The catastrophic events are characterized by “low frequency and high severity”. Nevertheless, during the last decades, both the frequency and the magnitude of these events have been significantly rising worldwide. In 2021, the European Commission adopted a new Strategy on Adaptation to Climate Change aiming to reinforce the adaptive capacity and minimize vulnerability to the effects of climate change and natural catastrophes. In a continuous time framework over an infinite horizon, we solve in closed form the problem of a representative consumer who holds a production technology (firm) and who optimises with respect to both the intertemporal consumption and the mix between an insurance (adaptation) against the magnitude of the catastrophic losses, and an effort strategy (mitigation) aimed at reducing the frequency of such losses. The catastrophic events are modelled as a Poisson jump process. We then propose some numerical simulations calibrated to the country-specific data of the five main European economies (Germany, France, Italy, Spain, and Netherlands). Our model demonstrates that an optimal mix of mitigation/effort strategies allows to reduce the volatility of the economic growth rate, even if its level may be lowered due to the effort costs. Simulations allow us to also conclude that different countries must optimally react differently to catastrophes, which means that a one-for-all policy does not seem to be optimal.
The development of subsurface exploitation projects, including CO2 storage processes, requires a large number of numerical simulations where fluid and transport in porous media are coupled, at a certain stage, with the solution of the Biot problem, for instance, to evaluate the potential of faults destabilization and associated induced seismicity. These simulations are computationally expensive, therefore we consider the possibility of using a surrogate for full-physics simulations. We follow a data-driven approach based on neural networks, denoted as deep learning reduced order modeling (DL-ROM), to build a reduced model where the reduced space is identified using an autoencoder trained on full-physics numerical solutions. In this work, we consider physical uncertain parameters, such as Young's moduli, permeabilities, and fault transmissibility, as well as process controls, such as the injection rate of CO2, and we train the DL-ROM using numerical simulations of reservoir operations. We apply the surrogate modeling to two synthetic problems developed in the context of underground CO2 storage, where the injection sites are characterized by a sloping fault that could be destabilized during operations. High-fidelity simulations are performed using a one-way coupled strategy: the flow in porous media is simulated with a finite volume-based commercial software for both cases, while the solid problem is solved either with commercial finite element-based software or through a multi-point stress finite volume approximation available in the open-source library PorePy. We investigate the capability of the reduced model to accurately reproduce new scenarios by comparing the outcome with a standard numerical solution. We can observe how the DL-ROM can be used in place of full-physics modeling to rapidly compute the stress state along a fault and the characteristic quantities used to estimate whether subsurface operations can destabilize or not the fault itself. After training, the DL-ROM is computationally inexpensive, enabling multi-query analysis for statistical investigations on fault stability. Even though this is not the first example ofa surrogate model for fault stability problems, to the best of the authors’ knowledge, this is at least one of the first examples of a data-driven DL-ROM used in this context.
Coastal aquifers serve as a lifeline for agriculture, drinking water and ecosystems. However, they are increasingly under threat worldwide from seawater intrusion, primarily driven by excessive groundwater extraction, intensified land use, population growth, and climate change, including sea-level rise. This study applies an integrated, multi-seasonal hydrochemical and isotopic approach to assess groundwater and surface water quality in the Metaponto coastal plain (southern Italy). Groundwater and surface water were sampled seasonally over one year and analysed for major ions, trace metals, stable isotopes (delta O-18, delta H-2), and radioactive isotopes (H-3, C-14), supported by hydrochemical diagrams, ionic deviation analysis, seawater fraction (f(sea)), and multivariate statistics. Electrical conductivity in groundwater ranged from freshwater values (<1000 mu S/cm) inland to >10,000 mu S/cm in coastal and deep wells, with maximum seawater fractions reaching similar to 0.41. Hydrochemical facies evolve from Ca-HCO3 inland to Na-Cl dominance near the coast, reflecting salinization driven by seawater mixing and cation exchange. Stable isotopes indicate predominantly meteoric recharge, while enriched delta O-18 and delta H-2 values (up to -2.5 parts per thousand and -15 parts per thousand, respectively) in coastal and deep groundwater reflect evaporation and saline mixing. Tritium (<= 0.5-4 TU) and radiocarbon ages (<1500 to >10,000 years BP) reveal a vertically stratified aquifer, with modern shallow groundwater overlying confined paleo-groundwater. Despite elevated salinity, Heavy Metal Pollution Index (HPI) values remained low (approximate to 10-15), indicating negligible metal contamination. These results demonstrate that groundwater quality degradation in the Metaponto coastal aquifer is primarily controlled by salinization rather than metal pollution and highlight the value of integrated hydrochemical-isotopic tools for identifying aquifer vulnerability and supporting sustainable groundwater management in coastal plains.
Volatile organic compounds (VOCs) are common environmental and occupational pollutants, yet evidence on their health impacts remains fragmented. This umbrella review aimed to integrate and evaluate the credibility of associations between exposures to major VOCs and adverse health outcomes. We conducted an umbrella review of meta-analyses, systematically searching PubMed/MEDLINE, Embase, CINAHL, and Google Scholar from inception to May 30, 2025. Eligible studies were meta-analyses of observational studies (cohort, case-control, and cross-sectional) that investigated exposure to VOCs and health outcomes. For each association, effect sizes were recalculated using random-effects models and expressed as equivalent odds ratios (eORs) with 95% confidence intervals (CIs). Methodological quality was assessed using AMSTAR 2, and the class of evidence (CE) for each association was graded using predefined criteria for observational evidence (Classes I-IV). A total of 27 meta-analyses met the inclusion criteria, covering 50 exposure-outcome associations across 35 countries. Methodological quality was varied substantially (6 high, 6 moderate, 6 low, and 9 critically low). For occupational exposure, benzene was associated with a range of outcomes, including micronucleus frequency (eOR, 2.31 [95% CI, 1.04-5.13]; CE=IV), nervous system cancers (1.21 [1.05-1.38]; CE=IV), and several solid tumors. Styrene was associated with dyschromatopsia (2.79 [1.81-4.31]; CE=IV), and formaldehyde was linked to sinonasal cancer (1.79 [1.23-2.60]; CE=IV). In environmental exposure settings, benzene was associated with childhood hematologic malignancies, asthma, and low birth weight. Xylene showed a modest association with asthma (1.04 [1.01-1.07]; CE=IV), while formaldehyde was associated with childhood asthma (1.27 [1.05-1.53]; CE=IV). Styrene exposure was also associated with autism spectrum disorder (1.37 [1.12-1.68]; CE=III). Exposure to VOCs is associated with multiple health outcomes, although the overall credibility of evidence remains limited by low methodological quality. Strengthened emission control, improved exposure assessment, and well-designed prospective biomonitoring studies are needed to clarify causal relationships. Study Registration: CRD420251240414.
