As one of main directions of green mining, short-wall block backfill mining (SBBM) could provide active control of water-conducting fractures development and strata movement. Furthermore, it could ...solve the problem of gangue accumulation on surface. According to the physical similarity criterion and the characteristics of SBBM technology, the protection effect for surface water resources of SBBM was studied by physical similarity simulation tests. The results of tests had shown that SBBM decreased the water-conducting fractures development caused by strata movement after coal mining, and it has a significant effect in protecting surface water resources above the working face. Therefore, based on movement characteristics of overlying strata using SBBM, a mechanical analysis model was established under SBBM for a superimposed beams in elastic foundation with extended water-conducting fractures in overlying strata, furthermore, a method to calculate the height of water-conducting fractured zone (HWFZ) in SBBM was given, and the mechanical mechanism of water-conducting fractures development in overlying strata was revealed. The calculated HWFZ after SBM was only 2.0 m according to the mechanical model, whereas the measured HWFZ of the washing fluid loss and drilling TV imaging was 6.3 m in experimental SBBM working face. The field-measured data was closely consistent with the results of the tests (7.9 m) and the mechanical calculation (2.0 m), which verified the accuracy of physical similarity simulation tests and the mechanical model. The results of the study will enhance the recovery rate of coal resources, and they have a significant for protection of the ecological environment.
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•SBBM was proposed to recover coal seam under surface water.•Surface water resources protection effect using SBBM was analyzed by physical tests.•Mechanical mechanism of water-conducting fractures development was explained.•A method was proposed to calculate HWFZ in SBBM.
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•Clarification of the core objective of the original publication•Surface water monitoring data was included in defining/validating the PM concept.•PM substances are not more likely to ...reach drinking water than non-PM ones.•Ground- and sources of drinking water monitoring data leads to the same conclusion.
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Water contamination with heavy metal ions and organic compounds such as citrate, ethylenediaminetetraacetic acid, tartrate, pharmaceuticals, surfactants and natural organic matter, is a serious ...problem in the natural environment. Although many methods have been effectively applied to the removal of heavy metal complexes from aqueous solution, there is a lack of information available on the mechanisms, advantages and disadvantages of these various methods. This review summarizes the various treatment methods applied to the removal of heavy metal complexes, with a summary of the mechanisms of action and recent research progress. The methods reviewed in detail include electrolysis, membrane separation, adsorption, precipitation, replacement-coprecipitation, TiO2 photocatalysis and Fenton oxidation-precipitation, with the advantages and disadvantages of each method discussed. Furthermore, the heavy metal complex removal mechanisms are analyzed comprehensively. Results show that the adsorption method exhibited unique merits, showing much promise for future development. Finally, this review comprehensively analyzes future prospects and developments in methods for removal of chelated heavy metals.
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•Methods and mechanisms for treating metal complexes wastewater are summarized.•The merits and defects of each method are analyzed and compared carefully.•Adsorption methods are promising due to high efficiency and low operational costs.•TiO2 photocatalysis is an efficient and environmentally-friendly method.•Multi-method synergistic action would become a trend to treat metal complexes.
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To tackle the symptoms of eutrophication in the open Baltic Sea and Finnish coastal waters, Finland has agreed to reduce both total nitrogen (TN) and total phosphorus (TP) inputs. Due to large ...investments in treatment of municipal and industrial wastewaters, TP loads started to decrease already in the mid-1970s and the respective TN loads in the mid-1990s. During the last two decades, much effort has been spent in decreasing the load originating from diffuse sources. Trend analyses in 1995–2016 showed that, despite various mitigation measures, riverine nutrient export has not substantially decreased, and especially the export from rivers draining agricultural lands remains high. In some areas TN concentrations and export were increasing and we found evidence that it was linked to ditching of peatlands. Several factors connected to climate/weather (e.g. temperature and precipitation) have counteracted the mitigation measures, and therefore Finland will not achieve the nutrient reduction targets by 2021.
Groundwater contamination induced by anthropogenic activities has long been a global issue. Characterizing and modeling contaminant transport processes is crucial to groundwater protection and ...management. However, challenges still exist in process complexity, data constraint, and computational cost. In the era of big data, the growth of machine learning has led to new opportunities in studying contaminant transport in groundwater systems. In this work, we introduce a new attention‐based graph neural network (aGNN) for modeling contaminant transport with limited monitoring data and quantifying causal connections between contaminant sources (drivers) and their spreading (outcomes). In five synthetic case studies that involve varying monitoring networks in heterogeneous aquifers, aGNN is shown to outperform LSTM‐based (long‐short term memory) and CNN‐ based (convolutional neural network) methods in multistep predictions (i.e., transductive learning). It also demonstrates a high level of applicability in inferring observations for unmonitored sites (i.e., inductive learning). Furthermore, an explanatory analysis based on aGNN quantifies the influence of each contaminant source, which has been validated by a physics‐based model with consistent outcomes with an R2 value exceeding 92%. The major advantage of aGNN is that it not only has a high level of predictive power in multiple scenario evaluations but also substantially reduces computational cost. Overall, this study shows that aGNN is efficient and robust for highly nonlinear spatiotemporal learning in subsurface contaminant transport, and provides a promising tool for groundwater management involving contaminant source attribution.
Plain Language Summary
Groundwater contamination caused by human activities is a longstanding global challenge. Accurately characterizing and modeling the movement of contaminants is crucial for the protection and management of groundwater resources. However, the complexity of the processes, limitations in data availability, and high computational demands pose significant challenges. In the age of big data, machine learning offers new avenues for exploring contaminant transport in groundwater. In this study, we introduce a novel machine learning model called an attention‐based graph neural network (aGNN) designed to model contaminant transport with sparse monitoring data and to analyze the causal relationships between contaminant sources and observed concentrations at specific locations. We conducted five synthetic case studies across diverse aquifer systems with varying monitoring setups, where aGNN demonstrated superior performance over models based on other approaches. It also proved highly capable of making inferences about pollution levels at unmonitored sites. Moreover, an explanatory analysis using aGNN effectively quantified the impact of each contaminant source, with results validated by a physics‐based model. Overall, this study establishes aGNN as an efficient and robust method for complex spatiotemporal learning in subsurface contaminant transport, making it a valuable tool for groundwater management and contaminant source identification.
Key Points
A novel graph‐based deep learning method is proposed for modeling contaminant transport constrained by monitoring data
The proposed model quantifies the contribution of each potential contaminant source to the observed concentration at an arbitrary location
The deep learning method substantially reduces the computational cost compared with a physics‐based contaminant transport model
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•Rural source water protection often requires protecting private well sources.•There are source water protection capacity gaps in Ontario, Canada in rural areas.•Rural appropriate policy options and ...programs are needed.
This research examined the capacity for source water protection (SWP) of privately-serviced rural areas in Ontario. Privately-serviced areas refer to communities where households and public buildings either fully or partially derive their drinking water from private water systems (i.e. private wells), which are not serviced by a municipal drinking water system. Capacity for SWP in these areas was explored through a framework consisting of the following elements of capacity: technical/human, financial, social, and institutional. A case study approach was employed using the Cataraqui Source Protection Area and the North Bay-Mattawa Source Protection Area. Thirty key informant interviews were conducted and analyzed, together with a literature review and member checking. It was found that privately-serviced rural communities often do not see the protection of drinking water as one of their mandated responsibilities and that there are institutional, technical/human, social, and financial capacity gaps for undertaking SWP in privately-serviced areas. Further investigation is needed regarding options for a new, integrated, implementable and context appropriate SWP framework for privately-serviced areas in rural Ontario. The findings of this research provide transferable lessons for the creation of rural policy in general, and the need for a rural (rather than an urban focused) approach to policies and programs to protect rural drinking water.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Waiting times for aquifers contaminated by agrochemicals are useful for management.•Lumped-parameter models can be used to estimate these waiting times.•Waiting times useful for management must not ...be confused with mean transit times.
In groundwater protection zones, water managers and water providers will typically need to know the time scale of recovery for aquifers contaminated by agrochemicals. Despite the large body of work on the topic in the scientific literature, there still seems to be some need to clarify which waiting times are most relevant for water management planning, and how they relate to metrics such as the mean transit time of tracer. Firstly, we propose a simple nomenclature for the different waiting times and how they relate to the evolution of agrochemical concentration (increasing and decreasing concentration trends, trend reversal, etc.). Secondly, we describe how to select and fit a lumped-parameter model to contaminant time series and environmental isotopes in order to characterize aquifer response to agrochemical contamination. Thirdly, we explain how waiting times can be calculated from the fitted lumped-parameter model. Finally, we present a case study focussing on the contamination of several springs of the city of Luxembourg by a fungicide transformation product combining environmental tracers and pesticide measurements. We find that on the study site, the parameters estimated separately from the environmental tracers and from the pesticide data are nearly the same although they correspond to different areas within the groundwatershed. Although this could be incidental, it might also indicate that robust waiting time estimates can be obtained from agrochemical time series and environmental tracer measurements, at least in simple cases.
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The rapid expansion of unconventional oil and gas development (UD), made possible by horizontal drilling and hydraulic fracturing, has triggered concerns over groundwater contamination and public ...health risks. To improve our understanding of the risks posed by UD, we develop a physically based, spatially explicit framework for evaluating groundwater well vulnerability to aqueous phase contaminants released from surface spills and leaks at UD well pad locations. The proposed framework utilizes the concept of capture probability and incorporates decision‐relevant planning horizons and acceptable risks to support goal‐oriented modeling for groundwater protection. We illustrate the approach in northeastern Pennsylvania, where a high intensity of UD activity overlaps with local dependence on domestic groundwater wells. Using two alternative models of the bedrock aquifer and a precautionary paradigm to integrate their results, we found that most domestic wells in the domain had low vulnerability as the extent of their modeled probabilistic capture zones were smaller than distances to the nearest existing UD well pad. We also found that simulated capture probability and vulnerability were most sensitive to the model parameters of matrix hydraulic conductivity, porosity, pumping rate, and the ratio of fracture to matrix conductivity. Our analysis demonstrated the potential inadequacy of current state‐mandated setback distances that allow UD within the boundaries of delineated capture zones. The proposed framework, while limited to aqueous phase contamination, emphasizes the need to incorporate information on flow paths and transport timescales into policies aiming to protect groundwater from contamination by UD.
Key Points
Capture probability is used to assess domestic well vulnerability to aqueous phase contamination from unconventional oil and gas development
Multiple groundwater model conceptualizations provide more conservative risk estimates than a single calibrated model
The approach provides a scientifically defensible basis for groundwater and human health protection regulations such as setback distances
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The Chemical Strategy for Sustainability (CSS) includes actions to ensure the protection of drinking water resources from chemical pollution. To proactively identify potential pollutants, the German ...Environment Agency (UBA) proposed the Persistent and Mobile (PM) concept according to which Persistence (criteria of REACH Annex XIII) and Mobility (log Koc < 4) would be proxies for a substance's degradation potential and transport velocity, two processes believed to drive the potential for contamination of surface and groundwater as drinking water sources. Two studies identified hundreds of PM substances while three subsequent studies have selected some of these substances for monitoring in surface, ground- and/or drinking water to support the concept. In the present work, the Persistence of the aforementioned substances was reassessed based on all experimental data publicly available. Depending on the exact study examined, it was found that 15 % to 40 % of the substances were erroneously concluded as P. The reinterpretation of the data indicates that a PM substance does not have a higher likelihood to be detected in surface or groundwater than a non-PM substance. In addition, the PM properties do not have any influence on the level of contamination. Twenty-six to 75 % of the substances selected because they were identified as PM were not found in surface or ground water despite being selected for their high emission pattern. Regulations based primarily on the PM concept, like the CLP and possibly REACH and UN-GHS, are unlikely to appropriately identify substances of concern for drinking water sources. It is more likely that chemical presence in surface and groundwater is driven by emission patterns or local factors. The development of specific exposure models would better contribute to the protection of drinking water resources and consumers.
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•PM substances do not have a higher likelihood of detection than non-PM substances.•Likelihood of detecting a P substance in water is independent of the M criterion.•There is no indication that PM substance accumulate in water bodies.•No evidence of log Dow or log Koc as driver of contamination of water.
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Fluid flow in fracture porosity in the Earth's crust is in general accompanied by crystallization or dissolution depending on the state of saturation. The evolution of the microstructure in turn ...affects the transport and mechanical properties of the rock, but the understanding of this coupled system is incomplete. Here, we aim to simulate spatio‐temporal observations of laboratory experiments at the grain scale (using potash alumn), where crystals grow in a fracture during reactive flow, and show a varying growth rate along the fracture due to saturation differences. We use a multiphase‐field modeling approach, where reactive fluid flow and crystal growth is computed and couple the chemical driving force for grain growth to the local saturation state of the fluid. The supersaturation of the fluid is characterized by a concentration field which is advected by fluid flow and in turn affects the crystal growth with anisotropic growth kinetics. The simulations exhibit good agreement with the experimental results, providing the basis for upscaling our results to larger scale computations of combined multi‐physical processes in fractured porous media for applications as groundwater protection, geothermal, and hydrocarbon reservoir prediction, water recovery, or storing H2 or CO2 in the subsurface.
Plain Language Summary
In the Earth's crust fluid flow can occur in fractured rock and depending on the composition of the fluid and physical conditions minerals can precipitate or dissolve. This affects the properties of the rock system and is for example, of interest to subsurface engineering applications. In this work we simulate observations of laboratory experiments at the grain scale, where crystals grow in an open fracture during fluid flow. In these experiments, the growth rate of the crystals varies along the fracture since the supersaturation of the fluid decreases due to the crystallization. We use a multiphase‐field model for the numerical simulation of crystal growth in the open fracture and combine it with reactive fluid flow. With the presented model the driving force for grain growth is coupled to the local supersaturation, which enables the incorporation of reactive mass transport in open fractures. Our phase‐field simulations agree with the laboratory experiments. The presented simulative approach can be used for upscaling the results on microscale to larger length and time scales and can help to better predict the subsurface behavior for example, of groundwater, fractured geothermal, and hydrocarbon reservoirs.
Key Points
Reactive fluid flow with advective mass transfer causes locally variable precipitation rate in open fracture
A higher flow velocity or a higher supersaturation results in faster precipitation along the flow channel
Phase‐field modeling allows reproduction of laboratory crystal growth experiments from an advecting fluid using transmitted light microscopy
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