Kathmandu has one of the highest particulate matter air pollution levels in the world. However, few direct measurement data are available for long-term analyses, limiting policy interventions and public health advisories. Remote sensing-based data sets provide an alternative approach to address this issue. In this paper, we present an approach to analyze and understand the diurnal, seasonal, annual, and multi-annual variability of pollution levels based on in situ measurements of particulate matter (PM_2.5), remote sensing data based on Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra Aerosol Optical Depth (AOD), and aerosol mass concentration retrievals, as well as AOD and PM_2.5 data from Copernicus Atmosphere Monitoring Service (CAMS) and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data sets. The analysis of the direct measurements revealed distinct annual patterns, characterized by winter maxima and summer minima. With the exception of the summer monsoon season, public health guidelines are frequently exceeded significantly throughout the year, particularly in winter. The analysis furthermore pointed toward distinct daily patterns with primary maxima in the mornings, secondary maxima in the late evenings, and minima in the afternoons. The annual pattern of AOD derived from the MODIS data is markedly different from that. Due to the coarse spatial resolution and the fact that MODIS AOD is a column integrated property, it does not reflect the small scale phenomenon of the Kathmandu urban pollution pattern but instead shows a maximum in the spring. The same pattern was observed with the CAMS and MERRA-2 reanalysis AOD data, even though MERRA-2 captures pollution levels during the summer monsoon season very well. The CAMS reanalysis PM_2.5 data are generally well-aligned with the near-surface measurement data, even though they overestimate the daily and monthly maxima and do not capture the morning maxima in the diurnal course. Nevertheless, CAMS PM_2.5 data can be adjusted via linear regression to reasonably mirror the measurements. It shows that PM_2.5 concentrations in Kathmandu have increased significantly in the past decades (almost 2 μg/m³ annually), mainly after the monsoon season from September to February. Our results indicate that around 85% of all winter days in Kathmandu are categorized as “unhealthy” according to the Air Quality Index (AQI).

加德满都拥有世界上最高的颗粒物空气污染水平之一。但是，很少有直接测量数据可用于长期分析，从而限制了政策干预措施和公共卫生咨询。基于遥感的数据集提供了解决此问题的替代方法。在本文中，我们提出了一种基于颗粒物原位测量（PM _2.5），基于中等分辨率成像光谱仪（RS）的遥感数据来分析和理解污染水平的昼夜，季节，年度和多年变化的方法。MODIS）Aqua和Terra气溶胶光学深度（AOD），气溶胶质量浓度检索以及AOD和PM _2.5数据来自哥白尼大气监测局（CAMS）和研究和应用的现代时代回顾分析，版本2（MERRA-2）重新分析数据集。对直接测量的分析揭示了不同的年度模式，其特征是冬季最大值和夏季最小值。除夏季季风季节外，全年（尤其是在冬季）经常会大大超出公共卫生准则。该分析还指出了不同的每日模式，早上有一次最高值，晚上有次要最高值，下午有最低值。从MODIS数据得出的AOD年度模式与之明显不同。由于粗略的空间分辨率以及MODIS AOD是列综合属性，它没有反映加德满都城市污染格局的小规模现象，而是在春季表现出最大值。尽管MERRA-2很好地捕获了夏季风季节的污染水平，但CAMS和MERRA-2再分析AOD数据也观察到了相同的模式。CAMS再分析PM_2.5数据通常与近地表测量数据高度吻合，即使它们高估了每日和每月的最大值，也没有捕捉到昼夜过程中的早晨最大值。但是，可以通过线性回归调整CAMS PM _2.5数据，以合理反映测量结果。结果表明，在过去的几十年中，加德满都的PM _2.5浓度显着增加（每年近2μg/ m ³），主要是在9月至2月的季风季节之后。我们的结果表明，根据空气质量指数（AQI），加德满都所有冬季中约有85％被归类为“不健康”。