Modeling and mapping of cadmium in soils based on qualitative and quantitative auxiliary variables in a cadmium contaminated area

Cao, Shanshan; Lu, Anxiang; Wang, Jihua; Huo, Lili

doi:10.1016/j.scitotenv.2016.10.088

Cited by 46 publications

(16 citation statements)

References 37 publications

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“…It also produced maps with higher accuracy. This is in agreement with other studies comparing regression kriging with ordinary kriging if a strong relationship between main variable and covariate exists [34,36,50]. We also corroborated that the improvement in accuracy and also improved modelling or correlation, with the agronomic parameters of biomass and, to a lesser extent, crop yield.…”

Section: Discussionsupporting

confidence: 92%

“…Regression kriging was used intensively as a spatial modeling tool in soil science and environmental science when there was a sparse variable sampled alongside correlated exhaustively available variables to improve the spatial mapping of the sparse variable. Case studies involved the mapping of soil properties with remote sensing data, geological data or terrain attributes [34,35]. Specifically, in precision agriculture, soil electrical conductivity, a parameter that can be easily obtained from the bare soil with proximal soil sensors, was often used with regression kriging for improving the spatial estimation of various soil properties within the field [36,37].…”

Section: Introductionmentioning

confidence: 99%

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Regression Kriging for Improving Crop Height Models Fusing Ultra-Sonic Sensing with UAV Imagery

et al. 2017

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A crop height model (CHM) can be an important element of the decision making process in agriculture, because it relates well with many agronomic parameters, e.g., crop height, plant biomass or crop yield. Today, CHMs can be inexpensively obtained from overlapping imagery captured from unmanned aerial vehicle (UAV) platforms or from proximal sensors attached to ground-based vehicles used for regular management. Both approaches have their limitations and combining them with a data fusion may overcome some of these limitations. Therefore, the objective of this study was to investigate if regression kriging, as a geostatistical data fusion approach, can be used to improve the interpolation of ground-based ultrasonic measurements with UAV imagery as covariate. Regression kriging might be suitable because we have a sparse data set (ultrasound) and an exhaustive data set (UAV) and both data sets have favorable properties for geostatistical analysis. To confirm this, we conducted four missions in two different fields in total, where we collected UAV imagery and ultrasonic data alongside. From the overlapping UAV images, surface models and ortho-images were generated with photogrammetric processing. The maps generated by regression kriging were of much higher detail than the smooth maps generated by ordinary kriging, because regression kriging ensures that for each prediction point information from the UAV, imagery is given. The relationship with crop height, fresh biomass and, to a lesser extent, with crop yield, was stronger using CHMs generated by regression kriging than by ordinary kriging. The use of UAV data from the prior mission was also of benefit and could improve map accuracy and quality. Thus, regression kriging is a flexible approach for the integration of UAV imagery with ground-based sensor data, with benefits for precision agriculture-oriented farmers and agricultural service providers.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Regression Kriging for Improving Crop Height Models Fusing Ultra-Sonic Sensing with UAV Imagery

et al. 2017

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“…Traditionally, soil heavy metals are measured by several analysis methods conducted in the laboratory, including atomic absorption spectrophotometry (AAS), atomic fluorescence spectrometry (AFS), inductively coupled plasma-atomic emission spectrometry (ICP-AES) and inductively coupled plasma-mass spectroscopy (ICP-MS) [16]. These methods have high accuracy and are widely used in many related studies [17,18,19,20]; however, they are complex, labor intensive, expensive, and require a long amount of time. In addition, the sampling process is destructive and results in discharge of waste that could lead to additional pollution.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Spatial Modeling of Cr, Pb, Cd, As and Ni in Soil Based on Portable X-ray Fluorescence (PXRF) and Geostatistics: A Case Study in East China

Xia

Shao

et al. 2019

IJERPH

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To verify the feasibility of portable X-ray ﬂuorescence (PXRF) for rapidly analyzing, assessing and improving soil heavy metals mapping, 351 samples were collected from Fuyang District, Hangzhou City, in eastern China. Ordinary kriging (OK) and co-ordinary kriging (COK) combined with PXRF measurements were used to explore spatial patterns of heavy metals content in the soil. The Getis-Ord index was calculated to discern hot spots of heavy metals. Finally, multi-variable indicator kriging was conducted to obtain a map of multi-heavy metals pollution. The results indicated Cd is the primary pollution element in Fuyang, followed by As and Pb. Application of PXRF measurements as covariates in COK improved model accuracy, especially for Pb and Cd. Heavy metals pollution hot spots were mainly detected in northern Fuyang and plains along the Fuchun River in southern Fuyang because of mining, industrial and traffic activities, and irrigation with polluted water. Area with high risk of multi-heavy metals pollution mainly distributed in plain along the Fuchun River and the eastern Fuyang. These findings certified the feasibility of using PXRF as an efficient and reliable method for soil heavy metals pollution assessment and mapping, which could contribute to reduce the cost of surveys and pollution remediation.

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“…Toxic metal contamination in soil has become a global issue due to its environmental impacts and potential risks and hazards to human beings. Among toxic metals in soils, Cadmium (Cd) has been of great concern and is extensively studied around the world [1][2][3]. Cd affects human health by leaching into streams and groundwater but is also of concern in agricultural soils [4].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Models for Spatial Distribution and Prediction of Cadmium in Subtropical Forest Soils, Guangdong, China

Ding

Zhao

Xing³

et al. 2021

Land

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Cadmium (Cd) is a toxic metal and found in various soils, including forest soils. The great spatial heterogeneity in soil Cd makes it difficult to determine its distribution. Both traditional soil surveys and spatial modeling have been used to study the natural distribution of Cd. However, traditional methods are highly labor-intensive and expensive, while modeling is often encumbered by the need to select the proper predictors. In this study, based on intensive soil sampling (385 soil pits plus 64 verification soil pits) in subtropical forests in Yunfu, Guangdong, China, we examined the impacting factors and the possibility of combining existing soil information with digital elevation model (DEM)-derived variables to predict the Cd concentration at different soil depths along the landscape. A well-developed artificial neural network model (ANN), multi-variate analysis, and principal component analysis were used and compared using the same dataset. The results show that soil Cd concentration varied with soil depth and was affected by the top 0–20 cm soil properties, such as soil sand or clay content, and some DEM-related variables (e.g., slope and vertical slope position, varying with depth). The vertical variability in Cd content was found to be correlated with metal contents (e.g., Cu, Zn, Pb, Ni) and Cd contents in the layer immediately above. The selection of candidate predictors differed among different prediction models. The ANN models showed acceptable accuracy (around 30% of predictions have a relative error of less than 10%) and could be used to assess the large-scale Cd impact on environmental quality in the context of intensifying industrialization and climate change, particularly for ecosystem management in this region or other regions with similar conditions.

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Modeling and mapping of cadmium in soils based on qualitative and quantitative auxiliary variables in a cadmium contaminated area

Cited by 46 publications

References 37 publications

Regression Kriging for Improving Crop Height Models Fusing Ultra-Sonic Sensing with UAV Imagery

Regression Kriging for Improving Crop Height Models Fusing Ultra-Sonic Sensing with UAV Imagery

Improvement of Spatial Modeling of Cr, Pb, Cd, As and Ni in Soil Based on Portable X-ray Fluorescence (PXRF) and Geostatistics: A Case Study in East China

Comparison of Models for Spatial Distribution and Prediction of Cadmium in Subtropical Forest Soils, Guangdong, China

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