Case Study:

Waterloo, Iowa

Conclusions

Limitations

Some limitations also must be noted with respect to the technical methods utilized and any conclusions that can be drawn from this type of analysis:

• The methods described here can be used to identify environmental impacts on local populations in a relatively precise manner. The authors do not, however, outline a complete methodology for comparing the distribution of burdens or draw specific conclusions with respect to environmental justice. Furthermore, local populations may experience other benefits or costs resulting from the project, such as increased accessibility or economic development, that need to be weighed against the environmental impacts. Identifying the environmental justice implications of any project or action raises difficult questions. Analysts adopting the methodology described here will need to develop a method for assessing the fairness of the various project impacts across population groups, based on local goals and objectives.

• Additional caveats apply to any analysis conducted using micro-scale emissions and noise dispersion models to calculate exposure. First, the contours are only an approximation; diffusion of noise and pollutants will be affected by structures and topography as well as specific traffic conditions and meteorological conditions at any given time. Exposure at the individual level will also vary depending upon a person's activity patterns (e.g., many people spend much of the day at work rather than at home). Conversely, the model does not consider exposure for people who spend significant time at commercial establishments in the corridor. Furthermore, annoyance from noise varies considerably from person to person. The SPARTACUS Case Study identified research on the percentage of population affected by different noise levels.
• The analysis methodology described here also assumes that both total population and low-income and minority groups are distributed evenly across each block or block group. This may not accurately represent exposure; for example, if homes are clustered away from the road, exposure will be less. The representation of population data at the block level, however, is still a significant improvement over tract- or block-group-level data. New developments such as parcel-level land use databases and remote sensing to identify and map land uses may lead to still more accurate estimations of housing unit distributions in the future. In conjunction with the GIS-based emission and noise contour methodologies described above, these data sources may allow more precise assessment of population impacts. It is unlikely, however, that the socioeconomic characteristics associated with individual housing units will be known at a precise level of detail.

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