A Portable Tunable Diode Laser Absorption Spectroscopy System for Dissolved CO2 Detection Using a High-Efﬁciency Headspace Equilibrator

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Article A Portable Tunable Diode Laser Absorption Spectroscopy System for Dissolved CO2 Detection Using a High-Efﬁciency Headspace Equilibrator

Zhihao Zhang , Meng Li, Jinjia Guo *, Baolu Du and Ronger Zheng

College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China; [email protected] (Z.Z.); [email protected] (M.L.); [email protected] (B.D.); [email protected] (R.Z.) * Correspondence: [email protected]

Abstract: Continuous observation of aquatic pCO2 at the ocean surface, with a sensitive response time and high spatiotemporal resolution, is essential for research into the carbon biogeochemical cycle. In this work, a portable tunable diode laser absorption spectroscopy (TDLAS) system for

dissolved CO2 detection in surface seawater, coupled with a home-made headspace equilibrator, allowing real time underway measurements, is described. Both the optical detection part and sample extraction part were integrated together into a compact chamber. An empirical equation suitable for this system was acquired, which can convert the concentration from the gas-phase to the aqueous- phase. A monitoring precision of 0.5% was obtained with time-series measurement, and the detection limits of 2.3 ppmv and 0.1 ppmv were determined with 1 s and 128 s averaging time, respectively. Sampling device used in this work was ameliorated so that the response time of system reduced by about 50% compared to the traditional ‘shower head’ system. The fast response time reached Citation: Zhang, Z.; Li, M.; Guo, J.; the order of 41 s when the final concentration span was 3079 ppmv. For1902 ppmv, this figure was Du, B.; Zheng, R. A Portable Tunable as short as 20 s. Finally, a field underway measurement campaign was carried out and the results Diode Laser Absorption Spectroscopy were briefly analyzed. Our work proved the feasibility of the TDLAS system for dissolved CO System for Dissolved CO2 Detection 2 Using a High-Efficiency Headspace rapid detection. Equilibrator. Sensors 2021, 21, 1723. https://doi.org/10.3390/s21051723 Keywords: dissolved CO2; TDLAS; underway measurement; headspace equilibrator; portable system

Academic Editor: Bernhard Wilhelm Roth 1. Introduction Received: 11 January 2021 The present-day (2020) atmospheric carbon dioxide (CO2) levels of more than 410 ppmv Accepted: 25 February 2021 are nearly 50% higher than preindustrial concentrations, and the current elevated levels Published: 2 March 2021 and rapid growth rates are unprecedented in the past 55 million years of the geological record [1,2]. The oceans, which account for 71% of the Earth’s surface area, represent a Publisher’s Note: MDPI stays neutral signiﬁcant sink of anthropogenic CO2 emissions, making remarkable sense to global carbon with regard to jurisdictional claims in cycle and in mitigating anthropogenic climatic anomalies [3–5]. Some 30% of total anthro- published maps and institutional afﬁl- pogenic CO emissions were absorbed by the oceans [4,6] and the rate of ocean uptake of iations. 2 atmospheric CO2 has increased continuously for the last two decades on account of the ever-increasing concentration of CO2 in the atmosphere [7]. The anthropogenic carbon dioxide emissions have caused pronounced changes to the marine carbonate system [8], and are altering the surface seawater acid-base chemistry towards increased acidic glob- Copyright: © 2021 by the authors. ally [1,9]. However, many potential biogeochemical feedbacks from increasing seawater Licensee MDPI, Basel, Switzerland. CO2 are not well understood entirely, with quantities of high-quality data required to im- This article is an open access article prove the understanding of internal mechanisms [8,9]. In order to understand the ocean’s distributed under the terms and role in the uptake of atmospheric CO and thus the effects on climate and ocean carbon conditions of the Creative Commons 2 Attribution (CC BY) license (https:// cycling, as a primary approach, the air-sea exchange of CO2 in the surface seawater must creativecommons.org/licenses/by/ be measured [10]. The partial pressure of CO2 (pCO2) at the ocean surface varies geograph- 4.0/). ically and seasonally over a wide range [11]. Especially in some regions where pCO2 varies

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strongly, data with a higher resolution are needed to resolve the spatiotemporal variability of pCO2 fluxes [12]. Therefore, the continuous underway measurement of the spatial distribution and temporal variability of pCO2 dissolved in seawater, with high data point density, is essential and beneficial for understanding the ocean carbon biogeochemical cycle. To understand the complex processes of the carbon cycle at the sea-air interface, a crucial step is to implement real-time observation of dissolved pCO2 in surface seawater. Several geochemical analytical approaches have been successfully employed for this purpose [13]. As a mature method, gas chromatography (GC)-based pCO2 detection has proven itself via years of successful deployments in aqueous system because its low air sample consumption, a linear response over a wide range of concentrations, and high precision [14–16]. However, the application of GC-based pCO2 detection system in aqueous environment is limited by its poor sampling density [17], caused by time-consuming discrete sampling, strict requirements for stable temperatures, expensive catalysts for analysis, sample pollution during sampling, and time delays [8,18,19]. In marine research, membrane injection mass spectrometry (MIMS)-based pCO2 detection is also a typical approach used in aqueous environment observation tasks, such as monitoring of air-sea pCO2 fluxes [20] and oceanic inorganic carbon [21], allowing precise real time monitoring. Nevertheless, application of MIMS in underway measurements is limited by the enormous bulk of the instrumentation, high cost, complex sample preprocess operations and the membrane injection technique used [14]. In comparison to the approaches mentioned above, optical methods have many advan- tages in gas detection, and have gradually become a research hotspot. Non-dispersive infrared (NDIR)-based technology is increasingly being deployed in observation of pCO2 [22,23], yet, its applications are limited by the poor selectivity caused by the use of a wideband light source. As a technology that can realize in-situ measurements, a probe based on Raman spectroscopy has been successfully deployed for the detection of bicarbonate in hydrothermal areas [24], but for the special case of dissolved CO2 detection, its sensitivity is insufficient. In addition, cavity ring-down spectroscopy (CRDS)-based technology and off-axis integrated cavity output spectroscopy (OA-ICOS)-based technology are also increasingly being recognized and used in continuous CO2 and CH4 measurements both in surface water and atmosphere [25,26]. Despite the high sensitivity of sensors based on OA-ICOS and CRDS, however, they require rather complex setups with laser frequency locking to longitudinal cavity resonances [27]. Tunable diode laser absorption spectroscopy (TDLAS), based on molecule infrared spectroscopy, can distinguish diverse molecules precisely because absorption lines of the target gas are selected characteristically. It is advantageous for trace gas sensing in terms of cost performance, stability, environmental adaptability and no pretreatment. For the case of CO2, the background concentration in the atmosphere and seawater can reach the order of several hundreds of ppmv, which meant that the detection sensitivity of TDLAS is adequate for CO2 measurement, so TDLAS-based systems have a better cost performance than systems based on CRDS or ICOS. With remarkable response time and sensitivity, numerous employments have demonstrated that TDLAS is a potential alternative tool for underway measurement of pCO2 in surface seawater [13,28]. Combined with an appropriate gas-liquid separation device, the ability to measure at a high temporal and spatial resolution is obtained, allowing the dynamic variation of aquatic pCO2 to be revealed and regions with significant changes in carbonate system to be identified [8]. Despite these many superior features, however, TDLAS-based underway measurement of oceanic pCO2 has rarely been reported. With the aim of developing an underway measurement system which can capture the spatial-temporal distribution and dynamic variation of dissolved CO2 in ocean surface waters, a deck-based TDLAS system combined with a specially designed gas extraction device was developed. Both the optical detection part and sample extraction part were integrated into a compact chamber together that can realize dissolved CO2 measurements Sensors 2021, 21, 1723 3 of 15

directly. A multi-path gas cell (MPGC) was designed to achieve an optical path length of more than 11 m and an internal volume of less than 80 mL considering the need of small volume and long optical path length. In the special case of underway measurements, the headspace equilibrator was designed to work simultaneously with two methods of spray and bubble so that the response time of system was reduced by about 50% compared to traditional “shower head” headspace equilibrator. A series of experiments carried out in the laboratory were conducted to verify the reliability and performance of the system for aquatic CO2 detection. Finally, a ﬁeld application of continuous underway monitoring of oceanic pCO2 was deployed in Jiaozhou Bay, Qingdao, China. The results and details are discussed in this paper. Our work proved the feasibility of the TDLAS-based system for dissolved CO2 rapid detection, and can provide a reference for other researchers engaged in marine sensor development.

2. Apparatus and Methods

The developed dissolved CO2 detection system consisted of two main functional parts: the optical detection part and the sample extraction part. Both the above parts were fitted into a portable aluminum alloy chamber which was 40 cm in height, 50 cm in length and 30 cm in width. In this way, the gas pipeline volume was reduced and the response time was improved. In addition, this design could avoid the influence of high humidity and salinity on the optical and circuit devices. During underway measurements, the system can be powered through marine electric equipment, allowing long-term deployment. The schematic diagram of the optical design is depicted in Figure1. In this work, the absorption line at 4991.26 cm−1 was selected [29,30], which had a proper absorption intensity and no other interferential gases nearby. The optical detection part was based on the mature and traditional technology of TDLAS, using a continuous wave tunable diode laser (DFB-2004-3, nanoplus GmbH, Gerbrunn, Free State of Bavaria, Germany) operating at room temperature as the light source. Such a tunable laser output light with a single frequency and a narrow linewidth at the absorption line of target gas molecule, which was selected characteristically. In this way, TDLAS can distinguish diverse molecules precisely and provide an excellent selectivity for the detected gases. Under the tuning of current and temperature, the central wavelength of the laser scanned over a small range with a certain frequency (50 Hz in this work), so that the characteristic absorption line of the target gas was covered. The laser current was set at 80 mA with a temperature set at 3 ◦C for targeting the selected CO2 absorption line. Along the laser beam path, laser spectrum was modified by the light absorption at the wavelength of selected absorption line, and the concentration of target gas was obtained after the laser beam captured by the detector. The fundamental principle of TDLAS, Beer–Lambert law, indicated that the detection sensitivity was proportional to effective optical path length and molecular absorption line intensity, so that a MPGC was usually used to acquire a higher sensitivity. Both the increase of gas concentration and optical path in the MPGC can increase the intensity of particulate scattering (Rayleigh scattering). However, different from TDLAS, the Rayleigh scattering did not have wavelength selectivity, which just had the influence on the background of TDLAS second harmonic signal. And the intensity of Rayleigh scattering was small because it was inversely proportional to the fourth power of the wavelength. Therefore, the influence of particle scattering was ignored in this work. Additionally, tunable diode laser absorption spectroscopy-wavelength modulation spectroscopy (TDLAS-WMS), used extensively for weak signal detection, was another effective method to improve the sensitivity. SensorsSensors 20212021, 21, 21, ,x 1723 FOR PEER REVIEW 4 of 15 4 of 15

FigureFigure 1. 1.Schematic Schematic diagram diagram of the of opticalthe optical detection detection part of part the developedof the developed dissolved dissolved CO2 detection CO2 detection system.system. DFB: DFB: distributed distributed feedback feedback laser. laser.

TheThe diode diode current current and and temperature temperature of the of laser the laser were controlledwere controlled by a commercial by a commercial TEC TEC controllercontroller (CLD1015, (CLD1015, Thorlabs Thorlabs Inc., Newton,Inc., Newton, NJ, USA). NJ, WithUSA). the With injection the ofinjection a mixed currentof a mixed cur- of low-frequency sawtooth wave (50 Hz) and high-frequency sine wave (15 kHz), the laser rent of low-frequency sawtooth wave (50 Hz) and high-frequency sine wave (15 kHz), the wavelength was modulated. In order to meet the demands of miniaturization and high precision,laser wavelength a customized was MPGC modulated. designed In with order an to improved meet the Herriott demands type wasof miniaturization used in this and work.high precision, The MPGC a consisted customized of two MPGC 2” silver designed coated concave with an mirrors improved (d = 1 Herriott inch, f = 50type mm, was used Rin > this 97.5% work. at 2004 The nm) MPGC separated consisted by a distance of two of2″ 10.3silver cm, coated which concave allows the mirrors laser beam (d = to 1 inch, f = pass50 mm, 113 timesR > 97.5% between at two2004 mirrors nm) separated (leading to by an a effective distance optical of 10.3 pathlength cm, which of ~11.6 allows m). the laser Twobeam mirrors, to pass aligned 113 times parallel between to each two other, mirrors were (leading enclosed to within an effective the MPGC optical of 3.2 pathlength cm of inner~11.6 diameter m). Two and mirrors, 11 cm height,aligned resulting parallel in to a sampleeach other, volume were of lessenclosed than 78 within mL at 1the atm MPGC of (the3.2 cm volume inner of diameter the internal and structural 11 cm metalheight, was resulting removed). in Sucha sample a small volume volume of inside less thethan 78 mL MPGC could ensure a fast-dynamic response when measurement occurs under a certain at 1 atm (the volume of the internal structural metal was removed). Such a small volume pressure and gas velocity. To realize system integration, the gas inlet, gas outlet, pressure sensor,inside andthe MPGC temperature could sensor ensure were a fast-dynamic integrated on theresponse sidewall when of the measurement MPGC. To achieve occurs under aira certain impermeability, pressure twoand rubbergas velocity. O-rings To were realize used system to connect integration, the cavity, the flange gas andinlet, top gas outlet, cover.pressure On thesensor, top cover, and wetemperature used BaF2 as sensor the optical were window integrated material, on the with sidewall a transmittance of the MPGC. ofTo 94% achieve at 2004 air nm, impermeability, thus ensuring both two tightness rubber O-rings and transmitted were used light to intensity. connect In the order cavity, to flange eliminateand top thecover. negative On the effect top on cover, the system we used accuracy, BaF2 optical as the path optical outside window the MPGC material, was with a designedtransmittance to be sealed of 94% and at filled 2004 with nm, pure thus nitrogen, ensuring so asboth to keep tightness the CO and2 concentration transmitted in light in- thistensity. part wasIn order relatively to eliminate constant. the negative effect on the system accuracy, optical path out- The laser beam collimated with a fiber-coupled collimator subsequently injected side the MPGC was designed to be sealed and filled with pure nitrogen, so as to keep the the MPGC at a certain angle and position. Then, a fixed reflection mode formed inside theCO MPGC,2 concentration thus increasing in thisthe part absorption was relatively path and constant. improving the detection sensitivity. The demodulationThe laser beam of the collimated second harmonic with a fiber-coupled signal (2f-signal) collimator was performed subsequently by a lock-in injected the amplifierMPGC at (LIA-MV-200-H, a certain angle FEMTO and position. Messtechnik Then, GmbH, a fixed Berlin, reflection Germany). mode A lowformed power inside the consumption,MPGC, thus highincreasing performance the absorption embedded path industrial and improving computer (PCM-3365, the detection Advantech sensitivity. The Technologydemodulation (China) of the Co., second Ltd., Kunshan, harmonic Jiangsu, signal China) (2f-signal) was used was as performed the storage unitby a andlock-in am- operationplifier (LIA-MV-200-H, carrier of the host FEMTO system Messtechnik software. GmbH, Berlin, Germany). A low power consumption,The ocean high is a complicatedperformance aqueous embedded system, in anddustrial in many computer cases, sampling (PCM-3365, density Advantech is significant for marine underway measurements because low-frequency data cannot capture Technology (China) Co., Ltd., Kunshan, Jiangsu, China) was used as the storage unit and the dynamic variations of dissolved CO2. To obtain data with a high temporal-spatial resolution,operation acarrier home-made of the sample host system extraction software. device based on a headspace equilibrator was established.The ocean It was is able a complicated to realize continuous aqueous sampling system, inand marine in many surface cases, water, sampling as shown density is insignificant Figure2a. for marine underway measurements because low-frequency data cannot capture the dynamic variations of dissolved CO2. To obtain data with a high temporal-spatial resolution, a home-made sample extraction device based on a headspace equilibrator was established. It was able to realize continuous sampling in marine surface water, as shown in Figure 2a.

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Figure 2. Schematic diagram of the sample extraction part of the system. (a) Continuous underway Figure 2. Schematic diagram of the sample extraction part of the system. (a) Continuous underway observation of the surface seawater. (b) Details of the headspace equilibrator and gas exchange observation of the surface seawater. (b) Details of the headspace equilibrator and gas exchange model between the two gas-liquid phases. Gas Return: gas inlet for waste gas measured in the model between the two gas-liquid phases. Gas Return: gas inlet for waste gas measured in the MPGC; Gas Export: gas outlet for equilibrium gas; 𝑇 : temperature sensor of the water sample in MPGC; Gas Export: gas outlet for equilibrium gas; T : temperature sensor of the water sample in the equilibrator. sen the equilibrator.

DifferentDifferent from from the the traditional traditional “shower “shower head” head sampler” sampler which which just sprays just sprays droplets droplets in in thethe toptop space space for for gas-liquid gas-liquid equilibrium, equilibrium, the the sampling sampling device device deployed deployed in this in study this study was was speciallyspecially designed designed with with two two methods methods of gas-liquidof gas-liquid equilibrium—spray equilibrium—spray and bubble—as and bubble—as shownshown in in Figure Figure2b. 2b. Equilibrium Equilibrium gases gases were were pumped pumped from from the “gas the export”“gas export” to the MPGC,to the MPGC, wherewhere the the concentration concentration in in the the gas-phase gas-phase could could be acquired. be acquired. The exhaustThe exhaust gas was gas reversed was reversed backback to to the the “gas “gas return” return” inlet, inlet, and was and used was to generatesused to generates a large number a large of bubbles number through of bubbles thethrough bubble the generator, bubble generator, which are which pumped are from pump theed MPGCfrom the to MPGC the bottom to the water bottom of the water of equilibrator.the equilibrator. Tiny Tiny droplets droplets jetted jetted from from the top the of top the of headspace the headspace and bubbles and bubbles emerged emerged from the bottom, effectively increasing the contact area between the gas and liquid. In this from the bottom, effectively increasing the contact area between the gas and liquid. In this way, gas-liquid separation efficiency was improved and the response time of system was shortened.way, gas-liquid Thus, gasesseparation between effi theciency dual-phase was improved could reach and equilibrium the response more time quickly of system than was shortened. Thus, gases between the dual-phase could reach equilibrium more quickly with a “shower head” device reported before [31]. Tsen was the temperature sensor for the waterthan with sample a “shower in the equilibrator. head” device Theflow reported rate of before the sample [31]. gas,𝑇 which was the was temperature related to the sensor internalfor the water pressure sample of the in MPGC, the equilibrator. was controlled The throughflow rate a of mass the flowmeter sample gas, before which entering was related theto the MPGC. internal A vapor pressure filter was of the employed MPGC, to was remove controlled the influence through of the a mass vapor. flowmeter before enteringAs a classicalthe MPGC. method A vapor for sample filter extractionwas employed of dissolved to remove gas, athe headspace influence equilibrator of the vapor. is oftenAs used a classical to study method the air-sea for sample exchange extraction while an of underwaydissolved measurementgas, a headspace is carried equilibrator outis often [32]. used The right to study side ofthe Figure air-sea2b, exchange inset, shows while the an gas underway exchange modelmeasurement between is the carried air out and[32]. liquid The right [33]. side For theof Figure special 2b, case inset, in the sh equilibratorows the gas (left exchange side of model Figure 2betweenb), gases the dis- air and solvedliquid in[33]. water For and the gasesspecial in case the headspace in the equilibr exchangeator mutually(left side untilof Figure they reach2b), gases dynamic dissolved equilibrium. Dissolved gases in the liquid-phase are striped into the gas-phase. If the in water and gases in the headspace exchange mutually until they reach dynamic equilib- difference between the in-situ temperature of seawater (T − , °C) and the temperature rium. Dissolved gases in the liquid-phase are stripedin intositu the gas-phase. If the difference of the equilibrator (Teq, °C) is less than 2 °C, especially during the underway measurement, between the in-situ temperature of seawater (𝑇,℃) and the temperature of the equi- the pCO2 at the in-situ temperature ((pCO2)sw, µatm) could be calculated by the formula fromlibrator Takahashi (𝑇,℃ et) is al. less [18 than,34], as2 shown℃, especially in Equation during (1): the underway measurement, the 𝑝𝐶𝑂 at the in-situ temperature ((𝑝𝐶𝑂 ) ,𝜇𝑎𝑡𝑚) could be calculated by the formula from Takahashi et al.[18,34],(pCO2) assw shown= (pCO in2) eqEquation× exp 0.0423 (1): Tin−situ − Teq , (1)

(𝑝𝐶𝑂) =(𝑝𝐶𝑂) × 𝑒𝑥𝑝0.0423𝑇 −𝑇, (1)

The 𝑝𝐶𝑂 at equilibration temperature ((𝑝𝐶𝑂),μatm) can be expressed in terms of partial pressure in the gas-phase (𝑓,, 𝑝𝑝𝑚𝑣) while under equilibrium state using Equa- tion (2) [35]:

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The pCO2 at equilibration temperature ((pCO2)eq, µatm) can be expressed in terms of partial pressure in the gas-phase ( fc,eq, ppmv) while under equilibrium state using Equation (2) [35]: h i ( ) = × ( ) − pCO2 eq fc,eq Pb eq PH2O eq , (2)

where fc,eq was the CO2 mole fraction in dry air that equilibrated with the water sample and barometric pressure ((Pb)eq) in the equilibrator after correcting for the vapor pressure ( PH2O eq) at 100% relative humidity [36]. The fc,eq was the function of the amplitude of the 2f-signal (A2 f , fc,eq ∝ A2 f ). In addition, the value of the vapor partial pressure was calculated by Equation (3) at Teq and in-situ salinity (S‰)[35]. In this work, the direct measurement quantity we obtained was A2 f , which can be converted into fc,eq after the system is calibrated:

= − − − ln PH2O eq 24.4543 67.4509 100/Teq 4.8489ln Teq/100 0.000544S‰ (3)

3. Results and Discussion 3.1. System Calibration

To achieve quantitative monitoring of dissolved CO2, one should pay particular attention to calibration of the device under controllable conditions in the laboratory. In this experiment, we used four dry and certified calibration gases, whose concentrations were respectively 398 ppmv, 808 ppmv, 1006 ppmv, and 2019 ppmv, to calibrate the system. These calibration gases consisted of carrier gases of nitrogen and carbon dioxide. In this experiment, we pumped the MPGC below 700 Torr and flushed it with pure nitrogen gas for 20 min. In this way, the background noise level was obtained. Then, we sequentially filled the chamber with the calibration gases mentioned above and obtained the amplitude of the 2f-signal (A2 f ) averaged over 5 min. Obviously, the accuracy of the measurement results depends on good air-tightness and stable pressure within the MPGC. According to the data, the dependence of A2 f and fc,eq was fitted as the following equation, with the R-squared value > 0.99:

fc,eq = 3800.65 × A2 f − 142.35, in ppmv (4)

After the calibration model of this system was established in Equation (4), the A2 f could be transformed into the gas-phase concentration ( fc,eq). For the TDLAS system, the empirical formula applicable to the calculation of (pCO2)sw was obtained through the above formulas, as expressed in Equation (5). The (pCO2)sw was the function of A2 f , (Pb)eq, Teq, Tin−situ and S. Obviously, this method relied on the precise quantiﬁcation of A2 f [14]:

(pCO2)sw = 3800.65 × A2 f − 142.35 × {(Pb)eq − exp[24.4543 − 67.4509 100/Teq − 4.8489 ln Teq/100 − 0.000544S‰]} × exp 0.0423 Tin−situ − Teq , (5) In order to verify the reliability of this model, the above calibration gases were measured again. In addition, we measured another standard gas of 601 ppmv as a checkpoint (as “Test point 1” in Figure3b), and the measured value was 595 ppmv. The linear fitting result is shown in Figure3b. There was a good agreement between fc,eq and the corresponding calibration gas concentration (Ccal), with a R-squared value more than 0.999. Within the range of 398–2019 ppmv, the fitting relative error of the CO2 concentration ((measured value-standard value)/standard value) was less than 2%, and the fluctuation range was from 1.7% (398 ppmv) to 0.4% (2019 ppmv). Then, a gas sample under equilibrium state, stripped by the equilibrator from fresh tap-water, was measured and is displayed as “Test point 2” in Figure3a. The calculated gas-phase concentration of “Test point 2” was ~906 ppmv, which was within a reasonable range of CO2 concentration dissolved in fresh tap-water. Finally, the reliability of the calibration model was demonstrated. SensorsSensors2021 2021, 21, 21, 1723, x FOR PEER REVIEW 77 of of 15 15 Sensors 2021, 21, x FOR PEER REVIEW 7 of 15

Figure 3. The measured 2f-signal versus corresponding concentrations of calibration gases. (a) Figure 3. The measured 2f-signal versus corresponding concentrations of calibration gases. (a) Figure 3. The measured 2f-signalSystem versus calibration corresponding experiment concentrations with four ofcalibration calibration gases. gases. C (cala): Systemconcentration calibration of calibration experiment gases. System calibration experiment with four calibration gases. Ccal: concentration of calibration gases. with four calibration gases. C(calb) :Verification concentration results of calibration of the system gases. calibration (b) Verification model. results fc: gas-phase of the system concen calibrationtration; Test model. point 1: fc: gas-phase(b) concentration; Verification results Testanother point of thestandard 1: system another gas calibration standard of 601 ppmv gasmodel. ofused 601 fc: as ppmvgas-phase a checkpoint. used concen as a checkpoint.tration; Test point 1: another standard gas of 601 ppmv used as a checkpoint. 3.2.3.2. SystemSystem PrecisionPrecision andand AccuracyAccuracy 3.2. System Precision and Accuracy ToTo evaluateevaluate thethe precisionprecision andand thethe stabilitystability ofof thethe sensor,sensor, a a time-series time-series continuouscontinuous To evaluate the precision and the stability of the sensor, a time-series continuous measurementmeasurement ofof thethe gas sample of of 810.6 810.6 ppmv ppmv was was carried carried out out over over a atime time period period of of31 measurement of the gas sample of 810.6 ppmv was carried out over a time period of 31 31min. min. The The gas gas sample sample was was sealed sealed in the in theMPGC MPGC so that so thatits concentration its concentration was wasconstant. constant. Dur- min. The gas sample was sealed in the MPGC so that its concentration was constant. Dur- Duringing the themeasurement, measurement, 1880 1880 datadata points points were were obtained obtained with with1 s interval, 1 s interval, as shown as shown in Figure in ing the measurement, 1880 data points were obtained with 1 s interval, as shown in Figure Figure4a. After4a . the After comparison the comparison of the measured of the measured concentration concentration between between the beginning the beginning and end, 4a. After the comparison of the measured concentration between the beginning and end, andthe result end, the did result not show didnot significant show significant drift. The drift.variation The range variation of the range measured of the measuredconcentra- the result did not show significant drift. The variation range of the measured concentra- concentrationtion was ~802–813 was ~802–813 ppmv for ppmv the 31 for min the observation, 31 min observation, and the and average the average value of value the ofmeas- the tion was ~802–813 ppmv for the 31 min observation, and the average value of the meas- measuredured concentration concentration was 807 was ppmv. 807 ppmv. A histogram A histogram of the of frequency the frequency distribution distribution of theof meas- the ured concentration was 807 ppmv. A histogram of the frequency distribution of the meas- measuredured results results is shown is shown in Figure in Figure 4b. 4Accordingb. According to the to theresults results fitted fitted by bythe the Gauss Gauss profile, profile, the ured results is shownthedistribution distribution in Figure satisfied satisfied4b. According a Gaussian a Gaussian to the profile profileresults an andfittedd the the by full full the width width Gauss at profile, half maximummaximum the (FWHM)(FWHM) distribution satisfied a Gaussian profile and the full width at half maximum (FWHM) valuevalue waswas 7.77.7 ppmv,ppmv, which which determined determined the the measurement measurement precision. precision. WeWe took took the the relative relative value was 7.7 ppmv, which determined the measurement precision. We took the relative error,error, half half of of the the ratio ratio of of FWHM FWHM to to the the average average value value of theof the measured measured concentration, concentration, as the as error, half of theprecisionthe ratio precision of ofFWHM the of system, the to system, the so aver a precisionsoage a precisionvalue of of 0.5% theof 0.5% wasmeasured obtained.was obtained. concentration, as the precision of the system, so a precision of 0.5% was obtained.

FigureFigure 4.4.Results Results ofof time-series time-series continuous continuous measurement. measurement. (a ()a Continuous) Continuous measurement measurement of of a mixturea mix- Figure 4. Results of time-series continuous measurement. (a) Continuous measurement of a mixture of 810.6 ppmv CO2 sealed in the MPGC, a total of 1880 data points acquired with 1 s interval. of 810.6 ppmv CO2 sealed in the MPGC, a total of 1880 data points acquired with 1 s interval. ture of 810.6 ppmv(b CO) Histogram2 sealed in plot the andMPGC, Gaussian a total profile, of 1880 to da assessta points the acquiredprecision with of the 1 ssystem. interval. (b) Histogram plot(b) and Histogram Gaussian plot profile, and Gaussian to assess proﬁle,the precision to assess of the the system. precision of the system. An Allan analysis was performed to investigate the limit of detection (LOD) and sta- An Allan analysisAn was Allan performed analysis to was investigate performed the to limit investigate of detection the (LOD) limit of and detection sta- (LOD) and stability,bility, as as shown shown in in Figure Figure 5.5. The The Allan Allan variance, variance, along with time,time, werewere plottedplotted onon a a loglog- bility, as shown in Figure 5. The Allan variance, along with time, were plotted on a log- loglog scale. scale. FromFrom thethe plot,plot, wewe foundfound thethe sensitivitysensitivity was was 2.3 2.3 ppmvppmv with with a a 11 ss integrationintegration time,time, log scale. From the plot, we found the sensitivity was 2.3 ppmv with a 1 s integration time, whilewhile thethe LODLOD waswas 0.10.1 ppmv ppmv withwith a a 128 128 s s integration integration time. time. TheThe resultsresults ofof precision precision and and while the LOD was 0.1 ppmv with a 128 s integration time. The results of precision and

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LODLOD illustrated illustrated that that the the performance performance of of this this system system was was sufﬁcient sufficient to to apply apply it it to to detection detection of dissolved CO in seawater. of dissolved CO2 2 in seawater.

FigureFigure 5. 5.Allan Allan deviation deviation from from the the time-series time-series measurement. measurement.

TheThe CO CO2 2standard standard gas gas (810.6 (810.6 ppmv) ppmv) was was measured measured discretely discretely with with 10 10 times times for for the the calculationcalculation of of the the system system accuracy, accuracy, as shownas shown in Table in Table1, and 1, thenand then the accuracy the accuracy of 0.5% of of0.5% the of systemthe system was obtainedwas obtained with thewith consideration the consideratio of then of uncertainty the uncertainty propagation. propagation. In addition, In addi- thetion, repeatability the repeatability of the systemof the system was characterized was characterized by the experimentalby the experimental standard standard deviation de- (σviation) obtained (σ) obtained under the under repeatability the repeatability condition. condit So, weion. acquired So, we acquired the repeatability the repeatability of the systemof the (systemσ) of 1.3 (σ ppmv) of 1.3 according ppmv according to the Bessel to the formula. Bessel formula.

1 TableTable 1. 1.Result Result of of discrete discrete measurement measurement of of CO CO2 standard2 standard gas gas (810.6 (810.6 ppmv) ppmv). 1.

No.No. 1 1 22 33 44 55 66 77 88 99 1010 MeanMean Value 804.2 806.3 805.9 805.1 807.0 808.9 807.4 806.4 807.0 806.9 806.5 Value 804.2 806.3 805.9 805.1 807.0 808.9 807.4 806.4 807.0 806.9 806.5 1 Units: parts per million by volume (ppmv). 1 Units: parts per million by volume (ppmv). 3.3. System Response Time 3.3. System Response Time The dynamic response time is a significant parameter for underway measurements The dynamic response time is a significant parameter for underway measurements because a faster instrument response implies a higher temporal resolution. This is related because a faster instrument response implies a higher temporal resolution. This is related to the structure of the MPGC, length of the gas circuit, gas velocity, and efficiency of the to the structure of the MPGC, length of the gas circuit, gas velocity, and efficiency of sample extraction device. In this experiment, a submerged pump was placed into a con- the sample extraction device. In this experiment, a submerged pump was placed into a tainer with water open to the atmosphere. Firstly, the container was filled with ~15 L of container with water open to the atmosphere. Firstly, the container was filled with ~15 L of tap water, and then placed stably in the room for 2 h until the CO2 dissolved in the tap tap water, and then placed stably in the room for 2 h until the CO2 dissolved in the tap water reachedwater reached a dynamic a dynamic equilibrium equilibrium with the with atmosphere. the atmosphere. Subsequently, Subsequently, a concentration a concentration change waschange performed was performed by way of by putting way of a putting reaction a reagent reaction into reagent the water into the sample. water The sample. reagent The wasreagent a powdered was a powdered mixture ofmixture the reagents of the reagen sodiumts sodium bicarbonate bicarbonate and anhydrous and anhydrous citric acid. citric acid. The reagent reacted violently with water and a large amount of CO2 was released in The reagent reacted violently with water and a large amount of CO2 was released in a short a short time so that the concentration of dissolved CO2 in the water changed rapidly, as time so that the concentration of dissolved CO2 in the water changed rapidly, as shown in Figureshown6. in The Figure chemical 6. The reaction chemical is shown reaction in Equationis shown in (6): Equation (6):

NaHCO +CHO =CHONa +3HO+3CO ↑ (6) NaHCO3 + C6H8O7 = C6H5O7Na3 + 3H2O + 3CO2 ↑ (6) As we can obtain from Figure 6, at the beginning of experiment (at 0 s), a background concentrationAs we can obtainof ~860 from ppmv Figure of tap-water6, at the beginning in the container of experiment was measured (at 0 s), a backgroundby the instru- concentrationment. At the oftime ~860 𝑡 ppmv, the reagents of tap-water were inadded the container to the water was sample. measured Weby observed the instrument. a distinct Atrising the time of thet0, theconcentration, reagents were and added the sensor to the took water 55 sample. s and 34 We s observedto reach 90% a distinct (at 𝑡 rising=𝑡 + = + of𝜏 the) and concentration, 63% of the final and theconcentration sensor took span 55 s ( and𝜏=55 34 ss, to 𝜏 reach=34 s), 90% respectively. (at t1 t0 τ90) and 63% of the ﬁnal concentration span (τ90 = 55 s, τ63=34 s), respectively.

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FigureFigure 6. 6.Typical Typical response response of of the the system system in in conjunction conjunction with with a home-madea home-made sample sample extraction extraction device device whilst the reaction reagent was added to the analyzed water sample at 𝑡. 𝜏 and 𝜏 were whilst the reaction reagent was added to the analyzed water sample at t0. τ90 and τ63 were the times the times taken for the signal to reach 90% and 63% of the final concentration span, respectively. taken for the signal to reach 90% and 63% of the final concentration span, respectively. The blue area The blue area was the whole rising process from background concentration to maximum concen- was the whole rising process from background concentration to maximum concentration. tration. However, in this experiment, τ included not only the dynamic response time of However, in this experiment, 90𝜏 included not only the dynamic response time of the sensor, but also the reaction time between the reagent and water and the mixing the sensor, but also the reaction time between the reagent and water and the mixing time time of the upper water and lower water in the container, which were collectively called theof delaythe upper time water [17]. Sinceand lower these water two processesin the container, do not existwhich in were natural collectively aquatic systems,called the thedelay delay time time [17]. should Since these be subtracted two processes when do calculating not exist in thenatural response aquatic time systems, of the the sensor. delay Finally,time should we studied be subtracted 0–90% of when the whole calculating rise process, the response taking time 0–10% of the of thesensor. total Finally, rise time we studied 0–90% of the whole rise process, taking 0–10% of the total rise time as the delay as the delay time (τdelay = ~12 s), and 10–90% as the fastest response time of the system time (𝜏 = ~12 s), and 10–90% as the fastest response time of the system (𝑇 =𝜏 − (T90 = τ90 − τdelay)[17,37]. It was concluded that the fastest response time of dynamic 𝜏 ) [17,37]. It was concluded that the fastest response time of dynamic measurement measurement of dissolved CO2 in water was ~41 s when the final concentration span was 3079of dissolved ppmv. CO2 in water was ~41 s when the final concentration span was 3079 ppmv. TheThe concentration concentration increase increase process process described described in in Figure Figure6 was6 was only only part part of of the the whole whole experiment,experiment, shown shown in in blue blue area area of of Figure Figure7a. 7a. In In this this experiment, experiment, three three sets sets of of reagents reagents with with separateseparate doses doses were were added added into into the the water water sample sample at theat the times times of t 0,1of , 𝑡t,0,2,, and𝑡,, tand0,3, respec-𝑡,, re- tively.spectively. Before Before the next the additionnext addition of reagent, of reag theent, concentration the concentration was was reduced reduced by replacing by replac- parting ofpart the of water the water sample sample in the in container the container with fresh withtap fresh water. tap water. Therefore, Therefore, a total ofa total three of concentrationthree concentration mutation mutation processes processes (blue area, (blue yellow area, area yellow and greenarea and area green in Figure area7 a)in wereFigure obtained,7a) were and obtained, their final and concentration their final concentration spans were ∆ spansfc,1 (3079 were ppmv), Δ𝑓, ∆(3079fc,2 (2206 ppmv), ppmv), Δ𝑓, and(2206∆ f ppmv),c,3 (1902 and ppmv), Δ𝑓, respectively. (1902 ppmv), Three respectively. rising response Three rising processes response under processes the above under concentrationthe above concentration spans are compared spans are compared in Figure7 inb. Figure Obviously, 7b. Obviously, we can see we that can there see that was there a tendencywas a tendency between between∆ fc and Δ𝑓τ90, whichand 𝜏 could, which be describedcould be described as follows: as with follows: the decreasewith the ofde- thecrease value of of the∆ fvaluec from of 3079 Δ𝑓 ppmv from 3079 to 1902 ppmv ppmv, to 1902 the valueppmv, of theτ90 valuetended of to𝜏 decrease tendedfrom to de- 55crease s to 30from s (as 55 showns to 30 s in (as the shown inset in of the Figure inset7b). of Figure Referring 7b). to Referring the calculation to the calculation of T90,1 in of Figure𝑇, 6in, the FigureT90,2 6,of the 27 s𝑇 and, Tof90,3 27of s20 and s were 𝑇, obtained of 20 s whenwere ∆obtainedfc,2 of 2206 when ppmv Δ𝑓, and of∆ 2206fc,3 ofppmv 1902 ppmv,and Δ𝑓 respectively., of 1902 ppmv, In addition, respectively. as shown In inaddition, the red circleas shown in Figure in the7b, red the circle three in responseFigure 7b, processes the three had response good consistency, processes which had good indicated consistency, that the gaswhich tripped indicated efficiency that ofthe thegas sample tripped extraction efficiency device of the in sample this system extraction was relativelydevice in this stable. system was relatively stable. InIn this this experiment, experiment, the the available available datadata ofof ∆Δ𝑓fcand andτ 90𝜏was was insufficient insufficient to to support support a a moremore quantitative quantitative conclusion. conclusion. However, However, we we were were able able to to confirm confirm that that when when∆ Δ𝑓fc became became veryvery small, small, the the response response time time of of this this sensor sensor would would become become very very fast, fast which, which was was especially especially importantimportant for for underwayunderway measurements because because it it meant meant a higher a higher density density of effective of effective data datapoints. points.

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Figure 7. ExperimentalFigure 7. Experimental process and process results and of response results of time response under differenttime under final different concentration final concentration spans. (a) Three concentra- spans. (a) Three concentration rising processes caused by separate doses of reagents. Δ𝑓 , Δ𝑓 tion rising processes caused by separate doses of reagents. ∆ fc,1, ∆ fc,2 and ∆ fc,3 were the variation range, of, concentrations and Δ𝑓, were the variation range of concentrations after the reagent was put into the water sam- after the reagent was put into the water sample. t0,1, t0,2, and t0,3 was the time the reagent was added. (b) Comparison of ple. 𝑡,, 𝑡,, and 𝑡, was the time the reagent was added. (b) Comparison of three concentration three concentration rising processes (∆ fc,1, ∆ fc,2 and ∆ fc,3). t1 = t0,1 + τ90,1, t2 = t0,2 + τ90,2, and t3 = t0,3 + τ90,3 were the rising processes (Δ𝑓,, Δ𝑓, and Δ𝑓,). 𝑡 =𝑡, +𝜏,, 𝑡 =𝑡, +𝜏,, and 𝑡 =𝑡, +𝜏, were time taken to reached 90% of ∆ fc, respectively. the time taken to reached 90% of Δ𝑓, respectively. Figure 7. Experimental process and results of response time under different final concentration spans. (a) Three concentration rising processes caused by separate doses of reagents. Δ𝑓,, Δ𝑓, 3.4.and Δ𝑓 Atmosphere, were the variation Monitoring range of concentrations in the after Laboratory the reagent was put into the water sam- 3.4. Atmosphere Monitoringple. 𝑡,, 𝑡,, and 𝑡in, wasthe the Laboratory time the reagent was added. (b) Comparison of three concentration rising processesThe sensor (Δ𝑓,, Δ𝑓, was and Δ𝑓 evaluated,). 𝑡 =𝑡, +𝜏, by, 𝑡 comparison=𝑡, +𝜏,, and 𝑡 monitoring=𝑡, +𝜏, were in a laboratory environment The sensor was evaluated by comparison monitoring in a laboratory environment withthe timecommercial taken to reached 90% instruments of Δ𝑓, respectively. (GGA, ABB, San Jose, CA, USA), based on off-axis integrated with commercial instruments (GGA, ABB, San Jose, CA, USA), based on off-axis inte- cavity3.4. Atmosphere output Monitoring spectroscopy. in the Laboratory For continuous day and night monitoring, the experiment grated cavity outputwasThe conducted spectroscopy. sensor was evaluated from For 18:00by comparison cont CSTinuous (China) monitoring day on in and a 5 laboratory December night environment monitoring, 2018, to 10:00 the experi- CST on 7 December ment was conductedwith commercial from 18:00instruments CST (GGA, (China) ABB, San on Jose, 5 DecemberCA, USA), based 2018, on off-axis to 10:00 inte- CST on 7 De- 2018grated cavity (~40 output h sampling). spectroscopy. For We cont usedinuous a day Y-joint and night for monitoring, sampling the experi- for both systems, thus ensuring cember 2018 (~40thement h consistencywassampling). conducted from ofWe 18:00 the used gasCST (China)a samples. Y-jo onint 5 December for The sampling results 2018, to were10:00 for CST measuredboth on 7 De-systems, by the thus TDLAS en- system and suring the consistencycember 2018 of (~40 the h sampling). gas samples. We used aThe Y-joint results for sampling were for both measured systems, thus by en- the TDLAS sys- bysuring the the GGA,consistency and of the compared gas samples. The in Figureresults were8. measured Their linearby the TDLAS fitting sys- correlation coefficient value tem and by the GGA,(R-squared)tem and byand the GGA,compared was and 0.973. compared in Figure in Figure 8.8. Their Their linear linear fitting correlation fitting coefficientcorrelation coefficient value (R-squared)value was (R-squared) 0.973. was 0.973.

Figure 8. Comparison measurement of ambient CO2 in the laboratory between the TDLAS system (black curve) and GGA (red curve). The inset is a detailed plot of a concentration mutation.

Figure 8. Comparison measurement of ambient CO2 in the laboratory between the TDLAS system As can be seen(black from curve) Figure and GGA 8, CO (red2 concentration curve). The inset fluctuations is a detailed occurred plot of a concentration in the labora- mutation. tory atmosphere, and a high consistency was found for both systems. When the concentration of CO2 mutatedAs can(as shown be seen in from the Figure inset),8 ,both CO 2 theconcentration TDLAS system ﬂuctuations and GGA occurred showed in the laboratory a rapid responseatmosphere, to the concentration and a high mutation consistency. The was variation found range for both of systems.concentrations When thewas concentration from 428 ppmv oftoCO 7502 mutatedppmv. During (as shown this inperiod, the inset), the bothCO2 concentrations the TDLAS system were and relatively GGA showed a rapid high in the dayresponse time (gray to thearea), concentration and then dropped mutation. gradually The variation to the range minimum of concentrations value in was from the early morning428 (orange ppmv to and 750 ppmv.blue areas). During There thisperiod, were two the COincrease2 concentrations processes wereand two relatively high in decrease processes due to laboratory personnel influence. Although both instruments were able to describe the variation tendency of CO2 concentrations accurately, however,

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Sensors 2021, 21, x FOR PEER REVIEW 11 of 15 the day time (gray area), and then dropped gradually to the minimum value in the early morning (orange and blue areas). There were two increase processes and two decrease processes due to laboratory personnel influence. Although both instruments were able the results of the TDLAS system were more detailed, which is particularly significant for to describe the variation tendency of CO concentrations accurately, however, the results underway measurements. 2 of the TDLAS system were more detailed, which is particularly significant for underway In addition, for the marine real time measurement application, there were some ad- measurements. vantagesIn addition, compared for with the marineGGA. Firstly, real time the op measurementtical detection application, part and gas there sampling were somedevice advantageswere integrated compared in the with TDLAS-based GGA. Firstly, system the optical, which detection realized part the andunderway gas sampling measurement device wereof dissolved integrated CO in2 thedirectly. TDLAS-based In this way, system, the integration which realized level the and underway portability measurement of the instru- of ment were improved, and the gas path length between the sampling device and MPGC dissolved CO2 directly. In this way, the integration level and portability of the instrument werewas improved,saved (gas and sample the gas demand path length can betweenbe reduce thed). sampling With such device short and gas MPGC path waslength, saved the (gas“memory sample effect” demand of canthe begas reduced). circuit was With also such weakened. short gas Meanwhile, path length, it the weakened “memory the effect” char- ofacteristic the gas circuitchanges was of also the weakened.gas sample Meanwhile, (such as temperature) it weakened caused the characteristic by the long changes gas path. of theSecondly, gas sample the TDLAS-based (such as temperature) system had caused the be bytter the environmental long gas path. adaptability Secondly, the because TDLAS- of basedits lower system reflectivity had the requirement better environmental of concave adaptability mirrors than because GGA (more of its than lower 99.9%). reflectivity There- requirementfore, this system of concave was more mirrors suitable than GGAfor long-term (more than gas 99.9%). detection Therefore, in marine this environments, system was morebecause suitable the gas for sample long-term with gas high detection salinity in and marine high environments, humidity could because decrease the the gas reflectiv- sample withity of high the salinitycavity mirrors. and high humidity could decrease the reflectivity of the cavity mirrors.

3.5.3.5. Field Field Application Application InIn orderorder toto verify verify and and improve improve the the performa performancence of the of thesystem, system, a field a ﬁeld real time real appli- time applicationcation was carried was carried out with out the with system the system deployed deployed on deck onon deck18 September on 18 September 2019. The 2019.meas- ◦ 0 ◦ 0 ◦ 0 ◦ 0 Theurement measurement site was sitelocated was in located Jiaozhou in Jiaozhou Bay (35°57 Bay′–36°18 (35 57′ N,–36 120°0418 N,′–120°23 120 04′ –120E), Qingdao,23 E), Qingdao,which is a which semi-closed is a semi-closed bay on the bay west on side the of west downtown side of Qingdao, downtown China. Qingdao, As shown China. in AsFigure shown 9, inwe Figure chose9 ,the we route chose according the route according to the following to the following factors: surface factors: runoff surface (stations runoff (stationsA1, A2 and A1, A4), A2 and human A4), activities human activities (stations (stations A0 and A3) A0 andand A3)special and geographic special geographic positions positions(stations (stationsA5, A6 and A5, A7), A6 which and A7), correspond which correspond to estuaries, to human estuaries, living human areas, living the bay areas, cen- thetre bayand centre the bay and mouth, the bay respectively. mouth, respectively.

FigureFigure 9. 9.Experimental Experimental stations stations of of underway underway measurement. measurement. TheThe A0A0 stationstation (wharf)(wharf) waswas wherewhere thethe employmentemployment began. began. The The experiment experiment was was performed performed in in an an anticlockwise anticlockwise direction. direction. The The stations stations were were chosen by the following factors: surface runoff, human activities and special geographic posi- chosen by the following factors: surface runoff, human activities and special geographic positions tions (the bay center and the bay mouth). (the bay center and the bay mouth).

TheThe ﬁeldfield measurementmeasurement of the above above stations stations was was performed performed in in an an anticlockwise anticlockwise di- directionrection and and the the concentration-time concentration-time plot plot of ofthis this experiment experiment can can be befound found in Figure in Figure 10. 10We. found that the 𝑝𝐶𝑂 in surface seawater varied greatly (from 550 ppmv to 820 ppmv) in We found that the pCO 2 in surface seawater varied greatly (from 550 ppmv to 820 ppmv) inJiaozhou Jiaozhou Bay, Bay, and and the the spatial spatial distribution distribution overalloverall satisﬁedsatisfied thethe followingfollowing trends:trends: the the east east andand northeast northeast areas areas of of the the bay bay had had the the highest highest concentration, concentration, and and the the concentration concentration grad- grad- uallyually decreaseddecreased fromfrom the bay center center to to the the bay bay mouth. mouth. Many Many interesting interesting variations variations of con- of concentrationcentration were were measured measured in inthe the vicinity vicinity of of these these stations, stations, which which will will be be discussed discussed later. later.

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FigureFigure 10. 10.Underway Underway measurementmeasurement in Jiaozhou Bay. Bay. Th Thee As As mark mark the the measurement measurement stations. stations. For For the subsequentthe subsequent analysis, analysis, the resultsthe results were were divided divided into into many many sections sections (a-k) (a-k) with with different different colors. colors.

TheThe resultsresults werewere divided into into several several sectio sections,ns, according according to tothe the sequence sequence of stations, of stations, forfor subsequent subsequent analysis analysis and and discussion. discussion. During During field field monitoring, monitoring, the the instruments instruments were were first testedfirst tested with with the surface the surface water water within within the the wharf, wharf, allowing allowing for for equipment equipment debugging debugging of of the systemthe system and instrumentand instrument warm warm up(section up (section a in a Figure in Figure 10). 10). In addition,In addition, section section g and g and j show j theshow static the measurement static measurement from thefrom stopped the stopped vessel vessel at stations at stations A5 andA5 and A7. A7. There There were were some some concentration mutations appeared in Sections b, c, d, f, and the spatial distribution concentration mutations appeared in Sections b, c, d, f, and the spatial distribution of pCO2 couldof 𝑝𝐶𝑂 be briefly could be explained briefly explained by the following by the following three factors: three factors: surface surface runoff, runoff, human human activities andactivities geographic and geographic position. position. • Surface Runoff & Human Activities • Surface Runoff & Human Activities On the one hand, surface runoff brings an abundance of diluted water with higher On the one hand, surface runoff brings an abundance of diluted water with higher 𝑝𝐶𝑂 than seawater in the summer and autumn. On the other hand, rivers carry abundant pCO than seawater in the summer and autumn. On the other hand, rivers carry abundant nutrients2 that lead to a high primary production of phytoplankton, and cause the 𝑝𝐶𝑂 nutrientsto go down. that At lead the tosame a high time, primary the rivers production passing through of phytoplankton, the human living and areas cause carry the pCOan 2 toabundance go down. of At particulate the same organic time, the carbon rivers (POC) passing and throughbiodegradable the human organic living matter areas (BOM) carry anin abundancethe form of sewage, of particulate which leads organic to the carbon enhancement (POC) andof the biodegradable aerobic respiration organic process matter (BOM)and causes in the the form 𝑝𝐶𝑂 of sewage,to go up. which Actually, leads in to Septembe the enhancementr, primary of production the aerobic of respiration phyto- processplankton and begins causes to weaken, the pCO and2 to aerobic go up. respiration Actually, inbegins September, to control primary the distribution production of of phytoplankton𝑝𝐶𝑂 in the bay. begins In addition, to weaken, theand seawater aerobic near respiration human living begins areas to control have thehigher distribution 𝑝𝐶𝑂 oflevelspCO 2duein theto human bay. In activity. addition, Therefore, the seawater there near were human some significant living areas increase have higherprocessespCO 2 levels(section due b, toc, humand and f) activity. of the concentratio Therefore,n there when were across some the significantestuaries and increase human processes living (sectionareas (Stations b, c, d and A1–A4). f) of the concentration when across the estuaries and human living areas (Stations• Geographic A1–A4). Position • GeographicAt the bay center, Position because it is far away from estuary and human living areas, station A5 hasAt thea lower bay center,𝑝𝐶𝑂. becauseAt the bay it ismouth far away (Section from h estuary and i in and Figure human 10), living𝑝𝐶𝑂 isareas, at a stationlow level relative to its higher salinity than other areas in the bay. The salinity of seawater at A5 has a lower pCO2. At the bay mouth (Section h and i in Figure 10), pCO2 is at a low level relativethe bay to mouth its higher is higher salinity because than of other its freque areasnt in exchange the bay. The with salinity seawater of seawateroutside the at thebay. bay The tidal variation of Jiaozhou Bay on 18 September 2019, is displayed in Table 2. From mouth is higher because of its frequent exchange with seawater outside the bay. The tidal ~13:30 p.m. CST, seawater with high salinity begin to pour into the bay, so the salinity of variation of Jiaozhou Bay on 18 September 2019, is displayed in Table2. From ~13:30 p.m. the seawater at the bay mouth increases continuously. CST, seawater with high salinity begin to pour into the bay, so the salinity of the seawater atTable the bay2. Tidal mouth chart increases of Jiaozhou continuously. Bay on 18 September 2019 1.

Tidal 1 Table 2. Tidal chart of08:00 Jiaozhou 09:00 Bay10:00 on 1811:00 September 12:00 201913:00 . 13:30 14:00 15:00 16:00 17:00 Time(hh:mm) TidalTidal height(cm) Time 364 304 257 208 146 98 90 98 150 236 330 08:00 09:00 10:00 11:00 12:00 13:00 13:30 14:00 15:00 16:00 17:00 1 Data(hh:mm) from the National Marine Information Center, Global Tidal Service Platform of China. (http://global-tide.nmdis.org.cn accessed on 2 March 2021). Time zone: −800. Tidal datum: 239.0 Tidal height cm below the mean364 sea level. 304 257 208 146 98 90 98 150 236 330 (cm) 1 Data from the National Marine Information Center, Global Tidal Service Platform of China. (http://global-tide. nmdis.org.cn accessed on 2 March 2021). Time zone: −800. Tidal datum: 239.0 cm below the mean sea level.

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It is worth noting that the distribution of pCO2 in the bay was the results of the com- prehensive actions of physical and biogeochemical processes. This paper only undertook a brief analysis.

4. Conclusions In this paper, we demonstrated a compact deck-based TDLAS system for dissolved CO2 detection in surface seawater, coupled with a home-made headspace equilibrator, allowing continuous and online underway measurements. Both the optical detection part and sample extraction part were fitted together into a portable aluminum alloy chamber that can realize the dissolved CO2 measurement directly. The headspace equilibrator used in this work has been improved so that the response time of system was reduced by about 50% compared to traditional “shower head” headspace equilibrator. Under the special design, the two methods of spray and bubble for gas-liquid equilibrium, when the final concentration span was equal to 3079 ppmv, achieved a fast response time on the order of 41 s. For 1902 ppmv, this figure was as little as 20 s. The MPGC was specially designed under the considering the need of small volume and long optical path length. We obtained an empirical equation suitable for the system by deducing theories about the headspace equilibrator, which can convert the concentration of the gas-phase to the liquid-phase. With a time-series (about 31 min) continuous measurement of a gas sample of 810.6 ppmv, a monitoring precision of 0.5% was obtained. The LOD were 2.3 ppmv at 1 s averaging time, and 0.1 ppmv at 128 s averaging time, respectively. Finally, a field underway measurement was carried out and the result was briefly analyzed with the system deployed in Jiaozhou Bay, Qingdao. Our work proved the feasibility of the TDLAS-based system for dissolved CO2 rapid detection, and can provide a reference for other researchers engaged in marine sensor development. Despite thr fact only pCO2 dissolved in water is being measured by the sensor in this paper, however, many other dissolved gases could be stripped using the sample extraction device. Therefore, this system also has great potential to detect other dissolved gases, such as methane, and this could be explored in future research.

Author Contributions: Z.Z. and J.G. proposed the idea; then developed the TDLAS system of dissolved CO2 detection in surface seawater. Z.Z., M.L. and B.D.; completed the test experiments in laboratory and deployment in Jiaozhou Bay. Z.Z. and M.L.; performed the data process. Z.Z.; wrote the draft of the manuscript. R.Z. and J.G.; supervised the research project and revised the draft. All authors have read and agreed to the published version of the manuscript. Funding: This work was financially supported by the National Natural Science Foundation of China (Grant No. 41527901), the Provincial Key Research and Development Program of Shandong, China (2019JZZY010417), and the Fundamental Research Funds for the Central Universities (Grant No. 841962003). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data sharing is not applicable to this article. Acknowledgments: The authors would like to thank the research vessel for their enthusiastic support and efficient work. Zhihao Zhang would like to give his thanks to Wangquan Ye and Guanxiang Du for their useful suggestions. Conflicts of Interest: The authors declare no conflict of interest.

References 1. Doney, S.C.; Busch, D.S.; Cooley, S.R.; Kroeker, K.J. The impacts of ocean acidiﬁcation on marine ecosystems and reliant human communities. Annu. Rev. Environ. Resour. 2020, 45, 83–112. [CrossRef] 2. Gingerich, P.D. Temporal scaling of carbon emission and accumulation rates: Modern anthropogenic emissions compared to estimates of PETM onset accumulation. Paleoceanogr. Paleoclimatol. 2019, 34, 329–335. [CrossRef] Sensors 2021, 21, 1723 14 of 15

3. DeVries, T. The oceanic anthropogenic CO2 sink: Storage, air-sea fluxes, and transports over the industrial era. Glob. Biogeochem. Cycles 2014, 28, 631–647. [CrossRef] 4. IPCC. Climate Change 2013—The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovern- mental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2014; pp. 465–570. 5. Wang, S.; Moore, J.; Primeau, F.; Khatiwala, S. Simulation of anthropogenic CO2 uptake in the CCSM3. 1 ocean circulation- biogeochemical model: Comparison with data-based estimates. Biogeosciences 2012, 9, 1321–1336. [CrossRef] 6. Perez, F.F.; Fontela, M.; Garcia-Ibanez, M.I.; Mercier, H.; Velo, A.; Lherminier, P.; Zunino, P.; de la Paz, M.; Alonso-Perez, F.; Guallart, E.F.; et al. Meridional overturning circulation conveys fast acidification to the deep Atlantic Ocean. Nature 2018, 554, 515–518. [CrossRef][PubMed] 7. Bindoff, N.L.; Cheung, W.W.L.; Kairo, J.G.; Arístegui, J.; Guinder, V.A.; Hallberg, R.; Hilmi, N.; Jiao, N.; Karim, M.S.; Levin, L.; et al. Changing Ocean, Marine Ecosystems, and Dependent Communities. In IPCC Special Report on the Ocean and Cryosphere in a Changing Climate; Intergovernmental Panel on Climate Change: Geneva, Switzerland, 2019. 8. Clarke, J.S.; Achterberg, E.P.; Connelly, D.P.; Schuster, U.; Mowlem, M. Developments in marine pCO2 measurement technology; towards sustained in situ observations. TrAC Trends Anal. Chem. 2017, 88, 53–61. [CrossRef] 9. Doney, S.C.; Fabry, V.J.; Feely, R.A.; Kleypas, J.A. Ocean acidification: The other CO2 problem. Annu. Rev. Mar. Sci. 2009, 1, 169–192. [CrossRef][PubMed] 10. Arruda, R.; Atamanchuk, D.; Cronin, M.; Steinhoff, T.; Wallace, D.W.R. At-sea intercomparison of three underway pCO2 systems: Intercomparison of pCO2 systems. Limnol. Oceanogr. Methods 2019, 18, 63–76. [CrossRef] 11. Takahashi, T.; Sutherland, S.; Sweeney, C.; Poisson, A.; Metzl, N.; Tilbrook, B.; Bates, N.; Wanninkhof, R.; Feely, R.; Chris, S.; et al. Global sea-air CO2 flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects. Deep Sea Res. Part II Top. Stud. Oceanogr. 2002, 49, 1601–1622. [CrossRef] 12. Friedrich, T.; Oschlies, A. Neural network-based estimates of North Atlantic surface pCO2 from satellite data: A methodological study. J. Geophys. Res. Ocean. 2009, 114, 3020. [CrossRef] 13. Liu, Z.; Zheng, C.; Zhang, T.; Li, Y.; Ren, Q.; Chen, C.; Ye, W.; Zhang, Y.; Wang, Y.; Tittel, F.K. Midinfrared Sensor System Based on Tunable Laser Absorption Spectroscopy for Dissolved Carbon Dioxide Analysis in the South China Sea: System-Level Integration and Deployment. Anal. Chem. 2020, 92, 8178–8185. [CrossRef] 14. Sepulveda-Jauregui, A.; Martinez-Cruz, K.; Strohm, A.; Walter Anthony, K.M.; Thalasso, F. A new method for field measurement of dissolved methane in water using infrared tunable diode laser absorption spectroscopy. Limnol. Oceanogr. Methods 2012, 10, 560–567. [CrossRef] 15. You, O.-R.; Son, S.K.; Baker, E.T.; Son, J.; Kim, M.J.; Barcelona, M.J.; Kim, M. Bathymetric influence on dissolved methane in hydrothermal plumes revealed by concentration and stable carbon isotope measurements at newly discovered venting sites on the Central Indian Ridge (11–13◦S). Deep Sea Res. Part I Oceanogr. Res. Pap. 2014, 91, 17–26. [CrossRef] 16. Dickson, A.G.; Sabine, C.L.; Christian, J.R. Guide to Best Practices for Ocean CO2 Measurements; North Pacific Marine Science Organization: Sidney, BC, USA, 2007. 17. Grilli, R.; Triest, J.; Chappellaz, J.; Calzas, M.; Desbois, T.; Jansson, P.; Guillerm, C.; Ferré, B.; Lechevallier, L.; Ledoux, V.; et al. Sub-Ocean: Subsea Dissolved Methane Measurements Using an Embedded Laser Spectrometer Technology. Environ. Sci. Technol. 2018, 52, 10543–10551. [CrossRef] 18. Takahashi, T.; Olafsson, J.; Goddard, J.G.; Chipman, D.W.; Sutherland, S.C. Seasonal variation of CO2 and nutrients in the high-latitude surface oceans: A comparative study. Glob. Biogeochem. Cycles 1993, 7, 843–878. [CrossRef] 19. Liu, Y.; Sharma, K.R.; Fluggen, M.; O’Halloran, K.; Murthy, S.; Yuan, Z. Online dissolved methane and total dissolved sulfide measurement in sewers. Water Res. 2015, 68, 109–118. [CrossRef] 20. Vautier, C.; Abhervé, R.; Labasque, T.; Laverman, A.M.; Guillou, A.; Chatton, E.; Dupont, P.; Aquilina, L.; de Dreuzy, J.-R. Mapping gas exchanges in headwater streams with membrane inlet mass spectrometry. J. Hydrol. 2020, 581, 124398. [CrossRef] 21. Douchi, D.; Liang, F.; Cano, M.; Xiong, W.; Wang, B.; Maness, P.C.; Lindblad, P.; Yu, J. Membrane-Inlet Mass Spectrometry Enables a Quantitative Understanding of Inorganic Carbon Uptake Flux and Carbon Concentrating Mechanisms in Metabolically Engineered Cyanobacteria. Front. Microbiol. 2019, 10, 1356. [CrossRef][PubMed] 22. Prytherch, J.; Yelland, M.J.; Pascal, R.W.; Moat, B.I.; Skjelvan, I.; Neill, C.C. Direct measurements of the CO2 flux over the ocean: Development of a novel method. Geophys. Res. Lett. 2010, 37.[CrossRef] 23. Sutton, A.J.; Sabine, C.L.; Maenner-Jones, S.; Lawrence-Slavas, N.; Meinig, C.; Feely, R.; Mathis, J.; Musielewicz, S.; Bott, R.; McLain, P. A high-frequency atmospheric and seawater pCO2 data set from 14 open-ocean sites using a moored autonomous system. Earth Syst. Sci. Data 2014, 6, 353–366. [CrossRef] 24. Guo, J.; Ye, W.; Liu, Q.; Qi, F.; Cheng, K.; Yang, D.; Zheng, R. Development of a compact deep-sea Raman spectroscopy system and direct bicarbonate detection in sea trials. Appl. Opt. 2019, 58, 2630–2634. [CrossRef] 25. Humborg, C.; Geibel, M.; Sun, X.; McCrackin, M.; Mörth, C.-M.; Stranne, C.; Jakobsson, M.; Gustafsson, B.; Sokolov, A.; Norkko, A.; et al. High Emissions of Carbon Dioxide and Methane From the Coastal Baltic Sea at the End of a Summer Heat Wave. Front. Mar. Sci. 2019, 6, 493. [CrossRef] 26. Grefe, I.; Kaiser, J. Equilibrator-based measurements of dissolved nitrous oxide in the surface ocean using an integrated cavity output laser absorption spectrometer. Ocean Sci. 2014, 10, 501. [CrossRef] Sensors 2021, 21, 1723 15 of 15

27. Xia, J.; Zhu, F.; Zhang, S.; Kolomenskii, A.; Schuessler, H. A ppb level sensitive sensor for atmospheric methane detection. Infrared Phys. Technol. 2017, 86, 194. [CrossRef] 28. Ren, Q.; Chen, C.; Wang, Y.; Li, C.; Wang, Y. A prototype of ppbv-level mid-infrared CO2 sensor for potential application in deep-sea natural-gas-hydrate exploration. IEEE Trans. Instrum. Meas. 2020, 69, 7200–7208. [CrossRef] 29. Gordon, I.E.; Rothman, L.S.; Hill, C.; Kochanov, R.V.; Tan, Y.; Bernath, P.F.; Birk, M.; Boudon, V.; Campargue, A.; Chance, K.V.; et al. The HITRAN2016 molecular spectroscopic database. J. Quant. Spectrosc. Radiat. Transf. 2017, 203, 3–69. [CrossRef] 30. Šimeˇcková, M.; Jacquemart, D.; Rothman, L.S.; Gamache, R.R.; Goldman, A. Einstein A-coefficients and statistical weights for molecular absorption transitions in the HITRAN database. J. Quant. Spectrosc. Radiat. Transf. 2006, 98, 130–155. [CrossRef] 31. Webb, J.R.; Maher, D.T.; Santos, I.R.J.L.; Methods, O. Automated, in situ measurements of dissolved CO2, CH4, and δ13C values using cavity enhanced laser absorption spectrometry: Comparing response times of air-water equilibrators. Limnol. Oceanogr. Methods 2016, 14, 323–337. [CrossRef] 32. Frankignoulle, M.; Borges, A.; Biondo, R. A new design of equilibrator to monitor carbon dioxide in highly dynamic and turbid environments. Water Res. 2001, 35, 1344–1347. [CrossRef] 33. Liss, P.S.; Slater, P.G. Flux of Gases across the Air-Sea Interface. Nature 1974, 247, 181–184. [CrossRef] 34. Takahashi, T.; Sutherland, S.C.; Wanninkhof, R.; Sweeney, C.; Feely, R.A.; Chipman, D.W.; Hales, B.; Friederich, G.; Chavez, F.; Sabine, C.; et al. Climatological mean and decadal change in surface ocean pCO2, and net sea–air CO2 flux over the global oceans. Deep Sea Res. Part II Top. Stud. Oceanogr. 2009, 56, 554–577. [CrossRef] 35. Weiss, R.F.; Price, B.A. Nitrous oxide solubility in water and seawater. Mar. Chem. 1980, 8, 347–359. [CrossRef] 36. Wu, Y.; Dai, M.; Guo, X.; Chen, J.; Xu, Y.; Xu, D.; Dai, J.; Zhang, Z. High-frequency time-series autonomous observations of sea surface pCO2 and pH. Limnol. Oceanogr. 2020.[CrossRef] 37. Li, J.; Luo, G.; Du, Z.; Ma, Y. Hollow waveguide enhanced dimethyl sulfide sensor based on a 3.3µm interband cascade laser. Sens. Actuators B Chem. 2018, 255, 3550–3557. [CrossRef]