AI Medical Compendium Topic:
Environmental Monitoring

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Potential risk assessment and occurrence characteristic of heavy metals based on artificial neural network model along the Yangtze River Estuary, China.

Environmental science and pollution research international
Pollution from heavy metals in estuaries poses potential risks to the aquatic environment and public health. The complexity of the estuarine water environment limits the accurate understanding of its pollution prediction. Field observations were cond...

Impact of long-term mining activity on groundwater dynamics in a mining district in Xinjiang coal Mine Base, Northwest China: insight from geochemical fingerprint and machine learning.

Environmental science and pollution research international
Long-term coal mining could lead to a serious of geo-environmental problems. However, less comprehensive identification of factors controlling the groundwater dynamics were involved in previous studies. This study focused on 68 groundwater samples co...

Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review.

Environmental science and pollution research international
Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) algorithms have been utilized to predict them. However, review st...

Application of machine learning and multivariate approaches for assessing microplastic pollution and its associated risks in the urban outdoor environment of Bangladesh.

Journal of hazardous materials
Microplastics (MPs) are an emerging global concern due to severe toxicological risks for ecosystems and public health. Therefore, this is the first study in Bangladesh to assess MP pollution and its associated risks for ecosystems and human health in...

Stacking Machine Learning Models Empowered High Time-Height-Resolved Ozone Profiling from the Ground to the Stratopause Based on MAX-DOAS Observation.

Environmental science & technology
Ozone (O) profiles are crucial for comprehending the intricate interplay among O sources, sinks, and transport. However, conventional O monitoring approaches often suffer from limitations such as low spatiotemporal resolution, high cost, and cumberso...

Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi province, Thailand.

Environmental research
Exploring the potential of new models for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore t...

Spatio-temporal prediction of groundwater vulnerability based on CNN-LSTM model with self-attention mechanism: A case study in Hetao Plain, northern China.

Journal of environmental sciences (China)
Located in northern China, the Hetao Plain is an important agro-economic zone and population centre. The deterioration of local groundwater quality has had a serious impact on human health and economic development. Nowadays, the groundwater vulnerabi...

Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.

Water science and technology : a journal of the International Association on Water Pollution Research
Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore...

Explainable geospatial-artificial intelligence models for the estimation of PM concentration variation during commuting rush hours in Taiwan.

Environmental pollution (Barking, Essex : 1987)
PM concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM concentration and its spatial distribution during rush hours using machine learning mo...

Machine learning-assisted chromium speciation using a single-well ratiometric fluorescent nanoprobe.

Chemosphere
Chromium is widely recognized as a significant pollutant discharged into the environment by various industrial activities. The toxicity of this element is dependent on its oxidation state, making speciation analysis crucial for monitoring the quality...