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Volatile organic compounds (VOCs) significantly impact air quality and atmospheric chemistry, influencing ozone formation and secondary organic aerosol production. Despite their importance, the uncertainties associated with representing VOCs in atmospheric emission inventories are considerable. This work presents a spatiotemporal assessment and evaluation of benzene, toluene, and xylene (BTX) emissions and concentrations in Spain by combining bottom-up emissions, air quality modelling techniques, and ground-based observations. The emissions produced by High-Elective Resolution Modelling Emission System (HERMESv3) were used as input to the Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) chemical transport model to simulate surface concentrations across Spain. Comparing modelled and observed levels revealed uncertainty in the anthropogenic emissions, which were further explored through sensitivity tests. The largest levels of observed benzene and xylene were found in industrial sites near coke ovens, refineries, and car manufacturing facilities, where the modelling results show large underestimations. Official emissions reported for these facilities were replaced by alternative estimates, resulting in varied improvements in the model's performance across different stations. However, uncertainties associated with industrial emission processes persist, emphasising the need for further refinement. For toluene, consistent overestimations in background stations were mainly related to uncertainties in the spatial disaggregation of emissions from industrial-use solvent activities, mainly wood paint applications. Observed benzene levels in Barcelona's urban traffic areas were 5 times larger than the ones observed in Madrid. MONARCH failed to reproduce the observed gradient between the two cities due to uncertainties arising from estimating emissions from motorcycles and mopeds, as well as from different measurement methods and the model's capacity to accurately simulate meteorological conditions. Our results are constrained by the spatial and temporal coverage of available BTX observations, posing a key challenge in evaluating the spatial distribution of modelled levels and associated emissions.

Summary: Background & aims: A 2-hour postprandial glucose (2-h PG) concentration greater than 140 mg/dL is associated with a greater risk of type-2 diabetes (TD2) and cardiovascular disease. An oral glucose tolerance test (OGTT) is the method most used for detecting either prediabetes or T2D; however, its use in clinical practice is limited due to the 2-h testing period. Therefore, the aim of the study was to assess dysglycaemia, through a prognostic assessment of body composition, biochemical and clinical parameters as predictors of an abnormal 2-h PG. Methods: Participants underwent a physical examination in which their anthropometric measurements were obtained, and a blood sample was taken for biochemical and hormonal analysis. All patients underwent a 75-g OGTT after 8 h of fasting, and the levels of glucose and insulin were measured. The results were compared statistically by dividing the participants' 2-h postprandial glucose (2-h PG) into the following groups: normoglycaemia (NGT),

Many studies have shown that emission inventories are one of the inputs with the most critical influences on the results of air quality modelling. Comparing emission inventories among themselves is, therefore, essential to build confidence in emission estimates. In this work, we extend the approach of Thunis et al. (2022) to compare emission inventories by building a benchmark that serves as a reference for comparisons. This benchmark is an ensemble that is based on three state-of-the-art EU-wide inventories: CAMS-REG, EMEP and EDGAR. The ensemble-based methodology screens differences between inventories and the ensemble. It excludes differences that are not relevant and identifies among the remaining ones those that need special attention. We applied the ensemble-based screening to both an EU-wide and a local (Poland) inventory. The EU-wide analysis highlighted a large number of inconsistencies. While the origin of some differences between EDGAR and the ensemble can be identified, their magnitude remains to be explained. These differences mostly occur for SO2 (sulfur oxides), PM (particulate matter) and NMVOC (non-methane volatile organic carbon) for the industrial and residential sectors and reach a factor of 10 in some instances. Spatial inconsistencies mostly occur for the industry and other sectors. At the local scale, inconsistencies relate mostly to differences in country sectorial shares that result from different sectors/activities being accounted for in the two types of inventories. This is explained by the fact that some emission sources are omitted in the local inventory due to a lack of appropriate geographically allocated activity data. We identified sectors and pollutants for which discussion between local and EU-wide emission compilers would be needed in order to reduce the magnitude of the observed differences (e.g. in the residential and industrial sectors). The ensemble-based screening proved to be a useful approach to spot inconsistencies by reducing the number of necessary inventory comparisons. With the progressive resolution of inconsistencies and associated inventory improvements, the ensemble will improve. In this sense, we see the ensemble as a useful tool to motivate the community around a single common benchmark and monitor progress towards the improvement of regionally and locally developed emission inventories.

Anthropogenic emissions are the result of many different economic sectors, including transportation, power generation, industrial, residential and commercial activities, waste treatment and agricultural practices. Air quality models are used to forecast the atmospheric composition, analyze observations and reconstruct the chemical composition of the atmosphere during the previous decades. In order to drive these models, gridded emissions of all compounds need to be provided. This paper describes a new global inventory of emissions called CAMS-GLOB-ANT, developed as part of the Copernicus Atmosphere Monitoring Service (CAMS; https://doi.org/10.24380/eets-qd81, Soulie et al., 2023). The inventory provides monthly averages of the global emissions of 36 compounds, including the main air pollutants and greenhouse gases, at a spatial resolution of 0.1° × 0.1° in latitude and longitude, for 17 emission sectors. The methodology to generate the emissions for the 2000–2023 period is explained, and the datasets are analyzed and compared with publicly available global and regional inventories for selected world regions. Depending on the species and regions, good agreements as well as significant differences are highlighted, which can be further explained through an analysis of different sectors as shown in the figures in the Supplement.

Spatial soil databases can help model complex phenomena in which soils are a decisive factor – for example, evaluating agricultural potential or estimating carbon storage capacity. The Latin America and Caribbean Soil Information System, SISLAC, is a regional initiative promoted by the Food and Agriculture Organization's (FAO) Latin America and the Caribbean Soil Partnership to contribute to sustainable management of soil. SISLAC includes data from 49 084 soil profiles distributed unevenly across the continent, making it the region's largest soil database. In addition, there are other soil databases in the region with about 40 000 soil profiles that can be integrated into SISLAC and improve it. However, some problems hinder its usages, such as the quality of the data and their high dimensionality. The objective of this research is evaluate the quality of the SISLAC data and the other available soil databases to generate a new improved version that meets the minimum quality requirements to be used for different purposes or practical applications. The results show that 15 % of the existing soil profiles had an inaccurate description of the diagnostic horizons and 17 % of the additional profiles already existed in SISLAC; therefore, a total of 32 % of profiles were excluded for these two reasons. Further correction of an additional 4.5 % of existing inconsistencies improved overall data quality. The improved database consists of 66 746 profiles and is available for public use at https://doi.org/10.5281/zenodo.7876731 (Díaz-Guadarrama and Guevara, 2023). This revised version of SISLAC data offers the opportunity to generate information that helps decision-making on issues in which soils are a decisive factor. It can also be used to plan future soil surveys in areas with low density or where updated information is required.

Gridded bottom-up inventories of CO2 emissions are needed in global CO2 inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO2 emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1.0 provides gridded (0.1∘ × 0.1∘) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO2ff (36.7 Gt) and CO2bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions generally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations. CoCO2-MOSAIC 1.0 is freely available at zenodo (https://doi.org/10.5281/zenodo.7092358; Urraca et al., 2023) and at the JRC Data Catalogue (https://data.jrc.ec.europa.eu/dataset/6c8f9148-ce09-4dca-a4d5-422fb3682389, last access: 15 May 2023; Urraca Valle et al., 2023).

We present a high-resolution global emission catalogue of CO2 and co-emitted species (NOx, SO2, CO, CH4) from thermal power plants for the year 2018. The construction of the database follows a bottom-up approach, which combines plant-specific information with national energy consumption statistics and fuel-dependent emission factors for CO2 and emission ratios for co-emitted species (e.g. the amount of NOx emitted relative to CO2: NOx/CO2). The resulting catalogue contains annual emission information for more than 16 000 individual facilities at their exact geographical locations. Each facility is linked to a country- and fuel-dependent temporal profile (i.e. monthly, day of the week and hourly) and a plant-level vertical profile, which were derived from national electricity generation statistics and plume rise calculations that combine stack parameters with meteorological information. The combination of the aforementioned information allows us to derive high-resolution spatial and temporal emissions for modelling purposes. Estimated annual emissions were compared against independent plant- and country-level inventories, including Carbon Monitoring for Action (CARMA), the Global Infrastructure emission Database (GID) and the Emissions Database for Global Atmospheric Research (EDGAR), as well as officially reported emission data. Overall good agreement is observed between datasets when comparing the CO2 emissions. The main discrepancies are related to the non-inclusion of auto-producer or heat-only facilities in certain countries due to a lack of data. Larger inconsistencies are obtained when comparing emissions from co-emitted species due to uncertainties in the fuel-, country- and region-dependent emission ratios and gap-filling procedures. The temporal distribution of emissions obtained in this work was compared against traditional sector-dependent profiles that are widely used in modelling efforts. This highlighted important differences and the need to consider country dependencies when temporally distributing emissions. The resulting catalogue (https://doi.org/10.24380/0a9o-v7xe, Guevara et al., 2023) is developed in the framework of the Prototype System for a Copernicus CO2 service (CoCO2) European Union (EU)-funded project to support the development of the Copernicus CO2 Monitoring and Verification Support capacity (CO2MVS).

The 2020 COVID-19 crisis caused an unprecedented drop in anthropogenic emissions of air pollutants and greenhouse gases. Given that emissions estimates from official national inventories for the year 2020 were not reported until 2 years later, new and non-traditional datasets to estimate near-real-time emissions became particularly relevant and widely used in international monitoring and modelling activities during the pandemic. This study investigates the impact of the COVID-19 pandemic on 2020 European (the 27 EU member states and the UK) emissions by comparing a selection of such near-real-time emission estimates, with the official inventories that were subsequently reported in 2022 under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) and the United Nations Framework Convention on Climate Change (UNFCCC). Results indicate that annual changes in total 2020 emissions reported by official and near-real-time estimates are fairly in line for most of the chemical species, with NOx and fossil fuel CO2 being reported as the ones that experienced the largest reduction in Europe in all cases. However, large discrepancies arise between the official and non-official datasets when comparing annual results at the sector and country level, indicating that caution should be exercised when estimating changes in emissions using specific near-real-time activity datasets, such as time mobility data derived from smartphones. The main examples of these differences are observed for the manufacturing industry NOx (relative changes ranging between −21.4 % and −5.4 %) and road transport CO2 (relative changes ranging between −29.3 % and −5.6 %) total European emissions. Additionally, significant discrepancies are observed between the quarterly and monthly distribution of emissions drops reported by the various near-real-time inventories, with differences of up to a factor of 1.5 for total NOx during April 2020, when restrictions were at their maximum. For residential combustion, shipping and the public energy industry, results indicate that changes in emissions that occurred between 2019 and 2020 were mainly dominated by non-COVID-19 factors, including meteorology, the implementation of the Global Sulphur Cap and the shutdown of coal-fired power plants as part of national decarbonization efforts, respectively. The potential increase in NMVOC emissions from the intensive use of personal protective equipment such as hand sanitizer gels is considered in a heterogeneous way across countries in officially reported inventories, indicating the need for some countries to base their calculations on more advanced methods. The findings of this study can be used to better understand the uncertainties in near-real-time emissions and how such emissions could be used in the future to provide timely updates to emission datasets that are critical for modelling and monitoring applications.

Abstract Background Excess body fatness and hyperinsulinemia are both associated with an increased risk of postmenopausal breast cancer. However, whether women with high body fatness but normal insulin levels or those with normal body fatness and high levels of insulin are at elevated risk of breast cancer is not known. We investigated the associations of metabolically defined body size and shape phenotypes with the risk of postmenopausal breast cancer in a nested case–control study within the European Prospective Investigation into Cancer and Nutrition. Methods Concentrations of C‐peptide—a marker for insulin secretion—were measured at inclusion prior to cancer diagnosis in serum from 610 incident postmenopausal breast cancer cases and 1130 matched controls. C‐peptide concentrations among the control participants were used to define metabolically healthy (MH; in first tertile) and metabolically unhealthy (MU; >1st tertile) status. We created four metabolic health/body size phenotype categories by combining the metabolic health definitions with normal weight (NW; BMI

Comprehensive monitoring of NO2 exceedances is imperative for protecting human health, especially in urban areas with traffic. However, an accurate spatial characterization of the exceedances is challenging due to the typically low density of air quality monitoring stations and the inherent uncertainties in urban air quality models. We study how observational data from different sources and timescales can be combined with a dispersion air quality model to obtain bias-corrected NO2 hourly maps at the street scale. We present a kriging-based data fusion workflow that merges dispersion model output with continuous hourly observations and uses a machine-learning-based land use regression (LUR) model constrained with past short intensive passive dosimeter campaign measurements. While the hourly observations allow the bias adjustment of the temporal variability in the dispersion model, the microscale LUR model adds information on the NO2 spatial patterns. Our method includes an uncertainty calculation based on the estimated error variance of the universal kriging technique, which is subsequently used to produce urban maps of probability of exceeding the 200 µg m−3 hourly and the 40 µg m−3 annual NO2 average limits. We assess the statistical performance of this approach in the city of Barcelona for the year 2019. Our results show that simply merging the monitoring stations with the model output already significantly increases the correlation coefficient (r) by +29 % and decreases the root mean square error (RMSE) by −32 %. When adding the time-invariant microscale LUR model in the data fusion workflow, the improvement is even more remarkable, with +46 % and −48 % for the r and RMSE, respectively. Our work highlights the usefulness of high-resolution spatial information in data fusion methods to better estimate exceedances at the street scale.

In orbit since late 2017, the Tropospheric Monitoring Instrument (TROPOMI) is offering new outstanding opportunities for better understanding the emission and fate of nitrogen dioxide (NO2) pollution in the troposphere. In this study, we provide a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula during 2018–2021, considering the recently developed Product Algorithm Laboratory (PAL) product. We complement our analysis with estimates of NOx anthropogenic and natural soil emissions. Closely related to cloud cover, the data availability of TROPOMI observations ranges from 30 %–45 % during April and November to 70 %–80 % during summertime, with strong variations between northern and southern Spain. Strongest TrC-NO2 hotspots are located over Madrid and Barcelona, while TrC-NO2 enhancements are also observed along international maritime routes close the strait of Gibraltar, and to a lesser extent along specific major highways. TROPOMI TrC-NO2 appear reasonably well correlated with collocated surface NO2 mixing ratios, with correlations around 0.7–0.8 depending on the averaging time. We investigate the changes of weekly and monthly variability of TROPOMI TrC-NO2 depending on the urban cover fraction. Weekly profiles show a reduction of TrC-NO2 during the weekend ranging from −10 % to −40 % from least to most urbanized areas, in reasonable agreement with surface NO2. In the largest agglomerations like Madrid or Barcelona, this weekend effect peaks not in the city center but in specific suburban areas/cities, suggesting a larger relative contribution of commuting to total NOx anthropogenic emissions. The TROPOMI TrC-NO2 monthly variability also strongly varies with the level of urbanization, with monthly differences relative to annual mean ranging from −40 % in summer to +60 % in winter in the most urbanized areas, and from −10 % to +20 % in the least urbanized areas. When focusing on agricultural areas, TROPOMI observations depict an enhancement in June–July that could come from natural soil NO emissions. Some specific analysis of surface NO2 observations in Madrid show that the relatively sharp NO2 minimum used to occur in August (drop of road transport during holidays) has now evolved into a much broader minimum partly de-coupled from the observed local road traffic counting; this change started in 2018, thus before the COVID-19 outbreak. Over 2019–2021, a reasonable consistency of the inter-annual variability of NO2 is also found between both datasets. Our study illustrates the strong potential of TROPOMI TrC-NO2 observations for complementing the existing surface NO2 monitoring stations, especially in the poorly covered rural and maritime areas where NOx can play a key role, notably for the production of tropospheric O3.

One of the largest challenges with soil information around the world is how to harmonize archived soil data from different sources and how to make it accessible to soil scientist. In Ecuador, there have been two major projects that have provided soil information, but the methodology of these projects, although comparable, did not coincide, especially with respect to how information was reported. Here, we present a new soil database for Ecuador, comprising 13 542 soil profiles with 51 713 measured soil horizons, including 92 different edaphic variables. The original data were in a non-editable format (i.e., PDF), which made it difficult to access and process the information. Our study provides an integrated framework that combines multiple analytic tools for automatically converting legacy soil information from an analog format into usable digital soil mapping inputs across Ecuador. This framework allowed us to incorporate quantitative information on a broad set of soil properties and retrieve qualitative information on soil morphological properties collected in the profile description phase, which is rarely included in soil databases. We present a new harmonized national soil database using a specific methodology to preserve relevant information. The national representativeness of soil information has been enhanced compared with other international databases, and this new database contributes to filling the gaps in publicly available soil information across the country. The database is freely available at https://doi.org/10.6073/pasta/1560e803953c839e7aedef78ff7d3f6c (Armas et al., 2022).

Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost the bias, they are often coupled with model output statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate the extent to which AQ forecasts can be improved using a variety of MOS methods, including moving average, quantile mapping, Kalman filter, analogs and gradient boosting machine methods, and consider as well the persistence method as a reference. We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a comprehensive set of continuous and categorical metrics at various timescales, along different lead times and using different meteorological input datasets. Our results show that O3 forecasts can be substantially improved using such MOS corrections and that improvements go well beyond the correction of the systematic bias. Depending on the timescale and lead time, root mean square errors decreased from 20 %–40 % to 10 %–30 %, while Pearson correlation coefficients increased from 0.7–0.8 to 0.8–0.9. Although the improvement typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. The MOS methods relying on meteorological data were found to provide relatively similar performance with two different meteorological inputs. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O3 mixing ratios overall, with the lowest errors and highest correlations. However, they are not necessarily the best in predicting the peak O3 episodes, for which simpler MOS methods can achieve better results. Although the complex impact of MOS methods on the distribution of and variability in raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest.

In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

Some studies show that significant uncertainties affect emission inventories, which may impeach conclusions based on air-quality model results. These uncertainties result from the need to compile a wide variety of information to estimate an emission inventory. In this work, we propose and discuss a screening method to compare two emission inventories, with the overall goal of improving the quality of emission inventories by feeding back the results of the screening to inventory compilers who can check the inconsistencies found and, where applicable, resolve errors. The method targets three different aspects: (1) the total emissions assigned to a series of large geographical areas, countries in our application; (2) the way these country total emissions are shared in terms of sector of activity; and (3) the way inventories spatially distribute emissions from countries to smaller areas, cities in our application. The first step of the screening approach consists of sorting the data and keeping only emission contributions that are relevant enough. In a second step, the method identifies, among those significant differences, the most important ones that provide evidence of methodological divergence and/or errors that can be found and resolved in at least one of the inventories. The approach has been used to compare two versions of the CAMS-REG European-scale inventory over 150 cities in Europe for selected activity sectors. Among the 4500 screened pollutant sectors, about 450 were kept as relevant, among which 46 showed inconsistencies. The analysis indicated that these inconsistencies arose almost equally from large-scale reporting and spatial distribution differences. They mostly affect SO2 and PM coarse emissions from the industrial and residential sectors. The screening approach is general and can be used for other types of applications related to emission inventories.

IntroductionComparable indicators on complete cancer prevalence are increasingly needed in Europe to support survivorship care planning. Direct measures can be biased by limited registration time and estimates are needed to recover long term survivors. The completeness index method, based on incidence and survival modelling, is the standard most validated approach.MethodsWithin this framework, we consider two alternative approaches that do not require any direct modelling activity: i) empirical indices derived from long established European registries; ii) pre-calculated indices derived from US-SEER cancer registries. Relying on the EUROCARE-6 study dataset we compare standard vs alternative complete prevalence estimates using data from 62 registries in 27 countries by sex, cancer type and registration time.ResultsFor tumours mostly diagnosed in the elderly the empirical estimates differ little from standard estimates (on average less than 5% after 10-15 years of registration), especially for low prognosis cancers. For early-onset cancers (bone, brain, cervix uteri, testis, Hodgkin disease, soft tissues) the empirical method may produce substantial underestimations of complete prevalence (up to 20%) even when based on 35-year observations. SEER estimates are comparable to the standard ones for most cancers, including many early-onset tumours, even when derived from short time series (10-15 years). Longer observations are however needed when cancer-specific incidence and prognosis differ remarkably between US and European populations (endometrium, thyroid or stomach).DiscussionThese results may facilitate the dissemination of complete prevalence estimates across Europe and help bridge the current information gaps.

We present a European dataset of daily sector-, pollutant- and country-dependent emission adjustment factors associated with the COVID-19 mobility restrictions for the year 2020. We considered metrics traditionally used to estimate emissions, such as energy statistics or traffic counts, as well as information derived from new mobility indicators and machine learning techniques. The resulting dataset covers a total of nine emission sectors, including road transport, the energy industry, the manufacturing industry, residential and commercial combustion, aviation, shipping, off-road transport, use of solvents, and fugitive emissions from transportation and distribution of fossil fuels. The dataset was produced to be combined with the Copernicus CAMS-REG_v5.1 2020 business-as-usual (BAU) inventory, which provides high-resolution (0.1∘×0.05∘) emission estimates for 2020 omitting the impact of the COVID-19 restrictions. The combination of both datasets allows quantifying spatially and temporally resolved reductions in primary emissions from both criteria pollutants (NOx, SO2, non-methane volatile organic compounds – NMVOCs, NH3, CO, PM10 and PM2.5) and greenhouse gases (CO2 fossil fuel, CO2 biofuel and CH4), as well as assessing the contribution of each emission sector and European country to the overall emission changes. Estimated overall emission changes in 2020 relative to BAU emissions were as follows: −10.5 % for NOx (−602 kt), −7.8 % (−260.2 Mt) for CO2 from fossil fuels, −4.7 % (−808.5 kt) for CO, −4.6 % (−80 kt) for SO2, −3.3 % (−19.1 Mt) for CO2 from biofuels, −3.0 % (−56.3 kt) for PM10, −2.5 % (−173.3 kt) for NMVOCs, −2.1 % (−24.3 kt) for PM2.5, −0.9 % (−156.1 kt) for CH4 and −0.2 % (−8.6 kt) for NH3. The most pronounced drop in emissions occurred in April (up to −32.8 % on average for NOx) when mobility restrictions were at their maxima. The emission reductions during the second epidemic wave between October and December were 3 to 4 times lower than those occurred during the spring lockdown, as mobility restrictions were generally softer (e.g. curfews, limited social gatherings). Italy, France, Spain, the United Kingdom and Germany were, together, the largest contributors to the total EU27 + UK (27 member states of the European Union and the UK) absolute emission decreases. At the sectoral level, the largest emission declines were found for aviation (−51 % to −56 %), followed by road transport (−15.5 % to −18.8 %), the latter being the main driver of the estimated reductions for the majority of pollutants. The collection of COVID-19 emission adjustment factors (https://doi.org/10.24380/k966-3957, Guevara et al., 2022) and the CAMS-REG_v5.1 2020 BAU gridded inventory (https://doi.org/10.24380/eptm-kn40, Kuenen et al., 2022b) have been produced in support of air quality modelling studies.

Tyrosine kinase inhibitors (TKIs) have been improving the prognosis of patients with chronic myeloid leukemia (CML), but there are still large differences in survival among European countries. This raises questions on the added value of results from population-based studies, which use real-world data, compared to results of randomized controlled trials (RCTs) involving patients with CML. There are also questions about the extent of the findings on RCTs effectiveness for patients in the general population. We compare survival data extracted from our previous systematic review and meta-analysis of CML RCTs with the latest updated population-based survival data of EUROCARE-6, the widest collaborative study on cancer survival in Europe. The EUROCARE-6 CML survival estimated in patients (15–64 years) diagnosed in 2000–2006 vs. 2007–2013 revealed that the prognostic improvement highlighted by RCTs was confirmed in real-world settings, too. The study shows, evaluating for the first time all European regions, that the optimal outcome figures obtained in controlled settings for CML are also achievable (and indeed achieved) in real-world settings with prompt introduction of TKIs in daily clinical practice. However, some differences still persist, particularly in Eastern European countries, where overall survival values are lower than elsewhere, probably due to a delayed introduction of TKIs. Our results suggest an insufficient adoption of adequate protocols in daily clinical practice in those countries where CML survival values remain lower in real life than the values obtained in RCTs. New high-resolution population-based studies may help to identify failures in the clinical pathways followed there.

This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a soil moisture pattern recognition framework to increase the spatial resolution and fill gaps of the ESA-CCI (European Space Agency Climate Change Initiative v4.5) soil moisture dataset, which contains > 40 years of satellite soil moisture global grids with a spatial resolution of ∼ 27 km. We use terrain parameters coupled with bioclimatic and soil type information to predict finer-grained (i.e., downscaled) satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating downscaled soil moisture with and without the support of bioclimatic and soil type information. The outcome is a dataset of gap-free global mean annual soil moisture predictions and associated prediction variances for 28 years (1991–2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, 987 stations) and in situ precipitation records (171 additional stations) only for evaluating the new dataset. Cross-validated correlation between observed and predicted soil moisture values varies from r= 0.69 to r= 0.87 with root mean squared errors (RMSEs, m3 m−3) around 0.03 and 0.04. Our soil moisture predictions improve (a) the correlation with the ISMN (when compared with the original ESA-CCI dataset) from r= 0.30 (RMSE = 0.09, unbiased RMSE (ubRMSE) = 0.37) to r= 0.66 (RMSE = 0.05, ubRMSE = 0.18) and (b) the correlation with local precipitation records across boreal (from r= < 0.3 up to r= 0.49) or tropical areas (from r= < 0.3 to r= 0.46) which are currently poorly represented in the ISMN. Temporal trends show a decline of global annual soil moisture using (a) data from the ISMN (-1.5[-1.8,-1.24] %), (b) associated locations from the original ESA-CCI dataset (-0.87[-1.54,-0.17] %), (c) associated locations from predictions based on terrain parameters (-0.85[-1.01,-0.49] %), and (d) associated locations from predictions including bioclimatic and soil type information (-0.68[-0.91,-0.45] %). We provide a new soil moisture dataset that has no gaps and higher granularity together with validation methods and a modeling approach that can be applied worldwide (Guevara et al., 2020, https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e).

Background: Since returning to in-person instruction after the emergence of COVID-19 schools have seen a dramatic increase in the number of students chronically absent, with data indicating a greater increase for low-income, Black, and Hispanic students. Given the role of school attendance in both promoting positive educational outcomes as well as providing students with physical and mental health supports, it is critical to identify ways to re-engage families in a manner that is culturally responsive and equitable. Contributions to Theory: Current attendance interventions focus primarily on school-based academic and behavioral supports for students while excluding the family. Additionally, traditional family engagement models do not address the sociocultural realities of low-income and/or minoritized families. We present a strengths-based model of family engagement to support attendance. Implications for School Health Policy, Practice, and Equity: Along with our model, we provide concrete supports that schools can provide, including example measurement items. In this way, schools and districts can self-assess as well as identify action steps to promote positive school-family partnerships for equitable family engagement. Conclusions: Without consistent attendance, schools cannot support positive educational outcomes nor provide important safety net services for students. Attendance is a family engagement challenge, which addressing holistically can reduce racial and socioeconomic educational and health disparities.

Over the past decade, Brazil has experienced severe droughts across its territory, with important implications for soil moisture dynamics. Soil moisture variability has a direct impact on agriculture, water security and ecosystem services. Nevertheless, there is currently little information on how soil moisture across different biomes responds to drought. In this study, we used satellite soil moisture data from the European Space Agency, from 2009 to 2015, to analyze differences in soil moisture responses to drought for each biome of Brazil: Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa and Pantanal. We found an overall soil moisture decline of −0.5 % yr−1 (p) at the national level. At the biome level, Caatinga presented the most severe soil moisture decline (−4.4 % yr−1), whereas the Atlantic Forest and Cerrado biomes showed no significant trend. The Amazon biome showed no trend but had a sharp reduction of soil moisture from 2013 to 2015. In contrast, the Pampa and Pantanal biomes presented a positive trend (1.6 % yr−1 and 4.3 % yr−1, respectively). These trends are consistent with vegetation productivity trends across each biome. This information provides insights into drought risk reduction and soil conservation activities to minimize the impact of drought in the most vulnerable biomes. Furthermore, improving our understanding of soil moisture trends during periods of drought is crucial to enhance the national drought early warning system and develop customized strategies for adaptation to climate change in each biome.

We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx; SOx; NMVOC, non-methane volatile organic compound; NH3; CO; PM10; and PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1 × 0.1∘ and regional European 0.1 × 0.05∘ grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS programme. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production and traffic counts) at national and local levels were collected and combined with existing meteorology-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily and hourly) the resulting profiles are pollutant-dependent, year-dependent (i.e. time series from 2010 to 2017) and/or spatially dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Guevara et al., 2020a) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68, Guevara et al., 2020b) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: February 2021).

We quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Our estimates are provided in the form of a dataset of reduction factors varying per country and day that will allow the modelling and identification of the associated impacts upon air quality. The country- and daily-resolved reduction factors are provided for each of the following source categories: energy industry (power plants), manufacturing industry, road traffic and aviation (landing and take-off cycle). We computed the reduction factors based on open-access and near-real-time measured activity data from a wide range of information sources. We also trained a machine learning model with meteorological data to derive weather-normalized electricity consumption reductions. The time period covered is from 21 February, when the first European localized lockdown was implemented in the region of Lombardy (Italy), until 26 April 2020. This period includes 5 weeks (23 March until 26 April) with the most severe and relatively unchanged restrictions upon mobility and socio-economic activities across Europe. The computed reduction factors were combined with the Copernicus Atmosphere Monitoring Service's European emission inventory using adjusted temporal emission profiles in order to derive time-resolved emission reductions per country and pollutant sector. During the most severe lockdown period, we estimate the average emission reductions to be −33 % for NOx, −8 % for non-methane volatile organic compounds (NMVOCs), −7 % for SOx and −7 % for PM2.5 at the EU-30 level (EU-28 plus Norway and Switzerland). For all pollutants more than 85 % of the total reduction is attributable to road transport, except SOx. The reductions reached −50 % (NOx), −14 % (NMVOCs), −12 % (SOx) and −15 % (PM2.5) in countries where the lockdown restrictions were more severe such as Italy, France or Spain. To show the potential for air quality modelling, we simulated and evaluated NO2 concentration decreases in rural and urban background regions across Europe (Italy, Spain, France, Germany, United-Kingdom and Sweden). We found the lockdown measures to be responsible for NO2 reductions of up to −58 % at urban background locations (Madrid, Spain) and −44 % at rural background areas (France), with an average contribution of the traffic sector to total reductions of 86 % and 93 %, respectively. A clear improvement of the modelled results was found when considering the emission reduction factors, especially in Madrid, Paris and London where the bias is reduced by more than 90 %. Future updates will include the extension of the COVID-19 lockdown period covered, the addition of other pollutant sectors potentially affected by the restrictions (commercial and residential combustion and shipping) and the evaluation of other air quality pollutants such as O3 and PM2.5. All the emission reduction factors are provided in the Supplement.

Abstract A geochemometric study based on a multi-criteria decision analysis was applied, for the first time, for the optimal evaluation and selection of artificial neural networks, and the prediction of geothermal reservoir temperatures. Eight new gas geothermometers (GasG1 to GasG8) were derived from this study. For an effective and practical application of these geothermometers, a new computer program GaS_GeoT was developed. The prediction efficiency of the new geothermometers was compared with temperature estimates inferred from twenty-five existing geothermometers using gas-phase compositions of fluids from liquid- (LIQDR) and vapour-dominated (VAPDR) reservoirs. After applying evaluation statistical metrics (DIFF%, RMSE, MAE, MAPE, and the Theil's U test) to the temperature estimates obtained by using all the geothermometers, the following inferences were accomplished: (1) the new eight gas geothermometers (GasG1 to GasG8) provided reliable and systematic temperature estimates with performance wise occupying the first eight positions for LIQDR; (2) the GasG3 and GasG1 geothermometers exhibited consistency as the best predictor models by occupying the first two positions over all the geothermometers for VAPDR; (3) the GasG3 geothermometer exhibited a wider applicability, and a better prediction efficiency over all geothermometers in terms of a large number of samples used (up to 96% and 85% for LIQDR and VAPDR, respectively), and showed the smallest differences between predicted and measured temperatures in VAPDR and LIQDR; and lastly (4) for the VAPDR, the existing geothermometers ND84c, A98c, and ND98b sometimes showed a better prediction than some of the new gas geothermometers, except for GasG3 and GasG1. These results indicate that the new gas geothermometers may have the potential to become one of the most preferred tools for the estimation of the reservoir temperatures in geothermal systems.

The spread of the new coronavirus SARS-CoV-2 that causes COVID-19 forced the Spanish Government to implement extensive lockdown measures to reduce the number of hospital admissions, starting on 14 March 2020. Over the following days and weeks, strong reductions in nitrogen dioxide (NO2) pollution were reported in many regions of Spain. A substantial part of these reductions was obviously due to decreased local and regional anthropogenic emissions. Yet, the confounding effect of meteorological variability hinders a reliable quantification of the lockdown's impact upon the observed pollution levels. Our study uses machine-learning (ML) models fed by meteorological data along with other time features to estimate the “business-as-usual” NO2 mixing ratios that would have been observed in the absence of the lockdown. We then quantify the so-called meteorology-normalized NO2 reductions induced by the lockdown measures by comparing the estimated business-as-usual values with the observed NO2 mixing ratios. We applied this analysis for a selection of urban background and traffic stations covering the more than 50 Spanish provinces and islands. The ML predictive models were found to perform remarkably well in most locations, with an overall bias, root mean square error and correlation of +4 %, 29 % and 0.86, respectively. During the period of study, from the enforcement of the state of alarm in Spain on 14 March to 23 April, we found the lockdown measures to be responsible for a 50 % reduction in NO2 levels on average over all provinces and islands. The lockdown in Spain has gone through several phases with different levels of severity with respect to mobility restrictions. As expected, the meteorology-normalized change in NO2 was found to be stronger during phase II (the most stringent phase) and phase III of the lockdown than during phase I. In the largest agglomerations, where both urban background and traffic stations were available, a stronger meteorology-normalized NO2 change is highlighted at traffic stations compared with urban background sites. Our results are consistent with foreseen (although still uncertain) changes in anthropogenic emissions induced by the lockdown. We also show the importance of taking the meteorological variability into account for accurately assessing the impact of the lockdown on NO2 levels, in particular at fine spatial and temporal scales. Meteorology-normalized estimates such as those presented here are crucial to reliably quantify the health implications of the lockdown due to reduced air pollution.

We describe the bottom–up module of the High-Elective Resolution Modelling Emission System version 3 (HERMESv3), a Python-based and multi-scale modelling tool intended for the processing and computation of atmospheric emissions for air quality modelling. HERMESv3 is composed of two separate modules: the global_regional module and the bottom_up module. In a companion paper (Part 1, Guevara et al., 2019a) we presented the global_regional module. The bottom_up module described in this contribution is an emission model that estimates anthropogenic emissions at high spatial- (e.g. road link level,) and temporal- (hourly) resolution using state-of-the-art calculation methods that combine local activity and emission factors along with meteorological data. The model computes bottom–up emissions from point sources, road transport, residential and commercial combustion, other mobile sources, and agricultural activities. The computed pollutants include the main criteria pollutants (i.e. NOx, CO, NMVOCs (non-methane volatile organic compounds), SOx, NH3, PM10 and PM2.5) and greenhouse gases (i.e. CO2 and CH4, only related to combustion processes). Specific emission estimation methodologies are provided for each source and are mostly based on (but not limited to) the calculation methodologies reported by the European EMEP/EEA air pollutant emission inventory guidebook. Meteorologically dependent functions are also included to take into account the dynamical component of the emission processes. The model also provides several functionalities for automatically manipulating and performing spatial operations on georeferenced objects (shapefiles and raster files). The model is designed so that it can be applicable to any European country or region where the required input data are available. As in the case of the global_regional module, emissions can be estimated on several user-defined grids, mapped to multiple chemical mechanisms and adapted to the input requirements of different atmospheric chemistry models (CMAQ, WRF-Chem and MONARCH) as well as a street-level dispersion model (R-LINE). Specific emission outputs generated by the model are presented and discussed to illustrate its capabilities.

A critical aspect of predicting soil organic carbon (SOC) concentrations is the lack of available soil information; where information on soil characteristics is available, it is usually focused on regions of high agricultural interest. To date, in Chile, a large proportion of the SOC data have been collected in areas of intensive agricultural or forestry use; however, vast areas beyond these forms of land use have few or no soil data available. Here we present a new SOC database for the country, which is the result of an unprecedented national effort under the framework of the Global Soil Partnership. This partnership has helped build the largest database of SOC to date in Chile, named the Chilean Soil Organic Carbon database (CHLSOC), comprising 13 612 data points compiled from numerous sources, including unpublished and difficult-to-access data. The database will allow users to fill spatial gaps where no SOC estimates were publicly available previously. Presented values of SOC range from 6×10-5 % to 83.3 %, reflecting the variety of ecosystems that exist in Chile. The database has the potential to inform and test current models that predict SOC stocks and dynamics at larger spatial scales, thus enabling benefits from the richness of geochemical, topographic and climatic variability in Chile. The database is freely available to registered users at https://doi.org/10.17605/OSF.IO/NMYS3 (Pfeiffer et al., 2019b) under the Creative Commons Attribution 4.0 International Public License.

Historical and cultural building conservation is often limited to high value buildings and only receives financial support when serious issues arise. Modern technologies are able to support and encourage a preventive approach, diminishing costs and avoiding possibly irreparable damage. One way of doing so is by improving the work-flow of building inspectors during their inspection r outines. In that regard, mixed reality presents itself as an attractive framework for easing maintenance tasks. This paper describes our current implementation of such a solution, levering the spatial capabilities of Microsoft’s HoloLens glasses.

The NO2 annual air quality limit value is systematically exceeded in many European cities. In this context, understanding human exposure, improving policy and planning, and providing forecasts requires the development of accurate air quality models at the urban (street level) scale. We describe CALIOPE-Urban, a system coupling CALIOPE – an operational mesoscale air quality forecast system based on the HERMES (emissions), WRF (meteorology) and CMAQ (chemistry) models – with the urban roadway dispersion model R-LINE. Our developments have focused on Barcelona city (Spain), but the methodology may be replicated for other cities in the future. WRF drives pollutant dispersion and CMAQ provides background concentrations to R-LINE. Key features of our system include the adaptation of R-LINE to street canyons, the use of a new methodology that considers upwind grid cells in CMAQ to avoid double counting traffic emissions, a new method to estimate local surface roughness within street canyons, and a vertical mixing parameterisation that considers urban geometry and atmospheric stability to calculate surface level background concentrations. We show that the latter is critical to correct the night-time overestimations in our system. Both CALIOPE and CALIOPE-Urban are evaluated using two sets of observations. The temporal variability is evaluated against measurements from five traffic sites and one urban background site for April–May 2013. While both systems show a fairly good agreement at the urban background site, CALIOPE-Urban shows a better agreement at traffic sites. The spatial variability is evaluated using 182 passive dosimeters that were distributed across Barcelona during 2 weeks for February–March 2017. In this case, the coupled system also shows a more realistic distribution than the mesoscale system, which systematically underpredicts NO2 close to traffic emission sources. Overall CALIOPE-Urban improves mesoscale model results, demonstrating that the combination of both scales provides a more realistic representation of NO2 spatio-temporal variability in Barcelona.

We present the High-Elective Resolution Modelling Emission System version 3 (HERMESv3), an open source, parallel and stand-alone multi-scale atmospheric emission modelling framework that computes gaseous and aerosol emissions for use in atmospheric chemistry models. HERMESv3 is coded in Python and consists of a global_regional module and a bottom_up module that can be either combined or executed separately. In this contribution (Part 1) we describe the global_regional module, a customizable emission processing system that calculates emissions from different sources, regions and pollutants on a user-specified global or regional grid. The user can flexibly define combinations of existing up-to-date global and regional emission inventories and apply country-specific scaling factors and masks. Each emission inventory is individually processed using user-defined vertical, temporal and speciation profiles that allow obtaining emission outputs compatible with multiple chemical mechanisms (e.g. Carbon-Bond 05). The selection and combination of emission inventories and databases is done through detailed configuration files providing the user with a widely applicable framework for designing, choosing and adjusting the emission modelling experiment without modifying the HERMESv3 source code. The generated emission fields have been successfully tested in different atmospheric chemistry models (i.e. CMAQ, WRF-Chem and NMMB-MONARCH) at multiple spatial and temporal resolutions. In a companion article (Part 2; Guevara et al., 2019) we describe the bottom_up module, which estimates emissions at the source level (e.g. road link) combining state-of-the-art bottom–up methods with local activity and emission factors.

It is well established that in Europe, high O3 concentrations are most pronounced in southern/Mediterranean countries due to the more favourable climatological conditions for its formation. However, the contribution of the different sources of precursors to O3 formation within each country relative to the imported (regional and hemispheric) O3 is poorly quantified. This lack of quantitative knowledge prevents local authorities from effectively designing plans that reduce the exceedances of the O3 target value set by the European air quality directive. O3 source attribution is a challenge because the concentration at each location and time results not only from local biogenic and anthropogenic precursors, but also from the transport of O3 and precursors from neighbouring regions, O3 regional and hemispheric transport and stratospheric O3 injections. The main goal of this study is to provide a first quantitative estimation of the contribution of the main anthropogenic activity sectors to peak O3 events in Spain relative to the contribution of imported (regional and hemispheric) O3. We also assess the potential of our source apportionment method to improve O3 modelling. Our study applies and thoroughly evaluates a countrywide O3 source apportionment method implemented in the CALIOPE air quality forecast system for Spain at high resolution (4 × 4 km2) over a 10-day period characterized by typical summer conditions in the Iberian Peninsula (IP). The method tags both O3 and its gas precursor emissions from source sectors within one simulation, and each tagged species is subject to the typical physico-chemical processes (advection, vertical mixing, deposition, emission and chemistry) as the actual conditions remain unperturbed. We quantify the individual contributions of the largest NOx local sources to high O3 concentrations compared with the contribution of imported O3. We show, for the first time, that imported O3 is the largest input to the ground-level O3 concentration in the IP, accounting for 46 %–68 % of the daily mean O3 concentration during exceedances of the European target value. The hourly imported O3 increases during typical northwestern advections (70 %–90 %, 60–80 µg m−3), and decreases during typical stagnant conditions (30 %–40 %, 30–60 µg m−3) due to the local NO titration. During stagnant conditions, the local anthropogenic precursors control the O3 peaks in areas downwind of the main urban and industrial regions (up to 40 % in hourly peaks). We also show that ground-level O3 concentrations are strongly affected by vertical mixing of O3-rich layers present in the free troposphere, which result from local/regional layering and accumulation, and continental/hemispheric transport. Indeed, vertical mixing largely explains the presence of imported O3 at ground level in the IP. Our results demonstrate the need for detailed quantification of the local and remote contributions to high O3 concentrations for local O3 management, and show O3 source apportionment to be an essential analysis prior to the design of O3 mitigation plans in any non-attainment area. Achieving the European O3 objectives in southern Europe requires not only ad hoc local actions but also decided national and European-wide strategies.

Country-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM). We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5  ×  5 km pixel resolution) were obtained from ISRIC – World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ∼  1 and ∼  60 %, with no universal predictive algorithm among countries. A regional (n  =  11 268 SOC estimates) ensemble of these five algorithms was able to explain ∼  39 % of SOC variability from repeated 5-fold cross-validation. We report a combined SOC stock of 77.8 ± 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 ± 16.5 Pg) and croplands (13 ± 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 ± 42.2 and 76.8 ± 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates.

Riverine wetlands are created and transformed by geomorphological processes that determine their vegetation composition, primary production and soil accretion, all of which are likely to influence C stocks. Here, we compared ecosystem C stocks (trees, soil and downed wood) and soil N stocks of different types of riverine wetlands (marsh, peat swamp forest and mangroves) whose distribution spans from an environment dominated by river forces to an estuarine environment dominated by coastal processes. We also estimated soil C sequestration rates of mangroves on the basis of soil C accumulation. We predicted that C stocks in mangroves and peat swamps would be larger than marshes, and that C, N stocks and C sequestration rates would be larger in the upper compared to the lower estuary. Mean C stocks in mangroves and peat swamps (784.5 ± 73.5 and 722.2 ± 63.6 MgC ha−1, respectively) were higher than those of marshes (336.5 ± 38.3 MgC ha−1). Soil C and N stocks of mangroves were highest in the upper estuary and decreased towards the lower estuary. C stock variability within mangroves was much lower in the upper estuary (range 744–912 MgC ha−1) compared to the intermediate and lower estuary (range 537–1115 MgC ha−1) probably as a result of a highly dynamic coastline. Soil C sequestration values were 1.3 ± 0.2 MgC ha−1 yr−1 and were similar across sites. Estimations of C stocks within large areas need to include spatial variability related to vegetation composition and geomorphological setting to accurately reflect variability within riverine wetlands.

The CALIOPE Air Quality Forecast System (CALIOPE-AQFS) represents the current state of the art in air quality forecasting systems of high-resolution running on high-performance computing platforms. It provides a 48 h forecast of NO2, O3, SO2, PM10, PM2.5, CO, and C6H6 at a 4 km horizontal resolution over all of Spain, and at a 1 km horizontal resolution over the most populated areas in Spain with complex terrains (the Barcelona (BCN), Madrid (MAD) and Andalusia (AND) domains). Increased horizontal resolution from 4 to 1 km over the aforementioned domains leads to finer textures and more realistic concentration maps, which is justified by the increase in NO2/O3 spatial correlation coefficients from 0.79/0.69 (4 km) to 0.81/0.73 (1 km). High-resolution emissions using the bottom-up HERMESv2.0 model are essential for improving model performance when increasing resolution on an urban scale, but it is still insufficient. Decreasing grid spacing does not reveal the expected improvement in hourly statistics, i.e., decreasing NO2 bias by only ~ 2 μg m−3 and increasing O3 bias by ~ 1 μg m−3. The grid effect is less pronounced for PM10, because part of its mass consists of secondary aerosols, which are less affected than the locally emitted primary components by a decreasing grid size. The resolution increase has the highest impact over Barcelona, where air flow is controlled mainly by mesoscale phenomena and a lower planetary boundary layer (PBL). Despite the merits and potential uses of the 1-km simulation, the limitations of current model formulations do not allow confirmation of their expected superiority close to highly urbanized areas and large emissions sources. Future work should combine high grid resolutions with techniques that decrease subgrid variability (e.g., stochastic field methods), and also include models that consider urban morphology and thermal parameters.

The aim of the present work was to analyse the multitemporal variability of sediment yield in an agro-forestry system of the Penedès region (NE Spain). In this area high erodible soil parent materials, frequent high intensity rainfall, as well as other external factor such as land use management changes and abandonment of traditional soil conservation measures, favor erosion processes. The Soil and Water Assessment Tool (SWAT) was used to estimate runoff and soil erosion at basin scale during the decade 2001-2010, using detailed soil, land use and relief data. Important differences of runoff and sediment yield along the analyzed years were found. Forest and natural vegetated areas were the responsible for 48.8% of the sediments exported from the basin, being 82.0% of these sediments generated in the bare gully sidewalls of these areas. Agricultural lands produced the rest 51.2% of the total soil losses. In these lands, particularly in vineyards, traditional agricultural practices were removed during past land transformations and the soil is maintained bare most of the year. The selected decade (2001-2010) represent an example of the situation that more and more frequently are observed in the area under the climate change conditions.

We investigate the variation of the oscillation frequency of the Mg2+ and O2− ions in the magnesium oxide lattice due to the interactions of the surface with water monolayers by means of Low Energy Electron Diffraction. Our key result is a new technique to determine the adsorbate vibrations produced by the water monolayers on the surface lattice as a consequence of their change in the surface Debye temperature and its chemical shift. The latter was systematically investigated for different annealing times and for a constant external thermal perturbation in the range of 110–300 K in order to accomplish adsorption or desorption of water monolayers in the surface lattice.

Objectives: To investigate the frequency of malnutrition and associated factors in patients over 65 years of age in a hospital. Methods: We conducted an observational, crosssectional and descriptive study. Department of Nutritional Support, Hospital Medica Sur, Mexico, we evaluated patients over 65 years of age within the first 24 hours of admission. Results: We evaluated 769 patients, 49% of whom were women and 51% were men, with an average age of 75.3 ± 7.7 years. Among the patients evaluated, 53.6% exhibited an altered nutritional state. In addition, 9% were diagnosed as obese and 15% as overweight. Their risk of malnutrition was determined to be 22.5%, and at the time of admission, 7% were malnourished. Conclusion: The prevalence of malnutrition in hospitalized patients over 65 years of age was high. Thus, the early diagnosis of patients who are at risk for malnutrition or who are malnourished is essential and allows for prompt treatment.Objetivos: Investigar la frecuencia de la desnutrición y factores asociados en pacientes mayores de 65 años de edad en un hospital. Métodos: Se realizó un estudio observacional, transversal y descriptivo. En el departamento de Soporte Nutricional del Hospital Médica Sur, México, DF, se evaluaron los pacientes mayores de 65 años de edad en las primeras 24 horas de su ingreso. Resultados: En nuestro estudio evaluamos a 769 pacientes, 49% mujeres y 51% hombres, con una edad promedio de 75,3 ± 7,7 años. Encontramos un estado de nutrición alterado en el 53,6% del total. El 9% fueron diagnosticados con obesidad y el 15% con sobrepeso. El riesgo de desnutrición fue del 22,5% y al ingreso el 7% estaban desnutridos. Conclusión: La prevalencia de un mal estado de nutrición al ingreso es alta. Es indispensable un diagnóstico temprano de los pacientes que se encuentran en riesgo de desnutrición o desnutridos lo cual permitirá el inicio del tratamiento adecuado.

Background: Mexico has the highest rates of childhood obesity of any country in the world. As an environmental risk factor, family perception and behavior concerning child obesity is thought to have an important influence on the prevention and treatment of obesity. Objective: the aim of this study was to evaluate maternal perceptions of children´s weight and to determine whether these perceptions are associated with the actual weight of the children. Methods: A descriptive study was conducted that assessed 273 children and included interviews with their mothers. Maternal perceptions of the children´s weight status were compared with the measured weights, statures and body mass indices (BMI). Results: The mean BMIs of the children and mothers were 18.1 ± 3.6 and 27.7 ± 4.7 kg/m², respectively. The perceptions of mothers of children´s weight are not associated with actual BMI category of children (p < 0.0001). We found 74 children who were overweight and obese (27%); 62.2% of the mothers of these children considered them to be overweight and obese and 37.8% of these mothers felt that their sons were of normal weight when in fact they were overweight (p = 0.038). Conclusions: We found no association between actual BMI category of children and their mother´s perceptions of their weight. In addition, found that third of mothers with obese or overweight children reported that their children had a normal nutritional status.Antecedentes: México tiene una alta prevalencia de obesidad infantil incluso colocándolo en el primer lugar a nivel mundial. Como un factor de riesgo ambiental, la percepción de la familia y el comportamiento relativo a la obesidad infantil se cree que tiene una influencia importante en la prevención y el tratamiento de la obesidad. Métodos: Estudio descriptivo mediante el que se evaluaron 273 niños y se realizaron entrevistas con sus madres. La percepción materna del estado de nutrición o el peso de los niños se compararon con los pesos medidos, tallas e índices de masa corporal (IMC). Resultados: El IMC promedio de los niños y las madres fueron 18,1 ± 3,6 y 27,7 ± 4,75 kg/m², respectivamente. Las percepciones de las madres acerca del peso de los niños no se correlacionaron con la categoría real del IMC de los niños (p < 0,0001). Se encontraron 74 niños (27%) con sobrepeso y obesidad; 62,2% de las madres de estos niños les consideró en la categoría correcta al mencionar que los percibían con sobrepeso y obesidad y 37,8% de estas madres percibieron, que sus hijos tenían un peso normal cuando en realidad tenían sobrepeso (p = 0,038). Conclusiones: No se encontró asociación entre la categoría del IMC de los niños y las percepciones de la madre. Además, la tercera parte de las madres con niños obesos o con sobrepeso informaron que sus hijos tenían un estado nutricional normal.

Antecedentes: Los cambios demográficos en México están aumentando la proporción de adultos mayores de 60 años en riesgo de inseguridad alimentaria. La desnutrición en este grupo de edad es frecuente en la comunidad pero se exacerba durante las hospitalizaciones frecuentes, lo que aumenta la morbi-mortalidad y los costos en los servicios de salud. Objetivo: Describir la frecuencia de desnutrición al ingreso hospitalario de pacientes mayores de 60 años. Diseño: Observacional, transversal y descriptivo. Ubicación: Hospital de tercer nivel de referencia. Sujetos: 97 pacientes mayores de 60 años de edad ingresados consecutivamente a los sectores de hospitalización durante un periodo de seis meses del año 2006. Intervenciones: Al ingreso se realizó la valoración nutricional mediante la aplicación de la Mini Valoración Nutricional (MNA: Mini Nutritional Assessment) y la Valoración Global Subjetiva más la recolección de datos de laboratorio rutinarios (albúmina sérica y linfocitos totales) y del estado socioeconómico y educativo. Resultados: De 97 pacientes incluidos, 48% sólo completaron educación primaria y 66% tenían ingresos económicos medios. La Mini Valoración Nutricional clasificó a 69% de los pacientes con riesgo franco asociado a desnutrición (18% severa y 50% moderada) lo que se correlacion o a la versión corta de este instrumento que identificó a 73% de los individuos en riesgo. La Valoración Global Subjetiva clasificó a 46% de pacientes con desnutrición moderada y 20% con desnutrición severa. El valor de Kappa entre ambos métodos fue del 42%. El Índice de Riesgo Nutricio promedio fue de 85,9 ± 11 puntos, con 80% de los pacientes con riesgo asociado a desnutrición moderada y severa, siendo la concordancia con la Mini Valoración Nutricional de sólo 11%. 70% de los pacientes tuvieron valores de albúmina menores de 3 g/dl. Según el método Chang 52% tuvieron desnutrición calórica, 29% proteica y 18% mixta. Conclusiones: La desnutrición al ingreso hospitalario de los adultos mayores es un hallazgo frecuente a partir de los diferentes métodos aplicados. La Mini Valoración Nutricional y la Valoración Global Subjetiva son métodos estructurados de bajo costo que pueden ser fácilmente aplicados y replicados, útiles para establecer la frecuencia de desnutrición en esta población de pacientes. La Mini Valoración Nutricional podría tener más utilidad como predictor de morbi-mortalidad tanto en los ancianos institucionalizados como los hospitalizados.Background: The population older than 60 years in Mexico is growing changing the classic pyramidal demographic structure. This fact is increasing the risk of malnutrition in the elderly, specially under nutrition which is a common problem among elderly people living at home and during hospitalizations, condition that is closely related to the increasing of morbidity, mortality and costs. Objective: Describe the prevalence of malnourished elderly who needs hospital admission. Design: Cross-sectional observational study. Setting: Third-level reference hospital. Subjects: Ninety seven consecutive subjects older than 60 years admitted to hospitalization. Interventions: During the first three days of admission all subjects were evaluated to determine their nutritional status using Mini Nutritional Assessment and Subjective Global Assessment; albumin, total lymphocytes, level of income and school grade were also included. Results: Just 48% of patients have finished primary school and 66% had middle economic incomes. According to Mini Nutritional Assessment 69% of patients had risk associated to malnutrition (18% at high risk and 50% at moderate risk). The short form of the Mini Nutritional Assessment described 73% of patients at risk related to malnutrition in correlation with the complete Mini Nutritional Assessment. 46% and 20% of patients were classified at moderate malnutrition and severe malnutrition respectively using the Subjective Global Assessment. Kappa between Mini Nutritional Assessment and Subjective Global Assessment was of 42%. The Nutritional Risk Index mean value was of 85.9 ± 11, with 80% of patients at risk associated with malnutrition when moderate and severe risk was included. Kappa between Nutritional Risk Index and Mini Nutritional Assessment was 11%. 70% of patients had serum albumin values under 3 g/dl. According to Chang's method 52% had caloric undernutrition, 29% protein undernutrition and 18% mixed undernutrition. Conclusions: Malnutrition is a common problem in elderly population at hospital admissions according to different methods used. Mini Nutritional Assessment and Subjective Global Assessment are useful low cost and replicable nutritional evaluation tools in elderly population. Mini Nutritional Assessment could have a better value to predict morbidity and mortality in institutionalized and community elderly subjects.

Se presenta la metodología empleada en la calibración y evaluación de la técnica de espectrometría de rayos-X aplicada a la determinación de elementos traza y mayores en muestras geológicas. Esta metodología se ha probado analizando muestras internacionales de referencia geoquímica. Se comparan los datos obtenidos con valores recomendados en la literatura. Con base en esta información, se ha evaluado el error relativo para cada elemento, obteniéndose en general valores de 10% a 20% para trazas y < 5% para mayores. Se presentan en forma gráfica los resultados de comparación para algunas muestras, con el fin de ejemplificar el comportamiento de los elementos calibrados. doi: https://doi.org/10.22201/igeof.00167169p.1993.32.2.564