Temporal–Spatial Distributions and Influencing Factors of Heavy Metals As, Cd, Pb, and Zn in Alluvial Soils on a Regional Scale in Guangxi, China

Understanding the temporal–spatial distribution and influencing factors of heavy metals on a regional scale is crucial for assessing the anthropogenic impacts and natural variations in elemental geochemical behavior. This study evaluated the spatial distributions of the heavy metals As, Cd, Pb, and Zn as well as the driving mechanisms over the past 31 years in Guangxi, China, using three geochemical baseline projects (the Environmental Geochemical Monitoring Network Project (EGMON) project 1992–1996; the Geochemical Baseline (CGB) 1 project 2008–2012; and the CGB2 project 2015–2019). By calculating the variable importance using the random forest algorithm, it was found that natural factors are the primary drivers of the spatial distribution of heavy metals in the EGMON project, especially precipitation for As, the digital elevation model (DEM) for Cd and Pb, and temperature for Zn. Surface alluvial soils showed obvious heavy metal enrichment in the CGB1 project, with the gross domestic product (GDP) driving the spatial distribution of all heavy metals. In addition, the anomalous intensity and range of heavy metals in the CGB2 project decreased significantly compared with the CGB1 project, especially owing to the normalized difference vegetation index (NDVI) as a positive anthropogenic factor that improves the degree of rocky desertification, thus reducing the heavy metal contents of As and Pb, and the precipitation promoting the decomposition of Fe–Mn concretions and thus the migration of Cd and Zn. This research promotes an understanding of anthropogenic and natural influences on the spatiotemporal distribution of heavy metals and is of great significance for environmental monitoring and governance.

[1]  Xueqiu Wang,et al.  Spatial-temporal variability and influence factors of Cd in soils of Guangxi, China , 2023, PloS one.

[2]  Xueqiu Wang,et al.  Temporal Variations of Sediment Provenance in a Karst Watershed, China , 2023, Applied Sciences.

[3]  Shuyi Ren,et al.  The spatiotemporal variation in heavy metals in China's farmland soil over the past 20 years: A meta-analysis. , 2021, The Science of the total environment.

[4]  Yan Zhang,et al.  Quantitative source apportionment, risk assessment and distribution of heavy metals in agricultural soils from southern Shandong Peninsula of China. , 2021, The Science of the total environment.

[5]  Syed Abdul Wadood,et al.  Source apportionment of cadmium pollution in agricultural soil based on cadmium isotope ratio analysis , 2020 .

[6]  N. Jain,et al.  A review on soil heavy metals contamination: Effects, sources and remedies , 2020 .

[7]  Kelin Wang,et al.  Fingerprinting sediment sources in a typical karst catchment of southwest China , 2020, International Soil and Water Conservation Research.

[8]  Chutian Zhang,et al.  Modeling the spatial variations in anthropogenic factors of soil heavy metal accumulation by geographically weighted logistic regression. , 2020, The Science of the total environment.

[9]  Yongzhang Zhou,et al.  Heavy metal(loid)s in the topsoil of urban parks in Beijing, China: Concentrations, potential sources, and risk assessment. , 2020, Environmental pollution.

[10]  Xiang-dong Li,et al.  Deciphering source contributions of trace metal contamination in urban soil, road dust, and foliar dust of Guangzhou, southern China. , 2019, The Science of the total environment.

[11]  Wei Li,et al.  Evaluation of various approaches to predict cadmium bioavailability to rice grown in soils with high geochemical background in the karst region, Southwestern China. , 2019, Environmental pollution.

[12]  R. Price,et al.  Evolution of carbonate and karst critical zones , 2019, Chemical Geology.

[13]  Sucai Yang,et al.  Quantitative analysis of the factors influencing spatial distribution of soil heavy metals based on geographical detector. , 2019, The Science of the total environment.

[14]  Yan Li,et al.  [Impacts of Land Use and Landscape Patterns on Heavy Metal Accumulation in Soil]. , 2019, Huan jing ke xue= Huanjing kexue.

[15]  Q. Fan,et al.  Lead isotopic fingerprinting as a tracer to identify the pollution sources of heavy metals in the southeastern zone of Baiyin, China. , 2019, The Science of the total environment.

[16]  M. Moradi,et al.  The effect of land use configurations on concentration, spatial distribution, and ecological risk of heavy metals in coastal sediments of northern part along the Persian Gulf. , 2019, The Science of the total environment.

[17]  Hanlian Liu,et al.  Spatial distributions and the identification of ore-related anomalies of Cu across the boundary area of China and Mongolia , 2019, Journal of Geochemical Exploration.

[18]  Lei Huang,et al.  A review of soil heavy metal pollution from industrial and agricultural regions in China: Pollution and risk assessment. , 2018, The Science of the total environment.

[19]  Wei Wu,et al.  Assessment of heavy metal pollution and human health risks in urban soils around an electronics manufacturing facility. , 2018, The Science of the total environment.

[20]  Yongfei Gao,et al.  The spatial distribution and accumulation characteristics of heavy metals in steppe soils around three mining areas in Xilinhot in Inner Mongolia, China , 2017, Environmental Science and Pollution Research.

[21]  G. Han,et al.  Characteristics of heavy metals in soils under different land use in a typical karst area, Southwest China , 2017, Acta Geochimica.

[22]  T. Nishimura,et al.  A review of source tracking techniques for fine sediment within a catchment , 2017, Environmental Geochemistry and Health.

[23]  J. Ji,et al.  Temporal-spatial variation and source apportionment of soil heavy metals in the representative river-alluviation depositional system. , 2016, Environmental pollution.

[24]  L. Zeng,et al.  County-scale temporal–spatial distribution and variability tendency of heavy metals in arable soils influenced by policy adjustment during the last decade: a case study of Changxing, China , 2015, Environmental Science and Pollution Research.

[25]  E. Solgi,et al.  Analysis and assessment of nickel and chromium pollution in soils around Baghejar Chromite Mine of Sabzevar Ophiolite Belt, Northeastern Iran , 2015 .

[26]  R. Pesch,et al.  Modelling and mapping spatio-temporal trends of heavy metal accumulation in moss and natural surface soil monitored 1990–2010 throughout Norway by multivariate generalized linear models and geostatistics , 2014 .

[27]  S. Qureshi,et al.  Heavy metal content in urban soils as an indicator of anthropogenic and natural influences on landscape of Karachi—A multivariate spatio-temporal analysis , 2014 .

[28]  M. Cracknell,et al.  Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer–Mt Charter region, Tasmania, using Random Forests™ and Self-Organising Maps , 2014 .

[29]  Zongwei Ma,et al.  A review of soil heavy metal pollution from mines in China: pollution and health risk assessment. , 2014, The Science of the total environment.

[30]  M. Wiesmeier,et al.  Degradation and small-scale spatial homogenization of topsoils in intensively-grazed steppes of Northern China , 2009 .

[31]  R. M. Lark,et al.  Spatio-temporal variability of some metal concentrations in the soil of eastern England, and implications for soil monitoring. , 2006 .

[32]  S. Qi,et al.  Heavy metals in agricultural soils of the Pearl River Delta, South China. , 2002, Environmental pollution.

[33]  Xie Xuejing,et al.  Geochemical mapping in China , 1997 .

[34]  Xuejing Xie,et al.  The suitability of floodplain sediment as a global sampling medium: evidence from China , 1997 .

[35]  Xie Xuejing,et al.  Usable Values for Chinese Standard Reference Samples of Stream Sediments, Soils, and Rocks: GSD 9‐12, GSS 1‐8 and GSR 1‐6 , 1985 .

[36]  Emmanuel John M. Carranza,et al.  Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) , 2015, Comput. Geosci..

[37]  J. Ramos-Miras,et al.  Impact of 70 years urban growth associated with heavy metal pollution. , 2015, Environmental pollution.

[38]  Xueqiu Wang,et al.  China geochemical baselines: Sampling methodology , 2015 .

[39]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[40]  Wang Xueqiu.  Global Geochemical Baselines: Understanding the past and predicting the future , 2012 .

[41]  Z. Qin,et al.  Analytical scheme and quality monitoring system for China Geochemical Baselines , 2012 .

[42]  W. Ping,et al.  Occurrence, speciation, source and geochemical cycle of arsenic. , 2010 .

[43]  Luo Li-qiang.  Geochemical Characteristics and Research Direction of Arsenic , 2009 .

[44]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[45]  L. Breiman.  Random Forests , 2001, Machine Learning.

[46]  Zhu Hui.  Lead and its variation in wet deposition of Qingdao , 2001 .