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Contemporary US and European approaches to ecosystems and mental health have left many feeling disempowered in the face of the climate crisis. This article advocates for holistic approaches to supporting natural complex systems with contemplation and care. It explores how environmental metaphors and nature-based art therapy can foster human-nature connections, call for deep listening, reshape practitioner perceptions, and enhance understandings of internal and external worlds. Further, how existing frameworks such as the Expressive Therapies Continuum (ETC), can highlight the therapeutic qualities of nature-based art therapy and the potential for adaptation through the expressed paradigm shift. This article proposes that responsive mental health care can be adapted to better address the necessary changes required to support the global community and environment as we listen and adapt to climate change.

Regionalization has emerged as a crucial research area for the past 50 years, including aggregating smaller areas into larger, contiguous, and/or homogeneous regions. Spatial optimization techniques are advantageous for solving regionalization problems, yet their nondeterministic polynomial-time (NP) hard nature leads to computational complexity and time consumption, especially with extensive datasets. Although regionalization studies play a pivotal role in defining boundaries for multi-scalar analysis and modeling complex socio-environmental systems (SES), current approaches lack integrated consideration of raster and vector data. We introduce a unified, structured framework integrating Geographic Information Systems (GIS), image segmentation, and regionalization to identify and characterize socio-environmental units accounting for various data models and types. We use a public geodatabase of the Rio Grande/Bravo basin, an SES covering diverse cultures, ecosystems, and economies, to demonstrate the functionality of our newly developed method, which effectively identifies spatial units for subsequent SES analysis and modeling. The delineation process accounts for various factors, including administrative boundaries, estimated total quantities, compactness, spatial contiguity, and similarity in socio-environmental characteristics. To make this work reproducible, replicable, and expandable, we developed the approach entirely based on open-source Python packages. Our method is easily transferable to other research using various data formats and spatial scales to delineate spatial unit boundaries effectively and efficiently.

ABSTRACTChanges to the Earth's environment, increasing anthropogenic pressure, and the global decline of biodiversity bring the need to establish spatial frameworks for the monitoring and assessment of such dynamic processes. Several environmental stratifications have been developed at the global level; however, most of them only include natural conditions in the classification process. Incorporating spatial patterns of biodiversity and the degree of anthropogenic pressure seems to be essential in an era of significant environmental transformation. We developed a new comprehensive classification of Global Environmental Systems based on general abiotic gradients, distribution of biodiversity, and spatial differentiation of human impact. This classification is based on 22 variables covering abiotic, biotic, and anthropogenic factors. We identified 10 abiotic, biotic, and anthropogenic classes using cluster analysis; their combination results in 169 unique Global Environmental Systems (GES) showing human-environment interactions. Each class shows an area with similar abiotic and biotic background and human pressure..

Purpose: This study aims to illustrate how environmental systems shape the peer interactions of an autistic student within the classroom. Method: Drawing on the bioecological model of human development, this situated discourse analysis used thematic coding and microanalysis to examine data from semistructured interviews and 10 sessions of direct classroom observations of a 9-year-old autistic student and his classroom communication partners. Results: Convergent data across participants, time, and data sources revealed the following systemic influences on peer interaction: predominant medicalized view of autism (macrosystem), educational practices (exosystem), misaligned roles across adults and peers in the classroom (mesosystem), and multimodal opportunities for direct interaction that were supported by objects and physical contact and inhibited by rapid pacing (microsystem). Conclusions: Findings illustrate the environmental complexities associated with the development of peer interactions for autistic students. We offer explicit clinical implications for how environmental factors can be addressed in the school-based eligibility determination process and in the Individualized Education Program.

Nearly a decade ago, the "Framework for K-12 Science Education" argued for the need to intertwine science and engineering practices, disciplinary core ideas, and crosscutting concepts in performance expectations. However, there are few empirical examples for how intertwining three dimensions facilitates learning. In this study, we used a learning progressions approach to examine how student engagement in computational thinking (science and engineering practice) intertwines with learning about the flow of water through environmental systems (disciplinary core ideas) and understanding of systems and system models (crosscutting concept). We developed three secondary-level curriculum units situated in current groundwater contamination and urban flooding contexts. Units included specially designed NetLogo computational models. Post-assessments measured student performances in computational thinking processes and understanding of hydrologic systems. Using item response theory in our analysis, we identified distinct levels of performance on a learning progression. At the lower end, literal model users interacted with models and manipulated model interfaces to achieve a specified goal. In the middle, Model Technicians used computational models to solve real-world problems. At the upper end, principle-based model users used computational thinking processes and principles related to systems modeling and hydrology to explain how the models worked to predict water flow. Differences between performances of literal model users, model technicians, and principle-based model users reflected shifts in how students made sense of the systems and system models crosscutting concept. These shifts in performances aligned with progress in computational thinking practices and finally with use of hydrology disciplinary core ideas. These findings contribute to understanding of how science and engineering practices, disciplinary core ideas, and crosscutting concepts intertwine during learning; how computational thinking practices develop; and how computational thinking about system models facilitates learning for environmental science literacy.

Abstract Background Discovering causality in environmental systems is challenging because frequently controlled experiments or numerical simulations are difficult. Algorithms to learn directed acyclic graphs from system data are powerful, but they often result in too many possible causal structures that cannot be properly evaluated. Results An approach to this problem proposed here is to initially restrict the system to a target variable with its two major drivers. Subsequently, testable causal structures are obtained from rules to infer directed acyclic graphs and expert knowledge. The proposed approach, which is essentially based on correlation and regression, was applied to understand drivers of a toxic algal bloom in the Odra River in summer 2022. Through this application, useful insight on the interplay between river flow and salt inputs that likely caused the algal bloom was obtained. Conclusions The Odra River example demonstrated that carefully applied correlation and regression techniques together with expert knowledge can help to discover reliable casual structures in environmental systems.

Purely natural land formations are increasingly rare in today’s world, as most areas have been shaped, to varying degrees, by human influence over time. To better understand ongoing changes in the natural environment, we adopted an approach that involves identifying global systems with a significant anthropogenic component. In this study, we developed a new classification of Global Environmental Systems based on over 20 high-resolution datasets, covering abiotic, biotic, and anthropogenic conditions. We created abiotic, biotic, and anthropogenic classifications, each with ten classes. The combinations of these classes result in 169 distinct classes of Global Environmental Systems. This classification provides a suitable spatial framework for monitoring land use dynamics, biodiversity changes, global climate change impacts, and various processes exhibiting complex spatial patterns.

The investigation of dynamic fully integrated cultural-environmental systems is one grand challenge facing archaeologists in this century. In the Midwest and Southeast United States, archaeologists recently increased their study of Mississippian social systems (ca. AD 1000–1600) in relationship to paleoclimate and paleoenvironmental data. Significant differences in chronological control between archaeological chronologies and paleoenvironmental records pose challenges to the study of cultural-environmental systems in this region and often result in equifinal results. Three major lines of paleoenvironmental records are reviewed: bald cypress tree-ring records, the Living Blended Drought Atlas (LBDA), and lake-bottom sediment cores. The strongest approaches include local and regional multiproxy environmental records from the same location as a well-investigated archaeological site(s) or region(s). In the rare case where the cores also encode a regional population history, it may be possible to develop stronger inferences that consider variation within and between communities and their vulnerability to climate change and environmental catastrophes. [ABSTRACT FROM AUTHOR]

Games with environmental feedback have become a crucial area of study across various scientific domains, modelling the dynamic interplay between human decisions and environmental changes, and highlighting the consequences of our choices on natural resources and biodiversity. In this work, we propose a co-evolutionary model for human-environment systems that incorporates the effects of knowledge feedback and social interaction on the sustainability of common pool resources (CPRs). The model represents consumers as agents who adjust their resource extraction based on the resource’s state. These agents are connected through social networks, where links symbolize either affinity or aversion among them. The interplay between social dynamics and resource dynamics is explored, with the system’s evolution analyzed across various network topologies and initial conditions. We find that knowledge feedback can independently sustain CPRs. However, the impact of social interactions on sustainability is dual-faceted: it can either support or impede sustainability, influenced by the network’s connectivity and heterogeneity. A notable finding is the identification of a critical network mean degree, beyond which a depletion/repletion transition parallels an absorbing/active state transition in social dynamics, i.e. individual agents and their connections are/are not prone to being frozen in their social states. Furthermore, the study examines the evolution of the social network, revealing the emergence of two polarized groups where agents within each community have the same affinity. Finally, we observe an inverse relationship between system complexity and sustainability. Comparative analyses using Monte–Carlo simulations and rate equations are employed, along with analytical arguments, to reinforce the study’s findings. The model successfully captures key aspects of the human-environment system, offering valuable insights to understand how both the spread of information and social dynamics may impact the sustainability of CPRs.

Agent-based modeling (ABM) has been successfully used, since its emergence in the 1990s, to model and simulate the dynamics at work in complex socio-environmental systems, in many domains and applications where interactions between people and their environments give rise to emergent phenomena that are difficult to study otherwise (urban planning, land-use change, adaptation to environmental changes, biodiversity protection in socio-ecosystems, environmental pollution control, etc.). The inclusion of multiple levels of analysis, abstraction, and representation in these models, however, is much more recent and is still the subject of many proposals and discussions within a relatively informal field, Multilevel Agent-Based Modeling (ML-ABM), which is most often presented as an approach that extends the classical ABM paradigm to include multilevel concepts. Over the past decade, ML-ABM has been increasingly adopted and explored by researchers as an effective paradigm for framing and defining the mechanisms underlying multilevel dynamics. However, due to the youth of the field, no single definition, methodology, or tool unifies studies in this rapidly expanding area. This review will begin with an introduction to socio-environmental systems (SES) and the challenges that modeling approaches face in representing them properly, especially regarding the complexity of human behaviors and organizations. ABM presents opportunities for modeling SESs with respect to these challenges, including the simulation of individual and social behavior and their ability to provide a descriptive and generative representation of the simulated system. However, ABM is limited in its ability to represent levels and scales, as these concepts are absent from the classical ABM metamodel. A complete review of the ML-ABM literature will be carried out, structured around a continuum that emerged during the review: that of the distribution of behaviors (and thus, from a software engineering perspective, of control) across the levels, from approaches that allow only one level to be active at a time, to approaches that rely on simultaneous activity and feedback loops between several levels. Different design choices will, thus, be presented to meet the different needs of multi-level representation, focusing on the interest on modelers and the strengths and limitations of each. In particular, we will highlight a limitation shared by all the reviewed approaches, namely their inability to represent several parallel hierarchies of levels and their interactions, a capability that appears more and more crucial to finely represent social behaviors in SES. A new perspective on the interest that the AGR approach could represent to allow this representation of hierarchies allows us to conclude on the research perspectives are still open.

The pollution of environmental systems with heavy metals is becoming a serious problem worldwide. These contaminants are one of the main issues in sludge (which is considered waste) and can even have harmful effects if the sludge is not treated properly. Thus, the development of a novel functional magnetic nanoadsorbent based on a derived lysine is reported here, which can be efficiently applied for metal removal from sludge. Magnetic nanoparticles were coated with silica layer and further modified by covalent bonding of derived lysine. The morphology of the nanomaterial, its nano-size and the silica layer thickness were analyzed by transmission electron microscopy. The successful silanization of the lysine derivative to the silica-coated magnetic nanostructures was investigated by several physicochemical characterization techniques, while the magnetic properties were measured with a vibrating sample magnetometer. The synthesized nanostructures were used as adsorbents for simultaneous removal of most critical heavy metals (Cr, Zn, Cu) from real complex sludge suspensions. The main practical adsorption parameters, pH of the native stabilized sludge, adsorbent amount, time, and adsorbent regeneration were investigated. The results show promising adsorption properties among currently available adsorbents (the total equilibrium adsorption capacity was 24.5 mg/g) from the sludge with satisfactory nanoadsorbent reusability and its rapid removal. The stability of the nanoadsorbent in the sludge, an important but often neglected practical parameter for efficient removal, was verified. This work opens up new possibilities for the development of high-quality magnetic nanoadsorbents for metal pollutants applied in various complicated environmental fields and enables waste recovery.

Most current water allocation strategies fail to address the unbalanced development among multiple systems. How to optimize the coordination development relationships among the social, economic and environmental systems has always been the focus. To bridge this gap, a water optimal allocation model for coordinated development was innovatively constructed and applied to the main stream of the Xiangjiang River Basin, China. The results showed the following. (i) From 2025 to 2030, the water deficit ratio of the study area will increase from 3.21 to 5.50% when P = 50% and from 4.59 to 6.85% when P = 75%. The existing water supply capacity will not be able to meet the increasing water demand. (ii) Agricultural and industrial water will account for a large proportion of the total water consumption. Due to the transformation of industrial structure, measures should be formulated to bring the best benefits. (iii) Restricted by different systems, the coordinated development in each city will present spatial and temporal differences. (iv) The proposed model was proved to overcome the backwards of uncoordinated development and achieve a balance of the regional social, economic and environmental benefits. Also, some recommendations and limitations were discussed. This study provides an effective basis for enhancing regional sustainable water resources planning and management. HIGHLIGHTS A new water optimal allocation model for coordinated development (WOAM-CD) is designed in this study.; The WOAM-CD can effectively deal with the uncoordinated development among the regional social-economic-environmental systems.; According to the regional water endowment and industrial characteristics, the WOAM-CD can draw up corresponding water allocation schemes to balance the social, economic and environmental benefits.;