Abstract
Abstract
Human-induced environmental change increasingly threatens the stability of socio-ecological systems. Careful statistical characterization of environmental concentrations is critical to quantify and predict the consequences of such changes on human and ecosystems conditions. However, while concentrations are naturally defined as the ratio between solute mass and solvent volume, they have rarely been treated as such, typically limiting the analysis to familiar distributions generically used for any other environmental variable. To address this gap, we propose a more general framework that leverages their definition explicitly as ratios of random variables. We show that the resulting models accurately describe the behavior of nitrate plus nitrite in US rivers and salt concentration in estuaries in the Everglades by accounting for heavy tails potentially emerging when the water volume fluctuates around low values. Models that preclude the presence of heavy tails and the related high probability of extreme concentrations could significantly undermine the accuracy of diagnostic frameworks and the effectiveness of mitigation interventions, especially for soil contamination characterized by a water volume (i.e. soil moisture) frequently approaching zero.
Funder
US National Science Foundation
Princeton University’s Dean for Research, High Meadows Environmental Institute, Andlinger Center for Energy and the Provost International Fund
Carbon Mitigation Initiative (CMI) at Princeton University, and the Moore Foundation.
Subject
Public Health, Environmental and Occupational Health,General Environmental Science,Renewable Energy, Sustainability and the Environment
Cited by
4 articles.
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