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 μm (PM 10 ) and less than 2.5 μm (PM 2.5 ). 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.

中文翻译：

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控制矿山作业中扬尘的数据驱动方法

颗粒物 (PM) 是与露天采矿造成的空气污染相关的主要参与者之一。扬尘被视为无法通过常规措施收集的颗粒物，按粒径分类。环境保护署 (EPA) 根据粒径小于 10 μm (PM 10 ) 和小于 2.5 μm (PM 2.5 ) 将 PM 分为粗颗粒和细颗粒。露天采矿的基本操作，如钻孔和爆破、装载、运输和加工，都是可能产生扬尘的过程。法规和立法强制采矿业使用环境监测系统，其中通过 PM 10 水平测量的扬尘水平作为其中的一部分。空气质量监测器位于露天煤矿周围的不同位置，并在生产过程中跟踪空气质量水平。本研究介绍了一种数据驱动的方法来处理与露天煤矿扬尘有关的空气质量问题。数据实时来自不同的矿山设备，并与空气质量监测系统集成，以提供信息以支持扬尘决策。该方法在大型露天煤矿的案例研究中得到实施和演示。