Nutrient enrichment and biological invasions frequently co-occur in freshwater ecosystems, yet it remains unclear whether stability of entire planktonic groups responds uniformly to these combined stressors. To address this question, we conducted a fully factorial mesocosm experiment that crossed three nutrient regimes with two invasive species that impose distinct disturbance pathways. We quantified temporal stability, resistance, resilience, and recovery across functional and compositional facets in zooplankton, phytoplankton, and bacterioplankton communities. Our results revealed highly taxon- and dimension-specific responses. Snail bioturbation significantly reduced the functional resistance of zooplankton under high nutrient conditions. Meanwhile, the functional resistance of bacterioplankton declined with increasing nutrient levels. This suggests that functionally distinct stressors may affect different trophic guilds through distinct mechanisms. Species richness was consistently positively associated with compositional temporal stability, resistance and recovery across all trophic levels, but was negatively associated with resilience, revealing a fundamental trade-off between maintaining community robustness and enabling rapid adaptability. Functional and compositional stability were positively correlated only in the temporal stability dimension, but exhibited negative associations in the resistance and recovery dimensions. This indicates that biomass-related functions and community structure can follow divergent trajectories throughout perturbation and recovery. Most critically, the multivariate analyses and pairwise correlations among trophic levels indicated generally weak cross-trophic coupling, suggesting that, under the conditions of this mesocosm experiment, no single planktonic group provided a consistently reliable proxy for multidimensional whole-community stability. These findings highlight the value of assessing multiple dimensions of ecological stability across trophic groups. Consideration of stability indicators across multitrophic groups may provide complementary information for adaptive lake management as global pressures from nutrient loading and biological invasions continue to intensify.

Natural coastal ecosystems hold substantial capacity for blue carbon storage, but habitat replacement with harbour and shoreline infrastructure has severely diminished their potential to mitigate climate change. Enhancement of topographic complexity on coastal infrastructure through eco-engineering can increase biota abundance, such as the standing stock of long-lived seaweeds, thus positively impacting carbon capture and mitigating the carbon footprint of concrete-based infrastructure. However, ecosystem services of eco-engineered structures remain largely unquantified. Here, we assessed the seaweed biomass on a large-scale, eco-engineered seawall and show that topographically complex panels supported up to 19-fold greater blue carbon storage than flat surfaces. However, estimated panel CO2 emissions exceeded CO2 captures by 3- to 64-fold, after two years of seaweed growth. No retrofitted panel was realistically expected to achieve carbon neutrality within its 20-year design life, despite seaweed turnover and sequestration supporting long-term blue carbon potential. Predictive calculations, using six topographic designs, four concrete mixtures, two installation scenarios, and two detritus sequestration rates, suggested that the most efficient topography for seaweed growth, produced from low-carbon concrete and integrated during the construction phase, may achieve carbon neutrality within 15-32 years. Less effective or less sustainable scenarios were expected to require 52-277 years until carbon neutrality. An active transition towards low-carbon, integrated designs is necessary to align biodiversity-focused thinking in green infrastructure design with broader principles of sustainability and climate change mitigation.

Physics-Informed Neural Networks (PINNs) demonstrate considerable potential for solving partial differential equation (PDE)-based inverse problems in various practical scenarios. This study leveraged PINN for identifying dispersion coefficients (D) in homogeneous and heterogeneous porous media. The difficulty in inverting dispersion coefficients poses challenges for PINN, covering absence of real-world data validation and low accuracy caused by large dimensional differences. To address these issues, this study proposed an improved groundwater solute transport physics-informed neural network (GT-PINN) for dispersion coefficients inversion, using PDE-scaling to adjust the effective contribution of the governing-equation residual in the loss function and using the real-world breakthrough curve (BTC) data to define boundary conditions. Based on the optimized sensor layout and observation-number strategy, the proposed GT-PINN framework identified the D from short time-series BTC data, and then employed the D for forward prediction of solute transport processes over longer time scales. Four one-dimension studies were introduced to benchmark the coefficient inversion capabilities of the GT-PINN, including two homogeneous linear-transport cases with different inlet boundary conditions, one layered heterogeneous linear-transport case and one homogeneous nonlinear Freundlich sorption case. Additionally, the analysis exploring the influence of the initial conditions, initial coefficient guess, observation noise and random seeds on the coefficient inversion accuracy was presented. The laboratory experiment further indicated improvement of the method in practical coefficient estimation, with small absolute and relative errors. The findings highlight the potential of GT-PINN in solute transport systems.

This study proposes a novel framework for soil heavy metal pollution monitoring and source apportionment, namely "multi-source data fusion-spectral correction-feature selection-inversion mapping-uncertainty assessment-source apportionment." In this study, GF-5A and Sentinel-2B images were fused using the 3DT-NET algorithm, and the fused imagery was subsequently corrected using the direct standardization (DS) spectral correction method. Fractional-order differentiation was then applied to enhance soil spectral features, while the Boruta algorithm was employed to select informative bands for heavy metal modeling. Machine learning models were developed to predict heavy metal concentrations, and the associated prediction uncertainty was quantitatively assessed. Finally, soil heavy metal pollution sources in specific sampling areas were identified through correlation analysis. Compared with GF-5A imagery alone, the fused and DS-corrected spectra increased the best prediction accuracy (R2) from 0.476 to 0.611 for Pb (+28.4%), from 0.448 to 0.551 for Zn (+23.0%), and from 0.654 to 0.764 for Cu (+16.8%). Relative to Sentinel-2B imagery, the improvements were even greater, with R2 increasing from 0.285 to 0.611 for Pb (+114.4%), from 0.215 to 0.551 for Zn (+156.3%), and from 0.458 to 0.764 for Cu (+66.8%). Source apportionment results further showed that Zn is jointly controlled by elevation and slope, whereas Pb and Cu are mainly affected by slope. Overall, the proposed framework provides an effective and transferable solution for overcoming the spectral-spatial trade-off in satellite remote sensing, and offers strong support for regional pollution screening, risk zoning, source tracing, and precision land management.

Assessing Cd behavior in agricultural soils requires consideration of total soil Cd, its operational partitioning, and crop accumulation. A 122-day greenhouse pot experiment evaluated coal gangue, air-dried cattle manure, and their mixtures in two contrasting composite soils: an acidic lower-Cd soil (LS) and a neutral-to-alkaline higher-Cd soil (HS). After cultivation, total soil Cd averaged 0.648 and 1.825 mg kg-1 in LS and HS, respectively, whereas operationally labile Cd concentrations were similar (0.195 and 0.207 mg kg-1). Residual Cd accounted for 19.97% of fraction-sum Cd in LS and 57.50% in HS, while mean root-associated, stem, and leaf Cd concentrations were consistently higher in LS. Among grain-bearing amended pots, mixed coal gangue-manure treatments had lower grain Cd concentrations than manure-only treatments in both soils. Mean grain Cd mass decreased from 8.94 to 2.49 μg pot-1 in LS and from 6.92 to 1.50 μg pot-1 in HS for manure-only and mixed treatments, respectively. Grain dry mass was comparable between these treatment groups in LS and slightly lower under mixed treatments in HS, indicating that the lower grain Cd was not attributable solely to biomass dilution. Ten of 18 manure-only grain samples, but none of the 29 mixed-treatment samples, exceeded the 0.10 mg kg-1 reference threshold. Because the unamended planted controls produced no grain at harvest, grain-based comparisons were restricted to grain-bearing amended pots. The contrasting soil and plant responses demonstrate that total soil Cd alone does not adequately represent crop Cd behavior. Integrating soil Cd partitioning with grain Cd concentration and Cd mass provides a more informative basis for evaluating amendment responses in soil-maize systems.

Invasive aquatic plants are associated with substantial economic costs. In North America, a large number of lakes have been invaded by Eurasian watermilfoil (EWM; Myriophyllum spicatum), a species widely reported to reduce lakefront property values by 1-40%. We reassessed the impact of EWM on property values using a causal identification strategy based on an event-study framework. This reassessment relied on more than 11,000 lakefront real estate transactions conducted in Québec (Canada) between 2000 and 2023. We tested the hypothesis that EWM exerted a significant negative effect on property values and that this effect was further amplified following media coverage of the invasion. Contrary to findings from studies conducted in the United States, we did not detect a robust or statistically significant effect of EWM on lakefront house prices. Likewise, we did not find a significant price response associated with either the initial detection of EWM or its first mention in a local newspaper. Differences may be explained by limitations in previous studies that relied on hedonic price models without an explicit causal identification strategy. In addition, the potential effect of EWM on prices may be attenuated by strong demand for lakefront properties combined with highly constrained housing supply. Overall, our findings suggest that the effect of invasive aquatic plants on property values reflects a complex buyer-seller decision-making process, particularly in settings where other amenities remain highly attractive to prospective buyers.

With agricultural activities widely cited as drivers of biodiversity loss globally, it is important to distinguish between agricultural practices that have negative impacts and those with positive impacts on biodiversity. To measure on-farm biodiversity outcomes, the direction and magnitude of impacts need to be assessed. Outcomes can be measured using indicators. However, there is currently a multitude of biodiversity indicators available. This abundance makes monitoring biodiversity outcomes challenging, as farmers lack clarity on what to measure. This study presents a shortlist of biodiversity indicators selected via an expert panel as part of a modified Delphi process. The modified Delphi implemented two scoring approaches across four rounds, exploring the decision-making behind indicator selection. Thirty-nine experts scored 58 indicators based on their appropriateness for evaluating on-farm biodiversity and the practicality of data collection by non-specialists. The variation in scoring approaches helped combat respondent fatigue and build robustness into the indicator selection process. At the end of the study, nine indicators were chosen for monitoring on-farm biodiversity: area of habitat types, a connectivity index, a diversity index (landscape), both a crop and livestock diversity index, in-field crop temporal heterogeneity, indicator species, species richness, and species rarity. A final step considering each indicator's ability to inform on-farm decision-making is likely to have increased the efficacy of the chosen indicators in driving practice change. This would ensure the selected indicators are multifunctional, providing an evaluation of impact while also highlighting the desired direction of travel for the farmer or landowner.

Cattle grazing is a known source of water pollution. Identifying areas within grazinglands where cattle activity contributes most to downstream water quality impairment is important for effective management. This study presents a framework to detect these runoff pollutant hotspots. The framework is based on Pearson correlation coefficients between cattle presence across the catchment and water quality at the outlet. Cattle locations were recorded at 5-60 min time intervals using Virtual Fencing (VF) technology in three experimental catchments located in north-central Oklahoma, USA, two under rotational grazing and one continuously grazed. Escherichia coli (E. coli), total Kjeldahl nitrogen (TKN), and total phosphorus (TP) were measured during runoff events between March 2022, and June 2024. The spatial and temporal scales at which runoff quality was most affected by cattle presence were determined through a sensitivity analysis of grid cell size (GCS), time interval (ΔT) between consecutive cattle Global Positioning System (GPS) records, and pollutant half-life (HL) representing manure decay. The strongest grid cell-scale correlations between water quality parameters and cattle presence ranged from 0.48 to 0.83 for E. coli, 0.70 to 0.86 for TKN, and 0.67 to 0.90 for TP. These hotspots were detected using grid cell sizes ranging from 70 m × 70 m to 100 m × 100 m, and half-life values ranging from 3 to 12 days for E. coli, 3-23 days for TKN, and 3-20 days for TP. Compared to the continuously grazed catchment, hotspots in rotationally grazed, VF-managed catchments with cattle exclusion from riparian buffers were fewer, diffused, and located away from streams. Significant positive spatial autocorrelation was observed in five hotspot maps. Hotspot identification was influenced by pollutant persistence and grazing management, with rotational grazing showing fewer and more diffuse hotspots than continuous grazing.

Long-term intensive fertilization has accumulated large legacy phosphorus (P) pools in greenhouse vegetable soils, while substituting chemical fertilizers with livestock manure may exacerbate soil P loading. We evaluated pakchoi growth and legacy P utilization in an acidified, P-enriched greenhouse soil, comparing an unfertilized, unamended control (CK) with equal-carbon inputs of low-C:P, high-P chicken manure (OF) and high-C:P, low-P plant-derived amendments: alkali lignin (AL), straw (ST), and alkali-lignin-modified straw (AST). Compared with the OF, shoot dry weight was 47.0% and 12.4% higher under ST and AST, respectively, while shoot P uptake increased by 80.2% and 76.8%. OF produced an apparent P surplus, whereas ST and AST generated negative apparent P balances, indicating current-season amendment P inputs could not offset shoot P removal and supporting enhanced legacy P utilization without further accumulation of available or labile inorganic P. Plant-derived amendments increased soil C:P and pH, generally enhanced C- and N-acquiring enzyme activities, and reduced ecoenzymatic vector angles, alleviating relative microbial P-acquisition limitation. Metagenomic and random forest analyses showed that enhanced P acquisition was not primarily linked to a single P-cycling process, but more closely associated with resource stoichiometry and soluble N status, alongside coordinated restructuring of microbial C degradation, N transformation, and P-cycling functions. Overall, AL most strongly ameliorated acidity but elicited weak plant responses; ST produced the strongest biomass response, while AST maintained comparably high P acquisition and markedly improved soil pH. These high-C:P amendments provide a low-P alternative to high-P manure inputs, offering a strategy for reduced-P management and legacy P utilization.

This paper examines whether firms' use of data is associated with lower carbon intensity. We construct a firm-level measure of data elements (DE) from the annual reports of Chinese A-share listed firms from 2010 to 2023. The baseline outcome is corporate carbon intensity (CI). It is measured as estimated CO2 emissions per unit of operating revenue. Higher DE is associated with lower CI. A one-standard-deviation increase in DE corresponds to an approximately 0.35% decline in CI. The result is robust to alternative measures and specifications. The evidence is consistent with better asset use, stronger green innovation, and improved environmental information governance. The relationship is more evident under stronger environmental regulation and among firms with weaker initial digital capability. Lower CI, however, does not imply lower total emissions.

Algal bloom prediction remains challenging in data-scarce lake systems where continuous in-situ bloom observations and long-term external loading records are limited. This study developed an integrated multi-scale framework for predicting algal bloom occurrence in Lake Burdur over the 1987-2024 period by combining satellite-based deep learning (DL), catchment-scale hydrological and water-quality simulation, machine learning, and explainable artificial intelligence. Long-term Landsat and Sentinel-2 imagery was used with U-Net DL models to detect algal blooms, achieving test accuracies of 94.91% and 91.53%, respectively. The Soil and Water Assessment Tool (SWAT) was implemented to simulate catchment-derived hydrological and water-quality inputs entering the lake, including point and non-point source contributions. These SWAT-derived variables, together with meteorological parameters, were used to train Random Forest, Support Vector Machine (SVM), eXtreme Gradient Boosting, and Categorical Boosting models under three temporal lag scenarios, namely short-term (t-1 to t-3), medium-term (t-1 to t-6), and long-term (t-1 to t-12). The short-term lag scenario provided the most favorable overall balance between predictive performance, stability, and generalization, with the SVM model achieving an overall accuracy of 89.74% and a balanced accuracy of 90.42%. SHapley Additive exPlanations analysis indicated that short-term bloom prediction was mainly associated with meteorological predictors, particularly Logarithmic Solar Radiation-Wind Speed Index and relative humidity, whereas SWAT-derived external Total Nitrogen to Total Phosphorus loading ratio became more influential under the medium-term scenario. These findings indicate that bloom prediction in Lake Burdur was associated with interacting recent atmospheric conditions and antecedent catchment-derived nutrient inputs. The proposed framework provides an interpretable basis for algal bloom prediction and supports future development toward early-warning applications in data-scarce lake systems.

Green finance and environmental protection policies serve as the "dual engines" that drive green innovation and facilitate green development. Clarifying their synergistic innovation incentive effect is essential for optimizing top-level policy design and addressing market failures in green innovation. Using a sample of A-share listed companies between 2010 and 2023, this research constructs a triple difference model to systematically evaluate the manifestation of the "dual synergy" between green finance reform pilots and environmental protection policies on enterprises' green technological innovation. The results indicate that the synergistic impact of dual policies markedly stimulates green technological innovation at the firm level. This finding holds consistently across a battery of robustness checks; mechanism tests indicate that the synergistic policies promote enterprises' green innovation through the dual channels of "strengthening environmental supervision" and "suppressing greenwashing risks"; exploratory analysis reveals that the synergistic policy impact is stronger for firms with stringent internal environmental standards, state-owned enterprises, and those operating in regions with advanced environmental governance systems. These findings underscore the necessity of continually refining the legal and regulatory frameworks for green finance and environmental protection, amplify the effectiveness of individual policies while enhancing the synergy and coupling between the two; additionally, conduct "targeted" design based on enterprise endowments and regional differences to improve policy accuracy and implementation rate, thereby injecting sustained momentum into high-quality green development.

A novel gas-liquid multichannel bioreactor based on cordierite honeycomb monoliths was engineered and operated under internal gas and liquid recirculation for methane bioconversion into ectoine. The assessment of the volumetric gas-liquid mass transfer coefficients for O2 ([Formula: see text]) in this novel bioreactor showed values up to 1472.7 ± 111.6 h[-1], representing an increase of one order of magnitude over conventional bubble column reactors. Bubble measurements were consistent with a flow regime compatible with Taylor-flow operation, suggesting that liquid-film renewal was likely a major contributor to mass-transfer enhancement. During diluted-methane bioconversion into ectoine, stable operation at relatively high gas and liquid recirculation rates was achieved after gradual enrichment of the halotolerant mixed methanotrophic culture, potentially because of the associated hydrodynamic shear stress. After inoculum adaptation, methane bioconversion efficiencies and capacities of up to 72.9% and 45 g m[3] h[-1], with an ectoine concentration of 32.1 mg gVSS[-1], were achieved when operating with a gas residence time of 30 min, and gas and liquid recirculation of 40 L min[-1] and 25 L min[-1], respectively (corresponding to a [Formula: see text] of 679.5 ± 57.3 h[-1]). Process performance was supported by a well-adapted inoculum dominated by Methylophaga, Methylonatrum and Methylotuvimicrobium, which accumulated 44% of the relative abundances. Monoliths as microchannels in high gas-liquid mass transfer reactors represent a promising platform for methane valorisation, but further optimisation of operational parameters is required to intensify ectoine production from methane.

Red gypsum (RG) is the calcium-sulfate residue precipitated when the weak-acid stream of sulfate-route titanium dioxide manufacture is neutralised with lime or limestone. This critical review of 47 documents connects the provenance and variability of RG to the design and environmental performance of its treatment flowsheets. Across 20 feedstock records from twelve sites, RG spans 23.1-40.3 wt% CaO, 27.0-47.0 wt% SO3, 3.5-29.0 wt% Fe2O3 and 0.8-7.6 wt% TiO2; site-level and pooled medians agree within one percentage point, so the ranges, not a typical composition, are the design specification. Iron, a co-precipitated, poorly crystalline hydroxide, varies by factors of 1.7-2.1 between campaigns at one plant. Purification, phase transformation, integrated waste-acid recovery, thermal reduction, carbonation and direct use are compared on harmonised reagent, water, energy, secondary-stream and technology-readiness bases. Within the reviewed corpus, routes using the plant's own waste acid give the most selective iron removal and the only purified gypsum tested against a product standard. Indirect carbonation yields 93-99% pure carbonate and gross uptakes of 173-176 kg CO2 per tonne, about 74% of the corpus-median stoichiometric capacity, but reagent inventories of 0.4-8.9 t and a sulfate-salt co-product of 0.4-0.8 t per tonne make reagent circularity, not chemistry, the binding constraint; non-selective leaching transfers iron, titanium, thorium and uranium into the carbonate. No reviewed study reports a net CO2 balance, a life-cycle assessment or operation beyond laboratory scale, and seven of eight construction-type studies omit leaching or radiological testing. A conditional route-selection framework with explicit criteria and confidence levels is proposed.

Urine represents a critical water resource for closing the water and nitrogen loop in life support systems during manned space exploration. This work focuses on the impacts of pretreatment agents on urine progression and dissolved organic matter (DOM) transformation during long-term storage, with a perspective towards technology innovation for nitrogen-water synergistic recovery from urine in space. Urea hydrolysis, driven by microbial activity, was identified as the primary driver of urine variation, initiating rapid changes within 8 h of storage. The time point of significant DOM variation was prior to urea hydrolysis, and fresh humic-like substance from recent biological activity, which were dominated the variations of DOM. Meanwhile, the increasing NH4[+]-N and pH can resulted in precipitation, such as MgNH4PO4, which showed potential risks to the urine transport and treatment devices. Both H2SO4 and CrO3 showed excellent urine stabilization performance with urea hydrolysis rates of 0.10∼0.17 within 336 h. However, there have divergent DOM transformation pathways and sediment components: H2SO4 influenced the fresh DOM by microbial inhibition through acidic environment, and uric acid and CaSO4 were the main sediments; CrO3 could enhance the degradation of protein-like substances by strong oxidation, and generate Cr-containing dense sediments. Therefore, the selection criteria for technologies and pretreatment agents on urine recovery are proposed, which encourage the integration of membrane separation and biosynthesis. This work reveals the dynamic variation and the mechanism of pretreatment agents during urine storage, which offers the basis for innovation in urine recovery for deep space exploration.