June 15 2009

Purdue-developed tool can get most pollution control for the money

WEST LAFAYETTE, Ind. - There may be thousands of things large and small that can be done to better control pollution on even the smallest waterways, and a new tool developed at Purdue University may help sort out how to choose the best ones.

Indrajeet Chaubey, an associate professor of agricultural and biological engineering, combined a best management practices tool with a complex genetic algorithm that can search out the best solutions for non-point source pollution control in a watershed. By analyzing data from an area, in just a few hours the tool can compute the most cost-effective pollution-control strategies for water resources affected by agriculture, a process that currently takes weeks or months.

A paper on the work appeared this past week in the journal Water Resources Research.

"When you have got limited resources to control non-point pollution in an area, you have to decide where to best use your resources," Chaubey said. "At the same time, you want to be sure you don't disrupt the agricultural production in an area."

Chaubey has spent the last several years developing a best management practices tool that takes into consideration all feasible solutions for decreasing non-point source pollution, or pollution that gets into water through runoff. The tool determines the best solution - such as changes in tillage practices, grass coverage and structural changes on the land - based on the amount of pollution that can be eliminated, the economic impact to agricultural land and other factors. The calculations used include soil, water, topography and other data usually collected by governmental agencies.

The algorithm assesses which of those practices will result in the most pollution control for the amount of money available with as little disruption to agriculture as possible.

"You have to look at the economic information at the same time. If the solution we provide will negatively impact farmers, it will not be adopted," Chaubey said. "Combining economic analysis with environmental analysis gives solutions that are more likely to be acceptable to farmers and watershed managers."

Current methods used to choose watershed-management practices include funding projects based on a first-come basis or spending on the project or projects seen as most beneficial. The problem is that one major project might break the budget, while several smaller projects could result in better pollution control for the same money.

Chaubey said the system was tested with information from the L'Anguille River Watershed in eastern Arkansas. Further testing is being done on six locations in Indiana. The U.S. Department of Agriculture funded the research.

Chaubey also expects to develop the tool in a format accessible by government officials to evaluate projects in their jurisdictions.

Writer: Brian Wallheimer, (765) 496-2050, bwallhei@purdue.edu

Source: Indrajeet Chaubey, (765) 494-1162, ichaubey@purdue.edu

Ag Communications: (765) 494-8415;
Steve Leer, sleer@purdue.edu
Agriculture News Page


Development of a Multi-Objective Optimization Tool for the Selection and Placement of BMPs for Nonpoint Source Pollution Control

 Chetan Maringanti, Indrajeet Chaubey, Jennie Popp

Best management practices (BMPs) are effective in reducing the transport of agricultural nonpoint source pollutants to receiving water bodies. However, selection of BMPs for placement in a watershed requires optimization of the available resources to obtain maximum possible pollution reduction. In this study, an optimization methodology is developed to select and place BMPs in a watershed to provide solutions that are both economically and ecologically effective. This novel approach develops and utilizes BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. BMP tool replaces the dynamic linkage of distributed parameter watershed model during optimization and therefore reduces the computation time considerably. Total pollutant load from the watershed, and net cost increase from the baseline were the two objective functions minimized during the optimization process. The optimization model, consisting of a multi-objective genetic algorithm (NSGA-II) in combination with watershed simulation tool (SWAT), was developed and tested for NPS pollution control in L'Anguille River Watershed located in Eastern Arkansas. The optimized solutions provided a tradeoff between the two objective functions for sediment, phosphorus, and nitrogen reduction. The results indicated that buffer strips were very effective in controlling the NPS pollutants from leaving the crop lands. The optimized BMP plans resulted in potential reduction of 33%, 32%, and 13% in sediment, phosphorus, and nitrogen loads, respectively from the watershed.


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