An ecological remediation system and method for acid heavy metal contaminated soil

Abstract

The application relates to the field of contaminated soil ecological restoration, and discloses an acid heavy metal contaminated soil ecological restoration system and method, wherein the acid heavy metal contaminated soil ecological restoration method comprises the following steps: constructing an initial pollution state set representing acidification intensity, metal activity and migration driving force; calling a rhizosphere modulation capacity mapping unit to generate a preliminary intervention scheme; when a monitoring result shows that the rhizosphere modulation effect enters a preset structure transformation interval, taking the structure transformation interval as a trigger condition, starting an aboveground part removal operation corresponding to the partition; after the aboveground part removal operation is completed, using a time sequence window formed by rhizosphere structure exposure and interface flux redistribution to form a composite substrate layer with buffer, adsorption and migration retardation capacity on the pollution interface; inputting the migration flux of the composite substrate layer into a regulation and control logic unit to generate a vegetation configuration scheme and an intervention time strategy. The application has the advantages of improving the soil vegetation restoration efficiency of a mining area.

Description

Technical Field

[0001] This invention relates to the field of ecological remediation of contaminated soil, specifically to an ecological remediation system and method for acidic heavy metal contaminated soil. Background Technology

[0002] Southern my country, including provinces such as Hunan, Jiangxi, Guangdong, and Anhui, is rich in metallic mineral resources, with numerous polymetallic mines such as copper and iron ore. Long-term mining operations have resulted in tailings and waste rock often stored in open-pit piles. Under natural weathering and leaching by precipitation, the sulfide minerals in the tailings, primarily pyrite, undergo oxidation reactions catalyzed by microorganisms such as *Thiobacillus ferrooxidans*, producing sulfuric acid and releasing heavy metal ions such as Fe²⁺, Mn²⁺, Cu²⁺, Zn²⁺, and Cd²⁺. This leads to severe soil acidification in the mining areas, with pH values ​​often between 2.5 and 4.0, forming typical acidic polluted soils.

[0003] Under these conditions of combined acid and heavy metal pollution, traditional vegetation restoration faces multiple challenges: Limited plant growth: High soil acidity and heavy metal concentrations lead to low seed germination rates, hindered root development, and difficulty in biomass accumulation; A conflict between tolerance and biomass: Acid-tolerant and heavy metal-accumulating plants typically have limited biomass and poor adaptability; while remediation plants with high biomass lack tolerance and struggle to survive long-term in acidic heavy metal environments; Defective vegetation configuration: Existing vegetation is often structurally simple, has weak ecological functions, and poor system stability, making it prone to degradation and hindering long-term ecological restoration goals; High risk of secondary pollution: Acidic mine wastewater and heavy metals migrate with rainfall runoff, and existing soil and vegetation measures are insufficient to effectively block pollution diffusion, causing secondary pollution of surface water and groundwater; Existing vegetation restoration methods cannot simultaneously address stress resistance, biomass accumulation, and system stability, making it difficult to achieve long-term, sustainable, and systematic ecological restoration goals.

[0004] Therefore, it is essential to design an ecological remediation system and method for acidic heavy metal contaminated soil that can improve the efficiency of vegetation restoration in mining areas. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an ecological remediation system and method for acidic heavy metal contaminated soil, which has the advantage of improving the efficiency of soil vegetation restoration in mining areas and solves the problems mentioned in the background technology.

[0006] To achieve the aforementioned goal of improving the efficiency of soil vegetation restoration in mining areas, this invention provides the following technical solution: a method for ecological remediation of acidic heavy metal contaminated soil, comprising the following steps:

[0007] The distribution of hydrogen ion activity, the concentration distribution of mobile and complexed heavy metal ions, and the characteristics of metal migration flux under rainfall and runoff conditions were collected in the soil of the area to be remediated to construct an initial pollution state set characterizing acidification intensity, metal activity, and migration driving force.

[0008] Based on the initial pollution state set, acidity regulating media, mineral adsorbents and complexation inhibition components are applied to the soil. An initial buffer and fixation structure is formed by mixing, injection or layered covering. The rhizosphere modulation capacity mapping unit is called to match the root systems of candidate plants in terms of pore disturbance, ion complexation, pH buffering and heavy metal fixation capacity to form a root type combination strategy. Combined with the spatial layout density and coverage ratio of the first round of intervention vegetation, a preliminary intervention plan is generated.

[0009] Planting was carried out according to the preliminary intervention plan, and the changes in rhizosphere oxygen flux, carbon dioxide release rate, dissolved organic carbon and aboveground functional decline index were continuously monitored. When the monitoring results showed that the rhizosphere modulation effect entered the preset structural transformation interval, the aboveground removal operation of the corresponding zone was initiated using the structural transformation interval as the trigger condition.

[0010] After the aboveground removal operation is completed, the time window formed by the exposure of rhizosphere structure and the redistribution of interface flux is used to apply functional microbial combinations, organic matrix replenishment and water-air boundary regulation conditions to the rhizosphere interface in a targeted manner, so as to form a composite matrix layer with buffering, adsorption and migration blocking capabilities at the polluted interface.

[0011] The migration flux of the composite matrix layer is input into the control logic unit, and the vegetation configuration and intervention rhythm are dynamically corrected based on the deviation between the target stable intervals to generate vegetation configuration schemes and intervention timing strategies.

[0012] Preferably, the process of constructing an initial contamination state set characterizing acidification intensity, metal activity, and migration driving forces is as follows:

[0013] A multi-point soil sampling network was deployed in the area to be remediated to collect soil samples from different depths for pH value, concentration of exchangeable heavy metal ions, and composition of complexed heavy metals.

[0014] Metal migration flux parameters under rainfall and runoff conditions were obtained through microfluidic solution infiltration experiments. Combined with on-site temperature and humidity, soil porosity, and integrated soil samples, pH value, heavy metal concentration, and migration flux parameters, a multidimensional pollution factor matrix was formed.

[0015] Based on the various indicators in the multidimensional pollution factor matrix, the acidification index, heavy metal activity index, and migration potential index are calculated and integrated into an initial pollution state set.

[0016] The preferred process for forming a root-type combination strategy is as follows:

[0017] The acidification intensity, heavy metal activity, and migration potential of the initial pollution state are input into the rhizosphere modulation capability mapping unit.

[0018] By simulating the regulatory effects of different plant root systems under soil pore disturbance and ion complexation conditions, a coupled dataset of root systems and pollutants is generated.

[0019] By combining quantitative indicators of the root system's pH buffering capacity and heavy metal fixation capacity, a multi-objective optimization algorithm is used to form the optimal root type combination strategy.

[0020] Preferably, the process of generating a preliminary intervention plan is as follows:

[0021] Based on the root type combination strategy, the area to be remediated is spatially divided, and soil heterogeneity, porosity and local moisture distribution information are comprehensively marked with pollution intensity indicators to form zoning management units.

[0022] For each zone management unit, the candidate root type is initially determined based on root modulation capacity, pH buffer index and heavy metal fixation potential, and then fine-tuned according to soil moisture and porosity distribution.

[0023] Set vegetation cover ratio gradients within each zone;

[0024] The deployment density and coverage ratio are evaluated by zoning and superimposing, and the potential contribution of different combinations to pollution buffering and migration inhibition is predicted by simulating the intervention effect, so as to formulate adjustment strategies.

[0025] Finally, the root type deployment density, coverage ratio, and control strategy of each zone are integrated to generate a preliminary intervention plan that includes a spatial distribution layout map, initial coverage ratio, and execution sequence.

[0026] Preferably, the process of continuously monitoring rhizosphere oxygen flux, carbon dioxide release rate, dissolved organic carbon changes, and aboveground functional decline index is as follows:

[0027] According to the preliminary intervention plan, the root type layout density, coverage ratio and spatial layout of each zone management unit will be determined.

[0028] After the vegetation is laid out, a soil micro-sensor network and plant physiological monitoring nodes are deployed to collect high-frequency data on rhizosphere oxygen flux, carbon dioxide release rate and soil moisture dynamics in each zone.

[0029] By using optical sensors and periodic sampling analysis, the time-series changes of dissolved organic carbon content and aboveground functional decline index are obtained, forming a dynamic monitoring dataset across spatial zones;

[0030] The collected monitoring data is correlated with the root type distribution and coverage ratio information in the preliminary intervention plan to assess the progress of the rhizosphere modulation effect and identify hotspot areas in each zone that do not reach the preset threshold.

[0031] Preferably, the process of triggering the aboveground removal operation when the rhizosphere modulation effect is detected to enter the preset structural transformation range is as follows:

[0032] Based on the identified hotspot area information, vegetation type of each zone, root type density and coverage ratio, the vegetation units in the hotspot area are classified and divided into hierarchical divisions, and a removal execution sequence is generated in order of priority.

[0033] Guided by the removal execution sequence, partial removal and whole-plant pruning are carried out in stages for each vegetation unit according to plant species, root depth, aboveground growth stage and spatial distribution.

[0034] Preferably, the process of forming a composite matrix layer with buffering, adsorption, and migration-blocking capabilities at the contaminated interface is as follows:

[0035] The removed rhizosphere area was designated as a functional enhancement zone to accurately locate pollution hotspots and acidification intensity gradients.

[0036] Functional microbial combinations and organic matrix replenishment are applied according to the characteristics of the pollution interface, while soil moisture and gas boundary conditions are regulated.

[0037] A composite matrix layer is formed by continuously monitoring the density of microbial communities, the distribution of organic matrix, and migration flux.

[0038] Preferably, the process of inputting the composite matrix layer migration flux into the control logic unit is as follows:

[0039] High-frequency sampling was performed on the heavy metal migration flux, pH buffer changes and soil moisture dynamics in the composite matrix layer. The data were normalized according to the sampling location, depth and time sequence to form the composite matrix layer migration flux data.

[0040] The migration flux data of the composite matrix layer is input into the control logic unit, and the deviation value and correction coefficient are calculated by combining the pollution stability target range, historical intervention operation data and rhizosphere modulation process.

[0041] Based on the calculation results, a zoning correction strategy is generated, including vegetation adjustment suggestions, microbial deployment amount, and organic substrate replenishment amount.

[0042] Preferably, the process of generating vegetation configuration schemes and intervention timing strategies is as follows:

[0043] Based on the zoning correction strategy, determine the root layout scheme and adjustment range for each zoning;

[0044] Based on the evolution curve of rhizosphere modulation effect and pollution buffering progress, dynamically adjust vegetation configuration density, coverage ratio and the time window for the next round of intervention;

[0045] The adjustment results of each zone are summarized to generate the final comprehensive vegetation configuration plan and intervention time strategy.

[0046] This invention also discloses another technical solution, an ecological remediation system for acidic heavy metal contaminated soil, comprising:

[0047] Status acquisition module: Collects soil pH, heavy metal concentration and migration flux to form an initial pollution status set;

[0048] Root matching module: Matches plant root capabilities to the initial pollution state and generates a preliminary intervention plan based on spatial layout;

[0049] Monitoring and control module: Implements planting and monitors rhizosphere and aboveground indicators; triggers removal operation when the modulation effect reaches the preset range.

[0050] Composite building blocks: Microorganisms and organic matrices are applied to the rhizosphere interface to form a composite matrix layer;

[0051] Strategy control module: Input the composite matrix migration flux and dynamically adjust the vegetation configuration and intervention rhythm to generate vegetation configuration schemes and intervention timing strategies.

[0052] Compared with existing technologies, the present invention provides an ecological remediation system and method for acidic heavy metal contaminated soil, which has the following beneficial effects:

[0053] This invention constructs an initial pollution state set based on high-frequency data acquisition of hydrogen ion activity, mobile and complexed heavy metal concentrations, and metal migration flux under rainfall and runoff conditions in the soil of the area to be remediated. This set is then combined with a rhizosphere modulation capacity mapping unit to optimize and match candidate plant root systems in terms of pore disturbance, ion complexation, pH buffering, and heavy metal fixation capacity, forming a root type combination strategy. Furthermore, vegetation density and coverage are dynamically adjusted according to zonal management units to generate a preliminary intervention plan. During planting, rhizosphere oxygen flux, carbon dioxide release rate, dissolved organic carbon changes, and aboveground functional decline index are continuously monitored. When the rhizosphere modulation effect... When the system enters the preset structural transformation zone, it triggers the aboveground removal operation. Simultaneously, it applies functional microbial combinations, organic matrix replenishment, and water-air boundary regulation conditions to the rhizosphere interface, forming a composite matrix layer with buffering, adsorption, and migration-blocking capabilities. Through the regulation logic unit, the migration flux of the composite matrix layer is dynamically analyzed to achieve deviation correction and optimization adjustment of vegetation configuration and intervention rhythm. This can effectively improve the soil buffering capacity and heavy metal fixation capacity in mining areas, optimize vegetation stress resistance, biomass accumulation, and ecosystem stability, and achieve efficient and sustainable ecological restoration of acidic heavy metal contaminated soil. It significantly overcomes the shortcomings of traditional vegetation restoration in terms of long-term survival, system stability, and secondary pollution control. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the method of the present invention;

[0055] Figure 2 This is a schematic diagram of the structure of the present invention;

[0056] Figure 3 This is a schematic diagram of the composite matrix layer structure of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Example 1: Please refer to Figure 1 An ecological remediation method for acidic heavy metal contaminated soil according to an embodiment of the present invention includes the following steps:

[0059] S1: Collect the hydrogen ion activity distribution, the concentration distribution of mobile and complexed heavy metal ions, and the characteristics of metal migration flux under rainfall and runoff conditions in the soil of the area to be remediated, and construct an initial pollution state set to characterize acidification intensity, metal activity, and migration driving force.

[0060] The process of constructing the initial contamination state set characterizing acidification intensity, metal activity, and migration driving forces in S1 is as follows:

[0061] A multi-point soil sampling network was established in the remediation area to collect soil samples from different depths for pH values, exchangeable heavy metal ion concentrations, and complexed heavy metal components. To comprehensively reflect the characteristics of soil pollution, sampling points were set up in the remediation area at equal intervals or according to topographical zones, typically one sampling point per 50-100 square meters. Different depth layers were set according to soil thickness, such as 0–10 cm, 10–20 cm, and 20–30 cm. A certain amount of soil samples were collected from each layer. The hydrogen ion activity of the collected soil samples was measured using a pH meter, and the exchangeable heavy metal concentrations were determined using standardized chemical analysis methods, such as EDTA chelate titration. At the same time, the composition of complexed heavy metals was determined by acid hydrolysis or complexation extraction methods to ensure that the samples can fully reflect the soil acidification intensity and heavy metal speciation distribution.

[0062] Metal migration flux parameters under rainfall and runoff conditions were obtained through microfluidic solution infiltration experiments. These parameters, combined with on-site temperature and humidity, soil porosity, and integrated soil samples (pH, heavy metal concentration, and migration flux parameters), formed a multidimensional pollution factor matrix. To reduce the discrepancy between the indoor microfluidic solution infiltration experiments and actual field soil conditions, in-situ field infiltration experiments were conducted simultaneously in the target area while obtaining metal migration flux parameters under microfluidic conditions. In-situ infiltration experiments involved setting up infiltration loops or in-situ infiltration columns in typical zones and collecting leachate samples under natural or semi-artificial rainfall conditions. The heavy metal ion migration flux under actual rainfall and runoff was measured and used as on-site control data for the microfluidic experiment results. The migration flux parameters obtained from the in-situ field infiltration experiments were correlated with the microfluidic experiment results at corresponding sampling points to construct a parameter correction model to characterize the differences between indoor and outdoor conditions. The parameter correction model used soil porosity distribution, structural stability index, and temperature... Humidity fluctuation amplitude is used as a correction factor to scale and adjust the weight of metal migration flux under microfluidic experimental conditions, making it closer to the actual migration behavior under heterogeneous field conditions. According to different soil types and structural characteristics, the sampling area is divided into several representative soil units, and corresponding experimental-field mapping relationships are established for each unit. When the field infiltration experimental data of a certain soil unit is insufficient, historical monitoring data or long-term observation statistical parameters of similar soil units are used to compensate and correct the microfluidic experimental results, thereby avoiding the systematic bias of single-point experiments in the overall migration flux assessment. By incorporating the corrected microfluidic migration flux parameters and field in-situ infiltration experimental data into the multidimensional pollution factor matrix, the matrix simultaneously contains high-precision parameters under controllable experimental conditions and realistic constraints under complex field environments, realizing a multi-scale fusion expression of heavy metal migration characteristics, thereby improving the representativeness and reliability of the pollution factor matrix in practical applications.

[0063] Based on the indicators in the multidimensional pollution factor matrix, the acidification index, heavy metal activity index, and migration potential index were calculated and integrated into an initial pollution state set. The original indicators such as pH, buffer capacity, exchangeable and complexed heavy metal concentrations, migration flux, soil porosity, and moisture were dimensionless and standardized to eliminate the influence of dimensional differences on index calculation. Principal component analysis (PCA) was used to perform eigenvalue decomposition on the standardized pollution factor matrix, extracting several principal components that characterize the main variation features of the pollution state. These principal components were used to replace the original highly correlated indicators in index construction. During PCA, the number of principal components was determined by the eigenvalue magnitude and cumulative contribution rate. Based on the loading distribution of each principal component on different pollution factors, corresponding acidification-related principal components, heavy metal activity-related principal components, and migration behavior-related principal components were constructed to reduce the index superposition bias caused by the same pollution mechanism being included multiple times. In the calculation of the heavy metal activity index, the BCR continuous extraction method was introduced. The study classifies and assesses heavy metal speciation, using the contents of exchangeable, reducible, oxidizable, and residual heavy metals as basic inputs and integrating them with bioavailability coefficients for weighted integration. This allows the heavy metal activity index to reflect the actual risk level of different metal speciations in the environment, reducing redundant representations with the migration potential index at the physical migration level. Using principal component scores after dimensionality reduction and migration flux parameters after standardization, the acidification index, heavy metal activity index, and migration potential index are calculated respectively, and the indices are integrated to form an initial pollution state set. Each sampling point and depth layer corresponds to a set of pollution characteristic parameters after redundancy removal, which are used for rhizosphere modulation effect assessment and vegetation intervention strategy simulation. Through the combination of standardization, dimensionality reduction, and speciation classification, the calculation process of pollution indices has a clear mathematical basis and is supported by internationally accepted methods, avoiding redundant measurement and coupling interference between indicators, thereby improving the comparability, stability, and scientific rationality of the initial pollution state set under different scenarios.

[0064] After constructing the initial pollution state set, the concentration of functional microorganisms was set in stages according to the acidification index, heavy metal activity index, and migration potential index corresponding to each sampling point and depth layer. The microbial concentration was expressed as the number of viable bacteria per unit dry soil mass and correlated with the pollution index level. The microbial concentration was set to 1×10⁻⁶. 6 ~1×10 8 When the acidification index and heavy metal activity index are both higher than the preset high-risk threshold, and the migration potential index is in the upper limit range, the microbial release concentration is set at 5 × 10 CFU / g (based on dry soil mass). 7 ~1×10 8The concentration of microorganisms was set at CFU / g to enhance their rapid buffering, fixation, and precipitation capabilities for hydrogen ions and reactive heavy metals. When the pollution index was at a moderate level and the migration potential index showed a downward trend, the concentration of microorganisms was controlled at 1×10⁻⁶. 7 ~5×10 7 CFU / g is used to maintain the functional stability of the microbial community within the composite substrate layer; once the acidification index, heavy metal activity index, and migration potential index all enter the target stable range, the microbial concentration is adjusted to 1×10⁻⁶. 6 ~1×10 7 CFU / g is used as a maintenance replenishment level to avoid excessive metabolism that consumes oxygen flux and organic matrix. During continuous monitoring, the concentration of microorganisms is dynamically adjusted based on changes in principal component scores and the time series trend of pollution indices. When any index deviates from the target range by more than a preset margin during the continuous monitoring period, the concentration is adjusted by increasing or decreasing by 10% to 30% from the original concentration. This achieves a closed-loop match between the buffering, adsorption, and migration inhibition capacity of the composite matrix layer and changes in pollution status. Through parameterization, the concentration of microorganisms is directly driven by quantitative indices from the initial pollution state, enhancing the feasibility, repeatability, and cross-regional comparability of microbial control measures during ecological restoration.

[0065] S2: Based on the initial pollution state set, acidity regulating media, mineral adsorbents and complexation inhibition components are applied to the soil. An initial buffer and fixation structure is formed by mixing, injection or layered covering. The rhizosphere modulation capacity mapping unit is called to match the root systems of candidate plants in terms of pore disturbance, ion complexation, pH buffering and heavy metal fixation capacity to form a root type combination strategy. Combined with the spatial layout density and coverage ratio of the first round of intervention vegetation, a preliminary intervention plan is generated.

[0066] The process of forming a radical combination strategy in S2 is as follows:

[0067] The initial pollution state set, including acidification intensity, heavy metal activity, and migration potential, is input into the rhizosphere modulation capability mapping unit. The initial improvement process involves applying acidity regulating media, mineral adsorbents, and complexation inhibition components to the target soil based on these parameters. Through mixing, targeted injection, or layered coverage, the introduced components form a stable initial buffer and fixation structure within the polluted soil. The acidity regulating media adjusts the soil pH and reduces acidification. The mineral adsorbent provides surface active sites for adsorbing and fixing heavy metal ions. The complexation inhibition component weakens the migration ability of heavy metals in the soil pore water, thus forming a basic stable structure within the soil with pH buffering capacity, ion adsorption capacity, and complexation inhibition capacity. After the initial improved buffer and fixation structure is formed, the distribution concentration of the acidity regulating media, the adsorption capacity of the mineral adsorbent, and the complexation inhibition component are monitored within the soil. The effective range of action of the components is parameterized to generate a set of buffer fixation structure parameters. This set of parameters, along with the acidification intensity, heavy metal activity, and migration potential of the initially improved pollution state set, is input into the rhizosphere modulation capacity mapping unit. The rhizosphere modulation capacity mapping unit constructs a quantitative mapping model based on multidimensional root functional parameters, transforming root pore disturbance intensity, ion complexation capacity, pH buffering capacity, and heavy metal fixation capacity into standardized feature vectors. Through weighted evaluation matrix and similarity matching calculations, the adaptation relationship between different plant root types and the parameter space of the initially improved pollution state is determined to output root type combination strategies. This unit has multidimensional data processing capabilities and can analyze the distribution characteristics of soil acidification degree, heavy metal activity, and migration potential at different spatial points and depth layers. Based on various indicators, a soil pollution-root system interaction simulation environment is established to ensure that the pollution characteristics of each sampling point can be mapped to the subsequent root selection and modulation capacity assessment, providing basic data support for the generation of root type combination strategies.

[0068] By simulating the regulatory effects of different plant root systems under soil pore disturbance and ion complexation conditions, a coupled dataset of root systems and pollutants is generated. In the mapping unit, simulation experiments are conducted on candidate plant root types to examine their rhizosphere effects under different soil pore structures and ion complexation environments, including the root system's ability to disturb soil micropores, ion complexation and capture efficiency, and regulatory ability on the surrounding soil microenvironment. Through numerical simulation or experimental verification, the regulatory ability of each root type is correlated with the initial pollution state, generating a coupled dataset of root systems and pollutants, providing a quantifiable basis for combinatorial optimization.

[0069] Combining quantitative indicators of root system pH buffering capacity and heavy metal fixation capacity, a multi-objective optimization algorithm is employed to formulate the optimal root type combination strategy. The multi-objective optimization algorithm uses quantitative indicators of root system's ability to regulate the soil environment as input to the objective function. The objective function includes, but is not limited to: an acidification buffering index characterizing the effect of rhizosphere pH stabilization; a metal activity inhibition index characterizing heavy metal fixation and adsorption capacity; and an ecological adaptation index reflecting the sustainability of root growth under specific soil conditions. Each objective function is normalized to a comparable dimensionless parameter. The weight allocation of each objective function is set based on the dominant pollution characteristics of the target area. When the acidification level of the area exceeds a preset threshold, the weight of the acidification buffering index in the overall objective function is increased; when the risk of heavy metal migration is dominant, the weight of the metal activity inhibition index is increased. The weight ratio is determined jointly through historical monitoring data, field experimental results, and expert experience rules, and is dynamically fine-tuned during the optimization iteration process based on feedback from the regulation effect. Taking a genetic algorithm as an example, the optimization process of the root type combination strategy includes encoding candidate root type combinations into a chromosome structure, where each... Each gene locus corresponds to a root type and its distribution ratio. A weighted objective function is used as the fitness function to evaluate the comprehensive performance of each root type combination in terms of pH buffering capacity, heavy metal fixation capacity, and ecological adaptability. Under preset population size, crossover probability, and mutation probability parameters, multiple generations of iterations are performed to gradually screen and evolve the root type combination with the best fitness. The acidification index and heavy metal activity coefficient of the sampling area are used as input parameters and substituted into the calculation models of acidification buffering index, metal activity inhibition index, and ecological adaptability index, respectively. After multiple generations of optimization iterations, the optimal root type combination results corresponding to different pollution scenarios are output. For example, in the high acidification and high migration risk scenario, a combination strategy with deep root type and high buffering capacity root system as the main components and shallow root type as the auxiliary components is generated. In the moderate pollution scenario, a mixed root type configuration scheme that takes into account both coverage efficiency and ecological stability is generated. The source and adjustment mechanism of the objective function, weight allocation, and parameter settings in the multi-objective optimization algorithm are clarified, so that the generation process of root type combination strategy has clear quantitative basis and reproducibility, thereby improving the applicability and scientific rationality of the initial vegetation intervention scheme under different pollution conditions.

[0070] The process of generating the initial intervention plan in S2 is as follows:

[0071] Based on the root type combination strategy, the area to be remediated is spatially divided. Soil heterogeneity, porosity, and local moisture distribution information are comprehensively marked with pollution intensity indicators to form zonal management units. Based on the root type combination strategy, the area to be remediated is spatially divided. Combining soil heterogeneity information, porosity, and local moisture distribution, each small area is marked. Pollution intensity indicators are superimposed to form zonal management units. In actual operation, each zonal management unit corresponds to an independently controllable vegetation layout unit, which facilitates differentiated management and control of different soil conditions.

[0072] For each zone management unit, the candidate root types are initially determined based on root modulation capacity, pH buffering index, and heavy metal fixation potential, and then fine-tuned according to soil moisture and porosity distribution. Within each zone management unit, the candidate root types are initially deployed, and an initial deployment density range is assigned to each root type based on root modulation capacity, pH buffering capacity, and heavy metal fixation potential. At the same time, the deployment density is fine-tuned in combination with soil moisture content and porosity to ensure that the plant roots in each zone can fully play their role in pollution buffering and metal fixation.

[0073] A vegetation cover ratio gradient is set within each zone. Within each zone, a vegetation cover ratio gradient is set according to the planting density and soil carrying capacity. The cover ratio gradient is distributed from low to high to facilitate the simulation of the impact of different cover levels on the pollution buffering effect and to provide a parameter basis for optimization.

[0074] A zoned overlay assessment of planting density and coverage ratio was conducted. By simulating intervention effects, the potential contributions of different combinations to pollution buffering and migration inhibition were predicted, leading to adjustment strategies. The root type planting density and coverage ratio within each zone were overlaid for assessment. The contributions of each combination to acidification intensity, metal activity, and migration inhibition capacity were calculated. The overall pollution buffering capacity was predicted using a simulated intervention effect method, and adjustment strategies for different combinations were generated. This provides a basis for optimizing the initial intervention plan. Vegetation cover ratio gradients include, but are not limited to, the following ranges:

[0075] Low cover gradient zone: vegetation cover ratio is 20% to 35%, which is suitable for zones with high acidification and high risk of heavy metal migration. It is used to reduce rhizosphere oxygen consumption intensity and reserve interface space for subsequent structural transformation and composite matrix construction.

[0076] Medium cover gradient range: vegetation cover ratio is 35% to 55%, suitable for areas with moderate acidification and migration risk, used to balance rhizosphere modulation capacity and soil aeration conditions, forming a stable rhizosphere regulation state.

[0077] High coverage gradient zone: The vegetation coverage ratio is 55% to 75%, which is suitable for zones with low acidification and stable pollution. It is used to enhance the root buffering effect and heavy metal fixation capacity, and improve the efficiency of ecological coverage.

[0078] In the actual deployment process, based on the comprehensive levels of the initial pollution state of each zone, including the acidification index, heavy metal activity index, and migration potential index, the vegetation cover configuration scheme is refined within the above-mentioned coverage ratio gradient range to form a zone-specific vegetation cover configuration scheme. When the rhizosphere modulation effect is detected to be close to the preset structural transformation range, the coverage ratio gradient can be reduced to enter the low coverage range to trigger the removal of the aboveground parts and the directional construction of the subsequent composite matrix layer. By setting the range of coverage ratio gradient values, vegetation cover regulation has clear parameter boundaries and dynamic adjustment basis, avoiding the problems of rhizosphere hypoxia due to excessively high coverage ratio or insufficient modulation capacity due to excessively low coverage ratio.

[0079] Finally, the root type density, coverage ratio, and control strategies of each zone are integrated to generate a preliminary intervention plan that includes a spatial distribution layout map, initial coverage ratio, and execution sequence. The root type density, vegetation coverage ratio, and control strategies of each zone are integrated to form a complete preliminary intervention plan, including a spatial distribution layout map, initial coverage ratio, and execution sequence, to ensure that planting and control can be carried out in accordance with the plan during actual planting, facilitating monitoring of effects and dynamic adjustments.

[0080] S3: Planting is carried out according to the preliminary intervention plan, and the changes in rhizosphere oxygen flux, carbon dioxide release rate, dissolved organic carbon and aboveground functional decline index are continuously monitored. When the monitoring results show that the rhizosphere modulation effect enters the preset structural transformation interval, the aboveground removal operation of the corresponding zone is initiated using the structural transformation interval as the trigger condition.

[0081] The process of continuously monitoring rhizosphere oxygen flux, carbon dioxide release rate, dissolved organic carbon changes, and aboveground functional decline index in S3 is as follows:

[0082] According to the preliminary intervention plan, the root type density, coverage ratio and spatial layout of each zone management unit will be implemented on site. By clarifying the planting location and root type combination of each zone, spatial reference and comparison basis will be provided for monitoring, ensuring that the monitoring data can accurately correspond to the vegetation layout plan of each zone.

[0083] After vegetation deployment, a soil microsensor network and plant physiological monitoring nodes are deployed to collect high-frequency data on rhizosphere oxygen flux, carbon dioxide release rate, and soil moisture dynamics in each zone. The soil microsensor network, which includes oxygen sensors, carbon dioxide sensors, and moisture sensors, is evenly distributed in each zone to collect high-frequency data on rhizosphere oxygen flux, carbon dioxide release rate, and soil moisture dynamics, ensuring the spatiotemporal continuity and accuracy of the data.

[0084] By using optical sensors and regular sampling analysis, time-series changes in dissolved organic carbon content and aboveground functional decline index are obtained, forming a dynamic monitoring dataset across spatial zones. Using optical sensors and regular soil sampling analysis, time-series data on dissolved organic carbon content and aboveground functional decline index are acquired, including determination of organic carbon content after soil solution extraction, and assessment of aboveground decline degree through leaf vigor measurement, canopy photosynthetic rate, or biomass changes. This provides continuous physiological indicator support for the rhizosphere modulation effect in each zone. The collected high-frequency rhizosphere oxygen flux, carbon dioxide release rate, soil moisture, dissolved organic carbon, and aboveground functional decline index data are integrated to form a dynamic monitoring dataset across spatial zones. Time-series analysis and spatial mapping of the data are performed to ensure synchronous comparison of monitoring indicators across zones, reflecting the dynamic changes in the rhizosphere modulation effect.

[0085] The collected monitoring data is correlated with the root type distribution and coverage ratio information in the preliminary intervention plan to assess the progress of the rhizosphere modulation effect and identify hotspot areas in each zone that do not reach the preset threshold. The dynamic monitoring data is correlated with the root type layout, coverage ratio and zoning information in the preliminary intervention plan to assess the actual progress of the rhizosphere modulation effect in each zone. By comparing the indicators of each zone with the preset threshold, hotspot areas that do not achieve the target control effect are identified, providing a basis for adjustment of aboveground removal operations or composite matrix application.

[0086] Soil microsensor networks for monitoring rhizosphere oxygen flux and carbon dioxide release rate preferably employ acid-resistant solid-state electrode sensors or optical sensors. The sensor's sensitive elements are coated with an inert protective coating or ceramic encapsulation layer to resist the corrosive effects of high hydrogen ion activity and dissolved metal ions in highly acidic soil environments, ensuring long-term stability and signal reliability under pH 2.5–4.0 conditions. Regarding sensor deployment, the sensor nodes within each zone are arranged in a gridded or semi-grid pattern based on the area of ​​the zoned management unit, root type density, and spatial heterogeneity. In a preferred embodiment, rhizosphere oxygen flux and carbon dioxide release rate monitoring nodes are deployed in a gridded pattern within the remediation area, with at least one rhizosphere oxygen flux and one carbon dioxide release rate monitoring node in each monitoring grid unit. For areas with concentrated root distribution or significant changes in pollution gradients, supplementary data collection is achieved by densifying the node deployment or using mobile monitoring devices to improve local rhizosphere monitoring. To improve the spatial resolution of process parameters, the soil microsensor network also includes acid-resistant moisture sensors paired with oxygen and carbon dioxide sensors. These sensors are used to synchronously collect soil moisture data, avoiding interference from drastic moisture fluctuations in gas diffusion rate measurements. This improves the accuracy of rhizosphere oxygen flux and carbon dioxide release rate calculations. To ensure data consistency and comparability during long-term high-frequency data acquisition, a periodic calibration and maintenance strategy is implemented for various sensors during system operation. This includes on-site calibration using standard gases or control soil samples at preset time intervals, zero-point and sensitivity correction of sensor output signals, and identification of drift or failure points based on sensor self-test results for timely replacement or repair. This ensures the stability and traceability of continuous monitoring data in highly acidic soil environments. Through the selection of acid-resistant sensors, deployment density control, and calibration and maintenance mechanisms, high-frequency acquisition of key parameters such as rhizosphere oxygen flux and carbon dioxide release rate has clear implementation conditions and engineering feasibility.

[0087] The process of initiating the aboveground removal operation for the corresponding partition in S3 is as follows:

[0088] Based on the identified hotspot area information, vegetation type, root type density, and coverage ratio of each zone, vegetation units within the hotspot area are classified and a priority-ordered removal execution sequence is generated. Based on the information of hotspot areas where the rhizosphere modulation effect is not up to standard identified by dynamic monitoring, combined with the vegetation type, root type density, and coverage ratio within each zone, vegetation units within the hotspot area are classified and a priority-ordered removal execution sequence is generated according to the size of the modulation effect gap, vegetation growth stage, and spatial connectivity, providing a clear execution order and key areas for aboveground removal operations.

[0089] Guided by the removal execution sequence, partial removal and whole-plant pruning are carried out in stages for each vegetation unit according to plant species, root depth, aboveground growth stage and spatial distribution. According to the removal execution sequence, each vegetation unit is classified and managed according to plant species, root depth, aboveground growth stage and spatial distribution, and partial removal or whole-plant pruning operations are carried out in stages, including partial pruning with shears or mowing equipment, and mechanical whole-plant removal when necessary, in order to regulate rhizosphere oxygen flux and soil chemical environment, ensure that the rhizosphere modulation effect enters the preset structural transformation range, and at the same time ensure the accuracy and safety of the operation process.

[0090] The pre-defined structural transformation interval is a target interval obtained through continuous monitoring of key modulatory indicators of the rhizosphere environment and experimental calibration. It characterizes the critical stage of the rhizosphere environment transitioning from a normal steady state to a structural remodeling state. Key modulatory indicators include, but are not limited to, one or more of rhizosphere oxygen flux, dissolved active carbon concentration, and microbial metabolic activity intensity. Rhizosphere oxygen flux is characterized by the rate of oxygen diffusion through the rhizosphere interface per unit time. When the monitored value consistently falls below the pre-calibrated first threshold interval, it indicates a shift in the local rhizosphere environment from an aerobic-dominated state to a hypoxic or microanaerobic state, with a structural imbalance in root respiration and microbial oxygen consumption. The first threshold interval can be obtained through control experiments on the target vegetation type under different cover conditions and determined based on relevant soil ecological experimental results. Dissolved active carbon concentration characterizes the release level of mineral surface-bound carbon components and the carbon source pulse characteristics at the rhizosphere interface under strongly acidic conditions. When the monitored value abnormally increases in the pore water per unit volume of soil and exceeds the pre-defined threshold interval, it indicates a shift in the local rhizosphere environment from an aerobic-dominated state to a hypoxic or microanaerobic state. The second threshold range indicates that under low pH conditions, mineral-bound organic residues, root exudates, or microbial metabolites undergo desorption, reactivation, or rapid release, causing the rhizosphere carbon source supply mode to change from a slow-release state to a pulsed input state. The second threshold range is determined based on the results of rhizosphere interface simulation experiments under strongly acidic soil conditions and statistical results of the dynamic fluctuation range of dissolved active carbon in low-carbon soils from relevant literature. The preset structural transformation range is identified through a multi-indicator joint judgment method. That is, when the rhizosphere oxygen flux is lower than the first threshold range and the dissolved active carbon concentration is higher than the second threshold range, and continues to exceed the preset time window, it is determined that the rhizosphere modulation effect has entered the structural transformation range, thereby triggering the aboveground removal operation in the corresponding hot spot area. Through quantitative judgment, the aboveground removal operation no longer relies on experience judgment, but is based on monitorable and repeatable rhizosphere environmental parameters, realizing the fine regulation of rhizosphere oxygen supply status, interface reaction intensity, and soil chemical structure evolution process.

[0091] S4: After the aboveground removal operation is completed, the time window formed by the exposure of the rhizosphere structure and the redistribution of the interface flux is used to apply functional microbial combinations, organic matrix replenishment and water-air boundary regulation conditions to the rhizosphere interface in a targeted manner, so as to form a composite matrix layer with buffering, adsorption and migration blocking capabilities at the polluted interface.

[0092] The process of forming a composite matrix layer with buffering, adsorption, and migration-blocking capabilities at the contamination interface in S4 is as follows:

[0093] The removed rhizosphere area is designated as the functional enhancement zone, and pollution hotspots and acidification intensity gradients are precisely located. After the aboveground removal operation is completed, the removed rhizosphere area is spatially delineated, and pollution hotspots and soil acidification intensity gradients are used as the basis for the division of the functional enhancement zone. Combined with soil pore distribution and root residue location, precise positioning is achieved, providing a clear regional boundary and pollution characteristic basis for the construction of the composite matrix layer. The functional enhancement zone can be digitally divided using a combination of GPS positioning and soil conductivity measurement, forming an executable spatial management map.

[0094] Functional microbial combinations and organic matrix replenishment are deployed according to the characteristics of the pollution interface, while simultaneously regulating soil moisture and gas boundary conditions. In the functional enhancement zone, functional microbial combinations and organic matrix replenishment are deployed in a directional manner according to the spatial characteristics of the pollution interface and the pollution intensity gradient, while simultaneously regulating soil moisture and gas boundary conditions to promote the uniformity of microbial activity and matrix distribution. This includes using micro-injectors to uniformly inject microbial solutions into soil pores, laying organic matrix particles, and regulating moisture through irrigation or spraying, while utilizing soil aeration pores to regulate oxygen and carbon dioxide concentrations to ensure the formation conditions of the composite matrix layer.

[0095] A composite matrix layer is formed by continuously monitoring microbial community density, organic matrix distribution, and migration flux. The buffering, adsorption, and migration blocking capabilities of the composite matrix layer are dynamically evaluated by continuously monitoring microbial community density, organic matrix distribution, and metal migration flux. Microbial supply and water and air conditions are adjusted according to the monitoring results until a uniform and functionally stable composite matrix layer is formed. A micro-sensor network and soil micro-sampling wells can be deployed to periodically analyze microbial activity, organic matrix content, and migration flux parameters, thereby achieving precise construction and functional verification of the composite matrix layer.

[0096] To verify the long-term buffering, adsorption, and migration-blocking capabilities of the composite matrix layer under strong acid conditions, a stability verification mechanism combining accelerated aging tests and long-term in-situ monitoring was introduced after the composite matrix layer was constructed. The accelerated aging test simulated the structural and functional evolution of the composite matrix layer under years of rainfall, runoff, and acidification by increasing the frequency of temperature and humidity fluctuations, enhancing the scouring intensity of the acidic solution, and increasing the number of wet-dry cycles under controlled conditions. During the test, changes in the microbial community composition, organic matrix degradation rate, and heavy metal adsorption capacity in the composite matrix were periodically monitored to assess the functional decline trend. The rate of change under accelerated aging conditions was scaled against the annual average change parameters of the actual environment to estimate the effective service life of the composite matrix layer under natural conditions. Long-term monitoring sample areas were set up within the functional enhancement zone to monitor the composite matrix layer. For no less than five years, in-situ continuous monitoring will be conducted. The long-term monitoring content includes the microbial community density and community structure succession characteristics, organic matrix content and physicochemical stability indicators, and the long-term trend of heavy metal migration flux. Time series data will be generated through annual or seasonal sampling to verify the functional sustainability of the composite matrix layer in actual strongly acidic soil environments. During the monitoring and evaluation process, when significant functional shifts in the microbial community or degradation of the organic matrix leading to a decrease in adsorption capacity are detected, the composite matrix layer will be regenerated and maintained by supplementing microorganisms or matrix materials and adjusting water and air conditions, thereby extending its effective migration inhibition time window. Through a verification method combining accelerated aging tests and long-term in-situ monitoring, it will be demonstrated that the composite matrix layer has at least five years of structural stability and functional durability in strongly acidic soil environments with pH 2.5 to 4.0.

[0097] S5: Input the migration flux of the composite matrix layer into the control logic unit, and dynamically correct the vegetation configuration and intervention rhythm based on the deviation between the target stable intervals to generate vegetation configuration schemes and intervention timing strategies.

[0098] The process of inputting the composite matrix layer migration flux into the control logic unit in S5 is as follows:

[0099] High-frequency sampling was conducted on the heavy metal migration flux, pH buffer changes, and soil moisture dynamics within the composite matrix layer. The data were normalized according to sampling location, depth, and time sequence to form composite matrix layer migration flux data. The sampling covered different depths and spatial locations, and was recorded chronologically. The collected data underwent normalization to eliminate differences in measurement units and the influence of environmental fluctuations, forming a composite matrix layer migration flux dataset suitable for subsequent analysis. This included deploying multiple miniature soil sensors in the functional enhancement area to automatically collect pH, conductivity, moisture content, and heavy metal concentration at preset time intervals, and uploading the data to a centralized processing system for standardization via a data acquisition terminal.

[0100] The composite matrix layer migration flux data is input into the control logic unit. Combined with the pollution stabilization target range, historical intervention operation data, and rhizosphere modulation process, the deviation value and correction coefficient are calculated. The control logic unit constructs a feedback control model based on the deviation between the composite matrix layer migration flux and the target stabilization range. Through the state prediction function and error feedback function, the adjustment parameters of vegetation configuration and intervention rhythm are calculated to realize dynamic closed-loop optimization decision-making on vegetation configuration scheme and intervention timing strategy. The normalized composite matrix layer migration flux data is input into the control logic unit. Combined with the pollution stabilization target range, historical vegetation intervention and microbial release data, and rhizosphere modulation process, the deviation value and corresponding correction coefficient are calculated to evaluate the actual effect of the composite matrix layer on pollution buffering and migration inhibition. Through the software interface of the control logic unit, the sampled data can be imported into the calculation module in real time. The reference curve and early warning threshold are established using historical data, and the deviation index of each zone is automatically generated.

[0101] Based on the calculation results, a zoning correction strategy is generated, including suggestions for vegetation adjustment, microbial input, and organic substrate replenishment. Based on the deviation analysis results, a zoning correction strategy is generated, including suggestions for adjusting vegetation type or coverage ratio, determining microbial replenishment, and organic substrate input, in order to dynamically optimize the ecological restoration effect. Zoning strategies can be established in the control logic unit, mapping deviation values ​​to specific operation parameters, realizing executable adjustment plans for each management unit, and providing execution priority and operation time window to guide on-site implementation.

[0102] Lightweight edge computing modules are integrated into miniature soil sensor nodes or data acquisition terminals deployed within the enhanced functional area. These modules perform local preprocessing operations on raw monitoring data, including outlier removal, short-cycle mean calculation, and initial trend assessment. The preprocessed key indicator data is then uploaded to the centralized processing system via wireless or wired communication, significantly reducing data transmission volume and cloud computing load, and improving overall response efficiency. At the edge computing level, rapid discrimination rules are set for heavy metal migration flux, pH buffer changes, and soil moisture dynamics. When migration flux exceeds a preset safety threshold or shows a sudden increase within a short time window, the edge computing module can directly trigger an emergency response signal, prioritizing the activation of local intervention plans, including temporarily increasing organic matrix replenishment, adjusting water supply, or limiting... External disturbances are controlled to initially suppress pollution risks before the centralized control strategy is generated. At the centralized processing level, the control logic unit performs fine calculations of deviation values ​​and correction coefficients based on the pre-processed data, combined with the pollution stability target range, historical intervention data, and rhizosphere modulation process, and generates a zonal correction strategy. For control needs in non-emergency situations, the centralized processing system updates the strategy according to a preset calculation cycle to avoid excessive system load caused by frequent calculations. Through the introduction of edge computing pre-processing and emergency response mechanisms, the control logic unit can balance real-time performance and computational stability when facing high-frequency monitoring data, reducing the impact of data transmission latency and model calculation time on the ecological restoration control effect, and ensuring the continuous and effective operation of the composite matrix layer migration inhibition function.

[0103] The process of generating vegetation configuration schemes and intervention timing strategies in S5 is as follows:

[0104] Based on the zoning correction strategy, the root type layout scheme and adjustment range for each zone are determined. Based on the zoning correction strategy, the deviation value of each management unit and the buffering effect of the composite matrix layer are analyzed to determine the appropriate root type combination, layout density and coverage ratio adjustment range in each zone in order to optimize the rhizosphere modulation capability. This includes mapping the deviation value to the specific vegetation parameter range, automatically generating adjustment suggestions for each zone through the control logic unit, and outputting an operation parameter table for on-site personnel to refer to.

[0105] Based on the evolution curve of rhizosphere modulation effect and the progress of pollution buffering, the vegetation configuration density, coverage ratio and the time window for the next round of intervention are dynamically adjusted. Combining the evolution curve of rhizosphere modulation effect and the progress of pollution buffering, the vegetation configuration density, coverage ratio and the time window for the next round of intervention in each zone are dynamically determined to ensure that the intervention operation is synchronized with the pollution buffering effect. Real-time rhizosphere modulation and pollution migration data can be obtained using data acquisition and monitoring systems. The impact of different configuration schemes on pollution buffering can be predicted through models, and operation triggering conditions and timing can be set in the control system.

[0106] The adjustment results of each zone are summarized to generate the final integrated vegetation configuration plan and intervention time strategy. The root type layout plan, vegetation density, coverage ratio and intervention time window are integrated to form the final integrated vegetation configuration plan and intervention time strategy. The operation guide or visual layout map is output. The management software can generate the zone layout map and operation plan table, marking the vegetation type, layout density, coverage ratio and corresponding operation time of each zone, which is convenient for on-site implementation and tracking.

[0107] Example 2: Please refer to Figure 2 An ecological remediation system for acidic heavy metal contaminated soil, comprising:

[0108] Status acquisition module: Collects soil pH, heavy metal concentration and migration flux to form an initial pollution status set;

[0109] Root matching module: Matches plant root capabilities to the initial pollution state and generates a preliminary intervention plan based on spatial layout;

[0110] Monitoring and control module: Implements planting and monitors rhizosphere and aboveground indicators; triggers removal operation when the modulation effect reaches the preset range.

[0111] Composite building blocks: Microorganisms and organic matrices are applied to the rhizosphere interface to form a composite matrix layer;

[0112] Strategy control module: Input the composite matrix migration flux and dynamically adjust the vegetation configuration and intervention rhythm to generate vegetation configuration schemes and intervention timing strategies.

[0113] Example 3: Please refer to Figure 3 The construction process of the composite matrix layer includes:

[0114] Structural transformation interval judgment module: Determines the trigger timing for removing the above-ground portion;

[0115] Aboveground removal trigger module: exposes the rhizosphere interface and forms an application window;

[0116] Targeted application module: Constructs a composite matrix layer with migration-blocking capabilities at the rhizosphere interface.

[0117] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0118] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for ecological remediation of acidic heavy metal contaminated soil, characterized in that, Includes the following steps: The distribution of hydrogen ion activity, the concentration distribution of mobile and complexed heavy metal ions, and the characteristics of metal migration flux under rainfall and runoff conditions were collected in the soil of the area to be remediated to construct an initial pollution state set characterizing acidification intensity, metal activity, and migration driving force. Based on the initial pollution state set, acidity regulating media, mineral adsorbents and complexation inhibition components are applied to the soil. An initial buffer and fixation structure is formed by mixing, injection or layered covering. The rhizosphere modulation capacity mapping unit is called to match the root systems of candidate plants in terms of pore disturbance, ion complexation, pH buffering and heavy metal fixation capacity to form a root type combination strategy. Combined with the spatial layout density and coverage ratio of the first round of intervention vegetation, a preliminary intervention plan is generated. Planting was carried out according to the preliminary intervention plan, and the changes in rhizosphere oxygen flux, carbon dioxide release rate, dissolved organic carbon and aboveground functional decline index were continuously monitored. When the monitoring results showed that the rhizosphere modulation effect entered the preset structural transformation interval, the aboveground removal operation of the corresponding zone was initiated using the structural transformation interval as the trigger condition. After the aboveground removal operation is completed, the time window formed by the exposure of rhizosphere structure and the redistribution of interface flux is used to apply functional microbial combinations, organic matrix replenishment and water-air boundary regulation conditions to the rhizosphere interface in a targeted manner, so as to form a composite matrix layer with buffering, adsorption and migration blocking capabilities at the polluted interface. The migration flux of the composite matrix layer is input into the control logic unit, and the vegetation configuration and intervention rhythm are dynamically corrected based on the deviation between the target stable intervals to generate vegetation configuration schemes and intervention timing strategies.

2. The method for ecological remediation of acidic heavy metal contaminated soil according to claim 1, characterized in that, The process of constructing an initial contamination state set characterizing acidification intensity, metal activity, and migration driving forces is as follows: A multi-point soil sampling network was deployed in the area to be remediated to collect soil samples from different depths for pH value, concentration of exchangeable heavy metal ions, and composition of complexed heavy metals. Metal migration flux parameters under rainfall and runoff conditions were obtained through microfluidic solution infiltration experiments. Combined with on-site temperature and humidity, soil porosity, and integrated soil samples, pH value, heavy metal concentration, and migration flux parameters, a multidimensional pollution factor matrix was formed. Based on the various indicators in the multidimensional pollution factor matrix, the acidification index, heavy metal activity index, and migration potential index are calculated and integrated into an initial pollution state set.

3. The method for ecological remediation of acidic heavy metal contaminated soil according to claim 2, characterized in that, The process of forming a root-type combination strategy is as follows: The acidification intensity, heavy metal activity, and migration potential of the initial pollution state are input into the rhizosphere modulation capability mapping unit. By simulating the regulatory effects of different plant root systems under soil pore disturbance and ion complexation conditions, a coupled dataset of root systems and pollutants is generated. By combining quantitative indicators of the root system's pH buffering capacity and heavy metal fixation capacity, a multi-objective optimization algorithm is used to form the optimal root type combination strategy.

4. The method for ecological remediation of acidic heavy metal contaminated soil according to claim 3, characterized in that, The process of generating a preliminary intervention plan is as follows: Based on the root type combination strategy, the area to be remediated is spatially divided, and soil heterogeneity, porosity and local moisture distribution information are comprehensively marked with pollution intensity indicators to form zoning management units. For each candidate root type within a zone management unit, the initial density range is determined based on root modulation capacity, pH buffering index, and heavy metal fixation potential, and then fine-tuned according to soil moisture and porosity distribution. Set vegetation cover ratio gradients within each zone; The deployment density and coverage ratio are evaluated by zoning and superimposing, and the potential contribution of different combinations to pollution buffering and migration inhibition is predicted by simulating the intervention effect, so as to formulate adjustment strategies. Finally, the root type deployment density, coverage ratio, and control strategy of each zone are integrated to generate a preliminary intervention plan that includes a spatial distribution layout map, initial coverage ratio, and execution sequence.

5. A method for ecological remediation of acidic heavy metal contaminated soil according to claim 4, characterized in that, The process of continuously monitoring rhizosphere oxygen flux, carbon dioxide release rate, dissolved organic carbon changes, and aboveground functional decline index is as follows: According to the preliminary intervention plan, the root type layout density, coverage ratio and spatial layout of each zone management unit will be determined. After the vegetation is laid out, a soil micro-sensor network and plant physiological monitoring nodes are deployed to collect high-frequency data on rhizosphere oxygen flux, carbon dioxide release rate and soil moisture dynamics in each zone. By using optical sensors and periodic sampling analysis, the time-series changes of dissolved organic carbon content and aboveground functional decline index are obtained, forming a dynamic monitoring dataset across spatial zones; The collected monitoring data is correlated with the root type distribution and coverage ratio information in the preliminary intervention plan to assess the progress of the rhizosphere modulation effect and identify hotspot areas in each zone that do not reach the preset threshold.

6. The method for ecological remediation of acidic heavy metal contaminated soil according to claim 5, characterized in that, The process of initiating the aboveground removal operation for the corresponding partition is as follows: Based on the identified hotspot area information, vegetation type of each zone, root type density and coverage ratio, the vegetation units in the hotspot area are classified and divided into hierarchical groups, and a removal execution sequence is generated in order of priority. Guided by the removal execution sequence, partial removal and whole-plant pruning are carried out in stages for each vegetation unit according to plant species, root depth, aboveground growth stage and spatial distribution.

7. A method for ecological remediation of acidic heavy metal contaminated soil according to claim 6, characterized in that, The process of forming a composite matrix layer with buffering, adsorption, and migration-blocking capabilities at the contaminated interface is as follows: The removed rhizosphere area was designated as a functional enhancement zone to accurately locate pollution hotspots and acidification intensity gradients. Functional microbial combinations and organic matrix replenishment are applied according to the characteristics of the pollution interface, while soil moisture and gas boundary conditions are regulated. A composite matrix layer is formed by continuously monitoring the density of microbial communities, the distribution of organic matrix, and migration flux.

8. A method for ecological remediation of acidic heavy metal contaminated soil according to claim 7, characterized in that, The process of inputting the composite matrix layer migration flux into the control logic unit is as follows: High-frequency sampling was performed on the heavy metal migration flux, pH buffer changes and soil moisture dynamics in the composite matrix layer. The data were normalized according to the sampling location, depth and time sequence to form the composite matrix layer migration flux data. The migration flux data of the composite matrix layer is input into the control logic unit, and the deviation value and correction coefficient are calculated by combining the pollution stability target range, historical intervention operation data and rhizosphere modulation process. Based on the calculation results, a zoning correction strategy is generated, including vegetation adjustment suggestions, microbial deployment amount, and organic substrate replenishment amount.

9. A method for ecological remediation of acidic heavy metal contaminated soil according to claim 8, characterized in that, The process of generating vegetation configuration schemes and intervention timing strategies is as follows: Based on the zoning correction strategy, determine the root layout scheme and adjustment range for each zoning; Based on the evolution curve of rhizosphere modulation effect and pollution buffering progress, dynamically adjust vegetation configuration density, coverage ratio and the time window for the next round of intervention; The adjustment results of each zone are summarized to generate the final comprehensive vegetation configuration plan and intervention time strategy.

10. An ecological remediation system for acidic heavy metal contaminated soil, applied to the ecological remediation method for acidic heavy metal contaminated soil as described in any one of claims 1-9, characterized in that, include: Status acquisition module: Collects soil pH, heavy metal concentration and migration flux to form an initial pollution status set; Root matching module: Matches plant root capabilities to the initial pollution state and generates a preliminary intervention plan based on spatial layout; Monitoring and control module: Implements planting and monitors rhizosphere and aboveground indicators; triggers removal operation when the modulation effect reaches the preset range. Composite building blocks: Microorganisms and organic matrices are applied to the rhizosphere interface to form a composite matrix layer; Strategy control module: Input the composite matrix migration flux and dynamically adjust the vegetation configuration and intervention rhythm to generate vegetation configuration schemes and intervention timing strategies.

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