Soil parameters affecting the levels of potentially harmful metals in Thessaly area, Greece: a robust quadratic regression approach of soil pollution prediction

Golia, Evangelia E.; Diakoloukas, Vassilios

doi:10.1007/s11356-021-14673-0

Cited by 30 publications

(23 citation statements)

References 53 publications

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“…Similar to our results, it was reported that calibration, validation, and prediction processes can be complicated and dependent on soil types and its physiochemical properties, the soil management history, chemical characteristics, and the analytical method, along with the sample size and distribution, as well as the prediction approaches [4,7,13,14,[27][28][29]54,61]. Based on 2250 soil samples that were collected in a 5-year period from four soil groups varying in clay mineralogy (Alfisols, Inceptisols, Entisols, and Vertisols) and a series of robust regression analyses, Golia and Diakoloukas [61] concluded that several factors (pH, electric conductivity, SOC, soil texture, exchangeable cations, and metal oxides along with coexisting metals) affect the total concentrations of Fe and Cd in soils. For instance, total Cd was reliably predicted with Cu and Zn in Entisols; clay, Pb, and CEC in Alfisols; and clay, Pb, and CEC in Vertisols [61].…”

Section: Accuracy Analysis Of the Plsr Modelssupporting

confidence: 89%

“…The results suggested that the M3 elements are related to the variation in soil type or texture and basic soil properties (mostly CaCO 3 and pH), along with coexisting metals that can reliably assist as predictive factors [61]. Moreover, the role of CaCO 3 (e.g., Ca and electrolyte sources, and pH modifier) on soil biogeochemistry is still overlooked in mountain soils and needs greater attention for the monitoring of land degradation and quality [2,60].…”

Section: Reference Datamentioning

confidence: 99%

“…Based on 2250 soil samples that were collected in a 5-year period from four soil groups varying in clay mineralogy (Alfisols, Inceptisols, Entisols, and Vertisols) and a series of robust regression analyses, Golia and Diakoloukas [61] concluded that several factors (pH, electric conductivity, SOC, soil texture, exchangeable cations, and metal oxides along with coexisting metals) affect the total concentrations of Fe and Cd in soils. For instance, total Cd was reliably predicted with Cu and Zn in Entisols; clay, Pb, and CEC in Alfisols; and clay, Pb, and CEC in Vertisols [61]. These results are in line with our findings (Supplementary Table S1, Figure 4).…”

Section: Accuracy Analysis Of the Plsr Modelsmentioning

confidence: 99%

“…SOC has a broad absorption in the VIS region and shows a pattern in spectra as linear, concave, or an upward shift shape that also depends on soil mineralogy [69]. Fe showed even stronger correlations than SOC that are most likely related to features of Fe oxides linked with soil types [61]. Equally, the concentrations of spectrally featureless heavy metals in soils were estimated via the correlations among Fe oxides [30], organic matter [69], and clay minerals [70].…”

Section: Analysis Of Prediction Mechanisms 431 Correlation Pca and Pa...mentioning

confidence: 99%

“…The small influence of SOC on M3-extracted metals was not fully consistent with previous studies. Correlations with Pb, Zn, and Cd or As, Cr, and Cu are governed by soil organic matter or Fe (oxides) along with coexisting metals and soil texture that can reliably assist as predictive factors [18,19,29,61]. Therefore, based on our analyses, M3-extractable elements are indirectly bound to iron oxides, clay minerals, or organic matter and can be assessed using the spectral features of these soil constituents, which is in line with other studies [4,24,26].…”

Section: Analysis Of Prediction Mechanisms 431 Correlation Pca and Pa...mentioning

confidence: 99%

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Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains

Mammadov

Denk

Riedel

et al. 2022

Land

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Soil spectroscopy is a promising alternative to evaluate and monitor soil and water quality, particularly in mountainous agricultural lands characterized by intense degradation and limited soil tests reports; a few studies have evaluated the feasibility of VIS-NIR spectroscopy to predict Mehlich 3 (M3) extractable nutrients. This study aimed to (i) examine the potential of VIS-NIR spectroscopy in combination with partial least squares regression to predict M3-extractable elements (Ca, K, Mg, P, Fe, Cd, Cu, Mn, Pb, and Zn) and basic soil properties (clay, silt, sand, CaCO3, pH, and soil organic carbon-SOC), (ii) find optimal pre-processing techniques, and (iii) determine primary prediction mechanisms for spectrally featureless soil properties. Topsoil samples were collected from a representative area (114 samples from 525 ha) located in the mountainous region of NW Azerbaijan. A series of pre-processing steps and transformations were applied to the spectral data, and the models were calibrated and evaluated based on the coefficient of determination (R2), root mean square error (RMSE), and the residual prediction deviation (RPD). The leave-one-out cross-validated predictions showed that the first derivative spectra produce higher prediction accuracies (R2 = 0.51–0.91; RPD = 1.20–2.29) for most soil properties. The evaluation of the model performance with optimal pre-processing techniques revealed that both calibration and validation models produce considerable differences in RPD values associated with sample size and the random partition of the calibration or validation subsets. The prediction models were excellent or very good (RPD > 2.0) for CaCO3, SOC, sand, silt, Ca, and Pb, good or fair (1.4 < RPD < 2.0) for clay, K, Cd, pH, Fe, Mn, and Cu, and poor (1.0 < RPD < 1.4) for Mg, P, and Zn. Principal component and correlation, stepwise regression analysis, and variable importance in projection procedures allowed to elucidate the underlying prediction mechanisms. Unlike the previous studies, the spectral estimations of pH, Ca, Mg, P, Fe, Pb, and Cd concentrations were linked to their correlation with CaCO3 rather than soil organic matter, whereas Mg and P concentrations were also connected to Fe-oxides. Soil particle sizes contributed to predicting K concentration but confounded the prediction of P and Zn concentration. The weaker correlations of Mn, Cu or Zn with CaCO3, particle sizes, SOC, Fe, and spectral data yielded to their lower prediction accuracy. The major prediction mechanisms for M3-extractable elements relied on their relations with CaCO3, pH, clay content and mineralogy, and exchangeable cations in the context of their association with land use. The results can be used in mountain lands to evaluate and control the effect of management on soil quality indices and land degradation neutrality. Further studies are needed to develop most advantageous sampling schemes and modeling.

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Section: Accuracy Analysis Of the Plsr Modelssupporting

confidence: 89%

Section: Reference Datamentioning

confidence: 99%

Section: Accuracy Analysis Of the Plsr Modelsmentioning

confidence: 99%

Section: Analysis Of Prediction Mechanisms 431 Correlation Pca and Pa...mentioning

confidence: 99%

Section: Analysis Of Prediction Mechanisms 431 Correlation Pca and Pa...mentioning

confidence: 99%

See 3 more Smart Citations

Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains

Mammadov

Denk

Riedel

et al. 2022

Land

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Appraisal of contamination, source identification and health risk assessment of selected metals in the agricultural soil of Chakwal, Pakistan

Batool,

Shah

2023

Environ Geochem Health

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Exploring effective network design for monitoring soil pH and potentially toxic elements based on geochemical surveys in economically developed area

Xu,

Fu,

Pan

et al. 2023

Environ Geochem Health

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Periodically visiting soil monitoring sites, i.e., sampling and analysis, is recognized as one of the most important ways to monitor soil quality. However, reconciling the monitoring costs with monitoring precision of the soil monitoring network (SMN) is a key technical problem to be solved. A statistically sound method, which depends on the spatial variation in monitoring indicators, was adopted to determine the number of monitoring sites and the monitoring interval as well as their ability to detect a particular change under an economically feasible scenario.The spatial variation in soil monitoring indicators was inquired from the "Multi-Purpose Regional Geochemical Survey in Zhejiang Province (MRGSZ)" project. Based on the data for soil pH and concentration of potentially toxic elements, the number of monitoring sites and the monitoring intervals that might be used for soil monitoring were determined with the administrative region as the monitoring unit. The results showed that there was great spatial variation in the MRGSZ region, which resulted in discrepancies in the minimum detectable changes (MDCs), monitoring site numbers, and temporal monitoring intervals for revisiting. Our research proposes a number of monitoring sites (n r ) that could reconcile the monitoring costs, practicability, and monitoring precision; thus, it was recommended for the design of SMNs. Under n r , the MDC values of each monitoring indicator were acceptable for all administrative regions, and the temporal monitoring intervals were practical with variations of 6.7 to 14.8 years.

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Soil parameters affecting the levels of potentially harmful metals in Thessaly area, Greece: a robust quadratic regression approach of soil pollution prediction

Cited by 30 publications

References 53 publications

Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains

Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains

Appraisal of contamination, source identification and health risk assessment of selected metals in the agricultural soil of Chakwal, Pakistan

Exploring effective network design for monitoring soil pH and potentially toxic elements based on geochemical surveys in economically developed area

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