Surveillance spilled Chironomidae (Diptera) larvae from drinking water treatment plants in South Korea using morphogenetic species analysis and eDNA metabarcoding

Park, Jae-Won; Park, Kiyun; Kwak, Ihn‐Sil

doi:10.1016/j.scitotenv.2023.165241

Cited by 2 publications

(2 citation statements)

References 46 publications

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“…Therefore, the utilization of eDNA in ecological research, biomonitoring, and environmental management can be beneficial and bring about a transformative impact on the field of conservation science [404]. Several studies have reported the use of eDNA analysis to investigate the biotic composition, abundance, and distribution of aquatic species, such as macroinvertebrates, plants, fishes, and others in freshwater and marine ecosystems [348][349][350][351][352][353][354].…”

Section: Effects Of Pharmaceuticals On Aquatic Invertebrates Plants A...mentioning

confidence: 99%

Insights in Pharmaceutical Pollution: The Prospective Role of eDNA Metabarcoding

Papaioannou,

Geladakis,

Kommata

et al. 2023

Toxics

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Environmental pollution is a growing threat to natural ecosystems and one of the world’s most pressing concerns. The increasing worldwide use of pharmaceuticals has elevated their status as significant emerging contaminants. Pharmaceuticals enter aquatic environments through multiple pathways related to anthropogenic activity. Their high consumption, insufficient waste treatment, and the incapacity of organisms to completely metabolize them contribute to their accumulation in aquatic environments, posing a threat to all life forms. Various analytical methods have been used to quantify pharmaceuticals. Biotechnology advancements based on next-generation sequencing (NGS) techniques, like eDNA metabarcoding, have enabled the development of new methods for assessing and monitoring the ecotoxicological effects of pharmaceuticals. eDNA metabarcoding is a valuable biomonitoring tool for pharmaceutical pollution because it (a) provides an efficient method to assess and predict pollution status, (b) identifies pollution sources, (c) tracks changes in pharmaceutical pollution levels over time, (d) assesses the ecological impact of pharmaceutical pollution, (e) helps prioritize cleanup and mitigation efforts, and (f) offers insights into the diversity and composition of microbial and other bioindicator communities. This review highlights the issue of aquatic pharmaceutical pollution while emphasizing the importance of using modern NGS-based biomonitoring actions to assess its environmental effects more consistently and effectively.

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Section: Effects Of Pharmaceuticals On Aquatic Invertebrates Plants A...mentioning

confidence: 99%

Insights in Pharmaceutical Pollution: The Prospective Role of eDNA Metabarcoding

Papaioannou,

Geladakis,

Kommata

et al. 2023

Toxics

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show abstract

“…In recent times, chironomid larva outbreaks have become a frequent concern in countries such as South Korea, United States, Scotland, and South Africa [ 2 ]. Consequently, it is imperative to gain a precise understanding of the occurrence of chironomid larvae in water treatment plants and actively explore appropriate technologies and methods for their monitoring and control [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

RT-CLAD: Artificial Intelligence-Based Real-Time Chironomid Larva Detection in Drinking Water Treatment Plants

Jang,

Yeo,

Park

et al. 2023

Sensors

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The presence of chironomid larvae in tap water has sparked public concern regarding the water supply system in South Korea. Despite ongoing efforts to establish a safe water supply system, entirely preventing larval occurrences remains a significant challenge. Therefore, we developed a real-time chironomid larva detection system (RT-CLAD) based on deep learning technology, which was implemented in drinking water treatment plants. The acquisition of larval images was facilitated by a multi-spectral camera with a wide spectral range, enabling the capture of unique wavelet bands associated with larvae. Three state-of-the-art deep learning algorithms, namely the convolutional neural network (CNN), you only look once (YOLO), and residual neural network (ResNet), renowned for their exceptional performance in object detection tasks, were employed. Following a comparative analysis of these algorithms, the most accurate and rapid model was selected for RT-CLAD. To achieve the efficient and accurate detection of larvae, the original images were transformed into a specific wavelet format, followed by preprocessing to minimize data size. Consequently, the CNN, YOLO, and ResNet algorithms successfully detected larvae with 100% accuracy. In comparison to YOLO and ResNet, the CNN algorithm demonstrated greater efficiency because of its faster processing and simpler architecture. We anticipate that our RT-CLAD will address larva detection challenges in water treatment plants, thereby enhancing water supply security.

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Surveillance spilled Chironomidae (Diptera) larvae from drinking water treatment plants in South Korea using morphogenetic species analysis and eDNA metabarcoding

Cited by 2 publications

References 46 publications

Insights in Pharmaceutical Pollution: The Prospective Role of eDNA Metabarcoding

Insights in Pharmaceutical Pollution: The Prospective Role of eDNA Metabarcoding

RT-CLAD: Artificial Intelligence-Based Real-Time Chironomid Larva Detection in Drinking Water Treatment Plants

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