A rapid detection system and method for microplastics in soil

Abstract

This invention relates to a rapid detection system and method for microplastics in soil. The system includes a soil laser ablation system and an infrared spectroscopy detection system. The soil laser ablation system includes a laser, a focusing lens, a sample ablation chamber, and a two-dimensional displacement platform. The infrared spectroscopy detection system includes an infrared light source, a gas cell, and an infrared detector. Before the soil laser ablation system ablates the soil sample, a pressure difference is created between the gas cell and the sample ablation chamber to transfer volatiles generated during the ablation of the soil sample into the gas cell. The infrared detector acquires the infrared characteristic spectral data of the volatiles, and the spectral characteristics and intensity information of the characteristic bands of the volatiles are selected. Qualitative and quantitative analyses of the types and contents of microplastics in the soil are then performed based on microplastic classification and quantification models, respectively. This invention enables rapid, on-site detection of microplastics in soil.

Description

Technical Field

[0001] This invention belongs to the field of microplastic detection, and in particular relates to a rapid detection system and method for microplastics in soil. Background Technology

[0002] Plastics play a vital role in various industries due to their good chemical stability, lightweight nature, and excellent insulation properties. However, the resulting waste plastics are causing increasingly serious environmental pollution. In these environments, plastics gradually decompose into microplastics with a diameter of less than 5 mm under the influence of long-term sunlight exposure and microbial degradation. In recent years, as microplastics have been increasingly discovered in soil, the problem of microplastic pollution in soil has received growing attention.

[0003] Currently, methods for detecting microplastics in soil mainly fall into two categories. The first is visual analysis, which involves examining screened and enriched samples using microscopes or scanning electron microscopes (SEM) to identify and quantify microplastic particles. While simple, this method is prone to bias and error due to the size of the microplastic particles, and cannot provide qualitative analysis of microplastics; it is typically used as a supplement to other methods. The second method, based on the thermal decomposition characteristics of plastics, has been applied to soil microplastic detection. Examples include differential scanning calorimetry (DSC), thermogravimetric analysis combined with differential scanning calorimetry (TGA-DSC), and thermal decomposition gas chromatography-mass spectrometry (Pyr-GC-MS). However, these methods require large, complex equipment and time-consuming analysis, making them unsuitable for analyzing large numbers of samples and hindering rapid measurement of microplastics in soil.

[0004] For example, the invention patent application "A Device and Method for Detecting and Analyzing Microplastics in Soil" (Publication No.: CN117949406 A, Application No.: 202410122746.3) adopts a traditional separation approach. After drying and crushing, density separation is performed using a sodium chloride solution. After a long period of settling, the microplastics float to the surface and are finally extracted onto a nylon filter membrane for infrared spectroscopy detection. However, this method has the following drawbacks: First, the sample pretreatment is extremely cumbersome and time-consuming, requiring a long drying and solution settling process (up to several hours), resulting in a very long detection cycle that cannot meet the needs of rapid screening of large batches of samples. Second, the microplastics extracted onto the filter membrane through density separation often inevitably contain soil organic matter and fine mineral particles. When directly scanning the filter membrane with infrared spectroscopy, there is severe background scattering and signal overlap, leading to poor accuracy in quantitative and qualitative analysis.

[0005] For example, the invention patent application "Method for Detecting Microplastics in Soil Based on Programmable Pyrolysis-Time-of-Flight Mass Spectrometry" (Publication No.: CN116794149 A, Application No.: 202310455460.2) places the pretreated microplastic sample in a heating conduit, and vaporizes it by programmable heating using a resistance wire. The vaporized products are then detected by a time-of-flight mass spectrometer. However, this method also has significant drawbacks: firstly, it requires demanding equipment and has high maintenance costs, relying on a high-precision time-of-flight mass spectrometer, and the entire pyrolysis and detection process must be performed in a strictly high-vacuum environment (5×10⁻⁶). −3 The method involves heating the sample at a rate below 10°C / min using resistance wire, which is complex and has high equipment and operating costs. Secondly, the method involves slow, controlled heating of the sample using resistance wire, which can cause secondary cracking or cross-reaction of complex soil organic matter during slow heating. The resulting mixed gas can interfere with the spectral analysis. Furthermore, this method still requires complex purification pretreatment with reagents such as hydrogen peroxide before pyrolysis.

[0006] In order to overcome the technical defects of the existing technologies, such as "density separation and extraction is time-consuming and laborious and easily interfered with by solid matrix" and "traditional pyrolysis mass spectrometry equipment is expensive, relies on high vacuum and is prone to secondary reactions", there is an urgent need for a new soil microplastic detection technology that is simple to pretreatment, fast to detect, strong anti-interference ability and does not require an extremely harsh detection environment. Summary of the Invention

[0007] To address the shortcomings of current methods for detecting microplastics in soil, the present invention aims to provide a rapid detection system and method for microplastics in soil. This system utilizes laser ablation heating of soil to excite characteristic volatiles, and further combines absorption spectroscopy to detect these volatiles. The spectral characteristic data of the obtained volatiles are analyzed using qualitative and quantitative models to determine the types and content of microplastics in the soil, thus enabling rapid, on-site detection of microplastics in soil.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] This invention provides a rapid detection system for microplastics in soil, comprising a soil laser ablation system and an infrared spectroscopy detection system.

[0010] The soil laser ablation system includes a laser, a focusing lens, a sample ablation chamber, and a two-dimensional displacement platform. The laser focuses a laser beam onto the surface of a soil sample within the sample ablation chamber through the focusing lens, achieving laser ablation of the soil sample. The sample ablation chamber is an aluminum cuboid with an air inlet, an air outlet, a pull-out soil sample support base, and an openable chamber door. The air inlet is equipped with an air inlet valve, and the air outlet is equipped with an exhaust valve. The top of the sample ablation chamber is covered with a quartz glass cover. The sample ablation chamber is positioned on the two-dimensional displacement platform.

[0011] The infrared spectroscopy detection system includes an infrared light source, a gas cell, and an infrared detector; the gas cell has a gas cell inlet and a gas cell outlet; the gas cell inlet is equipped with an inlet valve, and the gas cell outlet is equipped with an exhaust valve; the gas cell inlet is connected to the outlet of the sample ablation chamber.

[0012] Before ablation of soil samples using the soil laser ablation system, the inlet valve of the gas cell is closed and the outlet valve of the gas cell is opened. The gas pressure in the gas cell is adjusted to negative pressure using a vacuum pump, and then the outlet valve of the gas cell is closed to create a pressure difference between the gas cell and the sample ablation chamber. This difference is used to send the volatiles generated from the ablation of the soil sample in the sample ablation chamber into the gas cell. Infrared spectral data of the volatiles are obtained using an infrared detector. The spectral characteristics and intensity information of the characteristic bands of the volatiles are then selected. The types and contents of microplastics in the soil are qualitatively and quantitatively analyzed according to the microplastic classification model and the microplastic quantification model, respectively.

[0013] When ablation soil samples, the soil samples are dried, sieved, and then laid flat in the soil sample support plate of the sample ablation chamber. The air inlet valve of the sample ablation chamber and the exhaust valve of the sample ablation chamber are closed, the sample ablation chamber is sealed, and the standard atmospheric pressure is maintained.

[0014] When ablation soil samples, the two-dimensional displacement platform moves the sample ablation chamber in an “S”-shaped path at a constant displacement speed, so that the laser beam traverses the entire soil surface.

[0015] The two-dimensional displacement platform includes an electrically translatable X-axis and an electrically translatable Y-axis.

[0016] The microplastics include PE, PP, PS, and PBAT.

[0017] This invention provides a method for rapid detection of microplastics in soil using the aforementioned rapid detection system for microplastics in soil, comprising the following steps:

[0018] S1. Establish microplastic classification and quantitative models for microplastics;

[0019] S1.1 Establish a microplastic classification model;

[0020] S1.1.1 Prepare standard soil samples of known types and concentrations of microplastics;

[0021] Various single microplastics and their mixtures at different concentration gradients were added to blank soil to prepare standard samples. The standard samples were then tested using a rapid microplastic detection system in soil to obtain a large amount of infrared spectral data of volatiles containing known microplastic species.

[0022] S1.1.2 Then, the obtained volatile infrared spectral data are baseline calibrated, and the peak heights at the M×3 characteristic bands corresponding to the M types of microplastics are extracted to form an M×3 dimensional feature vector.

[0023] S1.1.3. Using the M×3 dimensional feature vector X as input and the type of microplastic as the multiple output label Y, the support vector machine algorithm is optimized and combined using a multi-output gradient boosting machine model.

[0024] S1.2 Establish a quantitative model for microplastics;

[0025] S1.2.1 For the microplastics identified by the microplastic classification model, extract the infrared spectral data of samples with different concentration gradients of the microplastics in the standard sample library.

[0026] S1.2.2. Using the intensity of the three characteristic bands x1, x2, and x3 corresponding to this type of microplastic as input independent variables and the actual content C of the corresponding microplastic as dependent variable, a multiple linear model is established.

[0027] S1.2.3 Calculate the coefficient of determination R of the model 2 and root mean square error (RMSE); when the established quantitative model for microplastics R 2 When the value is greater than 0.95, the quantitative model of microplastics is considered to have been successfully established.

[0028] S1.2.4 Finally, using the blank standard deviation method, the detection results are converted into the concentration of the microplastic by measuring multiple blank samples.

[0029] S2. Detection of soil ablation and characteristic volatiles;

[0030] S2.1 After drying and sieving the soil to be tested, spread it evenly on the soil sample support plate and push it into the sample ablation chamber. Cover the sample ablation chamber door, close the air inlet valve of the sample ablation chamber and the exhaust valve of the sample ablation chamber outlet, seal the sample ablation chamber, and maintain standard atmospheric pressure. The laser emitted by the laser is focused on the surface of the soil to be tested through the focusing lens and the quartz glass cover on the top of the sample ablation chamber. The step size of the two-dimensional displacement platform is controlled by the operating program, so that the two-dimensional displacement platform moves at a constant speed along a preset path, so that the laser beam traverses the entire soil surface, excites the volatiles in it, and collects the volatiles in the sample ablation chamber.

[0031] S2.2 Before ablating the soil sample, close the inlet valve of the gas cell and open the outlet valve of the gas cell. Use a vacuum pump to adjust the gas pressure in the gas cell to negative pressure and then close the outlet valve of the gas cell. After the ablation of the soil sample is completed, open the inlet valve of the sample ablation chamber and the inlet valve of the gas cell. Use the pressure difference to send the volatiles in the sample ablation chamber into the gas cell. After the gas pressure in the gas cell returns to one atmosphere, turn on the infrared light source and use an infrared detector to collect the infrared characteristic spectrum data of the volatiles.

[0032] S3. Analysis of the types and content of microplastics;

[0033] Based on the infrared characteristic spectral data of volatiles, spectral characteristic information and intensity information of characteristic bands of volatiles were screened out, and qualitative and quantitative analysis of the types and contents of microplastics in soil was carried out according to the microplastic classification model and the microplastic quantification model, respectively.

[0034] In step S1.1.3, the weights of the M×3 features are first evaluated and nonlinearly mapped using a gradient boosting machine. Then, the mapped features are input into multiple parallel SVM classifiers for hyperplane partitioning. The model parameters are iteratively updated using training set data until the model achieves a classification accuracy of 95% on the validation set. Finally, the microplastic classification model is saved.

[0035] In step S1.2.2, the mathematical expression of the multiple linear model is:

[0036] C = β0 + β1x1 + β2x2 + β3x3

[0037] Where C is the actual microplastic content in ‰; β0 is the intercept; β1, β2, and β3 are the regression coefficients of each characteristic band; and x1, x2, and x3 are the intensities of the three characteristic bands.

[0038] The least squares method was used to fit and solve the above multiple linear model to determine the regression coefficients.

[0039] Before soil ablation and characteristic volatile matter detection, the gas pressure in the gas cell is maintained at standard atmospheric pressure. Infrared spectral data of the current air is detected using an infrared detector. This air infrared spectral data is used as a benchmark and control when analyzing the infrared spectral data characteristics of soil microplastic ablation volatile matter samples, ensuring that there are no abnormal volatile components in the air that would interfere with the detection results.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] (1) The pretreatment process is greatly simplified, and the detection efficiency and sample representativeness are significantly improved. Compared with existing detection methods based on chemical digestion or density separation to extract microplastics, this invention uses laser ablation to excite soil samples to generate volatiles, eliminating the need for complex reagent pretreatment and microplastic particle enrichment of soil samples, and completely avoiding the tedious "organic matter removal" and "filtration" steps. The laser heating energy is concentrated and highly efficient, which shortens the complete detection time of a single sample to less than 15 minutes; at the same time, this method supports a large sample volume, with a single sample detection volume of up to 5g, effectively overcoming the defect of poor sample representativeness caused by the extremely small sample volume (usually milligram level) of traditional micro-area spectroscopy methods (such as micro-infrared spectroscopy), and ensuring the macroscopic accuracy of soil detection results.

[0042] (2) This invention innovates the spectral feature extraction and data dimensionality reduction methods, requiring fewer resources for model computation, resulting in faster speed and clear chemical interpretability. Existing spectral analysis methods typically combine all acquired spectral data directly with machine learning for full-spectrum dimensionality reduction analysis, which suffers from high data dimensionality and long computation time. Furthermore, the extracted mathematical features often lack practical chemical and physical meaning, making them "black box" models. In contrast, this application targets all types of microplastics in the soil, based on their specific volatiles produced after laser ablation (e.g., styrene produced by PS ablation), and compares them with the ablation volatiles of the soil matrix. Based on this, the invention precisely selects the three strongest characteristic peak bands in the spectrum of each microplastic ablation volatile (e.g., the example selects four microplastics (PE, PP, PS, PBAT), totaling only 12 characteristic bands, such as the characteristic band of styrene from PS ablation volatiles: 3042.22 cm⁻¹). -1 990.77 cm -1 909.29 cm -1This technology not only significantly reduces the model input dimension from thousands of full-spectrum data points to M×3 (M being the number of microplastic species), greatly reducing the model's computational complexity and processing time; more importantly, because these M×3 feature peaks directly correspond to the functional group structures of specific pyrolysis products of microplastics, it eliminates signal interference from other non-volatile or non-characteristic volatile substances in the soil, giving the classification and quantitative model extremely strong chemical interpretability and resistance to matrix interference.

[0043] (3) A highly accurate classification and quantitative integrated prediction model was constructed. Based on the precise extraction of M×3 characteristic bands, this invention utilizes a multi-output gradient boosting machine model to optimize the support vector machine (SVM) classification algorithm, achieving accurate classification of various microplastics in soil. Simultaneously, since a good linear relationship was found between the content of specific microplastics and the intensity of characteristic peaks of specific volatiles, this invention establishes a quantitative relationship model between microplastic content and the intensity of characteristic peaks in the absorption spectrum based on a multivariate linear model. This integrated model can quickly and accurately output the prediction results of the types and contents of microplastics in soil, achieving a significant advancement in analytical methods.

[0044] In summary, compared with current methods for detecting microplastics in soil, the laser ablation method of this invention for generating volatiles from soil samples eliminates the need for complex pretreatment and microplastic particle enrichment, simplifying the detection process. It offers high laser heating efficiency, short detection time (less than 15 minutes per sample), and a large sample volume per test (up to 5g), ensuring representative results. Further analysis of the generated volatiles using spectroscopic methods, focusing on characteristic peaks in the spectral data, avoids interference from other substances in the soil. Combined with classification and quantitative models, it enables prediction of the types and content of microplastic particles in the soil. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the system composition of the rapid detection system for microplastics in soil according to the present invention;

[0046] Figure 2 The spectrum and characteristic bands of volatiles from soil-PE microplastic samples after ablation are shown in this embodiment of the invention.

[0047] Figure 3 The following are characteristic volatile spectra of four common microplastics (PE, PP, PS, PBAT) produced by ablation after being mixed with soil according to embodiments of the present invention.

[0048] Figure 4aThe results of a quantitative model established between the content of microplastic PE in soil and the intensity of characteristic peaks in the spectral characteristics of volatiles in this embodiment of the invention;

[0049] Figure 4b The results of a quantitative model established between the content of microplastic PP in soil and the intensity of characteristic peaks in the spectral characteristics of volatiles in an embodiment of the present invention;

[0050] Figure 4c The results of a quantitative model established between the content of microplastic PS in soil and the intensity of characteristic peaks in the spectral composition of volatiles in this embodiment of the invention;

[0051] Figure 4d The results of a quantitative model established between the content of microplastic PBAT in soil and the intensity of characteristic peaks in the spectral composition of volatiles in this embodiment of the invention;

[0052] Figure 5 The results of the microplastic classification model established between the microplastic content in soil and the characteristic peak bands of volatile matter spectra in this embodiment of the invention.

[0053] The attached figures are labeled as follows: 1. Laser; 2. Focusing lens; 3. Sample ablation chamber; 4. Motorized X-axis translation; 5. Motorized Y-axis translation; 6. Sample ablation chamber air inlet; 7. Volatile matter; 8. Quartz glass cover; 9. Sample ablation chamber air outlet; 10. Soil sample; 11. Soil sample support base plate; 12. Sample ablation chamber door; 13. Infrared light source; 14. Gas cell air inlet; 15. Gas cell; 16. Gas cell air outlet; 17. Infrared detector. Detailed Implementation

[0054] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0055] like Figure 1 As shown, a rapid detection system for microplastics in soil is disclosed. The system includes a soil laser ablation system and an infrared spectroscopy detection system.

[0056] The soil laser ablation system includes a laser 1, a focusing lens 2, a sample ablation chamber 3, and a two-dimensional displacement platform. The laser 1 focuses a laser beam onto the surface of a soil sample 10 within the sample ablation chamber 3 via the focusing lens 2, achieving laser ablation of the soil sample 10. The sample ablation chamber 3 is an aluminum cuboid with an air inlet 6, an air outlet 9, a pull-out soil sample support base plate 11, and an openable chamber door 12. The air inlet 6 is equipped with an air inlet valve, and the air outlet 9 is equipped with an exhaust valve. The top of the sample ablation chamber 3 is equipped with a quartz glass cover 8 to facilitate laser ablation of the soil sample 10 within the chamber. The sample ablation chamber 3 is positioned on a two-dimensional displacement platform, which includes an electrically driven X-axis 4 and an electrically driven Y-axis 5 for precise control of the movement path of the sample ablation chamber 3.

[0057] During soil sample ablation, the dried and sieved soil sample 10 is laid flat in the soil sample support plate 11 of the sample ablation chamber 3. The air inlet valve 6 and the exhaust valve 9 of the sample ablation chamber are closed, sealing the sample ablation chamber 3 and maintaining standard atmospheric pressure. The laser emitted by the laser 1 is focused on the soil surface. The two-dimensional displacement platform moves the sample ablation chamber 3 along a preset path at a constant displacement speed, heating and ablating the entire soil surface. The excited volatiles 7 are collected in the sample ablation chamber 3.

[0058] The infrared spectroscopy detection system includes an infrared light source 13, a gas cell 15, and an infrared detector 17; the gas cell 15 has a gas cell inlet 14 and a gas cell outlet 16; the gas cell inlet 14 is equipped with an inlet valve, and the gas cell outlet 16 is equipped with an exhaust valve; the gas cell inlet 14 is connected to the sample ablation chamber outlet 9.

[0059] Before the soil laser ablation system ablates the soil sample, the inlet valve of the gas pool inlet 14 is closed and the exhaust valve of the gas pool outlet 16 is opened. The gas pressure in the gas pool 15 is adjusted to negative pressure (-50 kPa) using a vacuum pump and then the exhaust valve is closed. After the soil laser ablation system completes the ablation of the soil sample 10, the inlet valve of the sample ablation chamber inlet 6 and the inlet valve of the gas pool inlet 14 are opened. Using the pressure difference between the gas pool 15 and the sample ablation chamber 3, the volatiles 7 in the sample ablation chamber 3 are sent into the gas pool 15. Then, the infrared light source 13 is turned on, and the infrared characteristic spectral data of the volatiles 7 are obtained using the infrared detector 17. The spectral characteristic information and the intensity information of the characteristic band of the volatiles are screened out. The types and contents of microplastics in the soil are qualitatively and quantitatively analyzed according to the microplastic classification model and the microplastic quantification model, respectively.

[0060] The microplastics include, but are not limited to, PE (polyethylene), PP (polypropylene), PS (polystyrene), and PBAT (polybutylene adipate).

[0061] A rapid detection method for microplastics in soil includes the following steps:

[0062] S1. Establish microplastic classification and quantitative models for microplastics;

[0063] S1.1 Establish a microplastic classification model;

[0064] S1.1.1 Prepare standard soil samples of known types and concentrations of microplastics;

[0065] Standard samples were prepared by adding single microplastics and mixtures thereof at different concentration gradients to blank soil; wherein the concentration gradients included 1‰, 5‰ and 10‰.

[0066] A rapid detection system for microplastics in soil was used to test standard samples and obtain a large amount of infrared spectral data of volatiles containing known microplastic species.

[0067] S1.1.2 Then, the obtained volatile infrared spectral data are baseline calibrated, and the peak heights at the M×3 characteristic bands corresponding to the M types of microplastics are extracted to form an M×3 dimensional feature vector.

[0068] In this embodiment of the invention, the wavelength corresponding to PE is 3085.62 cm. -1 2967.98 cm -1 911.70 cm -1 The band corresponding to PS is 3042.22 cm. -1 990.77 cm -1 909.29 cm -1 As shown in Table 2.

[0069] S1.1.3. Considering that multiple microplastics may coexist in actual soil samples, this invention adopts a multi-output classification architecture. Using an M×3 feature vector X as input and the type of microplastic as the multi-output label Y, a multi-output gradient boosting machine (MVM) model is used to optimize and combine the support vector machine (SVM) algorithm. Specifically, the gradient boosting machine is first used to evaluate and nonlinearly map the weights of the M×3 features, and then the mapped features are input into multiple parallel SVM classifiers for hyperplane partitioning. The model parameters are iteratively updated using the training set data until the model achieves a classification accuracy of 95% on the validation set, at which point the microplastic classification model is saved.

[0070] S1.2 Establish a quantitative model for microplastics;

[0071] S1.2.1 For the microplastics identified by the microplastic classification model, extract the infrared spectral data of samples with different concentration gradients of the microplastics in the standard sample library.

[0072] S1.2.2 Since the concentration of characteristic volatiles generated by laser ablation and the original mass fraction of microplastics in the soil follow a derived linear relationship according to the Lambert-Beer law, the intensity of the characteristic peak is highly positively correlated with the content. Using the intensities x1, x2, and x3 of the three characteristic bands corresponding to this type of microplastic as input variables and the actual microplastic content C as the dependent variable, a multiple linear model is established. Its mathematical expression is:

[0073] C = β0 + β1x1 + β2x2 + β3x3

[0074] Where C is the actual microplastic content in ‰; β0 is the intercept; β1, β2, and β3 are the regression coefficients of each characteristic band; and x1, x2, and x3 are the intensities of the three characteristic bands.

[0075] The least squares method was used to fit and solve the above multiple linear model to determine the regression coefficients.

[0076] S1.2.3 Calculate the coefficient of determination R of the model 2 And the root mean square error (RMSE). When the established quantitative model for microplastics R... 2 When the value is greater than 0.95, the quantitative model for microplastics is considered to have been successfully established and can be used to predict the content of microplastics in actual unknown soil samples.

[0077] S1.2.4 Finally, using the blank standard deviation method, the detection results were converted into the concentration of the microplastic after measuring multiple (10) blank samples.

[0078] Calculate the standard deviation of 10 parallel samples, and then calculate the method detection limit (MDL) using the following formula:

[0079] MDL=t (n-1,0.99)×S

[0080] Where MDL is the method detection limit; n is the number of parallel determinations of the sample; t is the t-distribution value (one-sided) with n-1 degrees of freedom and 99% confidence level; S is the standard deviation of n parallel determinations. The t-values ​​with n-1 degrees of freedom and 99% confidence level are taken from Table 1.

[0081] Table 1. Reference table of t-values ​​when the degrees of freedom are n-1 and the confidence level is 99%.

[0082] Number of parallel determinations (n) Degrees of freedom (n-1) <![CDATA[t (n-1,0.99) ]]> 7 6 3.143 8 7 2.998 10 9 2.821 11 10 2.764 16 15 2.602

[0083] S2. Detection of soil ablation and characteristic volatiles;

[0084] S2.1 After drying and sieving the soil to be tested, spread it evenly on the soil sample support plate 11, push it into the sample ablation chamber 3, cover the sample ablation chamber door 12, close the air inlet valve of the sample ablation chamber 6 and the exhaust valve of the sample ablation chamber outlet 9, seal the sample ablation chamber 3, and maintain standard atmospheric pressure; the laser emitted from the laser 1 passes through the focusing lens 2 and the quartz glass cover 8 on the top of the sample ablation chamber 3 and is focused on the surface of the soil to be tested. The step size of the two-dimensional displacement platform is controlled by the operation program, so that the electric translation X-axis 4 and electric translation Y-axis 5 of the two-dimensional displacement platform move in an "S" shaped path at a constant speed, so that the laser ablates and heats the entire soil to be tested, exciting the volatiles 7, and collecting the volatiles in the sample ablation chamber 3;

[0085] S2.2 Before ablating the soil sample, the inlet valve of the gas pool inlet 14 is closed and the exhaust valve of the gas pool outlet 16 is opened. The gas pressure in the gas pool 15 is adjusted to -50 kPa using a vacuum pump, and then the exhaust valve of the gas pool outlet 16 is closed. After the ablation of the soil sample 10 is completed, the inlet valve of the sample ablation chamber inlet 6 and the inlet valve of the gas pool inlet 14 are opened. The volatiles 7 in the sample ablation chamber 3 are sent into the gas pool 15 using the pressure difference. After the gas pressure in the gas pool 15 is restored to 1 atmosphere, the infrared light source 13 is turned on, and the infrared detector 17 is used to collect the infrared characteristic spectral data of the volatiles 7.

[0086] S3. Analysis of the types and content of microplastics;

[0087] Based on the infrared characteristic spectral data of volatile 7, the spectral characteristic information and intensity information of volatile characteristic bands were screened out, and the types and contents of microplastics in the soil were qualitatively and quantitatively analyzed according to the microplastic classification model and the microplastic quantification model, respectively.

[0088] Preferably, before soil ablation and characteristic volatiles detection, the air pressure in the gas cell 15 is maintained at standard atmospheric pressure, and the infrared spectral data of the current air is detected by the infrared detector 17. This infrared spectral data of the air is used as a benchmark and control when analyzing the infrared spectral data characteristics of soil microplastic ablation volatiles samples to ensure that there are no abnormal volatile components in the air that interfere with the detection results.

[0089] Example 1

[0090] In this embodiment, the sample materials include: standard soil (NST-2) and polyethylene (PE) microplastic particles with a particle size of 100 μm.

[0091] 1) Volatile excitation of soil samples

[0092] A laser ablation system was used to excite and collect soil microplastic volatiles. 5 g of standard soil was mixed evenly with 0.005 g, 0.01 g, and 0.05 g of PE microplastic particles, respectively. The mixture was then spread evenly in the bottom plate 11 of the sample ablation chamber at a size of 5 × 10 cm, pushed into the sample ablation chamber 3, and the chamber door 12 was closed to seal the sample ablation chamber 3. The output power of the laser 1 was set to 5 W, and the focusing lens 2 was adjusted to focus the laser through the quartz glass cover 8 onto the surface of the soil sample 10. The X-axis 4 and Y-axis 5 were moved at a constant speed (2 mm / s) in an "S" shaped path to achieve overall heating and ablation of the soil sample. The excited volatiles 7 were collected in the sample ablation chamber 3 for analysis.

[0093] 2) Acquisition of infrared spectral characteristics of volatiles

[0094] The spectrometer in the long-path infrared detector 17 is a Bruker V70, with a spectral detection range of 400–4000 cm⁻¹. -1 The spectral resolution was set to 2 cm. -1 Gas cell 15 is a Pike gas cell with an optical path length of 20 m. Before ablation of the soil sample, the inlet valve of gas cell inlet 14 is closed, and the exhaust valve of gas cell outlet 16 is opened. The gas pressure in gas cell 15 is adjusted to -50 kPa using a vacuum pump. Then, the exhaust valve of gas cell outlet 16 is closed, the inlet valve of gas cell inlet 14 is opened, and the exhaust valve of sample ablation chamber outlet 9 and the inlet valve of sample ablation chamber inlet 6 are opened. The volatiles 7 in sample ablation chamber 3 are sent into gas cell 15 using the pressure difference. After the gas pressure in gas cell 15 is restored to 1 atmosphere, the infrared characteristic spectrum data of the volatiles are collected.

[0095] 3) Spectral analysis of volatiles released from soil after heating and ablation of microplastics

[0096] Analysis of the obtained infrared characteristic spectral data revealed that, compared with the ablation volatiles spectrum of standard soil, the volatiles spectrum of the mixture after adding PE showed a higher concentration at 3085.62 cm⁻¹. -1 2976.89 cm -1 and 911.70 cm -1 The characteristic peak band is located at ( Figure 2 The characteristic peak band data from the selected samples are fed into a pre-established microplastic classification model based on the characteristic peak positions using a support vector machine (SVM). Figure 5 ) and a multivariate linear model for the quantitative analysis of microplastics based on characteristic peak intensities ( Figure 4a The results showed that all soil samples were correctly identified as containing PE microplastics. The quantitative results of PE in the three samples were 0.0047 g, 0.0106 g, and 0.0512 g, respectively.

[0097] Example 2

[0098] In this embodiment, the main sample materials are: standard soil (NST-2) and four types of microplastic particles with a particle size of 100 μm (PE, PP, PS, PBAT).

[0099] 1) Volatile excitation of soil samples

[0100] A laser ablation system was used to excite and collect soil microplastic volatiles. 5 g of standard soil was mixed evenly with microplastic particles of 0.005 g PE, 0.005 g PP, 0.005 g PS, and 0.005 g PBAT, respectively. The mixture was then spread evenly in the bottom plate 11 of the sample ablation chamber at a size of 5 × 10 cm, pushed into the sample ablation chamber 3, and the chamber door 12 was closed to seal the sample ablation chamber 3. The output power of the laser 1 was set to 5 W, and the focusing lens 2 was adjusted to focus the laser light through the quartz glass cover 8 onto the surface of the soil sample 10. The X-axis 4 and Y-axis 5 were moved at a constant speed (2 mm / s) in an "S" shaped path to achieve overall heating and ablation of the soil sample 10. The excited volatiles 7 were collected in the sample ablation chamber 3 for analysis.

[0101] 2) Acquisition of infrared spectral characteristics of volatiles

[0102] The spectrometer in the long-path infrared detector 17 is a Bruker V70, with a spectral detection range of 400–4000 cm⁻¹. -1 The spectral resolution was set to 2 cm. -1Gas cell 15 is a Pike gas cell with an optical path length of 20 m. Before ablation of the soil sample, the inlet valve of gas cell inlet 14 is closed, and the exhaust valve of gas cell outlet 16 is opened. The gas pressure inside gas cell 15 is adjusted to -50 kPa using a vacuum pump. Then, the exhaust valve of gas cell outlet 16 is closed, the inlet valve of gas cell inlet 14 is opened, and the exhaust valves of sample ablation chamber exhaust port 9 and sample ablation chamber inlet 6 are opened. The volatiles 7 in sample ablation chamber 3 are sent into gas cell 15 using the pressure difference. After the gas pressure in gas cell 15 returns to 1 atmosphere, the infrared characteristic spectrum data of the volatiles are collected, such as... Figure 3 As shown.

[0103] 3) Spectral analysis of volatiles released from soil after heating and ablation of microplastics

[0104] After filtering out the characteristic peak bands from the collected infrared characteristic spectral data, the data is fed into a pre-established microplastic classification model based on the characteristic peak positions using a support vector machine (SVM). Figure 5 ) and a multivariate linear model for the quantitative analysis of microplastics based on characteristic peak intensities ( Figure 4a , Figure 4b , Figure 4c and Figure 4d The results showed that all four soil samples were correctly identified as containing PE, PP, PS, and PBAT, respectively. The quantitative results for the four samples were 0.0042 g, 0.0053 g, 0.0048 g, and 0.0054 g, respectively.

[0105] Table 2. Characteristic volatiles and characteristic wavelengths produced after ablation of four soil-microplastic mixtures.

[0106] Types of microplastics in soil Characteristic volatiles Characteristic peak band PE 1,4-Octadiene <![CDATA[3085.62 cm -1 ; 2967.98 cm -1 ; 911.70 cm -1 ]]> PP 2,3-Dimethyl-1-hexene <![CDATA[2970.39 cm -1 ; 2882.16 cm -1 ; 894.35 cm -1 ]]> PS styrene <![CDATA[3042.22 cm -1 ; 990.77 cm -1 ; 909.29 cm -1 ]]> PBAT propylene <![CDATA[3103.45 cm -1 ; 2953.51 cm -1 ; 912.18 cm -1 ]]>

[0107] The above description represents two preferred embodiments of the present invention, but the present invention is not limited to the contents disclosed in these embodiments and accompanying drawings. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

[0108] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A rapid detection system for microplastics in soil, characterized in that, The rapid detection system for microplastics in soil includes a soil laser ablation system and an infrared spectroscopy detection system. The soil laser ablation system includes a laser (1), a focusing lens (2), a sample ablation chamber (3), and a two-dimensional displacement platform. The laser (1) focuses the laser beam onto the surface of the soil sample (10) in the sample ablation chamber (3) through the focusing lens (2) to achieve laser ablation of the soil sample (10). The sample ablation chamber (3) is an aluminum cuboid with a sample ablation chamber air inlet (6), a sample ablation chamber air outlet (9), a pull-out soil sample support base plate (11), and an openable sample ablation chamber door (12). The sample ablation chamber air inlet (6) is equipped with an air inlet valve, and the sample ablation chamber air outlet (9) is equipped with an exhaust valve. The top of the sample ablation chamber (3) is equipped with a quartz glass cover (8). The sample ablation chamber (3) is arranged on a two-dimensional displacement platform. The infrared spectroscopy detection system includes an infrared light source (13), a gas cell (15), and an infrared detector (17); the gas cell (15) has a gas cell inlet (14) and a gas cell outlet (16); the gas cell inlet (14) is equipped with an inlet valve, and the gas cell outlet (16) is equipped with an exhaust valve; the gas cell inlet (14) is connected to the sample ablation chamber outlet (9); Before ablation of soil samples by the soil laser ablation system, the inlet valve of the gas pool inlet (14) is closed in advance, and the exhaust valve of the gas pool outlet (16) is opened. The gas pressure in the gas pool (15) is adjusted to negative pressure by a vacuum pump and then the exhaust valve of the gas pool outlet (16) is closed, so that a pressure difference is formed between the gas pool (15) and the sample ablation chamber (3), which is used to send the volatiles (7) generated by ablation of soil samples in the sample ablation chamber (3) into the gas pool (15). The infrared characteristic spectrum data of the volatiles (7) are obtained by using an infrared detector (17), and the spectral characteristic information and characteristic band intensity information of the volatiles are screened out. The types and contents of microplastics in the soil are qualitatively and quantitatively analyzed according to the microplastic classification model and the microplastic quantification model, respectively.

2. The rapid detection system for microplastics in soil according to claim 1, characterized in that, When ablation soil samples, the soil sample (10) is dried and sieved and then laid flat in the soil sample support plate (11) of the sample ablation chamber (3). The air inlet valve (6) of the sample ablation chamber and the exhaust valve (9) of the sample ablation chamber are closed to seal the sample ablation chamber (3) and maintain standard atmospheric pressure.

3. The rapid detection system for microplastics in soil according to claim 1, characterized in that, When ablation of soil samples, the two-dimensional displacement platform moves the sample ablation chamber (3) in an "S"-shaped moving path at a constant displacement speed, so that the laser beam travels across the entire soil surface.

4. The rapid detection system for microplastics in soil according to claim 1, characterized in that, The two-dimensional displacement platform includes an electrically translatable X-axis (4) and an electrically translatable Y-axis (5).

5. The rapid detection system for microplastics in soil according to claim 1, characterized in that, The microplastics include PE, PP, PS, and PBAT.

6. A method for rapid detection of microplastics in soil using the rapid detection system for microplastics in soil as described in any one of claims 1-5, characterized in that, The rapid detection method for microplastics in soil includes the following steps: S1. Establish microplastic classification and quantitative models for microplastics; S1.1 Establish a microplastic classification model; S1.1.1 Prepare standard soil samples of known types and concentrations of microplastics; Various single microplastics and their mixtures with different concentration gradients were added to blank soil to prepare standard samples; the standard samples were tested using a rapid detection system for microplastics in soil to obtain a large amount of infrared spectral data of volatiles containing known microplastic species. S1.1.2 Then, the obtained volatile infrared spectral data are baseline calibrated, and the peak heights at the M×3 characteristic bands corresponding to the M types of microplastics are extracted to form an M×3 dimensional feature vector. S1.1.

3. Using the M×3 dimensional feature vector X as input and the type of microplastic as the multi-output label Y, the support vector machine algorithm is optimized and combined using a multi-output gradient boosting machine model. S1.2 Establish a quantitative model for microplastics; S1.2.1 For the microplastics identified by the microplastic classification model, extract the infrared spectral data of samples with different concentration gradients of the microplastics in the standard sample library. S1.2.

2. Using the intensities of the three characteristic bands corresponding to this type of microplastic, x1, x2, and x3, as input independent variables and the actual content of the corresponding microplastic, C, as the dependent variable, a multivariate linear model is established. S1.2.3 Calculate the coefficient of determination R of the model 2 and root mean square error (RMSE); when the established quantitative model for microplastics R 2 A value >0.95 indicates that the quantitative model for this type of microplastics has been successfully established; S1.2.4 Finally, using the blank standard deviation method, the detection results were converted into the concentration of the microplastic after measuring multiple blank samples. S2. Detection of soil ablation and characteristic volatiles; S2.1 After drying and sieving the soil to be tested, spread it flat on the soil sample support plate (11), push it into the sample ablation chamber (3), cover the sample ablation chamber door (12), close the air inlet valve (6) of the sample ablation chamber and the exhaust valve (9) of the sample ablation chamber, seal the sample ablation chamber (3), and maintain standard atmospheric pressure; the laser emitted by the laser (1) passes through the focusing lens (2) and the quartz glass cover (8) on the top of the sample ablation chamber (3) and focuses on the surface of the soil to be tested. By controlling the step size of the two-dimensional displacement platform through the operation program, the two-dimensional displacement platform moves at a constant speed along a preset path, so that the laser beam travels across the entire soil surface, excites the volatiles (7) therein, and collects the volatiles in the sample ablation chamber (3); S2.2 Before ablation of the soil sample, the inlet valve of the gas pool inlet (14) is closed in advance, and the exhaust valve of the gas pool outlet (16) is opened. The gas pressure in the gas pool (15) is adjusted to negative pressure by a vacuum pump and then the exhaust valve of the gas pool outlet (16) is closed. After the ablation of the soil sample (10) is completed, the inlet valve of the sample ablation chamber inlet (6) and the inlet valve of the gas pool inlet (14) are opened. The volatiles (7) in the sample ablation chamber (3) are sent into the gas pool (15) by the pressure difference. After the gas pressure in the gas pool (15) is restored to one atmosphere, the infrared light source (13) is turned on and the infrared characteristic spectrum data of the volatiles (7) are collected by the infrared detector (17). S3. Analysis of the types and content of microplastics; Based on the infrared characteristic spectral data of volatiles (7), the spectral characteristic information and the intensity information of the characteristic band of volatiles were screened out, and the types and contents of microplastics in the soil were qualitatively and quantitatively analyzed according to the microplastic classification model and the microplastic quantitative model, respectively.

7. The rapid detection method for microplastics in soil according to claim 6, characterized in that, In step S1.1.3, the weights of the M×3 features are first evaluated and nonlinearly mapped using a gradient boosting machine. Then, the mapped features are input into multiple parallel SVM classifiers for hyperplane partitioning. The model parameters are iteratively updated using training set data until the model achieves a classification accuracy of 95% on the validation set. Finally, the microplastic classification model is saved.

8. The rapid detection method for microplastics in soil according to claim 6, characterized in that, In step S1.2.2, the mathematical expression of the multiple linear model is: C = β0 + β1x1 + β2x2 + β3x3 Where C is the actual microplastic content in ‰; β0 is the intercept; β1, β2, and β3 are the regression coefficients of each characteristic band; and x1, x2, and x3 are the intensities of the three characteristic bands. The least squares method was used to fit and solve the above multiple linear model to determine the regression coefficients.

9. The rapid detection method for microplastics in soil according to claim 6, characterized in that, Before soil ablation and characteristic volatiles detection, the gas pressure in the gas cell (15) is maintained at standard atmospheric pressure, and the infrared spectral data of the current air is detected by infrared detector (17). When analyzing the infrared spectral data characteristics of soil microplastic ablation volatiles samples, the infrared spectral data of the air is used as a benchmark and control to ensure that there are no abnormal volatile components in the air that interfere with the detection results.

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