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<p>Urban air quality forecasting is vital for managing pollution exposure and protecting public health. This study introduces AQNet, a spatiotemporal deep learning framework that integrates adaptive mesh-based graph construction with attention-driven temporal modeling to enhance prediction accuracy. The model is tested on the Beijing Multi-Site Air Quality Dataset containing hourly pollutant and meteorological records from 12 monitoring stations. AQNet captures spatial dependencies through graph convolution over nonuniform mesh partitions and models temporal dynamics using gated recurrent units. A cross-fusion layer integrates these representations before final prediction. The framework surpasses existing models such as those by Han et al. and Chen et al., achieving a Mean Absolute Error of 2.50, Root Mean Square Error of 3.35, and an <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R^2\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mi>R</mi> <mn>2</mn> </msup> </math></EquationSource> </InlineEquation> of 0.96, with precision, recall, and F1-score above 0.95. The adaptive mesh and attention modules enable efficient scaling across cities with different sensor densities, as confirmed through runtime and GPU profiling across Beijing, Delhi, Bangkok, and Kathmandu datasets. AQNet maintains high-resolution forecasting accuracy with minimal computational cost, confirming its suitability for deployment in diverse urban environments.</p>

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Aqnet adaptive mesh attention framework for spatiotemporal air quality prediction in diverse urban environments

  • 期刊论文

  • 发表日期：

    2026年1月19日

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  • Prashaya Fusiripong,
  • Herison Surbakti

摘要

Urban air quality forecasting is vital for managing pollution exposure and protecting public health. This study introduces AQNet, a spatiotemporal deep learning framework that integrates adaptive mesh-based graph construction with attention-driven temporal modeling to enhance prediction accuracy. The model is tested on the Beijing Multi-Site Air Quality Dataset containing hourly pollutant and meteorological records from 12 monitoring stations. AQNet captures spatial dependencies through graph convolution over nonuniform mesh partitions and models temporal dynamics using gated recurrent units. A cross-fusion layer integrates these representations before final prediction. The framework surpasses existing models such as those by Han et al. and Chen et al., achieving a Mean Absolute Error of 2.50, Root Mean Square Error of 3.35, and an \(R^2\) R 2 of 0.96, with precision, recall, and F1-score above 0.95. The adaptive mesh and attention modules enable efficient scaling across cities with different sensor densities, as confirmed through runtime and GPU profiling across Beijing, Delhi, Bangkok, and Kathmandu datasets. AQNet maintains high-resolution forecasting accuracy with minimal computational cost, confirming its suitability for deployment in diverse urban environments.