用卫星数据追踪厄瓜多尔城市氮氧化物污染，识别高污染区域与极端值模式。
Tracking Urban Atmospheric Pollutants using Sentinel-5P Satellite Data
- 以哨兵5号卫星的对流层柱浓度数据为基础，用中位数和尾部百分位数刻画污染分布。
- 发现高度城市化地区极端氮氧化物浓度更高、波动更大，低度城市化地区则更稳定均匀。
- 无需预设阈值，通过聚类分析自动识别污染模式，适合数据缺乏地区的空气质量评估。
城市氮氧化物（NO₂）是燃烧相关空气污染的关键指标，具有显著的空间和时间变异性。本研究利用厄瓜多尔瓜亚斯省的哨兵5号/TROPOMI对流层柱观测数据，提出一种基于卫星的城区NO₂污染追踪框架。方法不直接估算地表浓度，而是强调稳健的分布特征指标，包括中位数及上尾百分位数（P90、P95、P99），用于刻画县级尺度下的背景水平和局部污染极端情况。多年卫星数据按年聚合，并采用无监督K-means聚类分析，无需预设阈值即可识别典型的污染状态。结果表明，高度城市化县份持续表现出更高的极端NO₂值和更大的变异性，而低度城市化区域则呈现较低且更均一的模式。该方法为数据匮乏地区仅依赖卫星观测进行城市空气质量评估提供了一种可解释、可扩展的工具。实现代码已公开于GitHub：https://hvelesaca.github.io/sentinel-5P-clustering/。
原文摘要 · Abstract (English)
Urban nitrogen dioxide ($NO_2$) is a key indicator of combustion-related air pollution and exhibits strong spatial and temporal variability in cities. This study presents a satellite-based framework for tracking urban $NO_2$ pollution using tropospheric column observations from Sentinel-5P/TROPOMI over Guayas Province, Ecuador. Rather than estimating surface concentrations, the methodology emphasizes robust distributional metrics, including the median and upper-tail percentiles ($P_{90}$, $P_{95}$, and $P_{99}$), to characterize background conditions and localized pollution extremes at the canton scale. Multi-year satellite observations are aggregated annually and analyzed using unsupervised K-means clustering to identify characteristic pollution regimes without predefined thresholds. Results show that highly urbanized cantons consistently exhibit elevated extreme $NO_2$ values and greater variability, while less urbanized areas display lower and more homogeneous patterns. The proposed approach provides an interpretable and scalable tool for urban air-quality assessment in data-scarce regions using satellite observations alone. The implementation is publicly available on GitHub https://hvelesaca.github.io/sentinel-5P-clustering/.
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