Stream water quality management: A stochastic mixed-integer programming model.

摘要

Water quality management under the US Environmental Protection Agency's watershed approach requires that water quality standards be maintained throughout the year. The main purpose of this research was to develop a methodology that incorporates inter-temporal variations in stream conditions through statistical distributions of pollution loading variables. This was demonstrated through a general stochastic cost minimization mixed-integer linear programming (MIP) model. Traditional approaches for addressing variability in stream conditions are unlikely to satisfy the assumptions on which these methodologies are founded or are inadequate in addressing the problem correctly when distributions are not normal.; The MIP model solves for the location and maximum capacity of treatment plants to be built throughout the watershed which will provide the optimal level of treatment throughout the year.; The proposed methodology involves estimating the parameters of the distribution of pollution loading variables from simulated data and using those parameters to regenerate a suitable number of random observations in the optimization process such that the new data preserve the same distribution parameters. All stream segments in the watershed are assigned the same randomly drawn value in a particular draw to reflect the high spatial correlation in loadings between segments. The methodology was tested with water quality data for the Paint Creek watershed in West Virginia. The objective of the empirical model was to minimize costs for implementing pH TMDLs for the watershed by determining the level of treatment required to attain water quality standards under stochastic stream conditions. The output of the model provided total minimum costs for treatment and selection of the spatial pattern of the least-cost technologies for treatment. To minimize costs, the model utilized a spatial network of streams in the watershed, which provides opportunities for reducing costs by trading pollution control among different sources. The results were used to estimate the costs attributable to intertemporal variations and the costs of different settings for the ‘margin of safety’.; The application of the methodology, however, is not limited to the estimation of TMDL implementation costs. For example, it could be utilized to estimate costs of antidegradation policies for water quality management and other watershed management issues.