Sensitivity Analysis Study Considering the Selection of Appropriate Land-Use Parameters in AERMOD Modeling Analyses

Modeling Software for EHS Professionals

Sensitivity Analysis Study Considering the Selection of Appropriate Land-Use Parameters in AERMOD Modeling

Analyses

Prepared By:

Elizabeth CarperEri Ottersburg

BREEZE SOFTWARE 12700 Park Central Drive,

Suite 2100 Dallas, TX 75251

+1 (972) 661-8881 breeze-software.com

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Sensitivity Analysis Study Considering the Selection of Appropriate Land-Use Parameters in AERMOD Modeling Analyses

Control # 167

Elizabeth Carper Trinity Consultants, 20819 72nd Avenue South Suite 610, Kent, Washington, 98032

Eri Ottersburg Trinity Consultants, 20819 72nd Avenue South Suite 610, Kent, Washington, 98032

ABSTRACT

In the near future AERMOD PRIME will officially replace the Industrial Source Complex Short-Term model, Version 3 (ISCST3) as the preferred Environmental Protection Agency (EPA) air dispersion model. Unlike the ISCST3 model that requires the simple selection of urban or rural default settings, the AERMOD dispersion model has a meteorological pre-processor (AERMET) that requires the input of site-specific land use parameters corresponding to land-use categories, including albedo, Bowen ratio, and surface roughness. AERMET then “adjusts” the meteorological data based on these site-specific micrometeorological parameters for use in AERMOD. AERMOD’s sensitivity to these land-use parameters has become a hot topic in the air dispersion community because of the significant impact on ambient concentrations that may result due to sensitivity to input parameters. This paper investigates the relationship between surface characteristics and air dispersion impacts as well how these relationships are addressed by regulatory agencies. Furthermore, this paper presents case studies of how the evaluation of surface characteristics can play a significant role in regulatory review of air dispersion modeling.

INTRODUCTION

The American Meteorological Society (AMS)/United States Environmental Protection Agency (U.S. EPA) Regulatory Model Improvement Committee (AERMIC) has developed AERMOD-PRIME (AERMOD, hereafter) to become the new Guideline model, replacing ISCST3. The goal of the model is to provide a more representative depiction of air dispersion modeling by using superior air dispersion algorithms. AERMOD was designed with the intent that meteorological data from sources such as the Nationa l Weather Service (NWS), commonly used for models such as ISCST3, would still be adequate input to the model and additional site-specific measurements would not be a mandatory requirement. AERMOD incorporates the effects of surface characteristics of the land on meteorological data using a processor called AERMET. However at this time, there is little guidance on the best method for determining the appropriate surface characteristics of a modeling domain to input to the AERMET model,

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and there is no consistent guidance for such applications across the regulatory agencies within the U.S. AERMET processes NWS and/or site-specific meteorological data with additional surface characteristics parameters to provide meteorological data specific to the modeling domain. Surface characteristics are obtained by determining the land use type such as urban, agricultural, deciduous, etc within a circular area extending approximately three (3) kilometers from the point of interest. Assuming that the meteorological observation site is representative of the facility being studied, the land use analysis is intended to be performed about the meteorological data collection site.1 The land use type can by classified in as many as 12 pie-shaped sectors within the three-kilometer circle. There are a total of eight classified land use types identified within the AERMOD user’s manual including: coniferous forest, cultivated, desert, deciduous forest, grasslands, swamp, urban, and water. Each land use type is defined by three surface characteristics. These three characteristics are albedo, Bowen ratio, and surface roughness. The albedo is the proportion of the sunlight that is reflected back into space. The Bowen ratio is the ratio of solar radiation received that is available for sensible heating to that available for latent heating.2 The surface roughness length is an indicator of the amount of drag the ground surface exerts on the wind. SENSITIVITY ANALYSES Model Setup Revision 03273 of AERMET and AERMOD version 02222 are used in this analysis, which is a recent beta version of the models.3 The AERMOD model setup is the same for all scenarios in the sensitivity analysis. Furthermore, within the meteorological processing, only the surface characteristics are varied. The model includes three source types: an area source, a momentum dominant point source, and a buoyancy dominant point source.4 Each source type is modeled at several release heights. Building downwash effects are not modeled in the analysis. Table 1 summarizes the source parameters for each modeled source.

1 Minimum Meteorological Data Requirements for AERMOD –Study and Recommendations draft document

Version: 98314 (AERMOD & AERMET) 98022 (AERMAP) (December 14, 1998) from SCRAM. 2 In meteorology, sensible heat is heat that is utilized in the form of a temperature increase. Latent heat is the

portion of energy received that is used for evaporation of surface moisture. 3The most recent beta version was released on March 23,2004 (Version 04079) 4 The plume exiting from a stack rises from either momentum or buoyancy or a combination of the two.

Exhaust gases with high exiting velocities eject the plume from the stack giving it momentum to rise. Similarly, exhaust gases that are high in temperature cause the plume to rise because the exiting gas temperature is higher than the ambient temperature. In the analyses presented in this report the momentum source is representative of a baghouse with an exhaust temperature approximately equal to ambient air and the buoyant source is representative of a combustion unit.

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Table 1. Source Parameters.

Emission Temperature Velocity Diameter Release Height Source Rate (K) (F) (m/s) (ft/s) (m) (ft) (m) (ft) Area 0.001 g/s-m2 -- -- -- -- 12.2 x

12.2 40 x 40

0 4.572 9.144

0 15 30

Momentum 1 g/s 293.15 68 9.144 30 0.9144 3 0 4.572 9.144 30.48 45.72

0 15 30 100 150

Buoyancy 1 g/s 533.15 500 9.144 30 0.9144 3 0 4.572 9.1443 30.48 45.72

0 15 30 100 150

Receptors are placed at 5-degree intervals on 10 concentric circles at the following distance from the sources: 50, 100, 150, 300, 500, 1000, 1500, 2000, 2500, and 3000 meters. The model is run with flat terrain (elevation of zero for all modeled objects and the meteorological station). Figure 1 is a plot of the receptor rings. For this analysis the maximum concentrations for annual and 1-hour averaging periods are analyzed, because these are the averaging periods often required when modeling criteria pollutants.

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Figure 1: Receptor Rings

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Surface Characteristic Comparison This analysis compares the effect of each surface characteristic (albedo, Bowen ratio, and surface roughness) on modeled concentrations. Within AERMET one surface characteristic is varied while the other characteristics are kept constant for the entire modeling domain. The value of the surface characteristics for each model run is provided in Table 2.

Table 2. Surface Characteristics of Each Model Run.

Model Run Albedo Bowen Ratio Surface Roughness Base 0.18 0.85 0.35 Low Albedo 0.10 0.85 0.35 Average Albedo 0.35 0.85 0.35 High Albedo 0.60 0.85 0.35 Low Bowen Ratio 0.18 0.10 0.35 Average Bowen Ratio 0.18 3.05 0.35 High Bowen Ratio 0.18 6.00 0.35 Low Surface Roughness 0.18 0.85 0.0001 Average Surface Roughness 0.18 0.85 0.65 High Surface Roughness 0.18 0.85 1.30

Tables 3 and 4 show the variability in modeled concentration for each scenario and source type. The variability is defined as the difference between the maximum and minimum first high concentrations of each scenario. Results indicate Bowen ratio has very little effect on model concentrations and surface roughness has the greatest effect.

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Table 3. Range of Concentration Variability Annual Averaging Period.

Stack Variability of Concentration (µg/m3) Source Type Height

(ft) While varying

surface roughness While varying

albedo While varying Bowen ratio

0 539.68 4.25 1.34 15 33.36 1.63 1.03

Area

30 13.36 0.37 0.58 0 238.47 11.81 4.92 15 89.21 7.26 0.33 30 40.23 0.97 0.15 100 2.16 0.07 0.02

Momentum Point Source

150 0.88 0.03 0.01 0 35.55 3.78 1.58 15 27.08 1.13 0.87 30 16.05 0.11 0.54 100 1.50 0.08 0.09

Buoyant Point Source

150 0.67 0.04 0.03

Table 4. Range of Concentration Variability 1-Hour Averaging Period.

Stack Variability of Concentration (µg/m3) Source Type Height

(ft) While varying

surface roughness While varying

albedo While varying Bowen ratio

0 88,546.46 0.00 0.00 15 532.93 234.81 0.00

Area

30 449.28 125.40 88.58 0 478,142.76 0.00 0.00 15 2,026.76 9.98 0.00 30 964.52 634.81 2.58 100 151.36 38.52 0.09

Momentum Point Source

150 36.04 0.75 0.77 0 172.38 0.00 0.00 15 122.43 0.00 0.00 30 70.37 0.86 0.01 100 3.03 1.12 3.16

Buoyant Point Source

150 5.27 0.90 1.41

Table 3 demonstrates that the effect of surface characteristics on ambient emissions decreases as stack height increases for the annual averaging period. However, for the 1- hour averaging period this relationship is only observed while varying surface roughness. This is probably due to the hourly fluctuations of the meteorological data. These fluctuations are less noticeable for long averaging periods. The relationship is maintained for the 1-hour averaging period while varying the surface roughness

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characteristic because the surface roughness is constant throughout the domain. In other words change in hourly wind direction will not change the surface roughness characteristic. The buoyancy dominated point sources shows the smallest change in concentration with changing surface characteristics. In general a buoyancy dominated source will have lower concentrations. The high temperature of the plume allows it to rise higher above the ground than a plume with a low temperature. A buoyant plume is therefore less affected by surface conditions. Further investigation demonstrates that surface roughness also has an affect on the distance a plume will travel. Wind speeds are greater when the surface roughness is low because there is less friction from land features. Wind speed has two effects that compete in terms of concentration. First, increased wind speed causes increased dilution, which makes the concentration lower. Second, increased wind speed also increases plume spread, which results in higher concentrations at ground level because the plume spreads toward the ground faster. For plumes released at heights that are close to the ground, low surface roughness will result in higher peak concentrations and higher concentrations at more distant receptors because there is little vertical plume spread; therefore, the surface release will not spread away from ground level receptors. For plumes released further away from the ground, the peak concentration is less with a low surface roughness because the plume is dispersing before it reaches the ground. Figure 2 shows the difference between modeled concentrations with low surface roughness and high surface roughness compared with distance from the source. These effects are analyzed in more detail later in this report. Figure 2 also demonstrates that surface roughness has a greater affect on distance for sources with a lower release height.

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Figure 2: Surface Roughness Affect on Concentration and Distance

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Land Use Type Comparison The surface characteristics that are used for each land use type are the suggested values provided in the AERMET User Guide. An analysis of all eight land types is performed to investigate how each land use type will affect plume dispersion. The results of these analyses are presented in Figure 3.

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Figure 3: Comparison of Land Use Type on Emission Impacts

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Figure 3 demonstrates an interesting phenomenon. Land use types that displayed the highest ambient concentrations at a release height of zero show the lowest concentrations at higher release heights. This result can be explained by examining surface roughness impacts on wind speeds and mixing heights. If a land type has a low surface roughness (e.g., water) there is less wind friction allowing a concentrated plume to be dispersed and transported at a faster pace. As discussed previously, there is little vertical plume dispersion at low release heights; therefore the plume will not spread away from ground level receptors, resulting in high concentrations especially for land use types with low surface roughness. For high release heights at low surface roughness the plume will disperse before in reaches the ground receptors at a faster pace than it would at a high surface roughness. Furthermore, low surface roughness also results in lower mechanically driven mixing heights that can sometimes trap a concentrated plume close to the ground. If a land use type has a high sur face roughness (e.g., urban) then the wind friction will create more turbulence and subsequently a higher mechanically-driven mixing height; therefore, instances where concentrated plumes are trapped close to the ground by low mixing heights are not as likely to occur. When examining the impact on buoyant point sources from surface roughness, it is observed that even when the surface roughness is low, there are low concentrations from a zero release height. This can be explained because the buoyancy of the stack lifts the plume high enough that plume is dispersed before it reaches the ground receptors. Figure 4 and Figure 5 compare the difference in concentration from the lowest release point to the highest release point with the surface roughness characteristics. These figures demonstrate that the difference in concentration is greater for land use types with low surface roughness and smaller for land types with higher surface roughness for both the area and momentum dominated point source. For buoyant sources with higher plumes, land use types with low surface roughness have faster dispersion than source types with higher surface roughness.

Figure 4: Sensitivity to Release Heights vs. Surface Roughness Annual Averaging Period

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Figure 5: Sensitivity to Release Heights vs. Surface Roughness 1-Hour Averaging Period

REGULATORY SIGNIFICANCE The modeling algorithms of AERMOD are more advanced than those of ISCST3, making AERMOD the preferred regulatory model to assess and predict compliance with air quality standards. As such, AERMOD requires more complicated meteorological inputs than ISCST3. Since these meteorological inputs are site and case specific it is not practical to consider general guidance for selecting appropriate parameters that can be applied in every case. Under current modeling guidance, five years of data from the nearest or most representative meteorological station may be used. Meteorological data typically often comes from airports located in urban areas, while most industrial facilities are located in rural areas. One question that arises is whether or not it is more appropriate to use the surrounding surface characteristics of the meteorological station or the surrounding surface characteristics of the facility being modeled. The differences in surface characteristics of meteorological stations and industrial sites may be considerable, and can have a significant impact on modeled concentrations. Therefore, the selection of surface characteristics is an important part of the modeling process that is receiving more scrutiny by regulatory agencies. One proposed method to address these differences is to model both sets of meteorological data and use the worst-case scenario to determine ambient impacts. The success of this method is subject to the opinion of the regulating agency. Other methods, such as the “up-over-down” technique, adjust meteorological data to simulate the effect on meteorology during the transition from one set of surface conditions to another set of

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surface conditions.5 In addition to being cumbersome, there is no clear guidance on how to conduct this method. A future version of the AERMOD-PRIME model may incorporate algorithms to address this issue, but meanwhile it remains an important and significant regulatory concern. The following section of this paper describes two separate case studies. These case studies demonstrate how different regulating agencies responded to a modeling study where the surface characteristics of the meteorological stations are different from the industrial sites. Case Study I – Meteorological Station in Urban Location Vs. Industrial Facility in Rural Location Case Study I demonstrates a classic scenario where the meteorological station is located at an airport in an urban area and the industrial facility is located in a mostly rural area. Figure 6 illustrates these two sites and their surrounding surface characteristics.

Figure 6: U.S. Geological Survey Land Use Land Cover Analysis

a This land analysis is prepared by processing U.S. Geological Survey (USGS) with the MAKEGEO utility; a

component of the CALPUFF model.

5 The concept of this technique is to adjusts the hourly wind speed by profiling up (using the surface roughness length near the meteorological measurement site) to a height at which the wind speed is assumed to be the same over the distance to the source and then profiling down to the stack release height (using the surface roughness length near the source).

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When reviewing the project the regulatory agency was concerned about modeling with AERMOD because AERMET meteorological data is heavily influenced by surface characteristics. They did not feel comfortable using either the airports surface characteristics or the industrial facility’s surface characteristics. It was proposed to the agency that both surface characteristics would be modeled and the worst-case scenario would be used. The results of this initial modeling are shown in Figure 7.

Figure 7: Dispersion Analysis Comparison

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In spite of AERMOD’s superior modeling capabilities, the state agency responsible for issuing this specific permit decided that they were not comfortable with using the “worst-case” method, and insisted that the facility use ISC PRIME to estimate emission impacts. The results of the ISC PRIME modeling are compared with the AERMOD modeling in Figure 8. As demonstrated in Figure 8, ISC PRIME concentrations are between the initial AERMOD results for the short term averaging periods but are significantly higher than both for the annual averaging periods.

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Figure 8: Comparison of ISC PRIME with AERMOD

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Case Study II - Meteorological Station Surrounded by Grasslands Vs. Industrial Facility Located Near River In Case Study II a company proposes to install a greenfield facility that is to be located immediately next to a major river. The meteorological data used in air dispersion modeling is less than eight kilometers from the location of the proposed facility. However, the proposed facility is immediately next to a river while the meteorological station is further south and surrounded by land. When evaluating the surface characteristics around both the meteorological station and the proposed facility it was observed that the land surrounding the proposed facility is approximately 50% water and 50% grasslands, while the land surrounding the meteorological station is entirely made of grasslands. The regulatory agency was concerned that this difference would make a significant impact on the modeling results and required additional justification that the meteorological data is representative of the area. Land surrounding the proposed meteorological station and the industrial facility is analyzed using images available on the TerraServer website. The TerraServer images for these sites are shown in Figure 9.

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Figure 9: TerraServer Land Use and Land Cover Analysis

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Meteorological Station Industrial Facility

To better understand the scenario the windrose from the meteorological data is evaluated. The windrose is presented in Figure 10.

Figure 10: Windrose

This windrose indicates that for the dominant wind direction the land type surrounding the proposed facility is the same as the land type surrounding the meteorological station. Therefore, it is argued that the meteorological data is representative of the industrial site.

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This argument was accepted by the regulating agency responsible for this project and no further modeling was required. To demonstrate the concept of Case Study II, a hypothetical modeling analysis is performed. Three scenarios are analyzed using the same model set up that was used in the sensitivity section of this report. The first scenario assumes the surface characteristics of the airport. The second scenario assumes the surface characteristics of the proposed facility located south of the river. The third scenario assumes the surface characteristics of a hypothetical facility located north of the river. The land use and land cover analyses are presented in Figure 11.

Figure 11: Proposed Land Use Land Covers

If the above argument is correct, the results of this analysis will show that the first and second scenario will have similar results while third scenario will vary. Figure 12 present the results of analysis.

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Figure 12: Comparison of Surface Characteristics vs. Primary Wind Direction

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The charts for the of the momentum dominated and buoyancy dominated point sources in Figure 12 support the argument of Case Study II. These charts demonstrate that if the surface characteristics are the same for the primary wind directions, then dispersion impacts are approximately the same. Furthermore, as the stack height increases, the surface characteristics have less influence on dispersion results. Some variability is noticed in the charts from the hourly averaging. For releases at zero feet the area source and the momentum source show similar results for scenario 2 and 3. The highs of these receptors are on opposite sides of the modeling domain; therefore, the same impact is observed when the wind blows over the same land use type. Further investigation of the meteorological conditions is necessary to determine other factors that may be contributing to this variability. CONCLUSION The results of this analysis conclude that of all three surface characteristics, surface roughness has the greatest impact on emission impacts. Albedo and Bowen ratio have a much lower impact on plume dispersion and show more fluctuation in hourly averaging periods. Surface roughness also affects the speed and distance of which a plume can travel and the mechanically driven mixing height. Lower surface roughness allows for higher winds speeds that can transport a concentrated plume faster whereas higher heights increase plume dispersion. The interpretations of these relationships will play an important role in determining the best way to choose surface characteristics that are representative of each facility’s surroundings. However, further analysis is still needed to understand which characteristics are the most representative for a specific facility. Until there is better guidance available, each project will be subject to regulatory interpretation on a case-by-case basis. REFERENCES

1. Ramakrishnan, D.; Wall, D. Current and Future Challenges in Conducting an AERMOD-PRIME Analysis. # 03-A-26-AWMA

2. Ramakrishnan, D.; Zwicke, G.; Wall, D.; Remsburg, A. M. A Performance Comparison of AERMOD vs. Current Guideline Models in a Real World Scenario, # 03-A-27-AWMA