Skip to main content

Advertisement

SpringerLink
Log in

Application of GIS and feedforward back-propagated ANN models for predicting the ecological and health risk of potentially toxic elements in soils in Northwestern Nigeria

  • Original Paper
  • Published:
Environmental Geochemistry and Health Aims and scope Submit manuscript

Abstract

Potentially toxic elements (PTEs) occur naturally in most geologic materials. However, recent anthropogenic disturbances such as ore mining have contributed significantly to their enrichment in soils. Their occurrence in soil may portend a myriad of related risks to the environment and biota. Most traditional soil quality evaluation methods involve comparing the background values of the elements to the established guideline values, which is often time-consuming and fraught with computational errors. As a result, to conduct a comprehensive and unbiased evaluation of soil quality and its effects on the ecosystem and human health, this research combined geochemical, numerical, and GIS data for a composite health risk zonation of the entire study area. Furthermore, the multilayer perceptron artificial neural network (MLP-NN) was used to forecast the most important toxic components influencing soil quality. Geochemical, statistical, and quantitative soil pollution evaluation (pollution index and ecological risk index) showed that apart from mining, the spread and association of trace elements and oxides occur as a consequence of surface environmental conditions (e.g., leaching, weathering, and organo-metallic complexation). The hazard quotients (HQs) and hazard index (HI) of all PTEs were greater than one. This indicates that residents (particularly children) are more susceptible to risks from toxic element ingestion than dermal exposure and inhalation. Ingestion of As and Cr resulted in higher cancer risks and lifetime cancer risk levels (> 1.0E 04), with risk levels increasing toward the northeastern, western, and southeastern directions of the study area. The low modeling errors observed from the sum of square errors, relative errors, and coefficient of determination confirmed the efficiency of the MLP-NN in pollution load prediction. Based on the sensitivity analysis, Hg, Sr, Zn, Ba, As, and Zr showed the greatest influence on soil quality. Focus on remediation should therefore be placed on the removal of these elements from the soil.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

Not applicable.

References

  • Acharya, U., Daigh, A. L. M., & Oduor, P. G. (2021). Machine learning for predicting field soil moisture using soil, crop, and nearby weather station data in the Red River Valley of the North. Soil System, 5, 57. https://doi.org/10.3390/soilsystems5040057

    Article  Google Scholar 

  • Adimalla, N., Li, P., & Venkatayogi, S. (2018). Hydrogeochemical evaluation of groundwater quality for drinking and irrigation purposes and integrated interpretation with water quality index studies. Environmental Process, 5, 363–383.

    Article  CAS  Google Scholar 

  • Aghamelu, O. P., Omeka, M. E., & Unigwe, C. O. (2023). Modeling the vulnerability of groundwater to pollution in a fractured shale aquifer in SE Nigeria using information entropy theory, geospatial, and statistical modeling approaches. Modeling Earth Systems and Environment, 9(2), 2385–2406.

    Article  Google Scholar 

  • American Public Health Association (APHA). (2017). Standard methods for the examination of water and wastewater (23rd ed.). Washington, D.C.: American Public Health Association.

    Google Scholar 

  • Bhutiani, R., Kulkarni, D. B., Khanna, D. R., & Gautam, A. (2017). Geochemical distribution and environmental risk assessment of heavy metals in groundwater of an industrial area and its surroundings, Haridwar, India. Energy, Ecology and Environment, 2(2), 155–167.

    Article  Google Scholar 

  • Bouslihim, Y., Rochdi, A., & Paaza, N. E. A. (2021). Machine learning approaches for the prediction of soil aggregate stability. Heliyon, 7(3), e06480.

    Article  Google Scholar 

  • CCME (Canadian Council of Ministers of the Environment), (1999). Canadian environmental quality guidelines. Soil Quality Guidelines for the Protection of Environmental and Human Health. Last updated 2007

  • Dessureault-Rompre, J., Zebarth, B. J., Burton, D. L., & Georgallas, A. (2015). Predicting soil nitrogen supply from soil properties. Canadian Journal of Soil Science, 95(1), 63–75.

    Article  CAS  Google Scholar 

  • DPR Department of Petroleum Resources. (Ministry of Petroleum and Natural Resources, Abuja, 2002).

  • EA (British Environment Agency), 2009. Land contamination: Soil guideline values (SGVs). Science Report SC050021, Bristol, UK. https://www.gov.uk/government/publications/ land-contamination-soil-guideline-valuessgvspublications/ land-contamination-soil-guideline-valuessgvs. Accessed 24 Nov 2014.

  • Egbueri, J. C., Igwe, O., Omeka, M. E., & Agbasi, J. C. (2023). Development of MLR and variedly optimized ANN models for forecasting the detachability and liquefaction potential index of erodible soils. Geosystems and Geoenvironment, 2(1), 100104.

    Article  Google Scholar 

  • Emadi, M., Taghizadeh-Mehrjardi, R., Cherati, A., Danesh, M., Mosavi, A., & Scholten, T. (2020). Predicting and mapping of soil organic carbon using machine learning algorithms in Northern Iran. Remote Sensing, 12(14), 2234.

    Article  Google Scholar 

  • Hakanson, L. (1980). An ecological risk index for aquatic pollution control. A Sedimentological Approach. Water Resources, 14, 975–1001.

    Google Scholar 

  • IARC. (2011). Overall evaluations of carcinogenicity to humans: as evaluated in IARC monographs volumes 1–82 (a total of 900 agents, mixtures, and exposures). International Agency for Research on Cancer, Lyon

  • Igwe, O., & Omeka, M. E. (2022). Hydrogeochemical and pollution assessment of water resources within a mining area, SE Nigeria, using an integrated approach. International Journal of Energy and Water Resources, 6(2), 161–182.

  • Jahromi, M. A., Jamshidi-Zanjani, A., & Darban, A. K. (2020). Heavy metal pollution and human health risk assessment for exposure to surface soil of mining area: A comprehensive study. Environmental Earth Sciences, 79(14), 1–18. https://doi.org/10.1007/s12665-020-09110-3

    Article  CAS  Google Scholar 

  • Jamshidi-Zanjani, A., Saeedi, M., & Li, L. Y. (2015). A risk assessment index for bioavailability of metals in sediments: Anzali International Wetland case study. Environmental Earth Sciences, 73, 2115–2126. https://doi.org/10.1007/s12665-014-3562-5

    Article  CAS  Google Scholar 

  • Jia, Z., Li, S., & Wang, L. (2018). Assessment of soil heavy metals for eco-environment and human health in a rapid urbanization area of the upper Yangtze Basin. Scientific Reports, 8(1), 1–14.

    Article  Google Scholar 

  • Kaiser, H. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141–151. https://doi.org/10.1177/001316446002000116

    Article  Google Scholar 

  • Kalantar, B., Pradhan, B., Naghibi, S. A., Motevalli, A., & Mansor, S. (2018). Assessment of the effects of training data selection on the landslide susceptibility mapping: A comparison between support vector machine (SVM), logistic regression (LR), and artificial neural networks (ANN). Geomatics, Natural Hazard and Risk, 9, 49–69. https://doi.org/10.1080/19475705.2017.1407368

    Article  Google Scholar 

  • Keskin, H., Grunwald, S., & Harris, W. G. (2019). Digital mapping of soil carbon fractions with machine learning. Geoderma, 339, 40–58.

    Article  CAS  Google Scholar 

  • Khan, M. U., Malik, R. N., & Mohammed, S. (2013). Human health risk assessment from heavy metal via food crops consumption with wastewater irrigation practices in Pakistan. Chemosphere, 93, 2230–2238. https://doi.org/10.1016/j.chemosphere.2013.07.067

    Article  CAS  Google Scholar 

  • Kolawole, T. O., Ajibade, O. M., Olajide-Kayode, J. O., & Fomba, K. W. (2023b). Level, distribution, ecological, and human health risk assessment of heavy metals in soils and stream sediments around a used-automobile spare part market in Nigeria. Environmental Geochemistry and Health, 45(5), 1573–1598.

    Article  CAS  Google Scholar 

  • Kolawole, T. O., Laniyan, T. A., Bello, E. O., & Isibor, R. A. (2022). Assessment of some potentially harmful elements and their associated health risk in stream sediments of rural gold-mining communities in Southwestern Nigeria. Arabian Journal of Geosciences, 15, 941. https://doi.org/10.1007/s12517-022-10164-9

    Article  CAS  Google Scholar 

  • Kolawole, T. O., Oyelami, C. A., Olajide-Kayode, J. O., Jimoh, M. T., Fomba, K. W., Anifowose, A. J., & Akinde, S. B. (2023a). Contamination and risk surveillance of potentially toxic elements in different land-use urban soils of Osogbo, Southwestern Nigeria. Environmental Geochemistry and Health, 45, 4603–4629. https://doi.org/10.1007/s10653-023-01518-7

    Article  CAS  Google Scholar 

  • Kouadri, S., & Samir, K. (2021). Hydro-chemical study with geospatial analysis of groundwater Quality Illizi Region South-East of Algeria. Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 40(4), 1315–1333. https://doi.org/10.30492/ijcce.2020.39800

    Article  CAS  Google Scholar 

  • Lü, J., Jiao, W. B., Qiu, H. Y., Chen, B., Huang, X. X., & Kang, B. (2018). Origin and spatial distribution of heavy metals and carcinogenic risk assessment in mining areas at You’xi County southeast China. Geoderma, 310, 99–106. https://doi.org/10.1016/j.geoderma.2017.09.016

    Article  CAS  Google Scholar 

  • Migaszewski, Z. M., & Gałuszka, A. (2015). The characteristics, occurrence, and geochemical behavior of rare earth elements in the environment: A review. Critical Review Environmental Science and Technology, 45, 429–471. https://doi.org/10.1080/10643389.2013.866622

    Article  CAS  Google Scholar 

  • Neris, J. B., Olivares, D. M., Velasco, F. G., Luzardo, F. H. M., Correia, L. O., & González, L. N. (2019). HHRISK: A code for assessment of human health risk due to environmental chemical pollution. Ecotoxicology and Environmental Safety, 170, 538–547. https://doi.org/10.1016/j.ecoenv.2018.12.017

    Article  CAS  Google Scholar 

  • Nganje, T. N., Adamu, C. I., & Ukpong, E. E. (2010). Heavy metal concentrations in soils and plants in the vicinity of Arufu lead–zinc mine, Middle Benue Trough, Nigeria. Chinese Journal of Geochemistry, 29, 167–174. https://doi.org/10.1007/s11631-010-0167-x

    Article  CAS  Google Scholar 

  • Nganje, T. N., Adamu, C. I., Ekwere, A. S., Ibe, K. A., Edet, A., & Hursthouse, A. S. (2023). Environmental geochemical signature of shale in pollution assessment of trace metals in soil and water in parts of southern Benue trough, southeastern Nigeria. International Journal of Environmental Analytical Chemistry, 103(12), 2858–2878.

    Article  CAS  Google Scholar 

  • Okamkpa, J. R., Omeka, M. E., Igwe, O., & Iyiokwu, M. U. (2022). An integrated geochemical and spatiotemporal assessment of groundwater resources within an industrial suburb, Southeastern Nigeria. International Journal of Energy and Water Resources. https://doi.org/10.1007/s42108-022-00183-3

    Article  Google Scholar 

  • Okon, E. E., Kudamnya, E. A., Oyeyemi, K. D., Omang, B. O., Ojo, O., & Metwaly, M. (2022). Field observations and geophysical research applied to the detection of manganese (Mn) Deposits in the Eastern Part of Oban Massif. SouthEastern Nigeria: an Integrated Approach. Minerals, 12, 1250. https://doi.org/10.3390/min12101250

    Article  CAS  Google Scholar 

  • Olade, M. A. (1976). On the genesis of the lead-zinc deposits in Nigeria’s Benue Rift (aulacogen): A re-interpretation. Journal of Mining and Geology, 13(2), 2–27.

    Article  CAS  Google Scholar 

  • Olajide-Kayode, J. O., Kolawole, T. O., Oyaniran, O. O., Mustapha, S. O., & Olatunji, A. S. (2023). Potentially Harmful Element toxicity in Geophagic clays consumed in parts of southeastern Nigeria. Journal of Trace Elements and Minerals, 4, 100050. https://doi.org/10.1016/j.jtemin.2023.100050

    Article  Google Scholar 

  • Oluwakayode, A. O., Omang, B. O., Adesola, B. M., & Olubusola, I. S. (2021). Evaluation of gold mineralization potential using electrical resistivity method Along River Chanchaga, Minna, North Central Nigeria. IJESKA, 3(3), 289–300.

    Google Scholar 

  • Omang, B. O., Asinya, E. A., Udinmwen, E., & Oyetade, O. P. (2022). Structural framework and deformation episodes in the Igarra schist belt Southwestern Nigeria. Global Journal Series, 20(1), 1–17.

    Google Scholar 

  • Omang, B. O., Vishiti, A., Kave, G. T., Enah, E. A., & Suh, C. E. (2023). Microchemical characterization and stream sediment composition of alluvial gold particles from the Rafin Gora drainage system, Kushaka schist belt, North Western Nigeria. Communication in Physical Sciences, 9(3).

  • Omeka, M. E., & Egbueri, J. C. (2022). Hydrogeochemical assessment and health-related risks due to toxic element ingestion and dermal contact within the Nnewi-Awka urban areas, Nigeria. Environmental Geochemistry and Health, 1, 1–29. https://doi.org/10.1007/s10653-022-01332-7

    Article  CAS  Google Scholar 

  • Omeka, M. E., Egbueri, J. C., & Unigwe, C. O. (2022a). Investigating the hydrogeochemistry, corrosivity, and scaling tendencies of groundwater in an agrarian area (Nigeria) using graphical, indexical, and statistical modeling. Arabian Journal of Geosciences, 15(13), 1–24. https://doi.org/10.1007/s10661-022-09856-2

    Article  CAS  Google Scholar 

  • Omeka, M. E., & Igwe, O. (2021). Heavy metals concentration in soils and crop plants within the vicinity of abandoned mine sites in Nigeria: An integrated indexical and chemometric approach. International Journal of Environmental Analytical Chemistry. https://doi.org/10.1080/03067319.2021.1922683

    Article  Google Scholar 

  • Omeka, M. E., Igwe, O., Onwuka, O. S., et al. (2023). Efficacy of GIS-based AHP and data-driven intelligent machine learning algorithms for irrigation water quality prediction in an agricultural-mine district within the Lower Benue Trough, Nigeria. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-023-25291-3

    Article  Google Scholar 

  • Omeka, M. E., Igwe, O., & Unigwe, C. O. (2022b). An integrated approach to the bioavailability, ecological, and health risk assessment of potentially toxic elements in soils within a barite mining area, SE Nigeria. Environmental Monitoring and Assessment, 194(3), 1–30. https://doi.org/10.1007/s10661-022-09856-2

    Article  CAS  Google Scholar 

  • Pavilonis, B., Grassman, J., Johnson, G., et al. (2017). Characterization and risk of exposure to elements from artisanal gold mining operations in the Bolivian Andes. Environmental Research, 154, 1–9. https://doi.org/10.1016/j.envres.2016.12.010

    Article  CAS  Google Scholar 

  • Pourret, O., Bollinger, J. C., & Hursthouse, A. (2021). Heavy Metal: A misused term? Acta Geochimica, 40(3), 466–471.

    Article  CAS  Google Scholar 

  • Pourret, O., & Hursthouse, A. (2019). It’s Time to replace the term “Heavy Metals” with “Potentially Toxic Elements” when reporting environmental research. International Journal of Environmental Resources and Public Health, 16, 4446. https://doi.org/10.3390/ijerph16224446

    Article  CAS  Google Scholar 

  • Qingjie, G., Jun, D., Yunchuan, X., Qingfei, W., & Liqiang, Y. (2008). Calculating pollution indices by heavy metals in ecological geochemistry assessment and a case study in parks of Beijing. Journal of China University of Geosciences, 19, 230–241.

    Article  Google Scholar 

  • Ray, A., Kumar, V., Kumar, A., Rai, R., Khandelwal, M., & Singh, T. N. (2020). Stability prediction of Himalayan residual soil slope using artificial neural network. Natural Hazards. https://doi.org/10.1007/s11069-020-04141-2

    Article  Google Scholar 

  • Santra, P., Kumar, M., Kumawat, R. N., Painuli, D. K., Hati, K. M., Heuvelink, G. B. M., & Batjes, N. H. (2018). Pedotran functions to estimate soil water content at field capacity and permanent wilting points in hot Arid Western India. Journal of Earth System Science. https://doi.org/10.1007/s12040-018-0937-0

    Article  Google Scholar 

  • Tchounwou, P. B., Yedjou, C. G., Patlolla, A. K., & Sutton, D. J. (2012). Heavy metal toxicity and the environment. In A. Luch (Ed.), Molecular, clinical and environmental toxicology (pp. 133–164). Basel: Springer.

    Chapter  Google Scholar 

  • Tomlinson, D. L., Wilson, J. G., Harris, C. R., & Jeffrey, D. W. (1980). Problems in the assessment of heavy metals levels in estuaries and the formation of a pollution index. Helgoländer Meeresuntersuchungen, 33(1–4), 566–575. https://doi.org/10.1007/BF02414780

    Article  Google Scholar 

  • Tyler, G. (2004). Rare earth elements in soil and plant systems—A review. Plant and Soil, 267, 191–206. https://doi.org/10.1007/s11104-005-4888-2

    Article  CAS  Google Scholar 

  • Unigwe, C. O., Igwe, O., Onwuka, O. S., Egbueri, J. C., & Omeka, M. E. (2022). Roles of hydro-geotechnical and slope stability characteristics in the erosion of Ajali and Nanka geologic formations in southeastern Nigeria. Arabian Journal of Geosciences, 15(18), 1492.

    Article  Google Scholar 

  • US EPA (United States Environment Protection Agency). (2005). Ecological soil screening level (Eco-SSL) guidance and documents. Washington DC. Available at: http://www.epa.gov/oswer/riskassessment/ecorisk/ecossl. html. Accessed 24 Nov 2014.

  • US EPA IRIS (US Environmental Protection Agency)’s Inter grated Risk Information System. (2011). Environmental Protection Agency Region I, Washington DC 20460.US EPA, 2012. http://www.epa.gov/iris

  • US-EPA (US Environmental Protection Agency). (2017). National recommended water quality criteria-aquatic life criteria table and human health criteria table. https://www.epa.gov.wqc/nationalrecommended-water-quality criteria-aquatic-life-criteria-table

  • Valadares, S. V., Cropper, W. P., Jr., Lima Neves, J. C., Leite, H. G., Barros, N. F., & Gerber, S. (2017). Pedotransfer functions to estimate parameters for soil phosphorus models. Soil Science Society of America Journal, 81(1), 210–213.

    Article  CAS  Google Scholar 

Download references

Funding

No funding was received for this research.

Author information

Authors and Affiliations

  1. Department of Geology, University of Calabar, P.M.B. 1115, Calabar, Cross River State, Nigeria

    Benjamin Odey Omang, Michael Ekuru Omeka & Enah Asinya Asinya

  2. Department of Geology, Faculty of Physical Sciences, University of Nigeria, Nsukka, Nigeria

    Victor Chukwuemeka Aluma

  3. Department of Environmental Resources Management, University of Calabar, Calabar, Cross River State, Nigeria

    Peter Ereh Oko

Authors
  1. Benjamin Odey Omang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Ekuru Omeka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Enah Asinya Asinya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter Ereh Oko
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Victor Chukwuemeka Aluma
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MEO conceived and designed the project and experiment; analyzed and interpreted the data; performed health risk assessment, and machine learning modeling and wrote the manuscript; BOO contributed to field data collection and laboratory analysis, manuscript editing, and review; EAA contributed to filed data collection and analysis and manuscript review; VCA contributed to numerical data analysis and multivariate statistical analysis; PEO contributed to manuscript editing and review.

Corresponding author

Correspondence to Michael Ekuru Omeka.

Ethics declarations

Conflict of interests

The authors declare that there are no competing interests in this research.

Animal research

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 76 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Omang, B.O., Omeka, M.E., Asinya, E.A. et al. Application of GIS and feedforward back-propagated ANN models for predicting the ecological and health risk of potentially toxic elements in soils in Northwestern Nigeria. Environ Geochem Health 45, 8599–8631 (2023). https://doi.org/10.1007/s10653-023-01737-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10653-023-01737-y

Keywords

Search

Navigation