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The chemical pollution crisis severely threatens human and environmental health globally. To tackle this challenge the establishment of an overarching international science–policy body has recently been suggested. We strongly support this initiative based on the awareness that humanity has already likely left the safe operating space within planetary boundaries for novel entities including chemical pollution. Immediate action is essential and needs to be informed by sound scientific knowledge and data compiled and critically evaluated by an overarching science–policy interface body. Major challenges for such a body are (i) to foster global knowledge production on exposure, impacts and governance going beyond data-rich regions (e.g., Europe and North America), (ii) to cover the entirety of hazardous chemicals, mixtures and wastes, (iii) to follow a one-health perspective considering the risks posed by chemicals and waste on ecosystem and human health, and (iv) to strive for solution-oriented assessments based on systems thinking. Based on multiple evidence on urgent action on a global scale, we call scientists and practitioners to mobilize their scientific networks and to intensify science–policy interaction with national governments to support the negotiations on the establishment of an intergovernmental body based on scientific knowledge explaining the anticipated benefit for human and environmental health.
2022
2015
2010
Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem wherein the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties, e.g., in measurements or atmospheric transport modeling; hence, all state-of-the-art methods are based on some form of regularization of the problem using additional information. We consider two kinds of additional information: the prior source term, also known as the first guess, and regularization parameters for the shape of the source term. While the first guess is based on information independent of the measurements, such as the physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term and regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3.
2020
2001
2021
2022
On the robustness of field calibration for smart air quality monitors
The robustness of field calibrated Air Quality Multi-sensors (AQM) performances to long term and/or mobile operation is still debated. Though accuracy generally exceeds the one of laboratory calibrations models, experimental results show that field calibration models cannot sustain optimal field performances due to changes occurring in operative conditions. Among them, the relocation of calibrated multi-sensors platforms and sensor drift are considered as the most relevant. In this work, we want to provide an answer to the general issue of field calibration robustness assessement. Analysing theoretical foundations and providing tools for determining the calibration model validity domain. In particular, by leveraging the probability distribution of target and interferent gas as well as environmental variables, measures of dissimilarity between calibration and operative phase conditions are considered to quantitatively capture the occurring change. A 6 months multiple nodes dataset including node relocations events in several sites have been processed for deriving nonlinear multivariate field calibrations whose robustness to changing conditions have been analysed. Kullback-Leibler, Euclidean and Hellinger dissimilarity measurements have been correlated with recorded performance degradation. Results show that quantifying relevant factors probability distribution changes allows to explain and predict performances of in field data driven calibration models. They also highlight the role of concept drift in explaining field performances ameliorating our capability to select optimal conditions in which a field calibration should be derived. Finally, smart air quality monitors could now autonomously detect the need for re-calibration.
2020
On the reliability of seasonal snow forecasts
Reliable information on seasonal snow conditions is important for long-range weather forecasting and climate modeling. The reliability of winter-mean hindcasts of snow water equivalent (SWE) produced by the ECMWF for the period 1993–2022 within the CopERnIcus climate change Service Evolution (CERISE) project is evaluated in this study. In probabilistic forecasting, reliability is defined as the consistency between forecast probabilities and observed frequencies for a binary event. Here, reliability is assessed using two independent SWE datasets (ERA5-Land and ESA Snow-CCI v4) across eight land regions in the Northern Hemisphere non-mountainous regions. The reliability assessment is performed for two tercile-based binary events representing low- and high snow accumulation winters. Reliability is quantified using a weighted linear regression applied to reliability diagrams and is grouped into five categories from perfect to dangerous. The results show that the ECMWF seasonal snow hindcasts consistently yield marginally useful to perfect reliability categories for both low- and high-snow conditions independently to the chosen benchmark. The assessment shows sensitivity to the choice of verification dataset, with ERA5-Land yielding higher reliability categories than ESA Snow-CCI, typically 1 to 2 categories higher. It is found that differences in hindcasts reliability between regions and between verification datasets may be linked to snow variability, model representation, and observational uncertainty.
2026
2011
2010
2006
2006
2015
2015
2016
On the coupling between polar and tropical regions during springtime: variability of tropical intrusion and Frozen In Anticyclones. Poster P-1108-04
Abstract : Recent observational and modeling transport studies of Arctic stratospheric final warming have shown that tropical/subtropical air masses can be transported to high latitudes and remain confined within a long-lived ¿frozen- in¿ anticyclone (FriaC), embedded in the summer easterlies for several months. We first present a climatology of these sporadic events over the period 1960-2011 using era-40 and era interim reanalyses. this study highlights stratospheric favorable preconditioning for FrIACs occurrence, that is: i) early and abrupt final warming, ii) no stratospheric major warming during the previous winter, and iii) east phase of the Quasi-Biennial Oscillation. We will present in detail the FriaC in spring 2011, which was the largest ever recorded. Our climatology further suggests that the frequency of occurrence of FriaCs has increased over the last decade (among the nine cases detected over the period 1960- 2011, five occurred between 2002 and 2011). A chemistry climate model is then used for the first time to investigate FriaCs characteristics and variability. simulations were performed with the nCar¿s Community earth system Model (CesM, version 1.0.2), a coupled model system including the Whole Atmosphere Community Climate Model (WACCM). FrIACs characteristics (i.e. spatial extent and duration), are overall consistent by comparing with FriaCs detected era-40 meteorological reanalyses. Dynamical analysis reveals that FriaCs are associated with an abrupt and early winter-to-summer stratospheric circulation transition, characterized by an amplification of planetary wave activity. Furthermore, our model results confirm that FrIACs occur preferentially under the easterly phase of the QBO and in absence of MSW during the preceding
2015
Large-eddy simulation (LES) experiments have been performed using the Parallelized LES Model (PALM). A methodology for validating and understanding LES results for plume dispersion and concentration fluctuations in an atmospheric-like flow is presented. A wide range of grid resolutions is shown to be necessary for investigating the convergence of statistical characteristics of velocity and scalar fields. For the scalar, the statistical moments up to the fourth order and the shape of the concentration probability density function (p.d.f.) are examined. The mean concentration is influenced by grid resolution, with the highest resolution simulation showing a lower mean concentration, linked to larger turbulent structures. However, a clear tendency to convergence of the concentration variance is observed at the two higher resolutions. This behaviour is explained by showing that the mechanisms driving the mean and the variance are differently influenced by the grid resolution. The analysis of skewness and kurtosis allows also the obtaining of general results on plume concentration fluctuations. Irrespective of grid resolution, a family of Gamma p.d.f.s well represents the shape of the concentration p.d.f. but only beyond the peak of the concentration fluctuation intensity. In the early plume dispersion phases, the moments of the p.d.f. are in good agreement with those generated by a fluctuating plume model. To the best of our knowledge, our study demonstrates for the first time that, if resolution and averaging time are adequate, atmospheric LES provides a trustworthy representation of the high order moments of the concentration field, up to the fourth order, for a dispersing plume.
2020
2019