IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i3p799-d136123.html
   My bibliography  Save this article

Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm

Author

Listed:
  • Asma Shaheen

    (Institute of Geographical Information Systems, School of Civil & Environmental Engineering, National University of Sciences and Technology, 44000 Islamabad, Pakistan)

  • Javed Iqbal

    (Institute of Geographical Information Systems, School of Civil & Environmental Engineering, National University of Sciences and Technology, 44000 Islamabad, Pakistan)

Abstract

In third world countries, industries mainly cause environmental contamination due to lack of environmental policies or oversight during their implementation. The Sheikhupura industrial zone, which includes industries such as tanneries, leather, chemical, textiles, and colour and dyes, contributes massive amounts of untreated effluents that are released directly into drains and used for the irrigation of crops and vegetables. This practice causes not only soil contamination with an excessive amount of heavy metals, but is also considered a source of toxicity in the food chain, i.e., bioaccumulation in plants and ultimately in human body organs. The objective of this research study was to assess the spatial distribution of the heavy metals chromium (Cr), cadmium (Cd), and lead (Pb), at three depths of soil using geostatistics and the selection of significant contributing variables to soil contamination using the Random Forest (RF) function of the Boruta Algorithm. A total of 60 sampling locations were selected in the study area to collect soil samples (180 samples) at three depths (0–15 cm, 15–30 cm, and 60–90 cm). The soil samples were analysed for their physico-chemical properties, i.e., soil saturation, electrical conductivity (EC), organic matter (OM), pH, phosphorus (P), potassium (K), and Cr, Cd, and Pb using standard laboratory procedures. The data were analysed with comprehensive statistics and geostatistical techniques. The correlation coefficient matrix between the heavy metals and the physico-chemical properties revealed that electrical conductivity (EC) had a significant ( p ≤ 0.05) negative correlation with Cr, Cd, and Pb. The RF function of the Boruta Algorithm employed soil depth as a classifier and ranked the significant soil contamination parameters (Cr, Cd, Pb, EC, and P) in relation to depth. The mobility factor indicated the leachate percentage of heavy metals at different vertical depths of soil. The spatial distribution pattern of Cr, Cd, and Pb revealed spatial variability regarding subsoil horizons. Significant contamination was discovered near the Deg drain and the Bed Nallah irrigated area that indicated a high Cr topsoil contamination, and in a homogenous pattern in Cd and Pb ( p < 0.05). Consequently, different soil management strategies can be adopted in an industrial irrigated area to reduce the contamination load of heavy metals in soil.

Suggested Citation

  • Asma Shaheen & Javed Iqbal, 2018. "Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:799-:d:136123
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/3/799/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/3/799/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Chao Song & Mei-Po Kwan & Weiguo Song & Jiping Zhu, 2017. "A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence," Sustainability, MDPI, vol. 9(5), pages 1-21, May.
    3. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
    4. Min Jiang & Guangming Zeng & Chang Zhang & Xiaoying Ma & Ming Chen & Jiachao Zhang & Lunhui Lu & Qian Yu & Langping Hu & Lifeng Liu, 2013. "Assessment of Heavy Metal Contamination in the Surrounding Soils and Surface Sediments in Xiawangang River, Qingshuitang District," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-11, August.
    5. David Stephens & Markus Diesing, 2014. "A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.
    6. José Rato Nunes & José Ramos-Miras & António Lopez-Piñeiro & Luís Loures & Carlos Gil & José Coelho & Ana Loures, 2014. "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, MDPI, vol. 6(12), pages 1-15, December.
    7. Jun Yang & Fuhong Lv & Jingcheng Zhou & Yongwei Song & Fei Li, 2017. "Health Risk Assessment of Vegetables Grown on the Contaminated Soils in Daye City of Hubei Province, China," Sustainability, MDPI, vol. 9(11), pages 1-14, November.
    8. Jie Yang & Linglong Cao & Jianhua Wang & Chunlian Liu & Chuguang Huang & Weixu Cai & Hongda Fang & Xiaojuan Peng, 2014. "Speciation of Metals and Assessment of Contamination in Surface Sediments from Daya Bay, South China Sea," Sustainability, MDPI, vol. 6(12), pages 1-18, December.
    9. Fei Li & Ying Cai & Jingdong Zhang, 2018. "Spatial Characteristics, Health Risk Assessment and Sustainable Management of Heavy Metals and Metalloids in Soils from Central China," Sustainability, MDPI, vol. 10(1), pages 1-24, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aman Fang & Jihong Dong & Yingli An, 2019. "Distribution Characteristics and Pollution Assessment of Soil Heavy Metals under Different Land-Use Types in Xuzhou City, China," Sustainability, MDPI, vol. 11(7), pages 1-12, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jin Li & Maggie Tran & Justy Siwabessy, 2016. "Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-29, February.
    2. Tong, Jianfeng & Liu, Zhenxing & Zhang, Yong & Zheng, Xiujuan & Jin, Junyang, 2023. "Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load," Energy, Elsevier, vol. 282(C).
    3. Ramón Ferri-García & María del Mar Rueda, 2022. "Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys," Statistical Papers, Springer, vol. 63(6), pages 1829-1881, December.
    4. Yvan Devaux & Lu Zhang & Andrew I. Lumley & Kanita Karaduzovic-Hadziabdic & Vincent Mooser & Simon Rousseau & Muhammad Shoaib & Venkata Satagopam & Muhamed Adilovic & Prashant Kumar Srivastava & Costa, 2024. "Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    5. Ghosh, Indranil & Chaudhuri, Tamal Datta & Alfaro-Cortés, Esteban & Gámez, Matías & García, Noelia, 2022. "A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    6. Conor Waldock & Bernhard Wegscheider & Dario Josi & Bárbara Borges Calegari & Jakob Brodersen & Luiz Jardim de Queiroz & Ole Seehausen, 2024. "Deconstructing the geography of human impacts on species’ natural distribution," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    7. Abdullah Al Saim & Mohamed H. Aly, 2022. "Machine Learning for Modeling Wildfire Susceptibility at the State Level: An Example from Arkansas, USA," Geographies, MDPI, vol. 2(1), pages 1-17, January.
    8. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    9. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    10. Matteo Rucco & Giovanna Viticchi & Lorenzo Falsetti, 2020. "Towards Personalized Diagnosis of Glioblastoma in Fluid-Attenuated Inversion Recovery (FLAIR) by Topological Interpretable Machine Learning," Mathematics, MDPI, vol. 8(5), pages 1-27, May.
    11. Sangjin Kim & Jong-Min Kim, 2019. "Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data," Mathematics, MDPI, vol. 7(6), pages 1-16, May.
    12. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    13. Foutzopoulos, Giorgos & Pandis, Nikolaos & Tsagris, Michail, 2024. "Predicting full retirement attainment of NBA players," MPRA Paper 121540, University Library of Munich, Germany.
    14. Zhao-Yue Chen & Hervé Petetin & Raúl Fernando Méndez Turrubiates & Hicham Achebak & Carlos Pérez García-Pando & Joan Ballester, 2024. "Population exposure to multiple air pollutants and its compound episodes in Europe," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    15. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.
    16. Schrader, Silja & Graham, Sonia & Campbell, Rebecca & Height, Kaitlyn & Hawkes, Gina, 2024. "Grower attitudes and practices toward area-wide management of cropping weeds in Australia," Land Use Policy, Elsevier, vol. 137(C).
    17. Bram Janssens & Matthias Bogaert & Mathijs Maton, 2023. "Predicting the next Pogačar: a data analytical approach to detect young professional cycling talents," Annals of Operations Research, Springer, vol. 325(1), pages 557-588, June.
    18. Mohamed Zine & Fouzi Harrou & Mohammed Terbeche & Mohammed Bellahcene & Abdelkader Dairi & Ying Sun, 2023. "E-Learning Readiness Assessment Using Machine Learning Methods," Sustainability, MDPI, vol. 15(11), pages 1-22, June.
    19. Cooray, Upul & Watt, Richard G. & Tsakos, Georgios & Heilmann, Anja & Hariyama, Masanori & Yamamoto, Takafumi & Kuruppuarachchige, Isuruni & Kondo, Katsunori & Osaka, Ken & Aida, Jun, 2021. "Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis," Social Science & Medicine, Elsevier, vol. 291(C).
    20. Simon Besnard & Nuno Carvalhais & M Altaf Arain & Andrew Black & Benjamin Brede & Nina Buchmann & Jiquan Chen & Jan G P W Clevers & Loïc P Dutrieux & Fabian Gans & Martin Herold & Martin Jung & Yoshik, 2019. "Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:799-:d:136123. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.