Environmental Earth Sciences最新文献

The combined extraction of gas from close-distance coal seams can be an effective means of preventing and controlling the outburst of coal and gas. It can improve the production efficiency of coal mines and make effective use of coal methane resources. In order to determine the source of the combined extraction of gas in the coal seam group and the proportion of gas extraction in each coal seam, in this work, the combination of carbon isotope measurement, numerical simulation and field layered measurement test are adopted to study the traceability of combined extraction of gas in outburst coal seam group. When the four-layer coal seam is jointly extracted, the gas mixing ratios of the coal seams from top to bottom calculated by the carbon isotope method account for about 23%, 56%, 7% and 13%, respectively. According to the field layered measurement test, the proportion of each coalbed methane in the mixture is about 22%, 55%, 7% and 15% in the top-down four layers of coal seam. In accordance with the numerical simulation study, the top-to-bottom ratio of gas extracted from each coal seam is about 3.4:7.4:1:1.8 when the multi-holes are arranged in parallel to extract the four-layer coal seam. Under the geological conditions of the coal seam in this coal mine, the three research methods all confirm that when the four-layer coal seam is jointly extracted, the amount of gas extracted from the second-layer coal seam is the largest, followed by the amount of gas extracted from the first-layer coal seam, and the amount of gas extracted from the third and fourth-layer coal seams is relatively small. The research offers a theoretical foundation for evaluating the accuracy of measuring the volume of gas extracted from multi-coal seam combined extraction, and provides a new research idea for solving the problem of combined extraction of close-spaced bursting coal seams, which has guiding significance for the accurate measurement of mine gas control.

Mining activity is an important source of heavy metals in agricultural soils, threatening food safety and human health. In the present study, an integrated approach of Nemerow pollution index (P_N), Geo-accumulation index (I_geo), potential ecological risk index (PERI), principal component analysis (PCA), cluster analysis (CA), and positive matrix factorization (PMF) was applied to comprehensively illustrate the pollution, potential ecological risk, and sources of heavy metals in agricultural soils surrounding a legacy Pb-Zn mine. The agricultural soils were seriously polluted and suffered high risks of heavy metals with the order of Cd > Pb > Hg > As > Zn > Cu > Ni > Cr. According to the PMF model, Cd (85.5%) and Zn (15.9%) originated from irrigation of polluted water. As (76.2%) and Pb (26.4%) were related to Pb/Zn ores transportation. Pb (52.5%), Zn (51.1%), and Cu (38.8%) were associated with atmospheric deposition during Pb-Zn mining. Hg (67.3%) was mainly from agricultural sources. Cr (59.5%), Ni (59%), and Cu (28.7%) came from the natural parent materials. Pb-Zn mining activity was the priority source accounting for 57.7% of heavy metals pollution in the agricultural soil. Additionally, agricultural and natural sources contributed 19.8% and 22.5%, respectively. These results provide valuable information for future prevention, remediation, and management of soil heavy metals pollution surrounding the Pb-Zn mining region.

The southern Mediterranean coast suffers from limited water resources as a result of exploitation of water supply, population growth, and climate change. Spatial lineaments and Seawater Intrusion (SWI) were detected at the southeast portion of Ras El Dabaa, on Egypt’s northwest coast, using the direct current resistivity (DCR) method. The Vertical Electrical Sounding (VES) data were acquired using Schlumberger array along four profiles and inverted both independently and jointly, aiming to obtain Two Dimensional (2D) geoelectrical images. The results of the one Dimensional (1D) inversion of VES data at each profile were stitched to form pseudo-2D sections on which the resistivity values and aquifer thickness in the southwest of the region appeared to be generally increasing, indicating a potential improvement in water quality.However, the results did not fully image the lateral variation but focused on the horizontal boundaries of the subsurface. On the contrary, the results of 2D inversion of the same data sets successfully managed to provide images that depicted resistivity distribution in both lateral and vertical directions. The detected sets of lineaments and fractured zones within the oolitic limestone and fossiliferous limestone units control the occurrence of groundwater in the region. The 2D inversion scheme revealed a low resistivity zone that indicated the presence of SWI and/or the dissolution of marine salts from the marine limestone bedrock of these aquifers in the northern portions of the studied area. Additionally, analysis of the 2D apparent porosity section shows how aquifers are connected by secondary porosity, which is defined by structures that resemble channels. The current approach offers valuable structural information for future planning and development of such complex geological coastal locations, taking into consideration the vulnerability of the groundwater system.

The Kingdom of Saudi Arabia is facing challenges related to water scarcity, however, the Cambrian-Ordovician Saq Aquifer System, in Al Qassim Province, provides vital water resources. This article assesses the petrophysical properties and hydrochemical characteristics of the aquifer system utilizing downhole cam recording and geostatistical analysis. The evaluation aims to assign the hydrogeological attributes, groundwater potential, and associated risks using an open-petrophysical aquifer system approach. The petrophysical evaluation appraises the prevailing lithology, zonation, hydrogeological properties, and salinity patterns. Sandstones with low shale content and dispersed distribution possess effective porosity that is fully saturated with groundwater containing calcium and bicarbonate ions. The majority of groundwater samples exhibit simple dissolution or lack prevailing ionic concentrations, indicating an ancient marine water genesis and a fossilized water type. The resistivity-depth profiles reveals three potential water-bearing aquifers including a disconnected compartment of an unconfined aquifer, a continuous compartment of a confined aquifer, and a continuous compartment of an unconfined aquifer. Petrophysical and hydrochemical parameters have been analyzed using geostatistical methods to assess their spatial variability and reduce potential sampling errors. Three distinct risk segments (RSs) with varying levels of risk characterize the aquifer system. RS-A represents a potential aquifer with low risk, RS-B poses moderate risks, and RS-C carries high risks. A fairway map of the aquifer system assigns geologic-hydro related factors that influence aquifer assessment and risk mapping. Segment-A is deemed an attractive long-term investment opportunity with low risk, while Segment-B offers a good investment opportunity with moderate risk. Segment-C provides a fair investment opportunity but entails high risks related to petrophysical qualities, hydrochemical characterizations, and irrigation utilities. The extraction and utilization of groundwater present promising investment opportunities, while employing a petrophysical approach can effectively evaluate and manage groundwater resources for sustainable utilization.

Quantitative landslide hazard models provide estimations of the number of landslides per area and time that might be expected in the near future. These models are essential to calculate landslide risk in monetary terms. Although they are very useful tools for managing the activity of unstable slopes, their production calls for a vast amount of spatial and temporal data. Here, we present a case where this was possible producing the quantitative landslide hazard map for the municipality of Loja, Ecuador. It is based on a model that integrates six causal factors (distance to faults, lithology, slope, geomorphology, topographic position index, land use) and a comprehensive multi-temporal inventory of landslides. First, a susceptibility map was generated with a good prediction capability (Area under prediction rate curve, AUPRC: 0.8) combining two widely used and tested probabilistic methods: “Matrix” and “Likelihood ratio”. Subsequently, this map was transformed into a hazard map by including the temporal frequency of landslides. The map assesses the annual probability of each pixel to be set in motion within one of these landslides. The preliminary temporal validation of the hazard map indicates that the pixels mobilized during two years after the map production fit reasonably well with our spatio-temporal forecast. The findings emphasize that classical spatial prediction methods, when augmented by robust and extensive data on landslide distribution and activity, can yield hazard models with reliable predictive capabilities. This suggests that in practical applications, models based on relatively simple calculations can provide effective and reliable starting points for managing landslide risks.

The empirical formula (EF) method, which do not rely on topographic data, stands as the prevailing technique for estimating lake water storage variation (LWSV). However, for smaller lakes, the sporadic monitoring frequency of satellite altimetry fails to adequately support this method, presenting a challenge in accurately gauging LWSV. Using Lake Chahannur, a lake in China with an area smaller than 50 km², as a case study, seven schemes based on the EF method and the Area-Volume-Height (A-V-H) curve method were designed to estimate the LWSV of this undersized lake. The efficacy and precision of each scheme were evaluated against field-measured elevations. Findings reveal that due to the limited satellite altimetry monitoring, both the EF method and the H-driven A-V-H curve schemes struggle to provide consistent and comprehensive estimations. In the A-driven A-V-H curve schemes, terrain data from SRTM DEM suffers from mask processing and substantial errors, with the former posing challenges for shrinking lakes and the latter significantly compromising estimation accuracy. While field-measured elevations boast high precision, the interpolation process leads to terrain maps lacking in detail, with site density becoming a crucial factor influencing the accuracy of LWSV estimation. The combination of terrain reconstruction and A-driven pattern emerges as the most promising, boasting high accuracy, rich detail, and significantly reduced reliance on satellite altimetry monitoring, making it particularly suitable for small lakes. Chahannur’s bottom elevation ranges between 1271.71 and 1273.44 m, and the lake shows a downward trend in water volume from 1991 to 2020, with fluctuations totaling approximately 35 million m³. This study serves as a vital addition to the field of LWSV estimation, potentially broadening the scope of estimation from large-scale lakes to a wider array of global surface water bodies.

The dams and reservoirs built in the valleys play a key role in water management and contribute to a society’s quality of life. But they are also a potential risk, with often tragic downstream consequences. Keddara dam on the Boudouaou wadi, in central Algeria, is just an example. Its historical water level record shows that it has overflowed on several occasions, resulting in a real danger of flooding for people living downstream, particularly those living in the town of Boudouaou. In this respect, hazard mapping represents a very effective means of prevention, through the delimitation of the Public Hydraulic Domain (PHD). To do this, we were interested in the predetermination of extreme flood flows and hydraulic simulation in a natural environment. In fact, the particularity of our study lies in the strategy adopted for the case of an ungauged catchment, led by a dam located in its upstream part, and characterized by water inflows outside the catchment, This situation of the dam has led to devastating spills, which leads us to consider the entire Boudouaou basin to determine the flow rates and thus the hazard zones downstream the basin. In order to solve this problem a very high spatial resolution data was used, with Digital Terrain Model and Digital Surface Model of 1 m, as well as orthorectified satellite image of 50 cm. On the other hand, the peak flow data for different return periods were estimated using empirical formulas, using frequency maximum precipitation and the physical characteristics of the basins. However, roughness data, bridges as well as details of all hydraulic structures are the subject of several field visits. The results of the hydraulic simulation obtained after calibration and validation of the HEC-RAS model, by using the peak flows caused by the overflow of the Keddara dam on the one hand, and the Boudouaou sub-basin downstream on the other hand, seem to give better results for the various retained recurrences (high, medium and low), i.e. simulated water levels correspond exactly or almost exactly to the heights measured in the field.

Land users and policy makers recognize importance of soil health and these types of evaluations are welcomed within sustainable land management. The aim of this study was to establish minimum and maximum values of soil health index across the breadth of agricultural used soils of Slovakia. Core objectives included (1) identifying the range of soil health benchmarks; (2) defining benchmarks for different land uses, and (3) defining benchmarks of soil health index for main groups of agricultural soils in Slovakia. These benchmarks represent the first soil health metrics of their kind in Slovakia using data from 266 locations. The soil health index (SHI) approach has been used as a quantitative tool to establish linkage between soil health and soil ecosystem services. We recorded the highest average SHI value in the very warm climatic region of the Slovak Republic, where the majority of agriculturally used arable soils (with an optimal pH value without contamination) are located. The locality with lowest SHI value is in a slightly warm area on soil used as arable soil with a very small depth of humus horizon, with by the pH value in an acidic area, and with a high content of clay. The typical SHI in humus-rich soil groups of Slovakia (Mollic Fluvisols, Chernozems and Cutanhic Luvisols) is higher contrasted with the typical SHI in other soil groups of Slovakia.

Presently, online ride-hailing firms and municipal transportation systems substantially influence urban traffic management. Considering the increasing use of these services, it is essential to assess and predict travel demand to optimize service efficiency. This study analyzes Uber data from New York City, employing automated machine learning algorithms to assess and forecast ride demand inside the city. In conventional transportation planning, forecasting travelers’ destination selections is a crucial stage. Traditional methodologies, such as physical models like gravity models, are constrained in their capacity to encompass the comprehensive array of elements affecting travel behavior, frequently addressing just two or three variables. This study employs machine learning methodologies for predicting passenger destination choices, illustrating that these algorithms can include a wider array of variables, hence providing enhanced forecast accuracy relative to conventional approaches. The utilization of ArcGIS Pro and Python modules for automation enhanced the efficiency of spatial analysis. A range of machine learning methodologies, such as decision trees, Light-GBM, XG-Boost, Cat-Boost, and hybrid models, were utilized to predict demand. The selected source demand forecasting model is an ensemble model, with a R² of 0.94 and a Mean Absolute Error of 91.63. The optimal model for predicting destination demand was identified as a composite model comprising eight distinct algorithms. The model’s R² is 0.95, while the Mean Absolute Error is 67.12. Moreover, the examination of environmental factors indicated that proximity to recreational activities, median age, and population density had the most substantial influence on predicting travel demand.

The scarcity of freshwater resources in coastal plains has raised global concerns. Understanding the chemical characteristics and evolution of groundwater in coastal plains is crucial for ensuring sustainable water supply for residents and irrigation. This study focuses on a representative profile of the Luanhe plain, systematically analyzing hydrochemistry and stable isotopes of seawater, river water, and groundwater samples to elucidate groundwater chemistry evolution and investigate the influence of evaporation and mixing processes. Results showed that shallow groundwater was predominantly of the Ca-HCO₃ type, formed through the dissolution of halite, carbonate, and silicate minerals in the aquifer. Under the influence of evaporation and mixing processes, there was a transition in the chemical type towards Ca-Na-HCO₃ type. Especially in areas influenced by seawater intrusion at estuaries, a rapid shift towards Na-Cl type occurred. In addition, deep aquifers evolved predominantly into Ca-HCO₃ type groundwater through the leaching of silicate and carbonate minerals within the aquifer matrix. Along the flow path, influenced by cation exchange and mixing processes, the water chemistry underwent a shift from the Ca-HCO₃ type to the Na-HCO₃ type. The river water chemistry was primarily influenced by evaporation and the erosion of silicate and sulfate minerals from the riverbed. At estuarine zones, intense seawater intrusion significantly increased salinity and shifted the water chemistry from a Ca-Cl type to a Na-Cl type. This study elucidated the evolution mechanisms and influencing factors of coastal groundwater chemistry, providing scientific insights for local groundwater resource management.