清远市清城区土壤中重金属的空间分布、来源解析和健康评价:基于PCA和PMF模型的对比

doi:10.13745/j.esf.sf.2023.2.46

摘要/Abstract

摘要：

本研究在中国东南部一个典型的快速转型工业城区采集了122个土壤样品。我们采用富集因子(EF)、地质累积指数法(I_geo)、斯皮尔曼相关性分析、潜在生态风险综合指数(RI)和人体健康风险模型(HHR)多种分析方法对研究区 9种重金属(As、Co、Cr、Cu、Hg、Ni、Pb、Ti和Zn)的污染特征进行评估,并结合主成分分析(PCA)、正矩阵分解(PMF)模型和地统计学法对研究区土壤进行来源解析。结果表明,土壤中As、Cu、Hg、Pb、Zn存在明显富集,但研究区整体处于清洁状态。由于PCA和PMF模型对重金属聚类分组的结果并不完全一致,故分别将9种重金属元素划分为两个来源和3个来源,以便互相验证元素划分的准确性。在潜在生态风险评价中,研究区整体处于轻微生态风险水平;值得注意的是,Hg元素带来的生态风险是所有元素中最高的。对于人体健康风险评价,在该研究区中,成人和儿童均不存在非致癌风险和致癌风险,但是我们发现儿童比成人在面对重金属带来的健康风险时表现得更敏感,应当引起重视。

关键词: 土壤重金属, 来源解析, 主成分分析(PCA), 正定矩阵模型(PMF), 健康评价

Abstract:

In this study we collected 122 soil samples from a typical, rapidly transforming industrial urban area in southeastern China to evaluate pollution characteristics of 9 heavy metals (As, Co, Cr, Cu, Hg, Ni, Pb, Ti, and Zn) in soil using enrichment factor (EF), geological accumulation index (I_geo), Spearman correlation analysis, potential ecological risk comprehensive index (RI), and human health risk model (HHR); combined with principal component analysis (PCA), positive matrix factorization (PMF) model and geostatistical analysis, the source of heavy metals was investigated. The results showed that As, Cu, Hg, Pb, and Zn were obviously enriched in soil, but the study area as a whole is clean from heavy metal pollution. Hierarchical clustering and grouping results of heavy metals by PCA and PMF models identified 2 and 3 source areas respectively, which helped to improve the accuracy of source analysis. According to ecological risk assessment the study area as a whole is at slight ecological risk, and Hg poses the highest ecological risk among all elements. By human health risk assessment neither adults nor children in the study area are at health risks from heavy metal pollution, including non-carcinogenic and carcinogenic risks, but we found that heavy metal pollution poses greater health risks to children and should be taken seriously.

Key words: soil heavy metals, source analysis, principal component analysis (PCA), positive matrix factorization model (PMF), health assessment

中图分类号:

宁文婧, 谢先明, 严丽萍. 清远市清城区土壤中重金属的空间分布、来源解析和健康评价:基于PCA和PMF模型的对比[J]. 地学前缘, 2023, 30(4): 470-484.

NING Wenjing, XIE Xianming, YAN Liping. Spatial distribution, sources and health risks of heavy metals in soil in Qingcheng District, Qingyuan City: Comparison of PCA and PMF model results[J]. Earth Science Frontiers, 2023, 30(4): 470-484.

图/表 16

参考文献 56

[1]	HU N J, LIU J H, HUANG P, et al. Sources, geochemical speciation, and risk assessment of metals in coastal sediments: a case study in the Bohai Sea, China[J]. Environmental Earth Sciences, 2017, 76(8): 1-14. DOI URL
[2]	HUANG K X, LUO X Z, ZHENG Z. Application of a combined approach including contamination indexes, geographic information system and multivariate statistical models in levels, distribution and sources study of metals in soils in Northern China[J]. PLoS One, 2018, 13(2): e0190906. DOI URL
[3]	中华人民共和国环境保护部, 中华人民共和国国土资源部. 全国土壤污染状况调查公报[R/OL]. (2014-2-17) [2017-2-17]. http://www.gov.cn/foot/2014-04/17/content_2661768.htm.
[4]	周俊驰. 基于GIS及地统计学的县域耕地重金属污染评价预警与源解析[D]. 长沙: 湖南农业大学, 2017.
[5]	GAN Y D, MIAO Y J, WANG L H, et al. Source contribution analysis and collaborative assessment of heavy metals in vegetable-growing soils[J]. Journal of Agricultural and Food Chemistry, 2018, 66(42): 10943-10951. DOI PMID
[6]	LIANG J, FENG C T, ZENG G M, et al. Spatial distribution and source identification of heavy metals in surface soils in a typical coal mine city, Lianyuan, China[J]. Environmental Pollution, 2017, 225: 681-690. DOI PMID
[7]	ADEMUYIWA O, AKINWUNMI F, ATOBATELE Z, et al. Heavy metal burdens of public primary school children in Ibadan north-west local government area, Nigeria related to playground soils and classroom dusts[J]. Toxicology Letters, 2013, 221: S107-S108.
[8]	李海燕, 王垚森, 张晓然, 等. 北京城乡接合部地表颗粒物中重金属污染负荷及健康风险评价[J]. 地学前缘, 2019, 26(6): 199-206. DOI
[9]	LIU R, WANG M, CHEN W P, et al. Spatial pattern of heavy metals accumulation risk in urban soils of Beijing and its influencing factors[J]. Environmental Pollution, 2016, 210: 174-181. DOI PMID
[10]	张素荣, 王昌宇, 刘继红, 等. 雄安新区西南部土壤重金属污染特征及生态风险评价[J]. 地学前缘, 2021, 28(4): 238-249. DOI
[11]	REN Y J, LIN M, LIU Q M, et al. Contamination assessment, health risk evaluation, and source identification of heavy metals in the soil-rice system of typical agricultural regions on the southeast coast of China[J]. Environmental Science and Pollution Research, 2021, 28(10): 12870-12880. DOI
[12]	WANG Y Y, WEN A B, GUO J, et al. Spatial distribution, sources and ecological risk assessment of heavy metals in Shenjia River watershed of the Three Gorges Reservoir Area[J]. Journal of Mountain Science, 2017, 14(2): 325-335. DOI URL
[13]	REHMAN Z U, KHAN S, BRUSSEAU M L, et al. Lead and cadmium contamination and exposure risk assessment via consumption of vegetables grown in agricultural soils of five-selected regions of Pakistan[J]. Chemosphere, 2017, 168: 1589-1596. DOI PMID
[14]	赵卫东, 赵芦, 龚建师, 等. 宿州矿区浅层地下水污染评价及源解析[J]. 地学前缘, 2021, 28(5): 1-14. DOI
[15]	ZHU X, YU W X, LI F, et al. Spatio-temporal distribution and source identification of heavy metals in particle size fractions of road dust from a typical industrial district[J]. Science of the Total Environment, 2021, 780: 146357. DOI URL
[16]	ZENG F, SHI G L, LI X, et al. Application of acombined model to study the source apportionment of PM₁₀ in Taiyuan, China[J]. Aerosol and Air Quality Research, 2010, 10(2): 177-184. DOI URL
[17]	ĐORĐEVIĆ D, PETROVIĆ S, RELIĆ D, et al. Applying receptor models Unmix and PMF on real data set of elements in PM for sources evaluation of the sea coastal side region (Southeast Adriatic Sea)[J]. Atmospheric Measurement Techniques Discussions, 2013, 6(3): 4941-4969.
[18]	LEE S, LIU W, WANG Y H, et al. Source apportionment of PM_2.5: comparing PMF and CMB results for four ambient monitoring sites in the southeastern United States[J]. Atmospheric Environment, 2008, 42(18): 4126-4137. DOI URL
[19]	DUAN X C, YU H H, YE T R, et al. Geostatistical mapping and quantitative source apportionment of potentially toxic elements in top- and sub-soils: a case of suburban area in Beijing, China[J]. Ecological Indicators, 2020, 112: 106085. 1-106085.11.
[20]	ABUZAID A S, JAHIN H S. Profile distribution and source identification of potentially toxic elements in North Nile delta, Egypt[J]. Soil and Sediment Contamination: An International Journal, 2019, 28(6): 582-600. DOI URL
[21]	鲍丽然, 龚媛媛, 严明书, 等. 渝西经济区土壤地球化学基准值与背景值及元素分布特征[J]. 地球与环境, 2015, 43(1): 31-40.
[22]	ZOLLER W H, GLADNEY E S, DUCE R A. Atmospheric concentrations and sources of trace metals at thesouth pole[J]. Science, 1974, 183(4121): 198-200. DOI URL
[23]	JIANG YX, CHAO S H, LIU J W, et al. Source apportionment and health risk assessment of heavy metals in soil for a township in Jiangsu Province, China[J]. Chemosphere, 2017, 168: 1658-1668. DOI PMID
[24]	MIRI M, ALLAHABADI A, GHAFFARI H R, et al. Ecological risk assessment of heavy metal (HM) pollution in the ambient air using a new bio-indicator[J]. Environmental Science and Pollution Research, 2016, 23(14): 14210-14220. DOI URL
[25]	DONG B, ZHANG R Z, GAN Y D, et al. Multiple methods for the identification of heavy metal sources in cropland soils from a resource-based region[J]. Science of the Total Environment, 2019, 651: 3127-3138. DOI URL
[26]	MÜLLER G. Index of geoaccumulation in sediments of the Rhine River[J]. Geology Journal, 1969, 2(3): 108-118.
[27]	WU J T, MARGENOT A J, WEI X, et al. Source apportionment of soil heavy metals in fluvial Islands, Anhui section of the Lower Yangtze River: comparison of APCS-MLR and PMF[J]. Journal of Soils and Sediments, 2020, 20(9): 3380-3393. DOI
[28]	HAKANSON L. An ecological risk index for aquatic pollution control: a sedimentological approach[J]. Water Research, 1980, 14(8): 975-1001. DOI URL
[29]	曹伟, 张雷, 秦延文, 等. 云蒙湖表层沉积物重金属分布特征及风险评价[J]. 地学前缘, 2021, 28(5): 448-455. DOI
[30]	陈怡先, 姜小三, 王勇, 等. 基于GIS矿区土壤重金属生态环境及人体健康评价[J]. 环境科学学报, 2018, 38(4): 1642-1652.
[31]	李春芳, 曹见飞, 吕建树, 等. 不同土地利用类型土壤重金属生态风险与人体健康风险[J]. 环境科学, 2018, 39(12): 5628-5638.
[32]	WANG H Z, CAI L M, WANG S, et al. A comprehensive exploration on pollution characteristics and health risks of potentially toxic elements in indoor dust from a large Cu smelting area, Central China[J]. Environmental Science and Pollution Research, 2021, 28(41): 57569-57581. DOI
[33]	XIAO R, GUO D, ALI A, et al. Accumulation, ecological-health risks assessment, and source apportionment of heavy metals in paddy soils:a case study in Hanzhong, Shaanxi, China[J]. Environmental Pollution, 2019, 248: 349-357. DOI URL
[34]	CASTRO-GONZÁLEZ N P, CALDERÓN-SÁNCHEZ F, MORENO-ROJAS R, et al. Health risks in rural populations due to heavy metals found in agricultural soils irrigated with wastewater in the Alto Balsas sub-basin in Tlaxcala and Puebla, Mexico[J]. International Journal of Environmental Health Research, 2017, 27(6): 476-486. DOI URL
[35]	SUN C Y, LIU J S, 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: 517-523. DOI PMID
[36]	WANG S, CAI L M, WEN H H, et al. Spatial distribution and source apportionment of heavy metals in soil from a typical county-level city of Guangdong Province, China[J]. Science of the Total Environment, 2019, 655: 92-101. DOI URL
[37]	FEI X F, CHRISTAKOS G, XIAO R, et al. Improved heavy metal mapping and pollution source apportionment in Shanghai City soils using auxiliary information[J]. Science of the Total Environment, 2019, 661: 168-177. DOI URL
[38]	ESMAEILI A, MOORE F, KESHAVARZI B, et al. A geochemical survey of heavy metals in agricultural and background soils of the Isfahan industrial zone, Iran[J]. CATENA, 2014, 121: 88-98. DOI URL
[39]	WONG C S C, LI X D, ZHANG G, et al. Atmospheric deposition of heavy metals in the Pearl River Delta, China[J]. Atmospheric Environment, 2003, 37(6): 767-776. DOI URL
[40]	CHEN X, XIA X H, ZHAO Y, et al. Heavy metal concentrations in roadside soils and correlation with urban traffic in Beijing, China[J]. Journal of Hazardous Materials, 2010, 181(1/2/3): 640-646. DOI URL
[41]	李鱼, 董德明, 吕晓君, 等. 汽车尾气中铅对公路两侧土壤的污染特征[J]. 生态环境, 2004, 13(4): 549-552.
[42]	ZHAO L, XU Y F, HOU H, et al. Source identification and health risk assessment of metals in urban soils around the Tanggu chemical industrial district, Tianjin, China[J]. Science of the Total Environment, 2014, 468/469: 654-662. DOI URL
[43]	LI F, ZHANG J D, HUANG J H, et al. Heavy metals in road dust from Xiandao District, Changsha City, China: characteristics, health risk assessment, and integrated source identification[J]. Environmental Science and Pollution Research, 2016, 23(13): 13100-13113. DOI URL
[44]	钱建平, 张力, 张爽, 等. 桂林市汽车尾气汞污染[J]. 生态学杂志, 2011, 30(5): 944-950.
[45]	WU Y, ZHOU X Y, LEI M, et al. Migration and transformation of arsenic:contamination control and remediation in realgar mining areas[J]. Applied Geochemistry, 2017, 77: 44-51. DOI URL
[46]	MATSCHULLAT J. Arsenic in the geosphere: a review[J]. Science of the Total Environment, 2000, 249(1/2/3): 297-312. DOI URL
[47]	王婷, 张倩, 杨海雪, 等. 农田土壤中铜的来源分析及控制阈值研究[J]. 生态毒理学报, 2014, 9(4): 774-784.
[48]	管滨, 孙虎. 不同土地利用类型下土壤铜累积状况及其污染风险控制[J]. 山东农业科学, 2015, 47(4): 83-87.
[49]	王林, 徐强, 宋仲容. 农药研究现状及环境影响评价[J]. 科技信息, 2006(10): 19-20.
[50]	SILVA F BV, NASCIMENTO C W A, ARAÚJO P R M, et al. Assessing heavy metal sources in sugarcane Brazilian soils: an approach using multivariate analysis[J]. Environmental Monitoring and Assessment, 2016, 188(8): 1-12. DOI URL
[51]	HUANG J H, GUO S T, ZENG G M, et al. A new exploration of health risk assessment quantification from sources of soil heavy metals under different land use[J]. Environmental Pollution, 2018, 243: 49-58. DOI PMID
[52]	HUY N, HE K L, SUN Z H, et al. Quantitative source apportionment of heavy metal(loid)s in the agricultural soils of an industrializing region and associated model uncertainty[J]. Journal of Hazardous Materials, 2020, 391: 122244. DOI URL
[53]	LI J, TENG Y G, WU J, et al. Source apportionment of soil heavy metal in the middle and upper reaches of Le’an River based on PMF model and geostatistics[J]. Research of Environmental Sciences, 2019, 32(6): 984-992.
[54]	ZHANG L, BAI Y S, WANG J Z, et al. Identification and determination of the contribution of iron-steel manufacturing industry to sediment-associated polycyclic aromatic hydrocarbons (PAHs) in a large shallow lake of Eastern China[J]. Environmental Science and Pollution Research, 2016, 23(21): 22037-22046. DOI URL
[55]	WANG H Z, CAI L M, WANG Q S, et al. A comprehensive exploration of risk assessment and source quantification of potentially toxic elements in road dust:a case study from a large Cu smelter in central China[J]. CATENA, 2021, 196: 104930. DOI URL
[56]	JIANG H H, CAI L M, HU G C, et al. An integrated exploration on health risk assessment quantification of potentially hazardous elements in soils from the perspective of sources[J]. Ecotoxicology and Environmental Safety, 2021, 208:111489. DOI URL

参数	描述	单位	值
IngR	儿童(成人)对土壤的口腔摄入	mg/d	200 (100)
ED	儿童(成人)暴露时间	a	6 (26)
EF	儿童(成人)暴露频率	d/a	350
BW	儿童(成人)的平均体重	kg	15.9(56.8)
ABS	皮肤吸收因子	量纲为一	0.001
AT	致癌(非致癌)作用的平均时间	d	25 550 (365×ED)
InhR	儿童(成人)土壤口鼻吸入率	m³/d	7.5 (15)
SA	儿童(成人)暴露皮肤区域	cm²	2 373 (5 700)
AF	儿童(成人)皮肤表面积土壤黏附系数	mg/cm²	0.2 (0.07)
PEF	粒子排放因子	m³/kg	1.36×10⁹

参数	描述	单位	值
IngR	儿童(成人)对土壤的口腔摄入	mg/d	200 (100)
ED	儿童(成人)暴露时间	a	6 (26)
EF	儿童(成人)暴露频率	d/a	350
BW	儿童(成人)的平均体重	kg	15.9(56.8)
ABS	皮肤吸收因子	量纲为一	0.001
AT	致癌(非致癌)作用的平均时间	d	25 550 (365×ED)
InhR	儿童(成人)土壤口鼻吸入率	m³/d	7.5 (15)
SA	儿童(成人)暴露皮肤区域	cm²	2 373 (5 700)
AF	儿童(成人)皮肤表面积土壤黏附系数	mg/cm²	0.2 (0.07)
PEF	粒子排放因子	m³/kg	1.36×10⁹

参数	Ni	Cu	Zn	Hg	As	Cr	Pb
口腔摄入的剂量	0.02	0.04	0.30	0.000 3	0.000 3	0.003	0.003 5
皮肤接触的剂量	0.005 4	0.012	0.06	0.000 021	0.000 123	0.000 06	0.000 525
口鼻吸入的剂量	0.020 6	0.040 2	0.30	0.000 0857	0.000 3	0.000 028 6	0.003 5
口腔摄入的斜率因子	-	-	-	-	1.50	0.50	0.008 5
皮肤接触的斜率因子	-	-	-	-	3.66	20	-
口鼻吸入的斜率因子	84	-	-	-	15.1	42	-

参数	Ni	Cu	Zn	Hg	As	Cr	Pb
口腔摄入的剂量	0.02	0.04	0.30	0.000 3	0.000 3	0.003	0.003 5
皮肤接触的剂量	0.005 4	0.012	0.06	0.000 021	0.000 123	0.000 06	0.000 525
口鼻吸入的剂量	0.020 6	0.040 2	0.30	0.000 0857	0.000 3	0.000 028 6	0.003 5
口腔摄入的斜率因子	-	-	-	-	1.50	0.50	0.008 5
皮肤接触的斜率因子	-	-	-	-	3.66	20	-
口鼻吸入的斜率因子	84	-	-	-	15.1	42	-

特征值	元素含量w_B/(mg·kg^-1)
特征值	As	Co	Cr	Cu	Hg	Ni	Pb	Ti	Zn
最小值	1.17	1.59	5.30	1.40	0.038	0.94	9.00	964	14.20
最大值	121.22	14.88	84.60	138.00	0.381	108.60	129.20	7 104	239.70
平均值	20.77	6.73	39.66	19.76	0.101 8	13.33	46.65	3 544	68.96
中位数	14.21	6.58	39.60	15.90	0.089	12.20	40.90	3 730	52.20
标准差	21.04	2.71	21.74	18.95	0.051 5	11.23	25.62	1 128	45.90
变异系数	1.01	0.40	0.55	0.96	0.51	0.84	0.55	0.32	0.67
研究区背景值	10.40	6.40	38.20	13.50	0.081 8	10.997	38.67	3 707	44.58