Spatial Differentiation and Influencing Factors of Heavy Metal Content in Soils of Typical River Valley City

doi:10.16258/j.cnki.1674-5906.2021.06.019

Ecology and Environment ›› 2021, Vol. 30 ›› Issue (6): 1276-1285.DOI: 10.16258/j.cnki.1674-5906.2021.06.019

• Research Articles • Previous Articles Next Articles

Spatial Differentiation and Influencing Factors of Heavy Metal Content in Soils of Typical River Valley City

ZHANG Jun¹^,²(), GAO Yu¹, WANG Guolan¹, JING Zihan¹, YANG Minghang¹

1. Shaanxi Key Laboratory of Disaster Monitoring and Mechanism Simulation/Baoji University of Arts and Sciences, Baoji 721013, China
2. Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education/Chang'an University, Xi'an 710064, China

Received:2021-01-26 Online:2021-06-18 Published:2021-09-10

典型河谷城市土壤重金属含量空间分异及其影响因素

张军¹^,²(), 高煜¹, 王国兰¹, 金梓函¹, 杨明航¹

1.宝鸡文理学院/陕西省灾害监测与机理模拟重点实验室,陕西宝鸡 721013
2.长安大学/旱区地下水文与生态效应教育部重点实验室,陕西西安 710064

作者简介:张军（1974年生）,男,副教授,博士,硕士研究生导师,主要研究方向为区域重金属污染控制与风险评价。E-mail: zhangjun1190@126.com
基金资助:
国家自然科学基金项目(41771215);旱区地下水文与生态效应教育部重点实验室（长安大学）开放基金项目(300102290504);陕西省社科界重大理论与现实问题研究项目(2020Z042);陕西省灾害监测与机理模拟重点实验室项目(20JS012)

Abstract

Abstract:

It is important to analyze the spatial pollution characteristics and influencing factors of heavy metals in soils of typical river valley cities. Therefore, the contents of 8 heavy metals (As, Cd, Cr, Cu, Mn, Ni, Pb, Zn) were determined by inductively coupled plasma mass spectrometry (ICP-MS) in 62 topsoil samples collected in Baoji City. And based on geostatistical spatial interpolation and geographic detector method, 11 factors such as geography, environment and meteorology were taken as independent variables and the content of each heavy metal element was taken as dependent variables to explore the spatial differentiation and influencing factors of heavy metal content in soils of typical river valley cities. The results showed that the eight heavy metal elements in the soil of Baoji urban area were all higher than the soil background values of Shaanxi Province, and the mean mass fractions of Cd and Zn were 0.77 and 261.17 mg∙kg^-1, which were 8.19 and 3.76 times of the soil background values of Shaanxi Province. The pollution of Cd, Zn, As and Cu were more serious, and the distribution of Cd, Zn, As and Cu were mainly concentrated in Panjiawan overpass, Binhe Road and Majiapo, showing a certain spatial aggregation. Precipitation repressented the greatest influence on the spatial distribution of Cd, while other heavy metals were mainly affected by altitude. Precipitation had the strongest explanatory power to the spatial distribution of Cd content (P_D_,_H=0.188). Except Cd, elevation (DEM) showed the strongest explanatory power to the spatial distribution of the other seven heavy metal elements. In addition, P_D_,_H values of the interaction were higher than those of the single factor, indicating that complex environmental conditions intensified the accumulation of heavy metals in soil. The spatial distribution of heavy metals was significantly different, which is closely related to the environmental and meteorological conditions in the study area.

Key words: heavy metal, spatial distribution, geographic detector, risk detection, interaction, Baoji City

摘要：

探析典型河谷城市土壤重金属空间污染特征及其影响因素,对城市污染防治具有重要意义。采集宝鸡市区62份表层土壤,使用电感耦合等离子体质谱仪（ICP-MS）测定8种重金属（As、Cd、Cr、Cu、Mn、Ni、Pb、Zn）含量,基于地统计空间插值与地理探测器方法,以地理、环境、气象等11种因子为自变量,各元素含量为因变量,探析典型河谷城市土壤重金属含量的空间分异及其影响因素。结果表明,宝鸡市区土壤8种重金属元素均超过陕西省土壤背景值,其中Cd和Zn质量分数均值达0.77、261.17 mg∙kg^-1,为陕西省土壤背景值的8.19倍和3.76倍;Cd、Zn、As、Cu污染较严重,其分布主要集中在潘家湾立交桥、滨河路及马家坡,在空间上呈现一定的聚集性;降水对Cd的空间分布影响最大,而其他重金属主要受海拔高程影响。降水因子对Cd空间分布具有最强解释力(因子解释力P_D_,_H达0.188),除Cd外,DEM（高程）对其余7种重金属含量空间分布均具有最强解释力;此外,交互作用的P_D_,_H值均高于单个因素,表明复杂的环境条件加剧了土壤中重金属的积累。重金属的空间分布差异显著,这与研究区的环境和气象条件密切相关。

关键词: 重金属, 空间分布, 地理探测器, 风险探测, 交互, 宝鸡市

CLC Number:

ZHANG Jun, GAO Yu, WANG Guolan, JING Zihan, YANG Minghang. Spatial Differentiation and Influencing Factors of Heavy Metal Content in Soils of Typical River Valley City[J]. Ecology and Environment, 2021, 30(6): 1276-1285.

张军, 高煜, 王国兰, 金梓函, 杨明航. 典型河谷城市土壤重金属含量空间分异及其影响因素[J]. 生态环境学报, 2021, 30(6): 1276-1285.

Figures/Tables 9

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交互作用 Interaction	解释力 Explanation
非线性减弱 Nonlinear reduction	P_D_,_H (x∩y)<Min[P_D_,_H (x) P_D_,_H (y)]
单因子减弱 Single factor reduction	Min[P_D_,_H (x) P_D_,_H (y)]<P_D_,_H (x∩y)<Max[P_D_,_H (x) P_D_,_H (y)]
相互独立 Independent	P_D_,_H (x∩y)=P_D_,_H (x)+P_D_,_H (y)
双因子增强 Double factor enhancement	P_D_,_H (x∩y)>Max[P_D_,_H (x) P_D_,_H (y)]
非线性增强 Non-linear enhancement	P_D_,_H (x∩y)>P_D_,_H (x)+P_D_,_H (y)

交互作用 Interaction	解释力 Explanation
非线性减弱 Nonlinear reduction	P_D_,_H (x∩y)<Min[P_D_,_H (x) P_D_,_H (y)]
单因子减弱 Single factor reduction	Min[P_D_,_H (x) P_D_,_H (y)]<P_D_,_H (x∩y)<Max[P_D_,_H (x) P_D_,_H (y)]
相互独立 Independent	P_D_,_H (x∩y)=P_D_,_H (x)+P_D_,_H (y)
双因子增强 Double factor enhancement	P_D_,_H (x∩y)>Max[P_D_,_H (x) P_D_,_H (y)]
非线性增强 Non-linear enhancement	P_D_,_H (x∩y)>P_D_,_H (x)+P_D_,_H (y)

元素 Element	Cd	Zn	As	Cu	Ni	Pb	Cr	Mn
均值 Mean value/(mg∙kg^-1)	0.77	261.17	17.03	40.52	30.52	16.75	49.18	331.23
最小值 Minimum value/(mg∙kg^-1)	0.14	22.9	2.9	4.7	6.73	未检出 No detection	8.4	未检出 No detection
最大值 Maximum value/(mg∙kg^-1)	5.5	1882	67.93	214	198	49.2	249	614
变异系数 Coefficient of variation	1.34	1.14	1.08	1.18	0.82	0.67	0.72	0.65
陕西省土壤背景值 Soil background values in Shaanxi Province/(mg∙kg^-1)	0.094	69.4	11.2	21.4	28.8	21.4	62.5	557
与陕西省土壤背景值比值 Ratio of soil background value to Shaanxi Province	8.19	3.76	1.52	1.89	1.05	0.78	0.78	0.59

元素 Element	Cd	Zn	As	Cu	Ni	Pb	Cr	Mn
均值 Mean value/(mg∙kg^-1)	0.77	261.17	17.03	40.52	30.52	16.75	49.18	331.23
最小值 Minimum value/(mg∙kg^-1)	0.14	22.9	2.9	4.7	6.73	未检出 No detection	8.4	未检出 No detection
最大值 Maximum value/(mg∙kg^-1)	5.5	1882	67.93	214	198	49.2	249	614
变异系数 Coefficient of variation	1.34	1.14	1.08	1.18	0.82	0.67	0.72	0.65
陕西省土壤背景值 Soil background values in Shaanxi Province/(mg∙kg^-1)	0.094	69.4	11.2	21.4	28.8	21.4	62.5	557
与陕西省土壤背景值比值 Ratio of soil background value to Shaanxi Province	8.19	3.76	1.52	1.89	1.05	0.78	0.78	0.59

元素 Element	因子Factor
元素 Element	DEM	NDVI	降水Precipitation	大气温度Atmospheric temperature	土地利用Land use type	土壤类型Soil type	土壤质地 Soil texture	距公路距离Distance from road	距河流距离Distance from river	距工厂距离Distance from factory	距铁路距离Distance from railway
Cd	0.141	0.058	0.188	0.187	0.005	0.159	0.021	0.006	0.001	0.039	0.016
Zn	0.203	0.007	0.009	0.01	0.077	0.029	0.003	0.005	0.006	0.017	0.002
As	0.355	0.066	0.093	0.093	0.092	0.133	0.009	0.001	0.001	0.042	0.024
Cu	0.082	0.047	0.012	0.011	0.036	0.04	0.002	0.013	0.021	0.045	0.003
Ni	0.279	0.015	0.054	0.054	0.076	0.032	0.006	0.001	0.001	0.000	0.007
Pb	0.142	0.001	0.01	0.01	0.01	0.039	0.009	0.001	0.001	0.001	0.003
Cr	0.095	0.054	0.037	0.036	0.052	0.025	0.003	0.007	0.019	0.053	0.004
Mn	0.305	0.091	0.204	0.204	0.068	0.066	0.012	0.001	0.007	0.044	0.033

Spatial Differentiation and Influencing Factors of Heavy Metal Content in Soils of Typical River Valley City

典型河谷城市土壤重金属含量空间分异及其影响因素

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交互探测 Interaction Detection	Cd	Zn	As	Cu	Ni	Pb	Cr	Mn
第一主因子 First principal factor	降水 Precipitation	DEM	DEM	DEM	DEM	DEM	DEM	DEM
P_D_,_H	0.188	0.203	0.355	0.082	0.279	0.142	0.095	0.305
交互作用1 Interaction Function 1	降水∩土壤类型Precipitation ∩ Soil type	DEM ∩大气温度 DEM ∩ Atmospheric temperature	DEM ∩降水 DEM ∩ Precipitation	DEM ∩降水 DEM ∩ Precipitation	DEM ∩ 土地利用 DEM ∩ Land use type	DEM ∩ 土壤类型 DEM ∩ Soil type	DEM ∩降水 DEM ∩ Precipitation	DEM ∩降水 DEM ∩ Precipitation
P_D_,_H	0.307	0.298	0.441	0.251	0.331	0.227	0.244	0.424
交互作用2 Interaction Function 2	降水∩土壤质地Precipitation ∩ Soil texture	DEM ∩降水 DEM ∩ Precipitation	DEM ∩ 大气温度 DEM ∩ Atmospheric temperature	DEM ∩ 大气温度 DEM ∩ Atmospheric temperature	DEM ∩ 大气温度 DEM ∩ Atmospheric temperature	DEM ∩降水 DEM ∩ Precipitation	DEM ∩ 大气温度 DEM ∩ Atmospheric temperature	DEM ∩ 大气温度 DEM ∩ Atmospheric temperature
P_D_,_H	0.251	0.298	0.439	0.251	0.324	0.213	0.243	0.422
交互作用3 Interaction Function 3	降水∩ 土地利用Precipitation ∩ Land use type	DEM ∩ NDVI	DEM ∩ 土壤类型 DEM ∩ Soil type	DEM ∩ 土壤类型 DEM ∩ Soil type	DEM ∩降水 DEM ∩ Atmospheric temperature	DEM ∩大气温度 DEM ∩ Atmospheric temperature	DEM ∩ 土壤类型 DEM ∩ Soil type	DEM ∩ 土壤类型 DEM ∩ Soil type
P_D_,_H	0.222	0.273	0.417	0.201	0.331	0.213	0.184	0.363

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