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Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources

Mobile source emissions account for more than 80% of carbon monoxide and hydrocarbons and more than 90% of nitrogen oxides and solid particles in urban air pollutants. Also, mobile source emissions have become the main source of urban air pollution, causing serious damage to the social ecological en...

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Formato: Online
Lenguaje:inglés
Publicado: MDPI - Multidisciplinary Digital Publishing Institute 2026
Materias:
Air pollution forecasting
External factor optimization
Genetic algorithm
Autoformer
Temporal variation
Multivariate dependencies
Relationships analysis
Model interpretability
OBD
Emission factors
COPERT
Two-stream network
Time-frequency features
Atmospheric pollutants
Wavelet decomposition
Multi-layer perceptron model
Atmospheric carbonyls
Sampling methods
Offline analytical methods
Online analytical methods
Problems and challenges
Prospect
Air pollution
Ozone
Houston
Forecasting
Quantum machine learning
Quantum neural network
Graph neural network
Surface plasmon resonance (SPR)
Particulate matter (PM)
Sensors
Machine learning (ML)
Deep learning (DL)
Artificial intelligence (AI)
Environmental monitoring
Air quality
Deep learning
WRF
Weather forecasting
Computational fluid dynamics
Artificial intelligence
Spatiotemporal convolution
Air quality prediction
Generative CNN
Multi-scale fusion
Feature correlation
Digital twins
Machine learning calibration
Urban air pollution
Graph neural networks (GNNs)
N
A
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
Acceso en línea:ONIX_20260416T142754_9783725863303_24
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