Root concentration factor estimation

A method for estimating the TSCF for equation 14.24 is given in Table 14.10. The root concentration factor is also defined in Table 14.10 as the ratio of the contaminant in the roots to the concentration dissolved in the soil water (pg/kg root per pg/L). This is important in estimating the mass of contaminant sorbed to roots in phytoremediation systems. The values of TSCF and RCF for metals depend on the metals redox states and chemical speciation in soil and groundwater. 

Estimating the Transpiration Stream Concentration Factor (TSCF) and Root Concentration Factor (RCF) for Some Typical Contaminants (8)... 

Root Mean Square Error of Prediction (RMSEP) Plot (Model Diagnostic) Prediction error is a useful metric for selecting the optimum number of factors to include in the model. This is because the models are most often used to predict the concentrations in future unknown samples. There are two approaches for generating a validation set for estimating the prediction error internal validation (i.e., cross-validation with the calibration data), or external validation (i.e., perform prediction on a separate validation set). Samples are usually at a premium, and so we most often use a cross- validation approach. 

Root Mean Square Error of Prediction (RMSEP) Plot (Model Diagnostic) The RMSEP versus number of factors plot in Figure 5.113 shows a break at three factors and a leveling off after six factors. Tlie RMSEP value with six factors (0,04) is comparable to the estimated error in the reported concentrations (0.033), indicating the model is predicting well At this point we tentatively choose a rank six model. The rank three model shows an RMSEP of 0.07 and may well have been considered to be an adequate model, depending on how well the reference values are known. 

Table 12.3 compares the estimated analyte concentrations for DIED, PARAFAC, and PARAFAC x 3 noise (PARAFAC with the addition of a factor of three greater random errors) applied to the same calibration problem. Table 12.4 is analogous to Table 12.3, except that it also presents the squared correlation coefficients between the true and estimated X-way and Y-way profiles for all three species present in the six samples. It is first evident that PARAFAC slightly outperforms DTLD when applied to the same calibration problem. However, the improvement often lies in the third or fourth decimal place and is hardly significant when compared with the overall precision of the data. This near equivalence of DTLD and PARAFAC is rooted in the fact that DTLD performs admirably, and there is little room for... 

Improvements in the detection limit can only be achieved if the S/N ratio is improved. Unfortunately, different methods have been used to quantify the variations in a background signal. One approach is simply to measime the average difference between the maximum and minimmn (referred to as peak to peak, p-p) whereas a second approach approximates the variations in the noise by a sine wave. If the sine wave approximation is vahd, the average is the root mean square of the signal (abbreviated as rms). The difference between these approaches result in estimates which vary by a factor of approximately 3 [i.e., S/N(rms) = 2.8 S/N(p-p)]. Fortunately, most authors specify their method for estimating the noise. It is also important to recognise that the hmit of detection is an extrapolated value and is calculated based on the best possible conditions with virtually no interference. In practice, the lowest amount (or concentration) which can be rehably detected is some three to five times this estimate. 

Empirical data describing the extent of chemical uptake by plants roots are generally expressed as ratios of chemical concentrations in the plant compartment of interest (e.g., shoots, roots, xylem sap) to that in the exposure medium (soil, soil pore water, hydroponic solution) measured at the time the samples are collected. These ratios are generally referred to as bioconcentration factors (BCFs) but they may or may not reflect equilibrium conditions. Plant BCF values are widely used to provide direct and approximate estimates of plant tissue concentrations from measured exposure... 

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