← 研踪 PaperTrailarXiv:2511.00532 · v2
AQBuc: Air Pollution Forecasting in Bucharest
首次提交 2025-11-01 20:24
版本更新 2026-11-01 06:01
雷达观测 2026-11-04 23:43
摘要
In recent years, air pollution, especially the particulate matter $2.5\,μ\text{m}$ (PM2.5), has become a growing concern mainly in urban areas. Exposure to air pollution is linked to the development of numerous health conditions, such as the aggravation of respiratory diseases, cardiovascular disorders, impaired lung function, and even cancer or early death. Forecasting future levels of PM2.5 has become increasingly important, as it can provide early warnings and help prevent diseases. We propose AQBuc, a dataset for predicting PM2.5 pollution in Bucharest, Romania, introducing an understudied location, as most work in this field has focused on Asian cities. Our dataset allows us to benchmark models for predicting future levels of PM2.5 across various time horizons. We evaluate a broad range of methods, including machine learning models, such as linear regression and ensemble-based methods, deep learning models based on recurrent networks and transformers, and large language models (LLMs). Specialized time-series models handle temporal data better, especially on longer horizons, while tree ensembles are more cost-effective for short horizons. Meanwhile, LLMs struggle to capture continuous sensor dynamics without specialized architectural changes.
作者备注：15 pages, 3 figures, Accepted at ICONIP 2026
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v2 · 2026-11-01 06:01 · 最新观测版本
AQBuc: Air Pollution Forecasting in Bucharest
In recent years, air pollution, especially the particulate matter $2.5\,μ\text{m}$ (PM2.5), has become a growing concern mainly in urban areas. Exposure to air pollution is linked to the development of numerous health conditions, such as the aggravation of respiratory diseases, cardiovascular disorders, impaired lung function, and even cancer or early death. Forecasting future levels of PM2.5 has become increasingly important, as it can provide early warnings and help prevent diseases. We propose AQBuc, a dataset for predicting PM2.5 pollution in Bucharest, Romania, introducing an understudied location, as most work in this field has focused on Asian cities. Our dataset allows us to benchmark models for predicting future levels of PM2.5 across various time horizons. We evaluate a broad range of methods, including machine learning models, such as linear regression and ensemble-based methods, deep learning models based on recurrent networks and transformers, and large language models (LLMs). Specialized time-series models handle temporal data better, especially on longer horizons, while tree ensembles are more cost-effective for short horizons. Meanwhile, LLMs struggle to capture continuous sensor dynamics without specialized architectural changes.
查看此版本解读 →查看此版本原文 ↗