Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy

Cheng, Hang; Shen, Ruili; Chen, Yiyun; Wan, Qijin; Shi, Tiezhu; Wang, Junjie; Yuan, Weihua; Hong, Yongsheng; Li, Xiaocui

doi:10.1016/j.geoderma.2018.08.010

Cited by 108 publications

(75 citation statements)

References 47 publications

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“…For example, Zheng et al [12] suggested that it is feasible to predict AS element contents in soils using reflectance spectral, and the model results of 4 nm + multiplicative scatter correction (MSC) + partial least squares regression (PLSR) were the best (R 2 = 0.711, residual predictive deviation (RPD) = 1.827); Cheng et al [13] reported that AS contents in surface soils were detectable using visible/near-infrared spectral, and Savitzky-Golay (SG)+PLSR had the best effect (R 2 = 0.75, RPD = 1.81). Many previous studies have investigated models for the estimation of AS content from visible and neat infrared (VNIR) hyperspectral [12][13][14][15][16][17][18][19][20]. PLSR was usually chosen as the estimation model [20].…”

Section: Introductionmentioning

confidence: 99%

Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance

et al. 2020

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Soil arsenic (AS) contamination has attracted a great deal of attention because of its detrimental effects on environments and humans. AS and inorganic AS compounds have been classified as a class of carcinogens by the World Health Organization. In order to select a high-precision method for predicting the soil AS content using hyperspectral techniques, we collected 90 soil samples from six different land use types to obtain the soil AS content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A partial least squares regression (PLSR), a support vector regression (SVR), and a back propagation neural network (BPNN) were used to establish a relationship between the hyperspectral and the soil AS content to predict the soil AS content. In addition, the feasibility and modeling accuracy of different interval spectral resampling, different spectral pretreatment methods, feature bands, and full-band were compared and discussed to explore the best inversion method for estimating soil AS content by hyperspectral. The results show that 10 nm + second derivative (SD) + BPNN is the optimum method to predict soil AS content estimation; R 2 v is 0.846 and residual predictive deviation (RPD) is 2.536. These results can expand the representativeness and practicability of the model to a certain extent and provide a scientific basis and technical reference for soil pollution monitoring. Sustainability 2020, 12, x FOR PEER REVIEW 3 of 19 polluting enterprises in the suburb, such as chemical plants, funeral homes, medical waste treatment 95 plants, and so on. These polluting enterprises are also direct discharge areas of wastewater, waste 96 gas, and waste slag in Weinan City. In recent years, heavy metal pollution caused by city expansion 97 has drastically affects the soil ecological environment and human health. 98 2.2. Acquisition and Processing of Soil Data 99 The results of previous field investigations indicated that the soil AS content in the mining area 100 is mainly affected by human activities and shows a large difference, and the suburban soil AS 101 content is mainly affected by land use types and shows a small difference. Therefore, from 102 November to December, 2018, 90 soil samples were collected in accordance with the uniform grid 103 method with a higher sampling density in the mining area (50 m) and a lower density in the suburb 104 (200 m). The 50 soil samples were collected from the mining area and the rest form the suburb. There105 were six members in the research group with unified training on the sampling method. First, sample 106 points ware selected on the satellite map, and then the six members were divided into two groups to 107 finish the sampling job in each area. A real-time kinematic (RTK) was used to precisely locate every 108 sampling location. Figure 1 shows the position of the sampling points. The sampling was conducted 109 at a depth of 0-20 cm. Stones, plant residues, and other large debris were removed from each fresh 110 sample, which were then mixed tho...

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Section: Introductionmentioning

confidence: 99%

Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance

et al. 2020

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“…The density of the heavy metal is bigger than 4.5 g/cm 3 or more, and atomic numbers are from 23 (V) to 92 (U) of heavy metal elements, such as lead, nickel, zinc, copper, iron, cadmium, chromium, etc. [1].…”

Section: Introductionmentioning

confidence: 99%

“…The pollution of heavy metals in soil and water can lead to the degradation of soil fertility, the reduction of crop yields, and the decline in quality, which seriously affects the environmental quality and sustainable economic development and threatens people's food safety [2]. Heavy metal pollution has become a global concern [1,[3][4][5][6][7][8][9]. Whether in the soil or water, different heavy metals enter the bottom of the human food chain and finally enter the human body [2].…”

Section: Introductionmentioning

confidence: 99%

Phosphate-Mineralization Microbe Repairs Heavy Metal Ions That Formed Nanomaterials in Soil and Water

Yu¹,

Zhan²

2020

Nanomaterials - Toxicity, Human Health and Environment

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This chapter presents a new method for treatment of heavy metal ions in soil or water. Heavy metal pollution in soil and water has become one of the serious environmental problems. Heavy metal pollution can degrade soil quality and ecosystems, contaminate crops, and threaten human health. At present, there are three ways to repair heavy metals in soil or water, including physical, chemical, and biological technologies. The microbial mineralization technology can be applied to remove heavy metal pollutants which contaminated soil and water and has been paid with more attention in recent years. Heavy metal ions can be mineralized by phosphate-mineralization microbe to form stable phosphate nanomaterials compared to mineralization of carbonate-mineralization microbe in the environments. Therefore, heavy metal pollution can well be removed from soil or water by microbial mineralization method.

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“…Previous studies on soil heavy metal pollution were mainly focus on source analysis, pollution assessment methods, spatial distribution and pollution remediation, etc. [3][4][5][6] As the classical methods applied to soil heavy metals spatial distribution, geostatistics and multivariate statistical methods coupled with GIS technique have been widely used in spatial distribution, influencing factors and sources of heavy metals in soils [7][8][9]. Yutian County in Hotan District in Xinjiang Uygur Autonomous Region (hereinafter referred to as "Xinjiang") had cultivated area of 335.8 km 2 and cultivated area per capita of 0.116 hm 2 , which is one of the key counties of the national poverty alleviation plan.…”

Section: Introductionmentioning

confidence: 99%

Factors that influence the spatial distribution of heavy metals in soil of the Yutian County, Xinjiang, China

Chen

Zhou

et al. 2019

E3S Web Conf.

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In this study, 1165 surface soil samples for heavy metal analysis were collected in the Yutian County, Xinjiang, China. The factors that influence the spatial distribution of heavy metal elements in soils in the study area were analyzed by means of multivariate statistical analysis, geostatistics, spatial autocorrelation, spatial analysis and GIS technology. Results show that among 1165 soil samples, three of which had As contents greater than the risk screening values. The theoretical models for variation function of Cd and Pb were exponential model, while the theoretical models for variation function of Hg, As, Cr, Cu, Ni and Zn were spherical model. Nugget value of Cd was less than 25%, indicated a relatively strong spatial correlation. Nugget value of other elements ranged between 25% and 50%, indicated significant spatial correlations. The spatial autocorrelation Moran's I index of soil heavy metal contents in the Yutian County was greater than 0. There was a positive spatial correlation distribution in the county scale. The spatial distribution of soil heavy metal contents in the Yutian County showed a general decreasing trend from the center of the study area to surrounding areas. Distribution of soil heavy metal contents in the Yutian County varied in different parent materials, soil types and land use patterns. Hg, As, Pb, Cr, Cu, Ni and Zn in soil derived from the same source, contents of which were affected by soil texture.

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Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy

Cited by 108 publications

References 47 publications

Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance

Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance

Phosphate-Mineralization Microbe Repairs Heavy Metal Ions That Formed Nanomaterials in Soil and Water

Factors that influence the spatial distribution of heavy metals in soil of the Yutian County, Xinjiang, China

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