STREAM WATER QUALITY MANAGEMENT: A STOCHASTIC MIXED-INTEGER PROGRAMMING MODEL

Water quality management under the watershed approach of Total Maximum Daily Load (TMDL) programs 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 cost minimization mixed-integer linear programming (MIP) model that maintains the spatial integrity of the watershed problem. 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 the 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 estimation of parameters of the distribution of pollution loading variables from simulated data and use of those parameters to re-generate a suitable number of random observations in the optimization process such that the new data preserve the same distribution parameters. The objective of the empirical model was to minimize costs for implementing pH TMDLs for a watershed by determining the level of treatment required to attain water quality standards under stochastic stream conditions. The output of the model was 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 cost-reduction through trading of pollution among sources and/or least-cost treatment. The results were used to estimate the costs attributable to inter-temporal variations and the costs of different settings for the margin of safety. The methodology was tested with water quality data for the Paint Creek watershed in West Virginia. The stochastic model included nine streams in the optimal solution. An estimate of inter-temporal variations in stream conditions was calculated by comparing total costs under the stochastic model and a deterministic version of the stochastic model estimated with mean values of the loading variables. It was observed that the deterministic model underestimates total treatment cost by about 45 percent relative to the 97th percentile stochastic model. Estimates of different margin of safety were calculated by comparing total costs for the 99.9th percentile treatment (instead of an idealistic absolute treatment) with that of the 95th to 99th percentile treatment. The differential costs represent the savings due to the knowledge of the statistical distribution of pollution and an explicit margin of safety. Results indicate that treatment costs are about 7 percent lower when the level of assurance is reduced from 99.9 to 99 percent and 21 percent lower when 95 percent assurance is selected. 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 anti-degradation policies for water quality management and other watershed management issues.Resource /Energy Economics and Policy,

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

Water quality management under the US Environmental Protection Agency\u27s 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

Implications of Current and Alternative Water Allocation Policies in the Bow River Sub Basin of Southern Alberta

In this study, economic implications of allocating surface water with the existing policy (seniority rule) and three other alternative (People First, proportional reduction, and trading) policies are investigated to address potential water scarcities in the Bow River Sub Basin (BRSB) of Southern Alberta using a mathematical programming model. The model used an improved calibration technique and 2008 data for three irrigation and three non-irrigation sector users in the BRSB. Results indicate that while the seniority rule favours senior license holding irrigation users and the People First policy favors municipal sector users, irrigation users are better off with the proportional allocation policy even though it affects all users across-the-board. Moreover, if the users can participate in a costless trade, then non-irrigation users tend to buy water as they place high value of water at the margin. Some irrigation users find selling water more profitable than utilizing their allocations for crop production

STREAM WATER QUALITY MANAGEMENT: A STOCHASTIC MIXED-INTEGER PROGRAMMING MODEL

Water quality management under the watershed approach of Total Maximum Daily Load (TMDL) programs 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 cost minimization mixed-integer linear programming (MIP) model that maintains the spatial integrity of the watershed problem. 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 the 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 estimation of parameters of the distribution of pollution loading variables from simulated data and use of those parameters to re-generate a suitable number of random observations in the optimization process such that the new data preserve the same distribution parameters. The objective of the empirical model was to minimize costs for implementing pH TMDLs for a watershed by determining the level of treatment required to attain water quality standards under stochastic stream conditions. The output of the model was 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 cost-reduction through trading of pollution among sources and/or least-cost treatment. The results were used to estimate the costs attributable to inter-temporal variations and the costs of different settings for the 'margin of safety'. The methodology was tested with water quality data for the Paint Creek watershed in West Virginia. The stochastic model included nine streams in the optimal solution. An estimate of inter-temporal variations in stream conditions was calculated by comparing total costs under the stochastic model and a deterministic version of the stochastic model estimated with mean values of the loading variables. It was observed that the deterministic model underestimates total treatment cost by about 45 percent relative to the 97th percentile stochastic model. Estimates of different margin of safety were calculated by comparing total costs for the 99.9th percentile treatment (instead of an idealistic absolute treatment) with that of the 95th to 99th percentile treatment. The differential costs represent the savings due to the knowledge of the statistical distribution of pollution and an explicit margin of safety. Results indicate that treatment costs are about 7 percent lower when the level of assurance is reduced from 99.9 to 99 percent and 21 percent lower when 95 percent assurance is selected. 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 anti-degradation policies for water quality management and other watershed management issues

Analysis of the fatty acid composition of Caulerpa lentillifera using gas chromatography mass spectrometry

Caulerpa lentillifera or which also known as Sea Grape or Green Caviar is a type of green seaweed from the class of Caurlepacea, order of Caulerpales, family of Caulerpaceae, genus of Caulerpa and species of Lentillifera They are common components of seaweed communities in tropical and subtropical waters. The presence of various phytoconstituents has been from various seaweed species. Yet, the study on phytochemical components and the biological activity of C. lentillifera are not fully understood yet. Hence, this study was done to determine the best extraction solvents for C. lentillifera and to evaluate the phytocomponent (%) in the n- hexane, Dichloromethane (DCM) and methanol extract of C. lentillifera using Gas Chromatography-Mass Spectrometry (GC-MS) analysis. C. lentillifera was collected from the coastal area of Sabah, Malaysia. Then, it was subjected for purification, drying and soxhlet extraction process using n- hexane, DCM and methanol. Only fatty acid compound was analysed using a Perkin Elmer Turbo Mass Spectrophotometer. Based on the result, , it is proved that, methanol is the most efficient solvent as it recorded the highest extraction yield in C. lentillifera Twenty phytocomponents have been identified from all extracts of C. lentillifera by GCMS analysis. This analysis discovered the presence of major constituents like palmitic acid, oleic acid, pentadecanoic acid, behenic acid, myristic acid,etc. Many studies has shown that, most of the identified major compounds were proven to exhibit antibacterial, antifungal, anti-inflammatory, antiviral, etc. Thus, it is apparent that C. lentillifera have the potential to be used as seaweed of phytopharmaceutical importance as it contains numerous bioactive compounds