The dataset builds upon data from CDP , the Bonn Center for Local Climate Action and Reporting , and data collected by Peking University (83 cities in China). The CDP data being self-reported by cities, we applied quality control procedures, documented the type of emissions and reporting method used, and made a correction to separate CO2 emissions from those of other greenhouse gases. Further, a set of ancillary data that have a direct or potentially indirect impact on CO2 emissions were collected from other datasets (e.g. socio-economic and traffic indices) or calculated , then combined with the emission data. We applied several quality controls and validation comparisons with independent datasets. The dataset presented here is not intended to be comprehensive or a representative sample of cities in general, as the choice of cities is based on self-reporting not a designed sampling procedure.We present a global dataset of anthropogenic carbon dioxide (CO Cities concentrate population, energy use2 and economic output, hence are important focal points for investigating emission drivers and mitigation options, prompting across-city comparative analyses of greenhouse gases (GHG) emissions4 and energy use2.Cities are hotspots of the global carbon cycle, with considerable fossil fuel and cement CO2 emissions estimates are needed from local inventories, but also underlying ancillary socio-economic data. Scope-1 emissions cover GHGs emitted in the city territory, including emissions from grid-supplied energy produced within cities5. Scope-2 emissions include grid-supplied energy used by cities and produced by power plants outside the city boundary. The distinction about power plants emissions included in Scope-1 is not always precise in city-reported data. Scope-1 GHG emissions of cities5 include transport, industrial, waste and local power plants emissions, and allow for a more direct comparison between cities, as uncertain additional assumptions are needed for Scope-2 emissions about the GHG mix of electricity consumed by each city.To understand city emission drivers in different global regions, not only city-scale CO5. Among the different types of GHG emissions in cities, fossil fuel CO2 emissions are more robustly comparable between cities as they constitute the largest share of total GHG emissions and are estimated by more cities than non-CO2 gas emissions. Additionally, Scope-1 CO2 emissions from inventories could ultimately be compared to observation-based estimates using atmospheric CO2 measurements and tracer transport models7, provided emissions from power plants within a city territory are included in reported Scope-1 emissions.City-level GHG emissions are mostly self-reported using different inventory methods based on energy and fuel statistics and follow different protocols2 emission patterns between cities. Key drivers are population and Gross Domestic Product (GDP) per capita. For instance, analysis of the relationships between per-capita consumption and per-capita GDP was applied to energy consumption in cities by Creutzig et al.1, who derived a typology of urban energy use and mitigation potentials and highlighted city compactness and high gasoline prices as explanatory variables for lower energy use in affluent cities.Ancillary emission drivers data related to processes impacting the use of carbon fuels allow us to better understand differences in CO2 emissions with data related to emission drivers is still missing, although there are several regional datasets8–13 as well as recent efforts to estimate city carbon footprints using regional14 and national15 consumption-based models. Sorting out the best available information on data sources and a better understanding of the methods used to produce them help address inconsistencies across the set of cities studied; however, full traceability to the original data used in each city emission inventory is often not possible, and city-level CO2 emissions and driver-related data are not always consistent across space and time. Further, the scope of gases and sectors covered by the datasets are often uneven and activity data could have been scaled down from national data when local activity data are unavailable.A comprehensive global city dataset with Scope-1 CO2 emissions based on CDP16, the carbonn Climate Registry of the Bonn Center for Local Climate Action and Reporting (http://carbonn.org), and a set of Chinese cities compiled by Beijing University (private communication). CO2 emission data are complemented with key ancillary data associated with emissions. The dataset includes 343 cities in nine geographic regions. The majority (88%) of these cities reported emissions between 2010–2015. When the original data reported total emissions from multiple GHGs, we developed a simple correction procedure to remove the contribution of methane (CH4). Scope-2 emission data are provided as additional information when available. Wherever possible, independent consistency checks of the data are performed. The final dataset of Scope-1 CO2 emissions and ancillary data is organized as a tab-separated values (TSV) file of dimensions 343 rows (cities) × 179 columns.Here, we present a global dataset of Scope-1 city CO2 emissions from this study with independent datasets for US and Japanese cities.In the following, we describe the methodology used to produce the final dataset (Methods) and details about each individual dataset (Data Records). The Technical Validation section includes analyses to support the technical quality of the dataset. That is, the Quality Assurance/Quality Control (QA/QC) applied to data records of the same variable when different datasets include this variable, and the comparison of Scope-1 CO2 emissions and ancillary data. http://jupyter.org/, open-source web application for code, visualizations, etc.) using pandas DataFrames (http://pandas.pydata.org/), a data structure that allows data sorting, counting or conditional replacement to be executed on tabular data in lines of code. 16 (DCDP2016) provides annual mean GHG emissions in CO2 equivalents from 187 cities in eight geographic regions. The CDP data are collected through an Online Response System that allows cities to report climate hazards, climate actions, targets and emission inventories. The emissions disclosed to CDP are provided directly by city governments. Cities first identify the inventory boundary and their emissions sources. Most cities use the scopes framework to report Scope-1 and Scope-2 emissions.The 2016 edition of the CDP emissions dataset5. Scope-2 are the emissions from purchased energy generated upstream from the city, mainly electricity5. Total Emissions are the sum of the two scopes. Emission data in DCDP2016 were examined manually to extract only Scope-1 emissions, after correcting for inconsistencies by either consulting with the city data providers or by comparing with a more recent version17 of the CDP data called DCDP2017. Based on quality control procedures comparing Scope-1, Scope-2 and total emissions , quality flags were assigned to the GHG Scope-1 emissions of each city. The quality-controlled data are denoted DCDP. CDP, summarized in Scope-1 are the direct territorial emissions from residential and industrial heating, transport, industrial sectors and power plants within the territory of citiesTo calculate their emissions, cities collect data for a variety of sources, including city departments, their country’s national inventory, local utilities, statistics agencies, and universities. Methods such as scaling can be used for national data to estimate the cities inventory. Once cities have the appropriate data, they multiply the activity data by an emission factor associated with the activity being measured. Cities then compile all this information and provide CDP their inventory and a summary of their inventory data.2 sources from other GHG contributions in DCDP. In total, 44 cities (25%) reported only emissions from fossil fuel and cement CO2, 6 cities did not specify the GHGs covered, 4 reported CO2 and CH4, and 133 included all GHGs, that is, CO2, CH4, N2O, HFCs, PFCs, SF6, and/or NF3  = f the Scope-1 CO2-equivalent emissions (f) as a function of the set of GHGs considered. Then:Since emissions of different greenhouse gases (GHGs) are included under Scope-1 emissions, we separated the fraction of emissions from fossil fuel and cement CO NF3 see . We sepaf(gi) is the emission of each GHG, and αi its warming potential. The emission of CO2 denoted by Where 2 GHGs compared to those of CH4, denoted by Neglecting emissions of other non-CO2 emissions were 4 emissions from waste in each city, approximated by national per capita methane waste emissions multiplied by city population, using country emissions data from the EDGAR inventory (Data Citation 1), country population from The World Factbook , and a GWP of 28 for CH4, time horizon 100 years18. We highlight that this is a simplification; however, deriving city-specific estimates from country-specific emission factors seems reasonable and agrees with the approach taken by UNFCCC for the official national reporting. Regarding whether per capita waste generation (solid waste and waste water) might differ between rural and urban areas, we assume that for OECD countries, there is only a minor difference since consumption and income levels are closely correlated. For emerging countries, the difference in per capita waste of rural and urban population could be larger or CH4 emissions from waste might not even be covered in all rural areas as managed, and this accounted for in the used CH4 emission dataset.An upper bound of Scope-1 CO2 emissions was obtained using the same approach, considering this time CH4 emissions from waste and from natural gas.A lower bound of β) to account for import and export. If the volume of domestic natural gas production, import and export are represented by vproduction, vimport and vexport, respectively, then:Note that figures for national methane emissions from natural gas production should be scaled by the remaining fraction  Scope-1 GHG emissions reported by 48 large cities from the carbonn Climate Registry  with 6 cities in Africa, 5 in East Asia, 10 in Latin America & Caribbean, 3 in North Africa, Middle East, West Asia, 19 in South Asia, and 18 in Southeast Asia, and 25 other cities for which we had ancillary data from Creutzig et al.1; 2) Scope-1 CO2 emissions from the new data compilation of 83 large cities in China from Peking University (DPKU). The emissions were grouped with DCDP into a single dataset denoted Demissions covering 343 cities, for which ancillary data was then gathered in the following steps. Note that DCDP, Dcarbonn and DPKU contained some ancillary data that we preserved (see Data Records section).Most of the cities that disclosed their emissions to CDP are from USA, Canada, Australia, New Zealand (43%) and Europe (31%). Therefore, the D2 emissions. Spatial units of these ancillary data were checked for consistency with emissions data. The consistency of spatial and temporal units between ancillary data and emissions, and within ancillary data themselves, were checked and assigned a Quality Flag . Ancillary data related to emissions were collected to match Scope-1 COet al.1 , UITP (https://trid.trb.org/view/708144) , and WB20 data . These datasets contain energy-use, other socio-economic and climate-related attributes. We integrated the GEA, UITP and WB datasets that comprise 273 cities into DGEA+, but this dataset only matched emissions from 57 cities from DCDP, 25 from Dcarbonn and 35 from DPKU. In DGEA+, duplicate identical values of the same attribute in DGEA, DUITP and DWB were merged into one common column tagged with label ‘GEA+’. If the values were different, they were investigated further to resolve discrepancies (e.g. data entry errors) or left as is if the values were obtained from different sources; any such discrepancies are noted in the Data Records.First, we used three datasets previously reported in Creutzig al.1 see : GEA19 , an urbanization index (http://www.un.org/en/development/desa/population/publications/pdf/urbanization/WUP2011_Report.pdf), and climate indices of heating and cooling degree days (HDD and CDD). HDD and CDD are commonly used to estimate the climate-dependent demand for energy needed to heat or cool a building, respectively, and are defined as the yearly sum of the difference between a base temperature Tb and daily temperature, Tdaily, whenever the daily temperature is lower (HDD) or greater (CDD) than Tb:Altogether, the DGEA, DUITP and DWB, HDD and CDD were given for a 5-year average over 2007–2011 for 13 base temperatures in increments of 0.5 °C (Tb = 12.5 °C–18.5 °C for HDD and Tb = 17 °C–23 °C for CDD). Those values were computed by the online software tool Degree Days.net (www.degreedays.net) created by BizEE Software (http://www.bizeesoftware.com/) using daily temperature data obtained from Weather Underground local weather stations in different cities (www.wunderground.com). The software returns monthly averages across the year range specified.In DGEA+ with Demissions. Missing ancillary data in this merged dataset were then completed from external sources and merged again into Demissions in the order listed below .We merged DOTHERS to fill gaps in DGEA+ and to add new attributes related to emissions. Note that DOTHERS data were not used to replace any data – the names of these independent, updated or new attributes were tagged with an ‘others’ label and added alongside existing attributes (themselves tagged with their dataset of origin). City population and area from multiple sources were obtained for all cities in Demissions. Diesel and gasoline prices provided in DGEA+ for year 200921 were updated to 2014 values22 for all the cities in Demissions. Average household size was added for 119 out of 139 cities in Demissions that did not have this attribute from DGEA+, and 48 DGEA+ values were updated. The mean travel time to work was obtained from external sources for 152 of the 187 CDP cities in Demissions. City area, population, average household size, and mean travel time were checked for spatial consistency before being included in DOTHERS. Gross Domestic Product (GDP) estimates (at Purchasing Power Parity (PPP) and nominal) along with population values for corresponding years, where available, were obtained manually from various external data sources (see Data Records). Altogether, GDP-PPP  estimates were found for 153 (211) cities. The original GDP estimates given in DPKU for all 83 Chinese cities were not checked against independent sources.We obtained external ancillary data denoted as D23 (DINRIX) covering in total 1064 cities of which 118 matched Demissions, and from TomTom , covering 390 cities of which 123 matched Demissionss.Congestion-related data was obtained from INRIX24 based on 2015 data (DIESE) for 181 cities, of which 85 cities matched Demissions.Socio-economic indicators related to transportation, environment, economy, technology, governance and human capital was obtained from the IESE Cities in Motion datasetUEX, from high-resolution satellite land cover imagery in 282 urban area clusters that correspond to 343 cities in Demissions. The DUEX attributes relate to, for each year, high and low built-up areas , the BUA fraction (%) out of the total BUA for high and low BUAs, and low/high BUA population density per km2. DUEX was derived from Landsat imagery collections, pre-processed by the Global Human Settlement (http://data.europa.eu/89h/jrc-ghsl-ghs_smod_pop_globe_r2016a) and built-up (http://data.europa.eu/89h/jrc-ghsl-ghs_built_ldsmt_globe_r2015b) grids, and population data from the Center for International Earth Science Information Network (CIESIN) Gridded Population of the World (GPWv4) (http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a). Please see the Data Records section for more details.Urban area expansion in units of percentage of total built-up area (BUA) for low and high BUAs reflects the increase in the built environment due to activities related to urban life, such as housing construction. Here, we calculated 18 urban area expansion attributes pertaining to three different years , denoted DGEA+ had CDD and HDD indices for some cities, these indices were recomputed (Dclim) from a global harmonized gridded climate dataset25 sampled for the grid-cell containing each city. Dclim contains HDD for a base temperature (Tb) of 15.5 °C and CDD for a base temperature (Tb) of 23 °C.Although Demissions merged with all ancillary datasets  is denoted Dfinal and has dimensions of 343 rows (cites) × 179 columns . The data is publicly available on PANGAEA (Data Citation 2).The dataset containing Dfinal, To summarize the coverage of the different data contained in Dfinal covers emissions and ancillary data in a wide range of countries and 83 cities in China, allowing data to be analyzed for differences between cities worldwide . In the interest of “data discoverability”, we developed an interactive, web-based data visualization, available at the temporary URL https://katirg.github.io/GlobalCarbonCities/. Permanent visualization of Dfinal will be available in the Global Carbon Atlas at www.globalcarbonatlas.org in March, 2019.The new dataset DGEA+, DINRIX, DTomTom, DIESE, Dclim, DUEX, and DOTHERS). The notebooks import the following modules: pandas (version 0.22.0), numpy (version 1.14.0), matplotlib (version 2.1.2), and csv (version 1.0).The Jupyter notebooks (IPython version 5.1.0) used in this section are publicly archived with the final dataset on PANGAEA (Data Citation 2), along with the necessary input files  , good quality Scope-1 GHG emissions  were found for 144 of these cities. Carbonn: Scope-1 GHG values were obtained for 73 cities. PKU: Scope-1 GHG values were obtained for 83 cities.Common column to store Scope-1 GHG emissions values in Dfinal, i.e., ‘CDP’, ‘carbonn’, or ‘PKU’.Common column to store the name of the dataset corresponding to the Scope-1 emissions in Dfinal. CDP: Units ‘tCO2’ are used for cities that reported only CO2 (refer to the ‘Gases included (CDP)’ attribute), and units ‘tCO2-eq’ are used for cities that specified gases other than CO2 or did not specify whether non-CO2 gases were included. Carbonn: units ‘tCO2-eq’ are used since a gas species breakdown was not available. PKU: units ‘tCO2’ are used for all cities since emissions related to CO2 only.Common column to store the units of the Scope-1 GHG emissions values in Dfinal. CDP: emission years ranged from 1990–2016, with 88% of the data reported for the period 2010–2015. Carbonn: emission years ranged from 1994–2017, with 78% of the data reported for the period 2010–2015. PKU: all emissions were estimated for the year 2010.Common column to store the year of the Scope-1 GHG emissions values in Dfinal.Common column to store the emissions protocol used to obtain the Scope-1 GHG emissions values in DCDP: The protocols used to estimate emissions in DCDP are described in the CDP submission guidelines . The majority of cities report their inventory to CDP using the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC)5 launched in 2014 by C40, WRI and ICLEI, after a pilot in 2011 with 35 cities. The GPC aims to enable a consistent, transparent and internationally recognized approach for cities to measure and report emissions, allowing for credible comparison and aggregation across timescales and geographies. Cities that commit to the Compact of Mayors, now the Global Covenant of Mayors for Climate and Energy, must complete an emissions inventory using the GPC methodology.Within GPC, cities can choose to either report BASIC (Scope 1 + Scope 2 + some waste Scope 3 emissions) or BASIC+ emissions . The breakdown was as follows:1. Global Protocol for Community-Scale Greenhouse Gas Emissions Inventories (GPC), , 64 cities (34%). Comprises emissions from stationary energy, transportation, waste, Industrial Processes and Product Use (IPPU), Agriculture, Forestry and Other Land Use (AFOLU), Total Scope 1 , and Total BASIC emissions.2. International Emissions Analysis Protocol (ICLEI), 18 cities (10%). Comprises emissions from stationary energy, transport, fugitive emissions, industrial processes, agriculture, land use, land use change and forestry, solid waste disposal, wastewater treatment and discharge, and other sources.3. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 29 cities (16%). Comprises emissions from energy, IPPU, AFOLU, waste and other sectors.4. U.S. Community Protocol for Accounting and Reporting of Greenhouse Gas Emissions (ICLEI), 26 cities (15%). Comprises emissions from built environment, transportation and other mobile sources, solid waste; wastewater and water, agricultural livestock, and upstream impacts of community-wide activities.5. “Other”, 50 cities (27%). This category includes combinations or subsets of methodologies, or propitiatory methodologies specific to a region/city. Comprises emissions from buildings, water, waste, transport/residential/commercial/industrial/ institutional use, stationary combustion, mobile combustion, industrial processes, waste, and any other classification.Carbonn: cities reported emissions based on the GPC protocol (http://e-lib.iclei.org/wp-content/uploads/2015/12/cCR2015_5Year_Report.pdf).PKU: Scope-1 CO2 emissions were calculated from direct energy consumption statistics26 with CO2 emission factors of diesel oil, coal gas, natural gas, and liquefied petroleum gas calculated according to the guidelines of IPCC 200627.The three common geo-descriptive columns are:final. Ambiguous city names were corrected .Consistent city name assigned to each city in Dfinal.Consistent country name used in Dfinal using the regions defined in Dcarbonn.A geographic region assigned to each city in D2 emissions separated as described in Methods from total GHG emissions reported by cities in DCDP were estimated between 1990–2016, with 88% of the data being in the period 2010–2015. Some cities also reported change in their emissions between the most recent and previous reporting periods, explanations for this change, methodology details, and gases included, as well as ancillary data such as population, boundary definition, land area, mean annual temperature, mean altitude, and Gross Domestic Product (GDP). The following records were obtained from DCDP.Scope-1 COName provided by each city.Boundary of the city provided by each city. Boundary types are diverse and correspond to metropolitan area for 25 cities, administrative boundary of a local government: 147; combination of administrative divisions: 2; geopolitical Boundary - physical areas over which local government has jurisdictional control: 3; municipal boundary: 1; Other boundary types: 4 cities.The year when a city reported data to CDP, that is, 2016 for 162 cities and 2017 for 25 cities.CDP match the recent report from . Also, according to the Instituto Nacional de Estatística Portugal, the population of Moita near Lisbon is 66,029 (https://en.wikipedia.org/wiki/Moita).Latitude and longitude coordinates provided by each city. The following discrepancies were noted in these coordinates. Wellington referred to Wellington, Florida instead of Wellington, New Zealand. Moita in Portugal did not localize to the city. We set the Moita city near Lisbon, since the population  and emissions  in DReported city population.Year corresponding to reported city population data.CDP.City land area available for 184 out of 187 cities in D2, 6 cities did not specify, 4 cities included CO2 and CH4, and the remaining 133 cities included CO2, CH4 plus a combination of one or more of N2O, HFCs, PFCs, SF6, and/or NF3  of cities reduced their emissions with respect to the previous reporting year. Emissions increased for 36 (19%) cities, and stayed the same for 12 (6%) cities. Of the remaining cities, 24 (13%) cities were reporting their emissions for the first time in 2016 (thus had no previous comparison year), 7 (4%) cities reported “other” to indicate that their emission data was not directly comparable to previous estimates due to protocol differences, and 20 cities (11%) did not specify.CDP2016 dataset to explore the reasons provided for changes in emissions. Most reasons tended to be due to a methodological change, population growth, change in policy, or increase in renewable energy and electricity.The interested reader may refer to the original DCDP in units of metric ton CO2-eq. Note that some cities reported only CO2, effectively making this unit tCO2 (refer to the ‘Gases included (CDP)’ attribute). Scope-2 emissions exist for 141 out of 187 cities which were of good quality .Scope-2 GHG emissions from D2 emissions reported in 2016 in units of metric ton CO2-eq. Note that some cities reported only CO2, effectively making this unit tCO2 (refer to the ‘Gases included (CDP)’ attribute). Depending on the methodology selected, Scope-1 and Scope-2 emissions are included in total emissions.The city total COCDP. Values provided by source for DCDP cities.Average altitude of the city in meters above sea level, available for 168 out of the 187 cities in DCDP.Average annual temperature in degrees C, available for 177 out of the 187 cities in DGDP of the city in the currency reported by the city (given in next column).Currency of the GDP value.The year for which the GDP value was obtained.Source of reported GDP data.For cities whose CDP2016 data were replaced by CDP2017 values; reasons are indicated in this column. Please see the Technical Validation section.Quality flags (A–E) assigned to emissions as described in section ‘Technical Validation’.Lower and upper bound estimates of Scope-1 emissions resulting from the methane corrections described in Methods Step 2.Lower and upper bound estimates of Scope-1 emissions resulting from the methane corrections described in Methods Step 2.Lower and upper bound estimates of total emissions resulting from the methane corrections described in Methods Step 2.Lower and upper bound estimates of Total emissions resulting from the methane corrections described in Methods Step 2.Defined as (Scope 1 + Scope 2)/Total emissions. See ‘Quality Flags’ section and carbonn (see Methods) were estimated for years in the period 1994–2017, with 78% of the data in 2010–2015. Emissions correspond to all activities occurring throughout the local government’s entire geographic area (http://e-lib.iclei.org/wp-content/uploads/2015/12/cCR2015_5Year_Report.pdf). Cities also reported ancillary data such as city type, population, GDP and nominal GDP. The following records were obtained from Dcarbonn.Scope-1 GHG emissions from 73 cities selected in DCity name.City type: Municipality: 58; Independent municipality: 7; Special municipality/Federal district: 3; State/Region: 2; Independent intercommunality: 1; Intercommunality:1; Sovereign city-state: 1.City population.Year corresponding to reported city population.GDP of the city in the units reported by the city (see next column).Currency unit of GDP.Year corresponding to reported GDP.Nominal GDP in units specified in ‘GDP unit (carbonn)’ above.carbonn.Average altitude of the city, in meters above sea level, available for 2 out of the 73 cities in DPKU correspond to the year 2010. Cities also reported ancillary data such as urban population, built-up area, GDP and income per capita. The following records were obtained from DPKU.All of the emissions reported by the 83 cities in DCity name.Urban population.PKU .Built-up area was defined by interpreting Landsat images for each city in DGDP of the city in renminbi currency units.Per capita income of the city in renminbi currency units.2 emissions provided by source.Residential COGEA+ is the fusion of DGEA, DUITP and DWB containing the following attributes:As explained in Methods, DGEA.City name as given in DUITP.City name as given in DWB.City name as given in DWB cities: City: 12; Greater metropolitan area: 3; Government Administered Area (Province): 3; City and County: 1; County: 1.City type for DWB cities (1998–2006).Study year for emissions for DGEA, available for 41 of the 187 cities in DCDP.City population from DUITP, available for 42 of the 187 cities in DCDP.City population from DWB, available for 15 of the 187 cities in DCDP.City population from DWB, available for 15 of the 187 cities in DCDP.Year corresponding to city population in Dyear = 1950, 1990 and 2010 from DWB, available for 15 of the 187 cities in DCDP.Population of urban agglomerations in year1 = 1950 and 1990 and year2 = 2010 from DWB, available for 15 of the 187 cities in DCDP.Population growth rate between GEA, available for 30 of the 187 cities in DCDP.City area from DWB, available for 15 of the 187 cities in DCDP. Used Wikipedia values corresponding to definition of the city.City area from DGEA, available for 41 of the 187 cities in DCDP.Population per area from DGEA, available for 30 of the 187 cities in DCDP.Population per sqrt(area) from DUITP, available for 42 of the 187 cities in DCDP.Population per area from DWB, available for 15 of the 187 cities in DCDP.Population per area from DWB, available for 15 of the 187 cities in DCDP.Population per sqrt(area) from DCDP. One city in DGEA+ (Brasília) was missing a weather station ID.The ID of the weather station used to calculate HDD and CDD, available for 56 of the 187 cities in DGEA+. HDD values across DGEA, DUITP and DWB were the same for all cities except Sao Paulo in DUITP, which disagreed with the values in the other two datasets. The DUITP values were considered anomalous and were removed. No HDD data were available for one city (Brasília) in DGEA+.HDD values for a base temperature of 15.5 °C obtained from the weather station specified in the ‘weather_station_id’ attribute of DGEA+. CDD values across DGEA, DUITP and DWB were the same for all cities except Sao Paulo in DUITP, which disagreed with the values in the other two datasets. The DUITP values were considered anomalous and were removed. No CDD data were available for one city (Brasília) in DGEA+.CDD values for a base temperature of 23 °C obtained from the weather station specified in the ‘weather_station_id’ attribute of D1 that these temperature thresholds were the most predictive of energy use, given the data available. .These base temperature values for HDD and CDD were selected since it was shown in a 2015 studyTotal final energy consumption in GJ/capita/yr. Provided by source.2-eq/capita/yr, excluding aviation and marine sources. Provided by source.Per capita emissions for energy use in tCO21. In the final consolidated dataset, these values were not used; updated 2014 values22 were used instead (see Methods Step 4).November 2008 diesel price 21. In the final consolidated dataset, these values were not used; updated 2014 values22 were used instead (see Methods Step 4).November 2008 gasoline price UITP were anomalous compared with the corresponding DGEA and DWB values; cross-checking with the reference cited in the datasets revealed that these DUITP values were incorrect and they were therefore removed.Fuel prices for two cities (Rio de Janeiro and Sao Paulo) in DCDP. The following discrepancies/uncertainties were noted for household size in DGEA+:Average number of persons per household, obtained from multiple sources, available for 48 of the 187 cities in DSydney: The 2011 household size reported in DGEA+ corresponds to the population of the Greater Sydney Area, whereas the corresponding city population in Demissions corresponds to the ‘City of Sydney’.Stockholm: It could not be confirmed that household size in DGEA+ represents the corresponding city population in Demissions. Household size in DOTHERS was estimated by a manual calculation based on values from Statistics Sweden .Lisbon: It appears that the 2011 population for household size in DGEA+ does not match the corresponding city population in Demissions.Melbourne: The household size reference link in DGEA+ is actually for Sydney.Seoul. The household size reference link in DGEA+ is invalid.GEA+ that matched cities in DCDP.It was also found that the household size source reference no longer exists for 14 of the 57 cities in DYear corresponding to household size values.Reference corresponding to household size values obtained from multiple sources.Comments regarding household size computations.http://www.un.org/en/development/desa/population/publications/pdf/urbanization/WUP2011_Report.pdf), available for 57 of the 187 cities in DCDP.Percentage of Population Residing in Urban Areas in 2005 (https://www.scribd.com/document/17016734/MasterCard-Worldwide-Centers-of-Commerce-Index-2008), obtained by considering 43 indicators and 74 sub-indicators across seven dimensions , available for 33 of the 187 cities in DCDP.Center of commerce index . Source: derived visually from Google maps, available for 51 of the 187 cities in DCDP.Indicates type of bounding geography . Despite the title of this column, strings for the geographical features are used as values, not 0 or 1. Source: derived visually from Google maps, available for 18 of the 187 cities in DGEA.GDP per capita per year at Purchasing Paring Parity (PPP) provided by DUITP.GDP per capita per year (PPP) provided by DWB.GDP per capita per year (PPP) provided by Demissions to indicate whether the city has corresponding ancillary data in DGEA+  or if no matching city exists in DGEA+ .Computed for each city in DINRIX. The attributes relate to congestion rate, defined as the ratio of the total drive time in congested vs. free flow traffic, in percent.The following congestion-based attributes were obtained from DThe average peak period congestion rate is applied to travel times to derive daily time spent in peak period congestion. The average number of hours spent in congestion during peak hours is then estimated assuming 240 working days a year.Average of seven congestion rates: peak periods on highways in and out of the city; peak periods within a city; day time travel on highways in and out of a city; day time travel within a city; late night on highways in and out of a city; late night within a city; weekend travel on all roads.Determined by the number of peak hours that drivers spent in congestion in 2016.The seven congestion rates (see above) weighted by relative volumes to reflect typical driving patterns, which is then weighted by the Median Travel Time, effectively adjusting the congestion rate by the city’s size and associated average journey times.TomTom. The attributes relate to congestion level, defined as the percent increase in overall travel times when compared to uncongested traffic.The following congestion-based attributes were obtained from DIncrease in overall travel times when compared to uncongested traffic. Travel times were calculated using speed measurements on individual road segments and entire networks in TomTom’s historical traffic database, weighted by the number of measurements.Percent change in congestion level compared to the previous year.Percent increase in travel time compared with uncongested traffic during morning peak times .Percent increase in travel time compared with uncongested traffic during evening peak times .Ranking based on Congestion Level; applied only to cities with population greater than 800,000.IESE: Economy, Environment, Governance, Human capital, International impact, Mobility and transportation, Public management, Social cohesion, Technology, Urban planning, the Cities in Motion index (CIMI), CIMI ranking, and CIMI performance. These indicators reflect the sustainability and standard of living of a city.Thirteen socio-economic indicators were obtained from DThe IESE indicators include both city and national level variables from several sources with objective measures  as well as subjective measure . The calculation of the synthetic indicators produces values that were standardized in order to note the differences amongst the various cities between positions more intuitively. In order to modify the scale of each city, the city with the highest score (first in the ranking) was assigned the number 100. Cities with a high performance (H) are considered to be those with an index greater than 90; relatively high (RH), between 60 and 90; average (A), between 45 and 60; and low (L), below 45.The IESE economy index includes aspects that promote economic development. This index takes into account four positively-contributing indicators – labour productivity calculated as GDP/working population [thousands], number of headquarters of publicly traded companies, percentage of 18 to 64-year-old population who are new entrepreneurs or owners/managers of a new business [per capita], and gross domestic product [millions USD at 2014 prices], and two negatively-contributing indicators – number of calendar days needed so a business can operate legally, and ease of starting a business [rank].2 emissions (fossil fuel burning and cement manufacture [kt]), CO2 emission index, methane emissions arising from human activities , PM2.5 , PM10 , and pollution index.The IESE environment index aims to reflect environmental sustainability/sustainable development. This index takes into account two positively-contributing indicators – percentage of the population with access to the water supply, and environmental performance index (1 = poor to 100 = good), and six negatively-contributing indicators – COThe IESE governance index aims to reflect the effectiveness, quality and sound guidance of state intervention. This index takes into account five positively-contributing indicators: the strength of legal rights for borrowers and lenders, corruption perceptions index (0 = very corrupt to 100 = very transparent), number of functions of the city’s innovation department, range of government Web services, and whether a city has an open data platform.The IESE human capital index aims to reflect a city’s talent and ability to create/support/retain it. The index takes into account seven indicators, considered as positive contributors to the index: the proportion of the population with secondary education, the number of business schools, the international movement of higher-level students, the number of universities, the number of museums per city, the number of art galleries per city, and the expenditure on leisure and recreation [millions USD at 2014 prices].The IESE international impact index aims to reflect the international renown of a city . This index takes into account four positively-contributing indicators: the number of international tourists, the number of passengers who travel with airlines, number of hotels per capita, and the number of international conferences and meetings hosted in a city, and one negatively-contributing indicator – ranking of cities according to the number of photos taken in the city and uploaded to Panoramio .2 consumption and other traffic system inefficiencies), traffic inefficiency index , number of road accidents per 100,000 inhabitants, and commute index (considering travel time to work).The IESE mobility and transportation index aims to reflect how well a city facilitates movement and access to public transportation services. This index takes into account three positively-contributing indicators – the number of metro stations per city, number of arrival and departure flights in a city, means of transportation (increases with increasing public transportation options), and four negatively-contributing indicators – traffic index , varies from 0 to 100, with 0 being a situation of perfectly equitable income distribution and 100 that of perfect inequality), price of property as percentage of income) – and one negatively-contributing indicator, the ratio of women workers in the public administration.The IESE social cohesion index aims to reflect the degree of social interaction and sense of belonging to a common situation or project. This index takes into account five positively-contributing indicators – the ratio of death per 100,000 inhabitants, crime rate, health index, unemployment rate (number of unemployed / labor force), the Gini index , innovation index , number of smartphones per capita, and the number of wireless access points.The IESE urban planning index aims to reflect the habitability of a city in terms of e.g. quality of health infrastructure, housing policies, design of public spaces. This index takes into account four positively-contributing indicators – percentage of the population with access to sanitation facilities, number of bicycle shops per capita, number of architecture firms per capita, and the number of cycling enthusiasts per capita, and one negatively-contributing indicator – number of people per household.The IESE Cities in Motion Index (CIMI) is a weighted aggregation of the 10 indices described above: economy: 1; human capital: 0.4814; international outreach: 0.6212; urban planning: 0.841; environment: 0.6215; technology: 0.3763; governance: 0.4047; social cohesion: 0.5941; mobility and transportation: 0.4707; and public management: 0.571.The IESE CIMI ranking is a 1–181 ranking (since there are 181 cities) of the CIMI, where 1 = highest, 181 = lowest.The IESE CIMI performance classifies a city’s performance based on its CIMI: high ; relatively high ; average ; low .emissions that are not part of DGEA+ do not have climate indices, therefore we calculated average HDD and CDD over the same period (2007–2011) as in DGEA+ for all 343 cities in Demissions for the optimal temperature thresholds of 15.5 °C for HDD and 23 °C for CDD, as explained above. The temperature data were based on the three-hourly ERA-interim reanalysis available from 1979 and updated in real time29, sampled at a spatial resolution of ≈ 80 km. Calculating climate indices from gridded data that is treated homogeneously allows comparison of different locations without possible biases such as weather station locations within a city. However, at the relatively coarse resolution of the grid, coastal and orographic effects on surface temperature may have been missed25. The city locations were obtained from the latitude and longitude coordinates in DOTHERS.The cities in Dclim at grid points closest to the lat/lon coordinates above, averaged over the same five-year period (2007–2011) as for the climate indices in DGEA+. HDD 15.5 C is defined as the sum over one year of the difference between 15.5 °C (base temperature) and the daily temperature, whenever the daily temperature is lower than the base temperature.HDD calculated for a base temperature of 15.5 °C from DGEA+ for 25 cities out of 56 cities with climate indices in DGEA+. Twelve cities are colder than predicted by the global model, and 5 of these cities are at high altitudes (>1000 m). The relatively course resolution of the ERA-interm dataset may not account for coastal and orographic effects25; this could lead to the prediction of fewer colder days than for the same coordinates but at higher altitude. The remaining 7 cities are at lower altitudes (13–935 m) and thus altitude effects likely do not explain why they are colder than the model predictions.We noted that our computed HDD 15.5 C had poor correspondence (30–90%) with observations-based HDD 15.5 C in Dclim at grid points closest to the lat/lon coordinates, averaged over the same five-year period (2007–2011) as for the climate indices in DGEA+. CDD 23 C is defined as the sum over one year of the difference between 23 °C (base temperature) and the daily temperature, whenever the daily temperature is higher than the base temperature.CDD calculated for a baseline temperature of 23 °C from DGEA+ for 53 cities out of 56 cities with climate indices in DGEA+. Forty-eight cities were warmer than predicted by the global model except for four cities , perhaps attributable to the urban heat island effect.We noted that our computed CDD 23 C had poor correspondence (30–90%) with observations-based CDD 23 C in DUEX contains three types of urban area expansion attributes for two built-up areas (BUA) values (low and high) pertaining to three different years: 1990, 2000, and 2014. This results in 3 types×2 BUAs×3 years = 18 DUEX attributes. The three types of attributes are BUAs [km2], BUA fraction [%] out of the total city area, and BUA population density [people/km2], as described below. The methodology for the preparation of DUEX, completed beforehand using ArcGIS (www.arcgis.com) and R using the city coordinates in Dclim, is as follows:Dclim. UACs are approximations, reflecting estimates of urban catchment areas and do not depict administrative borders. UACs are selected based on the global human settlement (GHS) dataset (http://data.europa.eu/89h/jrc-ghsl-ghs_smod_pop_globe_r2016a). GHS is classified into high- and low-density and rural clusters, defined as:The UAC is the study area for each city. The selection is based on city coordinates described above for DHigh-density clusters (HDCs) comprise “contiguous cells  with a density of at least 1,500 inhabitants/km2 or a built-up density greater than 50%, and a minimum of 50,000 inhabitants per cluster”30.Low-density clusters (LDCs) comprise “contiguous cells  with a density of at least 300 inhabitant/km2 and minimum of 5,000 inhabitants per cluster”30.Rural clusters include all cells outside high- and low-density clusters30. From these areas, only the areas that intersect with one of the leftover city points are selected.CDP  are classified as rural and therefore were not considered in this analysis.In the first round, contiguous areas from the HDC that intersect with one of the city points were identified and extracted. In the second round, contiguous LDC areas were identified and extracted for the city points that did not intersect with HDC in the first round. Note: two cities in D2) within the following ranges: 8,000,000–100,000,000; 100,000,001–300,000,000; 300,000,001–2,000,000,000; 2,000,000,001–4,000,000,000; 4,000,000,001–6,015,000,000 were assigned buffer distances (km) of 3, 5, 7, 10, and 13, respectively.A buffer was applied, whereby the buffer distance was chosen based on the size of the urban cluster . Classes . Datasets were classified into ‘low’ and ‘high’ for values between 0–25% and >25% of the built-up area (BUA), respectively, for the years 1990, 2000 and 2014. Classified raster data were added up for the change analysis and the areas calculated for high and low BUA.Built-up intensity changes for each UAC were calculated based on built-up area raster grids (GHG BUILT-UP_GRID) (http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a) depicts the 2015 population, whereas the latest built-up area layer is from 2014. The 18 attributes obtained from DUEX for 282 urban clusters corresponding to the 343 cities in Dfinal are described below:Population densities are extracted  within the classified built-up areas, high and low, for the 1990 baseline. Changes are captured from 1990–2000 and from 2000–2015. Note that the population raster dataset (GPWv4)  for year = 1990, 2000, and 2014; three attributes in total.High BUA for year = 1990, 2000, and 2014, where total BUA is the sum of low and high BUA; three attributes in total.Percentage of low BUA of the total BUA for year = 1990, 2000, and 2014, where total BUA is the sum of low and high BUA; three attributes in total.Percentage of high BUA of the total BUA for year = 1990, 2000, and 2014; three attributes in total.The population density in the low BUA for year = 1990, 2000, and 2014; three attributes in total.The population density in the high BUA for The attributes obtained from other external sources are as follows:City latitude. Used for HDD/CDD calculations.City longitude . Used for HDD/CDD calculations.https://www.mediawiki.org/wiki/GeoHack), obtained from the city’s Wikipedia page.Source of city coordinates: GeoHack (emissions where possible. We noted the largest discrepancies (30–90%) in population values in DOTHERS vs. DCDP in three cities:Population values obtained from external sources, consistent with the population and city definition in DBrasília: The population value in DCDP was 1,409,671 (2015 estimate) vs. 2,977,216 (2016 estimate) in DOTHERS (https://en.wikipedia.org/wiki/Bras%C3%ADlia#cite_note-pop-1). The IBGE average household size value from the 2010 census31 also corresponds to a population of 2,977,216.Caracas: The population value in DCDP was 3,518,590 (2015 estimate) vs. 5,290,000 (2016 estimate) in DOTHERS .Santiago de Guayaquil: The population value in DCDP was 2,350,915 (2010 estimate) vs. 3,500,000 (2014 estimate) in DOTHERS (https://en.wikipedia.org/wiki/Guayaquil).Year corresponding to the external population values.Source for external population values.emissions.City area obtained from external sources, consistent with population, area and city definition in DYear corresponding to the area value.Source for area value.22.2014 diesel price, from national values22.2014 gasoline price, from national valuesemissions. Note that the following assumptions were made to estimate household size in the cities listed below:Average number of people per household obtained from multiple sources. All updated household size values we collected were obtained for populations that matched the city population in DAddis Ababa: The 1984 value for average household size32 was used since a more recent value could not be found.Aspen and Pitkin county: Average household size for “Aspen and Pitkin county”  was assumed to be close enough to household size of “Aspen” , since population values are close (https://www.census.gov/quickfacts/fact/table/aspencitycolorado/PST045216).Bogor: The average household size of West Java in 2012 survey report33 was assumed to be representative of the average household size of Bogor, a city in West Java, since a value for Bogor itself could not be found.  which is consistent with the value obtained for a 2014 Jakarta survey34 (4.5).)Bornova: The average household size of Izmir province found in the 2011 census35 was used for Bornova.Caracas: The population of Caracas in DCDP  corresponds to the population of the Metropolitan Region of Caracas (https://en.wikipedia.org/wiki/Metropolitan_Region_of_Caracas) which is made up of the Metropolitan District of Caracas  plus 11 municipalities over Miranda and Vargas states. Therefore, to estimate household size, the household sizes of Distrito Capital, Miranda and Vargas states were averaged: 1/3×(3.41 + 3.52 + 3.58) = 3.50.Durban: The population of Durban in DCDP is 3,555,868, but actually this corresponds to the population of eThekwini Metropolitan Municipality . Therefore, the average household size for eThekwini  and not Durban  was used.Incheon: Average household size in the 2015 census report36 is stated as 2.65 for “Incheon Province”; this value was assumed to be valid for Incheon city.Jakarta: Average household size for Jakarta was based on a survey34 of 297 households in 2014.Pretoria Tshwane: The most recent household size statistic that could be found (3.40)37 corresponds to year 2007 and has a similar value as in the original DGEA+.Quito and Santiago de Guayaquil: The 1990 value for average household size38 was used since a more recent value could not be found. Note also that this value is listed as “Average number of persons per living quarters”.Suwon: Only the average household size for Gyeonggi province (2.68) could be found36. Since Suwon is the largest metropolis of Gyeonggi province, this value was assumed to be representative of the average household size in Suwon.Santiago de Cali: The average household size for Valle de Cauca in the 2005 census39 was used. Santiago de Cali is the capital of the Valle del Cauca department, and the most populous city in southwest Colombia.Year for which the mean household size above is given.Source reference for updated household sizes.GEA+ for city ‘Birmingham’ was “Calculation: pop/household (1036900/410700)”. The updated household size for Birmingham, however, was obtained directly from EUROSTAT (http://ec.europa.eu/eurostat/) without the need for calculation; therefore, this comment was removed.Updated comments pertaining to household size. For example, the original comment in Demissions. The following assumptions that were made in estimating mean commute time for some cities.Mean one-way travel time to travel to work for city citizens. All values we collected were obtained for populations that matched the city population in DAjax, ON: The mean commute time for Durham region was used since a value could not be found for the city of Ajax, Ontario.Birmingham: The mean commute time was taken from Telegraph article (http://www.telegraph.co.uk/motoring/news/3122428/Birmingham-worst-place-for-commuting-survey.html and https://www.findaphd.com/search/projectdetails.aspx?PJID=61628), but the original reference could not be found.Leicester, Manchester: The mean commute time was taken from Telegraph article (http://www.telegraph.co.uk/motoring/news/3122428/Birmingham-worst-place-for-commuting-survey.html and https://www.findaphd.com/search/projectdetails.aspx?PJID=61628), but the original reference could not be found.Seoul: Two sources for mean commute time were found: one value (41.2 min) from the 2010 Statistics Korea census referenced by a 2011 KBS World Radio article (http://world.kbs.co.kr/english/news/news_Ec_detail.htm?No=84738), and another value (48.0 min) from a “big data analysis conducted by the nation’s No. 2 mobile carrier KT” referenced in the a 2017 Korea Herald article . The latter value was used since it is most recent of the two values.Suwon: The reference above for Seoul also reports an average mean commute time (43.5 min) for Gyeonggi province. Since Suwon is the largest metropolis of Gyeonggi province, it was assumed that this was representative of Suwon.Rotterdam: The average commute time for MRDH (Metropoolregio Rotterdam Den Haag) across education levels was used40. However, MRDH comprises 23 out of 60 municipalities in Zuid-Holland province, and its population and area are much larger than Rotterdam itself .Mexico City: The mean commute time (75 min) was obtained from a 2017 Mexico News Daily article (http://mexiconewsdaily.com/news/45-days-a-year-spent-commuting-in-cdmx/), but the original El Universal report cited could not be found. However, this value is consistent with the 61–90 minutes estimation in a 2008 report from the Universidad Nacional Autónoma de México41.Caracas, Lima: mean travel time was based on 16 contributors only (www.numbeo.com).Ljubljana: mean travel time was based on 30 contributors only (www.numbeo.com).Vlinius: mean travel time was based on 46 contributors only (www.numbeo.com).Reykijavik: mean travel time was based on 22 contributors only (www.numbeo.com).Athens: mean travel time was based on 117 contributors only (www.numbeo.com).Buenos Aires: mean travel time was based on 42 contributors only (www.numbeo.com).Adelaide: mean travel time was based on 37 contributors only (www.numbeo.com).Canberra: mean travel time was based on 31 contributors only (www.numbeo.com).Melbourne: mean travel time was based on 81 contributors only (www.numbeo.com).Santiago: mean travel time was based on 36 contributors only (www.numbeo.com).Hong Kong: mean travel time was based on 53 contributors only (www.numbeo.com).Copenhagen: mean travel time was based on 49 contributors only (www.numbeo.com).Torino: mean travel time was based on 23 contributors only (www.numbeo.com).Amman: mean travel time was based on 25 contributors only (www.numbeo.com).Warsaw: mean travel time was based on 83 contributors only (www.numbeo.com).Cape Town: mean travel time was based on 74 contributors only (www.numbeo.com).Durban: mean travel time was based on 17 contributors only (www.numbeo.com).Johannesburg: mean travel time was based on 81 contributors only (www.numbeo.com).Pretoria Tshwane: mean travel time was based on 21 contributors only (www.numbeo.com).Taipei City: mean travel time was based on 26 contributors only (www.numbeo.com)Quito: calculated based on the following estimation based on four time windows weighted by the fraction of time spent for each38:0.2×(15 min/2) + 0.39×(15 min + 7.5 min) + 0.24×(30 min + 15 min) + 0.08×(60 min + 30 min)Year for which mean travel time to work is given.Source for mean travel time values.final includes GDP values at Purchasing Paring Parity (GDP-PPP) and nominal GDP (nGDP) obtained from multiple GDP assessments, as specified in the ‘source’ columns. Altogether, these references gap-filled the GDP-PPP attribute for 153 cities , and gap-filled the nGDP attribute for 211 cities .DGDP-PPP values in billions USD.GDP-PPP normalized by population values referenced in the same source.The following sources were used to obtain GDP-PPP in the order listed below:https://www.brookings.edu/research/global-metro-monitor); 140 cities matched Dfinal. The original units [$Million] were converted to [$Billion] for consistency with other GDP datasets. The population values were obtained from the same data source for the same reference year (2014).Brookings Institution for 2014 ; 5 cities matched Dfinal. The population values were obtained from the same data source for the same reference year (2015).Brookings Institution for 2015 (https://web.archive.org/web/20110504031739/https://www.ukmediacentre.pwc.com/imagelibrary/downloadMedia.ashx?MediaDetailsID=1562), obtained for 8 cities in Dfinal for which a value from Brookings Institution for 2014 was not available.PricewaterhouseCoopers (PwC) for the year 2008  Report  in billions USD.Nominal GDP normalized by population values referenced in the same source.The following sources were used to obtain nGDP in the order listed below:http://foreignpolicy.com/2012/08/07/the-most-dynamic-cities-of-2025/, or displayed as an interactive map http://www.mckinsey.com/tools/Wrappers/Redesign/InteractiveWrapper.aspx?sc_itemid={C84CB74F-A3B1-47B1-8265-6252F6D85B68}), of which 168 cities matched Dfinal.McKinsey for the year 2010 (https://stats.oecd.org/). The OECD GDP values were found for 27 cities in Dfinal that did not have a value from McKinsey 2010.The Organisation for Economic Co-Operation and Development (OECD) (available at https://en.wikipedia.org/wiki/List_of_cities_by_GDP#cite_ref-42), found for 14 cities that did not have a value from McKinsey 2010 or from OECD.Various external sources, principally from Wikipedia .https://www.cia.gov/library/publications/the-world-factbook/rankorder/rankorderguide.html).National per capita methane emissions from waste and natural gas production multiplied by city population using country emissions data from the EDGAR inventory for year 2010 (Data Citation 1) and population data from the World Factbook .Year of national natural gas export data published in the CIA World Fact Book 2014  and household size (HQF), respectively.City area  differs between datasets, which led us to define an area quality flag (AQF). Differences of the year for which population household size and Scope-1 COCDP to DGEA (attributes ‘City area (CDP) [km2]’ and ‘City area (GEA) [km2]’), AQF (CDP/GEA) = 1 is set for ratios within range  and AQF (CDP/GEA) = 0 otherwise. Inconsistent areas between DCDP and DGEA with AQF(CDP/GEA) = 0 were found for 10 out of 30 common cities. Four inconsistencies were attributable to different city boundary definitions:Determined from the ratio of city areas of DStockholm: DCDP area is for ‘City of Stockholm’, while DGEA area is for ‘Stockholm County’.Toronto: DCDP area is for ‘City of Toronto’, while DGEA area is for ‘Greater Toronto Area’.Cardiff: DCDP area is for ‘City of Cardiff’, while DGEA area is for ‘Cardiff and Vale of Glamorgan’.Birmingham: DCDP area is for ‘Birhmingham City Council’, but city boundary is not specified in DGEA.CDP and DWB, DCDP and DOTHERS, DPKU and DGEA, DPKU and DGEA, and DPKU and DOTHERS.Similarly to AQF (CDP/GEA), other AQF (X/Y) flags were computed for cities areas across pairs of datasets (X/Y), namely DCDP to DWB, AQF (CDP/WB) = 0 were found for 3 out of 15 cities. Two inconsistencies are attributable to different city boundary definitions and a data entry error:From the ratio of areas in DLos Angeles: DCDP is for ‘City of Los Angeles’ while DWB for ‘Los Angeles County’.Toronto: DCDP is for ‘City of Toronto’ whereas DWB is for ‘Greater Toronto Area’.Seattle: the DCDP area value was a typo .CDP vs. DOTHERS areas, AQF (CDP/OTHERS) = 0, were found in 19 out of 184 cities. Areas were not reported for four DCDP cities . Three data entry errors in DCDP areas were found and corrected for Faro, Porto Alegre, Salvador, and manually corrected in Dfinal. The DCDP area of Kadiovacik is likely not 5,947 km2 as reported and was estimated manually to be 0.23 km2 by drawing a square around its center using https://www.daftlogic.com/projects-google-maps-area-calculator-tool.htm). It is unclear if remaining area inconsistencies were due to different city boundary definitions or e.g. data entry errors.From the ratio of areas in DPKU vs. DGEA, AQF (PKU/GEA) = 0 were found for all 19 common cities. The built-up area reported in DPKU was consistently much smaller than the area reported in DGEA, likely due to city boundary definitions.From the ratio of areas in DPKU vs. DWB area, AQF (PKU/WB) = 0 were found for all 3 common cities. The built-up area reported in DPKU is consistently much smaller than in DWB, likely due to city boundary definitions.From the ratio of areas in DPKU vs. DOTHERS area, AQF (PKU/others) = 0, were found for 73 out of 77 cities. The built-up area reported in DPKU is consistently much smaller than the area reported in DOTHERS, likely due to city boundary definitions.From the ratio of areas in D2 emissions were estimated, which defines temporal quality flags for population (PQF) and household size (HQF).Temporal consistency means similarity of the year for which population household size and Scope-1 COemissions  and the year of DCDP population. TQF value = 1 for differences less than 5 years, and 0 otherwise. Inconsistent values were found for 14 out of 187 cities. Most of these inconsistencies were on or near the cutoff 5-year point . The remaining two cities  have recent population years  but not very recent emission years .Determined from the absolute difference between the year of Scope-1 emissions in DInconsistent values were found for 11 out of 68 cities. Most of these inconsistencies were on or near the cutoff 5-year point . The remaining city (Graz) has a recent population year (2014) but a quite out-dated emission year (1994).Inconsistent values were found for 17 out of 20 cities. In these cases, the difference in population and emission years ranged from 6–15 years.WB (attribute ‘Population 2010 (WB)’ in Dfinal). Inconsistent values were found for 2 out of 20 cities. In these cases, the difference in population and emission years was on the cutoff point of 5 years.Note that this PQF was determined from the 2010 population value in DOTHERS (difference range 5–22 years).Inconsistent values were found for 59 out of 269 cities in Demissions and the year of DGEA+ household size, where these values exist. HQF value = 1 for differences less than 15 years (given the slower changes in this variable), and 0 otherwise. No inconsistent HQF (GEA+) values were found for all 103 cities for which household size values exist in DGEA+.Determined from the absolute difference between the year of Scope-1 emissions in DOTHERS. Inconsistent values were found for two out of 176 cities for which external household size values were obtained (Graz and Piacenza). These inconsistencies are due to quite out-dated emission years [1994 for Graz (Dcarbonn) and 1990 for Piacenza (DCDP)].Defined as in HQF (GEA+) above but for household sizes in DCDP  and scope-specific values  as shown in CDP2016 values were replaced by DCDP2017 values if the latter had a better quality flag.Emission Quality Flags (EQF) were computed for DCDP2016 by those from DCDP2017, and to have EQF = D for 36 cities missing both scopes. Special case for Rotterdam: DCDP2016 data was EQF = A, but included emissions from several facilities in the port and were replaced by DCDP2017 values .EQF = A, B, C, D, or E were assigned based on the steps shown in CDP emission data, systematic errors were also introduced when removing non-CO2 gases emissions. Further, whether Scope-1 data from DCDP, and Dcarbonn and DPKU included all power plants in the territory of each city was not verified systematically against independent sources. Many of these city power plants can be small and may not be reported by each city.The uncertainty of reported emissions from cities is particularly difficult to estimate since no formal uncertainty analysis is applied by cities on the bottom-up activity and emission factor data that they collect for inventories. In our processing of DAccurate estimates for city-scale emissions uncertainties depends on a clear understanding of system boundaries, i.e. emitting activities included in the accounting for Scope-1. A large source of errors between city emission estimates is the use of distinct systems boundaries . Once these inconsistencies are corrected, the between-estimates uncertainty could be reduced.A detailed evaluation against independent estimates is arguably the best practical way to assess uncertainties when looking at a multiple-cities dataset. This is the approach we followed in this section.et al.42 provides U.S. fossil fuel CO2 emissions for the year 2002 on a 10 × 10 km grid at the level of fuel type, economic sub-sector, and county/state identification over the entire US landscape. VULCAN includes individual factories, power plants, roadways and neighborhoods. To compare with our Scope-1 values for US cities, we considered two scenarios: 1) summed VULCAN emissions from all the sectors of aircraft, cement, commercial, industrial, non-road, on-road, residential, and electricity production and 2) we removed from the sum of all-sectors the emissions from electricity production. Emissions at the location of 64 US cities in Dfinal (63 from DCDP and 1 from Dcarbonn) were collocated with the gridded VULCAN data, assuming that each city is approximately a circle of area corresponding to the reported administrative area in our dataset. Scope-1 emissions and Scope-1 emission density (emissions/area) in Dfinal were regressed against the two corresponding VULCAN scenarios including and excluding emissions from electricity production. The correlation coefficient (R2) when electricity production emissions were included was 0.69 (respectively 0.70 when excluded) for Scope-1 emissions and 0.56 (0.59) for Scope-1 emission density. The lower correlation coefficient in emission density for both cases is due to the coarse approximation of the city shape (using circles for cities areas instead of detailed GIS administrative area shape files overlaid with VULCAN gridded data) and the low resolution of VULCAN, and/or the different years they represent.The VULCAN dataset from Gurney final Scope-1 emissions. MAD = 99% and 82%, including and excluding power plant electricity generation, respectively. Most of this error is of a systematic nature due to the following reasons: emissions are for year 2002 in VULCAN and for more recent years in Dfinal, the VULCAN gridded data were not sampled for the precise administrative area of Dfinal in each US city, we lack precise knowledge on whether all local power plant emissions were included in Scope-1 data in Dfinal as compared to the comprehensive set of city power plants included in VULCAN. Scope-1 emissions of at least 6 out of 14 US cities in Dfinal include power plants; but 20 cities in Dfinal reported zero within-city power plant emissions in their Scope-1 emissions. Other cities likely reported power plants but we could not verify it from their declarations.We also calculated the median absolute deviation (MAD) between VULCAN and D2 emissions in Dfinal for US cities is described in Markolf et al.43. The authors compiled publicly available national datasets for estimating emissions in the 100 most populated metropolitan areas in the US in 2014. Twenty-eight cities in Markolf et al. overlapped with Dfinal. Markolf et al. documented separately CO2 emissions from on-road transportation, electricity generation, industrial processes, residential buildings, commercial buildings and waste. Then, the emissions were divided by population because the metropolitan areas in Markolf et al. generally encompass a wider territory than in Dfinal. The ratio between per capita Scope-1 emissions of Markolf et al. to Dfinal was calculated, both including and excluding electricity generation in Markolf et al. When electricity generation was excluded, there were three outliers with a ratio ≥2 ; the mean ratio excluding these outliers was 1.2 ± 0.4. When electricity generation was included, there were 11 outliers with ratios ≥2; the mean ratio excluding these outliers was 1.3 ± 0.4.The second dataset against which we compared Scope-1 COfinal per-capita Scope-1 emissions for the 28 common US cities is 393% and 175% . These ratios were reduced when the outliers (see above) were removed, giving MAD of 197% and 161% . We note that some of the inventory data used by Markolf et al. may be common with DCDP and Dcarbonn, thus Markolf et al. cannot be proven to be strictly independent from our data.The MAD statistics between Markolf and Dfinal Scope-1 CO2 emissions was developed for all municipalities in Japan and described in Nakamichi et al.44 The authors computed direct CO2 emissions using a bottom-up (built-up) approach based on emission factors and other statistics attributed to four civilian categories: residential, commercial, industrial (including electricity production) and transportation, as well as the total integrated direct emissions. The year of analysis was 2005 for all categories. Six cities in Nakamichi et al. overlapped with Dfinal. The ratio between Scope-1 emissions of Nakamichi et al. to Dfinal was calculated, taking into account the ratios between reported population and city areas, which are in agreement . The mean Scope-1 emission ratio was 0.8 ± 0.4 (range 0.4–1.3). Excluding Tokyo, corresponding to the worst ratio (0.4) despite consistent population and area ratios, the mean Scope-1 emission ratio was 0.9 ± 0.3 (range 0.5–1.3).The third dataset against which we compared DHow to cite this article: Nangini, C. et al. A global dataset of CO2 emissions and ancillary data related to emissions for 343 cities. Sci. Data. 6:180280 doi: 10.1038/sdata.2018.280 (2019).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Several studies have identified a negative association between serum glycine (Gly) levels and metabolic syndrome (MetS). However, this association has not been fully established in the elderly.A total of 472 Chinese individuals  participated in a population-based, cross-sectional survey in Beijing Hospital. The MetS and its components were defined based on the 2006 International Diabetes Federation (IDF) standard for Asians. Serum Gly concentration was determined using isotope dilution liquid chromatography tandem mass spectrometry.p for trend < 0.001), and serum Gly concentrations declined gradually with increasing numbers of MetS components (p = 0.03 for trend). After adjusting for age and gender, lower Gly levels were significantly associated with MetS and central obesity, with OR (95% CI) of 0.40 (0.25–0.65) and 0.46 (0.28–0.74). The stratified analysis conducted according to age showed that the OR between serum Gly levels and MetS was greater in those older than 65  than in those younger than 65 . In the stratified analysis, using other age cut-off points, the results consistently showed that the association between serum Gly levels and MetS was more remarkable in the older groups.The proportion of patients with MetS decreased gradually with increasing Gly levels (Gly levels are associated with cardiometabolic characteristics and MetS in the elderly, and the association is more pronounced in very old people than in younger old people. In recent years, the improvements in living standards, economic development, and lifestyle changes have led to a longer life expectancy for older people, and the global elderly population has continued to increase. With the increase of age, the incidence of cardiometabolic diseases in the elderly may rise gradually , 2. HoweMetabolic syndrome (MetS) is reported to be a risk factor for increased risk of type 2 diabetes and cardiovascular disease and a proven risk factor for cardiovascular morbidity, especially stroke and coronary heart disease (CHD), and mortality in the elderly , 5. AccoGly is a major and the simplest non-essential amino acid in humans, animals, and many mammals. It is mainly generated in the liver and kidney and is used to produce creatine, purine, glucose, and collagen and is involved in anti-inflammatory processes, the immune function, and anti-oxidation reactions . RecentlTherefore, we proposed to use a cross-sectional study of 472 Chinese subjects, of whom about 80% are older than 65 years old, to investigate the association between serum Gly levels and MetS in the elderly and whether this association in the elderly is different from relatively young adults.The design of and recruitment for the population-based cross-sectional study has been described in detail elsewhere . Briefly5, Cambridge Isotope Laboratories, USA) solution (312.5 μmol/l). Serum Gly was extracted with 0.4 ml of acetonitrile containing 0.1% formic acid and analyzed using liquid chromatography method associated with mass spectrometry (LC-MS/MS). The LC separation was performed on an Agilent 1200 series LC system. Two microliter aliquots of the prepared samples were injected onto a Waters Shield C18 column  maintained at 20 °C and eluted with a mobile phase of 0.01% formic acid in water-acetonitrile (90:10) at a flow rate of 0.3 ml/min. An API 4000 triple quadrupole mass spectrometer (Sciex Applied Biosystems) was used for the MS/MS detection. The detection was performed with positive electronic spray ionization (ESI) in multiple reaction monitoring (MRM) mode at a source temperature of 700 °C and a voltage of 5500 V. The dwell times were 0.08 s for MRM. Nitrogen was used as the curtain, nebulizer, and collision gas at pressures of 50, 60, and 70 psi, respectively. Certain ion transitions for Gly and the internal standards were m/z 76.0 → 30.0 and 78.0 → 32.0, respectively. The calibration curve was generated using a linear regression of the peak area ratios (y) versus the amino acid concentrations (x) of the calibrators.Serum Gly concentrations were determined using isotope dilution liquid–chromatography tandem mass spectrometry. Briefly, 0.05 ml aliquots of Gly  calibrators  or serum samples were mixed with 0.05 ml of the isotope-labeled internal standard , and sitting blood pressure (BP) were measured. The serum samples were also tested for fasting blood glucose (FBG), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein (apo) AI and B, and high-sensitive C-reactive protein (CRP), using assay kits from Sekisui Medical Technologies  on a Hitachi 7180 chemistry analyzer.MetS and its components were defined based on the 2006 International Diabetes Federation standard for Asians with central obesity , in addition to two or more of the following criteria : (1) BP P values < 0.05 were considered statistically significant. SPSS 21.0 software (SPSS Inc.) was used for statistical analysis of the data, and PASS 15.0 software was used for calculating the statistical power.The main continuous variables were all tested for normality. Normally distributed data were expressed as means ± SD, whereas variables obeying a skewed distribution were shown as median (interquartile range). Count data were reported as frequencies and percentages. For quantitative data, the variance analysis was conducted to compute the difference across the Gly tertiles, whereas for count data, the Chi-square test was used. Correlation coefficients between Gly levels and metabolic traits were calculated by Spearman partial correlation after adjustment for age and gender. Serum Gly levels were depicted according to the number of MetS components, using a linear regression model. Multivariate logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs), which adopted the entry method and the maximum likelihood ratio test for MetS and its components. Potential confounding variables, including age; gender; smoking; alcohol drinking; family history of diabetes, hypertension, and CAD; and CRP, were controlled in the regression models. The performance of Gly measurements for classifying MetS status was assessed by logistic regression and receiver operating characteristic analysis. Stratified analyses were performed according to age and serum CRP levels [< 0.09 (median) and ≥ 0.09 mg/dL]. Two-sided p < 0.05). With respect to metabolic traits, BMI, WC, and TG significantly declined with increasing Gly levels, which had a dose–response relationship ; in contrast, HDL-C levels increased notably (p < 0.01 for trend). However, increased Gly levels did not show association with changes in BP, TC, LDL-C, andCRP levels. Remarkably, the proportion of patients with MetS decreased gradually with increasing Gly levels (p for trend < 0.001).The Gly levels of 472 subjects had skewed distribution , and they could not be normally distributed after natural logarithm, logarithmic transformation of 10, and square root (SQRT) transformation. The prevalence of metabolic syndrome in our study was 39.0%. As shown in Table p = 0.03 for trend)  Fig. . The medp < 0.05) . Regarding the patterns of dichotomous cardiometabolic traits, serum Gly levels were found to be lower in subjects with MetS, central obesity, hypertriglyceridemia, hypertension, and IFG than in the corresponding control groups (05) Fig. . No diffp < 0.001, R = − 0.197). Serum Gly concentration was significantly negatively associated with most of the cardiovascular risk factors, such as BMI, WC, TG, and CRP (p < 0.05) and were positively associated with protective factors, including HDL-C (p < 0.01).As shown in Table p < 0.05). When Gly levels were used as categorical variables, the crude ORs for MetS, central obesity, and hypertriglyceridemia were lower with increasing Gly levels in model 1, which had a dose–response relationship (p < 0.05 for trend). After adjusting for age and gender (model 2), lower Gly levels were significantly associated with MetS and central obesity, with ORs (95% CI) of 0.40 (0.25–0.65) and 0.46 (0.28–0.74). Further controlling for smoking status; alcohol drinking; CRP; and family history of diabetes, hypertension, and CAD only slightly attenuated the strength of the association for MetS, central obesity, and hypertriglyceridemia. The results were the same when the Gly levels were used as a continuous variable. The sensitivity and specificity of Gly measurements for MetS diagnosis were 71.2 and 65.5%, respectively.As presented in Table Stratified analysis of the associations of serum Gly levels and MetS was conducted according to a 65-year age cut-off point. The results showed that the OR between serum Gly levels and MetS (95% CI) was 0.71 (0.55–0.91) in the group of those older than 65, whereas in the group of those younger than 65 years old, the OR was 0.92 0.60–1.42) in model 1, which means that the association was more pronounced in the groups of those aged ≥65 regardless of Gly levels used as a continuous variable or a categorical variable , and dietary supplementation with Gly reduces concentrations of free fatty acids and TG in an animal model of intraabdominal obesity . MoreoveBased on cross-sectional study of the elderly population, the present study is the first to examine the relationship between Gly levels and MetS and its components in the elderly. However, several limitations should be considered in the future. First, we were unable to obtain causality because of the cross-sectional design of the study. Thus, it is critical to conduct prospective studies in the future. Second, because of the relatively small sample size , even though there was a clear trend of the associations of Gly levels and MetS and its components, only MetS and central obesity remained statistically significant after adjustment. Third, we did not record physical activity and other possible risk factors of metabolic diseases in the subjects, so possible confounding of unmeasured factors may exist, even after adjustment.Our study demonstrated for the first time that serum Gly levels are associated with cardiometabolic characteristics and MetS in the elderly. Patients with MetS have lower Gly levels than do normal subjects. What’s more, the association between Gly levels and MetS is more pronounced in very old people than in younger old people. Our study emphasizes that it is significantly essential for the elderly population to maintain a higher Gly level in serum. Further studies are needed to confirm that Gly is a protective factor for cardiometabolic diseases in the elderly.


This study aimed to compare the diffusion kurtosis imaging (DKI) versus diffusion weighted imaging (DWI) in predicting the recurrence of early stage single nodules of hepatocellular carcinoma (HCC) treated by radiofrequency ablation (RFA).A retrospective analysis of 107 patients with early stage single nodules of HCC was performed, all patients treated by RFA. Recurrence rate of HCC was recorded after a median follow-up of 36 months. During follow-up, the data of magnetic resonance imaging (MRI), DWI and DKI were obtained in multiple time points. The predictive values of DWI and DKI were analyzed using ROC curves.P < 0.05) and MD .The overall recurrence rate was 66.3% (71/107). The sensitivity, specificity, and AUC for ADC, MD and MK after RFA  were higher than before RFA . The sensitivity, specificity, and AUC for MK after RFA were 85.7, 96.7%, and 0.956, respectively, which were significantly greater than those of ADC (78.6, 73.3% and 0.842; The prediction efficacy of DKI for the recurrence of early stage single nodules of HCC was better than that of DWI. And, MK was the most sensitive predictor among the DKI. Hepatocellular carcinoma (HCC) is a major health problem worldwide due to its high incidence  and poor prognosis . Early sOver the last decades, several studies on functional magnetic resonance imaging, such as diffusion-weighted imaging (DWI), have demonstrated the value in diagnosis and preoperative and postoperative evaluation of HCC . Tumors At present, studies on the evaluation of HCC by DKI were fewer. Thus, our study aimed to compare DKI versus DWI in predicting the recurrence of early stage single nodules of hepatocellular carcinoma treated by radiofrequency ablation.This study was approved by the local institute review board, and each patient signed the written informed consent. 178 consecutive patients with HCC based on clinical history or previously performed MRI between December 2016 and January 2018 in my hospital were recruited. We excluded 15 patients for they were been diagnosed as advanced HCC. 163 patients were diagnosed as early stage HCC. 26 patients were excluded with multiple nodules. Among the 137 patients with single nodules, 30 patients were excluded from our study population for the following reasons: 16 patients were treated by transcatheter arterial chemoembolization (TACE), 9 patients were treated by surgical therapies and 5 patients were untreated. Radiofrequency ablation was the first choice for 107 patients , and three-phase dynamic contrast-enhanced (DCE) and DKI sequence . All scan parameters were displayed in the Table Conventional MRI and DKI examinations of the liver were performed on all subjects before RFA and one month later after RFA, then followed-up every 3 months. The study was performed with a 3.0 T magnetic resonance imaging (MRI) scanner . A standard 18-channel mode was employed for the body phased array coil. The regular MRI scan sequence included axial T1WI, axial fat-saturated T2WI, axial DWI sequence . Depending on the tumor size, location and the relationships with surrounding organizations, ablation was undertaken by the percutaneous route, laparoscopic or open approach. The procedure was done by an experienced interventional radiologist. It was effective in producing complete tumor necrosis, with ablation of a margin of non-tumors tissue of 1 cm after RFA. All patients had complete ablation for the treated lesions by RFA. MRI was performed to evaluate the treatment efficacy after RFA procedure. Multiple overlapping ablations were performed in order to achieve an adequate ablation if needed.Each subject performed contrast-enhanced MRI and DKI after initiating RFA one month, and then followed-up every 3 months. In this study, one month was considered as the earliest time point to assess the tumors, which may guide timely decision-making for subsequent therapies. The HCCs were classified as either recurrence groups or non-recurrence groups, which was assessed according to the overall mRECIST . Non-rec0·exp. (−b·D + b2·D2·K/6), where S and S0 was the signal intensity of the images acquired at b and b0, respectively (s/mm2), and b is the b-value (s/mm2); D is the corrected apparent diffusion parameter of the Gaussian distribution, and K is the apparent kurtosis coefficient. DKI parameters  were recorded using b values . Standard ADC (mm2/s) was obtained using a conventional mono-exponential fit with the following equation: S(b) = S0·exp.(−b·ADC). The ADC was calculated using b values of 0 and 800 s/mm2. The parameters were measured three times. The region of interest (ROI) was then selected. Two radiologists with different years of experience in abdominal imaging independently placed ROIs for each lesion. The principle of ROI selection were as follows: ① combined with conventional T1WI and T2WI images, the large lesion dimension was set as the ROI, where the scope of the lesion should be as large as possible; ②the internal areas that were necrotic or adjacent to blood vessels or bile ducts were excluded; ③the lesion ROIs were set at three different positions for each case to reduce error and the selected range was consistently maintained for each patient; ④ the maximum diameter of the HCC was measured on an axial T2WI on the basis of the reference T1WI enhanced image. The ROI of the lesions ranged from 1.0 cm2 to 2.5cm2. Statistical analyses were performed to compare the results of clinical and laboratory examinations. The differences in the corresponding parameters and the correlation with HCC prognosis were analyzed.The original data was transferred to PHILIPS workstation synchronously and post-processed by MRI Toolbox software package. According to DKI theories, S(b) = St-test. The sensitivity, specificity, and AUC under the ROC curve were calculated using MedCalc software (version11.4.2.0).Data obtained from DKI and ADC was statistically analyzed with SPSS 20.0 statistics software . Quantitative data were expressed as the mean ± standard deviation. ADC, MK, and MD of pre-RFA and post-RFA were analyzed using paired − 3 mm2/s, 1.347 × 10− 3 mm2/s, 0.726) and non-recurrence groups  . The ADC and MD values of the recurrence groups  were lower than those of the non-recurrence groups  after RFA, the MK of the recurrence groups (0.678) was higher than that of the non-recurrence groups (0.424) than pre-RFA ; MK value was lower after RFA (0.505) than pre-RFA (0.732) , among the 107 HCCs treated by RFA, 36 HCCs were classed as non-recurrence groups. Among the 36 HCC nodules, 27 HCCs were complete necrosis, 7 HCCs were partial necrosis and 2 HCCs were stable disease. The recurrence rate was 66.3% (71/107). No obvious differences were observed in ADC, MD, and MK of pre-RFA between the recurrence (0.98 × 10P < 0.05)  for ADC, MD and MK after RFA  were higher than before RFA . The sensitivity, specificity, and AUC for MK after RFAwere 85.7, 96.7%, and 0.956 (Fig. As a minimally invasive procedure, RFA is increasingly used to treat for early stage HCCs . RFA carThe most commonly used DKI indexes in clinical studies are MK and MD. The extent of signal attenuation is shown by diffusion kurtosis. MK is considered as an evaluation criterion for the complexity of tissue microstructure. MK is closely related to tumor grade . The comMany studies have used DKI to evaluate cerebral infarction, glioma, multiple sclerosis, Parkinson’s disease, and attention-deficit hyperactivity disorder –27. ReceMK based on the DKI model may have better predictive accuracy in the prognosis of early stage single nodule HCCs after RFA and increased statistical power to reflect the microstructural complexity of the tumor compared with ADC derived from the conventional DWI model. The increased MK value may be due to the complex microenvironment of the tumor. The presence of multiple tissue types, such as tumor cell, necrosis, and inflammation, reflect more peaked distributions of tissue diffusivities in non-Gaussian diffusion behavior than in Gaussian diffusion behavior. Kurtosis of tissues has been reported to partially represent the interaction of water molecules with cell membranes and intracellular compounds. Study has described a significant decrease in MK in HCCs that were completely necrotic after RFA compared with those of background liver parenchyma . The livThe MK value, which is a significant parameter, allows preliminary parameter-based pre-operation pathological grading for patients with HCC and effectively predicts tumor recurrence. The values of MK are valuable for predicting recurrence in clinical trials for HCC.Our study had several limitations. First, the relatively small population in our study might result in bias in the results. Second, RFA was performed by the percutaneous route, laparoscopic or open approach. Different ablation methods may affect the prognosis of HCCs. Third, the reference criteria for defining recurrent nodules is relatively weak.DKI is an independent predictor of the recurrence of early stage single HCCs treated by RFA. In addition, the prediction efficacy of MK is better than MD.


The antiphospholipid syndrome (APS) is an important cause of acquired thromboembolic complications and pregnancy morbidity. The pathogenic mechanisms that damage the fetal–maternal unit and cause abnormal placental development are incompletely understood in APS patients. Liquid Chromatography/Mass Spectrometry (LC/MS) based metabolomics are applied for the mechanism of disease and further supporting the research of diagnosis and management in recent years. The aim of this research was to investigate the difference of serum metabolic profiles in recurrent abortion women with APS and healthy women to explore the mechanism of this disease.Serum samples of 25 recurrent abortion women with APS and 25 healthy women were collected and analyzed by LC/MS in this study. Potential biomarkers were discovered by multivariate statistical analysis and then identified based on analysis results.Totally, we identified five biomarkers that involved in different metabolic pathway such as purine metabolism, amino acid metabolism and tyrosine metabolism. These biomarkers showed different roles in disease development.Metabolomics was proved as a powerful tool in understanding the mechanism of recurrent abortion caused by APS. The antiphospholipid syndrome (APS) is a chronic autoimmune disease with rheumatic inflammatory, which can induce state of hypercoagulation. APS includes two clinical manifestations: vascular thrombosis and pregnancy complications . APS is Multiple studies have shown that the relationship between immune response and metabolism. Metabolomic analysis aims at a comprehensive characterization of biological samples . The infIn this study, LC/MS based metabolomics method was applied to investigate the metabolic profile change between recurrent abortion women with APS and healthy women. This study is aim to identify the potential biomarkers for APS and explore the biological pathways change. Furthermore, the study results could provide a new insight on the diagnosis and management of pregnant women with APS.25 recurrent abortion women with APS and 25 healthy women as healthy control were recruited from Center for Reproductive Medicine, Jilin Province People's Hospital, from Jan 2017 to Aug 2018. All the tests were performed in Sep 2018. The study had been approved by the Ethics Committee of Jilin Province People's Hospital. Prior to inclusion in this study, informed consent was got from all participants. All the authors had no access to information that could identify individual participants during or after data collection.APS patients had been previously diagnosed based on the updated Sapporo classification criteria , whereasIn 1999, the proposed Sapporo classification criteria for APS were first published . To meetSerum samples were collected and stored at −80°C until analysis.Using Bio-Rad Anti-Cardiolipin IgG EIA Kit #425–2000 to test the Serum anticardiolipin (aCL) antibody of IgG of clinical samples. Testing procedure followed the instruction of the Kit.Serum samples were melted at room temperature. 100μL serum were added 400μL acetonitrile and vertex 30 seconds, then put it at room temperature for 10 minutes, centrifuge for 10 minutes in 12000rpm, using 0.22μm filter to filter the supernatant. The filtered samples were finally analyzed by LC/MS.Agilent 1200 rapid resolution liquid chromatography (RRLC) coupled with Agilent 6520 Q-TOF mass spectrometer  was used for the LC/MS analysis. Sample volume was 10μL. Column: Agilent Eclipse Plus C18, 3.5μm, 2.1mm×150mm. Column temperature: 30°C.The LC condition was as below, Solvent A: water with 0.1% formic acid, v/v; Solvent B: acetonitrile with 0.1% formic acid, v/v. Flow rate: 0.3 mL/min. Solvent gradient: 0–5 min, 95%-80% solvent A; 5–10 min, 80%-60% solvent A; 10–15 min, 60%-2% solvent A; 15–16 min 2%-0% solvent A; and 16–17 min, 0% solvent A. 30 min for post run.m/z 50 to 1000.Mass spectrometry condition: Electrospray ionization (ESI) source; Positive mode: gas temperature, 350°C; drying gas, 9L/min; nebulizer, 40psig; fragmentor, 150V; skimmer, 60V; capillary, 3500V. Negative mode: gas temperature, 350°C; drying gas, 9L/min; nebulizer, 40psig; fragmentor, 150V; skimmer, 60V; capillary, 3500V. Centroid mode for data collection, from http://www.hmdb.ca/) and KEGG (http://www.kegg.com/).Data normalization was performed and then imported the data files into the statistic software . After further processing, using T-test and principal component analysis (PCA) for the comparison of the metabolites profiles between APS group and healthy group. Biomarker identification used public databases in internet such as HMDB , Total T4 (TT4), Free T3 (FT3), Free T4 (FT4), reverse T3 (rT3) and thyroid stimulating hormone (TSH) were tested using ThyroChek thyroid testing kit.The Serum aCL, Serum anti-b2GPI, IgG aCL and IgG anti-b2GPI profile of the 25 APS patients are shown in Using Agilent RRLC/Q-TOF/MS to analyze the serum metabolites profiles of the samples collected from APS and control groups. The representative base peak intensity (BPI) chromatograms of the serum sample of an APS patient in positive and negative mode were shown in m/z 315.2368, 259.1455, 169.5847, 469.3568 and 366.5248) and five ions in negative mode  were selected for the extraction of the ion chromatographic peaks. The system stability (RSDs %) of m/z, peak areas and retention times of the ten selected ions were 0.02–0.14%, 0.0003–0.0007% and 5.9–8.5%, respectively. During the analysis, the QC results achieve perfect system stability, reproducibility of chromatographic separation and mass measurement that demonstrated the analysis results are reliable.Method validation was performed using quality control (QC) sample, which was prepared by mixing 10μL for each serum from all the collected samples in this study. QC sample was analyzed to evaluate the system stability following every five injection. Five ions in positive mode  can increase the production of trophoblast uric acid. Meanwhile, the uric acid play a role as endogenous secondary signals to activate the trophoblast inflammation by stimulating the inflammasome in trophoblasts . AnotherArginine is a semiessential amino acid that can be used for protein biosynthesis. The synthesis mainly occurs from citrulline both in the liver and in kidney. Nitric oxide (NO)’s immediate precursor is arginine. NO act as the second messenger that was considered as an important signaling molecule, and NO is an intercellular messenger regulating vasodilation. At the same time, in the immune system, NO has the functions of response reaction to infection. Immune cells show the ability of NO synthesis. Considering that NO is potent immunomodulators, arginine metabolism play significant roles in physiological and pathological conditions . L-arginThyroid function testing results shows TT3, TT4, FT4 and TSH were significantly decreased in APS patients. Triiodothyronine (T3) and thyroxine (T4) are two tyrosine-based thyroid hormones, which are produced by the thyroid gland and then released in the body.The two hormones are mainly responsible for metabolism regulation. Thyroid hormones are critical in many target tissues such as the brain and skeleton for their growth and maturation. Maternal thyroxine is crucial in fetal development due to it supplies thyroid hormone-dependent tissues in critical periods during the first trimester of pregnancy . TransthCreatine’s function is adenosine triphosphate (ATP) recycling. ATP is the cell energy currency, which is primarily existed in brain and muscle tissue in vertebrates. Serum creatinine testing is a common used test for the indication of renal function. In addition, the low levels of creatinine in the body could be a sign that the liver or muscles are not working well. Creatine and creatinine metabolism is associated with a variety of diseases . Some anIn present study, the LC-MS based metabolomics method was applied to analyze the metabolic profile changes between recurrent abortion women with APS and control subjects. The results of identification of five potential biomarkers have shown that the APS is closely related to purine metabolism, amino acid metabolism and tyrosine metabolism. Furthermore, the potential biomarkers showed the metabolism disorder of APS and may be further investigated for diagnosis and therapy. It is proved that the LC/MS metabolomics method is useful for APS mechanism study. The application of metabolomics can provide more metabolic information to support the research on diagnosis and therapy of recurrent abortion caused by APS.S1 File(XLSX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.