废弃铅锌冶炼场地土壤和地下水重金属迁移及预测

Impact of migration and prediction on heavy metals from soil to groundwater in an abandoned lead/zinc smelting site

摘要

长期的金属冶炼活动会导致重金属在场地土壤和地下水中的富集和扩散。基于机器学习模型研 究了某废弃铅锌冶炼场地土壤中重金属向地下水的迁移规律并进行了含量预测。结果表明,场地土壤 重金属主要累积在杂填土和素填土层,并存在垂向迁移到地下水的现象。场地土壤中As、Cd和Pb 含 量平均值均明显超过场地土壤风险筛选值。地下水中Zn、Cd、Pb 和As的平均浓度均超过了相应的地 下水VI 类限值。土壤重金属污染主要集中在火法冶炼区、湿法冶炼区和原料堆存区。土壤中Cd以活 性态为主, Pb 以还原态为主。当冶炼场地地下水位低于5 m且土壤Cd含量超过344 mg/kg 时,或土壤 中活性Pb含量超过5425 mg/kg时,需要对场地土壤和地下水进行协同修复。

Abstract

Long-term metal smelting activities can lead to enrichment and dispersion of heavy metals in the site soil and groundwater. The migration and prediction of heavy metals from soil to groundwater in an abandoned lead/zinc smelting site were studied using machine learning model. The results showed that heavy metals in site soil mainly accumulated in the fill layer, and vertically migrated to groundwater significantly. The mean of Pb, As, and Cd in site soils significantly exceeded the screening value of risk control standard for soil contamination of development land. The mean of Zn, Cd, Pb and As in groundwater exceeded the corresponding groundwater Class VI limit of standard for groundwater quality of China. Soil contamination of heavy metals was serious in the pyrometallurgical area, hydrometallurgical area and raw material storage area, and Cd and Pb in the upper soil layer had a strong migration potential downward with active and reducible state. The synergistic remediation for site soil and groundwater in smelting site was suggested when groundwater level was below 5 m and soil Cd concentration exceeded 344 mg/kg, or when the soil active Pb concentration exceeded 5425 mg/kg.

Keywords

lead/zinc smelting site / migration and transformation / machine learning / heavy metals / prediction

参考文献

[[1]] Sharma K, Janardhana Raju N, Singh N, et al.. Heavy metal pollution in groundwater of urban Delhi environs: Pollution indices and health risk assessment [J]. Urban Climate, 2022, 45: 101233,

[[2]] Sheng D, Meng X, Wen X, et al.. Contamination characteristics, source identification, and source-specific health risks of heavy metal(loid)s in groundwater of an arid oasis region in Northwest China [J]. The Science of the Total Environment, 2022, 841: 156733,

[[3]] Zhou Q, Song C, Wang P, et al.. Generating dual-active species by triple-atom sites through peroxymonosulfate activation for treating micropollutants in complex water [J]. Proceedings of the National Academy of Sciences of the United States of America, 2023, 120(13): e2300085120,

[[4]] Jiang W, Liu H, Sheng Y, et al.. Distribution, source apportionment, and health risk assessment of heavy metals in groundwater in a multimineral resource area, North China [J]. Exposure and Health, 2022, 14(4): 807-827,

[[5]] Zeng J, Tabelin C B, Gao W, et al.. Heterogeneous distributions of heavy metals in the soil-groundwater system empowers the knowledge of the pollution migration at a smelting site [J]. Chemical Engineering Journal, 2023, 454: 140307,

[[6]] Li C, Li M, Zeng J, et al.. Migration and distribution characteristics of soil heavy metal(loid)s at a lead smelting site [J]. Journal of Environmental Sciences, 2024, 135: 600-609,

[[7]] Tang L, Liu J, Zeng J, et al.. Anthropogenic processes drive heterogeneous distributions of toxic elements in shallow groundwater around a smelting site [J]. Journal of Hazardous Materials, 2023, 453: 131377,

[[8]] Megremi I, Vasilatos C, Vassilakis E, et al.. Spatial diversity of Cr distribution in soil and groundwater sites in relation with land use management in a Mediterranean Region: The case of C. Evia and Assopos-Thiva Basins, Greece [J]. Science of the Total Environment, 2019, 651: 656-667,

[[9]] Wang Z, Su Q, Wang S, et al.. Spatial distribution and health risk assessment of dissolved heavy metals in groundwater of Eastern China coastal zone [J]. Environmental Pollution, 2021, 290: 118016,

[[10]] Zhang Y, Wu Y, Song B, et al.. Spatial distribution and main controlling factor of cadmium accumulation in agricultural soils in Guizhou, China [J]. Journal of Hazardous Materials A, 2022, 424: 127308,

[[11]] Guo Z, Zhang Y, Xu R, et al.. Contamination vertical distribution and key factors identification of metal(loid)s in site soil from an abandoned Pb/Zn smelter using machine learning [J]. The Science of the Total Environment, 2023, 856(2): 159264,

[[12]] Huang C, Guo Z, Li T, et al.. Source identification and migration fate of metal(loid)s in soil and groundwater from an abandoned Pb/Zn Mine [J]. The Science of the Total Environment, 2023, 895: 165037,

[[13]] Ma Y, Li Y, Fang T, et al.. Analysis of driving factors of spatial distribution of heavy metals in soil of non-ferrous metal smelting sites: Screening the geodetector calculation results combined with correlation analysis [J]. Journal of Hazardous Materials, 2023, 445: 130614,

[[14]] Bandyopadhyay S, Maiti S K. Application of statistical and machine learning approach for prediction of soil quality index formulated to evaluate trajectory of ecosystem recovery in coal mine degraded land [J]. Ecological Engineering, 2021, 170: 106351,

[[15]] Zhang Y, Li F, Li J, et al.. Spatial distribution, potential sources, and risk assessment of trace metals of groundwater in the North China Plain [J]. Human and Ecological Risk Assessment: An International Journal, 2015, 21: 726-743,

[[16]] Zhang Y, Li T, Guo Z, et al.. Spatial heterogeneity and source apportionment of soil metal (loid)s in an abandoned lead/zinc smelter [J]. Journal of Environmental Sciences (China), 2023, 127: 519-529,

[[17]] Kim D, Yun S, Cho Y, et al.. Hydrochemical assessment of environmental status of surface and ground water in mine areas in South Korea: Emphasis on geochemical behaviors of metals and sulfate in ground water [J]. Journal of Geochemical Exploration (Journal of the Association of Exploration Geochemists), 2017, 183(Pt.A): 33-45,

[[18]] Ran H, Guo Z, Yi L, et al.. Spatial variability of arsenic fractionation in an abandoned arsenic-containing mine: Insights into soil particle sizes and quantitative mineralogical analysis [J]. Science of the Total Environment, 2023, 889: 164145,

[[19]] Drahota P, Grösslová Z, Kindlová H. Selectivity assessment of an arsenic sequential extraction procedure for evaluating mobility in mine wastes [J]. Analytica Chimica Acta, 2014, 839: 34-43,

[[20]] Li Z, Pan F, Xiao K, et al.. An integrated study of the spatiotemporal character, pollution assessment, and migration mechanism of heavy metals in the groundwater of a subtropical mangrove wetland [J]. Journal of Hydrology, 2022, 612: 128251,

[[21]] Gui H, Yang Q, Lu X, et al.. Spatial distribution, contamination characteristics and ecological-health risk assessment of toxic heavy metals in soils near a smelting area [J]. Environmental Research, 2023, 222: 115328,

[[22]] Jiang C, Zhao Q, Zheng L, et al.. Distribution, source and health risk assessment based on the Monte Carlo method of heavy metals in shallow groundwater in an area affected by mining activities, China [J]. Ecotoxicology and Environmental Safety, 2021, 224: 112679,

[[23]] Jafarzadeh N, Heidari K, Meshkinian A, et al.. Non-carcinogenic risk assessment of exposure to heavy metals in underground water resources in Saraven, Iran: Spatial distribution, monte-carlo simulation, sensitive analysis [J]. Environmental Research A, 2022, 204: 112002,

[[24]] Du S, Zhang X, Jin X, et al.. A review of multi-scale modelling, assessment, and improvement methods of the urban thermal and wind environment [J]. Building and Environment, 2022, 213: 108860,

[[25]] Gao B, Stein A, Wang J-feng. A two-point machine learning method for the spatial prediction of soil pollution [J]. International Journal of Applied Earth Observations and Geoinformation, 2022, 108: 102742,

[[26]] Cao J, Guo Z, Ran H, et al.. Risk source identification and diffusion trends of metal(loid)s in stream sediments from an abandoned arsenic-containing mine [J]. Environmental Pollution, 2023, 329: 121713,

[[27]] Podgorski J, Araya D, Berg M. Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh−Machine learning spatial prediction modeling and comparison with arsenic [J]. Science of the Total Environment, 2022, 833: 155131,

[[28]] Sumdang N, Chotpantarat S, Cho K H, et al.. The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach [J]. Ecotoxicology and Environmental Safety, 2023, 253: 114665,

[[29]] LY/T 1237-1999. Determination of organic matter in forest soil and calculation carbon-nitrogen ratio [S]. (in Chinese)

[[30]] LY/T 1243-1999. Determination of cation exchange capacity in forest soil [S]. (in Chinese)

[[31]] DZ/T 0279.28-2016. Analysis methods for regional geochemical sample-Part 28: Determination of sulfur contents by burning-iodine quantity method [S]. (in Chinese)

[[32]] LY/T 1225-1999. Determination of forest soil particle-size composition (mechanical composition) [S]. (in Chinese)

[[33]] Polakowski C, Mako A, Sochan A, et al.. Hand-feel soil texture and particle-size distribution in central France. Relationships and implications [J]. Geoderma, 2023, 430: 116358

[[34]] Dabek-Zlotorzynska E, Kelly M, Chen H, et al.. Application of capillary electrophoresis combined with a modified BCR sequential extraction for estimating of distribution of selected trace metals in PM_2.5 fractions of urban airborne particulate matter [J]. Chemosphere, 2005, 58(10): 1365-1376,

[[35]] Wang Z, Wang J, Han J-jun. Spatial prediction of groundwater potential and driving factor analysis based on deep learning and geographical detector in an arid endorheic basin [J]. Ecological Indicators, 2022, 142: 109256,

[[36]] Xu D, Xu Z, Mu Z, et al.. Mechanistic insights into the migration behavior of cadmium (Cd) in the soil andgroundwater systems at a construction site: Experimental and numerical analysis [J]. Journal of Environmental Chemical Engineering, 2023, 11(3): 109712,

[[37]] Zhu G, Wu X, Ge J, et al.. Influence of mining activities on groundwater hydrochemistry and heavy metal migration using a self-organizing map (SOM) [J]. Journal of Cleaner Production, 2020, 257: 120664,

[[38]] China National Environmental Monitoring Centre.. . The element background values of Chinese soil [M], 1990 Beijing China Environment Science Press (in Chinese)

[[39]] GB36600—2018. Soil environmental quality risk control standard for soil contamination of development land [S]. (in Chinese)

[[40]] Zhang Y, Song B, Zhou Z-yang. Pollution assessment and source apportionment of heavy metals in soil from lead-zinc mining areas of South China [J]. Journal of Environmental Chemical Engineering, 2023, 11(2): 109320,

[[41]] Chaithanya M, Das B, Vidya R. Distribution, chemical speciation and human health risk assessment of metals in soil particle size fractions from an industrial area [J]. Journal of Hazardous Materials Advances, 2023, 9: 100237,

[[42]] Gao Z, Niu F, Lin Z, et al.. Fractal and multifractal analysis of soil particle-size distribution and correlation with soil hydrological properties in active layer of Qinghai-Tibet Plateau, China [J]. Catena, 2021, 203: 105373,

[[43]] Ma J, Li Y, Liu Y, et al.. Effects of soil particle size on metal bioaccessibility and health risk assessment [J]. Ecotoxicology and Environmental Safety, 2019, 186: 109748,

[[44]] Liu G, Wang J, Liu X, et al.. Partitioning and geochemical fractions of heavy metals from geogenic and anthropogenic sources in various soil particle size fractions [J]. Geoderma, 2018, 312: 104-113,

[[45]] Tian Z, Pan Y, Chen M, et al.. The relationships between fractal parameters of soil particle size and heavy-metal content on alluvial-proluvial fan [J]. Journal of Contaminant Hydrology, 2023, 254: 104140,

[[46]] Wang B, Gao F, Li Y, et al.. Necessity of introducing particle size distribution of hand-adhered soil on the estimation of oral exposure to metals in soil: Comparison with the traditional method [J]. Journal of Hazardous Materials, 2023, 448: 130891,

[[47]] Xu D, Fu R-bing. Mechanistic insight into the release behavior of arsenic (As) based on its geochemical fractions in the contaminated soils around lead/zinc (Pb/Zn) smelters [J]. Journal of Cleaner Production, 2022, 363: 132348,

[[48]] GB/T 14848-2017. Standard for groundwater quality [S]. (in Chinese)

[[49]] Afzal M, Shabir G, Iqbal S, et al.. Assessment of heavy metal contamination in soil and groundwater at leather industrial area of Kasur, Pakistan [J]. CLEAN − Soil, Air, Water, 2014, 42(8): 1133-1139,

[[50]] Bux R K, Haider S I, Mallah A, et al.. Spatial analysis and human health risk assessment of elements in ground water of District Hyderabad, Pakistan using ArcGIS and multivariate statistical analysis [J]. Environmental Research, 2022, 210: 112915,

[[51]] Ewusi A, Sunkari E D, Seidu J, et al.. Hydrogeochemical characteristics, sources and human health risk assessment of heavy metal dispersion in the mine pit water–surface water–groundwater system in the largest manganese mine in Ghana [J]. Environmental Technology & Innovation, 2022, 26: 102312,

[[52]] Sun Y, Zhang Y, Lu L, et al.. The application of machine learning methods for prediction of metal immobilization remediation by biochar amendment in soil [J]. The Science of the Total Environment, 2022, 829: 154668,

[[53]] Yang Z, Gong H, He F, et al.. Iron-doped hydroxyapatite for the simultaneous remediation of lead-, cadmium- and arsenic-co-contaminated soil [J]. Environmental Pollution, 2022, 312: 119953,