Land pollution simulation prediction method and system, storage medium and electronic equipment

Abstract

The invention provides a land pollution simulation prediction method and system, a storage medium and electronic equipment, and relates to the technical field of pollutant prediction. The land pollution simulation and prediction method comprises the following steps: determining an experiment site, collecting basic information, constructing a soil pollution list, and performing soil pollution accumulation simulation and prediction. According to the land pollution simulation and prediction method, the site soil pollution list can be normalized and visually expressed, a pollutant migration simulation mathematical model is established for prediction result analysis, and the site pollutant condition can be conveniently and rapidly simulated and predicted.

Description

Technical Field

[0001] The present invention provides a method, system, storage medium and electronic device for simulating and predicting land pollution, which relates to the technical field of pollutant prediction. Background Art

[0002] With the development of industrialization and urbanization, the amount of polluted liquid discharged from the soil has increased exponentially.

[0003] Chinese Patent CN112434076A discloses a method and system for simulating the migration and early warning of soil pollutants. By sampling the suspected polluted area, the soil in the polluted area can be analyzed separately according to different attributes, so as to simulate the migration of pollutants in soils with different attributes in a targeted manner; by obtaining the simulation function of the pollutant migration speed for different attribute soils respectively, predicting the concentration of pollutants in the soil at a certain moment, and combining with soil data, a visual spatial distribution model diagram is made, so as to facilitate intuitively observing the coverage of pollutants. The simulation and prediction of this patent rely on sampling the suspected polluted area, and it is impossible to establish a simulated pollution situation only through information collection and pollutant list, and the steps are cumbersome.

[0004] Chinese Patent CN116756130A discloses a method for screening and processing soil cadmium environmental ecological toxicity data and its application. By conducting ecological toxicology experiments or retrieving existing literature to obtain ecological toxicity data, a species sensitivity distribution model is established to realize the research on various proportions of species. The model constructed by this patent has a low correct rate and a high error rate in specific implementation, and still needs to be optimized.

[0005] Aiming at the problems existing in the prior art, the present invention provides a method, system, storage medium and electronic device for simulating and predicting land pollution, which is reliable in technology, simple in process, convenient to control experimental conditions, and has less investment. Summary of the Invention

[0006] Aiming at the problems existing in the above-mentioned prior art, the present invention provides a method, system, storage medium and electronic device for simulating and predicting land pollution, and develops a visual and simplified list compilation system tool by determining the experimental site and collecting basic information, constructing a soil pollution list and simulating and predicting soil pollution accumulation.

[0007] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] In the first aspect, the present invention provides a method for simulating and predicting land pollution, including the following steps:

[0009] (1) Determine the experimental site and collect basic information;

[0010] (2) Construct a soil pollution list;

[0011] (3) Soil pollution accumulation simulation and prediction.

[0012] The construction method described in step (2) includes the following steps:

[0013] ① Identify the characteristic pollutants of the site based on the basic information collected in step (1);

[0014] ② Complete the site functional area division and merger based on the characteristic pollutants of the site obtained in step ①;

[0015] ③ Calculate the pollutant input amount into the soil based on the basic information collected in step (1) to obtain the pollutant emissions of different emission pathways;

[0016] ④ Digitalize the characteristic pollutants obtained in step ①, the functional area division and merger obtained in step ②, and the pollutant emissions obtained in step ③ to obtain a soil pollution inventory.

[0017] Specifically, the soil pollution accumulation simulation and prediction described in step (3) include: constructing a site conceptual model through the basic information obtained in step (1), the soil pollution inventory obtained in step (2), establishing a pollutant migration simulation mathematical model and prediction result analysis.

[0018] Specifically, the collection of basic information described in step (1) includes: enterprise basic information, pollution source information, and natural information;

[0019] Specifically, the enterprise basic information includes: enterprise geographical location, enterprise layout, enterprise site utilization history and current situation.

[0020] Specifically, the pollution source information includes: production process, pollution generation characteristics, and pollution discharge characteristics.

[0021] Specifically, the natural information includes: site meteorological conditions, site topography and geomorphology, site surface hydrology, site stratigraphic lithology, and hydrogeology around the site.

[0022] Specifically, the identification of site characteristic pollutants in step ① is: conduct potential pollution identification for each functional unit, determine the functional units, pollution sources, pollution pathways, pollutant types, and pollutant migration pathways with pollution, and identify and summarize the potential characteristic pollutants within the local plot and surrounding areas.

[0023] Specifically, the functional area division and merger in step ② are: combine the pollution source information and enterprise basic information for functional area division and merger.

[0024] Specifically, the different emission pathways in step ③ are: atmospheric deposition, wastewater leakage, solid waste leaching, and storage tank leakage.

[0025] Specifically, the atmospheric deposition includes the emissions from the waste gas discharge outlets and the particulate matter emissions from the solid material storage yards.

[0026] Preferably, the emissions from the waste gas discharge outlets are referred to HJ 772-2022, and the monitoring data method is preferably adopted. When the monitoring data is lacking, the pollution discharge coefficient method can be used for estimation.

[0027] The calculation formula of the monitoring data method is: M = ΣM i *k

[0028] In the formula: M—the sedimentation amount of a certain pollutant in the waste gas, mg / a; M i —the emissions from the main discharge outlets, mg / a; k—the sedimentation coefficient, [0,1], obtained by calculating with the AERMOD model.

[0029] Preferably, the formula of Mi is: M i = Q i × C i × T i / 1000;

[0030] In the formula: M i —the emissions from the main discharge outlets, mg / a; Q i —the air volume (standard condition) of the i-th main discharge outlet, m 3 / h; C i —the emission concentration (standard condition) of a certain pollutant, mg / m 3 ; T i the production time of the i-th discharge outlet, h / a.

[0031] Specifically, the calculation formula of the pollution discharge coefficient method is: M = ΣE i *k;

[0032] In the formula: M—the sedimentation amount of a certain pollutant in the waste gas, mg / a; E i —the emissions of a certain pollutant in process i, mg / a; k—the sedimentation coefficient, [0,1], obtained by calculating with the AERMOD model.

[0033] Preferably, the E i is: E i = ∑(G i - R i ); G i —the average output of a certain pollutant in process i; R i —the removal amount of a certain pollutant in process i.

[0034] Preferably, the G i is: G i = P × M i ;

[0035] Where: G i —— The average output of a certain pollutant in Process i; P—— The pollutant generation coefficient of a certain pollutant in Process i; M i —— The actual product output or production time of Process i.

[0036] Specifically, the R i is: R i = G i × η T × k T ; η T —— The average removal efficiency of a certain pollutant treatment technology in Process i, %; k T —— The actual operation rate of the treatment facility for obtaining a certain pollutant in Process i; k T —— The operation time of the pollution treatment facility (h) / the operation time of the production facility (h).

[0037] Specifically, the calculation formula for the particulate matter emission from the solid material storage yard is: P i,atmo = U c × C 01 × k i,atmo ;

[0038] Where: P i,atmo —— The generation amount of the pollutant, t; C 01 —— The pollutant content, obtained by on-site measurement; k i,atmo —— The sedimentation coefficient, [0,1], calculated using the AERMOD model.

[0039] Specifically, the wastewater leakage includes pipeline leakage, channel leakage, and tank leakage.

[0040] Specifically, the wastewater leakage is preferably accounted for by the monitoring data method (the difference between the wastewater generation amount and the sewage treatment amount). When monitoring data is lacking, the formula can be used for accounting.

[0041] Specifically, the generation amount of the pollutant from pipeline leakage is: P wl1 = Q 1 × C 02

[0042] Where: P wl1 —— The generation amount of the pollutant, mg / d; C 02 —— The pollutant content, mg / m 3 , obtained by on-site measurement.

[0043] Specifically, the Q 1 is the pipeline wastewater seepage volume; Q 1 = α × β × q × L;

[0044] Where: Q 1 —— The pipeline wastewater seepage volume, m3 / d; L—the length of the pipeline, km; α—the coefficient of variation, generally taken as 0.1 - 1.0, and selected according to the anti-seepage ability when special anti-seepage measures are taken for the pipeline; β—the adjustment coefficient, which is given for the dimensional difference of the unit leakage of different pressure pipelines. The value for pressurized pipelines is 3.6, and the value for non-pressurized pipelines and channels is 0.001; q—the unit leakage, L / min×km or L / d×km, which is the unit leakage of pressurized pipelines and non-pressurized pipelines made of different materials.

[0045] Specifically, the leakage amount of pollutants leaked from the channel is: P wl2 =(q 1 +q 2 )×C 03 ;

[0046] In the formula: P wl2 —the leakage amount of pollutants, mg / d; C 03 —the pollutant content, mg / m 3 , obtained through actual measurement.

[0047] Specifically, the q 1 is the allowable seepage amount of the pressure pipe and channel, q 1 =0.014D = 0.014×s / π; the q 2 is the allowable seepage amount of the non-pressurized pipe and channel, q 2 =1.25D = 1.25×s / π;

[0048] In the formula: q 1 —the allowable seepage amount of the pressure pipe and channel, L / (min×km); q 2 —the allowable seepage amount of the non-pressurized pipe and channel, m 3 / (d×km); D—the inner diameter of the pipeline, mm; s—the wetted perimeter of the pipe and channel, mm.

[0049] Specifically, the leakage amount of pollutants leaked from the pool: P wl3 =Q 2 ×C 04 ;

[0050] In the formula: P wl3 —the leakage amount of pollutants, mg / d; C 04 —the pollutant content, mg / m 3 , obtained through actual measurement.

[0051] Specifically, the Q 2 is the seepage amount of the pool, Q 2 =α×q×(S 底 +S 侧 )×10 -3 ;

[0052] In the formula: Q 2 —— Leakage volume of the pond body, m 3 / d; S 底 —— Bottom area of the pond, m 2 ; S 侧 —— Wetted area of the pond wall, m 2 ; α —— Variation coefficient, generally taken as 0.1 - 1.0. When special anti-seepage measures such as anti-seepage coating and anti-seepage cement are adopted for the pond structure, it is selected according to the anti-seepage ability; q —— Unit leakage volume, referring to the leakage volume per unit time and per unit area, m 3 / (m 2 ×d).

[0053] Specifically, the solid waste leaching includes without anti-seepage measures and with anti-seepage measures.

[0054] Specifically, the generation amount of pollutants without anti-seepage measures is: P sl1 =Q 3 ×C 05 ;

[0055] In the formula: P sl1 —— Generation amount of pollutants, mg / d; C 05 —— Pollutant content, mg / m 3 .

[0056] Specifically, the Q 3 is the leakage volume, Q 3 =λ×F×X×10 -3 ;

[0057] In the formula: Q 3 —— Leakage volume, m 3 / d or m 3 / a; λ —— Rainfall infiltration coefficient at the location of the construction project site; F —— Vertical projection area of the pollution source coverage area, m 2 ; X —— Local maximum daily precipitation or average annual rainfall, mm.

[0058] Specifically, the generation amount of pollutants with anti-seepage measures is: P sl2 =Q 4 ×C 06 ;

[0059] In the formula: P sl2 —— Generation amount of pollutants, mg / d; C 06 —— Pollutant content, mg / m 3 , obtained through on-site measurement.

[0060] Specifically, the Q 4 is the leakage volume,

[0061] Where: Q 4 —— leakage rate, m 3 / d or m 3 / a; K—— equivalent permeability coefficient of the anti-seepage system, m / d; I—— hydraulic gradient, the permeating groundwater is perpendicular to the anti-seepage layer, and the value here is 1; A—— anti-seepage area, m 2 ; φ—— failure rate of the anti-seepage structure, usually 0.007% - 0.013% for single-layer membrane structure anti-seepage, and 0 for double-layer membrane structure.

[0062] Specifically, the leakage of the storage tank includes the storage of volatile organic compounds (static breathing loss, working loss) and the leakage during transfer.

[0063] Specifically, the emission of a certain volatile organic compound in the leakage is: E 年 =(D 1 +D 2 )(1 - η 去除 ×k);

[0064] Where: E 年 : the emission of a certain volatile organic compound; η 去除 : the removal efficiency of the pollution control technology (covering the collection efficiency), obtained according to the collection method and end-treatment measures. k: the operation rate of the pollution control technology, up to 100%, that is, the ratio of the operation time of the process waste gas purification device to the normal production time.

[0065] The D 1 is the generation amount of organic liquid storage, D 1 =∑(k 1 ×Q i +n×k 2 );

[0066] Where: D 1 is the generation amount of organic liquid storage, kg / a; k 1 : working loss emission coefficient, kg / t×turnover; k 2 : static loss emission coefficient, kg / a; n: the number of storage tanks under the same material, storage tank type, storage tank volume, and storage temperature; Q i : annual turnover of the material, t / a; the corresponding coefficients are determined according to the province / city, material name, and loading method.

[0067] Specifically, the D 2 is the generation amount of organic liquid transfer, D 2 =∑(k×Qi);

[0068] Where: D 2 is the generation amount of organic liquid transfer, kg / a; k: loading coefficient, kg / t×loading amount; Q i: Annual loading capacity of materials, t / a.

[0069] Specifically, the digital method described in step ④ is: using points, lines, and polygons to mark the input positions of pollutant emission pathways in the study area and converting them into a grid map.

[0070] Specifically, the construction of the site conceptual model described in step (3) is: conceptualizing the information of the experimental site through the basic information obtained in step (1).

[0071] Specifically, the establishment of the mathematical model for simulating pollutant migration described in step (3) is: according to different sites, through the site conceptual model, selecting a commercial model, and inputting the soil pollution inventory obtained in step (2) into the commercial model to establish a mathematical model for simulating pollutant migration.

[0072] Specifically, the analysis of the prediction results described in step (3) is: establishing a pollution scenario, running the mathematical model for simulating pollutant migration to obtain results, and verifying the obtained results.

[0073] Specifically, the verification is model uncertainty analysis and model result verification.

[0074] Specifically, the conceptualization is: defining the boundary conditions, initial conditions, and soil moisture and solute characteristics of the site conceptual model area.

[0075] Specifically, the commercial model is at least one of RAIDAR, fugacity model, Cal TOX, EEMMS, and Hydrus.

[0076] Specifically, the verification of the model results is to compare the measured values of the site with the simulated values. If the model is reliable, a database is established and the operation steps are written. If the model is not reliable, return to the establishment of the mathematical model for simulating pollutant migration described in step (3), reconstruct the model, and conduct the analysis of the prediction results until the model is reliable.

[0077] Specifically, the measured values of the site are obtained by arranging points, sampling, and analyzing and testing pollutants at the site.

[0078] Specifically, the model uncertainty analysis is: using the coefficient of determination: R 2 , root mean square error: RMSE to test the simulation effect of the model. The calculation formulas are as follows:

[0079]

[0080] Specifically, the SST is the total sum of squares, the SSR is the regression sum of squares, and the SSE is the sum of squared residuals;

[0081]

[0082] Specifically, n is the number of samples, Y i is the true value, y i is the predicted value.

[0083] In a second aspect, the present invention provides a land pollution simulation and prediction system, including the following modules constructed by using the land pollution simulation and prediction method as described above: a basic information collection module, a soil pollution inventory module, and a soil pollution accumulation simulation and prediction module.

[0084] In a third aspect, the present invention provides a computer-readable storage medium, on which computer program instructions are stored, characterized in that the computer program instructions can implement the land pollution simulation and prediction method as described above.

[0085] In a fourth aspect, the present invention provides an electronic device, including a memory and a processor, where the memory is used to store one or more computer program instructions, and wherein the one or more computer program instructions are executed by the processor to implement the land pollution simulation and prediction method as described above.

[0086] The technical effects achieved by the present invention are as follows:

[0087] (1) The present invention develops a visual and simplified inventory compilation system tool by determining the experimental site and collecting basic information, constructing a soil pollution inventory, and simulating and predicting soil pollution accumulation.

[0088] (2) The present invention constructs a site conceptual model through basic information, and analyzes the prediction results by establishing a mathematical model for simulating pollutant migration through the soil pollution inventory, so as to conveniently and quickly simulate and predict the situation of site pollutants.

[0089] (3) The land pollution simulation and prediction method of the present invention is easy to operate, low in cost, and easy to promote. Description of the Drawings

[0090] Figure 1 It is a conceptual model diagram of site pollutants. The site is generalized as an area of 1800m * 1000m, with a horizontal length of 1800m and a vertical length of 1000m. The gray area represents pollutant leakage, and the blue area represents the input of waste residue landfill or pollutant accumulation. The gray area in the blue area is: storage leakage, running and dripping, pollutant leakage, depression / landfill, and storage leakage.

[0091] Figure 2 It is a conceptual diagram of soil layer properties. The layer from 0 - 3m is miscellaneous fill, the layer from 3 - 7m is silty clay, and the layer from 7 - 10m is silty sand.

[0092] Figure 3 It is the distribution of benzo(a)pyrene pollution in site cumulative simulation, and the darker the color, the higher the pollution concentration.

[0093] Figure 4 The simulation results of the solid waste leaching pollution of benzo(a)pyrene in the site.

[0094] Figure 5 The simulation results of the atmospheric deposition pollution of benzo(a)pyrene in the site.

[0095] Figure 6 The simulation results of the wastewater leakage pollution of benzo(a)pyrene in the site.

[0096] Figure 7 The comparison between the measured values and the simulated values of the whole site simulation.

[0097] Figure 8 The scatter plot of the verification results of different soil layer depths in the site.

[0098] Among them, Figure 5 、 6 Due to computer system reasons, it is impossible to continue to zoom in, but it does not affect the technical effect of this application. Specific embodiments

[0099] The present invention will be further described in detail below in conjunction with the accompanying drawings and preferred embodiments, but those skilled in the art will understand that these are only for the purpose of explaining the preferred embodiments and should not therefore be construed as limiting the scope of the present invention.

[0100] Example 1

[0101] 1. Determine the experimental site and collect basic information

[0102] In this embodiment, the experimental site is determined as: an iron and steel smelting enterprise.

[0103] The basic information collected in this embodiment includes: geographical location, enterprise layout, historical and current land use, regional natural environment, topography and geomorphology, surface hydrology, regional stratigraphic lithology, regional hydrogeological conditions, hydrogeology of surrounding plots, hydrogeology of this plot, and production process and pollution generation and discharge characteristics.

[0104] 2. Construct a soil pollution inventory

[0105] In this embodiment, a soil pollution inventory is constructed, which specifically includes the following steps:

[0106] (1) Identify the characteristic pollutants in the site: conduct potential pollution identification for each functional unit, determine the functional units with pollution, pollution sources, pollution pathways, types of pollutants, and pollutant migration pathways, and summarize the potential characteristic pollutants within this plot and the surrounding areas.

[0107] Among them, the identified site includes: oxygen production unit, steel rolling unit, stacking unit, sintering unit, ironmaking unit, steelmaking unit, lime unit, power station and pelletizing unit.

[0108] Through the analysis of the collected data, on-site inspection and personnel interviews, the following conclusions are drawn:

[0109] ① It is initially judged that the key suspected polluted areas of the site are: sintering unit: sintering machine room, desulfurization water tank; ironmaking unit: blast furnace, slag flushing water tank; steelmaking unit: converter, continuous caster, torpedo ladle, turbid water treatment area, waste residue treatment area; production auxiliary facilities area: stockyard, gas holder and solid waste room, etc. The main pollution pathways are material spillage during the production process, and during the stacking of raw materials and pollutants, pollutants seep into the ground through the surface, causing pollution; sewage pipelines and sewage tanks leak, and pollutants seep into the ground through the surface, causing pollution; dust and floating dust particles settle; pollutants migrate horizontally and vertically, etc. The site may be contaminated with pH, heavy metals (arsenic, cadmium, copper, lead, mercury, nickel, manganese, antimony, zinc, vanadium, cobalt, beryllium, thallium), volatile organic compounds, semi-volatile organic compounds, soluble fluorides, cyanides, sulfides, phenolic compounds, polycyclic aromatic hydrocarbons, ammonia nitrogen, benzo[a]pyrene, aniline polychlorinated biphenyls, dioxins, etc.

[0110] ② Surrounding enterprises have caused certain pollution to this project plot. It is initially considered that the main reasons for possible soil pollution are the leakage, emission and settlement of dust, soot and organic waste gas during the production process; on the basis of understanding the possible pollution sources, combined with relevant factors such as the type and distribution of pollutants, pollution migration pathways and receptor information, the representative pollutants are pH, heavy metals (arsenic, cadmium, copper, lead, mercury, nickel, manganese, antimony, zinc, vanadium, cobalt, beryllium, thallium), cyanides, fluorides, sulfides, volatile organic compounds, semi-volatile organic compounds, phenolic compounds, polycyclic aromatic hydrocarbons, benzo[a]pyrene, ammonia nitrogen, polychlorinated biphenyls, dioxins, etc.

[0111] (2) Functional area division and combination: The functional area division and combination are carried out in combination with the main pollution discharge links and the distribution of each functional unit within the enterprise. Combining the main production pollution discharge links and the distribution of each functional unit within the enterprise, this plot is divided into ironmaking unit, steelmaking unit, stacking unit, sintering unit, oxygen production unit, and steel rolling unit.

[0112] (3) Accounting for the input amount of pollutants into the soil.

[0113] Taking benzo[a]pyrene with relatively serious pollution in the plot as an example below.

[0114] Accounting for the input amount of pollutants into the soil: The emission amount of benzo[a]pyrene is accounted for for each pollution source pathway.

[0115] Through investigations, the technological processes of benzo[a]pyrene emissions and their sources and pathways of release to the soil surface were determined. Based on the atmospheric emission function, wastewater leakage function, and solid waste leaching function, detailed emission calculation methods, pollutant input calculation parameters, drainage input settings, and grouping designs are shown in the table.

[0116] Through on-site investigations, personnel interviews, historical records, standards, and literature, etc., the input methods of pollutants were described. The emission coefficients of atmospheric, waste, and wastewater pollutant inputs mainly come from the "Accounting Methods and Coefficient Manual for Pollutant Emissions and Discharges in the Statistical Survey of Emission Sources". For each pollution source pathway, the emissions of benzo[a]pyrene were accounted for, and the specific parameters required for the calculation of various pollutant input pathways are shown in Tables 1 to 4.

[0117] Table 1 Atmospheric Emission Type Parameters

[0118] Process flow BaP emission (mg / a) Pollution discharge coefficient (kg / t) Sedimentation coefficient Sintering 50 4.88 0.29 Stockpiling unit 10 0.34 0.27 Converter steelmaking 50 44 0.92

[0119] Table 2 Solid Waste Leaching Type Parameters

[0120]

[0121] Table 3 Wastewater (Pipeline) Leakage Type Parameters

[0122] Length (km) <![CDATA[Unit leakage rate L / (m 2 ·d)]]> Coefficient of variation (α) Adjustment coefficient (β) 0.2 2 0.3 3.6

[0123] Table 4 Wastewater (Pool) Leakage Type Parameters

[0124]

[0125] The historical reconstruction of the benzo[a]pyrene pollution process was carried out, and the results are shown in Table 5.

[0126] Table 5 Summary of Pollutant Input Pathways

[0127]

[0128]

[0129] The calculated results of the quantitative benzo[a]pyrene fluxes for different pollution pathways are shown in Table 6.

[0130] The sintering ore and pelletizing processes, blast furnace ironmaking, and converter steelmaking production processes generate wastes carrying transportable benzo(a)pyrene. Since 1984, solid wastes have been seeping into the environment through precipitation, and the waste residue stacking routes involve the sintering ore, pelletizing production, and blast furnace ironmaking processes. The annual benzo(a)pyrene input from the leaching of waste residues is 13.55 kg. The waste gases generated during the sintering, raw material transfer and feeding, and converter steelmaking processes also carry transportable benzo(a)pyrene. From 1984 to 2021, the average annual emission of benzo(a)pyrene input through atmospheric deposition is 20.05 kg. The pipeline leakage and sewage tank leakage routes mainly result in the input of benzo(a)pyrene due to the blast furnace ironmaking process. During the period from 1984 to 2021, the average annual benzo(a)pyrene inputs through these two routes are 0.43 kg and 4.61 kg respectively.

[0131] Table 6 Benzo(a)pyrene Input into Soil

[0132]

[0133]

[0134] III. Cumulative Simulation Prediction of the Temporal and Spatial Distribution of Soil Pollution

[0135] 1. Determine the simulation objectives: The core objectives are field simulation prediction and refined simulation of the underground environment.

[0136] 2. Construct a site conceptual model: Establish a regional site pollution conceptual model to identify characteristic pollutants, depict the temporal and spatial characteristics of pollutants, and depict site information. Qualitatively judge the input of pollutants based on functional areas, processes, storage tanks, and waste residue stacking conditions, etc.

[0137] Run the Hydrus-3D model. Through comprehensive data collection, summarization, and conceptualization, information available for the site is obtained. The X, Y, and Z axes of the model framework of Hydrus-3D in the study area are 1800, 1000, and 10 m respectively. The resolution of the X and Y axes is 5 m. The resolution of the Z axis is 1 m. The initial benzo(a)pyrene concentration is zero.

[0138] Atmospheric boundary conditions (precipitation, evaporation, and wet atmospheric deposition): The precipitation at this site basically all comes from atmospheric precipitation, so it is set as the atmospheric boundary condition.

[0139] No-flux boundary: Since most areas of this site are surface-hardened, the moisture migration is largely restricted, so it is set as the no-flux boundary.

[0140] Dry atmospheric deposition has no water input. Pollutant leakage is described by variable flux boundaries. There are multiple slag piles on the site, and benzo(a)pyrene pollution enters the soil through leaching. Considering the groundwater, the bottom boundary is set as free drainage, affected by the groundwater level.

[0141] As Figure 1 For the conceptualization and digitization of the spatial input of site pollutants, the site is generalized as an area of 1800m * 1000m. The gray area is mainly for pollutant leakage, and the blue area is mainly for the input of waste residue landfill or pollutant accumulation. Within the drilling depth range, the soil layer properties are conceptualized, as Figure 2 shown.

[0142] 3. Establish a mathematical model for simulating pollutant migration:

[0143] (1) Select the model: After a literature review and comparison of models, Hydrus is selected as the main model. The Hydrus model is mainly applicable to saturated-unsaturated Darcy flow in porous media, considering up to 15 solutes, with coupled or independent solute transport in a one-way chain. Physical non-equilibrium solute transport is constructed by two-region and dual-porosity formulas, and the liquid phase is divided into mobile and immobile regions. Additional modules include the DualPerm module, C-Ride module, and HP2 module.

[0144] (2) Run the model: First, determine the model parameters and boundary conditions. The flow parameters of miscellaneous fill and other soil types are from the literature and the model parameter data of Hydrus. The density parameters are obtained based on the average values of each soil layer type in geotechnical experiments. The parameters of benzo(a)pyrene dispersion coefficient, molecular diffusion coefficient, and adsorption-desorption process are from the literature. The parameters related to model iteration remain default. The summary of Hydrrus-3D model parameters is shown in Table 7-8.

[0145] Table 7 Site model parameters

[0146]

[0147] Table 8 Default soil hydraulic parameters of Hydrus model

[0148]

[0149]

[0150] Part of the upper boundary of the model is the atmospheric boundary, and the other part of the upper boundary is the fixed concentration boundary. The lower boundary is the Dirichlet boundary. The initial water head is -100 cm. The initial concentration of benzo(a)pyrene is 0 mg / L. The settings for numerical model calculation are default values.

[0151] Model prediction: Finally, the environmental process simulation of multi-media migration of site pollutants was realized through Hydrus, and the historical dynamic accumulation of the site pollution process was demonstrated through visualization software, realizing the backtracking of the pollutant migration process. Through this experiment, the feasibility of the designed scenario for quantifying the input of site pollutants was tested, and the historical cumulative migration process of pollutants was reconstructed.

[0152] Figure 3 It shows the pollution distribution of benzo[a]pyrene in the site cumulative simulation. The darker the color, the higher the pollution concentration. At a depth of 0-2m, it can be seen that the benzo[a]pyrene pollution shows an uneven distribution. In this soil layer, the pollution by benzo[a]pyrene is relatively serious in the mid-west and central regions, and the concentration is mainly concentrated between 190-230mg / kg, while the pollution in the eastern and southern regions is relatively light, and the pollution concentration in some areas is below 50mg / kg. At a depth of 2-4m, compared with the soil layer of 0-2m, the pollution area at this depth level is slightly different. The pollution in the south-west region is relatively serious, and the pollution is concentrated between 150-200mg / kg, especially the concentration in the south-west is relatively high, while the pollution concentration in the eastern region is relatively light, and the pollution is mainly concentrated below 100mg / kg. The pollution conditions of the two soil layers show different distribution characteristics of benzo[a]pyrene pollution in this site.

[0153] Figures 4 - 6 It shows the pollution simulation results of benzo[a]pyrene in the site, including the pollution conditions of three emission pathways. First, the pollution of waste gas is mainly concentrated in the left-middle region. As the depth increases, the diffusion range of pollutants increases, but it is still concentrated on the left side. Second, the pollution caused by wastewater leakage is concentrated in the central region. However, since the emission amount of benzo[a]pyrene through the wastewater emission pathway is small, there is no pollution display after reaching a depth of 5-6m. Third, the soil benzo[a]pyrene pollution caused by solid waste leaching is concentrated in the central region of the site, and the pollutant concentration is relatively high. Through the pollution distribution shown in the figure at different depths and different pollution pathways. Although the site is closed, as the pollutants continue to migrate downward, they accumulate at greater depths. Over time, the pollution intensifies. Due to the strong adsorption capacity of clay and silty clay for pollutants, the residence time of pollutants is relatively long, increasing the difficulty of land reuse and remediation.

[0154] (2) Analysis of prediction results: Model uncertainty analysis and model result verification

[0155] ① Determine the measured value

[0156] Sampling point layout, analysis and testing in this embodiment: According to the sampling point layout principle and the pollution identification and analysis of the target plot, and based on the functions and pollution characteristics of each sub-region within the factory area, facilities and equipment that may be heavily polluted or parts with obvious pollution are selected as the basis for judgment method sampling point layout; for areas with uniform pollution distribution, low pollution degree and severely damaged landforms (including demolition and historical change damage), a combined method of systematic sampling point layout and systematic random sampling point layout is adopted, and the number of sampling points is determined according to the area of the region, and at the same time, combined with the actual situation of the plot, based on the functions and pollution characteristics of each sub-region within the factory area, in key areas such as sintering machines, blast furnaces, converters, slag flushing pools and turbidity recycling pools, facilities and equipment that may be heavily polluted or parts with obvious pollution are selected as the basis for judgment method sampling point layout; for other areas such as office areas, finished product warehouses and open spaces with uniform pollution distribution and low pollution degree, a combined method of systematic sampling point layout and systematic random sampling point layout is adopted for sampling point layout. According to the above sampling point layout principle, a total of 261 sampling points are laid out, including 211 soil sampling points and 50 soil-water collaborative sampling points.

[0157] When considering the basic items (mandatory items) in the Soil Environmental Quality - Risk Control Standards for Soil Pollution of Construction Land

[0158] (GB36600 - 2018), for judgment sampling points, other items (optional items) and characteristic pollution factors determined according to pollution identification are also considered based on the potential pollution characteristics of different regions.

[0159] Based on the previous pollutant identification, considering comprehensively, the test factors for this plot are pH, heavy metals (arsenic, cadmium, copper, lead, mercury, nickel, manganese, antimony, zinc, vanadium, cobalt, beryllium, thallium, hexavalent chromium), cyanide, fluoride, sulfide, volatile organic compounds (full items), semi-volatile organic compounds (full items), phenolic compounds (full items), polycyclic aromatic hydrocarbons, petroleum hydrocarbon benzo[a]pyrene, ammonia nitrogen, aniline, polychlorinated biphenyls, dioxins, etc.

[0160] For all soil samples within the plot, the pH value ranges from 7.14 to 12; among the heavy metal and inorganic matter indicators, 9 kinds are detected, including arsenic, cadmium, copper, lead, mercury, nickel, cobalt, beryllium and antimony; among the semi-volatile organic compound indicators, 8 kinds are detected, including naphthalene, benzo[a]anthracene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, indeno[1,2,3-cd]pyrene, dibenzo[a,h]anthracene; petroleum hydrocarbon benzo(a)pyrene (C10 - C40) is detected, and subsequently, benzo(a)pyrene is used as the measured value for comparison with the simulation data.

[0161] The test results are shown in Table 9.

[0162] Table 9 Pollutant Test Results

[0163]

[0164]

[0165] ② Model uncertainty analysis and model result verification:

[0166] The coefficient of determination: R 2 To test the simulation effect of the model, the calculation formula is as follows:

[0167]

[0168] SST is the total sum of squares, SSR is the regression sum of squares, and SSE is the sum of squared residuals.

[0169] Root mean square error: RMSE:

[0170]

[0171] Where n is the number of samples, Y i is the true value, and y i is the predicted value;

[0172] Finally, the historical dynamic pollutant accumulation process was displayed through visualization software, realizing the backtracking and dynamic monitoring of the site pollution process. Figure 7 The comparison between the measured values and the simulated values of the entire site simulation is shown. Specifically, the points in the scatter plot are evenly distributed on both sides of the 1:1 line, indicating a good linear relationship between the measured values and the simulated values. The red shaded area in the figure represents the two-fold error range, and the gray shaded area represents the five-fold error range. It can be seen that most of the points fall within the red shaded area, that is, within the two-fold error range, and some high-concentration value points exceed the two-fold error range. Overall, it shows that the model can accurately simulate the pollutant concentration in most cases. The R 2 of the model = 0.568, further indicating that the model has a good fitting effect. Overall, the model can better simulate the benzo(a)pyrene concentration distribution of the site.

[0173] Figure 8 The scatter plot of the verification results at different soil depths of the site is shown.

[0174] (3) Model optimization and improvement include: improvement of the emission inventory and correction of model parameters

[0175] ① Emission inventory formulation

[0176] Some information of the steel plant, data such as emission factors related to the process flow and the properties of pollutants are still missing. For example, there may be errors in some physical and chemical properties of pollutants, which affects the accuracy of the inventory compilation. By consulting more literature for comparison, relatively accurate parameters related to the process flow are obtained to further clarify and improve the sources of characteristic pollutants.

[0177] ② Model parameter correction

[0178] In the current model, some parameters may not accurately reflect the actual situation, especially the parameters related to soil moisture migration and pollutant emissions. To improve the credibility of the model, the following optimizations will be carried out:

[0179] Literature parameter comparison: In terms of the model results, incorrect input of some parameters may be one of the reasons for the large difference between the model results and the actual situation. In the current model, the default values of Hydrus are used for the environmental process parameters of some pollutants, which may deviate greatly from the actual situation. Subsequently, the rationality and accuracy of each parameter will be confirmed by reading the literature.

[0180] Optimizing emissions: For the site-scale model, emissions are the most influential factor on the results. Therefore, it is a very important step to adjust emissions by improving the information of various sites.

[0181] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than a limitation on the protection scope of the present invention. Simple modifications or equivalent replacements made by those of ordinary skill in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A land pollution simulation prediction method, characterized in that: The following steps are involved: (1) Determine the experimental site and collect basic information; (2) Construct a soil pollution inventory; (3) Simulation and prediction of soil pollution accumulation; The method of constructing described in step (2) comprises the following steps: ① Complete the identification of site characteristic pollutants based on the basic information collected in step (1); ②Complete the division and merging of site functional areas according to the site characteristic pollutants obtained in step ①; ③ Based on the basic information collected in step (1), complete the soil input calculation of pollutants to obtain the pollutant emissions from different emission pathways; ④Digitize the site characteristic pollutants obtained in step ①, the site functional area division and combination obtained in step ②, and the pollutant emissions obtained in step ③ to obtain a soil pollution inventory; The soil pollution accumulation simulation prediction described in step (3) includes: constructing a site conceptual model through the basic information obtained in step (1), establishing a soil pollution inventory obtained in step (2), establishing a pollutant migration simulation mathematical model and analyzing the prediction results.

2. The land pollution simulation prediction method according to claim 1 is characterized in that: The basic information collected in step (1) includes: basic information of the enterprise, information on pollution sources, and natural information; The basic information of the enterprise includes: the geographical location of the enterprise, the layout of the enterprise, the history and current status of the use of the enterprise's site; The pollution source information includes: production process, pollution nodes and pollutant emission pathways; The natural information includes: site meteorological conditions, site topography, site surface hydrology, site stratum lithology, site stratum lithology and site surrounding hydrogeology; Step ① The identification of the characteristic pollutants of the site is as follows: potential pollution identification is performed on each functional unit, the functional unit with pollution, pollution source, pollution pathway, pollutant type and pollutant migration pathway are determined, and potential characteristic pollutants in the experimental site and surrounding areas are identified and summarized; The site functional area division and merging in step ② is as follows: functional area division and merging are performed in combination with pollution source information and enterprise basic information; The different emission pathways in step ③ are atmospheric deposition, wastewater leakage, solid waste infiltration, and storage tank leakage; The atmospheric deposition includes the emission from the exhaust outlet and the emission of particulate matter from the solid material storage yard; The wastewater leakage includes pipeline leakage, channel leakage and pool leakage; The solid waste infiltration includes those without anti-seepage measures and those with anti-seepage measures; The tank leakage is the leakage during the storage and transfer of volatile organic compounds.

3. The land pollution simulation prediction method according to claim 1, characterized in that: The digitization method described in step ④ is: using points, lines and polygons to mark the input locations of pollutant emission pathways in the study area and converting them into a grid map.

4. The land pollution simulation prediction method according to claim 1 is characterized in that: The step (3) of constructing the site conceptual model is as follows: conceptualizing the information of the experimental site using the basic information obtained in the step (1); The step (3) of establishing a pollutant migration simulation mathematical model comprises: selecting a business model based on different sites through a site concept model, inputting the soil pollution inventory obtained in step (2) into the business model, and establishing a pollutant migration simulation mathematical model; The prediction result analysis in step (3) is as follows: establishing a pollution scenario, running a pollutant migration simulation mathematical model to obtain results, and verifying the obtained results; The verification is model uncertainty analysis and model result verification.

5. The land pollution simulation prediction method according to claim 4 is characterized in that: The conceptualization is to define the boundary conditions, initial conditions, and water and solute properties of the soil in the conceptual model area of ​​the site.

6. The land pollution simulation prediction method according to claim 4 is characterized in that: The commercial model is at least one of RAIDAR, fugacity model, Cal TOX, EEMMS and Hydrus.

7. The land pollution simulation prediction method according to claim 4 is characterized in that: The model result verification is to compare the measured value of the site with the simulated value. If the model is reliable, a database is established and the operation steps are written; If the model is unreliable, return to step (3) to establish a mathematical model for pollutant migration simulation, rebuild the model and perform prediction result analysis until the model is reliable; The method of measuring the site value is to set up points on the site, take samples, analyze and test pollutants to obtain the site measured value.

8. A land pollution simulation prediction system, characterized in that: The method comprises the following modules constructed by using the land pollution simulation prediction method as described in any one of claims 1 to 7: a basic information collection module, a soil pollution inventory module and a soil pollution accumulation simulation prediction module.

9. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions can implement the land pollution simulation prediction method as described in any one of claims 1-7.

10. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions; The processor executes one or more computer program instructions in the memory to implement the land pollution simulation and prediction method as described in any one of claims 1-7.

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