ASSESSING HUMAN HEALTH AND ENVIRONMENTAL IMPACTS OF AIR POLLUTANTS
James Kuiper, Michael Lazaro, and Young-Soo Chang Argonne National Laboratory Environmental Assessment Division 9700 South Cass Avenue Argonne, Illinois 60439
for presentation at the Twenty-Third Annual ESRI User Conference San Diego, California, USA July 7-11, 2003
sponsored by Environmental Systems Research Institute
The submitted manuscript has been created by the University of Chicago as Operator of Argonne National Laboratory (“Argonne”) under contract No. W-31-109-ENG-38 with the U.S. Department of Energy. The U.S. Government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.
ABSTRACT Air pollutants generated routinely or accidentally by human activities and by natural processes can threaten human health and the environment. We have completed numerous projects that required consequence assessment and risk quantification for air quality issues. A variety of models and tools are available to examine the distribution, transport, and effects of air pollutants, ranging from dust to toxic or radioactive substances. GIS software is used to manage data, analyze impacts, and visualize results. This paper presents examples of how these various and seemingly dissimilar models and tools have been employed in an integrated framework to address complex air pollution issues.
state and international boundaries, and are a matter of concern and negotiations between governments.
INTRODUCTION Integration of air pollution assessment, government regulation of air quality, modeling of air dispersion, and the implementation of GIS tools to support modeling and impact analysis is a broad and complex subject. This paper provides introductory material about the science of air quality modeling, current U.S. legislation and trends that have influenced development and implementation of the models, and a broad overview of the types of models in use. With that baseline, some of the mechanics of coupling the models with GIS and analyzing impacts to human health and the environment are discussed. Examples of specific projects and techniques from our experience are provided.
An air pollutant that is transported and deposited near its source generally has only a small effect on air quality and minimal impacts to receptors, unless the pollutant is highly toxic or the receptors are highly sensitive. When air pollutants are transported over relatively long distances and/or are airborne for a longer time they can have more complex effects, and there is a greater likelihood of chemical reactions that result in more toxic pollutants. Air pollutants can damage human health and the environment in many ways, including causing respiratory ailments, structural property damage, degradation of terrestrial and aquatic resources, and reduction of the stratospheric ozone layer. In particular, acid deposition, resulting from NOx and SOx from combustion sources, damages lakes, trees, and terrestrial and aquatic resources. Air pollutants can also cause regional haze, which reduces visibility in pristine national parks and wilderness areas and sometimes interferes with aviation. Some toxic chemicals found in polluted urban air cause cancer and other adverse human health conditions, such as birth defects and long-term injury to respiratory and pulmonary systems. Toxic air pollutants from accidental releases can cause serious injury or even death. In 1984, an accidental gas leak at a Union Carbide plant in Bhopal, India killed 3,800 people; 40 persons experienced permanent total disability; and 2,680 persons experienced permanent partial disability (Union Carbide 2003).
Background Air pollutants come from many different anthropogenic (man-made) sources, which include stationary sources (ranging in size from gas stations and dry cleaners to power plants, and industrial facilities) and mobile sources (such as cars, trucks, planes, locomotives, and agricultural and construction equipment). In addition, natural sources such as windblown dust, wildfires, and volcanic eruptions emit pollutants into the atmosphere. Once airborne, air pollutants are transported over varying distances throughout the atmosphere. For example, large particles of 10 µm suspended into the air by prevailing winds to an altitude of more than 50 meters will remain suspended about an hour before settling back to an altitude of 10 meters. On the other hand, ozone or fine particulates can travel to the upper atmosphere and be transported hundreds or thousands of miles under favorable meteorological conditions. Air pollutants cross
Legislation The Clean Air Act (CAA) was enacted in 1970 to improve air quality in the United States and was
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most recently amended in 1990. The primary objective of the Act is to regulate air pollutant emissions to protect human health and the environment. In general, the CAA delegates responsibility to state and local governments for the prevention and control of air pollutants at their sources. Federal assistance and leadership were considered essential in helping state and local governments to implement programs to prevent and control air pollution.
permit. The permit includes information on emission limits and control requirements, stack emission test requirements, emission monitoring requirements, operational and emission inventory reporting requirements, and ambient monitoring requirements. In addition, the permit includes provisions for enforcement and permit fees. The CAA empowers the EPA to fine violators and revoke the permit for successive violations. Also, based on actual emissions from the facility, permit applicants must pay permit fees that will be used for other state air pollution control activities.
Under the CAA, the U.S. Environmental Protection Agency (EPA) established healthbased national ambient air quality standards (NAAQS) for six criteria pollutants (SO2, NO2, CO, O3, particulate matter [PM10 and PM2.5], and Pb). Nonattainment areas were identified for locations having a concentration level of a criteria air pollutant higher than the NAAQS. Under the CAA, the state is responsible for the preparation of State Implementation Plans (SIP) for a nonattainment area to attain the NAAQS at the earliest practical date. Writing about the 1990 amendments to the Clean Air Act, Ruch (1991) noted actions that industrial facilities could investigate to more smoothly transition to future requirements and avoid the costs of failing to meet the related deadlines. This included preparing emission inventories and identifying voluntary reductions, with the benefits including possibly being dropped from the regulation/permitting process or getting a compliance date extension.
In addition, the EPA established national emission standards, such as the National Emission Standards for Hazardous Air Pollutants (NESHAP), which list hazardous air pollutants, and the New Source Performance Standards (NSPS) to limit emissions at their sources. The 1990 CAA Act introduced market-based flexible programs to clean up air pollutants as efficiently and economically as possible through trading of pollution allowances and emission credits. Measuring Trends in Air Pollution During last two decades, total U.S. emissions of the six criteria pollutants have decreased by approximately 25%. National ambient air quality has improved despite population growth, industry growth, and attendant energy consumption and vehicle miles traveled. These emissions improvements are a result of effective implementation of clean air laws and regulations, as well as improvements in the efficiency of industrial technologies. However, despite this great progress in air quality improvements, approximately half of U.S. citizens still live in nonattainment areas for one or more of the six criteria pollutants. Figure 1 shows a May 2003 map of the United States with the number of NAAQS pollutants at nonattainment levels, by county.
Another feature of the CAA is public participation in the SIP development, such as hearings and public comments. For an attainment area, the Prevention of Significant Deterioration (PSD) program requires a business owner to obtain a permit before a major air pollutant source is constructed or an existing major source is modified. This program is designed to enhance protection of air quality resources. In this program, maximum allowable increments, as an absolute ceiling, have established for SO2, NO2, and PM10 in that emission sources are allowed to consume only part of the available increments over a baseline concentration for new sources and modification of existing major sources.
Benefits of Air Quality Modeling To track trends of control measures for fulfillment of the CAA, actual air quality measurements have been made at monitoring sites across the country. However, direct measurement of air quality is expensive when compared to air quality modeling. In cases where an acceptable level of accuracy can be achieved with an air quality model, costs can be reduced by using modeling instead of
One of the striking breakthroughs in the 1990 CAA is the Clean Air Act Permit Program (CAAPP), the most extensive and demanding of the operating permits for larger and more significant pollution emitters. Once issued, the CAAPP permit replaces an earlier operating
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Figure 1. Number of NAAQS Pollutants by County at Nonattainment Levels, as of May 2003 (EPA 2003) direct measurement. Also, modeling is the only way to assess potential future impacts of most proposed construction projects, or operational changes to existing ones. Modeling provides the best approach for estimating impacts under various hypothetical accidental release scenarios.
AIR DISPERSION MODELS A large number of air dispersion models have been developed and applied in support of research or regulatory applications related to air quality. The models have been used to address routine atmospheric releases and air quality impacts (for example, from smoke stacks or unintended or accidental releases of hazardous chemicals from industrial process). One way to categorize air quality models is by their computational approach. Three broad groups are (1) straight-line Gaussian, (2) Lagrangian puff and (3) Eulerian grid. Within these groups, air dispersion models can be further characterized by their inherent level of complexity as required or appropriate for consideration for a particular application. The simplest and easy to use tools are referred to as (1) screening models, followed by (2) health impact-consequence assessment models for more refined analysis, then by (3) applied
Accordingly, modeling is widely used to assess potential impacts on air quality and air-quality-related values, such as acid deposition and visibility. As models have become more numerous, complex, and diverse, the powerful and robust techniques available in GIS have been increasingly used for spatial data management, visualization, user interface, analysis of impacts, and, sometimes, the modeling itself. The combined use of air quality models and GIS has become standard procedure and an indispensable tool to assess potential air quality impacts.
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Numerous accidental release scenarios for nerve (e.g. Sarin) or blistering (e.g. mustard) agents have been evaluated to identify hazard zones and to assess population exposures to various levels or doses of an agent released from an Army chemical weapons depot. We have also applied models in this subgroup that are becoming the more accepted tool for application in hazard assessment, especially in complex terrain settings or in support of emergency response actions. These models are of a Lagrangian puff formulation and have the capability to better handle shifts in winds from encounters with terrain obstacles or due to passage of a weather front. Examples of GIS applications of puff models include the newly released D2-Puff model, developed for the U.S Army at Aberdeen Proving Ground, and the HPAC modeling system used for military applications at the Defense Threat Reduction Agency (DTRA). The D2-Puff model is intended to eventually replace D2PC as the Army’s realtime emergency response and preparedness model during the remaining duration of demilitarization operations at the Army’s chemical weapons depots.
research models or codes for special regulatory applications, and finally to most complex (4) large-eddy simulation and computational fluid dynamics codes. Examples of models in each of these groups and subgroups, including a sample of tools used in GIS applications covered in this paper, are given below. Further discussion on model types and on the need for guidance in the selection and application of specific accidental release models for risk management planning can be found in Lazaro et al. (1998). Screening Models Screening models are very simple tools that can be used with any PC or at times with a hand calculator. Results from such calculations are considered to be very conservative and are typically used as a “first-cut” screening to determine whether more detailed analysis is warranted, or to justify a conclusion that the impacts of the proposed action would not be significant in terms of air quality impacts or health exposure concerns. Examples of screening models are the SCREEN and TSCREEN models developed by the EPA. SCREEN estimates maximum impacts from a single source, while TSCREEN is used to estimate the hazard from an accidental chemical release. The output from these codes is typically an estimate of a concentration at a single point or a limited group of receptors, which prohibits practical use in GIS display or visualization applications.
In connection with these operations, we are also developing a GIS-based emergency planning system for the Alabama Chemical Stockpile Emergency Preparedness Program (Alabama CSEPP) that uses D2PC and D2-Puff outputs as aids in planning for “special needs” residents in communities around the Anniston Army Depot in Alabama.
Health-Consequence Assessment Models Applied Research – Special Application Models
Codes referred to as “refined models” by the EPA (2002) are typically applied for regulatory purposes or in emergency response applications, for example, to acquire a construction permit or to identify evacuation zones in the use of or accidental release of a hazardous chemical, radiological, or biological substance. Regulatory models for routine release, or in some cases models used for emergency planning, in this subgroup are typically straight-line Gaussian models. The EPA ISC3 model is widely used for regulatory applications for routine releases from stacks (e.g., power plants or industrial facilities) or area sources (e.g., fugitive dust impacts from construction site activities). An example of a straight-line Gaussian model that Argonne National Laboratory researchers have used in GIS applications is the Army’s D2PC model.
Specialized codes typically have a narrow research oriented application or are designed to address unique environmental constraints (e.g., ice fog and impacts from chromium and salt drift from cooling tower plumes) in regulatory applications. They tend to contain special features or details that most widely used routine release models do not possess. This would include the ability to simulate dense or heavy vapor cloud behavior or the channeling influence on plume transport in complex terrain. An illustration of the latter is depicted in the plume aerial photo shown in Figure 2. Argonne National Laboratory’s SMOKE model was developed to simulate the movement of plumes released in such complex environmental settings as a means of studying the influences of terrain
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on the transport and dispersion of military smoke used as an obscurant during battlefield operations. The SMOKE model is a PC-based puff dispersion model for predicting the transport of fog-oil smoke plumes produced in U.S. Army training exercises (Lazaro et al. 1999).
profile over a large tank farm containment dike fire assessed for TAPS. The CMAQ model is an Eulerian grid model currently being applied at Argonne to study urban and regional air pollution in the Las Vegas area (Argonne National Laboratory 2003a). Eulerian grid models use a fixed cell-based coordinate system that assumes a uniform concentration within cells at a given time step. These models often include a height dimension, simulating changes in atmospheric conditions at different altitudes. The study is being conducted in support of the Bureau of Land Management’s (BLM’s) Resource Management Plan for Las Vegas. It is intended to provide decision makers with information to ensure the protection of air resources in the Las Vegas Valley by providing a baseline and future assessment of urban air pollution connected with an expected rapid growth in population in and around Las Vegas. The study will also include the development of a GIS software package that will aid BLM land managers in tracking sales and assessing the impacts of the land use resulting from current and future land sale or lease actions.
Figure 2. Aerial View of Terrain Channeling During Early Morning Smoke Release under Stable Conditions
Model Input and Output Data
Large-Eddy Simulation and Computational Fluid Dynamics Models
The level of detail in the required level of analysis, the inputs needed, and the output generated, can vary considerably across various air dispersion model types. Depending on the application and the model selected to address site-specific environmental constraints, the data required as input to a particular air quality model can range from a few source release and environmental parameters to an extensive set of facility and area-specific data. Simple screening tools require small amounts of input data and can be easily applied by non-modelers to obtain quick results on most, even low end, PCs. On the other hand, the more complex CFD or LES models require large computers, often equipped with many processors. Such codes are typically written to take advantage of massively parallel computers that can simultaneously run many linked model calculations. Some models, such as the CMAQ code, need to be coupled with other models in order to generate the required detailed input data sets necessary in the complex simulations. For example, the CMAQ model requires very detailed three-dimensional wind fields in order to accurately simulate the complex flow patterns inherent in linked-urban and -valley influences. The meteorological
Models in this group have very detailed and often comprehensive mathematical formulations involving explicit numerical solutions of the Navier-Stokes equations. The solutions not only often include the details of flow around obstacles and complex dynamic atmospheric physics processes (e.g., precipitation, deposition, intense mesoscale convection), but also complex atmospheric chemistry. Some codes also include fire heat transfer and thermodynamic equations. Two examples of this category include the EPA Community Multi-scale Air Quality (CMAQ) model and the National Institute of Standards and Technology’s (NIST’s) Fire Dynamics Simulator (FDS). The FDS model has been applied in simulating the dynamics and buoyancy in large crude oil fires, and the output from such simulations were used as input to FIREPLUME to estimate the transport and dispersion of smoke, soot, and other combustion products. These assessments were conducted in support of the renewal of the right-of-way for the continued operation of the Trans-Alaska Pipeline System (TAPS). Figure 3 shows the FDS-simulated crude oil plume temperature
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Figure 3. Fire Dynamics Simulator Results for a Large Crude Oil Tank Farm Containment Dike Fire. Scale on Right Indicates Increasing Temperature from Blue to Red. model being used to generate these wind fields in Las Vegas and the surrounding area is a prognostic model called the Mesoscale Meteorological (MM5) model. The source or air pollution emission data requirements for CMAQ are also very detailed, often including spatial and temporal variations.
source release physics typically encountered in industrial accidents, environmentally driven dispersion physics and chemistry, and the physics at the human-environmental interface (e.g., removal or resuspension at the ground surface). More information about these hazard assessment models can be found in Lazaro and Hesse (1998).
Some simple models only provide a single concentration estimate at some downwind distance from the source release point, typically the maximum concentration resulting from the specified release quantity and its duration. Others provide detailed visualizations of regional hotspots such as depicted in Figure 4.
ASSESSING IMPACTS TO RECEPTORS Once a model has been used to determine the extent and characteristics of an air pollutant plume, the next step is often to examine the effects of the plume on the environment or human health. Some models have built-in calculations for specific analyses or have an integrated map viewer that displays the plume and other information. In most cases, however, it is necessary to import the output plume into a GIS system and to overlay it on GIS layers representing the receptors being affected.
Air dispersion models can also vary considerably in terms of their built-in capabilities and design features. Examples of some codes typically applied to industrial and military chemical and biological accidents are given in Table 1. The table identifies which codes have specific capabilities to handle some of the complex
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values, or even the distribution of concentrations in a volume. This plume layer can be superimposed on other ones for a visual assessment, or processed with other GIS layers for a more analytical one. A major source of demographic data in the United States is available from the U.S. Bureau of the Census in the form of county, tract, or block group polygons with statistical data in the linked database table (U.S. Bureau of the Census 2003). One drawback of these census data is that they locate populations at their residences only; they do not account for movement of individuals as they conduct their daily activities. When necessary, some researchers apply techniques to correct for this or use other data sources. In studies of industrial complexes or similar situations, it is often important to examine effects to the worker population as well as to the population surrounding the facility. One approach for representing the distribution of the worker population is to use building footprints for locations and a count of workers for each building. These counts might be available from the installation, or they might be estimated from phone book data or a similar source. Table 2 lists additional examples of GIS layers used to represent receptors and assess impacts.
Figure 4. Example CMAQ Model Output Showing an Ozone Air Pollution Event in the Midwest United States. Shades of Light Blue and Green Indicate Higher Ozone Concentations Importing Air Dispersion Model Results Into a GIS Many of the straight-line Gaussian models produce a text file containing coordinates for a plume in the form of distances from the source and a half-width for the plume for a particular concentration. Frequently the user must manually import these files into the GIS, first converting the format to one that can be parsed into the GIS, then transforming the origin to the location of the source and rotating the plume to match the wind direction. In the case of ArcGIS software, these steps can be accomplished with the GENERATE command to import the plume, the TRANSFORM command to move it to the proper location, and the ROTATE command in ArcEdit to align it to the wind direction. These steps can be automated relatively easily, and there are many other approaches that can be used for this step. For the Alabama CSEPP project described later in this paper, a tool in ArcView 3.x was produced to parse these types of files from the D2PC model, produce a GIS Shapefile, and display it on a base map.
GIS Methods for Assessing Impacts A wide variety of GIS techniques can be applied to analyze the impact of a plume on a particular receptor. Examples of some of the possible approaches are discussed below. Spatial Query: A spatial query simply uses the plume polygon to make a selection on another layer of the features it covers. Figure 5 depicts a hypothetical example of an accident along a road. Red points in the figure represent businesses in the area. Roads and businesses intersecting the plume footprint have been identified and are highlighted in yellow. In an emergency response system, such a capability might include an automatic callback system that would alert affected businesses to the emergency and that could aid in identifying locations for roadblocks. In ArcView 3.x, this query can be made by making the business and road layers active, then choosing Select by Theme from the Theme menu. Then choose the plume layer and click the New Set button. In ArcMap 8.x, the same operation can be
GIS Layers for Receptors Air dispersion model output usually indicates a “footprint” of a plume for a particular concentration, a grid of surface concentration
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Yes Yes Yes
(Hazard zone/footprint)
(Concentrations, tabular or space/time history plots)d
Time-integrated concentrations
Yes
(See next page for footnotes.)
Developer
Yes
Yes
(Variable averaging time)
Performance Evaluatione Independent
No No
Concentrations fluctuations
No
Dry/Wet deposition
Consequence Physics (output)
Concentration fluctuations
c
Yes
In-cloud chemistry
Plume meander
No
Land/water interface Yes
No
Complex terrain
b
No
Mesoscale transport
No
No
No
No
No
- Time-varying
Plume lift-off
No
Yes
- Elevated
Plume rise (momentum/buoyancy) Dispersion Physics (Ch i twake ) effects Building
Yes Yes
- Two-phased/entrained aerosol - Ground level
No
No
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
Yes
No
Yes Yes
No
Yes
- Liquid jet release/flashing
Yes
-
Yes
Yes
-
Dense gas
Yes
Passive gas only
Yes
Yes
No
No
No
No
No
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
No
No No
No
No
Yes
No
No
No
No
No
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No No
Yes
No
Yes
Yes
ADAM ALOHA CALPUFF CASRAM
Release Physics Spills front end
a
Feature/Model
Yes
Yes
No
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
No Yes
Yes
Yes
-
No
8
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes/No
Yes
No
No
No
Yes
No
No
Yes
Yes
Yes
Yes
Yes Yes
No
Yes
-
No
DEGADIS FEM3C
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
No
Yes (UF6 ver.) No
Yes
Yes
Yes
Yes
Yes Yes
Yes
Yes
-
Yes
HGSYSTEM
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No
No
Yes
Yes
No
No No
No
No
Yes
No
Yes
Yes
Yes
Yes
No
Yes
No
Yes/No
No
No
No
No
No
No
No
No
Yes
Yes
No
No No
No
No
Yes
No
HOTMAC/ INPUFF RAPTAD
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes/No
Yes
Yes
No
No
Yes
Yes
No
No
Yes
Yes
No
No No
No
No
Yes
No
SCIPUFF
Yes
Yes
No
Yes
No
Yes
Yes
No
Yes
Yes
No
No
No
No
No
No
Yes
Yes
Yes
No Yes
No
Yes
-
No
SLAB
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes/No No
Yes
Yes
-
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
No
Yes
No
No
No
No
No
No
No
Yes
Yes
No Yes
No
Yes
-
-
TSCREEN VLSTRAC
Table 1. Selected Air Dispersion Models Used for Industrial and Military Chemical and Biological Accidents and Their Major Characteristics (Lazaro and Hesse 1998)
Footnotes for Table 1 a Features also cover release thermodynamics. Some of these physical source processes, such as plume rise, also influence vapor cloud dispersion. b Models that treat plume meander take into account the enhancement of horizontal plume spread caused by wind direction fluctuations.
Models that implicitly account for concentration fluctuations within the model computations. SCIPUFF also explicitly uses the computation to express uncertainty in the model predictions.
c
d Models that treat intermittent variations in concentrations calculate the variation as a function of time for a given spatial location. e Performance evaluation conducted with EPA’s recommended, or other recognized or acceptable, set of performance measures.
Table 2. Examples of GIS Layers Used to Represent Receptor Data Receptor Human Populations
Source U.S. Census county, block group, or tract: http://factfinder.census.gov
Installation Worker Population Environmental Justice
Building polygons with worker counts derived from employer information U.S. Census county, block group, or tract: http://factfinder.census.gov
Protected Animal Species
Nest, den, etc. location from surveys, or habitat location from analysis
Forests, Water Bodies
Land cover data
performed by choosing Select by Location from the Selection menu and specifying the settings in the dialog.
Assumptions / Limitations Uses residence to indicate location, so does not address movement to work or other activities. Often must be manually collected and simplifies location to building location. Uses residence to indicate location, so does not address movement to work or other activities. May not sufficiently account for movement, or may generalize locations of individuals to their habitat. Often derived from analysis of satellite imagery, which can have limited accuracy in separating some land cover types.
distributed, and second, that the population density in the block groups is correct for the time of day the event would occur. The actual population might be significantly different if individuals had moved into or out of the area at the time of the accident.
Polygon Intersection: A common need is to estimate the total population that might be impacted by a plume. Figure 6a depicts a plume superimposed on U.S. Census block group polygons shaded according to population density. Darker shades indicate higher population density. Intersecting the plume polygon yields the layer shown in Figure 6b. In the data table linked to the new layer, the area of the intersected polygons is updated, then multiplied by the population density. Summing these values yields an estimate of the total population affected by the plume. This technique has some important assumptions: First, that the populations in the block groups are uniformly
A further refinement of this technique would be to weight the effects according to the type of polygon intersected. For example, in a study of acid rain the density of a certain species of fish, as well as the baseline water pH and temperature might be known for the water bodies in the study area. If acid rain had a stronger effect on the species under certain pH and temperature conditions, these values could be used as weights to refine the estimate of affected fish.
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Figure 5. Hypothetical Plume from Road Accident with Intersecting Roads and Businesses Highlighted population level is shown with a thicker outline.
Direction or Location of Maximum Impact: In dose or risk analysis for catastrophic or routine events, it may be necessary to estimate the maximum impact for a particular scenario or to specify the conditions that would lead to the maximum impact. Many air quality models simulate the size and concentration of a plume under specific meteorological conditions. Once the plume footprint has been determined, there may be other important factors, such as wind direction and source location, that greatly influence the level of impact. To examine these impacts, an iterative polygon intersection process can be employed in which the plume is rotated around a source to examine the relationship of wind direction to impact level, or the source moved to examine the relationship of location to impact level. Figure 7 depicts an example of rotating a plume around a fixed source, with a backdrop of U.S. Census block group data representing the receptor information. The footprint with the maximum
In the GIS, this analysis can be performed with a program that loops over the range of angles or source locations, performing an intersection at each position, and tracking the results. This process can be time consuming, so it is helpful to reduce the number of iterations to the extent possible. For example, a larger angle increment can be used for short and wide plumes vs. long and narrow ones, and the range of angles considered can be reduced if there is an direction range that will clearly produce the maximum impact. Population Rose: A population rose is a circular pattern of distances from a central source point, subdivided by sectors for cardinal directions. An estimate of the population in each sector is made by intersecting the population rose with demographic data. One air quality model that makes use of population rose information is
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Figure 6. Hypothetical Plume Displayed Over US Census Block Group Polygons (A), and After an Intersection Operation. Darker Shades of Red Indicate Higher Population Density. of 16 cardinal directions. Each population rose cell is coded with the distance range and cardinal direction. A helpful step is to code each cell with a sequential identification (ID) number so the table can be easily sorted in the order needed for the final text file. Next, a demographic layer is produced from U.S. Census block groups or a similar source. A population density field is added to the table for this layer and filled with the total population divided by the polygon areas. The center of the population rose is then moved to the source location, and the two layers are intersected. Areas for the intersected polygons are recalculated, then multiplied by the population density to provide subtotals. A summary operation is used to combine the polygons back into population rose cells, summing the subtotals. The table for this layer can then be sorted to fit the order needed and output to an ASCII file.
CAP-88. The CAP-88 model is a set of computer programs, databases, and associated utility programs for estimation of dose and risk from radionuclide emissions to air (Trinity 2002). Population rose data are input to the CAP-88 model as a table of numbers in a text file. Population roses are also used in analysis of acute effects in accident analyses. Figure 8 depicts an example of a population rose superimposed on a U.S. Census block group demographic layer, and centered on a hypothetical source location. Increasing population density is shown as darker shades of red. The resulting layer with estimated populations for each population rose cell is shown in Figure 9. Creating the population rose input file takes several steps. The population rose diagram is first made as a polygon layer using circles for the distance ranges and radial sectors for each
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Figure 7. Example of Determining the Direction of Maximum Impact. Darker Shades of Red Indicate Higher Population Density. The intersection step is unnecessary to collect information about a set of point features under a grid of cells. The process starts by linking the polygon table to the point table using a spatial join. This identifies the polygon each point is within. Then a summary operation is performed on the point table for each PID, including a summation of a statistic in the point table. For example, if airports were indicated as points, the total number of airports in each cell is determined by adding a flag field containing a value of 1 and summing it for each PID. This step produces a non-spatial table containing the PID and the calculated statistic. The table is then joined to the original grid layer using the PID.
Gridded data: Input data representing air pollution sources for Eulerian grid models can be gathered with the same process as the population rose section above, except that a grid of square cells is used instead of the radial population rose diagram. Polygon layers such as land cover and population density are examples of input data that might be collected. In some cases the input layers could be represented as line (such as roads and railroads) or point features (such as airports). For line layers, the intersection operation can still be performed, but the output contains only the lines split by the cell edges and not the grid polygon geometry. The summation step is used on the intersected line table for each unique polygon identifier (PID) to obtain a total length for each cell and those results can be joined to the original grid layer. For example the number of miles of railroads in each cell is determined in this manner.
These are just some examples of how air quality models and GIS analysis can be used together to estimate impacts of air pollutants and use the results for decision making. The examples focus primarily on human receptors represented by
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Figure 8. Example Population Rose Displayed over U.S. Census Block Group Polygons. Darker Shades of Red Indicate Higher Population Density. census data, but the techniques could be applied to other environmental impacts as well.
focused on analyzing compliance with national and state air quality standards in relation to development of coal-bed methane in the Powder River Basin (Environmental Assessment Division 2002). Development is expected to accelerate and continue over the next 10 to 20 years. Argonne conducted an assessment of potential impacts on ambient air quality and air-quality-related values associated with coalbed methane and conventional oil and gas development using the CALPUFF modeling system (Scire 1999). Figure 10 shows the extent of the areas concerned (the modeling domain) and associated population centers and sensitive receptor locations.
PROJECT EXAMPLES Air quality modeling is a component of many of the assessment projects and environmental studies conducted by our organization, and the applications vary significantly. Included here are brief descriptions of recent projects conducted by Argonne to illustrate how air quality models coupled with GIS have been used. Many of our larger projects involve producing or supporting environmental impact statements (EISs) for large federal projects. These projects often have elements from several of the following areas.
Accident Analysis Compliance with National and State Air Quality Standards
A component of many EIS projects is analysis of possible accidents and the risks they pose to human health and the environment. Accidents may produce air pollution in many ways, such
A recent project for the Montana and Wyoming offices of the Bureau of Land Management
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Figure 9. Example Population with Population Counts Derived from U.S. Block Group Polygons after an Intersection Operation. Darker Shades of Green Indicate Higher Population within the Cell Area. as from gas releases, evaporation of volatile compounds from liquids, or smoke plumes from fires. Therefore, a variety of models are employed, depending on the specific circumstances. Normally these analyses focus on accidents that cause severe consequences. One recent EIS project included analysis of possible chemical and radiological accidents at a proposed industrial facility designed to convert depleted uranium hexafluoride to a more stable chemical form (Argonne National Laboratory 2003b). Hydrogen fluoride and ammonia are two highly toxic chemicals that would be used at the facility. A second EIS project concerned the renewal of the grant for the Trans-Alaska Pipeline Right of Way (BLM 2002). In this case, accident analysis for air quality issues included releases of pollutants to the air through smoke plumes from fires, and evaporation from oil spills to land and water.
Risk Assessment and Environmental Justice In addition to accident analysis for acute exposures, risk assessments for chronic exposures focus on long-term effects and routine releases of air pollutants from normal operations. Each of the EIS projects mentioned above included risk assessments. In some cases, it is sufficient to determine the maximum possible exposure for a particular contaminant, without necessarily looking at the spatial location of the source or receptor. When there is a more significant issue, however, the GIS may be employed to analyze the number of individuals affected around the source. In recent years, environmental justice has emerged as another issue considered in EISs. This involves examining whether minority populations such as a particular racial, ethnic, or economic group, are disproportionately affected by the impact.
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Figure 10. Modeling Domain, Project Areas, Population Centers, and Sensitive Receptor Locations for the Montana and Wyoming Powder River Projects (EAD 2002) developed by Argonne National Laboratory, includes the ability to import air dispersion plume results from the D2PC model and is currently being augmented to accept output from the D2Puff model. In the emergency management offices, these models are run with real-time meteorology information within GIS systems. The Argonne software enables planners to visualize the model results and the planning zones affected by a possible accident or emergency; to query demographic data and tables of facilities and special-needs individuals; to designate evacuation routes and traffic control points; and to output maps and reports regarding these plans. Figure 12 shows an example map display from the SPP system. In this example, a hypothetical plume and hazard wedge are superimposed on a facilities layer (blue). Facilities within the hazard wedge have been selected and highlighted in yellow. A key capability of this system is the ability for an emergency planner to effectively work with model results and relevant databases without
User Interfaces Coupling GIS and Air Quality Models Some projects have involved coupling an air quality model with a GIS to provide the user with an integrated tool. One example is RISKIND, which models the effects of a transportation accident involving radiological materials (Biwer 2001). In this case, a GIS interface was added to the model to allow users to examine the results over a base map without needing to purchase GIS software. Figure 11 shows the map display dialog within RISKIND with a hypothetical accident plume superimposed on a street map and other base map layers. A second project supports the Chemical Stockpile Emergency Preparedness Program (CSEPP) in northeastern Alabama. Argonne has assembled databases to support emergency planning, and produced a GIS interface to be used for emergency planning in seven state and county emergency management offices. Special Population Planner (SPP), the GIS software
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Figure 11. RISKIND Map Display with Hypothetical Accident Plume Displayed over a Base Map. in-depth knowledge of the GIS system. Further information about this project is available at http://www.dis.anl.gov/ep/alabama/ep_alabama _home.html, and in papers by Kuiper and coworkers (2002, 2001, and 2000).
include data preparation, user interfaces, visualization of results, and analysis of impacts. Our experience has shown that close collaboration among air quality and GIS experts provides many opportunities for unique and useful decision making that helps minimize the effects of air pollution on human health and the environment.
More information about these and other recent air quality modeling projects at Argonne National Laboratory is available at http://www.ead.anl.gov.
ACKNOWLEDGEMENTS
CONCLUSION
This work was supported by the U.S. Department of Energy, the U.S. Bureau of Land Management, the U.S. Department of Defense, and the State of Alabama, through U.S. Department of Energy contract W-31-109-Eng-38.
This paper has provided a broad overview of air quality modeling and the assessment of its impacts on the environment and human health. Since the issues and physical processes involved are spatial in nature, there are many ways that GIS is used in this work. These uses
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Figure 12. Special Population Planner Map View Showing Hypothetical Plume and Hazard Wedge Superimposed on Facilities Point Layer for Montana and Wyoming State Offices of the Bureau of Land Management, December, 170 pp.
REFERENCES Argonne National Laboratory, 2003a, unpublished material, May.
Kuiper, J., T. Allison, D. Miller, W. Metz, L. Nieves, and J. Stache, 2000, Special Population Planner User Documentation Manual, prepared for State of Alabama Emergency Management Agency, Clanton, AL, 59 pp.
Argonne National Laboratory, 2003b, unpublished material, June. Biwer, B., D. LePoire, J. Kuiper, and S.Y. Chen, 2001, Integration of GIS with a Radiological Transportation Accident Consequence Health Risk Model, presented at Waste Management 2001, Tucson, AZ, 11 pp.
Kuiper, J., W. Metz, and D. Miller, 2001, Special Population Planner - A GIS-Based Emergency Planning System, presented at the 2001 Advanced Simulation Technologies Conference, Seattle, WA, 6 pp.
Environmental Assessment Division, 2002, Air Quality Impact Assessment for the Montana Final Statewide Oil and Gas EIS and Proposed Amendment of the Powder River and Billings Resource Management Plans and the Wyoming Final EIS and Planning Amendment for the Powder River Basin Oil and Gas Development Project, technical support document prepared
Kuiper, J., and D. Miller, 2002, "Special Population Planner: An Emergency Planning GIS," Illinois GIS Notes, Vol. 3, No. 2, Summer. Lazaro, M.A., H. Wang, R.C. Wells, and L.A. Corio, 1998, Comparison of Consequence Assessment Models Applicable to RMP Hazard
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Assessment – Hypothetical Petroleum Industry Accident Scenario, proceedings of the Air & Waste Management Association's 91st Annual Meeting & Exhibition, 98-TA36.04, San Diego, CA, 12 pp., June 14-18.
U.S. Environmental Protection Agency (EPA), 1999, Benefits and Costs of the Clean Air Act 1990 to 2010, EPA Report to Congress, EPA410-R-99-001, November.
Lazaro, M.A., and Hesse, D.J., 1998, An RMP Modeling Guidance Development Framework – Extensions from Atmospheric Consequence Assessment Model Reviews and Evaluations, proceedings of the Air & Waste Management Association's 91st Annual Meeting & Exhibition, 98-RA50C.03, San Diego, CA, 16 pp., June 14-18.
EPA, 2002, Requirements for Preparation, Adoption, and Submittal of State Implementation Plans (Guideline on Air Quality Models). Available at http://www.access.gpo.gov/nara/cfr/waisidx_02/ 40cfr51_02.html, Accessed June, 2003. July. U.S. EPA, 2003, EPA National Ambient Air Quality Standards. Available at http://www.epa.gov/ttn/naaqs/. Accessed June, 2003.
Lazaro, M.A., Brown, D.F., Dunn, W., and A.J. Policastro, 1999, The SMOKE-FIREPLUME Model: Tool for Application to Prescribed Burns and Wildland Fires, published by the International Association of Wildland Fire in the proceedings of the Joint Fire Science Program’s conference, Crossing the Millennium: Integrating Spatial Technologies and Ecological Principals for a New Age in Fire Management, June 17-19.
AUTHOR INFORMATION James Kuiper GIS Analyst / Biogeographer Environmental Assessment Division - 900/H03 Argonne National Laboratory 9700 South Cass Avenue Argonne, Illinois 60439-4832 Office: (630) 252-6206 FAX: (630) 252-6090 E-mail:
[email protected]
Ruch, R.B., Jr. and J.S. Howell, Jr., 1991, Proactive Industrial Strategies for the Clean Air Act Amendments of 1990, Journal of the Air and Waste Management Association, Volume 41, No. 7, July.
Michael Lazaro Atmospheric Scientist Environmental Assessment Division Argonne National Laboratory E-mail:
[email protected]
Scire, J.S., et al., 1999, A User’s Guide for the CALPUFF Dispersion Model (Version 5.0), Earth Tech, Inc., Concord, Mass., October. Trinity Engineering Associates, Inc., 2002, CAP88-PC Version 3.0 User Guide, Draft Revision 1, Cincinnati, OH. Available at http://www.epa.gov/radiation/assessment/docs/v 3userguide1.pdf. Accessed June 2003. 141 pp.
Young-Soo Chang, Ph.D. Environmental Systems Engineer Environmental Assessment Division Argonne National Laboratory E-mail:
[email protected]
Union Carbide, 2003, Bhopal. Available at http://www.bhopal.com. Accessed June 2003. U.S. Bureau of the Census, 2003, U.S. Census American FactFinder. Available at http://factfinder.census.gov. Accessed June 2003. U.S. Department of the Interior, Bureau of Land Management, 2002, Renewal of the Federal Grant for the Trans-Alaska Pipeline System Right of Way, Final Environmental Impact Statement, BLM/AK/PT-03/005+2880+990, Anchorage, Alaska, November.
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