INNOVATIVE DISPERSION MODELING PRACTICES TO ACHIEVE A REASONABLE LEVEL OF CONSERVATISM IN AERMODMODELING DEMONSTRATIONSCASE STUDY TO EVALUATE EMVAP, AND BACKGROUND CONCENTRATIONS

29th Annual Conference on the Environment-St. Paul, MNNovember 19, 2014

Sergio A. Guerra - Wenck Associates, Inc.

All truth passes through three stages.

First, it is ridiculed.

Second, it is violently opposed.

Third, it is accepted as being self-evident.

Arthur Schopenhauer

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Challenge of new short-term NAAQS

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AERMOD Model AccuracyAppendix 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.

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Perfect Model

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MONITORED CONCENTRATIONS

AE

RM

OD

CO

NC

EN

TRAT

ION

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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 IndianaKali D. Frost Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014

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SO2 Concentrations Paired in Time & Space

Probability analyses of combining background concentrations with model-predicted concentrationsDouglas R. Murray, Michael B. Newman Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014

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SO2 Concentrations Paired in Time Only

Probability analyses of combining background concentrations with model-predicted concentrationsDouglas R. Murray, Michael B. Newman Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014

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EMVAP• Problem: Currently assume continuous emissions from

proposed project or modification• Current modeling practices prescribe that an emission

source (e.g., power plant) be modeled as if in continuous operation at maximum capacity.

• EMVAP assigns emission rates at random over numerous iterations.

• The resulting distribution from EMVAP yields a more representative approximation of actual impacts

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Background Concentrations

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Siting of Ambient MonitorsAccording 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; and3) 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.

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Exceptional Events

http://blogs.mprnews.org/updraft/2012/06/co_smoke_plume_now_visible_abo/

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Exceptional Events

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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

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Probability of two unusual events

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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 / 10,000Equivalent to one exceedance every 27 years!

= 99.99th percentile of the combined distribution

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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 formationSergio A. Guerra, Shannon R. Olsen, Jared J. Anderson Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014

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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 formationSergio A. Guerra, Shannon R. Olsen, Jared J. Anderson Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014

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Positively Skewed Distribution

http://www.agilegeoscience.com

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Case Study: Three cases evaluated

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1. Using AERMOD by assuming a constant maximum emission rate (current modeling practice)

2. Using AERMOD by assuming a variable emission rate3. Using EMVAP to account for emission variability

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Three cases used to model the power plant

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Input parameter Case 1 Case 2 Case 3Description of

Dispersion Modeling

Current Modeling Practices

AERMOD with hourly emission

EMVAP (500 iterations)

SO2 Emission rate (g/s)

478.7

Actual emission rates from CEMS

data

Bin1: 478.7 (5.0% time) Bin 2: 228.7 (95% time)

Stack height (m) 122Exit temperature

(degrees K)416

Diameter (m) 5.2Exit velocity (m/s) 23

Results of 1-hour SO2 concentrations for the three cases

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Case 1 (µg/m3)

Case 2 (µg/m3)

Case 3 (µg/m3)

Description of

Dispersion Modeling

Current Modeling Practices

AERMODwith hourly

emission

EMVAP(500

iterations)

H4H 229.9 78.6 179.3Percent of NAAQS

117% 40% 92%

St. Paul Park 436 ambient monitor location

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Concentrations at different percentiles for the St. Paul Park 436 monitor (2011-2013)

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Percentile g/m3

50th 2.660th 3.570th 5.280th 6.190th 9.695th 12.998th 20.199th 25.6

99.9th 69.599.99th 84.7Max. 86.4

Case 3 with three different background values

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Case 3 with Max. Bkg

(µg/m3)

Case 3 with 99th % Bkg

(µg/m3)

Case 3 with 50th % Bkg

(µg/m3)

H4H 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• 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 while still providing a reasonable level of conservatism

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QUESTIONS…

Sergio A. Guerra, PhDEnvironmental EngineerPhone: (952) 837-3340sguerra@wenck.com

www.SergioAGuerra.com

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