Academic Journal
Wave-driven resuspension as the dominant environmental predictor of enterococci at Chicago beaches.
| Τίτλος: | Wave-driven resuspension as the dominant environmental predictor of enterococci at Chicago beaches. |
|---|---|
| Συγγραφείς: | Ferron L; Illinois Mathematics and Science Academy (IMSA), Aurora, IL, USA., Shrestha A; School of Public Health, University of Illinois Chicago (UIC), Chicago, IL, USA., Silva MR; AIHealth4All, College of Medicine, University of Illinois Chicago, Chicago, IL, USA. mqcft@umkc.edu.; Office of Research and Innovation, University of Missouri - Kansas City, Kansas City, MO, USA. mqcft@umkc.edu. |
| Πηγή: | Environmental monitoring and assessment [Environ Monit Assess] 2026 Aug 27; Vol. 198 (9). Date of Electronic Publication: 2026 Aug 27. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2959 (Electronic) Linking ISSN: 01676369 NLM ISO Abbreviation: Environ Monit Assess Subsets: MEDLINE |
| Imprint Name(s): | Publication: 1998- : Dordrecht : Springer Original Publication: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981- |
| Ιατρικοί όροι (MeSH): | Bathing Beaches*/statistics & numerical data , Enterococcus*/growth & development , Enterococcus*/isolation & purification , Environmental Monitoring*/methods , Water Microbiology*, Chicago ; Random Forest ; Water Movements |
| Περίληψη: | Predicting fecal indicator bacteria (FIB) to protect public health at Chicago beaches remains a significant challenge due to complex, site-specific environmental dynamics. This study presents a machine learning pipeline utilizing site-specific random forest regressor models to predict log-transformed enterococci concentrations. The models integrate meteorological, hydrodynamic, and temporal-lag features. A hybrid evaluation framework is implemented; models are trained for regression but evaluated on their ability to classify bacterial exceedances at a 320 CCE/100 mL threshold, chosen as a conservative value given data sparsity at the Chicago Park District's Beach Action Value of 1000 CCE/100 mL, which is used to issue swim advisories at these recreational beaches. This is achieved by dynamically identifying an optimal F1 score threshold for each model's raw count predictions, linking regression performance to classification utility. Model interpretability was a primary focus, employing SHAP (SHapley Additive exPlanations) for each beach and in aggregate. This analysis provided critical insights into the distinct drivers at each location. For the Chicago beaches, the models identified hydrodynamic factors, specifically maximum wave height and maximum dominant wave period, as the most significant hydrometeorological predictors of enterococci concentrations, highlighting the importance of nearshore physical processes in this region. The resulting pipeline provides an interpretable, validated, and site-specific forecasting system capable of identifying high-risk contamination events with moderate accuracy but struggling with sensitivity. (© 2026. The Author(s).) |
| Competing Interests: | Declarations. Competing interests: The authors have no relevant financial or non-financial interests to disclose. Ethical responsibilities of authors: All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. |
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| Contributed Indexing: | Keywords: Beach monitoring; Fecal indicator bacteria; Hydrodynamics; Lake Michigan; Random forest; SHAP |
| Entry Date(s): | Date Created: 20260827 Date Completed: 20260827 Latest Revision: 20260923 |
| Update Code: | 20260923 |
| PubMed Central ID: | PMC13522073 |
| DOI: | 10.1007/s10661-026-15791-3 |
| PMID: | 42658341 |
| Βάση Δεδομένων: | MEDLINE |
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