Investigation
of Dust Emission in Limestone Mines and its Statistical Prediction using Supervised
Machine Learning (Regression) Modelling
Pal Rajib, Vardhan Harsha, Shanmugam Bharath Kumar, Hanumanthappa Harish and Senapati
Amrites
Disaster Advances; Vol. 18(5); 114-125;
doi: https://doi.org/10.25303/185da1140125; (2025)
Abstract
In India, the fugitive dust emissions in the processing plant and mining area of
limestone mines are very high. The dust emission of (particulate matter) PM10 and
PM2.5 forms an unsafe working environment for workers in processing plant areas
and mining areas. The excessive emission of PM10 and PM2.5 will cause lung-related
diseases to the workers and the people existing in the adjacent areas of the mine.
The dust emission majorly causes air pollution to occur due to the distribution
of particulate matter in the work area. This study majorly investigates the dust
emission levels of PM10 and PM2.5 in the limestone mine of Kadapa, Andra Prasad,
India. The investigation on the dust emission of PM10 and PM2.5 was carried out
as per the guidelines of DGMS and MoEF and CC guidelines, with a specific focus
on PM10 and PM2.5 particulate matter.
From the study, it was clear that the dust emission levels of PM10 and PM2.5 in
the mine area and some parts of the processing area were below the permissible limit
of 1200 μg/m³ as per the National Ambient Air Quality Standards (NAAQS, 2009). It
was also found that the dust emission levels of PM10 and PM2.5 in the crushing and
screening area of the processing plant were above the permissible limit of 1200
μg/m³. Further the statistical prediction model was developed using linear, quadratic
and cubic supervised machine learning (regression) modelling. The results indicated
that the cubic regression model will provide the accurate prediction of fugitive
dust emission with lower error and standard deviation.
