Mine Site Research Articles

Arsenic content in tissues, Fungus combs, Herbs, Termite mounds and Soils of termites of the genus Macrotermes and assessment of the health risk associated with their food use in the industrial zone of the Societe Nouvelle Des phosphates du Togo (SNPT)

Arsenic is present in different environmental matrices of the phosphate mining and processing area of the Société Nouvelle des Phosphates du Togo; including termite tissues, termite waste, termite mounds, termite Fungus combs, soils and nearby herbs. Arsenic levels vary across matrices, with termite waste having the highest concentration (approximately 27.01 mg/kg) compared to the outside herbs, which have the lowest. Phosphate mining sites, including Hahotoé-Kpogamé and Akoumapé, have higher levels of As contamination in soils and termite tissues compared to the phosphate processing site. A positive correlation is observed between As levels in termite tissues and termite waste; suggesting an ability of termites to eliminate arsenic via their waste. Regarding health risks, the hazard quotient (HQ) for arsenic in termites exceeds 1, and for non-threshold effects, the excess individual risk (EIR) assessment gives EIR > 10-4 in both adults and children; which would mean that termite consumption poses a significant health risk, including cancer risks for human populations, particularly children. However, it should be noted that the estimation in this study includes total As (organic and inorganic). Since the toxicological reference values were calculated on the basis of inorganic arsenic, which presents more hazard and whose fraction represents 10% of total As, the daily exposure dose values for adults and children represent 1.40.10-4 mg/kg/d and 1.02.10-4 mg/kg/d of inorganic arsenic, respectively. These doses will then become lower than the toxicological reference value (TRV) with the consequence of hazard quotient (HQ) lower than 1 and resulting in low risks of carcinogenic effects. It is therefore necessary to specifically determine the inorganic fraction to refine the risk assessment and to extend this study to other trace elements in the prospected area.

Constructing a Coal Mine Safety Knowledge Graph to Promote the Association and Reuse of Risk Management Empirical Knowledge

Coal mining production processes are complex and prone to frequent accidents. With the continuous improvement of safety management systems in China’s coal mining industry, a vast amount of coal mine safety experience knowledge (CMSEK) has been accumulated, originating from on site operations. This knowledge has been recorded and stored in paper or electronic documents but it remains unconnected, and the increasing volume of documents further complicates the reuse and sharing of this knowledge. In the era of large models and digitalization, this knowledge has yet to be fully developed and utilized. To address these issues, a risk management checklist was derived from coal mining site data. By integrating intelligent algorithm models and the coal industry knowledge engineering design, a coal mine safety experience knowledge graph (CMSEKG) was developed to enhance the efficiency of utilizing coal mine safety experience knowledge. Specifically, we creatively developed a coal mine safety experience knowledge representation framework, capable of representing coal mine risk inspection records from different sources and of various types. Furthermore, we proposed a deep learning-based coal mine safety entity recognition model (CMSNER), which can effectively extract coal mine safety experience knowledge from text. Finally, the CMSEKG was stored using the Neo4j graph database, and a knowledge graph was constructed using selected case information as examples. The CMSEKG effectively integrates fragmented safety management experience and professional knowledge, promoting knowledge services and intelligent applications in coal mining operations, thereby providing knowledge support for the prevention and management of coal mine risks.

Enhancing phytoremediation of bauxite mine subsoil by Jatropha curcas L. using sewage sludge and poultry sludge

Phytoremediation is a sustainable technology for cleaning up heavy metal contamination at mining sites. However, degraded soils at these sites create a harsh environment for plants to survive and properly yield biomass. In this study, sewage sludge and poultry sludge were applied as soil amendments in bauxite mine subsoil to determine their impact on the growth and heavy metal uptake of Jatropha curcas L. Both sewage sludge and poultry sludge were applied at 25% and 50%. J. curcas was grown in the amended soils for 120 days under greenhouse conditions. Changes in soil physico-chemical properties, plant growth and heavy metal uptake of J. curcas were determined after that. An increase in EC, CEC, total C, total N, total available P and total extractable K was detected in the amended soils. These improvements enhanced the growth of J. curcas, particularly in the development of above-ground plant biomass. Increased plant biomass subsequently led to higher bioaccumulation and translocation efficiency of Al, Fe, Pb and Zn. As a result, higher heavy metal removal of up to 98.03% was detected in the amended treatments. The findings indicated that the application of sewage sludge and poultry sludge improves soil conditions for plant development.

Ecological risk assessment of heavy metals contaminated mining sites of eastern india using soil and moss.

The blooming industrialization and urbanization is leading to increased mining operations. These intensified mining activities emit heavy metals into the environment, posing serious threats to ecosystems. Hence, this study focused on assessing heavy metal pollution in mining soil, utilizing mosses as bioindicators. The ecological risk, geo-accumulation factor, and contamination factor have been calculated to know the harmful effect of heavy metals on ecosystem. The study covered three distinct mining sites of eastern India within Odisha: Jajpur's Sukinda Valley (SP1, Cr), Keonjhar's Joda-Barbil (SP2, Fe and Mn), and Sundargarh's Koira-Joda (SP3, Fe). The collection of 48 soil samples through random sampling revealed significant variations in heavy metal concentrations. SP1 recorded Cr concentration of 6572 ± 445mg/kg and Ni of 8042.47 ± 501.38mg/kg, surpassing eco-toxicological levels. The storage site in SP2 exhibited the highest Fe concentration at 9872 ± 502mg/kg, and Mn levels in SP3 were at 7884 ± 432mg/kg. Storage areas in all three regions held the highest concentrations of heavy metals. Mosses in studied area demonstrated as potential bioindicators for monitoring heavy metal pollution. EF and Igeo assessments showed Cd, Pb, Hg, and other heavy metal contamination compared to earlier investigations. This study indicated higher ecological risks for Pb, As, Cu, Ni, and Zn. The Hyophila involuta accumulates Mn, Cr, Cd, Pb, Fe, and Hg, while Barbula arcuata accumulates Mn, As, and Cu in SP1. Hyophila involuta and Trematodon longicollis accumulate Mn, Cr, Cd, Pb, Fe, Hg, and Zn in SP2. Trematodon ambiguous accumulates Cd, Fe, and Ni, while Fissidens diversifolius accumulates Mn, Cr, Hg, As, Cu, and Zn in SP3. These findings emphasize the necessity of monitoring heavy metal pollution in contaminated zones using moss as a potential bioindicator.

Machine learning-assisted risk evaluation of heavy metals in the Hainan gold mining region, China.

This study employed machine learning (ML) to thoroughly investigate the impact of informal mining activities on the distribution and pollution status of heavy metals in soils near private gold mines in Hainan Province, southern China, a region known for its ecological sensitivity and economic importance. By systematically collecting surface soil samples and samples at depths of 0.5-1m from 175 drilling sites, a comprehensive quantitative analysis was conducted on major heavy metal elements, including lead (Pb), copper (Cu), cadmium (Cd), nickel (Ni), mercury (Hg), chromium (Cr), arsenic (As), and zinc (Zn). Combined with evaluation methods such as the Pollution Load Index (PLI), Normalized Pollution Index (NIPI), and Ecological Risk Index (ERI), the study revealed a high level of soil pollution at informal mining sites. The findings indicated that the average concentrations of Pb, Cd, Hg, As, and Zn in surface soils significantly exceeded the background values for soils in China, with a pronounced positive correlation observed between these heavy metal elements in both surface and deep soil profiles (r > 0.5). Furthermore, leveraging the heavy metal content in surface soils and the constructed environmental indicators, the predictive accuracy for metal content in deep soils was found to range from R2 = 0.27 to 0.68, suggesting that informal mining activities have led to substantial variations in metal content across different soil profiles. Through the application of a random forest model for predictive analysis of the PLI, NIPI, and ERI, high prediction accuracy was achieved (R2 = 0.78, 0.86, and 0.60, respectively). The study demonstrates that informal mining activities not only elevate the risk of soil pollution but also alter the distribution patterns of heavy metals. Also, this study provides a crucial foundation for the scientific assessment of soil quality and potential environmental hazards, while also affirming the efficacy of ML techniques in forecasting soil quality parameters.

MINERAL PRODUCTION AND ENVIRONMENTAL POLLUTION IN NIGERIA

This study empirically determined the effect of mineral production on environmental pollution in Nigeria from 1985 to 2022. The research utilised time series data obtained from the Central Bank of Nigeria (CBN) data bulletin and the World Development Indicators (WDI) of the World Bank. The data analysis methodologies utilised include descriptive statistical analysis, ADF Unit Root test, ARDL Bound Co-integration test, and ARDL methodology. The result of the ADF unit root test indicated the presence of a combination of stationary and non-stationary elements. That is a mixture of I(0) and I(1). Furthermore, the ARDL limits co-integration test has shown the presence of a long-term link between the variables. Finally, the ARDL estimation revealed that crude petroleum and natural gas production, likewise mining and quarrying had significant and positive implication on emission of CO2 in both long and short runs. Coal production had positively significant impact on CO2 emission in the long run but positively insignificant impact in the short run. In addition, metal ores positively and insignificantly impacted on CO2 emission in both the long and short runs. The study's results indicated that mineral mining in Nigeria resulted in a rise in environmental contamination. The report proposed that the Nigerian government should thoroughly evaluate and revise current environmental laws and regulations in order to effectively address the environmental consequences of mineral development. This should include establishing clear standards, guidelines, and permits for mining operations, waste management, pollution control, and rehabilitation of mining sites. KEYWORDS: Mineral Production, Environmental Pollution, CO2 Emission

Study on failure mechanism of cracked coal rock and law of gas migration

China possesses abundant coal resources and has extensive potential for exploitation. Nevertheless, the coal rock exhibits low strength, and the coal seam fractures due to mining activities, leading to an increased rate of gas emission from the coal seam. This poses significant obstacles to the exploration and development of the coal seam. This paper focuses on studying the failure mechanism of fractured coal rock by conducting uniaxial and triaxial compression experiments on the coal rock found at the Wangpo coal mine site. Simultaneously, in conjunction with the findings from the field experiment, a gas migration model of the mining fracture field is constructed to elucidate the pattern of coal seam gas distribution during mining-induced disturbances. The study structure reveals that coal rock exhibits three distinct failure modes: tensile failure, shear failure, and tension-shear failure. The intricate fissure in the rock layer will intensify the unpredictability of rock collapse patterns. The compressive strength of coal rock diminishes as the confining pressure drops. The coal rock in the working face area will collapse as a result of the lack of confining pressure. In the rock strata above the mining fracture zone, the gas pressure is first higher and then significantly falls with time. After 100 days of ventilation, the low gas pressure area changes little, so to ensure the safety of the project, the ventilation time of the fully mechanized mining surface is at least 100 days. The research results will help to establish the core technology system of coal seam development and improve the competitiveness of coal seam resources in China.

The Speech Act of Criticism Strategy Analysis on the Hashtag #Wadasmelawan

The study aims to describe the function of the hashtag #WadasMelawan and the criticism speech act strategy in voicing social justice for Wadas residents. Wadas is a village in the Central Java Province of Indonesia that is projected to be developed as an adhesive mining site—a government plan that the local Wadas villagers reject. The data in this study were collected on February 8 and 9, 2022, and processed using NVivo software. A total of 1550 tweets were analyzed using the hashtag function (Simpson, 2018) and the speech acts of criticism strategy (Nguyen, 2005). The results indicated that the #WadasMelawan has functions in expressing victimhood, solidarity, integration (victimhood and solidarity), and hashtag alone (expressing either solidarity or empathy). The speech acts of criticism strategy are negative evaluation, disapproval, disagreement, statement of difficulty, and consequences. In addition, indirect strategies are correction, indicating standards, preaching, demand for change, advice on change, uncertainty, asking, sarcasm, and the use of emojis or pictures. The #WadasMelawan and criticism strategy involved the media voicing justice for Wadas on X (formerly known as Twitter).

Dynamic fluid flow model for phosphate slurry pipeline: OCP main pipeline as case study

The transportation of phosphate slurry through large-scale pipelines presents significant challenges due to the complex behavior of multiphase flows, particularly with varying solid content, density, and dynamic viscosity. Efficient and accurate prediction of flow behavior is critical for optimizing the operation of such pipelines. This work aims to develop a dynamic computational model to simulate phosphate slurry flow in pipelines. The case study focuses on the OCP Group’s main slurry pipeline, which links the mining sites at Khouribga to the industrial plants at Jorf Lasfar, Morocco. This pipeline system spans a total length of 187.124 km, consisting of 5237 pipes with an inner diameter ranging from 0.8546 m to 0.8578 m, and features several elevation changes along its ground level. Using a section-averaged, dynamic approach and the Finite Volume scheme, the model computes essential flow parameters, including density, dynamic viscosity, and pressure, for the incompressible and non-Newtonian slurry and process water flows. The model’s accuracy is validated against on-site measured data, showing an average deviation of ±0.32% for outlet density, and below 10% for pressure along the pipeline, underscoring the model’s reliability. Additionally, a sensitivity analysis was conducted to illustrate the impact of key parameters on the predicted head losses and pressures along the pipeline. This analysis shows that the slurry viscosity is the most critical parameter, significantly influencing these predictions. This model provides high accuracy and reasonable CPU time for real-time simulation and monitoring, while also offering significant potential for optimizing pipeline operations and ensuring the reliability of phosphate transport.

Towards decarbonizing large-scale industries: A decision support framework for optimizing grid-connected PV-battery energy systems planning – Case study of an OCP mining site, Morocco, North Africa

Incorporating renewable energy into forthcoming grid-connected or decentralized energy systems assumes an escalating significance and can potentially enhance endeavors toward accomplishing Sustainable Development Goal 7 (SDG7). Nevertheless, deploying sustainable renewable energy-intensive systems may present challenges related to their intermittency and cost instability. This paper proposes the development of a decision support model aimed at planning an optimal on-grid PV battery system. The model builds upon the Open Energy Modeling Framework (OEMOF) and defines asset capacities, energy dispatch, operational strategies, and optimal costs for the designated system. Key factors considered include energy demand profile, photovoltaic potential, electricity tariffs, and the availability of the National Grid. Furthermore, the model evaluation incorporates the computation of pertinent performance, feasibility, and viability indicators, notably the Levelized Cost of Energy (LCOE). To validate the framework, the article conducts a case study to determine the optimal sizing and planning of a grid-connected PV battery energy system. The objective is to cater to the electricity needs of an OCP (Office Chérifien des Phosphates) mining site in Morocco. The study considers the characteristics of the national power grid, incorporating time-varying electricity tariffs based on a Demand-Response program taking into account various rates according to the time of use (ToU). The case study includes a sensitivity analysis that examines different factors, namely the proportion of renewable energy, the investment costs of photovoltaic systems and batteries, and the financial parameter known as the weighted average cost of capital (WACC). Through this analysis, the study assesses the impact of these variables on the calculated cost of energy. Experimental results revealed a range of LCOE values from $0.07111/kWh to $0.11847/kWh for high renewable energies integration.

Assessment of Natural Radioactivity and Radiation Hazard in Soil and Rock Samples from Mining Sites within North-Eastern Nigeria

Assessment of Natural Radioactivity and Radiation Hazard in Soil and Rock Samples from Mining Sites within North-Eastern Nigeria

Erratum: Amycolatopsis nalaikhensis sp. nov. and Amycolatopsis carbonis sp. nov., two novel actinobacteria with antimicrobial activity isolated from a coal mining site in Mongolia.

Erratum: Amycolatopsis nalaikhensis sp. nov. and Amycolatopsis carbonis sp. nov., two novel actinobacteria with antimicrobial activity isolated from a coal mining site in Mongolia.

Assessement of the physico-chemical quality of Groundwater in the Zagora area, Morocco

Abstract The Zagora region heavily relies on groundwater as a crucial water resource for a range of human activities. Mining activities can have significant impacts on groundwater quality in the areas surrounding mining sites. The primary objective of this study is to analyze and evaluate the effects of pollution on the quality of groundwater in the Zagora area, with a particular focus on mapping and identifying its sources. It is based on the use of Geographic Information Systems (GIS) and the spatial variation of the physico-chemical parameters of the studied water sources. Assessing the physico-chemical quality of groundwater in Zagora, involves evaluating several parameters that can affect its quality. These parameters include temperature (T°C), pH, electrical conductivity (EC), dissolved oxygen (O2), chlorides (Cl−), sulfates (SO4 2−), nitrates (NO3 −), nitrites (NO2 −), ammonium (NH4 +), and orthophosphates (PO4 3−). The results of the physico-chemical analysis have shown that, the electrical conductivity ranged from 501.59 to 3173.92 μs/cm, while sulfate concentrations ranged from 91.87 to 363.02 mg/L. The spatial distribution of the results indicates that the water samples were collected from different locations, and there are variations in the levels of parameters analyzed. Some of the parameters, such as electrical conductivity, and sulfate, have higher levels in some locations compared to others, indicating potential sources of pollution or natural variability in the water composition. The GIS analysis also revealed that the areas with poor water quality were clustered around specific locations, indicating potential sources of contamination. Therefore, further investigation and monitoring of the water sources may be necessary to ensure safe and healthy water consumption.

Extent and drivers of land productivity degradation at the territorial level in Eastern DR Congo

Land degradation is a global environmental problem that affects the livelihoods of communities. Understanding the extent and causative factors of this phenomenon is important to design the cause-targeted strategies to cope with this problem. This study aimed to assess the effects of biophysical and socio-economic factors on the occurrence of land productivity degradation at the territorial level in Eastern DR Congo, using the Kalehe territory as a case study. The land productivity dynamics were determined based on the trend analysis of Landsat-based NDVI annual composites over the 1987-2020 period. Additionally, a binary logistic regression model was developed to identify the determinant factors and to map the susceptibility of this degradation at the territorial level. The results revealed that 31.25% (1303.175 km2) of the territory had experienced a decrease in land productivity, with 10.8% (453.83 km2) characterized by a significant decrease at the confidence level of 90%, representing the hotspots area of land productivity degradation where conservation actions should be prioritized. This decrease in land productivity is location-specific as it depends on the land cover types and health zones. It is likely to occur at high altitudes, in areas close to artisanal mining sites, rivers, and administrative centers. These results imply that policy interventions to cope with the problem of land productivity degradation should be developed following the heterogeneity of the landscape, and sustainable land management practices that take into account both the biophysical and socio-economic drivers of land degradation should be implemented in this region to achieve the land degradation neutrality by 2030.

Drill Hole Spacing Analysis for Evaluation of Quartz Sand Resources as Residual Material from On-Land Alluvial Tin Processing based on Global Estimation Variance and Kriging Variance, with Case Study in the Bangka and Belitung Islands, Indonesia

On-land alluvial tin mining activities produce residual materials from mineral processing or mineral washing; the major one of these is quartz sand, which can be utilized in the manufacturing industry. As time goes by, the reserves of alluvial tin are decreasing, which is in line with the increasing residual material from abundant mineral processing. Mining procedures are carried out following the Indonesian government regulations, covering mining as well as reprocessing. This motivated us to evaluate the potential of quartz sand at two mining sites in Bangka and Belitung Islands. This study evaluated alluvial tin processing residual quartz sand through drill hole spacing analysis (DHSA) by comparing two geostatistics parameters, namely global estimation variance (GEV) and kriging variance (KV). Drill hole samples were taken with varying spacing, ranging from 50 to 200 m, after which geostatistical analysis was carried out. With several simulations, the incorporation of GEV and KV was able to produce the optimal drill hole spacing with measured resource categories in the range of 40 to 55 m, indicated resources in the range of 55 to 85 m, and inferred resources >85 m. Accordingly, the total estimated quartz sand resources of both sites were obtained.

Evaluating CO2 sequestration through Technosol constructed from coal mining waste

This paper proposes enhancing CO2 sequestration and recovering degraded areas using Technosols constructed with coal waste and strategic materials. The evaluation of carbon capture considered four methods: (i) plant dry mass produced by Megathyrsus Maximus cv. Mombasa (ii) carbon incorporation via biochar, (iii) microbial biomass carbon, and (iv) mineral sequestration by lime. Local soil from the mine site was used as a control. Results show soil fertility parameters of the Technosols according to Brazilian guidelines. Plant growth increased with biochar incorporation but was still lower than in control soil. The pH, chemical, and mineralogical composition data were combined with geochemical reactions, showing the interaction of coal waste compounds, mainly pyrite with lime. Regarding calculating the CO2 capture potential, biochar and mineral carbonation were the most relevant means for the designed system. The best performance obtained can capture approximately 65 t CO2eq ha−1, about four times greater than the systems traditionally used to recover waste disposal areas. The study demonstrates the feasibility of incorporating mining site recovery, waste disposal, and emissions mitigation in a closed system, providing sustainable options for the coal mining industry.

A cross-sectional study investigating malaria prevalence and associated predictors of infection among migrants to a newly established gold mining settlement in the Gambella Region of Ethiopia.

Malaria is a major disease burden in Ethiopia. Migration can influence malaria transmission dynamics, with individuals relocating from malaria-free highland regions to malarious lowlands potentially facing elevated risks of contracting malaria. Migrants may find it difficult to protect themselves against malaria and have limited access to diagnosis or treatment. Settlers in gold mining sites are one type of migrant and are often neglected in malaria research yet may have particularly high malaria risk. This study was a malaria prevalence survey among settlers in a new gold mining settlement in the highly malarious Gambella Region, Ethiopia. n = 590 people were surveyed for demographic information and their knowledge and practices of malaria. Participants were tested for malaria using rapid diagnostic tests and microscopy. Using logistic regressions, the influence of demographic characteristics on malaria infections and bed net access were analysed. A sub-sample of participants was interviewed to comprehend settlement living conditions and healthcare accessibility. The overall prevalence of Plasmodium falciparum was 37.5% (CI 32.4-42.3%). Young children were most likely to have malaria, with individuals aged 15-24 having 67% lower odds (aOR: 0.33; CI 0.13-0.86) of infection compared to those aged 1-4years old. Meanwhile, those age 25-plus had 75% decreased odds of malaria infection (aOR 0.25; CI 0.10-0.65). Individuals with bed nets had ~50% decreased odds of testing positive for falciparum malaria than those reporting having no bed net (aOR: 0.47; CI 0.22-0.97). Individuals who relocated from low elevation with high malaria test positivity rate areas were more prone to testing positive for malaria, as were those residing in densely populated households with multiple malaria cases. Conversely, individuals from higher elevations with low malaria test positivity rates, and those living in households with 5-10 occupants and < 2 malaria infections, were more likely to possess bed nets. This gold mining settlement provides an example of an oft-neglected atypical community where malaria is a significant, but under-addressed, health problem. Within this community, future interventions focused on distributing bed nets, particularly to larger households and those with children, have great potential to alleviate the malaria burden. Efforts should also be made to provide affordable, and accessible, early diagnosis and treatment.

Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock Distance Prediction

AbstractIn the field of civil and mining engineering, blasting operations are widely and frequently used for rock excavation, However, some undesirable environmental problems induced by blasting operations cannot be ignored. Blast-induced flyrock is one important issue induced by blasting operation, which needs to be well predicted to identify the blasting zone’s safety zone. This study introduces an adaptive weighted multi-kernel learning model (AW-MKL) to provide an accurate prediction of blast-induced flyrock distance in Sungun Copper Mine site. The proposed model uses a combination of multi-kernel learning (MKL) approach and adaptive weighting strategy based on weighted Euclidean distance and modified local outlier factor (MLOF) to maximally improve the predictive ability of kernel ridge regression (KRR). To demonstrate the superiority of the proposed approach, six machine learning models were developed as comparisons, i.e., KRR, RF, GBDT, SVM, M5 Tree, MARS and AdaBoost. The outcomes of the proposed method achieved the highest accuracy in testing phase, with RMSE of 2.05, MAE of 0.98 and VAF of 99.92, which confirmed the strong predictive capability of the proposed AW-MKL in predicting blast-induced flyrock distance.

Tilapia as a model fish for biomonitoring of metal pollution in dams associated with mining watersheds: contrasting diagnosis from different tissues and health risk assessment.

Tilapia is a model fish species used as a pollution biomonitor due to its tolerance and availability in many contaminated sites. Blue tilapia Oreochromis aureus specimens (n = 320) were collected in eleven dams influenced by mining in the SE Gulf of California region (dams 1, 2 and, 3 comprise 55 mining sites; dam 4 comprises 8; dams 6, 8, 10, and 11, ≤ 6; and dams 5, 7, and 9 include 19, 20, and 16 mining sites, respectively). Cadmium, Cu, Pb, and Zn concentrations were analyzed in the muscle, liver, gills, and guts to identify metal pollution and evaluate risks and seasonal changes. The distinct tissues exhibited different metal accumulation capacities, therefore allowed develop a diagnosis comparative between the eleven dams. In general, metal concentrations were higher in dams 1, 2, 5, and 9, which are associated with more mining sites in their sub-basins. The four metals exhibited the highest levels in the tilapia liver in dams 1 and 2, which can be related to the present and past mining activity in the lower watershed (55 sites) and the geothermal activity in these dams. In general, Zn exhibited the highest level in the tilapia livers from dams 1, 2, 3, 4, 5, and 10 compared to the maximum mean (220µg/g) concentrations previously recorded. The non-carcinogenic risks indicated that the Pb risk was enhanced when the intake was ≥ 231.5gweek-1 of tilapia muscle, indicating a potential risk of adverse health effects for the entire population.

Resource Valuation of Kalilingseng-Cave Manganese Mining Site as a Tourist Destination Using the Travel Cost Method

General Background: Kalilingseng Cave, a former Dutch-era manganese mine in Kulon Progo, has untapped potential for educational and cultural tourism. Specific Background: Despite its historical and aesthetic value, the cave remains underdeveloped as a tourist site, lacking comprehensive economic valuation. Knowledge Gap: Prior research has not addressed the cave's economic potential as a tourism destination, leaving a gap in understanding its full value. Aims: This study aims to evaluate the tourism potential of Kalilingseng Cave using SWOT analysis and estimate its economic value through the Travel Cost Method (TCM). Results: The SWOT analysis highlights the cave’s accessibility and proximity to public facilities. TCM results show an average WTP of IDR 67,000.00, with annual economic benefits of IDR 21,775,000.00. Novelty: This research uniquely integrates SWOT and TCM to assess the tourism potential of an abandoned mining site. Implications: Proper development could make Kalilingseng Cave a sustainable tourist destination, benefiting the local economy and supporting geotourism education. Highlights: Tourism Potential: Kalilingseng Cave's historical and aesthetic value positions it as an educational and geotourism destination. Economic Valuation: The Travel Cost Method reveals an annual benefit of IDR 21,775,000.00 from visitors, indicating potential for community development. SWOT Insights: The SWOT analysis highlights strengths like accessibility and opportunities for geotourism while addressing weaknesses such as poor air circulation and lack of official management. Keywords: Ex-Manganense Mining, Kalilingseng, SWOT, Travel Cost Method