mlsanityData to 7 Oct 2026

Machine Learning for Urban Air Quality Analytics: A Survey

arXiv 2310.09620 ↗First posted 14 Oct 2023cs.LGcited by 8

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Abstract (arXiv)

The increasing air pollution poses an urgent global concern with far-reaching consequences, such as premature mortality and reduced crop yield, which significantly impact various aspects of our daily lives. Accurate and timely analysis of air pollution is crucial for understanding its underlying mechanisms and implementing necessary precautions to mitigate potential socio-economic losses. Traditional analytical methodologies, such as atmospheric modeling, heavily rely on domain expertise and often make simplified assumptions that may not be applicable to complex air pollution problems. In contrast, Machine Learning (ML) models are able to capture the intrinsic physical and chemical rules by automatically learning from a large amount of historical observational data, showing great promise in various air quality analytical tasks. In this article, we present a comprehensive survey of ML-based air quality analytics, following a roadmap spanning from data acquisition to pre-processing, and encompassing various analytical tasks such as pollution pattern mining, air quality inference, and forecasting. Moreover, we offer a systematic categorization and summary of existing methodologies and applications, while also providing a list of publicly available air quality datasets to ease the research in this direction. Finally, we identify several promising future research directions. This survey can serve as a valuable resource for professionals seeking suitable solutions for their specific challenges and advancing their research at the cutting edge.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 39 of its 158 references that are in the map. First posted on arXiv 14 Oct 2023.

Builds on

39 of 158 references.

References · 39

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27 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 1; 2026: 1.

8 papers.

  1. 2024Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systemscited by 25
  2. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
  3. 2024Spatio-Temporal Field Neural Networks for Air Quality Inferencecited by 12
  4. 2024NRFormer: Nationwide Nuclear Radiation Forecasting with Spatio-Temporal Transformercited by 4
  5. 2024RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogrammingcited by 2
  6. 2024Spatio-Temporal Forecasting of PM2.5 via Spatial-Diffusion guided Encoder-Decoder Architecturecited by 0
  7. 2025Inductive Spatio-Temporal Kriging with Physics-Guided Increment Training Strategy for Air Quality Inferencecited by 0
  8. 2026A temporal deep learning framework for calibration of low-cost air quality sensorscited by 0