Environmental Health

A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities.

The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with prolonged exposure to electromagnetic fields. Our objective is to determine the optimal machine learni...

Achieving pollution abatement and carbon reduction synergistically: How can industrial robots play a role?

Intelligent manufacturing and green development are pivotal issues in China's pursuit of high-quality economic growth. As the core carrier of artificial intelligence-driven production transformations, industrial robots' role in synergizing enterprise pollution control and carbon reduction has rarely been discussed in the literature. Leveraging a unique micro dataset with robot adoption, energy use...

Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning.

Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosyst...

Mapping of high-resolution daily particulate matter (PM) concentration at the city level through a machine learning-based downscaling approach.

PM pollution is a major global concern, especially in Vietnam, due to its harmful effects on health and the environment. Monitoring local PM levels is...

Improving the prediction of chemotherapy dose-limiting toxicity in colon cancer patients using an AI-CT-based 3D body composition of the entire L1-L5 lumbar spine.

PURPOSE: Chemotherapy dose-limiting toxicities (DLT) pose a significant challenge in successful colon cancer treatment. Body composition analysis may ...

Recent advances in groundwater pollution research using machine learning from 2000 to 2023: A bibliometric analysis.

Groundwater pollution has become a global challenge, posing significant threats to human health and ecological environments. Machine learning, with it...

Advanced Mass-Spectra-Based Machine Learning for Predicting the Toxicity of Traditional Chinese Medicines.

Traditional Chinese medicine (TCM) has been a cornerstone of health care for centuries, valued for its preventive and therapeutic properties. However,...

Spatiotemporal modeling of long-term PM concentrations and population exposure in Greece, using machine learning and statistical methods.

The lack of high-resolution, long-term PM observations in Greece and the Eastern Mediterranean hampers the development of spatial models that are cruc...

Accurate prediction of pollution and health risks of iodinated X-ray contrast media in Taihu Lake with machine learning and revealing key environmental factors.

Iodinated X-ray contrast media (ICM) are commonly detected at considerable concentrations in aquatic environments. The long-term pollution trends in I...

Wastewater treatment plant site selection using advanced decision tree machine learning and remote sensing techniques.

Wastewater treatment plants in Coimbatore South are under pressure from rapid urbanization, inadequate infrastructure, and industrial pollution, leadi...

Coffee Leaf Rust Disease Detection and Implementation of an Edge Device for Pruning Infected Leaves via Deep Learning Algorithms.

Global warming and extreme climate conditions caused by unsuitable temperature and humidity lead to coffee leaf rust () diseases in coffee plantations...

Endocrine disruptor identification and multitoxicity level assessment of organic chemicals: An example of multiple machine learning models.

Endocrine-disrupting chemicals (EDCs) pollution is a major global environmental issue. Assessing the multiple toxic effects of EDCs is key to managing...

Ensemble learning-assisted quantitative identifying influencing factors of cadmium and arsenic concentration in rice grain based multiplexed data.

Rapid and accurate prediction of rice Cd (rCd) and rice As (rAs) bioaccumulation are important for assessing the safe utilization of rice. Currently, ...

Predicting the adsorption of ammonia nitrogen by biochar in water bodies using machine learning strategies: Model optimization and analysis of key characteristic variables.

Biochar adsorption technology has been widely used to remove ammonia nitrogen from water bodies. However, existing methods for predicting adsorption e...

Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics.

Microplastics (MPs), the plastic debris smaller than 5 mm, are ubiquitous in waterbodies and have been shown to be toxic to aquatic organisms, especia...

Machine Learning-Assisted "Shrink-Restricted" SERS Strategy for Classification of Environmental Nanoplastic-Induced Cell Death.

The biotoxicity of nanoplastics (NPs), especially from environmental sources, and "NPs carrier effect" are in the early stages of research. This study...

Modeling the global ocean distribution of dissolved cadmium based on machine learning-SHAP algorithm.

Cadmium (Cd) is a bio-essential trace metal in the ocean that can be toxic at high concentrations, significantly impacting the marine environment and ...

Revolutionizing the construction industry by cutting edge artificial intelligence approaches: a review.

The construction industry is rapidly adopting Industry 4.0 technologies, creating new opportunities to address persistent environmental and operationa...

Cyto-Safe: A Machine Learning Tool for Early Identification of Cytotoxic Compounds in Drug Discovery.

Cytotoxicity is essential in drug discovery, enabling early evaluation of toxic compounds during screenings to minimize toxicological risks. assays s...

Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution.

Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply...