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A FRAMEWORK FOR POLLUTANT TRADING DURING THE TMDL ALLOCATION PHASE

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Paper number  701P0904,  . (doi: 10.13031/2013.17419)
Authors:   A. Z. Zaidi, S. M. deMonsabert, R. El-Farhan, and S. Choudhury
Keywords:   TMDL Trading, Fecal Coliform TMDL, Economic Analysis, Optimization, Mathematical Modeling

The Environmental Protection Agency (EPA) encourages pollutant trading programs that help achieve Total Maximum Daily Load (TMDL) implementation goals. Such trades need to be consistent with water quality standards. For an approved TMDL, EPA recommends that the point and/or nonpoint source waste load allocations be used as the baseline for trading credits. The complexity inherent in modeling the effects of pollutants on receiving bodies makes it difficult to understand the implications associated with trading pollutant loads from different sources. The reduction of one credit from one source does not equal the reduction of one credit at another location in the watershed. Similarly, unit costs for load reductions vary considerably depending on the control strategy and the level of reduction. Though trading ratios may account for some uncertainties associated with estimates of nonpoint source loads and long-term performance of the control measures, the selection of an effective trading ratio is not very straightforward and does not fully address the environmental impacts. Although higher trading ratios may be used, this alone cannot guarantee that the water quality standard will be met and may unnecessarily increase mitigation costs. This paper proposes an alternative strategy for the TMDL trading framework. Instead of explicitly determining trading ratios, a trading scenario selection method is utilized. Water quality is simulated for the alternate trading scenarios based on various pollutant loads obtained from accepted models such as HSPF. The costs associated with the nonpoint reductions are compared for various load allocation strategies. This comparison enables an efficient screening of watersheds to identify those well-suited for a pollutant trading strategy. Watersheds with high cost variations and flexibility among allocation strategies are ideal candidates for trading. The methodology is demonstrated using the results of the TMDL allocation for the Muddy Creek WAR1 subwatershed in Rockingham County, Virginia.

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