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2012
How Atmospheric Chemistry and Transport Drive Surface Variability of N2O and CFC-11
Nitrous oxide (N2O) is a long-lived greenhouse gas that affects atmospheric chemistry and climate. In this work, we use satellite measurements of N2O, ozone (O3), and temperature from the Aura Microwave Limb Sounder (MLS) instrument to calculate stratospheric loss of N2O, and thus its atmospheric lifetime. Using three chemistry transport models simulating the Aura period 2005–2017, we verify the stratospheric sink using MLS data and follow that loss signal down to the surface and compare with surface observations. Stratospheric loss has a strong seasonal cycle and is further modulated by the Quasi-Biennial Oscillation (QBO); these cycles are seen equally in both observations and the models. When filtered for interannual variability, the modeled surface signal is QBO-caused, and it reproduces the observed pattern, highlighting the potential role of the QBO in tropospheric chemistry and composition, as well as in model evaluation. The observed annual surface signal in the northern hemisphere matches well with the models run without emissions, indicating the annual cycle is driven mostly by stratosphere-troposphere exchange (STE) flux of N2O-depleted air and not surface N2O emissions. In the southern hemisphere (SH), all three models disagree and thus provide no guidance, except for indicating that modeling annual STE in the SH remains a major model uncertainty. Parallel model simulations of CFCl3, which has greater stratospheric loss that N2O and possibly surreptitious emissions, show that its interannual variations parallel those of N2O, and thus the observed N2O variability can identify the stratospheric component of the observed CFCl3 variability.
American Geophysical Union (AGU)
2021
2024
2016
2014
2008
Thyroid hormone disrupting chemicals (THDCs) are of major concern in ecotoxicology. With the increased number of emerging chemicals on the market there is a need to screen for potential THDCs in a cost-efficient way, and in silico modeling is an alternative to address this issue. In this study homology modeling and docking was used to screen a list of 626 compounds for potential thyroid hormone disrupting properties in two gull species. The tested compounds were known contaminants or emerging contaminants predicted to have the potential to reach the Arctic. Models of transthyretin (TTR) and thyroid hormone receptor α and β (TRα and TRβ) from the Arctic top predator glaucous gull (Larus hyperboreus) and temperate predator herring gull (Larus argentatus) were constructed and used to predict the binding affinity of the compounds to the thyroid hormone (TH) binding sites. The modeling predicted that 28, 4 and 330 of the contaminants would bind to TRα, TRβ and TTR respectively. These compounds were in general halogenated, aromatic and had polar functional groups, like that of THs. However, the predicted binders did not necessarily have all these properties, such as the per- and polyfluoroalkyl substances that are not aromatic and still bind to the proteins.
Elsevier
2020