Co‐occurrences of EDCs/PPCPs in Surface Water Using Chemometrics

Hagemann, Mark; Park, Minji; Srinivasan, V.; Reckhow, David A.; Lavine, Michael; Rosenfeldt, Erik J.; Stanford, Benjamin D.; Park, Mi‐Hyun

doi:10.5942/jawwa.2016.108.0042

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…Multivariate predictive approaches could offer an accessible supplement for high-frequency monitoring. Multivariate statistical analysis of water quality data has been applied to predict other contaminants in surface waters, including endocrine-disrupting compounds and pharmaceuticals …”

Section: Introductionmentioning

confidence: 99%

“…Multivariate statistical analysis of water quality data has been applied to predict other contaminants in surface waters, including endocrine-disrupting compounds and pharmaceuticals. 18 Bench-top fluorescence spectroscopy has recently gained attention as a tool for analysis of natural organic matter (NOM). 19−21 It is nondestructive, sensitive, rapid, relatively inexpensive, 22−24 and allows for collection of a large volume of data in a single measurement.…”

Section: ■ Introductionmentioning

confidence: 99%

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Fluorescence Spectra Predict Microcystin-LR and Disinfection Byproduct Formation Potential in Lake Water

Brophy

Trueman

Park

et al. 2018

Environ. Sci. Technol.

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Disinfection byproducts (DBPs) and algal toxins can be expensive to monitor and represent significant potential risks to human health. DBPs, including haloacetic acids and trihalomethanes, are possible or probable human carcinogens. Microcystin-LRproduced by cyanobacteriais linked with various adverse health effects. Here we show that fluorescence spectra predict both microcystin-LR occurrence and DBP formation potential (DBPfp) in lake water. We compared models with either fluorescence spectra or a suite of water quality predictors as inputs. A regularized logistic regression model with fluorescence spectral inputs correctly classified 94% of test data with respect to microcystin-LR occurrence, with a 96% probability of correctly ranking a detect/nondetect pair. Regularized linear regression predicted DBPfp based on fluorescence inputs with a combined R 2 of 0.83 on test data. A gradient-boosted classifier with seven water quality inputs was comparable in detecting microcystin-LR (91% correct), as was UV 254 in predicting DBPfp (combined test R 2 = 0.84), but no single parameter matched fluorescence spectra over both predictive tasks. Results highlight the potential for multiparameter monitoring via fluorescence spectroscopy, extending previous work on predicting DBPs alone. As a highfrequency monitoring tool, this approach could supplement mass spectrometric methods that may only be applicable at low frequency due to resource limitations.

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Section: Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%