Agent-based models typically treat systems in isolation, discarding environmental coupling as either computationally prohibitive or dynamically irrelevant. We demonstrate that this neglect misses essential physics: environmental degrees of freedom create memory effects that fundamentally alter system dynamics. By systematically transforming linear update rules into exact generalized Langevin equations, we show that unobserved environmental agents manifest as memory kernels whose timescales and coupling strengths are determined by the environmental interaction spectrum. Network topology shapes this memory structure in distinct ways: small-world rewiring drives dynamics toward a single dominant relaxation mode, while fragmented environments sustain multiple persistent modes corresponding to isolated subpopulations. We apply this framework to covert influence operations where adversaries manipulate target populations exclusively via environmental intermediaries. The steady-state response admits a random-walk interpretation through hitting probabilities, revealing how zealot opinions diffuse through the environment to shift system agent opinions toward the zealot mean - even when zealots never directly contact targets.
System outputs such as eigenfrequencies or strain data, often used in structural health monitoring (SHM), not only react to damage but also depend on environmental conditions. When trying to correct for these confounding effects, it is often (at least implicitly) assumed that only the expected, i.e., mean, output values are affected by environmental conditions. However, the evaluation of real-world SHM data indicates that environmental conditions may influence not only the mean output but also higher-order statistical moments, particularly the variances of and the covariances and correlations between the output quantities, such as eigenfrequencies of different modes or strain sensors at different locations. To address these issues, we discuss two approaches for identifying and quantifying multivariate confounding effects on output covariances and correlations: a random forest and a nonparametric, kernel-based approach. We compare the two competing methods on both artificial and real-world SHM data, finding that the kernel-based approach achieves higher accuracy, but the random forest produces estimates that are more robust and sometimes easier to interpret.
Micha Wehrli, Nahum Ashfield, Isabel Navarro Law
et al.
Abstract The 13th Young Environmental Scientists (YES) Meeting, held in York, United Kingdom, from the 11-15th August 2025, continued the tradition of fostering interdisciplinary collaboration and professional development among early-career researchers in environmental sciences. Organised by the SETAC Europe Student Advisory Council (SAC), this student-led conference brought together 111 participants from 22 countries, offering a dynamic platform for scientific exchange across all different disciplines that exist within SETAC. Under the theme, “Between Grinding Gears—Students and Early Career Scientists Under Pressure”, the meeting featured over 100 scientific contributions, four interactive workshops and three keynote lectures that addressed scientific challenges as well as personal- and professional realities faced by students and early career scientists. Beside the academic program, there were also social events to bolster networking among peers, strengthening the community that defines the YES meetings and securing its role as a cornerstone for early-career engagement within SETAC. This commentary highlights the scientific and social achievements of the 2025 edition, the financial support that made it possible, and the collaborative spirit that brought it to life. As we look to the future, the continued success of the YES Meeting depends on the passion and voluntary engagement of students and early-career researchers, whose energy and commitment will continue to advance this unique, student-driven platform thriving as a space for growth, collaboration, and lasting friendships.
Building upon the eco-evolutionary game dynamics framework established by Tilman et al., we investigate stochastic fluctuations in a two-strategy system incorporating environmental feedback mechanisms, where the payoff matrix exhibits population size dependence. We adopt a systematic approach which is the so-called $Ω$-expansion. When the stochastic factor is integrated, it is shown that the population size for each strategy fluctuates around the interior equilibrium of the macroscopic equations (corresponding to the deterministic model of the eco-evolutionary game) and its variance converges to a constant that is proportional to the environmental carrying capacity if the interior equilibrium is asymptotically stable. The simulation results demonstrate that the $Ω$ expansion provides a valid approximation, and the reliability of the aforementioned conclusions is verified. Therefore, analogous to Fudenberg and Harris' s stochastic replicator dynamics for infinite populations under external noise (\emph{J. Econ. Theory 57, 420-441}), the dynamic stability of the eco-evolutionary game can be extended to the stochastic regime when the environmental carrying capacity is sufficiently large.
Public opinion on environmental issues remains polarized in many countries, posing a significant barrier to the implementation of effective policies. Behind this polarization, empirical studies have identified social susceptibility, personal prejudice, and personal experience as dominant factors in opinion formation on environmental issues. However, current coupled human-environment models have not yet incorporated all three factors in polarized populations. We developed a stylized coupled human-environment model to investigate how social susceptibility, personal prejudice, and personal experience shape opinion formation and the environment in polarized populations. Using analytical and numerical methods, we characterized the conditions under which polarization, consensus, opinion changes, and cyclic dynamics emerge depending on the costs of mitigation, environmental damage, and the factors influencing opinion formation. Our model shows that prejudice is the key driver of persistent polarization, with even slightly prejudiced populations maintaining indefinite polarization independent of their level of objectivity. We predict that polarization can be reduced by decreasing the role of prejudice or increasing the willingness to consider opposing opinions. Finally, our model shows that cost reduction methods are less effective at reducing environmental impact in prejudiced populations. Our model generates thresholds for when reducing costs or emissions is more useful depending on the factors which influence the population's opinion formation. Overall, our model provides a framework for investigating the importance of cognitive and social structures in determining human-environment dynamics.
Rasha M. Reda, Eman M. Zaki, Ahmed A. A. Aioub
et al.
Abstract The aquaculture sector is facing environmental issues because of rising chemical pollution of aquatic systems, which may be caused by man-made or natural causes. The combined action of these mixtures can produce an effect that is either severalfold higher or severalfold lower than the overall toxicity of the individual components. The objective of this study is to evaluate the impact of three individual toxicants: lead (Pb 0.088 mg L−1), mercury (Hg 0.084 mg L−1), and pendimethalin (PM 0.355 mg L−1) and their combinations on the behavior, oxidative stress, immunology, biochemistry, and histopathological changes in Nile tilapia. The results show that mortality was evident across all treatment groups and was highest in the mixture (MIX) group, at 33.4%. Some of these behavioral changes included increased mucus secretion, fin rot, and dark skin coloration of the fish, all of which were evidently exacerbated in the MIX group. The erythrocyte and hemoglobin indices presented marked reductions in the MIX and PM groups in comparison to the control group. The MIX group had higher levels of serum creatinine and urea, as well as the liver enzymes alanine aminotransferase, aspartate transaminase, and alkaline phosphatase. They also had higher levels of malondialdehyde and lower levels of total antioxidant capacity, which showed oxidative stress. The stress biomarkers, cortisol and glucose, showed greater impact on the MIX and PM groups. The neurobehavioral assessment demonstrated noticeable decreased acetylcholinesterase levels in PM and MIX-exposed groups. In the serum immunological parameters, levels of nitric oxide and lysozyme were lower, especially in the MIX and PM groups. In all experimental groups, especially the MIX group, the levels of spleen pro-inflammatory cytokine genes were higher. The histopathological examination revealed marked lesions in the gills, liver, and brains in the form of inflammation, necrosis, and circulatory alterations. Among the exposed groups, the MIX group exhibited the highest histopathological changes. Collectively, our findings demonstrated significant toxic effects of heavy metals and herbicides on Nile tilapia, revealing a synergistic impact on both physiological and histopathological responses resulting from combined exposures. The data evidently indicate the ecological hazards associated with heavy metals and herbicides in aquatic ecosystems.
ABSTRACT The impacts of disasters and climate change have been particularly devastating for vulnerable and marginalised communities. This study explores the resilience of ageing individuals, persons with disabilities, and people living with HIV during and after Cyclone Idai in Zimbabwe. The cyclone, which struck in March 2019, was one of the most destructive in the Southern Hemisphere, causing widespread destruction and loss of life. It exposed critical vulnerabilities among marginalised populations, including disrupted access to healthcare, mobility barriers, and increased health risks. These groups often struggle to access essential medication and adapt to altered environments in the aftermath of disasters. Using a case study of Manicaland Province, this research highlights the specific challenges faced by these populations and underscores the importance of inclusive disaster preparedness and response strategies. The study amplifies the voices of those often overlooked in disaster risk narratives and contributes to a broader understanding of resilience in the context of climate‐related crises. As climate change intensifies, it is vital to design adaptive and equitable frameworks that address the unique needs of marginalised groups to build sustainable and resilient communities.
Urban development is the planned growth of cities that takes into account ecological issues, the needs of urban life, social and technical equipment standards, and quality of life. However, as a result of policies implemented by decision-makers and users, both planned and unplanned, urban space is expanding spatially outwards from the city, while also experiencing densification in vacant areas within the city and functional transformations in land use. This process, known as urban sprawl, has been intensely debated over the past century. Making the negative effects of urban sprawl measurable and understandable from a scientific perspective is critically important for sustainable urban planning and management. Transportation surfaces hold a significant share in the land use patterns of expanding cities in physical space, and accessibility is one of the main driving forces behind land use change. Therefore, the most significant consequence of urban sprawl is the increase in urban mobility, which is shaped by the needs of urban residents to access urban functions. This increase poses risk factors for the planning period in terms of time, cost, and especially environmental impact. Urban space has a dynamic and complex structure. Planning is based on being able to guess how this structure will change over time. At first, geometric models were used to study cities, but as time went on and the network of relationships became more complicated, more modern and technological methods were needed. Artificial Neural Networks, Support Vector Machines, Agent-Based Models, Markov Chain Models, and Cellular Automata, developed using computer-aided design technologies, can be cited as examples of these approaches. In this study, the temporal change in urban sprawl and its relationship with influencing factors will be revealed using the SLEUTH model, which is one of the cellular automata-based urban simulation models. Erzurum, one of the medium-sized metropolitan cities that gained importance after the conversion of provincial borders into municipal borders with the Metropolitan Law No. 6360, has been selected as the case study area for this research. The urban sprawl process and determining factors of Erzurum will be analyzed using the SLEUTH model. By creating a simulation model of the current situation within the specified time periods and generating future scenarios, the aim is to develop planning decisions with sustainable, ecological, and optimal size and density values.
Yvonne Jansen, Federica Bucchieri, Pierre Dragicevic
et al.
We present the results of a brainstorming exercise focused on how situated visualizations could be used to better understand the state of the environment and our personal behavioral impact on it. Specifically, we conducted a day long workshop in the French city of Bordeaux where we envisioned situated visualizations of urban environmental footprints. We explored the city and took photos and notes about possible situated visualizations of environmental footprints that could be embedded near places, people, or objects of interest. We found that our designs targeted four purposes and used four different methods that could be further explored to test situated visualizations for the protection of the environment.
Aiden Price, Kerrie Mengersen, Michael Rigby
et al.
Extreme natural hazards are increasing in frequency and intensity. These natural changes in our environment, combined with man-made pollution, have substantial economic, social and health impacts globally. The impact of the environment on human health (environmental health) is becoming well understood in international research literature. However, there are significant barriers to understanding key characteristics of this impact, related to substantial data volumes, data access rights and the time required to compile and compare data over regions and time. This study aims to reduce these barriers in Australia by creating an open data repository of national environmental health data and presenting a methodology for the production of health outcome-weighted population vulnerability indices related to extreme heat, extreme cold and air pollution at various temporal and geographical resolutions. Current state-of-the-art methods for the calculation of vulnerability indices include equal weight percentile ranking and the use of principal component analysis (PCA). The weighted vulnerability index methodology proposed in this study offers an advantage over others in the literature by considering health outcomes in the calculation process. The resulting vulnerability percentiles more clearly align population sensitivity and adaptive capacity with health risks. The temporal and spatial resolutions of the indices enable national monitoring on a scale never before seen across Australia. Additionally, we show that a weekly temporal resolution can be used to identify spikes in vulnerability due to changes in relative national environmental exposure.
<i>Aspergillus cristatus</i> is a crucial edible fungus used in tea fermentation. In the industrial fermentation process, the fungus experiences a low to high osmotic pressure environment. To explore the law of material metabolism changes during osmotic pressure changes, NaCl was used here to construct different osmotic pressure environments. Liquid chromatography–mass spectrometry (LC–MS) combined with multivariate analysis was performed to analyze the distribution and composition of <i>A. cristatus</i> under different salt concentrations. At the same time, the in vitro antioxidant activity was evaluated. The LC–MS metabolomics analysis revealed significant differences between three <i>A. cristatus</i> mycelium samples grown on media with and without NaCl concentrations of 8% and 18%. The contents of gibberellin A3, A124, and prostaglandin A2 related to mycelial growth and those of arabitol and fructose-1,6-diphosphate related to osmotic pressure regulation were significantly reduced at high NaCl concentrations. The biosynthesis of energy-related pantothenol and pantothenic acid and antagonism-related fluvastatin, aflatoxin, and alternariol significantly increased at high NaCl concentrations. Several antioxidant capacities of <i>A. cristatus</i> mycelia were directly related to osmotic pressure and exhibited a significant downward trend with an increase in environmental osmotic pressure. The aforementioned results indicate that <i>A. cristatus</i> adapts to changes in salt concentration by adjusting their metabolite synthesis. At the same time, a unique set of strategies was developed to cope with high salt stress, including growth restriction, osmotic pressure balance, oxidative stress response, antioxidant defense, and survival competition.
Abstract The Paris Agreement advances a heterogeneous approach to international climate cooperation. Such an approach may be undermined by carbon leakage—the displacement of emissions from states with more to less stringent climate policy constraints. Border carbon adjustments offer a promising response to leakage, but they also raise concerns about their compatibility with international trade law. This Article provides a comprehensive analysis of border carbon adjustments and proposes a way to design them that balances legal, administrative, and environmental considerations.
Cross-border illegal water pollution discharge (CIPD) occurs much frequently due to weak environmental supervision and unclear environmental rights. This paper attempts to explore the impact of cross-border ecological compensation policy (CBEC) on CIPD. By using day-night difference of pollution data at administrative boundary monitoring station, illegal water pollution discharge is calculated. Difference-in-differences-in-differences (DDD) model is used to estimate the impact of CBEC on CIPD. First, DDD results show that CBEC can effectively curb CIPD. Second, environmental regulatory effect, environmental governance effect and environmental awareness effect produced by CBEC are tested. CBEC inhibits CIPD by strengthening media supervision and public pollution reporting in environmental regulatory effect, and reduces CIPD by increasing environmental protection investment and environmental law enforcement efficiency in environmental governance effect, and curbs CIPD by promoting environmental information disclosure and reducing environmental corruption in environmental awareness effect. Third, by further analyzing different stages of CBEC, CIPD generated by environmental action coordination and environmental governance mechanism coordination is highly inhibited. Fourth, CBEC has a stronger inhibition effect on CIPD as upstream region is economically underdeveloped and downstream is economically developed. The conclusion provides policy implications from inter-government coordination for restraining CIPD.
Laws that govern land acquisition can lock in old paradigms. We study one such case, the Coal Bearing Areas Act of 1957 (CBAA) which provides minimal social and environmental safegaurds, and deviates in important ways from the Right to Fair Compensation and Transparency in Land Acquisition, Rehabilitation and Resettlement Act 2013 (LARR). The lack of due diligence protocol in the CBAA confers an undue comparative advantage to coal development, which is inconsistent with India's stance to phase down coal use, reduce air pollution, and advance modern sources of energy. We argue that the premise under which the CBAA was historically justified is no longer valid due to a significant change in the local context. Namely, the environmental and social costs of coal energy are far more salient and the market has cleaner energy alternatives that are cost competitive. We recommend updating land acquisition laws to bring coal under the general purview of LARR or, at minimum, amending the CBAA to ensure adequate environmental and social safeguards are in place, both in letter and practice.
Environmental variables that fluctuate randomly and dynamically over time, such as water quality indices, are considered to be stochastic. They exhibit sub-exponential memory structures that should be accounted for in their modeling and analysis. Furthermore, risk assessments based on these environmental variables should consider limited data availability, which may introduce errors, e.g., model misspecifications, into their modeling. In this study, we present a pair of risk measures to determine the exponential disutility of a generic environmental variable both from below and above. The generic environmental variable is modelled as an infinite-dimensional nonlinear as well as affine stochastic differential equation and its moments and sub-exponential autocorrelations are estimated analytically. Novel risk measures, called dynamic robustified Orlicz risks, are formulated subsequently, and long, sub-exponential memory is efficiently addressed using them. The worst-case upper and lower bounds of the disutility are identified in closed form from the Hamilton-Jacobi-Bellman equations associated with the Orlicz risks. Finally, the proposed methodology is applied to weekly water quality data in a river environment in Japan.
Dario Spiller, Gabriele Santin, Alessandro Sebastianelli
et al.
COVID-19 had a strong and disruptive impact on our society, and yet further analyses on most relevant factors explaining the spread of the pandemic are needed. Interdisciplinary studies linking epidemiological, mobility, environmental, and socio-demographic data analysis can help understanding how historical conditions, concurrent social policies and environmental factors impacted on the evolution of the pandemic crisis. This work deals with a regression analysis linking COVID-19 mortality to socio-demographic, mobility, and environmental data in the US during the first half of 2020, i.e., during the COVID-19 pandemic first wave. This study can provide very useful insights about risk factors enhancing mortality rates before non-pharmaceutical interventions or vaccination campaigns took place. Our cross-sectional ecological regression analysis demonstrates that, when considering the entire US area, the socio-demographic variables globally play the most important role with respect to environmental and mobility variables in describing COVID-19 mortality. Compared to the complete generalized linear model considering all socio-demographic, mobility, and environmental data, the regression based only on socio-demographic data provides a better approximation and proves to be a better explanatory model when compared to the mobility-based and environmental-based models. However, when looking at single entries within each of the three groups, we see that the mobility data can become relevant descriptive predictors at local scale, as in New Jersey where the time spent at work is one of the most relevant explanatory variables, while environmental data play contradictory roles.