In mining operations, the era of dust is a frequent phenomenon, resulting in the presence of airborne dust suspended within the mine environment. This airborne dust primarily contains mineral particles and, within the presence of moisture, offers rise to particulate matter, which consists of a posh combination of strong and liquid parts. The dimension of those particles ranges from 10 µ to 2.5 µ, rendering them invisible to the bare eye. Inhalation of such particles can pose important well being hazards to employees, particularly upon persistent publicity. Particulate matter consists of a mix of natural and inorganic particles, together with dust, pollen, soot, smoke, and liquid droplets, making it extraordinarily hazardous to human respiratory well being. Thus, monitoring the degrees of particulate matter in mining sites is of utmost significance for making certain the protection and well-being of employees. This monitoring performs an important function within the prevention and prediction of well being hazards related to inhalation12.Understanding particulate matter in mining environments: sources, traits, and well being impressionUnderstanding particulate matter (PM) in mining environments is crucial for recognizing its sources, traits, and the potential impression it poses within the mining context. PM originates from each pure and anthropogenic sources, encompassing sea salt, pollen, volcanic eruptions, airborne dust, and varied industrial actions. Among industrial operations, mining considerably contributes to PM emissions on account of processes comparable to drilling, blasting, transportation, and dealing with of supplies. Drilling operations generate suspended airborne dust particles, whereas blasting releases particles and gasoline emissions, together with NOx, which might pose well being risks13. Additionally, open-pit coal mining contributes to elevated PM ranges, facilitated by wind-driven dispersion of coal dust, necessitating the implementation of efficient mitigation methods to safeguard human well being and the environment14.Based on their formation mechanisms, PM is assessed into varied sorts, together with dust, smoke, fumes, fly ash, mist, and spray (see Table 1). These completely different PM sorts exhibit distinct dimension ranges, with fantastic and ultrafine particles able to reaching the alveoli within the respiratory system, whereas PM10-sized particles primarily settle within the higher airways. The share of inhaled airborne particles that enter the respiratory tract is represented by whole inhalable dust15. Other measures, comparable to thoracic and respirable dust, consult with particles that cross by the larynx into the thoracic cavity and attain the gasoline trade area of the lungs, respectively. Hazardous dusts may chemically work together with the respiratory system, permitting poisonous substances like lead and arsenic to cross by alveolar partitions into the bloodstream16. A complete understanding of those PM classifications is essential for assessing their impression on human well being.Table 1 Size ranges of pm sorts.Importance and strategies of monitoring particulate matter in varied environmentsExposure to particulate matter (PM) poses important well being dangers to miners, as they inhale ambient air of their office. PM’s mineralogical composition can result in extreme well being points, comparable to asbestosis and silicosis3. Effective monitoring of PM is essential not solely for environmental permits and planning but additionally for safeguarding miners’ well being. However, present monitoring methods in mining areas encounter limitations, necessitating the implementation of quick and correct air monitoring methods. Inadequate monitoring of PM dust focus (starting from PM 2.5 µ to PM 10 µ) can result in employee publicity and varied well being issues, together with respiratory issues, lung illnesses, respiratory difficulties, non-fatal coronary heart assaults, and cardiac arrhythmias. Therefore, complete and exact monitoring methods are important for making certain the well-being of miners17,18.Monitoring particulate matter (PM) in mining sites entails gathering air high quality information whereas contemplating wind course. This monitoring could be divided into three elements: (1) monitoring the mine environment away from gear operations however inside the website, (2) monitoring PM dust at working sites, together with drilling, blasting, loading, transportation, and services, and (3) monitoring PM dust outdoors the mining area19.IoT-based monitoring methods for particulate matter: a evaluation of previous analysis workIn mining operations, varied dust-forming actions happen at completely different places, necessitating the monitoring of particulate matter (PM) concentrations at a number of sites. The fast development of Internet of Things (IoT) know-how has led to the event of IoT-based PM monitoring methods, which function a promising various to conventional monitoring methods20. Conventional monitoring methods typically require important human intervention, are time-consuming, and might lead to handbook errors, emphasizing the necessity for improved monitoring options. IoT-based PM monitoring methods gather information by measurement units (sensors) and transmit it by way of the community, making them extra environment friendly and dependable. These methods are designed to allow mine operators to promptly examine dust-causing sites and implement needed preventive measures. To be efficient, these methods must be straightforward to put in at a number of sites and exhibit enough endurance, contemplating that the primary dust-generating areas might change over time, and employees are uncovered to harsh out of doors circumstances throughout mining operations. This research investigates the efficiency of IoT measurement units and the community in current operations, together with an open-pit mine website.A mess of research has explored the appliance of the Internet of Things (IoT) in monitoring visitors stream and monitoring air high quality. For occasion, a research in 2022 launched an affordable IoT-based system for monitoring visitors stream and figuring out the air high quality index (AQI)21. This research utilized machine learning strategies, which eradicated the necessity for complicated calibration, permitting the measurement of pollutant gases and correct dedication of AQI. Similarly, one other research in 2020 demonstrated an IoT-based indoor air high quality monitoring platform, storing information within the cloud and offering sources for additional indoor air high quality studies22.In line with this, researchers in 2020 developed an IoT system for monitoring air high quality, able to monitoring native air high quality and offering information for person evaluation by way of an built-in buzzer23. Additionally, one other research in 2020 mentioned the usage of IoT within the mining area, highlighting how IoT serves as a wi-fi community for gathering info from digital units and sensors24.Over the previous decade, advances in wi-fi sensor networks (WSN), radio frequency identification (RFID), and cloud computing have facilitated the combination of the Internet of Things (IoT) in harsh work environments like mining25. This integration has considerably improved the accuracy, effectivity, cost-effectiveness, and real-time capabilities of the monitoring course of. Notably, these developments have enabled computerized occasion detection, management, and distant information trade, making monitoring possible in in any other case inaccessible places. Several profitable implementations of WSN-based monitoring methods have been reported, comparable to early detection of fires in coal mines and detection of poisonous mine gases within the surroundings. Furthermore, IoT know-how has enabled the correct measurement of particulate matter inside a short while. Given that point and price are essential components in managing these initiatives, this work goals to develop a low-cost IoT-based PM monitoring gadget able to monitoring pollution of lower than 2.5 µ. By utilising these applied sciences, mining operations could be made safer and extra environment friendly, whereas concurrently decreasing prices and environmental impacts26.Predicting particulate matter: an software of AI and MLNumerous research have proposed varied machine learning algorithms for the prediction of airborne particulate matter. Li et al. launched a real-time prediction method primarily based on weighted excessive learning machine (WELM) and adaptive neuro-fuzzy inference system (ANFIS)27. Choubin et al. developed machine learning fashions, together with Random Forest (RF), Bagged Classification and Regression Trees (Bagged CART), and Mixture Discriminant Analysis (MDA), for forecasting PM10-induced risk28. Rutherford et al. utilized excitation-emission matrix (EEM) fluorescence spectroscopy and a machine learning algorithm to localize PM sources29.In the context of PM2.5 prediction, Just et al. proposed a brand new technique using machine learning techniques30. Yang et al. put ahead hybrid fashions by combining completely different deep learning approaches31. Stirnberg et al. developed a technique integrating satellite-based Aerosol Optical Depth (AOD) with meteorological and land use components for predicting PM10 concentrations32. Additionally, Gilik et al. constructed a supervised mannequin for air air pollution prediction using sensor information and explored mannequin transferability between cities33.These complete research collectively exhibit the potential and effectiveness of machine learning in air air pollution prediction, offering beneficial insights for future analysis and purposes on this area.In the context of this literature evaluation, the part prominently highlights the novelty and scientific contribution of the present analysis work—an IoT-based monitoring and ML powered dust prediction system. The proposed system not solely provides real-time monitoring of varied PM particle sizes, together with PM1.0, PM2.5, PM4.0, and PM10.0, but additionally integrates a environment friendly prediction mannequin to make sure exact and correct PM measurements. With {hardware} integration and sturdy software program protocols, the system addresses the constraints of conventional monitoring techniques, facilitating environment friendly and complete monitoring of PM dust focus in mining environments. This analysis work goals to considerably contribute for bettering mine air high quality by successfully monitored and prediction of PM dust air pollution in surface mine sites by utilising the innovative know-how like IoT and ML. The proposed IoT-based Dust Monitoring System stands as a novel and sensible answer that advances the sphere of air high quality monitoring and holds promising potential for widespread implementation in mining and past.
https://www.nature.com/articles/s41598-024-58021-x
Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques
