dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast, the results from LSH areas were more diverse and matched the three SOFD patterns. Therefore, care should be taken when extrapolating our results to other landscape development types. Land-sparing urban areas have many rare satellite species that increase the breeding bird diversity at the city level. However, many satellite species occur only at a few restricted sites; city inhabitants seldom encounter them. Thus, we assume that easily observed core species, such as sparrows, blue and great tits, and European blackbirds, are important for people connected with nature in city areas. Our data suggest that urban planners should select an LSH development type to increase the number of common bird species but an LSP development type if the aim is to promote a higher number of rare species.
Analytical techniques X-ray diffraction (XRD) has been widely used for the characterization of crystalline materials (Kohli and Mittal, 2019). Therefore, the mineralogical compositions of the aerosol and soil samples were determined using an Empyrean X-ray diffractometer (Malvern Panalytical, with CuKα radiation) operated with a PIXcel 3D detector. The mineral phases were identified and quantified by the Rietveld method  (Rietveld, 1969)  using the HIGHScore v4. 5 software and the ICDD (International Center for Diffraction Data) and ICSD (Inorganic Crystal Structure Database) databases. The OC content was derived using a thermal-optical technique (Sunset Lab) based on the procedure from  Birch and Cary (1996). Briefly, the quartz filters were introduced into an oven, where the samples were volatilized and oxidized to CO 2.
Spectra from 10 separate epochs were taken over a period of 11. 3 hr centered on 2006 July 24 at 10:52 UTC. The observations used the low-  resolution module (R = Δλ/λ ∼ 60-130) spanning the wavelengths 7. 5-40 μm in four separate spectral segments ("spectral orders" in IRS terminology). We extracted spectra from pipeline-processed data products (version S18. 18. 0) using the SPICE tool. Each of the extracted spectral orders (40 spectra each, 587 s exposure time) were scaled to the median flux density and averaged together, primarily to account for rotational variability but also misalignment between the slits and asteroid position. A potential artifact known as the "teardrop" (Spitzer Science Center 2012) was avoided by removing the 13. 5-14. 3 μm region from the shortest order in our data set.
Small discontinuities remained between spectral orders. We therefore extrapolated neighboring spectral orders to a common wavelength to derive order-to-order scaling factors and produce a near-continuous spectrum. The largest correction was 6%. To determine the emissivity spectra from the SEDs, we fit a modeled SED, which is essentially a blackbody spectrum, to each asteroid SED using C++ code written by  Mueller (2018)  based on the Near-Earth Asteroid Thermal Model (NEATM; Harris 1998) and using the same NEATM parameters (H v , G, r, Δ, and α) as  Emery et al. (2006;  see Table  1 ). The optimal fit of the modeled SED to the asteroid SED for each asteroid was achieved by adding a χ 2 minimization routine to the Mueller (2018) code to iterate over values within the uncertainties of p v and η.
Note that the uncertainties provided in Table  1  for (944) Hidalgo are only the values we iterated in between for p v and η. They are not uncertainties in the values of p v and η. This is because we obtained the values (H v , G, r, Δ, and α) for (944) Hidalgo from the JPL Small-Body Database Browser 2021, and there are no uncertainties associated with the values. We then divided the asteroid SED by the modeled SED to obtain the emissivity spectrum for each asteroid shown in Figure  2. The NEATM fit for each asteroid was over the TIR (∼5-38 μm). We have restricted the MSTM and Hapke modeling to the wavelength region around ∼7. 5-13. 5 μm shown in panels (b), (d), (f), and (h) in Figure  2  because the 10 μm plateau region is the most prominent spectral feature of the asteroid spectra (Figure  1 ).
2. 1. Additional user-specified parameters for MSTM may be found in the Appendix. The minerals in Table  2  were chosen for modeling based on those present in comet Hale-Bopp, C03 carbonaceous chondrite meteorites, and CY chondrite meteorites, which show a prominent 10 μm feature in their spectra  (Crovisier et al. 2000; Emery et al. 2006; Bates et al. 2021). These planetary bodies are composed of silicates, mainly olivine and pyroxene, with minor amounts of other silicates, including phyllosilicates, sulfides, and carbonates  (Brearley & Jones 1998, pp. 313-398; Crovisier et al. 2000; Bates et al. 2021). The particle positions and radii were specified using a Python script we wrote that generates the x, y, and z coordinates of each monodispersed, spherical particle in a rectangular prism (the size of the prism was based on the assumption that ρV m = nV p , where ρ is the packing density, V m is the volume of the whole medium, n is the number of particles, and V p is the volume of particles).
References. (1) See Tables  1  and 2  of  Emery et al. (2006)  for Trojan asteroid parameters; (2) See JPL Small-Body Database Browser 2021 for (944) Hidalgo parameters. Initially, we modeled each mineral at the 0. 5 μm radius size. We used ∼200-300 nonoverlapping particles (to avoid strange transmission effects when k is low). There are two main parameters that affect the computational time. One is the size parameter (ratio of the particle radius to the wavelength of light), so the code will take longer to run at short wavelengths (where the particle size/wavelength ratio is larger) than at longer wavelengths. The other parameter that increases the computational time is whether MSTM is run in fixed or random orientation of light (see the Appendix for more details). A rectangular prism of 200-300 particles at the 0.
The H(μ o ) and H(μ) in Equation (1) are the Ambartsumian-Chandrasekhar H function (Equation (8. 53) in Hapke 2012), approximated by m m m @ + + ( ) ( ) H 1 2 1 2 2 and m m m @ + + ( ) ( ) H 1 2 1 2. 3 o o o Python code written by Dr. Jessica Arnold (J. Arnold, personal communication, 2020 July 17) that computes Equations (1), (  2 ) and (3) and implements the miepython module  6  was used to determine the Hapke bidirectional reflectance. After the calculation of the bidirectional reflectance, the modeled emissivity spectrum for a single mineral is obtained by inverting the reflectance using Kirchhoff's law (E = 1 -R, where E is the emissivity and R is the reflectance).
Special thanks to Neil Bowles, Timothy Glotch, Audrey Martin, and Helena Bates for their assistance in this work. V. C. L. , K. L. D. H. , and H. C. are supported under NASA cooperative agreement 80NSSC19M0214 to the Center for Lunar and Asteroid Surface Science (CLASS) funded by NASA's Solar System Exploration Research Virtual Institute at the University of Central Florida. H. C. also acknowledges support from NASA's Near-Earth Object Observations program. G. I. is supported by a CNRS Momentum grant.
PUBLIC SUMMARY 1. Here we present for the first time the near-real-time Global Gridded Daily CO 2 Emissions Dataset (called GRACED). 2. GRACED can be updated almost in near-real-time with a spatial-resolution of 0. 1° by 0. 1° and a temporal-resolution of 1-day. 3. GRACED shows gridded emissions of seven sectors, including power, industry, residential consumption, ground transport, domestic aviation, international aviation, and international shipping. 4. Regular updates of GRACED will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt on various spatial scales.
INTRODUCTION Although human emissions of carbon dioxide (CO 2 ) to the atmosphere are the main cause of global climate change, detailed and spatially-explicit estimates of such emissions are updated infrequently, typically lagging emissions by at least a year. However, with the rising ambition of climate policies and mitigation efforts, 1,2 a reliable, spatially-explicit and up-to-date dataset of fossil CO 2 emissions is becoming increasingly important. For example, such detailed data is necessary to link emissions to observable atmospheric concentration signals and constrain regional CO 2 fluxes, and can help decision makers to more quickly assess both the effectiveness of policies and local priorities for further mitigation. 3, 4 ince the end of 2019, the COVID-19 pandemic has caused major disruptions of human activities and energy use.
Governments around the world have imposed compulsory lockdowns that restrict in-person educational and commercial activities to reduce the spread of coronavirus. 5] [6]  As lockdown restrictions have relaxed in many countries and economic activities have recovered in some sectors, the effect of the pandemic on CO 2 emissions has weakened, even during large "second waves" of cases. A timely and finely-gridded emissions dataset enables quantitative analysis of how temporal and spatial changes in CO 2 emissions in each country in response to emergencies (e. g. COVID-19) and other disturbances of human activities, and helps constraining predictions of future trends. Existing datasets of global gridded (i. e. 1] [12] [13]  Another prominent example is the Emission Database for Global Atmospheric Research (EDGAR).
EDGAR estimates J o u r n a l P r e -p r o o f emissions based on national CO 2 emissions reported by the Global Carbon Project (GCP) and emission factors, broken down to IPCC-relevant source-sector levels. EDGAR uses spatial geospatial proxies such as point and line source locations at a 0. 1°×0. 1° ] [16] [17]  More recently, The Global Carbon Grid (http://gidmodel. org) establishes high-resolution maps of global CO 2 emissions from fossil fuel combustion and cement production based on a framework that integrates multiple data flows including point sources, country-level sectoral activities and emissions, and transport emissions and distributions. The Global Carbon Grid v1. 0 provides global 0. 1°×0. 1° ] [20]  Even the most current of the gridded CO 2 emissions datasets described above lag emissions by a year or more and do not reflect sub-monthly temporal variations related to seasonality, weather, economic activities, or policies.
Nassar et al. made a first attempt to further downscale these global datasets at the weekly and diurnal scale using static local temporal scaling factors. 21 However, during a normal year, day-to-day variations are due mainly to weather impacting heating/cooling demands of residences and commercial buildings and the generation of renewable energy, as well as weekends and holidays. Since the pandemic began in early 2020, though, daily variations have been perturbed by a multitude of other factors, including lockdowns, industrial production drops and recoveries, and changes in human behavior. Timely and quantitative analysis on the effects of these COVID-related changes on CO 2 emissions using tools such as inversion systems thus requires dynamic knowledge of global CO 2 emissions. It was this need for data that led to our development of the Carbon Monitor, a near-real-time daily dataset of global CO 2 emission at the national level (https://carbonmonitor.
org). 3, 4 Chevallier et al. disaggregated the daily national Carbon Monitor totals on a worldwide uniform grid using satellite retrievals of a pollutant co-emitted with CO 2 as a spatial proxy, without sectoral distinction. 22 Here, we considerably refine the approach by downscaling the daily national emissions from Carbon Monitor into a 0. 1° × 0. 1° grid for each of the seven sectors (power, industry, residential, ground transportation, domestic aviation, international aviation, and international shipping), using sector-specific geospatial data from the Global Carbon Grid (GID) v1. 0, the EDGARv5. 0_FT2019 database for 2019, and NO 2 retrievals from the Tropospheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite to provide a new spatially-explicit dataset of daily global CO 2 emissions covering the last two years since January 1, 2019, which we name GRACED.
Datasets used in the study (1) A near-real-time daily dataset of global sectoral CO 2 emission from fossil fuel and cement production at national level since January 1, 2019 published as Carbon Monitor (data available at https://carbonmonitor. org/). 4 [23] [24] [25]  (3) Global monthly gridded emissions at a 0. 1°×0. 1° resolution in 2019 defined for a large number of IPCC sub-sectors provided by the Emission Database for Global Atmospheric Research (EDGAR) (https://edgar. jrc. ec. europa. eu/overview. php?v=50_GHG). 16, 26 (4) Daily NO 2 TCVD retrievals data in 2019 and 2020 from the Tropospheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite, launched in October 2017. The ground resolution of the TROPOMI NO 2 retrievals was 7 × 3. 5 km 2 at nadir until 5 August 2019 and has been 5.
Grouping the GID and EDGAR sectors into Carbon Monitor categories. Firstly, we link the Carbon Monitor emission sectors to GID and EDGAR sectors according to Table  S1. We consider that GID has the highest accuracy in source location and we rely on this database as much as possible. However, for the domestic aviation, international aviation, and international shipping sector, GID does not distinguish between related domestic and international sub-sectors: we therefore directly use EDGAR's monthly spatial patterns for the spatial distribution in these sectors. Spatially gridding procedure. Secondly, we do spatially gridding procedure. We use the global annual spatial patterns of CO 2 emission from the GID sub-sectors and global monthly CO 2 emission spatial patterns from EDGAR sub-sectors for the year 2019 for spatially downscaling Carbon Monitor daily national-level emissions.
We assume that the spatial pattern of emissions remained unchanged after the last year of GID and EDGAR (2019). The validity of this assumption will depend on the country and on the time horizon for the adjustment. While, the sub-national emission may change rapidly within a country from 2019 to 2020 as there was a great difference in J o u r n a l P r e -p r o o f the timing and degree of the impact of COVID-19 in various regions. Therefore, for large emitters that have a significant impact on global total emissions, we use sub-national proxy based on TROPOMI NO 2 retrievals data to allocate national Carbon Emission totals into regional totals, before doing a second down-scaling at 0. 1° based on the GID and EDGAR spatial patterns. The analysis can be updated consistently with the latest high-resolution emission maps and other spatial proxies for each year.
(2) For large emitters, subnational emission patterns can vary significantly from one year to the next, which has a great impact on the global total emissions. This was particularly obvious in 2020 with regional variations in the COVID-19 crisis, for instance between eastern and western US, or between eastern and western China. Capturing those sub-national emission changes is important to having a competitive product that could avoid the negative impact on dataset's accuracy caused by ignoring significant variations of large emitters' subnational emission patterns, and is not addressed by Eq. 1 and Eq. 2 which use a climatological emission pattern. It is reported that the global changes in emissions are also consistent with global changes in the NO 2 inventory from satellite data. 6 Therefore, we assume that the sub-national emission changes follow the pattern of the differences in NO 2 column concentration between 2020 and 2019.
In detail, we calculate an index R of each province in large emitters, which is the averaged NO 2 concentration of each province, according to TROPOMI NO 2 retrievals data in year y. 𝑅 𝑝,𝑦 = 𝑁𝑂 2 𝑝,𝑦 #(3) Where p represents province (state); y represents the year. 2 , is the satellite NO 2 concentration averaged temporally over rolling 14 day-period in year y for province p(as explained above) and spatially over the 5% grid points within each province(state) that have the largest NO 2 average over the year. The choice of the 5% largest values allows extracting clear patterns very close to emission location. In the following step, we remove any negative NO 2 value for the 5% grid points over the year 2019 and J o u r n a l P r e -p r o o f 2020 that may be generated and attribute the mass gain to the other 5% pixels.
Uncertainty analysis The uncertainties are from Carbon Monitor, GID, and EDGAR dataset we used. The uncertainty analysis of Carbon Monitor was presented in our related paper recently published at Nature Communications. 4. We followed the 2006 IPCC Guidelines for National Greenhouse Gas Inventories to conduct an uncertainty analysis of the data. First, the uncertainties were calculated for each sector. The uncertainties ranges of the Power, Ground transport, Industry, Residential, Aviation, International shipping sector are ±14. 0%, ±9. 3%, ±36. 0%, ±40. 0%, ±10. 2%, and ±13. 0%, respectively. The uncertainty in the emission projection for 2019 is estimated as 2. 2% by combining the reported uncertainty of the projected growth rates and the EDGAR estimates in 2018. Then, we combine all the uncertainties by following the error propagation equation from the IPCC.
Eq. 5 and calculated that the overall uncertainty range of Carbon Monitor is ±7. 2%. As for GID and EDGAR, uncertainty is introduced in the magnitude of national-level total emissions, the magnitude and location of large point sources, the magnitude and distribution of non-point sources, and from the use of proxy data to characterize emissions. As pointed out by Hogue et al. , the largest uncertainty contribution in gridded emission data sets comes from how well the distribution of the proxy used for spatial disaggregation represents the distribution of emissions. 27 So for the gridded data from GID and EDGAR used in this research, the largest contribution to uncertainty comes from the spatial disaggregation process of national-level emissions and the accuracy of the spatial proxy parameters.
DISCUSSION This research presents for the first time the near-real-time high-resolution gridded fossil CO 2 emissions from fossil fuel and cement production, which is based on the Carbon Monitor project. 4 In this work, we developed a near real-time global J o u r n a l P r e -p r o o f gridded emission dataset called Carbon Monitor-G to provide a high-quality, fine-grained dataset since January 1, 2019. This dataset is a daily gridded map with a spatial resolution of 0. 1°×0. 1°. One of the advantages of GRACED is that it can support global near real-time carbon emission monitoring on various fine spatial scales (such as cities) at sub-national level, which can further improve our understanding of the spatio-temporal variability in emissions and human activities. Through the long time series of GRACED, we provide important daily-scale input for the analysis of emission trends during the COVID-19 pandemics, which will help to carry out more local and adaptive management of climate change mitigation in post-COVID era.
We found that carbon emissions are mainly concentrated in eastern U. S. , western Europe, southeastern China, South Korea, Japan, and India spatially. A sharp decline of CO 2 emissions in 2020 was identified in the central and eastern U. S. , the United Kingdom, France, Germany in Europe, India, Japan, South Korea and eastern China. Various sectors show different spatial distribution characteristics, which is mainly explained by the emission sources. In general, the current statistical data cannot fully grasp the fine-grained dynamics of CO 2 emissions under the COVID-19 pandemics, and further monitoring, observation and data collecting are urgently needed. The ability of near-real-time fine-grained monitoring of daily emission trends we demonstrate here help to take timely local actions in regional, sub-national or urban areas, and have policy implications for local climate change mitigation and earth system management.
GRACED provides the first global near real-time gridded carbon emissions data. This globality and timeliness comes at the expense of reduced accuracy due to near real-time spatial allocation information. Therefore, it is recommended that potential users of GRACED carefully consider these limitations when using this dataset. Inevitably, with the updated version of proxy data, the accuracy of emission spatial allocation in future versions of GRACED can be further improved. With Carbon Monitor national-level data and satellite retrievals data publicly updated in near real time, there are no restrictions on continuing to produce updated future versions of GRACED products within the same model framework.
,,1,2020 #(4) Where  ,,1,2019 means the first version of the emission value of a province in day d and for sector s1 in 2019. np is the number of provinces of the country. In detail, firstly, we calculate the ratio of change in the R index in 2020 compared to 2019, which is  ,2020  ,2019. Secondly, multiply the provincial emission value aggregated from our first version dataset for 2019,  ,,,2019 , to update the provincial emission value for 2020. Last, divide the updated provincial emission value by the sum of the updated provincial emission value ∑  ,,1,2019 *  ,2020  ,2019  =1in 2020 to do the normalization processing in the Eq. 4. So, the sum of the updated provincial emissions within a country can be consistent with the national-level emission value from Carbon Monitor in 2020 after multiplying the national-level emission  ,,1,2020 from Carbon Monitor.
Commercial bans due to harmful algal blooms (HABs), which are natural events, question the sustainability of human activities in marine and coastal areas. A risk assessment of these bans is important to support decision-making to better manage and mitigate their impacts. However, data are sparse and difficult to collect. The dataset presented in this paper includes "estimated closures of scallop fishing areas" due to HAB toxicity along the French coasts of the English Channel. The closure data were simulated for each scallop (Pecten maximus) fishing area through an algorithm applied to the in situ dataset from the French monitoring network REPHYTOX. The methodology of the production of closure data consists of comparing phycotoxin concentration in scallop to regulatory thresholds of phycotoxins, and then, simulating the number and duration of closures based on the monitoring strategies and closure mechanisms as defined in the regulations.
These data only cover closures related to regulatory threshold exceedances of phycotoxins in shellfish. Closures induced by the lack of sampling or other reasons (e. g. , failures in toxin analysis) are not included in the dataset because of the lack of information. Data are produced during the scallop fishing season. Facing the non-existence of such a closure database due to the lack of centralized management of local closure decrees, this dataset can be used to analyse the management strategies to deal with HABs and to highlight the governance challenges related to these strategies. It is also useful to study the link between the ecological and the socioeconomic dimensions of HABs, and to describe how toxin concentrations in shellfish translate into socioeconomic impacts and management challenges.