Emission of respirable dust from stone quarrying, potential health effects, and its management

Spatial distribution and radiological hazards assessment of naturally occurring radionuclide materials in soil from quarry sites in Ogun State, Nigeria

Workers and dwellers around quarrying sites are exposed to naturally occurring radioactive materials (NORMs) during various activities done on the rock and earth crust. This study investigated the spatial distribution and radiological health effects of quarrying activities in ten quarry sites in three districts (Odeda, Ajebo, and Ijebu Ode) around Ogun State, Nigeria. The NORMs (40K, 238U, 232Th) were assessed using a gamma spectrometer with a NaI(Tl) detector. The radiological hazards of NORMs were assessed and statistically analyzed. The activity concentration of NORMs (Bq/kg) ranged from 40K (76.8 ± 44.8-2647.9 ± 179.4), 238U (3.2 ± 1.8-55.4 ± 24.9), and 232Th (5.2 ± 3.9-244.4 ± 89.8) revealing 70% of all samples above the world average limit 420(40K), 33(238U), and 45 (232Th). The activity concentration of NORMs in all the sites followed in the order 238U < 232Th < 40K. The radiological and health parameter ranges for the adsorbed dose rate (DR) 3.0-339.92 (nGy/h), radium equivalent (Raeq) 5.88-739.4 (Bq/kg), annual effective dose equivalent outdoor (AEDEout) 3.72-417.16(µSvy-1), excess lifetime cancer risk (ELCR × 10-3) 0.01-1.46, and exposure rate (ER) 13.10-1531.47(µRh-1). The radiological hazard parameters are 2-3 times higher than their world averages in most of the samples thus discouraging the usage of the soil for building and ecological activities. This study showed that radionuclides are priority pollutants with high impact and with high exposure risk tendencies in all the quarry sites investigated and therefore unsuitable for ecological and building activities.

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques

The mining industry confronts significant challenges in mitigating airborne particulate matter (PM) pollution, necessitating innovative approaches for effective monitoring and prediction. This research focuses on the design and development of an Internet of Things (IoT)-based real-time monitoring system tailored for PM pollutants in surface mines, specifically PM 1.0, PM 2.5, PM 4.0, and PM 10.0. The novelty of this work lies in the integration of IoT technology for real-time measurement and the application of machine learning (ML) techniques for accurate prediction based on recorded dust pollutants data. The study's findings indicate that PM 1.0 pollutants exhibited the highest concentration in the atmosphere of the ball clay surface mine sites, with the stockyard site registering the maximum levels of PM pollutants (28.45 µg/m3, 27.89 µg/m3, 26.17 µg/m3, and 27.24 µg/m3, respectively) due to the dry nature of clay materials. Additionally, the research establishes four ML models-Decision Tree (DT), Gradient Boosting Regression (GBR), Random Forest (RF), and Linear Regression (LR)-for predicting PM pollutant concentrations. Notably, Random Forest demonstrates superior performance with the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) at 1.079 and 1.497, respectively. This comprehensive solution, combining IoT-based monitoring and ML-based prediction, contributes to sustainable mining practices, safeguarding worker well-being, and preserving the environment.

Reversing the damage: ecological restoration of polluted water bodies affected by pollutants due to anthropogenic activities

Aquatic ecosystems provide a large number of cultural, regulating, and supporting services to humans and play a pivotal role in sustaining freshwater-dependent ecosystems. However, an increase in human population coupled with economic growth in the last few decades has severely affected their functioning and ecological health. This has led to an increase in concentrations of pollutants originating from anthropogenic activities such as heavy metals, plastics, semi-volatile organic compounds, and endocrine disruptors. These pollutants provoke deleterious impacts on aquatic biodiversity and affect the water quality and functioning. In this paper, we discuss the sources and impacts of such pollutants as well as restoration techniques for reducing their impact on aquatic ecosystems. Several physical and chemical ecological restoration techniques, such as dredging, sediment capping, water diversion, adsorption, aeration, and flushing, can be employed to improve the water quality of water bodies. Additionally, biological techniques such as phytoremediation, phycoremediation, the use of biomembranes, and the construction of ecological floating beds can be employed to increase the population of aquatic organisms and improve the overall ecological health of aquatic ecosystems. Restoration techniques can effectively reduce the concentrations of suspended solids and dissolved phosphorus and increase the levels of dissolved oxygen. The restoration techniques for improving the ecological health of water bodies should not be limited to simply improving the water quality but should also focus on improving the biological processes and ecosystem functioning since it is essential to mitigate the adverse effects of pollutants and restore the vital ecosystem services provided by water bodies for future generations.

Exposure to Respirable Particulate Matter and Its Association with Respiratory Outcomes in Beauty Salon Personnel

We aimed to assess exposure to respirable particulate matter (PM) of beauty salon personnel, identify its determinants and ascertain the associated respiratory effects. We collected 122 full-day respirable PM samples from 12 beauty salons (floor area ranging from 24 to 550 m³, staff from 4 to 8) in Almaty, Kazakhstan, taking 10 samples from each place using a portable SidePak AM520 monitor. We also assessed lifestyle (smoking, etc.), respiratory symptoms and health-related quality of life (HRQL) of the personnel using questionnaires. Out of 11,831 5-min data points, daily median respirable PM concentrations were highly variable and ranged from 0.013 to 0.666 mg/m³ with 8.5-times difference in the median concentrations between the venue with the highest median (0.29 mg/m³) and the least median (0.034 mg/m³). In a multivariate linear regression modelling, ambient PM_2.5 concentration was the strongest predictor of daily median respirable PM concentration (beta 2.12; 95% CI 1.89; 2.39), and R² of the model was 0.63. We also found a positive association of the median respirable PM with respiratory symptoms and seasonal allergy, but not with HRQL. Short-term respirable PM levels in the beauty salons may be very high, but the median concentrations are mainly determined by the ambient air pollution.

Dust pollution hazard and harmful airborne dust exposure assessment for remote LHD operator in underground lead-zinc ore mine open stope

Underground mines embroil several occupational hazards, including airborne dust generation from various mining operations. Line-of-sight remote Load Haul Dumper (LHD) mucking is adopted to draw the blasted muck from unsupported open stopes in underground metalliferous mines. Assessment of particulate matter (PM) concentrations and remote LHD operator's exposure is crucial for devising appropriate dust control measures. In this study, PM generated due to mucking in longhole open stope by line-of-sight remote LHD during downcast airflow was measured using real-time aerosol spectrometers. The particulate concentrations at upstream and downstream of dust source were analysed for various particle sizes as well as occupational dust types, such as alveolic and thoracic. The airborne dust concentration of ≤ 10 μm (PM₁₀), ≤ 5 μm, and ≤ 1 μm (PM₁) size at operator's location in downstream was measured 71.3%, 28.5%, and 3.0%, respectively. The alveolic and thoracic dust types, respectively, were determined 25.1% and 74.2% in downstream and 48.9% and 84.6% in upstream total airborne dust concentration (311 ± 246 μg/m³). Dilution of airborne dust generated due to muck sliding inside the stope was analysed with time. Moreover, dust concentrations under typical airflow scenarios encountered in open stope were simulated using Ventsim software to identify the potential dust exposure hazard for remote LHD operator. The simulation revealed that downcast airflow causes maximum exposure of harmful airborne dust for remote LHD operator. This study enhanced the understanding of exposure potential of airborne dust during remote LHD mucking. Moreover, it emphasised adoption of tele-remote-operated LHD and automated mucking operation in open stopes.

Occupational Exposure to Mineral Dust in Mining and Earthmoving Works: A Scoping Review

Anthropogenic activity is related to several environmental imbalances, including dust. Particulate matter can also hinder humans with numerous health consequences, such as asthma, cancer, and pneumoconiosis. With a particular focus on mineral dust, this review is intended to determine in which circumstances occupational exposure occurs in the mining and earthmoving industries. Research followed the guidelines provided by the preferred reporting items for systematic review and meta-analysis protocols and its extension for scoping reviews. Of the 8993 records identified, only 24 passed both exclusion and inclusion criteria. Within the pool of results, it was possible to identify the following variables related to dust exposure: job-related (activity, job category, and site), engineering (equipment, transport system), technical (distance), and physical (season and weather) variables. Due to the significant variance in protocol settings, it was challenging to perform a general analysis, resulting in a study-by-study approach. The most significant conclusion of this study is not related to the setting of occupational exposure, although it derives from it. The necessity of adopting standard procedures for data collection, independent of research objective, was demonstrated within the context of occupational exposure to mineral dust.