National Academies Press: OpenBook

Effective Methods for Environmental Justice Assessment (2004)

Chapter: Chapter 3 - Air Quality

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59 CHAPTER 3. AIR QUALITY OVERVIEW Air quality is important to human health, the vitality of the natural environment, and the quality of life in general. Poor air is of special concern for sensitive populations with potential health issues, such as asthmatics, people with other pulmonary health problems, children, and the elderly. From an environmental justice perspective, there is some evidence that certain ailments exacerbated by poor air quality have a higher incidence rate in minority and low-income populations than in the general population. Poor air quality can also degrade the natural environment by decreasing visibility and damaging animals, crops, vegetation, and buildings. Although quality of life is subjective, poor visibility, dust, odors, and the emotional impacts of exhaust smells have a negative impact on nearly everyone. In this chapter, we focus on air quality issues related to human activity, but natural sources of pollutants also can worsen an area’s air quality problems. The important point is that the worse the general air quality in an area is due to whatever sources, the greater the harm that additional emissions are likely to bring about. Transportation projects can affect ground-level air quality (microscale or “hot-spot”) due to increased concentrations of carbon monoxide caused by idling vehicles and congestion or to particulate matter caused by diesel engine emissions and stirred dust and dirt that become airborne due to disturbance by vehicles. Environmental justice assessment of micro-scale air quality impacts is a straightforward process of combining information about micro-scale effects and demographics for affected areas. While greenhouse gases and particulate emissions may affect regional air quality, their distribution is generally assumed to be uniform across large areas. The typical regional air quality assessment methods do not provide geographic distinctions. Therefore, environmental justice assessment of regional air quality is less informative than assessment of micro-scale issues unless experimental, resource-intensive methods are used. In cases where protected populations are very concerned about air quality, it may not be enough to assess the impact from transportation system changes. Because it is the cumulative exposure to all air pollutants that affect human health and quality of life, many environmental justice proponents have recommended evaluating the distribution of pollutants from all sources. This form of assessment can be time consuming and resource intensive due to the large amounts of monitoring equipment and data required to develop an understanding of cumulative ground-level concentrations. STATE OF THE PRACTICE The most common techniques being used to assess air quality are described in this section along with examples of successful environmental justice assessments. We discuss air quality

60 regulation, regional air quality assessment, micro-scale air quality assessment, and mitigation strategies. Air quality has been regulated for many years, and transportation policies, programs, and projects must meet comprehensive federal and state standards. The current state of the practice is to identify both specific sites (i.e., hot spots) and regions (usually large metropolitan or multi- county areas) where these standards may be exceeded and to determine strategies for meeting the standards. Environmental justice assessments most often are performed when air quality standards are not met or would potentially not be met if a proposed project were built. The basic assumption is that, unless the standards are violated, there is no adverse effect to be evaluated for distributive effects to protected populations. Given this assumption, some argue that transportation air quality is not an important environmental justice issue because policies, projects, and programs cannot be implemented if they violate the standards. Many practitioners and community representatives do not accept this argument, however. Proponents of environmental justice argue that protected populations experience a disproportionate level of adverse health effects due to differing levels of exposure and differences in lifestyle, among other factors. There is also a considerable body of evidence indicating that protected population groups tend to live and work closer to sources of air pollution than does the general population (Bullard 1996; Bryant and Mohai 1992). It is beyond the scope of this guidebook to explore this argument fully or to propose alternative air quality standards that would be more protective of protected populations. Instead, the methods presented in this chapter are designed to be used independently of established air quality standards. In this way, practitioners can be responsive to air quality concerns raised by communities that argue they are experiencing adverse effects even when all air quality standards are being met. Air quality regulation Procedures for evaluating air quality primarily are guided by regional pollution control agencies, departments of health, and metropolitan planning organizations (MPOs). These agencies are responsible for monitoring air quality, which includes six common criteria pollutants: ozone (O3), particulate matter (PM), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2). A brief summary of the adverse effects of each pollutant is provided in Table 3-1. State and local agencies monitor air quality to determine if it complies with the National Ambient Air Quality Standards (NAAQS). As directed by the 1970 Clean Air Act, the U.S. Environmental Protection Agency (U.S. EPA) created the NAAQS to protect human health and the public welfare. Primary standards are designed to protect human health, whereas secondary standards protect public welfare. The current primary and secondary standards are provided in Table 3-2. When monitoring indicates that the concentration of one of the five criteria pollutants violates the NAAQS, the air quality status of the region may be changed from “attainment” to “non- attainment.” If an area previously in the nonattainment category achieves attainment, it is

61 Table 3-1. Effects of criteria pollutants Pollutant Description Ozone (O3) Ozone can irritate lung airways and cause wheezing, coughing, and pain when taking deep breaths. It also aggravates asthma and increases susceptibility to diseases such as pneumonia and bronchitis (U.S. EPA 2003a). Particulate matter (PM) Of all measured pollutants, PM may be the most detrimental to human health. PM has been linked to increased mortality rates (Lave and Seskin 1977). Children and seniors with respiratory problems such as asthma are at greatest risk (Schwartz and Dockery 1992). Asthma rates are higher in low-income populations and mortality rates are highest among African Americans (U.S. EPA 1996). Carbon monoxide (CO) Exposure reduces the amount of oxygen in the bloodstream (U.S. EPA 1995). People with heart disease are at greatest risk. Seniors are at risk. Heart disease rates are higher for most African American age groups compared to Caucasians (National Center for Health Statistics 1995). Nitrogen oxides (NOX) NOX reacts with sunlight to create ozone. NOX has been linked to acute respiratory problems (U.S. EPA 1996). Sulfur Dioxide dioxide (SO2) Primarily emitted by diesel engines, SO2 is a serious irritant to asthmatics, and contributes to particulate formation and to acid rain (U.S. EPA 1994a). Table 3-2. National ambient air quality standards Pollutant Statistic Standard value* Standard type 1-hour average 0.12 ppm (235 µg/m3) Primary & secondaryOzone (O3) 8-hour average 0.08 ppm (157 µg/m3) Primary & secondary Annual arithmetic mean 50 µg/m3 Primary & secondaryParticulate (PM10)** 24-hour average 150 µg/m3 Primary & secondary Annual arithmetic mean 15 µg/m3 Primary & secondaryParticulate (PM2.5)*** 24-hour average 65 µg/m3 Primary & secondary 8-hour average 9 ppm (10 mg/m3) PrimaryCarbon monoxide (CO) 1-hour average 35 ppm (40 mg/m3) Primary Nitrogen dioxide (NO2) Annual arithmetic mean 0.053 ppm (100 µg/m 3) Primary & secondary Annual arithmetic mean 0.030 ppm (80 µg/m3) Primary 24-hour average 0.14 ppm (365 µg/m3) Primary Sulfur dioxide (SO2) 3-hour average 0.50 ppm (1300 µg/m3) Secondary * Parenthetical value is an approximately equivalent concentration. ** Particles with aerodynamic diameters of 10 micrometers or less *** Particles with aerodynamic diameters of 2.5 micrometers or less Source: U.S. EPA 2003.

62 designated as having “maintenance” status for that particular pollutant. For regions designated as nonattainment areas, state implementation plans (SIP) must be prepared by the responsible agencies. The SIP ensures that no transportation project or policy will result in an increase in regional emissions nor cause a pollutant violation (FHWA 2001). Transportation conformity refers to the coordination of the transportation planning and air quality planning processes. To achieve transportation conformity, Transportation Improvement Programs (TIPs) must be consistent with SIPs. Transportation conformity with the NAAQS only applies to O3, CO, PM, and NO2 non- attainment and maintenance areas. Note that an exceedance of a pollutant does not automatically constitute a violation. For example, CO must exceed the criteria two times in a year to be considered a violation. Nonattainment or maintenance status often results in rules stating that transportation projects must not cause an increase in a specified pollutant or that more stringent analysis procedures must be followed. State and local agencies then must enforce these rules and procedures (FHWA 2001). The models used to determine whether a transportation project or TIP would result in an air quality impact include EPA’s MOBILE5 and the new MOBILE6. MOBILE6 was being phased into use nationwide at the time this document was created. These models are used to estimate emission factors for vehicles. Emission factors are the rate at which an average vehicle emits pollutants, usually expressed in grams/mile (moving vehicles) or grams/hour (idling vehicles). Emission factors determined by the MOBILE6 model often are stratified by speed and year. MPOs or state pollution control agencies usually determine the parameters used in the MOBILE6 model for application to a given location. These parameters can include vehicle age, mileage by vehicle type, inspection and maintenance programs, and specific fuel makeup characteristics. The MOBILE6 model output emission factors are incorporated into either or both microscale (hot-spot) and regional analyses. The microscale and regional analyses provide more meaningful results for use in quantifying project impacts. Regional air quality assessments Based on ISTEA and TEA-21 requirements, MPOs develop 20-year plans and 3 to 5-year TIPs. The TIP is a prioritized list of projects for which the MPO will seek FHWA or DOT approval. A regional air quality assessment is conducted to ensure that the TIP is in conformance with the SIP. This evaluation assesses the regional impacts that transportation investments will have on emissions in nonattainment or maintenance areas. Information required to perform a regional air quality assessment includes the following: • Estimates of current and future population and employment; • Estimates of current and future travel and congestion; • Assumptions about current and future background pollutant concentrations;

63 • Transit operating policies and transit ridership and expected future changes in fares and level of service; and • Effectiveness of SIP measures that have already been implemented. Regional air quality analyses incorporate travel demand information and emission factors to calculate total regional emissions. Depending on the attainment status for various pollutants and the population in the region, network-based travel demand models, local vehicle miles traveled (VMT) forecasts from the Highway Performance Monitoring System (HPMS), traffic speed and delay estimates, and/or local counts of all traffic in a region are used to evaluate regional air quality. The emission factors must be approved by the U.S. EPA. Currently, MOBILE6 is used to generate emission factors outside California, and the current version of EMFAC (short for emissions factor) is used within California. Regional travel demand models can project VMT and average speed on each roadway link of a road network. Multiplying the link VMT by the emission factor for a given link speed results in the total emissions for the link. The sum of emissions for all links gives a value for total regional emissions. Figure 3-1 provides an overview of the regional conformity assessment process (FHWA 2001). Base Year Future Year Regional travel demand model Modeled VMT by roadway class Congested speeds by roadway class HPMS-adjusted VMT by roadway class Emissions factors by roadway class MOBILE Regional travel demand model Future year modeled VMT by roadway class Base year modeled VMT by roadway class Base year HPMS-adjusted VMT by roadway class O ff -m o d e l p o s t p ro c e s s in g Regional emissions HPMS VMT by roadway class Base year HPMS to model adjustment factors by roadway class HPMS-to-model VMT adjustment Modeled VMT growth rate by roadway class Emissions computations Travel computations Highway performance monitoring system (HPMS)  Future year HPMS-adjusted VMT by roadway class Congested speeds by roadway class Emission factors by roadway class Regional emissions MOBILE Figure 3-1. Regional conformity assessment process Source: FHWA 2001.

64 Regional analyses focus on estimating emissions of transportation-related pollutants, which include CO, NO2, and volatile organic compounds (VOC). When VOCs interact with NO2 , heat, and sunlight, they form ground-level ozone (O3). Any increase in these pollutants is detrimental to the environment and, depending on the attainment status of the area, an increase could prevent a transportation project from moving forward. Micro-scale air quality assessment Motor vehicles are among the major contributors to criteria pollutant levels. They are the number one source of CO and NO2, the number two source of VOC, the number three source of PM, and the number four source of SO2. In total, highway vehicles and off-highway vehicles generate an estimated 77 percent of all CO emissions in the United States (U.S. EPA 1994b). Because CO is the most prevalent criteria pollutant, microscale analyses often screen for air quality violations by evaluating CO levels. Figure 3-2 provides an overview of the microscale air quality assessment process. This example is based on an approved process for meeting microscale transportation conformity regulations. This is just one example, however, and the process can vary from jurisdiction to jurisdiction. The most frequently used air dispersion models for localized analyses are CAL3QHC or one of the CALINE series models. The model results provide estimated carbon monoxide concentrations at discrete receptors near worst-case intersections. Analyses are performed at intersections because vehicles produce greater emissions when they are idling or traveling at slow speeds. The assumption is that if worst-case intersections do not exceed CO limits, there will be no exceedances for any of the criteria pollutants. The model incorporates the emission factors from the MOBILE6 model, along with intersection operating characteristics such as signal timing, traffic volume, and intersection geometry. Two scenarios must be evaluated to determine transportation conformity: • If there are no projected exceedances or violations in the area affected by the project, the project’s future effect is compared to the standard because the test is whether the project causes an exceedance of the standard. • If there is a projected violation or exceedance in the area affected by the project, the project cannot worsen an existing violation, so a no-build/build comparison is required (FHWA 2001, Section F). For phased projects, it may be necessary to perform a microscale analysis for each significant project phase. This is done to ensure that interim phases do not cause NAAQS violations that might be eliminated once a project is fully implemented. The intent of the microscale analysis is to ensure that transportation system changes, in combination with existing or foreseeable future background concentrations, do not result in NAAQS violations. Although the results of these analyses generally are considered to be reasonably accurate, the highly localized nature of the assessment makes it difficult to directly relate any violation to disparate effects on protected populations. If an air quality impact were

65 predicted to result from a planned project, the impact would be at discrete receptor locations, usually near a congested intersection. The discrete receptors used for the microscale assessments typically are on sidewalks or beside buildings very near to intersections. Project/intersection description Determine air quality/regulatory objectives Assemble all data pertaining to intersection-related traffic conditions Multiple intersection screening/ranking Individual intersection modeling Assemble data on traffic, meteorology, site characteristics, background No further analysis required unless in top 3 based on traffic volumes Rank top 20 by traffic volumes Calculate LOS for top 20 Model top 3 based on traffic volumes Locate receptors Compute 1-hour peak- traffic concentration using CAL3QHC Apply persistence factor and background Compare results with NAAQS Conformity determination Rank by LOS LOS = A,B,C Model top 3 based on LOS LOS = D,E,F Figure 3-2. Example of a local conformity assessment process Source: FHWA 2001. Mitigation measures Local air quality mitigation measures. If violations of local standards or the NAAQS are predicted to result from a proposed transportation project, mitigation measures would be required. Mitigation measures could include increasing intersection capacity by adding traffic lanes, optimizing signal timing for air quality purposes, or diverting traffic to other locations. It is possible that these mitigation measures could cause impacts themselves. Such impacts could include right-of-way acquisition for additional lanes or an increase in pedestrian conflict areas

66 due to greater crossing distances. Diverting traffic may cause impacts at other intersections that may not be equipped to handle greater traffic volumes. Regional air quality mitigation measures. Regional measures may be considered to address nonattainment of pollutants. Regional air quality mitigation measures may include roadway or transit projects to reduce congestion or inspection and maintenance programs to reduce vehicle emissions. SELECTING AN APPROPRIATE METHOD OF ANALYSIS Most of the air quality and environmental justice assessment methods presented in this chapter examine each of the major categories of vehicle-generated air pollution individually. Although these techniques are the ones most readily combined with standard air quality models and assessment processes, they do not allow practitioners to consider additive or synergistic effects of pollutants or cumulative effects from all sources. One method, however, does allow for evaluation of cumulative pollutant levels from all sources. This method can be implemented as an extension of the most commonly used air quality assessment models. Examples are provided showing how results for estimated pollutant levels can be evaluated independently of current criteria pollutant standards. The methods presented here do not directly address questions about the causal connection between vehicle-generated pollutants and observed health effects such as asthma or increased cancer rates. Understanding this connection between environmental air quality (outdoor and indoor) and health effects is an emerging field of research, although most studies have not focused solely on transportation sources. This type of research is very time consuming and requires considerable expertise in health and epidemiology. Because the cost of health effects- based methods would be beyond the scope of all but a very few, extreme transportation situations, they are not included in this guidebook. See Carlin and Xia (1996), Hockman and Morris (1998), and Waller, Louis, and Carlin (1999) for examples of exposure-based environmental justice research on the health effects of air quality. METHODS Table 3-3 provides a summary of the four methods presented in this chapter. The methods used to analyze air quality impacts from transportation projects range from simple to complex, and from well understood to experimental and not yet prescribed by regulatory authority. Micro-scale analyses for transportation-related projects nearly always focus on CO and possibly PM, whereas regional analyses generally address CO, NO2, PM, and VOC. Method 1. General air quality review When projects do not warrant an air quality analysis, the assessment may extend only to a general discussion about air quality rules and the existing air quality environment. For purposes of environmental justice assessment, concentrations should be evaluated in terms of potential NAAQS violations to identify situations where alteration of the planned policy, program, or

67 project must be considered. In addition, the net change in concentrations, independent of NAAQS thresholds, can be determined to identify situations where there would be either beneficial or adverse effects to protected populations. Table 3-3. Summary of methods for analyzing air quality Method Assessment level Appropriate uses Use when Data needs Expertise required 1. General air quality review Screening Project/corridor/system Initial analysis and when air quality effects are expected to be minimal Low Spreadsheet 2. Detailed microscale analysis Detailed Project/corridor Project is controversial or there are potential environmental justice concerns and more detail of microscale effects is required by local guidance Medium Modeling (CAL3QHC) 3. Detailed regional analysis Detailed Large projects/systems Transportation conformity analysis is required and there is potential for environmental justice concern Medium Modeling (MOBILE6) 4. Analysis using pollution surfaces Detailed Project/corridor/system Air quality is a major issue with protected population groups and previous methods have not addressed all issues High Modeling, database, geographic information systems (GIS), statistical analysis When to use. This approach is advised in situations where detailed analysis is not warranted. It is intended to document both local and regional air quality in an area, as well as the presence of protected populations. The level of concern that there could potentially be distributive effects to protected populations is based on review of demographic patterns. Information gathered for this assessment can be used to perform detailed microscale analysis and detailed regional air quality analysis if findings indicate the potential for adverse effects to protected populations. Detailed microscale or regional air quality analyses may not be necessary if the project does not trigger the requirements for a conformity determination or if the project is within an attainment area or a maintenance area with specific guidance that does not require a detailed microscale analysis. A detailed air quality analysis may not be necessary within a nonattainment or maintenance area if a project is not regionally significant and if the project does not affect any transportation control measures.

68 Even within maintenance or nonattainment areas, programs may be in place that provide thresholds indicating whether a microscale air quality analysis is needed. An example guidance would require evaluation of the 10 highest volume intersections in a region. The local guidance may state that a microscale air quality analysis is required if the highest volume intersection within the project area is less than the 10th highest intersection volume in the region. Similar guidance may specify that intersections operating at a particular level of service (LOS) or better may not require micro-scale air quality analysis. Analysis. This analysis is a process of documenting regional and local air quality issues, and then documenting the presence of protected populations in the study area to determine (on a qualitative basis) whether the proposed transportation system change could potentially cause distributive effects. Based on local guidance, this analysis may include evaluating the highest traffic volume intersections within the project area, and/or evaluating the worst intersection LOS. Step 1 – Document regional air quality. The first step is to document regulations and regional air quality in the study area. Much of the information needed for this step can be obtained from state and local agencies. At a national level, the EPA provides detailed information on regional air quality throughout the United States. Available information includes lists of regions that are in violation of the NAAQS, air pollution data and maps for ozone, PM, NO2, SO2, and air quality index (AQI) reports. The AQI is an index for reporting air quality to the public. The AQI is calculated daily for each criteria pollutant using standard formulas approved by the EPA and is tied to relative levels of acute health concern. For example, an AQI of 101 to 150 is unhealthy for sensitive groups or individuals, such as those with asthma. AQI values greater than 150 are considered unhealthy (151-200), very unhealthy (201-300), or hazardous (301-500) to all persons. The AQI is calculated on the basis of measurements taken at more than a thousand locations across the country each day. In large metropolitan areas, state and local agencies must report the AQI to the public daily. When the AQI is above 100, they must also report which groups, such as children, people with asthma, or people with heart disease, may be sensitive to the specific pollutant. Although it is not required, many smaller communities also report the AQI as a public health service (U.S. EPA 2003a). To complete Step 1, describe the regional pollutants of highest concern in the study area, common sources of those pollutants, and provide an indication of the general level of regional air quality based on state and local information and on information available from the U.S. EPA. The data developed for this step could include the region’s attainment/nonattainment status and number of days above the NAAQS for the various pollutants. Step 2 – Document local air quality. The second step is to document local air quality rules and potential locations with relatively higher pollutant levels in the study area. Discuss the regulations and guides that are used to evaluate local transportation air quality and transportation conformity. Describe how the attainment status in the area determines the level of air quality assessment that is necessary. Next, identify locations in the study area where air quality from mobile and point sources may be of greater concern. At a minimum, you should document areas where air pollution levels could be higher because of proximity to transportation facilities. These

69 locations could be areas near large concentrations of mobile sources, such as freeways, arterials, and congested intersections. If time and resources allow, it would be advisable to also identify other significant sources of air pollution that may affect the project area, such as electrical generation facilities and other point sources. Step 3 – Document protected populations in the project area. Use any of a number of qualitative or quantitative methods for identifying protected populations in the project area. Many suitable techniques are discussed in Chapter 2. The best technique to use depends on the size of the project being evaluated. For policies, programs, and large projects with a sizeable area of potential effects, it is appropriate to use census data and any of the evaluation techniques that use census data, such as the Environmental Justice Index (EJI) or threshold analysis. For projects with smaller study areas, it is more appropriate to use data gathering techniques to characterize protected populations. Suggested techniques include local knowledge, field surveys, and public participation-based techniques such as focus groups. Step 4 – Evaluate potential for unequal distributive effects. A map is probably the best way to evaluate the distribution of air pollution sources in the vicinity of protected populations. Either desktop methods or GIS can be used to map the location of significant point and mobile sources depending on available resources, the size of the study area, and the number of sources and demographic analysis units. Then, overlay the source information on maps showing the location of protected population groups. Because this is a screening test, the objective is not to identify statistically significant unequal location patterns. Rather, the intent is to identify areas where there is spatial clustering of air pollution sources and where protected populations are in very close proximity to air pollution sources. Document these sites as locations where local air quality and its effects on protected populations could be of greater concern than in other portions of the study area. Commonly used regional air quality assessment methods assume that pollutants at this scale are distributed relatively uniformly across large areas. Therefore, it will not usually be worthwhile to evaluate unequal geographic distributions unless the study area includes multiple regions. Rather, you will need to discuss regional air quality as mentioned above and describe the adverse effects that may be experienced by various population groups. Data needs, assumptions, and limitations. This type of analysis requires the following basic information on the project and the geographic area or region of the project: • Project traffic study results, including traffic volumes and LOS; • Maps showing transportation system intersections and links; • Information on the location of point sources that emit air pollution; • Regional attainment status, and local project analysis guidance; and • Information on the location of protected populations. Information on point sources that emit air pollution can be found in numerous sources. Many states publish an annual database of permitted facilities. Some of these databases include information on the level of emissions, making it possible to map not only the locations of facilities but also to categorize facilities based on the volume and types of pollutants they emit.

70 At the national level, the EPA annually releases this type of information in the Toxics Release Inventory (TRI) (U.S. EPA 2003b). This technique assumes that proximity to air pollution sources is an indication of possibly higher local air pollution levels. This may not be a valid assumption in all situations because of factors such as wind speed and direction, topography, and pollutant dispersion characteristics, among others. Without further analysis, this technique merely documents local and regional air quality issues in the study area and identifies locations where air quality may be of greater concern to protected populations. Using this method, one approach to evaluating the distributive air quality effects of a project would be to assess whether the project would affect local air quality in the areas of greater concern. If so, a more detailed analysis in those areas should be considered. However, if the project would not have an effect in these areas and air quality is not a major issue to protected populations in the study area, it should generally be safe to conclude that the project would have no distributive air quality effects. Obviously, this method has major limitations. It relies on very basic data to assess the potential for localized air quality effects, and it assumes that the distribution of regional air quality is uniform. For areas where the potential for unequal effects is identified, the actual level of effect cannot be quantified. Because of these limitations, it is safe to assume that any results from this method of assessment would be challenged if a project were controversial. This technique is therefore best used as a screening technique only, and further assessment using more robust techniques should be performed unless no potential for unequal effects is found and the project is not controversial. Results and their presentation. The results of this analysis could include a table showing intersection volumes or LOS, where higher volumes indicate a greater potential for localized air quality effects. A second table showing total stack and fugitive emissions from point sources could be used to display sites with high emissions levels. A general discussion of the regional attainment status and guidance for air quality should be included. Probably the most useful presentation aid would be a map showing locations of protected populations and nearby mobile and point sources of air pollution. Assessment. A general air quality review is intended to disclose local and regional air quality concerns and regulations. For environmental justice assessment, comparison of mobile and stationary air quality sources and their proximity to protected populations is included. Due to the many limitations of this approach, further detailed analysis should be performed in any situation where protected populations could be affected by the policy, program, or project or where the transportation system change is controversial. Because no quantitative air quality analysis is conducted, this technique provides qualitative results and any conclusions are subjective. Method 2. Detailed microscale analysis The detailed microscale analysis is an extension of the hot spot analysis used to determine NAAQS conformance. The additional information and modeling needed to perform

71 environmental justice assessment are reasonable and should be within the resources of most agencies that must currently perform local air quality analysis under Clean Air Act and transportation conformity guidelines. When to use. A detailed analysis should be conducted for any regionally significant transportation project within a nonattainment or maintenance area. Because the additional data requirements necessary to perform environmental justice assessment are relatively minor, this method can be used to evaluate both projects and plans, such as TIPs or major investment studies. This analysis should also be conducted if a state agency requires it on the basis of its SIP, guidance, or rules. The analysis also is needed if the project is controversial and will be subjected to substantial public scrutiny due to perceived potential air quality impacts. Such an analysis is especially useful in situations where a general air quality review (Method 1) indicates the potential for air quality effects to protected populations. This type of analysis should be conducted (a) to determine that no established criteria pollutant standards are violated and (b) to evaluate effects to protected populations. Although this form of detailed assessment is only required in specific circumstances according to the Clean Air Act and transportation conformity requirements, the method is broadly applicable to all transportation plans and projects that would benefit from microscale air quality assessment. The microscale analysis is typically performed at selected worst case, or “hot spot” intersections. Environmental justice assessment involves evaluating the pattern of where these intersections are located in relation to the activity spaces of protected population groups and then, at an even finer level, the effects of hot spot intersections on specific sensitive receptors. Analysis. The following steps are specific to the environmental justice component of the local air quality assessment. For an overview of a standard procedure for performing the hot-spot analysis, see “Microscale air quality assessment,” above. FHWA (2001) also provides a detailed discussion of the requirements for performing hot-spot analysis. Figure 3-3 provides an example of how a microscale analysis process can be used to evaluate environmental justice. Step 1 – Describe the transportation system change and gather necessary data. The detailed microscale assessment can be performed for policies, plans, and projects down to the level of a specific intersection. You must define the nature of the transportation system change and the air quality objectives by which the change should be evaluated. For general NAAQS conformance with an environmental justice component, the objective could be stated as “document NAAQS compliance ensuring that the proposed projects cause no violations at worst case intersections and in areas where there is possible environmental justice concern.” In situations where air quality is highly controversial and there is a history of concern over impacts to protected populations, the objective could be to obtain emissions reductions. In this case, the goal would be to “evaluate project performance and ensure that the transportation facility yields a net emissions reductions once it is fully implemented.” Once the transportation system change and objectives are described, you must assemble all the data necessary to describe intersection-related traffic conditions and demographic characteristics of the intersections. The specific data needed are described later under “Data needs, assumptions, and limitations.”

72 Step 2 – Review demographics for all intersections. Once each intersection has been assigned a score characterizing its level of environmental justice concern, rank the intersections either categorically or numerically. If an environmental justice concern was quantified using a measure such as the EJI, select a threshold value denoting areas of relatively higher concern based on expert opinion and knowledge of both system user demographics and activity space demographics near the intersections. Tabulate the number of intersections with a high level of environmental justice (EJ) concern. Step 1 Step 2 Step 3 Step 4 1a 1b 1c 1d 3a 3b 3c 4a 4b 4c 4d 4f 4g 4e 3d 2b2a Policy/plan/project/intersection description Determine air quality/regulatory objectives Assemble all data pertaining to intersection-related traffic conditions Multiple intersection demographic review Multiple intersection screening/ranking Individual intersection modeling Assemble data on traffic, meteorology, site characteristics, background Compute 1-hour peak traffic concentration using CAL3QHC Apply persistence factor and background Rank by concentrationModel top 3 based on EJ concern Tabulate number of intersections of EJ concern Disproportionate effects determination Disproportionate effects determination Identify relationship between EJ score and concentration Locate receptors Model top 3 based on traffic volumes Rank top 20 by traffic volumes Model top 3 based on LOS Calculate LOS Assemble all data pertaining to intersection-related demographics Figure 3-3. Example of a microscale environmental justice assessment process Source: FHWA 2001. The results of this step would lead to a finding of distributive effects to protected populations in two cases:

73 • Fifty percent or more of the affected intersections in the study area are in areas with high environmental justice concern. • The proportion of intersections in the study area with high environmental justice concern is larger than would be expected for the comparison region. The expected proportion of intersections with high environmental justice concern can be estimated from reviewing the demographic characteristics of a large sample of similar intersections in the comparison area. The comparison area should be a jurisdiction large enough to encompass the entire study area, such as a county, metropolitan area, or state. Step 3 – Screen and rank multiple intersections. As suggested in the FHWA guide, rank the intersections by traffic volumes and select the top 20 intersections. Then calculate the level of service for those intersections. Select for individual intersection modeling, at a minimum, the top three intersections based on traffic volume ranking, the top three intersections based on LOS ranking, and the top three intersections based on EJ concern ranking. The purpose of adding at least three intersections of environmental justice concern is to allow comparison of results for worst case intersections obtained in Step 4. Step 4 – Model individual intersection. The purpose of this step is to compute 1-hour peak traffic-related concentrations at sensitive receptors through application of a hot-spot model such as CAL3QHC with the addition of any persistence or background air quality factors. The screening and ranking step (Step 3) is recommended because the level of modeling effort increases linearly with the number of intersections being evaluated. By applying the screening process, you can be reasonably certain that the worst case intersections are being evaluated. Once you have calculated emissions scores for each modeled intersection, rank the intersections in order of concentration. Interpretation of the results can be relatively simple, although various statistical techniques also can be used to compute rank-order correlations and determine significance of the findings. Initial findings, however, can be made simply by identifying where the “high-EJ concern” intersections fall in relation to the worst case intersections. In general, a finding of adverse distributive effects to protected populations would be warranted based on the following results: • Fifty percent or more of the worst case intersections are in areas of high environmental justice concern. • The proportion of worst case intersections with high environmental justice concern is larger than would be expected for the comparison region. • A violation or exceedance is indicated at any intersection of high environmental justice concern. • Projected pollutant concentration increases will be measurable between baseline and future-year scenarios, and the areas with the greatest increases are characterized as having high environmental justice concern. In contrast, results indicating that concentrations are lower at intersections characterized as having high environmental justice concern when compared to the worst case intersections may support a finding that protected populations are not adversely or disproportionately affected. A

74 finding of beneficial distributive effects would be supported by a result indicating that concentrations at the modeled intersections with high environmental justice concern will be measurably lower for future-year scenarios than for the baseline condition. Data needs, assumptions, and limitations. This analysis requires the following types of information: • Background concentration levels (although this can often be estimated); • Traffic impact study, including traffic volume, intersection geometry, signal timing, traffic speeds, location of sensitive receivers; • Regional vehicle parameters and fuel information, including: – Vehicle classification and mileage data, – Vehicle age distribution, and – Vehicle inspection/maintenance program details; • Specific fuel characteristics (oxygenated fuel, or ethanol); and • Geometric layout of intersections and locations of sidewalks and nearby sensitive receptors. In addition, information is needed to characterize the level of environmental justice concern of each intersection being analyzed. Any number of methods presented in Chapter 2 can be used to identify the relative level of environmental justice concern. As an initial screening step, or for large projects or system-wide analyses to review policies or programs, it would be useful and possibly necessary to use a census-data-based approach such as the Environmental Justice Index. However, due to the localized nature of effects being addressed by the microscale analysis, it is more appropriate to use demographic data collected for specific receptors using field survey, local knowledge, public input, and other detailed data-gathering techniques. A microscale intersection analysis is limited by the fact that only discrete receptors are analyzed. These locations are on such a small scale that the approach does not allow any means of generalizing or characterizing the overall air quality in an area. Thus, the approach can only be used to identify if any microscale distributive effects would result from the proposed transportation system change. It cannot be extended to address the general air quality of the local study area. It should be noted, however, that this approach does evaluate the maximum impact at intersections affected by a proposed project. So if the maximum impact is below the NAAQS, you can be reasonably confident that the project’s impacts elsewhere also will be lower than the NAAQS. Pollutant concentrations typically decrease very rapidly at greater distances from a roadway, but it would be difficult to precisely extrapolate results beyond the specific receptors being evaluated. It could also be possible that a roadway is to be moved closer to a receptor. Under these conditions, a slight shift in roadway alignment closer to a receptor could result in an increased concentration, even if an overall air quality benefit is achieved. For these reasons, occasions may arise where an increase from no-build occurs, but a build analysis verifies that concentrations do not violate the NAAQS.

75 Results and their presentation. Microscale intersection analyses predict the pollutant concentrations at discrete receptors near worst case intersections and intersections of high environmental justice concern. The number of receptors can vary significantly (e.g., 1 to 20). The location of these receptors is usually close (within 200 ft) to the roadway. Most analyses of this type include a figure showing the receptor locations, along with a table detailing the concentrations at each receptor. The results will always include the project alternative results and sometimes existing and no-build scenarios. Assessment. Policy, program, and project microscale results are most often compared to the NAAQS. However, results are occasionally compared to a no-build scenario. This microscale environmental justice assessment method is based on FHWA microscale or “hot spot” NAAQS conformity analysis, and shows expected concentrations at discreet receptors in parts per million (ppm). Environmental justice is evaluated by comparing and ranking expected concentrations and level of environmental justice concern at the worst case intersections as defined by traffic volumes, LOS, and level of environmental justice concern. If an exceedance of the NAAQS is predicted or if it is found that areas with relatively greater environmental justice concern experience relatively higher emissions, mitigation measures should be included to prevent the exceedances and unequal emissions levels. Mitigation could be required depending on attainment status and local rules. Mitigation may include adding intersection capacity with additional traffic lanes, optimizing signal timing for air quality purposes, or diverting traffic to other locations. Potential impacts associated with these measures could include right-of-way acquisition, increased pedestrian conflict areas, or increased traffic volumes at other locations. Mitigation measures often must be completed before a project is finished and sometimes may be a result of a nearby site development. Each of these mitigation measures could have environmental justice-related issues other than air quality that would need to be evaluated using appropriate methods. Method 3. Detailed regional analysis When to use. A regional air quality analysis is conducted for regionally significant projects and when required by local guidance. This type of analysis might be conducted for a major transit project, for a new freeway connection, or even for adding capacity to a major regional connection in an urban area. Because this technique provides only regional estimates and does not provide geographic distinction below the regional level, environmental justice assessment of regional air quality merely involves documenting the protected populations in the region and the air quality concerns that have been raised by those populations. Many of the analysis steps and issues discussed as part of the general air quality review (Method 1) apply to this technique as well. Analysis. A detailed regional analysis is carried out in two steps: Step 1 – Perform regional air quality analysis. Regional air quality analyses use regional travel demand models and MOBILE6 emission modeling. Regional travel demand models provide traffic volumes and speed on a link level. The MOBILE6 model provides emission rates that vary by speed and incorporates vehicle age, vehicle classifications, and other operating

76 characteristics. By applying these emission rates to each link of the regional travel demand model, total regional emissions can be determined. Generally, if capacity is added to a transportation system, delay decreases. While this may result in more trips or longer trips, the reduced delay results in an overall decrease in emissions because vehicles operate least efficiently when idling or in congested environments. Step 2 – Document air quality concerns of protected populations. Once the regional air quality analysis is completed, present the findings to protected population groups and address any questions or concerns. Except in cases where the geographic scope of the policy, program, or project covers more than one air quality region, it is very difficult to assess unequal distributive effects using this method. That does not mean, however, that regional air quality has no environmental justice implications. Because unequal effects cannot be determined in most cases and regional air quality is similar across broad areas, the environmental justice assessment effort should focus on sharing information with protected population groups and understanding any air quality concerns that these groups may have. To understand how protected populations perceive regional air quality and its health effects, consider either conducting a survey or focus groups. You must be sure to obtain the demographic characteristics of each participant that are relevant to characterizing protected populations. Recommended survey questions are presented in Chapter 2. Data needs, assumptions, and limitations. This type of analysis requires the following information: • Results of applying a regional travel demand model, • MOBILE6 modeling variables and regional characteristics, and • Information on protected populations. The models that must be used (according to regulation) assume a uniform distribution of the vehicle fleet across a region. The greatest limitation of this type of analysis for environmental justice assessment is that it provides no geographic distinction for results. As a result, extension of the technique to evaluate environmental justice is merely a matter of disclosure. You can do little more than share the results with protected population groups and understand their most important air quality concerns and how those concerns may be different (e.g., lesser, greater, or focused on specific health effects such as asthma) from the general population. Suggestions for understanding locations in a study area where there may be relatively higher pollution concentrations are explained under Method 1, but can be applied here as well. Results and their presentation. A regional air quality analysis generally includes a description of the air quality status of the region, a description of the guidance, and a tabular presentation of total pollutant emissions. The pollutants include CO, NO2, PM, and VOC. The results of the analysis would include a no-build and build comparison of annual tons of pollutant. Again, transportation projects usually represent an increase in capacity, which usually results in reduced pollutant emissions because free flowing traffic pollutes less than stop-and-go traffic. Assessment. This method can provide regional estimates of air quality impacts that would result from a proposed major transportation project, but it cannot give an indication of how protected

77 populations living or moving about in particular areas of the community would be affected differently than people in other parts of the community. Its principal use would be to estimate whether the build scenario’s emissions are higher than those of the no-build scenario. Method 4. Analysis using pollution surfaces The previous methods presented in this chapter each have relatively severe limitations when it comes to performing environmental justice assessment. The general air quality review is suitable as a screening technique, but it only provides information as to whether a transportation policy, program, or project may have effects to protected populations, in which case a more detailed assessment technique must be used to characterize the effects. The detailed microscale analysis provides an indication as to how protected populations would be affected at worst case sites, commonly intersections, within a study area. Its greatest limitation is that results cannot be extrapolated beyond the worst case sites that are evaluated. In other words, it does not provide results that can be used to assess the variability in pollutant levels across a study area to which all protected populations and the general population are exposed. The regional air quality review, on the other hand, is used to characterize air quality that is assumed to be relatively uniform over large areas, and it does not yield results that are geographically variable within the region of interest. Although regional impacts to protected populations can be described in general terms, this technique is limited by the fact that its results cannot be disaggregated to evaluate variable air quality patterns within the region. With all of the previous methods discussed, it is difficult to develop estimates of the overall, or cumulative, air quality picture because the techniques are focused on assessing only transportation air quality. What is needed is a method that can be used to assess the geographic variability of air quality within a region. The method would allow air quality assessment at a subregional or local scale larger than the microscale sites within a few hundred feet of hot-spot intersections but smaller than the large air quality regions that commonly cover multiple counties or large metropolitan areas. In addition, the method needs to be able to evaluate how transportation system changes affect subregional air quality from mobile sources, as well as the cumulative subregional air quality picture. When to use. This method is more data intensive than the previous methods discussed and is based on techniques that are less commonly used in current practice. It should therefore be used in situations where policies, programs, or projects are controversial and where the common methods for assessing local and regional transportation conformity do not address all of the concerns expressed by protected population groups. Analysis. Commonly used air quality assessment methods rely on travel demand models (discussed in Chapter 7), mobile source emission rate models (such as MOBILE6), microscale air quality dispersion models (such as CAL3QHC), and photochemical models for calculating regional air quality. Photochemical models use emissions estimates from all human and biological mobile and point sources, combined with meteorological data, to predict regional ambient pollution levels and to determine NAAQS conformance. These commonly used techniques can be combined with ancillary data sources to develop a map showing the variation

78 in pollutant concentrations across space and time. Such maps are called pollution surfaces because they provide a ground-level concentration estimate for each grid cell in a study area. It has been argued that traditional transportation air quality assessment methods have three important limitations: • The estimates of vehicle activity (vehicle-miles traveled and average speed) lack the accuracy and spatial resolution needed to evaluate control measures. • The mobile source emission rate modeling process uses highly aggregated fleet estimates and biased emission rates. • The modeling process is not oriented to the needs of transportation planners and engineers who design and implement emissions control strategies. These users require more feedback from typical transportation system improvement strategies than is provided in current methods (Bachman et al. 2000, p. 207). For these and other reasons described earlier, the commonly used assessment methods also severely limit the ability to perform an environmental justice assessment. Probably the most significant reason is the third limitation cited by Bachman: namely that transportation planners cannot obtain from the existing methods the level of feedback necessary to evaluate spatial variability and to design effective control measures. Both of these factors—the ability to evaluate spatial variability and the ability to determine the effectiveness of control measures—are key to environmental justice assessment of transportation-related air quality. Two basic approaches are used to overcome the limitations of the most commonly used air quality assessment methods. These include model-based methods and statistical methods. Model- based methods extend existing models to account for the spatial and temporal variability in vehicle emissions. They are based on more detailed information than is currently used. Statistical methods are techniques that use a combination of regression analysis and known concentrations from monitoring sites to predict pollutant concentrations across the entire study area. Recent examples of model-based research and statistical method research are described below. One prototype model-based method is known as the Mobile Emission Assessment System for Urban and Regional Evaluation (MEASURE). The details of the MEASURE model design and architecture can be found in U.S. EPA (1998a). This method for developing pollution surfaces improves upon regulatory emissions models such as MOBILE6 in two ways. First, MEASURE is modal in that emissions rates are specific to particular modes of vehicle operation, such as engine starts, normal operation, and rapid acceleration. Second, for each grid cell, the model computes the characteristics of the vehicle fleet and the proportion of the time that the fleet is in each of the vehicle operation modes. MEASURE therefore inherently captures the spatial variability in transportation air quality. In practice, emissions estimates are calculated for each vehicle mode, and then total emissions estimates are calculated by summing across modes. Once the total emissions estimates are computed for each cell, the output can be used in photochemical models (Bachman et al. 2000). The MEASURE emissions modeling process is depicted in Figure 3-4.

79 Pollution surface Photochemical model (optional) Emissions surface Zone-based emissions Evaporation Zonal activity Road segment activity Spatial data Engine start Zonal running exhaust Road segment running exhaust Road-based emissions Figure 3-4. The MEASURE emissions modeling process Source: Bachman et al. 2000. See Table 6.2. Statistical methods to predict pollutant levels most commonly use least squares regression analysis to develop predictive models based on monitored pollution data and information such as land use, population, and vehicle miles traveled. This form of prediction is preferred over global and local interpolation techniques. This is because monitoring networks generally are sparse and thus do not accurately depict pollutant distributions that are affected by complex topography, complex meteorology, and rapid distance decay functions. Many approaches are used to develop regression models for estimating pollution surfaces. A project known as Small Area Variations in Air Quality and Health (SAVIAH), funded by the European Union, is used as an example. Details of the SAVIAH study can be found in Briggs et al. (1997). The SAVIAH approach used in Huddersfield, United Kingdom, is depicted in Figure 3-5. The study used standard air quality monitoring devices (i.e., samplers) to record pollutant levels at a number of locations within the cities that were studied. The following is a procedure for implementing the Method 4 analysis. Step 1 – Develop pollution surface. Generate a pollution surface using either model-based or statistical techniques. One important benefit of the model-based technique is that it can be used very effectively to evaluate transportation-related emissions and proposed control measures. The greatest benefit of the statistical technique, on the other hand, is that it is better for evaluating

80 cumulative pollutant concentrations from all sources. Both techniques result in a geographically variable pollution surface that can be evaluated for distributive effects to protected populations. Suggested steps for performing the model-based technique are presented as Steps 1a-1f. Suggested steps for performing the statistical technique are presented as Steps 1g-1j. Land cover High density residential Measured pollutant concentrations Monitoring site adjusted mean concentration Weighted built land factor Weighted traffic factor Altitude Regression model Grid Pollution surface Sampler height Industrial Traffic flows Road network Traffic volume Figure 3-5. The SAVIAH statistical pollution surface development process Source: Briggs, et al. 1997. See Table 6.2. Step 1a (MEASURE example). Organize the spatial environment. Determine analysis units for each of the necessary input variables. For example, TAZs could be the unit of analysis for estimating trip origins and engine starts. Alternatively, it would be possible to disaggregate trip origins to a smaller analysis unit using land use information to identify residential areas and census block information to identify household densities. Travel demand model network links need to be related to an accurate GIS road layer. It is also necessary to select the output grid cell size. Cell size selection should be based on resolution of the input data, data processing requirements such as file size limitations, desired resolution of the outputs, and computational efficiency. Step 1b. Estimate vehicle fleet characteristics. This step involves determining the characteristics of the vehicle fleet and its spatial variability. Thus, the fleet characteristics could be different for each output grid cell in the model. In MEASURE, a “technology group” distribution is calculated

81 for each zip code in the study area. Technology groups are combinations of vehicle characteristics and operating characteristics that have been identified through empirical research as being highly predictive. For engine start and engine off modes, vehicle fleet characteristics are estimated for zonal analysis units such as TAZs and/or zip codes. Estimates for operating vehicles (i.e., vehicles in motion) associated with road segments are based on analysis of travel demand model links. In MEASURE, the mix of vehicles on various roadways is identified by developing estimates of local and regional fleet characteristics based on zip-code-level vehicle registration data. Step 1c. Estimate vehicle activity. Use the travel demand model to predict regional travel, including the number and location of peak hour or daily trip origins, road segment volumes, and average speeds based on volume-to-capacity ratios. Use this information to estimate distributions of speed and acceleration, and vehicle mode operations. Step 1d. Predict facility-level emissions. Transportation “facilities” include major roads, minor roads, and trip origin zones. Engine start emissions are linked to the zone (i.e., TAZ, census block group, or census block) of origin. Minor roads not usually included in travel demand models are assigned running exhaust emissions as zonal averages based on travel time, local road speeds, fleet composition estimates, and vehicle miles traveled. Major roads included in the travel demand model are assigned running exhaust emissions as linear (i.e., road segment) averages based on speed and acceleration characteristics and fleet composition estimates. Step 1e. Generate the mobile emissions inventory. This step involves converting the facility- based emissions estimates generated in Step 1d to grid-cell-based emissions estimates. Convert estimates for each polygon facility (origin zone or minor road zone) to a rate expressed as grams/square kilometer, and convert major road facilities to a rate expressed as grams per kilometer. The polygon and line facilities are overlaid on the output grid cells and the rates are allocated to the grid cells based on the proportion of a facility that falls into each cell. Compute emissions per grid cell by multiplying the rates by the areas and lengths of the facility/grid cell portions. Step 1f. Apply photochemical model (Optional). Use an appropriate photochemical model to generate estimates of ground-level pollutant concentrations across the study area. The ground- level pollutant concentrations are based on meteorological conditions and the emissions estimates obtained for each grid cell. The physical properties of the pollutant and distance decay functions are also accounted for in the model. Perform this step if you wish to evaluate the distribution of ground-level pollutant concentrations rather than transportation-related emissions (the output of Step 1e). By incorporating emissions information for point sources into the photochemical model, you can evaluate cumulative ground-level concentrations in addition to ground-level concentrations from transportation-related sources. However, application of photochemical modeling requires collection and processing of far more input data (including regional speciated emission inventories and three-dimensional fields and boundary conditions of all meteorological and air quality parameters) than any of the other methods described here. Therefore, this step generally would be impractical for evaluating individual transportation projects. Step 1g (SAVIAH example). Data collection and preparation. Unlike a model-based method that estimates emissions based on roadway geometry, traffic volumes, and vehicle fleet

82 emissions characteristics, statistical methods predict population surfaces by fitting regression models to observations at monitoring sites based on known values for predictor variables. Because existing monitoring sites (such as stations used to evaluate regional air quality and to develop AQI scores) are relatively sparse in most areas, you may want to consider additional monitoring over a prolonged period of time (i.e., many weeks or months). A larger monitoring network and a larger number of samples will yield a more accurate regression model. In addition, you will need to collect the necessary information to compute predictor variables. Commonly used information is listed in “data needs, assumptions, and limitations.” Step 1h. Conduct exploratory study. The result of a multivariate analysis is a best-fit curve allowing estimates of the response variable to be derived from known values of predictor variables. The purpose of the exploratory study is to reduce the set of candidate predictor variables described in Step 1g to the set to be used in the regression model. Highly intercorrelated predictor variables should be eliminated, as should other variables found to have low correlation with the response variable. Use a variable reduction procedure to develop correlation matrices and identify candidate predictors that should be retained in the final model. Comrie and Diem (2001), for example, used principal component analysis to evaluate an initial set of candidate predictor variables. Although it is beyond the scope of this guidebook to provide an in-depth discussion of variable reduction, numerous books on applied regression analysis are available, such as Neter et al. (1996). Although this approach is not always applied, it may be worthwhile to consider developing multiple regression models, one for each commonly observed set of meteorological conditions. Comrie and Diem (2001), for example, developed independent regression models for four distinct clusters of CO monitoring data, where observed concentrations, temperature ranges, wind speeds, and atmospheric pressure were relatively constant within each cluster. Step 1i. Refine and select model. The result of Step 1h is a small subset of candidate regression models with a limited number of explanatory variables that provide good predictive ability. Step 1i results in selection of the final model, based on review of residual plots and analyses to identify lack of fit, outliers, and influential observations. The SAVIAH study (Briggs et al. 1997), for example, developed regression models to predict NO2 concentrations in Huddersfield, the United Kingdom; Amsterdam, the Netherlands; and Prague, the Czech Republic. In each city, the regression models were based on local predictors that provided results with the most predictive power. However, two constraints were placed on the regression models developed for each city. First, the models had to include terms for traffic volume, land cover, and topography because each of these is known to affect pollutant dispersion. Second, a common GIS-based buffering approach was used to compute predictor variables that had a spatial component, such as total traffic volume within 300 meters of a monitoring site. The resulting regression equation for average annual NO2 concentration in Huddersfield included traffic volume within 300 meters, high-density housing, and industrial land use proportions within 300 meters, altitude, and sampler height. Step 1j. Apply the model. Based on computed predictor variable values for each grid cell, use the selected regression equation to compute predicted pollutant concentrations. This yields a pollution surface for the entire study area.

83 The output of either the model-based or statistical approach is a pollution surface that provides estimates of either emissions or pollutant concentrations for each cell in the study area grid. This output is then combined with a population surface to perform an environmental justice assessment. Step 2 – Develop population surface. A population surface is a raster, or grid-cell-based, representation of a population. Population surfaces are produced in GIS using any of various algorithms to convert census polygon-based, or zonal, population data to a grid cell-based format. A population surface is the best form of demographic data to use with model-based and statistical air quality prediction techniques because their results are also grid-based. By using the same set of grid cells to produce pollution surfaces and population surfaces, you create an information-rich dataset that can be evaluated to assess unequal distributive effects to protected populations. More detail on the process for developing population surfaces is provided in Chapter 2. The output of this step is a set of GIS grids depicting the distribution of protected populations, nonprotected or other populations, and total population. Protected populations can include race, income, age, sex, or any other protected population group. It is also possible to produce a “protected population surface” that is merely the sum of all population groups of interest. From these datasets, standard map algebra routines can be used to compute estimates of affected populations and population percentages for the entire study area or for smaller areas within a larger region. Step 3 – Overlay pollution surface and population surface. The next step is to overlay the pollution surface created in Step 1 with the various population surfaces created in Step 2. You can do this using GIS and relational database software. Both tabular and map-based results are needed. Figure 3-6 depicts the process of combining population surfaces and pollution surfaces and the resulting outputs. Step 4 – Visualize results. The power of this technique is the rich dataset it produces for evaluating geographically distributed effects. With additional processing of time-series data, such as prebuild, build, and future-year scenarios, temporal environmental justice aspects can also be assessed. Because of the richness of the dataset, it is both unwise and unnecessary to base conclusions about distributive effects on a single statistical test or data visualization approach. Rather, it is important to analyze and visualize the data in many different ways. This will lead to a detailed understanding of distributive effects patterns and effective control measures for altering present or future distributive patterns. The following assessment and data visualization routines are described below: • Relative emissions burden calculation, • Relative pollution burden graphs, • Regional effects mapping and analysis, and • Local effects analysis. Relative emissions burden calculation. Relative emissions burden is defined as the ratio between (a) the average ground-level pollutant concentration (or emissions level) available to

84 members of protected population groups and (b) the concentration available to members of non- protected population groups. Note that this is a measure of pollutant “availability,” not exposure. “Burden” therefore means that pollutants are present and available for persons to be exposed to. The relative emissions burden evaluation was first used in 1998 by the U.S. EPA to determine if pollutant distributions in the Louisiana Industrial Corridor disproportionately affected minority populations (U.S. EPA 1998b). Inputs Population surfaces Outputs From Step 1 Overlay Zonal census blocks Zonal census block groups Pollution surface Zonal/Surface conversion Grid cells Protected population (PP) Nonprotected population (NPP) Total population (TP) Tabulated population characteristics and concentrations for each grid cell Determine distributive effects and evaluate significance Average emissions burden for PP, NPP, and TP Map PP and pollutant concentration Comparison charts Figure 3-6. GIS process for combining pollution surfaces and population surfaces The EPA approach based estimates of emissions burden on proximity to pollution sources and on estimates of pollutant emissions in pounds. The approach did not account for pollutant dispersion based on meteorological and chemical properties, nor did it develop estimates of ground-level pollutant concentrations. Unlike the approach presented here, the U.S. EPA study used zonal census data, buffers of pollutant-emitting facilities, and information on emissions volumes to evaluate distributive emissions patterns. One benefit of working with population surfaces is that

85 calculation of relative emissions burden is relatively simple and straightforward when compared to working with vector GIS datasets. Relative emissions burden is computed using the following equation: R PC NPP NPP PC NNP NNP i i i N i i N i = ×     ( ) ×     ( ) = = ∑ ∑ 1 1 / / where R = relative emissions burden N = number of cells in grid PC = pollutant concentration NPP = number of persons in protected population group NNP = number of persons in non-protected population group Evaluation of results is also straightforward. If R is greater than 1.0, the average level of emissions experienced by members of protected population groups is greater than the average level experienced by members of other population groups. If, for example, R is computed to be 1.25, this means that the average emissions burden to members of protected populations is 25 percent greater than the average emissions burden to members of other population groups. Although it is worthwhile to evaluate relative emissions burden, comparison of average burden levels across a study area is only of limited value in determining to what extent there may be unequal distributive patterns and where those patterns are evident. This more detailed evaluation is better performed by evaluating the distributions in charts and maps. Examples are provided below. Relative pollution burden graphs. When evaluating the codistribution of air quality effects and protected populations, visualizing the information patterns in graphs and maps is often the most insightful form of analysis. To produce graphs, the pollution surface must be combined with a population surface, which yields an estimate of pollutant concentration and protected population characteristics for each grid cell. This dataset can be used to identify any areas where standards may be violated and to identify locations where strategies to reduce pollutant concentrations are required. For purposes of environmental justice assessment, it is also important to understand that many communities may have concern over pollutant concentrations that meet regulatory air quality standards. This could be because a community feels that the NAAQS are not protective enough of sensitive individuals or of individuals that receive greater exposure due to lifestyle. Or, it could be that a community is concerned about the additive and synergistic effects of exposure to multiple pollutants.

86 Evaluating distributive effects at concentration levels below the NAAQS is also a useful approach for dealing with concerns about measurement error. It is often practical to evaluate effects above the NAAQS, as well as to identify a “threshold of concern” for values that approach the NAAQS. The threshold of concern can be based on policy, expert information, or even community input. Figure 3-7 depicts results of an analysis to estimate annual average PM10 concentrations (see page 61) in a five-county metropolitan region. The annual average NAAQS for PM10 is 50 micrograms per cubic meter (µg/m3). A decision was made, based on public input, that areas with concentrations above a 40 µg/m3 threshold of concern should also be evaluated for distributive effects. The graphs show the regional pattern of concentrations available to protected population groups compared to other individuals. The top chart shows that a greater proportion of protected population groups reside in areas with concentrations between 31 and 60 µg/m3meter. The bottom chart shows that a greater number of persons in protected populations also reside within areas with concentrations in this range. It is important when using this technique to compare to the rest of the population both (a) the percent of the protected population and (b) the total number of individuals in the protected population group that would experience adverse effects. This can be done by preparing one set of graphs with percent of population as the vertical axis and another set of graphs with the number of persons as the vertical axis (as in Figure 3-7). Both evaluations are necessary to determine distributive effects because in certain study areas a majority of the affected population may belong to protected groups. In the case of this particular dataset, evaluations by number of persons and population proportions both show patterns of unequal distribution. For other study areas, however, this may not be the case. This topic is addressed again in Chapter 10. Regional effects mapping and analysis. The graphic visualization of pollution and population surface results presented in Figure 3-7 is a very useful assessment technique for characterizing the distribution of pollutant levels among population groups. The graphs are even more useful, however, when combined with maps showing the pollution surface overlaid on the population surface. Figure 3-8 provides an example for the same five-county area discussed above. The top map depicts areas where there is a high proportion of protected population. The bottom map depicts areas where a large total number of members of protected population groups reside. As with the graphs, it is necessary to evaluate the population distribution patterns in both ways. The benefit of using both maps and graphs is that one form of visualization overcomes the limitations of the other: maps are very good at depicting geographic patterns, but all detail is lost when you try to quantify the geographic patterns. On the other hand, by viewing the graphs it is easy to speak in quantitative terms about the disproportionate pattern that seems evident in the map. If you rely on just the graphs, however, it is easy enough to understand that a disproportionate pattern exists but impossible, without the map, to determine where the patterns are located. Regional analysis is thus a combination of determining relative emissions burden and visualizing any potential disparities using graphs and maps. In the examples provided above, it could be concluded that areas where annual average PM10 concentrations exceed 40 µg/m3 are of concern and that strategies must be implemented to reduce these concentration levels. It could also be concluded that there is an environmental justice issue because those concentration levels burden

87 a greater proportion of the protected population and a greater number of individuals in protected population groups. This form of analysis is useful for policies, programs, long-range planning, and regionally significant projects. Proportion of Specific Population Group 0 5 10 15 20 25 30 35 40 45 0-5 6-10 11-30 31-40 41-50 51-55 56-60 61-65 PM10 Concentration (micrograms/cubic meter) P er ce n t o f P o p u la ti o n Protected Population Other Population Threshold of Concern Above PM10 NAAQS Unequal exposure at levels between 31 & 60 micrograms/cubic meter Total Persons 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000 0-5 6-10 11-30 31-40 41-50 51-55 56-60 61-65 PM10 Concentration (micrograms/cubic meter) P o p u la ti o n C o u n t Protected Population Other Population Threshold of Concern Above PM10 NAAQS Unequal exposure at levels between 31 & 60 micrograms/cubic meter Figure 3-7. Distribution of annual average PM10 concentrations in a regional study area

88 Percent of Total Population Number of Persons Figure 3-8. PM10 concentration and protected population patterns in a regional study area

89 By performing prebuild and postbuild modeling runs, this method also allows you to evaluate policy, program, and project impacts. With some change in current regulation, the geographic variability of pollutant concentrations that results from this method would also give transportation planners a much broader set of control measures to consider for achieving transportation conformity. Local effects analysis. The pollution surface technique can be used to evaluate regionally significant projects and transportation corridors. However, for smaller projects with the potential only for localized effects, it is more appropriate to combine regional analysis of the pollution surface with microscale analysis (Method 2). Regional pollution surface analysis is useful to help understand whether the area of effects is in a location where pollutant concentrations are predicted to be relatively high. Microscale analysis can then be used to evaluate the localized effects of small projects. To evaluate localized effects of regionally significant projects and transportation corridors, the project area of effects must be identified. Using the pollution surface technique, a reasonable approach is to apply a dispersion model to identify the area within which the project would have a measurable (or meaningful) effect on concentrations. This level would depend on the pollutant being evaluated as well as on the sensitivity of the model. For CO, the area potentially affected by the project would be highly localized, whereas for PM the area of effects could be far- reaching. Figure 3-9 shows the distribution of predicted postimplementation annual average PM10 concentrations. The regional pollution surface described above was used to tabulate results within the project area of effects defined for two alternative alignments. Alternative 1 is depicted in the top two graphs and Alternative 2 in the bottom two graphs. For each alternative, percent of population is depicted in the left-hand chart, and population count in the right-hand chart. A review of these graphs indicates that Alternative 1 may be preferable from an environmental justice perspective because there would be no unequal impact to protected population groups above 40 µg/m3. In contrast, Alternative 2 would result in a greater proportion of protected population groups being exposed at these levels. Both the total number of persons and the total number of persons in protected population groups in areas with concentrations above 40 µg/m3 would be similar under both alternatives. The biggest drawback of the pollution surface approach is that it requires a great deal of time and effort to produce the necessary data. That is why, although limited in their predictive power, the previous three methods are likely to be used in all but the most controversial situations. Both statistical and model-based methods for estimating the regional and local variability in air quality are highly experimental. Unlike the micro-scale analysis and regional air quality assessment methods, this method has not yet been mandated, or even widely accepted, as part of the regulatory process. The assessment of relative emissions burden is not the same as determining if people in the study area are exposed or if the level of exposure varies by person. To measure exposure, you must consider factors such as the amount of time persons spend outdoors and the ventilation properties

90 of buildings. Tests to evaluate the statistical significance of the relative emissions burden statistic (R) are available, but their application is problematic. This is because in most situations the sample set, in this case grid cells, will be large and even slight differences in average emissions burden would be deemed significant. It is also uncertain whether you can assume that the sample population is normally distributed about the mean, which is a requirement of most parametric tests. Because of these concerns, it is better to merely evaluate the value of R to determine the direction of the difference in average burden levels. Proportion of Specific Population Group Alternative 1 0 5 10 15 20 25 30 35 40 45 0-5 6-10 11-30 31-40 41-50 51-55 56-60 61-65 PM10 Concentration (micrograms/cubic meter) P er ce n t o f P o p u la ti o n Protected Population Other Population Threshold of Concern Above PM10 NAAQS No unequal exposure at levels above 40 micrograms/cubic meter Total Persons Alternative 1 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 0-5 6-10 11-30 31-40 41-50 51-55 56-60 61-65 PM10 Concentration (micrograms/cubic meter) P o p u la ti o n C o u n t Protected Population Other Population Threshold of Concern Above PM10 NAAQS No unequal exposure at levels above 30 micrograms/cubic meter Alternative 2 0 5 10 15 20 25 30 35 40 0-5 6-10 11-30 31-40 41-50 51-55 56-60 61-65 PM10 Concentration (micrograms/cubic meter) P er ce n t o f P o p u la ti o n Protected Population Other Population Threshold of Concern Above PM10 NAAQS Unequal exposure at levels between 30 & 50 micrograms/cubic meter Alternative 2 0 500 1,000 1,500 2,000 2,500 3,000 0-5 6-10 11-30 31-40 41-50 51-55 56-60 61-65 PM10 Concentration (micrograms/cubic meter) P o p u la ti o n C o u n t Protected Population Other Population Threshold of Concern Above PM10 NAAQS Unequal exposure at levels between 6 & 55 micrograms/cubic meter Figure 3-9. Comparison of alternative project alignments Data needs, assumptions, and limitations of population surfaces are discussed in Chapter 2. Results and their presentation. The graphics included in the discussion of Step 4, above, provide examples of results and how they may be presented. Depending on the audience, it may be necessary to simplify the presentation of the graphs and maps. Data needs, assumptions, and limitations. Application of a dispersion model such as MEASURE would require the following data to be collected (Bachman et al. 2000):

91 • Spatial character information • Vehicle mode activity information — Land use boundaries — Idle — Census blocks — Cruise — TAZs — Acceleration — Roads — Deceleration — Travel demand model network — Starts — Output grid cells — Engine off • Temporal character information • Trip generation information — Hour of the day — Land use • Vehicle technology information — Housing units — Model year — Socioeconomic characteristics — Engine displacement — Home-based work trips — Transmission type — Home-based shopping trips — Fuel delivery technology — Home-based school trips — Supplemental air injection system — Home-based other trips — Catalyst configuration — Nonhome-based trips — Exhaust gas recirculation Application of a regression model such as the one implemented in the SAVIAH project would generate the following data needs (Briggs et al. 1997): • Road network • Topography • Road type • Concentrations from monitoring sites • Distance-to-road • Sample height • Traffic volume • Site exposure • Land use/land cover • Topographical exposure Assessment. Analysis of local and regional air quality using pollution surfaces and population surfaces is a very useful technique for evaluating distributive air quality effects to protected populations. Pollution surfaces can be developed using models that extend commonly used regulatory models to account for geographic variability in pollutant concentrations. Data visualization techniques using graphs and maps and findings based on expert opinion and public input generally are recommended over statistical tests that reduce the information-rich dataset to a single test for statistical significance. Although these methods are not widely used in current practice, considerable research has been performed. The drawback of this method is that it is extremely data intensive and has not yet received the level of regulatory approval that has been given to traditional microscale and regional air quality assessment methods. Given that techniques for understanding the geographic variability of air quality are important both for

92 implementing more effective transportation control measures and for thoroughly evaluating environmental justice, it is expected that use of these techniques will increase in the future. RESOURCES 1) Federal Highway Administration (FHWA). 2001. Transportation Conformity Reference Guide. Available at http://www.fhwa.dot.gov/environment/conformity/ref_guid. The transportation conformity reference guide provides a thorough review of the transportation conformity process. It includes information on NAAQS, approved models for evaluating transportation conformity, a discussion on how to perform a regional air quality analysis, and information on how to perform a microscale analysis. 2) U.S. Environmental Protection Agency (U.S. EPA). 2003b. Toxics Release Inventory Program. Available at http://www.epa.gov/tri/. This resource provides detailed information on the TRI program and discusses common uses of TRI reports. Detailed TRI data files are also available by state. REFERENCES Bachman, W., W. Sarasua, S. Hallmark, and R. Guensler. 2000. “Modeling Regional Mobile Source Emissions in a Geographic Information System Framework.” Transportation Research, Vol. 8C, Nos. 1-6, pp. 205-229. Briggs, D.J., S. Collins, P. Elliott, P. Fischer, S. Kingham, E. Lebret, K. Pryl, H. Van Reeuwijk, K. Smallbone, and A. Van Der Veen. 1997. International Journal of Geographical Information Science. Vol. 11, No. 7, pp. 699-718. Bryant, Bunyan, and Paul Mohai. 1992. Race and the Incidence of Environmental Hazards. Boulder, Colorado: Westview. Bullard, Robert. 1996. “Environmental Justice: It’s More Than Just Waste Facility Siting.” Social Science Quarterly. Vol. 77, No. 3 (September), pp. 493-499. Carlin, B.P., and H. Xia. 1996. “Assessing Environmental Justice Using Bayesian Hierarchical Models: Two Case Studies.” Journal of Exposure Analysis and Environmental Epidemiology. Vol. 9, No. 1, pp. 66-78. Comrie, Andrew, and Jeremy Diem. 2001. SMOGMAP, System for Management, Observation, and GIS Modeling of Air Pollution: Final Report, Phase IV. Prepared for Pima Association of Governments. Tucson, AZ: The University of Arizona, Department of Geography and Regional Development. Additional SMOGMAP information available online: http://geog.arizona.edu/~diem/smogmap. Lave, L.B., and E.P. Seskin. 1977. Air Pollution and Human Mortality. Baltimore, MD: Johns Hopkins University Press. National Center for Health Statistics. 1995. Health, United States, 1995. Hyattsville, MD: Public Health Service.

93 Neter, John, Michael H. Kutner, Christopher Nachtsheim, and William Wasserman. 1996. Applied Linear Regression Models. Third edition. Chicago, IL: Irwin. Schwartz, Joel, and Douglas W. Dockery. 1992. “Increased Mortality in Philadelphia Associated with Daily Air Pollution Concentrations.” American Review of Respiratory Disease. Vol. 145, No. 3 (March), pp. 600-604. United States Environmental Protection Agency (U.S. EPA). 2003a. National Ambient Air Quality Standards (NAAQS). Office of Air and Radiation Fact Sheet, U.S. Environmental Protection Agency. Available at http://www.epa.gov/air/criteria.html. U.S. EPA. 1998a. A GIS-Based Modal Model of Automobile Exhaust Emissions. Report number EPA-600-98-097, Research Triangle Park, North Carolina. Available at http://www.epa.gov/ordntrnt/ORD/NRMRL/Pubs/600R98097/600R98097.htm. U.S. EPA. 1998b. Title VI Administrative Complaint Regarding Louisiana Department of Environmental Quality, Permit for Proposed Shintech Facility: Draft Revised Demographic Information. Washington, DC: U.S. EPA, Office of Civil Rights. U.S. EPA. 1996. EPA’s Proposal on the Particulate Matter Standard. Office of Air and Radiation Fact Sheet, U.S. Environmental Protection Agency. Available at http://capita.wustl.edu/OTAG/OTAGActivities/OTAGDocuments/NEWSTAND/pmFACT.h tml. U.S. EPA. 1995. Automobile Emissions: An Overview. National Vehicle and Fuel Emissions Laboratory. Washington, DC: Government Printing Office. U.S. EPA. 1994a. Supplement to the Second Addendum (1986) to Air Quality Criteria for Particulate Matter and Sulfur Oxides (1982): Assessment of New Findings on Sulfur Dioxide Acute Exposure Health Effects in Asthmatic Individuals. EPA-600/FP-93/002. Washington, DC. U.S. EPA. 1994b. National Air Pollutant Emission Trends, 1900-1993. Washington, DC: Government Printing Office. Waller, L.A., T.A. Lewis, and B.P. Carlin. 1999. “Environmental Justice and Statistical Summaries of Differences in Exposure Distributions.” Journal of Exposure Analysis and Environmental Epidemiology. Vol. 9, No. 1, pp. 56-65.

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TRB’s National Cooperative Highway Research Program (NCHRP) Report 532: Effective Methods for Environmental Justice Assessment is designed to enhance understanding and to facilitate consideration and incorporation of environmental justice into all elements of the transportation planning process, from long-range transportation systems planning through priority programming, project development, and policy decisions. The report offers practitioners an analytical framework to facilitate comprehensive assessments of a proposed transportation project’s impacts on affected populations and communities.

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