Multiple regression analysis to assess the contamination with metals and metalloids in surface sediments (Aveiro Lagoon, Portugal)
Graphical abstract
Introduction
Estuarine catchments have been strategic settings throughout human history, either as places of navigation, agricultural abundance or as locations of the biggest cities in the World (Kennish, 1996; McLusky and Elliot, 2004). Usually used as repositories of industrial and domestic effluents, estuaries are the end-point of numerous contaminants, the vast majority of which tend to settle and are thus stored in estuarine and marine sediments (Laurier et al., 2003; Prego and Cobelo-Garcia, 2003; Kim et al., 2004). Due to their toxicity to organisms and persistence in the environment, growing international consciousness has developed with regards to assessing the contamination status and protecting these ecosystems.
Traditionally, the dynamic of metals/metalloids in sediments is evaluated through the utilization of enrichment factors (EFs) (Abrahim and Parker, 2008), but recently, novel methodologies have been developed to evaluate the composition and dynamics of sediment characteristics. For example, artificial neural networks combined with residual kriging have been used to predict the spatial distribution of Cr in soil (Tarasov et al., 2018).
Multiple regression analysis allows for the verification of simple and higher order effects of several explanatory variables and their interactions. Upon simplification of the starting models, only a few significant explanatory variables will remain. It has been applied recently to evaluate biogeochemical processes in estuarine water (Stoichev et al., 2016, Stoichev et al., 2020). Multiple regression was used to quantify major components of lake sediments by near infrared spectra (Russell et al., 2019) or to find out the relative importance of hydrous iron and manganese oxides on the retention of trace metals in estuarine sediments (Turner, 2000). Multiple regression analysis has been successfully applied to study the behavior of Hg species in surface sediments from the Aveiro Lagoon (Stoichev et al., 2019). A relatively small area of the lagoon, the Laranjo Bay, suffered from chlor-alkali mercury pollution coming from a single upstream source (Pereira et al., 2009; Stoichev et al., 2019). However, sediment contamination in the system is not restricted to mercury, with reports of significant concentrations of other metals and metalloids, such as As, Pb, Zn (Costa and Jesus-Rydin, 2001).
The aim of this work was to evaluate the effectiveness of multiple regression analysis to study spatial variations in heavy metal/metalloid concentrations in surface sediments from contaminated shallow tidal environments. One type of explanatory variables depended on geographical distances. Another type of variables was related to sediment geochemistry, which, in coastal environments, should include indicators of both terrestrial (e.g. Al or Fe) and oceanic (e.g. Ca) influence (Perez et al., 2016; Gredilla et al., 2015a; Dias et al., 2007). An attempt was made to separate and quantitatively evaluate the effects of contaminant dispersion from a point source from those of geochemical processes as a potential influence for spatial distribution of contamination. This is possible if additive statistical effects of some explanatory variables (responsible for metal/metalloid dispersion and for the geochemistry of the sediments) on the dependent variables (e.g. concentration of contaminant) are found. For this purpose, equations were developed modelling metal/metalloid concentrations depending on different distance variables or geochemical explanatory variables.
Section snippets
Study area
Aveiro Lagoon is a coastal lagoon in the North of Portugal (Fig. 1) comprised of a network of channels, opening into the Atlantic Ocean by way of a single narrow channel. The lagoon covers an area of 83 km2 at high tide (HT) and 66 km2 at low tide (LT) and has an average depth of 1 m. The tidal range is minimum 0.6 m during neap tide; maximum 3.2 m during spring tide. The water residence time in the lagoon is approximately 2 days, however, it is more than two weeks in the contaminated area
Potential existence of seasonal trends
Analysis of covariance ANCOVA (season, geochemical variables) for surface sediments from the Aveiro Lagoon (nine samples taken in February and August 2012) demonstrated that seasonal trends for YE (metal/metalloid concentrations) were not observed (results not shown). When, for some sampling sites, winter/summer differences existed, they followed concentration changes observed for geochemical variables Xi. For example, the concentrations of As could be expressed by a combination of TC and the
Conclusions
The metal/metalloids, coming from a single upstream source, were suspected of contaminating surface sediments from a shallow coastal lagoon. Multiple regression analysis demonstrated that, from the studied chemical elements, only As, Cu, Zn and Pb were influenced both by geochemical and geographical distance variables, the latter representing the anthropogenic influence and the extent of transport of contaminants away from the upstream source. It is possible that for Pb, an additional source
CRediT authorship contribution statement
Teodor Stoichev:Conceptualization, Methodology, Writing - original draft.João Pedro Coelho:Formal analysis, Funding acquisition.Alberto De Diego:Conceptualization.Maria Gabriela Lobos Valenzuela:Formal analysis, Funding acquisition.Maria Eduarda Pereira:Funding acquisition.Aubin Thibault de Chanvalon:Conceptualization.David Amouroux:Conceptualization, Funding acquisition.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
This research was supported by national funds through FCT (Foundation for Science and Technology) (Portugal) within the scope of UIDB/04423/2020 and UIDP/04423/2020. The financial support of European SUDOE Interreg IVB Program through the Orque-Sudoe project and of FONDECYT project (1150855) is acknowledged. João Pedro Coelho is funded by CESAM (UIDP/50017/2020+UIDB/50017/2020) and the Integrated Program of SR&TD ‘Smart Valorization of Endogenous Marine Biological Resources Under a Changing
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