Construction the Statistics Distributions for Characterizing the Transfer Factors of Metals from Soil to Plant (TFsp) Using Bayesian Method

Abstract

Plants have the faculty of  levy the metals in the soil. The consumption of this plants can represent in some situations a health risk to be assessed. The transfer of contaminants from soil to food crops is a major route connecting the soil contamination to human exposure. The Transfer Factors Soil-Plant (TF_sp) (the ratio between the concentration of contaminants in plants and the concentration of contaminants in the soil) is a value commonly used in the assessment of exposure and health risks. This research use the BAPPET database (database contents the informations of elements metal traces plants and vegetables). The goal of this research is for define the variable that influent the variability of TF_sp and for characterizing their effects from their posteriors distributions using bayesian methods, Metropolis-Hastings. There are 3 metals (Cd, As and Pb), 4 plant types (leaf, fruit, root and tuber) and 2 analysis (using 4 plant types and 3 plant types, without tuber) with 4 models of analysis of varians (ANOVA, using normal and lognormal distribution for likelihood) that used in this research. The results of analysis for 4 plant types is chosing the model II with lognormal distribution for likelihood (y_i ~ LN(µ_i, σ_i²)) for the best model and for 3 plant types is chosing the model IV with lognormal distribution for likelihood (y_i ~ LN(µ_i, σ²), µ_i = µ + α_i + B_j + δ_k, B_j~ N(0, σ_B²)) for the best model. The contains of metal Cd, As and Pb in leaf has the highest risk for the health because that has the biggest posterior mean of TF_sp.