Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 503867
Title Identification of soil heavy metal sources and improvement in spatial mapping based on soil spectral information: A case study in northwest China
Author(s) Chen, Tao; Chang, Qingrui; Liu, Jing; Clevers, J.G.P.W.; Kooistra, L.
Source Science of the Total Environment 565 (2016). - ISSN 0048-9697 - p. 155 - 164.
DOI http://dx.doi.org/10.1016/j.scitotenv.2016.04.163
Department(s) Laboratory of Geo-information Science and Remote Sensing
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2016
Keyword(s) Co-kriging - Reflectance spectra - Soil heavy metals - Source identification - Spatial distribution
Abstract

In a sewage irrigation area of northwest China, 52 topsoil samples were collected to measure the contents of arsenic (As), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn). To identify their sources, multivariate statistics and geostatistics were applied to separate pedogenic elements (As and Mn) from anthropogenic elements (Cr, Cu, Hg, Ni, Pb and Zn). The accumulation of soil Hg was mainly attributed to long-term sewage irrigation, whereas Cr, Ni and Zn were mainly from industrial activities and dust deposition. In addition to the impacts of industry and dust, traffic-related factors were the main sources of Pb and Cu contamination. Based on the relationships of heavy metals with various soil properties and reflectance spectra, co-kriging (CK) was used to improve the interpolation of heavy metals. Comparatively, soil spectra were more suitable as covariates due to their ease and low-cost of collecting as features.

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