The influence of geometry and draught shields on the performance of passive samplers

Passive samplers provide an excellent opportunity to perform indicative measurements or establish a dense network of measuring sites. A drawback compared with conventional active measuring methods is the larger spread of results. This variation can, to a large extent, be attributed to the influence of temperature, sampler geometry and wind on sampling results. A proper design of sampler geometry and optimum choice of draught shield can reduce the influence of wind velocity on a badge type sampler to less than 10%. Wire mesh screens prove to be inadequate in damping turbulence. Filters give good results. Attention should be paid to the size and isolation value of the walls of the sampler to prevent thermal updrafts occurring within the sampler. Tube type samplers are less influenced by wind, provided that turbulence is prevented from influencing diffusion within the sampler.     

Seasonal variations in nickel and vanadium in Mont Blanc snow and ice dated from the 1960s and 1990s

Ni and V have been determined in snow and ice collected at a high altitude location (Col du D?me) near the summit of Mont Blanc on the French-Italian border; dated from the 1960s and 1990s. Ni and V were simultaneously determined by inductively coupled plasma sector field mass spectrometry. Measured concentrations range from 6 to 700 pg g(-1) and 4 to 1,100 pg g(-1) for Ni and V, respectively. The results show pronounced seasonal variations in the concentrations of both metals, with high concentration values in summer layers and much lower values for winter layers. These seasonal variations are linked especially with the existence of inversion layers during winter months. Ni and V concentrations in excess of the contributions from rock and soil dust (Ni(excess), V(excess)) appear to be mainly associated with anthropogenic inputs, with pronounced seasonal variations. Large variations in the V(excess)/Ni(excess) ratio are observed, with a higher ratio in summer than in winter. This shows differences in anthropogenic inputs at Col du D?me during the different parts of the year. The above ratio was compared with the corresponding ratios for oil combustion from stationary sources and the exhaust from gasoline and diesel engines. It appears that Ni and V concentrations at Col du D?me are probably the result of changing combinations of contributions from oil combustion for power generation, industrial and residential uses, on one side, and automobile and truck traffic, on the other side, with possibly a significant contribution from Ni smelters in Russia during winter months.     

Collaborative study to improve the quality control of rare earth element determinations in environmental matrices

In order to control the quality of rare earth determinations in environmental matrices, the Standards, Measurements and Testing Programme (formerly Community Bureau of Reference, BCR) of the European Commission has started a project, the final aim of which is to certify four types of matrices (tuna muscle, mussel tissue, aquatic plant and estuarine sediment) for their contents of a range of rare earth elements (Sc, Y and the lanthanides: La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb and Lu). The elements U and Th were added to the study. The first part of this project consisted of an interlaboratory study which aimed to test the feasibility of preparation of environmental reference materials and to detect and remove most of the pitfalls observed in rare earth determinations. This paper presents the preparation of the four matrices for the intercomparison study and for the candidate reference material. The main results are presented of the interlaboratory study that was carried out prior to the certification campaign. This collaborative trial is the first attempt ever carried out at this scale to evaluate the state-of-the-art of rare earth determinations in the environment. Its impact on the improvement of chemical measurements will have positive effects on the comparability of data necessary for environmental monitoring.