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Combining spatial autocorrelation with artificial intelligence models to estimate spatial distribution and risks of heavy metal pollution in agricultural soils

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Abstract

Information on spatial distribution and potential sources of heavy metals in agricultural lands is very important for human health and food safety. In this study, pollution degree of lead (Pb), cadmium (Cd), and nickel (Ni) in Yüksekova Plain, located on the border in the southeastern part of Turkey, was evaluated by geoaccumulation index (Igeo), modified contamination factor (mCdeg), and Nemerow pollution index (PINemerow) combined with spatial autocorrelation using deep learning algorithms. A total of 304 soil samples were collected from two different depths (0–20 and 20–40 cm) in the study area, which covered 17.5 thousand ha land. Covariates were determined for spatial distribution models of Pb, Cd, and Ni by factor analysis (FA). Spatial distribution models for surface soils were developed using pedovariables (silt, sand, clay lime, organic matter, electrical conductivity, pH, Ca, and Na) determined by the FA and Igeo and mCdeg values by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. The estimation success of models for different depths was assessed by root mean square error (RMSE), mean absolute percent error (MAPE), and Taylor diagrams. The RMSE and MAPE values showed a strong correlation between heavy metal contents and the covariates. The RMSE values of ANN-Ni0-20, ANN-Ni20-40, ANN-Pb0-20, ANN-Cd0-20, and ANN-Cd20-40 models (0.01240, 0.07257, 0.0039, 0.00045, 0.00044, and 0.04607, respectively) confirmed the success of the models. Likewise, the MAPE values between 0.2 and 8.5% indicated that all models were very good predictors. In addition, the Taylor diagrams showed that the estimation performance of ANFIS and ANN models are compatible. The IgeoNi and IgeoPb values in both models at both depths indicated that strongly to extremely polluted (4–5) areas are quite high in the study area, while the IgeoCd values revealed that unpolluted areas are widespread. The mCdeg index value showed a moderate to high contamination at the first depth, while very high contamination at the second depth in most of the study area. Spatial distribution of PINemerow revealed that moderate pollution (2–3) is common in both soil depths of the study area. The PINemerow of subsurface layer was between 0.91 and 1 (warning limit class) in a small part of the study area. The results showed that vertical mobility of heavy metals is closely related to pedovariables. In addition, the ANN and ANFIS models are capable of exhibiting the heterogeneity in the spatial distribution pattern of high variation in the data. Thus, the locations with extreme contamination have been accurately determined. The pollution indices calculated considering the commonly used international reference values revealed that heavy metal pollution in some part of the study area reached the detrimental levels for human health and food safety. The results suggested that the pollution indices were more successful than simple heavy metal concentrations in interpreting the pollution risk levels. High-resolution spatial information reported in this study can help policy makers and authorities to reduce heavy metal emissions of pollutants or, if possible, to eliminate the pollution.

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Data availability

The datasets used in the study can be obtained on a reasonable request from the corresponding author.

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Acknowledgements

The authors are grateful for the financial support provided by the Hakkari Governorship Special Provincial Administration.

Funding

This study was funded by the Hakkari Governorship Special Provincial Administration.

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Authors and Affiliations

  1. Sanliurfa, Turkey

    Elif Günal

  2. Soil Science Department, Faculty of Agriculture, Siirt University, Siirt, Turkey

    Mesut Budak

  3. Department of Plant and Animal Production, Vocational School of Kahta, Adiyaman University, Adiyaman, Turkey

    Miraç Kılıç

  4. Department of Agricultural Structures and Irrigation, Faculty of Agriculture, Samsun, Turkey

    Bilal Cemek

  5. Department of Plant and Animal Production, Kurtalan Vocational High School, Siirt University, Siirt, Turkey

    Mesut Sırrı

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  1. Elif Günal
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Contributions

E.G., M.B., and M.K. conceived of the presented idea. E.G. and M.K. developed the theory and performed the computations. B.C. and M.B. verified the analytical methods. All authors discussed the results and contributed to the final manuscript. E.G., M.B, and M.S. carried out the experiment field studies and laboratory analysis. E.G., M.B., B.C., and M.K. conceived the original idea. E.G. and M.B. supervised the project. E.G. and M.K. developed the theoretical formalism, performed the analytic calculations, and performed the numerical simulations. E.G., M.B., and M.S. conceived and planned the experiments. M.B. and M.S. carried out the experiments. M.K and B.C. planned and carried out the simulations. E.G., M.B., M.S., and M.K. contributed to sample preparation. E.G., M.B., M.K., and M.S. contributed to the interpretation of the results. E.G., M.B., and M.K. took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis, and manuscript. E.G., M.B., and M.K. designed the model and the computational framework and analyzed the data. M.K. and B.C. performed the calculations. E.G. and M.K. wrote the manuscript with input from all authors. E.G. and M.B. conceived the study and were in charge of overall direction and planning.

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Correspondence to Miraç Kılıç.

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Günal, E., Budak, M., Kılıç, M. et al. Combining spatial autocorrelation with artificial intelligence models to estimate spatial distribution and risks of heavy metal pollution in agricultural soils. Environ Monit Assess 195, 317 (2023). https://doi.org/10.1007/s10661-022-10813-2

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