Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
PAIRING AERMOD CONCENTRATIONS WITH 
THE 50TH PERCENTILE MONITORED VALUE 
Background Concentrations Workgroup for Air Dispe...
Road Map 
• 1-Screening monitoring data 
• 2-AERMOD’s time-space mismatch 
• 3-Proposed 50th % Bkg Method 
2
1. Sitting of Ambient Monitors 
According to the Ambient Monitoring Guidelines for Prevention of 
Significant Deterioratio...
1. Exceptional Events 
http://blogs.mprnews.org/updraft/2012/06/co_smoke_plume_now_visible_abo/ 
4
1. Exceptional Events 
5
1. Exceptional Events 
6
1. Example Tracer (SF6) Array 
Probability analyses of combining background concentrations with model-predicted concentrat...
1. Summary of Tracer and SO2 
Observed Outside 90° Downwind 
Sector 
Probability analyses of combining background concentr...
1. 24-hr PM2.5 Santa Fe, NM Airport 
Background Concentration and Methods to Establish Background Concentrations in Modeli...
1. Positively Skewed Distribution 
http://www.agilegeoscience.com 
10
1. 24-hr PM2.5 observations at Shakopee 
2008-2010 
Evaluation of the SO2 and NOX offset ratio method to account for secon...
2. AERMOD Model Accuracy 
Appendix W: 9.1.2 Studies of Model Accuracy 
a. A number of studies have been conducted to exami...
2. Perfect Model 
13 
MONITORED CONCENTRATIONS 
AERMOD CONCENTRATIONS
2. Monitored vs Modeled Data: 
Paired in time and space 
AERMOD performance evaluation of three coal-fired electrical gene...
2. Kincaid Power Station and 28 SO2 Monitors 
Probability analyses of combining background concentrations with model-predi...
2. SO2 Concentrations Paired in Time & Space 
Probability analyses of combining background concentrations with model-predi...
2. SO2 Concentrations Paired in Time Only 
Probability analyses of combining background concentrations with model-predicte...
18 
3. Current Practice for Pairing Bkg and 
Mod 
• Add maximum monitored concentration 
• Add 98th (or 99th) monitored co...
3. 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...
3. 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...
3. Proposed Approach to Combine 
Modeled and Monitored Concentrations 
• Combining the 98th (or 99th for 1-hr SO2) % monit...
3. 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 /...
3. 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 ...
24 
3. Blaine ambient monitor location.
3. 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...
3. Advantages 
1. Simplicity and ease of use 
2. Overcomes bias introduced by “exceptional” events 
3. Provides a combined...
Conclusion 
27 
• Use of 50th % monitored concentration is statistically 
conservative when pairing it with the 98th (or 9...
QUESTIONS… 
Sergio A. Guerra, PhD 
Environmental Engineer 
Phone: (952) 837-3340 
sguerra@wenck.com 
www.SergioAGuerra.com...
Upcoming SlideShare
Loading in …5
×

Pairing aermod concentrations with the 50th percentile monitored value

707 views

Published on

Presentation delivered to the Background Concentrations Workgroup for Air Dispersion Modeling organized by the Minnesota Pollution Control Agency. delivered on May 29, 2014. Three topics covered include 1) Screening monitoring data, 2) AERMOD’s time-space mismatch, and
3) Proposed 50th % Bkg Method

Published in: Engineering
  • Be the first to comment

Pairing aermod concentrations with the 50th percentile monitored value

  1. 1. PAIRING AERMOD CONCENTRATIONS WITH THE 50TH PERCENTILE MONITORED VALUE Background Concentrations Workgroup for Air Dispersion Modeling Minnesota Pollution Control Agency May 29, 2014 Sergio A. Guerra - Wenck Associates, Inc.
  2. 2. Road Map • 1-Screening monitoring data • 2-AERMOD’s time-space mismatch • 3-Proposed 50th % Bkg Method 2
  3. 3. 1. 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. 3
  4. 4. 1. Exceptional Events http://blogs.mprnews.org/updraft/2012/06/co_smoke_plume_now_visible_abo/ 4
  5. 5. 1. Exceptional Events 5
  6. 6. 1. Exceptional Events 6
  7. 7. 1. Example Tracer (SF6) Array 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. 1. Summary of Tracer and SO2 Observed Outside 90° Downwind Sector 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. 1. 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 9
  10. 10. 1. Positively Skewed Distribution http://www.agilegeoscience.com 10
  11. 11. 1. 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 11
  12. 12. 2. 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. 12
  13. 13. 2. Perfect Model 13 MONITORED CONCENTRATIONS AERMOD CONCENTRATIONS
  14. 14. 2. 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 14
  15. 15. 2. Kincaid Power Station and 28 SO2 Monitors 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 15
  16. 16. 2. 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 16
  17. 17. 2. 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 17
  18. 18. 18 3. Current Practice for Pairing Bkg and Mod • Add maximum monitored concentration • Add 98th (or 99th) monitored concentration • Add 98th (or 99th) seasonal concentration
  19. 19. 3. 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 19
  20. 20. 3. 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 20
  21. 21. 3. 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 21
  22. 22. 3. 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 22
  23. 23. 3. 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 23
  24. 24. 24 3. Blaine ambient monitor location.
  25. 25. 3. 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 25
  26. 26. 3. Advantages 1. Simplicity and ease of use 2. Overcomes bias introduced by “exceptional” events 3. Provides a combined probability that is more conservative than the form of the short-term standards 4. Not based on temporal pairing (e.g., paired sums, seasonal pairing, etc.) that is inappropriate based on AERMOD’s mismatch in time and space 5. Allows for flexibility to use higher percentile on a case-by- case basis 26
  27. 27. Conclusion 27 • Use of 50th % monitored concentration is statistically conservative when pairing it with the 98th (or 99th) % predicted concentration • Independence of Bkg and Mod distributions is evident from accuracy evaluations showing lack of correlation between Pred and Obs values • Methods is protective of the NAAQS while still providing a reasonable level of conservatism
  28. 28. QUESTIONS… Sergio A. Guerra, PhD Environmental Engineer Phone: (952) 837-3340 sguerra@wenck.com www.SergioAGuerra.com 28

×