# Deep Spatio-Temporal Neural Network for Air Quality Reanalysis

arXiv: https://arxiv.org/abs/2502.11941 · HTML: https://mlsanity.com/paper/2502.11941/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Spatial-Temporal Transformer Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2001.02908/) (2020)
- [Variational AutoEncoder for Reference based Image Super-Resolution](https://mlsanity.com/paper/2106.04090/) (2021)
- [Image Super-Resolution via Attention based Back Projection Networks](https://mlsanity.com/paper/1910.04476/) (2019)
- [Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder](https://mlsanity.com/paper/2004.12811/) (2020)

## Built on by (most cited first, 4 of 4)

- [TopoFlow: Topography-aware Pollutant Flow Learning for High-Resolution Air Quality Prediction](https://mlsanity.com/paper/2602.16821/) (2026)
- [Beyond the Hype: Comparing Lightweight and Deep Learning Models for Air Quality Forecasting](https://mlsanity.com/paper/2512.09076/) (2025)
- [Cross-Resolution Attention Network for High-Resolution PM2.5 Prediction](https://mlsanity.com/paper/2603.11725/) (2026)
- [Interpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Operational Time-Series Models](https://mlsanity.com/paper/2603.25495/) (2026)

## Abstract (arXiv)

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.