A Data-Driven Approach to Control Fugitive Dust in Mine Operations

Abstract

Particulate matter (PM) is one of the main actors related to air pollution caused by surface mining. Fugitive dust, considered as particulate matter that cannot be collected by conventional measures, is classified by the particle size. The Environmental Protection Agency (EPA) categorizes PM as coarse and fine particles based on the particle size being less than 10 mu m (PM10) and less than 2.5 mu m (PM2.5). Basic operations of surface mining such as drilling and blasting, loading, haulage, and processing are processes that can potentially generate fugitive dust. Regulations and legislations enforce the mining industry to use environmental monitoring systems, fugitive dust level measured by PM(10)level as part of this. Air quality monitors are positioned at different locations around surface coal mines and track air quality levels during production. This study introduces a data-driven methodology to handle air quality issues related to fugitive dust at surface coal mines. Data is sourced from different mine equipment in real-time and they are integrated with air quality monitoring systems to provide information to support decisions for fugitive dust. The method is implemented and demonstrated in a case study at a large surface coal mine.