Effects of moisture, clay and oil concentration on calibration

Three sample outliers were eliminated from the cross-validation dataset in each subset of samples. All six subset of sample generated a total of 16 cross- validation models (Table 3-4). When all calibration models were compared, the best model was the 30,000-mgkg-1 oil concentration with the least RMSE of cross-validation and the highest RPD values (Table 3-4). As shown in Table 3-4, the PLS models used different numbers of latent factors in the cross- validation models. Ideally, the optimal number of factors might be expected to be three or less if Beer’s law applies and the pre-processing removed unwanted

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physical effects (Naes et al., 2002). Nevertheless, because of the systematic stratification of the samples and the interaction effect of the experimental design parameters, the variability in the number of latent factors can be explained. Although most authors suggest that a smaller number of these factors are used, many more have reported even higher factors with a related sample matrix, e.g. Aske et al. (2001). An equally important fact, nonetheless, is that the validation, when done on a separate sample set, should give stable and good prediction accuracy.

Table 3-4 Summary of calibration results of polycyclic aromatic hydrocarbons using partial lease squares cross-validation models at different oil concentrations, moisture and clay contents

Variable Calibration quality indicators Number of LV RMSE (mgkg-1) RPD r2 Moisture content (%) 0 (dry) 0.36 1.99 0.72 1 5 0.48 1.53 0.61 2 10 0.47 1.59 0.62 5 15 0.44 1.62 0.61 3 20 0.30 2.53 0.82 7 Field-moist (mixed MC) 0.39 1.93 0.72 4 Clay content (%) 9 0.39 1.99 0.70 5 20 0.48 1.55 0.51 3 26 0.41 1.51 0.54 4 35 0.44 1.72 0.64 2 74 0.35 1.79 0.63 5 Oil concentration (mgkg-1) 30,000 0.12 3.20 0.89 3 60,000 0.26 2.21 0.79 2 90,000 0.40 2.05 0.72 2 120,000 0.35 2.04 0.75 1 150,000 0.44 1.67 0.59 1

RMSE, root-mean-square error; RPD, ratio of the standard error of prediction to the standard deviation of the reference data in the validation set; LV latent variables

88 3.4.2.1 Effect of Moisture Content

Although views expressed in the literature show that calibration models developed for dry samples always are better than those for wet samples because water is known to decrease the accuracy of vis-NIR-predicted soil properties (Tekin et al., 2012; Nocita et al., 2013), the lower model accuracy achieved in the current study for the dry samples as compared to the 20% moisture samples is in line with the findings of Charkraborty et al. (2010) who reported very large prediction error (0.55 log10 mgkg-1) and even lower RPDs (1.34 and 1.06) for air-dried ground scans for reflectance and first-derivative TPH models, respectively. Moreover, Tekin et al. (2012) reported an excellent model performance with wet samples of up to 10% moisture for soil organic carbon. Nonetheless, the performance achieved in this study based on RPD values can be classified as good for the dried ground samples, whereas the performance for the 20% moisture and field-moist intact samples can be classified as excellent and almost good, respectively (Chang et al., 2001). Nevertheless, between the fairly performing 5% and 10% moisture models, the 5% moisture model showed worse performance with lower RPD and higher RMSE values as confirmed in Figure 3-2a. Tekin et al. (2012) and Nocita et al. (2013) also reported similar results with soil organic carbon for 5 and 15% moisture, respectively. The good model performance of the field-moist samples suggests the possibility of quantitative predictions without recourse to lengthy prior sample preparations.

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Figure 3-2 Variation of RPD and RMSE values of partial least-squares cross- validation models developed for polycyclic aromatic hydrocarbons (PAH) of diesel-contaminated soils

(a) With moisture content, (b) With clay content, and (c) With oil concentration 3.4.2.2 Effect of clay content

The good calibration performance observed with the least clay content model, 9% clay (Table 3-4), agrees with the findings of Forrester et al. (2010) that minor amounts of clay can seriously affect the apparent intensity of sorbed hydrocarbon (TPH) spectral signals and hence the calibration performance with vis-NIR spectroscopy. As shown in Figure 3-2b, the poorest calibration performance occurred at 26% clay content. This result is in agreement with the

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report of Forrester et al. (2010) that a dramatic signal reduction by up to a factor of 100 for sand containing up to 25% clay was observed for TPH calibration. Nevertheless, the unexpected relatively high calibration performance of the 35 and 74% clay models may not be unconnected with the interaction effect of water and oil.

3.4.2.3 Effect of Oil Concentration

Table 3-4 shows that calibration performance, in terms of the RPD, of PAH models with vis-NIR spectroscopy decreased with increasing oil concentration. As shown in Figure 3-2c, the RPD value decreased progressively with increasing oil concentration and attaining saturation at about 90,000 mgkg-1 oil con-centration. It thus appears that for measurement of PAHs with vis-NIR spectroscopy, best models performance can be expected at very low clay content and oil concentrations at moisture of no more than 20% (Figure 3-2).

The ANOVA results in Table 3-5 showed that the combined effect of oil concentration, clay and/or moisture resulted in significant (p<0.05) differences in the quality of the PAH calibration models. This implies that the interaction of the experimental design parameters significantly (p<0.05) affected the precision, in terms of the RMSE and RPD values, of measurement of PAH with vis-NIR spectroscopy. Nevertheless, Table 3-5 showed that the accuracy of vis- NIR spectroscopy was mostly affected by the combined influence of the three design parameters, namely oil concentration, moisture and clay contents as shown by the p values.

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Table 3-5 One-way ANOVA on the analysis of the significance of the combined effect of oil concentration, moisture and clay contents on the accuracy of prediction of polycyclic aromatic hydrocarbons using results of partial least squares cross-validation.

Option Source of variation S.S. d.f. M.S. F p value F crit. A Between groups 14.5348 3 4.8449 36.61* 2.1×10−7 3.24 Within groups 2.1174 16 0.1323 – – – Totals 16.6522 19 – – – – B Between groups 13.6446 3 4.5482 47.00* 3.7×10−8 3.24 Within groups 1.5484 16 0.0968 – – – Totals 15.1930 19 – – – – C Between groups 9.4512 3 3.1504 56.72* 9.6×10−9 3.24 Within groups 0.8887 16 0.0555 – – – Totals 10.3399 19 – – – – D Among groups 18.8735 5 3.7747 39.78* 7.5×10−11 2.62 Within groups 2.2773 24 0.0949 – – – Totals 21.1508 29 – – – –

A combination of moisture content and oil concentration, B combination of clay content and oil

concentration, C combination of moisture content and clay content, D combination of oil concentration, moisture and clay contents; S.S., sum of squares, M.S., mean squares, d.f., degree of freedom;

*p<0.05 (significant)

3.4.3 Accuracy of prediction of polycyclic aromatic hydrocarbons