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<p>Coal is critical to energy generation, accounting for one-third of global electricity output. However, coal mining emits pollutants into the atmosphere, which harm health. As a result, it is critical to estimate air quality in industrial locations to implement preventative measures. Artificial neural network (ANN) modelling is a popular multilayer network for predicting air pollution. The study assessed air quality in the Singrauli coalmine complex, comprising nine mines, using climatic parameters and emission rates. Concentrations of the pollutants&#xa0;in the region exceed permissible values in several mines, especially during dry periods. The ANN models for PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>X</sub>, and SO<sub>2</sub> performed best when constructed using the annual dataset that included the entire mine complex, with regression coefficient values of 0.92, 0.91, 0.89, and 0.81, respectively. The SO<sub>2</sub> ANN model slightly underpredicts the actual SO<sub>2</sub> values, particularly during testing, due to insufficient data diversity, imbalanced training data, and suboptimal model tuning. The sensitivity study found that cloud cover, precipitation, wind direction, wind speed, and solar radiation are crucial for accurate pollutant concentration estimates. The findings emphasize the importance of weather conditions in predicting pollution and highlight the potential of ANN models in predicting environmental risks in industrial zones.</p>

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Artificial neural network modeling for predicting PM10, PM2.5, NOX, and SO2 in coal mining areas

  • 期刊论文

  • 发表日期：

    2025年6月7日

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  • Akash Mishra,
  • Navin Prasad,
  • Tanushree Bhattacharya,
  • Bindhu Lal

摘要

Coal is critical to energy generation, accounting for one-third of global electricity output. However, coal mining emits pollutants into the atmosphere, which harm health. As a result, it is critical to estimate air quality in industrial locations to implement preventative measures. Artificial neural network (ANN) modelling is a popular multilayer network for predicting air pollution. The study assessed air quality in the Singrauli coalmine complex, comprising nine mines, using climatic parameters and emission rates. Concentrations of the pollutants in the region exceed permissible values in several mines, especially during dry periods. The ANN models for PM10, PM2.5, NOX, and SO2 performed best when constructed using the annual dataset that included the entire mine complex, with regression coefficient values of 0.92, 0.91, 0.89, and 0.81, respectively. The SO2 ANN model slightly underpredicts the actual SO2 values, particularly during testing, due to insufficient data diversity, imbalanced training data, and suboptimal model tuning. The sensitivity study found that cloud cover, precipitation, wind direction, wind speed, and solar radiation are crucial for accurate pollutant concentration estimates. The findings emphasize the importance of weather conditions in predicting pollution and highlight the potential of ANN models in predicting environmental risks in industrial zones.