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Assessment of dispersion of respirable particles emitted from opencast mining operations: development and validation of stepwise regression models

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Abstract

This study gives insight into the spatiotemporal variability of respirable PM concentrations around typically highly mechanized opencast coal mines in India and its influence on ambient air quality, which at present is scant in the literature. The results suggest that at a distance of 500 m from the pit boundary, the respirable PM concentrations were found higher than the background concentration (1.52, 1.80 and 1.89 times for PM10, PM2.5 and PM1 at Mine 1 and 1.4, 1.35 and 1.35 times for PM10, PM2.5 and PM1 at Mine 2) which suggests that residents up to and beyond 500 m from the mine are exposed to the PM emitted from mining activities. For PM2.5 and PM1 concentrations, RH was the most important determinant (PM2.5: 24.8%; PM1: 30.1%). Pit boundary PM concentration was the weakest determinant (7%) for the PM2.5 concentration in mine surroundings, and wind speed was found as the weakest determinant (6.6%) for PM1 concentration. Conversely, distance (20.8%) was the important determinant of PM10 concentrations. All the four predictors (RH, wind speed, distance and Pit boundary PM concentration) could explain 46–54% variability in the PM concentrations. Inclusion of pit boundary PM concentration as a predictor increased the prediction capability of the models (all the developed models have R2 > 0.40 at significance level p < 0.05). PM prediction models can be used by the mining and regulatory authorities to assess the respirable PM level in mine surroundings.

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Authors and Affiliations

  1. Mahanadi Coalfields Limited, Sambalpur, 768020, India

    Satya Prakash Sahu

  2. Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Aditya Kumar Patra

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  1. Satya Prakash Sahu
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  2. Aditya Kumar Patra
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Sahu, S.P., Patra, A.K. Assessment of dispersion of respirable particles emitted from opencast mining operations: development and validation of stepwise regression models. Environ Dev Sustain 24, 9139–9164 (2022). https://doi.org/10.1007/s10668-021-01816-z

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