Natural background levels and threshold values of chemical species in three large-scale groundwater bodies in Northern Italy

Molinari, Agnese; Guadagnini, Laura; Marcaccio, Marco; Guadagnini, Alberto

doi:10.1016/j.scitotenv.2012.03.015

Cited by 71 publications

(42 citation statements)

References 30 publications

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“…In this study, to find a suitable set of p attributes for clustering, both the primal concentration variables and their principle components (PCs) were considered candidate attributes. Hydrochemical data often follow non-normal distributions, and therefore appropriate data processing is necessary before any parametric analyses [48]. In this study, PCA was conducted for the standardized logarithms of the concentrations, as is commonly done in hydrochemical studies [16].…”

Section: Model Selectionmentioning

confidence: 99%

“…The well-known EM algorithm can solve MLE problems using unobserved variables, and it has previously been applied to GMM clustering [48,49]. In this case, Θ = {θ 1 , · · · θ k , ω 1 , · · · ω k } is the parameter set to be estimated via MLE, where θ k = {µ k , D k } represents the parameters of the kth Gaussian distribution.…”

Section: Expectation-maximization Algorithmmentioning

confidence: 99%

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Investigating Hydrochemical Groundwater Processes in an Inland Agricultural Area with Limited Data: A Clustering Approach

Zheng

Zhang

et al. 2017

Water

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Abstract:Groundwater chemistry data are normally scarce in remote inland areas. Effective statistical approaches are highly desired to extract important information about hydrochemical processes from the limited data. This study applied a clustering approach based on the Gaussian Mixture Model (GMM) to a hydrochemical dataset of groundwater collected in the middle Heihe River Basin (HRB) of northwestern China. Independent hydrological data were introduced to examine whether the clustering results led to an appropriate interpretation on the hydrochemical processes. The main findings include the following. First, in the middle HRB, although groundwater chemistry reflects primarily a natural salinization process, there are evidence for significant anthropogenic influence such as irrigation and fertilization. Second, the regional hydrological cycle, particularly surface water-groundwater interaction, has a profound and spatially variable impact on groundwater chemistry. Third, the interaction between the regional agricultural development and the groundwater quality is complicated. Overall, this study demonstrates that the GMM clustering can effectively analyze hydrochemical datasets and that these clustering results can provide insights into hydrochemical processes, even with a limited number of observations. The clustering approach introduced in this study represents a cost-effective way to investigate groundwater chemistry in remote inland areas where groundwater monitoring is difficult and costly.

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Section: Model Selectionmentioning

confidence: 99%

Section: Expectation-maximization Algorithmmentioning

confidence: 99%

Investigating Hydrochemical Groundwater Processes in an Inland Agricultural Area with Limited Data: A Clustering Approach

Zheng

Zhang

et al. 2017

Water

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show abstract

“…Accordingly, those contaminant concentrations outside the interval defined by the mean ± three times the standard deviation of the variable (concentration) should be excluded from the dataset and not considered in consequent steps (Molinari et al, 2012).…”

Section: Outlier Detectionmentioning

confidence: 99%

“…Nevertheless, "background" is undoubtedly the most used one, with a large number of citations in different areas of environmental science (de Paula Filho et al, 2015;Fiore et al, 2014;Molinari et al, 2012;Preziosi et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

A chemical status predictor. A methodology based on World-Wide sediment samples

Gredilla

Vallejuelo

Diego

et al. 2015

Journal of Environmental Management

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“…By defining the NBL, it is necessary to know the extension and the hydro-geochemical characteristics of the examined groundwater body, paying attention to collect pristine water samples of the same aquifer body far from anthropogenic sources of contamination (Coetsiers et al, 2009;Hinsby et al, 2008;Wendland et al, 2008aWendland et al, , 2008b. Many studies treat the problem of the NBL definition testing different approaches for its estimation (Coetsiers et al, 2009;Ducci et al, 2016;Molinari et al, 2012;Preziosi et al, 2010;Fumagalli, 2013, Rotiroti et al, 2015), since the NBL assumes importance discriminating the anthropogenic contamination from the natural occurrence. A first approach to estimate the NBL value into European groundwater bodies was suggested by Edmunds and Shand (2008), and it was based on a geochemical prospective rather than a statistical one.…”

Section: Introductionmentioning

confidence: 99%

Geostatistics as a tool to improve the natural background level definition: An application in groundwater

Libera

Fabbri

Mason³

et al. 2017

Science of The Total Environment

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The Natural Background Level (NBL), suggested by UE BRIDGE project, is suited for spatially distributed datasets providing a regional value that could be higher than the Threshold Value (TV) set by every country. In hydro-geochemically dis-homogeneous areas, the use of a unique regional NBL, higher than TV, could arise problems to distinguish between natural occurrences and anthropogenic contaminant sources. Hence, the goal of this study is to improve the NBL definition employing a geostatistical approach, which reconstructs the contaminant spatial structure accounting geochemical and hydrogeological relationships. This integrated mapping is fundamental to evaluate the contaminant's distribution impact on the NBL, giving indications to improve it. We decided to test this method on the Drainage Basin of Venice Lagoon (DBVL, NE Italy), where the existing NBL is seven times higher than the TV. This area is notoriously affected by naturally occurring arsenic contamination. An available geochemical dataset collected by 50 piezometers was used to reconstruct the spatial distribution of arsenic in the densely populated area of the DBVL. A cokriging approach was applied exploiting the geochemical relationships among As, Fe and NH4+. The obtained spatial predictions of arsenic concentrations were divided into three different zones: i) areas with an As concentration lower than the TV, ii) areas with an As concentration between the TV and the median of the values higher than the TV, and iii) areas with an As concentration higher than the median. Following the BRIDGE suggestions, where enough samples were available, the 90th percentile for each zone was calculated to obtain a local NBL (LNBL). Differently from the original NBL, this local value gives more detailed water quality information accounting the hydrogeological and geochemical setting, and contaminant spatial variation. Hence, the LNBL could give more indications about the distinction between natural occurrence and anthropogenic contamination.

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Natural background levels and threshold values of chemical species in three large-scale groundwater bodies in Northern Italy

Cited by 71 publications

References 30 publications

Investigating Hydrochemical Groundwater Processes in an Inland Agricultural Area with Limited Data: A Clustering Approach

Investigating Hydrochemical Groundwater Processes in an Inland Agricultural Area with Limited Data: A Clustering Approach

A chemical status predictor. A methodology based on World-Wide sediment samples

Geostatistics as a tool to improve the natural background level definition: An application in groundwater

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