Search Results (98)

Environmental degradation and soil desertification are among the most severe environmental issues of recent decades worldwide. Over time, these processes have led to increasingly extreme and highly dynamic climatic conditions. In Brazil, the Northeast Region is characterized by semi-arid and arid areas that exhibit high climatic variability and are extremely vulnerable to environmental changes and pressures from human activities. The application of geotechnologies and geographic information system (GIS) modeling is essential to mitigate the impacts and pressures on the various ecosystems of Northeastern Brazil (NEB), where the Caatinga biome is predominant and critically threatened by these factors. In this context, the objective was to map and assess the spatiotemporal patterns of land use and land cover (LULC), detecting significant trends of loss and gain, based on surface reflectance data and precipitation data over two decades (2000–2019). Remote sensing datasets were utilized, including Landsat satellite data (LULC data), MODIS sensor data (surface reflectance product) and TRMM data (precipitation data). The Google Earth Engine (GEE) software was used to process orbital images and determine surface albedo and acquisition of the LULC dataset. Satellite data were subjected to multivariate analysis, descriptive statistics, dispersion and variability assessments. The results indicated a significant loss trend over the time series (2000–2019) for forest areas (Z_MK = −5.872; Tau = −0.958; p < 0.01) with an annual loss of −3705.853 km² and a total loss of −74,117.06 km². Conversely, farming areas (agriculture and pasture) exhibited a significant gain trend (Z_MK = 5.807; Tau = 0.947; p < 0.01), with an annual gain of +3978.898 km² and a total gain of +79,577.96 km², indicating a substantial expansion of these areas over time. However, it is important to emphasize that deforestation of the region’s native vegetation contributes to reduced water production and availability. The trend analysis identified an increase in environmental degradation due to the rapid expansion of land use. LULC and albedo data confirmed the intensification of deforestation in the Northern, Northwestern, Southern and Southeastern regions of NEB. The Northwestern region was the most directly impacted by this increase due to anthropogenic pressures. Over two decades (2000–2019), forested areas in the NEB lost approximately 80.000 km². Principal component analysis (PCA) identified a significant cumulative variance of 87.15%. It is concluded, then, that the spatiotemporal relationship between biophysical conditions and regional climate helps us to understand and evaluate the impacts and environmental dynamics, especially of the vegetation cover of the NEB. Full article

To address the issue of significant perturbations caused by the limited flexibility of clean energy grid integration, along with the combined effects of electric vehicle charging demand and the uncertainty of high-penetration intermittent energy in the integrated energy system (IES), a capacity optimization method for the IES subsystem of a hydrogen-containing chemical park, accounting for strong perturbations, is proposed in the context of the park’s energy usage. Firstly, a typical scenario involving source-load disturbances is characterized using Latin hypercube sampling and Euclidean distance reduction techniques. An energy management strategy for subsystem coordination is then developed. Building on this, a capacity optimization model is established, with the objective of minimizing daily integrated costs, carbon emissions, and system load variance. The Pareto optimal solution set is derived using a non-dominated genetic algorithm, and the optimal allocation case is selected through a combination of ideal solution similarity ranking and a subjective–objective weighting method. The results demonstrate that the proposed approach effectively balances economic efficiency, carbon reduction, and system stability while managing strong perturbations. When compared to relying solely on external hydrogen procurement, the integration of hydrogen storage in chemical production can offset high investment costs and deliver substantial environmental benefits. Full article

Generational approaches to climate engagement among older adults remain limited. This study examines the evolution of eco-anxiety, environmental activism, and pro-environmental behavior from a life course perspective, focusing on older adults in Spain. A nationwide CATI survey of 3000 residents aged 18 and older was conducted, employing validated multidimensional scales for eco-anxiety, environmental activism, and pro-environmental behavior, each rescaled to a 0–10 range. Data were analyzed using descriptive statistics, analyses of variance, and hierarchical regression models to estimate linear and quadratic age effects beyond sex, education, and subjective social class. Results show that (1) eco-anxiety follows an inverted-U pattern, peaking at ages 45–49 and declining significantly after 60; (2) environmental activism remains high until the late sixties, while everyday pro-environmental behaviors sharply decline after retirement; and (3) eco-anxiety and environmental action in older adults are partially decoupled, reflecting the role of supportive personal and contextual factors beyond emotional concern. The findings challenge prevailing stereotypes of passive older adults by demonstrating that older age can constitute a significant period of climate engagement. Despite a slight decline in climate concern following retirement, the willingness to take action remains notably resilient. Older adults maintain consistent involvement in environmental volunteering and activism, often motivated by a desire to leave a lasting legacy and shaped by personal experiences of past crises and collective struggles. However, pro-environmental behaviors show a marked decrease in older adults, not due to diminished interest but likely as a result of structural constraints such as declining health, limited income, and inadequate housing conditions. This study suggests that, in the context of liquid modernity marked by rapid change and uncertainty, older adults may serve as societal anchors—preserving narratives, emotional bonds, and civic networks. Through policies that address structural barriers, this anchor role can be supported, empowering older adults to improve their well-being and strengthening community resilience in the face of climate change. Full article

Rapid urbanization in semi-arid heritage cities is accelerating land use/land cover (LULC) transitions, with critical implications for local climate regulation, surface energy balance, and environmental sustainability. This study investigates Jaipur, Jodhpur, and Udaipur (Rajasthan, India) between 2018 and 2024 to assess the influence of spatio-temporal dynamics of LULC on urban surface metrics. Multi-temporal satellite datasets were used to derive the index-based built-up index (IBI), surface urban heat island intensity (SUHI), Albedo, urban thermal field variance index (UTFVI), and bare soil index (BSI). The results reveal substantial built-up expansion—most pronounced in Udaipur (+26.7%)—coupled with vegetation loss (up to −23.8% in Jaipur) and progressive albedo decline (Sen’s slope ≈ −0.002 yr⁻¹). These transformations highlight suppressed surface reflectivity and enhanced heat absorption. A key and novel finding is the emergence of a counter-intuitive surface urban cool island (SUCI) effect, whereby urban cores exhibited daytime cooling and nighttime warming relative to rural surroundings. This anomaly is attributed to the rapid heating and poor nocturnal heat retention of bare, sparsely vegetated rural soils, contrasted with the thermal inertia and shading of urban surfaces. By documenting negative SUHI patterns and explicitly linking them to LULC trajectories, this study advances the understanding of urban climate dynamics in semi-arid contexts. The findings underscore the need for climate-sensitive planning—strengthening peri-urban green belts, regulating impervious expansion, and adopting albedo-enhancing construction materials—while safeguarding cultural heritage. More broadly, the study contributes empirical evidence from climatically vulnerable yet culturally significant cities, offering insights relevant to global SUHI research and sustainable urban development. Full article

Time-series forecasting is widely recognized as essential for integrating renewable energy, managing emissions, and optimizing demand across energy and environmental applications. Initially, traditional forecasting methods are hindered by limitations including poor interpretability, limited generalization to diverse scenarios, and substantial computational demands. Consequently, a novel minimum-variance self-tuning (MVST) method is proposed, grounded in adaptive control theory, to overcome these challenges. The method utilizes recursive least squares with self-tuning parameter updates, delivering high prediction accuracy, rapid computation, and robust multi-step forecasting without pre-training requirements. Testing is performed on CO₂ emissions (annual), transformer load (15 min), and building electric load (hourly) datasets, comparing MVST against LSTM, ARDL, fixed-PID, XGBoost, and Prophet across varied scales and contexts. Significant improvements are observed, with prediction errors reduced by 3–8 times and computational time decreased by up to 2000 times compared to these methods. Finally, these advancements facilitate real-time power system dispatch, enhance energy planning, and support carbon emission management, demonstrating substantial research and practical value. Full article

Understanding the social drivers of pro-environmental behavior in urban forests and green spaces is critical for addressing sustainability challenges. Subjective norms serve as a key pathway through which social expectations influence individuals’ behavioral intentions. Despite mixed findings in the literature regarding the impact of subjective norms on individuals’ intentions, there is a research gap about the determinants of this construct. This study was conducted to explore how social expectations shape perceived subjective norms among visitors of urban forests. A theoretical model was developed with subjective norms at its center, incorporating their predictors including social identity, media influence, interpersonal influence, and institutional trust, personal norms as a mediator, and behavioral intention as the outcome variable. Using structural equation modeling, data was collected and analyzed from a sample of visitors of urban forests in Tehran, Iran. The results revealed that subjective norms play a central mediating role in linking external social factors to behavioral intention. Social identity emerged as the strongest predictor of subjective norms, followed by media and interpersonal influence, while institutional trust had no significant effect. Subjective norms significantly influenced both personal norms and intentions, and personal norms also directly predicted intention. The model explained 50.9% of the variance in subjective norms and 39.0% in behavioral intention, highlighting its relatively high explanatory power. These findings underscore the importance of social context and internalized norms in shaping sustainable behavior. Policy and managerial implications suggest that strategies should prioritize community-based identity reinforcement, media engagement, and peer influence over top-down institutional messaging. This study contributes to environmental psychology and the behavior change literature by offering an integrated, empirically validated model. It also provides practical guidance for designing interventions that target both social and moral dimensions of environmental action. Full article

Sport volunteering plays an important role in achieving the goals of sustainable development by supporting the social dimension of sustainability, fostering social integration, and promoting a healthy lifestyle. However, there is a lack of systematic research in Poland on the motivations of sport volunteers, particularly in the context of mass endurance events. This study employed a quantitative, cross-sectional design involving 148 sport volunteers engaged in mass endurance events in Poznan, Poland. To measure motivation, the Polish adaptation of the VMS-ISE scale was used. Data analysis was conducted using one-way analysis of variance (ANOVA). The results showed that volunteer motivations were relatively homogeneous regardless of gender and education level, with the exception of passion for sport, which was significantly stronger among men (p = 0.037). Significant differences were found based on place of residence: residents of medium-sized cities demonstrated the highest motivation for personal development (p < 0.001), whereas individuals from rural areas exhibited stronger patriotism, a greater need for interpersonal interaction, and a higher valuation of external rewards (p < 0.05). The motivations of sport volunteers in Poland are complex and sensitive to environmental factors. Understanding these differences allows for better alignment of recruitment and volunteer management strategies, which can enhance both the effectiveness and sustainability of volunteer engagement. It is recommended to develop volunteer programs that take into account the demographic and socio-cultural characteristics of participants. Full article

Coffee beverage quality is determined by a complex interaction of genetic and environmental factors, including specific biochemical characteristics. In this context, the present study aimed to estimate the genetic parameters of elite irrigated Conilon coffee genotypes in the Cerrado over two consecutive years based on the biochemical characteristics of the beans, assessed by near-infrared spectroscopy (NIRS). The research was conducted at the Embrapa Cerrados experimental field, using the unit’s elite collection. Levels of chlorogenic acid (5-ACQ), caffeine, sucrose, citric acid and trigonelline were analyzed in the raw beans of 18 genotypes harvested in two consecutive years. Data were subjected to analysis of variance in a time-subdivided plot design, considering genotypes as plots and years as subplots, with means grouped by the Scott-Knott test at 5% significance. Results showed significant genetic variability for caffeine, sucrose and trigonelline, while chlorogenic and citric acid levels did not differ significantly among genotypes. A significant genotype × year interaction was observed for caffeine, sucrose, and 5-ACQ. Estimated heritabilities were high for caffeine (85.5%), trigonelline (80.1%), sucrose (62%) and citric acid (60%). Selection gains were positive for sucrose (5.58%), citric acid (10.01%) and trigonelline (8.27%), and negative for caffeine (−6.87%) and 5-ACQ (−0.47%). It is concluded that among the compounds evaluated, caffeine shows the greatest potential for selection, enabling effective gains in raw bean composition, while sucrose and trigonelline present moderate potential for genetic improvement. Full article

This exploratory study investigated how health concerns related to chronic environmental contamination and how satisfaction with the human biomonitoring (HBM) process influence the perceived quality of life in the context of per- and polyfluoroalkyl substances (PFAS) contamination in the Veneto Region (Italy). We administered a questionnaire to 84 residents of the Red Area, where PFAS exposure is classified as most severe. The main findings revealed that satisfaction with HBM was positively correlated with perceived quality of life and showed a statistically significant but modest moderation effect on the relationship between PFAS-related health concerns and quality of life (explaining 17.4% of the variance). Particularly, it attenuates the negative effect that PFAS health concerns have on quality of life. Differences between subgroups revealed heightened concern regarding PFAS health risks among women vs. men and participants with children vs. those without. These results underscore the central role of relational and communication aspects of HBM programs to mitigate psychological distress and possibly contribute to higher perceived well-being. The study highlights the need for tailored public health interventions, including transparent communication, empathetic support, and community engagement, to address the psychosocial dimensions of environmental contamination. Full article

The primary aim of this study was to explore the influence of abiotic factors on weed development in maize fields, with the goal of informing more effective weed management practices. We focused on identifying key environmental, edaphic, and agricultural variables that contribute to weed infestations, particularly before the application of spring herbicide treatments. Field investigations were conducted from 2018 to 2021 across selected maize-growing regions in Hungary. Over the four-year period, a total of 51 weed species were recorded, with Echinochloa crus-galli, Chenopodium album, Portulaca oleracea, and Hibiscus trionum emerging as the most prevalent taxa. Collectively, these four species accounted for more than half (52%) of the total weed cover. Altogether, the 20 most dominant species contributed 95% of the overall weed coverage. The analysis revealed that weed cover, species richness, and weed diversity were significantly affected by soil properties, nutrient levels, geographic location, and tillage systems. The results confirm that the composition of weed species was influenced by several environmental and management-related factors, including soil parameters, geographical location, annual precipitation, tillage method, and fertilizer application. Environmental factors collectively explained a slightly higher proportion of the variance (13.37%) than farming factors (12.66%) at a 90% significance level. Seasonal dynamics and crop rotation history also played a notable role in species distribution. Nutrient inputs, particularly nitrogen, phosphorus, and potassium, influenced both species diversity and floristic composition. Deep tillage practices favored the proliferation of perennial species, whereas shallow cultivation tended to promote annual weeds. Overall, the composition of weed vegetation proved to be a valuable indicator of site-specific soil conditions and agricultural practices. These findings underscore the need to tailor weed management strategies to local environmental and soil contexts for sustainable crop production. Full article

This study examines how beliefs about the causes of climate change relate to emotions, perceptions of its effects, and willingness to engage in collective action among residents of the Valparaíso Region in Chile, a territory particularly vulnerable to this phenomenon. A survey was conducted with 809 individuals using stratified probabilistic sampling. Analysis of variance revealed significant differences among those who attribute climate change to human, mixed, or natural causes. The results show that individuals who believe in the anthropogenic origin of climate change report higher levels of negative emotions, anxiety, perceived impacts, and willingness to participate in both direct and institutional collective actions. Moreover, these individuals perceive greater negative effects of climate change on their surroundings and daily lives. In contrast, those who attribute the phenomenon to natural causes show a lower predisposition to act and a lower risk perception. The study concludes that causal attribution of climate change significantly influences people’s emotional and behavioral responses, highlighting the importance of strengthening climate education and communication based on scientific evidence as key tools for fostering civic engagement in the face of the environmental crisis. The findings contribute to sustainability by strengthening environmental education, participatory governance, and collective action in vulnerable contexts. Full article

With the increasing complexity of ecological and environmental problems, eco-anxiety is increasingly recognized as an essential problem in China. Despite its prevalence, there is a lack of valid measurements in China. The purpose of the present study was to expand the Hogg Eco-anxiety Scale (HEAS) under the Chinese context and evaluate the psychometric attributes of the expanded scale. Specifically, a qualitative study was conducted in Study 1 (n = 17) to expand the HEAS in the Chinese context. Exploratory factor analysis in Study 2 (n = 297) and confirmatory factor analysis in Study 3 (n = 374) were conducted to validate the scale. The climate change anxiety scale and pro-environmental behavior scale were used to assess criterion-related validity in Study 4 (n = 305). Results indicated that a new eco-anxiety scale (i.e., EAS-20) including 20 items attributed to four dimension (somatic symptoms, affective symptoms, rumination, and behavioral symptoms) was developed. It showed satisfactory psychometric properties, including high internal consistency (α = 0.97) and a four-factor structure explaining 84.36% of the variance. The criterion-related validity was acceptable (0.25 ≤ r ≤ 0.37). The article concludes that the 20-item Eco-Anxiety Scale (EAS-20) has good psychometric properties and can be applied to measure eco-anxiety in the Chinese adult population. Full article

Subtropical plateau lakes, which are distinguished by their elevated altitudes and subtropical climates, display distinct ecological dynamics. Nevertheless, the spatial and seasonal variations in the plankton community structure, as well as their interactions with environmental factors, remain inadequately understood. This study investigated the alterations in the phytoplankton and zooplankton community structure across different geographical regions (southern, central, and northern) and seasonal periods (rainy and dry) in Erhai lake, located in a subtropical plateau in China. The results indicated that the average values of total nitrogen (TN), total phosphorus (TP), chlorophyll-a (Chla), pH, and conductivity are significantly higher during the rainy season in comparison to the dry season. Furthermore, during the rainy season, there were significant differences in the concentrations of TN, TP, and Chla among the three designated water areas. Notable differences were also observed in the distribution of Microcystis, the density of Cladocera and copepods, and the biomass of copepods across the three regions during this season. Conversely, in the dry season, only the biomass of Cladocera exhibited significant variation among the three water areas. The redundancy analysis (RDA) and variance partitioning analysis demonstrated that the distribution of plankton groups (Cyanophyta, Cryptophyta, and Cladocera) is significantly associated with TN, Secchi depth (SD), and Chla during the rainy season, whereas it is significantly correlated with TP and SD during the dry season. These findings underscore the critical influence of environmental factors, shaped by rainfall patterns, in driving these ecological changes. In the context of the early stages of eutrophication in Lake Erhai, it is essential to ascertain the spatial distribution of water quality parameters, as well as phytoplankton and zooplankton density and biomass, during both the rainy and dry seasons. Full article

Understanding the influence of environmental factors on cotton performance is crucial for enhancing yield and fiber quality in the context of climate change. This study investigates genotype-by-environment (G×E) interactions in cotton, using data from 250 recombinant inbred lines (CCRI70 RILs) cultivated across 14 diverse environments in China’s major cotton cultivation areas. Our findings reveal that environmental effects predominantly influenced yield-related traits (boll weight, lint percentage, and the seed index), contributing to 34.7% to 55.7% of their variance. In contrast fiber quality traits showed lower environmental sensitivity (12.3–27.0%), with notable phenotypic plasticity observed in the boll weight, lint percentage, and fiber micronaire. Employing six machine learning models, Random Forest demonstrated superior predictive ability (R² = 0.40–0.72; predictive Pearson correlation = 0.63–0.86). Through SHAP-based interpretation and sliding-window regression, we identified key environmental drivers primarily active during mid-to-late growth stages. This approach effectively reduced the number of influential input variables to just 0.1–2.4% of the original dataset, spanning 2–9 critical time windows per trait. Incorporating these identified drivers significantly improved cross-environment predictions, enhancing Random Forest accuracy by 0.02–0.15. These results underscore the strong potential of machine learning to uncover critical temporal environmental factors underlying G×E interactions and to substantially improve predictive modeling in cotton breeding programs, ultimately contributing to more resilient and productive cotton cultivation. Full article

To counter the consequences of climate change on both planetary and human health, a greater adoption of sustainable behaviors is required. In this context, two factors emerge as potentially crucial: Readiness to Change (RTC) and environmental sensitivity. The study aimed to investigate the interaction between these two constructs and their impact on the engagement of pro-environmental behaviors and levels of eco-anxiety, in order to assess potential differences in behavioral and affective factors that may support the improvement of sustainable habits. Data were anonymously collected online from 947 participants. A Random Forest Clustering (RFC) analysis was performed as well as Analysis of Variance (ANOVA) to explore differences between the identified clusters in terms of sustainable behaviors and eco-anxiety. The RFC revealed the presence of seven distinct clusters and highlighted that environmental sensitivity plays a key role in defining them. Moreover, the findings showed that high RTC combined with high environmental sensitivity is associated with greater engagement in pro-environmental behaviors and higher levels of eco-anxiety. These results represent a promising groundwork for the development of both future studies in this field of research and targeted educational and awareness programs addressing the climate crisis. Full article