Light-absorbing carbon in Europe – Measurement and modelling, with a focus on residential wood combustion emissions

The atmospheric concentration of elemental carbon (EC) in Europe during the six-year period 2005–2010 has been simulated with the EMEP MSC-W model. The model bias compared to EC measurements was less than 20% for most of the examined sites. The model results suggest that fossil fuel combustion is the dominant source of EC in most of Europe but that there are important contributions also from residential wood burning during the cold seasons and, during certain episodes, also from open biomass burning (wildfires and agricultural fires). The modelled contributions from open biomass fires to ground level concentrations of EC were small at the sites included in the present study, <3% of the long-term average of EC in PM10. The modelling of this EC source is subject to many uncertainties, and it was likely underestimated for some episodes. EC measurements and modelled EC were also compared to optical measurements of black carbon (BC). The relationships between EC and BC (as given by mass absorption cross section, MAC, values) differed widely between the sites, and the correlation between observed EC and BC is sometimes poor, making it difficult to compare results using the two techniques and limiting the comparability of BC measurements to model EC results. A new bottom-up emission inventory for carbonaceous aerosol from residential wood combustion has been applied. For some countries the new inventory has substantially different EC emissions compared to earlier estimates. For northern Europe the most significant changes are much lower emissions in Norway and higher emissions in neighbouring Sweden and Finland. For Norway and Sweden, comparisons to source-apportionment data from winter campaigns indicate that the new inventory may improve model-calculated EC from wood burning. Finally, three different model setups were tested with variable atmospheric lifetimes of EC in order to evaluate the model sensitivity to the assumptions regarding hygroscopicity and atmospheric ageing of EC. The standard ageing scheme leads to a rapid transformation of the emitted hydrophobic EC to hygroscopic particles, and generates similar results when assuming that all EC is aged at the point of emission. Assuming hydrophobic emissions and no ageing leads to higher EC concentrations. For the more remote sites, the observed EC concentration was in between the modelled EC using standard ageing and the scenario treating EC as hydrophobic. This could indicate too-rapid EC ageing in the model in relatively clean parts of the atmosphere

Early growth response 1 regulates hematopoietic support and proliferation in human primary bone marrow stromal cells

Human bone marrow stromal cells (BMSC) are key elements of the hematopoietic environment and they play a central role in bone and bone marrow physiology. However, how key stromal cell functions are regulated is largely unknown. We analyzed the role of the immediate early response transcription factor EGR1 as key stromal cell regulator and found that EGR1 was highly expressed in prospectivelyisolated primary BMSC, down-regulated upon culture, and low in noncolony-forming CD45neg stromal cells. Furthermore, EGR1 expression was lower in proliferative regenerating adult and fetal primary cells compared to adult steady-state BMSC. Overexpression of EGR1 in stromal cells induced potent hematopoietic stroma support as indicated by an increased production of transplantable CD34+ CD90+ hematopoietic stem cells in expansion co-cultures. The improvement in bone marrow stroma support function was mediated by increased expression of hematopoietic supporting genes, such as VCAM1 and CCL28. Furthermore, EGR1 overexpression markedly decreased stromal cell proliferation whereas EGR1 knoc

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

On Statistical Model Checking of Stochastic Systems

Statistical methods to model check stochastic systems have been, thus far, developed only for a sublogic of continuous stochastic logic (CSL) that does not have steady state operators and unbounded until formulas. In this paper, we present a statistical model checking algorithm that also verifies CSL formulas with unbounded untils. The algorithm is based on Monte Carlo simulation of the model and hypothesis testing of the samples, as opposed to sequential hypothesis testing. The use of statistical hypothesis testing allows us to exploit the inherent parallelism in this approach. We have implemented the algorithm in a tool called VESTA, and found it to be effective in verifying several examples

Laboratory simulations and parameterization of the primary marine aerosol production

A major source of the primary marine aerosol is the bursting of air bubbles produced by breaking waves. Several source parameterizations are available from the literature, usually limited to particles with a dry diameter Dp > 1 μm. The objective of this work is to extend the current knowledge to submicrometer particles. Bubbles were generated in synthetic seawater using a sintered glass filter, with a size spectra that are only partly the same spectra as measured in the field. Bubble spectra, and size distributions of the resulting aerosol (0.020-20.0 μm Dp) of the resulting aerosol, were measured for different salinity, water temperature (Tw), and bubble flux. The spectra show a minimum at ∼1 μm Dp, which separates two modes, one at ∼0.1 μm, with the largest number of particles, and one at 2.5 μm Dp. The modes show different behavior with the variation of salinity and water temperature. When the water temperature increases, the number concentration Np decreases for Dp 0.35 μm, Np increases. The salinity effect suggests different droplet formation processes for droplets smaller and larger than 0.2 μm Dp. The number of particles produced per size increment, time unit, and whitecap surface (φ) is described as a linear function of Tw and a polynomial function of Dp. Combining φ with the whitecap coverage fraction W (in percent), an expression results for the primary marine aerosol source flux dFo/dlogDp = W φ (m-2 s-1 ). The results are compared with other commonly used formulations as well as with recent field observations. Implications for aerosol-induced effects on climate are discussed

Characterizing the international carbon capture and storage community

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Cirrus Cloud Occurrence as Function of Ambient Relative Humidity: A Comparison of Observations Obtained during the INCA Experiment

Based on in-situ observations performed during the Interhemispheric differences in cirrus properties from anthropogenic emissions (INCA) experiment, we introduce and discuss the cloud presence fraction (CPF) defined as the ratio between the number of data points determined to represent cloud at a given ambient relative humidity over ice (RHI) divided by the total number of data points at that value of RHI. The CPFs are measured with four different cloud probes. Within similar ranges of detected particle sizes and concentrations, it is shown that different cloud probes yield results that are in good agreement with each other. The CPFs taken at Southern Hemisphere (SH) and Northern Hemisphere (NH) midlatitudes differ from each other. Above ice saturation, clouds occurred more frequently during the NH campaign. Local minima in the CPF as a function of RHI are interpreted as a systematic underestimation of cloud presence when cloud particles become invisible to cloud probes. Based on this interpretation, we find that clouds during the SH campaign formed preferentially at RHIs between 140 and 155%, whereas clouds in the NH campaign formed at RHIs somewhat below 130%. The data show that interstitial aerosol and ice particles coexist down to RHIs of 70–90%, demonstrating that the ability to distinguish between different particle types in cirrus conditions depends on the sensors used to probe the aerosol/cirrus system. Observed distributions of cloud water content differ only slightly between the NH and SH campaigns and seem to be only weakly, if at all, affected by the freezing aerosols