Three-dimensional (3D) geological modeling technology offers intricate geological information services and technical support for the research of urban geological conditions and their evolutionary mechanisms. It further facilitates the analysis of environmental geological problems, encompassing their distinguishing features and genetic mechanisms. The precision of such three-dimensional models is predominantly influenced by the caliber of modeling data, the chosen modeling methodologies, and the intricacy of the modeled geological objects. This research, exemplified by the three-dimensional geological structure modeling of the Wuhan metropolitan development area, aims to explore a comprehensive three-dimensional modeling framework for urban geology, particularly in scenarios with large-scale and complex geological conditions. This study holds significant technical value in enhancing the application of 3D geological modeling, as well as practical importance for urban geological investigations and research.

To clarify effects of weathered coal humic acid on water-stable aggregates and pore characteristics of reclaimed cambisol, this research analyzed the evolution characteristics of soil aggregates and pores. Effects of different humic acid dosages (0, 1%, 3%, and 5% by weight) and application period (1 year, 2 years, and 3 years) on soil aggregates and organic carbon components in soil water-stable aggregates were investigated. The results showed that water-stable macroaggregates (>0.25 mm) had the highest content in the first year; but in the third year, water-stable microaggregates (0.053–0.25 mm) had the highest content, and silt and clay (<0.053 mm) were at a maximum. In the third year of 5% treatment, mean weight and geometric mean diameters were the largest, soil structure was the most stable, and TOC and water-stable microaggregates (0.053–0.25 mm) in soil had the highest proportions. For 5% treatment, the majority of irregular pores were independent in the first year, and number of pores decreased in the second year, but average pore volume increased, forming robust connected pores. In the third year, the number of elongated and irregular pores increased. In the third year of 5% treatment, most pores were irregular and of size >100 μm, and soil structure was the most stable.

Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and El Niño phenomenon. This paper proposes an improved double-output Residual Neural Network (DO-ResNet) model to concurrently estimate the ocean subsurface temperature (OST) and ocean subsurface salinity (OSS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly(SSHA), sea surface wind (SSW), and geographical information (including longitude (LON) and latitude (LAT)). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R²) were employed to evaluate the model's performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R² values for estimating OST (OSS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of OST and OSS across different depths with Argo-derived data, demonstrating the model's ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model displays strong seasonal adaptability. In conclusion, this research introduces a novel artificial intelligence technique for estimating OST and OSS in the tropical Western Pacific Ocean.

As an important part of coastal wetlands, tidal flats provide unique ecosystem services and functions. China's tidal flats are significantly threatened by industrialization, urbanization, aquaculture expansion and coastline reconstruction, and there is an urgent need for sustainable strategies to balance the protection and utilization of tidal flats. Remote sensing technology can realize large-scale spatiotemporal research of tidal flats, comprehensively improve the ecological environment of coastal zones, and help achieve the overall planning goals of major projects for the protection and restoration of important ecosystems in China. In this study, based on GEE (Google Earth Engine) platform, Sentinel-2 (MSI), Landsat 8 (OLI) and Landsat 9 (OLI-2) remote sensing images were used to construct multi-source intensive time series remote sensing image collection, combined with MSIC-OA algorithm. The tidal flat data of China in 2018 and 2023 are extracted and analyzed. The results show as follows: 1) The overall classification accuracy of the tidal flat in 2023 is 95.19%, and the Kappa coefficient is 0.89; In 2018, they were 92.77% and 0.83 respectively. 2) The total tidal flat area in 2018 and 2023 is 8300.34km2 and 8151.54km2, respectively, a decrease of 148.80km2; 3) In 2023, estuarine and bay tidal flats will account for 54.88% of the total area, and most of the tidal flats will be distributed near river inlets and bays; 4) In 2023, the total length of the coastline adjacent to the tidal flat is 10196.17km, of which the artificial shoreline accounts for 67.06%, and the development degree of the tidal flat is 2.04, indicating that most of the tidal flat has been developed and utilized. The research results provide valuable reference for the scientific planning and rational utilization of tidal flat resources in China.

Coppicing has been neglected in recent decades leaving gaps in knowledge on silvicultural interventions, especially the long-term effects on coppices of South-East Europe. Thus, our work aims to define the long-term effects of thinning in sessile oak (Quercus petraea (Matt.) Liebl.) and European beech (Fagus sylvatica L.) sub-mountainous thermophilic low coppices in the Croatian Dinarides. The experiment includes two localities with thinning plots and control plots. Thinning was done in 2002 with 17.71% of wood volume removed in the European beech coppice and 26.09% in the sessile oak coppice. 1276 trees were marked, measured (DBH, tree height, number of stems per stump) and assessed for vitality, origin and six silvicultural features revealing tree quality. Descriptive statistics for all variables, RMANOVA for DBH and height and for continuous variables mean+-+ standard deviation for categorical frequency and relative frequency variables were carried out. The results show a statistically significant positive long-term effect of thinning on tree growth, stem and crown features and support findings that thinning, by increasing growth and quality, is a necessary intervention in European beech and sessile oak low coppices. The recent economic crisis, the sudden increase in energy prices, and the increased demand for assorted wood products have initiated higher interest in coppices in Croatia.

The study delves into the Zaalklapspruit valley-bottom wetland in South Africa, a site designed through ecological engineering and currently impacted by acid mine drainage due to an upstream defunct coal mine. The research conducted in 2022, aimed to uncover the dynamics of contaminant dispersal within such a wetland setting, focusing on the origins, routes, and ultimate recipients of various metals and sulfur compounds. Analysis revealed that the bottom sediment is rich in organic material, with pH levels observed between 6.05 and 6.59, and exhibited notably low oxidation-reduction potentials, reaching down to −219.67 mV at sample point S3. Notably, Manganese displayed the highest adsorption rates, whereas Iron was predominantly absorbed by the roots of Typha capensis and the algae Klebsormidium acidophilum. The wetland's macrophyte rhizospheres hosted a rich array of microbiota, including families like Helicobacteraceae and Hydrogenophilaceae, which are crucial in metal and sulfur processing. This investigation sheds light on the intricate biogeochemical processes taking place involving the wetland's sediment, macrophyte root systems, periphyton, and microbial populations, underscoring the potential of ecologically engineered wetlands in the remediation of waters affected by acid mine drainage.

The purpose of this study is to identify, and examine the potential characteristics of Siompu Tangerines to be given legal protection for Geographical Indications and examine the efforts of local governments in encouraging legal protection of Siompu Tangerines Geographical Indications, this research uses a type of empirical legal research. The conclusion of this study explains that characteristics of Siompu Tangerines have the potential to be given legal protection for Geographical Indications because they have a distinctive sweet, slightly sour taste and strong aroma with a sugar content scale of 9-10º Brix, the orange has been named the sweetest orange in the National Citrus fruit contest and several times served at official ceremonies at the State palace, the reputation of Siompu Tangerines makes it a National Flagship Orange through the Decree of the Minister of Agriculture number 742 / Kpts / TP. 240/7/1997. Geographical environmental factors from natural conditions and human factors make Siompu Tangerines can only grow well in the Siompu island area. The Regional Government of South Buton Regency has made efforts in terms of the development of Siompu Tangerines but has not been optimal in carrying out legal protection of Geographical Indications, due to a lack of community understanding, and the Regional Government regarding the potential of Siompu Tangerines which can be given protection by registering as a Geographical Indication product, the importance of obtaining legal certainty and obtaining exclusive rights is very useful in improving the community's economy and Farmer Tangerines Siompu.

Through analysis of the constituent components of the observation data from 12 geoe-lectric field stations located approximately 600 km form the epicenter of the Jishishan Ms6.2 earthquake, this study reveals that during earthquake preparation and occurrence, the geoelec-tric field at each station exhibit non-uniform variations. These variations are characterized by significant decreases or increases in correlation coefficients between different azimuths (δxy) and between long and short polar distances within the same azimuth (δxx). Additionally, there are discrete or extruding changes observed in the dominant azimuth angles of the telluric field at the stations, ranging approximately from 40° to 90°. These anomalies indicate local site effects and exhibit quasi-synchronous temporal characteristics. The study found that stations located in regions with intense tectonic activities, such as Menyuan and Jingtai, experience more significant anomalous changes influenced by the tectonic activities of the Qaidam-Qilian Mountain block. When multiple stations with anomalous changes are used to determine the earthquake epicenter, earthquakes often occur within or near the intersection area of the predicted ranges of these sta-tions. The specific time of earthquake occurrence shows no clear relationship with the locations or tectonic activities of the stations.

Since the 1960s, forest planners have used linear programming models to develop management plans for large, forested areas. Hundreds of academic papers have presented such models, incorporating multiple objectives, a growing diversity of management interventions, and uncertainty, among other things. Three basic ways to formulate these models have been used: Model I, III, and III. We define these models based on the sequence of management unit states represented by the variables. In Model I, variables represent a sequence of states from the beginning of the planning horizon to the end. In Model II, variables represent a sequence of states from one intervention to the next. Finally, in Model III, variables represent a single arc in a management unit’s decision tree, including only a beginning and an ending state. We formulate each type of model for a case study with three increasingly complex scenarios incorporating additional ecosystem services. Our results indicate that, despite requiring more variables and constraints, Model III requires the least time to formulate, largely because it has the least dense parameter matrix. Model II has the shortest solution times, with Model III close behind. Model I requires both the longest formulation and solution times.

The sustainable development of the country, particularly in hilly regions, encounters formidable challenges attributed to the gradual degradation of environmental conditions in these areas. Over time, disputes have arisen concerning forests, water sources, and other natural assets. The present analysis focuses on the environmental conflicts surrounding the Chamera Stage-I hydroelectric project in the Chamba region of Himachal Pradesh. This analysis is based on perception surveys conducted within local communities and field studies examining ecological and environmental parameters. These parameters include the loss of green cover, decline in biodiversity, depletion of natural resources, disruption of access to common property, disturbances in the ecological cycle, alterations in microclimate, changes in land use patterns, and the loss of ethnobotanical and wild edible fruit plants. Local communities overwhelmingly assert that these conflicts have arisen due to the construction of the dam. They firmly hold the view that higher authorities disregard the environmental and ecological impact as a crucial consideration in any project. Consequently, as communities grapple with survival challenges, conflicts with authorities emerge over environmental issues. The analysis also highlights measures to mitigate these adverse impacts and proposes an enhanced collaborative approach in handling such projects to address conflicts more effectively.

Springs play a significant role in maintaining hydrological balance in the mountain ecosystem. The communities living in the mountain tops rely entirely on spring water for their daily needs. In recent decades, spring water discharges have decreased drastically in the Himalayan regions and become seasonal. Remote sensing and geographical information system techniques have proved to be essential for understanding the sustainable development of groundwater, particularly where people rely solely on spring water in the mountainous regions of the Himalayas. This study employs the Analytical Hierarchy Process (AHP) to identify groundwater potential zones. It involves overlaying seven thematic layers concerning elements of physical features and land-use/land-cover of the region, using the weighted overlay toolbox in ArcGIS, with each layer being assigned a weight based on its importance. Based on the results, groundwater potential zones have been classified into five categories: poor, fair, good, very good, and excellent. The study found that poor groundwater potential zones cover 1.1% of the area, fair zones cover 27.8%, good zones cover 23.3%, very good zones cover 26.6%, and excellent zones cover 21.2% of the total area. The excellent to very good potential zones are associated with perennial springs that have higher discharge (4.30-30.92 l/m), lineaments, rainfall, forested land, and porous soils, favoring the potentiality of the springs. The fair and poor groundwater potential zones are associated with seasonal flowing springs, consisting of barren land, less rainfall, and low fracture density, which influence the potentiality of groundwater. The validation of the groundwater potential zones indicated an Area Under Curve (AUC) value of 0.88, which shows good agreement with observed spring water locations. The identified groundwater potential zones are valuable for prioritizing the area for the sustainable development of spring water. Particularly, it is helpful for policymakers and government bodies to be involved in developing strategies for sustainable water management at the micro-watershed level and safeguarding spring water resources using integrated watershed management in the headwater region of the Himalayas.

This study was conducted to investigate heavy metal contamination and potential health risk of ground water of selected municipal solid waste (MSW) disposal sites of Khyber Pakhtunkhwa (KP) Pakistan. For this purpose a total of 30 composite samples of both surface and ground water were collected from dumpsites of Abbottabad, Bannu and Peshawar. The collected samples were analyzed for physico-chemical parameters and heavy metals by following standard methods. Results indicated that pH values were found within the acceptable limits specified by national environmental quality standards (NEQS) and world health organization (WHO). Mean concentrations of heavy metals were within NEQS and WHO limits, except for Ni which exceeded the NEQS and WHO limit of 0.02 mg/L in all samples, Similarly, Mn exceeded the limit set by NEQS and WHO (0.5 mg/L) in the ground water samples collected from Bannu dumpsite. The multivariate analysis indicated that the heavy metal contamination of the groundwater of the selected dumpsites was anthropogenic. The pollution index (PI) values (PI≥100) of Mn and Ni indicated very high pollution at individual levels while collectively based on heavy metals pollution index (HPI) values (HPI ≥ 200) the ground water of all the selected dumpsites were found highly polluted. Whereas, the hazard index (HI) values (HI ≥ 5) suggested adverse health effects. It was concluded that the ground water of all the dumpsites was highly polluted with heavy metals particularly Ni and Mn thus, posing a high health risk.

Species distribution models (SDMs) are usually used to predict current species’ geographic distributions and to forecast the impact of climate change, with different aims, such as conservation, and biodiversity management. SDMs use has been increasing in the last decades, however, they are vulnerable to parametrization and data quality input. Thus, inappropriate input can lead to potential unreliability in results. In this context, the most used data and methodologies in SDM, and putative deviations from the consensual best practices, were identified, by analysing recent literature (2018 to 2022). Results show that the parameters presented more consistently are the chosen algorithm (MaxEnt was used in 98% of the studies), the accuracy measures, and the time windows. Many papers fail to specify other parameters, limiting the reproducibility of the studies. Some papers also fail to provide information about the target species: only a fraction of the species' range is considered, or no justification for including specific variables in the model is provided. These options can decrease reliability in predictions under future scenarios since data provided to the model is inaccurate from the start or there is insufficient information for outputs discussion.

Like many coastal lagoons in several countries, “Navío Quebrado” lagoon (La Guajira-Colombia) is a very delicate and precious environment; indeed, it is a nationally recognized Flora and Fauna Sanctuary. Several factors, amongst which climate change, are threatening its existence because of changes in the governing hydro-morphological processes. A first step to address this problem certainly is to understand its hydrological behavior and to be able to replicate via simulation its recent history and then infer likely futures. These potential futures will be marked by changes in the water input by its tributary, the Camarones River, and by a modified water exchange with the sea, according to a foreseen sea level rise pattern, as well as by a different evaporation from the free surface, according to temperature changes. In order achieve the required ability to simulate future scenarios, data on the actual behavior have to be gathered, i.e. a monitoring system has to be set up, which to date is not existent. Conceptually, designing a suitable monitoring system is not a complex issue and seems easy to implement. However, the environmental and socio-cultural and socio-economic context makes every little step a hard climb. An extremely simple –almost “primitive”- monitoring system has been set up in this case which is based on very basic measurements of current velocity and water levels and a direct participation of local stakeholders, first of all the National Park unit of the Sanctuary. All this may clash with the latest groovy advances of science, like in situ automatized sensors, remote sensing, machine learning and digital twins, and certainly several improvements are possible and desirable. But it has strong positive points: it is providing surprisingly reasonable data and…it is working at almost zero additional cost. Several technical difficulties made this exercise interesting in itself and worth being shared. Its novelty lies in showing how old, simple methods may offer a working solution to new challenges. This humble experience may be of help in several other similar situations across the world.

This study investigates the anthropogenic activities, water quality, and ichthyofaunal diversity of the Navotas Riverine Ecosystem. It examines the anthropogenic influences, analyzes the physicochemical and microbiological properties of water, identifies fish species, evaluates the water quality index, and determines ichthyofaunal diversity using various indices. Water samples and fish collections were conducted from January to April 2024 at three sites. Key findings indicate that water quality parameters such as temperature (23.20-37.5°C), dissolved oxygen (6.22-7.84 mg/L), phosphate concentration (0.55-3.72 mg/L), and fecal coliform (350-160,000 MPN/100 mL) failed to meet DENR standards for Class C water. The TDS (1153.33-10700 ppm) and conductivity (17653.33-21333.33 µS/cm) were also elevated, indicating significant pollution. The study identified low ichthyofaunal diversity, with six native and one invasive fish species. A total of 269 fish were collected: 161 from Site 1, 46 from Site 2, and 62 from Site 3. The most abundant species was Scatophagus argus (19% occurrence), while the invasive Sarotherodon melanotheron ranked third (17% occurrence). Notably, pH (r = 0.753) correlated highly with nitrate concentration. S. melanotheron significantly correlated with pH (r = 0.602, p = 0.019) and nitrates (r = 0.555, p = 0.031). Lutjanus argentimaculatus also had significant correlations with pH (r = 0.527, p = 0.039) and nitrates (r = 0.651, p = 0.011). Conversely, S. argus had a negative moderate correlation with temperature (r = -0.529, p = 0.039) and conductivity (r = -0.536, p = 0.036). These correlations suggest that the water parameters significantly influence the diversity and abundance of fish in the river. The study concludes that the river's water quality is heavily influenced by anthropogenic activities which negatively affect ichthyofaunal diversity. Recommendations include continuous monitoring, gut and heavy metal analysis of fish, assessment of avifauna diversity, and strict implementation of conservation regulations.

Our research assesses the effects of four forest species, namely Swietenia macrophylla King, Swietenia mahogany (L.) Jack., Pinus occidentalis Swartz, and Pinus caribaea Morelet var. Caribaea, on organic carbon (OC) dynamics and carbon dioxide equivalent balance (BCO2 Eq.) in the soils beneath these species. Reforestation projects in the study region cover 1,200, 543, 770, and 1,152 hectares, with these four species, respectively, being the most relevant species in reforestation projects within the country. To determine the BCO2 Eq. per unit area, we compared the greenhouse gas (GHG) fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) with the OC reserves found in the mineral soil to a depth of 30 cm and in the forest litter. For 18 months, we conducted field measurements in sixteen stands, four for each species. Our results indicate that S. macrophylla absorbed the highest amount of CO2, while S. mahogany released the highest amount into the atmosphere. BCO2 Eq. from S. macrophylla soils was -23.19 metric tons of CO2 Eq. ha-1 year-1, while for P. occidentalis, S. mahogany, and P. caribaea, the corresponding quantities were -3.838, -2.299, and +0.982, respectively. During the measurement period, soils under S. macrophylla, P. occidentalis, and P. caribaea were net sinks of CO2 Eq., while soils under S. mahogany behaved as a source. The absorption rate of CO2 Eq. from the atmosphere was approximately 6, 10, and 24 times higher in S. macrophylla when compared to the respective rates of P. occidentalis, P. caribaea, and S. mahagony.

Nitrates can cause severe ecological imbalances in aquatic ecosystems, with considerable consequences for human health. Therefore, monitoring this inorganic form of nitrogen becomes essential for any water quality management structure. This research aimed at developing a novel Nitrate Portable Measurement System (NPMS) for monitoring nitrate concentrations in water samples. NPMS is a reagent-free ultraviolet system developed using low-cost electronic components. Its operation principle is based on Beer-Lambert law for measuring Nitrate concentrations in water samples through light absorption in the spectral range 295-315 nm. The system is equipped with ready to use ultraviolet sensor, Light Emission Diode (LED), op-amp, microcontroller, liquid crystal display, quartz cuvette, temperature sensor, and battery. All the components are assembled in a 3D-printed enclosure box which allows a very compact self-contained equipment, with high portability, enabling field and near real time measurements. The proposed methodology and the developed instrument were used to analyse multiple nitrate standard solutions. The performance was evaluated in comparison with the Nicolet Evolution 300, a classical UV-Vis spectrophotometer. The results demonstrate a strong correlation between the retrieved measurements by both instruments within the investigated spectral band and for concentrations above 5 mg NO3- /l.

It is well established that the reaction cycles involving some halogenated alkanes (so-called ozone-depleting substances, ODS) contribute to the depletion of ozone in the stratosphere, thus causing the Montreal Protocol (initially signed in 1987) and later amendments. The Protocol called for the scheduled phase-out of ODS, including chlorofluorocarbons (CFC), hydrochloro-fluorocarbons (HCFC), carbon tetrachloride (CCl4), halon, methyl chloroform (CH3CCl3), me-thyl chloride (CH3Cl), and even hydrofluorocarbons (HFCs). In view of the urgent importance of ozone layer protection to the globally ecological environment, the Taiwan government took regulatory actions on reducing ODS consumption since 1993 by the joint-venture of the central competent authorities. Under the regulatory requirements and the industry’s efforts by adopt-ing the alternatives to ODS and abatement technologies, the phase-out of some ODS (i.e., CFC, CCl4, halon, and CH3CCl3) have been achieved prior to 2010. The consumptions of HCFCs and methyl chloride have been significantly declined over the past three decades (1993-2022). However, HFC emission indicated a V-type variation during the period. Due to the local pro-duction and extensive use of HFC in Taiwan, its emissions increased from 663 kilotons of carbon dioxide equivalents (CO2eq) in 1993 to 2,330 kilotons of CO2eq in 2001, and then decreased to 373 kilotons of CO2eq in 2011. Since then, the emissions of HFC largely used as the alternatives to ODS showed an upward trend, increasing to 1,555 kilotons of CO2eq in 2022. To be in compliance with the Kigali Amendment (KA-2015) to the Montreal Protocol for mitigating global warming, the Taiwan government has taken regulatory actions in reducing the consumption of some HFC substances with high global warming potential (GWP) under the authorization of the Climate Change Response Act in 2023, aiming at the baseline consumption in 2024 by 80 % reduction by 2045.

Background: Land use and land cover change (LULCC) in the Ruhuhu River Sub-Basin (RRSB), a crucial watershed within the Lake Nyasa Basin in Tanzania, poses a significant threat to water resource sustainability and ecological integrity. However, a lack of comprehensive data on long-term LULCC trends has hindered effective conservation efforts. Methods: This study utilized Landsat 5 TM and Landsat 8 OLI data within Google Earth Engine to quantify LULCC dynamics from 1990 to 2023. Six land cover classes were analyzed: water, forest, barren land, grassland, shrubland, and farmland. Results: Results revealed a substantial decline in forest cover (56.92% to 28.60%), primarily converted to farmland (2.99% to 20.22%). Shrubland expanded initially but then declined, while barren land steadily increased. These changes have profound implications for water resources, biodiversity, and ecosystem services within the RRSB and the broader Lake Nyasa Basin. Conclusions: This research highlights the urgency of implementing sustainable land management practices to mitigate the adverse impacts of LULCC in the RRSB. The high classification accuracies achieved (90.9% to 98.7%) validate the robustness of the methodology. This study provides crucial insights for policymakers, land managers, and conservation practitioners, emphasizing the need for targeted interventions to promote sustainable land use and ensure the long-term resilience of the RRSB ecosystem.

The main goal was to investigate the ability of non-thermal plasma reactor with gliding arc discharge to remove the diesel exhaust particulates (DEPs). A conventional knifed-shaped LTP GA reactor was utilized. Three cases were studied: 140 L/min, 70 L/min and 14 L/min of air drawn through the reactor and diesel exhaust fumes were sampled continuously. They were assayed in terms of concentration and number particle size distribution. The higher the residence time’s, the higher the energy input that may be utilized for DEPs’ removal. Reactor’s performance definitely lowered the concentration of DEPs (250 - 580 nm) and altered their number size distribution. There was no effect on the number concentration, nor on the particle size distribution, of DEP particulates of 10 – 250 nm in size. The effectiveness of DEPs’ removal: Decreasing flow rate from 140 L/min to 70 L/min somehow altered the values. Achieving the airflow of 14 L/min led to a substantial improvement (even to a fourfold increase for 300 – 480 nm particles). Non-thermal plasma reactors with gliding arc discharge may be successfully adapted in the process of DEPs’ treatment. Their performance may be optimized by adjusting the airflow at the inlet of reactor to guarantee the longest aerosol residence times and the highest removal efficiency.

The use of low cost sensors has dramatically increased in recent years in all the engineering sectors. In the field of buildings and automotive, low cost sensors open very interesting perspectives, because they allow to monitor temperature and humidity distributions together with air quality, in a widespread and punctual way and allow control of all energy parameters. The main issue still open remains the validation of the measurements. In this work, we propose an innovative approach to verify the measurements given by some low-cost systems built ad hoc for automotive applications. Two independent low-cost measurement systems have been set to measure Particulate air Matter (PM) concentration, TVOC concentration, CO2 concentration, formaldehyde concentration, air temperature, relative humidity, pressure, air flow velocity and GPS position. These systems have been calibrated by the comparison with standard and certified sensors used by the regional authority of Emilia-Romagna region (ARPAE, Italy) for characterising the air-quality. The innovation of this approach lies in both the in-field comparison of low-cost and high quality sensors and the use of proper conversion approaches for mass concentration measurements. A quantitative analysis of the sensors performance is given, with a focus on effects of time-granularity, relative humidity, mass conversion from particle counts and size detection response. The results show that the low-cost sensors measurements of air temperature, relative humidity and particle number concentration are in a good agreement with high quality sensors measurements, with a strong impact of relative humidity on performance indicators. Overall, a good quality and consistency of the data among the sensors has been achieved.

Popular geo-computer vision works make use of aerial imager with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the lack of information near image boundaries. In this work, the impact of three image sizes (256 × 256, 512 × 512, and 1024 × 1024 pixels) and two image overlap levels (no overlap and 12.5% overlap) on the performance of road classification models was statistically evaluated. For this, two convolutional neural networks used in various tasks of geospatial object extraction were trained (using the same hyperparameters) on a large dataset (containing aerial image data covering 8650 km² of the Spanish territory that was labelled with binary road information) under twelve different scenarios, each scenario featuring a different combination of tile size and overlap. To assess their generalisation capacity, the performance of all resulting models was evaluated on a data from novel areas covering approximately 825 km². The performance metrics obtained were analysed using appropriate descriptive and inferential statistical techniques to evaluate the impact of the performance at distinct levels of the fixed factors (tile size, tile overlap, neural network architecture). Statistical tests were applied to study the main and interaction effects of the fixed factors on the performance. A significance level of 0.05 was applied to all the null hypothesis tests. The results were highly significant for the main effects (p-values lower than 0.001), while the two-way and three-way interaction effects among them had different levels of significance. The results indicate that the training of road classification models on images with a higher tile size (more semantic context) and tile overlap (additional border context and continuity) significantly impacts their performance. The best model was trained on a dataset featuring tiles with a size of 1024 × 1024 pixels and a 12.5% overlap and achieved a loss value of 0.0984, an F1 score of 0.8728, and an ROC-AUC score of 0.9766, together with an error rate of 3.5% on the test set.

Early reporting of the introduction and establishment of exotic species is of paramount importance for successful management. Here we report the introduction and rapid spread of the Bare-eyed Pigeon, Patagioenas corensis, on the binational island of Saint-Martin, West Indies. This range-restricted species naturally occurs in the arid coastal areas of Columbia and Venezuela and nearby islands. Its introduction on Saint-Martin represents an expansion of about 1000 km beyond its established native range. Using observations recorded in e-bird and results from a recent field survey, we show that since its introduction, most probably between late 2012 and early 2013, the species is expanding fast in Saint-Martin and has recently broadened its habitat to include anthropized, built areas. The expansion of Bare-eyed Pigeon on Saint-Martin and the neighbouring Leeward Islands, possibly facilitated by climate change in the future, could be a threat to both native columbid species and other bird species, through competition for resources. We therefore recommend to local authorities and stakeholders to rapidly eradicate the species or at least prevent its further spread on Saint-Martin, possibly though listing it as a game species, while it is still possible to do so.

The catastrophic twin earthquakes that struck southern Turkey and northern Syria on February 6 2023, caused massive casualties and extensive damage to infrastructure, with the province of Hatay bearing the brunt of the impact. To investigate the potential of nighttime light (NTL) data for earthquake disaster assessment, we performed an analysis of pre- and post-earthquake NPP-VIIRS and SDGSAT-1 NTL data. The NTL change ratio (NCR) was employed to identify the extent of damage in Hatay Province following the earthquake to assess the impact on affected buildings and populations. NPP-VIIRS daily NTL Time-series data was used to establish an economic recovery framework for earthquake disasters in Hatay Province, assessing regional economic recovery after earthquake. The results revealed a strong correlation between the total reduced NTLs of NPP-VIIRS and SDGSAT-1 and the presence of severely and moderately damaged buildings. Conversely, the correlation with slightly damaged buildings was relatively weaker. Furthermore, the reduced NTLs of NPP-VIIRS provide a more precise identification of all earthquake damage compared to the SDGSAT-1 data, achieving an overall accuracy of 86.21% in earthquake damage identification and a Kappa coefficient of 0.72. In accordance with the earthquake economic recovery framework, as of the end of March 2024, neither the provincial economy nor any of its districts have fully restored to their pre-earthquake levels. The findings offered new and valuable insights into earthquake disaster assessment, assisting decision-makers in accurately assessing the level of disaster risk following an earthquake and efficiently allocating post-earthquake relief funds.

Plastics, once regarded as a revolutionary material shaping modern society, now poses an unprecedented threat to our environment. Household solid waste sorting stations produce several fractions, one of which contains a high concentration of Low-Density Polyethylene (LDPE) film waste (packaging, sunscreen film, etc.). This fraction is difficult to recycle because it contains quite a lot of impurities. Usually, it is sent to the cement factories that burn it together with other fuels. However, with some processing techniques such catalytic pyrolysis this fraction could be valorized. In this paper the experiments which were carried out at a laboratory scale installation, in batches with a processing capacity of 1-3 kg of waste. The pyrolysis reactor is connected to a distillation column giving the possibility to separate the fractions. The gaseous and liquid fractions were characterized by GC-FID-TCD (gases) and GC-MS (liquids) analysis. Natural catalysts such as bentonite or clinoptilolite were studied and used in the melting of plastic mass to simplify the process as much as possible. To test the activity of the catalysts, the pyrolysis of LDPE granules was initially studied. It was found that natural zeolites are much more active than bentonite and that a minimum concentration of 5-10% is needed to have a positive effect on the composition of the fractions (increasing the weight of the light fractions C1-C6, C6-C10 and C11-C13) in relation to the heavy fractions C13-C20 and C20+. Catalytic pyrolysis gives a completely different distribution of light hydrocarbons. The best catalyst selected from LDPE lab experiments, was then tested upon the pyrolysis of plastic film waste obtained by a waste treatment plant. The research objective reported in this paper was to obtain a fraction of combustible gases in the largest possible proportion, which can be much easier exploited by burning in an engine that drives an electric generator.

Global climate change primarily affects the spatiotemporal variation of physical quantities such as relative humidity, atmospheric pressure, ambient temperature, and notably, precipitation levels. Accurate precipitation predictions remain elusive, necessitating tools for detailed spatiotemporal analysis to better understand climate impacts on the environment, agriculture, and society. This study compares three learning models: ARIMA (Autoregressive Integrated Moving Average), RF-R (Random Forest Regression), and LSTM-NN (Long Short-Term Memory Neural Network), utilizing monthly precipitation data (in millimeters) from 757 locations in Boyacá, Colombia. The LSTM-NN model outperformed others, precisely replicating precipitation observations in both training and testing datasets, significantly reducing the root mean square error (RMSE) with average monthly deviations of approximately 19 mm per location. Evaluation metrics (RMSE, MAE, R², MSE) underscore the LSTM model’s robustness and accuracy in capturing precipitation patterns. Consequently, the LSTM model was chosen to predict precipitation over a 16-month period starting from August 2023, offering a reliable tool for future meteorological forecasting and planning in the region.

Soil naturally contains fluorine, but concerns arise when its levels or bioavailability are increased by human activities or specific compounds. South Korea faces challenges in regulating soil fluorine pollution despite implementing stricter total fluorine standards than many developed countries. Moreover, these regulations can be economically unfeasible because of the naturally high background levels of fluorine in the soil in Korea. This review explores the global landscape of soil fluorine management strategies and examines potential solutions that reconcile environmental protection with economic concerns. We recommend a shift towards regulating specific and hazardous fluorine compounds rather than total content, and prioritizing remediation efforts based on assessments of bioavailable fluorine. This approach may help Korea establish a more effective and sustainable strategy for managing soil fluorine pollution.

The effect of land use on sandy soils of southern Botswana was carried out by comparing the composition and properties of soil organic matter. Virgin and disturbed soils were sampled from savanna ecosystems (Central District and Kweneng District). The biodegradability of organic matter was evaluated by incubation in the laboratory. Humic fractions were quantified and humic acids were analyzed by visible and infrared spectroscopy. The results indicate that continued disturbance, whether due to grazing or subsistence farming, has resulted in small significant changes in the concentration of available nutrients and organic matter in the soil. Nevertheless, substantial changes could be established in soil C/N ratio, humic acid/fulvic acid ratio and in the biodegradability of soil organic matter and the structural characteristics of humic acids. The increased aromaticity of humic acid (visible and IR spectroscopies) following disturbance, suggests increased biogeochemical activity and/or the impact of abiotic processes (such as periodic fires) selectively removing aliphatic constituents. The overall results indicate a low potential soil fertility, the sustainable preservation of which depending more on features related to quality than to the total amount of the soil organic matter, which shows considerable aromatization parallel to its degree of association with the mineral fraction.

This study investigates the anthropogenic activities, water quality, and ichthyofaunal diversity of the Navotas Riverine Ecosystem. It examines the anthropogenic influences, analyzes the physicochemical and microbiological properties of water, identifies fish species, evaluates the water quality index, and determines ichthyofaunal diversity using various indices. Water samples and fish collections were conducted from January to April 2024 at three sites. Key findings indicate that water quality parameters such as temperature (23.20-37.5°C), dissolved oxygen (6.22-7.84 mg/L), phosphate concentration (0.55-3.72 mg/L), and fecal coliform (350-160,000 MPN/100 mL) failed to meet DENR standards for Class C water. The TDS (1153.33-10700 ppm) and conductivity (17653.33-21333.33 µS/cm) were also elevated, indicating significant pollution. The study identified low ichthyofaunal diversity, with six native and one invasive fish species. A total of 269 fish were collected: 161 from Site 1, 46 from Site 2, and 62 from Site 3. The most abundant species was Scatophagus argus (19% occurrence), while the invasive Sarotherodon melanotheron ranked third (17% occurrence). Notably, pH (r = 0.753) correlated highly with nitrate concentration. S. melanotheron significantly correlated with pH (r = 0.602, p = 0.019) and nitrates (r = 0.555, p = 0.031). Lutjanus argentimaculatus also had significant correlations with pH (r = 0.527, p = 0.039) and nitrates (r = 0.651, p = 0.011). Conversely, S. argus had a negative moderate correlation with temperature (r = -0.529, p = 0.039) and conductivity (r = -0.536, p = 0.036). These correlations suggest that the water parameters significantly influence the diversity and abundance of fish in the river. The study concludes that the river's water quality is heavily influenced by anthropogenic activities which negatively affect ichthyofaunal diversity. Recommendations include continuous monitoring, gut and heavy metal analysis of fish, assessment of avifauna diversity, and strict implementation of conservation regulations.

Ecological Society is based on socioeconomic systems directed to the equilibrium of inter-relationships between environment, climate, production systems, technologies and human communities to promote the overall well-being of people and the Earth planet. It has a main goal for supporting sustainable development and equitable societies and achieving optimal health outcomes recognizing the interconnection between people, technologies, animals, plants, and their shared environment for the one health. Using data of scientific publications and patents until the year 2024, this study shows new technologies that can support the energy transition with the shift from fossil fuels to renewable energy sources in an effort to reduce CO₂ emissions for an ecological society having, whenever possible, zero-carbon economies to reduce environmental pollution and climate change and foster health and wellbeing of people. Results reveal that sustainable technologies supporting the transformation of the energy sector, economies and societies, and having a rapid growth, are blue hydrogen, floating photovoltaic systems, carbon capture and utilization, green hydrogen and liquid metal batteries. These findings can guide decisions of policymakers and stakeholders towards fruitful investments driving the transition from fossil-based to zero-carbon socioeconomic systems to have a long-run sustainable development of overall world system that has to direct towards an ecological society associated with one health.

The Range–Doppler (R-D) model is extensively employed for the geometric processing of Synthetic Aperture Radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R-D model, comprising a process that involves numerous parameters and complex computations. In this study, we introduce a novel approach to improve the positioning accuracy of the R-D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also design low-order polynomial compensation models with different parameters. The models are evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and GF3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of Ground Control Points (GCPs) are conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R-D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images.

Hydrogen has emerged as a promising energy carrier, offering a viable solution to address our current global energy demands. Solar energy stands out as a primary source of renewable power and has the potential to produce hydrogen through the utilization of solar cells. The quest for efficient, durable, and cost-effective photocatalysts is imperative for the advancement of solar-driven hydrogen generation. Copper slag, a by-product stemming from copper smelting and refining processes, primarily consists of metal oxides like hematite, silica, and alumina. This composition renders it a compelling secondary resource for deployment as a photocatalyst, thereby diverting copper slag from landfills. This review aims to comprehensively examine copper slag as a photocatalytic material, exploring its chemical, physical, photocatalytic, and electrochemical properties. Furthermore, it assesses its suitability for water treatment and its potential as an emerging material for large-scale solar hydrogen production.

This article aims to assess Pakistan's National Spatial Data Infrastructure implementation efforts through well-established approaches including the SDI readiness model, organizational aspects, and state of play. The findings underscored challenges related to human resources, SDI education/culture, long-term vision, GI awareness, sustainable funding, metadata availability, online geospatial services, and geospatial standards hindering the NSDI development in Pakistan. However, certain factors, such as the legal framework for NSDI establishment, web connectivity, geospatial software availability, core spatial dataset availability, and institutional leadership, exhibit favorable standings. Thus, to enhance the development of NSDI for Pakistan, recommendations include bolstering financial and human resources, improving online geospatial presence, and fostering a long-term vision for NSDI.

Small and Medium Enterprises (SMEs) are recognized as the main drivers of economic growth and development in many countries throughout the world. SMEs represent about 90% of the manufacturing sector and contribute about 14% of the total manufacturing contribution to GDP. Despite SMEs’ potential to change Nigeria’s status from a consumer economy to a production economy, policies that will ensure that environmentally- friendly manufacturing processes and production technology are not implemented or complied with. SMEs’ operational activities may be deleterious to human health and the ecosystem if environmental management practices are not adopted or prioritized. This paper evaluated the perception and adoption of the Environmental Management System, mainly ISO 14001, by SMEs in Nigeria. One hundred and fifty (150) SMEs in the manufacturing sector in Lagos and Ogun State, Southwestern Nigeria, were randomly selected for this study. The paper posits that environmental management literacy and advocacy should be carried out in SMEs. Environmental management practices involve engaging both industries and product end-users in sustainable practices. The paper concludes that the implementation of environmental management practices and culture by SMEs will abate environmental pollution and its concomitant health risks to humans, the environment, flora, and fauna. Mandatory and voluntary environmental measures are suggested as a means of engendering eco-friendly innovation for sustainable development, triggering employment, job security, and gross domestic product.

Flood frequency analysis at large scales, essential for the development of flood risk maps, is hindered by the uncertainty in outputs of process-based hydrological models and the scarcity of gauge flow data. We develop a Bayesian hierarchical model (BHM) based on the Generalized Extreme Value (GEV) distribution for regional flood frequency analysis at high resolution across North America. Our model leverages annual maximum flow data from ≈20000 gauged stations and a dataset of 130 static catchment-specific covariates to predict extreme flows at all catchments over the continent as well as their associated statistical uncertainty. Additionally, a modification is made to the data layer of the BHM to include daily gauge discharge data when available, which improves the precision of the discharge level estimates. We validated the model using a hold-out approach and found that its predictive power is very good for the GEV distribution location and scale parameters and improveable for the shape parameter, which is notoriously hard to estimate. The resulting discharge return levels yield satisfying agreement when compared with available design peak discharge from various government sources. Assessment of the covariates’ contributions to the model also informs on the most relevant underlying factors influencing flood-inducing peak flows. The key covariates in our model are temperature-related bioindicators, the catchment drainage area and geographical location.

The transition to renewable energy sources is pivotal in addressing the escalating challenges of climate change and environmental degradation. Solar energy, particularly photovoltaic (PV) technology, stands out as a prominent solution due to its potential for clean and sustainable electricity generation with minimal greenhouse gas emissions. However, accurately assessing the carbon footprint of PV modules is essential for guiding policy, industry practices, and research. This paper reviews the state of current literature and highlights the difficulties in estimating the carbon footprint of PV modules manufactured in China. It emphasizes the inherent limitations of Process-Based Life Cycle Assessment (PLCA), including data collection challenges, dynamic environmental changes, and subjective methodological choices. The study underscores the need for improved transparency, standardization, and reproducibility in Life Cycle Assessments (LCAs) to provide more accurate and reliable environmental impact evaluations.

The StarDICE experiment strives to establish an instrumental metrology chain with a targeted accuracy of 1 mmag in griz bandpasses to meet the calibration requirements of next-generation cosmological surveys. Atmospheric transmission stands out as a significant source of systematic uncertainty. Specifically, gray extinction induces spurious variations of photometric fluxes. We propose a solution relying on an uncooled long-wave infrared thermal camera to evaluate it. This type of camera can provide real-time insights into atmospheric conditions and detect cirrus clouds. However, achieving accurate measurements with thermal imaging systems necessitates prior calibration due to the influence of temperature-induced effects, which compromise their spatial and temporal precision. Moreover, these systems cannot provide scene radiance values in physical units by default. This study introduces a new calibration process utilizing a tailored forward modeling approach. The method is applied to an uncooled FLIR Tau2 thermal camera. It incorporates sensor, housing, flat-field support, and ambient temperatures, along with raw digital response, as input data. Experimental measurements are conducted inside a climatic chamber, with the camera imaging a thermoregulated blackbody source. Results demonstrate the calibration effectiveness, achieving precise radiance measurements with a temporal pixel root-mean-square error (RMSE) of 0.09 W/m2/srand residual spatial noise of 0.03 W/m2/sr.

Soil water balance (SWB) in woody crops is sometimes difficult to estimate with one-dimensional models because these crops do not completely cover the soil and usually have a deep root system, particularly when cropped under rainfed conditions in a Mediterranean climate. In this study, the actual crop evapotranspiration (ETc act) is estimated with the soil water balance model SIMDualKc which uses the dual-Kc approach (relating the fraction of soil cover with the crop coefficients) to improve the estimation of the water requirements of a rainfed vineyard, using data from a deep soil profile. The actual basal crop coefficient (Kcb act) obtained using the SIMDualKc model was compared with the Kcb act estimated using the A&P approach, which is a simplified approach based on measurements of the fraction of ground cover and crop height. Spectral vegetation indices derived from Landsat-5 satellite data were used to determine the fraction of ground cover (fc VI) and thus, the density coefficient (Kd). The SIMDualKc model was calibrated using soil water content (SWC) measurements down to a depth of 1.85 meters, which significantly improved the conditions for using a SWB estimation model. The test of the model was performed using a different SWC dataset. A good agreement between simulated and field measured SWC was observed for both data sets along the crop season, with RMSE<12.0 mm, NRMSE <13%. The calibrated Kcb values were 0.15, 0.60 and 0.52 for the initial, mid-season, and end season, respectively. The ratio between actual (ETc act) and crop evapotranspiration (ETc) was quite low between veraison and maturity (mid-season), corresponding to 36%, indicating that the rainfall was not sufficient to satisfy the vineyards water requirements. Spectral vegetation indices used to compute fc VI were unable to fully track the plants’ conditions when water stress. However, ingestion of data from remote sensing showed promising results that could be used to support decisions making in irrigation scheduling. Further studies on the use of the A&P approach using remote sensing data are required.

The Brazilian Pantanal region suffers intensely from biomass burning during the dry season. Large quantities of gases and particles are emitted into the atmosphere during this period, with serious implications for fauna and flora. Understanding the dynamics of these emissions is es-sential for mitigating the ecosystem, its functioning, and possible anthropogenic disturbances. In this sense, this study analyzed emissions over the north of the Brazilian Pantanal during the 2022 drought. Measurements of concentration of fine particulate matter (PM2.5), Black carbon (BC), and elemental concentration (25 chemical elements) were determined by gravimetry, reflectance analysis, and X-ray fluorescence, respectively, for samples collected between August and Octo-ber 2022. The average concentrations of PM2.5 and BC had their values increased by approxi-mately 4 and 2.5 times, respectively, when compared to the averages from a decade ago. The maximum concentrations found are close to values typical of the southern Amazon region, a re-gion with high rates of deforestation and changes in land use. The elemental analysis showed large changes in concentrations, mainly associated with biomass burning emission (BB) and soil resuspension. Furthermore, analyses of lead (Pb) concentrations showed strong anthropization at the study site.

Crop coefficient (Kc), the ratio of crop evapotranspiration (ETc) to reference evapotranspiration (ETo), is used to schedule an efficient irrigation regime. This research was conducted to investigate variations of ETc and growth-stage-specific Kc in a flood-irrigated winter wheat as a forage crop from 2021 to 2023 in the Lower Rio Grande Valley of southern New Mexico, USA, and evaluate the performances of the two temperature-based ETo estimation methods of Hargreaves-Samani and Blaney-Criddle with the widely used Penman-Monteith method. Results indicated that the total ETc over the whole growth stage for flood-irrigated winter wheat was 556.4 mm on a two-year average, while the average deep percolation (DP) was 2.93 cm and 2.77 cm, accounting for 28.8% and 27.2% of applied irrigation water, respectively in the 2021-22 and 2022-23 growing seasons. The ETo over the growing season computed by Penman-Monteith, Hargreaves-Samani, and Blaney-Criddle equations were 867.0 mm, 1015.0 mm, and 856.2 mm in 2021-22, and 785.6 mm, 947.0 mm, and 800.1 mm in 2022-23, respectively. The Hargreaves-Samani method performed better than the Blaney-Criddle method with higher statistical indicators of model efficiency coefficient (CE), and smaller mean bias error (MBE), mean absolute error (MAE), and the root mean square error (RMSE) in both growing seasons. The result of a global sensitivity analysis showed that the mean temper-ature is main driving factor for estimated ETo based on Blaney-Criddle and Hargreaves-Samani, but the sensitivity percentage for Blaney-Criddle was 76.9%, which was much higher than that of 48.9% for Hargreaves-Samani, given that Blaney-Criddle method is less accurate in ETo estimation for this area especially during the hottest season from May to August. In contrast, wind speed and maximum temperature were the main driving factors for the Penman-Monteith method, with sensitivity percentages of 70.9% and 21.9%, respectively. The two-year average crop coefficient (Kc) values at the initial, mid, and late growth stage were 0.54, 1.1, and 0.54 based on Penman-Monteith, 0.51, 1.0 and 0.46 based on Blaney-Criddle, and 0.52, 1.2 and 0.56 based on Hargreaves-Samani. Results showed that the Hargreaves-Samani equation serves as an alternative tool to predict ETo when fewer meteorological variables are available. The calculated local growth-stage-specific Kc can help improve irrigation water management in this region.

This study investigates the utilization of Eco-Innovation practices within Nigerian industries to mitigate urban soil contamination. Soil samples were collected from various urban land uses in Ijebu Ode town, specifically mechanic workshops (MW), roadsides (RD), industrial areas (IL), and market zones (MT). Ten composite soil samples were randomly gathered from each land use at a depth of 0-30cm and subjected to analysis for heavy metal concentrations (copper Cu, iron Fe, lead Pb, zinc Zn, cadmium Cd, manganese Mn) using an Atomic Absorption Spectrophotometer. This facilitated the calculation of contamination levels and pollution load index. The findings indicate that the concentration of Cu, Zn, Fe, and Mn is highest in soils from mechanic workshops, with the degree of soil contamination ranking as follows: Mechanic Workshop > Industrial land use > Roadside > Market land use. The study concludes that elevated levels of heavy metals in the sampled urban soil are closely linked to improper land use practices and other human activities. It advocates for the promotion of sustainable environmental practices through comprehensive stakeholder engagement aimed at educating the populace on the management and preservation of urban soils against harmful practices.

Earthquake-triggered landslides represent a significant seismic-related disaster, posing threats to both the lives and properties of individuals in affected areas. Furthermore, they can result in road and river blockages, as well as other secondary disasters, significantly impacting post-earthquake rescue efforts. Efficient and accurate rapid assessment of high-risk landslide zones carries important implications for decision-making in disaster response and for mitigating potential secondary disasters. This study uses the high-intensity Zone VII to IX of the 5 September 2022 Luding Ms6.8 earthquake as a case study. The "difference method" and the "cumulative displacement method," based on the simple Newmark model, are employed to assess earthquake-triggered landslides. The evaluation results from both methods demonstrate that the areas posing an ex-tremely high risk of earthquake-triggered landslides are predominantly situated on the western side of the Xianshuihe Fault. Actual landslide data verification shows that both models have high predictive accuracy, with the difference method model slightly outper-forming the cumulative displacement method model. Moreover, the study recommends determining threshold values for each landslide hazard interval with significant physical meaning using past strong earthquake data as soon as possible when utilizing the difference method to assess the risk of earthquake-triggered landslides.

PFAS are a variety of ecologically persistent compounds of anthropogenic origin loosely included in many industrial products, in which the carbon chain can be fully (perfluoroalkyl substances) or partially (polyfluoroalkyl substances) fluorinated. Their ubiquitous presence in many environmental compartments over the years, and highly long-lasting nature, arose concern because of the possible adverse effects of PFAS on ecosystem and human health. Between the remediation technologies, adsorption has been demonstrated to be a maneageble and cost-effective method for the removal of PFAS in aqueous media. This study tested two novel and eco-friendly adsorbents (pinewood and date seeds biochar) on 6 different PFAS (PFOS, GenX, PFHxA, PFOA, PFDA, PFTeDA). Batch sorption tests (24 hours) were carried out to evaluate the removal efficiency of each PFAS substance in relation to the two biochars. All samples of liquid phase were analysed by a developed and then well-established method: i) pre-treatment (centrifugation and filtration); ii) determination by high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS/MS). Results evidenced a comparable adsorption capacity of both materials, but greater for the long-chain PFAS. Such findings may lead to a promising path for the use of waste origin materials in the PFAS remediation field.

Keywords: emerging pollutants; pharmaceuticals; plant uptake; soil behavior; soil remediation.

Granulites and associated dykes from the less well studied southern Ivrea-Verbano Zone (IVZ; around Ivrea town) are characterized by combining field, macro, micro and chemical (major and trace-element mineral composition) data in order to identify chemical and rheological variations in the lower crust that could be relevant for geodynamic implications. The Ivrea granulites are similar to those belonging to the Lower Mafic Complex of the central IVZ. The mafic lithologies experienced a stealth metasomatism (pargasitic amphibole and An-rich plagioclase) that occurred at suprasol-idus conditions by a pervasive reactive porous flow of mantle-derived orogenic (hydrous) basaltic melts infiltrated along relatively few, likely deformation-assisted, channels. Similar metasomatic melts are already reported in the central and northern IVZ, suggesting a massive regional hydration of the lowermost Southalpine mafic crust during the Early Mesozoic. The plastic behavior of the lowermost part of the crust, induced by its metasomatic hydration, could have promoted the Tri-assic extension of the Variscan crust and the beginning of the Alpine cycle.

This study aimed to evaluate the effects of plasma treated metal contaminated water, used for irrigation, on plant growth. Zinc (Zn) is one of the more commonly used metals. It can enter the environment from industrial processes as particles released into the atmosphere or as wastewater discharged into waterways or the ground. Exposure to large amounts of zinc, even for a short time, can seriously affect human health. In this experiment, three different DBD O2 plasma treated zinc contaminated water and a control (tap water) were used, with Arabidopsis thaliana as the model plant. The treatments were: i) Control, ii) Zn water, iii) Zn+O3(30 min), and iv) Zn+O3(60 min). Arabidopsis plant exhibited maximum growth in the Zn+O3(30 min) treatment. All growth parameters, except leaf area, followed this trend: Zn+O3(30 min) > Control > Zn water > Zn+O3(60 min). Gene expression analysis revealed that reduced metal ion stress and controlled oxidation due to active oxygen species contributed to favorable/improved growth of Arabidopsis in the Zn+O3(30 min) treatment. Therefore, 30 minutes of DBD O2 plasma treated zinc contaminated water [Zn+O3(30 min)] can mitigate the adverse effects of excess zinc ions and promote the growth of Arabidopsis plants.

Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century has been considerably affecting short-term weather conditions, such as increasing weather variability, and incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, Spatial Error Model (SEM) and Spatial Durbin Model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time-varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches and then over that threshold milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance.

The exponential growth of user-contributed data provides a comprehensive basis for assessing collective perceptions of landscape change. A variety of possible public data sources exist, such as geospatial data from social media or Volunteered Geographic Information (VGI). Key challenges with such ‘opportunistic’ data sampling are variability in platform popularity and bias due to changing user groups and contribution rules. In this study, we use five case studies to demonstrate how intra- and inter-dataset comparisons can help to assess the temporality of landscape scenic resources, such as identifying seasonal characteristics for a given area, or testing hypotheses about shifting popularity trends observed in the field. By focusing on the consistency and reproducibility of temporal patterns for selected scenic resources and comparisons across different dimensions of data, we aim to contribute to the development of systematic methods for disentangling the perceived impact of events and trends from other technological and social phenomena included in the data. The proposed techniques may help to draw attention to overlooked or underestimated patterns of landscape change, fill in missing data between periodic surveys, or corroborate and support field observations. Despite limitations, the results already provide a comprehensive basis for developing indicators with a high degree of timeliness for monitoring perceived landscape change over time.

With the rapid developments that have been made in computer vision and computer technology, semantic segmentation of high-resolution images has emerged as a mainstream and challenging task. The, segmentation of water areas remains a complex task due to the similar features between water areas or between water areas and other land objects. Meanwhile, since coastlines and riverbanks are typically presented in irregular shapes, it is difficult to obtain the categories of pixels at boundaries. This paper proposed the use of a Water-Land Boundary Attention Network (WLBANet) to improve the accuracy of pixels at boundaries between water and land. This network also reinforced the performance of contextual information extraction into distinguishing between water areas and other land objects. To prove the validity of this proposed network, WLBANet was performed on the Water Land Dataset (WLD), which is also by this paper, and which contains water areas and other land objects categories. The results demonstrates that the proposed method has significant effectiveness.

In this study the impact of climate change on drought conditions in Serbia is investigated trough the multifractal analysis of standardized precipitation evapotranspiration index (SPEI). The SPEI time series for accumulation time scale of 1, 3, 6 and 12 months were calculated using the high resolution meteorological gridded dataset E-OBS with a resolution of 0.10 in regular latitude/longitude coordinates and time span from 1961 to 2020. The multifractal analysis was performed for two standard climatic periods (1961−1990 and 1991−2020) using multifractal detrended fluctuation analysis method (MFDFA) which is suitable for nonstationary temporal series. In each of 1278 grid cells that cover Serbian territory we analyzed 4 SPEI time series for two 30 years periods (which totals 10224 MFDFA runs) and calculated multifractal spectrum parameters that describe persistence, degree of multifractality and the influence of small/large fluctuations. We found that all the analyzed SPEI series show multifractal properties with the dominance of small fluctuations. The short and medium drought conditions described by SPEI-1, SPEI-3 and SPEI-6 are persistent (the position of maximum of multifractal spectrum is in the range 0.5<α0<1), stronger persistence is found at higher accumulation time scales, while SPEI-12 time series are antipersistent (0<α0-1<0.5). The degree of multifractality (width of multifractal spectrum W) increases from SPEI-1 to SPEI-6 and decreases for SPEI-12. For all series multifractal spectrum is right skewed (skew parameter r>0) indicating that small SPEI temporal fluctuations contribute more to multifractality than large fluctuations. In the second period SPEI-1 SPEI-3 and SPEI-6 series become more persistent with weaker multifractality, indicating that short and medium drought conditions (which are related to soil moisture and crop stress) become easier to predict, while SPEI-12 changed toward random regime and stronger multifractality in eastern and central part of country, indicating that long term drought conditions (related to streamflow, reservoir levels, and groundwater levels) become more difficult for modeling and prediction.

This manuscript reviews three case studies from Brazil, Panama, and Portugal that illustrate Nature-based Solutions. The Tijuca Forest in Rio de Janeiro is a remarkable story of centuries of forest management that today is recognized as a UNESCO World Heritage Site. The Panama Canal Watershed produces water for Canal operations, electricity generation, and drinking water for half the country´s population. Traditional windmills and weirs near streams in the Alentejo Region, Portugal, have largely been abandoned due to the damming of the Guadiana River. Yet today, weirs are increasingly recognized for their important contribution to water provisioning in this dry region. All have a primary goal related to water provisioning, yet their ecosystem benefits are multiple. The cases offer important lessons for adaptation to climate change, cultural benefits from traditional human activities, and concerns about social equity.

(1) Background: Depressional wetlands are highly vulnerable to changes in temperature and rainfall, affecting littoral vegetation, thus HydroGeoMorphic (HGM) wetland types. This study aimed to assess the utility of Sentinel-2A vegetation indices in enhancing delineation and sub-classify depressional HGM types. (2) Methods: We sampled 84 pairs of four vegetation indices from 10x10m plots along 14 belt transects in eight depressional wetlands in Mpumalanga, South Africa. We evaluated vegetation index differences between and within wetlands, focusing on lit-toral gradients. (3) Results: Significant differences (Bonferroni p

This work mapped the research chronology and conceptual trends on conservation agriculture-soil health-sustainable development goals nexus by analyzing data from related literature. This is the first-time different bibliometric methods such as VOSviewer, Flourish, as well as Bibliometrix and Biblioshiny models in RStudio were simultaneously used to investigate the research impacts and/or interactions between conservation agriculture (CA), soil health and sustainable development at global scale. On 20th February 2024, a search was launched on the web of science core collection using related search terms to extract relevant data. After the screening and elimination, the search produced 835 papers which were used in the bibliometric analysis. The revealed that USA (31%), India (27%), Australia (7%), England (6%), China (6%), Canada (5%), and other countries had below 5% of the published documents. Many of the papers covered zero hunger (38%), climate action (30%), life on land (26%), while other SDGs had relatively low coverage. The study found the adoption of no till, cover cropping, and organic amendments by the authors as the common CA practices. The hybrid bibliometric approach provided a clear roadmap in understanding the global research trajectory on CA-soil health and SDGs nexus, as well as the roles of countries, authors, institutions, publishing journals. This knowledge could support in championing future debates on CA potential for effective discussion and policies, especially in the developing countries where there have been low publications.

Fine particulate matter (PM_2.5) can enter the lungs and cause premature death in millions of people worldwide every year. More and more international scholars are paying attention to the potential harm of PM_2.5 and how to reduce its negative impact. Reducing air pollution and its negative impacts is an important function of green spaces. Many scholars have conducted research on the impact of green spaces on PM_2.5. This article attempts to comprehensively review past related research. This is undoubtedly beneficial for people to better understand the impact of green spaces on PM_2.5 and optimize green spaces accordingly to reduce PM_2.5 concentration. This article first provides a detailed introduction to the impact of plants on PM_2.5 pollution from the perspective of the mechanism of action. Then, this article provides a review of the different impacts of green space types and morphological factors on the surrounding environment PM_2.5. In addition, this article outlines the range of the impact of green spaces on PM_2.5 in the environment. This article categorizes green spaces into different types such as road green spaces, commercial and residential green spaces, and parks and squares, and explores the impact of green spaces on PM_2.5 in the environment. This article analyzes the impact of various parts of a single plant on PM_2.5 in the environment from a single plant perspective. Finally, this article analyzes which factors in the surrounding environment can affect the impact of green spaces on PM_2.5 pollution in the environment. A review of relevant research is also beneficial for people to have a more comprehensive understanding of current research progress and guide future research to better carry out.

The Third U.S. National Marine Environmental DNA Workshop on June 2–5, 2024 brought together researchers, practitioners, and policymakers to overview and discuss: (1) environmental (e)DNA – defined as “DNA in the environment” – as a strategic U.S. national priority, (2) eDNA tool readiness and approaches to support decision-making, (3) emerging eDNA technologies, and (4) plans for implementation and adoption for environmental monitoring and ecosystem assessment “from microbes through whales”. Uses and applications of eDNA have been revolutionizing exploration, measurement, and monitoring of biodiversity across ecosystems, including the marine and freshwater aquatic environments emphasized in the Workshop, as well as terrestrial and aerial systems. The Workshop featured the launch of the new U.S. National Aquatic Environmental DNA Strategy from the White House Office of Science and Technology Policy (2024), with aquatic defined as freshwater to ocean waters, across all salinities; the Strategy was developed using outputs and a taskforce from the 2022 Second Workshop. The 2.5-day Third Workshop included speaker presentations, panel discussions, and networking on eDNA tools, applications, use, upscaling, and their future. The Workshop led into Capitol Hill Ocean Week (CHOW) events (June 5–6, 2024), during World Ocean Week. Note that this summary paper represents the author’s take-away view and interpretation, and not necessarily the overall views or consensus of the U.S. Government, Smithsonian Institution, workshop organizers, or speakers.

The evaluation of Eco-Environmental Quality (EEQ) is important to maintain the quality of ecology and is crucial for the protection of the environment and promote sustainable development. In the past 30 years, Rupandehi district has gone into rapid urbanization and population growth due to the migration of people causing drastic changes in Land use. This study takes Rupandehi district as a study area and analyses the land use change dynamics and its impact on the ecological environment from 1993 to 2023. The study utilizes the cloud computing capability of Google Earth Engine (GEE) to extract multiple indices using Landsat remote sensing data. We also applied the Thresholding approach to various spectral indices to extract land use information in GEE. The Remote Sensing Ecological Indices (RSEI) was constructed using Principal Component Analysis (PCA) to monitor the ecological quality and the effect of Land Use Land Cover (LULC) change on the quality of ecology was analysed. The result shows that the overall ecological quality of the Rupandehi district is good ranging from 0.59 in 1993 to 0.67 in 2023 with a significant impact of land use change. The eco-environmental quality of the study area is deteriorating because of a sharp increase in built-up areas and deforestation as well as the loss of agricultural land. Meanwhile, the spatial and temporal distribution of land use change collides with the change in the ecological quality of the study area. This study provides an accurate and rapid assessment of ecological quality and monitors the impact of land use change on it providing a scientific basis for researchers, government, and others to preserve the ecology and promote sustainable and eco-friendly development.

Prunus armeniaca L. is the native species of cultivated apricot, which is important information for studying the origin, succession pattern, pest control, sustainable development of biodiversity of economic tree species of the genus Apricot, etc. However, with global climate change and increasingly intense human activities, the survival environment of Prunus armeniaca L. is under serious stress, and exploring the potential survival space of Prunus armeniaca L. is an important way to protect the health and development of Prunus armeniaca L.. Exploring the potential living space of Prunus armeniaca L. is an important way to protect the healthy development of Prunus armeniaca L. The Yili River Valley is a typical distribution area of Prunus armeniaca L. . Exploring the potential distribution suitable area of Prunus armeniaca L. in the Yili River Valley and analyzing its future change trend can provide scientific guidance for the protection, development, and utilization of Prunus armeniaca L.. Based on the Geographic Information System software platform combined with the MaxEnt Model, we simulated the potential distribution areas and the degree of adaptation of Prunus armeniaca L. in the Yili River Valley, and predicted the potential distribution changes of Prunus armeniaca L. in the future under different situations. Based on the above methods, the following results were obtained: (1) The MaxEnt Model simulated the geographic distribution of Prunus armeniaca L. with high accuracy and an AUC value of 0.969, and the area of the currently suitable area for Prunus armeniaca L. in the Yili River Valley was about 10407.77 km2, accounting for about 18.45% of the overall land area of the Yili River Valley; the area of the highly suitable area was about 1380.9 km², accounting for about 13.26%, the area of the medium suitable zone is about 2830.15km², and the area of low suitable zone is about 6196.05km². (2) The maximum increase in the total area of the suitable zone under the future low-radiation forcing scenario is 11.49%, and the minimum increase under the future medium-high forcing scenario is 4.94%. (3) The overall migration was in the southwest direction, with the largest migration angle under the SSP245 scenario and the largest migration distance under the SSP585 scenario. Under the background of future climate change, there is a tendency for the potential habitat of Prunus armeniaca L. in the Yili River Valley to expand from low altitude to high altitude, and altitude is the dominant geographic factor influencing the distribution of Prunus armeniaca L.. By simulating and studying the potential geographic distribution of Prunus armeniaca L. in the Yili River Valley under the background of climate change, we can reveal the response mechanism between the geographic distribution of Prunus armeniaca L. and the natural environment, which can provide scientific guidance and data support for the exploitation and protection of Prunus armeniaca L. in the future.

Land degradation, desertification and tree mortality related to global climate change are in the spotlight of Remote Sensing research in recent decades, since extreme climatic events could affect the composition, structure and biogeography of forests. However, the complexity of tree mortality processes requires a holistic approach. Herein, we present the first global assessment and a historical perspective of tree mortality by reviewing both Remote Sensing and ground-based studies. We compiled 254 papers on tree mortality that make use of remotely sensed products, ground-based monitoring and climatic drivers, focusing on their spatial and temporal patterns, in the methods applied, while highlighting research gaps. Forested ecosystems, especially in the Mediterranean area, appear to have experienced often climatic-induced physiological stress under extreme droughts and warming, raising concern that forests may become increasingly vulnerable to tree mortality. Our core results indicated that international publications on tree mortality are on the increase with the main hotspots being North America (39%) and Europe (26%). Wetness indicators appear as the barometer in explaining tree mortality on a local scale, while vegetation indicators derived from multispectral optical sensors are promising for large scale assessment. We observed that almost all of the studies we have reviewed were based on less than 25 years of data and were of local scale. Longer timeframes and regional scale investigations that will include multiple tree species analysis could have a significant impact on future research.

By sinking and storing carbon from the atmosphere via photosynthesis, forests play a significant role in combating climate change. Global warming is currently the most pressing global issue. Forests play an important role in the global carbon cycle, covering 30% of the world's terrestrial land area and conserving 81% of the earth's terrestrial carbon biomass. The aim of the study was to quantify and map the Carbon Sink and Stock Values of the Yeraba state forest ecosystem. Primary and secondary data sources were used in this study. Primary data such as plot length and width, DBH (diameter at breast height), soil data, and ground control point (GCP) were obtained in the field using a Garmin 72 GPS, a clinometer, and a diameter tape. Diameter tape, caliper and Soil ogre materials. Beside from this sentimental 2 satellite image, there was also another important primary data. In addition to the primary data source, secondary data sources were used. It includes published books, journals and studies, reports, website sources, and other materials that are publicly available. The systematic sampling technique for identifying each intersection point in the area at regular intervals was the most appropriate sampling design for this study. The study used data from four forest carbon pools, including AGB, BGB, soil, and litter, to calculate the carbon sink and stock values of the Yeraba state forest ecosystem. A total of 17 quadrats, each measuring 20m x 20m and spaced 428 m apart, were used to collect vegetation data. For litter and soil sample collection, five 1 m x 1 m sub-quadrats were established at each quadrat's four corners and center. This data was correlated with five Sentinel 2A level 1c imagery data derived vegetation indices of 2023 to obtain a model and estimate of AGB and AGC at the study site. The total mean carbon stock density of Yeraba state Forest was 404.895 t/ha, with 223.656t/ha, 58.15t/ha, 3.41t/ha, and 119.679 t/ha in the above ground carbon, below ground carbon, litter carbon, and soil carbon pools, respectively. The three environmental factors that influence the distribution of different carbon pools in the forest are altitudinal gradient, slope, and aspect. Standing from the result and inputs the researcher recommended that, Future research may find that using LiDAR and Radar imageries, which can address the saturation and canopy penetration issues, will increase the accuracy of carbon stock estimation.

The hydrocarbon generation potential of Chia Gara strata and their genetic link with produced oils from the Zagros basin are still not comprehensively investigated. Forty-seven rock samples from the Tithonian–Berriasian interval were subjected to geochemical analyses to infer organic matter enrichment, hydrocarbon potential, thermal maturity, and timing for hydrocarbon generation/expulsion and their implications regarding upcoming petroleum explorations in the Zagros basin. The results showed that the Chia Gara rock samples have fair to very-good generation potential, as confirmed by higher total organic carbon (TOC=0.68–3.95 wt%) and Rock-Eval (S1+S2=3.37–8.52 mg HC/g rock). The hydrogen index (HI=171–462) and Rock-Eval Tmax over 430 °C support the presence of Types II and mixed Types II/III kerogen. These results confirm the high generation potential of the Chia Gara Formation in the study region. 1D basin models of the KK–109 well show that over 55% of kerogens were converted into oil in the Cretaceous (77–67 Ma). Expulsion started from the Miocene until now (22–0 Ma), consistent with 0.71–0.80 of modeled Easy%Ro and 55–80 %TR. Consequently, this project confirms the potential oil expulsion from the Chia Gara Formation, which can be a resource for upcoming hydrocarbon investigation in the Zagros basin, Iraq.

Advanced 1H Nuclear Magnetic Resonance (NMR) relaxometry and diffusometry methods and VIS-nearIR spectroscopy combined with pH, electric conductivity (EC) and totally dissolved solids (TDS) measurements were used to assess the properties of wastewater collected from a chicken slaughterhouse in each step of the treatment process (wastewater before treatment, biologically treated wastewater, chemically treated wastewater and discharged wastewater) and from sludge. The 1H NMR Carr-Purcell-Meiboom-Gill (CPMG) and Pulsed-Gradient-Stimulated-Echo (PGSE) decay curves recorded for all samples of wastewater were analyzed by inverse Laplace transform (ILT) to obtain the distributions of transverse relaxation times T2 and diffusion coefficient D. The VIS-nearIR total absorbance, T2-values, D-values, pH, EC and TDS parameters were used for statistical analysis in principal component analysis (PCA). The 1H T2-distributions measured for the slaughterhouse wastewater lie in two main regions reflecting the amount of dissolved solids or the distribution of undissolved solids mobility. The PCA analysis successfully differentiates between polluted and less polluted types of wastewater and sludge. The wastewater treatment applied by the slaughterhouse is efficient. The recommended methods for wastewater monitoring are the NMR T2- and D-distributions and EC, TDS and NMR-D diffusion coefficient. Finally, Machine Learning AI-algorithms are used to provide prediction maps of wastewater treatment stage.

The article adds new policy insights to the way in which formal teaching methods of environmental stewardship education areas are evaluated in Tuvalu. We aimed to answer three questions: 1. What are the formal policies shaping environmental stewardship education in Tuvalu? 2. Are national educational and environmental policies mutually consistent? and 3. Are these national policies consistent with regional and global policies? These questions were addressed using a study of Tuvaluan online-available documentary assessments of national policies, frameworks, and curricula in conjunction with those obtained from the Education Department. Our findings revealed limitations and weaknesses regarding the provisions of environmental stewardship education at the national scale, including misaligned curricula, policy documents, and formal educational provisions with Tuvalu’s national environmental policy. We also found inconsistencies with regional and global policies such as the Pacific Islands Framework for Nature Conservation, the Framework for Resilient Development in the Pacific, and international strategic plans for biodiversity and climate change. These cross-scalar policy weaknesses and limitations highlight the need for the Tuvaluan government to align educational and environmental policies at different scales. We recommend a multi-pronged strategy where modes of informal environmental education can support and complement formal government policymaking on education and the environment.

The present study analyzes students' weather-related hazard and risk literacy in Greece, a climate change hotspot region in the Eastern Mediterranean. In this context, we examine the students' level in two core literacy variables, namely knowledge and competency. In addition, we explore how knowledge, attitudes and socio-demographic variables influence students' competencies related to weather and climate risk assessment and adaptability. A questionnaire-based survey was conducted on 474 students aged 12-16. The regression results showed that knowledge significantly affects the level of competency. Self-belief and confidence in science were the most influential among the attitudinal variables. We conclude by discussing educational and behavioural issues highlighted as essential to address with targeted policies and measures in formal education complemented by non-formal educational activities.

As the number of end-of-life vehicles continues to grow, the field of separation technology for broken copper and aluminium parts of end-of-life vehicles has emerged as a significant area of research. As a key technology for the separation of non-magnetic non-ferrous metals, such as copper and aluminium, eddy current separation can achieve efficient separation of non-magnetic non-ferrous metals according to their differing conductivity and density. Nevertheless, further research is required in the separation of large-scale broken copper and aluminium parts of end-of-life vehicles. This paper presents a novel magnetic roller model comprising circumferential and axial periodic arrangements of permanent magnets. A single permanent magnet was constructed as an equivalent current model, and the magnetic scalar potential equations were solved using simulation tools. This allowed the distribution of the magnetic field outside the magnetic roller to be studied. The results demonstrate that the novel permanent magnet array magnetic roller can enhance the magnetic field intensity and action distance of the external magnetic field, which is more conducive to the separation of large-scale broken copper and aluminium parts of scrap cars. The findings of this research provide a theoretical foundation for the separation of scrap car broken copper and aluminium parts.

In this study the distribution of heavy metal in the agricultural soils in the vicinity of 3 large mine-landfills around Kizhnica mine in Kosovo was investigated. The mining sector is one of the most important sectors of the economic development of Kosovo. The Kizhnica mine is one of the main ore producers in Kosovo. In addition to the positive aspects of production development it has its own side effects such as: industrial waste and the possibility of contamination of sur-rounding areas including the agricultural lands in the surrounding region. Ten sampling sites close to the Kizhnica mining complex were selected. These sampling sites have been characterized and evaluated as the most critical points, due to the anthropogenic influences of the discharge of industrial water from mining flotation and open tailing dumps in the Kizhnica. Concentration of Pb, Zn, Cu, As, Cd, Ni, Mn and Sb in selected samples was carried out by inductively coupled plasma–optical emission spectrometry (ICP–OES). Obtained data were used in order to generate geochemical maps and for calculatation the contamination factor (CF), pollution load index (PLI) and geo-accumulation index (Igeo). The statistical clustering analysis (CA), Pearson correlation coefficient (r) and air spatial distribution patterns using AERMOD View-Gaussian air dispersion model are used to perform evaluation within the area. The results demonstrated higher contents of the heavy metals in the vicinity of the mining area. It may be concluded that the heavy metal contents are impacted by anthropogenic nature of the contamination that confirms a current eco-logical threat of the mining activities in the region. Finally, a paper proposes the potential reas-signment of the deposed waste material into different combinations in construction in order to enhance the waste management and reduce the continued risk to: residents, lands and all living beings in the region.

Polarimetric Synthetic Aperture Radar (PolSAR) images encompass valuable information that can facilitate extensive land cover interpretation and generate diverse output products. Extracting meaningful features from PolSAR data poses challenges distinct from those encountered in optical imagery. Deep Learning (DL) methods offer effective solutions for overcoming these challenges in PolSAR feature extraction. Convolutional Neural Networks (CNNs) play a crucial role in capturing PolSAR image characteristics by leveraging kernel capabilities to consider local information and the complex-valued nature of PolSAR data. In this study, a novel three-branch fusion of complex-valued CNN named Shallow to Deep Feature Fusion Network (SDF2Net) is proposed for PolSAR image classification. To validate the performance of the proposed method, classification results are compared against multiple state-of-the-art approaches using the Airborne Synthetic Aperture Radar (AIRSAR) datasets of Flevoland, San Francisco, and ESAR Oberpfaffenhofen dataset. The results indicate that the proposed approach demonstrates improvements in OA, with 1.3% and 0.8% enhancement for the AIRSAR datasets and 0.5% improvement for the ESAR dataset. The analyses conducted on the Flevoland data underscore the effectiveness of the SDF2Net model, revealing a promising OA of 96.01% even with only 1% sampling ratio.

This study examined local communities' perceptions of mangroves in coastal southeastern Cuba. A variety of methods were employed, including mixed and structured questionnaire surveys, interviews with key informants, and document reviews. Data were gathered from 334 respondents living in communities adjacent to four mangrove social-ecological systems (SESs). The analysis focused on five variables: community use of mangrove resources, ecosystem services, threats to the ecosystem, management activities, and social-ecological relationships. To qualitatively assess the influence of social-ecological relationships and governance, a matrix was created based on anthropogenic activities identified by respondents and their perceptions of ecosystem services. The results indicated that while local people recognize the uses and ecosystem services of mangroves, they do not rely on them for their livelihoods. They also showed moderate to full awareness of management responsibilities and activities at each site. The most commonly identified threats were climate change, drought, and deforestation. Three types of social-ecological relationships were identified based on the characteristics of the communities, their economic activities, and their impacts on the mangroves: urban-industrial, rural-agricultural, and rural-agricultural/tourism. Finally, the research proposed a set of five recommendations and eight associated indicators to ensure that mangrove management framework become more inclusive and participatory, thereby contributing to the articulation of mangrove SES governance in the southeastern region of Cuba.

Significant changes in Land Use and Land Cover (LULC) have widespread implications for the environment, economy, and society; influencing future sustainability and development of a region. This study aimed to assess LULC changes for a 30 year period (1990 – 2020) and project future LULC from 2030 to 2100 for the Irrawaddy Delta using remote sensing and simulations with the artificial neural networks-cellular automata method. The findings showed significant LULC changes in the Delta, particularly for the mangrove forests and cropland (rice paddies). Mangrove coverage was 1,471 km² in 1990 but decreased to 1,282 km² in 2020, and simulations predict a further reduction to 1,277 km² in 2100. Cropland areas increased from 10,550 km² to 10,618 km² in 2020, with simulations projecting a slight decrease to 10,586 km² by 2100. Human activities and cyclonic events, especially in the southern part of the region, have led to barren land and impervious areas replacing dense mangrove and forested areas. These changes threaten the ecological integrity of the Delta and impact local livelihoods and biodiversity. The study underscores the need for sustainable land management practices and policies to mitigate the adverse effects of LULC changes and ensure the resilience of the Irrawaddy Delta against ongoing environmental challenges.

Effective and sustainable water resource management requires flexibility and adaptation to local contexts. Our study analyzes how local water self-governing associations have emerged, using an adapted version of the Combined IAD-SES framework, also known as CIS. Through a compara-tive analysis of two distinct Chilean cases, the research highlights the critical role of historical factors alongside institutional support, political landscapes, and financial realities in shaping current water management practices. The findings suggest that when these elements are aligned and supportive of local water users’ associations, positive outcomes emerge, leading to more effi-cient, sustainable, and user-centered water resource management. Furthermore, this study reveals how the experiences and successes of these local user associations have shaped national policies, particularly regarding the development of monitoring mechanisms and the promotion of public-private cooperation in water governance.

During 2021 and 2022 summers, we measured the total and dissolved (< 0.45 mm) concentration of 25 elements in Utah Lake using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). Utah regulates twelve of these elements. ICP-OES sensitivity is at the ppb-level but is not the approved regulatory method. All regulations are for dissolved concentrations; except aluminum (Al) and phosphorus (P) which are for total recovery. We found total Al concentrations above allowable, but dissolved concentrations were well below allowable concentrations. We attribute high total concentrations to suspended clays. Dissolved copper (Cu) concentrations were below regulatory levels in 2021, but some samples were above regulatory levels in 2022. This could be related to the use of Cu-based algaecide treatments, or from other sources. Lead (Pb) data were inconclusive; dissolved Pb concentrations were well below the acute (1-hour average) limit, but the chronic concentration limit (4-hour average) is below the ICP-OES minimal detection limit. Arsenic (As) concentrations exhibit a seasonal trend that we attribute to groundwater inflows. This ppb-level study provides insight into regulated elements in Utah Lake previously not available due to the high sensitivity of the method and measurements of both total and dissolved concentrations.

The marine region of the Zhongjieshan Islands is an essential component and location for enhancement and release in Zhoushan Fishing Ground. The scientific assessment of the target species' ecological carrying capacity serves as the foundation for augmentation and release efforts. Based on data from the 2021-2022 survey of biological resources in the waters of Zhongjieshan islands, the Ecopath model was built to analyze the trophic structure, energy flow, and overall characteristics of the ecosystem, as well as the ecological carrying capacity of the main enhancement and releasing species, Larimichthys crocea and Sepiella maindroni. The ecosystem's functional groups had trophic levels ranging from 1 to 3.48, with an average energy transfer efficiency of 8.11% and total flow of 2125.905 t·km-2. The ratio of total primary output to total respiration is 1.713. Zhongjieshan Islands' ecosystem was currently at an embryonic stage. The ecological carrying capacity of Larimichthys crocea and Sepiella maindroni was calculated to be 0.2325 t·km-2 and 0.1617 t·km-2, separately or individually. Larimichthys crocea and Sepiella maindroni showed enormous potential for development and release. The findings offered essential data for ecological studies on the waters of Zhongjieshan Islands, as well as a reference for enhancement and release efforts in this area.

Relevant observations suggest that in geothermal power resources and volcanic regions in Earth the mean heat flow is either comparable or greater than the current global mean heat flow of this planet. We have inferred that critical or minimum heat flow required to sustain geothermal provinces (T >150⁰C) is at least 90 mWm^-2 and the same required to sustain active volcanoes in Earth is at least 100 mWm^-2.The volcanism in Venus is found have declined significantly during the past 300 Ma. It is suggested that Earth may lose significant part of its current active volcanoes and geothermal power resources in the next 300-500 Ma if the global mean heat flow in Earth is decreasing at a rate of 5.76 mW per 100 Ma. At the current inferred maximum rate of mantle cooling (20^o C per 100 Myrs) the plate tectonics in Earth is likely to cease within 750 Myrs from now. The critical mean surface heat flux for sustaining plate tectonics in Earth is inferred to be around 50-55 mW/m².

One of the important air pollutants is ammonia, which is mainly emitted from the agriculture sector. This sector is responsible for over 81% of global ammonia emissions. The main threat related to ammonia emissions is water eutrophication and soil acidification. The aim of the research was a preliminary assessment of the implementation status of methods for reducing ammonia emissions on farms and to learn the views and awareness of agricultural producers on reducing emissions of pollutants into the air. The research was conducted using a survey questionnaire that was made available to farmers in various ways. Based on the results of pilot surveys, it can be concluded that farmers have general knowledge of environmental protection and generally agree that people have an impact on the state of the environment. Low-emission practices to reduce ammonia emissions from agricultural sources are not widely used. The best situation is regarding reduction practices in the storage of natural fertilizers and the use of low-emission fertilizer application techniques. Some reduction activities are not obligatory, therefore it is difficult to determine the scope of application of these practices. Therefore, such periodic research will allow monitoring the activity of agricultural producers in implementing reduction methods.

The implementation of circular economy principles is increasingly seen as a viable way to promote sustainable development and reduce environmental impact. This case study examines the implementation of circular economy principles at Taiwan Sugar Company (TSC), a leading sugar manufacturer in Taiwan. The study analyses the company's efforts to redesign its production processes, develop closed-loop systems, and promote resource efficiency. It also explores the challenges and opportunities associated with implementing circular economy principles in the sugar industry, such as the complexity of supply chains, the need for stakeholder engagement, and the availability of sustainable technologies. This study follows the principle of British standard 8001:2017 to implement the concept of circular economy in the organization. The study finds that the Taiwan Sugar Company has made significant progress in implementing circular economy principles, including adopting renewable energy sources, using byproducts as raw materials, developing sustainable packaging solutions, and using the output products of the company to make another useful product for industrial or agricultural use. These initiatives will result in the reduction of waste, an increase in resource efficiency, and enhanced environmental performance. However, the study also identifies several challenges that the company faces in implementing circular economy principles, such as the lack of standardized regulations and guidelines, the need for investment in sustainable technologies, and the need for stakeholder collaboration. Overall, the case study provides valuable insights into the implementation of circular economy principles in the sugar industry and offers practical recommendations for other organizations seeking to adopt circular economy principles. The study highlights the importance of stakeholder engagement, technological innovation, and regulatory support in promoting the transition toward a more sustainable and circular economy.

Lanthanides and actinides are emerging contaminants, but little is known about their uptake and distribution by plants and their interactions in the rhizosphere. To better understand the fate of these metals in plants, we assessed the bioassociation of 2, 20 and 200 µM Eu(III) by five hydroponically grown crops endemic to Europe. The metal’s concentration and its speciation was monitored by inductively coupled plasma mass spectrometry and laser spectroscopy, whereas root exudation was investigated by chromatographic methods. It has been shown, that Eu(III) bioassociation is a two-stage process, involving rapid biosorption followed by accumulation in root tissue and distribution to the stem and leaves. Within 96 h of exposure time, the plant induces a change of Eu(III) speciation in the liquid medium, from a predominant Eu(III) aquo species, as calculated by thermodynamic modelling, to a species with longer luminescence lifetime. Root exudates such as citric, malic, and fumaric acid were identified in the cultivation medium and affect Eu(III) speciation in solution, as was shown by a change in the thermodynamic model. These results contribute to a comprehensive understanding of the fate of lanthanides in the biosphere and provide a basis for further investigations with the chemical analogues Cm(III) and Am(III).

The Katerini-Kolindros aquifer system (KKas) is a critical freshwater resource, essential for the local and regional economy. To understand contamination sources, groundwater flow paths, and assess the aquifer's health, an extensive dataset of 751 samples from 113 wells collected between 2010 and 2020 was analyzed using multivariate statistical analysis (MVSA) and hydrogeochemical diagrams. The prevailing water types identified were Ca-HCO₃ and Mg-HCO₃, with mixed water types resulting from various factors affecting groundwater quality. The chemistry of groundwater in the KKas is shown to be affected by ion exchange processes and to a lesser extent by reverse ion exchange processes. No indications of sea-water intrusion have been identified in the water wells monitored. On the other hand, nitrate contamination has been detected in specific water wells spatially associated with urban-type land uses and activities. It is considered that nitrate contamination originates primarily from septic tanks and urban sewage networks or secondarily from fertilization of football pitches, cemeteries or urban waste management practices.

Urbanization is a dynamic process. At different stages of urbanization, polices of managing car-bon emissions should be adjusted. In this paper, two types of nighttime light data and statistical data of 68 cities in China from 2002 to 2021 are used to establish an urban development evalua-tion system comprising population, economy, construction, and society subsystems. Then, cities were categorized into expanding, contracting, and stable types to understand the uncertainty of urbanization's impact on carbon emissions. The main conclusions are as follows: (1) Integrated nighttime light data is used to extract built-up area boundaries, achieving an overall accuracy ranging from 68.75% to 98.4%, with kappa coefficients ranging between 0.60 and 0.94. (2) Ex-panding cities were concentrated in the eastern coastal regions, while contracting cities were mainly located in inland cities with traditional industries. Urban agglomerations in the Beijing-Tianjin-Hebei region, Yangtze River Delta region, and Chengdu-Chongqing metropolitan demonstrate synchronized characteristic of urbanization development. (3)Economy and con-struction have a greater impact on carbon emissions than population and society. For expanding cities, the focus of carbon emission governance should prioritize rational land use planning. Conversely, for contracting cities, emphasis should be placed on adjusting economic structures.

In the context of global urbanization and climatic change, the frequency of various uncertainties and disturbances faced by urban system is increasing, and enhancing urban resilience has become one of the vital components of sustainable development of modern cities. At the same time, urban resilience has become a hot topic in interdisciplinary research such as economics, ecology, and geography. This paper constructs urban resilience evaluation indicator system based on four dimensions—economy, ecology, society, and infrastructure. The evaluation indicator is used to measure the resilience levels of Yichang-Jingzhou-Jingmen-Enshi (YJJE) urban agglomeration in China from 2010 to 2023. Using the entropy weight method, the Getis-Ord Gi* model, and the robustness testing, this paper analyzes the spatiotemporal evolution characteristics of urban resilience in YJJE urban agglomeration. Then, the factor contribution model is used to explore the influencing factors of urban resilience. Finally, the CA-Markov model is used to predict urban resilience in 2030. The results are as follows: From 2010 to 2023, the urban resilience values of all prefecture-level and county-level cities in YJJE urban agglomeration showed an upward trend, and it indicated that the resilience of urban system is improving. Meanwhile, the urban resilience of YJJE urban agglomeration increased with an average annual growth rate of 3.25%. Furthermore, the urban resilience exhibited a significant spatially heterogeneity distribution, with Xiling, Wujiagang, Xiaoting, Yidu, Zhijiang, Dianjun, Dangyang, Yuan’an, Yiling, Duodao being the high-value agglomerations of urban resilience, and Hefeng, Jianli, Shishou, Wufeng being the low-value agglomerations of urban resilience. Then, we identified critical driving factors of urban resilience in YJJE urban agglomeration. Finally, the urban resilience of all cities within YJJE urban agglomeration will reach the medium level or higher than medium level in 2030. Xiling, Wujiagang, Xiaoting, Zhijiang, Dianjun, Dangyang, Yuan’an will remain hot spots of urban resilience, while Jianli will remain cold spots of urban resilience in the future. This study can provide the scientific reference and policy recommendation for the government to build resilient cities and improve sustainable urban development.

Because Canadian Nitrogen (N) fertilizer use has more than doubled since 1990 (1.2 to 2.9 MtN by 2021), a better understanding of this trend is needed. A comprehensive set of Recommended N Rates (RNR) that agreed with fertilizer sales data from 1996 and 2001 was compared with the Fertilizer Use Survey (FUS). The FUS was conducted from 2014 to 2021, with 2017 being the most representative year for these data. Using non-parametric statistics, confidence intervals were derived from the histograms used to present the FUS data. N application rates from the RNR for Canola, Spring and Duram wheat, and Oats in the west were all below their respective FUS confidence intervals, whereas N application rates for Grain corn showed almost no difference in N use between the RNR and FUS. Crop-specific N application rates interpolated from the RNR and FUS were integrated over their respective crop areas and plotted against national fertilizer sales records from 1990 to 2021. The rapid increase in N use between 2001 and 2017 (0.89 MtN), 90% of it (0.80 MtN) in Western Canada, was primarily due to increased application rates per crop, rather than crop area changes. The FUS-RNR interpolations were a good approximation of N sales records and could improve farm GHG emissions modelling. The economically important crops in Western Canada should be the main focus for N-related GHG reduction measures, but production losses need to be avoided.

Event data loggers are frequently used to record the date and time of tip events in tipping-bucket rain gauges. The HOBO® pendant event data logger is one such commercially-available device commonly used for this purpose. It can record the contact closure of a TBGR reed switch at a maximum timing resolution of 1 second, tied to the timing of the logger clock, which is set each time the logger is launched. These event loggers are ideal for the routine recording of rainfall. This paper addresses the issue of whether they can also be relied upon when estimating short-term intensities, for which they were not designed. New experiments show that for a series of switch closures at fixed intervals other than exact multiples of 1 s, the HOBO® logger fails to record evenly-spaced tip events. Thus, for example, with pulses at fixed 2.75 s intervals, the logger records some events as occurring at 2 s intervals, and others at 3 s intervals. The duration between logged events is correct only when the mean is calculated for a long series of logged events. In natural rainfall, tip events can occur at any time, and inter-tip times, from which intensity can be estimated, will generally not be an integral number of seconds. Consequently, particularly in intense rain, the logger behaviour just described can lead to erroneous estimates of the rainfall rate estimated from the duration of individual inter-tip times. Possible solutions are discussed.

AbstractLarge numbers of urban lakes are facing issues related to cyanobacteria, commonly known as blue-green algae, resulting in significant threats to humans and animal health due to their production of microtoxins and Anatoxin-A. These toxins induce acute effects including gastrointestinal distress, and respiratory problems, underscoring the need to protect urban lakes to safeguard community health and ecosystem well-being. A comprehensive monitoring program is a critical step to address cyanobacteria-related issues. Monitoring results of dissolved phosphorous, nitrates(N), Total Kjeldahl Nitrogen (TKN), total phosphorous (TP), TSS, and Escherichia coli (E. coli) concentrations, are used to understand the importance of different pathways events during dry conditions (no prior rainfall) and wet events (rainfall during or proceeding monitoring events) plus a microbial source tracking procedure, to understand impacts on growth characteristics of cyanobacteria and microtoxins. The Mann-Kendall Trend Test is used to characterize trends of various parameters (using transitions of ‘wet’ and ‘dry’ to the Lake and within the Lake), to identify the impact of various management strategies and/or the impact of various sources from overland land uses for a) wet events to the Lake, b) dry events to the Lake, c) wet events in the Lake, and d) dry events within the Lake. Microbial source tracking methods are used along the shoreline of Fairy Lake to interpret elevated levels of fecal pollution and E. coli bacteria in water samples, aiding in the identification of pollution sources. Blue-green algae have been observed at least once each year from 2018 to 2023. Microtoxins and Anatoxin-A were present in nine samples with a maximum of 1.72 mg/L and 0.02 mg/L, respectively. Frequent beach closures have occurred every year, raising public concerns regarding both ecological and recreational aspects for Fairy Lake.

Soil organic matter (SOM) is essential for nutrient cycling and soil carbon (C) accumu-lation, both of which are heavily influenced by the quality and quantity of plant litter. Since SOM dynamics in relation to plant diversity are poorly understood we investigated the effects of willow variety and mixture, and site on the soil C stocks, SOM chemical composition and ther-mal stability. Using pyrolysis-field ionization mass spectrometry we analysed the top 10 cm of soil from two 7-year-old experimental sites in Germany and Sweden, with monocultures and mixtures of two willow varieties (Salix spp.) belonging to different species. Overall, site had the strongest effect on SOM quality. Results showed significant variability across sites for willow identity and mixture effects on C accumulation and SOM chemistry. In the German site (Ros-tock), yearly soil C accumulation was higher (p < 0.05) for variety ‘Loden’ (1.0 Mg C ha-1 year-1) compared to ‘Tora’ (0.5 Mg C ha-1 year-1), whilst in the Swedish site (Uppsala), both varieties ex-hibited similar soil C accumulation rates of around 0.6 Mg C ha-1 year-1. Willow variety identity significantly affected SOM quality at both sites, while mixing had minor effects. Our findings emphasize the significance of site-specific context and variety or species identity in shaping soil C accumulation in willow plantations.

Land degradation, desertification and tree mortality related to global climate change are in the spotlight of Remote Sensing research in recent decades, since extreme climat-ic events could affect the composition, structure and biogeography of forests. Howev-er, the complexity of tree mortality processes requires a holistic approach. Herein, we present the first global assessment and a historical perspective of tree mortality by re-viewing both Remote Sensing and ground-based studies. We compiled 254 papers on tree mortality that make use of remotely sensed products, ground-based monitoring and climatic drivers, focusing on their spatial and temporal patterns, in the methods applied, while highlighting research gaps. Forested ecosystems, especially in the Medi-terranean area, appear to have experienced often climatic-induced physiological stress under extreme droughts and warming, raising concern that forests may become in-creasingly vulnerable to tree mortality. Our core results indicated that international publications on tree mortality are on the increase with the main hotspots being North America (39%) and Europe (26%). Wetness indicators appear as the barometer in ex-plaining tree mortality on a local scale, while vegetation indicators derived from mul-tispectral optical sensors are promising for large scale assessment. We observed that almost all of the studies we have reviewed were based on less than 25 years of data and were of local scale. Longer timeframes and regional scale investigations that will include multiple tree species analysis could have a significant impact on future re-search.

Permafrost thaw is an important aspect of Earth’s carbon cycles. Initial estimates suggest that permafrost thaw could contribute anywhere between 20 to 500 Gt of CO2-eq by 2100. These estimates are all fundamentally centered around one number: active layer thickness (ALT). The deeper the ALT, the more emissions. Unfortunately, ALT is a highly spatially heterogeneous number and determined by numerous thermal, soil hydrology, and geomorphological effects. The remotely sensed active layer thickness (ReSALT) algorithm was introduced in 2010 to provide scientists with a way to model ALT heterogeneously at meter-scale resolution. However, upon inspection, this work shows that ReSALT’s modeling approach is self-inconsistent when using a variable soil porosity model. This work then introduces SCReSALT (Self-Consistent ReSALT) to solve that problem. Experimental comparisons to a past study in Utqiagvik (formerly Barrow), Alaska show significant improvement and suggest that SCReSALT should become a standard tool in permafrost thaw modeling. Code to reproduce all results can be found at https://github. com/jmathur25/permafrost-prediction.

Long-distance Real-Time Kinematic (RTK) positioning is crucial for applications in remote areas, such as maritime environments. Achieving 2-3 cm accuracy with RTK requires successful ambiguity resolution, which involves two main two steps: identifying the best integer ambiguity candidate and confirming its validity. While previous research has largely concentrated on the first step, including the development of Cascading Ambiguity Resolution methods, and reducing tropospheric delay, studies on the validation of ambiguity for long-distance RTK are limited. This study conducts a thorough examination of ambiguity validation for long-distance RTK, focusing on two prevalent methods: the theoretical success rate and the R-ratio test. The results reveal several key insights. Firstly, the six commonly used bounds for the theoretical success rate are not accurate reflection of the actual success rate, making them unsuitable for long-distance RTK applications. Secondly, the R-ratio test proves to be dependable when the threshold is set above 1.7, assuming there is a minimum observation period of one minute and at least ten satellites are visible. However, the probability of successfully resolving ambiguities with the R-ratio test does not surpass 50%. Additionally, if ambiguity resolution is not achieved within 20 minutes, simply prolonging the observation time is generally unproductive. To improve the performance of ambiguity resolution in practical situations that require extended observation times, this research proposes a novel ambiguity validation method. This new approach is based the duration for which an integer ambiguity resolution candidate maintaining the best status. This method aims to provide a reliable means of validating ambiguities in case that the R-ratio test fails.

Hyperspectral images (HSI) contain abundant spectral information. Efficient extraction and utilization of this information for image classification remain prominent research topics. Previously, hyperspectral classification techniques primarily relied on statistical attributes and mathematical models of spectral data. Deep learning classification techniques have recently been extensively utilized for hyperspectral data classification, yielding promising outcomes. This study proposes a deep learning approach that uses polarization feature maps for classification. Initially, the polar coordinate transformation method is employed to convert the spectral information of all pixels in the image into spectral feature maps. Subsequently, the proposed Deep Context Feature Fusion Network (DCFF-NET) is utilized to classify these feature maps. The model is validated using three open-source hyperspectral datasets: Indian Pines, Pavia University, and Salinas. The experimental results indicated that DCFF-NET achieves excellent classification performance. Experimental results on three public HSI datasets demonstrate that the proposed method accurately recognizes different objects with an overall accuracy (OA) of 86.68%, 94.73%, and 95.14% based on the pixel method, and 98.15%, 99.86%, and 99.98% based on the pixel-patch method.

The complexity of plastic polymers and even more so of additives has increased enormously in recent years. This makes the high-quality recycling of mixed plastic waste considerably more difficult. Some additives have now been strictly regulated or even completely banned for good reasons ('legacy additives'). Material or mechanical recycling generally utilises old plastics that still contain these substances. Consequently, products that are manufactured using such recyclates are contaminated with these harmful substances. We therefore recommend avoiding the use of these recyclates for products with intensive contact with consumers until further notice. We also show that the climate policy challenges for the plastics (and chemical) industry necessitate a defossilisation ('feedstock change'). This turnaround can only succeed if solely high-quality recycling takes place in future; recyclates should primarily replace virgin plastics. This can only work if used plastics with a high degree of homogeneity and known formulation are collected separately, as is already the case today with PET bottles. In this context, we would like to point out inconsistencies in the current legislation on the European emissions trading system.

Unquestionably the rapidly changing climate and, therefore, alterations in the associated bioclimate, constitute an alarming reality with implications in daily practice and natural capital management. In this research the present and projected bioclimate evolution over the phytogeographical regions of Greece is displayed. For this purpose, ultrahigh-resolution computation results on the spatial distribution of the Emberger index’s Q2 classes of bioclimatic characterisation are analysed and illustrated for the first time. The assessments are performed over the reference period (1970–2000) and two future time frames (2021–2040; 2041–2060) under the RCP4.5 and RCP8.5 emission scenarios. By 2060 and under the extreme RCP8.5, intense xerothermic trends are demonstrated owing to the resulting significant spatial evolution mainly of the Arid-Hot, Semi-Arid-Very Hot, Semi-Arid-Hot and Semi-Arid-Temperate Q2 classes, respectively, over the phytogeographical regions of Kiklades (up to 29% occupation), Kriti and Karpathos (up to 30%), West Aegean Islands (up to 26%), North East (up to 56%)–North Central (up to 31%). The RCP8.5 long-term period exhibits the strongest impacts over approximately the right half of the Greek territory the bioclimate of which appears as more dry-thermal in the future.

The Tonle Sap Lake (TSL) landscape is a region of vast natural resources and biological diversity in the heart of Southeast Asia. In addition to serving as the foundation for a highly productive fisheries system, this landscape is home to numerous globally threatened species. Despite recognition by several governmental and international agencies for decades, nine protected areas have been established within this region, natural landcover such as grasslands have experienced considerable declines since the turn of the century. This project used local expert knowledge to train and validate a random forest supervised classification of Landsat satellite imagery in Google Earth Engine. The time series of thematic maps was then used to quantify the conversion of grasslands to croplands between 2004 and 2023. The classification encompassed a 10-kilometer buffer surrounding the landscape, an area of nearly 3 million hectares. The average overall accuracy for these thematic maps was 82.5% (78.5% - 87.9%), with grasslands averaging a 76.1% user’s accuracy. The change detection indicated that over 207,281 ha of grasslands were lost over this period (&gt; 59.5% of the 2004 area), with approx. 89.3% of this loss could be attributed to cropland expansion. The results of this project will inform conservation efforts focused on local scale planning and management of commercial agriculture.

Source water quality is a key determinant of drinking water quality. The recast European Union 2020/2184 drinking water directive (DWD) introduced the obligation for comprehensive risk assessment in drinking water supplies, including the water source and incorporated new elements of natural origin in the list of parameters to be monitored. The current study is the first comprehensive assessment of 15 natural elements (B, Ba, Be, Ca, Co, K, Li, Mg, Mo, Na, Se, Sr, Ti, U and V) in 1155 (82%) Hungarian drinking water sources, including surface water, bank filtered and groundwater sources. Parameters posing a risk to health (Se, V and U) were typically below the lower limit of quantification (LOQ), but higher concentrations (max. 7.0, 17 and 41 µg/L, respectively) may occur in confined locations, the latter exceeding the DWD parametric value in one water supply. Li in a small geographical area, Mg and Ca in the majority of the water supplies reached the concentration range assumed to be protective to health. Water sources were grouped in 6 clusters based on their element distribution, some of them also showing clear geographical patterns. Water types were not differentiated, with the exception of karstic waters (dominated by Ca and Mg). None of the investigated parameters are expected to be a source of public health concern.

Previous studies on the water-energy nexus mainly focused on the calculation and comparison of resource consumption at the national or regional level, lacking interprovincial sector-sector transfer analysis. In this study, the intensity of water and energy consumption of various sectors in China were calculated, the key nodes and paths of resource networks were identified, and countermeasures for resource conservation were proposed from the new perspective of the “dual saving” and “bidirectional saving” of water and energy. The results showed that the metallurgical industry (Me) in Jiangsu and the chemical industry (Ch) in Hebei and Jiangsu had high node strength in the water and energy network and were key sectors in China with “dual saving” effects of water and energy. The construction industry, Ch, Me in Jiangsu, electricity and hot water production and supply industry in Beijing, and Me in Hebei had high node strength in the water-related energy network and energy-related water network, significantly supporting the “bidirectional saving” effect of water and energy in China. The electrical equipment industry (El) in Jiangsu→El in Zhejiang, El in Zhejiang→El in Shanghai frequently appeared in key paths, which could effectively reduce the resource consumption of the entire network.

Recent omics technologies have revolutionized the study of molecular plant responses to air pollution, overcoming previous limitations. This review synthesizes the latest advancements in molecular plant responses to major air pollutants, emphasizing ozone (O3), nitrogen oxides (NOX) and particulate matter (PM) research. These pollutants induce stress responses common to other abiotic and biotic stresses, including the activation of reactive oxygen species (ROS)-scavenging enzymes and hormone signaling pathways. New evidence showed the central role of antioxidant phenolic compound biosynthesis via phenylpropanoids pathway in air pollution stress responses. Transcription factors like WRKY, AP2/ERF, and MYB, which connect hormones signaling to antioxidant biosynthesis, were also affected. To date, research has predominantly focused on laboratory studies analyzing individual pollutants. This review highlights the need for comprehensive field studies and the identification of molecular tolerance traits, which are crucial for the identification of tolerant plant species aimed to the development of sustainable Nature Based Solutions (NBS) to mitigate urban air pollution.

This research investigates precision enhancement in regional quasi-geoid refinement through ADS80 tri-linear array scanning stereoscopic imagery for aerial triangulation, coupled with GNSS gravity-potential modeling. Acquiring stereoscopic imagery and analyzing triangulation accuracy with the ADS80 camera, we performed this study over the Qinghai-Tibet Plateau's elevated, desolate terrain, collecting 593 GNSS points following high-precision stereoscopic imagery model. Utilizing 12 gravity satellite models, we computed geoid heights and China 1985 Yellow Sea elevations for 28 benchmarks and GNSS points, thereby refining the Qinghai Province Quasi-Geoid Model (QPQM) using geometric techniques. The findings reveal that POS-assisted ADS80 stereoscopic imagery yields high-precision triangulation with maximal horizontal and elevation accuracies of 0.083/0.116 cm and 0.053/0.09 cm, respectively, across five control point arrangements. The RMSE of normal heights for 1985, processed via these GNSS points, achieved decimeter precision. By applying error corrections from benchmarks to the 1985 elevation data from gravity satellites and performing weighted averaging, the precision of EGM2008, SGG-UGM-2, and SGG-UGM-1 models improved to 8.61 cm, 9.09 cm, and 9.38 cm, respectively, surpassing the QPQM by 9.22 cm to 9.99 cm. This research demonstrates that the proposed methods can significantly enhance the precision of regional quasi-geoid surfaces. Additionally, these methods offer a novel approach for rapidly establishing regional quasi-geoid models in the uninhabited areas of the Qinghai-Tibet Plateau.

Over the last fifty years, management plans have become more descriptive with regard to potential sustainability of forest systems, raising questions about the feasibility of implementing management activities and ensuring sustainability of a wide variety of ecosystem services. To assess this issue, we conducted a survey among forest planning and operation research communities to understand their perceptions regarding the potential of a sample of sustainability statements currently used in forest plans to be incorporated into optimization models or other mathematical operations. The results revealed that only a few statements from the sample were deemed to have relatively mature or firm methodologies and data to enable inclusion in modern mathematical models for land use optimization. These statements were mostly related to economic sustainability, offering quantifiable information such as a non-declining flow of wood products over time and limits on the amount of timber harvested per decade. In contrast, sociocultural and, to some extent, ecological statements regarding sustainability were generally perceived to be more difficult to translate into mathematical modeling efforts. Particularly challenging were statements corresponding to sustaining natural or scenic characteristics of a forest. These findings may be attributed to various factors, including a lack of measurable indicators for sustainability and a potential lack of understanding about the modeling components and their interactions with planned management activities.

Plant biomass could become a new world-class "currency" because almost everything can be made of it: fuels, foods, feeds, constructing materials, furniture, etc. Currently, growing biomass for energy needs is particularly relevant in so complicated socioeconomical situation. There are important its productivity and the lowest possible ecological footprint. Can increasing crop biodiversity address these challenges? A field experiment was started at the Vytautas Magnus University Agricultural Academy in 2020-2022. Maize, hemp and faba bean were grown as single, binary and ternary crops to determine its effect on soil properties. Studies have shown that most intercrops failed to stabilize soil aggregate composition, NPK levels and pH, and enzyme urease activity. Binary maize-hemp and the ternary crops reduced the amount of the main macroelements in the soil the most because of the highest yields, which were removed from the fields. Regardless of the ecological services provided by faba beans, biomass removal leads to degradation of most soil properties and making the soil vulnerable to erosion. In the future, the cultivation of such multicrops should be investigated under conditions of more abundant fertilization or the effect of green manure should be used by increasing the number of fast- and short-growing interior plant species in the cultivation.

With LiDAR (Light Detection and Ranging) time series being used for various applications, optimal realisation of a common geodetic datum over many epochs is a highly important prerequisite with direct impact on accuracy and reliability of derived measures. In our work, we develop and define several approaches to the adjustment of multi-temporal LiDAR data in a given software framework. These approaches ranging from pragmatic to more rigorous solutions are applied to an 8 year time series with 21 individual epochs. The analysis of the respective results suggests that a sequence of bi-temporal adjustments brings the best results while being more flexible and computationally viable than the most extensive approach of using all epochs in one single multi-temporal adjustment. With a combination of sparse control patches measured in the field and one selected reference block, we obtain relative datum discrepancies in the range of 1-2 cm for the complete time series. Based on our findings, we formulate design criteria for setting up and adjusting future time series with the proposed method.

Building a new-type power system is the key core of the green and low-carbon development of energy, the restructuring of the power system will inevitably have a linked impact on the coordinated development of the economy-energy-environment (3E) system. This paper constructs a "Dual-Triangle" theoretical framework consisting of the “Energy Trilemma” and the "Sustainable Development Triangle", by combining historical statistical analysis with scenario predictive analysis, analyzes the historical characteristics of the "Dual Triangle" and future development trends under different scenarios, and studies the coupled interaction relationship of the "Dual-Triangle" to explore the optimal transformation path of the power system that is most beneficial to the coordinated development of the 3E system. The conclusions are as follows: (1) The energy system gradually inclined towards economy and cleanliness from 2010 to 2022, the level of coordinated development of the 3E system fluctuated upwards, and the operation status of the "Dual Triangle" was stable. (2) Positive coupling relationship between "Dual Triangle" of scenario S1 is close, which can be regarded as the preferred path for the reconstruction of the power system. Due to phased development has clear development priorities, scenario S2 will artificially break the balance of the “Energy Trilemma”, which is a development model that dynamically mitigates the challenges of the “Energy Trilemma”.(3)The contradiction between economy and cleanliness is continuously weakened, and security has become the core contradiction of the "Impossible Triangle". The combination of renewable energy and energy storage is the key to resolving this contradiction. This paper conducts an integrated and innovative application of the "Dual-Triangle" theory, and the results provide a case basis for how to achieve the coupled interaction of the "Dual-Triangle", promote high-quality energy development, and support the sustainable development of the economy and society.

Marine sand, in addition to oil and gas resources, is the second largest marine mineral resource. The rational development and utilization of marine sand resources are conducive to the growth of the marine economy. Marine sand can be categorized into surface marine sand and buried marine sand. Buried marine sand deposits are buried beneath the sea floor, making it challenging to locate them due to their thin distribution. Consequently, there exist numerous technical difficulties associated with marine sand exploration. In this study, various geophysical methods such as sub-bottom profile survey and single-channel seismic survey were employed in the northern offshore waters of the South China Sea along with offshore engineering geological drilling. As a result, two distinct marine sand bodies were delineated within the study area. Additionally, five reflective interfaces (R0, R1, R2, R3 and R4) were identified from top to bottom. These interfaces can be divided into five seismic sequences: A2, B2, C2, D2 and E2 respectively. Three sets of strata were recognized: the Quaternary Holocene Marine sedimentary layer (Q4m), the Upper Pleistocene alluvial layer, and the diluvial sedimentary layer (Q3al+pl) as well as the Upper Pleistocene Marine sedimentary layer (Q3m). Sequence A2 and B2 corresponded to Quaternary Holocene Formation (Q4m) within the study area while sequence C2 corresponded to B2 as well as upper Pleistocene alluvial and alluvial deposits (Q3al+pl), which represent one of the main occurring layers of sand in this region. Lastly, E2 represents a layer composed mainly of silty clay and round gravel known as upper Pleistocene marine sediment layer (Q3m). In total, two ore bodies containing marine sand have been discovered during this study. The estimated total resource amounts approximately 46 million m³ with an average thickness around 11 meters.

South Africa (SA) is grappling with a severe food insecurity crisis exacerbated by socioeconomic challenges. This resulted in the Department of Basic Education (DBE) advocating for the establishment of vegetable gardens at schools. However, information on the suitability of the soils in SA for sustainably growing Beetroot is unavailable. This study aimed to assess the suitability of soils for sustainable Beetroot production at various schools in the Kgakotlou circuit. Four Secondary Schools were selected: Mothimako, Madipoane, Klaas Mothapo and Phomolong. A detailed soil survey was carried out to study soils in vegetable gardens. Soil samples were collected for laboratory analysis of physicochemical properties. The soil suitability was evaluated using the physicochemical properties of the soils and optimal soil requirements for Beetroot production. We found various soils in the schools, including Cambisols (Hutton and Nkonkoni), Leptosol (Glenrosa), Plinthosol (Westleigh), and Acrisols (Tshiombo and Tubatse). Portions of the gardens on Hutton and Westleigh soil were moderately suitable (S2), limited by electrical conductivity (EC). Meanwhile, portions of Tshiombo, Tubatse, and Nkonkoni were marginally suitable (S3), limited by pH and EC. Portions on Glenrosa soil at Klaas Mothapo and Phomolong were not suitable (NS) and were limited by shallow depth, soil pH and EC. The results reveal the importance of considering soil suitability before establishing vegetable gardens in school settings. Indexing soil suitability using the physicochemical properties of soil resources for sustainable beetroot production is proposed for schools in the Kgakatlou circuit and other parts of SA.

Human-elephant conflict poses serious conservation challenge in Asia and Africa. We deployed an AI-based camera-alert system in a conflict hotspot in West Bengal, India that accurately detected elephants near villages (266 events) and transmitted real-time alerts ( = 42 seconds). Among detections of separate age and sex classes, lone adult bulls were the most prone to conflict. Real-time alerts permitted rapid response by field personnel to prevent conflict at the site ( = 18.2, SD = 12.4 min to arrival). Frequent detections of humans (33,217 events) on elephant trails highlighted the value of real-time alerts. Four human fatalities and five serious injuries occurred in the study area over a two-year period prior, but none during the study (2023-2024) after deploying the AI-alerting system. Loss of lives and livelihoods underscore the urgency of applying this approach to other elephant ranges and adapting AI models to other conflict-pronespecies to promote coexistence.