Shield muck organic pollution environmental risk classification assessment method

Abstract

A kind of shield muck organic pollution environmental risk grading evaluation method based on physicochemical property response mechanism, comprising: collecting multiple source shield muck samples and constructing basic database;Multi-dimensional physicochemical characterization is carried out on the sample, the characteristic factor is extracted using principal component analysis method and the comprehensive score of physicochemical property is calculated;The maximum adsorption capacity of muck to characteristic organic pollutants (such as sodium alpha-alkyl sulfonate) is obtained by isothermal adsorption experiment;Establish the quantitative response prediction model between the comprehensive score of physicochemical property and the adsorption capacity;Based on the ratio of measured concentration of pollutants and predicted adsorption capacity, the environmental migration risk index is calculated, and then the risk grade of muck is determined.The present application overcomes the limitation that traditional evaluation only relies on total concentration, reveals the control effect of muck medium characteristics on pollutant migration, can quickly and accurately identify high-risk muck, and provides scientific decision-making basis for classification disposal and resource utilization of shield muck.

Description

Technical Field

[0001] This invention relates to the field of environmental geotechnical monitoring technology, specifically to environmental geotechnical engineering and solid waste resource utilization, and more specifically to a quantitative assessment and classification method for the environmental risks of exogenous organic modifiers in the slag generated during tunnel boring machine (TBM) construction. Background Technology

[0002] Shield tunneling has become the mainstream construction method for tunnels and underground engineering projects due to its safety and efficiency. During earth pressure balance shield tunneling, a large amount of chemical modifiers are typically injected into the soil chamber to improve the plasticity of the excavation face, reduce permeability, and minimize wear on the cutterhead. Among these, foaming agents, primarily composed of anionic surfactants such as sodium α-alkenyl sulfonate (AOS) and sodium dodecyl sulfate (SDS), are the most widely used. If these foaming agent-containing excavated soils are not properly disposed of, they can easily become new sources of environmental pollution.

[0003] Currently, the main methods for disposing of tunnel boring machine (TBM) excavated soil are landfilling or off-site transportation and disposal. Because the residual foaming agents in the excavated soil have high water solubility and an amphiphilic structure, they are highly susceptible to leaching under conditions of natural rainfall or changes in moisture levels in the storage environment, and can migrate with leachate, thereby polluting the surrounding soil and groundwater systems. However, existing solid waste or soil environmental quality assessment systems primarily focus on risk management of heavy metals, petroleum hydrocarbons, or persistent organic pollutants (POPs), lacking specific evaluation standards and testing specifications for specific exogenous organic amendments (especially anionic surfactants) in TBM excavated soil.

[0004] For example, Chinese patent application CN121168828A discloses an ecological impact assessment method for shield tunneling excavated soil based on Hakanson-LCA. This method combines the Hakanson ecological risk assessment method with the LCA method to simultaneously assess the potential ecological risks of heavy metal elements in shield tunneling excavated soil and the carbon emission impact generated during resource utilization and landfill disposal. This dual assessment method can comprehensively analyze the excavated soil from the two dimensions of environmental pollution and climate change, providing a more comprehensive ecological impact assessment and a scientific basis for environmental protection and waste management.

[0005] In summary, existing technologies are insufficient to accurately reflect the actual environmental risk level of multi-source tunnel boring machine (TBM) excavated soil under different physical and chemical properties. Therefore, this invention is proposed. Summary of the Invention

[0006] One objective of this invention is to address the problems in existing environmental risk assessments of tunnel boring machine (TBM) slag, such as the use of single evaluation indicators, neglect of differences in the physicochemical properties of the slag medium, and the lack of a "soil-pollution" interaction response mechanism. This invention establishes a quantitative response model between comprehensive physicochemical properties of slag and pollutant adsorption capacity, enabling accurate identification and classification of slag environmental risks, and providing a scientific basis for the resource utilization and classified disposal of slag.

[0007] On the other hand, the evaluation method of the present invention can reduce detection costs while accurately identifying risks.

[0008] This invention provides a method for assessing the environmental risk of organic pollution from tunnel boring machine (TBM) slag, comprising: S1. Pre-treat the tunnel boring machine excavation soil sample to prepare the excavation soil sample to be tested; S2. Measure the multidimensional indicators of the waste soil sample, extract characteristic factors reflecting the properties of the waste soil using multivariate statistical analysis, and calculate the comprehensive evaluation F-value characterizing the properties of the waste soil itself. S3. Measure the adsorption curves of organic pollutants on different types of slag and obtain parameters characterizing the maximum retention capacity of organic pollutants on slag. S4. Regression analysis is performed on the comprehensive evaluation F-value of the slag soil properties obtained in step S2 and the parameters of the maximum holding capacity obtained in step S3 to predict the theoretical adsorption capacity (Q) of the slag soil sample to be tested. pred ) S5. Based on the measured initial concentration (C0) and predicted adsorption capacity (Q) of organic pollutants in the waste soil. pred The Environmental Migration Risk Index (MRI) was obtained, and the waste soil was classified into different risk levels.

[0009] The assessment method of this invention establishes a quantitative response model between multidimensional indicators (such as physicochemical properties) of construction waste and the adsorption capacity of organic pollutants, taking into account the characteristics of construction waste itself, to achieve accurate identification and classification of environmental risks of construction waste, and to provide a scientific basis for the resource utilization and classified disposal of construction waste. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of the overall process flow of the environmental risk classification assessment method for organic pollution of tunnel boring machine slag based on the physicochemical property response mechanism proposed in this invention. Figure 2 This is a Sips isothermal adsorption fitting curve of characteristic pollutant (sodium α-olefin sulfonate, AOS) on three typical physicochemical properties of shield tunnel slag (high adsorption S1, medium adsorption S8, and low adsorption S10) selected in Example 1 of the present invention. Detailed Implementation

[0011] The method for assessing the environmental risk of organic pollution from tunnel boring machine (TBM) slag is described in further detail below. This does not limit the scope of protection of the invention; rather, the scope is defined by the claims. Certain specific details disclosed provide a comprehensive understanding of the various disclosed embodiments. However, those skilled in the art will recognize that embodiments can be implemented using other materials, etc., without employing one or more of these specific details.

[0012] Unless the context otherwise requires, the terms “comprising” and “including” in the specification and claims shall be understood as open-ended and inclusive, meaning “including, but not limited to”.

[0013] The terms "implementation," "an implementation," "another implementation," or "certain implementations" used in this specification refer to specific features, structures, or characteristics described in relation to the implementation, which are included in at least one implementation. Therefore, "implementation," "an implementation," "another implementation," or "certain implementations" do not necessarily all refer to the same implementation. Furthermore, specific features, structures, or characteristics can be combined in any way within one or more implementations. Each feature disclosed in this specification can be replaced by any alternative feature that provides the same, equivalent, or similar purpose. Therefore, unless otherwise specified, the disclosed features are merely general examples of equivalent or similar features.

[0014] Existing environmental risk assessment methods generally suffer from the limitation of "emphasizing total amount and neglecting medium." Traditional assessment models typically determine the risk level solely based on whether the total concentration of pollutants in the waste soil exceeds a certain limit, while ignoring the decisive influence of the physicochemical properties of the waste soil itself (i.e., "medium characteristics") on the environmental behavior of pollutants. In reality, the geological conditions of the strata traversed by tunnel boring machines are complex and varied, resulting in waste soil with significant differences in physicochemical indicators such as particle size distribution (e.g., the ratio of clay to sand), organic matter content, mineral composition, and cation exchange capacity.

[0015] High-clay, high-organic-matter-content construction waste soils can strongly retain and lock in surfactants through physical adsorption, ion exchange, and hydrophobic bonding, thus significantly reducing their mobility and bioavailability. Conversely, sandy, organic-poor construction waste soils have weak pollutant retention capacity; even at low initial pollutant concentrations, weak adsorption can lead to a high risk of leaching and diffusion. Ignoring this physicochemical "adsorption response mechanism" and simply evaluating based on concentration can easily lead to over-remediation of highly adsorbent construction waste soils, resulting in resource waste, or under-identification of low-adsorbent, high-risk construction waste soils, creating environmental hazards.

[0016] The present invention provides a method for assessing the environmental risk of organic pollution from tunnel boring machine (TBM) slag. This method is a comprehensive assessment method that organically combines the physical and chemical properties of the slag with its adsorption response to organic pollutants. It quantifies the slag's potential for retaining pollutants, accurately classifies and scientifically categorizes the environmental risks of TBM slag, and provides a more precise assessment for the formulation of subsequent treatment plans.

[0017] This invention provides a method for assessing the environmental risk of organic pollution from tunnel boring machine (TBM) slag, comprising: S1. Pre-treat the tunnel boring machine excavation soil sample to prepare the excavation soil sample to be tested; S2. Measure the multidimensional indicators of the waste soil sample, extract characteristic factors reflecting the properties of the waste soil using multivariate statistical analysis, and calculate the comprehensive evaluation F-value characterizing the properties of the waste soil itself. S3. Measure the adsorption curves of organic pollutants on different types of slag and obtain parameters characterizing the maximum retention capacity of organic pollutants on slag. S4. Regression analysis is performed on the comprehensive evaluation F-value of the slag soil properties obtained in step S2 and the parameters of the maximum holding capacity obtained in step S3 to predict the theoretical adsorption capacity (Q) of the slag soil sample to be tested. pred ); S5. Based on the measured initial concentration (C0) and predicted adsorption capacity (Q) of organic pollutants in the waste soil. pred The Environmental Migration Risk Index (MRI) was obtained, and the waste soil was classified into different risk levels.

[0018] During the evaluation process, the order of steps S2 and S3 can be reversed, that is, S3 is performed first, and then S2 is performed.

[0019] The organic pollutant of the present invention is anionic surfactant, the main component of shield tunneling foam agent; optionally, the organic pollutant mainly includes sodium α-alkenyl sulfonate (AOS) and / or sodium dodecyl sulfate (SDS).

[0020] In some embodiments, the pretreatment of the tunnel boring machine excavation sample in step S1 includes: removing large-diameter impurities (e.g., stones with a diameter greater than 2 mm).

[0021] In an optional implementation, uncontaminated undisturbed soil is used as a background control sample. Multidimensional indicators of the uncontaminated undisturbed soil (such as organic matter content, particle size distribution, and specific surface area) are detected. The multidimensional indicators of the shield tunneling excavation soil are subtracted from the multidimensional indicators of the uncontaminated undisturbed soil (i.e., the background values ​​of physicochemical indicators such as naturally inherent organic matter (TOC) in the strata are subtracted), thereby further improving the accuracy of the assessment of the adsorption impact of characteristic organic pollutants (such as foaming agents) injected from external sources during the shield tunneling process.

[0022] In some implementations, step S2 includes: detecting multidimensional indicators of the slag soil sample, using principal component analysis to reduce the dimensionality of the multidimensional indicators, extracting principal components reflecting the properties of the slag soil, and calculating the comprehensive evaluation F-value of the slag soil properties using formula I. Formula I Where n is the number of principal components, w i The variance contribution rate weight of the i-th principal component, FAC i Let be the score of the i-th principal component.

[0023] In some implementations, in step S2, the multidimensional indicators of the slag soil sample include physical and chemical indicators.

[0024] Optional multidimensional indicators for waste soil samples include pH value, total organic matter content (TOC), particle size distribution, and specific surface area (SSA).

[0025] Further optional multidimensional indicators for waste soil samples include pH value, total organic matter content (TOC), particle size distribution, cation exchange capacity (CEC), and specific surface area (SSA).

[0026] Preferably, the multidimensional indicators of the slag soil sample include pH value, total organic matter content (TOC), particle size distribution, cation exchange capacity (CEC), specific surface area (SSA), and minerals.

[0027] The particle size distribution includes the content of clay, silt, and sand. That is, classified by particle size, sand particles have a larger particle size (>0.075mm), silt particles have a medium particle size (0.005-0.075mm), and clay particles have a smaller particle size (<0.005mm).

[0028] In some implementations, n is 2-3. That is, the number of principal components is 2 or 3.

[0029] The optional number of principal components is 2, with the first principal component having a variance contribution weight of 50-70% and the second principal component having a variance contribution weight of 15-30%.

[0030] In some embodiments, the key loading parameters of the first principal component include clay content, total organic matter (TOC), particle size distribution, cation exchange capacity (CEC), and specific surface area (SSA).

[0031] The main loading indicators for the second principal component include sand content and pH value.

[0032] For the testing of the physical and chemical properties of construction waste, any methods commonly used in the relevant fields can be employed. For example, particle size distribution can be determined using a laser particle size analyzer, organic matter content can be determined using the potassium dichromate volumetric method, and cation exchange capacity can be determined using the ammonium acetate exchange method. There are no restrictions on the testing methods, as long as the corresponding parameters can be measured.

[0033] In some embodiments, step S3 includes: measuring the adsorption curves of organic pollutants on different types of construction waste, fitting the adsorption data using the Sips model (Sips Isotherm), and obtaining parameters characterizing the maximum retention capacity of construction waste for organic pollutants.

[0034] Optional parameters for maximum retention capacity include maximum adsorption capacity (Q). max ) or allocation coefficient (K d ).

[0035] In some implementations, step S4 includes: performing regression analysis on the comprehensive evaluation F-value of the slag soil properties obtained in step S2 and the parameters of the maximum holding capacity obtained in step S3 to obtain Formula II. Formula II Among them, Q pred Let be the predicted adsorption capacity of the slag, a be the response coefficient, and b be the intercept constant.

[0036] Optionally, the comprehensive evaluation value F of the slag properties obtained in step S2 can be used as the independent variable, and the maximum adsorption capacity (Q) obtained in step S3 can be used as the independent variable. max Using as the dependent variable, a predictive adsorption capacity model as shown in Formula II is established through regression analysis.

[0037] Both a and b are derived from the sample database. The sample database refers to the database used to determine the physicochemical properties of each soil sample, perform PCA analysis to calculate the F-value, and simultaneously perform adsorption measurements and Sips model fitting to obtain the maximum adsorption capacity (Q). max ), and the physicochemical properties, F-value, and Q-value of each sample. max Value association forms a complete sample database. The F-value of each sample in the database is used as the independent variable, Q... max The value is the dependent variable. Through linear regression analysis, the response coefficient 'a' and the intercept constant 'b' of the model are obtained.

[0038] Step S1 also includes the stage of constructing a predictive model (i.e., establishing a sample database). To ensure the statistical significance and generalizability of the principal component analysis (PCA) and regression models, the collection of reference samples must meet certain requirements for sample size and diversity. Specifically, the number of reference samples should be greater than the number of dimensions of the measured multidimensional physicochemical indicators, preferably more than 10. In terms of sampling scope, the reference samples should cover the main typical strata lithologies involved in the shield tunneling construction within the target assessment area, and should at least include high-clay-content cohesive soil layers, medium-grained silt layers, and low-clay-content sandy soil or weathered rock layers. By covering multi-dimensional geological samples with significant differences in physicochemical properties, it can be ensured that the extracted characteristic factors have sufficient data representativeness and model generalization ability.

[0039] The prediction model established based on regression analysis is used to quickly predict the theoretical retention threshold of organic pollutants (such as predicting adsorption capacity) of the slag soil based on its physicochemical properties.

[0040] In some embodiments, in step S5, the measured initial concentration (C0) of organic pollutants in the slag and the predicted adsorption capacity (Q) are used to determine the adsorption capacity. pred Methods for classifying risk levels include: when C0 <k1× Q pred When k1 is at a certain level, it is classified as Level I low risk, where k1 is the safety factor threshold; when k1 × Q pred ≤ C0≤Q pred When C0 > Q, it is classified as Level II medium risk; pred At that time, it was determined to be a Level III high-risk area.

[0041] Where k1 is the safety factor threshold.

[0042] Due to the dynamic effects of rainfall erosion and the heterogeneity of the slag medium in actual storage environments, desorption is highly likely to occur when the pollutant concentration approaches the theoretical maximum adsorption capacity. The value of k1 is typically empirically set between 0.75 and 0.90, but k1 = 0.80 is optional. This value setting considers both the prediction error tolerance of the multiple regression model and reserves a safety buffer for practical engineering applications.

[0043] In some embodiments, step S5 includes: based on the measured initial concentration (C0) of organic pollutants in the slag and the predicted adsorption capacity (Q) pred The ratio of the environmental migration risk index (MRI) to the environmental migration risk index (MRI) is obtained by comprehensively evaluating the F value of the waste soil and the properties of the waste soil, and the waste soil is classified into different risk levels.

[0044] The measured initial concentration (C0) of organic pollutants in the test slag can be determined at any step.

[0045] The calculation results show that the F-value of cohesive slag is generally greater than 1.0, while the F-value of sandy slag is generally less than -1.0.

[0046] In one implementation, the Environmental Migration Risk Index (MRI) is divided into three levels: Level I (Low-Risk / Exempt Utilization Zone): F-value of waste soil greater than 1, C0 / Q pred The ratio is less than 80%. Therefore, the organic pollution risk of this waste soil can be considered to be Level I.

[0047] At this level, a high F-value indicates strong adsorption capacity of the slag, and that the adsorbed organic pollutants are in an unsaturated state. The organic pollutants are firmly locked on the surface of the soil particles, and the risk of leaching is extremely low. It can be directly utilized as a resource (such as roadbed filler or brick making).

[0048] Level II (Medium Risk / Restricted Utilization Area): The F-value of the waste soil is less than 1 and greater than -1.0; or the F-value of the waste soil is greater than 1.0, but C0 / Q pred The ratio is greater than 80% and less than 100%. Therefore, the organic pollution risk of the waste soil can be classified as Level II.

[0049] At this level, there is a certain risk of desorption of the construction waste, meaning that the organic pollutants adsorbed by the construction waste may desorb from the construction waste. It is recommended to perform simple drying or add adsorbents to improve the waste before use.

[0050] Level III (High-Risk / Strictly Controlled Area): The F-value of the construction waste is less than -1.0, or C0 / Q pred The percentage is greater than 100%. Therefore, the organic pollution risk of this waste soil can be considered to be Level III.

[0051] At this level, a low F-value for slag indicates that the slag (e.g., sandy soil) has weak adsorption capacity, or that CO exceeds Q. pred Organic pollutants are highly susceptible to migration with water. Ex-situ remediation (such as rinsing or thermal desorption) or safe landfilling is necessary.

[0052] The range of the above F-values ​​is established based on the PCA statistical distribution of a multivariate geological sample database; while C0 / Q pred The 80% threshold (i.e., safety factor k1=0.8) was determined based on indoor dynamic leaching experiments on soil columns. This means that when the actual concentration reaches 80% of the limit adsorption capacity, the pollutants in the leachate will exhibit a significant "penetration effect".

[0053] In some preferred embodiments, to more accurately assess the environmental risks of the waste soil in the actual site, step S5 also introduces the hydrogeological parameters of the waste soil storage site as a risk level correction mechanism. The hydrogeological parameters mainly include the groundwater depth (D) and the formation permeability coefficient (K) of the site.

[0054] Specifically, the implementation plan is as follows: Set an extremely sensitive environmental threshold (groundwater depth D < 1.5m, or permeability coefficient K > 1.0 × 10⁻⁶). -4 (cm / s). When the measured hydrogeological parameters at the site reach the aforementioned extremely high sensitivity threshold, the environmental vulnerability of the system increases significantly. At this time, the original risk level determined according to the above criteria needs to be forcibly upgraded or downgraded: if the original assessment was Level I low risk, it will be automatically upgraded to Level II medium risk; if the original assessment was Level II medium risk, it will be upgraded to Level III high risk, thereby ensuring the absolute safety of groundwater quality.

[0055] In some embodiments, the present invention provides a method for assessing the environmental risk of organic pollution from tunnel boring machine (TBM) slag (see attached diagram). Figure 1 ),include: S1. Pre-process the tunnel boring machine (TBM) slag samples, prepare the slag samples to be tested, and construct the database; S2. Detect multidimensional indicators (such as physicochemical properties) of the waste soil samples. Use principal component analysis to reduce the dimensionality of these indicators, extract the principal components reflecting the properties of the waste soil, and calculate the comprehensive evaluation F-value of the waste soil properties using formula I. Formula I Where n is the number of principal components, w i The variance contribution rate weight of the i-th principal component, FAC i The score of the i-th principal component; S3. Determine the adsorption curves of organic pollutants on different types of construction waste, and use the Sips model (Sips Isotherm) to fit the adsorption data to obtain the maximum adsorption capacity of organic pollutants on the construction waste. S4. Regression analysis is performed between the F-value of the comprehensive evaluation of the properties of the slag obtained in step S2 and the parameter of the maximum holding capacity obtained in step S3, to obtain Formula II. (i.e. Q) pred = f(F))Formula II Among them, Q pred Let be the predicted adsorption capacity of the slag, a be the response coefficient, and b be the intercept constant. S5. Based on the measured initial concentration (C0) and predicted adsorption capacity (Q) of organic pollutants in the waste soil. pred The environmental migration risk index (MRI) is obtained by comprehensively evaluating the ratio of the waste soil to its properties and the resulting F-value. S6. Classify construction waste into different risk levels and corresponding decision-making processes. For example, risk levels include Level 1: low risk, can be utilized as a resource; Level 2: medium risk, can be utilized under certain conditions; Level 3: high risk, requires treatment / safe disposal.

[0056] Compared with existing technologies, the method for assessing the environmental risk of organic pollution from tunnel boring machine (TBM) slag has the following advantages: By controlling and investigating the adsorption behavior of surfactants by the inherent properties of slag soil, such as clay particles and organic matter, we accurately distinguished between two easily misjudged scenarios: "high concentration-low risk" (strong adsorption soil) and "low concentration-high risk" (weak adsorption soil), which significantly improved the scientific nature of risk assessment.

[0057] A "physicochemical properties-adsorption response" prediction model was established for a specific region. In the subsequent evaluation process, only conventional physicochemical indicators (such as particle size distribution, TOC, etc.) need to be measured to quickly calculate the F value and deduce the adsorption capacity. There is no need to carry out time-consuming and expensive adsorption experiments one by one, which greatly reduces the detection cost and time cycle.

[0058] The proposed risk classification strategy is directly linked to subsequent engineering treatment measures, avoiding blind, one-size-fits-all remediation. Waste soil assessed as low-risk can be directly utilized, saving unnecessary remediation costs; high-risk waste soil is subject to strict control, effectively preventing environmental pollution accidents.

[0059] In addition, the evaluation method of the present invention is applicable to the evaluation of shield tunneling spoil under different geological backgrounds, and the evaluation model can be dynamically updated and locally modified according to soil characteristic data of different regions, which has broad application value.

[0060] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods in the following embodiments, unless otherwise specified, are generally performed under conventional conditions or as recommended by the manufacturer. Unless otherwise stated, all percentages, ratios, proportions, or parts are by weight. Example 1

[0061] Construction of a physicochemical response model for multi-source shield tunneling excavated soil This example aims to illustrate how to establish a "physicochemical properties-adsorption response" prediction model, which forms the basis for subsequent rapid risk assessment.

[0062] Step 1: Sample collection and preparation. Eleven types of tunnel boring machine (TBM) excavation samples (numbered S1 to S11) were collected from construction sites in six typical cities with active TBM construction: Beijing, Guangzhou, Shenzhen, Jinan, Hangzhou, and Changsha. These samples consisted of different geological strata (including silty clay, silt, medium-coarse sand, completely weathered granite, and mudstone). All reference samples were air-dried, and gravel and engineering impurities larger than 2mm were removed. The samples were then ground using an agate grinder and passed through a 2mm standard sieve before use.

[0063] Step 2: Perform multidimensional characterization of the physicochemical properties and calculate the F-value of the shield tunneling slag samples processed in Step 1.

[0064] Physicochemical property testing: Particle size distribution: determined using a Malvern laser particle size analyzer (Mastersizer 2000), with sodium hexametaphosphate as the dispersant. The percentage content of clay particles (<0.005 mm), powder particles (0.005-0.075 mm), and sand particles (>0.075 mm) was obtained.

[0065] Organic matter content (TOC): The oxidation was carried out using the potassium dichromate-sulfuric acid oxidation method, which involved heating and oxidation in an oil bath at 170-180℃.

[0066] Cation exchange capacity (CEC): Determined using the 1 mol / L ammonium acetate exchange method.

[0067] Specific surface area (SSA): Measured using a fully automated specific surface area analyzer based on the N2 adsorption BET multi-point method.

[0068] pH value: Measured using a pH meter, with the soil-to-water ratio controlled at 1:2.5 (by mass).

[0069] The test results were standardized using Z-score (zero-mean normalization) software via SPSS to eliminate dimensional differences among the indicators. Factor analysis was then performed on the standardized data to extract principal components with eigenvalues ​​greater than 1. The results showed that the first principal component (PC1) had a variance contribution rate of 62.4%, with its main loading indicators being clay content, CEC, TOC, and SSA, representing "colloidal chemisorption activity"; the second principal component (PC2) had a variance contribution rate of 18.6%, with its main loading indicators being sand content and pH value, representing "physical structure and acid-base characteristics". Based on the score coefficient matrix of each principal component, the comprehensive evaluation F-value of each sample was calculated. The calculation formula is as follows: F = 0.624 × PC1 score + 0.186× PC2 score The physicochemical properties of each sample and the calculated F-values ​​are shown in Table 1 below: Table 1: Summary of Physicochemical Properties and Overall Scores (F-values) of 11 Reference Samples

[0070] Step 3: Determine the adsorption response characteristics and fit the model to the shield tunnel slag samples.

[0071] Sodium α-olefin sulfonate (AOS), the most commonly used pollutant in tunnel boring machine (TBM) engineering, was selected as the characteristic contaminant. Batch equilibrium adsorption experiments were conducted under a constant temperature of 25℃, with initial concentration ranges of 50, 100, 200, 500, 800, 1200, 1500, and 2000 mg / L. This concentration range was chosen because the typical residual concentration of foaming agent in the excavated soil during actual TBM construction is usually 100-500 mg / kg. Setting the concentration to a maximum of 2000 mg / L (approximately 4-5 times the actual residual concentration) ensures that the experiment covers both the linear and fully saturated regions of the adsorption curve, thereby accurately obtaining the maximum adsorption capacity (Q). max The adsorption isotherms were fitted using the Sips equation, and the correlation coefficient R for each sample was calculated. 2 All values ​​are greater than 0.98. (That is, over 98% of the variation in AOS adsorption by slag can be explained by the Sips adsorption isotherm model. See attached figure.) Figure 2 A comparison of Slips isotherm adsorption curves for the characteristic pollutant sodium α-olefin sulfonate in three typical physicochemical soil samples S1, S5, and S11. The horizontal axis represents the equilibrium concentration C of the solution. e (mg / L), with the vertical axis representing the adsorption capacity Q of the slag. e (mg / kg), the table in the upper right corner of the figure shows the parameter results obtained after nonlinear fitting of the three scatter curves using the Sips adsorption isotherm model, where Qmax is the theoretical maximum adsorption capacity parameter characterizing the maximum retention capacity of the slag, R 2 The correlation coefficient is used to fit the model. The maximum adsorption capacity Q of each sample is calculated. max (Results are shown in Table 2 below). The results show that the maximum adsorption capacity (Q) of the slag soil sample S1 is... max The concentration of sulfur dioxide in the first sample was as high as 2540 mg / kg, while that in the second sample (S11) was only 280 mg / kg. The combined scores F and Q of the 11 samples were compared. max Linear regression analysis was performed to obtain the response model equation: Q pred = 2.0 × F + 4.8. The coefficient of determination R for this equation is... 2 The F-value, reaching 0.992, indicates that the comprehensive property score is highly correlated with the soil's ability to retain pollutants, and the adsorption capacity can be reliably predicted through physicochemical indicators.

[0072] Table 2: Reference Sample F-value and Measured Maximum Adsorption Capacity Q max Correspondence and Regression Analysis Table

[0073] Example 2 Application of rapid on-site assessment of shield tunneling muck soil physicochemical properties model constructed based on assessment method and Example 1 (low-risk scenario) A subway tunnel section traverses a water-rich silty clay layer, and it is urgent to determine the environmental risk level of the generated waste soil to determine its disposal destination.

[0074] According to the evaluation method of this invention, samples were first rapidly taken on-site and sent for testing of routine physicochemical indicators. The measured multidimensional physicochemical indicators of this batch of slag were as follows: clay content 28%, sand content 18%, TOC content 1.1%, and cation exchange capacity (CEC) 14.5 cmol. + / kg, specific surface area (SSA) 35.6 m² 2 / g, pH value 8.2. Substituting the above standardized data into the PCA characteristic score formula determined in Example 1, the comprehensive physicochemical property score F of the slag soil was calculated to be 1.25, indicating that it belongs to a highly adsorbent medium.

[0075] Using the prediction model Q pred The theoretical maximum adsorption capacity Q of the slag soil for AOS was calculated using the formula: = 2.0 × F + 4.8. pred The concentration was approximately 7.3 mg / kg. Meanwhile, the actual residual concentration (C0) of AOS in the slag was measured on-site using methylene blue spectrophotometry and found to be 2.5 mg / g.

[0076] Calculate the environmental migration risk index (MRI). The pollutant saturation level at this point (C0 / Q) is considered. pred The actual concentration was only 2.5 / 7.3 ≈ 34.2%. Given the high F value (>1.0) and the fact that the actual concentration was far below 80% of the predicted adsorption threshold, it indicates that most pollutants were strongly locked in by the colloidal components of the soil particles, making them difficult to leach and dissolve by rainwater. Based on this, the assessment system classified this batch of construction waste as "Level I Risk (Exempted Utilization Area)".

[0077] Based on the assessment results, the construction unit directly transported the batch of excavated soil to a nearby roadbed backfill site for resource utilization, without the need for expensive washing treatment, which saved disposal costs and achieved resource utilization. Example 3

[0078] Application of this assessment method and the corresponding model of the physical and chemical properties of shield tunneling excavated soil constructed in Example 1 for rapid on-site assessment (high-risk scenario) Another section of the subway project traverses a highly weathered granite stratum, and the excavated soil exhibits distinct sandy characteristics.

[0079] Rapid sampling and testing were also conducted on-site, and the multidimensional physicochemical indicators were measured as follows: clay content was only 6%, sand content was as high as 75%, TOC content was 0.15%, and cation exchange capacity (CEC) was 3.5 cmol. + / kg, with a specific surface area (SSA) of only 9.2m². 2 / g, pH value 9.5 (high alkalinity due to foaming agent). Substituting the data into the PCA algorithm determined in Example 1, the comprehensive score F was calculated to be -1.1, indicating that the physical structure of the slag soil is loose and the colloidal chemical activity is extremely low.

[0080] The theoretical maximum adsorption capacity Q of the slag for AOS was calculated using a predictive model. pred The concentration was only 2.0 × (-1.1) + 4.8 ≈ 2.6 mg / kg. However, the actual measured AOS residual concentration (C0) on site was 3.0 mg / kg.

[0081] The comparison revealed that the actual pollutant concentration (3.0 mg / kg) exceeded the theoretical retention threshold of the construction waste (2.6 mg / kg), i.e., C0 > Q. pred This means that the soil adsorption sites are completely saturated, and the excess pollutants are in a free state, which can easily migrate into the groundwater system under rainfall conditions. Based on this, the assessment system classifies this batch of construction waste as "Level III Risk (Strictly Controlled Area)".

[0082] Based on the assessment results, the environmental protection department immediately halted the plan to transport and backfill the construction waste, and mandated that the construction company set up temporary rinsing equipment on-site to reduce the AOS concentration in the waste to below 200 mg / kg. Only after a retest showed that the concentration met the standard could the waste be treated as ordinary solid waste. This measure effectively prevented a potential groundwater pollution incident.

[0083] Comparative Example 1 This comparative study uses the traditional "total concentration threshold evaluation method" to conduct parallel evaluations of two specific batches of tunnel boring machine (TBM) excavated soil, and compares them with the results of actual indoor dynamic leaching experiments on soil columns (referencing current national wastewater standards) as an objective factual benchmark. It is assumed that the total safe threshold for anionic surfactants (such as AOS) in the excavated soil of a certain project is set at 3.0 mg / g. That is: when the measured total concentration C0 > 3.0 mg / g, the traditional method determines it to be exceeding the standard (high risk, requiring restricted use or remediation); when C0 ≤ 3.0 mg / g, it is determined to be compliant (low risk, can be directly utilized as a resource).

[0084] Scenario A: Misjudgment of "high concentration-strong adsorption" media (false positives and over-repair) Silty clay slag with the same geological background as in Example 2 (F value = 1.25, predicted maximum adsorption capacity Q) was selected. pred=7.3 mg / g), and the actual AOS total residual concentration (C0) of this batch of construction waste was determined to be 3.5 mg / g through direct extraction. If the traditional total concentration method is used for assessment, since the actual concentration of 3.5 mg / g is greater than the control limit of 3.0 mg / g, this method would directly determine that this batch of construction waste is in a high-risk state of exceeding the standard and require mandatory ex-situ washing and remediation. However, using the assessment method of this invention, the pollution saturation (C0 / Q) is calculated. pred The concentration of anionic surfactants in the bottom leachate was approximately 47.9%. Because the F-value was greater than 1.0 and the saturation was far below the safety threshold of 80%, this system classified it as a Class I low-risk product, recommending exemption from direct use. To verify the accuracy of the above two assessment results, a 15-day standard rainfall simulation soil column leaching experiment was conducted on this batch of waste soil, and the leachate was collected for testing. The objective results showed that the concentration of anionic surfactants in the bottom leachate was below 0.5 mg / L throughout the entire rainfall cycle, far below the Class I discharge limit (5.0 mg / L) specified in the "Integrated Wastewater Discharge Standard" (GB 8978-1996). This proves that the assessment results of this invention are completely consistent with the facts. The traditional full-volume evaluation method ignored the extremely strong chemical adsorption and locking capacity of clay, resulting in a "false positive" misjudgment. If the traditional method were followed, it would lead to an extremely high and unnecessary waste of remediation funds.

[0085] Scenario B: False negatives and environmental accidents in "low concentration - weak adsorption" media. Strongly weathered granite sandy soil with the same geological background as in Example 3 (F value = -1.1, predicted maximum adsorption capacity Q) was selected. pred =2.6 mg / g), the actual AOS total residual concentration (C0) of this batch of construction waste was measured to be 2.8 mg / g. If assessed using traditional methods, since the actual concentration of 2.8 mg / g does not exceed the control limit of 3.0 mg / g, this method would determine that this batch of construction waste is in a low-risk state and safe for direct transport for roadbed backfilling. However, using the assessment method of this invention, its pollution saturation (C0 / Q) is calculated to be... pred The concentration of anionic surfactant in the bottom leachate was approximately 107.7%. Because the F-value was less than 0 and the saturation exceeded 80%, approaching the theoretical adsorption limit, this system determined it to have an extremely high risk of desorption and release, classifying it as a Level III high-risk area (strictly controlled zone). An objective soil column rainfall leaching experiment was conducted to verify this. The results showed that only 3 hours after the start of rainfall, the concentration of anionic surfactant in the bottom leachate surged to 18.5 mg / L, exceeding the Class I limit (5.0 mg / L) of the "Integrated Wastewater Discharge Standard" by more than three times. This comparison shows that the traditional method, simply because the total concentration was barely below the empirical standard, ignored the inherent weakness of sandy soil with no holding capacity, resulting in a "false negative" judgment. If implemented in this way, it would inevitably lead to a serious groundwater pollution incident.

[0086] In summary, traditional full-concentration evaluation methods sever the coupling constraint relationship between pollutants and the soil medium. The method of this invention, by constructing a quantitative response model of multidimensional physicochemical properties, completely overcomes the limitations of the "one-size-fits-all" approach in traditional assessment systems. The assessment results are highly consistent with actual leaching and infiltration water quality indicators (wastewater standards), demonstrating significant scientific validity, accuracy, and substantial engineering and economic benefits.

[0087] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

Claims

1. A method for assessing the environmental risk of organic pollution from tunnel boring machine (TBM) slag, comprising: S1. Pre-treat the tunnel boring machine excavation soil sample to prepare the excavation soil sample to be tested; S2. Measure the multidimensional indicators of the soil sample to be tested, use multivariate statistical analysis to extract the characteristic factors reflecting the properties of the soil, and calculate the comprehensive evaluation F value of the properties that characterize the soil itself. S3. Measure the adsorption curves of organic pollutants on different types of slag and obtain parameters characterizing the maximum retention capacity of organic pollutants on slag. S4. Regression analysis is performed on the comprehensive evaluation F value of the slag soil obtained in step S2 and the parameters of the maximum holding capacity obtained in step S3 to predict the theoretical adsorption capacity Q of the slag soil sample to be tested. pred ; S5. Based on the measured initial concentration (C0) of organic pollutants in the slag and the predicted adsorption capacity Q pred The environmental migration risk index MRI was obtained, and the construction waste was classified into different risk levels. During the evaluation process, the order of steps S2 and S3 can be reversed, that is, S3 is performed first, and then S2 is performed. Preferably, the organic pollutant is anionic surfactant, the main component of the shield tunnel foam agent; More preferably, the organic pollutants mainly include sodium α-alkenyl sulfonate and / or sodium dodecyl sulfate.

2. The evaluation method according to claim 1, characterized in that, Step S2 includes: detecting multidimensional indicators of the slag soil sample, using principal component analysis to reduce the dimensionality of the multidimensional indicators, extracting the principal components reflecting the properties of the slag soil, and calculating the comprehensive evaluation F value of the properties of the slag soil. Preferably, the F-value for the comprehensive evaluation of the properties of the slag and soil is calculated using Formula I. Formula I Where n is the number of principal components, w i The variance contribution rate weight of the i-th principal component, FAC i The score of the i-th principal component; Preferably, in step S2, the multidimensional indicators of the slag soil sample include physical and chemical indicators.

3. The evaluation method according to claim 2, characterized in that, The multidimensional indicators of the slag soil samples include pH value, organic matter content, particle size distribution, and specific surface area; Preferably, the multidimensional indicators of the slag soil sample include pH value, organic matter content, particle size distribution, cation exchange capacity, and specific surface area; Preferably, the multidimensional indicators of the slag soil sample include pH value, organic matter content, particle size distribution, cation exchange capacity, specific surface area, and minerals; More preferably, the particle size distribution includes the content of clay particles, powder particles, and sand particles.

4. The evaluation method according to claim 2 or 3, characterized in that, n is 2-3. That is, the number of principal components is 2 or 3; Preferably, the number of principal components is 2, the variance contribution weight of the first principal component is 50-70%, and the variance contribution weight of the second principal component is 15-30%. More preferably, the main loading indicators of the first principal component include clay content, organic matter content, particle size distribution, and cation exchange capacity; The main loading indicators for the second principal component include sand content and pH value.

5. The evaluation method according to any one of claims 1-4, characterized in that, Step S3 includes: measuring the adsorption curves of organic pollutants on different types of construction waste, fitting the adsorption data using the Sips model, and obtaining parameters characterizing the maximum retention capacity of construction waste for organic pollutants. Preferably, the parameter for maximum retention capacity includes the maximum adsorption capacity Q. max ) or allocation coefficient K d .

6. The evaluation method according to any one of claims 1-5, characterized in that, Step S4 includes: performing regression analysis between the comprehensive evaluation F-value of the slag soil properties obtained in step S2 and the parameters of the maximum holding capacity obtained in step S3, to obtain Formula II. Official II Among them, Q pred Let be the predicted adsorption capacity of the slag, a be the response coefficient, and b be the intercept constant. Preferably, the comprehensive evaluation value F of the properties of the slag obtained in step S2 is used as the independent variable, and the maximum adsorption capacity Q in step S3 is used as the independent variable. max Using the dependent variable, a predictive adsorption capacity model as shown in Formula II was established through linear regression analysis.

7. The evaluation method according to any one of claims 1-6, characterized in that, In step S5, based on the measured initial concentration C0 of organic pollutants in the slag and the predicted adsorption capacity Q... pred Methods for classifying risk levels include: when C0 < k1 × Q pred When k1 is at a certain level, it is classified as Level I low risk, where k1 is the safety factor threshold; when k1 × Q pred ≤ C0≤Q pred When C0 > Q, it is classified as Level II medium risk; pred At that time, it was determined to be a Level III high-risk area; Where k1 is the safety factor threshold; Preferably, k1 is 0.75 to 0.90 (preferably, k1 = 0.80).

8. The evaluation method according to any one of claims 1-6, characterized in that, Step S5 includes: based on the measured initial concentration C0 of organic pollutants in the slag and the predicted adsorption capacity Q pred The ratio of the waste soil to the environmental migration risk index (MRI) is obtained by comprehensively evaluating the F value based on the properties of the waste soil and the ratio of the waste soil to the environmental migration risk index, and the waste soil is classified into different risk levels.

9. The evaluation method according to claim 8, characterized in that, The environmental migration risk index MRI classifies it into three levels: Grade I: High F-value of slag and C0 is much smaller than Q. pred ; Grade II: Slag muck F value is moderate, or C0 is close to Q. pred ; Grade III: Low F-value of construction waste, or C0 exceeding Q. pred ; Preferably, the F-value of the slag is greater than 1, and C0 / Q pred If the ratio is less than 80%, the organic pollution risk of the waste soil is determined to be Level I. Preferably, the F-value of the slag is less than 1 and greater than -1.0; or the F-value of the slag is greater than 1, but C0 / Q pred If the ratio is greater than 80% and less than 100%, the organic pollution risk of the waste soil is determined to be Level II. Preferably, the F-value of the slag is less than -1.0, or the CO / Q ratio is less than 1.

0. pred If the percentage is greater than 100%, the organic pollution risk of the waste soil is determined to be Level III.

10. The evaluation method according to any one of claims 1-6, characterized in that, The hydrogeological parameters of the environment where the slag was located were also introduced, including the groundwater depth and / or permeability coefficient. Preferably, if the groundwater depth D < 1.5m, or the permeability coefficient K > 1.0×10⁻⁶, the groundwater depth is preferably lower than 1.5m. -4 cm / s, and the original risk level is adjusted based on the environmental migration risk index MRI judgment standard.

Images