WO2021093769A1 - 一种耕地土壤重金属的空间分布及来源解析方法及装置 - Google Patents

一种耕地土壤重金属的空间分布及来源解析方法及装置 Download PDF

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WO2021093769A1
WO2021093769A1 PCT/CN2020/128105 CN2020128105W WO2021093769A1 WO 2021093769 A1 WO2021093769 A1 WO 2021093769A1 CN 2020128105 W CN2020128105 W CN 2020128105W WO 2021093769 A1 WO2021093769 A1 WO 2021093769A1
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heavy metals
soil
spatial distribution
analysis
source
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PCT/CN2020/128105
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French (fr)
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胡月明
唐宗
彭丽梅
赵理
周悟
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华南农业大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/71—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited
    • G01N21/74—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited using flameless atomising, e.g. graphite furnaces
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6402—Atomic fluorescence; Laser induced fluorescence
    • G01N21/6404—Atomic fluorescence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/71—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited
    • G01N21/72—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited using flame burners
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6402—Atomic fluorescence; Laser induced fluorescence
    • G01N21/6404—Atomic fluorescence
    • G01N2021/6406—Atomic fluorescence multi-element

Definitions

  • the invention relates to the technical field of data analysis, in particular to a method and device for spatial distribution and source analysis of heavy metals in cultivated soil.
  • Soil is the most precious natural resource for human survival. It has a buffering and purifying effect on environmental pollutants. Understanding the spatial distribution and source analysis of heavy metals in the soil is an important prerequisite for monitoring and evaluating the ecological environment of the study area; spatial distribution is used to assess the accumulation of heavy metals Possible sources and a powerful way to identify hot spots in the study area, and source analysis is a key step to prevent or reduce heavy metal pollution; in the existing sampling process, due to the limited number of sampling points, the spatial distribution of the entire study area cannot be fully expressed Circumstances, leading to a certain deviation in the analysis results.
  • the deterministic interpolation method is to use mathematical functions for interpolation, to study the similarity within the study area (such as inverse distance weighted interpolation), or based on smoothness (such as radial basis function interpolation) to create from known samples Interpolation methods for predicting the surface; deterministic interpolation methods can be divided into two categories: global methods and local methods; global methods use the entire data set to calculate predicted values; local methods use measurement points in the neighborhood to calculate predicted values; deterministic interpolation methods are simple The characteristic of high efficiency, the deterministic interpolation method should be selected for the non-normally distributed sample point data.
  • the sample points are uniformly distributed and the heavy metal content conforms to the non-normal distribution.
  • the results of comprehensive multivariate statistical analysis and spatial variability of heavy metals indicate that the sources of heavy metal content in the study area are mainly derived from soil-forming parent material, vehicle emissions, human activities and agricultural materials using inverse distance weighting (IDW) interpolation to draw geochemical maps.
  • IDW inverse distance weighting
  • the deterministic interpolation method does not provide the error test of the predicted value, and the interpolation effect for the sample data conforming to the normal distribution is poor, and it is susceptible to the influence of the sample extreme value; the limitation of the deterministic interpolation has caused a problem.
  • Statistical Kriging interpolation does not provide the error test of the predicted value, and the interpolation effect for the sample data conforming to the normal distribution is poor, and it is susceptible to the influence of the sample extreme value; the limitation of the deterministic interpolation has caused a problem.
  • the key to the Kriging method is the determination of the weight coefficient.
  • the method dynamically determines the value of the variable according to a certain optimization criterion function, so that the interpolation function is in the best state.
  • the Kriging method considers the position relationship between the observed point and the estimated point, and also considers the relative position relationship between the observation points. When the points are scarce, the interpolation effect is better than the inverse distance weighting method; so the Kriging method is used Interpolation of spatial data often achieves ideal results.
  • the purpose of the present invention is to overcome the shortcomings of the prior art.
  • the present invention provides a method and device for spatial distribution and source analysis of heavy metals in cultivated soils, which can obtain various metal cultivated soil heavy metal spatial simulation maps that basically conform to the actual spatial distribution; Quantitative analysis of soil heavy metal sources and their contributions provides an effective theoretical basis for the identification and control of soil heavy metal sources.
  • the present invention provides the following solutions:
  • a method for spatial distribution and source analysis of heavy metals in farmland soil comprising:
  • the extracted data including heavy metal concentration data and soil pH data, the heavy metals including Cd, Hg, Pb, Cr, and As;
  • the spatial distribution and source analysis of the heavy metals in the cultivated soil are processed to obtain the spatial distribution characteristics and source correlations of the heavy metals in the cultivated soil.
  • the step of uniformly selecting corresponding areas based on GPS positioning to sample cultivated land soil samples to obtain cultivated land soil sample sampling includes:
  • the corresponding area to be sampled is uniformly selected based on GPS positioning
  • the performing data extraction processing on the cultivated soil sample sampling to obtain the extracted data includes:
  • Data extraction processing is performed on the dissolved sample solution to obtain extraction data.
  • the performing data extraction processing on the dissolved sample solution to obtain extracted data includes:
  • the concentration of Cd in the dissolved sample solution is measured based on graphite furnace atomic absorption spectrometry, the concentration of Hg and As in the dissolved sample solution is measured based on the original gasification-atomic fluorescence spectrometry, and the Pb and As in the dissolved sample solution are measured based on the flame atomic absorption spectrometry.
  • the concentration of Cr is determined by using a portable meter to measure the pH value of the dissolved sample solution based on the potentiometric method to obtain the extracted data.
  • the processing of spatial distribution and source analysis of heavy metals in cultivated soil based on the extracted data to obtain the spatial distribution characteristics and source correlation of heavy metals in cultivated soil includes:
  • a spatial interpolation method is used to process the spatial distribution of heavy metals in the cultivated soil to obtain the spatial distribution characteristics of the heavy metals in the cultivated soil.
  • the performing source analysis processing of heavy metals on farmland soil based on the extracted data to obtain the source correlation of heavy metals in farmland soil includes:
  • Descriptive statistical analysis processing is performed on the extracted data to obtain statistical descriptive characteristics, and the descriptive statistical analysis includes statistical analysis of maximum value, minimum value, average value, standard deviation, and coefficient of variation;
  • Multivariate statistical analysis processing is performed on the statistical description characteristics, and the source correlation of heavy metals in the cultivated soil is obtained based on the multivariate statistical analysis results.
  • the performing multivariate statistical analysis processing on the statistical description characteristics, and obtaining the source relevance of heavy metals in cultivated soil based on the multivariate statistical analysis result includes:
  • the source correlation of the heavy metals in the cultivated soil is obtained.
  • using spatial interpolation to process the spatial distribution of heavy metals in cultivated soil based on the extracted data to obtain the spatial distribution characteristics of heavy metals in cultivated soil includes:
  • the GS+9.0 software is used to analyze the spatial variability of heavy metals conforming to normal or lognormal distribution to obtain the spatial variability of heavy metals;
  • the spatial distribution characteristics of heavy metals in the cultivated soil are obtained.
  • the embodiment of the present invention also provides a device for spatial distribution and source analysis of heavy metals in cultivated soil, the device includes:
  • Sampling module used to uniformly select the corresponding area to sample cultivated land soil samples based on GPS positioning, and obtain cultivated land soil sample sampling;
  • Data extraction module used to perform data extraction processing on the cultivated soil samples to obtain extracted data, the extracted data including heavy metal concentration data and soil pH data, the heavy metals including Cd, Hg, Pb, Cr, and As;
  • Spatial distribution and source analysis module used to process the spatial distribution and source analysis of heavy metals in the cultivated soil based on the extracted data, and obtain the spatial distribution characteristics and source correlation of heavy metals in the cultivated soil.
  • the method and device for spatial distribution and source analysis of heavy metals in cultivated soil of the present invention analyzes and compares the results of various spatial interpolation methods based on the content and spatial variability of heavy metals in cultivated soil.
  • the prediction error ME is closest to 0 and the RMSE is the smallest ( Priority)
  • the geostatistical kriging interpolation method is compared with the deterministic spatial interpolation method, and the most suitable interpolation method for different soil heavy metals is determined
  • the verified spatial simulation maps of various metal farmland soil heavy metals basically conform to Actual spatial distribution
  • quantitative analysis of the sources of heavy metals in cultivated soil and their contributions overcomes the defects of traditional qualitative identification of pollution sources, and provides an effective theoretical basis for the identification and control of sources of heavy metals in soil.
  • FIG. 1 is a schematic flowchart of a method for spatial distribution and source analysis of heavy metals in cultivated soil in an embodiment of the present invention
  • Figure 2 is a diagram of heavy metal loading in the main component in an embodiment of the present invention.
  • Figure 3 is an analysis spectrum of soil heavy metal settlements in an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of the structure and composition of a device for spatial distribution and source analysis of heavy metals in cultivated soil in an embodiment of the present invention.
  • the purpose of the present invention is to overcome the shortcomings of the prior art.
  • the present invention provides a method and device for spatial distribution and source analysis of heavy metals in cultivated soils, which can obtain various metal cultivated soil heavy metal spatial simulation maps that basically conform to the actual spatial distribution; Quantitative analysis of soil heavy metal sources and their contributions provides an effective theoretical basis for the identification and control of soil heavy metal sources.
  • FIG. 1 is a schematic flowchart of a method for spatial distribution and source analysis of heavy metals in cultivated soil in an embodiment of the present invention.
  • a method for spatial distribution and source analysis of heavy metals in farmland soil includes:
  • the uniform selection of the corresponding area based on GPS positioning for sampling of cultivated soil samples to obtain the sampling of cultivated soil samples includes: uniform selection of the corresponding area to be sampled based on GPS positioning; selection based on quincunx sampling mode Determine the corresponding area to be sampled, and select 5 sampled soils with the sampling depth as the preset value in sequence to mix, and obtain soil samples of cultivated land.
  • GPS positioning is used to uniformly select the corresponding area to be sampled. For example, a certain district (county) area of a city is selected by GPS, and the field sampling of cultivated soil in the area will generally adopt the corresponding sampling area.
  • a plum-shaped sampling mode is adopted. 5 soil sub-samples with a depth of 0-20 cm are selected in sequence and mixed to a weight of about 300 g; then all soil samples Take samples and record them back to the laboratory for physical and chemical analysis.
  • S12 Perform data extraction processing on the cultivated soil sample sampling to obtain extracted data, the extracted data including heavy metal concentration data and soil pH data, the heavy metals including Cd, Hg, Pb, Cr, and As;
  • the performing data extraction processing on the cultivated soil sample sampling to obtain the extracted data includes: sequentially performing impurity removal and grinding processing on the cultivated soil sample sampling to obtain the grinding sample;
  • the ground sample is dissolved in HNO 3 -HF-HClO 4 at high temperature to obtain a dissolved sample solution; data extraction processing is performed on the dissolved sample solution to obtain extraction data.
  • the performing data extraction processing on the dissolved sample solution to obtain the extracted data includes: measuring the Cd concentration of the dissolved sample solution based on graphite furnace atomic absorption spectrometry, and measuring the dissolved sample based on the original gasification-atomic fluorescence spectroscopy method.
  • the concentration of Hg and As is measured for the solution
  • the concentration of Pb and Cr is measured for the dissolved sample solution based on flame atomic absorption spectrometry
  • the pH value of the dissolved sample solution is measured with a portable meter based on the potentiometric method to obtain the extracted data.
  • samples of each cultivated soil sample were air-dried in the laboratory, after removing stones and other debris, crushed with a plastic rod; after that, the sample was filtered with a 1mm nylon sieve, mixed evenly, and then placed flat on the plastic sheet ; The samples were further ground until they passed a 0.15mm nylon sieve; the ground samples were sealed in bags and analyzed; the soil samples were digested with HNO 3 -HF-HClO 4 high-temperature dissolved samples, and graphite furnace atomic absorption spectrometry was used for determination Cd concentration; reduction gasification-atomic fluorescence spectrometry is used to determine the concentration of Hg and As; flame atomic absorption spectrometry is used to determine the concentration of Pb and Cr; potentiometric method is used to measure soil pH using a portable meter; to ensure sample quality, The heavy metal analysis process is monitored for accuracy and precision according to China's national first-level reference materials. The ratio of repeated sampling is maintained at 10%-15%, the recovery rate is between 97.1%-102
  • S13 Perform spatial distribution and source analysis of heavy metals in the cultivated soil based on the extracted data, and obtain the spatial distribution characteristics and source correlation of heavy metals in the cultivated soil.
  • the processing of the spatial distribution and source analysis of heavy metals in cultivated soil based on the extracted data to obtain the spatial distribution characteristics and source correlation of heavy metals in cultivated soil includes: pairing based on the extracted data Cultivated soil is subjected to source analysis processing of heavy metals to obtain the source correlation of heavy metals in the cultivated soil; based on the extracted data, spatial interpolation is used to process the spatial distribution of heavy metals in the cultivated soil to obtain the spatial distribution characteristics of heavy metals in the cultivated soil.
  • the performing source analysis processing of heavy metals in the cultivated soil based on the extracted data to obtain the source relevance of heavy metals in the cultivated soil includes: performing descriptive statistical analysis processing on the extracted data to obtain statistical description characteristics, the Descriptive statistical analysis includes statistical analysis of maximum value, minimum value, average value, standard deviation, and coefficient of variation; multivariate statistical analysis is performed on the statistical descriptive characteristics, and the source correlation of heavy metals in cultivated soil is obtained based on the results of multivariate statistical analysis.
  • the performing multivariate statistical analysis processing on the statistical description characteristics, and obtaining the source correlation of heavy metals in cultivated soil based on the results of the multivariate statistical analysis includes: performing Pearson correlation coefficient analysis processing on the heavy metals based on the statistical description characteristics to obtain The relevance of the heavy metals; performing principal component analysis processing on the heavy metals based on the statistical description characteristics to obtain the principal component factors of the heavy metals; performing cluster analysis on the heavy metals based on the statistical description characteristics using the correlation coefficient farthest neighbor method, Obtain the cluster dendrogram of the heavy metals; obtain the source correlation of the heavy metals in the cultivated soil based on the correlation of the heavy metals, the principal component factors of the heavy metals, and the cluster dendrogram of the heavy metals.
  • the use of spatial interpolation to process the spatial distribution of heavy metals in the cultivated soil based on the extracted data to obtain the spatial distribution characteristics of heavy metals in the cultivated soil includes: using GS+9.0 software based on the extracted data to perform normal or correct analysis Perform spatial variability analysis on heavy metals with a number normally distributed to obtain the spatial variability of heavy metals; perform geostatistical Kriging interpolation analysis on the spatial variability of the heavy metals to obtain the first interpolation analysis result; perform spatial variability of the heavy metals The deterministic spatial interpolation analysis is performed to obtain a second interpolation analysis result; the spatial distribution characteristics of heavy metals in the cultivated soil are obtained based on the first interpolation analysis result and the second interpolation analysis result.
  • the pH range of soil samples is 5.03-7.32, with an average of 5.71, which is slightly acidic soil. Therefore, the standard value of heavy metal elements with pH ⁇ 6.5 in the soil environmental quality standard is selected for subsequent analysis and evaluation; from Table 1, it can be seen that the content of each heavy metal element is determined by The descending order is: Cr(78.692 ⁇ 38.164 mg/kg)>Pb(54.299 ⁇ 18.124mg/kg)>As(10.280 ⁇ 7.233mg/kg)>Hg(0.163 ⁇ 0.051mg/kg)>Cd(0.16 ⁇ 0.05mg/kg).
  • the coefficient of variation can reflect the influence of human activities on the content of heavy metals. The larger the coefficient of variation, the stronger the interference by human activities.
  • the coefficient of variation in this region from large to small is heavy metal As (70.36%)> Cr (48.50%)> Pb (33.38%)>Hg(31.29%)>Cd(31.25%), all of which are of moderate intensity, and are less disturbed by human activities.
  • the skewness and kurtosis coefficients of the five heavy metal contents are all close to 0, and the K-S test P values of heavy metals Cd, Hg, Pb and Cr are all greater than 0.05, and they pass the K-S test and conform to the normal distribution. Therefore, the heavy metals Cd, Hg, Pb, Cr can be interpolated in the arable land, and the heavy metals As cannot be interpolated.
  • the maximum value of the above five heavy metals is lower than the soil secondary standard limit, and the exceeding rate is zero; compare the soil background values in Guangdong province, where the background value refers to each region The normal content of elements in various natural bodies (rocks, weathering products, soil, sediments, natural water, near-ground atmosphere, etc.) under normal geographical conditions and geochemical conditions.
  • the soil background value refers to the content of elements in the soil that has not been or has not been polluted or destroyed under the influence of human activities. The value can be obtained by looking up China's national soil environmental quality standards.
  • the average values of the above five heavy metals are all higher than the background value.
  • the over-standard rate of heavy metal Cd points is 98.67%; the over-standard rate of Hg points is 97.33%; the over-standard rate of As points is 40.0%; the over-standard rate of Pb points is 80.0%; The over-standard rate of Cr samples is 76.0%, which indicates that the problem of heavy metal pollution in the soil in this area is prominent.
  • the present invention uses the Pearson correlation coefficient Analyzed the correlation of heavy metals in 5 types of cultivated soils. As shown in Table 2, the correlation coefficient of heavy metals Cd and As reached 0.362, which was significant at the 0.01 level, and the correlation coefficient between Cd and Cr reached 0.289, and the correlation was at the 0.05 level. Significant; the correlation coefficient between As and Cr reaches 0.279, the correlation is significant at the 0.05 level; the heavy metals Hg and Pb are not correlated with other heavy metals. The results show that the heavy metals Cd, As and Pb may have the same source, and the sources of Hg and Pb may be different from other heavy metals and have a certain degree of independence.
  • the intuitive load classification diagrams of 5 heavy metals are obtained.
  • the distance between the heavy metals in the figure reflects the correlation between the elements.
  • the distances between the heavy metals Cd, As and Cr are relatively close; it further shows that these three heavy metals can have the same source;
  • the heavy metals are further analyzed by clustering, and the tree diagram is obtained by using the farthest neighbor method based on the correlation coefficient, as shown in Figure 3.
  • the abscissa in Figure 3 represents the relative distance between the heavy metals.
  • the five heavy metal elements Cd and As are first polymerized, then polymerized with Cr, then polymerized with Hg, and finally polymerized with Pb.
  • the results are consistent with the principal component analysis results.
  • the results show that the heavy metals Cd, As and Pb may have the same source, and the sources of Hg and Pb have a certain degree of independence.
  • the sources of heavy metals in the five cultivated soils are divided into three aspects: (1) The impact of agricultural production activities; the heavy metal Cd is usually considered as a symbol element of agricultural production, mainly Due to the use of pesticides and chemical fertilizers; Cd is mainly present in phosphate fertilizers, because Cd is usually present in phosphate rock as an impurity; inorganic As compounds such as calcium arsenate, lead arsenate, sodium arsenate and many others are used as insecticides or weeding Cr is mainly found in agricultural chemical fertilizers and fertilizers made of municipal waste and sludge.
  • GS+9.0 software was used to analyze the spatial variability of four heavy metal elements (Cd, Hg, Pb, and Cr) conforming to normal or lognormal distribution; the four heavy metals were fitted by the three variance function models of spherical, exponential and Gaussian The content data, the results are shown in Table 5; comprehensively considering the fitting index residual square sum RSS and the determination coefficient R2, the spherical model can better fit the heavy metals Cd and Pb; the exponential model can better fit the heavy metals Hg; Gaussian model fitting can better fit the heavy metal Cr; under the best model fitting, the fitting effects of the four heavy metal elements are better, the coefficient of determination R2 is all above 0.538, and the value of C0/(C+C0) The descending order is: Cd(0.515)>Cr(0.386)>Hg(0.347)>Pb(0.312); according to the grading standard of the spatial correlation degree of regionalization variables, it can be seen that the spatial correlation of the four heavy metals is moderate. , Can be
  • the geostatistical kriging interpolation method and the deterministic spatial interpolation method are used for comparison, as shown in Table 6;
  • the optimal interpolation methods for heavy metals Cd and As are deterministic spatial interpolation (Radial basis function, RBF);
  • the optimal interpolation methods for heavy metals Hg, Pb and Cr are all disjunctive Kerry Golden interpolation method (Disjunctive Kriging, DK); among them, GPI (Global Polynomial Interpolation) and IDW (Inverse Distance Weight) are the global polynomial and inverse distance weighting methods of deterministic spatial interpolation methods, respectively.
  • the results of various spatial interpolation methods are analyzed and compared according to the heavy metal content and spatial variability of cultivated soil.
  • the geostatistics The Kriging interpolation method is compared with the deterministic spatial interpolation method, and the interpolation method that is most suitable for different soil heavy metals is determined; the verified spatial simulation map of various metal farmland soil heavy metals basically conforms to the actual spatial distribution; the source of heavy metals in farmland soil Quantitative analysis of its contributions overcomes the traditional qualitative identification of pollution sources and provides an effective theoretical basis for the identification and control of soil heavy metal sources.
  • FIG. 4 is a schematic diagram of the structure and composition of the spatial distribution and source analysis device of heavy metals in cultivated soil in an embodiment of the present invention.
  • a device for spatial distribution and source analysis of heavy metals in farmland soil includes:
  • Sampling module 11 used to uniformly select the corresponding area based on GPS positioning to sample cultivated land soil samples, and obtain cultivated land soil sample samples;
  • Data extraction module 12 used to perform data extraction processing on the cultivated soil samples to obtain extracted data.
  • the extracted data includes heavy metal concentration data and soil pH data.
  • the heavy metals include Cd, Hg, Pb, Cr, and As ;
  • Spatial distribution and source analysis module 13 used to process the spatial distribution and source analysis of heavy metals in the cultivated soil based on the extracted data, and obtain the spatial distribution characteristics and source correlation of heavy metals in the cultivated soil.
  • the results of various spatial interpolation methods are analyzed and compared according to the heavy metal content and spatial variability of cultivated soil.
  • the geostatistics The Kriging interpolation method is compared with the deterministic spatial interpolation method, and the interpolation method that is most suitable for different soil heavy metals is determined; the verified spatial simulation map of various metal farmland soil heavy metals basically conforms to the actual spatial distribution; the source of heavy metals in farmland soil Quantitative analysis of its contributions overcomes the traditional qualitative identification of pollution sources and provides an effective theoretical basis for the identification and control of soil heavy metal sources.
  • the program can be stored in a computer-readable storage medium, and the storage medium can include: Read only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

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Abstract

一种耕地土壤重金属的空间分布及来源解析方法及装置，方法包括：基于GPS定位均匀选择对应区域进行耕地土壤样品采样，获取耕地土壤样品采样(S11)；对耕地土壤样品采样进行数据提取处理，获取提取数据(S12)，提取数据包括重金属浓度数据和土壤pH值数据，重金属包括Cd、Hg、Pb、Cr和As；基于提取数据对耕地土壤进行重金属的空间分布及来源解析处理，获取耕地土壤中重金属的空间分布特征以及来源相关性(S13)。由该方法得到的耕地土壤重金属空间模拟图基本符合实际空间分布，对耕地土壤重金属源及其贡献进行定量解析，为土壤重金属源头识别控制提供有效理论依据。

Description

一种耕地土壤重金属的空间分布及来源解析方法及装置
本申请要求于2019年11月12日提交中国专利局、申请号为201911102806.0、发明名称为“一种耕地土壤重金属的空间分布及来源解析方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及数据分析技术领域，尤其涉及一种耕地土壤重金属的空间分布及来源解析方法及装置。
背景技术
土壤是人类生存最宝贵的自然资源，对环境污染物有缓冲和净化作用，了解土壤中重金属的空间分布及来源解析是监测和评价研究区生态环境的重要前提；空间分布是评估重金属富集的可能来源并确定研究区域内热点的有力途径，而来源解析则是防止或减少重金属污染的关键步骤；在现有的采样过程中，由于采样点数量有限，不能完整的表达整个研究区的空间分布情况，导致分析结果存在一定的偏差。
确定性插值方法，是使用数学函数进行插值，以研究区域内部的相似性(如反距离加权插值法)、或者以平滑度为基础(如径向基函数插值法)由已知样点来创建预测表面的插值方法；确定性插值方法可以划分为两类：全局方法和局部方法；全局方法使用整个数据集计算预测值；局部方法由邻域内的测量点计算预测值；确定性插值方法具有简单且效率高的特点，对于非正态分布的样点数据应选择确定性插值方法，Ungureanu为了描述以罗马尼亚瓦斯路伊地区土壤中研究元素的空间分布，由于样本点分布均匀且重金属含量符合非正态分布，使用反距离加权(IDW)插值法绘制地球化学图，综合多变量统计分析和重金属空间变异性的结果表明研究区重金属含量来源主要由成土母质、车辆排放、人类活动和农业材料的影响；但确定性插值方法不提供预测值的误差检验，且对于符合正态分布的样点数据插值效果较差，易受样点极值的影响；针对确定性插值的局限性， 产生了地统计克里金插值法。这种方法认为在空间连续变化的属性是非常不规则的，用简单的平滑函数进行模拟将出现误差，用随机表面函数给予描述会比较恰当，克里金方法的关键在于权重系数的确定，该方法在插值过程中根据某种优化准则函数来动态地决定变量的数值，从而使内插函数处于最佳状态。克里金方法考虑了观测的点和被估计点的位置关系，并且也考虑各观测点之间的相对位置关系，在点稀少时插值效果比反距离权重等方法要好；所以利用克里金方法进行空间数据插值往往取得理想的效果。
近年来相关技术学者开展了大量关于土壤重金属累积特征、重金属污染评价以及源解析等方面的研究；Yang等在武汉市青山区、Neda等在伊朗砖厂、Hu等在黄海沿岸的蔬菜大棚种植区，采用主成分分析、聚类分析等方法结合空间分布特征等开展了土壤重金属污染评价和源解析。
现有对耕地土壤重金属空间分布研究大多都是采用克里金插值法进行插值，缺少对耕地土壤重金属含量和耕地土壤重金属的空间变异性的考虑，如对于某些含量不符合正态分布的土壤重金属应选择确定性插值法进行插值；大多数研究只定性地推测土壤重金属潜在的污染源，未对其进行定量化的解析，且对土壤重金属与制约因素之间的关系缺乏深入的认识、污染源分析往往聚焦于某种或有限的几种因素。
发明内容
本发明的目的在于克服现有技术的不足，本发明提供了一种耕地土壤重金属的空间分布及来源解析方法及装置，可以得到各种金属耕地土壤重金属空间模拟图基本符合实际空间分布；对耕地土壤重金属源及其贡献进行定量解析，为土壤重金属源头识别控制提供了有效理论依据。
为实现所述目的，本发明提供了如下方案：
一种耕地土壤重金属的空间分布及来源解析方法，所述方法包括：
基于GPS定位均匀选择对应区域进行耕地土壤样品采样，获取耕地土壤样品采样；
对所述耕地土壤样品采样进行数据提取处理，获取提取数据，所述提取数据包括重金属浓度数据和土壤PH值数据，所述重金属包括Cd、Hg、 Pb、Cr和As；
基于所述提取数据对耕地土壤进行重金属的空间分布及来源解析处理，获取耕地土壤中重金属的空间分布特征以及来源相关性。
可选的，所述基于GPS定位均匀选择对应区域进行耕地土壤样品采样，获取耕地土壤样品采样，包括：
基于GPS定位均匀选定对应的待采样区域；
基于梅花形采样模式对选定对应的待采样区域依次选择5个采样深度为预设值的采样土壤进行混合，获得耕地土壤样品采样。
可选的，所述对所述耕地土壤样品采样进行数据提取处理，获取提取数据，包括：
对所述耕地土壤样品采样依次进行除杂质及研磨处理，获取研磨样品；
对所述研磨样品进行HNO 3-HF-HClO 4高温溶解，获取溶解样品溶液；
对所述溶解样品溶液进行数据提取处理，获取提取数据。
可选的，所述对所述溶解样品溶液进行数据提取处理，获取提取数据，包括：
基于石墨炉原子吸收光谱法对溶解样品溶液进行Cd浓度测定，基于原气化—原子荧光光谱法对溶解样品溶液进行Hg和As的浓度测定，基于火焰原子吸收光谱法对溶解样品溶液进行Pb和Cr的浓度测定，基于电位法利用便携计测定溶解样品溶液的PH值，获得提取数据。
可选的，所述基于所述提取数据对耕地土壤进行重金属的空间分布及来源解析处理，获取耕地土壤中重金属的空间分布特征以及来源相关性，包括：
基于所述提取数据对耕地土壤进行重金属的来源解析处理，获取耕地土壤中重金属的来源相关性；
基于所述提取数据采用空间插值法对耕地土壤进行重金属的空间分布处理，获取耕地土壤中重金属的空间分布特征。
可选的，所述基于所述提取数据对耕地土壤进行重金属的来源解析处理，获取耕地土壤中重金属的来源相关性，包括：
对所述提取数据进行描述统计分析处理，获取统计描述特征，所述描 述统计分析包括最大值、最小值、平均值、标准差和变异系数统计分析；
对所述统计描述特征进行多元统计分析处理，基于多元统计分析结果获得耕地土壤中重金属的来源相关性。
可选的，所述对所述统计描述特征进行多元统计分析处理，基于多元统计分析结果获得耕地土壤中重金属的来源相关性，包括：
基于所述统计描述特征对重金属进行Pearson相关系数分析处理，获得所述重金属的相关性；
基于所述统计描述特征对重金属进行主成分分析处理，获得所述重金属的主成分因子；
基于所述统计描述特征对重金属利用相关系数最远邻法进行聚类分析，获得所述重金属的聚类树状图；
基于所述重金属的相关性、所述重金属的主成分因子以及所述重金属的聚类树状图获得耕地土壤中重金属的来源相关性。
可选的，所述基于所述提取数据采用空间插值法对耕地土壤进行重金属的空间分布处理，获取耕地土壤中重金属的空间分布特征，包括：
基于提取数据采用GS+9.0软件对符合正态或对数正态分布的重金属进行空间变异分析，获取重金属的空间变异性；
对所述重金属的空间变异性进行地统计学克里金插值分析，获取第一插值分析结果；
对所述重金属的空间变异性进行确定性空间插值法分析，获得第二插值分析结果；
基于所述第一插值分析结果和所述第二插值分析结果获得耕地土壤中重金属的空间分布特征。
另外，本发明实施例还提供了一种耕地土壤重金属的空间分布及来源解析装置，所述装置包括：
采样模块：用于基于GPS定位均匀选择对应区域进行耕地土壤样品采样，获取耕地土壤样品采样；
数据提取模块：用于对所述耕地土壤样品采样进行数据提取处理，获取提取数据，所述提取数据包括重金属浓度数据和土壤PH值数据，所述重金属包括Cd、Hg、Pb、Cr和As；
空间分布及来源解析模块：用于基于所述提取数据对耕地土壤进行重金属的空间分布及来源解析处理，获取耕地土壤中重金属的空间分布特征以及来源相关性。
本发明的有益效果如下：
本发明的耕地土壤重金属的空间分布及来源解析方法及装置，依据耕地土壤重金属含量和空间变异性，分析比较了多种空间插值方法结果，在依据使用预测误差ME最接近0并且RMSE是最小(优先级)的原理，将地统计克里金插值方法与确定性空间插值方法进行比较，确定了不同土壤重金属最适合使用的插值方法；经验证得到的各种金属耕地土壤重金属空间模拟图基本符合实际空间分布；对耕地土壤重金属源及其贡献进行定量解析，克服了传统定性识别污染源的缺陷，为土壤重金属源头识别控制提供了有效理论依据。
说明书附图
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实施例中的耕地土壤重金属的空间分布及来源解析方法的流程示意图；
图2是本发明实施例中的主成分中重金属载荷图；
图3是本发明实施例中的土壤重金属聚落分析谱图；
图4是本发明实施例中的耕地土壤重金属的空间分布及来源解析装置的结构组成示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本发明的目的在于克服现有技术的不足，本发明提供了一种耕地土壤重金属的空间分布及来源解析方法及装置，可以得到各种金属耕地土壤重金属空间模拟图基本符合实际空间分布；对耕地土壤重金属源及其贡献进行定量解析，为土壤重金属源头识别控制提供了有效理论依据。
为使本发明的所述目的、特征和优点能够更加明显易懂，下面结合附图和具体实施方式对本发明作进一步详细的说明。
实施例
请参阅图1，图1是本发明实施例中的耕地土壤重金属的空间分布及来源解析方法的流程示意图。
如图1所示，一种耕地土壤重金属的空间分布及来源解析方法，所述方法包括：
S11：基于GPS定位均匀选择对应区域进行耕地土壤样品采样，获取耕地土壤样品采样；
在本发明具体实施过程中，所述基于GPS定位均匀选择对应区域进行耕地土壤样品采样，获取耕地土壤样品采样，包括：基于GPS定位均匀选定对应的待采样区域；基于梅花形采样模式对选定对应的待采样区域依次选择5个采样深度为预设值的采样土壤进行混合，获得耕地土壤样品采样。
具体的，通过GPS定位来均匀选定对应的待采样区域，如通过GPS选定某市的某个区(县)区域内，对该区域内的耕地土壤进行的野外采样，一般都会采用相应个数的耕地土壤样品，在本发明中采集76个，在每个采样点采用梅花形采样模式，依次选择5个深度为0-20cm的土壤子样品并混合至重约300g；然后将所有土壤样品采样做好记录带回实验室进行物理和化学分析。
S12：对所述耕地土壤样品采样进行数据提取处理，获取提取数据，所述提取数据包括重金属浓度数据和土壤PH值数据，所述重金属包括Cd、Hg、Pb、Cr和As；
在本发明具体实施过程中，所述对所述耕地土壤样品采样进行数据提取处理，获取提取数据，包括：对所述耕地土壤样品采样依次进行除杂质 及研磨处理，获取研磨样品；对所述研磨样品进行HNO 3-HF-HClO 4高温溶解，获取溶解样品溶液；对所述溶解样品溶液进行数据提取处理，获取提取数据。
进一步的，所述对所述溶解样品溶液进行数据提取处理，获取提取数据，包括：基于石墨炉原子吸收光谱法对溶解样品溶液进行Cd浓度测定，基于原气化—原子荧光光谱法对溶解样品溶液进行Hg和As的浓度测定，基于火焰原子吸收光谱法对溶解样品溶液进行Pb和Cr的浓度测定，基于电位法利用便携计测定溶解样品溶液的PH值，获得提取数据。
具体的，对每一个耕地土壤样品采样在实验室中风干，除去石块和其他碎屑后，用塑料棒压碎；之后，将样品用1mm尼龙筛过滤，均匀混合，然后平放在塑料片上；将样品进一步研磨直至它们通过0.15mm尼龙筛；将研磨的样品密封在袋中并进行分析；土壤样品采用HNO 3-HF-HClO 4高温溶解样品进行消解，石墨炉原子吸收光谱法用于测定Cd浓度；还原气化—原子荧光光谱法用于测定Hg和As的浓度；火焰原子吸收光谱法用于测定Pb和Cr的浓度；采用电位法利用便携式计测定土壤pH值；为确保样品质量，重金属分析过程根据中国的国家一级标准物质进行准确度和精密度的监控，重复取样的比例保持在10％-15％，回收率介于97.1％-102.8％之间，相对偏差小于10％。
S13：基于所述提取数据对耕地土壤进行重金属的空间分布及来源解析处理，获取耕地土壤中重金属的空间分布特征以及来源相关性。
在本发明具体实施过程中，所述基于所述提取数据对耕地土壤进行重金属的空间分布及来源解析处理，获取耕地土壤中重金属的空间分布特征以及来源相关性，包括：基于所述提取数据对耕地土壤进行重金属的来源解析处理，获取耕地土壤中重金属的来源相关性；基于所述提取数据采用空间插值法对耕地土壤进行重金属的空间分布处理，获取耕地土壤中重金属的空间分布特征。
进一步的，所述基于所述提取数据对耕地土壤进行重金属的来源解析处理，获取耕地土壤中重金属的来源相关性，包括：对所述提取数据进行描述统计分析处理，获取统计描述特征，所述描述统计分析包括最大值、 最小值、平均值、标准差和变异系数统计分析；对所述统计描述特征进行多元统计分析处理，基于多元统计分析结果获得耕地土壤中重金属的来源相关性。
进一步的，所述对所述统计描述特征进行多元统计分析处理，基于多元统计分析结果获得耕地土壤中重金属的来源相关性，包括：基于所述统计描述特征对重金属进行Pearson相关系数分析处理，获得所述重金属的相关性；基于所述统计描述特征对重金属进行主成分分析处理，获得所述重金属的主成分因子；基于所述统计描述特征对重金属利用相关系数最远邻法进行聚类分析，获得所述重金属的聚类树状图；基于所述重金属的相关性、所述重金属的主成分因子以及所述重金属的聚类树状图获得耕地土壤中重金属的来源相关性。
进一步的，所述基于所述提取数据采用空间插值法对耕地土壤进行重金属的空间分布处理，获取耕地土壤中重金属的空间分布特征，包括：基于提取数据采用GS+9.0软件对符合正态或对数正态分布的重金属进行空间变异分析，获取重金属的空间变异性；对所述重金属的空间变异性进行地统计学克里金插值分析，获取第一插值分析结果；对所述重金属的空间变异性进行确定性空间插值法分析，获得第二插值分析结果；基于所述第一插值分析结果和所述第二插值分析结果获得耕地土壤中重金属的空间分布特征。
具体的，对于一组数据，在分析之前，需要探索数据的分布趋势并了解数据的基本特征，这需要对数据进行描述性统计分析；描述性统计分析是通过不同的描述性统计(例如最大值，最小值，平均值，标准差，变异系数等)分析数据的整体特征；还可以找出数据中的异常值，从整体上把握数据变化趋势，以便进行后续的分析；其中异常值分析方法如下：在所有的样本数据中，有的数据与平均值偏离较大，容易对分析结果产生较大影响，因此需要进行异常值分析，剔除异常性较大的数据。
土壤样本pH的范围5.03-7.32，平均5.71，呈微酸性土，因此，选择土壤环境质量标准中pH<6.5的重金属元素标准值进行后续分析评价；从表1可以看出，各重金属元素含量由大到小依次为：Cr(78.692±38.164 mg/kg)>Pb(54.299±18.124mg/kg)>As(10.280±7.233mg/kg)>Hg(0.163±0.051mg/kg)>Cd(0.16±0.05mg/kg)。变异系数可反映人为活动对重金属含量的影响，变异系数越大，表明受人为活动干扰越强烈，该区域的变异系数从大到小依次为重金属As(70.36％)>Cr(48.50％)>Pb(33.38％)>Hg(31.29％)>Cd(31.25％)，均为中等变异强度，受人为活动干扰较小。5种重金属含量的偏度和峰度系数都接近于0，且重金属Cd、Hg、Pb和Cr的K-S试验P值均大于0.05，并通过K-S试验，符合正态分布。因此在耕地土壤重金属Cd、Hg、Pb、Cr可进行地统计插值，重金属As不能进行地统计插值。
根据中国的国家土壤环境质量标准(GB15618-1995)，上述五种重金属的最大值低于土壤二次标准限值，超标率为零；比较广东省土壤背景值，其中，背景值是指各区域正常地理条件和地球化学条件下元素在各类自然体(岩石、风化产物、土壤、沉积物、天然水、近地大气等)中的正常含量。在环境科学中，土壤背景值是指在未受或少受人类活动影响下，尚未受或少受污染和破坏的土壤中元素的含量，其数值可通过查找中国的国家土壤环境质量标准获取。上述5种重金属的平均值均高于背景值，重金属Cd点的超标率为98.67％；Hg样点超标率为97.33％；As样点超标率为40.0％；Pb样点超标率为80.0％；Cr样点超标率为76.0％，因此表明该区域土壤重金属污染问题突出。
表1五种重金属含量统计描述
Figure PCTCN2020128105-appb-000001
由于不同重金属元素之间可能的相互作用，它们在土壤中的积累之间存在一定的相关性；相关分析可以进一步揭示土壤中重金属的来源是否具有同源性及其原因；本发明采用Pearson相关系数分析了耕地5种土壤中重金属的相关性如表2所示，重金属Cd和As的相关系数达到0.362，于0.01层的相关性显著，Cd与Cr的相关系数达到0.289，于0.05层的相关性显著；As与Cr的相关系数达到0.279，于0.05层的相关性显著；重金属Hg和Pb与其他重金属不存在相关性。结果表明，重金属Cd，As和Pb可能具有相同的来源，Hg和Pb的来源可能与其他重金属不同，具有一定的独立性。
表2耕地土壤重金属含量Pearson相关系数分析
Figure PCTCN2020128105-appb-000002
对五种重金属元素进行了主成分分析，以进一步反映土壤中重金属元素的来源；从表3可以看出，总共获得了三个主成分，方差贡献率为：31.582％、22.322％和20.988％，累积方差贡献率为74.891％；由表4可知， 经旋转后主成分F1中Cd、As和Cr的因子载荷较大，分别为0.727、0.609和0.831；主成分F2中Hg的因子载荷较大，载荷为0.881；主成分F3中Pb的因子载荷较大，载荷为0.882。利用主成分分析载荷矩阵所对应的特征向量分量，得到5种重金属的直观载荷分类图。如图2所示，图中重金属之间的距离反映了元素之间的相关性，重金属Cd，As和Cr之间的距离相对接近；进一步说明这三种重金属可具有相同的来源；对5种重金属进一步进行聚类分析，采用基于相关系数最远邻法进行聚类分析，得到树状图，如图3所示，图3中横坐标表示各重金属之间相对距离，根据聚类分析结果，5种重金属元素Cd和As先聚合后与Cr聚合，然后再与Hg聚合，最后和Pb聚合，其结果与主成分分析结果一致。综上分析，结果表明重金属Cd，As和Pb可能具有相同的来源，Hg和Pb的来源具有一定的独立性。
表3耕地土壤重金属含量因子分析
Figure PCTCN2020128105-appb-000003
表4耕地土壤重金属含量因子载荷矩阵
Figure PCTCN2020128105-appb-000004
综合以上相关性分析、因子分析和聚类分析结果，将5种耕地土壤重金属来源分为三个方面：(1)农业生产活动的影响；重金属Cd通常被认为是农业生产的标志元素，主要是由于农药和化肥的使用；Cd主要存 在于磷肥中，因为Cd通常作为杂质存在于磷酸盐岩中；无机As化合物如砷酸钙，砷酸铅，砷酸钠等许多用作杀虫剂或除草剂；Cr主要存在于农用化肥及以城市垃圾、污泥为原料的肥料中，长期施用这些肥料会增加土壤中Cr累积；所选取的地区为重要的粮农基地，是一个肥料、农药的高投入区，长期施用化肥和农药可导致土壤重金属Cd、As和Cr含量升高；(2)工业活动的影响；重金属Hg通常被定义为工业相关的人为因素，表土中的Hg累积通常与来自各种人类活动的大气沉积有关；燃烧化石燃料和金属冶炼是导致汞排放的最常见活动；与其他重金属不同，Hg是一种高度流动和稳定的环境污染物，由于高气压和低水溶性的结合，可以在大气中停留0.5—2年，因此可以清楚地证明Hg源自工业排放；(3)交通运输的影响；重金属Pb常被作为机动车污染源的标志性元素，主要是由于含Pb的汽油和柴油燃烧后尾气排放，并通过干沉降的方式影响周围土壤的Pb含量，其分布和集聚与主要交通道路一定的关系。
采用GS+9.0软件分析了符合正态或对数正态分布的四种重金属元素(Cd、Hg、Pb和Cr)空间变异性；通过球形、指数和高斯三种方差函数模型拟合四种重金属含量数据，结果显示在表5中；综合考虑拟合指标残差平方和RSS和决定系数R2，通过球形模型可更好地拟合重金属Cd和Pb；指数模型可更好地拟合重金属Hg；高斯模型拟合可更好地拟合重金属Cr；在最优模型拟合下，四种重金属元素的拟合效果均比较好，决定系数R2都在0.538以上，C0/(C+C0)的值从大到小依次为：Cd(0.515)>Cr(0.386)>Hg(0.347)>Pb(0.312)；根据区域化变量空间相关程度的分级标准可知，四种重金属的空间相关性均为中等程度，均可用于地统计学克里金插值分析。
表5耕地土壤重金属含量的空间变异性
Figure PCTCN2020128105-appb-000005
Figure PCTCN2020128105-appb-000006
使用预测平均值误差(Mean Error，ME)最接近0并且均方根误差(Root Mean Squared Error,RMSE)是最小(优先级)的原理，将地统计克里金插值方法与确定性空间插值方法进行比较，如表6所示；重金属Cd和As的最优插值方法均为确定性空间插值法(Radial basis function，RBF)；重金属Hg、Pb和Cr的最优插值方法均为析取克里金插值方法(Disjunctive Kriging,DK)；其中GPI(Global Polynomial Interpolation)和IDW(Inverse Distance Weight)分别是确定性空间插值方法的全局多项式和反距离权重法。
表6耕地土壤重金属克里金插值方法与确定性空间插值方法比较
Figure PCTCN2020128105-appb-000007
在本发明实施例中，依据耕地土壤重金属含量和空间变异性，分析比较了多种空间插值方法结果，在依据使用预测误差ME最接近0并且RMSE是最小(优先级)的原理，将地统计克里金插值方法与确定性空间插值方法进行比较，确定了不同土壤重金属最适合使用的插值方法；经验证得到的各种金属耕地土壤重金属空间模拟图基本符合实际空间分布；对耕地土壤重金属源及其贡献进行定量解析，克服了传统定性识别污染源的缺陷，为土壤重金属源头识别控制提供了有效理论依据。
实施例
请参阅图4，图4是本发明实施例中的耕地土壤重金属的空间分布及来源解析装置的结构组成示意图。
如图4所示，一种耕地土壤重金属的空间分布及来源解析装置，所述装置包括：
采样模块11：用于基于GPS定位均匀选择对应区域进行耕地土壤样品采样，获取耕地土壤样品采样；
数据提取模块12：用于对所述耕地土壤样品采样进行数据提取处理，获取提取数据，所述提取数据包括重金属浓度数据和土壤PH值数据，所述重金属包括Cd、Hg、Pb、Cr和As；
空间分布及来源解析模块13：用于基于所述提取数据对耕地土壤进行重金属的空间分布及来源解析处理，获取耕地土壤中重金属的空间分布特征以及来源相关性。
具体地，本发明实施例的装置相关功能模块的工作原理可参见方法实施例的相关描述，这里不再赘述。
在本发明实施例中，依据耕地土壤重金属含量和空间变异性，分析比较了多种空间插值方法结果，在依据使用预测误差ME最接近0并且RMSE是最小(优先级)的原理，将地统计克里金插值方法与确定性空间插值方法进行比较，确定了不同土壤重金属最适合使用的插值方法；经验证得到的各种金属耕地土壤重金属空间模拟图基本符合实际空间分布；对耕地土壤重金属源及其贡献进行定量解析，克服了传统定性识别污染源的缺陷，为土壤重金属源头识别控制提供了有效理论依据。
本领域普通技术人员可以理解上述实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储介质中，存储介质可以包括：只读存储器(ROM，Read Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁盘或光盘等。
另外，以上对本发明实施例所提供的一种耕地土壤重金属的空间分布及来源解析方法及装置进行了详细介绍，本文中应采用了具体个例对本发 明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本发明的限制。
以上结合附图对本发明的实施例进行了描述，但是本发明并不局限于上述的具体实施方式，上述的具体实施方式仅仅是示意性的，而不是限制性的，本领域的普通技术人员在本发明的启示下，在不脱离本发明宗旨和权利要求所保护的范围情况下，还可做出很多形式，这些均属于本发明的保护之内。

Claims (9)

  1. 一种耕地土壤重金属的空间分布及来源解析方法，其特征在于，所述方法包括：
    基于GPS定位均匀选择对应区域进行耕地土壤样品采样，获取耕地土壤样品采样；
    对所述耕地土壤样品采样进行数据提取处理，获取提取数据，所述提取数据包括重金属浓度数据和土壤PH值数据，所述重金属包括Cd、Hg、Pb、Cr和As；
    基于所述提取数据对耕地土壤进行重金属的空间分布及来源解析处理，获取耕地土壤中重金属的空间分布特征以及来源相关性。
  2. 根据权利要求1所述的空间分布及来源解析方法，其特征在于，所述基于GPS定位均匀选择对应区域进行耕地土壤样品采样，获取耕地土壤样品采样，包括：
    基于GPS定位均匀选定对应的待采样区域；
    基于梅花形采样模式对选定对应的待采样区域依次选择5个采样深度为预设值的采样土壤进行混合，获得耕地土壤样品采样。
  3. 根据权利要求1所述的空间分布及来源解析方法，其特征在于，所述对所述耕地土壤样品采样进行数据提取处理，获取提取数据，包括：
    对所述耕地土壤样品采样依次进行除杂质及研磨处理，获取研磨样品；
    对所述研磨样品进行HNO 3-HF-HClO 4高温溶解，获取溶解样品溶液；
    对所述溶解样品溶液进行数据提取处理，获取提取数据。
  4. 根据权利要3所述的空间分布及来源解析方法，其特征在于，所述对所述溶解样品溶液进行数据提取处理，获取提取数据，包括：
    基于石墨炉原子吸收光谱法对溶解样品溶液进行Cd浓度测定，基于原气化—原子荧光光谱法对溶解样品溶液进行Hg和As的浓度测定，基于火焰原子吸收光谱法对溶解样品溶液进行Pb和Cr的浓度测定，基于电位法利用便携计测定溶解样品溶液的PH值，获得提取数据。
  5. 根据权利要求1所述的空间分布及来源解析方法，其特征在于，所述基于所述提取数据对耕地土壤进行重金属的空间分布及来源解析处理，获取耕地土壤中重金属的空间分布特征以及来源相关性，包括：
    基于所述提取数据对耕地土壤进行重金属的来源解析处理，获取耕地土壤中重金属的来源相关性；
    基于所述提取数据采用空间插值法对耕地土壤进行重金属的空间分布处理，获取耕地土壤中重金属的空间分布特征。
  6. 根据权利要求5所述的空间分布及来源解析方法，其特征在于，所述基于所述提取数据对耕地土壤进行重金属的来源解析处理，获取耕地土壤中重金属的来源相关性，包括：
    对所述提取数据进行描述统计分析处理，获取统计描述特征，所述描述统计分析包括最大值、最小值、平均值、标准差和变异系数统计分析；
    对所述统计描述特征进行多元统计分析处理，基于多元统计分析结果获得耕地土壤中重金属的来源相关性。
  7. 根据权利要求6所述的空间分布及来源解析方法，其特征在于，所述对所述统计描述特征进行多元统计分析处理，基于多元统计分析结果获得耕地土壤中重金属的来源相关性，包括：
    基于所述统计描述特征对重金属进行Pearson相关系数分析处理，获得所述重金属的相关性；
    基于所述统计描述特征对重金属进行主成分分析处理，获得所述重金属的主成分因子；
    基于所述统计描述特征对重金属利用相关系数最远邻法进行聚类分析，获得所述重金属的聚类树状图；
    基于所述重金属的相关性、所述重金属的主成分因子以及所述重金属的聚类树状图获得耕地土壤中重金属的来源相关性。
  8. 根据权利要求5所述的空间分布及来源解析方法，其特征在于，所述基于所述提取数据采用空间插值法对耕地土壤进行重金属的空间分布处理，获取耕地土壤中重金属的空间分布特征，包括：
    基于提取数据采用GS+9.0软件对符合正态或对数正态分布的重金属进行空间变异分析，获取重金属的空间变异性；
    对所述重金属的空间变异性进行地统计学克里金插值分析，获取第一插值分析结果；
    对所述重金属的空间变异性进行确定性空间插值法分析，获得第二插 值分析结果；
    基于所述第一插值分析结果和所述第二插值分析结果获得耕地土壤中重金属的空间分布特征。
  9. 一种耕地土壤重金属的空间分布及来源解析装置，其特征在于，所述装置包括：
    采样模块：用于基于GPS定位均匀选择对应区域进行耕地土壤样品采样，获取耕地土壤样品采样；
    数据提取模块：用于对所述耕地土壤样品采样进行数据提取处理，获取提取数据，所述提取数据包括重金属浓度数据和土壤PH值数据，所述重金属包括Cd、Hg、Pb、Cr和As；
    空间分布及来源解析模块：用于基于所述提取数据对耕地土壤进行重金属的空间分布及来源解析处理，获取耕地土壤中重金属的空间分布特征以及来源相关性。
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