Impacts of phytoplankton blooms on trace metal recycling and bioavailability during dredging events in the Sado estuary (Portugal)

Journal Pre-proof Impacts of phytoplankton blooms on trace metal recycling and bioavailability during dredging events in the Sado estuary (Portugal) Maria Teresa Cabrita, Pedro Brito, Isabel Caçador, Bernardo Duarte PII:

S0141-1136(19)30391-5

DOI:

https://doi.org/10.1016/j.marenvres.2019.104837

Reference:

MERE 104837

To appear in:

Marine Environmental Research

Received Date: 24 June 2019 Revised Date:

30 October 2019

Accepted Date: 3 November 2019

Please cite this article as: Cabrita, M.T., Brito, P., Caçador, I., Duarte, B., Impacts of phytoplankton blooms on trace metal recycling and bioavailability during dredging events in the Sado estuary (Portugal), Marine Environmental Research (2019), doi: https://doi.org/10.1016/ j.marenvres.2019.104837. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

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Impacts of phytoplankton blooms on trace metal recycling and bioavailability during

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dredging events in the Sado estuary (Portugal)

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Maria Teresa Cabrita1,2*, Pedro Brito1,3, Isabel Caçador3, Bernardo Duarte3

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006 Algés, Lisboa, Portugal

Instituto do Mar e da Atmosfera (IPMA), Rua Alfredo Magalhães Ramalho, 6, 1495-

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Marine and Environmental Sciences Centre (MARE), Faculdade de Ciências,

Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal.

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Ordenamento do Território (IGOT), University of Lisbon, Rua Branca Edmée Marques,

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1600-276 Lisbon, Portugal)

Present affiliation: Centro de Estudos Geográficos (CEG), Instituto de Geografia e

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* Corresponding author. CEG/IGOT, University of Lisbon, Rua Branca Edmée Marques,

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1600-276 Lisbon, Portugal. Tel.: +351 210 442 962, Email: [email protected] (M.T.

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Cabrita)

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Abstract

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This work evaluates the impact of phytoplankton blooms on metal availability

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driven by dredging, in an area of the Sado estuary (Portugal), subject to ongoing

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dredging operations during the entire sampling period. In situ changes of chlorophyll a

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concentration, bioavailable trace metals (Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb) in the

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water column, metal content in particulate matter, and particulate metal to

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bioavailable metal ratios were investigated during pre-bloom, bloom and post-bloom

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conditions to evaluate the potential of the phytoplankton-mediated metal removal.

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Metals in particulate matter significantly enhanced concomitantly with the decline of

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metals (mostly Mn, Co, Cu, Zn, and Pb) in the water column during the bloom, in

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comparison with pre- and post-bloom periods. During the peak of the phytoplankton

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bloom, bioavailable Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb were reduced to 30, 99, 100, 87,

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98, 72, 84 and 88 % of their original levels (pre-bloom values). Copper and Pb, and to a

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lesser extent, Zn and Mn, were ranked as more particle reactive. Volume particulate

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matter concentrations of Mn, Ni, Cu and Pb much higher than the bioavailable

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concentrations, indicated that phytoplankton is likely to be a dominant sink of these

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metals during the bloom period. Thus, Mn, Ni, Cu and Pb are prone to be transferred

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and biomagnified into the marine food web. These results highlight phytoplankton

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blooms as important biological sinks of trace metals during dredging, which should be

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taken into consideration in planning and management of dredging, to minimise

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environmental impacts and protect estuarine and coastal ecosystems.

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Keywords: trace metals; bioavailability; phytoplankton blooms; estuarine and coastal

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systems; dredging

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1. Introduction

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The ever-present problem of trace metal contamination in estuaries and

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coastal areas is mostly linked to anthropogenic activities, such as recurrent dredging

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operations commonly taking place in these ecosystems to create and maintain

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shipping and navigation channels. Remobilization and increased bioavailability of trace

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metals, such as chromium (Cr), copper (Cu), zinc (Zn), cadmium (Cd), and lead (Pb), in

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the water column, occur in response to resuspension of sediments caused by dredging

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(Cabrita et al., 2013, 2014a; Caetano et al., 2003; Nayar et al., 2004; Ohimain et al.,

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2008). Dredging in shipyards is of particular concern since these areas are commonly

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contaminated with spilled petroleum, paints, solvents, and processed metal waste

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resulting, for instance, from cleaning and painting, steelwork, machinery and propeller

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repair, and tank cleaning (Chiu et al., 2006; OECD, 2010). The materials and practices

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employed are a source of a wide range of trace metals. For instance, anti-fouling and

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metal-based paints can contain up to 30 % trace metals, such as Cu, Zn and Pb (OECD,

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2010; OSHA, 2006). Chromium, manganese (Mn), cobalt (Co), nickel (Ni), Cd and Pb are

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also generated by thermal metal cutting, abrasive blasting, surface plating, coating and

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finishing activities (OECD, 2010; OSHA, 2006). Dredging in these particular areas poses

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a serious environmental risk to coastal and marine ecosystems due to the persistence

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and toxicity of several elements (Deforest et al., 2007; Pan and Wang, 2012) that may

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be transferred in high amounts into the water column, and often far exceed

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background values (Nriagu 1990; Eggleton and Thomas 2004; Nayar et al. 2004; Cabrita

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et al., 2013, 2014a). Trace elements can accumulate in marine organisms (Cabrita et

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al., 2013, 2014a; Rainbow, 2007), become toxic at high concentrations (Ansari et al.,

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2004; Cabrita et al., 2016, 2018; Kumar and Achyuthan, 2007; Wei at al., 2014), and

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can eventually be transferred and biomagnified in food webs (Cabrita et al., 2017;

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Cheung and Wang, 2008; Wang, 2002). Phytoplankton, at the very base of marine food

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webs, are highly efficient primary producers, and although only account for about 1-2

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% of the total global photosynthetic biomass, they are responsible for approximately

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40 % of total global CO2 fixation (Falkowski, 1994; Field et al., 1998). Furthermore,

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phytoplankton are efficient scavengers of trace elements and have an important role

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in trace element cycling in marine ecosystems (González-Dávila, 1995; Luengen et al.,

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2007; Luoma et al., 1998). These organisms also rapidly respond to changes in metal

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availability in the water column (e.g. Cabrita et al., 2013, 2014a), which is particularly

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relevant when pulses of trace elements are made available in short timescales in the

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water column due to dredging events. During phytoplankton blooms, fast production

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and accumulation of microalgae biomass takes place, resulting in the rapid

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transformation and incorporation of inorganic elements into organic forms, which can

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lead to changes in the chemical form and toxicity of trace metals such as Ni, Zn, arsenic

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(As) and Cd (Cloern, 1996). Studies focused on understanding the impact of

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phytoplankton blooms on metal cycling in estuaries, have shown that these biological

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events can have a huge impact in reducing dissolved levels of some metals, namely Ni,

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Zn, Cd (Luengen et al., 2007; Luoma et al., 1998; Reynolds and Hamilton-Taylor, 1992;

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Sharp et al., 1984; Slauenwhite and Wangersky, 1991), and of methylmercury

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(Luengen and Flegal, 2009). By contrast, dissolved concentrations of other elements,

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such as Mn, Co, Pb and total Hg were found to rise at the end of the phytoplankton

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bloom, showing that the bloom decay also affected metal cycling (Luengen et al., 2007;

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Luengen and Flegal, 2009). Nevertheless, the ecological role of phytoplankton blooms

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as biological vectors of metal biogeochemical variability in estuaries and coastal areas

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during dredging events has not been established yet. In dredging impacted areas, the

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direct link between primary production and metal dynamics can be difficult to assess

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due to the complexity of physical and chemical processes occurring during dredging

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that hinder isolation of biological influences on metal cycling. The intense sediment

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resuspension occurring during dredging, and the size similarity of phytoplankton and

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resuspended sediment particles makes the collection of sediment-free phytoplankton

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samples extremely difficult (Ho et al., 2007). Although dissolved and particulate metal

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fractions of the water column, combined with phosphorus uptake data, have been

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used to estimate the significance of phytoplankton metal uptake during blooms

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(Luengen et al., 2007; Luengen and Flegal, 2009; Luoma et al., 1998), increases in

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metal concentrations during dredging may not result in the accumulation of toxic

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metals by phytoplankton. This is mostly due to the environmental chemical conditions

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and on the microalgae themselves, regarding, for instance, synergistic and antagonistic

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interactions between multiple trace metals, and differences in metal requirements

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among species (Campbell et al., 2006; González-Dávila, 1995; Sunda, 1989, 2012).

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Metal chelators or competing metals (e.g. Mn) may also be released and can decrease

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the uptake of toxic metals such as Cd, Cu and Zn. Levels of metals accumulated within

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the phytoplankton cells will provide a more accurate measure to evaluate the impact

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of these biological events on the metal cycling (Rainbow, 2006).

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Due to the high levels of metals made available in the water column through

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dredging, and the role of phytoplankton blooms in the transfer of toxic metals into the

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food web, it is paramount to evaluate bloom impact on bioavailable trace metals and

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potential risk of transfer into marine food webs via phytoplankton when dredging

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events coincide with blooms. This will have repercussions on metal fate in the marine

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environment, and on dredging management options in compliance with the protection

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of estuarine and coastal areas. To help fill this knowledge gap regarding the impacts of

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phytoplankton blooms during dredging, the present study was designed to investigate

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whether phytoplankton blooms have an impact on metal (Cr, Mn, Co, Ni, Cu, Zn, Cd,

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and Pb) bioavailability and may be a link to metal transfer into marine food webs, in a

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coastal environment. The hypothesis to be tested herein is based on the idea that

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bioavailable trace metals are considerably removed by phytoplankton when bloom

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and dredging events coincide and affect metal bioavailability in the water column. To

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the best of our knowledge, this is the first study showing phytoplankton bloom

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impacts on enhanced metal availability driven by dredging, to provide applicable

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scientific evidence to support stakeholder capacity building regarding coastal dredging

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management.

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The field work was performed in the Sado estuary (Portugal), the second largest

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estuary in the west coast of Portugal (Figure 1). This mesotidal estuary consists of a

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wide bay with a narrow channel marking the entrance of the Sado river and has an

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area of approximately 180 km2 and average depth of 8 m (Martins et al., 2001). The

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bay has a North Channel with weak residual currents, low hydrodynamics, and thus

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has high levels of sediment deposition, and a South Channel where the water

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circulation is mainly driven by tidal action, with both channels being separated by

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intertidal sand banks (Martins et al., 2001; Neves, 1985). The Sado estuary is a well-

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mixed estuary due to low average depth, strong tidal currents and low freshwater

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discharge, with a typical water residence time of one month (Ferreira et al., 2002). The

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estuary supports a variety of activities ranging from industrial, shipyard and urban

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waste disposal, harbour associated activities, agriculture, fisheries and aquaculture,

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and recreational activities. It is also a RAMSAR (Ramsar Convention on Wetlands of

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International Importance especially as Waterfowl Habitat) area that includes two

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Special Protection Zones, and a Natural Reserve (RNES). Dredging operations, related

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to the maintenance of navigation channels, are a common feature of this estuary

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(Carvalho et al., 2001), and are usually carried out in the North and South Channels

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and estuarine entrance.

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2. Materials and Methods

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2.1 Sampling design and procedures

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Sampling was performed in a shipyard located in the North Channel, where

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sediments were intermittently dredged over a five-month period, for maintenance of

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the depth of waterways accesses and quay basins. Following legislation on the

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classification of dredged sediment contamination levels (DR, 1995), sediments within

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the shipyard area have been considered clean for Ni and Pb (lower than 30 and 50 μg

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g-1 d.w., respectively), slightly contaminated for Cd (3-5 μg g-1 d.w.), and trace

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contaminated for Cr, Cu and Zn (lower than 50-100, 35-150 and 100-600 μg g-1 d.w.,

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respectively) (Caeiro et al., 2005). In this area, sediment metals, in particular Cu and

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Zn, can exceed chemical concentrations above which adverse biological effects are

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likely to occur (Caeiro, et al. 2009). In situ measurements and water sampling were

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performed in one site located in the shipyard (S1; 38°28'20.3"N; 8°47'30.5"W), at a

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distance of 0.1 to 0.5 km from the dredging areas, in two different periods (April and

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July 2013), during ongoing dredging operations. Because the sampling design and

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schedule required the occurrence of a phytoplankton bloom within the dredging

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period, surface water (0.5 m) was analysed for chlorophyll a (Chl a) concentration,

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intermittently between April and July 2013, until a bloom could be detected. The onset

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of a bloom was detected in July. Sampling was carried out from 23 to 30 April (pre-

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bloom), and from 4 to 12 July (bloom and post-bloom). Post-bloom conditions were

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considered when Chl a was reduced to values below 13 µg L-1. Dredging operations

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were taking place at the shipyard within the sampling periods.

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Measurements and sampling were always performed at high tide to avoid any

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bias due to variations in phytoplankton community associated with the semi-diurnal

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tidal cycle. Temperature (Temp), salinity (S), dissolved oxygen (DO) and turbidity of

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surface water (0.5 m depth) were determined in situ with a 650 MDS (YSI

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Incorporated) and taken in six replicate sample readings. Surface water was sampled

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using 2-L ultra-clean acid-washed polypropylene bottles for the determination of

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bioavailable Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb, by submerging the bottles beneath the

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water’s surface. Phytoplankton were sampled from surface waters (0.5 m depth), with

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a 10-L Niskin bottle. The collected samples were immediately pre-filtered on a 200 µm

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mesh-size net to remove larger zooplankton, stored in 5-L polyethylene ultra-clean

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acid-washed containers, kept inside black plastic bags to avoid contact with light

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during transportation to the laboratory for further processing. Surface water was also

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sampled into 5-L ultra-clean acid-washed polyethylene bottles for determination of

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suspended particulate matter (SPM) and Chl a. Sampling materials were rinsed

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thoroughly with water from the sampling site before sample collection. Manipulations

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were performed using powder-free latex gloves.

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2.2 Analytical determinations

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Suspended particulate matter (SPM) Suspended particulate matter was obtained by filtration in pre-weighed polycarbonate filters (0.45 µm) and determined gravimetrically.

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Phytoplankton biomass analysis

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Phytoplankton biomass was measured as chlorophyll a (Chl a) concentration.

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Water samples were filtered through Whatman GF/F filters which were immediately

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frozen at -20 °C, until pigment analysis which was carried out. Chlorophyll a was

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extracted using 90 % (v/v) acetone overnight, and determined spectrophotometrically

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by using the method of Ritchie (2008).

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Bioavailable trace metal concentrations

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Trace metals (Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb) concentrations in the water

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column were determined using diffusive gradient in thin-films (DGTs), which indicate

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the bioavailable trace metal fraction in the water column (Zhang and Davison, 1995).

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The technique of DGT provides an in situ means of quantitatively measuring labile

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species in aqueous systems. The technique measures dissolved species with molecular

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sizes sufficiently smaller than the pore size of the hydrogel to allow them to diffuse

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freely through it, as well as a fraction of larger molecules which will be partly impeded

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(Zhang and Davison, 1995). The labile fraction of dissolved metal sampled by DGT

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includes free ions and all inorganic complexes (Davison, 1978; Tusseau-Vuillemin et al.,

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2003). The DGTs have been applied to the in situ measurement of metals in coastal

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and open seawater, and as a pollution monitoring tool, as a surrogate for

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bioavailability. The term “bioavailable trace metal” is here used to indicate the DGT-

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metal reactive fraction in the water column. In the laboratory, a DGT unit was

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suspended with a nylon thread inside each 2-L polypropylene bottle. All DGT holders,

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Chelex-100 resins and diffusive gels (type APA, 0.8 mm thickness, open pore) (Zhang

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and Davison, 1999) were obtained from DGT Research. In the DGT assembly, the resin

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gel is sequentially covered by the diffusion gel and a 0.13 mm cellulose nitrate filter

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membrane (0.45 μm pore size, Whatman, Maidstone, UK) (Zhang and Davison, 1999).

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After a stirring period of 48 h, the DGTs were cautiously removed from the bottles, the

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resins detached and immersed in 5 mL of 1 M HNO3, in decontaminated

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polytetrafluoroethylene tubes. Metal concentration was directly quantified in resin

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eluates obtained from the field and blank DGTs, by an Inductively Coupled Plasma

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Mass Spectrometer, ICP-MS (Thermo Elemental, X-Series), equipped with a Peltier

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impact bead spray chamber and a concentric Meinhard nebuliser. Procedural DGT

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blanks were prepared in triplicate using the same analytical procedure and reagents,

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and included within each batch of samples to check for contamination of blank DGTs.

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Eluates were analysed with reagent blanks to control eventual contaminations during

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the analytical procedure, and with a certified reference material of river water (SLRS-5,

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from the National Research Council of Canada) to control the accuracy of the

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procedure. All blank values were below detection limits, showing that there was no

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contamination introduced during sample transport to the laboratory, or during the

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analytical procedure. The precision expressed as relative standard deviation (SD) of

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three replicates was confirmed to be below 5 % (p < 0.05) and found acceptable. Used

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materials were cleaned with HNO3 (20%) for two days and rinsed thoroughly with Milli-

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Q water (18.2 MΩ cm), to avoid contamination.

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Trace metal concentrations in particulate matter

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In the laboratory, particulate matter samples were obtained by filtration into

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decontaminated polycarbonate filters (0.45 µm) which were dried at 40 °C. Filters

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were digested as previously described for sediments (Caetano et al., 2007), as

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particulate matter samples from the sampling site were mainly composed of diatoms

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(data not shown), and diatoms have a high silica percentage by dry weight (Sicko-Goad

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et al. 1984). Therefore, the most complete decomposition procedure possible is

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required, with the use of HF to fully dissolve the silicates and the organic content

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(Polkowska-Motrenko et al., 2000), and HNO3 to efficiently solubilise metals from the

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cells (Meeravali and Kumar, 2000). Reagents blanks and certified reference materials

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(Plankton BCR 414 and Ulva lactuca BCR 279, from the Community Bureau of

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Reference) were prepared in triplicate using the same analytical procedure to control

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the accuracy of the procedure. The concentrations of Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb

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were determined by ICP-MS. Concentrations obtained for the certified reference

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materials were consistently within the ranges of certified values, according to the t-

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test for a 95% confidence level. The precision expressed as relative standard deviation

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(SD) of three duplicates was less than 6 % (p < 0.05) and found adequate. Ultra-clean

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acid-decontaminated materials were employed to avoid metal contamination.

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Particulate metal to bioavailable metal ratios (L Kg-1) were calculated by

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dividing metal (Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb) concentrations in particulate matter

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(µg g-1 d.w.) by the bioavailable concentrations of each metal in the water column (µg

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L-1), in order to highlight which metals were more particle reactive, and to better

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evaluate the potential differences in metal bioaccumulation in the microalgae

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between pre-bloom, bloom and post-bloom periods. Observation under a light

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microscope showed that suspended particle material other than phytoplanktonic cells

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was negligible in the water samples, suggesting that the metal levels obtained were

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mostly associated with the phytoplankton and thus particulate metal to bioavailable

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metal ratios reflected actual incorporation into the microalgae and thus can be

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inferred as bioaccumulation factors.

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2.3 Statistical analysis

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Due to the lack of data normality, differences in metal concentrations between

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pre-bloom, bloom and post-bloom conditions were compared through Kruskal Wallis

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non-parametric test. Results yielding p < 0.05 were considered statistically significant.

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These statistical analyses were performed with Statistica 6.1 Software (StatSoft, Inc.).

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A Principal Component Analysis (PCA) was performed to help understand possible

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relationships between the phytoplankton assemblages and trace metals, under pre-

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bloom, bloom and post-bloom conditions. The PCA was applied to a matrix of 20

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variables (temperature, turbidity, dissolved oxygen (DO), Chl a concentration,

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bioavailable Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb, and concentrations of these trace

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metals in particulate matter, and 20 objects (samples collected under pre-bloom,

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bloom and post-bloom conditions). The software used was NTSYS PC (Numerical

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Taxonomy and Multivariate System Analysis) Version 2.0 software package.

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3. Results

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3.1 Environmental conditions in surface waters

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Table 1 displays the average and standard deviation values of physical and

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chemical parameters measured during dredging, under phytoplankton pre-bloom,

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bloom and post-bloom conditions. Water temperature ranged between 18 and 22 °C

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between pre-bloom, bloom and post-bloom conditions. Salinity showed no

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considerable variations (35 PSU), reflecting the mixing of seawater and freshwater in

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the estuary, during the entire sampling period. Dissolved oxygen values suggested that

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surface water remained relatively well oxygenated during the sampling period,

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although during the bloom and post-bloom periods, DO was lower (94 ± 13.91 - 89 ±

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12.85 %) than during pre-bloom (111 ± 6.17 %). Relatively similar average values were

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found for turbidity and suspended particulate matter concentration in surface water,

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between pre-bloom (31 ± 1.83 NTU; 6.1 ± 0.31 mg L-1), bloom (34 ± 0.50 NTU; 6.9 ±

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0.42 mg L-1) and post-bloom (35 ± 0.22 NTU; 5.5 ± 0.53 mg L-1) conditions.

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3.2 Chl a concentration in surface waters

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Figure 2 shows phytoplankton biomass, measured as Chl a concentration, in

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surface water, during pre-bloom, bloom and post-bloom conditions. Phytoplankton

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concentration averaged 9.26 ± 0.88 µg L-1 during pre-bloom, increasing significantly

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(p<0.05) to 25.95 ± 5.25 µg L-1 during the bloom, with a significant reduction to 12.03 ±

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1.65 µg L-1 during the post-bloom period.

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3.3 Bioavailable trace metal concentration in surface waters

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Figure 3 illustrates bioavailable Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb concentrations (µg L-1)

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in surface waters, under pre-bloom, bloom and post-bloom conditions. Average values

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of all trace element concentrations were significantly higher (p<0.05) during pre-bloom

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than bloom period, decreasing from 1.14 ± 0.30 to 0.79 ± 0.13 µg L-1 for Cr, 26.96 ±

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7.26 to 0.38 ± 0.33 µg L-1 for Mn, 28.72 ± 0.87 to 0.050 ± 0.039 µg L-1 for Co, 0.310 ±

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0.065 to 0.039 ± 0.038 µg L-1 for Ni, 12.76 ± 11.22 to 0.22 ± 0.15 µg L-1 for Cu, 39.5 ±

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14.2 to 11 ± 6.5 µg L-1 for Zn, 0.064 ± 0.020 to 0.011 ± 0.009 µg L-1 for Cd, and 1.31 ±

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0.57 to 0.15 ± 0.15 µg L-1 Pb. These reductions in Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb

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concentration corresponded to a decline of 30, 99, 100, 87, 98, 72, 84 and 88 % of

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their original levels (pre-bloom value). After the bloom, trace element levels in the

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water column were maintained (Zn: 12.34 ± 4.99 µg L-1) or increased (Mn: 6.28 ± 5.55

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µg L-1, Co: 0.13 ± 0.10 µg L-1, Ni: 0.18 ± 0.03 µg L-1, Cu: 1.23 ± 0.61 µg L-1, Cd: 0.055 ±

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0.014 µg L-1, and Pb: 0.67 ± 0.37 µg L-1) in comparison with bloom levels, or even

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reached pre-bloom concentrations (Cr: 1.14 ± 0.10 µg L-1).

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3.4 Trace metal concentration in particulate matter

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The concentration of Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb in particulate matter,

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under pre-bloom, bloom and post-bloom conditions, is depicted in Figure 4. Average

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values increased significantly (p<0.05) between pre-bloom and bloom conditions, for

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all trace metals. Average concentrations changed between 39 ± 4.0 and 70 ± 14 µg g-1

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d.w. for Cr, 131 ± 46 and 284 ± 49 µg g-1 d.w. for Mn, 3.3 ± 0.65 and 6.2 ± 1.1 µg g-1

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d.w. for Co, 8.8 ± 1.6 and 15 ± 4.0 µg g-1 d.w. for Ni, 167 ± 38 and 336 ± 105 µg g-1 d.w.

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for Cu, 162 ± 30 and 288 ± 71 µg g-1 d.w. for Zn, 0.14 ± 0.04 µg g-1 d.w. and 0.22 ± 0.058

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µg g-1 d.w. for Cd, and 64 ± 18 and 212 ± 66 µg g-1 d.w. for Pb, between pre-bloom and

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bloom conditions, respectively. During post-bloom, the trace metal levels in the

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particulate matter declined again, to values lower than those observed in pre-bloom

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conditions, namely to 26 ± 8.2, 59 ± 18, 1.8 ± 0.56, 5.5 ± 1.4, 70 ± 33, 82 ± 24, 0.10 ±

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0.066 and 31 ± 6.9 for µg g-1 d.w. for Cr, Mn, Co, Ni, Cu, Zn, Cd, and Pb, respectively.

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3.5 Effect of trace metal enhancement on bioaccumulation in phytoplankton

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assemblages under pre-bloom, bloom and post-bloom conditions

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A PCA was performed to highlight possible relationships between trace metal

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levels and phytoplankton potential bioaccumulation, under pre-bloom, bloom and

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post-bloom conditions, and thus evaluate the possible impact of blooms on metal

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availability in the water column (Figure 5). The PC1 explained 53 % of the variance and

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evidently separated pre-bloom, bloom and post-bloom phytoplankton assemblages,

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due to a combination of metal availability in the water column, temperature, turbidity,

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and Chl a as a proxy of phytoplankton biomass, associated with phytoplankton trace

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metal content. Bloom phytoplankton assemblages gathered in one group were

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projected in the opposite side to the pre- and post-bloom microalgae groups, and

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linked to enhanced metal (Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb) accumulation in

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particulate matter, lowered bioavailable metal levels and high Chl a concentrations

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and water temperature during bloom events. This pointed to the impact of

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phytoplankton trace element accumulation in reducing metal concentrations in the

350

water column during bloom events.

351

The PCA also showed decoupling of pre- and post-bloom assemblages. The pre-

352

bloom samples were projected close to bioavailable metals (mostly Co, Cu and Zn) and

353

particulate matter trace metals, which suggested that these assemblages had no

354

considerable impact on metal levels in the water column. Contrastingly, post-bloom

355

microalgae samples gathered in the opposite side to pre-bloom assemblages, and

356

therefore associated with low trace metal bioavailability and particulate matter metal

357

content. This suggest that either the amount of metals incorporated in post-bloom

358

cells was lower in comparison with the pre-bloom and bloom phytoplankton most

359

likely because metal levels in the water column were low, or there was lesser

360

incorporation of non-biogenic material following the bloom most likely because metal

361

levels in the water column were low.

362

To further elucidate if the bioavailable metals in the water column could be

363

accounted for the metals found in the phytoplankton samples, and thus assess if

364

phytoplankton is a dominant sink of metals, the volume concentrations of the

365

particulate fractions (ug L-1) were calculated and compared to the bioavailable metal

366

levels (Figure 6). The results obtained showed that volume concentrations of the

367

particulate fraction were higher than the bioavailable metal concentrations, for Mn,

368

Ni, Cu and Pb.

369 370

3.6 Particulate metal to bioavailable metal ratios

371

Particulate metal to bioavailable metal ratios (metal concentration in

372

suspended particulate matter/bioavailable metal concentration in water column),

373

under pre-bloom, bloom and post-bloom conditions, are presented in Table 2. The

374

ratios for all trace metals varied between 1.3×103 L Kg-1 (Cd during post-bloom period)

375

to 995×103 L Kg-1 (Cu during bloom period), which highlights phytoplankton as

376

potential accumulator of trace metals during dredging. For all elements, the

377

particulate metal to bioavailable metal ratios were significantly higher (p<0.05) during

378

bloom (Cr: 95 ± 8.9, Mn: 507 ± 140, Co: 289 ± 124, Ni: 417 ± 173, Cu: 995 ± 295, Zn:

379

746 ± 50, Cd: 14 ± 5.2, Pb: 872 ± 136 L Kg-1 × 103) than during pre-bloom (Cr: 36 ± 5.2,

380

Mn: 4.5 ± 0.67, Co: 8.5 ± 1.2, Ni: 27 ± 3.5, Cu: 34 ± 9.0, Zn: 4.9 ± 0.99, Cd: 1.9 ± 0.17,

381

Pb: 59 ± 19 L Kg-1 × 103). The particulate metal to bioavailable metal ratio was again

382

reduced to values generally similar to pre-bloom accumulation factors, namely, 22 ±

383

6.2, 6.0 ± 0.33, 26 ± 21, 28 ± 3.4, 52 ± 14, 7.1 ± 1.6, 1.3 ± 0.47, and 51 ± 16 L Kg-1 × 103

384

for Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb, respectively.

385 386

4. Discussion

387

Scientific understanding of the impacts of dredging operations, in parallel with

388

planning, environmental assessment, licensing, and monitoring of coastal dredging,

389

have significantly evolved in recent years. Although guidance for best practice has

390

increasingly became available in several countries, mitigation and management to

391

minimise dredging impact and protect the marine environment typically contemplate

392

environmental dimensions related to seabed habitats and benthic communities, fish

393

and shellfish, seabirds and coastal birds, and marine mammals, with less focus on the

394

base of the marine food web. The present study shows how phytoplankton

395

communities potentially affect trace metal availability in the water column and may be

396

the link to metal transfer within marine food webs.

397

Suspended particulate matter changed little between pre-bloom (6.1±0.31 mg

398

L-1), bloom (6.9±0.42 mg L-1), and post-bloom (5.5±0.53 mg L-1). These results are in

399

accordance with other studies also showing that during algal blooms and occurrence of

400

maximal phytoplankton biomass levels, SPM concentrations can remain low (He 2017;

401

Guinder, et al., 2009; Staats et al., 2001). Staats et al. (2001) found that the sediment

402

bed benthic microalgae produced large amounts of extracellular carbohydrates leading

403

to an increase in bed strength and a reduction of resuspension, which explained low

404

SPM concentrations found during the algal bloom.

405

The microscopic observation of the samples showed that the amount of

406

particles, other than phytoplankton cells, was very reduced during the entire sampling

407

period, suggesting that the SPM samples can be regarded as phytoplankton biomass,

408

which needs to be supported by the remaining results from this study.

409 410

The Chl a concentration, averaging 26 ± 5.3 µg L-1 during the bloom period, was

411

comparable to the chlorophyll maxima previously reported for the same area of the

412

Sado estuary (around 20 µg L-1), also occurring in the same time of the year (Coutinho,

413

2003; Oliveira and Coutinho, 1992) under no dredging conditions. Likewise, the

414

biomass (9.26 ± 0.88 µg L-1) determined during pre-bloom period was within the range

415

(5 - 10 µg L-1) reported for no-bloom periods in the Sado estuary, in those studies. The

416

Chl a values herein obtained are also within the no-bloom and bloom ranges reported

417

from a 1978 - 1999 time series for the Sado estuary (Ferreira et al. 2003). This indicates

418

that dredging, even if extended in time, had no impact on the seasonal variability of

419

phytoplankton biomass, as already found in other estuarine systems subject to

420

dredging (Cabrita, 2014b; Kromkamp and Peene, 2005). This was probably related to

421

the fact that phytoplankton growth was not light limited throughout the sampling

422

period. Even though sediment resuspension and turbidity triggered by dredging may

423

limit phytoplankton biomass through light attenuation (Burford and O'Donohue,

424

2006), the levels of SPM and turbidity herein observed were below those reported for

425

turbid macrotidal estuaries (e.g. Uncles et al., 2006) able to constrain phytoplankton

426

productivity (Cloern, 1987; Irigoien and Castel, 1997).

427

The highest bioavailable trace metal concentrations found in the water column

428

during the entire dredging period, occurred during the pre-bloom period, and were

429

relatively higher than those previously observed during dredging in the Tagus estuary

430

subjected to similar intermittent dredging operation scheme (Cabrita et al., 2013,

431

2014a), but lower than those reported for heavily contaminated estuaries (Pan and

432

Wang, 2012). Bioavailable Zn, Mn, Co and Cu displayed the highest concentrations in

433

the water column during the pre-bloom period, probably reflecting the metal

434

abundance in the bottom sediments subjected to dredging, that were trace metal

435

contaminated, in particular regarding Cu and Zn which can exceed chemical levels

436

leading to adverse biological effects (Caeiro, et al. 2009). Sources of these metals in

437

the sediments were possibly associated with historical contamination due to the

438

shipyard activities (OECD, 2010; OSHA, 2006). However, the concentrations of all

439

bioavailable trace metals were significantly reduced during the bloom period, with

440

decline of pre-bloom values ranging from 72 to 100 % for all metals except Cr (30 %).

441

After the bloom, metal levels in the water column were maintained (Zn), increased

442

(Mn, Co, Ni, Cu, Cd, and Pb) in comparison with bloom period levels, or even reached

443

pre-bloom concentrations (Cr). This is a first indication that phytoplankton may have

444

had an influence on the bioavailable metal levels in the water column, during the

445

period of high phytoplankton productivity. Nevertheless, the observed bioavailable

446

metal decline during the bloom period could be due to a combination of accumulation

447

and complexation of metals by exudates (Davies, 1978), phytoplankton uptake,

448

adsorption of abiotic particles onto settling phytoplankton cells, or abiotic particle

449

ingestion by zooplankton during grazing, with subsequent faecal pellet sedimentation,

450

all processes removing metals from the water column (Schoemann et al., 1998).

451

Therefore, it is fundamental to investigate metal accumulation in the particulate metal

452

during the pre-bloom, bloom and post-bloom periods to elucidate the phytoplankton

453

role on the removal of bioavailable metals, as metal bioavailability may not directly

454

result into accumulated levels in phytoplankton (Rainbow, 2006; Sunda, 1989).

455

Comparison between the obtained metal concentrations in particulate matter

456

from this study and published phytoplankton-associated metal concentrations,

457

showed that values vary considerably. Nevertheless, our values are reasonably within

458

the published ranges obtained (Qiu, 2015; Vilhena et al., 2014; Li et al. 2001). Inorganic

459

material often much smaller (< 5 µm), non-descript, unpigmented, and often of higher

460

metal concentration than algal biomass is unlikely to have contributed to the metal

461

levels measures in the particulate matter, as the pore size of the filters used for both

462

suspended particulate matter and the metal concentrations in particulate matter was

463

the same. Our results seem to suggest that the bioavailability of trace metals in the

464

water column had repercussions on the amount of metals found in the particulate

465

matter, during pre-bloom. The close association of the pre-bloom phytoplankton with

466

the bioavailable dissolved metals (mostly Co, Cu and Zn) highlighted by the PCA (Figure

467

5), suggests that these assemblages responded to the metal bioavailability by

468

incorporating metals, although not causing a considerable impact on metal levels in

469

the water column. During this period, phytoplankton accumulated metals at much

470

higher levels (39, 131, 3.3, 167, 162, 0.14, 64 µg g-1 d.w., for Cr, Mn, Co, Cu, Zn, Cd and

471

Pb, respectively), than those commonly found in natural phytoplankton assemblages

472

and species that were not subject to the influence of dredging and consequent rise in

473

dissolved trace metal bioavailability (0.6, 50, 0.2, 3.9, 5.9, 0.002, and 0.2 µg g-1 d.w. for

474

Cr, Mn, Co, Cu, Zn, Cd and Pb, respectively) (Cabrita et al., 427 2013, 2014a). The metal

475

accumulation appeared to have occurred in particulate matter as a response to the

476

metal levels in the water column (in particular Zn, Mn, Co and Cu), driven by dredging,

477

as previously found in immobilised Phaeodactylum tricornuntum subjected to dredging

478

(Cabrita et al., 2013, 2014a).

479

During the bloom, the concentration of all metals in the particulate matter further

480

increased, being significantly higher than the levels found during the pre-bloom

481

period, occurring concomitantly with a significant reduction in the water column metal

482

levels. During this period, particulate matter metals occurred at similar levels (70, 284,

483

6.2, 332, 228, 0.22, 212 µg g-1 d.w., for Cr, Mn, Co, Cu, Zn, Cd and Pb, respectively), to

484

those reported for species growing under the influence of dredging driven trace metal

485

bioavailability (76, 259, 10, 53, 227, 0.45, 72 µg g-1 d.w., for Cr, Mn, Co, Cu, Zn, Cd and

486

Pb, respectively) (Cabrita et al., 2013, 2014a). The metal accumulation occurred in the

487

particulate matter as a response to the high metal levels in the water column (in

488

particular Zn, Mn, Co and Cu), as previously found in immobilised Phaeodactylum

489

tricornuntum subjected to increased metal availability (Cabrita et al., 2013, 2014a).

490

Metals are present in the microalgae cells to support vital cell biochemical and

491

physiological processes (Sunda, 1989) and the phytoplankton metal quotas are largely

492

driven by the biochemical demands of the cells (Twining and Baines, 2013). The

493

generalized metal abundance ranking of Zn > Mn ≈ Ni ≈ Cu ≫ Co ≈ Cd reported for

494

natural phytoplankton assemblages from temperate, equatorial, and Antarctic waters

495

in the Pacific and Atlantic Oceans, and of Zn > Mn ≫ Cu ≈ Cr ≫ Co ≫ Cd found for P.

496

tricornuntum (Cabrita et al., 2013, 2014a), were changed to Cu ≈ Zn > Mn ≫ Pb > Cr ≫

497

Ni > Co ≫ Cd, and Cu > Zn ≈ Mn > Pb ≫ Cr > Ni ≫ Co ≫ Cd in the phytoplankton

498

communities of the Sado estuary exposed to increased metal availability, during pre-

499

bloom and bloom periods, respectively. During these periods, Cu and Pb were

500

incorporated at high rates, resulting in accumulated levels comparable to Mn and Zn,

501

which changed the overall metal abundance ranking of natural phytoplankton

502

assemblages. Whereas Mn, Co, Ni, Cu, Zn and Cd are essential trace metals playing

503

important roles on phytoplankton metabolism (Twinning and Baines, 2013; Wolfe-

504

Simon et al., 2005; Lane et al., 2000, 2005; Lee and Morel, 1995), and therefore the

505

increase in their concentrations during the pre-bloom and bloom was expected, Cr and

506

Pb have no known metabolic function, and are particularly toxic even at very low

507

concentrations (Sunda, 1989; Cervantes et al., 2001). However, the levels of both

508

elements (Cr and Pb) in the cells were significantly higher during the bloom than pre-

509

bloom period because they can enter phytoplankton cells using the same transport

510

systems of essential elements (Sunda and Huntsman, 1998) and are remarkably

511

bioaccumulative (Sunda, 1989, Jaishankar et al., 2014). In particular, Cr species can

512

incorporate and also be photoreduced by marine phytoplankton (Li et al., 2009;

513

Semeniuk et al., 2016). These results indicating that phytoplankton uptake significantly

514

enhances the decline of metals (mostly Mn, Co, Cu, Zn, and Pb) in the water column

515

during the bloom periods, in the Sado estuary, are corroborated by the results from

516

the PCA (Figure 5) and previous findings reporting that phytoplankton intracellular

517

uptake may be an important sink of metals during phytoplankton blooms (Li et al.,

518

2011; Luengen et al., 2007; Luoma et al., 1998; Reynolds and Hamilton-Taylor, 1992;

519

Sharp et al., 1984; Slauenwhite and Wangersky, 1991). The comparison between metal

520

volume concentrations of the phytoplankton and the bioavailable concentrations,

521

showing that volume concentrations of Mn, Ni, Cu and Pb in the phytoplankton were

522

higher than the bioavailable concentrations, further supports that phytoplankton is

523

likely a dominant sink of these metals during the bloom period. This suggests that the

524

impact of other factors also responsible for the large decreases in DGT-metals, such as

525

adsorption of metals onto microbially formed Mn and Co oxides, which would also

526

explain the large loss of bioavailable Mn and Co during the bloom, as well as other

527

trace metals that adsorb to these oxides, and the release of strong trace metal

528

chelators by the microbial community during the phytoplankton bloom, if present, was

529

less important during the bloom. In spite of phytoplankton metal uptake during

530

blooms being estimated from the water column bioavailable and particulate metal

531

fractions combined with phosphorus uptake data, and not directly from phytoplankton

532

metal content, the studies conducted in the South San Francisco Bay, showed that this

533

phytoplankton-mediated removal process was true for Mn, Co, Ni, Zn, Cd and Pb, but

534

not for Cu (Luengen et al., 2007; Luoma et al., 1998). In this case, Cu levels in the water

535

column were not reduced during the bloom, and authors suggested that any metal

536

removal by phytoplankton that might have occurred was masked by Cu input from

537

resuspension or by complexation to organic ligands. It is well known that this element

538

has a high affinity for organic matter and ligands both in dissolved and particulate

539

forms (Duarte et al., 2008, 2011). Contrastingly, in our study, 98 % of bioavailable Cu

540

was removed during the bloom, with high levels in phytoplankton along with negligible

541

amounts of suspended particulate matter in the water column supporting a

542

phytoplankton-derived Cu removal process.

543

As the bloom receded, a sharp decline in the phytoplankton trace metal

544

content was observed, concomitant with a rise in metal levels in the water column,

545

contributed to the decoupling of pre- and post-bloom assemblages disclosed by the

546

PCA, suggesting that microalgae were settling out of surface water and being dragged

547

down into the bottom sediments and consequently, the impact of phytoplankton on

548

surface water metal removal became limited. The metal abundance ranking in

549

phytoplankton during this period was similar to the observed during the bloom,

550

namely Cu > Zn ≈ Mn > Pb ≫ Cr > Ni ≫ Co ≫ Cd. While not excluding the possibility of

551

microalgal remineralization as a source of metals to the water column, dredging still

552

taking place during the post-bloom period was, most likely, the main mechanism

553

responsible for the increase in bioavailable metals in the short term. In Luengen et al.

554

(2007) and Luoma et al. (1998) studies, conducted under no dredging influence,

555

decomposing bloom phytoplankton, presumably triggered sub-oxic conditions in

556

surface sediments, which resulted in metal release to overlying waters during

557

reductive dissolution of Mn and Fe oxyhydroxides, leading to dissolved Mn, Co, Zn, and

558

Pb increase. On the other hand, large amounts of organic carbon (DOC) are typically

559

released into the water column during the decay phase of the bloom, and able to

560

complex metals, and thus reduce their bioavailability in the water column (Luengen

561

and Flegal 2009; Lorenzo et al., 2007; Luengen et al. 2007). For instance, an

562

experiment simulating a bloom of the marine diatom Skeletonema costatum showed

563

that, during the decay, refractory humic-like material presumably produced in situ as a

564

by-product of bacterial degradation of labile dissolved organic carbon, contributed to

565

the complexation of Cu, thus reducing the levels of this metal in the water column.

566

Metal reduction during bloom decay was not noticed in this study, most likely because

567

ongoing dredging counterbalanced this DOC-mediated removal process.

568

The particulate metal to bioavailable metal ratios were used to better evaluate

569

which metals were more particle reactive and the potential risk of transfer into marine

570

food webs via phytoplankton when dredging events coincided with blooms. The

571

particulate metal to bioavailable metal ratios obtained were within the range (103 -

572

106) previously found for phytoplankton (Fisher, 1986; Li et al., 2011), supporting the

573

fact that phytoplankton are efficient scavengers of trace elements. Copper and Pb

574

were ranked as more particle reactive and thus more prone to be transferred and

575

biomagnified into the marine food web before, during and after the bloom. This

576

enhanced risk was extended to Zn and Mn during the bloom event, although Co and Ni

577

also presented high particulate metal to bioavailable metal ratios. Those metals (Mn,

578

Zn and Pb) were also reported as highly particle reactive in the IAEA report (2004),

579

with concentration factors higher for Pb (1 × 105), Mn (5 × 104) and Zn (1 × 104) than

580

for the other metals (Cr, Co, Ni and Cd) considered as less particle reactive

581

(concentration factors on the order of 103). Copper was not included in the IAEA report

582

(2004) concerning phytoplankton, but other studies have also reported high

583

particulate metal to bioavailable metal ratio values for this metal comparable to those

584

of Pb in suspended particulate matter and phytoplankton samples (Noriki et al., 1985;

585

Qiu, 2015). Even though particulate metal to bioavailable metal ratios are useful, it is

586

important to keep in mind that these computational tool depends upon a variety of

587

factors such as phytoplankton metabolic activity, exposure period, chelators present in

588

the water column, other environmental variables such as pH, salinity nutrients, organic

589

matter, and factors influencing metal bioavailability (e.g. Reinfelder, et al., 1998), such

590

as dredging in the present study.

591

Although the estuarine and coastal processes are highly complex, and isolation

592

of biological effects on metal cycling is challenging, our study has clearly showed that

593

removal of trace metals, in particular Mn, Ni, Cu and Pb, by phytoplankton during

594

blooms is effective and substantial, highlighting the role of phytoplankton blooms as

595

biological vectors of trace elements, and the potential risk of metal transfer into

596

marine food webs (Cheung and Wang, 2008; Connell and Sanders 1999; Wang, 2002).

597

Several studies have tackled the metal transfer from phytoplankton into zooplankton

598

(e.g. Reinfelder and Fisher, 1991; Reinfelder and Fisher, 1994; Wang et al. 1996;

599

Reinfelder et al., 1998; Mathews and Fisher, 2008), showing that the bioaccumulation

600

of trace elements in aquatic organisms is largely a function of the cytological

601

distribution of metals in the phytoplankton. This means that higher levels of metals in

602

phytoplankton do not necessarily translate into higher metal levels in animals. The

603

application of a kinetic model taking into account weight-specific ingestion rate,

604

assimilation efficiency, physiological loss rate constant and weight-specific growth rate

605

showed that trophic transfer potential and biomagnification is limited for most trace

606

metals, for instance Cr, Cu and Pb (Reinfelder et al., 1998; Mathews and Fisher, 2008).

607

In contrast, caesium, methylmercury and selenium biomagnification are commonly

608

observed (Reinfelder et al., 1998; Mathews and Fisher, 2008), and biomagnification of

609

Cd might be expected for organisms (e.g. filter feeders) which assimilate this element

610

from phytoplankton with assimilation efficiencies in excess of 20% (Reinfelder et al.,

611

1997). Furthermore, biomagnification may not always occur among feeding categories,

612

but comparison between specific predator-prey pairs suggest that Cd and Zn may be

613

magnified between trophic levels (Timmermans et al., 1989). Whereas the

614

biomagnification risk of many metals is almost absent, Zn, Cd, Cs and Hg build-up in

615

the phytoplankton can have consequences in the food chain, with may have a stronger

616

impact in polluted or episodically dredged estuarine and coastal systems.

617

The impact of phytoplankton on metal availability in estuarine and coastal

618

ecosystems, though occurring during a small window of opportunity, may be of

619

particular concern when dredging coincide with blooms. One approach to reduce

620

potential adverse impacts, particularly regarding Zn, Cd, Cs and Hg, from dredging on

621

estuarine and coastal ecosystems can be the implementation of environmental work

622

windows, as already used in other estuaries, for example, the San Franscisco Bay

623

(Levine-Fricke, 2004; Robinson and Jabusch, 2013). These are periods in the year when

624

specific marine organisms are presumed to be least vulnerable to possible impacts. As

625

the timing of phytoplankton blooms in the Sado estuary, as well as in other estuarine

626

areas, is roughly predictable because these events are subject to changing physical

627

forcings associated with the coastal ocean (e.g., tides), the atmosphere (wind), or on

628

the land surface (precipitation and river flow) (Cloern et al., 1996), monitoring

629

phytoplankton biomass in systems where dredging activities are needed and frequent

630

is paramount to establish environmental work windows outside the bloom periods.

631

Although environmental work windows can also cause difficulties to the dredging

632

schedules, blooms are short-term episodic events (Cloern et al., 1996), and work

633

window establishment should be considered in planning and best practice guidance to

634

minimise dredging impacts and provide protection of the estuarine and coastal

635

resources.

636 637

5. Conclusions

638

The metal bioavailability was impacted during the bloom period occurring

639

concomitantly with ongoing dredging activities. A significant reduction of metals,

640

mostly Mn, Co, Cu, Zn, and Pb, from the water column, was observed, in comparison

641

with pre- and post-bloom periods, in the Sado estuary. At the height of the bloom,

642

bioavailable Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb were reduced to 30, 99, 100, 87, 98, 72,

643

84 and 88 % of their pre-bloom levels. Manganese, Ni, Cu and Pb in were shown to be

644

the most likely metals to be transferred and biomagnified into the estuarine food web.

645

To minimise dredging impacts and better protect estuarine and coastal ecosystems

646

subject to dredging, particularly in metal contaminated areas, such as shipyards,

647

environmental work windows should be established within the scope of coastal

648

dredging planning and contribute to increase stakeholder capacity building regarding

649

coastal dredging management.

650 651

Acknowledgments

652

Cabrita M.T. and Duarte B. wish to acknowledge individual grants by Fundação para a

653

Ciência e a Tecnologia (FCT, Grants SFRH/BPD/50348/2009 and CEECIND/00511/2017,

654

respectively)

655

UID/MAR/04292/2019. Authors would like to thank Rui Silva for valuable assistance in

656

the field work.

and

project

grants

PTDC/CTA-AMB/30056/2017

(OPTOX)

and

657 658

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924 925 926

Figure captions

927

Figure 1 - Location of the sampling site (S1; 38°28'20.3"N, 8°47'30.5"W) in the Sado

928

estuary (Portugal).

929 930

Figure 2 - Mean, standard error, standard deviation, maximum and minimum of

931

phytoplankton biomass, measured as Chl a concentration (µg L-1), in surface water,

932

during pre-bloom, bloom and post-bloom periods, at the sampling site subject to

933

dredging, in the Sado estuary (Portugal). Results of Kruskal-Wallis test for comparison

934

of pre-bloom, bloom and post-bloom periods are presented.

935 936

Figure 3 - Mean, standard error, standard deviation, maximum and minimum of

937

bioavailable Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb concentrations (µg L-1), in surface water,

938

during pre-bloom, bloom and post-bloom periods, at the sampling site subject to

939

dredging, in the Sado estuary (Portugal). Results of Kruskal-Wallis test for comparison

940

of pre-bloom, bloom and post-bloom periods are presented.

941 942

Figure 4 - Mean, standard error, standard deviation, maximum and minimum of Cr,

943

Mn, Co, Ni, Cu, Zn, Cd and Pb concentrations in particulate matter (µg g-1 d.w.), in

944

surface water, during pre-bloom, bloom and post-bloom periods, at the sampling site

945

subject to dredging, in the Sado estuary (Portugal). Results of Kruskal-Wallis test for

946

comparison of pre-bloom, bloom and post-bloom periods are presented.

947 948

Figure 5 - Projection of temperature, turbidity, dissolved oxygen (DO), Chl a

949

concentration, bioavailable Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb in the water column, and

950

concentrations of these trace metals (Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb) in particulate

951

matter, and samples collected during pre-bloom, bloom and post-bloom periods,

952

obtained from the Principal Component Analysis (PCA). Pre-bloom, bloom and post-

953

bloom phytoplankton assemblages are highlighted by circles. Percentage of total

954

variance is indicated in brackets close to principal component axes.

955 956

Figure 6 - Average metal concentrations (ug L-1) in particulate matter samples versus

957

average bioavailable metal concentrations (ug L-1) in surface water, obtained for the

958

pre-bloom, bloom and post-bloom periods, at the sampling site subject to dredging, in

959

the Sado estuary (Portugal). The line represents metal concentrations in particulate

960

matter equal to bioavailable metal concentrations in surface water.

Table 1 - Temperature (T, °C), salinity (S, PSU), dissolved oxygen (DO, %), turbidity (NTU), and suspended particulate matter (SPM, mg L-1), at surface water (0.5 m) of the sampling area (Sado estuary) during dredging, under phytoplankton pre-bloom, bloom and post-bloom conditions (average ± standard error, N=3 for SPM, N=6 for other variables.

Conditions

T (°C)

S (PSU)

DO (%)

Turbidity (NTU)

SPM (mg L-1)

Pre-bloom

18±1.28

35±0.35

111±6.17

31±1.83

6.1±0.31

Bloom

22±1.42

35±0.46

94±13.91

34±0.50

6.9±0.42

Post-bloom

22±0.20

35±0.15

89±12.85

35±0.22

5.5±0.53

Table 2 - Particulate metal to bioavailable metal ratios (metal concentration in suspended particulate matter/bioavailable metal concentration in water column) of Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb (L Kg-1 × 103), in phytoplankton collected at surface water (0.5 m) of the sampling area (Sado estuary), during dredging, under pre-bloom, bloom and postbloom conditions (average ± standard error, N=7), and Kruskal-Wallis p-values obtained by comparing averages.

Kruskal-Wallis

Particulate metal to bioavailable metal ratio

Pre-Bloom

Bloom

Post-bloom

Cr

36±5.2

95±8.9

22±6.2

0.0006

Mn

4.5±0.67

507±140

6.0±0.33

0.0035

Co

8.5±1.2

289±124

26±21

0.0028

Ni

27±3.5

417±173

28±3.4

0.0087

Cu

34±9.0

995±295

52±14

0.0065

Zn

4.9±0.99

746±50

7.1±1.6

0.0008

Cd

1.9±0.17

14±5.2

1.3±0.47

0.0022

Pb

59±19

872±136

51±16

0.0159

p-value

N PORTUGAL

SETÚBAL

Sado Estuary

S1 SADO ESTUARY 200 km

ATLANTIC OCEAN 2 km

Figure 1

40

Chl a concentration (µg L-1)

KW-H(2;24) = 19.2485; p = 0.00007

Mean Mean±SE Mean±2*SD

30 25.95

20

12.03

10

9.26

0 Pre-bloom

Bloom

Sampling conditions

Figure 2

Post-bloom

2,0

40 KW-H(2;22) = 13.8197; p = 0.0010

KW-H(2;23) = 19.1739; p = 0.00007

30

Bioavailable Dissolved Cu (µg L-1)

Bioavailable Dissolved Cr (µg L-1)

1,5

1,0

0,5

20

10

0

Mean Mean±SE Mean±2*SD

0,0 50

Pre-bloom Bloom KW-H(2;20) = 13.3889; Sampling p = 0.0012 conditions

Post-Bloom

80

Pre-bloom

Bloom

Post-Bloom

KW-H(2;23) = 15.7307; Sampling p = 0.0004 conditions

40

Dissolved Zn (µg L-1) Bioavailable

Dissolved Mn (µg L-1) Bioavailable

60 30

20

10

40

20

0 0 40

Pre-bloom Bloom KW-H(2;18) = 8.544; p = 0.0140 Sampling conditions

Post-Bloom

0,20

Dissolved Cd (µg L-1) Bioavailable

Dissolved Co (µg L-1) Bioavailable

20

10

0,10

0,05

0,00

0 Pre-bloom Bloom KW-H(2;19) = 15.4737; p = 0.0004

Post-Bloom

3,0

Sampling conditions

Pre-bloom Bloom KW-H(2;19) = 13.4667; p = 0.0012

Post-Bloom

Sampling conditions

2,5

Dissolved Pb (µg L-1) Bioavailable

0,4

Dissolved Ni (µg L-1) Bioavailable

Post-Bloom

0,15

30

0,5

Pre-bloom Bloom KW-H(2;22) = 15.3379;Sampling p = 0.0005conditions

0,3

0,2

0,1

0,0

2,0

1,5

1,0

0,5

0,0 Pre-bloom

Bloom

Post-Bloom

Pre-bloom

Sampling conditions

1.14

1.14 0.79

Bloom

Sampling conditions

Figure 3 12.76

Post-Bloom

600

100 KW-H(2;19) = 15.2286; p = 0.0005

500 -1 in phytoplankton particulate matter (µg g ) Cu in

80

-1 particulate matter (µg g ) Cr inin phytoplankton

KW-H(2;19) = 15.2947; p = 0.0005

60

40

20

400

300

200

100 Mean Mean±SE Mean±2*SD

0 400

Pre-bloom Bloom KW-H(2;18) = 14.1838; p = 0.0008

0 500

Post-Bloom

Pre-bloom Bloom KW-H(2;18) = 15.1579; p = 0.0005

Post-Bloom

Pre-bloom Bloom KW-H(2;18) = 8.3805; p = 0.0151

Post-Bloom

Sampling conditions

Sampling conditions particulate matter (µg g-1) Zn inin phytoplankton

in phytoplankton particulate matter (µg g-1) Mn in

350 300 250 200 150 100

400

300

200

100

50 0 10

0 Pre-bloom Bloom KW-H(2;20) = 16.1925; p = 0.0003

Post-Bloom

0,4

Sampling conditions

8

particulate matter (µg g-1) Cd inin phytoplankton

particulate matter (µg g-1 ) Co inin phytoplankton

Sampling conditions

6

70.0 4

2

39.4 25.9

0 25

Pre-bloom Bloom KW-H(2;19) = 14.4391; p = 0.0007

0,3

0,2

335.7 0,1

0,0

Post-Bloom

400

20

15

284.0 10

5

Post-Bloom 69.5

300

200

288.0 100

161.6

131.1 0 Pre-bloom

Pre-bloom Bloom KW-H(2;20) = 15.8653; p = 0.0004

Sampling conditions

-1 particulate matter (µg g ) Pb inin phytoplankton

in phytoplankton particulate matter (µg g-1 ) Ni in

Sampling conditions

166.7

Bloom

59.3 Post-Bloom

Sampling conditions

0 Pre-bloom

Bloom

Sampling conditions

Figure 4 6.2

0.22

81.9

Post-Bloom

PC2 (30%) 1.0

DO

WCo WCu

PZn PCu PMn PCr PCo PNi PPb PCd

WZn 0.5

WMn WNi WPb WCd

PC1 (53%)

0.0 -1.0

-0.5

0.0

0.5

1.0

1.5

Chl

WCr -0.5

Tur

Temp

-1.0

-1.5

1.0

Pre-bloom

PC2 (30%)

Bloom Post-Bloom 0.5

0.0 -1.0

-0.5

0.0

0.5

-0.5

Figure 5 -1.0

1.0

1.5

PC1 (53%)

Cconcentration of metals in particulate matter (µg L-1)

2,5

Pre-bloom Bloom Post-bloom

Cu 2,0

Mn 1,5

Pb

1,0

0,5

Ni 0,0 0

10

20

Bioavailable metal concentration (µg L-1)

Figure 6

30

40

Highlights

•

Phytoplankton bloom impact on metal bioavailability investigated during dredging

•

Bioavailable metals significantly decline when blooms and dredging coincide

•

Phytoplankton blooms appear as important biological sinks of metals during dredging

•

Manganese, Ni, Cu and Pb with potential to accumulate in food webs during blooms

Declaration of interests ☒ 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. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: