arXiv AI
Jul 29
Long-Term PM2.5 Forecasting Using a DTW-Enhanced CNN-GRU Model
arXiv:2510. 22863v2 Announce Type: replace-cross Abstract: Reliable long-term forecasting of PM2.
From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations
arXiv:2607. 25687v1 Announce Type: cross Abstract: Full-field reconstruction of air pollution is essential for evaluating pollution exposure and supporting public health decision-making.
Related stories
arXiv Machine Learning
Jun 9
TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation
arXiv:2606. 07569v1 Announce Type: new Abstract: Accurate carbon emission monitoring is critical for climate policy and emerging regulatory mechanisms such as the EU Carbon Border Adjustment Mechanism, yet city-level high-frequency monitoring data remain extremely scarce, severely limiting data-hungry deep learning models.
arXiv Machine Learning
Jun 5
Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia
arXiv:2606. 06174v1 Announce Type: new Abstract: Childhood asthma is a common illness exacerbated by air pollution as well as meteorological and neighborhood-level socioeconomic factors.
arXiv Machine Learning
Jul 23
Air Quality Arena: A Large-Scale Multi-Region Ground Monitoring Dataset and Benchmark for Air Quality Forecasting with Time-Series Foundation Models
arXiv:2607. 19381v1 Announce Type: new Abstract: Air pollution causes an estimated 7.
arXiv AI
Jun 11
FOCUS on Contamination: Hydrology-Informed Noise-Aware Learning for Geospatial PFAS Mapping
arXiv:2502. 14894v5 Announce Type: replace-cross Abstract: Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with significant public health impacts, yet large-scale monitoring remains severely limited due to the high cost and logistical challenges of field sampling.
Hugging Face Trending Papers
Aug 12
Air Quality Station Simulation via LSTM and Attention-Based Modelling
Poor air quality in urban areas is driven by a complex chain of processes and presents a significant public health concern. To better understand and control the mechanisms that determine air quality, cities deploy networks of measurement stations, and launch initiatives for collecting denser data about the concentration of pollutants in the atmosphere.
arXiv AI
Jul 10
GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance
arXiv:2601. 11440v3 Announce Type: replace-cross Abstract: Urban wind flow reconstruction is essential for assessing air quality, heat dispersion, and pedestrian comfort, yet remains challenging when only sparse sensor data are available.
arXiv Machine Learning
Aug 13
Air Quality Station Simulation via LSTM and Attention-Based Modelling
arXiv:2608. 11839v1 Announce Type: new Abstract: Poor air quality in urban areas is driven by a complex chain of processes and presents a significant public health concern.
arXiv AI
2d ago
Benchmarking Generative Models for Weather Data Assimilation on Real Station Observations
This study introduces the first controlled benchmark of generative models for weather data assimilation using real station observations from 11,849 NOAA MADIS stations across the U.S. It evaluates key design choices—diffusion vs. flow matching, pixel vs. latent-space formulations, and inference-time conditioning strategies—against a classical 3D-Var baseline. The benchmark finds that learned generative priors and full-gradient guidance improve RMSE over ERA5, while other design variations offer minimal benefit, especially under sparse observation conditions.
arXiv AI
Aug 11
AirFlow: Context Preserving and Multi-Rate State Modeling for Air Quality Forecasting
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.
arXiv AI
Jun 9
AQIFormer: A Transformer-Based Multi-View Architecture for Cross-City Air Quality Classification
arXiv:2606. 07648v1 Announce Type: cross Abstract: Air pollution represents one of the most critical environmental and public health challenges globally, with traditional sensor-based monitoring systems facing significant scalability and economic constraints.
arXiv AI
Sep 7
Predicting Spatiotemporal Mobile Sensing-Based PM2.5 Concentrations Using Low-Rank Adapted Spatially Attentive Graph Neural Network
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.