Reciclaje de materiales varios

X_VARIABLE Y_VARIABLE VALUE SCALE

Average Stream Flow (m³/s) Average Fecal Coliform 94-05 (cfu/100mL) 150 m² 3 Average Stream Flow (m³/s)

Max Fecal Coliform 94-05 (cfu/100mL)

150 m² 3 Swine Density (#/km²) Average Fecal Coliform 94-05 (cfu/100mL)

Swine Density (#/km²) Max Fecal Coliform 94-05 (cfu/100mL)

Given the values for each variable, their ranking among the three levels of importance and their relative scaling, the policy maker in this situation could use this DSS to decide where to focus his or her efforts. Highly developed land area within the example watershed is likely to be the most important variable to consider in microbial loading of surface waters. Given the results of this DSS, the policy maker would first want to focus efforts on best management practices for mitigating stormwater runoff in urban areas. The ranking of soil permeability as a Level 1 variable with a scale of 6 makes it somewhat less of a priority for policy development when compared to high intensity land use, but the policy maker should also consider addressing both variables in a combination approach that may, for example, limit development on permeable soils. The same approach could be used when considering high intensity land use and slope- limiting construction on large gradients. Addressing more than one important variable at a time, depending on how they rank in the DSS framework, allows for integrated policies that could help curb potential surface water contamination.

Conclusion

Gastrointestinal illnesses caused by pathogenic microbes result in a loss of productivity in healthy people, and in the case of the very old, young and immuno-

compromised, can be fatal. Waterborne disease outbreaks have occurred in the US due to contact with or ingestion of fecally contaminated water. Certain stormwater variables have been identified as being related to or predictive of contamination and include precipitation, slope, soil type, land cover, stream flow velocity, and proximity to sources of fecal matter, such as concentrated animal operations. Many watershed models exist that allow for the computation of microbial loading given a specific set of variables. However, these models

are often complex and require that the user have a significant understanding of mathematics and the ability to collect copious amounts of data. There existed a need for a method of approaching the problem of microbial loading that focused on the use of available data and estimating risk in a semi-quantitative way to provide an understanding of risk that falls between a qualitative framework and complex algorithms.

The decision support system presented above presents a basic framework for approaching microbial loading in the Neuse watershed and can be improved upon in the future. First of all, swine density is the only variable considered that presents a source of microbes in a watershed. The other five variables address the transport of microbes into receiving waters. The decision to include only swine density was based on the large number of swine operations in the Neuse Basin. The results of the variable analysis presented in Chapter II, however, indicate that swine may not contribute a significant loading of microbes. The negative slopes of the linear regression lines suggest current swine regulations and

containment measures are effective in preventing loading from swine operations. The importance of highly developed land area indicates microbial sources associated with urbanized areas may correlate better with fecal coliform values in nearby surface waters in the Neuse River Basin. Sources such as municipal discharges and failing septic systems should be considered in further work concerning the improvement of this decision support system. Additionally, examination of possible interactions between overlapping variables, such as highly developed land area and areas of high slope may reveal a more complex relationship regarding their correlations to fecal coliform values. These interactions between variables should be considered both in the improvement of the correlations and in the

A verification of the results of this DSS would be a helpful next step in establishing this method as a viable alternative to the more complex model driven approaches in the field of watershed management. The variable rankings presented in this DSS could be compared to the outputs of a proven model for the Neuse watershed to establish consistencies in the conclusions made by the two approaches. Finally, if this method is to be employed in a different watershed, the scales may need to be modified according to the new dynamic ranges of the variables for the given watershed.

The decision support system detailed above presents an alternate approach that is aimed at policy makers and presents a way to structure their reasoning as they approach the problem of microbial loading in a watershed. This framework has been compiled with readily available data and common computer programs. Important variables are identified and the user is provided with a method for ranking and scaling the variables given the data available in his or her watershed. The end result is a semi-quantitative risk estimate that can be used as a guide for future policies and planning. The levels of importance and scales in the decision support system above can be improved upon in the future through the use of more accurate and recent data. The use of this semi-quantitative approach provides a middle ground in the field of decision support systems. It provides a reasonable and user friendly alternative to the qualitative and algorithmic extremes in DSS already in place in watershed management.

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