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Analysis of Ozone Concentrations Using Probability Distributions
Ozone: Science & Engineering ( IF 2.1 ) Pub Date : 2020-03-18 , DOI: 10.1080/01919512.2020.1736987
Amaury de Souza 1 , Flavio Aristone 1 , Widinei A. Fernandes 1 , Ana Paula Garcia Oliveira 2 , Zaccheus Olaofe 3 , Marcel Carvalho Abreu 4 , José Francisco de Oliveira Junior 5 , Guilherme Cavazzana 6 , Cicero Manoel dos Santos 7 , Ivana Pobocikova 8
Affiliation  

ABSTRACT This present study aims to evaluate the stratospheric ozone that was continuously measured during 2016 over Campo Grande, the capital of South Mato Grosso state, Brazil. To determine the best-adjusted distribution describing the ozone (O3) co-generation data in Campo Grande, 15 functions were used while modeling the numerical results. Five sets of data were used: the entire year, spring (September to December), summer (December to March, high solar radiation data), autumn (March to June), and winter (June to September, low solar radiation data) to study the seasonal variation in the statistical behavior of the probability distribution functions. The distribution performances are evaluated using three tests of quality, namely Kolmogorov–Smirnov (K-S), Anderson–Darling (A-D), and Chi-square tests. Finally, all the results of the fitted quality tests have been compared. It has been observed that the generalized extreme value distribution provides a good fit all along the year, while for specific seasons the best distributions vary. The best distributions, according to the seasons, are Gamma 3P for the winter, lognormal 3P for spring, Weibull for summer and Gamma 3P for autumn, respectively. There was a coincidence in the probability distribution function adjustment in winter and autumn, period with lower O3 concentrations, kurtosis, and skewness. In the summer and spring, it was observed higher concentrations of O3, kurtosis, and asymmetry and different probability distribution functions.

中文翻译:

使用概率分布分析臭氧浓度

摘要 本研究旨在评估 2016 年在巴西南马托格罗索州首府坎波格兰德上空连续测量的平流层臭氧。为了确定描述坎波格兰德臭氧 (O3) 热电联产数据的最佳调整分布,在对数值结果进行建模时使用了 15 个函数。使用了五组数据:全年,春季(9-12月),夏季(12-3月,高太阳辐射数据),秋季(3-6月),冬季(6-9月,低太阳辐射数据)研究概率分布函数统计行为的季节性变化。分布性能使用三种质量检验进行评估,即 Kolmogorov-Smirnov (KS)、Anderson-Darling (AD) 和卡方检验。最后,已比较所有拟合质量测试的结果。已经观察到,广义极值分布提供了全年的良好拟合,而对于特定季节,最佳分布各不相同。根据季节,最佳分布分别是冬季的 Gamma 3P、春季的对数正态 3P、夏季的 Weibull 和秋季的 Gamma 3P。冬季和秋季,O3 浓度、峰度和偏度较低的时期,概率分布函数的调整存在巧合。在夏季和春季,观察到较高的 O3 浓度、峰度和不对称性以及不同的概率分布函数。而对于特定季节,最佳分布各不相同。根据季节,最佳分布分别是冬季的 Gamma 3P、春季的对数正态 3P、夏季的 Weibull 和秋季的 Gamma 3P。冬季和秋季,O3 浓度、峰度和偏度较低的时期,概率分布函数的调整存在巧合。在夏季和春季,观察到较高的 O3 浓度、峰度和不对称性以及不同的概率分布函数。而对于特定季节,最佳分布各不相同。根据季节,最佳分布分别是冬季的 Gamma 3P、春季的对数正态 3P、夏季的 Weibull 和秋季的 Gamma 3P。冬季和秋季,O3 浓度、峰度和偏度较低的时期,概率分布函数的调整存在巧合。在夏季和春季,观察到较高的 O3 浓度、峰度和不对称性以及不同的概率分布函数。和偏度。在夏季和春季,观察到较高的 O3 浓度、峰度和不对称性以及不同的概率分布函数。和偏度。在夏季和春季,观察到较高的 O3 浓度、峰度和不对称性以及不同的概率分布函数。
更新日期:2020-03-18
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