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Integration of fluorescence quenching correction into trihalomethane formation prediction models

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Abstract

Despite there being numerous models of trihalomethane (THM) formation, they are limited by high estimation errors, which can be close to the regulatory limits for THMs, due to the fluorescence quenching effect. In this research, the estimation error for THM formation was reduced by correcting the quenching effect. The trihalomethane formation potential (THMFP) test was conducted in the presence of chlorine and bromine, individually and in mixtures. The THM precursors used in this study were protein (bovine serum albumin; BSA), amino acids (tryptophan and tyrosine), chlorine, bromine, and Suwannee River natural organic matter (SWNOM). BSA tended to form bromodichloromethane (BDCM) rather than trichloromethane (TCM) during chlorination in the presence of bromide (Br−). In contrast, SWNOM tended to form chlorinated THMs (TCM) rather than brominated THMs (BDCM and dibromochloromethane; DBCM), and no TBMs were formed in these processes. BSA with SWNOM decreased the formation of TCM due to the decrease in the amount of TCM precursor in SWNOM through binding with BSA. The concentration of each THM species was predicted from the fluorescence intensity of peak C, corrected fluorescence intensity of peak T, and Br− concentration. The use of humic-like and corrected protein-like fluorescence in the excitation–emission matrix model for predicting THM species reduced the prediction error. In this research, correction of the fluorescence quenching decreased the mean percentage estimation error for TCM, BDCM, and DBCM from 47%, 35%, and > 100% in classical approaches to 6.6%, 26.9%, and 2.0%, respectively. This study is expected to make contributions in reporting the relationship between the concentration of natural organic matter compositions and the formation of THM species.

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Data availability

All data in this manuscript are available on reasonable request. All the analyses were performed in triplicate, and the data reported are average with standard deviation.

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Acknowledgements

We thank the Open Research Facilities for Life Science and Technology of the Tokyo Institute of Technology for technical support in measuring THMs. Thanks also to Jimmy in discussing the comments.

Funding

This study was financially supported by the Ministry of the Environment of Japan [S-13–2-3] and JSPS KAKENHI [grant number 18H01566].

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, School of Environment and Society, Tokyo Institute of Technology, 2-12-1-M1-4, Ookayama, Meguro-ku, Tokyo, 152-8552, Japan

    Kornravee Saipetch, Rajendra Khanal & Chihiro Yoshimura

  2. 3rd Floor of Federal Secretariat Construction and Management Building, Policy Research Institute, A Think-Tank of the Government of Nepal, Sano Gaucharan-5, Kathmandu, 44600, Nepal

    Rajendra Khanal

Authors
  1. Kornravee Saipetch
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  2. Rajendra Khanal
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  3. Chihiro Yoshimura
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Highlights

•BSA tends to form BDCM rather than TCM during chlorination in the presence of Br−.

•SWNOM tends to form chlorinated THMs (TCM) rather than brominated THMs (BDCM and DBCM).

•BSA and SWNOM decrease the formation of TCM, possibly by decreasing the amount of TCM precursor in SWNOM by binding to BSA.

•Using humic-like and corrected protein-like fluorescence in the EEM model for predicting THM species reduces the prediction error.

Appendix 1

Appendix 1

  • A1 GC-BID running program.

  • Column: SH-Rtx-5 (Crossbond 5% diphenyl/95% dimethyl polysiloxane.

  • Auto sampler settings (AOC-20i) as follows:

Injection volume

1.5 μL

Washing with solvent before

3 times

Washing with solvent after injection

1 time

Wash with sample

2 times

  • Injection temperature of 200 °C in split mode.

  • BID detector temperature of 300 °C.

  • Oven temperatures as follows:

Rate

Temperature (°C)

Hold (min)

 

30

5.5

10

35

4

10

40

3

15

150

0

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Saipetch, K., Khanal, R. & Yoshimura, C. Integration of fluorescence quenching correction into trihalomethane formation prediction models. Environ Monit Assess 193, 845 (2021). https://doi.org/10.1007/s10661-021-09649-z

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  • DOI: https://doi.org/10.1007/s10661-021-09649-z

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