Spatial prediction and influencing factors identification of potential toxic element contamination in soil of different karst landform regions using integration model.

[1]  Jiyan Shi,et al.  Fe-biochar for simultaneous stabilization of chromium and arsenic in soil: Rational design and long-term performance. , 2022, The Science of the total environment.

[2]  J. Podgorski,et al.  Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh - Machine learning spatial prediction modeling and comparison with arsenic. , 2022, The Science of the total environment.

[3]  A. Baccarelli,et al.  Predicting chemical ecotoxicity by learning latent space chemical representations , 2022, Environment international.

[4]  Chaosheng Zhang,et al.  Carbonate bedrock control of soil Cd background in Southwestern China: Its extent and influencing factors based on spatial analysis. , 2021, Chemosphere.

[5]  Tao Yu,et al.  Transferability of heavy metal(loid)s from karstic soils with high geochemical background to peanut seeds. , 2022, Environmental pollution.

[6]  Xiong Liu,et al.  A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology. , 2021, Environment international.

[7]  Gerard B. M. Heuvelink,et al.  Spatial cross-validation is not the right way to evaluate map accuracy , 2021 .

[8]  Zheng-Ping Yu,et al.  Concentrations and health risks of heavy metals in five major marketed marine bivalves from three coastal cities in Guangxi, China. , 2021, Ecotoxicology and environmental safety.

[9]  Fencun Xie,et al.  Highly efficient removal of As(III) by Fe-Mn-Ca composites with the synergistic effect of oxidation and adsorption , 2021, Science of The Total Environment.

[10]  Long Zhao,et al.  Application of stochastic model to assessment of heavy metal(loid)s source apportionment and bio-availability in rice fields of karst area. , 2021, The Science of the total environment.

[11]  J. Ji,et al.  Ecological risk assessment of Cd and other heavy metals in soil-rice system in the karst areas with high geochemical background of Guangxi, China , 2021, Science China Earth Sciences.

[12]  Z. Yang,et al.  Linking rocky desertification to soil erosion by investigating changes in soil magnetic susceptibility profiles on karst slopes , 2021 .

[13]  Tao Yu,et al.  Distribution and secondary enrichment of heavy metal elements in karstic soils with high geochemical background in Guangxi, China , 2021 .

[14]  Zhongfa Zhou,et al.  Global trends in karst-related studies from 1990 to 2016: A bibliometric analysis , 2021 .

[15]  Fei Li,et al.  Spatio-temporal distribution and source identification of heavy metals in particle size fractions of road dust from a typical industrial district. , 2021, The Science of the total environment.

[16]  M. Lei,et al.  An integrated method for source apportionment of heavy metal(loid)s in agricultural soils and model uncertainty analysis. , 2021, Environmental pollution.

[17]  J. Podgorski,et al.  Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling , 2020, International journal of environmental research and public health.

[18]  Chao Zhang,et al.  Spatial distribution prediction of soil As in a large-scale arsenic slag contaminated site based on an integrated model and multi-source environmental data. , 2020, Environmental pollution.

[19]  Wei Zhang,et al.  Cadmium risk in the soil-plant system caused by weathering of carbonate bedrock. , 2020, Chemosphere.

[20]  J. Podgorski,et al.  Global threat of arsenic in groundwater , 2020, Science.

[21]  Yiwei Gong,et al.  Comparison of the concentrations, sources, and distributions of heavy metal(loid)s in agricultural soils of two provinces in the Yangtze River Delta, China. , 2020, Environmental pollution.

[22]  Yinjun Zhao,et al.  Cadmium source identification in soils and high-risk regions predicted by geographical detector method. , 2020, Environmental pollution.

[23]  Yi Li,et al.  Heavy metal concentration, potential ecological risk assessment and enzyme activity in soils affected by a lead-zinc tailing spill in Guangxi, China. , 2020, Chemosphere.

[24]  Zhenming Zhang,et al.  Applicability of GIS-based spatial interpolation and simulation for estimating the soil organic carbon storage in karst regions , 2020 .

[25]  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.

[26]  Yiwei Gong,et al.  Heavy Metal(loids) in typical Chinese tobacco-growing soils: Concentrations, influence factors and potential health risks. , 2019, Chemosphere.

[27]  Wei Li,et al.  Enrichment and source identification of Cd and other heavy metals in soils with high geochemical background in the karst region, Southwestern China. , 2019, Chemosphere.

[28]  Xingwang Liu,et al.  Cupriavidus sp. strain Cd02-mediated pH increase favoring bioprecipitation of Cd2+ in medium and reduction of cadmium bioavailability in paddy soil. , 2019, Ecotoxicology and environmental safety.

[29]  Shenglu Zhou,et al.  Identification of the sources and influencing factors of potentially toxic elements accumulation in the soil from a typical karst region in Guangxi, Southwest China. , 2019, Environmental pollution.

[30]  Hong Hou,et al.  Status of cadmium accumulation in agricultural soils across China (1975-2016): From temporal and spatial variations to risk assessment. , 2019, Chemosphere.

[31]  Alexander Gribov,et al.  Evaluation of empirical Bayesian kriging , 2019, Spatial Statistics.

[32]  Ru-tian Bi,et al.  Multi-scale spatial relationships between soil total nitrogen and influencing factors in a basin landscape based on multivariate empirical mode decomposition , 2019, Journal of Arid Land.

[33]  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.

[34]  E.M. Baglaeva,et al.  Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals , 2019, CATENA.

[35]  Jie Luo,et al.  Heavy metals in agricultural soils from a typical township in Guangdong Province, China: Occurrences and spatial distribution. , 2019, Ecotoxicology and environmental safety.

[36]  B. Sengupta,et al.  Arsenic contamination in irrigation water, agricultural soil and maize crop from an abandoned smelter site in Matehuala, Mexico. , 2017, Journal of hazardous materials.

[37]  Heqing Shen,et al.  Extensive arsenic contamination in high-pH unconfined aquifers in the Indus Valley , 2017, Science Advances.

[38]  T. Makino,et al.  Functional effects of manganese and iron oxides on the dynamics of trace elements in soils with a special focus on arsenic and cadmium: A review , 2016 .

[39]  Dianjie Wang,et al.  Rock outcrops redistribute water to nearby soil patches in karst landscapes , 2016, Environmental Science and Pollution Research.

[40]  J. Liu,et al.  Predictive soil mapping with limited sample data , 2015 .

[41]  É. Verrecchia,et al.  Speciation and multivariable analyses of geogenic cadmium in soils at Le Gurnigel, Swiss Jura Mountains , 2015 .

[42]  H. Ji,et al.  The formation of representative lateritic weathering covers in south-central Guangxi (southern China) , 2014 .

[43]  Q. Zhang,et al.  Multi-element geochemical mapping in Southern China , 2014, Journal of geochemical exploration.

[44]  F. Liang,et al.  Discrimination of tower-, cockpit-, and non-karst landforms in Guilin, Southern China, based on morphometric characteristics , 2014 .

[45]  Y. Zhang,et al.  6.27 Tower Karst and Cone Karst , 2013 .

[46]  B. Xia,et al.  Human health risk from soil heavy metal contamination under different land uses near Dabaoshan Mine, Southern China. , 2012, The Science of the total environment.

[47]  T. Bishop,et al.  Evaluation of spatial variability of soil arsenic adjacent to a disused cattle-dip site, using model-based geostatistics. , 2011, Environmental science & technology.

[48]  Martial Bernoux,et al.  Mapping organic carbon stocks in eucalyptus plantations of the central highlands of Madagascar: A multiple regression approach , 2011 .

[49]  Tibor Németh,et al.  Association of individual soil mineral constituents and heavy metals as studied by sorption experiments and analytical electron microscopy analyses. , 2009, Journal of hazardous materials.

[50]  Liu Yansui,et al.  Rocky land desertification and its driving forces in the karst areas of rural Guangxi, Southwest China , 2008 .

[51]  R. V. Rossel,et al.  Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study , 2008 .

[52]  J. Huff,et al.  Cadmium-induced Cancers in Animals and in Humans , 2007, International journal of occupational and environmental health.

[53]  Y. Lucas,et al.  Fe, Al and Si species and organic matter leached off a ferrallitic and podzolic soil system from Central Amazonia , 2007 .

[54]  Z. Ouyang,et al.  Geochemistry of red residua underlying dolomites in karst terrains of Yunnan-Guizhou Plateau I. The formation of the Pingba profile , 2004 .

[55]  Chen Wenjun.  The study of Disu underground river system, Du'an county, Guangxi , 1988 .