Modelling the Coastal Fecal Bacteria Contamination in the Eastern Mediterranean Region: Lebanon as a Case Study

Open Access

Issue		E3S Web Conf. Volume 669, 2025 6^th International Conference on Environmental Design and Health (ICED2025)


Article Number		07003
Number of page(s)		6
Section		Pollution-Land Erosion
DOI		https://doi.org/10.1051/e3sconf/202566907003
Published online		26 November 2025

W. Musie, G. Gonfa, Fresh water resource, scarcity, water salinity challenges and possible remedies: review. Heliyon 9, 18685 (2023). https://doi.org/10.1016/j.heliyon.2023.e18685 [Google Scholar]
A. G. Cosby, V. Lebakula, C. N. Smith, D. W. Wanik, K. Bergene, A. N. Rose, D. Swanson, D. E. Bloom, Accelerating growth of human coastal populations at the global and continent levels: 2000–2018. Sci. Rep. 14, 1 (2024). https://doi.org/10.1038/s41598-024- 73287-x [CrossRef] [Google Scholar]
E. Hadjisolomou, M. Rousou, K. Antoniadis, L. Vasiliades, I. Kyriakides, H. Herodotou, M. Michaelides, Data-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus. Water 15, 4097 (2023). https://doi.org/10.3390/w15234097 [CrossRef] [Google Scholar]
N. Da Le, T. T. H. Hoang, T. M. H. Nguyen, et al., Microbial contamination in the coastal aquaculture zone of the Ba Lat river mouth, Vietnam. Mar. Pollut. Bull. 192, 115078 (2023). https://doi.org/10.1016/j.marpolbul.2023.115078 [Google Scholar]
L. Grbčić, S. Družeta, G. Mauša, et al., Coastal water quality prediction based on machine learning with feature interpretation and spatio-temporal analysis. Environ. Model. Softw. 155, 105458 (2022). https://doi.org/10.1016/j.envsoft.2022.105458 [Google Scholar]
A. Tiwari, D. M. Oliver, A. Bivins, S. P. Sherchan, T. Pitkänen, Bathing Water Quality Monitoring Practices in Europe and the United States. IJERPH 18, 11 (2021). https://doi.org/10.3390/ijerph18115513 [Google Scholar]
S. DeFlorio-Barker, C. Wing, R. M. Jones, S. Dorevitch, Estimate of incidence and cost of recreational waterborne illness on United States surface waters. Environ. Health 17, 1 (2018). https://doi.org/10.1186/s12940-017-0347-9 [Google Scholar]
E. S. van der Meulen, E. S., Tertienko, A., Blauw, et al., A review of prediction models for E. coli in urban surface waters. Urban Water J 21, 5 (2024). https://doi.org/10.1080/1573062X.2024.2313634 [Google Scholar]
M. Naloufi, F. S. Lucas, S. Souihi, P. Servais, A. Janne, T. W. M. De Abreu, Evaluating the Performance of Machine Learning Approaches to Predict the Microbial Quality of Surface Waters and to Optimize the Sampling Effort. Water 13, 18 (2021). https://doi.org/10.3390/w13182457 [Google Scholar]
D. Džal, I. N. Kosović, T. Mastelić, D. Ivanković, T. Puljak, S. Jozić, Modelling Bathing Water Quality Using Official Monitoring Data. Water 13, 21 (2021). https://doi.org/10.3390/w13213005 [Google Scholar]
Y. Park, M. Kim, Y. Pachepsky, et al., Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea. J. Environ. Qual. 47, 5 (2018). https://doi.org/10.2134/jeq2017.11.0425 [Google Scholar]
D. C. Love, G.L. Lovelace, E.S. Money, et al., Microbial Fecal Indicator Concentrations in Water and Their Correlation to Environmental Parameters in Nine Geographically Diverse Estuaries. Water Qual. Expo. Health 2, 85–95 (2010). https://doi.org/10.1007/s12403-010-0026-3 [Google Scholar]
G. Nikjou-Helabad, A. Kambo, A Correlational Study of Escherichia coli Concentrations and Coastal Water Temperatures in Metro Vancouver’s Routine Beach Water Quality Reports from 2016-2022. BCIT Environ. Public Health. J., (2023). https://doi:10.47339/EPHJ.2023.224 [Google Scholar]
M. D. Stocker, Y. A. Pachepsky, R. L. Hill, K. G. Sellner, D. Macarisin, K. W. Staver, Intraseasonal variation of E. coli and environmental covariates in two irrigation ponds in Maryland, USA. Sci. Total Environ. 670, 732–740 (2019). https://doi.org/10.1016/j.scitotenv.2019.03.121 [Google Scholar]
A. Tselemponis, C. Stefanis, E. Giorgi, A. Kalmpourtzi, I. Olmpasalis, et al., Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms. IJERPH 20, 6216 (2023). https://doi.org/10.3390/ijerph20136216 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.