Environmental Health

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. Tackling this problem requires reliable, consistent methods for monitoring plastic pollution ingested by seabirds and other marine fauna, including methods for identifying different types of plastic. This study presents a robust method for the rapid, r...

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 toxic to humans and the environment. The Malaysian peat is often acidic or extremely acidic having high levels of chlorine and/or other organic acids that act as catalysts or precursors in PCDD/Fs formation. This study aims to predict PCDD/Fs emissions i...

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...

Machine Learning Applied to the Detection of Mycotoxin in Food: A Systematic Review.

Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds c...

Remotely sensed estimates of long-term biochemical oxygen demand over Hong Kong marine waters using machine learning enhanced by imbalanced label optimisation.

In many coastal cities around the world, continuing water degradation threatens the living environment of humans and aquatic organisms. To assess and ...

Advancing ecotoxicity assessment: Leveraging pre-trained model for bee toxicity and compound degradability prediction.

The prediction of ecological toxicity plays an increasingly important role in modern society. However, the existing models often suffer from poor perf...

Coastal ozone dynamics and formation regime in Eastern China: Integrating trend decomposition and machine learning techniques.

Machine-learning is a robust technique for understanding pollution characteristics of surface ozone, which are at high levels in urban China. This stu...

Screening structure and predicting toxicity of pesticide adjuvants using molecular dynamics simulation and machine learning for minimizing environmental impacts.

Surfactants as synergistic agents are necessary to improve the stability and utilization of pesticides, while their use is often accompanied by unexpe...

Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting.

Accurate multi-step ahead flood forecasting is crucial for flood prevention and mitigation efforts as well as optimizing water resource management. In...

Environmental impact assessment of ocean energy converters using quantum machine learning.

The depletion of fossil energy reserves and the environmental pollution caused by these sources highlight the need to harness renewable energy sources...

Blockchain and IoT integration for secure short-term and long-term air quality monitoring system using optimized neural network.

Accurate air pollution prediction is vital for residents' well-being. This research introduces a secure air quality monitoring system using neural net...

Identification of biological indicators for human exposure toxicology in smart cities based on public health data and deep learning.

With the acceleration of urbanization, the risk of urban population exposure to environmental pollutants is increasing. Protecting public health is th...