WATER QUALITY

The Impact of Agricultural Systems on Surface and Ground Water Quality
M. C. Hirschi,* J. K. Mitchell, R. A. Cooke
University of Illinois; U.S. Department of Agriculture

An examination of the hydraulic performance of small-scale sediment controls was begun to better understand their sediment-trapping characteristics. Two specific practices have been at least partially examined: filter fabric (also called ``silt fence'') and a rock check dam. A unified conceptual model, using orifice flow relationships, shows promise for describing both control structures. Hydraulic capacity determinations, based on design runoff events, will lead to recommendations for contributing area limits based on structure size. Specifications for such control structures are sorely needed by erosion control practitioners and have been requested by soil conservation agency personnel.

Best Management Practices (BMPs) for Controlling Field-to-Stream Delivery of Agrochemicals Field Validation in the Little Vermilion River Watershed


J. K. Mitchell,* M. C. Hirschi
U.S. Department of Agriculture; University of Illinois

The root zone water quality model was used as a tool in developing a model for predicting flow and nitrate loading in a surface drainage ditch at the outlet of a tile-drained watershed. A sensitivity analysis guided the calibration of the model for two tile-drained sites. Linear regression models related measured surface drainage ditch response to measured tile system response. The analysis led to the consideration of watershed outlet response as a linear combination of field tile inputs and drainage flow to the surface drainage system. Coefficients for the linear model are related to the drainage characteristics of the watershed.

Machine Vision as a Sensor for Microbial Contamination in Water

J. F. Reid,* J. G. O'Brien
USDA National Needs Fellowship; University of Illinois

The focus of this research is to determine the feasibility for using machine vision in the identification and enumeration of some pathogenic protozoans in water. Microfluo rescence techniques can be employed to enhance morphological feature recognition in samples through image analysis. Computer recognition of the microorganisms Giardia lamblia and Cryptosporidium could aid in the development of automated detection systems for water quality monitoring. Such systems could be a fundamental control sensor of quality control in potable water systems.

* Denotes principal investigator.