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Realistic estimates of evaporation from unsaturated soils, which are important for many geotechnical and geoenvironmental applications, need to be considered in the context of soil settlement, and require thermohydromechanical (THM) analysis. Evaporation also depends on environmental parameters, including air temperature, air relative humidity, net radiation, and wind speed. Therefore, a consideration of atmospheric coupling in predicting evaporation is also necessary. In this study, the two-dimensional model EVAP1––which numerically estimates evaporation from unsaturated soil using THM and employing a soil–atmosphere model––was used to conduct a parametric study in order to investigate the effects of variation in environmental parameters and to study the effects of climate change on potential and actual evaporation and soil settlement. We found that the evaporation rate increased nonlinearly with increases in air temperature, net radiation, and wind speed, but decreases with an increase in relative humidity. However, the effect of a change in wind speed was less than the effect of a change in the three other environmental parameters. In addition, the change in evaporation rate differed in different regions with different air temperatures. For example, the temperature change had more of an effect on the evaporation rate at higher temperatures. In addition, neglecting soil settlement led to an overestimation of evaporation, albeit the amount of evaporation was almost the same whether soil settlement was considered or not, both at the beginning of the evaporation process, when potential evaporation was dominant, and at the end, when the water content in both cases had decreased almost to the residual water content, and the evaporation was minimized. The difference in the amount of evaporation was greater in the middle of the evaporation process, when actual evaporation dominated. [ABSTRACT FROM AUTHOR]

Worldwide concern regarding the need for more sustainable textile supply chain prompted emphasis on innovation in the coloration process, often considered the most problematic segment of the supply chain. Digital textile printing, an emerging coloration technology, has the potential to improve sustainability significantly. This study undertakes an environmental sustainability analysis that compares the impacts of rotary-screen printing, the traditional, more established printing method, and digital textile printing. Researchers partnered with Creditex S.A.A., a vertically integrated textile company from Peru, to perform the research in a realistic factory setting. Creditex printed a 16-color design for an order of 1000m cotton fabric with reactive dye occupying both rotary-screen printing and digital textile printing and collected environmental impact data throughout the production process, including consumption, usage, and wastage. The findings suggest that digital textile printing is favorable to rotary-screen printing in terms of environmental sustainability impacts within the research context. [ABSTRACT FROM AUTHOR]

The transition towards renewable energy sources necessitates accurate monitoring of environmental parameters to estimate power generation from renewable energy systems. The rapid integration of renewable energy sources into the power grid has necessitated the development of efficient monitoring systems to optimise power generation and enhance overall system performance. This paper provides a comprehensive review of environmental parameters monitoring systems designed for estimating power generation from renewable energy sources. The focus is on the advancements in technology and methodologies employed in monitoring crucial environmental factors that influence the output of renewable energy systems. It explores the significance of environmental parameters, including solar irradiance, wind speed, temperature, and humidity, in determining the efficiency of solar and wind power generation. Various monitoring techniques and sensors used for real-time data acquisition are discussed, highlighting their accuracy and reliability in capturing diverse environmental conditions. It delves into the integration of advanced data analytics and machine learning algorithms for processing the vast amounts of data collected from monitoring systems. These techniques play a pivotal role in predicting power generation patterns, optimising energy output, and facilitating proactive maintenance of renewable energy infrastructure. Furthermore, the review discusses the challenges associated with environmental monitoring, such as data accuracy, sensor calibration, and communication issues. It also explores emerging technologies, such as Internet of Things (IoT) devices and remote sensing, that promise to address these challenges and enhance the robustness of environmental monitoring systems. It focuses on case studies and practical applications of environmental monitoring systems in different renewable energy projects. These case studies provide insights into the successful implementation of monitoring systems, their impact on energy yield, and the economic benefits derived from improved system efficiency. It consolidates the current state of environmental parameters monitoring systems for power generation estimation from renewable energy sources. It highlights the interdisciplinary nature of these systems, incorporating elements of meteorology, data science, and engineering. The synthesis of existing knowledge and identification of research gaps contribute to the ongoing efforts to enhance the reliability and efficiency of renewable energy systems in the face of a dynamic and changing climate. This review paper provides a comprehensive analysis of the existing literature on environmental parameters monitoring systems for estimating power generation from renewable energy sources. The paper explores various renewable energy systems, including solar, wind, and hydroelectric, and highlights the significance of monitoring environmental factors such as solar irradiance, wind speed, temperature, humidity, and water flow rate. More than 50+ papers are taken into consideration; they examine the methodologies, sensors, data acquisition techniques, and modeling approaches employed for power estimation. Additionally, the challenges, advancements, and future directions in this field are discussed to guide further research and development. [ABSTRACT FROM AUTHOR]

Potato, a component of global food security and economic stability, is cultivated extensively worldwide due to its adaptability to diverse climates and soil types. Ongoing research and technological advancements, including the use of unconventional products destined to fight the most harmful pathogens, are essential for enhancing productivity and resilience in potato farming. The current study aimed to identify the most appropriate fertilizers and phytosanitary treatments in order to attain optimal potato yields, dry matter, and starch contents, under climate-specific environments. The experiment was conducted in the northwest of Romania in 2023. The research was organized as a trifactorial experiment with the factors variety, fertilization, and phytosanitary treatment. XLSTAT (2022.2.1v.) was utilized for data processing. Mineral fertilization combined with conventional phytosanitary treatment led to the highest average yields in both potato varieties. Also, a combination of organic fertilization and treatment with 4% A. cepa extracts led to notable results, which suggests the possibility of successfully using these inputs in organic agriculture. The study shows the importance of selecting appropriate agricultural inputs to optimize potato yields, achieve specific levels of dry matter and starch content in a specific area, and promote sustainability. [ABSTRACT FROM AUTHOR]

Geomorphic environments play a crucial role in influencing the preservation and characteristics of tsunami deposits. This paper introduces a global database of tsunami deposits, encompassing information on deposit locations, thematic data such as geomorphic environments and proxies and bibliographic details. Additionally, the database features maps incorporating environmental parameters and the precise locations of tsunami deposits. The primary utility of this database lies in assessing progress and identifying gaps in knowledge. It also involves analysing the relationship between environmental parameters and interpreting areas with varying probabilities of tsunami deposit preservation. The files are readily compatible with GIS software and can seamlessly integrate into spatial databases associated with tsunamis or other hazards. This contributes significantly to disaster risk management, enhancing preparedness and response efforts by providing a comprehensive historical dataset on tsunamis. Future applications of the database include the incorporation of modern deposits, boulders and new data from paleotsunami and historical studies. By enhancing data with thematic information, such as dating techniques and creating timelines, the database facilitates a more comprehensive understanding. The correlation between geomorphic environments and proxies aids in selecting sampling sites and identifying suitable proxies for analysis. Encouraging an open‐access approach, this database invites all interested researchers to include and modify additional information. The information compiled for this database serves multiple purposes: (1) assessing the global distribution of tsunami deposits; (2) identifying knowledge gaps in tsunami deposits; (3) guiding the selection of study areas for further research and finally; (4) enabling a meta‐analysis of the information gathered. [ABSTRACT FROM AUTHOR]

Environmental parameters along elevational gradients affect the number of butterflies and the variety of species. However, which variables play significant roles and how they operate can be difficult to untangle. Here, we examine the relationships between observed butterfly richness (overall and subgroups) at different elevation gradients and remotely sensed environmental variables (vegetation productivity, surface temperature, landscape heterogeneity, and moisture stress) using generalized linear models. We surveyed butterflies with a fixed‐point count method in 19 elevation bands within 1600–5200 m above sea level in Manang district, trans‐Himalayan region, north‐central Nepal. The number of butterflies in each elevation band was studied and estimated, then interpolated across the lowest and highest elevation to estimate butterfly species richness. Then, the selection of models was performed on butterfly richness and elevations to test the best model support based on the lowest value of the Akaike information criterion and a multimodel averaging for other environmental variables. Altogether, 94 butterfly species, representing 20 subfamilies and six families, were recorded throughout the study periods. We obtained cubic model support for overall species richness, Papilionidae, and Hesperiidae, quadratic to Nymphalidae and Pieridae, and the linear model to Lycaenidae. In our study, vegetation productivity was found to have a significant positive impact on butterfly communities. Our study further suggests species richness of Papilionidae and Hesperiidae has a strong positive correlation with surface temperature and landscape heterogeneity and negative associations with moisture stress but other subgroups of butterfly communities including overall species richness showed insignificant relationships with these variables. This study provides significant information related to the responses of montane butterflies to environmental variables along elevational gradients from the Himalayas Nepal. However, further detailed studies on the functional behaviors of butterflies potentially offer more insights into their distribution patterns and ecological relationship in the montane environment. [ABSTRACT FROM AUTHOR]

Stem water content serves as a pivotal parameter that reflects the plant vitality and maintains their internal water balance. Given the insufficient comprehension regarding the stem water content characteristics and its influencing factors during different stages of the overwintering period, the study focused on Acer truncatum Bunge and developed an Internet of Things (IoT)-based ecological information monitoring system. The system incorporated a proprietary stem water content sensor, allowing non-invasive, in-situ and real time acquisition of stem water content while monitoring diverse environmental parameters. We conducted a detailed elucidation of stem water content variation characteristics and their responses to diverse environmental factors. The results showed: (1) During the overwintering period, stem water content exhibited diurnal variations characterized by " daytime ascent and nighttime descent" across the three stages, exhibiting differences in the moment when the stem water content reaches extremal values and daily fluctuations ranges. Stem water content exhibited minimal fluctuations during deciduous and bud-breaking stages but experienced significant freezing-thawing alternations during the dormant stage, leading to an increased daily fluctuation range. (2) The Pearson correlation coefficients between environmental parameters and stem water content varied dynamically across stages. Path analysis revealed that during the deciduous stage, stem temperature and saturation vapor pressure deficit were dominant factors influencing stem water content; during dormant stage, air temperature and saturation vapor pressure deficit directly impacted stem water content; during the bud-breaking stage, the primary parameters affecting stem water content were saturation vapor pressure deficit and stem temperature. The study provides valuable insights into unveiling the water transport patterns within tree stems tissue and their environmental adaptation mechanisms during the overwintering period, aiding in the scientific development of winter management strategies to protect trees from severe cold and freezing damage, while fostering healthy growth in the subsequent year. [ABSTRACT FROM AUTHOR]

Freshwater invertebrates and zooplankton (FIZ) play a key role in freshwater ecosystems and in food webs, but there is little research on their life cycle, ecology, and conservation. Therefore, this study focused on FIZ' annual dynamics (abundance, diversity, and gross energy) in 30 ponds located in two wetlands: the Marais breton and Marais poitevin (Atlantic coast, France, Europe). These ponds had a FIZ abundance of 50,412 ind/m3 per site [range of 28,864–80,690] in a year. The study defined four typologies of ponds according to the environmental variables and the presence of some key FIZ taxa. The first group had a long flooding time, high silt content, and aquatic vegetation cover. The associated taxa were Chironomidae, Gammaridae and Atyidae. The second group of ponds had moderate slopes, high sand content in the sediment and a rich abundance of microcrustacea. The third group contained shallow ponds with soft slopes and a high aquatic vegetation cover (100%), which result in high FIZ diversity. Finally, the fourth group contained ponds with steep slopes, high silt content in the sediment, and Oligochaeta as the major taxon in this environment. Defining pond typologies based on environmental factors and FIZ is essential to adapting FIZ management and conservation measures implemented on ponds. In a context of global change, a reduction or even disappearance of FIZ communities would have a catastrophic impact on all faunal taxa dependent on FIZ as a food resource. [ABSTRACT FROM AUTHOR]

Nitrogen-cycling processes in the deep sea remain understudied. This study investigates the distribution of nitrogen-cycling microbial communities in the deep-sea surface sediments of the western South China Sea, using metagenomic sequencing and real-time fluorescent quantitative PCR techniques to analyze their composition and abundance, and the effects of 11 environmental parameters, including NH4+-N, NO3−-N, NO2−-N, PO43−-P, total nitrogen (TN), total organic carbon (TOC), C/N ratio, pH, electrical conductivity (EC), SO42−, and Cl−. The phylum- and species-level microbial community compositions show that five sites can be grouped as a major cluster, with sites S1 and S9 forming a sub-cluster, and sites S13, S19, and S26 forming the other; whereas sites S3 and S5 constitute a separate cluster. This is also evident for nitrogen-cycling functional genes, where their abundance is influenced by distinct environmental conditions, including water depths (shallower at sites S1 and S9 against deeper at sites S13, S19, and S26) and unique geological features (sites S3 and S5), whereas the vertical distribution of nitrogen-cycling gene abundance generally shows a decreasing trend against sediment depth. Redundancy analysis (RDA) exploring the correlation between the 11 environmental parameters and microbial communities revealed that the NO2−-N, C/N ratio, and TN significantly affect microbial community composition (p < 0.05). This study assesses the survival strategies of microorganisms within deep-sea surface sediments and their role in the marine nitrogen cycle. [ABSTRACT FROM AUTHOR]

Abstract Geomorphic environments play a crucial role in influencing the preservation and characteristics of tsunami deposits. This paper introduces a global database of tsunami deposits, encompassing information on deposit locations, thematic data such as geomorphic environments and proxies and bibliographic details. Additionally, the database features maps incorporating environmental parameters and the precise locations of tsunami deposits. The primary utility of this database lies in assessing progress and identifying gaps in knowledge. It also involves analysing the relationship between environmental parameters and interpreting areas with varying probabilities of tsunami deposit preservation. The files are readily compatible with GIS software and can seamlessly integrate into spatial databases associated with tsunamis or other hazards. This contributes significantly to disaster risk management, enhancing preparedness and response efforts by providing a comprehensive historical dataset on tsunamis. Future applications of the database include the incorporation of modern deposits, boulders and new data from paleotsunami and historical studies. By enhancing data with thematic information, such as dating techniques and creating timelines, the database facilitates a more comprehensive understanding. The correlation between geomorphic environments and proxies aids in selecting sampling sites and identifying suitable proxies for analysis. Encouraging an open‐access approach, this database invites all interested researchers to include and modify additional information. The information compiled for this database serves multiple purposes: (1) assessing the global distribution of tsunami deposits; (2) identifying knowledge gaps in tsunami deposits; (3) guiding the selection of study areas for further research and finally; (4) enabling a meta‐analysis of the information gathered.

Soil salinity is one of the significant environmental issues that can reduce crop growth and productivity, ultimately leading to land degradation. Therefore, accurate monitoring and mapping of soil salinity are essential for implementing effective measures to combat increasing salinity. This study aims to estimate the spatial distribution of soil salinity using machine learning methods in Huludao City, located in northeastern China. By meticulously collecting data, soil salinity was measured in 310 soil samples. Subsequently, environmental parameters were calculated using remote sensing data. In the next step, soil salinity was modeled using machine learning methods, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). Additionally, to estimate uncertainty, the lower limit (5%) and upper limit (95%) prediction intervals were used. The results indicated that accurate maps for predicting soil salinity could be obtained using machine learning methods. By comparing the methods employed, it was determined that the RF model is the most accurate approach for estimating soil salinity (RMSE=0.03, AIC=-919, BIS=-891, and R2=0.84). Furthermore, the results from the prediction interval coverage probability (PICP) index, utilizing the uncertainty maps, demonstrated the high predictive accuracy of the methods employed in this study. Moreover, it was revealed that the environmental parameters, including NDVI, GNDVI, standh, and BI, are the main controllers of the spatial patterns of soil salinity in the study area. However, there remains a need to explore more precise methods for estimating soil salinity and identifying salinity patterns, as soil salinity has intensified with increased human activities, necessitating more detailed investigations. [ABSTRACT FROM AUTHOR]

With the advancement of communication technology, many innovative applications have developed in agriculture as a result of the integration of the Internet of Things (IoT) with unmanned aerial vehicles (UAVs), leading to the modernization of agriculture. This study seeks to propose an effective and low-cost platform for comprehensive monitoring of environmental parameters using IoT and drones.The preparation of this paper was based on a platform that was tested in a realistic environment on a farm near the city of Al-Median in Tunisia, where the platform was built to suit the realistic environment of a farm in Baghdad, through the use of sensors above and below the ground, which meets the experimental work and standards for automated and real-time monitoring. For environmental standards, the unified theory of acceptance and use of technology model was used, which is a model based on four basic elements: 1) expected performance, 2) expected effort, 3) social impact, and 4) facilitating conditions for obtaining results. The unique integration of IoT sensors with drones has shown impressive experimental results, indicating the possibility of performing both automatic and manual actions by humans. These smart moves contribute significantly to promoting precision agriculture, leading to a significant increase in agricultural production and conservation of natural resources.

Salinity, light intensity, and oxygen concentration are key environmental factors that significantly affect biological processes and the composition and dispersion of Moina biomass. Evaluating the effectiveness of probiotic enrichment in improving population density, growth rate, and neonate production can provide valuable details on the effectiveness of probiotics in enhancing the resilience and viability of Moina micrura under suboptimal circumstances. The purpose of this research project is to assess the efficacy of two probiotics, Bacillus pocheonensis strain S2 and Lysinibacillus fusiformis strain A1, in improving the population density, growth rate, and reproductive output in M. micrura across various environmental conditions. Moina micrura were treated with each probiotic at a volume of 5 x 105 CFU/ml under different levels of salinity (0, 2, 4, and 6 ppt), light intensity (800, 1,000, 1,500, and 2,000 lux), and oxygen concentration (80, 70, 60, and 50%). The results indicated that M. micrura treated with L. fusiformis A1 at 0 ppt attained the highest population density (6 ± 0.90 Ind./ml), growth rate (0.355 ± 0.030 µ), and number of offspring production (5 ± 0.75 Ind./ml). The highest point of population density (5 ± 0.07 Ind./ml), growth rate (0.381 ± 0.002 µ) and number of offspring (7 ± 0.41 Ind./ml) of M. micrura were obtained while treated with B. pocheonensis S2 at light intensity of 1,500 lux. Similarly, the highest population density (5 ± 0.60 Ind./ml), growth rate (0.365 ± 0.190 µ), and offspring production (2 ± 0.25 Ind./ml) of M. micrura were observed during enrichment with B. pocheonensis S2 at 70% oxygen concentration. Therefore, these results suggested that the optimum conditions for enriching M. micrura with B. pocheonensis S2 are salinity of 0 ppt, 70% oxygen concentration, and a light intensity level of 1,500 lux. [ABSTRACT FROM AUTHOR]

Artificial waterways are characterized by dams and locks that slow water flow. Such conditions can influence the environment of freshwater organisms, including the zooplankton community. Our research aimed to assess the impact of environmental and hydrological conditions on the zooplankton of sites upstream and downstream of canal locks. The study was carried out in 2021 and 2022. Water samples were collected monthly from two canals during the growing season. We evaluated how water flow velocity and selected environmental parameters (i.e., water temperature, Secchi disk visibility, conductivity, oxygen concentration, saturation, pH and chl-a concentrations) influence the zooplankton diversity (T) density (N) and biomass (B). Our study showed that the zooplankton of the canals was highly diverse. Rotifers dominated over crustaceans in diversity and density at all sites of canals. The statistical analysis revealed differences in environmental and biological conditions between habitats located upstream and downstream of locks. Rotifers density and biomass changed approximately proportionally to changes in chlorophyll at sites upstream of locks. It supports the hypothesis that rotifer communities are defined by the bottom-up effect linked to food supply (phytoplankton). The growth of crustaceans (density and biomass), as well as total zooplankton biomass, was affected by water temperature at sites downstream of the lock. [ABSTRACT FROM AUTHOR]

The study proposes to discuss the seasonal water bird diversity, habitat preference, assemblages of water bird species and their conservation problems in different agroecosystems. Field visits were done once in a week during Jan. 2021-Jan. 2023. Freshwater and saline agroecosystems are the study areas used for the water bird survey. The water bird study was conducted by direct observation method, point count method and line transect method. Observations were made using binoculars (10 × 50 Nikon) and video camera (Nikon Coolpix p1000). Based on the pilot study in 2020, we decided to concentrate on 16 species of waterbirds coming in the 3 different orders i.e., Order Pelecaniformes, Ciconiiformes and Suliformes. Compared to the different study areas, a greater abundance of water birds was recorded in the winter season, while a lesser abundance was observed in the monsoon season. The species richness was highest in saline agroecosystem and water bird congregation was more in freshwater agroecosystem. Saline agroecosystem was the most preferred habitat used for water bird species. Agroecosystems support the globally threatened water bird species such as spot billed pelican, darter, black headed ibis and painted stork. Many factors that are listed below threaten the agroecosystem and it causes the decreasing assemblages of water bird species. They are high water intake due to heavy rain, electric lines, high growth of water hyacinth, fishing nets, solid wastes and garbage deposited from toddy shops, pesticides used in fresh water rice farming, tourism, crackers, high level of fishing activities, habitat destruction, communication towers, predators and white flags were introduced by farmers. [ABSTRACT FROM AUTHOR]

LoRa systems are emerging as a promising technology for wireless sensor networks due to their exceptional range and low power consumption. The successful deployment of LoRa networks relies on accurate propagation models to facilitate effective network planning. Therefore, this review explores the landscape of propagation models supporting LoRa networks. Specifically, we examine empirical propagation models commonly employed in communication systems, assessing their applicability across various environments such as outdoor, indoor, and within vegetation. Our investigation underscores the prevalence of logarithmic decay in most empirical models. In addition, we survey the relationship between model parameters and environmental factors, clearing their nuanced interplay. Analyzing published measurement results, we extract the log-distance model parameters to decipher environmental influences comprehensively. Drawing insights from published measurement results for LoRa, we compare them with the model's outcomes, highlighting successes and limitations. We additionally explore the application of multi-slope models to LoRa measurements to evaluate its effectiveness in enhancing the accuracy of path loss prediction. Finally, we propose new lines for future research in propagation modelling to improve empirical models. [ABSTRACT FROM AUTHOR]

Abstract Environmental parameters along elevational gradients affect the number of butterflies and the variety of species. However, which variables play significant roles and how they operate can be difficult to untangle. Here, we examine the relationships between observed butterfly richness (overall and subgroups) at different elevation gradients and remotely sensed environmental variables (vegetation productivity, surface temperature, landscape heterogeneity, and moisture stress) using generalized linear models. We surveyed butterflies with a fixed‐point count method in 19 elevation bands within 1600–5200 m above sea level in Manang district, trans‐Himalayan region, north‐central Nepal. The number of butterflies in each elevation band was studied and estimated, then interpolated across the lowest and highest elevation to estimate butterfly species richness. Then, the selection of models was performed on butterfly richness and elevations to test the best model support based on the lowest value of the Akaike information criterion and a multimodel averaging for other environmental variables. Altogether, 94 butterfly species, representing 20 subfamilies and six families, were recorded throughout the study periods. We obtained cubic model support for overall species richness, Papilionidae, and Hesperiidae, quadratic to Nymphalidae and Pieridae, and the linear model to Lycaenidae. In our study, vegetation productivity was found to have a significant positive impact on butterfly communities. Our study further suggests species richness of Papilionidae and Hesperiidae has a strong positive correlation with surface temperature and landscape heterogeneity and negative associations with moisture stress but other subgroups of butterfly communities including overall species richness showed insignificant relationships with these variables. This study provides significant information related to the responses of montane butterflies to environmental variables along elevational gradients from the Himalayas Nepal. However, further detailed studies on the functional behaviors of butterflies potentially offer more insights into their distribution patterns and ecological relationship in the montane environment.

Abstract The presence, abundance, and distribution of aquatic macrophytes and their growth forms in the river Krka in Slovenia were studied. The studied slow‐flowing lowland River Krka is also called a »green river« since it is overgrown with macrophytes from its source to its outflow to the river Sava. The environmental parameters of the river ecosystem were also assessed using the modified version of the RCE (Riparian, Channel, and Environmental Inventory) method. Within 24 sections reaching from 100 to 250 m, 23 taxa of macrophytes were found. Sections were distributed noncontinuously from the source to the outflow more or less equally along the river course. The invasive alien species Elodea canadensis reached the highest relative abundance, followed by Ranunculus trichophyllus, Potamogeton crispus, Myriophyllum spicatum, Ceratophyllum demersum, and Potamogeton nodosus. Potamogeton crispus was present in most of the studied sections followed by Nasturtium officinale and M. spicatum. The river offers suitable conditions for the high diversity of macrophytes regarding the type of substrate, low flow velocity, heterogeneity of habitats, and relatively high concentrations of nutrients. In comparison to the survey performed in 2003, when the river Krka was surveyed continuously from the source to the outflow, we found that seven of the submerged and natant macrophytes were not detected in 2020. Macrophytes, growing in the river Krka, indicated meso‐ to eutrophic conditions. Five species recorded in the river Krka are listed on the Red list of endangered species in Slovenia, indicating the need to conserve the river ecosystem.

Abstract The community structure and co-occurrence pattern of eukaryotic algae in Yuncheng Salt Lake were analyzed based on marker gene analysis of the 18S rRNA V4 region to understand the species composition and their synergistic adaptations to the environmental factors in different salinity waters. The results showed indicated that the overall algal composition of Yuncheng Salt Lake showed a Chlorophyta-Pyrrophyta-Bacillariophyta type structure. Chlorophyta showed an absolute advantage in all salinity waters. In addition, Cryptophyta dominated in the least saline waters; Pyrrophyta and Bacillariophyta were the dominant phyla in the waters with salinity ranging from 13.2 to 18%. Picochlorum, Nannochloris, Ulva, and Tetraselmis of Chlorophyta, Biecheleria and Oxyrrhis of Pyrrophyta, Halamphora, Psammothidium, and Navicula of Bacillariophyta, Guillardia and Rhodomonas of Cryptophyta were not observed in previous surveys of the Yuncheng Salt Lake, suggesting that the algae are undergoing a constant turnover as the water environment of the Salt Lake continues to change. The network diagram demonstrated that the algae were strongly influenced by salinity, NO3 −, and pH, changes in these environmental factors would lead to changes in the algal community structure, thus affecting the stability of the network structure.

Freshwater insects like Ephemeroptera are more comprehensive and direct indicators of the biological impacts of pollution. During the study period (2018-2020), a total of 4,374 individuals of mayflies were collected and categorized under nine families, 27 genera, and 36 species. The family Leptophlebiidae was found dominant with 13 species. In the post-monsoon season, a higher species diversity of Ephemeroptera was noticed in the river's upstream section with a Shannon-Wiener index value of H' = 1.814. ANOVA revealed a significant difference (p <0.05) except for Ephemeridae (p >0.05). Protecting rivers requires a holistic approach and collaboration among stakeholders is essential for successful implementation. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate the seasonal evolution of lipid classes (polar (PL) and neutral lipids (NL)) in the gonad–visceral mass (GV) and adductor muscle (AM) of Mactra stultorum collected from the northern coast of Tunisia. Our results have shown that NL was the major lipids in GV. However, the PL characterizes the AM. Significant decreases in the PL contents of both tissues were recorded during the summer season. Besides, NL was accumulated mostly in GV during winter and spring seasons, which correspond to the gametogenetic and ripe stages. The related fatty acid composition of NL and PL changed significantly between seasons. Our data demonstrated that PUFAs and SFA were the major FA's in PL and NL fractions, respectively. The seasonal fluctuation of PUFA-PL levels in GV was highly during spring as compared to summer. The percent of NL and PUFA-NL levels of GV appeared to be relate to the food availability which was confirmed by a positive correlation with chlorophyll a content. Specific variations were noted in GV as evidence by higher significant levels of DHA-PL and EPA-PL during autumn. Inversely, AM exhibited lower levels of DHA-PL during spring that could be explained by the probably transfer of fatty acids from AM to the GV to sustain the gametogenesis process. [ABSTRACT FROM AUTHOR]

This paper describes an integrated monitoring system designed for individualized environmental risk assessment and management in the workplace. The system incorporates monitoring devices that measure dust, noise, ultraviolet radiation, illuminance, temperature, humidity, and flammable gases. Comprising monitoring devices, a server-based web application for employers, and a mobile application for workers, the system integrates the registration of workers' health histories, such as common diseases and symptoms related to the monitored agents, and a web-based recommendation system. The recommendation system application uses classifiers to decide the risk/no risk per sensor and crosses this information with fixed rules to define recommendations. The system generates actionable alerts for companies to improve decision-making regarding professional activities and long-term safety planning by analyzing health information through fixed rules and exposure data through machine learning algorithms. As the system must handle sensitive data, data privacy is addressed in communication and data storage. The study provides test results that evaluate the performance of different machine learning models in building an effective recommendation system. Since it was not possible to find public datasets with all the sensor data needed to train artificial intelligence models, it was necessary to build a data generator for this work. By proposing an approach that focuses on individualized environmental risk assessment and management, considering workers' health histories, this work is expected to contribute to enhancing occupational safety through computational technologies in the Industry 5.0 approach. [ABSTRACT FROM AUTHOR]

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency. [ABSTRACT FROM AUTHOR]

The community structure and co-occurrence pattern of eukaryotic algae in Yuncheng Salt Lake were analyzed based on marker gene analysis of the 18S rRNA V4 region to understand the species composition and their synergistic adaptations to the environmental factors in different salinity waters. The results showed indicated that the overall algal composition of Yuncheng Salt Lake showed a Chlorophyta-Pyrrophyta-Bacillariophyta type structure. Chlorophyta showed an absolute advantage in all salinity waters. In addition, Cryptophyta dominated in the least saline waters; Pyrrophyta and Bacillariophyta were the dominant phyla in the waters with salinity ranging from 13.2 to 18%. Picochlorum, Nannochloris, Ulva, and Tetraselmis of Chlorophyta, Biecheleria and Oxyrrhis of Pyrrophyta, Halamphora, Psammothidium, and Navicula of Bacillariophyta, Guillardia and Rhodomonas of Cryptophyta were not observed in previous surveys of the Yuncheng Salt Lake, suggesting that the algae are undergoing a constant turnover as the water environment of the Salt Lake continues to change. The network diagram demonstrated that the algae were strongly influenced by salinity, NO3−, and pH, changes in these environmental factors would lead to changes in the algal community structure, thus affecting the stability of the network structure. [ABSTRACT FROM AUTHOR]

Bacterial pollution is the presence of harmful bacteria in aquatic ecosystems, which can cause health problems if ingested. These bacteria originate from a variety of sources, including human and animal waste, agricultural runoff, and untreated sewage. This study aimed to assess the levels of bioindicator bacteria and their associations with various environmental parameters in surface water samples. The samples were collected seasonally from five stations along Riva Stream between March 2018 and January 2019. Indicator bacterial levels were determined using membrane filtration. Temperature, salinity, pH, dissolved oxygen, and conductivity values of the stations were measured in situ during sampling using a multiparameter (YSI). As a result of the sampling, maximum total coliform, fecal coliform, and intestinal enterococci levels were recorded as 24 x 103 CFU/100ml, 18 x 103 CFU/100ml and 24 x 102 CFU/100 ml, respectively. During sampling, the indicator bacterial levels were above the limit at all stations. When the FC/IE ratios were analyzed, the maximum FC/IE ratio was recorded as 52.10 during the winter sampling period at the station. The detection of bioindicator bacteria levels above the standard limit throughout the sampling period (especially during the summer period) was associated with a high level of domestic waste in the region. The results showed that more frequent and detailed monitoring studies should be conducted in this region. [ABSTRACT FROM AUTHOR]

Beverages are an important part of the diet, but their environmental impact has been scarcely assessed. The aim of this study was to assess how changes in beverage consumption over a one-year period can impact the environmental sustainability of the diet. This is a one-year longitudinal study of 55–75-year-old participants with metabolic syndrome (n = 1122) within the frame of the PREDIMED-Plus study. Food and beverage intake were assessed using a validated food frequency questionnaire and a validated beverage-specific questionnaire. The Agribalyse® 3.0.1 database was used to calculate environmental impact parameters such as greenhouse gas emission, energy, water, and land use. A sustainability beverage score was created by considering the evaluated environmental markers. A higher beverage sustainability score was obtained when decreasing the consumption of bottled water, natural and packed fruit juice, milk, and drinkable dairy, soups and broths, sorbets and jellies, soft drinks, tea without sugar, beer (with and without alcohol), and wine, as well as when increasing the consumption of tap water and coffee with milk and without sugar. Beverage consumption should be considered when assessing the environmental impact of a diet. Trial registration: ISRCTN, ISRCTN89898870. Registered 5 September 2013. [ABSTRACT FROM AUTHOR]

In this study, polyvinylpyrrolidone ultrafine fibers were produced by the Forcespinning process. As the functional performance of forcespun fibers is strongly dependent on their diameter, response surface methodology and artificial neural network combined with principal component analysis were applied, for the first time in this study, to predict the relationships between fiber diameter and a set of combined chemical, environmental and spinning parameters. With this aim in view, 40 sets of Forcespinning trials were performed by combining six main variables, namely: solution concentration, syringe orifice size, relative humidity rate, ambient temperature, rotational speed and collection distance. The collected samples appeared as a random network of smooth fibers with a mean fiber diameter ranging from 461 nm to 8.978 μm. Interestingly, the optimized neural network consisting of six input variables, five hidden layers with ten neurons and one output layer exhibited the best prediction accuracy, thereby providing a remarkable Mean Squared Error of 0.0498 and a correlation coefficient of 0.9954, between observed and predicted fiber diameter. Additionally, 3D plots of ANN computed data, allowed pointing out mutual interactions between effects of chemical, spinning and environmental parameters, in relation to fiber diameter. Furthermore, principal component analysis conducted on ANN computed data, enabled classifying the contribution of each parameter as follows: solution concentration, followed by syringe orifice size, relative humidity rate, ambient temperature, rotational speed and collection distance. These findings demonstrate, for the first time, the key role of environmental parameters in the Forcespinning process. [ABSTRACT FROM AUTHOR]

Mangrove forests support a wide diversity of habitats for numerous macrofauna species that are crucial to the health of mangrove ecosystems, as well as a vast variety of macroinvertebrate species. Therefore, it is necessary to periodically update our knowledge of the diversity of macrofaunal communities found throughout the mangrove forests of the Aqaba Gulf. This study seeks to examine biodiversity and identify macroinvertebrate fauna in various mangrove-associated habitats, i.e., genuine mangrove forests and neighboring coastal mangrove zones with varying levels of ecological heterogeneity. The belt transects and quadrate approach were used to collect samples from five distinct locations along the Aqaba Gulf mangrove. Overall, 8186 organisms belong to 118 species. Eighty-four molluscan species, 23 crustacean species, and 11 echinoderm species were recorded throughout the present investigation. The diversity of species was estimated for each site. Rowaisseia station showed significantly greater numbers for taxonomic richness, and the lowest number was present in Gharqana station. The statistical assessment of the various environmental factors and sediment characteristics in terms of abundance and variety of species of macroinvertebrate fauna revealed that the composition of bed sediment has a strong influence on the distribution and variation of macrofaunal communities. The current study's findings will aid in the conservation of mangrove forests by providing a better understanding of the ecological patterns and functions of benthic macrofaunal communities, which will be useful in future studies for habitat management and monitoring programs in the Gulf of Aqaba mangrove forests. [ABSTRACT FROM AUTHOR]

Invasive species threaten native biodiversity, potentially leading to a decline in endemic aquatic species. Thus, understanding their physiological responses and evaluating ecosystem impacts is crucial. The study investigates adaptive responses to oxidative stress in the invasive Sinanodonta woodiana in Lake Candia (northwest Italy). This approach represents a novel perspective in environmental monitoring, utilizing invasive species to gain insights into ecosystem health and potential ecological threats. The investigation delved into oxidative stress biomarkers, including superoxide dismutase, catalase, glutathione S-transferase, glyoxalase I, glyoxalase II, and lactate dehydrogenase, in mussel tissues (gills and digestive gland). Concurrently, environmental factors such as temperature, dissolved oxygen, pH, and conductivity were measured at four stations in Lake Candia during the summer. Environmental factors exhibited homogeneity across selected stations, with positive correlations observed only for temperature and conductivity with catalase and glyoxalase I. A distinct pattern of oxidative stress response was consistently observed in both mussel tissues throughout the sampling period across all monitoring stations, with more pronounced alterations observed in one specific station. This observation aligns with IBRv2 scores, indicating the station's highest impact. Gills exhibited a transient and severe oxidative stress response, while heightened antioxidant defenses in digestive glands suggested the adaptive ability of Chinese pond mussels to environmental disturbances. Proactive inclusion of invasive species in biomonitoring programs reduces reliance on native populations, enhancing strategies for controlling non-native mussels in protected areas like Lake Candia.

The objective of the investigation was to explore the abundance, composition, and diversity patterns of the fish fauna along the temporal and spatial scale and study the influence of environmental parameters on the fish assemblage in the Rihand reservoir, a large sub-tropical Indian reservoir in India. On the temporal scale, the highest abundance was recorded in the summer season and the lowest in the monsoon season, while on the spatial scale, the highest abundance was recorded from the riverine and the minimum from the lacustrine zone. The highest species richness and diversity were recorded from the riverine zone followed by the transitional and lacustrine zones, respectively. The analysis of the variance of fish abundance revealed insignificant differences between the riverine and the transitional zones. The trophic guild analysis indicated the dominance of carnivores. The output of canonical correspondence analysis depicted the significant influence of the physico-chemical traits on the abundance of fish species. The transparency and specific conductivity were found to be the most critical factors affecting fish assemblages in the Rihand reservoir. This study generated important baseline ecological information that would be useful for the monitoring, conservation, and management of the reservoir ecosystem. HIGHLIGHTS This study quantified the abundance and diversity patterns of the fish fauna in the Rihand reservoir.; The species richness and diversity are arranged in GIS.; The trophic guild analysis indicated the dominance of carnivores.; This study suggested the inclusion of the riverine and transitional zones in management and conservation strategies.; The new information on fisheries and ecological complexity would be useful for sustainable management.;

The structure of plant communities and their response to temperature variations are an essential basis for evaluating the ecological structure and function of tea plantations. However, field surveys and quantitative evaluation of plant communities and ecotea plantations remain challenging. In this study, a novel strategy was proposed for rapid surveillance of plant community structure and its response to changes in weather conditions in tea plantations. This strategy aims to construct the normalized tea plantation ecological index (NTEI) by synergizing environmental parameters with multisource remote sensing data; establish the fitting and inversion model of NTEI by cascading the Fourier function with the convolutional neural networks gate recurrent unit (CNN-GRU) network; and evaluate the variability of the plant community in tea plantations by analyzing the variation characteristics of the NTEI and the measured temperature. The study revealed the following: First, the NTEI can objectively characterize the plant communities of tea plantations, and its variation characteristics were consistent with the changes in vegetation phenology and temperature; second, the Fourier function has the potential to quantify NTEI, and it is fitting R2 for the NTEI of nine plant communities ranged from 0.840 to 0.921; third, the CNN-GRU has the most advantage in establishing the prediction model of NTEI, and its prediction accuracy was Rp2 = 0.955 and RMSEP = 0.314; and fourth, the plant communities with high species richness increased regional ecological stability, had a strong buffering capacity against temperature changes, and had less variability in NTEI. The results provide significant guidance for building plant community structures and improving the ecological benefits of tea plantations.

Abstract We investigated the spatio-temporal dynamics of phytoplankton composition, chlorophyll-a as a proxy for algal biomass, and abundance in relation to environmental parameters in four Ethiopian soda lakes: Arenguade, Beseka, Chittu, and Shala. Triplicate water samples were collected from each lake from January to December 2020, four times in different seasons. Lake Chittu had the highest chlorophyll-a concentration, followed by Lake Arenguade, Beseka and Shala. Chlorophyll-a concentrations generally increased during the post rainy and dry season. The results of LR models are high for lakes Arengude, Beseka and Chittu. Lakes Shala and Beseka had the highest number of phytoplankton taxa, with both taxa composition and abundance dominated by Bacillariophyceae. Cyanoprokaryota, particularly Limnospira fusiformis, predominated in the abundance of Lakes Arenguade and Chittu. Water temperature, Secchi depth, turbidity, electrical conductivity, soluble reactive phosphorus, nitrate and silica significantly influenced the phytoplankton community structure. Long-term trend analysis revealed changes in phytoplankton biomass and lake taxonomic composition. The alteration in phytoplankton biomass and species composition of the lakes could be attributed to three factors: (1) frequent high-velocity explosions conducted for seismological studies in the past. This impact caused a dramatic increase in lake level in the case of Lake Beseka leading to a drop in nutrient concentration; (2) climate change and (3) salt content. Overall, our findings suggest that phytoplankton composition, biomass, and abundance varied according to seasonal fluctuations, emphasizing the possible effects of anthropogenic and natural causes on their community structure.

In underground mining, the real-time monitoring of environmental parameters plays a pivotal role in ensuring the safety of mining operations and personnel. This article explores the integration of Long Range (LoRa) wireless communication technology and the Internet of Things (IoT) to bolster safety measures and prevent potential accidents within underground mines. The environmental parameters in underground mines include Oxygen (O2), Carbon Dioxide (CO2), Carbon Monoxide (CO), Methane (CH4), Nitric Oxide (NO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Hydrogen Sulfide (H2S), Ethylene Oxide (EO), Temperature and Humidity. Currently, underground mines in India use portable multi-gas detector devices to measure environmental parameters. HPD13A LoRa 868 MHz based Real Time Environmental Parameters Monitoring System (RTEPMS) is designed and developed to facilitate real-time data collection in underground mines. In addition, the developed RTEPMS system is tested and evaluated at the open surface level and in one of the underground mines in India. The experimental results represent successful LoRa-based wireless communication established in an underground mine with data acquisition and real-time processing. Major parameters exceeding threshold limits in the underground mine environment include O2, CO, CO2, NO2, and EO. The data correlation between LoRa-based RTEPMS and multi-gas detector devices is 69.47% for CO2 and 72.38% for CO, while the values for CH4 and H2S are nearly zero, indicating their presence in underground mines is almost negligible. The RTEPMS is an affordable solution for smaller and less affluent underground mines. It alerts mine workers if environmental parameters exceed threshold limits during emergencies.

Seagrasses demonstrate complex ecological functions, and their morphological and physiological indicators are affected by various environmental factors. In this study, we conducted six sampling surveys of a Thalassia hemprichii seagrass bed in Xincun Bay, Lingshui, Hainan in 2019. The morphological and physiological characteristics of seagrasses in January, March, May, July, September, and November of a single year were studied, and the effects of various environmental factors on the morphological and physiological characteristics of T. hemprichii were analyzed. The results demonstrated markedly differences in leaf length and width, rhizome length and thickness, and root length across different months. The highest value [(15.05±6.13) cm] of leaf length occurred in May, and the lowest value [(7.19±2.55) cm] in September. The highest [(11.93±1.68) mm] and the lowest [(8.73±1.96) mm] values for leaf width were recorded in November and September, respectively. The highest value [(5.22± 1.71) mm] for rhizome diameter occurred in November, and the lowest value [(4.06±0.74) mm] in March. The C content of the underground seagrass tissues was significantly different across months, with the highest value (31.23%±0.94%) in November and the lowest value (24.90%±3.48%) in March. Seawater temperature had a significantly positive correlation with leaf width and C content of the underground tissues. Salinity was significantly positively correlated with the leaf and rhizome lengths of T. hemprichii and had a negative relationship with the C content of the underground tissues. There was a significant positive correlation between pH and leaf length, leaf width, and C and N content of the underground tissues. The ammonium level in seawater was significantly positively correlated with the N content of aboveground tissues. Nitrate level of seawater was negatively correlated with the C/N ratio of the aboveground parts. There was a significant positive correlation between the dissolved inorganic nitrogen (DIN) in seawater and the N content of the underground parts. Sediment organic carbon had a negative relationship with leaf length and width, and rhizome growth. The results demonstrated that the growth of T. hemprichii in Xincun Bay was greatly affected by temperature, salinity, and pH. A high nutrient level in Xincun Bay may have a negative impact on T. hemprichii. In order to protect the T. hemprichii meadow, measures should be taken to limit the input of nutrients in Xincun Bay. The results of this study may provide data support and serve as a scientific basis for the restoration of declining seagrass.

Nitrogen-cycling processes in the deep sea remain understudied. This study investigates the distribution of nitrogen-cycling microbial communities in the deep-sea surface sediments of the western South China Sea, using metagenomic sequencing and real-time fluorescent quantitative PCR techniques to analyze their composition and abundance, and the effects of 11 environmental parameters, including NH4+-N, NO3−-N, NO2−-N, PO43−-P, total nitrogen (TN), total organic carbon (TOC), C/N ratio, pH, electrical conductivity (EC), SO42−, and Cl−. The phylum- and species-level microbial community compositions show that five sites can be grouped as a major cluster, with sites S1 and S9 forming a sub-cluster, and sites S13, S19, and S26 forming the other; whereas sites S3 and S5 constitute a separate cluster. This is also evident for nitrogen-cycling functional genes, where their abundance is influenced by distinct environmental conditions, including water depths (shallower at sites S1 and S9 against deeper at sites S13, S19, and S26) and unique geological features (sites S3 and S5), whereas the vertical distribution of nitrogen-cycling gene abundance generally shows a decreasing trend against sediment depth. Redundancy analysis (RDA) exploring the correlation between the 11 environmental parameters and microbial communities revealed that the NO2−-N, C/N ratio, and TN significantly affect microbial community composition (p < 0.05). This study assesses the survival strategies of microorganisms within deep-sea surface sediments and their role in the marine nitrogen cycle.

Weather Forecasting is very essential as it is helpful in saving lives and materials by predicting disasters such as cyclonic storms, tsunamis, extreme rainfall, etc. Within the defined range of rainfall rate approximation, this study investigates the application of Fuzzy Logic (FL) to forecast rainfall using the Interval Type –2 Fuzzy Inference System (IT2FIS). Environmental parameters which influence rainfall have been applied in this analysis and every implementation is carried out using MATLAB 9.13. The performance of IT2FIS model is compared with the actual data. Correlation coefficient (R2) and Root Mean-Squared Error (RMSE) have been used to evaluate the performance metrics of the proposed model. The results suggest that the IT2FIS model can capture the dynamic behavior of rainfall data and generate reasonable results, implying that it might be beneficial in long-term rainfall prediction. [ABSTRACT FROM AUTHOR]

To preserve their collections, heritage institutions must understand and mitigate causes of damage, including environmental factors. Historically at the British Library this was addressed by policies developed over decades—frequently without overarching guidance—and in response to changing requirements. As a result, these policies were often disjointed, complex and unrepresentative of current practice. This article addresses the review, revision and application of policy. This is informed by a focus on collection needs that incorporate human, operational and practical requirements; the pragmatic understanding of contributory issues; and sufficient flexibility to encourage engagement and support Library activities without jeopardising collection safety. The revised policy has afforded a variety of benefits, not least that it is a single, concise document. It provides explicit reference to material types and vulnerabilities, and the requirements of operational activities, emphasising preventive conservation concerns. It is accessible to other stakeholders, encouraging effective collaboration and leading to achievable outcomes, benefitting the institution as a whole. As such it has strengthened the role of preventive conservation within the Library, and provided a solid and adaptable basis for future work. [ABSTRACT FROM AUTHOR]

We investigated the spatio-temporal dynamics of phytoplankton composition, chlorophyll-a as a proxy for algal biomass, and abundance in relation to environmental parameters in four Ethiopian soda lakes: Arenguade, Beseka, Chittu, and Shala. Triplicate water samples were collected from each lake from January to December 2020, four times in different seasons. Lake Chittu had the highest chlorophyll-a concentration, followed by Lake Arenguade, Beseka and Shala. Chlorophyll-a concentrations generally increased during the post rainy and dry season. The results of LR models are high for lakes Arengude, Beseka and Chittu. Lakes Shala and Beseka had the highest number of phytoplankton taxa, with both taxa composition and abundance dominated by Bacillariophyceae. Cyanoprokaryota, particularly Limnospira fusiformis, predominated in the abundance of Lakes Arenguade and Chittu. Water temperature, Secchi depth, turbidity, electrical conductivity, soluble reactive phosphorus, nitrate and silica significantly influenced the phytoplankton community structure. Long-term trend analysis revealed changes in phytoplankton biomass and lake taxonomic composition. The alteration in phytoplankton biomass and species composition of the lakes could be attributed to three factors: (1) frequent high-velocity explosions conducted for seismological studies in the past. This impact caused a dramatic increase in lake level in the case of Lake Beseka leading to a drop in nutrient concentration; (2) climate change and (3) salt content. Overall, our findings suggest that phytoplankton composition, biomass, and abundance varied according to seasonal fluctuations, emphasizing the possible effects of anthropogenic and natural causes on their community structure. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the frequency of spicule malformations in freshwater sponges in relation to selected environmental parameters of the streams and the presence of river pollutants. A total of 50 sponge samples were collected from ten rivers in Serbia. Selected parameters of the water varied considerably at every site where sponges were found. After spicule preparation, the samples were subjected to morphological analysis by light and scanning electron microscopy, and the number of anomalies were recorded (spicules with bulbous enlargements, sharply bent, bifurcated, scissor- and cross-like, and t-shaped). The frequencies and types of malformations within the analyzed specimens varied from 1 to 100 per 1000 spicules, with an average number of 12 per 1000. The main types of anomalies were single- and double-bent spicules. The highest number of anomalies was found in a specimen of Eunapius fragilis collected at Markovac (Velika Morava River), and the lowest number was found in a specimen of Ephydatia fluviatilis from Kanjiža (Tisa River). The sites with the lowest and the highest numbers of anomalies showed statistically significant differences in concentrations of ammonia, orthophosphates, sodium, chloride, manganese, and lead. This study indicates that several pollutants potentially affect the occurrence of spicule anomalies. [ABSTRACT FROM AUTHOR]

The relationship between water quality and bacterial composition may determine the sustainability level in an integrated multi-trophic aquaculture (IMTA) system. In this study, the effects of different shrimp stocking densities on water quality and bacterial community structure were studied in a sea cucumber-shrimp-crab-fish IMTA system. The results showed correlations between bacterial community structure and water quality parameters (dissolved oxygen, salinity, pH, ammonia nitrogen, nitrite nitrogen, and phosphate phosphorus) under different stocking densities of Penaeus japonicus. Temperature, salinity, nitrite nitrogen, ammonia nitrogen, and phosphate phosphorus were significant factors (p < 0.05) influencing the bacterial community in the samples. Nitrite nitrogen showed the greatest effect on bacterial community diversity and richness. Flavobacteriaceae and Cryomorphaceae play important roles in the denitrification process, and these bacteria were significantly more abundant in low-density ponds than in high-density ponds. This study provides practical knowledge on shrimp stocking density management and its effect on IMTA system biosecurity. [ABSTRACT FROM AUTHOR]

This work describes the design and implementation of an Internet of Things architecture that focuses on monitoring temperature and humidity. The system is based on the strategic selection of technologies, including Arduino, LoRa modules, DHT11 sensors, and ESP32 microcontrollers. Python Flask was used to develop a user interface for the web application, ensuring an easy and accessible user experience. Moreover, to enhance the scalability and security of the system, it is recommended to implement modular algorithms and authentication measures. The article concludes by emphasising the advantages and effectiveness of open source technology in data acquisition, processing, and monitoring across various application scenarios. Suggestions for future improvements are provided, with particular attention to expanding data visualisation and environmental monitoring. The limitations and advantages of the system are addressed, highlighting its potential application in remote or resource-limited areas. The results obtained in this work can be scaled to the different stages of a mining project in which environmental parameters must be monitored. [ABSTRACT FROM AUTHOR]

In the Mediterranean, one of the main reasons for discarding commercial species is when fishers catch fish smaller than the minimum conservation reference size (MCRS). In reality though, other drivers that vary by country/region have greater impact on the discarding process. A notable illustration of the coexistence of different motives is the Greek bottom trawl fishery. Present work focused on the spatial analysis of discards of three commercial species subjected to MCRS with low market price and often discarded independent of their size (i.e., Trachurus trachurus, Parapenaeus longirostris, Sardina pilchardus). To model and map the spatial distribution of species discards, hierarchical Bayesian spatial models were utilized employing discards dependence on environmental inputs. Further information was gained, by calculating the spatial overlap between the spatial distribution of species discards and their known nurseries. Results showed that S. pilchardus discards had the highest estimated values in shallower waters, whereas T. trachurus and P. longirostris discards were distributed over greater depths. Only a small portion of species nurseries and discard grounds coincided, verifying that the reasons behind discarding were market-driven. Area-specific and species-specific management is essential to reduce discards, since local market demand and fishing restrictions have an impact on the discarding process. [ABSTRACT FROM AUTHOR]

The curve fitting analysis is performed on time-stamped signal values. Then a situational analysis graphical user interface (GUI) is developed to analyze the data which is from a mining area. The resulting waveform plots and the designed GUI are presented. The data is from the mining area namely Singrauli. Five environmental parameters related to pollution, namely CO, SO2, PM10, PM2.5, and Ozone are considered for the analysis using neural network. [ABSTRACT FROM AUTHOR]

Cenderawasih Bay is one of the territorial waters of Indonesia with high fishery potential. In 2022, the Indonesian Goverment revealed that the bay's small pelagic fishery potential reached 135, 140 t, yet the utilization rate remains at only 0.3, indicating underutilization. This shows the necessity of estimating fish distribution in the bay to maximize the use of small pelagic fisheries resources. The hydroacoustic method, known for its extensive survey coverage and minimal environmental impact, can be employed to estimate the distribution of small pelagic fish. Therfore, this research aims to investigate the distribution of acoustic parameters of small pelagic fish using a split beam echosounder, specifically the SIMRAD EY-60, to obtain fish SV value and distribution data. SV (Scattering Volume) values were coverted to TS (Target Strength) values and projected onto a spatial distribution map. The distribution of fish exhibited a varied range of values, with the highest SV value ranging from -59.7 to -57.4 dB and the highest TS values ranging from -59.4 to -46.7 dB. Fish distribution was more concentrated in areas with warmer temperatures and dispersed evenly across different salinity ranges, with a higher concentration of fish detected in areas with elevated chlorophyll levels. [ABSTRACT FROM AUTHOR]

To investigate the influence of climate on hospitalizations of sickle cell anemia (SCA) adults and children, we analyzed the health and meteorological parameters from a metropolis (1999-2018). 1462 hospitalizations were coded for SCA patients in crisis (M:F = 715:747) and 1354 hospitalizations for SCA patients without crisis (M:F = 698:656) [age = 22.9 vs 15.2 years and duration of hospitalization (DoH) = 5.7 vs 4.4 days, respectively,]. More hospitalizations were for adults than children in crisis, and for children than adults without crisis. More children and adults were hospitalized in winter andspring than in summer and autumn Hospitalizations correlated positively with humidity (lag −5), maximum pressure (lag −2), mean pressure (lag −2), and thermal amplitude (lag −2), and negatively with maximum temperature (lag −3). DoH positively correlated with minimum temperature (lag −4). Understanding these complex associations would induce attitudinal/behavioral modifications among patients and their caregivers. [ABSTRACT FROM AUTHOR]

Influence of seasonal variations in environmental parameters on fish assemblage and species diversity patterns was studied in a tropical reservoir of Central India during March 2021 to February 2022. A total of 41 species belonging to 7 orders, 15 families and 29 genera were recorded with highest diversity during post‐monsoon season compared to the pre‐monsoon season and monsoon season. The fish abundance similarity between the monsoon and post‐monsoon reached a maximum of 89%. Seasonal variations in the mean species abundance, Simpson dominance index, Shannon diversity index, Pielou's evenness index and Margalef's richness index were determined. One‐way ANOVA revealed a significant difference (P < 0.05) at the temporal scale. There was a significant seasonal difference in species abundance (P = 0.0006) and evenness index (P = 0.02) among the diversity indices. During the study period, three 'vulnerable' species, three 'near threatened' species and two 'endangered' species were caught. Carnivore dominance was evident throughout the year in the trophic guild of fishes. The Pearson correlation test revealed that water pH, dissolved oxygen, sediment organic carbon and clay percentage were positively correlated to all four diversity indices. Simpson and Margalef species richness diversity indices were positively correlated to sediment pH. Fish abundance was positively associated with sediment pH, silt, phosphorus and nitrogen content. Canonical correspondence analysis was used to investigate the relationships between fish assemblages and environmental conditions. Among the 19 physicochemical parameters, nine (dissolved oxygen, total dissolved solids, conductivity, transparency, hardness, phosphate and chlorophyll‐a, sediment nitrogen and phosphorus) characteristics were shown to be statistically significant (P < 0.05) in determining fish diversity. Dissolved oxygen, total dissolved solids, conductivity, transparency, hardness, phosphate and chlorophyll‐a of water and sediment nitrogen and phosphorus qualities were the key factors for the species abundance. The reservoir boasts a diverse fish population that allows multi‐species fishing and generates revenue for numerous local fishers. Fish habitats should be protected in order to maintain diversity and long‐term ecological benefits. [ABSTRACT FROM AUTHOR]

The mining industry relies on extracting valuable minerals through underground mining. Many industries have implemented automation to enhance workplace safety, optimize operations, improve responses to events, and achieve cost-effectiveness. A real-time communication and monitoring system is indispensable in underground mines to prevent significant hazards and improve safety in underground mines. However, the environmental conditions of underground mines are affected by toxic, flammable, combustible gases and dust. The harmful gases are a significant concern as they can cause gas explosions. Internet of Things (IoT) enabled real-time communication system with Long Range (LoRa) transceiver module is designed and developed to measure the underground mine environmental parameters, temperature, and humidity. The LoRa-based proof of concept (POC) system is tested and evaluated at the surface level and in two underground mines. The LoRa module radio waves range test is carried out to measure the received signal strength indicator (RSSI) value at the surface level. In addition, the developed system is tested and evaluated at different positions of underground mines to measure environmental parameters in straight and curved tunnels. The experimental results represent successful IoT with LoRa-based wireless communication between underground mine tunnels to the surface, wireless transmission of parameters at the straight tunnels, and curved tunnels of underground mines. [ABSTRACT FROM AUTHOR]