Search Results (646)

Environmental health services play a critical role in communicable disease outbreaks by addressing environmental determinants of disease transmission. However, the scope, impact, and challenges of Environmental Health Practitioner (EHP)-led interventions remain insufficiently documented. Aim and objectives: This systematic review objectively assessed the role, impacts, and challenges of municipal environmental health services in outbreak response, with a focus on South Africa, to inform the standardisation and strengthening of disease surveillance and prevention. Methods: The PICO framework guided the development of search terms and research questions. PubMed, Scopus, Google Scholar, and Web of Science were searched for English-language, full-text studies published between 2010 and 2024. Studies not meeting these inclusion criteria were excluded. Screening and reporting followed PRISMA guidelines, and data were synthesised using a standardised extraction tool. Results: A total of 58 studies were included. The key EHP functions identified were water quality monitoring, vector control, food safety, waste management, and outbreak response. While South Africa demonstrated comparatively advanced systems, persistent implementation challenges remain, including the integration of environmental monitoring with disease surveillance. The findings emphasised the need for integrating environmental monitoring with disease surveillance systems and integrating WASH and climate-responsive strategies. Conclusions and recommendation: The review recommends strengthening guidelines and advancing evidence-based practice. Enhancing EHP roles within surveillance frameworks is essential for improving outbreak preparedness and public health resilience. Full article

Background and Objectives: Knee osteoarthritis (KOA) is a degenerative joint disease that affects quality of life through pain, impaired functional performance, and altered gait patterns. Hydrotherapy is a well-tolerated form of physical rehabilitation, especially suitable for patients with severe pain, as water’s properties support movement while reducing joint load. Its effects have been widely studied, primarily focusing on patient-reported outcomes, with limited synthesis of functional performance and gait-related outcomes. Materials and Methods: A systematic search was conducted in PubMed, Web of Science, Cochrane, PEDro, SpringerLink, ScienceDirect, and Google Scholar, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search strategy included a combination of Medical Subject Headings (MeSH) terms and keywords. For example, the PubMed search strategy was as follows: (“knee osteoarthritis” OR “knee OA”) AND (“hydrotherapy” OR “aquatic therapy” OR “water-based exercise”) AND (“gait” OR “walking” OR “functional performance”). Randomized controlled trials (RCTs) from the last 10 years involving patients with KOA undergoing aquatic therapy were included. Primary outcomes included functional performance assessed by measures such as the 6 min walking test (6MWT), the Timed Up and Go (TUG) test, the five sit-to-stand (5 STS) and stair climb (SC) tests, and by using gait-related parameters (e.g., speed, cadence, and step length) assessed clinically or using technology. Patient-reported outcomes, including the Visual Analog Scale (VAS), Western Ontario and McMaster University’s Osteoarthritis Index (WOMAC), and Knee Injury and Osteoarthritis Outcome Score (KOOS), were analyzed as a secondary objective. Results: A total of 479 studies were identified, of which 13 met the eligibility criteria. The results revealed improvements in functional performance, with increases in 6MWT in five studies, the TUG test in four trials, and better performance in the 5-STS and SC tests in five studies. Benefits in gait parameters were noted in four studies. Additionally, one of the articles reported improvements in static and dynamic balance, another showed enhanced proprioception, and a third described more efficient muscle activation during gait following hydrotherapy. Consistent benefits in pain reduction, joint stiffness, and activities of daily living, as reflected by VAS, WOMAC, and KOOS, were also noted immediately and maintained at follow-up. The variability in outcome measures and intervention characteristics limited the possibility of data integration and the calculation of effect sizes. Conclusions: Hydrotherapy as a rehabilitation intervention may be associated with improvements in functional capacity, mobility, and self-reported physical ability in patients with KOA, with some evidence supporting a beneficial effect on gait; however, the certainty of evidence remains low to moderate due to heterogeneity among studies and limited sample sizes. These findings should be interpreted in light of the methodological limitations identified across the included trials. Full article

Additive manufacturing by Fused Filament Fabrication (FFF) enables the fabrication of complex polymer components, although limitations in surface quality and dimensional accuracy often require post-processing. Abrasive water jet machining (AWJM) is a non-thermal technique suitable for improving surface integrity in polymers and composites without inducing thermal damage. This study investigates the AWJM performance on FFF-printed polylactic acid (PLA) and carbon-fiber-reinforced PLA (PLA–CF), focusing on the influence of water pressure (WP), traverse feed rate (TFR), and abrasive mass flow rate (AMFR). A full factorial design was implemented, and surface integrity was evaluated through surface roughness (Ra) and kerf taper (T), considering their variation across characteristic cutting regions: initial damage region (IDR), smooth cutting region (SCR), and rough cutting region (RCR). Results show that WP and TFR are the dominant parameters, while AMFR has a limited effect within the studied range. The SCR exhibits the lowest roughness, whereas the RCR shows significant degradation due to energy loss. Both materials present similar behavior, with only minor improvements in PLA–CF. ANOVA confirms that process parameters have a stronger influence than material type, providing useful criteria for AWJM optimization in FFF polymers. Full article

Recycled aggregates (RAs) from construction and demolition waste (CDW) provide substantial circular-economy benefits, yet their elevated porosity, adhered mortar, and heterogeneity typically impair the mechanical performance and durability of recycled aggregate concrete (RAC). This PRISMA 2020-compliant systematic review synthesises 2180 records (2015–2026) to evaluate advanced strategies for enhancing RA quality prior to structural use. This paper critically compares removal-based treatments (mechanical, thermal, acid cleaning) with strengthening and densification approaches, including accelerated carbonation, pozzolanic and nano-silica coatings, polymer impregnation, microbial-induced calcium carbonate precipitation (MICP), and modified mixing methods such as triple-stage mixing (TSMA). Evidence shows that while all RA types (including recycled fine aggregate (RFA), recycled coarse aggregate (RCA), and their combination (RFCA)) can slightly reduce compressive strength and 30% replacement serves as a critical threshold, beyond this, strength loss accelerates, particularly in RCA and RFCA mixes. However, accelerated carbonation and TSMA consistently refine the interfacial transition zone, reduce water absorption by 17–30%, and recover 85–94% of natural aggregate concrete strength. Bio-deposition reduces water absorption by 13–21%, while acid/silica fume treatments improve late-age strength but carry environmental trade-offs. This review formulates a practice-oriented implementation framework for structural-grade RAC. Sustainability analyses indicate that carbonated RA can achieve net-positive CO₂ abatement when under low-carbon energy supply. A mechanistic schematic is presented to synthesise treatment-to-pore-structure/durability pathways across the four principal treatment routes, and a quantitative synthesis plot compares water absorption reductions across all treatment types using 13 data points drawn from included studies. A structured treatment comparison evaluates the energy intensity, industrial scalability, CO₂ footprint, and technology readiness level for each strategy. The remaining challenges include a lack of hybrid treatment studies, limited real-scale durability data, and insufficient mechanistic models linking treatment to pore structure evolution. This review recommends harmonised durability-based criteria and updates to standards (e.g., BS 8500, EN 12620) to support the scalable deployment of treated RA. Full article

Urban water scarcity and climate change pose significant challenges for sustainable development, particularly in rapidly expanding metropolitan areas. In cities like Casablanca, these pressures also threaten the preservation of cultural heritage sites such as traditional public bathhouses (Hammams). This study investigates how Hammams can integrate sustainable water management solutions in alignment with Sustainable Development Goal 11 (SDG 11), focusing on the treatment and reuse of graywater. This study compares three graywater treatment systems, a Membrane Bioreactor (MBR), a Sequencing Batch Reactor (SBR), and a Moving Bed Biofilm Reactor (MBBR), evaluated through literature review and dimensioning calculations, and also integrates an existing treatment plant in Berlin that functions as a real-scale laboratory. The comparison is based on a set of technical, economic, and environmental criteria used for comparative engineering design assessment and evaluation for the selected Hammam water reuse applications. All systems are technically feasible but show distinct trade-offs. The SBR has the lowest energy demand and highest cost savings, the MBBR offers a compact and simple design, and the MBR provides the highest effluent quality at a higher energy cost. Heat recovery provides a significant thermal energy recovery potential but is reported separately from the electrical energy demand of the treatment systems. Full article

Nature-based solutions (NBS) are increasingly promoted in environmental management to address water, climate, biodiversity, and pollution challenges while delivering social and economic co-benefits. Yet decision-makers still face uncertainty about what works where, for whom, and how reliably over time. This narrative review synthesizes cross-cutting, peer-reviewed evidence on three decision-critical domains: NBS effectiveness for key environmental management objectives; co-benefits, trade-offs, and equity (including distributional risks across groups and places); and monitoring and evaluation (M&E). This review is not a systematic review, not a semi-systematic review with a fixed, protocol-driven study inventory, and not a meta-analysis; “comprehensiveness” refers to breadth of themes and management objectives addressed, not to exhaustive capture of all published sources. A distinguishing contribution is an intervention–pathway–endpoint typology oriented to measurement and M&E: it links broad NBS categories to dominant biophysical mechanisms and to concrete indicator families. Unlike criteria-first verification frameworks, this typology is organized around measurement logic (what to monitor, and how endpoints chain from processes to management decisions). It complements criteria- and process-oriented NbS quality frameworks (e.g., the IUCN Global Standard’s criteria and indicators for verification, design, and scaling) by foregrounding an explicit indicator logic chain for appraisal, monitoring, and cross-project comparability. The review assesses effectiveness for water quality, flood and flow regulation, heat mitigation, biodiversity, and carbon/climate mitigation; consolidates social, economic, and ecological co-benefits; reviews recurring M&E weaknesses; proposes a pragmatic minimum indicator set and feasible evaluation designs; and outlines an implementation-oriented NBS environmental management cycle. The aim is to strengthen transparent, climate-aware, evidence-based, and equity-aware environmental management. Full article

To reveal the mechanism underlying the effects of dominant spoilage fungi on wheat quality during storage and provide a theoretical basis for targeted microbial control in wheat storage, this study characterized the structural features of fungal communities on the surface of stored wheat and at different depths of the grain bulk via high-throughput sequencing. Additionally, screening was performed for stably existing dominant spoilage fungi in a wheat storage environment. Subsequently, four isolated dominant spoilage fungal strains, Fusarium lateritium, Aspergillus niger, Penicillium citrinum and Talaromyces islandicus, were back-inoculated onto wheat kernels sterilized by ⁶⁰Co gamma irradiation. Simulated storage trials were conducted at 28 °C and 80% relative humidity to investigate their impacts on wheat quality. The results show that F. lateritium and A. niger exhibited faster growth rates and were able to colonize the entire surface of wheat kernels within 8 days. After infection by these two fungi, wheat superoxide dismutase (SOD) activity decreased by 33.83 U/g and 21.90 U/g, peroxidase (POD) activity decreased by 1408 U/(g·min) and 745 U/(g·min), and electrical conductivity (EC) increased by 11.17 μS/(cm·g) and 7.74 μS/(cm·g), respectively. After 10 days of storage, A. niger significantly reduced the water absorption of wheat gluten to 175.91% and elevated the fatty acid value to 74.20 mg/100g, rendering the wheat unsuitable for storage. P. citrinum exerted the most significant effect on the solvent retention capacity (SRC) of wheat flour in water, sucrose, sodium carbonate, and lactic acid solutions. This study clarified the screening criteria for dominant spoilage fungi in stored wheat, as well as the threshold values and differential characteristics of the impacts of different dominant spoilage fungi on wheat quality, providing critical theoretical support for targeted microbial control during wheat storage. Full article

Freshwater ecosystems are increasingly threatened by eutrophication and other anthropogenic and climate-driven pressures that undermine ecological functioning and biodiversity. This study evaluates the transferability of a GIS-based multi-criteria decision analysis (GIS–MCDA) framework with Fuzzy Analytic Hierarchy Process (F-AHP), originally developed for a shallow coastal lake, to a morphologically distinct deep upland lake (Lough Tay, Ireland). Monthly in situ measurements at a single monitoring point in 2024 were analysed together with meteorological variables using Spearman rank correlations. Because spatial interpolation of in-lake water quality parameters was not feasible, eutrophication susceptibility was mapped using four external spatial drivers: distance from water resources (River Cloghoge inflows), land-based nitrogen export potential, distance from environmental pollutants represented by the transportation network, and a wind exposure index derived from a DEM and wind-rose analysis. Criteria were standardized with fuzzy membership functions, weighted using F-AHP (consistency index 0.056), and aggregated using weighted linear combination at 25 m resolution. The resulting Eutrophication Susceptibility Index (ESI) ranged from 0.18 to 0.81, indicating generally moderate to good conditions, with higher ESI values concentrated in the northern lake sector near inflow zones. The results demonstrate that GIS–MCDA can be adapted to lakes with limited monitoring by relying on external drivers, providing a spatial proxy for susceptibility rather than measured trophic status. Full article

Framed within data-centric artificial intelligence, this study integrates statistics, geotechnologies and AI to improve water quality prediction. The primary objective was to identify the minimum sample size required to train robust and accurate machine learning models. Based on 30 sampling points in a tropical wetland in northern Colombia, ammonium concentration was selected as the target variable, and total dissolved solids, suspended solids, phosphate, dissolved oxygen, nitrate and chemical oxygen demand were chosen as predictors. Because 30 observations are insufficient to train robust models, data augmentation was performed using ordinary kriging (OK) and empirical Bayesian kriging (EBK). From the kriging-interpolated surfaces, 1000 synthetic points (randomly and spatially distributed while preserving the estimated spatial structure) were sampled; from this expanded dataset, subsamples of varying sizes were drawn to train six algorithms: multiple linear regression (MLR), random forest (RF), k-nearest neighbours (k-NN), gradient boosting machines (GBM), multilayer perceptron (MLP) and radial basis function neural network (RBF-NN). The RF, k-NN, MLP, RBF-NN and GBM models trained on the interpolated data exhibited excellent performance: in the testing phase, they achieved adjusted coefficients of determination > 0.95 and symmetric mean absolute percentage errors (SMAPEs) < 10%, and the resulting predictive surfaces showed comparable performance under external validation. According to the criteria of stability, goodness of fit, and external validation, the optimal minimum sample size for most algorithms was 104 observations. These results represent a significant advance in mitigating data scarcity in water quality modelling. The identification of effective data augmentation methods and the determination of appropriate sample sizes, as demonstrated here, support the robust application of AI techniques in water quality prediction. The proposed strategy is transferable to other quantitative, spatially continuous environmental variables and thus contributes to the development of the emerging subdiscipline of geospatial artificial intelligence (GeoAI). Full article

This study aimed to optimize the formulation of gluten-free bread (GFB) based on rice flour (RF) and Quercus rotundifolia acorn flour (AF) by evaluating the combined effects of flour substitution (0%, 50%, and 100%) and water addition (90%, 100%, and 110%) on technological, textural, colorimetric, structural, and sensory properties. A three-level full factorial design (3²) combined with response surface methodology (RSM) was used to model and optimize product quality. The developed models showed high predictive performance (R² = 0.714–0.999; non-significant lack of fit), confirming their suitability for describing complex interactions in gluten-free systems. Water addition was the dominant factor influencing moisture, crumb structure, and textural softness, while AF mainly affected color, structure, and sensory attributes. Increasing acorn content significantly decreased lightness (L*) and increased redness (a*) and darkness index (DI), reflecting higher phenolic compound content and more intense Maillard reactions. Specific volume (1.85–2.41 cm³/g) was maximized at higher hydration levels, especially when combined with intermediate to high acorn substitution, indicating a synergistic interaction between fiber-rich flour and water availability. Texture analysis showed that AF increased hardness and reduced cohesiveness, while water addition significantly improved softness, elasticity, and overall mouthfeel. Image analysis of crumb structure demonstrated that higher hydration promoted larger pore size and porosity, whereas AF increased cell density, resulting in a finer crumb structure under low hydration conditions. Sensory evaluation confirmed that breads with high acorn content were well accepted due to their characteristic nutty flavor. Multi-response desirability optimization yielded an optimal formulation with approximately 83% AF and 108% water, representing the best achievable compromise among the evaluated quality criteria. The results demonstrate that AF can serve as a key functional ingredient in GFB, provided that hydration is carefully adjusted. This study highlights the effectiveness of RSM combined with image-based analysis as a robust approach for developing high-quality gluten-free bakery products. Full article

Landscape visual impact assessment is a key component of environmental impact studies, as it enables the identification and management of negative effects on the territory. Traditional methods are often subjective, rely on expert judgement, and consider limited criteria. To address these limitations, this study proposes a quantitative index based on the integration of grey clustering and Shannon entropy complemented with Geographic Information System (GIS). This approach allows classification under uncertainty and the objective weighting of indicators related to physiographic, biotic, and anthropic factors of visual quality, fragility, and accessibility. The methodology was applied to an open-pit mine in Peru. Results show that terrain modifications, presence of artificial elements, and the alteration of water bodies significantly affect visual quality, while the absence of restoration measures, observer exposure, and vegetation type increase fragility and reduce landscape resilience. The proposed method provides a robust, transparent, and reproducible framework that overcomes subjectivity in traditional approaches, supporting more reliable environmental planning and management. Full article

High Andean lagoons in southern Peru have critical hydrological and ecological functions; however, long-term time series integrating trophic, integral quality, and metal contamination metrics to support adaptive management are lacking. A total of 1846 records (2015–2024) from four systems (3100–4600 m a.s.l.) were analyzed using seven indices assessing trophic status (TSItsr, TRIX), general water quality (OWQI, WQIHA, CCME-WQI), and metal contamination (HPI, CD). Temporal trends were assessed using Mann–Kendall and Theil–Sen slope; spatial heterogeneity using Kruskal–Wallis and Dunn–Bonferroni comparisons; controlling factors using distance-based redundancy analysis (999 permutations); and functional typology using Ward’s hierarchical clustering on Z-standardized data. 93% of the series lacked monotonic trends (52/56 lagoon–stratum × index combinations), demonstrating high interannual stability; spatial variance was marked (ε² = 0.73 in CCME-WQI). Distance-based redundancy analysis (db-RDA) explained 24.6% of total variability, with lake identity as the dominant driver (~45%), followed by temporal change (~8%). Four functional archetypes emerged, including a metal-eutrophic hotspot (HPI ≈ 213; CD ≈ 19) and recovering reservoirs with intermediate water quality indicators. Joint thresholds (TSItsr ≥ 60 + HPI ≥ 100) establish early-warning criteria, with Paucarani (HPI = 213) approaching the critical domain where metal-driven stress may facilitate cyanobacterial dominance. Systems show temporal resilience but strong spatial divergence induced by local pressures. The proposed typology and thresholds provide an operational basis for early warnings and prioritization of remediation actions in high-mountain ecosystems subject to increasing anthropogenic stress. Full article

Precipitation Use Efficiency (PUE) is a key ecological indicator for evaluating how vegetation converts precipitation into biomass or productivity. A thorough analysis of its quantification methods and driving mechanisms is of great significance for improving regional precipitation use efficiency and ensuring agricultural and ecological water security. In this study, we conducted a comprehensive literature search without time restrictions in the Web of Science and China National Knowledge Infrastructure (CNKI) databases, using “Precipitation Use Efficiency” and “PUE” as core keywords. After retrieval, a strict “independent dual-screening plus cross-checking” procedure was adopted with unified inclusion and exclusion criteria to ensure literature quality. Only highly relevant and methodologically rigorous studies were retained, resulting in a final set of 80 eligible publications. Key information was systematically extracted using content analysis, followed by integrated summarization and inductive analysis. This paper systematically illustrates the ecological connotation of PUE, compares diverse quantification and research methods with their applicable conditions, analyzes spatiotemporal differentiation characteristics and multidimensional driving mechanisms, summarizes practical approaches for PUE improvement, and reviews current research limitations. It represents a systematic integration and refinement of the research framework of precipitation use efficiency. The results can provide targeted theoretical support for revealing the driving mechanisms of PUE and promoting the efficient utilization of precipitation resources. Full article

This is the first systematic review evaluating Black Sea plankton as biosensor organisms for Biological Early Warning Systems (BEWS)—real-time monitoring approaches that detect sublethal behavioral or physiological responses to pollutants before irreversible ecosystem damage occurs. The systematic literature review was guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach, ensuring methodological transparency and applicability. A total of 140 publications from databases (Web of Science Core Collection, Scopus, PubMed, and Google Scholar databases) were included in the final analysis. We assess nine native planktonic taxa as candidates for automated video-based water quality monitoring, using a multi-criteria framework encompassing biological sensitivity, technical detectability, and practical feasibility. Three species emerge as the most suitable candidates: Aurelia aurita as a universal indicator (sensitive to copper, surfactants, petroleum, and microplastics; its large size enables standard video detection); Acartia tonsa for trace contamination (reproductive toxicity at metal concentrations 4–33× below regulatory standards); and Mnemiopsis leidyi for metal-specific discrimination (bioluminescent responses: 650% Zn, 430% Cu, and 350% Hg at 0.001 mg/L). Analysis of 140 publications reveals critical gaps: 33% of species lack toxicological data, 95% of studies test single toxicants despite natural mixture exposure, and microplastic methodology varies 1000-fold in particle size. Threshold analysis suggests planktonic sublethal stress at “safe” concentrations under current standards, suggesting inadequate protection of marine food webs. A complementary monitoring approach integrating these species with computer vision algorithms offers autonomous early-warning capability for Black Sea environmental management. Full article

Transitional water ecosystems exhibit pronounced spatio-temporal variability and increasing anthropogenic pressures, posing substantial challenges for ecological monitoring and management. Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has emerged as a powerful framework for addressing the structural complexity of these systems. This systematic review synthesizes peer-reviewed studies applying ML and DL to ecological research and monitoring in transitional waters. A structured search of the Scopus^® database was conducted up to 31 December 2024, and studies were screened according to predefined eligibility criteria and PRISMA 2020 guidance; methodological quality was appraised using a structured assessment framework. Ninety-six studies met the inclusion criteria. Regression was the most frequent analytical task (44.1%), followed by classification (36.2%) and clustering (19.7%), with water quality monitoring representing the dominant thematic domain. Tree-based and kernel-based ML models prevailed overall, whereas DL architectures increased markedly after 2020, particularly in remote sensing and high-dimensional applications. Despite methodological heterogeneity and variable validation practices, the evidence indicates that ML and DL approaches effectively accommodate non-linearity, data heterogeneity, and scale mismatches typical of transitional waters. Standardized validation strategies and improved model interpretability remain essential for robust ecological inference and operational implementation. Full article