use of probabilistic statistical techniques in aermod modeling evaluations

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

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CO

NC

EN

TR

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ION

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100

100 0

-

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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

Positively Skewed Distribution

http://www.agilegeoscience.com

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

sguerra@cppwind.com

Direct: + 970 360 6020

www.SergioAGuerra.com

www.cppwind.com @CPPWindExperts

Thank You!