Temporal and spatial distributions of particulate matters around mining areas under two coal mining methods in arid desert region of northwest China

The large-scale development and utilization of coal resources have brought great challenges to the ecological environment of coal-mining areas. Therefore, this paper has used scientific and effective methods to monitor and evaluate whether changes in ecological environment quality in coal-mining areas are helpful to alleviate the contradiction between human and nature and realize the sustainable development of such coal-mining areas. Firstly, in order to quantify the degree of coal dust pollution in coal-mining areas, an index-based coal dust index (ICDI) is proposed. Secondly, based on the pressure-state-response (PSR) framework, a new coal-mine ecological index (CMEI) was established by using the principal component analysis (PCA) method. Finally, the coal-mine ecological index (CMEI) was used to evaluate and detect the temporal and spatial changes of the ecological environment quality of the Ningwu Coalfield from 1987 to 2021. The research shows that ICDI has a strong ability to extract coal dust with an overall accuracy of over 96% and a Kappa coefficient of over 0.9. As a normalized difference index, ICDI can better quantify the pollution degree of coal dust. The effectiveness of CMEI was evaluated by four methods: sample image-based, classification-based, correlation-based, and distance-based. From 1987 to 2021, the ecological environment quality of Ningwu Coalfield was improved, and the mean of CMEI increased by 0.1189. The percentages of improvement and degradation of ecological environment quality were 71.85% and 27.01%, respectively. The areas with obvious degradation were mainly concentrated in coal-mining areas and built-up areas. The ecological environment quality of Pingshuo Coal Mine, Shuonan Coal Mine, Xuangang Coal Mine, and Lanxian Coal Mine also showed improvement. The results of Moran’s Index show that CMEI has a strong positive spatial correlation, and its spatial distribution is clustered rather than random. Coal-mining areas and built-up areas showed low–low clustering (LL), while other areas showed high–high clustering (HH). The utilization and popularization of CMEI provides an important reference for decision makers to formulate ecological protection policies and implement regional coordinated development strategies.

Download Full-text

Research on Temporal and Spatial Resolution and the Driving Forces of Ecological Environment Quality in Coal Mining Areas Considering Topographic Correction

Remote Sensing ◽

10.3390/rs13142815 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2815

Author(s):

Xinran Nie ◽

Zhenqi Hu ◽

Qi Zhu ◽

Mengying Ruan

Keyword(s):

Remote Sensing ◽

Coal Mining ◽

Coal Mine ◽

Ecological Environment ◽

Topographic Correction ◽

Ecological Index ◽

Mining Areas ◽

Environment Quality ◽

Temporal And Spatial

Over the last few years, under the combined effects of climate change and human factors, the ecological environment of coal mining areas has undergone tremendous changes. Therefore, the rapid and accurate quantitative assessments of the temporal and spatial evolution of the ecological environment quality is of great significance for the ecological restoration and development planning of coal mining areas. This study applied the ecological environment index after topographic correction to improve the remote sensing ecological index (RSEI). Based on a series of Landsat images, the ecological environment quality of Yangquan Coal Mine in Shanxi Province from 1987 to 2020 was monitored and evaluated by an improved remote sensing ecological index. The results show that after topographic correction, the topographic effect of the remote sensing ecological index was greatly reduced, and its practicability was improved. From 1987 to 2020, the ecological environment quality of Yangquan Coal Mine was improved, and the mean of the RSEI increased from 0.4294 to 0.6379. The ecological environment quality of the six coal mines in the study area was improved. Among the six coal gangue dumps, the ecological environmental quality of D1, D2, D3, and D4 has improved, and the ecological environment quality of D5 and D6 worsened. The percentages of improved, unchanged, and degraded ecological environment quality in the entire coal mining area were 77.08%, 0.99%, and 21.93%, respectively. The global Moran’s index was between 0.7929 and 0.9057, and it was shown that there was a strong positive correlation between the ecological environmental qualities of the study area, and that its spatial distribution was clustered rather than random. The LISA cluster map showed that the aggregation and dispersion degree of ecological environment quality was mainly high–high clustering and low–low clustering over the whole stage. During the study period, temperature and precipitation had limited impacts on the ecological environment quality of Yangquan Coal Mine, while the coal mining activities and urbanization construction seriously affected the local ecological environment quality and the implementation of ecological restoration policies, regulations, and measures was the main reason for the improvement of the ecological environment quality.

Download Full-text

Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI

The Science of The Total Environment ◽

10.1016/j.scitotenv.2019.136092 ◽

2020 ◽

Vol 707 ◽

pp. 136092 ◽

Cited By ~ 12

Author(s):

Jingzhe Wang ◽

Jianli Ding ◽

Danlin Yu ◽

Dexiong Teng ◽

Bin He ◽

...

Keyword(s):

Machine Learning ◽

Soil Salinity ◽

Northwest China ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Desert Region ◽

Arid Desert ◽

Sentinel 2

Download Full-text

Comparison of three evapotranspiration models to Bowen ratio-energy balance method for a vineyard in an arid desert region of northwest China

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2008.05.016 ◽

2008 ◽

Vol 148 (10) ◽

pp. 1629-1640 ◽

Cited By ~ 124

Author(s):

Baozhong Zhang ◽

Shaozhong Kang ◽

Fusheng Li ◽

Lu Zhang

Keyword(s):

Energy Balance ◽

Bowen Ratio ◽

Northwest China ◽

Balance Method ◽

Energy Balance Method ◽

Desert Region ◽

Arid Desert ◽

Ratio Energy

Download Full-text

Temporal and Spatial Distributions of Particulate Matters under Two Coal Mining Methods in Arid Desert Region of Northwest China

10.5194/egusphere-egu2020-6590 ◽

2020 ◽

Author(s):

Ruoshui Wang ◽

Yun Liu

Keyword(s):

Particulate Matter ◽

Coal Mining ◽

Coal Mine ◽

Fine Particles ◽

Barometric Pressure ◽

Mining Area ◽

Underground Mine ◽

Particulate Matters ◽

Opencast Mine ◽

Mining Methods

Abstract:&#160;The particulate matter (PM)&#160;in coal mining can bring pollution to the surrounding environment and have adverse effect on human health. In order to prevent and control the PM&#160;pollution in coal mine and better understand the PM&#160;transportation in the air, spatial and temporal distribution of PM&#160;concentration in two typical coal mining methods were studied&#160;in the arid desert region of northwest China. The mass concentrations of particulate matters, i.e., PM1, PM2.5, PM10 and TSP (total suspended particulate), were monitored by portable environmental particulate matter meter during two windy seasons&#8212;spring and winter in a typical opencast coal mine and an underground coal mine.&#160;The results show that:(1) In the opencast mine, high concentrations of PM appeared in the mining area (MA) .&#160;Average&#160;PM10 and TSP concentration were 1950.18 &#956;g&#183;m-3&#160;and 2393.56 &#956;g&#183;m-3&#160;respectively&#160;in spring, while PM1 and PM2.5 concentration&#160;were 6.22 &#956;g&#183;m-3&#160;and 42.58 &#956;g&#183;m-3&#160;in winter.&#160;In the underground mine, it was concentrated in the coal yard (CY), average&#160;PM10 and TSP concentration were 920.95 &#956;g&#183;m-3&#160;and 1225.89 &#956;g&#183;m-3&#160;respectively&#160;in spring, while PM1 and PM2.5 concentration&#160;were 8.64 &#956;g&#183;m-3&#160;and&#160;35.93 &#956;g&#183;m-3&#160;&#160;in winter.(2) The variations of pollution index (PI) showed similar patterns in both spring and winter&#8212; that is, high in the morning&#160;then achieved maximum value exceeded 10, and decreasing from noon at the opencast mine entrance (ME), the mining area (MA), road in the mine&#160;(RM), and the coal storage yard (CS). However, the PI rose in the evening in spring, but decreased in winter. In the CY of the underground mine, the PI was high during the day; whereas in the evening it decreased in spring and increased in winter.(3) In the opencast mine, the PM10 and TSP concentrations varied more obviously from season to season and from area to area than the concentrations of PM1 and PM2.5. Barometric pressure had the most significant influence on PM1, PM2.5 and PM10. Wind speed had the greatest influence on TSP. In the underground mine, the variation patterns of the concentration of the four different-sized particulate matters were basically the same from area to area. The concentrations of PM1 and PM2.5 had greater seasonal variation than PM10 and TSP. The most important meteorological factors were temperature and barometric pressure for PM1 and PM2.5, while air humidity had the greatest impact on PM10 and TSP.Considering the above results, it is recommended to control the daily occurrence and spread of particulate matter at 08:00 and 18:00&#160;in the opencast mine, and from 08:00 to 16:00 in the underground mine. Primary attention should be given to the influence of wind speed and relative humidity changes on the diffusion of coarse particles(PM10 and TSP) in spring, while the influence of changes in barometric pressure on the diffusion of fine particles(PM1 and PM2.5) should be considered in the mining area in winter for both the two typical coal mining methods. The diffusion of coarse particulate matter in the opencast mine and of fine particulate matter in the underground mine are the main issues to be considered, while it is essential to prevent and control the spread of fine particles in the areas of roadways.

Download Full-text