摘要：空气污染估计每年导致 790 万人过早死亡，使准确预测成为一项关键的公共卫生优先事项。机器学习正日益被用于预测空气污染水平，但现有基准在地理覆盖范围和污染物覆盖范围上仍较为狭窄，且未能在真实世界的大规模数据上评估最新一代时间序列基础模型（TSFM）。我们提出 Air Quality Arena（AQA），其中包括一个大规模、多国家、多污染物数据集（AQA-Data）以及基准测试集（AQA-Bench），以弥补这一空白。AQA 覆盖 7 个具有多样性的国家和 4 个大洲，在连续三年期间采集了 6 类主要污染物的数据，包含超过 14,000 条站点—污染物时间序列，旨在为空气质量任务提供全面的评测基准。我们利用 11 个领先的时间序列基础模型和传统基线，在该数据集上评估短期空气质量预测性能。结果表明，TSFM 是有效的零样本预测器，并且持续优于传统基线；其中，表现最佳的模型采用跨模态架构，利用视觉基础模型进行时间序列预测。AQA 已在此 HTTP 链接公开发布。

Abstract:Air pollution causes an estimated 7.9 million premature deaths annually, making accurate forecasting a critical public health priority. Machine learning is increasingly being applied to forecast air pollution levels, yet existing benchmarks remain narrow in both geographic scope and pollutant coverage, and fail to evaluate the latest generation of time series foundation models (TSFMs) on real world, large scale data. We present Air Quality Arena (AQA), a large scale multi-country and multi-pollutant dataset (AQA-Data) and benchmark (AQA-Bench) to address this gap. AQA covers 6 major pollutants over a three year period across 7 diverse countries and 4 continents, with more than 14,000 station-pollutant series, aiming to provide a comprehensive benchmark for air quality tasks. We benchmark this dataset across 11 leading time series foundation models and classical baselines to assess performance on short-term air quality forecasting. Our results demonstrate that TSFMs are effective zero-shot forecasters and consistently outperform classical baselines, with our top-performing model employing a cross-modal architecture that leverages a vision foundation model for time series forecasting. AQA is publicly released at this http URL