CN111552924A - Method for evaluating heavy metal pollution characteristics and potential ecological risks of soil on scale of villages and towns - Google Patents
Method for evaluating heavy metal pollution characteristics and potential ecological risks of soil on scale of villages and towns Download PDFInfo
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Abstract
The invention discloses a method for evaluating the heavy metal pollution characteristics and the potential ecological risk of soil in a village and town scale, which adjusts the evaluation domains of a single potential ecological risk index and a comprehensive potential ecological risk index according to the type and the number of selected heavy metals, and combines a method after the evaluation standard is improved with an ArcGIS-based interpolation technology to realize the visualization of the pollution condition. The ArcGIS technology can predict the pollution condition in the research area through the actual value of the sampling point, can further grade the soil heavy metal pollution in the research area by combining an ecological risk evaluation method, and can know the total pollution condition of the soil heavy metal pollution.
Description
Technical Field
The invention relates to the technical field of soil detection, in particular to a method for evaluating heavy metal pollution characteristics and potential ecological risks of soil in a village and town scale.
Background
Soil is generally considered to be the most susceptible part of the environment to the accumulation of heavy metals. The heavy metal content of soil is mainly influenced by human activity intensity and background content in natural environment, the heavy metal in soil is difficult to rapidly migrate and remove due to the self characteristics of the heavy metal in soil, the heavy metal in soil is continuously accumulated, particularly, the heavy metal pollution of the soil around a non-ferrous metal mining area mainly comes from the migration and sedimentation of slag, waste water and fly ash in the processes of collection, transportation, processing and the like of metal ores, the accumulation of high-content heavy metal usually occurs in a surface soil layer with the maximum heavy metal binding force, the high-content heavy metal accumulation can affect the physical and chemical properties of the soil, soil microorganisms and the like to different degrees, the soil quality and the regional environment are finally obviously changed, and the human health is directly or.
The content distribution of heavy metals in soil is different and mainly depends on several processes of soil. The surface soil is generally considered to be the layer with the highest heavy metal content, and the surface soil (1-20cm) is selected as a research layer in the research because the high-content organic matter of the layer can be combined with heavy metals in a large amount.
In recent years, researches on heavy metals in mining soil mainly focus on heavy metal migration (Cong LU et al, 2016), soil heavy metal pollution evaluation (Tian K et al, 2017) and soil and crop heavy metal research (Khan Z Iet al, 2017). Among them, evaluation of soil heavy metal pollution as one of important directions for regional environmental quality evaluation is gradually concerned by researchers in the fields of soil science, geography, environmental science and the like, and related research methods tend to be diversified. At present, methods for evaluating heavy metal pollution are more, such as a single factor evaluation method, an inner merlo index method, an enrichment factor index method (Buat Menard P et al, 1979), a potential ecological hazard index method (Hakanson L,1980), a pollution load index method (Liu W H et al, 2005), an earth accumulation index method (G.M muller et al, 1969), a fuzzy coefficient method (Onkal-Engin G et al, 2004), a gray clustering method (Fan L Q et al, 2012) and the like, and all the methods have advantages and disadvantages and should be selected according to different research purposes and regional characteristics. With the continuous improvement of the pollution evaluation method, the assessment of the potential ecological risks of the region becomes an effective quantitative tool for further depicting the quality of the ecological environment of the region. The potential ecological risk index method is a method for evaluating the heavy metal pollution degree in soil and sediments, the method comprehensively considers the behavior rules of heavy metal toxicity, migration, conversion and deposition in the environment and the like while considering the heavy metal content of the soil, comprehensively reflects the potential of the heavy metal on the ecological environment from the aspect of sedimentology, and is suitable for evaluating the pollution of the soil and the sediments in a large area range (Hakanson L, 1980).
Most of the researches in the prior art only surround farmland pollution of a few typical mining areas, the large-scale research based on the village and town level is not carried out, the soil heavy metal pollution characteristics are revealed only from a local range, the range and the degree of the pollution influence are difficult to determine, and the description and the pollution management are inconvenient.
Disclosure of Invention
The invention overcomes the defects of the prior art, provides a method for evaluating the heavy metal pollution characteristics and potential ecological risks of soil at a village and town scale, and can at least solve the problems that the toxicity and the types of the evaluated heavy metals are not comprehensively considered, the pollution grading result is not accurate and the like in the prior art.
In order to achieve the above purpose, the invention provides the following technical scheme:
a method for evaluating the heavy metal pollution characteristics and potential ecological risks of soil on a village and town scale comprises the following steps:
determining a soil sampling point according to the distance from a mining area in a research area, wherein the soil sampling point is far away from the edge of a field block and the roadside and avoids fertilizer and pesticide application points;
secondly, arranging a plurality of sampling points around each soil sampling point, collecting samples at each sampling point, mixing the samples collected by all the sampling points to obtain soil samples corresponding to the soil sampling points; detecting heavy metal data in each of the soil samples;
calculating a single-factor potential ecological hazard index of each heavy metal at each soil sampling point according to the heavy metal data, and calculating a comprehensive potential ecological hazard index at each soil sampling point;
and step four, adjusting the evaluation domain of the comprehensive potential ecological hazard index according to the types and the quantity of the heavy metals in the research area, and evaluating the potential risk condition of heavy metal pollution in the research area according to the adjusted evaluation domain.
Further, carrying out interpolation processing on the heavy metal data of the soil sampling point, wherein the interpolation processing comprises the following steps: taking the distance between the interpolation point and the soil sampling point as a weight to carry out weighted average, wherein the closer the distance to the interpolation point, the soil sampling point is endowed with a higher weight value; setting a series of sampling points distributed on the plane of the research area, wherein the coordinate value of the sampling points is Xi,Yi,Zi(i 1, 2.. once, n), calculating a Z point value by a weighted value, wherein Z is a weighted average value, and the formula is as follows:
wherein Z (x) is a predicted value of an interpolation point, ziIs the value of the known point, n is the total number of interpolations, diIs the distance, W, between the known point and the predicted pointiIs the weight between the known point and the interpolated point, the closer to the known point the greater the weight, the inverse ratio of the weight to the distance, u is the determining coefficient, determines WiDecreasing speed with increasing distance.
Further, the single-factor potential ecological hazard index E is calculated according to the formula:
the comprehensive potential ecological hazard index RIjCalculating the formula:
wherein:
is a single item of heavy metal i at a sampling point jA potential ecological risk index; RI (Ri)jComprehensive potential ecological risk indexes of the heavy metals at the sampling point j are obtained; t isiThe toxicity response coefficient of the heavy metal i reflects the toxicity level and the sensitivity of organisms to the pollution;
the pollution coefficient of the heavy metal i at the sampling point j is shown;
the actual measured value of the heavy metal i at the sampling point j is obtained;
is the reference content of heavy metal i.
Further, after interpolation processing is carried out on each heavy metal data at the soil sampling point, a soil heavy metal distribution map of the whole research area is drawn according to the interpolation processing result, and therefore the distribution area of each heavy metal in the research area is judged.
Furthermore, according to the soil heavy metal distribution diagram and in combination with the single-factor potential ecological hazard index, a surface integral distribution diagram of the ecological risk level of each heavy metal in the research area in each village and town is drawn, so that the pollution risk level of each heavy metal in each village and town in the research area is divided.
Furthermore, a comprehensive ecological risk level map of each heavy metal is drawn according to the comprehensive potential ecological hazard index, and the area occupied by different risk levels of each village and town in the research area is counted according to the comprehensive ecological risk level map, so that the total ecological risk evaluation result of the heavy metal potential of each village and town soil is obtained.
Further, the evaluation domain of the comprehensive potential ecological hazard index is as follows: an index of less than 70 is a slight risk; an index between 70 and 140 is moderate risk; an index between 140 and 280 is a strong risk; an index of 280 or more is a strong risk; an index greater than a set value is extremely risky.
Compared with the closest prior art, the technical scheme provided by the invention has the following excellent effects:
the invention provides a method for evaluating the heavy metal pollution characteristics and the potential ecological risk of soil in a village and town scale, which adjusts the evaluation domains of a single potential ecological risk index and a comprehensive potential ecological risk index according to the variety and the number of selected heavy metals, and combines a method after improving the evaluation standard with an ArcGIS-based interpolation technology to realize the visualization of the pollution condition. The evaluation method based on ArcGIS interpolation pushes pollution feature analysis and ecological risk assessment to a new research category.
The ArcGIS technology can predict the pollution condition in the research area through the actual value of the sampling point, can further grade the soil heavy metal pollution in the research area by combining an ecological risk evaluation method, and can know the total pollution condition of the soil heavy metal pollution.
Drawings
FIG. 1 is a flow chart of an ecological risk assessment method in an embodiment of the present invention;
FIG. 2 is a schematic diagram of an overview of a research area and locations of spots in an embodiment of the present invention;
FIG. 3 is a distribution diagram of heavy metal As content in soil in a research area according to an embodiment of the invention;
FIG. 4 is a distribution diagram of the heavy metal Cu content in soil of a research area in an embodiment of the invention;
FIG. 5 is a distribution diagram of the content of heavy metal Hg in soil in a research area in an embodiment of the invention;
FIG. 6 is a distribution diagram of heavy metal Pb content in soil of a research area according to an embodiment of the present invention;
FIG. 7 is a distribution diagram of the heavy metal Zn content in soil of a research area in the embodiment of the invention;
FIG. 8 is a single-factor ecological risk index distribution diagram of soil heavy metal As in the embodiment of the invention;
FIG. 9 is a soil heavy metal Cu single-factor ecological risk index distribution diagram in an embodiment of the present invention;
FIG. 10 is a single-factor ecological risk index distribution diagram of soil heavy metal Hg in the embodiment of the invention;
FIG. 11 is a single-factor ecological risk index distribution diagram of soil heavy metal Pb in the embodiment of the invention;
FIG. 12 is a soil heavy metal Zn single-factor ecological risk index distribution diagram in the embodiment of the present invention;
FIG. 13 is a diagram of a distribution of potential ecological total risk indexes in a research area after adjustment of an assessment domain according to an embodiment of the present invention;
FIG. 14 is a distribution diagram of the total risk index of potential ecology in the research area before the adjustment of the assessment domain in the embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In the description of the present invention, the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", etc., indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of describing the present invention but do not require that the present invention must be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
As shown in figure 1, the invention provides a method for evaluating the heavy metal pollution characteristics and potential ecological risks of soil in a village and town scale, which comprises the following steps:
determining a soil sampling point according to the distance from a mining area in a research area, wherein the soil sampling point is far away from the edge of a field and the roadside and avoids fertilizer and pesticide application points;
secondly, arranging a plurality of sampling points around each soil sampling point, collecting samples at each sampling point, mixing the samples collected by all the sampling points to obtain soil samples corresponding to the soil sampling points; detecting heavy metal data in each soil sample;
calculating the single-factor potential ecological hazard index of each heavy metal at each soil sampling point according to the heavy metal data, and calculating the comprehensive potential ecological hazard index at each soil sampling point;
and step four, adjusting an evaluation domain of the comprehensive potential ecological hazard index according to the types and the quantity of the heavy metals in the research area, and evaluating the potential risk condition of heavy metal pollution in the research area according to the adjusted evaluation domain.
A specific example is given below to illustrate the content of the ecological risk assessment method of the present invention.
1. Determining a region of investigation
The Suxian is located in the south of Hunan province (see FIG. 2) and has geographic locations ranging from 112 degrees 53 '55' to 113 degrees 16 '20' in east longitude, 25 degrees 30 '21' to 26 degrees 03 '29' in north latitude, 37.4 kilometers in east and south China and 61.0 kilometers in north and south China. The terrain of the land is complex, and the overall terrain inclines from south to north. The Suxian area has mild climate, belongs to a humid climate area of the middle and subtropical monsoon, has mild climate and abundant rainfall, and has the average temperature of 17.8 ℃ over the years and the average rainfall of 1469.8 mm over the years. The region has abundant mineral resources, including non-ferrous metal ores in persimmon bamboo garden, large-scale lead-zinc ores at bridge mouths, agate manganese ores, large-scale numerous holes, street holes, roosting coal mines and the like which are reputed in the world. The whole area has already found that nonferrous metals 7, 50 and more varieties, and nonferrous metals such as lead, zinc, tungsten, tin, bismuth, molybdenum and the like occupy important positions in global mineral resources. The common deposit 18 in the Suxian district, wherein 4 oversize deposits are mainly distributed in white open ponds, pond streams, great quince and upper towns. 143 kinds of minerals are stored in non-ferrous metal ores in a persimmon bamboo garden, are the largest multi-metallic ores found in the world, and are known as a world non-ferrous metal museum by foreign mineralogists and foreign miners.
By 31 days 12 months in 2010, 2 streets (Suxianling street, south tower street) in the Suxian district, 8 towns (dwelling phoenix ferry town, Wuli brand town, permit hole town, bridge entrance town, white deer hole town, white exposed pond town, down-pressing town and fertile farmland town), 9 villages (Liao Wang Ping village, Du foot village, Taiping village, Matouling village, Nelumbo Ping village, Tangxi village, Daqu Shangxiang, Deng Pong village, Liao Bay village).
2. Soil sample collection
According to the method, through field visit and reference of relevant data, soil sampling point collection is completed in a random and uniform point distribution mode in 2015 7-9 months with the assistance of local farmers. Sampling principle: the closer the sampling points are to the mining area, the denser the sampling points are, and the farther the sampling points are, the sparser the sampling points are; sampling is finished in the field and is far away from the edges of the field blocks and the roadside; investigation is carried out before sampling, and fertilizer and pesticide application points are avoided. 4-5 sampling points are arranged in a range of 3m × 3m around each sampling point, the sampling points are randomly and uniformly distributed, about 100g of surface soil (0-20cm) is shoveled by plastic for each sampling point, a sample bag is packaged after uniform mixing, and the sampling points, the sampling date, the soil type and the sampling point number are marked for storage. 311 soil is collected, 166 samples are randomly selected and sent to a professional organization with heavy metal detection qualification for analysis, and the heavy metal detection items of the soil are As, Hg, Cu, Pb and Zn. FIG. 2 shows the sampling range and point location of the heavy metals in the soil in the research area.
The basic geographic data comprises data such as administrative areas at the county and town level of the Suxian district, a village map of the Suxian district, a mining area distribution map of the Suxian district and the like, which are provided by a resource and environment scientific data center of China academy of sciences, and the accuracy of the data can be guaranteed. The coordinates of the sampling points are recorded by a GPS instrument. The sampled and geographic data were processed using Micosoft Excel 2010, IBM SPSS statics 19, ArcGISI 10.1 (ESRIInc.).
3. Research method
3.1 potential ecological Risk index method
Large metals and coal mine areas existing in the Suxian area are easy to threaten heavy metal pollution to the surrounding soil environment, and in order to quantitatively depict the potential pollution degree of the heavy metals, the risk of the heavy metal pollution of the Suxian area is discussed by adopting an internationally recognized Hakanson pollution evaluation method based on the consideration of the toxicity of the heavy metals. The method is proposed by Swedish scholars Hakanson in 1980 and is widely used for soil heavy metal pollution evaluation, and the principle of the method is shown in a paper (Hakanson L,1980), and the calculation formula is as follows:
the single-factor potential ecological hazard index E calculation formula is as follows:
the comprehensive potential ecological hazard index RIjCalculating the formula:
wherein:
the index is a single potential ecological risk index of the heavy metal i at the sampling point j; RI (Ri)jComprehensive potential ecological risk indexes of the heavy metals at the sampling point j are obtained; t isiThe toxicity response coefficient of the heavy metal i reflects the toxicity level and the sensitivity of organisms to the pollution;
the pollution coefficient of the heavy metal i at the sampling point j is shown;
the actual measured value of the heavy metal i at the sampling point j is obtained;
is the reference content of heavy metal i.
The Hakanson classification standard was developed while considering the toxicity coefficients of 8 pollutants at the same time, and calculating the amount of pollutant having the largest toxicity coefficient among 8 heavy metals and heavy metals. The invention only has 5 heavy metals, the quantity and the types of the heavy metals are changed, and the result is easy to deviate if the evaluation standard is not adjusted. At present, most of researches applying potential ecological risk evaluation rarely consider the types and the quantities of heavy metals to correspondingly adjust the heavy metals, and the evaluation domain is adjusted according to the characteristics of the research objects, so that the evaluation result is more accurate, and the E value and the RI value before and after adjustment are compared in a table 1.
TABLE 1 Hakanson rating Scale in comparison to the E and RI rating scales for this study
The threshold value of the extremely strong risk can be set as required, and is set to 600 in this embodiment.
3.2 soil heavy Metal IDW interpolation
According to the method, based on data characteristics, research purposes and sampling point setting consideration, IDW is selected for research by combining with the experience of previous research, inverse distance weighted Interpolation (IDW) is one of the methods commonly used for soil heavy metal research at present, and the spatial estimation has high precision (Shepard D, 1968). Briefly describing the interpolation principle: and taking the distance between the interpolation point and the sample point as a weight to carry out weighted average, wherein the closer the sample point is to the interpolation point, the greater the weight is given to the sample point. Let a series of discrete points distributed on the plane, whose coordinate value is known as Xi,Yi,Zi(i 1, 2.. once, n) calculating a Z point value by a weighted value, wherein Z is a weighted average value, and the formula is as follows:
z (x) is the predicted value of the interpolation point, ziIs the value of the known point, n is the total number of interpolations, diIs the distance, W, between the known point and the predicted pointiIs the weight between the known point and the interpolated point, the closer to the known point the greater the weight, the inverse ratio of the weight to the distance, u is the determining coefficient, determines WiDecreasing speed with increasing distance.
4. Heavy metal contaminated condition of soil
4.1 soil heavy Metal data eigenvalue analysis
Statistical characterization of soil heavy metals is presented in table 2. The average value of the five heavy metals far exceeds the background value of the heavy metals in the soil in Suxian district, and the standard exceeding multiples are respectively as follows: 5.6, 1.5, 3.2, 5.9 and 2.1, indicating that suxian soil is contaminated with heavy metals to varying degrees. In the secondary standard of the soil environmental quality standard, the limit values of As, Cu, Hg, Pb and Zn are respectively 40mg/Kg, 150mg/Kg, 0.3mg/Kg, 250mg/Kg and 200mg/Kg, and the limit values are the highest values in the secondary standard of the national soil environmental quality standard; the average values of As and Zn determined by the experiment of the invention are 78.70mg/Kg and 160.19mg/Kg respectively, and when the average values are beyond the standard range, Hg is near the standard value, so that the pollution risk is higher, and the average value is the average value of the measured values of all sampling points.
The single-factor pollution index is one of the methods commonly adopted for evaluating the heavy metal pollution at home and abroad at present, and a certain evaluation standard is selected to analyze the accumulation degree of the heavy metal in the soil. The method selects the background value of the soil in the research area as an evaluation standard, calculates the single-factor pollution index of the soil in Suxian area, and the background value is the value (also an empirical value for many years) of the content of certain heavy metal in a certain area specified by the state; the calculation formula can be found in the paper (Guo et al, 2011). The ratio of the heavy metal single-factor pollution indexes of five soils in the research area is obtained through calculation: hg is greater than Pb, As is greater than Zn, Cu and 1 (the single-factor pollution index is greater than 1, which indicates that soil heavy metals are interfered by the outside and accumulated to a certain extent, the heavy metal accumulation degree is higher As the index is larger), the single-term pollution index of Hg is the maximum (10.41), and the accumulation is the most serious. On the basis of applying a single-factor pollution evaluation method, an inner-Metro comprehensive index evaluation method is adopted to evaluate the overall condition of the soil quality in the research area. The method simultaneously considers the average value and the maximum value of the single pollution indexes, highlights the influence of high-concentration pollutants on the soil environment quality, and the calculation formula can be seen in a paper (Nemerow N L, 1974). P represents the inner Merlot index, and if P is less than or equal to 1, the product is non-pollution; p is more than 1 and less than or equal to 2, which is light pollution; p is more than 2 and less than or equal to 3, which is moderate pollution; p > 3 is a heavy contamination. The calculation result shows that the heavy metal pollution condition of the soil in the Suxian district is heavy pollution, the comprehensive index of inner Metro is 11.60, and the heavy metal pollution condition of the research district, which is influenced by human, is serious.
The distribution of the data population can be measured by skewness and kurtosis value. The data close to the normal distribution has a skewness value close to 0 and a kurtosis value close to 3. The data distribution characteristics of the five heavy metals are shown in table 2.
TABLE 2 heavy metal contamination of soil
Therefore, the five heavy metals have right bias (bias value is more than 0) to a certain degree, wherein the high bias values of Cu and Pb indicate that the two heavy metal elements are influenced by artificial activities such as mining of mineral resources and industrial production to cause the content of local heavy metal to exceed the background value. The coefficient of variation in table 2 is commonly used to reflect the average degree of variation of each sampling point in the research area and the accumulation of each element in soil. Generally, the variation is strong when the variation coefficient is larger than 1, and the large variation coefficient can indicate that the heavy metal in the soil is influenced by stronger human activities. The variation coefficients of the five heavy metals are respectively Pb, Cu, As, Zn and Hg, are more than 50 percent and belong to strong variation, which shows that the five heavy metals have large local mass fraction variation and obvious spatial variation, and the high variation can be attributed to the strong influence of artificial activities such As mining of mineral resources and the like.
4.2 heavy Metal contamination status in research area
And analyzing the data and obtaining the heavy metal distribution condition of the soil in the research area by an IDW interpolation technology based on ArcGIS. Fig. 3 to 7 show the interpolation results of five heavy metals, and the dark color range in the graphs is the mining area. Except Hg, the distribution trends of the contents of the other four heavy metals are similar, and the high-value aggregation areas are located at junctions of three villages and towns, namely a white dew pond town, a pond stream town and a big Kuixiang town; as, Pb and Zn appear in local areas with high value areas with small-scale distribution caused by small mining activities. The Hg content of the research area is generally high, and the high value distribution is relatively dispersed, and mainly exists in Wuli town, roost town, Matouling town, Qiaokou town, fertile field town and upper town. According to investigation, a high-value area is near a persimmon bamboo garden-agate mountain large-scale metal mining area, and the heavy metal content of soil around the mining area is increased due to a large number of mining, transporting, processing and other processes; the local area has sporadic distribution high value area, which is mainly distributed near the small mining area; the phoenix crossing town and the cave towns are the main coal mine area, but the soil of the two towns is less affected by heavy metal pollution, and the fertile field towns are the lead-zinc ore area, but the heavy metal content of the soil is lower, and no high-value accumulation area is formed.
The periphery of a large mining area is influenced by large-scale strong artificial activities, and a large-area high-value gathering area is presented. The small mining area mainly influences local small-range soil of the villages and towns, and high-value gathering areas mostly appear at junctions of the villages and the towns.
5. Soil heavy metal ecological risk pollution evaluation
5.1 soil heavy metal single factor ecological Risk evaluation
And (3) evaluating the potential ecological risks of five heavy metal pollution in the research area by utilizing an ArcGIS interpolation technology and combining a potential ecological risk evaluation method. Fig. 8 to 12 show the distribution of the ecological risk level of five heavy metals in the research area in various towns and towns respectively. The ecological risk grade of Zn and Cu in the whole region range is slight (grade 1), and the ecological pollution risk is low; the Hg risk index distribution is darker in color, which indicates that Hg is serious in pollution condition in the whole area and high in ecological risk level. As and Pb high-risk polluted areas appear in local areas, especially the pollution of villages and towns (on white exposed ponds, pond streams and great quails) near large metal mining areas in persimmon bamboo gardens is the most serious, the pollution risk level reaches more than 3 grades, and the pollution belongs to heavy pollution. In addition, high risk areas for As are also present in coal production areas located in the cross-green area of Fengdu. The heavy metal ecological risk result obtained through GIS interpolation can only visually describe the approximate distribution trend of the heavy metal ecological risk result, and the pollution risk cannot be evaluated on each village and town specifically. Therefore, the method analyzes the occupied areas of different pollution risk grades of all villages and towns, and simultaneously, because the Zn and Cu pollution risk indexes of the Suxian are low, the risk grade division based on the villages and the towns is not needed, the method only performs single-factor ecological risk grade area statistics and analysis on the three heavy metals of As, Hg and Pb of all the villages and the towns.
5.11 evaluation of potential ecological Risk of As
Table 3 the areas of various towns and towns with different risk levels of As contamination were counted, and were divided into 5 total levels, each level corresponding to an ecological risk level (see table 3). Overall, 59.37% of the area occupying the whole area is at slight risk of contamination, with distribution at various towns. 2. The proportion of the 3-level ecological risk area to the total area of the research area is 24.46%, 10.05% and 6.10%, the proportion of the 5-level ecological risk area to the total area of the research area is only 0.02%, and the Suxian area can be considered to have no extremely strong ecological risk area due to small occupied area; as contamination is mainly localized. Looking at table 3 in the lateral direction, for each town, towns occupying more than half of the area of the town at moderate risk and above include Liaowang village, Gongzu village, Matouling village, white deer hole town, Bailu pond town, Tangxi village. Looking at table 3 in the longitudinal direction, the moderate (level 2) ecological risk areas are mainly concentrated in the town of Matouling, the town of white deer cave, the town of white exposed pond and the town of pond stream, and the proportion of the total area occupying the risk level in the research area is more than 10%. The 3 grades and the 4 grades are strong and very strong risk grades, and the large-area 3-grade risk is mainly positioned in the white open pond town, the pond village and the big quan village and respectively occupies 40.50 percent, 20.07 percent and 19.39 percent of the total area of the risk grade; the Bailu pond town and the pond xi village are also the places where the main 4-level risk pollution areas are located, the area ratio in the grade is more than 40%, and is respectively 40.05% and 20.07%, and in addition, 19.39% of the area of the Daqiuyou village is at the 4-level pollution risk. According to investigation, the distribution of coal mines is mainly concentrated on roosting ferry-a lot of holes, and coal mining is accompanied by As pollution, so local strong pollution risk areas exist in malting villages, hills and foot villages, bridgehead towns, white deer hole towns and the like.
Major As moderate pollution risk areas of a Matouling town, a white deer cave town, a white exposed pond town and a pond stream town are required to enhance the monitoring, prevention and control of soil heavy metals in the towns; bailu pond, Tangxi and Daqin are villages and towns with main strength and above risks. A more accurate village-based division is performed on villages and towns in which strong (4-level) risk areas exist, and comprises the following steps: horse head ridge village: the board building village; pond xi village: violence village, quinma ridge village, shanghe village, and stone tiger village; and (3) ballast of the Bailu pond: DOUBOMUN, BAIZUOYUANUN, BAILUOTAN, XIANGSHAPINGCUN; big quai shangxiang: paradise village and taipan head village. The high content is related to the activities of large mining areas, is greatly influenced by the activities of mining and the like, is uniformly distributed around the mining areas, and is used As an important As pollution monitoring and treating area.
TABLE 3 As potential ecological Risk status in various towns
% except the total column indicates the proportion of the area of a certain risk class occupying the total area of the risk class in the study area in the township; the total column% represents the proportion of the risk grade area to the area.
5.12Hg assessment of potential ecological Risk
The areas of the Hg pollution risk levels in various towns are shown in table 4. Overall, there is more severe Hg pollution in suxian, 56.17% of the area is at strong (class 3) Hg pollution risk, and 26.43% of the area is at strong (class 4) Hg pollution risk; table 4 looking transversely, more than half of the area in the rest towns is a strong risk pollution area except for the hills and the towns, Liao family, the gulf village and two streets. Hg pollution in rural areas is mainly moderate risk, and the moderate risk grade area accounts for 84.5% of the area in the township. Table 4 looking longitudinally, the three towns on the whitepond, lagoon and great quail are distribution areas of major strong Hg pollution risk, occupying the risk class (class 3) areas of 13.37%, 14.49% and 16.48%, respectively. The 4-level pollution risks mainly occupy towns, namely a Wuli town (16.32%), a bridge town (14.76%) and a town (10.79%), and the occupied areas of the rest towns are less than 10%. The ecological risk of extremely strong (grade 5) pollution locally appears in bridge opening towns, which accounts for 65.38% of the total area of the grade 5 risk, and the rest areas of extremely strong risk are located in Wuli Town and Liang Town, which account for 14.06% and 4.64% of the grade respectively
The pollution condition of Hg in Suxian district is serious, more than 50% of area in most towns has moderate and more pollution risks, and each town should do good monitoring work. Wherein, the major remediation areas are mainly 6 villages and towns around the nonferrous metal mining area, such as the whitepond, the pool stream and the great town, the five-mile card, the bridge junction and the depression are mainly used, the corresponding heavy metal remediation and management measures are taken, and the activities of mining, transportation and the like of mineral resources are standardized.
TABLE 4 evaluation of potential ecological risk of soil Hg in various towns
% except the total column indicates the proportion of the area of a certain risk class occupying the total area of the risk class in the study area in the township; the total column% represents the proportion of the risk grade area to the area.
5.13 potential ecological Risk assessment of Pb
Table 5 shows the area statistics of the Pb pollution risk levels in each town. The areas of high risk of Pb in suxian are small, with 80.23% of the area being a slight risk of contamination. Liao Wang Pong, Taiping, Wuli brand, Chili cave, Suxianling and south Tower street, Liang Tian, Deng Jia Dan and Liao Jiawan are all slight risks of Pb throughout the entire area. The main distribution areas of moderate risk (grade 2) and strong risk (grade 3) of Pb are the same As the main distribution areas of strong risk (grade 3) of As, and are located in the peripheral towns of large-scale mines at the penma ridge zone of the Yangxi, namely the Bailu Yangxi (grade 2 30.40%, grade 3 33.20%), Yangxi (grade 2 28.84%, grade 3 33.49%) and Daqushangxi (grade 2 13.62%, grade 3 23.65%). In addition, there are fractional level 2 and level 3 pollution risk areas at the bridge portal and on the towns. The Bailu pond town and the Daqishangxiang are locations of the ecological risk areas with strong (4-level) and strong (5-level), which indicates that the ecological risk of extremely high Pb exists locally in the jurisdiction areas of the two towns.
Although Pb does not cause large-area pollution in a research area, similar to a main strong pollution risk area of As, the Lou Pond, the Yangxi town and the Daqu village town are ecological risk high-value areas mainly storing Pb, and the local content is overhigh. The key Pb monitoring and managing areas are a Yanglongcun, a Wuma Ridge village, a Shanghe village and a Shihu village of the Yangxi village; dongbourcun of Bailu pond town, Shi Zhu Yuan village, Bailu pond village, Xiangshan lawn village; the Liangyanping village and Taiping head village of Daqixiang, and the Baoanling village and Baixi village of Qiaokou town.
TABLE 5 potential ecological risk status of Pb in various villages and towns
% except the total column indicates the proportion of the area of a certain risk class occupying the total area of the risk class in the study area in the township; the total column% represents the proportion of the risk grade area to the area.
5.2 comprehensive ecological Risk assessment of soil heavy metals
On the whole, the comprehensive ecological risk index grade of the Suxian is higher, particularly, the area near a metal mine in a persimmon bamboo garden is the most serious, most of the area is in a very strong (4-grade) ecological risk grade, and the villages and towns mainly related to the Suxian comprise a white exposed pond, a pool stream and a big quail; other level 4 ecological risk areas are located primarily in coal mines, including bridgehead towns, matouling towns, and wuli town. Fig. 13 is a comprehensive ecological risk grade chart of five heavy metals, and fig. 13 shows that most areas are ecological risks of more than grade 3, and the bailu pond town, the pond xi village and the great quai shangxiang form a large-range ecological risk pollution area of grade 4, and the soil at the center of the range, namely the location of the large mining area, is ecological risk of grade 5. In order to further clarify the comprehensive ecological risk level status of each village and town, the area occupied by different risk levels of each village and town is counted, and the result is shown in table 6.
TABLE 6 Total ecological risk evaluation of heavy metal potential in soil of various villages and towns
% except the total column indicates the proportion of the area of a certain risk class occupying the total area of the risk class in the study area in the township; the total column% represents the proportion of the risk grade area to the area.
The comprehensive pollution risk level of the Suxian district is high, the pollution condition is obvious, only 2.34% of the area is slight risk, more than 50% of the area (65.80%) is 3-grade strong pollution risk, the 4-grade very strong pollution risk area accounts for 18.33%, and the small amount of very strong pollution risk area is 0.75%; in a transverse view, except for the Liao Wang Pong village, Suxianling village, south Tower street, Deng Pong village and Liao Bay village, most areas of other villages and towns mainly have pollution risks of grade 3 and above. Wherein, the local areas with strong local pollution risk are mainly on the Matouling (4 level 7.40%), bridge entrance (4 level 8.83%, 5 level 17.72%), white pond (4 level 24.68%, 5 level 59.60%), pond stream (4 level 26.89%), down-strike (4 level 5.97%, 5 level 2.29%) and Daqu (4 level 15.13%, 5 level 19.95%)
The high ecological risks of the Bailu and Yanxi towns are possibly related to the large mining areas existing at the periphery, so that the two towns are used as areas for controlling, preventing and controlling the heavy metal key pollution of the soil, and the monitoring strength of the heavy metal in the soil at the periphery of the large mining areas is increased.
6. Evaluation of heavy metal pollution of soil based on village and town scale
The evaluation of the heavy metal pollution of the mining area soil is mostly concentrated on the large area range around the mining area, no specific area division standard exists, although the degree and the approximate area range of the mining area pollution can be known, the large area is not divided into smaller areas, the specific description and analysis are not convenient, and the positioning of the small area pollution area cannot be realized, so that the local small area pollution is easy to ignore (Ryszard m., 2016; Obiora s.c., 2016; i.m.h.r.antunes et., 2016). Heavy metal pollution evaluation is also performed on soil heavy metal pollution in a special environment which is possibly affected by a mining area, and although the method is highly targeted, the pollution generated by the mining area cannot be comprehensively known in a limited research area range (Yan Wet al, 2015). The method also aims at the mining area soil, and aims to comprehensively know the heavy metal pollution condition of the soil in a large area, and simultaneously consider the pollution condition in a small area to realize the positioning of the high-risk pollution area.
Taking the evaluation of the ecological pollution risk of the As in the soil As an example, the As single-factor potential ecological risk level chart (figure 3) and the As potential ecological risk conditions (table 3) of various towns in the evaluation result are combined for analysis. The approximate extent of the high risk areas can be seen in fig. 3, but further high risk location positioning and area statistics require segmentation of the level map with administrative divisions. Table 7 shows the segmentation results, and the first-level key pollution remediation village (very high ecological risk) and the second-level pollution remediation village (high ecological risk) are listed for the high risk pollution area, and the rest of the towns have lower risk levels and are only used as monitoring areas. Due to the fact that pollution division areas are reduced, pollution management of the mining area is specifically distributed to all villages, execution difficulty of pollution control of decision makers is reduced, and meanwhile attention can be paid to a high-risk pollution area in a small range. The guiding significance of this study was ignored by the previous studies.
TABLE 7As high-risk pollution key treating village and grade table
6.1 adjustment of potential ecological Risk assessment Domain
The determination of the comprehensive potential ecological hazard grade (RI) is related to the kind and amount of pollutants, and since the Hakanson method is an evaluation standard based on the determination of 8 kinds of heavy metals, the evaluation standard is no longer applicable when the number of heavy metals is less than or more than 8. In this respect, the invention properly adjusts the evaluation domain of the Hakanson method, so that the evaluation result is closer to the true value. Comparing the total risk distribution map (fig. 13) after adjusting the evaluation domain 3 with the unadjusted fig. 14, the position of the relatively high-risk villages and towns does not change significantly, but it can be clearly seen that the total pollution risk of the suxian area before the unadjusted is smaller, only 4 risk levels appear, and most areas are moderate risk (level 2); the adjusted risk level increased to 5 levels and most areas were at strong risk (level 3). In order to quantitatively express the influence of the adjustment evaluation domain on the potential ecological risk evaluation, the area conditions of different comprehensive risk levels of Suxian district before the adjustment evaluation domain are counted and compared with the adjusted statistical result (table 6), the risk levels are greatly changed in the research area, and the area ratio of each level before and after the adjustment is shown in table 8. Comparison shows that after the evaluation domain is adjusted, the grade 1 and grade 2 risk grade areas are obviously reduced from 17.92 percent and 66.28 percent to 2.34 percent and 12.77 percent respectively, the high risk area is obviously increased, the grade 3 strong risk is the dominant pollution risk grade (65.80 percent) of Suxian, and a small part of the area has grade 5 pollution risk, and the grade is not appeared before the adjustment. The soil ecological risk level is easily weakened without adjustment of an evaluation domain, and the real situation is not easily reflected.
TABLE 8 Suxian area statistics of risk level before and after adjustment of assessment Domain
| Area ratio (%) | Grade 1 (slight) | Grade 2 (middle) | Grade 3 (Strong) | Grade 4 (very strong) | Grade 5 (extremely strong) |
| Before adjustment | 17.92 | 66.28 | 15.43 | 0.37 | — |
| After adjustment | 2.34 | 12.77 | 65.80 | 18.33 | 0.75 |
| Increase/decrease rate | —86.9％ | —80.73％ | +76.55％ | +97.98％ |
6.2 selection of soil heavy Metal spatial interpolation method
According to the method, the IDW interpolation is used for predicting and analyzing the heavy metals in the soil, a plurality of interpolation methods such as IDW, Spline and Kriging can be selected at present, and although three common interpolation methods are largely summarized by the former people, the conclusion is not consistent. There are methods in which artificial IDW has the best interpolation effect (Ferguson R B et al, 1996; Weber D et al, 1992), but there are also kriging interpolation superior to the rest of the interpolation methods (Laslett G M et al, 1987; Panagopoulos T et al, 2006). Therefore, there is no uniform standard for the selection of various interpolation methods, and there is no absolute advantage of one method over the others (Milillo T M et al, 2008). The interpolation methods used for different interpolation objects also differ (Bhunia G S et al, 2016; Mcshane L M et al, 1997; Dlamini P et al, 2012). Inverse distance weighted Interpolation (IDW) is applicable to non-normal distribution data, does not require the computation of high-precision half-variogram, and is simple and easy to operate (Xia et al, 1968). Table 9 shows the results of the single-sample K-S test, which can visually indicate whether the data are normally distributed. The table shows that the bilateral significance values of the five heavy metals are less than 0.10, so that the null hypothesis is denied, the data do not accord with the significance test, and the five heavy metals are considered not to obey normal distribution; meanwhile, the smoothing effect of the interpolation method should be considered, and as various interpolation methods have smoothing effects with different sizes, the precision of prediction of pollution of a research area is reduced, and the complexity of reflection of the overall pollution condition of the area is also reduced (Xie Y et al, 2011). The IDW interpolation has a smaller smoothing effect, and researchers compare several interpolation methods to find that the average pollution index calculated by the IDW interpolation is closer to the average pollution index of the sampling points. Therefore, the invention adopts an inverse distance weighting method (IDW) to carry out interpolation analysis on the heavy metal sampling points in the research area.
In future research, there is a large room for research on improving interpolation accuracy. If the semivariance function with higher precision is considered, the kriging interpolation can be used; improving IDW interpolation accuracy requires selection of exponent values, estimation of neighborhood sample numbers, etc. (Kravchenko A et al, 1999; Weber D et al, 1992); continuous variables or category information related to soil attributes can also be used as auxiliary information to improve interpolation precision, so that the result is closer to a true value; for different research objects, the result precision can be improved by combining several interpolation methods (dlimini P et al, 2012); researchers apply high-progress surface modeling (HASM) to soil attribute interpolation based on a traditional interpolation method, and the method has the advantage of more remarkable precision.
TABLE 9 Single sample Kolmogorov-Smirnov test
Wherein, the a test distribution is normal distribution; b is calculated according to the data.
The method takes a typical mining area (Suxian district, Chenzhou city) as an example, selects the soil heavy metal as a research object to evaluate the potential ecological risk of the soil heavy metal in a research area, cuts a potential ecological risk grade graph by using administrative division based on the grade of a village and a town, counts the risk grade area by the village and the town, can position a high-risk polluted area of the soil heavy metal from a smaller scope, and enables the research result to have practical guiding significance. The following are several results that the present invention ultimately achieves:
(1) by means of IDW interpolation, the Hakanson potential ecological risk method of the adjustment evaluation domain can be used for effectively evaluating the pollution of the heavy metals in the soil of the mining area. According to the invention, the Hakanson evaluation standard is adjusted according to the actual number and types of heavy metals, the risk evaluation result is more serious before improvement, the pollution level is high, and the adjustment of the evaluation domain is more in line with the pollution condition of a research area.
(2) And cutting the pollution evaluation result of the Suxian district based on the village and town scale, counting areas with different risk levels in the range of each village and town, positioning a high-risk area by using a village boundary, and depicting the pollution condition from a finer granularity, so that the research result has practical guiding significance.
(3) The distribution of the content of the five heavy metals is similar to that of the four other heavy metals except Hg, high-value gathering areas are located at the south of a white open pond town, a pond stream town and the junction of the white open pond town and a big quintic town, the content of Hg in a research area is generally high, and high-value gathering areas are relatively dispersed and mainly exist in a Wuli town, a roost crossing town, a mad green, a bridge entrance town, a fertile field town and a downward-rising town. The single factor and the internal Merlot pollution index show that the heavy metal pollution condition of the five soils in Suxian is obvious.
(4) The single-factor ecological risk index shows that large areas of As and Pb high-risk polluted areas are distributed in the white dew pond town, the pond stream town and the Daqiuxiang town; most of Suxian area soil is in Hg intensity and above with major ecological risks, the villages and towns with strong ecological risks are white open ponds, pond streams and great quails, and very strong (level 4) ecological risk areas appear in Wuli cards, Matouling and Qiaokou towns; bridgehead, Wuli brand and fertile farmland are also the locations of the main extremely strong (5-grade) ecological risk areas; the single-factor ecological risk evaluation result shows that Zn and Cu are in slight pollution risk level in the whole region range; the comprehensive ecological risk index shows that: the comprehensive ecological risk index grade of the Suxian district is higher, 82.9% of soil area in the research district is in intensity (grade 3) or above ecological risk, the soil is seriously polluted by heavy metal, and the strong (grade 4) risk area is mainly concentrated in a polymetallic mining area and a coal mining area of a persimmon bamboo garden.
Other embodiments of the present technology will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the technology following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the technology pertains and as may be applied to the essential features hereinbefore set forth. The specification and examples are to be considered as exemplary only, and the technical scope of the present invention is not limited to the contents of the specification, and must be determined in accordance with the scope of protection of the present application.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is only limited by the content of the appended representative protection scope.
Claims (7)
1. A method for evaluating the heavy metal pollution characteristics and potential ecological risks of soil on a village and town scale is characterized by comprising the following steps of:
determining a soil sampling point according to the distance from a mining area in a research area, wherein the soil sampling point is far away from the edge of a field block and the roadside and avoids fertilizer and pesticide application points;
secondly, arranging a plurality of sampling points around each soil sampling point, collecting samples at each sampling point, mixing the samples collected by all the sampling points to obtain soil samples corresponding to the soil sampling points; detecting heavy metal data in each of the soil samples;
calculating a single-factor potential ecological hazard index of each heavy metal at each soil sampling point according to the heavy metal data, and calculating a comprehensive potential ecological hazard index at each soil sampling point;
and step four, adjusting the evaluation domain of the comprehensive potential ecological hazard index according to the types and the quantity of the heavy metals in the research area, and evaluating the potential risk condition of heavy metal pollution in the research area according to the adjusted evaluation domain.
2. The ecological risk assessment method according to claim 1, wherein the heavy metal data of the soil sampling points are subjected to interpolation processing, and the interpolation processing comprises the following steps: taking the distance between the interpolation point and the soil sampling point as a weight to carry out weighted average, wherein the closer the distance to the interpolation point, the soil sampling point is endowed with a higher weight value; setting a series of sampling points distributed on the plane of the research area, wherein the coordinate value of the sampling points is Xi,Yi,Zi(i 1, 2.. times.n), solving a Z point value through a weighted value, wherein the formula is as follows:
wherein Z (x) is a predicted value of an interpolation point, ziIs the value of the known point, n is the total number of interpolations, diIs the distance, W, between the known point and the predicted pointiIs the weight between the known point and the interpolated point, the closer to the known point the greater the weight, the inverse ratio of the weight to the distance, u is the determining coefficient, determines WiDecreasing speed with increasing distance.
3. The ecological risk assessment method according to claim 1, wherein the single-factor potential ecological hazard index E is calculated by the formula:
the comprehensive potential ecological hazard index RIjCalculating the formula:
wherein:
the index is a single potential ecological risk index of the heavy metal i at the sampling point j; RI (Ri)jComprehensive potential ecological risk indexes of the heavy metals at the sampling point j are obtained; t isiThe toxicity response coefficient of the heavy metal i reflects the toxicity level and the sensitivity of organisms to the pollution;
the pollution coefficient of the heavy metal i at the sampling point j is shown;
the actual measured value of the heavy metal i at the sampling point j is obtained;
is the reference content of heavy metal i.
4. The ecological risk assessment method according to claim 2, wherein after interpolation processing is performed on each heavy metal data at the soil sampling point, a soil heavy metal distribution map of the whole research area is drawn according to the interpolation processing result, so as to determine the distribution area of each heavy metal in the research area.
5. The ecological risk assessment method according to claim 4, wherein a surface plot of the ecological risk level of each heavy metal in the research area in each village and town is drawn according to the soil heavy metal distribution map and the single-factor potential ecological hazard index, so that the pollution risk level of each heavy metal in each village and town in the research area is divided.
6. The ecological risk evaluation method according to claim 1, wherein a comprehensive ecological risk level map of each heavy metal is drawn according to the comprehensive potential ecological hazard index, and areas occupied by different risk levels of all villages and towns in the research area are counted according to the comprehensive ecological risk level map, so that a total ecological risk evaluation result of the heavy metal potential of the soil of each village and town is obtained.
7. The ecological risk assessment method according to claim 1, wherein the evaluation domain of the comprehensive potential ecological hazard index is: an index of less than 70 is a slight risk; an index between 70 and 140 is moderate risk; an index between 140 and 280 is a strong risk; an index of 280 or more is a strong risk; an index greater than a set value is extremely risky.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112686497A (en) * | 2020-12-05 | 2021-04-20 | 辽宁大学 | Method for quantizing field soil environmental damage identification object based on kriging interpolation |
| CN112966416A (en) * | 2021-03-08 | 2021-06-15 | 河海大学 | Resource utilization method for soil compaction shield fine sand and silty clay mixed muck |
| CN113313300A (en) * | 2021-05-25 | 2021-08-27 | 辽宁大学 | Method for identifying soil environment damage physical quantity based on reverse distance weighted interpolation |
| CN113406025A (en) * | 2021-06-09 | 2021-09-17 | 水利部交通运输部国家能源局南京水利科学研究院 | Method for determining dredging range of ecological dredging project of water area |
| CN114054486A (en) * | 2021-11-15 | 2022-02-18 | 四川大学 | Method for retarding heavy metal migration and application thereof |
| CN114443982A (en) * | 2021-05-06 | 2022-05-06 | 中南大学 | Large-area soil heavy metal detection and space-time distribution characteristic analysis method and system |
| CN116679033A (en) * | 2023-06-07 | 2023-09-01 | 生态环境部华南环境科学研究所(生态环境部生态环境应急研究所) | Method and system for judging arsenic environmental risk of soil of industrial contaminated site |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104931574A (en) * | 2015-06-05 | 2015-09-23 | 浙江中烟工业有限责任公司 | Visualization method for regional distribution of contents of heavy metals in tobacco |
| WO2015149408A1 (en) * | 2014-04-04 | 2015-10-08 | 江苏省环境科学研究院 | Ecological risk determining method for heavy metal pollution in river and lake sediments |
| CN107066831A (en) * | 2017-05-19 | 2017-08-18 | 君晟合众（北京）科技有限公司 | A kind of regional complex environmental assessment techniques, apparatus and system |
-
2020
- 2020-04-22 CN CN202010323657.7A patent/CN111552924A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015149408A1 (en) * | 2014-04-04 | 2015-10-08 | 江苏省环境科学研究院 | Ecological risk determining method for heavy metal pollution in river and lake sediments |
| CN104931574A (en) * | 2015-06-05 | 2015-09-23 | 浙江中烟工业有限责任公司 | Visualization method for regional distribution of contents of heavy metals in tobacco |
| CN107066831A (en) * | 2017-05-19 | 2017-08-18 | 君晟合众（北京）科技有限公司 | A kind of regional complex environmental assessment techniques, apparatus and system |
Non-Patent Citations (2)
| Title |
|---|
| 付亚星: "石家庄市土壤重金属空间分布特征及污染评价研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》 * |
| 熊仕娟 等: "辣椒产地土壤重金属潜在生态风险评价——以贵州省百宜镇为例", 《西南大学学报（自然科学版）》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112686497A (en) * | 2020-12-05 | 2021-04-20 | 辽宁大学 | Method for quantizing field soil environmental damage identification object based on kriging interpolation |
| CN112966416A (en) * | 2021-03-08 | 2021-06-15 | 河海大学 | Resource utilization method for soil compaction shield fine sand and silty clay mixed muck |
| CN114443982A (en) * | 2021-05-06 | 2022-05-06 | 中南大学 | Large-area soil heavy metal detection and space-time distribution characteristic analysis method and system |
| CN113313300A (en) * | 2021-05-25 | 2021-08-27 | 辽宁大学 | Method for identifying soil environment damage physical quantity based on reverse distance weighted interpolation |
| CN113406025A (en) * | 2021-06-09 | 2021-09-17 | 水利部交通运输部国家能源局南京水利科学研究院 | Method for determining dredging range of ecological dredging project of water area |
| CN113406025B (en) * | 2021-06-09 | 2024-02-09 | 水利部交通运输部国家能源局南京水利科学研究院 | Determination method for dredging range of water area ecological dredging engineering |
| CN114054486A (en) * | 2021-11-15 | 2022-02-18 | 四川大学 | Method for retarding heavy metal migration and application thereof |
| CN116679033A (en) * | 2023-06-07 | 2023-09-01 | 生态环境部华南环境科学研究所(生态环境部生态环境应急研究所) | Method and system for judging arsenic environmental risk of soil of industrial contaminated site |
| CN116679033B (en) * | 2023-06-07 | 2024-01-23 | 生态环境部华南环境科学研究所(生态环境部生态环境应急研究所) | Method and system for judging arsenic environmental risk of soil of industrial contaminated site |
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Application publication date: 20200818 |