Deep Spatio-Temporal Neural Network for Air Quality Reanalysis

• URL: http://arxiv.org/abs/2502.11941v1
• Date: Mon, 17 Feb 2025 15:52:22 GMT
• Title: Deep Spatio-Temporal Neural Network for Air Quality Reanalysis
• Authors: Ammar Kheder, Benjamin Foreback, Lili Wang, Zhi-Song Liu, Michael Boy,
• Abstract summary: We propose AQ-Net, atemporal reanalysis model for both observed and unobserved stations in the near future. To learn encoding fine-grained spatial air quality estimation, we incorporate AQ-Net with the neural kNN.
• Score: 17.089907362560197
• License:
• Abstract: Air quality prediction is key to mitigating health impacts and guiding decisions, yet existing models tend to focus on temporal trends while overlooking spatial generalization. We propose AQ-Net, a spatiotemporal reanalysis model for both observed and unobserved stations in the near future. AQ-Net utilizes the LSTM and multi-head attention for the temporal regression. We also propose a cyclic encoding technique to ensure continuous time representation. To learn fine-grained spatial air quality estimation, we incorporate AQ-Net with the neural kNN to explore feature-based interpolation, such that we can fill the spatial gaps given coarse observation stations. To demonstrate the efficiency of our model for spatiotemporal reanalysis, we use data from 2013-2017 collected in northern China for PM2.5 analysis. Extensive experiments show that AQ-Net excels in air quality reanalysis, highlighting the potential of hybrid spatio-temporal models to better capture environmental dynamics, especially in urban areas where both spatial and temporal variability are critical.

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