Beyond Water Quality Monitoring: Toward AI-Enabled Early Detection of Hidden Environmental Health Risks from Emerging Contaminants

References

[1] Campos, D., Galvão, V., de Rezende, M. L., Braga, A., Bodini, M., Aires, U. R. V., Yonaba, R., & Goliatt, L. (2026). Automated machine learning achieves accurate water quality prediction with reduced parameter requirements. Scientific Reports, 16(1), 4431. https://doi.org/10.1038/s41598-025-34448-8

[2] Chang, S. Y., Aboelyazeed, D., Sawadekar, K., Chavda, D., Shen, C., & Van Meter, K. J. (2026). A community dataset for large-scale river nitrogen modeling in the United States. Scientific Data, 13(1), 836. https://doi.org/10.1038/s41597-026-06873-5

[3] Cui, S., Gao, Y., Huang, Y., Shen, L., Zhao, Q., Pan, Y., & Zhuang, S. (2023). Advances and applications of machine learning and deep learning in environmental ecology and health. Environmental Pollution, 335, 122358. https://doi.org/10.1016/j.envpol.2023.122358

[4] de Paula Souza, J., Blum, J., Maran, U., Sild, S., Dawson, L., Čavoški, A., Holden, L., Lee, R., Karnel, V., Meusburger, L., Fraize-Frontier, S., Walsh, A., Rivière, G., Raitano, G., Roncaglioni, A., Di Consiglio, E., Tcheremenskaia, O., Bossa, C., Wendt-Rasch, L., Puzyn, T., & Fritsche, E. (2026). Advancing the implementation of artificial intelligence in regulatory frameworks for chemical safety assessment by defining robust readiness criteria. Frontiers in Artificial Intelligence, 8, 1738770. https://doi.org/10.3389/frai.2025.1738770

[5] Egon, S. S. (2025). Investigating algal toxin occurrence and water quality correlations in a freshwater-estuarine system [Master's thesis, University of Central Florida]. STARS. https://stars.library.ucf.edu/etd2024/444

[6] Fan, Y., Li, J., Ren, Y., Liu, C., & Zhang, W. (2026). Where machine learning fails in predicting emerging contaminant adsorption: A decision-oriented framework for model credibility and transferability. Environmental Science & Technology, 60(27), 18954-18974. https://doi.org/10.1021/acs.est.6c00882

[7] Gago-Ferrero, P., Boehm, A. B., Hsu-Kim, H., Li, X., Gibson, M., Vrijheid, M., Wang, B., & Zimmerman, J. (2026). The human exposome: Integrating the environment, human health, and society for the next 60 years. Environmental Science & Technology, 60(19), 13817-13824. https://doi.org/10.1021/acs.est.6c03080

[8] Gaillard, L., Barouki, R., Blanc, E., Coumoul, X., & Andréau, K. (2025). Per- and polyfluoroalkyl substances as persistent pollutants with metabolic and endocrine-disrupting impacts. Trends in Endocrinology and Metabolism, 36(3), 249-261. https://doi.org/10.1016/j.tem.2024.07.021

[9] Kapoor, S., & Narayanan, A. (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns, 4(9), 100804. https://doi.org/10.1016/j.patter.2023.100804

[10] Khan, M. S., Umer, H., & Faruqe, F. (2024). Artificial intelligence for low income countries. Humanities and Social Sciences Communications, 11(1), 1422. https://doi.org/10.1057/s41599-024-03947-w

[11] Kim, M., Adjei-Nimoh, S., Park, J., Egon, S. S., Choi, E., Pak, G., You, K., Sadmani, A. H. M. A., Kim, K. T., & Lee, W. H. (2026). Calibration-free on-site detection of microcystin-LR using integrated biosensing, multi-parameter water quality monitoring, and machine learning. Water Research, 298, 125832. https://doi.org/10.1016/j.watres.2026.125832

[12] Lawal, O. P., Opara, I. J., Ayo-ige, A., Eboh, N. A., Cos-Ibe, U., Forson, K. A. M., Mensah, E. K., Olaitan, O. F., Nii-Okai, E., Yeboah, A., Gabriels, N., & Olaniyi, A. O. (2025). Artificial intelligence-integrated biosensors for antimicrobial resistance detection and surveillance: A review and future perspectives for global biosecurity. Cureus, 17(11), e98098. https://doi.org/10.7759/cureus.98098

[13] Mallek, M., & Barceló, D. (2026). Analysis and risks of emerging contaminants and microplastics in natural and treated waters and human health: A critical review. Journal of Xenobiotics, 16(3), 93. https://doi.org/10.3390/jox16030093

[14] National Research Council (US) Committee on Drinking Water Contaminants. (2001). Drinking water contaminant candidate list: Past, present, and future. In Classifying drinking water contaminants for regulatory consideration. National Academies Press. https://www.ncbi.nlm.nih.gov/books/NBK207426/

[15] Olawade, D. B., Osborne, A., Soladoye, A. A., Oluwadare, O. E., Awogbindin, E. O., & Wada, O. Z. (2026). Smart insurance analytics: A novel ensemble feature selection approach to unlock health insurance coverage predictions in Sierra Leone. International Journal of Medical Informatics. https://www.sciencedirect.com/science/article/pii/S1386505626000535

[16] Oluwadare, O. E., Adeoba, M. I., Akor, J. T., Yusuff, T. A., & Milimo, P. (2026). The rise of smart hospitals: Biomedical engineering innovations in automation, monitoring, and digital healthcare. Journal of Medicine and Health Research, 11(2), 121-138. https://doi.org/10.56557/jomahr/2026/v11i210794

[17] Onyijen, O. H., Olaitan, E. O., Olayinka, T. C., & Oyelola, S. (2023). Data-driven machine learning techniques for the prediction of cholera outbreak in West Africa. International Journal of Applied and Natural Sciences. https://bluemarkpublishers.com/index.php/IJANS

[18] Qiu, W., Gago-Ferrero, P., Hollender, J., Leusch, F. D. L., Richardson, S. D., Samanipour, S., Shi, H., & Wang, Z. (2026). Advancing the discovery of emerging contaminants: A leap in technology and data. Environmental Science & Technology, 60(22), 15530-15543. https://doi.org/10.1021/acs.est.6c03027

[19] Scheringer, M., & Schulz, R. (2025). The state of the world's chemical pollution. Annual Review of Environment and Resources, 50, 381-408. https://doi.org/10.1146/annurev-environ-111523-102318

[20] Sillé, F. C. M., Prasse, C., Luechtefeld, T., & Hartung, T. (2025). AI redefines mass spectrometry chemicals identification: Retention time prediction in metabolomics and for a Human Exposome Project. Frontiers in Public Health, 13, 1687056. https://doi.org/10.3389/fpubh.2025.1687056

[21] Turkina, V., Gringhuis, J. T., Boot, S., Petrignani, A., Corthals, G. L., Praetorius, A., O'Brien, J., & Samanipour, S. (2025). Prioritization of unknown LC-HRMS features based on predicted toxicity categories. Environmental Science & Technology, 59(16), 8004-8015. https://doi.org/10.1021/acs.est.4c13026

[22] Wang, Q., Zhang, Y., Wang, W., Wu, X., Zhou, H., Chen, L., & Wu, B. (2025). A review of AI-driven monitoring, forecasting, and source attribution of aquatic biocontaminants. Biocontaminant, 1, e025. https://doi.org/10.48130/biocontam-0025-0025

[23] Yuan, R., Zhang, H., Liu, J., Yu, M., Hou, X., & Jiang, G. (2026). From target-nontarget to nontarget screening: A review on screening methods for organic pollutants based on high-resolution mass spectrometry. Environment & Health, 4(5), 843-861. https://doi.org/10.1021/envhealth.5c00520

[24] Zhu, J.-J., Yang, M., & Ren, Z. J. (2023). Machine learning in environmental research: Common pitfalls and best practices. Environmental Science & Technology, 57(46), 17671-17689. https://doi.org/10.1021/acs.est.3c00026

@article{1722830,
      author = {Ishola Abdul Dimeji, Jumoke Oluwafunmito Omotosho, Linda Egbubine, Adejoke Olowookere-Oduyebo},
      title = {Beyond Water Quality Monitoring: Toward AI-Enabled Early Detection of Hidden Environmental Health Risks from Emerging Contaminants},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {681-691},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722830.pdf},
      abstract = {Water quality monitoring relies on a fixed, regulated list of things to test for, measured against limits set decades ago. But the chemicals actually present in water and in the human body are far more numerous, and increasingly dominated by substances routine testing does not cover, including PFAS ("forever chemicals"), pharmaceutical residues, microplastics, endocrine-disrupting chemicals, and pesticide breakdown products. This review brings together research from analytical chemistry, exposure science, and artificial intelligence (AI) to ask a question no existing review has directly answered: can AI move environmental health monitoring beyond routine compliance testing toward genuinely detecting hidden risks, meaning contaminants that are chemically real and biologically plausible but currently invisible to regulators? We examine four areas where AI is being used, finding unknown chemicals, real-time sensor monitoring, linking exposure to biological effects, and forecasting contamination, and identify where the field agrees and where claims remain unproven. The evidence is mostly early-stage: small datasets, testing on the same data used to build the model, limited interpretability, and little connection to real health outcomes. Building on these gaps, we propose a three-step AI-Enabled Sentinel Framework that keeps chemical detection separate from health-risk assessment and requires models to be interpretable and independently tested from the outset. We close with a research agenda and implications for regulators, utilities, and communities facing unequal exposure. Our central point: the next stage of water quality monitoring will come not from testing the same known list more precisely, but from AI systems that can responsibly flag the unknown, once the field closes the gaps described here.},
      keywords = {emerging contaminants; artificial intelligence; machine learning; early warning systems; exposome; non-target screening; environmental health surveillance; water quality},
      month = {September},
      doi = {https://doi.org/10.64388/IREV10I3-1722830}
  }