ESTIMATING THE POLLUTANT TRADING RATIO FROM THE TMDL ALLOCATION MATRIX

An innovative approach for TMDL allocation and implementation is the watershed-based pollutant trading. One of the most important technical challenges to establishing a pollutant trading program, i.e. setting of trading ratios, can be a very contentious issue and has been already listed as an obstacle by several pollutant trading programs. Given the inherent scientific uncertainty and lack of technical guidance in trading program, industries would generally like to see the ratios set as low as possible to lower the cost of complying with environmental regulation, while environmental groups would like to set the ratios as high as possible to ensure that trading programs will result in water quality improvement. However, most of the available studies did not provide an approach to explicitly addressing the issue of trading ratio determination. The objective of this paper is to introduce a practical methodology in estimating trading ratio from a TMDL allocation matrix of feasible scenarios. By examining different “equivalent trading ratios (ETRs)”, which generally correlate non-linearly with selected pairs of allocation scenarios, the discussions focuses on the primary factors influencing trading ratio estimation and their potential impacts on TMDL implementation. A case study is presented, which is based on the EPA-approved Nitrate TMDL Development for Muddy Creek, Virginia. The results show that with the increase in the relative percentage of nonpoint source load reduction, the ETR also increases. Determination of the ETR can provide a fast quantitative evaluation of the trade-offs among various combinations of point and nonpoint source control measures and their impacts on ambient water quality improvement. By further incorporating economic considerations (e.g. marginal treatment cost), this practical methodology will help enhance the scientific basis of trading ratio estimation and thus the public perception of TMDL-based pollutant trading program.