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Use of Probabilistic Statistical Techniques in AERMOD Modeling Evaluations

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The advent of the short term National Ambient Air Quality Standards (NAAQS) prompted modelers to reassess the common practices in dispersion modeling analyses. The probabilistic nature of the new short term standards also opens the door to alternative modeling techniques that are based on probability. One of these is the Monte Carlo technique that can be used to account for emission variability in permit modeling.
Currently, it is assumed that a given emission unit is in operation at its maximum capacity every hour of the year. This assumption may be appropriate for facilities that operate at full capacity most of the time. However, in most cases, emission units operate at variable loads that produce variable emissions. Thus, assuming constant maximum emissions is overly conservative for facilities such as power plants that are not in operation all the time and which exhibit high concentrations during very short periods of time.
Another element of conservatism in NAAQS demonstrations relates to combining predicted concentrations from the AMS/EPA Regulatory Model (AERMOD) with observed (monitored) background concentrations. Normally, some of the highest monitored observations are added to the AERMOD results yielding a very conservative combined concentration.
A case study is presented to evaluate the use of alternative probabilistic methods to complement the shortcomings of current dispersion modeling practices. This case study includes the use of the Monte Carlo technique and the use of a reasonable background concentration to combine with the AERMOD predicted concentrations. The use of these methods is in harmony with the probabilistic nature of the NAAQS and can help demonstrate compliance through dispersion modeling analyses, while still being protective of the NAAQS.

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Use of Probabilistic Statistical Techniques in AERMOD Modeling Evaluations

  1. 1. Use of Probabilistic Statistical Techniques in AERMOD Modeling Evaluations A&WMA’s 108th Annual Conference & Exhibition – Raleigh, NC June 24, 2015 Sergio A. Guerra, Ph.D. - CPP, Inc. Jesse Thé, Ph.D., P.Eng. - Lakes Environmental Software
  2. 2. Outline • AERMOD’s Probabilistic Performance Evaluation • Monte Carlo Statistical Technique • Combining Modeled Results and Background Concentrations • Case Study Example
  3. 3. Model’s 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.
  4. 4. Perfect Model MONITORED CONCENTRATIONS AERMODCONCENTRATIONS 100 1000 - -
  5. 5. 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
  6. 6. 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
  7. 7. 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
  8. 8. AERMOD’s Evaluation
  9. 9. Are We Using the Model Correctly? Temporal matching is not justifiable Perfect model AERMOD
  10. 10. Solutions to AERMOD’s Limitations Advanced Modeling Techniques Traditional Modeling Technique Variable emissions Use EMVAP to account for variability Assume continuous maximum emissions Background Concentrations Combine AERMOD’s concentration with the 50th % observed Tier 1: Combine AERMOD’s concentration with max. or design value (e.g., 98th % observed for SO2) Tier 2: Combine predicted and observed values based on temporal matching (e.g., by season or hour of day).
  11. 11. Monte Carlo Approach • Pioneered by the Manhattan Project scientists in 1940’s • Technique is widely used in science and industry • EPA has approved this technique for risk assessments • Used by EPA in the Guidance for 1-hour SO2 Nonattainment Area SIP Submissions (2014)
  12. 12. Emission Variability Processor • Assuming fixed peak 1‐hour emissions on a continuous basis will result in unrealistic modeled results • Better approach is to assume a prescribed distribution of emission rates • EMVAP assigns emission rates at random over numerous iterations • The resulting distribution from EMVAP yields a more representative approximation of actual impacts • Incorporate transient and variable emissions in modeling analysis • EMVAP uses this information to develop alternative ways to indicate modeled compliance using a range of emission rates instead of just one value
  13. 13. Background Concentrations
  14. 14. 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.
  15. 15. Exceptional Events http://blogs.mprnews.org/updraft/2012/06/co_smoke_plume_now_visible_abo/
  16. 16. Exceptional Events
  17. 17. 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
  18. 18. Probability of Two Unusual Events Happening at the Same Time
  19. 19. Combining 99th Percentile Pre and Bkg (1-hr SO2) 99th percentile is 1st rank out of 100 days = 0.01 P(Pre ∩ Bkg) = P(Pre) * P(Bkg) = (1-0.99) * (1-0.99) = (0.01) * (0.01) = 0.0001 = 1 / 10,000 days Equivalent to one exceedance every 27 years! = 99.99th percentile of the combined distribution
  20. 20. 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
  21. 21. Combining 99th Pre and 50th Bkg 50th Percentile is 50th rank out of 100 days = 0.50 P(Pre ∩ Bkg) = P(Pre) * P(Bkg) = (1-0.99) * (1-0.50) = (0.01) * (0.50) = 0.005 = 1 / 200 days Equivalent to 1.8 exceedances every year = 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
  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
  23. 23. 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 hourly emission rates from CEMS data Bin1: 478.7 (5.0% time) Bin 2: 228.7 (95% time) Stack height (m) 122 Exit temperature (degrees K) 416 Diameter (m) 5.2 Exit velocity (m/s) 23
  24. 24. Results of 1-hour SO2 Concentrations 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 NAAQS 117% 40% 92%
  25. 25. St. Paul Park 436 Ambient Monitor Location
  26. 26. Positively Skewed Distribution http://www.agilegeoscience.com
  27. 27. Histogram of 1-hr SO2 Observations Innovative Dispersion Modeling Practices to Achieve a Reasonable Level of Conservatism in AERMOD Modeling Demonstrations. Sergio A. Guerra EM Magazine, December 2014.
  28. 28. Concentrations at Different Percentiles for the St. Paul Park 436 monitor (2011-2013) Percentile µg/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. Case 3 with Three Different Background Values Case 3 with 99th % Bkg (µg/m3) Case 3 with 50th % Bkg (µg/m3) 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 • Probabilistic standards provide a stringent level of protection based on the likelihood of complying with the NAAQS • AERMOD’s evaluations are based on the probability of a maximum occurrence happening sometime and somewhere in the modeling domain • Probabilistic methods can be used to achieve more reasonable results • 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, • provide a reasonable level of conservatism, • are in harmony with probabilistic nature of 1-hr standards 32
  31. 31. Advanced Model Input Analysis Solutions • Emission Variability Processor (EMVAP) • Evaluation of background concentrations EM Magazine, December 2014 Guerra, S.A. “Innovative Dispersion Modeling Practices to Achieve a Reasonable Level of Conservatism in AERMOD Modeling Demonstrations.” EM Magazine, December 2014.
  32. 32. Sergio Guerra, PhD sguerra@cppwind.com Direct: + 970 360 6020 www.SergioAGuerra.com www.cppwind.com @CPPWindExperts Thank You!

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