This study presents a new mobile‑sensing dataset from Surat, India, capturing PM2.5 concentrations along with meteorological and land‑use variables. The authors model the data as a graph using two node‑definition strategies—uniform segmentation and DBSCAN clustering—and introduce a Spatially Attentive Graph Neural Network (SA‑GNN) that combines cluster‑specific GRUs with a Graph Attention Network to forecast fine‑grained, short‑term PM2.5 levels. SA‑GNN outperforms traditional LSTM, RNN, GRU, and ANN baselines, achieving an R² of 0.95, RMSE of 6.8, and MAE of 4.2 µg/m³ on the dataset.
By Om Chiddarwar, Priyanka Mandal, Praveen Kumar Chandaliya, Shriniwas Arkatkar
arXiv:2609.24634v1 Announce Type: new
Abstract: Nitrogen dioxide (NO2) is a common air pollutant, released into the atmosphere through the incomplete burning of fossil fuels, and associated with resp...
By Linus Scheibenreif, Konrad Schindler
arXiv:2605.03795v3 Announce Type: replace-cross
Abstract: Urban air quality forecasting is challenging because pollutant concentrations are nonlinear, nonstationary, spatiotemporally dependent, and o...
By Nourin Jahan, Muhammed Navas T, Tanujit Chakraborty, Madhurima Panja
arXiv:2606. 24347v1 Announce Type: new Abstract: Accurate short-term PM$_{2.
By Cheng Huang, Muyao Guan, Jairus Yougui Railey, Ning Xu, Honghui Xu, Changjiang Zhang, Zhen Zhang, Shiqing Zhang, Cong Bai
arXiv:2511. 19277v2 Announce Type: replace Abstract: Global greenhouse gas emissions estimates are essential for monitoring and mitigation planning.
By Brittany V. Lancellotti, Jordan M. Malof, Aaron Davitt, Gavin McCormick, Shelby Anderson, Pol Carb\'o-Mestre, Gary Collins, Verity Crane, Zoheyr Doctor, George Ebri, Kevin Foster, Trey M. Gowdy, Michael Guzzardi, John Heal, Heather Hunter, David Kroodsma, Khandekar Mahammad Galib, Paul J. Markakis, Gavin McDonald, Daniel P. Moore, Eric D. Nguyen, Sabina Parvu, Michael Pekala, Christine D. Piatko, Amy Piscopo, Mark Powell, Krsna Raniga, Elizabeth P. Reilly, Michael Robinette, Ishan Saraswat, Patrick Sicurello, Isabella S\"oldner-Rembold, Raymond Song, Charlotte Underwood, Kyle Bradbury
arXiv:2607. 19381v1 Announce Type: new Abstract: Air pollution causes an estimated 7.
By Rishi Bharadwaj, Manik Gupta, Pandarasamy Arjunan
arXiv:2608. 12001v1 Announce Type: cross Abstract: Rapid urbanization in Dhaka District, Bangladesh has triggered substantial alterations in land use and environmental conditions, necessitating systematic monitoring for informed urban planning and ecological sustainability.
By Muhammad Masud Tarek, Md. Alamgir Hossain, Md. Samiul Islam, Muntasir Hasan Kanchan
The paper introduces a multi‑sensor deep learning framework for mapping informal settlements in Córdoba, Argentina, using high‑resolution PlanetScope multispectral imagery, COSMO‑SkyMed SAR data, and medium‑resolution PRISMA hyperspectral observations. It compares SAR‑only, MS‑only, and various fusion strategies (early, middle, late) and finds that late fusion with hyperspectral data (LF+HS) delivers the best balance of classification accuracy and spatial precision. The study also demonstrates that detections outside official polygons align with broader municipal vulnerability layers and that identified settlements show higher surface temperatures during a heatwave, highlighting localized heat amplification.
By Luigi Russo, Anabella Ferral, Silvia Liberata Ullo, Paolo Gamba
arXiv:2608. 09775v1 Announce Type: new Abstract: Accurate air quality forecasting is essential for public health and urban environmental management, but remains challenging because pollutant channels differ in periodicity and distribution drift, while their concentration trajectories contain both multi-scale dependencies and rapid changes.
By Fan Yang, Nan Chen, Yijie Dong, Yuchen Zhang, Wei Zhang
arXiv:2607. 17037v1 Announce Type: new Abstract: High-resolution atmospheric data are required to resolve mesoscale and localized meteorological structures, however such datasets remain limited in many regions of the world.
By Evangelia Rafaela Frastali, Achyut Paudel, Maryam Golbazi, Frank Liu
arXiv:2609.39833v1 Announce Type: new
Abstract: Extreme rainfall events are increasing in intensity and frequency as climate change accelerates. While kilometer-scale precipitation forecasts are crit...
By Pierre-Louis Lemaire, Luca Schmidt, Wietze Suijker, Alex Hernandez-Garcia, David Rolnick
DeepC4 is a deep learning-based spatial disaggregation method that uses local census statistics as cluster-level constraints and incorporates multiple conditional label relationships in a multitask learning framework. Applied to Rwandan urban morphology, it achieves macro‑F1 scores of 0.63, 0.78, and 0.45 for roof, wall, and height prediction, respectively, and estimates national dwelling and occupant counts within about 1.1% error compared to census records. The approach outperforms existing GEM and METEOR methods and covers 32‑49% more 500‑meter grid pixels across provinces.
By Joshua Dimasaka, Christian Gei{\ss}, Emily So