Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: Accuracy and uncertainty analysis

Xie, Yonghong; Chen, Tongbin; Lei, Mei; Yang, Jun; Guo, Qingjun; Song, Bo; Zhou, Xiaoyong

doi:10.1016/j.chemosphere.2010.09.053

Cited by 373 publications

(222 citation statements)

References 24 publications

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“…The smallest influences on the amplitude (0.1%) were found for the pH in the maps generated by ISD, and the biggest influence was for Fe (-80.2%) by kriging. Generally, the kriging presented a greater influence at the increasing of the minimum value and at the reduction of the maximum value when comparing the interpolated values to the original values (Table 3), corroborating the results of Xie et al (2011). However, the kriging was the method that influenced the mean and median the least.…”

supporting

confidence: 77%

Interpolation methods for thematic maps of soybean yield and soil chemical attributes

Betzek¹,

Souza²,

Bazzi³

et al. 2017

SCA

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The application of precision agriculture considers the values of non-sampled places by the interpolation of sample data. The accuracy with which the maps of spatial distribution of yield and the soil attributes are produced in the interpolation process influences their application and utilization. This paper aimed to compare three interpolation methods (inverse of the distance, inverse of the square distance, and ordinary kriging) in the construction of thematic maps of soybean yield and soil chemical attributes. A set of data referred to 55 sampling units for the construction maps of soybean yield and of eight soil chemical attributes, by different interpolation methods. The comparison was made based on the error matrix, by calculating the Kappa and Tau indices, beyond the relative deviation coefficient (RDC). It was noticed that the inverse of the square distance was the interpolator that less influenced the data behavior, and the best interpolation method dependent of the variability of the studied attribute. The kriging and the inverse of the square distance were considered the methods that presented the best results in the interpolation of data. Key words: Geostatistics. Precision agriculture. Spatial variability. ResumoA aplicação da agricultura de precisão considera os valores mensurados para lugares não amostrados obtidos por meio da interpolação dos dados amostrais. A precisão com que os mapas de distribuição espacial da produtividade e atributos do solo são produzidos no processo de interpolação influencia a sua aplicação e utilização. Assim, este trabalho teve como objetivo comparar três métodos de interpolação (inverso da distância, inverso do quadrado da distância e krigagem ordinária) na construção de mapas temáticos de produtividade de soja e atributos químicos do solo. Um conjunto de dados referentes a 55 unidades de amostragem foi utilizado para a construção dos mapas de oito atributos químicos do solo e da produtividade de soja, por diferentes métodos de interpolação. A comparação foi feita com base na matriz de erro, por meio do cálculo dos índices Kappa e Tau, além do coeficiente de desvio relativo

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supporting

confidence: 77%

Interpolation methods for thematic maps of soybean yield and soil chemical attributes

Betzek¹,

Souza²,

Bazzi³

et al. 2017

SCA

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“…They also provided two possible explanations for the low accuracy: (1) regression-kriging and ordinary kriging are not capable of dealing with many hotspots (sporadic high values); and (2) a relatively coarse resolution of the predictors is a limitation to mapping accuracy. Xie et al [23] applied four different interpolation methods (inverse distance weighting, local polynomial, ordinary kriging and radial basis functions) to map the spatial distribution of three toxic metals (Cd, Cu, Pb) in soil in a small area (605 km 2 ) in Beijing. The results showed that all of the interpolation methods provided a high accuracy based on cross-validation.…”

Section: Introductionmentioning

confidence: 99%

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Kheir

Adhikari

et al. 2016

Remote Sensing

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After decades of mining and industrialization in Qatar, it is important to estimate their impact on soil pollution with toxic metals. The study utilized 300 topsoil (0-30 cm) samples, multi-spectral images (Landsat 8), spectral indices and environmental variables to model and map the spatial distribution of arsenic (As), chromium (Cr), nickel (Ni), copper (Cu), lead (Pb) and zinc (Zn) in Qatari soils. The prediction model used condition-based rules generated in the Cubist tool. In terms of R 2 and the ratio of performance to interquartile distance (RPIQ), the models showed good predictive capabilities for all elements. Of all of the prediction results, Cu had the highest R 2 = 0.74, followed by As > Pb > Cr > Zn > Ni. This study found that all of the models only chose images from January and February as predictors, which indicates that images from these two months are important for soil toxic metals' monitoring in arid soils, due to the climate and the vegetation cover during this season. Topsoil maps of the six toxic metals were generated. The maps can be used to prioritize the choice of remediation measures and can be applied to other arid areas of similar environmental/socio-economic conditions and pollution causes.

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“…Al analizar el contraste de cada mapa se encontró que con KO se tuvo el mayor ajuste (X 2* KO = 0.48) menor que 0.86 de IDW y por ende KO es más ef iciente en la predicción de la presencia de As en la región. Este resultado es acorde con lo reportado por otros investigadores, quienes mencionan que el interpolador más popular y rápido es Kriging (Isaaks y Srivastava, 1989;Xie et al, 2011).…”

Section: Arsénico En Los Suelos Del Dr017 En La Comarca Laguneraunclassified

Arsénico edáfico y su distribución en el distrito de riego 017: uso de métodos de interpolación.

Garrido¹,

Castruita

Vidal

et al. 2017

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RESUMENLos constituyentes del suelo (arcillas, carbonatos, materia orgánica y óxidos e hidróxidos), intervienen en la movilidad del arsénico (As) y determinan su variación espacial; por lo cual, su cartografía puede contribuir al entendimiento de la presencia de este metaloide en el suelo. Los objetivos de esta investigación fueron determinar la concentración de As en los suelos del Distrito de Riego 017 (DR017) de México y establecer su distribución espacial, utilizando dos métodos de interpolación. Cuatro etapas fueron realizadas; la primera consistió en trabajo de gabinete y campo, en la que 33 sitios de muestreo fueron localizados en un polígono del DR017, posteriormente en cada sitio, muestras simples de suelo (0-30 cm de profundidad) fueron colectadas. La segunda y tercera se llevaron a cabo en laboratorio, en las cuales, a cada muestra se determinó la concentración de As disponible, contenidos de arcilla, carbonatos de calcio y fósforo disponible y su regresión. La cuarta etapa tuvo dos partes, la elaboración de dos mapas con dos métodos de interpolación (Interpolador Ponderación Inversa de la Distancia, IDW en sus siglas en inglés y Kriging ordinario, KO), así como la verif icación de la precisión de cada mapa. Los resultados mostraron que, la distribución del As en el DR017 no es uniforme y está asociada con el contenido de arcilla y CaCO 3 . De tal manera que, las concentraciones de As fluctuaron de 0.07 a 1.89 mg kg -1 , donde las mayores concentraciones de este metaloide (1.39-1.89 mg kg -1 ) se relacionaron positivamente (R 2 = 0.9058) con altos contenidos de arcilla (39.44 a 43.44%). El mapa de distribución espacial de As obtenido con KO, tuvo la mayor precisión (75.7%), en el cual se muestra que la concentración aumenta en un sentido sur-norte, donde el área de mayor concentración está en la parte norte del distrito. Palabras clave: arsenatos, kriging, IDW, geoestadístico. SUMMARYSoil constituents (clays, carbonates, organic matter and oxides and hydroxides), involved in the mobility of arsenic (As), determine its spatial variation; thus, the mapping can contribute to the understanding of the presence of this metalloid on the soil. The objectives of this research were to determine the concentration of As in soils of Irrigation District 017 (DR017) of Mexico and establish its spatial distribution using two interpolation methods. Four stages were performed; the f irst consisted of desk work and f ield, in which 33 sampling sites were located in a polygon of DR017, then at each site, simple soil samples (0-30 cm depth) were collected. The second and third were carried out in laboratory, in which, in each sample the concentration of As available, clay content, carbonates of calcium and phosphorus available and their regression were determined. The fourth stage had two parts, the development of two maps with two interpolation methods (Interpolator Weighting Inverse Distance, IDW its acronym in English and ordinary Kriging, OK) and verif ication of the accuracy of each map. The results showed that the d...

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Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: Accuracy and uncertainty analysis

Cited by 373 publications

References 24 publications

Interpolation methods for thematic maps of soybean yield and soil chemical attributes

Interpolation methods for thematic maps of soybean yield and soil chemical attributes

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Arsénico edáfico y su distribución en el distrito de riego 017: uso de métodos de interpolación.

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