Method for analyzing heavy metal pollution of sediments

Abstract

The present application provides a kind of sediment heavy metal pollution analysis method, it is related to environmental monitoring field, the present application is by sediment collection module using adjustable depth column sampler, internal multilayer cabin is independently closed, sample is collected according to predetermined depth;Depth control module monitors the position and water pressure change of sampler through depth sensor, ensures accurate sampling, and adjusts to target depth in real time;Sample processing module automatically processes sediment sample, carries out drying, crushing, homogenization in turn, generates sample quality coefficient, improves detection accuracy;Detection analysis module determines heavy metal concentration using spectral analysis technology, calculates element content by light absorption intensity;Risk assessment module generates ecological risk index according to concentration data, risk weight and quality coefficient, sets risk level to assess pollution degree;The present application realizes accurate detection of sediment heavy metal pollution and accurate assessment of pollution degree by accurate sampling, automatic processing and risk assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a sediment heavy metal pollution analysis method. Background Art

[0002] Analysis of heavy metal pollution in sediments can help identify the sources of pollutants, such as industrial emissions, agricultural runoff, and urban wastewater. Sediments are also the main source of heavy metal pollution in water bodies. Due to their close connection with aquatic ecosystems, the accumulation and distribution of heavy metals in sediments can reflect the pollution status of water bodies and identify the main types of pollutants. This is of great significance for formulating pollution prevention and control measures, policy making, and evaluating the effectiveness of governance. Heavy metals in sediments can enter aquatic organisms through the food chain, thereby affecting the entire ecosystem and human health. Heavy metals are bioaccumulative and toxic. Long-term exposure may lead to a decrease in aquatic populations, a decline in biodiversity, and even affect human health problems caused by eating contaminated aquatic products.

[0003] Prior art, publication number CN114674811A discloses a method for analyzing heavy metal concentrations in sediments from agricultural ditches to rivers. The method sets a starting point and multiple sampling points, measures the heavy metal concentrations in the sediments at the sampling points, and records the distances from the sampling points to the starting point to obtain overall sample data. This overall sample data is then analyzed for differences. Regression analysis is used to analyze the relationship between heavy metal concentration and distance, generating a linear regression equation. Based on the linear regression equation, the trend of heavy metal concentration changes with distance is determined. By measuring and analyzing heavy metal concentrations in sediments at different sampling points, the correlation between concentration and distance, as well as the significance of regional differences, is determined, thereby predicting the changing trend of heavy metal concentrations in sediments from agricultural ditches to rivers.

[0004] Insufficient existing technology:

[0005] In the existing technology, by setting a starting point and multiple sampling points, recording the distance from the sampling point to the starting point, and obtaining overall sample data, the main focus is on sampling along the distance, and the sampling depth and accuracy are not considered in detail. Sampling is limited to a single depth, ignoring the potential impact of changes in heavy metal concentrations in sediments with depth.

[0006] Existing technologies mainly use linear regression to analyze the relationship between heavy metal concentration and distance. They do not mention what detection technology is used to measure heavy metal concentration, nor do they clarify whether they can effectively distinguish or simultaneously detect multiple heavy metal elements. In addition, they use linear regression equations to analyze the trend of concentration changes with distance, but do not deeply explore the comprehensive risk assessment of heavy metal pollution to ecosystems and human health. Their methods are relatively one-sided in analyzing the significance of regional differences and fail to fully consider the comprehensive analysis of multiple factors.

[0007] Therefore, it is necessary to provide a sediment heavy metal pollution analysis method to solve the problem.

[0008] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0009] The purpose of the present invention is to provide a method for analyzing heavy metal pollution in sediments to solve the problems raised in the above background technology.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A method for analyzing heavy metal pollution in sediments, comprising the following steps:

[0012] Step 1: Collect sediment samples at different depths in the water using a depth-adjustable cylindrical sampler with multiple, independently sealed sampling chambers. Each chamber can be opened and closed at a predetermined depth to obtain sediment samples.

[0013] Step 2: Monitor the depth and position of the sampler in the water in real time to ensure that the sampler accurately samples at the designated location and depth. Use depth sensor technology to measure water pressure changes and convert them into depth data. Set the target depth, compare it with the current depth to determine whether the target position has been reached, and make adjustments.

[0014] Step 3: Automated pre-processing of the collected sediment samples. Each sub-unit in the module performs specific sample pre-processing, sequentially drying, crushing, and homogenizing the sediment samples to generate a sample quality coefficient to improve the accuracy of subsequent testing and analysis.

[0015] Step 4: Detect the treated sediment using spectral analysis technology. Using the properties of heavy metal atoms in the sample absorbing light under radiation of a specific wavelength, the light of a specific wavelength emitted by the light source passes through the decomposed elements in the sample, and the light absorption intensity is measured to calculate the concentration data of the heavy metal elements in the sediment.

[0016] Step 5: Assess the potential risks of sediments to ecosystems and human health. Generate an ecological risk index based on heavy metal concentration data, combined with its environmental risk weight and the obtained sample quality coefficient. Set the risk level and compare it with the ecological risk index to determine the degree of heavy metal pollution in the sediments and the waters in which they are located.

[0017] Furthermore, sediment samples were collected at different depths in the water body according to the following method:

[0018] The stratified sampling method is used to collect sediment samples at different depths by setting up multiple independent closed sampling chambers. The sampler sinks to a predetermined depth, and each chamber automatically opens and collects samples when it reaches the predetermined depth, ensuring a comprehensive analysis of the pollution situation at different levels of the water body. The formula is:

[0019]

[0020] The predetermined sampling depth of the i-th sampling chamber is set to H i , L represents the total sinking depth of the sampler, N represents the number of chambers, and i represents the index of the sampling chamber sequence.

[0021] Furthermore, the water pressure change is measured based on depth sensor technology and converted into depth data according to the following method:

[0022] The water pressure at the depth of the sampler is measured in real time by a depth sensor installed on the sampler. The measured water pressure is converted into the current depth data of the sampler using the relationship between water pressure and depth. The formula is as follows:

[0023]

[0024] Where d(t) represents the depth of the sampler at time t, P(t) represents the water pressure measured at time t, ρ represents the density of water, g represents the acceleration of gravity, and P0 represents the atmospheric pressure.

[0025] Furthermore, the target depth is set, compared with the current depth to determine whether the target position has been reached, and adjustments are made based on the following method:

[0026] First, the target depth is preset. Based on the real-time measurement of the current water pressure, the current depth is calculated using the relationship between water pressure and water depth. The difference between the target depth and the current depth is obtained. Based on this difference, it is determined whether the depth of the sampler needs to be adjusted and the direction of the sampler adjustment. The proportional-integral-differential control algorithm is used to gradually make the sampler approach the target depth. The logic is as follows:

[0027] Δd(i, t) = d(t) - H i

[0028]

[0029] Wherein, Δd(i, t) represents the depth error of the i-th sampling chamber at time t, Q(i, t) represents the adjustment judgment value of the sampler at time t when the i-th sampling chamber needs to be used for sampling, u(i, t) represents the control signal issued at time t when the i-th sampling chamber needs to be used for sampling, and the control signal is used to adjust the movement of the sampler, Kp , K i , K d Represent the proportional, integral and differential control coefficients respectively, H i Indicates the predetermined sampling depth.

[0030] Furthermore, the sediment samples were dried, crushed, and homogenized in order to improve the accuracy of subsequent testing and analysis, based on the following methods:

[0031] The sample is placed in a desiccator, and the water in the sample is evaporated by controlling the temperature and time until the sample mass no longer changes. The dried sediment sample is crushed to a certain particle size using a mechanical crushing device to increase the surface area of ​​the sample, ensure the uniformity of the sample and the sufficiency of the subsequent chemical reaction. The sample is fully mixed by stirring to ensure that the composition of the sample is evenly distributed in space to eliminate the discreteness between samples. The sample mass coefficient is calculated based on the following formula:

[0032]

[0033] Among them, δ represents the sample quality coefficient, m h Indicates the mass of the sample after drying, m q Indicates the mass of the sample before drying.

[0034] Furthermore, the light absorption intensity was measured and the concentration of heavy metal elements in the sediment was calculated according to the following method:

[0035] After the treated sample is atomized, the heavy metals in the sample are decomposed into free atoms. The free atoms absorb light of a specific wavelength in the ground state. The heavy metal atoms in the sample absorb part of the light, resulting in a decrease in the sample light intensity. By measuring the absorbed light intensity, the concentration data of the heavy metal elements in the sample is calculated based on the following formula:

[0036]

[0037] Among them, C m (i, j) represents the concentration of the jth heavy metal in the sample of the i-th sampling chamber, A(i, j) represents the absorbance of the jth heavy metal element in the sample of the i-th sampling chamber, ε(j) represents the molar absorption coefficient of the jth heavy metal, and l represents the optical path length of the sample.

[0038] Furthermore, to assess the potential risks of sediments to ecosystems and human health, an ecological risk index was generated based on the heavy metal concentration data, combined with its environmental risk weights and the obtained sample quality coefficients. The method was based on:

[0039] The heavy metal concentration components were measured, and the heavy metal background values ​​and heavy metal toxicity coefficients of the waters where the sediments were located were collected as reference benchmarks for the assessment. The pollution index of a single heavy metal and the average pollution index of all heavy metals were calculated. The ecological risk index was generated by combining the environmental risk weight and the sample quality coefficient. The formula is as follows:

[0040]

[0041] Among them, PI j represents the pollution index of the jth heavy metal, B m (i, j) represents the background value of the jth heavy metal in the sample of the i-th sampling cabin, represents the toxicity coefficient of the jth heavy metal, represents the environmental risk weight of the jth heavy metal, CPI represents the comprehensively generated ecological risk index, j represents the index of heavy metals, m represents the number of heavy metal types, and N represents the number of compartments.

[0042] Furthermore, the risk level is compared with the ecological risk index to determine the degree of heavy metal pollution in sediments and waters. The method is based on:

[0043] According to the size of the comprehensive ecological risk index, the risk level is divided into the following categories: when CPI≤150, it is a low risk level, indicating that the degree of heavy metal pollution is low, the ecological environment of sediments and waters is relatively safe, and the impact on aquatic organisms and ecosystems can be ignored; when 150≤CPI≤300, it is a medium risk level, indicating that the degree of heavy metal pollution is moderate, which may have certain negative impacts on the ecosystems of sediments and waters, and there are certain ecological risks; when CPI≥300, it is a high risk level, indicating that heavy metal pollution is serious, posing a significant threat to the ecosystems of sediments and waters, and causing harm to aquatic organisms and human health.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] Taking into account the potential impact of changes in heavy metal concentrations in sediments with depth, the present invention uses a depth-adjustable columnar sampler and multiple layers of independently enclosed sampling chambers to obtain sediment samples at different depths to provide more accurate and comprehensive heavy metal distribution data. This multi-level sampling helps to fully understand the pollution of sediments at different depths, rather than being limited to samples at the surface or a single depth.

[0046] The present invention uses a detection method based on spectral analysis technology, which can more accurately determine the concentrations of multiple heavy metal elements. The light absorption characteristics improve the sensitivity and accuracy of detection. This technology is more suitable for complex multi-element analysis and provides a more reliable data basis for risk assessment. Finally, by combining heavy metal concentrations, environmental risk weights and sample quality factors, an ecological risk index is generated, and a comprehensive risk level assessment is conducted based on this. This method more comprehensively reflects the potential ecological and health risks of heavy metals in sediments.

[0047] This invention not only improves the accuracy of heavy metal pollution analysis in sediments, but also ensures the comprehensiveness and reliability of the results. It can automatically process large amounts of data, reduce human errors, and provide multi-dimensional pollution assessments, helping researchers and decision makers to more accurately understand environmental risks and develop more scientific governance plans, thereby promoting the efficiency and effectiveness of pollution control work. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of the overall method of the present invention. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0050] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0051] Example:

[0052] See also Figure 1 The present invention provides a method for analyzing heavy metal pollution in sediments, which specifically comprises the following steps:

[0053] Step 1: Collect sediment samples at different depths in the water using a depth-adjustable cylindrical sampler with multiple, independently sealed sampling chambers. Each chamber can be opened and closed at a predetermined depth to obtain sediment samples.

[0054] Step 2: Monitor the depth and position of the sampler in the water in real time to ensure that the sampler accurately samples at the designated location and depth. Use depth sensor technology to measure water pressure changes and convert them into depth data. Set the target depth, compare it with the current depth to determine whether the target position has been reached, and make adjustments.

[0055] Step 3: Automated pre-processing of the collected sediment samples. Each sub-unit in the module performs specific sample pre-processing, sequentially drying, crushing, and homogenizing the sediment samples to generate a sample quality coefficient to improve the accuracy of subsequent testing and analysis.

[0056] Step 4: Detect the treated sediment using spectral analysis technology. Using the properties of heavy metal atoms in the sample absorbing light under radiation of a specific wavelength, the light of a specific wavelength emitted by the light source passes through the decomposed elements in the sample, and the light absorption intensity is measured to calculate the concentration data of the heavy metal elements in the sediment.

[0057] Step 5: Assess the potential risks of sediments to ecosystems and human health. Generate an ecological risk index based on heavy metal concentration data, combined with its environmental risk weight and the obtained sample quality coefficient. Set the risk level and compare it with the ecological risk index to determine the degree of heavy metal pollution in the sediments and the waters in which they are located.

[0058] It should be noted that since sediments may have different pollution characteristics at different depths, if only sediments at a certain depth are collected, the data may become monotonous, which is not conducive to comprehensive analysis. A device is needed to capture the vertical distribution of heavy metal pollution, help to more comprehensively analyze the pollution status of sediments, reveal the temporal and spatial variation characteristics of pollution sources, and accurately collect sediment samples from different depths in the water. This multi-layer, independently enclosed sampling chamber design ensures that samples collected at a predetermined depth are not contaminated by other layers.

[0059] Therefore, it is necessary to collect sediment samples at different depths in the water body, based on the following methods:

[0060] The stratified sampling method is used to collect sediment samples at different depths by setting up multiple independent closed sampling chambers. The sampler sinks to a predetermined depth, and each chamber automatically opens and collects samples when it reaches the predetermined depth, ensuring a comprehensive analysis of the pollution situation at different levels of the water body. The formula is:

[0061]

[0062] The predetermined sampling depth of the i-th sampling chamber is set to H i , L represents the total sinking depth of the sampler, N represents the number of cabins, and i represents the index of the sampling cabin sequence; the above formula can help determine the specific depth position of the sampler in the water body at a specific point in time. By subtracting the distance from the cabin to the top of the sampler from the total depth, the exact water depth of the cabin at the time of sampling can be determined, ensuring that each cabin collects samples in different water layers.

[0063] It should be noted that in water bodies, environmental conditions at different depths may vary significantly, such as temperature, pressure, chemical composition, etc. The depth control module ensures that the sampler can reach and maintain the specified depth to obtain accurate samples. In the absence of precise depth control, the sampler may not be able to accurately reach the target depth, resulting in sediments or water from other water layers being mixed into the sample, affecting the experimental results and the accuracy of the data. The depth control module can effectively avoid this situation and ensure that the collected samples are pure and representative.

[0064] Therefore, it is necessary to measure water pressure changes based on depth sensor technology and convert them into depth data based on the following method:

[0065] The water pressure at the depth of the sampler is measured in real time by a depth sensor installed on the sampler. The measured water pressure is converted into the current depth data of the sampler using the relationship between water pressure and depth. The formula is as follows:

[0066]

[0067] Among them, d(t) represents the depth of the sampler at time t, P(t) represents the water pressure measured at time t, ρ represents the density of water, g represents the acceleration of gravity, and P0 represents the atmospheric pressure. In the above formula, it is reflected that the water pressure changes positively with the change of depth. By measuring the water pressure, the current depth can be inferred and calculated by inverse operation. The water pressure value monitored in real time by the depth sensor is converted into depth data for controlling and analyzing the position of the sampler.

[0068] It should be noted that accurately reaching the target depth is the key to underwater sampling, because the position of the sampler directly affects the representativeness of the sampling and the reliability of the experimental results. By measuring and calculating the current depth in real time, the system can correct the deviation in time to ensure that the sampler stably approaches and stays at the target depth.

[0069] Therefore, it is necessary to set the target depth, compare it with the current depth to determine whether the target position has been reached, and make adjustments based on the following method:

[0070] First, the target depth is preset. Based on the real-time measurement of the current water pressure, the current depth is calculated using the relationship between water pressure and water depth. The difference between the target depth and the current depth is obtained. Based on this difference, it is determined whether the depth of the sampler needs to be adjusted and the direction of the sampler adjustment. The proportional-integral-differential control algorithm is used to gradually make the sampler approach the target depth. The logic is as follows:

[0071] Δd(i, t) = d(t) - H i

[0072]

[0073] Wherein, Δd(i, t) represents the depth error of the i-th sampling chamber at time t, Q(i, t) represents the adjustment judgment value of the sampler at time t when the i-th sampling chamber needs to be used for sampling, u(i, t) represents the control signal issued at time t when the i-th sampling chamber needs to be used for sampling, and the control signal is used to adjust the movement of the sampler, K p , K i , K d Represent the proportional, integral and differential control coefficients respectively, H i Indicates the predetermined sampling depth; From the above formula, we can see that Δd is the core variable of the control system, which reflects the deviation between the current depth of the sampler and the target depth. Q is a specific logical judgment used to determine whether the sampler needs to be adjusted and the direction of adjustment. It is the preliminary decision of the control algorithm. u(t) is the PID control algorithm, which accurately controls the movement of the sampler. K p , K i , K d The size relationship is: K p >K i >K d The specific reason is that K p ×Δd(i, t) is proportional control, which adjusts the output by the current error and has high response sensitivity. p If it is less than K i and K d , which will cause the system to respond slowly and the system to receive signals late; K i ×∫Δd(i, t)dt is the integral control, which is adjusted according to the accumulated value of the error to eliminate the continuous deviation, and K i Should be an intermediate value, if K i Value greater than K p , the system will be too sensitive to the cumulative error, resulting in system instability and integral saturation. If it is less than K d , which will result in a slower system error accumulation speed. It is difficult for the system to adjust the control algorithm immediately after Δd is generated. It is a differential control, which is adjusted based on the rate of change of the error, K d is minimum, because differential control is used to suppress the rate of change of error and prevent system overshoot and oscillation. If K d Greater than K p and K i , which will make the system too sensitive to noise, so K d Setting it to the minimum value can effectively reduce system overshoot and oscillation and improve system stability.

[0074] It should be noted that sediment samples usually contain moisture. The main purpose of drying is to remove this moisture to ensure that the sample will not introduce errors due to different moisture content during subsequent processing and analysis. The crushing process can break the sediment sample into smaller particles, increase the surface area of ​​the sample, and thus improve the rate and uniformity of subsequent chemical reactions. In order to ensure the consistency of the sample, the sample should be homogenized so that the components of the sample are evenly distributed throughout the sample, thereby ensuring that each time the sample is sampled and analyzed, the composition of the sample represents the whole, rather than the deviation of a certain part.

[0075] Therefore, it is necessary to dry, crush, and homogenize the sediment samples in order to improve the accuracy of subsequent testing and analysis. The method is based on:

[0076] The sample is placed in a desiccator, and the water in the sample is evaporated by controlling the temperature and time until the sample mass no longer changes. The dried sediment sample is crushed to a certain particle size using a mechanical crushing device to increase the surface area of ​​the sample, ensure the uniformity of the sample and the sufficiency of the subsequent chemical reaction. The sample is fully mixed by stirring to ensure that the composition of the sample is evenly distributed in space to eliminate the discreteness between samples. The sample mass coefficient is calculated based on the following formula:

[0077]

[0078] Among them, δ represents the sample quality coefficient, m h Indicates the mass of the sample after drying, m q Indicates the mass of the sample before drying.

[0079] It should be noted that the detection module based on spectral analysis technology can accurately determine the concentration of heavy metal elements in samples. Spectral analysis technology has high sensitivity and can detect trace or even trace amounts of heavy metal elements in samples. It can also provide high specificity and can distinguish the absorption peaks of different elements, which makes multi-element analysis of complex samples possible. High sensitivity and specificity ensure the reliability and accuracy of the test results, which is especially critical when it is necessary to identify and measure low-concentration pollutants.

[0080] Therefore, it is necessary to measure the light absorption intensity and calculate the concentration data of heavy metal elements in the elements contained in the sediment. The method is based on:

[0081] After the treated sample is atomized, the heavy metals in the sample are decomposed into free atoms. The free atoms absorb light of a specific wavelength in the ground state. The heavy metal atoms in the sample absorb part of the light, resulting in a decrease in the sample light intensity. By measuring the absorbed light intensity, the concentration data of the heavy metal elements in the sample is calculated based on the following formula:

[0082]

[0083] Among them, c m (i, j) represents the concentration of the jth heavy metal in the sample of the i-th sampling cabin, A(i, j) represents the absorbance of the jth heavy metal element in the sample of the i-th sampling cabin, ε(j) represents the molar absorptivity of the jth heavy metal, and l represents the optical path length of the sample; in the above formula, A(i, j) is the absorbance, which is a dimensionless quantity used to indicate how much light the sample absorbs. The higher the absorbance, the more light the sample absorbs. ε(j) represents the ability of each mole of substance to absorb light at a specific wavelength. This value is inherent in the substance and determines its absorption efficiency for light of a specific wavelength in its ground state. The basic principle formula of light absorption describes the degree to which light is weakened due to absorption when passing through a sample. By measuring the intensity difference between the incident light and the transmitted light, the absorbance A(i, j) of the sample can be calculated. The absorbance is directly related to the concentration of heavy metals in the sample. The molar absorptivity is a known constant. Therefore, the concentration of heavy metals in the sample can be inferred by measuring the absorbance and optical path length.

[0084] It should be noted that a quantitative ecological risk index is generated by combining heavy metal concentrations, environmental risk weights and sample quality coefficients. This quantitative assessment method can convert complex pollution conditions into an easy-to-understand numerical value or level. It not only reflects the actual situation of heavy metal pollution, but also provides a unified scale so that the pollution conditions in different regions or time periods can be objectively compared.

[0085] Therefore, it is necessary to assess the potential risks of sediments to ecosystems and human health. Based on the heavy metal concentration data, combined with their environmental risk weights and the obtained sample quality coefficient, an ecological risk index is generated. The method is based on:

[0086] The heavy metal concentration components were measured, and the heavy metal background values ​​and heavy metal toxicity coefficients of the waters where the sediments were located were collected as reference benchmarks for the assessment. The pollution index of a single heavy metal and the average pollution index of all heavy metals were calculated. The ecological risk index was generated by combining the environmental risk weight and the sample quality coefficient. The formula is as follows:

[0087]

[0088]

[0089] Among them, PI j represents the pollution index of the jth heavy metal, B m (i, j) represents the background value of the jth heavy metal in the sample of the i-th sampling cabin, represents the toxicity coefficient of the jth heavy metal, Represents the environmental risk weight of the jth heavy metal, CPI represents the comprehensively generated ecological risk index, j represents the index of heavy metals, m represents the number of heavy metal types, and N represents the number of compartments; in the above formula, the background value of heavy metals can be obtained by utilizing the existing historical monitoring data to obtain the background value of heavy metals in the target waters or similar waters; the toxicity coefficient is a parameter that measures the toxicity of a certain heavy metal to the environment and organisms, which is usually derived from ecotoxicological studies and reflects the potential harm of heavy metals to the ecosystem. The toxicity coefficient is usually based on laboratory toxicology studies to determine the median lethal concentration or median lethal dose of a certain heavy metal to a specific organism. The following is the value range of the common heavy metal toxicity coefficient. The toxicity coefficient of mercury ranges from (40-100). Mercury is highly toxic and can The toxicity coefficient of cadmium ranges from 30 to 70, which is harmful to the kidneys and bones. Long-term exposure can lead to serious health problems. The toxicity coefficient of lead ranges from 20 to 50, which can cause damage to the nervous system, especially for children. The toxicity coefficient of arsenic ranges from 10 to 30, which is carcinogenic. Long-term intake can cause skin lesions and cancer. The toxicity coefficient of chromium ranges from 5 to 20, which is highly toxic. Long-term exposure can cause skin and respiratory diseases. It should be noted that the range of toxicity coefficients depends on the chemical properties of each heavy metal and its mechanism of influence on the organism. For example, mercury and cadmium are particularly toxic to the nervous system and kidneys, and the higher the toxicity of the heavy metal, the smaller the range of toxicity coefficients.

[0090] It should be noted that through this comprehensive assessment, potential environmental problems can be identified earlier and early warnings can be issued in a timely manner. This forward-looking risk management helps to take measures before pollution problems worsen and reduce the long-term harm of environmental pollution to ecosystems and human health. The visualization of risk levels and comparative analysis with the ecological risk index can more intuitively convey the current status and risks of pollution to the public. The public can use this information to more clearly understand the extent of environmental risks, thereby enhancing their environmental awareness.

[0091] Therefore, it is necessary to set the risk level and compare it with the ecological risk index to determine the degree of heavy metal pollution in sediments and waters. The method is based on:

[0092] According to the size of the comprehensive ecological risk index, the risk level is divided into the following categories: when CPI≤150, it is a low risk level, indicating that the degree of heavy metal pollution is low, the ecological environment of sediments and waters is relatively safe, and the impact on aquatic organisms and ecosystems can be ignored; when 150≤CPI≤300, it is a medium risk level, indicating that the degree of heavy metal pollution is moderate, which may have certain negative impacts on the ecosystems of sediments and waters, and there are certain ecological risks; when CPI≥300, it is a high risk level, indicating that heavy metal pollution is serious, posing a significant threat to the ecosystems of sediments and waters, and causing harm to aquatic organisms and human health.

[0093] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0094] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0095] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0096] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for analyzing heavy metal pollution in sediments, characterized in that: The analysis method comprises: Step 1: Collect sediment samples at different depths in the water using a depth-adjustable cylindrical sampler with multiple, independently sealed sampling chambers. Each chamber can be opened and closed at a predetermined depth to obtain sediment samples. Step 2: Monitor the depth and position of the sampler in the water in real time to ensure that the sampler accurately samples at the designated location and depth. Use depth sensor technology to measure water pressure changes and convert them into depth data. Set the target depth, compare it with the current depth to determine whether the target position has been reached, and make adjustments. Step 3: Automated pre-processing of the collected sediment samples. Each sub-unit in the module performs specific sample pre-processing, sequentially drying, crushing, and homogenizing the sediment samples to generate a sample quality coefficient to improve the accuracy of subsequent testing and analysis. Step 4: Detect the treated sediment using spectral analysis technology. Using the properties of heavy metal atoms in the sample absorbing light under radiation of a specific wavelength, the light of a specific wavelength emitted by the light source passes through the decomposed elements in the sample, and the light absorption intensity is measured to calculate the concentration data of the heavy metal elements in the sediment. Step 5: Assess the potential risks of sediments to ecosystems and human health. Generate an ecological risk index based on heavy metal concentration data, combined with environmental risk weights and the obtained sample quality coefficient. Set a risk level and compare it with the ecological risk index to determine the extent of heavy metal pollution in the sediments and the surrounding waters. Sediment samples were dried, crushed, and homogenized in the following order to improve the accuracy of subsequent analysis. The methods used were: The sample is placed in a desiccator, and the water in the sample is evaporated by controlling the temperature and time until the sample mass no longer changes. The dried sediment sample is crushed to a certain particle size using a mechanical crushing device to increase the surface area of ​​the sample, ensure the sample uniformity and the sufficiency of subsequent chemical reactions. The sample is fully mixed by stirring to ensure that the sample composition is evenly distributed in space to eliminate the discreteness between samples. The sample mass coefficient is calculated based on the following formula: Among them, δ represents the sample quality coefficient, m h Indicates the mass of the sample after drying, m q Indicates the mass of the sample before drying; The light absorption intensity is measured and the concentration of heavy metal elements in the sediment is calculated based on the following method: After the treated sample is atomized, the heavy metals in the sample are decomposed into free atoms. The free atoms absorb light of a specific wavelength in the ground state. The heavy metal atoms in the sample absorb part of the light, resulting in a decrease in the sample light intensity. By measuring the absorbed light intensity, the concentration data of the heavy metal elements in the sample is calculated based on the following formula: Among them, C m (i, j) represents the concentration of the jth heavy metal in the sample of the i-th sampling cabin, A(i, j) represents the absorbance of the jth heavy metal element in the sample of the i-th sampling cabin, ε(j) represents the molar absorptivity of the jth heavy metal, and l represents the optical path length of the sample; To assess the potential risks of sediments to ecosystems and human health, an ecological risk index is generated based on heavy metal concentration data, combined with environmental risk weights and the obtained sample quality coefficients. The method is based on: The heavy metal concentration components were measured, and the heavy metal background values ​​and heavy metal toxicity coefficients of the waters where the sediments were located were collected as reference benchmarks for the assessment. The pollution index of a single heavy metal and the average pollution index of all heavy metals were calculated. The ecological risk index was generated by combining the environmental risk weight and the sample quality coefficient. The formula is as follows: Among them, PI j represents the pollution index of the jth heavy metal, B m (i, j) represents the background value of the jth heavy metal in the sample of the i-th sampling cabin, represents the toxicity coefficient of the jth heavy metal, represents the environmental risk weight of the jth heavy metal, CPI represents the comprehensively generated ecological risk index, j represents the index of heavy metals, m represents the number of heavy metal types, and N represents the number of compartments; 2. The method for analyzing heavy metal pollution in sediments according to claim 1, characterized in that: Sediment samples were collected at different depths in the water body, based on the following methods: The stratified sampling method is used to collect sediment samples at different depths by setting up multiple independent closed sampling chambers. The sampler sinks to a predetermined depth, and each chamber automatically opens and collects samples when it reaches the predetermined depth, ensuring a comprehensive analysis of the pollution situation at different levels of the water body. The formula is: The predetermined sampling depth of the i-th sampling chamber is set to H i , L represents the total sinking depth of the sampler, N represents the number of chambers, and i represents the index of the sampling chamber sequence.

3. The method for analyzing heavy metal pollution in sediments according to claim 1, wherein: The depth sensor technology is used to measure water pressure changes and convert them into depth data. The method is based on: The water pressure at the depth of the sampler is measured in real time by a depth sensor installed on the sampler. The measured water pressure is converted into the current depth data of the sampler using the relationship between water pressure and depth. The formula is as follows: Where d(t) represents the depth of the sampler at time t, P(t) represents the water pressure measured at time t, ρ represents the density of water, g represents the acceleration of gravity, and P0 represents the atmospheric pressure.

4. A sediment heavy metal pollution analysis method according to claim 3, characterized in that: Set the target depth, compare it with the current depth to determine whether the target position has been reached, and make adjustments based on the following method: First, the target depth is preset. Based on the real-time measurement of the current water pressure, the current depth is calculated using the relationship between water pressure and water depth. The difference between the target depth and the current depth is obtained. Based on this difference, it is determined whether the depth of the sampler needs to be adjusted and the direction of the sampler adjustment. The proportional-integral-differential control algorithm is used to gradually make the sampler approach the target depth. The logic is as follows: Δd(i,t)=d(t)-H i Wherein, Δd(i, t) represents the depth error of the oth sampling chamber at time t, Q(i, t) represents the adjustment judgment value of the sampler at time t when the i-th sampling chamber needs to be used for sampling, u(i, t) represents the control signal issued at time t when the i-th sampling chamber needs to be used for sampling, and the control signal is used to adjust the movement of the sampler, K p , K i , K d Represent the proportional, integral and differential control coefficients respectively, H i Indicates the predetermined sampling depth.

5. The method for analyzing heavy metal pollution in sediments according to claim 1, characterized in that: The risk level is set and compared with the ecological risk index to determine the degree of heavy metal pollution in sediments and waters. The method is based on: According to the size of the comprehensive ecological risk index, the risk level is divided into the following categories: when CPI≤150, it is a low risk level, indicating that the degree of heavy metal pollution is low, the ecological environment of sediments and waters is relatively safe, and the impact on aquatic organisms and ecosystems can be ignored; when 150≤CPI≤300, it is a medium risk level, indicating that the degree of heavy metal pollution is moderate, which may have certain negative impacts on the ecosystems of sediments and waters, and there are certain ecological risks; when CPI≥300, it is a high risk level, indicating that heavy metal pollution is serious, posing a significant threat to the ecosystems of sediments and waters, and causing harm to aquatic organisms and human health.

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