Atmospheric trace element concentrations were measured from March 1999 to December 2003 at the Air Chemistry Observatory of the German Antarctic station Neumayer, by inductively coupled plasma–quadrupol mass spectrometry (ICP–QMS) and ion chromatography (IC). This continuous five‐year long record derived from weekly aerosol sampling revealed a distinct seasonal summer maximum for elements linked with mineral dust entry (Al, La, Ce, Nd) and a winter maximum for the mostly sea salt derived elements Li, Na, K, Mg, Ca and Sr. The relative seasonal amplitude was around 1.7 and 1.4 for mineral dust (La) and sea salt aerosol (Na), respectively. On average, a significant deviation regarding mean ocean water composition was apparent for Li, Mg and Sr, which could hardly be explained by mirabilite precipitation on freshly formed sea ice. In addition, we observed all over the year, a not clarified high variability of element ratios Li/Na, K/Na, Mg/Na, Ca/Na and Sr/Na. We found an intriguing co‐variation of Se concentrations with biogenic sulphur aerosols (methane sulphonate and non‐sea salt sulphate), indicating a dominant marine biogenic source for this element, linked with the marine biogenic sulphur source.

Atmospheric trace element concentrations were measured from March 1999 through December 2003 at the Air Chemistry Observatory of the German Antarctic station Neumayer by inductively coupled plasma -quadrupol mass spectrometry (ICP-QMS) and ion chromatography (IC). This continuous five year long record derived from weekly aerosol sampling revealed a distinct seasonal summer maximum for elements linked with mineral dust entry (Al, La, Ce, Nd) and a winter maximum for the mostly sea salt derived elements Li, Na, K, Mg, Ca, and Sr. The relative seasonal amplitude was around 1.7 and 1.4 for mineral dust (La) and sea salt aerosol (Na), respectively. On average a significant deviation regarding mean ocean water composition was apparent for Li, Mg, and Sr which could hardly be explained by mirabilite precipitation on freshly formed sea ice. In addition we observed all over the year a not clarified high variability of element ratios Li/Na, K/Na, Mg/Na, Ca/Na, and Sr/Na. We found an intriguing co-variation of Se concentrations with biogenic sulfur aerosols (methane sulfonate and non-sea salt sulfate), indicating a dominant marine biogenic source for this element linked with the marine biogenic sulfur source.

European regulatory frameworks for chemicals (i.e. registered under REACH, plant protection products (PPPs), biocides, human and veterinary medicinal products) require that substances undergo an assessment to identify whether they are persistent (P), bioaccumulative (B) and toxic (T), or very persistent (vP) and very bioaccumulative (vB), i.e. to identify them as PBT substances or vPvB substances according to their properties. We screened current practices, evaluated possibilities and made a proposal for a harmonised assessment. Our proposal assumes that it should be possible to identify PBT and vPvB substances on the basis of the data available according to the requirements of the respective legal frameworks. For substances registered as PPPs and mostly also biocides and medicinal products, a 'definitive assessment' is often possible. Within REACH, the registrant has to provide all information necessary for PBT assessment regardless of the yearly tonnage of chemicals. But in cases of limited data availability, we suggest using a weight of evidence approach to account for such differences in data availability and type of data across different frameworks and to make use of valuable additional information. We propose to base the evaluation of persistence on degradation half-lives and to normalise a number of parameters (e.g. type of kinetics used, temperature). But further work is needed, e.g. for deriving DegT 50 for water and sediment compartments. For the B-criterion, information other than BCF in fish could be considered and more information related to bioaccumulation processes should be gathered (e.g. in species other than fish, different uptake routes). Testing for T identification is focused on standard aquatic species but could also be complemented by e.g. information from other species. Information such as those read-across from structurally related substances and QSAR are often of importance for screening assessments. The aim of PBT and vPvB identification is to reliably target the problematic substances, with as few false negatives or positives as possible, regardless of the regulatory framework. Each aspect was thus considered in the context of the others for a final balanced decision. As the need for conservatism is interpreted differently under the various frameworks, harmonizing this identification is a challenging task.

BackgroundFor evaluating the fate of xenobiotics in the environment, a variety of degradation or environmental metabolism experiments are routinely conducted. The data generated in such experiments are evaluated by optimizing the parameters of kinetic models in a way that the model simulation fits the data. No comparison of the main software tools currently in use has been published to date. This article shows a comparison of numerical results as well as an overall, somewhat subjective comparison based on a scoring system using a set of criteria. The scoring was separately performed for two types of uses. Uses of type I are routine evaluations involving standard kinetic models and up to three metabolites in a single compartment. Evaluations involving non-standard model components, more than three metabolites or more than a single compartment belong to use type II. For use type I, usability is most important, while the flexibility of the model definition is most important for use type II.ResultsTest datasets were assembled that can be used to compare the numerical results for different software tools. These datasets can also be used to ensure that no unintended or erroneous behaviour is introduced in newer versions. In the comparison of numerical results, good agreement between the parameter estimates was observed for datasets with up to three metabolites. For the now unmaintained reference software DegKinManager/ModelMaker, and for OpenModel which is still under development, user options were identified that should be taken care of in order to obtain results that are as reliable as possible. Based on the scoring system mentioned above, the software tools gmkin, KinGUII and CAKE received the best scores for use type I. Out of the 15 software packages compared with respect to use type II, again gmkin and KinGUII were the first two, followed by the script based tool mkin, which is the technical basis for gmkin, and by OpenModel.ConclusionsBased on the evaluation using the system of criteria mentioned above and the comparison of numerical results for the suite of test datasets, the software tools gmkin, KinGUII and CAKE are recommended for use type I, and gmkin and KinGUII for use type II. For users that prefer to work with scripts instead of graphical user interfaces, mkin is recommended. For future software evaluations, it is recommended to include a measure for the total time that a typical user needs for a kinetic evaluation into the scoring scheme. It is the hope of the authors that the publication of test data, source code and overall rankings foster the evolution of useful and reliable software in the field.Electronic supplementary materialThe online version of this article (10.1186/s12302-018-0145-1) contains supplementary material, which is available to authorized users.

When data on the degradation of a chemical substance have been collected in a number of environmental media (e.g., in different soils), two strategies can be followed for data evaluation. Currently, each individual dataset is evaluated separately, and representative degradation parameters are obtained by calculating averages of the kinetic parameters. However, such averages often take on unrealistic values if certain degradation parameters are ill-defined in some of the datasets. Moreover, the most appropriate degradation model is selected for each individual dataset, which is time consuming and then requires workarounds for averaging parameters from different models. Therefore, a simultaneous evaluation of all available data is desirable. If the environmental media are viewed as random samples from a population, an advanced strategy based on assumptions about the statistical distribution of the kinetic parameters across the population can be used. Here, we show the advantages of such simultaneous evaluations based on nonlinear mixed-effects models that incorporate such assumptions in the evaluation process. The advantages of this approach are demonstrated using synthetically generated data with known statistical properties and using publicly available experimental degradation data on two pesticidal active substances.