Possibility of optimized indices for the assessment of heavy metal contents in soil around an open pit coal mine area

Sawut, Rukeya; Kasim, Nijat; Abliz, Abdugheni; Li, Hu; Yalkun, Ahunaji; Maihemuti, Balati; Shi, Qingdong

doi:10.1016/j.jag.2018.05.018

Cited by 43 publications

(25 citation statements)

References 52 publications

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“…Equations (7)-(9) were then used to estimate the contents of the soil heavy metals in the validation area. The heavy metal contents were calculated for the validation area-Conghua using Equations (7)- (9). Figure 13b-d shows the spatial distributions of three heavy metal content estimates.…”

Section: Regional-scale Soil Heavy Metal Contents Retrieved From Hj-1mentioning

confidence: 99%

“…At present, because soil spectral reflectance is a cumulative property which derives from the inherent spectral behaviors of the heterogeneous combination of soil's physical and chemical properties, hyperspectral visible and near-infrared reflectance (VNIR) spectroscopy coupled with calibration techniques has been developed to predict various soil properties and soil heavy metal content [9][10][11][12]. In contrast with traditional in situ measurements of soil heavy metal content, hyperspectral remote sensing techniques provide advantages in rapidly monitoring soil heavy metal content at a regional scale, such as near real-time detection, relatively low cost, and environmental friendliness [9][10][11][12][13][14][15][16][17]. These methods mainly use a 0-5 cm soil sampling depth, which does not result in a reliable measurement of soil heavy metal content in the studied areas because the distributions of heavy metals in soils are not homogenous from the surface (0 m) to the depth (1 m) [18,19].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Soil Heavy Metal Content Using Hyperspectral Data

et al. 2019

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Quickly and efficiently monitoring soil heavy metal content is crucial for protecting the natural environment and for human health. Estimating heavy metal content in soils using hyperspectral data is a cost-efficient method but challenging due to the effects of complex landscapes and soil properties. One of the challenges is how to make a lab-derived model based on soil samples applicable to mapping the contents of heavy metals in soil using air-borne or space-borne hyperspectral imagery at a regional scale. For this purpose, our study proposed a novel method using hyperspectral data from soil samples and the HuanJing-1A (HJ-1A) HyperSpectral Imager (HSI). In this method, estimation models were first developed using optimal relevant spectral variables from dry soil spectral reflectance (DSSR) data and field observations of soil heavy metal content. The relationship of the ratio of DSSR to moisture soil spectral reflectance (MSSR) with soil moisture content was then derived, which built up the linkage of DSSR with MSSR and provided the potential of applying the models developed in the laboratory to map soil heavy metal content at a regional scale using hyperspectral imagery. The optimal relevant spectral variables were obtained by combining the Boruta algorithm with a stepwise regression and variance inflation factor. This method was developed, validated, and applied to estimate the content of heavy metals in soil (As, Cd, and Hg) in Guangdong, China, and the Conghua district of Guangzhou city. The results showed that based on the validation datasets, the content of Cd could be reliably estimated and mapped by the proposed method, with relative root mean square error (RMSE) values of 17.41% for the point measurements of soil samples from Guangdong province and 17.10% for the Conghua district at the regional scale, while the content of heavy metals As and Hg in soil were relatively difficult to predict with the relative RMSE values of 32.27% and 28.72% at the soil sample level and 51.55% and 36.34% at the regional scale. Moreover, the relationship of the DSSR/MSSR ratio with soil moisture content varied greatly before the wavelength of 1029 nm and became stable after that, which linked DSSR with MSSR and provided the possibility of applying the DSSR-based models to map the soil heavy metal content at the regional scale using the HJ-1A images. In addition, it was found that overall there were only a few soil samples with the content of heavy metals exceeding the health standards in Guangdong province, while in Conghua the seriously polluted areas were mainly distributed in the cities and of 26 croplands. This study implies that the new approach provides the potential to map the content of heavy metals in soil, but the estimation model of Cd was more accurate than those of As and Hg.

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Section: Regional-scale Soil Heavy Metal Contents Retrieved From Hj-1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

et al. 2019

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“…In fact, some studies used GWR model to analyse the local driving mechanism of environmental factors [33][34][35][36], but the research on ESV was very rare. In our study, OLS and GWR were integrated used to quantitatively analyse the impact of meteorological factors, which can provide targeted information with policy making.…”

Section: Discussionmentioning

confidence: 99%

Influence of meteorological factors on ecosystem services value: a case study of Beijing-Tianjin-Hebei region, China

et al. 2020

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Meteorological factors are one of the natural factors, which affect ecosystem services value (ESV). Influence of meteorological factors was studied in Beijing-Tianjin-Hebei region using ordinary least square (OLS) with geographical weighted regression (GWR). The main aim of this study was to reveal the differences in the influence mechanism at the global and local levels. The main meteorological factors influencing ESV were temperature and precipitation, followed by humidity. Days with annual daily precipitation≥0.1mm, annual minimum precipitation and annual average relative humidity were three important meteorological factors. Annual temperature range, annual minimum precipitation, days with annual daily precipitation≥0.1mm, in particular, the last one had an obvious positive effect. The positive and negative effects of annual average relative humidity were coexisting, and the negative effect was the main. It was obvious that the spatial distribution characteristics of the local influence mechanism. The local model of GWR can better solve the spatial non-stationarity of the dependent and independent variables, thus it was better than the global model of OLS. The results also provide detailed field information on the different effects of meteorological factors at different locations.E3S Web of Conferences 158, 06003 (2020)

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“…Geographically weighted regression models are widely used in urban landscape pattern analysis [35,37,38], PM 2.5 concentration estimation [39], carbon emissions [40], and ecosystem services [41,42]. Sawut et al [43] also estimated the heavy metal arsenic (As) contents of an open-pit coal mine in soil on the basis of GWR.…”

Section: Introductionmentioning

confidence: 99%

Spatial Heterogeneity of Vegetation Response to Mining Activities in Resource Regions of Northwestern China

Xie

Wang

et al. 2020

Remote Sensing

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Aggregated mining development has direct and indirect impacts on vegetation changes. This impact shows spatial differences due to the complex influence of multiple mines, which is a common issue in resource regions. To estimate the spatial heterogeneity of vegetation response to mining activities, we coupled vegetation changes and mining development through a geographically weighted regression (GWR) model for three cumulative periods between 1999 and 2018 in integrated resource regions of northwestern China. Vegetation changes were monitored by Sen’s slope and the Mann–Kendall test according to a total of 72 Landsat images. Spatial distribution of mining development was quantified, due to four land-use maps in 2000, 2005, 2010, and 2017. The results showed that 80% of vegetation in the study area experienced different degrees of degradation, more serious in the overlapping areas of multiple mines and mining areas. The scope of influence for single mines on vegetation shrunk by about 48%, and the mean coefficients increased by 20%, closer to mining areas. The scope of influence for multiple mines on vegetation gradually expanded to 86% from the outer edge to the inner overlapping areas of mining areas, where the mean coefficients increased by 92%. The correlation between elevation and vegetation changes varied according to the average elevation of the total mining areas. Ultimately, the available ecological remediation should be systematically considered for local conditions and mining consequences.

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Possibility of optimized indices for the assessment of heavy metal contents in soil around an open pit coal mine area

Cited by 43 publications

References 52 publications

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

Influence of meteorological factors on ecosystem services value: a case study of Beijing-Tianjin-Hebei region, China

Spatial Heterogeneity of Vegetation Response to Mining Activities in Resource Regions of Northwestern China

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