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Dissolved copper (dCu) biogeochemical cycling in the subarctic Northeast Pacific and a call for improving methodologies
Accepted Manuscript Dissolved copper (dCu) biogeochemical cycling in the subarctic Northeast Pacific and a call for improving methodologies
Anna M. Posacka, David M. Semeniuk, Whitby Hannah, Constant M.G. van den Berg, Jay T. Cullen, Kristin Orians, Maria T. Maldonado PII: DOI: Reference:
S0304-4203(16)30224-9 doi: 10.1016/j.marchem.2017.05.007 MARCHE 3456
To appear in:
Marine Chemistry
Received date: Revised date: Accepted date:
29 November 2016 24 May 2017 25 May 2017
Please cite this article as: Anna M. Posacka, David M. Semeniuk, Whitby Hannah, Constant M.G. van den Berg, Jay T. Cullen, Kristin Orians, Maria T. Maldonado , Dissolved copper (dCu) biogeochemical cycling in the subarctic Northeast Pacific and a call for improving methodologies, Marine Chemistry (2017), doi: 10.1016/ j.marchem.2017.05.007
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ACCEPTED MANUSCRIPT Dissolved copper (dCu) biogeochemical cycling in the subarctic Northeast Pacific and a call for improving methodologies. Anna M. Posackaa, David M. Semeniuka, Whitby Hannahb, Constant M.G. van den Bergb, Jay T.
Department of Earth, Ocean, and Atmospheric Sciences, University of British Columbia, 2207 Main
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a
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Cullenc, Kristin Oriansa, Maria T Maldonadoa
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Mall, Vancouver, British Columbia, Canada V6T 1Z4 University of Liverpool, United Kingdom, L69 3GP, Liverpool, UK
c
School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, Canada V8W 2Y2;
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b
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Corresponding author: Anna M. Posacka, [email protected]
Abstract
ACCEPTED MANUSCRIPT
We investigated biogeochemical cycling of dissolved copper (dCu) along the Line P transect, spanning from the coastal waters of British Columbia, Canada, to the High Nutrient, Low Chlorophyll (HNLC) open Ocean Station Papa (OSP or P26), in the subarctic Northeast Pacific. DCu concentrations ranged
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from 1.4 – 3.7 nmol kg-1 throughout the water column along the transect, and were elevated in the upper
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and bottom waters near the continental margin (< 300 m and > 1100m respectively) as well as in the
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upper waters offshore (< 300 m). These trends were attributed to the fluvial and sedimentary sources near the coast of BC, and upwelling of deep, dCu rich waters in the Alaska gyre offshore. In addition, we
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conducted a temporal investigation of dCu at OSP over three consecutive years (2010‒2012), which
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revealed dynamic variability in the top 300 m that was accompanied by elevated sub-surface concentrations, indicating Cu supply from atmospheric deposition. We explore atmospheric inputs in the
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Gulf of Alaska and suggest that they may play a significant role in moderating dCu distribution in this
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region. Consistent with previous investigations in the North Pacific, dCu distributions in the nutricline throughout the transect were strongly linked to those of phosphate and silicate. However, within the
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Northeast Pacific Oxygen Minimum Zone (OMZ) silicate and dCu distributions were noticeably
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decoupled suggesting a deficit or loss of dCu in these deep, oxygen depleted waters. In this study, we also assessed the requirement for UV oxidation of our samples (pH 2) prior to dCu analysis by flow
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injection-chemiluminescence (FIA-CL). We found that UV oxidation leads to a significant increase in labile Cu (6.5‒43%), however, the variation in this increase is largely dictated by the length of sample storage under acidic conditions. Our results suggest that UV oxidation is essential prior to FIA-CL analysis of young samples (aged for 48 h‒2 months), but may not be required if samples have been stored for an extended period at low pH (≥ 4 years). We found a good agreement between dCu values at station P26 obtained with FIA-CL and those obtained with cathodic stripping voltammetry (CSV).
ACCEPTED MANUSCRIPT However, comparison of our dataset with all published dCu profiles in the North Pacific revealed some discrepancies in dCu values in this region. Here, we briefly discuss the role of sample storage period, UV oxidation and differences in analytical methodologies as factors causing uncertainties in dCu values.
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1. Introduction
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Copper (Cu) is an essential micronutrient for marine organisms and, like other bioactive trace elements, its vertical distribution is governed by its interactions with biota. In laboratory settings, low availability
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of Cu has been shown to impair growth and metabolism of eukaryotic phytoplankton (Annett et al.,
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2008; Guo et al., 2012; Peers et al., 2005), ammonium oxidizing archaeon Nitrosopumilus maritimus (Amin et al., 2013), denitrifying bacteria (Granger and Ward, 2003) as well as marine heterotrophic
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bacterium Dokdonia sp. Dokd-P16 (Posacka et al, unpublished). Furthermore, some iron-stressed
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phytoplankton in culture have an increased metabolic demand for Cu, through the upregulation of a high-affinity iron (Fe) uptake transport system (HAFeTS) that requires Cu (Annett et al., 2008; Guo et
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al., 2012; Maldonado et al., 2006) or/and substitution of Fe-containing enzymes with Cu-containing
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homologs (Peers and Price, 2006). Thus, Cu nutrition is likely to be important in the High Nutrient Low Chlorophyll (HNLC) waters, where low Fe concentrations limit the growth of primary producers.
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Indeed, recent field work demonstrates such metabolic coupling between Cu and Fe in the HNLC waters of the subarctic Northeast Pacific (Semeniuk et al., 2016b). However, while low Cu may hinder metabolic functions, exposure to Cu levels that exceed cellular requirements causes toxicity (Brand et al., 1986; Debelius et al., 2011; Gordon et al., 1994; Mann et al., 2002; Paytan et al., 2009). Because of a narrow range where Cu supports optimal growth and the distinct Cu requirements of different taxa, Cu
ACCEPTED MANUSCRIPT bioavailability may influence the taxonomic composition of the marine microbial community (Brand et al., 1986; Mann et al., 2002; Paytan et al., 2009). In the global ocean, vertical distribution of dCu is typically characterized by low surface water concentrations that are accompanied by a vertical gradient of increasing dCu towards the ocean seafloor.
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Such a behaviour has been classified as hybrid nutrient-like (Bruland and Lohan, 2003), and may be
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attributed to the collective control of internal cycles (biological utilization and regeneration processes),
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particle scavenging in intermediate waters and fluxes from bottom sediments (Boyle et al., 1977; Bruland, 1980; Bruland and Lohan, 2003; Takano et al., 2014). Dissolved Cu concentrations in the
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interior waters of the North Pacific are enriched relative to other oceans, with concentrations being
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nearly twofold of those measured in the North Atlantic, due to global ocean circulation patterns. Profiles of dCu in different parts of the North Pacific are consistent with its hybrid nutrient-like behaviour (Boyle
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et al., 1977; Bruland, 1980; Ezoe et al., 2004; Fujishima et al., 2001; Martin et al., 1989; Takano et al.,
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2014; Tanita et al., 2015). Here, strong correlations between dCu and major nutrients (NO3-, PO43-) can be observed in the oceanic nutricline (Bruland, 1980; Martin et al., 1989), indicating a tight influence of
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biological processes on Cu distribution in the North Pacific. However, our understanding of sources and
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sinks of Cu is still fairly limited in many parts of the basin, including the Northeast subarctic region. In particular, the role of systems such as the Oxygen Minimum Zone (OMZ) of the subarctic NE
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Pacific in moderating dCu cycles is not well understood, although this zone has been proposed to affect redox-sensitive elements such as Cd, Zn as well as Cu (Janssen et al., 2014; Janssen and Cullen, 2015). NE Pacific OMZ is an intermediate water feature (~ 300‒2000 m) characterized by persistently low, although non-zero, dissolved O2 levels. Such conditions may favour scavenging of Cu via formation of insoluble sulfides precipitates, proposed to be facilitated by sulfidic microenvironments on sinking
ACCEPTED MANUSCRIPT particles in low O2 zones (Janssen et al., 2014; Janssen and Cullen, 2015). Thus, OMZ may have an important role in reducing the dCu inventory in the NE Pacific. In addition, control of atmospheric deposition on dCu in the NE subarctic Pacific also remains to be explored. This process may represent an important source of Cu across the subarctic North Pacific,
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which experiences a seasonal transport of Asian dust from desert and loess region in central Asia (Duce
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and Tindale, 1991) In addition to being the second largest source of dust in the world (Han et al., 2011),
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Asia is also a significant emitter of anthropogenic pollutants which may enhance the atmospheric content of elements, such as Cu (Lee et al., 2013). Influence of atmospheric sources on surface Cu distribution is
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evident in the profiles from the western Pacific (eg. Ezoe et al., 2004; Tanita et al., 2015). In contrast,
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little is known regarding the influence of atmospheric inputs on the dCu in the remote waters of the NE Pacific, where Asian sources may reach episodically (Boyd et al., 1998).
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Our ability to obtain reliable measurements of the dCu is instrumental to the interpretation of Cu
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cycling in aquatic environments and its impact on microbial communities. Recently, some uncertainties have emerged in dCu analysis due to the observation that UV oxidation of acidified oceanic samples
Standards
&
Reference
material
statement,
2013,
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GEOTRACES
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leads to increases in labile Cu (e.g. Biller and Bruland, 2012; Middag et al., 2015; Milne et al., 2010;
http://www.geotraces.org/images/stories/documents/intercalibration/Files/Reference_Samples_May13/SAFe_
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Ref_Cu_05_13.pdf). Typically, samples for total dCu are acidified (pH 1.7‒2) in order to dissociate any
organically bound Cu (ligands or colloids) prior to analysis. However, recent evidence suggests that some organic Cu complexes may persist under acidic conditions even after prolonged storage, thus requiring UV oxidation (Biller and Bruland, 2012; Middag et al., 2015; Milne et al., 2010). Lack of UV oxidation was used to explain the underestimation of dCu values in the GEOTRACES reference material
ACCEPTED MANUSCRIPT SAFe (D2) (e.g. Boye et al., 2012; Milne et al., 2010). However, effects of UV on labile Cu have been variable in recent investigations, and it remains unclear how best to obtain accurate dCu measurements. In the present study, we focus on characterizing Cu biogeochemistry along Line P, a coastal-open ocean zonal transect in the subarctic Northeast Pacific spanning from the continental shelf of Vancouver
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Island (BC) to Ocean Station Papa (OSP, ~ 1600 km offshore) in the Alaskan gyre, where primary
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productivity is Fe-limited (Fig. 1). Previous work there examined the role of Cu nutrition and its
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availability to resident plankton communities, the interactive control of Cu and Fe availability on Felimited phytoplankton, as well as the distribution of dCu within the mixed layer (Semeniuk et al., 2015,
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2016a, 2016b, 2009). However, there exists a dearth of dCu measurements at depth across the transect
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nor are there systematic investigations of its temporal variability. Here, we report high-resolution dCu profiles down to 2000 m at five major stations of Line P, and examine Cu behaviour in the context of
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oceanographic features encountered along this transect including: a) the Northeast Pacific Oxygen
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Minimum Zone (OMZ, 300‒2000 m); b) the coastal California Undercurrent (CUC) intruding waters at stations P4 and P12; c) the North Pacific Intermediate Water (NPIW) at station P26 (McAlister, 2015),
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and d) the offshore upwelling Alaskan gyre. In addition, temporal variability of dCu was examined over
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3 sampling cruises (in 2010, 2011 and 2012) at the offshore station P26 (OSP). In this study, we also
material
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investigated the requirement for UV oxidation of samples (GEOTRACES Standards & Reference statement,
2013,
http://www.geotraces.org/images/stories/documents/intercalibration/Files/Reference_Samples_May13/S AFe_Ref_Cu_05_13.pdf) prior to our analysis of dCu by flow injection-chemiluminescence (FIA-CL). Furthermore, we compare our results to published dCu profiles in the North Pacific and discuss variability inherent in the dataset. We propose that methodological factors explored in this study
ACCEPTED MANUSCRIPT (acidified sample storage, UV oxidation and analytical methods) likely contribute to this variability, and warrant more detailed investigation into how to improve current dCu methodologies.
2. Methodology
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2.1 Sampling and study transect
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Samples were collected at five stations along the Line P (P4-P26) during the August 17‒26th cruise on board the C.C.G.S. John P. Tully (Cruise, 2011-27) (Fig.1). For a temporal study of dCu at OSP we used
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samples from three cruises: Aug 17th to Sept 3rd 2010 (cruise 2010-14), August 17th to 26th 2011 (Cruise,
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2011-27) and Aug 13th to 30th 2012 (cruise 2012-13). We obtained 18-22 samples at each station within the depth range of 10-2000 m of the water column using a 12 bottle powder coated trace metal clean
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rosette system equipped with 12 L Teflon- coated GO-FLO bottles (General Oceanics, FL, USA)
metal
clean
500
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(Measures et al., 2008) Filtered seawater (0.22 µm AcroPak , Pall Corporation) was collected into trace mL
BellArt
bottles
(according
to
GEOTRACES
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http://www.geotraces.org/libraries/documents/Intercalibration/Cookbook.pdf).
Samples
protocols, for
total
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dissolved analysis were acidified at sea to pH 2 using 1 mL of ultrapure 12N HCl per litre of sample (Seastar Chemicals, Sidney BC). Another set of dCu samples was collected during the 2011 and 2012
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Line P cruises as described above, but were immediately frozen after filtration at ambient pH and stored at ‒20°C until analysis. Macronutrients Si(OH)4, NO3-, PO43- were analyzed according to Barwell-Clarke and Whitney (1996). Macronutrient and CTD data are courtesy of Institute of Ocean Sciences, Department
of
Ocean
&
Fisheries
(https://www.waterproperties.ca/linep/index.php). 2.2 Dissolved Cu analysis
and
are
publicly
available
ACCEPTED MANUSCRIPT Samples from the Aug 2011 cruise were initially analyzed for dCu by flow injectionchemiluminescence (FIA-CL), without prior UV oxidation (~ 3 months of sample storage at pH 2). We found a good agreement between these data and a historical profile of dCu at OSP (P26, Fig.2, Martin et al., 1989). Furthermore, our values of GEOTRACES inter-calibration standard SAFe D2 and a reference
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material NASS-6 were in good agreement with the consensus values and certified values, respectively
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(Table 1). Following a recent recommendation of UV oxidation of samples prior to dCu analysis by the
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GEOTRACES community, all the transect samples were re-analyzed between July‒August 2015 (~ 4 years of storage at pH 2) after 2 hours of UV oxidation (twice in 60 min bursts). UV apparatus used in
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this study consisted of 30 mL silica sample tubes with PTFE capping units surrounding a high -pressure
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mercury lamp (125 W) (van den Berg, 2017). Once UV oxidized, samples were left for at least 2 hours or overnight before analysis to remove the effect of hypochlorite or hypobromite, which can be produced
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by UV oxidation (Achterberg & van den Berg, 1994).
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There was a large difference between our new dCu dataset and the initial analysis at all stations across the transect (6.5‒43% more dCu), with no apparent trend (e.g. onshore-offshore, Supplementary Figure
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S1). Originally, we attributed this increase to the effect of UV oxidation. However, we also found higher
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dCu values in a small subset of non-UV oxidized samples in this most recent re-analysis. Nevertheless, UV oxidation of those aged samples was associated with a minor increase in dCu (7 out of 9 samples
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showed an increase in dCu after UV ranging 3.4-18 %, Supplementary Material, Table S1). This led us to hypothesize that both long sample storage at low pH and UV oxidation can lead to increases in FIACL labile dCu. Subsequently, we sought to investigate how the length of sample storage and UV oxidation affect Cu lability in FIA-CL analysis. For these experiments we used August 2011 samples, from different depths at station P26, which were not acidified, but instead were stored at – 20°C until 2015. After acidification to pH 2, corresponding measurements of dCu in UV and non-UV oxidized
ACCEPTED MANUSCRIPT sample aliquots were performed at different time points (48 h, 2 weeks and 2 months) and compared to the 2015 data. Next, we compared FIA-CL measurements of dCu at P26 (2015 re-analysis, 4 years of storage and 2 hr of UV oxidation) with values obtained by CSV (no storage, 45 mins of UV oxidation), and previous dCu measurements by Semeniuk et al. (2016a). Lastly, we compared our results with other
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published dCu datasets in the North Pacific.
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2.2.1 Flow injection analysis, chemiluminescence (FIA-CL)
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For measurements of the total dissolved Cu we used a flow injection analysis, chemiluminescence (FIA-CL) method of Zamzow et al (1998). This method is based on the oxidation of a complex formed
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between Cu and 1,10-phenanthroline in the presence of hydrogen peroxide. A working standard of Cu
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was prepared in MilliQ from 1000 ppm Cu ICP-MS standard, and was used to quantify dCu in the samples by the standard additions method. A five-standard calibration curve was acquired every 8‒10
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samples. Each day a number of samples previously analyzed (≥ 5) were run to determine the precision of
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the method. Precision based on percent relative standard deviation (% RSD) of n = 36 replicate sample was 2.93 % RSD, median = 1.36 % RSD, and range = 0.14 – 10.2 % RSD. The accuracy of the method assessed
by
analyses
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was
of
SAFe
D1
&
D2
(GEOTRACES
reference
samples,
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http://www.geotraces.org/science/intercalibration/322-standards-and-reference-materials) and NASS-6 (National Research Council Canada). Additionally, GEOTRACES inter-calibration sample GSC 318 (for which there is no current consensus) was also analyzed (Table 1). We run the GEOTRACES intercalibration standards with and without UV oxidation. All reference materials were in good agreement with the consensus values and we found no statistical difference between UV oxidized and non-UV oxidized SAFe samples (t-test, p=0.582). System blanks for FIA-CL were 0.12 ± 0.022 nmol kg-1 (n=14), yielding a detection limit (3 × blank standard deviation) of 0.064 nmol kg-1. Data was plotted
ACCEPTED MANUSCRIPT using packages “m_map” (Pawlowicz, 2015) in commercial software Matlab (The Mathworks, Inc, Natick, Massachusetts, United States), Sigmaplot (Systat Software, San Jose, CA), and “ggplot2” (Wickham, 2009) in open source programming language R (R Core Team, 2016). The dCu dataset along with the ancillary data, as well as scripts used to produce the plots in this study can be downloaded from
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a public repository: https://github.com/AnnaMagdalena/DCu_LineP-Subarctic-Pacific.
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2.2.2 Cathodic Stripping Voltammetry (CSV)
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Cathodic Stripping Voltammetry (CSV) was performed on non-acidified samples collected during the
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August 2012 cruise. Samples were defrosted overnight at 4C, swirled gently to re-dissolve any particulate matter formed during freezing, and poured into conditioned sterilin containers to warm up to
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room temperature (20C) in the dark. Total dissolved copper was measured by CSV at natural pH. In
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addition, surface (10 m) and deep (1200 m and 1400 m) samples were analyzed by anodic stripping
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voltammetry (ASV) at pH 1.9, allowing inter-comparison with reference seawater of the same pH (NASS-6). UV digestion at natural pH avoids the formation of hypochlorite which interferes with CSV
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Cu determinations (Campos and van den Berg, 1994). For CSV measurements, the sample was poured
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into conditioned silica 40 mL UV tubes or into a conditioned silica cell. UV oxidation was performed for 45 minutes using the same UV apparatus as for the FIA-CL method, with the lamp either surrounded by four 40 mL sample tubes with PTFE caps, or positioned horizontally above a sample aliquot in the voltammetric cell. Dissolved copper was then measured in the cell by CSV in the presence of 20 μM salicylaldoxime (SA) and 0.01 M borate/ammonia pH buffer (pH 8.15 on NBS scale) (Campos and van den Berg, 1994) The borate/ammonia pH buffer (1 M boric acid/0.3 M ammonia) was UV oxidized to remove organic matter and contaminating metals were removed by equilibration with 100 μM
ACCEPTED MANUSCRIPT manganese dioxide (MnO2) followed by filtration (van den Berg, 1982). Sample was purged with N2 for 5 minutes to remove dissolved oxygen prior to analysis. The voltammetric equipment was a μ-Autolab III potentiostat (Ecochemie, Netherlands) connected to a 663 VA stand (Metrohm) with hanging mercury drop electrode (HMDE). The system used a glassy carbon counter electrode, an Ag/AgCl
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reference electrode with a 3 M KCl salt bridge, with a rotating polytetrafluoroethylene (PTFE) rod for
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stirring solutions. The software was modified to discard 2, instead of the usual 4, drops of Hg between
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scans, to minimize Hg usage. CSV measurements were in the differential pulse mode, at a deposition potential of -0.15 V, a deposition time of 30 s, and a 1s potential jump to -1.2 V to desorb any residual
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organic matter. For ASV measurements, acid-cleaned quarts UV tubes were not conditioned, and prior to
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UV digestion, the sample was acidified to pH 1.9 with trace metal grade HCl. Voltammetric scans for ASV used the square-wave mode with a 5-minute deposition time, with no reagents added. Comparative
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measurements between CSV and ASV were found to give the same result within the standard deviation
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of three repeat measurements, and ASV measurements on NASS-6 reference material gave results within
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5% of the certified value (3.94 ± 0.32 nmol L-1 versus 3.90 nmol L-1 for NASS-6).
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3. Results
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3.1 Methodological considerations of dCu analyses 3.1.1 Effect of storage time and UV oxidation on labile dCu. Time dependent analysis of dCu in non-UV oxidized acidified samples demonstrated that labile Cu increases progressively with the length of storage period (48 h‒2 months, Fig 3, dataset in Supplementary Material, Table S2). Subjecting those same samples to UV oxidation generated an additional increase in labile Cu at each time point, which was greatest for the ‘youngest’ samples (~ 40
ACCEPTED MANUSCRIPT % offset between UV and non-UV oxidized samples at 48 h). However, the amount of Cu released by UV oxidation decreased when measurements were performed after a longer storage, either 2 weeks or 2 months (~ 6‒30%). After 2 months of acidified sample storage, the profile of dCu after UV oxidation is on par with the more recent analyses done in 2015 (~ 4 years of storage with 2 h of UV oxidation),
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although upper water column samples are still somewhat lower (50 m sample ~24 % lower; 75 m sample
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~11% lower than samples for these depths).
3.1.2. Comparison of datasets obtained by FIA-CL, CSV and the historical data at OSP.
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Measurements of dCu at P26 performed with distinct sample pre-treatment, hardware and modes of
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detection (FIA-CL and CSV) were in good agreement, particularly at depths below 250 m (Fig. 4A, Supplementary Material, Table S3). However, there is some disparity between the two profiles in surface
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waters (0‒250 m), likely due to the seasonal variation in dCu, as samples analyzed by CSV were
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collected in Aug 2012, while those analyzed by FIA-CL were collected in Aug 2011. Our dataset is also in good agreement with the surface dCu measured along the transect (P4‒P26) by Semeniuk et al
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(2016a) for the same cruise and analytical method. The various measurements are linearly correlated (p
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=<0.001 Fig 4B). In contrast, dCu profiles at P26 from our most recent analysis (2015) are noticeably offset from the historical data from Martin et al (1989) (Fig. 2). The differences between the two profiles
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are up to 40 % in the surface and approximately 14 % at depths below 250 m, likely reflecting differences in methodological approaches. 3.1.3 Comparison of published dCu profiles in the North Pacific. To compare published vertical profiles of dCu in the North Pacific we grouped data into oceanographically similar regions (Region 1‒7, Fig. 5). Within these regions, we see substantial disagreement between studies with dCu differences of ~10-100 %, even for profiles collected at the same
ACCEPTED MANUSCRIPT sampling location. For instance, at Ocean Station Papa (Region 3) our dCu values are 15‒40 % and ~ 60 % higher than those of Martin et al (1989) and Coale and Bruland (1990), respectively. Interestingly, the results for these two published studies disagree even though samples were obtained during the same cruise (Vertex VII) and analyzed with a similar method, although with differences in filtration (0.45 and
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0.3 µm for Martin et al (1989) and Coale and Bruland (1990), respectively). However, we also note
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some agreement between studies when comparing stations farther away (note that not all vertical dCu
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profiles are plotted). The profiles from Tanita et al. (2015) (station 22, profile not plotted; station 5) and Moffett and Dupont (2007) (stations 6 and 4) agree with each other and are more similar to the dCu
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values reported in this study. On the other hand, the results of Fujishima et al (2001) (station 19, profile
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not plotted; station 17), Takano et al., (2014) and Ezoe et al (2004) (stations BO01‒07 stations, profiles not plotted) agree with one another, but are much lower than our dCu values. Finally, at the
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GEOTRACES inter-calibration station SAFe (region 6) there is an excellent agreement between the
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analyses of Bruland (1980), Coale and Bruland (1990), and Biller & Bruland (2012). While in the near surface waters these differences likely reflect temporal variability in dCu, this is
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unlikely to be the cause for the variations in the deep-water values of nearby stations. Thus,
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methodological differences such as the analytical approaches used, sample storage time at low pH, filtration and whether a UV step was utilized or not, may help explain these differences (methodological
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details of each study are listed in Supplementary Material, Table S4). For instance, the dCu profile at OSP from Martin et al (1989) was obtained using non-UV oxidized samples and is similar to our profile also obtained without UV oxidation (2012 analysis, ~ 3 months of sample storage). In contrast, the difference between UV and non-UV treated samples is not observed when the datasets of Bruland (1980) at station 17 and Biller and Bruland (2012) at SAFe are compared (ADPC-DDC without UV and
ACCEPTED MANUSCRIPT NOBIAS P1A resin extraction with UV, respectively). Thus, UV oxidation alone is not the sole cause of these disagreements.
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3.2 Copper biogeochemistry in the subarctic Northeast Pacific.
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3.2.1 Line P hydrography.
Surface waters along Line P are characterized by low salinity and a strong density stratification (Fig. 6),
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with a mixed layer of ~ 20‒30 m (Fig. 7). In the eastern reaches of the transect (P4 and P12), the
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permanent pycnocline is centered on the σt= 26.5 kg m-3, and is shifted to the σt= 26.8 kg m-3 in the water column of station P26. Coastal stations (P4‒P12) are influenced by the California Under Current
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(CUC) along σt = 26.5 – 26.8 kg m-3 (Pierce et al., 2000) characterized as warm and salty (McAlister,
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2015). At the western terminus of the transect, waters along the σt = 26.8 kg m-3 (P26) represent the fresher and cooler waters of the North Pacific Intermediate Water (NPIW) (McAlister, 2015; Talley,
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1993). In this system, an oxygen minimum zone (OMZ) extends from ~ 300‒2000 m with O2
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concentrations between ~7 – 60 µmol kg-1, with the lowest O2 occurring at depths ~ 1000 m.
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3.2.2 General trends in dCu along Line P transect.
Vertical profiles of dissolved Cu along the transect were typical of a nutrient-like element with surface depletions relative to deep-water and increasing concentrations with depth (Fig. 7A & B), consistent with dCu behaviour in other ocean provinces (Boye et al., 2012; Heller and Croot, 2015; Jacquot and Moffett, 2015; Martin et al., 1989; Vu and Sohrin, 2013). Surface dCu concentrations were highest at station P4 (2.16 nmol kg-1), gradually declined towards P16 (1.49 nmol kg-1), and increased west of P16
ACCEPTED MANUSCRIPT towards stations P20 and P26 (1.78 and 1.83 nmol kg-1, respectively). This surface dCu trend is consistent with a previous investigation along Line P during the same cruise (Semeniuk et al., 2016a). In contrast, the onshore-offshore trend in deep water dCu (> 300 m) was characterized by increasing concentrations from P4 to P12 followed by a decline at P16, and minor dCu increases at P20 and P26
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(Fig. 7B). In the upper 300 m, patterns of dCu distribution matched well with those of PO43- at the
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offshore stations P16, P20 and P26. In contrast, at the two stations closer to the coast (P4 and P12), dCu
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levels were in excess of PO43- at the shallowest depths (0‒40 m). Vertical distributions of dCu and PO43followed the density trend at P26 with a summer (0‒29 m) and winter mixed layers (50‒130 m) derived
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from sigma-t profiles (Fig. 7A). As such, the presence of two ‘cuproclines’ at this station can be
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observed; one between 20‒75 m and another between 135‒200 m.
Deep dCu profile values at P12 (400‒2000 m) were higher (2.99‒3.72 nmol kg-1) than those at P16,
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P20 & P26 (2.74‒3.48 nmol kg-1) (Fig. 7B). Across the transect, dCu levels within the OMZ (~ 300–
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2000 m with O2 concentrations ranging between ~7 – 60 µmol kg-1) were consistent. However, sharp
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increases in dCu can be seen below 1000 m at P4, likely reflecting sedimentary sources.
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3.2.3 Annual variability in dCu values in upper waters at Ocean Station Papa (OSP, P26).
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Dissolved Cu distribution at the offshore station P26 during three consecutive August Line P cruises: 2010‒2012 were characterized by dynamic shifts in dCu in the upper 300 m (Fig. 8), while below this depth dCu patterns and concentrations remain relatively homogenous (Supplementary Material, Table S3). Between 40 and 200 m, dCu levels were typically lower in 2010 (≤ 2 nmol kg-1), than in 2011 and 2012 (> 2 nmol kg-1). Surface enrichments in dCu at P26 were observed in 2010 and 2012 data, with values of 2.3‒2.4 nmol kg-1 (5‒10 m) relative to 1.83 nmol kg-1 in 2011 (10 m) (Fig. 8, Supplementary Material, Table S3). This could be an indication of an atmospheric deposition event in 2010 and 2012.
ACCEPTED MANUSCRIPT The shape of the dCu profile in 2011 can be partially explained by the density structure and agrees somewhat with the distribution of phosphate, while this is not observed in 2010 and 2012. However, lower sampling resolution for the latter years limit our ability to relate water column structure and dCu
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distribution.
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3.2.3 Dissolved Cu and macronutrient relationships across the transect.
Metal-macronutrient relationships are useful tools for examining the importance of both biological and
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abiotic processes in influencing the distribution of hybrid type metals such as Cu. As such, strong metal-
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macronutrient correlations are suggestive of trace metal distribution being largely driven by biological processes (uptake and remineralization), while deviations from those correlations imply abiotic effects
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on metal distribution (e.g. particle scavenging). Here, we observed a strong correlation of dCu with PO43-
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and Si(OH)4 in the upper 400 m of the transect (P4‒P26, [dCu] = 0.5533[PO43-] + 1.26, r2 =0.826; [dCu] = 0.016[Si(OH)4] + 1.69, r2=0.72., Fig. 9A & B, respectively). However, dCu distribution in deeper
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waters was uncoupled from that of both macronutrients. In the Cu-PO43- relationship, dCu values below
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400 m (~2.7 µmol kg-1 PO43-) begin to increase abruptly above the nutricline trend, suggesting an excess of dCu concentrations relative to phosphate (Fig. 9A). On the other hand, decoupling between dCu and Si begins at a shallower depth (300 m) with Cu values falling well below the nutricline trend (Fig. 9B). A noticeable plateau can be seen in copper-silicate relationship between ~ 75 µmol kg-1 to ~ 150 µmol kg-1 Si(OH)4 (~ 300‒1600 m), suggesting scavenging of dCu in the deeper waters. 4. Discussion
ACCEPTED MANUSCRIPT This discussion is divided into 2 distinct sections. First, we discuss methodological aspects accounting for variability among dCu values from different laboratories and analytical methods (section 4.1), followed by a section focusing on the biogeochemistry of dCu in the NE Pacific Ocean (section 4.2).
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4.1 Methodological uncertainties of dCu analysis
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The variability in the North Pacific dCu datasets we highlight here, indicate that there is some uncertainty regarding the concentration and distribution of Cu in this basin, and perhaps in other oceans.
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There have been other reports on the variability in dCu analysis in recent literature, and this has so far
2012;
GEOTRACES
Standards
&
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been attributed primarily to UV oxidation (e.g. Milne et al., 2010; Biller and Bruland 2012; Boye et al., Reference
material
statement,
2013,
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http://www.geotraces.org/images/stories/documents/intercalibration/Files/Reference_Samples_May13/SAFe_
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Ref_Cu_05_13.pdf). However, as we explored here, dCu results can also be largely influenced by sample
storage time and the choice of analytical method. Below, we discuss the interplay between these three
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4.1.1 UV oxidation
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factors and make a call for an improvement of current dCu analyses.
Recent studies, examining the importance of a UV oxidation step to obtain quantitative detection and accurate dCu, report variable recovery of Cu from acidified seawater samples. For instance, a study by Milne et al., (2010) found ~ 10% increase in labile Cu in the SAFe D2, Biller and Bruland (2012) report ~ 30 % increase in an offshore California Current sample, while we found between 6.4 to 43 % increase in P26 samples following UV oxidation, depending on acidic storage period. In a recent inter-
ACCEPTED MANUSCRIPT comparison study at Bermuda Atlantic Time Series station, dCu measurements throughout the water column obtained with and without UV oxidation were compared by two separate labs (Middag et al., 2015). Increases in labile Cu due to UV oxidation were on average 12 % and 16 % for US and Netherlands GEOTRACES labs, respectively (Middag et al. 2015). This variability could be partially
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explained by differences in sample origins (and hence the amount of organic compounds), and UV
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oxidation conditions.
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In UV oxidation, the interaction between UV light with dissolved oxygen (DO) and water leads to production of ozone, superoxide radicals (• O2- ) and hydroxyl radicals (• OH) (Achterberg and van den
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Berg, 1994). The superoxide will ultimately disproportionate to hydrogen peroxide (H2O2), unless it
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reacts with redox-active species. The energetic UV radiation combined with the superoxide acts to break down dissolved organic matter, ultimately to CO2 and water. A side-reaction of the superoxide with
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chloride (abundant in seawater) leads to formation of hypochlorite (ClO -) (Haag and Hoigne, 1983).
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Hypochlorite formation and the radiative heating of the water lead to a rapid decrease in the residual concentration of DO, which is the end of the oxidative destruction of organic matter, typically after 30 to
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45 minutes of irradiation (Achterberg and van den Berg, 1994). Once the DO in the sample is exhausted
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H2O2 may be added to continue the process. The efficacy of UV oxidation is influenced by several factors such as the intensity of the UV lamp, the distance between the UV lamp and the sample, as well
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as the sample volume irradiated. For instance, using on-line UV oxidation methods where surface-tovolume ratio is high requires much shorter UV exposure time to destroy DOM than batch techniques (minutes vs hours, respectively; e.g. Achterberg and van den Berg (1994) and Achterberg et al.,(2001)). Thus, there is a need to establish consistent UV oxidation protocols for future studies. In our experiments, dCu values obtained after UV oxidation of samples stored in acidic conditions for 48 h and 2 weeks were substantially lower relative to the values obtained after ~ 4 years of acidic storage
ACCEPTED MANUSCRIPT with UV oxidation. A possible explanation for this could be that early in the sample storage period, when Cu complexation by organics is expected to be highest, our UV oxidation process was not effective. This may explain why we did not observe any significant increases in labile Cu following UV oxidation using longer exposure time (4 h on samples from 50 m and 75 m stored for 2 months, Supplementary Material,
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Table S2). Further investigation is required to determine whether adding H2O2 or O2 to these ‘young’
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samples would cause greater increase in labile Cu after UV oxidation. However, given the low DOC
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content of waters along Line P (80‒100 µmol kg-1, Wong et al., (2000)) addition of H2O2 should not be required (Achterberg and van den Berg, 1994). We considered that the differences between dCu values
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of short-term and long-term stored acidic samples (48 h‒2 months vs 4 years) could be caused by Cu
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leaching from sampling bottles during storage. While we were not able to directly test for Cu leakage from sampling bottles, we eliminated this possibility on the basis that we found an agreement between
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4.1.2 Sample storage period
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years) and CSV (non-acidified and frozen).
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the dataset obtained in 2015 for station P26 with two different methods: FIA-CL (stored acidified for 4
As we demonstrated here (using FIA-CL analytical method for dCu), changes in labile Cu in acidified
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samples are highly dependent on their storage time. Long storage at low pH promotes the release of Cu from organic complexes in samples, which introduces a potential for variable results depending on the time from collection to analysis. This has implications for whether a UV oxidation of samples is necessary prior to analysis. SAFe reference materials were collected and acidified in 2004, thus were much older than our Line P samples (stored in acidic conditions for 48 h‒4 years). We found that the consensus value for dCu in the SAFe D2 agreed with the value from our initial analysis in 2011 without UV oxidation (at this point SAFe D2 sample age was ~ 7 years), and with our values in the 2015
ACCEPTED MANUSCRIPT analyses, with and without UV oxidation. Similarly, Tanita et al., (2015; using CSV analytical method for dCu) and Butler et al. (2013; using solid-phase extraction with Toyopearl AF chelate 650 M, followed by ICPMS analytical method for dCu) without UV oxidation, obtained values on par with the consensus value (Supplementary Material, Table S4). In contrast, Milne et al. (2010; using pre-
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concentration step with Toyopearl AF chelate 650 M resin, followed by isotope dilution/ICPMS) did
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find a small increase (~ 10 %) in labile Cu in UV irradiated SAFe D2 sample (collected from 1000 m),
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despite its long storage. Taken together, these studies suggest that long storage period potentially leads to degradation of organic complexes binding Cu in the SAFe sample, and values for dCu in agreement with
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each other, though some differences may emerge due to specific analytical methodologies (discussed in
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4.1.3). The hypothesis that aging of samples can destroy inert Cu is further supported by our observations of minor (if none) increases in labile dCu in samples from station P26, stored for ~ 4 years
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and UV oxidized relative to non-UV oxidized (Supplementary Material, Table S1). Our study suggests
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that UV oxidation is critical for the determination of dCu by FIA-CL in acidified samples stored for ≤ 3 months, but may not be required for samples stored for more than ~ 4 years.
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Middag et al. (2015) compared dCu values at the Bermuda Atlantic Time Series station, with and
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without UV oxidation, obtained by two research groups (US GEOTRACES and Netherlands GEOTRACES), and analyzed with the same method (solid-phase column extraction (Nobias-chelate
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PA1) with ICP-MS, Biller and Bruland, 2012). As in our study, UV oxidation enhanced labile Cu in acidified samples. However, the percent enhancement was not significantly different between the two datasets (US group: mean = 11.7 ± 10.1 %; Netherlands group, mean = 16.2 ± 10.5 %, two sample t-test, p-value=0.15), even though their samples were stored for different time (1 and 2 years, respectively). This suggests that 2 years of acidic storage may not be sufficient to destroy all the inert dissolved Cu.
ACCEPTED MANUSCRIPT Interestingly, for several samples in their study (Middag et al., 2015), dCu values with and without UV oxidation were within analytical uncertainty. Similarly, we found no significant change in dCu values for a small number of samples after UV oxidation, coupled with ~ 4 years’ acidic storage (Supplementary Material, Fig. S1). Finally, Milne et al. 2010 found that UV treatment had no effect on
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the dCu concentration in SAFe D1 sample (also collected from 1000 m as D2 sample). These
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inconsistent results with the idea that UV oxidation is needed for accurate dCu determinations, especially
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in young samples, may be exceptions, and may reflect differences in analytical methods or simply nature
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of the samples.
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4.1.3 Role of methodologies in current dCu uncertainties.
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Methodologies employed in dCu analysis include: solid phase extraction with quantification via either standard additions or external calibration (e.g. Biller and Bruland, 2012; Lohan et al., 2005; O’Sullivan
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et al., 2013; Sohrin et al., 2008) or isotope dilution (Lagerström et al., 2013; e.g. Lee et al., 2011; Milne
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et al., 2010), electrochemical methods (e.g. Buck and Bruland, 2005; Campos and van den Berg, 1994), and flow injection analysis methods, chemiluminesence methods (e.g. Achterberg et al., 2001; Zamzow
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et al., 1998). These methodologies and their variations differ in terms of their recovery of Cu (and its species) in the seawater sample. For instance, analysis by cathodic stripping voltammetry (CSV) using a ligand with lower Cu-complex stability, such as tropolene, can cause greater underestimation of the total dCu, than the use of stronger ligands such as oxine in non-UV treated seawater (Achterberg and van den Berg, 1994). For solid phase extraction methods, underestimation of dCu may result from the need to buffer the sample to a higher pH, possibly allowing re-complexation of Cu with organic ligands
ACCEPTED MANUSCRIPT (Ndung’u et al., 2003); although, it has been suggested this may be prevented with the use of in-line buffering (O’Sullivan et al., 2013). Thus, in addition to storage time of acidified samples, analytical methodologies may introduce another source of variability in dCu values. Indeed, our comparison of two distinct methodologies in terms of sample preparation and mode of
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detection for dCu analysis supports this. Here, the values for dCu obtained with FIA-CL and CSV
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methods agreed only when samples for FIA-CL analysis were stored for ≥ 2 months and UV oxidized.
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This suggest these two methods have distinct efficiencies of Cu recovery from the sample. In the FIACL system, mostly labile and some weakly associated Cu species are measured, due to the short reaction
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time (fraction of a second) with the synthetic ligand 1,10 phenanthroline (48 µM) before detection
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(Zamzow et al., 1998). On the other hand, in CSV system there is a longer reaction time (4‒5 min) between Cu and the competing ligand (SA, 20 µM), which may result in greater Cu acquisition from the
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organic Cu pool. The higher efficiency of CSV is further supported by the similarity between the values
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for dCu in a non-UV treated sample (from 100 m at P26) analyzed by CSV (1.6 ± 0.1 nmol kg-1) and that for same sample, aged for ~ 3 months (non-UV oxidized), and analyzed by FIA-CL (1.5 ± 0.05 nmol
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kg-1). Overall, our results indicate that CSV is far more efficient at accessing the strongly complexed
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organic Cu than the FIA-CL, and that for accurate measurements of dCu, in addition to UV oxidation aging of samples is required for the latter method.
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In recent studies, underestimation dCu values were mainly explained in terms of UV oxidation (Biller and Bruland, 2012; Boye et al., 2012; Milne et al., 2010). However, we surveyed recent dCu studies that employ the GEOTRACES inter-calibration standards, and observed variability that cannot be fully explained by the presence or absence of a UV oxidation step (Supplementary Material, Table S5). For instance, some methods obtain reference material values within the current GEOTRACES consensus value without UV oxidation (Butler et al., 2013; Jacquot et al., 2013; Jacquot and Moffett, 2015;
ACCEPTED MANUSCRIPT Lagerström et al., 2013; Lee et al., 2011; Vu and Sohrin, 2013), while others underestimate the reference value if UV oxidation is omitted (Boye et al., 2012; Milne et al., 2010; Sohrin et al., 2008). Methods incorporating isotope dilution (ID) may underestimate dCu in non-UV oxidized samples (e.g. Milne et al., 2010, Boye et al., 2012), despite the recommendation that this analytical method does not require UV (GEOTRACES
Reference
&
Standards
Statement,
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oxidation
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http://www.geotraces.org/science/intercalibration/322-standards-and-reference-materials).
2013, In
isotope
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dilution methods, unlike those based solely on solid phase partitioning, extraction efficiency of metals is not crucial for metal quantification (Milne et al., 2010). However, in principle metals must be in an
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isotopically exchangeable form before equilibration with the isotope spike for ID methods. Otherwise,
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the isotopic ratio measured in the sample will not reflect the ratio of the spiked sample, and consequently the original sample concentration will be underestimated. Thus, attention should be paid to dCu sample
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pre-treatment (storage and UV oxidation) prior to analysis with ID methods.
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Given the methodological issues explored here and the observed variability in dCu values in the literature, we believe that there is an urgent need to establish an optimal and consistent treatment of dCu
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samples. Future studies should: 1) compare dCu values with different analytical methods using acidified
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samples of the same age; 2) establish standard UV conditions to be used in future studies (intensity, time, use/no use of H2O2), and which methods may require this treatment; 3) address the identity of the inert
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Cu species in acidified samples (e.g. colloidal vs dissolved Cu-L species).
4.2 Copper cycling in the subarctic Northeast Pacific Although exploration of Cu cycling along Line P is relatively new, a number of processes are known to moderate the distribution of nutrients and other trace metals (e.g. Cd, Fe, Zn, Ag, Mn, Ga, Pb) in this region, and are likely to influence Cu as well. These include: coastal upwelling, river discharge (Whitney
ACCEPTED MANUSCRIPT et al., 2005), atmospheric deposition (Boyd et al., 1998; Hamme et al., 2010), onshore-offshore shelf transport (Cullen et al., 2009; Lam et al., 2006), and mesoscale eddies (Brown et al., 2012; Johnson et al., 2005; Xiu et al., 2011). Trace metals could also be sourced to this region via water masses penetrating the western and eastern reaches of the Line P transect (CUC (P4‒P12) and NPIW (P26),
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respectively, McAlister, 2015). In addition, recent work has shown that the extensive Oxygen Minimum
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Zone (300-2000 m) of the subarctic NE Pacific plays an important role in the cycling and speciation of
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redox-sensitive metals such as Cd, Zn, Ag and Fe (Janssen and Cullen, 2015; Kramer et al., 2011;
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Schallenberg et al., 2015), and may also affect Cu (Janssen et al., 2014).
4.2.1.1 Water masses and continental shelf
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4.2.1 Controls of dCu concentrations in the upper waters (< 600 m)
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The continental shelf of BC (~ 150‒200 m) is an important source of both dissolved and particulate Fe
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that can be transported long distances offshore (Cullen et al., 2009; Lam et al., 2006; Schallenberg et al., 2015). In addition, coastal stations of Line P are influenced by the intrusion of warm and salty waters of
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the California Undercurrent (CUC, 150‒200 m), which acts as a source of metals such as Ga to stations
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P4 and P12 (McAlister, 2015). In this study, however, we did not observe dCu enrichments along isopycnals surfaces associated with either the CUC or the continental shelf (~ 200 m, σt = 26.5-26.8, Fig.7A). While Cu is known to have strong sedimentary source, it also has a strong affinity for organic matter (Fischer et al., 1986). Inputs from the CUC or the continental shelf could be reduced by scavenging of Cu near the coast of BC where productivity in the summer is high (Peña and Bograd, 2007). Instead, remineralization processes appeared to be the dominant source of dCu in waters between 40‒400 m as maxima were mostly associated with the bottom of the nutricline. This depth also coincides
ACCEPTED MANUSCRIPT with the upper boundary of the Oxygen Minimum Zone (~ 400 m) in this region (Fig.7B). Thus, our data suggests that neither the continental margin nor advected water masses were major sources of dCu to Line P.
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4.2.1.2 Sources of dCu to the mixed layer along the transect.
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The mixed layer distribution of dCu along Line P was examined in relation to that of dissolved
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aluminum (dAl) (Cain, 2014, Fig. 10A & B), a proxy for atmospheric deposition and continental sources. The gradual decline of dCu from the coastal station P4 to the offshore station P16 (2.19 nmol
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kg-1 and 1.6 nmol kg-1, respectively) is in agreement with the dAl trend. This suggests that North
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American continental sources, such as mineral aerosol deposition or fluvial runoff, may partially explain the elevated dCu at our coastal stations. A recent study suggests that fluvial inputs, rather than
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atmospheric deposition, from North America are more significant sources of Pb to stations P4 and P12
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(McAlister, 2015). Furthermore, Semeniuk et al., (2016a) found a decreasing gradient of surface dCu (7‒10 m) with increasing salinity between coastal station P3 and offshore station P16, suggesting that
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indeed fluvial sources are most likely responsible for the dCu trends observed near the coast.
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Westward of station P16, the latitudinal trends of dCu and dAl in the mixed layer diverge, with dAl continuing its east-west decline (> 3-fold between P4 and P26), while dCu starts to increase towards P26
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(OSP) (~ 0.4 nmol kg-1 between station P16 to P26). Low dAl concentrations at OSP indicate the relatively small atmospheric inputs to this station. Thus, the disparity in the dAl and dCu trends at the offshore stations (P20 and P26) suggests that atmospheric deposition alone is unlikely to account for the increasing dCu towards OSP. Instead, upwelling of dCu rich waters in the Gulf of Alaska (GoA) is likely driving this trend (Fig. 11). Upwelling would bring high dCu concentrations to the surface while lowering dAl, as deep waters are typically depleted in dAl, due to its short residence time (Orians and Bruland, 1985).
ACCEPTED MANUSCRIPT Transport of copper by mesoscale eddies is also a possible mechanism for increased dCu levels offshore, however satellite altimetry anomalies did not indicate the presence of an eddy near P26 during our study (Semeniuk et al., 2016a). Hence, we propose that the trend of increasing dCu levels towards the offshore stations is largely controlled by the upwelling, which is in agreement with previous
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explanations for the high surface water dCu in the Gulf of Alaska (Vertex VII, Martin et al., 1989).
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Using a simple-one dimensional model, we estimate that upwelling is responsible for supplying
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0.58‒1.95 nmol L-1 y-1 dCu to the euphotic zone at P26, assuming the summer and winter mixed layer of
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30 and 100 m, respectively (Supplementary Material, Table S7).
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4.2.1.3 Temporal variability of dCu at Ocean Station Papa (P26). We showed that dCu concentrations in upper waters at OSP are temporally variable and identified sub-
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surface dCu maxima in Aug 2010 and 2012, that are suggestive of atmospheric inputs (Fig. 8). To
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explore the possibility of atmospheric aerosol deposition of Cu at OSP, we used Aerosol Optical Depth measurements (AOD, λ=550 nm) by Moderate Optical Imaging Spectroradiometer (MODIS) on NASA's
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Aqua satellite, a proxy for aerosol concentration in the atmosphere. The area averaged AOD plots
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between July-August for all three years indicate the presence of elevated atmospheric aerosol concentrations in the western region of GoA near OSP and diminishing levels towards North America (Fig. 12). The potential deposition of these atmospheric aerosols could explain high dCu in the subsurface waters at OSP in 2010 and 2012, but would not corroborate with the lower values measured in 2011. It is possible that atmospheric Cu inputs occurred in 2011 as well, but that we did not observe any effects on dCu surface distribution that year due to either Cu consumption by the resident community or the low solubility of Cu in deposited material.
ACCEPTED MANUSCRIPT The identification of the aerosol plumes we observed in the GoA requires further study. However we take an opportunity to examine their potential origins by assessing the atmospheric sources that are known to influence this region including : glacial flour from coastal Alaska (Crusius et al., 2011), volcanic ash from the Aleutian Islands (Hamme et al., 2010) and Asian dust (Bishop et al., 2002; Boyd
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et al., 1998). Deposition events associated with these sources may enhance local primary productivity,
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likely by inducing Fe-fertilization (Bishop et al., 2002, Hamme et al., 2010). Yet, due to the episodic
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character of such events, as well as their dependence on prevailing wind patterns (Takeda, 2011) and local hydrology (Crusius et al., 2011), deposition of these sources to surface waters in the GoA is highly
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unpredictable, while their metal content remains unknown. It is unlikely that the high AOD we observed
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in this study was associated with the glacial flour from river valleys in Alaska as its transport tends to occur in autumn, when coastal river levels are low and riverbed sediments are exposed (Crusius et al.,
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2011). While major eruptions from Aleutian Island volcanos can provide a spectacular amount of air-
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borne ash into the GoA as seen during the Kasatochi volcano eruption in August 2008 (Hamme et al., 2010), such events did not occur during our study. We did note, however, that some eruptive activity
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associated with small ash cloud formation for Mt. Cleveland volcano were reported in June 2010 and
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July 2012 (Herrick et al., 2014; Neal et al., 2014), but the role of this atmospheric source requires further exploration. A possible source of atmospheric inputs prior to our sampling may be dust from Asia,
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typically reaching the remote waters of the subarctic NE Pacific in early spring and summer (Uematsu et al., 1983). Asian dust originates from arid and semi-arid desert area in Northern China, the second largest dust source in the world (Han et al., 2011) contributing ~ 400 Tg dust to the North Pacific and beyond (Zhang et al., 1997). Indeed, the deposition of Asian sources in the GoA is supported by the distinct signature of lead isotopes measured in the mixed layer at OSP (McAlister, 2015).
ACCEPTED MANUSCRIPT Our observations suggest that surface dCu at OSP may be moderated via atmospheric deposition events in the Gulf of Alaska. There is a need to quantify dCu inputs from such events, which are likely to vary in time and space along the Line P transect. Indeed, the Hovmoller-longitude time series of monthly averaged AOD levels over the area covering Line P indicate annual, seasonal as well as spatial
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variability in the atmospheric aerosols across the transect (Supplementary Material, Fig. S2). Generally,
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aerosol plumes tend to be associated with spring-summer periods and are confined to the western section
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of the transect. In contrast, the easternmost locations are characterised by persistently low aerosol levels
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all year round.
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4.2.1.4 Biological implications of potential atmospheric inputs of Cu at OSP.
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In certain oceanographic settings atmospheric inputs can represent an important source of nutrients like
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nitrogen (Duce et al., 2008; Mackey et al., 2010), phosphorus (Hsu et al., 2014; Markaki et al., 2003), Fe (Boyd et al., 1998; Duce and Tindale, 1991; Hamme et al., 2010; Jickells, 1995, 2005), Co, Mn and Ni
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(Mackey et al., 2012) to the surface ocean. While aerosol deposition can be beneficial to phytoplankton
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by increasing availability of essential elements, it may also inhibit microbial growth if the concentration of potentially toxic metals, such as Cu, are too high (Jordi et al., 2012; Paytan et al., 2009). Although future work is needed to assess the role of atmospheric deposition in dCu cycling at OSP and elucidate its origins, we explore the potential biological effects associated with inputs of Asian sources at this station, given the recent evidence for their deposition there (McAlister, 2015). Highly urbanized and industrialized areas of east Asia are a source of harmful pollutants that may mix with mineral dust from deserts on the trajectory of its long-range transport across the North Pacific (Hoell et al., 1996; Hsu et
ACCEPTED MANUSCRIPT al., 2010; Li et al., 2012). For instance, dust samples collected over urbanized areas such as Daejeon, Korea during Asian dust (AD) events (when strong monsoons carry dust from deserts) contain Cu levels that are on average 23‒57 times higher than uncontaminated Chinese desert soil (12.9 mg kg-1 versus 291‒740 mg kg-1, Lee et al., 2013). Furthermore, anthropogenically modified aerosols are often
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characterized by a high fractional solubility of Cu, hence have a greater potential to be solubilized in
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surface waters in deposition areas than mineral aerosols. Indeed, as much as half of Cu in aerosols
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deposited over the East China Sea during the AD period is soluble (Hsu et al., 2010), compared to 1‒7% solubility of mineral aerosols from Saharan deserts (Sholkovitz et al., 2010). Thus, enhanced Cu content
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and high fractional solubility of contaminated aerosols that may be advected from Asia could have
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inhibitory effects on phytoplankton communities in deposition areas across the North Pacific, including potentially the GoA. A number of factors are likely to control such effects including: Cu dissolution time
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from aerosols (Mackey et al., 2015), the buffering capacity of the in situ ligand pool, and taxonomic
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composition of the resident community. Abundance of smaller sized groups such as cyanobacteria and dinoflagellates, which are very sensitive to Cu toxicity may be diminished relative to more resilient
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diatoms (Brand et al., 1986). In our study region (station P16), a reduction in Cu bio-availability using an
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artificial, strong organic ligand cyclam, increased the abundance of cyanobacteria, suggesting that these organisms may be experiencing Cu stress (Semeniuk, 2014). Thus, cyanobacteria may be vulnerable to
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any potential increases in ambient Cu that could result from atmospheric deposition events in the GoA. Our current understanding of the composition, deposition rates and biological effects of atmospheric sources deposited in this region is, however, still profoundly limited. As such, future aerosol studies would be highly beneficial from an ecological standpoint as well as to better understand the cycling of Cu and other bioactive elements in the GoA.
ACCEPTED MANUSCRIPT 4.2.3. Correlations of dCu with macronutrients. As in previous investigations in the North Pacific, distribution of dCu was strongly coupled to that of the macronutrients phosphate and silicate in the oceanic nutricline (e.g. Martin et al.,1984, Bruland, 1980). The Cu:P nutricline ratio (0.55 mmol:mol, using data from all stations, depths 0‒400 m) in our
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study is in the range of ratios in this region and in others (e.g. 0.3‒0.8 mmol:mol for Central and North
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Pacific, Boyle et al., 1977, Martin et al, 1989, Bruland, 1980; 0.41‒0.47 mmol:mol for North Atlantic, Bruland and Franks, 1983; and 0.33‒0.54 mmol:mol for Indian Ocean, Morley et al., 1993). In contrast
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to our findings, recent studies in the Atlantic (Jacquot and Moffett, 2015, Roshan and Wu, 2015) and Southern Oceans (Heller and Croot, 2015) found poor correlations between Cu and PO34-, whereas its
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distribution showed a better correlation with Si(OH)4 throughout the water column. In our study, the
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Cu:Si ratio in the nutricline (0.016 mmol:mol, all stations, depths 0‒300 m) is similar to that reported for the Atlantic sector of the Southern Ocean (0.012-0.020 mmol:mol, depths 10-5300 m, Heller and Croot,
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2015) and for the Indian Ocean (0.02 mmol:mol, depths 0-5000 m, Vu and Sohrin, 2015), yet lower than
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the ratio in the Atlantic Ocean (0.035 mmol:mol, depths 0-5000 m, Roshan and Wu, 2015). The correlations between Cu and macronutrients observed here suggest that the distribution of Cu
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along Line P is strongly coupled to biological assimilation and regeneration cycles. Indeed, converting
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the Cu-PO34- for the entire transect using the Redfield ratio of 106C:1P yields a Cu:C ratio of 5.1 µmol:mol, which is in the range of assimilation ratios determined in cultured phytoplankton (Annett et al., 2008; Guo et al., 2012). Interestingly, the Cu:C of diatoms cells in field samples (4.2 µmol:mol) is also on par with the assimilation ratio determined here, while those of flagellates were substantially elevated (25-33 µmol:mol, Twining et al., 2015). This raises some interesting questions regarding the potential role of diatoms in influencing the Cu-PO34- ratios in the oceanic nutricline in the North Pacific.
ACCEPTED MANUSCRIPT 4.2.1 Processes affecting dCu in deep waters (> 600 m) 4.2.2. Sedimentary sources of dCu. Sediments are considered to represent an important source of Cu to the overlying waters, as supported
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by the benthic flux estimates using water column and sediment pore-water Cu profiles (Boyle et al.,
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1977; Heggie et al., 1987; Klinkhammer et al., 1982; Klinkhammer, 1980). Copper may be liberated
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from sedimentary material via aerobic respiration of organic matter (oxic sediments: Klinkhammer et al., 1982; Shaw et al., 1990) or via microbially mediated reductive dissolution of Mn/Fe oxides to which Cu
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is bound (suboxic sediments: Murray, 1975). In contrast, anoxic sediments may to trap Cu due to the
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formation of highly insoluble precipitates with sulfide minerals, which tends to occur under reducing conditions (Morse and Luther, 1999).
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Given the large distance from the deepest sample and the seafloor at most of our stations (~ 1000‒2000
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m, P12‒P26), we only examine the role of benthic sources at the shallowest station, P4 (deepest sample 200 m above the seafloor). The bottom enrichment of dCu at this station (1100 and 1200 m, Fig. 7B) is
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consistent with sedimentary input sources. Bottom sediments at station P4 may be reducing as enhanced
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Fe (II) levels have been measured in deep waters at this station (1000 and 1200 m, Schallenberg et al. 2015), although during a different cruise than those in our study (June 2012 and Aug 2013). Reducing
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conditions in these sediments would likely favor formation of insoluble copper sulfides, thus diminishing the dCu inputs from bottom sediments. However, resuspension of bottom sediments and oxidation of CuS in overlaying waters may help explain the enrichment in bottom dCu concentrations at P4. We did not find evidence of sedimentary inputs of Cu from the continental shelf (~ 200 m), in contrast with other regions of the North Pacific where shelf source of Cu was suggested (Biller and Bruland, 2013). It is possible that scavenging of dCu by sinking particles, which is likely to be intense at P4 given its high productivity in the summer (Whitney et al., 2005), reduced the signature of dCu input from the
ACCEPTED MANUSCRIPT continental shelf. Alternatively, Cu may be scavenged from the dissolved phase upon sediment resuspension (Fischer et al., 1986) on the shelf, or scavenged by sulfide minerals if shelf sediments are sufficiently reducing, both of which would limit Cu inputs into water column at P4.
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4.2.4 dCu in the Oxygen Minimum Zone (OMZ).
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Previous studies strongly suggest that the OMZ of the subarctic NE Pacific acts as a sink for elements such as Ag, Zn, Cd, and potentially Cu (Janssen et al., 2014; Janssen and Cullen, 2015; Kramer et al.,
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2011). Both thermodynamic considerations and field observations suggest that these class B metals are depleted under anoxic conditions via formation of insoluble sulfide solid phases (Jacobs et al., 1985;
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Jacobs and Emerson, 1982; Landing and Lewis, 1991). Since waters within the OMZ of the subarctic NE
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Pacific still contain traces of O2 (lowest in this study O2 = 7 µmol kg-1), the presence of free sulfide in the water column is unlikely as it would become rapidly oxidized by O2. However, sulfidic conditions
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may develop within microenvironments on sinking particles in the OMZ’s (Janssen & Cullen, 2015).
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Formation of insoluble ZnS within these particles may explain the decoupling between Zn and Si distributions within the OMZ waters of subarctic NE Pacific (Janssen & Cullen, 2015).
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Similarly, to the recent Zn study, we also observed a noticeable break in the relationship between Cu
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and Si that coincided with the Oxygen Minimum Zone (300‒2000 m) (Fig. 9B). Analogous to the Zn:Si, decoupling between Cu and Si within these O2 deficient waters might be indicative of the scavenging process described by Janssen and Cullen (2015). However, unlike Zn & Si, the water column behaviour of Cu and Si are not as tightly coupled in the world's oceans, creating some uncertainties in such an interpretation. Furthermore, behaviour of Cu in low O2 settings is not straightforward because processes that act to release and remove dCu may be occurring simultaneously. For instance, dissolution of Mn/Fe-oxyhydroxide minerals (under hypoxic and suboxic conditions), as well as POM
ACCEPTED MANUSCRIPT remineralization, would enhance dCu in the water column of OMZ. Both processes may partially explain the accumulation of dCu we observed in the upper reaches of the OMZ (400 m) where low O2 levels might be favouring reductive dissolution of Cu, and where remineralization of POM is highest. In contrast, formation of CuS within microenvironments on sinking particles would act as a sink of dCu in
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the OMZ. However, it is currently uncertain how effective this mechanism might be in the presence of
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strong organic ligands binding most of the dissolved Cu, even in these deep waters (Moffett and Dupont,
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2007). Furthermore, Cu redox chemistry, which is poorly understood under such conditions, adds another level of uncertainty because Cu in +1 state is a definite class B acceptor (soft metal forming
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stable complexes with donor atoms such as S), while Cu in +2 state can behave as both soft and hard
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metal (forming stable complexes with donor atoms such as N, O, and F; Ahrland et al., 1958). However, along Line P, the dCu concentrations at depths corresponding to the OMZ were generally uniform.
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Similar behaviour of dCu can be seen in a previous study at station P26 by Martin et al. (1989)
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(Supplementary Material, Fig. S3). In contrast, it is typical for dCu concentrations to gradually increase with depth (e.g. Jacquot and Moffett, 2015, Fig. S2), which can be explained by the flux of Cu from
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bottom sediments and particle scavenging throughout the water column (Boyle et al., 1977; Takano et
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al., 2014). The behaviour of dCu within the OMZ of the NE Pacific suggests that it may be scavenged more intensely in these low O2 waters by the process described by Janssen and Cullen (2015).
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Cu distributions within the OMZ waters near Mauritania (Jacquot and Moffett, 2015, Roshan and Wu,2015) and eastern subtropical North Pacific (Nameroff et al., 2002) did not show any anomalies associated with low O2 conditions. In contrast, accumulation of dCu was observed within the secondary nitrite maximum of the eastern tropical South Pacific OMZ, although shelf sources were likely responsible for this trend rather than low O2 conditions (Jacquot et al, 2013). Interpreting Cu behaviour within OMZ’s in continentally influenced study sites, as is the case of these previous investigations, is
ACCEPTED MANUSCRIPT challenging because of strong dCu sources there that may be masking potential removal processes. While further research is required to confirm this, our dataset may support the recent hypothesis for the scavenging of Cu within the OMZ of the Northeast Pacific (Janssen and Cullen, 2015). Using the Cu:Si relationship for each station along Line P, we estimated that approximately 0.01‒1.3 nmol kg-1 of dCu is
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role in reducing the dissolved Cu inventory in the subarctic NE Pacific.
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‘missing’ within the OMZ, suggesting these intermediate low O2 waters may potentially have significant
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5. Conclusions
In the present study, we identified major biogeochemical and physical processes that drive the spatial
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and temporal patterns of dCu along Line P. Our dataset also adds to the growing body of dCu
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measurements across the North Pacific, and aims to improve our understanding of Cu behaviour throughout this basin. In the eastern sections of our transect, fluvial rather than atmospheric inputs from
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North America, were responsible for enhanced dCu levels in surface waters. Remote, Fe-deplete stations
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along Line P were also found to be enriched in dCu, a feature we propose to be largely driven by upwelling in the Gulf of Alaska. Atmospheric deposition may represent an additional source of Cu to
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these remote waters as indicated by the sub-surface dCu enrichments at station Papa, which coincide
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with elevated atmospheric aerosol levels. Furthermore, we explored the possible sources of atmospheric Cu to this region and discuss the potential ecological impacts of these inputs in surface waters at OSP. Across our transect, Cu was strongly correlated with both phosphate and silicate in the nutricline, reflecting the influence of biological processes (uptake and remineralization) on the Cu distribution in this region. We also provided some evidence that dCu may be sensitive to scavenging within the OMZ, behaving similarly to other soft metals such as Cd and Zn in these O2 deficient waters (Janssen et al. 2014, Janssen & Cullen, 2015). Finally, our study addressed some of the recent methodological
ACCEPTED MANUSCRIPT uncertainties regarding dCu analysis. We observed that the length of acidified sample storage largely impacts the outcome of dCu analysis by FIA-CL. With shorter storage periods (up to 2 months) a UV oxidation pre-treatment of the samples is required for accurate measurements of dCu, while this treatment was found to be less important if samples were stored for an extended length of time (several
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years). In addition, we also compared different analytical methods to measure dCu and discussed how
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various methodologies may also contribute to current uncertainties in dCu values. We proposed that the
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interplay between the three factors briefly explored in our study (sample storage, UV oxidation, analytical methodologies) may explain the inconsistencies in dCu concentrations of GEOTRACES
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reference materials and in dCu profiles in the North Pacific.
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Acknowledgements: We would like to hugely thank the Line P cruise participants, especially Jason
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McAllister & Nari Sim for collection of dCu samples, chief scientist Marie Robert, and captains and crews of the CCGS J.P. Tully. Great thanks to Dave Janssen for helpful advice on the interpretation of
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copper and silicate relationship in the OMZ. We would like to thank Rob Middag, Maeve Lohan, Jessica
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Fitzsimmons, Kristen Buck, Travis Mellett and Claire Parker for helpful discussions on the issue of UV oxidation in dCu analysis. Nutrient data were analyzed by the Institute of Ocean Sciences and dissolved
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O2 data is courtesy of the Line P program, which is run by the Institute of Ocean Sciences, Sidney BC.
grant to MM.
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Funding: AP and DS were funded by a Natural Sciences and Engineering Council of Canada Discovery
Author contributions: Sample analysis was performed by AP, DS & HW. All authors contributed to data interpretation, and the manuscript was primarily written by AP.
(Bruland and Franks, 1983; Morley et al., 1993)
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Table 1. Comparison of dCu values (nmol kg-1) determined using FIA-CL with the GEOTRACES intercalibration materials consensus values as of May 2013 (SAFe S, D1 and D2) and NASS-6 certified reference material (gamma-irradiated) for ocean water (National Research Council Canada, http://www.nrc-cnrc.gc.ca/eng/solutions/advisory/crm/certificates/nass_6.html). Also, included values for the new GEOTRACES standard, GSC 318 for which there is no consensus.
Reference material
+ UV ( 2hr)
No UV
SAFe D1 (2015)
2.24 ± 0.19 (n=2)
2.31 ± 0.11 (n=3)
Consensus/ Certified 2.28 ± 0.15
SAFe D2 (2012)
nd
2.34 ± 0.16 (n=3)
2.27 ± 0.11
SAFe D2 (2015)
2.33 ± 0.12 (n=4)
2.27 ± 0.15 (n=3)
2.27 ± 0.11
GSC 318 (2015)
1.46 ± 0.11 (n=5)
1.38 ± 0.02 (n=2)
No consensus
NASS-6 (2012)
nd
3.88 ± 0.15 (n=4)
3.81 ± 0.03
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Figure 1. Map of the Gulf of Alaska showing Line P transect and the 5 sampling stations (P4‒P26). Bathymetry was contoured at 1000 m intervals.
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Figure 2. Profiles of dCu (nmol kg-1) at station P26 (Ocean Station Papa). Samples from August 2011 cruise analyzed with FIA-CL without UV oxidation between Jan‒Feb 2012 (open circles) and analysed again between June‒August 2015 with 2 hr of UV oxidation (closed circles). Also plotted is the dCu dataset reported by Martin et al. (1989) using ADDC-PDDC without UV treatment (triangles).
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Figure 3. Changes in labile Cu with increasing storage time of: (A) 48 hrs, (B) 2 weeks and (C) 2 months. At each time point acidified samples were analyzed without (closed circles) and with 2 hr of UV treatment (open circles) and analyzed by FIA-CL. DCu profile obtained in 2015 (~ 4 years of storage at pH 2) is also shown for reference (triangles). The dataset plotted can be found in Supplementary Material, Table S2.
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Figure 4. Comparison of dCu datasets at station P26 analyzed by FIA-CL with 2 hr of UV oxidation and sample storage at pH 2 of ~ 4 yrs (Aug 2011 cruise, circles) and CSV with UV oxidation and no acidified storage (Aug 2012 cruise, gray triangles) (A). Linear regressions of data obtained in this study by FIA-CL (AP), versus CSV (HW, gray triangles), and versus surface transect data from Semeniuk et al. (2016a) (DS, open squares) (B). The regression fits are: y=1.058x-0.26, r2=0.78, p<0.001 between datasets of AP & HW (using all data points), and y= 0.959x-0.104, r2=0.7, p<0.001 between datasets of AP & DS, excluding the outlier data point at 2 nmol kg-1 dCu.
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Figure 5. Comparison o dCu datasets in the North Pacific. Upper and bottom panel shows plots of selected dCu profiles from different regions (R1‒R7) as indicated in the map. The details of methodologies used in each study are provided in the Supplementary Material, Table S4.
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Figure 6. Potential temperature - salinity plot (θ-S) including all stations along the transect (P4 - ◊, P12□; P16 - x; P20 - ☆; P26 - ∆) with dissolved oxygen (µmol kg-1) as symbol colors.
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Figure 7. Top panel (A) shows profiles of dCu (nmol kg-1), PO43- (µmol kg-1) and sigma-t (kg m-3) in the upper 300 m of stations along the Line P transect. Bottom panel (B) shows depth profiles of dCu, PO43and O2 (µmol kg-1) throughout the entire water column sampled (10‒2000 m) with the bottom depths at each station.
ACCEPTED MANUSCRIPT Figure 8. Profiles of dCu (nmol kg-1), PO43- (µmol kg-1) and sigma-t (kg m-3) at station P26 obtained with samples collected in Aug 2010, 2011, and 2012. Samples from 2010 & 2011 were analyzed with FIA-CL (2 hr of UV), and samples from 2012 were analyzed by CSV (45 min of UV). Density and PO 43-data are courtesy of the Line P program, Institute of Ocean Sciences, Department of Oceans & Fisheries, Canada. The dataset along with the storage conditions can be found in Supplementary Table S6.
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Figure 9. Macronutrient relationships: dissolved copper with PO43- (A) and dissolved copper with Si(OH)4 (B) using data from the entire transect. Linear regressions (solid lines) were performed using data between 50-400 m for phosphate ([DCu]=0.5533[PO43-] + 1.26, r2 =0.826) and 0-300 m for silicic acid ([dCu]= 0.016[Si(OH)4] + 1.69, r2=0.72). Horizontal dashed lines indicate the extension of linear regressions assuming dCu data was to follow the nutricline trends. Vertical dashed lines represent data points used for the linear regressions.
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Figure 10. Mean mixed layer dCu and dAl trends along the Line P transect in August 2011 (dAl data from Cain, 2013) with an inset showing the spatial surface dCu distribution along the transect.
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Figure 11. Contour plot of dCu in the upper 600 m along the Line P transect in August 2011. White dashed lines with labels represent dissolved PO43- (µmol kg-1). Shoaling of high dCu and phosphate can be seen at the offshore stations (P16‒P26) being suggestive of the upwelling conditions.
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Figure 12. Area averaged Aerosol Optical Depth (AOD at λ=550 nm, AquaMODIS, 1ᵒ resolution) in the Gulf of Alaska between June-August 2010, 2011 and 2012. AOD data was downloaded from NASA Giovanni visualization and analysis online data system, developed and maintained by the NASA GES DISC (http://giovanni.gsfc.nasa.gov/giovanni/) and plotted using MATLAB. The script used to produce the plot can be found in a public repository: https://github.com/AnnaMagdalena/DCu_LineP-Subarctic-Pacific.
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ACCEPTED MANUSCRIPT Highlights: Distribution of dissolved Cu along Line P is presented
Main sources of copper include fluvial inputs near the coast and upwelling of the Alaskan gyre offshore
Atmospheric sources may moderate surface Cu at Ocean Station Papa
Evidence of Cu scavenging within the Oxygen Minimum Zone (OMZ)
For short-term stored acidified samples (≤ 4 months) UV oxidation is crucial prior to Cu analysis by FIA-CL
Aging of acidified samples for several years reduces the need for UV oxidation of samples
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Anna M. Posacka, David M. Semeniuk, Whitby Hannah, Constant M.G. van den Berg, Jay T. Cullen, Kristin Orians, Maria T. Maldonado PII: DOI: Reference:
S0304-4203(16)30224-9 doi: 10.1016/j.marchem.2017.05.007 MARCHE 3456
To appear in:
Marine Chemistry
Received date: Revised date: Accepted date:
29 November 2016 24 May 2017 25 May 2017
Please cite this article as: Anna M. Posacka, David M. Semeniuk, Whitby Hannah, Constant M.G. van den Berg, Jay T. Cullen, Kristin Orians, Maria T. Maldonado , Dissolved copper (dCu) biogeochemical cycling in the subarctic Northeast Pacific and a call for improving methodologies, Marine Chemistry (2017), doi: 10.1016/ j.marchem.2017.05.007
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ACCEPTED MANUSCRIPT Dissolved copper (dCu) biogeochemical cycling in the subarctic Northeast Pacific and a call for improving methodologies. Anna M. Posackaa, David M. Semeniuka, Whitby Hannahb, Constant M.G. van den Bergb, Jay T.
Department of Earth, Ocean, and Atmospheric Sciences, University of British Columbia, 2207 Main
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Cullenc, Kristin Oriansa, Maria T Maldonadoa
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Mall, Vancouver, British Columbia, Canada V6T 1Z4 University of Liverpool, United Kingdom, L69 3GP, Liverpool, UK
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School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, Canada V8W 2Y2;
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Corresponding author: Anna M. Posacka, [email protected]
Abstract
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We investigated biogeochemical cycling of dissolved copper (dCu) along the Line P transect, spanning from the coastal waters of British Columbia, Canada, to the High Nutrient, Low Chlorophyll (HNLC) open Ocean Station Papa (OSP or P26), in the subarctic Northeast Pacific. DCu concentrations ranged
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from 1.4 – 3.7 nmol kg-1 throughout the water column along the transect, and were elevated in the upper
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and bottom waters near the continental margin (< 300 m and > 1100m respectively) as well as in the
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upper waters offshore (< 300 m). These trends were attributed to the fluvial and sedimentary sources near the coast of BC, and upwelling of deep, dCu rich waters in the Alaska gyre offshore. In addition, we
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conducted a temporal investigation of dCu at OSP over three consecutive years (2010‒2012), which
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revealed dynamic variability in the top 300 m that was accompanied by elevated sub-surface concentrations, indicating Cu supply from atmospheric deposition. We explore atmospheric inputs in the
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Gulf of Alaska and suggest that they may play a significant role in moderating dCu distribution in this
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region. Consistent with previous investigations in the North Pacific, dCu distributions in the nutricline throughout the transect were strongly linked to those of phosphate and silicate. However, within the
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Northeast Pacific Oxygen Minimum Zone (OMZ) silicate and dCu distributions were noticeably
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decoupled suggesting a deficit or loss of dCu in these deep, oxygen depleted waters. In this study, we also assessed the requirement for UV oxidation of our samples (pH 2) prior to dCu analysis by flow
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injection-chemiluminescence (FIA-CL). We found that UV oxidation leads to a significant increase in labile Cu (6.5‒43%), however, the variation in this increase is largely dictated by the length of sample storage under acidic conditions. Our results suggest that UV oxidation is essential prior to FIA-CL analysis of young samples (aged for 48 h‒2 months), but may not be required if samples have been stored for an extended period at low pH (≥ 4 years). We found a good agreement between dCu values at station P26 obtained with FIA-CL and those obtained with cathodic stripping voltammetry (CSV).
ACCEPTED MANUSCRIPT However, comparison of our dataset with all published dCu profiles in the North Pacific revealed some discrepancies in dCu values in this region. Here, we briefly discuss the role of sample storage period, UV oxidation and differences in analytical methodologies as factors causing uncertainties in dCu values.
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1. Introduction
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Copper (Cu) is an essential micronutrient for marine organisms and, like other bioactive trace elements, its vertical distribution is governed by its interactions with biota. In laboratory settings, low availability
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of Cu has been shown to impair growth and metabolism of eukaryotic phytoplankton (Annett et al.,
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2008; Guo et al., 2012; Peers et al., 2005), ammonium oxidizing archaeon Nitrosopumilus maritimus (Amin et al., 2013), denitrifying bacteria (Granger and Ward, 2003) as well as marine heterotrophic
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bacterium Dokdonia sp. Dokd-P16 (Posacka et al, unpublished). Furthermore, some iron-stressed
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phytoplankton in culture have an increased metabolic demand for Cu, through the upregulation of a high-affinity iron (Fe) uptake transport system (HAFeTS) that requires Cu (Annett et al., 2008; Guo et
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al., 2012; Maldonado et al., 2006) or/and substitution of Fe-containing enzymes with Cu-containing
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homologs (Peers and Price, 2006). Thus, Cu nutrition is likely to be important in the High Nutrient Low Chlorophyll (HNLC) waters, where low Fe concentrations limit the growth of primary producers.
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Indeed, recent field work demonstrates such metabolic coupling between Cu and Fe in the HNLC waters of the subarctic Northeast Pacific (Semeniuk et al., 2016b). However, while low Cu may hinder metabolic functions, exposure to Cu levels that exceed cellular requirements causes toxicity (Brand et al., 1986; Debelius et al., 2011; Gordon et al., 1994; Mann et al., 2002; Paytan et al., 2009). Because of a narrow range where Cu supports optimal growth and the distinct Cu requirements of different taxa, Cu
ACCEPTED MANUSCRIPT bioavailability may influence the taxonomic composition of the marine microbial community (Brand et al., 1986; Mann et al., 2002; Paytan et al., 2009). In the global ocean, vertical distribution of dCu is typically characterized by low surface water concentrations that are accompanied by a vertical gradient of increasing dCu towards the ocean seafloor.
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Such a behaviour has been classified as hybrid nutrient-like (Bruland and Lohan, 2003), and may be
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attributed to the collective control of internal cycles (biological utilization and regeneration processes),
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particle scavenging in intermediate waters and fluxes from bottom sediments (Boyle et al., 1977; Bruland, 1980; Bruland and Lohan, 2003; Takano et al., 2014). Dissolved Cu concentrations in the
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interior waters of the North Pacific are enriched relative to other oceans, with concentrations being
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nearly twofold of those measured in the North Atlantic, due to global ocean circulation patterns. Profiles of dCu in different parts of the North Pacific are consistent with its hybrid nutrient-like behaviour (Boyle
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et al., 1977; Bruland, 1980; Ezoe et al., 2004; Fujishima et al., 2001; Martin et al., 1989; Takano et al.,
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2014; Tanita et al., 2015). Here, strong correlations between dCu and major nutrients (NO3-, PO43-) can be observed in the oceanic nutricline (Bruland, 1980; Martin et al., 1989), indicating a tight influence of
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biological processes on Cu distribution in the North Pacific. However, our understanding of sources and
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sinks of Cu is still fairly limited in many parts of the basin, including the Northeast subarctic region. In particular, the role of systems such as the Oxygen Minimum Zone (OMZ) of the subarctic NE
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Pacific in moderating dCu cycles is not well understood, although this zone has been proposed to affect redox-sensitive elements such as Cd, Zn as well as Cu (Janssen et al., 2014; Janssen and Cullen, 2015). NE Pacific OMZ is an intermediate water feature (~ 300‒2000 m) characterized by persistently low, although non-zero, dissolved O2 levels. Such conditions may favour scavenging of Cu via formation of insoluble sulfides precipitates, proposed to be facilitated by sulfidic microenvironments on sinking
ACCEPTED MANUSCRIPT particles in low O2 zones (Janssen et al., 2014; Janssen and Cullen, 2015). Thus, OMZ may have an important role in reducing the dCu inventory in the NE Pacific. In addition, control of atmospheric deposition on dCu in the NE subarctic Pacific also remains to be explored. This process may represent an important source of Cu across the subarctic North Pacific,
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which experiences a seasonal transport of Asian dust from desert and loess region in central Asia (Duce
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and Tindale, 1991) In addition to being the second largest source of dust in the world (Han et al., 2011),
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Asia is also a significant emitter of anthropogenic pollutants which may enhance the atmospheric content of elements, such as Cu (Lee et al., 2013). Influence of atmospheric sources on surface Cu distribution is
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evident in the profiles from the western Pacific (eg. Ezoe et al., 2004; Tanita et al., 2015). In contrast,
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little is known regarding the influence of atmospheric inputs on the dCu in the remote waters of the NE Pacific, where Asian sources may reach episodically (Boyd et al., 1998).
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Our ability to obtain reliable measurements of the dCu is instrumental to the interpretation of Cu
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cycling in aquatic environments and its impact on microbial communities. Recently, some uncertainties have emerged in dCu analysis due to the observation that UV oxidation of acidified oceanic samples
Standards
&
Reference
material
statement,
2013,
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GEOTRACES
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leads to increases in labile Cu (e.g. Biller and Bruland, 2012; Middag et al., 2015; Milne et al., 2010;
http://www.geotraces.org/images/stories/documents/intercalibration/Files/Reference_Samples_May13/SAFe_
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Ref_Cu_05_13.pdf). Typically, samples for total dCu are acidified (pH 1.7‒2) in order to dissociate any
organically bound Cu (ligands or colloids) prior to analysis. However, recent evidence suggests that some organic Cu complexes may persist under acidic conditions even after prolonged storage, thus requiring UV oxidation (Biller and Bruland, 2012; Middag et al., 2015; Milne et al., 2010). Lack of UV oxidation was used to explain the underestimation of dCu values in the GEOTRACES reference material
ACCEPTED MANUSCRIPT SAFe (D2) (e.g. Boye et al., 2012; Milne et al., 2010). However, effects of UV on labile Cu have been variable in recent investigations, and it remains unclear how best to obtain accurate dCu measurements. In the present study, we focus on characterizing Cu biogeochemistry along Line P, a coastal-open ocean zonal transect in the subarctic Northeast Pacific spanning from the continental shelf of Vancouver
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Island (BC) to Ocean Station Papa (OSP, ~ 1600 km offshore) in the Alaskan gyre, where primary
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productivity is Fe-limited (Fig. 1). Previous work there examined the role of Cu nutrition and its
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availability to resident plankton communities, the interactive control of Cu and Fe availability on Felimited phytoplankton, as well as the distribution of dCu within the mixed layer (Semeniuk et al., 2015,
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2016a, 2016b, 2009). However, there exists a dearth of dCu measurements at depth across the transect
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nor are there systematic investigations of its temporal variability. Here, we report high-resolution dCu profiles down to 2000 m at five major stations of Line P, and examine Cu behaviour in the context of
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oceanographic features encountered along this transect including: a) the Northeast Pacific Oxygen
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Minimum Zone (OMZ, 300‒2000 m); b) the coastal California Undercurrent (CUC) intruding waters at stations P4 and P12; c) the North Pacific Intermediate Water (NPIW) at station P26 (McAlister, 2015),
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and d) the offshore upwelling Alaskan gyre. In addition, temporal variability of dCu was examined over
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3 sampling cruises (in 2010, 2011 and 2012) at the offshore station P26 (OSP). In this study, we also
material
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investigated the requirement for UV oxidation of samples (GEOTRACES Standards & Reference statement,
2013,
http://www.geotraces.org/images/stories/documents/intercalibration/Files/Reference_Samples_May13/S AFe_Ref_Cu_05_13.pdf) prior to our analysis of dCu by flow injection-chemiluminescence (FIA-CL). Furthermore, we compare our results to published dCu profiles in the North Pacific and discuss variability inherent in the dataset. We propose that methodological factors explored in this study
ACCEPTED MANUSCRIPT (acidified sample storage, UV oxidation and analytical methods) likely contribute to this variability, and warrant more detailed investigation into how to improve current dCu methodologies.
2. Methodology
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2.1 Sampling and study transect
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Samples were collected at five stations along the Line P (P4-P26) during the August 17‒26th cruise on board the C.C.G.S. John P. Tully (Cruise, 2011-27) (Fig.1). For a temporal study of dCu at OSP we used
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samples from three cruises: Aug 17th to Sept 3rd 2010 (cruise 2010-14), August 17th to 26th 2011 (Cruise,
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2011-27) and Aug 13th to 30th 2012 (cruise 2012-13). We obtained 18-22 samples at each station within the depth range of 10-2000 m of the water column using a 12 bottle powder coated trace metal clean
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rosette system equipped with 12 L Teflon- coated GO-FLO bottles (General Oceanics, FL, USA)
metal
clean
500
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(Measures et al., 2008) Filtered seawater (0.22 µm AcroPak , Pall Corporation) was collected into trace mL
BellArt
bottles
(according
to
GEOTRACES
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http://www.geotraces.org/libraries/documents/Intercalibration/Cookbook.pdf).
Samples
protocols, for
total
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dissolved analysis were acidified at sea to pH 2 using 1 mL of ultrapure 12N HCl per litre of sample (Seastar Chemicals, Sidney BC). Another set of dCu samples was collected during the 2011 and 2012
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Line P cruises as described above, but were immediately frozen after filtration at ambient pH and stored at ‒20°C until analysis. Macronutrients Si(OH)4, NO3-, PO43- were analyzed according to Barwell-Clarke and Whitney (1996). Macronutrient and CTD data are courtesy of Institute of Ocean Sciences, Department
of
Ocean
&
Fisheries
(https://www.waterproperties.ca/linep/index.php). 2.2 Dissolved Cu analysis
and
are
publicly
available
ACCEPTED MANUSCRIPT Samples from the Aug 2011 cruise were initially analyzed for dCu by flow injectionchemiluminescence (FIA-CL), without prior UV oxidation (~ 3 months of sample storage at pH 2). We found a good agreement between these data and a historical profile of dCu at OSP (P26, Fig.2, Martin et al., 1989). Furthermore, our values of GEOTRACES inter-calibration standard SAFe D2 and a reference
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material NASS-6 were in good agreement with the consensus values and certified values, respectively
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(Table 1). Following a recent recommendation of UV oxidation of samples prior to dCu analysis by the
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GEOTRACES community, all the transect samples were re-analyzed between July‒August 2015 (~ 4 years of storage at pH 2) after 2 hours of UV oxidation (twice in 60 min bursts). UV apparatus used in
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this study consisted of 30 mL silica sample tubes with PTFE capping units surrounding a high -pressure
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mercury lamp (125 W) (van den Berg, 2017). Once UV oxidized, samples were left for at least 2 hours or overnight before analysis to remove the effect of hypochlorite or hypobromite, which can be produced
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by UV oxidation (Achterberg & van den Berg, 1994).
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There was a large difference between our new dCu dataset and the initial analysis at all stations across the transect (6.5‒43% more dCu), with no apparent trend (e.g. onshore-offshore, Supplementary Figure
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S1). Originally, we attributed this increase to the effect of UV oxidation. However, we also found higher
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dCu values in a small subset of non-UV oxidized samples in this most recent re-analysis. Nevertheless, UV oxidation of those aged samples was associated with a minor increase in dCu (7 out of 9 samples
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showed an increase in dCu after UV ranging 3.4-18 %, Supplementary Material, Table S1). This led us to hypothesize that both long sample storage at low pH and UV oxidation can lead to increases in FIACL labile dCu. Subsequently, we sought to investigate how the length of sample storage and UV oxidation affect Cu lability in FIA-CL analysis. For these experiments we used August 2011 samples, from different depths at station P26, which were not acidified, but instead were stored at – 20°C until 2015. After acidification to pH 2, corresponding measurements of dCu in UV and non-UV oxidized
ACCEPTED MANUSCRIPT sample aliquots were performed at different time points (48 h, 2 weeks and 2 months) and compared to the 2015 data. Next, we compared FIA-CL measurements of dCu at P26 (2015 re-analysis, 4 years of storage and 2 hr of UV oxidation) with values obtained by CSV (no storage, 45 mins of UV oxidation), and previous dCu measurements by Semeniuk et al. (2016a). Lastly, we compared our results with other
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published dCu datasets in the North Pacific.
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2.2.1 Flow injection analysis, chemiluminescence (FIA-CL)
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For measurements of the total dissolved Cu we used a flow injection analysis, chemiluminescence (FIA-CL) method of Zamzow et al (1998). This method is based on the oxidation of a complex formed
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between Cu and 1,10-phenanthroline in the presence of hydrogen peroxide. A working standard of Cu
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was prepared in MilliQ from 1000 ppm Cu ICP-MS standard, and was used to quantify dCu in the samples by the standard additions method. A five-standard calibration curve was acquired every 8‒10
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samples. Each day a number of samples previously analyzed (≥ 5) were run to determine the precision of
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the method. Precision based on percent relative standard deviation (% RSD) of n = 36 replicate sample was 2.93 % RSD, median = 1.36 % RSD, and range = 0.14 – 10.2 % RSD. The accuracy of the method assessed
by
analyses
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was
of
SAFe
D1
&
D2
(GEOTRACES
reference
samples,
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http://www.geotraces.org/science/intercalibration/322-standards-and-reference-materials) and NASS-6 (National Research Council Canada). Additionally, GEOTRACES inter-calibration sample GSC 318 (for which there is no current consensus) was also analyzed (Table 1). We run the GEOTRACES intercalibration standards with and without UV oxidation. All reference materials were in good agreement with the consensus values and we found no statistical difference between UV oxidized and non-UV oxidized SAFe samples (t-test, p=0.582). System blanks for FIA-CL were 0.12 ± 0.022 nmol kg-1 (n=14), yielding a detection limit (3 × blank standard deviation) of 0.064 nmol kg-1. Data was plotted
ACCEPTED MANUSCRIPT using packages “m_map” (Pawlowicz, 2015) in commercial software Matlab (The Mathworks, Inc, Natick, Massachusetts, United States), Sigmaplot (Systat Software, San Jose, CA), and “ggplot2” (Wickham, 2009) in open source programming language R (R Core Team, 2016). The dCu dataset along with the ancillary data, as well as scripts used to produce the plots in this study can be downloaded from
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a public repository: https://github.com/AnnaMagdalena/DCu_LineP-Subarctic-Pacific.
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2.2.2 Cathodic Stripping Voltammetry (CSV)
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Cathodic Stripping Voltammetry (CSV) was performed on non-acidified samples collected during the
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August 2012 cruise. Samples were defrosted overnight at 4C, swirled gently to re-dissolve any particulate matter formed during freezing, and poured into conditioned sterilin containers to warm up to
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room temperature (20C) in the dark. Total dissolved copper was measured by CSV at natural pH. In
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addition, surface (10 m) and deep (1200 m and 1400 m) samples were analyzed by anodic stripping
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voltammetry (ASV) at pH 1.9, allowing inter-comparison with reference seawater of the same pH (NASS-6). UV digestion at natural pH avoids the formation of hypochlorite which interferes with CSV
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Cu determinations (Campos and van den Berg, 1994). For CSV measurements, the sample was poured
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into conditioned silica 40 mL UV tubes or into a conditioned silica cell. UV oxidation was performed for 45 minutes using the same UV apparatus as for the FIA-CL method, with the lamp either surrounded by four 40 mL sample tubes with PTFE caps, or positioned horizontally above a sample aliquot in the voltammetric cell. Dissolved copper was then measured in the cell by CSV in the presence of 20 μM salicylaldoxime (SA) and 0.01 M borate/ammonia pH buffer (pH 8.15 on NBS scale) (Campos and van den Berg, 1994) The borate/ammonia pH buffer (1 M boric acid/0.3 M ammonia) was UV oxidized to remove organic matter and contaminating metals were removed by equilibration with 100 μM
ACCEPTED MANUSCRIPT manganese dioxide (MnO2) followed by filtration (van den Berg, 1982). Sample was purged with N2 for 5 minutes to remove dissolved oxygen prior to analysis. The voltammetric equipment was a μ-Autolab III potentiostat (Ecochemie, Netherlands) connected to a 663 VA stand (Metrohm) with hanging mercury drop electrode (HMDE). The system used a glassy carbon counter electrode, an Ag/AgCl
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reference electrode with a 3 M KCl salt bridge, with a rotating polytetrafluoroethylene (PTFE) rod for
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stirring solutions. The software was modified to discard 2, instead of the usual 4, drops of Hg between
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scans, to minimize Hg usage. CSV measurements were in the differential pulse mode, at a deposition potential of -0.15 V, a deposition time of 30 s, and a 1s potential jump to -1.2 V to desorb any residual
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organic matter. For ASV measurements, acid-cleaned quarts UV tubes were not conditioned, and prior to
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UV digestion, the sample was acidified to pH 1.9 with trace metal grade HCl. Voltammetric scans for ASV used the square-wave mode with a 5-minute deposition time, with no reagents added. Comparative
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measurements between CSV and ASV were found to give the same result within the standard deviation
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of three repeat measurements, and ASV measurements on NASS-6 reference material gave results within
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5% of the certified value (3.94 ± 0.32 nmol L-1 versus 3.90 nmol L-1 for NASS-6).
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3. Results
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3.1 Methodological considerations of dCu analyses 3.1.1 Effect of storage time and UV oxidation on labile dCu. Time dependent analysis of dCu in non-UV oxidized acidified samples demonstrated that labile Cu increases progressively with the length of storage period (48 h‒2 months, Fig 3, dataset in Supplementary Material, Table S2). Subjecting those same samples to UV oxidation generated an additional increase in labile Cu at each time point, which was greatest for the ‘youngest’ samples (~ 40
ACCEPTED MANUSCRIPT % offset between UV and non-UV oxidized samples at 48 h). However, the amount of Cu released by UV oxidation decreased when measurements were performed after a longer storage, either 2 weeks or 2 months (~ 6‒30%). After 2 months of acidified sample storage, the profile of dCu after UV oxidation is on par with the more recent analyses done in 2015 (~ 4 years of storage with 2 h of UV oxidation),
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although upper water column samples are still somewhat lower (50 m sample ~24 % lower; 75 m sample
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~11% lower than samples for these depths).
3.1.2. Comparison of datasets obtained by FIA-CL, CSV and the historical data at OSP.
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Measurements of dCu at P26 performed with distinct sample pre-treatment, hardware and modes of
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detection (FIA-CL and CSV) were in good agreement, particularly at depths below 250 m (Fig. 4A, Supplementary Material, Table S3). However, there is some disparity between the two profiles in surface
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waters (0‒250 m), likely due to the seasonal variation in dCu, as samples analyzed by CSV were
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collected in Aug 2012, while those analyzed by FIA-CL were collected in Aug 2011. Our dataset is also in good agreement with the surface dCu measured along the transect (P4‒P26) by Semeniuk et al
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(2016a) for the same cruise and analytical method. The various measurements are linearly correlated (p
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=<0.001 Fig 4B). In contrast, dCu profiles at P26 from our most recent analysis (2015) are noticeably offset from the historical data from Martin et al (1989) (Fig. 2). The differences between the two profiles
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are up to 40 % in the surface and approximately 14 % at depths below 250 m, likely reflecting differences in methodological approaches. 3.1.3 Comparison of published dCu profiles in the North Pacific. To compare published vertical profiles of dCu in the North Pacific we grouped data into oceanographically similar regions (Region 1‒7, Fig. 5). Within these regions, we see substantial disagreement between studies with dCu differences of ~10-100 %, even for profiles collected at the same
ACCEPTED MANUSCRIPT sampling location. For instance, at Ocean Station Papa (Region 3) our dCu values are 15‒40 % and ~ 60 % higher than those of Martin et al (1989) and Coale and Bruland (1990), respectively. Interestingly, the results for these two published studies disagree even though samples were obtained during the same cruise (Vertex VII) and analyzed with a similar method, although with differences in filtration (0.45 and
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0.3 µm for Martin et al (1989) and Coale and Bruland (1990), respectively). However, we also note
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some agreement between studies when comparing stations farther away (note that not all vertical dCu
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profiles are plotted). The profiles from Tanita et al. (2015) (station 22, profile not plotted; station 5) and Moffett and Dupont (2007) (stations 6 and 4) agree with each other and are more similar to the dCu
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values reported in this study. On the other hand, the results of Fujishima et al (2001) (station 19, profile
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not plotted; station 17), Takano et al., (2014) and Ezoe et al (2004) (stations BO01‒07 stations, profiles not plotted) agree with one another, but are much lower than our dCu values. Finally, at the
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GEOTRACES inter-calibration station SAFe (region 6) there is an excellent agreement between the
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analyses of Bruland (1980), Coale and Bruland (1990), and Biller & Bruland (2012). While in the near surface waters these differences likely reflect temporal variability in dCu, this is
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unlikely to be the cause for the variations in the deep-water values of nearby stations. Thus,
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methodological differences such as the analytical approaches used, sample storage time at low pH, filtration and whether a UV step was utilized or not, may help explain these differences (methodological
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details of each study are listed in Supplementary Material, Table S4). For instance, the dCu profile at OSP from Martin et al (1989) was obtained using non-UV oxidized samples and is similar to our profile also obtained without UV oxidation (2012 analysis, ~ 3 months of sample storage). In contrast, the difference between UV and non-UV treated samples is not observed when the datasets of Bruland (1980) at station 17 and Biller and Bruland (2012) at SAFe are compared (ADPC-DDC without UV and
ACCEPTED MANUSCRIPT NOBIAS P1A resin extraction with UV, respectively). Thus, UV oxidation alone is not the sole cause of these disagreements.
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3.2 Copper biogeochemistry in the subarctic Northeast Pacific.
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3.2.1 Line P hydrography.
Surface waters along Line P are characterized by low salinity and a strong density stratification (Fig. 6),
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with a mixed layer of ~ 20‒30 m (Fig. 7). In the eastern reaches of the transect (P4 and P12), the
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permanent pycnocline is centered on the σt= 26.5 kg m-3, and is shifted to the σt= 26.8 kg m-3 in the water column of station P26. Coastal stations (P4‒P12) are influenced by the California Under Current
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(CUC) along σt = 26.5 – 26.8 kg m-3 (Pierce et al., 2000) characterized as warm and salty (McAlister,
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2015). At the western terminus of the transect, waters along the σt = 26.8 kg m-3 (P26) represent the fresher and cooler waters of the North Pacific Intermediate Water (NPIW) (McAlister, 2015; Talley,
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1993). In this system, an oxygen minimum zone (OMZ) extends from ~ 300‒2000 m with O2
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concentrations between ~7 – 60 µmol kg-1, with the lowest O2 occurring at depths ~ 1000 m.
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3.2.2 General trends in dCu along Line P transect.
Vertical profiles of dissolved Cu along the transect were typical of a nutrient-like element with surface depletions relative to deep-water and increasing concentrations with depth (Fig. 7A & B), consistent with dCu behaviour in other ocean provinces (Boye et al., 2012; Heller and Croot, 2015; Jacquot and Moffett, 2015; Martin et al., 1989; Vu and Sohrin, 2013). Surface dCu concentrations were highest at station P4 (2.16 nmol kg-1), gradually declined towards P16 (1.49 nmol kg-1), and increased west of P16
ACCEPTED MANUSCRIPT towards stations P20 and P26 (1.78 and 1.83 nmol kg-1, respectively). This surface dCu trend is consistent with a previous investigation along Line P during the same cruise (Semeniuk et al., 2016a). In contrast, the onshore-offshore trend in deep water dCu (> 300 m) was characterized by increasing concentrations from P4 to P12 followed by a decline at P16, and minor dCu increases at P20 and P26
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(Fig. 7B). In the upper 300 m, patterns of dCu distribution matched well with those of PO43- at the
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offshore stations P16, P20 and P26. In contrast, at the two stations closer to the coast (P4 and P12), dCu
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levels were in excess of PO43- at the shallowest depths (0‒40 m). Vertical distributions of dCu and PO43followed the density trend at P26 with a summer (0‒29 m) and winter mixed layers (50‒130 m) derived
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from sigma-t profiles (Fig. 7A). As such, the presence of two ‘cuproclines’ at this station can be
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observed; one between 20‒75 m and another between 135‒200 m.
Deep dCu profile values at P12 (400‒2000 m) were higher (2.99‒3.72 nmol kg-1) than those at P16,
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P20 & P26 (2.74‒3.48 nmol kg-1) (Fig. 7B). Across the transect, dCu levels within the OMZ (~ 300–
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2000 m with O2 concentrations ranging between ~7 – 60 µmol kg-1) were consistent. However, sharp
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increases in dCu can be seen below 1000 m at P4, likely reflecting sedimentary sources.
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3.2.3 Annual variability in dCu values in upper waters at Ocean Station Papa (OSP, P26).
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Dissolved Cu distribution at the offshore station P26 during three consecutive August Line P cruises: 2010‒2012 were characterized by dynamic shifts in dCu in the upper 300 m (Fig. 8), while below this depth dCu patterns and concentrations remain relatively homogenous (Supplementary Material, Table S3). Between 40 and 200 m, dCu levels were typically lower in 2010 (≤ 2 nmol kg-1), than in 2011 and 2012 (> 2 nmol kg-1). Surface enrichments in dCu at P26 were observed in 2010 and 2012 data, with values of 2.3‒2.4 nmol kg-1 (5‒10 m) relative to 1.83 nmol kg-1 in 2011 (10 m) (Fig. 8, Supplementary Material, Table S3). This could be an indication of an atmospheric deposition event in 2010 and 2012.
ACCEPTED MANUSCRIPT The shape of the dCu profile in 2011 can be partially explained by the density structure and agrees somewhat with the distribution of phosphate, while this is not observed in 2010 and 2012. However, lower sampling resolution for the latter years limit our ability to relate water column structure and dCu
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distribution.
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3.2.3 Dissolved Cu and macronutrient relationships across the transect.
Metal-macronutrient relationships are useful tools for examining the importance of both biological and
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abiotic processes in influencing the distribution of hybrid type metals such as Cu. As such, strong metal-
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macronutrient correlations are suggestive of trace metal distribution being largely driven by biological processes (uptake and remineralization), while deviations from those correlations imply abiotic effects
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on metal distribution (e.g. particle scavenging). Here, we observed a strong correlation of dCu with PO43-
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and Si(OH)4 in the upper 400 m of the transect (P4‒P26, [dCu] = 0.5533[PO43-] + 1.26, r2 =0.826; [dCu] = 0.016[Si(OH)4] + 1.69, r2=0.72., Fig. 9A & B, respectively). However, dCu distribution in deeper
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waters was uncoupled from that of both macronutrients. In the Cu-PO43- relationship, dCu values below
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400 m (~2.7 µmol kg-1 PO43-) begin to increase abruptly above the nutricline trend, suggesting an excess of dCu concentrations relative to phosphate (Fig. 9A). On the other hand, decoupling between dCu and Si begins at a shallower depth (300 m) with Cu values falling well below the nutricline trend (Fig. 9B). A noticeable plateau can be seen in copper-silicate relationship between ~ 75 µmol kg-1 to ~ 150 µmol kg-1 Si(OH)4 (~ 300‒1600 m), suggesting scavenging of dCu in the deeper waters. 4. Discussion
ACCEPTED MANUSCRIPT This discussion is divided into 2 distinct sections. First, we discuss methodological aspects accounting for variability among dCu values from different laboratories and analytical methods (section 4.1), followed by a section focusing on the biogeochemistry of dCu in the NE Pacific Ocean (section 4.2).
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4.1 Methodological uncertainties of dCu analysis
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The variability in the North Pacific dCu datasets we highlight here, indicate that there is some uncertainty regarding the concentration and distribution of Cu in this basin, and perhaps in other oceans.
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There have been other reports on the variability in dCu analysis in recent literature, and this has so far
2012;
GEOTRACES
Standards
&
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been attributed primarily to UV oxidation (e.g. Milne et al., 2010; Biller and Bruland 2012; Boye et al., Reference
material
statement,
2013,
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http://www.geotraces.org/images/stories/documents/intercalibration/Files/Reference_Samples_May13/SAFe_
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Ref_Cu_05_13.pdf). However, as we explored here, dCu results can also be largely influenced by sample
storage time and the choice of analytical method. Below, we discuss the interplay between these three
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4.1.1 UV oxidation
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factors and make a call for an improvement of current dCu analyses.
Recent studies, examining the importance of a UV oxidation step to obtain quantitative detection and accurate dCu, report variable recovery of Cu from acidified seawater samples. For instance, a study by Milne et al., (2010) found ~ 10% increase in labile Cu in the SAFe D2, Biller and Bruland (2012) report ~ 30 % increase in an offshore California Current sample, while we found between 6.4 to 43 % increase in P26 samples following UV oxidation, depending on acidic storage period. In a recent inter-
ACCEPTED MANUSCRIPT comparison study at Bermuda Atlantic Time Series station, dCu measurements throughout the water column obtained with and without UV oxidation were compared by two separate labs (Middag et al., 2015). Increases in labile Cu due to UV oxidation were on average 12 % and 16 % for US and Netherlands GEOTRACES labs, respectively (Middag et al. 2015). This variability could be partially
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explained by differences in sample origins (and hence the amount of organic compounds), and UV
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oxidation conditions.
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In UV oxidation, the interaction between UV light with dissolved oxygen (DO) and water leads to production of ozone, superoxide radicals (• O2- ) and hydroxyl radicals (• OH) (Achterberg and van den
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Berg, 1994). The superoxide will ultimately disproportionate to hydrogen peroxide (H2O2), unless it
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reacts with redox-active species. The energetic UV radiation combined with the superoxide acts to break down dissolved organic matter, ultimately to CO2 and water. A side-reaction of the superoxide with
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chloride (abundant in seawater) leads to formation of hypochlorite (ClO -) (Haag and Hoigne, 1983).
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Hypochlorite formation and the radiative heating of the water lead to a rapid decrease in the residual concentration of DO, which is the end of the oxidative destruction of organic matter, typically after 30 to
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45 minutes of irradiation (Achterberg and van den Berg, 1994). Once the DO in the sample is exhausted
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H2O2 may be added to continue the process. The efficacy of UV oxidation is influenced by several factors such as the intensity of the UV lamp, the distance between the UV lamp and the sample, as well
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as the sample volume irradiated. For instance, using on-line UV oxidation methods where surface-tovolume ratio is high requires much shorter UV exposure time to destroy DOM than batch techniques (minutes vs hours, respectively; e.g. Achterberg and van den Berg (1994) and Achterberg et al.,(2001)). Thus, there is a need to establish consistent UV oxidation protocols for future studies. In our experiments, dCu values obtained after UV oxidation of samples stored in acidic conditions for 48 h and 2 weeks were substantially lower relative to the values obtained after ~ 4 years of acidic storage
ACCEPTED MANUSCRIPT with UV oxidation. A possible explanation for this could be that early in the sample storage period, when Cu complexation by organics is expected to be highest, our UV oxidation process was not effective. This may explain why we did not observe any significant increases in labile Cu following UV oxidation using longer exposure time (4 h on samples from 50 m and 75 m stored for 2 months, Supplementary Material,
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Table S2). Further investigation is required to determine whether adding H2O2 or O2 to these ‘young’
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samples would cause greater increase in labile Cu after UV oxidation. However, given the low DOC
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content of waters along Line P (80‒100 µmol kg-1, Wong et al., (2000)) addition of H2O2 should not be required (Achterberg and van den Berg, 1994). We considered that the differences between dCu values
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of short-term and long-term stored acidic samples (48 h‒2 months vs 4 years) could be caused by Cu
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leaching from sampling bottles during storage. While we were not able to directly test for Cu leakage from sampling bottles, we eliminated this possibility on the basis that we found an agreement between
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4.1.2 Sample storage period
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years) and CSV (non-acidified and frozen).
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the dataset obtained in 2015 for station P26 with two different methods: FIA-CL (stored acidified for 4
As we demonstrated here (using FIA-CL analytical method for dCu), changes in labile Cu in acidified
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samples are highly dependent on their storage time. Long storage at low pH promotes the release of Cu from organic complexes in samples, which introduces a potential for variable results depending on the time from collection to analysis. This has implications for whether a UV oxidation of samples is necessary prior to analysis. SAFe reference materials were collected and acidified in 2004, thus were much older than our Line P samples (stored in acidic conditions for 48 h‒4 years). We found that the consensus value for dCu in the SAFe D2 agreed with the value from our initial analysis in 2011 without UV oxidation (at this point SAFe D2 sample age was ~ 7 years), and with our values in the 2015
ACCEPTED MANUSCRIPT analyses, with and without UV oxidation. Similarly, Tanita et al., (2015; using CSV analytical method for dCu) and Butler et al. (2013; using solid-phase extraction with Toyopearl AF chelate 650 M, followed by ICPMS analytical method for dCu) without UV oxidation, obtained values on par with the consensus value (Supplementary Material, Table S4). In contrast, Milne et al. (2010; using pre-
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concentration step with Toyopearl AF chelate 650 M resin, followed by isotope dilution/ICPMS) did
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find a small increase (~ 10 %) in labile Cu in UV irradiated SAFe D2 sample (collected from 1000 m),
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despite its long storage. Taken together, these studies suggest that long storage period potentially leads to degradation of organic complexes binding Cu in the SAFe sample, and values for dCu in agreement with
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each other, though some differences may emerge due to specific analytical methodologies (discussed in
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4.1.3). The hypothesis that aging of samples can destroy inert Cu is further supported by our observations of minor (if none) increases in labile dCu in samples from station P26, stored for ~ 4 years
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and UV oxidized relative to non-UV oxidized (Supplementary Material, Table S1). Our study suggests
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that UV oxidation is critical for the determination of dCu by FIA-CL in acidified samples stored for ≤ 3 months, but may not be required for samples stored for more than ~ 4 years.
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Middag et al. (2015) compared dCu values at the Bermuda Atlantic Time Series station, with and
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without UV oxidation, obtained by two research groups (US GEOTRACES and Netherlands GEOTRACES), and analyzed with the same method (solid-phase column extraction (Nobias-chelate
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PA1) with ICP-MS, Biller and Bruland, 2012). As in our study, UV oxidation enhanced labile Cu in acidified samples. However, the percent enhancement was not significantly different between the two datasets (US group: mean = 11.7 ± 10.1 %; Netherlands group, mean = 16.2 ± 10.5 %, two sample t-test, p-value=0.15), even though their samples were stored for different time (1 and 2 years, respectively). This suggests that 2 years of acidic storage may not be sufficient to destroy all the inert dissolved Cu.
ACCEPTED MANUSCRIPT Interestingly, for several samples in their study (Middag et al., 2015), dCu values with and without UV oxidation were within analytical uncertainty. Similarly, we found no significant change in dCu values for a small number of samples after UV oxidation, coupled with ~ 4 years’ acidic storage (Supplementary Material, Fig. S1). Finally, Milne et al. 2010 found that UV treatment had no effect on
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the dCu concentration in SAFe D1 sample (also collected from 1000 m as D2 sample). These
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inconsistent results with the idea that UV oxidation is needed for accurate dCu determinations, especially
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in young samples, may be exceptions, and may reflect differences in analytical methods or simply nature
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of the samples.
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4.1.3 Role of methodologies in current dCu uncertainties.
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Methodologies employed in dCu analysis include: solid phase extraction with quantification via either standard additions or external calibration (e.g. Biller and Bruland, 2012; Lohan et al., 2005; O’Sullivan
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et al., 2013; Sohrin et al., 2008) or isotope dilution (Lagerström et al., 2013; e.g. Lee et al., 2011; Milne
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et al., 2010), electrochemical methods (e.g. Buck and Bruland, 2005; Campos and van den Berg, 1994), and flow injection analysis methods, chemiluminesence methods (e.g. Achterberg et al., 2001; Zamzow
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et al., 1998). These methodologies and their variations differ in terms of their recovery of Cu (and its species) in the seawater sample. For instance, analysis by cathodic stripping voltammetry (CSV) using a ligand with lower Cu-complex stability, such as tropolene, can cause greater underestimation of the total dCu, than the use of stronger ligands such as oxine in non-UV treated seawater (Achterberg and van den Berg, 1994). For solid phase extraction methods, underestimation of dCu may result from the need to buffer the sample to a higher pH, possibly allowing re-complexation of Cu with organic ligands
ACCEPTED MANUSCRIPT (Ndung’u et al., 2003); although, it has been suggested this may be prevented with the use of in-line buffering (O’Sullivan et al., 2013). Thus, in addition to storage time of acidified samples, analytical methodologies may introduce another source of variability in dCu values. Indeed, our comparison of two distinct methodologies in terms of sample preparation and mode of
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detection for dCu analysis supports this. Here, the values for dCu obtained with FIA-CL and CSV
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methods agreed only when samples for FIA-CL analysis were stored for ≥ 2 months and UV oxidized.
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This suggest these two methods have distinct efficiencies of Cu recovery from the sample. In the FIACL system, mostly labile and some weakly associated Cu species are measured, due to the short reaction
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time (fraction of a second) with the synthetic ligand 1,10 phenanthroline (48 µM) before detection
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(Zamzow et al., 1998). On the other hand, in CSV system there is a longer reaction time (4‒5 min) between Cu and the competing ligand (SA, 20 µM), which may result in greater Cu acquisition from the
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organic Cu pool. The higher efficiency of CSV is further supported by the similarity between the values
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for dCu in a non-UV treated sample (from 100 m at P26) analyzed by CSV (1.6 ± 0.1 nmol kg-1) and that for same sample, aged for ~ 3 months (non-UV oxidized), and analyzed by FIA-CL (1.5 ± 0.05 nmol
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kg-1). Overall, our results indicate that CSV is far more efficient at accessing the strongly complexed
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organic Cu than the FIA-CL, and that for accurate measurements of dCu, in addition to UV oxidation aging of samples is required for the latter method.
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In recent studies, underestimation dCu values were mainly explained in terms of UV oxidation (Biller and Bruland, 2012; Boye et al., 2012; Milne et al., 2010). However, we surveyed recent dCu studies that employ the GEOTRACES inter-calibration standards, and observed variability that cannot be fully explained by the presence or absence of a UV oxidation step (Supplementary Material, Table S5). For instance, some methods obtain reference material values within the current GEOTRACES consensus value without UV oxidation (Butler et al., 2013; Jacquot et al., 2013; Jacquot and Moffett, 2015;
ACCEPTED MANUSCRIPT Lagerström et al., 2013; Lee et al., 2011; Vu and Sohrin, 2013), while others underestimate the reference value if UV oxidation is omitted (Boye et al., 2012; Milne et al., 2010; Sohrin et al., 2008). Methods incorporating isotope dilution (ID) may underestimate dCu in non-UV oxidized samples (e.g. Milne et al., 2010, Boye et al., 2012), despite the recommendation that this analytical method does not require UV (GEOTRACES
Reference
&
Standards
Statement,
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oxidation
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http://www.geotraces.org/science/intercalibration/322-standards-and-reference-materials).
2013, In
isotope
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dilution methods, unlike those based solely on solid phase partitioning, extraction efficiency of metals is not crucial for metal quantification (Milne et al., 2010). However, in principle metals must be in an
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isotopically exchangeable form before equilibration with the isotope spike for ID methods. Otherwise,
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the isotopic ratio measured in the sample will not reflect the ratio of the spiked sample, and consequently the original sample concentration will be underestimated. Thus, attention should be paid to dCu sample
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pre-treatment (storage and UV oxidation) prior to analysis with ID methods.
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Given the methodological issues explored here and the observed variability in dCu values in the literature, we believe that there is an urgent need to establish an optimal and consistent treatment of dCu
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samples. Future studies should: 1) compare dCu values with different analytical methods using acidified
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samples of the same age; 2) establish standard UV conditions to be used in future studies (intensity, time, use/no use of H2O2), and which methods may require this treatment; 3) address the identity of the inert
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Cu species in acidified samples (e.g. colloidal vs dissolved Cu-L species).
4.2 Copper cycling in the subarctic Northeast Pacific Although exploration of Cu cycling along Line P is relatively new, a number of processes are known to moderate the distribution of nutrients and other trace metals (e.g. Cd, Fe, Zn, Ag, Mn, Ga, Pb) in this region, and are likely to influence Cu as well. These include: coastal upwelling, river discharge (Whitney
ACCEPTED MANUSCRIPT et al., 2005), atmospheric deposition (Boyd et al., 1998; Hamme et al., 2010), onshore-offshore shelf transport (Cullen et al., 2009; Lam et al., 2006), and mesoscale eddies (Brown et al., 2012; Johnson et al., 2005; Xiu et al., 2011). Trace metals could also be sourced to this region via water masses penetrating the western and eastern reaches of the Line P transect (CUC (P4‒P12) and NPIW (P26),
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respectively, McAlister, 2015). In addition, recent work has shown that the extensive Oxygen Minimum
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Zone (300-2000 m) of the subarctic NE Pacific plays an important role in the cycling and speciation of
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redox-sensitive metals such as Cd, Zn, Ag and Fe (Janssen and Cullen, 2015; Kramer et al., 2011;
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Schallenberg et al., 2015), and may also affect Cu (Janssen et al., 2014).
4.2.1.1 Water masses and continental shelf
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4.2.1 Controls of dCu concentrations in the upper waters (< 600 m)
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The continental shelf of BC (~ 150‒200 m) is an important source of both dissolved and particulate Fe
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that can be transported long distances offshore (Cullen et al., 2009; Lam et al., 2006; Schallenberg et al., 2015). In addition, coastal stations of Line P are influenced by the intrusion of warm and salty waters of
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the California Undercurrent (CUC, 150‒200 m), which acts as a source of metals such as Ga to stations
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P4 and P12 (McAlister, 2015). In this study, however, we did not observe dCu enrichments along isopycnals surfaces associated with either the CUC or the continental shelf (~ 200 m, σt = 26.5-26.8, Fig.7A). While Cu is known to have strong sedimentary source, it also has a strong affinity for organic matter (Fischer et al., 1986). Inputs from the CUC or the continental shelf could be reduced by scavenging of Cu near the coast of BC where productivity in the summer is high (Peña and Bograd, 2007). Instead, remineralization processes appeared to be the dominant source of dCu in waters between 40‒400 m as maxima were mostly associated with the bottom of the nutricline. This depth also coincides
ACCEPTED MANUSCRIPT with the upper boundary of the Oxygen Minimum Zone (~ 400 m) in this region (Fig.7B). Thus, our data suggests that neither the continental margin nor advected water masses were major sources of dCu to Line P.
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4.2.1.2 Sources of dCu to the mixed layer along the transect.
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The mixed layer distribution of dCu along Line P was examined in relation to that of dissolved
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aluminum (dAl) (Cain, 2014, Fig. 10A & B), a proxy for atmospheric deposition and continental sources. The gradual decline of dCu from the coastal station P4 to the offshore station P16 (2.19 nmol
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kg-1 and 1.6 nmol kg-1, respectively) is in agreement with the dAl trend. This suggests that North
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American continental sources, such as mineral aerosol deposition or fluvial runoff, may partially explain the elevated dCu at our coastal stations. A recent study suggests that fluvial inputs, rather than
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atmospheric deposition, from North America are more significant sources of Pb to stations P4 and P12
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(McAlister, 2015). Furthermore, Semeniuk et al., (2016a) found a decreasing gradient of surface dCu (7‒10 m) with increasing salinity between coastal station P3 and offshore station P16, suggesting that
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indeed fluvial sources are most likely responsible for the dCu trends observed near the coast.
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Westward of station P16, the latitudinal trends of dCu and dAl in the mixed layer diverge, with dAl continuing its east-west decline (> 3-fold between P4 and P26), while dCu starts to increase towards P26
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(OSP) (~ 0.4 nmol kg-1 between station P16 to P26). Low dAl concentrations at OSP indicate the relatively small atmospheric inputs to this station. Thus, the disparity in the dAl and dCu trends at the offshore stations (P20 and P26) suggests that atmospheric deposition alone is unlikely to account for the increasing dCu towards OSP. Instead, upwelling of dCu rich waters in the Gulf of Alaska (GoA) is likely driving this trend (Fig. 11). Upwelling would bring high dCu concentrations to the surface while lowering dAl, as deep waters are typically depleted in dAl, due to its short residence time (Orians and Bruland, 1985).
ACCEPTED MANUSCRIPT Transport of copper by mesoscale eddies is also a possible mechanism for increased dCu levels offshore, however satellite altimetry anomalies did not indicate the presence of an eddy near P26 during our study (Semeniuk et al., 2016a). Hence, we propose that the trend of increasing dCu levels towards the offshore stations is largely controlled by the upwelling, which is in agreement with previous
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explanations for the high surface water dCu in the Gulf of Alaska (Vertex VII, Martin et al., 1989).
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Using a simple-one dimensional model, we estimate that upwelling is responsible for supplying
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0.58‒1.95 nmol L-1 y-1 dCu to the euphotic zone at P26, assuming the summer and winter mixed layer of
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30 and 100 m, respectively (Supplementary Material, Table S7).
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4.2.1.3 Temporal variability of dCu at Ocean Station Papa (P26). We showed that dCu concentrations in upper waters at OSP are temporally variable and identified sub-
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surface dCu maxima in Aug 2010 and 2012, that are suggestive of atmospheric inputs (Fig. 8). To
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explore the possibility of atmospheric aerosol deposition of Cu at OSP, we used Aerosol Optical Depth measurements (AOD, λ=550 nm) by Moderate Optical Imaging Spectroradiometer (MODIS) on NASA's
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Aqua satellite, a proxy for aerosol concentration in the atmosphere. The area averaged AOD plots
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between July-August for all three years indicate the presence of elevated atmospheric aerosol concentrations in the western region of GoA near OSP and diminishing levels towards North America (Fig. 12). The potential deposition of these atmospheric aerosols could explain high dCu in the subsurface waters at OSP in 2010 and 2012, but would not corroborate with the lower values measured in 2011. It is possible that atmospheric Cu inputs occurred in 2011 as well, but that we did not observe any effects on dCu surface distribution that year due to either Cu consumption by the resident community or the low solubility of Cu in deposited material.
ACCEPTED MANUSCRIPT The identification of the aerosol plumes we observed in the GoA requires further study. However we take an opportunity to examine their potential origins by assessing the atmospheric sources that are known to influence this region including : glacial flour from coastal Alaska (Crusius et al., 2011), volcanic ash from the Aleutian Islands (Hamme et al., 2010) and Asian dust (Bishop et al., 2002; Boyd
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et al., 1998). Deposition events associated with these sources may enhance local primary productivity,
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likely by inducing Fe-fertilization (Bishop et al., 2002, Hamme et al., 2010). Yet, due to the episodic
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character of such events, as well as their dependence on prevailing wind patterns (Takeda, 2011) and local hydrology (Crusius et al., 2011), deposition of these sources to surface waters in the GoA is highly
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unpredictable, while their metal content remains unknown. It is unlikely that the high AOD we observed
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in this study was associated with the glacial flour from river valleys in Alaska as its transport tends to occur in autumn, when coastal river levels are low and riverbed sediments are exposed (Crusius et al.,
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2011). While major eruptions from Aleutian Island volcanos can provide a spectacular amount of air-
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borne ash into the GoA as seen during the Kasatochi volcano eruption in August 2008 (Hamme et al., 2010), such events did not occur during our study. We did note, however, that some eruptive activity
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associated with small ash cloud formation for Mt. Cleveland volcano were reported in June 2010 and
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July 2012 (Herrick et al., 2014; Neal et al., 2014), but the role of this atmospheric source requires further exploration. A possible source of atmospheric inputs prior to our sampling may be dust from Asia,
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typically reaching the remote waters of the subarctic NE Pacific in early spring and summer (Uematsu et al., 1983). Asian dust originates from arid and semi-arid desert area in Northern China, the second largest dust source in the world (Han et al., 2011) contributing ~ 400 Tg dust to the North Pacific and beyond (Zhang et al., 1997). Indeed, the deposition of Asian sources in the GoA is supported by the distinct signature of lead isotopes measured in the mixed layer at OSP (McAlister, 2015).
ACCEPTED MANUSCRIPT Our observations suggest that surface dCu at OSP may be moderated via atmospheric deposition events in the Gulf of Alaska. There is a need to quantify dCu inputs from such events, which are likely to vary in time and space along the Line P transect. Indeed, the Hovmoller-longitude time series of monthly averaged AOD levels over the area covering Line P indicate annual, seasonal as well as spatial
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variability in the atmospheric aerosols across the transect (Supplementary Material, Fig. S2). Generally,
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aerosol plumes tend to be associated with spring-summer periods and are confined to the western section
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of the transect. In contrast, the easternmost locations are characterised by persistently low aerosol levels
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all year round.
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4.2.1.4 Biological implications of potential atmospheric inputs of Cu at OSP.
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In certain oceanographic settings atmospheric inputs can represent an important source of nutrients like
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nitrogen (Duce et al., 2008; Mackey et al., 2010), phosphorus (Hsu et al., 2014; Markaki et al., 2003), Fe (Boyd et al., 1998; Duce and Tindale, 1991; Hamme et al., 2010; Jickells, 1995, 2005), Co, Mn and Ni
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(Mackey et al., 2012) to the surface ocean. While aerosol deposition can be beneficial to phytoplankton
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by increasing availability of essential elements, it may also inhibit microbial growth if the concentration of potentially toxic metals, such as Cu, are too high (Jordi et al., 2012; Paytan et al., 2009). Although future work is needed to assess the role of atmospheric deposition in dCu cycling at OSP and elucidate its origins, we explore the potential biological effects associated with inputs of Asian sources at this station, given the recent evidence for their deposition there (McAlister, 2015). Highly urbanized and industrialized areas of east Asia are a source of harmful pollutants that may mix with mineral dust from deserts on the trajectory of its long-range transport across the North Pacific (Hoell et al., 1996; Hsu et
ACCEPTED MANUSCRIPT al., 2010; Li et al., 2012). For instance, dust samples collected over urbanized areas such as Daejeon, Korea during Asian dust (AD) events (when strong monsoons carry dust from deserts) contain Cu levels that are on average 23‒57 times higher than uncontaminated Chinese desert soil (12.9 mg kg-1 versus 291‒740 mg kg-1, Lee et al., 2013). Furthermore, anthropogenically modified aerosols are often
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characterized by a high fractional solubility of Cu, hence have a greater potential to be solubilized in
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surface waters in deposition areas than mineral aerosols. Indeed, as much as half of Cu in aerosols
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deposited over the East China Sea during the AD period is soluble (Hsu et al., 2010), compared to 1‒7% solubility of mineral aerosols from Saharan deserts (Sholkovitz et al., 2010). Thus, enhanced Cu content
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and high fractional solubility of contaminated aerosols that may be advected from Asia could have
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inhibitory effects on phytoplankton communities in deposition areas across the North Pacific, including potentially the GoA. A number of factors are likely to control such effects including: Cu dissolution time
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from aerosols (Mackey et al., 2015), the buffering capacity of the in situ ligand pool, and taxonomic
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composition of the resident community. Abundance of smaller sized groups such as cyanobacteria and dinoflagellates, which are very sensitive to Cu toxicity may be diminished relative to more resilient
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diatoms (Brand et al., 1986). In our study region (station P16), a reduction in Cu bio-availability using an
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artificial, strong organic ligand cyclam, increased the abundance of cyanobacteria, suggesting that these organisms may be experiencing Cu stress (Semeniuk, 2014). Thus, cyanobacteria may be vulnerable to
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any potential increases in ambient Cu that could result from atmospheric deposition events in the GoA. Our current understanding of the composition, deposition rates and biological effects of atmospheric sources deposited in this region is, however, still profoundly limited. As such, future aerosol studies would be highly beneficial from an ecological standpoint as well as to better understand the cycling of Cu and other bioactive elements in the GoA.
ACCEPTED MANUSCRIPT 4.2.3. Correlations of dCu with macronutrients. As in previous investigations in the North Pacific, distribution of dCu was strongly coupled to that of the macronutrients phosphate and silicate in the oceanic nutricline (e.g. Martin et al.,1984, Bruland, 1980). The Cu:P nutricline ratio (0.55 mmol:mol, using data from all stations, depths 0‒400 m) in our
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study is in the range of ratios in this region and in others (e.g. 0.3‒0.8 mmol:mol for Central and North
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Pacific, Boyle et al., 1977, Martin et al, 1989, Bruland, 1980; 0.41‒0.47 mmol:mol for North Atlantic, Bruland and Franks, 1983; and 0.33‒0.54 mmol:mol for Indian Ocean, Morley et al., 1993). In contrast
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to our findings, recent studies in the Atlantic (Jacquot and Moffett, 2015, Roshan and Wu, 2015) and Southern Oceans (Heller and Croot, 2015) found poor correlations between Cu and PO34-, whereas its
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distribution showed a better correlation with Si(OH)4 throughout the water column. In our study, the
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Cu:Si ratio in the nutricline (0.016 mmol:mol, all stations, depths 0‒300 m) is similar to that reported for the Atlantic sector of the Southern Ocean (0.012-0.020 mmol:mol, depths 10-5300 m, Heller and Croot,
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2015) and for the Indian Ocean (0.02 mmol:mol, depths 0-5000 m, Vu and Sohrin, 2015), yet lower than
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the ratio in the Atlantic Ocean (0.035 mmol:mol, depths 0-5000 m, Roshan and Wu, 2015). The correlations between Cu and macronutrients observed here suggest that the distribution of Cu
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along Line P is strongly coupled to biological assimilation and regeneration cycles. Indeed, converting
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the Cu-PO34- for the entire transect using the Redfield ratio of 106C:1P yields a Cu:C ratio of 5.1 µmol:mol, which is in the range of assimilation ratios determined in cultured phytoplankton (Annett et al., 2008; Guo et al., 2012). Interestingly, the Cu:C of diatoms cells in field samples (4.2 µmol:mol) is also on par with the assimilation ratio determined here, while those of flagellates were substantially elevated (25-33 µmol:mol, Twining et al., 2015). This raises some interesting questions regarding the potential role of diatoms in influencing the Cu-PO34- ratios in the oceanic nutricline in the North Pacific.
ACCEPTED MANUSCRIPT 4.2.1 Processes affecting dCu in deep waters (> 600 m) 4.2.2. Sedimentary sources of dCu. Sediments are considered to represent an important source of Cu to the overlying waters, as supported
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by the benthic flux estimates using water column and sediment pore-water Cu profiles (Boyle et al.,
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1977; Heggie et al., 1987; Klinkhammer et al., 1982; Klinkhammer, 1980). Copper may be liberated
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from sedimentary material via aerobic respiration of organic matter (oxic sediments: Klinkhammer et al., 1982; Shaw et al., 1990) or via microbially mediated reductive dissolution of Mn/Fe oxides to which Cu
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is bound (suboxic sediments: Murray, 1975). In contrast, anoxic sediments may to trap Cu due to the
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formation of highly insoluble precipitates with sulfide minerals, which tends to occur under reducing conditions (Morse and Luther, 1999).
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Given the large distance from the deepest sample and the seafloor at most of our stations (~ 1000‒2000
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m, P12‒P26), we only examine the role of benthic sources at the shallowest station, P4 (deepest sample 200 m above the seafloor). The bottom enrichment of dCu at this station (1100 and 1200 m, Fig. 7B) is
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consistent with sedimentary input sources. Bottom sediments at station P4 may be reducing as enhanced
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Fe (II) levels have been measured in deep waters at this station (1000 and 1200 m, Schallenberg et al. 2015), although during a different cruise than those in our study (June 2012 and Aug 2013). Reducing
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conditions in these sediments would likely favor formation of insoluble copper sulfides, thus diminishing the dCu inputs from bottom sediments. However, resuspension of bottom sediments and oxidation of CuS in overlaying waters may help explain the enrichment in bottom dCu concentrations at P4. We did not find evidence of sedimentary inputs of Cu from the continental shelf (~ 200 m), in contrast with other regions of the North Pacific where shelf source of Cu was suggested (Biller and Bruland, 2013). It is possible that scavenging of dCu by sinking particles, which is likely to be intense at P4 given its high productivity in the summer (Whitney et al., 2005), reduced the signature of dCu input from the
ACCEPTED MANUSCRIPT continental shelf. Alternatively, Cu may be scavenged from the dissolved phase upon sediment resuspension (Fischer et al., 1986) on the shelf, or scavenged by sulfide minerals if shelf sediments are sufficiently reducing, both of which would limit Cu inputs into water column at P4.
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4.2.4 dCu in the Oxygen Minimum Zone (OMZ).
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Previous studies strongly suggest that the OMZ of the subarctic NE Pacific acts as a sink for elements such as Ag, Zn, Cd, and potentially Cu (Janssen et al., 2014; Janssen and Cullen, 2015; Kramer et al.,
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2011). Both thermodynamic considerations and field observations suggest that these class B metals are depleted under anoxic conditions via formation of insoluble sulfide solid phases (Jacobs et al., 1985;
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Jacobs and Emerson, 1982; Landing and Lewis, 1991). Since waters within the OMZ of the subarctic NE
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Pacific still contain traces of O2 (lowest in this study O2 = 7 µmol kg-1), the presence of free sulfide in the water column is unlikely as it would become rapidly oxidized by O2. However, sulfidic conditions
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may develop within microenvironments on sinking particles in the OMZ’s (Janssen & Cullen, 2015).
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Formation of insoluble ZnS within these particles may explain the decoupling between Zn and Si distributions within the OMZ waters of subarctic NE Pacific (Janssen & Cullen, 2015).
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Similarly, to the recent Zn study, we also observed a noticeable break in the relationship between Cu
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and Si that coincided with the Oxygen Minimum Zone (300‒2000 m) (Fig. 9B). Analogous to the Zn:Si, decoupling between Cu and Si within these O2 deficient waters might be indicative of the scavenging process described by Janssen and Cullen (2015). However, unlike Zn & Si, the water column behaviour of Cu and Si are not as tightly coupled in the world's oceans, creating some uncertainties in such an interpretation. Furthermore, behaviour of Cu in low O2 settings is not straightforward because processes that act to release and remove dCu may be occurring simultaneously. For instance, dissolution of Mn/Fe-oxyhydroxide minerals (under hypoxic and suboxic conditions), as well as POM
ACCEPTED MANUSCRIPT remineralization, would enhance dCu in the water column of OMZ. Both processes may partially explain the accumulation of dCu we observed in the upper reaches of the OMZ (400 m) where low O2 levels might be favouring reductive dissolution of Cu, and where remineralization of POM is highest. In contrast, formation of CuS within microenvironments on sinking particles would act as a sink of dCu in
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the OMZ. However, it is currently uncertain how effective this mechanism might be in the presence of
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strong organic ligands binding most of the dissolved Cu, even in these deep waters (Moffett and Dupont,
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2007). Furthermore, Cu redox chemistry, which is poorly understood under such conditions, adds another level of uncertainty because Cu in +1 state is a definite class B acceptor (soft metal forming
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stable complexes with donor atoms such as S), while Cu in +2 state can behave as both soft and hard
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metal (forming stable complexes with donor atoms such as N, O, and F; Ahrland et al., 1958). However, along Line P, the dCu concentrations at depths corresponding to the OMZ were generally uniform.
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Similar behaviour of dCu can be seen in a previous study at station P26 by Martin et al. (1989)
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(Supplementary Material, Fig. S3). In contrast, it is typical for dCu concentrations to gradually increase with depth (e.g. Jacquot and Moffett, 2015, Fig. S2), which can be explained by the flux of Cu from
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bottom sediments and particle scavenging throughout the water column (Boyle et al., 1977; Takano et
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al., 2014). The behaviour of dCu within the OMZ of the NE Pacific suggests that it may be scavenged more intensely in these low O2 waters by the process described by Janssen and Cullen (2015).
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Cu distributions within the OMZ waters near Mauritania (Jacquot and Moffett, 2015, Roshan and Wu,2015) and eastern subtropical North Pacific (Nameroff et al., 2002) did not show any anomalies associated with low O2 conditions. In contrast, accumulation of dCu was observed within the secondary nitrite maximum of the eastern tropical South Pacific OMZ, although shelf sources were likely responsible for this trend rather than low O2 conditions (Jacquot et al, 2013). Interpreting Cu behaviour within OMZ’s in continentally influenced study sites, as is the case of these previous investigations, is
ACCEPTED MANUSCRIPT challenging because of strong dCu sources there that may be masking potential removal processes. While further research is required to confirm this, our dataset may support the recent hypothesis for the scavenging of Cu within the OMZ of the Northeast Pacific (Janssen and Cullen, 2015). Using the Cu:Si relationship for each station along Line P, we estimated that approximately 0.01‒1.3 nmol kg-1 of dCu is
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role in reducing the dissolved Cu inventory in the subarctic NE Pacific.
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‘missing’ within the OMZ, suggesting these intermediate low O2 waters may potentially have significant
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5. Conclusions
In the present study, we identified major biogeochemical and physical processes that drive the spatial
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and temporal patterns of dCu along Line P. Our dataset also adds to the growing body of dCu
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measurements across the North Pacific, and aims to improve our understanding of Cu behaviour throughout this basin. In the eastern sections of our transect, fluvial rather than atmospheric inputs from
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North America, were responsible for enhanced dCu levels in surface waters. Remote, Fe-deplete stations
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along Line P were also found to be enriched in dCu, a feature we propose to be largely driven by upwelling in the Gulf of Alaska. Atmospheric deposition may represent an additional source of Cu to
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these remote waters as indicated by the sub-surface dCu enrichments at station Papa, which coincide
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with elevated atmospheric aerosol levels. Furthermore, we explored the possible sources of atmospheric Cu to this region and discuss the potential ecological impacts of these inputs in surface waters at OSP. Across our transect, Cu was strongly correlated with both phosphate and silicate in the nutricline, reflecting the influence of biological processes (uptake and remineralization) on the Cu distribution in this region. We also provided some evidence that dCu may be sensitive to scavenging within the OMZ, behaving similarly to other soft metals such as Cd and Zn in these O2 deficient waters (Janssen et al. 2014, Janssen & Cullen, 2015). Finally, our study addressed some of the recent methodological
ACCEPTED MANUSCRIPT uncertainties regarding dCu analysis. We observed that the length of acidified sample storage largely impacts the outcome of dCu analysis by FIA-CL. With shorter storage periods (up to 2 months) a UV oxidation pre-treatment of the samples is required for accurate measurements of dCu, while this treatment was found to be less important if samples were stored for an extended length of time (several
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years). In addition, we also compared different analytical methods to measure dCu and discussed how
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various methodologies may also contribute to current uncertainties in dCu values. We proposed that the
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interplay between the three factors briefly explored in our study (sample storage, UV oxidation, analytical methodologies) may explain the inconsistencies in dCu concentrations of GEOTRACES
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reference materials and in dCu profiles in the North Pacific.
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Acknowledgements: We would like to hugely thank the Line P cruise participants, especially Jason
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McAllister & Nari Sim for collection of dCu samples, chief scientist Marie Robert, and captains and crews of the CCGS J.P. Tully. Great thanks to Dave Janssen for helpful advice on the interpretation of
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copper and silicate relationship in the OMZ. We would like to thank Rob Middag, Maeve Lohan, Jessica
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Fitzsimmons, Kristen Buck, Travis Mellett and Claire Parker for helpful discussions on the issue of UV oxidation in dCu analysis. Nutrient data were analyzed by the Institute of Ocean Sciences and dissolved
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O2 data is courtesy of the Line P program, which is run by the Institute of Ocean Sciences, Sidney BC.
grant to MM.
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Funding: AP and DS were funded by a Natural Sciences and Engineering Council of Canada Discovery
Author contributions: Sample analysis was performed by AP, DS & HW. All authors contributed to data interpretation, and the manuscript was primarily written by AP.
(Bruland and Franks, 1983; Morley et al., 1993)
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Table 1. Comparison of dCu values (nmol kg-1) determined using FIA-CL with the GEOTRACES intercalibration materials consensus values as of May 2013 (SAFe S, D1 and D2) and NASS-6 certified reference material (gamma-irradiated) for ocean water (National Research Council Canada, http://www.nrc-cnrc.gc.ca/eng/solutions/advisory/crm/certificates/nass_6.html). Also, included values for the new GEOTRACES standard, GSC 318 for which there is no consensus.
Reference material
+ UV ( 2hr)
No UV
SAFe D1 (2015)
2.24 ± 0.19 (n=2)
2.31 ± 0.11 (n=3)
Consensus/ Certified 2.28 ± 0.15
SAFe D2 (2012)
nd
2.34 ± 0.16 (n=3)
2.27 ± 0.11
SAFe D2 (2015)
2.33 ± 0.12 (n=4)
2.27 ± 0.15 (n=3)
2.27 ± 0.11
GSC 318 (2015)
1.46 ± 0.11 (n=5)
1.38 ± 0.02 (n=2)
No consensus
NASS-6 (2012)
nd
3.88 ± 0.15 (n=4)
3.81 ± 0.03
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Figure 1. Map of the Gulf of Alaska showing Line P transect and the 5 sampling stations (P4‒P26). Bathymetry was contoured at 1000 m intervals.
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Figure 2. Profiles of dCu (nmol kg-1) at station P26 (Ocean Station Papa). Samples from August 2011 cruise analyzed with FIA-CL without UV oxidation between Jan‒Feb 2012 (open circles) and analysed again between June‒August 2015 with 2 hr of UV oxidation (closed circles). Also plotted is the dCu dataset reported by Martin et al. (1989) using ADDC-PDDC without UV treatment (triangles).
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Figure 3. Changes in labile Cu with increasing storage time of: (A) 48 hrs, (B) 2 weeks and (C) 2 months. At each time point acidified samples were analyzed without (closed circles) and with 2 hr of UV treatment (open circles) and analyzed by FIA-CL. DCu profile obtained in 2015 (~ 4 years of storage at pH 2) is also shown for reference (triangles). The dataset plotted can be found in Supplementary Material, Table S2.
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Figure 4. Comparison of dCu datasets at station P26 analyzed by FIA-CL with 2 hr of UV oxidation and sample storage at pH 2 of ~ 4 yrs (Aug 2011 cruise, circles) and CSV with UV oxidation and no acidified storage (Aug 2012 cruise, gray triangles) (A). Linear regressions of data obtained in this study by FIA-CL (AP), versus CSV (HW, gray triangles), and versus surface transect data from Semeniuk et al. (2016a) (DS, open squares) (B). The regression fits are: y=1.058x-0.26, r2=0.78, p<0.001 between datasets of AP & HW (using all data points), and y= 0.959x-0.104, r2=0.7, p<0.001 between datasets of AP & DS, excluding the outlier data point at 2 nmol kg-1 dCu.
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Figure 5. Comparison o dCu datasets in the North Pacific. Upper and bottom panel shows plots of selected dCu profiles from different regions (R1‒R7) as indicated in the map. The details of methodologies used in each study are provided in the Supplementary Material, Table S4.
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Figure 6. Potential temperature - salinity plot (θ-S) including all stations along the transect (P4 - ◊, P12□; P16 - x; P20 - ☆; P26 - ∆) with dissolved oxygen (µmol kg-1) as symbol colors.
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Figure 7. Top panel (A) shows profiles of dCu (nmol kg-1), PO43- (µmol kg-1) and sigma-t (kg m-3) in the upper 300 m of stations along the Line P transect. Bottom panel (B) shows depth profiles of dCu, PO43and O2 (µmol kg-1) throughout the entire water column sampled (10‒2000 m) with the bottom depths at each station.
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Figure 9. Macronutrient relationships: dissolved copper with PO43- (A) and dissolved copper with Si(OH)4 (B) using data from the entire transect. Linear regressions (solid lines) were performed using data between 50-400 m for phosphate ([DCu]=0.5533[PO43-] + 1.26, r2 =0.826) and 0-300 m for silicic acid ([dCu]= 0.016[Si(OH)4] + 1.69, r2=0.72). Horizontal dashed lines indicate the extension of linear regressions assuming dCu data was to follow the nutricline trends. Vertical dashed lines represent data points used for the linear regressions.
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Figure 10. Mean mixed layer dCu and dAl trends along the Line P transect in August 2011 (dAl data from Cain, 2013) with an inset showing the spatial surface dCu distribution along the transect.
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Figure 11. Contour plot of dCu in the upper 600 m along the Line P transect in August 2011. White dashed lines with labels represent dissolved PO43- (µmol kg-1). Shoaling of high dCu and phosphate can be seen at the offshore stations (P16‒P26) being suggestive of the upwelling conditions.
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Figure 12. Area averaged Aerosol Optical Depth (AOD at λ=550 nm, AquaMODIS, 1ᵒ resolution) in the Gulf of Alaska between June-August 2010, 2011 and 2012. AOD data was downloaded from NASA Giovanni visualization and analysis online data system, developed and maintained by the NASA GES DISC (http://giovanni.gsfc.nasa.gov/giovanni/) and plotted using MATLAB. The script used to produce the plot can be found in a public repository: https://github.com/AnnaMagdalena/DCu_LineP-Subarctic-Pacific.
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ACCEPTED MANUSCRIPT Highlights: Distribution of dissolved Cu along Line P is presented
Main sources of copper include fluvial inputs near the coast and upwelling of the Alaskan gyre offshore
Atmospheric sources may moderate surface Cu at Ocean Station Papa
Evidence of Cu scavenging within the Oxygen Minimum Zone (OMZ)
For short-term stored acidified samples (≤ 4 months) UV oxidation is crucial prior to Cu analysis by FIA-CL
Aging of acidified samples for several years reduces the need for UV oxidation of samples
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