Analysis and Prediction of the Meteorological Characteristics of Dust Concentrations in Open-Pit Mines

Zhigao Liu (), Ruixin Zhang, Jiayi Ma, Wenyu Zhang and Lin Li
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Zhigao Liu: School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Ruixin Zhang: School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Jiayi Ma: School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Wenyu Zhang: School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Lin Li: China National Energy Investment Group Co., Ltd., Beijing 100011, China

Sustainability, 2023, vol. 15, issue 6, 1-16

Abstract: Based on the dust concentration data and meteorological environment data monitored at the open-pit mine site, the characteristics of dust concentration and the influence of temperature, humidity, wind speed, air pressure and other meteorological conditions on dust concentration were analyzed, and the causes of the change of dust concentration were clarified. Meanwhile, a dust concentration prediction model based on LSTM neural network is established. The results show that the dust concentration of the open-pit mine is high in March, November and the whole winter, and it is low in summer and autumn. The daily variation of humidity and temperature in different seasons showed the trend of “herringbone” and “inverted herringbone”, respectively. In addition, the wind speed was the highest in spring and the air pressure distribution was uniform, which basically maintained at 86–88 kPa. The peak humidity gradually deviates with each month and is obviously affected by seasonality. The higher the humidity, the lower the temperature and the higher the concentration of dust. In different seasons, the wind speed is the highest around 20:00 at night, and the dust is easy to disperse. The R 2 values of PM2.5, PM10 and TSP concentrations predicted by LSTM model are 0.88, 0.87 and 0.87, respectively, which were smaller than the MAE, MAPE and RMSE values of other prediction models, and the prediction effect was better with lower error. The research results can provide a theoretical basis for dust distribution law, concentration prediction and dust removal measures of main dust sources in open-pit mines.

Keywords: neural network; open-pit dust; meteorological factors; machine learning; prediction of concentration (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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