dataesr/scientific-paragraphs-categorization huggingface dataset:
Meteorological observations Continuous surface meteorological measurements were made onboard the Polarstern at various locations and heights. Measurements of air temperature and relative humidity were made at a height of 29 m above sea level using a Vaisala HMP155 probe. Wind speed and wind direction were measured at 39 m above sea level using an ultrasonic anemometer. The meteorological measurement data used here to evaluate the WRF-Chem model are available at Schmithu ¨sen (2021). Radiosondes (model Vaisala RS41-SGP) measuring temperature, humidity, and winds were routinely launched every 6 hours throughout the whole expedition  (Maturilli et al. , 2021). During synoptic events of special interest, such as major storms, the launch frequency was enhanced up to three-hourly. The launches were performed from the helicopter deck of the Polarstern, approximately 12 m above the sea level, so the data do not capture the lowermost part of the boundary layer.
Elemental mercury observations As described in  Angot et al. (2022b) , Hg(0) measurements were performed in the University of Colorado seacontainer laboratory using a Tekran 2537B analyzer. Only Hg(0) was collected and analyzed (as opposed to total gaseous mercury) as cation-exchange membranes were used to remove potential divalent Hg species. All instruments located within this container were automatically backflushed with zero-air when wind direction was more than ±130 degrees from the ship bow to prevent any contamination of the sampling line by the ship exhaust. This backflushing explains the presence of gaps in the Hg(0) time series. The Hg(0) dataset can be accessed at  Angot et al. (2022a).
Ozone observations Ozone ambient-air mole fractions were monitored in three different sea-container laboratories using commercial instruments. The three individual ozone time series were cross-evaluated and used to generate the hourly averaged merged dataset used in this study. This merged dataset limits gaps in the ozone time series (as opposed to the Hg(0) time series; see above). More information can be found in  Angot et al. (2022b). The O 3 dataset can be accessed at  Angot et al. (2022c).
Emissions Model Input Hg emissions Global Mercury Assessment 2018  (Steenhuisen and Wilson, 2019, 2022)  Anthropogenic emissions ECLIPSEv6b  (Klimont et al. , 2017)  Fire emissisions FINNv2. 5  (Wiedinmyer et al. , 2011; Wiedinmyer et al. , 2023)  Biogenic emissions MEGAN  (Guenther et al. , 2012)  Art. 11(1) page 8 of 35 Ahmed et al: Modelling coupled mercury-halogen-ozone cycles in the Arctic analysed using the QDOAS software  (Fayt et al. , 2011)  to retrieve the absorption due to the oxygen dimer (O 4 ) and BrO at different viewing elevation angles, using the zenith spectra as a reference (see Figure  S2 ). The resulting Differential Slant Column Densities (DSCDs) are the difference of the slant column densities (SCDs) in the viewing direction and the SCD in the zenith direction. The DOAS retrieval settings are given in Table  S1 , and an example of the DOAS fit can be found in  Benavent et al.
Ozone observations Surface ozone data from Villum (Greenland) and Zeppelin (Svalbard) were retrieved from the EBAS database (http:// ebas-data. nilu. no/default. aspx). All measurements were performed using commercial UV absorption instruments (detection limit of 1 ppb). Surface ozone data from Utqia g : vik (Alaska), Summit (Greenland), and the Polar Environment Atmospheric Research Laboratory (Eureka, Nunavut, Canada) were provided by the NOAA Global Monitoring Laboratory (https://www. esrl. noaa. gov/gmd/ ozwv/surfoz/data. html). These measurements are also performed using a commercial UV absorption instrument. More information can be found in  Platt et al. (2022).
Satellite BrO observations Satellite BrO data of the high-resolution TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite were used to retrieve total BrO columns by applying the method from  Seo et al. (2019). To obtain the tropospheric BrO Slant Column Density (SCD), the stratospheric part was removed from the total column using the stratospheric correction method described in  Theys et al. (2011). It requires stratospheric ozone and NO 2 columns from TROPOMI and the tropopause height, which were taken from the NCEP Reanalysis 1 product  (Kalnay et al. , 1996). The tropospheric BrO Vertical Column Densities (VCDs) were obtained from the tropospheric SCDs using an air mass factor suitable for a surface BrO layer of 400 m thickness over a bright surface. As a result, boundary layer BrO over dark surfaces such as open oceans may be underestimated.
Model evaluation at Arctic stations and with satellite retrievals In addition to the model comparison with MOSAiC observations, we further evaluated the model with measurement data from other Arctic locations. Here, we compare the simulated surface Hg and O 3 concentrations to observations at Arctic stations, as well as modelled tropospheric BrO vertical column density with satellite-derived measurements. An additional comparison of meteorology at 3 Arctic stations is also provided in Figures  S6-S8. (Canada). Overall, we find relatively good agreement between the model and observations in capturing the timing of AMDEs at each measurement site. There is a general negative bias in the simulated Hg(0) time series compared to the observations (Table  4 ), suggesting either too much net Hg(0) oxidation (via high bromine concentrations) or low/missing sources (including re-emission from snow, open ocean sources, and transport from midlatitudes).
, 2014). These amounts are within the range of our model results, demonstrating a higher fraction of modelled Hg(p) during early spring. From the same long-term dataset, Hg(p) concentrations reached an annual maximum in April, while Hg(II) concentrations peaked during May. The reason for the springtime peak in Hg(p) has been investigated and associated with several variables. Low temperatures have been shown to increase the transition of gaseous Hg(II) to Hg(p), as well as a high fraction of organic and chloride containing aerosols  (Amos et al. , 2012; Steffen et al. , 2014; Xu et al. , 2020). In addition, the transport of air pollution from midlatitudes to the Arctic during winter and spring (known as Arctic haze) brings high aerosol concentrations, contributing to the predominance of Hg(p) in spring.
Data accessibility statement The updated WRF-Chem model code used in this study is available on Zenodo at  Ahmed et al. (2022)  Surface ozone data at Utqia g : vik (Alaska), Summit (Greenland), and Eureka (Canada) were provided by the NOAA Global Monitoring Laboratory (https://www. esrl. noaa. gov/ gmd/ozwv/surfoz/data. html). Satellite BrO VCD data from TROPOMI are available on Zenodo at  Zilker and Richter (2023) , https://doi. org/10. 5281/zenodo. 7711532.
Acknowledgments We would like to thank the following individuals for their contributions during MOSAiC:  Jacques Hueber ,  Matthew D. Shupe ,  Marion Maturilli , and  Holger Schmithu  ¨sen. We also thank all those who contributed to MOSAiC and made this endeavour possible (Nixdorf et al. , 2021). We thank all those involved in the operation and maintenance of the Polarstern research vessel (Knust, 2017). Computer analyses benefited from access to IDRIS HPC resources (GENCI allocation  A011017141 ) and the IPSL mesoscale computing center. Some observations used here were provided by the  Atmospheric Radiation Measurement (ARM) Climate Research Facility , a  US Department of Energy (DOE) Office of Science User Facility  sponsored by the  Office of Biological and Environmental Research. We thank the many ARM operators who supported the field observations.
, https://doi. org/10. 5281/zenodo. 7137482. The surface meteorology measurements made onboard the Polarstern can be accessed at Schmithu ¨sen (2021). The MOSAiC radiosonde data are available at Maturilli et al. (2021). The elemental mercury dataset from MOSAiC is available atAngot et al. (2022a). The merged ozone dataset from MOSAiC is available atAngot et al. (2022a). BrO MAX-DOAS measurement data are available at Mahajan (2022). Surface elemental mercury and ozone data at Villum (Greenland) and Zeppelin (Svalbard) are available at the EBAS website https:// ebas. nilu. no/. The surface total gaseous mercury measurement dataset at Alert (Canada) is available at https://datadonnees. ec. gc. ca/data/air/monitor/monitoring-ofatmospheric-gases/total-gaseous-mercury-tgm/. The speciated mercury data at Alert (Canada) are available at https://data-donnees.
Epidemiological Information National epidemiological data derived from the governmental SARS-CoV-2 surveillance program in Mexico is made available by the General Epidemiological Directorate (Dirección General de Epidemiología) through the Secretaría de Salud website (https: //www. gob. mx/salud/documentos/datos-abiertos-152127), which was accessed on 24 April 2022. Confirmed positive cases of COVID-19 and the associated deaths were collated per day, from 25 March 2020 to 8 April 2022, for the epidemiological analyses. The information was plotted at national and regional levels using a 7-day rolling average on a sliding window with a step of one day. Epidemiological data processing and graphs were created using R v. 4. 2. 1  [23].
Sequences Data Source All sequences and metadata from the Omicron variant available in the Global Initiative on Sharing All Influenza Data (GISAID) database (accessed on 18 May 2022) were downloaded with a submission dateline on 31 March 2022. In total, the set had 13,562 sequences from Mexico and 3,191,009 from the rest of the world. Regarding Mexican sequences, 5752 (42. 4%) were generated by the National Institute for Genomic Medicine (INMEGEN), 4097 (30. 2%) by the Mexican Consortium for Genomic Surveillance (CoViGen-Mex), 2851 (20. 0%) by the Institute for Diagnostics and Epidemiological Reference (InDRE), and the remaining (6. 4%) by other public and private institutions. The information on these Mexican genomes is included in Table  S1  (Supplementary Materials). Regarding the CoViGen-Mex sequences, samples were taken and sequenced, then consensus genomes were generated as described in  Taboada et al.
, 2022 [24]. To evaluate the displacement of the Delta variant by BA. 1. x lineages, sequences and metadata from this variant prior to and during the transition period (November 2021 to January 2022) were also retrieved from GISAID; in total, 5489 Mexican sequences of the Delta variant were obtained (Table  S2 ), with 2590 being generated by CoViGen-Mex. All sequences and metadata are available through GISAID EPI_SET_220927gw and genome sequences from CoViGEn-Mex were also being deposited in GenBank (Tables  S1  and S2 ).
A Spearman correlation analysis was performed to analyze differences in lineage distribution between regions. Heatmaps were generated to show the abundance of lineages throughout time, using the pandas v. 1. 3. 5, seaborn v. 0. 11. 0, and matplotlib v. 3. 5. 2 libraries of Python v. 3. 7. 6. Finally, using the Shannon's index, the diversity of Delta lineages prior to the entry of BA. 1. x in each region was determined. The significance of any differences in the Shannon diversity between regions was evaluated using the Wilcoxon signed-rank test and its associated p-value with an alpha = 0. 05.
Acknowledgments: We are grateful to the authors from the originating laboratories responsible for obtaining the samples and the submitting laboratories responsible for generating the genomes shared via GISAID, on which this research is based. For the full list of contributors of each sequence and its details, including the  Originating Lab, Submitting Lab , and the list of Authors, visit https: //doi. org/10. 55876/gis8. 220927gwaccessed on 27 September 2022. We appreciate the computer assistance provided by  Jerome Verleyen ,  Roberto Bahena ,  Juan Manuel Hurtado , and  Verónica Jacinto. We thank  Julissa Enciso-Ibarra  and  Karla G. Aguilar-Rendon  for technical help. The project  LANCAD-UNAM-DGTIC-350 of the Dirección General de Cómputo y Tecnologías de la Información (DGTIC-UNAM ) provided Supercomputing resources in Miztli.
Data Availability Statement: This work was carried out using data shared through the GISAID initiative. All sequences and metadata are available through GISAID with the accession EPI_SET_220927gw; additionally, sequences generated by the authors have also been deposited in the GenBank NCBI database. Accession numbers are reported in the Supplemental Materials of this article. Epidemiological data was obtained though the Secretaría de Salud website (https://www. gob. mx/salud/ documentos/datos-abiertos-152127), accessed on 24 April 2022.
Top-down CO 2 emission estimates based on satellite observations are of great importance for independently verifying the accuracy of reported emissions and emission inventories. Difficulties in verifying these satellite-derived emissions arise from the fact that emission inventories often provide annual mean emissions, while estimates from satellites are available only for a limited number of overpasses. Previous studies have derived CO 2 emissions for power plants from the Orbiting Carbon Observatory-2 and 3 (OCO-2 and OCO-3) satellite observations of their exhaust plumes, but the accuracy and the factors affecting these emissions are uncertain. Here we advance monitoring and quantifying point source carbon emissions by focusing on how to improve the accuracy of carbon emission using different wind data estimates.
Zheng et al. (2020a)  used 5 years' worth of OCO-2 XCO 2 data to estimate emissions from urban and industrial areas in China and compared these local emissions with the Multi-resolution Emission Inventory for China (MEIC;  Zheng et al. , 2018) , the Emissions Database for Global Atmospheric Research (EDGAR;  Crippa et al. , 2020)  and the Open-source Data Inventory for Anthropogenic Carbon dioxide (ODIAC;  Oda et al. , 2018)  inventory estimations. The approach was extended to the entire globe and to OCO-3 by  Chevallier et al. (2020 Chevallier et al. ( , 2022)). Satellite instruments that directly detect CO 2 concentrations, such as OCO-2 and OCO-3, can be used for CO 2 emission estimates for power plants and other point sources. However, the limited track width, low revisit rate and clouds mean these instruments only incidentally provide useful snapshot observations over a point source (see Sect.
, 2020 (Chevallier et al. , , 2022;; Zheng et al. , 2020a) , but it is difficult to attribute these signals to specific emission sources. Velazco et al. (2011)  quantified errors of power plant annual emission estimates by a hypothetical Carbon Monitoring Satellite (CarbonSat) constellation. Hill and Nassar (2019)  assessed pixel size and revisit rate requirements for monitoring power plant CO 2 emissions from space. In addition, instantaneous emissions at satellite overpass times are difficult to compare with an inventory of annual emissions because of the intermittence and variability of power production and CO 2 emissions. This is why previous studies had to use either instantaneous emission reports or temporally disaggregated inventories. In this study, we advance the research of monitoring and quantifying point source carbon emissions by focusing on how to improve the accuracy of carbon emission using different wind data estimates, assessing these emission estimates by comparing it with the US Environmental Protection Agency (EPA) emission data, and identifying and exploring suitable cases elsewhere in the world.
Satellite data The Orbiting Carbon Observatory-2 (OCO-2) launched in July 2014 collects high-resolution spectra of reflected sunlight in the bands centered near 0. 765, 1. 61 and 2. 06 µm  (Crisp et al. , 2017). OCO-2 flies on a near-polar sunsynchronous orbit and crosses the Equator at a fixed local time (LT) near 13:36 with a repeat cycle of 16 d. About 10 % of the approximately 1 million daily CO 2 observations are cloudless and can be used to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO 2 ) with the Atmospheric CO 2 Observations from Space (ACOS) algorithm at a spatial resolution of about 1. 29 × 2. 25 km 2 across swaths which are up to 10 km wide  (O'Dell et al. , 2018). The Orbiting Carbon Observatory-3 (OCO-3) launched in May 2019 is mounted on the Japanese Experiment Module -Exposed Facility (JEM-EF) of the International Space Station (ISS) and views the Earth at all latitudes less than about 52 • with a footprint size of about 1.
Power plant database We collected reported hourly CO 2 emission data for US power plants from the US Environmental Protection Agency (EPA) from 2018 to 2021 (https://www. epa. gov/airmarkets/ power-sector-emissions-data, last access: 5 March 2022) as truth to validate the satellite-estimated emission. We sorted out the list from EPA for all 1631 power plants operated during this period. The publicly available Global Power Plant Database (GPPD, v1. 3. 0) from the World Resources Institute was used to automatically identify CO 2 emission signals from power plants upwind of satellite tracks  (Yin et al. , 2021). The GPPD includes 34 936 power plants (https://datasets. wri. org/ dataset/globalpowerplantdatabase, last access: 2 June 2021) with 15 fuel types, such as biomass, geothermal, hydro, nuclear and solar.
Wind fields We used three types of wind field data in the CO 2 emission estimation procedures described in Sect. 3. Hourly horizontal wind fields u and v at 10 m are taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) nextgeneration reanalysis ERA5 dataset (0. 25 • × 0. 25 • ) and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) dataset (0. 5 • × 0. 625 • ). The effective wind speed proposed by  Varon et al. (2018)  and used by  Reuter et al. (2019)  and  Hakkarainen et al. (2021)  was calculated from the 10 m wind by applying the empirical scaling factor 1. 4. Varon et al. (2018)  derived this scaling factor from a linear fit between effective wind speed and 10 m wind speed. The effective wind speeds from ERA5 (WERA) and MERRA-2 (WMERRA) are derived using factor 1.
1a ) and performed a visual selection to identify cases of enhancement from plumes of isolated power plants like in Fig. 1. The screening criterion is able to select a clear plume profile in the XCO 2 observations downwind of the power plant, such as in Fig. 1e-h , while other cases are rejected due to insignificant XCO 2 enhancement, missing data and emission source cluster interference, such as in Fig. S10. In the end, we arrived at 50 cases where the power plant was operating and the emission plume crossed the satellite track, including 30 cases from OCO-2 and 20 cases from OCO-3. When the distance between two adjacent power plants did not exceed the range of 1 pixel of the satellite, it was regarded as a single isolated emission source, and their names were connected with commas (Table  1 ).
Detection and estimation of global power plant emission signals Figure  5  shows the number of cases retained for each processing step of the automatic detection of global power plant emission signals using the GPPD. We obtained 1387 d of XCO 2 observation data from OCO-2 from January 2018 to December 2021 and 766 d of XCO 2 observation data from OCO-3 from August 2019 to November 2021. For 8660 power plants in the world, all tracks from OCO-2 and OCO-3 were scanned near the power plants. The number of cases with more than 10 observations (step 2 in Sect. 3. 3) near the Gaussian peak is 39 365 and 42 932 for OCO-2 and OCO-3. A total of 40. 71 % of the XCO 2 cases are located in the downwind direction of the power plant. Among them, 24. 94 % of cases contain at least five observations that are significantly enhanced relative to the background.
We used the Gaussian plume model method to evaluate the impact of three kinds of wind field datasets (WPBL, WERA and WMERRA) on the accuracy of emission estimates of isolated power plants. The results show that, for a single case, the correlation between reported emission and estimated emission driven by WPBL is the highest. When there are multiple observations of the same power plant, the correlation between the average and total estimated emissions of the power plant and the reported emissions is significantly improved, from R 2 = 0. 40 to 0. 87. No matter what kind of wind field data are used, the Gaussian plume model has a high correlation R 2 of the total emissions from power plants from multiple observations, which is above 0. 5. In general, obtaining more observation data from more instruments can significantly reduce the uncertainty of estimated emissions of power plants.
This study has only considered three sources of uncertainty. Future research may investigate additional sources, such as the assumption of steady-state conditions and the plume rise, to better understand their impact on the results. With the future increase in observation sensors with improved spatial-temporal resolution, such as the planned Copernicus Carbon Dioxide Monitoring mission (CO2M) and the Japanese Global Observing Satellite for Greenhouse gases and Water cycle (GOSAT-GW), the probability of observing a CO 2 plume will greatly increase. The abundant observation data obtained by the new generation of satellites will contribute to the monitoring of power plant emissions worldwide. The emissions of power plants in the background of other emission signals may also be monitored due to highresolution observations and increased swath width, and the uncertainty of the estimated emissions of power plants will further decrease.
Acknowledgements. The support provided by the  China Scholarship Council (CSC)  during a visit by  Xiaojuan Lin  to the  Royal Netherlands Meteorological Institute (KNMI)  is acknowledged. Financial support. This research has been supported by the  National Natural Science Foundation of China  (grant nos. 71874097 ,  41921005 ,  71904007  and  71904104 ). Review statement. This paper was edited by  Eduardo Landulfo  and reviewed by  Ray Nassar  and  Gerrit Kuhlmann.
Data availability. Version 10r of OCO-2 and Version 10. 4r of OCO-3 bias-corrected XCO 2 retrievals was downloaded from the data archive maintained at the NASA Goddard Earth Science Data and Information Services Center (https://doi. org/10. 5067/E4E140XDMPO2, OCO-2 Science Team, 2020, https://doi. org/10. 5067/970BCC4DHH24, OCO-2/OCO-3 Science Team, 2022). Versions 1. 3. 2 and 2. 2. 0 of the TROPOMI L2 offline products were obtained online (https://doi. org/10. 5194/amt- 15-203715- -202215- , van Geffen et al. , 2022)). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-23-6599-2023-supplement.
3b , Extended Data Fig. 3  and Supplementary Table  4 ). The K356T mutation is likely to abolish a crucial salt bridge formed with S309 heavy chain variable region (Vh) E108 (as resolved in PDB entry 7TN0  5  ), as (1) the BN. 1 RBD harbouring the T356K reversion bound S309 similarly to the BA. 2. 75. 2 RBD (the BN. 1 RBD is distinguished by K356T, S486F and F490S, with only residue 356 being involved in the epitope); and (2) sotrovimab did not neutralize BA. 2 S(K356T) VSV pseudovirus (Extended Data Fig. 3a ,b and Supplementary Table  3 ). Moreover, deglycosylation of the BN. 1 RBD with peptide:N-glycosidase F (PNGase) did not improve S309 binding compared with untreated BN. 1, despite complete removal of the N354 glycan introduced by the K356T mutation, as confirmed by mass spectrometry (Extended Data Fig.