Single-Element and Competitive Metal Mobility Measured with Column Infiltration and Batch Tests

The distribution coefficient, K-d, is often used to quantify heavy metal mobility in soils. Batch sorption or column infiltration tests may be used to measure K-d. The latter are closer to natural soil conditions, but are difficult to conduct in clays. This difficulty can be overcome by using a laboratory centrifuge. An acceleration of 2600 gravities was applied to columns of London Clay, an Eocene clay sub-stratum, and Cu, Ni, and Zn mobility was measured in centrifuge infiltration tests, both as single elements and in dual competition. Single-element K-d values were also obtained from batch sorption tests, and the results from the two techniques were compared. It was found that K-d values obtained by batch tests vary considerably depending on the metal concentration, while infiltration tests provided a single K-d value for each metal. This was typically in the lower end of the range of the batch test K-d values. For both tests, the order of mobility was Ni > Zn > Cu. Metals became more mobile in competition than when in single-element systems: Ni K-d decreased 3.3 times and Zn K-d 3.4 times when they competed with Cu, while Cu decreased only 1.2 times when in competition with either Ni or Zn. Our study showed that competitive sorption between metals increases the mobility of those metals less strongly bound more than it increases the mobility of more strongly bound metals.

The structure of the Statistics Anxiety Rating Scale: A confirmatory factor analysis using UK psychology students

This study aimed to examine the structure of the statistics anxiety rating scale. Responses from 650 undergraduate psychology students throughout the UK were collected through an on-line study. Based on previous research three different models were specified and estimated using confirmatory factor analysis. Fit indices were used to determine if the model fitted the data and a likelihood ratio difference test was used to determine the best fitting model. The original six factor model was the best explanation of the data. All six subscales were intercorrelated and internally consistent. It was concluded that the statistics anxiety rating scale was found to measure the six subscales it was designed to assess in a UK population.