Sensitivity of AERMOD to Meteorological Data Sets Based on Varying Surface Roughness. Paper No A-168-AWMA

Transcription

1 Sensitivity of AERMOD to Meteorological Data Sets Based on Varying Surface Roughness Paper No A-168-AWMA Prepared by: Anthony J. Schroeder, CCM Senior Consultant George J. Schewe, CCM, QEP Principal Consultant Trinity Consultants 1717 Dixie Highway Suite 900 Covington, KY (859) June 18,

2 ABSTRACT Dispersion modeling in support of regulatory programs for federal, regional, state and local permitting relies on the U.S. Environmental Protection Agency s AERMOD Model as specified in the Guideline on Air Quality Models (40 CFR 51, Appendix W). 1 Internal calculations in AERMOD for daytime mixing in the convective boundary layer and night time mixing in the stable boundary layer rely on surface land use. Three variables are derived from land use data using EPA s AERSURFACE 2 Program, namely, albedo, Bowen ratio, and the surface roughness parameter. The purpose of this study was to demonstrate the sensitivity of the AERMOD 3 Model in modeling identical sources with meteorological data sets derived using both airport and industrial site land use characteristics. This paper (using the results of a companion study of land use and the derivation of these three variables with variable surface roughness) presents combinations of meteorological data sets with representative industrial facility point and volume sources representing stacks, transfer points, storage piles and roads. Meteorology from various U.S. regions is used along with the land use characteristics for each airport and an urban complex where industrial activities are likely. Flat terrain is assumed in all cases. Comparisons between estimated concentrations for the various source types and meteorological data sets on both an annual basis and on a short-term averaging time basis show the importance of selecting specific land use and the possible variability that will result when following modeling guidance from states and regions on the selection of the appropriate location to use to determine the representative land use. INTRODUCTION The AERMOD Model 3,4 was introduced to the regulatory dispersion modeling community in the late 1990s. AERMOD was developed specifically by the AMS/EPA Regulatory Model Improvement Committee (AERMIC) to employ best state-of-practice parameterizations for characterizing the meteorological influences on dispersion. Section b of the Guideline on Air Quality Models (GAQM), Appendix W, 40 CFR Part 51 1 states that AERMOD is the recommended model for a wide range of regulatory applications in all types of terrain thus, officially replacing the Industrial Source Complex Model as the primary refined analytical technique for modeling traditional stationary sources. Provided along with the AERMOD Model are a number of preprocessors for preparing data sets applicable to running the AERMOD algorithms for transport, dispersion, convective boundary layer turbulence, stable boundary layer, terrain influences, building downwash, and land use. These are AERMAP, AERSURFACE, and AERMET. AERMAP is used to process elevation data from digitized data sets to generate elevations of receptors, sources, and structures as well the critical height for each receptor. AERSURFACE uses land use land cover (LULC) data to calculate albedo, Bowen ratio, and the surface roughness parameter which can vary on an annual, seasonal, or monthly basis for one or up to twelve sectors around a site. AERMET is the meteorological data processor that uses a combination of surface observation data from the National Weather Service (NWS), upper air data from NWS, onsite data if available and meeting prescribed collection and quality assurance criteria, and albedo, Bowen ratio, and surface roughness parameters from AERSURFACE. 2

3 Current guidance on the use of AERSURFACE for deriving the three meteorological variables is to apply the program for the LULC data set at the location of the weather data collection. This is generally the location of the weather instruments at the data collection site. As seen in the companion paper to this paper, namely, Sensitivity of AERSURFACE Results to Study Area and Location, 5 albedo and Bowen ratio vary but do not have a significant impact on air concentrations. Surface roughness values, however, which do vary widely 1) when comparing sites at different locations on the airport, 2) when using the AERSURFACE recommended 1 km radius to determine surface roughness versus the 3 km radius recommended in previous EPA modeling guidance, and 3) when comparing airport sites to industrial facility sites (where the meteorological data would be deemed representative considering geographical setting and general meteorological concerns), have been shown to have significant impact on modeled concentrations. The Environmental Protection Agency (EPA) has recognized this situation in recent meetings. At the June 11, 2008 meeting of the EPA Regional/State/Local Modelers Workshop, the AERMOD Implementation Workgroup (AIWG), Surface Characteristics Subgroup presented their findings 6 concerning three surface characteristics issues when comparing a meteorological collection site to an application site for a source: 1) lack of representative meteorological data, 2) parameter determination (albedo, Bowen ratio, and surface roughness), and 3) representativeness of using meteorological data from an airport to an application (industrial) site with dissimilar land use. Graphical findings were presented with indicated differences over many source types ranging from higher concentrations to lower concentrations when comparing the concentration ratios at the airport with a 1 km radius and a 3 km radius for the surface roughness calculations. The current analysis expands this work by looking at both the 1 km and 3 km radius for surface roughness calculation at the airport site but considering differences in the National Weather Service (NWS) coordinates (from the National Data Climatic Center, NCDC) and estimated locations of the weather data collection. This paper also considers an alternative non-airport site such as at a potential industrial site also at both a 1 km and 3 km radius. The derived surface characteristics are used in the AERMET processor with meteorological data and thereafter to perform dispersion modeling using AERMOD. The results of these analyses were used to discern the sensitivity of air concentration estimates for three NWS sites, three source types, and three averaging periods (including short term and long term) using surface characteristics for a three possible locations near each airport (airport and non-airport) and two radii distances. METHODOLOGY The basic methodology conducted in this analysis followed the recommendations of the GAQM 1 for application of the AERMOD Model. These recommendations included the use of regulatory options, the characterization of sources appropriately, hourly meteorological data based on nearby NWS data and processed in the AERMET program, surface characteristics based on AERSURFACE processing of NLCD92, local land use data (used in AERMET), and the tabulation of concentrations over 3-hr, 24-hr, and annual average time periods. To minimize the 3

4 effects of other influencing modeling features, terrain was assumed to be flat in all cases and no building downwash was considered. Notable differences in this analysis to that of EPA 6 were the use of fewer source types and the use of a nested 100m and 250m Cartesian receptor grid covering a domain of 10km by 10km. Also to minimize the effects of high impacts due to the surface-based volume source in the extreme near field, a square fence line at a distance of 100m was positioned around all sources. Locations and Land Use Study locations were defined in three general areas of the United States, the Eastern U.S., the Central U.S., and the Western U.S. Three airports were chosen as representative of these three general locations, namely, the Albany Airport (ALB, NWS No ) located in Albany, New York, the Jackson Julian Carroll Airport (JKL, NWS No ) located near Jackson, Kentucky, and the Pocatello Regional Airport (PIH, NWS No ) located in Pocatello, Idaho. To represent surface characteristics of typical industrial operations in each of these regions, an industrial facility located in the vicinity of each airport was chosen for inclusion in this study. The area surrounding the Albany airport consisted of primarily both high and low intensity residential and commercial/industrial/transportation land use with smaller areas of deciduous and evergreen forest, pasture/hay, and small grains. The area surrounding the nearby selected plant site consisted primarily of industrial quarries, haul roads and industrial operations along with mixed forest land use categories and smaller areas of pasture/hay, commercial/industrial/transportation, and evergreen forest land use. The area surrounding the Jackson airport was rather homogeneous and primarily contained deciduous forest. Some smaller areas of pasture/hay, mixed forest, and evergreen forest land use types are also located near Jackson airport. The nearby selected plant site location was surrounded by a much more complex mixture of land use types compared with the airport site. Primary land use types were transitional barren to the northeast, deciduous forest and open water to the southeast, pasture/hay and deciduous forest to the southwest, and deciduous forest pasture/hay, and open water to the northwest. The area surrounding the Pocatello airport consisted generally of shrubland in all directions, with some row crops to the southeast, commercial/industrial/transportation to the southwest (airport buildings), and orchards/vineyards/other to the northwest. The nearby selected plant site location consisted of a patchwork of row crops, orchards/vineyards/other, shrubland, pasture/hay, and an actual facility which included commercial/industrial/transportation land use within the study area. The AERSURFACE 2 tool was used to process the land use in the vicinity of each airport. Five AERSURFACE applications were processed using a 1 km and 3 km radius at the NCDCspecified tower location, a 1 km radius at an alternative tower location at the airport, and a 1 km and 3 km radius at a pseudo industrial location. The results of this application of AERSURFACE are reported in the report Sensitivity of AERSURFACE Results to Study Area and Location. 4 Two of the sites, namely, Albany and Pocatello, have greater surface roughness 4

5 elements for the 3 km radius compared to the 1 km radius namely, 15 and 20 percent, while the third site in Jackson with relatively consistent deciduous forest land use has only about a 1 percent increase. For Albany the selected pseudo plant site had higher roughness that the airport while for Jackson and Pocatello, the pseudo plant sites had lower surface roughness (cleared land versus forested airport site at Jackson and open range versus buildings at the airport). Thus, the determinations of surface roughness gave a good cross section of variable land use on and off airports. Meteorological Processing The AERMET program was used along with the AERSURFACE results for albedo, Bowen ratio, and surface roughness parameter to generate 15 sets of meteorological data. A 1992 data set of SCRAM formatted surface data for Albany, Jackson, and Pocatello and fixed format TD upper air profiles for Albany, Huntington, and Boise were used for each set of surface roughness parameters. Figures 1 through 3 show wind roses for each site for the NCDC tower location and a 1 km radius. Wind roses for these 1 km radius meteorological data sets showed little variation to those at alternative airport or industrial locations or at a 3 km radius for surface roughness. Wind speed and wind direction are little affected by the surface roughness parameter and convective and stable boundary layer parameters. Each site is characterized by multiple dominant wind directions and speeds and are mildly affected by local geographical features (Albany by the Hudson and Mohawk River Valleys, Jackson by the surrounding forested land, and Pocatello by the Snake River Valley). Sources Three sources were modeled in this analysis representing a tall buoyant stack, a shorter less buoyant stack, and a small storage pile represented by a volume source. All sources were assumed to be collocated at the center of a coordinate system located at the NCDC coordinates, he alternate airport location, or at the pseudo plant site. The influences of building or structure downwash and wakes were not included in the analysis. Parameters defining the physical characteristics of each source are shown in Table 1. Figure 1. Wind Rose for Albany, NY. Figure 2. Wind Rose for Jackson, KY. 5

6 Figure 3. Wind Rose for Pocatello, ID. 6

7 Table 1. Source Characteristics Used in Modeling Sensitivity Analysis. Point Source ID Stack height (m) Stack gas exit temp. (K) Stack gas exit velocity (m/s) Stack gas diameter (m) Emission rate (g/s) STACK STACK Volume Source ID Release height (m) Physical height (m) Horizontal dimensions (m) Volume X Emission rate (g/s) Receptors An array of receptors were placed around each airport and plant site. A fence line was assumed around each set of sources at a 100m distance in a square. A fence line receptor grid was arranged at a 50m spacing, a 100m grid out to 2km around each site, and a 250m grid out to 5km. A total of about 3,000 receptors were used in the modeling. Model s and Analysis Each set of source parameters were modeled using AERMOD (Version 07026) along with each set of meteorological data. Concentrations were calculated for a 3hr, 24hr, and annual averaging period. The concentration associated with the meteorological data set using the NCDC 1 km radius surface roughness parameters was considered as the baseline for each site. This baseline was selected because this scenario followed the AERSURFACE application guidance. Concentration differences between each scenario and the baseline were then tabulated. RESULTS Tables 2 through 10 present the comparisons between air concentrations derived on the basis of the baseline location meteorological data and a 1 km radius with concentrations derived for each meteorological scenario for considering surface roughness. All source types are described in each set of tables. The tables are arranged in sets of three by meteorological data location, Tables 2 through 4 are for Albany, Tables 5 through 7 are for Jackson, and Table 8 through 10 are for Pocatello. Within each table, which applies to one source type, are found five scenarios of surface roughness/meteorology. The only difference between meteorological data sets is the surface roughness parameters applicable to each scenario. All other AERMET processing related operations are identical. Concentrations are estimated using AERMOD with each source for each meteorological scenario. 7

8 Tables 2 through 4 show the comparison of concentrations for the Albany airport sites and a nearby pseudo industrial site. The surface roughness at this airport includes grassy areas and buildings at the airport or just beyond the airport property. Thus, as the radius at the airport is increased, the surface roughness is greater and the associated turbulence in AERMOD increases. The increased turbulence causes more mixing of all source plumes and is seen as increased concentrations for the tall point sources (with some contribution from increased scaling of turbulence and wind speed with height as well) and decreased concentrations for the lower emitting volume source. As surface roughness increases to its greatest value at the 3 km radius at the pseudo plant site, point source concentrations are at their highest concentrations when Table 2. Ratio of to NCDC 1 km Concentrations at Albany 35m Stack. Ratio of to NCDC 1 km Concentrations 35m Stack Albany Alternative Tower Albany NCDC 1km Albany NCDC 3km Albany Plant 1km Albany Plant 3km Table 3. Ratio of to NCDC 1 km Concentrations at Albany 65m Stack. Ratio of to NCDC 1 km Concentrations 65m Stack Albany Alternative Tower Albany NCDC 1km Albany NCDC 3km Albany Plant 1km Albany Plant 3km Table 4. Ratio of to NCDC 1 km Concentrations at Albany Volume. Ratio of to NCDC 1 km Concentrations Volume Albany Alternative Tower Albany NCDC 1km Albany NCDC 3km Albany Plant 1km Albany Plant 3km

9 compared to the baseline (more turbulence with better mixing to ground) and the volume source at its lowest concentrations (also better mixing but at ground level which reduces ground level concentrations). Tables 5 through 7 show the comparison of concentrations for the Jackson airport and nearby area. The surface roughness at this airport includes forested areas and buildings at the airport or just beyond the airport property. Thus, as the radius at the airport is increased, the surface roughness stays about the same and the associated turbulence in AERMOD also stays about the same. This causes little change in the turbulence and mixing and thus, concentrations are not significantly affected. At the pseudo industrial location, surface roughness is decreased at Table 5. Ratio of to NCDC 1 km Concentrations at Jackson 35m Stack. Ratio of to NCDC 1 km Concentrations 35m Stack Jackson Alternative Tower Jackson NCDC 1km Jackson NCDC 3km Jackson Plant 1km Jackson Plant 3km Table 6. Ratio of to NCDC 1 km Concentrations at Jackson 65m Stack. Ratio of to NCDC 1 km Concentrations 65m Stack Jackson Alternative Tower Jackson NCDC 1km Jackson NCDC 3km Jackson Plant 1km Jackson Plant 3km Table 7. Ratio of to NCDC 1 km Concentrations at Jackson Volume. Ratio of to NCDC 1 km Concentrations Volume Jackson Alternative Tower Jackson NCDC 1km Jackson NCDC 3km Jackson Plant 1km Jackson Plant 3km

10 Jackson in the suburban and rural areas and thus turbulence and mixing are decreased. Point source concentrations are lower at this location than the base site and volume source concentrations are increased (poorer mixing but at ground level which increases ground level concentrations). Tables 8 through 10 show the comparison of concentrations for the Pocatello airport and nearby area. The surface roughness at this airport includes grassy areas and a few scattered buildings at the airport. Just beyond the airport property lies open range, farming, scrub areas. Thus, as the Table 8. Ratio of to NCDC 1 km Concentrations at Pocatello 35m Stack. Ratio of to NCDC 1 km Concentrations 35m Stack Pocatello Alternative Tower Pocatello NCDC 1km Pocatello NCDC 3km Pocatello Plant 1km Pocatello Plant 3km Table 9. Ratio of to NCDC 1 km Concentrations at Pocatello 65m Stack. Ratio of to NCDC 1 km Concentrations 65m Stack Pocatello Alternative Tower Pocatello NCDC 1km Pocatello NCDC 3km Pocatello Plant 1km Pocatello Plant 3km Table 10. Ratio of to NCDC 1 km Concentrations at Pocatello Volume. Ratio of to NCDC 1 km Concentrations Volume Pocatello Alternative Tower Pocatello NCDC 1km Pocatello NCDC 3km Pocatello Plant 1km Pocatello Plant 3km

11 radius at the airport is increased, the surface roughness slightly decreases with subsequent decreased turbulence. This causes less mixing of all sources which causes slightly decreased concentrations for the point sources and increased concentrations for the lower volume source. The off airport pseudo industrial location has even less roughness over the area and thus, turbulence is diminished from the base case and concentrations increase. These tables indicated that the magnitude of air concentration differences may be potentially significant depending on both the airport location, its situation within specific land use types, the type of source and the consideration of either the airport or the industrial location. As an additional indication of the comparison of the air concentrations of pollutant at various averaging times for different locations or radius of influence, Figures 4-9 present comparisons. Figures 4 through 6 present all alternative scenario-based concentrations compared to the base case for all airport sites combined for all stacks combined. The annual comparisons in Figure 4 show concentrations that are nearly equal between the scenario impacts and the base case. A few outliers for the plant 1 km and 3 km radii indicate a significant change in surface roughness which yielded some concentrations lower for the scenario (Jackson) and a few higher (Albany). In Figures 5 and 6 a similar pattern emerges and the grouping of the concentration comparisons shows the differences in the airport locations. Figure 4. Comparison of All Concentrations Versus the Base Case for Annual Average and All Stacks. 11

12 Figure 5. Comparison of All Concentrations Versus the Base Case for 24hr Average and All Stacks. Figure 6. Comparison of All Concentrations Versus the Base Case for 3hr Average and All Stacks. 12

13 For volume sources, Figures 7-9 show that for areas with less surface roughness, volume sources are estimated to have higher concentrations than the base case and conversely for areas with more surface roughness, the concentrations due to volume sources are less than the base case. A similar pattern occurs across all averaging periods. Figure 7. Comparison of All Concentrations Versus the Base Case for Annual Average and All Volume Sources. Figure 8. Comparison of All Concentrations Versus the Base Case for 24hr Average and All Volume Sources. 13

14 Figure 9. Comparison of All Concentrations Versus the Base Case for 3hr Average and All Volume Sources. SUMMARY The selection of the appropriate radius around a meteorological data site for the determination of surface roughness makes a significant difference in the magnitude of the related air concentrations. The AERSURFACE guidance as well as the latest AERMOD implementation guidance suggests the use of a 1 km radius for this determination. The guidance also indicates that the location of the data collection site at the airport should be the center of the radius. The use of the 1 km radius when compared to a 3 km radius has been shown to yield different results in terms of the surface roughness elements as well as the associated air concentrations. The identification of the correct location of the meteorological tower was also shown to affect the surface roughness and air concentrations. Finally, the selection of land use at the airport versus a potential industrial facility site shows that the differences can give different air concentrration results depending on the source type. 14

15 REFERENCES 1. Guideline on Air Quality Models. Appendix W to 40 CFR Parts 51 and 52. Federal Register, November 9, pp AERSURFACE Users Guide. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. January AERMOD Implementation Guide. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Revised January User s Guide for the AMS/EPA Regulatory Model - AERMOD. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Revised September Schroeder, Tony and G. Schewe, Sensitivity of AERSURFACE Results to Study Area and Location, presented at the 102nd Air & Waste Management Association Conference, Detroit, Michigan, June 15-19, Surface Characteristics. Presentation by the AERMOD Implementation Work Group, Surface Characteristics Subgroup, Environmental Protection Agency Regional/State/Local Modelers Workshop, Denver, Colorado, June 11, KEYWORDS AERMOD, AERSURFACE, Dispersion, Meteorology 15