Long-term Temperature Monitoring Research Articles

Evidence-based strategies for optimizing long-term temperature monitoring in offices

Long-term monitoring of the thermal environment in office buildings has become increasingly relevant with the rise of wireless sensor networks. However, there is a notable absence of explicit guidelines for implementing monitoring strategies in such contexts. This lack of direction can lead to inconsistent deployment of sensor networks, resulting in higher maintenance costs and inaccurate long-term assessments of thermal conditions. Based on data analyses of high-accuracy, high-frequency field measurements conducted over a year or longer across multiple offices in Sydney and Shanghai, this study proposes a strategy for long-term temperature monitoring. The strategy advises practitioners to prioritize considerations such as air-conditioning type, room size, and space function when selecting "representative" sensor locations. Typically, sampling every 30minutes is deemed adequate for shared offices where an error margin of ±0.5°C is acceptable. For environments with stable indoor temperatures, less frequent sampling intervals suffice. A power regression model tailored for offices equipped with central AC and no operable windows was developed to predict the maximum allowable sampling interval based on several days of indoor temperature monitoring in winter. Regarding monitoring duration, the study advocates a preferred sampling period of one year to comprehensively capture seasonal variations. Alternatively, a minimum monitoring period of four to six months commencing in late spring or early summer is identified as potentially sufficient. These findings offer valuable insights for optimizing long-term thermal monitoring practices in offices and may contribute to expanding the scope of thermal comfort standards.

An in-situ hybrid laser-induced integrated sensor system with antioxidative copper

Integration of sensors with engineering thermoplastics allows to track their health and surrounding stimuli. As one of vital backbones to construct sensor systems, copper (Cu) is highly conductive and cost-effective, yet tends to easily oxidize during and after processing. Herein, an in-situ integrated sensor system on engineering thermoplastics via hybrid laser direct writing is proposed, which primarily consists of laser-passivated functional Cu interconnects and laser-induced carbon-based sensors. Through a one-step photothermal treatment, the resulting functional Cu interconnects after reductive sintering and passivation are capable of resisting long-term oxidation failure at high temperatures (up to 170 °C) without additional encapsulations. Interfacing with signal processing units, such an all-in-one system is applied for long-term and real-time temperature monitoring. This integrated sensor system with facile laser manufacturing strategies holds potentials for health monitoring and fault diagnosis of advanced equipment such as aircrafts, automobiles, high-speed trains, and medical devices.

Hybrid Twins Modeling of a High-Level Radioactive Waste Cell Demonstrator for Long-Term Temperature Monitoring and Forecasting.

Monitoring a deep geological repository for radioactive waste during the operational phases relies on a combination of fit-for-purpose numerical simulations and online sensor measurements, both producing complementary massive data, which can then be compared to predict reliable and integrated information (e.g., in a digital twin) reflecting the actual physical evolution of the installation over the long term (i.e., a century), the ultimate objective being to assess that the repository components/processes are effectively following the expected trajectory towards the closure phase. Data prediction involves using historical data and statistical methods to forecast future outcomes, but it faces challenges such as data quality issues, the complexity of real-world data, and the difficulty in balancing model complexity. Feature selection, overfitting, and the interpretability of complex models further contribute to the complexity. Data reconciliation involves aligning model with in situ data, but a major challenge is to create models capturing all the complexity of the real world, encompassing dynamic variables, as well as the residual and complex near-field effects on measurements (e.g., sensors coupling). This difficulty can result in residual discrepancies between simulated and real data, highlighting the challenge of accurately estimating real-world intricacies within predictive models during the reconciliation process. The paper delves into these challenges for complex and instrumented systems (multi-scale, multi-physics, and multi-media), discussing practical applications of machine and deep learning methods in the case study of thermal loading monitoring of a high-level waste (HLW) cell demonstrator (called ALC1605) implemented at Andra's underground research laboratory.

Modeling and Data Mining Analysis for Long-Term Temperature-Stress-Strain Monitoring Data of a Concrete Gravity Dam

The safety condition of concrete gravity dams is influenced by multiple factors, and assessing their safety solely based on a single factor is difficult to comprehensively evaluate. Therefore, this paper proposes a comprehensive modeling and analysis approach to assess dam safety by considering long-term temperature, stress, and strain monitoring data of actual concrete gravity dams. Firstly, the K-means clustering algorithm is utilized to classify the data. Then, the study area of the dam is meshed and three indicator evaluation values for all the elements are calculated. The other elements’ evaluation values can be obtained by the Inverse Distance Weighting (IDW) method. Finally, the analytic hierarchy process extended by the D numbers preference relation (D-AHP) method is applied to compute the weights of temperature, stress, and strain and evaluate the dam’s safety comprehensively. The effectiveness of this method is validated through application to specific engineering cases. The results demonstrate that compared to assessing methods considering only single factors, the comprehensive evaluation method proposed in this paper can more comprehensively and accurately reflect the actual safety condition of concrete gravity dams, providing important references for engineering decision-making.

Assessing and predicting indoor environmental quality in 13 naturally ventilated urban residential dwellings

This research presents the assessments of IEQ in 13 naturally ventilated urban residential dwellings based on the long-term monitoring of CO2, PM2.5, formaldehyde, temperature, and relative humidity within a year. In addition to the costly monitoring techniques, we utilized time series prediction methods as an economical and efficient approach to comprehend the quality of indoor environment. Nevertheless, choosing a prediction method proves to be a challenge due to the inconclusive nature of the advantages and disadvantages associated with various methodologies. Thus, we conducted a comprehensive comparison of the LSTM, SVM, ARIMA, and BPNN algorithms for predicting IEQ. The results indicate that for short-term prediction (one day), the SVM algorithm has the shortest fitting time, the program occupies the least memory, and the overfitting problem is not readily apparent, while the prediction accuracy is acceptable. Long-term (one year) predictions can be executed with greater precision using LSTM. In regard to robustness, prediction time and accuracy of SVM in different dwellings varies greatly and the robustness is weak. When considering parameters, the discrepancy between the prediction error of PM2.5 and the sensor error is more pronounced, resulting in an even lower level of predictability. As for the input data, it was observed that the computation time of the four algorithms increased linearly with the sample size. While the accuracy of predictions is predominantly influenced by the sampling frequency of the original data. The investigation results will benefit future decision-making and control strategies regarding the enhancement of thermal comfort and indoor air quality.

Temporal Dynamics of Negative Air Ion Concentrations in Nanjing Tulou Scenic Area

Negative air ions (NAIs) are crucial for assessing the impact of forests on wellbeing and enhancing the physical and mental health of individuals. They serve as pivotal indicators for assessing air quality. Comprehensive research into the distribution patterns of NAI concentrations, especially the correlation between NAI concentrations and meteorological elements in tourist environments, necessitates the accumulation of additional long-term monitoring data. In this paper, long-term on-site monitoring of NAI concentrations, air temperature, relative humidity, and other factors was conducted in real time over 24 h, from April 2020 to May 2022, to explore the temporal dynamic patterns of NAIs and their influencing factors. The results showed that (1) the daily dynamics of NAI concentrations followed a U-shaped curve. The peak concentrations usually occurred in the early morning (4:30–8:00) and evening (19:10–22:00), and the lowest concentrations usually occurred at noon (12:50–14:45). (2) At the monthly scale, NAI concentrations were relatively high in February, August, and September and low in January, June, and December. At the seasonal scale, NAI concentrations were significantly higher in winter than in other seasons, with higher concentrations occurring in the summer and autumn. (3) Relative humidity, air temperature, and air quality index (AQI) were the primary factors that influenced NAI concentrations. Relative humidity showed a significant positive correlation with NAI concentrations, while air temperature and AQI both exhibited a significant negative correlation with NAI concentrations. Higher air quality corresponds to higher NAI concentrations. Our research provides new insights into NAI temporal dynamics patterns and their driving factors, and it will aid in scheduling outdoor recreation and forest health activities.

Sediment temperature characteristics and their relation to distribution patterns of two sentinel crab species in intertidal flats in western Japan

In the context of global climate change, monitoring focused on temperature and benthic animals in intertidal flats and the development of metrics to assess climate change and ecosystem responses are essential for a sustainable society. However, few studies have assessed the relationship between intertidal sediment temperature and the distribution of benthic animals. To address this gap, in the present study, intertidal sediment temperatures were observed in 12 intertidal flats in 11 survey areas over 335 days, from October 2, 2019, to August 31, 2020, using water temperature data loggers. The characteristics of intertidal sediment temperatures were variable among the survey areas, and a correlation analysis suggested that such characteristics are possibly influenced by various spatial-scale factors, such as geographical, basin, and habitat scales. Furthermore, two sentinel crab species, Macrophthalmus japonicus and Macrophthalmus banzai were collected, and the number of wintering individuals of each species was estimated based on their carapace width to analyze the changes in abundances of the two species in each survey area. The results show that the number of days with daily minimum temperature ≥ 19 °C was the factor that influenced the abundance rate, suggesting that M. japonicus and M. banzai populations may decrease and increase, respectively, according to future climate change in Japan. Our findings emphasize the importance of long-term monitoring of sediment temperatures and benthic animals in intertidal flats to evaluate the influence of future climate change.

基于热传输与水混合的持续监测和数值模拟的泉水温度变异性解释: 以波兰南部Pieniny山脉Czarny Potok泉水为例

AbstractA numerical model of heat conduction and water mixing was developed, enabling a quantitative description of water temperature variability at a spring outflow. The study examined the Czarny Potok spring, located in the Pieniny Mountains of southern Poland, which was the subject of a 4-year series of water temperature observations. The presented model describes the soil and water environment in the immediate vicinity of the spring, assuming that the spring water temperature is shaped by the mixing of water flowing through the shallow zone that experiences seasonal fluctuation and the deeper neutral zone. It was also assumed that the conductive heat flow in the tested medium is conditioned by seasonal heating and cooling of the land surface. The thermal diffusivity of the bedrock was calculated on the basis of the phase shift and the attenuation of thermal amplitude at different depths, based on long-term monitoring of soil temperature. The heat conduction and water mixing models enabled calculation of the water temperature at the outflow. The obtained results are close to the empirical spring water temperatures. The estimated mean error was 0.075 °C and the mean absolute error was 0.188 °C. The results of the calculations suggest that the tested spring is recharged primarily by water flowing through the seasonal fluctuation zone (75%), while the remaining 25% captures a deeper circulation system associated with the neutral zone.

Analysis of the subsurface and fluid temperature monitoring by long-term operation of borehole heat exchangers

Owing to the prevailing escalation in energy costs, escalating energy demands, depletion of conventional resources, and escalating greenhouse gas emissions, there has been an expeditious upsurge in the worldwide proliferation of alternative energy sources. Geothermal systems, in particular, are highly coveted as environmentally friendly and sustainable renewable energy reservoirs, offering efficacious cooling and heating capabilities for various structures (Xu et al., 2020; Ghasemi-Fare & Basu, 2013). The energy exchange efficiency of geothermal systems is significantly contingent upon the ground temperature, enabling the discharge of energy into the ground for cooling purposes in summer and its retrieval for heating requirements during winter. However, long-term operation of geothermal energy systems can engender an imbalance in thermal recovery through the heating and cooling processes, consequently influencing the thermomechanical behavior of the subsurface over time (Baek et al., 2017). Geothermal systems, also known as ground source heat pumps (GSHPs), are designed to operate for around 20 years, while the heat source component can be utilized for an extended duration ranging from 30 to 50 years. Understanding heat transfer characteristics and long-term performance of geothermal energy systems is crucial for optimal utilization and efficiency. However, research often lacks field investigation data, specifically for long-term monitoring and validation of fluid temperatures and subsurface temperature profiles. This research conducted comprehensive long-term measurements to assess fluid and subsurface temperature behavior in a borehole heat exchanger (BHE) system. The study examined the impact of sustained BHE performance on surface and subsurface temperature dynamics. A sophisticated three-dimensional numerical model was developed based on empirical insights from the field study and validated using monitored data. The analysis included inlet/outlet fluid temperature, spatial heterogeneity of ground temperature profiles, thermal recovery processes, and alterations caused by residual thermal energy.
 Furthermore, additional temperature sensors were deployed at various depths to monitor temperature changes at distinct levels within the subground.
 In this study, an advanced three-dimensional (3D) numerical model was developed utilizing the Finite Element Method (FEM) to effectively capture the intricate heat transfer processes occurring around BHEs. The geometry mesh was generated using a specialized triangular mesh generator, enabling spatial variation of element sizes to accurately represent the geometry and temperature distribution patterns. To address the specific requirements of U-tube heat exchanger loops, where the steepest temperature gradient is anticipated, a meticulously chosen element size of 0.7 mm, was employed. As the model extends towards the lateral boundaries, particularly in the radial direction, the element size gradually increases to ensure faithful representation of the system's characteristics. To properly account for diverse vertical temperature gradients, the number of elements and the vertical distance between slices were systematically adjusted within a range of 0.01 to 5 m. This meticulous variation facilitated accurate consideration of the distinct vertical temperature profiles. Notably, the finest discretization was applied near the bottom of the BHE (approximately z = -155 m) and at the top surface, characterized by a significant temperature gradient.
 The depicted Figure 2 showcases the model's performance in analyzing the fluid profile in comparison to the monitored data over a duration of 600 hours. This comparison reveals that the maximum relative difference between the monitored and simulated values for the inlet and outlet fluid temperature does not exceed 2.649 and 2.296 degrees Celsius, respectively. These findings offer compelling evidence of the exceptional accuracy of the proposed numerical modeling approach, thus affirming its suitability for examining the long-term sustainability of BHE systems.

Comparison of the corrugated steel web composite box-girder and traditional girders regarding temperature field under solar radiation

The corrugated web composite box-girder (CWCB) is a novel structural form and has been widely adopted in bridge engineering due to its excellent mechanical properties and construction convenience. However, researches on the temperature field in CWCB is very limited, which may augment the risk of concrete cracking and steel web bucking and deteriorate the durability of bridge structures. This paper thereby experimentally investigates the time-varying and spatial temperature distribution features of CWCB based on a long-term temperature monitoring system. The experimental results indicate that the temperature field in CWCB is non-uniformly distributed and the vertical and horizontal temperature differences can reach up to 13.4 ℃ and 19.1 ℃, respectively. Meanwhile, the observed temperature distribution profile of the experimental CWCB is varying with seasons owing to the different exposure to solar radiation. Then, a refined numerical thermal simulation approach is developed and its reliability and accuracy have been validated by the experimental temperature data of three types of box-girders, i.e., CWCB, ordinary composite box-girder (OCB), and conventional concrete box-girder (CCB). Based on the validated numerical approach, the temperature fields of three full-sized box-girders are therewith compared and the results demonstrate that the temperature field in CWCB is much more complicated than the traditional box-girders. Besides, the obtained cross-sectional temperature differences of CWCB are significantly larger than that of the traditional box-girders. Finally, two temperature profiles have been proposed to characterize the thermal actions in CWCB, which are composed of parabolas and polylines. The outcome of this study can provide a useful tool for analyzing the temperature field and calculating the design thermal load of CWCB.

Contribution of temperature measurements to the hydrogeological model in the Snowy 2.0 deep tunnels

This paper refers to experiments developed on the deep tunnels design of the Snowy 2.0 pumped-storage hydroelectric project in NSW, Australia. The 27 km long waterway and the power station complex are about 800 m below ground, and are in a complex tectonic and lithological setting. Long-term temperature monitoring in boreholes has been used to infer an empirical thermal model along the deep tunnels, which is presented as an example of input data for the ventilation design during construction, and for durability design during operation. An innovative aspect of this paper is related to the graphical interpretative method of borehole temperature logs that is proposed to infer downward or upward groundwater flows and predict the hydrogeological behaviour of fractured and faulted zones that cross the tunnel alignment. A large campaign of thermal acquisition in boreholes, using an acoustic televiewer probe, identified local anomalies of the geothermal gradient, which were correlated to permeability and pressure data measured in the boreholes, to identify zones of greater permeability that intersect the tunnels. Furthermore, the application of thermal measurements during tunnel excavation is proposed as an additional tool for the prediction of water inflow or identification of zones to be pre-grouted and/or drained. Thematic collection: This article is part of the Leading to Innovative Engineering Geology Practices collection available at: https://www.lyellcollection.org/topic/collections/leading-to-innovative-engineering-geology-practices

A Microwave Stripline Ring Resonator Sensor Exploiting the Thermal Coefficient of Dielectric Constant for High-Temperature Sensing

A stripline transmission-line (TL)-based temperature sensor for use in harsh environments is designed to exploit the thermal coefficient of the dielectric constant (TCD) of the microwave (MW) substrate material. Rogers 3210 substrate is selected owing to its high TCD of −459 ppm/°C and a dielectric constant of 10.8. A ring resonator is designed for 2.4 GHz, with the ring selected for the simple geometric dependence of its resonant frequency on radius. TLs are gap-coupled to the ring, with widths designed for 50- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> impedance matching and lengths optimized for a compact form factor. Upper and lower ground planes shield the dielectric, resonator, and TLs from environmental disturbances, such as debris and moisture, which could otherwise disrupt temperature measurement. Furthermore, the electric field is completely contained within the homogeneous dielectric, providing improved sensitivity compared to similar designs using microstrip technology. The fabricated sensor requires uniform compression of layers to mitigate the effects of air pockets and thermal expansion of materials. It is found that the sensor requires temperature-conditioning, approximately 70 h cycling between 30 °C and 80 °C, before its resonant frequency reaches a steady state suitable for instantaneous temperature measurement. Subsequent 10 °C- and 2 °C-step experiments are performed in the 30 °C–80 °C and 30 °C–40 °C ranges, respectively. As a result, a linear sensitivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 500$ </tex-math></inline-formula> kHz/°C is identified. The duration of these experiments and time-based data are representative of applications where long-term temperature monitoring is required.

Thermal fatigue load models for fatigue design of steel box girder bridges

Coupling with traffic loads, thermal loads can also contribute to the fatigue damage cumulation of bridges. Referring to traffic and wind fatigue load models, the concepts of two kinds of probabilistic thermal fatigue load models for the first system of steel box girder bridges were proposed. Specifically, the basic forms of the two models are 1) equivalent vertical temperature gradient models based on the damage equivalence rule and 2) the effect ratio curves based on a statistical analysis of thermal actions. Then, two design probabilistic thermal fatigue load models following the given procedures are set up using an extended dataset based on a 350-day long-term in-suit temperature monitoring, and parametric analysis on three damage equivalence factors including geometric features, design life, and degree of indeterminacy was conducted. To validate the proposed models and related approaches, two kinds of fatigue details in a case steel box girder bridge were evaluated. The results show that thermal effects on fatigue damage cumulation are significant and should be taken into consideration, and the proposed thermal fatigue load models are applicable for fatigue design and evaluation.

Swamp Wetlands in Degraded Permafrost Areas Release Large Amounts of Methane and May Promote Wildfires through Friction Electrification

Affected by global warming, permafrost degradation releases a large amount of methane gas, and this part of flammable methane may increase the frequency of wildfires. To study the influence mechanism of methane emission on wildfires in degraded permafrost regions, we selected the northwest section of Xiaoxing’an Mountains in China as the study area, and combined with remote sensing data, we conducted long-term monitoring of atmospheric electric field, temperature, methane concentration, and other observation parameters, and further carried out indoor gas–solid friction tests. The study shows that methane gas (the concentration of methane at the centralized leakage point is higher than 10,000 ppm) in the permafrost degradation area will release rapidly in spring, and friction with soil, surface plant residues, and water vapor will accelerate atmospheric convection and generate electrostatic and atmospheric electrodischarge phenomena on the surface. The electrostatic and atmospheric electrodischarge accumulated on the surface will further ignite the combustibles near the surface, such as methane gas and plant residues. Therefore, the gradual release of methane gas into the air promotes the feedback mechanism of lightning–wildfire–vegetation, and increases the risk of wildfire in degraded permafrost areas through frictional electrification (i.e., electrostatic and atmospheric electrodischarge).

Monitoring body temperature of cattle using an innovative infrared photodiode thermometer

Body temperature monitoring, which plays a pivotal role in diagnosing the physiological state of an animal, depends on the methods used and their applicability within the production systems. Recent research has examined several methods, such as rectal probes, intra-vaginal radio transmitters, data loggers, reticular boluses, digital rectal, and microchip transponder thermometry RFID tags, for detecting body temperature in several locations, including the skin surface, vagina, subcutis, and rumen. However, the potential for device loss and the potential for stress induction are the drawbacks to these approaches. In this report, we propose, for the first time, the use of an innovative infrared (IR) photodiode thermometer to detect the temperature of cattle in real-time. Totally, seventy-five adult cows (Red Sindhi Cross (n = 25), Holstein Fresian (HF) Cross (n = 25), and kangayam (n = 25)), with an average age of (43.0 ± 0.4) months and a weight of 487.0 kg ± 42.5 kg, were monitored for 30 days. The surface temperatures of the rectal across the tail base (TB) and the abdominal region (ADT) were measured using an innovative infrared photodiode thermometer. The invasive rectal temperature (RT) was determined using a clinical thermometer, which is the gold standard for measuring cow temperature. The rectal temperature (RT) of n = 75 cows was strongly correlated with the temperature of tail base (TB) and closely correlated with the abdominal region (ADT) with r = 0.85, and r = 0.74 respectively. The emissivity (ε), measured by reflection, is 98.9 ± 1%. The emissivity on the cattle surface varied from 0.975 to 0.989. Our innovative infrared photo diode thermometers get rid of the problems that come with measuring temperature, such as invasive, less invasive, stressful methods, it could be used for long-term monitoring of core body temperature, which is shown to be safe as it provides better precision for inferring the core body temperature of cows. The remote server gets the cows' temperatures through Bluetooth and sends them to a central location so that the cows' health can be checked on all the time.

THE REGIONAL TRENDS OF GLOBAL CLIMATE CHANGE IN THE TERNOPIL REGION

Climate change, its causes and consequences are an extremely relevant topic today. It is constantly discussed not only in the circles of scientific experts but also in almost all spheres of society because the modern economy is largely dependent on many climatic factors. Climate change is happening quite rapidly around the world as well as in Ukraine. Evidence of climate change is supported by numerous hydrometeorological observations, observations of the amount and intensity of precipitation, temperature indicators and their absolute and average values. These indicators allow us to determine climate change. Long-term monitoring of air temperature makes it possible to characterize the climate change trend and the extent of its spread. Ukraine has a large network of observation points that have their data expertly assessed by the Central Geophysical Observatory. We would like to point out that in recent decades the temperature in the region is continuing to rise based on the archival data from the Ternopil Regional Center for Hydrometeorology and the research of the Central Geophysical Observatory and the Ukrainian Research Hydrometeorological Institute under the Ministry for Emergencies and NAS of Ukraine.&#x0D; The average annual air temperature rises due to significant warming of summer and winter periods. Regarding the climatic norm, which was calculated from 1961 to 1990, in almost all warm months the temperature exceeds up to 4°C, and in the winter months it exceeds up to 6°C. Having analyzed the observations of the Ternopil Regional Center of Hydrometeorology from 1946 to 2021, we notice that until 1989 the average annual air temperature in the region was much lower than the climatic norm, which is +8,4°C. In the Ternopil region, 2019 was ranked first among the warmest years since 1946, its average temperature was + 10,1°C, which is 1.7°C higher than the climatic norm. According to the Kremenets meteorological station, the average annual air temperature was + 10,5°С in the northern areas. According to the Ternopil Civil Aviation Meteorological Station, the average annual temperature was +9,6°С in the central areas.&#x0D; The analysis of changes in precipitation over the study periods demonstrates that, depending on the region, the amount of precipitation is unevenly distributed. For example, in the northern districts of the Ternopil region, the number of years that exceed the annual average rainfall norm increases with each passing decade. The years with the highest precipitation for the entire period of our research were 2001, 2008 and 2012. A similar trend of increase in precipitation was not observed in the central areas of the region. The amount of precipitation throughout the period 1992-2020 was constantly changing between dry and wet years. Therefore, no steady growth trend has been recorded. According to the Ternopil Civil Aviation Meteorological Station, in the last decade, the amount of precipitation has decreased significantly. Throughout 2015-2020 the annual amount did not exceed 564 mm. Typical of the territory of our region is the predominance of summer over winter precipitation - almost 70% of the annual amount of precipitation falls in the summer season. Having analyzed the results of observations from 1992 to 2021, we note that throughout 1992-2011 a significant increase in precipitation compared to the climatic norm in July and August was observed. In the following years, the amount of precipitation in these months sharply decreased, which became one of the factors of hydrological drought.&#x0D; Key words: climate, climate change, climatic norm, Ternopil region.

Temporal dynamics of negative air ion concentration and its relationship with environmental factors: Results from long-term on-site monitoring

Negative air ions (NAIs) play an important role in evaluating forest health effects and promoting human physical and mental health. In this paper, long-term on-site monitoring of NAI concentration, air temperature, and relative humidity was conducted in real time over 24 h, from July 2019 to March 2021, to explore the temporal dynamic patterns of NAIs. We found that the daily dynamics of NAI concentration showed a bimodal curve. The peak concentrations usually occurred in the early morning (5:00–7:00) and afternoon (15:00–17:00), and the lowest concentrations usually occurred at noon (11:00–13:00). At the monthly scale, NAI concentrations were relatively high in February and August and low in May and December, and at the seasonal scale, NAI concentration was significantly higher in summer than in other seasons. Autumn had the second highest NAI concentration. There was no significant difference in NAI concentration between winter and spring. A comprehensive analysis shows that the AQI was the most key factor affecting NAI concentrations compared to temperature and relative humidity, especially the two indicators of particulate matter and ozone, and that NAI concentration had a negative correlation with these indicators and was significantly higher under favorable air quality conditions than under polluted air conditions. NAI concentrations and air temperature showed marked piecewise characteristics, with NAIs increasing linearly with rising temperature only if the Ta was separated into three ranges of −5 °C–10 °C, 10 °C–30 °C, and 30 °C–40 °C. With rising relative humidity, NAI concentration increased in accordance with a quadratic function. Our research provides new insights into the NAI temporal dynamics patterns and its driving factors, and will aid in scheduling outdoor recreation and forest health activities for urban people.

Tribute to Scientifc Contributions of Vladimir Cermak

Dr. Vladimír Čermák is currently one of the well-known research scientists in the field of global Geothermics. Initiated his early works measuring terresttial heat flow, first in Czechoslovakia but later extemnding into various other European countries. At the Geophysical Institute (Prague) he defended his PhD work (1967) and his DrSc thesis (1981). Under broad international cooperation these activities lead to the construction of the first continental scale heat flow map of Europe. Later, he become the Director of the Institute of Geophysics (1990-1998), now of the Academy of Science of the Czech Republic, where at present serves as Emeritus Professor of Sciences. He is author of more than 300 publications in Geothermics. He is well known for the pioneering works on long-term monitoring of subsurface temperatures in the famous geothermal borehole of Prague since 1990. Systematic monitoring of temperatures in this borehole continues to the present day. He was honoured at the 2015 International Union of Geodesy and Geophysics (IUGG) meeting. The prposed 2022 meeting of the Internatioanl Heat flow Commission – IHFC is named after Vladimir Cermak.

Horizontal wellbore production profile evaluation by distributed fiber-optic temperature surveillance

The main goal of the paper is demonstration of permanent downhole long-term monitoring capabilities for oil and gas production profile along horizontal wellbore in case of natural flow. The informational basis of the results obtained is the data of long-term temperature and acoustic monitoring in the borehole using a distributed fiber-optic sensor (DTS + DAS). &#x0D; &#x0D; Materials and methods. At the same time, flowing bottom-hole pressure and surface rates were monitored at the well for rate transient analysis, as well as acoustic cross-well interference testing [1], based on the results of which “well-reservoir” system properties were evaluated, the cross-well reservoir properties of the were estimated, and the possibility of cross-well testing using downhole DTS-DAS equipment was justified. &#x0D; &#x0D; The research results made it possible to assess reliability of DTS-DAS long-term monitoring analysis results in case of multiphase inflow and multiphase wellbore content. In particular, DTS-DAS results was strongly affected by the phase segregation in the near-wellbore zone of the formation. &#x0D; &#x0D; Conclusions. In the process of study, the tasks of inflow profile for each fluid phase evaluation, as well as its changes during the well production, were solved. The reservoir intervals with dominantly gas production have been reliably revealed, and the distribution of production along the wellbore has been quantified for time periods at the start of production and after production stabilization.

Long-Term Variability in Ground Thermal State in Central Yakutia’s Tuymaada Valley

This paper presents the results of long-term temperature monitoring at the Yakutsk and Zeleny Lug stations, which are experimental sites, for the thermal state of valley permafrost landscapes under the conditions of modern climate warming. An analysis of the long-term data from meteorological stations in the region clearly showed one of the highest trends of increase in the mean annual air temperature in the north of Russia. Here, we established quantitative regularities in the long-term variability of the ground temperature at the bottom of the active layer and at zero amplitude. The dynamics of the ground temperature of the layer of zero amplitude during climate warming indicate the thermal stability of permafrost. The main regulating factor of the thermal state of grounds in permafrost landscapes is short-term fluctuations in the regime of snow accumulation. Active layer thickness is characterized by low interannual variability, weak climate warming responses, and insignificant trends. The results of studies of the thermal regime of soils can be extended to the same types of valley landscapes in the Lena River, and are a reliable basis for predicting heat transfer in natural and disturbed landscapes.