Field Studies on the Influence of High‐Voltage Direct Transmission Underground Cables on Soils Along the SuedLink Route in Southwest Germany

Thermal and aging performance characteristics of pouch-type lithium-ion battery using heat pipes under nonuniform heating conditions

We are observing a visible change in climate patterns. Its significant effects are overall temperature increase, sea-level rise because of massive ice melting, and other extreme weather events.Wildfires are a significant issue in the whole world and cause devastation year after year. Due to this change in climate pattern, we are observing increase rates of Wildfire. Now, the question arises of the increase in the overall world's temperature, the reason for this cause, or any other reason for it.Initially, we are handling the Earth surface temperature data, which has data ranging back to 1750. From this data, we are trying to visualize the increase in average temperature around the world. Accordingly, these visualizations lead us to create machine learning models for estimation and prediction purposes for our future work.Secondly, we have a government-funded dataset of wildfire data that we are using to generate visualizations of the wildfire scope, frequency, and causation. And eventually, these visualizations guided us to create some machine learning models for estimation and prediction.Next, we combined the above datasets based on the unique features present in them for our final observation correlating the increase in temperature and wildfire causes.In brief, we want to know which cause of the Wildfire affects the increase in temperature and how much in terms of some values.There is a dynamic web page deployed with cloud service. The application supports user input to generate visualizations for parameters of interest dynamically; also, users can provide details like latitude, longitude, year, month, state, fires, and perform causation predictions with the trained models.

Time‐domain reflectometry (TDR) is increasingly used for field soil water estimation because the measurement is non‐destructive and less affected by soil texture, bulk density and temperature. However, with the increase in instrument resolution, the influences of soil bulk density and temperature on TDR soil moisture measurements have been reported. The influence is primarily caused by changes in soil and water dielectric permittivity when soil compaction and temperature varies. The objective of this study is to quantify the influence of soil bulk density and temperature, and to provide the corresponding correction methods. Data collected from sand, sandy loam, loam and clay loam show a linear relationship between the square root of dielectric constant of dry soil and bulk density, and a bulk density correction formula has been developed. The dielectric permittivity of soil solids estimated using this formula is close to that of oxides of aluminium, silicon, magnesium and calcium. Data collection from sandy loam show a noticeable decrease in measured soil moisture with increase in temperature when the volumetric soil water content is above 0·30 m3 m−3. A temperature‐correction equation has been developed, which could provide the corrected soil moisture based on soil temperature and TDR‐measured moisture. The effect of clay content has been detected, but it is not statistically significant. High clay contents cause the underestimation of soil water content in the low moisture range and overestimation of soil water content in the high moisture range. Copyright © 2003 John Wiley & Sons, Ltd.

This paper emphasizes on the development of a new predictive modeling based on interval valued Markov integrated Rhotrix optimization using genetic algorithm. Interval valued partitioning was used for development of predictive modeling of future behavior based on the most important genetic algorithm optimizer. The proposed predictive model is trained and tested on the datasets taken from multiple websites for short and decadal long weather prediction. The initialization of the weather parameters is done in first phase using interval valued Markov integrated Rhotrix predictive model. Optimized estimation of weather parameters is performed in second phase using Genetic Algorithm improving the error in predictive modeling. The experimental result shows that the increase in the greenhouse gases per year is 1.915 ppm, the average increase in temperature for each year is 0.055 ℃ and the average increase in temperature for each year due to the impact of greenhouse gases is −0.137 ℃. Further it was also analyzed and estimated that for 2020–2029, the average concentration of greenhouse gases will be 414.1015 ppm, the average increase in temperature will be 13.696 ℃, the average increase in temperature due to greenhouse gases will be 13.6413 ℃. The actual global temperature was computed by adding 12.7 ℃ (20th century global average temperature) to the temperature anomaly.

Since single-integration climate models only provide one possible realization of climate variability, ensembles are a promising way to estimate the uncertainty in climate modeling. A statistical model is presented that extracts information from an ensemble of regional climate simulations to estimate probability distributions of future temperature change in Southwest Germany in the following two decades. The method used here is related to kernel dressing which has been extended to a multivariate approach in order to estimate the temporal autocovariance in the ensemble system. It has been applied to annual and seasonal mean temperatures given by ensembles of the coupled general circulation model ECHAM5/MPI-OM as well as the regional climate simulations using the COSMO-CLM model. The results are interpreted in terms of the bivariate probability density of mean and trend within the period 2011–2030 with respect to 1961–1990. Throughout the study region one can observe an average increase in annual mean temperature of approximately +0.6K and a corresponding trend of +0.15K/20a. While the increase in 20-year mean temperature is virtually certain, the 20-year trend still shows a 20% chance for negative values. This indicates that the natural variability of the climate system, as far as it is reflected by the ensemble system, can produce negative trends even in the presence of longer-term warming. Winter temperatures are clearly more affected and for both quantities we observe a north-to-south pattern where the increase in the very southern part is less intense.

The difference between soil and air temperatures (ΔT) in a specified time is dependent on meteorological conditions, properties of soil and land covers. Understanding ΔT is critical in assessing land–atmosphere thermal interactions in changing environment. However, systematic knowledge of interannual variations and responses of ΔT to the environmental changes (e.g., snow cover and soil moisture) at decadal scales remain limited. Here, variations of the mean annual air and soil temperatures, and ΔT were investigated at 217 sites in northern Eurasia during 1981–2015. It is found that changes in the mean annual air and soil temperatures were inconsistent as the average increase in soil temperature was generally less than that of air. The relationships between trends in soil and air temperatures were significant in the upper ground (0.2 and 0.8 m) over the seasonal frost region but insignificant in the permafrost regions and deeper ground. During the period, widespread changes in ΔT occurred and closely responded to the environmental changes, but the relationships varied with soil depth. Among the tested factors, trends in snow cover thickness dominantly control trends in ΔT, followed by trends in snow cover duration and solar radiation. Both linear and nonlinear analyses indicate enhanced relationships between trends in snow depth and ΔT as depth increases. This study provides the first view of decadal trends in ΔT and conjunctions with the environmental changes during 1981–2015 over northern Eurasia. The findings are relevant to quantify land–atmosphere thermal interactions given impacts of future environmental changes.

Most cities have climate goals such as lowering the Urban Heat Island (UHI) effect and reducing flood damages. Green infrastructure (GI) can help mitigate the UHI effects and has the potential to locally delay flood peaks. However, in dense urban infrastructure there is often little space for ground based vegetation. Green roofs are therefore a feasible implementation option even within city centres. While there is a myriad of types of green roofs available for flat roofs, and their performance is tested in various environments, sloped roofs as of yet have few design options available. To this end, Green Panels were developed as a novel type of GI for sloped roofs. As it is a novel design, its performance can be estimated only by literature results of GI applying different designs and materials. To overcome this research gap, in this case study a Green Panel prototype was constructed and its performance was monitored over a period of 3-4 months at the University of Twente, the Netherlands.The experimental setup consisted of 1 m2 of Green Panels, and 1 m2 of regular roof tiles as control area, both at a slope of 45 degrees. The materials of the Green Panels, mounted on the same railing as solar panels, is High Density Polyethylene, while different substrates were tested: soil, rock wool, recycled fabric, and combinations thereof. Applied sensors were a soil moisture and temperature sensor (Truebner SMT50) and an environmental sensor (BME680), including a thermal sensor, both connected to Sensebox Mini dataloggers. The soil moisture sensors were placed in each of 6 Green Panel trays. The environmental sensors were placed above and below the roof tiles in both control and Green Panel locations, as well as above and below the Green Panels themselves. The measured parameters were air temperature, humidity, atmospheric pressure, VOC, soil temperature, and soil moisture. These values were compared to meteorological data from a local weather station at 5km distance. Other benefits such as increased biodiversity were not monitored, though species such as ladybug (family Coccinellidae) and fly (family Muscidae) were observed.Analysis of the results shows that there is a small effect of Green Panels on reducing extreme temperatures, and errors in measurement setup and gaps in data continuity did not affect validity. The implications of this analysis were extrapolated to the urban scale for the city of Enschede to help answer what an appropriate performance monitoring scheme is for cities intending to implement GI and still uncertain about when their climate goals are being met.

With the development of many earth‐observing remote sensing (RS) platforms, spatially distributed remote sensing products are becoming critical inputs to many hydrologic and meteorological models. Remotely sensed soil moisture (SM) and evapotranspiration (ET) including ground‐based data have the potential to be used for estimating pixel‐scale soil hydraulic parameters. However, only a few studies have been conducted to better understand the impact of assimilating both SM and ET in estimating soil hydraulic properties of the root zone. In this study, we used inverse modeling based on the Noisy Monte Carlo Genetic Algorithm by linking RS SM and ET derived from the Surface Energy Balance Algorithm for Land for estimating pixel‐scale effective soil hydraulic properties. Walnut Creek (Iowa), Brown (Illinois), and Lubbock (Texas) test sites were selected to assess the performance of this approach from point to satellite scales using synthetic and validation experiments. For comparison purposes, inverse modeling results were analyzed under three scenarios (ET only, SM only, and SM + ET in the optimization criteria). These results showed that considering both SM and ET components improved the estimations of effective soil hydraulic properties and reduced their uncertainties better than SM or ET only. Overall, although uncertainty exists, our proposed SM + ET based scheme performed well in estimating effective soil hydraulic properties at multiple spatial scales (point, airborne, and satellite footprints) under various hydroclimatic conditions.

Frost heave in seasonally frozen soil surrounding natural gas pipelines (NGPs) can cause severe damage to adjacent infrastructure, including road surfaces and buildings. Based on the stratigraphic characteristics of seasonal frozen soil in Beijing, a soil–natural gas pipeline–heat pipe heat transfer model was developed to investigate the mitigation effect of the soil-freezing phenomenon by transferring shallow geothermal energy utilizing heat pipes. Results reveal that heat pipe configurations (distance, inclination angle, etc.) significantly affect soil temperature distribution and the soil frost heave mitigation effect. When the distance between the heat pipe wall and the NGP wall reaches 200 mm, or when the inclined angle between the heat pipe axis and the model centerline is 15°, the soil temperature above the NGP increases by 9.7 K and 17.7 K, respectively, demonstrating effective mitigation of the soil frost heave problem. In the range of 2500–40,000 W/(m·K), the thermal conductivity of heat pipes substantially impacts heat transfer efficiency, but the efficiency improvement plateaus beyond 20,000 W/(m·K). Furthermore, adding fins to the heat pipe condensation sections elevates local soil temperature peaks above the NGP to 274.2 K, which is 5.5 K higher than that without fins, indicating enhanced heat transfer performance. These findings show that utilizing heat pipes to transfer shallow geothermal energy can significantly raise soil temperatures above the NGP and effectively mitigate the soil frost heave problem, providing theoretical support for the practical applications of heat pipes in soil frost heave management.

Conservation tillage and biochar improve soil water content and moderate soil temperature in a tropical Acrisol

Photo source: Flickr/WorldFish Arsenic is a widely distributed toxic element that naturally occurs in minerals. One of the most common pathways for exposure is when arsenic leaches into drinking water supplies, often through natural weathering/breakdown. People are also exposed to arsenic through food and dust. Long-term exposure is linked to health problems, including skin lesions, cardiovascular disease, and cancer. Research from Bangladesh shows long-term exposure through drinking water increases the rate of morbidity by up to 60% (http://bit.ly/2fFhq6w). Understanding exposure routes and minimizing human consumption and contact with arsenic has widespread human health benefits. One crop known to take up arsenic when the element is available in soils or irrigation water is rice (Oryza sativa L.). Arsenic accumulates throughout the plant tissues including the grain that is consumed. This is concerning for people whose diets include a high proportion of rice or rice products such as regions and cultures where rice is served with most meals, those on a gluten-free diet using rice substitutes (e.g., rice milk or rice flour), and young children who are given rice crackers and rice cereals. Rice plants may be exposed to arsenic through soil or irrigation water. Because of this, the hydrological and biogeochemical processes that take place in the rhizosphere, or the soil zone around the roots of plants, influence arsenic uptake, according to Rebecca Neumann, Assistant Professor in the Department of Civil and Environmental Engineering at the University of Washington. Specifically, rice plants release oxygen from their roots when flooded, which reacts with iron, forming “plaques” along root surfaces. The iron oxide plaques scavenge arsenic, and the plants take up arsenic released from the plaques or dissolved in the soil solution. One factor that can influence rhizosphere processes, which ultimately affect arsenic accumulation in the rice grain, is soil temperature. Because global climate models predict an increase in average temperatures, Neumann and colleagues set up an experiment to determine if elevated soil temperatures would have an effect on plaque formation and arsenic uptake. Their results were recently published in Agricultural & Environmental Letters (see http://bit.ly/2vCtRUO). “We purposefully grew our plants in a system where we could image and visualize the rhizosphere,” Neumann says. The researchers grew plants in rhizoboxes (30 cm 3 20 cm 3 2.5 cm), which enabled them to sample soil porewater and perform synchrotron imaging on the plant root zone at the end of the experiment. Rice plants were kept in growing chambers, where the atmospheric and ambient soil conditions were designed to mimic conditions in Bangladesh. The elevated soil temperature treatment was 5°C higher, which is the upper limit of expected warming in the region. Globally, Bangladesh does not have the highest concentration of arsenic in rice grains; however, “arsenic contamination of rice poses a large human health problem [in Bangladesh] because rice is the staple food. So that was part of the motivation for focusing on and trying to mimic conditions that are relevant in that context,” Neumann explains. X-ray fluorescence data collected for (a,c) one plant from the 26.1°C soil-temperature treatment and (b,d) one plant from the 30.5°C soil temperature treatment. (a–b) Solid-phase iron concentrations in counts per second (cps); concentrations in both panels scale with color bar in panel (a). (c–d) Solid-phase arsenic concentrations in counts per second; concentrations in both panels scale with color bar in panel (c). The synchrotron imaging provided additional information about arsenic in the rhizosphere. The authors report that with warmer soil temperature, there was more iron plaque on roots and more arsenic sequestration. Neumann explains, “There is more arsenic mobilized and made available in the warmer temperatures. This excess arsenic, some of it [is] sequestered in the plaques, and then some of it [is taken up through] the roots.” At the end of the experiment, plants were destructively sampled to test for arsenic levels in plant tissues. The authors report the plants grown in warmer soil temperatures had greater concentrations of arsenic in the porewater, root tissue, straw tissue, and husk. However, arsenic concentrations in the grain did not change with soil temperature. Read the full study in Agricultural & Environmental Letters http://bit.ly/2vCtRUO. While this is a small data set, using a single rice cultivar and focusing only on elevated soil temperature in laboratory conditions, Neumann says the results show, “it's not just plant physiology that can change [with increased temperatures], but actually these processes in the rhizosphere.” Having established this response, Neumann hopes to investigate rhizosphere interactions in other rice cultivars and to scale up to field experiments. Understanding what changes may occur under future climate conditions enables researchers to help mitigate arsenic exposure through rice before it happens.

In this paper, the finite volume method is used to investigate the laminar forced convection of water–copper nanofluid between two porous horizontal concentric cylinders. The effects of Reynolds number, the volume fraction of nanoparticles, the geometry, and the porous medium porosity on heat transfer have been studied. The problem is investigated in two different geometries and Re = 10, 25, 50, 75,100, and volume fraction of nanoparticles 0, 0.2, 0.5, 2, and 5% that were related to Copper nanoparticles and the porous medium porosity of 0.5, 0.9. The results indicated that in each geometry, the corresponding Nusselt number increase in the porosity of 0.9 is greater than that of the case with the porosity of 0.5. The results show that the increase in the heat transfer coefficient in the second geometry is greater than the first geometry and in porosity 0.9 is greater than porosity 0.5. These increase values are 6 and 3%, respectively. The increase in the average temperature of the inner cylinder surface in the five mentioned Reynolds values and both geometries is investigated. This increase in temperature in Re = 10 is greater than other Reynolds numbers. The corresponding temperature increases of Re = 10, for the first and second geometries, are 1.8 and 2.2%, respectively. Investigation of the effects of volume fraction of nanoparticles on Nusselt number and heat transfer coefficient shows that both parameters increase by increasing in the volume fraction of nanoparticles. The results show that the increase in the volume fraction of nanoparticles causes the increase in average temperature of the surface. The results show that these increases of temperature that take place in the volume fractions of 0.5, 2, and 5% of nanoparticles are equal to 0.6, 1.14, and 2.3% and relative to the water, respectively.

SW—Soil and Water: Climate Change and Water Resources Management in Arid and Semi-arid Regions: Prospective and Challenges for the 21st Century

Increases in intrapulpal temperature during polymerization of composite resin

Urbanization coincides with remarkable environmental changes including vegetation, soil and climate. An urban heat island is a well-known urban mesoclimatic anomaly, however its effect on functions of urban soils considering their spatial-temporal variability remain overlooked. The research aimed to explain the urban heat island effect on soil microbial respiration by linking meso-climatic CCLM and TERRA_URB models and the digital soil mapping approach for the case of Moscow megalopolis. Moscow megalopolis is the biggest city in Russia, which experiences on-going urbanization within the New Moscow project. As a result, a clear urbanization gradient was observed in land-use structure, climate and soil properties. For the study period (May–October 2019), the increase in average monthly temperature of urban soils compared to rural reached 1.7 and 2.0 °C in June and September with the maximal values obtained in the city center. The significant effect of soil temperature, carbon content and pH on soil microbial (basal) respiration was estimated based on the lab experiment with a representative sample of urban topsoils (n = 140). The resulting equation was integrated with the mesoclimatic data in two ways: i) considering urban heat islands (based on the modelled 500 m grid soil temperature maps) and ii) non-considering urban heat islands (based on the average monthly rural soil temperature). Comparison of the results showed that urban heat island increased microbial respiration by 5–10% on average and up to 25% in the most affected locations. The additional amount of CO2 which could be potentially emitted by soil microbes as a result of the urban heat island in Moscow megapolis was on average 0.2 Mg C ha−1 (0–10 cm layer; 1.1 g cm−3 bulk density). Small scale green zones in the city center (public gardens, residential court yards and green lawns along the roads) where high soil C contents coincided with the maximal urban heat island effect were the hotspots of microbial respiration. Although the estimated absolute values could be rather uncertain, the research highlights the vulnerability of urban topsoil C stocks to mineralization intensified by mesoclimatic anomalies.