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Accurate modeling of ash clouds from volcanic eruptions requires knowledge about the eruption source parameters including eruption onset, duration, mass eruption rates, particle size distribution, and vertical-emission profiles. However, most of these parameters are unknown and must be estimated somehow. Some are estimated based on observed correlations and known volcano parameters. However, a more accurate estimate is often needed to bring the model into closer agreement with observations. This paper describes the inversion procedure implemented at the Norwegian Meteorological Institute for estimating ash emission rates from retrieved satellite ash column amounts and a priori knowledge. The overall procedure consists of five stages: (1) generate a priori emission estimates, (2) run forward simulations with a set of unit emission profiles, (3) collocate/match observations with emission simulations, (4) build system of linear equations, and (5) solve overdetermined systems. We go through the mathematical foundations for the inversion procedure, performance for synthetic cases, and performance for real-world cases. The novelties of this paper include a memory efficient formulation of the inversion problem, a detailed description and illustrations of the mathematical formulations, evaluation of the inversion method using synthetic known-truth data as well as real data, and inclusion of observations of ash cloud-top height. The source code used in this work is freely available under an open-source license and is able to be used for other similar applications.

This paper outlines the structure and usage of the GenChem system, which includes a chemical pre-processor GenChem.py) and a simple box model (boxChem). GenChem provides scripts and input files for converting chemical equations into differential form for use in atmospheric chemical transport models (CTMs) and/or the boxChem system. Although GenChem is primarily intended for users of the Meteorological Synthesizing Centre – West of the European Monitoring and Evaluation Programme (EMEP MSC-W) CTM and related systems, boxChem can be run as a stand-alone chemical solver, enabling for example easy testing of chemical mechanisms against each other. This paper presents an outline of the usage of the GenChem system, explaining input and output files, and presents some examples of usage. The code needed to run GenChem is released as open-source code under the GNU license.

A description of the new air quality downscaling model – the urban EMEP (uEMEP) and its combination with the EMEP MSC-W model (European Monitoring and Evaluation Programme Meteorological Synthesising Centre West) – is presented. uEMEP is based on well-known Gaussian modelling principles. The uniqueness of the system is in its combination with the EMEP MSC-W model and the “local fraction” calculation contained within it. This allows the uEMEP model to be imbedded in the EMEP MSC-W model and downscaling can be carried out anywhere within the EMEP model domain, without any double counting of emissions, if appropriate proxy data are available that describe the spatial distribution of the emissions. This makes the model suitable for high-resolution calculations, down to 50 m, over entire countries. An example application, the Norwegian air quality forecasting and assessment system, is described where the entire country is modelled at a resolution of between 250 and 50 m. The model is validated against all available monitoring data, including traffic sites, in Norway. The results of the validation show good results for NO2, which has the best known emissions, and moderately good for PM10 and PM2.5. In Norway, the largest contributor to PM, even in cities, is long-range transport followed by road dust and domestic heating emissions. These contributors to PM are more difficult to quantify than NOx exhaust emission from traffic, which is the major contributor to NO2 concentrations. In addition to the validation results, a number of verification and sensitivity results are summarised. One verification showed that single annual mean calculations with a rotationally symmetric dispersion kernel give very similar results to the average of an entire year of hourly calculations, reducing the runtime for annual means by 4 orders of magnitude. The uEMEP model, in combination with EMEP MSC-W model, provides a new tool for assessing local-scale concentrations and exposure over large regions in a consistent and homogenous way and is suitable for large-scale policy applications.

A large fraction of the urban population in Europe is exposed to particulate matter levels above the WHO guideline value. To make more effective mitigation strategies, it is important to understand the influence on particulate matter (PM) from pollutants emitted in different European nations. In this study, we evaluate a country source contribution forecasting system aimed at assessing the domestic and transboundary contributions to PM in major European cities for an episode in December 2016. The system is composed of two models (EMEP/MSC-W rv4.15 and LOTOS-EUROS v2.0), which allows the consideration of differences in the source attribution. We also compared the PM10 concentrations, and both models present satisfactory agreement in the 4 d forecasts of the surface concentrations, since the hourly concentrations can be highly correlated with in situ observations. The correlation coefficients reach values of up to 0.58 for LOTOS-EUROS and 0.50 for EMEP for the urban stations; the values are 0.58 for LOTOS-EUROS and 0.72 for EMEP for the rural stations. However, the models underpredict the highest hourly concentrations measured by the urban stations (mean underestimation of 36 %), which is to be expected given the relatively coarse model resolution used (0.25∘ longitude × 0.125∘ latitude). For the source attribution calculations, LOTOS-EUROS uses a labelling technique, while the EMEP/MSC-W model uses a scenario having reduced anthropogenic emissions, and then it is compared to a reference run where no changes are applied. Different percentages (5 %, 15 %, and 50 %) for the reduced emissions in the EMEP/MSC-W model were used to test the robustness of the methodology. The impact of the different ways to define the urban area for the studied cities was also investigated (i.e. one model grid cell, nine grid cells, and grid cells covering the definition given by the Global Administrative Areas – GADM). We found that the combination of a 15 % emission reduction and a larger domain (nine grid cells or GADM) helps to preserve the linearity between emission and concentrations changes. The nonlinearity, related to the emission reduction scenario used, is suggested by the nature of the mismatch between the total concentration and the sum of the concentrations from different calculated sources. Even limited, this nonlinearity is observed in the NO3-, NH4+, and H2O concentrations, which is related to gas–aerosol partitioning of the species. The use of a 15 % emission reduction and of a larger city domain also causes better agreement on the determination of the main country contributors between both country source calculations. Over the 34 European cities investigated, PM10 was dominated by domestic emissions for the studied episode (1–9 December 2016). The two models generally agree on the dominant external country contributor (68 % on an hourly basis) to PM10 concentrations. Overall, 75 % of the hourly predicted PM10 concentrations of both models have the same top five main country contributors. Better agreement on the dominant country contributor for primary (emitted) species (70 % is found for primary organic matter (POM) and 80 % for elemental carbon – EC) than for the inorganic secondary component of the aerosol (50 %), which is predictable due to the conceptual differences in the source attribution used by both models. The country contribution calculated by the scenario approach depends on the chemical regime, which largely impacts the secondary components, unlike the calculation using the labelling approach.

Pedagogical content knowledge (PCK) consists of a set of understandings, knowledge, skills, and dispositions necessary for effective performance in specific teaching and learning situations. Using Scopus, EBSCO, and Web of Science databases, the study examines the progress of the PCK in science teacher education between 2011 and 2021. In total, 59 articles were reviewed, and 13 were selected according to the inclusion criteria. Among the findings, it stands out that the articles emphasize a series of tools used when teaching applied sciences, such as the use of educational technologies beyond the textbook or the integration of students' thinking. The articles state that PCK transcends subject knowledge and leads to subject knowledge for teaching. Finally, the literature has tried to answer how science teachers use PCK in the classroom, demonstrating strategies and practical value, both of which are vital for the functioning and application of their educational work.

This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in the MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The paper gives an overall picture of its status at the end of MACC-II (summer 2014) and analyses the performance of the multi-model ensemble. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs (non-methane volatile organic compounds) and PAN+PAN precursors) over eight vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performance of the system is assessed daily, weekly and every 3 months (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the ensemble median to forecast regional ozone pollution events. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10. The statistical indicators for ozone in summer 2014 show that the ensemble median gives on average the best performances compared to the seven models. There is very little degradation of the scores with the forecast day but there is a marked diurnal cycle, similarly to the individual models, that can be related partly to the prescribed diurnal variations of anthropogenic emissions in the models. During summer 2014, the diurnal ozone maximum is underestimated by the ensemble median by about 4 μg m−3 on average. Locally, during the studied ozone episodes, the maxima from the ensemble median are often lower than observations by 30–50 μg m−3. Overall, ozone scores are generally good with average values for the normalised indicators of 0.14 for the modified normalised mean bias and of 0.30 for the fractional gross error. Tests have also shown that the ensemble median is robust to reduction of ensemble size by one, that is, if predictions are unavailable from one model. Scores are also discussed for PM10 for winter 2013–1014. There is an underestimation of most models leading the ensemble median to a mean bias of −4.5 μg m−3. The ensemble median fractional gross error is larger for PM10 (~ 0.52) than for ozone and the correlation is lower (~ 0.35 for PM10 and ~ 0.54 for ozone). This is related to a larger spread of the seven model scores for PM10 than for ozone linked to different levels of complexity of aerosol representation in the individual models. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion.

Deciphering the mechanisms underlying viral persistence is critical to achieving a cure for human immunodeficiency virus (HIV) infection. Here, we implement a systems approach to discover molecular signatures of HIV latently infected CD4+ T cells, identifying the immunosuppressive, adenosine-producing ectonucleotidase CD73 as a key surface marker of latent cells. Hypoxic conditioning, reflecting the lymphoid tissue microenvironment, increases the frequency of CD73+ CD4+ T cells and promotes HIV latency. Transcriptomic profiles of CD73+ CD4+ T cells favor viral quiescence, immune evasion, and cell survival. CD73+ CD4+ T cells are capable of harboring a functional HIV reservoir and reinitiating productive infection ex vivo. CD73 or adenosine receptor blockade facilitates latent HIV reactivation in vitro, mechanistically linking adenosine signaling to viral quiescence. Finally, tissue imaging of lymph nodes from HIV-infected individuals on antiretroviral therapy reveals spatial association between CD73 expression and HIV persistence in vivo. Our findings warrant development of HIV-cure strategies targeting the hypoxia-CD73-adenosine axis.

The Meteorological Synthesizing Centre-West (MSC-W) of the European Monitoring and Evaluation Programme (EMEP) has been performing model calculations in support of the Convention on Long Range Transboundary Air Pollution (CLRTAP) for more than 30 years. The EMEP MSC-W chemical transport model is still one of the key tools within European air pollution policy assessments. Traditionally, the model has covered all of Europe with a resolution of about 50 km × 50 km, and extending vertically from ground level to the tropopause (100 hPa). The model has changed extensively over the last ten years, however, with flexible processing of chemical schemes, meteorological inputs, and with nesting capability: the code is now applied on scales ranging from local (ca. 5 km grid size) to global (with 1 degree resolution). The model is used to simulate photo-oxidants and both inorganic and organic aerosols. In 2008 the EMEP model was released for the first time as public domain code, along with all required input data for model runs for one year. The second release of the EMEP MSC-W model became available in mid 2011, and a new release is targeted for summer 2012. This publication is intended to document this third release of the EMEP MSC-W model. The model formulations are given, along with details of input data-sets which are used, and a brief background on some of the choices made in the formulation is presented. The model code itself is available at www.emep.int, along with the data required to run for a full year over Europe.

A detailed 3-D evaluation of an ensemble of five regional Chemistry Transport Models (RCTM) and one global CTM with focus on free tropospheric ozone over Europe is presented. It is performed over a summer period (June to August 2008) in the context of the GEMS-RAQ project. A data set of about 400 vertical ozone profiles from balloon soundings and commercial aircraft at 11 different locations is used for model evaluation, in addition to satellite measurements with the infrared nadir sounder (IASI) showing largest sensitivity to free tropospheric ozone. In the middle troposphere, the four regional models using the same top and boundary conditions from IFS-MOZART exhibit a systematic negative bias with respect to observed profiles of about −20%. Root Mean Square Error (RMSE) values are constantly growing with altitude, from 22% to 32% to 53%, respectively for 0–2 km, 2–8 km and 8–10 km height ranges. Lowest correlation is found in the middle troposphere, with minimum coefficients (R) between 0.2 to 0.45 near 8 km, as compared to 0.7 near the surface and similar values around 10 km. A sensitivity test made with the CHIMERE mode also shows that using hourly instead of monthly chemical boundary conditions generally improves the model skill (i.e. improve RMSE and correlation). Lower tropospheric 0–6 km partial ozone columns derived from IASI show a clear North-South gradient over Europe, which is qualitatively reproduced by the models. Also the temporal variability showing decreasing ozone concentrations in the lower troposphere (0–6 km columns) during summer is well reproduced by models even if systematic bias remains (the value of the bias being also controlled by the type of used boundary conditions). A multi-day case study of a trough with low tropopause was conducted and showed that both IASI and models were able to resolve strong horizontal gradients of middle and upper tropospheric ozone occurring in the vicinity of an upper tropospheric frontal zone.

A multi-model study of the long-range transport of ozone and its precursors from major anthropogenic source regions was coordinated by the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) under the Convention on Long-range Transboundary Air Pollution (LRTAP). Vertical profiles of ozone at 12-h intervals from 2001 are available from twelve of the models contributing to this study and are compared here with observed profiles from ozonesondes. The contributions from each major source region are analysed for selected sondes, and this analysis is supplemented by retroplume calculations using the FLEXPART Lagrangian particle dispersion model to provide insight into the origin of ozone transport events and the cause of differences between the models and observations. In the boundary layer ozone levels are in general strongly affected by regional sources and sinks. With a considerably longer lifetime in the free troposphere, ozone here is to a much larger extent affected by processes on a larger scale such as intercontinental transport and exchange with the stratosphere. Such individual events are difficult to trace over several days or weeks of transport. This may explain why statistical relationships between models and ozonesonde measurements are far less satisfactory than shown in previous studies for surface measurements at all seasons. The lowest bias between model-calculated ozone profiles and the ozonesonde measurements is seen in the winter and autumn months. Following the increase in photochemical activity in the spring and summer months, the spread in model results increases, and the agreement between ozonesonde measurements and the individual models deteriorates further. At selected sites calculated contributions to ozone levels in the free troposphere from intercontinental transport are shown. Intercontinental transport is identified based on differences in model calculations with unperturbed emissions and emissions reduced by 20% by region. Intercontinental transport of ozone is finally determined based on differences in model ensemble calculations. With emissions perturbed by 20% per region, calculated intercontinental contributions to ozone in the free troposphere range from less than 1 ppb to 3 ppb, with small contributions in winter. The results are corroborated by the retroplume calculations. At several locations the seasonal contributions to ozone in the free troposphere from intercontinental transport differ from what was shown earlier at the surface using the same dataset. The large spread in model results points to a need of further evaluation of the chemical and physical processes in order to improve the credibility of global model results.

We present a comparison of tropospheric NO2 from OMI measurements to the median of an ensemble of Regional Air Quality (RAQ) models, and an intercomparison of the contributing RAQ models and two global models for the period July 2008–June 2009 over Europe. The model forecasts were produced routinely on a daily basis in the context of the European GEMS ("Global and regional Earth-system (atmosphere) Monitoring using Satellite and in-situ data") project. The tropospheric vertical column of the RAQ ensemble median shows a spatial distribution which agrees well with the OMI NO2 observations, with a correlation r=0.8. This is higher than the correlations from any one of the individual RAQ models, which supports the use of a model ensemble approach for regional air pollution forecasting. The global models show high correlations compared to OMI, but with significantly less spatial detail, due to their coarser resolution. Deviations in the tropospheric NO2 columns of individual RAQ models from the mean were in the range of 20–34% in winter and 40–62% in summer, suggesting that the RAQ ensemble prediction is relatively more uncertain in the summer months. The ensemble median shows a stronger seasonal cycle of NO2 columns than OMI, and the ensemble is on average 50% below the OMI observations in summer, whereas in winter the bias is small. On the other hand the ensemble median shows a somewhat weaker seasonal cycle than NO2 surface observations from the Dutch Air Quality Network, and on average a negative bias of 14%. Full profile information was available for two RAQ models and for the global models. For these models the retrieval averaging kernel was applied. Minor differences are found for area-averaged model columns with and without applying the kernel, which shows that the impact of replacing the a priori profiles by the RAQ model profiles is on average small. However, the contrast between major hotspots and rural areas is stronger for the direct modeled vertical columns than the columns where the averaging kernels are applied, related to a larger relative contribution of the free troposphere and the coarse horizontal resolution in the a priori profiles compared to the RAQ models. In line with validation results reported in the literature, summertime concentrations in the lowermost boundary layer in the a priori profiles from the DOMINO product are significantly larger than the RAQ model concentrations and surface observations over the Netherlands. This affects the profile shape, and contributes to a high bias in OMI tropospheric columns over polluted regions. The global models indicate that the upper troposphere may contribute significantly to the total column and it is important to account for this in comparisons with RAQ models. A combination of upper troposphere model biases, the a priori profile effects and DOMINO product retrieval issues could explain the discrepancy observed between the OMI observations and the ensemble median in summer.

The primary goal of the present mixed methods review is to systematically examine the available evidence for the effectiveness of different types of school-based interventions for reducing disciplinary school exclusion. Quantitative evidence will help to understand the overall size of the impact, as well as the factors that better explain it. Qualitative evidence will help to better understand how these programmes may work, and what factors aid or hinder implementation and success. The research questions underlying the quantitative review are as follows: (1) Do school-based programmes reduce the use of exclusionary sanctions in schools?; (2) Are some school-based approaches more effective than others in reducing exclusionary sanctions?; (3) Do participants' characteristics (e.g., age, sex, or ethnicity) affect the impact of school-based programmes on exclusionary sanctions in schools?; (4) Do characteristics of the interventions, implementation, and methodology affect the impact of school-based programmes on exclusionary sanctions in schools?; (5) Do school-based programmes have an impact on reducing the involvement of children and young people in crime and violence?; and (6) Do participants' characteristics (e.g., age, gender, ethnicity) affect the impact of school-based programmes on crime and violence? If sufficient data are available, we will compare different approaches (e.g., school-wide management, classroom management, restorative justice, cognitive-behavioural interventions) and identify those that could potentially demonstrate larger effects. We will also (potentially) run analysis controlling for characteristics of participants (e.g., age, ethnicity, level of risk); interventions (e.g., theoretical bases, components); implementation (e.g., facilitators' training, doses, quality); and methodology (e.g., research design). The research questions underlying the qualitative review are defined as follows: (1) What are the barriers and facilitators to implementation of interventions to reduce school exclusions? and (2) What are the barriers and facilitators to implementation of interventions to reduce the involvement of children and young people in crime and violence?

On May 13, 2022, the World Health Organization raised an alert caused by a large increase in the number of infections by monkeypox virus (MPXV), which causes mpox, a zoonotic [...]