Reservoir sediment heavy metal pollution assessment method

Abstract

The invention discloses a reservoir sediment heavy metal pollution assessment method which comprises the following steps: collecting reservoir basic attribute data, and setting a reservoir sediment collection point location; reservoir in-situ sediments are collected on a large scale, and reservoir sediment physicochemical index data are obtained; and obtaining a heavy metal pollution evaluation result based on the reservoir sediment physicochemical index data. The method fully considers the spatial difference of the reservoir sediment heavy metal content and the current reservoir surrounding land utilization situation, comprehensively and truly evaluates the current reservoir sediment heavy metal pollution situation and the historical source, and overcomes the limitation of a sediment heavy metal single evaluation index result and the uncertainty of heavy metal pollution source spatial distribution; the reservoir sediment heavy metal pollution level and cause are reasonably evaluated, and a basis is provided for effective treatment of reservoir sediment heavy metal pollution.

Description

Technical Field

[0001] The invention belongs to the technical field of environmental pollution assessment, and in particular relates to a method for assessing heavy metal pollution in reservoir sediments. Background Art

[0002] More than 45,000 reservoirs around the world undertake important functions such as flood control, water supply, irrigation, and power generation. Due to the uneven temporal and spatial distribution of water resources, more than 1.7 billion people rely on reservoirs for safe drinking water, and reservoir water quality is crucial to human health and sustainable development. Reservoir pollution is closely related to life and production activities in the surrounding areas. In recent years, increasingly frequent human activities have inevitably led to the migration of heavy metals into reservoirs, seriously threatening aquatic ecosystems. Reservoir sediments are in a closed environment, which makes heavy metals and other pollutants more persistent. Environmental disturbances will cause heavy metals and other pollutants in reservoir sediments to be released into the water body, bringing greater ecological risks. At the same time, the construction of reservoir dams has destroyed the natural flow pattern of rivers and formed a new ecosystem with both lake and river characteristics, which makes the water quality and physical and chemical properties of reservoir sediments have obvious idle heterogeneity. Therefore, it is of great significance to assess the degree of heavy metal pollution, ecological risks and pollution sources of reservoir sediments on a large scale.

[0003] However, in previous assessments, the degree of heavy metal pollution in reservoir sediments, ecological risks and pollution sources were mostly evaluated based on the results of sediments at local points in the reservoir. The randomness of the sampling points made the results unrepresentative. In addition, the land use type around the reservoir was not considered in the identification of the source of heavy metal pollution in sediments, resulting in a deviation between the assessment results and the actual situation. Summary of the invention

[0004] Purpose of the invention: In order to overcome the deficiencies in the prior art, a method for assessing heavy metal pollution in reservoir sediments is provided, which fully considers the spatial differences in the heavy metal content in reservoir sediments and the current land use status around the reservoir, comprehensively and truly assesses the current status and historical sources of heavy metal pollution in reservoir sediments, overcomes the spatial heterogeneity of heavy metals in reservoir sediments and the uncertainty of pollution source tracing, reasonably assesses the level and causes of heavy metal pollution in reservoir sediments, and provides a basis for the control of heavy metal pollution in reservoir sediments.

[0005] Technical solution: To achieve the above purpose, the present invention provides a method for evaluating heavy metal pollution in reservoir sediments, comprising the following steps:

[0006] S1: Collect basic reservoir attribute data and set reservoir sediment collection points;

[0007] S2: Obtain reservoir sediment physical and chemical index data through large-scale collection of reservoir in-situ sediments;

[0008] S3: Obtain the heavy metal pollution assessment results based on the physicochemical index data of reservoir sediments.

[0009] When reasonably setting the sampling points of reservoir sediments in step S1, key sensitive areas such as the confluence areas of the main tributaries of the reservoir, drinking water source protection areas, industrial pollution confluence areas, farmland concentration areas, and urban living concentration areas should be considered.

[0010] Furthermore, the physicochemical index data of reservoir sediments in step S2 includes organic matter content, particle size composition, and heavy metal content. The particle size composition is divided into three categories: sand grains, silt grains, and clay grains. Heavy metals include arsenic, cadmium, chromium, copper, iron, mercury, manganese, nickel, lead, and zinc.

[0011] Furthermore, the heavy metal pollution assessment in step S3 includes heavy metal pollution degree assessment, ecological toxicity risk assessment, and pollution source assessment.

[0012] Furthermore, the method for assessing the heavy metal pollution degree in step S3 is: calculate the single-factor pollution index of sediment heavy metals to judge the heavy metal pollution degree of sediments;

[0013] The calculation formula for the single-factor pollution index of sediment heavy metals is as follows:

[0014]

[0015] In formula (1), CF ij is the single-factor pollution index of the jth heavy metal in the ith sediment sample, C ij is the content of the jth heavy metal in the ith sediment sample, and S j is the background value of the jth heavy metal in the ith sediment sample.

[0016] The heavy metal pollution degree of sediments can be divided into four levels: CF ≤ 1 is low pollution, 1 < CF ≤ 3 is moderate pollution, 3 < CF ≤ 6 is obvious pollution, and CF > 6 is high pollution.

[0017] Furthermore, the method for assessing the ecological toxicity risk in step S3 is: combine the sediment quality criteria and the toxicity risk index to judge the ecological toxicity risk of sediment heavy metals;

[0018] The sediment quality criteria include the lower end of the effect range and the median of the effect range (ERL / ERM), the threshold effect level and the possible effect level (TEL / PEL). If the content of a certain heavy metal in the sediment is lower than the lower end of the effect range ERL or the threshold effect level TEL, it indicates that this heavy metal will hardly produce toxicity to aquatic organisms. If the content of a certain heavy metal in the sediment exceeds the median of the effect range ERM or the possible effect level PEL, it indicates that this heavy metal will produce toxicity to aquatic organisms.

[0019] Furthermore, the median mean of the effect range mERM in step S3 q and mean possible effect level (mPEL) q It is used to comprehensively evaluate the potential ecological risks of multiple heavy metals in sediments. The calculation formula is as follows:

[0020]

[0021] In formulas (2) and (3), C j is the average content of the jth heavy metal, n is the number of heavy metals investigated, ERM j and PEL j are the median effect range and possible effect level of the jth heavy metal, respectively;

[0022] Mean possible effect level (mPEL) q The risk category and toxicity probability are divided into four levels, mPEL q ≤0.1 is low priority, 8% toxicity probability, 0.1 <mPEL q ≤1.5 is medium-low priority, 21% toxicity probability, 1.5 <mPEL q ≤2.3 is medium-high priority, 49% toxicity probability, mPEL q >2.3 is high priority, 73% toxicity probability.

[0023] The calculation formula of toxicity risk index TRI is as follows:

[0024]

[0025] In formulas (4) and (5), TRI j is the toxicity risk index of the jth heavy metal, n is the number of heavy metal types investigated, and mTRI is the average toxicity risk index.

[0026] Furthermore, the method for assessing pollution sources in step S3 comprises the following steps:

[0027] A1: Conduct correlation analysis on sediment physical and chemical index data to determine whether heavy metals in sediments have multiple sources and identify the influence of sediment particle size composition and organic matter content on the distribution of heavy metals;

[0028] A2: The number of major pollution factors of heavy metals in sediments was determined through factor analysis based on principal components, and then the spatial distribution of the scores of major pollution factors in the entire reservoir was obtained using the Kriging interpolation method;

[0029] A3: Combined with the correlation coefficient between the main pollution factors of heavy metals in sediments and each heavy metal, the pollution source categories corresponding to the main pollution factors are analyzed, and through the land use types around the reservoir, the real pollution sources of heavy metals in reservoir sediments are finally identified.

[0030] Furthermore, in step A1, correlation analysis is performed on the physical and chemical index data of all point sediments to obtain correlation coefficients and significant p values. When the correlation coefficient is greater than 0, it indicates that there is a positive correlation between different physical and chemical indicators. When p≤0.05, it is a significant correlation, and p≤0.01 is an extremely significant correlation.

[0031] If the heavy metal contents in sediments are extremely significantly or significantly positively correlated, then different heavy metals in sediments have the same source; otherwise, heavy metals in sediments have multiple sources.

[0032] If the heavy metal content in sediments is extremely significantly or significantly positively correlated with the particle size composition (sand / silt / clay), the distribution of heavy metals in sediments is controlled by the particle size composition and is closely related to the distribution of sand, silt or clay.

[0033] If the heavy metal content in sediments is extremely significantly or significantly positively correlated with the organic matter content in sediments, the distribution of heavy metals in sediments is controlled by organic matter.

[0034] Furthermore, the step A2 is specifically as follows:

[0035] A2-1: Use the Keiser-Meyer-Olkin (KMO) test and Barlett's sphericity test to determine whether there is a correlation between heavy metals in sediments;

[0036] A2-2: When the KMO value is greater than 0.7 and the significant p value of the Barlett sphericity test is less than 0.05, it indicates that there is a correlation between different heavy metals in the sediments and factor analysis can be performed. Otherwise, factor analysis cannot be performed.

[0037] A2-3: Through principal component analysis, extract the components with eigenvalues ​​greater than 1 as principal components. If the explained rate of the variance of the extracted principal components is less than 70%, it is necessary to extract additional principal components with eigenvalues ​​less than but close to 1 to ensure that the explained rate of the variance of the extracted principal components is higher than 70%;

[0038] A2-4: Use Kaiser standardized orthogonal rotation method to maximize the variance of each principal component, obtain the rotated component matrix, and output the principal component factor score data;

[0039] The expression of the rotation component matrix is:

[0040] L'=L×T (6)

[0041] In formula (6), L is the initial loading matrix, which represents the loading of the variable on each principal component; T is the rotation matrix, which is used to rotate the loading matrix to maximize the variance of each principal component; L' is the rotation component matrix. The purpose of this rotation method is to make the loading of each principal component clearer, that is, a variable has a high loading on one principal component and a low loading on other principal components.

[0042] The expression of principal component factor score is:

[0043] F i =∑ j w ij × j (7)

[0044] In formula (7), F i is the score of the i-th principal component factor; w ij is the load in the rotation component matrix, that is, the weight of variable j on the i-th principal component; z j is the value of the standardized, post-variable j;

[0045] A2-5: The components in the rotated component matrix are the main pollution factors of heavy metals in sediments, and the coefficients in the rotated component matrix are the correlation coefficients between the main pollution factors of heavy metals in sediments and each heavy metal;

[0046] A2-6: The principal component factor score data is the score of the main pollution factor of heavy metals in sediments at each sampling point. The spatial distribution of the main pollution factor scores of heavy metals in sediments of the entire reservoir is obtained using the Kriging interpolation method.

[0047] Furthermore, the step A3 is specifically as follows:

[0048] A3-1: According to the correlation coefficient between the main pollution factors of heavy metals in sediments and heavy metals, screen out the heavy metals that are closely related to each main pollution factor;

[0049] A3-2: Consult data and journal articles to preliminarily determine the common sources of different types of heavy metals, such as natural, agricultural, industrial, forestry and other pollution sources;

[0050] A3-3: Find out the areas with high scores for each major pollution factor, combine them with the land use types around the reservoir, and finally identify the real source of heavy metal pollution in the reservoir sediments.

[0051] The data analysis and processing software in the present invention includes SPSS, Origin, and QGis; the number of sediment samples is large enough, usually required to be greater than 100.

[0052] Beneficial effects: Compared with the prior art, the present invention takes into account the spatial differences in the heavy metal content of reservoir sediments and the current land use status around the reservoir, and combines a variety of environmental risk assessment indicators to comprehensively and truly assess the degree of heavy metal pollution in reservoir sediments, ecological risks and pollution sources, overcoming the limitations of the results of a single evaluation indicator for heavy metals in sediments and the uncertainty of the spatial distribution of heavy metal pollution sources, and can provide a scientific basis for the effective prevention and control of heavy metal pollution in reservoir sediments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the evaluation method of the present invention;

[0054] Figure 2 Schematic diagram of 129 sediment sampling points in Dawangtan Reservoir selected in the embodiment of the present invention;

[0055] Figure 3 It is the land use type around Dawangtan Reservoir;

[0056] Figure 4 This is the spatial distribution map of the heavy metal pollution factor scores of four sediments in Dawangtan Reservoir. DETAILED DESCRIPTION

[0057] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0058] like Figure 1 As shown, the present invention provides a method for evaluating heavy metal pollution in reservoir sediments, comprising the following steps:

[0059] S1: Collect basic reservoir attribute data and set reservoir sediment collection points:

[0060] When rationally setting reservoir sediment collection points in the present invention, key consideration is given to sensitive areas such as the confluence area of ​​the main tributaries of the reservoir, drinking water source protection areas, industrial pollution confluence areas, farmland concentration areas, and urban living concentration areas.

[0061] S2: Obtain reservoir sediment physical and chemical index data through large-scale collection of reservoir in-situ sediments:

[0062] The physical and chemical indicator data of reservoir sediments include organic matter content, particle size composition, and heavy metal content. The particle size composition is divided into three categories: sand, silt and clay. The heavy metals include arsenic, cadmium, chromium, copper, iron, mercury, manganese, nickel, lead and zinc.

[0063] In the present invention, the data analysis and processing software includes SPSS, Origin, and QGis; the number of sediment samples is greater than 100.

[0064] S3: Based on the physicochemical index data of reservoir sediments, obtain the heavy metal pollution assessment results:

[0065] Heavy metal pollution assessment includes heavy metal pollution degree assessment, ecological toxicity risk assessment, and pollution source assessment.

[0066] The method for heavy metal pollution degree assessment is: calculate the single-factor pollution index of sediment heavy metals to determine the heavy metal pollution degree of sediments;

[0067] The calculation formula for the single-factor pollution index of sediment heavy metals is as follows:

[0068]

[0069] In formula (1), CF ij is the single-factor pollution index of the jth heavy metal in the ith sediment sample, C ij is the content of the jth heavy metal in the ith sediment sample, S j is the background value of the jth heavy metal in the ith sediment sample.

[0070] The heavy metal pollution degree of sediments can be divided into four levels: CF ≤ 1 is low pollution, 1 < CF ≤ 3 is moderate pollution, 3 < CF ≤ 6 is obvious pollution, and CF > 6 is high pollution.

[0071] The method for ecological toxicity risk assessment is: combine the sediment quality criteria and the toxicity risk index to judge the ecological toxicity risk of sediment heavy metals;

[0072] The sediment quality criteria include the lower end of the effect range and the median of the effect range (ERL / ERM), the threshold effect level and the possible effect level (TEL / PEL). If the content of a certain heavy metal in the sediment is lower than the lower end of the effect range ERL or the threshold effect level TEL, it indicates that this heavy metal will hardly produce toxicity to aquatic organisms. If the content of a certain heavy metal in the sediment exceeds the median of the effect range ERM or the possible effect level PEL, it indicates that this heavy metal will produce toxicity to aquatic organisms.

[0073] The average of the median of the effect range mERM q and the average of the possible effect level mPEL q are used to comprehensively evaluate the potential ecological risks of multiple heavy metals in sediments, and the calculation formula is as follows:

[0074]

[0075] In formulas (2) and (3), C jis the average content of the jth heavy metal, n is the number of heavy metals investigated, ERM j and PEL j are the median effect range and possible effect level of the jth heavy metal, respectively;

[0076] Mean possible effect level (mPEL) q The risk category and toxicity probability are divided into four levels, mPEL q ≤0.1 is low priority, 8% toxicity probability, 0.1 <mPEL q ≤1.5 is medium-low priority, 21% toxicity probability, 1.5 <mPEL q ≤2.3 is medium-high priority, 49% toxicity probability, mPEL q >2.3 is high priority, 73% toxicity probability.

[0077] The calculation formula of toxicity risk index TRI is as follows:

[0078]

[0079] In formulas (4) and (5), TRI j is the toxicity risk index of the jth heavy metal, n is the number of heavy metal types investigated, and mTRI is the average toxicity risk index.

[0080] The method of pollution source assessment includes the following steps:

[0081] A1: Conduct correlation analysis on sediment physical and chemical index data to determine whether heavy metals in sediments have multiple sources and identify the influence of sediment particle size composition and organic matter content on the distribution of heavy metals;

[0082] Correlation analysis was performed on the physical and chemical index data of all deposition points to obtain the correlation coefficient and significant p value. When the correlation coefficient is greater than 0, it indicates that there is a positive correlation between different physical and chemical indicators. When p≤0.05 is a significant correlation, and p≤0.01 is an extremely significant correlation.

[0083] If the heavy metal contents in sediments are extremely significantly or significantly positively correlated, then different heavy metals in sediments have the same source; otherwise, heavy metals in sediments have multiple sources.

[0084] If the heavy metal content in sediments is extremely significantly or significantly positively correlated with the particle size composition (sand / silt / clay), the distribution of heavy metals in sediments is controlled by the particle size composition and is closely related to the distribution of sand, silt or clay.

[0085] If the heavy metal content in sediments is extremely significantly or significantly positively correlated with the organic matter content in sediments, the distribution of heavy metals in sediments is controlled by organic matter.

[0086] A2: The number of major pollution factors of heavy metals in sediments was determined through factor analysis based on principal components, and then the spatial distribution of the scores of major pollution factors in the entire reservoir was obtained using the Kriging interpolation method;

[0087] Step A2 is specifically as follows:

[0088] A2-1: Use the Keiser-Meyer-Olkin (KMO) test and Barlett's sphericity test to determine whether there is a correlation between heavy metals in sediments;

[0089] A2-2: When the KMO value is greater than 0.7 and the significant p value of the Barlett sphericity test is less than 0.05, it indicates that there is a correlation between different heavy metals in the sediments and factor analysis can be performed. Otherwise, factor analysis cannot be performed.

[0090] A2-3: Through principal component analysis, extract the components with eigenvalues ​​greater than 1 as principal components. If the explained rate of the variance of the extracted principal components is less than 70%, it is necessary to extract additional principal components with eigenvalues ​​less than but close to 1 to ensure that the explained rate of the variance of the extracted principal components is higher than 70%;

[0091] Eigenvalue acquisition: Eigenvalues ​​are obtained by eigendecomposing the data covariance matrix or correlation matrix during principal component analysis. The specific steps are as follows:

[0092] 1) Standardize the data so that the mean of each variable is 0 and the variance is 1;

[0093] 2) Calculate the covariance matrix or correlation matrix of the standardized data matrix;

[0094] 3) Perform eigendecomposition on the covariance matrix or correlation matrix to obtain eigenvalues ​​and corresponding eigenvectors; the eigenvalues ​​represent the explained variance of each principal component, and larger eigenvalues ​​correspond to more important principal components;

[0095] Typically, principal components with eigenvalues ​​greater than 1 are considered to explain sufficient variance and are therefore retained.

[0096] A2-4: Use Kaiser standardized orthogonal rotation method to maximize the variance of each principal component, obtain the rotated component matrix, and output the principal component factor score data;

[0097] The expression of the rotation component matrix is:

[0098] L'=L×T (6)

[0099] In formula (6), L is the initial loading matrix, which represents the loading of the variable on each principal component; T is the rotation matrix, which is used to rotate the loading matrix to maximize the variance of each principal component; L' is the rotation component matrix. The purpose of this rotation method is to make the loading of each principal component clearer, that is, a variable has a high loading on one principal component and a low loading on other principal components.

[0100] The expression of principal component factor score is:

[0101] F i =∑ j w ij ×Z j (7)

[0102] In formula (7), F i is the score of the i-th principal component factor; w ij is the load in the rotation component matrix, that is, the weight of variable j on the i-th principal component; z j is the value of the standardized variable j.

[0103] A2-5: The components in the rotated component matrix are the main pollution factors of heavy metals in sediments, and the coefficients in the rotated component matrix are the correlation coefficients between the main pollution factors of heavy metals in sediments and each heavy metal;

[0104] A2-6: The principal component factor score data is the score of the main pollution factor of heavy metals in sediments at each sampling point. The spatial distribution of the main pollution factor scores of heavy metals in sediments of the entire reservoir is obtained using the Kriging interpolation method.

[0105] A3: Combined with the correlation coefficient between the main pollution factors of heavy metals in sediments and each heavy metal, the pollution source categories corresponding to the main pollution factors are analyzed, and through the land use types around the reservoir, the real pollution sources of heavy metals in reservoir sediments are finally identified.

[0106] Step A3 is specifically as follows:

[0107] A3-1: According to the correlation coefficient between the main pollution factors of heavy metals in sediments and heavy metals, screen out the heavy metals that are closely related to each main pollution factor;

[0108] A3-2: Consult data and journal articles to preliminarily determine the common sources of different types of heavy metals, such as natural, agricultural, industrial, forestry and other pollution sources;

[0109] A3-3: Find out the areas with high scores for each major pollution factor, combine them with the land use types around the reservoir, and finally identify the real source of heavy metal pollution in the reservoir sediments.

[0110] Based on the above content, in order to verify the effectiveness and evaluation effect of the method of the present invention, the method of the present invention is applied in an example in this embodiment, as follows:

[0111] The research object of this embodiment is the Dawangtan Reservoir in Nanning City, Guangxi Zhuang Autonomous Region, and the heavy metal pollution degree, ecological risk and pollution source of the reservoir sediment are evaluated. The steps are as follows:

[0112] 1. Collect basic reservoir attribute data

[0113] The reservoir is a national large (II) type water conservancy project that is mainly used for agricultural irrigation, and also has comprehensive uses such as flood control, power generation, and urban water supply. The water intake points of Dawangtan Reservoir that provide drinking water sources for surrounding towns include Dashatian Water Plant, Minghu Water Plant and Nachen Water Plant. The water and land within a radius of 500 meters around the water intake are all first-level water source protection areas. The main sources of reservoir pollution include agricultural non-point source pollution, village and town life pollution, and industrial emission pollution. Although Dawangtan Reservoir has carried out comprehensive water surface management since 2013, and the water quality has been significantly improved, the content of heavy metals, nutrients, and organic matter in sediments is still very high, which can easily lead to hypoxia in the reservoir water body.

[0114] The important sensitive areas of Dawangtan Reservoir include drinking water source protection areas (Dashatin Water Plant, Minghu Water Plant and Nachen Water Plant), industrial pollution inflow areas and urban living pollution areas. The sediment collection points are arranged in a grid or staggered plum blossom shape with a spacing of 100 to 200 meters, with a total of 87 points. The sediment collection points in other areas are gridded at 1 km and arranged in a staggered plum blossom shape, with a total of 42 points.

[0115] 2. Obtaining physical and chemical index data of reservoir sedimentation

[0116] 129 in situ sediments were collected from the Dawangtan Reservoir, and the contents of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb), zinc (Zn), organic matter (TOC) and the composition ratios of sand (Sand), silt (Silt), and clay (Clay) in the sediments were determined. The specific data are shown in Table 1.

[0117] Table 1 Heavy metals, organic matter and particle size composition of sediments (particle size and iron content are in %, others are in mg / kg)

[0118]

[0119] 3. Determine the degree of heavy metal pollution in sediments

[0120] The background value data of heavy metals in sediments are derived from the journal “Background Values ​​and Benchmark Values ​​of Chemical Elements in Urban Soils in China”, and are taken according to the soil geochemical background values ​​and benchmark values ​​in Nanning. The specific data are shown in Table 1. Then the single factor pollution index CF of heavy metals in sediments is calculated, as shown in Table 2.

[0121] Table 2 Single factor pollution index CF of heavy metals in sediments and the number of sampling points at different pollution levels

[0122]

[0123] The single factor pollution index CF of heavy metals in the sediments of Dawangtan Reservoir is as follows: Fe>Cd>Mn>Zn>Hg>Cu>Ni>Cr>As>Pb. The average CF of Fe shows a significant pollution level, the average CF of Cd, Mn, Zn, Hg, Cu, and Ni shows a moderate pollution level, and the average CF of Cr, As, and Pb shows a low pollution level. Cd, Fe, and Mn in the local sediments of Dawangtan Reservoir are at a high pollution level.

[0124] 4. Determine the ecotoxicity risk of heavy metals in sediments

[0125] The sediment quality benchmark data comes from the journal article "Development and evaluation of sediment quality guidelines for Florida coastal waters". The sediment quality benchmark includes the lower limit of the effect range and the median of the effect range (ERL / ERM), the threshold effect level and the possible effect level (TEL / PEL) as shown in Table 3.

[0126] Table 3 Heavy metal content limits corresponding to sediment quality standards (unit: mg / kg)

[0127]

[0128] The evaluation results based on the sediment quality benchmark are shown in Table 4. The contents of As (51.16%), Cd (100%), Cr (78.29%), Cu (77.52%), Hg (71.32%), Ni (37.98%), Pb (99.22%), and Zn (89.15% of sediment samples) are all below the ERL, indicating that they are almost not toxic to aquatic organisms; except for Ni, the contents of heavy metals in other sediment samples are between the ERL and ERM, indicating that they are occasionally toxic to aquatic organisms. It is worth noting that the Ni content of 0.78% of the sediment samples exceeds the ERM, which is toxic to aquatic organisms. The contents of As (41.86%), Cd (100%), Cr (35.66%), Cu (30.23%), Hg (55.81%), Ni (17.05%), Pb (96.12%), and Zn (79.07% of sediment samples) were all below the TEL, indicating that they were almost not toxic to aquatic organisms. Except for Ni and Zn, the contents of other heavy metals in sediment samples were between the TEL and PEL, indicating that they were occasionally toxic to aquatic organisms. It is worth noting that the Zn content of 3.10% of sediment samples and the Ni content of 1.55% of sediment samples exceeded the PEL, which would be toxic to aquatic organisms.

[0129] Table 4 Evaluation results based on sediment quality benchmarks and the number of sampling points at different levels

[0130]

[0131] According to mERM q The results showed that 91.47% of the sediment samples in Dawangtan Reservoir were at low priority and 8% toxicity probability; 10 sediment samples were at medium-low priority and 21% toxicity probability. One sediment sample was at medium-high priority and 49% toxicity probability, requiring special attention. According to mPEL q As a result, all 129 sediment samples were at medium-low priority, with a 21% toxicity probability. According to the mTRI results, 129 sediment samples had no toxicity risk.

[0132] Table 5 Ecotoxicity risk assessment results of heavy metals in sediments

[0133]

[0134] 5. Correlation analysis of sedimentary physical and chemical indicators

[0135] SPSS software was used to conduct correlation analysis on organic matter content, particle size composition, and heavy metal content. The results are shown in Table 6. Most heavy metals (Cr, Cu, Fe, Mn, Ni, and Zn) in the Dawangtan Reservoir sediments showed extremely significant positive correlations with each other, indicating that they may have the same source; Cd had a weak correlation with heavy metals other than Cr, indicating that the reservoir sediments had an independent source of Cd pollution. Hg in the reservoir sediments showed extremely significant positive correlations with As, Cu, Fe, Ni, and Zn, indicating that Cu, Fe, Ni, and Zn in the sediments had multiple pollution sources.

[0136] Except for Cd, sediment TOC showed extremely significant positive correlation with all heavy metals, and the correlation coefficients with Fe and Cu were the largest, indicating that the organic matter in the reservoir sediments played an important role in regulating the distribution of heavy metals. The distribution of sediment Cu and Hg content was significantly affected by sand, the distribution of Hg content was extremely significantly affected by silt, and the distribution of Cr content was extremely significantly affected by clay.

[0137] Table 6 Correlation analysis results of sedimentary physical and chemical indicators

[0138]

[0139] *p≤0.05 is significant

[0140] **p≤0.01 is extremely significant

[0141] 6. Spatial distribution of the scores of the main pollution factors of reservoirs

[0142] Through the Keiser-Meyer-Olkin test and the Barlett sphericity test, the KMO value was 0.789 (>0.7), and the significance p value of the Barlett sphericity test was 0.000 (<0.05). The two indicators showed that there was a correlation between different heavy species of sediments. At the same time, the number of sediment samples (129) was greater than 100, which met the sample conditions for factor analysis, so factor analysis based on principal components could be performed.

[0143] The results of principal component analysis are shown in Table 7. Four components with eigenvalues ​​greater than 1 were extracted as principal components, which can explain 39.148%, 12.280%, 10.892% and 10.222% of the variance respectively. The total explanation rate is 72.542%, which is higher than 70%, indicating that these four principal components can well explain the overall characteristics. These four principal components correspond to four sediment heavy metal pollution factors.

[0144] Table 7 Eigenvalues ​​and contribution rates of principal components

[0145]

[0146]

[0147] The variance of each principal component was maximized by the Kaiser standardized orthogonal rotation method, and the rotation component matrix was obtained, as shown in Table 8. At the same time, the score data of the four principal component factors were output, and the spatial distribution of the scores of the four sediment heavy metal pollution factors in Dawangtan Reservoir was obtained by the Kriging interpolation method.

[0148] Table 8 Correlation coefficients between heavy metal pollution factors and individual heavy metals obtained based on the rotation component matrix

[0149]

[0150] 7. Determine the true source of heavy metal pollution in reservoir sediments

[0151] Combining the correlation coefficients between the four sediment heavy metal pollution factors and each heavy metal, it can be seen that pollution factor 1 is closely related to As, Hg, and Zn, pollution factor 2 is closely related to Cr, Fe, Mn, and Ni, pollution factor 3 is closely related to Cu and Pb, and pollution factor 4 is closely related to Cd. By consulting journal literature, it can be seen that As, Hg, and Zn are the main components of industrial wastewater, Cr, Fe, and Mn are the main components of natural parent rocks, As, Cu, and Pb are the main components of pesticides, Cu is the main component of fertilizers, and Cd has multiple sources, including industrial sources (electronics industry, chemical industry, and electroplating) and agricultural sources (pesticides and fertilizers). Through the spatial characteristics of the scores of the four sediment heavy metal pollution factors, it can be seen that pollution factors 1 and 3 may be point source pollution, and pollution factors 2 and 4 may be non-point source pollution; based on the land use type around the reservoir, pollution factor 1 is locked as an industrial pollution source, pollution factor 3 is a forestry pollution source, pollution factor 2 is natural soil erosion, and pollution factor 4 is an agricultural pollution source.

Claims

1. A method for assessing heavy metal pollution in reservoir sediments, characterized in that: The steps include: S1: Collect basic reservoir attribute data and set reservoir sediment collection points; S2: Obtain reservoir sediment physical and chemical index data by collecting in-situ reservoir sediments; S3: Based on the physical and chemical indicator data of reservoir sediments, the heavy metal pollution assessment results are obtained.

2. A reservoir sediment heavy metal pollution assessment method according to claim 1, characterized in that: The physical and chemical index data of reservoir sediments in step S2 include organic matter content, particle size composition, and heavy metal content. The particle size composition is divided into three categories: sand, silt and clay. The heavy metals include arsenic, cadmium, chromium, copper, iron, mercury, manganese, nickel, lead and zinc.

3. A reservoir sediment heavy metal pollution assessment method according to claim 1, characterized in that: The heavy metal pollution assessment in step S3 includes heavy metal pollution degree assessment, ecotoxicity risk assessment and pollution source assessment.

4. A reservoir sediment heavy metal pollution assessment method according to claim 3, characterized in that: The method of evaluating the heavy metal pollution degree in step S3 is: calculating the sediment heavy metal single factor pollution index to determine the sediment heavy metal pollution degree; The calculation formula of the single factor pollution index of sediment heavy metals is as follows: In formula (1), CF ij is the single factor pollution index of the jth heavy metal in the ith sediment sample, C ij is the jth heavy metal content in the ith sediment sample, S j is the background value of the jth heavy metal in the ith sediment sample.

5. A reservoir sediment heavy metal pollution assessment method according to claim 3, characterized in that: The method of ecotoxicity risk assessment in step S3 is: combining the sediment quality benchmark and the toxicity risk index of the sediment to determine the ecotoxicity risk of heavy metals in the sediment; Sediment quality standards include the lower limit of the effect range and the median of the effect range (ERL / ERM), the threshold effect level and the possible effect level (TEL / PEL). If the content of a certain heavy metal in the sediment is lower than the lower limit of the effect range ERL or the threshold effect level TEL, it indicates that the heavy metal has almost no toxicity to aquatic organisms. If the content of a certain heavy metal in the sediment exceeds the median of the effect range ERM or the possible effect level PEL, it indicates that the heavy metal will be toxic to aquatic organisms.

6. A reservoir sediment heavy metal pollution assessment method according to claim 5, characterized in that: The median mean of the effect range mERM in step S3 q and mean possible effect level (mPEL) q It is used to comprehensively evaluate the potential ecological risks of multiple heavy metals in sediments. The calculation formula is as follows: In formulas (2) and (3), C j is the average content of the jth heavy metal, n is the number of heavy metals investigated, ERM j and PEL j are the median effect range and possible effect level of the jth heavy metal, respectively; The calculation formula of toxicity risk index TRI is as follows: In formulas (4) and (5), TRI j is the toxicity risk index of the jth heavy metal, n is the number of heavy metal types investigated, and mTRI is the average toxicity risk index.

7. A reservoir sediment heavy metal pollution assessment method according to claim 3, characterized in that: The method for pollution source assessment in step S3 comprises the following steps: A1: Conduct correlation analysis on sediment physical and chemical index data to determine whether heavy metals in sediments have multiple sources and identify the influence of sediment particle size composition and organic matter content on the distribution of heavy metals; A2: The number of major pollution factors of heavy metals in sediments was determined through factor analysis based on principal components, and then the spatial distribution of the scores of major pollution factors in the entire reservoir was obtained using the Kriging interpolation method; A3: Combined with the correlation coefficient between the main pollution factors of heavy metals in sediments and each heavy metal, the pollution source categories corresponding to the main pollution factors are analyzed, and through the land use types around the reservoir, the real pollution sources of heavy metals in reservoir sediments are finally identified.

8. A reservoir sediment heavy metal pollution assessment method according to claim 7, characterized in that: In the step A1, correlation analysis is performed on the physical and chemical index data of all the deposition points to obtain the correlation coefficient and the significant p value. When the correlation coefficient is greater than 0, it indicates that there is a positive correlation between the different physical and chemical indicators. When p≤0.05, it is a significant correlation, and p≤0.01 is an extremely significant correlation. If the heavy metal contents in sediments are extremely significantly or significantly positively correlated, then different heavy metals in sediments have the same source; otherwise, heavy metals in sediments have multiple sources. If the heavy metal content in sediments is extremely significantly or significantly positively correlated with the particle size composition, the distribution of heavy metals in sediments is controlled by the particle size composition and is closely related to the distribution of sand particles, silt particles or clay particles. If the heavy metal content in sediments is extremely significantly or significantly positively correlated with the organic matter content in sediments, the distribution of heavy metals in sediments is controlled by organic matter.

9. A reservoir sediment heavy metal pollution assessment method according to claim 7, characterized in that: The step A2 is specifically as follows: A2-1: Use the Keiser-Meyer-Olkin test and Barlett's sphericity test to determine whether there is a correlation between heavy metals in sediments; A2-2: When the KMO value is greater than 0.7 and the significant p value of the Barlett sphericity test is less than 0.05, it indicates that there is a correlation between different heavy metals in the sediments and factor analysis can be performed. Otherwise, factor analysis cannot be performed. A2-3: Through principal component analysis, extract the components with eigenvalues ​​greater than 1 as principal components. If the explained rate of the variance of the extracted principal components is less than 70%, it is necessary to extract additional principal components with eigenvalues ​​less than but close to 1 to ensure that the explained rate of the variance of the extracted principal components is higher than 70%; A2-4: Use Kaiser standardized orthogonal rotation method to maximize the variance of each principal component, obtain the rotated component matrix, and output the principal component factor score data; The expression of the rotation component matrix is: L'=L×T (6) In formula (6), L is the initial loading matrix, which represents the loading of the variable on each principal component; T is the rotation matrix, which is used to rotate the loading matrix to maximize the variance of each principal component; L' is the rotation component matrix. The purpose of this rotation method is to make the loading of each principal component clearer, that is, a variable has a high loading on one principal component and a low loading on other principal components. The expression of the principal component factor score is: F i =∑ j w ij ×z j (7) In formula (7), F i Score the i-th principal component factor; w ij is the load in the rotation component matrix, that is, the weight of variable j on the i-th principal component; z j is the value of the standardized, post-variable j; A2-5: The components in the rotated component matrix are the main pollution factors of heavy metals in sediments, and the coefficients in the rotated component matrix are the correlation coefficients between the main pollution factors of heavy metals in sediments and each heavy metal; A2-6: The principal component factor score data is the score of the main pollution factor of heavy metals in sediments at each sampling point. The spatial distribution of the main pollution factor scores of heavy metals in sediments of the entire reservoir is obtained using the Kriging interpolation method.

10. A reservoir sediment heavy metal pollution assessment method according to claim 7, characterized in that: The step A3 is specifically as follows: A3-1: According to the correlation coefficient between the main pollution factors of heavy metals in sediments and heavy metals, screen out the heavy metals that are closely related to each main pollution factor; A3-2: Consult data and journal articles to preliminarily determine the common sources of different types of heavy metals; A3-3: Find out the areas with high scores for each major pollution factor, combine them with the land use types around the reservoir, and finally identify the real source of heavy metal pollution in the reservoir sediments.

Images