The statistical behavior of PM10 events over guadeloupean archipelago: Stationarity, modelling and extreme events

Multifractal characterisation of particulate matter (PM10) time series in the Caribbean basin using visibility graphs

“…Table 1 shows that ∆ D q value is higher for low dust season, followed by "overall" and high dust season. These results are consistent with those previously found by Plocoste et al (2020b) with descriptive statistics. Indeed, by computing the kurtosis parameter (K) which is a useful indicator of intermittency in pollution studies (Windsor and Toumi, 2001;Plocoste et al, 2018), they found K Low (33.0) > K Overall (11.8) > K High (5.9).…”

“…Overall, one can notice that spectra are asymmetrical in shape with a i.e. the high dust season (Prospero et al, 2014;Velasco-Merino et al, 2018;Euphrasie-Clotilde et al, 2020;Plocoste et al, 2020b).…”

Application of Statistical Distributions to PM10 Concentrations: Van, Türkiye

“…Concentrations of air pollutants can be modeled with different distribution functions as well. Each area may have its own characteristic depending on various factors such as emission levels or meteorological conditions (Plocoste et al, 2020). Consequently, motivated by the reasons mentioned above modeling the distribution of the PM 10 the concentration of Van province is beneficial in controlling risks arising from air pollutants, which can be harmful to the environment and human health.…”

“…Amongst the other distribution functions, the lognormal distribution stands out in modeling air pollutant levels. In addition, the distributions such as the Weibull, Gamma, Rayleigh, and Gumbel are utilized in modeling PM 10 concentrations in the literature (See in Mijić et al 2009;Yusof et al 2010;Plocoste et al 2020). In this study, the lognormal, Gamma, and Weibull distributions are considered in the modeling PM 10 concentrations of Van province due to their well-acceptance and satisfactory performances in modeling PM 10 levels.…”