Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Soil Sampling and Lab Analysis
2.3. Descriptive Statistics and the Correlation Matrix of Soil Parameters
2.4. Selection of Soil Parameters Using the Random Forest Classifier
2.5. Metal Mobility Ratio in Subsoil Profiles
2.6. Geostatistical Analysis and Variogram Modelling for Soil Heavy Metals
Kriging Interpolation
3. Results and Discussion
3.1. The Exploratory Analysis of Soil Variables
3.2. Assessment of Soil Contaminant Parameters
3.3. Estimation of Metal Mobility and Variability at Vertical Soil Profile
3.4. Assessment of Appropriate Variogram Models
3.5. Spatial Distribution of Heavy Metals in Soil
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Saha, N.; Rahman, M.S.; Ahmed, M.B.; Zhou, J.L.; Ngo, H.H.; Guo, W. Industrial metal pollution in water and probabilistic assessment of human health risk. J. Environ. Manag. 2017, 185, 70–78. [Google Scholar] [CrossRef] [PubMed]
- Zhu, Y.; Zhang, Z.; Zhao, X.; Lian, J. Accumulation and potential sources of heavy metals in the soils of the Hetao Irrigation District, Inner Mongolia, China. Pedosphere 2017, in press. [Google Scholar] [CrossRef]
- Cai, L.; Xu, Z.; Ren, M.; Guo, Q.; Hu, X.; Hu, G.; Wan, H.; Peng, P. Source identification of eight hazardous heavy metals in agricultural soils of Huizhou, Guangdong Province, China. Ecotoxicol. Environ. Saf. 2012, 78, 2–8. [Google Scholar] [CrossRef] [PubMed]
- Moral, R.; Gilkes, R.; Jordán, M. Distribution of heavy metals in calcareous and non-calcareous soils in Spain. Water Air Soil Pollut. 2005, 162, 127–142. [Google Scholar] [CrossRef]
- Mapanda, F.; Mangwayana, E.N.; Nyamangara, J.; Giller, K.E. The effect of long-term irrigation using wastewater on heavy metal contents of soils under vegetables in Harare, Zimbabwe. Agric. Ecosyst. Environ. 2005, 107, 151–165. [Google Scholar] [CrossRef]
- Yang, J.; Lv, F.; Zhou, J.; Song, Y.; Li, F. Health risk assessment of vegetables grown on the contaminated soils in daye city of Hubei Province, China. Sustainability 2017, 9, 2141. [Google Scholar] [CrossRef]
- Stankovic, S.; Kalaba, P.; Stankovic, A.R. Biota as toxic metal indicators. Environ. Chem. Lett. 2014, 12, 63–84. [Google Scholar] [CrossRef]
- Li, F.; Cai, Y.; Zhang, J. Spatial characteristics, health risk assessment and sustainable management of heavy metals and metalloids in soils from Central China. Sustainability 2018, 10, 91. [Google Scholar] [CrossRef]
- Oliveira, H. Chromium as an environmental pollutant: Insights on induced plant toxicity. J. Bot. 2012, 2012, 375843. [Google Scholar] [CrossRef]
- Zhao, H.; Xia, B.; Fan, C.; Zhao, P.; Shen, S. Human health risk from soil heavy metal contamination under different land uses near Dabaoshan Mine, Southern China. Sci. Total Environ. 2012, 417, 45–54. [Google Scholar] [CrossRef] [PubMed]
- Kirman, C.R.; Suh, M.; Hays, S.M.; Gürleyük, H.; Gerads, R.; De Flora, S.; Parker, W.; Lin, S.; Haws, L.C.; Harris, M.A. Reduction of hexavalent chromium by fasted and fed human gastric fluid. II. Ex vivo gastric reduction modeling. Toxicol. Appl. Pharmacol. 2016, 306, 120–133. [Google Scholar] [CrossRef] [PubMed]
- Yang, J.; Cao, L.; Wang, J.; Liu, C.; Huang, C.; Cai, W.; Fang, H.; Peng, X. Speciation of metals and assessment of contamination in surface sediments from Daya Bay, South China Sea. Sustainability 2014, 6, 9096–9113. [Google Scholar] [CrossRef]
- Fifi, U.; Winiarski, T.; Emmanuel, E. Assessing the mobility of lead, copper and cadmium in a calcareous soil of Port-au-Prince, Haiti. Int. J. Environ. Res. Public Health 2013, 10, 5830–5843. [Google Scholar] [CrossRef] [PubMed]
- Hou, D.; O’Connor, D.; Nathanail, P.; Tian, L.; Ma, Y. Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review. Environ. Pollut. 2017, 231, 1188–1200. [Google Scholar] [CrossRef] [PubMed]
- Santos-Francés, F.; Martínez-Graña, A.; Zarza, C.Á.; Sánchez, A.G.; Rojo, P.A. Spatial distribution of heavy metals and the environmental quality of soil in the Northern Plateau of Spain by geostatistical methods. Int. J. Environ. Res. Public Health 2017, 14, 568. [Google Scholar] [CrossRef] [PubMed]
- Wang, G.; Zhang, S.; Xiao, L.; Zhong, Q.; Li, L.; Xu, G.; Deng, O.; Pu, Y. Heavy metals in soils from a typical industrial area in Sichuan, China: Spatial distribution, source identification, and ecological risk assessment. Environ. Sci. Pollut. Res. 2017, 24, 16618–16630. [Google Scholar] [CrossRef] [PubMed]
- Ravankhah, N.; Mirzaei, R.; Masoum, S. Spatial eco-risk assessment of heavy metals in the surface soils of industrial city of Aran-o-Bidgol, Iran. Bull. Environ. Contam. Toxicol. 2016, 96, 516–523. [Google Scholar] [CrossRef] [PubMed]
- Hossain, M.A.; Ali, N.M.; Islam, M.S.; Hossain, H.Z. Spatial distribution and source apportionment of heavy metals in soils of Gebeng industrial city, Malaysia. Environ. Earth Sci. 2015, 73, 115–126. [Google Scholar] [CrossRef]
- Ağca, N. Spatial distribution of heavy metal content in soils around an industrial area in Southern Turkey. Arab. J. Geosci. 2015, 8, 1111–1123. [Google Scholar] [CrossRef]
- Mostert, M.M.; Ayoko, G.A.; Kokot, S. Application of chemometrics to analysis of soil pollutants. TrAC Trends Anal. Chem. 2010, 29, 430–445. [Google Scholar] [CrossRef] [Green Version]
- Da Silva, F.B.V.; do Nascimento, C.W.A.; Araújo, P.R.M.; da Silva, L.H.V.; da Silva, R.F. Assessing heavy metal sources in sugarcane brazilian soils: An approach using multivariate analysis. Environ. Monit. Assess. 2016, 188, 457. [Google Scholar] [CrossRef] [PubMed]
- Nakamura, K.; Kuwatani, T.; Kawabe, Y.; Komai, T. Extraction of heavy metals characteristics of the 2011 Tohoku tsunami deposits using multiple classification analysis. Chemosphere 2016, 144, 1241–1248. [Google Scholar] [CrossRef] [PubMed]
- Calce, S.E.; Kurki, H.K.; Weston, D.A.; Gould, L. Principal component analysis in the evaluation of osteoarthritis. Am. J. Phys. Anthropol. 2017, 162, 476–490. [Google Scholar] [CrossRef] [PubMed]
- Song, C.; Kwan, M.-P.; Song, W.; Zhu, J. A comparison between spatial econometric models and random forest for modeling fire occurrence. Sustainability 2017, 9, 819. [Google Scholar] [CrossRef]
- Pardo-Iguzquiza, E.; Chica-Olmo, M. Geostatistics with the Matern semivariogram model: A library of computer programs for inference, kriging and simulation. Comput. Geosci. 2008, 34, 1073–1079. [Google Scholar] [CrossRef]
- Varouchakis, Ε.; Hristopulos, D. Comparison of stochastic and deterministic methods for mapping groundwater level spatial variability in sparsely monitored basins. Environ. Monit. Assess. 2013, 185, 1–19. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.; Jiang, X.; Wang, Y.; Zhuang, D. Spatial characteristics of heavy metal pollution and the potential ecological risk of a typical mining area: A case study in China. Process Saf. Environ. Prot. 2017, 113, 204–219. [Google Scholar] [CrossRef]
- Li, J.; Heap, A.D. Spatial interpolation methods applied in the environmental sciences: A review. Environ. Model. Softw. 2014, 53, 173–189. [Google Scholar] [CrossRef]
- Saeed, R.; Sattar, A.; Iqbal, Z.; Imran, M.; Nadeem, R. Environmental impact assessment (EIA): An overlooked instrument for sustainable development in Pakistan. Environ. Monit. Assess. 2012, 184, 1909–1919. [Google Scholar] [CrossRef] [PubMed]
- Atlas, I. An Atlas: Surface Water Industrial and Municipal Pollution in Punjab; Irrigation and Power Department, Directorate of Land Reclamation Punjab: Lahore, Pakistan, 2015. [Google Scholar]
- Li, X.; Liu, L.; Wang, Y.; Luo, G.; Chen, X.; Yang, X.; Hall, M.H.; Guo, R.; Wang, H.; Cui, J. Heavy metal contamination of urban soil in an old industrial city (Shenyang) in Northeast China. Geoderma 2013, 192, 50–58. [Google Scholar] [CrossRef]
- Lu, X.; Wang, L.; Li, L.Y.; Lei, K.; Huang, L.; Kang, D. Multivariate statistical analysis of heavy metals in street dust of Baoji, NW China. J. Hazard. Mater. 2010, 173, 744–749. [Google Scholar] [CrossRef] [PubMed]
- Margesin, R.; Schinner, F. Manual for Soil Analysis-Monitoring and Assessing Soil Bioremediation; Springer Science & Business Media: Berlin, Germany, 2005; Volume 5. [Google Scholar]
- Klute, A.; Dinauer, R.C. Physical and mineralogical methods. Planning 1986, 8, 79. [Google Scholar]
- Ali, Z.; Malik, R.; Shinwari, Z.; Qadir, A. Enrichment, risk assessment, and statistical apportionment of heavy metals in tannery-affected areas. Int. J. Environ. Sci. Technol. 2015, 12, 537–550. [Google Scholar] [CrossRef]
- Carter, M.R. Soil Sampling and Methods of Analysis; CRC Press: Boca Raton, FL, USA, 1993. [Google Scholar]
- Gowd, S.S.; Reddy, M.R.; Govil, P. Assessment of heavy metal contamination in soils at Jajmau (kanpur) and Unnao industrial areas of the Ganga Plain, Uttar Pradesh, India. J. Hazard. Mater. 2010, 174, 113–121. [Google Scholar] [CrossRef] [PubMed]
- Edgell, K. Usepa Method Study 37 SW-846 Method 3050 Acid Digestion of Sediments, Sludges, and Soils; US Environmental Protection Agency, Environmental Monitoring Systems Laboratory: Cincinnati, OH, USA, 1989.
- Tiwari, M.K.; Bajpai, S.; Dewangan, U.; Tamrakar, R.K. Assessment of heavy metal concentrations in surface water sources in an industrial region of central India. Karbala Int. J. Mod. Sci. 2015, 1, 9–14. [Google Scholar] [CrossRef]
- Kimbrough, D.E.; Wakakuwa, J.R. Acid digestion for sediments, sludges, soils, and solid wastes. A proposed alternative to EPA SW 846 Method 3050. Environ. Sci. Technol. 1989, 23, 898–900. [Google Scholar] [CrossRef]
- IBM Corp. IBM SPSS Statistics for Windows, version 22.0; IBM Corp.: Armonk, NY, USA, 2013. [Google Scholar]
- Liu, Z.; Zhou, W.; Shen, J.; He, P.; Lei, Q.; Liang, G. A simple assessment on spatial variability of rice yield and selected soil chemical properties of paddy fields in South China. Geoderma 2014, 235, 39–47. [Google Scholar] [CrossRef]
- Ditzler, G.C. Scalable Subset Selection with Filters and Its Applications. Ph.D. Thesis, Drexel University, Philadelphia, PA, USA, 2015. [Google Scholar]
- Ghimire, B.R.; Nagai, M.; Tripathi, N.K.; Witayangkurn, A.; Mishara, B.; Sasaki, N. Mapping of Shorea robusta forest using time series MODIS data. Forests 2017, 8, 384. [Google Scholar] [CrossRef]
- Stephens, D.; Diesing, M. A comparison of supervised classification methods for the prediction of substrate type using multibeam acoustic and legacy grain-size data. PLoS ONE 2014, 9, e93950. [Google Scholar] [CrossRef] [PubMed]
- Kursa, M.B.; Rudnicki, W.R. Feature selection with the Boruta package. J. Stat. Softw. 2010, 36, 1–13. [Google Scholar] [CrossRef]
- Chandrashekar, G.; Sahin, F. A survey on feature selection methods. Comput. Electr. Eng. 2014, 40, 16–28. [Google Scholar] [CrossRef]
- Ghayoraneh, M.; Qishlaqi, A. Concentration, distribution and speciation of toxic metals in soils along a transect around a Zn/Pb smelter in the northwest of Iran. J. Geochem. Explor. 2017, 180, 1–14. [Google Scholar] [CrossRef]
- Jiang, M.; Zeng, G.; Zhang, C.; Ma, X.; Chen, M.; Zhang, J.; Lu, L.; Yu, Q.; Hu, L.; Liu, L. Assessment of heavy metal contamination in the surrounding soils and surface sediments in Xiawangang River, Qingshuitang District. PLoS ONE 2013, 8, e71176. [Google Scholar] [CrossRef] [PubMed]
- Minasny, B.; McBratney, A.B. The Matérn function as a general model for soil variograms. Geoderma 2005, 128, 192–207. [Google Scholar] [CrossRef]
- Paulo, J.R., Jr.; Peter, J.D. Geor: A Package for Geostatistical Analysis. R J. 2001. Available online: https://cran.r-project.org/doc/Rnews/ (accessed on 13 March 2018).
- ESRI. Arcgis Desktop, version 10.4; ESRI: Redlands, CA, USA, 2016. [Google Scholar]
- Hengl, T.; de Jesus, J.M.; MacMillan, R.A.; Batjes, N.H.; Heuvelink, G.B.; Ribeiro, E.; Samuel-Rosa, A.; Kempen, B.; Leenaars, J.G.; Walsh, M.G. Soilgrids1km—Global soil information based on automated mapping. PLoS ONE 2014, 9, e105992. [Google Scholar] [CrossRef] [PubMed]
- Bogunovic, I.; Trevisani, S.; Seput, M.; Juzbasic, D.; Durdevic, B. Short-range and regional spatial variability of soil chemical properties in an agro-ecosystem in Eastern Croatia. Catena 2017, 154, 50–62. [Google Scholar] [CrossRef]
- Nunes, J.R.; Ramos-Miras, J.; Lopez-Piñeiro, A.; Loures, L.; Gil, C.; Coelho, J.; Loures, A. Concentrations of available heavy metals in Mediterranean agricultural soils and their relation with some soil selected properties: A case study in typical Mediterranean soils. Sustainability 2014, 6, 9124–9138. [Google Scholar] [CrossRef]
- Andersson, H.; Bergström, L.; Ulén, B.; Djodjic, F.; Kirchmann, H. The role of subsoil as a source or sink for phosphorus leaching. J. Environ. Qual. 2015, 44, 535–544. [Google Scholar] [CrossRef] [PubMed]
- Candeias, C.; da Silva, E.F.; Ávila, P.F.; Teixeira, J.P. Identifying sources and assessing potential risk of exposure to heavy metals and hazardous materials in mining areas: The case study of Panasqueira Mine (Central Portugal) as an example. Geosciences 2014, 4, 240–268. [Google Scholar] [CrossRef]
- Chakraborty, R.; Ghosh, A.; Ghosh, S.; Mukherjee, S. Evaluation of contaminant transport parameters for hexavalent chromium migration through saturated soil media. Environ. Earth Sci. 2015, 74, 5687–5697. [Google Scholar] [CrossRef]
- Aide, M.T.; Cummings, M.F. The influence of pH and phosphorus on the adsorption of chromium (VI) on boehmite. Soil Sci. 1997, 162, 599–603. [Google Scholar] [CrossRef]
- Violante, A.; Cozzolino, V.; Perelomov, L.; Caporale, A.; Pigna, M. Mobility and bioavailability of heavy metals and metalloids in soil environments. J. Soil Sci. Plant Nutr. 2010, 10, 268–292. [Google Scholar] [CrossRef]
- Tom, M.; Fletcher, T.D.; McCarthy, D.T. Heavy metal contamination of vegetables irrigated by urban stormwater: A matter of time? PLoS ONE 2014, 9, e112441. [Google Scholar] [CrossRef] [PubMed]
- Hasan, M.; Kausar, D.; Akhter, G.; Shah, M.H. Evaluation of the mobility and pollution index of selected essential/toxic metals in paddy soil by sequential extraction method. Ecotoxicol. Environ. Saf. 2017, 147, 283–291. [Google Scholar] [CrossRef] [PubMed]
- Wei, P.; Lu, Z.; Song, J. Variable importance analysis: A comprehensive review. Reliab. Eng. Syst. Saf. 2015, 142, 399–432. [Google Scholar] [CrossRef]
- Ren, B.; Chen, Y.; Zhu, G.; Wang, Z.; Zheng, X. Spatial variability and distribution of the metals in surface runoff in a nonferrous metal mine. J. Anal. Methods Chem. 2016, 2016, 4515673. [Google Scholar] [CrossRef] [PubMed]
- Adhikari, D.; Jiang, T.; Kawagoe, T.; Kai, T.; Kubota, K.; Araki, K.S.; Kubo, M. Relationship among phosphorus circulation activity, bacterial biomass, pH, and mineral concentration in agricultural soil. Microorganisms 2017, 5, 79. [Google Scholar] [CrossRef] [PubMed]
- Zhiyuan, W.; Dengfeng, W.; Huiping, Z.; Zhiping, Q. Assessment of soil heavy metal pollution with principal component analysis and geoaccumulation index. Procedia Environ. Sci. 2011, 10, 1946–1952. [Google Scholar] [CrossRef]
- Allué, J.; Garcés, A.M.; Bech, J.; Barceló, J.; Poschenrieder, C. Fractionation of chromium in tannery sludge-amended soil and its availability to fenugreek plants. J. Soils Sediment. 2014, 14, 697–702. [Google Scholar] [CrossRef]
- Roger, A.; Libohova, Z.; Rossier, N.; Joost, S.; Maltas, A.; Frossard, E.; Sinaj, S. Spatial variability of soil phosphorus in the Fribourg Canton, Switzerland. Geoderma 2014, 217, 26–36. [Google Scholar] [CrossRef]
- Guo, G.; Wu, F.; Xie, F.; Zhang, R. Spatial distribution and pollution assessment of heavy metals in urban soils from Southwest China. J. Environ. Sci. 2012, 24, 410–418. [Google Scholar] [CrossRef]
- Yewale, P.P.; Rahman, A.; Nahar, N.; Saha, A.; Jass, J.; Mandal, A.; Nawani, N.N. Sources of Metal Pollution, Global Status, and Conventional Bioremediation Practices. In Handbook of Metal–Microbe Interactions and Bioremediation; CRC Press: Boca Raton, FL, USA, 2017; pp. 25–40. [Google Scholar]
- Malandrino, M.; Abollino, O.; Buoso, S.; Giacomino, A.; La Gioia, C.; Mentasti, E. Accumulation of heavy metals from contaminated soil to plants and evaluation of soil remediation by vermiculite. Chemosphere 2011, 82, 169–178. [Google Scholar] [CrossRef] [PubMed]
- Mwamburi, J. Chromium distribution and spatial variations in the finer sediment grain size fraction and unfractioned surficial sediments on Nyanza Gulf, of Lake Victoria (East Africa). J. Waste Manag. 2016, 2016, 7528263. [Google Scholar] [CrossRef]
- Onweremadu, E.; Amaechi, J.; Ndukwu, B. Vertical distribution of cadmium and lead on soils affected by metropolitan refuse disposal in Owerri, Southeastern Nigeria. Iran. J. Energy Environ. 2011, 2, 62–67. [Google Scholar]
- Coppola, E.; Capra, G.F.; Odierna, P.; Vacca, S.; Buondonno, A. Lead distribution as related to pedological features of soils in the Volturno River low Basin (Campania, Italy). Geoderma 2010, 159, 342–349. [Google Scholar] [CrossRef]
- Akpen, G.; Ekanem, E.; Agunwamba, J. The effects of sewage effluent discharges on the water quality of Wupa River in Abuja, Nigeria. J. Sci. Technol. 2016, 36, 86–95. [Google Scholar] [CrossRef]
- Zhao, F.-J.; Ma, Y.; Zhu, Y.-G.; Tang, Z.; McGrath, S.P. Soil contamination in China: Current status and mitigation strategies. Environ. Sci. Technol. 2014, 49, 750–759. [Google Scholar] [CrossRef] [PubMed]
Depth of Soil | Soil Quality Parameters | Min | Max | Mean | Median | SD | Kurtosis | Skewness | FAO/WHO Standards |
---|---|---|---|---|---|---|---|---|---|
0–15 cm | EC (µS cm−1) | 1 | 15 | 4.9 | 3 | 3.9 | −0.1 | 1 | |
pH | 7.2 | 9.6 | 8.2 | 8.2 | 0.5 | −0.5 | 0.4 | 6.5–8.5 | |
OM (%) | 0.3 | 2 | 1.2 | 1.4 | 0.4 | −0.7 | −0.4 | ||
P (mg kg−1) | 0.7 | 55 | 17.3 | 16.7 | 14 | 0.3 | 0.7 | ||
K (mg kg−1) | 68 | 806 | 212.8 | 193 | 131.4 | 7.4 | 2.4 | ||
Saturation (%) | 28 | 48 | 38.2 | 38 | 3.6 | 1.4 | −0.4 | ||
Pb (mg kg−1) | 7.9 | 22.8 | 14.1 | 13.6 | 4.6 | 1.3 | 0.3 | 100 | |
Cr (mg kg−1) | 21.8 | 31.6 | 26 | 25.9 | 2.2 | 0.4 | 0.4 | 30 | |
Cd (mg kg−1) | 2.5 | 9 | 6 | 5.6 | 1.6 | 0.4 | 0.2 | 300 | |
15–30 cm | EC (µS cm−1) | 1 | 13.5 | 4.56 | 2.75 | 3.6 | −0.2 | 1 | - |
pH | 7 | 9.6 | 7.98 | 8 | 0.6 | 0.2 | 0.7 | - | |
OM (%) | 0.55 | 2.02 | 1.24 | 1.25 | 0.4 | −0.6 | −0.4 | - | |
P (mg kg−1) | 1 | 55 | 19.22 | 17.75 | 16 | −1 | 0.5 | - | |
K (mg kg−1) | 36 | 806 | 202.18 | 184 | 137.6 | 6 | 2.1 | - | |
Saturation (%) | 24 | 44 | 37.47 | 38 | 3.8 | 2.6 | −1.3 | - | |
Pb (mg kg−1) | 2.98 | 9.57 | 6.16 | 6.27 | 1.9 | −1.3 | 0 | - | |
Cr (mg kg−1) | 10.3 | 24.8 | 17.55 | 17.3 | 4.5 | −1.4 | 0.1 | - | |
Cd (mg kg−1) | 2.4 | 8 | 5.22 | 4.64 | 1 | −0.9 | 0.6 | - | |
60–90 cm | EC (µS cm−1) | 1 | 12.3 | 4 | 2.2 | 3.4 | −0.4 | 0.9 | - |
pH | 6.5 | 9.4 | 7.8 | 7.7 | 0.6 | 0.6 | 0.7 | - | |
OM (%) | 0.3 | 2 | 1.2 | 1.2 | 0.4 | −0.6 | −0.2 | - | |
P (mg kg−1) | 2.7 | 55 | 20.3 | 18.2 | 13.9 | −0.2 | 0.8 | - | |
K (mg kg−1) | 36 | 806 | 181.8 | 124 | 143.9 | 7.5 | 2.6 | - | |
Saturation (%) | 19 | 42 | 36.5 | 38 | 5.2 | 3.1 | −1.7 | - | |
Pb (mg kg−1) | 3 | 9.6 | 5.8 | 5.5 | 2 | −1.2 | 0.3 | - | |
Cr (mg kg−1) | 2.2 | 24.8 | 10.4 | 7.8 | 8.2 | −1.1 | 0.7 | - | |
Cd (mg kg−1) | 2.8 | 5.4 | 4 | 4.4 | 0.7 | −1 | −0.7 | - |
Soil Parameters | EC | pH | OM | P | K | Sat (%) | Pb Total | Cr Total | Cd Total |
---|---|---|---|---|---|---|---|---|---|
EC | 1.00 | ||||||||
pH | 0.55 ** | 1.00 | |||||||
OM | 0.04 ns | 0.12 ns | 1.00 | ||||||
P | 0.26 * | 0.08 ns | 0.78 ** | 1.00 | |||||
K | 0.06 ns | −0.07 ns | 0.47 ** | 0.40 ** | 1.00 | ||||
Sat (%) | 0.01 ns | 0.06 ns | 0.05 ns | −0.02 ns | −0.01 ns | 1.00 | |||
Pb Total | −0.33 ** | −0.20 * | 0.05 ns | −0.17 ns | 0.09 ns | 0.10 ns | 1.00 | ||
Cr Total | −0.32 ** | −0.19 ns | 0.01 ns | −0.18 ns | 0.06 ns | 0.10 ns | 0.99 ** | 1.00 | |
Cd Total | −0.39 ** | 0.03 ns | 0.00 ns | −0.45 ** | 0.16 ns | 0.09 ns | 0.22 * | 0.20 * | 1.00 |
Soil Depths | Soil Heavy Metals | Nugget (m2/h)2 | Sill | Nugget % * | Range (m) | RMSE | Model |
---|---|---|---|---|---|---|---|
Soil Depth (0–15 cm) | Pb | 1.0 | 4 | 25 | 0.4 | 6.372 | Matern |
6.4 | 11.25 | 57 | 0.425 | 6.803 | Spherical | ||
6.5 | 30.83 | 21 | 0.771 | 6.872 | Exponential | ||
8.01 | 21 | 38 | 0.214 | 6.496 | Gaussian | ||
Cr | 1.2 | 5 | 24 | 0.16 | 7.238 | Matern | |
0.02 | 0.08 | 25 | 0.03 | 7.488 | Spherical | ||
4.7 | 14 | 34 | 0.21 | 7.429 | Exponential | ||
4.66 | 14.06 | 33 | 0.94 | 7.325 | Gaussian | ||
Cd | 1.2 | 4 | 30 | 0.17 | 0.589 | Matern | |
2.13 | 4.5 | 47 | 0.00 | 0.693 | Spherical | ||
2.1 | 6.03 | 35 | 0.586 | 0.691 | Exponential | ||
2.06 | 5.64 | 37 | 0.17 | 0.735 | Gaussian | ||
Soil Depth 15–30 cm | Pb | 3.6 | 10 | 36 | 0.01 | 3.24 | Matern |
3.73 | 9.28 | 40 | 2.38 | 4.486 | Spherical | ||
3.17 | 11.52 | 28 | 3.13 | 4.348 | Exponential | ||
4.13 | 13.24 | 31 | 38.12 | 8.404 | Gaussian | ||
Cr | 12 | 31 | 39 | 0.04 | 3.330 | Matern | |
4.8 | 19.53 | 25 | 0.29 | 3.295 | Spherical | ||
3.23 | 11.31 | 29 | 0.18 | 4.410 | Exponential | ||
8.38 | 23.13 | 36 | 0.14 | 3.316 | Gaussian | ||
Cd | 0.63 | 1.9 | 33 | 17 | 0.207 | Matern | |
0.3 | 1.41 | 21 | 0.09 | 0.244 | Spherical | ||
0.5 | 1.94 | 26 | 0.207 | 0.397 | Exponential | ||
0.38 | 1.22 | 31 | 0.098 | 0.686 | Gaussian | ||
Soil Depth 30–90 cm | Pb | 1.8 | 4.2 | 43 | 0.28 | 3.225 | Matern |
2.8 | 7.65 | 37 | 3.20 | 4.014 | Spherical | ||
2.23 | 6.85 | 33 | 5.94 | 3.512 | Exponential | ||
2.43 | 9.74 | 25 | 9.43 | 3.408 | Gaussian | ||
Cr | 0.97 | 4.9 | 20 | 0.28 | 7.970 | Matern | |
12.7 | 55 | 23 | 4.85 | 10.885 | Spherical | ||
18.2 | 81.56 | 22 | 11.8 | 13.850 | Exponential | ||
9.6 | 29.75 | 32 | 4.30 | 8.014 | Gaussian | ||
Cd | 0.56 | 1.8 | 31 | 0.34 | 0.127 | Matern | |
1.08 | 2.85 | 38 | 1.25 | 0.148 | Spherical | ||
1.07 | 4.79 | 22 | 3.36 | 0.148 | Exponential | ||
0.19 | 0.78 | 24 | 0.21 | 0.184 | Gaussian |
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Shaheen, A.; Iqbal, J. Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm. Sustainability 2018, 10, 799. https://doi.org/10.3390/su10030799
Shaheen A, Iqbal J. Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm. Sustainability. 2018; 10(3):799. https://doi.org/10.3390/su10030799
Chicago/Turabian StyleShaheen, Asma, and Javed Iqbal. 2018. "Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm" Sustainability 10, no. 3: 799. https://doi.org/10.3390/su10030799