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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
Outline
• AERMOD’s Probabilistic Performance Evaluation
• Monte Carlo Statistical Technique
• Combining Modeled Results and Background
Concentrations
• Case Study Example
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.
Perfect Model
MONITORED CONCENTRATIONS
AE
RM
OD
CO
NC
EN
TR
AT
ION
S
100
100 0
-
-
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
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
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
AERMOD’s Evaluation
Are We Using the Model Correctly?
Temporal matching is not justifiable
Perfect model AERMOD
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).
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)
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
Background Concentrations
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.
Exceptional Events
http://blogs.mprnews.org/updraft/2012/06/co_smoke_plume_now_visible_abo/
Exceptional Events
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
Probability of Two Unusual Events
Happening at the Same Time
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
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
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
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
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
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%
St. Paul Park 436 Ambient Monitor Location
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.
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
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%
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
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.
Sergio Guerra, PhD
Direct: + 970 360 6020
www.SergioAGuerra.com
www.cppwind.com @CPPWindExperts
Thank You!
