Mining Research Articles

Heavy metal contamination in eggs on poultry farms and ecological risk assessment around a gold mine area in northern Thailand

The aim of this study was to analyze and compare the Hg, Pb, Cd, and Mn levels in egg feed, soil, and water among laying hens, laying ducks, and free-grazing duck farms in contaminated and uncontaminated areas. This study revealed that the Hg concentration in the eggs of free-grazing ducks was significantly greater than that in the eggs of laying hens and ducks in both contaminated and uncontaminated areas. However, the Pb and Mn levels in the eggs of laying ducks and free-grazing ducks were significantly greater than those in the eggs of laying hens in the contaminated area. Unfortunately, the Hg, Pb, Cd, and Mn concentrations in the feed, soil, and water from these three farms in both areas were not significantly different (P > 0.05). Hg and Cd were confirmed to be enriched in the egg albumin fraction, while Pb and Mn were found mainly in the egg yolk. However, egg consumption from free-grazing duck farms was the riskiest to Hg, Pb, and Mn contamination in the contaminated area. Additionally, the ecological risk factor (ER) in the soil revealed that all the farms were at considerable to high environmental risk for Cd except for Hg and Pb. Although the potential ecological risk index (RI) indicated a moderate risk for all farms in both contaminated and uncontaminated areas, these results were not consistent with our hypothesis. Therefore, the information gained in this study could be useful for setting up mitigation strategies and making decisions about public health concerns related to health hazards, especially for ecological risk assessments of heavy metal contamination.

Research on Coal Mine Environmental Pollution Management and Ecological Restoration Technology

This paper analyses the current situation of environmental pollution in coal mines, its causes, and its impact on the ecological environment, and systematically researches the management of environmental pollution in coal mines and ecological restoration technology. The paper analyses the main categories of coal mine environmental pollution, including water pollution, air pollution, and solid waste pollution, and discusses the causes of these pollution and their destructive effects on the ecological environment. Immediately after that, the article elaborates on the treatment technologies of coal mine environmental pollution, including physical, chemical and biological treatment technologies for water pollution control, blocking, adsorption and covering technologies for air pollution control, and landfill, incineration and resource utilization strategies for solid waste disposal. This paper also discusses coal mine ecological restoration techniques, including species selection and allocation for vegetation restoration, vegetation restoration models, physical, chemical and biological methods for soil improvement, and engineering, vegetation and agricultural measures for soil and water conservation. Through the research of this paper, the researchers aim to provide scientific basis and technical support for the management of coal mine environmental pollution and ecological restoration and to promote the restoration of the ecological environment in mining areas and the sustainable development of the coal industry.

Enhanced path tracking control of hydraulic support pushing mechanism via adaptive sliding mode technique in coal mine backfill operations

The hydraulic support pushing mechanism is the primary equipment utilized in coal mine backfill operations, playing a crucial role in enhancing filling efficiency, ensuring a stable filling body, and managing gob safety. This paper focuses on analyzing the dynamic model and the interrelationship of the hydraulic cylinder, which serves as the power source for the pushing mechanism. To address the intricate coupling effects arising from the hydraulic cylinders and the displacement-force induced by the shared pump, this study employs feedforward compensation for decoupling analysis. Additionally, this article introduces an adaptive sliding mode approach law and an adaptive synovial controller to combat issues such as buffeting and interference. The simulation results demonstrate that the sliding mode reaching law proposed in this paper can achieve a stable state in approximately 3 seconds, which is significantly better than other methods. Combining the experimental equipment information from a mining area in Hebei Province with Amesim-Simulink simulation results, it is evident that the adaptive sliding mode controller exhibits an error range between approximately 1.33E-4 and 1.5E-4 during the stable phase. This performance surpasses traditional PI and fuzzy PID controllers in terms of path tracking ability, effectively enabling precise control of the filling support pushing mechanism.

Cerium(III)-induced structural transformation of hexagonal birnessite: Effect of mineral phase transition on arsenite transport and valence changes

The widespread mining and application of rare earth elements (REEs) have led to their continuous accumulation in the environment, with increasing concentrations in soil. The interaction between the most abundant REEs, cerium (Ce), and the prevalent hexagonal birnessite (HB) in the environment is worth attention. HB is one of the most effective metal oxides for the oxidation of arsenite [As(III)] and subsequent adsorption, and thus for arsenic (As) immobilization. Therefore, in this study, we investigated the effect of the presence of Ce(III) ion on the HB formation process and the influence of generating minerals on the oxidation and removal of As(III). Research has found that the interfacial reactions of REEs in manganese (Mn) minerals not only affect their cycling but also alter the properties of the Mn minerals, thereby affecting the environmental fate of As. The results indicated that the presence of Ce ions affected the structure of HB during mineral synthesis and reduced the crystallinity of the conversion products. Their substitution for Mn(IV) in the lattice increased the specific surface area of minerals, reduced particle size, and produced more hydroxyl groups that were conducive to the immobilization of As(III). Meanwhile, Ce(III) was oxidized to Ce(IV) during the formation of Ce-bearing hexagonal birnessite (Ce-HB), and CeO2 nanoparticles were formed on the mineral surface and the removal rate of As(III) by Ce-HB was greatly improved. When the As concentration was lower than 6 mg·L−1, the removal effect of Ce-HB could reach the drinking water standard. However, the oxidation rate decreased due to the decrease in the proportion of Mn(IV). This study fundamentally reveals the behavior of HB coexisting with Ce in the migration and transformation of As(III) in the environment.

Environment-compatible heavy metal risk prediction method created with multilevel ensemble learning

Accurate health risk prediction (HRP) is an effective means of reducing the hazards of heavy metal (HM) exposure. It can address the drawbacks of lag and passivity faced by health risk assessment. This study innovatively proposed an HRP method, MEL-HR, based on multilevel ensemble learning (MEL) technology and environment compatibility. We conducted point and interval prediction experiments on health risks using 490 sets of data covering 17 environment factors. The point prediction results indicated that when the model predicts HI and TCR, the R2 values were 0.707 and 0.619, respectively. For P5, P50, and P95 in interval prediction, the R2 values of the model were 0.706, 0.703, and 0.672 for HI, and that for TCR were 0.620, 0.607, and 0.616, respectively. The analysis of feature importance indicated that, in addition to HM factors, longitude, mining area coefficient, and soil organic matter were key environmental factors affecting the MEL-HR model. Comparative experiments showed that compared to soil HMs-based MEL-HR, environment compatibility-based MEL-HR has improved the accuracy for HI and TCR by 19.83 % and 40.36 % for the point prediction and 22.06 % and 40.01 % for interval prediction. This study can provide technical support for targeted and resilient prevention and control of health risks.

Mineral prospectivity mapping susceptibility evaluation based on interpretable ensemble learning

In the present study, an interpretable ensemble learning-based method for mineral prediction mapping is proposed to address the limitations of traditional mineralization prediction modeling. A stacking ensemble learning model was constructed, employing random forest (RF), extreme gradient boosting (XGBoost), and AdaBoost as primary learners, and logistic regression as the secondary learner. The model’s interpretability was analyzed using local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) algorithms. The lead–zinc deposits in the Changba mining area of Gansu Province served as a case study. By integrating geological and geochemical data, and selecting 18 evaluation factors, the effectiveness and interpretability of the ensemble learning model in mineralization prediction were validated. The results demonstrate that the lead–zinc prospecting map generated using the stacking model effectively correlates geological and geochemical data with known lead–zinc deposit locations, significantly enhancing the accuracy of identifying potential lead–zinc prospecting areas.

Effects of groundwater level changes on soil characteristics and vegetation response in arid and semiarid coal mining areas.

Coal mining in arid and semiarid regions often leads to numerous ecological and environmental problems, such as aquifer depletion, lake shrinkage, vegetation degradation, and surface desertification. The drainage from coal mining activities is a major driving force in the evolution of the groundwater-soil-vegetation system. In order to explore the effect of groundwater level fluctuation on soil properties and the response mechanism of surface vegetation in coal mining areas, this study is based on hydrogeological and ecological vegetation investigations in the Bojianghaizi Basin, and soil and vegetation samples are collected in the areas with different groundwater levels, and soil and vegetation indexes are analyzed with the aid of methods such as numerical statistics, linear regression, and correlation analysis with the aid of the Origin software. The results show that there is a significant negative correlation between groundwater table (GWT) and soil water content (SWC), soil conductivity, soil organic matter (SOM), soil available nitrogen (SAN), and soil available potassium (SAK). Mining activities have led to the destruction of the soil structure, greatly reducing its ability to retain water and fertilizer. The contents of SWC, SOM, and SAN in the mining area are significantly reduced, which are at least 49.73%, 47.56% and 59.90% lower than those around the mining area. On the northern and southern sides of the lake, serious soil salinization exists in the lakeshore zone where the depth to the water table is <0.5m, and the water required for the growth of vegetation here mainly comes from the groundwater, so there are only a few water-loving and saline-resistant plants; when the depth to the water table is 0.5-7m, the growth of surface vegetation is influenced by the double impacts of the water table and atmospheric precipitation with a high degree of species richness; when the depth to the water table is >7m, the surface vegetation is only dependent on the limited atmospheric precipitation for water. When the depth of groundwater is >7m, the surface vegetation only relies on limited atmospheric precipitation for water, and drought-tolerant plants mainly grow in these areas. This study not only provides a scientific basis for the sustainable development and environmental protection of similar mines in the world, but also has important significance in guiding the ecological management and rational utilization of water resources in coal mine areas. What is more, This study provides valuable insights into sustainable water resource management in arid and semi-arid regions, crucial for mitigating the ecological impacts of coal mining activities.

Green tech soil analysis: a comparison of potentially toxic element contents among urban, agricultural, and gold mining areas in the Eastern Brazilian Amazon.

Artisanal gold mining can lead to soil contamination with potentially toxic elements (PTEs), necessitating soil quality monitoring due to environmental and human health risks. However, determining PTE levels through acid digestion is time-consuming, generates chemical waste, and requires significant resources. As an alternative, portable X-ray fluorescence (pXRF) offers a faster, more cost-effective, and sustainable analysis. This study compared total As, Ba, Cr, Cu, Fe, Mn, Ni, Pb, Sr, Ti, V, and Zn obtained from pXRF with their pseudo-total contents obtained through acid digestion (USEPA method 3051A) in areas influenced by artisanal gold mining in the Eastern Amazon, Brazil. pXRF data and machine learning algorithms were used to predict extractable Cu, Fe, Mn, and Zn. Linear regression models were fitted to compare the two methods, and random forest and support vector machine techniques were used to predict extractable contents. The best regression model fits for the pseudo-total PTE contents were those for Cu, Fe, Mn and Pb in agricultural areas (R2 > 0.80); Fe and Mn in gold mining residue (R2 > 0.70); and Ba, Cu and Mn in urban areas (R2 > 0.80). The best models for predicting the extractable PTE contents were those for Cu (R2 = 0.72; RMSE = 2.58mgdm-3) and Zn (R2 = 0.71; RMSE = 1.44mgdm-3) in agricultural areas and for Zn (R2 = 0.72; RMSE = 0.43mgdm-3) in gold mining residue. The results demonstrated that pXRF can characterize and predict PTE contents in mining-impacted areas, offering a sustainable approach to soil quality analysis.

Assessment of seismic station performance in north Chhattisgarh, India: a comprehensive ambient noise analysis

A new network of 16 broadband seismic stations is operational in the northern Chhattisgarh region to monitor the seismicity arising in the otherwise seismically quiescent zone. Ambient noise is comprehensively analyzed to evaluate station performance and validate the data quality. Power spectral densities are computed for all stations, comparing ambient noise levels against global limits for data from November 2022 to December 2023. Results indicate that noise levels consistently fall within global noise limits. The study emphasizes the pronounced effect of instrumental tilt on the horizontal components of broadband seismometers. Spatial variation of ambient noise levels across various period bands of the noise spectrum highlights the contribution of diverse noise sources such as anthropogenic activities, surface waves, body waves, and barometric effects. Seasonal variations in short-period, microseism, and long-period bands reveal temporal variation of noise levels. Diurnal variation in the short-period band indicates a prominent effect of cultural noise. Furthermore, the proximity of seismic stations to major mining areas induces high noise levels in the very short-period band during blasting activities compared to non-blasting periods. Our study thus explores the variability of the ambient noise environment while validating the robustness and stability of the seismic installation across the study region.

Legal Review of Procedures for Granting Rock Mining Business Permits

This research is motivated by the granting of mining permits originally granted by the Central Government, with the issuance of Presidential Decree Number 55 of 2022 concerning the Delegation of Granting of Business Licensing in the Mineral and Coal Mining Sector, some mining permits are granted by the Central Government to the Provincial Government, but only limited to the granting of Mining Business Permits (IUP) for non-metallic mineral groups, certain types of non-metallic minerals, and rocks. The legal research method used in this research is normative juridical research which is research that is carried out or aimed only at written regulations with a descriptive analytical research nature which is a method that functions to describe or provide an overview of the object being studied. The data source used is secondary data with quantitative data analysis. The results of this study are: The Procedure for Rock Mining Business Permits is carried out by means of an application to the regent or mayor if the mining area is in a district or city, to the governor if the mining area is across district or city borders, to the minister if the mining area is in a cross-provincial border area. The procedures carried out in the procedures for rock mining permits are the determination of the mining area carried out by authorized officials, determination of mining business areas, mining business permits, regulation of mining permits if there is a violation of mining permits. Supervision of Rock Mining Activities After Obtaining a Business Permit in the Mining Sector, it is carried out by the ESDM Service where the mining permit is located. Settlement of Rock Mining Permit Disputes can be carried out through a non-adjudication process.

Assessing bioavailability risks of heavy metals in polymetallic mining regions: a comprehensive analysis of soils with varied land uses.

To accurately assess the bioavailability risk of heavy metals (HMs) in a representative polymetallic mining region, we undertook an exhaustive analysis of Cu, Pb, Ni, Co, Cd, Zn, Mn, and Cr in soils from diverse land-use types, encompassing agricultural, forest, residential, and mining areas. We employed speciation analysis methods and a modified risk assessment approach to ascertain potential ecological threats posed by the HMs. Our findings reveal that both the total potential ecological risk and the modified bioavailability risks are most pronounced in the soil of the mining area. The modified bioavailability threats are primarily caused by Pb, Ni, Cd, and Co. Although the total potential ecological risk of Cu is high in the local soil, the predominance of its stable forms reduces its mobility, thereby mitigating its detrimental impact on the ecosystem. Additionally, medium modified bioavailability risks were identified in the peripheries of agricultural and forest areas, potentially attributable to geological processes and agricultural activities. Within the urban district, medium risks were observed in residential and mining areas, likely resulting from mining, metallurgy, industrial operations, and traffic-related activities. This study provides critical insights that can assist governmental authorities in devising targeted policies to alleviate health hazards associated with soils in polymetallic mining regions.

Mineral exploration and environmental impact assessment in the Jabal Hamadat Area, Central Eastern Desert, Egypt, using remote sensing and airborne radiometric data

Mineral exploration is essential for economic growth, but it must be conducted with careful consideration of health and environmental impacts. This study focuses on mapping potential mineralization areas and evaluating environmental consequences in the Jabal Hamadat area. By integrating ASTER, Landsat-9 and airborne radiometric data, the study includes: (1) identification of alteration mineral indicators including iron oxides (gossanized zone), chlorite (propylitic zone), kaolinite, sericite, montmorillonite/illite (argillic zone), and alunite (advanced argillic zone) via the Band Ratio (BR) technique; (2) preparation of a lineament density map through an automated lineament extraction technique; and (3) identification of areas with elevated F-parameter values exceeding 10. Ten areas with high mineralization potential are delineated for further exploration. Additionally, the study assesses environmental radiation exposure, finding that certain areas exceed the safe radiation limit of 1.0 mSv/year. Peak radiation levels range from 0.75 to 1.25 mSv/year, with mining sites showing the highest readings at 1.25 mSv/year and 0.64 mSv/year. These findings highlight elevated radiological hazards, emphasizing the need for comprehensive monitoring and effective mitigation strategies to protect human health and minimize environmental impact. The methodology's success in this area indicates its potential applicability to other mining areas, contributing to enhanced safety and environmental protection.

Exploring soil pollution patterns in Ghana's northeastern mining zone using machine learning models

This study assessed the pollution status and effectiveness of machine learning models in predicting pollution indices in soils from a mining area in Northeastern Ghana. 552 soil samples were analysed with an Energy Dispersive X-ray Fluorescence (ED-XRF) spectrometer for their elemental concentrations. Four pollution indices; Nemerow Integrated Pollution Index (NIPI), degree of contamination (Cdeg), modified degree of contamination (mCdeg) and Pollution Load Index (PLI). Additionally, the Multivariate Adaptive Regression Splines (MARS) machine learning approach were used. The high CV%, skewness, and kurtosis values show a high degree of variability and uneven distribution patterns which denotes dispersed hotspots that can be interpreted as an influence of gold anomalies and illegal mining activities in the area. V (120.86 mg/L), Cr (242.42 mg/L), Co (30.92 mg/L) Ba (337.62 mg/L), and Zn (35.42 mg/L) recorded values higher than the global and regional contaminant thresholds. The NIPI shows that 46.74% and 26.81% of samples are slightly and moderately polluted respectively. The Cdeg analysis supports these findings, with 36.96% and 41.49% of samples classified as having “moderate” to “considerable” contamination, respectively. The PLI indicates progressive soil quality deterioration (43.84%) of samples reflecting substantial environmental disturbance. The pollution indices show the effect of illegal mining on Shaega, Buin and other areas in the eastern boundary of the study. The MARS models developed for the study demonstrated high predictive capabilities with an R2 value of 0.9665 for model 1 (NIPI), and RMSE and MAE values of 0.8227 and 0.4287 respectively. For model 2 (Cdeg), R2 value of 0.9863, RMSE and MAE of 1.0416 and 0.6181, respectively. Model 3 (mCdeg) produced an R2 value of 0.9844, RMSE and MAE of 0.1225 and 0.0670. These findings suggest MARS models can be an integral tool for soil quality analysis in cooperation with pollution indices. The study suggests that remedial and legislative measures be implemented to address the issue of illegal mining in the area.

Identification and Characterization of Reclaimed and Underclaimed Mine Features Using Lidar and Temporal Remote Sensing Methods within the Coastal Plain Uranium Mining Region of Texas

We developed a spatiotemporal mapping approach utilizing multiple techniques for distinguishing and mapping known reclaimed mine sites from “unreclaimed” mine sites in a historic uranium mining district along the South Texas Coastal Plains. Lidar laser scanning penetrates the vegetation canopy to expose anthropogenic modifications to the landscape. The Lidar analysis (bare earth elevation surface, slope, topographic contours, topographic textures, and overland-flow hydrography) revealed mine features. Visual interpretation of Landsat imagery and time-series analysis augmented the Lidar analysis revealing the temporal life cycle of mining. The combination of bare earth texture with time-lapse and time-series analyses revealed areas of disturbance for reclaimed mines. The spatiotemporal mapping approach proved to be most useful in identifying and characterizing the known mine pit and pile features, reclamation status, and areas of disturbance due to mining. Two mine waste volume estimation methods resulted in a 21% difference indicating that although the approach helps to map mine features and areas of mining disturbance for the purposes of mine land inventory, additional information is needed to improve the estimation of buried mine waste at reclaimed mine sites.

Optimization design of unmanned mining truck path tracking controller based on road adhesion coefficient estimation

Abstract In response to the issues of inaccurate tracking precision caused by continuous turning and variable road adhesion coefficients on complex roads in mining areas for unmanned mining trucks, this paper adopts unscented Kalman filter (UKF) to estimate the road adhesion coefficient. Based on this, an adaptive preview feedforward linear quadratic regulator (LQR) control algorithm is designed using genetic algorithm, feedforward control, and linear quadratic optimal path tracking control methods. Firstly, a nine degree of freedom vehicle dynamics model and a two degree of freedom vehicle lateral dynamics model are established, and the Dugoff model to calculate tire forces is introduced; secondly, utilizing UKF to estimate the road adhesion coefficient, and leveraging active disturbance rejection control for rapid tracking of road curvature, an optimized design of the LQR controller is carried out. Then, the effectiveness of the designed controller was verified through Trucksim/Simulink joint simulation testing. Finally, the hardware in the loop test results showed that the designed controller had good tracking accuracy and strong adaptability in complex road and special working conditions in mining areas.

Comparison and regional suitability evaluation of different backfill source heat pumps using TRNSYS

As global energy demand and carbon emissions continues to rise year by year, the use of renewable energy for heating becomes increasingly urgent. This study employs solar and geothermal energy to supply heat to mining facilities. Initially, three distinct heating systems for mining areas were modeled in TRNSYS: the Backfill Source Heat Pump (BSHP), the Solar Assisted Backfill Source Heat Pump (SABSHP), and the Solar Assisted Backfill Source Heat Pump with Seasonal Heat Storage (SABSHP-SHS). Following a decade of simulation, the optimal system was identified through a comparative analysis that encompassed backfill temperature equilibrium, the inlet and outlet water temperatures for buried pipes, Coefficient of Performance (COP), Partial Load Ratio (PLR), and economic factors. Over the 10-year simulation period, the COP and PLR of the SABSHP-SHS system outperformed both the BSHP and SABSHP systems, showing higher energy efficiency and better heat pump performance. To further assess the regional adaptability of the SABSHP-SHS system, simulations were conducted across four regions with diverse climatic profiles: Shijiazhuang, Yan’an, Xining, and Hegang. The analysis focused on heat collection, efficiency, backfill temperature variation, system energy consumption, and the energy efficiency ratio. The findings indicate that Hegang exhibits the highest solar heat collection efficiency at 89.9% during the non-heating season, while Xining achieves a significantly higher heat collection efficiency of 54.1% during the heating season. These results suggest that the SABSHP-SHS system is particularly effective in regions with pronounced seasonal variations, characterized by extended cold winters and brief, intense summers.

High prevalence of alcohol use disorder & psychiatric comorbidity among coal mine workers: Observations from a cross-sectional study in West Bengal.

Background & objectives Tobacco (30%), alcohol (21.4%) and cannabis (3%) are the three most commonly abused substances in India. Tobacco and alcohol abuse have higher prevalence rates among different occupational groups as compared to that in general population in the country. Both tobacco and alcohol lead to significant occupational harm in terms of absenteeism, injuries, sickness and lost productivity. This study estimated the patterns of substance use and associated common psychiatric comorbidity in a sample of coal mine workers. Methods Coal mine workers at the age of 18 yr or above engaged in mining activities (skilled/semi-skilled/unskilled) were recruited from the Raniganj - Asansol coal mining areas of Eastern Coalfields Limited (ECL). Participants were screened to identify patterns of substance use and other common mental health problems. All participants were administered ASSIST (The Alcohol, Smoking and Substance Involvement Screening Test); GENACIS (Gender, Alcohol and Culture: An International Study) modified questionnaire; and Hospital Anxiety and Depression Scale (HADS) for primary screening for anxiety and depression. Results Out of 202 participants 69 per cent (n=140) were found with either 'hazardous and harmful' (moderate risk); or 'dependent' (high risk) patterns of use of either tobacco or alcohol or cannabis or of more than one substance. Only 31 per cent (n=62) were 'low-risk' for all substances. Almost 65 per cent (n=132) and 49 per cent (n=99) participants out of the whole sample (n=202) belonged to 'hazardous and harmful' (moderate risk) and 'dependent' (high risk) pattern of tobacco and alcohol consumption, respectively. About 28.8 per cent (n=38) and 23.7 per cent (n=31) of all participants had anxiety and depression, respectively. Combined moderate to high use of both alcohol and tobacco use was significantly associated with the risk of having anxiety [adjusted odds ratio (AOR)=4.896, P<0.015] and depression (AOR=5.335, P<0.012). Interpretation & conclusions Alcohol and tobacco are major substance abuse problems among coal mine workers. This population requires early community and primary care based brief intervention as well as additional community-based pharmacotherapy for substance dependence problems as well as intervention for common mental health issues.

The evaluations of oxidative stress and neurotoxicity in threatened endemic fish Barbus meridionalis from Osor River (Spain)

Mediterranean barbel (Barbus meridionalis) an endemic species is currently facing habitat destruction and pollution in Osor River (Spain) due to mining runoff that has severely deteriorated the water quality by metals, primarily zinc (Zn). In order to assess the potential risk of metal contamination and hydrological changes in the Osor River by using oxidative stress and neurotoxicity biomarkers via IBR analyses in the barbel, five different stations were chosen: upstream (S1: reference site and S2: hydrologically changed), mine (S3), and downstream (S4 and S5). The highest tissue metal levels were measured particularly at S3 and following downstream sites. SOD activity and the GSH system parameters (GPX, GST, and GSH) were the most sensitive oxidative stress indicators among the antioxidant system parameters. The organs with the greatest changes in antioxidant biomarkers were the liver and gill. As a sign of neurotoxicity, AChE activities significantly raised in the brain and muscle but drastically lowered in the kidney, liver, and gill particularly in the area of mine and downstream compared to reference site. Integrated biomarker response index (IBR) method was applied to visualize the affect of metal and hydrological alterations with biomarker response according to sites in the Osor River. IBR analyzes together with correlations between metal levels and oxidative stress biomarkers, emphasized that S2 and S3 have the greatest impact on the biomarker levels due to mine activity and hydrological changes highlighting the vulnerability to extinction of native fish B. meridionalis. It is also critical to assess the current data based on the multi-biomarker approach for a range of detrimental effects on fish fitness at the individual level as well as population persistence from an ecological standpoint.

Estimating rare earth elements at various scales with bastnäsite indices for Mountain Pass

Numerous researchers are exploring innovative ways to map rare earth elements (REEs) critical to components in high technology devices precisely, and one approach that has made significant advances in recent years involves imaging spectroscopy. This research focuses on the utilization of this technique to map REEs across the Sulfide Queen mine and Birthday claim area located in Mountain Pass, California, where the highest concentrations of REEs within a carbonatite deposit in the United States have been reported. New spectral indices based on reflectance measurements of eleven representative ore samples under visible illumination in a laboratory setting were developed and applied to data collected by eight airborne and spaceborne imaging spectrometers. The results show that these indices can effectively detect REEs in any rock formation where an adequate amount of rare earth fluorocarbonates have accumulated. Identification can be achieved using airborne and spaceborne hyperspectral sensors with a spatial resolution of up to 30 m. Geochemical analyses confirm that a REE+Y ore grade of 3.25 wt% or greater is sufficient for detection using these indices.

Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data

Abstract. Methane (CH4) is an important anthropogenic greenhouse gas, and its rising concentration in the atmosphere contributes significantly to global warming. A comparatively small number of highly emitting persistent methane sources are responsible for a large share of global methane emissions. The identification and quantification of these sources, which often show large uncertainties regarding their emissions or locations, are important to support mitigating climate change. Daily global column-averaged dry air mole fractions of atmospheric methane (XCH4) are retrieved from radiance measurements of the TROPOspheric Monitoring Instrument (TROPOMI) on board on the Sentinel-5 Precursor (S5P) satellite with a moderately high spatial resolution, enabling the detection and quantification of localized methane sources. We developed a fully automated algorithm to detect regions with persistent methane enhancement and to quantify their emissions using a monthly TROPOMI XCH4 dataset from the years 2018–2021. We detect 217 potential persistent source regions (PPSRs), which account for approximately 20 % of the total bottom-up emissions. By comparing the PPSRs in a spatial analysis with anthropogenic and natural emission databases, we conclude that 7.8 % of the detected source regions are dominated by coal, 7.8 % by oil and gas, 30.4 % by other anthropogenic sources like landfills or agriculture, 7.3 % by wetlands, and 46.5 % by unknown sources. Many of the identified PPSRs are in well-known source regions, like the Permian Basin in the USA, which is a large production area for oil and gas; the Bowen Basin coal mining area in Australia; or the Pantanal Wetlands in Brazil. We perform a detailed analysis of the PPSRs with the 10 highest emission estimates, including the Sudd Wetland in South Sudan, an oil- and gas-dominated area on the west coast in Turkmenistan, and one of the largest coal production areas in the world, the Kuznetsk Basin in Russia. The calculated emission estimates of these source regions are in agreement within the uncertainties in results from other studies but are in most of the cases higher than the emissions reported by emission databases. We demonstrate that our algorithm is able to automatically detect and quantify persistent localized methane sources of different source type and shape, including larger-scale enhancements such as wetlands or extensive oil- and gas-production basins.