四川省沐川县典型土壤剖面重金属分布特征和生态风险评价

廖碧霞; 沈文灵; 贺灵

doi:10.15898/j.ykcs.202305090062

四川省沐川县典型土壤剖面重金属分布特征和生态风险评价

廖碧霞^{1, 2,},
沈文灵^{1, 2},
贺灵^1, ,

1.
中国地质科学院地球物理地球化学勘查研究所，河北廊坊 065000
2.
桂林理工大学，广西桂林 541006

基金项目: 中国地质调查局地质调查项目(DD20190522-03，DD20221641-02)

详细信息

作者简介:
廖碧霞，硕士研究生，主要研究方向为环境地球化学。E-mail：1470949680@qq.com

通讯作者:
贺灵，博士，高级工程师，主要研究方向为生态地球化学调查与评价。E-mail：lingh1237@163.com。

中图分类号: S151.93；X820.4
计量
- 文章访问数: 116
- HTML全文浏览量: 150
- PDF下载量: 28
出版历程
- 收稿日期: 2023-05-08
- 修回日期: 2023-07-05
- 录用日期: 2023-08-05
- 网络出版日期: 2023-12-07
- 刊出日期: 2023-12-30

Distribution Characteristics and Ecological Risk Assessment of Heavy Metals in Typical Soil Profiles of Muchuan County, Sichuan Province

LIAO Bixia^{1, 2,},
SHEN Wenling^{1, 2},
HE Ling^1, ,

1.
Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, Langfang 065000, China
2.
Guilin University of Technology, Guilin 541006, China

摘要

摘要:
根据全国土壤污染状况调查显示，全国土壤的环境状况总体不容乐观，耕地土壤环境质量令人担忧，已对粮食安全构成威胁。已有的研究工作多集中于土壤重金属的空间分布特征及污染源分析、重金属污染风险评估以及评估方法，但对于不同土壤深度重金属在耕地中的积累与剖面分布的变化及其生态风险分析相对较少。为研究四川省沐川县土壤剖面重金属分布特征和生态风险，本文在研究区选择三个不同地质背景区采集了土柱剖面样品开展相关工作。结果表明：样品中As、Cd、Hg、Pb、Ni、Cu、Zn七项指标中，除了Cu外，其余重金属元素含量都高于国家和四川省土壤背景值，表明这些元素在土壤中呈现不同程度地富集。土壤中7种重金属的浓度与土壤养分(氮、磷、钾)，土壤有机碳和pH值存在相关性，如在玉米地剖面中，氮和磷与Cd呈显著正相关，相关系数分别为0.845、0.747。大量研究表明，磷肥中含有一定量的重金属。磷肥中重金属含量高低与磷矿及其来源有关，磷肥能够增加土壤 Cd 含量。土壤有机碳与Cd呈显著正相关，相关系数为0.934，其原因是土壤有机质对重金属的吸附作用，有机碳对土壤中重金属的保留起了重要作用。pH值与Cd呈显著负相关，相关系数为-0.964，随着pH值的增加，土壤对重金属离子的吸附会增加，从而导致土壤中活性重金属离子减少。土壤重金属之间存在显著的正相关关系，表明它们普遍存在同源性。采用地质积累指数( ${I}_{\mathrm{g}\mathrm{e}\mathrm{o}}$ )评价土壤重金属污染程度，并选取潜在生态风险指数( $RI$ )评价其潜在生态风险，结果表明土壤中主要污染元素为Cd。生态风险指数显示，玉米地的潜在生态风险较大，其中Cd、Hg的生态风险较高，潜在生态风险指数( $RI$ )随着剖面深度的增加而降低。当地应采取适当措施，加强对该地区污染的防治工作，避免对人体健康造成危害。
- 土壤 /
- 重金属 /
- 含量分布 /
- 污染评价 /
- 电感耦合等离子体质谱法
Abstract:
BACKGROUND
Soil is a precious resource for human survival and social development. The quality of the soil environment is impacted by a variety of issues due to the social economy’s rapid expansion, and the issue of heavy metal contamination in farmed land has garnered great attention globally. Heavy metals in soil pose a severe risk to the security of agricultural products and public health due to their persistence, latency, and ease of entry into the food chain.　　In recent years, many scholars have carried out research on soil heavy metal pollution and ecological risk assessment under different conditions such as natural conditions, industrial and mining industries and developed transportation in different regions. Zhou et al.^[11] found that Xiong’an New Area was affected by the production activities of surrounding enterprises. The contents of As, Cd, Cu, Pb and Zn in some root soil samples exceeded the screening value standard for soil pollution risk of agricultural land (GB 15618—2018), and the exceeding ratios were 23.33%, 96.67%, 33.33%, 33.33% and 10.00%, respectively. Song et al.^[12] evaluated the characteristics of heavy metal pollution in the surface soil of Fuping County, Hebei Province, and found that As and Cd exceeded the acceptable carcinogenic risk level (As is 10⁻⁵, Cd is 10⁻⁶). Kumar et al.^[10] collected data on heavy metal-contaminated soils in India from 1991 to 2018. The average Cd content of all soil types exceeded the limit values, and the potential ecological risk values of Cd were greater than 320, reflecting a higher ecological risk. For the heavily polluted soil, according to the different pollution situation in our country, the remediation measures are taken according to local conditions. However, due to the wide area of contaminated soil and the complex composition of pollution sources, the current soil remediation work still faces huge problems.
OBJECTIVES
To study the vertical distribution characteristics of heavy metals in soil, the relationship between soil heavy metals and soil nutrient elements, as well as the degree of pollution and potential ecological risks.
METHODS
The contents of Cd, Cu, Ni, Pb, Zn were measured using inductively coupled plasma-mass spectrometry (ICP-MS); As content was determined by hydride generation atomic fluorescence spectrometry (HG-AFS); P and K₂O contents were determined by X-ray fluorescence spectrometry (XRF); N content was determined by oxidation combustion gas chromatography (GC); Hg content was determined by cold vapor atomic fluorescence spectrometry (CV-AFS); Organic carbon content was determined by high-frequency combustion infrared absorption method (IR); potentiometric method (POT) was used to measure soil pH value. Statistical analysis and calculation of soil heavy metal content, pollution index, and ecological risk index were conducted using Excel 2016. Pearson correlation analysis was conducted using SPSS 26, and the degree of soil heavy metal pollution was evaluated using the geoaccumulation index (I_geo). Potential ecological risk index (RI) values were selected to evaluate potential ecological risks.
RESULTS
The average contents of As, Cd, Cu, Hg, Ni, Pb, and Zn in the soil of YS plot were 20.8mg/kg, 0.35mg/kg, 26.38mg/kg, 0.121mg/kg, 33.29mg/kg, 42.37mg/kg, and 94.47mg/kg, respectively; The average contents of As, Cd, Cu, Hg, Ni, Pb, and Zn in the soil of PS plot were 7.21mg/kg, 0.32mg/kg, 28.32mg/kg, 0.028mg/kg, 47.34mg/kg, 33.29mg/kg, and 116.45mg/kg, respectively; The average contents of As, Cd, Cu, Hg, Ni, Pb, and Zn in the soil of GS plot were 5.42mg/kg, 0.16mg/kg, 22.38mg/kg, 0.08mg/kg, 31.8mg/kg, 30mg/kg, and 75.03mg/kg, respectively. The concentrations of As, Cd, Hg, Ni, Pb, and Zn were higher than the national and Sichuan soil background values, indicating that these metals were relatively enriched in the soil of Muchuan County. The relationship between seven heavy metals at different soil depths was evaluated through Pearson correlation analysis (seen in Table 4). There was a significant positive correlation between heavy metals, indicating their widespread homology. In the PS profile, the correlation between Cd, Hg and organic carbon was very high, with correlation coefficients of 0.934 and 0.955, respectively (Fig.5); As, Cd, Cu, Hg, Zn showed a highly significant negative correlation with pH, and the correlation between Cd, Hg content and soil pH was shown in Fig.5, with correlation coefficients of −0.964 and −0.944, respectively. The content of heavy metals in soil was closely related to organic carbon and pH value, which should be attributed to the adsorption of organic matter and the fact that pH not only affected the electrostatic adsorption of heavy metals by soil particles, but also damaged the inert part of the parent material. Soil organic matter and pH value are important factors affecting the migration of heavy metals in soil. The surface soil had a high content of organic matter, multiple adsorption sites, and a high soil pH value, which reduced the solubility of heavy metals and thus the metal migration rate.　　Soil pollution assessment results. The I_geo values of Cu and Zn in all soil profiles were less than 0, indicating that the soil in the study area was not contaminated by these heavy metals. The I_geo value of Cd at four depths was significantly reduced. Except that the I_geo value at GS point was less than 1, YS and PS were greater than 1, indicating that the Cd pollution degree of corn land (YS, PS) was more serious than that of tea garden land (GS). This may be due to the difference of tillage conditions, and the I_geo value of surface soil at YS point was between 2 and 3, showing moderate-strong pollution. The I_geo values of As, Hg, Ni and Pb at four depths were all less than 1 and close to 0, indicating that the soil pollution was slight, which may be caused by human input or natural changes. In general, conventional agricultural practices lead to the enrichment of heavy metals in soils due to excessive use of fertilizers and pesticides, wastewater irrigation and atmospheric deposition. Zhao et al.^[42] found that use of fertilizers and manure increased the content of heavy metals (Cd, Cu, Pb, and Zn) by approximately 3% per year. The order of heavy metal pollution degree from high to low is Cd>Hg>As>Pb>Ni.　　Potential ecological risk assessment. According to the description of risk level, the YS plot had the highest potential risk index for Cd and Hg, and there was a significant ecological risk of Cd and Hg at depths of 0-140cm (80≤E_i<160), among which the surface soil Cd had a strong ecological risk (160≤E_i<320). It indicates that Cd pollution sources in the region may be affected by past agricultural activities, including fertilizers and pesticides. The soil Cd of PS plot exhibited strong ecological hazards (160≤E_i<320) at the depth of 0-30cm while exhibiting strong ecological hazards (80≤E_i<160) at 60-110cm. The Cd and Hg in surface soil at the GS plot site had moderate ecological risks (40≤E_i<80). The value of RI showed a strong ecological risk (300≤RI<600) at 0-10cm of the YS plot, and a moderate ecological risk (150≤RI<300) at 30-140cm. Moderate ecological hazards (150≤RI<300) were present in the PS plot, while mild ecological hazards (RI<150) were present at 60-110cm. The ecological hazards of GS plot at 0-130cm were relatively weak. The E_i values of heavy metals in soil decreased with the increase of depth, which was consistent with the evaluation results of I_geo pollution. The E_i values of Cd in the three profiles were relatively high, indicating that special attention should be paid to the control of heavy metal pollution.
CONCLUSIONS
According to the results of soil vertical profile data, it can be concluded that heavy metal content tends to accumulate in the surface soil, and its content decreases with increasing depth. The I_geo value and E_i value also decrease with the increase of formation depth. The geoaccumulation index and potential ecological risk analysis indicate that Cd poses significant ecological risks to the local soil, and appropriate measures should be taken to strengthen pollution prevention and control in the area to avoid harm to human health. The content of heavy metals is closely related to soil nutrients and physicochemical properties, positively correlated with organic carbon content, and negatively correlated with pH value. According to the research results, it is suggested to carry out further research on the accumulation of heavy metals in soil, rationally assess its ecological harm, and ensure the safe use of land.
- soil /
- heavy metals /
- content distribution /
- pollution assessment /
- inductively coupled plasma-mass spectrometry

HTML全文

地热水的水化学特征包含其形成过程中地质、构造、断裂、蚀变以及环境变化等多种信息，是研究地热流体形成和物质来源最基本和最重要的特征之一^［1］。近年来许多学者对地热水的水化学特征开展研究，如西藏查孜、觉拥、加查象牙泉等水化学特征分析^［2-4］；谭梦如等^［5］、刘成龙等^［6］对云南勐海县勐阿街温泉、硫磺洞温泉水文地球化学特征和成因进行了分析；刘明亮等^［7］研究了雄安新区地热水化学特征及其指示意义。较多学者^［8-11］开展了地热水中化学元素的分析测试工作，而对地热水中的胶体粒子的分析研究工作有待加强。

目前，对天然水中胶体粒子的研究已经开展了一些工作，已有学者发现胶体粒子可以通过吸附-解吸过程影响着物质和元素的生物地球化学循环过程^［12-13］。日本地热工作者Tanaka等^［14］用快速原子轰击质谱法在地热水中检测到二氧化硅的单体、二聚体、三聚体、四聚体等多聚体，但未能检测到二氧化硅球形胶体粒子；该温泉中的鲕粒蛋白石是由二氧化硅微球组成的，Tanaka等认为二氧化硅球形胶体粒子是形成鲕粒蛋白石的关键一环。Otsu等^［15］对地热水中SiO₂胶体粒子的粒径分析方法进行了模拟研究。罗雯等^［16］验证了硅华中铯(Cs)的含量与蛋白石中的Q³结构有关。地热水中SiO₂胶体粒子研究对热储温标公式的应用有重要的参考价值，深入研究自然条件下胶体粒子在地热水中生成和沉淀的原因十分有意义。基于此背景，开展地热水中胶体粒子的测试研究很有必要。

西藏地区地热活动强烈，地热水资源丰富，且以富含锂、铷、铯、硼等元素为特征。羊八井地热田是中国著名的高温地热田，其位于西藏自治区拉萨市西北当雄县羊八井区西侧，念青唐古拉山山前凹陷盆地的西南段，海拔4200～4500m，距拉萨市约90km。研究该地区地热水中胶体粒子的分析方法有助于更好地认识西藏高温地热田的水化学特点，并对西藏高温地热水的分析测试提供经验。本工作通过激光粒度仪测定了羊八井地热水中胶体粒子的粒径；透射电镜和扫描电镜表征胶体粒子的形貌；红外光谱测定胶体粒子的特征谱峰。通过实验分析得到了胶体粒子的结构成分。在此基础上，讨论了地热水中硅实验室之间分析结果相差较大原因及胶体粒子对地热水中有价元素的富集情况，为深入研究羊八井地热水中胶体粒子对铯的吸附方式和铯硅华矿床的研究提供借鉴。

1. 实验部分

1.1 仪器与设备

Mastersizer 2000型激光粒度仪(英国Malvern公司)；Tecnai G2 F20热场发射透射电镜(美国FEI公司)；JSM-6700F扫描电子显微镜(日本电子JEOL公司)；X-MaxN型X射线能谱仪(英国OXFORD公司)；VERTEX70傅里叶变换近红外光谱仪(德国Bruker公司)；Lambda35紫外可见分光光度计(美国PerkinElmer公司)；Thermo iCAP6300 Duo电感耦合等离子体发射光谱仪(美国ThermoFisher公司)；WGZ-200型台式浊度仪(中国昕瑞公司)。

1.2 样品采集与处理

本实验选取羊八井地热旅游区蓝色天国晾晒池中地热水作为研究对象。该地热水为羊八井深层地热井ZK4001尾水，在日光下有明显的丁达尔效应，经0.45µm膜过滤后保存于聚乙烯瓶中。该地热水的主要特征是胶体粒子稳定、不沉淀且含量巨大。只有稳定的胶体溶液才能用各种仪器表征。胶体样品在超纯水稀释的过程中仍能保持稳定为稀释实验提供了保障。大量的胶体粒子使得过滤收集胶体粒子变得容易，并使进一步表征滤出的胶体粒子成为现实。

1.3 样品测试

为探明胶体粒子在地热水中的粒径、形貌、化学成分。通过激光粒度仪测定该地热水中胶体粒子的平均粒径；并用透射电镜(TEM)观察该地热水中胶体粒子的形貌。用电感耦合等离子体发射光谱法(ICP-OES)分别检测该地热水胶体粒子过滤前后主要化学成分。对比地热水过滤前后的主要化学成分的差别推测出胶体粒子的化学成分。

将经过0.1µm滤膜滤出的胶体粒子经自然风干后进行能谱仪(EDS)成分分析、红外光谱(FTIR)结构特征分析、扫描电镜(SEM)形貌表征，进一步佐证胶体粒子的成分、结构、形貌。

1.4 测试数据质量控制

电导率按《地下水质分析方法》(DZ/T 0064—2021)分析，其他检测项目按照《食品安全国家标准饮用天然矿泉水检验方法》(GB 8538—2022)分析。样品分析质量依照《地质矿产实验室测试质量管理规范》(DZ/T 0130—2006)第6部分：水样分析质量管理要求。通过测定国家标准物质来控制准确度，精密度采用样品平行测定，判断依据数学模型：

$Y=11.0\times C\times X^{-0.28}$

式中：Y为重复分析相对偏差允许限(%)；X为各组分分析结果浓度值(mg/L)；C为重复分析相对偏差允许限系数。

2. 结果与讨论

2.1 浑浊度的意义

地热水经过0.45µm和0.22µm滤膜过滤后，丁达尔效应依然明显(浑浊度41.7NTU)，但经0.1µm滤膜过滤后变为无色，丁达尔现象微弱(浑浊度2.42NTU)，可见地热水中胶体粒子无法通过0.1µm的滤膜。由此推测水体中胶体粒子的粒径大小为100nm左右。胶体粒子被过滤后，地热水的浑浊度明显降低。浑浊度是由于水中存在悬浮物或者胶态物造成的光学散射或吸收行为引起的。通过观察地热水不同稀释比例的浊度变化趋势，证实随着稀释比例的增大，地热水的浊度随之变小，为水样报告中浊度值的解读提供了经验。分析结果见表1。

表 1 样品过滤前后及稀释后分析结果

Table 1. Analysis results of samples after different treatments.

样品及处理	pH	Na (mg/L)	K (mg/L)	Ca (mg/L)	Mg (mg/L)	Fe (mg/L)	Mn (mg/L)	Al (mg/L)	浑浊度 (NTU)
地热水	8.61	1658	259.1	15.49	0.12	0.81	0.0099	5.13	41.7
过滤后	8.63	1520	248.8	7.31	0.13	ND	0.0059	0.16	2.42
稀释2倍	8.81	593.5	137.8	4.55	0.048	0.40	0.0021	1.82	17.4
稀释5倍	8.83	240.2	54.46	1.82	0.019	0.17	0.00092	0.70	5.92
稀释10倍	8.84	133.4	29.42	1.11	ND	0.078	0.00085	0.36	2.41
稀释20倍	8.81	64.64	13.84	0.45	ND	0.052	0.00078	0.18	0.94
稀释50倍	8.74	31.42	5.83	0.41	0.40	0.17	ND	0.070	0.53
稀释100倍	8.70	14.75	2.95	2.88	0.40	0.010	ND	0.029	ND

样品及处理	HCO₃ ⁻ (mg/L)	CO₃ ²⁻ (mg/L)	SO₄ ²⁻ (mg/L)	Cl⁻ (mg/L)	SiO₂ (mg/L)	H₂SiO₃ (mg/L)	HBO₂ (mg/L)	TDS (mg/L)	电导率 (mS/cm)
地热水	169.3	406.8	190.4	1973	1509	121.5	1175	7340	7.28
过滤后	211.0	384.0	192.6	2015	120.8	79.15	1115	5554	7.21
稀释2倍	56.74	210.9	88.14	927.5	671.0	92.70	540.7	2571	3.14
稀释5倍	27.24	84.26	41.68	376.1	262.8	77.33	208.4	1325	1.35
稀释10倍	13.62	41.85	18.41	196.1	138.2	55.45	110.7	767.8	0.69
稀释20倍	18.72	12.28	9.03	92.27	69.11	33.85	54.68	336.4	0.35
稀释50倍	7.38	7.25	3.90	39.32	29.34	7.07	22.25	134.2	0.18
稀释100倍	8.51	2.23	1.91	20.21	15.13	3.50	11.25	70.26	0.067
注：“ND”表示未检出，除pH、浑浊度、电导率外其他组分含量的单位为mg/L。过滤后为地热水经过0.1µm滤膜过滤胶体粒子后的样品；稀释2倍、稀释5倍、稀释10倍、稀释20倍、稀释50倍、稀释100倍分别为地热水通过超纯水稀释，稀释后的样品摇匀后静置20天然后进行分析测试。

下载: 导出CSV

| 显示表格

2.2 胶体粒子对电导率的影响

通过比较表1地热水过滤前后的测定结果，发现过滤后SiO₂减少了1388mg/L，溶解性固体总量(TDS)减少了1786mg/L。因过滤前后地热水中其他离子变化不大，由此推断出被过滤掉的胶体粒子主要成分是SiO₂。同时发现过滤后钾(K)、钠(Na)、钙(Ca)、铝(Al)的含量变少，说明这部分阳离子有可能吸附在胶体粒子表面随胶体一起被滤出。很多地热工作者通常用电导率来计算TDS^［17］，然而这种方法只适用基体简单的水样，对于胶体粒子含量较高的地热水其计算结果和实际结果差值较大。羊八井地热水过滤前后其电导率变化很小，但过滤前后TDS相差很大。这是由于胶体粒子是以双电层结构存在地热水中的，这种双电层结构使整个胶团呈现电中性。在SiO₂胶体粒子制备实验中，胶体粒子成长后电导率值趋于最小并保持稳定^［18］，因而胶体粒子的存在对样品的电导率贡献较小，但是对TDS贡献较大。

2.3 SiO₂和H₂SiO₃分析结果的不同意义

由于目前没有地热水检测的国家标准，地热水中硅的检测方法是依据《食品安全国家标准饮用天然矿泉水检验方法》(GB 8538—2022)，而标准中硅的检测方法有两种方法：一种是ICP-OES分析的SiO₂；另一种是硅钼黄(蓝)光谱法即紫外可见分光光度计(UV-Vis)测得的H₂SiO₃。一般来说，UV-Vis测得的H₂SiO₃为大家熟知的偏硅酸即可溶性硅酸^［19-20］，而ICP-OES测得的SiO₂为地热水中的全硅。一般矿泉水中的硅含量较低，其主要以偏硅酸的形式存在，这种情况下两种方法测得的硅含量接近。但一些地热水中的硅含量高，存在形式复杂，这类地热水中同时存在着偏硅酸和硅酸的多聚体^［14］和胶体粒子，这时两种方法测得的硅含量差别较大。而个别科研人员将ICP-OES测得的SiO₂和UV-Vis法测得的H₂SiO₃二者结果相互转换使用。这是地热水中硅的实验室间比对结果差别较大^［21］的一个原因。通过对比表1中ICP-OES测得的SiO₂和UV-Vis测得H₂SiO₃的分析结果相差较大。而经过0.1µm滤膜滤出胶体粒子后，两种方法的硅分析结果差值由1387.5mg/L缩小到41.65mg/L，这种巨大的变化说明该胶体粒子可以被ICP-OES检测，但不被UV-Vis检测。其原因是胶体粒子可以被等离子体产生的高温激发，但不能与钼酸铵显色，因此可以通过ICP-OES和UV-Vis的差值来判断样品中胶体粒子的含量。地热水稀释后UV-Vis测定值会明显增加，但即使稀释到100倍ICP-OES测定值仍然大于UV-Vis测定值，说明通过稀释可以使部分的SiO₂胶体粒子转化为H₂SiO₃。

2.4 样品表征结果

对地热水中的胶体粒子进行了一系列的表征。首先通过激光粒度仪测定了地热水胶体粒子的平均粒径为80.83nm。随后进行透射电镜(TEM)表征，将地热水滴到待测铜网上，待自然挥发后上机测定。从图1中a、b中可以看到羊八井地热水中含有大量的胶体粒子，因样品未经超声等任何处理，该电镜图片中胶体粒子的形态接近于胶体粒子在地热水中的形态，胶体粒子的粒径集中分布在50～100nm之间。同时将地热水中的滤出物进行了扫描电镜(SEM)分析，从图1中c、d中可以看出滤出物呈球形，且粒径分布在100nm附近，这也与之前激光粒度仪分析的结果相吻合。

图 1 胶体粒子的透射电镜和扫描电镜图像

a、b—胶体粒子的透射电镜图像；c、d—胶体粒子的扫描电镜图像。

Figure 1. The TEM patterns and SEM patterns of colloidal particles.

下载: 全尺寸图片幻灯片

为了进一步确定滤出物的成分，进行了能谱仪(EDS)分析(图2)，结果表明滤出物主要为硅氧化合物，且硅氧质量比为0.83，接近SiO₂的理论值0.88，从而也印证了胶体粒子的主要成分为SiO₂。同时能够发现，除了含有Si、O元素外，还含有少量Na、K、Cl、Ca元素，说明胶体粒子在滤出时会携带其他离子。

图 2 胶体粒子的能谱分析(EDS)图谱

Figure 2. The EDS spectrum of colloidal particles.

下载: 全尺寸图片幻灯片

为了进一步确定胶体粒子的结构，将滤出物自然风干后进行红外光谱(FTIR)表征。从图3可以看出在1073cm⁻¹、796cm⁻¹、593cm⁻¹三处有十分强烈的吸收，1073cm⁻¹处为Si—O—Si反对称伸缩振动，796cm⁻¹和593cm⁻¹处为Si—O键对称伸缩振动，该滤出物的特征吸附峰和蛋白石的红外吸收峰位相吻合^［22-24］。证实了该胶体粒子与蛋白石有密切的关系^［14］。

图 3 胶体粒子的红外光谱图

Figure 3. The FTIR spectrum of colloidal particles.

下载: 全尺寸图片幻灯片

对于一些文献中认为不可溶性硅是硅聚合物、SiO₂胶体粒子^［25］的猜想，通过对地热水中SiO₂胶体粒子的TEM、SEM、FTIR表征证实了该猜想。

2.5 胶体粒子行为的影响

地热水中SiO₂含量与热储温度有特殊关系，地热工作者建立了不同SiO₂地热温标公式，如无蒸汽散失的石英温标、玉髓温标、无定形SiO₂温标计算地热水温度^［26］，不同的温标公式对应着不同应用条件。但地热水中SiO₂有不同的聚合度，聚合度大的形成胶体粒子，而胶体粒子容易沉淀从而导致该类地热水的热储温度偏低。上述研究可以计算地热水中胶体粒子的含量，因而本研究对地热温标应用有重要参考价值。

由于SiO₂胶体粒子有吸附地热水中金属离子的能力，这为铯硅华的形成机理提供依据^［25］，即SiO₂胶体粒子表面大量的活性羟基基团可以吸附铯而形成含铯硅华^［27］。通过ICP-OES测定胶体粒子滤出物中的铯，结果表明胶体中铯含量为0.15%。SiO₂胶体粒子对铯的优异富集能力是西藏铯硅华形成的原因之一。由于SiO₂胶体粒子对高价阳离子具有更强的吸附能力^［28］且西藏地区部分泉华中发现了稀土元素的存在，因而可以进一步研究稀土元素与SiO₂含量之间的关系。多格错仁南岸地区共有100个以上的盐泉钙华沉积点，盐泉平均盐度能达到41.22g/L^［29］。本次研究发现西藏多格错仁南岸钙华中的轻稀土元素与SiO₂含量呈显著正相关(图4)，这一发现意味着盐泉水中的SiO₂和轻稀土元素的富集有密切关系，SiO₂在高盐度下富集轻稀土元素的行为，为廉价易得的SiO₂胶体^［30］富集地热水、盐湖水中有价元素提供了可能，同时也为盐湖资源、地热水资源开发提供一个新的解决方案。

图 4 稀土元素与SiO₂关系图

Figure 4. The relationship between rare earth elements and SiO₂.

下载: 全尺寸图片幻灯片

3. 结论

地热水中天然存在的SiO₂胶体粒子常因为含量少、易沉淀、难收集等原因，导致对胶体粒子的研究进度偏慢。而本实验选取的羊八井地热水胶体含量大、稳定、不沉淀，能把天然胶体粒子的各种特性展现在地热工作者面前。研究显示胶体粒子的存在增加了地热水的浑浊度，但并未增加地热水的电导率。采用TEM、SEM、激光粒度仪、ICP-OES等分析方法研究了羊八井地热水中胶体粒子的组成。结果表明：地热水中高含量的SiO₂以偏硅酸和胶体粒子的形式共同存在于水中，该胶体粒子可以被ICP-OES法分析，但不会与钼酸铵显色。地热水中SiO₂胶体含量可以通过计算SiO₂和H₂SiO₃的差值而得，SiO₂胶体粒子相对稳定不宜沉淀，通过稀释可以使部分的SiO₂胶体粒子转化为H₂SiO₃。SiO₂胶体粒子在滤出的过程中会吸附地热水中的金属离子。

本工作揭示了地热水中硅的两种常见存在形式(偏硅酸和胶体粒子)，为地热水中硅实验室间比对结果差别较大作了合理解释。但除了这两种常见的存在形式，地热水中SiO₂的其他存在形式还有待开展研究。影响SiO₂胶体粒子在地热水中稳定存在的条件还未厘清，如何消除SiO₂胶体粒子对地热水中Ca、Al、Cs等分析元素的干扰值得研究，进一步研究SiO₂富集铯和轻稀土元素的机理十分有意义。

图 1 研究区位置和地质简图

Figure 1. Location and geological map of the study area.

下载: 全尺寸图片幻灯片

图 2 三个采样点土壤剖面重金属平均含量对比

黑色虚线表示全国土壤背景值，红色虚线表示四川土壤背景值。

Figure 2. Comparison of average heavy metal contents in soil profiles at three sampling points. The black dotted line indicates the background value of national soil, and the red dotted line indicates the background value of Sichuan soil.

下载: 全尺寸图片幻灯片

图 3 三个采样点土壤剖面重金属垂向分布特征

Figure 3. Vertical distribution characteristics of heavy metals in soil profiles at three sampling points.

下载: 全尺寸图片幻灯片

图 4 三个采样点土壤剖面有机碳(C_org)、氮(N)、磷(P)、氧化钾(K₂O)含量和pH的垂向分布特征

Figure 4. Vertical distribution characteristics of organic carbon (C_org), nitrogen (N), phosphorus (P), potassium oxide (K₂O) contents and pH in soil profiles at three sampling points.

下载: 全尺寸图片幻灯片

图 5 紫色黏质土(PS剖面) Cd与C_org (a)、Hg与C_org(b)、Cd与pH值(c)、Hg与pH值(d)相关关系

Figure 5. The correlation between Cd and C_org (a), Hg and C_org (b), Cd and pH (c), Hg and pH (d) in purple clay soil (profile PS).

下载: 全尺寸图片幻灯片

三个采样点剖面土壤0～10cm深度(a)、30～40cm深度(b)、70～80cm深度(c)和100～110cm深度(d)的重金属地质累积指数( ${\mathit{I}}_{\mathbf{g}\mathbf{e}\mathbf{o}}$ )

Geo-accumulation indexes ( ${\mathit{I}}_{\mathbf{g}\mathbf{e}\mathbf{o}}$ ) of heavy metals in profile soils at 0-10cm depth (a), 30-40cm depth (b), 70-80cm depth (c) and 100-110cm depth (d) at the three sampling sites.

下载: 全尺寸图片幻灯片

表 1 研究区不同类型土柱剖面取样点概况

Table 1 Sampling points of different types of soil column profiles in the study area.

采样地点	采样深度(cm)	土地利用类型	土壤类型	地质背景	可见特征描述
剖面YS	140	山坡旱地，种植玉米	黄色黏质土	三叠系雷口坡组(T₂l)，岩性为粉砂岩与白云岩、泥质灰岩互层，夹黑色碳质页岩	0～50cm灰黄色黏质土，50～140cm黄色黏质土
剖面PS	110	山坡旱地，种植玉米	紫色黏质土	侏罗系蓬莱镇组(J₃p)岩性以泥岩、砂岩和粉砂岩为主	0～80cm紫色黏质土，80～110cm紫色黏质土，土壤水分降低
剖面GS	130	茶园地	灰色黏质土	三叠系须家河组(T₃x) ，岩性主要为砂岩、粉砂岩、泥岩及煤层组成的沉积旋回	0～40cm，灰色黏质土，有机质较丰富；40～70cm，土壤颜色变黄，细砂成分增多；80～90cm，青灰色淤泥，水分增多；90～100cm，土壤变灰黑色，水分变少；100～120cm，青灰色黏质土；120～130cm，灰绿色，底部为页岩、泥岩

下载: 导出CSV

表 2 各指标分析测试检出限

Table 2 Detection limit of each index analysis.

分析项目	检出限	分析项目	检出限
As	0.5 $\mathrm{m}$ g/kg	Zn	4 $\mathrm{m}$ g/kg
Cd	0.03 $\mathrm{m}$ g/kg	P	10 $\mathrm{m}$ g/kg
Cu	1. $0\mathrm{ }$ mg/kg	C_org	0.10%
Hg	0.0005 $\mathrm{m}$ g/kg	K₂O	0.05%
Pb	2 $\mathrm{m}$ g/kg	pH	0.1
Ni	2 $\mathrm{m}$ g/kg	N	20 $\mathrm{m}$ g/kg

下载: 导出CSV

表 3 三个采样点土壤剖面重金属与土壤养分指标的Pearson相关性

Table 3 Pearson correlation between heavy metals in soil profiles and soil nutrient indicators at three sampling points.

采样地点	养分元素	As	Cd	Cu	Hg	Ni	Pb	Zn
剖面YS	N	0.186	0.813^**	0.706^**	−0.419	−0.147	0.724^**	0.135
	P	0.401	0.947^**	0.758^**	−0.188	−0.120	0.828^**	0.307
	K₂O	0.155	−0.399	−0.486	0.795^**	0.526	−0.669^**	0.638^*
	C_org	0.003	0.759^**	0.764^**	−0.721^**	−0.340	0.861^**	−0.150
	pH	−0.492	−0.455	−0.120	−0.627^*	−0.088	−0.265	−0.836^**
剖面PS	N	0.814^**	0.845^**	0.828^**	0.924^**	−0.160	−0.072	0.724^*
	P	0.458	0.747^**	0.504	0.632^*	−0.209	0.481	0.695^*
	K₂O	−0.113	−0.266	0.047	−0.295	0.924^**	−0.121	0.424
	C_org	0.865^**	0.934^**	0.865^**	0.955^**	−0.283	0.065	0.717^*
	pH	−0.897^**	−0.964^**	−0.884^**	−0.944^**	0.287	−0.223	−0.735^**
剖面GS	N	0.934^**	0.448	−0.262	0.899^**	−0.765^**	0.897^**	−0.612^*
	P	0.186	0.144	0.661^*	0.084	0.265	0.485	0.425
	K₂O	−0.713^**	−0.237	0.801^**	−0.721^**	0.937^**	−0.366	0.863^**
	C_org	0.947^**	0.552	−0.303	0.953^**	−0.824^**	0.893^**	−0.673^*
	pH	−0.451	−0.039	−0.054	−0.358	0.227	−0.649^*	0.110
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。

下载: 导出CSV

表 4 土壤剖面重金属之间的Pearson相关性

Table 4 Pearson correlation between heavy metals in soil profiles.

元素	As	Cd	Cu	Hg	Ni	Pb	Zn
As	1
Cd	0.546^**	1
Cu	0.187	0.644^**	1
Hg	0.772^**	0.141	−0.288	1
Ni	−0.369^*	0.141	0.683^**	−0.769^**	1
Pb	0.822^**	0.672^**	0.310	0.566^**	−0.311	1
Zn	0.029	0.500^**	0.783^**	−0.484^**	0.864^**	0.065	1
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。

下载: 导出CSV

表 5 三个采样点土壤剖面重金属潜在生态风险指数

Table 5 Potential ecological risk index of heavy metals in soil profiles of three sampling points.

采样地点	采样深度 (cm)	${E}_{i}$							RI
采样地点	采样深度 (cm)	As	Cd	Cu	Hg	Ni	Pb	Zn	RI
剖面YS	10	21.9	278.4	5.0	83.3	5.0	8.7	1.2	403.5
	30	21.1	141.6	4.3	72.9	4.9	7.8	1.0	253.6
	60	20.0	125.3	4.5	66.7	5.1	6.9	1.0	229.6
	90	18.0	95.7	3.7	88.6	4.7	6.4	1.1	218.1
	110	20.6	96.5	3.8	96.9	5.1	6.2	1.1	230.1
	140	20.7	146.6	4.3	90.5	5.6	6.0	1.2	274.9
剖面PS	10	7.7	177.3	4.8	27.8	6.9	5.5	1.4	231.4
	30	8.0	171.6	5.0	22.5	7.2	6.0	1.4	221.7
	60	7.0	103.3	4.7	16.8	7.4	5.0	1.3	145.5
	90	6.3	80.5	4.1	15.2	7.0	4.8	1.3	147.6
	110	6.3	80.1	4.3	13.7	8.0	5.4	1.4	119.2
剖面GS	10	9.6	56.6	3.4	74.2	3.4	6.3	0.7	154.3
	30	9.7	101.8	3.8	78.6	3.9	6.2	0.8	204.8
	60	6.9	33.8	3.5	64.8	4.2	5.0	0.8	119.1
	90	2.3	64.2	3.0	31.6	5.2	3.9	0.9	111.0
	110	0.9	21.6	3.9	30.4	6.0	3.6	0.9	67.5
	130	1.5	85.1	5.0	25.7	7.4	3.8	1.2	129.7

下载: 导出CSV

参考文献(46)

[1]	Chai L, Wang Y, Wang X, et al. Pollution characteristics, spatial distributions, and source apportionment of heavy metals in cultivated soil in Lanzhou, China[J]. Ecological Indicators, 2021, 125: 107507. doi: 10.1016/j.ecolind.2021.107507
[2]	Zhang L X, Zhu G Y, Ge X, et al. Novel insights into heavy metal pollution of farmland based on reactive heavy metals (RHMs): Pollution characteristics, predictive models, and quantitative source apportionment[J]. Journal of Hazardous Materials, 2018, 360: 32−42. doi: 10.1016/j.jhazmat.2018.07.075
[3]	Zeng F, Ali S, Zhang H, et al. The influence of pH and organic matter content in paddy soil on heavy metal availability and their uptake by rice plants[J]. Environmental Pollution, 2011, 159(1): 84−91. doi: 10.1016/j.envpol.2010.09.019
[4]	Zhuo H, Wang X, Liu H, et al. Source analysis and risk assessment of heavy metals in development zones: A case study in Rizhao, China[J]. Environmental Geochemistry and Health, 2019, 42: 135−146.
[5]	Pecina V, Brtnický M, Baltazár T, et al. Human health and ecological risk assessment of trace elements in urban soils of 101 cities in China: A meta-analysis[J]. Chemosphere, 2020, 267: 129215.
[6]	Yunus K, Zuraidah M A, John A. A review on the accumulation of heavy metals in coastal sediment of Peninsular Malaysia[J]. Ecofeminism and Climate Change, 2020, 1(1): 21−35. doi: 10.1108/EFCC-03-2020-0003
[7]	贺灵, 吴超, 曾道明, 等. 中国西南典型地质背景区土壤重金属分布及生态风险特征 [J]. 岩矿测试, 2021, 40(3): 384-396. He L, Wu C, Zeng D M, et al. Soil heavy metal distribution and ecological risk characteristics in typical geological background areas of Southwestern China[J]. Rock and Mineral Analysis, 2021, 40(3): 384-396.
[8]	Zhang Q, Han G, Liu M, et al. Distribution and contamination assessment of soil heavy metals in the Jiulongjiang River catchment, Southeast China[J]. International Journal of Environmental Research and Public Health, 2019, 16: 4674. doi: 10.3390/ijerph16234674
[9]	Frišták V, Pipíška M, Lesný J, et al. Utilization of biochar sorbents for Cd²⁺, Zn²⁺, and Cu²⁺ ions separation from aqueous solutions: Comparative study[J]. Environmental Monitoring and Assessment, 2014, 187(1): 4093.
[10]	Kumar V, Sharma A, Kaur P, et al. Pollution assessment of heavy metals in soils of India and ecological risk assessment: A state-of-the-art[J]. Chemosphere, 2019, 216: 449−462. doi: 10.1016/j.chemosphere.2018.10.066
[11]	周亚龙, 王乔林, 王成文, 等. 雄安新区企业周边农田土壤-作物系统重金属污染风险及累积效应[J]. 环境科学, 2021, 42(12): 5977-5987. Zhou Y L, Wang Q L, Wang C W, et al. Risk and cumulative effect of heavy metal pollution in farmland soil crop system around enterprises in Xiong’an[J]. Environmental Science, 2021, 42 (12): 5977-5987.
[12]	宋绵, 龚磊, 王艳, 等. 河北阜平县表层土壤重金属对人体健康的风险评估[J]. 岩矿测试, 2022, 41(1): 133-144. Song M, Gong L, Wang Y, et al. Risk assessment of heavy metals in surface soil of Fuping County, Hebei Province on human health[J]. Rock and Mineral Testing, 2022, 41(1): 133-144.
[13]	Huang H B, Lin C Q, Yu R L, et al. Contamination assessment, source apportionment and health risk assessment of heavy metals in paddy soils of Jiulong River Basin, Southeast China[J]. RSC Advances, 2019, 9: 14736−14744. doi: 10.1039/C9RA02333J
[14]	Barrena-González J, Contador J F L, Fernández M P. Mapping soil properties at a regional scale: Assessing deterministic vs. geostatistical interpolation methods at different soil depths[J]. Sustainability, 2022, 14: 10049. doi: 10.3390/su141610049
[15]	谢龙涛, 潘剑君, 白浩然, 等. 基于GIS的农田土壤重金属空间分布及污染评价——以南京市江宁区某乡镇为例[J]. 土壤学报, 2020, 57(2): 316-325. Xie L T, Pan J J, Bai H R, et al. GIS based spatial distribution and pollution assessment of heavy metals in farmland soil—Taking a township in Jiangning District of Nanjing as an example[J]. Journal of Soil Science, 2020, 57 (2): 316-325.
[16]	Ye X, Li H, Ma Y, et al. The bioaccumulation of Cd in rice grains in paddy soils as affected and predicted by soil properties[J]. Journal of Soils and Sediments, 2014, 14: 1407−1416. doi: 10.1007/s11368-014-0901-9
[17]	成晓梦, 孙彬彬, 贺灵, 等. 四川省沐川县西部地区土壤硒含量特征及影响因素[J]. 岩矿测试, 2021, 40(6): 808-819. Cheng X M, Sun B B, He L, et al. Characteristics and influencing factors of soil selenium content in Western Muchuan County, Sichuan Province[J]. Rock and Mineral Analysis, 2021, 40(6): 808-819.
[18]	韩伟, 王乔林, 宋云涛, 等. 四川省沐川县北部土壤硒地球化学特征与成因探讨[J]. 物探与化探, 2021, 45(1): 215-222. Han W, Wang Q L, Song Y T, et al. Geochemical characteristics and genesis of soil selenium in Northern Muchuan County, Sichuan Province[J]. Geophysical and Geochemical Exploration, 2021, 45(1): 215-222.
[19]	Müller G. Index of geoaccumulation in sediments of the Rhine River[J]. GeoJournal, 1969, 2: 108−118.
[20]	Hakanson L. An ecological risk index for aquatic pollution control—A sedimentological approach[J]. Water Research, 1980, 14(8): 975−1001. doi: 10.1016/0043-1354(80)90143-8
[21]	魏复盛, 陈静生, 吴燕玉, 等. 中国土壤环境背景值研究[J]. 环境科学, 1991(4): 12−19,94. Wei F S, Chen J S, Wu Y Y, et al. Study on the background values of soil environment in China[J]. Environmental Science, 1991(4): 12−19,94.
[22]	徐争启, 倪师军, 庹先国, 等. 潜在生态危害指数法评价中重金属毒性系数计算[J]. 环境科学与技术, 2008, 21(2): 112-115. Xu Z Q, Ni S J, Tuo X G, et al. Calculation of heavy metal toxicity coefficient in potential ecological hazard index evaluation[J]. Environmental Science and Technology, 2008, 21(2): 112-115.
[23]	成杭新, 彭敏, 赵传冬, 等. 表生地球化学动力学与中国西南土壤中化学元素分布模式的驱动机制[J]. 地学前缘, 2019, 26(6): 159-191. Cheng H X, Peng M, Zhao C D, et al. The driving mechanism of supergene chemical kinetics and the distribution pattern of chemical elements in soils in Southwest China[J]. Geoscience Frontiers, 2019, 26(6): 159-191.
[24]	Huang Y, Li T, Wu C, et al. An integrated approach to assess heavy metal source apportionment in peri-urban agricultural soils[J]. Journal of Hazardous Materials, 2015, 299: 540−549. doi: 10.1016/j.jhazmat.2015.07.041
[25]	Sun C, Liu J, Wang Y, et al. Multivariate and geostatistical analyses of the spatial distribution and sources of heavy metals in agricultural soil in Dehui, Northeast China[J]. Chemosphere, 2013, 92(5): 517−523. doi: 10.1016/j.chemosphere.2013.02.063
[26]	Pan L B, Ma J, Wang X L, et al. Heavy metals in soils from a typical county in Shanxi Province, China: Levels, sources and spatial distribution[J]. Chemosphere, 2016, 148: 248−254. doi: 10.1016/j.chemosphere.2015.12.049
[27]	Dumat C, Quenea K, Bermond A, et al. Study of the trace metal ion influence on the turnover of soil organic matter in cultivated contaminated soils[J]. Environmental Pollution, 2006, 142(3): 521−529. doi: 10.1016/j.envpol.2005.10.027
[28]	Huang B, Li Z, Li D, et al. Distribution characteristics of heavy metal(loid)s in aggregates of different size fractions along contaminated paddy soil profile[J]. Environmental Science and Pollution Research, 2017, 24(30): 23939−23952. doi: 10.1007/s11356-017-0012-4
[29]	Kelepertzis E, Paraskevopoulou V, Argyraki A, et al. Evaluation of single extraction procedures for the assessment of heavy metal extractability in citrus agricultural soil of a typical Mediterranean environment (Argolida, Greece)[J]. 2015, 15(11): 2275.
[30]	Sun R, Yang J, Xia P, et al. Contamination features and ecological risks of heavy metals in the farmland along shoreline of Caohai Plateau wetland, China[J]. Chemosphere, 2020, 254: 126828. doi: 10.1016/j.chemosphere.2020.126828
[31]	王美, 李书田. 肥料重金属含量状况及施肥对土壤和作物重金属富集的影响[J]. 植物营养与肥料学报, 2014, 20(2): 466−480. Wang M, Li S T. The status of heavy metal content in fertilizers and the impact of fertilization on heavy metal enrichment in soil and crops[J]. Journal of Plant Nutrition and Fertilizers, 2014, 20(2): 466−480.
[32]	Jiang B, Adebayo A, Jia J, et al. Impacts of heavy metals and soil properties at a Nigerian e-waste site on soil microbial community[J]. Journal of Hazardous Materials, 2019, 362: 187−195. doi: 10.1016/j.jhazmat.2018.08.060
[33]	Khaledian Y, Pereira P, Brevik E C, et al. The influence of organic carbon and pH on heavy metals, potassium, and magnesium levels in lithuanian podzols[J]. Land Degradation & Development, 2017, 28(1): 345−354.
[34]	Xu J, Kleja D B, Biester H, et al. Influence of particle size distribution, organic carbon, pH and chlorides on washing of mercury contaminated soil[J]. Chemosphere, 2014, 109: 99−105. doi: 10.1016/j.chemosphere.2014.02.058
[35]	Ahmad M, Soo lee S, Yang J E, et al. Effects of soil dilution and amendments (mussel shell, cow bone, and biochar) on Pb availability and phytotoxicity in military shooting range soil[J]. Ecotoxicology and Environmental Safety, 2012, 79: 225−231. doi: 10.1016/j.ecoenv.2012.01.003
[36]	Huang B, Li Z, Huang J, et al. Aging effect on the leaching behavior of heavy metals (Cu, Zn, and Cd) in red paddy soil[J]. Environmental Science and Pollution Research, 2015, 22(15): 11467−11477. doi: 10.1007/s11356-015-4386-x
[37]	Zhang H, Luo Y, Makino T, et al. The heavy metal partition in size-fractions of the fine particles in agricultural soils contaminated by waste water and smelter dust[J]. Journal of Hazardous Materials, 2013, 248: 303−312.
[38]	Li X P, Feng L. Multivariate and geostatistical analyzes of metals in urban soil of Weinan industrial areas, northwest of China[J]. Atmospheric Environment, 2012, 47: 58−65. doi: 10.1016/j.atmosenv.2011.11.041
[39]	Li X, Yang H, Zhang C, et al. Spatial distribution and transport characteristics of heavy metals around an antimony mine area in Central China[J]. Chemosphere, 2017, 170: 17−24. doi: 10.1016/j.chemosphere.2016.12.011
[40]	Wang X P, Wang L Q, Zhang Q, et al. Integrated assessment of the impact of land use types on soil pollution by potentially toxic elements and the associated ecological and human health risk[J]. Environmental Pollution, 2022, 299: 118911.1−118911.9.
[41]	Novara A, Ruehl J, la Mantia T, et al. Litter contribution to soil organic carbon in the processes of agriculture abandon[J]. Solid Earth, 2015, 6(2): 425−432. doi: 10.5194/se-6-425-2015
[42]	Zhao S, Qiu S, He P. Changes of heavy metals in soil and wheat grain under long-term environmental impact and fertilization practices in North China[J]. Journal of Plant Nutrition, 2018, 41(15): 1970−1979. doi: 10.1080/01904167.2018.1485158
[43]	陈文轩, 李茜, 王珍, 等. 中国农田土壤重金属空间分布特征及污染评价[J]. 环境科学, 2020, 41(6): 2822-2833. Chen W X, Li Q, Wang Z, et al. Spatial distribution characteristics and pollution assessment of heavy metals in farmland soils in China[J]. Environmental Science, 2020, 41(6): 2822-2833.
[44]	Yang Q, Li Z, Lu X, et al. A review of soil heavy metal pollution from industrial and agricultural regions in China: Pollution and risk assessment[J]. Science of the Total Environment, 2018, 642: 690−700. doi: 10.1016/j.scitotenv.2018.06.068
[45]	Wei M, Pan A, Ma R, et al. Distribution characteristics, source analysis and health risk assessment of heavy metals in farmland soil in Shiquan County, Shaanxi Province[J]. Process Safety and Environmental Protection, 2023, 171: 225−237. doi: 10.1016/j.psep.2022.12.089
[46]	张小敏, 张秀英, 钟太洋, 等. 中国农田土壤重金属富集状况及其空间分布研究[J]. 环境科学, 2014, 35(2): 692−703. doi: 10.13227/j.hjkx.2014.02.047 Zhang X M, Zhang X Y, Zhong T Y, et al. Study on the enrichment and spatial distribution of heavy metals in farmland soils in China[J]. Environmental Science, 2014, 35(2): 692−703. doi: 10.13227/j.hjkx.2014.02.047

施引文献(3)

期刊类型引用(2)

1.	涂家润，周红英，崔玉荣，李国占，耿建珍，张健. 磷灰石ID-TIMS高精度U-Pb定年方法. 岩矿测试. 2024(04): 533-545 . 本站查看
2.	宁泽，徐磊，林学辉，张媛媛，张勇. 矿物特征自动分析系统对碎屑矿物定量识别及与传统镜下鉴定的误差分析. 岩矿测试. 2024(05): 713-722 . 本站查看

其他类型引用(1)

资源附件(1)

其他相关附件

在线预览下载 (1.7MB)

图(6) / 表(5)

计量

文章访问数: 116
HTML全文浏览量: 150
PDF下载量: 28
被引次数: 3

1. 实验部分
1.1 仪器与设备
1.2 样品采集与处理
1.3 样品测试
1.4 测试数据质量控制
2. 结果与讨论
2.1 浑浊度的意义
2.2 胶体粒子对电导率的影响
2.3 SiO2和H2SiO3分析结果的不同意义
2.4 样品表征结果
2.5 胶体粒子行为的影响
3. 结论

四川省沐川县典型土壤剖面重金属分布特征和生态风险评价

作者简介:
廖碧霞，硕士研究生，主要研究方向为环境地球化学。E-mail：1470949680@qq.com

通讯作者:
贺灵，博士，高级工程师，主要研究方向为生态地球化学调查与评价。E-mail：lingh1237@163.com。

计量