The Superstatistical Nature and Interoccurrence Time of Atmospheric Mercury Concentration Fluctuations

Carbone, Francesco; Bruno, Antonio Giovanni; Naccarato, Attilio; Simone, Francesco De; Gencarelli, Christian N.; Sprovieri, Francesca; Hedgecock, Ian M.; Landis, Matthew S.; Skov, Henrik; Pfaffhuber, Katrine Aspmo; Read, Katie A.; Martin, Lynwill; Angot, Hélène; Dommergue, Aurélien; Magand, Olivier; Pirrone, Nicola

doi:10.1002/2017jd027384

Cited by 6 publications

(8 citation statements)

References 65 publications

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“…The relative q-exponential fit (dashed line) obtained from Equation (8), at the maximum threshold Q reported in legend, is also shown. All the PDFs collapse on to the same q-exponential distribution characterized by an average q ≈ 1.65 ± 0.03, in a range of thresholds Q ∈ [0.3, 2.3], which, in terms of IMF periods, are T j ≈ 30 min up to T j ≈ 0.5 days, demonstrating the universality of the process as reported in [35,36]. This q-value is significant since a similar value has been reported in a different context, namely velocity fluctuations in fully-developed turbulence experiments for the inertial sub-range [35], where q ≈ 1.62 was obtained from the IOT statistics.…”

Section: Interoccurrence Times Statistics In Mesoscale Windsupporting

confidence: 62%

“…Here, the scale-dependent features of the wind speed have been using different methods, and the results compared with the classical values found in the literature. Initially, the Hurst exponent H has been evaluated by using Empirical Mode Decomposition (EMD) [30][31][32], and interoccurrence times (IOT) statistics [33][34][35][36]. The value obtained is in good agreement with the classical estimation of H obtained via the first order structure function for the inertial sub-range [35,37], without the necessity of employing the extended self-similarity (ESS) procedure [38].…”

Section: Introductionmentioning

confidence: 63%

“…A further method used to obtain information on the scaling properties of a stochastic processes, is theIOT τ analysis [33][34][35][36]. IOTs are a measure of the time between the occurrence of two or more subsequent extreme events in the data that exceed a fixed threshold Q (Figure 7, upper panel).…”

Section: Interoccurrence Times Statistics In Mesoscale Windmentioning

confidence: 99%

“…Empirical studies have shown that the PDFs of the IOTs can be described by a universal Tsallis q-exponential function, if a long-term memory exists in the data (i.e., power-law decay of the autocorrelation function) [34][35][36]82] of the form:…”

Section: Interoccurrence Times Statistics In Mesoscale Windmentioning

confidence: 99%

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Scaling Properties of Atmospheric Wind Speed in Mesoscale Range

et al. 2019

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The scaling properties of turbulent flows are well established in the inertial sub-range. However, those of the synoptic-scale motions are less known, also because of the difficult analysis of data presenting nonstationary and periodic features. Extensive analysis of experimental wind speed data, collected at the Mauna Loa Observatory of Hawaii, is performed using different methods. Empirical Mode Decomposition, interoccurrence times statistics, and arbitrary-order Hilbert spectral analysis allow to eliminate effects of large-scale modulations, and provide scaling properties of the field fluctuations (Hurst exponent, interoccurrence distribution, and intermittency correction). The obtained results suggest that the mesoscale wind dynamics owns features which are typical of the inertial sub-range turbulence, thus extending the validity of the turbulent cascade phenomenology to scales larger than observed before.

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Section: Interoccurrence Times Statistics In Mesoscale Windsupporting

confidence: 62%

Section: Introductionmentioning

confidence: 63%

Section: Interoccurrence Times Statistics In Mesoscale Windmentioning

confidence: 99%

Section: Interoccurrence Times Statistics In Mesoscale Windmentioning

confidence: 99%

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Scaling Properties of Atmospheric Wind Speed in Mesoscale Range

et al. 2019

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“…Also worth mentioning are selected entropic applications beyond BG in other areas of knowledge: complex networks [ 16 , 17 , 18 ]; economics [ 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 ]; geophysics (earthquakes, atmosphere) [ 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 ]; general and quantum chemistry [ 139 , 167 , 168 , 169 , 170 , 171 ]; hydrology and engineering (water engineering [ 172 ] and materials engineering [ 173 , 174 ]); power grids [ 175 ]; the environment [ 176 ]; medicine [ 177 , 178 , 179 ]; biology [ 180 , 181 ]; computational processing of medical images (microcalcifications in mammograms [ 182 ] and magnetic resonance for multiple sclerosis [ 183 ]) and time series (e.g., ECG in coronary disease [ 184 ] and EEG in epilepsy [ 185 , 186 ]); train delays [ 187 ]; citations of scientific publications and scientometrics [ 188 , 189 ]; global optimization techniques [ 190 ...…”

Section: Non-boltzmannian Entropy Measures and Distributionsmentioning

confidence: 99%

Beyond Boltzmann–Gibbs–Shannon in Physics and Elsewhere

Tsallis

2019

Entropy

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The pillars of contemporary theoretical physics are classical mechanics, Maxwell electromagnetism, relativity, quantum mechanics, and Boltzmann–Gibbs (BG) statistical mechanics –including its connection with thermodynamics. The BG theory describes amazingly well the thermal equilibrium of a plethora of so-called simple systems. However, BG statistical mechanics and its basic additive entropy S B G started, in recent decades, to exhibit failures or inadequacies in an increasing number of complex systems. The emergence of such intriguing features became apparent in quantum systems as well, such as black holes and other area-law-like scenarios for the von Neumann entropy. In a different arena, the efficiency of the Shannon entropy—as the BG functional is currently called in engineering and communication theory—started to be perceived as not necessarily optimal in the processing of images (e.g., medical ones) and time series (e.g., economic ones). Such is the case in the presence of generic long-range space correlations, long memory, sub-exponential sensitivity to the initial conditions (hence vanishing largest Lyapunov exponents), and similar features. Finally, we witnessed, during the last two decades, an explosion of asymptotically scale-free complex networks. This wide range of important systems eventually gave support, since 1988, to the generalization of the BG theory. Nonadditive entropies generalizing the BG one and their consequences have been introduced and intensively studied worldwide. The present review focuses on these concepts and their predictions, verifications, and applications in physics and elsewhere. Some selected examples (in quantum information, high- and low-energy physics, low-dimensional nonlinear dynamical systems, earthquakes, turbulence, long-range interacting systems, and scale-free networks) illustrate successful applications. The grounding thermodynamical framework is briefly described as well.

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First atmospheric mercury measurements at a coastal site in the Apulia region: seasonal variability and source analysis

Martino

Tassone

Angiuli

et al. 2022

Environ Sci Pollut Res

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In the framework of the Italian Special Network for Mercury (ISNM) “Reti Speciali”, a sampling campaign to monitor atmospheric mercury (Hg) was carried out at Monte Sant’Angelo (MSA). This is a coastal monitoring station in the Apulia region, representative of the Southern Adriatic area, within the Mediterranean basin. This work presents continuous Gaseous Elemental Mercury (GEM) measurements over about three years at MSA, using the Lumex RA-915AM mercury analyzer. The aim was to obtain a dataset suitable for the analysis of Hg concentrations in terms of source and transport variation. Diurnal cycles of GEM were evaluated to observe the influence of local atmospheric temperature and wind speed on potential re-emissions from surrounding sea and soil surfaces. Data were also analyzed in terms of long-range transport, using backward trajectory cluster analysis. The spatial distribution of potential sources, contributing to higher measured GEM values, was obtained employing Potential Source Contribution Function (PSCF) statistics. The influence of major Hg anthropogenic point sources, such as mining activities and coal-fuel power plants, both regionally and continentally, from mainland Europe, was observed. The role of the vegetation GEM uptake in modulating the seasonal GEM variability was also investigated. The potential of wildfire influence over the highest detected GEM levels was further examined using active fire data and the evaluation of the vegetation dryness index during the selected episodes. Graphical abstract

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The Superstatistical Nature and Interoccurrence Time of Atmospheric Mercury Concentration Fluctuations

Cited by 6 publications

References 65 publications

Scaling Properties of Atmospheric Wind Speed in Mesoscale Range

Scaling Properties of Atmospheric Wind Speed in Mesoscale Range

Beyond Boltzmann–Gibbs–Shannon in Physics and Elsewhere

First atmospheric mercury measurements at a coastal site in the Apulia region: seasonal variability and source analysis

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