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.

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.

This paper highlights the critical role of pH or proton activity measurements in environmental studies and emphasises the importance of applying proper statistical approaches when handling pH data. This allows for more informed decisions to effectively manage environmental data such as from mining influenced water. Both the pH and {H+} of the same system display different distributions, with pH mostly displaying a normal or bimodal distribution and {H+} showing a lognormal distribution. It is therefore a challenge of whether to use pH or {H+} to compute the mean or measures of central tendency for further environmental statistical analyses. In this study, different statistical techniques were applied to understand the distribution of pH and {H+} from four different mine sites, Metsämonttu in Finland, Felsendome Rabenstein in Germany, Eastrand and Westrand mine water treatment plants in South Africa. Based on the statistical results, the geometric mean can be used to calculate the average of pH if the distribution is unimodal. For a multimodal pH data distribution, peak identifying methods can be applied to extract the mean for each data population and use them for further statistical analyses.

Microplastics (MPs), as a new type of environmental pollutant, have attracted extensive attention in recent years. However, there has been relatively little research specifically focusing on MPs in the Yellow River Basin, China, particularly regarding MP migration patterns. Based on surface water and sediment samples from 19 sampling sites in the Wuding River (WDR), the abundances and characteristic distributions of MPs were analyzed, and the environmental factors affecting their distribution and potential ecological risks were evaluated. The results showed that the MP abundances in surface water and sediments of the WDR were significantly different (P < 0.05), with mean values of 2.98 ± 0.69 items/L and 419.47 ± 75.61 items/kg, respectively. In terms of MP characteristics, the most common size class was 0.1-0.5 mm in surface water. Polyethylene (PE, 32.50%) and polypropylene (PP, 27.50%) were the main polymer types of MPs in surface water. Although similar MP characteristics were observed in sediments, there were significantly more particles in the <0.1 mm particle size (P < 0.05), which was 15.0% higher than in surface water. Also, more high-density MP fragments were observed in sediment samples. The retention of MPs in sediments was influenced by the MP characteristics (density, shape, particle size) and sediment particle size. In contrast, the MP abundance in surface water was more closely related to the presence of other environmental pollutants, such as total phosphorus (WTP) and ammonia nitrogen (WAN). Temperature (T), agricultural land (AGR), and residential land (RES) only had significant effects on the distribution of MPs in surface water (P < 0.05). Potential ecological risk assessments revealed that MP pollution in sediments was more serious than in surface water, especially in the middle and lower reaches. The results of this study are important for understanding MP transport in a sandy river and for eliminating potential sources of MPs.

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.

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.

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.

BACKGROUND: The aim of the study was to investigate the relationship between the number of positive cases of COVID-19 and the environmental parameters of temperature and humidity in five departments of the province of Catamarca, Argentina.
METHODS: The departments Capital, Andalgalá, Paclín, Ambato and Tinogasta, Catamarca, Argentina, were studied. Between April 2021 and April 2022 variables analyzed were: number of COVID-19 cases, temperature, and percentage of environmental humidity. The correlation of variables was analyzed by the coefficient of Spearman.
RESULTS: The temperature with the numbers of COVID-19 cases show inverse correlation values that ranged between -0.56 and -0.34. The humidity percentages show a slightly positive relationship only for Capital and Andalgalá, with values of 0.34 and 0.40 with the number of cases.
CONCLUSIONS: The results showed a relationship between the number of positive cases of COVID-19 and the environmental parameters of temperature and humidity in five departments in the province of Catamarca.
Introducción: El objetivo del trabajo fue estudiar la relación entre el número de casos positivos de COVID-19 y los parámetros ambientales de temperatura y humedad en cinco departamentos de la provincia de Catamarca. Métodos: Los departamentos estudiados fueron Capital, Andalgalá, Paclín, Ambato y Tinogasta, Catamarca, Argentina. Las variables número de casos COVID-19, temperatura y porcentaje de humedad ambiental fueron obtenidos entre abril de 2021 y abril de 2022. La correlación de variables se analizó mediante el coeficiente de Spearman. Resultados: La temperatura, en relación con los nú- meros de casos por COVID-19 mostró valores de correlación inversa que oscilaron entre -0.56 y -0.34. El porcentaje de humedad, indicó una relación levemente positiva con el número de casos solo para Capital y Andalgalá con valores de 0.34 y 0.40. Conclusión: Los resultados muestran una relación entre el número de casos positivos de COVID-19 y los parámetros ambientales de temperatura y humedad en cinco departamentos de la provincia de Catamarca.

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.

This dataset provides detailed electricity demand forecasting metrics for the Sharjah Electricity and Water Authority (SEWA) over 2020 and 2021. Data encompasses both hourly and daily demand patterns, enriched with specific environmental parameters such as temperature, humidity, and solar irradiance. Additionally, SEWA\'s unique load metrics and lagged demand values, representing previous hour demand, are included. Data was procured using advanced electrical load meters and standardized weather data acquisition systems. Preliminary and advanced data processing was conducted via Excel tool. This comprehensive dataset is invaluable for stakeholders in electricity provisioning and policy-making. Its granular detail makes it a pivotal resource for modelling and forecasting electricity demand, aiding in infrastructure planning, renewable energy considerations, and demand-side management. The potential applications span across academic, policy, and industry domains, rendering it a versatile tool for future electricity demand research.