Method for evaluating influence of microplastics on greenhouse soil function under temperature mediation

Abstract

The invention discloses a method for evaluating the influence of microplastics on greenhouse soil functions under temperature mediation, and relates to the technical field of environmental pollutant behavior and soil ecological response evaluation. The device simulates the amplification effect of a facility agriculture high-temperature scene on a micro-plastic disturbance behavior, and is closer to the actual state of a micro-pollution behavior in an agricultural greenhouse and other semi-closed spaces. By selecting four types of PE micro-plastics with different forms, experiments are carried out under the same quality and same ultraviolet irradiation conditions, and on the premise that variables such as plastic types, aging time, irradiation uniformity and the like are kept consistent, the synergistic effect of colors and forms on aging reaction can be revealed, so that the structural dimension precision of plastic aging sensitivity evaluation is improved. The aluminum foil tray has the characteristic of reflection spectrum consistency, so that the influence of local hot spots or shadows is effectively reduced, and the problem of experimental errors caused by non-uniform illumination in a traditional aging experiment is solved.

Description

Technical Field

[0001] This invention relates to the field of environmental pollutant behavior and soil ecological response assessment technology, specifically to an evaluation method for the impact of temperature-mediated microplastics on facility soil function. Background Technology

[0002] In the research on the migration and functional disturbance mechanisms of micropollutants in agricultural soil systems, microplastic particles, as a novel environmental pollutant, are increasingly becoming a focus of attention in precision agriculture and soil health due to their physicochemical characterization, ecological intervention capabilities, and coupling behavior with temperature and humidity environments. Among these, microplastic aging behavior parameters are crucial indicators for revealing the differentiated impact of their actual environmental behavior, directly determining their biological exposure risk, enzymatic reactivity, and degree of functional disturbance in soil systems.

[0003] Currently, most microplastic soil studies often use unaged, static new plastics as experimental materials, neglecting the changes in the surface physicochemical properties of microplastics in the environment due to aging processes such as light exposure, oxidation, and hydrolysis. This approach fails to accurately reflect the actual risk behavior of microplastics in the natural environment, resulting in poor extrapolation of experimental results and an underestimation of their interference capabilities.

[0004] This experimental setup, which ignores the aging effect, stems primarily from the research inertia of convenient material preparation and the use of a single control variable. However, it seriously affects the scientific rigor of microplastic ecological risk assessment. Aging microplastics often exhibit changes such as oxidative cracking, an increase in polar functional groups, and the appearance of nanoscale uneven structures, leading to enhanced biofilm adsorption capacity, higher enzyme-catalyzed response sensitivity, and significant microbial community disturbance effects. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for evaluating the impact of temperature-mediated microplastics on facility soil function, thus solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating the impact of temperature-mediated microplastics on facility soil function, comprising the following steps: S1. Four types of PE microplastics were placed under ultraviolet light for 96 hours and rotated daily to stimulate surface aging morphology and generate typical functional groups. S2. Data on irradiated PE microplastics were collected through multi-instrument composite testing and preprocessed to obtain the plastic aging dataset SLW. S3. Based on the plastic aging dataset SLW, comprehensively measure the changes in the ability of aging degree to affect environmental behavior and obtain the plastic aging activity index Ψag. S4. Based on the plastic aging activity index Ψag, the four types of aged PE microplastics were mixed into the facility agriculture soil at a mass ratio of 1%, and cultured in temperature setting groups with a cycle of 0~180 days, while soil parameters were collected simultaneously. S5. The obtained soil parameters and plastic aging activity index Ψag are coupled and analyzed to construct the soil function disturbance index Φsoil. S6. Estimate the negative impact on soil multifunctionality using the soil function disturbance index Φsoil, obtain the function loss rate L, and provide feedback.

[0007] Preferably, S1 includes S11 and S12; S11. Take four types of PE microplastics, including black flake PE, black granular PE, white flake PE and white granular PE; prepare samples of each type with a uniform mass of 10g. Cut PE microplastics into 5×5mm sizes, and maintain 0.15mm particle size for granular microplastics; evenly spread each type of microplastic thin layer in an aluminum foil tray, keeping them non-overlapping to ensure uniform UV irradiation; Among them, the aluminum foil surface has a uniform reflectance spectrum to avoid local overheating or shading and ensure consistent aging. S12. During the 96-hour continuous irradiation period, the daily cumulative ultraviolet irradiation intensity I is recorded, and the total ultraviolet irradiation energy Qto received per unit area of ​​the microplastic sample is calculated. The total ultraviolet radiation energy Qto is obtained using the following formula: ; In the formula, Itx represents the ultraviolet irradiation intensity at hour tx, T represents the total irradiation duration, and Δt represents the time interval. In this experiment, Δt = 1h.

[0008] Preferably, S2 includes S21 and S22; S21. Collect data on the irradiated PE microplastics using a data acquisition device, including surface contact angle Ac, surface Zeta potential Aze, crystallinity Ax, functional group intensity distribution function AFT, and surface roughness index As, and fit it into an initial dataset CW. The surface contact angle Ac was acquired using a contact angle measuring instrument. The acquisition steps were as follows: the aged microplastics were laid flat on a glass slide, keeping the surface smooth; a drop of deionized water was added to the sample surface using a micro-syringe; the contact angle test module was activated to capture the droplet profile in real time; the Young-Laplace fitting method was used to calculate the contact angles on the left and right sides of the droplet; each sample was measured at least 5 times, and the average value was taken. The surface zeta potential (Aze) reflects the surface charge characteristics of microplastics and their stability and adsorption capacity in an aqueous environment. It is acquired using a zeta potential analyzer. The acquisition steps are as follows: a certain mass of microplastic (about 20 mg) is added to deionized water; ultrasonic dispersion is performed for 10 min to form a uniform suspension; the sample is injected into an electrophoretic pool; the electrophoretic mobility is measured at 25℃; and the zeta potential value is automatically calculated by the instrument. Crystallinity Ax reflects the degree of orderliness of the molecular chains in microplastics and the degree of damage to crystalline regions caused by ultraviolet aging. It is obtained by X-ray diffraction. The microplastic sample is pressed into a pellet and fixed. The scanning angle range is set to 5°-60°. The diffraction peak pattern is obtained. The peaks in the crystalline region are separated from those in the amorphous region. The crystallinity is then calculated. Functional group intensity distribution function (AFT) was used to characterize the changes in the type and content of functional groups on the surface of microplastics, reflecting changes in their chemical activity; it was acquired using Fourier transform infrared spectroscopy; the microplastics were pressed into thin sheets; the scanning wavenumber range was 4000–4000 cm⁻¹. -1 ; Collect absorption spectra; Extract from 600–1800 cm⁻¹ -1 Interval functional group peaks; perform integral calculations; The surface roughness index As characterizes the degree of microstructure damage and specific surface area change of microplastics; it is acquired using a scanning electron microscope. The microplastics are sputter-coated with gold; multi-magnification SEM images are acquired; the images are imported into analysis software; the grayscale variation matrix is ​​extracted; and surface undulation characteristic parameters are calculated.

[0009] Preferably, S2 also includes S22; S22. Clean and normalize the obtained initial dataset CW to obtain the plastic aging dataset SLW; Cleaning includes outlier detection and removal: statistical distribution analysis is performed on each class of parameters in the initial dataset CW; significant deviations are initially screened using box plots or the 3σ rule. For suspected outliers, retest and confirm. If the measurement error does exist, remove the data point. For systematic anomalies, such as sample contamination or surface collapse, the entire sample set is marked as invalid; The formula for normalization is as follows: ; In the formula, SLWo represents the o-th data in the plastic aging dataset, CWo represents the o-th data in the initial dataset, minCWo represents the valley value of the o-th data in the initial dataset, and maxCWo represents the peak value of the o-th data in the initial dataset.

[0010] Preferably, S3 includes S31; S31. Based on the plastic aging dataset SLW, the surface contact angle Ac, surface Zeta potential Aze, crystallinity Ax, functional group intensity distribution function AFT and surface roughness index As are fused to construct the plastic aging activity index Ψag, which reflects the comprehensive environmental behavior of microplastics after aging. The plastic aging activity index Ψag is obtained using the following formula: ; In the formula, ln represents the logarithmic function, [v2, v1] represents the FTIR integral wavenumber interval, and d represents the integral sign.

[0011] Preferably, S3 also includes S32; S32. Based on the historical sample distribution characteristics of the plastic aging activity index Ψag, construct the aging critical threshold Tag; based on all plastic aging activity index Ψag samples, calculate and obtain the mean pΨ and standard deviation σΨ of the activity index. Based on the obtained mean activity index pΨ and standard deviation of activity index σΨ, the aging critical threshold Tag is calculated; The aging critical threshold Tag is obtained by adding the mean of the activity index pΨ to the standard deviation of the activity index σΨ; The aging activity index Ψag of plastics is compared with the aging critical threshold Tag to determine the aging state of plastics. The aging condition of plastics is obtained by matching in the following ways: When the plastic aging activity index Ψag < the aging critical threshold Tag, it indicates a normal aging state with weak changes in behavior. When the plastic aging activity index Ψag is greater than or equal to the aging critical threshold Tag, it indicates an enhanced aging state with potential for environmental disturbance.

[0012] Preferably, S4 includes S41; S41. Mix the four types of PE microplastics with obtained plastic aging activity index Ψag into the facility agriculture soil at a mass ratio of 1%, based on soil quality. Three sets of temperature-controlled experimental environments were established: 25℃, 30℃, and 35℃. Each set had five treatments, including black flakes, black granules, white flakes, white granules, and a control (CK) blank. The mixed samples were placed in 1000mL soil culture containers, and the culture period was set to 180 days. Soil moisture was kept stable at 70% of field capacity, and daily regulation was carried out using the weighing-watering method. The incubation time points were set at 0, 15, 30, 60, 90, 120, 150, and 180 days. Soil parameters were collected in 8 rounds, and the soil pollution intensity index was obtained. The soil pollution input intensity index is obtained using the following formula: The soil pollution input intensity index (Input) is obtained by multiplying the plastic aging activity index (Ψag), the total ultraviolet radiation energy (Qto), and the mass of the microplastic added (Mpe), and then dividing by the mass of the soil sample.

[0013] Preferably, S4 also includes S42; S42. At each incubation time point, collect and process the following three types of soil ecological function response parameters, including soil enzyme activity response group, aggregate structure disturbance parameter Agg, and biomass fluctuation parameter Δbio. The soil enzyme activity response group includes β-1,4-glucosidase BG, β-1,4-N-acetylglucosidase NAG, leucine aminopeptidase LAP, and acid phosphatase ACP; and the average soil enzyme response value Eavg is calculated. The average soil enzyme response value Eavg was obtained by summing the values ​​of β-1,4-glucosidase BG, β-1,4-N-acetylglucosidase NAG, leucine aminopeptidase LAP, and acid phosphatase ACP, and then dividing by 4. The aggregate structure disturbance parameter Agg was obtained as follows: First, at a set time point, the mass of aggregates in the soil of the treatment group was measured and recorded as the aggregate mass of the treatment group; at the same time point, the aggregate mass of the blank control group without microplastics was also measured and recorded as the aggregate mass of the control group; then, the aggregate mass of the treatment group was subtracted from the aggregate mass of the control group to obtain the absolute difference between the two; finally, this difference was divided by the aggregate mass of the control group to obtain the aggregate structure disturbance parameter Agg. The biomass fluctuation parameter Δbio was obtained as follows: First, on the set 30th or 120th day, the microbial biomass of the soil in the treatment group with added microplastics was measured and recorded as the treatment group microbial biomass; second, at the same time point, the microbial biomass of the control group soil without added microplastics was measured and recorded as the control group microbial biomass; then, the microbial biomass of the treatment group was subtracted from the microbial biomass of the control group, and the difference between the two was calculated; finally, this difference was divided by the microbial biomass of the control group to obtain the biomass fluctuation parameter Δbio.

[0014] Preferably, S5 includes S51 and S52; S51. The obtained soil pollution input intensity index Input, average soil enzyme response value Eavg, aggregate structure disturbance parameter Agg, biomass fluctuation parameter Δbio, and plastic aging activity index Ψag are normalized to unify the dimensions of the data. S52. Perform coupled analysis on the processed data according to the proportion, and construct the soil functional disturbance index Φsoil. The soil function disturbance index Φsoil is obtained using the following formula: ; In the formula, ln represents the logarithmic function.

[0015] Preferably, S6 includes S61 and S62; S61. Combine the obtained soil function disturbance index Φsoil with the average soil enzyme response value Eavg to construct a loss expression function of functional decay amount and time evolution, and obtain the functional loss rate L. The functional loss rate L is obtained by the following formula: ; In the formula, exp represents the exponential function, L(t) represents the functional loss rate at time t, Φsoil(t) represents the soil functional disturbance index at time t, ex represents a non-zero constant, and λt represents the time response coefficient. The time response coefficient λt is obtained by calculating the change ΔΦ(t) of the soil function disturbance index between every two adjacent time points. The absolute changes over all time periods are summed up. Dividing the accumulated result by the observation time t yields the total disturbance response per unit time. We obtain λt = cumulative perturbation change amplitude / culture time; S62. Analyze the acquired functional loss rate L, determine the loss status, and generate feedback suggestions; The state of loss is obtained by matching in the following way: When the functional loss rate L < 0.4, it indicates a low loss state, and the feedback suggestion is: no intervention is required. When 0.4 ≤ functional loss rate L ≤ 0.7, it indicates a medium loss state, and the feedback suggestion is to temporarily suspend the addition of microplastics and strengthen enzyme activity regulation. When 0.7 < functional loss rate L, it indicates a high-risk loss state and an alarm is issued, with feedback and recommendations: suspend plastic addition and implement soil remodeling measures.

[0016] This invention provides a method for evaluating the impact of temperature-mediated microplastics on facility soil function, which has the following beneficial effects: (1) By selecting four different types of PE microplastics and conducting experiments under the same mass and UV irradiation conditions, it was ensured that the synergistic effect of color and morphology on the aging response could be revealed under the premise that variables such as plastic type, aging time, and irradiation uniformity remain consistent, thereby improving the structural dimension accuracy of plastic aging sensitivity evaluation. The aluminum foil tray has the characteristic of consistent reflectance spectrum, which effectively reduces the influence of local hot spots or shadow formation and solves the experimental error problem caused by uneven illumination in traditional aging experiments.

[0017] The total irradiation energy Qto is calculated by accumulating daily light intensity data, enabling a dynamic cumulative expression of aging intensity. This provides a unified energy benchmark for subsequent response analysis of plastic structures, improving the scientific rigor and timeliness of experimental comparison results.

[0018] (2) The collected parameters cover physical properties, chemical properties and structural state, forming an aging state characterization across physical-chemical-structural dimensions, avoiding misjudgment caused by the distortion of a single indicator.

[0019] The zeta potential (Aze) not only reflects electrical properties but is also closely related to adsorption performance and migration behavior; crystallinity (Ax) is associated with crystalline region destruction and is core evidence for UV-induced structural depolymerization. This mechanistic correspondence between these properties gives the results high explanatory power. Statistical methods were used to clean the initial collected data (CW) to improve the stability and reliability of the subsequent normalized data (SLW), enhancing the robustness against interference in subsequent index calculations and modeling.

[0020] (3) From obtaining the degree of plastic aging from UV aging experiments to modeling the soil response state, and then to deducing functional loss and forming feedback suggestions, a complete model of the impact of microplastics on the soil system was constructed, which is more systematic than the traditional method of analyzing only single parameters or stage responses.

[0021] A standardized processing path for cross-scale and cross-dimensional data has been established: Through normalization operations such as SLW and Φsoil, the data fusion problem between different acquisition devices and different dimensional parameters has been solved, providing a foundation for subsequent universality in multiple scenarios. Attached Figure Description

[0022] Figure 1 This is a schematic diagram illustrating the steps of a method for evaluating the impact of temperature-mediated microplastics on facility soil function according to the present invention. Figure 2 This is a flowchart of the plastic aging activity index determination process of the present invention; Figure 3 This is a flowchart for determining the functional loss rate of the present invention; Figure 4 This is a flowchart illustrating the process of obtaining the plastic aging dataset according to the present invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] Example 1 This invention provides a method for evaluating the impact of temperature-mediated microplastics on facility soil function. Please refer to [link to relevant documentation]. Figures 1 to 4 This includes the following steps: S1. Four types of PE microplastics were placed under ultraviolet light for 96 hours and rotated daily to stimulate surface aging morphology and generate typical functional groups. S2. Data on irradiated PE microplastics were collected through multi-instrument composite testing and preprocessed to obtain the plastic aging dataset SLW. S3. Based on the plastic aging dataset SLW, comprehensively measure the changes in the ability of aging degree to affect environmental behavior and obtain the plastic aging activity index Ψag. S4. Based on the plastic aging activity index Ψag, the four types of aged PE microplastics were mixed into the facility agriculture soil at a mass ratio of 1%, and cultured in temperature setting groups with a cycle of 0~180 days, while soil parameters were collected simultaneously. S5. The obtained soil parameters and plastic aging activity index Ψag are coupled and analyzed to construct the soil function disturbance index Φsoil. S6. Estimate the negative impact on soil multifunctionality using the soil function disturbance index Φsoil, obtain the function loss rate L, and provide feedback.

[0025] In this embodiment, the traditional research breaks through the assumption of using only fresh microplastics and incorporates the aging process into the entire experimental design of microplastic intervention for the first time. By using 96 hours of ultraviolet irradiation and combining it with a variety of physicochemical analysis methods, the authenticity of aging characterization is significantly improved. The constructed plastic aging activity index Ψag integrates nonlinear response parameters such as contact angle, Zeta potential, crystallinity, functional group distribution and surface roughness, which can be used to quantitatively assess the ecological intervention potential of aged microplastics and fill the gap of lack of coupled quantitative models between "structural parameters and functional perturbation response".

[0026] By cultivating microplastic-soil mixtures under multiple temperature settings (25℃ / 30℃ / 35℃) for extended periods, the amplification effect of high-temperature scenarios in facility agriculture on microplastic disturbance behavior was simulated, more closely reflecting the actual state of micro-pollution behavior in semi-enclosed spaces such as agricultural greenhouses. Simultaneously, soil parameters (enzyme activity, aggregate changes, and biomass fluctuations) were dynamically collected, and coupled with the aging activity index, the response process was modeled, providing mathematical support for revealing the three-factor linkage of microplastic aging characteristics, temperature conditions, and soil function changes.

[0027] This method establishes a calculation framework for the soil function disturbance index Φsoil and the function loss rate L. It does not rely on artificially set thresholds or weight parameters, but directly derives the real impact of key parameter changes on soil function from observation data. Through the linkage analysis of the disturbance index and the function loss rate, the evolution trend of problems such as enzyme system suppression, aggregate structure disintegration, and biomass decline caused by microplastics under different temperature conditions can be reversed, thus realizing a complete chain from pollution behavior identification to functional risk estimation.

[0028] This invention constructs an experimental evaluation model that closely matches the scenario of facility agriculture through a three-stage treatment path of ultraviolet aging, temperature incubation, and functional perturbation, which significantly improves the environmental representativeness and extrapolability of the research results.

[0029] Example 2 Please refer to Figure 1 and Figure 4 Specifically: S1 includes S11 and S12; S11. Take four types of PE microplastics, including black flake PE, black granular PE, white flake PE and white granular PE; prepare samples of each type with a uniform mass of 10g. Cut PE microplastics into 5×5mm sizes, and maintain 0.15mm particle size for granular microplastics; evenly spread each type of microplastic thin layer in an aluminum foil tray, keeping them non-overlapping to ensure uniform UV irradiation; Among them, the aluminum foil surface has a uniform reflectance spectrum to avoid local overheating or shading and ensure consistent aging. S12. During the 96-hour continuous irradiation period, the daily cumulative ultraviolet irradiation intensity I is recorded, and the total ultraviolet irradiation energy Qto received per unit area of ​​the microplastic sample is calculated. The total ultraviolet radiation energy Qto is obtained using the following formula: ; In the formula, Itx represents the ultraviolet irradiation intensity at hour tx, T represents the total irradiation duration, and Δt represents the time interval. In this experiment, Δt = 1h.

[0030] In this embodiment, four representative types of polyethylene (PE) microplastics—black flakes, black granules, white flakes, and white granules—were selected to effectively cover the typical microplastic pollution forms with complex sources and heterogeneous appearances in facility agriculture, making the experimental samples more representative. By uniformly cutting the flake microplastics (5×5mm) and uniformly controlling the diameter of the granules (0.15mm), the specific surface area of ​​different types of microplastics during ultraviolet irradiation was ensured to be consistent, reducing the interference of factors affecting aging efficiency from the source and improving data comparability. Using an aluminum foil substrate with uniform reflectivity, ultraviolet rays can be reflected and scattered to the back of the microplastics, reducing the probability of shadow formation, improving the uniformity of ultraviolet irradiation coverage, effectively avoiding aging deviations caused by local overheating or shading, and ensuring the reproducibility and scalability of the experimental design. The light intensity during the irradiation process is continuously recorded hourly, and the total ultraviolet irradiation energy Qto received per unit area is introduced as a time integral index to quantify the ultraviolet energy input intensity, eliminating the use of the crude index of "irradiation duration" and establishing a physical basis for the aging degree of microplastics. During the irradiation process, the microplastics are rotated daily to ensure that both sides receive light of similar intensity. Combined with the quantitative integration of Qto, this helps to establish a unified evaluation benchmark for subsequent aging behavior and structural characterization, laying a high-quality input foundation for the construction of the plastic aging activity index Ψag. Compared with the static room temperature aging method, this embodiment, through a combination strategy of continuous ultraviolet irradiation + spectral control + heat distribution optimization, more closely approximates the actual aging path of microplastics under greenhouses or agricultural films. Using the total ultraviolet irradiation energy Qto as a unified physical starting point can avoid model deviations caused by inconsistent initial aging levels in subsequent experimental results, thereby enhancing the physical consistency and robustness of the data-driven model.

[0031] Example 3 Please refer to Figure 4 Specifically: S2 includes S21 and S22; S21. Collect data on the irradiated PE microplastics using a data acquisition device, including surface contact angle Ac, surface Zeta potential Aze, crystallinity Ax, functional group intensity distribution function AFT, and surface roughness index As, and fit it into an initial dataset CW. Among them, the surface contact angle Ac is acquired by a contact angle measuring instrument; The surface zeta potential (Aze) reflects the electrical properties of the microplastic surface and its stability and adsorption capacity in an aqueous environment; it is acquired using a zeta potential analyzer. Crystallinity Ax reflects the degree of orderliness of the microplastic molecular chains and the degree of damage to the crystalline regions caused by ultraviolet aging; it is obtained by X-ray diffraction. Functional group intensity distribution function (AFT) is used to characterize the changes in the type and content of functional groups on the surface of microplastics, reflecting changes in their chemical activity; it is acquired by Fourier transform infrared spectroscopy. The surface roughness index As characterizes the degree of damage to the microstructure and the change in specific surface area of ​​the microplastic surface; it was obtained by scanning electron microscopy.

[0032] S2 also includes S22; S22. Clean and normalize the obtained initial dataset CW to obtain the plastic aging dataset SLW; Cleaning includes outlier detection and removal: statistical distribution analysis is performed on each class of parameters in the initial dataset CW; significant deviations are initially screened using box plots or the 3σ rule. Suspected outliers are retested for confirmation. If measurement error is indeed found, the data points are removed. For systematic anomalies, the entire sample set is marked as invalid; The formula for normalization is as follows: ; In the formula, SLWo represents the o-th data in the plastic aging dataset, CWo represents the o-th data in the initial dataset, minCWo represents the valley value of the o-th data in the initial dataset, and maxCWo represents the peak value of the o-th data in the initial dataset.

[0033] In this embodiment, a comprehensive behavioral characterization system was established: for aged PE microplastics, five key parameters, including contact angle, Zeta potential, crystallinity, functional group strength and surface roughness, were collected to construct a comprehensive aging behavior description system from physical, chemical, electrical to structural dimensions; each index was matched with a specific high-precision device to avoid the use of indirect estimation or model substitution, which significantly enhanced the physical traceability of the characterization data and the experimental standardization basis; The parameters not only describe the surface morphology, but are also closely related to the migration, adsorption, and aggregation of microplastics in the soil-water environment. This helps to provide an input basis with environmental significance for subsequent functional perturbation mechanism analysis and improves the interpretability of the model.

[0034] Preliminary screening was conducted by introducing statistical distribution analysis, box plot method and 3σ rule, and then confirmed by experimental retesting. This distinguished the sources of sporadic measurement errors and systematic abnormal data, and avoided the reduction of data credibility due to accidental deletion or incorrect retention. If a systematic error or parameter drift is found in the entire sample set, it is directly marked as an invalid batch instead of being forcibly corrected, so as to ensure the parameter independence and result stability of the subsequent construction of the plastic aging activity index. The original parameters of different dimensions and scales are uniformly converted into dimensionless data, which is conducive to cross-index modeling and subsequent fusion analysis, and ensures that the plastic aging dataset SLW has a unified space in mathematics, providing a unified vector basis for multi-parameter coupling models.

[0035] Since the aging behavior of plastics is strongly correlated with their surface chemical / physical state, it is possible to establish the link between structural parameters, environmental behavior, and soil effects. This helps to achieve accurate alignment of input data responses when constructing indicators such as the soil function perturbation index in subsequent steps. The highly standardized and structured output of the SLW dataset can be used to train multiple types of models and has the potential to be extended to other plastic types or aging scenarios.

[0036] Example 4 Please refer to Figure 2 Specifically: S3 includes S31; S31. Based on the plastic aging dataset SLW, the surface contact angle Ac, surface Zeta potential Aze, crystallinity Ax, functional group intensity distribution function AFT and surface roughness index As are fused to construct the plastic aging activity index Ψag, which reflects the comprehensive environmental behavior of microplastics after aging. The plastic aging activity index Ψag is obtained using the following formula: ; In the formula, ln represents the logarithmic function, [v2, v1] represents the FTIR integral wavenumber interval, and d represents the integral sign.

[0037] S3 also includes S32; S32. Based on the historical sample distribution characteristics of the plastic aging activity index Ψag, construct the aging critical threshold Tag; based on all plastic aging activity index Ψag samples, calculate and obtain the mean pΨ and standard deviation σΨ of the activity index. Based on the obtained mean activity index pΨ and standard deviation of activity index σΨ, the aging critical threshold Tag is calculated; The aging critical threshold Tag is obtained by adding the mean of the activity index pΨ to the standard deviation of the activity index σΨ; The aging activity index Ψag of plastics is compared with the aging critical threshold Tag to determine the aging state of plastics. The aging condition of plastics is obtained by matching in the following ways: When the plastic aging activity index Ψag < the aging critical threshold Tag, it indicates a normal aging state with weak changes in behavior. When the plastic aging activity index Ψag is greater than or equal to the aging critical threshold Tag, it indicates an enhanced aging state with potential for environmental disturbance.

[0038] S4 includes S41; S41. Mix the four types of PE microplastics with the obtained plastic aging activity index Ψag into the facility agriculture soil at a mass ratio of 1%. Three sets of temperature-controlled experimental environments were established: 25℃, 30℃, and 35℃. Each set had five treatments, including black flakes, black granules, white flakes, white granules, and a control (CK) blank. The mixed samples were placed in 1000mL soil culture containers, and the culture period was set to 180 days. Soil moisture was kept stable at 70% of field capacity, and daily regulation was carried out using the weighing-watering method. The incubation time points were set at 0, 15, 30, 60, 90, 120, 150, and 180 days. Soil parameters were collected in 8 rounds, and the soil pollution intensity index was obtained. The soil pollution input intensity index is obtained using the following formula: The soil pollution input intensity index (Input) is obtained by multiplying the plastic aging activity index (Ψag), the total ultraviolet radiation energy (Qto), and the mass of added microplastics (Mpe), and then dividing by the mass of the soil sample.

[0039] S4 also includes S42; S42. At each incubation time point, collect and process the following three types of soil ecological function response parameters, including soil enzyme activity response group, aggregate structure disturbance parameter Agg, and biomass fluctuation parameter Δbio. The soil enzyme activity response group includes β-1,4-glucosidase BG, β-1,4-N-acetylglucosidase NAG, leucine aminopeptidase LAP, and acid phosphatase ACP; and the average soil enzyme response value Eavg is calculated. The average soil enzyme response value Eavg was obtained by summing the values ​​of β-1,4-glucosidase BG, β-1,4-N-acetylglucosidase NAG, leucine aminopeptidase LAP, and acid phosphatase ACP, and then dividing by 4. The aggregate structure disturbance parameter Agg was obtained as follows: First, at a set time point, the mass of aggregates in the soil of the treatment group was measured and recorded as the aggregate mass of the treatment group; at the same time point, the aggregate mass of the blank control group without microplastics was also measured and recorded as the aggregate mass of the control group; then, the aggregate mass of the treatment group was subtracted from the aggregate mass of the control group to obtain the absolute difference between the two; finally, this difference was divided by the aggregate mass of the control group to obtain the aggregate structure disturbance parameter Agg. The biomass fluctuation parameter Δbio was obtained as follows: First, on the set 30th or 120th day, the microbial biomass of the soil in the treatment group with added microplastics was measured and recorded as the treatment group microbial biomass; second, at the same time point, the microbial biomass of the control group soil without added microplastics was measured and recorded as the control group microbial biomass; then, the microbial biomass of the treatment group was subtracted from the microbial biomass of the control group, and the difference between the two was calculated; finally, this difference was divided by the microbial biomass of the control group to obtain the biomass fluctuation parameter Δbio.

[0040] In this embodiment, by integrating multiple physical-chemical parameters such as surface contact angle, Zeta potential, crystallinity, functional group intensity distribution, and surface roughness, a plastic aging activity index Ψag with structure-behavior mapping capability is formed, breaking the technical bottleneck of traditionally relying on a single functional group or surface morphology to judge the degree of aging. The integration method not only focuses on the physical changes in surface morphology, but also on its potential migration, adsorption, and reaction behavior in the ecological environment, and has a high correlation with ecological behavior prediction.

[0041] An aging critical threshold Tag is constructed, and the aging state of the current sample is dynamically classified by using the mean and standard deviation of the distribution statistics parameters of historical samples. This enables the plastic aging activity index Ψag to have discriminative ability and can be used to distinguish between two states: one where the behavior has changed significantly and the other where it has not. The construction method of this threshold has good reusability and can be easily extended to data analysis systems under different microplastic types or different aging mechanisms.

[0042] Three temperature experimental groups were set up at 25℃, 30℃, and 35℃, and the plants were continuously cultured for 180 days with eight rounds of sample collection. This conformed to the actual ground temperature dynamics in facility agriculture, improving the realism of the experiment. Combined with daily water replenishment and precise management to maintain 70% field water holding capacity, the comparability and stability of the experimental data under variable control conditions were ensured.

[0043] Based on Ψag, irradiation energy Qto, and PE mass, a soil pollution input intensity is constructed to effectively establish a quantifiable channel for microplastic aging state, irradiation intensity, and disturbance intensity, supporting the unification of the input layer among different experimental treatment groups. Enzyme activity response group (reflecting changes in metabolic capacity); Aggregate structure disturbance Agg (reflecting physical structure stability); Biomass fluctuation Δbio (reflecting microbial ecological state); The parameters are reasonably sourced and the quantification methods are clear, reflecting the need for modeling soil ecological processes from three perspectives: functionality, dynamics, and restoration; The three-parameter collection is coupled with the pollution input intensity index Input, further strengthening the logical path of "input-response-effect" and providing a clear mathematical structure for the subsequent establishment of the disturbance index Φsoil.

[0044] A systematic experimental platform with temperature as the control variable was established to reveal the full-process characteristics of microplastic disturbance paths under typical variable temperature scenarios in facility agriculture. A coupling mechanism between pollution input intensity and functional response parameters was introduced to form a key bridge for quantifiable tracking of disturbance paths, solving the technical breakpoint problem of non-quantifiable input and non-concrete output in existing research. This supports the subsequent construction of disturbance index and the establishment of functional loss assessment models, laying the foundation for the application of this invention in multiple fields such as ecological risk quantification, farmland management optimization, and analysis of micropollutant soil migration mechanisms.

[0045] Example 5 Please refer to Figure 3 Specifically: S5 includes S51 and S52; S51. The obtained soil pollution input intensity index Input, average soil enzyme response value Eavg, aggregate structure disturbance parameter Agg, biomass fluctuation parameter Δbio, and plastic aging activity index Ψag are normalized to unify the dimensions of the data. S52. Perform coupled analysis on the processed data according to the proportion, and construct the soil functional disturbance index Φsoil. The soil function disturbance index Φsoil is obtained using the following formula: ; In the formula, ln represents the logarithmic function.

[0046] S6 includes S61 and S62; S61. Combine the obtained soil function disturbance index Φsoil with the average soil enzyme response value Eavg to construct a loss expression function of functional decay amount and time evolution, and obtain the functional loss rate L. The functional loss rate L is obtained by the following formula: ; In the formula, exp represents the exponential function, L(t) represents the functional loss rate at time t, Φsoil(t) represents the soil functional disturbance index at time t, ex represents a non-zero constant, and λt represents the time response coefficient. S62. Analyze the acquired functional loss rate L, determine the loss status, and generate feedback suggestions; The state of loss is obtained by matching in the following way: When the functional loss rate L < 0.4, it indicates a low loss state, and the feedback suggestion is: no intervention is required. When 0.4 ≤ functional loss rate L ≤ 0.7, it indicates a medium loss state, and the feedback suggestion is to temporarily suspend the addition of microplastics and strengthen enzyme activity regulation. When 0.7 < functional loss rate L, it indicates a high-risk loss state and an alarm is issued, with feedback and recommendations: suspend plastic addition and implement soil remodeling measures.

[0047] In this embodiment, the pollution input intensity index (Input), enzyme response, aggregate structure disturbance, biomass fluctuation, and aging activity are uniformly processed in terms of dimensions. This solves the problem of direct superposition and modeling difficulties between disturbance parameters from different sources and with different dimensions, and provides a foundation for the stability and universality of subsequent calculation models. It also avoids misjudgments caused by excessive changes in a single variable and improves the transferability of the soil function disturbance index (Φsoil) across scenarios and samples.

[0048] Based on multiple dimensions of disturbance parameters, a soil function disturbance index Φsoil is constructed to comprehensively characterize soil ecological functions, breaking away from the traditional ecological response discrimination model based solely on a single enzyme activity or structural parameter. The index includes physical disturbances, chemical inputs, and biological responses, possessing the ability to track complete ecological disturbance paths. It supports consistent comparison of disturbance intensity under the combined influence of factors such as plastic type, aging state, and ambient temperature, supporting quantitative comparison and regional attribution analysis.

[0049] Using Φsoil as the disturbance intensity and Eavg to represent the functional state, a dynamic evolution function is used to output the functional loss rate L. This supports the prediction of the trend of functional degradation caused by microplastic pollution over time, making the pollution response no longer a static judgment but an evolutionary process tracking. It helps to determine whether the current disturbance is a short-term fluctuation or a precursor to medium- to long-term ecological function degradation. The functional loss rate L is divided into three levels (low loss, medium loss, and high-risk loss), corresponding to three levels of response measures: no intervention, enzyme regulation intervention, and suspension of addition + soil reconstruction. The hierarchical feedback mechanism enables the system to not only have monitoring functions, but also the ability to output context-driven response commands, making it practical for the management of pollution in actual facility agriculture.

[0050] The complete closed-loop technical path from perturbation data → normalization modeling → exponential expression → trend loss → response suggestions has been established, forming a pollution effect evaluation chain for facility agriculture scenarios; the Φsoil+L system supports the methodological leap from point value assessment to trend-driven decision-making, enabling the system to have the technical characteristics of integrating the three elements of timeliness, dynamism, and decision-making. It enables the evaluation of the real ecological disturbance potential of microplastics at different aging levels by taking aging characteristics as the entry point, and has strong capabilities in pollution risk prediction, behavior classification and discrimination and scenario adaptation. Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended technical solutions and their equivalents.

Claims

1. A method for evaluating the impact of temperature-mediated microplastics on facility soil function, characterized in that: Includes the following steps: S1. Four types of PE microplastics were placed under ultraviolet light for 96 hours and rotated daily to stimulate surface aging morphology and generate typical functional groups. S2. Data on irradiated PE microplastics were collected through multi-instrument composite testing and preprocessed to obtain the plastic aging dataset SLW. S3. Based on the plastic aging dataset SLW, comprehensively measure the changes in the ability of aging degree to affect environmental behavior and obtain the plastic aging activity index Ψag. S4. Based on the plastic aging activity index Ψag, the four types of aged PE microplastics were mixed into the facility agriculture soil at a mass ratio of 1%, and cultured in temperature setting groups with a cycle of 0~180 days, while soil parameters were collected simultaneously. S5. The obtained soil parameters and plastic aging activity index Ψag are coupled and analyzed to construct the soil function disturbance index Φsoil. S6. Estimate the negative impact on soil multifunctionality using the soil function disturbance index Φsoil, obtain the function loss rate L, and provide feedback.

2. The method for evaluating the impact of temperature-mediated microplastics on facility soil function according to claim 1, characterized in that: S1 includes S11 and S12; S11. Take four types of PE microplastics, including black flake PE, black granular PE, white flake PE and white granular PE; prepare samples of each type with a uniform mass of 10g. Cut PE microplastics into 5×5mm sizes, and maintain 0.15mm particle size for granular microplastics; evenly spread each type of microplastic thin layer in an aluminum foil tray, keeping them non-overlapping to ensure uniform UV irradiation; Among them, the aluminum foil surface has a uniform reflectance spectrum to avoid local overheating or shading and ensure consistent aging. S12. During the 96-hour continuous irradiation period, the daily cumulative ultraviolet irradiation intensity I is recorded, and the total ultraviolet irradiation energy Qto received per unit area of ​​the microplastic sample is calculated. The total ultraviolet radiation energy Qto is obtained using the following formula: ； In the formula, Itx represents the ultraviolet irradiation intensity at hour tx, T represents the total irradiation duration, and Δt represents the time interval. In this experiment, Δt = 1h.

3. The method for evaluating the impact of temperature-mediated microplastics on facility soil function according to claim 2, characterized in that: S2 includes S21 and S22; S21. Collect data on the irradiated PE microplastics using a data acquisition device, including surface contact angle Ac, surface Zeta potential Aze, crystallinity Ax, functional group intensity distribution function AFT, and surface roughness index As, and fit it into an initial dataset CW. Among them, the surface contact angle Ac is acquired by a contact angle measuring instrument; The surface zeta potential (Aze) reflects the electrical properties of the microplastic surface and its stability and adsorption capacity in an aqueous environment; it is acquired using a zeta potential analyzer. Crystallinity Ax reflects the degree of orderliness of the microplastic molecular chains and the degree of damage to the crystalline regions caused by ultraviolet aging; it is obtained by X-ray diffraction. Functional group intensity distribution function (AFT) is used to characterize the changes in the type and content of functional groups on the surface of microplastics, reflecting changes in their chemical activity; it is acquired by Fourier transform infrared spectroscopy. The surface roughness index As characterizes the degree of damage to the microstructure and the change in specific surface area of ​​the microplastic surface; it was obtained by scanning electron microscopy.

4. The method for evaluating the impact of temperature-mediated microplastics on facility soil function according to claim 3, characterized in that: S2 also includes S22; S22. Clean and normalize the obtained initial dataset CW to obtain the plastic aging dataset SLW; Cleaning includes outlier detection and removal: statistical distribution analysis is performed on each class of parameters in the initial dataset CW; significant deviations are initially screened using box plots or the 3σ rule. Suspected outliers are retested for confirmation. If measurement error is indeed found, the data points are removed. For systematic anomalies, the entire sample set is marked as invalid; The formula for normalization is as follows: ； In the formula, SLWo represents the o-th data in the plastic aging dataset, CWo represents the o-th data in the initial dataset, minCWo represents the valley value of the o-th data in the initial dataset, and maxCWo represents the peak value of the o-th data in the initial dataset.

5. The method for evaluating the impact of temperature-mediated microplastics on facility soil function according to claim 4, characterized in that: S3 includes S31; S31. Based on the plastic aging dataset SLW, the surface contact angle Ac, surface Zeta potential Aze, crystallinity Ax, functional group intensity distribution function AFT and surface roughness index As are fused to construct the plastic aging activity index Ψag, which reflects the comprehensive environmental behavior of microplastics after aging. The plastic aging activity index Ψag is obtained using the following formula: ； In the formula, ln represents the logarithmic function, [v2, v1] represents the FTIR integral wavenumber interval, and d represents the integral sign.

6. The method for evaluating the impact of temperature-mediated microplastics on facility soil function according to claim 5, characterized in that: S3 also includes S32; S32. Based on the historical sample distribution characteristics of the plastic aging activity index Ψag, construct the aging critical threshold Tag; based on all plastic aging activity index Ψag samples, calculate and obtain the mean pΨ and standard deviation σΨ of the activity index. Based on the obtained mean activity index pΨ and standard deviation of activity index σΨ, the aging critical threshold Tag is calculated; The aging critical threshold Tag is obtained by adding the mean of the activity index pΨ to the standard deviation of the activity index σΨ; The aging activity index Ψag of plastics is compared with the aging critical threshold Tag to determine the aging state of plastics. The aging condition of plastics is obtained by matching in the following ways: When the plastic aging activity index Ψag < the aging critical threshold Tag, it indicates a normal aging state with weak changes in behavior. When the plastic aging activity index Ψag is greater than or equal to the aging critical threshold Tag, it indicates an enhanced aging state with potential for environmental disturbance.

7. The method for evaluating the impact of temperature-mediated microplastics on facility soil function according to claim 6, characterized in that: S4 includes S41; S41. Mix the four types of PE microplastics with the obtained plastic aging activity index Ψag into the facility agriculture soil at a mass ratio of 1%. Three sets of temperature-controlled experimental environments were established: 25℃, 30℃, and 35℃. Each set had five treatments, including black flakes, black granules, white flakes, white granules, and a control (CK) blank. The mixed samples were placed in 1000mL soil culture containers, and the culture period was set to 180 days. Soil moisture was kept stable at 70% of field capacity, and daily regulation was carried out using the weighing-watering method. The incubation time points were set at 0, 15, 30, 60, 90, 120, 150, and 180 days. Soil parameters were collected in 8 rounds, and the soil pollution intensity index was obtained. The soil pollution input intensity index is obtained using the following formula: The soil pollution input intensity index (Input) is obtained by multiplying the plastic aging activity index (Ψag), the total ultraviolet radiation energy (Qto), and the mass of added microplastics (Mpe), and then dividing by the mass of the soil sample.

8. The method for evaluating the impact of temperature-mediated microplastics on facility soil function according to claim 7, characterized in that: S4 also includes S42; S42. At each incubation time point, collect and process the following three types of soil ecological function response parameters, including soil enzyme activity response group, aggregate structure disturbance parameter Agg, and biomass fluctuation parameter Δbio. The soil enzyme activity response group includes β-1,4-glucosidase BG, β-1,4-N-acetylglucosidase NAG, leucine aminopeptidase LAP, and acid phosphatase ACP; and the average soil enzyme response value Eavg is calculated. The average soil enzyme response value Eavg was obtained by summing the values ​​of β-1,4-glucosidase BG, β-1,4-N-acetylglucosidase NAG, leucine aminopeptidase LAP, and acid phosphatase ACP, and then dividing by 4. The aggregate structure disturbance parameter Agg was obtained as follows: First, at a set time point, the mass of aggregates in the soil of the treatment group was measured and recorded as the aggregate mass of the treatment group; at the same time point, the aggregate mass of the blank control group without microplastics was also measured and recorded as the aggregate mass of the control group; then, the aggregate mass of the treatment group was subtracted from the aggregate mass of the control group to obtain the absolute difference between the two; finally, this difference was divided by the aggregate mass of the control group to obtain the aggregate structure disturbance parameter Agg. The biomass fluctuation parameter Δbio was obtained as follows: First, on the set 30th or 120th day, the microbial biomass of the soil in the treatment group with added microplastics was measured and recorded as the treatment group microbial biomass; second, at the same time point, the microbial biomass of the control group soil without added microplastics was measured and recorded as the control group microbial biomass; then, the microbial biomass of the treatment group was subtracted from the microbial biomass of the control group, and the difference between the two was calculated; finally, this difference was divided by the microbial biomass of the control group to obtain the biomass fluctuation parameter Δbio.

9. The method for evaluating the impact of temperature-mediated microplastics on facility soil function according to claim 8, characterized in that: S5 includes S51 and S52; S51. The obtained soil pollution input intensity index Input, average soil enzyme response value Eavg, aggregate structure disturbance parameter Agg, biomass fluctuation parameter Δbio, and plastic aging activity index Ψag are normalized to unify the dimensions of the data. S52. Perform coupled analysis on the processed data according to the proportion, and construct the soil functional disturbance index Φsoil. The soil function disturbance index Φsoil is obtained using the following formula: ； In the formula, ln represents the logarithmic function.

10. The method for evaluating the impact of temperature-mediated microplastics on facility soil function according to claim 9, characterized in that: S6 includes S61 and S62; S61. Combine the obtained soil function disturbance index Φsoil with the average soil enzyme response value Eavg to construct a loss expression function of functional decay amount and time evolution, and obtain the functional loss rate L. The functional loss rate L is obtained by the following formula: ； In the formula, exp represents the exponential function, L(t) represents the functional loss rate at time t, Φsoil(t) represents the soil functional disturbance index at time t, ex represents a non-zero constant, and λt represents the time response coefficient. S62. Analyze the acquired functional loss rate L, determine the loss status, and generate feedback suggestions; The state of loss is obtained by matching in the following way: When the functional loss rate L < 0.4, it indicates a low loss state, and the feedback suggestion is: no intervention is required. When 0.4 ≤ functional loss rate L ≤ 0.7, it indicates a medium loss state, and the feedback suggestion is to temporarily suspend the addition of microplastics and strengthen enzyme activity regulation. When 0.7 < functional loss rate L, it indicates a high-risk loss state and an alarm is issued, with feedback and recommendations: suspend plastic addition and implement soil remodeling measures.

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