A method for correlation analysis of groundwater arsenic content and microbial diversity

Abstract

The application relates to the technical field of groundwater environment monitoring, and discloses a method for correlatively analyzing the arsenic content and microbial diversity of groundwater. A semi-closed diffusion buffer probe is placed in an aquifer, a microporous ceramic semi-permeable membrane is used to build a low Peclet number environment, and a microfluidic unit is used to inject a substrate according to a sine rule to build a periodic concentration field. A double-channel electrochemical sensing array is used in combination with an asynchronous stroboscopic sampling strategy to collect arsenic concentration and biofilm impedance signals respectively, and response waveforms are reconstructed in an equivalent modulation period. Further, signal amplitude and phase are extracted through digital lock-in amplification, a dynamic hysteresis loop is constructed, and geometric features are calculated to obtain a functional diversity index. The application effectively solves the problem that in-situ electrochemical detection is time-consuming and it is difficult to track fast-changing signals, and realizes dynamic correlational analysis of arsenic speciation transformation and microbial metabolic activity.

Description

Technical Field

[0001] This invention relates to the field of groundwater environmental monitoring technology, specifically a correlation analysis method for groundwater arsenic content and microbial diversity. Background Technology

[0002] Arsenic pollution in groundwater is a global environmental and geological concern, and microbial metabolic activity is a key factor driving the transformation, migration, and release of arsenic from groundwater sediments. To elucidate the biogeochemical cycling mechanisms of arsenic in groundwater systems, it is necessary to simultaneously monitor the concentration of arsenic species in groundwater and the functional activity of microbial communities.

[0003] Traditional monitoring methods mainly rely on periodically extracting groundwater samples and sending them to laboratories for chemical analysis and high-throughput sequencing. While this offline analysis mode can provide static data at a specific moment, the sampling process can easily disrupt the original redox environment of the groundwater, leading to changes in sample properties. Furthermore, the analysis results have a significant time lag, making it difficult to reflect the real-time dynamic process of microbial interactions with arsenic in the groundwater environment.

[0004] Currently, although in-situ sensors based on electrochemical principles are used for monitoring heavy metals in groundwater, they still face many technical bottlenecks in practical applications. In in-situ environments, groundwater is usually in a continuous flow state. Directly injecting substrates into the aquifer to stimulate microbial activity is easily diluted by groundwater runoff and turbulence, resulting in unstable local substrate concentrations and difficulty in constructing accurate excitation signals. More importantly, to achieve high sensitivity detection of low concentrations of arsenic, electrochemical methods typically employ differential pulse anodic stripping voltammetry. This method requires a long enrichment and deposition time to accumulate the signal, leading to long single measurement cycles and low sampling frequencies. When attempting to detect the transient metabolic response of microorganisms through rapidly changing substrate concentration fields, the sensor's sampling rate cannot match the rate of change of the chemical signal. This insufficient temporal resolution prevents the monitoring system from fully recording the dynamic waveform of arsenic speciation, making it difficult to establish an accurate kinetic correlation between microbial metabolic activity and arsenic speciation. Summary of the Invention

[0005] This invention provides a correlation analysis method for arsenic content and microbial diversity in groundwater, aiming to solve the problem that existing technologies cannot simultaneously achieve high temporal resolution dynamic monitoring and long-term electrochemical detection in in-situ environments, and to realize dynamic correlation analysis of arsenic speciation process and microbial community metabolic activities in groundwater.

[0006] The technical solution adopted in this invention is as follows:

[0007] A method for correlation analysis of arsenic content and microbial diversity in groundwater, comprising the following steps:

[0008] The buffer probe is placed in the groundwater aquifer to be tested. The buffer chamber inside the buffer probe is used to establish chemical equilibrium with the external groundwater environment. The baseline arsenic speciation concentration and the baseline impedance modulus of the biofilm are measured using a dual-channel electrochemical sensing array located in the buffer chamber.

[0009] The substrate solution is continuously injected into the buffer chamber using an injection unit, and the injection flow rate is controlled according to a preset sine wave function to construct a substrate concentration field that changes periodically with time in the buffer chamber.

[0010] While the injection unit is running continuously, the first and second electrodes of the dual-channel electrochemical sensing array are used to collect arsenic concentration detection data and biofilm impedance signals respectively, perform asynchronous stroboscopic sampling, and control the sampling trigger time of the first electrode to generate a phase step delay relative to the injection cycle of the injection unit, so as to complete the discrete point coverage of a complete signal cycle within multiple injection cycles.

[0011] The baseline arsenic speciation concentration and the baseline impedance modulus of the biomembrane are used to perform baseline subtraction processing on the discretely acquired arsenic concentration detection data and the biomembrane impedance signal, respectively. The processed data are then mapped to the same equivalent modulation period according to the corresponding phase information to reconstruct the arsenic speciation conversion response waveform and the biomembrane metabolism response waveform.

[0012] The arsenic speciation response waveform and the biomembrane metabolic response waveform are digitally phase-locked amplified to extract the amplitude and phase of the arsenic speciation response with the same frequency as the injection frequency, as well as the amplitude and phase of the biomembrane metabolic response.

[0013] Using the biofilm metabolic response waveform as the input variable and the arsenic speciation response waveform as the output variable, a dynamic hysteresis loop is constructed in a two-dimensional coordinate system. Based on the geometric characteristics of the dynamic hysteresis loop, a substitution index characterizing the functional diversity of the microbial community is calculated and generated.

[0014] Furthermore, the buffer probe includes a support frame and a microporous ceramic semi-permeable membrane covering the outside of the support frame;

[0015] The microporous ceramic semi-permeable membrane is used to provide fluid damping inside the buffer cavity, blocking the runoff and turbulence of external groundwater from directly impacting the inside of the buffer cavity, so that the fluid inside the buffer cavity is in a low Pecklet number state.

[0016] Furthermore, the variation of the injection flow rate of the injection unit with time is determined by three parameters: the basic DC bias flow rate, the modulation amplitude, and the modulation period.

[0017] The base DC bias velocity is greater than the modulation amplitude, and the value of the modulation period is configured to satisfy the diffusion steady-state condition and the biological response matching condition, ensuring the spatial uniformity of the substrate concentration in the buffer cavity and that the excitation frequency is within the passband range of the biological community.

[0018] Furthermore, the step-delay of the sampling trigger time of the first electrode relative to the injection period of the injection unit specifically includes:

[0019] Calculate the ratio of the duration of a single detection cycle to the injection cycle, and determine the number of complete injection cycles that need to be traversed between two adjacent samplings, i.e., the cycle jump factor;

[0020] Set a target number of sampling points within a reconstruction cycle, and calculate the phase step time accordingly;

[0021] A physical trigger timing sequence is generated based on the periodic jump factor and the phase step time, such that the trigger time of each sample is increased by a fixed step in phase compared to the trigger time of the previous sample.

[0022] Furthermore, a nested acquisition timing sequence is constructed when acquiring the biomembrane impedance signal, specifically including:

[0023] Identify the enrichment deposition time window of the first electrode during differential pulse anodic stripping voltammetry;

[0024] The electrochemical impedance spectroscopy measurement task of the second electrode is embedded at the midpoint of the enrichment deposition time window;

[0025] The duration of a single scan of the electrochemical impedance spectroscopy is configured to be shorter than the duration of the enrichment deposition time window, and the amplitude of the AC excitation signal of the second electrode is limited to a preset range.

[0026] Furthermore, the reconstructed arsenic speciation transformation response waveform and biomembrane metabolic response waveform specifically include:

[0027] The signal control and processing unit calls the baseline arsenic speciation concentration and performs differential calculation on the discretely acquired arsenic concentration detection data to subtract the background concentration and obtain dynamic arsenic response data;

[0028] Call the baseline impedance modulus of the biofilm and calculate the normalized offset of the discretely acquired biofilm impedance signal relative to the baseline impedance modulus of the biofilm.

[0029] Using modulo operation, the dynamic arsenic response data and the normalized offset are mapped to a single equivalent time interval with a duration equal to the modulation period based on the corresponding physical sampling absolute trigger time, and then arranged in ascending order according to the size of the equivalent time coordinates to generate an ordered measurement sequence.

[0030] The first and last data points of the ordered measurement sequence are periodically extended, and the extended sequence is fitted using a cubic spline interpolation algorithm to generate high temporal resolution waveforms of the arsenic speciation response and the biomembrane metabolism response, respectively.

[0031] Furthermore, the digital phase-locked amplification process specifically includes:

[0032] Two orthogonal digital reference signals are generated based on the injection frequency;

[0033] The arsenic speciation response waveform and the biomembrane metabolism response waveform are respectively multiplied point-to-point with the two orthogonal digital reference signals to calculate the in-phase component product and the orthogonal component product.

[0034] The in-phase component product and the quadrature component product are integrated using the full-cycle numerical integration method to obtain the in-phase component value and the quadrature component value, and the phase angle and amplitude are calculated.

[0035] The difference between the phase angle of the biomembrane metabolic response and the phase angle of the arsenic speciation response is calculated as the phase difference.

[0036] Furthermore, the construction and feature extraction of the dynamic hysteresis loop include:

[0037] The arsenic speciation response waveform and the biomembrane metabolic response waveform were normalized to map the data to a dimensionless interval.

[0038] Normalized biomembrane metabolic response data were used as the x-axis, and normalized arsenic speciation transformation response data were used as the y-axis. The point sets were arranged in chronological order and connected end to end to form a closed phase space trajectory.

[0039] The area enclosed by the dynamic hysteresis loop is calculated using the discretized Green's formula and taken as the hysteresis area.

[0040] The absolute value of the difference between the actual hysteresis loop area and the area of ​​an ideal ellipse with the same major and minor axes is calculated as the degree of nonlinear distortion.

[0041] This invention provides a method for correlation analysis of arsenic content and microbial diversity in groundwater. It has the following beneficial effects:

[0042] 1. This invention solves the problem of long detection time and difficulty in matching fast-changing signals in anodic stripping voltammetry by controlling the sampling trigger time of electrochemical detection with a fixed phase step delay relative to the substrate injection cycle through an asynchronous stroboscopic sampling strategy. This method reconstructs a complete high-resolution signal cycle using discrete data points from multiple injection cycles, enabling high-precision capture of the dynamic process of arsenic speciation transformation in groundwater without changing the sensor hardware response speed.

[0043] 2. This invention utilizes a microporous ceramic semi-permeable membrane to construct a semi-closed diffusion buffer environment, effectively blocking the hydraulic impact of external groundwater runoff and turbulence on the probe's interior, maintaining a stable diffusion state with a low Pecklet number within the buffer chamber. This structural feature ensures that the substrate injected via microfluidic technology can form a spatially uniform concentration field within the chamber that varies sinusoidally over time, preventing the substrate from being instantly washed away or diluted due to fluctuations in external groundwater flow velocity, thus guaranteeing the accuracy of the chemical modulation signal.

[0044] 3. This invention establishes a correlation analysis model based on a dynamic hysteresis loop, using biofilm metabolic impedance as the input variable and arsenic speciation response as the output variable. Data is quantified by calculating the area and nonlinear distortion of the hysteresis loop. This method can intuitively reflect the hysteresis effect of microbial communities in response to environmental stimuli and the nonlinear characteristics of metabolic processes, providing specific quantitative indicators for assessing the functional diversity of groundwater microbial communities and their metabolic kinetics of arsenic. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the system structure according to an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of a method flow according to an embodiment of the present invention.

[0047] Among them, 100 is a buffer probe; 110 is a support frame; 120 is a microporous ceramic semi-permeable membrane; 130 is a buffer chamber; 200 is an injection unit; 210 is a drive pump; 220 is a nozzle; 300 is a dual-channel electrochemical sensing array; 310 is a first electrode; 320 is a second electrode; 330 is a reference electrode; 340 is an auxiliary electrode; and 400 is a signal control and processing unit. Detailed Implementation

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] See attached document Figure 1 The present invention provides a correlation analysis system for groundwater arsenic content and microbial diversity, which mainly includes: a buffer probe 100, an injection unit 200, a dual-channel electrochemical sensor array 300, and a signal control and processing unit 400.

[0050] The buffer probe 100 constitutes the physical interface between the system and the groundwater environment and is configured to be directly inserted into the aquifer. The buffer probe 100 is equipped with a support frame 110, the exterior of which is covered by a microporous ceramic semi-permeable membrane 120. The support frame 110 and the microporous ceramic semi-permeable membrane 120 together form a buffer cavity 130 with a fixed volume. The internal volume of the buffer cavity 130 is set to be between 5 ml and 20 ml.

[0051] The microporous ceramic semi-permeable membrane 120 has a pore size ranging from 0.2 micrometers to 0.5 micrometers. This pore size allows dissolved ions in the groundwater to diffuse into and out of the buffer chamber 130, maintaining the ion concentration, pH value, and redox potential inside the buffer chamber 130 in chemical equilibrium with the external aquifer. The microporous ceramic semi-permeable membrane 120 provides fluid damping inside the buffer chamber 130, preventing the runoff and turbulence of external groundwater from directly impacting the interior of the buffer chamber 130, thus keeping the fluid inside the buffer chamber 130 in a low Peckley number state.

[0052] The injection unit 200 is used to apply substrate concentration field excitation to the buffer chamber 130. The injection unit 200 includes a drive pump 210 and a nozzle 220. The drive pump 210 is located at the monitoring wellhead or at the tail of the buffer probe 100 after waterproof encapsulation. The drive pump 210 is in fluid communication with the nozzle 220 located inside the buffer chamber 130 through a pressure-resistant infusion line.

[0053] The nozzle 220 is positioned so that its outlet is aligned with the sensing surface of the dual-channel electrochemical sensing array 300, with the distance between them maintained between 2 and 5 millimeters. The drive pump 210 controls the injection flow rate according to a preset sine wave function, thereby constructing a substrate concentration field that varies periodically with time within the buffer chamber 130.

[0054] A dual-channel electrochemical sensor array 300 is integrated and mounted at the geometric center of the buffer chamber 130. The dual-channel electrochemical sensor array 300 includes a first electrode 310, a second electrode 320, a reference electrode 330, and an auxiliary electrode 340. All electrodes are located in the same electrolyte environment within the buffer chamber 130.

[0055] The first electrode 310 is made of glassy carbon electrode material modified with gold nanoparticles. The first electrode 310 is configured to perform differential pulse anodic stripping voltammetry to determine the arsenic speciation and instantaneous concentration in the water within the buffer chamber 130.

[0056] The second electrode 320 employs an interdigitated microelectrode structure. A hydrophilic porous media layer is attached to the surface of the second electrode 320 to facilitate the attachment and growth of indigenous microorganisms in the groundwater, forming a biofilm. The second electrode 320 is configured to perform electrochemical impedance spectroscopy analysis to monitor changes in the impedance modulus of the biofilm in the low-frequency region.

[0057] The signal control and processing unit 400 is electrically connected to the injection unit 200 and the dual-channel electrochemical sensor array 300 via a shielded cable. The signal control and processing unit 400 includes a multi-channel potentiostat module and a digital signal processor.

[0058] The signal control and processing unit 400 sends a flow control command to the drive pump 210 and simultaneously triggers data acquisition from the dual-channel electrochemical sensor array 300. The signal control and processing unit 400 executes asynchronous stroboscopic sampling logic, controlling the sampling trigger time of the first electrode 310 to have a phase-step delay relative to the injection cycle of the drive pump 210. The signal control and processing unit 400 receives the raw electrical signal from the dual-channel electrochemical sensor array 300 and performs equivalent waveform reconstruction and phase-locked demodulation operations.

[0059] See attached document Figure 2 This invention provides a method for correlation analysis of arsenic content and microbial diversity in groundwater, mainly including the following steps:

[0060] Step S100: Establish in-situ diffusion equilibrium and determine system baseline parameters. Insert the buffer probe 100 into the predetermined depth of the groundwater aquifer to be measured. Allow the buffer probe 100 to stand in the well for a preset equilibrium time, allowing the chemical components of the water inside the buffer chamber 130 to reach concentration equilibrium with the external groundwater environment through the microporous ceramic semi-permeable membrane 120.

[0061] During the resting period, the signal control and processing unit 400 controls the injection unit 200 to be in the off state. The signal control and processing unit 400 controls the dual-channel electrochemical sensing array 300 to perform baseline scanning. The first electrode 310 measures the background arsenic speciation concentration in the buffer chamber 130. The second electrode 320 measures the baseline impedance modulus of the biofilm under undisturbed conditions until the impedance deviation value of three consecutive scans is less than a preset threshold, thus establishing the system's zero-time baseline.

[0062] In step S200, a continuous sinusoidal substrate concentration field excitation is applied. The signal control and processing unit 400 sends a start command to the injection unit 200. The pump 210 is driven to continuously inject the substrate solution into the buffer chamber 130 via the nozzle 220.

[0063] The injection flow rate of the drive pump 210 is periodically modulated according to a preset sine wave function. The variation of the injection flow rate with time is determined by three parameters: the base DC bias flow rate, the modulation amplitude, and the modulation period. This continuous injection process constructs a nutrient concentration field that fluctuates periodically with time within the buffer chamber 130, serving as an input signal to induce a metabolic response in the microbial community. The value of the modulation period is set according to the metabolic response time constant of the target microbial community.

[0064] In step S300, dual-channel asynchronous stroboscopic equivalent sampling is performed. While the injection unit 200 continues to operate, the signal control and processing unit 400 controls the dual-channel electrochemical sensing array 300 to acquire data. The second electrode 320 continuously acquires biomembrane impedance signals at a sampling rate higher than the modulation frequency.

[0065] For the first electrode 310, the signal control and processing unit 400 executes stroboscopic sampling logic. Since the single detection time of the differential pulse anodic stripping voltammetry is close to or exceeds the modulation period, the signal control and processing unit 400 controls the sampling trigger time of the first electrode 310 to have a phase step delay relative to the injection period. Within multiple consecutive modulation periods, each sampling trigger time is increased by a fixed step in phase compared to the previous sampling trigger time. After a preset number of modulation periods, discrete point coverage of a complete signal period is achieved.

[0066] In step S400, the equivalent response waveform is reconstructed and phase-locked demodulation is performed. The signal control and processing unit 400 maps the discrete arsenic concentration detection data obtained in step S300 to the same equivalent modulation period based on their corresponding phase information. The signal control and processing unit 400 performs interpolation fitting on the mapped data points to reconstruct a high-time-resolution arsenic speciation response waveform.

[0067] The signal control and processing unit 400 performs digital phase-locked amplification on the reconstructed arsenic speciation response waveform and the real-time acquired biomembrane impedance signal waveform. The signal control and processing unit 400 extracts the signal components with the same frequency as the modulation frequency from the two waveforms and calculates the amplitude and phase of the arsenic speciation response, as well as the amplitude and phase of the biomembrane metabolic response.

[0068] Step S500: Construct a hysteresis dynamic model and invert the functional diversity index. The signal control and processing unit 400 constructs a dynamic hysteresis loop in a two-dimensional coordinate system, using the biomembrane metabolic response as the input variable and the arsenic speciation response as the output variable.

[0069] The signal control and processing unit 400 calculates the closed area of ​​the hysteresis loop and the phase difference between the input and output variables. Based on the weighted calculation results of the closed area of ​​the hysteresis loop and the phase difference, the signal control and processing unit 400 generates a substitution index characterizing the functional diversity of the microbial community. In this embodiment, the substitution index is specifically defined as the functional diversity index. The functional diversity index value is used to quantitatively assess the metabolic regulation capacity and functional complexity of the microbial community in the current groundwater environment regarding arsenic speciation.

[0070] In step S100, establishing in-situ diffusion equilibrium and determining the system baseline parameters are prerequisites for implementing this method. This step aims to eliminate the interference of groundwater environmental background fluctuations on micro-disturbance signals and ensure that the subsequently applied sinusoidal excitation signal dominates within the buffer cavity 130.

[0071] Step S101: Probe Deployment and Physical Isolation. The buffer probe 100 is vertically inserted into the filter section of the monitoring well, ensuring it is completely submerged in the saturated aquifer. The buffer chamber 130 contacts the external water body through a microporous ceramic semi-permeable membrane 120. The microporous ceramic semi-permeable membrane 120 is made of alumina ceramic, silicon carbide ceramic, or sintered glass, possessing a rigid structure capable of resisting underground hydrostatic pressure without deformation, thus maintaining a constant internal volume of the buffer chamber 130. The porosity of the microporous ceramic semi-permeable membrane 120 is set to 30% to 50%, and its tortuosity is set to 1.5 to 3.0, ensuring both the mass transfer flux of dissolved substances and providing fluid resistance through the tortuosity of the pores.

[0072] Step S102, chemical equilibrium establishment. The buffer probe 100 is left to stand downhole for a preset equilibrium time. During this period, solutes in the external groundwater diffuse into the buffer chamber 130 through the microporous ceramic semi-permeable membrane 120, driven by the concentration gradient. This process follows Fick's diffusion law until the chemical potentials inside and outside the chamber are equal. Equilibrium time The setting is calculated based on the characteristic diffusion length and effective diffusion coefficient of the buffer cavity 130, and it satisfies the following relationship:

[0073] ,in, The characteristic diffusion length of the buffer cavity 130 is specifically the maximum straight-line distance from the inner wall of the microporous ceramic semi-permeable membrane 120 to the surface of the dual-channel electrochemical sensing array 300. This represents the effective diffusion coefficient of the solute in the microporous ceramic semi-permeable membrane 120 and the medium within the cavity. It should be noted that... Due to the inherent properties of the microporous ceramic semi-permeable membrane 120, its calculation requires the introduction of a porosity correction factor. (i.e., the aforementioned 30%-50%) and tortuosity correction factor ,Right now ,in is the free diffusion coefficient of the solute in pure water.

[0074] Step S103, fluid dynamic steady-state confirmation. During the settling process, the microporous ceramic semi-permeable membrane 120 generates a fluid damping effect on the rapid velocity fluctuations of the external groundwater, smoothing out instantaneous pressure pulsations. Through pore damping, the fluid state inside the buffer cavity 130 is confined to a low Pecklet number region. Pecklet number The definition is as follows:

[0075] ,in, The velocity of the residual fluid inside the buffer chamber 130; The characteristic diffusion length defined in step S102; This is the effective diffusion coefficient defined in step S102. This system ensures this through physical structural constraints. Under these conditions, the mass transport process is dominated by diffusion mechanisms, and the influence of convective transport is suppressed. This ensures that the subsequent substrate concentration field distribution generated by the injection unit 200 depends primarily on the injection rate and diffusion law, rather than an uncontrollable external hydraulic gradient.

[0076] Step S104, Electrochemical baseline scan and determination. When the preset equilibrium time is reached... Subsequently, the signal control and processing unit 400 initiates the baseline verification procedure. The second electrode 320 performs an electrochemical impedance spectroscopy scan on the attached biofilm. The scan frequency range covers 0.1 Hz to 100 kHz, with a focus on monitoring the impedance modulus in the low-frequency region from 0.1 Hz to 10 Hz. The system performs multiple scans continuously at fixed time intervals and calculates the relative deviation rate between two adjacent scans. :

[0077] ,in, For the first The low-frequency impedance modulus of the next scan; For the first The low-frequency impedance modulus of the next scan. When the relative deviation rate... When the arsenic concentration falls below the preset threshold three times consecutively, the system is considered to have reached a steady state, indicating that the biofilm's physiological state is stable and the intracavitary aquatic environment is balanced. At this point, the system records the current arsenic speciation concentration and biofilm impedance as the zero-point baseline for subsequent differential calculations. For the specific parameter settings and operating procedures of differential pulse anodic stripping voltammetry and electrochemical impedance spectroscopy, those skilled in the art can perform conventional configurations according to the actual detection object, and will not be elaborated further here.

[0078] Based on the establishment of the system's physical and chemical equilibrium, this embodiment further details the method for confirming the electrochemical activity state of the biofilm in step S100. This process is configured to quantify the growth maturity and electrochemical stability of the biofilm on the surface of the second electrode 320, providing a zero-time reference benchmark for subsequent micro-perturbation correlation analysis.

[0079] Step S105: Apply a micro-amplitude AC excitation. The signal control and processing unit 400 controls the potentiostat module to apply a sinusoidal AC potential signal to the second electrode 320. The amplitude of this AC potential signal is set to 5 mV to 10 mV. This amplitude range is selected to ensure that the electrochemical system is in the linear response region, avoiding irreversible redox damage to microbial cells within the biofilm caused by large amplitude voltages. The scanning frequency range is set to 10. -2 Hertz to 10 5 Hertz, sampling points are distributed logarithmically, and the density is set to collect 10 data points per ten octaves.

[0080] Step S106: Equivalent circuit model fitting and key parameter extraction. The signal control and processing unit 400 acquires the current response signal and calculates the complex impedance data. The system uses a simplified Randle equivalent circuit model to fit the impedance spectrum data using nonlinear least squares method. This equivalent circuit model includes the solution resistors in series. and parallel-connected double-layer capacitors and charge transfer resistance .

[0081] In the low-frequency region from 0.1 Hz to 10 Hz, since the mass transfer process of the biomembrane has not yet entered the semi-infinite diffusion control region, the Warburg diffusion impedance term can be ignored, and the electrochemical properties of the biomembrane are mainly determined by the charge transfer resistance. Dominant. This parameter characterizes the rate of extracellular electron transport in microorganisms and the intensity of metabolic activity in biomembranes. Complex impedance The mathematical relationship with the equivalent circuit elements is expressed as follows:

[0082] ,in, Indicates angular frequency as Complex impedance at time; It represents the resistance of the solution, reflecting the conductivity of the electrolyte solution within the buffer chamber 130; It represents charge transfer resistance, reflecting the resistance to electron exchange at the biomembrane interface; It represents the double-layer capacitance, reflecting the charging characteristics of the electrode-biomembrane interface; Represents the imaginary unit, satisfying ; Represents angular frequency, satisfying ,in The excitation frequency.

[0083] Step S107, Biomembrane Steady-State Determination and Baseline Locking. To ensure that the impedance change induced by subsequent sinusoidal substrate injection originates from substrate excitation rather than biomembrane growth drift, the system needs to confirm the charge transfer resistance. Temporal stability. The system operates at fixed inspection intervals. (Set to 10 minutes) Repeat steps S105 and S106 to obtain a time-varying charge transfer resistance sequence. The system calculates the impedance drift rate between two adjacent time points. : ,in, For the current moment Extracted charge transfer resistance value; For the previous moment Extracted charge transfer resistance value.

[0084] When impedance drift ratio When the charge transfer resistance is less than a preset stability threshold (ranging from 2% to 5%) for three consecutive times, the signal control and processing unit 400 determines that the biomembrane has entered a physiological homeostasis. At this time, the system locks the most recently measured charge transfer resistance value as the baseline charge transfer resistance. And lock the corresponding impedance magnitude to the baseline impedance magnitude. The baseline parameter is stored and used as a normalized reference value when calculating the functional diversity index in subsequent step S500. For the specific algorithm of electrochemical equivalent circuit fitting, those skilled in the art can use the Levenberg-Marquardt algorithm, which will not be elaborated here.

[0085] After completing system stabilization and baseline construction, this embodiment elaborates on the flow control model of the injection unit 200 in step S200. This model is configured to construct a dynamic chemical field with controllable frequency and small amplitude inside the buffer chamber 130 through fluid dynamics control, thereby serving as the frequency domain input signal to stimulate microbial metabolism.

[0086] Step S201: Establish the sinusoidal flow control equation. The signal control and processing unit 400 has a pre-set waveform generation algorithm configured to generate a time-series signal driving the pump 210. The instantaneous injection velocity of the pump 210 follows a sinusoidal modulation law. The flow control equation is as follows:

[0087] ,in, Indicates time The instantaneous injection flow rate, expressed in microliters per minute; This indicates the basic DC bias flow rate, used to maintain continuous positive delivery of the substrate into the buffer chamber 130; This indicates the amplitude of the flow modulation, which determines the dynamic range of substrate concentration fluctuations; This represents the modulation period, the value of which is determined by the preset excitation frequency. Decision, satisfaction ; This represents the initial phase, which is set to 0 in this embodiment; Indicates the time.

[0088] Step S202: Set flow rate parameter constraints. To ensure the physical stability of the fluid injection process and the effectiveness of the biological stimulation, the parameters in the above equations must meet physical constraints. First, the basic DC bias flow rate... The numerical range is set from 1 μL / min to 10 μL / min. This flow rate range is chosen to match the volume of the buffer chamber 130 (5 mL to 20 mL), ensuring that the fluid convection velocity caused by injection is much lower than the solute diffusion velocity, maintaining a low Peckley number within the chamber. Secondly, the base DC bias flow rate... It must be greater than the flow modulation amplitude. ,Right now To prevent fluid backflow. Again, flow modulation amplitude. The value range is set based on the DC bias velocity. The substrate concentration should be between 10% and 30%. This limit ensures that any resulting fluctuations in substrate concentration are within a small range. Within this range, the metabolic response of the microbial community maintains an approximately linear relationship with the substrate concentration, avoiding nonlinear metabolic saturation or substrate inhibition effects caused by excessively high substrate concentrations.

[0089] Step S203: Determine the matching relationship between the excitation frequency and the biological response timescale. Modulation period. The values ​​were set between 50 and 500 seconds. This timescale was based on the characteristic response time of the extracellular electron transport chain of groundwater microorganisms. The substrate injection frequency was set at 2 × 10⁻⁶. -3 Hertz to 2×10 -2 The low-frequency band of Hertz can stimulate the dynamic activity of electron transport proteins on the surface of microbial cell membranes and the periplasmic space enzyme system, while simultaneously separating nanosecond or microsecond-level pure electrochemical double-layer charging and discharging processes in the frequency domain. The signal control and processing unit 400 controls the drive pump 210 via a timer to ensure the instantaneous injection flow rate of the actual output. The tracking error for the above control equations is less than 1%.

[0090] To ensure that the chemical signals generated by microfluidic injection can be effectively recognized by the microbial community and converted into metabolic electrical signals, while ensuring the spatial uniformity of substrate concentration within the buffer chamber 130, the excitation frequency involved in step S200 needs to be quantitatively selected based on the matching relationship between the fluid diffusion timescale and the biological metabolic timescale.

[0091] Step S204: Calculate the lower limit of the substrate diffusion mixing time within the chamber. The straight-line distance from the nozzle 220 outlet to the surface of the dual-channel electrochemical sensing array 300 is defined as the effective diffusion transport distance. In this embodiment, to match the requirements of rapid modulation, the distance is configured to be between 200 and 500 micrometers. Substrate molecules are transported from the nozzle exit to the biofilm surface primarily via molecular diffusion. The signal control and processing unit 400 calculates the diffusion-mixing time constant. The formula is as follows:

[0092] ,in, Indicates the diffusion mixing time constant; Indicates the effective diffusion transmission distance; This represents the substrate diffusion coefficient. For commonly used acetate or lactate substrates, the value ranges from 1.0 × 10⁻⁶ for groundwater at 25°C. -9 Square meters per second up to 1.5 × 10 -9 Square meters per second.

[0093] To ensure that the local substrate concentration on the surface of the dual-channel electrochemical sensing array 300 represents the average concentration within the buffer cavity 130 at every phase point of the sinusoidal modulation, phase distortion caused by substrate transport delay must be eliminated. The system sets the modulation period. The following diffusion steady-state conditions must be met:

[0094] ,in, The modulation period is defined in step S201. This condition causes the mass transport rate to be much faster than the concentration modulation rate, thereby establishing a quasi-uniform dynamic concentration field within the buffer cavity 130.

[0095] Step S205: Determine the upper time limit of the microbial metabolic response. Microbial extracellular electron transport involves substrate transmembrane transport, intracellular enzymatic reactions, and electronic transitions of cytochrome proteins; this series of biochemical reactions exhibits kinetic lag. The system incorporates characteristic frequencies of the biological response. When the external excitation frequency is higher than this characteristic frequency, the microbial community exhibits low-pass filtering characteristics, and the amplitude of the output metabolic impedance signal will be significantly attenuated. Based on the electrochemical characteristics of the arsenic metabolism functional bacteria in groundwater, its biological response characteristic frequency... It is distributed in the range of 0.05 Hz to 0.1 Hz.

[0096] To obtain a metabolic response signal with a high signal-to-noise ratio, the excitation frequency... It must be located within the band gap of the biological community. The system sets biological response matching conditions as follows: ,in, To excite the frequency, satisfy This condition ensures that the rate of change of the microfluidic excitation signal is slow enough, allowing the redox state inside the microorganism to reach a quasi-equilibrium state in response to changes in the concentration of the external substrate.

[0097] Step S206: Determine the optimal operating frequency window. The signal control and processing unit 400 combines the physical diffusion limitations of step S204 and the biodynamic limitations of step S205, and considers the environmental background drift noise in the extremely low frequency band, to calculate the excitation frequency. Effective working window:

[0098] ;

[0099] Based on the above calculations, this embodiment will use the excitation frequency. The specific values ​​are set to 0.002 Hz to 0.02 Hz (corresponding to the modulation period). The frequency range is from 50 seconds to 500 seconds. This frequency range avoids the diffusion delay and biological response attenuation of high-frequency bands, while also setting a lower frequency limit ( This avoids the inefficiency caused by excessively long testing cycles and interference from drifting environmental background parameters.

[0100] This invention provides an analytical method for solving long-period detection time delays in dynamic environments. In step S300, the physical limitations of conventional real-time sampling in a microfluidic modulation environment are first defined, namely, the technical principle of sampling conflict. This part aims to clarify why synchronous sampling cannot resolve the low-frequency sinusoidal chemical field constructed by this system, thereby introducing the necessity of asynchronous stroboscopic equivalent sampling.

[0101] Step S301: Analyze the time-domain characteristics of stripping voltammetry. The first electrode 310 performs differential pulse anodic stripping voltammetry to determine trace arsenic. This electrochemical method is a sequential process involving multiple consecutive actions. The signal control and processing unit 400 controls the first electrode 310 to sequentially perform four stages: cleaning, enrichment, settling, and stripping scan. Define the duration of a single complete detection cycle. The calculation formula is as follows: ;in, This is the electrochemical cleaning time, used to remove residual impurities on the electrode surface, and is typically set to 30 to 60 seconds. To enrich the deposition time, in order to obtain a signal-to-noise ratio that meets the detection limit requirements in low-concentration groundwater environments at the nanomolar level, this time must be maintained between 60 and 300 seconds to ensure that sufficient analytes are reduced and deposited on the electrode surface. Set the settling and balancing time to 10 to 20 seconds. The dissolution scan time depends on the scan potential range and scan rate, and is typically 10 to 30 seconds.

[0102] Step S302, quantify the sampling aperture effect. Based on the settings in step S206, the modulation period generated by the injection unit 200... The range is from 50 seconds to 500 seconds. The detection cycle duration is... With modulation period A comparison reveals significant overlap in time scales. The system defines the sampling aperture ratio. like: ;

[0103] Under the operating parameters of this system, The numerical range falls between 0.2 and 6.0. When When the concentration is close to or greater than 1, the substrate concentration field within the buffer chamber 130 undergoes a significant phase change during a single measurement performed by the first electrode 310. At this point, the sensor's detection result is no longer the instantaneous concentration at a specific moment, but rather the concentration over the entire enrichment and deposition time. The integral average of the concentration changes during the period. This phenomenon, known as the sampling aperture effect, causes severe amplitude attenuation and phase lag in the detected signal waveform.

[0104] Step S303: Establish the failure boundary for real-time sampling. To demonstrate the infeasibility of real-time sampling, assume the actual arsenic speciation response concentration within buffer chamber 130. It follows a sinusoidal variation. If continuous real-time sampling is used, the enrichment deposition time should be the primary consideration. Due to the integral effect, the measured value output by the first electrode 310 amplitude response coefficient for: ,in, The excitation frequency is defined in step S201. When the excitation frequency... With enrichment sedimentation time As the product increases, the amplitude response coefficient A rapid decline. For example, when enrichment deposition time... Equal to modulation period When the amplitude is half of the original value, the signal amplitude will decrease by about 36%, and the original phase information will be lost due to the integral effect, making it impossible to recover the true dynamic waveform through linear compensation.

[0105] Step S304: Define the technical requirements for asynchronous sampling. Based on the above analysis, the signal control and processing unit 400 determines that the sampling theorem cannot be satisfied by increasing the sampling rate because the time consumed by a single physical detection, i.e., the detection cycle length... This is an inherent physical characteristic determined by electrochemical reaction kinetics, and cannot be compressed. To achieve this, the detection cycle duration... Approaching or exceeding the modulation period To reconstruct a complete dynamic waveform under these conditions, continuous monitoring of a single cycle must be abandoned in favor of a cross-cycle discrete sampling strategy. This requires the signal generated by the injection unit 200 to have high periodic repeatability, enabling the signal control and processing unit 400 to utilize the equivalent time sampling principle, namely the asynchronous stroboscopic equivalent sampling mechanism detailed later.

[0106] In view of the sampling conflict problem analyzed in step S300, this embodiment elaborates on the core stroboscopic sampling timing algorithm in step S300. This algorithm is executed by the signal control and processing unit 400 and is configured to reconstruct a complete high-resolution signal cycle by discretely acquiring data points in multiple signal cycles and using phase rearrangement technology, thereby avoiding the limitation of insufficient physical sampling rate.

[0107] Step S305: Set the equivalent reconstruction resolution parameters. To restore the dynamic waveform of arsenic morphology transformation within buffer cavity 130, the system first defines a target number of sampling points within a reconstruction cycle. . The value of determines the phase resolution of the reconstructed waveform. In this embodiment, The time is set to 12 to 24. Based on the target number of sampling points, the signal control and processing unit 400 calculates the phase step time. : ,in, The modulation period set in step S201. Phase step time. It represents the time increment between two adjacent data points on the equivalent time axis.

[0108] Step S306: Determine the cycle jump factor. This is based on the duration of a single detection cycle. It may be greater than the modulation period. Alternatively, a system reset time may be required between two detections. The system calculates the number of complete signal cycles that need to be traversed between two adjacent samples, i.e., the cycle jump factor. . The integer must be a positive integer, and its value must satisfy the following physical constraints:

[0109] ,in, This indicates the rounding up operation; The detection cycle duration defined in step S301; The system preparation time, encompassing the buffer time required for data transmission, electrode potential reset, and command processing, is set to 5 to 10 seconds. This formula ensures that the actual physical sampling interval is always greater than the sensor's minimum duty cycle time, preventing sampling commands from overlapping in the time domain.

[0110] Step S307: Generate a physical trigger timing sequence. The signal control and processing unit 400 generates a physical trigger timing table for controlling the first electrode 310 based on the calculated parameters. The zero-time point is set at the start time of the injection unit 200 or the zero-phase point of the sine wave. =0,th Absolute trigger time of the next sample The calculation formula is as follows:

[0111] ,in, This is the sampling sequence index, with a value range of [value range missing]. ; This represents the actual physical sampling interval. The formula shows that each sampling spans [time period]. A complete modulation cycle, with a phase step time superimposed on it. This timing strategy causes a sequential shift in the relative phase position of the sampling points within each cycle.

[0112] Step S308, perform equivalent time mapping. After completion... After the first physical sampling, the system obtains a set of discrete measurement data sequences. The signal control and processing unit 400 maps all the data points acquired across cycles back to a single modulation cycle through modulo operations. Within, obtain equivalent time coordinates : ;

[0113] Substituting the formula from step S307 into the above formula, since... It is the modulation period Integer multiples of are zero in modulo operations, therefore: ;

[0114] The calculation results show that, although the physical sampling process spans a long time span, the data points are uniformly distributed within the equivalent period and cover the period from 0 to the modulation period. The complete waveform. The signal control and processing unit 400 compares the measured value with the corresponding equivalent time coordinate. The arsenic morphology response waveform is constructed by pairing the components. The specific implementation of the modulus operation can be accomplished by those skilled in the art using standard mathematical library functions, and will not be elaborated upon here.

[0115] To simultaneously acquire chemical speciation data of arsenic ions and physiological activity data of biological membranes within the same buffer chamber 130, and to ensure no signal crosstalk occurs between the two highly sensitive electrochemical measurements, step S300 further includes collaborative acquisition logic. This logic is configured to embed EIS measurements using the potential steady-state window during the ASV process, thereby achieving synchronous acquisition of multidimensional data.

[0116] Step S309: Construct a nested acquisition timing sequence. The signal control and processing unit 400 identifies the enrichment deposition time based on the ASV timing characteristics described in step S301. A steady-state window is established to maintain a constant potential. During this window, the first electrode 310 maintains a constant negative potential, and the system is in a DC polarized state. The signal control and processing unit 400 embeds the EIS measurement task of the second electrode 320 into this window. A sampling sequence index is defined. EIS trigger time like: ,in, The first one calculated in step S307 The absolute trigger time of the next sample corresponds to the start point of the ASV cycle; Electrochemical cleaning time; For enrichment of sedimentation time; This represents the duration of a single EIS scan. This formula positions the EIS measurement period at the midpoint of the enrichment deposition time, thereby aligning the acquired biofilm impedance data with the center of the arsenic ion deposition process on the time axis and reducing phase errors caused by time deviations.

[0117] Step S310: Set anti-crosstalk physical constraints. To prevent AC disturbance signals applied by the second detection channel from coupling to the first detection channel and causing instability in the ASV deposition layer, the system sets signal isolation constraints. First, the EIS single scan duration... It must be less than the enrichment deposition time. That is, satisfying To meet this time constraint, this embodiment configures the EIS scan in a discrete frequency point mode, scanning only 5 to 10 key characteristic frequency points in the range of 0.1 Hz to 1000 Hz, or employs a fast EIS mode with multiple sine wave superposition excitation. This setting retains the low-frequency information required to extract charge transfer resistance while reducing measurement time. Secondly, when performing the EIS scan, the signal control and processing unit 400 limits the amplitude of the AC excitation signal to within 5 millivolts, reducing electric field interference to the DC polarization of the first detection channel.

[0118] Step S311: Generate synchronization data pairs. When corresponding to the sampling sequence index... After the physical sampling process is completed, the signal control and processing unit 400 extracts the dissolution peak current value of the first detection channel and the characteristic impedance value of the second detection channel, and combines them to generate a synchronous data vector. : , , ,in, Indicates the first The ASV dissolution peak current obtained from the second sampling corresponds to the concentration response of arsenic ions; Indicates the first The biofilm impedance modulus obtained from the second sampling is taken from the real part or modulus of the impedance in the low frequency range of 0.1 Hz to 10 Hz, which corresponds to the instantaneous active state of the biofilm. This refers to the equivalent time coordinates calculated in step S308. Through this data structure, the system logically establishes a correspondence between chemical signals and biological signals at the same phase point.

[0119] Step S312: Perform blank baseline subtraction. To improve the analytical accuracy of the dynamic response, the signal control and processing unit 400 utilizes the baseline impedance magnitude locked in step S107. The measurement data were standardized. The normalized bioactivity offset was calculated. :

[0120] This step removes the inherent static impedance background of the biomembrane while preserving the dynamic metabolic response induced by microfluidic substrate injection. For ASV data, the system also performs background current baseline subtraction. The resulting calibration dataset will then be fed into the subsequent micro-perturbation correlation analysis module.

[0121] In step S300, the signal control and processing unit 400 executes a data reconstruction algorithm. This step is configured to transform the time-discrete and scrambled synchronization data vector obtained in step S300 into a waveform function that changes continuously within a modulation period, providing a high-resolution digital signal input for subsequent phase analysis.

[0122] Step S401: Perform time-domain reordering. The signal control and processing unit 400 receives the accumulated set of synchronization data vectors in the memory. ,in This is the sampling sequence index, with values ​​ranging from 0 to... According to step S311, each data vector contains the ASV dissolution peak current. Biomembrane impedance modulus and equivalent time coordinates The processor is based on the equivalent time coordinate. Based on the numerical values, the dataset is sorted in ascending order to generate an ordered time series. and the corresponding ordered measurement sequence (Corresponding to current or impedance data respectively):

[0123] ;

[0124] ;

[0125] in, Indicates the sorted order of the first... At a certain point in time, satisfying This step logically maps the physical data acquired across multiple cycles back to a single 0-to-modulation cycle. Within the interval, the original topological structure of the signal as the phase changes is restored.

[0126] Step S402: Construct periodic boundary extension. Since the measured signal is physically a periodic sinusoidal response, in order to eliminate the limitations of subsequent interpolation algorithms at the interval endpoints (i.e.,...) and To address the discontinuity error at the endpoints (points), the system performs a cyclic extension operation. The signal control and processing unit 400 copies and splices the first and last data points of the ordered measurement sequence to both ends of the sequence, constructing an extended time series. and extended measurement sequence :

[0127] ;

[0128] ;

[0129] This step utilizes the periodicity of the signal. This ensures the continuity of the derivative at the beginning and end of the reconstructed waveform, preventing issues arising during transitions. A pseudo-oscillation occurs at the phase point.

[0130] Step S403: Perform cubic spline interpolation reconstruction. To obtain a high sampling rate signal that meets the requirements of digital phase-locked loop demodulation, the signal control and processing unit 400 uses a cubic spline interpolation algorithm to fit the expanded sequence. The system first defines the number of high-resolution sampling points after reconstruction. This value is set between 1024 and 4096 to reduce quantization noise. The reconstruction sampling interval is then calculated. : ;

[0131] Based on extended time series and extended measurement sequence The processor calculates the interpolation function. And generate reconstructed discrete sequences on the new high-resolution time grid. : ,in, The expression is a piecewise cubic polynomial function that satisfies the condition that the second derivative is continuous at each original data point. For the specific coefficients of the cubic spline interpolation, those skilled in the art can use standard numerical computation libraries.

[0132] Step S404: Generate a dual-channel reconstructed waveform. The signal control and processing unit 400 targets the ASV dissolution peak current. and normalized bioactivity offset Perform steps S401 to S403 as described above. The final output will be two synchronized reconstructed waveforms:

[0133] Reconstructing the arsenic response waveform Characterizes the dynamic change of arsenic ion concentration in buffer chamber 130 over time;

[0134] Reconstructing bioactive waveforms This characterizes the dynamic changes in the metabolic activity of the microbial community over time. The two waveforms are aligned on the time axis and have the same modulation period. The data length provides a standardized mathematical object for subsequent calculations of the phase difference between the two.

[0135] In step S400, the signal control and processing unit 400 executes a digital lock-in amplification algorithm. This algorithm is configured to extract the amplitude and phase information of a single fundamental frequency component from the reconstructed waveform output in step S404, which is used to shield broadband noise and calculate the phase hysteresis between arsenic speciation and biological activity.

[0136] Step S405: Generate quadrature reference signals. To achieve phase-sensitive detection, the signal control and processing unit 400 generates two quadrature digital reference signals based on its internal clock. The frequencies of these two signals are locked to the excitation frequency set in step S201. Based on the reconstruction sampling interval determined in step S403 and the number of high-resolution sampling points The processor generates discrete sinusoidal reference sequences. Sum and cosine reference sequence :

[0137] ;

[0138] ;

[0139] in, To reconstruct the sequence time index, the value range is: These two sequences correspond to the in-phase branch and the quadrature branch in a lock-in amplifier, respectively.

[0140] Step S406: Perform dual-channel phase-sensitive detection. The signal control and processing unit 400 reconstructs the arsenic response waveform output in step S404. and reconstructed bioactive waveforms Perform point-to-point multiplication operations with the aforementioned orthogonal reference signals respectively. For reconstructing the arsenic response waveform... (Here, discretization is denoted as...) ), calculate the product of in-phase components and orthogonal component product :

[0141] ;

[0142] ;

[0143] For reconstructing bioactive waveforms (Here, discretization is denoted as...) Similarly, perform the above multiplication operation to obtain the corresponding in-phase component product of the biological signal. Orthogonal component product of biological signals This step performs a frequency shift on the input signal, converting the fundamental frequency signal to be measured into a DC component, while noise and higher harmonics are converted into high-frequency AC components.

[0144] Step S407: Perform integral low-pass filtering. The system utilizes the time length of the reconstructed waveform, which is exactly one complete modulation cycle. This method utilizes the characteristics of full-cycle numerical integration to replace the traditional RC low-pass filter. In the time domain, this method represents the summation and averaging of the product sequence, exhibiting notch characteristics for integer multiples of the fundamental frequency, and is able to suppress... High-order harmonic interference. The signal control and processing unit 400 calculates the in-phase component value of the arsenic response. and orthogonal component values :

[0145] ;

[0146] ;

[0147] Similarly, calculate the in-phase component value of biological activity. and orthogonal component values Coefficients in the formula Used for normalization processing to compensate for the amplitude coefficient introduced by the root mean square operation of sine waves.

[0148] Step S408: Calculate the phase difference and amplitude. Based on the component values ​​in a Cartesian coordinate system, the signal control and processing unit 400 calculates the absolute phase angle of each signal using the four-quadrant arctangent function. Calculate the arsenic response phase angle. and bioactive phase angle :

[0149] ;

[0150] ;

[0151] Subsequently, the metabolic response phase difference between the two was calculated. : ;

[0152] If the calculation result exceeds In the given interval, the system performs phase decoupling to map it back to the principal value interval. Metabolic response phase difference. This reflects the degree of lag in the response of the microbial community to changes in environmental arsenic concentration and serves as a kinetic indicator for assessing the expression activity of genes involved in arsenic metabolism. Simultaneously, the system calculates the amplitude of the biological response. : ;

[0153] This amplitude parameter is used to quantify the metabolic intensity of microorganisms under unit concentration perturbation. Through the above digital demodulation process, the system achieves high signal-to-noise ratio extraction of weak signals at millihertz frequencies, effectively suppressing power frequency interference and broadband noise in the groundwater environment.

[0154] In step S500, the signal control and processing unit 400 uses the synchronous waveform data demodulated in the previous step to construct a phase space trajectory, i.e., a dynamic hysteresis loop, reflecting the response characteristics of the microbial community to changes in environmental arsenic concentration. The geometry of this hysteresis loop corresponds to the nonlinear dynamic characteristics of biogeochemical processes in the groundwater system.

[0155] Step S501: Perform waveform normalization mapping. To eliminate the influence of the dimensional difference between current and impedance units on the geometry, the signal control and processing unit 400 reconstructs the arsenic response waveform output in step S404. and reconstructed bioactive waveforms Perform normalization. Define the reconstructed sequence time index as... (Value range 0 to) ), calculate the standardized sequence of x-coordinates and ordinate sequence :

[0156] ;

[0157] ;

[0158] in, and These are the discrete sampled values ​​at the corresponding time points; , These represent the maximum and minimum values ​​of the reconstructed arsenic response waveform within one modulation period, respectively. , These represent the maximum and minimum values ​​of the reconstructed bioactive waveform within one modulation period. This step maps both dimensions of the signal to the dimensionless interval [0,1], constructing a unit phase plane region.

[0159] Step S502, synthesize phase space trajectories. The system uses... As the horizontal axis (driving variable), with The vertical axis (response variable) is used to represent the point set arranged in chronological order in a two-dimensional Cartesian coordinate system. Because the signal is periodic and has undergone the boundary expansion process in step S402, this point set is connected end-to-end, forming a closed dynamic hysteresis loop. The trajectory equation of this closed curve can be parametrically expressed as: ;

[0160] In an ideal linear system with no delay, the trajectory appears as a straight line; however, in biological systems with metabolic lag or nonlinear damping, the trajectory appears as an ellipse or a distorted elliptical ring structure.

[0161] Step S503: Calculate the geometric area of ​​the hysteresis loop. To quantify the hysteresis of the microbial metabolic response, the signal control and processing unit 400 calculates the hysteresis area enclosed by the dynamic hysteresis loop based on the discretized form of Green's formula. The processor executes the following polygon area calculation formula:

[0162] In the formula, when the index hour, Modulo zeroing, i.e. and Hysteresis area The physical meaning represents the hysteresis loss or system memory effect intensity of a biological community adapting to changes in environmental arsenic concentration within a single modulation cycle. For the specific algorithm implementation of polygon area calculation, those skilled in the art can use standard geometric algorithms such as the shoelace formula.

[0163] Step S504: Extract the morphology factor. In addition to the area, the system further calculates the tilt angle of the hysteresis loop. and nonlinear distortion Inclination angle Defined as the angle between the major axis and the horizontal axis of the hysteresis loop, it reflects the sensitivity gain of biological activity to changes in arsenic concentration. The signal control and processing unit 400 calculates the point set... The covariance matrix is ​​calculated and eigenvalue decomposition is performed. The direction of the eigenvector corresponding to the largest eigenvalue is taken as the direction of the major axis, thereby determining... Nonlinear distortion The area of ​​the hysteresis loop is obtained by calculating the absolute value of the difference between the actual area of ​​the hysteresis loop and the area of ​​the ideal ellipse with the same major and minor axes: ,in, and These are the major and minor semi-axis of the ellipse fitted by the hysteresis loop, respectively. In this embodiment, the parameters are... and These are set to multiples of the square roots of the largest and smallest eigenvalues ​​of the covariance matrix, respectively. Nonlinear distortion degree. This is used to characterize whether there are high-order nonlinear biochemical processes in the system, such as substrate inhibition effects or multi-species cooperative competition mechanisms that cause response waveform distortion. By extracting the above geometric parameters, the system transforms time-varying biochemical signals into a set of quantitative morphological feature vectors, providing feature inputs for subsequent microbial diversity index inversion.

[0164] In the subsequent processing of step S500, the signal control and processing unit 400 executes steps S505 to S507, which are configured to convert the geometric morphological features extracted in steps S503 and S504 into quantitative indicators characterizing the ecological functions of the microbial community.

[0165] Step S505: Establish a multi-parameter coupled inversion model. The signal control and processing unit 400 retrieves a preset algorithm from the memory and constructs a functional diversity index based on the geometric features of the dynamic hysteresis loop. The computational equation is as follows. This index characterizes the complexity of metabolic pathways within a microbial community. The processor performs the following linear weighted operation: ,in, The hysteresis area calculated in step S503 characterizes the system memory effect of community metabolism. The nonlinear distortion degree calculated in step S504 characterizes the nonlinear interaction strength of the metabolic response network. The metabolic response phase difference calculated in step S408 The absolute value of represents the overall hysteresis of the community's response to environmental disturbances; , and These are the weight coefficients for the corresponding feature terms; To correct for the intercept, this formula transforms the geometric and topological information in phase space into a dimensionless ecological index.

[0166] Step S506, Physical meaning analysis and classification determination. The system determines the physical meaning and classification based on the calculated functional diversity index. The numerical value of the functional diversity index is used to assess the functional status of the microbial community in the current groundwater environment. It shows a positive correlation with the Shannon-Wiener index. When the threshold is higher than the preset threshold, it corresponds to the presence of multiple microbial populations with arsenic metabolism functions within the biofilm (such as the coexistence of arsenic-oxidizing bacteria and arsenic-reducing bacteria). The community has a high functional redundancy and a high hysteresis area. and nonlinear distortion Presenting a large value; when When the threshold is below a preset low threshold, it corresponds to a simple community structure or suppression by environmental stress, simple metabolic pathways, a linear dynamic response, and a small hysteresis area. The signal control and processing unit 400 will calculate in real time... By comparing with historical baseline data, community succession trend data are generated.

[0167] Step S507, Coefficient Calibration and Model Training. To determine the weight coefficients in step S505 ( , , ) and corrected intercept The specific values ​​are determined using an offline supervised learning process in this embodiment. During system initialization, biodiversity analysis is performed on the effluent sample discharged from buffer chamber 130 using gene sequencing technology to obtain community diversity data as a standard reference value. A partial least squares regression algorithm is used to establish the mapping relationship between electrochemical geometric characteristics and the standard reference value, and the aforementioned coefficients are calculated. In a preferred embodiment, the value ranges of each coefficient are set as follows: The value ranges from 0.5 to 2.0. The value ranges from 1.5 to 3.0. The values ​​range from 0.1 to 0.8. Through this indirect measurement method, the system achieves the technical effect of real-time monitoring of changes in the ecological function of microbial communities using electrochemical signals.

Claims

1. A method for correlation analysis of groundwater arsenic content and microbial diversity, characterized in that, The method comprises the following steps: placing a buffer probe into a groundwater aquifer to be tested, using a buffer cavity inside the buffer probe to establish chemical equilibrium with the external groundwater environment, and using a double-channel electrochemical sensing array located in the buffer cavity to measure baseline arsenic speciation concentration and baseline impedance modulus of the biofilm; using an injection unit to continuously inject substrate solution into the buffer cavity, controlling the injection flow rate according to a preset sinusoidal function, and constructing a substrate concentration field that changes periodically with time in the buffer cavity; while the injection unit is continuously operating, using the first electrode and the second electrode of the double-channel electrochemical sensing array to collect arsenic concentration detection data and biofilm impedance signals respectively, controlling the sampling trigger time of the first electrode to produce a phase step delay relative to the injection period of the injection unit, and completing the discrete point coverage of one complete signal period within multiple injection periods; using the baseline arsenic speciation concentration and the baseline impedance modulus of the biofilm to perform baseline subtraction processing on the discrete collected arsenic concentration detection data and biofilm impedance signals respectively, and mapping the processed data according to the corresponding phase information into the same equivalent modulation period to reconstruct arsenic speciation transformation response waveforms and biofilm metabolism response waveforms; performing digital lock-in amplification processing on the arsenic speciation transformation response waveforms and the biofilm metabolism response waveforms, extracting the amplitude and phase of the arsenic speciation transformation response and the amplitude and phase of the biofilm metabolism response at the same frequency as the injection frequency; using the biofilm metabolism response waveform as an input variable and the arsenic speciation transformation response waveform as an output variable, constructing a dynamic hysteresis loop in a two-dimensional coordinate system, and calculating a substitute index representing the functional diversity of the microbial community according to the geometric characteristics of the dynamic hysteresis loop.

2. The method according to claim 1, wherein, The buffer probe comprises a support framework and a microporous ceramic semi-permeable membrane wrapped outside the support framework; the microporous ceramic semi-permeable membrane is used to provide fluid damping inside the buffer cavity and block the direct impact of the external groundwater flow on the buffer cavity, so that the fluid inside the buffer cavity is in a low Peclet number state.

3. The method according to claim 1, wherein, The variation of the injection flow rate of the injection unit with time is determined by three parameters: a basic direct current bias flow rate, a modulation amplitude, and a modulation period; the basic direct current bias flow rate is greater than the modulation amplitude, and the value of the modulation period is configured to satisfy the diffusion steady-state condition and the biological response matching condition, ensuring the spatial uniformity of the substrate concentration in the buffer cavity and the excitation frequency within the passband range of the biological community.

4. The method according to claim 1, wherein, The first electrode performs differential pulse anodic stripping voltammetry detection, and the second electrode performs electrochemical impedance spectroscopy analysis; the control of the sampling trigger time of the first electrode relative to the injection period of the injection unit to produce a phase step delay specifically includes: calculating the ratio of the length of a single detection period to the injection period to determine the number of complete injection periods that need to be crossed between adjacent two samplings, i.e., the period jump factor; setting the target number of sampling points in a reconstruction period, and calculating the phase step time accordingly; The physical trigger timing sequence is generated according to the cycle jump factor and the phase step time, so that each sampling trigger time is increased by a fixed step in phase compared with the previous sampling trigger time.

5. The method according to claim 4, wherein, The specific steps for constructing the nested acquisition timing sequence when collecting the biofilm impedance signal include: identifying an enrichment deposition time window of the first electrode when performing the differential pulse anodic stripping voltammetry; embedding the electrochemical impedance spectroscopy measurement task of the second electrode into the midpoint of the enrichment deposition time window; configuring the single-scan duration of the electrochemical impedance spectroscopy to be less than the duration of the enrichment deposition time window, and limiting the amplitude of the alternating current excitation signal of the second electrode within a preset range to prevent signal crosstalk.

6. The method according to claim 1, wherein, The steps for reconstructing the arsenic speciation transformation response waveform and the biofilm metabolism response waveform specifically include: The signal control and processing unit calls the baseline arsenic speciation concentration, performs differential calculation on the discrete collected arsenic concentration detection data to eliminate the background concentration, and obtains dynamic arsenic response data; The signal control and processing unit calls the biofilm baseline impedance modulus, calculates the normalized offset of the discrete collected biofilm impedance signal relative to the biofilm baseline impedance modulus; The dynamic arsenic response data and the normalized offset are mapped into a single equivalent time interval with a duration of the modulation period according to the corresponding physical sampling absolute trigger time by using a modulo operation, and are arranged in ascending order according to the size of the equivalent time coordinates to generate an ordered measurement sequence; The first and last data points of the ordered measurement sequence are periodically boundary extended, and the extended sequence is fitted by using a cubic spline interpolation algorithm to generate the arsenic speciation transformation response waveform and the biofilm metabolism response waveform with high time resolution, respectively.

7. The method according to claim 1, wherein, The digital phase-locked amplification processing specifically includes: generating two orthogonal digital reference signals based on the injection frequency; performing point-to-point multiplication operation on the arsenic speciation transformation response waveform and the biofilm metabolism response waveform, respectively, and the two orthogonal digital reference signals to calculate in-phase component products and quadrature component products; integrating the in-phase component products and the quadrature component products by using a full-cycle numerical integration method to obtain in-phase component values and quadrature component values, and then solving the phase angle and the amplitude; and calculating the difference between the phase angle of the biofilm metabolism response and the phase angle of the arsenic speciation transformation response as the phase difference.

8. The method according to claim 7, wherein, The construction method of the dynamic hysteresis loop includes: normalizing the arsenic speciation transformation response waveform and the biofilm metabolism response waveform, respectively, and mapping the data to a dimensionless interval; arranging the point set in time sequence and connecting the first and last points to form a closed phase space trajectory with the normalized biofilm metabolism response data as the abscissa and the normalized arsenic speciation transformation response data as the ordinate.

9. The method according to claim 8, wherein, The geometric morphological characteristics of the dynamic hysteresis loop at least include: calculating the area of the region surrounded by the dynamic hysteresis loop as the hysteresis area by using the discretized Green's formula; calculating the absolute value of the difference between the actual hysteresis loop area and the area of an ideal ellipse with the same major and minor axes as the non-linear distortion degree.

10. The method according to claim 9, wherein, The alternative index for characterizing the functional diversity of the microbial community is specifically a functional diversity index obtained by linearly weighting the lag area, the non-linear distortion degree and the phase difference; wherein the numerical value of the functional diversity index is used for quantitatively evaluating the metabolic regulation ability and the functional complexity of the microbial community in the groundwater environment to the arsenic element form conversion.

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