Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model

Abstract

Springer Science+Business Media Dordrecht 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., PM<inf>0.23–0.3</inf>, PM<inf>0.3–0.4</inf>, PM<inf>0.4–0.5</inf>, PM<inf>0.5–0.65</inf>, PM<inf>0.65–0.8</inf>, PM<inf>0.8–1</inf>, and PM<inf>1–1.6</inf>) 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

Status and chemical characteristics of ambient PM2.5 pollutions in China: a review

“…Gurjar et al (2010) have suggested the exposure to PM 2.5 is a more severe problem in the urban area as compared to the rural area. PM 2.5 pollution is defined by both quantitative and qualitative measurements of a number of activities conducted by different groups at particular places for short time intervals (Gautam et al 2016a, b;Patra et al 2016b;Gautam and Patra 2015). Moreover, most of the research work on PM 2.5 has paid more attention to urban areas than to rural areas for the exploration of the sources and adverse effects (Gautam et al 2016b;Lin et al 2015a;Vela et al 2015;Kumar et al 2013).…”

Micro‐ to macro‐scaling analysis of PM2.5 in sensitive environment of Himalaya, India

“…Dynamic behaviour of particulate emission can be studied by various methods such as artificial neural networks (ANNs), regression analysis, classification and regression tree (CART), ARIMA modelling, rescaling (R/S), goodness‐fit‐test comprising chi‐square statistic, Kolmogorov–Smirnov statistics, Anderson–Darling statistics, linear unbiased estimates, Monte‐Carlo maximum likelihood estimator (MCMLE), correlation analyses, and power spectrum analyses and so forth. (Diaz‐Robles et al, 2008; Gautam, Patra, et al, 2021; Liu, 2017; Patra et al, 2016; Roy, 2021; Taheri et al, 2016). All the statistical methods used are for finding correlation between meteorological parameters and pollutant concentrations, as well as to evaluate the amount of persistence or randomness in the time series (de Medrano et al, 2021).…”

Dispersion of Particulate Matters from Separated Plume Stacks using Discrete Phase Modelling

“…Particulate matter present in the gases poses an immediate hazard to health. A study on the dispersion of particulate matter in and around mines and the duration of exposure to these PMs was conducted by Patra et al (2015) [21].An artificial neural network (ANN) study was done in the premises of the Hindustan Coal Limited (HCL), India. They studied the effect of wind speed, wind direction, ambient temperature, relative humidity, and PM concentration.…”