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Cited by 431

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Surrogate Parameters for Monitoring Organic Matter and THM Precursors

Edzwald, James K., Becker, William C., Wattier, Kevin L.·1985·Journal AWWA

Abstract

UV absorbance at 254 nm is an excellent surrogate parameter for estimating the raw water concentrations of organic carbon (NPTOC—nonpurgeable total organic carbon) and THM precursors (TTHMFP—total trihalomethane formation potential) in the Grasse River (highly colored water) and the Glenmore Reservoir (protected, upland source). The raw water surrogate equations developed from the Grasse River data were tested successfully on other waters. Direct filtration pilot‐plant data demonstrated that UV can be used to monitor pilot‐plant performance for removals of NPTOC and THM precursors. For two plants studied, UV was an excellent predictor of plant performance for removals of NPTOC and THM precursors. Instantaneous TTHMs of the finished waters can be predicted from raw water UV, finished water pH, chlorine consumption, and temperature data. Particular attention should be given to the selection of sampling locations and to the sample‐handling conditions.

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Crossref · 10.1002/j.1551-8833.1985.tb05521.x

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Metadata

Authors
Edzwald, J. K., Becker, W. C., & Wattier, K. L.
Venue
Journal AWWA
Publisher
Wiley
Published
1985-04
Volume
77
Issue
4
Pages
122-132
ISSN
  • 0003-150X (ISSN-L)
  • 1551-8833
OpenAlex
W1571497471
Cited by
431
References
7
FWCI
3.56
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Surrogate Parameters for Monitoring Organic Matter and THM Precursors. https://app.cochat.ai/papers/10.1002/j.1551-8833.1985.tb05521.x
The paper
Edzwald, J. K., Becker, W. C., & Wattier, K. L. (1985). Surrogate Parameters for Monitoring Organic Matter and THM Precursors. Journal AWWA. https://doi.org/10.1002/j.1551-8833.1985.tb05521.x

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.