The study uses the Remote Sensing Ecological Index, or RSEI, to represent ecological quality through four components: greenness, wetness, dryness and heat. Because the index is calculated from observations acquired at the target date, it is retrospective: it identifies ecological conditions and changes after observations become available but does not itself forecast the following period. Its conventional construction through principal component analysis is also linear and does not explicitly capture nonlinear temporal interactions between ecological indicators and external environmental drivers. The proposed framework was designed to address these limitations by assigning feature extraction, temporal prediction and multivariable fusion to separate neural networks.

Within the staged convolutional neural network–long short-term memory–backpropagation neural network framework, or CLB, a convolutional neural network extracts features from multivariable time series. A long short-term memory network then models temporal dependence and predicts target-period variables. Finally, a backpropagation neural network fuses the predicted remote-sensing indicators with climatic, topographic and spatial variables to estimate the target-period RSEI. The architecture therefore produces a forecast from earlier observations and supporting environmental information rather than simply calculating an index directly from imagery for the date being evaluated. This design also allows the three networks to represent different parts of the prediction process: pattern extraction, time-dependent forecasting and nonlinear integration.

Landsat-8 imagery from 2015 to 2020 was used to quantify ecological change in the Wangpo Mine Field. Across that observed period, the mean RSEI increased by 12.1%, from 0.6136 to 0.6879. Areas classified as excellent quality expanded, while poor-quality areas contracted. These results describe a measured improvement in the ecological index for the study area and period. They do not show that the forecasting framework caused the landscape change, and the reported values remain specific to the mine field, the selected indicators and the six-year Landsat-8 record.

For the held-out 2020 data, the RSEI grades predicted by the CLB framework were consistent with grades calculated from Landsat-8 imagery, with reported grade-specific accuracies ranging from 87.3% to 94.1%. The test demonstrates agreement between one-step-ahead predictions and direct image-based classifications for this dataset. The supplied study abstract does not report uncertainty intervals, error distributions or comparisons with alternative forecasting models, and it does not establish how the framework would perform in other mining regions, under different time scales or under future climatic and land-use conditions. The authors therefore present CLB as a methodological reference for one-step-ahead ecological-quality prediction in mining areas rather than as a universally validated forecasting system.

Subject of Research: One-step-ahead ecological-quality prediction in a Chinese mining area using Landsat-8 data and a multi-neural-network framework

Article Title: Mine ecological assessment and prediction based on RSEI and a multi-neural-network framework

Article References: Li, P., Wang, B., Guo, Y., Liang, Y., & Yan, J. (2026). Mine ecological assessment and prediction based on RSEI and a multi-neural-network framework. Scientific Reports. https://doi.org/10.1038/s41598-026-73044-2

Image Credits: AI Generated

DOI: 10.1038/s41598-026-73044-2

Keywords: mine ecology, ecological forecasting, Remote Sensing Ecological Index, Landsat-8, convolutional neural network, long short-term memory, backpropagation network, Wangpo Mine Field, Shanxi, machine learning, Mine, ecological

The study, published in Frontiers in Environmental Science, describes a laboratory-scale reactor in which aluminum plates are wired in a monopolar parallel configuration and immersed in synthetic acid mine drainage. When direct current flows through the plates, the sacrificial aluminum anodes dissolve, releasing metal cations that react with hydroxide ions generated by cathodic water electrolysis to form amorphous aluminum hydroxide flocs. These freshly generated flocs sweep through the solution, capturing dissolved metals through adsorption, surface complexation, precipitation and coprecipitation. The twist is the addition of pyrolysis ash, a mineral-rich solid produced by heating dewatered waste activated sludge to 600 degrees Celsius under oxygen-limited conditions for two hours. Ground and sieved to below 0.15 millimeters, this ash supplies alkalinity, mineral exchange sites and heterogeneous nucleation surfaces that the electrocoagulation process alone cannot provide.

The researchers began with single-factor experiments designed to isolate the influence of each operating parameter. Electrode material proved decisive. Iron electrodes achieved a maximum manganese removal efficiency of 90.15 percent, outperforming the 81.50 percent obtained with aluminum, but their performance on ferrous iron told a different story. With iron plates, iron removal peaked at 67.89 percent after roughly 30 minutes and then declined to 51.01 percent at 60 minutes, because the dissolving iron anode continuously reintroduces ferrous and ferric species into solution, offsetting the removal of the original dissolved iron. Aluminum electrodes, by contrast, sustained iron removal at 81.96 percent after an hour and raised the pH to 6.31, making them the clear choice for the coupled experiments.

Electrode spacing emerged as a second critical variable. When the gap between plates widened from 10 to 20 millimeters, removal efficiencies for both metals and the final pH all declined, reflecting increased ohmic resistance, longer ion migration distances and weakened mass transfer between electrodes. Tighter spacing improves performance by shortening the distance ions must travel, but going too narrow risks floc accumulation between plates, concentration polarization and short-circuiting. Current density, which governs the rate at which sacrificial anodes dissolve and hydroxide flocs form, showed a pronounced quadratic effect. Manganese removal exceeded 80 percent whenever current density surpassed 15 milliamperes per square centimeter, but pushing the current too high induced electrode passivation, bubble coalescence and floc fragmentation that reduced current utilization efficiency.

The pyrolysis ash dosage experiment provided the most striking demonstration of the coupled process. Holding all other conditions constant, raising the ash dosage from zero to 0.4 grams per liter increased ferrous iron removal by roughly 8 percentage points and manganese removal by roughly 22 percentage points within 30 minutes. The ash also raised the pH of the acidic wastewater, which started at 3.89, toward neutrality within a relatively short reaction period. Beyond the optimal dosage, however, removal efficiencies plateaued or declined slightly, because excessive particle loading causes agglomeration, partial blockage of adsorption sites and increased mass transfer resistance. The ash functions as a consumable mineral-assisted coagulant aid rather than a conventional packed-bed adsorbent, becoming incorporated into the composite flocs through precipitation, coprecipitation and sweep flocculation.

To move beyond trial and error, the team employed response surface methodology using a Box-Behnken design with three factors and three levels, treating ferrous iron removal, manganese removal and final pH as response variables. The resulting quadratic regression models were highly significant, with F-values of 52.92, 64.19 and 76.26 for the three responses and p-values below 0.0001, while nonsignificant lack-of-fit tests confirmed the models adequately described the experimental data. All pairwise interactions among electrode spacing, current density and ash dosage proved statistically significant for every response, revealing genuine synergies that single-factor experiments could not capture. The response surfaces showed that ferrous iron removal generally decreased with wider electrode spacing, rose and then fell with increasing current density, and climbed to a plateau with increasing ash dosage.

According to the fitted model, the optimal operating conditions were an electrode spacing of 5 millimeters, a current density of 20.73 milliamperes per square centimeter and a pyrolysis ash dosage of 0.46 grams per liter. Validation experiments run at a rounded current density of 21 milliamperes per square centimeter delivered average removal efficiencies of 83.62 percent for ferrous iron and 92.71 percent for manganese, with the final effluent pH climbing to 7.04. Relative errors between predicted and measured values were just 1.30 percent, 0.13 percent and 0.85 percent for the three responses, demonstrating close agreement between model and experiment. Under these conditions, the process transferred 85.71 milligrams of iron and 3.606 milligrams of manganese out of each liter of treated water, leaving residual concentrations of 16.79 and 0.284 milligrams per liter respectively.

A preliminary cost assessment suggests the process could be economically viable. At the optimized current density and an effective anodic area of roughly 440 square centimeters, the operating current is 9.24 amperes, corresponding to a specific electrical energy demand of 23.1 to 69.3 kilowatt-hours per cubic meter depending on cell voltage. Faraday’s law puts the theoretical aluminum consumption at approximately 1.55 kilograms per cubic meter, while the ash dosage corresponds to 0.46 kilograms per cubic meter. Using scenario prices of 10 cents per kilowatt-hour for electricity, 2.50 dollars per kilogram for aluminum and 10 to 50 cents per kilogram for ash preparation, the estimated operating cost falls between 6.23 and 11.04 dollars per cubic meter, excluding depreciation, labor and solids management.

Characterization of the recovered composite flocs revealed the underlying removal mechanisms. Scanning electron microscopy showed heterogeneous, irregular agglomerates with rough, flocculent surfaces and compact granular domains, consistent with the aggregation of electro-generated hydroxide flocs and ash particles. Energy-dispersive X-ray spectroscopy detected oxygen, iron, carbon, manganese, silicon, calcium and aluminum, with iron and manganese accounting for 47.28 and 1.92 weight percent of the analyzed region. X-ray diffraction identified major peaks assigned to FeOOH and Fe3O4, alongside silicate and carbonate mineral phases from the ash, and weak peaks suggesting MnOOH and Mn3O4. Together these observations support a combined mechanism in which electro-generated hydroxide flocculation, adsorption and surface complexation, precipitation and coprecipitation, and heterogeneous nucleation promoted by the ash all contribute to metal immobilization.

The authors acknowledge that their study used synthetic acid mine drainage rather than field-collected water, so the results should be regarded as laboratory-scale evidence of feasibility rather than direct validation of field performance. Real mine drainage carries high sulfate concentrations, dissolved organic matter and coexisting ions that may influence conductivity, metal speciation, electrode passivation and competitive adsorption. Future work will need to validate the process on water from different mine sites and seasonal conditions, integrate continuous-flow operation, measure energy demand and electrode consumption, and conduct standardized leaching evaluations of the recovered solids. Still, the vision is compelling: a treatment train that turns two waste streams, sewage sludge and abandoned mine water, into a single solution, powered by electricity and guided by statistical optimization.

Subject of Research: Experimental study on the coupled treatment of acid mine drainage from closed coal mines by electrocoagulation and pyrolysis-ash adsorption

Article Title: Experimental study on the coupled treatment of acid mine drainage from closed coal mines by electrocoagulation and pyrolysis-ash adsorption

Article References: Yuan, D., Chang, Y., Li, X., Qin, B., Zhang, M., Gao, C., & Wu, X. (2026). Experimental study on the coupled treatment of acid mine drainage from closed coal mines by electrocoagulation and pyrolysis-ash adsorption. Frontiers in Environmental Science, 14, Article 1964982. https://doi.org/10.3389/fenvs.2026.1964982

Image Credits: AI Generated

DOI: 10.3389/fenvs.2026.1964982

Keywords: Experimental, coupled, treatment, acid, mine, drainage, closed, coal, mines, electrocoagulation, pyrolysis-ash, adsorption

The research, published in Frontiers in Geochemistry, analyzed 71 samples spanning nearly every environmental compartment of the Guanajuato River Basin: bedrock, mine tailings, soils, stream sediments, ancient overbank sediments, surface water, and groundwater. The goal was ambitious. Rather than simply measuring how much arsenic or lead was present, the team wanted to determine where those elements came from, whether from the hydrothermal mineralization that formed the district’s famous silver veins, or from the centuries of mining that exploited them. This distinction matters enormously, because elevated concentrations of toxic elements alone cannot discriminate between natural geological enrichment and human contamination, particularly in terrain where both share a common origin.

The study area is geochemically extraordinary. The Guanajuato Mining District hosts low-sulfidation epithermal silver-gold vein systems controlled by the Veta Madre fault zone, a structure that geologists have described as a fossil geothermal system. Hydrothermal activity generated mineral assemblages enriched in silver, gold, lead, zinc, copper, arsenic, antimony, and mercury. In other words, the basin’s rocks were naturally loaded with potentially toxic elements long before the first shovel struck ore. Historical records indicate that Guanajuato was a premier silver-producing region during the eighteenth century, generating substantial volumes of mining waste and profoundly altering the basin’s fluvial architecture. The legacy tailings were derived directly from the same hydrothermal mineralization that naturally enriches the bedrock, creating substantial overlap between natural and mining-related geochemical signatures.

To disentangle these overlapping signals, the researchers deployed a two-pronged analytical strategy. First, they applied multivariate statistics, including Spearman rank correlation, principal component analysis, and hierarchical cluster analysis, to identify elemental associations across the solid and aqueous matrices. Second, they trained an exploratory Random Forest classifier, a machine learning algorithm that builds hundreds of decision trees, using bedrock samples as a geogenic reference class and mine tailings samples as a mining-related reference class. The model achieved an out-of-bag error of 0.12, meaning it correctly classified about 88 percent of training samples it had not seen during model fitting. The researchers then applied the trained model to soils, stream sediments, and overbank sediments to estimate their relative geochemical affinity to each reference class.

The multivariate statistics revealed two principal geochemical associations in the solid samples. The first, comprising aluminum, iron, chromium, vanadium, and nickel, showed strong positive correlations and was interpreted as geogenic, reflecting the natural composition of the basin’s mafic-ultramafic and volcano-sedimentary lithologies. The second association, involving sulfur, arsenic, cadmium, lead, copper, and zinc, was interpreted as mineralized and mining-related. The Random Forest analysis identified sulfur, cadmium, and arsenic as the leading predictors for discriminating between the two reference classes. At the matrix level, median affinity scores for overbank sediments and soils shifted toward the geogenic reference class at 61 and 63 percent respectively, while stream sediments occupied an intermediate position with nearly equal median scores of 49 percent geogenic and 51 percent mining-related affinity.

This intermediate position of stream sediments is geochemically telling. It suggests that modern stream sediments integrate both natural and mining-derived materials, reflecting the long-term influence of weathering, erosion, and sediment transport processes operating throughout the basin. Historical records indicate that large volumes of finely ground wastes were deposited in tailings piles or discharged directly into drainage channels during ore processing. Because these materials are more susceptible to weathering and erosion than the original bedrock, they serve as an efficient mechanism for the downstream redistribution of metal(loid)s and contribute to the persistence of mining-related signatures in modern sediments. Spatially, stream sediment samples located near the historical mining district generally showed lower median geogenic affinity scores and coincided with higher values of a standardized sulfur-plus-cadmium-plus-arsenic index, while elevated index values also occurred at some downstream locations, indicating that this geochemical signature was not restricted to the immediate vicinity of historical tailings deposits.

Perhaps the most striking finding concerns what the preindustrial overbank sediments reveal. These samples were obtained from a roughly two-meter-high floodplain terrace previously dated to pre-industrial depositional periods ranging from 2450 to 9410 years before present. Their concentrations of arsenic, cadmium, copper, and zinc demonstrate that weathering of naturally mineralized lithologies supplied trace elements to the fluvial system long before the onset of intensive mining. The authors conclude that historical mining has amplified and redistributed preexisting hydrothermal geochemical signatures rather than introducing entirely new elemental assemblages. This is a crucial insight for environmental management in naturally mineralized basins worldwide, where elevated elemental concentrations may be misclassified as contamination when they actually reflect geological inheritance.

The water chemistry told a different story. Bicarbonate-type waters dominate the basin, but groundwater is enriched in sodium, silicon, fluoride, arsenic, and zinc, whereas surface waters exhibit higher overall mineralization with calcium-magnesium-sulfate signatures. Most dissolved metal(loid) concentrations remained below the analytical limit of quantification in both surface and groundwater, a contrast the researchers attribute to the carbonate-rich composition of Guanajuato mine tailings, which buffers acidity and limits acid mine drainage generation, favoring metal(loid) retention through interactions with iron-manganese oxyhydroxides and carbonate phases. Arsenic represents an important exception because its mobility may increase under circumneutral to alkaline conditions through desorption from mineral surfaces, making it one of the few elements occurring at measurable dissolved concentrations while also showing enrichment in solid matrices.

The risk assessment revealed spatially heterogeneous environmental concerns. Ecological risk was highest in mine tailings, driven primarily by elevated arsenic, cadmium, and copper, with two specific tailings deposits classified as moderate ecological risk while the remaining deposits were classified as low risk. Water quality and human health risks were restricted to localized groundwater and downstream surface water sites. Among surface waters, particularly high pollution index values coincided with elevated manganese concentrations, whereas high index values in groundwater coincided with arsenic concentrations exceeding drinking-water guideline values. For groundwater samples with quantified arsenic, carcinogenic risk estimates exceeded the upper acceptable level of 1.0E-04, with the highest values observed in two specific production wells. The authors emphasize that these estimates indicate potential carcinogenic concern under the evaluated long-term exposure scenario but require careful interpretation given the high proportion of arsenic measurements below the quantification limit.

The researchers are transparent about the limitations of their approach. The Random Forest model was trained on a limited dataset of only 12 lithological and 13 mine tailings samples, making the findings exploratory rather than definitive. Variable importance measures based on mean decrease in impurity may be biased when predictors are highly correlated, and the Random Forest is primarily a predictive classification algorithm rather than a mechanistic source-apportionment model. Water sampling was restricted to the dry season, so the hydrochemical patterns represent low-flow conditions and may not capture seasonal variations in metal(loid) mobility. Nevertheless, the integrated evaluation of multiple environmental matrices provides a screening framework for interpreting mining-related impacts in naturally mineralized basins, identifying priority areas for continued monitoring, particularly specific groundwater sites and tailings deposits with elevated ecological risk. For a basin shaped by five centuries of mining, the message is clear: the past is not buried, it is flowing downstream, one sediment particle at a time.

Subject of Research: Geochemical connectivity among mine tailings, soils, sediments, and waters in a historical mining basin integrating multivariate statistics and exploratory random forest modeling

Article Title: Geochemical connectivity among mine tailings, soils, sediments, and waters in a historical mining basin integrating multivariate statistics and exploratory random forest modeling

Article References: Rueda-Garzon, L. F., Carrillo-Chávez, A., Miranda-Avilés, R., Calvo-Ramos, D., Morales-Martínez, J. L., Muñoz-Torres, C., & García-Martínez, R. (2026). Geochemical connectivity among mine tailings, soils, sediments, and waters in a historical mining basin integrating multivariate statistics and exploratory random forest modeling. Frontiers in Geochemistry, 4, Article 1919883. https://doi.org/10.3389/fgeoc.2026.1919883

Image Credits: AI Generated

DOI: 10.3389/fgeoc.2026.1919883

Keywords: geochemistry, mining, machine learning, arsenic, Guanajuato, random forest, environmental risk, source apportionment, Geochemical, connectivity, mine, tailings

Tailings storage facilities, known in the industry as TSFs, are the embankments and impoundments built to contain the fine-grained residue that remains after gold ore is crushed and processed. On the Witwatersrand, the gold-bearing basin that underlies much of Gauteng Province, these facilities rise as pale, treeless hills that dominate the horizon of townships and informal settlements. Under dry Highveld conditions, particularly during the windy winter months, fine dust can be lifted from tailings surfaces and carried into nearby communities. Residents may inhale respirable dust containing crystalline silica and, in some cases, low concentrations of uranium and other metals associated with gold-bearing ore. Assessing the health risks of such exposure requires not only measurements of dust concentration but also a clear picture of how long, and in what settings, people are actually exposed to whatever the wind carries off the dumps.

That second ingredient is precisely what time-activity data provide, and it is precisely what has been missing for the communities of the West Rand. Exposure assessment in environmental epidemiology depends fundamentally on the product of concentration and duration: a person who spends twelve hours a day outdoors near a dust source faces a very different exposure profile from someone who works in an office across town and sleeps behind closed windows. Yet regulatory models and risk assessments in South Africa, as in many countries, have often relied on generic assumptions imported from international guidelines, typically European or North American time budgets that assume substantial time indoors, in sealed buildings, with mechanical ventilation. Such assumptions map poorly onto communities where many homes are informal structures, where cooking and socializing happen outdoors, where unemployment keeps large numbers of people at home all day, and where children play on unpaved ground close to the tailings themselves.

The research team set out to replace those assumptions with locally observed data. Their study focused on residential communities situated in proximity to tailings storage facilities along the West Rand, the western arm of the Witwatersrand gold field that runs through municipalities such as Merafong and the areas around Randfontein and Westonaria. Participants across a range of age groups were asked to document and recall how they spent their time across a representative day, capturing the categories of activity that exposure scientists use to partition daily life: time indoors at home, time indoors elsewhere such as school or work, time outdoors near the residence, time spent further afield, and time in transit. By collecting this information separately for children of different ages, working-age adults, and older residents, the study was able to construct age-stratified time budgets rather than a single community average.

The age-stratified approach matters because exposure risk is not distributed evenly across a lifetime. Infants and young children breathe more air per unit of body weight than adults, their respiratory tracts are still developing, and their behaviors, crawling on floors, playing in dust, hand-to-mouth contact, can amplify contact with contaminated soil and particulates. School-age children spend large portions of the day in classrooms that may or may not offer protection from outdoor dust, and many walk routes to and from school that pass close to tailings footprints. Adults of working age may be away from the residential exposure zone for much of the day, or, where unemployment is high, may remain in it continuously. Elderly residents, particularly those with pre-existing respiratory or cardiovascular conditions, are among the most vulnerable to particulate pollution yet may spend nearly all of their time within a few hundred meters of home. Without age-specific data, a risk model that averages across these very different patterns will underestimate exposure for some groups and overestimate it for others.

While the detailed numerical results reside in the full publication, the study’s contribution can be understood at two levels. At the level of raw evidence, it provides measured, locally grounded estimates of the hours per day that West Rand residents of different ages spend in locations and microenvironments relevant to tailings-derived dust exposure. At the level of method, it demonstrates a practical protocol for gathering such data in resource-constrained, high-exposure settings, where survey-based recall and structured questionnaires are often the only feasible instruments. Studies of this kind commonly reveal that total time outdoors near the home is substantially higher than default values assumed in international exposure models, particularly for children and for adults who are not formally employed, and that time in enclosed, mechanically ventilated environments is correspondingly lower. The West Rand findings are significant precisely because they anchor risk calculations in the actual rhythms of life in mining-adjacent communities rather than in borrowed assumptions.

The broader context makes the work urgent. The Witwatersrand has produced gold since 1886, and the region’s legacy tailings footprint extends across hundreds of square kilometers, much of it now surrounded or penetrated by residential development that expanded during and after apartheid-era planning, when Black communities were frequently located on marginal land close to mine dumps. Re-mining operations, in which companies reprocess old tailings to extract residual gold, add a contemporary layer of activity: trucks, conveyors, and processing plants that can re-suspend dust from deposits that had partially stabilized. Community organizations and environmental justice groups on the West Rand have for years raised concerns about respiratory illness, silicosis-like symptoms, and the long shadow of mining waste, while regulatory attention to residential proximity has grown through frameworks such as South Africa’s National Environmental Management Act and guidelines addressing buffer distances around tailings facilities. Sound policy in this contested space depends on defensible exposure estimates, and defensible exposure estimates depend on exactly the kind of time-activity data this study supplies.

The findings also speak to an international audience of exposure scientists, because time-activity patterns are among the most context-dependent variables in all of environmental health research. A time budget measured in a North American suburb, with its sealed houses, air conditioning, and car commutes, is simply a different object from one measured in a settlement of informal dwellings where daily life unfolds outdoors. Researchers in the exposure sciences have increasingly recognized that microenvironment models, which calculate total exposure as the sum of concentrations multiplied by time spent in each distinct setting, are only as good as the time-location inputs they receive. Studies from low- and middle-income countries remain underrepresented in this literature relative to the size of the populations potentially affected, which makes locally generated datasets from places like the West Rand valuable well beyond their immediate geography. They offer reference points for other mining-affected regions of southern Africa, and for the wider Global South, where tailings facilities and residential communities frequently coexist.

For residents themselves, the practical significance lies in what follows from measurement. Age-specific time budgets can inform where and when dust-control interventions will deliver the greatest benefit: rehabilitating tailings surfaces with vegetation or covers, adjusting re-mining activities during high-wind seasons, considering buffer zones in land-use planning, and targeting school and household-level measures, such as improved ventilation practices and dust-suppression around play areas, toward the groups whose daily routines place them most directly in the exposure pathway. Public health authorities can use the same data to refine health risk assessments and to prioritize environmental monitoring locations. Community advocates gain a documented, quantified account of how mining infrastructure shapes the daily lives of those living in its shadow, evidence that can carry weight in consultations, licensing processes, and debates over land use.

The study also underscores a deceptively simple point that recurs throughout exposure science: people are not passive receptors of pollution, and the dose they receive reflects the texture of their days. Understanding that texture, hour by hour and age group by age group, is an unglamorous but essential foundation for protecting public health in landscapes shaped by extraction. For the communities of the West Rand, where gold mine waste has been a neighbor for generations, having their actual patterns of movement and activity captured in the peer-reviewed literature represents a step toward risk assessments that see them as they are. It is a reminder that the path from a mine dump to a person’s lungs runs not only through the air, but through the ordinary details of where people live, work, learn, and play, and that measuring those details is where credible environmental health protection begins.

As gold mining continues its long retreat across the Witwatersrand, the waste it leaves behind will outlast the industry itself, and the populations living alongside that waste will continue to grow. Research that documents, with precision, how those populations inhabit their environment converts a legacy of neglect into an evidence base for action. The age-specific time-spent patterns reported for the West Rand provide a template for how such evidence can be gathered and a foundation on which dust management, urban planning, and public health interventions can now be built, in South Africa and in every other region where communities and mine tailings share the same ground.

Subject of Research: Age-specific time-spent patterns among residents living near gold mine tailings storage facilities in the West Rand, South Africa

Article Title: Age-specific time-spent patterns among residents living near gold mine tailings storage facilities in the West Rand, South Africa

Article References: Kalumbi, L. R., Masekameni, M. D., Utembe, W., & Brouwer, D. (2026). Age-specific time-spent patterns among residents living near gold mine tailings storage facilities in the West Rand, South Africa. Journal of Exposure Science & Environmental Epidemiology. https://doi.org/10.1038/s41370-026-00972-6

Image Credits: AI Generated

DOI: 10.1038/s41370-026-00972-6

Keywords: Age-specific, time-spent, patterns, residents, living, near, gold, mine, tailings, storage, facilities, West