Published in

Elsevier, Science of the Total Environment, (539), p. 583-591

DOI: 10.1016/j.scitotenv.2015.07.108

Links

Tools

Export citation

Search in Google Scholar

Rainfall intensity effects on removal of fecal indicator bacteria from solid dairy manure applied over grass-covered soil

This paper is available in a repository.
This paper is available in a repository.

Full text: Download

Green circle
Preprint: archiving allowed
Red circle
Postprint: archiving forbidden
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

The rainfall-induced release of pathogens and microbial indicators from land-applied manure and their subsequent removal with runoff and infiltration precedes the impairment of surface and groundwater resources. It has been assumed that rainfall intensity and changes in intensity during rainfall do not affect microbial removal when expressed as a function of rainfall depth. The objective of this work was to test this assumption by measuring the removal of Escherichia coli, enterococci, total coliforms, and chloride ion from dairy manure applied in soil boxes containing fescue, under 3, 6, and 9cmh(-1) of rainfall. Runoff and leachate were collected at increasing time intervals during rainfall, and post-rainfall soil samples were taken at 0, 2, 5, and 10cm depths. Three kinetic-based models were fitted to the data on manure-constituent removal with runoff. Rainfall intensity appeared to have positive effects on rainwater partitioning to runoff, and removal with this effluent type occurred in two stages. While rainfall intensity generally did not impact the parameters of runoff-removal models, it had significant, inverse effects on the numbers of bacteria remaining in soil after rainfall. As rainfall intensity and soil profile depth increased, the numbers of indicator bacteria tended to decrease. The cumulative removal of E. coli from manure exceeded that of enterococci, especially in the form of removal with infiltration. This work may be used to improve the parameterization of models for bacteria removal with runoff and to advance estimations of depths of bacteria removal with infiltration, both of which are critical to risk assessment of microbial fate and transport in the environment.