Earth and Environmental Sciences

Over the past 15 years, the planetary boundaries (PB) framework has advanced as a scientific approach to define limits for Earth’s system processes, including boundaries for nitrogen (N) and phosphorus (P). These boundaries have been conceptualized and quantified using various approaches, leading to varying estimates and interpretations. In this paper, we review existing control variables and methods used to define and quantify ‘safe’ N and P boundaries for environmental impacts and ‘just’ N and P boundaries for food production. Based on our analysis, we propose that ‘N losses’ (N delivery to surface water, N leaching to groundwater, and NH 3 emissions to air) and ‘P delivery to surface water’ are the most suitable control variables for defining ‘safe’ boundaries for biogeochemical flows of these elements. We present quantitative estimates for both ‘safe’ planetary N and P boundaries, for N based on previous studies and for P based on new calculations. We then assess the contribution of the food system (including agricultural production, aquaculture, and human wastewater streams) to N and P losses. We propose to define ‘just’ boundaries for N and P based on the required input of these nutrients to meet the dietary needs of the population. We indicate possible values for such ‘just’ N and P boundaries and provide options to attain those boundaries while staying with ‘safe’ N and P boundaries.

Background The built environment plays a pivotal role in shaping cardiometabolic risk by influencing lifestyle behaviours. The 20-Minute Neighbourhood (20MN) concept, promoting accessible, walkable urban areas, has gained traction as a strategy to improve community health. Objectives This study examined whether neighbourhood built environment features, operationalised within a 20-min, are associated with cardiometabolic risk through indirect behavioural pathways over time, using Bayesian Network Analysis (BNA). Our aim was to investigate causal pathways linking the built environment with cardiometabolic risk. Methods A Bayesian network model was constructed using longitudinal data from the North-West Adelaide Health Study (NWAHS), spanning three waves over ten years. Built environment indices were developed using expert input, fuzzy logic, and GIS data within a 1600 m street-network buffer of participants' residences. Results The BNA identified indirect pathways linking built environment variables, particularly the food facilities and public facilities indices, to cardiometabolic risk through modifiable behavioural mediators and downstream biometric change. Specifically, food and public facilities were linked to HbA1c indirectly through fruit and vegetable intake, physical activity, and BMI. The model explained 32.1% of HbA1c variance, with BMI serving as a strong predictor. Centrality measures also identified physical activity and BMI as key bridging nodes within the network. Discussion These findings show that the contribution of neighbourhood built environment features to cardiometabolic risk may operate primarily through indirect behavioural and biometric pathways rather than through simple direct associations. BNA provided a useful framework for identifying these pathways in a longitudinal, policy-relevant urban health context.

Under neoliberal governance paradigms, interactions between state agencies and other urban actors -when they occur- are typically channeled through private-public partnerships and depoliticized “participatory” or “multi-stakeholder” processes. This paper explores various dimensions that enable socially innovative multi-actor collaboration (IMAC), understood as a process of (re)articulation and transformation of networks of actors and collaborative institutional spaces to achieve social innovation. It examines how IMAC may thrive and contribute to transforming hegemonic urban governance models into more democratic and collaborative ones, particularly in contexts of socio-political change. To do so, the paper develops a seven-dimension framework, which combines institutionalist theories from critical political sciences and planning, to provide a nuanced analysis of transformations of and interrelations among actors, institutions and their governance environment. It is applied to investigate transformations in urban food governance in Madrid between 2015 and 2023, a critical case of socially innovative civil-public collaboration in a context of political disruptions and discontinuities. Methodologically, the work combines document analysis, participatory research and interviews. The findings show how different urban actors contribute to and are transformed by IMAC revealing the complexities of their changing and overlapping roles and identities in urban governance. They highlight interdependencies between flexible, evolving, yet stable structures that enable thinking and acting together, fostering IMAC. The study also emphasizes the relevance of hybrid actors like activist-practitioner-(action)researchers, intermediate levels of actor aggregation, and supra-local collaboration networks in consolidating IMAC and ensuring its continuity during political change.

This study advances observed accessibility as a complement to traditional potential accessibility approaches, showing that access to jobs is shaped by employment subcenter-specific mechanisms that mediate who can obtain jobs in those locations. It examines the determinants of functional linkages between workers' places of residence and the three main employment subcenters in the Mexico City Metropolitan Zone: the Central Agglomeration, Santa Fe, and Naucalpan. The analysis uses secondary data on employment subcenters in 2019, a subsample of public transport commuting trips from the 2017 Origin-Destination Survey, and Spatial Autoregressive Combined (SAC) models. Findings reveal differentiated accessibility regimes for public transport commuters. The Central Agglomeration functions as an inclusive metropolitan hub, drawing commuters from across the region, with access not structured by distance but associated with integration into formal labor markets and public transport connectivity. Santa Fe emerges as a socially selective enclave, serving higher-income and more educated commuters. Naucalpan operates as a proximity-based industrial subcenter, attracting workers primarily from adjacent areas. The study makes four contributions to the job accessibility literature. First, it advances observed accessibility as a complement to potential accessibility, shifting the focus from jobs that are theoretically reachable to those that workers actually obtain. Second, it shows that accessibility operates through subcenter-specific regimes rather than as a metropolitan-wide condition. Third, it demonstrates that spatial mismatch varies across employment subcenters. Fourth, it shows that the benefits of public transport depend on the inclusiveness and labor market role of the destinations it connects.

Aboveground carbon (AGC) fluxes from deforestation and subsequent regrowth in tropical moist forest (TMF) are increasingly well characterized, but carbon losses and gains following partial disturbance are uncertain. We synthesized 146 studies quantifying postdisturbance AGC changes relative to undisturbed forests across TMF. Immediate AGC losses (mean ± 1 SD; 2.5 ± 2.3 years after disturbance) following partial anthropogenic disturbances were greatest for forest fires (49 ± 26%), selective logging (34 ± 20%), and edge effects (31 ± 19%). Higher-frequency and -intensity disturbances significantly increased carbon loss. After 20 years of regeneration, AGC stock was higher in recovering degraded forests (41 to 117%) compared to secondary regrowth forests after complete deforestation (1 to 74%), indicating greater regeneration potential when forest structure is preserved. Our compiled database and associated meta-analysis improve accuracy and completeness for carbon inventory reporting and modeling. Substantial AGC losses and gains from distinct degradation and recovery processes are now better characterized, serving as an evidence base for policies to halt degradation and foster recovery for climate mitigation.

Despite the well-established role of interleukin-17 (IL-17)–producing γδ T cells (γδT17 cells) in autoimmune inflammations, key factors to trigger the activation of γδT17 cells remain largely unknown. Here, we show that aryl hydrocarbon receptor (AhR) is markedly reduced upon γδT17 cell activation. AhR deficiency and pharmacological activation promotes and suppresses γδT17 cell activation, respectively. Mechanistically, AhR deficiency strengthens heat shock protein family A member 9 (HSPA9)-mediated competition with F-box and WD-40 domain protein 11(FBXW11) for binding to RBP-j-associated molecule (RAM) domain of Notch intracellular domain (NICD), which impairs FBXW11-dependent NICD ubiquitination to increase NICD occupancy at the Rorc enhancer to drive γδT17 cell activation. Consistently, AhR deficiency increases the proportion of γδT17 cells and the disease severity in psoriasis-like dermatitis mice and colitis mice, which is almost entirely reversed by pretreatment with either adeno-associated virus (AAV)-sh Hspa9 or AAV-sh Notch1 . Collectively, these findings identify AhR deficiency as a key driver of aberrant γδT17 activation, uncover a γδT17 activation mechanism via HSPA9/FBXW11-mediated suppression of NICD ubiquitination, and provide a strong mechanistic basis for AhR agonists as therapeutics in autoimmune inflammations.

Deep convective clouds are frequently associated with extreme weather events such as torrential rainfall and thunderstorms, making their accurate identification critical for improving short-term weather forecasting and enabling effective disaster early warning. Most existing deep learning approaches for cloud classification rely on conventional convolutional neural networks (CNNs), which suffer from limited receptive fields and struggle to capture long-range contextual dependencies essential for robust cloud segmentation. To overcome this limitation, we propose DIF-UNet, a novel U-Net architecture that combines a Swin Transformer and a CNN-based residual network as dual encoders. The model introduces two key components: (1) a Semantic Information Fusion Module (SIFM) that adaptively integrates heterogeneous semantic features from both encoders using learnable parameters and (2) a Cross-Layer Feature Interaction Module (CLFI) that enhances hierarchical feature synergy to preserve fine-grained spatial details while reducing information loss. Evaluated on FY-4A multi-channel satellite observations, DIF-UNet achieves state-of-the-art performance with an intersection-over-union (IoU) score of 77.78% and an F1 score of 87.39%, outperforming baseline models by 3.84% and 2.49%, respectively. These results demonstrate that explicitly modelling both local and global contextual information significantly improves deep convective cloud recognition accuracy.

Dark clayey (‘old blue’) tills occur at multiple locations throughout central Sweden. They have drawn scientific interest due to their over-consolidated state from ice sheet over-riding and high clay contents. These tills are overlain by younger glacial sediments and therefore predate the last deglaciation, but their absolute ages remain uncertain. This study evaluates the residence time and chemical weathering of a dark clayey till at Forsmark, east-central Sweden, using meteoric 10 Be and major oxide and trace element geochemistry, in the context of assessments of post closure safety of a planned repository for long-lived high-level spent nuclear fuel. The Forsmark landscape in the late Quaternary has periodically been a subglacial, subaquatic, or subaerial environment. Because 10 Be has been supplied to sediments in each of these environments, we also measured meteoric 10 Be in younger tills at Forsmark and tills from three sites above the highest postglacial shoreline, including dark clayey till at Bograngen. We then evaluated the 10 Be inventory of the Forsmark dark clayey till in the context of two end-member till-deposition scenarios; early last ice sheet advance (MIS 2-3) or before MIS 3. The most likely scenario is that the residence period for the dark clayey till at Forsmark starts in early MIS 2-late MIS 3. In this scenario, the advancing last ice sheet sourced fine-grained till components from sedimentary rocks and clay-rich sediments deposited in the Bothnian Sea during the MIS 3 interstadial. These sediments were then compacted as ice thickened to its Last Glacial Maximum configuration and subsequently overlain by a deglacial sandy-silty till. Dark clayey tills may not represent one specific date prior to the last deglaciation. Rather, they are a distinct sediment facies sourced from water-lain deposits, which accumulated during interstadial or interglacial conditions, and fine-grained sedimentary rocks, and were emplaced and compacted during ice sheet advances. All sediments studied at Forsmark and Bograngen display minimal chemical weathering. This conclusion is best elucidated from ACNK diagrams, where the sediment compositions approximate those of parent rocks, and is supported by a lack of indications of leaching or enrichment of elements contained in major oxides or of REE-Y along vertical sediment profiles. Minimal chemical weathering in these sediments is consistent with young ages (MIS 1 – late MIS 3) but is not in itself age diagnostic. Sediments at the remaining two sites from above the highest shoreline have the most 10 Be and show slight illitization of plagioclase, which together provide evidence that these samples have a longer (pre-Holocene) history of subaerial exposure. Our interpretation that the dark clayey till at Forsmark dates from early in the last period of ice cover indicates active erosion of bedrock and sediments by the ice sheet. In the context of performing assessments of post-closure repository safety over future glacial cycles, the Forsmark dark clayey till is therefore an inappropriate analogue for soil development through multiple ice-free interglacial/interstadial periods and for the long-term evolution of groundwater buffering capacity, which is governed by bicarbonate generated through chemical weathering.

Urbanization is a major driver of land-use change and ecological shifts, especially in semi-arid regions with high environmental sensitivity. This study examined urban land growth and its ecological impacts in Astana, Kazakhstan, from 2000 to 2020 and forecasted trends for 2030. Landsat imagery was classified using a Support Vector Machine (SVM) approach, and ecological conditions were assessed through spectral indices, including Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), a Tasseled Cap Wetness index (Wet), and a Normalized Difference Bare-Soil and Built-up Index (NDBSI). The Future Land Use Simulation (CA–Markov) model simulated land use under Business-as-Usual (BAU) and Ecological Priority (EP) scenarios. The results showed a significant increase in built-up land, mainly at the expense of cropland and grassland, with increased landscape fragmentation and rising LST, indicating intensifying urban heat. Ecological indices showed spatially varied responses, with localized greening in protected areas and overall environmental pressure in expanding zones. Scenario simulations suggest that policy interventions under the EP scenario can mitigate cropland loss, limit fragmentation, and enhance ecological connectivity compared with BAU. Overall, the findings show that integrating remote sensing, machine learning, and scenario modeling offers an effective framework for assessing urban–ecological dynamics and supports evidence-based planning for sustainable urban development in semi-arid cities.

Although climate-smart agricultural practices are increasingly promoted, comparative environmental and economic assessments across multiple practices and crops remain limited. This study evaluates five climate-smart agricultural practices in Dutch potato and onion production, including soil management, biodiversity enhancement, sustainable irrigation systems, crop protection, and green energy use. It compares them with conventional production systems using integrated Life Cycle Assessment and Life Cycle Costing. Specifically, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methodologies were applied to assess the environmental and economic sustainability of the studied systems, respectively. Among the evaluated practices, soil management exhibited the best overall environmental performance for both crops, achieving reductions of up to 42% and 66% in greenhouse gas emissions for potatoes and onions, respectively, compared with the baseline under the modelled conditions. Biodiversity measures significantly reduced freshwater eutrophication and ecotoxicity-related impacts, particularly in potato cultivation, while crop protection practices mainly improved pesticide-related toxicity categories. Similarly, soil management and biodiversity demonstrated the best economic performance, with profits increasing to approximately €3318/ha and €3121/ha for potatoes and €3898/ha and €3694/ha for onions, respectively, compared with baseline profits of €2879/ha and €3526/ha. The results suggest that the implementation of CSA practices can improve both the environmental and economic sustainability of intensive vegetable production systems under the modelled Dutch conditions, although the effectiveness of each practice depends strongly on crop-specific environmental hotspots and management assumptions. The findings provide evidence to support farmers and policymakers in selecting cost-effective climate-smart practices while identifying priorities for future field validation and uncertainty assessment.

Environmental risk assessment of landfill solid waste should include higher plants as indicators of toxicity to support sustainable waste management. This study evaluated solid waste from mining and power-generating industries using SANS 10234, a multidisciplinary approach that combined physicochemical analysis of waste samples and the Phytotoxkit bioassay to assess plant-based toxicity. Leachate extractions from samples identified as wastes of concern were evaluated using standard toxicity tests. Based on the Phytotoxkit results, Coal solid waste A, Clinker ash, and Chrome solid waste were identified as wastes of concern, whilst the whole effluent toxicity test results of the leachate indicated that these three samples pose a low risk to aquatic ecosystems. The phytotoxicity was attributed to the waste’s physicochemical parameters. Coal solid waste A expressed a low pH of 2.60 and a high electrical conductivity of 14,557 µS/cm, while Clinker ash presented a pH of 6.72 and an electrical conductivity value of 3685 µS/cm. These values were outside the optimal range for many plants to thrive. The presence of toxic elements in some samples may have contributed to the observed phytotoxicity. These results highlight that solid waste can present varying risks to both terrestrial and aquatic systems, reinforcing the importance of including higher plants in landfill risk assessments for sustainable waste management.

This study addresses the disconnect between environmental impact assessment (EIA) outputs and construction contract management, which limits the practical effectiveness of environmental decision-making in project delivery. To bridge this gap, ImpactPredict—a data-driven decision-support framework—is developed to integrate environmental impact data with environmental-based contractual risk assessment. The methodology combines: (1) severity–likelihood environmental scoring with contractual weighting to generate quantitative indicators of claim likelihood before and after mitigation; (2) developing the proposed framework using Microsoft Excel and Power BI; (3) validation using six case study energy projects in Egypt, enabling cross-case comparative analysis; and (4) statistical analysis to test the model’s sensitivity and uncertainty. The results show consistent reductions across all projects, with mitigation leading to an average 40% risk reduction across all case studies, and significant decreases in predicted claims. Linear regression analysis between initial contractual risk (CR) and residual contractual risk (RCR) produced the predictive equation RC^R = 4.93 + 0.351(CR). The regression coefficient and hypothesis testing (t = 3.367, p = 0.028 < 0.05) provide preliminary evidence that initial contractual risk is a statistically significant predictor of residual contractual risk. The coefficient of determination (R2 = 0.758) indicates that approximately 75.8% of the variance in residual risk is explained by the initial risk conditions. In addition, low prediction error values (mean absolute error = 1.17; root mean square error = 1.28) demonstrate satisfactory predictive stability and model reliability. The sensitivity analysis indicates that the model exhibits proportional responsiveness to all input variables, with severity and likelihood identified as dominant drivers of risk magnitude, while contractual weighting governs risk translation into project performance outcomes. These findings confirm that environmental impacts can be operationalized as quantifiable contractual risk drivers. The study concludes that embedding contract-integrated environmental intelligence within accessible analytical platforms enhances decision-making, supports measurable performance improvement, and transforms EIA into a proactive risk management tool.

Extreme heatwaves threaten public health and economies. Using multi-source data from 1964–2023 and the Excess Heat Factor (EHF), we identified heatwaves and, with a generalized linear mixed model and Hurst-based intensity forecasting, assessed drivers and future trends across Belt and Road Initiative (BRI) regions. (1) Duration, frequency, and the number of events increased by 18.7 days, 21.2 days, and 5.5 events, respectively. During the 2004–2023 period, HWD, HWF, and HWN accelerated, expanding from South Asia/Middle East to Central Asia, the Caucasus, and North Asia. In 1994–2023, centroids shifted west/south: frequency 2.54° W, 1.83° S; and intensity 1.17° W, 2.79° S. (2) Between 2000 and 2019, interaction effects exceeded single effects; dominant drivers shifted from SPEI and wind speed to shortwave radiation and NDVI. (3) Future intensification peaks in East Asia, the Iranian Plateau, and China’s east coast; with H ≥ 0.7, enhanced areas exceed 33% (max 37%), concentrated in Central and western West Asia.

Under global climate change, shifts in the suitable distribution of forest vegetation have become an important issue in ecology and biogeography, closely linked to forest biodiversity conservation and terrestrial ecosystem sustainable development. Birch forests are widely distributed across cold-temperate, temperate, and montane regions in China, but different birch forest types may vary in their environmental adaptations and spatial responses to climate change. In this study, three representative birch forest vegetation types in China, namely Betula utilis forest, Betula albosinensis forest, and Betula ermanii krummholz, were selected for comparative analysis. Based on vegetation distribution records and environmental variables, an optimized MaxEnt model was constructed using ENMeval to identify current suitable distribution patterns, key environmental drivers, and future habitat changes under climate change scenarios. The results showed that the three birch forest types differed markedly in current suitable distribution patterns. Betula utilis forest was mainly concentrated in the Qinling Mountains, Betula albosinensis forest showed a broader montane distribution pattern, and Betula ermanii krummholz was restricted to high-altitude or high-latitude cold habitats. Climatic factors were the dominant drivers of suitability, but the key environmental variables differed among the three vegetation types, indicating niche differentiation along temperature, precipitation, and elevation gradients. Under future climate scenarios, the suitable habitats of the three types showed type-specific changes in area, spatial stability, and centroid migration. Betula utilis forest and Betula albosinensis forest mainly exhibited regional spatial adjustment and partial expansion, whereas Betula ermanii krummholz showed stronger dependence on high-elevation cold habitats and more limited spatial adjustment capacity. These findings indicate that different birch forest vegetation types in China do not respond uniformly to climate change. The study provides a vegetation-type-specific basis for identifying stable suitable areas, potential expansion areas, and climate-sensitive habitats, and can support adaptive management and conservation planning for montane forest vegetation helping advance the implementation of Sustainable Development Goal 15 (SDG15) and long-term sustainability of mountain forest ecosystems under future climate change.

This study aims to evaluate the cradle-to-gate environmental impacts of industrial-scale lyocell fiber production in Türkiye using site-specific foreground data. The assessment was conducted in accordance with ISO 14040 and ISO 14044 using SimaPro 9.4 software and the Ecoinvent v3.7.1 database, with a declared unit of 1 kg of lyocell fiber at the facility gate. The results indicate that climate change, fossil resource use, freshwater use, and land use are the most relevant impact categories within the evaluated system. The total Global Warming Potential was calculated as 4.13 kg CO2 eq/kg fiber. Contribution analysis showed that the production stage was the dominant source of climate change impacts, followed by raw material supply, transportation, pulp production, and waste management. Electricity consumption, steam generation, dissolving pulp production, and transportation logistics were identified as the main environmental hotspots. A screening-level sensitivity assessment further indicated that electricity supply is a key improvement lever, with photovoltaic electricity substitution showing substantial potential for reducing GWP. The findings provide site-specific evidence for industrial lyocell production in Türkiye and demonstrate the value of primary LCA datasets for hotspot identification, product-level environmental reporting, sustainability benchmarking, and possible future EPD development.

The global necessity for sustainable development has prompted a significant re-evaluation of practices across diverse industries, notably influencing the cosmetic sector’s approach to ingredient management. To address this imperative, the L’Oréal Group launched its “Green Sciences” initiative, aiming to support the transition toward more sustainable cosmetic ingredients. This article introduces the Green Sciences Index (GSI), an assessment tool designed for a structured and standardized evaluation of cosmetic ingredient sustainability to assist innovators in selecting ingredients during the early stages of development. Adopting a life-cycle perspective, the proposed framework assesses feedstock origin, ingredient manufacturing processes, and the environmental impact of an ingredient’s end-of-life. The GSI was specifically designed with a focus on the global manufacturing pathway, from primary feedstocks to the final ingredients. It integrates an assessment framework inspired by the Principles of Green Chemistry for cosmetic ingredients, encouraging specific sustainable manufacturing practices, structured around “Biotechnology and Fermentation”, “Eco-extraction and Physical Processes”, and synthesis by “Green Chemistry”. The practical application of the GSI is demonstrated through a series of concrete case studies featuring diverse ingredients. The GSI is an eco-design lever serving as a pre-screening orientation tool at the ingredient level, complementing comprehensive Life Cycle Assessments to guide development and innovation toward more sustainable cosmetic products.