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OLS Field Name OLS Field Data
Main Title Data Base for Influent Heavy Metals in Publicly Owned Treatment Works.
Author Minear, Roger A. ; Ball, Roy O. ; Church, Richard L. ;
CORP Author Tennessee Univ., Knoxville. Dept. of Civil Engineering.;Municipal Environmental Research Lab., Cincinnati, OH.
Year Published 1981
Report Number EPA-R-805606; EPA-600/2-81-220;
Stock Number PB82-108168
Additional Subjects Information systems ; Water pollution control ; Metals ; Industrial waste treatment ; Concentration(Composition) ; Heavy metals ; Publicly owned treatment works
Holdings
Library Call Number Additional Info Location Last
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Status
NTIS  PB82-108168 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 06/23/1988
Collation 285p
Abstract
Results are presented of a 2-year study involving the identification and assembly of a data base existing within the Publicly Owned Treatment Works (POTW) of the U.S. on influent heavy metals. The general character of the data base with respect to associated descriptors (among which are % industrial contribution, % combined sewers, % infiltration/inflow, sample frequency, sample type, analytical method) is provided. Wastewater treatment plant laboratories were evaluated in the conduct of the study and generally found to be satisfactory within the framework of the evaluation method. Of the 80,000 individual pieces of data from 239 wastewater treatment plants, a substantial data base was generated for cadmium, copper, nickel, lead and zinc. Extensive data (but reduced in total volume) were obtained for silver, arsenic and mercury. Limited data were obtained for beryllium, cobalt, antimony and selenium. Aluminum, iron and manganese data were received as well. Median and mean (computed in varying manner) are reported and compared. Generally, log normal distributions were observed for all but the data extremes. While no associations could be demonstrated between the median or mean values for the composite data base and the individual descriptors using Pearson and Spearman techniques, isolation of the low percent industrial waste category from the parent data base did show significant differences for 6 of 7 metals. Lead was the metal not showing a difference.