dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). This study aims at exploring ammonia emission potential and volatilization in Europe, using infrared satellite data of ammonia columns, reanalysis temperature data, and chemical transport model simulations to provide information on chemical sources and sinks. We specifically study the relationship between satellite-derived ammonia concentration at the start of the growing season, soil emission potentials, and their spatial variability over Europe in March of 2011. Section 2 provides the methods and datasets used. The results are described in Sect. 3, including simulation from GEOS-Chem in Sect. 3. 1. Regional emission potentials are shown and discussed in Sect. 3. 2. Using a climate model, future projections of ammonia columns are investigated under different climate scenarios in Sect.
, 2009). The IASI Fourier-transform spectrometer monitors the atmosphere in the spectral range between 645 and 2760 cm -1 (thermal infrared) and is nadir-looking. IASI has a swath width that measures 2200 km, with a pixel size of ∼ 12 km. Ammonia was first detected with IASI using the ν 2 vibrational band (∼ 950 cm -1 )  (Clerbaux et al. , 2009; Coheur et al. , 2009). The ammonia total columns used in this study are the product of an artificial neural network and reanalyzed temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) product ERA5 ANNI-NH 3 -v3R-ERA5  (Van Damme et al. , 2021). Several studies used ammonia data from IASI to study hotspots of ammonia of different source types, including both natural and anthropogenic sources  (Clarisse et al. , 2019a, b; Dammers et al.
In order to assign a mass transfer coefficient k to each land type, we used data from the Moderate Resolution Imaging Spectroradiometer (MODIS), a series of instruments orbiting the Earth aboard the Aqua and Terra satellites. The data product MCD12Q1 (version 6) is a combined Aqua-Terra land cover type product, with a spatial resolution of 500 m. This product provides maps of land cover type from 2001 through 2019  (Sulla-Menashe and Friedl, 2018). From the land use categories included in the MOD12Q1 product  (Belward et al. , 1999)  we focus on croplands, forests, shrublands, and grasslands. We do not include bare lands, snow cover, and urban areas in our analysis; we are not interested in studying these surfaces, since we focus on ammonia volatilization from the soil in areas where fertilizers are applied.
Output includes the hourly mean for selected diagnostics. Anthropogenic emissions are taken primarily from the global Community Emissions Data System (CEDS) inventory  (Hoesly et al. , 2018). Biogenic non-agricultural ammonia, as well as ocean ammonia sources, are taken from the Global Emission Inventories Activities database (GEIA;  Bouwman et al. , 1997). Open-fire emissions are generated using the GFED 4. 1s inventory  (Randerson et al. , 2015). We used the Harmonized Emissions Component module (HEMCO) to obtain the ammonia emissions over Europe  (Keller et al. , 2014).
GEOS-Chem validation with IASI To analyze how well the model simulates atmospheric ammonia, we compare the simulated GEOS-Chem hourly averaged (March 2011) ammonia total column output (Sect. 2. 3. 1) with the IASI NH 3 total columns gridded at the same horizontal resolution (0. 5 • × 0. 625 • ) and during the same month. We applied a temporal coincidence criterion to GEOS-Chem outputs in order to compare them with IASI morning observations. For instance, we selected data between 08:30 and 11:30 UTC in the GEOS-Chem model output. Figure  1  shows the IASI NH 3 distribution (Fig. 1a ) and that from GEOS-Chem (Fig. 1b ), the bias between the two (Fig. 1c ), and the mean relative difference MRD (Fig. 1d ), all in March 2011. MRD is calculated as the mean of the ratio (GeosChem NH 3 -IASI NH 3 )×100 IASI NH 3 at each grid point.
Generally, both GEOS-Chem and IASI show coincident sources of ammonia, reflecting the good ability of the model to reproduce ammonia columns over major agricultural source regions in Europe. The bias between IASI and GEOS-Chem and the mean relative difference (MRD) are shown in Fig. 1c  and d. Ammonia columns from GEOS-Chem are underestimated by up to 2 × 10 16 molec. cm -2 in some source regions/hotspots, especially in England, northeastern France, the North European Plain (the Netherlands, Belgium), and Spain (around Barcelona). Similar results were found in the study of  Whitburn et al. (2016) , in which they show that GEOS-Chem underestimates ammonia columns by up to 1 × 10 16 molec. cm -2 in Europe on average in 2009, notably in the North European Plain. It is important to note that, in our study, we compare only 1 month of data  (March, 2011) , which marks the start of the growing season in the majority of the countries of interest (FAO, 2022; USDA, 2022).
The differences are most likely due to the fact that, with IASI, cloud-free data are used to retrieve ammonia. In most of the studied regions, the MRD is around 50 % (± 7 %) in absolute value; for instance, in Brittany MRD = -43 %, whereas in both Barcelona and Valladolid in Spain, it is -57 %. While England shows the highest MRD value in absolute terms (-79 %), the best-represented region is the Po Valley (+0. 1 %), followed by the region of New Aquitaine in the southwest of France (-34. 1 %) (see Table  1 ). A summary of the results of this study, including the MRD over some source regions, is listed in Table  1. Although the biases and MRD values can be considered to be high, the spatial distribution is consistent between IASI and GEOS-Chem. Therefore, we assume that meteorological and soil parameters affecting one dataset (e.
6  and listed in Table  1. Table  1  shows the average lifetime from GEOS-Chem (hours), the average T skin from the two datasets that we used ( • C), the average ammonia emission potential in all the cases examined (dimensionless), and the average ammonia columns from IASI and GEOS-Chem (molec. cm -2 ). The four cases show a similar pattern, with the North European Plain exhibiting the highest emission potential. This is shown in Figs. 1, 2,  and 5 , as well as in Table  1 , where soil is higher in regions with high ammonia columns. This is expected in fertilized lands (croplands), since soil is proportional to the concentration of ammonia near the surface. The latter increases when the soil content of ammonium (NH + 4 ) increases following the application of nitrogen-based fertilizers. Figure  6  also shows that for cases 1 and 2 (GEOS-Chem) the emission potential in the Po Valley is almost equal to case 3 (IASI), with soil = 0.
Conclusions Agriculture worldwide has fed the human race for thousands of years and will continue to do so, as humankind highly relies on it. Emissions from agricultural activities will inevitably increase in order to meet the expected yield. In this study, we use a variety of state-of-the-art datasets (satellite, reanalysis, and model simulation) to calculate the first regional map of ammonia emission potential during the start of the growing season in Europe. The emission potential can be used as a proxy to calculate ammonia columns in the atmosphere and as such to assess its deposition, atmospheric transport, and contribution to PM formation. First, we show that the GEOS-Chem chemistry transport model is able to reproduce key spatio-temporal patterns of ammonia levels over Europe. The ammonia budget is governed by the emissions over source regions (North European Plain, Brittany, and the Po Valley), as well as by key loss processes.
Data availability. The IASI NH 3 data used in this study are retrieved from the Aeris data infrastructure (https://iasi. aeris-data. fr/ nh3r-era5/, LATMOS-ULB, 2023). ERA5 skin temperature from 1979 to present is available for download from the following DOI: https://doi. org/10. 24381/cds. adbb2d47  (Hersbach et al. , 2023). The GEOS-Chem outputs used in this study are only available upon request. EC-Earth3 model output prepared for CMIP6 ScenarioMIP is retrieved here: https://doi. org/10. 22033/ESGF/CMIP6. 727 (EC-Earth Consortium, 2019). The MODIS land cover data are available for download from the following link: https://doi. org/10. 5067/ MODIS/MCD12Q1. 006  (Friedl and Sulla-Menashe, 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10.
For the supermicron size, lters were changed less frequently (every 2-13 days) and sampling was not done continuously, giving rise to signicant data gaps (see ESI Fig. S1 † ). Mass concentrations of supermicron aerosols reported here correspond to the sum of the top four stages (aerodynamic diameter > 0. 95 mm) of the impactor. Samples were analyzed for water-soluble inorganic ions (SO 4 2-, NH 4 + , K + , Na + , Mg 2+ , Ca 2+ , Cl -, Br -, NO 2 -, NO 3 -) and organic carbon (both water-soluble and insoluble). Watersoluble aerosols were extracted from the lters using ultrapure water, followed by ltration through a syringe lter (0. 22 mm). Inorganic ions in the extract solution were quantied using an ion chromatograph (Model 761 compact IC, Metrohm). 2. 2. 1. 6. BrO, IO, and SO 2 slant column densities.
Spectra are then analyzed by DOAS to obtain slant column densities of SO 2 , BrO, and other species relative to the background. Evaluations were done in the 314-327 nm and 331-353 nm for SO 2 and BrO, respectively, and included cross-sections for O 3 absorption, the Ring-effect, a 5th-degree polynomial, and generic corrections for dark current, offset, stray-light, and wavelength shis (against a synthetic solar-atlas). The line density obtained by integrating the columns in the cross-section of the plume is multiplied by the normal component of wind velocity at plume height to derive the ux. The height and direction of the center of mass of the plume are calculated by triangulation from simultaneous measurements by two stations. For this study, we used plume velocity data from the ERA5 re-analysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF), with a time resolution of 1 h.
Data availability Maïdo L2 GEM data (https://doi. org/10. 25326/352 )are freely available 116 at https://gmos. aeris-data. fr/(last access:  30 January 2023 ) from the GMOS-FR data portal coordinated by  IGE (Institut des Géosciences de l'Environnement -Grenoble , France; technical PI:  Olivier Magand ) and maintained by the French national center for atmospheric data and services (AERIS). O 2 -O 2 , SO 2 ,  BrO , and IO differential slant column densities (dSCDs) measured by the  University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS ) are free available at https://doi. org/10. 5281/zenodo. 7864745. DMPS data are available from the EBAS Data Centre (https://ebas. nilu. no/;last access April 28th, 2023). MultiGas and DOAS data from OVPF (IPGP) are available upon request.
Acknowledgements This publication is part of the  GMOS - Train  project that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No. 860497. Support was received from the  European Union 's  Horizon 2020 Research and Innovation programme (ACTRIS TNA ; grant agreement no. 654109 ). The support from the  Academy of Finland  ( 331207 ) is appreciated. R. V. acknowledges funding from the  US National Science Foundation  ( AGS-1620530 ,  AGS-1951514 ). TJR acknowledges  ANR PRC VOLC-HAL-CLIM ANR-18-CE01-0018. S. A acknowledges funding from the  Swedish National Space Agency (Dnr. 149/18 ). Maïdo GEM data were collected via instruments coordinated by the IGE-PTICHA technical platform dedicated to atmospheric chemistry eld instrumentation.
Meanwhile, Mrs. Russo's children have organized a home nursing service for her that comes twice a day to support her while her husband is away, along with a domestic help to assist her with the housework. Dr. Andersson receives discharge letters from both Ploetzberg Hospital as well as the Kreikebohm Rehabilitation Centre. She adapts his medication according to the cardiologists' advice. Along with his wife, Mr. Russo enrolls in a support program arranged by his health insurance company where he uses an app on his mobile phone to enter data on his physical and mental well-being and his weight. Furthermore, he receives an activity tracker that also measures his heart rate. Among other things, Dr. Andersson uses this data to manage the course of his disease and for adapting her treatment. Researchers from Ploetzberg Hospital ask Mr.
Population and study design This study was embedded in the Generation R Study, a prospective population-based cohort from early in pregnancy onwards in the city of Rotterdam, the Netherlands. (Kooijman et al. , 2016)  The study design and participant sampling has been described in detail previously. (Jaddoe et al. , 2006)  In brief, mothers were eligible for the cohort if they had an expected delivery date from April 2002 until January 2006 and were living in the study area. In total, 8879 mothers were enrolled in the full Generation R study during pregnancy. For the present study, we included a total of 7947 women (Fig. 1 ). We excluded twin pregnancies, terminated pregnancies and intra-uterine deaths. Moreover, pregnancies without any data on noise exposure during pregnancy and with no measurements of embryonic size, fetal growth or birth outcomes were excluded.
Noise levels were estimated at a height of 4 m at the most exposed façade of the residential addresses. An intersect was done from the buildings noise data with the geocodes. In case the geocode was outside the building, but in less than 50 m, it was assigned to the closest building (3. 8% of the addresses). For each noise source, the day-evening-night noise indicator (L DEN ) was calculated as the A-weighted average sound level over the entire 24-hour day with penalties for the evening (+5 decibel (dB); 4 h, 19:00-23:00) and the night (+10 dB, 8 h, 23:00-07:00). (Gan et al. , 2012)  The specific equations used for the noise estimations of each noise source and the total noise exposure are described in Methods S1. The levels of L DEN of each noise exposure source and of the total noise exposure were applied to each geocoded participant's address reported for the pregnancy period.
Traffic-related air pollution exposure The concentrations of nitrogen dioxide (NO 2 ) and particulate matter with an aerodynamic diameter of less than 2. 5 μm (PM 2. 5 ) during pregnancy (i. e. , from conception until birth) were estimated using landuse regression models. (Beelen et al. , 2013; Eeftens et al. , 2012)  Between February 2009 and February 2010, NO 2 was measured in 80 sites spread throughout the Netherlands and Belgium and PM 2. 5 in 40 of these sites. An annual mean concentration for each pollutant was calculated averaging the results of all measurements, while using data from a continuous reference site to adjust for temporal trends. Next, land-use regression models were developed, and air pollution levels were estimated at each geocoded participant's address reported for the pregnancy period.
Statistical analyses 2. 8. 1. Missing data The percentage of missing values of covariates was lower than 20%, except for information on monthly household income and folic acid use during pregnancy (22. 9% and 25. 5%, respectively). Missing data of the covariates were imputed using the Markov chain Monte Carlo approach for multiple imputations to limit selection bias and loss of statistical power. A total of 25 complete datasets were obtained and analyzed using standard procedures for multiple imputation (i. e. , Rubin's rules). (Graham et al. , 2007; Sterne et al. , 2009)  Distributions in the imputed and observed datasets were comparable (Table  S1 ).
Mònica Guxens received funding from the Spanish Institute of Health Carlos III (PI17/01340 including FEDER funds, CPII18/00018). We thank DCMR Milieudienst Rijnmond for providing the noise data. The geocodification of the addresses of the Dutch study participants was done within the framework of a project funded by the Health Effects Institute (HEI) (Assistance Award No. R-82811201). We acknowledge support from the Spanish Ministry of Science and Innovation and the State Research Agency through the "Centro de Excelencia Severo Ochoa 2019-2023" Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. Esmée Essers was funded by the EU commission via a fellowship within the LifeCycle Project (733206) and by the Agence Nationale de Securité de Sanitaire de l'Alimentation de l.
Ecological Indicators from Remote Sensing Phytoplankton chlorophyll concentration is not the only piece of information on ocean ecology and ecosystems that is accessible to remote sensing. Over the years, a formidable range of ecological indicators have been developed from ocean-colour data (Table  3 ). Chlorophyll concentration underlies many of these products, though not necessarily all of them. For example, combining chlorophyll data with light available at the sea surface and photosynthesis models, it has been possible to compute marine primary production. The spectrally-resolved radiance data have been used to develop algorithms for detection of phytoplankton functional types and phytoplankton size structure (IOCCG 2014). Information on other environmental variables have been used to improve ocean-colour algorithms; for example, light and temperature have been used to refine models for detection of phytoplankton size classes from space  (Brewin et al.
2018; Skákala et al. 2018 ). In the climate context, in the absence of long time series of ocean-colour products, one relied heavily on models for understanding threats to the marine ecosystems from climate change, and to predict future changes. But as the time series of ocean-colour data grows-for example, the ocean-colour time series from the Climate Change Initiative is now 24 years long-the satellite data are being used increasingly to test model outputs and to examine whether model structures are appropriate for detection of climate impacts on the ocean biota. Until recently, quantitative comparisons of model outputs and satellite products have been hampered by the differences in the currencies used in the two approaches: ecosystem models use biogeochemically-important variables, such as carbon or nitrogen, as their main currency.
Acknowledgements This work is a contribution to the  Simons Foundation   Project Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES , number  549947 , S. S. ). This paper is also a contribution to the  ESA projects   Ocean Colour Climate Change Initiative (OC-CCI) ;  Biological Pump and Carbon Exchange Processes (BICEP ) ;  Biodiversity of the Coastal Ocean: Monitoring with Earth Observation (BiCOME) ; and  Biodiversity in the Open Ocean: Mapping, Monitoring and Modelling (BOOMS ). Additional support from the  National Centre for Earth Observations (UK)  is also gratefully acknowledged.
Acknowledgements We thank Stan Finney and Chen Xu  for their review comments that together with those of the editor  Ian Percival  improved our paper. We also thank  Stan Finney  for his many years of engagement in discussions with us on Ordovician rocks and their fossils which are reflected in our paper. Harper thanks the  Leverhulme Trust  for support and Meidla is grateful for financial support from the  University of Tartu , Estonia. This paper is a contribution to the  International Geoscience Programme (IGCP)  projects  653  'The onset of the  Great Ordovician Biodiversification Event' and 735 'Rocks  and the  Rise of Ordovician Life: Filling knowledge gaps in the Early Palaeozoic Biodiversification '.
Data collection Research was conducted to extract salient information from secondary data sources such as organisational reports, published articles and reports. Table  1  provides an illustration of the different data sources used in this study. The reference energy system (RES) is a simplified graphical representation of the energy system under consideration. It represents the different fuel inputs, conversion technologies, transmission and distribution, and the final electricity demand. The RES can be built in compliance with current and predicted energy policies. Fig. 1  depicts the RES of the Mauritius model with the OSeMOSYS standard nomenclature.
• Techno-economic parameters The different costs associated with the power plant technologies and the technology performance parameters are important considerations that define the overall performance of the energy model optimisation given that OSeMOSYS aims at finding the least cost energy mix to sustain the demand. The cost of fuel importation (variable costs of fuel imports) includes the purchasing cost, the insurance and freight price. The average costs of fuel imports were calculated using data for the years 2011-2020 from the Statistics Mauritius Energy Report.
More precisely, the purpose of this study was to gather daily information from a representative sample of Facebook's Active User Base (FAUB), who were invited to participate in the survey to consult them about various COVID-19-related characteristics, including symptoms, PCR test outcomes, vaccination acceptance, isolation measures, and demographics. Questionnaires were translated into 56 languages, and data were collected from 114 countries/territories, reaching a wide range of social and economic groups. Note also that the UMD-CTIS data provide fine-grained coverage of pandemic trends, which permits estimating various health indicator trends for different regions. In this paper, we introduce a machine-learning approach that includes tree-based supervised classifiers and featureselection strategies to detect COVID-19-active cases.
Study population We evaluate the performance of the proposed detection approach using datasets extracted from the UMD-CTIS collected for four countries: Brazil (BR), Canada (CA), Japan (JP), and South Africa (ZA). Geographic diversity and the availability of sufficient data samples were considered when selecting these countries. In addition, we evaluate the detection methods for two periods: 2020 (from April 23 to December 31, 2020), and 2021 (from January 1 to December 31, 2021). We selected these periods to observe the impact of the vaccination campaigns on the selection of relevant features during the training stage and the estimation accuracy. Afterward, for each country and period, we extract responses from participants who have reported at least one symptom during the previous 24 hours and have provided a test result within the past 14 days.
Machine Learning Methods Machine-learning (ML) models used in this work are tree-based classifiers  [24, 25]. Specifically, we will use random forest (RF), extreme gradient boosting (XGB), and light gradient boosting (LGB). These models provide relevance rankings of the input variables, which serves as the basis for performing an explainability analysis process. Now, each model is presented: • Random Forest (RF)  [26, 24, 25] : RF consists of a set of decision trees, each generated by a bagging algorithm. These trees form a "forest" of trees voting for a specific result. This algorithm uses bootstrapping to fit decision trees with sub-samples of the original dataset. For each tree, the algorithm uses averaging to improve predictive accuracy and control overfitting. In classification algorithms, the most common output will be chosen as the final output of the algorithm.