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INNOVATIVE DISPERSION MODELING 
PRACTICES TO ACHIEVE A REASONABLE 
LEVEL OF CONSERVATISM IN AERMOD 
MODELING DEMONSTRATION...
2 
Challenge of new short-term NAAQS
AERMOD Model Accuracy 
Appendix W: 9.1.2 Studies of Model Accuracy 
a. A number of studies have been conducted to examine ...
Perfect Model 
4 
MONITORED CONCENTRATIONS 
AERMOD CONCENTRATIONS
Monitored vs Modeled Data: 
Paired in time and space 
AERMOD performance evaluation of three coal-fired electrical generat...
SO2 Concentrations Paired in Time & Space 
Probability analyses of combining background concentrations with model-predicte...
SO2 Concentrations Paired in Time Only 
Probability analyses of combining background concentrations with model-predicted c...
8 
Roadmap 
• Case study based on 4 reciprocating internal combustion 
engines (RICE) used for emergency purposes 
• Engin...
9 
EMVAP 
• Problem: Currently assume continuous emissions from 
proposed project or modification 
• In this case study an...
10 
ARM2 
• Emission sources emit mostly NOx that is gradually 
converted to NO2 
• Chemical reactions are based on plume ...
Podrez, M. “Ambient Ratio Method Version 2 (ARM2) for use with ARMOD 1-hr NO2 Modeling”, 2013. 
11
Four cases evaluated 
Input parameter Case 1 Case 2 Case 3 Case 4 
Description of 
Dispersion 
Modeling 
Current 
Modeling...
Engine Input Parameters 
Input parameters per engine 
Stack height (m) 10 
NOx Emission rate (g/s) 5.0 
Exit temperature 
...
14 
Results of 1-hour NO2 Concentrations 
Case 1 
(μg/m3) 
Case 2 
(μg/m3) 
Case 3 
(μg/m3) 
Case 4 
(μg/m3) 
Case Descrip...
Background Concentrations 
15
Sitting of Ambient Monitors 
According to the Ambient Monitoring Guidelines for Prevention of 
Significant Deterioration (...
Exceptional Events 
http://blogs.mprnews.org/updraft/2012/06/co_smoke_plume_now_visible_abo/ 
17
Exceptional Events 
18
Exceptional Events 
19
24-hr PM2.5 Santa Fe, NM Airport 
Background Concentration and Methods to Establish Background Concentrations in Modeling....
Probability of two unusual events 
21
Combining 98th percentile Pre and Bkg 
(1-hr NO2 and 24-hr PM2.5) 
P(Pre ∩ Bkg) = P(Pre) * P(Bkg) 
= (1-0.98) * (1-0.98) 
...
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...
Proposed Approach to Combine Modeled 
and Monitored Concentrations 
• Combining the 98th (or 99th for 1-hr SO2) % monitore...
Combining 98th Pre and 50th Bkg 
P(Pre ∩ Bkg) = P(Pre) * P(Bkg) 
= (1-0.98) * (1-0.50) 
= (0.02) * (0.50) 
= 0.01 = 1 / 10...
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 / 2...
Positively Skewed Distribution 
http://www.agilegeoscience.com 
27
24-hr PM2.5 observations at Shakopee 
2008-2010 
Evaluation of the SO2 and NOX offset ratio method to account for secondar...
Background concentrations 
1) Bkg 1: Maximum 1-hour NO2 observations from the 
Blaine monitor averaged over three years. 
...
30 
Case 4 with three different backgrounds 
Case 4 with 
Bkg 1 
(μg/m3) 
Case 4 with 
Bkg 2 
(μg/m3) 
Case 4 with 
Bkg 3 ...
Blaine ambient monitor location. 
31
Histogram of 1-hour NO2 observations 
Percentile g/m3 
50th 9.4 
60th 13.2 
70th 16.9 
80th 26.4 
90th 39.5 
95th 52.7 
9...
Conclusion 
33 
• Use of EMVAP and ARM2 can help achieve more 
realistic concentrations 
• Use of 50th % monitored concent...
QUESTIONS… 
Sergio A. Guerra, PhD 
Environmental Engineer 
Phone: (952) 837-3340 
sguerra@wenck.com 
www.SergioAGuerra.com...
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INNOVATIVE DISPERSION MODELING PRACTICES TO ACHIEVE A REASONABLE LEVEL OF CONSERVATISM IN AERMOD MODELING DEMONSTRATIONS CASE STUDY TO EVALUATE EMVAP, AMR2, AND BACKGROUND CONCENTRATIONS

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Presentation delivered at the Annual Air and Waste Management Association conference in Long beach, California on June 26, 2014.
Innovative dispersion modeling techniques are presented including ARM2, EMVAP and the 50th percentile background concentration. Case study involves peaking engines that are used 250 hour per year. These intermittent sources are required to undergo a modeling evaluation in many states. Current modeling techniques grossly overestimate the emissions from these sporadic sources.

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INNOVATIVE DISPERSION MODELING PRACTICES TO ACHIEVE A REASONABLE LEVEL OF CONSERVATISM IN AERMOD MODELING DEMONSTRATIONS CASE STUDY TO EVALUATE EMVAP, AMR2, AND BACKGROUND CONCENTRATIONS

  1. 1. INNOVATIVE DISPERSION MODELING PRACTICES TO ACHIEVE A REASONABLE LEVEL OF CONSERVATISM IN AERMOD MODELING DEMONSTRATIONS CASE STUDY TO EVALUATE EMVAP, AMR2, AND BACKGROUND CONCENTRATIONS A&WMA’s 107th Annual Conference and Exhibition-Long Beach, CA June 26, 2014 Sergio A. Guerra - Wenck Associates, Inc.
  2. 2. 2 Challenge of new short-term NAAQS
  3. 3. 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. 3
  4. 4. Perfect Model 4 MONITORED CONCENTRATIONS AERMOD CONCENTRATIONS
  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 5
  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 6
  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 7
  8. 8. 8 Roadmap • Case study based on 4 reciprocating internal combustion engines (RICE) used for emergency purposes • Engines are also part of a peaking shaving agreement and may be required to operate 250 hour per year • 3 Modeling techniques are presented • EMVAP • ARM2 • The use of the 50th percentile monitored concentration as Bkg
  9. 9. 9 EMVAP • Problem: Currently assume continuous emissions from proposed project or modification • In this case study an applicant is requesting to load shave 250 hour per year. • Current modeling practices prescribe that the engines be modeled as if in continuous operation(i.e., 8760 hour/year). • EMVAP assigns emission rates at random over numerous iterations. • The resulting distribution from EMVAP yields a more representative approximation of actual impacts
  10. 10. 10 ARM2 • Emission sources emit mostly NOx that is gradually converted to NO2 • Chemical reactions are based on plume entrapment and contact time • Chu and Meyers* identified that higher NOx concentrations and lower NO2/NOx ambient ratios were present in the near proximity of the source, and lower NOx and higher NO2/NOx ratios occurred as distance increased. * Chu and Meyers, “Use of Ambient Ratios to Estimate Impact of NOx Sources on Annual NO2 Concentration”, presented at the 1991 Air and Waste Management Association annual meeting.
  11. 11. Podrez, M. “Ambient Ratio Method Version 2 (ARM2) for use with ARMOD 1-hr NO2 Modeling”, 2013. 11
  12. 12. Four cases evaluated Input parameter Case 1 Case 2 Case 3 Case 4 Description of Dispersion Modeling Current Modeling Practices EMVAP (500 iterations) ARM2 Method EMVAP and ARM2 Method Maximum peak shaving hours per year 250 250 250 250 Hours of operation assigned in the model 8760 250 8760 250 NOx to NO2 Conversion Assumed 100% conversion Assumed 100% conversion Calculated based on the ARM2 equation Calculated based on the ARM2 equation 12
  13. 13. Engine Input Parameters Input parameters per engine Stack height (m) 10 NOx Emission rate (g/s) 5.0 Exit temperature (degrees K) 700 Diameter (m) 0.305 Exit velocity (m/s) 50 13
  14. 14. 14 Results of 1-hour NO2 Concentrations Case 1 (μg/m3) Case 2 (μg/m3) Case 3 (μg/m3) Case 4 (μg/m3) Case Description Current Modeling Practices EMVAP (500 itr.) ARM2 Method EMVAP and ARM2 Method H8H 2,455.6 577.8 491.1 157.7 Percent of 1,306% 307% 261% 84% NAAQS
  15. 15. Background Concentrations 15
  16. 16. Sitting 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. 16
  17. 17. Exceptional Events http://blogs.mprnews.org/updraft/2012/06/co_smoke_plume_now_visible_abo/ 17
  18. 18. Exceptional Events 18
  19. 19. Exceptional Events 19
  20. 20. 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 20
  21. 21. Probability of two unusual events 21
  22. 22. Combining 98th percentile Pre and Bkg (1-hr NO2 and 24-hr PM2.5) P(Pre ∩ Bkg) = P(Pre) * P(Bkg) = (1-0.98) * (1-0.98) = (0.02) * (0.02) = 0.0004 = 1 / 2,500 Equivalent to one exceedance every 6.8 years! = 99.96th percentile of the combined distribution 22
  23. 23. 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 23
  24. 24. Proposed Approach to Combine Modeled and Monitored Concentrations • Combining the 98th (or 99th for 1-hr SO2) % monitored concentration with the 98th % 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 24
  25. 25. Combining 98th Pre and 50th Bkg P(Pre ∩ Bkg) = P(Pre) * P(Bkg) = (1-0.98) * (1-0.50) = (0.02) * (0.50) = 0.01 = 1 / 100 = 99th 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 25
  26. 26. 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 26
  27. 27. Positively Skewed Distribution http://www.agilegeoscience.com 27
  28. 28. 24-hr PM2.5 observations at Shakopee 2008-2010 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 28
  29. 29. Background concentrations 1) Bkg 1: Maximum 1-hour NO2 observations from the Blaine monitor averaged over three years. 2) Bkg 2: Average of the annual 98th percentile daily maximum 1-hour NO2 concentrations for years 2010- 2012. 3) Bkg 3: 50th percentile concentration from the 2010- 2012 hourly observations. 29
  30. 30. 30 Case 4 with three different backgrounds Case 4 with Bkg 1 (μg/m3) Case 4 with Bkg 2 (μg/m3) Case 4 with Bkg 3 (μg/m3) Max. 98th% 50th % H8H 157.7 157.7 157.7 Background 106.6 86.6 9.4 Total 264.3 244.2 167.1 Percent of NAAQS 140.6% 130.0% 88.9%
  31. 31. Blaine ambient monitor location. 31
  32. 32. Histogram of 1-hour NO2 observations Percentile g/m3 50th 9.4 60th 13.2 70th 16.9 80th 26.4 90th 39.5 95th 52.7 98th 67.7 99.9th 97.8 32
  33. 33. Conclusion 33 • Use of EMVAP and ARM2 can help achieve more realistic concentrations • Use of 50th % monitored concentration is statistically conservative when pairing it with the 98th (or 99th) % predicted concentration • 3 Methods are protective of the NAAQS while still providing a reasonable level of conservatism
  34. 34. QUESTIONS… Sergio A. Guerra, PhD Environmental Engineer Phone: (952) 837-3340 sguerra@wenck.com www.SergioAGuerra.com 34

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