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CADDIS Volume 3: Examples & Applications

State & Other Regulatory Examples

The principles of causal analysis have proven to be useful in a number of applications where establishing a cause-effect relationship is required for environmental decision-making.

Applications to the Clean Water Act 303d/TMDL program

map of states using stressor identification
Figure 1. States that have used or are currently using methods from the Stressor Identification process, shown in green.

In the sequence of reporting on the status of streams and rivers, Stressor Identification (and use of the tools in CADDIS) most often occurs after a water body is listed as impaired by biological or unknown causes and before the development of a TMDL or watershed management plan. U.S. EPA does not require documentation of how pollutants or the targets of watershed management are identified; however, we have found some evidence of the adoption of our methods (Figure 1). This list does not include states which have conducted full stressor identification case studies (see the Case Studies page for detailed descriptions of these assessments).


State Example of Stressor Identification Use
Arizona Biocriteria Implementation Procedures
Idaho Hellroaring Creek Stressor Identification
Indiana  Stressor Identification Process for the Limberlost Watershed (Morris et al. 2006)
Iowa  Total Maximum Daily Load For Sediment and Nutrients Camp Creek Polk County, Iowa
Maine  Urban Streams Project Report
Maryland  TMDL Elements to Review Prior to Implementation Planning
Minnesota  2007 Guidance Manual for Assessing the Quality of Minnesota Surface Waters for the Determination of Impairment
Mississippi  Phase 1 Total Maximum Daily Load Organic Enrichment/Low Dissolved Oxygen and Ammonia Nitrogen Little Tangipahoa River South Independent Basin
New Jersey  The Use of Benthic Macroinvertebrate Assessments in the Stressor Identification Process to Reduce Chemical Analytical Costs
North Carolina  DRAFT Total Maximum Daily Load for Addressing Impaired Biological Integrity in the Headwaters of Swift Creek Watershed, Neuse River Basin
Virginia  Benthic TMDL Development: Stressor Identification for the Jackson River, Virginia; Potomac/Shenandoah River Fish Kill

Derivation of water quality criteria

Controlled laboratory tests have provided the exposure-response relationships needed to develop U.S. EPA’s ambient water quality criteria. Some stressors, however, are not easily tested in the laboratory. A weight of evidence approach similar to that used in Stressor Identification may provide a way to develop criteria based on field associations between exposure and effects. This approach was applied to suspended and bedded sediment (U.S. EPA 2006).

Hazardous waste site assessments

Environmental assessments for Superfund, the Oil Pollution Act, and other legislation that deal with wastes in the environment have traditionally followed the conventional risk assessment framework—that is, they characterize the contaminants and use laboratory toxicity data to estimate the risks that they pose to humans and nonhuman populations and communities. However, assessors of contaminated sites have increasingly used biological surveys to identify impairments at those sites and then used some sort of causal inference to determine whether the wastes or some other stressor is the cause. Because these ecoepidemiological assessments might improve the quality of contaminated site assessments, we have performed two demonstration assessments: the Elk Hills Naval Petroleum Reserve, CA and California Gulch, CO.

Endangered species act

Assessments of the viability of threatened and endangered species can protect those species' futures. Historically, they have tended to focus on habitat loss and degradation. The causal assessment of the decline in the population of Vulpes macrotis mutica (San Joaquin kit fox) on the Elk Hills Naval Petroleum Reserve took the approach of comparing possible causes of a past decline to determine whether toxicity from oil field contaminants was responsible for a specific multi-year decline in a population. The assessment compared possible causes: habitat alteration by oil development, climate, prey abundance, disease, contaminants, and predation. Predation was found to be the cause of population decline, but predator (coyote) abundance appeared to have increased in response to oil development.

References

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