Record Display for the EPA National Library Catalog
RECORD NUMBER: 547 OF 739Main Title | Report of the CCL Classification Process Work Group to the National Drinking Water Advisory Council. | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Year Published | 2004 | ||||||||||||||||
Report Number | 816R04009 | ||||||||||||||||
OCLC Number | 57363599 | ||||||||||||||||
Subjects | Water--Pollution ; Water--Standards | ||||||||||||||||
Internet Access | |||||||||||||||||
Holdings |
|
||||||||||||||||
Collation | {188} p. ; 28 cm. | ||||||||||||||||
Notes | "April 30, 2004." |
||||||||||||||||
Contents Notes | Introduction.-- Overview of recommended CCL classification process and overarching issues.-- CCL classification approach for microbial contaminants.-- CCL classification approach for chemical contaminants.-- Moving from PCCL onto the CCL.-- Introduction.-- Background on the Contaminant Candidate List and the National Research Council Recommendations.-- Convening and membership of the NDWAC CCL Classification Work Group.-- NDWAC CCL Classification Process Work Group Guiding principles.-- Summary of the NDWAC CCL Work Group Deliberation Process.-- Role of the CCL in protecting public health and implications of inclusion on the PCCL or CCL.-- Overviw of process and overarching issues.-- Transparency and public participation.-- Overview of recommended CCL classification process.-- Overarching issues.-- CCL classification approach for microbial contaminants.-- Identifying the microbial CCL universe.-- Microbial CCL universe to PCCL.-- Use of attributes to classify microbial contaminants.-- Applications to genomics to the CCL classification process.-- CCL classification approach for chemical contaminants.-- Building the chemical CCL universe.-- Process and criteria for screening agents from the Chemical CCL universe to the PCCL.-- Use of attributes to classify chemical contaminants.-- Moving from the PCCL onto the CCL.-- Quantifying attributes for use as inputs to classification models.-- Overview of classification approaches.-- Recommended approach to selecting the CCL.-- Training data set. |