Search Results (584)

The resistivity method is widely used to address long-term monitoring challenges in fields such as environmental protection, ecological restoration, seawater intrusion, and geological hazard assessment. However, external environmental changes can influence monitoring data, resulting in inversion results that fail to accurately reflect subsurface variations. Furthermore, the data volume required for such monitoring is several times larger than that for conventional single-point observations, leading to excessively long inversion times and low computational efficiency. To address these issues, we develop a three-dimensional inversion algorithm for the resistivity method that incorporates time-lapse constraints. Additionally, MPI parallelization is integrated into the program to increase computational efficiency. Through the design of theoretical models and the synthesis of data to test the algorithm, the results show that, compared with those of separate inversion, the shapes and values of time-lapse inversion results at different time points are more consistent, maintaining temporal continuity, and the computational efficiency of MPI parallel inversion is greatly improved. Particularly in high-noise environments, time-lapse inversion effectively suppresses background noise interference, reduces false anomalies, and produces results that closely align with the true model, thus confirming the algorithm’s effectiveness and superiority. Full article

Citizen science is increasingly utilized for environmental monitoring and educational purposes. For lotic ecosystems, this approach could be used to implement traditional methods and gain more data on local biodiversity, particularly in areas where professional monitoring is limited. This study, conducted in Italy, aimed to complement data on river macroinvertebrates collected by the Regional Environmental Protection Agency (ARPA) with additional data gained by volunteers. Our results revealed taxonomic differences between the macroinvertebrate communities of ARPA and citizen science sites. ARPA sites host 34.4% of the total biodiversity, with 22 exclusive taxa, while citizen science sites, with 6 exclusive taxa, represent 9.4% of the total gamma diversity. Compositional differences are mainly explained by taxa turnover between sites. ARPA sites, located along the main river stretches, are richer in alpha and gamma diversity, while volunteer-monitored sites, mostly in agricultural ditches, show lower richness at the local and regional scales but host some unique taxa, increasing the total biodiversity. This study supports the implementation of volunteer programs to increase the number of monitored rivers, enhancing information on macroinvertebrate diversity and distribution and generating relevant data to support decision-making and develop strategies for river conservation and ecosystem restoration at a local scale. Full article

This study investigates the anatomical adaptations and ecological plasticity of C. ambiguus in extreme environmental conditions by analyzing the structural characteristics of its leaves and annual shoots collected from 12 populations in the arid regions of Mangystau, including Western Karatau, Northern Aktau, and the Tyubkaragan Peninsula. Microscopic and statistical analyses revealed significant variability in key anatomical traits, including epidermal thickness, collenchyma, primary cortex, and vascular bundle area, highlighting the species’ adaptive responses to drought, high solar radiation, and limited water availability. The epidermal thickness ranged from 14.85 µm (Pop_12 Botakan) to 22.51 µm (Pop_6 Samal), demonstrating xeromorphic adaptations for reducing transpiration. At the same time, the vascular bundle area varied from 286.06 × 10⁻³ mm² (Pop_3 Emdikorgan) to 528.51 × 10⁻³ mm² (Samal), indicating differences in water transport efficiency across populations. Despite substantial anatomical variation, the low coefficients of variation (0.31%–6.31%) suggested structural stability, reinforcing C. ambigua’s ability to maintain functional integrity under environmental stress. Canonical Correlation Analysis (CCA) confirmed that environmental factors such as soil type, elevation, and water availability significantly influenced anatomical traits. Floristic analysis revealed distinct patterns of species richness, with the highest diversity recorded in Pop_4 and Pop_7, while Pop_12 and Pop_9 exhibited lower diversity, indicating potential vulnerability. Furthermore, the identified anatomical traits could serve as key markers for selecting drought-resistant genotypes in afforestation and restoration programs. This study also highlighted the need for the long-term monitoring of C. ambigua populations to assess the impact of climate change on structural adaptations. These findings offer a framework for integrating ecological and genetic studies to refine conservation strategies for xerophytic species. Full article

Astana, Kazakhstan’s capital city since 1997, gained from substantial public investment, achieving relatively low poverty, high income, and broad access to social services. Implementation of the state infrastructure programs, which were aligned with China’s 2013 Belt and Road Initiative, allowed Astana to become a transport hub, attract people, and improve housing conditions. However, our analysis indicates that Astana’s construction boom resulted in intensive use of financial and natural resources. Moreover, the loss of green and blue lands, accelerated during the implementation of the state infrastructure programs, raises concerns about the environmental impacts of infrastructure spending. As a result, our study highlights the importance of further research and broader stakeholder engagement for bringing Astana’s development path into closer alignment with the principles of sustainability. Specifically, Astana’s stakeholders should adhere to best practices of urban ecosystem preservation, managing sprawl, and efficient use of resources. Finally, integrating green and blue infrastructure in setting targets, allocating funding, and monitoring, improving, and reporting on traditional infrastructure initiatives becomes increasingly important for sustainable urban development. Full article

Increasing pollution of aquatic ecosystems due to anthropogenic activities underscores the urgent need for effective water quality monitoring. This study evaluates the use of fish scales from chub (Squalius cephalus) and nase (Chondrostoma nasus) as non-invasive bioindicators of trace and macroelement pollution in the Maros/Mureș River. We conducted qualitative and quantitative elemental analyses using X-ray fluorescence (XRF) and inductively coupled plasma optical emission spectrometry (ICP-OES). Scanning electron microscopy (SEM) revealed distinct scale structures among the species studied. Concentrations of Ca, K, Mg, Na, P, S, Al, Ba, Cu, Cr, Fe, Mn, Sr, and Zn were measured by ICP-OES. Our findings indicate significant variability in elemental accumulation in fish scales, supporting their potential use as bioindicators of environmental pollution, with variability depending on the ecological characteristics of the species. This methodology offers promising applications for modern interdisciplinary monitoring programs. Full article

Strategically important objects, such as dams, tunnels, bridges, and others, require long-term structural health monitoring programs in order to preserve their structural integrity with minimal downtime, financial expenses, and increased safety for civilians. The current study focuses on developing a damage detection methodology that is applicable to the long-term monitoring of such structures. It is based on the identification of resonant frequencies from operational modal analysis, removing the effect of environmental factors on the resonant frequencies through support vector regression with optimized hyperparameters and, finally, classifying the global structural state as either healthy or damaged, utilizing the Mahalanobis distance. The novelty lies in two additional steps that supplement this procedure, namely, the nonlinear estimation of the relative effects of various environmental factors, such as temperature, humidity, and ambient loads on the resonant frequencies, and the selection of the most informative resonant frequency features using a non-parametric neighborhood component analysis algorithm. This methodology is validated on a wooden two-story truss structure with different artificial structural modifications that simulate damage in a non-destructive manner. It is found that, firstly, out of all environmental factors, temperature has a dominating decreasing effect on resonance frequencies, followed by humidity, wind speed, and precipitation. Secondly, the selection of only a handful of the most informative resonance frequency features not only reduces the feature space, but also increases the classification performance, albeit with a trade-off between false alarms and missed damage detection. The proposed approach effectively minimizes false alarms and ensures consistent damage detection under varying environmental conditions, offering tangible benefits for long-term SHM applications. Full article

Anthropogenic activities and rapidly increasing climate change have led to a significant loss of genetic diversity in domestic animals. Genealogical data have traditionally been used to monitor genetic diversity. However, due to dependency on pedigree completeness and significant errors that can occur in genealogical records, inaccurate estimation of population parameters, such as the inbreeding coefficient or effective population size, might occur. To reduce possible errors, it is necessary to combine genealogical data with molecular data. An integrated approach using genealogical and molecular data leads to the optimization of breeding programs while controlling the inbreeding that can occur within the population of domestic animals. Molecular techniques such as single nucleotide polymorphism (SNP) genotyping, whole-genome sequencing (WGS), or genome-wide association studies (GWASs) enable a detailed understanding of breed-specific genetic profiles and their use in conservation programs. In addition, molecular methods such as structural variation (SV) analysis and pangenome and epigenomic profiling provide a more comprehensive insight into genetic diversity. The conservation of genetic diversity is of particular importance for an autochthonous domestic breed due to its resilience to unfavorable climatic conditions, its specific productive traits, and its disease resistance. A combined approach of genealogical and molecular data helps to maintain genetic diversity and sustainable agricultural systems under evolving environmental challenges. Full article

Wireless localization is a fundamental component of modern sensor networks, with applications spanning environmental monitoring and smart cities. Ensuring accurate and efficient localization is critical for enhancing network performance and reliability, particularly in the presence of signal attenuation and noise. This study proposes a novel two-step localization framework, SD/SOCP-GTRS, to improve the precision of target localization using received signal strength (RSS) measurements. In the first step (SD/SOCP), semidefinite programming (SDP) and second-order cone programming (SOCP)-based convex relaxation are applied to the maximum likelihood (ML) estimator, generating an initial coarse estimate. The second step (GTRS) refines this estimate using weighted least squares (WLS) and the generalized trust region subproblem (GTRS), mitigating performance degradation caused by relaxation. Monte Carlo simulations validate that the proposed SD/SOCP-GTRS approach effectively reduces root mean square error (RMSE) compared to other methods. These findings demonstrate that the SD/SOCP-GTRS framework consistently outperforms existing techniques, approaching the theoretical performance limit and offering a robust solution for high-precision localization in wireless sensor networks. Full article

Climate change intensifies biotic and abiotic stresses, threatening global crop productivity. High-throughput phenotyping (HTP) technologies provide a non-destructive approach to monitor plant responses to environmental stresses, offering new opportunities for both crop stress resilience and breeding research. Innovations, such as hyperspectral imaging, unmanned aerial vehicles, and machine learning, enhance our ability to assess plant traits under various environmental stresses, including drought, salinity, extreme temperatures, and pest and disease infestations. These tools facilitate the identification of stress-tolerant genotypes within large segregating populations, improving selection efficiency for breeding programs. HTP can also play a vital role by accelerating genetic gain through precise trait evaluation for hybridization and genetic enhancement. However, challenges such as data standardization, phenotyping data management, high costs of HTP equipment, and the complexity of linking phenotypic observations to genetic improvements limit its broader application. Additionally, environmental variability and genotype-by-environment interactions complicate reliable trait selection. Despite these challenges, advancements in robotics, artificial intelligence, and automation are improving the precision and scalability of phenotypic data analyses. This review critically examines the dual role of HTP in assessment of plant stress tolerance and crop performance, highlighting both its transformative potential and existing limitations. By addressing key challenges and leveraging technological advancements, HTP can significantly enhance genetic research, including trait discovery, parental selection, and hybridization scheme optimization. While current methodologies still face constraints in fully translating phenotypic insights into practical breeding applications, continuous innovation in high-throughput precision phenotyping holds promise for revolutionizing crop resilience and ensuring sustainable agricultural production in a changing climate. Full article

Environmental monitoring requires reliable bioindicators to assess the genotoxic effects of pollutants in aquatic ecosystems. In this study, the marine fish Thalassophryne maculosa was evaluated as a bioindicator of genotoxicity through the application of the micronucleus test. Fish were exposed to varying concentrations of mercuric chloride (HgCl₂) (0.1, 0.25, and 0.5 µg HgCl₂/g body weight) over different time intervals (24, 48, 72, and 96 h). A dose- and time-dependent increase in nuclear abnormalities, including micronuclei, was observed, with significant chromosomal damage detected at 0.25 and 0.5 µg HgCl₂/g body weight. These results demonstrate the sensitivity of T. maculosa to mercury exposure, even at concentrations below regulatory safety thresholds, emphasizing its suitability as a bioindicator for detecting genotoxic contamination in coastal ecosystems. This study provides critical insights into the ecological risks posed by mercury and highlights the potential of T. maculosa to enhance environmental monitoring programs, particularly in regions vulnerable to heavy metal pollution. Full article

In forested watersheds, suspended sediment concentration (SSC) is an important parameter that impacts water quality and beneficial use. Water quality also has impacts beyond the stream channel, as elevated SSC can violate Indigenous sovereignty, treaty rights, and environmental law. To address elevated SSC, watershed partners must understand the dynamics of the sediment regime in the basins they steward. Collection of additional data is expensive, so this study presents modeling and analysis techniques to leverage existing data on SSC. Using data from the South Fork Clearwater River in Idaho County, Idaho, USA, we modeled SSC over water years 1986–2011 and we applied regression techniques to evaluate correlations between SSC and natural disturbances (channel-building flow events) and anthropogenic disturbances (timber harvesting, hazardous fuel management, controlled burns, and wildfire). Analysis shows that SSC did not change over the period of record. This study provides a monitoring program design to support future decision making leading to reductions in SSC. Full article

This paper examines the impact of government-led green certification on enterprise green transformation, utilizing data from A-share-listed firms in China and focusing on the Green Factory Certification program launched by the Ministry of Industry and Information Technology in 2016. We find that government-led green certification has a significant environmental incentive effect, with certified green factories significantly accelerating enterprise green transformation compared to non-certified enterprises. These findings provide a strong response to the ongoing debate regarding the environmental performance of green certification policies. Unlike general green certification, government-led green certification incorporates both internal incentives and external monitoring. Green factory certification reduces financing costs and promotes internal green innovation, while also attracting the attention of green investors and media scrutiny, which in turn accelerates enterprise green transformation. Additionally, green factory certification has differential effects on enterprise green transformation. The effect of green factory certification on enterprise green transformation is more pronounced in regions with high public environmental concern, non-heavily polluting industries, non-state-owned enterprises, and large-scale enterprises. This paper broadens the policy pathways for enterprise green transformation from the perspective of government-led green certification, offering valuable insights for promoting such transformations. Full article

Achieving the ambitious economic, social, and environmental goals of the UN Sustainable Development Goals (SDGs) requires strategic digital governance improvements that promote long-term and equitable participation in emerging technologies. However, research lacks clear confirmation regarding how governments’ varied policy investments in spheres, like infrastructure expansion, skills programming, and cybersecurity, specifically contribute to holistic sustainable development progress monitored across international benchmarks. Addressing persistent uncertainties, this study statistically modeled if national expenditures directed toward information and communications technology (ICT) access, digital literacy initiatives, and online privacy protections predict higher performances meeting SDGs longitudinally. Analyzing recent country-level data across 27 European nations, structural equation modeling uncovered positive relationships between all three complementary digital governance priority areas and national SDG Index achievement over time. Beyond theoretically validating the instrumental role of availability, capabilities, and security advancements for balanced digitization, findings offer policymakers vital empirical guidance to amplify social returns on ICT investments. The results also demonstrate practical tools to track implementation impacts amidst unrelenting technological shifts. Ultimately, equitably mainstreaming technologies’ vast problem-solving potential necessitates evidence-based digital governance carefully expanding equitable participation. This work aims to equip leaders to purposefully craft enabling, empowering ICT policy ecosystems advancing urgent development aims. Full article

Although the Universal Salt Iodization (USI) program has been highly successful, it remains relevant due to the continued risk of Iodine Deficiency Disorders (IDDs) in vulnerable groups, such as children and pregnant women. This program empowers the relevant authority to continuously monitor iodine levels in iodized salt. Our study reports on the use of a Salt Iodate Micro-Method Reagent (SIMR) detection kit for this purpose. The kit was validated, with a linearity of 5.0–60.0 mg/Kg, at a detection limit of 6.8 mg/Kg, with excellent recovery ranging from 93.0 to 108.3%, whereas the repeatability, intermediate precision, and reproducibility achieved a mean coefficient of variation (CV) of 5.3%, 6.8%, and 5.9%, respectively. The stability of the reagents used in the kit was tested using freshly prepared iodine standard quality control (QC) samples of 20.0 mg/Kg and 40.0 mg/Kg, all of which were observed to be stable, within the range of the mean ± 2 × (standard deviation, SD), for 10 days. The suitability of the kit was proven when no difference was found in the mean results of 70 salt samples, using a paired t-test and the Bland–Altman plot, compared to the reference method, at a 95% confidence interval (CI). Thus, the SIMR detection kit is a highly feasible alternative method for iodine monitoring, with a fast analysis time, as well as being cost effective, and environmentally friendly. Full article

Negative impacts of human activities on stream ecosystems include the reduction/modification of the connectivity between surface water and groundwater and the contamination of these resources. Vertical hydrological exchanges principally occur through the coarse surface sediments and the hyporheic zone (porous matrix) and these compartments have the property to store pollutants. Such hydrological exchanges participate in the self-purification of the stream and infiltration of polluted surface water can lead to the contamination of groundwater. A complete environmental monitoring program should therefore include the assessment of the biological quality of the porous matrix and of the dynamics of vertical hydrological exchanges. The Functional trait (FTR) method based on the study of oligochaete communities in the coarse surface sediments and the hyporheic zone, allows simultaneous assessment of the effects of pollutants present in these compartments and the dynamics of vertical hydrological exchanges. Here, we applied the FTR method upstream and downstream of the effluents of three different wastewater treatment plants (WWTPs) whose discharges were significantly polluted, and for one of them (Oberglatt), before and after its upgrading. We could clearly observe negative effects of the effluents of each of these WWTPs on oligochaete communities and the Oberglatt WWTP upgrading resulted, compared to the state before the upgrading, in a significant reduction of the polluted sludge effect at the downstream site of the effluent. In addition, the method allowed us to identify several sites where the stream had a high capacity to self-purify (through exfiltration of groundwater) and other sites where groundwater was vulnerable to pollution by surface water. Full article