Particulate Matter Emissions in Mining Operations

Particulate matter (PM) emissions arise throughout the life cycle of mining operations, from excavation and blasting to transport and stockpile management. The size distribution of particles—from coarse dust (> PM10) to fine and ultrafine fractions (PM2.5 and PM1)—determines their atmospheric residence time, transport distance and health impacts. Sources include wind erosion of exposed pits and waste heaps, mechanical crushing and screening of ore, vehicle movements on haul roads and emissions from ancillary equipment. The mineral composition of emitted dust often reflects local geology but can also contain trace metals and process residues, posing risks to respiratory health and ecosystem integrity. Regulatory limits on ambient PM concentrations vary globally, driving the adoption of water sprays, chemical suppressants, wind fences and enclosed handling to reduce fugitive dust. Advances in remote sensing, atmospheric dispersion modelling and sensor networks have improved quantification of emissions and informed site-specific control measures. Integration of monitoring data with predictive analytics is enabling real-time management and optimisation of dust suppression, with the twin goals of safeguarding local communities and meeting tightening environmental standards.

Research from Nature Portfolio

Recent studies have demonstrated the potential of combining Internet of Things (IoT) sensor networks with machine learning to monitor and predict dust levels in surface mine environments. A real-time IoT platform was deployed across multiple work zones to measure particulate fractions (PM1.0, PM2.5, PM4.0 and PM10.0). Data from these sensors fed four prediction models—Decision Tree, Gradient Boosting Regression, Random Forest and Linear Regression. The Random Forest algorithm achieved the lowest prediction errors, enabling accurate short-term forecasts of dust concentrations under variable meteorological conditions. This integrated system illustrates how predictive analytics can guide proactive dust control, optimise resource allocation and enhance worker safety in mining operations.

Particulate Matter Emissions in Mining Operations publication trend

The graph below shows the total number of articles in particulate matter emissions in mining operations across all publications each year (not limited to Nature Index journals).

Technical terms

Particulate matter (PM): A mixture of solid particles and liquid droplets suspended in air, classified by aerodynamic diameter (e.g. PM10, PM2.5, PM1).

PM10 / PM2.5: Particles with diameters of 10 micrometres or less / 2.5 micrometres or less; critical for assessing respiratory health impacts.

Dust resuspension: Re-entrainment of previously settled particles into the air by wind or vehicular activity.

Internet of Things (IoT): Network of interconnected sensors and devices that collect and exchange data for real-time monitoring.

Machine learning (ML): Data-driven algorithms that learn patterns from historical data to make predictions or classifications.

References

1. A Novel Landsat-Derived Multispectral Index for Coal Dust Detection: Spatiotemporal Dispersion Patterns and Natural Driving Forces. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2025).
2. Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques. Scientific Reports (2024).
3. Analyzing Characteristics of Particulate Matter Pollution in Open-Pit Coal Mines: Implications for Green Mining. Energies (2021).
4. Air Pollution Emissions 2008–2018 from Australian Coal Mining: Implications for Public and Occupational Health. International Journal of Environmental Research and Public Health (2020).