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        <title>Frontiers in Environmental Science | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/environmental-science</link>
        <description>RSS Feed for Frontiers in Environmental Science | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-07T07:03:21.952+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1800122</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1800122</link>
        <title><![CDATA[The impact of transverse grass strip distribution patterns on slope erosion and hydrodynamic mechanisms]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yiwen Liang</author><author>Sijia Feng</author><author>Ke Li</author>
        <description><![CDATA[Vegetation distribution patterns are key drivers of surface runoff and soil erosion in arid and semi-arid regions. However, quantitative studies on the parameterization of Transverse Grass Strip Combinations (TGSC) patterns are still scarce. Through controlled movable-bed erosion experiments conducted under a fixed 50% vegetation cover, we quantified the effects of different TGSC patterns on slope hydrodynamic characteristics and erosion rates under various slopes and flow conditions. Results show hydrodynamic parameters and slope erosion rate increase with the increase of slope and flow rate. In addition, the TGSC significantly reduced slope erosion, and there was a significant power-law relationship between erosion rates and hydrodynamic parameters (with flow rate exponent being the best indicator). We further parameterized TGSC patterns based on the Path Connectivity Index (PCI), developed and validated a high-performance erosion prediction model, and identified a critical grass strip width threshold of 0.3 m to achieve optimal erosion control. This study provides theoretical support for vegetation-based soil and water conservation in arid and semi-arid regions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1798412</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1798412</link>
        <title><![CDATA[Research trends and conservation implications of ecological water levels in Poyang Lake]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Yanhui Zhang</author><author>Ruoqi Xia</author><author>Jinhui Zhu</author><author>Jinliang Liu</author><author>Bing Li</author>
        <description><![CDATA[This study comprehensively reviews domestic and international research on the ecological water level (EWL) of Poyang Lake by integrating bibliometric analysis and quantitative synthesis. Using CiteSpace software for bibliometric mapping of publications from the CNKI and Web of Science Core Collection databases, we identified key research hotspots, trends, and characteristics. Concurrently, statistical methods were applied to analyze variations in EWLs, which were estimated using multiple methods across annual and monthly scales. Our bibliometric findings reveal three primary research clusters: (1) methodologies for EWL determination, (2) impacts of water level fluctuations on wetland ecosystems, and (3) mechanisms by which the Three Gorges Dam and climate change influence EWL. The field has evolved from initial hydrological analyses toward assessments of biological impacts. Methodologically, it has shifted from statistical approaches to model-based simulations. Furthermore, EWL findings are increasingly presented as interval-based thresholds rather than fixed values. First, data precision is insufficient; for instance, hydrological station data lack the spatial resolution to accurately characterize the hydrological regimes of critical micro-geomorphic units like sub-lakes and mudflats. Second, the link between water levels and biotic requirements remains weak, with few studies quantifying the needs of specific species during key life stages, such as migratory birds. Future research priorities should focus on enhancing data quality through remote sensing technologies and advancing practical applications in water resource management. Quantitatively, the annual minimum ecological water level (Min-EWL) was determined to be 9.71 ± 1.94 m, while the annual optimal ecological water level (O-EWL) was 12.53 ± 2.01 m, both exhibiting substantial inter-annual variability. At the monthly scale, ecological water level requirements peaked from July to August and reached their lowest in January. Although trends derived from different accounting methods were generally consistent, significant variations in the ranges of minimum and optimal EWL values were observed across different months.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1798930</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1798930</link>
        <title><![CDATA[Integrating InSAR-derived deformation into landslide susceptibility mapping in the Ninglang–Yongsheng expressway region, Yunnan, China]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shi Xu</author><author>Fei Wang</author><author>Jiayu Wang</author>
        <description><![CDATA[A Landslide susceptibility mapping along mountain highways is often based on static predisposing factors and historical inventories, which may fail to identify slopes currently destabilized by recent engineering activities. This study adapts and extends a deformation enhanced landslide susceptibility framework for the Ninglang to Yongsheng Expressway corridor in Yunnan Province, Southwest China. A landslide inventory comprising 256 mapped events was established from historical records and high resolution optical imagery. Susceptibility models were developed using Random Forest and XGBoost classifiers based on a suite of static conditioning factors describing topography, land cover, geology, hydrology, and human activity. To account for ongoing slope processes, multi temporal InSAR derived deformation information was integrated into the modeling framework to represent present day slope instability. The inclusion of deformation features leads to a clear improvement in predictive performance, with ROC AUC increasing from 0.764 to 0.826 for the Random Forest model and from 0.758 to 0.820 for the XGBoost model, while PR AUC reaches approximately 0.876–0.878 for the deformation enhanced models. Crucially, a multi-phase SHAP evaluation across the engineering lifecycle revealed that the predictive importance of InSAR deformation surged during active excavation, confirming that the models captured actual physical kinematics rather than statistical artifacts. SHAP based interpretation indicates that distance to road and elevation are the most influential predictors, while deformation information provides complementary dynamic evidence of active slope instability. The adapted framework enhances the operational value of landslide susceptibility mapping for inspection prioritization and geohazard management along newly constructed mountain highways.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1793022</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1793022</link>
        <title><![CDATA[Geomorphology-informed habitat quality dynamics and connectivity-based restoration prioritization in semi-arid coal-mining landscapes]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhanrong Zhu</author><author>Yangyang He</author><author>Husheng Cao</author><author>Shiyue Fang</author><author>Kehua Li</author><author>Huadong Du</author><author>Congying Yu</author>
        <description><![CDATA[IntroductionHabitat degradation in semi-arid coal-mining landscapes is strongly conditioned by geomorphology, yet restoration planning often relies on habitat maps that insufficiently capture landform-specific constraints, shifting drivers, and connectivity bottlenecks.MethodsWe developed a geomorphology-informed, multi-source framework to assess habitat quality (HQ) dynamics and restoration priorities in the Yushenfu mining area from 1995 to 2023 by integrating InVEST-based HQ assessment, spatial autocorrelation and trend analysis, structural equation modeling, and connectivity modeling based on MSPA, graph indices, and circuit theory.ResultsHQ exhibited persistent geomorphology-dependent heterogeneity, with loess hilly-gully terrain maintaining comparatively cohesive high-quality habitat mosaics, whereas aeolian sandy terrain remained dominated by aggregated low-quality patches. Regionally, the area share of very low-quality habitats increased from 18.01% to 26.13%, while high-quality habitats declined from 60.34% to 54.59%. Driver analyses indicated a shift from vegetation-mediated regulation in earlier stages to stronger cumulative human modification and topographic control in later stages. Core habitat area contracted from 648.39 km2 to 349.01 km2, indicating progressive simplification of ecological network structure and increasing dependence on a limited set of sources, corridors, and bottlenecks.DiscussionRestoration should shift from patch-based intervention to landform-stratified, connectivity-oriented prioritization, emphasizing source protection and corridor consolidation in loess terrain and resistance reduction through substrate stabilization and disturbance mitigation in aeolian sandy terrain.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1833310</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1833310</link>
        <title><![CDATA[Feasibility of microbially-solidified coal gangue particles as vegetation substrate for vegetation restoration]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Erxing Peng</author><author>Xiaoying Hu</author><author>Zuojun Ning</author><author>Fansheng Zhou</author><author>Yaling Chou</author><author>Kaichun Zhou</author><author>Qifan Yang</author>
        <description><![CDATA[The feasibility of coal gangue particles solidified with microbially-induced carbonate precipitation technology (MICP) as vegetation substrate for vegetation restoration is studied by solidification tests, microscopic tests, and pot tests. Test results show that the strength, water retention, and anti-wind ability of coal gangue particles can be improved by MICP. The main products of MICP solidifying coal gangue particles are calcite and vaterite. Under low solidification degrees, the shape of CaCO3 is an irregular granule. With an increase in the solidification degree, CaCO3 becomes a uniform sphere. Ryegrass can grow in solidified coal gangue particles. When cementation solution concentration and cementation cycles are low, the germination and growth of ryegrass can be improved. With a surge in the solidification degree and ion concentration, salt stress becomes more obvious, and its germination and growth are inhibited. The combined use of coal gangue, MICP treatment, and ryegrass cultivation can provide a potential approach for environmental restoration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1837937</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1837937</link>
        <title><![CDATA[Editorial: New artificial intelligence methods for remote sensing monitoring of coastal cities and environment]]></title>
        <pubdate>2026-05-06T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Peng Liu</author><author>Fang Huang</author><author>Gary Zarillo</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1782580</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1782580</link>
        <title><![CDATA[Multi-temporal remote sensing diagnosis of urban cooling networks: mechanisms and pathways of thermal degradation in Dongguan]]></title>
        <pubdate>2026-05-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qi Wang</author><author>Qian He</author><author>Jing Qiu</author>
        <description><![CDATA[IntroductionUnder the double pressure of climate change and rapid urbanization, the degradation of the urban thermal environment has become a major obstacle to regional sustainable development. As a manufacturing-leading metropolis, Dongguan has undergone a drastic land use transformation and ecological space compression since 2000, potentially undermining its urban cooling system.MethodsIn this study, multi-temporal Landsat imagery was used to characterize the spatiotemporal evolution of land surface temperature (LST) and to examine the long-term dynamics of an urban cold island network from a source–corridor–matrix perspective. Cold island sources were identified using morphological spatial pattern analysis (MSPA), resistance surfaces were weighted using the CRITIC method, and potential cooling corridors were extracted using the minimum cumulative resistance (MCR) model.ResultsThe results show that from 2000 to 2025, LST increased markedly and the urban heat island expanded from fragmented hotspots to a more continuous pattern, forming multi-core high-temperature clusters along major urban corridors and industrial belts. Meanwhile, core cold sources progressively contracted and became increasingly concentrated, with remaining major cold sources retreating to the southern hilly areas and the western waterfront. The thermal resistance surface shifted from a dispersed low-resistance structure to a more connected high-resistance pattern. Correspondingly, the cold island corridor network simplified from a multi-level configuration to a more linear framework, indicating a degradation sequence characterized by core source degradation–corridor fragmentation–functional decline.DiscussionThese findings highlight that blue–green space fragmentation and the intensification of high-resistance surfaces are key factors associated with the weakening of urban cooling connectivity. Rebuilding low-resistance ventilation corridors and strengthening ecological links among cold sources, supported by nature-based solutions, are critical for restoring cooling network functions and enhancing urban climate resilience.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1816065</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1816065</link>
        <title><![CDATA[Circular valorization of acid-hydrolyzed fishery wastewater: integrated remediation and enhanced C-phycocyanin production by Spirulina sp.]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Charith Akalanka Dodangodage</author><author>Geethaka Nethsara Gamage</author><author>Ranoda Hasandee Halwatura</author><author>Jagath C. Kasturiarachchi</author><author>Thilini A. Perera</author><author>Sanjitha Dilan Rajapakshe</author><author>Sayuri S. Niyangoda</author><author>Rangika Umesh Halwatura</author>
        <description><![CDATA[The valorization of fishery-derived wastes through microalgal biotechnology provides a practical route to integrate wastewater remediation with high-value bioproduct generation. This study demonstrates an integrated process in which solid fish waste was converted into an algal growth medium via sulfuric acid hydrolysis (3% H2SO4; 1:8, w/v) combined with autoclave-assisted pretreatment (121 °C, 20 min), and subsequently utilized for the mixotrophic cultivation of Spirulina sp. in bench-scale photobioreactors. The resulting hydrolysate contained a chemical oxygen demand (COD) of 2,897 ± 28 mg L-1, NO3−–N of 284.89 ± 11.04 mg L-1, and PO43-–P of 130.23 ± 0.47 mg L-1 at an adjusted pH of 9.10 ± 0.05. Process optimization identified a 75% (v/v) hydrolysate concentration and an irradiance of 180 μmol photons m-2 s-1 as the most effective operating condition. Under this optimized regime, the culture reached a maximum biomass concentration of 2.10 ± 0.03 g L-1, representing an 11.1-fold increase relative to the autotrophic BG-11 control. Simultaneously, robust nutrient polishing was achieved, with NO3−–N and PO43-–P removal efficiencies of 96.6% and 93.2%, respectively. Concurrently, pigment synthesis was significantly enhanced, delivering an intracellular C-phycocyanin content of 66.00 ± 0.85 mg g-1 DW (a 14.7-fold increase over the control) and a volumetric productivity of 10.66 ± 0.20 mg L-1 d-1. Lipid accumulation also increased in the hydrolysate-grown biomass (12.25%) versus the control (5.87%). Overall, the proposed circular bioprocess confirms that acid-hydrolyzed fishery waste serves as a highly effective substrate for simultaneous bioremediation and high-yield C-phycocyanin production, establishing a scalable resource recovery strategy for the fishery industry.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1797545</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1797545</link>
        <title><![CDATA[Enhanced weather classification using xception with SENet and attention mechanisms]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gunjan Shandilya</author><author>Sheifali Gupta</author><author>Abdul Khader Jilani Saudagar</author><author>Sunnia Ikram</author><author>Ateeq Ur Rehman</author><author>Isabel De la Torre Díez</author><author>Heba G. Mohamed</author><author>Ramón Pali Casanova</author><author>Ángel Kuc Castilla</author><author>Upinder Kaur</author>
        <description><![CDATA[IntroductionWeather classification plays a crucial role in applications such as environmental monitoring, disaster management, and smart city infrastructure. Accurate and efficient classification of weather conditions from images remains a challenging task due to variations in illumination, texture, and atmospheric conditions.MethodsThis study proposes an efficient deep learning framework for multi-class weather classification by integrating the Xception architecture with Squeeze-and-Excitation (SE) blocks and a spatial attention mechanism. Transfer learning with pre-trained ImageNet weights was employed, and a comparative analysis was conducted using EfficientNet-B3, ResNet152V2, and Xception architectures. The proposed enhanced Xception model incorporates channel-wise recalibration and spatial feature refinement to improve representational capability. The model was trained and evaluated on the Multi-Class Weather Dataset (MWD), which consists of 1,125 images categorized into four classes: sunshine, cloudy, rain, and sunrise. To ensure robustness and generalization, 5-fold cross-validation, statistical significance testing, calibration analysis, and robustness evaluation under image perturbations were performed.ResultsThe proposed model achieved a classification accuracy of 99.06% on the test set. Additionally, it attained a macro precision of 98.3%, macro recall of 97.7%, and macro F1-score of 98.0%. The model demonstrated strong generalization capability and robustness under varying perturbation conditions, with only moderate computational overhead.DiscussionThe integration of SE blocks and spatial attention significantly enhances feature representation by emphasizing informative channels and spatial regions. Compared to baseline architectures, the proposed framework shows superior performance in terms of accuracy and robustness. These results indicate that the model is well-suited for real-world weather classification applications, particularly in intelligent environmental monitoring systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1757914</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1757914</link>
        <title><![CDATA[Forecasting the total carbon allowance cap under emission-reduction targets using a hybrid path analysis and supervised machine learning framework]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xin Wang</author><author>Wenxiu Hu</author><author>Li Bai</author><author>Wei Chang</author><author>Xinli Yu</author>
        <description><![CDATA[Carbon allowances constitute a foundational component of national carbon emission control frameworks, as they govern the equitable distribution of subsequent allocations and directly shape the overall effectiveness of greenhouse gas mitigation strategies. However, the temporal evolution of carbon allowances is inherently complex, high-dimensional, and nonlinear, thereby posing substantial challenges to the rigorous prediction of the aggregate allowance cap. Although artificial intelligence technologies have achieved substantial advances in environmental forecasting in recent years, existing predictive approaches often prioritize predictive accuracy while neglecting systematic variable selection and structured modeling procedures, thereby constraining their utility for policy-oriented decision support. To address this limitation, we propose a hybrid modeling framework that integrates path analysis with supervised machine learning to forecast China’s future carbon allowance cap. Path analysis was first applied to disentangle both direct and indirect relationships among the variables, thereby enabling the identification of structurally significant predictors with substantial explanatory power. Based on the selected indicators, a standardized dataset was constructed to train and systematically compare multiple supervised machine learning algorithms. Empirical results demonstrate that, under uniformly regularized data conditions, Gaussian Process Regression (GPR) consistently outperforms alternative supervised learning algorithms in terms of predictive accuracy and robustness. The integrated forecasting framework developed herein provides a robust analytical foundation for identifying the determinants of carbon allowance trajectories and illustrates how machine learning can be effectively integrated with environmental datasets to inform carbon governance, strengthen climate mitigation pathways, and advance data-driven environmental decision-making under realistic emission reduction targets.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1803239</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1803239</link>
        <title><![CDATA[Protected areas and renewable energy consumption: evidence from panel data across multiple countries]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chunbu Du</author><author>Yuhao Li</author><author>Hangda Li</author>
        <description><![CDATA[Against the global backdrop of accelerating climate change and the urgent need for energy transition, understanding the role of protected areas in shaping renewable energy consumption has become increasingly important. Using panel data covering more than 150 countries worldwide from 2013 to 2022, this study examines the extent to which protected areas shape national renewable energy consumption patterns. The results show that countries with larger shares of terrestrial and marine protected areas tend to exhibit significantly higher proportions of renewable energy in final energy consumption, indicating a robust positive relationship between protected areas and the greening of national energy systems. Further analysis reveals that the expansion of protected areas promotes renewable energy consumption primarily by constraining the development space of resource-intensive industries and reducing economies’ dependence on natural resource rents. This effect is more pronounced in countries with stronger green technological capabilities, where institutional constraints are more effectively translated into technological substitution. In addition, higher accessibility to clean fuels enables environmental and health preferences to be more readily converted into actual clean energy consumption, thereby strengthening demand-side dynamics. Moreover, differences in urban development levels, governance capacity, and income levels across countries lead to heterogeneous effects of protected areas on energy structures. Overall, the findings suggest that protected areas not only function as critical instruments for biodiversity conservation but also provide important institutional foundations for advancing low-carbon energy systems by reshaping resource utilization patterns, promoting green technological adoption, and strengthening societal environmental awareness.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1807424</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1807424</link>
        <title><![CDATA[Environmental criticism in light of environmental law legislation]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Noor Alhendi</author><author>Asem Baniamer</author>
        <description><![CDATA[This study examines environmental criticism as a contemporary analytical approach and explores its relationship with environmental law. It adopts a comparative analytical framework to assess the extent of convergence and divergence between ecocritical principles and legal rules governing environmental protection. The study identifies key elements of environmental criticism and traces their presence within environmental law at both international and national levels. The findings reveal partial alignment between the two fields, particularly in principles such as sustainability and intergenerational justice, while highlighting significant gaps in ethical and ecocentric dimensions. The study concludes by proposing an ecocritical framework to enhance environmental law and strengthen its responsiveness to contemporary ecological challenges.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1766468</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1766468</link>
        <title><![CDATA[The Atriplex genus: a promising resource for phytoremediation of heavy metal and metalloid-contaminated sites in arid regions]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Stanley Lutts</author><author>Rania Zaghdoudi</author><author>Nolan Regnier</author><author>Walid Zorrig</author><author>Souhir Sghayar</author><author>Salima Benazzouk</author><author>Chedly Abdelly</author><author>Ahmed Debez</author><author>Juan-Pablo Martínez</author><author>Monika Patel</author>
        <description><![CDATA[Phytoremediation of areas polluted by heavy metals and toxic metalloids is challenging, particularly in arid regions where limited water availability compromises plant establishment. This study shows that several Atriplex species (A. halimus, A. canescens, A. nummularia, A. atacamensis, A. hortensis, etc.) can be used in a phytostabilization and/or phytoextraction strategy. Many of these species are xero-halophytic plants with C4 metabolism that are adapted to harsh conditions. A deep root system allows contaminants to be removed from deep within the soil. These species can accumulate heavy metals and toxic metalloid elements and develop tolerance mechanisms associated with the synthesis of osmoprotective compounds (proline and glycinebetaine), phytochelatins and metallothioneins, and endogenous antioxidant compounds. Sequestration of toxic elements in cell walls or excretion into leaf trichomes contributes to this tolerance. In many cases, transfer factors greater than 1 suggest that these plants can be used to decontaminate polluted sites. Adult plants can produce 3 t.ha-1 of dry matter, and the quantities of pollutants effectively removed from the soil can be significant (from 500 g to several kg per hectare in a single harvest, depending on the pollutant). The application of chelating agents can be useful in increasing the bioavailability of toxic elements, and fertilization, mainly with nitrogen, may be required when the soil is not very fertile and a high plant density (up to 4,000 plants per hectare) is used to help combat erosion. Species of the genus Atriplex are characterized by significant intraspecific genetic variability, and their use therefore requires prior identification of the material best suited to the various pollutants present. Many species of this fascinating genus constitute particularly promising plant material for the low-cost management of large areas of polluted land in arid regions, helping to combat erosion, gradually decontaminate the soil, and restore the ecological balance of marginal areas.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1799981</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1799981</link>
        <title><![CDATA[Policy-driven industrial transformation and associated agriculture–ecology–economy nexus shifts within social–ecological system: a case of the greenest coastal province of China]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chong Jiang</author><author>Mingrui Liao</author><author>Yunzhe Hu</author><author>Yixin Wang</author><author>Yutong Guan</author><author>Buqing Wang</author><author>Yuexin Xiao</author>
        <description><![CDATA[A rising human impact alters landscape patterns and determines the agriculture–ecology–economy nexus (AEEN), which further threatens the sustainability of social–ecological system (SES). However, the mechanisms through which policy interventions drive AEEN evolution and regime shifts are not sufficiently elucidated. We adopted the Fujian Province, which is undergoing intensive policy interventions, rapid socioeconomic development, and ecosystem restoration, as a case study to examine SES evolution and its responses to diverse interventions. Vegetation greening enhanced carbon sequestration and reduced soil loss, while built-up area expansion rapidly occupied croplands, and aquaculture development squeezed coastal zones. Macroeconomic policies and industrial structure upgrades necessarily promote economic growth, which results in employment structure adjustments and population redistribution. The unequal urban–rural developments led to rural population loss and urban–rural resident migration; thus, the dominant role of rural residents in the employment structure for primary sector was gradually substituted by urban employment population for second and tertiary sectors. Regarding agricultural production, the sown area declined, and planting structure adjustments since 1997 led to a substantial decline in grain crop productivity. Regime shifts in nexus sectors reversed the pairwise interactions from co-benefits in the agriculture- (1978–1997) and ecology- (2017–2022) dominated phases to tradeoffs in the economy-dominated (1998–2016) phase, which were driven by compound policy interventions and historical events. Our findings are expected to enrich the understanding of AEEN interplay, SES governance, and support strategy formulations for regionally coordinated development of agriculture, ecology, and economy in Fujian and beyond.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2025.1633679</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2025.1633679</link>
        <title><![CDATA[Wetland ecological compensation: a study on willingness to pay and its determinants]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shuaiyu Lu</author>
        <description><![CDATA[IntroductionCoastal wetlands provide critical ecosystem services but face increasing threats from human activities. Although ecological compensation mechanisms are globally recognized, research on tourist willingness-to-pay (WTP) for wetland conservation remains limited, particularly in developing countries. This study investigates this gap using the Red Beach Wetland in China, an internationally important coastal ecosystem, as a case study.MethodsA contingent valuation survey was administered to 320 visitors at the Red Beach Wetland in 2023. The questionnaire collected data on demographics, attitudes, and payment preferences. Binary logistic regression was employed to identify factors influencing WTP, while descriptive statistics were used to analyze payment levels and allocation preferences.ResultsThe results indicated that 83.75% of tourists were willing to pay for conservation, with an average annual WTP of 26.81 CNY (3.78 USD). Significant positive predictors of WTP included higher income (OR = 1.38), education level (OR = 1.47), environmental concern (OR = 1.37), and trust in governance (OR = 1.29). The primary reasons for non-participation were financial constraints (58% of non-payers) and distrust in management (13%). Visitors showed a strong preference for allocating funds to water quality improvement (30%) and favored an entrance fee surcharge (42%) as the payment mechanism.DiscussionThe high WTP underscores the potential of tourists as a key stakeholder group in financing coastal wetland conservation. The findings support the implementation of tiered payment options, enhanced visitor education, transparent fund management, and targeted communication strategies to optimize participation and address equity concerns. This study provides both theoretical insights into WTP determinants and practical tools for designing effective ecological compensation programs. Future research should examine longitudinal WTP patterns and test innovative payment mechanisms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1769038</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1769038</link>
        <title><![CDATA[Carbon emission reduction under import competition: a green breakthrough or burdened endeavor?]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mingrong Wang</author><author>Longnan Ma</author>
        <description><![CDATA[In the dual context of trade liberalization and the low-carbon transition, understanding how import competition shapes corporate carbon emissions has become a critical question. This study introduces an innovative “dual-track competition” framework and uses panel data of Chinese listed companies from 2010 to 2023 to examine the impact of import competition on corporate carbon emissions, yielding four key findings: First, import competition significantly increases firms’ carbon emissions. Second, import competition promotes green innovation, which reduces carbon emissions and generates a “green breakthrough effect.” However, it simultaneously reduces corporate energy utilization efficiency and compresses cost-plus markups, generating a “heavy burden effect.” Third, the emission-increasing impact of import competition is primarily driven by the dominance of the “cost-survival race” over the “green innovation race.” Finally, the emission-increasing effect of import competition is weaker for firms engaging in green mergers and acquisitions, firms facing relatively mild financing constraints, and firms operating in clean and low-technology industries. These findings provide important insights into the dual nature of import competition on carbon emissions, thereby advancing the understanding of how trade liberalization interacts with corporate environmental performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1751718</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1751718</link>
        <title><![CDATA[Atmospheric and meteorological responses during the April 8, 2024, total solar eclipse: advancing workforce development through experiential learning]]></title>
        <pubdate>2026-04-30T00:00:00Z</pubdate>
        <category>Community Case Study</category>
        <author>Debanjana Das</author><author>Ricardo Sakai</author><author>Adrian Flores</author><author>Nakul N. Karle</author><author>Zhifeng Yang</author><author>Ujjawal Shah</author><author>Rocio D. Rossi</author><author>Ivan Sloan</author><author>Sen Chiao</author><author>Belay B. Demoz</author>
        <description><![CDATA[On 8 April 2024, a total solar eclipse affected the eastern United States, creating a natural experiment to examine short-term atmospheric responses to an abrupt reduction in solar radiation. We collected observations at the Howard University Beltsville Research Campus (HUBC; 39.05° N, 76.88° W), where ground-based and upper-air measurements captured pre-, during-, and post-eclipse conditions under mostly clear skies. At HUBC, peak obscuration reached ∼89% at 15:20 EDT. Downwelling shortwave irradiance decreased from clear-sky values >900 W m-2 to <20 W m-2 (≈98% reduction). Near-surface air temperature declined from 16.3 °C to 13.5 °C (−2.8 °C), lagging maximum obscuration by ∼10 min, while relative humidity increased from ∼28% to ∼34% (a ∼22% increase), consistent with cooling. Surface wind speeds weakened by ∼1 m s-1. HALO Doppler lidar indicated suppressed vertical velocity variance (SD (w) ∼0.5–0.8 m s-1 at peak), consistent with reduced turbulent mixing and transient stabilization of the convective boundary layer. Microwave radiometer profiles showed cooling strongest near the surface with diminished lapse rates aloft. These findings are consistent with an eclipse-induced stabilization of the lower atmosphere and disruption of the normal diurnal cycle. Representing a detailed case study from the Mid-Atlantic, our results contribute to the growing body of eclipse meteorology literature. This effort also served as a powerful STEM education opportunity, engaging students in hands-on fieldwork and data collection, thereby highlighting the value of eclipse events as natural laboratories for both scientific investigation and public engagement.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1734724</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1734724</link>
        <title><![CDATA[Exploring land use/land cover changes and their effect on land surface temperature and urban heat island: a case study of Lahore, Pakistan]]></title>
        <pubdate>2026-04-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fahad Shah</author><author>Suiliang Huang</author><author>Hui-Gwang Yun</author><author>Md. Omar Sarif</author><author>Ayyoob Sharifi</author>
        <description><![CDATA[This study investigates the intensity of Land Surface Temperature (LST) and the Urban Heat Island (UHI) effect across Lahore District and its surrounding urban and peri-urban areas. Landsat satellite data were utilized to examine the spatiotemporal patterns of LST, identify UHI zones, and assess changes in land use and land cover (LULC), the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), and the Urban Thermal Field Variance Index (UTFVI). The built-up area exhibited rapid expansion, increasing from 22.99% in 2000 to 36.06% in 2010 and 47.17% in 2020, while the vegetation/agriculture land cover declined from 53.21% in 2000 to 46.16% in 2020. A substantial rise in mean LST was also observed, from 33 °C in 2000 to 34.8 °C in 2020. The results revealed a strong positive correlation between LST and NDBI, with R2 values of 0.7549 in 2000 and 0.6192 in 2020, whereas a strong negative correlation was found between LST and NDVI, with R2 values of 0.6141 in 2000 and 0.4753 in 2020. These findings provide comprehensive insights into the influence of LULC dynamics on LST and UHI intensity, offering valuable guidance for environmental engineers and urban planners in promoting sustainable urban development.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1811768</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1811768</link>
        <title><![CDATA[Multi-criteria ranking of renewable energy alternatives for smart grids using expert-weighted TOPSIS]]></title>
        <pubdate>2026-04-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wenfeng Geng</author><author>Sen Song</author>
        <description><![CDATA[IntroductionHigh penetration of renewable energy sources increases operational variability and planning uncertainty in smart grids. Transparent prioritization of technologies is required before detailed sizing and dispatch analysis.MethodsA decision support framework based on expert weighted Technique for Order Preference by Similarity to Ideal Solution is applied. Four alternatives, solar PV, wind, hydropower, and biomass, are assessed using four criteria: efficiency, cost, emissions, and resource availability. Criteria weights from pairwise comparisons are 0.562, 0.254, 0.125, and 0.059.ResultsCloseness coefficients from the weighted normalized matrix rank the options as follows: solar PV (0.9211), wind (0.6220), hydropower (0.3759), and biomass (0.0253). Sensitivity analysis with ±30% weight variation and equal weighting preserves the ranking.DiscussionThe framework is shown to be transparent, robust, and easy to replicate. Adaptation to other regions is enabled through updated local data and stakeholder defined weights.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fenvs.2026.1799258</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fenvs.2026.1799258</link>
        <title><![CDATA[Research on photovoltaic power generation based on multi-dimensional indicators and models]]></title>
        <pubdate>2026-04-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Pengying Fan</author><author>Zhenlin Chen</author><author>Yile Wang</author>
        <description><![CDATA[IntroductionPhotovoltaic (PV) power generation is vital for sustainable energy and carbon reduction, yet existing studies often focus on single aspects, lacking integrated planning support.MethodsThis study develops a framework combining power forecasting, optimization, and carbon assessment using a multidimensional indicator system, PCA, t-SNE, and PSO.ResultsA 1% increase in PV generation could reduce China’s power sector carbon emissions by 2.05% by 2035; the model achieved an R2=0.9975.DiscussionThe framework supports regional energy planning, though future models should incorporate policy shifts and market dynamics.]]></description>
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