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/.

翻译：城市中的二氧化氮（$NO_2$）是燃烧相关空气污染的关键指标，且在城区内表现出显著的时空变化性。本研究提出一种基于卫星的框架，利用Sentinel-5P/TROPOMI的对流层柱浓度观测数据，追踪厄瓜多尔瓜亚斯省的城市$NO_2$污染。该方法不依赖于估算地表浓度，而是强调使用稳健的分布指标——包括中位数及上尾百分位数（$P_{90}$、$P_{95}$和$P_{99}$）——来表征省级行政区尺度下的背景条件与局部污染极端值。通过整合多年卫星观测数据的年度聚合结果，并采用无监督的K均值聚类分析，无需预设阈值即可识别典型的污染模式。结果显示，高度城市化区域始终呈现出更高的$NO_2$极端值与更强的变异性，而城市化程度较低的区域则表现为更低的污染水平与更均匀的空间分布。该研究提出的框架仅依赖卫星观测，即可为数据稀缺区域的城市空气质量评估提供一种可解释且可扩展的工具。相关实现代码已在GitHub上公开，可通过 https://hvelesaca.github.io/sentinel-5P-clustering/ 获取。