Gaseous radiocarbon measurements of small samples

Radiocarbon dating by means of accelerator mass spectrometry (AMS) is a well-established method for samples containing carbon in the milligram range. However, the measurement of small samples containing less than 50 lg carbon often fails. It is difficult to graphitise these samples and the preparation is prone to contamination. To avoid graphitisation, a solution can be the direct measurement of carbon dioxide. The MICADAS, the smallest accelerator for radiocarbon dating in Zurich, is equipped with a hybrid Cs sputter ion source. It allows the measurement of both, graphite targets and gaseous CO2 samples, without any rebuilding. This work presents experiences dealing with small samples containing 1-40 lg carbon. 500 unknown samples of different environmental research fields have been measured yet. Most of the samples were measured with the gas ion source. These data are compared with earlier measurements of small graphite samples. The performance of the two different techniques is discussed and main contributions to the blank determined. An analysis of blank and standard data measured within years allowed a quantification of the contamination, which was found to be of the order of 55 ng and 750 ng carbon (50 pMC) for the gaseous and the graphite samples, respectively. For quality control, a number of certified standards were measured using the gas ion source to demonstrate reliability of the data.

Effect of low count sums on quantitative environmental reconstructions: an example using subfossil chironomids

The concentrations of chironomid remains in lake sediments are very variable and, therefore, chironomid stratigraphies often include samples with a low number of counts. Thus, the effect of low count sums on reconstructed temperatures is an important issue when applying chironomid‐temperature inference models. Using an existing data set, we simulated low count sums by randomly picking subsets of head capsules from surface‐sediment samples with a high number of specimens. Subsequently, a chironomid‐temperature inference model was used to assess how the inferred temperatures are affected by low counts. The simulations indicate that the variability of inferred temperatures increases progressively with decreasing count sums. At counts below 50 specimens, a further reduction in count sum can cause a disproportionate increase in the variation of inferred temperatures, whereas at higher count sums the inferences are more stable. Furthermore, low count samples may consistently infer too low or too high temperatures and, therefore, produce a systematic error in a reconstruction. Smoothing reconstructed temperatures downcore is proposed as a possible way to compensate for the high variability due to low count sums. By combining adjacent samples in a stratigraphy, to produce samples of a more reliable size, it is possible to assess if low counts cause a systematic error in inferred temperatures.

Modern diatom, cladocera, chironomid, and chrysophyte cyst assemblages as quantitative indicators for the reconstruction of past environmental conditions in the Alps. II. Nutrients

Surface sediments from 68 small lakes in the Alps and 9 well-dated sediment core samples that cover a gradient of total phosphorus (TP) concentrations of 6 to 520 μg TP l-1 were studied for diatom, chrysophyte cyst, cladocera, and chironomid assemblages. Inference models for mean circulation log10 TP were developed for diatoms, chironomids, and benthic cladocera using weighted-averaging partial least squares. After screening for outliers, the final transfer functions have coefficients of determination (r2, as assessed by cross-validation, of 0.79 (diatoms), 0.68 (chironomids), and 0.49 (benthic cladocera). Planktonic cladocera and chrysophytes show very weak relationships to TP and no TP inference models were developed for these biota. Diatoms showed the best relationship with TP, whereas the other biota all have large secondary gradients, suggesting that variables other than TP have a strong influence on their composition and abundance. Comparison with other diatom – TP inference models shows that our model has high predictive power and a low root mean squared error of prediction, as assessed by cross-validation.