Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model
Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model
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DOI：
10.1007/s11869-015-0369-9
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发表时间：
2016-09-01
影响因子：
5.1
通讯作者：
Kumar, Prashant
中科院分区：
文献类型：
--
作者：
Patra, Aditya Kumar;Gautam, Sneha;Kumar, Prashant
颗粒物（PM）是露天矿及其周围地区的主要污染物。露天矿开采造成的空气质量恶化问题比地下矿开采更为严重。粉尘浓度的预测必须知道实施控制策略和技术，以控制工作场所环境中的空气质量下降。有限的研究报告了PM在矿井内台阶之间的分散分布和旅行时间。本文对印度最深的露天铜矿之一Malanjkhand铜矿项目（MCP）的PM浓度进行了测量和建模。气象参数（风速、温度、相对湿度）和七个尺度范围内的PM浓度（即，PM0.23-0.3、PM0.3-0.4、PM0.4-0.5、PM0.5-0.65、PM0.65-0.8、PM0.8-1和PM 1 -1.6）已测量8天。实地研究的结果提供了一个了解的分散所产生的PM由于采矿活动。本研究工作提出了一种方法，以评估增加的PM浓度水平的矿山工人的暴露及其随深度的变化。相关性研究表明，PM从源到地表的传播过程中，其浓度与深度有关。经验方程来表示PM浓度和深度之间的关系。人工神经网络（ANN）模型显示PM浓度和气象参数之间的关系已经开发。神经网络模型的性能进行评估的真实的和预测数据之间的相关系数。结果表明，实验数据和建模输出之间的高度一致。这项工作的结果是重要的，在了解精细PM的变化，在工作场所内的矿井和相关的暴露的矿井工人。
Particulate matter (PM) is a major pollutant in and around opencast mine areas. The problem of degradation of air quality due to opencast mine is more severe than those in underground mine. Prediction of dust concentration must be known to implement control strategies and techniques to control air quality degradation in the workplace environment. Limited studies have reported the dispersion profile and travel time of PM between the benches inside the mine. In this paper, PM concentration has been measured and modeled in Malanjkhand Copper Project (MCP), which is one of the deepest opencast copper mines in India. Meteorological parameters (wind speed, temperature, relative humidity) and PM concentration in seven size ranges (i.e., PM0.23-0.3, PM0.3-0.4, PM0.4-0.5, PM0.5-0.65, PM0.65-0.8, PM0.8-1, and PM1-1.6) have been measured for 8 days. The results of the field study provide an understanding of the dispersion of the PM generated due to mining activities. This research work presents an approach to assess the exposure of enhanced level of PM concentration on mine workers and its variation with depth. The correlations study shows that concentration of PM during its travel from source to surface is associated with depth. Empirical equations are developed to represent relationships between concentrations of PM and depth. Artificial neural network (ANN) model showing the relationship between PM concentration and meteorological parameters has been developed. The performance of the ANN model is evaluated in terms of the correlation coefficient between the real and the forecasted data. The results show strong agreement between the experimental data and the modeled output. The findings of this work are important in understanding fine PM variation inside the mine at the workplace and the associated exposure of mine workers.