Given the strong impact of air quality on health, environment, and economy, Morocco has implemented an air quality network to assess air pollutants including PM₁₀ (particulate matter with a diameter less than 10 μm). This network which is composed of 29 fixed measurement stations is spatially limited and does not provide sufficient time resolution. The scarcity of measured air quality data led to seek an optimal alternative source to conduct related data-based studies. This represents the primary objective of this paper. PM₁₀ concentrations of global Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA) data (4D Variational analysis “4v” and analysis “an”), as well as regional CAMSRA data, were examined against the average daily PM₁₀ concentrations collected from six fixed Moroccan air quality measurement stations in 2016 (i.e., observation data). The verification is carried out by studying and analyzing seasonal, extreme, and annual values. The study shows a strong seasonal dependence with a positive bias in winter and a negative bias during summer. For the study of extreme values, global CAMSRA “an” and “4v” data record significant bias of approximately 184 and 161 μg/m³, respectively. However, the annual analysis shows that the CAMSRA global “an” data have the smallest average bias (20.008 μg/m³) and hence has the closest representation of observation data. We conclude that the CAMSRA global analysis data could be used to compute climatology, study trends, evaluate models, benchmark other reanalysis, or serve as boundary conditions for regional models for past periods.

鉴于空气质量对健康，环境和经济的巨大影响，摩洛哥已实施了空气质量网络以评估包括PM ₁₀（直径小于10μm的颗粒物）在内的空气污染物。由29个固定测量站组成的网络空间有限，无法提供足够的时间分辨率。所测量的空气质量数据的稀缺性导致寻求进行相关数据研究的最佳替代来源。这代表了本文的主要目标。针对每日平均PM ₁₀检查了全球哥白尼大气监测服务再分析（CAMSRA）数据（4D变异分析“ 4v”和分析“ an”）以及区域CAMSRA数据的PM ₁₀浓度2016年从六个固定的摩洛哥空气质量测量站收集的浓度（即观测数据）。通过研究和分析季节，极值和年度值来进行验证。研究显示强烈的季节依赖性，冬季为正偏见，夏季为负偏见。为了研究极值，全球CAMSRA“ an”和“ 4v”数据分别记录了大约184和161μg/ m ^3的显着偏差。但是，年度分析显示，CAMSSA全球“ an”数据的平均偏差最小（20.008μg/ m ³），因此具有最接近的观测数据表示。我们得出的结论是，CAMSSA全球分析数据可用于计算气候，研究趋势，评估模型，对其他再分析进行基准测试，或用作过去时期区域模型的边界条件。