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.     

One-year monitoring survey of organic compounds (PAHs, PCBs, TBT), heavy metals and biomarkers in blue mussels from the Arcachon Bay, France

Marine mussels Mytilus sp. were transplanted on a monthly basis in cages over one year to oyster farms and harbours in the Arcachon Bay (France) in order to assess the water quality of the bay. Contaminant levels (organotin compounds, trace metals, PCBs and PAHs) were measured in tissues of transplanted mussels and mussels from a reference station, along with physiological parameters of the mussels (condition indexes, lipid content and dry weight). Four biomarkers (AChE: acetylcholinesterase activity, GST: gluthathione S-transferase activity, CAT: catalase activity and TBARS: thiobarbituric acid-reactive substance content) were also monitored. The remote stations monitored (oyster parks) exhibited no accumulation pattern of pollutants. Their respective concentrations therefore constitute a background level of the contamination in the bay ([TBT]= 30 ng Sn g(-1) dw, [SigmaHAPs]= 100 ng g(-1) dw, [SigmaPCBs]= 35 ng g(-1) dw). The elevated chemical contamination of the largest harbour of the bay, the Arcachon harbour, can be interpreted in terms of persistence of organotin compounds ([SigmaOTs]= 1500-2000 ng Sn g(-1) dw) and PAHs ([SigmaHAPs]= 4500-5000 ng g(-1) dw) in sediments and, to a lesser extent, of direct inputs of copper ([Cu]= 20 microg g(-1) dw in harbours versus 7 in oyster parks) and petrogenic PAHs ([methylphenanthrenes]= 1600 ng g(-1) dw in the dockyard versus 170 at the gas stations), related to the use of copper-based antifouling paints and to dockyard activity, respectively. However, the Arcachon Bay presents a low contamination level by PCBs and metals, including harbour stations. Furthermore, higher levels of other PAHs (particularly alkyl PAHs such as methylphenanthrenes/1600 ng g(-1) dw) not included in the 16 PAHs from the EPA priority list (usually studied in biomonitoring programmes/1500 ng g(-1) dw) in the Arcachon harbour underline the need to integrate these compounds in biomonitoring of highly PAH-polluted areas such as harbours in order to avoid misinterpretation of the biological responses observed. Biomarker responses were not able to discriminate the different chemical contamination levels recorded in the Arcachon Bay and rather reflected changes in environmental factors. Furthermore, the strong intraspecies variability of biological responses could be due to genetic differences of mussels from the Arcachon Bay. It is the first time that such an integrated monitoring is performed in the Arcachon Bay, also taking into account seasonal variations of chemical contents and biomarkers levels in mussel tissues.     

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.     

Application of a checklist for quality assistance in environmental modelling to an energy model

Large, complex energy models present considerable challenges to develop and test. Uncertainty assessments of such models provide only partial guidance on the quality of the results. We have developed a model quality assistance checklist to aid in this purpose. The model checklist provides diagnostic output in the form of a set of pitfalls for the model application. The checklist is applied here to an energy model for the problem of assessing energy use and greenhouse gas emissions. Use of the checklist suggests that results on this issue are contingent on a number of assumptions that are highly value-laden. When these assumptions are held fixed, the model is deemed capable of producing moderately robust results of relevance to climate policy over the longer term. Checklist responses also indicate that a number of details critical to policy choices or outcomes on this issue are not captured in the model, and model results should therefore be supplemented with alternative analyses.