Regional soil pollution health risk assessment method based on POI data

Abstract

The invention relates to the technical field of regional soil pollution health risk assessment, in particular to a regional soil pollution health risk assessment method based on POI data, and the method comprises the steps: firstly obtaining and preprocessing multi-source heterogeneous data, and constructing a POI spatial database; adopting a Kriging spatial interpolation method to generate a pollutant concentration spatial distribution grid map layer; then combining kernel density estimation and dynamic weight correction to construct a population activity intensity index surface; dynamically generating an exposure evaluation unit, and calculating the multi-path daily average exposure dose of people of different ages; and finally, quantifying the multi-path health risk, generating a risk level distribution map, and identifying the dominant POI category of the high-risk area. According to the method, deep fusion of multi-source data is realized, the association between population activity characteristics and pollution exposure is accurately depicted, the evaluation accuracy and suitability are improved, and a scientific decision basis is provided for pollution prevention and control and risk management and control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of regional soil pollution health risk assessment, in particular to a regional soil pollution health risk assessment method based on POI data. BACKGROUND

[0002] The current common regional soil pollution health risk assessment method has many limitations. In terms of data utilization, the traditional method relies on soil pollution sampling point data and population statistics data, the data source is relatively single, and the multi-type data reflecting the spatial characteristics of population activities cannot be fully integrated, resulting in insufficient description of the correlation between population activities and pollution exposure.

[0003] In terms of exposure assessment unit division, the existing method often uses fixed grid or administrative region as the assessment unit, which does not consider the spatial difference of population activity intensity in the region, ignores the population aggregation characteristics of residential, educational, commercial and industrial areas, and makes the assessment unit not match the actual activity range of the population, thereby affecting the accuracy of exposure dose calculation.

[0004] In terms of population activity intensity description, the traditional method often uses static population density data, which cannot combine the attraction effect of various places on population activities, and lacks dynamic adjustment mechanism for population activity intensity weight, making it difficult to accurately reflect the spatio-temporal distribution characteristics of population, resulting in deviation of health risk assessment results from the actual situation.

[0005] In addition, the result presentation form of the existing assessment method is relatively single, mainly in the form of data table or simple chart, which is difficult to intuitively show the spatial distribution of high-risk areas and the risk causes, and brings inconvenience to the relevant departments to formulate targeted pollution prevention and control measures and risk control strategies.

[0006] Therefore, it is urgent to develop a regional soil pollution health risk assessment method that can integrate multi-source data, accurately describe population activity intensity, dynamically generate exposure assessment units, and realize risk quantification and visualization, in order to solve the problems of insufficient precision, poor adaptability and weak decision support of traditional assessment methods. SUMMARY

[0007] The present application relates to the technical field of regional soil pollution health risk assessment, in particular to a regional soil pollution health risk assessment method based on POI data.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0009] A regional soil pollution health risk assessment method based on POI data, comprising the following steps:

[0010] Step S1: Multi-source heterogeneous data acquisition and preprocessing: Obtain soil pollution sampling point data, digital elevation model geographic spatial data, population census grid data, and point of interest (POI) data containing residential, educational, medical, commercial, catering, park, and industrial facility categories in the target area; classify and clean the POI data, perform geocoding and spatialization, and establish a POI spatial database;

[0011] Step S2: Soil pollutant spatial distribution simulation: Based on the soil pollution sampling point data, use the Kriging spatial interpolation method to generate a concentration spatial distribution raster layer of a specific pollutant in the target area;

[0012] Step S3: Dynamic weight population activity intensity surface construction: Based on the POI spatial database, preset an initial activity intensity weight value for each POI category; use the kernel density estimation algorithm to generate a density distribution raster layer for each type of POI; dynamically correct the initial weight of each type of POI according to the population census grid data, weight and superimpose the corrected weight and the corresponding density layer to generate a population activity intensity index surface that comprehensively reflects the spatiotemporal distribution characteristics of the population;

[0013] Step S4: Dynamic generation of exposure assessment unit and calculation of pollution exposure dose: Divide the population activity intensity index surface into high, medium, and low activity zones according to a preset threshold, and use the spatial boundaries of each activity zone as a dynamic exposure assessment unit; for each exposure assessment unit, superimpose the concentration spatial distribution raster layer to extract the average pollutant concentration within the unit; combine the preset exposure parameters to calculate the daily average pollutant exposure dose for different age groups in the unit through oral ingestion, skin contact, and respiratory inhalation;

[0014] Step S5: Multi-path health risk quantification and visualization: Substitute the pollutant exposure dose calculated in each exposure assessment unit and the toxicity parameters of the corresponding pollutant into the health risk assessment model to calculate the carcinogenic risk value and hazard quotient of each unit; map the calculated risk values back to the spatial range of the corresponding dynamic exposure assessment unit to generate a regional soil pollution health risk level distribution map, and identify the dominant POI category information in the high-risk area.

[0015] As a preferred scheme, step S1 specifically includes:

[0016] Obtain soil pollution sampling point data through an environmental monitoring network, obtain digital elevation model geographic spatial data and population census grid data through a geographic information platform, and obtain original point of interest (POI) data containing residential, educational, medical, commercial, catering, park, and industrial facility categories through a commercial map service interface or public data set;

[0017] The original Points of Interest (POI) data is classified and cleaned. Based on the preset POI category system, the attribute information of each POI record is classified and filtered to remove duplicate, erroneous, and irrelevant POI records.

[0018] The cleaned POI records are geocoded and spatialized, converting the text address information of each POI record into precise geographic coordinates and unifying them to the same spatial reference coordinate system as the digital elevation model geospatial data and the census grid data.

[0019] Based on the spatialized POI records, geographic coordinates, and corresponding attribute category information, a structured POI spatial database is constructed. This database supports fast querying and statistical analysis based on spatial location and POI category, and provides an input data source for subsequent kernel density estimation algorithms.

[0020] As a preferred embodiment, step S2 specifically includes:

[0021] Based on soil pollution sampling point data, spatial structure analysis and outlier detection are performed to generate a standardized sampling point dataset that meets the requirements of spatial interpolation.

[0022] Based on a standardized sampling point dataset, a variogram model is constructed to characterize the spatial autocorrelation features of specific pollutant concentrations.

[0023] Based on the variogram model, the Kriging spatial interpolation algorithm is used to make the optimal unbiased estimate of the pollutant concentration at unknown spatial locations within the target area, and generate a spatial prediction point set of pollutant concentration.

[0024] The spatial prediction point set is rasterized to generate a preliminary raster layer of pollutant concentration spatial distribution with continuous concentration values ​​covering the entire target area.

[0025] Spatial reference and pixel attribute standardization processing are performed on the preliminary raster layer of pollutant concentration spatial distribution to generate a concentration spatial distribution raster layer that is spatially registered with the geospatial data of the digital elevation model and the subsequent surface spatial registration of the population activity intensity index.

[0026] As a preferred approach, based on a POI spatial database, an initial activity intensity weight value is preset for each POI category; a kernel density estimation algorithm is used to generate density distribution raster layers for each type of POI; specifically including:

[0027] Based on the POI spatial database, POI spatial points belonging to the categories of residential, educational, medical, commercial, catering, park and industrial facilities are extracted respectively;

[0028] For each POI category, an initial activity intensity weight value representing its relative importance is assigned according to its attraction and aggregation effect on the daily activities of the population in the region;

[0029] For each set of POI spatial points of a category, a kernel density estimation algorithm is independently used for spatial smoothing processing to generate a density distribution raster layer reflecting the spatial aggregation degree of the POI facilities of the category in the target region, wherein the search radius of the kernel density estimation is set based on the average service range or spatial distribution characteristics of the POI facilities of the category;

[0030] The generated density distribution raster layers of each category of POI are normalized to make the density values in a unified numerical interval, forming standardized density distribution raster layers of each category of POI.

[0031] As a preferred scheme, the initial weights of each category of POI are dynamically corrected based on the population census grid data, and the corrected weights are weighted and superimposed on the corresponding density layers to generate a population activity intensity index surface that comprehensively reflects the spatiotemporal distribution characteristics of the population; specifically including:

[0032] The standardized density distribution raster layers of each category of POI are spatially superimposed and analyzed with the population census grid data to calculate the spatial correlation between the normalized density values of each category of POI in each grid cell and the number of permanent residents in the grid;

[0033] Based on the results of the spatial correlation analysis, a dynamic weight correction model is established; for the POI categories that show significant positive correlation with the population, the initial activity intensity weight values are increased; for the POI categories that show no significant correlation or negative correlation, the initial weight values are maintained or decreased, thereby obtaining a set of dynamically corrected POI category activity intensity weight values;

[0034] The dynamically corrected POI category activity intensity weight values are respectively weighted and multiplied with the corresponding standardized density distribution raster layers of each category of POI to generate weighted activity intensity raster layers of each category;

[0035] The weighted activity intensity raster layers of each category are spatially superimposed and summed to generate a population activity intensity index surface raster layer that comprehensively reflects the combined effect of population distribution and multiple categories of POI facilities.

[0036] As a preferred scheme, the population activity intensity index surface is divided into high, medium and low activity zones according to a preset threshold, and the spatial boundaries of each activity zone are used as dynamic exposure assessment units; specifically including:

[0037] The pixel values of the population activity intensity index surface raster layer are extracted, and the statistical quantiles are calculated;

[0038] Two thresholds for dividing high, medium and low population activity zones are set based on statistical quantiles, wherein the high activity zone threshold is greater than or equal to the first high quantile, and the medium activity zone threshold is between the first low quantile and the second low quantile;

[0039] According to the two thresholds, the pixel reclassification is performed on the population activity intensity index surface raster layer to generate a population activity intensity zoning raster layer with high, medium and low levels of attributes;

[0040] The population activity intensity zoning raster layer is subjected to raster-to-vector processing to extract the continuous spatial boundaries of the high, medium and low activity zones respectively, and a spatial vector boundary file is generated as the basis for subsequent calculation;

[0041] The spatial vector boundary file is subjected to topological inspection and correction to ensure that the boundaries of the activity zones of different levels are free of overlap and gaps, and the corrected spatial vector boundaries of the activity zones of different levels are defined as high, medium and low dynamic exposure assessment units respectively to form a dynamic exposure assessment unit vector layer.

[0042] As a preferred solution, for each exposure assessment unit, the concentration spatial distribution raster layer is superimposed to extract the average pollutant concentration within the unit; combined with the preset exposure parameters, the daily average pollutant exposure dose of different age groups in the unit is calculated under three routes of oral ingestion, skin contact and respiratory inhalation; specifically including:

[0043] The dynamic exposure assessment unit vector layer and the concentration spatial distribution raster layer are subjected to spatial superposition analysis;

[0044] For each dynamic exposure assessment unit, the arithmetic mean of the pollutant concentrations of all grid pixels completely falling within the boundary range of the unit is calculated using the zoning statistical tool, and the mean is defined as the representative average pollutant concentration of the dynamic exposure assessment unit;

[0045] Based on the activity zone level corresponding to the dynamic exposure assessment unit and the dominant POI category within it, combined with the typical activity patterns of different age groups in residential, educational, medical, commercial, catering, park and industrial facility type POI sites, a set of exposure parameters matched with the population activity characteristics of the unit are determined and assigned, and the exposure parameters at least include the daily residence time of different groups in each site, soil ingestion rate, skin exposure surface area and soil adhesion coefficient, and respiratory rate;

[0046] The representative average pollutant concentration of the dynamic exposure assessment unit, the exposure parameters matched with the population activity characteristics of the unit, and the specific calculation formula of different exposure routes are combined to independently calculate the daily average pollutant exposure dose of children and adults in the dynamic exposure assessment unit under the routes of oral ingestion, skin contact and respiratory inhalation respectively;

[0047] Summarize the daily average pollutant exposure dose of each dynamic exposure assessment unit, which is calculated by three exposure pathways, to form a pollutant multi-pathway, multi-population exposure dose result set with the dynamic exposure assessment unit as the basic recording unit.

[0048] As a preferred solution, the calculated pollutant exposure dose in each exposure assessment unit is substituted into the health risk assessment model with the toxicity parameters of the corresponding pollutant to calculate the carcinogenic risk value and hazard quotient of each unit, specifically including:

[0049] Based on the pollutant multi-pathway, multi-population exposure dose result set, obtain the toxicity parameters of the preset specific pollutant, and the toxicity parameters at least include the oral intake carcinogenic slope factor, the respiratory inhalation unit risk factor, and the reference dose of each exposure pathway;

[0050] For each dynamic exposure assessment unit in the dynamic exposure assessment unit vector layer, combine the calculated daily oral intake exposure dose for children and adults in the unit with the oral intake carcinogenic slope factor to calculate the carcinogenic risk value of the oral intake pathway in the unit;

[0051] For each dynamic exposure assessment unit in the dynamic exposure assessment unit vector layer, combine the calculated daily respiratory inhalation exposure dose for children and adults in the unit with the respiratory inhalation unit risk factor to calculate the carcinogenic risk value of the respiratory inhalation pathway in the unit;

[0052] For each dynamic exposure assessment unit in the dynamic exposure assessment unit vector layer, combine the calculated daily oral intake, skin contact, and respiratory inhalation exposure dose for children and adults in the unit with the reference dose of the corresponding exposure pathway to calculate the hazard quotient of each pathway;

[0053] For each dynamic exposure assessment unit, summarize the carcinogenic risk values of the oral intake and respiratory inhalation pathways to obtain the comprehensive carcinogenic risk value of the unit; and summarize the hazard quotients of the oral intake, skin contact, and respiratory inhalation pathways to obtain the comprehensive hazard quotient index of the unit;

[0054] Based on the comprehensive carcinogenic risk values and comprehensive hazard quotient indexes of all dynamic exposure assessment units, construct a regional soil pollution health risk assessment result set.

[0055] As a preferred solution, map the calculated risk values back to the spatial range of the corresponding dynamic exposure assessment unit to generate a regional soil pollution health risk level distribution map, and associate the dominant POI category information in the high-risk area for identification; specifically including:

[0056] Based on the set of regional soil pollution health risk assessment results, according to the preset risk level division threshold of the comprehensive carcinogenic risk value and the comprehensive hazard quotient index, the risk level of each dynamic exposure assessment unit is evaluated;

[0057] The evaluated risk level attribute is associated with the corresponding spatial feature in the dynamic exposure assessment unit vector layer to generate a dynamic exposure assessment unit risk zoning vector layer with a risk level attribute;

[0058] The dynamic exposure assessment unit risk zoning vector layer is symbolically rendered, different colors and legends are given according to the risk level, and a regional soil pollution health risk level distribution map is generated;

[0059] For the dynamic exposure assessment unit in the dynamic exposure assessment unit risk zoning vector layer that is evaluated as high risk, the spatial range is superimposed and analyzed with the POI spatial database, and the dominant POI category located in the spatial range of the unit is identified and extracted.

[0060] In the regional soil pollution health risk level distribution map, the high-risk dynamic exposure assessment unit is labeled, and the dominant POI category information is associated and identified as risk cause auxiliary information.

[0061] As can be seen from the technical solutions provided by the above-mentioned application, the regional soil pollution health risk assessment method based on POI data provided by the application has the beneficial effects of:

[0062] The evaluation accuracy is significantly improved: the application integrates soil pollution sampling point data, digital elevation model geographic spatial data, population census grid data and multi-category POI data, realizes deep fusion of multi-source heterogeneous data, and breaks the limitation of single traditional evaluation data; through POI data classification, cleaning, geocoding and spatialization processing, combined with kernel density estimation algorithm and dynamic weight correction model, the spatio-temporal distribution characteristics of population activity intensity are accurately described, the pollution exposure assessment is highly matched with the actual activity law of the population, and the accuracy and reliability of the health risk assessment results are greatly improved.

[0063] The dynamic evaluation mechanism has strong adaptability: the application innovatively adopts a dynamic exposure assessment unit generation method, divides high, medium and low activity zones based on the statistical quantile of the population activity intensity index surface, uses the spatial boundary of the activity zone as the evaluation unit, replaces the traditional fixed grid unit, and is more suitable for the spatial difference characteristics of regional population activities; at the same time, according to the typical activity mode of different age groups in various POI places, the differentiated exposure parameters are matched, the multi-path daily exposure dose is calculated, the evaluation process fully considers the heterogeneity of the population, and is suitable for the evaluation needs of different regions and different scenes, and has a wider application range.

[0064] The system demonstrates outstanding risk management support capabilities: through multi-channel health risk quantification calculations, it generates comprehensive carcinogenic risk values ​​and comprehensive hazard quotient indices, and combines them with symbolic rendering technology to form an intuitive regional soil pollution health risk level distribution map. At the same time, it identifies the dominant POI category information in high-risk areas, clearly revealing the spatial distribution and causal relationships of high-risk areas. This provides relevant departments with clear data support and decision-making basis for accurately locating key pollution prevention and control areas, formulating targeted control measures, and optimizing regional land use planning, significantly improving the scientific nature and efficiency of risk management. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the steps in a regional soil pollution health risk assessment method based on POI data according to the present invention. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0067] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific embodiments.

[0068] like Figure 1 As shown, this embodiment of the invention provides a method for regional soil pollution health risk assessment based on POI data, including the following steps:

[0069] Step S1: Acquisition and preprocessing of multi-source heterogeneous data: Acquire soil pollution sampling point data, digital elevation model geospatial data, population census grid data, and Points of Interest (POI) data including categories such as residential, educational, medical, commercial, catering, park, and industrial facilities in the target area; classify, clean, geocode, and spatialize the POI data to establish a POI spatial database;

[0070] Step S2: Spatial distribution simulation of soil pollutants: Based on soil pollution sampling point data, a raster layer of the spatial distribution of specific pollutant concentrations within the target area is generated using the Kriging spatial interpolation method.

[0071] Step S3: Construction of dynamic weighted population activity intensity surface: Based on the POI spatial database, an initial activity intensity weight value is preset for each POI category; the kernel density estimation algorithm is used to generate density distribution raster layers for each type of POI; the initial weights of each type of POI are dynamically corrected according to the census grid data, and the corrected weights are weighted and superimposed with the corresponding density layers to generate a population activity intensity index surface that comprehensively reflects the spatiotemporal distribution characteristics of the population.

[0072] Step S4: Exposure assessment unit dynamically generates and calculates pollution exposure dose: The population activity intensity index surface is divided into high, medium and low activity areas according to the preset threshold, and the spatial boundaries of the activity areas at each level are taken as the dynamic exposure assessment unit. For each exposure assessment unit, the average pollutant concentration in the unit is extracted by superimposing the concentration spatial distribution grid layer. Combined with the preset exposure parameters, the daily average pollutant exposure dose of different age groups in the unit is calculated under the three ways of oral ingestion, skin contact and respiratory inhalation.

[0073] Step S5: Multi-path health risk quantification and visualization: The pollutant exposure dose calculated in each exposure assessment unit is substituted into the health risk assessment model with the toxicity parameters of the corresponding pollutant to calculate the carcinogenic risk value and hazard quotient of each unit. The calculated risk value is mapped back to the corresponding dynamic exposure assessment unit spatial range to generate a regional soil pollution health risk level distribution map, and the dominant POI category information in the high-risk area is identified.

[0074] In this embodiment, the role of step S1 is to comprehensively collect multi-type data related to soil pollution in the target area, and through classification cleaning, geocoding and spatialization processing, a standardized and structured POI spatial database is constructed, providing complete and accurate basic data support for subsequent soil pollutant spatial distribution simulation, population activity intensity analysis and risk assessment. The following are the detailed steps:

[0075] Step S1-1: Comprehensive acquisition of multi-source heterogeneous data

[0076] Based on the core needs of regional soil pollution health risk assessment, four types of core data are obtained through multiple channels. The first type is soil pollution sampling point data, which is collected through regional environmental monitoring network, including geographic coordinates, sampling depth, monitored pollutant types and corresponding concentration values, etc. The second type is digital elevation model geographic spatial data, which is obtained through professional geographic information platform, covering terrain elevation, slope, slope direction and other topographic and geomorphic spatial information of the target area. The third type is population census grid data, also from the geographic information platform, recording the number of permanent population and population density in each grid. The fourth type is multi-category point of interest (POI) data, collected through commercial map service interface or public data set, covering seven core categories of residence, education, medical care, business, catering, park and industrial facilities. Each data contains attribute information such as POI name, address and category. During data acquisition, the system automatically records metadata information such as acquisition time and data precision of each data source to ensure data traceability.

[0077] Step S1-2: POI data classification and cleaning

[0078] Based on the preset POI category system, the collected original POI data is classified and cleaned. First, according to the classification standard of residential, education, medical, commercial, catering, park and industrial facilities, the attribute information of each original POI record is compared and classified one by one, and the target category of each record is determined. Then, the data cleaning process is started, and the repeated record identification algorithm is used to remove POI records with completely consistent or repeated core attributes. Based on the address format validity checking rules, error records are screened, including records with missing address information or logical contradictions. According to the evaluation range boundary, POI records unrelated to the target area are screened out, and only effective records located in the evaluation area or closely related to the regional population activity are retained. After cleaning, a POI intermediate data set with clear classification, no redundancy and no error is generated, and a data cleaning report is output to clearly indicate the total number of original data, the number of removed records and the number of effective records.

[0079] Step S1-3: POI data geocoding and spatialization:

[0080] The cleaned POI intermediate data set is geocoded and spatialized. First, call the geocoding tool to convert the text address information of each POI record into accurate geographic coordinates, ensuring that the coordinate precision meets the spatial analysis requirements. Then, perform spatial reference coordinate system processing to convert the converted POI geographic coordinates, digital elevation model geographic spatial data and population census grid data into the same preset spatial reference coordinate system, eliminating the spatial reference differences of different data sources. During the coordinate conversion and coordinate system processing, the system automatically checks the conversion accuracy. If the conversion error of a single POI coordinate exceeds the preset threshold, it is marked as an abnormal record and re-encoded. If the overall data coordinate system conversion consistency does not meet the standard, the conversion process is re-executed to ensure that all spatial data have uniform spatial reference and accurate matching.

[0081] Step S1-4: Structured construction of POI spatial database:

[0082] Based on the spatialized POI data, digital elevation model geographic spatial data and core information of population census grid data, a structured POI spatial database is constructed; the database includes a spatial data layer and an attribute data layer, the spatial data layer stores the coordinate information spatial topological relationship of POI and other geographic data, the attribute data layer stores the attribute information of POI such as category name and address, and the terrain attribute of digital elevation model and the population attribute data of population census grid; the database supports two core query statistical functions, one is the query analysis based on spatial position, which can quickly locate all POIs and corresponding geographic and population data within a specific spatial range; the other is the screening statistics based on POI category, which can batch extract the spatial distribution and attribute information of a certain category of POI; after the database is constructed, data integrity and query response speed test is automatically performed to ensure that the data is complete and the query statistics is efficient, providing stable input data source for subsequent kernel density estimation algorithm and other spatial analysis processes.

[0083] In this embodiment, the role of step S2 is to generate a pollutant concentration spatial distribution raster layer covering the entire area, which is continuous in space and registered with other geographic data, based on the soil pollution sampling point data of the target area, through data normalization processing, variogram modeling, Kriging spatial interpolation calculation and layer standardization optimization, to provide accurate pollutant concentration spatial data support for the subsequent pollution exposure dose calculation of the exposure assessment unit; the following are the detailed steps:

[0084] Step S2-1: Normalization processing of sampling point data:

[0085] Based on the collected soil pollution sampling point data, spatial structure analysis and outlier detection are carried out to generate a normalized sampling point data set that meets the requirements of spatial interpolation; first, spatial autocorrelation analysis method is used to explore the spatial distribution pattern of sampling point data, to judge the aggregation dispersion or random distribution characteristics of pollutant concentration in space, and to clarify the spatial structure rationality of data; then, Grubbs test method is used to check the pollutant concentration values of sampling points one by one, to identify and eliminate extreme abnormal data caused by sampling error, instrument failure or abnormal pollution events; after outlier detection, the remaining valid sampling point data is integrated, and the key attribute information such as geographic coordinates and pollutant concentration values of each sampling point is supplemented and improved, to form a structured and unified normalized sampling point data set, ensuring that the data meets the basic requirements of subsequent spatial interpolation modeling;

[0086] Step S2-2: Variogram model construction:

[0087] Based on the normalized sampling point data set, a variogram model representing the spatial autocorrelation characteristics of a specific pollutant concentration is constructed; the variogram is a core tool for describing the spatial variation law of pollutant concentration, and its calculation formula is: wherein, is the variogram value of the distance is the number of sample point pairs with spatial distance equal to is the spatial position of the th sample point, is the spatial position of the th sample point, is the pollutant concentration value at the spatial position of the th sample point; in the calculation process, first, according to the spatial coordinates of the normalized sample point dataset, a reasonable distance grouping interval is determined, the number of sample point pairs in each distance interval and the corresponding concentration difference square sum are counted, and the formula is substituted to calculate the variogram value corresponding to different distances ; then based on these discrete variogram values, a nonlinear fitting method is used to construct a theoretical variogram function model that meets the spatial variation characteristics of the pollutant concentration, and the key parameters such as nugget value and range of the model are determined, which provides model support for subsequent Kriging spatial interpolation;

[0088] Step S2-3: Kriging spatial interpolation calculation:

[0089] Based on the constructed variogram function model, the Kriging spatial interpolation algorithm is used to make optimal unbiased estimation of the pollutant concentration at unknown spatial positions in the target area, and a spatial prediction point set of the pollutant concentration is generated; first, the spatial prediction grid of the target area is divided, and the grid size is reasonably set according to the evaluation accuracy requirement and the range of the region, so as to ensure that the prediction result can meet the accuracy requirement and will not excessively increase the calculation amount; for the center point of each prediction grid, the Kriging interpolation algorithm is used to calculate the optimal unbiased estimation value of the pollutant concentration at the center point according to the spatial position relationship between the center point and all surrounding sample points, and combining the spatial variation information provided by the variogram function model; in the calculation process, the algorithm automatically gives different weights to sample points with different distances, and the closer the sample point, the greater the weight, and the farther the sample point, the smaller the weight, so as to ensure that the estimation result can fully reflect the spatial correlation of the pollutant concentration; after completing the concentration estimation for all prediction grid center points one by one, all prediction results are integrated to form a spatial prediction point set of the pollutant concentration covering all unknown spatial positions in the target area;

[0090] Step S2-4: preliminary grid layer generation:

[0091] ​​​The obtained pollutant concentration spatial prediction point set is rasterized and converted to generate a preliminary grid layer of pollutant concentration spatial distribution covering the entire target area and having continuous concentration values; in the rasterization and conversion process, the pollutant concentration estimate value of each prediction grid center point is assigned to the corresponding grid pixel based on the prediction grid set in step S2-3, so that the discrete prediction point set is converted into a continuous grid data structure; at the same time, the spatial range of the grid layer is set to be completely consistent with the target area, so as to ensure that the layer can completely cover the evaluation area without data loss or out-of-range situations; in the generated preliminary grid layer, the value of each pixel represents the pollutant concentration estimate value of the spatial position, and the spatial distribution trend of the pollutant in the target area is intuitively presented through the continuous distribution of pixel values;

[0092] Step S2-5: Layer standardization and spatial registration

[0093] The preliminary grid layer of pollutant concentration spatial distribution is subjected to spatial reference and pixel attribute standardization processing to generate a concentration spatial distribution grid layer that is spatially registered with the digital elevation model geographic spatial data and the subsequent population activity intensity index surface; in terms of spatial reference standardization, the spatial reference coordinate system of the preliminary grid layer is adjusted to be the same as that of the digital elevation model geographic spatial data and the population census grid data, so as to ensure that all geographic data are consistent in spatial position and avoid deviations in subsequent overlay analysis due to differences in coordinate systems; in terms of pixel attribute standardization, the pixel size data format and concentration value unit of the grid layer are unified, the concentration values are reasonably checked, and abnormal values are removed, so as to ensure that the pixel attributes meet the requirements of subsequent data processing and analysis; after the standardization processing, the final concentration spatial distribution grid layer is output, which not only has continuous and accurate pollutant concentration information, but also can be accurately spatially registered with other core geographic data, thereby laying a foundation for concentration extraction and dose calculation of the subsequent exposure assessment unit.

[0094] In this embodiment, the role of step S3 is to construct a population activity intensity index surface that comprehensively reflects the spatiotemporal distribution characteristics of the population based on the POI spatial database and the population census grid data, through POI category extraction, initial weight presetting, kernel density estimation, dynamic weight correction, and weighted overlay, so as to provide accurate population activity spatial data support for the subsequent division of the dynamic exposure assessment unit; the following are the detailed steps:

[0095] Step S3-1: POI category extraction and initial activity intensity weight presetting

[0096] Based on the POI spatial database, POI locations belonging to seven categories—residential, educational, medical, commercial, catering, park, and industrial facilities—are extracted through category filtering to ensure that the attribute category of each POI location fully matches the preset category system. Then, based on the attraction and agglomeration effect of each type of POI facility on the daily activities of the regional population, initial activity intensity weights representing their relative importance are assigned. For example, residential POIs are given higher initial weights due to their long dwell time and strong agglomeration effect, industrial facility POIs are given moderate initial weights due to their relatively low population activity frequency, and park POIs are given corresponding initial weights based on their opening hours and the size of their user base. After the weight assignment is completed, a correlation table between POI categories and initial weights is established to ensure that the initial weights of each POI category are traceable and adjustable, providing basic data for subsequent dynamic correction.

[0097] Step S3-2: POI kernel density estimation and density distribution raster layer generation:

[0098] For each category of POI spatial location set, a kernel density estimation algorithm is independently initiated for spatial smoothing. First, based on the average service range or spatial distribution characteristics of each type of POI facility, a search radius for kernel density estimation is set. For example, the search radius for residential POIs is set based on the average service radius of urban residential areas, while the search radius for commercial POIs is determined by referring to the radiation range of commercial complexes. Then, the kernel density estimation algorithm calculates the POI clustering degree at each spatial location, using the following formula: ,in, For spatial location The kernel density value at that location, This represents the total number of spatial points in this category of POIs. The set search radius, For the first The coordinates of each POI spatial point are obtained; the spatial location within the target area is calculated one by one using this formula to generate a density distribution raster layer that reflects the spatial agglomeration degree of this type of POI facility; finally, the generated POI density distribution raster layers are normalized by using a linear normalization method to convert all density values ​​to a unified numerical range of 0 to 1, forming standardized POI density distribution raster layers for various types of POIs and eliminating the dimensional differences in density values ​​of different types of POIs.

[0099] Step S3-3: Dynamic weight correction and generation of weighted activity intensity raster layer:

[0100] The standardized POI density distribution grid layer of each type is overlaid with the population census grid data to analyze the space, so that each grid unit is associated with the normalized density value of each type of POI and the number of permanent population in the grid; then the spatial correlation coefficient of the normalized density value of each type of POI in each grid unit and the number of permanent population is calculated, and the correlation quantifies the correlation degree of the two; based on the spatial correlation analysis result, a dynamic weight correction model is established, for the POI category with an absolute value of the correlation coefficient greater than a preset threshold and showing a significant positive correlation, the initial activity intensity weight value is adjusted upward by a preset proportion; for the POI category with an absolute value of the correlation coefficient less than a preset threshold and showing no significant correlation or showing a negative correlation, the initial weight value is maintained or adjusted downward by a preset proportion; after the weight correction of all POI categories is completed, a set of dynamically corrected POI category activity intensity weight values are obtained; the dynamically corrected weight values are multiplied by the corresponding standardized POI density distribution grid layer respectively, that is, the density value of each pixel is multiplied by the corrected weight of the corresponding category, to generate a weighted activity intensity grid layer of each category, so that the layer reflects the aggregation degree of POI and the weight priority at the same time.

[0101] Step S3-4: Surface integration of population activity intensity index:

[0102] The weighted activity intensity grid layers of each category are overlaid and summed to generate a comprehensive grid layer by adding the weighted activity intensity pixel values of each category corresponding to each spatial position in the target area one by one; each pixel value of the layer is the population activity intensity index of the corresponding spatial position, and the numerical size comprehensively reflects the population activity intensity under the joint action of population distribution and multiple types of POI facilities; after generation, the spatial reference of the grid layer is checked to ensure that it is consistent with the digital elevation model geographic spatial data, population census grid data and the concentration spatial distribution grid layer to be generated subsequently; finally, the population activity intensity index surface grid layer is formed, which provides core population activity spatial distribution data support for the division of the dynamic exposure assessment unit in step S4.

[0103] In this embodiment, the role of step S4 is to form a standardized exposure dose result set based on the population activity intensity index surface generated in step S3 and the concentration spatial distribution grid layer generated in step S2, through activity area level division, dynamic exposure assessment unit construction, average pollutant concentration extraction in the unit and multi-path multi-population exposure dose calculation, to provide accurate exposure data support for the health risk quantification in step S5; the following are the detailed steps:

[0104] Step S4-1: Population activity intensity pixel value statistics and threshold setting:

[0105] Extract all pixel values in the population activity intensity index surface raster layer to construct a pixel value dataset; calculate the statistical quantiles of the dataset using statistical analysis methods, including the first high quantile, the second low quantile, and the first low quantile, wherein the first high quantile is usually the 75th quantile, the second low quantile is the 50th quantile, and the first low quantile is the 25th quantile; set two thresholds for dividing high, medium, and low population activity zones based on these statistical quantiles; the first threshold is the high activity zone threshold, which is greater than or equal to the first high quantile, and the second threshold is the medium activity zone threshold, which is between the first low quantile and the second low quantile; the region corresponding to the pixel value below the first low quantile is designated as the low activity zone; after the threshold setting is completed, a threshold division standard table is formed, which clearly defines the pixel value range corresponding to each activity zone, providing a basis for subsequent pixel reclassification;

[0106] Step S4-2: Population activity intensity zoning raster layer generation:

[0107] According to the two thresholds set, perform pixel reclassification on the population activity intensity index surface raster layer; assign the pixels with a value greater than or equal to the high activity zone threshold to the high activity zone attribute, the pixels with a value between the medium activity zone threshold and the high activity zone threshold to the medium activity zone attribute, and the pixels with a value below the medium activity zone threshold to the low activity zone attribute; during the reclassification process, the system automatically checks the accuracy of the attribute assignment of each pixel to ensure that there are no missing or misclassified pixels; after the reclassification is completed, a population activity intensity zoning raster layer with high, medium, and low level attribute identifiers is generated, which directly presents the spatial classification characteristics of the population activity intensity in the target region;

[0108] Step S4-3: Dynamic exposure assessment unit vector layer construction:

[0109] Start the raster-to-vector processing tool to convert the population activity intensity zoning raster layer; during the conversion process, extract the continuous spatial boundaries of high, medium, and low activity zones based on their attributes to generate corresponding spatial vector boundary files, each containing the boundary coordinates, topological relationships, and other spatial information of the corresponding level activity zone; perform topological checking on the generated spatial vector boundary files, focusing on checking whether the boundaries have topological errors such as overlap, gaps, or hanging nodes; for the errors found in the checking, use the topological correction tool to repair them to ensure that the boundaries of each level activity zone are free of overlap, gaps, and correct topological relationships; define the corrected high, medium, and low level activity zone spatial vector boundaries as high, medium, and low level dynamic exposure assessment units, respectively, and integrate them to form a dynamic exposure assessment unit vector layer, which provides a spatial unit basis for subsequent concentration extraction and dose calculation;

[0110] Step S4-4: Exposure assessment unit average pollutant concentration extraction:

[0111] The dynamic exposure assessment unit vector layer is spatially overlaid with the concentration spatial distribution grid layer to establish the spatial correlation between the two; for each dynamic exposure assessment unit, the partition statistical tool is called to filter out all grid cells that fall completely within the unit boundary range, and the pollutant concentration values corresponding to these cells are extracted; the arithmetic mean method is used to calculate the average of these concentration values, and the formula is: , wherein, is the representative average pollutant concentration of the dynamic exposure assessment unit, is the total number of grid cells that fall completely within the unit, is the pollutant concentration value of the th grid cell; the calculated is taken as the core concentration parameter of the dynamic exposure assessment unit and is stored in the attribute table of the dynamic exposure assessment unit vector layer to form a comprehensive data set containing spatial information and concentration information;

[0112] Step S4-5: Exposure parameter matching and multi-path multi-population daily exposure dose calculation:

[0113] Based on the activity area level corresponding to the dynamic exposure assessment unit and the dominant POI category within it, combined with the typical activity patterns of different age groups in various POI places, matching exposure parameters are determined and assigned; the exposure parameters include the daily residence time of different populations in various places, soil intake rate, skin exposure surface area, soil adhesion coefficient and respiratory rate;

[0114] The authoritative source of exposure parameters:

[0115] All exposure parameters prefer to use the national general recommended values in the "China Population Exposure Parameter Manual" (Children's Volume, Adult Volume) issued by the Ministry of Ecology and Environment, and can be combined with the population activity characteristics of the target area for local fine-tuning; for parameters without domestic recommended values, the recommended values in the "Technical Guidelines for Risk Assessment of Soil Pollution in Construction Land" (HJ25.3-2019) are used to ensure the compliance, authority and reproducibility of the parameters;

[0116] Definition of core parameters, population differentiation assignment basis and recommended value range:

[0117] Daily residence time : refers to the daily residence time of the population in the corresponding Pol type assessment unit, with the unit of h / d; the differentiation assignment basis of children and adults is different from the daily activity patterns of different populations, for example, in residential POI units, the recommended value for adults is 16h / d, and the recommended value for children is 20h / d; in commercial POI units, the The recommended value is 2 hours / day for children. The recommended value is 1h / d, and the specific value should be determined based on the dominant POI category within the cell.

[0118] Soil intake rate This refers to the amount of soil ingested orally by a population each day, measured in units of... According to the "Chinese Population Exposure Parameter Manual", the recommended value for children is... Recommended value for adults ;

[0119] Exposed surface area of ​​skin : refers to the skin surface area in contact with soil, measured in units of According to the "Chinese Population Exposure Parameter Manual", the recommended value for children is... Recommended value for adults ;

[0120] Soil adhesion coefficient This refers to the amount of soil adhering to a unit area of ​​skin surface, measured in units of... According to HJ25.3-2019, the recommended value for children is... Recommended value for adults ;

[0121] Skin absorption coefficient This refers to the proportion of pollutants absorbed through the skin, and is dimensionless. Based on HJ25.3-2019, values ​​are assigned for different types of pollution; the recommended value for heavy metal pollutants is... The recommended value for volatile organic compounds is determined by combining the octanol-water partition coefficient of the pollutant;

[0122] respiratory rate This refers to the volume of air a person breathes daily, measured in units of... According to the "Chinese Population Exposure Parameter Manual", the recommended value for children is... Recommended value for adults ;

[0123] Exposure frequency This refers to the number of days per year that a person is exposed to soil pollutants, expressed in units of... The general recommended value for regional-scale assessment is ;

[0124] Exposure duration This refers to the number of years a person is continuously exposed to soil pollutants, measured in units of... In the carcinogenic risk assessment, adults Recommended value ,child Recommended value In the non-carcinogenic risk assessment, The value is equal to the exposure period of the corresponding population;

[0125] Body weight : refers to the average body weight of the corresponding population, and the unit is kg; According to the "Manual of Exposure Parameters for Chinese Population", the recommended value for children is 50 kg, and the recommended value for adults is 70 kg; ;

[0126] Average exposure time : refers to the average time of pollutant exposure, and the unit is a; In the carcinogenic risk assessment, the value is 70 years (the average life expectancy of the corresponding population); in the non-carcinogenic risk assessment, the value is 24 hours; ;

[0127] Parameter matching rules:

[0128] For dynamic exposure assessment units of high, medium and low different activity intensity, combined with the dominant POI category in the unit, the daily residence time is differentiated and valued, and the rest of the parameters use the above recommended values, to ensure that the evaluation results fit the actual exposure characteristics of the population in the region;

[0129] The representative average pollutant concentration of the dynamic exposure assessment unit, the matched exposure parameters and the specific calculation formula of different exposure pathways are combined to calculate the daily average exposure dose under the three pathways of oral ingestion, skin contact and respiratory inhalation;

[0130] The daily average exposure dose calculation formula for the oral ingestion pathway is: , wherein, is the daily average exposure dose of the oral ingestion pathway, is the soil ingestion rate, is the daily residence time, is the exposure frequency, is the exposure duration, is the body weight, is the average exposure time; this formula and parameter definition meet the specification requirements of "Technical Guidelines for Risk Assessment of Soil Pollution in Construction Land" (HJ 25.3-2019);

[0131] The daily average exposure dose calculation formula for the skin contact pathway is: , wherein, is the daily average exposure dose of the skin contact pathway, is the skin exposure surface area, is the soil adhesion coefficient, ​​​is the skin absorption coefficient, and the remaining symbols have the same meanings as the oral ingestion pathway formula; this formula and parameter definition meet the specification requirements of the Technical Guidelines for Risk Assessment of Construction Land Soil Pollution (HJ 25.3-2019);

[0132] The daily average exposure dose calculation formula for the respiratory inhalation pathway is: wherein, is the daily average exposure dose for the respiratory inhalation pathway, is the respiratory rate, and the remaining symbols have the same meanings as the oral ingestion pathway formula; this formula and parameter definition meet the specification requirements of the Technical Guidelines for Risk Assessment of Construction Land Soil Pollution (HJ 25.3-2019);

[0133] After the daily average exposure doses of the three pathways are independently calculated for the two types of populations, i.e., children and adults, all exposure dose data of the populations in each dynamic exposure assessment unit are summarized to form a pollutant multi-pathway, multi-population exposure dose result set with the dynamic exposure assessment unit as the basic recording unit. The result set contains unit identification, population type, exposure dose of each pathway, and total exposure dose, and other key information.

[0134] In this embodiment, the role of step S5 is to combine the toxicity parameters of a specific pollutant based on the pollutant multi-pathway, multi-population exposure dose result set generated in step S4, to realize the quantification and intuitive display of the health risk of regional soil pollution by calculating the carcinogenic risk value and hazard quotient, assessing the risk level, visualizing the presentation, and identifying the cause of the high-risk area, and to provide accurate decision-making basis for pollution prevention and risk control. The following are the detailed steps:

[0135] Step S5-1: Toxicity parameter acquisition and association:

[0136] Based on the pollutant multi-pathway, multi-population exposure dose result set, the specific pollutant species involved in the assessment is determined, and the toxicity parameters of the corresponding pollutant are directly obtained from the preset toxicity parameter database. The toxicity parameters at least include the oral ingestion carcinogenic slope factor, respiratory inhalation unit risk factor, and oral ingestion reference dose, skin contact reference dose, and respiratory inhalation reference dose.

[0137] The authoritative source of core toxicity parameters:

[0138] Oral ingestion carcinogenic slope factor : Preferably, the recommended values of pollutants published in the appendix of the Soil Environmental Quality Risk Control Standards for Construction Land Soil Pollution (GB 15618-2018) and the Technical Guidelines for Risk Assessment of Construction Land Soil Pollution (HJ 25.3-2019) are adopted. If there is no recommended value in the domestic standard, the authoritative toxicity parameter value published by the U.S. EPA Integrated Risk Information System (IRIS) is adopted.

[0139] Unit risk factor for respiratory inhalation : Preferentially use the recommended values of pollutants published in the appendix of the Technical Guidelines for Risk Assessment of Soil Pollution in Construction Sites (HJ 25.3-2019); if there is no recommended value in the domestic standard, use the recommended value of the unit risk factor published by the U.S. EPA Integrated Risk Information System (IRIS);

[0140] Reference amount for each exposure route , , : Preferentially use the domestic recommended values published in the Technical Guidelines for Risk Assessment of Soil Pollution in Construction Sites (HJ 25.3-2019); if there is no domestic recommended value, use the authoritative value of the U.S. EPA IRIS database;

[0141] Explanation of population differences:

[0142] The above toxicity parameters are inherent properties of pollutants, which are determined by the physicochemical and toxicological properties of the pollutants themselves and do not change with the age of the population; the difference in risk values between children and adults is realized through the differentiated exposure dose of the two types of population, and is irrelevant to the toxicity parameters themselves, and there is no population-differentiated toxicity parameter setting;

[0143] During the acquisition process, the system automatically verifies the applicability and timeliness of the toxicity parameters to ensure that the parameters are completely matched with the types of pollutants and meet the requirements of the current industry standards or specifications for environmental health risk assessment; the acquired toxicity parameters are associated with the exposure dose result set of the pollutants in multiple ways and multiple populations, and a comprehensive data set is established with the dynamic exposure assessment unit as the core index, associated with the types of pollutants, exposure dose and corresponding toxicity parameters, providing complete parameter support for subsequent risk calculation;

[0144] Step S5-2: Calculation of carcinogenic risk values in multiple pathways

[0145] For each dynamic exposure assessment unit in the vector layer of the dynamic exposure assessment unit, based on the associated comprehensive data set, the carcinogenic risk values of the oral ingestion, skin contact and respiratory inhalation pathways are calculated respectively;

[0146] The formula for calculating the carcinogenic risk value of the oral ingestion pathway is: , wherein, is the carcinogenic risk value of the oral ingestion pathway, is the daily average exposure dose of oral ingestion, is the oral ingestion carcinogenic slope factor;

[0147] The formula for calculating the carcinogenic risk value of the skin contact pathway is: , wherein, Carcinogenic risk value for dermal contact pathway, Daily average exposure dose for dermal contact pathway, Carcinogenic slope factor for dermal contact pathway;

[0148] The formula for calculating the carcinogenic risk value for respiratory inhalation pathway is: wherein, Carcinogenic risk value for respiratory inhalation pathway, Daily average exposure dose for respiratory inhalation pathway, Unit risk factor for respiratory inhalation pathway;

[0149] For children and adults respectively, the carcinogenic risk values of the above two pathways are calculated independently, and for each dynamic exposure assessment unit, the carcinogenic risk values of the two pathways of the two types of people are summarized to obtain the comprehensive carcinogenic risk value of the unit, and the formula is: wherein, Comprehensive carcinogenic risk value of the dynamic exposure assessment unit, Carcinogenic risk value for oral ingestion pathway for children, Carcinogenic risk value for oral ingestion pathway for adults, Carcinogenic risk value for dermal contact pathway for children, Carcinogenic risk value for dermal contact pathway for adults, Carcinogenic risk value for respiratory inhalation pathway for children, Carcinogenic risk value for respiratory inhalation pathway for adults;

[0150] Step S5-3: Multi-pathway hazard quotient calculation and comprehensive index summary:

[0151] For each dynamic exposure assessment unit in the vector layer of dynamic exposure assessment units, the hazard quotient of oral ingestion, dermal contact and respiratory inhalation is calculated based on the associated comprehensive data set;

[0152] The formula for calculating the hazard quotient of oral ingestion pathway is: wherein, Hazard quotient of oral ingestion pathway, Daily average exposure dose for oral ingestion pathway, Reference dose for oral ingestion pathway;

[0153] The formula for calculating the hazard quotient of dermal contact pathway is: wherein, Hazard quotient of dermal contact pathway, Daily average exposure dose for dermal contact pathway, Reference dose for dermal contact pathway;

[0154] The hazard quotient of the respiratory inhalation route is calculated by the formula: wherein, is the hazard quotient of the respiratory inhalation route, is the daily average exposure dose of the respiratory inhalation, is the respiratory inhalation reference dose;

[0155] For the two types of populations of children and adults, the hazard quotients of the above three routes are independently calculated, and for each dynamic exposure assessment unit, the hazard quotients of the three routes of the two types of populations are summarized to obtain the comprehensive hazard quotient index of the unit, and the formula is: wherein, is the comprehensive hazard quotient index of the dynamic exposure assessment unit, is the hazard quotient of the oral ingestion route of children, is the hazard quotient of the oral ingestion route of adults, is the hazard quotient of the skin contact route of children, is the hazard quotient of the skin contact route of adults, is the hazard quotient of the respiratory inhalation route of children, is the hazard quotient of the respiratory inhalation route of adults;

[0156] Step S5-4: Construction of the health risk assessment result set of regional soil pollution:

[0157] Based on the comprehensive carcinogenic risk value and the comprehensive hazard quotient index of all dynamic exposure assessment units, the unit identifier, the spatial coordinates, the population exposure dose, the risk value of each route and the comprehensive risk index of each unit are integrated to construct the health risk assessment result set of regional soil pollution. The result set is stored in a structured data format, supports multi-dimensional query and statistical analysis according to dynamic exposure assessment units, risk levels, POI categories and the like, and after the construction is completed, the system automatically performs data integrity check to ensure that the risk calculation results of each dynamic exposure assessment unit are not missing and abnormal, thereby providing a reliable data basis for subsequent risk level assessment and visualization;

[0158] Step S5-5: Risk level assessment and risk zoning vector layer generation:

[0159] Based on the set of regional soil pollution health risk assessment results, according to the preset risk level division threshold values of the comprehensive carcinogenic risk value and the comprehensive hazard quotient index, the risk level of each dynamic exposure assessment unit is evaluated; the risk level is usually divided into three levels of low risk, medium risk and high risk, wherein the low risk corresponds to the comprehensive carcinogenic risk value being lower than the first threshold value and the comprehensive hazard quotient index being lower than the second threshold value, the medium risk corresponds to the comprehensive carcinogenic risk value being between the first threshold value and the third threshold value and the comprehensive hazard quotient index being between the second threshold value and the fourth threshold value, and the high risk corresponds to the comprehensive carcinogenic risk value being higher than the third threshold value or the comprehensive hazard quotient index being higher than the fourth threshold value;

[0160] The evaluated risk level attribute is associated with the corresponding spatial feature in the dynamic exposure assessment unit vector layer through spatial association technology to generate a dynamic exposure assessment unit risk zoning vector layer with a risk level attribute; in the association process, the risk level attribute and the spatial feature are one-to-one corresponding, without mismatching or missing, and the layer contains the spatial boundary information and the risk level attribute of the dynamic exposure assessment unit, providing core vector data for subsequent visualization rendering;

[0161] Step S5-6: regional soil pollution health risk level distribution map generation:

[0162] The dynamic exposure assessment unit risk zoning vector layer is symbolized and rendered; different rendering rules are set according to the risk level, for example, the low-risk area is assigned green, the medium-risk area is assigned yellow, and the high-risk area is assigned red, and corresponding legend marks are configured for different risk levels; in the rendering process, the visual presentation effect of the layer is optimized to ensure that the boundaries of different risk levels are clear and distinguishable, and the color transition is natural without visual confusion; after rendering, a regional soil pollution health risk level distribution map covering the target region is generated, which directly presents the spatial distribution pattern of soil pollution health risk in the target region and facilitates quick identification of high-risk aggregation areas;

[0163] Step S5-7: high-risk area dominant POI category identification and association identification:

[0164] For the dynamic exposure assessment unit in the dynamic exposure assessment unit risk zoning vector layer that is evaluated as high risk, a spatial overlay analysis tool is started to overlay the spatial range with a POI spatial database; by counting the proportion or density proportion of each type of POI in the overlaid area, the POI category with the highest proportion is identified and extracted as the dominant POI category of the high-risk unit;

[0165] In the regional soil pollution health risk level distribution map, the high-risk dynamic exposure assessment unit is labeled using a labeling tool. The labeling content includes the dynamic exposure assessment unit identification, the comprehensive carcinogenic risk value, the comprehensive hazard quotient index, and the dominant POI category information. The labeling position is selected as the geometric center or a prominent position of the high-risk area to ensure that the labeling information is clear and visible and does not block key map elements. Through the association identification, the risk degree and potential cause of the high-risk area are clear, which provides a clear direction for precisely formulating pollution prevention and control measures and risk management strategies.

[0166] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely divergences, modifications, replacements and variations of the embodiments, and the scope of the application is defined by the appended claims and their equivalents.

Claims

1. A method for assessing regional soil pollution health risks based on POI data, characterized in that: Includes the following steps: Step S1: Acquisition and preprocessing of multi-source heterogeneous data: Acquire soil pollution sampling point data, digital elevation model geospatial data, population census grid data, and Points of Interest (POI) data including categories such as residential, educational, medical, commercial, catering, park, and industrial facilities in the target area; classify, clean, geocode, and spatialize the POI data to establish a POI spatial database; Step S2: Spatial distribution simulation of soil pollutants: Based on soil pollution sampling point data, a raster layer of the spatial distribution of specific pollutant concentrations within the target area is generated using the Kriging spatial interpolation method. Step S3: Construction of dynamic weighted population activity intensity surface: Based on the POI spatial database, an initial activity intensity weight value is preset for each POI category; the kernel density estimation algorithm is used to generate density distribution raster layers for each type of POI; the initial weights of each type of POI are dynamically corrected according to the census grid data, and the corrected weights are weighted and superimposed with the corresponding density layers to generate a population activity intensity index surface that comprehensively reflects the spatiotemporal distribution characteristics of the population. Step S4: Dynamic generation of exposure assessment units and calculation of pollution exposure dose: The population activity intensity index surface is divided into three levels of activity areas: high, medium and low, according to a preset threshold, and the spatial boundary of each level of activity area is used as a dynamic exposure assessment unit; for each exposure assessment unit, a concentration spatial distribution raster layer is superimposed, and the average pollutant concentration within the unit is extracted; combined with preset exposure parameters, the daily average pollutant exposure dose for different age groups within the unit is calculated for the three routes of oral ingestion, skin contact and inhalation. Step S5: Multi-pathway health risk quantification and visualization: Substitute the pollutant exposure doses calculated in each exposure assessment unit and the corresponding pollutant toxicity parameters into the health risk assessment model to calculate the carcinogenic risk value and hazard quotient of each unit; map the calculated risk values ​​back to the corresponding dynamic exposure assessment unit spatial range to generate a regional soil pollution health risk level distribution map, and identify the dominant POI category information in the high-risk area.

2. The method for regional soil pollution health risk assessment based on POI data according to claim 1, characterized in that: Step S1 specifically includes: Soil pollution sampling point data is obtained through environmental monitoring networks, digital elevation model geospatial data and population census grid data are obtained through geographic information platforms, and raw point of interest (POI) data including categories such as residential, educational, medical, commercial, catering, park and industrial facilities are obtained through commercial map service interfaces or public datasets. The original Points of Interest (POI) data is classified and cleaned. Based on the preset POI category system, the attribute information of each POI record is classified and filtered to remove duplicate, erroneous, and irrelevant POI records. The cleaned POI records are geocoded and spatialized, converting the text address information of each POI record into precise geographic coordinates and unifying them to the same spatial reference coordinate system as the digital elevation model geospatial data and the census grid data. Based on the spatialized POI records, geographic coordinates, and corresponding attribute category information, a structured POI spatial database is constructed. This database supports fast querying and statistical analysis based on spatial location and POI category, and provides an input data source for subsequent kernel density estimation algorithms.

3. The method for regional soil pollution health risk assessment based on POI data according to claim 1, characterized in that: Step S2 specifically includes: Based on soil pollution sampling point data, spatial structure analysis and outlier detection are performed to generate a standardized sampling point dataset that meets the requirements of spatial interpolation. Based on a standardized sampling point dataset, a variogram model is constructed to characterize the spatial autocorrelation features of specific pollutant concentrations. Based on the variogram model, the Kriging spatial interpolation algorithm is used to make the optimal unbiased estimate of the pollutant concentration at unknown spatial locations within the target area, and generate a spatial prediction point set of pollutant concentration. The spatial prediction point set is rasterized to generate a preliminary raster layer of pollutant concentration spatial distribution with continuous concentration values ​​covering the entire target area. Spatial reference and pixel attribute standardization processing are performed on the preliminary raster layer of pollutant concentration spatial distribution to generate a concentration spatial distribution raster layer that is spatially registered with the geospatial data of the digital elevation model and the subsequent surface spatial registration of the population activity intensity index.

4. The method for regional soil pollution health risk assessment based on POI data according to claim 1, characterized in that: Based on the POI spatial database, an initial activity intensity weight value is preset for each POI category; a kernel density estimation algorithm is used to generate density distribution raster layers for each type of POI; specifically including: Based on the POI spatial database, POI spatial points belonging to the categories of residential, educational, medical, commercial, catering, park and industrial facilities are extracted respectively; For each POI category, an initial activity intensity weight value is assigned to characterize its relative importance based on its attraction and agglomeration effect on the daily activities of the population in the area. For each category of POI spatial point set, a kernel density estimation algorithm is used independently for spatial smoothing to generate a density distribution raster layer that reflects the degree of spatial agglomeration of POI facilities of that type in the target area. The search radius of the kernel density estimation is set based on the average service range or spatial distribution characteristics of POI facilities of that type. The generated density distribution raster layers of various POIs are normalized to ensure that their density values ​​are within a uniform range, thus forming standardized density distribution raster layers of various POIs.

5. The method for regional soil pollution health risk assessment based on POI data according to claim 4, characterized in that: The initial weights of various POIs are dynamically corrected based on census grid data. The corrected weights are then weighted and superimposed with the corresponding density layers to generate a population activity intensity index surface that comprehensively reflects the spatiotemporal distribution characteristics of the population. Specifically, this includes: The standardized POI density distribution raster layers are spatially overlaid with census grid data to calculate the spatial correlation between the normalized density values ​​of various POIs in each grid cell and the number of permanent residents in the grid. Based on the spatial correlation analysis results, a dynamic weight correction model is established. For POI categories that show a significant positive correlation with population size, their initial activity intensity weight values ​​are increased. For POI categories that show no significant correlation or a negative correlation, their initial weight values ​​are maintained or decreased, thus obtaining a set of dynamically corrected POI category activity intensity weight values. The dynamically corrected POI category activity intensity weight values ​​are then multiplied by their corresponding standardized POI density distribution raster layers to generate weighted activity intensity raster layers for each category. Spatially overlay and sum the weighted activity intensity raster layers for each category to generate a surface raster layer that comprehensively reflects the population activity intensity index caused by the combined effects of population distribution and multiple types of POI facilities.

6. The method for regional soil pollution health risk assessment based on POI data according to claim 1, characterized in that: The population activity intensity index surface is divided into three levels of activity zones—high, medium, and low—according to a preset threshold, and the spatial boundaries of each activity zone are used as dynamic exposure assessment units; specifically including: Extract the cell values ​​of the surface raster layer of the population activity intensity index and calculate its statistical quantiles; Based on statistical quantiles, two thresholds are set to divide the population activity areas into high, medium and low levels. The threshold for the high activity area is greater than or equal to the first high quantile, and the threshold for the medium activity area is between the first low quantile and the second low quantile. Based on two thresholds, the surface raster layer of the population activity intensity index is reclassified to generate a population activity intensity partition raster layer with high, medium and low attributes. The raster layer of population activity intensity is converted from raster to vector, and the continuous spatial boundaries of the high, medium and low activity zones are extracted to generate spatial vector boundary files as the basis for subsequent calculations. The spatial vector boundary files are topologically checked and corrected to ensure that the boundaries of each level of activity area are non-overlapping and seamless. The corrected spatial vector boundaries of each level of activity area are defined as high, medium and low dynamic exposure assessment units, forming a dynamic exposure assessment unit vector layer.

7. The method for regional soil pollution health risk assessment based on POI data according to claim 6, characterized in that: For each exposure assessment unit, a concentration spatial distribution raster layer is overlaid to extract the average pollutant concentration within the unit; combined with preset exposure parameters, the average daily pollutant exposure dose for different age groups within the unit is calculated for oral ingestion, skin contact, and inhalation. Specifically, it includes: Spatial overlay analysis was performed between the dynamic exposure assessment unit vector layer and the concentration spatial distribution raster layer; For each dynamic exposure assessment unit, the arithmetic mean of pollutant concentrations of all raster cells that fall completely within the boundary of the unit is calculated using a zonal statistical tool. This mean is defined as the representative average pollutant concentration of the dynamic exposure assessment unit. Based on the activity zone level corresponding to the dynamic exposure assessment unit and the dominant POI category within it, and combined with the typical activity patterns of different age groups in POI locations of residential, educational, medical, commercial, catering, park and industrial facilities, a set of exposure parameters matching the population activity characteristics of the unit are determined and assigned. The exposure parameters include at least the daily stay time of different groups in each location, soil ingestion rate, skin exposure surface area and soil adhesion coefficient, and respiration rate. The representative average pollutant concentration of the dynamic exposure assessment unit, the exposure parameters matched with the population activity characteristics of the unit, and the specific calculation formulas for different exposure routes were combined to independently calculate the average daily pollutant exposure dose for children and adults in the dynamic exposure assessment unit via oral ingestion, skin contact, and inhalation. The daily average pollutant exposure doses calculated from three exposure pathways within each dynamic exposure assessment unit and for different population groups are aggregated to form a set of pollutant exposure dose results from multiple pathways and for multiple population groups, with the dynamic exposure assessment unit as the basic recording unit.

8. The method for regional soil pollution health risk assessment based on POI data according to claim 1, characterized in that: The pollutant exposure doses calculated in each exposure assessment unit and the corresponding pollutant toxicity parameters are substituted into the health risk assessment model to calculate the carcinogenic risk value and hazard quotient for each unit. Specifically, it includes: Based on the exposure dose results set of pollutants from multiple routes and multiple populations, the toxicity parameters of a specific pollutant are obtained. The toxicity parameters include at least the carcinogenicity slope factor of oral ingestion, the carcinogenicity slope factor of skin contact, the risk factor of inhalation, and the reference dose for each exposure route. For each dynamic exposure assessment unit in the vector layer of the dynamic exposure assessment unit, the average daily oral exposure dose calculated within the unit for both children and adults is combined with the oral intake carcinogenic slope factor to calculate the carcinogenic risk value of the oral intake route for that unit. For each dynamic exposure assessment unit in the vector layer of the dynamic exposure assessment unit, the average daily skin exposure dose calculated for both children and adults within the unit is combined with the skin exposure carcinogenic slope factor to calculate the carcinogenic risk value of the skin exposure route for that unit. For each dynamic exposure assessment unit in the vector layer of the dynamic exposure assessment unit, the average daily exposure dose of respiratory inhalation calculated within the unit for both children and adults is combined with the risk factor of the respiratory inhalation unit to calculate the carcinogenic risk value of the respiratory inhalation route for that unit. For each dynamic exposure assessment unit in the vector layer of the dynamic exposure assessment unit, the average daily exposure doses calculated within the unit for both children and adults for oral ingestion, skin contact, and inhalation are combined with the reference doses for the corresponding exposure routes to calculate the hazard quotient for each route. For each dynamic exposure assessment unit, the carcinogenic risk values ​​of the three routes of oral ingestion, skin contact, and inhalation are summarized to obtain the comprehensive carcinogenic risk value of the unit; the hazard quotients of the three routes of oral ingestion, skin contact, and inhalation are summarized to obtain the comprehensive hazard quotient index of the unit. Based on the comprehensive carcinogenic risk value and comprehensive hazard quotient of all dynamic exposure assessment units, a set of regional soil pollution health risk assessment results is constructed.

9. The method for regional soil pollution health risk assessment based on POI data according to claim 8, characterized in that: The calculated risk values ​​are mapped back to the corresponding spatial range of the dynamic exposure assessment unit to generate a regional soil pollution health risk level distribution map, and the dominant POI category information in the high-risk area is associated with it for identification. Specifically, it includes: Based on the regional soil pollution health risk assessment results set, and according to the preset risk level classification thresholds of comprehensive carcinogenic risk value and comprehensive hazard quotient index, the risk level of each dynamic exposure assessment unit is assessed. The assessed risk level attributes are associated with the corresponding spatial elements in the dynamic exposure assessment unit vector layer to generate a dynamic exposure assessment unit risk zoning vector layer with risk level attributes. Symbolic rendering is performed on the vector map layer of risk zoning of dynamic exposure assessment units, and different colors and legends are assigned according to the risk level to generate a regional soil pollution health risk level distribution map. For dynamic exposure assessment units that are rated as high-risk in the risk zoning vector layer of dynamic exposure assessment units, their spatial range is overlaid with the POI spatial database to identify and extract the dominant POI category within the spatial range of the unit. In the regional soil pollution health risk level distribution map, high-risk dynamic exposure assessment units are marked, and their dominant POI category information is used as auxiliary information for risk causes for association identification.

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