Using regression model to identify and evaluate heavy metal pollution sources in an open pit coal mine area, Eastern Junggar, China

Sawut, Rukeya; Tiyip, Tashpolat; Abliz, Abdugheni; Kasim, Nijat; Nurmemet, Ilyas; Sawut, Memet; Tashpolat, Nigara; Ablimit, Arzune

doi:10.1007/s12665-017-7035-5

Cited by 16 publications

(4 citation statements)

References 36 publications

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“…The area has a typical temperate continental arid climate. It is windy all year round, with wind force mostly levels 4-5 [19]. The average annual precipitation is approximately 100 mm.…”

Section: Study Areamentioning

confidence: 99%

Identification and Monitoring of Surface Elements in Open-Pit Coal Mine Area Based on Multi-Source Remote Sensing Images

Hai¹,

Xia²,

Song³

et al. 2022

Pol. J. Environ. Stud.

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Coal mining has brought a series of environmental problems. Local government departments have issued relevant governance policies, but the premise of scientific prevention and control is to correctly grasp the actual distribution of various ground objects in the mining area. Using classification methods to extract ground object information based on remote sensing images can effectively realize mining area monitoring and provide reference for land and space planning and environmental protection in the mining area. Therefore, it is very important to select the appropriate scale and method to identify the ground object information of remote sensing image. in this paper, Landsat 8 images of the Wucaiwan mining area and GF-2 images of the Tebian coal mine were taken as the research objects, and unsupervised classification, supervised classification and object-oriented classification were used to identify and monitor the mining area's surface. The results showed that: (1) the classification effect of the Mahalanobis distance method was the best in terms of comprehensive operation process and classification accuracy. This method had high classification accuracy for GF-2 and Landsat 8 images. When classifying GF-2 images, the kappa coefficient reached 0.90, and the overall classification accuracy was 94.27%. When classifying Landsat 8 images, the kappa coefficient reached 0.85, and the overall classification accuracy was 90.02%. (2) The factors causing the classification error were 'homospectral foreign bodies' and 'mixed pixels'. (3) When combined with the actual needs and image characteristics, the extensive use of medium and high-resolution remote sensing images to identify and monitor the surface elements of mining areas can greatly improve the work efficiency and minimize the image costs. (4) The construction layout of tailings pond in the Tebian coal mine was conducive

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“…The area has a typical temperate continental arid climate. It is windy all year round, with wind force mostly levels 4-5 [19]. The average annual precipitation is approximately 100 mm.…”

Section: Study Areamentioning

confidence: 99%

Identification and Monitoring of Surface Elements in Open-Pit Coal Mine Area Based on Multi-Source Remote Sensing Images

Hai¹,

Xia²,

Song³

et al. 2022

Pol. J. Environ. Stud.

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“…The Wucaiwan open-pit coal mine area is located in Jimusar County in the eastern part of the Junggar Basin (44˚42'-44˚57' N, 89˚5'-89˚16' E), Xinjiang Province, the People's Republic of China (Fig 1) [23]. The coal bearing area is 901.05 km 2 [26]. There are five large open-pit coal mines distributed in an L-shape in the mining area, with crisscrossing roads and convenient transportation [27].…”

Section: Plos Onementioning

confidence: 99%

Identification and monitoring of coal dust pollution in Wucaiwan mining area, Xinjiang (China) using Landsat derived enhanced coal dust index

et al. 2022

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Coal dust is the main pollutant in coal mining areas. Such pollutants easily diffuse and are difficult to monitor, which increases the cost of environmental pollution control. Remote sensing technology can be used to dynamically monitor mining areas at a low cost, and thus, this is a common means of mining area management. According to the spectral characteristics of various ground objects in remote sensing images, a variety of remote sensing indexes can be constructed to extract the required information. In this study, the Wucaiwan open-pit coal mine was selected as the study area, and the Enhanced Coal Dust Index (ECDI) was established to extract the coal dust pollution information for the mining area. A new mining area pollution monitoring method was developed, which can provide technical support for environmental treatment and mining planning in Zhundong. The results of this study revealed the following: (1) Compared with the normalized difference coal index, the ECDI can expand the difference between the spectral information about the coal dust and the surrounding features, so it has a significant recognition ability for coal dust information. (2) From 2010 to 2021, the coal dust pollution in the study area initially increased and then decreased. With the continued exploitation of the coal mines in the study area, the coal dust pollution area increased from 14.77 km2 in 2010 to 69.49 km2 in 2014. After 2014, the local government issued various environmental pollution control policies, which had remarkable results. The coal dust pollution area decreased to 36.85 km2 and 17.85 km2 in 2018 and 2021, respectively. (3) There was a great deal of pollution around mines and roads, around which the pollution was more serious. Various factors, such as wind, coal type, and the mining, processing, and transportation modes, affect the distribution of the coal dust pollution.

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“…Geodiversity and its integration into society are considered by providing economic, social, cultural, and environmental benefits [59] and the importance of geological heritage [60]. Other researchers consider the pollution caused by abandoned mining sites [61], agricultural soils by heavy metals [62,63], and natural and anthropogenic sources [64].…”

Section: Scientific Production Analysismentioning

confidence: 99%

Geodiversity and Mining Towards the Development of Geotourism: A Global Perspective

Carrión-Mero¹,

Montalván-Burbano²,

Herrera-Narváez³

et al. 2021

IJDNE

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Geodiversity and geological heritage are considered to be parallel and closely related concepts of biodiversity and biological heritage. Biodiversity is based and exists on the geodiversity of a territory. In the same way, mining activity and mining heritage are distinguished as contributing to social and economic development. The mining heritage is linked to the geological heritage and has become increasingly important for its social value and relationship with tourist activities. This study aims to analyze these fields' intellectual structure through bibliometrics as a current and innovative methodology that allows a full understanding of their structure and the relationships between these fields of cultural heritage, geodiversity, mining and geotourism. The following methodology considered: i) Search for classified information with scientific rigour in an indexed database, using search descriptors that cover the objective set, ii) analysis of scientific articles through graphical and statistical output schemes, and iii) discussion of the contributions, methodologies used, and trends on the subject. 709 documents of 29 years (1990-2019) from the Scopus database were analyzed. The results show the main themes, countries and influential institutions in cultural heritage, geodiversity, mining and tourism, which address six major research topics ranging from sustainable mining to geodiversity. Extensive information and understanding are provided that other research has partially addressed, allowing current and potential research areas to be identified.

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Using regression model to identify and evaluate heavy metal pollution sources in an open pit coal mine area, Eastern Junggar, China

Cited by 16 publications

References 36 publications

Identification and Monitoring of Surface Elements in Open-Pit Coal Mine Area Based on Multi-Source Remote Sensing Images

Identification and Monitoring of Surface Elements in Open-Pit Coal Mine Area Based on Multi-Source Remote Sensing Images

Identification and monitoring of coal dust pollution in Wucaiwan mining area, Xinjiang (China) using Landsat derived enhanced coal dust index

Geodiversity and Mining Towards the Development of Geotourism: A Global Perspective

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