Heavy metal quality comprehensive evaluation method in combination with regional sediment heterogeneity

Abstract

The invention belongs to the technical field of environmental monitoring, and particularly relates to a heavy metal quality comprehensive evaluation method in combination with regional sediment heterogeneity. The method comprises the following steps: collecting heavy metal content data, sedimentary chronology data and sample longitude and latitude data of regional sediments; calculating a geochemical baseline of the regional sediment, and analyzing pollution source characteristics of heavy metals according to the geochemical baseline; establishing a spatial pattern of a regional geochemical baseline by utilizing a geostatistical analysis function of the GIS, and quantifying and extracting a baseline value of any grid through a fishing net analysis function of the GIS; and calculating a corrected pollution index and a comprehensive ecological risk index based on the sampling point distribution and the baseline value of the corresponding grid, and judging the heavy metal pollution level and risk level of the regional surface sediment according to an evaluation standard. According to the method, the problem that part of evaluation areas are lack of geochemical reference values is solved, the accuracy and timeliness of sediment heavy metal environment quality evaluation are improved, and the uncertainty caused by subjective selection of the geochemical reference values is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of environmental monitoring, and in particular relates to a comprehensive evaluation method for heavy metal quality. Background Art

[0002] Heavy metals have attracted much attention as a persistent, accumulative and biotoxic pollutant. Sediments are the main environmental medium for studying and assessing regional heavy metal pollution risks. The quality evaluation of heavy metals in sediments will be beneficial to the source control of key pollutants and subsequent environmental risk management.

[0003] However, most current evaluations are based on the ratio of the detected concentration of heavy metals in sediments to the geochemical reference value, and the geochemical reference value is usually selected as a fixed value provided by geological or environmental standards, ignoring its spatial matching and applicability to specific regions. Due to the temporal and spatial heterogeneity of sediments, the selection of geochemical reference values ​​will seriously affect the accuracy and timeliness of regional sediment environmental quality evaluation results at different spatial scales. Summary of the invention

[0004] The purpose of the present invention is to provide a comprehensive evaluation method for heavy metal quality in combination with regional sediment heterogeneity to improve spatial matching and applicability in specific areas.

[0005] The comprehensive evaluation method of heavy metal quality combined with regional sediment heterogeneity provided by the present invention comprises the following specific steps:

[0006] (1) Collect data on heavy metal content, sedimentary chronology, and latitude and longitude of regional sediments;

[0007] (2) Calculate the geochemical baseline of regional sediments and analyze the pollution source characteristics of heavy metals based on the geochemical baseline;

[0008] (3) Use the geostatistical analysis function of GIS to establish the spatial pattern of regional geochemical baselines, and use the fishing net analysis function of GIS to quantify and extract baseline values ​​for any grid;

[0009] (4) Calculate the modified pollution index based on the distribution of sampling points and the baseline value of the corresponding grid, and determine the heavy metal pollution level of regional surface sediments according to the evaluation criteria;

[0010] (5) The comprehensive ecological risk index was calculated based on the distribution of sampling points and the baseline values ​​of the corresponding grids, and the heavy metal risk level of regional surface sediments was determined according to the evaluation criteria.

[0011] Further:

[0012] In step (1), the heavy metal content data, sedimentary chronology data and sample longitude and latitude data of the collected regional sediments specifically include:

[0013] The heavy metal data of sediments in the area include surface sediments and core sediments;

[0014] The sedimentary chronology data are sedimentation rate data of local sediments or dating data of core sediments at different depths;

[0015] The sample longitude and latitude data are the sampling longitude and latitude of each core sediment;

[0016] The heavy metals include vanadium, chromium, nickel, copper, zinc, arsenic, cadmium and lead.

[0017] In step (2), the calculation of the geochemical baseline of regional sediments specifically includes:

[0018] The relative cumulative frequency method was used to calculate the geochemical baselines of heavy metals in surface sediments and core sediments.

[0019] In step (2), the heavy metal pollution source characteristics are analyzed according to the geochemical baseline, specifically including:

[0020] The geochemical baseline databases of heavy metals in surface sediments and core sediments determined by the relative cumulative frequency method are denoted as BS and BC, respectively;

[0021] The significant difference between BS and BC was analyzed by independent sample t test. If BS is significantly higher than BC, it means that there is non-point source pollution of heavy metals; otherwise, there is no non-point source pollution.

[0022] Obtain the concentration database DS of heavy metals in surface sediments from field sampling surveys in the target area;

[0023] The significant difference between DS and BS was analyzed again by independent sample t-test. If DS was significantly higher than BS, it indicated the existence of point source pollution of heavy metals; otherwise, it was from natural sources.

[0024] In step (3), the spatial pattern of regional geochemical baselines is established by using the geostatistical analysis function of GIS, and the baseline values ​​of any grid are quantified and extracted by using the fishing net analysis function of GIS, which specifically includes:

[0025] The pre-industrial background of heavy metals in sediments of each core was calculated based on local sedimentation rate data, and the concentration data of the pre-industrial background were calculated and expressed as mean ± standard deviation, which was defined as the geochemical baseline value;

[0026] Calculate the geochemical baseline of each core sediment according to the relative cumulative frequency method, and obtain the geochemical baseline value of each core sediment according to the calculation result;

[0027] The geochemical baseline value of the pre-industrial background of the core sediments is compared with the geochemical baseline value calculated by the relative cumulative frequency method, and the final geochemical baseline value of the core sediments is determined according to the comparison results;

[0028] The geochemical baseline values ​​and longitude and latitude data of the core sediments were imported into ArcGIS analysis software, and the Geostatistical Analyst function was used to perform interpolation analysis to predict the regional geochemical baseline and perform visualization.

[0029] The fishing net creation function of ArcGIS is used to create the fishing net, the interpolation image data is segmented according to the created fishing net, and the grid classification and extraction of the interpolation data are performed based on the segmentation results.

[0030] In step (4), the modified pollution index is calculated based on the sampling point distribution and the baseline value of the corresponding grid. The specific calculation method is as follows:

[0031]

[0032] Among them, C i is the heavy metal concentration of the i-th sample, GBV i is the geochemical baseline of the grid where the i-th sample is located, CI i is the heavy metal pollution index, and MCI is the modified pollution index.

[0033] In step (4), the comprehensive ecological risk index is calculated based on the distribution of sampling points and the baseline value of the corresponding grid. The specific calculation method is as follows:

[0034]

[0035] Where T is the toxicity response coefficient of heavy metals in sediments, C i is the heavy metal concentration of the i-th sample, GBV i is the geochemical baseline of the grid where the i-th sample is located, ER ave is the average value of heavy metal ecological risk index of the i-th sample, ER max is the maximum ecological risk index of heavy metals in the ith sample, ER is the ecological risk index of a single heavy metal, and NIR is the comprehensive ecological risk index.

[0036] The present invention also provides a computer-readable storage medium, which includes a program for a comprehensive evaluation method for heavy metal quality in combination with regional sediment heterogeneity. When the program for a comprehensive evaluation method for heavy metal quality in combination with regional sediment heterogeneity is executed by a processor, the steps of the comprehensive evaluation method for heavy metal quality in combination with regional sediment heterogeneity as described in any one of the above items are implemented.

[0037] Compared with the prior art, the present invention solves the problem of lack of geochemical reference values ​​in some evaluation areas by creating a geochemical baseline, and takes into account the temporal and spatial heterogeneity of regional sediments. The geochemical baseline predicted by the GIS method has spatial attributes, which is convenient for comparison with subsequent sediment monitoring samples, improves the accuracy and timeliness of the environmental quality evaluation of heavy metals in sediments, and reduces the uncertainty caused by the subjective selection of geochemical reference values. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flow chart of the comprehensive evaluation method for heavy metal quality combined with regional sediment heterogeneity provided by the present invention.

[0039] Figure 2 A schematic diagram of the analysis results of the characteristics of heavy metal pollution sources in surface sediments provided by one embodiment of the present invention.

[0040] Figure 3 A schematic diagram of the spatial pattern of the heavy metal geochemical baseline of Daya Bay sediments provided in one embodiment of the present invention.

[0041] Figure 4 A schematic diagram of the evaluation results of the heavy metal-corrected pollution index of surface sediments provided in one embodiment of the present invention.

[0042] Figure 5 A schematic diagram of the evaluation results of the comprehensive ecological risk index of heavy metals in surface sediments provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0043] The present invention is further described below through specific embodiments in conjunction with the accompanying drawings. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0044] The present invention provides a comprehensive evaluation method for heavy metal quality combined with regional sediment heterogeneity, and its process is shown in Figure 1 Shown; including:

[0045] S1, collect heavy metal content data, sedimentary chronology data and sample longitude and latitude data of regional sediments;

[0046] S2, calculate the geochemical baseline of regional sediments and analyze the pollution source characteristics of heavy metals based on the geochemical baseline;

[0047] S3, using the geostatistical analysis function of GIS to establish the spatial pattern of regional geochemical baselines, and quantifying and extracting baseline values ​​of any grid through the fishing net analysis function of GIS;

[0048] S4, calculate the modified pollution index based on the distribution of sampling points and the baseline value of the corresponding grid, and determine the heavy metal pollution level of regional surface sediments according to the evaluation criteria;

[0049] S5, calculate the comprehensive ecological risk index based on the distribution of sampling points and the baseline value of the corresponding grid, and determine the risk level of heavy metals in regional surface sediments according to the evaluation criteria.

[0050] The present invention solves the problem of lack of geochemical reference values ​​in some evaluation areas, and takes into account the temporal and spatial heterogeneity of regional sediments. The geochemical baseline predicted by the GIS method has spatial attributes, which is convenient for comparison with subsequent sediment monitoring samples, improves the accuracy and timeliness of sediment heavy metal environmental quality evaluation, and reduces the uncertainty caused by the subjective selection of geochemical reference values.

[0051] Further:

[0052] The heavy metal content data, sedimentary chronology data and sample longitude and latitude data of the collected regional sediments specifically include:

[0053] The heavy metal data of sediments in the area include surface sediments and core sediments;

[0054] The sedimentary chronology data are sedimentation rate data of local sediments or dating data of core sediments at different depths;

[0055] The sample longitude and latitude data are the sampling longitude and latitude of each core sediment;

[0056] The heavy metals include vanadium, chromium, nickel, copper, zinc, arsenic, cadmium and lead.

[0057] The heavy metal content data, sedimentary chronology data and sample longitude and latitude data of the collected regional sediments are obtained through big data retrieval means, and the data are derived from previous field survey materials, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform and Web of Science.

[0058] The geochemical baseline of the sediment in the calculation area specifically includes:

[0059] The relative cumulative frequency method was used to calculate the geochemical baselines of heavy metals in surface sediments and core sediments.

[0060] The relative cumulative frequency method is to establish the concentration data and relative cumulative frequency of heavy metals on the X-axis and Y-axis respectively, and judge the distribution pattern of heavy metal concentration under natural, artificial or abnormal conditions based on the visualization results of the data. The heavy metal data characterized by the relative cumulative frequency method usually have 0, 1 or 2 inflection points. When there is 0 inflection point, all concentration data distribution is approximately straight line, and all data belong to the geochemical baseline range at this time; when there is only 1 inflection point, the data set on the inflection point can represent the concentration of significant human influence, and the data set under the inflection point represents the range of the geochemical baseline; when there are two inflection points, the data between the two inflection points may belong to the range of the geochemical baseline, and there may also be the influence of human activities. At this time, the data between the two inflection points can be compared with the data outside the two inflection points. The similarity of the cumulative frequency curve can be compared to make a judgment.

[0061] The characteristics of heavy metal pollution sources analyzed based on geochemical baselines specifically include:

[0062] The geochemical baseline databases of heavy metals in surface sediments and core sediments determined by the relative cumulative frequency method are denoted as BS and BC, respectively;

[0063] The significant difference between BS and BC was analyzed by independent sample t test. If BS is significantly higher than BC, it means that there is non-point source pollution of heavy metals; otherwise, there is no non-point source pollution.

[0064] Obtain the concentration database DS of heavy metals in surface sediments from field sampling surveys in the target area;

[0065] The significant difference between DS and BS was analyzed again by independent sample t-test. If DS was significantly higher than BS, it indicated the existence of point source pollution of heavy metals; otherwise, it was from natural sources.

[0066] The software for the independent sample t-test analysis was Excel 2016.

[0067] The use of the geostatistical analysis function of GIS to establish the spatial pattern of regional geochemical baselines and the quantification and extraction of baseline values ​​of any grid by the fishing net analysis function of GIS specifically include:

[0068] The pre-industrial background of heavy metals in sediments of each core was calculated based on local sedimentation rate data, and the concentration data of the pre-industrial background were calculated and expressed as mean ± standard deviation, which was defined as the geochemical baseline value;

[0069] Calculate the geochemical baseline of each core sediment according to the relative cumulative frequency method, and obtain the geochemical baseline value of each core sediment according to the calculation result;

[0070] The geochemical baseline value of the pre-industrial background of the core sediments is compared with the geochemical baseline value calculated by the relative cumulative frequency method, and the final geochemical baseline value of the core sediments is determined according to the comparison results;

[0071] The geochemical baseline values ​​and longitude and latitude data of the core sediments were imported into ArcGIS analysis software, and the Geostatistical Analyst function was used to perform interpolation analysis to predict the regional geochemical baseline and perform visualization.

[0072] The fishing net creation function of ArcGIS is used to create the fishing net, the interpolation image data is segmented according to the created fishing net, and the grid classification and extraction of the interpolation data are performed based on the segmentation results.

[0073] The specific calculation method for calculating the pre-industrial background of heavy metals in each core sediment based on local sedimentation rate data is as follows: assuming that the sedimentation rate is a cm / year and the local industry started in 1980, the heavy metal concentration in the core sediment before 1980 is the pre-industrial background.

[0074] When creating a fishing net using ArcGIS's Create Fishnet function, the grid size is the custom space size.

[0075] The modified pollution index is calculated based on the sampling point distribution and the baseline value of the corresponding grid. The specific calculation method is as follows:

[0076]

[0077] Among them, C i is the heavy metal concentration of the i-th sample, GBV i is the geochemical baseline of the grid where the i-th sample is located, CI i is the heavy metal pollution index, and MCI is the modified pollution index.

[0078] The heavy metal pollution level of regional surface sediments is judged based on the calculated modified pollution index (MCI) using evaluation criteria, wherein the evaluation criteria are as follows: if MCI<1.5, it is considered to be pollution-free; if 1.5≤MCI<2, it is considered to be low pollution; if 2≤MCI<4, it is considered to be moderate pollution; if 4≤MCI<8, it is considered to be high pollution; if 8≤MCI<16, it is considered to be extremely high pollution.

[0079] The comprehensive ecological risk index is calculated based on the sampling point distribution and the baseline value of the corresponding grid. The specific calculation method is as follows:

[0080]

[0081] Where T is the toxicity response coefficient of heavy metals in sediments, C iis the heavy metal concentration of the i-th sample, GBV i is the geochemical baseline of the grid where the i-th sample is located, ER ave is the average value of heavy metal ecological risk index of the i-th sample, ER max is the maximum ecological risk index of heavy metals in the ith sample, ER is the ecological risk index of a single heavy metal, and NIR is the comprehensive ecological risk index.

[0082] Based on the calculated comprehensive ecological risk index (NIR), the evaluation criteria are used to judge the level of heavy metal pollution in the regional surface sediments, wherein the specific evaluation criteria are: if NIR<30, it is considered as low risk; if 30≤NIR<50, it is considered as medium risk; if 50≤NIR<100, it is considered as high risk; if 100≤NIR<150, it is considered as high risk; if NIR>150, it is considered as extremely high risk.

[0083] Taking Daya Bay Nature Reserve in Guangdong, China as an example, the comprehensive evaluation of heavy metal quality of sediments is carried out by using the comprehensive evaluation method of heavy metal quality combined with regional sediment heterogeneity provided by the present invention. The specific analysis steps are as follows:

[0084] The heavy metal content data of Daya Bay sediments were collected, and the geochemical baselines of surface sediments and core sediments were calculated by the cumulative frequency distribution method, including vanadium (V), chromium (Cr), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), and lead (Pb), as shown in Table 1 below:

[0085] Table 1 Geochemical baseline values ​​of heavy metals in sediments (mg / kg)

[0086]

[0087] Furthermore, 42 random samples were collected from the surface sediments of Daya Bay to measure the content of heavy metal elements, including vanadium, chromium, nickel, copper, zinc, arsenic, cadmium, and lead. The pollution source characteristics of heavy metals in the collected sediment samples were analyzed using independent sample t-test. Figure 2 As shown, Figure 2 A schematic diagram of the analysis results of the characteristics of heavy metal pollution sources in surface sediments provided for an embodiment of the present invention shows that there is point source pollution for Cu and Pb in Daya Bay sediments; there is non-point source pollution for As and V, and there is a significant combined effect of point source and non-point source pollution for Zn and Cd. There is no significant difference between all geochemical baseline data of Cr and Ni and the measured concentrations of samples, indicating that Cr and Ni are of natural origin.

[0088] Furthermore, the geochemical baseline of each core was calculated based on the collected core sediment data, and the geochemical baseline and longitude and latitude of each core were imported into ArcGIS. The spatial pattern of the geochemical baseline of Daya Bay sediments was predicted through the geostatistical analysis function, and the baseline value of any grid can be quantified and extracted through the fishing net analysis function of GIS. Each grid unit is 1km×1km. The spatial pattern of the geochemical baseline of heavy metals in Daya Bay sediments is shown in Figure 2. Figure 3 shown.

[0089] Furthermore, based on the longitude and latitude of the 42 random samples collected and the grid of their corresponding geochemical baseline values, the corrected pollution index of these 42 surface sediment samples was calculated, and the heavy metal pollution level of the regional surface sediments was evaluated.

[0090] The evaluation results of the heavy metal modified pollution index of surface sediments are as follows: Figure 4 As shown, the results indicate that S1 and S3 have low contamination, S9 has moderate contamination, and the remaining sediment samples can be considered as non-contaminated.

[0091] Furthermore, based on the longitude and latitude of the 42 random samples collected and the grid of their corresponding geochemical baseline values, the comprehensive ecological risk index of these 42 surface sediment samples was calculated, and the risk level of heavy metals in regional surface sediments was evaluated.

[0092] The evaluation results of the comprehensive ecological risk index of heavy metals in surface sediments are as follows: Figure 5 As shown, the results indicate that S9 has an extremely high ecological risk, S1 has a high risk, S2 has a relatively high risk, S3, S6, S8, S10, S11, and S12 have a moderate risk, and the remaining sediment samples can be considered as low risk.

[0093] Based on the above analysis, it can be seen that the spatial pattern of the geochemical baseline created can accurately evaluate any subsequent survey and monitoring samples of sediments in the area, thereby giving a more accurate heavy metal environmental quality evaluation result, providing a scientific basis for future environmental monitoring and pollution remediation work.

[0094] The present invention also provides a computer-readable storage medium, which includes a program for a comprehensive evaluation method for heavy metal quality in combination with regional sediment heterogeneity. When the program for a comprehensive evaluation method for heavy metal quality in combination with regional sediment heterogeneity is executed by a processor, the steps of the comprehensive evaluation method for heavy metal quality in combination with regional sediment heterogeneity as described above are implemented.

[0095] It should be understood that in the embodiments provided by the present invention, the devices and methods involved can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0096] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0097] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A comprehensive evaluation method for heavy metal quality combined with regional sediment heterogeneity, characterized in that: The specific steps are: (1) Collect data on heavy metal content, sedimentary chronology, and latitude and longitude of regional sediments; (2) Calculate the geochemical baseline of regional sediments and analyze the pollution source characteristics of heavy metals based on the geochemical baseline; (3) Use the geostatistical analysis function of GIS to establish the spatial pattern of regional geochemical baselines, and use the fishing net analysis function of GIS to quantify and extract baseline values ​​for any grid; (4) Calculate the modified pollution index based on the distribution of sampling points and the baseline value of the corresponding grid, and determine the heavy metal pollution level of regional surface sediments according to the evaluation criteria; (5) The comprehensive ecological risk index was calculated based on the distribution of sampling points and the baseline values ​​of the corresponding grids, and the heavy metal risk level of regional surface sediments was determined according to the evaluation criteria.

2. The method for comprehensive evaluation of heavy metal quality according to claim 1, characterized in that: The heavy metal content data, sedimentary chronology data and sample longitude and latitude data of the collected regional sediments in step (1) are specifically: The heavy metal data of sediments in the area include surface sediments and core sediments; The sedimentary chronology data are sedimentation rate data of local sediments or dating data of core sediments at different depths; The sample longitude and latitude data are the sampling longitude and latitude of each core sediment; The heavy metals include vanadium, chromium, nickel, copper, zinc, arsenic, cadmium and lead.

3. The comprehensive evaluation method for heavy metal quality according to claim 2, characterized in that: In step (2): (1) The geochemical baseline of the sediments in the calculation area is specifically calculated by using the relative cumulative frequency method to calculate the geochemical baseline of heavy metals in surface sediments and core sediments respectively; (2) The characteristics of heavy metal pollution sources analyzed based on the geochemical baseline include: The geochemical baseline databases of heavy metals in surface sediments and core sediments determined by the relative cumulative frequency method are denoted as BS and BC, respectively; The significant difference between BS and BC was analyzed by independent sample t test. If BS is significantly higher than BC, it means that there is non-point source pollution of heavy metals; otherwise, there is no non-point source pollution. Obtain the concentration database DS of heavy metals in surface sediments from field sampling surveys in the target area; The significant difference between DS and BS was analyzed again by independent sample t-test. If DS is significantly higher than BS, it indicates the existence of point source pollution of heavy metals; otherwise, it is from natural sources.

4. The comprehensive evaluation method for heavy metal quality according to claim 3 is characterized in that: Step (3) uses the geostatistical analysis function of GIS to establish the spatial pattern of regional geochemical baselines, and uses the fishing net analysis function of GIS to quantify and extract the baseline values ​​of any grid, specifically including: The pre-industrial background of heavy metals in sediments of each core was calculated based on local sedimentation rate data, and the concentration data of the pre-industrial background were calculated and expressed as mean ± standard deviation, which was defined as the geochemical baseline value; Calculate the geochemical baseline of each core sediment according to the relative cumulative frequency method, and obtain the geochemical baseline value of each core sediment according to the calculation result; The geochemical baseline value of the pre-industrial background of the core sediments is compared with the geochemical baseline value calculated by the relative cumulative frequency method, and the final geochemical baseline value of the core sediments is determined according to the comparison results; The geochemical baseline values ​​and longitude and latitude data of the core sediments were imported into ArcGIS analysis software, and the Geostatistical Analyst function was used to perform interpolation analysis to predict the regional geochemical baseline and perform visualization. The fishing net creation function of ArcGIS is used to create the fishing net, the interpolation image data is segmented according to the created fishing net, and the grid classification and extraction of the interpolation data are performed based on the segmentation results.

5. The method for comprehensive evaluation of heavy metal quality according to claim 3, characterized in that: In step (4): The modified pollution index is calculated based on the sampling point distribution and the baseline value of the corresponding grid. The specific method is as follows: Among them, C i is the heavy metal concentration of the i-th sample, GBV i is the geochemical baseline of the grid where the i-th sample is located, CI i is the heavy metal pollution index, MCI is the modified pollution index; The comprehensive ecological risk index is calculated based on the distribution of sampling points and the baseline value of the corresponding grid. The specific method is as follows: Where T is the toxicity response coefficient of heavy metals in sediments, C i is the heavy metal concentration of the i-th sample, GBV i is the geochemical baseline of the grid where the i-th sample is located, ER ave is the average value of heavy metal ecological risk index of the i-th sample, ER max is the maximum ecological risk index of heavy metals in the ith sample, ER is the ecological risk index of a single heavy metal, and NIR is the comprehensive ecological risk index.

6. A computer-readable storage medium, characterized in that: A program comprising a method for comprehensive evaluation of heavy metal quality in combination with regional sediment heterogeneity as described in any one of claims 1 to 5, wherein when the program is executed by a processor, each step of the method for comprehensive evaluation of heavy metal quality is implemented.

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