ArticleiScience2021
Fluctuations of water quality time series in rivers follow superstatistics.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- A seasonally explicit counterfactual framework reveals incomplete turbidity recovery in a tropical river seven years after a major mine-tailings disaster.Environmental monitoring and assessment · 2026Article
- Analyzing spatio-temporal dynamics of dissolved oxygen for the River Thames using superstatistical methods and machine learning.Scientific reports · 2024Article
- Research on water environmental indicators prediction method based on EEMD decomposition with CNN-BiLSTM.Scientific reports · 2024Article
- Video-based marker-free tracking and multi-scale analysis of mouse locomotor activity and behavioral aspects in an open field arena: A perspective approach to the quantification of complex gait disturbances associated with Alzheimer's disease.Frontiers in neuroinformatics · 2023Article
- Monitoring water quality: A citizen science success story.iScience · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Superstatistics is a general method from nonequilibrium statistical physics which has been applied to a variety of complex systems, ranging from hydrodynamic turbulence to traffic delays and air pollution dynamics. Here, we investigate water quality time series (such as dissolved oxygen concentrations and electrical conductivity) as measured in rivers and provide evidence that they exhibit superstatistical behavior. Our main example is time series as recorded in the River Chess in South East England. Specifically, we use seasonal detrending and empirical mode decomposition to separate trends from fluctuations for the measured data. With either detrending method, we observe heavy-tailed fluctuation distributions, which are well described by log-normal superstatistics for dissolved oxygen. Contrarily, we find a double peaked non-standard superstatistics for the electrical conductivity data, which we model using two combined
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Registered trials
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