Mapping soil Pb stocks and availability in mainland France combining regression trees with robust geostatistics

Lacarce, Eva; Saby, Nicolas P.A.; Martín, Manuel; Marchant, B. P.; Boulonne, Line; Meersmans, Jeroen; Jolivet, Claudy; Bispo, Antonio; Arrouays, Dominique

doi:10.1016/j.geoderma.2011.11.014

Cited by 39 publications

(17 citation statements)

References 38 publications

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“…The study by Lacarce et al (2012) also found a relationship between population density and Pb; however the study did not quantify Zn concentrations. The significance of population density is most likely a reflection of the longer history of industrial activity in areas closer to the Sydney CBD which also have a higher population density.…”

Section: Drivers Of Pb and Zn Distribution In The Sydney Estuary Catcmentioning

confidence: 92%

“…Use of spatially dense covariates allows finer scale predictions, rather than estimating mean metal concentration, or interpolating over a broader area. Integrating covariates into a model helps determine drivers of contaminant distribution, which may enable investigators to improve source management, thus reducing further contaminant input and exposure (Lacarce et al, 2012;Maas et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Modelling drivers and distribution of lead and zinc concentrations in soils of an urban catchment (Sydney estuary, Australia)

Johnson

Bishop

Birch

2017

Science of The Total Environment

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Section: Drivers Of Pb and Zn Distribution In The Sydney Estuary Catcmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Modelling drivers and distribution of lead and zinc concentrations in soils of an urban catchment (Sydney estuary, Australia)

Johnson

Bishop

Birch

2017

Science of The Total Environment

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“…In international literature, there are many examples considering the use of geostatistics to assess risk for soils and waters in waste disposal sites or in the vicinity of industrial sites [26][27][28][29][30][31]. To our knowledge, this is the first attempt to use geostatistics and estimate risk for both soil and water in the vicinity of an OMW disposal site.…”

Section: Desalination and Water Treatmentmentioning

confidence: 99%

Geostatistical estimation of risk for soil and water in the vicinity of olive mill wastewater disposal sites

Komnitsas

Modis

Doula

et al. 2014

Desalination and Water Treatment

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Olive mill wastewater (OMW) is often disposed of in poorly engineered evaporation ponds or various environmental receptors in most Mediterranean countries causing contamination of soils and water bodies. The aim of this work is to estimate the risk for soils and waters as a result of OMW disposal in an area of 15 km 2 at Rethymnon, island of Crete, Greece. Soil, surface, and groundwater samples were collected over a period of five years and geostatistics using the kriging approach was considered for the assessment of risk. Risk maps for several pollutants, namely phenols, Ni, Cr, and available P, in soil and water were produced. The results of the study indicate the presence of hot spots in the area under investigation, mainly in the vicinity of OMW disposal sites. Finally, the fate of contaminants in affected media is discussed and a framework for monitoring of soils and water bodies in areas affected by OMW disposal is proposed.

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“…Pringle et al, 2011), as do concentrations of heavy metals, trace elements and plant nutrients (e.g. Marchant et al, 2009;Lacarce et al, 2012). When data for a lognormal variable have been determined from composite samples, the sample support affects not only the variance but also the mean.…”

Section: Introductionmentioning

confidence: 99%

A geostatistical method to account for the number of aliquots in composite samples for normal and lognormal random variables

Orton

Pringle

Allen

et al. 2015

European J Soil Science

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Summary Geostatistical methods can be used to calculate predictions of soil variables at unsampled locations, but the methodology is typically based on samples collected on identical sample supports. In this paper, we provide and test theory that allows the inclusion of data from mixed sample supports in a single analysis. In particular, we consider composite sample supports that are defined by the number of aliquots used to form a single composite sample, ni, and the set of locations, xi, from which the aliquots were collected. We allow both ni and xi to vary between samples (xi can vary in the extent and geometry of the aliquot locations), and thereby show how point data (a special case of composite data, defined by ni = 1 and xi as the known sample point) can be included in the same geostatistical analysis as composite data. A further complication arises when data are not normally distributed, rather their logarithm is. When composite sampling is used for such lognormal data, the sample support affects not only the variance but also the mean. We give the theory for normally distributed variables, and also derive an approximation that can be used when the point‐support variable is lognormal. We focus on this latter case, and test the approach with a series of simulation experiments. Finally, we illustrate the approach on a dataset of soil organic carbon (SOC) values from a grazing property in Queensland, Australia, where soil information from two measurement phases was obtained on different supports.

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Mapping soil Pb stocks and availability in mainland France combining regression trees with robust geostatistics

Cited by 39 publications

References 38 publications

Modelling drivers and distribution of lead and zinc concentrations in soils of an urban catchment (Sydney estuary, Australia)

Modelling drivers and distribution of lead and zinc concentrations in soils of an urban catchment (Sydney estuary, Australia)

Geostatistical estimation of risk for soil and water in the vicinity of olive mill wastewater disposal sites

A geostatistical method to account for the number of aliquots in composite samples for normal and lognormal random variables

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