Record Display for the EPA National Library Catalog

RECORD NUMBER: 3 OF 4

Main Title Sewage Sludge Pathogen Transport Model Project.
Author Dawson, J. F. ; Hain, K. E. ; McClure, B. ; Sheridan, R. E. ; Yeager, J. G. ;
CORP Author BDM Corp., Albuquerque, NM.;Health Effects Research Lab., Cincinnati, OH.;Department of Energy, Washington, DC.
Year Published 1981
Report Number EPA/DF-81/006A ; EPA-600/1-81-049A
Stock Number PB82-109000
Additional Subjects Sludge ; Transport ; Models ; Salmonella ; Bacteria ; Polioviruses ; Sewage sludge ; Pathogens ; Ascaris ; Biological transport ; Environmental factors
Holdings
Library Call Number Additional Info Location Last
Modified
Checkout
Status
NTIS  PB82-109000 Some EPA libraries have a fiche copy filed under the call number shown. 07/26/2022
Collation 485p
Abstract
The sewage sludge pathogen transport model predicts the number of Salmonella, Ascaris, and polioviruses which might be expected to occur at various points in the environment along 13 defined pathways. These pathways describe the use of dried or liquid, raw or anaerobically digested sludge as a cropland fertilizer, dried raw sludge as an animal feed supplement, and composted sludge as a residential soil amendment. The model uses a compartment-vector approach in which a mathematical state represents a discrete point in a treatment or application pathway where pathogen populations are computed as a function of time. Within these compartments, mathematical process functions describe population changes due to environmental factors. Pathogen exchanges between compartments are described by transfer functions. The model permits user specification of various parameters in both process and transfer functions, enabling him to simulate a unique set of environmental conditions. Five separate exposure risk calculations provide risk assessment determinations for pathogens associated with airborne particulates, residue and soil, vegetable crops, meat, and milk. Certain of the exposure risk calculations can be modified by the model user to simulate unique exposure conditions. The model can be progressively modified to accommodate new information, thus constantly enhancing its predictive accuracy.