Soil heavy metal risk assessment method for fuzzy region

Abstract

The invention relates to a soil heavy metal risk assessment method for a fuzzy region, fully considers spatial heterogeneity of soil heavy metals and a complex relationship between the soil heavy metals and agricultural products, and provides a fuzzy region averaging method, an improved potential ecological risk index method and an agricultural product risk index method. Therefore, soil heavy metal risk assessment of the fuzzy region is realized, and technical reference is provided for regional soil heavy metal risk assessment.

Description

Technical Field

[0001] This invention relates to the field of soil remediation technology, and specifically to a method for assessing the risk of heavy metals in soil in ambiguous areas. Background Technology

[0002] Soil is a vital component of terrestrial ecosystems, and its quality significantly impacts agricultural product safety, human health, and ecological security. However, in recent years, heavy metal pollution in soil has become increasingly serious, posing a significant threat to human health and safety. Therefore, it is imperative to conduct soil pollution surveys and implement preventative measures.

[0003] Risk assessment, based on soil pollution investigations and in conjunction with agricultural product quality and safety, analyzes the status of soil pollution and assesses the risk of pollutant contamination exceeding standards in agricultural products. Soil heavy metal risk assessment is highly systematic, and the evaluation indicators and methods used vary depending on the assessment objectives and scales. Currently, methods for studying soil heavy metal risk include index methods and model methods, but both have limitations in practical applications and cannot provide accurate and comprehensive results. Inconsistent and imperfect assessment methods have led to phenomena such as "soil exceeding standards, agricultural products not exceeding standards" and "soil not exceeding standards, agricultural products exceeding standards" in some arable land areas—i.e., ambiguous areas. Currently, risk classification for such areas often uses dominant location methods or interpolation methods. While the dominant location method struggles to provide accurate regional risk assessment results, interpolation methods, although solving this problem, suffer from inaccurate results under conditions of sparse location data.

[0004] Current soil heavy metal risk assessments primarily consider two aspects: the potential risks posed by heavy metals in soil and the actual risks from heavy metals in agricultural products. From the perspective of soil safety, The potential ecological hazard index method, as a classic method for assessing ecological risk, has been gradually adopted in soil assessment. (Han Ping, Wang Jihua, Feng Xiaoyuan, et al.) The ecological risk assessment of heavy metal pollution in soil in Shunyi District, Beijing was conducted using the geoaccumulation index method and the potential ecological hazard index method. Zhang Haoran (Zhang Haoran, Zhang Yuling, Zhang Yanjie, et al. Ecological risk assessment of heavy metal pollution in soil around typical coal gangue hills in Fengfeng mining area [J]. Green Technology, 2019 (24).) used the potential ecological hazard index method to conduct a risk assessment of heavy metal pollution in soil around coal gangue hills in Fengfeng mining area. Pang Wenpin et al. (Pang Wenpin, Qin Fanxin, Lü Yachao, et al. Chemical speciation and risk assessment of heavy metals in farmland soil in Xingren coal mining area, Guizhou [J]. Chinese Journal of Applied Ecology, 2016, 27 (005): 1468-1478). The risk assessment of heavy metals in farmland soil in Xingren coal mining area, Guizhou was conducted using the single-factor index method, the potential ecological risk index method and the risk assessment coding method. However, this method has the following problems in practical applications: The regional risk assessment results directly use the mean of all monitoring points in the region. However, regional soil heavy metal pollution has a multi-layered spatial structure; the purpose and sampling density of monitoring surveys vary at different times, and the representativeness of monitoring points is inconsistent. Directly using classical statistical methods to estimate the mean often leads to results that are either "inflated" or "inflated." The assessment criteria directly adopt relevant parameters from abroad, resulting in a lack of specificity in the regional soil heavy metal risk assessment. The exposure parameters are not precise enough; appropriate revision of the parameters is necessary to make the assessment results more reliable. Although some studies have improved... The potential ecological risk index method improves the accuracy of evaluation results, but it is complex and costly to operate, and it is difficult to adapt to the assessment of potential heavy metal risks in soil under large-scale, multi-scenario, and multi-data conditions.

[0005] Soil heavy metal risk assessment needs to consider not only the soil itself but also the quality and safety of agricultural products, assessing it from the perspective of real-world risks. Xiong Zi (Xiong Zi. Research on the Characteristics and Risk Assessment of Heavy Metal Pollution in Farmland Soil in Hebei Province [D]. Chinese Academy of Agricultural Sciences.) proposed a three-pronged assessment method involving soil, plants, and accumulation trends. However, when assessing the degree of heavy metal contamination in agricultural products, he did not consider spatial heterogeneity, directly using the average values ​​of all locations for regional agricultural product risk assessment, resulting in inaccurate assessment results. Furthermore, it suffers from difficulties in obtaining soil heavy metal input and output values, is time-consuming, and is impractical in real-world applications. Li Jiarui et al. (Li Jiarui. Evaluation of Heavy Metal Pollution in Cultivated Land and Health Risk Assessment Based on the Soil-Crop-Human System [D]. Zhejiang University.) used a human health risk assessment model to evaluate the human health risks of heavy metals in soil and crops. However, they did not consider the contamination rate of soil heavy metals at the same location at different time dimensions, and this indiscriminate treatment led to inaccurate assessment results.

[0006] Therefore, the key to scientifically and accurately delineating soil heavy metal risk assessment in ambiguous areas is to combine data from co-monitoring points of soil and agricultural products, comprehensively consider the spatial differentiation and spatial autocorrelation of soil heavy metals, and select appropriate risk assessment methods. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the present invention aims to provide a method for assessing the risk of heavy metals in soil in fuzzy regions. This method fully considers the spatial heterogeneity of heavy metals in soil and their complex relationship with agricultural products, and proposes an improved fuzzy region mean method. The potential ecological risk index method and the agricultural product risk index method were used to achieve soil heavy metal risk assessment in ambiguous areas, aiming to provide a technical reference for regional soil heavy metal risk assessment.

[0008] To achieve this objective, the present invention adopts the following technical solution:

[0009] This invention provides a method for assessing the risk of heavy metals in soil in ambiguous areas, the method comprising the following steps:

[0010] (1) Select fuzzy regions from cultivated land areas to form a study area, use a geographic detector to screen the main influencing factors of soil heavy metal content, and then obtain the average soil heavy metal content of the study area based on the fuzzy region mean method.

[0011] (2) Calculate the potential ecological risk index of heavy metal elements in the edible parts of crops and the feed parts of livestock and poultry in the study area respectively, and assess the potential risk of heavy metals in soil in the ambiguous area based on the index classification standard.

[0012] The agricultural product risk index method was used to calculate the degree and rate of heavy metal contamination in agricultural products in the study area. Based on the risk level classification standard, the actual risk of heavy metal contamination in agricultural products in ambiguous areas was assessed.

[0013] (3) Based on the potential risk assessment results of heavy metals in soil in the fuzzy area and the actual risk assessment results of heavy metals in agricultural products in the fuzzy area, the risk assessment results of heavy metals in soil in the fuzzy area are obtained.

[0014] This invention provides a method for assessing the risk of heavy metals in soil in fuzzy regions, evaluating the risk from both potential and actual perspectives. For the potential risk assessment, a fuzzy region mean method and an improved method are proposed. The potential ecological risk index method first analyzes the main influencing factors affecting the distribution of heavy metal content in the soil of the study area using geographic detectors. Then, it derives the mean values ​​of heavy metal elements in the soil of the study area using the fuzzy regional mean method, which considers spatial heterogeneity and solves the problem of inconsistent weights for monitoring points caused by inconsistent sampling densities. Finally, it proposes improvements from the perspective of soil environmental quality. The potential ecological risk index method not only combines the actual situation of the study area but also improves the credibility of the assessment results. Secondly, from the perspective of agricultural product environmental quality, an agricultural product risk index method is proposed, which comprehensively considers the degree of exceedance of agricultural products and the exceedance rate of agricultural products at the same monitoring point at different time dimensions, thereby improving the accuracy of soil heavy metal risk assessment in the study area. Finally, by combining the results of the two methods and in accordance with relevant assessment rules, the soil heavy metal risk assessment results of the ambiguous area are obtained from the perspectives of potential risk and actual risk, which can provide convenience for subsequent risk avoidance and remediation.

[0015] Preferably, the screening step of the fuzzy area in step (1) specifically includes: taking administrative villages as units, screening cultivated land areas with soil exceeding the standard but agricultural products not exceeding the standard, and / or cultivated land areas with soil not exceeding the standard but agricultural products exceeding the standard as fuzzy areas, and forming study areas at the county level for the fuzzy areas.

[0016] Preferably, the main influencing factors in step (1) include soil type, land use type, elevation, and soil pH.

[0017] The spatial differentiation and distribution of heavy metals in soil are influenced by many factors. By reviewing literature and screening influencing factors, an influencing factor dataset S{x1, x2, ..., X} was established. n}, where x1, x2, ..., X n The values ​​used include soil type, elevation, land use type, pollution source, soil pH, and organic matter. This invention mainly investigates the effects of four influencing factors—soil type, land use type, elevation, and soil pH—on soil heavy metals.

[0018] Preferably, in the screening in step (1), the main influencing factors are used as independent variables, and the soil heavy metal element contents are used as dependent variables, and the data of the main influencing factors are classified.

[0019] The soil heavy metal elements include four heavy metals, namely cadmium, arsenic, lead, and chromium.

[0020] The geographical detector is a set of statistical methods that reveal geographical spatial differentiation and analyze the interaction between independent variables and dependent variables. It can not only perform quantitative data analysis but also process qualitative data. The geographical detector includes 4 detectors. Among them, the differentiation and factor detection are used to detect the degree to which each independent variable X explains the spatial differentiation of the dependent variable Y, which is measured by the q value, and the expression is:

[0021]

[0022]

[0023] .

[0024] where: h = 1,..., L is the stratification of factor X; and N are the number of units in layer h and the whole region respectively; and are the variances of the Y values in layer h and the whole region respectively; SSW and SST are the sum of the variances within the layer and the total variance of the whole region respectively; the value range of the q value is [0, 1], and the larger the q value, the more obvious the influence of X on Y.

[0025] The four main influencing factors of the present invention are obtained through the screening by the above geographical detector.

[0026] Preferably, the soil type is set as X 11 , X 12 , ……, X 1n , and the corresponding data classification is 1, 2, ……, n; the land use type is set as X 21 , X 22 , ……, X 2n , and the corresponding data classification is 1, 2, ……, n; the elevation is divided into X3 < 50, 50 ≤ X3 < 200, 200 ≤ X3 < 500, 500 ≤ X3 < 1000, and X3 ≥ 1000, and the corresponding data classifications are 1, 2, 3, 4, 5 respectively; the soil pH is divided into pH ≤ 5.5, 5.5 < pH ≤ 6.5, 6.5 < pH ≤ 7.5, and pH > x7.5, and the corresponding data classifications are 1, 2, 3, 4 respectively.

[0027] In the screening, X 11 , X 12 , ……, X 1nIndicates soil type, such as alluvial soil, red soil, calcareous soil, etc.; X 21 X 22 ..., X 2n X3 indicates the land use type, such as paddy field, dry land, and irrigated land; X3 indicates the elevation of the study area; and pH indicates the pH of the soil in the study area.

[0028] Preferably, the formula for calculating the average heavy metal content of the soil in the study area in step (1) is shown in equation (a):

[0029] (a)

[0030] In formula (a): The average content of heavy metal element j in the soil of the study area is expressed in mg·kg⁻¹. -1 The heavy metal elements j are cadmium, arsenic, lead, and chromium. The measured concentration of heavy metal element j in the soil of the study area is given in mg·kg⁻¹. -1 m represents the number of heavy metal elements j in the soil of the study area under different data classifications; The percentage of cultivated land area in the total cultivated land area of ​​the study area is represented by the n-level data partition.

[0031] Preferably, the formula for calculating the potential ecological risk index of heavy metal elements in the edible parts of crops in the study area described in step (2) is as shown in equations (b) to (d):

[0032] (b)

[0033] (c)

[0034] (d)

[0035] In equations (b) to (d): is the comprehensive potential ecological risk index of the edible parts of crops in the study area; n represents the types of heavy metal elements. It is a single potential ecological risk index of heavy metal element j in the edible parts of the soil in the study area; The content of heavy metal element j in the edible parts of crops in the study area is the average value in mg·kg⁻¹. -1 ; denoted as the toxicity response coefficient of heavy metal element j; This refers to the single pollution coefficient; The value represents the average content of heavy metal element j in the root system of plants in the study area, in mg·kg⁻¹. -1 ; The background value of heavy metal element j in the soil of the study area is given in mg·kg⁻¹. -1 .

[0036] This invention focuses on a study area and uses data from soil heavy metal synergistic monitoring points as a basis to investigate the effects of soil heavy metals on human health through the edible parts of commonly cultivated crops. The heavy metal content in plant roots is used to represent the heavy metal content in the soil, and an enrichment coefficient is introduced to improve the accuracy of the assessment results.

[0037] Preferably, the formula for calculating the potential ecological risk index of heavy metal elements in the livestock feed parts of crops in the study area described in step (2) is as shown in equations (e) to (g):

[0038] (e)

[0039] (f)

[0040] (g)

[0041] In equations (e) to (g): is the comprehensive potential ecological risk index of the livestock and poultry feed parts of crops in the study area; n represents the types of heavy metal elements. It is a single potential ecological risk index of heavy metal element j in the soil of the study area in the feed parts of livestock and poultry. The content of heavy metal element j in the livestock feed portion of crops in the study area is the average value in mg·kg⁻¹. -1 ; denoted as the toxicity response coefficient of heavy metal element j; This refers to the single pollution coefficient; The value represents the average content of heavy metal element j in the root system of plants in the study area, in mg·kg⁻¹. -1 ; The reference value for feed hygiene standards for heavy metal element j is mg·kg⁻¹. -1 .

[0042] When plant roots transport nutrients upwards, they trap most heavy metals in the root and stem regions. Ignoring this factor and considering only the impact of soil heavy metals on edible parts of the soil may lead to inaccurate assessments. This invention comprehensively considers the potential impact of soil heavy metals on human health from a food chain perspective, measuring the contents of cadmium, arsenic, lead, and chromium in livestock and poultry feed parts in the study area. The parameters were modified to use the "Feed Hygiene Standard" (GB13078-2017) as the standard to replace the background value of heavy metal element j in the soil of the study area, which can more accurately calculate the single pollution coefficient of livestock and poultry feed parts.

[0043] Preferably, the index grading standard in step (2) uses cadmium, arsenic, lead, and chromium as evaluation indicators for classification, specifically including: when <30 and / or When the risk level is <60, the risk level is slight ecological risk, and the potential risk assessment result is Class I; when 30≤ <60 and / or 60≤ When the value is less than 120, the risk level is medium ecological risk, and the potential risk assessment result is Class II; when the value is less than or equal to 60, the risk level is medium ecological risk. <120 and / or 120≤ When the value is less than 240, the risk level is considered relatively high ecological risk, and the potential risk assessment result is Class III; when the value is less than or equal to 120... When the value is less than 240, the risk level is classified as severe ecological risk, and the potential risk assessment result is Class IV; when When the value is ≥240, the risk level is extremely high ecological risk, and the potential risk assessment result is Class IV; when When the value is ≥240, the risk level is extremely high ecological risk, and the potential risk assessment result is Class V.

[0044] This invention uses the toxicity coefficient calculated by Xu Zhengqi (Xu Zhengqi, Ni Shijun, Tuo Xianguo, et al. Calculation of heavy metal toxicity coefficient in evaluation by potential ecological hazard index method [J]. Environmental Science and Technology, 2008, 31(2):112-115.) instead of the toxicity response coefficient, where Cd=30, As=10, Pb=5, Cr=2. The grading standard adjustment scheme first proposed by Ma Jianhua et al. (Ma Jianhua, Wang Xiaoyun, Hou Qian, et al. Heavy metal pollution and potential ecological risk of surface dust in a kindergarten in a certain city [J]. Geographical Research, 2011, 30(003):486-495.) is adopted. The classification method is as follows: the maximum toxicity coefficient among all pollutants in this invention is taken as... The first-level threshold is 30. The upper limits for other risk levels are obtained by multiplying the value of the previous level by 2. The grading standards. The grading method is as follows: First, according to The first classification threshold value of 150 is divided by the total toxicity response coefficient of the eight pollutants, 133, to obtain a unit toxicity response coefficient classification value of 1.13; then, the total toxicity coefficient of the four heavy metals of this invention, 47, is multiplied by 1.13 and rounded to the tens digit to obtain the first level of this invention. The grading threshold is 60, and the upper limit of the other risk grades is obtained by multiplying the grading value of the next higher level by 2.

[0045] It should be noted that the delineation of potential ecological risks of soil heavy metals needs to comprehensively consider the potential risks that soil heavy metal elements may pose to human health. Both direct and indirect factors that threaten human health should be considered. The risk assessment results of edible parts and livestock feed parts are determined by the worst factor among cadmium, arsenic, lead and chromium in the topsoil, respectively. The worst result is selected as the potential risk assessment result of soil heavy metals in the fuzzy area.

[0046] This invention explores the potential impact of soil heavy metals on human health from a food chain perspective, through crop feed parts and edible parts. Since plants primarily absorb heavy metals via their roots, and this absorption is mainly related to the form of the heavy metals—exchangeable and carbonate-bound forms are more readily absorbed, while residual forms are ineffective—this invention... Potential ecological risk index method The parameters were corrected to approximate the total amount of highly bioavailable heavy metals by using the heavy metal content in the roots of plants in the study area. This replaced the heavy metal content in the soil, effectively avoiding situations where soil with high heavy metal content, which is not easily absorbed by organisms, was classified as high-risk. Furthermore, different crops or different parts of the same crop have varying rates of heavy metal accumulation, resulting in different levels of harm. This invention corrects these differences by... Potential ecological risk index method Parameters, introducing enrichment coefficients into The potential ecological risk index method can greatly improve the accuracy of assessment results. Furthermore, this invention redefines the grading standards based on the types and quantities of heavy metals being assessed, avoiding the direct application of existing methods. Errors caused by ecological risk levels.

[0047] Preferably, the degree of heavy metal contamination in agricultural products in the study area described in step (2) is obtained using the fuzzy regional mean method, and the calculation formulas for the degree of contamination are shown in equations (h) and (i):

[0048] (h)

[0049] (i)

[0050] In equations (h) and (i): The degree of contamination of heavy metal element j in agricultural products in the study area; is the single-factor index of heavy metal element j in agricultural products at collaborative monitoring point i; m is the number of heavy metal elements j in agricultural products in the study area under different soil data classifications; The percentage of cultivated land area in the total cultivated land area of ​​the study area for the n-level data partition is %. The measured average concentration of heavy metal element j in agricultural products at monitoring point i is expressed in mg·kg⁻¹. -1 ; The limit standard value for heavy metal element j in agricultural products, in mg·kg -1 .

[0051] The assessment indicators for the degree of contamination in agricultural products are the four heavy metals: cadmium, arsenic, lead, and chromium, and their respective limit standards. See the heavy metal limits in the "Limits of Contaminants in Food" (GB2762-2017).

[0052] Preferably, the excess rate of heavy metal elements in agricultural products in the study area in step (2) is obtained based on the fuzzy regional mean method, and the calculation formula for the excess rate of agricultural products is shown in equations (j) and (k):

[0053] (j)

[0054] (k)

[0055] In equations (j) and (k): The percentage of agricultural products containing heavy metal element j in the study area was %. The percentage of agricultural products exceeding the standard for heavy metal element j at monitoring point i (%) The number of times agricultural products containing heavy metal element j at the collaborative monitoring point i exceeded the standard; The number of times heavy metal element j is detected in agricultural products at monitoring point i.

[0056] In the calculation of the agricultural product exceedance rate described in this invention, the contents of heavy metal elements cadmium, arsenic, lead, and chromium in the main crops grown in the study area are measured once a year for n consecutive years, and the exceedance rate of agricultural products at each point is calculated according to formulas (j) and (k).

[0057] Preferably, based on the calculation results of the degree and rate of heavy metal contamination in agricultural products in the study area described in step (2), the risk level of heavy metal contamination in agricultural products in the study area is obtained, and the calculation formula is shown in equation (m):

[0058] (m)

[0059] In formula (m): To determine the risk level of heavy metal element j in agricultural products in the study area.

[0060] The agricultural product risk index method proposed in this invention comprehensively considers the degree of agricultural product exceeding standards and the rate of heavy metal exceeding standards in agricultural products at the same location at different time dimensions, thus effectively reflecting the real risks that agricultural product exceeding standards may cause.

[0061] Preferably, the risk level grading standard in step (2) is divided as follows: when 0 < When ≤1, it is classified as Risk Level I; when 1 < When ≤2, it is classified as Risk Level II; when 2 < When the risk level is ≤3, it is classified as risk level III. When the value is greater than 3, it is classified as risk level IV.

[0062] In assessing the actual risk of heavy metals in agricultural products in ambiguous areas, this invention determines the risk level of heavy metals in agricultural products in the study area based on the worst-performing factor among cadmium, arsenic, lead, and chromium.

[0063] Preferably, the soil heavy metal risk assessment results of the fuzzy area in step (2) are classified as follows: the potential risk assessment result of soil heavy metals in the fuzzy area is Class I, and the actual risk assessment result of agricultural products in the fuzzy area is Class I; the potential risk assessment result of soil heavy metals in the fuzzy area is Class II, and the actual risk assessment result of agricultural products in the fuzzy area is Class II; the potential risk assessment result of soil heavy metals in the fuzzy area is Class III, and the actual risk assessment result of agricultural products in the fuzzy area is Class III; the potential risk assessment result of soil heavy metals in the fuzzy area is Class IV, and the actual risk assessment result of agricultural products in the fuzzy area is Class IV; and the potential risk assessment result of soil heavy metals in the fuzzy area is Class V.

[0064] This invention selects fuzzy areas as the research object, uses data from collaborative monitoring points as a basis, and proposes a comprehensive risk assessment method for fuzzy areas using four heavy metals—cadmium, arsenic, lead, and chromium—as evaluation indicators. The soil heavy metal risk in fuzzy areas is assessed from both the potential risks to human health and the actual risks from agricultural product contamination. Finally, the combined results of both assessments are used as the final risk assessment result for the study area.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] This invention provides a method for assessing the risk of heavy metals in soil in fuzzy regions, evaluating the risk from both potential and actual perspectives. For the potential risk assessment, a fuzzy region mean method and an improved method are proposed. The potential ecological risk index method first analyzes the main influencing factors affecting the distribution of heavy metal content in the soil of the study area using geographic detectors. Then, it derives the mean values ​​of heavy metal elements in the soil of the study area using the fuzzy regional mean method, which considers spatial heterogeneity and solves the problem of inconsistent weights for monitoring points caused by inconsistent sampling densities. Finally, it proposes improvements from the perspective of soil environmental quality. The potential ecological risk index method, by introducing enrichment coefficients and revising relevant parameters and assessment standards in the formula, can more accurately reflect the degree of ecological risk in the study area. Secondly, from the perspective of agricultural product environmental quality, an agricultural product risk index method is proposed, which comprehensively considers the degree of exceedance of agricultural products and the exceedance rate of agricultural products at the same monitoring point at different time dimensions, thereby improving the accuracy of soil heavy metal risk assessment in the study area. Finally, by combining the results of the two methods and based on relevant assessment rules, the soil heavy metal risk assessment results for ambiguous areas are obtained from the perspectives of potential risk and actual risk, which can facilitate subsequent risk avoidance and remediation.

[0067] Compared to existing methods for assessing soil heavy metal risk, this invention comprehensively considers soil spatial heterogeneity and the representativeness of monitoring point weights, and more comprehensively takes into account the harm of soil heavy metals to the human body through different pathways, starting from edible parts for humans and feed parts for livestock and poultry. The potential ecological risk index method has been improved. This method also considers the heavy metal contamination levels of agricultural products over different time periods. It is simple, widely applicable, and suitable for soil heavy metal risk assessment in ambiguous areas nationwide. Attached Figure Description

[0068] Figure 1 This is a schematic diagram of fuzzy region determination provided in Embodiment 1 of the present invention. Detailed Implementation

[0069] The technical solution of the present invention will be further illustrated below through specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the present invention and should not be construed as limiting the invention in any way.

[0070] This invention provides a method for assessing the risk of heavy metals in soil in ambiguous areas, the method comprising the following steps:

[0071] (1) Taking administrative villages as units, select fuzzy areas from cultivated land areas where soil exceeds standards but agricultural products do not exceed standards, and / or where soil does not exceed standards but agricultural products exceed standards. Then, fuzzy areas are used to form study areas at the county level.

[0072] The main influencing factors of soil heavy metal content were identified using a geographic detector, including soil type, land use type, elevation, and soil pH. Then, using these main influencing factors as independent variables and soil heavy metal element content as the dependent variable, the main influencing factors were classified. Specifically, the soil type was set as X. 11 X 12 ..., X 1n The corresponding data levels are 1, 2, ..., n; the land use type is set as X. 21 X22 , ……, X 2n , and the corresponding data classifications are 1, 2, ……, n; the elevation is divided into X3 < 50, 50 ≤ X3 < 200, 200 ≤ X3 < 500, 500 ≤ X3 < 1000, and X3 ≥ 1000, and the corresponding data classifications are 1, 2, 3, 4, 5 respectively; the soil pH is divided into pH ≤ 5.5, 5.5 < pH ≤ 6.5, 6.5 < pH ≤ 7.5, and pH > 7.5, and the corresponding data classifications are 1, 2, 3, 4 respectively.

[0073] Then, according to the fuzzy regional mean method, the mean content of heavy metals in the soil of the study area is obtained, and the calculation formula is shown in formula (a):

[0074] . (a)

[0075] In formula (a): is the mean content of heavy metal element j in the soil of the study area, mg·kg -1 ; heavy metal element j is cadmium, arsenic, lead, and chromium; is the measured concentration of heavy metal element j in the soil of the study area, mg·kg -1 ; m is the number of heavy metal element j in the soil of the study area under different data classifications; is the proportion of the cultivated land area in the n-level data partition to the total cultivated land area of the study area, %.

[0076] (2) Calculate the potential ecological risk index of heavy metal elements in the human edible parts of the crops in the study area, and the calculation formula is shown in formulas (b) - (d):

[0077] (b)

[0078] (c)

[0079] . (d)

[0080] In formulas (b) - (d): is the comprehensive potential ecological risk index of the human edible parts of the crops in the study area; n is the number of heavy metal element types; is the single potential ecological risk index of heavy metal element j in the human edible parts of the soil in the study area; is the mean content of heavy metal element j in the human edible parts of the crops in the study area, mg·kg -1 ; is the toxicity response coefficient of heavy metal element j; is the single pollution coefficient; is the mean content of heavy metal element j in the plant root parts of the study area, mg·kg -1 [[ID= The background value of heavy metal element j in the soil of the study area is given in mg·kg⁻¹. -1 .

[0081] The potential ecological risk index of heavy metal elements in the livestock feed parts of crops in the study area was calculated using the formulas shown in equations (e) to (g):

[0082] (e)

[0083] (f)

[0084] (g)

[0085] In equations (e) to (g): is the comprehensive potential ecological risk index of the livestock and poultry feed parts of crops in the study area; n represents the types of heavy metal elements. It is a single potential ecological risk index of heavy metal element j in the soil of the study area in the feed parts of livestock and poultry. The content of heavy metal element j in the livestock feed portion of crops in the study area is the average value in mg·kg⁻¹. -1 ; denoted as the toxicity response coefficient of heavy metal element j; This refers to the single pollution coefficient; The value represents the average content of heavy metal element j in the root system of plants in the study area, in mg·kg⁻¹. -1 ; The reference value for feed hygiene standards for heavy metal element j is mg·kg⁻¹. -1 .

[0086] Based on an index-based grading standard, the potential heavy metal risk in soils of ambiguous areas is assessed. This index-based grading standard uses cadmium, arsenic, lead, and chromium as assessment indicators, specifically including: when... <30 and / or When the risk level is <60, the risk level is slight ecological risk, and the potential risk assessment result is Class I; when 30≤ <60 and / or 60≤ When the value is less than 120, the risk level is medium ecological risk, and the potential risk assessment result is Class II; when the value is less than or equal to 60, the risk level is medium ecological risk. <120 and / or 120≤ When the value is less than 240, the risk level is considered relatively high ecological risk, and the potential risk assessment result is Class III; when the value is less than or equal to 120... When the value is less than 240, the risk level is classified as severe ecological risk, and the potential risk assessment result is Class IV; when When the value is ≥240, the risk level is extremely high ecological risk, and the potential risk assessment result is Class IV; when When the value is ≥240, the risk level is extremely high ecological risk, and the potential risk assessment result is Class V.

[0087] The degree and rate of heavy metal contamination in agricultural products in the study area were calculated using the agricultural product risk index method. The degree of heavy metal contamination in agricultural products in the study area was obtained based on the fuzzy regional mean method, and the calculation formulas for the degree of contamination are shown in equations (h) and (i).

[0088] (h)

[0089] (i)

[0090] In equations (h) and (i): The degree of contamination of heavy metal element j in agricultural products in the study area; is the single-factor index of heavy metal element j in agricultural products at collaborative monitoring point i; m is the number of heavy metal elements j in agricultural products in the study area under different soil data classifications; The percentage of cultivated land area in the total cultivated land area of ​​the study area for the n-level data partition is %. The measured average concentration of heavy metal element j in agricultural products at monitoring point i is expressed in mg·kg⁻¹. -1 ; The limit standard value for heavy metal element j in agricultural products, in mg·kg -1 .

[0091] The rate of heavy metal contamination in agricultural products in the study area was obtained using the fuzzy regional mean method. The calculation formulas for the rate of contamination are shown in equations (j) and (k):

[0092] (j)

[0093] (k)

[0094] In equations (j) and (k): The percentage of agricultural products containing heavy metal element j in the study area was %. The percentage of agricultural products exceeding the standard for heavy metal element j at monitoring point i (%) The number of times agricultural products containing heavy metal element j at the collaborative monitoring point i exceeded the standard; The number of times heavy metal element j is detected in agricultural products at monitoring point i.

[0095] Based on the calculation results of the degree and rate of heavy metal contamination in agricultural products in the study area, the risk level of heavy metal contamination in agricultural products in the study area is obtained, and the calculation formula is shown in equation (m):

[0096] (m)

[0097] In formula (m): To determine the risk level of heavy metal element j in agricultural products in the study area.

[0098] Based on a risk level grading standard, the actual risk of heavy metal contamination in agricultural products in ambiguous areas is assessed; the risk level grading standard is divided into: when 0 < When ≤1, it is classified as Risk Level I; when 1 < When ≤2, it is classified as Risk Level II; when 2 < When the risk level is ≤3, it is classified as risk level III. When the value is greater than 3, it is classified as risk level IV.

[0099] (3) Based on the potential risk assessment results of heavy metals in soil in fuzzy areas and the actual risk assessment results of heavy metals in agricultural products in fuzzy areas, the risk assessment results of heavy metals in soil in fuzzy areas are as follows: the potential risk assessment result of heavy metals in soil in fuzzy areas is Class I, and the actual risk assessment result of heavy metals in agricultural products in fuzzy areas is Class I; the potential risk assessment result of heavy metals in soil in fuzzy areas is Class II, and the actual risk assessment result of heavy metals in agricultural products in fuzzy areas is Class II; the potential risk assessment result of heavy metals in soil in fuzzy areas is Class III, and the actual risk assessment result of heavy metals in agricultural products in fuzzy areas is Class III; the potential risk assessment result of heavy metals in soil in fuzzy areas is Class IV, and the actual risk assessment result of heavy metals in agricultural products in fuzzy areas is Class IV; the potential risk assessment result of heavy metals in soil in fuzzy areas is Class V.

[0100] Example 1

[0101] This embodiment provides a method for soil heavy metal risk assessment in ambiguous areas, the method comprising the following steps:

[0102] (1) Taking administrative villages as units, fuzzy areas were selected from cultivated land areas where soil quality exceeded standards but agricultural products did not, and fuzzy areas where soil quality did not exceed standards but agricultural products exceeded standards. Taking a certain region in northern China as an example, fuzzy areas were selected by village as units, and the fuzzy areas were combined into study areas by county level. The schematic diagram of fuzzy area determination is shown below. Figure 1 As shown.

[0103] The main influencing factors of soil heavy metal content were screened using a geographic detector, including soil type, land use type, elevation, and soil pH. Then, using these main influencing factors, the average contents of heavy metals cadmium, arsenic, lead, and chromium in the soil of the study area were calculated. Analysis showed that elevation was the main influencing factor for cadmium and chromium, while soil pH was the main influencing factor for arsenic and lead. Some parameters for the estimated mean values ​​of cadmium and chromium are shown in Table 1, and some parameters for the estimated mean values ​​of arsenic and lead are shown in Table 2.

[0104] Table 1

[0105]

[0106] Table 2

[0107]

[0108] Then, based on the fuzzy regional mean method, the average cadmium content in the soil of the study area was determined to be 0.80 mg·kg⁻¹. -1 The average arsenic content was 10.81 mg·kg. -1 The average lead content was 41.92 mg·kg⁻¹. -1 The average chromium content was 55.18 mg·kg⁻¹. -1 .

[0109] (2) The potential ecological risk indices for cadmium in the edible parts of crops in the study area were calculated to be 1.34, for arsenic 0.001, for lead 0.003, and for chromium 0.0005. The potential ecological risk indices for cadmium in the forage parts of crops in the study area were calculated to be 1.12, for arsenic 0.045, for lead 0.047, and for chromium 0.049. Based on the index classification standard, the potential heavy metal risk in the soil of the ambiguous area was assessed as Class I.

[0110] Using the agricultural product risk index method, the exceedance rate of cadmium in agricultural products in the study area was calculated to be 0.14, with an exceedance rate of 0.03, and a risk level of I; the exceedance rate of arsenic was 0.01, with an exceedance rate of 0, and a risk level of I; the exceedance rate of lead was 0.31, with an exceedance rate of 0, and a risk level of I; and the exceedance rate of chromium was 0.09, with an exceedance rate of 0, and a risk level of I. Based on the risk level classification standard, the actual risk of heavy metals in agricultural products in the ambiguous area was assessed as Class I.

[0111] (3) Based on the potential risk assessment results of heavy metals in soil in the fuzzy area and the actual risk assessment results of heavy metals in agricultural products in the fuzzy area, the risk assessment results of heavy metals in soil in the fuzzy area are as follows: the potential risk assessment results of heavy metals in soil in the fuzzy area are Class I, and the actual risk assessment results of heavy metals in agricultural products in the fuzzy area are Class I.

[0112] In summary, the soil heavy metal risk assessment method for fuzzy regions provided by this invention assesses the soil heavy metal risk in fuzzy regions from both the perspectives of potential risk and actual risk. For potential risk, the fuzzy region mean method and an improved method are proposed. The potential ecological risk index method first analyzes the main influencing factors affecting the distribution of heavy metal content in the soil of the study area using geographic detectors. Then, it derives the mean values ​​of heavy metal elements in the soil of the study area using the fuzzy regional mean method, which considers spatial heterogeneity and solves the problem of inconsistent weights for monitoring points caused by inconsistent sampling densities. Finally, it proposes improvements from the perspective of soil environmental quality. The potential ecological risk index method, by introducing enrichment coefficients and revising relevant parameters and assessment standards in the formula, can more accurately reflect the degree of ecological risk in the study area. Secondly, from the perspective of agricultural product environmental quality, an agricultural product risk index method is proposed, which comprehensively considers the degree of exceedance of agricultural products and the exceedance rate of agricultural products at the same monitoring point at different time dimensions, thereby improving the accuracy of soil heavy metal risk assessment in the study area. Finally, by combining the results of the two methods and based on relevant assessment rules, the soil heavy metal risk assessment results for ambiguous areas are obtained from the perspectives of potential risk and actual risk, which can facilitate subsequent risk avoidance and remediation.

[0113] Compared to existing methods for assessing soil heavy metal risk, this invention comprehensively considers soil spatial heterogeneity and the representativeness of monitoring point weights, and more comprehensively takes into account the harm of soil heavy metals to the human body through different pathways, starting from edible parts for humans and feed parts for livestock and poultry. The potential ecological risk index method has been improved. This method also considers the heavy metal contamination levels of agricultural products over different time periods. It is simple, widely applicable, and suitable for soil heavy metal risk assessment in ambiguous areas nationwide.

[0114] The above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for assessing the risk of heavy metals in soil in ambiguous areas, characterized in that, The soil heavy metal risk assessment method includes the following steps: (1) Select fuzzy regions from cultivated land areas to form a study area, use a geographic detector to screen the main influencing factors of soil heavy metal content, and then obtain the average soil heavy metal content of the study area based on the fuzzy region mean method. (2) Calculate the potential ecological risk index of heavy metal elements in the edible parts of crops and the feed parts of livestock and poultry in the study area respectively, and assess the potential risk of heavy metals in soil in ambiguous areas based on the index classification standard. The agricultural product risk index method was used to calculate the degree and rate of heavy metal contamination in agricultural products in the study area. Based on the risk level classification standard, the actual risk of heavy metal contamination in agricultural products in ambiguous areas was assessed. (3) Based on the potential risk assessment results of heavy metals in soil in the fuzzy area and the actual risk assessment results of heavy metals in agricultural products in the fuzzy area, the risk assessment results of heavy metals in soil in the fuzzy area are obtained.

2. The method for assessing the risk of heavy metals in soil in ambiguous areas according to claim 1, characterized in that, The specific steps of screening the fuzzy areas in step (1) include: taking administrative villages as units, screening arable land areas where soil exceeds the standard but agricultural products do not, and / or where soil does not exceed the standard but agricultural products exceed the standard as fuzzy areas, and forming study areas at the county level for the fuzzy areas.

3. The method for assessing the risk of heavy metals in soil in ambiguous areas according to claim 1 or 2, characterized in that, The main influencing factors mentioned in step (1) include soil type, land use type, elevation, and soil pH; Preferably, in the screening process described in step (1), the main influencing factors are used as independent variables and the soil heavy metal content is used as the dependent variable to classify the main influencing factors. Preferably, the soil type is set to X 11 , X 12 , ……, X 1n , and the corresponding data classifications are 1, 2, ……, n; the land use type is set to X 21 , X 22 , ……, X 2n , and the corresponding data classifications are 1, 2, ……, n; the elevation is divided into X3 < 50, 50 ≤ X3 < 200, 200 ≤ X3 < 500, 500 ≤ X3 < 1000, and X3 ≥ 1000, and the corresponding data classifications are 1, 2, 3, 4, 5 respectively; the soil pH is divided into pH ≤ 5.5, 5.5 < pH ≤ 6.5, 6.5 < pH ≤ 7.5, and pH > 7.5, and the corresponding data classifications are 1, 2, 3, 4 respectively.

4. The method for assessing the risk of heavy metals in soil in ambiguous areas according to claim 3, characterized in that, The formula for calculating the average heavy metal content in the soil of the study area in step (1) is shown in equation (a): ； (a) In formula (a): The average content of heavy metal element j in the soil of the study area is expressed in mg·kg⁻¹. -1 The heavy metal elements j are cadmium, arsenic, lead, and chromium. The measured concentration of heavy metal element j in the soil of the study area is given in mg·kg⁻¹. -1 m represents the number of heavy metal elements j in the soil of the study area under different data classifications; The percentage of cultivated land area in the total cultivated land area of ​​the study area is represented by the n-level data partition.

5. The method for assessing the risk of heavy metals in soil in ambiguous areas according to claim 4, characterized in that, The formula for calculating the potential ecological risk index of heavy metal elements in the edible parts of crops in the study area described in step (2) is shown in equations (b) to (d): (b) (c) ； (d) In equations (b) to (d): is the comprehensive potential ecological risk index of the edible parts of crops in the study area; n represents the types of heavy metal elements. It is a single potential ecological risk index of heavy metal element j in the edible parts of the soil in the study area; The content of heavy metal element j in the edible parts of crops in the study area is the average value in mg·kg⁻¹. -1 ; denoted as the toxicity response coefficient of heavy metal element j; This refers to the single pollution coefficient; The value represents the average content of heavy metal element j in the root system of plants in the study area, in mg·kg⁻¹. -1 ; The background value of heavy metal element j in the soil of the study area is given in mg·kg⁻¹. -1 .

6. The method for assessing the risk of heavy metals in soil in ambiguous areas according to claim 5, characterized in that, The formulas for calculating the potential ecological risk index of heavy metal elements in the livestock feed parts of crops in the study area described in step (2) are shown in equations (e) to (g): (e) (f) ； (g) In equations (e) to (g): is the comprehensive potential ecological risk index of the livestock and poultry feed parts of crops in the study area; n represents the types of heavy metal elements. It is a single potential ecological risk index of heavy metal element j in the soil of the study area in the feed parts of livestock and poultry. The content of heavy metal element j in the livestock feed portion of crops in the study area is the average value in mg·kg⁻¹. -1 ; denoted as the toxicity response coefficient of heavy metal element j; This refers to the single pollution coefficient; The value represents the average content of heavy metal element j in the root system of plants in the study area, in mg·kg⁻¹. -1 ; The reference value for feed hygiene standards for heavy metal element j is mg·kg⁻¹. -1 ; Preferably, the index grading standard in step (2) uses cadmium, arsenic, lead, and chromium as evaluation indicators for classification, specifically including: when <30 and / or When the risk level is <60, the risk level is slight ecological risk, and the potential risk assessment result is Class I; when 30≤ <60 and / or 60≤ When the value is less than 120, the risk level is medium ecological risk, and the potential risk assessment result is Class II; when the value is less than or equal to 60, the risk level is medium ecological risk. <120 and / or 120≤ When the value is less than 240, the risk level is considered relatively high ecological risk, and the potential risk assessment result is Class III; when the value is less than or equal to 120... When the value is less than 240, the risk level is classified as severe ecological risk, and the potential risk assessment result is Class IV; when When the value is ≥240, the risk level is extremely high ecological risk, and the potential risk assessment result is Class IV; when When the value is ≥240, the risk level is extremely high ecological risk, and the potential risk assessment result is Class V.

7. The method for assessing the risk of heavy metals in soil in ambiguous areas according to claim 6, characterized in that, The degree of heavy metal contamination in agricultural products in the study area described in step (2) is determined using the fuzzy regional mean method. The calculation formulas for the degree of contamination in agricultural products are shown in equations (h) and (i): (h) ； (i) In equations (h) and (i): The degree of contamination of heavy metal element j in agricultural products in the study area; is the single-factor index of heavy metal element j in agricultural products at collaborative monitoring point i; m is the number of heavy metal elements j in agricultural products in the study area under different soil data classifications; The percentage of cultivated land area in the total cultivated land area of ​​the study area for the n-level data partition is %. The measured average concentration of heavy metal element j in agricultural products at monitoring point i is expressed in mg·kg⁻¹. -1 ; The limit standard value for heavy metal element j in agricultural products, in mg·kg -1 .

8. The method for assessing the risk of heavy metals in soil in ambiguous areas according to claim 7, characterized in that, The excess rate of heavy metal elements in agricultural products in the study area in step (2) is obtained based on the fuzzy regional mean method. The calculation formula for the excess rate of agricultural products is shown in equations (j) and (k): (j) ； (k) In equations (j) and (k): The percentage of agricultural products containing heavy metal element j in the study area was %. The percentage of agricultural products exceeding the standard for heavy metal element j at monitoring point i (%) The number of times agricultural products containing heavy metal element j at the collaborative monitoring point i exceeded the standard; The number of times heavy metal element j is detected in agricultural products at monitoring point i.

9. The method for assessing the risk of heavy metals in soil in ambiguous areas according to claim 8, characterized in that, Based on the calculation results of the degree and rate of heavy metal contamination in agricultural products in the study area described in step (2), the risk level of heavy metal contamination in agricultural products in the study area is obtained, and the calculation formula is shown in equation (m): ； (m) In formula (m): To determine the risk level of heavy metal element j in agricultural products in the study area; Preferably, the risk level grading standard in step (2) is divided as follows: when 0 < When ≤1, it is classified as Risk Level I; when 1 < When ≤2, it is classified as Risk Level II; when 2 < When the risk level is ≤3, it is classified as risk level III. When the value is greater than 3, it is classified as risk level IV.

10. The method for assessing the risk of heavy metals in soil in ambiguous areas according to claim 9, characterized in that, The soil heavy metal risk assessment results of the fuzzy area in step (2) are classified as follows: the potential risk assessment result of soil heavy metals in the fuzzy area is Class I, and the actual risk assessment result of agricultural products in the fuzzy area is Class I; the potential risk assessment result of soil heavy metals in the fuzzy area is Class II, and the actual risk assessment result of agricultural products in the fuzzy area is Class II; the potential risk assessment result of soil heavy metals in the fuzzy area is Class III, and the actual risk assessment result of agricultural products in the fuzzy area is Class III; the potential risk assessment result of soil heavy metals in the fuzzy area is Class IV, and the actual risk assessment result of agricultural products in the fuzzy area is Class IV; the potential risk assessment result of soil heavy metals in the fuzzy area is Class V.

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