A novel method for multi-pollutant monitoring in water supply systems using chemical machine vision

Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over brief intervals. This paper proposes a direct anal...

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Bibliographic Details
Published in:Environmental science and pollution research international 2024-04, Vol.31 (18), p.26555-26566
Main Authors: Yan, Jiacong, Lee, Jianchao, Liu, Lu, Duan, Qiannan, Lei, Jingzheng, Fu, Zhizhi, Zhou, Chi, Wu, WeiDong, Wang, Fei
Format: Article
Language:English
Subjects:
Aquatic Pollution
Arsenic
Arsenic - analysis
Atmospheric Protection/Air Quality Control/Air Pollution
Cadmium
Cadmium - analysis
Contaminants
Datasets
Deep learning
Drinking water
Drinking Water - chemistry
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental monitoring
Environmental Monitoring - methods
Food industry
Food safety
High definition
Indicators
Learning algorithms
Light sources
Machine Learning
Machine vision
Performance prediction
Pollution monitoring
Research Article
Selenium
Waste Water Technology
Water conveyance
Water Management
Water monitoring
Water Pollutants, Chemical - analysis
Water pollution
Water Pollution Control
Water Quality
Water quality management
Water Supply
Water supply systems
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Summary:Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over brief intervals. This paper proposes a direct analysis and rapid detection method of three indicators of arsenic, cadmium, and selenium in complex drinking water systems by combining a novel long-path spectral imager with machine learning models. Our technique can obtain multiple parameters in about 1 s. The experiment involved setting up samples from various drinking water backgrounds and mixed groups, totaling 9360 injections. A raw visible light source ranging from 380 to 780 nm was utilized, uniformly dispersing light into the sample cell through a filter. The residual beam was captured by a high-definition camera, forming a distinctive spectrum. Three deep learning models—ResNet-50, SqueezeNet V1.1, and GoogLeNet Inception V1—were employed. Datasets were divided into training, validation, and test sets in a 6:2:2 ratio, and prediction performance across different datasets was assessed using the coefficient of determination and root mean square error. The experimental results show that a well-trained machine learning model can extract a lot of feature image information and quickly predict multi-dimensional drinking water indicators with almost no preprocessing. The model’s prediction performance is stable under different background drinking water systems. The method is accurate, efficient, and real-time and can be widely used in actual water supply systems. This study can improve the efficiency of water quality monitoring and treatment in water supply systems, and the method’s potential for environmental monitoring, food safety, industrial testing, and other fields can be further explored in the future.
ISSN:1614-7499
0944-1344
1614-7499
DOI:10.1007/s11356-024-32791-3