Aquifers contain the largest store of unfrozen freshwater, making groundwater critical for life on Earth. Groundwater temperatures infl uence stream thermal regimes, groundwater-dependent ecosystems, aquatic biogeochemical processes, water quality, and the geothermal potential. Yet little is known about how groundwater responds to surface warming across spatial and temporal scales. We simulate current and projected groundwater temperatures at the global scale and show that groundwater at the depth of the water table is projected to warm on average by 3.3 ° C between 2000 and 2099 (RCP 8.5). However, regional groundwater warming patterns vary substantially due to spatial variability in climate and water table depth. The highest warming rates are projected in Central Russia, Northern China, and parts of North America and the Amazon rainforest. Results also show that by 2099, 234 million people are projected to live in areas where groundwater exceeds the highest threshold for drinking water temperatures set by any country.
The aim of this study was to perform a phytoscreening of per- and polyfluoroalkyl substances (PFAS) at a contaminated site in Germany, to investigate the applicability of this technique for PFAS contaminations. Foliage of three species, namely, white willow (Salix alba L.), black poplar (Populus nigra L.), and black alder (Alnus glutinosa L.), were sampled to evaluate seasonal and annual variations in PFAS concentrations. The results of the phytoscreening clearly indicated species and specific differences, with the highest PFAS sum concentrations ∑23 observed in October for white willow (0-1800 μg kg-1), followed by black poplar (6.7-32 μg kg-1) and black alder (0-13 μg kg-1). The bulk substances in leaves were highly mobile short-chain perfluoroalkyl carboxylic acids (PFCAs). In contrast, the PFAS composition in soil was dominated by long-chain PFCAs, perfluorooctanoic acid (PFOA) and perfluorodecanoic acid (PFDA), as a result of the lower mobility with ∑23PFAS ranging between 0.18 and 26 μg L-1 (eluate) and between 66 and 420 μg kg-1 (solid). However, the PFAS composition in groundwater was comparable to the spectrum observed in leaves. Spatial interpolations of PFAS in groundwater and foliage correspond well and demonstrate the successful application of phytoscreening to detect and delineate the impact of the studied PFAS on groundwater.
Modelling cooling demands still imposes a challenge in urban energy modelling. This study focusses on estimating cooling capacities of air-cooled and hybrid condensers, rooftop units and cooling towers and cooling demands of respective buildings. The units are detected in aerial images using specifically trained object detection models. Nominal capacities are estimated using regression analysis using the number of condenser fans and unit footprint as proxy. By estimating operating times of the units based on building type, the annual cooling demand is estimated. The approach is applied to the Manhattan Financial District in New York City, where a capacity of 1.46 GW and a cooling demand of 1.71 TWh are estimated.
Despite the global interest in green energy alternatives, little attention has focused on the large-scale viability of recycling the ground heat accumulated due to urbanization, industrialization and climate change. Here we show this theoretical heat potential at a multi-continental scale by first leveraging datasets of groundwater temperature and lithology to assess the distribution of subsurface thermal pollution. We then evaluate subsurface heat recycling for three scenarios: a status quo scenario representing present-day accumulated heat, a recycled scenario with ground temperatures returned to background values, and a climate change scenario representing projected warming impacts. Our analyses reveal that over 50% of sites show recyclable underground heat pollution in the status quo, 25% of locations would be feasible for long-term heat recycling for the recycled scenario, and at least 83% for the climate change scenario. Results highlight that subsurface heat recycling warrants consideration in the move to a low-carbon economy in a warmer world.
Abstract. Groundwater is an important source of freshwater; drinking water; and service water for irrigation, industrial and geothermal uses. It is also the largest terrestrial freshwater biome in the world. In many areas, this habitat is naturally or anthropogenically threatened. This study uses long-term groundwater data from southwestern Germany to identify shifts in groundwater fauna due to natural or anthropogenic impacts. Comprehensive analysis of metazoan groundwater fauna and abiotic parameters from 16 monitoring wells over 2 decades revealed no overall temporal trends in faunal abundance or biodiversity (in terms of number of species) and no significant large-scale trends in abiotic parameters. While 9 wells out of 16 show stable ecological and hydrochemical conditions at a local level, the remaining wells exhibit shifting or fluctuating faunal parameters. At some locations, these temporal changes are linked to natural causes, such as decreasing dissolved oxygen contents or fluctuating temperatures. A multivariate PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding) analysis suggests that, beside the hydrogeological setting, varying contents of sediment and detritus impact faunal abundance. By examining aerial images of the surroundings of individual wells, we found that anthropogenic impacts, such as construction sites and surface sealing, can cause significant shifts in groundwater fauna and changes in the ecological status in positive as well as negative direction. However, variable faunal compositions and abundances were also observed for sites with very stable abiotic conditions in anthropogenically less affected areas such as the Black Forest. These findings indicate that hydro(geo)logical changes and surface conditions, such as land use, should be assessed in line with hydrochemical parameters to better understand changes in groundwater fauna. Accordingly, reference sites for natural conditions in ecological assessment and biomonitoring schemes for groundwater protection should be selected carefully.
Heating and cooling of buildings is one of the largest final energy uses and largest sources of greenhouse gas emissions. To reduce the impact of heating and cooling on our climate, more efficient strategies are needed. Coupling and centralizing the production of heat and cold in combination with underground seasonal thermal storage (UTES) can significantly reduce CO2 emissions and costs. To plan and implement such strategies for heating and cooling, information on sources and sinks of heat and cold is essential for local authorities. However, spatial information on the cooling sector is rare and difficult to obtain. Often, the theoretical cooling demand of specific buildings and building types is modeled, but not met by air-conditioning equipment in reality. On the other hand, large-scale cooling demand models, which focus on entire countries, may use data from different countries as proxy or are not applicable below kilometer-scale.In this study, we present a method to identify air-conditioning equipment on the rooftops of buildings and quantify their cooling capacity. Thus, air-cooled and hybrid evaporative condensers, cooling towers and packaged rooftop units are detected on aerial images. Using manufacturer data, regression analyses are created to estimate the cooling capacity based on the size of the units and the number of condenser fans. The unit locations and all required parameters are obtained by convolutional neural network-based pixel classification models, which are easily executable within a geographical information system (GIS) framework. The approach is successfully evaluated by testing the capability of the detection models and comparing our estimated cooling capacities to the actual installed cooling capacities of air-conditioners for different locations. The detection performance strongly depends on the resolution of the used aerial images. At a resolution of 8 cm/pixel, the model detects 93% of the units and the pixel classification overestimates the relevant parameters for the regression by 0.7%. Using the regression analyses, the overall capacity in the evaluated areas is overestimated by 7-21%. To demonstrate the capability of our approach, we map the cooling capacity of air-conditioners in parts of Manhattan. In the Manhattan financial district alone, a cooling capacity of over 2 GW is estimated, which is equivalent to 1.3% of the summer peak load demand of the energy grid of the entire state of New York.The presented approach is a fast and easy to conduct method that requires little input data. It can detect individual air conditioners over large areas. The obtained information can support the creation of cooling cadastres and can serve as supplement or validation for other cooling demand models, such as building stock models, or example to include additional building types, such as industrial buildings.
Abstract This paper provides practical guidelines to the Bayesian calibration of building energy models using the probabilistic programming language Stan. While previous studies showed the applicability of the calibration method to building simulation, its practicality is still impeded by its complexity and the need to specify a whole range of information due to its Bayesian nature. We ease the reader into the practical application of Bayesian calibration to building energy models by providing the corresponding code and user guidelines with this paper. Using a case study, we demonstrate the application of Kennedy and O’Hagan’s (KOH) [1] Bayesian calibration framework to an EnergyPlus whole building energy model. The case study is used to analyze the sensitivity of the posterior distributions to the number of calibration parameters. The study also looks into the influence of prior specification on the resulting (1) posterior distributions; (2) calibrated predictions; and (3) model inadequacy that is revealed by a discrepancy between the observed data and the model predictions. Results from the case study suggest that over-parameterization can result in a significant loss of posterior precision. Additionally, using strong prior information for the calibration parameters may dominate any influence from the data leading to poor posterior inference of the calibration parameters. Lastly, this study shows that it may be misleading to assume that the posteriors of the calibration parameters are representative of their true values and their associated uncertainty simply because the calibrated predictions matches the measured output well.
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