Project Number: 1265-12630-003-00
Project Type: Appropriated

Start Date: Jun 14, 2005
End Date: Jun 13, 2010

Objective:
Objective 1: Determine dominant environmental parameters and processes involved in the fate and transport of manure-borne coliform bacteria at field and watershed scales in a hydrological context. Develop predictive models of the fate and transport of manure-borne coliform bacteria at field and watershed scales. Objective 2: Determine prevalence and diversity of pathogenic E. coli and Salmonella in watersheds with different land uses (urban/suburban, forested, animal agriculture) in the mid-Atlantic area. Measure airborne dissemination and survival of pathogenic bacteria and endotoxin from manures, compost, and wastewater treatment plant sludge. Evaluate methods combining immunological and genetic techniques for detection of water-borne pathogenic E. coli.

Approach:
An integrated laboratory research, field research at hillslope and watershed scales, and mathematical modeling will be used. The experimental research will include evaluating effect of manure particulates on transport of coliform microorganisms in soil, relating partitioning of coliform microorganisms between sediment and runoff to soil texture, manure properties and flow rate, establishing dependencies of coliform release rates from manure on rain intensity, manure type and composition, and manure application method, evaluating predictive efficiency of laboratory data on manure-borne coliform survival data for the field conditions, assessing phosphorus as a tracer of manure-borne transport in runoff; determining effect of background coliform populations and field manure application on coliform concentrations in runoff from fields and in a perennial creek in a riparian zone. Modeling research will include determining dominant mechanisms of manure-borne coliform transport at pedon, field, and watershed scales; develop and test models to simulate those mechanisms, performing uncertainty analysis to evaluate the reliability of coliform transport model predictions given available data on variation in input parameters, transforming model computers codes to make them compatible to existing and under-development user-friendly decision support tools.