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Predicting Spatiotemporal Mobile Sensing-Based PM2.5 Concentrations Using Low-Rank Adapted Spatially Attentive Graph Neural Network — arXiv:2609.04693

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.04693
License (as archived): CC0-1.0
Archived: 2026-10-16T22:20:51.059Z
SHA-256 of the archived text: 35cf96f068d10db37fa6c861ecf73860619dddadded7ef20cf1f2b74b9412212
Stable URL of this page: https://metraipolis.com/documento/dc_81af7f112ebe

How to cite

Predicting Spatiotemporal Mobile Sensing-Based PM2.5 Concentrations Using Low-Rank Adapted Spatially Attentive Graph Neural Network — arXiv:2609.04693 — arXiv (en), https://arxiv.org/abs/2609.04693 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_81af7f112ebe (Metraipolis archive dc_81af7f112ebe, sha256 35cf96f068d10db37fa6c861ecf73860619dddadded7ef20cf1f2b74b9412212)

BibTeX

@misc{dc81af7f112ebe,
  title = {Predicting Spatiotemporal Mobile Sensing-Based PM2.5 Concentrations Using Low-Rank Adapted Spatially Attentive Graph Neural Network — arXiv:2609.04693},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.04693},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_81af7f112ebe; sha256 35cf96f068d10db37fa6c861ecf73860619dddadded7ef20cf1f2b74b9412212},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.04693. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Urban air quality can vary significantly along transit corridors, necessitating high-resolution monitoring. This work introduces a novel mobile-sensing dataset from Surat, Gujarat, India, comprising PM$*{2.5}$ concentrations, meteorological variables (temperature, humidity, wind speed, wind direction), and land-use features. To represent the spatiotemporal data as a graph, two node-definition strategies were used: (i) uniform segmentation (200--400~m intervals) and (ii) DBSCAN clustering to adaptively group dense observations. For each node, rolling mean and standard deviation of meteorological variables were computed. To model this high-dimensional data, we propose a SA-GNN for fine-grained, short-term PM$*{2.5}$ forecasting and hotspot identification. We compared SA-GNN with LSTM, RNN, GRU, and ANN models. These models performed well on low-resolution data but had difficulty capturing rapidly changing patterns in urban air quality. SA-GNN employs cluster-specific GRUs to capture localized temporal dependencies and a Graph Attention Network to learn spatial heterogeneity. This hybrid architecture effectively models rapid fluctuations and complex spatial interactions. On our dataset, SA-GNN achieved $R^2 = 0.95$, RMSE $= 6.8$, and MAE $= 4.2~\si{\micro\gram\per\meter\cubed}$, outperforming all baseline models. Combining spatial clustering with adaptive attention significantly improves forecasting, enabling real-time, fine-grained monitoring and supporting personalized exposure tracking and timely alerts for healthier cities.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-07 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.04693 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T22:18:11.378Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).