Pairing aermod concentrations with the 50th percentile monitored value

PAIRING AERMOD CONCENTRATIONS WITH THE 50TH PERCENTILE MONITORED VALUEBackground Concentrations Workgroup for Air Dispersion ModelingMinnesota Pollution Control Agency

May 29, 2014

Sergio A. Guerra - Wenck Associates, Inc.

Road Map• 1-Screening monitoring data• 2-AERMOD’s time-space mismatch• 3-Proposed 50th % Bkg Method

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1. Sitting 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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1. Exceptional Events

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

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1. Example Tracer (SF6) Array

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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1. Summary of Tracer and SO2Observed Outside 90° Downwind Sector


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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

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1. Positively Skewed Distribution

http://www.agilegeoscience.com

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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 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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2. 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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2. Perfect Model

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MONITORED CONCENTRATIONS

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2. 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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2. Kincaid Power Station and 28 SO2 Monitors


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2. SO2 Concentrations Paired in Time & Space


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2. SO2 Concentrations Paired in Time Only


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3. Current Practice for Pairing Bkg and Mod • Add maximum monitored concentration• Add 98th (or 99th) monitored concentration• Add 98th (or 99th) seasonal concentration

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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 / 2,500Equivalent to one exceedance every 6.8 years!

= 99.96th percentile of the combined distribution

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


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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).


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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


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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



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3. Blaine ambient monitor location.

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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

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3. Advantages1. Simplicity and ease of use2. Overcomes bias introduced by “exceptional” events3. Provides a combined probability that is more

conservative than the form of the short-term standards4. 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

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Conclusion• 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

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QUESTIONS…

Sergio A. Guerra, PhDEnvironmental EngineerPhone: (952) [email protected]

www.SergioAGuerra.com

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Engineering

Pairing aermod concentrations with the 50th percentile monitored value