Distribution Characteristics and Source Analysis of Soil Heavy Metals in the Liaoyang—Dandong Region
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摘要:
辽阳—丹东地区是辽宁省重要粮食与经济作物生产区，以往土壤重金属研究主要集中在200cm以浅，掌握土壤0～500cm重金属分布特征及来源，对监测黑土地质量及研究土壤重金属迁移转化意义重大。本文采集了1381件土壤表层(0～20cm)、160件土壤中层(150～250cm)和75件土壤深层(250～500cm)样品，对其中8种重金属元素和Sc等元素含量进行了测定，探讨研究区土壤重金属在不同层分布规律并解析来源。调查结果显示：土壤表层、中层和深层As、Cd、Cr、Hg、Co、Pb、Sc、Zn和Cu平均含量均未超过污染风险筛选值；通过多元统计，Cd和Hg表层富集明显，Pb和Zn表现一定富集趋势，Cd、Hg和Pb在表层分布不均匀；通过空间分析方法和地累积指数，研究区土壤表层中Cd、Hg、Pb和Zn整体表现为轻微污染，中度污染点零星分布在人员集中和采矿活跃区域；土壤中层Pb元素轻微污染，深层元素均未污染；利用Pearson相关性分析、主成分分析(PCA)和人为贡献率(ACR)揭示Pb、As、Co、Cu、Cr、Sc和Zn主要受自然背景的影响，Pb和Zn局部轻微受人为因素的影响，Cd和Hg则受人为活动的影响显著。研究区需要加强对Hg、Cd、Pb和Zn元素在不同地块富集趋势的监测，中层深度需要关注Pb的富集，同时开展区内不同流域土壤的Cd和Hg在不同层位的迁移转化研究。
要点(1)变异系数、富集因子等多元统计方法和地理空间分析，相互印证揭示土壤重金属元素分布特点。
(2)地累积指数评价土壤重金属污染程度，表层土壤Cd、Hg、Pb和Zn表现为轻微污染，中层Pb轻微污染，深层元素均未污染。
(3) Pearson相关性分析、主成分分析和人为贡献率解析土壤重金属来源，Cd和Hg受人为活动的影响显著。
HIGHLIGHTS(1) Multivariate statistical methods such as variation coefficient and enrichment factor and geospatial analysis confirm each other and reveal the distribution characteristics of heavy metal elements in soil.
(2) Soil heavy metal pollution was evaluated by index of geoaccumulation. The surface soil was slightly polluted by Cd, Hg, Pb and Zn, the middle layer of soil was slightly polluted by Pb, and the deep soil was not polluted.
(3) The sources of heavy metals in soil were analyzed by Pearson correlation analysis, principal component analysis and anthropogenic contribution rate, and Cd and Hg were significantly affected by anthropogenic activities.
Abstract:The Liaoyang—Dandong region is an important grain and cash crop production area in Liaoning Province. It is of great significance for monitoring the quality of black soil and studying the migration and transformation of heavy metals in soil to understand the distribution characteristics and sources of heavy metals in soil from 0 to 500cm. Soil samples were collected from different layers, and the contents of 8 heavy metal elements and Sc elements were determined. The average contents did not exceed the pollution risk screening value, Cd and Hg were obviously enriched in the surface soil, and Pb and Zn showed a certain enrichment trend. Cd, Hg, Pb and Zn in the surface soil were slightly polluted, the middle layer of soil was slightly polluted by Pb, and the deep layer of soil was not polluted. Pb, As, Co, Cu, Cr, Sc and Zn were affected mainly by natural background, Pb and Zn were affected slightly by local human factors, and Cd and Hg were affected significantly by human activities. In the future, it will be necessary to strengthen the monitoring of the enrichment trend of Hg, Cd, Pb and Zn elements in different plots. The BRIEF REPORT is available for this paper at http://www.ykcs.ac.cn/en/article/doi/10.15898/j.ykcs.202404070080.
BRIEF REPORTSignificance: The Liaoyang—Dandong area is an important grain producing area in Liaoning Province. Due to the intensive use of black land and the industrial expansion of the city, a heavy metal enrichment trend began to appear in this area, attracting professional attention. The degradation of geochemical properties of black soil often shows heavy metal toxic elements (As, Cd, Cr, Hg, Pb, Ni, etc.) exceeding the normal content in terms of trace elements. The purpose of this study was to explore the distribution characteristics of heavy metal elements in soil in the Liaoyang—Dandong area from a shallow perspective of 500cm, and analyze the sources of these heavy metal elements, to provide scientific basis for the ecological environment protection of black soil in this area.
Methods: 1381 surface (0−20cm), 160 middle (150−250cm) and 75 deep soil samples were collected from the Liaoyang—Dandong area (Fig.1). The contents of heavy metals As, Cd, Cr, Hg, Co, Pb, Zn, Cu and Sc in soil were determined by XRF or ICP-MS/OES or AFS method (Table 1). Multivariate statistical and spatial distribution analysis methods were used to reveal the distribution characteristics of heavy metals in soil, and the geoaccumulation index, which isolates anthropogenic pollution from the total concentration determined within a sample［23］, was used to assess the pollution degree of different depths of soil. Pearson correlation analysis, principal component analysis (PCA) and anthropogenic contribution rate (ACR) were used to further explore the potential sources and effects of heavy metals.
Data and results: The variation coefficient of Cd and Hg in the surface soil was greater than or equal to 0.3, and the enrichment factor was greater than 1.9, showing the enrichment rule of the surface soil (Table 2 and Fig.2). The content of heavy metals in soil at different depths varied greatly. As, Cr, Co, Sc and Cu elements were almost identical in the peak area of the topsoil with those in the lower soil, which indicated that these elements had good inheritance characteristics. However, for Cd and Hg, their spatial distribution patterns were significantly different from those of other elements (Fig.3). Pearson correlation coefficient showed that Hg correlation was weak or not correlated in all regions, and Cd was not correlated in sedimentary rocks, moraines and quaternary alluvial regions (Table 3). Hg and Cd in surface soil were greatly affected by human activities. The evaluation results of geoaccumulation index showed that the surface soil was slightly polluted by Cd, Hg, Pb and Zn as a whole, the moderate pollution points were scattered in the areas where people were concentrated and mining was active, and the middle layer of soil was slightly polluted by Pb, which was due to the increase of ion exchange Pb content caused by topsoil acidification. According to the principal component analysis of heavy metal content in the surface and middle layers of soil and the calculation of anthropogenic contribution, As, Co, Cu, Cr, Pb and Zn originated mainly from the natural background, Pb and Zn were slightly affected by local human factors, while Cd and Hg were significantly affected by anthropogenic activities (Table 4, Fig.5). In addition, As was controlled mainly by sedimentary rocks, Pb was controlled mainly by granite, and Hg was partly attributed to metamorphic rocks.
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X射线荧光光谱定性分析技术经过长期的应用及发展，其应用范围也越来越广泛[1-4]。目前，XRF所带的定性分析软件(SQX)可自动对扫描谱图进行搜索和匹配，包括确定峰位、背景和峰位的净强度[5-7]，并从XRF特征谱线数据库中配对确定元素的谱线，这对从事XRF的分析者而言非常便利[8-10]。近年来刘岩等[11]采用XRF无标样分析法检测催化剂，测定结果的相对标准偏差小于1.3%；张红菊等[12]采用XRF无标样分析法检测轻合金铝合金中的主量元素，其测量值与认定值的相对误差低于±5%，测量结果都具有很好的可靠性和准确度。
自然界矿物种类复杂，应用XRF半定量分析软件(SQX)分析未知样品时，由于SQX软件仅对样品中9F~92U元素进行半定量分析，而对H2O、C这些参数不能直接测定。对于烧失量(LOI)、结晶水(H2O+)含量较高的铝土矿，二氧化碳含量较高的碳酸盐矿物，硫、碳含量较高的硫化物金属矿这类高烧失量矿物样品，平衡归一化计算时对未知样品中的Al2O3、SiO2、CaO、MgO、Fe等主要元素分析结果影响较大，半定量分析数据准确度较低。这就要求XRF分析人员需要掌握未知样品的来源及基本情况，根据测定结果对各元素在样品中的结构状态进行评估，选用更为合理的校正模式，提高半定量分析的准确性[13-15]。为了解决这个问题，本文提出了一种校正模式。该校正模式根据半定量分析初步结果，采用重量法、碘量法、酸碱测定法、红外光谱法有选择性地对未知样品中的LOI、S、C、H2O+等项目进行定量分析，然后将定量分析结果输入SQX该参数的固定结果中，二次平衡归一计算得出新的半定量分析结果。应用该校正模式校正后，铝土矿、碳酸盐矿物、硫化物金属矿等高烧失量矿物的半定量分析结果的准确度得到大幅度提高。
1. 实验部分
1.1 仪器与测量条件
ZSX PrimusⅣ型顺序扫描波长色散X射线荧光光谱仪(日本理学电机工业株式会社)，端窗铑靶X射线管，工作电压20~60kV，工作电流2~160mA，铍窗厚度30μm，视野光栏0.5~30mm，准直器: S2/S4，探测器: PC/SC，分光晶体:RX 25/Ge/PET/LiF200[16-19]。测量元素范围9F~92U。BP-1型压样机(丹东北方科学仪器公司)。各元素具体的测量条件见表 1。
表 1 仪器测量条件Table 1. Measuring conditions of the XRF equipment分析元素 数据库 靶材 电流
(kV)电压
(mA)滤光片 衰减器 准直器 晶体 探测器 PHA 重元素 Standard Rh 50 60 OUT 1/1 S2 LiF(200) SC 100~300 重元素(1) Sta-Ni400 Rh 50 60 Ni-400 1/1 S2 LiF(200) SC 150~250 Ca-Kα Standard Rh 40 75 OUT 1/1 S4 LiF(200) PC 100~300 K-Kα Standard Rh 40 75 OUT 1/1 S2 LiF(200) PC 100~300 Cl-Kα Standard Rh 30 100 OUT 1/1 S4 Ge PC 150~300 S-Kα Standard Rh 30 100 OUT 1/1 S4 Ge PC 150~300 P-Kα Standard Rh 30 100 OUT 1/1 S4 Ge PC 150~300 Si-Kα Standard Rh 30 100 OUT 1/1 S4 PET PC 100~300 Al-Kα Standard Rh 30 100 OUT 1/1 S4 PET PC 100~250 Mg-Kα Standard Rh 30 100 OUT 1/1 S4 RX25 PC 100~250 Na-Kα Standard Rh 30 100 OUT 1/1 S4 RX25 PC 100~250 F-Kα Standard Rh 40 75 OUT 1/1 S4 RX25 PC 100~300 1.2 SQX分析模拟计算流程
XRF半定量分析可选择测定未知样品中F~U或Ti~U之间的元素，分析测试程序完成后会自动报出大于仪检出限的各元素的分析结果，这时应根据测试结果作一个初步判断是否需要进行SQX计算；如不需要，则可以直接报出测定结果；如测定结果与样品实际结构状态有较大差别，则需选用更为合适的校正模式、平衡组分或添加其他方法测试结果后进行SQX计算，以得到更为合理的测定结果。定性分析的基本流程见图 1。
1.3 样品制备及实验方法
为验证本文提出的半定量分析模式分析校正效果，选用国家标准物质铝土矿GBW(E)70036、碳酸盐矿物GBW07131、硫化物多金属矿GBW07166作为待测样品，在105℃下烘干2h，称取4.5±0.1g，倒入放置于平板模具上的PVC塑料环(外径40mm，内径35mm，高5mm)中，在30t压力下加压30s压制成型，编号，置于样品盒内，用X射线荧光光谱仪半定量分析方法进行测试[20-22]。仪器自动计算出各元素的含量。
根据XRF半定量初步分析结果，按化学标准方法YS/T 575.19—2007、GB/T 3286.8—2014、GB/T 3286.7—2014、GB/T 14353.12—2010、GB/T 8151.2—2012、SN/T 3598—2013、GB/T 2469—1996、YS/T 575.18—2007选择性分析未知样品中的烧失量(LOI)、硫(S)、碳(C)、结晶水(H2O+)，计算出定量结果，备用。
将化学分析结果作为XRF半定量分析软件(SQX)中该元素的固定结果，重新进行平衡计算出新的半定量结果。
2. 结果与讨论
2.1 烧失量对铝土矿类型矿物半定量分析结果的影响
铝土矿是一种土状矿物，化学组成为Al2O3·nH2O，含水不定，多为单水或三水矿物[23-24]。由于XRF的局限性，对于H2O、C这些未定量的参数，其含量在铝土矿中较高[25]，平衡归一化计算时会对Al2O3、SiO2、Fe2O3等元素的影响较大。这时可采用烧失量校正的方法，添加烧失量(LOI)作为该样品的固定值，运行半定量分析软件(SQX)重新计算出新的结果。将GBW(E)70036作为未知样品用XRF定性分析方法进行分析，各种校正模式的计算值与认定值对照结果见表 2。
表 2 铝土矿标准物质GBW(E)70036各种校正模式计算值与认定值对比Table 2. Calculated values and standard values of bauxite standard material GBW(E)70036 in various correction models分析元素 氧化物模式测试
结果(%)添加LOI校正结果
(%)H2O作平衡
校正结果(%)GBW(E)70036
认定值(%)氧化物模式测试结果
相对误差(%)LOI校正结果
相对误差(%)MgO 0.136 0.121 0.116 0.120 13.33 0.83 Al2O3 76.94 67.51 64.46 69.74 10.32 -3.20 SiO2 7.91 6.62 6.12 4.88 62.09 35.66 P2O5 0.159 0.132 0.121 0.120 32.50 10.00 SO3 0.182 0.00 0.139 0.047 - - K2O 1.07 0.880 0.810 0.710 50.70 23.94 CaO 0.258 0.212 0.195 0.180 43.33 17.78 TiO2 5.10 4.17 3.81 3.97 28.46 5.04 Fe2O3 7.42 5.97 5.35 6.09 21.84 -1.97 LOI * 13.70 △ 13.74 - - H2O * △ 18.26 - - - 注：“*”表示XRF不能直接分析该参数，无数据；“△”表示在LOI或H2O其中一项参数有测量结果时，另一项结果不参与校正计算；“-”表示未定值或未统计计算。 据表 2可知，GBW(E)70036以氧化物模式的测试结果与认定值误差较大，当添加LOI校正计算后，其多个元素的平均相对误差由32.8%降至12.3%，准确度大幅提高。此外，在确定未知样品是未经高温灼烧的情况下，还可以采用H2O作为平衡组分直接计算，其计算结果也与认定值较为相近。
2.2 二氧化碳与烧失量对碳酸盐类型矿物半定量分析结果的影响
碳酸盐矿物中CO2的占比较高, 而CO2是SQX软件未能定量参数之一，给定性分析结果带来较大误差。为提高定性分析的准确度，可对CO2或烧失量进行定量分析[26]，添加烧失量或CO2定量分析结果作为该样品的固定值，运行SQX重新计算出新的结果。将GBW07131作为未知样品用XRF定性分析方法进行分析，各种校正模式的计算值与认定值对照结果见表 3。
表 3 碳酸盐标准物质GBW07131各种校正模式计算值与认定值对比Table 3. Calculated values and standard values of carbonate standard material GBW07131 in various correction models分析元素 氧化物模式
测试结果(%)CO2平衡
校正结果(%)添加LOI
校正结果(%)钙镁元素以碳酸盐
计平衡计算(%)GBW07131
认定值(%)氧化物模式测试
结果相对误差(%)LOI校正结果
相对误差(%)MgO 29.73 19.57 19.18 20.4 20.14 47.62 4.77 Al2O3 0.759 0.454 0.449 0.451 0.290 161.72 -54.83 SiO2 2.18 1.29 1.27 1.28 1.15 89.57 -10.43 P2O5 0.051 0.030 0.030 0.030 0.016 218.75 -87.50 SO3 0.442 0.256 0.00 0.254 - - - K2O 0.292 0.161 0.160 0.160 0.160 82.50 0.00 CaO 64.54 31.76 32.07 31.50 30.93 108.66 -3.69 TiO2 0.045 0.018 0.0186 0.0178 0.013 246.15 -43.08 MnO 0.038 0.015 0.016 0.011 0.012 216.67 -33.33 Fe2O3 0.435 0.169 0.176 0.167 0.170 155.88 -3.53 CO2 * 45.66 △ - - - - LOI * △ 45.67 - 45.73 - 0.13 注：“*”表示XRF不能直接分析该参数，无数据；“△”表示在LOI或CO2其中一项参数有测量结果时，另一项结果不参与校正计算；“-”表示未定值或未统计计算。 据表 3可知，GBW07131以氧化物模式测试结果较认定值误差较大。当添加LOI校正计算后，其多个元素的平均相对误差由122.4%降至27.2%，准确度大幅提高。此外，采用滴加稀盐酸确定未知样品是碳酸盐矿物的情况下，可以采用CO2作为平衡组分直接计算或者将CaO、MgO换算成为CaCO3、MgCO3计算模式重新平衡计算，其结果也与认定值较为相近。
2.3 碳硫元素对硫化物多金属矿类型矿物半定量分析结果的影响
硫化物多金属矿中的碳、硫元素含量较高，以氧化物模式对该类型样品进行半定量分析时误差较大。当采用化学法测定这类样品的烧失量时，硫化物金属矿中的硫在高温下会被空气中的氧替换，不仅会出现烧蚀减量，还会出现烧蚀增量，使得烧失量的结果是不准确的[27-28]，因此不能把烧失量校作为该未知样品的固定值对测定结果进行平衡计算。这时可以采用化学法测定该未知样品中的C、S元素，作为该样品的固定值，运行半定量分析软件(SQX)重新计算出新的结果。将GBW07166作为未知样品用XRF半定量程序进行分析，各种校正模式的计算值与认定值对照结果见表 4。
表 4 硫化矿多金属矿标准物质GBW07166各种校正模式计算值与认定值对比Table 4. Calculated values and standard values of sulfide polymetallic ore standard material GBW07166 in various correction models分析元素 氧化物模式测试
结果(%)总硫、总碳固定
平衡计算(%)LOI平衡计算
(%)Sulfide模式
校正结果(%)GBW07166
认定值(%)氧化物模式测试
结果相对误差(%)总硫、总碳校正
结果相对误差(%)MgO 0.360 0.350 0.675 0.505 0.310 16.13 12.90 Al2O3 1.60 1.55 3.03 2.29 1.25 28.00 24.00 SiO2 3.34 3.50 6.26 4.86 3.78 -11.64 7.41 S 18.43 33.80 0.00 27.75 33.80 - - K2O 0.306 0.433 0.387 0.484 0.320 -4.38 35.31 CaO 2.05 2.02 2.61 3.27 1.96 4.59 3.06 Fe 18.22 28.58 27.45 30.84 29.60 -38.45 -3.45 Cu 15.50 28.00 30.72 28.42 24.20 -35.95 15.70 Zn 0.025 0.057 0.049 0.055 0.057 -56.14 0.00 C * 0.138 △ - - - - LOI * △ 27.04 - - - - 注：“*”表示XRF不能直接分析该参数，无数据；“△”表示在LOI或C其中一项参数有测量结果时，另一项结果不参与校正计算；“-”表示未定值或未统计计算。 据表 4可知，GBW07166以氧化物模式或添加LOI校正计算结果后，测试结果较认定值误差较大，当添加全硫、全碳校正计算结果后，其多个元素的平均相对误差由27.2%降至9.5%，准确度大幅提高。此外，在没有条件测定全硫、全碳元素时，选用SQX软件中Sulfide校正模式重新平衡计算，其结果也与认定值较为相近。
3. 应用实例
选取3件不同类型的未知样品，应用XRF半定量程序分析，根据XRF半定量初步分析结果，计算对照结果见表 5。未知样品1、2在添加烧失量(LOI)校正计算后半定量分析结果与化学法分析结果比较，多个元素的平均相对误差分别由46.2%降至18.0%和37.6%降至7.1%。未知样品3添加总硫、总碳校正计算结果后，其多个元素的平均相对误差由28.1%降至10%，准确度得到了明显提高。若与DZ/T 130—2006《地质矿产实验室测试质量管理规范》要求定量分析规范中误差允许限(Yc)相比较，除少部分项目能满足规范要求外，大部分项目还是达不到定量分析要求。但是如铝土矿中的Al2O3，碳酸盐矿物中的CaO、MgO，硫化物多金属矿中Fe、Zn、Cu、Pb等元素的相对误差均在5%以内，与DZ/T 130—2006要求较为接近。
表 5 某未知样品各种校正模式的计算值与化学分析值对比Table 5. Calculated values and chemical analysis values of various correction modes for the unknown sample样品编号 分析元素 氧化物模式测试
结果(%)平衡校准计算
结果(%)化学法测定值
(%)氧化物模式测试
结果相对误差(%)平衡校准计算结果
相对误差(%)允许限Yc
(%)Al2O3 86.97 76.11 78.01 11.49 -2.44 0.63 SiO2 2.94 1.82 1.31 124.43 38.93 4.17 Fe2O3 3.26 2.54 2.55 24.84 -0.46 5.11 TiO2 4.29 3.4 3.10 38.38 9.78 4.80 未知样品1 K2O 0.19 0.17 0.16 18.75 6.25 10.45 CaO 0.33 0.31 0.31 6.45 0.00 9.00 MgO 0.29 0.25 0.20 45.00 25.00 9.95 P2O5 0.28 0.22 0.14 100.00 61.37 10.76 LOI * 14.6 14.6 - - 2.58 Na2O 0.76 0.71 0.781 -2.59 -8.72 7.17 MgO 0.29 0.24 0.21 39.14 14.95 9.84 Al2O3 0.48 0.39 0.31 54.13 26.16 9.00 SiO2 1.15 0.93 0.83 38.66 12.45 7.05 P2O5 1.24 1.00 0.97 28.34 2.88 6.77 Fe2O3 1.70 1.21 1.18 44.18 2.13 6.41 未知样品2 S 4.72 △ 3.21 47.04 - 4.74 CaO 1.13 0.81 0.83 36.64 -2.40 7.05 Cr 18.49 13.09 12.89 43.46 1.53 2.75 Ni 22.81 16.18 16 42.55 1.12 2.47 Cu 14.53 10.31 10.33 40.62 -0.23 3.04 Zn 3.04 2.16 2.02 43.24 5.94 5.49 LOI * 37.00 37.00 - - - MgO 0.21 0.200 0.185 14.49 8.11 10.12 Al2O3 0.53 0.472 0.427 23.87 10.61 8.34 SiO2 2.18 1.818 1.650 32.00 10.15 5.83 P2O5 0.02 0.024 0.028 -34.29 -13.21 14.91 S 18.84 34.02 34.02 - - - K2O 0.05 0.048 0.042 15.44 13.31 13.77 未知样品3 CaO 0.15 0.127 0.137 7.72 -7.50 10.81 TiO2 0.03 0.031 0.026 30.98 20.78 15.18 Fe 4.52 6.942 6.720 -32.81 3.31 3.64 Cu 0.68 1.057 1.218 -44.37 -13.18 6.36 Zn 32.24 50.517 48.250 -33.18 4.70 1.15 Pb 1.58 2.505 2.646 -40.46 -5.33 5.05 C * 1.21 1.21 - - - 注：“*”表示XRF不能直接分析该参数，无数据；“△”表示在LOI有测量结果时，该项结果不参与校正计算；“-”表示未定值或未统计计算。 4. 结论
实验证明采用本文提出的校正模式进行校正，分析铝土矿、碳酸盐矿物和硫化物多金属矿中多元素的平均准确度提高了2.6~4.5倍，半定量分析结果准确度大幅提高。其中，铝土矿中的Al2O3，碳酸盐矿物中的CaO、MgO，硫化物矿物中Fe、Zn、Cu、Pb等主量元素的相对误差均在5%以内，与化学法分析结果较为相近。本方法可快速、较为准确地测定铝土矿、碳酸盐矿物和硫化物矿物中多元素的含量。
这种化学法与半定量分析软件相结合的半定量校正模式，不仅可用于铝土矿、碳酸盐矿物和硫化物矿物，还适用于烧失量较高的锰矿、磷矿等矿物的压片半定量分析[29-30]。对于硫化物矿物等多金属矿的定量全分析，因为这类矿物容易腐蚀铂坩埚而很少采用熔片制样XRF分析[31]，通常采用化学分析法，但流程繁琐，本文研究方法可作为一种有效的矿石全分析的补充手段。
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图 1 研究区地质简图及采样点位图
Figure 1. Geological schematic map and sampling point bitmap of the study area
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图 3 土壤重金属元素含量空间分布图
Figure 3. Spatial distribution diagrams of heavy metal elements in soil
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图 4 不同层位土壤重金属元素地累积指数(Igeo)分布图
Figure 4. Distribution diagrams of geoaccumulation index (Igeo) of heavy metal elements in different depths of soil
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图 5 表层土壤对称-稳健多元成分数据分析
Figure 5. Symmetric and robust multi-component data analysis diagram of surface soil
表 1 分析方法质量监控
Table 1 Quality control of analysis methods
元素 分析方法 检出限(mg/kg) 准确度（△logC) RSD(%) 报出率(%) As AFS 0.20 0.004～0.024 3.06～6.51 100 Cd ICP-MS 0.02 0.004～0.039 3.34～8.88 100 Cr XRF 1.80 0.002～0.011 0.56～3.71 100 Cu XRF 0.90 0.002～0.036 0.59～6.00 100 Hg AFS 0.0003 0.000～0.031 2.69～8.59 100 Pb XRF 1.00 0.000～0.032 0.44～4.90 100 Zn ICP-OES 0.30 0.000～0.016 0.30～8.58 100 Sc ICP-OES 0.30 0.000～0.035 2.50～6.26 100 Co ICP-OES 0.60 0.000～0.035 1.79～6.21 100 
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表 2 土壤重金属元素含量统计
Table 2 Statistics of heavy metal element contents in soil
统计参数 样品数量 采样深度 As
(mg/kg)Cd
(mg/kg)Cr
(mg/kg)Hg
(mg/kg)Co
(mg/kg)Pb
(mg/kg)Sc
(mg/kg)Zn
(mg/kg)Cu
(mg/kg)平均值 1381 表层 9.643 0.243 70.311 0.063 14.991 39.856 11.695 85.196 24.827 160 中层 8.828 0.084 70.848 0.028 14.186 27.627 11.736 75.194 23.509 75 深层 9.442 0.107 70.709 0.033 14.081 30.484 11.752 76.426 24.246 中值 1381 表层 9.650 0.183 71.678 0.060 15.009 36.867 11.750 87.660 24.917 160 中层 8.435 0.078 70.700 0.025 14.000 27.300 11.800 75.950 22.100 75 深层 8.700 0.088 70.30 0.028 13.80 28.70 11.80 75.70 23.00 最小值 1381 表层 7.294 0.001 43.868 0.020 7.570 18.804 8.483 41.681 15.422 160 中层 1.410 0.021 19.700 0.006 3.250 2.260 4.350 21.700 6.640 75 深层 1.150 0.021 10.70 0.0047 2.260 2.260 3.090 12.00 3.54 最大值 1381 表层 11.79 1.093 142.353 0.181 20.673 99.575 15.357 140.552 32.513 160 中层 32.70 0.300 277 0.061 37.800 58.500 21.0 0 270 50.0 75 深层 49.50 1.480 516 0.250 51.90 231 31.40 402 115 标准偏差 1381 表层 0.984 0.150 13.144 0.019 2.505 14.862 1.483 14.606 3.432 160 中层 4.514 0.039 31.644 0.014 5.234 8.173 3.336 26.258 8.277 75 深层 5.470 0.092 33.293 0.024 5.279 14.082 3.381 27.646 10.446 变异系数 1381 表层 0.102 0.618 0.187 0.300 0.167 0.373 0.127 0.171 0.138 160 中层 0.511 0.464 0.447 0.517 0.369 0.296 0.284 0.349 0.352 75 深层 0.579 0.864 0.471 0.731 0.375 0.462 0.288 0.362 0.431 富集因子 − 表层/中层 1.096 2.903 0.996 2.249 1.060 1.448 1.000 1.137 1.060 − 表层/深层 1.026 2.282 0.999 1.903 1.070 1.314 1.000 1.120 1.029 背景值 − 表层 6.720 0.130 60.000 0.030 11.00 24.000 9.300 54.00 18.700
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表 3 土壤表层和中层重金属元素之间的相关系数
Table 3 Correlation coefficients between heavy metal elements in surface and deep soil
元素 Pearson系数 变质岩 沉积岩 第四系冰碛物 第四系冲积物 花岗岩 As 0.491** 0.408* 0.285* 0.530** 0.491** Cd 0.658** 0.112 0.045 0.208 0.613** Cr 0.713** 0.641** 0.742 ** 0.571** 0.596** Hg 0.283 0.027 0.073 0.215 0.236 Co 0.723** 0.774** 0.723** 0.654** 0.600** Pb 0.584** 0.477** 0.481** 0.539** 0.507** Sc 0.620** 0.891** 0.558** 0.521** 0.541** Zn 0.078 0.623** 0.658** 0.391** 0.326* Cu 0.393* 0.899** −0.049 0.506** 0.290* 注：“**”表示在 0.01 级别(双尾)相关性显著；“*”表示在 0.05 级别(双尾)相关性显著。
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表 4 表层土壤人为贡献率统计
Table 4 Statistics of anthropogenic contribution rate of surface soil
人为贡献率
数值范围样品数量(件) As Cd Cr Hg Co Pb Sc Zn Cu ACR＞40% 2 398 5 686 0 262 − 18 0 ACR＞60% 0 207 0 56 0 43 − 0 0
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期刊类型引用(5)
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