Nitrate and Nitrite Variability at the Seafloor of an Oxygen Minimum Zone Revealed by a Novel Microfluidic In-Situ Chemical Sensor

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Microfluidics, or lab-on-a-chip (LOC) is a promising technology that allows the development of miniaturized chemical sensors. In contrast to the surging interest in biomedical sciences, the utilization of LOC sensors in aquatic sciences is still in infancy but a wider use of such sensors could mitigate the undersampling problem of ocean biogeochemical processes. Here we describe the first underwater test of a novel LOC sensor to obtain in situ calibrated time-series (up to 40 h) of nitrate+nitrite (ΣNOx) and nitrite on the seafloor of the Mauritanian oxygen minimum zone, offshore Western Africa. Initial tests showed that the sensor successfully reproduced water column (160 m) nutrient profiles. Lander deployments at 50, 100 and 170 m depth indicated that the biogeochemical variability was high over the Mauritanian shelf: The 50 m site had the lowest ΣNOx concentration, with 15.2 to 23.4 μM (median=18.3 μM); while at the 100 site ΣNOx varied between 21.0 and 30.1 μM over 40 hours (median = 25.1μM). The 170 m site had the highest median ΣNOx level (25.8 μM) with less variability (22.8 to 27.7 μM). At the 50 m site, nitrite concentration decreased fivefold from 1 to 0.2 μM in just 30 hours accompanied by decreasing oxygen and increasing nitrate concentrations. Taken together with the time series of oxygen, temperature, pressure and current velocities, we propose that the episodic intrusion of deeper waters via cross-shelf transport leads to intrusion of nitrate-rich, but oxygen-poor waters to shallower locations, with consequences for benthic nitrogen cycling. This first validation of an LOC sensor at elevated water depths revealed that when deployed for longer periods and as a part of a sensor network, LOC technology has the potential to contribute to the understanding of the benthic biogeochemical dynamics.

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Citation: Yücel M, Beaton AD, Dengler M, Mowlem
MC, Sohl F, Sommer S (2015) Nitrate and Nitrite
Variability at the Seafloor of an Oxygen Minimum
Zone Revealed by a Novel Microfluidic In-Situ
Chemical Sensor. PLoS ONE 10(7): e0132785.
doi:10.1371/journal.pone.0132785

Editor: Wei-Chun Chin, University of California,
Merced, UNITED STATES

Received: March 13, 2015

Accepted: June 19, 2015

Published: July 10, 2015

Copyright: © 2015 Yücel et al. This is an open
access article distributed under the terms of the
[Creative Commons Attribution License, which permits](http://creativecommons.org/licenses/by/4.0/)
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.

Data Availability Statement: All relevant data are
within the paper and its Supporting Information files.

Funding: This study was supported by the
Helmholtz-Alliance ROBEX (Robotic Exploration of
[Extreme Environments - http://www.robex-allianz.de/](http://www.robex-allianz.de/en)
[en, funds to SS, FS) and the collaborative research](http://www.robex-allianz.de/en)
center Climate-Biogeochemistry Interactions in the
[Tropical Ocean (SFB754 - www.sfb754.de, funds to](http://www.sfb754.de)
SS, MD).

Competing Interests: The authors have declared
that no competing interests exist.

RESEARCH ARTICLE
# Nitrate and Nitrite Variability at the Seafloor of an Oxygen Minimum Zone Revealed by a Novel Microfluidic In-Situ Chemical Sensor

Mustafa Yücel 1,2 *, Alexander D. Beaton 3, Marcus Dengler 1, Matthew C. Mowlem 3,
Frank Sohl 4, Stefan Sommer 1

1 GEOMAR Helmholtz Centre for Ocean Research, Kiel, Germany, 2 Middle East Technical University
(METU), Institute of Marine Sciences, Erdemli, Mersin, Turkey, 3 National Oceanography Centre
Southampton, Ocean Technology and Engineering Group, Southampton, United Kingdom, 4 DLR German
Aerospace Center, Institute for Planetary Science, Berlin, Germany

- myucel@ims.metu.edu.tr

## Abstract

Microfluidics, or lab-on-a-chip (LOC) is a promising technology that allows the development

of miniaturized chemical sensors. In contrast to the surging interest in biomedical sciences,

the utilization of LOC sensors in aquatic sciences is still in infancy but a wider use of such

sensors could mitigate the undersampling problem of ocean biogeochemical processes.

Here we describe the first underwater test of a novel LOC sensor to obtain in situ calibrated
time-series (up to 40 h) of nitrate+nitrite (ΣNOx) and nitrite on the seafloor of the Mauritanian oxygen minimum zone, offshore Western Africa. Initial tests showed that the sensor

successfully reproduced water column (160 m) nutrient profiles. Lander deployments at

50, 100 and 170 m depth indicated that the biogeochemical variability was high over the
Mauritanian shelf: The 50 m site had the lowest ΣNOx concentration, with 15.2 to 23.4 μM
(median=18.3 μM); while at the 100 site ΣNOx varied between 21.0 and 30.1 μM over 40
hours (median = 25.1μM). The 170 m site had the highest median ΣNOx level (25.8 μM)
with less variability (22.8 to 27.7 μM). At the 50 m site, nitrite concentration decreased fivefold from 1 to 0.2 μM in just 30 hours accompanied by decreasing oxygen and increasing

nitrate concentrations. Taken together with the time series of oxygen, temperature, pres
sure and current velocities, we propose that the episodic intrusion of deeper waters via

cross-shelf transport leads to intrusion of nitrate-rich, but oxygen-poor waters to shallower

locations, with consequences for benthic nitrogen cycling. This first validation of an LOC

sensor at elevated water depths revealed that when deployed for longer periods and as a

part of a sensor network, LOC technology has the potential to contribute to the understand
ing of the benthic biogeochemical dynamics.

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 1 / 16

Lab-on-Chip Chemical Measurements at Seafloor

Introduction

In situ, high frequency observations are crucial to uncover the temporal and spatial complexity
in the aquatic environments [1]. Considerable progress has been made with the increasing use
of in situ sensors on a variety of platforms; however these devices mostly address physical
parameters such as temperature, light or pressure [2, 3]. On the other hand, with the notable
exception of oxygen [4,5], pH [6] and nitrate at low (μM) resolution (see below), routine calibrated and accurate measurements of chemical parameters still depend on spatially and temporally limited sampling schemes and subsequent analyses onboard research vessels or in shorebased laboratories. Hence, the biogeochemical variability of oceanic habitats is so far largely
unknown but is ultimately important to understand the productivity, elemental cycles and
ocean response to a changing global climate.

As a limiting nutrient of biological productivity, nitrate (NO3-) and nitrite (NO2-) are

among those parameters for which several sensor technologies already exist, such as a UVspectrophotometry based system reported by Johnson et al. [7] (also see [8, 9]), and the colorimetric system by Le Bris et al. [10] (for reviews see [11–13]). Some nitrate sensors have become
commercially available (such as Satlantic ISUS, Systea WIZ), and recent applications of these
sensors uncovered the high temporal variability in NO3- in the open ocean [8]. The temporal

-) and nitrite (NO2

As a limiting nutrient of biological productivity, nitrate (NO3

sensors uncovered the high temporal variability in NO3- in the open ocean [8]. The temporal

variability can be even higher in estuaries, where current can be fast and freshwater inputs are
significant [14]. Despite these improvements, the capacity of many nutrient sensors to perform
long term, stable measurements at elevated water depths may be limited due to shallow depth
range, large size, large power consumption and/or high detection limits.

The bottom waters of oceanic oxygen minimum zones (OMZ) are one of those environments for which there is almost no information on the short-term temporal nutrient variations.
Here, the availability of NO3-/NO2- drives the anaerobic degradation of organic matter in the

Here, the availability of NO3-/NO2- drives the anaerobic degradation of organic matter in the

water column and the sediment surface [15]. Moreover, these electron acceptors are involved
in the anaerobic oxidation of NH4+, which is released in high amounts from the seafloor under

-/NO2

in the anaerobic oxidation of NH4+, which is released in high amounts from the seafloor under

anoxic conditions [16, 17]. In organic-rich sediments NO3-/NO2- can be further involved in

the oxidation of sulfides [18]. Besides consumption and production, physical transport processes such as currents, submesoscale eddies or passage of internal waves can also drive the variability of these nutrients over continental shelves below OMZs [19].

To accurately constrain the variability of NO3-/NO2- at the seafloor of an OMZ, we need

pressure-insensitive, robust sensors with the capability of in situ calibration. The in situ calibration is particularly important for an application at a fixed point at the seafloor, where there is
potential for variability over short time scales. In this regard, microfluidics, or ‘lab-on-a-chip’
(LOC) is a promising technology that meets several demands needed for autonomous measurements at elevated depth such as low weight, low energy demand, and low volume consumption
of reagents and samples [20, 21] and ease of integration into underwater platforms. Having
been mostly developed for medical and pharmaceutical research [22], LOC technology is now
being transferred to aquatic environmental research as well [23–27]. Newly developed LOC
devices can measure not only NO3- and NO2-, [23, 26, 27] but also PO43- [28] and dissolved Fe

-/NO2

To accurately constrain the variability of NO3

-/NO2

devices can measure not only NO3- and NO2-, [23, 26, 27] but also PO43- [28] and dissolved Fe

and Mn [29]. Among these devices, the NO3- / NO2- LOC system is approaching maturity for

- and NO2

-, [23, 26, 27] but also PO4

and Mn [29]. Among these devices, the NO3- / NO2- LOC system is approaching maturity for

time-series applications but, prior to the work reported here, its suitability for the in situ
deployment at elevated depth has not been demonstrated.

- / NO2

Using a new generation LOC sensor that was capable of in situ calibration, we report the
first in situ time series (up to 40 h) of NO3-+ NO2- (SNOx) and NO2- at the seafloor (max.

first in situ time series (up to 40 h) of NO3-+ NO2- (SNOx) and NO2- at the seafloor (max.

170 m depth) in an upwelling region offshore Western Africa. We combine these data with
time series of oxygen, temperature and bottom currents to demonstrate that the LOC

-+ NO2

- (SNOx) and NO2

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 2 / 16

Lab-on-Chip Chemical Measurements at Seafloor

technology provides a previously unavailable window into the variability of nutrient concentrations in the bottom waters of a low-oxygen coastal ocean.

Methods

Study Area

The study site was near 18°N in the Mauritanian upwelling region which is part of the Canary
eastern boundary upwelling system that roughly extends between 43°N to 10°N [30, 31]. Fieldwork permit was obtained from Mauritanian Ministry of Fisheries and Maritime Economy
(Ministère des Pêches et de l'Economie Maritime, Permit No: 296, dated May 14, 2014). Coastal
upwelling near 18°N off Mauritania exhibits a pronounced seasonality where winds favorable
to upwelling prevail primarily from December to April. Due to the weak mean circulation in
the eastern tropical Atlantic, an oxygen minimum zone is situated below the surface layers.
While the core of the main OMZ is found at about 400 m depth, a secondary oxygen minimum
is situated below the surface mixed layer and above 200 m depth [32]. The ventilation of the
waters above the continental margin occurs primarily through the Mauritania Current [33] in
the near-surface layers and the Poleward Undercurrent below [34]. Both currents transport relatively oxygen-rich South Atlantic Central Water, which is supplied by the eastward flowing
North Equatorial Countercurrent and North Equatorial Undercurrent, northward into the
upwelling region.

A physical-biogeochemical measurement program was performed during the R/V Meteor
cruise M107 from May 28 to July 3, 2014 during the termination period of the upwelling season
as a part of a collaborative research center Climate-Biogeochemistry Interactions in the Tropical Ocean (SFB754) and the Helmholtz-Alliance ROBEX (Robotic Exploration of Extreme
Environments). The observational program included benthic lander deployments, conductivity-temperature-depth-oxygen (CTD/O2) profiling paired with water sampling and mooring
deployments measuring currents and hydrography along a transect at 18°N.

Lander deployments at the Bottom Boundary Layer

A GEOMAR benthic lander (thereafter as the Lander) was used as a platform to make autonomous in situ measurements [35]. The Lander was deployed on the sea floor at depths of 50, 100
and 170 m (Table 1). These depths were chosen as bottom water oxygen time series displayed
high variability during a previous cruise [36] (R/V MS Merian leg 17/4) and benthic nitrogen
turnover rates were high due to hypoxia [17]. At all locations, the LOC sensor (Ocean Technology and Engineering Group, National Oceanography Centre Southampton) was attached to
the lander and at the 50 and 100 m sites this LOC system was complemented with a CTD
probe (RBR, Ottawa, Canada) and an O2 optode (Aandrea, Bergen, Norway). For the deployment at the 50 m site an additional LOC sensor measuring NO2- was added. Prior to the Lander

Table 1. Details of underwater operations during R/VMeteor cruise M107 where autonomous measurements were performed.

Depth (m) Operation RV Meteor
Station

Longitude
(N)

Latitude
(W)

Measured Parameters Measurements Start
(2014, UTC)

Measurements End
(2014,UTC)

50 Lander 3 M107-687 18°17.0' 16°19.0' NO3-+ NO2

100 Lander 2 M107-633 18°14.7' 16°27.0' NO3-+ NO2

2-, NO2-, O2, CTD June 25, 15:00 June 26, 23:00

2-, NO2

-+NO2
-+NO2

170 Lander 1 M107-572 18°14.2' 16°31.0' NO3

170 CTD #16 M107-559 18°14.0' 16°31.0' NO3

2-, O2, CTD June 21, 15:00 June 23, 07:00

- June 13, 16:00 June 14, 14:00

- June 12, 15:00 June 12, 17:10

50 POZ Lander M107-505 18°16.0' 16°19.0' Current velocity June 8, 18:00 June 27, 07:00

50 BIGOII-4 Lander M107-665 18°17.1' 16°19.0' NO2-, syringe samples

doi:10.1371/journal.pone.0132785.t001

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 3 / 16

Lab-on-Chip Chemical Measurements at Seafloor

deployments, the LOC sensor was tested in situ on the CTD rosette to compare the sensor
results to concentration measurements from water samples analyzed on-board using an autoanalyzer (Quattro, Seal Analytical, UK).

Application of the Lab-on-Chip Nitrate/Nitrite Sensor

The LOC SNOx sensor was previously described in detail by Beaton et al. [23, 26]. That paper
as well as Ogilvie et al. [37] and Floquet et al. [38] described various aspects of the lab-on-achip approach for nutrients. In short, the chip used in the sensor was made up of PMMA and
harbored precision-milled microchannels (< 300 μm), mixers and optical components such as
LEDs (525 nm) and photodiodes. An integrated syringe pump, valves and electronics complemented the chip and are encased in mineral oil-filled housing (PVC, 12 cm diameter, 30 cm
height) with an internally fitted pressure-compensating bladder. The system ran autonomously
storing the data in a memory card and was powered by an external battery. The unit weighed
1.1 kg in water without and 1.5 kg with an internal battery.
The sensor uses attached standards and blank for regular in situ calibrations along with
sample (0.45 μm filtered) measurements, using the colorimetric Griess assay for NO2- detection

sample (0.45 μm filtered) measurements, using the colorimetric Griess assay for NO2- detection

[39]. The addition of an off-chip Cu-activated Cd column enabled NO3-+NO2- (SNOx) detec

[39]. The addition of an off-chip Cu-activated Cd column enabled NO3-+NO2- (SNOx) detec
tion through the reduction of NO3- to NO2- and subsequent on-chip analysis with a detection

tion through the reduction of NO3- to NO2- and subsequent on-chip analysis with a detection

limit of 20 nM, an accuracy of 0.6% and a precision of 7 nm at low concentrations or 0.5% at
high concentrations [23]. The deployment involved externally attached gas impermeable Flexboy bags (150 mL, Sartorius, UK) that contained two standard solutions (for either SNOx or
NO2-, as needed), Griess reagent, artificial seawater blank and imidazole buffer. The prepara

- to NO2

-+NO2

NO2-, as needed), Griess reagent, artificial seawater blank and imidazole buffer. The prepara
tion of the standards and reagents followed previous protocols [40, 23, and 26]. Waste was collected in a 500 mL Flexboy bag. For example, for a deployment to measure SNOx, the sensor
started to perform two sets of calibrations (artificial seawater blank and 11.3 and 33.9 μM standards for NO3-) followed by a repeating sequence involving the measurement of blank, sample,

dards for NO3-) followed by a repeating sequence involving the measurement of blank, sample,

standard solution and sample again. Each of these steps included 6 flushing cycles to avoid any
carryover. The final flush cycle was followed by a 100-second waiting stage that enabled color
development and the photometric measurement at 525 nm. A set of flushes and the waiting
stage altogether took about 7 minutes. The sensor recorded optical absorbance every 1 second
and the average of the last 3 reading of the waiting stage was used for calculations. Raw data
were processed in R 3.0.2 [41]. With this scheme, we obtained one blank-corrected sample
measurement every 14 minutes and one standard solution measurement every 28 minutes during deployment. For NO2- measurements, the off-chip Cd column was replaced with a short

ing deployment. For NO2- measurements, the off-chip Cd column was replaced with a short

tube. The sensor operated the same as SNOx sensor, with the only exception that the standard
solutions of 0.5 and 2 μM NO2- were used.

- were used.

Results and Discussion

First tests of the LOC analyzer at elevated depth

Two mid-depth O2 minima, sharp nitracline (increasing nitrate concentration over a relatively
small depth range in the water column) and a subsurface NO2- peak were persistent features in

different CTD casts at the Mauritanian Shelf (one example shown in Fig 1A). Before the Lander
deployments, we assessed the quality of the LOC sensor to reproduce vertical nutrient gradients by comparing it to concentrations obtained from water samples. It was therefore attached
to the CTD frame during a profile to a depth of 160 m (CTD #16, S1 Fig). The sensor started to
perform two sets of calibrations while the CTD was being lowered to 160 m. During the upcast,
the rosette was stopped for 14 minutes at 7 different depths to allow for the in situ analysis of
the blank (or a standard) and a sample. In the final minute of this period, a Niskin bottle

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 4 / 16

Lab-on-Chip Chemical Measurements at Seafloor

Fig 1. Water column chemistry in two stations in the Mauritanian OMZ (A) Water column profile of oxygen, NO3-+ NO2- (ΣNOx) and NO2- during a CTD

cast (#15) at 1200 m depth. Both casts were conducted on June 12, 2014 at 16°48.32' W and 18°09.00' N. (B) Profiles from CTD cast #16 at a depth of 160 m
where a LOC sensor was also attached to the CTD frame. “AA” stands for autoanalyer. These profiles were obtained on June 12, 2014 at 18°14.0' W and 16°
31.0' N.

-+ NO2

Fig 1. Water column chemistry in two stations in the Mauritanian OMZ (A) Water column profile of oxygen, NO3

- (ΣNOx) and NO2

doi:10.1371/journal.pone.0132785.g001

sample was taken. Overall, the SNOx distribution obtained from the LOC agreed well with the
distribution of SNOx obtained from shipboard autoanalyzer (AA) water samples (Fig 1B). All
profiles showed that SNOx was depleted at the surface with a nitracline located at about 20–40
m depth, below which SNOx increased to about 30 μM. The location of the nitracline coincided
with a NO2- concentration peak of 0.6 μM at 30 m, which was also the upper boundary of the

O2 minimum (29 μM after 60 m) extending down to 160 m.

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 5 / 16

Lab-on-Chip Chemical Measurements at Seafloor

Fig 2. Comparison of autoanalyzer (AA) and LOC measurements (A) Autoanalyzer and LOC ΣNOx measurements on samples obtained from hydrocast
CTD#2, from 1000 m. Here, LOC was operated on a shipboard laboratory bench. (B) Autoanalyzer and in situ LOC ΣNOx measurements on samples
obtained from hydrocast CTD#16, from 170 m.

doi:10.1371/journal.pone.0132785.g002

Analytical Uncertainty Assessment

In addition to the submerged LOC tests during hydrocasts, we have performed additional comparisons (LOC measurement also shipboard) on bottle samples from another hydrocast at
1000 m (CTD#2). The combined scatter plots showing autoanalyzer (AA) and LOC measurements during CTD#2 and 16 (Fig 2) indicate that the results from the two approaches are
highly correlated (R 2 - 0.99). The differences at high (>5 μM) concentrations were comparable to the estimated uncertainties of the two approaches, which were calculated from two times
the standard deviations of successive calibrations (on board for autoanalyzer and in situ for
LOC). This analysis yielded a value of ±0.1 μM for the autoanalyzer and ±0.4–1 μM for the
LOC analyzer (summarized in Table 2). Considering these inherent analytical uncertainties
with the differences in sample handling by different operators, we can assert that the LOC
nutrient sensor was capable of delivering in situ measurements at elevated depth with an analytical performance only slightly less precise than the established techniques.

Table 2. Summary of the analytical performance derived from in situ measurements of standard solutions (for LOC) and on board measurements
(Autoanalyzer).

Series Calibration solution, μM Number of measurements % Standard deviation from mean * Estimated uncertainty, μM **

170 m ΣNOx 33.9 46 0.6 0.40

100 m ΣNOx 11.3 85 4.3 0.98

50m ΣNOx 11.3 63 3.4 0.76

50m NO2- 2.0 63 1.6 0.08

Autoanalyzer 5.8 10 0.8 0.10

- Average value of the moving (5) standard deviations

** Calculated as the average of concentrations corresponding to 2 times the moving (5) standard deviations. These uncertainties are also shown on Figs

3 and 4.

doi:10.1371/journal.pone.0132785.t002

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 6 / 16

Lab-on-Chip Chemical Measurements at Seafloor

Fig 3. An example of the raw output of the lab-on-chip nitrate sensor. (A) Photodiode output showing the two sets of calibration performed in situ in the
beginning of the series at 170 m depth. After the measurement of a blank (Blk), two standards solutions of NO3- (Std1 and Std2) were measured and the

calibration plot in (B) was obtained. Panel (C) shows an excerpt of the photodiode output during the same time series at 170 m depth, 7–9 hours after the first
measurement. Blank solutions and the 33.9 μM standard solution were regularly measured in between sample (S) measurements, The fluctuating voltages
were recorded during the flushing of the microfluidic chip and do not affect the measurements themselves.

doi:10.1371/journal.pone.0132785.g003

In addition to the water sample comparison to validate sensor performance, it was also verified that the sensor performed reproducible in situ calibrations throughout all deployments,
essential to obtain reliable time series data. An excerpt of raw photodiode readings featuring in
situ blank, standard and sample measurements during the deployment at 170 m is shown in
Fig 3. During all deployments no significant drift in the in situ calibrations was observed, however a random variability was present between successive calibrations from which sensor
uncertainties were estimated (Table 2). The one blank-corrected sample per 14-minute measurement frequency used in this work should be considered as a minimum because we have
aimed to fully utilize the in situ calibration feature and we had to use relatively high number of

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 7 / 16

Lab-on-Chip Chemical Measurements at Seafloor

6 flushing cycles due to high NO3- concentrations. Although we have not investigated a full
optimization, we think that the frequency can increase to 1 measurement per 10 minutes with
less frequent blank/standard analyses. Using a smaller number of flushing cycles can further
increase the sample analysis frequency for low concentration samples (<5 μM). Use of newly
emerging microfluidic architectures such as multiplexed stop flow [42] can enable multiple
simultaneous measurements on the same chip, increasing the frequency to one sample per
minute or more.

Nitrate and nitrite dynamics in the bottom boundary layer

The CTD casts only provided a snapshot of a dynamic system while the time series for SNOx
from the bottom waters at 50, 100 and 170 m (Fig 4) revealed high variability. The 50 m site
had the lowest SNOx concentrations (15.2 to 23.4 μM) while the 100 m site had a larger SNOx
range (21 to 30.1 μM) recorded in 40 hours. Over a 20-hour period, the 170 m site had SNOx
levels ranging from 22.8 to 27.7 μM, with 5–8 hours of very stable levels interrupted by more
variable values. The median values of the time series measurements increased with depth, consistent with the trend suggested by the water column profiles. While the long-term (hours) variability was remarkable (discussed in detail in the next section), short-term variability on time
scales less than hour was also high in certain periods. To compare the inherent sensor precision
with natural variability, we have also plotted a ‘moving uncertainty’, calculated from two times
the moving (n = 5) standard deviations of the successive in situ measurements of a standard
solution (Fig 4 and Table 2). This comparison showed that most of the short-term variation
less than a micromolar could be attributed to sensor response. However at certain periods
there were marked deviations from the expected uncertainty, such as those in the 170 m time
series (Fig 4C). The most likely physical explanation for the short-term SNOx variability is the
propagation of bores and non-linear internal wave trains, observed at the upper continental
slope and the shelf off Mauritania at 18°N [19]. They are generated by interaction of barotropic
and low-mode baroclinic tides with topography [43] and propagate onshore. Individual nonlinear internal waves within the bores exhibited periods of 10 to 15 minutes and associated vertical velocities exceeded 0.15 m s -1 while on the continental slope they were particularly pronounced between 75 and 200m depth [19]. During their passage, they vertically displace water
over a depth range of more than 70m. Thus we hypothesize that the short-term variability was
due to the downward displacement of water having lower SNOx concentrations from above.
Non-linear internal waves have been observed on continental slopes in many continental
slopes in the world ocean [44]. We suggest that they cause elevated high-frequency variability
of near-bottom SNOx concentrations and other solutes having vertical concentration gradients
in the lower 50m to 100m of the water column, which may affect biogeochemical processes in
the sediments and within the bottom boundary layer.

At 50m, a location near the subsurface NO2- peak (Fig 1), we deployed a second LOC system

that measured NO2- only in addition to the LOC measuring SNOx. The real-time clocks of the

At 50m, a location near the subsurface NO2

that measured NO2- only in addition to the LOC measuring SNOx. The real-time clocks of the

two sensors were synchronized, which enabled measurement of NO2- simultaneously with

NO3- via subtracting NO2- from SNOx. In the series, shown in Fig 5, NO2- decreased from

1 μM to about 0.2 μM within 30 hours. Nitrate increased through the series with decreasing
NO2-, suggesting a different water mass moved in, possibly originating from deeper waters

- via subtracting NO2

- from SNOx. In the series, shown in Fig 5, NO2

NO2-, suggesting a different water mass moved in, possibly originating from deeper waters

where NO3- is high and NO2- is low. Remarkably, a similar magnitude of variability in the

NO3- NO2- relationship was documented by measurements of bottom water samples sequen

NO3- NO2- relationship was documented by measurements of bottom water samples sequen
tially taken via another benthic lander deployment (BIGOII-4, see Table 1) at the same position
measuring benthic fluxes. This was deployed for two days prior to the Lander deployment and
stopped sampling about 13 hours before the LOC sensors started measuring at the same depth.

- NO2

- is high and NO2

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 8 / 16

Lab-on-Chip Chemical Measurements at Seafloor

Fig 4. Time series measurements of NO3-+ NO2- (ΣNOx) in bottom waters. The time axis indicates time
starting from the first sensor measurement in each respective deployment (see Table 1 for dates). The gray
line in each series denotes a 12-point moving average. Also plotted as gray shading is ‘moving uncertainity’
corresponding to concentrations calculated from two times the moving (n = 5) standard deviations of the
successive in situ measurements of a standard solution (Table 2). Any point outside the gray area very likely
reflects natural variability.

doi:10.1371/journal.pone.0132785.g004

When LOC measurements are appended to the results of the discrete bottom water measurements (Fig 5, inset), a more complete picture emerges where the starting NO2- poor, NO3- rich

ments (Fig 5, inset), a more complete picture emerges where the starting NO2- poor, NO3- rich

waters were replaced by waters higher in NO3- and lower in NO2- through the middle of series.

At the end of this period, the bottom water chemistry returns to the starting composition, suggesting a ~3 day cycle.

- and lower in NO2

- poor, NO3

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 9 / 16

Lab-on-Chip Chemical Measurements at Seafloor

Fig 5. Simultaneous measurements of NO3- and NO2- at 50 m depth. Gray lines indicate 12-point moving averages. Also plotted as gray shading are

‘moving uncertainities’ calculated from two times the moving (n = 5) standard deviations of the successive in situ measurements of a standard calibration
solution. The data from bottom water samples from a previous lander deployment (BIGOII-4) at this depth are also given in the inset. The time zero is set at
the beginning of the BIGOII-4 deployment. When the time series from the lab-on-chip sensor are appended to the BIGOII-4 data (inset), a 3-day cycle
emerges where the original NO2- poor, NO3- rich bottom water is replaced by water of higher NO2- and lower NO3-. The water chemistry returns to original

Fig 5. Simultaneous measurements of NO3

- and NO2

- poor, NO3

- rich bottom water is replaced by water of higher NO2

- and lower NO3

emerges where the original NO2- poor, NO3- rich bottom water is replaced by water of higher NO2- and lower NO3-. The water chemistry returns to original

situation at the end of the 72-hour series.

doi:10.1371/journal.pone.0132785.g005

Drivers of the long-term (hours) benthic biogeochemical variability

In order to account for the observed variability of nutrients over several hours, we also
recorded physical parameters and oxygen at the 50 and 100m sites (Fig 6). In both sites, the
tides did not explain any of the nutrient variability. Cross correlation matrices of the series (S2
Fig) revealed that the 50 m site had more correlated pairs than the 100 m site. Considering this
difference, and the fact that our dataset is more complete for the 50 m site, we focus our discussion on the 50 m site. At the 50 m series, the NO2-, which correlated inversely with NO3
(Spearman Rank Correlation, r = -0.59, p<0.01), also had a significant inverse correlation with
temperature (T) (r = -0.52, p<0.01) and positive correlation with O2 (r = 0.58, p<0.01). Nitrate
had weaker, but still significant correlations (in opposite sign to that of NO2-) with these

-, which correlated inversely with NO3

had weaker, but still significant correlations (in opposite sign to that of NO2-) with these

parameters. Taken together, these findings suggest that during the course of the 50m time
series NO3- rich NO2- -poor cooler waters replaced the original warmer, O2-rich, NO2- rich

series NO3- rich NO2- -poor cooler waters replaced the original warmer, O2-rich, NO2- rich

waters. Therefore the overall trend was towards increasing temperatures, decreasing NO2

- rich NO2

- -poor cooler waters replaced the original warmer, O2-rich, NO2

waters. Therefore the overall trend was towards increasing temperatures, decreasing NO2- and

O2 while increasing NO3- (Fig 4 and Fig 6a).

What drives these non-tidal variations in the chemical and physical parameters? To attempt
an explanation we examined the velocity profiles from an acoustic Doppler current profiler
(ADCP) that was deployed on the sea floor at the depth of 50 m, close to the Lander deployment site (Table 1). Over a 15-day period, the alongshore and across-shore currents fluctuated

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 10 / 16

Lab-on-Chip Chemical Measurements at Seafloor

Fig 6. Time-Series of other parameters obtained during the lander deployments. (A) Time series of O2, pressure, salinity and temperature (black)
plotted with NO2- series (gray) in the 50 m site. (B) Time series of O2, pressure, salinity and temperature (black) plotted with ΣNOx series (gray) in the 100 m

site.

doi:10.1371/journal.pone.0132785.g006

significantly reaching velocities of 0.4 m s -1 (Fig 7). Close to the sea floor at 45–50 m depths, a
notable northward flow existed with intermittent onshore and offshore flow. Focusing on the
period of chemical time series at 50 m (June 23–27 2014, Fig 7); we found that the high- NO2
low- NO3- period coincided with the maximum northward alongshore flow in much of the

water column and elevated offshore flow in the near bottom layer. When the alongshore flow
decreased on June 26, an onshore flow in the near bottom layer became pronounced attaining
velocities up to 0.1 m s -1 . The change in the current direction is coincident with the steep
decrease in NO2-, and indicates deep-water movement towards the shore.

low- NO3

decrease in NO2-, and indicates deep-water movement towards the shore.

The correlation between near-bottom onshore flow variability and alongshore flow variability in the water column can be interpreted in terms of bottom-boundary layer Ekman dynamics. Due to bottom friction and the Coriolis force, a northward alongshore flow above the
bottom boundary layer will generate an offshore flow component in the bottom boundary
layer, which is in agreement with velocity time series (Fig 7). Thus, during the period of elevated northward flow on the shelf, the intrusion of deeper waters was blocked and water masses
from the shallower shelf regions that were low in NO3- and high in NO2- were advected off

from the shallower shelf regions that were low in NO3- and high in NO2- were advected off
shore. When the northward current ceased, deeper waters started to move on shore, which
resulted in higher NO3-, lower NO2- and lower O2 concentrations (Fig 6). While the northward

- and high in NO2

resulted in higher NO3-, lower NO2- and lower O2 concentrations (Fig 6). While the northward

flow on the shelf can generally be attributed to the Mauritanian current during this period [32],
the elevated northward velocities between June 22 and June 26 represent an intermittent feature that is probably due to local wind forcing. One biogeochemical implication is that these
fluctuating currents may lead to periodic pumping of deep waters at the 50 m site. At this shallow site this might be an important mechanism for nutrient transport to support primary production as well as for the supply of electron acceptors to drive denitrification and the

-, lower NO2

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 11 / 16

Lab-on-Chip Chemical Measurements at Seafloor

Fig 7. Alongshore and across shore velocities (m s -1 ) at the 50 m site. The velocities were measured with an upward-looking ADCP on a lander at 50m
on the 18° N transect. Positive alongshore velocities values stand for northward flow while positive across shore velocities represent flow towards the shore.
The NO2- (μM) series shown in Fig 4 are also added to indicate that the high- NO2- periods were associated with strong positive alongshore velocities and

The NO2- (μM) series shown in Fig 4 are also added to indicate that the high- NO2- periods were associated with strong positive alongshore velocities and

negative across shore velocities.

- (μM) series shown in Fig 4 are also added to indicate that the high- NO2

doi:10.1371/journal.pone.0132785.g007

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 12 / 16

Lab-on-Chip Chemical Measurements at Seafloor

subsequent nitrogen redox process in the upper sediments. While the hydrodynamicallydriven variability in NO3- concentrations is pronounced in the seafloor of the Mauritanian

driven variability in NO3- concentrations is pronounced in the seafloor of the Mauritanian

Upwelling, the variability is even more dramatic for NO2- when relative changes are considered

(Fig 5). At the 50 m site, the bottom water NO2- concentration decreased fivefold, from 1 μM

to 0.2 μM in just 30 hours. Porewater NO2- concentrations in the surface sediments at this site

were in the order of several hundred nanomolars (ref. [17] and unpubl. data from M107).
Hence, the dramatic change in the bottom water NO2- concentration can influence the direc

Hence, the dramatic change in the bottom water NO2- concentration can influence the direc
tion and magnitude of the benthic NO2- fluxes. Future longer-term deployments with a net

tion and magnitude of the benthic NO2- fluxes. Future longer-term deployments with a net
work of nutrient sensors can answer questions such as whether or not this dramatic variability
in NO2- exists at other sites and how this variability should be accounted for in determining

in NO2- exists at other sites and how this variability should be accounted for in determining

the benthic biogeochemical feedback in this productive coastal ocean.

Conclusions and Future Perspectives

We have successfully deployed a new prototype microfluidic lab-on-chip (LOC) NO3

-/ NO2

sensor in the bottom waters of a productive coastal ocean. To our knowledge this is the
first report of time series from a LOC NO3-/ NO2- sensor in a deep underwater setting. We

first report of time series from a LOC NO3-/ NO2- sensor in a deep underwater setting. We

found that the analytical performance of the sensor at elevated depth was comparable to a
conventional autoanalyzer. Coupled to time series measurements of oxygen and currents, the
LOC measurements recorded large variations in nutrients, probably linked to cross-shelf
water transport occurring in relation to the dynamics of the alongshore flow on the Mauritanian shelf. Relative variability of NO2- in the bottom waters was larger and this could affect

-/ NO2

nian shelf. Relative variability of NO2- in the bottom waters was larger and this could affect

the magnitude and direction of benthic biogeochemical fluxes in this low-oxygen coastal
ocean.

Although we have not tested the LOC sensor in situ at depths beyond 170 m, our results
show that the microfluidic sensor approach is not compromised by elevated pressures and
therefore has a high potential for deep-water applications. New developments in sensor technology (such as miniaturization and new microfluidic architectures) are expected to decrease
the size of these devices, allow the introduction of new chemical parameters, increase data
acquisition frequency and enhance the applicability of the sensor towards Lagrangian (moving)
platforms. These developments will not only be beneficial to Earth observation but also to
space and planetary exploration—indeed this motivation was the starting point of our sensor
testing work under the framework of Helmholtz Alliance ROBEX (Robotic Exploration of
Extreme Environments) [45]. Particularly, having robust sensors at high technological maturity for underwater applications could contribute to future missions to active icy moons such
as the Jovian moon Europa and Enceladus in the Saturnian system [46]. As the potential habitability of these moons is receiving increasing attention; the detection of biosignatures, dissolved
nutrients or energy-yielding chemicals in the subsurface oceans [47] of these icy satellites
would considerably benefit from in-situ analysis. In order to support this vision, and more specifically to increase the technological readiness of the new sensors, we need to continue to
bring new generation chemical sensors to extreme environments such as deep sea, assess their
capabilities and limitations and eventually implement sensor networks that will pave the way
for more extensive testing and the much-needed highly resolved in situ biogeochemical
datasets.

Supporting Information

[S1 Fig. The LOC sensor as attached to the CTD rosette and the benthic lander.](http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0132785.s001)
(TIFF)

PLOS ONE | DOI:10 1371/journal pone 0132785 July 10 2015 13 / 16

Lab-on-Chip Chemical Measurements at Seafloor

[S2 Fig. Cross correlation matrices for the multi-parameter datasets obtained from 50 and](http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0132785.s002)
100m. Units are μM for NO2-, NO3- and O2; dbars for pressure and °C for temperature. Stars

indicate significant correlation (p<0.01) between the pair.
(TIFF)

[S1 File. All data from the LOC sensors and autoanalyzer measurements on bottle samples.](http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0132785.s003)
(ZIP)

[S2 File. All data from the POZ landers on the current measurements in the bottom waters.](http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0132785.s004)
(ZIP)

Acknowledgments

We thank the Captain and crew of RV Meteor cruise M107 for a very productive research
cruise in the Eastern Atlantic. In situ sensor deployments would not have been possible without
the excellent support from Sonja Kriwanek, Matthias Türk and Asmus Petersen. We thank
Andy Dale, Bettina Domeyer and Anke Bleyer for the autoanalyzer nutrient measurements.
Lastly, MY would like to thank the members of the NOC OTEG group for their hospitality and
support during his research visits to Southampton.

Author Contributions

Conceived and designed the experiments: MY ADB MD MCM FS SS. Performed the experiments: MY MD SS. Analyzed the data: MY ADB MD SS. Contributed reagents/materials/analysis tools: ADB MD MCM FS. Wrote the paper: MY ADB MD MCM FS SS.

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