Analysis Methods for Heavy Metal Concentrations in Sediments from Agricultural Ditches to Rivers

Abstract

The present invention discloses a method for analyzing the heavy metal concentration of sediments from agricultural ditches to rivers, comprising the following steps: setting a starting point and multiple sampling points, measuring the heavy metal concentration of sediments at the sampling points, and recording the distance from the sampling point to the starting point to obtain overall sample data; performing a difference analysis on the overall sample data; using a regression analysis method to analyze the relationship between heavy metal concentration and distance to obtain a linear regression equation; and according to the linear regression equation, the changing trend of heavy metal concentration with distance. By measuring and analyzing the heavy metal concentration of sediments at different sampling points, the correlation between concentration and distance and the significance of differences between different regions are found, thereby predicting the changing trend of heavy metal concentration in sediments from agricultural ditches to rivers.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural pollution prevention and control, and in particular to a method for analyzing heavy metal concentrations in sediments from agricultural ditches to rivers. Background Art

[0002] Heavy metal pollution in agricultural ditch sediments will cause harm to surrounding plants, animals and microorganisms, and through the food chain, people will suffer harm and potential harm to human health after eating livestock and poultry products fed with agricultural products near the pollution source. Heavy metals refer to a group of metal elements with an atomic density greater than 5 grams per cubic centimeter, about 40 in total, mainly including cadmium (Cd), chromium (Cr), cobalt (Co), lead (Pb), copper (Cu), zinc (Zn), nickel (Ni), etc. It should be noted that elements such as selenium and aluminum, whose atomic density is less than 5 grams per cubic centimeter, may also be included in heavy metals if they are excessive, which may also make them toxic to the agricultural environment. In this article, heavy metal pollution refers to environmental pollution caused by heavy metals and their compounds.

[0003] Heavy metal pollution can affect the yield and quality of agricultural products, endanger the safety of the human living environment, threaten the ecological environment, and affect the utilization rate of fertilizers. Therefore, it is urgent to provide a method to predict heavy metal concentrations to prevent problems before they occur. Summary of the invention

[0004] The purpose of the present invention is to provide a method for analyzing the heavy metal concentration in sediments from agricultural ditches to rivers to solve the problems raised in the above-mentioned background technology. By measuring and analyzing the heavy metal concentration in sediments at different sampling points, the correlation between concentration and distance and the significance of differences between different regions are found, thereby predicting the heavy metal concentration in sediments from agricultural ditches to rivers.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for analyzing heavy metal concentrations in sediments from agricultural ditches to rivers, comprising the following steps:

[0007] S1: Set a starting point and multiple sampling points, measure the heavy metal concentration of the sediment at the sampling points, and record the distance from the sampling point to the starting point to obtain overall sample data;

[0008] S2: Perform difference analysis on the overall sample data;

[0009] S3: If the overall sample data conforms to the residual normality, the relationship between heavy metal concentration and distance is analyzed using regression analysis method to obtain a linear regression equation; if the overall sample data does not conform to the residual normality, field sampling is performed to measure the heavy metal concentration;

[0010] S4: Analyze and predict the changing trend of heavy metal concentrations at different distances based on the linear regression equation.

[0011] As a further solution of the present invention: in S1, the sampling points are distributed in three connected areas, namely the river area, the diffusion area and the long ditch area, and the adjacent sampling points in each area are arranged at equal distances.

[0012] As a further solution of the present invention: In S1, the determination method specifically includes sample pretreatment and sample determination; wherein, the sample pretreatment includes sample drying, microwave digestion, filtration, and titration, and the sample determination is spectrophotometric determination.

[0013] As a further solution of the present invention: in S2, the difference analysis is specifically: randomly assigning the overall sample data to a plurality of different treatment groups, and using a one-way analysis of variance to perform a difference analysis of heavy metal concentrations among regions, including the following steps:

[0014] The Shapiro-Wilk method was used to test whether the overall sample data conformed to the normal distribution; assuming that the overall sample obeyed the normal distribution, the degrees of freedom were set, the mean square between groups and the mean square within groups were calculated, and the F value was calculated according to the formula F = mean square between groups / mean square within groups. The P value under the current degrees of freedom was obtained by querying the F value distribution table. If P < 0.05, the hypothesis was rejected, that is, there were significant differences in the heavy metal concentrations in different regions.

[0015] It should be noted that multiple treatment groups refer to the concentration difference analysis of each heavy metal element in different areas of the river area, long ditch area and diffusion area as one group.

[0016] As a further solution of the present invention: the starting point is set at the point closest to the pollution source.

[0017] As a further embodiment of the present invention: the heavy metals include cadmium, chromium, cobalt, lead, copper, zinc, nickel, selenium and aluminum.

[0018] As a further solution of the present invention: In S3, the linear regression equation is: y=bx+a,

[0019] Among them, b is the regression coefficient, a is a constant, x is the distance from the prediction point to the starting point, and y is the heavy metal concentration at the prediction point;

[0020] First derive b: b=(x1y1+x2y2+...x n y n -nXY) / (x1+x2+...x n -nX)

[0021] Where: x n is the distance from the nth sampling point to the starting point; yn is the heavy metal element concentration at the nth sampling point; X is x i The average value of y i The average value of

[0022] Then derive a: a=Y-bX

[0023] Where X is x i The average value of y i The average value of .

[0024] As a further solution of the present invention: S4 is specifically: predicting the changing trend of heavy metal concentration with distance according to the positive or negative sign and size of coefficient b.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The analysis method provided by the present invention can be used to detect the content of heavy metals in sediments in diffusion zones, long ditch zones and stream areas. Compared with simply measuring the content of heavy metals in a single area of ​​the diffusion zone, long ditch zone or stream area, it can not only more accurately and objectively reflect the content of heavy metals in the comprehensive area of ​​the diffusion zone, long ditch zone or stream area, but also outline the impact of farm sewage, livestock manure and pesticides on heavy metal concentrations, the diffusion and migration and transformation process of heavy metal pollution in different areas, thereby providing comprehensive reference and guidance for farm management and heavy metal pollution prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a schematic diagram of a heavy metal concentration sample collection area in an embodiment of the present invention; DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1 In an embodiment of the present invention, a method for analyzing heavy metal concentrations in sediments from agricultural ditches to rivers comprises the following steps:

[0030] (1) Sample collection

[0031] like Figure 1As shown, the samples of heavy metal content in the sediment from agricultural ditches to rivers are collected from the long ditch area, river area, and diffusion area. The distance between adjacent sampling points in the diffusion area and the long ditch area is 25 meters, and the distance between adjacent sampling points in the river area is as equal as possible, which can be adjusted accordingly according to the actual terrain conditions. Sediments should be obtained from the bottom mud at a depth of 50cm to 150cm.

[0032] The 18 samples from point 1 to point 18 were collected from the ditch line in the Changgou area. The distance between adjacent samples was 25 meters. Point 18 was at the junction of the Changgou and the river. The Changgou area was divided into the upper ditch area, the middle ditch area and the lower ditch area.

[0033] The three samples from point 19 to 21 were collected from the river area, and the remaining samples from point 22 to 24 were collected from the diffusion area.

[0034] (2)ICP analysis

[0035] The determination of heavy metal content in sediments was completed in the laboratory.

[0036] Step 1: Drying

[0037] The obtained samples were placed in mortar and dried in a drying oven for 24 hours. After 24 hours, the dried samples were taken out, 0.5 g of each sample was placed in plastic mortar, and the actual weight was recorded.

[0038] Step 2: Microwave Digestion

[0039] Pour the sample into a test tube for microwave digestion and gently scrape the residue off the plastic surface with tweezers.

[0040] ① Predigestion

[0041] The operation of adding acid before microwave digestion is called pre-digestion, which is to transfer the sample particles to the tube for acid titration to remove unnecessary organic matter to ensure the safety of microwave digestion and eliminate its interference with the experimental results. When dropping acid, tilt the test tube so that the acid flows through the test tube wall, and slowly rotate the test tube to achieve the purpose of fully mixing the remaining sample in the test tube. All test tubes are placed on a disc for digestion, and the sample number corresponds to the label.

[0042] ②Microwave digestion

[0043] Set the digestion temperature to 200°C, the digestion time to 1.5h, and wait for cooling.

[0044] After microwave digestion is completed, pour the remaining solution into a conical flask filled with three layers of filter paper, slowly pour it from the side of the filter paper, wait for the filtration to be completed, and then make up the volume.

[0045] ③ Titration

[0046] Transfer the solution to a measuring cylinder and add distilled water until the concave surface of the solution is close to the 50 ml mark. Use a burette to titrate the remaining distilled water until the concave surface of the liquid is aligned with the mark.

[0047] ④Determination of heavy metal concentration

[0048] The heavy metal concentration in the sample was determined using an inductively coupled plasma spectrometer (ICP) to obtain the overall sample data X, X1, X2, …, X n , where X i is the heavy metal content in the sediment of the ith sampling point determined by ICP.

[0049] The measured concentration values ​​of heavy metals in sediments from agricultural ditches to rivers are shown in Tables 1-5. Table 1 shows the measured concentration values ​​of various heavy metals in sediments in the upper ditch area, Table 2 shows the measured concentration values ​​of various heavy metals in sediments in the middle ditch area, Table 3 shows the measured concentration values ​​of various heavy metals in sediments in the lower ditch area, Table 4 shows the measured concentration values ​​of various heavy metals in sediments in the river area, and Table 5 shows the measured concentration values ​​of heavy metals in sediments in the diffusion area.

[0050] Table 1 Determination of heavy metal concentrations in sediments in Shanggou area

[0051]

[0052] Table 2 Heavy metal concentrations in sediments in the middle ditch area

[0053]

[0054] Table 3 Determination of heavy metal concentrations in sediments in the Xiagou area

[0055]

[0056] Table 4 Heavy metal concentrations in river sediments

[0057]

[0058]

[0059] Table 5 Determination of heavy metal concentrations in the diffusion zone sediments

[0060]

[0061] (3) Residual normality analysis

[0062] ① Ensure that the sample data conforms to the Shapiro-Wilk normality test.

[0063] H0: X follows a normal distribution;

[0064] vs:X does not follow a normal distribution.

[0065] The test statistic W is:

[0066]

[0067] Where X = ∑ n i＝1 X i / n,X (i) is the i-th order statistic, i = 1, 2, ..., n, (a1, a2, ..., a n )=(m T V －1 ) / C,m=(m1,m2,……,m n ) T , let Y (i) is a sample Y1, Y2, ..., Y n The i-th order statistic, m i =E(Y (i) ), i= 1,2,...,n, V=COV(Y,Y),Y=(Y,Y2,...,Y n ) T Finally, the significant level α is set to 0.05. When W < α, H0 is rejected, otherwise, H0 is accepted.

[0068] (4) Analysis of regional differences

[0069] One-Way Anova was used to analyze the differences in the concentration of heavy metals between regions. The overall sample data was allocated to the diffusion area, long ditch area and river by a completely random method to compare whether there were differences in the concentration of heavy metals in the sediments of the diffusion area, long ditch area and river. The steps are as follows:

[0070] First, make the hypothesis H0: There is no significant difference in the effect indicators of multiple groups. Set the degree of freedom α = 0.05.

[0071] Calculate the mean square between groups (MSB) and the mean square error (MSE), use the formula: F = MSB / MSE to calculate the F value, and use SPSS software to calculate the P value under the current degrees of freedom; or query the F value distribution table, and then use SPSS to calculate the P value under the current degrees of freedom. If P < 0.05, reject the hypothesis H0, that is, there are significant differences in the concentration of the same heavy metal in different regions.

[0072] It should be noted that the F-value distribution table and SPSS software calculation are both commonly used statistical analysis processing methods. The F-value distribution table is a table obtained based on the F-distribution calculation. The required P value can be obtained by looking up the F-value distribution table. The P value refers to the probability of an event in a probability model where a statistical summary (such as the difference between the mean values ​​of two groups of samples) is the same as or even greater than the actual observed data. In other words, the P value is the probability that the null hypothesis of the test hypothesis is true or more serious. If the P value is smaller than the selected significance level (0.05 or 0.01), the null hypothesis will be rejected and unacceptable, but this does not directly indicate that the original hypothesis is correct. The P value is a random variable that follows a normal distribution.

[0073] The analysis results of the differences in the data in Tables 1 to 5 are shown in Table 6.

[0074] Table 6 Results of analysis of concentration differences among regions

[0075]

[0076]

[0077] (5) Regression analysis to analyze the trend of heavy metal concentration with distance

[0078] Specifically, the linear regression equation can be used, y = bx + a,

[0079] Among them, b is the regression coefficient, a is a constant, x is the distance from the prediction point to the starting point, and y is the heavy metal concentration at the prediction point;

[0080] First derive b: b=(x1y1+x2y2+...x n y n -nXY) / (x1+x2+...x n -nX)

[0081] Where: x n is the distance from the nth sampling point to the starting point; y n is the heavy metal element concentration at the nth sampling point; X is x i The average value of y i The average value of

[0082] Then derive a: a=Y-bX

[0083] Where X is x i The average value of y i The average value of .

[0084] The linear regression equation is calculated according to Table 1-5: y=bx+a. The analysis results of linear regression are shown in Table 7.

[0085] Table 7 Linear regression analysis results

[0086] Heavy Metal Elements area P-value Prediction Model Degree of fit (%) Cadmium Changgou District 0.01437* Y=-0.0183X+77.017 27.77 Cobalt Changgou District 0.001109** Y=-0.1416X+56.220 46.42 Chromium Element Cr Changgou District 0.0002513*** Y=-0.0438X+25.568 40.98 Copper Element Cu Changgou District 0.0001258*** Y=-0.3086X+123.495 58.73 Nickel Changgou District 0.0004316*** Y=-0.156X+77.017 52.13 Lead element Pb Changgou District 0.001643** Y=-0.0464X+37.731 43.86 Zinc Changgou District 0.0006028*** Y=-0.6735X+347.335 50.17

[0087] Table 7 takes Changgou area as an example to calculate the linear regression equations of the above heavy metals such as Cd, Co, Cr, etc. The higher the fitting degree, the more accurate the linear regression equation. It can be seen from Table 7 that the coefficients b of the linear regression equations of the seven heavy metals are all negative values, that is, the relationship between the heavy metal concentration and the distance is negatively correlated, which means that the change trend of the heavy metal concentration in this area is: gradually decreasing with the increase of distance.

[0088] The relationship between heavy metal concentration and collection point distance is as follows:

[0089] Input: Heavy metal data collected on-site from agricultural ditches to rivers

[0090] Output: Heavy metal content detection data in agricultural ditches to river sediments

[0091] Step 1: Enter the field collected data into the database of heavy metals in agricultural ditch to river sediments;

[0092] Step 2: Use ICP to measure the heavy metal content in the sediments from agricultural ditches to rivers, and enter the heavy metal content data into the database of heavy metal concentrations in sediments from agricultural ditches to rivers;

[0093] Step 3: Input the overall sample data X1, X2, ..., X n , the Shapiro-Wilk method was used to analyze the normality of residuals;

[0094] Step 4: Use one-way ANOVA to analyze the concentration differences between regions;

[0095] Step 5: Use regression analysis method to analyze the relationship between heavy metal concentration and distance;

[0096] Step 6: Use linear regression equation to predict and control the relationship between heavy metal concentration and distance of each point;

[0097] Step 7: Output the results, i.e., the detection data of heavy metal content in sediments from agricultural ditches to rivers;

[0098] Step 8: Generate heavy metal content detection and analysis report.

[0099] Although this specification is described according to implementation modes, not every implementation mode includes only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

[0100] Therefore, the above description is only a preferred embodiment of the present application and is not intended to limit the scope of implementation of the present application; that is, all equivalent changes made according to the scope of the claims of the present application are within the protection scope of the claims of the present application.

Claims

1. A method for analyzing heavy metal concentrations in sediments from agricultural ditches to rivers, characterized in that: The following steps are involved: S1: Set multiple sampling points from near to far away from the pollution source, and set the sampling point closest to the pollution source as the starting point; Determine the heavy metal concentration of sediment at each sampling point, and record the distance from each sampling point to the starting point to obtain overall sample data; S2: Perform residual normality test on the overall sample data; S3: If the overall sample data conforms to the residual normality, the relationship between heavy metal concentration and distance is analyzed using regression analysis method to obtain the linear regression equation; S4: Analyze and predict the changing trend of heavy metal concentration with distance based on linear regression equation; In step S1, the sampling points are distributed in three connected areas, namely the river area, the diffusion area and the long ditch area, and the adjacent sampling points in each area are arranged at equal distances.

2. The analysis method according to claim 1, characterized in that In S1, the determination method specifically includes sample pretreatment and sample determination; wherein, the sample pretreatment includes sample drying, microwave digestion, filtration, and titration, and the sample determination is spectrophotometric determination.

3. The analysis method according to claim 1, characterized in that The following steps are also included between S2 and S3: the overall sample data are randomly assigned to a plurality of different treatment groups, and the one-way analysis of variance is used to analyze the differences in heavy metal concentrations among regions, including the following steps: The Shapiro-Wilk test was used to test the overall sample data. It was assumed that the overall sample data followed a normal distribution, the degrees of freedom were set, the mean square between groups and the mean square within groups were calculated, and the F value was calculated according to the formula F = mean square between groups / mean square within groups. The P value under the current degrees of freedom was obtained by querying the F value distribution table. If P < 0.05, the hypothesis was rejected, that is, there were significant differences in the heavy metal concentrations in different regions.

4. The analysis method according to claim 1, characterized in that The starting point is located in the river area.

5. The analysis method according to claim 1, characterized in that The heavy metals include cadmium, chromium, cobalt, lead, copper, zinc, nickel, selenium and aluminum.

6. The analysis method according to claim 1, characterized in that In S3, the linear regression equation is: y=bx+a, Among them, b is the regression coefficient, a is a constant, x is the distance from the prediction point to the starting point, and y is the heavy metal concentration at the prediction point; First derive b: b = (x1y1+x2y2+...x n y n -nXY) / (x1+x2+...x n -nX) Where: x n is the distance from the nth sampling point to the starting point; y n is the heavy metal element concentration at the nth sampling point; X is x i The average value of y i The average value of Then derive a: a=Y - bX Where X is x i The average value of y i The average value of .

7. The analysis method according to claim 1, characterized in that S4 is specifically: predicting the changing trend of heavy metal concentration according to the value of coefficient b.

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