AIMC Topic: Atmosphere

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Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval.

Environmental science & technology Aug 3, 2024
Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for clim...
Deep Learning Satellite Imagery Atmosphere Environmental Monitoring Aerosols
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Identifying Driving Factors of Atmospheric NO with Machine Learning.

Environmental science & technology Jun 18, 2024
Dinitrogen pentoxide (NO) plays an essential role in tropospheric chemistry, serving as a nocturnal reservoir of reactive nitrogen and significantly promoting nitrate formations. However, identifying key environmental drivers of NO formation remains ...
Air Pollutants Air Pollution Machine Learning Environmental Monitoring Nitrogen Dioxide Nitrogen Oxides Atmosphere Ozone
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Deep learning bias correction of GEMS tropospheric NO: A comparative validation of NO from GEMS and TROPOMI using Pandora observations.

Environment international Jun 13, 2024
Despite advancements in satellite instruments, such as those in geostationary orbit, biases continue to affect the accuracy of satellite data. This research pioneers the use of a deep convolutional neural network to correct bias in tropospheric colum...
Nitrogen Dioxide Air Pollutants Atmosphere Deep Learning Environmental Monitoring Bias
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Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations.

The Science of the total environment Aug 23, 2022
Pollen is the most common cause of seasonal allergies, affecting over 33 % of the European population, even when considering only grasses. Informing the population and clinicians in real-time about the actual presence of pollen in the atmosphere is e...
Pollen Reproducibility of Results Poaceae Neural Networks, Computer Allergens Atmosphere
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A neural network based approach to classify VLF signals as rock rupture precursors.

Scientific reports Aug 12, 2022
The advent of novel technologies revealed that other geophysical signals than those directly related to fault motion could be used to probe the state of deformation of the Earth's crust. Electromagnetic signals belonging to this category have been in...
Atmosphere Earthquakes Neural Networks, Computer
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Generating a long-term (2003-2020) hourly 0.25° global PM dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS).

The Science of the total environment Jul 31, 2022
Generating a long-term high-spatiotemporal resolution global PM dataset is of great significance for environmental management to mitigate the air pollution concerns worldwide. However, the current long-term (2003-2020) global reanalysis dataset Coper...
Atmosphere Particulate Matter Deep Learning Environmental Monitoring Air Pollutants Air Pollution
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Design of experiments meets immersive environment: Optimising eating atmosphere using artificial neural network.

Appetite Jun 5, 2022
Design of experiments (DOE) is a family of statistical tools commonly used in food science to optimise recipes and facilitate new food development. In a novel cross-disciplinary twist, we propose to adapt DOE approach to the optimisation of restauran...
Food Atmosphere Humans Algorithms Neural Networks, Computer Reproducibility of Results
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Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data.

Scientific reports May 19, 2022
Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a supervised mach...
Weather Atmosphere Machine Learning Forecasting Neural Networks, Computer
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A novel multi-model data-driven ensemble approach for the prediction of particulate matter concentration.

Environmental science and pollution research international May 3, 2021
Accuracy in the prediction of the particulate matter (PM and PM) concentration in the atmosphere is essential for both its monitoring and control. In this study, a novel neuro fuzzy ensemble (NF-E) model was proposed for prediction of hourly PM and P...
Particulate Matter Neural Networks, Computer Atmosphere Environmental Monitoring
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A novel soft sensor based warning system for hazardous ground-level ozone using advanced damped least squares neural network.

Ecotoxicology and environmental safety Aug 22, 2020
Estimation of hazardous air pollutants in the urban environment for maintaining public safety is a significant concern to mankind. In this paper, we have developed an efficient air quality warning system based on a low-cost and robust ground-level oz...
Seasons Algorithms Atmosphere Environmental Monitoring Ozone Cities Air Pollutants Taiwan Neural Networks, Computer Least-Squares Analysis Air Pollution
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