Fingerprinting and tracing the sources of soils and sediments: Earth and ocean science, geoarchaeological, forensic, and human health applications

Owens, Philip N.; Blake, William H.; Gaspar, Leticia; Gateuille, David; Koiter, Alexander J.; Lobb, David A.; Petticrew, Ellen L.; Reiffarth, Dominic; Smith, Hugh G.; Woodward, Jamie

doi:10.1016/j.earscirev.2016.08.012

Cited by 203 publications

(127 citation statements)

References 228 publications

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“…Establishing the relative proportions of modern and historic contaminant sources is not straightforward Owens et al 2016). Principal component analysis (PCA) has been applied to discriminate be-tween natural and anthropogenic sources of contaminants in urban environments (e.g.…”

Section: Introductionmentioning

confidence: 99%

Metal contamination of bed sediments in the Irwell and Upper Mersey catchments, northwest England: exploring the legacy of industry and urban growth

2017

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Purpose Manchester is often heralded as the first industrial city. Large volumes of physical and liquid contaminants were released into its river network throughout the industrial period up to the latter part of the twentieth century. Water quality has improved dramatically in recent decades, but, given their environmental significance, it is important to ascertain the extent to which a legacy of contamination persists in the modern bed sediments. Materials and methods Fine-grained bed sediments were sampled at 40 sites in the Mersey and Irwell catchments. Sediments were wet sieved to isolate the <63-μm grain size fraction. Metal concentrations were determined using XRF. Particle size characteristics were also measured. Sediments were subjected to a five-step sequential extraction procedure to ascertain the environmental significance of metal concentrations. Alongside archival research of past industry, enrichment factors, multivariate statistical techniques and conditional inferences trees were used to identify sources of heavy metals. Results and discussion Bed sediment-associated heavy metal(-loid) concentrations were as follows: As (9.89-110 mg kg −1 ), Cr (76.5-413 mg kg −1 ), Cu (53.1-383 mg kg −1 ), Pb (80.4-442 mg kg −1 ) and Zn (282-1020 mg kg −1 ). Enrichment factors ranged from moderate to extremely severe, with Pb showing the greatest enrichment across the catchments. Chemical mobility was generally low, but metal(loid) partitioning identified the influence of anthropogenic sources. Statistical analysis highlighted a number of point sources associated with former industrial sites that operated during the industrial period. Conditional inference trees highlighted the role of the textile industry on Cu concentrations in addition to indicating the complexity of sources, fluxes and stores of sediment-associated contamination throughout the system. Conclusions Fine-grained sediment-associated metal(loid)s in the Mersey and Irwell catchments are anthropogenically enriched. Concentrations also exceed sediment quality guidelines. A lack of distinct spatial patterning points to a complex network of contaminant inputs across the catchments, even in the headwaters. Whilst potential modern urban sources are likely to be important, spatial patterns and multivariate/data mining techniques also highlighted the importance of releases from former industrial sites as well as the reworking of historically contaminated floodplains and soils.

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Section: Introductionmentioning

confidence: 99%

Metal contamination of bed sediments in the Irwell and Upper Mersey catchments, northwest England: exploring the legacy of industry and urban growth

2017

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“…Analysis of variance was conducted to test if five sources could be separated by each of 10 remaining colour fingerprint properties. According to the MixSIAR model, the property values of sediment data should fall within the range of the source data, the so-called "range-test" (Owens et al 2016;Collins et al 2017). All 45 possible pairwise plots from the 10 colour fingerprint variables indicated that only G, L, X, and Y met this requirement.…”

Section: Colour Coefficient Selectionmentioning

confidence: 99%

“…In brief, soil and sediment samples were air-dried and sieved through a 63 μm screen as the majority of actively transported sediment in this watershed is dominated by fine particles <63 μm (D. Lobb, unpublished data) and as this is the size fraction typically analysed in most sediment source fingerprinting studies (Walling 2013;Owens et al 2016;Collins et al 2017). The reflectance spectra were then determined using a diffuse spectroradiometer (ASD FieldSpec Pro, Analytical Spectral Device Inc., CO, USA).…”

Section: Determination Of Colour Coefficientsmentioning

confidence: 99%

“…Sediment source classification is one of the key steps in sediment fingerprinting (Walling 2013;Owens et al 2016;Collins et al 2017); however, the primary sediment sources are site specific and might vary within a watershed. For example, Massoudieh et al (2013) reported that streambank materials were the dominant (>95%) source of suspended sediment compared with forest and crop lands in the Chesapeake Bay agricultural watershed, MD, USA.…”

Section: Introductionmentioning

confidence: 99%

“…To quantify such uncertainties associated with the source contributions, a variety of mixing models such as MixSIR, SIAR, and mixing model of stable isotope analysis in R (MixSIAR) employ the Monte Carlo and (or) Bayesian approach to optimize the model and provide the posterior probability distributions of source contributions (Collins et al 2010;Parnell et al 2010;Phillips et al 2014). Compared with a frequentistbased model, the Bayesian statistical model offers advantages such as the reporting of uncertainties (Owens et al 2016), if the model assumptions are met. To improve the accuracy of source apportionment and reduce uncertainty, the latest version of the MixSIAR employs a Bayesian approach to incorporate the uncertainties into the posterior distribution of source ascription (Massoudieh et al 2013;Stock and Semmens 2013).…”

Section: Introductionmentioning

confidence: 99%

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Determining sources of fine-grained sediment for a reach of the Lower Little Bow River, Alberta, using a colour-based sediment fingerprinting approach

et al. 2017

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Identifying the predominant sources of sediment is a key requirement for soil erosion control within watersheds. A 4 yr study from 2009 to 2012 was conducted to apportion sediment sources in a subcatchment of the Lower Little Bow River watershed, AB, Canada. This study catchment lies along a 6 km reach of the river, having an upstream inlet and downstream outlet; as such, it represents the first application of the sediment fingerprinting technique in a reach setting. Six monitoring stations were established along this reach of river to collect sediments using passive time-integrated sediment samplers. Source material samples were collected throughout the study catchment. Reflectance spectra of source and sediment samples were determined using a diffuse reflectance spectrometer. Source materials were classified into five sources using canonical discriminant analysis. Following the normality test, analysis of variance and stepwise discriminant function analysis, four colour coefficients were selected and fitted to the Bayesian mixing model of stable isotope analysis in R. The results indicated that the unknown upstream sediment source was the single largest (37.4%) contributor of sediments, suggesting that there is sediment coming from a significant source or sources not represented within the study catchment. Irrigation return flow channels were the second largest contributor (25.6%), while the third largest contributor, agricultural land, had a minimal contribution (9.2%). Source contribution had a small spatial but large temporal variation. The results indicated that identifying the origin of secondary sediment sources of upstream and irrigation flow channels is a critical step for soil erosion control in the study subwatershed.Key words: colour coefficient, fingerprinting, MixSIAR, sediment sources.Résumé : Identifier les sources principales de sédiments est une exigence capitale lorsqu'on veut combattre l'érosion du sol dans un bassin hydrographique. De 2009 à 2012, les auteurs ont entrepris une étude visant à établir les sources de sédimentation d'un bassin versant secondaire de la Lower Little Bow River, en Alberta. Le bassin en question se situe le long d'un tronçon de six kilomètres de la rivière et compte une entrée en amont ainsi qu'un exutoire en aval. En tant que telle, l'étude correspond à la première application d'une technique de cartographie des sédiments dans un bief. Six postes de surveillance ont été aménagés le long du bief et les sédiments ont été recueillis au moyen de systèmes d'échantillonnage passif intégré dans le temps. Les échantillons de matériaux d'origine ont été glanés à la grandeur du bassin versant à l'étude. Le spectre de réflectance des échantillons prélevés à la source et des sédiments a été établi avec un spectromètre à réflectance diffuse. Les matériaux d'origine ont été répartis entre cinq sources par analyse discriminante canonique. À partir des résultats du test de normalité, de l'analyse de la variance et de l'analyse discriminante des fonctions pas à pas, les ...

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Aeolian sediment fingerprinting using a Bayesian mixing model

Gholami

Telfer

Blake

et al. 2017

Earth Surf Processes Landf

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Identifying sand provenance in depositional aeolian environments (e.g. dunefields) can elucidate sediment pathways and fluxes, and inform potential land management strategies where windblown sand and dust is a hazard to health and infrastructure. However, the complexity of these pathways typically makes this a challenging proposition, and uncertainties on the composition of mixed‐source sediments are often not reported. This study demonstrates that a quantitative fingerprinting method within the Bayesian Markov Chain Monte Carlo (MCMC) framework offers great potential for exploring the provenance and uncertainties associated with aeolian sands. Eight samples were taken from dunes of the small (~58 km2) Ashkzar erg, central Iran, and 49 from three distinct potential sediment sources in the surrounding area. These were analyzed for 61 tracers including 53 geochemical elements (trace, major and rare earth elements (REE)) and eight REE ratios. Kruskal–Wallis H‐tests and stepwise discriminant function analysis (DFA) allowed the identification of an optimum composite fingerprint based on six tracers (Rb, Sr, 87Sr, (La/Yb)n, Ga and δCe), and a Bayesian mixing model was applied to derive the source apportionment estimates within an uncertainty framework. There is substantial variation in the uncertainties in the fingerprinting results, with some samples yielding clear discrimination of components, and some with less clear fingerprints. Quaternary terraces and fans contribute the largest component to the dunes, but they are also the most extensive surrounding unit; clay flats and marls, however, contribute out of proportion to their small outcrop extent. The successful application of these methods to aeolian sediment deposits demonstrates their potential for providing quantitative estimates of aeolian sediment provenances in other mixed‐source arid settings, and may prove especially beneficial where sediment is derived from multiple sources, or where other methods of provenance (e.g. detrital zircon U–Pb dating) are not possible due to mineralogical constraints. Copyright © 2017 John Wiley & Sons, Ltd.

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Fingerprinting and tracing the sources of soils and sediments: Earth and ocean science, geoarchaeological, forensic, and human health applications

Abstract: International audienc

Cited by 203 publications

References 228 publications

Metal contamination of bed sediments in the Irwell and Upper Mersey catchments, northwest England: exploring the legacy of industry and urban growth

Metal contamination of bed sediments in the Irwell and Upper Mersey catchments, northwest England: exploring the legacy of industry and urban growth

Determining sources of fine-grained sediment for a reach of the Lower Little Bow River, Alberta, using a colour-based sediment fingerprinting approach

Aeolian sediment fingerprinting using a Bayesian mixing model

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