Impact Of Environmental Changes Research Articles

Community perceptions and mental burden among (former) residents at Europe’s largest lignite mine in Western Germany: a cross-sectional study

During the many years that coal has been used for energy production, most studies have focused on the global environmental impacts such as climate change or, when addressing health, rather on conditions of open pit coal miners. This study aims to assess the mental health impacts of lignite (brown coal) mining on communities living in proximity to an open pit mine, including resettlers. A questionnaire-based survey was used with items including socioeconomic effects of open pit mining and resettlement, environmental burden, attitudes, as well as scales for depressive and somatic symptoms. Data included n = 100 respondents from resettled villages and n = 235 participants from pit edge villages nearby Europe’s largest open pit mine Hambach in Western Germany. Both groups reported up to twice as high levels of depressive and somatic symptoms compared to the general population. Over 16.0% of all surveyed stated moderate-to-severe depressive symptoms, while 40.0% of respondents from pit edge villages self-reported moderate-to-severe somatic symptom levels (vs. 23.7% in resettled villages). Three out of four respondents from pit edge villages felt often to nearly always affected by dust followed by noise (41.9%). These results suggest that a changing home environment and residential proximity to an open pit mine could affect the mental health, leading to psychological strain. Further studies on the psychosocial impact of environmental change and loss of home are needed.

Impact of climate change on the waters off southwest Taiwan: predicted alterations in moonfish distribution and catch rates

IntroductionPrior research emphasizes the beneficiaries and detractors in fisheries due to climate change, focusing on alterations in biomass, species mix, and potential yields. Comprehending the potential impact of climate change on the fisheries income of maritime nations is an essential subsequent step in formulating effective socio-economic policies and food sustainability plans to mitigate and adapt to climate change. Globally, our understanding of small-scale fisheries remains inadequate, despite their significance for food security and livelihoods.MethodsThis study demonstrates by using global circulation model (GCM: IPSL) that moonfish catches react favorably to climate alterations in southwest Taiwan, through the analysis of primary fishery data from 2014-2020 spanning two seasons – northeast and southwest monsoons by using generalized additive models. The anticipated habitat shifts were utilized to examine the impact of future environmental changes on moonfish catch rates.ResultBy the 2060s, moonfish emerged as a winner of climate change in this study region. The habitats of moonfish are projected to shift westward during southwest monsoon, with a mean habitat centroid displacement of about 50-150 Km between RCP 2.6 and 8.5 during NE monsoon while 20-40 Km during the SW monsoon. A possible explanation could be that the Taiwan Strait (TS) may serve as a geographical barrier to the northward migration of south-dwelling moonfish. The study further demonstrates that moonfish fisheries catch rates may increase by 22.5% and 17.2% beyond the present catch rates by the 2060s under elevated CO2 emission scenarios during northeast and southwest monsoons respectively.DiscussionOur findings indicate the necessity for comprehensive economic evaluations regarding the potential impacts of climate change on regional small-scale marine fisheries, contributing to the adaptive conservation and management of fish habitats.

Climate transition and climate adaptation: The experiences of older immigrants from the former Soviet Union in Israel.

Climate transition and climate adaptation: The experiences of older immigrants from the former Soviet Union in Israel.

Development and Field Deployment of a Compact Dual-Range Infrared Carbon Dioxide Sensor System.

A dual-range mid-infrared carbon dioxide (CO2) sensor is developed with temperature and humidity compensation functionalities. Using the same optical path, the sensor employs dual-channel signal processing circuits to achieve measurements across two detection ranges of 200-3000 parts-per-million (ppm) (low concentration range) and 8-25% (high concentration range), respectively. The developed sensor, with a compact size of 8.5 × 5.5 × 3.5 cm3, shows a good linear response, with fitting goodness R2 = 0.99942 for the low range and R2 = 0.9993 for the high range. Under environmental conditions of 20 °C temperature and 30% relative humidity and with an averaging time of 1 s, the limits of detection are 0.15 ppm for the low range and 32.4 ppm for the high range, respectively. A temperature and humidity compensation scheme based on multiple linear regression is proposed to mitigate the impact of environmental temperature and humidity changes. The experimental results demonstrate that the relative error after compensation is reduced from 21% to 0.87%. Indoor and outdoor CO2 measurements are performed to validate the good characteristics of the sensor system.

Theory-Guided Design of N-Confused Porphyrinic Covalent Organic Frameworks for Oxygen Reduction Reaction.

Covalent organic frameworks (COFs) enable the precise and controlled synthesis of single-atom catalysts with uniformly distributed active metal centers, offering opportunities to elucidate the impact of subtle coordination environment changes on the catalytic performance. Inspired by N-confused porphyrins, we designed 20 metalloporphyrin-based COFs with either M-N3C1 or M-N4 centers, where M refers to 3d transition metals from Sc to Zn, for the electrocatalysis of oxygen reduction reaction (ORR). Density functional theory calculations predicted Co-based COFs to be the best ORR catalysts among the screened catalysts. Hence, Co-N3C1-COF and Co-N4-COF with a high crystallinity were synthesized. The Co-N3C1-COF exhibited improved ORR performance over the parent Co-N4-COF, as it had a reduced overpotential and increased four-electron selectivity, corroborating theoretical predictions. The enhanced performance was ascribed to the increase in electron density on Co as the coordination environment transits from Co-N4 to Co-N3C1. This not only facilitated the adsorption of O2 and critical intermediates but also changed the potential-determining step, which in turn made the ORR free energy profile of Co-N3C1-COF approach equilibrium for all elementary steps, thus leading to a reduced overpotential. This combined theoretical and experimental work exemplified carbon coordination in porphyrin-based COFs as an effective strategy to facilitate the catalytic capability for ORR. A descriptor was also provided to guide the design of coordination-varied Por-COFs catalysts.

Mathematical modeling of Chinese medicine by complex-valued five-agent network

Traditional Chinese medicine (TCM) is a holistic medicine that deals with the whole person, not just the disease or injury. Based on the Yin-Yang Philosophy and the Wuxing (Five Elements) network, TCM advocates that the human body has the self-regulation ability, which can make the internal organs of the human body operate in a coordinated manner, adapt to changes in the external environment, and achieve harmony between the human body and nature. However, for more than two thousand years, this argument about the human body's self-regulation and adaptation abilities has remained at the level of natural philosophy, lacking direct scientific evidence. This article applies the complex-valued multi-agent network theory to build a general mathematical model for the Yin-Yang Wuxing network based on which, we can quantitatively describe and compute the operation of TCM by linear algebra. The eigenstructure analysis of the established mathematical model clearly reveals the roles of the Yin-Yang philosophy and the Wuxing network in TCM and confirms their inherent feedback mechanism to ensure the balanced operation of organs in the human body. This paper opens the way for quantitative research on TCM, which can be used to identify unbalanced organs and predict the impact of environmental changes and medical treatments on the human body.

An AI-Based Framework for Characterizing the Atmospheric Fate of Air Pollutants Within Diverse Environmental Settings

This study introduces a novel artificial intelligence (AI) modeling framework that combines machine learning algorithms optimized through metaheuristics with explainable AI to capture complex interactions among pollutant concentrations, meteorological data, and socio-economic indicators. Applied to a COVID-19-related dataset comprising 404 variables, with benzene concentrations as the target—measured using proton transfer reaction–mass spectrometry in Belgrade, Serbia—the framework demonstrated exceptional sensitivity in assessing the impact of complex environmental and societal changes during the pandemic. Explainable AI techniques, such as SHAP and SAGE, were employed to reveal the influence of each predictor, while the clustering of SHAP values identified distinct environmental settings that influenced benzene behavior. Three distinct settings were identified regarding benzene levels during the onset of the state of emergency. The first, involving local petroleum-related activities, biomass burning, chemical manufacturing, and traffic, led to a 15.7% reduction in benzene levels. The second, characterized by non-combustion processes, nocturnal chemistry, and the specific meteorological context, resulted in a 51.9% increase. The third, driven by local industrial processes, contributed to a modest 2.33% reduction. The study underscored the critical role of environmental settings in shaping air pollutant behavior, emphasizing the importance of integrating broader environmental contexts into models to gain a more comprehensive understanding of air pollutants and their dynamics.

Breaking Commuting Habits: Are Unexpected Urban Disruptions an Opportunity for Shared Autonomous Vehicles?

While extensive research has examined how major life events affect travel habits, less attention has been paid to the impact of minor environmental changes on commuting behavior, particularly regarding shared autonomous vehicles (SAVs). This study investigated how daily disruptions and incremental environmental changes influence commuter behavior patterns and SAV adoption in Shanghai, applying the theory of interpersonal behavior framework. The study surveyed 517 Shanghai residents, examining travel satisfaction, commuting habits, psychological factors (such as habit strength and satisfaction), and attitudes towards SAVs. Structural equation modeling was employed to test hypotheses about psychological factors influencing SAV adoption, while logistic regression analyzed how these factors affected mode choice across different disruption contexts. Analysis revealed that psychological factors, particularly habit and satisfaction, were stronger predictors of SAV adoption than attitude-based factors. Route obstructions and workplace relocations significantly increased SAV consideration. Even minor, recurring disruptions, such as construction zones, showed strong effects on commuting behavior, supporting the habit discontinuity hypothesis and emphasizing the importance of minor disruptions in driving behavioral change. The study extends the theory of interpersonal behavior by integrating habit discontinuity theory to explain how minor disruptions drive SAV adoption. This research provides actionable insights for urban planners and policymakers, recommending that SAV trials and targeted interventions be implemented during infrastructure changes or other commuting disruptions to promote SAV adoption and foster more sustainable transportation systems.

The impact of environmental changes on infectious diseases among children in North America.

Climate change is no longer a distant threat but a present and accelerating force reshaping the epidemiology of infectious diseases in North America and elsewhere. Rising temperatures, shifting precipitation patterns, and extreme weather events are impacting the geographic distribution of pathogens and their vectors. Understanding these environmental influences on infectious diseases in children is essential for equipping healthcare providers to deliver competent care in the era of climate change. The northward spread of vector-borne diseases, warming waters and soils, and disease outbreaks following extreme weather events highlight the complex and multifactorial ways through which the environment influences the spread of infectious diseases. Continuing medical education and training on the evolving epidemiology of climate-sensitive diseases is critical for clinicians to address emerging health threats effectively. We advocate for more equitable funding and resource allocation to support innovative pediatric research on climate change and infectious diseases in the United States and other countries in North America.

The single-berry metabolomic clock paradigm reveals new stages and metabolic switches during grapevine berry development.

Asynchronicity causes metabolic chimerism in usual grapevine phenological stages, calling for a revisit of berry development at the individual fruit scale. To reveal the dynamics of metabolite composition in grapevine berries without phenological a priori, a dataset of 9,256 ions was obtained on 125 fruits at different stages by non-targeted ultra-performance liquid chromatography coupled to high-resolution mass spectrometry. This large metabolomic dataset was submitted to an analysis workflow combining classification and dimension reduction tools. This led to the clustering of metabolites into 12 specific kinetic patterns and a metabolome-based definition of the pericarp intrinsic clock outperforming expert timing procedures. Such increased temporal resolution enabled the identification of metabolite clusters that are annunciative of the onset of ripening, noticeably characterized by transient lipidic changes and the start of ABA accumulation. We also highlighted a cluster of stilbenes that accumulate during terminal fruit shriveling, after the arrest of phloem unloading. This non-targeted approach enables a more precise characterization of grapevine berry development through the concept of metabolomic clock. The discovery of new metabolic milestones of berry development paves the way toward a better assessment of the impact of environmental changes on the metabolism of non-climacteric fruit, in different genotypes.

Large-scale georeferenced neuroimaging and psychometry data link the urban environmental exposome with brain health.

Large-scale georeferenced neuroimaging and psychometry data link the urban environmental exposome with brain health.

Redefining Cell Culture Using a 3D Flipwell Co-culture System: A Mimetic for Gut Architecture and Dynamics In Vitro.

Gut mucosae are composed of stratified layers of microbes, a selectively permeable mucus, an epithelial lining, and connective tissue homing immune cells. Studying cellular and chemical interactions between the gut mucosal components has been limited without a good model system. We have engineered a three-dimensional (3D) multi-cellular co-culture system we coined "3D Flipwell system" using cell culture inserts stacked against each other. This system allows an assessment of the impact of a gut mucosal environmental change on interactions between gut bacteria, epithelia, and immune cells. As such, this system can be utilized in examining the effects of exogenous stimuli, such as dietary nutrients, bacterial infection, and drugs, on the gut mucosa that could predetermine how these stimuli might influence the rest of body. Here, we describe the methods of construction and application of the new 3D Flipwell system we utilized previously in assessing the crosstalk between the gut mucosa and macrophage polarization. We demonstrate the physiological responses of different components of the co-cultures to Sepiapterin (SEP), the precursor of the nitric oxide synthase cofactor tetrahydrobiopterin (BH4). We reported previously that SEP induces a pro-immunogenic shift of macrophages having acquired an immune suppressive phenotype. We also showed that SEP induces a defense mechanism of commensal gut bacteria. The protocol describing the assembly and use of the 3D Flipwell co-culture system herein would grant its utility in evaluating the concurrent effects of pharmacologic and microbiologic stimuli on gut mucosal components. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: 3D Flipwell construction, assembly, and collagen coating Basic Protocol 2: Flipwell cell seeding and cell culture Basic Protocol 3: Addition of bacterial culture to the Flipwell system Basic Protocol 4: Flipwell disassembly for scanning electron microscopy (SEM) studies Basic Protocol 5: Immunofluorescence antibody staining for confocal microscopy.

Impact of Environmental Changes on the Chemical Composition of Mosquitocidal Plants

Impact of Environmental Changes on the Chemical Composition of Mosquitocidal Plants

A supervised model to identify wolf behavior from tri-axial acceleration

BackgroundIn wildlife studies, animal behavior serves as a key indicator of the impact of environmental changes and anthropogenic disturbances. However, wild animals are elusive and traditional GPS studies only provide limited insight into their daily activities. To address this issue, behavior classification models have increasingly been used to detect specific behaviors in wildlife equipped with tri-axial accelerometers. Such models typically need to be trained on data from the target species. The present study focuses on developing a behavioral classification model tailored to the grey wolf (Canis lupus) and encompassing a variety of ecologically relevant behaviors.MethodsWe collected data from nine captive wolves equipped with collar-mounted tri-axial accelerometers recording continuous acceleration at 32 Hz (“fine-scale”) and averaged acceleration over 5-min intervals (“activity”). Using simultaneous video observations, we trained Random Forest models to classify wolf acceleration data into specific behaviors. We investigated the potential limits to the generalizability of these models to unlabeled data through individual-based cross-validation.ResultsWe present: (1) a model classifying fine-scale acceleration data (32 Hz) into 12 distinct behaviors (lying, trotting, stationary, galloping, walking, chewing, sniffing, climbing, howling, shaking, digging and jumping) with a class recall of 0.77–0.99 (0.01–0.91 in cross-validation), (2) a model classifying activity data (5-min averages) into 3 behavior categories (static, locomotion and miscellaneous) with a class recall of 0.43–0.91 (0.39–0.92 in cross-validation). Although classification performance decreased following cross-validation, recall scores for lying, trotting, stationary, galloping, walking and chewing individual behaviors (as well as static and locomotion categories) remained above 0.6. Classification performance was consistently poorer for rare behaviors, which constituted less than 1.1% of the training dataset.ConclusionsWe demonstrate the use of collar-mounted accelerometer to distinguish between 12 behaviors and 3 behavior categories in captive wolves, at fine-scale (32 Hz) and averaged 5-min resolutions, respectively. We also discuss the generalizability of the two models to free-ranging settings. These models can be employed to support future behavioral studies examining questions such as conflict mitigation, wolf responses to human disturbances, or specific activity budgets.

The mutation atlas of giant kelp (Macrocystis pyrifera): a mutation database resource for natural knockouts.

Giant kelp (Macrocystis pyrifera) is a paramount species of immense ecological and economic importance. It forms dense underwater forests, providing crucial habitat and serving as a foundation species for diverse marine ecosystems. Understanding the genetics of giant kelp is essential for conservation and sustainable farming, safeguarding these valuable ecosystems and their benefits. By analyzing mutations based on their impact, we can gain insights into the potential functional consequences and implications for the organism, helping to identify critical genes or regions that may play a significant role in adaptation, development, and environmental response. To achieve this, we annotated the effects and impact of spontaneous mutations in 559 giant kelp individuals from four different populations. We found over 15.9 million mutations in genes of giant kelp, and classified them into modifier, low, moderate, and high impact depending on their predicted effects. The creation of this mutation effect database, attached to the seedbank of these individuals, offers several applications, including enhancing breeding programs, aiding genetic engineering with naturally occurring mutations, and developing strategies to mitigate the impact of environmental changes.

Influence of environmental renewal on students’ mental health development under the background of long-term online teaching

In the wake of the COVID-19 pandemic, the prevalence of online education in primary education has exhibited an upward trajectory. Relative to traditional learning environments, online instruction has evolved into a pivotal pedagogical modality for contemporary students. Thus, to comprehensively comprehend the repercussions of environmental changes on students’ psychological well-being in the backdrop of prolonged online education, this study employs an innovative methodology. Founded upon three elemental feature sequences—images, acoustics, and text extracted from online learning data—the model ingeniously amalgamates these facets. The fusion methodology aims to synergistically harness information from diverse perceptual channels to capture the students’ psychological states more comprehensively and accurately. To discern emotional features, the model leverages support vector machines (SVM), exhibiting commendable proficiency in handling emotional information. Moreover, to enhance the efficacy of psychological well-being prediction, this study incorporates an attention mechanism into the traditional Convolutional Neural Network (CNN) architecture. By innovatively introducing this attention mechanism in CNN, the study observes a significant improvement in accuracy in identifying six psychological features, demonstrating the effectiveness of attention mechanisms in deep learning models. Finally, beyond model performance validation, this study delves into a profound analysis of the impact of environmental changes on students’ psychological well-being. This analysis furnishes valuable insights for formulating pertinent instructional strategies in the protracted context of online education, aiding educational institutions in better addressing the challenges posed to students’ psychological well-being in novel learning environments.

The Importance of Long-Term Data for Understanding How Environmental Change Impacts Mussel Communities and Their Ecosystem Effects

The Importance of Long-Term Data for Understanding How Environmental Change Impacts Mussel Communities and Their Ecosystem Effects

Phylogenetic relationships of Smilax astrosperma (Smilacaceae) with other Smilax species and its potential distribution patterns in karst areas of south China

The genus Smilax is adapted for growth in karst areas of south China, likely because of the karst habitat heterogeneity of the area and climate factors. However, the phylogenetic relationships among different species in this genus and impact of environmental changes on its geographic distribution patterns remain unclear. Therefore, we assembled the 11 chloroplast genomes of the genus Smilax and reconstructed its phylogeny using the maximum‐likelihood method to confirm the phylogenetic relationships of Smilax astrosperma among 28 other Smilax genus species. In addition, a maximum entropy (MaxEnt) model was applied to evaluate and predict the potential distribution of S. astrosperma using bioclimatic variables, soil environment, elevation, slope degree and aspect, and human footprints under present and predicted future climate scenarios. The results show that S. astrosperma has a close relationship with S. glabra in the genus Smilax. The average temperature of the coldest quarter is a crucial factor influencing the distribution of S. astrosperma in karst areas. The potential distribution range of S. astrosperma increases and decreases under the SSP1‐2.6 and SSP5‐8.5 scenarios, respectively. These findings highlight that S. astrosperma and other species of the karst areas of south China need special attention under climate warming since both warming itself and extreme weather events threaten their survival.

Application of a delta-generalized additive model to assess the impact of environmental changes on the spatial distribution of bigeye tuna (Thunnus obesus) in the Indian Ocean

Application of a delta-generalized additive model to assess the impact of environmental changes on the spatial distribution of bigeye tuna (Thunnus obesus) in the Indian Ocean

White lies for coral reefs: Dynamics of two-patch coral reefs model with asymmetric dispersal.

White lies for coral reefs: Dynamics of two-patch coral reefs model with asymmetric dispersal.