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Air Pollution

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Air pollution and prostate cancer: Unraveling the connection through network toxicology and machine learning.

Ecotoxicology and environmental safety
BACKGROUND: In recent years, air pollution has been demonstrated to be associated with the occurrence of various diseases. This study aims to explore the potential association between air pollutants and prostate cancer (PCa) and to identify key genes...

Distribution characteristics of volatile organic compounds and its multidimensional impact on ozone formation in arid regions based on machine learning algorithms.

Environmental pollution (Barking, Essex : 1987)
Volatile Organic Compounds (VOCs) are key components of atmospheric pollution and play a critical role in ozone (O) formation. Understanding their distribution and pollution sources is essential to grasping the multifaceted impact of VOCs on O produc...

Forecasting the concentration of the components of the particulate matter in Poland using neural networks.

Environmental science and pollution research international
Air pollution is a significant global challenge with profound impacts on human health and the environment. Elevated concentrations of various air pollutants contribute to numerous premature deaths each year. In Europe, and particularly in Poland, air...

AI-driven approaches for air pollution modelling: A comprehensive systematic review.

Environmental pollution (Barking, Essex : 1987)
In recent years, air quality levels have become a global issue with the rise of harmful pollutants and their effects on climate change. Urban areas are especially affected by air pollution, resulting in a deterioration of the environment and a surge ...

PM concentration prediction using machine learning algorithms: an approach to virtual monitoring stations.

Scientific reports
One of the most important pollutants is PM, which is particularly important to monitor pollutant levels to keep the pollutant concentration under control. In this research, an attempt has been made to predict the concentrations of PM using four Machi...

Uncovering key sources of regional ozone simulation biases using machine learning and SHAP analysis.

Environmental pollution (Barking, Essex : 1987)
Atmospheric chemical transport models (CTMs) are widely used in air quality management, but still have large biases in simulations. Accurately and efficiently identifying key sources of simulation biases is crucial for model improvement. However, tra...

PM concentration prediction using a whale optimization algorithm based hybrid deep learning model in Beijing, China.

Environmental pollution (Barking, Essex : 1987)
PM is a significant global atmospheric pollutant impacting visibility, climate, and public health. Accurate prediction of PM concentrations is critical for assessing air pollution risks and providing early warnings for effective management. This stud...

Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning.

BMC public health
BACKGROUND: Metabolic diseases (MDs), exemplified by diabetes, hypertension, and dyslipidemia, have become increasingly prevalent with rising living standards, posing significant public health challenges. The MDs are influenced by a complex interplay...

Advancing low-cost air quality monitor calibration with machine learning methods.

Environmental pollution (Barking, Essex : 1987)
Low-cost monitors for measuring airborne contaminants have gained popularity due to their affordability, portability, and ease of use. However, they often exhibit significant biases compared to high-cost reference instruments. For optimal accuracy, t...

Traffic-related air pollution backcasting using convolutional neural network and long short-term memory approach.

The Science of the total environment
Air pollution backcasting, especially nitrogen dioxide (NO), is crucial in epidemiological studies, thus enabling the reconstruction of historical exposure levels for assessing long-term health effects. Changes in NO concentrations in urban areas are...