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A swarm of semi-classical quantum mechanics/molecular mechanics molecular-dynamics simulations where OM2/MNDO is combined with the Gromacs program for consideration of explicit water is performed, solving the time-dependent Schrödinger equation in each step of the trajectories together with the Tully's fewest switches algorithm. Within this stochastic treatment, time dependent probabilities of the three lowest electronic states are determined. The fact that nucleobases are quickly deactivated is confirmed in the cytosine case where our best lifetime estimation is τ 1 =0.82 ps for the model with 100 water molecules with the SPCE force field and a time step of 0.1 fs. Lifetimes of the remaining molecules are visibly longer: 5-azacytosine, 2,4-diamino-1,3,5-triazine (DT), and 2,4,6-triamino-1,3,5-triazine (TT) molecules have an S 1 → S 0 de-excitation time of slightly above 10 ps. The lifetimes of the triazine family increases with the increasing number of exocyclic amino groups, that is, s -triazine < 2-amino-1,3,5-triazine < DT < TT. This can be explained by a higher mobility of the carbon-bonded hydrogen atoms in comparison with heavier amino groups since their movement is slowed down due to a substantially higher mass than hydrogen atoms, which can easier reach the out-of-plane positions required in the conical intersection structures. Moreover, bulkier NH 2 ligands suffer due to greater friction caused by the surrounding water environment. These mechanical aspects caused a change in the explored lifetime dependences in comparison with our previous gas-phase study.

We combine observations from Western USA and inverse modelling to constrain global atmospheric emissions of microplastics (MPs) and microfibers (MFs). The latter are used further to model their global atmospheric dynamics. Global annual MP emissions were calculated as 9.6 ± 3.6 Tg and MF emissions as 6.5 ± 2.9 Tg. Global average monthly MP concentrations were 47 ng m -3 and 33 ng m -3 for MFs, at maximum. The largest deposition of agricultural MPs occurred close to the world's largest agricultural regions. Road MPs mostly deposited in the East Coast of USA, Central Europe, and Southeastern Asia; MPs resuspended with mineral dust near Sahara and Middle East. Only 1.8% of the emitted mass of oceanic MPs was transferred to land, and 1.4% of land MPs to ocean; the rest were deposited in the same environment. Previous studies reported that 0.74-1.9 Tg y -1 of land-based atmospheric MPs/MFs (< 5 mm) are transported to the ocean, while riverine transport is between 3.3 and 14 Tg y -1 . We calculate that 0.418 ± 0.201 Tg y -1 MPs/MFs (size up to 250 and 2500 µm) were transported from the land to ocean (large particles were ignored). Model validation against observations showed that particle removal must be urgently updated in global models., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Estimation of a source term, i.e. release rate, of atmospheric radionuclide emissions is of key interest for nuclear emergency response and further accident analysis. The source term estimate is, however, often very inaccurate due to biases in atmospheric transport and used meteorological analysis. We propose a method for atmospheric plume bias correction which uses not only concentrations modeled at a measuring site but also the information on concentration gradient from the neighborhood of each measuring site, i.e. information already available from the atmospheric transport model. To properly regularize the model, we propose an elastic model of the plume bias correction based on regularization with the use of known topology of the measurement network. The proposed plume bias correction method can be coupled with an arbitrary source term estimation algorithm and can be instantly applied to any other atmospheric release of hazardous material. We demonstrate the method in two real cases. First, we use data from the European Tracer Experiment to validate the methodology. Second, we use data from the 106 Ru occurrence over Europe in 2017 to demonstrate the methodology in a more demanding case where agreement with state-of-the-art estimates is shown with much better reconstruction of measurements., (Copyright © 2021 Elsevier B.V. All rights reserved.)