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Water Pollutants, Chemical

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Satellite Remote Sensing-Implemented Nontargeted Screening of Emerging Contaminant Fingerprints in a River-to-Ocean Continuum through Interpretable Machine Learning: The Pivotal Intermediary Role of Dissolved Organic Matter.

Environmental science & technology
Emerging contaminants (ECs) can exert irreversible health impacts on humans, even at trace concentrations. Currently, nontargeted screening of ECs has been developed for their assessment, which requires sophisticated instrumentation. Although satelli...

Rapid and sensitive detection of pharmaceutical pollutants in aquaculture by aluminum foil substrate based SERS method combined with deep learning algorithm.

Analytica chimica acta
BACKGROUND: Pharmaceutical residual such as antibiotics and disinfectants in aquaculture wastewater have significant potential risks for environment and human health. Surface enhanced Raman spectroscopy (SERS) has been widely used for the detection o...

Prediction of surface water pollution using wavelet transform and 1D-CNN.

Water science and technology : a journal of the International Association on Water Pollution Research
Permanganate index (COD), total nitrogen, and ammonia nitrogen are important indicators that represent the degree of pollution of surface water. This study combined ultraviolet-visible (UV-vis) spectroscopy with a one-dimensional convolutional neural...

Sentinel-2 imagery coupled with machine learning to modelling water turbidity in the Doce River Basin, Brazil.

Environmental monitoring and assessment
Remote sensing and machine learning are techniques that can be used to monitor water quality properties, surpassing the limitations of the conventional techniques. Turbidity is an important water quality property directly influenced by the Fundão dam...

Artificial intelligence based detection and control strategies for river water pollution: A comprehensive review.

Journal of contaminant hydrology
Water quality (WQ) is a metric for assessing the overall health and safety of water bodies like a river. Owing to the habitation of anthropogenic habitation around its basin, the rivers can become one of the most contaminated water sources globally. ...

Integrating bioassay and machine learning data for ecological risk assessments of herbicide use on Ulva australis.

Marine pollution bulletin
Herbicide contamination of aquatic ecosystems poses a critical risk to biodiversity. Bioassays provide useful ecological insights on responses to herbicides; however, they require a model organism. Ulva australis is an ideal candidate for herbicide t...

Water quality parameters retrieval and nutrient status evaluation based on machine learning methods and Sentinel- 2 imagery: a case study of the Hongjiannao Lake.

Environmental monitoring and assessment
A timely and accurate understanding of lake water quality is significant for maintaining ecological balance, ensuring water resource security, and promoting regional sustainable development. However, due to the varying numerical ranges and characteri...

Decoding the Plastic Patch: Exploring the Global Microplastic Distribution in the Surface Layers of Marine Regions with Interpretable Machine Learning.

Environmental science & technology
The marine environment is grappling with microplastic (MP) pollution, necessitating an understanding of its distribution patterns, influencing factors, and potential ecological risks. However, the vast area of the ocean and budgetary constraints make...

Joint identification of hydraulic conductivity and groundwater pollution sources using unscented Kalman smoother with multiple data assimilation and deep learning.

Ecotoxicology and environmental safety
Identification of groundwater pollution sources (IGPSs) is a prerequisite for pollution remediation and pollution risk prediction. Data assimilation approaches have been used extensively in IGPSs field in recent years. A data assimilation approach-un...

Feasibility study of real-time virtual sensing for water quality parameters in river systems using synthetic data and deep learning models.

Journal of environmental management
With water quality management crucial for environmental sustainability, multiple techniques for real-time monitoring and estimation of water quality parameters have been developed. However, certain data types, such as airborne images, are only access...