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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]

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]

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]

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]

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]

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 latest improvements in infrared detectors enable the use of infrared thermography in many applications for outdoor temperature measurements through a low cost and easy to maintain solution. However, converting the radiative fluxes received by the infrared camera to the object of interests' apparent surface temperature is a challenging task. It requires us to consider the global radiative heat balance at the sensor level. Such a correction implies taking into account the background contributions (sky, sun, other elements on the scene), the involved transmissions (camera optics, atmosphere, participating media of the scene), etc. As a consequence, supplementary data are needed to achieve quantitative outdoor thermal monitoring. In this study, we propose a comparison of gathering those data from different observation scales: a local weather station, existing sensor networks such as Meteorological Aerodrome Report (METAR) and open source online satellite data from the European Copernicus program. Finally, the feasibility, advantages and limitations of the proposed methods are discussed. [ABSTRACT FROM AUTHOR]

Studying correlations between phytoplankton communities and environmental factors is critical for understanding how aquatic ecosystems function. The high sensitivity of phytoplankton to changes in these factors makes it possible to control the state of the ecosystem of water bodies. Artificial lakes often demonstrate increased trophic status, inducing changes in phytoplankton structure. In this paper, we studied phytoplankton in June 2023 (hydrological spring) in two ecosystems, South Baikal and the Irkutsk Reservoir, that are connected by a water course but have different environmental parameters. The gradient of environmental parameters from the lake towards the reservoir revealed peculiarities in the distribution of some microalgae species. Microscopy and statistical analysis showed that water temperature was the most important factor affecting the structure of the communities. The warmer water of the reservoir, in contrast to the lake, demonstrated a twofold increase in species number, abundance, and biomass. Downstream from the reservoir, we observed a succession in the dominating Baikal species complex, its supplementation, and replacement with other species typical of the summer period and Baikal bays. The trophic status of the reservoir during the study may be described as oligotrophic, with local traits of mesotrophicity; its water refers to Class I and Class II and may be qualified as clean. [ABSTRACT FROM AUTHOR]

In a saline semi-isolated lagoon on Cape Zeleny (White Sea), the annual dynamics of the vertical hydrological structure and the seasonal dynamics of phytoplankton were traced. Species composition, vertical distribution, abundance, nutrition type, and biomass were analyzed. In total, 293 species and supraspecific taxa of algae and cyanobacteria were found. Most of the identified species are marine, and 38 species are freshwater. Taxonomic composition changed in the lagoon throughout the year. Dinoflagellates dominated in winter and early June; unidentified cocci and flagellates in July; diatoms, dinoflagellates, and unidentified cells in August; dinoflagellates in September; and unidentified cocci and flagellates in October–November. The abundance of algae also changed in the lagoon throughout the year. The integrated biomass in the water column varied from 0.01 g C/m2 in January to 0.78 g C/m2 in early September. According to the environmental parameters, the water column of the lagoon was subdivided into several zones with different environmental conditions and corresponding phytoplankton communities. The similarity between the communities of different horizons was 32–46% in summer and 7% in winter. The chemocline layer was the most populous. It contained a maximum of phytoplankton biomass, 1–2 orders of magnitude higher than that in the overlying horizons. Despite the connection to the sea, the phytoplankton structure in the surface water layer in the lagoon and in the sea differed significantly in composition, quantitative parameters, and seasonal dynamics. The similarity between the communities never exceeded 50%. In terms of biomass dynamics, the lagoon lagged behind the sea until mid-summer, but, starting from August, it outnumbered it, and the phytoplankton development in the lagoon lasted longer, until late autumn. According to sequential tests DistLM, the phytoplankton structure and dynamics in the lagoon and in the sea were related to the daylength, water salinity, oxygen content, and pH by 24.5%. At the same time, the PhP structure did not depend on water temperature, underwater illuminance, or depth. Oxygen content and pH were defined by PhP activity. Salinity serves as a vector of the vertical sequence of ecological niches. The day length seems to be the crucial factor of the seasonal PhP dynamics in the semi-isolated coastal stratified lakes and lagoons. [ABSTRACT FROM AUTHOR]

Macro-benthos is commonly considered an indicator for evaluating the health of an aquatic ecosystem. Earlier research from sub-tropical mangrove estuaries, however, has primarily relied on conventional taxonomic methods to determine the pattern of macro-benthos diversity. Therefore, this study aimed to describe the pattern of both taxonomic and functional groups of macro-benthos with respect to ecological variables in three separate seasons (pre-monsoon, monsoon, and post-monsoon) from a mangrove-dominated Pasur River estuary, Bangladesh. The findings revealed significant seasonal variations in the water and sediment parameters (p < 0.05). During the study period, 47 species belonging to 35 families of macro-benthos were identified. The pollution indicator species, Capitella capitata complex was found to be dominant. The highest density of macro-benthos was recorded in post-monsoon (545 ± 13.76 ind./m2) followed by pre-monsoon (214 ± 5.57 ind./m2) and monsoon (63 ± 2.27 ind./m2). Diversity indices, Shannon, and evenness also displayed a similar seasonal trend. This pattern may be explained by the more stable bottom and higher food availability during post-monsoon, and on the other side, by erosion and higher turbidity during monsoon. Analysis of similarity (ANOSIM) detected a significant difference in community assemblage among the seasons (R = 0.7222, p = 0.0005), whereas similarity percentage analysis (SIMPER) identified Dendronereis aestuarina as the most contributory species for the overall average dissimilarity. Six functional feeding groups (FFGs) were identified where gathering collectors (GC) had the highest total density (221.83 ind./m2) and relative abundance (26.97%). The community was shown to be shaped by the amount of sedimentary silt and dissolved oxygen in the water main, according to a canonical correspondence analysis (CCA) study, they were positively correlated with the abundance of Pristinella acuminata, Lumbrineris sp., Cossura coasta, C. capitata complex, Neritina violacea, Laccotrephes griseus, Hydrometra butleri, Gomphus sp. and Libellula sp. CCA analysis also revealed a significant positive influence of pH, NO3-N, PO4-P, and organic matter, whereas, sand particles of sediments were found to have a negative effect on FFGs. Overall, the study suggests that the estuary is moderately diverse with macro-benthos and their functional feeding groups and influenced by monsoon strongly. The present study on FFGs of macro-benthos in an estuarine river of Bangladesh will provide baseline information for further investigation of other estuaries. [ABSTRACT FROM AUTHOR]

The advancement of the Internet of Things applications (technologies and enabling platforms), consisting of software and hardware (e.g., sensors, actuators, etc.), allows healthcare providers and users to analyze and measure physical environments at home or hospital. The measured physical environment parameters contribute to improving healthcare in real time. Researchers in this domain require existing representative datasets to develop machine-learning techniques to learn physical variables from the surrounding environments. The available environmental datasets are rare and need too much effort to be generated. To our knowledge, it has been noticed that no datasets are available for some countries, including Saudi Arabia. Therefore, this paper presents one of the first environmental data generated in Saudi Arabia's environment. The advantage of this dataset is to encourage researchers to investigate the effectiveness of machine learning in such an environment. The collected data will also help utilize the machine learning and deep learning algorithms in smart home and health care applications based on the Saudi Arabia environment. Saudi Arabia has a special environment in each session, especially in the northern area where we work, where it is too hot in the summer and cold in the winter. Therefore, environmental data measurements in both sessions are important for the research community, especially those working in smart and healthcare environments. The dataset is generated based on the indoor environment from six sensors (timestamps, light, temperature, humidity, pressure, and altitude sensors). The room data were collected for 31 days in July 2022, acquiring 8910 records. The datasets include six columns of different data types that represent sensor values. During the experiment, the sensors captured the data every 5 min, storing them in a comma-separated value file. The data are already validated and publicly available at PLOMS Press and can be applied for training, testing, and validating machine learning algorithms. This is the first dataset developed by the authors for the research community for such an environment, and other datasets will follow it in different environments and places. [ABSTRACT FROM AUTHOR]

Greenhouses, as important parts of facility agriculture, can reduce the restrictions on agricultural production imposed by the natural environment and make rational and efficient use of production resources. We conducted long-term, continuous testing of temperature, humidity, light intensity, and CO2 concentration parameters in a heliostat greenhouse in the central and western parts of the Inner Mongolia Autonomous Region, a cold and arid region of northern China. A large amount of data was processed by statistical observation, simulation analysis, and 3D reconstruction to obtain the overall distribution, variation pattern, and mathematical model of the regional greenhouse environment in time and space. The results show that the temperature, humidity, light intensity, and CO2 concentration in the greenhouse have significant daily variation patterns, that there are strong coupling relationships between light intensity–CO2 concentration–time and indoor temperature–light intensity–CO2 concentration, that the coefficients of determination (R2) of the mathematical models are 0.88 and 0.89, and that the standard errors (RMSE) are 49.67 ppm and 45.30 ppm, respectively. The environmental parameters were fitted with high accuracy in order to provide scientific data for the cultivation of heliostats in the region. [ABSTRACT FROM AUTHOR]

In the Mediterranean, the fleshy, mat-forming red alga Phyllophora crispa creates high-biodiversity habitats that influence light availability, water movement, and temperature. However, knowledge about its influence on other key environmental parameters, such as oxygen availability, chlorophyll, and turbidity, is missing. Therefore, we conducted an in situ study in the Western Mediterranean Sea using multiparameter probes that were placed multiple times on algal P. crispa mats, in adjacent Posidonia oceanica seagrass meadows, and on bare hard bottoms. We acquired a total of 17 full diel measurements for dissolved oxygen (DO), chlorophyll, and turbidity in September and October 2019. Results showed that P. crispa mats influence the investigated parameters differently when compared to P. oceanica meadows and that a monthly effect was observed. In September, general DO patterns measured for P. crispa mats and P. oceanica meadows follow the daily cycle depending on light availability, with the measured DO being lower in the P. oceanica meadows compared to the P. crispa mats and the hard-bottom habitats. In October, however, no significant difference in DO concentrations was observed between P. crispa mats and P. oceanica meadows. Results of this study corroborate that P. crispa mats can be viewed as an ecosystem engineering species, influencing environmental parameters and, hence, providing a habitat for outstanding associated biodiversity. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the seasonal variation of zooplankton diversity and abundance in the Nokoué Lagoon in southern Benin. Through extensive sampling, a total of 109 zooplanktonic taxa were identified and quantified. The average zooplankton abundance was found to be 60 individuals per liter, with copepods and rotifers being the most dominant groups, comprising 68.1% and 29.1% of the total abundance, respectively. The key factor identified as driving the structure of the zooplanktonic assemblages was salinity, which showed significant seasonal variation. The results revealed that during the high water period, when the lagoon was filled with fresh water, rotifers were dominant, zooplanktonic diversity was highest, and abundances were quite high. Conversely, during the low water period, when the lagoon was characterized by brackish water, diversity was minimal, and abundance decreased slightly. The study also found that some areas of the lagoon showed high abundances independent of salinity levels, suggesting that other factors such as riverine inputs or the presence of acadjas (home-made brush parks used as fish traps) may also have notable effects on the zooplankton community. Overall, the findings of this study provide valuable insights into the functioning of one of the most biologically productive lagoons in West Africa. [ABSTRACT FROM AUTHOR]

This work compares the contributions of synoptic, intraseasonal, and interannual components of large-scale parameters to tropical cyclone (TC) genesis over the North Indian Ocean (NIO) from April to December from 1979 to 2020. A composite analysis is employed with respect to TC genesis time and location. It is shown that most TCs occur when the total sea surface temperature (SST) is between 28 and 30 °C and SST anomalies in three time ranges are small (with the magnitude less than 0.2 °C). The TCs form mostly when the anomalies of vertical zonal wind shear are between −6 and 6 m s−1 and total vertical zonal wind shear falls within −12 and −3 m s−1, with the synoptic component being a positive contributor. The intraseasonal component of vorticity and convergence in the low level, vertical motion and specific humidity in the middle level, and convection contributes dominantly to the TC genesis. Synoptic-scale tropical disturbances obtain barotropic kinetic energy from the climatological mean and intraseasonal flows, with the former dominant in the southeastern sector, and the latter dominant in the northwestern sector. The contributions of the three temporal components of environmental factors are compared for TC genesis between the Arabian Sea (AS) and Bay of Bengal (BOB) and between the early season (April through June) and late season (September through December). The relative contributions of the three temporal components of factors are also compared for the TC formation among the NIO, northern tropical Atlantic Ocean (NTA), Northwestern Pacific (WNP), and Northeastern Pacific (ENP). [ABSTRACT FROM AUTHOR]

The internal temperature variation of ballastless track is very complicated under the effect of a sunlit environment, and there are serious transverse and vertical temperature gradients, which will cause cracking and deformation of the structure. In this paper, an ANSYS temperature effect analysis model for ballastless track, considering box girder structure, is established based on the environmental information of the bridge and the characteristics of the structural system. The model considers the influence of solar radiation intensity, wind speed, air temperature, geographical location, bridge orientation, material parameters, and other factors on the boundary conditions, and can meet the needs of the daylight temperature response analysis and calculation of any complex bridge structure. On this basis, the effect and applicability of a solar reflective coating on ballastless track cooling are studied. The results showed that the calculated results of the finite element model agree well with the measured results. Under the high-temperature conditions in summer, sunlight and ambient temperature mainly have significant effects on the temperature and temperature gradient of the track slab, and the maximum vertical temperature gradient reaches 74.48 °C/m. The reflective coating can significantly reduce the track slab's temperature and vertical temperature gradient, with a maximum temperature gradient reduction of 34%. The transverse temperature gradient of the track slab can be reduced by up to 54% by further application of the side reflective coating. This study can promote the application of reflective coatings on high-speed railway track structures. [ABSTRACT FROM AUTHOR]

Soiling significantly impacts PV systems' performance, but this can be mitigated through optimized frequency and timing of cleaning. This experimental study focused on the conditions leading to soiling. It utilized a novel method to evaluate the effectiveness of different cleaning frequencies. The transmittance of horizontally mounted glass coupons exposed outdoors in a warm and humid location was measured weekly and these measurements were used (i) to evaluate the variability of soiling and its seasonal correlations with environmental factors using linear regression models and (ii) to assess the effectiveness of the different cleaning cycles using statistical (F- and t-test) analysis. The minimum transmittance loss occurred during the season with the most frequent rainfall, which acted as the dominant natural cleaning agent. The experimental campaign showed that rainfalls do not completely clean soiling; a minimum intensity threshold has to be achieved to have a cleaning effect. The threshold rainfall was the highest for the weekly cleaned glass coupon and lowest for a coupon that was never cleaned. Based on the statistical analysis, it is suggested that weekly cleanings during winter and post-monsoon seasons and monthly cleanings during pre-monsoon and southwest monsoon seasons are optimal for areas in the Köppen–Geiger Cwa climate classification category. The correlation between soiling and environmental parameters was found to be highly dependent on the season. It may therefore not be possible to develop a simple, universal predictive relationship for soiling losses. The presented methodology is applicable to additional locations, even outside of the study area of India, to contribute to the understanding and mitigation of soiling. [ABSTRACT FROM AUTHOR]

Semi-outdoor space can be used as an alternative to short-term office activities to save office energy consumption and promote a healthy and nature-based working environment. This study evaluated the suitability of semi-outdoor space in four aspects including environmental measurements, physiological measurements, subjective measurements and cognitive performance tests. However, the manual processing and analysis of such multidimensional data can be time-consuming and error-prone. Hence, the objective of this study was to develop a semi-automatic method to manage and analyze the data from different instruments and platforms and two open-source applications (a stroop color and word test and a digit span test) for cognitive performance tests. These codes were critical to the success of the project, providing an effective framework for data extraction, data pre-processing, data analysis and performance tests. Eighty-nine people participated in the experiment of evaluation of thermal comfort, cognitive performance, physiological performance and environmental parameters in semi-outdoor spaces in a tropical setting. Each participant received cognitive tests to assess their selective attention, short-term memory, concentration and creativity quotient. Concurrently, qualitative measurements were conducted to assess thermal sensation, thermal comfort and thermal acceptability. The heart rate, skin temperature, and skin conductance of participants were measured throughout the experiments. Microclimatic variables such as illuminance, noise levels, dry-bulb air temperature, global temperature, relative humidity, air speed, and air direction were monitored simultaneously. To understand the effects of semi-outdoor spaces on participant performance, this study recorded participant performance in different environments through controlled experiments. Data related to participants in different settings include those shared (e.g., environmental measurement), and data unique to each participant (e.g., physiological performance). The results revealed that the subjects' cognitive and physiological performance did not change significantly after switching to the semi-outdoor space due to the availability of natural and mechanical ventilation, suggesting that short-term office activities in the semi-outdoor space are feasible in the tropics. [ABSTRACT FROM AUTHOR]

The type of single-slope solar greenhouse is mainly used for vegetable production in China. The coupling of heat storage and release courses and the dynamic change in the outdoor weather parameters momentarily affect the indoor environment. Due to the high cost of small weather stations, the environmental parameters monitored by the nearest meteorological stations are usually used as outdoor environmental parameters in China. In order to accurately predict the solar greenhouse and crop water demand, this paper proposes three deep learning models, including neural network regression (DNNR), long short-term memory (LSTM), and convolutional neural network-long- short-term memory (CNN-LSTM), and the hyperparameters of three models were determined by orthogonal experimental design (OD). The temperature and relative humidity monitored by the indoor sensors and outdoor weather station were taken as the inputs of models, the temperature and relative humidity 3, 6, 12 and 24 h in advance were taken as the output, 16 combinations of input and output data of two typical solar greenhouses were trained separately by three deep learning models, those models were trained 144, 144 and 288 times, respectively. The best model of three type models at four prediction time points were selected, respectively. For the forecast time point of 12 h in advance, the errors of the best LSTM and CNN-LSTM models in two greenhouses were all smaller than the DNNR models. For the three other time points, the results show that the DNNR models have excellent prediction accuracy among the three models. The maximum and minimum temperature, relative humidity, and ETo were also accurately predicted using the corresponding optimized models. In sum, this study provided an optimized deep learning prediction model for environmental parameters of greenhouse and provides technical support for irrigation decision-making and water allocation. [ABSTRACT FROM AUTHOR]

This paper calculates and analyzes the collision and freezing characteristics of water droplets on the cylindrical conductors with different diameters and deduces the variation law of icing weight change rate on different diameters rotating cylindrical conductors with wind speed, ambient temperature, liquid water content, and median volume diameter. Then, a prediction and value-seeking method of icing environment parameters based on rotating circular conductors is determined. The laboratory test results show that the measured values of ambient temperature T, wind speed U, liquid water content w and median volume diameter MVD are in good conformity with the reference values measured by other instruments, and the average errors of each parameter are 3.41%, 5.44%, 6.08% and 7.20%, respectively. The field natural experiment was carried out at Hunan Xuefengshan Energy Equipment Safety National Observation and Research Station. The obtained icing environmental parameters were substituted into the icing simulation calculation model of the LDQ-100 glass insulator. Compared with the field experiment results, the simulated ice shape and the field ice shape were in good agreement. The error between the numerical calculation results of the icing weight and the field icing weight of the experimental insulator was less than 6%. [ABSTRACT FROM AUTHOR]

Thin film thermo-responsive hydrogels have become a huge interest in applications such as smart drug-delivery systems or sensor/actuator technology. So far, mostly, the response of such hydrogels has been measured only by varying the temperature in a liquid environment, but studies of the response towards humidity and temperature are rare because of experimental limitations. Often the swelling measurements are performed on samples placed on a stage that can be heated/cooled, while vapors enter the permeation chamber at their own temperature. This thermal difference leads to some uncertainties on the exact relative humidity to which the sample is exposed to. In this study, we explored the possibility of performing swelling measurements under thermal equilibrium by placing the sample and an interferometer, as a detector, in an environmental chamber and therefore exposing the smart hydrogel to adjustable temperatures and relative humidity conditions while measuring the hydrogel's thin film thickness changes. As a case study, we used thin films of the thermo-responsive hydrogel, poly N-vinylcaprolactam deposited by initiated chemical vapor deposition (iCVD). Similar thin films were previously characterized by in situ ellipsometry while the sample was heated on a stage and exposed to humid air produced at room temperature. The comparison between the two measurement methods showed that while measurements in the presence of thermal gradients are limited mostly to low humidity, measurements in thermal equilibrium are restricted only by the operation limits of the used environmental chamber. [ABSTRACT FROM AUTHOR]

China's LNG import volume in 2021 reached 121.356 million tons, which makes China the largest importer in the world. The ambient air vaporizer (AAV) for LNG vaporization is an ideal selection for minimizing the average monetary value of terminals and maximizing operating efficiency. However, the heat and mass transfer performance of real multi-component LNG is not the same as that of the pure component in AAV; what is more, deep cryogenic and multi-component characteristics of LNG could lead to the deterioration of heat-transfer performance, which may cause catastrophic consequences. Thus, this difficulty should be concerning and solved in its operation. Based on the law of conservation of energy and mass, a heat and mass transfer model was established that can calculate the coupled heat transfer from the LNG to the air-side of the AAV along its own length. Empirical correlations are proposed to predict some basic properties of the frost layer at the outside of the tube, where the internal fluid is seen as a pure component and a multi-component. The numerical results show that the root–mean–square error (RMS) of the frost thickness is 0.749 mm, and the RMS of the outlet fluid temperature is 2.06 K and 2.21 K in summer and winter, respectively. That is in good agreement with the experimental data in the previous literature. The results show that the length of the finned tube in the AAV affected by pure CH 4 was increased by 42.5%; therefore, we recommend shortening the finned tube on the basis of safety to save costs. Compared to the pure components reference experiments, when the CH 4 content of multi-component in five regions was 78.48–96.91%, it is found that the different varieties of thermodynamic dryness degree x within the two types of components is the key point for the distinct mechanisms of the heat transfer characteristics of the AAV. Additionally, compared to changing the design pressure and flow rate, it is found that varying the flow rate has a much greater impact on the heat and mass transfer performance of the AAV than changing its pressure. The calculation of the coupled heat and mass transfer of the AAV can provide a theoretical basis for subsequent engineering designs. [ABSTRACT FROM AUTHOR]

Growing pumpkins in controlled environments, such as greenhouses, has become increasingly important due to the potential to optimise yield and quality. However, achieving optimal environmental conditions for pumpkin cultivation requires precise monitoring and control, which can be facilitated by modern sensor technologies. The objective of this study was to determine the optimal placement of sensors to determine the influence of external parameters on the maturity of pumpkins. The greenhouse used in the study consisted of a plastic film for growing pumpkins. Five different sensors labeled from A1 to A5 measured the air temperature, humidity, soil temperature, soil humidity, and illumination at five different locations. We used two methods, error-based sensor placement and entropy-based sensor placement, to evaluate optimisation. We selected A3 sensor locations where the monitored data were close to the reference value, i.e., the average data of all measurement locations and parameters. Using this method, we selected sensor positions to monitor the influence of external parameters on the maturity of pumpkins. These methods enable the determination of optimal sensor locations to represent the entire facility environment and detect areas with significant environmental disparities. Our study provides an accurate measurement of the internal environment of a greenhouse and properly selects the base installation locations of sensors in the pumpkin greenhouse.

Excessive anthropogenically-caused nutrient loading from both external and internal sources has promoted the growth of cyanobacteria in Lake Taihu from 2005 to 2014, suggesting increased production and release of cyanotoxins. In order to explain the spatial distribution and temporal variation of microcystins (MCs), the intracellular concentrations of MCs (MC-LR, -RR and -YR, L, R and Y are abbreviations of leucine, arginine and tyrosine) were monitored monthly from July 2013 to June 2014. Three MC variants are present simultaneously in Lake Taihu; the MC-LR and -RR variants were dominant (accounting for 40% and 39% of the total), followed by MC-YR (21%). However, MC-YR accounted for a higher proportion in colder months, especially in March. The highest concentrations of intracellular MCs were found in July and October when cyanobacteria cell density also reached the maximum. The average concentrations of MC-LR, -RR and -YR in July were 4.69, 4.23 and 2.01 µg/L, respectively. In terms of the entire lake, toxin concentrations in northern parts were significantly higher than the eastern part in summer, when MC concentrations were several times higher than the guideline value by WHO throughout much of Lake Taihu. Results from correlation and redundancy analysis (RDA) showed that total MCs, including all variants, were strongly and positively correlated with cyanobacteria cell density, water temperature, total phosphorus (TP) and pH, whereas each variant had different correlation coefficients with each of the considered environmental variables. MC-RR showed a stronger relationship with temperature, in contrast to MC-YR and -LR. Dissolved inorganic carbon (DIC) showed a negative relationship with each variant, suggesting that rising DIC concentrations may inhibit cyanobacterial growth and thereby reduce MC production in the future. [ABSTRACT FROM AUTHOR]

The aim of this study was to reveal the changes of macrophyte community over time and along the course of the Ižica River. In 1996, 2000, and 2016, we surveyed the distribution and abundance of macrophyte species in the lowland Ižica River, which originates in the town of Ig and then flows through an agricultural landscape. We calculated the River Macrophyte Index (RMI), which reflects the ecological status of the river. In 2016, ecomorphological conditions of the river, using the Riparian, Channel and Environmental inventory, were also assessed. In just 10.5 km of the river, we identified 27 taxa of macrophytes, among which Potamogeton natans, Sagittaria sagittifolia, and P. perfoliatus were the most abundant. Detrended correspondence analysis showed that, in 1996, the surveyed stretches differed more according to macrophyte composition than in the following years. The assessed environmental parameters explained 43% of the variability of the macrophyte species; riverbank stability explained 20%, riverbed structure 10%, while vegetation type of the riparian zone and bottom type explained 7 and 5%, respectively. The species composition of the macrophyte community revealed significant changes over the years of the riverine ecosystem. Comparison of RMIs in 1996 revealed better conditions in the upper and middle part of the river, while in 2016, the situation was the opposite, since the conditions in the upper part deteriorated significantly over time, while the lower part of the river had the best ecological status. These changes may be due to a considerable increase in the population of the settlement Ig, while better status in the lower course of the river may be a consequence of improvements in the infrastructure and the use of sustainable agricultural practices in the catchment due to the establishment of a formal area of protection. [ABSTRACT FROM AUTHOR]

This study focuses on the foraminiferal distribution on intertidal mudflats of two contrasted estuaries (Auray and Vie) along the French Atlantic coast. In both estuaries, the foraminiferal communities are dominated by Haynesina germanica and the Ammonia tepida group. Stations located near the outlets show a high diversity and abundance of species of the genus Elphidium. Stations in the inner estuary show a higher proportion of agglutinated species (Ammotium salsum, Ammobaculites agglutinans). Multivariate statistical analysis suggests that the distance to the sea and the percentage of fine sediment (<63 µm) are the two main parameters explaining the foraminiferal distribution. Chemical analyses of the sediment show that the two studied estuaries are not affected by major anthropogenic pollution, so that the faunas should mainly reflect the natural controlling parameters. Three indices of environmental quality commonly used in coastal areas show counter-intuitive differences between stations, suggesting that these indices may be less reliable for use in intertidal estuarine mudflats. The newly developed Marine Influence Index (MII) integrates three major ecological factors: the position of the sampling point on the salinity gradient, the emergence time at low tide and the relative importance of fresh water discharge. In our dataset, MII shows significant correlations with the controlling environmental parameters (distance to the sea, percentage grains < 63 µm), as well as with the foraminiferal patterns (PCA axis 1, species richness, percentage of Elphidium spp. and Quinqueloculina spp.). These results suggest that the MII explains a substantial part of the faunal variability on estuarine intertidal mudflats, and can be used to detect deviations from the natural distribution patterns in response to anthropogenic pollution. [ABSTRACT FROM AUTHOR]

The regulation of river flow in the Volga basin has caused irreversible changes to aquatic ecosystems. The transformation of the Volga into a cascade of hydraulic structures with a non-flow regime has resulted in a decrease in depth and flow, and an increase in the temperature and concentration of chemical elements, which has induced the process of eutrophication. The change in the species diversity of aquatic organisms under conditions of intense eutrophication was studied on models of water bodies from the Volga basin; the Kostroma section of the Gorky reservoir (Kostroma spill and the middle river section), and lakes Galichskoe and Chukhlomskoe were studied. Rheophilic biocenosis was replaced by a limnophilic one, the migration paths of fish were disrupted, and population characteristics were changed. In accordance with environmental conditions, the level of primary production and the calculated Carlson trophic index (TSI) and Broth-proposed index (ITS) (1987), the water bodies of the northern part of the upper Volga region are classified as follows: the middle river section of the Gorky reservoir is mesotrophic-eutrophic (TSI = 55.2, ITS = 16.2); the Kostroma spill is eutrophic with a tendency to hypertrophy (TSI = 67.4, ITS = 6.8); Lake Galichskoe is eutrophic with a tendency to dystrophy (TSI = 63.2, ITS = 8.4), and Lake Chukhlomskoe is hypertrophic with a tendency to dystrophy (TSI = 77.4, ITS = 8.0). In addition, frequent fluctuations in water level, reaching 1 m, have had an adverse effect on inhabitants of the littoral zone including the spawning fish, which may lead to disappearance of some of the region's most sensitive species. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems have become one of the most promising alternative energy sources, as they transform the sun's energy into electricity. This can frequently be achieved without causing any potential harm to the environment. Although their usage in residential places and building sectors has notably increased, PV systems are regarded as unpredictable, changeable, and irregular power sources. This is because, in line with the system's geographic region, the power output depends to a certain extent on the atmospheric environment, which can vary drastically. Therefore, artificial intelligence (AI)-based approaches are extensively employed to examine the effects of climate change on solar power. Then, the most optimal AI algorithm is used to predict the generated power. In this study, we used machine learning (ML)-based algorithms to predict the generated power of a PV system for residential buildings. Using a PV system, Pyranometers, and weather station data amassed from a station at King Khalid University, Abha (Saudi Arabia) with a residential setting, we conducted several experiments to evaluate the predictability of various well-known ML algorithms from the generated power. A backward feature-elimination technique was applied to find the most relevant set of features. Among all the ML prediction models used in the work, the deep-learning-based model provided the minimum errors with the minimum set of features (approximately seven features). When the feature set is greater than ten features, the polynomial regression model shows the best prediction, with minimal errors. Comparing all the prediction models, the highest errors were associated with the linear regression model. In general, it was observed that with a small number of features, the prediction models could minimize the generated power prediction's mean squared error value to approximately 0.15. [ABSTRACT FROM AUTHOR]

Simple Summary: Benthic fauna is an important element of the aquatic trophic chain, the basic element of biomonitoring. Our research was aimed at demonstrating the sensitivity of benthic fauna to the diversified morphometry of a single lake on the example of Lake Wicko (southern coast of the Baltic Sea). Our results show that lake morphometry plays a major role as a structuring factor for macroinvertebrates communities. Two segments of the lake, different in size and depth, show decreasing differences in the trophic state, abundance, diversity and number of indicator species of benthic fauna with the depth gradient. The greatest differences in abundance were observed in the shallowest zone of the bottom (eulittoral), where the chironomids differed the most. In the sublittoral of both lake segments, a simplified structure of benthic communities was found. Differences between the two segments within the intermediate zone (infralittoral zones) were recorded for mollusks and large crustaceans and the Oligochaeta/Chironomidae abundance ratio. The feeding groups were dominated by shredders in the eulittoral and infralittoral of the deeper lake segment. We recommend testing benthic macroinvertebrates in lakes with different morphometrics individually for each depth zone. We investigated the response of benthic macroinvertebrates in the eulittoral, infralittoral, and sublittoral zones, in two segments of the freshwater Lake Wicko on the coast of the Baltic Sea. Our results showed that the morphometry of lakes plays a major role as a factor structuring the macroinvertebrates communities. Two parts of the lake, different in size and depth, show decreasing differences in the trophic state, abundance, diversity and number of indicator species of benthic fauna with the depth gradient. The most significant differences were observed between the littoral zones of both segments. Similar environmental conditions in the sublittoral zones corresponded to the simplified structure of the benthic macroinvertebrates communities. In the infralittoral zone, the most significant differences between the two segments, were recorded for mollusks and large crustaceans as well as the Oligochaeta/Chironomidae abundance ratio. In the sublittoral zone, the diversity of chironomids differed most strongly. Lower species diversity was found in the part of the lake with a slight depth decrease. Shredders reached significantly higher values in eulittoral and infralittoral of the deeper lake segment. Average Score Per Taxon increased with a depth gradient. We recommend testing benthic macroinvertebrates in lakes with different morphometrics individually for each depth zone. [ABSTRACT FROM AUTHOR]

Silica-scaled chrysophytes have an ancient origin; nowadays they inhabit many northern water bodies. As the territories above the 60th parallel north were under the influence of glaciers during the Late Pleistocene, the local water bodies and their microalgal populations formed mainly during the Early Holocene. Now, the arctic, sub-arctic and temperate zones are located here and the water bodies in these regions have varying environmental characteristics. We analyzed the dispersal of silica-scaled chrysophytes in 193 water bodies in 21 northern regions, and for 135 of them determined the role of diverse environmental factors in their species composition and richness using statistical methods. Although the species composition and richness certainly depend on water body location, water temperature and conductivity, regions and individual water bodies with similar species composition can be significantly distant in latitudinal direction. Eighteen species and one variety from 165 taxa occurring here have clear affinities to fossil congeners; they have been encountered in all regions studied and amount to 6–54% of the total number of silica-scaled chrysophytes. We also compared the distribution of the species with a reconstruction of glacier-dammed lakes in the Northern Hemisphere in the Late Pleistocene–Early Holocene. The dispersal of silica-scaled chrysophytes in the northern water bodies could take place in the Late Pleistocene–Early Holocene over the circumpolar freshwater network of glacier-dammed lakes, the final Protista composition being subject to the environmental parameters of each individual water body and the region where the water body is located. This species dispersal scenario can also be valid for other microscopic aquatic organisms as well as for southerly water bodies of the Northern Hemisphere. [ABSTRACT FROM AUTHOR]

The present research aimed to determine the diversity of macrophyte taxa in the Ljubljanica River and its relationship with environmental parameters. In each of the 19 river sections, the presence and abundance of plant taxa were recorded, and basic physical and chemical parameters were measured. Additionally, selected environmental parameters were assessed using a modified version of the Riparian, Channel and Environmental (RCE) method. We compared the obtained data set with survey data from the year 2004. In 2019, a total of 34 macrophyte taxa were recorded. The dominant taxa with the highest abundance were Sparganium emersum, Callitriche sp., and the invasive alien species Elodea canadensis. The species richness and diversity of macrophytes decreased with distance from the source, an increase in pH, and alterations of the riverbed structure due to interference in the riverine ecosystem in the lower part of the Ljubljanica River and its catchment. The comparison of 2004 and 2019 surveys revealed a decrease in the overall presence and abundance of P. natans and in the frequency of occurrence of the species Myriophyllum spicatum and an increase in the presence and abundance of the invasive alien species Elodea canadensis. [ABSTRACT FROM AUTHOR]

When integrating the Internet of Things (IoT) with Unmanned Aerial Vehicles (UAVs) occurred, tens of applications including smart agriculture have emerged to offer innovative solutions to modernize the farming sector. This paper aims to present a low-cost platform for comprehensive environmental parameter monitoring using flying IoT. This platform is deployed and tested in a real scenario on a farm in Medenine, Tunisia, in the period of March 2020 to March 2021. The experimental work fulfills the requirements of automated and real-time monitoring of the environmental parameters using both under- and aboveground sensors. These IoT sensors are on a farm collecting vast amounts of environmental data, where it is sent to ground gateways every 1 h, after which the obtained data is collected and transmitted by a drone to the cloud for storage and analysis every 12 h. This low-cost platform can help farmers, governmental, or manufacturers to predict environmental data over the geographically large farm field, which leads to enhancement in crop productivity and farm management in a cost-effective, and timely manner. Obtained experimental results infer that automated and human-made sets of actions can be applied and/or suggested, due to the innovative integration between IoT sensors with the drone. These smart actions help in precision agriculture, which, in turn, intensely boost crop productivity, saving natural resources. [ABSTRACT FROM AUTHOR]

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants' health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939). [ABSTRACT FROM AUTHOR]

Simple Summary: Sows are susceptible to heat stress. Various indicators can be found in the literature assessing the level of heat stress in pigs, but none of them is specific to assess the sows' thermal condition. Moreover, previous thermal indices have been developed by considering only partial environment parameters, and the interaction between the index and the animal's physiological response are not always included. Therefore, this study aims to develop and assess a new thermal index specified for sows, called equivalent temperature index for sows (ETIS), with a comprehensive consideration of the influencing factors. An experiment was conducted, and the experimental data was applied for model development and validation. The equivalent temperatures have been transformed on the basis of equal effects of air velocity, relative humidity, floor heat conduction and indoor radiation on the thermal index, and used for the ETIS combination. The correlations between ETIS and sow's physiological parameters were performed. In the comparison with other thermal indices, the ETIS had the best performance (R = 0.82) using experimental data obtained from the sow house. In addition, the comfort threshold of ETIS has been classified for evaluating heat stress levels in the sow. This study concludes that the newly developed ETIS can be used to assess the degree of thermal comfort for sows. Heat stress affects the estrus time and conception rate of sows. Compared with other life stages of pigs, sows are more susceptible to heat stress because of their increased heat production. Various indicators can be found in the literature assessing the level of heat stress in pigs. However, none of them is specific to assess the sows' thermal condition. Moreover, thermal indices are mainly developed by considering partial environment parameters, and there is no interaction between the index and the animal's physiological response. Therefore, this study aims to develop a thermal index specified for sows, called equivalent temperature index for sows (ETIS), which includes parameters of air temperature, relative humidity and air velocity. Based on the heat transfer characteristics of sows, multiple regression analysis is used to combine air temperature, relative humidity and air velocity. Environmental data are used as independent variables, and physiological parameters are used as dependent variables. In 1029 sets of data, 70% of the data is used as the training set, and 30% of the data is used as the test set to create and develop a new thermal index. According to the correlation equation between ETIS and temperature-humidity index (THI), combined with the threshold of THI, ETIS was divided into thresholds. The results show that the ETIS heat stress threshold is classified as follows: suitable temperature ETIS < 33.1 °C, mild temperature 33.1 °C ≤ ETIS < 34.5 °C, moderate stress temperature 34.5 °C ≤ ETIS < 35.9 °C, and severe temperature ETIS ≥ 35.9 °C. The ETIS model can predict the sows' physiological response in a good manner. The correlation coefficients R of skin temperature was 0.82. Compared to early developed thermal indices, ETIS has the best predictive effect on skin temperature. This index could be a useful tool for assessing the thermal environment to ensure thermal comfort for sows. [ABSTRACT FROM AUTHOR]

Canine parvovirus type 2 (CPV-2) primarily infects dogs, which are the main host reservoir, causing severe gastrointestinal disease associated with immunosuppression. The present study was conducted in Thessaly, Greece and aimed to identify risk and environmental factors associated with CPV-2 infection in diarrheic dogs. Fecal samples were collected from 116 dogs presenting diarrhea and were tested by polymerase chain reaction (PCR) for the presence of CPV-2 DNA. Supplementary data regarding clinical symptoms, individual features, management factors and medical history were also gathered for each animal during clinical evaluation. Sixty-eight diarrheic dogs were found to be positive for the virus DNA in their feces. Statistical analysis revealed that CPV-2 DNA was less likely to be detected in senior dogs, while working dogs, namely hounds and shepherds, had higher odds to be positive for the virus. Livestock density and land uses, specifically the categories of discontinuous urban fabric and of human population density, were identified as significant environmental parameters associated with CPV-2 infection by using Geographical Information System (GIS) together with the Ecological Niche Model (ENM). This is the first description of the environmental variables associated with the presence of CPV-2 DNA in dogs' feces in Greece. [ABSTRACT FROM AUTHOR]

Vertical deformations of the Earth's surface result from a host of geophysical and geological processes. Identification and assessment of the induced signals is key to addressing outstanding scientific questions, such as those related to the role played by the changing climate on height variations. This study, focused on the European and Mediterranean area, analyzed the GPS height time series of 114 well-distributed stations with the aim of identifying spatially coherent signals likely related to variations of environmental parameters, such as atmospheric surface pressure (SP) and terrestrial water storage (TWS). Linear trends and seasonality were removed from all the time series before applying the principal component analysis (PCA) to identify the main patterns of the space/time interannual variability. Coherent height variations on timescales of about 5 and 10 years were identified by the first and second mode, respectively. They were explained by invoking loading of the crust. Single-value decomposition (SVD) was used to study the coupled interannual space/time variability between the variable pairs GPS height–SP and GPS height–TWS. A decadal timescale was identified that related height and TWS variations. Features common to the height series and to those of a few climate indices—namely, the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA), and the multivariate El Niño Southern Oscillation (ENSO) index (MEI)—were also investigated. We found significant correlations only with the MEI. The first height PCA mode of variability, showing a nearly 5-year fluctuation, was anticorrelated (−0.23) with MEI. The second mode, characterized by a decadal fluctuation, was well correlated (+0.58) with MEI; the spatial distribution of the correlation revealed, for Europe and the Mediterranean area, height decrease till 2015, followed by increase, while Scandinavian and Baltic countries showed the opposite behavior. [ABSTRACT FROM AUTHOR]

Based on severe weather reports, surface precipitation observations, surface routine observations, and the European Center for Medium-Range Weather Forecasts ERA5 reanalysis dataset during the warm seasons (May–September) of 2011–2018 over North China, this paper analyzes the statistical characteristics and environmental conditions of three types of severe convective events. Results are compared between events with different altitudes (i.e., mountains and plains), severities (i.e., ordinary and significant), and months. Hail and thunderstorm high winds (THWs) are more common over the mountains whereas short-duration heavy rainfall (SDHR) is more frequent over the plains. The occurrence frequency of severe convective events exhibits distinct monthly and diurnal variations. Analyses of the environmental parameters provide reference for the potential forecasting of severe convective events over this region. Specifically, the 850–500 hPa temperature lapse rate (LR85), pseudo-equivalent potential temperature at 500 hPa (thetase500), and precipitable water (PW) are skillful in distinguishing hail and THW environments from SDHR environments, and thetase500 is useful in discriminating between hail and THW environments. The convective environments over the plains are characterized by significantly higher (lower) PW (LR85) compared with mountains. The skill of these parameters in forecasting the severity of the convective hazards is limited. Probability distributions in the two parameters space indicate that the occurrence of significant hail requires both higher most unstable convective available potential energy (MUCAPE) and stronger 0–6 km bulk wind shear (SHR6) compared with ordinary hail. Compared with ordinary THWs, the significant THWs over the mountains depend more on the SHR6 whereas those over the plains rely more on the MUCAPE. The significant SDHR events over the plains tend to occur under a variety of instability conditions. The thermodynamic parameters (i.e., MUCAPE, thetase500, and downdraft convective available potential energy), and PW are significantly higher in July–August, whereas the LR85 and vertical wind shear are apparently higher in May, June and September. [ABSTRACT FROM AUTHOR]

Fungi are an important, yet often, neglected component of the aquatic microflora, and is responsible for primary decomposition and further processing of organic matter. By comparison, the ecological roles of terrestrial fungi have been well-studied, but the diversity and function of fungi that populate aquatic environments remain poorly understood. Here, the impact of urbanization on fungal diversity and community composition in the canal system of Suzhou was assessed by sequencing the internal transcribed spacer 1 (ITS1) region of the rRNA operon. It was amplified from environmental DNA that has been extracted from water samples and pre-deployed decomposing leaves collected from nine sampling locations (high, medium and low urbanization) over two seasons. The fungal diversity and community composition were determined by bioinformatic analysis of the large DNA sequence datasets generated to identify operational taxonomic units (OTUs) for phylogenetic assignment; over 1 million amplicons were sequenced from 36 samples. The alpha-diversity estimates showed high differences in fungal diversity between water and leaf samples, and winter versus summer. Higher numbers of fungal OTUs were identified in both water and leaf samples collected in the summer, and fungal diversity was also generally higher in water than on colonized leaves in both seasons. The fungal community on leaves was usually dominated by Ascomycetes, especially in winter, while water samples contained more diversity at phylum level with Chytridiomycetes often prominent, particularly in summer. At a genus level, a very high relative abundance of Alternaria on leaves was observed in winter at all locations, in contrast to very low abundance of this genus across all water samples. Fungal community composition also varied between sampling locations (i.e., urbanization); in cluster analysis, samples from high urbanization locations formed a distinct cluster, with medium and low urbanization samples clustering together or in some instances, separately. Redundancy analysis shed further light on the relationships between variation in fungal community composition and water physico-chemical properties. Fungal community diversity variation and correlation with different parameters is discussed in detail, but overall, the influence of season outweighed that of urbanization. This study is significant in cataloguing the impact of urbanization on fungal diversity to inform future restoration of urban canal systems on the importance of protecting the natural aquatic fungal flora. [ABSTRACT FROM AUTHOR]

Particulate pollution caused by urban traffic emissions has become a significant public hazard. Many urban roads of under-viaduct spaces (UVSs) have become concentrated areas of particulate pollution. This study aims to explore the effects of landscape parameters on particulate matter in UVSs in Wuhan, China. We selected 14 types of UVS sections and nine potential environmental parameters to monitor four types of particulate matter (PM 1.0 , PM 2.5 , PM 10 , and TSP). Finally, linear regression analysis was employed to quantify the relative contributions of environmental parameters to the reduction in the concentration of the four types of particulate matter in the summer and winter. The results showed that particulate matter concentrations exhibit spatial and seasonal differences in UVSs. A single landscape parameter was correlated with particulate matter concentration, while compound environmental parameters had significant effects on the particulate matter concentration in UVSs. Meteorological factors and greening structures had a dominant impact on the particulate matter concentrations in summer and winter, respectively. Therefore, adjusting and optimizing the environmental parameters could reduce particulate pollution in UVSs and could have practical significance for the planning and design of UVSs.

To optimize suppression, restoration, and prevention plans against wildfire, postfire assessment is a key input. Since little research has been carried out on applying Sentinel-2 imagery through an integrated approach to evaluate how environmental parameters affect fire severity, this work aims to fill this gap. A set of large forest fires that occurred in northwest Spain during extreme weather conditions were adopted as a case study. Sentinel-2 information was used to build the fire severity map and to evaluate the relation between it and a set of its driving factors: land cover, aspect, slope, proximity to the nearest stream, and fire recurrence. The cover types most affected by fire were scrubland, rocky areas, and Eucalyptus. The presence of streams was identified as a major cause of the reduced severity of fires in broadleaves. The occurrence of fires in the past is linked to the severity of fires, depending on the land cover. This research aims to help fire researchers, authority managers, and policy makers distinguish the conditions under which the damage by fire is minimized and optimize the resources allocated to restoration and future fire suppression. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is considered to be a potential indicator of photosynthesis. However, the impact of growth and environmental parameters on SIF at different time-scales remains unclear, which has greatly restricted the application of SIF in detecting photosynthesis variations. Thus, in this study, the impact of growth and environmental parameters on SIF was thoroughly clarified. Here, continuous time series of canopy SIF (760 nm, F760) over wheat and maize was measured based on an automated spectroscopy system. Meanwhile, field measurements of growth and environmental parameters were also collected using commercial-grade devices. Relationships of these parameters with F760, apparent SIF (F760/solar radiance, AF760), and SIF yield (F760/canopy radiance of 685 nm, Fy760) were analyzed using principal component analysis (PCA) and Pearson correlation to reveal their impacts on SIF. Results showed that F760 at seasonal and diurnal scales were mainly driven by solar radiation (SWR), leaf area index (LAI), chlorophyll content (Chl), mean leaf inclination angle (MTA), and relative water content (RWC). Other environmental parameters, including air temperature (Ta), relative humidity (Rh), vapor pressure deficit (VPD), and soil moisture (SM), contribute less to the variation of seasonal or diurnal F760. AF760 and Fy760 are likely to be less dependent on Ta, Rh, and VPD due to the removal of the impact from SWR, but an enhanced relationship of AF760 (and Fy760) with SM was observed, particularly under water stress. Compared with F760, wheat AF760 was better correlated to LAI and RWC as expected, while maize AF760 did not show an enhanced relationship with all growth parameters, probably due to its complicated canopy structure. The relationship of wheat Fy760 with canopy structure parameters was further reduced, except for maize measurements. Furthermore, SM-induced water stress and phenological stages should be taken into consideration when we interpret the seasonal and diurnal patterns of SIF since they were closely related to photosynthesis and plant growth (e.g., LAI in our study). To our knowledge, this is the first exploration of the impacts of growth and environmental parameters on SIF based on continuous ground measurements, not only at a seasonal scale but also at a diurnal scale. Our results could provide deep insight into the variation of SIF signals and also promote the further application of SIF in the health assessments of terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production. [ABSTRACT FROM AUTHOR]

Simple Summary: China harbors two lineages of cattle (Bos taurus and Bos indicus) that display pronounced geographical distribution differences. Northern Chinese cattle predominantly belong to B. taurus and southern Chinese cattle belong to B. indicus. Both B. taurus and B. indicus contribute to the admixture of cattle in central China. Thermal stress induces oxidative stress and DNA damage in mammals. In general, B. indicus are more resistant to thermal stress than B. taurus. Eukaryotic translation initiation factor 2-alpha kinase 4 (EIF2AK4), which pertains to the family of serine–threonine kinase, is a candidate gene for thermal stress. However, the effects of the bovine EIF2AK4 gene on the thermal tolerance traits of Chinese cattle breeds remain unknown. Our results suggest that a variant of the EIF2AK4 gene is associated with thermal tolerance traits in Chinese cattle. Eukaryotic translation initiation factor 2-alpha kinase 4 (EIF2AK4, also known as GCN2), which pertains to the family of serine–threonine kinase, is involved in oxidative stress and DNA damage repair. A missense single-nucleotide polymorphism (SNP) (NC_037337.1 g.35615224 T > G) in exon 6 of the EIF2AK4 gene which encodes a p.Ile205Ser substitution was observed in the Bovine Genome Variation Database and Selective Signatures (BGVD). The purpose of the current study is to determine the allelic frequency distribution of the locus and analyze its association with thermal tolerance in Chinese indigenous cattle. In our study, the allelic frequency distribution of the missense mutation (NC_037337.1 g.35615224 T > G) in Chinese cattle was analyzed by sequencing 1105 individuals of 37 breeds including 35 Chinese indigenous cattle breeds and two exotic breeds. In particular, association analysis was carried out between the genotypes and three environmental parameters including annual mean temperature (T), relative humidity (RH), and temperature–humidity index (THI). The frequency of the mutant allele G (NC_037337.1 g.35615224 T > G) gradually decreased from the southern cattle groups to the northern cattle groups, whereas the frequency of the wild-type allele T showed an opposite pattern, consistent with the distribution of indicine and taurine cattle in China. In accordance with the association analysis, genotypes were significantly associated with T (P < 0.01), RH (P < 0.01), and THI (P < 0.01), suggesting that the cattle with genotype GG were found in regions with higher T, RH, and THI. Thus, our results suggest that the mutation (NC_037337.1 g.35615224 T > G) of the EIF2AK4 gene is associated with thermal tolerance traits in Chinese cattle. [ABSTRACT FROM AUTHOR]

Enormous development of technology over the past two centuries has been associated with a considerable drawdown of non-renewable raw materials and an increase in the amount of harmful emissions. Thus, building direction in the field of construction implies a significant implementation of sustainability. The preference for environmentally friendly technology solutions is the right way to meet the sustainability trend. The main aim of this study was to analyze and verify selected construction systems of buildings designed for agricultural primary production in terms of their environmental impacts. The supporting construction systems of the comparative design variants was designed from a variety of material bases (concrete, steel, and wood). To evaluate environmental construction systems, the LCA methodology in mode "Cradle to Gate" was used. Parameters of sustainability related to the environmental impact were analyzed. These results could be a useful tool for the evaluation and comparison to an appropriate choice of the design option for an industrial building designed for agricultural primary production in terms of environmental impact. [ABSTRACT FROM AUTHOR]

The aim of the research presented in this exploratory paper is to present the sustainability priorities and measures focused on by craft beer SMEs and to highlight a number of innovative measures pursued by brewers to promote sustainability. The materials and methods for the study included an Internet survey of craft breweries with a sustainability profile, followed by an in-depth analysis of 70 shortlisted craft beer websites. The research finds that, as a whole, craft brewers have a broad interpretation of sustainability, encompassing environmental and socioeconomic parameters. Areas strongly profiled by brewers include energy & climate, water efficiency & conservation, spent grain reuse, and community involvement in its many forms. A strong focus on the "local", including both environmental and socioeconomic aspects, by this group points to unique sustainability priorities of SMEs. The findings also reveal that these priorities are often differentiated on company websites by environmental sustainability and community engagement activities. Furthermore, among the breweries analyzed, more specific discoveries also showed that the age of the brewing operation, in general, impacts how much emphasis is placed on company sustainability efforts, with breweries established in the 1980s and 1990s highlighting their sustainability efforts more profoundly. Despite the general broad interpretations of sustainability, numerous individual breweries profiled only a limited number of sustainability priorities. To broaden perspectives, it is suggested that active measures be taken in the industry promote more robust understandings of what sustainability is and how processes to promote sustainability in an integrated manner can be more concretely operationalized among this group of SMEs. [ABSTRACT FROM AUTHOR]

The production of energy at the global level is conditioned by the use of fossil fuels that have a great environmental impact. In the last decades, renewable energy production systems have been implemented, and networks of nearly zero-energy buildings have been created, with a consequent complexity in the design phase in order to optimize the results. In this way, electronic prototype development methods like the one that is proposed in this paper improve the tasks of design and modelling. Thus, a new weather station based on an Arduino platform has been developed to collect and store ambient temperature, relative humidity, barometric pressure, wind speed and air quality data, comparing the obtained data to those obtained using a validation station containing commercial sensors. The results show how the use of low cost Arduino sensors allow one to obtain similar values to those collected by more professional meteorological stations with insignificant scatter between both technologies. [ABSTRACT FROM AUTHOR]

Excessive anthropogenically-caused nutrient loading from both external and internal sources has promoted the growth of cyanobacteria in Lake Taihu from 2005 to 2014, suggesting increased production and release of cyanotoxins. In order to explain the spatial distribution and temporal variation of microcystins (MCs), the intracellular concentrations of MCs (MC-LR, -RR and -YR, L, R and Y are abbreviations of leucine, arginine and tyrosine) were monitored monthly from July 2013 to June 2014. Three MC variants are present simultaneously in Lake Taihu; the MC-LR and -RR variants were dominant (accounting for 40% and 39% of the total), followed by MC-YR (21%). However, MC-YR accounted for a higher proportion in colder months, especially in March. The highest concentrations of intracellular MCs were found in July and October when cyanobacteria cell density also reached the maximum. The average concentrations of MC-LR, -RR and -YR in July were 4.69, 4.23 and 2.01 μg/L, respectively. In terms of the entire lake, toxin concentrations in northern parts were significantly higher than the eastern part in summer, when MC concentrations were several times higher than the guideline value by WHO throughout much of Lake Taihu. Results from correlation and redundancy analysis (RDA) showed that total MCs, including all variants, were strongly and positively correlated with cyanobacteria cell density, water temperature, total phosphorus (TP) and pH, whereas each variant had different correlation coefficients with each of the considered environmental variables. MC-RR showed a stronger relationship with temperature, in contrast to MC-YR and -LR. Dissolved inorganic carbon (DIC) showed a negative relationship with each variant, suggesting that rising DIC concentrations may inhibit cyanobacterial growth and thereby reduce MC production in the future.