AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Air Pollution

Showing 41 to 50 of 231 articles

Clear Filters

Nordic environmental resilience: balancing air quality and energy efficiency by applying artificial neural network.

Frontiers in public health
Maintaining public health and environmental safety in the Nordic nations calls for a strict plan to define exact benchmarks on air quality and energy efficiency. This study investigates the complicated interaction of decentralized energy production (...

Meteorological and traffic effects on air pollutants using Bayesian networks and deep learning.

Journal of environmental sciences (China)
Traffic emissions have become the major air pollution source in urban areas. Therefore, understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air quality prediction models. Using re...

Regional PM prediction with hybrid directed graph neural networks and Spatio-temporal fusion of meteorological factors.

Environmental pollution (Barking, Essex : 1987)
Traditional statistical prediction methods on PM often focus on a single temporal or spatial dimension, with limited consideration for regional transport interactions among adjacent cities. To address this limitation, we propose a hybrid directed gra...

Fuzzy set-based decision support system for hydrogen sulfide removal technology selection in natural gas processing: a sustainability and efficiency perspective.

Environmental monitoring and assessment
Removing hydrogen sulfide (HS) toxic and corrosive gas from the natural gas processing and utilization industry is a challenging problem for managers of these industries. This problem involves different economic, environmental, and health issues. Var...

A hybrid deep learning model-based LSTM and modified genetic algorithm for air quality applications.

Environmental monitoring and assessment
Over time, computing power and storage resource advancements have enabled the widespread accumulation and utilization of data across various domains. In the field of air quality, analyzing data and developing air quality models have become pivotal in...

Spatiotemporal variations of PM and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning.

Environmental pollution (Barking, Essex : 1987)
Ozone pollution was widely reported along with PM reduction since 2013 in China. However, the meteorological drivers for ozone varying with different regions of China remains unknown using explainable machine learning, especially during the COVID-19 ...

MTLPM: a long-term fine-grained PM2.5 prediction method based on spatio-temporal graph neural network.

Environmental monitoring and assessment
The concentration of PM2.5 is one of the air quality indicators that the public pays the most attention to. Existing methods for PM2.5 prediction primarily analyze and forecast data from individual monitoring stations, without considering the mutual ...

Improving PM and PM predictions in China from WRF_Chem through a deep learning method: Multiscale depth-separable UNet.

Environmental pollution (Barking, Essex : 1987)
Accurate predictions of atmospheric particulate matter can be applied in providing services for air pollution prevention and control. However, the forecasting accuracy of traditional air quality models is limited owing to model uncertainties. In this...

High-resolution spatiotemporal prediction of PM concentration based on mobile monitoring and deep learning.

Environmental pollution (Barking, Essex : 1987)
Obtaining the high-resolution distribution characteristics of urban air pollutants is crucial for effective pollution control and public health. In order to fulfill it, mobile monitoring offers a novel and practical approach compared to traditional f...

Evaluating drivers of PM air pollution at urban scales using interpretable machine learning.

Waste management (New York, N.Y.)
Reducing urban fine particulate matter (PM) concentrations is essential for China to achieve the Sustainable Development Goals (SDGs). Identifying the key drivers of PM will enable the development of targeted strategies to reduce PM levels. This stud...