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In the context of “zero discharge” of mine water and the reverse distribution of coal and water resources in the western mining areas, the direct discharge of high-salinity mine water without proper treatment not only causes immeasurable damage to the local ecological environment, but also leads to serious water resource waste, ultimately constraining the green development of coal industry and the development of ecological civilization. The traditional zero discharge technology for high-salinity wastewater mainly relies on evaporation and crystallization to transform inorganic salts from a solution state to a crystalline state for recovery. From an economic perspective, this technology faces issues such as high investment, high operating costs, and low added value of inorganic salts. Based on this, this paper takes the mine water from a western mine as the raw water, aiming to achieve the resource utilization and desalination of mine water through the membrane integration technology, and focuses on exploring the influence of different operating conditions (operating voltage, feed solution concentration, temperature, acidity and alkalinity, current density, etc.) on the desalination performance of electrodialysis and the acid-base performance of bipolar membrane electrodialysis. Then, based on the experimental data and an economic evaluation model, the economic feasibility is explored, aiming to explore the membrane integration technology suitable for the treatment of high-salinity mine water in the western mining areas, and provide some new research ideas for the development of green resource utilization processes for high-salinity mine water. Through the exploration of the electrodialysis-bipolar membrane electrodialysis experimental process, it is found that the feed solution concentration of the bipolar membrane system should not exceed 80 g/L, the appropriate operating voltage is 18 V, the suitable feed solution temperature is 25 ℃, the feed solution pH should be kept neutral, and constant voltage operation is more suitable. In addition, it is confirmed that the membrane integration system centered on bipolar membrane electrodialysis can achieve the green resource utilization of high-salinity mine water. Taking the total salt content of the electrodialysis system feed solution as an example (20 g/L), the overall net profit is ￥18604299 per year, and it has a significant carbon dioxide emission reduction effect when coupled with a photovoltaic power supply system, with a carbon dioxide emission reduction of over 3162 tons per year, demonstrating good economic and social benefits.

Coal is the main energy source in China, and the western region in China is the main coal production area. The protection and utilization of coal mine water is the major technical challenge in the coal mining. The coal green mining research team of China Energy Group has pioneered the technology of underground coal mine reservoirs, which has been widely implemented in the Shendong mining area in Western China, providing over 95% of water needed for mining and ensuring the sustainable development of the mining area. To further enrich and improve the theoretical and technical system of underground coal mine reservoirs, this technology has been promoted and applied under different geological and working conditions in the western coal mining areas to ensure national energy security and water resources protection. The research team has built the original technology experimental platform system for coal mine underground reservoirs, including the comprehensive intelligent experimental platform for underground water transport and protection, the physical modelling platform for coal mine underground reservoirs under multiple coal seams, the dam structure experimental platform for coal mine underground reservoirs, the simulation experimental platform for deep well construction in western areas, the water-rock coupling mechanism experimental platform for underground water reservoirs, the integrated water treatment process experimental platform, and the impact experimental platforms for coal mine underground reservoirs, etc. On these platforms, the researches can be carried out consisting of the transport law of underground water, the optimization of dam structure parameters, the safety and stability of reservoirs, water-rock coupling mechanism, the optimization of mine water treatment, and the impact of caving rock mass on the dam structure under different coal seam occurrence conditions in the western mining areas of China. These platform systems provide a theoretical support and technical verification for the construction, operation, and safety of coal mine underground reservoirs. By utilizing these experimental platforms, various experimental research studies have been conducted, including the stability and seepage of coal mine underground reservoirs under multiple coal seams, the structural safety of coal mine underground reservoirs dam, and the water-rock coupling mechanism of coal mine underground reservoirs. These research results have been applied in actual engineering practice to ensure the safe and stable operation of coal mine underground reservoirs.

Coal mine underground reservoir is one of the key technologies to solve the protection and utilization of water resources in western mining areas. The basis for maintaining the safe operation of the reservoir is engineering safety, and the dam body is the core to ensure engineering safety. This paper studies the safety and stability of coal pillar dam of underground reservoir in coal mine. The stress variation (vertical stress and horizontal stress), fracture development and expansion, displacement variation and other multi-parameter variation laws during the failure of coal pillar dam are comprehensively analyzed based on mechanical model, similarity simulation and numerical calculation methods, from the angle of roof rock pressure (vertical stress) and water storage height (horizontal stress). This paper studies the variation characteristics of various parameters during the failure of coal pillar dam, and puts forward the key information of coal pillar dam failure precursors, which provides the basis for the safe operation monitoring and early warning of on-site coal mine underground reservoir. The results show that under the action of vertical stress, the location of coal pillar dam damage caused by overburden stress is the dam roof on the side of underground reservoir of coal mine. The smaller the size of coal pillar is, the easier it is to produce stress concentration. The greater the bearing stress is, the greater the possibility of damage is. Based on this, the failure critical point of coal pillar dam is obtained. Under the action of horizontal stress, the coal pillar dam body experiences three processes: elastic stage, initial failure stage and failure increase stage. The coal pillar dam body shows obvious displacement only after reaching the ultimate strength. At this time, the coal pillar dam body is damaged. After the horizontal stress decreases, the displacement has a rebound trend, but the overall stability of the coal pillar dam body is affected and it is not suitable to continue water storage. The analysis of the main factors causing the instability of the coal pillar dam shows that under the combined action of vertical stress and horizontal stress, the instability of the coal pillar dam is mainly at the top plate, bottom plate and middle position of the small-size dam near the reservoir. Taking Shangwan mine as an example, the maximum bearing head height of the coal pillar dam is 50m, but the coal pillar dam has been damaged at this time. Even if the water level is lowered, the overall stability of the coal pillar dam is irreparably damaged. Comprehensive study of the characteristics and laws of coal pillar dam instability in coal mine underground reservoirs, combined with displacement changes and the physical and mechanical properties of coal pillar dams, obtained the precursor information of coal pillar dam failure, that is, respond to the reservoir before reaching the ultimate strength under the action of external force. For safety measures, it is recommended to focus on monitoring the stress change information of the coal pillar roof with a smaller size on the reservoir side during on-site monitoring and early warning to ensure the safe operation of coal mine underground reservoirs. The research results provide a basis for monitoring and early warning methods for the safety and stability of coal pillar dams in coal mine underground reservoirs.

The weak bedding plane and the water-softening coal will lead to the decrease in mechanical properties, which has an important influence on the stability of underground reservoirs and the prevention and control of water inrush accidents in coal mine. To study the comprehensive influence of bedding and soaking on the mechanical properties of coal, the uniaxial compression deformation and failure characteristics of coal under different soaking conditions were analyzed through a non-destructive soaking test and uniaxial compression test and based on the Digital image correlation method (DIC). The mechanical property parameters and surface strain field information of axial parallel and vertical bedded coal samples in the natural state, natural water absorption and forced water absorption were obtained. The results shown that the water absorption characteristics of coal shown obvious stratification differences, and the mechanical properties were affected by the combined effects of stratification and soaking. The specific results were as follows, ① Capillary action led to stronger water adsorption in axial parallel-bedded coal than in axial vertical-bedded coal, and the natural limiting water absorption rate of axial parallel-bedded coal was 2.2 times higher than that of axial vertical-bedded coal. ② The uniaxial compressive strength and elastic modulus of coal were mainly affected by bedding and supplemented by soaking. The failure mode was related to the direction of bedding. The failure mode of axial vertical coal was shear failure, while that of axial parallel coal was split failure. ③ The coal failure was a process of transformation from uniform to non-uniform deformation. After water soaking, the coal appeared strain concentration area earlier, its compaction stage was prolonged, and the elastic deformation stage was shortened. The research results were helpful to further understand the water soaking characteristics, mechanical properties, and rupture evolution of axial parallel and vertical bedded coals, and can be used as a reference for the protection and stability analysis of reserved coal pillars in underground reservoirs.

Aquifer protection in high-intensity coal mining is difficult to green and safe-mining, due to less knowledge of the response law of local seepage-flow in the mining area after aquifer damaged for the protection method and techniques. Adopting the coupling analysis idea of local multi-field source, the study focuses on the response law of mining-seepage field and aimed to revel coupling relationship between locally mining and the seepage field response and construct local mining groundwater system used to analyze the time-space evolution law of mining-seepage field and its cumulative effects, with the limited hydrological data including ground and underground observation. Based on application analysis of typical case, the analysis method of mining-seepage field is pro-posed, suitable for groundwater protection and engineering restoration in the mining area. The results show: ① Based on coupling relation of “mining excitation-overburden strain-seepage field response” and the energy transfer relationship from mining kinetic and seepage potential energy, the mining-seepage coupling mechanism is proposed. The mining seepage system (MSS) is constructed for describing the state of the operative local seepage field being divided into conduction zone, disturbance zone and radiation zone; ② The simplified mining-seepage effect models are established, including the “columnar seepage model” of the conduction zone and the “well-seepage” state model of the disturbance-radiation zone. Relative analyzing parameters are proposed, such as the miming-seepage coupling coefficient, the mining-seepage flow, the apparent water conductivity, distance of source-measuring point for describing statues of mining-seepage field, and aquifer damage, the risk of water-reserving safe-mining and mining height used for the groundwater protection analysis; ③ Revealed that the mining-seepage coupling coefficient is the key factor affecting the characteristics of the local mining-seepage response, featured that the hard rock overburden is larger than soft rock in coupling strength and influence range, and that the low-conductivity aquifer is narrower than high-conductivity in mining seepage field response area, higher in the response frequency and closer in the cumulative influence distance. Timing variation of mining-seepage flow and the water conductivity are characteristics of the periodicity, amplitude fluctuation and in-rush-flow pulsation of the reactive response; ④ Based on mining-seepage coupling cumulative effect, the identification process of the conduction area is proposed, containing the equilibrium analyzing method for“suppling, run-off, excretion”of mining area and the constraint-equilibrium method for induced-seepage flow of the mining face. Analysis method of overburden thickness, mining risk and mining height proposed, aimed to control mining damage of heterogeneous aquifer; ⑤ The application indicates that the mining-seepage disturbs directly the III aquifer and affects the II aquifer, and the cumulative impact range exceeds respectively 2 km and 6 km, during mining period (2012–2019); that III’ mining-seepage flow is significantly lower than that of II aquifer with obvious synergistic response of mining-seepage, its total amount of the flow close to the actual mine water in the same period; that the initial section W01 mining face is the main conducting zone, with mining-seepage flow sourced from direct“excretion” of III aquifer and indirect supply of II aquifer; that total amount of other mining areas was stable in low-level, mainly from the seepage of III aquifer mining seepage and a small part from the cross-layer replenishment of II aquifer due to periodic subsidence fractures; that strategy of “Protecting II aquifer by Controlling III” and the flexible recovery mode are adopted in the case of large and thick multi-aquifers, in which safe mining height in the western and eastern areas was increased to about 8−12 m and 16 m, respectively, lead to obvious effect on the aquifers protection confirmed by the significantly decreased abnormal level of seepage in the continuation mining area and the phenomenon of no water inrush.

Underground reservoir technology for coal mines can realize the coordinated development of coal exploitation and water protection in water-shortage-prone areas. The seepage effect of the floor seriously affects the safety of underground reservoirs under the action of mining damage and seepage pressure. Focusing on the problem of floor seepage in underground reservoirs, a spatial mechanical model of underground reservoirs was established. The main factors affecting the seepage of the surrounding rock were studied. The seepage pressure law in different stages of spatial structure evolution of overlying strata was explored. The results showed that pressure change was the main factor affecting the stability of a reservoir's surrounding rock. The pore space between the broken and fractured rock in the water-flowing fractured zone was the main water storage space, which was directly related to the development of a breaking arch. According to the spatial structure evolution process of the overlying strata, the water storage state of an underground reservoir was divided into two stages and three situations. The seepage pressure was mainly affected by the water pressure and the overlying strata weight. The water pressure was affected by the reservoir head height, and the overlying strata weight was mainly affected by the overlying strata thickness.

Organophosphate esters (OPEs) are a group of pollutants that are widely detected in the environment at high concentrations. They can adversely affect human health through multiple routes of exposure, including dermal uptake. Although attention has been paid to achieving an accurate and complete quantification of the dermal uptake of OPEs, existing evaluation methods and parameters have obvious weaknesses. This study reviewed two main categories of methodologies, namely the relative absorption (RA) model and the permeability coefficient (PC) model, which are widely used to assess the dermal uptake of OPEs. Although the PC model is more accurate and is increasingly used, the most important parameter in this model, the permeability coefficient (Kp), has been poorly characterised for OPEs, resulting in considerable errors in the estimation of the dermal uptake of OPEs. Thus, the detailed in vitro methods for the determination of Kp are summarised and sorted. Furthermore, the commonly used skin membranes are identified and the factors affecting Kp and corresponding mechanisms are discussed. In addition, the experimental conditions, conclusions, and available data on Kp values of the OPEs are thoroughly summarised. Finally, the corresponding knowledge gaps are proposed, and a more accurate and sophisticated experimental system and unknown Kp values for OPEs are suggested.

Herein, the trapping effectiveness of N95, filter KN95, medical surgical masks (MSMs), and disposable medical masks (DMMs) against 19 airborne traditional and novel organophosphate esters (OPEs) was evaluated. Laboratory simulations (n = 24 for each type of mask) showed that time-dependent accumulation of ∑19OPEs on the four types of masks ranged between 30.1 and 86.6 ng in 24 h, with the highest and lowest median amounts trapped by the N95 masks (53.3 ng) and DMMs (43.2 ng), respectively. The trapping efficiency of the four types of masks for ∑19OPEs decreased over time from 84 % to 39 % in 24 h, with N95 masks showing the highest median efficiency (70 %). Further, field investigations were conducted in five types of microenvironments (train, hospital, bus, supermarket, and canteen), and an analysis of 200 samples showed that ∑19OPEs were accumulated in the masks with a variable amount from 3.7 to 117 ng/mask. Consistent with the laboratory simulations, the N95 masks (29.0 ng/mask) exhibited the highest hourly median amount of trapped OPEs, followed by the KN95 masks (24.5 ng/mask), MSMSs (17.4 ng/mask), and DMMs (15.8 ng/mask). Triethyl phosphate (TEP), tris(1-chloro-2-propyl) phosphate (TCIPP), tri-n-butyl phosphate (TNBP), and cresyl diphenyl phosphate (CDP) as well as 4-isopropylphenyl diphenyl phosphate (4IPPDPP) and 2,4-diisopropylphenyl diphenyl phosphate (24DIPPDPP) were the most commonly detected traditional and novel OPEs. Based on the amount of OPEs trapped on the masks, we estimated the concentration of ∑19OPEs in the train microenvironment to be the highest (222 ng/m3), which is approximately 2–5 times higher than that in the other microenvironments. The results of this study prove that masks can effectively protect humans from exposure to OPEs and act as low-cost indicators of indoor contamination.

Taigemiao Mining Area is rich in coal resources, but water resources are scarce and the ecological environment is fragile, and the use of hydrogeochemical methods to carry out research on the chemical characteristics and evolution law of groundwater in the whole basin of mining areas can provide scientific support for green mining in mining areas. Piper three-line diagram was used to analyze the types of Cretaceous groundwater, Jurassic groundwater, river water and lake water, and the water chemical characteristics met the characteristics of lake water mixed by river water and evolving groundwater; through Gibbs diagram analysis, it is concluded that the Cretaceous and Jurassic groundwater in the cyclic evolution process It is controlled by rock and evaporation; the ion ratio endmember method further shows that the Cretaceous and Jurassic groundwater is dominated by silicate rock salt and rock salt, accompanied by ion exchange; finally, the mass balance simulation shows that the Cretaceous groundwater circulation During the evolution process, dolomite, gypsum, and rock salt minerals are dissolved, CO2 is absorbed, and calcite is precipitated. With ion exchange, it can be mixed with different proportions of river water to form lake water, but Jurassic groundwater can only be mixed with a small amount of river water or not mixed with river water. The river water accounts for 83% of the water supply of Hongjiannao Lake. Jurassic groundwater cannot be the main source of water supply. The main sources of water in Hongjiannao Lake are Cretaceous groundwater and river water. Combined with the hydrogeological conditions of the mining area, the groundwater flow system of the mining area is divided into the Cretaceous groundwater flow system in the south, the Cretaceous groundwater flow system in the north and the Jurassic groundwater flow system in the deep. Coal mining directly affects the Jurassic groundwater flow system. On the premise of ensuring that the two Cretaceous groundwater flow systems are not damaged, when coal mining drainage only captures the outflow water from the Jurassic boundary or a small amount of Jurassic replenishment to Hongjiannao, coal mining will affect Hongjiannao and surrounding rivers. impact is minimized.

The conceptual hydrological model represented by the ABCD model is one of the main methods to quantify and analyze the hydrological cycle processes. However, few studies have examined the physical basis of the variables and parameters of the ABCD model and quantified the relationship between parameters in the conceptual hydrological model and traditional soil hydraulic parameters. In this study, we select the Taigemiao mining area in the Ordos basin as the research site. The HYDRUS-1D software is used to construct the soil-plant-atmosphere continuum (SPAC) model at a site scale for obtaining the “real” actual evapotranspiration during the non-freezing period. The actual evapotranspiration obtained from the SPAC model is considered as the objective function of the ABCD model for calibrating the ABCD model. The soil water storage and soil water leakage simulated by the ABCD model are compared with the results of the SPAC model. The relationships between the parameters of a and b in the ABCD model and the traditional soil hydraulic parameters are analyzed through a large number of scenario simulations. The results show that the ABCD model can better simulate the site-scale monthly actual evapotranspiration and soil water leakage during the non-freezing period. It is indicated that parameter a in the ABCD model has a strong linear correlation with the saturated water content, residual water content and curve shape parameters, while it shows a negative logarithmic relation to the saturated coefficient of permeability. It is also found that parameter b has a strong linear correlation with the saturated water content and curve shape parameter n, while the logarithmic relationships is revealed between parameter b and the saturated coefficient of permeability and curve shape parameter α. This study can improve the understanding of the physical basis of variables and parameters in conceptual hydrological models, and expands the application of hydrological models to hydrogeology.

The main coal producing areas in western China have the problems of dry climate and water shortage, and the mine water produced by coal mining generally has the characteristics of high salinity and high hardness. At present, the zero-discharge treatment of high-salinity mine water generally has the problems of complex process and high operating cost. In order to shorten the treatment process and improve the treatment efficiency, in this study, the vacuum membrane distillation technology was applied to replace the existing multi-stage reverse osmosis membrane concentration technology for the high-salinity mine water concentration and desalination treatment process. The high-salinity mine water after a lime-soda softening treatment was treated with different concentration ratios, and the membrane fouling mechanism and membrane cleaning method during the concentration process were studied. The results showed that with the increase of the concentration ratio, the membrane flux and desalination rate of the polyvinylidene fluoride (PVDF) hollow fiber membrane gradually decreased, while the conductivity of the produced water increased slowly. When the concentration ratio was up to 27 times, the membrane flux was 8.87 L/(m2·h), the salt rejection rate was 99.7%, and the water conductance was 26.6 μS/cm. The membrane distillation process was inhibited by the membrane fouling caused by the scaling of calcium and magnesium ions in mine water, and the inorganic salt deposits could not be removed by physical methods such as backwashing and ultrasonic cleaning. After 96 hours of continuous operation, the PVDF membrane flux reduced from 12.85 L/(m2·h) to 5.21 L/(m2·h). The pretreatment process of hardness removal had obvious influence on the calcium and magnesium scaling in the membrane distillation process. When the hardness removal rate exceeded 95%, the desalting rate and membrane flux attenuation of PVDF membrane were obviously weakened. The membrane fouling caused by the calcium and magnesium precipitation could be effectively removed by the citric acid cleaning agent. Under the optimized conditions of citric acid concentration of 0.87 mol/L, the cleaning temperature of 38 ℃ and the cleaning time of 40 min, the membrane cleaning efficiency could reach 99.5%. Atomic force microscopy (AFM) and scanning electron microscopy (SEM) were used to characterize the microscopic morphology and structure of the original film, the contaminated film and the film after chemical cleaning. The results showed that the deposited pollutants on the surface of the cleaned film were significantly reduced, but the surface morphology and microscopic structure were irreversibly changed.

The objective phenomenon of seepage reduction and self-healing is easy to occur in the mining fractured rock mass after long-term evolution process of coal seam after mining. In survey of this phenomenon, taking the No. 12401 work face of Bulianta Coal Mine and the No. 52306 work face of Daliuta Coal Mine in Shendong as typical test areas, the engineering exploration and theoretical research on the self-healing law in different areas of overburden were carried out. The evolution law of long-term self-healing process of overburden after mining was revealed. The results show that the fractured overburden of the two coal mines had achieved good self-healing effects 15 years and 7.3 years after mining, respectively. The areas with water conduction within the original water-conducting fractured zone have been significantly reduced. Although the caved zone still conducts water, the height has also been reduced. By taking the ratio of the vertical length of the self-healing section in overburden from the original water diversion to the water isolation to the height of the water-conducting fractured zone as the self-healing recovery rate for statistics, it is obtained that the self-healing recovery rate of No. 12401 work face in the overburden near the mining boundary and in the middle of the dip has reached 59.5% and 53.5% respectively, and the self-healing recovery rate of 52306 work face in the corresponding area is 25.8% and 47.5% respectively. Due to that No. 12401 work face has mined for a longer time and has a larger coal seam burial depth, its self-healing effect of fractured overburden is obviously better. Affected by the occurrence difference of different strata in the vertical direction of the overburden and the initial damasge degree in different areas in the horizontal direction, the distribution of the self-healing area is often discrete and discontinuous, in which the interval of the unrepaired area generally corresponds to the position of the key stratum or thick and hard rock layer, and the shorter the post mining life and the closer to the mining boundary, the more significant the dispersion. The self-healing effect of mining fractured rock mass is actually a process of permeability reduction and evolution under the comprehensive influence of multiple factors. For the mining conditions in Shendong coalfield, the residual settlement caused by stress compaction plays a leading role in the initial process within 1.5 to 2 years after coal mining, which is relatively significant in the overburden in the middle of the mining area. Then, the self-healing process has been dominated by the water-rock or water-gas-rock interactions in goaf. That is, the filling and sealing effect of the derivatives produced in the relevant process on the fractures and the roughness reduction of the fracture surface induced by hydraulic erosion directly affect the self-healing results. Therefore, whether there is long-term water seepage in the overburden after mining determines the difficulty of self-healing of fractured rock mass. Water conducting fractures in the overburden of most mines in Shendong are generally directly connected to the Quaternary loose layer or surface, and in recent years, there is abundant rainfall, which provides superior conditions for the generation of water-rock or water-gas-rock interactions and the promotion of self-healing effect.

The seepage characteristics of groundwater in the overlying fissures are the key factors to be considered in the later maintenance of coal mining and goaf, especially in the underground reservoir construction area of coal mines. It is also an important part of coal mine underground reservoir. In shallow groundwater-rich areas or under extreme summer rainfall conditions, underground reservoirs form vertical recharge, and analyzing the seepage characteristics of groundwater in overlying fissures provides a scientific basis for the safe operation of coal mine underground reservoirs and the protection of groundwater resources. In this study, the solid-liquid coupling similarity model test was used to obtain the development and stability of the overlying fissures in the mining area and the characteristics of groundwater seepage after the aquifer was connected. The analysis showed that the large abscission fissures and micro-fissures that extend far and wide are mostly water storage. , the vertical fissures running through multiple rock layers have strong water conductivity, and the hydraulic connection between the separation layer fissures is mainly formed by the vertical fissures in the two sides. Under the condition of vertical recharge, groundwater first infiltrates along the vertical fissures in an unsaturated manner, and is gradually saturated regionally from the upper and lower overlying fissures, finally forming a stable saturated infiltration form. On this basis, a mathematical model of groundwater seepage in saturated seepage state is established, and the numerical method is used to solve it. It is verified with similar simulation experiments that vertical fractures are the main water conduction channels, and their water conduction can account for up to 97%. At the same time, the migration speed of groundwater in vertical fractures is also many orders of magnitude higher than that in abscission fractures. Finally, through the sensitivity analysis, it is concluded that the vertical fracture seepage is positively correlated with the fracture development degree and the total water inflow, and negatively correlated with the rock permeability. The migration time of groundwater in overlying fissures is negatively correlated with the degree of fissure development, rock permeability and total water inflow.

In the long-term evolution process after the development of the water-conducting fractures in overburden, the self-healing phenomenon will occur, in which the water-conducting seepage capacity will gradually decrease. It is of great significance to study the self-healing mechanisms of water-conducting fractures for scientifically guiding the ecological function restoration of groundwater in mining areas. Based on the engineering detection results of self-healing of water-conducting fractures in No. 12401 working face of Bulianta coal mine in 15 years after mining, the mechanisms of repairing and reducing permeability of water-conducting fractures caused by chemical precipitation generated by the mixing of different groundwater from different layers after mining are studied. The results show that the water-conducting fractures in the overburden had directly developed into the Quaternary loose layer, which had connected multiple aquifers in the strata. Due to that the shallow groundwater contains more Ca2+, while the bedrock groundwater contains more CO3 2− and HCO3 −, the CaCO3 precipitation will be produced when the two kinds of groundwater meet and mix in the mining overburden. The sediment migrates with water and is continuously adsorbed on the fracture surface, the scaling process of inclusion co sedimentation consolidation occurs. After a long time of accumulation, the scale or inclusion with certain corrosion resistance is finally formed to plug and repair the fractures. The indoor test shows that the self-healing and permeability reduction effect of the water-conducting fractures caused by this process is stable and rapid. The absolute permeability of the fractured rock specimen is reduced from 0.09×10−15 m2 to 0.0025×10−15 m2 after nearly two months of mixed flow of simulated groundwater with two different water quality, and there is no obvious permeability fluctuation under the condition of 1.5 MPa water pressure. Because the confluence and mixing of different groundwater mainly occur in the fractured rock mass near the mining boundary, the self-healing process of water-conducting fractures and its effect in different areas of overburden will be significantly different. The self-healing of overburden water-conducting fractures in the middle area of No.12401 working face is mainly caused by the water-rock or water-gas-rock interactions caused by rainfall infiltration, while the water-conducting fractures near the mining boundary are dominated by the confluence mixed flow reaction of different groundwater and the water-rock or water-gas-rock interactions, so the self-healing effect of the latter should be obviously better. The field detection results show that after 15 years of self-healing evolution, the water-conducting fractures in the overburden near the mining boundary have been self-repaired in the depth range of 86.7 m to 179.1 m, while the overburden in the middle of the mining area still has local unrepaired phenomena in the corresponding self-healing area, with the depth range of 110.0 m to 118.6 m, which confirms the good repair effect of the chemical precipitation generated by the intersection and mixing of different groundwater.

Fluoride ions are widely distributed in surface rivers and groundwater bodies in China, especially in the mining areas along the Yellow River in the western Yellow River basin that there is a widespread problem of excessive fluoride in the mine water, which poses a potential threat to the local ecological environment and human health. The status quo of fluoride pollution in China is mostly at a low concentration pollution level, which leads to it difficult to remove efficiently through conventional water treatment technologies. The adsorption method is considered to be an effective way to remove low concentration fluoride ions because of its high adsorption efficiency and convenient operation. The research status of fluoride removal by commonly used adsorption materials such as carbon based, minerals, metals and metal organic frameworks (MOFs) was reviewed and summarized before summarizing the influence of different factors on the fluoride removal efficiency and adsorption mechanism of these adsorption materials. Then the application effect and operation cost of adsorption method in mine water treatment were emphatically analyzed, and the development direction of adsorption method in the treatment of low concentration (

Water storage capacity has always been the core component of the underground water reservoir in open-pit coalmines. Taking the western underground water reservoir of Baorixile open-pit coalmine as the research background, boreholes in the waste dump overlying the underground water reservoir were designed and drilled, and the capacity characteristics of the underground water reservoir were studied by a layered summation method. These test results revealed that: (1) the regional strata from shallow to deep are the near-surface loose layer, upper bench loess, mixed stripping material, glutenite water reservoir layer and rubble collecting ditch; and the glutenite water reservoir layer is the main water storage body of the underground water reservoir. (2) Permeability coefficient, porosity, layered and cumulative water storage of the western underground water reservoir in Baorixile open-pit coalmine increase with the elevation increase, and show a quadratic function relationship. (3) The total storage capacity of the underground water reservoir is 1.2234 Mm3, and the average porosity is 0.1003, that is, each volume of the water storage medium can store 0.1003 volume of water. These results provide a scientific basis for the evaluation and determination of the water storage capacity of the underground water reservoir in open-pit coalmines. HIGHLIGHTS The design and construction of the underground reservoir in Baorixile open-pit coalmine.; A calculation method of underground reservoir capacity in open-pit coalmines based on a field borehole water injection test.; The reservoir capacity characteristics of the western underground reservoir of Baorixile open-pit coalmine.;

This study focuses on the seismic safety of distributed underground reservoirs in coal mines, especially in scenarios involving the establishment of multiple reservoirs within the same mining area, spanning different coal seams. Dynamic similarity model tests and numerical simulations are employed to investigate the construction and operation of these reservoirs under extreme conditions, such as mine tremors or earthquakes. Utilizing the Daliuta coal mine underground reservoir as a case study, a similarity material model test platform is established to represent both upper and lower coal mine underground reservoirs. Stability tests are conducted on the interlayer rock mass under various levels of seismic intensity, and the safety of the interlayer rock mass at different safety distances is comparatively analyzed. Meanwhile, using the finite element method, the responses of the upper and lower coal mine underground reservoirs under different seismic intensity levels are simulated with the same conditions of model tests. Through the two types of simulations, the mechanical response and safety of the surrounding rock of the Daliuta coal mine underground reservoir under the influence of different seismic intensities are systematically analyzed, and the reasonable safety distances between the upper and lower reservoirs are obtained. This study provides a valuable scientific insight into the safe design of underground reservoir embankments in coal mines.

Few studies have considered the impacts of sample size and sample ratio of presence and absence points on the results of random forest (RF) testing. We applied this technique for the prediction of the spatial distribution of snail habitats based on a total of 15,000 sample points (5,000 presence samples and 10,000 control points). RF models were built using seven different sample ratios (1:1, 1:2, 1:3, 1:4, 2:1, 3:1, and 4:1) and the optimal ratio was identified via the Area Under the Curve (AUC) statistic. The impact of sample size was compared by RF models under the optimal ratio and the optimal sample size. When the sample size was small, the sampling ratios of 1:1, 1:2 and 1:3 were significantly better than the sample ratios of 4:1 and 3:1 at all four levels of sample sizes (p0.05). The sample ratio of 1:2 appeared to be optimal for a relatively large sample size with the lowest quartile deviation. In addition, increasing the sample size produced a higher AUC and a smaller slope and the most suitable sample size found in this study was 2400 (AUC=0.96). This study provides a feasible idea to select an appropriate sample size and sample ratio for ecological niche modelling (ENM) and also provides a scientific basis for the selection of samples to accurately identify and predict snail habitat distributions.

Abstract Background Phytoremediation is a potentially cost-effective way to remediate highly contaminated mine tailing sites. However, nutrient limitations, especially the deficiency of nitrogen (N), can hinder the growth of plants and impair the phytoremediation of mine tailings. Nevertheless, pioneer plants can successfully colonize mine tailings and exhibit potential for tailing phytoremediation. Diazotrophs, especially diazotrophic endophytes, can promote the growth of their host plants. This was tested in a mine-tailing habitat by a combination of field sampling, DNA-stable isotope probing (SIP) analysis, and pot experiments. Results Bacteria belonging to the genera Herbaspirillum, Rhizobium, Devosia, Pseudomonas, Microbacterium, and Delftia are crucial endophytes for Chinese silvergrass (Miscanthus sinensis) grown in the tailing, the model pioneer plant selected in this study. Further, DNA-SIP using 15N2 identified Pseudomonas, Rhizobium, and Exiguobacterium as putative diazotrophic endophytes of M. sinensis. Metagenomic-binning suggested that these bacteria contained essential genes for nitrogen fixation and plant growth promotion. Finally, two diazotrophic endophytes Rhizobium sp. G-14 and Pseudomonas sp. Y-5 were isolated from M. sinensis. Inoculation of another pioneer plant in mine tailings, Bidens pilosa, with diazotrophic endophytes resulted in successful plant colonization, significantly increased nitrogen fixation activity, and promotion of plant growth. Conclusions This study indicated that diazotrophic endophytes have the potential to promote the growth of pioneer plant B. pilosa in mine tailings. Video Abstract

The rapid development of nanotechnology has aroused considerable attentions toward understanding the effects of engineered nanomaterials (ENMs) on the propagation of antibiotic resistance. Molybdenum disulfide (MoS2) is an extensively used ENM and poses potential risks associated with environmental exposure; nevertheless, the role of MoS2 toward antibiotic resistance genes (ARGs) transfer remains largely unknown. Herein, it was discovered that MoS2 nanosheets accelerated the horizontal transfer of RP4 plasmid across Escherichia coli in a dose-dependent manner (0.5–10 mg/L), with the maximum transfer frequency 2.07-fold higher than that of the control. Integration of physiological, transcriptomics, and metabolomics analyses demonstrated that SOS response in bacteria was activated by MoS2 due to the elevation of oxidative damage, accompanied by cell membrane permeabilization. MoS2 promoted bacterial adhesion and intercellular contact via stimulating the secretion of extracellular polysaccharides. The ATP levels were maximally increased by 305.7 % upon exposure to MoS2, and the expression of plasmid transfer genes was up-regulated, contributing to the accelerated plasmid conjugation and increased ARG abundance in soil. Our findings highlight the roles of emerging ENMs (e.g., MoS2) in ARGs dissemination, which is significant for the safe applications and risk management of ENMs under the development scenarios of nanotechnology.

Abstract In order to solve the problem of water resources shortage in the Western mining area of China and make it reasonably and effectively allocated, the optimal allocation model of water resources was established based on Water Evaluation and Planning System in this paper. This model simulated the water resources changes of 8 scenarios from 2015 to 2035. These scenarios are consisted of different economic growth, population changes, water saving and greening intensity. The optimal development scenario applicable to the mining area and the water resources allocation results corresponding to the scenario were determined by calculating the comprehensive benefits of society, economy and ecology. The results show that the optimal development scenario for the mining area is high-speed economic growth, powerful water saving and moderate greening. Under this scenario, the total water demand of the mining area will reach 39.73 million cubic meters by 2035, the demand shortage will be 1.7 million cubic meters, and the water shortage rate will be 4.28%. The water resources allocation result corresponding to this scenario can be used as a reference for decision makers, which can provide a scientific basis for the rational use of water resources in the Western mining area.

The role of coal mine underground reservoirs (CMUR) in ‘guide storage and use’ greatly solves the problem of mine water waste. Replenishment and prediction of reservoir water sources provide an important support for the successful development of key technologies concerning CMUR. To study water replenishment for CMUR, the hydrogeological conditions of the Shendong mining area were investigated as an example. Based on the relationship between the development height of the ‘two belts’ of shallow coal seams and the relative occurrence of location of aquifers, the aquifers are generalized and classified according to occurrences location. Taking Bulianta coal mine as the research background, a discrete element fluid–solid coupling numerical simulation model is constructed to analyze the development characteristics of mining-induced fractures after coal seam mining, and the water replenishment channel of the CMUR is determined. On this basis, analysis is made on the change law of water pressure in the aquifer, aquifuge and coal seam roof under mining action. Taking hydraulic head pressure and water velocity as the judgment basis, prediction and evaluation are made on the water replenishment capacity of CMUR. The research results can provide references for coal mine safety production and mine water protection and utilization. HIGHLIGHTS Based on the water-conducting fractured zone, the occurrence type of ‘coal-water’ is generalized.; The change law of aquifer under different ‘coal-water’ conditions is revealed.; The source and amount of water supply for CMUR were obtained under different conditions.;

Spatio-temporal evolution laws of storage coefficient and contact network of crushed rock are of great significance for the construction and utilization of underground reservoirs in coal mines. Based on discrete element method and irregular rigid block model, spatio-temporal evolution laws of storage coefficient and contact network of crushed rock under different overburden stresses are investigated and the following main conclusions are obtained: (1) The average storage coefficient and the storage coefficient at different vertical heights of the crushed rock packing system decrease exponentially as the overburden stress increases. When the overburden stress ranges from 0 to 20 MPa, the average storage coefficient decreases by 48.947%. (2) The average void radius and throat radius of water storage space decrease exponentially as the overburden stress increases. The increase in overburden stress leads to the transformation of large voids into smaller voids, causing a gradual decrease in void connectivity and a tendency towards irregular void shapes. (3) With the increase of overburden stress, the number of strong contacts in the packing system of crushed rock increases and gradually expands from the top to the bottom. The average contact force of crushed rock increases exponentially, while the coordination number increases linearly. (4) As the overburden stress increases, the majority of contact directions are concentrated within the ±30° range in the loading direction. This increase results in an enhancement of the anisotropy of the packing system structure of crushed rock.

Rice plant counting is crucial for many applications in rice production, such as yield estimation, growth diagnosis, disaster loss assessment, etc. Currently, rice counting still heavily relies on tedious and time-consuming manual operation. To alleviate the workload of rice counting, we employed an UAV (unmanned aerial vehicle) to collect the RGB images of the paddy field. Then, we proposed a new rice plant counting, locating, and sizing method (RiceNet), which consists of one feature extractor frontend and 3 feature decoder modules, namely, density map estimator, plant location detector, and plant size estimator. In RiceNet, rice plant attention mechanism and positive–negative loss are designed to improve the ability to distinguish plants from background and the quality of the estimated density maps. To verify the validity of our method, we propose a new UAV-based rice counting dataset, which contains 355 images and 257,793 manual labeled points. Experiment results show that the mean absolute error and root mean square error of the proposed RiceNet are 8.6 and 11.2, respectively. Moreover, we validated the performance of our method with two other popular crop datasets. On these three datasets, our method significantly outperforms state-of-the-art methods. Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method.

Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People’s Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan–Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.

The occurrence and distribution of 10 brominated flame retardants (BFRs) and 10 organophosphate flame retardants (OPFRs) were investigated in indoor dust, total suspended particles (TSP), and vapour phase from offices (n = 10), homes (n = 9), and day-care centres (n = 10) in Beijing, China. Three types of samples were collected biweekly from one office and one home over a year to examine temporal trends. BFRs in dust significantly correlated with those in TSP, while OPFRs significantly correlated among all three matrices. In addition, BFRs in dust (ng/g) and TSP (pg/m3) exhibited similar temporal trends with higher levels in the cold season, whereas OPFRs in TSP and vapour phase (pg/m3) showed similar temporal trends with higher levels in the warm season. The geometric mean concentrations of BFRs and OPFRs in the three matrices from the above mentioned three types of indoor microenvironments were used for exposure and health risk estimation, and ∑7OPFRs showed much higher hazard index (HI) values than ∑10BFRs for all subpopulations, and inhalation of OPFRs was a major risk source. With the volatility of flame retardants (FRs) decreasing, the contribution of dust ingestion and dermal absorption showed an increasing trend, and the contribution of inhalation exhibited a gradual decreasing trend, which implied the dominant exposure pathway to FRs is strongly related to the vapour pressure (25 °C, Pa) of these substances. Using a single type of microenvironment or the collection of samples at a single point in time can lead to overestimation or underestimation of overall exposure and risk for people to some extent. The correlations of FRs in dust, TSP, and vapour phase from indoor microenvironments, as well as their temporal trends were first reported in this study, which will provide a basis for more accurate FR exposure assessments in the future.

The field of vision-based human hand three-dimensional (3D) shape and pose estimation has attracted significant attention recently owing to its key role in various applications, such as natural humancomputer interactions. With the availability of large-scale annotated hand datasets and the rapid developments of deep neural networks (DNNs), numerous DNN-based data-driven methods have been proposed for accurate and rapid hand shape and pose estimation. Nonetheless, the existence of complicated hand articulation, depth and scale ambiguities, occlusions, and finger similarity remain challenging. In this study, we present a comprehensive survey of state-of-the-art 3D hand shape and pose estimation approaches using RGB-D cameras. Related RGB-D cameras, hand datasets, and a performance analysis are also discussed to provide a holistic view of recent achievements. We also discuss the research potential of this rapidly growing field.

Abstract Cloud detection is one of important tasks in automatic ground‐based cloud observation systems with ground‐based cloud images. Most supervised methods need substantial annotated samples for model training, while the pixel‐level annotation costs a lot. In this letter, a self‐training mechanism is proposed to significantly reduce the requirement of annotated samples. With a number of original images, only a few images need to be annotated (even incompletely), and a local region classifier model can be initialized with the annotated samples. Then the model is retrained iteratively using unlabeled samples with high confidence pseudo labels given by a fusion decision. The finely trained model can classify the local regions into “cloud” or “sky”. The experiments show that the proposed mechanism is effective for several classifiers. The proposed method can outperform unsupervised methods and achieve comparable results with fully supervised learning methods but using much fewer annotated samples.

Abstract The exponential spread of COVID‐19 worldwide is evident, with devastating outbreaks primarily in the United States, Spain, Italy, the United Kingdom, France, Germany, Turkey and Russia. As of 1 May 2020, a total of 3,308,386 confirmed cases have been reported worldwide, with an accumulative mortality of 233,093. Due to the complexity and uncertainty of the pathology of COVID‐19, it is not easy for front‐line doctors to categorise severity levels of clinical COVID‐19 that are general and severe/critical cases, with consistency. The more than 300 laboratory features, coupled with underlying disease, all combine to complicate proper and rapid patient diagnosis. However, such screening is necessary for early triage, diagnosis, assignment of appropriate level of care facility, and institution of timely intervention. A machine learning analysis was carried out with confirmed COVID‐19 patient data from 10 January to 18 February 2020, who were admitted to Tongji Hospital, in Wuhan, China. A softmax neural network‐based machine learning model was established to categorise patient severity levels. According to the analysis of 2662 cases using clinical and laboratory data, the present model can be used to reveal the top 30 of more than 300 laboratory features, yielding 86.30% blind test accuracy, 0.8195 F1‐score, and 100% consistency using a two‐way patient classification of severe/critical to general. For severe/critical cases, F1‐score is 0.9081 (i.e. recall is 0.9050, and precision is 0.9113). This model for classification can be accomplished at a mini‐second‐level computational cost (in contrast to minute‐level manual). Based on available COVID‐19 patient diagnosis and therapy, an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100% consistency to significantly improve diagnostic and classification efficiency. The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines, thus creating a more comprehensive assessment of COVID‐19 cases during the early stages of infection. Such early differentiation will help the assignment of the appropriate level of care for individual patients.

Foot-mounted inertial navigation systems are suitable in GNSS denied environment for special tasks such as fire rescue, soldier, etc. These tasks are often accompanied by vigorous movements such as running and jumping, which is a challenge for the Zero Velocity Update (ZUPT) based inertial navigation system. As the speed increases, the inertial sensor drift increases, while the zero-velocity interval shortens, making the gait detection more difficult and resulting in a limited error correction. And as the motion intensifies, the stability of the system decreases. A multi-node inertial pedestrian navigation system based on the lower limb model constraint is proposed in this paper. Our first contribution in this paper is studying a simplified adaptive zero-bias estimation algorithm. The changes in velocity and angular velocity between two steps are used to estimate and compensate for the drift of accelerometers and gyroscopes. Our second contribution is proposing a novel gait segmentation method by lower limb motion statistics feature. A hybrid detection model of dual IMU is conducted to improve the accuracy of gait recognition. After that, the maximum distance inequality constraint is studied to correct the position error when the still phase is not available in vigorous movement. The final contribution is studying the chi-square fault detection method to improve the stability of KF. Multiple experiments prove that the proposed multi-node inertial pedestrian navigation system is effective in dealing with various challenging in walking, running, and hybrid motion pedestrian inertial navigation problems which could achieve less than 0.3% average distance error.

Using an abandoned underground goaf in coal mines as water reservoirs has been successfully applied for protecting mine water resources in western China. The water storage media are composed of broken rock masses and the voids between the rock masses. It is critical for reservoir capacity calculation that the deformation characteristics and seepage evolution of the water storage media under triaxial stress are theoretically described. In this study, broken rock masses and the space among the rock masses are simplified as two springs in a series. The mechanical behavior of the broken rock mass is described by Hooke’s law of linear elasticity, and the deformation characteristics of the space among the rock masses are represented by a nonlinear elastic constitutive model. The nonlinear stress-strain constitutive model of the water storage media is established by combining Hooke’s law and the fractional derivative stress-strain model. Similarly, a non-Darcy seepage model of the water storage media is obtained. The nonlinear stress-strain model is verified by mechanical experiments, physical simulation tests, and field measured data, and parameter sensitivity analysis is performed. The non-Darcy seepage equation is fitted and analyzed by using the seepage experimental data of the broken rock mass under triaxial compression conditions. The fractional non-Darcy model rather than the Forchheimer equation can more accurately describe the nonlinear seepage process in water storage media.

The safety of microplastics (MPs) and associated health effects has been one of the major concerns worldwide. However, the role of photoaging toward the risk of MPs in water ecosystems remains inconclusive yet. In this study, the size of polyamide (PA, ∼32.50 μm) MPs was obviously decreased after photoaging in water containing fulvic acid (FA) and humic acid (HA) (∼19.75 and ∼24.30 μm, respectively). Nanoplastics were formed (4.65% and 2.03%, respectively) and hydrophilia and colloidal stability was improved due to the formation of oxygen-containing functional groups. FA-aged PA exhibited higher inhibition on body length and weight of developing zebrafish than HA-aged and pristine PA. Photoaged MPs in intestine were more difficult to be depurated by zebrafish, leading to the disappearance of intestinal folding, shedding of more enterocytes, and emaciation of intestinal microvilli. Dietary lipid digestion in larvae was inhibited by aged PA due to oxidative stress-triggered lipid peroxidation and inhibition of lipase activities and bile acids secretion. Exposure of photoaged MPs down-regulated genes (cd36, dgat1a, dgat2, mttp, etc.) associated with triglyceride resynthesis and transportation, resulting in lipid maladsorption and growth inhibition. Our findings highlight the potential negative effects of environmentally aged MPs on diet digestion and nutrient assimilation in fish.

Water supply prediction and control is one of the key issues in exploitation of coal mine underground reservoirs (CMUR). In order to investigate the water supply for underground reservoirs in coal mines, the aquifers were classified into three types (types I, II, and III) according to the relative location of aquifers. The discrete element fluid structure coupling numerical simulation model is constructed according to the classification results. The numerical simulation parameters are inversed based on the surface subsidence data and the advance support pressure, and then, the disturbed characteristics and water pressure variation law of different types of aquifers are studied. The research results show that the maximum water inflow of type III, type II, and type I aquifers is 739 m3/h, 377 m3/h, and 279 m3/h, respectively. The dynamic calculation process of underground reservoir capacity under mining disturbance is refined, and the regulation and storage methods of underground reservoir are preliminarily put forward.

Wheat head detection can measure wheat traits such as head density and head characteristics. Standard wheat breeding largely relies on manual observation to detect wheat heads, yielding a tedious and inefficient procedure. The emergence of affordable camera platforms provides opportunities for deploying computer vision (CV) algorithms in wheat head detection, enabling automated measurements of wheat traits. Accurate wheat head detection, however, is challenging due to the variability of observation circumstances and the uncertainty of wheat head appearances. In this work, we propose a simple but effective idea—dynamic color transform (DCT)—for accurate wheat head detection. This idea is based on an observation that modifying the color channel of an input image can significantly alleviate false negatives and therefore improve detection results. DCT follows a linear color transform and can be easily implemented as a dynamic network. A key property of DCT is that the transform parameters are data-dependent such that illumination variations can be corrected adaptively. The DCT network can be incorporated into any existing object detectors. Experimental results on the Global Wheat Detection Dataset (GWHD) 2021 show that DCT can achieve notable improvements with negligible overhead parameters. In addition, DCT plays an important role in our solution participating in the Global Wheat Challenge (GWC) 2021, where our solution ranks the first on the initial public leaderboard, with an Average Domain Accuracy (ADA) of 0.821, and obtains the runner-up reward on the final private testing set, with an ADA of 0.695.

In order to study the seepage failure mechanism of roadway filling medium consisting of cohesive soil under complex hydrogeological conditions, a large-scale triaxial stress-seepage test system was utilized to investigate the influence of kaolin content and seepage loading rate on the seepage characteristics of filling medium. Through the analysis on the variation rules of sand loss and particle size distribution, the seepage characteristics and whole process of seepage instability of filling medium were explored in depth. It is concluded that (1) The seepage instability process of filling medium can be categorized into three stages: the initiation loss of fine clay, the accelerating loss of soil, and the stable status of soil loss. (2) The seepage failure process rate is proportional to the seepage loading rate and inversely proportional to the content of kaolin. (3) The kaolin and sand content of remaining mixture presented initial>bottom>middle>top status. The research results have guidance value for exploring the instability evolution mechanism of filling medium in deep roadway.

Abstract Background The population of plants is a crucial indicator in plant phenotyping and agricultural production, such as growth status monitoring, yield estimation, and grain depot management. To enhance the production efficiency and liberate labor force, many automated counting methods have been proposed, in which computer vision-based approaches show great potentials due to the feasibility of high-throughput processing and low cost. In particular, with the success of deep learning, more and more deeper learning-based approaches are introduced to deal with agriculture automation. Since different detection- and regression-based counting models have distinct characteristics, how to choose an appropriate model given the target task at hand remains unexplored and is important for practitioners. Results Targeting in-field maize tassels as a representative case study, the goal of this work is to present a comprehensive benchmark of state-of-the-art object detection and object counting methods, including Faster R-CNN, YOLOv3, FaceBoxes, RetinaNet, and the leading counting model of maize tassels—TasselNet. We create a Maize Tassel Detection Counting (MTDC) dataset by supplementing bounding box annotations to the Maize Tassels Counting (MTC) dataset to allow the training of detection models. We investigate key factors effecting the practical applications of the models, such as convergence behavior, scale robustness, speed-accuracy trade-off, as well as parameter sensitivity. Based on our benchmark, we summarise the advantages and limitations of each method and suggest several possible directions to improve current detection- and regression-based counting approaches to benefit next-generation intelligent agriculture. Conclusions Current state-of-the-art detection- and regression-based counting approaches can all achieve a relatively high degree of accuracy when dealing with in-field maize tassels, with at least 0.85 $$R^2$$ R 2 values and 28.2% rRMSE error. While detection-based methods are more robust than regression-based methods in scale variations and can infer extra information (e.g., object positions and sizes), the latter ones have significantly faster convergence behaviors and inference speed. To choose an appropriate in-filed plant counting method, accuracy, robustness, speed and some other algorithm-specific factors should be taken into account with the same priority. This work sheds light on different aspects of existing detection and counting approaches and provides guidance on how to tackle in-field plant counting. The MTDC dataset is made available at https://git.io/MTDC

This paper presents a thorough evaluation of twenty-one state-of-the-art widely-used crop segmentation algorithms, motived by their significance in vision tasks for further analysis. An ideal crop segmentation algorithm can effectively extract crop information, thus providing an important precondition for the application of intelligent agriculture analytics. In order to enable researchers in this field to fully understand various crop segmentation methods, this paper proposes a new classification strategy of object segmentation by dividing the algorithms into pixel-based and region-based approaches at first, and then systematically evaluating various crop segmentation methods with a unified data benchmark and four common metrics. A new dataset which incorporates crop variety, environment condition and observation distance into consideration is constructed for demonstrating the experiments and comparisons. The effectiveness and robustness of these algorithms were evaluated by three sets of comparative experiments. Based on the quantitative results, we summarize the advantages and disadvantages of the evaluated algorithms from the segmentation performances with four metric indicators. Furthermore, the discussion and evaluation results will provide great support for precision agriculture analysis.

Abstract Background Grain yield of wheat is greatly associated with the population of wheat spikes, i.e., $$spike~number~\text {m}^{-2}$$ spikenumberm-2 . To obtain this index in a reliable and efficient way, it is necessary to count wheat spikes accurately and automatically. Currently computer vision technologies have shown great potential to automate this task effectively in a low-end manner. In particular, counting wheat spikes is a typical visual counting problem, which is substantially studied under the name of object counting in Computer Vision. TasselNet, which represents one of the state-of-the-art counting approaches, is a convolutional neural network-based local regression model, and currently benchmarks the best record on counting maize tassels. However, when applying TasselNet to wheat spikes, it cannot predict accurate counts when spikes partially present. Results In this paper, we make an important observation that the counting performance of local regression networks can be significantly improved via adding visual context to the local patches. Meanwhile, such context can be treated as part of the receptive field without increasing the model capacity. We thus propose a simple yet effective contextual extension of TasselNet—TasselNetv2. If implementing TasselNetv2 in a fully convolutional form, both training and inference can be greatly sped up by reducing redundant computations. In particular, we collected and labeled a large-scale wheat spikes counting (WSC) dataset, with 1764 high-resolution images and 675,322 manually-annotated instances. Extensive experiments show that, TasselNetv2 not only achieves state-of-the-art performance on the WSC dataset ($$91.01\%$$ 91.01% counting accuracy) but also is more than an order of magnitude faster than TasselNet (13.82 fps on $$912\times 1216$$ 912×1216 images). The generality of TasselNetv2 is further demonstrated by advancing the state of the art on both the Maize Tassels Counting and ShanghaiTech Crowd Counting datasets. Conclusions This paper describes TasselNetv2 for counting wheat spikes, which simultaneously addresses two important use cases in plant counting: improving the counting accuracy without increasing model capacity, and improving efficiency without sacrificing accuracy. It is promising to be deployed in a real-time system with high-throughput demand. In particular, TasselNetv2 can achieve sufficiently accurate results when training from scratch with small networks, and adopting larger pre-trained networks can further boost accuracy. In practice, one can trade off the performance and efficiency according to certain application scenarios. Code and models are made available at: https://tinyurl.com/TasselNetv2.

High loads of fine particulate matter (PM2.5) and ozone (O3) pollution occurred frequently since early spring and led to an increasing contribution of secondary organic aerosols (SOA) in organic aerosols. However, the characteristics of precursor volatile organic compounds (VOCs) have rarely been studied. In this study, the continuous observation of VOCs was performed by an offline VOC monitoring system and gas chromatography-mass/flame ionization detector from 1 April 2020 to 31 July 2020; the characterization of VOCs and their contribution to SOA was explored. The results showed that during the observation period, the average mixing ratio of TVOCs was 42.6 ± 11.2 ppbv, and the major VOCs species were OVOCs, followed by alkanes, halocarbons, aromatics, alkenes and acetylene. When the west circulation pattern functioned, the value of aromatics increased, and the relation between PM2.5, O3 and VOCs increased when the high-pressure system controlled by anticyclone functioned. In combination with the results of positive matrix factorization, the main emission sources of ambient VOCs were complex, and the fuel combustion, industry-related emission, vehicle emission, biogenic emission and solvent volatilization accounted for 27.1%, 24.4%, 24.3%, 12.1% and 12.0%, respectively. Moreover, the industry-related emission contributed the greatest to the generation of SOA. This result indicated that the restrictions on aromatics during the industrial process are vital to reducing SOA formation.

This paper summarizes the factors affecting schistosomiasis transmission in the middle and lower reaches of the Yangtze River before and after the completion of the Three Gorges Dam and analyzes schistosomiasis prevalence trends to provide a basis for the application of the Three Gorges project methodologies in other areas. The Three Gorges Dam has demonstrated a positive effect on schistosomiasis control. Hubei, Hunan, Jiangxi, Anhui, and Jiangsu in the middle and lower reaches of the Yangtze River are currently regions with a high prevalence of schistosomiasis. These five provinces contained 97.62% of known snail areas and 93.66% of calculated schistosomiasis cases with 90.07% of counties (cities, districts) not yet meeting the criteria for schistosomiasis transmission interruption by the end of 2016. After the Three Gorges Dam was built, the prevalence of schistosomiasis in the middle and lower reaches of the Yangtze River decreased. By the end of 2016, the estimated number of cases decreased by 92.75% and the snail habitat areas decreased by 6.56% compared to 2004. No acute schistosomiasis cases have occurred for two consecutive years since 2015.

The occurrence levels of eight polybrominated diphenyl ethers (PBDEs) and four novel brominated flame retardants (NBFRs) were determined and compared in indoor dust from different microenvironments (21 homes, 23 offices and 16 day care centers) in Beijing, China. Concentrations of ∑8PBDEs in dust were 430–17,000 ng/g, 690–8600 ng/g, and 90–2300 ng/g for homes, offices, and day care centers, respectively, and were dominated by BDE-209. Concentrations of ∑4NBFRs ranged from 310 to 17,000 ng/g, 300 to 4300 ng/g, and not detected to 500 ng/g for homes, offices, and day care centers, respectively, and were dominated by bis(2-ethylhexyl)-3,4,5,6-tetrabromophthalate (BEH-TEBP) and decabromodiphenylethane (DBDPE) across microenvironments. The results showed an increasing detection and elevated concentration of NBFRs (especially BEH-TEBP), indicating that monitoring of NBFRs in dust samples should be of concern in future studies. A notable finding was that the BFR concentrations in dust samples from day care centers were generally one order of magnitude lower than those from homes and offices in the present study. This implies that previous estimates of toddler exposure via dust ingestion on data from homes may be overestimated. Concentrations of BDE-209 and ∑8PBDEs were found to be significantly higher in elevated surface dust than floor dust from day care centers. The estimates of daily intakes of BFRs via dust ingestion for Chinese adults and toddlers using Monte Carlo analysis were 2–5 orders of magnitude lower than the corresponding reference daily intakes. Keywords: Brominated flame retardants (BFRs), Floor dust, Elevated surface dust, Human exposure, Toddlers, Day care centers

Wastewater treatment plants (WWTPs) are considered to be the main sources of microplastic contaminants in the aquatic environment, and an in-depth understanding of the behavior of microplastics among the critical treatment technologies in WWTPs is urgently needed. In this paper, the characteristics and removal of microplastics in 38 WWTPs in 11 countries worldwide were reviewed. The abundance of microplastics in the influent, effluent, and sludge was compared. Then, based on existing data, the removal efficiency of microplastics in critical treatment technologies were compared by quantitative analysis. Particularly, detailed mechanisms of critical treatment technologies including primary settling treatment with flocculation, bioreactor system, advanced oxidation and membrane filtration were discussed. Thereafter, the abundance load and ecological hazard of the microplastics discharged from WWTPs into the aquatic and soil environments were summarized. The abundance of microplastics in the influent ranged from 0.28 particles L−1 to 3.14 × 104 particles L−1, while that in the effluent ranged from 0.01 particles L−1 to 2.97 × 102 particles L−1. The microplastic abundance in the sludge within the range of 4.40 × 103–2.40 × 105 particles kg−1. In addition, there are still 5.00 × 105–1.39 × 1010 microplastic particles discharged into the aquatic environment each day Moreover, among the critical treatment technologies, the quantitative analysis revealed that filter-based treatment technologies exhibited the best microplastics removal efficiency. Fibers and microplastics with large particle sizes (0.5–5 mm) were easily separated by primary settling. Polyethene and small-particle size microplastics (

In most mining areas of China, coal seams are characterized by low gas pressure, poor permeability, and high gas adsorption capacity, all of which have brought considerable difficulties to coal seam mining and coalbed methane (CBM) extraction. According to the multiyear scientific research and production practice of China, gas is migrated in quantity only after the coal body is directly mined, and the surrounding rocks deform and fracture under the mining influence. Thus, the key to effective control of gas migration and the coal and CBM comining technology lies in investigating the gas resolution, permeation, migration, and accumulation laws in the coal seams under the unloading confining pressure during mining. The MTS815.02 rock mechanics testing system and its supporting equipment are combined to test the permeability characteristics of coal and rock mass (postpeak fractured coal and sandstone specimens) under the loading and unloading of confining pressure using the steady method, and then, the permeation laws of the fractured coal and rock mass are obtained. Results show that after the postpeak rock crack propagation reaches a stable state, the confining pressure gradually increases, and the gas permeability presents an approximately linear reduction; in the postpeak unloading phase, the opening and coalescence degree of rock cracks gradually increase as the deformation extends. Thus, permeability reaches a peak value. The strain softening phase follows, where the cracks are closed and permeability declines to a certain extent. Moreover, the unloading step size of confining pressure has bearing on gas permeability. Specifically, as the unloading step size of confining pressure decreases, the change of gas permeability increases in stability.

The cement-plaster bonded rock-like material is one of the most commonly used materials to simulate different rocks in physical model tests. However, the applicability of this material in solid-fluid coupling model tests is not clear because there are few research studies on the water-physical properties of this material and its similarity to the actual rock is uncertain. This paper presents a systemic experimental study on the water-physical properties of the cement-plaster bonded rock-like materials. The parameters of rock-like materials, including water absorption, softening coefficient, and permeability coefficient, were compared with those of actual rocks to analyse the applicability of such material. Then, the influence of proportion on the water-physical properties of this material was discussed. By multiple regression analysis of the test results, empirical equations between the water-physical parameters and proportions were proposed. The equations can be used to estimate the water-physical properties of cement-plaster bonded rock-like materials with specific proportion and thus to select suitable materials in the solid-fluid coupling physical model tests.

Plant counting runs through almost every stage of agricultural production from seed breeding, germination, cultivation, fertilization, pollination to yield estimation, and harvesting. With the prevalence of digital cameras, graphics processing units and deep learning-based computer vision technology, plant counting has gradually shifted from traditional manual observation to vision-based automated solutions. One of popular solutions is a state-of-the-art object detection technique called Faster R-CNN where plant counts can be estimated from the number of bounding boxes detected. It has become a standard configuration for many plant counting systems in plant phenotyping. Faster R-CNN, however, is expensive in computation, particularly when dealing with high-resolution images. Unfortunately high-resolution imagery is frequently used in modern plant phenotyping platforms such as unmanned aerial vehicles, engendering inefficient image analysis. Such inefficiency largely limits the throughput of a phenotyping system. The goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery. In contrast to conventional object detection, we encourage another promising paradigm termed object counting where plant counts are directly regressed from images, without detecting bounding boxes. In this work, by profiling the computational bottleneck, we implement a fast version of a state-of-the-art plant counting model TasselNetV2 with several minor yet effective modifications. We also provide insights why these modifications make sense. This fast version, TasselNetV2+, runs an order of magnitude faster than TasselNetV2, achieving around 30 fps on image resolution of 1980 × 1080, while it still retains the same level of counting accuracy. We validate its effectiveness on three plant counting tasks, including wheat ears counting, maize tassels counting, and sorghum heads counting. To encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plus.

Rice density is closely related to yield estimation, growth diagnosis, cultivated area statistics, and management and damage evaluation. Currently, rice density estimation heavily relies on manual sampling and counting, which is inefficient and inaccurate. With the prevalence of digital imagery, computer vision (CV) technology emerges as a promising alternative to automate this task. However, challenges of an in-field environment, such as illumination, scale, and appearance variations, render gaps for deploying CV methods. To fill these gaps towards accurate rice density estimation, we propose a deep learning-based approach called the Scale-Fusion Counting Classification Network (SFC2Net) that integrates several state-of-the-art computer vision ideas. In particular, SFC2Net addresses appearance and illumination changes by employing a multicolumn pretrained network and multilayer feature fusion to enhance feature representation. To ameliorate sample imbalance engendered by scale, SFC2Net follows a recent blockwise classification idea. We validate SFC2Net on a new rice plant counting (RPC) dataset collected from two field sites in China from 2010 to 2013. Experimental results show that SFC2Net achieves highly accurate counting performance on the RPC dataset with a mean absolute error (MAE) of 25.51, a root mean square error (MSE) of 38.06, a relative MAE of 3.82%, and a R2 of 0.98, which exhibits a relative improvement of 48.2% w.r.t. MAE over the conventional counting approach CSRNet. Further, SFC2Net provides high-throughput processing capability, with 16.7 frames per second on 1024×1024 images. Our results suggest that manual rice counting can be safely replaced by SFC2Net at early growth stages. Code and models are available online at https://git.io/sfc2net.

In this study, the occurrence of 8 polybrominated diphenyl ethers (PBDEs), 5 alternative flame retardants (AFRs), and 7 organophosphate flame retardants (OPFRs) was determined in 50 pairs of human fingernail and indoor dust samples. The concentrations in fingernail were 9.79–242 ng/g, 17.7–926 ng/g, and 58.0–590 ng/g for PBDEs, AFRs, and OPFRs. Male fingernail showed significantly (p

Abstract Background Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. Results This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment. With 361 field images collected in four experimental fields across China between 2010 and 2015 and corresponding manually-labelled dotted annotations, a novel Maize Tassels Counting (MTC) dataset is created and will be released with this paper. To alleviate the in-field challenges, a deep convolutional neural network-based approach termed TasselNet is proposed. TasselNet can achieve good adaptability to in-field variations via modelling the local visual characteristics of field images and regressing the local counts of maize tassels. Extensive results on the MTC dataset demonstrate that TasselNet outperforms other state-of-the-art approaches by large margins and achieves the overall best counting performance, with a mean absolute error of 6.6 and a mean squared error of 9.6 averaged over 8 test sequences. Conclusions TasselNet can achieve robust in-field counting of maize tassels with a relatively high degree of accuracy. Our experimental evaluations also suggest several good practices for practitioners working on maize-tassel-like counting problems. It is worth noting that, though the counting errors have been greatly reduced by TasselNet, in-field counting of maize tassels remains an open and unsolved problem.

The quick and accurate picking of the first arrival on microseismic signals is one of the critical processing steps of microseismic monitoring. This study proposed a first arrival picking method for application to microseismic data with a low signal-to-noise ratio (SNR). This approach consisted of two steps: feature selection and clustering. First of all, the optimal feature was searched automatically using the ReliefF algorithm according to the weight distribution of the signal features, and without manual design. On that basis, a k-means clustering method was adopted to classify the microseismic data with symmetry (0–1), and the first arrival times were accurately picked. The proposed method was validated using the synthetic data with different noise levels and real microseismic data. The comparative study results indicated that the proposed method had obviously outperformed the classical STA/LTA and the k-means without feature selection. Finally, the microseismic localization of the first arrivals picked using the various methods were compared. The positioning errors were analyzed using box plots with symmetric effect, and those of the proposed method were the smallest, and stable (all of which were less than 0.5 m), which further verified the superiority of this study’s proposed method and its potential in processing complicated microseismic datasets.