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The data contain video traffic data (pedestrian, cyclists, vehicles) registered at Obroncow Wybrzeza (E) street. Date 11.08.2020, 13:00-20:00. The video camera was installed around bicycle crossings and pedestrian crossings at Obroncow Wybrzeza – Chlopska crossing. Camera was mounted on 7-meter-high mast. Collected data: the highest volume of vehicles passing the pedestrian and bicycle crossing were recorded at 03:00 p.m. - 04:00 p.m. most pedestrians were recorded between 04:00 p.m. - 05:00 p.m. and cyclists between 05:00 p.m. – 06:00 p.m. avg hourly intensity of vehicles within the pedestrian and bicycle crossing were 626 veh./h. avg of 186 ped./h on the pedestrian crossing. avg of 184 cyc./h on the bicycle crossing. GPS coordinates: 54.40824892413555, 18.589728949286908 This study is part of master thesis: Conflicts research in relationships vehicle - pedestrian and vehicle - a bicycle on selected crossings in Gdansk.

 The dataset contains seven columns as described below, including the data on the plant paramters (species name, dbh, height, quantity): Forest layer: The “Forest layer” represents the different plant layers, which is either “Adult” plant or “Seedling”. Microsite: The column labelled “Microsite” denotes the microsite (“Upland” or “Inundated”) from which the different adult plants or seedlings were recorded. Plot No.: This stands for the plot number for the various adult plants or plot number for the quadrats and seedlings recorded in them. The “Quadrat No”: This contains the numbers of quadrat for the enumerated seedlings. The “Species” column holds the names for the individual adult plants or seedlings of different species . The column “dbh” and “Height” respectively represent the diameter at breast heights in centimetres (cm) and heights in meters (m) for the individuals of specific adult plant species. The “Quantity” column contains the number of individuals for specific seedling species. When using the dataset, the “Filter” feature in Excel can easily be used to narrow down to the specific data. For examples: (a) the number individual stems of specific "Adult" plant "Species", say Elaeis guineensis with their "dbh" and "Height" that were recorded in a main plot, say "Plot No." 1 at the upland microsite (i.e. 4 stems) or inundated microsite (i.e. 33 stems); (b) to the number or "Quantity"of seedlings for a specific "Species", say Baphia nitida recorded in a particular "Quadrat No." of a particular "Plot No." say quadrat 2 of plot 1 in the inundated microsite (i.e. 3 seedlings) or quadrat 3 of plot 2 in the upland microsite (i.e. 2 seedlings). Note: The “Quadrat No” and “Quantity” columns shows have no data entries for rows with "Adult" plants as these are for only seedlings. Similarly the colums "dbh" and "Height" contain data for only rows with "Adult" plants.  This dataset represents the raw data collected for a study of plant composition and diversity in a12.9 ha moist semi-deciduous forest in the urban landcape of Kumasi, the city capital of the Ashanti Region of Ghana. Since 1960, the forest ecosytem has been gazetted and managed as a botanic garden, known as the Kwame Nkrumah University of Science and Technology (KNUST) Botanic Garden, to preserve its rich flora for environmental education, research and recreation. The KNUST Botanic Garden has therefore remained untouched, despite progressive land cover change or developmental activities (settlements and building facilities) that characterizes the KNUST campus within which the garden is located. The dataset was generated from a field survey conducted in December 2019 to to gather relevant information on plant parameters (species, dbh, height) from two distinct microsites (upland and inundated) of the garden. This dataset was first anlysed to generate results that permitted the writing of a master thesis. The robust methods and quality of data permitted subsequent use of the results for developing of a manuscript. This manuscript which is entiled "The Role of Urban Tropical Botanic Gardens in Biodiversity Conservation: An Example from the KNUST Botanic Garden in Kumasi, Ghana" has been accepted for publication in Biotropica.  The data was collected in two distinct microsites, dry upland and periodically flooded (inundated) microsites of the forest; the distinction was established by examining relevant factors such as topographic gradient, drainage conditions and flood channels during a reconnaissance survey. Three main plots, measuring 50×50 m each were randomly laid at each microsite for enumeration of adult plants (diameter at breast height, dbh > 5 cm). Five 2×2 m subplots were further laid from the center and corners of each main plot for enumeration of seedlings (dbh ≤ 5 cm and height < 1.5 m). Plant identification was made possible by physical observation and study of identification features such as crown shape, tree bole, bark texture and slash exudates, leaves and flowers. The dbh of all adult plants were measured with a diameter tape. Nikon Forestry Pro II laser hypsometer was used to measure the tree heights in meters. Information from this survey (species name, dbh, height, quantity) were organized in excel and pulled together to generate this dataset.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.NASA-GISS.GISS-E2-1-G.ssp460' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The GISS-E2.1G climate model, released in 2019, includes the following components: aerosol: Varies with physics-version (p==1 none, p==3 OMA, p==4 TOMAS, p==5 MATRIX), atmos: GISS-E2.1 (2.5x2 degree; 144 x 90 longitude/latitude; 40 levels; top level 0.1 hPa), atmosChem: Varies with physics-version (p==1 Non-interactive, p>1 GPUCCINI), land: GISS LSM, ocean: GISS Ocean (GO1, 1 degree; 360 x 180 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: GISS SI. The model was run by the Goddard Institute for Space Studies, New York, NY 10025, USA (NASA-GISS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, seaIce: 250 km.

Data contain risk classification on regional roads (voivodeship roads) in pomorskie voivodeship in 2017-2019, risk group: motorcyclists and mopeds. Measures used to assess the level of risk are (5 classes low, low to medium, medium, medium to high, high): -Social risk: density of accident costs [mln zl/km/3 years] and density of accidents with seriously injured and fatalities [accidents/km/3 years]. -Individual risk: accident costs ratio [mln zl/mln vkt/3years], accident ratio with seriously injured and fatalities [accidents/mln vkt/3years]. Number of [numbers per 3 years]: accidents - 159, injuries - 162, seriously injured 48, fatalities - 13, accident costs 146,7 mln zl. This study is a part of a research project: Risk classification for selected types of road accidents on regional roads (voivodeship roads) in pomorskie voivodeship in 2017-2019.

All fieldwork was conducted from November 23, 2019, to February 14, 2021 in two trapping periods. Trapping in Period 1 (November 2019 – March 2020) served as a control for Rattus presence under normal (pre-COVID) conditions and lasted for 14 weeks. Trapping during Period 2 (October 2020 – February 2021) took place during the shelter in place order and was conducted at the same sites as Trapping Period 1. Trapping Period 2 lasted for 14 weeks, and served as our experimental treatment approximately one year later during the COVID lockdown when human supplementation was absent. To test our hypothesis that invasive rats are dependent on human supplementation, the first site in each trapping transect originated at a semi-exposed source of human refuse (dining hall dumpster, compost pile, etc.). We placed each sequential site in a direction away from the initial refuse source, and towards natural habitat. To reduce the probability of capturing the same individual, each transect contained three sites spaced 75 m apart each, for a total transect length of 150 m (N = 15 sites) (Oca et al. 2017). We used camera traps (Bushnell Trophy Cam; Bushnell Corp., Overland Park, KS, USA) that were left on-site for four consecutive nights to document Rattus presence (N = 60 trap nights per trapping period). We programmed the cameras to capture three images every 30 seconds once movement was detected. Each site was baited with 30 grams of sunflower seeds (Helianthus annuus). All sites were originally open to the public (vehicles not allowed) but were closed from March 31, 2020 to September 2, 2020. To test the influence of habitat on Rattus presence and to account for errors in camera trap detection, we used occupancy modeling in the unmarked package in R (Fiske 2011). We binary-coded invasive Rattus presence or absence with a 1 or a 0, respectively, and indexed habitat composition at each site as a covariate (1 = mixed redwood, 2 = mixed oak, 3 = meadow) (Fiske 2011). We treated each individual night as a survey trial, which yielded four repeated surveys at each site. Finally, we back-transformed our detection and occupancy estimates and fitted 95% confidence intervals. To test the influence of habitat on Rattus presence, we created three models: one with variable occupancy, one with variable detection, and one with both variable occupancy and variable detection as a function of habitat. Globally, the genus Rattus is one of the most influential exotic species due to its high rates of competitive exclusion and large dietary breadth. However, the specific foraging strategies of urban and urban-adjacent populations remain largely unknown. We examined Rattus spp. dependency on human food supplementation in a peri-urban population. Through a natural experiment made possible by the COVID-19 shelter in place order in Santa Cruz California, USA, we measured changes in activity between invasive rats and native rodents with and without human supplementation. We measured invasive rat presence in normal (pre-COVID) conditions near dining halls and similar waste sources, and again under COVID lockdown conditions where all sources of human supplementation were removed. We found a decrease in Rattus presence after the removal of human refuse (p < 0.001), while native small mammal presence remained unchanged. These results have strong conservation implications, as they suggest that proper waste management is an effective, targeted, and less-invasive form of population control over conventional forms of poison. 

# CrIS PANs megacity dataset for São Paulo and Lagos [https://doi.org/10.5061/dryad.wpzgmsbtk](https://doi.org/10.5061/dryad.wpzgmsbtk) These data files contain all filtered CrIS PANs and CO tropospheric average mixing ratio retrievals over the São Paulo & Lagos urban megacity subregions. **Temporal Coverage of Data:** 2016-01-01 to 2021-05-20 Daily satellite data for the entire megacity area at 13:30 LT. **Spatial extent of data:** **latitude extents São Paulo:** 23.39838°S - 23.689°S **longitude extents São Paulo:** 46.36157°W - 46.80453°W **latitude extents Lagos:** 6.451°N - 6.64°N **longitude extents Lagos:** 3.23°E - 3.41°E Data has been filtered as described in the manuscript. To reproduce figures in submitted manuscript: Figures in the manuscript utilized daily means and monthly means. ## Description of the data and file structure seven columns of data: XPAN\_orig, Correction, XH2O, Lat, Lon, Year, Month, Day, XCO, XPAN. variables are described below. | Variable | Units | Description | | -------- | ----- | ----------- | | XPAN\_orig | parts per billion by volume [ppbv] | XPAN800 value reported in datafile. L2 variable called XPAN800. Tropospheric average of PANs from 800 hPa to the tropopause.Missing data have been filtered out for various reasons. | | Correction | parts per billion by volume [ppbv] | Correction value to be added to XPAN\_orig as described in Payne et al., 2022 | | XH2O | molecules/m2 | Reported total column water vapor value from L2 water valor files. Correction value uses this.Correction formula has been applied in XPAN variable (below). | | Lat | degrees | Latitude Max São Paulo: 23.39838Min São Paulo: -23.689Max Lagos: 6.64Min Lagos: 6.451 | | Lon | degrees | LongitudeMax São Paulo: -46.36157Min São Paulo: -46.80453Max Lagos: 3.41Min Lagos: 3.23 | | Year | Year | Format: YYYYMax: 2021Min: 2016 | | Month | Month | Format: MM | | Day | Day | Format: DD | | XCO | parts per billion by volume [ppbv] | Tropospheric average created using the L2 variable called “Species”. From 825 hPa - 215 hPa.Missing data have been filtered out. | | XPAN | parts per billion by volume [ppbv] | Corrected XPAN value. This is the XPAN value to use going forward. | ## Sharing/Access information **Other Megacity datasets:** Kevin W. Bowman (2023), TROPESS CrIS-SNPP L2 for Beijing Megacity, Summary Product V1, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: **[*August 2022*]**, [10.5067/GBMBES47TUOI](https://doi.org/10.5067/GBMBES47TUOI) Mexico City, Tokyo, Karachi, Delhi, and Los Angeles are also available on the NASA GES DISC. **Uncertainty, precision, and accuracy of measurements** CrIS PAN algorithm description and further information can be found in: Payne, Vivienne H., Kulawik, S. S., Fischer, E. V., Brewer, J. F., Huey, L. G., Miyazaki, K., Worden, J. R., Bowman, K. W., Hintsa, E. J., Moore, F., Elkins, J. W., & Juncosa Calahorrano, J. (2022). Satellite measurements of peroxyacetyl nitrate from the Cross-Track Infrared Sounder: comparison with ATom aircraft measurements. Atmospheric Measurement Techniques, 15(11), 3497–3511. https://doi.org/10.5194/amt-15-3497-2022 **Additional references:** Bowman, K. W., Rodgers, C. D., Kulawik, S. S., Worden, J., Sarkissian, E., Osterman, G., Steck, T., Lou, M., Eldering, A., Shephard, M., Worden, H., Lampel, M., Clough, S., Brown, P., Rinsland, C., Gunson, M., and Beer, R.: Tropospheric Emission Spectrometer: Retrieval method and error analysis. IEEE Trans. on Geosci. Remote Sensing, 44(5), 2006. Fu, D., Kulawik, S. S., Miyazaki, K., Bowman, K. W., Worden, J. R., Eldering, A., Livesey, N. J., Teixeira, J., Irion, F. W., Herman, R. L., Osterman, G. B., Liu, X., Levelt, P. F., Thompson, A. M. and Luo, M.: Retrievals of tropospheric ozone profiles from the synergism of AIRS and OMI: methodology and validation, Atmos. Meas. Tech., 11(10), 5587–5605, doi:10.5194/amt-11-5587- 2018-supplement, 2018. Worden, J., Kulawik, S., Frankenberg, C., Payne, V., Bowman, K., Cady-Pereira, K., Wecht, K., Lee, J.-E. and Noone, D.: Profiles of CH4, HDO, H2O, and N2O with improved lower tropospheric vertical resolution from Aura TES radiances, Atmos. Meas. Tech., 5(2), 397–411, doi:10.5194/amt-5-397-2012, 2012. The COVID-19 pandemic perturbed air pollutant emissions as cities shut down worldwide. Peroxyacyl nitrates (PANs) are important tracers of photochemistry that are formed through the oxidation of non-methane volatile organic compounds (NMVOCs) in the presence of nitrogen oxide radicals (NOx = NO + NO2). We use satellite measurements of free tropospheric PANs from the S-NPP Cross-Track Infrared Sounder (CrIS) over eight of the world’s megacities: Mexico City, Beijing, Los Angeles, Tokyo, São Paulo, Delhi, Lagos, and Karachi. We quantify the seasonal cycle of PANs over these megacities and find seasonal maxima in PANs correspond to seasonal peaks in local photochemistry. CrIS is used to explore changes in PANs in response to the COVID-19 lockdowns. Statistically significant changes to PANs occurred over two megacities: Los Angeles (PAN decreased) and Beijing (PAN increased). Our analysis suggests that large perturbations in NOx may not result in significant declines in NOx export potential of megacities.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.INM.INM-CM4-8.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The INM-CM4-8 climate model, released in 2016, includes the following components: aerosol: INM-AER1, atmos: INM-AM4-8 (2x1.5; 180 x 120 longitude/latitude; 21 levels; top level sigma = 0.01), land: INM-LND1, ocean: INM-OM5 (North Pole shifted to 60N, 90E; 360 x 318 longitude/latitude; 40 levels; sigma vertical coordinate), seaIce: INM-ICE1. The model was run by the Institute for Numerical Mathematics, Russian Academy of Science, Moscow 119991, Russia (INM) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

The dataset includes data on future activity-emission factor matrices for all the ICARUS participating cities and for the classical air pollutants (NH3, NMVOC, NOX, PM10, PM2.5, CO, SO2, BC and OC), GHGs (CO2, CO, N2O, CH4), heavy metals, PAHs and dioxins, relevant to the years 2015, 2020 and 2030. A detailed description of the structure of the DB can be found in deliverable D2.2 freely downloadable from the ICARUS website. Additionally, bottom-up activity data and emission factors are combined to sectoral emission grids (1x1 km) for each city. The sites targeted cover all the ICARUS pilot cities. The data are available in MS-Access format (accdb) to maximise the interoperability for use with a variety of analytical software. The sectoral emission grids are provided in standard GIS vector format (shp).

Data output from the tool set application for the assessment of the environmental (emissions, carbon footprint, ambient air concentrations), health (exposure and health effects), and economic impacts (e.g. health-related costs). Sosnowiec shapefile file: sosnowiec.7z Shapefile with the urban scale domain over Sosnowiec with 20 km x 20 km 2_SOS_Agriculture_baseline file: claircity_agricultureemissions_sos_jan2019.pdf Emissions (in kg/year) were based on EMEP emission inventory for livestock (emissions from agriculture are not available) at 0.1x0.1 degrees resolution (~ 10 km) for the year 2015 disaggregated for the urban domain of Sosnowiec by farms, meadows, vineyards land uses classified in the Open Street Map database. 3_SOS_Air Quality_Baseline (mesoscale/NO2 concentrations) file: no2_2010010100_2010123123_lcc.png Annual NO2 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2. 3_SOS_Air Quality_Baseline (mesoscale/PM2.5 concentrations) file: pm2.5_2010010100_2010123123_lcc.png Annual PM2.5 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2. 3_SOS_Air Quality_Baseline (mesoscale/PM10 concentrations) file: pm10_2010010100_2010123123_lcc.png Annual PM10 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2. 3_SOS_Air Quality_Baseline (urban scale/ NO2 concentrations) file: sos_aq2app_no2.txt Annual NO2 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters) 3_SOS_Air Quality_Baseline (urban scale/ PM10 concentrations) file: sos_aq2app_pm10.txt Annual PM10 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters) 3_SOS_Air Quality_Baseline (urban scale/ PM2.5 concentrations) file: sos_aq2app_pm2.txt Annual PM2.5 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters) 3_SOS_Air Quality_Baseline (mesoscale-report) file: sos_mesoscale.pdf This report provides an overview of the modelling approach used to characterize the air quality in the Sosnowiec region, which includes a detailed description of the air quality modelling system WRF-CAMx (section 1.1.) and a description of the methodology applied to evaluate the model performance (section 1.2.). It also includes results of concentration fields and a source apportionment for NO2, PM10 and PM2.5. 6_SOS_CarbonFootprint_Baseline file: ech.ma.15-fr2-wp5-carbon-footprint-ed2.pdf Carbon footprint methodologies and estimation for the baseline year for Sosnowiec 3_SOS_Air Quality_Baseline (urbanscale-report) file: sos_aq_urbanscale_report.pdf This report provides a brief overview oh the methodology used. It presents an analysis of concentration fields for NO2, PM10, PM2.5 of the total and by modeled sectors, it also includes an analysis of the source contribution and for the maximum values. 2_SOS_IRCI_baseline file: ech.ma.15-fr1-wp5-irc-ed5.3.pdf 2_SOS_IRCI_Scenarios file: ech.ma.15-fr3-wp5-irc-future-ed5.pdf 2_SOS_temporal_profiles file: sosnowiec-daily_hourlytd_res_comm_emi.xlsx Temporal profiles of Sosnowiec's residential sector and commercial sector scale in %: Daily emissions, typical days emissions and hourly typical days emissions of PM10 and NOX variables. 2_SOS_Transport_baseline_map file: roadnetwork_sos.zip Map with lines, link with emissions using filed "ID" 2_SOS_Transport_baseline_values file: emission_values_sos.zip Part 2 of 2 files that make the Sosnowiec transport emissions baseline: total emissions per link in .csv: To be linked to the road network file using the identifier "uniqueID" shapefile with road links units: g emissions at link level consisting of a zip file with 2 .csv-files, in the following format: first column: link-ID (link with shapefile of the road network) second column: pollutant (PM, PM non-exhaust or NOx) third column: mode ("BESTEL"= van or light freight, "MIDZWVR" = medium freight, "MOTOR" = motorcycles, "OVBUS" = bus, "PERSAUTO" = passenger cars, "ZWAARVR" = heavy freight columns D-AY: hourly intervals for weekday ("WD") and weekend ("WE") all units in g 2 files with values: aggregates over type, by type of day, per hour of day and a separate file for annual totals (at link level, per pollutant (including FC)) 2_SOS_transport_scenarios file: copy-of-190915_sos_scenario_results_summary_with_ups.xlsx This data-set reflects the relative changes of road transport emissions in different years and scenario's compared to the baseline. 2 sets of scenario's are given, one per tab: "SDW": BAU & scenario's established in the stakeholder dialogue workshop "UPS": updated BAU (if applicable) & final Unified Policy Scenario (UPS) selected in the policy workshop. reported for 3 future years compared to the 2015 baseline: 2025, 2035 and 2050 reported for NOx & PM for 6 modes: "MIDZWR": medium truck "ZWVR": heavy truck "BUS": busses "MOTO": motorcycles "CAR": passenger cars "VAN": light freight, assumed to be a mix of passenger cars and medium trucks all units: % 5_SOS_health_statistics file: sos_health-analysis.xlsx demographics and population data to calculate the health statistics 5_SOS_health_scenarios file: summary_results_sos.xlsx Health-related impacts (selected mortality and morbidity endpoints) related to exposure to PM2.5, NO2, and PM10, considering concentration-response functions and baseline concentrations recommended by WHO. 3_SOS_NO2_AQ_latlong file: sos_no2_latlong.rar The shapefile includes total NO2 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984 3_SOS_PM2_AQ_latlong file: sos_pm2_latlong.rar The shapefile includes total PM2 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984 3_SOS_PM10_AQ_latlong file: sos_pm10_latlong.rar The shapefile includes total PM10 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984 6_SOS_CarbonFootprint_Scenarios file: ech.ma.15-fr4-wp5-carbon-footprint-future-ed3-.pdf Carbon footprint business as usual and scenario projections for Sosnowiec 3_SOS_Air Quality_Baseline (mesoscale/SourceApportionment) file: sos_psat.xlsx Time series of daily average contributions for each source group for PM10, PM2.5 and NO2 concentrations from WRF-CAMx modelling system with the SA tool, for the Sosnowiec urban area. 3_SOS_Air Quality Scenarios_urban scale NO2 matrix file: sos_no2_scenarios.mat Annual NO2 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050. 3_SOS_Air Quality Scenarios_ urban scale PM10 matrix file: sos_pm10_scenarios.mat Annual PM10 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050. 3_SOS_Air Quality Scenarios_urban scale PM2 matrix file: sos_pm2_scenarios.mat Annual PM2.5 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050. 4_SOS_Exposure_EU_Baseline NO2 matrix file: sos_no2_exposureeu_baseline.mat Population potentially exposed to the annual NO2 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for the baseline year. 4_SOS_Exposure_EU_Baseline PM10 matrix file: sos_pm10_exposureeu_baseline.mat Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for the baseline year. 4_SOS_Exposure_WHO_Baseline PM10 matrix file: sos_pm10_exposurewho_baseline.mat Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 20 ug/m^3 for the baseline year. 4_SOS_Exposure_EU_Baseline PM2 matrix file: sos_pm2_exposureeu_baseline.mat Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 25 ug/m^3 for the baseline year. 4_SOS_Exposure_WHO_Baseline PM2 matrix file: sos_pm2_exposurewho_baseline.mat Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 10 ug/m^3 for the baseline year. 4_SOS_Exposure_EU_Scenarios NO2 matrix file: sos_no2_exposureeu_scenarios.mat Population potentially exposed to the annual NO2 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050. 4_SOS_Exposure_EU_Scenarios PM10 matrix file: sos_pm10_exposureeu_scenarios.mat Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050. 4_SOS_Exposure_WHO_Scenarios PM10 matrix file: sos_pm10_exposurewho_scenarios.mat Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 20 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050. 4_SOS_Exposure_EU_Scenarios PM2 matrix file: sos_pm2_exposureeu_scenarios.mat Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 25 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050. 4_SOS_Exposure_WHO_Scenarios PM2 matrix file: sos_pm2_exposurewho_scenarios.mat Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 10 ug/m^3 for BAU, scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.