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首页> 外文期刊>International Journal of Environmental Sciences >Prediction of dust dispersion during drilling operation in open cast coal mines: A multi regression model
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Prediction of dust dispersion during drilling operation in open cast coal mines: A multi regression model

机译:露天煤矿钻孔作业过程中粉尘扩散的预测:多元回归模型

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Dust pollution is one of the major concerns in mining operations. The workers and nearby human habitats prone to various respiratory diseases due to dust pollution. Prediction of dust dispersion is required to determine the pollution level of the ambient air and also to implement various control measures to reduce their concentration. Though there are various tools available for dust prediction, mathematical models are commonly used to predict the dust concentration, for its easy use. In the absence of specific mathematical models to predict the dust produced from drilling operations for Indian meteorological and geo-mining conditions, dust dispersion models were developed using multiple regression analysis method. Field investigations were carried out in two large opencast coal mines in India. First mine data was used to develop the models and the second mine data was used for validation of the models. It was found that the predicted dust concentration values of the developed models are more close to the field monitored values compared to the USEPA model predicted values. These models can be used for predicting the dust concentration level of PM10 in atmosphere in coal mines.
机译:粉尘污染是采矿作业中的主要问题之一。工人和附近的人类栖息地由于粉尘污染而容易发生各种呼吸道疾病。为了确定环境空气的污染水平并采取各种控制措施以降低其浓度,需要对粉尘的扩散进行预测。尽管有多种工具可用于粉尘预测,但是数学模型通常用于预测粉尘浓度,因为它易于使用。在缺乏专门的数学模型来预测印度气象和地质开采条件下钻井作业产生的粉尘的情况下,使用多元回归分析方法开发了粉尘扩散模型。在印度的两个大型露天煤矿进行了实地调查。第一个地雷数据用于开发模型,第二个地雷数据用于模型验证。已发现,与USEPA模型的预测值相比,已开发模型的预测粉尘浓度值更接近于现场监测值。这些模型可用于预测煤矿大气中PM10的粉尘浓度水平。

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