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When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
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Consider an external electric field propagating through a homogeneous medium. When the electric field crosses the surface boundary of the medium, it undergoes a discontinuity. The electric field can be resolved into normal and tangential components. The amount by which the field changes at any boundary is given by the difference between the field components above and below the surface boundary.
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Hybrid attention based deep learning for forecasting boundary layer ozone using satellite derived profiles.

Shahab S Band1, Sultan Noman Qasem2, Javad Ramezani3

  • 1Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan.

Ecotoxicology and Environmental Safety
|December 20, 2025
PubMed
Summary
This summary is machine-generated.

Accurate forecasting of ground-level ozone is crucial for environmental and health protection. This study introduces advanced deep learning models, with EMD-ConvBiGRU-AttentionNet showing the highest prediction accuracy for boundary layer ozone.

Keywords:
Artificial intelligenceAttention mechanismBig dataBoundary layer ozoneData ScienceDeep learningEmpirical Mode DecompositionMachine learningRemote sensing

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Area of Science:

  • Atmospheric chemistry and physics
  • Environmental science
  • Data science and machine learning

Background:

  • Ground-level ozone is a major air pollutant formed by photochemical reactions, posing risks to human health and ecosystems.
  • Forecasting boundary layer ozone is challenging due to complex non-linear relationships with meteorological and chemical factors, and a lack of fine-scale vertical data.
  • The Ozone Monitoring Instrument (OMI) provides valuable ozone profile data crucial for improving forecasting models.

Purpose of the Study:

  • To evaluate the effectiveness of various deep learning models for forecasting boundary layer ozone concentrations.
  • To develop and assess novel deep learning architectures, including attention mechanisms and Empirical Mode Decomposition, for enhanced ozone prediction.
  • To benchmark the performance of proposed models against conventional methods using key accuracy metrics.

Main Methods:

  • Utilized the OMPROFOZ ozone profile product from the Aura satellite's OMI instrument.
  • Evaluated recurrent neural networks (RNN), convolutional neural networks (CNN), gated recurrent units (GRU), long short-term memory (LSTM), and hybrid models (GRU-CNN, LSTM-CNN).
  • Developed and tested advanced models: ConvBiGRU-AttentionNet and EMD-ConvBiGRU-AttentionNet, incorporating attention mechanisms and Empirical Mode Decomposition.

Main Results:

  • The proposed deep learning models significantly outperformed conventional methods in ozone forecasting.
  • EMD-ConvBiGRU-AttentionNet demonstrated the highest prediction accuracy among all evaluated models.
  • Visual analyses, including residual plots and attention maps, confirmed the models' ability to capture complex spatio-temporal patterns.

Conclusions:

  • Advanced deep learning models, particularly EMD-ConvBiGRU-AttentionNet, offer a promising approach for accurate boundary layer ozone forecasting.
  • The integration of attention mechanisms and Empirical Mode Decomposition enhances the models' capacity to handle complex atmospheric data.
  • Improved ozone forecasting can aid in mitigating the adverse effects of this major air pollutant on health and the environment.