Abstract Environmental pollution management is one of the most important features in pollution risk assessment. Several studies have shown that exposure to particulate matter with an aerodynamic diameter of 10 μm or less, i.e. PM10, were associated to adverse health effects. To our knowledge, no study has yet investigated the modelling of PM10 frequency distribution and extreme events in the Caribbean basin. Here, the descriptive statistics and four theoretical distributions (lognormal, Weibull, Burr and stable) were used to fit the parent distribution of PM10 daily average concentrations in Guadeloupe archipelago with a database of 11 years. In order to determine the best distribution, the Kolmogorov–Smirnov statistic test (KS test) was computed as performance indicator value. With an annual average of 26.4 ± 16.1 μg/m3, the descriptive statistics highlighted that PM10 concentrations in Guadeloupe are lower than those measured in cities of Europe, Asia or Africa. Contrary to other megacities, we found that high PM10 levels in Guadeloupe are mainly due to natural large-scale sources, i.e. African dust. From May to September, i.e. high dust season, PM10 concentrations are 1.5 times larger since dust outbreaks are more frequent. A statistical stationarity threshold of 66 months is estimated using the distribution analysis. This underlines the cycle stability of African dust over this last decade. Concerning the statistical modelling, our results showed that Burr & Weibull mixture model is the best distribution to represent PM10 daily average concentrations with a first statistical behavior corresponding to the low dust season and an another to the high dust season. By analysing the extreme events statistic with the classical power-law distribution, we observed that Burr & Weibull mixture model could also improve the modelling of these events. In summary, the Burr & Weibull mixture model is suitable to model both classical and extreme events.

中文翻译：

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瓜德罗普群岛 PM10 事件的统计行为：平稳性、建模和极端事件

摘要 环境污染管理是污染风险评估的重要特征之一。多项研究表明，暴露于空气动力学直径为 10 微米或更小的颗粒物，即 PM10，与不利的健康影响有关。据我们所知，还没有研究调查加勒比盆地 PM10 频率分布和极端事件的建模。在这里，描述性统计量和四个理论分布（对数正态分布、威布尔分布、伯尔分布和稳定分布）被用来拟合瓜德罗普群岛 PM10 日平均浓度的母分布与 11 年的数据库。为了确定最佳分布，Kolmogorov-Smirnov 统计检验（KS 检验）被计算为性能指标值。年平均值为 26.4 ± 16.1 μg/m3，描述性统计数据强调，瓜德罗普岛的 PM10 浓度低于欧洲、亚洲或非洲城市的测量值。与其他特大城市相反，我们发现瓜德罗普岛 PM10 水平高主要是由于自然大规模来源，即非洲灰尘。5月至9月，即高尘季节，由于沙尘暴发更频繁，PM10浓度增加了1.5倍。使用分布分析估计 66 个月的统计平稳性阈值。这突显了过去十年非洲尘埃的循环稳定性。关于统计建模，我们的结果表明 Burr & Weibull 混合模型是表示 PM10 日平均浓度的最佳分布，第一个统计行为对应于低沙尘季节，另一个对应高沙尘季节。通过分析具有经典幂律分布的极端事件统计量，我们观察到 Burr & Weibull 混合模型也可以改进这些事件的建模。总之，Burr & Weibull 混合模型适用于对经典事件和极端事件进行建模。