3496 Modeling PM emissions from loading operations in mineral quarries with decision tree approach
DOI:
https://doi.org/10.17159/Abstract
The study aims to create a model for particulate matter emissions during the loading process, which is a crucial step in open-pit mining. The researchers conducted simultaneous measurements of total suspended particulates (TSP), PM10, PM2.5, and PM1, as well as factors related to thermal comfort, such as air temperature, relative humidity, station pressure, dew point temperature, wind speed, headwind, and crosswind speed. Additionally, they collected samples to determine the moisture content of the loaded materials in the laboratory. The analysis used 7895 measurement data from gypsum and limestone quarries, employing two data analysis programs, SPSS and WEKA, to derive equations for predicting PM release. In the modeling, the PM measurements were the dependent variables, while the thermal comfort parameters, laboratory measurement results, and loader bucket capacities were the independent variables. The linear regression models created with SPSS did not adequately capture the dependent variable, leading the researchers to explore the decision tree approach for further modeling. The M5P algorithm was employed to generate regression equations for the different data sets, and the findings demonstrated that the models possessed a satisfactory degree of predictive capability.
The amount of fine particles released during loading is influenced by various weather factors. Temperature, humidity, wind speed, and station pressure can all affect the dispersal of these particles. Additionally, the moisture content of the loaded material and the capabilities of the loading equipment contribute to this process.
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Copyright (c) 2026 Zekeriya Duran, Bülent Erdem, Tuğba Doğan, Mehmet Genç
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