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Li L, Zhang R, Li Q, Zhang K, Liu Z, Ren Z. Multidimensional spatial monitoring of open pit mine dust dispersion by unmanned aerial vehicle. Sci Rep 2023; 13:6815. [PMID: 37100866 PMCID: PMC10133240 DOI: 10.1038/s41598-023-33714-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/18/2023] [Indexed: 04/28/2023] Open
Abstract
Dust pollution is one of the most severe environmental issues in open pit mines, hindering green mining development. Open pit mine dust has characteristics of multiple dust-generating points, is irregular, influenced by climatic conditions, and has a high degree of distribution with a wide dispersion range in three dimensions. Consequently, evaluating the quantity of dust dispersion and controlling environmental pollution are crucial for supporting green mining. In this paper, dust monitoring above the open pit mine was carried out with an unmanned aerial vehicle (UAV) on board. The dust distribution patterns above the open pit mine were studied in different vertical and horizontal directions at different heights. The results show that the temperature changes less in the morning and more at noon in winter. At the same time, the isothermal layer becomes thinner and thinner as the temperature rises, which makes it easy for dust to spread. The horizontal dust is mainly concentrated at 1300 and 1550 elevations. The dust concentration is polarized at 1350-1450 elevation. The most serious exceedance is at 1400 elevation, with TSP (the concentration of total suspended particulate), PM10 (particulates with aerodynamic diameter < 10 μm), and PM2.5 (particulates with aerodynamic diameter < 2.5 μm) accounting for 188.8%, 139.5%, and 113.8%, respectively. The height is 1350-1450 elevation. Dust monitoring technology carried out by UAV can be applied to the study of dust distribution in the mining field, and the research results can provide reference for other open pit mines. It can also provide a basis for law enforcement part to carry out law enforcement, which has expanded and wide practical application value.
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Affiliation(s)
- Lin Li
- China Energy Investment Group Co., Ltd, Beijing, 100011, China
- State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing, 102209, China
- School of Energy and Mining, China University of Mining and Technology (Beijing), Beijing, 100083, China
| | - Ruixin Zhang
- School of Energy and Mining, China University of Mining and Technology (Beijing), Beijing, 100083, China
- School of Computer Science, North China University of Science and Technology, Sanhe, 065201, China
| | - Quansheng Li
- China Energy Investment Group Co., Ltd, Beijing, 100011, China.
- State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing, 102209, China.
- School of Energy and Mining, China University of Mining and Technology (Beijing), Beijing, 100083, China.
| | - Kai Zhang
- China Energy Investment Group Co., Ltd, Beijing, 100011, China
- State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing, 102209, China
| | - Zhigao Liu
- School of Energy and Mining, China University of Mining and Technology (Beijing), Beijing, 100083, China
| | - Zhicheng Ren
- China Energy Investment Group Co., Ltd, Beijing, 100011, China
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Feng X, Shen J, Yang H, Wang K, Wang Q, Zhou Z. Time-Frequency Analysis of Particulate Matter (PM 10) Concentration in Dry Bulk Ports Using the Hilbert-Huang Transform. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17165754. [PMID: 32784870 PMCID: PMC7460512 DOI: 10.3390/ijerph17165754] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 07/27/2020] [Accepted: 08/05/2020] [Indexed: 01/29/2023]
Abstract
To analyze the time–frequency characteristics of the particulate matter (PM10) concentration, data series measured at dry bulk ports were used to determine the contribution of various factors during different periods to the PM10 concentration level so as to support the formulation of air quality improvement plans around port areas. In this study, the Hilbert–Huang transform (HHT) method was used to analyze the time–frequency characteristics of the PM10 concentration data series measured at three different sites at the Xinglong Port of Zhenjiang, China, over three months. The HHT method consists of two main stages, namely, empirical mode decomposition (EMD) and Hilbert spectrum analysis (HSA), where the EMD technique is used to pre-process the HSA in order to determine the intrinsic mode function (IMF) components of the raw data series. The results show that the periods of the IMF components exhibit significant differences, and the short-period IMF component provides a modest contribution to all IMF components. Using HSA technology for these IMF components, we discovered that the variations in the amplitude of the PM10 concentration over time and frequency are discrete, and the range of this variation is mainly concentrated in the low-frequency band. We inferred that long-term influencing factors determine the PM10 concentration level in the port, and short-term influencing factors determine the difference in concentration data at different sites. Therefore, when formulating PM10 emission mitigation strategies, targeted measures must be implemented according to the period of the different influencing factors. The results of this study can help guide recommendations for port authorities when formulating the optimal layout of measurement devices.
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Affiliation(s)
- Xuejun Feng
- College of Habour, Coastal and Offshore Engineering, Hohai University, No.1, Xikang Road, Nanjing 210098, China; (X.F.); (K.W.)
| | - Jinxing Shen
- College of Civil and Transportation Engineering, Hohai University, No.1, Xikang Road, Nanjing 210098, China
- Correspondence:
| | - Haoming Yang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing 210044, China;
| | - Kang Wang
- College of Habour, Coastal and Offshore Engineering, Hohai University, No.1, Xikang Road, Nanjing 210098, China; (X.F.); (K.W.)
| | - Qiming Wang
- College of Science, Hohai University, No.1, Xikang Road, Nanjing 210098, China; (Q.W.); (Z.Z.)
| | - Zhongguo Zhou
- College of Science, Hohai University, No.1, Xikang Road, Nanjing 210098, China; (Q.W.); (Z.Z.)
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Sánchez Lasheras F, García Nieto PJ, García Gonzalo E, Bonavera L, de Cos Juez FJ. Evolution and forecasting of PM10 concentration at the Port of Gijon (Spain). Sci Rep 2020; 10:11716. [PMID: 32678178 PMCID: PMC7366928 DOI: 10.1038/s41598-020-68636-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 06/30/2020] [Indexed: 11/30/2022] Open
Abstract
The name PM10 refers to small particles with a diameter of less than 10 microns. The present research analyses different models capable of predicting PM10 concentration using the previous values of PM10, SO2, NO, NO2, CO and O3 as input variables. The information for model training uses data from January 2010 to December 2017. The models trained were autoregressive integrated moving average (ARIMA), vector autoregressive moving average (VARMA), multilayer perceptron neural networks (MLP), support vector machines as regressor (SVMR) and multivariate adaptive regression splines. Predictions were performed from 1 to 6 months in advance. The performance of the different models was measured in terms of root mean squared errors (RMSE). For forecasting 1 month ahead, the best results were obtained with the help of a SVMR model of six variables that gave a RMSE of 4.2649, but MLP results were very close, with a RMSE value of 4.3402. In the case of forecasts 6 months in advance, the best results correspond to an MLP model of six variables with a RMSE of 6.0873 followed by a SVMR also with six variables that gave an RMSE result of 6.1010. For forecasts both 1 and 6 months ahead, ARIMA outperformed VARMA models.
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Affiliation(s)
- Fernando Sánchez Lasheras
- Department of Mathematics, Faculty of Sciences, University of Oviedo, c/ Federico García Lorca 18, 33007, Oviedo, Spain.
| | - Paulino José García Nieto
- Department of Mathematics, Faculty of Sciences, University of Oviedo, c/ Federico García Lorca 18, 33007, Oviedo, Spain
| | - Esperanza García Gonzalo
- Department of Mathematics, Faculty of Sciences, University of Oviedo, c/ Federico García Lorca 18, 33007, Oviedo, Spain
| | - Laura Bonavera
- Department of Physics, Faculty of Sciences, University of Oviedo, c/ Federico García Lorca 18, 33007, Oviedo, Spain
| | - Francisco Javier de Cos Juez
- Department of Mining Exploitation and Prospecting, University of Oviedo, c/ Independencia 13, 33004, Oviedo, Spain
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