AIMC Topic: Water Microbiology
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An investigation of microbial groundwater contamination seasonality and extreme weather event interruptions using "big data", time-series analyses, and unsupervised machine learning.
Environmental pollution (Barking, Essex : 1987)
Feb 6, 2025
Temporal studies of groundwater potability have historically focused on E. coli detection rates, with non-E. coli coliforms (NEC) and microbial concentrations remaining understudied by comparison. Additionally, "big data" (i.e., large, diverse datase...
A calibration framework toward model generalization for bacteria concentration estimation in water resource recovery facilities.
Scientific reports
Dec 28, 2024
Reduced bacteria concentrations in wastewater is a key indicator of the efficacy of water resource recovery facilities (WRRFs). However, monitoring the presence of bacterial concentrations in real time at each stage of the WRRF is challenging as it r...
Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning.
Environment international
Dec 24, 2024
Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts...
Machine Learning-Assisted Liquid Crystal Optical Sensor Array Using Cysteine-Functionalized Silver Nanotriangles for Pathogen Detection in Food and Water.
ACS applied materials & interfaces
Dec 12, 2024
The challenge of rapid identification of bacteria in food and water still persists as a major health problem. To tackle this matter, we have developed a single-probe liquid crystal (LC)-based optical sensing platform for the differentiation of five c...
Integrating microbial profiling and machine learning for inference of drowning sites: a forensic investigation in the Northwest River.
Microbiology spectrum
Dec 9, 2024
Drowning incidents present significant challenges for forensic investigators in determining the exact site of occurrence. Traditional forensic methods often rely on physical evidence and circumstantial clues, but the emerging field of forensic microb...
Improving fecal bacteria estimation using machine learning and explainable AI in four major rivers, South Korea.
The Science of the total environment
Nov 19, 2024
This study addresses the critical public health issue of fecal coliform contamination in the four major rivers in South Korea (Han, Nakdong, Geum, and Yeongsan rivers) by applying advanced machine learning (ML) algorithms combined with Explainable Ar...
Development of a machine learning model to support low cost real-time Legionella monitoring in premise plumbing systems.
Water research
Sep 30, 2024
Legionella pneumophila (L. pneumophila) is a pathogenic bacterium primarily known for causing Legionnaires' Disease which is known for high mortality rates, particularly in the elderly. With caseloads continuing to increase, further research is neede...
A machine learning approach for rapid early detection of Campylobacter spp. using absorbance spectra collected from enrichment cultures.
PloS one
Sep 6, 2024
Enumeration of Campylobacter from environmental waters can be difficult due to its low concentrations, which can still pose a significant health risk. Spectrophotometry is an approach commonly used for fast detection of water-borne pollutants in wate...
Machine learning vs. regression models to predict the risk of Legionella contamination in a hospital water network.
Annali di igiene : medicina preventiva e di comunita
Jul 11, 2024
INTRODUCTION: The periodic monitoring of Legionella in hospital water networks allows preventive measures to be taken to avoid the risk of legionellosis to patients and healthcare workers.
Using machine learning models to estimate Escherichia coli concentration in an irrigation pond from water quality and drone-based RGB imagery data.
Water research
May 31, 2024
The rapid and efficient quantification of Escherichia coli concentrations is crucial for monitoring water quality. Remote sensing techniques and machine learning algorithms have been used to detect E. coli in water and estimate its concentrations. Th...
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