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Environmental Pollutants

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Application of gas chromatographic data and 2D molecular descriptors for accurate global mobility potential prediction.

Environmental pollution (Barking, Essex : 1987)
Mobility is a key feature affecting the environmental fate, which is of particular importance in the case of persistent organic pollutants (POPs) and emerging pollutants (EPs). In this study, the global mobility classification artificial neural netwo...

Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6).

Scientific reports
Euro 6 is the latest vehicle emission standards for pollutants such as CO, NO and PM, that all new vehicles must comply, and it was introduced in September 2015 in South Korea. This study examined the effect of Euro 6 by comparing the measured pollut...

A hybrid deep learning model for regional O and NO concentrations prediction based on spatiotemporal dependencies in air quality monitoring network.

Environmental pollution (Barking, Essex : 1987)
Short-term prediction of urban air quality is critical to pollution management and public health. However, existing studies have failed to make full use of the spatiotemporal correlations or topological relationships among air quality monitoring netw...

Deep Learning-Enabled Morphometric Analysis for Toxicity Screening Using Zebrafish Larvae.

Environmental science & technology
Toxicology studies heavily rely on morphometric analysis to detect abnormalities and diagnose disease processes. The emergence of ever-increasing varieties of environmental pollutants makes it difficult to perform timely assessments, especially using...

Prediction of atmospheric pollutants in urban environment based on coupled deep learning model and sensitivity analysis.

Chemosphere
Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for the scientific management of atmospheric pollution. This study develops a model that combines an attention mechanism, convolutional neural network (CNN), ...

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring.

Sensors (Basel, Switzerland)
Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a li...

Artificial intelligence in wastewater treatment: A data-driven analysis of status and trends.

Chemosphere
Wastewater treatment is a complex process that involves many uncertainties, leading to fluctuations in effluent quality and costs, and environmental risks. Artificial intelligence (AI) can handle complex nonlinear problems and has become a powerful t...

Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models.

Environment international
BACKGROUND: Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes.

Ocean Stratification Impacts on Dissolved Polycyclic Aromatic Hydrocarbons (PAHs): From Global Observation to Deep Learning.

Environmental science & technology
Ocean stratification plays a crucial role in many biogeochemical processes of dissolved matter, but our understanding of its impact on widespread organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), remains limited. By analyzing disso...

Developing machine learning approaches to identify candidate persistent, mobile and toxic (PMT) and very persistent and very mobile (vPvM) substances based on molecular structure.

Water research
Determining which substances on the global market could be classified as persistent, mobile and toxic (PMT) substances or very persistent, very mobile (vPvM) substances is essential to prevent or reduce drinking water contamination from them. This st...