Chlorine isotope evidence for the anthropogenic origin of tris-(4-chlorophenyl)methane

Compound-specific Cl-isotope analysis was performed on the persistent and bioaccumulating compound tris-(4-chlorophenyl)methane (4,4′,4″-TCPMe, referred to as TCPMe in this study) to elucidate whether its main source is natural or anthropogenic. Blubber from the Baltic grey seal (Halichoerus grypus) was extracted by continuous acetonitrile partitioning, and the TCPMe was isolated from the extract by preparative-capillary gas chromatography. Chlorine isotope analysis was subsequently performed by sealed-tube combustion in conjunction with thermal-ionization mass spectrometry (TIMS). The δ³⁷Cl of TCPMe was −3.5 ± 0.5‰, similar to the previously reported δ³⁷Cl of technical grade p,p′-DDT (referred to as DDT in this study). The data is not consistent with a putative marine natural source of TCPMe, as enzymatic (biotic) production is reported to give values of δ³⁷Cl < −10‰. The δ³⁷Cl–TCPMe data thus supports the hypothesis that TCPMe is produced as a byproduct during DDT synthesis and is released to the environment through the same pathways as DDT. It is also consistent with tris-(4-chlorophenyl)methanol as the primary biotransformation product of TCPMe.     

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning

We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012?–?2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude \({>}\,\mbox{M1}\) and \({>}\,\mbox{C1}\) within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy \(\mathrm{ACC}=0.93(0.00)\), true skill statistic \(\mathrm{TSS}=0.74(0.02)\), and Heidke skill score \(\mathrm{HSS}=0.49(0.01)\) for \({>}\,\mbox{M1}\) flare prediction with probability threshold 15% and \(\mathrm{ACC}=0.84(0.00)\), \(\mathrm{TSS}=0.60(0.01)\), and \(\mathrm{HSS}=0.59(0.01)\) for \({>}\,\mbox{C1}\) flare prediction with probability threshold 35%.     

The influence of anisotropy on compaction bands: The case of coaxiality between stress and fabric anisotropy tensors

Compaction bands are localized failure patterns that appear in highly porous rock material under the effect of relatively high confining pressure. Being affected mainly by volumetric compression, these bands appear to be almost perpendicular to the most compressive principal stress of a stress state at the so-called “cap” of the yield surface (YS). In this study, we focus on the mechanism that leads to the onset of compaction bands by using a viscoplasticity model able to describe the post-localization response of these materials. The proposed constitutive framework is based on the overstress theory of Perzyna (1966) and the anisotropic clay plasticity model of Dafalias (1986), which provides not only the necessary “cap” of the YS, but introduces a rotational hardening (RH) mechanism, thus, accounting for the effect of fabric anisotropy. Following the analysis of Veveakis and Regenauer-Lieb (2015), we identify the compaction bands as “static” cnoidal wave formations in the medium that occur at a post-yield regime, and we study the effect of rotational and isotropic hardening on their onset. Moreover, we determine a theoretical range of confining pressures in triaxial compression tests for the compaction bands to develop. Under the assumption of coaxiality between stress and anisotropy tensors, the results show that the isotropic hardening promotes compaction localization, whereas the RH has a slightly negative effect on the onset of compaction localization.     

The COVID-19 pandemic and global environmental change: Emerging research needs

The outbreak of COVID-19 raised numerous questions on the interactions between the occurrence of new infections, the environment, climate and health. The European Union requested the H2020 HERA project which aims at setting priorities in research on environment, climate and health, to identify relevant research needs regarding Covid-19. The emergence and spread of SARS-CoV-2 appears to be related to urbanization, habitat destruction, live animal trade, intensive livestock farming and global travel. The contribution of climate and air pollution requires additional studies. Importantly, the severity of COVID-19 depends on the interactions between the viral infection, ageing and chronic diseases such as metabolic, respiratory and cardiovascular diseases and obesity which are themselves influenced by environmental stressors. The mechanisms of these interactions deserve additional scrutiny. Both the pandemic and the social response to the disease have elicited an array of behavioural and societal changes that may remain long after the pandemic and that may have long term health effects including on mental health. Recovery plans are currently being discussed or implemented and the environmental and health impacts of those plans are not clearly foreseen. Clearly, COVID-19 will have a long-lasting impact on the environmental health field and will open new research perspectives and policy needs.     

Viscoelastic analysis of piles and pile groups

The axial and lateral load–displacement response of single piles and pile groups during the secondary consolidation (creep) phase of deformation is obtained by idealizing the soil as a linear viscoelastic material. The boundary element method is used to model the soil halfspace and the piles, which are assumed to behave elastically, are idealized as beam-columns. By using the correspondence principle, the problem is solved in the Laplace transform domain and the final solution is obtained by numerical inversion of the transformed data. The salient features of the viscoelastic response are identified in several non-dimensional plots which encompass the spectrum of geometrical and material parameters commonly encountered in practice. The results of the analysis compare favourably with the limited data available in the literature.     

Testing and Improving a Set of Morphological Predictors of Flaring Activity

Efficient prediction of solar flares relies on parameters that quantify the eruptive capability of solar active regions. Several such quantitative predictors have been proposed in the literature, inferred mostly from photospheric magnetograms and/or white-light observations. Two of them are the Ising energy and the sum of the total horizontal magnetic field gradient. The former has been developed from line-of-sight magnetograms, while the latter uses sunspot detections and characteristics, based on continuum images. Aiming to include these parameters in an automated prediction scheme, we test their applicability on regular photospheric magnetic field observations provided by the Helioseismic and Magnetic Imager (HMI) instrument onboard the Solar Dynamics Observatory (SDO). We test their efficiency as predictors of flaring activity on a representative sample of active regions and investigate possible modifications of these quantities. The Ising energy appears to be an efficient predictor, and the efficiency is even improved if it is modified to describe interacting magnetic partitions or sunspot umbrae. The sum of the horizontal magnetic field gradient appears to be slightly more promising than the three variations of the Ising energy we implement in this article. The new predictors are also compared with two very promising predictors: the effective connected magnetic field strength and the total unsigned non-neutralized current. Our analysis shows that the efficiency of morphological predictors depends on projection effects in a nontrivial way. All four new predictors are found useful for inclusion in an automated flare forecasting facility, such as the Flare Likelihood and Region Eruption Forecasting (FLARECAST), but their utility, among others, will ultimately be determined by the validation effort underway in the framework of the FLARECAST project.     

Palaeoflood discharge estimation using dendrogeomorphic methods,rainfall-runoff and hydraulic modelling—a case study from southern Crete

Natural Hazards - This study represents the first palaeoflood discharge estimation using dendrogeomorphic evidence from two ungauged catchments in southern Crete. Dendrogeomorphological methods,...