Review and Evaluation of the Plume Volume Molar Ratio Method (PVMRM) and Ozone Limiting Method (OLM) for short-term (1-hour average) NO2 Impacts

Transcription

1 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 Prepared by: Elizabeth Hendrick, CCM Dr. Steven Hanna, CCM Dr. Bruce Egan, CCM Vincent Tino, CCM Hanna Consultants Egan Environmental Inc. 7 Crescent Avenue 75 Lothrop Street 3 Clock Tower Place Kennebunkport, ME Beverly, MA Suite 250 Maynard, MA July 18, 2012

2 TABLE OF CONTENTS ES EXECUTIVE SUMMARY ES INTRODUCTION Background Overall Project Description IDENTIFICATION OF AVAILABLE NO2 FIELD STUDY OR MONITORING DATABASES Field data already used in Hanrahan (1999b) and MACTEC (2005) evaluations of the PVMRM model Search for Additional Field Data TECHNICAL REVIEW OF OLM AND PVMRM Review of the NO to NO2 Conversion FORTRAN Codes OLM PVMRM EVALUATION OF PVMRM AND OLM IN THE AERMOD MODEL USING THE WAINWRIGHT DATA Methodology Model Options Wainwright Source and Monitoring Data Description Choosing Hours to Model Model Performance Evaluation Methodology Results Comments on hours selected for analysis Variation in observed NO2/NOx ratio Summary of concentration predictions, including all hours Summary of quantitative performance measures for NO Quantile-Quantile (Q-Q) plots Results of evaluation of NO2 /NOx ratio and NO2 concentration for optional run with adjusted ozone levels Uncertainties and limitations that impact the analysis CONCLUSIONS & RECOMMENDATIONS Search for Model Evaluation Databases Review of PVMRM and OLM Model Formulation and Implementation Results of Model Evaluation of PVMRM and OLM in the AERMOD Model using the Wainwright Data Overview and methodology Key results REFERENCES 6-1 Evaluation of PVMRM and OLM i Index

3 LIST OF APPENDICIES Appendix A Appendix B Appendix C Appendix D Caterpillar Engine Performance Curves Excerpt of the Model Evaluation Spreadsheet Excerpt of the Model Evaluation Spreadsheet Excerpt of the Model Evaluation Spreadsheet AERMET Processed meteorological data for the same periods shown in Appendix B 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 = Table 3-2 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios: NO2/NOx in-stack = Table 3-3 Single Stack, Single Hour, Single Receptor Model Comparisons Table 3-4 Two Stacks, Single Hour, Single Receptor Model Comparisons Table 4-1 Stack locations and dimensions for the Wainwright Power Plant 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, 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/m 3 ) and when ambient heat flux was observed at the monitoring sites 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 Table 4-5 Statistical performance measures for hourly-averaged NO2 concentrations (in μg/m 3 ), for 381 hours with observed and both model predictions non-zero. A background of 2 μg/m 3 has been added to all modeled concentrations Table 4-6 Top-ten rankings for observed and AERMOD-predicted hourly-averaged NOx (unpaired). No background has been added to the AERMOD predictions 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 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 Evaluation of PVMRM and OLM ii Index

4 LIST OF FIGURES Figure 2-1 Figure 3-1 Figure 3-2 Figure 3-3 Figure 3-4 Figure 3-5 Houston petrochemical and power plant sources during TexAQS 2000, from Ryerson et al. (2003) 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.) 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) 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) 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) Moles of O3 and NOX, and NO2/NOX ratio as a function of increasing a secondary source s emission rate Figure 4-1 Village of Wainwright, Alaska, showing locations of power plant, monitoring station, and ASOS meteorological station Figure 4-2 Wainwright Power Plant, Wainwright, Alaska (front and side views) Figure 4-3 Wainwright Power Plant building with stack locations shown (red) with adjacent shop building and storage tanks included in BPIP-Prime 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/m 3 ) 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/m 3 ).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/m 3 ) 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) 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 Evaluation of PVMRM and OLM iii Index

5 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) Evaluation of PVMRM and OLM iv Index

6 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 3009/Evaluation of PVMRM and OLM ES-1 Executive Summary

7 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. 3009/Evaluation of PVMRM and OLM ES-2 Executive Summary

8 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 postprocessor 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. 3009/Evaluation of PVMRM and OLM ES-3 Executive Summary

9 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/m 3. 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. 3009/Evaluation of PVMRM and OLM ES-4 Executive Summary

10 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 for AERMOD/OLM and 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 10 th 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. 3009/Evaluation of PVMRM and OLM ES-5 Executive Summary

11 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. 3009/Evaluation of PVMRM and OLM ES-6 Executive Summary

12 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 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: 3009/Evaluation of PVMRM and OLM 1-1 Introduction

13 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. 3009/Evaluation of PVMRM and OLM 1-2 Introduction

14 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 NO 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). 3009/Evaluation of PVMRM and OLM 2-1 Identification of NO2 Data Sets

15 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 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. 3009/Evaluation of PVMRM and OLM 2-2 Identification of NO2 Data Sets

16 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 3009/Evaluation of PVMRM and OLM 2-3 Identification of NO2 Data Sets

17 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 2-4 Identification of NO2 Data Sets

18 Figure 2-1 Houston petrochemical and power plant sources during TexAQS 2000, from Ryerson et al. (2003) 3009/Evaluation of PVMRM and OLM 2-5 Identification of NO2 Data Sets

19 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 September 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. 3009/Evaluation of PVMRM and OLM 2-6 Identification of NO2 Data Sets

20 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 of AERMOD. The codes were examined line-by-line and numerous test runs were made to verify their implementation 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 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

21 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 3009/Evaluation of PVMRM and OLM 3-2 Code Technical Review

22 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 x 3/2 /zi 1/2 ]/[ (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) 3/2 x 3/2 /zi 1/2 (3) for large travel times (more than about 500 s), the solution approaches: σr = 0.94 (σwxzi/u) 1/2 (4) 3009/Evaluation of PVMRM and OLM 3-3 Code Technical Review

23 The x 3/2 behavior at small distances and the x 1/2 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 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 3009/Evaluation of PVMRM and OLM 3-4 Code Technical Review

24 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 n 2. 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, 3009/Evaluation of PVMRM and OLM 3-5 Code Technical Review

25 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 tophat plume flux (Cavgπ (nzr) 2 U). 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 tophat 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 3009/Evaluation of PVMRM and OLM 3-6 Code Technical Review

26 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 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 = 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. 3009/Evaluation of PVMRM and OLM 3-7 Code Technical Review

27 Table 3-1 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios: NO2/NOx in-stack = 0.0 Ozone concentration PPB Ozone concentration MOLES/m E E E E E E E E E E E E-06 Q NOx g/s Q NOx MOLES/s U m/s Instack fraction NO2/NOx Nd DOWNWIND DISTANCE m σy *σz (ISCST/PVMRM Class C) m Plume volume flux m 3 /s Ozone flux in Plume MOLES/s Ozone in Plume µg/m NOx flux in Plume MOLES/s NOx in Plume µg/m Nominal O3/NOx ratio for nz=1.414 nd /Evaluation of PVMRM and OLM 3-8 Code Technical Review

28 Table 3-1 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios: NO2/NOx in-stack = 0.0 (Continued) Ozone concentration PPB Ozone concentration MOLES/m E E E E E E E E E E E E-06 nz NO2 /NOx Ratio NO2 /NOx Ratio NO2 /NOx Ratio NO2 /NOx Ratio NO2 /NOx Ratio NO2 /NOx Ratio /Evaluation of PVMRM and OLM 3-9 Code Technical Review

29 NO2/NOx Ratio Nz=1.282 Nz=1.414 Nz=2 Nz=2.5 Nz=3 Nz= Downwind Distance (m) 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. 3009/Evaluation of PVMRM and OLM 3-10 Code Technical Review

30 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. 3009/Evaluation of PVMRM and OLM 3-11 Code Technical Review

31 Table 3-2 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios: NO2/NOx in-stack = 0.1 Ozone concentration PPB Ozone concentration MOLES/m E E E E E E E E E E E E-06 Q NOx g/s Q NOx MOLES/s U m/s Instack fraction NO2/NOx Nd DOWNWIND DISTANCE M σy *σz (ISCST/PVMRM Class C) m Plume volume flux m 3 /s Ozone flux in Plume MOLES/s Ozone in Plume µg/m NOx flux in Plume MOLES/s NOx in Plume µg/m Nominal O3/NOx ratio for nz=1.414 nd /Evaluation of PVMRM and OLM 3-12 Code Technical Review

32 Table 3-2 ISC/PVMRM Emulator Calculations of NO2/NOx Ratios: NO2/NOx in-stack = 0.1 (Continued) Ozone concentration PPB Ozone concentration MOLES/m E E E E E E E E E E E E-06 nz NO2 /NOx Ratio NO2 /NOx Ratio NO2 /NOx Ratio NO2 /NOx Ratio NO2 /NOx Ratio NO2 /NOx Ratio /Evaluation of PVMRM and OLM 3-13 Code Technical Review

33 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 A N/A N/A σr (min) (m) N/A N/A 5 15 Meteorological Data Wind Direction ( ) Wind Speed (m/s) Stack Data # of Stacks Distance Apart (m) N/A N/A N/A N/A Base Elevation (m) Emission Rate (g/s) Stack Height (m) Stack Temp (K) Stack Velocity (m/s) Stack Diameter (m) In-stack NO2 Ratio N/A Equilibrium. NO2 N/A N/A Ozone value (µg/m 3 ) N/A Receptor Data Rel. Location 5 km SSW 5 km SSW 5 km SSW 5 km SSW Elevation (m) Hill Height (m) Results NOX Conc. (µg/m 3 ) NO2 Conc. (µg/m 3 ) # of Contributing N/A N/A 1 1 Ozone (moles) N/A N/A NOX (moles) N/A N/A BHORIZ N/A N/A 0 0 BVERT N/A N/A 0 0 Plume Vol. (m 3 ) N/A N/A 5.91E E+07 % NO2 N/A N/A 42.1% 14.1% 3009/Evaluation of PVMRM and OLM 3-14 Code Technical Review

34 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 A N/A N/A σr (min) (m) N/A N/A 5 15 Meteorological Wind Direction ( ) Wind Speed (m/s) Stack Data # of Stacks Distance Apart (m) Base Elevation (m) Emission Rate (g/s) 100, , , , 50 Stack Height (m) Stack Temp (K) Stack Velocity Stack Diameter In-stack NO2 Ratio N/A Equilibrium. NO2 N/A N/A Ozone value N/A Receptor Data Rel. Location 5 km SSW 5 km SSW 5 km SSW 5 km SSW Elevation (m) Hill Height (m) Results NOX Conc. (µg/m 3 ) NO2 Conc. (µg/m 3 ) # of Contributing N/A N/A 2 2 Ozone (moles) N/A N/A NOX (moles) N/A N/A BHORIZ N/A N/A BVERT N/A N/A 0 0 Plume Vol. (m 3 ) N/A N/A 7.79E E+07 % NO2 N/A N/A 38.3% 14.1% 3009/Evaluation of PVMRM and OLM 3-15 Code Technical Review

35 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 = 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. 3009/Evaluation of PVMRM and OLM 3-16 Code Technical Review

36 Hanrahan s nz (maximum 1- hr NO2= µg/m 3 ) EPA s nz (maximum 1- hr NO2= µg/m 3 ) 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) 3009/Evaluation of PVMRM and OLM 3-17 Code Technical Review

37 Hanrahan s nz (maximum 1- hr NO2= µg/m 3 ) EPA s nz (maximum 1- hr NO2= µg/m 3 ) 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) 3009/Evaluation of PVMRM and OLM 3-18 Code Technical Review

38 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. 3009/Evaluation of PVMRM and OLM 3-19 Code Technical Review

39 Concentration (µg/m 3 ) NO 2 & NO X Concs. Total NO Total NO X Source2EmissionRate(g/s) 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). 3009/Evaluation of PVMRM and OLM 3-20 Code Technical Review

40 Moles & NO 2 /NO X Ratio O 3 moles NO X moles NO 2 /NO X Ratio Moles NO 2 /NO X ratio Source 2 Emission Rate (g/s) 0 Figure 3-5 Moles of O3 and NOX, and NO2/NOX ratio as a function of increasing a secondary source s emission rate. 3009/Evaluation of PVMRM and OLM 3-21 Code Technical Review

41 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 NO /Evaluation of PVMRM and OLM 3-22 Code Technical Review

42 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

43 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 NO 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 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. 3009/Evaluation of PVMRM and OLM 4-2 Wainwright Field Data Comparison

44 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. 3009/Evaluation of PVMRM and OLM 4-3 Wainwright Field Data Comparison

45 Figure 4-1 Village of Wainwright, Alaska, showing locations of power plant, monitoring station, and ASOS meteorological station. 3009/Evaluation of PVMRM and OLM 4-4 Wainwright Field Data Comparison

46 Figure 4-2 Wainwright Power Plant, Wainwright, Alaska (front and side views) 3009/Evaluation of PVMRM and OLM 4-5 Wainwright Field Data Comparison

47 Table 4-1 Stack locations and dimensions for the Wainwright Power Plant Unit UTM E UTM N Base Stack Stack (m) (km) Elevation (m) Height (m) Diameter (m) Unit Unit Unit Unit Unit Range of Hourly Source Parameters for the Engines Exhaust Gas Temperature (ºF) Exhaust Gas Flow Rate (CFM) 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. 3009/Evaluation of PVMRM and OLM 4-6 Wainwright Field Data Comparison

48 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, /Evaluation of PVMRM and OLM 4-7 Wainwright Field Data Comparison