A high-cold high-altitude atmospheric micro-plastic detection method based on pattern recognition

Abstract

The application discloses a kind of high-cold high-altitude atmospheric micro-plastic detection methods based on pattern recognition, it is related to pollutant monitoring technical field, the present application includes season difference highland atmospheric sampling, low-temperature low-loss sample pretreatment and particle separation, micro-plastic identification based on spectrum and form combined weighted pattern recognition, 72h air mass traceability and human respiratory exposure risk calculation step.The present application can adapt to the spectral degradation caused by ultraviolet aging and distinguish mineral dust interference by constructing highland aging micro-plastic and interference feature library, extracting particle spectrum and form combined features and using weighted pattern recognition classification, improve the recognition level of aging micro-plastic, while low-temperature pretreatment strategy reduces the loss of small and medium-sized particle micro-plastic, season sampling matches the meteorological difference of highland dry and wet season, and single process can output pollution abundance, transport source and health risk information simultaneously.

Description

Technical Field

[0001] This invention belongs to the field of pollutant monitoring technology, and in particular relates to a method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition. Background Technology

[0002] Atmospheric microplastics refer to plastic fibers, fragments, or particles with a diameter of less than 5 mm in the air. Because they can be transported over long distances and settle in remote ecosystems driven by atmospheric circulation, they have been listed as a key target for monitoring new environmental pollutants. High-altitude and cold regions specifically refer to areas with an average altitude exceeding 4500 m, such as the Qinghai-Tibet Plateau. These regions are characterized by low air pressure, strong ultraviolet radiation, large diurnal temperature variations, and frequent dust storms during the dry season. Ultraviolet aging refers to the process of molecular chain breakage and oxygen-containing functional group formation in plastic polymers under long-term strong ultraviolet light irradiation. Macroscopically, this manifests as material embrittlement and surface cracking; microscopically, it manifests as attenuation, broadening, or even disappearance of characteristic absorption peaks in the infrared spectrum. Mineral dust interference refers to the large amounts of mineral particles such as quartz, feldspar, and carbonates carried into the atmosphere by strong winds during the dry season in high-altitude and cold regions. These particles are collected along with microplastics in filter membrane samples. Because mineral particles also have absorption signals in the infrared fingerprint region, they constitute background interference for the accurate identification of microplastics.

[0003] Current methods for identifying atmospheric microplastics commonly employ laser infrared imaging systems combined with searches of commercial spectral libraries. By calculating the matching degree between the spectrum of the particle being tested and a standard spectral library, and based on a preset fixed threshold, it determines whether the particle is a microplastic and its polymer type. This method relies on complete, high signal-to-noise ratio standard spectra and can automate the identification of most microplastics under normal conditions.

[0004] However, directly applying the conventional infrared spectral library matching method to atmospheric samples from high-altitude and cold regions presents applicability issues. The strong ultraviolet radiation at high altitudes causes deep aging of suspended microplastics in the atmosphere, resulting in a significant decrease in the intensity of key identification signals such as the polyethylene methylene rocking vibration peak and the polypropylene methyl bending vibration peak in their infrared spectra. This naturally reduces the matching degree with the spectra of unaged plastics in the standard spectral library. If the conventional matching threshold is continued, a large number of aged microplastics with weakened characteristic signals will be judged as non-plastics and discarded. Simultaneously, a large number of mineral dust particles mixed in the sample exhibit broad and strong silica absorption bands in the infrared fingerprint region. The spectral shapes of some mineral particles overlap with the degraded spectra of certain aged polymers, making it difficult to effectively distinguish them using a single spectral similarity threshold. This can easily lead to misclassification of minerals as microplastics or aged microplastics as mineral interference. These problems make it difficult for existing general infrared spectral library matching detection methods to identify aged microplastics in the specific environment of high altitude and cold regions, affecting the integrity of the final detection results. Therefore, the following solution is proposed to address these issues. Summary of the Invention

[0005] The purpose of this invention is to provide a method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition. By constructing a standard feature library of plateau-aged microplastics and interfering substances, the method extracts the combined spectral and morphological features of the particles to be tested, and uses an adaptive weighted pattern recognition classification method to determine microplastics. This method can adapt to the spectral degradation characteristics caused by ultraviolet aging and distinguish between mineral and dust interference, thus solving the problems of existing infrared spectroscopy fixed threshold matching methods in identifying plateau-aged microplastics and being easily affected by mineral misjudgment.

[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0007] This invention relates to a method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition, comprising the following steps:

[0008] Step S1: Based on the climate characteristics of the high-altitude and cold region to be tested, the whole year is divided into dry season and wet season, and different active sampling flow rates are set for the dry season and wet season respectively. Atmospheric particulate matter is collected using a filter membrane. During the wet season, the sampling device is protected from rain.

[0009] Step S2: The sampled filter membrane is subjected to low-temperature ultrasonic peeling and density flotation separation in sequence to obtain the retained particles enriched with microplastics. Organic impurities are then removed by a low-temperature hydrogen peroxide digestion system, and the sample is concentrated and diluted to a fixed volume to prepare a monolayer dispersed particle sample.

[0010] Step S3: Simultaneously acquire infrared spectra and morphological images of individual particles in the particle sample, extract joint feature vectors containing spectral and morphological features, and classify each particle using a weighted distance metric pattern recognition algorithm based on a pre-constructed standard feature library of plateau-specific aging microplastics and interfering substances to determine whether it is a microplastic and its corresponding polymer type.

[0011] Step S4: Acquire global meteorological data during the sampling period, simulate the backward trajectory of the air mass starting from the sampling point and covering the predetermined retrospective period, correlate the microplastic polymer type information identified in Step S3 with the air mass trajectory, analyze the pollution sources and quantify the contribution ratio of each source.

[0012] Step S5: Based on the environmental concentration of microplastics of different particle sizes obtained in Step S3, construct a model of the average daily intake of respiratory exposure for different populations, calculate the intake, and classify the health risk level.

[0013] Furthermore, in step S1, the sampling flow rate during the dry season is set to be lower than that during the wet season; the filter membrane is a thickened, loss-resistant glass fiber filter membrane; during wet season sampling, a rainproof flow guiding device is installed on the sampling head; the sampling height is fixed at the preset human breathing zone height.

[0014] Further, step S2 specifically includes: immersing the filter membrane in anhydrous ethanol and ultrasonically treating it at a preset low temperature to remove particulate matter; adding zinc chloride flotation solution with a density greater than that of common sand and dust mineral particles and less than that of common microplastics to the resulting suspension, allowing it to stand and separate into layers, and collecting the supernatant; digesting the residue in the supernatant using a hydrogen peroxide solution with a preset mass fraction at a low temperature lower than the conventional organic matter digestion temperature; after digestion, vacuum filtering and rinsing, concentrating and adjusting the volume with ethanol, and finally drop-coating it onto an infrared glass slide and air-drying it at low temperature.

[0015] Further, in step S3, the extraction of the joint feature vector includes:

[0016] The spectral features that are relatively stable to plastic aging were extracted from the infrared spectrum, including the peak area of ​​the antisymmetric stretching vibration of methylene CH, the carbonyl index representing plastic aging, the peak area of ​​the stretching vibration of CO, and the absorption peak area of ​​Si-O representing mineral interference. The first derivative spectral features of the whole spectrum were also obtained.

[0017] Extract morphological features from the morphological image that do not change with particle rotation or translation, including aspect ratio, roundness, edge roughness, and texture entropy;

[0018] The spectral features and morphological features are concatenated to form the joint feature vector.

[0019] Furthermore, the carbonyl index is determined by the ratio of the integral area of ​​the carbonyl characteristic absorption peak generated by plastic aging to the integral area of ​​the characteristic absorption peak representing the carbon-hydrogen skeleton vibration; the Si-O absorption peak area is the integral area of ​​the characteristic absorption peak of the silicon-oxygen bond stretching vibration.

[0020] Furthermore, the aspect ratio is the ratio of the major axis to the minor axis of the particle's fitted ellipse; the roundness is the ratio of a set multiple of the particle's area to the square of its perimeter; the edge roughness is the ratio of the particle's convex hull perimeter to its actual perimeter; and the texture entropy is the information entropy calculated based on the gray-level co-occurrence matrix of the particle's transmission image.

[0021] Further, in step S3, the pattern recognition algorithm for the weighted distance metric is a weighted K-nearest neighbor classification algorithm; the weighted Euclidean distance between the joint feature vector of the particle to be tested and each standard feature vector in the standard feature library is calculated, wherein the weight of each feature dimension is pre-set according to the information gain of the feature in distinguishing between aging microplastics and plateau mineral interference; the K nearest neighbors with the smallest distance are selected for majority voting to determine the particle category.

[0022] Furthermore, step S3 also includes: extracting the equivalent projected circle diameter of particles identified as microplastics, and statistically analyzing the quantity abundance and mass abundance of microplastics of various polymer types according to several preset particle size ranges.

[0023] Further, step S4 specifically involves: retrieving GDAS global meteorological data and using the HYSPLIT model to simulate the backward trajectory of the air mass at the sampling point; matching the underlying surface characteristics of the area traversed by the trajectory with the polymer types identified in step S3 to identify cross-border transport sources, traffic wear sources, residential textile sources, and livestock dust sources; and calculating the contribution ratio of each pollution source to microplastics at the sampling point using the concentration weighted trajectory analysis method.

[0024] Furthermore, in step S5, the different population groups include at least adults and children; the calculation method of the average daily intake model of respiratory exposure is as follows: for each particle size range, the environmental quality concentration of microplastics is multiplied by the preset respiratory rate and preset average daily outdoor exposure time of the population, then divided by the preset average weight of the population, and summed over all particle size ranges to obtain the average daily microplastic inhalation amount per unit body weight; based on the relationship between the value of this inhalation amount and the preset risk threshold, low, medium and high exposure risk levels are divided.

[0025] The present invention has the following beneficial effects:

[0026] This invention employs a combined spectral and morphological weighted pattern recognition approach, adapting to the degradation characteristics of plastic functional groups and reducing interference from mineral particles in identification, thus improving the identification level of aged microplastics. A low-temperature pretreatment strategy reduces the loss of small- and medium-sized microplastics, and seasonal sampling parameters match the differences in meteorological conditions between dry and wet seasons. The method simultaneously integrates air mass trajectory tracing with human respiratory exposure calculations; a single pass can provide information on pollution abundance, transport source, and health risk, providing an environmentally adaptable detection route for atmospheric microplastic monitoring in high-altitude and cold regions.

[0027] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic flowchart of a method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition, according to the present invention. Detailed Implementation

[0030] The technical solutions of 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.

[0031] Please see Figure 1 As shown, this invention is a method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition, comprising the following steps:

[0032] Step S1: Based on the climate characteristics of the high-altitude and cold region to be tested, the whole year is divided into dry season and wet season, and different active sampling flow rates are set for the dry season and wet season respectively. Atmospheric particulate matter is collected using a filter membrane. During the wet season, the sampling device is protected from rain.

[0033] Step S2: The sampled filter membrane is subjected to low-temperature ultrasonic peeling and density flotation separation in sequence to obtain the retained particles enriched with microplastics. Organic impurities are then removed by a low-temperature hydrogen peroxide digestion system, and the sample is concentrated and diluted to a fixed volume to prepare a monolayer dispersed particle sample.

[0034] Step S3: Simultaneously acquire infrared spectra and morphological images of individual particles in the particle sample, extract joint feature vectors containing spectral and morphological features, and classify each particle using a weighted distance metric pattern recognition algorithm based on a pre-constructed standard feature library of plateau-specific aging microplastics and interfering substances to determine whether it is a microplastic and its corresponding polymer type.

[0035] Step S4: Acquire global meteorological data during the sampling period, simulate the backward trajectory of the air mass starting from the sampling point and covering the predetermined retrospective period, correlate the microplastic polymer type information identified in Step S3 with the air mass trajectory, analyze the pollution sources and quantify the contribution ratio of each source.

[0036] Step S5: Based on the environmental concentration of microplastics of different particle sizes obtained in Step S3, construct a model of the average daily intake of respiratory exposure for different populations, calculate the intake, and classify the health risk level.

[0037] In step S1, the sampling flow rate during the dry season is set to be lower than that during the wet season; the filter membrane is a thickened, loss-resistant glass fiber filter membrane; during sampling in the wet season, a rainproof flow guide device is installed on the sampling head; and the sampling height is fixed at the preset human breathing zone height.

[0038] Step S2 specifically includes: immersing the filter membrane in anhydrous ethanol and ultrasonically treating it at a preset low temperature to remove particulate matter; adding zinc chloride flotation solution with a density greater than that of common sand and dust mineral particles but less than that of common microplastics to the resulting suspension, allowing it to stand and separate into layers, and collecting the supernatant; digesting the residue in the supernatant using a hydrogen peroxide solution with a preset mass fraction at a low temperature lower than the conventional organic matter digestion temperature; after digestion, vacuum filtering and rinsing, concentrating and adjusting the volume with ethanol, and finally drop-coating it onto an infrared glass slide and air-drying it at low temperature.

[0039] In step S3, the extraction of the joint feature vector includes:

[0040] The spectral features that are relatively stable to plastic aging were extracted from the infrared spectrum, including the peak area of ​​the antisymmetric stretching vibration of methylene CH, the carbonyl index representing plastic aging, the peak area of ​​the stretching vibration of CO, and the absorption peak area of ​​Si-O representing mineral interference. The first derivative spectral features of the whole spectrum were also obtained.

[0041] Extract morphological features from the morphological image that do not change with particle rotation or translation, including aspect ratio, roundness, edge roughness, and texture entropy;

[0042] Spectral features and morphological features are combined to form a joint feature vector.

[0043] The carbonyl index is determined by the ratio of the integral area of ​​the characteristic absorption peak of carbonyl produced by plastic aging to the integral area of ​​the characteristic absorption peak representing the vibration of the hydrocarbon skeleton; the Si-O absorption peak area is the integral area of ​​the characteristic absorption peak of the stretching vibration of the silicon-oxygen bond.

[0044] Aspect ratio is the ratio of the major axis to the minor axis of the particle's fitted ellipse; roundness is the ratio of a set multiple of the particle's area to the square of its perimeter; edge roughness is the ratio of the particle's convex hull perimeter to its actual perimeter; texture entropy is the information entropy calculated based on the gray-level co-occurrence matrix of the particle's transmission image.

[0045] In step S3, the pattern recognition algorithm for weighted distance measurement is the weighted K-nearest neighbor classification algorithm; the weighted Euclidean distance between the joint feature vector of the particle to be tested and each standard feature vector in the standard feature library is calculated, wherein the weight of each feature dimension is pre-set according to the information gain of the feature in distinguishing between aging microplastics and plateau mineral interference; the K nearest neighbors with the smallest distance are selected for majority voting to determine the particle category.

[0046] Step S3 also includes: extracting the equivalent projected circle diameter of particles identified as microplastics, and statistically analyzing the quantity abundance and mass abundance of microplastics of various polymer types according to several preset particle size ranges.

[0047] Step S4 specifically involves: retrieving GDAS global meteorological data and using the HYSPLIT model to simulate the backward trajectory of air masses at the sampling points; matching the underlying surface characteristics of the areas traversed by the trajectory with the polymer types identified in step S3 to identify cross-border transport sources, traffic wear sources, residential textile sources, and livestock dust sources; and calculating the contribution percentage of each pollution source to microplastics at the sampling points using the concentration-weighted trajectory analysis method.

[0048] In step S5, the different population groups include at least adults and children; the calculation method of the daily average intake model of respiratory exposure is as follows: for each particle size range, the environmental quality concentration of microplastics is multiplied by the preset respiratory rate and preset daily average outdoor exposure time of the population, then divided by the preset average weight of the population, and summed over all particle size ranges to obtain the daily average microplastic inhalation amount per unit body weight; based on the relationship between the value of this inhalation amount and the preset risk threshold, low, medium and high exposure risk levels are divided.

[0049] The specific application of this embodiment is as follows:

[0050] Step S1: Seasonally Differentiated Plateau Atmospheric Particulate Matter Sampling

[0051] In the areas to be tested on the Qinghai-Tibet Plateau, the year is divided into a dry season (January-April and November-December) and a wet season (May-October) based on the characteristics of the plateau climate, and a differentiated sampling strategy is implemented.

[0052] During the dry season, the air is dry and dust concentrations are high. The sampling pump flow rate was set to 80 L / min to reduce the impact load of large dust particles on the filter membrane. During the wet season, precipitation and high humidity partially suppress ground dust, so the flow rate was increased to 100 L / min to obtain more representative aerosol samples within the same timeframe. A thickened, loss-resistant glass fiber filter membrane with a pore size of 0.45 μm was used for continuous 24-hour sampling, with the sampling head fixed at a height of 1.5 m above the ground.

[0053] During wet season operation, a rainproof deflector should be installed on the sampling head to prevent rainwater from directly washing away the filter membrane and causing sample loss. After sampling, the filter membrane containing atmospheric particulate matter should be sealed in a light-proof aluminum foil bag and frozen at -20°C to inhibit microbial activity and avoid further aging of the plastic caused by additional ultraviolet radiation.

[0054] Step S2: Low-temperature, low-loss sample pretreatment and particulate matter separation

[0055] Immerse the entire sampling filter membrane in a glass dish containing anhydrous ethanol and place it in a 40°C ultrasonic cleaner for 40 minutes to completely remove particulate matter such as sand, ice crystals, plant debris, and microplastics adhering to the filter membrane fibers. After removing the filter membrane, transfer the ethanol suspension containing all particulate matter to a separatory funnel.

[0056] Subsequently, a pre-prepared zinc chloride flotation solution with a density of 1.7-1.8 kg / L was added to the suspension, and after thorough stirring, the mixture was allowed to settle and separate into layers. The less dense microplastics and some lightweight organic debris floated to the surface, while the denser quartz, feldspar, and other sand and dust mineral particles settled to the bottom. The supernatant was collected and then vacuum-filtered through a 0.45 μm silver membrane to remove the target particles.

[0057] The retained material was resuspended in a 15% hydrogen peroxide solution and gently digested in a shaker at 40°C for 12 hours to selectively remove natural organic matter such as pollen and microbial residues, while avoiding the embrittlement and breakage of aging microplastics caused by conventional high-temperature digestion. After digestion, the material was filtered again and repeatedly washed with ultrapure water until neutral. Finally, the pure particles on the filter membrane were concentrated to a final volume of 150 μL with anhydrous ethanol. All the liquid was then drop-coated onto a high-reflectivity infrared glass slide and air-dried under a low-temperature drying airflow to form a monolayer dispersed array of test particles.

[0058] Step S3: Microplastic identification based on spectral-morphological joint weighted pattern recognition

[0059] Step S31: Acquisition of single-particle spectral and morphological data

[0060] A glass slide containing the particles to be tested was placed into an Agilent 8700 laser infrared imaging system. In automatic particle recognition mode, the system sequentially located each particle and acquired its infrared spectrum and high-resolution morphological microscopic images. For each particle... Wavenumber range of 4000-1000cm was obtained. Transmission spectral vector And orthographic projection images.

[0061] Step S32: Construct a standard feature library of plateau-specific aging microplastics and interfering substances.

[0062] A standard particle feature library specific to the Qinghai-Tibet Plateau was established in advance through a combination of field collection and laboratory-accelerated UV aging. The library includes high-frequency plastic particles from the plateau such as PE, PP, PVC, PU, ​​PVA, rubber, and PTFE at different aging levels, as well as typical non-plastic interfering particles from high-altitude and cold regions, such as quartz, feldspar, black carbon, fungal spores, and pasture fibers. For each known particle category in the library, its standard feature vector was calculated and stored.

[0063] Step S33: Extract the joint feature vector of the particles to be tested.

[0064] For each particle to be tested, perform the following feature extraction operations to construct a feature vector for pattern recognition. :

[0065] Spectral characteristics: To suppress peak distortion and reduce noise caused by high-altitude plastic aging, the full spectrum is not used directly. Instead, the absorption band characteristics of functional groups that are relatively stable and discriminative of the aging process are extracted. These include, but are not limited to, the peak area of ​​the methylene CH antisymmetric stretching vibration. Carbonyl index CO stretching vibration peak area And the area of ​​the Si-O absorption peak reflecting mineral interference. Simultaneously calculate 4000-1000cm The normalized first derivative spectrum of the entire spectrum is used to extract peak and valley position codes.

[0066] Morphological characteristics: Four geometric parameters that do not change with particle rotation or translation were extracted from the microscopic images: aspect ratio. (in Fit the major axis of the ellipse to the particles. (for the minor axis) and roundness Edge roughness and normalized transmittance texture entropy .

[0067] By combining the above spectral features with morphological features, particles are formed. joint eigenvectors And the normalized first derivative spectral principal component scores for each dimension.

[0068] Step S34: Adaptive Weighted K-Nearest Neighbor (K-NN) Classification and Recognition for High-Altitude Scenes

[0069] To address the high overlap in certain feature dimensions between aged microplastics and dust minerals in high-altitude environments (e.g., the carbonyl peak of severely aged PP is similar to that of some organic matter, and feldspar fragments and microplastic fragments have similar aspect ratios), a feature-weighted distance metric is designed to achieve accurate classification. For the feature vector of the particle to be tested... Calculate its relationship with each standard feature vector in the library. Weighted Euclidean distance :

[0070]

[0071] In the formula, This represents the total number of feature dimensions. and The test particles and particles in the library were respectively in the 1st... Standardized eigenvalues ​​of dimension; For the first The dimensional features have specific weights for high-altitude discrimination scenarios, and their values ​​are pre-set based on the information gain of the feature in distinguishing between "aging microplastics" and "high-altitude mineral interferences." For example, the carbonyl index CI and the Si-O peak area... High weights are assigned to enhance the ability to distinguish oxygen-containing functional groups in degradable plastics and mineral particles; while the weight of simple aspect ratio is adaptively reduced when dust debris and fibrous microplastics are easily confused. Find the five nearest neighbors with the smallest distance, and predict the category of the particle (specifically, the microplastic polymer type or non-plastic type) by majority voting based on the category labels of these neighbors.

[0072] Step S35: Microplastic determination and particle size abundance statistics

[0073] If a particle is identified by the classifier as belonging to a specific type of plastic polymer, it is recorded as a microplastic of the corresponding type, and its equivalent projected circle diameter measured by the system is extracted. All non-plastic particles are marked and excluded. After identification, the abundance and mass abundance of microplastics for each polymer type are statistically analyzed according to four particle size ranges: 20-25 μm, 25-50 μm, 50-100 μm, and 100-500 μm.

[0074] Step S4: Source tracing based on the 72-hour air mass trajectory

[0075] Global Data Assimilation System (GDAS) meteorological grid data corresponding to the sampling period were retrieved. Using the sampling point coordinates as the starting point, the HYSPLIT model was used to simulate the 72-hour backward trajectory of air masses every hour of the day. The abundance of microplastics of each polymer type identified in step S3 was used as chemical tracers for source apportionment, and the source contribution was calculated according to the following rules: high abundance of PET and PA fibers were associated with urban textile sources; irregularly shaped PE and PP fragments that matched the trajectory of air masses along major transportation routes were classified as traffic wear sources; air masses containing PTFE and rubber particles mainly indicated long-distance transboundary transport; and particles that overlapped with the trajectory of pastoral surfaces and were accompanied by PU and PVA were identified as sources of pastoral activities. A trajectory-based concentration weighting analysis model was used to quantify the contribution ratio of each type of pollution source in different seasons.

[0076] Step S5: Simultaneous Calculation of Human Respiratory Exposure Risk

[0077] Environmental mass concentration of microplastics in each particle size range obtained in step S3 (Unit: μg / m³) A dual-model approach was established for daily average respiratory intake in adults and children at high altitudes. Daily average microplastic inhalation per unit body weight was determined for different population groups. The calculation formula is:

[0078]

[0079] In the formula, Traverse the four particle size ranges; For the crowd The respiratory rate (0.83 m³ / h for adults, 0.53 m³ / h for children). The average daily outdoor exposure time (6 hours) is used. The average weight is 65kg for adults and 25kg for children.

[0080] according to The calculated values ​​classify respiratory exposure risk into three levels: low (EDI<0.1), medium (0.1≤EDI<1.0), and high (EDI≥1.0). Finally, these values ​​are combined with the pollution distribution map and source contribution table to generate a multi-dimensional integrated detection and assessment report that includes the pollution baseline, transport path, and human health risk.

[0081] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0082] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition, characterized in that, The detection method includes the following steps: Step S1: Based on the climate characteristics of the high-altitude and cold region to be tested, the whole year is divided into dry season and wet season, and different active sampling flow rates are set for the dry season and wet season respectively. Atmospheric particulate matter is collected using a filter membrane. During the wet season, the sampling device is protected from rain. Step S2: The sampled filter membrane is subjected to low-temperature ultrasonic peeling and density flotation separation in sequence to obtain the retained particles enriched with microplastics. Organic impurities are then removed by a low-temperature hydrogen peroxide digestion system, and the sample is concentrated and diluted to a fixed volume to prepare a monolayer dispersed particle sample. Step S3: Simultaneously acquire infrared spectra and morphological images of individual particles in the particle sample, extract joint feature vectors containing spectral and morphological features, and classify each particle using a weighted distance metric pattern recognition algorithm based on a pre-constructed standard feature library of plateau-specific aging microplastics and interfering substances to determine whether it is a microplastic and its corresponding polymer type. Step S4: Acquire global meteorological data during the sampling period, simulate the backward trajectory of the air mass starting from the sampling point and covering the predetermined retrospective period, correlate the microplastic polymer type information identified in Step S3 with the air mass trajectory, analyze the pollution sources and quantify the contribution ratio of each source. Step S5: Based on the environmental concentration of microplastics of different particle sizes obtained in Step S3, construct a model of the average daily intake of respiratory exposure for different populations, calculate the intake, and classify the health risk level.

2. The method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition according to claim 1, characterized in that, In step S1, the sampling flow rate during the dry season is set to be lower than that during the wet season; the filter membrane is a thickened, loss-resistant glass fiber filter membrane; during wet season sampling, a rainproof flow guiding device is installed on the sampling head; the sampling height is fixed at the preset human breathing zone height.

3. The method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition according to claim 1, characterized in that, Step S2 specifically includes: immersing the filter membrane in anhydrous ethanol and ultrasonically treating it at a preset low temperature to remove particulate matter; adding zinc chloride flotation solution with a density greater than that of common sand and dust mineral particles but less than that of common microplastics to the resulting suspension, allowing it to stand and separate into layers, and collecting the supernatant; digesting the residue in the supernatant using a hydrogen peroxide solution with a preset mass fraction at a low temperature lower than the conventional organic matter digestion temperature; after digestion, vacuum filtering and rinsing, concentrating and adjusting the volume with ethanol, and finally drop-coating it onto an infrared glass slide and air-drying it at low temperature.

4. The method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition according to claim 1, characterized in that, In step S3, the extraction of the joint feature vector includes: The spectral features that are relatively stable to plastic aging were extracted from the infrared spectrum, including the peak area of ​​the antisymmetric stretching vibration of methylene CH, the carbonyl index representing plastic aging, the peak area of ​​the stretching vibration of CO, and the absorption peak area of ​​Si-O representing mineral interference. The first derivative spectral features of the whole spectrum were also obtained. Extract morphological features from the morphological image that do not change with particle rotation or translation, including aspect ratio, roundness, edge roughness, and texture entropy; The spectral features and morphological features are concatenated to form the joint feature vector.

5. The method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition according to claim 4, characterized in that, The carbonyl index is determined by the ratio of the integral area of ​​the carbonyl characteristic absorption peak generated by plastic aging to the integral area of ​​the characteristic absorption peak representing the carbon-hydrogen skeleton vibration; the Si-O absorption peak area is the integral area of ​​the characteristic absorption peak of the silicon-oxygen bond stretching vibration.

6. The method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition according to claim 4, characterized in that, The aspect ratio is the ratio of the major axis to the minor axis of the particle's fitted ellipse; the roundness is the ratio of a set multiple of the particle's area to the square of its perimeter; the edge roughness is the ratio of the particle's convex hull perimeter to its actual perimeter; and the texture entropy is the information entropy calculated based on the gray-level co-occurrence matrix of the particle's transmission image.

7. The method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition according to claim 4, characterized in that, In step S3, the pattern recognition algorithm for the weighted distance metric is a weighted K-nearest neighbor classification algorithm; the weighted Euclidean distance between the joint feature vector of the particle to be tested and each standard feature vector in the standard feature library is calculated, wherein the weight of each feature dimension is pre-set according to the information gain of the feature in distinguishing between aging microplastics and plateau mineral interference; the K nearest neighbors with the smallest distance are selected for majority voting to determine the particle category.

8. The method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition according to claim 7, characterized in that, Step S3 also includes: extracting the equivalent projected circle diameter of particles identified as microplastics, and statistically analyzing the quantity abundance and mass abundance of microplastics of various polymer types according to several preset particle size ranges.

9. The method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition according to claim 1, characterized in that, Step S4 specifically involves: retrieving GDAS global meteorological data and using the HYSPLIT model to simulate the backward trajectory of air masses at the sampling points; matching the underlying surface characteristics of the areas traversed by the trajectory with the polymer types identified in step S3 to identify cross-border transport sources, traffic wear sources, residential textile sources, and livestock dust sources; and calculating the contribution percentage of each pollution source to microplastics at the sampling points using the concentration-weighted trajectory analysis method.

10. The method for detecting atmospheric microplastics in high-altitude and cold regions based on pattern recognition according to claim 1, characterized in that, In step S5, the different population groups include at least adults and children; the calculation method of the average daily intake model of respiratory exposure is as follows: for each particle size range, the concentration of microplastics in the environment is multiplied by the preset respiratory rate and preset average daily outdoor exposure time of the population, then divided by the preset average weight of the population, and summed over all particle size ranges to obtain the average daily microplastic inhalation per unit body weight. Based on the relationship between the inhalation volume and the preset risk threshold, low, medium, and high exposure risk levels are classified.

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