AERMOD Modeling of PM2.5 Impacts of the Proposed Highwood Generating Station

Transcription

1 AERMOD Modeling of PM2.5 Impacts of the Proposed Highwood Generating Station September 10, 2007 Prepared for Montana Environmental Information Center and Citizens for Clean Energy Prepared by Khanh T. Tran Principal AMI Environmental 206 Black Eagle Ave Henderson, NV Tel. (702)

2 Table of Contents Page I. Introduction 3 II. Qualifications 3 III. Background 4 IV. Modeling Methodologies 5 V.. Modeling Results 6 VI. References 8 2

3 I. INTRODUCTION This document presents the methodologies and results of an application of the AERMOD model to predict the air quality impacts of the particulate matter (PM10 and PM2.5) emissions from the proposed coal-fired Highwood Generating Station near Great Falls, Montana. Previous modeling was performed in December 2006 by Bison Engineering and copies of the AERMOD modeling files were provided to AMI Environmental (AMI) on a CD-ROM by Montana Environmental Information Center (MEIC, 2007). Based on my review of the 2006 modeling by Bison Engineering, I concluded that it was necessary to expand the grid of receptors used by Bison to accurately project peak concentrations of PM10, which are being used as a surrogate for PM2.5 for purposes of assessing compliance with the National Ambient Air Quality Standards. I performed several AERMOD modeling runs using the same meteorological data used by Bison and expanding the grid of receptors to reflect on-the-ground conditions such as prevailing winds and shifts in elevation that directly influence dispersion of particulate matter. The results of this modeling revealed significant violations on the National Ambient Air Quality Standards for PM2.5, as well as violations of the Class II increment for PM10. II. QUALIFICATIONS I am an Air Quality Consultant with over 30 years of experience in air quality modeling, visibility analysis, ozone and PM2.5, and air toxics health risk assessment. I am thoroughly familiar with air quality models approved by regulatory agencies, and I have developed my own models that have been approved and widely used. I have been a Principal of AMI Environmental (formerly Applied Modeling Inc.) since it was established in For the last 30 years, I have successfully managed over 200 air quality studies conducted by AMI on behalf of mostly government agencies (Arizona Department of Environmental Quality, Bureau of Land Management, Minerals Management Service, South Coast Air Quality Management District) and large Fortune 500 concerns including utilities such as (Duke Energy, Los Angeles Department of Water & Power, Southern California Edison) and oil companies (Arco, Texaco, Occidental Petroleum). My specialty is air quality modeling. I have extensive experience in the development, evaluation and applications of air quality simulation models, from simple Gaussian dispersion models to complex photochemical grid models. I have recently completed reviews of the air quality and visibility impact analysis for several proposed coal-fired power plants throughout the United States, including Georgia, Kentucky, and New Mexico. For some of these facilities, I also performed CALPUFF modeling for longrange transport to the nearest PSD Class I areas and a visibility impact analysis based on the FLAG procedures recommended by the United States Environmental Protection Agency (EPA) and Federal Land Managers. I have performed complex terrain dispersion modeling with AERMOD and ISCST3 for several power plants in Arizona, Georgia and southern California. 3

4 I have completed a comparative study of ISCST3 and the new ISC-PRIME and AERMOD models. I have also developed advanced models that have been reviewed and approved for use by regulatory agencies, such as the photochemical trajectory model TRACE, the Gaussian plume model, CMPOCS for onshore-offshore dispersion, and the multi-pathway health risk assessment model ACE2588 for air toxics. I have a M.S. in Mechanical Engineering from the University of California, Santa Barbara in 1974 and a B.S. in Mechanical Engineering from the University of California, Santa Barbara in III. BACKGROUND The 2007 Highwood Generating Station ( HGS ) Final EIS, Table 4-7 and the corresponding HGS air quality permit analysis state that particulate emissions from the proposed coal-fired power plant will result in a total concentration of 33.3 ug/m3 (including background) for PM10. This amounts to 22% of the 24-hour PM10 standard of 150 ug/m3 and 95% of the 24-hour PM2.5 of 35 ug/m 3. In this analysis, PM10 was used as a surrogate for PM2.5, i.e. PM2.5 project-related concentrations and background concentrations were assumed to be 100% PM2.5. This is an assumption that is frequently used in screening analysis. In the Final EIS, the proposed Highwood project passed this screening analysis. However, with maximum PM2.5 concentrations estimated at 95%, it was necessary to ensure that the underlying dispersion modeling for PM10 was properly performed to yield valid results. The PM10 dispersion modeling for the HGS did not yield valid results because it was not performed for a sufficient number of receptors over a sufficiently large area. Bison s 2006 dispersion modeling was performed with the AERMOD model. (Since December 2006, the AERMOD model has officially replaced the ISC and ISC-PRIME models that were used during the Draft EIS and Permit Application process.) The AERMOD modeling purported to use a grid of receptors located within 28 km of the proposed project site. However, based upon an examination of a Montana topographical map, the AERMOD receptor grid is not large enough to cover the elevated mountain ranges where high concentrations can occur due to plume impingement, e.g. the Highwood Mountains at 6000 ft and about 50 km east of the project site. High PM10/PM2.5 concentrations would be expected to occur there often since winds in the study area are frequently (over 50% of the time) from the south-southwest directions. In order to identify the highest concentrations of PM10 that may result from emissions from the HGS, it is therefore necessary to extend the modeling grid from a radius of 28 km to 50 km to cover the Highwood Mountains. The 50 km radius is also the maximum distance recommended by the US EPA for applying a Gaussian plume model such as AERMOD. It is further recommended that the AERMOD modeling be performed with an extended grid of receptors to include maximum concentrations of both PM10 and PM2.5. 4

5 IV. MODELING METHODOLOGIES This section documents the methodologies and assumptions used by AMI in the generation of modeling inputs such as source emissions, stack parameters, building wake dimensions, receptors and meteorological data. All input and output files are provided in the attached CD-ROM. A. Model Version I used version of the AERMOD model in this modeling study. It is currently the latest version of the model that has been approved by the US Environmental Protection Agency (US EPA). B. Source Emissions PM10 emissions for the Highwood facility and other cumulative sources were taken from the previous AERMOD modeling performed by Bison Engineering in December In the Bison study, PM10 was used as a surrogate for PM2.5, i.e. 100% PM10 was assumed to be PM2.5. A PM10 emissions rate of 83 lb/hr (10.47 g/s) has been calculated for the Highwood boilers. Other Highwood sources include the auxiliary boiler, emergency generator and coal handling. Cumulative facilities include Montana First Megawatts Plant (Northwestern Energy Development), Agri-Technology (ethanol plant), International Malting Company, Malmstrom AFB and Montana Refining Company. C. Stack Parameters and Building Wake Effects Stack parameters such as location, stack height, diameter, temperature and exit velocity for emissions sources from the proposed Highwood facility and other cumulative sources have been taken from the AERMOD modeling files used in the previous modeling study performed in December 2006 by Bison Engineering and provided to AMI on the CD- ROM by MEIC (MEIC, 2007). D. Receptors The previous modeling performed by Bison Engineering reported the use of a grid of receptors that are located within 28 km of the Highwood facility (Bison, 2005). However, the AERMOD input files provided on a CD-ROM to AMI only have 2,612 discrete Cartesian receptors located within 6.5 km of the Highwood facility (MEIC, 2007). AMI has extended the receptor grid to cover an area within a 50-km radius around the proposed facility. With a spatial resolution of one kilometer, there are 9,604 additional receptors. Terrain data at these additional receptors have been generated with the AERMAP preprocessor as recommended in the AERMOD user s guide (US EPA, 2004). There are a total of 12,216 receptors in the AERMOD modeling. 5

6 Following the AERMOD modeling runs with receptors at 1-km resolution, receptors at a finer resolution of 100 m are placed around the receptors with maximum concentrations. A grid of 400 receptors covering an area of 2 km x 2 km has been used to capture the peak concentrations. E. Meteorological Data The previous AERMOD modeling performed by Bison used a five-year dataset, from 1999 to It is comprised of meteorological data from surface observations and upper-air data from Great Falls International Airport. These data were processed by Bison with an outdated version of the AERMET program (version 04300). These datasets generated by Bison were not acceptable to the latest version of AERMOD. AMI has rerun the Great Falls Airport data with the latest version of AERMET (version 06431). We were able to rerun AERMET with the data from 1999 through The AERMET run for 2003 was not successful because of a formatting error in the raw surface data file. For the years , there are small differences in the Bison datasets and the new datasets that we generated with the latest version of AERMET. Hence, we have decided to change the AERMET label in the Bison datasets from to 06431, and use them in this modeling study. V. MODELING RESULTS The US EPA has recommended a special procedure for calculating the maximum 24- hour for comparison against the national ambient air quality standards (NAAQS) for PM10 and PM2.5 (US EPA, 2006). With five years of meteorological data, the US EPA recommends that the maximum 24-hour concentration is based on the high-sixth-high (H6H) for PM10 and the high-eighth-high (H8H) for PM2.5. However, the previous modeling performed by Bison Engineering used the high-second-high of each modeled year for comparing against the 24-hour NAAQS. I decided to use the same procedure since it has been reviewed and approved by Montana Department of Environmental Quality (MDEQ). The applicant Southern Montana Electric (SME) conducted 1 year of monitoring of PM10 at the Highwood proposed site. Data collected at the site from November 12, 2004 through November 11, 2005 show a maximum 24-hour average of 23 ug/m3. This maximum is taken to be the 24-hour background for comparing against the 24-hour PM2.5 standard of 35 ug/m3. Table 1 shows the peak 24-hour PM2.5 concentrations predicted by the AERMOD model with receptors located one kilometer apart. (Again, this assumes that all PM10 is PM2.5). The highest concentrations are calculated for the year 2000, with a maximum of ug/m3 and a second high of ug/m3. Both these peak concentrations occur at a receptor located about 13.7 km west of the Highwood site (UTM East = 484,000 m 6

7 and UTM North = 5,263,000 m, UTM Zone 12). A grid of 400 receptors at 100-m resolution has been placed around this maximum receptor. Two additional AERMOD runs have been performed for 2000 and 2003 with this 100-m grid. Table 2 shows the maximum and second high concentrations predicted by AERMOD for the modeled years 2000 and This table also shows the number of receptors with second high concentrations above 30 ug/m3 and above 12 ug/m3. For both 2003 and 2000, the high second high PM10 concentrations in Table 3 (40.53 ug/m3 for 2003 and ug/m3 for 2000) will largely exceed the PSD Class II increment of 30 ug/m3. Table 2 also shows exceedances of the PSD Class II increment are calculated to occur at two receptor locations for 2000 and three locations for With the background of 23 ug/m3, Table 4 indicates that the 24-hour PM2.5 NAAQS will be largely exceeded by the maximum total concentrations of ug/m3 for 2003 and ug/m3 for These maximum concentrations are predicted to occur west of the Highwood proposed site. These PM2.5 concentrations are much higher than the value 33.3 ug/m3 obtained in the Bison modeling (MEIC, 2007). Table 2 also shows exceedances of NAAQS are calculated to occur at several receptor locations (i.e. receptors with second high concentrations above 12 ug/m3). Due to the large numbers of these receptors (36 for 2003 and 32 for 2000) and their large magnitude, violations of the PM25 NAAQS will still occur even under the US EPA procedure that is based on the high-eighth-high (H8H) concentration (US EPA, 2006). /s/ Khanh Tran September 9, 2007 Table 1. Peak 24-Hour PM10 and PM2.5 concentrations predicted by AERMOD for 1-km Receptor Grid Year Maximum (First High) Second High Note: Peak concentrations calculated for 2000 and 2003 occur at (UTM East = 484,000 m and UTM North = 5,263,000 m, UTM Zone 12) 7

8 Table 2. Peak 24-Hour PM10/PM2.5 concentrations predicted by AERMOD for 100-m Receptor Grid Year Maximum (1st High) Second High Number of Receptors with 2 nd High Above 30 ug/m3 Number of Receptors with 2 nd High Above 12 ug/m Note: Peak concentrations calculated for 2000 and 2003 occur at (UTM East = 484,300 m and UTM North = 5,263,000 m, UTM Zone 12) Table 3. Comparison of 24-Hour PM10 Impacts (Second High) against PSD Class II Increment Year Second High Increment Increment Violation Yes Yes Table 4. Comparison of 24-Hour PM2.5 Impacts (Second High) against the NAAQS Year Project Conc. Background Conc. Total Conc. NAAQS NAAQS Violation Yes Yes VI. REFERENCES Bison Engineering Inc., Application for Air Quality and Operating Permits Southern Montana Electric Generation and Transmission Cooperative. November 30, Montana Environmental Information Center (MEIC), CD-ROM titled SME-Copy of Modeling Analysis. Provided by Anne Hedge to AMI Environmental on August 28,

9 U.S. EPA, Addendum to User s Guide for the AMS/EPA Regulatory Model AERMOD - Publication EPA-454/B December U.S. EPA, User s Guide for the AMS/EPA Regulatory Model AERMOD. Publication EPA-454/B September