Developing low-carbon cementitious materials is essential for reducing greenhouse gas emissions in the construction sector, particularly for marine infrastructure dedicated to renewable marine energies. However, their potential ecotoxicological effects remain poorly documented. This study evaluated the chemical and biological effects of leachates from three cement types (CEM I, CEM III, and CEM V) under controlled saline conditions simulating seawater exposure. Leachates were chemically characterized using inductively coupled plasma optical emission spectrometry (ICP-OES), and their ecotoxicity was assessed using a multi-species approach that included sea urchin fertilization (Paracentrotus lividus), microalgal growth (Phaeodactylum tricornutum), bacterial bioluminescence (Aliivibrio fischeri), and microbial functional responses (growth, biofilm formation, and quorum sensing). A Toxicological Priority Index-based approach was used to integrate toxicity across biological endpoints. Targeted trace elements were not quantified under the analytical conditions used, whereas concentrations of major elements, including Ca, Al, Si, and K, varied with binder composition. However, the sensitivity of ICP-OES in the saline matrix did not allow the presence of trace elements at lower concentrations to be excluded. Despite these relatively moderate dissolved concentrations, all leachates induced measurable biological effects. CEM I exhibited the highest overall toxicity, strongly inhibiting sea urchin fertilization, microalgal growth, and bacterial activity. In contrast, CEM III and CEM V showed lower overall toxicity but more pronounced effects on specific microbial functions. These results indicate that toxicity cannot be directly inferred from trace-element concentrations and likely reflects a combination of physicochemical changes associated with cement dissolution, including pH, alkalinity, ionic composition, and potentially other dissolved constituents. Therefore, although low-carbon cements reduce CO2 emissions, their distinct compositions can alter ecotoxicological profiles and, consequently, biological responses in marine environments. These findings highlight the importance of integrating ecotoxicological criteria into the evaluation of cementitious materials to support the development of more sustainable marine infrastructure.

Global wildfire activity has a major impact on regional climates because of the large amount of particulate matter injected into the atmosphere during and after the biomass burning. If its evolution has been extensively studied in specific regions of the globe, very few studies have been presented for the Southern Hemisphere, at a continental scale. This study aims to evaluate wildfire activity across three continents: Southern Africa (SAF), South America (SAM), and Oceania (OCE), and quantify its trend over the last two decades (2004-2024), using Active Fire MODIS satellite data. The first key outcome of this work is the heterogeneity of the wildfire seasonality among the three continents. The Oceanian continent, in particular, showed no clear seasonality during the studied time period, suggesting that different fire regimes co-exist in this area. The SAF and SAM continents exhibited clear seasonal patterns with a 20-week fire activity duration from May to October, characterised by low inter-annual variability for SAF, and a 14-week duration from July to October, marked by high inter-annual variability for SAM. Trend-changing points were determined using the robust Piecewise Linear Functions method, leading to different time slicing for each continent's trends. In terms of fire number (and summed FRP) tendencies, the results show a decrease of -13 (-21)% per decade for SAF (2010-2022), and an increase of +22 (+42)% per decade for SAM (2013-2022). The number (and summed FRP) of the most severe fires follows a slightly stronger decreasing trend for SAF, while their trend triples (almost doubles) for SAM compared to all fires. This may be attributed to an increase in fire activity driven by the sensitivity of wildfires in the SAM region to climatic conditions and their recent changes, as well as to regional land-use practices and fire management strategies.

Wildfire regimes in Mediterranean hotspots like Portugal are driven by anthropogenic factors and fuel build-up, causing severe socio-economic impacts. This study hypothesizes: 1) that seasonally-disaggregated models outperform an overall annual model by revealing shifts between weather- and fuel-driven regimes; and 2) that incorporating fuel memory, via the Fire Return Interval (FRI), enhances predictive accuracy across all weather conditions. Three fire seasons (High: Aug-Sep; Medium: Jul; Low: Oct-Jun) were statistically defined. Hazard maps were generated using a data-driven Spatial Multi-Criteria Evaluation (SMCE) and Simple Additive Weighting (SAW) approach. The FRI was calculated as the 10th percentile (P10) of re-burn intervals (1990-2024) per land cover class and geoclimatic zone; a temporal mask based on this threshold was applied to assign zero hazard to recently burned areas. All models were validated against independent 2019-2024 fire perimeters using the Area Under the Curve (AUC) method. The first hypothesis was partially validated: the seasonal High (AUC = 0.77) and Low (AUC = 0.81) models outperformed the Overall (AUC = 0.73). We found dominant hazard drivers shifted seasonally: the July model (AUC = 0.58) was linked to drought (low number of days per year with precipitation >10 mm, hereafter NDP10), whereas the High, Low, and Overall models were driven by topography, highly flammable land cover (shrublands) and high fuel load (proxied by high NDP10). However, the second hypothesis was refuted for the critical period, as the FRI mask decreased predictive performance in the High-season model. The findings indicate that while seasonality is essential for differentiating hazard drivers, this "fuel memory" is overridden by extreme weather conditions. Management strategies based solely on historical burn patterns are, therefore, insufficient to mitigate hazard in extreme events.

A thorough molecular and environmental evaluation of microbial isolates and indoor air quality (IAQ) at a place of worship is presented in this work. The study used a dual-methodological approach, including systematic environmental monitoring to assess occupant health concerns and 16S rRNA gene sequencing for taxonomic identification. Four different bacterial isolates were successfully identified by molecular analysis using BLAST and phylogenetic reconstruction: Bacillus tropicus (A11), Leclercia adecarboxylata (A12), Acinetobacter sp. (A13), and Escherichia coli (A14). The evolutionary position of isolate A13 indicated possible horizontal gene transfer, underscoring the existence of flexible, opportunistic pathogens within the indoor environment, whereas isolates A11 and A14 demonstrated significant genetic stability. Concurrently, six sessions of air quality monitoring showed a thermally demanding environment, with humidity (56.00-70.51%) and temperatures (29.21-33.79 °C) continuously surpassing ASHRAE guidelines. The average pollutant concentrations are within WHO and USEPA safety criteria, however, there were outliers that may suggest sporadic pollutant penetration (outdoor) or resuspension of dust. The results demonstrate a clear relationship between increased chemical pollutants (TVOCs) and reduced thermal comfort. To reduce the congregation's acute respiratory and cardiovascular risks, optimal ventilation systems and source-control measures are urgently needed, as evidenced by the presence of clinically relevant microorganisms combined with dangerous pollution levels.

The Seto Inland Sea (SIS), Japan's largest semi-enclosed coastal sea, hosts seagrass meadows that form an important and quantifiable reservoir of biomass-based carbon. Reliable mapping of these meadows is therefore essential for ecosystem assessment and coastal management. This study integrates a multiband depth-invariant bottom index (BI) derived from Sentinel-2 imagery with machine learning and deep learning classifiers to map seabed seagrass distribution and estimate the probabilistic seagrass biomass carbon potential of the inner SIS between 2019 and 2025. Sentinel-2 imagery was processed in Google Earth Engine, and Bands 2 (blue), 3 (green), and 4 (red) were combined into three BI configurations (BI2-3, BI3-4, and BI2-4). Of nine classifiers tested, Random Forest (RF) performed best, reaching 92.31% accuracy and κ = 0.81 under a single stratified split. More conservative validation across 50 repeated random splits gave 87.4 ± 9.5% accuracy and κ = 0.686 ± 0.225, while spatial leave-one-site-out (LOSO) validation gave 77.4% accuracy and κ = 0.50, indicating reduced transferability to unsurveyed sites. Among the three band-pair configurations, BI2-3 was the dominant predictor (variable importance = 0.93). Building on this classification, seagrass biomass carbon potential was estimated by combining satellite-derived seagrass probabilities with field-based biomass distributions through Monte Carlo uncertainty propagation. The resulting estimate reflects standing seagrass biomass carbon only, excluding sediment organic carbon, burial, decomposition, and lateral carbon export, and should be read as a biomass carbon potential rather than a total blue carbon stock. Monthly carbon potential followed a clear seasonal cycle, rising from winter into spring and peaking in May-June. Together, these results show that combining a multiband BI with artificial intelligence (AI)-based classification enables site-specific seagrass mapping and biomass-based carbon assessment in optically complex coastal waters, while underscoring the need for spatially independent validation before extrapolating beyond surveyed sites.

In endorheic basins with arid or semi-arid climates, lakebed geometry fundamentally controls water retention and regional atmospheric moisture feedbacks. However, quantifying these hydro-morphometric interactions to define environmental flow requirements remains challenging due to ambiguities in Stage-Area-Volume (S-A-V) relationships caused by spectral confusion between shallow brines and evaporite crusts. We propose a geometry-driven framework that resolves these limitations by integrating high-resolution bathymetric reconstructions with regional atmospheric moisture dynamics. Multi-decadal Landsat imagery (1972-2024) coupled with targeted RTK-GNSS surveys introduced a novel Temporal Stability Ratio (TSR) metric that substantially reduced saline-induced spectral biases. Analysis of the volume-area sensitivity coefficient (β) in a desiccating terminal saline lake delineated hydro-morphological functional zones, identifying key transition thresholds at 268.7 and 411.5 km[2] that separate evaporation-dominated from volume-dominated regimes. Time-series analysis revealed a post-2001 surface-dominated state with severely reduced hydrological memory. Variance decomposition of meteorological records demonstrated that each 100-km[2] increase in lake surface area elevates regional relative humidity by 1.35 percentage points, an effect that holds across monthly lags at the seasonal scale. The gradual loss of this microclimatic moderation exposes adjacent globally recognized agricultural heritage systems to extreme moisture deficits. These coupled hydro-climatic thresholds support phased filling strategies to reduce evaporative losses, increase storage reliability, and protect regional biodiversity alongside transnational regional economic security. The approach links physical lakebed geometry to atmospheric feedbacks and provides a transferable model for environmental water allocation in complex saline lake systems worldwide.

This study presents a hydrogeomorphic hazard and vulnerability assessment of the Astore river basin in the western Himalayan syntaxis. Hydrogeomorphic hazards i.e. debris flows, debris floods, and fluvial floods, pose significant risks to local communities and infrastructure. Prior recognition of specific flow types is crucial because different flow types require specific remedial measures. To achieve this objective, the Astore river basin was initially divided into 65 sub-basins using field data and high-resolution satellite imageries, enabling the identification of sub-basins generating debris flow, debris flood, and fluvial flood. This field-based classification was validated through statistical analysis of selected morphometric parameters, including basin Melton's ratio, basin relief ratio, basin area, basin length, basin gradient, basin channel length, and fan gradient. Threshold values were established for all the morphometric parameters through cross-correlation plots to differentiate sub-basin-scale hydrogeomorphic hazard types. Morphometric analysis of parameters with >90% predictive accuracy revealed distinct signatures for each sub-basin and hazard type. Debris flow-generating sub-basins are typically small (basin area < 10 km[2]), steep (fan gradient > 10°), with high basin Melton's ratio (> 0.60) and basin relief ratio (>0.37). Conversely, fluvial flood-generating sub-basins are larger, less steep, having low basin Melton's ratio (< 0.30), low basin relief ratio (< 0.37), large basin area (> 10 km[2]), and gentle fan gradient (< 5°). Debris flood-generating sub-basins occupy an intermediate range. Integration of morphometric analysis outcomes with glacial lake and human settlement inventories and the spatial distribution of loose debris cover was assessed to produce comprehensive hazard and vulnerability maps at sub-basins level, providing critical insights for risk reduction and sustainable development planning in the tectonically active and climate-vulnerable Himalayan regions.

Drought and water salinity are among the most severe environmental stressors associated with climate change, affecting food production systems and global food security. The aim of this study was to quantitatively synthesize available scientific evidence on the effects of drought and water salinity on the nutritional composition of food crops and to identify key moderators influencing these relationships. A systematic literature review and meta-analysis were conducted using observational and experimental studies that assessed the impacts of drought and salinity on nutrient composition of food crops and nutritional indicators. Random-effects models were applied to estimate pooled effect sizes, and heterogeneity was assessed using the Q statistic and τ[2] estimator. Moderator analyses were performed to examine the influence of geographic region, crop type, and salinity level. Potential publication bias was evaluated using scatter plots. The meta-analysis revealed a significant negative effect of both drought and salinity on crop nutritional outcomes, with drought showing a stronger and more consistent pooled effect than salinity. For salinity, effect sizes increased with salinity intensity, suggesting the presence of critical thresholds beyond which nutritional quality declines markedly. Moderator analyses indicated substantial regional differences and crop-specific responses, highlighting the context-dependent nature of these environmental stressors. These findings demonstrate that drought and water salinity represent important risk factors for food quality and crop nutritional quality, whose impacts are likely to intensify under future climate change scenarios. The study underscores the need to integrate agronomic and nutritional perspectives when assessing climate-related stressors and to develop targeted adaptation strategies aimed at safeguarding food security and nutritional quality.

Urban expansion reshapes ecological interactions in agroecosystems, yet its effects on plant-pollinator interactions remain poorly documented in tropical cities. We assessed how landscape urbanization and floral availability affect pollinator interactions across 16 urban farms in Havana, Cuba, along a gradient of impervious cover (38-97%). Farm area (0.01-11.2 ha) and plant richness (21-200 species) decreased significantly with urbanization, including a ∼90% decline in plant richness. In contrast, relative floral abundance showed a slight increase, suggesting management intensity may mitigate the effect of urbanization. We recorded 16,119 interactions involving eight pollinator morpho-groups, with interaction abundance varying substantially across sites (285-1735; a sixfold variation). Small wild bees, ants, and hoverflies consistently declined with urbanization across both years (2023 and 2024), whereas flies showed a positive response. Floral availability consistently enhanced visitation by Apis mellifera and other wild pollinator groups like small wild bees. Other morpho-groups exhibited strong interannual variability, including reversals in response direction to urbanization and floral resources. In 2023, urbanization reduced overall interaction number, while in 2024 the pattern was weaker and more heterogeneous, although several groups still showed negative responses to higher urban cover. Overall, floral resources consistently enhanced interactions, whereas urbanization effects were temporally variable. Our findings highlight contrasting temporal and taxon-specific responses of pollinators to urbanization. Despite high urban pressure, local floral management supports functional pollinator diversity and helps sustain pollination services in tropical urban agroecosystems. These results underscore the importance of integrating landscape planning and pollinator-friendly practices to enhance agroecological resilience under continued urban expansion.

Non-road mobile machinery (NRMM) is a comparatively under-regulated source of nitrogen oxides and fine particulate matter, with rising relative importance as on-road sources are progressively controlled, yet national-scale activity-validated inventories that quantify uncertainty and spatial heterogeneity remain rare. This study presents a calibrated, uncertainty-aware and spatially explicit emission inventory for New Zealand's agricultural diesel tractor fleet, an internationally relevant case because the country combines ultra-low-sulphur diesel and tightening on-road standards with the complete absence of NRMM-specific emission regulation. The inventory integrates row-level Motor Vehicle Register microdata for 43,367 diesel tractors, the official New Zealand Transport Agency licensing-status table that defines an active licensed fleet of 31,253 units, and 29,278 dealer sales records spanning 2017 to 2025. An IPCC Tier 2 fuel-anchored framework was applied with EMEP/EEA emission factors, tier hours empirically calibrated against a CARB monitoring study, and population-level fuel calibrated against a national 204 million litre tractor-specific consumption target. Monte Carlo uncertainty propagation with 10,000 iterations yielded annual emissions of 2258 t NOx (95% uncertainty interval 1609 to 3177), 115 t PM2.5 (95% uncertainty interval 73 to 190), and 534 kilotonnes CO2 (95% uncertainty interval 419 to 670). Tractor-level Lorenz analysis revealed a highly concentrated emission distribution with Gini coefficients of 0.42 for NOx and 0.51 for PM2.5; the highest-emitting 30% of tractors generated 58% of NOx and 69% of PM2.5. Spatial analysis across 67 territorial authorities identified strong clustering of intensity (Moran's I = 0.293, p = 0.001) and four significant Getis-Ord Gi* hotspots: three formed a Canterbury-Southland belt, with an additional, geographically separate hotspot in Auckland. Mann-Kendall and Theil-Sen tests rejected strict lock-in (p = 0.001) but showed modernisation of only +2.7 percentage points per year, implying that voluntary turnover will not deliver Tier 4 Final and Stage V dominance until the 2040s. The findings provide a potentially transferable evidence base to inform NRMM regulation.

Indoor allergens from pets and dust mites are persistent biological pollutants that pose significant risks to human health, yet effective and environmentally sustainable control strategies remain limited. Here, we isolated Bacillus amyloliquefaciens Ba2501, which secretes a subtilisin-family serine protease. Whole-cell co-culture assays demonstrated that Ba2501 reduces ELISA-detectable epitopes of three major indoor allergens-Fel d 1 (cat), Can f 1 (dog), and Der p 1 (dust mite). ELISA signals of Der p 1 and Fel d 1 became nearly undetectable within 48-72 h (99.86% and 100% reduction, respectively), while Can f 1 showed a more gradual decrease (67.83% at 72 h). To gain structural insights, we focused on Fel d 1 as a model and performed molecular docking, 200 ns molecular dynamics simulations, and MMPBSA analysis. The subtilisin-Fel d 1 complex exhibited a large buried interface (2392 Å[2]) stabilized by a salt bridge, short hydrogen bonds, and hydrophobic contacts. Residue-wise energy decomposition identified key interfacial residues driving the interaction. These findings provide a structural basis for protease-mediated allergen recognition and identify B. amyloliquefaciens Ba2501 as a promising candidate for developing environmentally sustainable, enzyme-based strategies to mitigate indoor allergen exposure and improve indoor air quality.

Soil erosion is a major land degradation process that affects ecosystem functions and services, requiring reliable methods for its assessment. The use of [137]Cs is valuable for estimating soil redistribution; however, its application is often limited by the lack of suitable reference sites. This study proposes a methodology to reconstruct the reference [137]Cs depth distribution and derive key erosion parameters using mechanically truncated profiles in situations where suitable undisturbed reference sites are unavailable. Two sites within the Panama Canal watershed were investigated: a truncated soil profile affected by land leveling and a pasture site under grazing. A reference [137]Cs depth distribution was reconstructed from the preserved lower portion of a truncated soil profile using a shifted exponential function, while the reference inventory (Aref) was independently estimated from precipitation-based fallout models. This approach allowed the estimation of the relaxation mass depth (h0) and the thickness of the removed soil layer. Erosion rates at the pasture site were quantified using the Profile Distribution Model (PDM) and the MODERN model. Both models yielded comparable erosion rates (1.5-2.5 Mg ha[-1] yr[-1]), with good agreement under low erosion, although a slight systematic overestimation by PDM was observed. The estimated erosion rates are consistent with those reported from erosion plot measurements, supporting the validity of the approach. Furthermore, the results highlight the importance of accounting for uncertainty in reference inventory estimates by using a range of plausible Aref values and expressing erosion rates as ranges. Overall, the proposed methodology provides a robust and practical alternative for applying the [137]Cs technique in disturbed landscapes, where suitable reference sites are unavailable and the underlying assumptions of the reconstruction are satisfied. The proposed approach could expand the applicability of the [137]Cs technique while reducing time, cost and field effort required for soil erosion assessment, compared to conventional monitoring approaches.

This study develops a novel water resources sustainability index based on actual water consumption (AWC, i.e., total evaporation and actual evapotranspiration) and environmental flow requirements in the Aji Chai Watershed, a sub-basin of Lake Urmia Basin in Iran. This index is defined as the ratio of AWC in a basin to its renewable water resources (RWR). The SWAT model was calibrated and validated from 1987 to 2021 to estimate average AWC and RWR. This study's novelty lies in developing an AWC-based sustainability index that integrates environmental flows, enabling basin-scale estimates of the withdrawal reductions needed to achieve sustainable water use. The results showed that during the validation period, correlation coefficient (R) and Nash-Sutcliffe Efficiency (NSE) values ranged from 0.4 to 0.93 and 0.01 to 0.74, respectively. Based on environmental flow allocations and groundwater depletion rates, the required decreases in surface water and groundwater withdrawals were estimated. Results show that the mean RWR and AWC are 582 MCM and 398 MCM, respectively, resulting in a sustainability index of 0.68, which exceeds the critical threshold of 0.40. To contribute the Aji Chai watershed's share toward meeting the environmental water requirement of Lake Urmia and sustain groundwater resources, annual surface and groundwater withdrawals should be reduced by 32% and 38%, respectively, mainly in the southern and western parts of the Aji Chai watershed. Additionally, when AWC surpasses its allowable limit, improving irrigation efficiency alone is not enough. Sustainable management instead requires changes in crop patterns, partial shift to rain-fed systems, deficit irrigation, or a combination of these strategies. A 32% reduction in surface water withdrawals would increase the average annual inflow to Lake Urmia by 19.5%, fully satisfying the lake's environmental flow requirements for this river. The methodology proposed has relevance for other stressed watersheds and drying lakes in arid and semi-arid regions.