Presentation delivered to the Background Concentrations Workgroup for Air Dispersion Modeling organized by the Minnesota Pollution Control Agency. delivered on March 25, 2014. Three topics covered include 1) Screening monitoring data, 2) AERMOD’s time-space mismatch, and
3) Proposed 50th % Bkg Method
Pairing aermod concentrations with the 50th percentile monitored value
1. PAIRING AERMOD CONCENTRATIONS WITH
THE 50TH PERCENTILE MONITORED VALUE
Background Concentrations Workgroup for Air Dispersion Modeling
Minnesota Pollution Control Agency
March 25, 2014
Sergio A. Guerra - Wenck Associates, Inc.
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.
4. 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. 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. 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. Hit it Big!!! Example
• You have 2 chances out of 100 to win the Powerball. Or
you have 98 chances out of a 100 of not winning the
power ball.
• You have 2 chances out of 100 to win the Mega Millions.
Or you have 98 chances out of a 100 of not winning the
Mega Millions.
• What are the chances of winning both the Powerball and
the Mega Millions?
8. Marginal Probability
P(PB ∩ Mega) = P(PB) * P(Mega)
Where:
P(PB ∩ Mega)= the marginal probability of winning the
PowerBall and at the same time winning the
Mega.
P(PB) = the marginal probability of winning the
Powerball (98th percentile).
P(Mega) = the marginal probability of winning the
Mega (98th percentile).
9. Probability of Winning both Lottos
P(PB ∩ Mega) = P(PB) * P(Mega)
= (1-0.98) * (1-0.98)
= (0.02) * (0.02) = (1/50) * (1/50)
= 0.0004 = 1 / 2,500
= 99.96th percentile of the combined
distribution
10. 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
= 99.96th percentile of the combined
distribution
11. 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
= 99.99th percentile of the combined
distribution
12. 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
13. 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
14. 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
16. 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
19. Conclusion
• Use of 50th % monitiored concentration is statistically
conservative when pairing it with the 98th (or 99th) %
predicted concentration
• Method is simple and statistically sound
• Method is protective of the NAAQS while providing a
reasonable level of conservatism