Environmental Health

Predicting abrupt depletion of dissolved oxygen in Chaohu lake using CNN-BiLSTM with improved attention mechanism.

Depletion of dissolved oxygen (DO) is a significant incentive for biological catastrophic events in freshwater lakes. Although predicting the DO concentrations in lakes with high-frequency real-time data to prevent hypoxic events is effective, few related experimental studies were made. In this study, a short-term predicting model was developed for DO concentrations in three problematic areas in C...

Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning.

Airborne trace elements (TEs) present in atmospheric fine particulate matter (PM) exert notable threats to human health and ecosystems. To explore the impact of meteorological conditions on shaping the pollution characteristics of TEs and the associated health risks, we quantified the variations in pollution characteristics and health risks of TEs due to meteorological impacts using weather normal...

Comprehensive river water quality monitoring using convolutional neural networks and gated recurrent units: A case study along the Vaigai River.

Effective monitoring of river water quality is required for long-term water resource management. Convolutional Neural Networks and Gated Recurrent Uni...

Application of machine learning in the study of development, behavior, nerve, and genotoxicity of zebrafish.

Machine learning (ML) as a novel model-based approach has been used in studying aquatic toxicology in the environmental field. Zebrafish, as an ideal ...

Quantification of litter in cities using a smartphone application and citizen science in conjunction with deep learning-based image processing.

Cities are a major source of litter pollution. Determination of the abundance and composition of plastic litter in cities is imperative for effective ...

An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning.

Brown sugar is a natural sweetener obtained by thermal processing, with interesting nutritional characteristics. However, it has significant sensory v...

Optimal biochar selection for cadmium pollution remediation in Chinese agricultural soils via optimized machine learning.

Biochar is effective in mitigating heavy metal pollution, and cadmium (Cd) is the primary pollutant in agricultural fields. However, traditional trial...

Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines.

Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial fo...

Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ.

Short-term exposure to ground-level ozone (O) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. Thi...

The use of vibrational spectroscopy and supervised machine learning for chemical identification of plastics ingested by seabirds.

Plastic pollution is now ubiquitous in the environment and represents a growing threat to wildlife, who can mistake plastic for food and ingest it. Ta...

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) prediction model based on limited peat samples using an evolved artificial neural network.

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) are involuntary by-products of incomplete combustion and are highly toxi...

Integrative approach for efficient detection of kidney stones based on improved deep neural network architecture.

In today's digital world, with growing population and increasing pollution, unhealthy lifestyle habits like irregular eating, junk food consumption, a...

Validation of the identification reliability of known and assumed UDMH transformation products using gas chromatographic retention indices and machine learning.

Thirty two commercially available standards were used to determine chromatographic retention indices for three different stationary phases (non-polar,...

X-ray transmission imaging of waste printed circuit boards for value estimation in recycling using machine learning.

The growing amount of electronic waste is a global challenge: on one hand, it poses a threat to the environment as it may contain toxic or hazardous s...

Prediction of developmental toxic effects of fine particulate matter (PM) water-soluble components via machine learning through observation of PM from diverse urban areas.

The global health implications of fine particulate matter (PM) underscore the imperative need for research into its toxicity and chemical composition....

Machine-learning-based corrections of CMIP6 historical surface ozone in China during 1950-2014.

Due to a lack of long-term observations in China, reports on historical ozone concentration are severely limited. In this study, by combining observat...

Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors.

This research utilized machine learning to analyze experiments conducted in an open channel laboratory setting to predict microplastic transport with ...

Identifying Driving Factors of Atmospheric NO with Machine Learning.

Dinitrogen pentoxide (NO) plays an essential role in tropospheric chemistry, serving as a nocturnal reservoir of reactive nitrogen and significantly p...

Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management.

In Saudi Arabia, water pollution and drinking water scarcity pose a major challenge and jeopardise the achievement of sustainable development goals. T...

Occurrence and Distribution of Antibacterial Quaternary Ammonium Compounds in Chinese Estuaries Revealed by Machine Learning-Assisted Mass Spectrometric Analysis.

Antimicrobial resistance (AMR) undermines the United Nations Sustainable Development Goals of good health and well-being. Antibiotics are known to exa...