A comprehensive decision-making and evaluation method for halogenated hydrocarbon contaminated soil and groundwater remediation strategy

Abstract

The application provides a comprehensive decision-making and evaluation method for halogenated hydrocarbon contaminated soil and groundwater remediation strategy, comprising: establishing a site baseline and conducting technical preliminary screening based on health and ecological risk assessment (HERA) and a three-level evidence system; conducting rapid resource efficiency assessment based on environmental footprint analysis (EFA); conducting global long-term assessment based on life cycle assessment (LCA) and cost-benefit analysis (CBA); and dynamic evaluation and closed-loop control based on digital twinning and multi-criteria decision analysis (MCDA) coupling. In the dynamic evaluation stage, an adaptive hybrid dynamic weighting mechanism is constructed, the subjective benchmark weight and the objective fluctuation weight calculated based on real-time data information entropy are integrated, and the comprehensive utility score is calculated; at the same time, the apparent degradation rate constant of the pollutant is solved in real time, and when it is lower than the target rate and the strategy is replaced, the intervention instruction is automatically output. The application realizes the deep integration of multi-dimensional indicators, dynamic adaptive evaluation and closed-loop intelligent regulation and control, and solves the problems of single, static and lack of feedback of the existing evaluation method.

Description

Technical Field

[0001] This invention relates to the field of environmental engineering and contaminated site remediation technology, specifically to a comprehensive decision-making and evaluation method for remediation strategies of halogenated hydrocarbon contaminated soil and groundwater. Background Technology

[0002] Due to historical industrial and agricultural activities, halogenated hydrocarbons (especially chlorinated hydrocarbons such as trichloroethylene and tetrachloroethylene) are widely present in soil and groundwater as heavy non-aqueous liquids (DNAPLs). Because of their density being greater than water, high toxicity, low solubility, and high mobility, they readily form complex pollution plumes in aquifers, posing long-term environmental and health risks.

[0003] While traditional physicochemical methods such as extraction and in-situ chemical oxidation are relatively mature, they often suffer from limitations including high energy consumption, high cost, long remediation cycles, and difficulty in ensuring complete removal of contaminants from deep aquifers. In recent years, near-natural attenuation (NA) and enhanced natural attenuation (ENA, such as biostimulation, bioaugmentation, phytoremediation, and microbial-mediated electrochemical techniques) have received widespread attention as low-energy, eco-compatible near-natural remediation technologies.

[0004] However, the comprehensive assessment of NA and ENA technologies still has significant shortcomings: First, existing assessment methods are often singular or fragmented. For example, Life Cycle Assessment (LCA) often ignores site-specific risk factors; Ecological Risk Assessment (ERA) has considerable uncertainty; and Health Risk Assessment (HERA) often ignores the trade-off between remediation costs and environmental benefits. Second, there is currently a lack of a unified and comprehensive assessment framework, both domestically and internationally, specifically for the remediation of halogenated hydrocarbon contaminated sites that can systematically integrate risk, environmental, and economic dimensions. Third, existing Multi-Criterion Decision Analysis (MCDA) methods are mostly static assessments, unable to adaptively adjust weights and provide strategy feedback based on the highly dynamic biogeochemical conditions and pollutant degradation rates during groundwater remediation. This results in post-remediation interventions often lagging behind the actual needs of the site.

[0005] Therefore, there is an urgent need to develop a comprehensive decision-making and evaluation method that can dynamically integrate multi-dimensional indicators, support transparent comparison, and have closed-loop feedback function. Summary of the Invention

[0006] This invention aims to overcome the shortcomings of existing technologies and provide a comprehensive decision-making and assessment method for the remediation of halogenated hydrocarbon-contaminated soil and groundwater.

[0007] This invention provides a comprehensive decision-making and evaluation method for remediation strategies of halogenated hydrocarbon-contaminated soil and groundwater, comprising the following steps: Step S1: Establishment of site baseline and initial technology screening based on Health and Ecological Risk Assessment (HERA). Collect time series data on site halogenated hydrocarbon concentrations, exposure pathways, toxicological reference values, and receptor distribution data; quantify the non-carcinogenic hazard index and carcinogenic risk level; and establish a site remediation baseline (refer to...). Figure 1 The three-tiered evidence system verifies the occurrence and intensity of natural decay (NA) as follows: 1) Preliminary evidence: assessing the spatial evolution and mass flux reduction of the pollution plume; 2) Intermediate evidence: Monitoring the redox potential (ORP), pH value, and electron acceptor (such as nitrate and sulfate) concentration characteristics in the groundwater system (see reference). Figure 2-7 ); 3) Advanced evidence: Obtaining dehalogenation microbial community functional data and isotopic fractionation evidence (see reference) Figure 8 ).

[0008] Based on the above three-level evidence system, the non-carcinogenic hazard index (HI) and carcinogenic risk (CR) were calculated; the Mantel test was used to calculate the correlation between the microbial community distance matrix and the environmental factor distance matrix, and environmental factors that significantly drive the dehalogenation process were screened (see reference). Figure 10 When HI > 1 or CR > 1E-6 and Level 3 evidence supports natural degradation, the site is retained for further evaluation; otherwise, other active remediation technologies are recommended. After calculation, a list of alternative near-natural remediation technologies is output, specifically covering four categories of near-natural remediation strategies optimized for halohydrocarbon degradation mechanisms: Biostimulation strategies: Activating the metabolic capacity of indigenous microorganisms by injecting nutrients or electron donors / acceptors into the aquifer. Specific methods include adding starch-based slow-release carbon sources, lactate, or whey to provide electron donors, or supplementing with specific electron acceptors such as nitrates or sulfates to regulate the redox environment. Biofortification strategy: targeting sites lacking key dehalogenation microorganisms (such as...) Dehalococcoides genus or Dehalobacter In cases where the microorganisms belong to the same category, highly efficient dehalogenating microbial agents that have been enriched and cultured can be directly inoculated into the contaminated area to improve the metabolic pathway of anaerobic reduction dehalogenation and solve the bottleneck of the accumulation of intermediate products such as cis-1,2-dichloroethylene. Microbial-mediated micro-electric field-assisted strategy: This strategy involves constructing an underground micro-electric field system by injecting conductive materials into groundwater or deploying them in situ. This strategy utilizes in-situ electrochemical regulation to directly promote electron transfer, accelerating not only the abiotic transformation of recalcitrant polyhalogenated hydrocarbons but also synergistically improving dehalogenation efficiency with the help of microorganisms. Phytoremediation and microecology combined strategy: In the boundary of pollution plume or shallow soil area, the microenvironment is regulated by the absorption, volatilization, rhizosphere degradation and filtration functions of plant roots, and the dehalogenation activity of rhizosphere microorganisms is indirectly stimulated through root exudates, so as to restore the ecological landscape of the site.

[0009] Step S2: Rapid resource efficiency assessment based on Environmental Footprint Analysis (EFA). For shortlisted candidate Enhanced Natural Attenuation (ENA) technologies (such as biostimulation, bioaugmentation, phytoremediation, and micro-electric field-assisted remediation), their carbon footprint, water footprint, energy footprint, and material footprint are quantified to obtain the direct environmental burden differences in resource consumption and emissions among the candidate technologies, eliminating high-energy-consuming and high-carbon-emission schemes. Specific quantitative indicators and comparative examples are as follows: Carbon and material footprint quantification: Assessing the greenhouse gas emission equivalent directly or indirectly caused by material consumption during remediation. For example, in the "lactate biostimulation" scheme, although its in-situ operation has low carbon emissions, the indirect carbon emissions and material consumption generated throughout the entire process of lactate agent production, packaging, and transportation to the site must be included in the accounting; while in the "in-situ chemical oxidation" or schemes using zero-valent iron, it is necessary to focus on quantifying the extremely high carbon footprint of the synthesis of energy-intensive chemicals; Energy footprint quantification: Assessing the electrical and fuel consumption during system operation. For example, for "micro-field assisted remediation," the core constraint is the long-term electrical energy consumption of the power supply and electrodes, requiring quantification of the number of kilowatt-hours (kWh) required to remove a unit mass of pollutants; Quantifying water and land footprints: For example, while "phytoremediation" has carbon sequestration potential, it may occupy a large area of ​​land surface (land footprint) and consume a large amount of groundwater / surface water for plant transpiration (water footprint). By comparison, EFA can quickly eliminate unsustainable solutions that, although they provide thorough remediation, will result in "high energy consumption, high carbon emissions, and serious secondary pollution".

[0010] Step S3: Global Long-Term Assessment Based on Life Cycle Assessment (LCA) and Cost-Benefit Analysis (CBA). LCA is used to conduct a cradle-to-grave life cycle assessment of the technologies selected in Step S2. The system boundary covers the entire process, including remediation equipment manufacturing, reagent resource extraction and production, transportation, on-site construction and in-situ injection, long-term operation and maintenance, equipment decommissioning, and site closure, tracing the cumulative environmental burden such as greenhouse gas emissions, ecotoxicity, and ecological damage. For example, when comparing conventional iron-based materials (such as zero-valent iron) with improved biostimulation strategies, in addition to assessing the effectiveness of in-situ pollutant removal, it is also necessary to calculate the resource and energy consumption (such as alkali consumption) during reagent preparation, emissions during transportation, and the implicit ecological impacts and cumulative greenhouse gas emission burden, such as changes in groundwater hardness after injection. Simultaneously, CBA is used to monetize and quantify the multidimensional inputs and outputs of the entire remediation project, using net present value (NPV) and benefit-cost ratio (BCR) as core evaluation indicators. The cost side includes upfront well construction, equipment procurement and capital investment, as well as long-term operation and maintenance costs. For example, the "micro-electric field-assisted strategy" needs to include costs such as replacement of highly conductive materials and electrode lifespan decay, while "biostimulation" needs to include long-term reagent dosage and long-term groundwater monitoring costs. The benefits include not only the restoration of land value and reuse revenue after site remediation, but also the benefits of reducing exposure risks obtained from Health Risk Assessment (HERA) (such as reducing the risk of cancer to 10). -6 The following ( ) are converted into the monetary value of avoided medical losses and health benefits, thereby completing the comprehensive quantification of multi-dimensional benchmark data. When a specific alternative satisfies NPV > 0 and the pollution risk can be controlled within the forecast period, the alternative is considered to have both economic and environmental feasibility.

[0011] Step S4: Dynamic evaluation and closed-loop control based on the coupling of digital twin and multi-criteria decision analysis (MCDA) (Reference) Figure 9 A digital twin of the remediation site is established, and the measured update data and numerical simulation prediction data of the site are spatiotemporally mapped to construct a dynamic index matrix with a time step of t. : ; Where m is the number of alternative strategies (usually ranging from m∈[3,6], such as the three strategies covered in the example: lactate biostimulation, zero-valent iron chemical reduction, and micro-electric field-assisted bioenhancement); n is the number of comprehensive indicators (usually ranging from n∈[10,20], such as 16 core evaluation indicators including health risk reduction rate, net carbon emissions, and present value of costs). Let be the original value of the i-th strategy on the j-th index. To eliminate differences in dimensions and orders of magnitude among different indices, the range standardization method is used to process the original matrix, resulting in a dimensionless standard value matrix. After standardization, individual indicator values The value range is strictly defined between [0,1].

[0012] An adaptive hybrid dynamic weighting mechanism is introduced, which determines the comprehensive weight of the j-th indicator at time t. for: ; in, Assign subjective baseline weights (e.g., expert weights determined by the Analytic Hierarchy Process (AHP)). This is an objective fluctuation weight calculated based on the information entropy of real-time data; when abnormal fluctuations in the measured data cause a decrease in information entropy, Increase.

[0013] α and β are the subjective preference adjustment coefficient and the objective data feedback adjustment coefficient, respectively. Both coefficients have values ​​within the range of α,β∈[0,1] and satisfy the constraint: α+β=1. The values ​​of α and β are set and adjusted as follows: During the initial operation or normal phase of the system, they are usually preset with equal weights (i.e., α=0.5, β=0.5). During the dynamic operation of the digital twin system, these two coefficients can be adaptively adjusted according to the reliability of the monitoring data. For example, when the amount of data collected by the site's high-frequency sensors is sufficient and the signal-to-noise ratio is high, the system can adaptively lower α and increase β (e.g., α=0.3, β=0.7) to enhance the driving force of objective measured data on decision feedback; conversely, if the monitoring well data is missing or highly uncertain due to physical faults, the system increases α (e.g., α=0.8, β=0.2) to rely more on expert prior knowledge to maintain the stability of the evaluation system.

[0014] The calculation method for the objective fluctuation weight based on real-time data information entropy is as follows: setting a time window. Among them, time window The value of is not a fixed constant, but rather adaptively defined based on the spatial scale of groundwater solute transport and hydrodynamic conditions. Specifically: in the small-scale indoor physical simulation stage (e.g., when conducting experiments using a two-dimensional groundwater sandbox with an effective length of 50cm), due to the rapid hydrodynamic circulation and high-frequency data acquisition, The value range is usually set to However, in the actual in-situ remediation stage of contaminated sites (such as large-scale groundwater pollution demonstration areas), considering the slow actual flow velocity, strong heterogeneity, and seasonal fluctuations of groundwater, The value range is usually set to (i.e., 1 week to 1 quarter). The system extracts the set time window. Calculate the probability weight of indicator j from the historical indicator matrix sequence within the matrix: ; Then, the information entropy is calculated: ; Finally, the objective fluctuation weights are obtained: ; Calculate the overall utility score: ; The system will use the strategy according to Sort in descending order, highest score The corresponding strategy is currently optimal.

[0015] Simultaneously, the twin system calculates the apparent degradation rate constant of the target pollutant in real time. : Using nonlinear least squares method for real-time access The curve is fitted with first-order decay kinetics, and the apparent rate is updated in real time. : ; Preset satisfaction threshold and expected target degradation rate Due to the overall utility score This is a dimensionless value calculated based on the range normalization index (theoretically with an absolute upper limit of 1.0). When the score is below 0.60, it means that the strategy has unacceptable shortcomings in risk reduction, environmental footprint, or economic costs (e.g., the carcinogenic risk has not been reduced to an acceptable level, or the net present value (NPV) < 0). Therefore, setting this threshold ensures that the system always operates above the "multidimensional benefit passing grade." Thus, its value range is typically set between 0.60 and 0.85. When the highest-scoring strategy... Greater than the preset satisfaction threshold, and (The expected target rate, whose value is usually set to 0.005d) ﹣1 up to 0.05d ﹣1 When ), maintain the current intervention intensity; when If a strategy is continuously exceeded for more than a preset time window, or if a core risk indicator exceeds a threshold, triggering a system recalculation that results in a strategy change (i.e., the original highest-scoring strategy is surpassed by another strategy), the system will trigger an alert and output intervention instructions. The preset intervention instructions specifically include: Instructions for biostimulation strategies: When the accumulation of intermediate products (such as cis-1,2-dichloroethylene) or the pH shows an acidification trend is detected, the instruction library outputs: "Increase the frequency of injection pump operation, quantitatively supplement specific electron donors (such as lactate or starch-based slow-release carbon sources), or automatically add alkaline buffer solution to adjust the microenvironment pH value". Instructions for bioaugmentation strategies: When advanced evidence detects that the abundance of core dehalogenated microorganisms is below the threshold required to maintain the decline, the instruction library outputs: "Trigger secondary targeted injection of functional microbial agents (such as enriched bacterial solutions containing dehalogenated cocci). Instructions for micro-electric field assisted strategies: When the degradation rate slows down and electron transfer is hindered, the instruction library outputs: "Increase the micro-electric field current density in the conductive medium region, or adaptively adjust the polarization reversal period of the anode and cathode". Instructions for phytoremediation combined strategies: Instruction library output: "Adjust irrigation patterns in the rhizosphere or apply synthetic root exudate analogues to reactivate rhizosphere degradation efficiency."

[0016] It should be noted that, in engineering applications, the underlying computational logic implementation of the adaptive hybrid dynamic weighting model in step S4 of this invention requires the mathematical evaluation model on which step S4 relies to be deployed using a high-efficiency computing platform (such as MATLAB or Python). Data cleaning and normalization: The collected sensor time-series data and test data are processed by range standardization after outlier removal to ensure uniformity of dimensions.

[0017] Objective weight calculation: Set a time window, extract the indicator matrix sequence within that time period, and calculate the probability weight of indicator j. Then calculate the information entropy to finally obtain the objective fluctuation weight.

[0018] Fitting and Iteration: The first-order decay dynamics of the real-time access curve are fitted using the nonlinear least squares method, and the apparent rate is updated in real time.

[0019] With a 12-hour polling cycle, it continuously compares with the satisfaction threshold. When the conditions meet the preset judgment logic branch, it outputs intervention signals through API interface or visual control screen, truly realizing intelligent remediation management of contaminated sites.

[0020] Compared with the prior art, the present invention has the following beneficial effects: This invention abandons the one-dimensional concentration control mindset and systematically integrates risk, resource efficiency, long-term environmental impact, and economics through a four-level progressive architecture of HERA→EFA→LCA / CBA→MCDA, avoiding the one-sidedness of single-dimensional assessment. It quantifies and couples a three-level evidence system (especially advanced microbial genetic and isotopic data) through Mantel verification and uses it as model input, greatly improving the accuracy of natural decay process assessment. A hybrid dynamic weighting mechanism based on information entropy enables the assessment model to automatically adjust indicator weights according to real-time site data (such as abnormal fluctuations in pH and ORP), ensuring that the evaluation results continuously reflect the actual site condition. Through real-time digital twin comparison and automatic triggering of instructions when strategies change, a closed loop of "monitoring-assessment-triggering-execution-remonitoring" is formed, achieving precise remediation and avoiding over- or under-remediation. Compared with traditional static multi-criteria evaluation methods, the closed-loop dynamic decision-making system of this invention shows significant quantitative advantages in parallel comparative experiments: the repair time is shortened by about 30%, the total amount of reagents is reduced by more than 25%, the overall operating cost is reduced by more than 20%, and the repair stagnation event caused by microenvironment deterioration is successfully avoided, achieving a high degree of unity between cost reduction and efficiency improvement and precise control. Attached Figure Description

[0021] Figure 1 This is a comprehensive evaluation framework diagram of the remediation strategy for halogenated hydrocarbon contaminated soil and groundwater according to the present invention. Figure 2 This is a curve showing the correlation between the natural decay effect (response ratio) and the change in dissolved oxygen in this invention. Figure 3 This is a curve showing the correlation between the natural decay effect (response ratio) and the change in conductivity in this invention. Figure 4 This is a curve showing the correlation between the natural decay effect (response ratio) and the change in redox potential in this invention. Figure 5 This is a curve showing the correlation between the natural decay effect (response ratio) and the pH change in this invention. Figure 6 This is a curve showing the correlation between the natural decay effect (response ratio) and the change in iron / manganese in this invention. Figure 7 This is a curve showing the correlation between the natural decay effect (response ratio) and the change in nitrate / sulfate levels in this invention. Figure 8 This is a curve showing the correlation between the natural decay effect (response ratio) and the microbial change in this invention. Figure 9 This is a flowchart of the dynamic simulation and decision feedback loop based on the coupling of digital twin and multi-criteria decision analysis (MCDA) in this invention. Figure 10 This is a Mantel test correlation network heatmap showing the relationship between the core microbial community (phylum, class, and genus level) of this invention and site environmental factors and halogenated hydrocarbon pollutants. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] Example 1 (Site-level Implementation) The method of this invention was applied to the remediation decision-making of a typical groundwater demonstration site contaminated with halogenated hydrocarbons in Shaoguan, Guangdong. The main pollutants at this site were trichloroethylene (TCE) and its intermediate metabolite cis-1,2-dichloroethylene (cis-DCE) (see reference). Figure 10 ).

[0024] Step S1: Grid sampling was performed on the site. Intermediate evidence (ORP, pH, sulfate concentration) and high-level evidence (microbial genus-level abundance obtained from high-throughput sequencing) were processed using the R language environment, and a Mantel test correlation analysis was performed. The results accurately located the core dehalogenated microbial community significantly driven by ORP and pH. Combined with the exposure model, it was determined that the non-carcinogenic risk of the current site condition far exceeded the acceptable level (HI > 1), necessitating ENA intervention.

[0025] Steps S2 & S3: Three strategies were initially selected: "Lactate Biostimulation," "Zero-Valence Iron (ZVI) Chemical Reduction," and "Micro-Electric Field-Assisted Bioenhancement." First, the functional unit was set as "treating 1 cubic meter of qualified groundwater," and inventory analysis and footprint calculations were performed using life cycle assessment software and the Ecoinvent database. Parallel calculations using EFA and LCA revealed that ZVI chemical reduction has extremely high greenhouse gas emissions equivalents in the upstream reagent preparation stage (iron ore mining, high-temperature smelting, and mechanical crushing), resulting in abnormally high carbon and energy footprints. While lactate biostimulation has extremely low carbon emissions during in-situ injection, the high-frequency reagent transportation and addition required to maintain a long-term reduction environment over several years leads to a significantly higher material footprint throughout its entire life cycle. Second, in terms of economic benefit assessment, a CBA economic model was constructed. Initial well construction investment, conductive material / reagent costs, and long-term manual operation and maintenance costs were accounted for as expenditures; health losses avoided through risk elimination (such as reducing carcinogenic risk to an acceptable level) and land appreciation after site remediation were accounted for as benefits. With a repair baseline of 10 years and a discount rate of 8%, Monte Carlo simulation software was used to fit a triangular distribution to each cost-benefit parameter. 10,000 random sampling simulations were conducted, and finally, the net present value and benefit-cost ratio of each option with a 95% confidence interval were calculated and output.

[0026]

[0027] Meanwhile, to eliminate the differences in dimensions and orders of magnitude among these 16 different indicators, the system uniformly adopts the range standardization method for normalization. Specifically, for benefit-type indicators where larger values ​​are generally better, a formula is used... For cost-related indicators where smaller values ​​are better, the formula is used. Subjective weights are set using AHP (Advanced Hierarchical Analysis), and the site's online monitoring data is integrated into the digital twin module.

[0028] The initial decision output "lactate biostimulation" had the highest overall utility score.

[0029] Result: The system successfully recommended the initial optimal strategy and established a baseline for subsequent dynamic adjustments.

[0030] Example 2 (Sandbox Dynamic Validation) To verify the reliability of the dynamic trigger control algorithm in step S4, a parallel verification was conducted using an indoor three-dimensional physical simulation device. A highly transparent acrylic groundwater sandbox simulation device with precise dimensions of 60cm×30cm×12cm was designed and assembled, and parallel comparative experiments were carried out. To meet the demand of the digital twin model for high-resolution spatiotemporal data, the device scientifically deployed 18 sampling ports in three-dimensional space (including specific valve height control to capture the vertical transport and dominant flow channels of DNAPL).

[0031] The "lactate biostimulation" strategy screened in Example 1 was applied to this sandbox. On day 45, three-dimensional concentration field data obtained through 18 sampling ports were fed back into the digital twin model. 1) Total pesticide usage saving rate: The control group adopted a pre-set fixed-cycle blind dosing mode, which led to the ineffective consumption of some pesticides in the middle and late stages and even caused the risk of secondary pollution; the experimental group only saw a change in the overall system utility score and When the level is below the threshold, the dosing command is triggered as needed, and the total consumption of lactate and other reagents is reduced by 25.4% throughout the cycle.

[0032] 2) Measured apparent degradation rate Reduced to the preset In contrast, the control group, due to its fixed weights, could not adjust its strategy in a timely manner according to the decay of the apparent degradation rate, and the total time for the TCE concentration to drop to the target limit was 120 days. In contrast, the dynamic system of the experimental group accurately triggered supplementary intervention after identifying the degradation bottleneck, and the overall time to reach the target was only 85 days, shortening the repair cycle by about 29.2%.

[0033] 3) At this point, the dynamic evaluation matrix The information entropy of the indicators representing "pH stability" and "risk of intermediate product accumulation" dropped sharply, and the objective fluctuation weights... Automatically and significantly improved.

[0034] 4) Number of failures avoided due to dynamic adjustments: During days 40 to 50 of operation, signs of "acidification" due to the accumulation of intermediate products (cis-DCE) appeared in some parts of the sandbox. The control group (static assessment) failed to respond in time, resulting in the complete cessation of the dehalogenation reaction (counted as one failure, requiring manual intervention); while in the experimental group, the information entropy of pH and ORP in the dynamic matrix dropped sharply, and the objective fluctuation weight increased dramatically. The system automatically issued an early warning on day 45 and triggered the "inject buffer to restore pH" command, successfully avoiding system crash.

[0035] It should be understood that the above embodiments are merely exemplary. Those skilled in the art can make various non-substantial improvements and substitutions based on the technical concept of this invention, and all such improvements and substitutions should be considered to fall within the protection scope of this application.

Claims

1. A comprehensive decision-making and evaluation method for remediation strategies of halogenated hydrocarbon-contaminated soil and groundwater, characterized in that, Includes the following steps: S1: Based on health and ecological risk assessment, establish site baselines and conduct initial technical screening, and couple and evaluate a three-level evidence system that includes information on the physical evolution of pollution plumes, physicochemical parameters, microbial community gene analysis, and isotope fractionation information; S2: Based on environmental footprint analysis, conduct rapid resource efficiency assessment of the candidate remediation technologies that have passed the initial screening in step S1; S3: Based on life cycle assessment and cost-benefit analysis, conduct a global long-term assessment of the technologies screened in step S2, trace the environmental impact of the entire life cycle and calculate the net present value or cost-benefit ratio. S4: Dynamic evaluation and closed-loop control based on the coupling of digital twin and multi-criteria decision analysis.

2. The comprehensive decision-making and evaluation method for remediation strategies of halogenated hydrocarbon-contaminated soil and groundwater according to claim 1, characterized in that, In step S1, the three-level evidence system is as follows: Preliminary evidence includes spatial evolution of the pollution plume and reduction in mass flux; Intermediate-level evidence includes redox potential, pH value, and electron acceptor concentration characteristics; Advanced evidence includes dehalogenated microbial community functional data and isotopic fractionation values.

3. The comprehensive decision-making and evaluation method for remediation strategies of halogenated hydrocarbon-contaminated soil and groundwater according to claim 2, characterized in that, Step S4 also includes: Construct a digital twin, and spatiotemporally map the measured site data with the numerical simulation prediction data to form a dynamic index matrix: ； Where m represents the number of alternative strategies and n represents the number of comprehensive indicators. This represents the original value of the i-th strategy on the j-th metric.

4. The comprehensive decision-making and evaluation method for remediation strategies of halogenated hydrocarbon contaminated soil and groundwater according to claim 3, characterized in that, Step S4 also includes: An adaptive hybrid dynamic weighting mechanism is introduced to calculate the comprehensive weight of the j-th index at time t. ； in, Subjective benchmark weights; The objective fluctuation weight is calculated based on the information entropy of real-time data, where α and β are preset coefficients, and α+β=1.

5. The comprehensive decision-making and evaluation method for remediation strategies of halogenated hydrocarbon-contaminated soil and groundwater according to claim 4, characterized in that, Step S4 also includes: Calculate the overall utility score for each alternative strategy: ； Simultaneously, the apparent degradation rate constant of the target pollutant is calculated in real time. : 。 6. The comprehensive decision-making and evaluation method for remediation strategies of halogenated hydrocarbon contaminated soil and groundwater according to claim 5, characterized in that, In step S4, the objective fluctuation weight Calculate using the following steps: Set time window Extract the indicator matrix sequence within this time period and calculate the probability weight of indicator j: ； Then, the information entropy is calculated: ； Finally, the objective fluctuation weights are obtained: 。 7. The comprehensive decision-making and evaluation method for remediation strategies of halogenated hydrocarbon contaminated soil and groundwater according to claim 5, characterized in that, The apparent degradation rate constant Using nonlinear least squares method for real-time access The curve is fitted with first-order decay dynamics.

8. The comprehensive decision-making and evaluation method for remediation strategies of halogenated hydrocarbon contaminated soil and groundwater according to claim 4, characterized in that, Subjective benchmark weight Determined using the Analytic Hierarchy Process (AHP).

9. The comprehensive decision-making and evaluation method for remediation strategies of halogenated hydrocarbon contaminated soil and groundwater according to claim 1, characterized in that, The environmental footprint in step S2 includes carbon footprint, water footprint, energy footprint, and material footprint.

10. The comprehensive decision-making and evaluation method for remediation strategies of halogenated hydrocarbon contaminated soil and groundwater according to claim 1, characterized in that, The cost-benefit analysis in step S3 converts the total cost of the repair project into net present value or cost-benefit ratio, along with the health risk reduction benefits and land value restoration.

Images