dataesr/scientific-paragraphs-categorization huggingface dataset:
Near-real-time daily emissions by sector. CM-Cities is downscaled from the GRACED dataset, in which spatial distribution and daily variations of emissions are combined. This section describes the methods for estimating NRT daily emissions from a temporal perspective, and the next section describes the spatial gridding procedure. The estimation of daily emission variations follows the Carbon Monitor national dataset  10, 36, 44  , which provides daily fossil fuel CO 2 emissions since January 1st, 2019 on the global and national levels, with detailed estimates in 7 main sectors, i. e. , power, industry, ground transport, residential (including commercial), domestic aviation, international aviation, and international shipping. Emissions from international bunkers (including the international aviation sector and international shipping sector) are only accounted for at the global level and usually excluded from the national territorial emissions according to the IPCC guidelines.
Therefore, CM-Cites considers the other 5 sectors, i. e. , power, industry, ground transport, residential, and domestic aviation. 1 ), which provides live power generation data with a daily or hourly resolution, and accounts for more than 70% of the total CO 2 emissions in the power sector  10. The emission factors are estimated using EDGAR's electricity emissions, divided by our collection of coal-fired electricity data in various countries. The daily emissions are estimated as: = Emis Emis AD AD ( / ), (1) power daily power yearly p ower daily p ower yearly , , , , where AD is the power generation. For emissions from other countries (countries not listed in Table  1 ), we assumed a linear relationship between daily global emission and daily total emissions from these countries, and then adjusted the emissions for countries that adopted lock-down measures during the COVID-19 following the method used by the Carbon Monitor national dataset  10.
Industry sector. For the industry sector, the daily emissions are calculated from the monthly industrial production index and the daily power generation data. Monthly industrial production data are acquired from several datasets (Table  2 ). The monthly CO 2 emissions estimated from the Industrial Production Index (IPI) are then disaggregated into a daily scale using daily power generation data. This approach is based on two assumptions: 1. A linear relationship exists between daily industrial production and industrial fossil fuel use. 2. A linear relationship exists between daily industry activity and daily electricity production  10. The monthly and daily industry emissions are estimated following: = Emis Emis IPI I PI ( / ), ( 2 ) ind monthly currentyear c i nd yearly c m onthly currentyear c y early c , , , , ,2019, , , ,2019, = Emis Emis Elec Elec ( / ), (3) ind daily ind monthly daily m onthly , , where Emis ind, monthly, currentyear, c is the monthly industry emissions for country c in current year, Emis ind, yearly, 2019, c is the yearly industry emissions for country c in 2019 (year of the latest update of baseline emissions), IPI is the corresponding Industrial Production Index.
Emis ind, daily and Emis ind, monthly are the daily and monthly industry emissions, respectively. Elec daily and Elec monthly are the daily and monthly electricity production, respectively. For countries not listed in Table  2 , the industry sector emissions are estimated in the same way as for the power sector. Ground transport sector. Daily emissions from ground transportation are estimated using TomTom live congestion index and EDGAR road transportation emissions. The TomTom traffic congestion level represents the extra time spent on a trip in congested conditions, as a percentage, compared to uncongested conditions. TomTom congestion level data were obtained for more than 400 cities around the world at a temporal resolution of one hour (https://www. tomtom. com/traffic-index/). This approach permits the estimation of NRT emissions from ground transportation with a temporal resolution up to one hour, and TomTom grants users permission for non-commercial usage.
The TomTom live congestion level data was proven to be highly accurate for most cities, and Carbon Monitor has successfully adopted this approach  10. Note that a zero-congestion level means the traffic is fluid or "normal" but does not mean there are no vehicles and zero emissions. The lower threshold of emissions when the congestion level is zero was estimated using real-time data from an average of 60 roads in the city of Paris. TomTom data accurately depicts the traffic volume using a sigmoid function-based regression (Eq. 4), and Fig. 3  is a comparison between the actual and TomTom estimated hourly car counts on the measured roads in Paris. The estimated traffic volume is then used to allocate the EDGAR on-road emissions to each day (Eq. 5). Q a X X , ( 4 ) d β λ = + + γ γ γ Emis Q Emis Q , ( 5 ) trans d d onroad d n d , 1 = ∑ = where Q d is the mean vehicle number per hour in day d, X is the daily mean TomTom congestion level data, and a, β, γ, λ are regression parameters, Emis trans, c, d is the ground transport emissions in day d, Emis onroad is the annual EDGAR road transportation emissions, n is the number of days in a year.
Aviation sector. Emissions in the aviation sector are computed from individual commercial flights data from the Flightradar24 database (https://www. flightradar24. com). This sector covers domestic flights, and all airports around a city were selected even if they are not part of the FUA, but some airports are not covered by the GADM, since we follow a territorial approach for emission allocation, if a city does not have an airport but emissions are present within the FUA boundary, (e. g. , the city of Dongguan does not have its own airport but has two nearby airports in Guangzhou and Shenzhen). In this case, we have attributed the daily patterns of the airport that is closest to the city. The daily CO 2 emissions were estimated as the product of distance flown and a constant emission factor (EF avi ).
= ⋅ Emis DF EF , ( 8 ) avi a vi where DF is distance flown, which is computed using great circle distance between the take-off, cruising, descent, and landing points for each flight and are cumulated over all flights. The emission factor per kilometer flown is assumed to be a constant for the mix of all aircraft from an airport (including regional, narrowbody passenger, widebody passenger, and freight operations) as the share of flight types has not significantly changed since 2019. Gridded daily CO 2 emissions. Carbon Monitor Cities disaggregates the Carbon Monitor national emissions to cities using the GRACED dataset developed by the Carbon Monitor team  40  , which consists of emission maps generated by spatializing and gridding the daily national emission inventories from Carbon Monitor into grid cells.
This was achieved by estimating spatial distribution proxies from satellite data and existing gridded products while maintaining consistency between bottom-up accounting results and the spatial sum of the  Given that GID has higher data quality in fine-grained spatial scales and point sources of industries and power plants, the GID-based point sources and the EDGAR emission maps were combined for constructing GRACED (Eq. 9). While the spatial emission patterns derived from GID and EDGAR (with latest updates in 2019) cannot accurately reflect the situation in 2020 and 2021, the NRT TROPOMI NO 2 retrievals were used as a proxy for CO 2 to capture the daily variability in CO 2 emission following GRACED  40. After several data processing steps, such as rolling-average and thresholding, the NO 2 data can reasonably indicate the spatial distribution of CO 2 sources  48.
EDGAR g, m, s represents the EDGAR gridded CO 2 emissions for grid g, sector s, and month m which date d belongs to. City-level spatial disaggregation. The spatial disaggregation is performed by first converting the city area shapefiles (FUA or GADM) into raster datasets with a unique ID assigned to each city. Then the raster city area maps are used as masks to extract the matching grid cells in GRACED emission maps. We then aggregate emission values for grid cells that correspond to the same city mask to yield the total sectoral emission value for a given city. For the aviation sector, emissions from all planes within the city's territory are included. The international shipping sector is not included in this dataset because most of the emissions from this sector occurred in the open ocean that cannot be allocated to specific cities.
The jurisdiction issue also applies to the aviation sector, but we keep the territorial-based allocation approach in the dataset for completeness. We use both the administrative areas and FUA because boundary definition has always been a problem in city-level inventory completion  17  , as the administrative city areas in most countries do not reflect emissions from the larger commuting zones of a city, which may constitute a large part of the emissions, meanwhile, FUA represents the most viable spatial dataset for covering the more complete urban areas. In addition, FUA is clearly-defined and produced using a consistent method for cities worldwide, while the definition of administrative city areas may vary significantly across different countries. Therefore, the use of both spatial scopes represents a potential solution to differentiate administrative emissions versus community-wide emissions and makes inter-dataset comparisons easier as demonstrated in the validation section.
City-level corrections. The disaggregation from EDGAR spatial distributions is insufficient especially for the residential and ground transport sectors, because EDGAR uses a disaggregation of national sectoral totals per population for residential, and per road network for ground transport, which introduces bias to cities. Therefore, we correct these two sectors at individual city level. The ground transport sector emission is corrected using city-specific TomTom data (by applying Eqs. 4,5 at city scale) for 416 cities worldwide that have their own NRT TomTom indices (list of these cities can be found in the documentation on Carbon Monitor website), which represent more accurate ground transport emission estimates for these cities. For cities that do not have their own TomTom data, we spatially disaggregate the national mean estimates following Eqs.
Outlier correction. Outliers exist in the data mainly due to errors in the source datasets, such as mistakes in unit conversions or data entry, etc. To correct these outliers, we apply a statistical method based on intrinsic properties of the distribution of the emissions in the database. This allows more accurate identification of outliers that are likely to be the results of incorrect data entry. Similar statistical approaches have been successfully applied to correct for outliers in emission datasets  25. The outlier identification method is based on standard deviation (STD). Specifically, we consider an emission value as an outlier if the differences between the current value and its daily neighbours are both greater than 3 times the yearly STD for that sector (Eq. 12). This threshold is determined by experimenting with data with known error and data with periodical high variation (e.
g. , weekday versus weekends for the ground transport emissions). These experiments determined the lower and upper bounds of the threshold such that it correctly identifies outliers and keeps the inherent variance within the data. -> ⋅ -> ⋅. + - x x STD and x x STD 3 3 (  12 ) n n 1 1 Limitations and future work. This dataset focuses on improving the timeliness, temporal resolution, and coverage of city-level inventories for studying NRT emission dynamics and also providing emission estimates for many cities in low-income regions. This dataset does not account for emissions related to land use, land use change, waste, and forestry, therefore, some emissions caused by long-term urban expansion are not captured. This dataset is constructed based on daily activity and models that can cover a majority rather than the entire daily emission-related activities due to data availability.
Therefore, we acknowledge that a small portion of daily variations in city emissions are not reflected in this dataset. This dataset is derived from the gridded Carbon Monitor which is based on downscaled national inventories, combined with point sources and spatial distributions from GID and EDGAR, therefore, one limitation is the lack of using city-specific bottom-up activity data except for the ground transport sector, which may introduce additional uncertainties. We also noted that some input data may contain inherent errors and missing values (other than the above-mentioned outliers), especially for cities in less developed nations, we do not intend to fix this kind of errors in the source data without enough background information of the specific city, but we consider our results represent a meaningful first-order estimate for many of these cities that are lacking any emission inventories.
Estimating NRT daily emissions for cities is a relatively new research direction and requires ongoing efforts to calibrate and update the workflow to improve data quality in the future. Further validation of the data is a crucial next step, and we plan to conduct more data validation and quality improvements in the future through multiple follow-up works. From a bottom-up perspective, we are collecting more city-level fossil fuel consumption data to better constrain the annual or monthly total emissions. From a top-down perspective, we plan to compare our results with field observations and satellite retrieval data. As proposed by previous study 1 , remotely sensed urban atmospheric measurements can help us estimate and predict CO 2 emission fluxes, and we plan to leverage these research outcomes to improve this city-level emissions dataset.
Data Records CM-Cities provides scope-1 NRT city-level emission inventories from 01/01/2019 to 31/12/2021 for 1500 cities in 46 counties. All data have gone through a validation process, in which we estimated the uncertainties and corrected errors. The attributes of the final dataset are listed in Table  4 , and the emission data are organized into spreadsheets. The definitions for sectors are consistent with the Carbon Monitor national inventories. Brief descriptions of the methods, sectors, coverage and uncertainty are also provided in Table  5. Latest updates for selected cities and related information are available for view and download on our website https://cities. carbonmonitor. org. At the time of writing this article, this dataset has been updated to December 31, 2021 and the full dataset can be downloaded at Figshare  50.
Technical Validation The quality of this dataset is evaluated by comparing it against existing datasets (Table  5 ). We also performed uncertainty analysis for our data and for each sector based on a synthesis analysis of input data uncertainties and the methodology used. Significant outliers were identified and corrected as shown in the examples of Fig. 7. The outlier occurrence rate for this dataset is 0. 012%. Validation against other datasets. Multiple datasets are used to validate our results, including 1. City inventories from the CDP-ICLEI Track, 2. Vulcan dataset for US counties, 3. CEADs and MEIC dataset for China, and 4. individual reports released by city governments. Note that only scope-1 emissions are compared. For cities in China, we validated our dataset by comparing it with the CEADs and MEIC datasets.
R 2 values between MEIC and CM-Cities are 0. 93 and 0. 62 for the power sector and ground transport sector, respectively, and the corresponding Rd are 21% and 31%. Other sectors were not compared due to the large differences in sector definition and coverage. The mean relative differences are all within the uncertainty ranges, which indicates a relatively high accuracy for Chinese cities. For cities in other countries, many datasets do not have recent (2019 or later) inventories, for example, the latest Vulcan dataset provides emissions in 2015 for United States counties, and the latest CDP-ICLEI Track inventories may range from 2010 to 2021 depending on reporting status of each city. For the completeness of the validation, we adjusted the area of accounting for CM-Cities to be as consistent as possible with these datasets and compared city inventories from all available data sources regardless of the time of accounting.
Note that GADM level-2 in the United States represents exactly the area of counties, so our GADM results were used for comparison with Vulcan county-level inventories. Figure  9  show examples of the annual total emission comparisons between CM-Cities and these datasets. Comparison between Vulcan (2015) and CM-Cities (2019) covers top 50 counties with the highest emissions in the United States. We used the coefficient of determination (R 2 ) and mean relative difference (Rd) to evaluate the comparison results. R 2 values are 0. 82, 0. 60, 0. 58, 0. 82, 0. 90, and 0. 69 for total, power, industry, residential (and commercial buildings), ground transport, and aviation sectors, respectively, and the corresponding Rd values are 26%, 114%, 67%, 35%, 41%, and 58%, respectively (Table  5 ). The differences are mainly due to 1.
Uncertainty analysis. The uncertainties in this dataset have two sources: 1. The uncertainties inherited from Carbon Monitor and GRACED. 2. The uncertainty introduced by the spatial downscaling process. The uncertainty analysis was conducted based on the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. For the power sector, uncertainty mainly comes from the emission factor and the variability of energy mix for power generation, the 1-sigma uncertainty of power emission from fossil fuel is estimated as ±10. 0%. For the industry sector, monthly production data is the main source of uncertainty, especially the production in China, which accounts for more than 60% of world total industrial CO 2 emissions. Monte Carlo simulations were used to determine the confidence interval based on regression models between estimated monthly emissions and officially reported emissions.
Spatial computations are based on raster (gridded) files, but most cities and urban areas have irregular-shaped boundaries that are not fully overlapped with gridded cells. Area discrepancies are found along all city boundaries, and smaller cities typically suffer from higher levels of dissimilarities because few grid cells account for a large portion of the total urban area. We computed the area discrepancies for all cities in the dataset (Fig. 10 ), and found that 44. 53% of cities show an area difference of 0%-5% and the count decreases as the discrepancy percentage gets higher. The mean area discrepancy for all cities is 13. 55%. We then estimated the overall uncertainty by first Fig. 10  The frequency distribution of city area (boundary) uncertainty ranges in the dataset. The mean area uncertainty for all cities is 13.
The uncertainty quantification of the daily-scale allocation of emissions requires real daily emissions from ground transportation, which is difficult to obtain and ignored in this study. Therefore, we focus on the uncertainty generated by the regression model. We used the 95% confidence interval of the regression model to estimate the uncertainty generated by the model. Given the high temporal resolution of this dataset, the uncertainties from daily activity will increase the overall uncertainties on top of the annual uncertainties, which is not inconsistent, as data are temporally auto-correlated.
Usage Notes The generated datasets  50  are available from https://doi. org/10. 6084/m9. figshare. 19425665. v1. The main data file has more than one million lines of data, which will take a long time to load in Excel. We recommend loading the data with a script that can handle large datasets. We have provided an example of Python code to help users read in and plot emissions for any city in the dataset (https://github. com/dh107/Carbon-Monitor-Cities/). Note that the raw TomTom and flightradar24 data are not included in this dataset as we only provide the estimated emissions. Users should also note that the unit of emissions in this dataset is ktCO 2. Filename indicates whether the data is based on administrative areas (GADM) or functional urban areas (default). The next update to this dataset is scheduled for May 2022, which will update the dataset to Feb 28, 2022.
List of gridded data used for producing Global Gridded Daily CO 2 Emissions Dataset (GRACED). gridded results. Three datasets were used in producing GRACED: 1. The Global Carbon Grid (GID), which provides global CO 2 emissions data from major industry and power plant point sources with a resolution of 0. 1° in 2019, 2. The Emissions Database for Global Atmospheric Research (EDGAR), which provides sectoral emissions as specified by the IPCC guidelines. 3. The NO 2 thermal chemical vapor deposition retrieval product from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite.
Country where the city is located. The following countries are covered in this dataset: Argentina, Australia, Austria, Bangladesh, Belgium, Brazil, Canada, Chile,China, Colombia, Denmark, Egypt, Finland, France, Germany, Greece, Hungary, India, Indonesia, Iran, Italy, Japan, Korea, Malaysia, Mexico, Myanmar, Netherlands, Nigeria, Norway, Pakistan, Peru, Philippines, Poland, Portugal, Russia, Singapore, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, United Arab Emirates, United Kingdom, United States, Vietnam Data attributes.
Global Grid in the Global Infrastructure Emission Database. Including power, industry, residential, transport, shipping, and aviation sectors with high data quality in spatial fine-grained maps (http://gidmodel. org) Data Data Description Resolution Global Carbon Grid (GID) v1. 0 0. 1°×0. 1° global, Annual Emission Database for Global Atmospheric Research (EDGARv5. 0) EDGAR v5. 0 FT2019, covers major fossil CO 2 sources globally, with monthly emissions provided per main source category (https://edgar. jrc. ec. europa. eu) 0. 1°×0. 1° global, Monthly TROPOMI NO 2 Retrievals Precursor satellite, launched in 2017 NO 2 thermal chemical vapor deposition retrievals acquired by the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 0. 1°×0. 1° global, Daily.
Materials and methods The starting point of this study was the authors' experience as telemedicine consultants and trainers in the context of their collaboration with Health for Development® (H4D®), a French private company that has developed a telemedicine booth (Consult-Station® equipped with numerous connected devices, run by the Consultaccess® software) and provides teleconsultations via a team of trained physicians. An empirical method was adopted  [11, 12]  based on the observation of telemedical practice. The impact of distance medical examination on the diagnostic process was analyzed using inductive reasoning, focusing on the difficulties encountered by the physician. A cause-and-effect analysis helped identifying possible solutions subsequently tested in a shared learning-by-doing process supported by the analysis of literature.
Similar conclusions can be drawn for listening. Aside from listening to the patient's story and complaints, hearing is used for cardiovascular and respiratory examination. Typically, body sounds are perceived with the help of a stethoscope, a mechanical, and more recently electronic, sound amplifier. In a videoconferencing context, the sounds are encoded by electronic stethoscopes, channeled through the net, decoded and listened through headphones or speakers. These devices determine the quality of the sound perceived by the physician and may also allow easy access to other visual data like phonograms, spectrograms  [38]  and computer-aided auscultation. In these conditions, the telemedical examination supplies the physician with better and a richer array of signs than traditional face-to-face examination.