review and evaluation of the plume volume molar … documents...for short term no2 impacts epsilon...

Review and Evaluation of the Plume Volume Molar Ratio Method (PVMRM) and

Ozone Limiting Method (OLM) for short-term (1-hour average) NO2 Impacts

Prepared for:

The American Petroleum Institute 1220 L Street, NW Washington, DC 20005

Prepared by:

Elizabeth Hendrick, CCM Dr. Steven Hanna, CCM Dr. Bruce Egan, CCM Vincent Tino, CCM Hanna Consultants Egan Environmental Inc. Epsilon Associates, Inc. 7 Crescent Avenue 75 Lothrop Street 3 Clock Tower Place Kennebunkport, ME 04046 Beverly, MA 01915 Suite 250 Maynard, MA 01754

July 18, 2012

TABLE OF CONTENTS

ES EXECUTIVE SUMMARY ES-1

1.0 INTRODUCTION 1-1 1.1 Background 1-1 1.2 Overall Project Description 1-1

2.0 IDENTIFICATION OF AVAILABLE NO2 FIELD STUDY OR MONITORING DATABASES 2-1 2.1 Field data already used in Hanrahan (1999b) and MACTEC (2005) evaluations

of the PVMRM model. 2-1 2.2 Search for Additional Field Data 2-2

3.0 TECHNICAL REVIEW OF OLM AND PVMRM 3-1 3.1 Review of the NO to NO2 Conversion FORTRAN Codes 2-1

3.1.1 OLM 2-1 3.1.2 PVMRM 2-1

4.0 EVALUATION OF PVMRM AND OLM IN THE AERMOD MODEL USING THE WAINWRIGHT DATA 4-1 4.1 Methodology 4-1 4.2 Model Options 4-2 4.3 Wainwright Source and Monitoring Data Description 4-3 4.4 Choosing Hours to Model 4-9 4.5 Model Performance Evaluation Methodology 4-10 4.6 Results 4-12

4.6.1 Comments on hours selected for analysis 4-12 4.6.2 Variation in observed NO2/NOx ratio 4-15 4.6.3 Summary of concentration predictions, including all hours 4-18 4.6.4 Summary of quantitative performance measures for NO2 4-22 4.6.5 Quantile-Quantile (Q-Q) plots 4-28 4.6.6 Results of evaluation of NO2 /NOx ratio and NO2 concentration for

optional run with adjusted ozone levels 4-31 4.7 Uncertainties and limitations that impact the analysis 4-31

5.0 CONCLUSIONS & RECOMMENDATIONS 5-1 5.1 Search for Model Evaluation Databases 5-1 5.2 Review of PVMRM and OLM Model Formulation and Implementation 5-1 5.3 Results of Model Evaluation of PVMRM and OLM in the AERMOD Model using the

Wainwright Data 5-2 5.3.1 Overview and methodology 5-2 5.3.2 Key results 5-3

6.0 REFERENCES 6-1

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LIST OF APPENDICIES

Appendix A Caterpillar Engine Performance Curves Excerpt of the Model Evaluation Spreadsheet

Appendix B Excerpt of the Model Evaluation Spreadsheet

Appendix C Excerpt of the Model Evaluation Spreadsheet AERMET Processed meteorological data for the same periods shown in Appendix B

Appendix D Example of output of BOOT model evaluation program

LIST OF TABLES

Table 3-1 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios: NO2/NOx in-stack = 0.0 ..................................................................................................................3-8

Table 3-2 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios: NO2/NOx in-stack = 0.1 ................................................................................................................3-12

Table 3-3 Single Stack, Single Hour, Single Receptor Model Comparisons.........................3-14 Table 3-4 Two Stacks, Single Hour, Single Receptor Model Comparisons ..........................3-15 Table 4-1 Stack locations and dimensions for the Wainwright Power Plant ..........................4-6 Table 4-2 Summary of hours analyzed in AERMOD/PVMRM and AERMOD/OLM evaluations

using the Wainwright data. The basic data period consists of 9144 hours from September 16, 2009 through September 30, 2010. ............................................4-14

Table 4-3 Summary of observed NO2/NOx ratios and ozone concentrations at Wainwright. These are for hours when observed NOx concentration ≥ 10 ppb (≈18.8 µg/m3) and when ambient heat flux was observed at the monitoring sites. ...........................4-15

Table 4-4 Summary of AERMOD/PVMRM and AERMOD/OLM predictions of NO2 concentrations for the 594 hours studied, including zero-zeros and false positives and false negatives.............................................................................................4-22

Table 4-5 Statistical performance measures for hourly-averaged NO2 concentrations (in μg/m3), for 381 hours with observed and both model predictions non-zero. A background of 2 μg/m3 has been added to all modeled concentrations..................................4-23

Table 4-6 Top-ten rankings for observed and AERMOD-predicted hourly-averaged NOx (unpaired). No background has been added to the AERMOD predictions..........4-25

Table 4-7 Top-ten rankings for observed and predicted hourly-averaged NO2 concentration (unpaired), using AERMOD/OLM and AERMOD/PVMRM. No background has been added to the AERMOD predictions. ..........................................................4-26

Table 4-8 Distribution (number of hours) by month and six-month season of top 50 and top 100 ranked hourly-averaged NO2 concentrations for observations and for AERMOD/PVMRM predictions (i.e., unpaired). No background has been added to the predictions...................................................................................................4-27

Evaluation of PVMRM and OLM ii Index for Short Term NO2 Impacts Epsilon Associates, Inc.

LIST OF FIGURES

Figure 2-1 Houston petrochemical and power plant sources during TexAQS 2000, from Ryerson et al. (2003)............................................................................................2-5

Figure 3-1 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios as a function of the value of

nz. (Data presented in Table 3-1 with in-stack NO2/NOx Ratio assumed to be 0.0.)....................................................................................................................... ..3-10

Figure 3-2 Comparison of 1-hr NO2 predicted for a full receptor grid using parameters listed in Table 3-3. (Hanrahan (1999a) versus EPA (2004) plume parameters) .................3-17

Figure 3-3 Comparison of 1-hr NO2 predicted for a full receptor grid using lower stack heights (24 meters) and unstable meteorology. (Hanrahan (1999a) versus EPA (2004) plume parameters)........................................................................................................3-18

Figure 3-4 Predicted concentration as a function of increasing a secondary source’s emission rate. (NOx concentration predicted by AERMOD and NO2 concentration predicted by AERMOD/PVMRM). .....................................................................................3-20

Figure 3-5 Moles of O3 and NOX, and NO2/NOX ratio as a function of increasing a secondary source’s emission rate........................................................................................3-21

Figure 4-1 Village of Wainwright, Alaska, showing locations of power plant, monitoring

station, and ASOS meteorological station.............................................................4-4 Figure 4-2 Wainwright Power Plant, Wainwright, Alaska (front and side views) ....................4-5 Figure 4-3 Wainwright Power Plant building with stack locations shown (red) with adjacent

shop building and storage tanks included in BPIP-Prime. .....................................4-7 Figure 4-4a PVMRM scatter plot of monitored and modeled NO2/NOx ratios, for 185 hours

(out of the 245) where monitored NOx concentration exceeded 10 ppb (18.8 μg/m3). ..............................................................................................................4-17

Figure 4-4b OLM scatter plot of monitored and modeled NO2/NOx ratios, for 185 hours (out of the 245) where monitored NOx concentration exceeded 10 ppb (18.8 μg/m3)….4-19

Figure 4-5a PVMRM scatter plot of monitored and modeled NO2/NOx ratios, for 99 hours where both monitored and modeled NOx concentration exceeded 10 ppb (18.8 μg/m3). ..............................................................................................................4-20

Figure 4-5b OLM scatter plot of monitored and modeled NO2/NOx ratios, for 99 hours where both monitored and modeled NOx concentration exceeded 10 ppb (18.8 μg/m3)...............................................................................................................4-21

Figure 4-6 Q-Q plot of ranked observed and predicted AERMOD NOx concentrations, for 381 hours when observed NOx ≥ 1 ppb and predicted NOx is non-zero....................4-29

Figure 4-7 Q-Q plot of ranked observed and predicted NO2 concentrations by AERMOD full conversion, current operational AERMOD/PVMRM with nz=4 and AERMOD\OLM, for 381 hours when observed and predicted NO2 is non-zero. ...........................4-30

Evaluation of PVMRM and OLM iii Index for Short Term NO2 Impacts Epsilon Associates, Inc.

Evaluation of PVMRM and OLM iv Index for Short Term NO2 Impacts Epsilon Associates, Inc.

Figure 4-8 Q-Q plot of ranked observed and predicted NO2 concentrations (sensitivity run for AERMOD/PVMRM with nz =1.282), for 381 hours when observed and predicted NO2 is non-zero. AERMOD full conversion and AERMOD/OLM NO2 concentrations are also shown (as in Fig. 4-7). ...................................................4-32

ES EXECUTIVE SUMMARY

In January of 2010 the U.S. EPA promulgated a new one-hour averaging period National Ambient Air Quality Standard (NAAQS) for nitrogen dioxide (NO2). Demonstrations of compliance with the one hour NO2 NAAQS require consideration of the role of ozone (O3) in the ambient air in converting nitrogen oxides (NOx) emissions to NO2. In addition to the Ambient Ratio Method (ARM) scaling approach, the U.S. EPA regulatory dispersion model AERMOD includes two methods, the Plume Volume Molar Ratio Method (PVMRM) and the Ozone Limiting Method (OLM), that consider available ambient ozone. Both methods are available as non-default options in AERMOD.

The American Petroleum Institute (API) contracted Epsilon Associates, in conjunction with Hanna Consultants and Egan Environmental (“the Team”) to review the PVMRM and OLM methods and conduct an independent model evaluation of these options. The first task was to conduct a search for available databases for use in a model evaluation. Secondly, a review of the implementation of each method’s formulation in the model codes was conducted. And lastly, an independent model evaluation of the predicted NOx to NO2 ratios and the resulting NO2 concentrations in both PVMRM and OLM was conducted using a new database.

Search for Field Data or Monitoring/Emissions Databases

A review of the technical literature has been conducted to determine if any NO2 field study databases exist that could be used for a complete model evaluation including dispersion and chemical transformation. In addition, the team also tried to obtain existing NO2, NOx and O3 monitoring data coupled with emission inventories and meteorological data to evaluate the chemical conversion of NOx into NO2. It is desirable to have near field data from fixed monitors operating continuously for a year or more around one or more short stacks where the stack parameters and emissions rates are well-known, as well as on-site meteorological observations required by AERMOD.

The team contacted many persons throughout the U.S. and elsewhere in the world, starting with persons who the team knew had been involved in industrial plant monitoring and PVMRM (or similar) model comparisons with the data. This search was only partially successful. Most persons said that they did not have any useful data of the type requested, but they were aware of confidential/proprietary observations. The team requested several such private data sets, but was not granted permission by the respondents to use their data. The respondents also pointed out that there were many observations of NO2 and NOx in urban or regional field programs, but these were generally complicated by multiple sources of unknown strengths.

A data set was identified that contained recent ambient monitoring of NOx, NO2, O3 and meteorological conditions for a year (Sept 2009-Sept 2010) for an offshore PSD project in Alaska. The monitor is located in Wainwright, Alaska, a small community north of the

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Arctic Circle. The primary source of NOx emissions in the area is the community power plant operated by the North Slope Borough Utilities: Power & Light Division. The facility consists of five diesel Caterpillar engines (three 450 kW engines and two 950 kW engines) with relatively short stacks (~30 ft). The team obtained hour-by-hour operator run logs indicating which units were operating and at what loads each hour. Using the log information, emissions estimates were made based on vendor information for each engine. Therefore, this database included monitoring data approximately 500 meters downwind of an isolated power plant where emissions were reasonably well quantified. This Wainwright data set was suitable for a model evaluation of PVMRM and OLM.

Technical Review of OLM and PVMRM

The purpose of the review was to ensure that the models were coded in a manner consistent with the OLM and PVMRM formulations and to assure that the model algorithms are well founded. OLM as described in Cole and Summerhays, 1979, was implemented in both the ISC model and AERMOD. PVMRM as described by Hanrahan (1999a) was originally coded as a postprocessor to the ISC model, and subsequently included in the AERMOD code.

The OLM involves an initial comparison of the estimated maximum NOX concentration and the ambient O3 concentration to determine which is the limiting factor to NO2 formation. A thorough review of the OLM code in both ISC3_OLM (96113) and AERMOD (09292) was performed. Testing in AERMOD and ISC was done for both a single source and two sources. No anomalies in the implementation were found and the code performs exactly the same in both models.

PVMRM considers both plume size and ambient O3 concentrations. A key concept in PVMRM is that the conversion of NO to NO2 is determined by the ratio of the number of moles of fresh ozone entrained into a plume to the number of moles of NO (NOx concentration in this context) in the plume as it reaches a receptor.

The Team reviewed the Hanrahan (1999a and b) papers and EPA’s (2004) Addendum to the AERMOD Model Formulation Document for PVMRM, as well as many other related documents. The team also checked the implementation of the PVMRM model in the FORTRAN codes. Several technical concerns have been identified with the implementation of PVMRM in the AERMOD model. These include:

a.) The relative dispersion formula in AERMOD is formulated for convective conditions, but is currently used for all stabilities in AERMOD. An appropriate relative dispersion formula must be added to properly handle neutral and stable atmospheric conditions.

b.) The relative and time-averaged sigma formulas must have consistent relations to each other, so that the relative sigma is never larger than the time-averaged sigma. This condition is not currently imposed in AERMOD.


c.) The number of standard deviations from the centerline used to define the plume volume

(nz) in AERMOD/PVMRM is too large. (Hanrahan used nz =1.282 in the PVMRM post-processor for ISC, AERMOD/PVMRM uses nz = 4)

d.) PVMRM does not adjust the relative plume sigmas to account for downwash.

e.) The merging of multiple plumes in PVMRM can lead to discontinuities in model predictions.

Evaluation of OLM and PVMRM using the Wainwright Data Set

While AERMOD has undergone many model evaluation studies in its default mode (Hanna et al., 1999), PVMRM and OLM are non-default model options and to date only three NO2 field data sets (Netherlands power plant study, Empire Abo and Palaau) have been used in their evaluations. Here a model evaluation exercise was conducted to assess the OLM and PVMRM modules in the AERMOD (11103) model using monitoring and emission data from a site in Wainwright, Alaska. The evaluation results at this new site are intended to add to the previous evaluations at other field sites reported by Hanrahan (1999b), MACTEC (2005), EPA (2004), and EPA (2011). For an evaluation of a model or model options to be robust, many data sets should be used in the evaluation so that a multitude of conditions, source data, etc. can be included. Approximately 12 months of observational data from Wainwright were reviewed, with data used for the evaluation limited to hourly periods when the wind was blowing from the power plant to the sector containing the monitoring station.

OLM and PVMRM predictions of the NO2/NOx ratio, AERMOD/PVMRM and AERMOD/OLM predictions of NO2 concentrations, and AERMOD predictions of NOx concentrations were evaluated. The results of the AERMOD NO2 evaluations depend not only on PVMRM or OLM but also on AERMOD. There could be good results because all model components are good, or because of compensating errors by modules, or there could be poor results due to the combination of good-performing modules with poorly-performing modules. Additionally, limitations and uncertainties in the evaluation data set and model inputs must be considered. For the Wainwright analysis, these include approximation of emissions using operating logs and vendor performance data, use of ambient ozone data from a single monitoring station, and the relatively low observed NOx and NO2 concentrations.


Key Results of Evaluation

Ratio of NO2/NOx paired-in-time at monitor location

PVMRM

♦ There is minimal mean bias between paired-in-time observed and PVMRM-predicted hourly averaged ratios; most of the observed and predicted ratios were in the range of 0.2 to 0.4.

♦ There is about a factor of two scatter in the PVMRM predictions when compared with observations. Furthermore, PVMRM has little skill in simulating deviations from the mean. Because of this deficiency, on a paired basis, PVMRM overpredicts the smaller range of observed NO2/NOx ratios and underpredicts the larger range, even though the model’s mean bias is not large.

♦ The Wainwright power plant sources were modeled by assuming an initial in-stack ratio of 0.2. Consequently, the modeled and observed ratios in the range of 0.2 to 0.4 suggest that, for most hours, there is minimal conversion of NO to NO2 in the plume before it reaches the monitor location.

OLM

♦ OLM-predicted hourly averaged ratios are almost always larger than observed ratios when paired-in-time. This result is caused by the fact that, unlike PVMRM, OLM immediately converts all possible NO to NO2, (depending on the ambient ozone concentration). OLM does not account for the gradual conversion of NO to NO2 by reactions with entrained ozone.

♦ On a paired basis, OLM-predicted ratios have minimal correlation with observed ratios.

AERMOD NO2 concentrations (BOOT evaluations using data paired in time and space)

The BOOT model evaluation software was applied to observed and predicted NO2 concentrations paired in time and space. There are 381 hours of data for use in these calculations, determined by whether the NO2 observation and prediction are both greater than 0.0 µg/m3.

♦ The AERMOD/PVMRM model has low mean relative bias, but its scatter is about two or three times the mean and there is little skill evident (i.e., correlations are not large).

♦ AERMOD/OLM has a mean relative bias of about 70% towards overprediction.


♦ When the log of the concentration (lnC) is considered and the geometric mean bias

(MG) and geometric variance (VG) are calculated, MG is slightly better for AERMOD/OLM than for AERMOD/PVMRM. MG and VG are influenced less by large errors at the largest concentrations.

♦ The fraction of predicted values within a factor of two of paired observed values (FAC2) is 0.402 for AERMOD/OLM and 0.470 for AERMOD/PVMRM. But since about 18% of the predicted and observed data pairs are very near background, FAC2 is inflated by this 18% figure. These performance measures are typical of those found for other models and other field data sets reported by Chang and Hanna (2004).

“Top Ten” AERMOD NOx and NO2 concentrations (unpaired comparisons)

♦ There is approximately a 52% overprediction by AERMOD of the highest observed concentration of NOx (unpaired); the tendency shifts to underprediction at lower observed concentrations (unpaired).

♦ AERMOD/PVMRM overpredicts the highest observed NO2 concentration (unpaired) by about 77%, but the magnitude of the overprediction decreases to about 50% by the 10th highest observed value.

♦ AERMOD/OLM overpredicts the highest observed NO2 concentration (unpaired) by a factor of 2.16 and an approximate factor of 2 over the remaining highest ten observed values.

Seasonal comparison of AERMOD/PVMRM with NO2 observations (unpaired)

♦ AERMOD/PVMRM captured the seasonal differences in observed NO2 concentrations, with higher concentrations in the winter.

AERMOD NO2 and NOx predictions – Quantile-Quantile (Q-Q) plots (unpaired comparisons)

♦ The hourly-averaged NOx Q-Q plot for AERMOD shows a 50% overprediction by AERMOD of the highest concentration, but this changes to an order of magnitude underprediction at low concentrations, where the background is questionable. These are unpaired concentrations that are simply ranked from highest to lowest, using the observations and the predictions. It is desirable that a model’s Q-Q plot follow the line of agreement as much as possible.

♦ AERMOD/PVMRM overpredicts the highest concentration in the NO2 Q-Q plot by about 75%, but then underpredicts by an order of magnitude at lower concentrations.



♦ A Q-Q plot for NO2 with PVMRM modified so that nz = 1.282, A = 0.8 and initial σr = 15 (Hanrahan assumptions) was similar to the Q-Q plot for the current operational AERMOD/PVMRM for the Wainwright analysis.

1.0 INTRODUCTION

1.1 Background

The U.S. EPA promulgated a new one-hour averaging period National Ambient Air Quality Standard (NAAQS) for nitrogen dioxide (1-hour NO2) in 2010. Demonstrations of compliance with the one hour NO2 NAAQS require consideration of the role of ozone (O3) in the ambient air in converting nitrogen oxides (NOx) emissions to NO2. Two methods which consider ambient ozone, the Ozone Limiting Method (OLM) and the Plume Volume Molar Ratio Method (PVMRM), have been available for several years, but they have not been fully evaluated for predicting short-term ambient concentrations of NO2.

Hanrahan (1999a) developed a technique, the Plume Volume Molar Ratio Method (PVMRM) to calculate the ratio of NO2 to NOx concentrations downwind from single and multiple sources of NOx. The PVMRM methodology as described by Hanrahan was coded as a postprocessor to the Industrial Source Complex (ISC) model. Hanrahan (1999b) provides the results of an evaluation study performed with the ISC/PVMRM model using two aircraft based data sets, a gas plant with multiple sources and two ground level monitors and a power plant with a single monitor. The results compared favorably to the use of simpler methods to estimate the conversion of NOx to NO2; namely, the Ambient Ratio Method (ARM) and the Ozone Limiting Method (OLM).

As of December 9, 2006, AERMOD was fully promulgated as a replacement to the ISC model as the recommended guideline model for New Source Review and other regulatory applications. A detailed description of the technical formulation of AERMOD and its processors was provided (Cimorelli, et al, 2004). The U.S. EPA has implemented the PVMRM technique into the AERMOD model. An addendum to the AERMOD Model Formulation Document (EPA, 2004) describes equations that have been coded into AERMOD/PVMRM. The PVMRM is implemented in AERMOD as a series of internal subroutines rather than as a postprocessor. The PVMRM and the OLM are both non-default options for converting NOx into NO2 available to users of AERMOD.

MACTEC (2004, 2005) provided some sensitivity analyses and an evaluation of the AERMOD/PVMRM model as applied to the annual average NAAQS for NO2. Updates and augmentations to the databases originally used by Hanrahan were used in these evaluations. Brode (2011) has recently reevaluated AERMOD/PVMRM with the databases with respect to the new 1-hour NO2 NAAQS.

1.2 Overall Project Description

The API requested an evaluation of the PVMRM and OLM methods, including a review of the model formulations and how they were implemented into AERMOD.

The project tasks were as follows:

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Task 1– Determine what databases are available for PVMRM and OLM model evaluation.

The Epsilon team has reviewed the technical literature to determine if any different NO2 field study databases exist that could be used for a complete model evaluation including dispersion and chemical transformation (in addition to the databases that was used in the Hanrahan evaluation). The team reached out to colleagues throughout the world in search of field databases as well as the possibility of obtaining existing NO2, NOx, and O3 monitoring data coupled with emission inventories and meteorological data to evaluate the chemical conversion of NOx into NO2. A description of the database search is presented in Section 2.

Task 2 – Review the Cole and Summerhays OLM formulation and Hanrahan PVMRM formulation assumptions that were implemented in AERMOD and ISC.

The purpose of the review of the ISC/OLM, ISC/PVMRM, AERMOD/OLM and AERMOD/PVMRM Fortran codes is to ensure that the models were coded in a manner consistent with the formulations and to assure that the model algorithms are well founded. The results of the investigation are presented in Section 3.

Task 3 – Perform a model evaluation for PVMRM and OLM with a new evaluation database.

For an evaluation of a model or model options to be robust, many data sets should be used in the evaluation so that a multitude of conditions, source data, etc. can be evaluated. This evaluation using the Wainwright data set should be considered as an additional piece of information to supplement the limited number of data sets used by EPA to date to evaluate the PVMRM and OLM options in AERMOD for predicting 1-hour NO2.

Model sensitivity runs were made and model performance statistics were computed for the evaluation of the PVMRM and OLM options in AERMOD (11103) using the Wainwright data set. Model performance was evaluated for the PVMRM and OLM-predicted and observed ratio of NO2/NOx, for the AERMOD predicted NOx concentrations and for the AERMOD/PVMRM and AERMOD/OLM predicted NO2 concentrations. Section 4 of this report describes the data set used in the evaluation, the limitations of the data set, the specific model runs that were made, the model performance statistics used, and the results of the evaluation.

2.0 IDENTIFICATION OF AVAILABLE NO2 FIELD STUDY OR

MONITORING DATABASES

The Epsilon team reviewed the literature describing the databases that were used in the Hanrahan’s (1999b) evaluation In addition a review of the technical literature has been conducted to determine if any different NO2 field study databases exist that could be used for a complete model evaluation including dispersion and chemical transformation. The team also tried to obtain existing NO2, NOx and O3 monitoring data coupled with emission inventories and meteorological data to evaluate the chemical conversion of NOx into NO2.

2.1 Field data already used in Hanrahan (1999b) and MACTEC (2005) evaluations of the PVMRM model.

There have been three NO2 field data sets used by Hanrahan (1999b) in his PVMRM evaluations. These same data sets were used by MACTEC (2005) in their evaluations of the AERMOD and ISC3 PVMRM modules. Since PVMRM focuses on calculation of only the ratio of NO2/NOx, the evaluations focus on that ratio, too. The three data sets used by Hanrahan (1999b) are:

Aircraft NO2/NOx measurements by Arellano et al. (1990) – Aircraft measurements were taken by the Dutch Electricity Generating Research Lab (KEMA) downwind of several large power plants in The Netherlands. Bange et al. (1991, p 2323) state that “Over a period of 10 years, KEMA carried out more than 60 measuring flights through plumes of power plants up to 25 km from the source. During these flights the plume was crossed at 3 different distances from the source, and at each distance 5-10 crossings were carried out. Among parameters such as pressure and temperature, continuous recordings of NOx, NO, and O3 were made during each crossing." Arellano et al. (1990) present an analysis of 12 different plumes from 5 power plants, where measurements were made at downwind distances from about 500 m out to about 16 km. Later, Bange et al. (1991) used the same 10-year KEMA aircraft data set but selected a different group of plume transects to test their revised model, which made use of instantaneous plume spread parameters. They do not list the specific days of measurements that they used but have grouped their selected observations into “summer” and “winter”. The several individual observations that are grouped at each distance are depicted by a median and a range. For winter, separate comparisons of model versus observations are given for instantaneous plumes and for hourly-averaged plumes.

Arellano et al. (1990) analyzed some of the 60 KEMA flights and Bange et al. (1991) analyzed others. It may be that the remaining flights would have useful data. Even so, if the original data could be obtained the team would be able to better specify the inputs (such as stack height) that were guessed at in the MACTEC (2005) PVMRM evaluations. A request for the original KEMA report was made, but those involved were unable to provide it at this time. The team reviewed the field data summary by Janssen et al. (1988).

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New Mexico Empire Abo Gas Plant 1993/1994 NO2/NOx measurements (Uhl et al. 1998) – The Empire Abo field study took place in order to assist in the development of a site-specific ARM ratio discussed by Chu and Meyer (1991). Uhl et al. (1998) discuss the two years of data collected at two fixed monitoring sites, one located 1.6 km north of the plant and the other located 2.5 km south of the plant. The plant had many sources, thus allowing the multiple source capability of PVMRM to be tested. The emissions were not well-known however. Tables of NO2/NOx ratios were compared, for cases with NOx > 20 ppb (the measurement threshold).

Hawaii-Palaau Generating Station 1993 NO2/NOx Data - The database included one year of observations of ozone, NOx and NO2 at a single monitor 220 m NW of the power generating station. There were four diesel-engine generators and an oil-fired combustion turbine.

Hanrahan (1999b) also tested his model against the predictions of a Large Eddy Simulation (LES) model that accounts for plume chemistry and was described by Sykes et al. (1998). However, no observations were involved.

In order to run the AERMOD/PVMRM model with the Arellano et al. (1990) and Bange et al. (1991) KEMA data, the MACTEC staff had to make several assumptions about missing input data, such as some stack parameters and meteorological inputs.

2.2 Search for Additional Field Data

The Dutch KEMA, Empire Abo, and Palaau data that have been used previously have several uncertainties, such as missing inputs, use of single aircraft cross-sections, locations too far away, insufficient monitor coverage, and so on. It is desirable to have near field data from fixed monitors operating continuously for a year or more around one or more short stacks where the stack parameters and emissions rates are well-known, as well as on-site meteorological observations required by AERMOD.

Requests were made via e-mail to many persons throughout the U.S. and elsewhere in the world, starting with persons who the team knew had been involved in industrial plant monitoring and PVMRM (or similar) model comparisons with the data. This search, described below, was only partially successful. Most persons said that they did not have any useful data of the type requested, but they were aware of confidential/proprietary observations that may be accessible by specifically requesting them from the industry sponsoring the data collection. The respondents also pointed out that there were many observations of NO2 and NOx in urban or regional field programs, but these were generally complicated by multiple sources of unknown strengths, such as urban street traffic and/or broad regional source areas.


A sampling of responses is provided below:

Several colleagues from the Netherlands who are knowledgeable about the KEMA aircraft studies were contacted. When asked if they knew of any more recent databases, they reported that all they have that is immediately accessible is some NO2 measurements from ship stacks at a distance of a few hundred meters. These ship data are complicated by downwash effects of the ship superstructure, and the emissions are not measured directly. The team also asked if they may still have a detailed report or electronic file with the Arellano aircraft data, which are used in evaluations of PVMRM by Hanrahan (1999b) and MACTEC (2005). The respondents indicated that they have a larger report in their file archives and that it should be possible to provide the detailed KEMA data. However since they have to search to locate it, this data was not made available during the course of this project.

A colleague from CEREA and Electricite de France who has worked with Dr. Hanna on setting up numerous comprehensive databases for model evaluation was contacted. He has led the set-up of two major European Union air quality field study data archives. He responded that he is aware of several regional European data sets where NO2 was measured but that there are far fewer public data sets of the type requested.

An ADMS developer at CERC in England who has worked with Dr. Hanna on several ADMS evaluation projects for the European Union and for the API was contacted. The chemistry module in ADMS (CERC, 2002) has been evaluated with many sets of field data (NOx, NO2, O3, VOC) but primarily in urban areas with much traffic. The urban traffic scenario is the major European concern for NO2. He could locate no public NO2 data for short stacks and the near-field, as requested. He sent a paper (Carruthers et al., 2008) where ADMS and OLM are compared for several hypothetical scenarios (i.e., not real data). The ADMS GRS chemistry scheme was proposed by Venkatram et al. (1994) as a simplified chemical scheme, not as complex as Carbon-IV yet slightly more complex than simple exponential formulas or as OLM.

Another colleague from the Univ. of Hertfordshire, England was contacted because he leads a European Union long term research study where air quality data are being archived and analyzed. He responded that he was unaware of data measured around specific NO2 point sources but that he would share our request with his colleagues. He pointed out that most European NO2 sampling is in urban areas with high traffic density.

TexAQS 2000 and 2006 Field Experiments were large interagency field experiments concerned mainly with summertime ozone problems in the Houston, TX area. One component of the study was aircraft sampling of power plant and refinery plumes by the Aeronomy Laboratory of NOAA, located in Boulder, CO. Many chemical components were sampled during many days of aircraft sampling, but power plant and refinery plumes were of major interest because of the ozone precursors emitted – NOx from power plants and refineries and VOCs from refineries. The various chemical processing plants in the


Houston Ship Channel and in Texas City were also of interest. Peischl et al. (2010) discuss estimation of NOx, SO2, and CO emissions from the aircraft sampling of power plant plumes such as the W.A. Parish Plant and the Oklaunion Plant. They devised and tested a method for estimating NO2 emissions at the Oklaunion plant using ambient data. Some aircraft transects were at downwind distance of about 1 km. The team was able to obtain these near field NO2 and NOx data from Dr. Ryerson, who devised and operated the sampling instrument.

Ryerson et al. (2003) discuss the aircraft measurements of reactive alkenes, NOx, and ozone during TexAQS 2000 downwind of “petrochemical industrial emissions”. Specific refineries studied included Sweeny, Freeport A and B, and Chocolate Bayou, as well as the Houston Ship Channel and Texas City industrial complexes. The W.A. Parish power plant was also studied. See Figure 2-1 for locations of the sources and relative magnitudes of VOC and NOx emissions. The paper often uses the term NOy, which is the total reactive nitrogen compounds, including NOx, HNO3 (nitric acid), and PAN. Plots are included in the Ryerson et al. (2003) paper that show the measurements along the aircraft flight path, allowing visualization of several basic effects, such as

1) the decrease in ozone when NOx is large in the plume within 1 or 2 km of the sources.

2) the subsequent increase in ozone in the plume at larger distances (beyond 10 km), due to generation of ozone by reactive nitrogen and by VOCs.

3) the enhanced production of ozone in petrochemical plant plumes (as compared with power plant plumes), due to the VOCs in the petrochemical plant plumes.


3009/Evaluation of PVMRM and OLM 2-5 Identification of NO2 Data Sets

Figure 2-1 Houston petrochemical and power plant sources during TexAQS 2000, from Ryerson et al. (2003)

for Short Term NO2 Impacts Epsilon Associates, Inc.

Eastern Refinery Data - An eastern refinery had historically operated a set of four ambient air monitoring stations surrounding the refinery, which included measurements for NOx, NO2 and O3 and meteorological data. Historical hourly emissions data for twelve larger refinery NOx sources were available from the EPA AirMarkets database. The refinery was contacted regarding use of the ambient monitoring data but declined to provide it for study purposes.

Wainwright, Alaska Community Power Plant data and monitoring Data – An offshore drilling company has conducted ambient monitoring for NOx , NO2, O3 and meteorological conditions for a year (Sept 2009-Sept-2010) for a PSD project. API has made that data available to us. The monitor is located in Wainwright, Alaska. This is a small community north of the Arctic Circle. The primary source of NOx emissions in the area is a community power plant. The power plant is operated by the North Slope Borough Utilities: Power & Light Division. It operates under a general permit for fuel limited diesel electric engines. The facility consists of five diesel Caterpillar engines (3 installed in 1988 (430 kW), and 2 (950 kW) installed in 2001 and 2002). The facility provided the team with hourly logs indicating which engines were operating and their output load. Logs were provided for the period of September 2009 - September 2010. The facility also provided physical source parameters and building dimensions. Hourly emission rates and exit parameters could be obtained from the Caterpillar vendor sheets for those particular engine models coupled with the operational data obtained from the hourly logs. Of the data sets investigated this data set had the most potential for use in the model evaluation of predicted ground level impacts of NO2 from an isolated source with short stacks.


3.0 TECHNICAL REVIEW OF OLM AND PVMRM

3.1 Review of the NO to NO2 Conversion FORTRAN Codes

The team performed a review of both the OLM and PVMRM codes implemented in ISC3_OLM, the PVMRM post-processor to ISC and version 09292 of AERMOD. The codes were examined line-by-line and numerous test runs were made to verify their implementation.

3.1.1 OLM

The OLM (Coles and Summerhays, 1979) involves an initial comparison of the estimated maximum NOX concentration and the ambient O3 concentration to determine which is the limiting factor to NO2 formation. The method calculates the amounts of initial NOX and NO2 in the plume based on the in-stack NO2/NOX ratio (10 percent was assumed). If the ambient moles of O3 are greater than the maximum moles of NOX, then total conversion of all emitted NOX to NO2 is assumed. Otherwise, the formation of NO2 is assumed to be the sum of the in-stack NO2 plus the emitted NOX limited by the available O3.

A thorough review of the OLM code in both ISC3_OLM (96113) and AERMOD (09292) was performed. Testing in AERMOD and ISC3_OLM was done for both a single source and two sources. No anomalies in the implementation were found and the code performs exactly the same in both models.

3.1.2 PVMRM

The Team reviewed the Hanrahan (1999a and b) papers and EPA’s (2004) Addendum to the AERMOD Model Formulation Document for PVMRM, as well as many other related documents. The team also checked the implementation of the PVMRM model in the FORTRAN codes. Sensitivity studies were carried out and are reported in later sections. The following subsections cover some specific technical concerns that have been identified.

The concept of the PVMRM is fairly simple. The production (titration) of NO2 from the reaction of NO in an effluent plume with ozone is proportional to the amount of fresh ozone that is entrained into the plume as it is advected downwind. The main concept in PVMRM is that the conversion of NO to NO2 is determined by the ratio of the number of moles of fresh ozone entrained into a plume to the number of moles of NO (NOx concentration in this context) in the plume as it reaches a receptor. The total NO2 concentration at the receptor location is the ratio of fresh moles of ozone to moles of NOx plus the fractional amount of NO2 emitted in the stack gas times the predicted NOx concentration. The moles of fresh ozone that are entrained are assumed to be proportional to the plume volume and the ambient air ozone background concentration. The amount of ozone available for reaction therefore increases as a plume grows through dispersion as it travels downwind.

3009/Evaluation of PVMRM and OLM 3-1 Code Technical Review for Short Term NO2 Impacts Epsilon Associates, Inc.

The concentration equation is:

NO2= NOx *((Moles O3)/(Moles NOx)+in-stack NO2/ NOx) (1)

The in-stack component will be most important immediately downwind of the source. The model implicitly assumes that fresh ozone is instantaneously entrained across the plume cross section and conversion of NOx to NO2 is instantaneous. These latter two assumptions are very conservative.

The primary output of the PVMRM code is the NO2/NOX ratio, which varies only from 0.1 to 0.9 (or as defined by the user). When comparing modeled estimates of NO2 to monitored values, both the model performance of NOx (NO + NO2) and the fraction of NOx converted into NO2 must be evaluated for the same time periods.

Technical Concerns with AERMOD/PVMRM

Definition of the Continuous Plume Volume compared to the Instantaneous Plume Volume

The most unique aspect of the AERMOD code is the fact that the dispersion rates in the AERMOD/PVMRM routines to calculate the ratio are different from those in AERMOD to calculate the NOX concentrations. As discussed below, some of the differences are explainable theoretically but some of the parameterization choices need further technical support.

The PVMRM is based on simulating the chemical reaction of ozone present in the ambient air as it is entrained and mixed with the NOx in an effluent plume. For these purposes, it is important to simulate the diffusion rate of the fresh ozone into the plume itself where the chemical reactions occur. This diffusion is described as relative diffusion (instantaneous plume volume). It includes the initial dispersion of the effluent as a volume type source as it exits a stack, the buoyancy-induced diffusion (BID) associated with the dynamics of plume rise, and the entrainment of ambient air directly into the plume as it is transported downwind. Hanrahan (1999a), Bange (1991) and Arellano (1990) all point out that the plume chemistry is mainly happening on a short time scale, for which relative dispersion coefficients are appropriate.

The implementation of PVMRM in the ISC post-processor did not use relative diffusion. While Hanrahan would have preferred to use actual relative dispersion coefficients (sigmas), instead he did some simple things such as using continuous plume sigmas for stability classes C, D, E, and F but not allowing class A and B sigmas. He used the plume sigmas from ISC.

The standard dispersion rates in AERMOD are described as continuous plume diffusion as they include not only the relative diffusion that occurs within an individual plume but also the time-averaged diffusion that occurs as a result of wind direction fluctuations that cause the plume to travel over an ever widening range of cross wind distances as it is advected


downwind over a time period of an hour. The effects of plume meander on ground level concentrations is an illustration of the effects of continuous plume diffusion as plume meander reduces ground level concentrations because the duration of direct contact at a receptor location is intermittent. Conversely the average concentrations within an average plume are lower than the instantaneous plume.

The concept of differentiating relative vs. continuous plume diffusion is very appropriate for the PVMRM. In AERMOD, the continuous plume diffusion rates are well established through theory and experimental data. The relative diffusion rates used in the PVMRM are, in contrast, not as well supported. The initial volume and buoyancy induced dispersion clearly fit the definition of relative diffusion. However, the inclusion of ambient air entrainment into the plume volume is treated with less rigor and technical support. The team’s comments focus on how the relative diffusion is implemented in the AERMOD code and how there are dispersion coefficient discrepancies between ISC/PVMRM and AERMOD/PVMRM.

The assumed relative (or instantaneous snapshot) plume dispersion parameters (e.g., σr) in PVMRM as implemented in AERMOD are very different from those used in ISC. In AERMOD, the PVMRM plume σr is based on Weil's (1996, 1998) relative dispersion formulation that was suggested for the U.S. Army’s SOBODM puff dispersion model. But that formula is for unstable conditions and does not vary with stability. Furthermore, the lateral and vertical dispersion parameters are assumed equal (i.e., σr = σy = σz) in PVMRM, an assumption that is not valid for stable conditions, when vertical dispersion is less than horizontal. In contrast, in ISC/PVMRM, the ISC model's plume σ values are used in PVMRM and are functions of stability. For stable conditions and for various combinations of unstable meteorological inputs, the difference in relative dispersion parameters in PVMRM in AERMOD and ISC will lead to differences in estimated NO2/NOX ratios. ISC would give smaller NO2 production in stable conditions because of the smaller plume and hence less ozone entrained.

The AERMOD/PVMRM relative dispersion formula reduces to:

σr = 0.74 [(σw/u) 3/2 x3/2 /zi

1/2]/[1 + 0.77 (σw/u) x/zi] (2)

where σw is the vertical turbulent standard deviation, u is the wind speed at plume level, zi is mixing depth, and x is downwind distance. This assumes a convective time scale of TLr = 0.46 zi/σw, which is about 500 seconds (over 8 minutes) for daytime convective conditions. For small travel times (less than about 500 s), the solution reduces to:

σr = 0.7(σw/u)3/2x3/2/zi1/2 (3)

for large travel times (more than about 500 s), the solution approaches:

σr = 0.94 (σwxzi/u)1/2 (4)


The x3/2 behavior at small distances and the x1/2 behavior at large distances is similar to that described in texts for relative dispersion (e.g., Gifford, 1968; Pasquill, 1974). The relative or instantaneous plume snapshot size (sigma) is much smaller than the time-averaged size near the source. But the instantaneous plume size has accelerated growth because it is continually encountering larger eddies. Eventually, at large distances, the size of the instantaneous plume approaches that of the time-averaged plume. But it is inherent in this concept that the instantaneous plume size never exceeds the time-averaged plume size. The latest PVMRM implementation in AERMOD violates this basic concept.

The solution in equation (2) is much different from the formulations used in AERMOD for time-averaged plume sizes. There is no evidence that the EPA has checked to be sure that the relative dispersion formulas always give a σr less than the AERMOD σy or σz.

Some spot checks of the AERMOD/PVMRM σr versus the Briggs-Turner σz in ISC were performed. Comparison with the AERMOD σz was not performed because its formulation is far more complicated.

As explained by Hanrahan (1999a), the reasoning behind using relative σr (for instantaneous times) as compared with time-averaged σz is that the instantaneous plume is what governs the chemical reactions of interest (the ozone-NOX reaction leading to formation of NO2). As explained in basic texts on atmospheric diffusion, the relative σr is smaller (usually by about a factor of two) than the (i.e., ISC or AERMOD) σz although they approach each other at large travel times.

But in AERMOD/PVMRM, the equations given above for convective (unstable conditions) are used for all stabilities. The developer of those formulas, Jeffrey Weil (1996, 1998), said in a private e-mail communication that he had expected that the EPA would have inserted different formulas for stable conditions, but this appears to not have been done. Also a condition should have been inserted to assure that the relative σr would never exceed the time-averaged plume spreads, even for unstable conditions.

The spot check results are widely varying because the Weil (1996, 1998) σr depends on σw, mixing height zi, and wind speed u. (plus the usual dependence on x). For example, for typical class B conditions (unstable) and a distance of 400 m, assume σw = 2 m/s and zi = 2000 m. The Weil formula (equation 2 above) gives σr = 128 m and the Turner-Briggs formula (should agree approximately with ISC) gives σz = 40 m (Turner, 1970) and 48 m (Briggs, 1971). Thus the relative σr is almost 3 times the time-averaged σz, which is against the spirit of the method as explained by Hanrahan (1999a) and originally by Bange et al. (1991).

For typical class E conditions (stable), it is assumed, as before, x = 400 m. But a stable σw would be 0.05 m/s and zi = 100 m. The Weil formula (not intended for stable conditions but used because the EPA does this in AERMOD/PVMRM) gives σr = 2.1 m. Turner gives 11 m and Briggs 12 m. Thus here the relative σr is indeed less (by a factor of 6) than the


time-averaged σz. But this behavior could change with other assumptions about σw

(increasing it) and zi (decreasing it), keeping in mind that the Weil formula doesn't really apply to stable conditions.

To keep with the spirit of Hanrahan's (1999a) assumption, the team advises that the EPA should consider:

1) adding a stable relative dispersion formula,

2) attempt to impose a condition that the relative and time-averaged sigma formulas have consistent relations to each other, so that the relative sigma is never larger than the time-averaged sigma, and

3) eliminate the isotropic condition for stable plumes which have smaller depth than width. It might take substantial effort to fix this correctly. In the meantime, perhaps use a simple relation such as relative sigma = 0.5 * (time-averaged sigma) near the source. Then have the ratio of sizes increase linearly from 0.5 so it reaches 1.0 at x = 1000 m.

As discussed, the above relative sigmas are used only for estimating the NO2/NOx ratio in the plume. The standard AERMOD is used for everything else including calculating the NOX concentration. Nevertheless, there can be factor of two variations in NO2/NOX ratio (and hence NO2 predictions) due to inconsistencies in the relative sigmas in PVMRM.

Number of standard deviations from plume centerline that define the plume volume (value of “n”)

The value of n chosen for plume size determines the amount of fresh ozone available for the conversion of NOX to NO2 in PVMRM. The PVMRM model entrains an amount of ozone into the plume based on the cross-sectional plume area defined by n*sigma. But since ambient ozone has a uniform concentration in space, the amount of ozone entrained does not have a Gaussian distribution but has essentially a top-hat distribution. The n chosen for the 2nσz assumed plume instantaneous depth and 2nσy plume instantaneous width wouldn't make much difference if the Gaussian cross-wind distribution were retained, since only 32% of the plume mass would be outside of plus and minus one σ in any direction. However, this plume width is assumed to have a top-hat distribution where ozone concentrations are spread across the plume uniformly. Therefore the value of n can make a big difference in the amount of ozone available, which is proportional to n2. Hanrahan (1999a) defines the horizontal and vertical spread radii as nz times the Gaussian plume standard deviations. He uses it to “identify a volume over which to define an average concentration”. For a circular top-hat, the radius would therefore be nz times the Gaussian standard deviation. Hanrahan notes that the choice of nz “is somewhat arbitrary” and chose a value of nz =1.282 noting that 80% of the area under the normal curve is between ±1.282 standard deviations of the mean value. In the AERMOD/PVMRM model,


a value of nz = 4.0 was chosen which corresponds to “about 99.99% of the volume under the normal curve”. When nz is 4 rather than 1.28, the amount of ozone entrained is increased by a factor of (4/1.28)2.

When converting back and forth from Gaussian to top-hat shapes, most models assume a top-hat width such that when the top-hat distribution is plotted on top of the Gaussian distribution and the areas are matched, it “looks” like there is the best match possible. Some people assume that the centerline concentrations have to match. Others assume that the σ must be the same for the top-hat and the Gaussian, which happens when σ equals the width of the top-hat distribution divided by √2. But a top-hat with a width assuming nz = 4 produces a much too broad plume shape.

For purposes of calculating a volume for a top-hat plume distribution, it is noted that, by definition, the flux of a contaminant in a bi-variate Gaussian distribution plume well away from the ground is equal to (2πσyσzU) where, σy and σz are the standard deviations of the plume distribution and U is the transport wind speed. This term is the denominator of the Gaussian plume equation. To equate the flux of contaminant within a Gaussian distribution plume to an elliptical top-hat distribution having an average value equal to the Gaussian plume peak value one would set the plume flux (Cmax2πσyσzU) equal to the equivalent top-hat plume flux (Cavgπ (nzR)2U).

For the average concentration for the top-hat distribution to be no larger than the maximum concentration for the equivalent flux Gaussian distribution, nz would take on a value of the √2. A value of nz =1.414 is used as a reference value for comparisons.

Sensitivity analyses were performed which confirm what has been stated based on the team’s scientific review.

Table 3-1 is from a spreadsheet simplification of the ISC/PVMRM equations set up to show the effect of different values of the parameter, nz, which defines the size of the PVMRM top-hat plume. The upper part of the table shows the different values of ambient air ozone concentrations, an emission rate of NOx, a wind speed and a value of the in-stack fraction of NO2 to NOx (0.0 in this case). An arbitrary ozone concentration value of 100 ppb is shown in the spreadsheet.

Note that Hanrahan (1999a) defines the plume volume as including a term delta x, as the plume segment thickness at the receptor which he comments will cancel out in a later equation for NOx concentration in terms of moles. As discussed above, the team feels that the plume volume should be expressed in terms of a flux of material across a vertical cross section and use the wind speed rather than a slice of the plume delta x. The AERMOD/PVMRM FORTRAN code in ’SUBROUTINE INTEGRATE’, integrates the plume volume along the wind direction from the source to the receptor location instead of using the plume segment delta x. Although it is not necessary to integrate along the entire length



of the plume, it does not have an affect on the result because the distance is used in the calculation of NOx moles in the plume, so the distance cancels out. This calculation is not included in the spreadsheet or tables presented below.

The values of the product of σy*σz as a function of downwind distance have been entered in the spreadsheet emulator from the ISC stability class C conditions. The spreadsheet displays the plume volume flow rate with the wind, equal to 2πσyσzU. This allows calculations of the concentrations and plume flux of NOx and ozone in the plume. The “Nominal Ratio of the moles of O3 to NOx for nz =1.414” is modified by the nz values for the ratio values in the rest of the table. This is the ratio of O3 to NOx in the effluent plume assuming all entrained air contains the ambient ozone concentration and the PVMRM defines the ratio of NO2 to NOx in Equation 1 to be applied to the NOx predictions at receptor points.

Figure 3-1 graphically depicts the NO2/NOx ratios computed in the spreadsheet shown in Table 3-1 as a function of downwind distance for various values of nz ranging from 1.282 to 4 based on a 3 m/s wind and ISC stability class C. It can be seen that nz =4 results in ratios that are 2 to 3 times greater than nz =1.282.

The important matter is that the choice of the nz value can make a substantial change in the magnitude of the plume volume modeled with the PVMRM. A value of nz between 1.2 and 1.5 is supported by different derivation estimates in atmospheric physics, and a value of 4 is too large.

Table 3-1 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios: NO2/NOx in-stack = 0.0

Ozone concentration PPB 100 100 100 100 100 100 100 100 100 100 100 100

Ozone concentration MOLES/m3 4.09E-06 4.09E-06 4.09E-06 4.09E-06 4.09E-06 4.09E-06 4.09E-06 4.09E-06 4.09E-06 4.09E-06 4.09E-06 4.09E-06

Q NOx g/s 100 100 100 100 100 100 100 100 100 100 100 100

Q NOx MOLES/s 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913

U m/s 3 3 3 3 3 3 3 3 3 3 3 3

Instack fraction NO2/NOx Nd 0 0 0 0 0 0 0 0 0 0 0 0

DOWNWIND DISTANCE m 100 200 300 400 500 700 1000 2000 3000 5000 7000 10000

σy *σz

(ISCST/PVMRM Class C) m2 225 370 619 779 943 1359 1885 6556 13161 32345 58760 110700

Plume volume flux m3/s 4241.15 6974.336 11667.88 14683.8 17775.13 25616.55 35531.41 123577.7 248079 609688.9 1107600 2086646

Ozone flux in Plume MOLES/s 0.017346 0.028525 0.047721 0.060056 0.0727 0.104771 0.145323 0.50543 1.014638 2.493615 4.530061 8.534339

Ozone in Plume µg/m3 196.319 196.319 196.319 196.319 196.319 196.319 196.319 196.319 196.319 196.319 196.319 196.319

NOx flux in Plume MOLES/s 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913

NOx in Plume µg/m3 23578.51 14338.28 8570.541 6810.224 5625.838 3903.727 2814.411 809.2076 403.0974 164.0181 90.28531 47.9238

Nominal O3/NOx ratio for

nz=1.414 nd 0.007979 0.013121 0.021952 0.027626 0.033442 0.048195 0.066848 0.232498 0.466734 1.147063 2.083828 3.925796



Table 3-1 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios: NO2/NOx in-stack = 0.0 (Continued)



nz

1.282 NO2 /NOx Ratio 0.00723 0.01189 0.01990 0.02504 0.03032 0.04369 0.06060 0.21076 0.42310 0.90000 0.90000 0.90000

1.414 NO2 /NOx Ratio 0.00798 0.01312 0.02195 0.02762 0.03344 0.04819 0.06684 0.23246 0.46666 0.90000 0.90000 0.90000

2 NO2 /NOx Ratio 0.01128 0.01856 0.03104 0.03907 0.04729 0.06816 0.09454 0.32880 0.66006 0.90000 0.90000 0.90000

2.5 NO2 /NOx Ratio 0.01411 0.02320 0.03881 0.04884 0.05912 0.08520 0.11817 0.41100 0.82508 0.90000 0.90000 0.90000

3 NO2 /NOx Ratio 0.01693 0.02783 0.04657 0.05860 0.07094 0.10224 0.14181 0.49320 0.90000 0.90000 0.90000 0.90000

4 NO2 /NOx Ratio 0.02257 0.03711 0.06209 0.07814 0.09459 0.13632 0.18908 0.65760 0.90000 0.90000 0.90000 0.90000


Figure 3-1 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios as a function of the value of nz. (Data presented in Table 3-1 with in-stack NO2/NOx Ratio assumed to be 0.0.)

Table 3-2 uses the same input parameters except that the value of the in-stack NO2/ NOx ratio is 0.1.

In both Tables 3-1 and 3-2, calculated values of the NO2 to NOx ratio that exceeded 0.9 have been replaced with a value of 0.9, the maximum value allowed in the ISCST/PVMRM code (AERMOD allows this as a user input).

Several observations can be made from this simple model:

♦ Table 3-1 (and Figure 3-1) show that the NO2 to NOx ratio is proportional to the value of nz if the in-stack ratio of NO2 to NOx is 0.0. The values at short distances downwind are relatively small but increase rapidly with downwind distance due to the increased volume of fresh ozone available. In this particular case (ISC stability class C) the shape of the curve shows that the ratio increases rapidly at a downwind distance of approximately 1000 meters. This is due to the increase in the plume volume at that distance based on the ISC sigmas which equates to more ozone available for reaction.

0

0 .1

0

0

0

.2

. 3

. 4

0 . 5

0 . 6

0 . 7

0 . 8

0 . 9

1

1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 7 0 0 1 0 0 0 2 0 0 0 3 0 0 0 5 0 0 0 7 0 0 0 1 0 0 0 0

N z = 1 .2 8 2

1 .4 1 4

N z = 2

N z = 2 .5

N z = 3

N z = 4

Downwind Distance (m)

NO

2/N

Ox

Rat

io Nz=1.282

Nz=1.414 Nz=2 Nz=2.5 Nz=3 Nz=4


♦ Table 3-2 shows that the in-stack contribution is the primary component to the ratio of NO2 to NOx for the first km or so downwind; the values of nz are of lesser importance close to the source but dominate the calculations at greater distances. For example, in Table 3-2 at 2 km downwind a value of nz =1.282 results in a value of NO2 to NOx of 0.31 whereas a value of nz =4 yields a ratio two and a half times as large.

As the NO2/NOx ratio gets larger, the reality is that there will be less NOx in a real world plume as NO2 is created from the NOx. The reactions also remove ozone but the supply of ozone is partially replenished with entrainment of the ambient air. Therefore, in the real world, there will be diminishing returns for NO2 production which is not simulated in the spreadsheet or in the ISC and AERMOD/PVMRM models.

The addendum to the AERMOD Model Formulation Document (MFD) (EPA,2004) states that the number of standard deviations from the plume centerline (nz) and the area under a normal curve (A) were revised from Hanrahan’s values nz =1.282 and A=0.8 to the EPA values of nz =4 and A=1.0. The presumption is that the EPA believed that by using 4 and 1.0, essentially the entire plume was captured in the volume calculations. However, the MFD doesn’t expand on the effect this simple change has on resulting concentrations. The team explored the magnitude of this change in terms of NO2/ NOx ratio and resulting predicted NO2 concentration.

The AERMOD code was modified, changing the nz and A parameters within the PVMRM_CALC subroutine, to determine the effects of these variations on the outputs of Hanrahan’s PVMRM model. The sensitivity runs defined a single receptor and one hour of meteorology with stable conditions. The meteorology for this hour (5/18/05 hr 1) had winds of 4.1 m/s from 26°, with a mixing height of 346 m and a 128 m Obukhov length (a moderately stable hour).

Table 3-3 presents the results of the comparison with both plume nz values for a single stack. Each column present the inputs and results of AERMOD run with flat terrain for 100% conversion, using the OLM, using AERMOD with the EPA’s plume nz definition, and using Hanrahan’s’ definition. In this case at this receptor, the OLM did not limit the production of NO2 because there was ample ozone for the conversion. Note that the plume volume computed by PVMRM is an order of magnitude larger when using the EPA nz and A values versus Hanrahan’s values. This results in the PVMRM predicted NO2 concentrations being approximately a factor of three times higher using the EPA’s plume nz and A values than the Hanrahan’s values when modeling the same stack inputs.

Table 3-4 presents results with two stacks 118 meters apart. The second stack had an emission rate equal to half that of the first stack. PVMRM considered both stacks merged in this case and again at the modeled receptor, for the selected hour, a difference of approximately a factor of 3 in predicted NO2 concentrations can be shown by changing the nz definition.

Table 3-2 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios: NO2/NOx in-stack = 0.1



Q NOx g/s 100 100 100 100 100 100 100 100 100 100 100 100

Q NOx MOLES/s 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913

U m/s 3 3 3 3 3 3 3 3 3 3 3 3

Instack fraction NO2/NOx Nd 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

DOWNWIND DISTANCE M 100 200 300 400 500 700 1000 2000 3000 5000 7000 10000

σy *σz

(ISCST/PVMRM Class C) m2 225 370 619 779 943 1359 1885 6556 13161 32345 58760 110700

Plume volume flux m3/s 4241.15 6974.336 11667.88 14683.8 17775.13 25616.55 35531.41 123577.7 248079 609688.9 1107600 2086646

Ozone flux in Plume MOLES/s 0.017346 0.028525 0.047721 0.060056 0.0727 0.104771 0.145323 0.50543 1.014638 2.493615 4.530061 8.534339

Ozone in Plume µg/m3 196.319 196.319 196.319 196.319 196.319 196.319 196.319 196.319 196.319 196.319 196.319 196.319

NOx flux in Plume MOLES/s 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913 2.173913

NOx in Plume µg/m3 23578.51 14338.28 8570.541 6810.224 5625.838 3903.727 2814.411 809.2076 403.0974 164.0181 90.28531 47.9238

Nominal O3/NOx ratio for

nz=1.414 nd 0.007979 0.013121 0.021952 0.027626 0.033442 0.048195 0.066848 0.232498 0.466734 1.147063 2.083828 3.925796



Table 3-2 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios: NO2/NOx in-stack = 0.1 (Continued)



nz

1.282 NO2 /NOx Ratio 0.10723 0.11189 0.11990 0.12504 0.13032 0.14369 0.16060 0.31076 0.52310 0.90000 0.90000 0.90000

1.414 NO2 /NOx Ratio 0.10798 0.11312 0.12195 0.12762 0.13344 0.14819 0.16684 0.33246 0.56666 0.90000 0.90000 0.90000

2 NO2 /NOx Ratio 0.11128 0.11856 0.13104 0.13907 0.14729 0.16816 0.19454 0.42880 0.76006 0.90000 0.90000 0.90000

2.5 NO2 /NOx Ratio 0.11411 0.12320 0.13881 0.14884 0.15912 0.18520 0.21817 0.51100 0.90000 0.90000 0.90000 0.90000

3 NO2 /NOx Ratio 0.11693 0.12783 0.14657 0.15860 0.17094 0.20224 0.24181 0.59320 0.90000 0.90000 0.90000 0.90000

4 NO2 /NOx Ratio 0.12257 0.13711 0.16209 0.17814 0.19459 0.23632 0.28908 0.75760 0.90000 0.90000 0.90000 0.90000

Table 3-3 Single Stack, Single Hour, Single Receptor Model Comparisons

AERMOD AERMOD AERMOD AERMOD

Base OLM PVMRM PVMRM

Description

Base case (100% NOx to

NO2)

Ozone Limiting Method

PVMRM with EPA Plume Parameters

PVMRM with Hanrahan Plume

Parameters

Model Settings nz N/A N/A 4 1.282 A N/A N/A 1 0.8

σr (min) (m) N/A N/A 5 15

Meteorological Data Wind Direction (°) 26 26 26 26 Wind Speed (m/s) 4.1 4.1 4.1 4.1

Stack Data # of Stacks 1 1 1 1

Distance Apart (m) N/A N/A N/A N/A Base Elevation (m) 0 0 0 0 Emission Rate (g/s) 100 100 100 100 Stack Height (m) 54 54 54 54 Stack Temp (K) 678 678 678 678

Stack Velocity (m/s) 9 9 9 9 Stack Diameter (m) 2.3 2.3 2.3 2.3 In-stack NO2 Ratio N/A 0.1 0.1 0.1 Equilibrium. NO2 N/A N/A 0.9 0.9

Ozone value (µg/m3) N/A 40 40 40

Receptor Data Rel. Location 5 km SSW 5 km SSW 5 km SSW 5 km SSW Elevation (m) 0 0 0 0

Hill Height (m) 0 0 0 0

Results NOX Conc. (µg/m3) 18.029 18.029 18.029 18.029 NO2 Conc. (µg/m3) 18.029 18.029 7.596 2.550 # of Contributing N/A N/A 1 1 Ozone (moles) N/A N/A 492.076 50.744 NOX (moles) N/A N/A 1531.269 1225.016

BHORIZ N/A N/A 0 0 BVERT N/A N/A 0 0

Plume Vol. (m3) N/A N/A 5.91E+08 6.09E+07 % NO2 N/A N/A 42.1% 14.1%


Table 3-4 Two Stacks, Single Hour, Single Receptor Model Comparisons

AERMOD AERMOD AERMOD AERMOD

Base OLM PVMRM PVMRM

Description

Base case (100% NOx to

NO2)

Ozone Limiting Method

PVMRM with EPA Plume Parameters

PVMRM with Hanrahan Plume

Parameters

Model Settings nz N/A N/A 4 1.282 A N/A N/A 1 0.8

σr (min) (m) N/A N/A 5 15

Meteorological Wind Direction (°) 26 26 26 26 Wind Speed (m/s) 4.1 4.1 4.1 4.1

Stack Data # of Stacks 2 2 2 2

Distance Apart (m) 118 118 118 118 Base Elevation (m) 0 0 0 0 Emission Rate (g/s) 100, 50 100, 50 100, 50 100, 50 Stack Height (m) 54 54 54 54 Stack Temp (K) 678 678 678 678 Stack Velocity 9 9 9 9 Stack Diameter 2.3 2.3 2.3 2.3

In-stack NO2 Ratio N/A 0.1 0.1 0.1 Equilibrium. NO2 N/A N/A 0.9 0.9

Ozone value N/A 40 40 40

Receptor Data Rel. Location 5 km SSW 5 km SSW 5 km SSW 5 km SSW Elevation (m) 0 0 0 0

Hill Height (m) 0 0 0 0

Results NOX Conc. (µg/m3) 27.384 27.384 27.384 27.384 NO2 Conc. (µg/m3) 27.384 27.384 10.478 3.873 # of Contributing N/A N/A 2 2 Ozone (moles) N/A N/A 649.215 50.744 NOX (moles) N/A N/A 2296.904 1225.016

BHORIZ N/A N/A 115.106 115.106 BVERT N/A N/A 0 0

Plume Vol. (m3) N/A N/A 7.79E+08 6.09E+07 % NO2 N/A N/A 38.3% 14.1%


A second set of testing with runs using larger numbers of receptors were made to provide a visual representation of the differences in plume concentration magnitude with downwind distance from the sources. Receptors were placed every 50 meters to produce a highly dense dataset with which concentrations were compared.

The first comparison set uses the stack parameters and meteorology shown in Table 3-3. A large dense grid of over 26,000 receptors spaced 50 meters apart was added. The plume is buoyant with a relatively high exit velocity, leading to the highest concentrations at considerable distances downwind. Figure 3-2 depicts the differences in NO2 concentration found between the plume nz values, and it is clearly seen that there are large differences. The maximum was found to increase 360% by using the nz =4 versus nz =1.282.

The second comparison was made to confirm that the anomaly wasn't caused by the stack configuration, or meteorology for the modeled hour. This comparison used two stacks with a lower height (24 meters) and a different, relatively unstable, meteorological hour. This case had over 3,100 receptors spaced 50m apart. This hour had wind of 7.7m/s from 180° with a CBL of 542 m, a mixing height of 1124 m, and a -318 m Obukhov length, a rather unstable hour. In this case the maximum concentration was found much closer to the sources than that shown in the first comparison in Figure 3-2. Although the maximum concentrations are relatively similar (within 7%) between the two nz cases, and the plume appears to have similar characteristics within about 2 kilometers downwind, the plume is significantly different beyond that. As seen in Figure 3-3, concentrations 2 to 5 km downwind using the EPA’s (2004) parameters are approximately twice that of those found using Hanrahan’s (1999a) parameters.

It can be concluded that short-range NO2/NOx ratios are not as sensitive to the plume nz definitions as are the ratios found further downwind. This was similar to the conclusion, based on the spreadsheets presented in Tables 3-1 and 3-2, that the NO2/NOx ratios are dominated by the in-stack conversion close to the source.


Hanrahan’s nz (maximum 1- hr NO2= 13.87 µg/m3)

EPA’s nz (maximum 1- hr NO2= 49.91 µg/m3)

Figure 3-2 Comparison of 1-hr NO2 predicted for a full receptor grid using parameters listed in Table 3-3. (Hanrahan (1999a) versus EPA (2004) plume parameters)


Hanrahan’s nz (maximum 1- hr NO2= 57.41 µg/m3)

EPA’s nz (maximum 1- hr NO2= 61.68 µg/m3)

Figure 3-3 Comparison of 1-hr NO2 predicted for a full receptor grid using lower stack heights (24 meters) and unstable meteorology. (Hanrahan (1999a) versus EPA (2004) plume parameters)


The Implementation of Multiple Plume Interactions

Hanrahan (1999a) extended the PVMRM to treat the impact of multiple sources in the vicinity of a ‘dominant’ source of NOX as a single source with an expanded plume volume. The approach considers the strength and spacing of ‘major contributing sources’, for each receptor location to determine the dimensions of the expanded plume. The expansion of the source configuration introduces an additional set of complexities associated with identifying the major dominant source for each receptor of interest. The PVMRM includes criteria whereby additional sources would be included to define a single wider and taller plume that includes the emissions of all neighboring “major contributing sources” that individually contribute concentrations that are at least 50% of the contribution from the one dominant source.

The enlarged plume has an increased volume based upon the crosswind extent of the group of sources. The horizontal plume size is increased by adding a width equal to the maximum distance between the “major contributing sources”. In the FORTRAN code, the inclusion and exclusion of sources into the single expanded source are based upon logical statements with specific true or false implications that can create discontinuities in the predicted downwind concentration values. Small changes to source emission rates or to individual source locations are capable of resulting in counterintuitive impacts. Although there may be some reductions of computation time by not treating the individual smaller plumes separately, the benefit of the method from a dispersion or chemistry standpoint is not clear. In practice, the method is likely to considerably increase the size of the new plume volume and therefore increase the amount of ozone in the plume which will then result in higher NO2/NOX ratios. While the method may work well for some source configurations, in reviewing the AERMOD/PVMRM model, some simple geometries were identified where the multiple plume methodology will result in unexpected predictions.

One case found that changing the emission rate of a second source may result in discontinuities of predicted NO2 concentrations. Figure 3-4 presents the total NOX and NO2 concentrations from two point sources, as the emission rate for the second source is increased from 5 to 100 grams per second. Figure 3-5 presents the total moles of NOX and O3 in the plume, as well as the calculated NO2/NOX ratio calculated by PVMRM. At 55 g/s, the second source exceeds the 50% threshold and becomes a “major contributing source” to the plume volume in addition to the dominant source. The number of moles of NOX jumps due to the addition of the second source’s emission rate then increases gradually, while the moles of O3 remains relatively constant, since the plume volume is relatively unchanged due to the proximity of the two sources to each other. This leads to a noticeable reduction in the NO2/NOX ratio calculated by PVMRM, and thus, lower NO2 concentrations at the point that plumes are merged, even though the emission rate of the second source is increasing.


0 10 20 30 40 50 60 70 80 90 100 110Source 2 Emission Rate (g/s)

0

5

10

15

20

25

30

35

40C

once

ntra

tion

(µg/

m3 )

7.96 8.31 8.67 9.03 9.38 9.7410.10 10.46

10.81 11.17

8.58 8.65 8.73 8.81 8.89 8.97 9.05 9.13 9.21 9.29

18.8819.72

20.5721.42

22.2723.11

23.9624.81

25.6526.50

27.3528.20

29.0429.89

30.7431.58

32.4333.28

34.1334.97

NO2 & NOX Concs.Total NO2

Total NOX

Figure 3-4 Predicted concentration as a function of increasing a secondary source’s emission rate. (NOx concentration predicted by AERMOD and NO2 concentration predicted by AERMOD/PVMRM).


0 10 20 30 40 50 60 70 80 90 100 110Source 2 Emission Rate (g/s)

0

500

1000

1500

2000

2500

3000

3500

Mol

es

0

0.2

0.4

0.6

0.8

1

NO

2 /NO

Xratio

492 492 492 492 492 492 492 492 492 492 507 507 507 507 507 507 507 507 507 507

1531 1531 1531 1531 1531 1531 1531 1531 1531 1531

23742450

25272603

26802756

28332910

29863063

0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42

0.31 0.31 0.30 0.29 0.29 0.28 0.28 0.27 0.27 0.27

Moles & NO2/NOX RatioO3 molesNOX molesNO2/NOX Ratio

Figure 3-5 Moles of O3 and NOX, and NO2/NOX ratio as a function of increasing a secondary source’s emission rate.



Definition of Plume Volume for Downwash Conditions

PVMRM does not adjust the plume sigmas to account for downwash. Downwash should be considered in both the relative diffusion and continuous plume diffusion categories because it occurs close to the source. It is already considered in the AERMOD main plume model (i.e., continuous plume diffusion). The net effect would be more entrainment of ambient ozone and hence more production of NO2.

4.0 EVALUATION OF PVMRM AND OLM IN THE AERMOD MODEL

USING THE WAINWRIGHT DATA

4.1 Methodology

A model evaluation exercise was conducted to assess the PVMRM and OLM modules in AERMOD. Model performance was evaluated for the PVMRM and OLM predicted and observed ratio of NO2/NOx, for the AERMOD predicted NOx concentrations and for the AERMOD/PVMRM and AERMOD/OLM predicted NO2 concentrations. The data set used in the evaluation, the limitations of the data set, the specific model runs that were made, the model performance statistics calculated, and the results of the evaluation are described in this section.

The model evaluation uses 12½ months of hourly-averaged observations from an ambient monitoring station close to a power plant site in Wainwright, Alaska. There are five stacks from diesel generators and the stack heights are about the same as the building height. The single monitoring station is located about 500 m to the east-south-east of the plant, and observes NO2, NO, NOx, and ozone concentrations, as well as meteorological variables such as wind speed, wind direction, temperature, and solar radiation.

Since the plume is likely to impact the monitoring station only when the wind direction is blowing towards the sector containing the monitor (corresponding to wind directions from about 250° to 310°), the evaluation took place only for those hours when the wind direction was in that sector. Additionally, the evaluations could be carried out only when the required input data and monitoring data were “non-missing”. This resulted in 594 hours of data available for evaluations.

The PVMRM and OLM modules in AERMOD predict the ratio, NO2/NOx, in plumes of NOx emitted to the atmosphere. AERMOD then predicts 1-hour NO2 concentrations by multiplying the PVMRM or OLM-predicted ratio NO2/NOx by the AERMOD-predicted NOx concentration. The concentration of NOx is usually the sum of the concentrations of NO and NO2. PVMRM and OLM employ slightly different assumptions concerning the conversion of emitted NO to NO2 as the plume moves downwind, in the presence of an ambient concentration of ozone.

The evaluation was conducted using the latest version of the U.S. EPA approved air quality model, AERMOD (11103). The AERMOD preprocessor codes and tools that have been used include AERMET, AERMINUTE and AERMAP. AERMET processes meteorological data for input to AERMOD. The AERMINUTE tool has been used to process the National Weather Service (NWS) 1-minute wind data that were incorporated into the AERMET processing. These NWS data represent back-up wind speed and direction data in the event that actual on-site wind measurements are missing. AERMAP processes terrain elevation data and generates receptor information for input to AERMOD.

3009/Evaluation of PVMRM and OLM 4-1 Wainwright Field Data Comparison for Short Term NO2 Impacts Epsilon Associates, Inc.

The results of the AERMOD NO2 evaluations depend not only on the PVMRM or OLM modules but also on the many other modules in AERMOD (e.g., plume rise, downwash, lateral and vertical dispersion, low wind parameterizations, and so on). There could be good results because all model components are good, or because of compensating errors by modules, or there could be poor results due to the combination of good-performing modules with poorly-performing modules. Additionally, limitations and uncertainties in the evaluation data set must be considered. For the Wainwright data set, these include, for example, approximation of emissions using operating logs and vendor performance data, use of ambient ozone data from a single monitoring station, and the relatively low observed NOx and NO2 concentrations.

For an evaluation of a model or model options to be robust, many data sets should be used in the evaluation so that a multitude of conditions, source data, etc. can be evaluated. This evaluation using the Wainwright data set should be considered as an additional piece of information to supplement the limited number of data sets used by EPA to date to evaluate the PVMRM and OLM options in AERMOD for predicting 1-hour NO2.

4.2 Model Options

Two sets of AERMOD runs have been used for primary analysis of the Wainwright data set. The first employs the PVMRM option, and the second employs the OLM option. Hourly ozone measurements from the ambient monitoring station have been input into AERMOD where they are used by the PVMRM and OLM modules to compute the conversion of NO into NO2. An initial plume in-stack NO2 to NOx ratio was set to 0.2 for this application based on the value recommended for diesel IC engines in Appendix C of the San Joaquin Valley Air Pollution Control District guidance document (SJVAPCD, 2010). The NO2/NOx ambient equilibrium ratio was set to 0.9 (the recommended default value).

As seen in the two photographs in Figure 4-2, the power plant building has a gently sloped roof and the five stack tops are approximately at the height of the building. The model runs assumed a flat topped building with a height equal to that of the roof crest (this is the standard recommended procedure).

In addition to the above-described two AERMOD runs with PVMRM and with OLM, an additional AERMOD run has been made using the PVMRM algorithms, but using the Hanrahan (1999a) assumed value of 1.282 for nz (a value of nz = 4 is in AERMOD (11103)). The AERMOD model code was modified to accommodate this change. The assigned value for nz is the number of standard deviations from the plume centerline that defines the plume volume in which ambient ozone is entrained in PVMRM. The nz value of 4 is coupled with A=1.0 and a minimum sigma value of 5 meters in AERMOD (11103); therefore, these values were changed to reflect Hanrahan’s values as described in the PVMRM model formulation paper (Hanrahan, 1999a) (i.e., nz =1.282 with A=0.8 and minimum sigma = 15 m). These are not user inputs, but are rather hardwired values in the source code.



Furthermore, sensitivity runs took place where AERMOD/OLM and AERMOD/PVMRM were run with the ambient ozone concentration recalculated to equal the sum of the observed ozone concentration and the observed NO2 concentration at the monitor. The reasoning is that, when the plume impacts the monitor, the observed ozone concentration is less than the ambient background ozone concentration because some of the ambient ozone has been used to convert NO to NO2 in the plume.

4.3 Wainwright Source and Monitoring Data Description

Wainwright, Alaska is a remote village north of the Arctic Circle. A company with offshore drilling operations is operating an ambient air quality and meteorological monitoring station near the local power plant in Wainwright. The monitoring station was designed and operated to comply with PSD requirements, with a Quality Assurance Project Plan approved by the EPA regional office. Figure 4-1 shows the area around the village with the locations of the power plant, the ambient monitoring station, and the NWS airport meteorological station marked. The ambient monitoring station is located 500 to 520 m (depending on which of the five stacks is used as the origin) to the east-southeast of the plant. The water area to the northwest is the Arctic Ocean, which extends hundreds of kilometers in that direction with no land areas present. The water area to the southeast is an inlet.

The power plant consists of five diesel generators each vented through its own stack. Figure 4-2 presents two photographs of the power plant building and its five stacks, showing that the stack heights are approximately at the height of the adjacent building. Units 1 through 3 are Caterpillar Model number 3508 engines with a rated design capacity (output) of 425 kW each. Units 4 and 5 are Caterpillar Model number 3512 engines with a rated design capacity of 910 kW each. The design capacity of each is based on the use of #2 diesel fuel; however, due to the extremely cold temperatures at Wainwright, #1 diesel fuel must be used since #2 diesel fuel will gel at those temperatures. Using #1 diesel fuel the engines are rated at 450 kW and 950 kW, respectively.

The physical stack parameters for Units 1 through 5 are presented in the top portion of Table 4-1. The NOx emission rate, exit temperature and exit flow rates were computed hourly based on the Wainwright Hourly Operator Logs which indicated which engines were running each hour and their output in kilowatts. Based on actual operating conditions, stack emissions, exit flow and exit temperature were interpolated from Caterpillar maximum design capacity data. The ranges of each operational hourly source parameter are presented in the bottom portion of Table 4-1. It is important to note that no stack test data exist for these engines and it is assumed that actual emissions are representative of Caterpillar data performance specifications. The curves of exit temperature, exit flow rate, and NOx emissions generated from the Caterpillar performance data for the 3508 and 3512 engines as a function of kilowatt output are presented in Appendix A.

Figure 4-1 Village of Wainwright, Alaska, showing locations of power plant, monitoring station, and ASOS meteorological station.


3009/Evaluation of PVMRM and OLM 4-5 Wainwright Field Data Comparison

Figure 4-2 Wainwright Power Plant, Wainwright, Alaska (front and side views)


Table 4-1 Stack locations and dimensions for the Wainwright Power Plant

Unit UTM E

(m)

UTM N

(km)

Base

Elevation (m)

Stack

Height (m)

Stack

Diameter (m)

Unit 1 462247.5 7837899.0 11.5 9.32 0.23

Unit 2 462247.1 7837899.4 11.5 9.32 0.23

Unit 3 462246.5 7837899.9 11.5 9.32 0.23

Unit 4 462263.0 7837887.5 11.5 9.02 0.31

Unit 5 462262.2 7837888.0 11.5 9.02 0.31

Range of Hourly Source Parameters for the Engines

Exhaust Gas Temperature (ºF) 490 – 880

Exhaust Gas Flow Rate (CFM) 1022 – 5070

NOx Emission Rate (lb/hr) 4 -28

Because the stacks are close in height to the building height, the stack plumes are likely to experience downwash. The latest version of the EPA Building Profile Input Program (BPIP-Prime) was run for all the stacks and buildings in the vicinity of the power plant to create the building parameter inputs to AERMOD. Figure 4-3 is an aerial photograph that highlights the sources and buildings whose locations and dimensions were entered in the BPIP-Prime program. In addition to the power plant building (height=9.35 m), the adjacent L-shaped shop building (height=10.67m) and two storage tanks (height=7.32 m) west of the power plant building were included. For each stack, the power plant and the shop buildings were the dominant structures for downwash depending on the wind direction.


Figure 4-3 Wainwright Power Plant building with stack locations shown (red) with adjacent shop building and storage tanks included in BPIP-Prime.

The Wainwright power plant is a relatively isolated source of NOx emissions in the village. Heating of homes, motor vehicles, snow mobiles and aircraft could be other sources of NOx emissions, but an emissions inventory of those other sources is not available. However, as indicated in Figure 4-1, because of the location of these other sources, emissions are not likely to impact the monitor at the same times as the power plant. Since the evaluation was only performed for time periods when wind directions were such that the power plant was expected to impact the monitor, the lack of an emission inventory for these sources will not negatively affect the results of this analysis.

At the monitoring station, hourly averaged measurements of NO, NOx, NO2 and O3 are recorded as well as meteorological parameters such as horizontal wind speed and wind direction, vertical wind speed, temperature difference, barometric pressure, and solar radiation. The pollutant and solar radiation are measured at a height of approximately 4 m, the winds are measured at approximately 10 m, temperature measurements are made at 2 and 9 m, and the barometric pressure is measured at 2 m. The team obtained the monitoring data for the period from September 16, 2009 through September 30, 2010.


The monitoring station uses a Thermo Electron Corporation Model 42i NO-NO2-NOx chemiluminescent gas analyzer (AECOM, 2010). It can measure the amount of nitrogen oxides in the air from sub-ppb levels up to 100 ppm. Its specification sheet (see www.thermo.com/air) states that its lower detectable limit is 0.4 ppb and its precision is ±0.4 ppb. At Wainwright, the concentrations were reported in an electronic file as whole units of ppb (i.e., 0 ppb, 1 ppb, 2 ppb, 3 ppb, etc.).

In the model evaluations, the team chose to consider observed NO2 concentrations of 1 ppb or greater. This threshold was selected based on the detection limit of the analyzer instrument and the very low ambient background concentrations (averaging slightly less than 1 ppb) in Wainwright.

The observed hourly-averaged NO2 concentrations during the 12½ month monitoring period that was used range from a minimum of 0 ppb to a maximum of 32 ppb, NOx concentrations ranged from 0 to 160 ppb and ambient ozone concentrations ranged from 0 to 46 ppb. For the wind direction sector of concern (250 to 310 degrees), the elevated NOx and NO2 concentration peaks tended to occur at the same hours.

In the remote Wainwright location, far from major NOx source regions, there were numerous hours recording 0 ppb NO2 concentrations and, as mentioned above, the average annual concentration of NO2 was about 1 ppb. Thus often the background was very low, close to the lower detectable limit (0.4 ppb) for the analyzer. There were only 30 hours out of the 121/2 month period with NO2 concentrations greater than 20 ppb. Despite the low background concentrations on an annual basis, there are a few days in the year with ozone and NOx concentrations that are more representative of areas closer to major source regions. For many years, regional air pollution scientists have been studying these brief excursions and can usually trace them to regional wind flow patterns that bring air masses to Alaska from the Eastern Asia industrial and urban areas. This study did not attempt to identify these periods with obvious high background concentrations at Wainwright. It is expected that trajectories from the southwest would be associated with flow from Eastern Asia, thus possibly overlapping with time periods when the wind would be blowing from the Wainwright power plant towards the ambient monitor.

Hourly surface meteorological data from the ambient monitoring station have been used as the primary source of meteorological data in the study. The nearby NWS ASOS station was used to provide back-up meteorological data. Global Hourly Surface data in TD3505 format from the ASOS station were purchased from the National Climatic Data Center (NCDC). In addition, 1-minute ASOS data were downloaded from the NCDC website. The ASOS 1-minute wind observations are rolling two-minute averages that are reported every minute and were processed with AERMINUTE and incorporated into the AERMET model. ASOS computes the true 2-minute average wind direction and then accounts for the magnetic declination for the site. To account for an approximate 17 degree difference in


http://www.thermo.com/air

direction between the “north” of the ambient monitoring station and the north of the ASOS station, the ASOS 1-minute data were adjusted by +17 degrees before inclusion in AERMET.

AERMET uses the ambient monitoring meteorological data as the primary source of data and uses the ASOS 1-minute wind data to substitute for missing wind data. The hourly ASOS data are also used for parameters that are not available from the ambient monitoring station.

The AERSURFACE tool is typically used to estimate the surface characteristics in the modeling domain that are used as inputs to AERMET. However, the data required for this tool are limited in Alaska, and are unavailable for the Wainwright area. Therefore, the land use values have been assigned manually. Between 218° and 37° (clockwise), “water” characteristics were used (see Figure 4-1, which shows the orientation of the coastline). Between 37° and 218° (clockwise) the “desert shrubland” characteristics were used. The surface meteorological data were combined in AERMET with concurrent upper air observations from the closest upper air site (Barrow, Alaska), which were also obtained through NCDC. The upper air observations are used to estimate the mixing depth.

4.4 Choosing Hours to Model

It is difficult to evaluate a dispersion model when there is only one monitoring station, since there are only a small fraction of the hours in a year when the wind direction will blow the plume towards the monitoring station.

From the 12½ month observation period, the team modeled those hours when the wind was blowing from a 60 degree sector approximately centered on the direction from the power plant to the monitoring station. Hours with wind directions of 250º through 310º were used if there were sufficient meteorological, ambient monitoring and power plant emissions data for those hours. Based on the ambient monitoring station meteorological data, there are 910 hours with winds blowing from the 250º - 310º sector. There were some hours when the wind measurements from the ambient monitor were missing, but there were concentration measurements reported. For those hours, the Wainwright NWS ASOS wind directions were used.

Hand-written hourly logs were provided by the operators of the Wainwright Power plant. The operations of those hours matching the wind direction sector were digitized and hourly NOx emissions, exit temperatures and flow rates were computed for each engine operating based on its kilowatt output and Caterpillar performance specifications. Some of the periods within the 250º -310º sector had missing operator log information. A total of 825 hours of emissions data were compiled. For the hours digitized, there were between one and three engines operating at any given hour.


In addition to the wind data and emissions data, monitored pollutant data for each hour were necessary. Therefore a final data set containing a total of 787 hours was compiled for the model evaluation. These are hours for which there were engine data, the winds were within the 250º -310º sector, and meteorological and pollutant monitoring data were available.

4.5 Model Performance Evaluation Methodology

The model evaluation exercise assesses the PVMRM and OLM modules in AERMOD. PVMRM and OLM predict 1-hour averaged NO2/NOx ratios. AERMOD predicts the 1-hour averaged NOx concentration and then multiplies that value by the PVMRM or OLM-predicted NO2/NOx ratio to obtain a prediction of the NO2 concentrations. In the basic model evaluations, AERMOD was run once with the PVMRM option and again with the OLM option for the evaluation hours satisfying the wind direction criterion. Both runs used the ambient measurements of ozone as inputs.

The model evaluation procedure employed a combination of qualitative and quantitative assessments, following the general guidance in Hanna (1989), Chang and Hanna (2004) and Hanna and Chang (2011). Their BOOT model evaluation software was used. A recent example of BOOT’s use by the current authors is the evaluation of four wind field–Lagrangian particle models with the JU2003 Oklahoma City SF6 tracer field data (Hanna et al., 2011).

The first step was to acquire the field data set and model predictions and place them in a data table in the format needed for BOOT. At this stage, a qualitative analysis of the data took place, such as by simply “looking” at the data in the table to try to discern outliers and certain types of behavior (e.g. peak concentrations, relations with month or season, etc.) Appendices B and C show a few hours of these data files. Several of these hours are on the “top 100” list for observed and AERMOD/PVMRM predicted concentrations described later. Quantile-Quantile (Q-Q) plots of NOx and NO2 concentrations were made to test the ability of the AERMOD model predictions to represent the frequency distribution of the observations, and especially to determine if the model can produce concentrations in the high range that match the observations. Scatter plots of paired (in time and space) observations and model predictions of the NO2/NOx ratio at the ambient monitoring location were prepared in order to determine if the model appears to produce minimal mean bias and scatter, and a positive correlation, based on a visual impression of each scatter plot. The next step was to apply the BOOT quantitative model evaluation software, which calculates a set of five standard performance measures used to evaluate the predictions of a model with observations. These performance measures include the fractional bias (FB), the geometric mean bias (MG), the normalized mean square error (NMSE), the geometric variance (VG), and the fraction of predictions within a factor of two of observations (FAC2). The software also has the capability to present many types of plots of the observed and modeled concentrations. The 95% confidence limits of the performance measures were calculated by BOOT. Furthermore, the software allowed


calculation of whether the performance measures for OLM were significantly different from those for PVMRM, and whether the mean bias measure (FB or MG) for a single model was significantly different from that for a perfect model (i.e, FB = 0.0 and MG = 1.0).

The performance measures used in the BOOT software (Hanna 1989, Chang and Hanna 2004) are defined below, where C is the concentration. Subscripts p and o refer to predicted and observed, and the overbar represents an average over the set of hours being evaluated.

Fractional Mean Bias:

)CC/()CC(FB popo +−= 2 (5)

Normalized Mean Square Error:

)C*C/())CC((NMSE popo

2−= (6)

Geometric Mean:

))Cln()Clnexp((MG po −= (7)

Geometric Variance:

))ClnC((lnexpVG po

2−= (8)

Fraction of Cp within a factor of two of Co:

FAC2 is the fraction of data that satisfy 0.5 ≤ Cp/Co ≤ 2.0 (9)

In addition, the median, average, and maximum of Co and Cp are listed in summary tables. Note that the above equations are generic and apply to any kinds of data pairings, including arc-maximum and paired-in-space comparisons; and any kinds of variables, including concentrations and ratios of concentrations.

Pairs of model predictions and observations are included in the above calculations only if both the predicted and observed concentration exceeded a threshold concentration, as defined below. This is because the MG and VG performance measures become very large (approaching infinity) for C approaching 0.0.


4.6 Results

The sets of model runs described in Section 4.2 were carried out and the predictions of the NO2/NOx ratio and NOx and NO2 concentrations were compared with the observations. For analysis purposes, the predicted and observed data were summarized in a spreadsheet. An excerpt of the spreadsheet of monitored data and modeled data is presented in Appendix B. The AERMET processed meteorological parameters for the same hours as presented in Appendix B are shown in Appendix C.

The concentrations reported below are sometimes expressed in units of ppb and sometimes in units of μg/m3. This is because the monitoring data are reported in ppb and the AERMOD predictions are given in μg/m3. The exact conversion of concentration from one unit to another depends on temperature and pressure for that hour, and this calculation is made for each hour when applying the BOOT software to predicted and observed data with a consistent set of units. However, at standard temperature and pressure, the relation 1 ppb = 1.88 μg/m3 is useful to know.

4.6.1 Comments on hours selected for analysis

As mentioned above, this model evaluation exercise involves only a single monitoring station and wind directions observed in a 60° sector from the direction from the source to the monitoring station. Additionally there were concerns about assigning a minimum (threshold) concentration for the monitoring data (observations), and determining a background concentration to add to the model predicted concentrations prior to comparisons with the observations.

The Epsilon team investigated the question of the threshold concentration for the monitoring instrument. The initial emphasis was on hours with observed NOx concentration exceeding 10 ppb (≈18.8 μg/m3). Observed concentrations above 10 ppb can confidently be thought to be caused by the industrial source. Observed concentrations below 10 ppb may be influenced by the regional background. The monitoring instrument threshold and the uncertainty are both equal to 0.4 ppb and therefore these instrument characteristics are unlikely to significantly affect the observed concentrations when they are above 10 ppb. The concentration data are reported by the monitoring instrument in increments of ppb. Reported NO2 concentrations are in the data file as whole numbers in units of ppb (i.e., 0, 1, 2, 3 ppb etc.). A reading of 0 means a value that is too small to be certain of. Readings of 1 ppb and higher are thought to be valid, although the contribution of background may be significant.

There were 245 hours that satisfied the condition that observed NOx ≥ 10 ppb (≈18.8 μg/m3). However, if those observations are compared with model predictions for those hours, the observed and predicted data sets are imbalanced, since predicted concentrations might be less than 10 ppb. Additionally there might be predicted concentrations that exceed 10 ppb in the other hours. Therefore the team also compared observations and


predictions for hours when concentrations were greater than zero (i.e., ≥1 ppb for observed and ≥ 0.000005 μg/m3 for modeled concentrations). In the range from 1 to 10 ppb, there may sometimes be a significant influence of regional background, but many of the hours in that range appeared to have valid observations.

To account for background in the predictions the team added 1 ppb (≈1.88 μg/m3) to the predicted concentrations. 1 ppb is approximately the annual average NO2 concentration at this monitor (based on 7188 hourly NO2 observations with wind directions outside the 250º-310º sector). The investigation of background could make use of only the single monitoring site, so it was not possible to use the usual method (when several monitors are available) of assuming that the background was given by the upwind monitor reading.

Table 4-2 contains a summary of the hours considered in the evaluations under different assumptions for threshold. The green section contains information on the 787 hours that satisfy the wind direction restriction and have available data. However, during some of those hours, the observed NOx concentration was reported as 0 ppb. During other hours, there were meteorological parameters missing, so AERMOD did not make predictions for that hour. Therefore the ratio NO2/NOx would be indeterminate (i.e., a “divide-by-zero”). Consequently there are 594 hours when the NO2/NOx ratio can be calculated.

The yellow section in Table 4-2 contains information on the 245 hours when observed NOx concentrations were greater than or equal to 10 ppb. Of these 245 hours, there were 185 for which the modeled NOx concentration was non-zero and therefore the NO2/NOx ratio could be calculated and compared for both observed and modeled. The yellow section also includes the number of hours classified as unstable (heat flux > 0) and stable (heat flux < 0). The heat flux is calculated by AERMET based on observed solar radiation, day of year and hour of day, land-use, and wind speed. Note that, in the land of the midnight sun (summer when the sun never sets) and in the winter when the sun never rises, it is possible to have positive heat fluxes at midnight in June and negative heat fluxes at noon in December. With this stability grouping (unstable or stable), there is no “neutral” class. Neutral conditions would be characterized by small magnitude heat fluxes (positive or negative) and/or strong winds.

A further complication at Wainwright is that the land-use is not spatially-homogeneous. For the wind direction sector studied here, the upwind fetch is from the Arctic Ocean. The power plant is only about 100 or 200 m from the shore and the monitoring station is only about 500 m downwind of the power plant. Thus the boundary layer has had minimal time to adjust to the land surface and may be more representative of the open ocean (or ice-covered ocean in the winter) much of the time.

The orange section in Table 4-2 focuses on the 381 hours when the observed and both model (AERMOD/PVMRM and AERMOD/OLM) predictions are non-zero. Because there was interest in the seasonal variations of the results, the team determined the distribution of these hours by month. The bottom row of Table 4-2 shows that the number of hours per


month has a quite wide range (from 1 to 92). The distributions by “spring-summer” (April – September, with 220 hrs) and “fall-winter” (Oct-March, with 161 hrs) are also listed. Thus the number of hours in these six month periods is within about ±30 % of being equally-divided. This information is used later in the comparisons of the highest observed and model simulated concentrations, and their seasonal dependence.

Table 4-2 Summary of hours analyzed in AERMOD/PVMRM and AERMOD/OLM evaluations using the Wainwright data. The basic data period consists of 9144 hours from September 16, 2009 through September 30, 2010.

Description # of hours

Total valid hours satisfying the conditions: 1.) wind direction in the 250° - 310° sector, 2.) non-missing pollutant monitoring data, 3.) non-zero emissions

787

Of the above 787 hours, hours when AERMOD computed concentrations (i.e., 193 hours had missing meteorological parameters, therefore AERMOD did not run for those hours)

594

Of the above 787 hours, hours with monitored NOx >10 ppb 245

Of the 245 hours, hours when modeled NO2/NOx ratios can be computed (i.e., modeled NOx >0.0)

185

Of the 245 hours, hours classified as unstable (heat flux > 0 W/m2) 54

Of the 245 hours, hours classified as stable (heat flux < 0 W/m2) 136

(Note: Based on monitoring data there were 190 hours out of the 245 that had non-missing heat flux data. There were 5 hours with modeled predictions of 0.0. Therefore the 185 hours were the only ones where it was possible to apply the unstable/stable classification to the ratio plots.)

Of the 594 hours, hours whose monitored NO2 concentration > 1 ppb and modeled NO2 concentration > 0.000005 µg/m3

381

Distribution of 381 hrs by month: Sept(92), Oct (1), Nov (11), Dec (22), Jan (78), Feb (30), Mar (19), Apr (14), May (5), June (24), Jul (61), Aug (24)

161 hrs Oct-Mar 220 hrs Apr-Sep


4.6.2 Variation in observed NO2/NOx ratio

Since the primary role of PVMRM is to estimate the ratio NO2/NOx for use in AERMOD, it is appropriate to first present the observed values of that ratio. Table 4-3 contains a listing of the number of data points (hours) with positive sensible heat fluxes (denoted “unstable”) and negative sensible heat fluxes (denoted “stable”) for hours with observed NOx concentration exceeding 10 ppb (≈18.8 μg/m3). The maximum, minimum, mean and standard deviation of the concentrations in each group are listed for observed NO2/NOx and ozone. Note that the overall mean value of NO2/NOx is 0.339 and the overall maximum is 0.818. The ratios and the observed ozone concentration are slightly larger for stable than for unstable periods.

The maximum stable observed NO2/NOx ratio (0.818) occurs on January 18, 2010, hr 14, when the measured ozone was 55.3 µg/m3, and sensible heat flux was -6.9 W/m2. This was classified as a “stable” hour, even though it was the middle of the afternoon. When the sun does not rise, the boundary layer can remain stable all afternoon.

The analysis shows that maximum observed concentrations of NO2 and the maximum values of the observed ratio, NO2/NOx, occur in the October through March period when the boundary layer at Wainwright is more stable. The background ozone concentration is higher during that period, too, though, providing more opportunity for the NO to react with ozone to form NO2.

Table 4-3 Summary of observed NO2/NOx ratios and ozone concentrations at Wainwright. These are for hours when observed NOx concentration ≥ 10 ppb (≈18.8 µg/m3) and when ambient heat flux was observed at the monitoring sites.

Observed NO2/NOx

ratio all hours

Unstable Observed NO2/NOx

ratio

Unstable Observed

Ozone (µg/m3)

Stable Observed NO2/NOx

ratio

Stable Observed

Ozone (µg/m3)

# of data points: 190 54 54 136 136 Max 0.818 0.438 77.6 0.818 82.2 Min 0.059 0.143 12.2 0.059 2.3

Mean 0.339 0.301 34.7 0.354 45.9 Std Dev 0.070 13.1 0.173 18.4


The NO2/NOx ratio is computed in PVMRM and OLM independently from the NOx concentration predicted by AERMOD and thus comparing these ratios to those observed is an important test of how well the PVMRM and OLM modules are performing. Scatter plots were prepared for the data set containing 185 hours of observed and modeled NO2/NOx ratio described above. The scatter plots contain points representing paired values (in time and space) of predicted and observed NO2/NOx ratios. Each point therefore represents one specific hour. The data are paired in space because the concentrations are predicted at the location of the single monitoring station. Figures 4-4a and 4-4b contain scatter plots for the PVMRM module and the OLM module in AERMOD for the 185 data points with observed NOx ≥ 10 ppb (≈18.8 μg/m3) where NO2/NOx ratios and sensible heat fluxes could be determined. To aid understanding, the unstable (positive heat flux) points are blue and the stable (negative heat flux) points are green in the figures. In addition, the magnitude of the observed NOx concentration on that hour is indicated by the shape of the symbol (see the legend).

In Figure 4-4a, for PVMRM, the points representing the NO2/NOx ratio are roughly evenly distributed on either side of the 45° line representing perfect agreement, but there is much scatter. There looks to be a slight average tendency towards overprediction. Recall that, in the simulations, the initial NO2/NOx ratio at the stack exit is assumed to be 0.20, so no predicted points on the figure are smaller than 0.20. If there happens to be minimal NO2 generated by PVMRM in the plume after release from the stack, the NO2/NOx ratio will always be close to 0.20. It is seen on the figure that there are a few (14) hours with observed NO2/NOx ratio less than 0.20.

The bulk of the observed and predicted points in Figure 4-4a are clustered in the box enclosed by NO2/NOx ratios ranging from 0.2 to 0.4. Therefore most of the time, there is minimal (less than 20 %) conversion of NO to NO2 in the Wainwright plume before it reaches the single monitor location.

Another obvious issue seen in Figure 4-4a is that there are several (15) hours with observed NO2/NOx ratios between 0.6 and 0.83, but there are no predicted NO2/NOx ratios larger than 0.6. Because the groups of predicted ratio points are mostly randomly distributed between 0.2 and 0.6 and show no variation with observed NO2/NOx ratios, the PVMRM module does not appear to be successful in producing good correlations at large and small extremes of NO2/NOx ratios. The model overpredicts the smaller range of observed NO2/NOx ratios and underpredicts the larger range, even though the model’s mean bias is not large.

It was hoped that by using different symbols for unstable and stable (upwards and downwards sensible heat fluxes) and for observed NOx concentration, trends could be seen in the scatter plot and reasons found for under or overpredictions. But there is no obvious trend for these subsets of data. As mentioned earlier and seen in Table 4-3, the largest observed NO2/NOx ratios occur under the stable category. However, after further



Figure 4-4a PVMRM scatter plot of monitored and modeled NO2/NOx ratios, for 185 hours (out of the 245) where monitored NOx concentration exceeded 10 ppb (18.8 μg/m3).

investigation, it was found that these stable points were actually occurring during the day in the winter, when the sun does not appear all day or barely appears at Wainwright. This generated an analysis of seasonal variations, and the results are reported later (see Table 4-8).

Figure 4-4b is the same as Figure 4-4a, but for the OLM module instead of the PVMRM module. The OLM module can produce more NO2 than the PVMRM module. About 2/3 of the time in Figure 4b, the OLM-predicted NO2/NOx ratio is 0.9, which is the maximum ratio allowed by the model. There are only three points where the OLM-predicted NO2/NOx ratio is less than the observed value. Thus this scatter plot suggests that there is nearly always a significant overprediction of the NO2/NOx ratio by OLM.

Figures 4-4a and 4-4b have a 10 ppb restriction only on observed NOx. For statistical comparisons, it is desirable that the same restriction be applied to both observed and predicted variables. Consequently, Figures 5a and 5b were prepared. They are the same as Figures 4-4a and 4-4b, except the further condition has been added that both observed and predicted NOx concentration must be ≥ 10 ppb. This is a more balanced data set than that plotted in Figures 4a and 4b. The overall results, however, are similar. In Figure 5a, for PVMRM, the NO2/NOx ratios for predicted and observed are still clustered in the box bounded by ratios between 0.2 and 0.4. The distribution of points leads to a visual impression that PVMRM is slightly overpredicting the NO2/NOx ratio (by about 0.1). The main difference is that most of the extreme (large) NO2/NOx ratios that were seen in Figure 4a have disappeared from Figure 4-5a. This simply means that large values of both observed and predicted NO2/NOx ratio are unlikely to occur during the same hour. For the same reason, in Figure 5b, for OLM, there are fewer extreme (large) NO2/NOx ratios. Except for one point, during all hours in Figure 4-5b, the OLM module overpredicts the NO2/NOx ratio.

4.6.3 Summary of concentration predictions, including all hours

This subsection focuses on the AERMOD NO2 concentration predictions. The PVMRM and OLM modules for predicting the NO2/NOx ratio are one component of the total AERMOD model, which contain many other modules (e.g., to calculate wind variation with height, plume rise, downwash, dispersion, and so on). For example, PVMRM might be perfectly predicting the NO2/NOx ratio but the AERMOD/PVMRM NO2 predictions might be poor because of problems in other modules, such as the building downwash module or the low wind parameterization module. Or, PVMRM might be poor and its errors might be compensated for by errors in other modules, resulting in good agreement with NO2 observations (the right answer for the wrong reasons).

As mentioned in subsection 4.6.1, the statistical performance measures were calculated only for the 381 hours when observed and predicted NO2 concentrations both exceeded a minimum threshold (64.1 % of the total 594 hours). These comparisons involve data paired in time and space. But the other 213 hours also contain useful information about false


Figure 4-4b OLM scatter plot of monitored and modeled NO2/NOx ratios, for 185 hours (out of the 245) where monitored NOx concentration exceeded 10 ppb (18.8 μg/m3).


Figure 4-5a PVMRM scatter plot of monitored and modeled NO2/NOx ratios, for 99 hours where both monitored and modeled NOx concentration exceeded 10 ppb (18.8 μg/m3).


Figure 4-5b OLM scatter plot of monitored and modeled NO2/NOx ratios, for 99 hours where both monitored and modeled NOx concentration exceeded 10 ppb (18.8 μg/m3).


positives (prediction greater than zero and observed equal zero), false negatives (prediction equal to zero and observed greater than zero) and “zero-zero” hours (both observed and predicted equal zero). Table 4-4 contains the numbers of hours (and the fraction) in each category.

Out of the 594 hours there are no hours when both observed and predicted concentrations equal zero. About 35% of the hours are false positive and about 1% of the hours are false negative. It is expected that only a small percentage of the hours fall into the category of false negatives because the hours chosen to model were those when the winds would carry the power plant plume toward the monitor. The percentage of hours (35%) that are false positive is close to that found at other sites (see Chang and Hanna, 2004). With a single monitor, it is easy for the observed wind direction to not exactly line up with the observed plume direction, and a 20 or 30° difference can lead to a plume hitting or missing a monitor. The numbers of hours for AERMOD/PVMRM and for AERMOD/OLM are the same because both models have similar assumptions about the plume dispersion.

Table 4-4 Summary of AERMOD/PVMRM and AERMOD/OLM predictions of NO2 concentrations for the 594 hours studied, including zero-zeros and false positives and false negatives.

Characteristic of model predictions Number of hours

Observed and AERMOD/PVMRM and AERMOD/OLM

predicted concentration all are greater than zero

381 (fraction 0.641)

Observed and AERMOD/PVMRM and AERMOD/OLM

predicted concentration all are zero

0 (fraction 0.0)

False positive for AERMOD/PVMRM and AERMOD/OLM

(observed C = 0 but predicted C > 0)

209 (fraction 0.352)

False negative AERMOD/PVMRM and AERMOD/OLM

(predicted C = 0 but observed C > 0)

4 (fraction 0.007)

4.6.4 Summary of quantitative performance measures for NO2

The PVMRM and OLM modules in AERMOD are predicting the NO2/NOx ratios as a function of downwind distance. Those ratios are then multiplied by the AERMOD-predicted NOx concentration to give the NO2 concentration. Table 4-5 summarizes the performance measures for PVMRM and OLM, linked with AERMOD, for hourly NO2


concentrations. The 381 hours with observed, AERMOD/PVMRM-predicted and AERMOD/OLM-predicted concentrations are included in the paired in time and space comparisons. An excerpt from the BOOT software output is presented in Appendix D. A background concentration of 2 μg/m3 (slightly larger than 1 ppb) has been added to the predictions. Units of μg/m3 are used here because they are the units of the AERMOD predictions.

Table 4-5 Statistical performance measures for hourly-averaged NO2 concentrations (in μg/m3), for 381 hours with observed and both model predictions non-zero. A background of 2 μg/m3 has been added to all modeled concentrations.

OBS AERMOD/OLM AERMOD/PVMRM

Highest NO2 (µg/m3) [unpaired] 72.5 158.3 130.5

2nd highest [unpaired] 65.7 142.2 112.0

Mean 12.9 21.5 12.7

Sigma 14.0 31.1 21.1

FB (perfect is 0.0) -0.50 0.01*

MG (perfect is 1.0) 0.94** 1.35

NMSE (perfect is 0.0) 3.60 3.09

VG (perfect is 1.0) 6.48 4.92

FAC2 (perfect is 1.0) 0.402 0.470

Notes: Number of hours with observed and both model predicted C’s at background = 69, or 69/381 = 0.181 of the 381 hours

* Not significantly different from 0.0 (for perfect model) at 95% confidence level. **Not significantly different from 1.0 (for perfect model) at 95% confidence level.

As expected, compared to PVMRM, the OLM module estimates a larger conversion of NO to NO2 in the presence of entrained ambient ozone. Thus the maximum AERMOD/OLM-predicted NO2 concentration is 27.8 μg/m3 larger (i.e., 21% larger) than that for AERMOD/PVMRM. The mean (average) predicted NO2 concentration is 8.8 μg/m3 larger (i.e., 69% larger) for AERMOD/OLM than for AERMOD/PVMRM. The PVMRM module


produces an NO2/NOx ratio such that, when linked with AERMOD, its mean predicted NO2 concentration is close to the observed value (12.7 vs. 12.9 μg/m3). However, the maximum NO2 concentration predicted by PVMRM/AERMOD is about 58 μg/m3 (or 80%) larger than the observed value.

NMSE is about 3.6 and 3.1 for AERMOD/OLM and AERMOD/PVMRM, respectively. This implies that the root mean square (RMS) error is about 1.7 times the mean concentration. This is typical of most model evaluation exercises (Chang and Hanna, 2004).

FAC2 is 0.40 and 0.47 for AERMOD/OLM and AERMOD/PVMRM, respectively. But since about 18% of the hours have both observed and predicted NO2 concentrations near the 2 μg/m3 background, those small values “count” as being within a factor of two and help to increase the FAC2 value.

The BOOT software uses bootstrap resampling to determine 95% confidence limits on the performance measures for any one model and for the difference in performance measures between two models. For the 381 points being analyzed in this exercise, there is a significant difference (at the 95% confidence level) between the AERMOD/OLM and the AERMOD/PVMRM model predictions of NO2 concentrations for the FB, MG, NMSE, VG, and FAC2 performance measures. The FB for AERMOD/PVMRM is not significantly different from 0.0 (for a perfect model) at the 95% confidence level. The MG for AERMOD/OLM is not significantly different from 1.0 (for a perfect model) at the 95% confidence level. This switch in results from one model to another for the FB and MG performance measures often occurs when the distribution of concentrations is non-normal (Chang and Hanna, 2004). The Wainwright data have many observed NO2 concentrations near the background and a few concentrations with large values.

It is also of interest to compare the highest five or ten observed concentrations with the highest five or ten predicted concentrations (an unpaired comparison). This is done for the “top ten” concentrations for NOx and NO2 in Tables 4-6 and 4-7, respectively. This analysis is for the 381 hours with non-zero observed and predicted concentrations (i.e., not restricted by a limitation such as NOx concentration ≥ 10 ppb). Table 4-6 shows that there is a 51.9% AERMOD overprediction of the highest NOx concentration, but there is a 6.4% underprediction of the 10th highest. Table 4-7 shows that there is a factor of 2.16 overprediction of the highest NO2 concentration by AERMOD/OLM and an approximate factor of two overprediction persists over the remaining highest ten values. There is a smaller overprediction (77%) by AERMOD/PVMRM of the highest concentration, but the magnitude of the overprediction gradually decreases to about 50% by the tenth highest value.


As seen in Table 4-7, the ambient ozone concentrations for the hours resulting in the “top ten” observed NO2 concentrations ranged from 28.4 to 49.7 ug/m3 (12 to 21 ppb). While these ozone concentrations are not very high (i.e., not at the 70 to 100 ppb level observed in polluted areas in the continental U.S.), they are at a similar magnitude as those observed in the field experiments used previously to test PVMRM (Hanrahan, 1999a).

Suspecting that there might be a seasonal dependence of NO2, the team tabulated the hours in each month that observed and AERMOD/PVMRM-predicted concentrations were in the “top 50” and “top 100” (unpaired). This analysis was complicated because the 381 hours were not evenly distributed among the 12 months. For example, September was expected to be unequal because there were 1½ September months in the total 12½ months. The other 11 months had only one month in the record. The hours available during a month are dependent on several factors: wind directions must be in the specified 60° sector, and monitoring, meteorological, and emissions data must be non-missing.

Table 4-6 Top-ten rankings for observed and AERMOD-predicted hourly-averaged NOx (unpaired). No background has been added to the AERMOD predictions.

Rank Observed NOx

(µg/m3)

AERMOD NOx

(µg/m3)

% Difference

1 368.63 559.95 52 %

2 341.97 405.70 19 %

3 297.72 381.61 28 %

4 289.94 332.63 15 %

5 283.34 286.54 1 %

6 262.65 271.54 3 %

7 256.79 262.57 2 %

8 244.64 253.34 4 %

9 233.04 250.68 8%

10 232.81 217.87 - 6 %


Table 4-7 Top-ten rankings for observed and predicted hourly-averaged NO2 concentration

(unpaired), using AERMOD/OLM and AERMOD/PVMRM. No background has been added to the AERMOD predictions.

Observed Observed AERMOD/OLM AERMOD/PVMRM

Rank NO2

(µg/m3)

Ozone

(µg/m3)

NO2

(µg/m3)

%

Difference

NO2

(µg/m3)

%

Difference

1 72.47 33.08 156.27 116 % 128.55 77 %

2 69.12 37.84 140.18 103 % 110.03 59 %

3 67.15 28.36 130.77 95 % 107.30 60 %

4 65.74 30.97 128.68 96 % 102.06 55 %

5 65.69 31.31 128.51 96 % 99.37 51 %

6 63.93 32.34 118.17 85 % 96.59 51 %

7 59.35 34.25 116.38 96 % 93.92 58 %

8 58.30 32.79 115.37 98 % 91.33 57 %

9 58.21 38.71 114.53 97 % 87.31 50 %

10 57.70 49.73 113.07 96 % 86.26 49 %

Table 4-8 lists the numbers of hours per month that have data sufficient for analysis and the number of hours per month that the observed hourly-averaged NO2 concentrations were in the “top 50” and “top 100”. The same information is listed for the predictions by AERMOD/PVMRM. The model predictions are able to reproduce the preponderance of high concentrations in the fall-winter period. In fact, there are no spring-summer hours appearing in the top 20 concentrations for either the observations or the AERMOD/PVMRM predictions. The last two rows in the table present the numbers of hours and the fraction of


total hours for each of the six-month seasons. This shows that, even though the model can capture the seasonal differences, it does produce slightly more hours in the spring-summer period with high concentrations.

Table 4-8 Distribution (number of hours) by month and six-month season of top 50 and top 100 ranked hourly-averaged NO2 concentrations for observations and for AERMOD/PVMRM predictions (i.e., unpaired). No background has been added to the predictions.

Month Number of

hours

available*

Observed

top 50

Observed

top 100

AERMOD/PVMRM

top 50

AERMOD/PVMRM

top 100

Sept 92 3 20 5 20

Oct 1 0 0 0 0

Nov 11 9 9 2 3

Dec 22 3 6 4 4

Jan 78 24 38 26 31

Feb 30 9 10 6 6

Mar 19 0 2 4 4

Apr 14 0 1 0 2

May 5 0 0 1 1

Jun 24 2 2 2 10

Jul 61 0 9 0 17

Aug 24 0 3 0 2

Spring-

Summer

(Apr-Sep)

220 5 (or 2.3%) 35 (or 15.9%) 8 (or 3.6%) 52 (or 23.6%)

Fall-Winter

(Oct-Mar) 161 45 (or 28%) 65 (or 40.3%) 42 (or 26.1%) 48 (or 29.8%)

*Hours with wind directions in 60° sector, with non-missing monitoring, meteorological, and emissions data and with

observed and predicted NO2 non-zero.


4.6.5 Quantile-Quantile (Q-Q) plots

In a Q-Q plot, the predicted and observed quantities are separately placed in order from largest to smallest. For example, if there are 381 hours of observed and predicted concentrations, separate lists of 381 numbers are created for the two sets of unpaired data. Then the data are plotted according to rank. For example, a point on the Q-Q plot may represent the 10th from the highest taken from the rankings for the observed and for the predicted data sets.

Figure 4-6 is a Q-Q plot for NOx, for the 381 hours with non-zero observed and predictions. This chemical is directly predicted by AERMOD without any assistance from PVMRM or OLM. It is seen that the predictions exceed the observations for the highest nine concentrations. The highest observed concentration is overpredicted by about 50%. But for the lower 98% of the range, the model underpredicts, with the amount of the underprediction increasing to about an order of magnitude at smaller and smaller concentrations. However, some of the apparent underprediction at small concentrations may be due to the fact that the background has not been added to these predictions.

Figure 4-7 is a Q-Q plot for NO2 concentrations using AERMOD with three optional “modules” for the NO2/NOx ratio. The three modules are PVMRM, OLM, and full conversion. The modules’ outputs of the NO2/NOx ratio are multiplied by the AERMOD NOx concentration predictions to give the NO2 predictions. In the full conversion case it is assumed that that there is an unlimited supply of ambient ozone so that all NOx is converted to NO2 instantaneously. It is seen that the OLM model curve follows the “full conversion” curve at small concentrations and is then more and more below that curve at large concentrations. This is because OLM (the ozone limiting model) by definition “limits” the production of NO2 at high concentrations when there is not sufficient ambient ozone available to react with the plume NO. The AERMOD/PVMRM NO2 curve is always below both curves, more so at smaller concentrations when PVMRM is accounting for the gradual entrainment of ambient ozone. The PVMRM curve approaches the OLM curve at large concentrations. The shape of the AERMOD/Full Conversion curve for NO2 has similar characteristics to that for NOx in Figure 4-6. That is, it overpredicts by 70% or so at the largest concentration but then underpredicts by an order of magnitude at smaller concentrations. Agreement would be better at small concentrations if the background of 2 μg/m3 were added to the predictions.

The shape of the AERMOD/PVMRM Q-Q curve for NO2 concentrations in Figure 4-7 was also found by the EPA (MACTEC 2004 and 2005, EPA 2004, EPA 2011) for the Empire Abo site in New Mexico, where the stack heights and monitor distances were similar to those at Wainwright.



Figure 4-6 Q-Q plot of ranked observed and predicted AERMOD NOx concentrations, for 381 hours when observed NOx ≥ 1 ppb and predicted NOx is non-zero.


Figure 4-7 Q-Q plot of ranked observed and predicted NO2 concentrations by AERMOD full conversion, current operational AERMOD/PVMRM with nz=4 and AERMOD\OLM, for 381 hours when observed and predicted NO2 is non-zero.

Figure 4-8 is similar to Figure 4-7 except that PVMRM has been changed so that nz = 1.28 instead of 4, A = 0.8 instead of 1.0, and σr (the initial plume standard deviation due to downwash and other effects) = 15 m instead of 5 m. This brings the PVMRM module closer to what Hanrahan (1999a) initially suggested. However the Weil relative dispersion coefficients used in AERMOD/PVMRM are retained. At first glance, the AERMOD/PVMRM Q-Q curves in Figures 4-7 and 4-8 look the same. However, closer inspection reveals that, in the middle part of the curve, the Figure 4-8 curve gives NO2 concentrations that are smaller by about 20%. There is less difference at the smallest concentrations and largest concentrations.

4.6.6 Results of evaluation of NO2 /NOx ratio and NO2 concentration for optional run with adjusted ozone levels

It is possible that the observed ozone concentration at the monitoring station may be slightly depressed, since the hours that are studied are those with wind directions blowing the plume from the power plant towards the monitor. When relatively large NOx and NO2 concentrations are observed, it is implied that the monitor is being impacted by the industrial plume, and hence some of the ambient ozone has been consumed in order to generate NO2. The model evaluation results reported above assumed that the measured ozone concentration at the monitoring site was equal to the ambient ozone concentration in the air upwind of the source. However an alternate and conservative (in the sense that it produces larger predicted NO2 concentrations) estimate of the actual ambient ozone concentration is that it equals the sum of the monitored ozone and NO2 concentrations. The range of predictions of NO2 can be bounded by these two assumptions regarding ambient ozone (i.e., the observed ozone concentration at the monitor, and the sum of the observed NO2

and observed ozone concentrations at the monitor). Thus, the team also ran the AERMOD/PVMRM and AERMOD/OLM models assuming that the input ambient ozone concentration is this larger value instead of the observed value. This change had no effect on the prediction of the maximum overall NO2 concentration for both AERMOD/PVMRM and AERMOD/OLM. However, the mean predicted NO2 concentrations over the hours modeled showed a small increase (7.0% increase for AERMOD/PVMRM and 5.7% increase for AERMOD/OLM). The mean NO2/NOx ratio for AERMOD/PVMRM increased by 5% and for AERMOD/OLM by 1.1%.

4.7 Uncertainties and limitations that impact the analysis

As with any model evaluation exercise, it is important to understand the limitations and uncertainties of the data used for evaluation. For the Wainwright evaluation, uncertainties and limitations include the following:


Figure 4-8 Q-Q plot of ranked observed and predicted NO2 concentrations (sensitivity run for AERMOD/PVMRM with nz =1.282), for 381 hours when observed and predicted NO2 is non-zero. AERMOD full conversion and AERMOD/OLM NO2 concentrations are also shown (as in Fig. 4-7).


3009/Evaluation of PVMRM and OLM 4-33 Wainwright Field Data Comparison

♦ Hourly power plant emissions and initial plume operational parameters were estimated using operating logs and vendor performance data. It is possible that actual emissions and initial plume parameters could be different than vendor specifications. There is no way to further refine the emissions estimates and it is not possible to quantify this uncertainty.

♦ Ambient ozone was only observed at the single monitoring location. Since the team only analyzed hours with wind directions in a sector that was downwind of the power plant, the ozone observation can be expected to often be in the NOx plume. It is unknown to what extent ambient ozone was scavenged by NO in the plume to form NO2. Thus the measured ozone concentrations should be considered as a lower limit for input into AERMOD/PVMRM or AERMOD/OLM (see section 4.6).

♦ Ambient NOx and NO2 concentrations were also only observed at the single location. These concentrations were relatively small and there were several periods where monitored concentrations were reported as 0. Over 90% of the monitored NOx concentrations were less than 10 ppb. Most of the time, observed concentrations were near the background, which exhibited variations in time that were not well understood.

♦ Meteorological conditions above the Arctic Circle are unique. For example, since the sun shines all day in June, the surface sensible heat flux is always upwards indicating unstable conditions. In December, the opposite happens and stable conditions occur all day (24 hrs). Also, upwind of the monitoring station, before the air flows over the power plant, its trajectory has been over the Arctic Ocean (open water in summer and frozen in winter). Thus the boundary layer is not likely to have reached equilibrium with the land surface when the air reaches the monitoring station.


5.0 CONCLUSIONS & RECOMMENDATIONS

5.1 Search for Model Evaluation Databases

The importance of having new and complete data sets to conduct model evaluations with cannot be emphasized enough. The team has identified the Wainwright data set and has conducted an evaluation of OLM and PVMRM in the AERMOD model with the Wainwright data as described in Section 4.

In the team’s search for available data sets, other options were also identified that have potential and should be further pursued (as discussed in Section 2), namely:

1. The team was hoping to obtain the full KEMA aircraft study data archives, which a few flights were used by each of Arellano et al. (1990) and Bange (1991). If the full KEMA report could be acquired then it would be valuable to see if there are more flight data that would be useful to incorporate, as well as to better define the source parameters of the releases in the Arellano and Bange studies.

2. The team did obtain the detailed near-field data from Dr. Ryerson TexAQS study near Houston, TX. Hourly emissions data would need to be obtained for these releases.

5.2 Review of PVMRM and OLM Model Formulation and Implementation

A review of the PVMRM and OLM codes as implemented in the ISC model and AERMOD was conducted. The purpose of the review was to ensure that the models were coded in a manner consistent with the PVMRM and OLM formulations. The OLM implementation in ISC and AERMOD was found to be consistent with the model formulation.

The PVMRM implementation in ISC as a post-processor was developed by Hanrahan and followed the formulation he documented (Hanrahan 1999a). The implementation of PVMRM in AERMOD uses relative dispersion, which is supported by comments by Hanrahan (1999), Arellano (1990) and Bange (1991). However the team has identified a few specific items of concern, mostly dealing with the relative dispersion calculation of the plume volume. The team reviewed the Hanrahan (1999a,b) and EPA (2004) formulation documents for PVMRM, as well as many other related documents. The team also checked the implementation of the PVMRM model in the FORTRAN codes to identify the items of concern.

To keep with the spirit of Hanrahan's (1999a) assumption, the team advises that the EPA should consider the following modifications to the AERMOD/PVMRM.

1) the team agrees with the approach of using relative dispersion for AERMOD/PVMRM, however since the relative dispersion formula used only applies to convective conditions, a neutral and stable relative dispersion formula must be added.

3009/Evaluation of PVMRM and OLM 5-1 Conclusions for Short Term NO2 Impacts Epsilon Associates, Inc.

2) attempt to impose a condition that the relative and time-averaged sigma formulas have

consistent relations to each other, so that the relative sigma is never larger than the time-averaged sigma,

3) eliminate the isotropic condition for stable plumes which are known have smaller depth than width. It might take a while to fix this correctly. In the meantime, perhaps use a simple relation such as relative sigma = 0.5 * (time-averaged sigma) near the source. Then have the ratio of sizes increase linearly from 0.5 until it reaches 1.0 at x = 1000 m.

4) use a more reasonable value for the number of standard deviations, nz, from the plume centerline that define the plume volume. A Gaussian distribution is best fitted by a top hat (constant concentration in space within the plume) by using a value between 1.2 and 1.5 for nz.

5) incorporate the effects of building downwash into the relative dispersion sigmas.

6.) incorporate an interpolation scheme to prevent discontinuities in model predictions when multiple plumes are merged in PVMRM.

Although PVMRM model evaluation results to date based on a limited sample of data (18 aircraft data points and 3 monitor locations) have shown the predicted to observed NO2/NOx ratios to be within a factor of two (MACTEC, 2005), the team is concerned that there may be compensating affects that may be resulting in good model performance for the wrong reasons (one effect causes over-predictions and another causes under-predictions, so together they cancel out to produce no bias). Additional evaluation data sets are necessary to have a more robust set of evaluations.

5.3 Results of Model Evaluation of PVMRM and OLM in the AERMOD Model using the Wainwright Data

5.3.1 Overview and methodology

♦ This model evaluation exercise uses 12½ months of hourly-averaged observations from an ambient monitoring station close to a power plant site in Wainwright, Alaska. There are five stacks from diesel generators and the stack heights are about the same as the building height. The single monitoring station is located about 500 m to the east-south-east of the plant, and observes NO2, NO, NOx, and ozone concentrations, as well as meteorological variables such as wind speed, wind direction, temperature, and solar radiation.

♦ The AERMOD model predicts NOx concentrations directly. The PVMRM and OLM modules in AERMOD predict the NO2/NOx ratio in a stack plume as a function of downwind distance, and as a function of the ambient ozone concentration. This report evaluates PVMRM and OLM predictions of the NO2/NOx ratio,


AERMOD/PVMRM and AERMOD/OLM predictions of NO2 concentrations, and AERMOD predictions of NOx concentrations. One-hour averaging times are used for all concentrations and meteorological variables.

♦ Since the plume is likely to blow towards the monitoring station only when the wind direction is blowing towards the sector containing the monitor (wind directions between about 250° and 310°), the evaluation used only those hours when the wind direction was in that sector. Furthermore the evaluations could be carried out only when the required input data and monitoring data were “non-missing”. This resulted in 594 hours of data available for evaluations.

♦ There are many sources of uncertainties in this analysis, such as the hourly emissions approximations based on operators’ logs and manufacturers’ specifications for the diesel generators, the monitored concentration uncertainties (including the effective thresholds and uncertainties (about 0.4 ppb) and the contribution of backgrounds), the heterogeneity of the surrounding terrain, and the AERMOD inputs (such as land use, which has been optimized for the contiguous U.S. states).

♦ As emphasized by Chang and Hanna (2004), for an evaluation of a model or model options to be robust, many data sets should be used in the evaluation so that a multitude of conditions, source data, etc. can be evaluated. This evaluation using the Wainwright data set should be considered as an additional piece of information to supplement the limited number of data sets used by EPA to date to evaluate the PVMRM and OLM options in AERMOD for predicting 1-hour NO2 (see Hanrahan 1999a, MACTEC 2005, EPA 2004 and EPA 2011).

5.3.2 Key results

Ratio of NO2/NOx in downwind plume – paired in time and space analysis

♦ It is found that most of the observed and the PVMRM-predicted ratios of hourly-averaged NO2/NOx are in the range from about 0.2 to 0.4. Thus there is minimal mean bias. However, the model predictions have much scatter and have minimal correlation with the observations. In particular, there are several high (0.6 to 0.8) ratios of NO2/NOx occurring in the winter that are not simulated by PVMRM. There are no hours where PVMRM predicted ratios exceeded about 0.6. The OLM-predicted hourly-averaged ratios are nearly all larger than the observed ratios, with many OLM-predicted ratios at the maximum permitted value of 0.9. The larger OLM ratios are expected because it conservatively allows conversion of more NO to NO2 by ambient ozone.


♦ AERMOD NO2 predictions – paired in time and space analysis using BOOT

software. The BOOT model evaluation software was applied to data paired in time and space where the observation and prediction both exceed a minimum threshold (>0 for NO2). Before carrying out the evaluations, a background of 2 μg/m3 (about 1 ppb) was added to the predictions. 381 (about 64%) of the 594 hours studied met this criterion. The remaining 36% of the 594 hours consisted of about 35% false positives, 1% false negatives, and no “zero-zeros”. This many false positives are typical of experiences with other models and field data sets (Chang and Hanna, 2004). The AERMOD/PVMRM model had low mean relative bias (indicated by a fractional mean bias, FB, of 0.01), but its scatter was about two or three times the mean and there was little skill evident (i.e., correlations were not significant). AERMOD/OLM, as expected, predicted larger values than AERMOD/PVMRM and thus had a mean relative bias of about 70 % towards overprediction. When the log of the concentration (lnC) was considered and the geometric mean (MG) and geometric variance (VG) were calculated (see equations 7 and 8), MG was slightly better for AERMOD/OLM than for AERMOD/PVMRM. MG and VG are influenced less by large errors at the largest concentrations. FAC2 (fraction of predicted values within a factor of two of observed values) was 0.402 for AERMOD/OLM and 0.470 for AERMOD/PVMRM. But since about 18% of the predicted and observed data pairs are very near background, FAC2 is inflated by this 18% figure.

AERMOD NO2 and NOx predictions – unpaired comparison of “top ten” observations and predictions

♦ The “top ten” observed and predicted NOx and NO2 concentrations were compared. This analysis involved the 381 hours with non-zero observed and predicted concentrations. There is a 51.9% overprediction by AERMOD of the top NOx concentration, but there is a 6.4% underprediction of the tenth highest NOx concentration. AERMOD/PVMRM overpredicts the highest NO2 concentration by 77% and the overprediction decreases to about 50% by the tenth highest value. AERMOD/OLM overpredicts NO2 by about a factor of two over the complete top-ten distribution.

Seasonal dependence of PVMRM/AERMOD comparisons with NO2 observations

♦ The ability of AERMOD/PVMRM to reproduce the seasonal dependence of the observed NO2 concentrations was investigated by studying the “top 100” observed and predicted hourly-averaged NO2 concentrations and their distribution by month. The top 100 were selected from the 381 hours with non-zero observed and predicted NO2 concentrations. The AERMOD/PVMRM model does quite well in this regard, since both the observed and predicted “top 20” are all from the winter season. The summer season has far fewer “top 100” occurrences for both the observed and modeled NO2.



AERMOD NOx and NO2 predictions – Q-Q plots (unpaired comparison)

The Q-Q plot is a check of the ability of the model to reproduce the ranked distribution of concentrations (unpaired). It does not address the ability to model the hour-to-hour variations (i.e., paired in time and space). Because the full distribution of data is listed, including low concentrations near the background, the low-concentration portion of the Q-Q plot is influenced by what is chosen for the background concentration.

Q-Q plot for NOx

♦ The hourly-averaged NOx Q-Q-plot for AERMOD shows a 50% overprediction by AERMOD of the highest concentration, but this changes to an order of magnitude underprediction at low concentrations, where the background is questionable.

Q-Q plots for NO2

♦ Q-Q plots were prepared for hourly-averaged NO2 predictions by AERMOD/PVMRM, AERMOD/OLM, and AERMOD/Full Conversion. AERMOD/PVMRM always predicts lower values than AERMOD/OLM, which predicts lower values than AERMOD/Full Conversion. AERMOD/PVMRM overpredicts the highest concentration by about 70%, but then underpredicts by an order of magnitude at lower concentrations. This characteristic shape of the NO2 Q-Q plots was also found for AERMOD/PVMRM for the Empire Abo field data (see MACTEC 2005, EPA 2004 and EPA 2011).

Q-Q plot for NO2 for case where PVMRM is modified so that nz = 1.282, A = 0.8 and initial σr = 15 m

♦ A sensitivity run was made incorporating parameters close to what Hanrahan (1999a) initially suggested when he developed PVMRM. However, the NO2 Q-Q plot looks very similar to that for the current operational AERMOD/PVMRM. The main difference is in the middle part of the curve, where the sensitivity run model version gives slightly smaller concentrations (by about 20%) than the current operational version.

6.0 REFERENCES

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3009/Evaluation of PVMRM and OLM 6-1 References for Short Term NO2 Impacts Epsilon Associates, Inc.

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http://www.ametsoc.org/

http://www.ametsoc.org/

Appendix A

Caterpillar Engine Performance Curves

Appendix B

Excerpt of the Model Evaluation Spreadsheet This page contains 10 of the top 100 observed NO2

and 6 of the top 100 predicted AERMOD/OLM and AERMOD/ PVMRM NO2.

yr mo dy hrMonitor Ozone

(ug/m3) NOX NO2 (OLM O3)NO2/NOX Ratio

(OLM O3)NO2 (PVMRM

O3)NO2/NOX Ratio

(PVMRM O3) Monitor NOX Monitor NO2Monitor

NO2/NOX ratio2010 1 26 14 32.33890 6.50077 5.85069 0.90000 2.66784 0.41039 256.79 57.56 0.224142010 1 26 15 53.57537 0.28609 0.25749 0.90003 0.11618 0.40610 140.64 37.95 0.269842010 1 30 12 71.49518 0.00126 0.00113 0.89683 0.00046 0.36508 15.47 11.05 0.714292010 1 30 13 69.13459 0.00008 0.00008 1.00000 0.00003 0.37500 26.50 15.46 0.583332010 1 30 22 66.01112 3.55679 3.20112 0.90000 1.23983 0.34858 82.89 21.81 0.263162010 1 30 23 70.78280 120.09956 82.24825 0.68483 42.78504 0.35625 65.65 17.51 0.266672010 1 30 24 73.15132 0.00000 0.00000 - 0.00000 - 56.96 13.14 0.230772010 1 31 1 84.64703 0.62651 0.56386 0.90000 0.21945 0.35027 4.38 0.00 0.000002010 1 31 2 75.49600 66.56196 59.90577 0.90000 22.75290 0.34183 43.85 13.15 0.300002010 1 31 3 64.08214 114.63813 92.42596 0.80624 39.25431 0.34242 85.54 28.51 0.333332010 1 31 4 75.55478 105.56034 90.61040 0.85838 36.13039 0.34227 39.49 13.16 0.333332010 1 31 7 64.10709 2.07489 1.86740 0.90000 0.79290 0.38214 89.96 24.14 0.268292010 1 31 8 61.79349 1.52560 1.37304 0.90000 0.57545 0.37720 96.50 30.71 0.318182010 1 31 9 54.97805 7.75391 6.97852 0.90000 2.71489 0.35013 153.67 41.71 0.271432010 1 31 10 64.06628 7.14886 6.43397 0.90000 2.47522 0.34624 109.64 35.08 0.320002010 2 1 11 82.68894 405.70125 156.27358 0.38519 99.37095 0.24494 6.99 4.66 0.666672010 2 2 5 40.62125 21.14107 19.02696 0.90000 8.25095 0.39028 190.06 45.80 0.240962010 2 2 6 76.43291 90.52113 81.46901 0.90000 37.03173 0.40909 41.20 13.73 0.333332010 2 2 7 88.19090 1.79723 1.61751 0.90000 0.71521 0.39795 4.57 0.00 0.000002010 2 2 8 88.12039 0.49412 0.44471 0.90000 0.19464 0.39391 4.56 2.28 0.500002010 2 2 9 88.01484 0.06579 0.05921 0.89998 0.02620 0.39824 4.56 2.28 0.500002010 2 2 10 87.83098 0.03955 0.03560 0.90013 0.01583 0.40025 4.55 2.27 0.500002010 2 2 11 85.49128 0.02632 0.02368 0.89970 0.01059 0.40236 4.55 2.28 0.500002010 2 2 12 85.14281 0.07361 0.06625 0.90001 0.03037 0.41258 9.07 4.53 0.500002010 2 2 13 85.07509 0.03458 0.03113 0.90023 0.01501 0.43407 9.06 4.53 0.500002010 2 2 14 85.21064 1.71636 1.54472 0.90000 0.72101 0.42008 6.81 2.27 0.333332010 2 2 15 85.16811 3.50336 3.15302 0.90000 1.55748 0.44457 4.53 2.27 0.500002010 2 2 16 82.80233 0.04362 0.03925 0.89982 0.01855 0.42526 6.80 2.27 0.333332010 2 2 17 82.59615 0.04653 0.04187 0.89985 0.02033 0.43692 4.52 2.26 0.500002010 2 2 18 80.07684 0.19542 0.17588 0.90001 0.08399 0.42979 2.26 0.00 0.000002010 2 2 19 77.34409 0.48321 0.43489 0.90000 0.20309 0.42029 2.25 0.00 0.000002010 2 2 20 79.34227 9.06202 8.15582 0.90000 3.94536 0.43537 2.24 0.00 0.000002010 2 2 21 81.12191 0.00000 0.00000 - 0.00000 - 2.22 0.00 0.000002010 2 2 22 80.83666 8.31450 7.48305 0.90000 3.61164 0.43438 2.21 0.00 0.00000

Monitor Values (ug/m3)Modeled Values (ug/m3)

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Appendix B - Excerpt of Spreadsheet of Modeled Concentrations and Monitored Concentrations Paired in Time and Space

Appendix C

Excerpt of the Model Evaluation Spreadsheet AERMET Processed meteorological data for the

same periods shown in Appendix B

yr mo dy hr j_day H u* w* VPTG Zic Zim L zo Bo r Ws Wd zref temp

j_day =

Julian day

H = sensible heat flux (W/m2)

u* = surface friction

velocity (m/s)

w* = convective

velocity scale (m/s)

vertical potential

temperature gradient

above Zic

gof

convectively-generated boundary layer (m)

gof

mechanically-generated boundary layer (m)

L = Monin-Obukhov

length (m)

z0 = surface

roughness length (m)

B0 = Bowen

ratior =

Albedo

Ws = reference

wind speed (m/s)

Wd = reference

wind direction (degrees)

zref = reference height for wind (m)

temp = reference

temperature (K)

2010 1 26 14 26 -36 0.286 -9 -9 -999 352 59.1 0.0001 1.5 0.77 7.2 304 2 255.22010 1 26 15 26 -34.2 0.269 -9 -9 -999 322 52.1 0.0001 1.5 0.78 6.8 308 2 253.42010 1 30 12 30 -1.5 0.024 -9 -9 -999 241 1 0.0001 1.5 1 1.2 276 2 254.62010 1 30 13 30 -2.7 0.028 -9 -9 -999 110 1 0.0001 1.5 0.77 1.4 251 2 254.82010 1 30 22 30 -13.6 0.219 -9 -9 -999 236 70.2 0.0001 1.5 1 5.5 306 2 258.42010 1 30 23 30 -14.9 0.239 -9 -9 -999 269 83.7 0.0001 1.5 1 6 294 2 257.52010 1 30 24 30 -999 -9 -9 -9 -999 -999 -99999 0.0001 1.5 1 5.1 296 2 257.22010 1 31 1 31 -11.6 0.186 -9 -9 -999 185 50.4 0.0001 1.5 1 4.7 280 2 2572010 1 31 2 31 -10.6 0.17 -9 -9 -999 161 41.9 0.0001 1.5 1 4.3 290 2 2572010 1 31 3 31 -10.3 0.165 -9 -9 -999 155 39.8 0.0001 1.5 1 4.2 294 2 256.92010 1 31 4 31 -10.1 0.161 -9 -9 -999 149 37.9 0.0001 1.5 1 4.1 292 2 256.92010 1 31 7 31 -12.9 0.207 -9 -9 -999 216 62.2 0.0001 1.5 1 5.2 304 2 256.92010 1 31 8 31 -12.4 0.198 -9 -9 -999 203 57.3 0.0001 1.5 1 5 304 2 256.92010 1 31 9 31 -12.7 0.203 -9 -9 -999 210 59.8 0.0001 1.5 1 5.1 304 2 2572010 1 31 10 31 -13.7 0.219 -9 -9 -999 236 69.9 0.0001 1.5 1 5.5 305 2 257.42010 2 1 11 32 -2.4 0.026 -9 -9 -999 10 1 0.0001 1.5 1 1.3 293 2 244.62010 2 2 5 33 -13.2 0.202 -9 -9 -999 210 58 0.0001 1.5 1 5.1 300 2 249.62010 2 2 6 33 -17.2 0.222 -9 -9 -999 241 58.8 0.0001 1.5 1 5.6 293 2 249.82010 2 2 7 33 -29.6 0.228 -9 -9 -999 250 36.8 0.0001 1.5 1 5.8 285 2 249.92010 2 2 8 33 -32.3 0.249 -9 -9 -999 285 43.8 0.0001 1.5 1 6.3 283 2 250.12010 2 2 9 33 -33.9 0.261 -9 -9 -999 307 48.4 0.0001 1.5 1 6.6 279 2 250.42010 2 2 10 33 -37 0.286 -9 -9 -999 351 58 0.0001 1.5 1 7.2 277 2 250.62010 2 2 11 33 -37.5 0.29 -9 -9 -999 359 59.7 0.0001 1.5 1 7.3 276 2 250.52010 2 2 12 33 -39.5 0.306 -9 -9 -999 390 66.8 0.0001 1.5 1 7.7 277 2 251.22010 2 2 13 33 -42.8 0.335 -9 -9 -999 446 80.7 0.0001 1.5 0.73 8.4 270 2 251.42010 2 2 14 33 -40.1 0.323 -9 -9 -999 422 77 0.0001 1.5 0.67 8.1 281 2 2512010 2 2 15 33 -43.4 0.347 -9 -9 -999 471 88.6 0.0001 1.5 0.68 8.7 283 2 250.82010 2 2 16 33 -42.6 0.331 -9 -9 -999 438 78 0.0001 1.5 0.75 8.3 274 2 250.82010 2 2 17 33 -46.3 0.36 -9 -9 -999 496 92 0.0001 1.5 1 9 274 2 251.12010 2 2 18 33 -47.7 0.372 -9 -9 -999 521 98.5 0.0001 1.5 1 9.3 277 2 251.62010 2 2 19 33 -47.5 0.372 -9 -9 -999 521 98.9 0.0001 1.5 1 9.3 279 2 252.52010 2 2 20 33 -29.2 0.386 -9 -9 -999 551 179.5 0.0001 1.5 1 9.6 284 2 253.62010 2 2 21 33 -999 -9 -9 -9 -999 -999 -99999 0.0001 1.5 1 9.1 282 2 2552010 2 2 22 33 -23.8 0.378 -9 -9 -999 534 207 0.0001 1.5 1 9.4 284 2 255.9

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Appendix C - Excerpt of Spreadsheet of AERMET Processed Meteorological Data Page 1 of 2

yr mo dy hr j_day ztemp ipcode pamt rh pres ccvr

j_day =

Julian day

ztemp = reference height for

temperature (m)

ipcode = precipitation

type code (0=none

pamt = precipitation

amount (mm/hr)

rh = relative

humidity (percent)

pres = station

pressure (mb)

ccvr = cloud cover

(tenths)2010 1 26 14 26 2 0 0 92 1021 02010 1 26 15 26 2 0 0 85 1023 02010 1 30 12 30 2 0 0 84 1017 92010 1 30 13 30 2 0 0 84 1017 02010 1 30 22 30 2 0 0 91 1019 102010 1 30 23 30 2 0 0 91 1019 102010 1 30 24 30 2 9999 -9 999 1019 992010 1 31 1 31 2 0 0 92 1019 102010 1 31 2 31 2 0 0 92 1019 102010 1 31 3 31 2 0 0 92 1019 102010 1 31 4 31 2 0 0 84 1019 102010 1 31 7 31 2 0 0 84 1019 102010 1 31 8 31 2 0 0 92 1019 102010 1 31 9 31 2 0 0 84 1020 102010 1 31 10 31 2 0 0 84 1020 102010 2 1 11 32 2 0 0 84 1031 02010 2 2 5 33 2 0 0 84 1033 102010 2 2 6 33 2 0 0 83 1033 92010 2 2 7 33 2 0 0 76 1032 02010 2 2 8 33 2 0 0 76 1032 02010 2 2 9 33 2 0 0 70 1032 02010 2 2 10 33 2 0 0 70 1031 02010 2 2 11 33 2 0 0 70 1031 02010 2 2 12 33 2 0 0 64 1029 02010 2 2 13 33 2 0 0 64 1029 02010 2 2 14 33 2 0 0 70 1029 02010 2 2 15 33 2 0 0 64 1028 02010 2 2 16 33 2 0 0 70 1028 02010 2 2 17 33 2 0 0 70 1027 02010 2 2 18 33 2 0 0 64 1027 02010 2 2 19 33 2 0 0 70 1025 02010 2 2 20 33 2 0 0 70 1025 92010 2 2 21 33 2 9999 -9 999 1024 992010 2 2 22 33 2 0 0 84 1024 10

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Appendix C (continued) - Excerpt of Spreadsheet of AERMET Processed Meteorological Data Page 2 of 2

Appendix D

Example of output of the BOOT model evaluation program for case where Cp and Co exceed 0 µg/m3 and where a background of

2 µg/m3 has been added to Cp. 381 hours satisfy these criteria.

OUTPUT OF THE BOOT PROGRAM, LEVEL 10/15/2004

No. of experiments = 381 No. of models = 3 (with the observed data counted as one) No. of observations = 381 No. of observations available for paried sampling = 380 (there might be odd number of observations in each block) No. of blocks (regimes) = 1 No. of experiments in each block (regime) 381

Out of the following options: (1) straight Co and Cp comparison (4) consider ln(Co) and ln(Cp) 1 was selected

Input data: Co, Cp1 (OLM), Cp2 (PVMRM) . (ug/m3)..

1 1 6.150 2.000 2.000 1 1 4.100 2.001 2.001 1 1 8.180 24.80 18.15 1 1 6.140 20.27 14.62 1 1 4.090 4.971 4.336 1 1 2.040 2.046 2.035 1 1 2.040 2.072 2.047 1 1 6.130 31.40 20.78 1 1 2.040 2.004 2.002 1 1 2.040 2.000 2.000 1 1 2.040 2.008 2.004 1 1 4.100 2.000 2.000 1 1 2.050 2.000 2.000 1 1 2.050 2.000 2.000 1 1 4.110 2.042 2.020 1 1 2.050 7.173 5.135 1 1 2.080 2.000 2.000 1 1 2.070 2.000 2.000 1 1 2.080 2.000 2.000 1 1 2.080 2.004 2.001 1 1 6.210 13.36 9.269 1 1 2.070 2.145 2.128

1 1 2.070 2.138 2.119 1 1 2.070 2.250 2.174 1 1 6.260 2.001 2.001 1 1 12.51 78.73 45.88 1 1 4.170 2.000 2.000 1 1 4.170 2.007 2.002 1 1 2.040 2.000 2.000 1 1 47.97 44.78 21.34 1 1 6.630 2.000 2.000 1 1 15.54 142.2 95.92 1 1 56.90 2.003 2.001 1 1 54.56 3.451 2.384 1 1 40.39 2.002 2.001 1 1 33.99 2.002 2.001 1 1 56.12 85.44 29.23 1 1 51.47 22.02 9.933 1 1 45.19 11.44 5.914 1 1 45.23 2.074 2.030 1 1 12.99 2.000 2.000 1 1 19.30 2.010 2.005 1 1 4.550 2.000 2.000 1 1 2.080 2.040 2.024 1 1 2.110 2.011 2.008 1 1 14.77 2.214 2.141 1 1 2.130 2.011 2.004 1 1 29.93 12.48 5.194 1 1 30.02 130.7 89.31 1 1 32.36 2.001 2.000 1 1 13.18 2.000 2.000 1 1 6.790 2.058 2.018 1 1 11.32 130.5 93.33 1 1 9.060 2.001 2.000 1 1 4.520 2.000 2.000 1 1 38.27 83.57 39.54 1 1 11.21 2.123 2.043 1 1 6.720 2.000 2.000 1 1 11.02 16.15 8.183 1 1 22.02 2.000 2.000 1 1 4.440 2.001 2.000 1 1 4.470 112.2 68.11 1 1 65.69 83.66 44.43 1 1 6.810 64.47 25.36 1 1 38.51 6.740 4.642

1 1 2.190 2.147 2.085 1 1 2.190 3.663 2.969 1 1 2.190 2.009 2.005 1 1 33.12 108.5 61.53 1 1 4.430 98.35 45.33 1 1 35.51 83.89 36.13 1 1 8.870 38.07 17.35 1 1 4.440 5.117 3.391 1 1 47.64 5.326 4.195 1 1 15.91 99.68 48.16 1 1 2.270 2.001 2.001 1 1 6.800 2.001 2.001 1 1 2.300 4.556 3.693 1 1 6.900 95.91 68.78 1 1 50.71 29.23 13.84 1 1 41.53 109.4 83.92 1 1 29.98 104.6 81.95 1 1 53.08 84.71 40.02 1 1 57.70 27.48 12.98 1 1 18.42 118.4 109.3 1 1 16.11 120.2 112.0 1 1 20.71 116.5 104.1 1 1 6.810 2.002 2.001 1 1 20.36 2.006 2.004 1 1 9.040 2.009 2.005 1 1 11.30 2.014 2.009 1 1 9.030 2.014 2.009 1 1 4.510 2.015 2.010 1 1 9.020 2.025 2.018 1 1 8.980 2.038 2.028 1 1 2.250 2.055 2.031 1 1 2.260 12.98 8.045 1 1 2.270 65.15 34.43 1 1 31.70 97.83 72.70 1 1 24.89 109.8 87.44 1 1 6.790 91.94 49.29 1 1 29.40 107.4 84.36 1 1 45.24 104.3 71.90 1 1 27.12 115.1 98.59 1 1 20.33 109.5 88.26 1 1 11.26 40.45 18.35 1 1 2.250 2.123 2.067 1 1 31.17 2.149 2.069

1 1 22.24 40.78 19.81 1 1 2.230 10.58 5.520 1 1 33.63 2.000 2.000 1 1 31.48 2.000 2.000 1 1 6.730 3.365 2.776 1 1 6.750 2.000 2.000 1 1 6.750 2.001 2.000 1 1 2.260 2.022 2.010 1 1 9.060 63.89 29.11 1 1 2.270 32.48 15.11 1 1 11.47 2.000 2.000 1 1 20.75 2.000 2.000 1 1 63.93 2.000 2.000 1 1 50.50 2.000 2.000 1 1 46.10 2.000 2.000 1 1 43.85 2.000 2.000 1 1 4.350 3.164 2.529 1 1 6.540 3.296 2.548 1 1 17.54 2.300 2.108 1 1 57.56 7.851 4.668 1 1 37.95 2.257 2.116 1 1 11.05 2.001 2.000 1 1 15.46 2.000 2.000 1 1 21.81 5.201 3.240 1 1 17.51 84.25 44.79 1 1 13.15 61.91 24.75 1 1 28.51 94.43 41.25 1 1 13.16 92.61 38.13 1 1 24.14 3.867 2.793 1 1 30.71 3.373 2.575 1 1 41.71 8.979 4.715 1 1 35.08 8.434 4.475 1 1 4.660 158.3 101.4 1 1 45.80 21.03 10.25 1 1 13.73 83.47 39.03 1 1 2.280 2.445 2.195 1 1 2.280 2.059 2.026 1 1 2.270 2.036 2.016 1 1 2.280 2.024 2.011 1 1 4.530 2.066 2.030 1 1 4.530 2.031 2.015 1 1 2.270 3.545 2.721 1 1 2.270 5.153 3.557

1 1 2.270 2.039 2.019 1 1 2.260 2.042 2.020 1 1 2.210 84.56 41.81 1 1 50.56 52.78 28.89 1 1 37.26 7.095 4.015 1 1 33.25 51.44 20.14 1 1 34.01 3.153 2.447 1 1 33.95 2.057 2.023 1 1 29.46 2.010 2.004 1 1 65.74 2.820 2.342 1 1 72.47 4.444 3.074 1 1 54.46 117.4 83.36 1 1 4.540 98.16 45.79 1 1 9.010 2.010 2.004 1 1 6.730 2.015 2.007 1 1 8.960 2.014 2.007 1 1 2.230 2.025 2.011 1 1 2.210 2.027 2.013 1 1 2.210 2.054 2.024 1 1 8.640 2.047 2.018 1 1 17.28 2.051 2.022 1 1 17.30 2.058 2.029 1 1 10.87 2.016 2.005 1 1 4.340 2.000 2.000 1 1 2.180 2.000 2.000 1 1 2.220 132.8 130.5 1 1 2.240 2.000 2.000 1 1 9.090 2.000 2.000 1 1 6.660 40.80 28.24 1 1 4.420 2.002 2.000 1 1 4.460 2.385 2.137 1 1 13.44 8.430 4.268 1 1 6.740 60.94 59.31 1 1 4.500 49.14 46.50 1 1 2.240 2.131 2.033 1 1 2.240 2.076 2.018 1 1 2.240 2.275 2.064 1 1 4.530 2.044 2.011 1 1 2.190 2.012 2.008 1 1 2.190 2.323 2.156 1 1 2.210 2.002 2.001 1 1 6.170 31.94 14.26 1 1 4.120 2.382 2.266

1 1 6.220 2.000 2.000 1 1 6.240 2.000 2.000 1 1 4.180 2.000 2.000 1 1 6.250 8.779 5.136 1 1 10.43 47.68 22.38 1 1 20.88 26.61 12.77 1 1 8.370 6.973 3.829 1 1 2.100 2.004 2.002 1 1 2.110 3.143 2.325 1 1 2.060 2.056 2.026 1 1 2.060 2.063 2.031 1 1 4.120 13.69 8.314 1 1 2.060 26.88 24.61 1 1 6.240 2.004 2.002 1 1 6.100 2.041 2.015 1 1 4.090 3.439 2.584 1 1 4.090 38.68 17.75 1 1 10.19 33.59 16.45 1 1 4.070 11.63 7.005 1 1 7.990 30.50 20.35 1 1 30.16 30.96 13.82 1 1 6.050 49.80 26.51 1 1 4.030 40.04 21.66 1 1 12.09 37.00 20.07 1 1 10.07 14.03 10.00 1 1 2.030 2.000 2.000 1 1 41.86 9.109 4.293 1 1 15.94 44.55 23.35 1 1 3.990 44.46 23.62 1 1 3.990 19.94 11.95 1 1 2.000 8.649 6.169 1 1 3.990 2.155 2.098 1 1 1.990 2.086 2.051 1 1 5.990 34.11 21.20 1 1 1.990 11.31 8.383 1 1 4.060 4.769 3.639 1 1 2.030 9.667 4.687 1 1 10.16 2.029 2.011 1 1 6.020 7.060 4.194 1 1 2.000 5.689 3.641 1 1 3.990 28.38 14.09 1 1 1.960 2.059 2.023 1 1 5.900 39.34 18.80

1 1 15.72 12.45 6.016 1 1 13.98 31.27 12.54 1 1 21.93 36.53 17.96 1 1 13.96 43.97 19.35 1 1 11.93 43.26 18.07 1 1 17.92 39.45 17.21 1 1 9.950 43.32 18.10 1 1 3.980 20.57 9.672 1 1 10.01 44.62 18.79 1 1 24.07 35.47 17.95 1 1 20.07 34.38 16.39 1 1 18.11 39.27 22.68 1 1 20.14 34.30 16.11 1 1 12.12 32.18 12.88 1 1 18.19 38.66 20.59 1 1 14.17 41.17 21.99 1 1 4.050 11.63 5.767 1 1 2.020 2.063 2.026 1 1 2.010 2.080 2.033 1 1 2.010 11.02 5.603 1 1 2.020 9.849 5.112 1 1 8.080 3.000 2.366 1 1 22.22 30.77 14.53 1 1 18.21 20.29 7.298 1 1 8.100 6.663 3.511 1 1 14.23 19.85 8.049 1 1 14.27 51.45 22.49 1 1 8.160 17.77 7.552 1 1 6.120 18.64 7.567 1 1 6.120 8.843 4.433 1 1 8.150 23.30 9.103 1 1 4.070 11.25 5.480 1 1 2.030 2.757 2.284 1 1 2.020 6.224 3.572 1 1 2.020 19.99 8.971 1 1 2.010 2.099 2.042 1 1 3.980 2.089 2.033 1 1 1.990 5.949 3.420 1 1 1.970 2.166 2.069 1 1 5.900 2.106 2.044 1 1 7.920 5.545 3.128 1 1 11.74 6.229 3.303 1 1 3.970 2.202 2.059

1 1 1.940 2.065 2.026 1 1 1.950 3.761 2.692 1 1 1.950 7.802 4.292 1 1 9.730 2.493 2.177 1 1 11.72 29.41 16.08 1 1 1.960 2.125 2.058 1 1 1.960 2.039 2.017 1 1 11.83 9.860 4.580 1 1 1.990 2.841 2.400 1 1 5.950 4.064 2.769 1 1 1.990 2.101 2.040 1 1 1.980 2.086 2.043 1 1 1.980 2.011 2.009 1 1 1.970 2.051 2.019 1 1 3.880 6.378 3.346 1 1 5.870 24.99 10.78 1 1 17.57 13.11 6.429 1 1 19.55 13.32 6.343 1 1 1.940 9.277 5.222 1 1 2.000 2.004 2.001 1 1 2.010 2.002 2.001 1 1 2.020 2.003 2.001 1 1 3.980 8.497 4.430 1 1 8.000 2.136 2.041 1 1 18.01 30.17 13.70 1 1 2.000 2.145 2.048 1 1 2.000 2.098 2.032 1 1 4.040 7.368 3.771 1 1 10.10 31.96 16.46 1 1 8.080 28.83 12.69 1 1 16.16 27.14 12.77 1 1 6.060 27.54 10.39 1 1 6.070 26.05 9.841 1 1 6.070 22.10 8.317 1 1 2.000 2.233 2.067 1 1 12.01 2.469 2.136 1 1 6.030 3.788 2.647 1 1 1.980 30.08 17.76 1 1 1.960 2.066 2.029 1 1 1.970 9.923 5.394 1 1 1.970 13.90 7.313 1 1 3.960 19.81 9.108 1 1 17.88 10.60 5.141

1 1 23.87 9.995 4.460 1 1 13.96 17.13 6.667 1 1 15.97 45.42 17.39 1 1 11.98 31.63 10.81 1 1 2.000 2.720 2.259 1 1 3.990 2.079 2.029 1 1 7.980 5.682 3.494 1 1 1.980 2.128 2.077 1 1 3.960 2.137 2.079 1 1 8.190 3.275 2.533 1 1 6.120 3.324 2.630 1 1 16.34 10.44 5.705 1 1 2.040 4.277 3.097 1 1 20.50 23.83 10.44 1 1 14.38 4.238 2.888 1 1 10.26 36.10 15.62 1 1 14.34 63.26 28.24 1 1 12.29 16.48 8.407 1 1 12.31 26.34 12.92 1 1 16.36 3.134 2.472 1 1 8.190 2.398 2.177 1 1 26.57 66.34 27.95 1 1 26.55 22.71 9.970 1 1 28.56 36.96 16.06 1 1 12.23 3.629 2.601 1 1 16.28 22.51 11.62 1 1 16.29 7.645 4.404 1 1 4.080 8.623 4.583 1 1 2.030 22.37 10.57 1 1 16.28 3.400 2.612 1 1 20.34 11.35 5.663 1 1 22.36 57.13 29.27 1 1 28.50 26.60 12.99 1 1 12.23 59.13 27.53 1 1 14.26 16.16 8.033 1 1 34.47 49.48 19.87 1 1 4.070 8.352 4.515 1 1 10.17 41.27 17.53 1 1 12.18 36.86 15.72 1 1 12.14 37.94 15.95 1 1 10.13 27.45 13.33 1 1 32.42 32.27 13.78 1 1 34.44 51.03 22.85

1 1 18.25 26.74 12.56 1 1 6.070 3.238 2.534 1 1 6.090 3.618 2.693 1 1 6.060 5.884 3.666 1 1 2.020 3.222 2.557 1 1 4.040 5.664 3.538 1 1 4.040 2.692 2.290 1 1 20.21 10.11 5.058 1 1 6.070 2.000 2.000 1 1 18.19 19.32 7.510 1 1 8.080 3.339 2.626 1 1 2.020 3.449 2.540 1 1 26.26 40.24 15.74 1 1 4.040 6.446 3.878 1 1 8.090 18.03 8.881

Regime averaged data: Co, Cp1, Cp2 ...

12.88 21.46 12.73

Nominal (median) results (No. of regimes = 1) MODEL MEAN SIGMA BIAS NMSE CORR FA2 FB HIGH 2nd HIGH PCOR OBS.NO2 13. 14.04 0.00 0.00 1.000 1.000 0.000 72. 66. n/a OLMNO2 21. 31.06 -8.58 3.60 0.274 0.402 -0.500 158. 142. n/a PVMRMNO2 13. 21.05 0.15 3.09 0.227 0.470 0.012 131. 112. n/a

Note: The Percentile 95% Confidence Limits are based on the 2.5th and 97.5th percentiles of the cumulative distribution function. The Student's t 95% Confidence Limits are based on calculated mean and standard deviation.

Student's t Percentile 95% Student 95% Model(s) Conf. limits t Mean S.D. Conf. limits------------------------------------------------------------------------------------- OBS.NO2 MEAN 11.469 14.314 17.816 12.891 0.724 11.477 14.351 OLMNO2 NMSE 2.870 4.298 9.873 3.584 0.363 2.903 4.356

FB -0.643 -0.346 -6.543 -0.494 0.076 -0.641 -0.350 CORR 0.167 0.384 4.982 0.275 0.055 0.175 0.385 PVMRMNO2 NMSE 2.422 3.730 9.245 3.076 0.333 2.491 3.767 FB -0.159 0.197 0.211 0.019 0.091 -0.156 0.189 CORR 0.125 0.332 4.326 0.228 0.053 0.133 0.336

Student's t Percentile 95% Student 95% Model(s) Conf. limits t Mean S.D. Conf. limits------------------------------------------------------------------------------------- OLMNO2 - PVMRMNO2 NMSE 0.078 0.939 2.320 0.508 0.219 0.073 0.936 FB -0.570 -0.457 -17.817 -0.513 0.029 -0.569 -0.457 CORR 0.012 0.082 2.675 0.047 0.018 0.014 0.083

SUMMARY OF CONFIDENCE LIMITS ANALYSES BASED ON PERCENTILE CONFIDENCE LIMITS ---------------------------------------------------------------------------

D(NMSE) among models: an 'X' indicates significantly different from zero at 95% confidence limits

O P L V M M N R O M 2 N O 2 -------- OLMNO2 | X

D(FB) among models: an 'X' indicates significantly different from zero at 95% confidence limits

O P L V M M N R O M 2 N O 2 --------

OLMNO2 | X

D(CORR) among models: an 'X' indicates significantly different from zero at 95% confidence limits

O P L V M M N R O M 2 N O 2 -------- OLMNO2 | X

FB for each model: an 'X' indicates significantly different from zero at 95% confidence limits

O P L V M M N R O M 2 N O 2 -------- X

CORR for each model: an 'X' indicates significantly different from zero at 95% confidence limits

O P L V M M N R O M 2 N O 2 -------- X X

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