AIMC Topic: Atmosphere

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Low-cost sensors for atmospheric NO measurement: A review.

Environmental pollution (Barking, Essex : 1987) Jul 15, 2025
Nitrogen dioxide (NO) is a major air pollutant in urban areas, prompting the development of numerous analytical methods for its monitoring. Among these, the chemiluminescence method stands out as the most commonly used and is widely regarded as a ref...
Atmosphere Nitrogen Dioxide Environmental Monitoring Air Pollutants Air Pollution
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Fate and speciation of NO in an arid climatic region: factors assessment.

Environmental monitoring and assessment May 8, 2025
NO and NO continuously recycle in the lower atmosphere through a complex series of reactions involving NO, VOCs, NO, and O. Therefore, the NO/NO ratio can be utilized in dispersion models as an important substitute to understand the fate of NO and NO...
Environmental Monitoring Air Pollutants Nitrogen Oxides Nitrogen Dioxide Air Pollution Atmosphere Kuwait
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Development of an automated photolysis rates prediction system based on machine learning.

Journal of environmental sciences (China) May 1, 2025
Based on observed meteorological elements, photolysis rates (J-values) and pollutant concentrations, an automated J-values predicting system by machine learning (J-ML) has been developed to reproduce and predict the J-values of OD, NO, HONO, HO, HCHO...
China Environmental Monitoring Machine Learning Photolysis Ozone Air Pollutants Aerosols Atmosphere Models, Chemical
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Rapid fiber-detection technique by artificial intelligence in phase-contrast microscope images of simulated atmospheric samples.

Annals of work exposures and health Apr 22, 2024
Since the manufacture, import, and use of asbestos products have been completely abolished in Japan, the main cause of asbestos emissions into the atmosphere is the demolition and removal of buildings built with asbestos-containing materials. To dete...
Environmental Monitoring Humans Japan Neural Networks, Computer Asbestos Artificial Intelligence Atmosphere Asbestos, Serpentine Microscopy, Phase-Contrast
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Development of rapid and highly accurate method to measure concentration of fibers in atmosphere using artificial intelligence and scanning electron microscopy.

Journal of occupational health Jan 1, 2021
AIM: We aimed to develop a measurement method that can count fibers rapidly by scanning electron microscopy equipped with an artificial intelligence image recognition system (AI-SEM), detecting thin fibers which cannot be observed by a conventional p...
Air Pollutants, Occupational Air Filters Microscopy, Electron, Scanning Atmosphere Asbestos Artificial Intelligence Occupational Exposure Particulate Matter Humans Image Interpretation, Computer-Assisted
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Bayesian framework for simulation of dynamical systems from multidimensional data using recurrent neural network.

Chaos (Woodbury, N.Y.) Dec 1, 2019
We suggest a new method for building data-driven dynamical models from observed multidimensional time series. The method is based on a recurrent neural network with specific structure, which allows for the joint reconstruction of both a low-dimension...
Models, Theoretical Atmosphere Algorithms Bayes Theorem Stochastic Processes Signal Processing, Computer-Assisted Neural Networks, Computer Computer Simulation Probability Data Analysis
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How AI and satellites could help predict volcanic eruptions.

Nature Mar 1, 2019
Satellite Communications Neural Networks, Computer Geographic Mapping Humans Forecasting Machine Learning Environmental Monitoring Atmosphere Artificial Intelligence Volcanic Eruptions
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