s) 23
  25. 25. 25 Results of 1-hour SO2 concentrations for the three cases Case 1 (μg/m3) Case 2 (μg/m3) Case 3 (μg/m3) Description of Dispersion Modeling Current Modeling Practices AERMOD with hourly emission EMVAP (500 iterations) H4H 229.9 78.6 179.3 Percent of 117% 40% 92% NAAQS
  26. 26. 26 St. Paul Park 436 ambient monitor location
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  28. 28. 28 Concentrations at different percentiles for the St. Paul Park 436 monitor (2011-2013) Percentile g/m3 50th 2.6 60th 3.5 70th 5.2 80th 6.1 90th 9.6 95th 12.9 98th 20.1 99th 25.6 99.9th 69.5 99.99th 84.7 Max. 86.4
  29. 29. 29 Case 3 with three different background values Case 3 with Max. Bkg (μg/m3) Case 3 with 99th % Bkg (μg/m3) Case 3 with 50th % Bkg (μg/m3) H4H 179.3 179.3 179.3 Background 86.4 25.6 2.6 Total 265.7 204.9 181.9 Percent of NAAQS 135.6% 104.5% 92.8%
  30. 30. Conclusion 30 • Use of EMVAP can help achieve more realistic concentrations • Use of 50th % monitored concentration is statistically conservative when pairing it with the 99th % predicted concentration • Methods are protective of the NAAQS while still providing a reasonable level of conservatism
  31. 31. QUESTIONS… Sergio A. Guerra, PhD Environmental Engineer Phone: (952) 837-3340 sguerra@wenck.com www.SergioAGuerra.com 31

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