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Abstract
An in-situ laser Doppler current probe (LDCP) for the simultaneous measurements of the micro-scale subsurface current speed and the characterizations of micron particles is dedicated in this paper. The LDCP performs as an extension sensor for the state-of-the-art laser Doppler anemometry (LDA). The all-fiber LDCP utilized a compact dual wavelength (491 nm and 532 nm) diode pumped solid state laser as the light source to achieve the simultaneous measurements of the two components of the current speed. Besides its ability for the measurements of the current speed, the LDCP is also capable of obtaining the equivalent spherical size distribution of the suspended particles within small size range. The micro-scale measurement volume formed by two intersecting coherent laser beams makes it possible to accurately estimate the size distribution of the micron suspended particles with high temporal and spatial resolution. With its deployment during the field campaign at Yellow Sea, the LDCP has been experimentally demonstrated as an effective instrument to capture the micro-scale subsurface ocean current speed. The algorithm for retrieving the size distribution of the small suspended particles (2∼7.5µm) has been developed and validated. The combined LDCP system could be applied to the continuous long-term observations of plankton community structure, ocean water optical parameter over a wide range, and useful to elucidate the processes and interactions of the carbon cycles in the upper ocean.
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1. Introduction
Algae, as aquatic organisms, together with other suspended particles (including microorganisms, organic matter, and mineral matter), play a dominant role in the carbon sequestration in the upper ocean [1–3]. It is paramount for studying the marine healthy condition, biodiversity, and the global carbon cycle [4,5]. However, the exact primary production information is difficult to estimate from the remote sensing production due to the complex composition and current-driven transportation of phytoplankton. The complicated cellular physiology, the mixed physical environment, the ambient irradiance field make it intricated to understand [6,7]. Efforts to understand and estimate the biological productivity of the upper ocean will be significantly improved once the capacity to rapidly determine the concentration and size distribution of the suspended particles over large regions is built [8]. Thus, the current speed and size distribution are important factors for estimating the transportation and growth rate of phytoplankton [9,10].
Extensive researches on the multi-scale dynamic characteristics and ecological environment parameters based on the advanced remote sensing and in-situ observation techniques have been carried out. Biogeochemical Argo (BGC-Argo) floats have been widely deployed to measure six marine core variables (including: irradiance, suspended particles, chlorophyll-a (chl a), oxygen (O2), nitrate (NO3) and pH) to analyze the phytoplankton growth rate and carbon accumulation rate [11,12]. In situ autonomous smart platforms such as floats and glider have also been utilized to capture high depth-resolved measurements of phytoplankton for the algae bloom monitor [13,14]. The most traditional method to characterize the information of particles (size, shape, morphology and structure) is the scanning electronic microscopy technique by utilizing high spatial resolution images of the microscopic particles [15]. However, the narrow sight of view of the scope limited the number of small-scale particles which can be analyzed simultaneously. It is difficult to achieve the in-situ observation for the tedious preparation of the samples to be placed under the microscope [16]. The differential electrical mobility analysis technique has been proved to be effective tool to quantify the size distribution and concentration of the aerosol particles in the size range from 10 nm to 1000 nm [17]. Light extinction spectroscopy (LES) technique has been dedicated to assess the characterization of submicron particles [18]. By integrating the flow cytometric and video technique, a submersible instrument named Imaging Flow Cytobot (IFCB) was designed by Woods Hole Oceanographic Institution (WHOI). The IFCB measures the optical properties and the images of particles with diameter between nano- and micro scale, which has been proved to be useful in oceanic plankton researches. However, it is difficult to hydrodynamic focusing to accurately position flowing sample particles due to the low sampling rate (5 ml seawater analyzed per 20 mins) [19,20]. The sampling rates of the traditional instruments are much lower than the current velocity inside the upper ocean, which limited the ability of in situ instruments to accurately obtain the abundance of algae.
Besides the influence due to the growth rate of local phytoplankton, the horizontal current-driven transportation of phytoplankton also has a significant impact on the assessment of primary productivity in a certain ocean area. Thus, the accurate measurements of the subsurface current speed are essential to study the current-driven transportation and time variable concentration of the phytoplankton in certain area. The lack of the non-invasive in-situ detection techniques for the high-resolution measurements of the marine micro-scale characteristics, biological, physical and chemical restricted the study of the microscale phenomenon and biological optics inside the ocean [21]. At present, the subsurface current speed profiles were often accomplished by acoustic Doppler current profiler (ADCP) and prototype ocean current meter [22,23]. Limited by the spatial resolution, the traditional instruments are not capable of monitoring the transportation and size distribution variability of the phytoplankton. Nowadays, optical measurement instruments such as laser Doppler anemometer (LDA), particle image velocimetry (PIV), phase Doppler anemometry (PDA) and shadow Doppler velocimetry (SDV) are becoming more and more popular for studying fluid mechanics. Laser Doppler anemometry is the most common non-invasive instrument for the airflow measurements with high accuracy, so far [24–26]. LDA techniques have been applied to measure the flow characterization in the towing tank and for full scale tests [27,28]. With the development of the laser technique and fiber technique, the submerged optical probe without power supply can be separated from the complex light source and control system and can be shaped with a streamlined shaped to decrease the perturbations of flow field [29,30].
This article focuses on the great demand for the high-resolution in-situ detection technique during the study of subsurface current flow and the size distribution of suspended particles. A LDCP system is designed for the measurement of distributed velocity with high spatial resolution. A dual wavelength LDCP system for the simultaneous measurement of water flow and particle size. The principle of the LDCP system is explained in Section 2. Section 3 describes the experimental setup of LDCP system and the methodology of data processing. Section 4 discusses the results of the velocity and particle size distribution. Section 5 presents the concluding remarks.
2. Specification and methodology
2.1 Specification of LDCP system
The LDCP system is a realization of the LDA principle for the velocity measurement of two component flow field. Figure 1 illustrates the configuration of the LDCP system. The laser utilized in the LDCP system is a compact diode pumped solid state (DPSS) laser (Cobolt Calypso 04-01, Cobolt Inc., Sweden), which transmits laser beam at two wavelengths (491 nm and 532 nm) with the power of 50 mW at each wavelength. The laser beam was divided into two beams by a fiber beam separator (BS, manufactured by Hanyu Inc., China), one beam was transmitted into profile sensor by single-mode fiber and the other beam passed through the AOM with a frequency shift of 40 MHz. Only the +1st diffraction orders of two wavelengths were used, all the other orders were blocked by beam stops. A fiber splitter (cut off 505 nm) was utilized to separate two different wavelengths laser from each other. The beams were then collimated into single-mode fiber which connected to the profile sensors (the max waist distance of the fiber collimator is up to 2349 mm, output 1/e2 waist diameter is 1.64 mm, PAF2P-A10A, Thorlabs Inc. USA). The electric vectors of polarization of the two laser beams were perpendicular to the plane of the beams. All the beams were brought to the measurement volume by a front lens (an aspherical lens with diameter of 150 mm manufactured by Jinlong Inc. China). The maximum value of the focal length is up to 2000 mm to avoid the possible hydrodynamic perturbations from the carrier and fixed optical cage. The distance from the measurement volume to the LDCP head can be changed by changing the front lens with different focal lengths, which makes it possible to adjust the fringe intervals and then to extend the measurement range of size distribution (the typical fringe intervals inside the measurement volume is 6.0 µm). The characteristic dimensions of the measurement volume have been illustrated in the Fig. 1, and the detailed information of the measurement volume could be calculated by the equation depicted in the figure once the beam waist is known. The front lens mentioned above also serves as the receiving unit. The most advantage of backward scattering system is the optical alignment consistency of the LDCP system when it is utilized for the speed measurements at different distance of the measurement volume to the LDCP system. The light scattered by the particles passing through the measurement volume was collected by a multi-mode fiber and transmitted to a photodetector (photomultiplier tube, PMT, H10723, Hamamatsu Inc., Japan). The data processor works out the signals involving the velocity component of the suspended particles. A trigger signal based on the intensity of the raw signal is set inside the signal analyzer to take full advantage of the capability of the signal analyzer. The signal with large intensity would be recorded and transferred to the data processor for further analysis. All parts (including the optical and electronic components) of the LDCP system are compactly installed in an aluminum alloy cylindrical housing with diameter of 220 mm. The laser beams transmit into the water medium through the end window of the cylindrical housing.
The signal received by the photon detectors are the scattering light of two laser beams which are considered to intersect at an angle 2α. Measurement volume of the LDCP system is the cross area of these two laser beams in the flow or other medium. The light intensity inside the measurement volume alternates with a distance equal to
This distance is known as the fringe intervals of the measurement volume. It is resulting from the interference created by the overlapping of the two light waves. Velocity measurements by means of LDA technique are based on the frequency analysis of the scattering light scattered by the seeding particles passing through the measurement volume as Fig. 2 shows.
The light signals received by the photodetector are sinusoid modified by a Gaussian envelope. The Doppler frequency f can be considered to be the alternating frequency in the intensity of light that is scattered out by a particle passing through the measurement volume. The velocity component of particles Vp passing through the measurement volume is calculated by
However, it is difficult to distinguish the flow direction of the particles due to the same Doppler frequency caused by a negative and a positive velocity of the same magnitude. In order to distinguish the flow direction from each Doppler burst, an acoustic optical modulator (AOM) is utilized to slightly shift the frequency in one or both of two laser beams. The intension of shifting the light frequency of one laser beam is to make the fringes inside the measurement volume moving towards fixed direction. In this condition, the frequency fPM measured by the receiving unit (photomultiplier) includes the fixed frequency fshift caused by utilizing the AOM and the frequency of the effective signals when the particles passing through the fringe intervals. The velocity Vp is resolved as
The velocity component Vp could be exactly calculated by directly comparing the measured frequency fPM and the preset shift frequency fshift. According to Eq. (3), the direction of the crossing particles is against the direction of the preset fringe pattern moving direction when there is Vp>0 from fPM>fshift. On the contrary, the direction of the particles passing through the measurement is along the direction of the preset fringe pattern moving direction.
2.2 Size measurement of micro-scale suspended particles
The work presented in this paper is based on the Lorenz–Mie theory to focus on the size distribution of the spherical and homogeneous suspended particles without taking the multiple scattering into consideration. In typical laser Doppler anemometer technique, the scattering characteristics of micro-scale particles with the size distribution closing to the laser wavelength are mainly related to the diameter, but not related to the ratio of the scattered incident light and the spatial distribution of the scattered light. Thus, the particle size information can be calculated by identifying this property. The methods for measuring particle size mainly include: 1) light absorption method based on total scattered light; and 2) light scattering method based on angular distribution of scattered light.
The LDCP system obtains the equivalent diameter information of the particles based on the visibility of the Doppler burst signal. The visibility of the Doppler burst signal can be expressed by
Among them, J1 is the first-order Bessel function, rp is the particle diameter, d is the fringe intervals of the measurement volume. Figure 3 illustrates the relationship between the burst signal visibility and the ratio of particle diameter and fringe intervals. By analyzing the curve depicted in the Fig. 3, we could conclude that the visibility comes to zero when the particle diameter is 1.62 times, 3.13 times, and 4.74 times the fringe interval. This characteristic has been proved by W. Farmer et al. for the glass spheres with a known diameter between 15 µm and 120 µm. In addition, the fluctuation of the visibility function cannot measure the particle diameter from a single value [32]. However, the influence of the shape and the refractive index of the particles were ignored in the model developed by W. Farmer [33]. Adrian et al. [34] and Naqwi [35] have developed a more sophisticated model for the Doppler burst signal properties by taking the reflecting process, refracting process and absorption coefficient into consideration.
The raw Doppler burst signal obtained by the LDCP system induced by one particle passing through the measurement volume, which contains background noise, effective burst signal and dark current of photonic detectors as Fig. 4 shows. The burst signal is mainly contributed by the reflected light rather than the refracted light due to the collection of scattering light at backward direction.
The autocorrelation data processing flow chart for the calculation of visibility is introduced in Fig. 5. The first step of the data processing is to reduce the influence of the background noise. The 500 sampling points at the end of the raw signal (8192 points in total) have been recognized as background noise. The levels of the background noise are related to the scattering light intensity collected by the detector and the dark current of the PMT. The velocity of the suspended particles could be calculated by identifying the Doppler shift information. The peak interval of the burst signal is function of velocity flowing through the measurement volume. The complexity of burst signal envelope and the uniformity of result after fast Fourier transform determine are important criteria for judging the particulate number in automatic algorithms. Multiple particles would be recognized by the algorithm if there are serval peaks in the FFT information or there are fluctuation in the envelop of the burst signals. Thus, we could model the burst signal based on the velocity information. The raw signals obtained by the photonic detector may be the convolution of the signals generated by more than one particle as Fig. 6 illustrated. After smoothing the background subtraction signal with a moving average filter, the peak position and intensity are identified by using processing algorithms. Then, the visibility of the burst signal could be obtained by the Eq. (4).
Fig. 6. Typical burst signal generated by two or more particles flowing through the measurement volume simultaneously. (a) typical burst signal type 1; (b) two particles with same velocity and different phases; (c) typical burst signal type 2; (d) two particles with same velocity and different phases; (e) typical burst signal type 3; (f) two particles with same velocity and different intensity; (g) typical burst signal type 4; (h) two particles with different velocity and different phases.
The visibility of the Doppler burst signal is determined by the particle size and the fringe intervals of the measurement volume. There are many factors that determine the reliability of the algorithm includes: 1) the envelope width of the raw signal, 2) the phase difference between the particles, 3) velocity of the particles, and 4) the particulate number that passing through the measurement at the same time. Generally, the time intervals of the two adjacent peaks inside the measurement volume are on order of milliseconds. Thus, the peaks and valleys of the signals generated by particles with different phases may form an aliasing signal. To reduce the probability of particles accumulation and the aliasing of multi-signal, the particle concentrations of flow studied in the experiment are relatively low. In this paper, the method is focus on the signal aliased by two particles. The raw signals obtained by the photonic detector may be the convolution of the signals generated by two particles as Fig. 6(a)–(d) illustrated. The burst signal curves are determined by the scattering light intensity, the time and velocity flowing through the measurement volume. The light intensity inside the measurement volume of LDCP system is ellipsoid. The intensity of the burst signal is determined by the position that the suspended particles flowing through the measurement volume. The raw burst signal may be generated by two or more particles flowing through the measurement volume from different positions as Fig. 6(e)–(f) show. The velocity of the particles passing through the measurement volume affect the peak intervals of the burst signal. Figure 6(g)–(h) depict the burst signal generated by two particles passing through the measurement volume with different velocity which can be obtained by Doppler shift information. In our demonstration campaign, the algae with low refraction index and small size range were utilized as seeding particles. Furthermore, the low particle concentrations of flow make it possible to reduce the possible aliasing of multi-signal. However, for the particles with large scale and complex refraction index, there is still more work to be done. The next steps will be to focus on the algorithm for the large-scale particle retrieval and the multiple particles recognition.
3. Experimental configuration
To verify the ability of the LDCP system to record the subsurface current speed and to obtain the size distribution of algae, demonstration experiments have been carried out. The configurations of field observation campaign and the laboratory experiment have been introduced in the following sections.
3.1 Field observation of micro-scale ocean current speed
A field observation of micro-scale oceanic subsurface current speed has been carried out by employing the LDCP system and traditional propeller current meter in a fixed cage which is powered by the Dongfonghong-2 scientific research vessel managed by Ocean University of China. The configuration of the field observation is illustrated in Fig. 7. The LDCP system and propeller current meter were rigidly connected to the cage to avoid the measurement error between two devices caused by the different basic measurement mechanism of current direction. The temporal resolution of the propeller current meter (SLC9-2) is 30s, while the data repetition rate of the LDCP system is up to 10 Hz.
3.2 Laboratory experiment of size distribution of particles
The configurations of the micro-scale suspended particle circulation system and dual wavelength heterodyne LDCP system in the laboratory experiment are depicted in Fig. 8. The suspended particles in the experiment were algal particles including Chlorella, Halophila, Diatom and Flat algal and the host medium is algae culture solution diluted with deionized water. The particle concentrations studied in the experiments are relatively low to reduce the probability of particles accumulation. The experiments have been carried out in a dark-light environment to reduce the effective of the algae from producing tiny dissolved oxygen bubbles, which would deteriorate their ability to follow liquid flow and distort particle size measurement results. The particle circulation system is achieved by means of adjustable speed peristaltic pump. The laser beams of the LDCP system intersects inside the measurement volume of the circulation system is utilized a square glass tube with high optical flatness.
4. Results and discussion
4.1 Time series velocity measurements of subsurface current
Field observations of subsurface current has been carried out by ultilizing the LDCP system and traditional propeller current meter in a fixed cage (approximately 1 m above the seafloor) for long term observations. The subsurface current profiles obtained by the LDCP system and the propeller current meter are illustrated in Fig. 9, with the red line represents the velocity measured by the LDCP system and the blue line depicts the results obtained by the traditional propeller type current meter. The local tide information is presented by the green line as well. Here, the measurements from the different instruments show similar trends; however, the LDCP system recorded higher speed values comparing to the traditional propeller-type current meter measurements.
Fig. 9. Current speed measured by Propeller type current meter and LDCP system at about 1 m above the seafloor over time.
The histogram graph is especially noteworthy because of its universal availability. The histogram is the default when it comes to dealing with massive data sets or real time streaming data. Histogram graphs (Fig. 10) reveal the systematic offset between the LDCP system and the propeller-type current meter. The yellow histogram graphs illustrate the results obtained by the propeller-type current meter, while the blue histogram graphs represent the values measured by the LDCP system. The results obtained by different instruments show the similar trend. The speed values recorded by the LDCP system are detached from the propeller type current meter with a mean deviation of 0.40 cm·s-1. The bias here may be due to the different sensitivity of two types of current meters to the flow within the low velocity range. It should be noted that the propeller-type current meter is an invasive single-point current meter. When the runners of the propeller type current meter start to rotate (within the low velocity range, against the internal and external resistances of the current meter), it is necessary to choose a flow with large speed. The minimum starting speed of the propeller-type current meter is 1 cm·s-1. However, the LDCP system is a non-invasive measurement equipment by collecting the scattering light with Doppler shift information, which is more sensitive to the flow within the low velocity range.
Fig. 10. Histogram of the subsurface current speed data measured by the LDCP system and propeller-type current meter. The yellow histograms represent the current speed obtained by the propeller-type current meter. The black line depicts the envelope of the yellow histograms. The blue histograms represent the current speed measured by the LDCP system. The blue line depicts the envelope of the blue histograms.
The observation site is an appropriate site for the cooperative observation due to the small water level range and the tide. Here we directly compare results obtained by the LDCP system and the propeller type current meter during the observation campaign (Fig. 11). The root-mean-square deviation between the speed determined with both methods is 0.381 cm·s-1, and thus in agreement with the error concluded by analyzing Fig. 10.
Fig. 11. Results obtained by the LDCP system and the propeller type current meter during the cooperative observation campaign.
4.2 Size distribution of the suspended particles
To validate the method of calculating the tracer particle diameter based on the visibility of the Doppler burst signals. Chlorella, Halophila, Diatom and Flat algal were used as tracer particles in the laboratory demonstration campaign as shown in Table 1.
Table 1. Categories of algae utilized in the laboratory observation campaign
The signal visibility distribution of different algae retrieval from the Doppler burst signal visibility have been illustrated in the first-order Bessel curve, as Fig. 12 shows. According to the characteristics of the first-order Bessel function, the visibility of Doppler burst signal decreases monotonically with the increase of the particle size within a specific range. The visibility results of Chlorella have been illustrated in Fig. 12 by black matchsticks. The blue matchsticks represent the results of Halophia, while the visibility fluctuation of the Diatom and the Flat algal are illustrated, respectively. From Fig. 12, we could conclude that the particle size distribution of Chlorella is the smallest, about 3∼5 µm; the particle size characteristics of Halophila and Diatoms overlap greatly, and the particle size range is about 5∼8 µm. The Doppler burst signal of the Flat algae shown in the figure has a visibility value between 0.18 and 0.42. When the signal visibility value is less than 0.23, the particle size corresponding to the visibility of the Doppler burst signal of the algal cannot be accurately determined (the same visibility value corresponds to several particle size values). When the signal visibility value is less than 0.23, the particle diameter may be between 7∼9 µm and may be between 9∼18 µm. This may be misjudgment in the process of automatic identification. Taking the inevitable particulate accumulation into consideration, the algorithm tends to output the smaller values rather than the larger values when it comes to the same visibility value corresponding to several particle size values.
Fig. 12. Relationship between visibility and the particle size and range of fringes. Images of particles obtained by the microscope. (a) Chlorella; (b) Halophia; (c) Diatom; (d) Flat algal.
Histogram graphs (Fig. 13) reveal the size distribution of the algae utilized in the demonstration campaign based on the visibility method. The size of the Chlorella ranges from 2.6 µm to 4.8 µm with more than 90 percent of values falling in 3.4 µm to 4.6 µm. The median value of the particle size distribution is approximately 3.93 µm. The results retrieved by the visibility method are smaller than the data obtained by the microscope (Table 1). The diameters of the Halophia are larger than the Chlorella with a closing Gaussian linear distribution in the range of 4.8 µm to 6.8 µm (as Fig. 13(b) shows). The Diatom has similar particle size distribution range from 4.8 µm to nearly 7 µm. However, the particle size distribution of Diatom is uniform over the whole range. The diameter of Diatom is larger than the Halophia according to the statics information in Table 1. The bias here may be caused by the aggregation of several unicellular algae and the limitation of visibility method. In this paper, we have paid great effect to distinguish the single particle from the aggregated particles, which would decrease the aliasing effects of the multiple particles. The limitation of the visibility method makes it difficult to capture the size larger than 7.5 µm.
Fig. 13. Size distribution of the suspended particles. (a) Chlorella; (b) Halophia; (c) Diatom; (d) Flat algae.
Generally, when it comes to measure the suspended particles with large scale such as cyanobacteria (the size of the suspended particles is larger than the fringe internals of the measurement volume), it is necessary to use a laser Doppler velocity signal in combination with a laser to excite the fluorescent signal or to combine the signal width information for suspension. laser-induced fluorescence method is suitable for the measurements of algae containing with chlorophyll, and signal width information method is suitable for inorganic particles or biochips that do not contain chlorophyll. The influence of trajectory effects and high concentration on the size distribution measurement must be considered. Also, when size distribution measurements are performed in a complex flow field, the influence of different particulate refractive index should be considered.
5. Conclusion and outlook
This article focuses on the great demand for the high-resolution in-situ detection technology during the study of micro-scale turbulent flow structure and the size distribution measurement of small-scale suspended particles. A LDCP system has been designed for the measurement of distributed subsurface velocity with high spatial resolution. The sensor is an extension of the principle of LDA. The LDCP system has been proved to be an effective tool to capture the micro-scale oceanic turbulence by carried out field campaign at Yellow Sea. Algorithm to calculate the distribution of the suspended particles with small scale (2∼7.5 µm) has developed and it has been validated by the laboratory experiment.
The specific conclusions are summarized below.
- 1. A LDCP system has been designed for the measurement of distributed subsurface velocity with high spatial resolution and size distribution of micro scale suspended particles simultaneously.
- 2. The speed values recorded by the LDCP system are detached from the propeller type current meter with a mean deviation of 0.4 cm·s-1, which has been proved by the field observation campaign.
- 3. Good quality measurements were demonstrated in laboratory measurement. Suspended particles with diameter from 2 µm to approximately 7.5 µm can be identified by the burst signal visibility method.
- 4. The LDCP system could obtain the velocity and concentration of the suspended particles (tracer particles or seeding particles) simultaneously, the fluxes of organic carbon could be calculated by integrating the velocity and concentration information.
- 5. The measurements range of size distribution based on the optical technique is limited due to the shapes and refractive index, and the concentration and size of the inclusions.
- 6. The LDCP system mentioned in this paper is intended to be miniaturized and intelligent to be configured at the mobile platforms, e.g., gliders or BGC-Argo systems for the concentration measurement of the particles and the estimation of the carbon flux with high depth resolution.
The present simultaneous measurements of suspended particles distribution and micro-scale subsurface current focus on the suspended particles within a small range with high temporal and spatial resolution. In terms of the large-scale suspended particles, size distribution measurement can be obtained by combining laser-induced fluorescence and laser Doppler technique. In this paper, the detection technology of small-scale tracer particles is explored by using Doppler burst signal visibility method. In the subsequent research, the technology will be used to detect the size and concentration of large suspended particles. By combining the positive remote sensing technique, it is expected to achieve the detection and monitor of the suspended carbon flux within the ocean subsurface with high depth resolution.
5.1 Outlook
Observations based on many instruments have put new sights on the phenomenon of plankton and zooplankton dynamics including temporal and spatial variations in biomass and species composition, early warning for harmful algal blooms and insights into carbon cycle transitions. However, we still cannot predict or explain the carbon cycle inside the upper ocean due to the particulate concentration with high spatial resolution. Traditional IFCB instruments could achieve the accurate description of the particulate shapes, however, the sampling rate limited the application to the estimation of biomass and carbon flux in the upper ocean. In the future, the LDCP system is intended to be configured at the mobile platforms, e.g., gliders or BGC-Argo systems for the describe the concentration of the particles and the estimation of the carbon flux with high depth resolution.
The LDCP system could increase the sampling rates by increasing the analog signal sampling rate, signal processing rate and data transmission rate. The particulate size distribution could be calculated by the visibility method and the laser induced fluorescence method. The concentration could be obtained by taking the velocity of particles and data acquisition rate into consideration. The water volume could be obtained by multiplying the measurement volume size and the velocity of the movement. The number of the suspended particles can be retrieved from the frequency of burst signal and the corrected number after deconvolution. The unit of the concentration mentioned here is cells·L-1. By combining the location and depth information of the instruments, the abundance of the particles in typical ocean area could be depicted with high resolution.
These combined observation instruments could allow continuous long-term observations of plankton community structure, ocean water optical parameter over a wide range, and useful to elucidate the processes and interactions of the carbon cycles in the upper ocean.
Funding
Laoshan Laboratory Science and Technology Innovation Projects (LSKJ202201202); Key Technologies Research and Development Program (2022YFB3901705); National Natural Science Foundation of China (41905022, 42106182, U2006217, U2106210); Natural Science Foundation of Shandong Province (ZR2021QD052).
Acknowledgments
We thank our colleagues for their kind support during the laboratory experiments including Yuanshuai Zhang from Ocean University of China for the contribution during the field observation campaign and Qichao Wang, Jintao Liu, Kailin Zhang, Shuguo Chen and Xuzhu Wang for the laboratory demonstration observation work.
Disclosures
The authors declare no conflicts of interest.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
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