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

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dc.authoridBUDAK, MESUT/0000-0001-5715-1246
dc.authoridCEMEK, Bilal/0000-0002-0503-6497
dc.authoridGUNAL, ELIF/0000-0003-0624-2919
dc.authoridSIRRI, MESUT/0000-0001-9793-9599
dc.authoridKILIC, Mirac/0000-0001-8026-5540
dc.contributor.authorGunal, Elif
dc.contributor.authorBudak, Mesut
dc.contributor.authorKilic, Mirac
dc.contributor.authorCemek, Bilal
dc.contributor.authorSirri, Mesut
dc.date.accessioned2024-12-24T19:24:37Z
dc.date.available2024-12-24T19:24:37Z
dc.date.issued2023
dc.departmentSiirt Üniversitesi
dc.description.abstractInformation 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 Yuksekova 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 Igeo(Ni) and Igeo(Pb) 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 Igeo(Cd) values revealed that unpolluted areas are widespread. The mC(deg) 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.
dc.identifier.doi10.1007/s10661-022-10813-2
dc.identifier.issn0167-6369
dc.identifier.issn1573-2959
dc.identifier.issue2
dc.identifier.pmid36680597
dc.identifier.scopus2-s2.0-85146734543
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s10661-022-10813-2
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6048
dc.identifier.volume195
dc.identifier.wosWOS:000927705400001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEnvironmental Monitoring and Assessment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectSpatial modeling
dc.subjectArtificial neural network
dc.subjectAdaptive neuro-fuzzy inference system
dc.subjectGeoaccumulation index
dc.subjectModified contamination factor
dc.subjectNemerow pollution index
dc.titleCombining spatial autocorrelation with artificial intelligence models to estimate spatial distribution and risks of heavy metal pollution in agricultural soils
dc.typeArticle

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