The influence of organic carbon and pH on heavy metals, potassium, and magnesium levels in Lithuanian podzols

Yones Khaledian, Paulo Alexandre da Silva Pereira, Eric C. Brevik, Neringa Pundyte, Dainius Paliulis

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Industrial activities can contribute to the accumulation of heavy metals in soils, which could potentially threaten public health and the environment. This research was conducted to investigate the relationships between pH and total organic carbon (TOC) with soil chemical parameters, including exchangeable and total Cu, Zn, Cd, Pb, K, and Mg concentrations in soils near Panevėžys and Kaunas, Lithuania. Principal component regression (PCR) and non-linear regression were used to find statistical relationships between pH, TOC, and the other soil properties studied. The results of correlation tests indicated that pH and TOC had strong relationships with most of the soil properties. The results of PCR [R2 = 0·87, RMSE = 0·046] and non-linear regression [R2 = 0·91, RMSE = 0·041] (pH and the entire parameters), PCR [R2 = 0·777, RMSE = 0·058] and non-linear regression [R2 = 0·871, RMSE = 0·046] (pH and the exchangeable parameters) to model the relationships between pH and soil chemical properties were promising and significant. Exchangeable heavy metal concentrations increased for pH > 5. Even though the relationships between TOC and heavy metals were significant, they were not as powerful as the relationships between pH and these metals. It was concluded that total metal concentrations in the study soils can be predicted by either pH or TOC. Metal mobility could most likely be controlled at the study site by manipulating soil pH and/or TOC. Finally, it is suggested that when there are financial and time limitations, assessment of total exchangeable metal concentrations using soil pH and/or TOC could be productive.
Original languageEnglish
Pages (from-to)345-354
Number of pages10
JournalLand Degradation and Development
Issue number1
Publication statusPublished - 2017



  • Non-linear regressionNon-linear regression ; principal component regression ; soil contamination ; soil degradation ;
  • principal component regression
  • soil contamination
  • soil degradation
  • statistical modeling

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