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18,658 results for “impacts”
Data from: Breaking Barriers? Ethnicity and socioeconomic background impact on early career progression in the fields of ecology and evolution
<p>The academic disciplines of Science, Technology, Engineering and Mathematics (STEM) have long suffered from a lack of diversity. While in recent years there has been some progress in addressing the underrepresentation of women in STEM subjects, other characteristics that have the potential to impact on equality of opportunity have received less attention. In this study, we surveyed 188 early career scientists (ECRs), defined as within ten years of completing their PhD, in the fields of ecology, evolutionary biology, behaviour, and related disciplines. We examined associations between ethnicity, age, sexual orientation, sex, socioeconomic background, and disability, with measures of career progression, namely publication record, number of applications made before obtaining a postdoc, type of contract, and number of grant applications made. We also queried respondents on perceived barriers to progression, and potential ways of overcoming them. Our key finding was that socioeconomic background and ethnicity were associated with measures of career progression. While there was no difference in the number of reported first-authored papers on PhD completion, ethnic minority respondents reported fewer other-authored papers. In addition, ECRs from a lower socioeconomic background were more likely to report being in teaching and research positions, rather than research only positions, the latter being perceived as more prestigious by some institutions. We discuss our findings in the context of possible inequality of opportunity. We hope that this study will stimulate wider discussion, and help to inform strategies to address the underrepresentation of minority groups in the fields of ecology and evolution, and STEM subjects more widely.</p>
Identifying functional impacts of heat-resistant fungi on boreal forest recovery after wildfire
<p>Fungi play key roles in carbon (C) dynamics of ecosystems: saprotrophs decompose organic material and return C in the nutrient cycle, and mycorrhizal species support plants that accumulate C through photosynthesis. The identities and functions of extremophile fungi present after fire can influence C dynamics, particularly because plant-fungal relationships are often species-specific. However, little is known about the function and distribution of fungi that survive fires. We aim to assess the distribution of heat-resistant soil fungi across burned stands of boreal forest in the Northwest Territories, Canada, and understand their functions in relation to decomposition and tree seedling growth. We cultured and identified fungi from heat-treated soils and linked sequences from known taxa with high throughput sequencing fungal data (Illumina MiSeq, ITS1) from soils collected in 47 plots. We assessed functions under controlled conditions by inoculating litter and seedlings with heat-resistant fungi to assess decomposition and effects on seedling growth, respectively, for black spruce (Picea mariana), birch (Betula papyrifera), and jack pine (Pinus banksiana). We also measured litter decomposition rates and seedling densities in the field without inoculation. We isolated seven taxa of heat-resistant fungi and found their relative abundances were not associated with environmental or fire characteristics. Under controlled conditions, Fayodia gracilipes and Penicillium arenicola decomposed birch, but no taxa decomposed black spruce litter significantly more than the control treatment. Seedlings showed reduced biomass and/or mortality when inoculated with at least one of the fungal taxa. Penicillium turbatum reduced growth and/or caused mortality of all three species of seedlings. In the field, birch litter decomposed faster in stands with greater pre-fire proportion of black spruce, while black spruce litter decomposed faster in stands experiencing longer fire-free intervals. Densities of seedlings that had germinated since fire were positively associated with ectomycorrhizal richness while there were fewer conifer seedlings with greater heat-resistant fungal abundance. Overall, our study suggests that extremophile fungi present after fires have multiple functions and may have unexpected negative effects on forest functioning and regeneration. In particular, heat-resistant fungi after fires may promote shifts away from conifer dominance that are observed in these boreal forests.</p> <p> </p> <p> </p>
Historians' Perspectives of the Importance and Impact of Mining History Scholarship
<p>In 2019, Eric C. Nystrom (Arizona State University) and Brian James Leech (Augustana College) conducted a survey of self-identified mining historians to determine their preferences for mining history scholarship. Our survey asked voluntary respondents to list their top three "classic" books in mining history, and also their top three "recent" mining history books. Respondents were also given an opportunity for optional comments, to accompany their choices. Basic demographic data about occupation, occupation status, and geographic location were collected. Geographic data was aggregated to remove personally identifying information.</p> <p>All responses were voluntary and anonymous. 40 complete responses were received, and 134 distinct books were mentioned. The data was then regularized, and complete bibliographic data generated. When a book has been issued in multiple editions, the first edition was used, unless a different edition was explicitly specified by the respondent.</p> <p><strong>Included files</strong></p> <p>This data release contains:</p> <ul> <li>Spreadsheet of responses (TSV), with responses regularized by BibTeX tag, geography grouped to prevent identification, and private comments removed (all in accordance with IRB)</li> <li>Bibliography of all books mentioned as either "classic" or "recent" works by study respondents, in Chicago format, alphabetical by author last name (PDF)</li> <li>BibTeX file containing complete bibliography of all books mentioned by respondents</li> </ul> <p><strong>Publications</strong></p> <p>The survey results were analyzed in the following works:</p> <p>Brian James Leech and Eric C. Nystrom, "Surveying the Minds: New Trends and Key Classics in Mining History," <em>Mining History Journal</em> 27 (2020): forthcoming.</p> <p>Brian James Leech and Eric C. Nystrom, "Surveying the Minds: New Trends and Key Classics in Mining History," paper presented at the Mining History Association Conference, Marquette, MI, June 8, 2019.</p> <p><strong>Notes and Errata</strong></p> <ul> <li> <p>For Rodman Paul's <em>Mining Frontiers</em>, one respondent explicitly listed the 2001 Paul & West edition; all others were assumed to be the first edition from 1963.</p> </li> <li> <p>One respondent explicitly mentioned the Herbert and Lou Henry Hoover translation of Agricola's 1556 work <em>De re Metallica</em>; since this English translation is the one almost always used in the modern era, references to Agricola were regularized as the Hoover edition.</p> </li> <li> <p>In a handful of cases, multi-volume works were listed by respondents, and were thus entered in the bibliography this way. See e.g. Lingenfelter, <em>Bonanzas & Borrascas</em>.</p> </li> <li> <p>ASU IRB exemption # STUDY00009309; Augustana IRB exemption # 462452423.</p> </li> </ul>
Cluster randomized controlled trial evaluating the impact on children's linear growth of an unconditional cash transfer program implemented in rural areas of North Togo
<p>A parallel cluster randomized controlled trial was implemented in North Togo. 162 rural landlocked villages were randomized into either an intervention arm (cash transfers + package of community activities, <em>n</em>=82) or a control arm (package of community activities only, <em>n</em>=80). </p> <p>Two different representative samples of children aged 6-29 months and their mothers were surveyed, one before the intervention (2014, <em>n</em>=2,658), the other two years afterwards (2016, <em>n</em>=2,031).</p> <p>You will found here:</p> <ul> <li>Two datasets, fully anonymized (French, CSV files): one for observations on households and the other for observations on mother-child pairs</li> <li>Two dictionaries of variables (French, Excel files): one for the household database and the other for the mother-child pairs database</li> <li>The study protocol as submitted to the Ministry of Health of Togo (French, PDF)</li> <li>The technical and financial proposal (to evaluate the CT program) as submitted to funders (English, PDF)</li> </ul> <p> </p>
d5-6-assessment-of-impacts-liguria
<p>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).</p> <ul> <li><strong>Liguria shapefile</strong><br> file: genova.7z<br> Shapefile with the urban scale domain over Genova/ Liguria with 25 km x 15 km</li> <li><strong>3_LIG_Air Quality_Baseline (mesoscale/NO2 concentrations)</strong><br> file: no2_2010010100_2010123123_lcc.png<br> Annual NO2 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_LIG_Air Quality_Baseline (mesoscale/PM2.5 concentrations)</strong><br> file: pm2.5_2010010100_2010123123_lcc.png<br> Annual PM2.5 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_LIG_Air Quality_Baseline (mesoscale/PM10 concentrations)</strong><br> file: pm10_2010010100_2010123123_lcc.png<br> Annual PM10 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_LIG_Air Quality_Baseline (urban scale/ NO2 concentrations)</strong><br> file: gen_aq2app_no2.txt<br> Annual NO2 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters)</li> <li><strong>3_LIG_Air Quality_Baseline (urban scale/ PM10 concentrations)</strong><br> file: gen_aq2app_pm10.txt<br> Annual PM10 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters)</li> <li><strong>3_LIG_Air Quality_Baseline (urban scale/ PM2.5 concentrations)</strong><br> file: gen_aq2app_pm2.5.txt<br> Annual PM2.5 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters)</li> <li><strong>6_LIG_CarbonFootprint_Baseline</strong><br> file: ech.ma.15-fr2-wp5-carbon-footprint-ed2.pdf<br> Carbon footprint methodologies and estimation for the baseline year for Liguria Region</li> <li><strong>3_LIG_Air Quality_Baseline (mesoscale-report)</strong><br> file: lig_mesoscale.pdf<br> This report provides an overview of the modelling approach used to characterize the air quality in the Liguria 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.</li> <li><strong>3_LIG_Air Quality_Baseline (urbanscale-report)</strong><br> file: lig_aq_urbanscale_report.pdf<br> 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.</li> <li><strong>2_LIG_IRCI_baseline</strong><br> file: ech.ma.15-fr1-wp5-irc-ed5.3.pdf</li> <li><strong>2_LIG_IRCI_Scenarios</strong><br> file: ech.ma.15-fr3-wp5-irc-future-ed5.pdf</li> <li><strong>2_LIG_temporal_profiles</strong><br> file: liguaria-daily_hourlytd_res_comm_emi.xlsx<br> Temporal profiles of Liguria's residential sector and commercial sector scale in %: Daily emissions, typical days emissions and hourly typical days emissions of PM10 and NOX variables.</li> <li><strong>2_LIG_Transport_baseline_map</strong><br> file: roadnetwork_lig.zip<br> Map with lines, link with emissions using filed "ID"</li> <li><strong>2_LIG_Transport_baseline_values</strong><br> file: emission_values_lig.zip<br> Part 2 of 2 files that make the Liguria 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 DAY: 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))</li> <li><strong>2_LIG_transport_scenarios</strong><br> file: 191121_gen_scenario_results_summary_v2.xlsx<br> 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: %</li> <li><strong>5_LIG_health_statistics</strong><br> file: lig_health-analysis.xlsx<br> demographics and population data to calculate the health statistics</li> <li><strong>5_LIG_health_scenarios</strong><br> file: summary_results_liguria.xlsx<br> 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.</li> <li><strong>3_LIG_NO2_AQ_latlong</strong><br> file: genova_no2_latlong.rar<br> The shapefile includes total NO2 concentrations (µg/m^3) , as well as concentrations by sector (transport, shipping, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_LIG_PM2_AQ_latlong</strong><br> file: genova_pm2_latlong.rar<br> The shapefile includes total PM2 concentrations (µg/m^3) , as well as concentrations by sector (transport, shipping, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_LIG_PM10_AQ_latlong</strong><br> file: genova_pm10_latlong.rar<br> The shapefile includes total PM10 concentrations (µg/m^3) , as well as concentrations by sector (transport, shipping, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>6_LIG_CarbonFootprint_Scenarios</strong><br> file: ech.ma.15-fr4-wp5-carbon-footprint-future-ed3-.pdf<br> Carbon footprint business as usual and scenario projections for Liguria Region.</li> <li><strong>3_LIG_Air Quality_Baseline (mesoscale/SourceApportionment)</strong><br> file: lig_psat.xlsx<br> 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 Genoa urban area.</li> <li><strong>3_LIG_Air Quality Scenarios_urban scale NO2 matrix</strong><br> file: lig_no2_scenarios.mat<br> 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.</li> <li><strong>3_LIG_Air Quality Scenarios_ urban scale PM10 matrix</strong><br> file: lig_pm10_scenarios.mat<br> 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.</li> <li><strong>3_LIG_Air Quality Scenarios_urban scale PM2 matrix</strong><br> file: lig_pm2_scenarios.mat<br> 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.</li> <li><strong>4_LIG_Exposure_EU_Baseline NO2 matrix</strong><br> file: lig_no2_exposureeu_baseline.mat<br> 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.</li> <li><strong>4_LIG_Exposure_EU_Baseline PM10 matrix</strong><br> file: lig_pm10_exposureeu_baseline.mat<br> 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.</li> <li><strong>4_LIG_Exposure_WHO_Baseline PM10 matrix</strong><br> file: lig_pm10_exposurewho_baseline.mat<br> 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.</li> <li><strong>4_LIG_Exposure_EU_Baseline PM2 matrix</strong><br> file: lig_pm2_exposureeu_baseline.mat<br> 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.</li> <li><strong>4_LIG_Exposure_WHO_Baseline PM2 matrix</strong><br> file: lig_pm2_exposurewho_baseline.mat<br> 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.</li> <li><strong>4_LIG_Exposure_EU_Scenarios NO2 matrix</strong><br> file: lig_no2_exposureeu_scenarios.mat<br> 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.</li> <li><strong>4_LIG_Exposure_EU_Scenarios PM10 matrix</strong><br> file: lig_pm10_exposureeu_scenarios.mat<br> 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.</li> <li><strong>4_LIG_Exposure_WHO_Scenarios PM10 matrix</strong><br> file: lig_pm2_exposurewho_scenarios.mat<br> 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.</li> <li><strong>4_LIG_Exposure_EU_Scenarios PM2 matrix</strong><br> file: lig_pm2_exposureeu_scenarios.mat<br> 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.</li> <li><strong>4_LIG_Exposure_WHO_Scenarios PM2 matrix</strong><br> file: lig_pm2_exposurewho_scenarios.mat<br> 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.</li> </ul>
d5-5-assessment-of-impacts-bristol
<p>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).</p> <ul> <li><strong>D5.5 Assessment of Impacts - First City</strong><br> file: d5.5-assessment-of-impacts-first-city_-revised-june-2020.pdf<br> This is an action deliverable consisting of the impact assessment work undertaken in WP5 encompassing: 1. the integrated urban module based on the household and dwelling characteristics; 2. the transport emission sector; 3. the industrial, residential, commercial and institutional emission sectores; 4. the energy/ power generation 5. the air quality and related population exposure; 6. the health-related impacts and costs; and 7. the carbon footprint. This report aims to describe the framework developed for the assessment of the environmental, health, and economic impacts for the first ClairCity case study (Bristol City Council). The impact assessment analysis data can be found on the ClairCity Data Portal and this report is the submitted formal deliverable to record that the work has been delivered.</li> <li><strong>specifications of modelling tool set</strong><br> file: module_specifications_report.pdf<br> The general purpose of this document is to come up with an aligned view of the model toolset developed to be applied to the 6 ClairCity case studies.</li> <li><strong>Bristol shapefile</strong><br> file: bristol_200mx200m.rar<br> Shapefile with the urban scale domain over Bristol with 20 km x 20 km, with a grid resolution of 200m x 200m</li> <li><strong>1_BRS_Integrated_household_BAU_Scenarios</strong><br> file: bristol_household_projections.xlsx<br> Official ONS projections of Bristol's household population by age group of household head, and household structure (household size and number of children)</li> <li><strong>1_BRS_Integrated_BAU_Scenarios_2025</strong><br> file: bristol_hh_energy_use_2025.xlsx<br> Household energy use projections for 2025, based on official ONS household population projections and the dataset</li> <li><strong>1_BRS_Integrated_BAU_Scenarios_2035</strong><br> file: bristol_hh_energy_use_2035.xlsx<br> Household energy use projections for 2035, based on official ONS household population projections and the dataset</li> <li><strong>1_BRS_Integrated_BAU_Scenarios_2050</strong><br> file: bristol_hh_energy_use_2050.xlsx<br> Household energy use projections for 2050, based on official ONS household population projections and the dataset</li> <li><strong>2_BRS_Natural_baseline</strong><br> file: claircity_naturalemissions_brs_jan2019.pdf<br> Emissions (in kg/year) were based on EMEP emission inventory for nature at 0.1x0.1 degrees resolution (~ 10 km) for the year 2015 disaggregated for the urban domain of Bristol by forest, grass, parks and nature reserve areas classified in the Open Street Map database.</li> <li><strong>2_BRS_Agriculture_baseline</strong><br> file: claircity_agricultureemissions_brs_jan2019.pdf<br> Emissions (in kg/year) were based on EMEP emission inventory for agriculture and livestock at 0.1x0.1 degrees resolution (~ 10 km) for the year 2015 disaggregated for the urban domain of Bristol by farms, meadows, vineyards land uses classified in the Open Street Map database</li> <li><strong>2_BRS_IRCI_baseline</strong><br> file: ech.ma.15-fr1-wp5-irc-ed5.3.pdf<br> This document reports about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular its scope is to: • Develop a specific tool to evaluate emissions using existing industrial emissions data (EMEP, E-PRTR, others) and EMEP/EEA Emission inventory Guidebook; • Develop a specific tool to estimate emissions from small combustion in residential, commercial and institutional sector.</li> <li><strong>2_BRS_IRCI_baseline_not_industry_area_fuel_cons</strong><br> file: 2_brs_irci_baseline_not_industry_area_fuel_cons.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports area fuel consumptions from small combustion in residential, commercial and institutional sector evaluated using UK statistical official data. The methodology and results are described in "2_BRS_IRCI_baseline2" document.</li> <li><strong>2_BRS_IRCI_baseline_not_industry_area_emi</strong><br> file: 2_brs_irci_baseline_not_industry_area_emi.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports area NOX and PM10 emissions from small combustion in residential, commercial and institutional sector evaluated using EMEP/EEA Emission inventory Guidebook and UK statistical official data. The methodology and results are described in "2_BRS_IRCI_baseline" document.</li> <li><strong>2_BRS_IRCI_baseline_industry_area_emi</strong><br> file: 2_brs_irci_baseline_industry_area_emi.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports NOX and PM10 Emissions from minor PRTR sources allocated to 20x20 grid. The methodology and results are described in "2_BRS_IRCI_baseline" document.</li> <li><strong>2_BRS_IRCI_baseline_industry_point_emi</strong><br> file: 2_brs_irci_baseline_industry_point_emi.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports NOX and PM10 Emissions from main PRTR sources by source. The methodology and results are described in "2_BRS_IRCI_baseline" document.</li> <li><strong>2_BRS_IRCI_BAU_Scenario</strong><br> file: ech.ma.15-fr3-wp5-irc-future-ed5.pdf<br> This document reports about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module activities related to BAU and scenarios definition. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In this report methodology and results are reported for: • Business as Usual (BAU): future situation without any policy interventions beyond what is decided upon at this point with three-time horizons: 2025, 2035 and 2050; • Scenario: added policy interventions to the BAU, same time horizon as results from Stakeholder Dialogue Workshop; • Unified Policy Scenario: final scenario as a results of Policy Workshop.</li> <li><strong>2_BRS_IRCI_BAU_not_industry_area_emi_2025</strong><br> file: 2_brs_irci_bau_not_industry_area_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) area NOX and PM10 emissions, in Business As Usual scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_BAU_not_industry_area_emi_2035</strong><br> file: 2_brs_irci_bau_not_industry_area_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) area NOX and PM10 emissions, in Business As Usual scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_BAU_not_industry_area_emi_2050</strong><br> file: 2_brs_irci_bau_not_industry_area_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) area NOX and PM10 emissions, in Business As Usual scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_BAU_industry_area_emi_2025</strong><br> file: 2_brs_irci_bau_industry_area_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) NOX and PM10 Emissions, in Business As Usual scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_BAU_industry_area_emi_2035</strong><br> file: 2_brs_irci_bau_industry_area_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) NOX and PM10 Emissions, in Business As Usual scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_BAU_industry_area_emi_2050</strong><br> file: 2_brs_irci_bau_industry_area_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) NOX and PM10 Emissions, in Business As Usual scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_BAU_industry_point_emi_2025</strong><br> file: 2_brs_irci_bau_industry_point_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) NOX and PM10 Emissions, in Business As Usual scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_BAU_industry_point_emi_2035</strong><br> file: 2_brs_irci_bau_industry_point_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) NOX and PM10 Emissions, in Business As Usual scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_BAU_industry_point_emi_2050</strong><br> file: 2_brs_irci_bau_industry_point_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) NOX and PM10 Emissions, in Business As Usual scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S1_not_industry_area_emi_2025</strong><br> file: 2_brs_irci_s1_not_industry_area_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) area NOX and PM10 emissions, in S1 scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S1_not_industry_area_emi_2035</strong><br> file: 2_brs_irci_s1_not_industry_area_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) area NOX and PM10 emissions, in S1 scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S1_not_industry_area_emi_2050</strong><br> file: 2_brs_irci_s1_not_industry_area_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) area NOX and PM10 emissions, in S1 scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S1_industry_area_emi_2025</strong><br> file: 2_brs_irci_s1_industry_area_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) NOX and PM10 Emissions, in S1 scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S1_industry_area_emi_2035</strong><br> file: 2_brs_irci_s1_industry_area_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) NOX and PM10 Emissions, in S1 scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S1_industry_area_emi_2050</strong><br> file: 2_brs_irci_s1_industry_area_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) NOX and PM10 Emissions, in S1 scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S1_industry_point_emi_2025</strong><br> file: 2_brs_irci_s1_industry_point_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) NOX and PM10 Emissions, in S1 scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S1_industry_point_emi_2035</strong><br> file: 2_brs_irci_s1_industry_point_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) NOX and PM10 Emissions, in S1 scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S1_industry_point_emi_2050</strong><br> file: 2_brs_irci_s1_industry_point_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) NOX and PM10 Emissions, in S1 scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S2_not_industry_area_emi_2025</strong><br> file: 2_brs_irci_s2_not_industry_area_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) area NOX and PM10 emissions, in S2 scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S2_not_industry_area_emi_2035</strong><br> file: 2_brs_irci_s2_not_industry_area_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) area NOX and PM10 emissions, in S2 scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S2_not_industry_area_emi_2050</strong><br> file: 2_brs_irci_s2_not_industry_area_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) area NOX and PM10 emissions, in S2 scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S2_industry_area_emi_2025</strong><br> file: 2_brs_irci_s2_industry_area_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) NOX and PM10 Emissions, in S2 scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S2_industry_area_emi_2035</strong><br> file: 2_brs_irci_s2_industry_area_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) NOX and PM10 Emissions, in S2 scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S2_industry_area_emi_2050</strong><br> file: 2_brs_irci_s2_industry_area_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) NOX and PM10 Emissions, in S2 scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S2_industry_point_emi_2025</strong><br> file: 2_brs_irci_s2_industry_point_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) NOX and PM10 Emissions, in S2 scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S2_industry_point_emi_2035</strong><br> file: 2_brs_irci_s2_industry_point_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) NOX and PM10 Emissions, in S2 scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S2_industry_point_emi_2050</strong><br> file: 2_brs_irci_s2_industry_point_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) NOX and PM10 Emissions, in S2 scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S3_not_industry_area_emi_2025</strong><br> file: 2_brs_irci_s3_not_industry_area_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) area NOX and PM10 emissions, in S3 scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S3_not_industry_area_emi_2035</strong><br> file: 2_brs_irci_s3_not_industry_area_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) area NOX and PM10 emissions, in S3 scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S3_not_industry_area_emi_2050</strong><br> file: 2_brs_irci_s3_not_industry_area_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) area NOX and PM10 emissions, in S3 scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S3_industry_area_emi_2025</strong><br> file: 2_brs_irci_s3_industry_area_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) NOX and PM10 Emissions, in S3 scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S3_industry_area_emi_2035</strong><br> file: 2_brs_irci_s3_industry_area_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) NOX and PM10 Emissions, in S3 scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S3_industry_area_emi_2050</strong><br> file: 2_brs_irci_s3_industry_area_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) NOX and PM10 Emissions, in S3 scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S3_industry_point_emi_2025</strong><br> file: 2_brs_irci_s3_industry_point_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) NOX and PM10 Emissions, in S3 scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S3_industry_point_emi_2035</strong><br> file: 2_brs_irci_s3_industry_point_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) NOX and PM10 Emissions, in S3 scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_S3_industry_point_emi_2050</strong><br> file: 2_brs_irci_s3_industry_point_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) NOX and PM10 Emissions, in S3 scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_UPS_not_industry_area_emi_2025</strong><br> file: 2_brs_irci_ups_not_industry_area_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) area NOX and PM10 emissions, in UPS scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_UPS_not_industry_area_emi_2035</strong><br> file: 2_brs_irci_ups_not_industry_area_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) area NOX and PM10 emissions, in UPS scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_UPS_not_industry_area_emi_2050</strong><br> file: 2_brs_irci_ups_not_industry_area_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) area NOX and PM10 emissions, in UPS scenario, from small combustion in residential, commercial and institutional sector evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_UPS_industry_point_emi_2025</strong><br> file: 2_brs_irci_ups_industry_point_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) NOX and PM10 Emissions, in UPS scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_UPS_industry_point_emi_2035</strong><br> file: 2_brs_irci_ups_industry_point_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) NOX and PM10 Emissions, in UPS scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_UPS_industry_point_emi_2050</strong><br> file: 2_brs_irci_ups_industry_point_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) NOX and PM10 Emissions, in UPS scenario, from main PRTR sources by source, evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_UPS_industry_area_emi_2025</strong><br> file: 2_brs_irci_ups_industry_area_emi_2025.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2025) NOX and PM10 Emissions, in UPS scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_UPS_industry_area_emi_2035</strong><br> file: 2_brs_irci_ups_industry_area_emi_2035.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2035) NOX and PM10 Emissions, in UPS scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_IRCI_UPS_industry_area_emi_2050</strong><br> file: 2_brs_irci_ups_industry_area_emi_2050.csv<br> This dataset reports results about the WP5 Task 5.2.4 Design & development of Industry Residential/Commercial & Other services module. The module integrates in the overall model the industrial, residential, commercial and institutional emissions sources. In particular the dataset reports future (2050) NOX and PM10 Emissions, in UPS scenario, from minor PRTR sources, allocated to 20x20 grid evaluated during the project as described in "2_BRS_IRCI_BAU_Scenario" document.</li> <li><strong>2_BRS_Transport_baseline_map</strong><br> file: baseline_map_v3.zip<br> Part 1 of 2 files that make the Bristol transport emissions baseline 1. total emissions per link in .csv: To be linked to the road network file using the identifier "uniqueID" 2. shapefile with road links units: g ___ GIS-map of Bristol road network.</li> <li><strong>2_BRS_Transport_baseline_values</strong><br> file: baseline_emissions_v3.zip<br> Part 2 of 2 files that make the Bristol transport emissions baseline: 1. total emissions per link in .csv: To be linked to the road network file using the identifier "uniqueID" 2. shapefile with road links units: g ___ emissions at link level consisting of a zip file with 18+1 .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 additional file added with all data combined "T_ALL_EMISSIONS_v3.xlsx"</li> <li><strong>2_BRS_Transport_source_apportionment</strong><br> file: 190919_behavior_v6_2015is1.xlsx<br> describes the share of road transport emissions by behavior-properties and population properties. Pure data & pivot-table for easy use: 1. male/female 2. 2 income-classes 3. 5 age groups 4. 3 car-ownership groups (0, 1, 2 or more) 5. Time of day: morning-, evening peak, midday and night 6. Type of day (weekend/weekday) 7. Motive: 8 classes 8. mode</li> <li><strong>2_BRS_transport_scenarios</strong><br> file: 190219_bristol_scenario_results_pw_summary.xlsx<br> 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: %</li> <li><strong>2_BRS_temporal_profiles</strong><br> file: bristol-daily_hourlytd_res_comm_emi.xlsx<br> Temporal profiles of Bristol's residential sector and commercial sector scale in %: Daily emissions, typical days emissions and hourly typical days emissions of PM10 and NOX variables. The data sources in synthesizing the temporal profiles are: 1. Monthly gas pattern (Gas) is available at Eurostat's energy database https://ec.europa.eu/eurostat/web/energy/data/database?p_p_id=NavTreeportletprod_WAR_NavTreeportletprod_INSTANCE_QAMy7Pe6HwI1&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view&p_p_col_id=column-2&p_p_col_count=1. The files are available in tab-delimited (.tsv) file extensions. In total, it has 129 variables and 815 columns. The variables consist of "unit,product,indic_nrg,geo", while the rest use the time in monthly periods from "2018M08" back to "2008M01". Since we have selected 2015 as the reference year, we selected "2015M01" to "2015M12". For each country selected, we choose all the observations marked with the country's ID, given that each country's ID is unique. 2. Hourly local temperature (Temp) is obtained from UK's Meteorological Office https://www.metoffice.gov.uk/ . The dataset is taken from the Filton station as an .xlsx file and it has three variables: "date", "hour" and "hourly temperature". 3. Hourly national electricity load (El) is available at open power system data https://data.open-power-system-data.org/time_series/ . The dataset has four variables: utc_timestamp is the start of time period in Coordinated Universal Time in datetime format, cet_cest_timestamp is the start of time period in Central European (Summer) Time in datetime format, Interpolated_values is the marker for missing data column in source data, which has been interpolated in string and the selected country's load in MW. 4. Share of fuel resources (%), especially wood heaters is calculated from Techne's emission area dataset. This dataset is available in .csv files and consists of eight variables: "year", "city", "zone", "codvariable", "namevariable", "pollutant", "emissions" and "unit".</li> <li><strong>3_BRS_Air Quality_Baseline (mesoscale/NO2 concentrations)</strong><br> file: no2_2010010100_2010123123_lcc.png<br> Annual NO2 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_BRS_Air Quality_Baseline (mesoscale/PM10 concentrations)</strong><br> file: pm10_2010010100_2010123123_lcc.png<br> Annual PM10 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_BRS_Air Quality_Baseline (mesoscale/PM2.5 concentrations)</strong><br> file: pm2.5_2010010100_2010123123_lcc.png<br> Annual PM2.5 average concentrations (µg/m^3) from WRF-CAMx modelling system for the mesoscale domain D2.</li> <li><strong>3_BRS_Air Quality_Baseline (mesoscale/SourceApportionment)</strong><br> file: psat_brs.xlsx<br> 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 Bristol urban area.</li> <li><strong>3_BRS_Air Quality_Baseline (mesoscale-report)</strong><br> file: brs_mesoscale.pdf<br> This report provides an overview of the modelling approach used to characterize the air quality in the Bristol 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.</li> <li><strong>3_BRS_Air Quality_Baseline (urban scale/ NO2 concentrations)</strong><br> file: brs_no2.mat<br> Annual NO2 average concentrations (µg/m^3) from URBAIR model (considering all the emission sectors: transport, industrial and IRCI) plus the background concentrations from the WRF-CAMx-SA system. The file contains X, Y coordinates from the LCP Clicurb coordinate system.</li> <li><strong>3_BRS_Air Quality_Baseline (urban scale/ PM10 concentrations)</strong><br> file: brs_pm10.mat<br> Annual PM10 average concentrations (µg/m^3) from URBAIR model (considering all the emission sectors: transport, industrial and IRCI) plus the background concentrations from the WRF-CAMx-SA system. The file contains X, Y coordinates from the LCP Clicurb coordinate system.</li> <li><strong>3_BRS_Air Quality_Baseline (urban scale/ PM2.5 concentrations)</strong><br> file: brs_pm2.mat<br> Annual PM2.5 average concentrations (µg/m^3) from URBAIR model (considering all the emission sectors: transport, industrial and IRCI) plus the background concentrations from the WRF-CAMx-SA system. The file contains X, Y coordinates from the LCP Clicurb coordinate system.</li> <li><strong>3_BRS_NO2_AQ_latlong</strong><br> file: brs_no2_latlong.rar<br> The shapefile includes total NO2 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_BRS_PM10_AQ_latlong</strong><br> file: brs_pm10_latlong.rar<br> The shapefile includes total PM10 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_BRS_PM2_AQ_latlong</strong><br> file: brs_pm2_latlong.rar<br> The shapefile includes total PM2 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_BRS_Air Quality_Baseline (urbanscale-report)</strong><br> file: brs_aq_urbanscale_report.pdf<br> 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.</li> <li><strong>3_BRS_Air Quality Scenarios_urban scale NO2 matrix</strong><br> file: brs_no2_scenarios.mat<br> 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 S1, S2, and S3 from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>3_BRS_Air Quality Scenarios_ urban scale PM10 matrix</strong><br> file: brs_pm10_scenarios.mat<br> 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 S1, S2, and S3 from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>3_BRS_Air Quality Scenarios_urban scale PM2 matrix</strong><br> file: brs_pm2_scenarios.mat<br> 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 S1, S2, and S3 from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_BRS_Exposure_EU_Baseline NO2 matrix</strong><br> file: brs_no2_exposureeu_baseline.mat<br> 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.</li> <li><strong>4_BRS_Exposure_EU_Baseline PM10 matrix</strong><br> file: brs_pm10_exposureeu_baseline.mat<br> 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.</li> <li><strong>4_BRS_Exposure_WHO_Baseline PM10 matrix</strong><br> file: brs_pm10_exposurewho_baseline.mat<br> 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.</li> <li><strong>4_BRS_Exposure_EU_Baseline PM2 matrix</strong><br> file: brs_pm2_exposureeu_baseline.mat<br> 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.</li> <li><strong>4_BRS_Exposure_WHO_Baseline PM2 matrix</strong><br> file: brs_pm2_exposurewho_baseline.mat<br> 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.</li> <li><strong>4_BRS_Exposure_EU_Scenarios NO2 matrix</strong><br> file: brs_no2_exposureeu_scenarios.mat<br> 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 S1, S2, and S3 from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_BRS_Exposure_EU_Scenarios PM10 matrix</strong><br> file: brs_pm10_exposureeu_scenarios.mat<br> 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 S1, S2, and S3 from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_BRS_Exposure_WHO_Scenarios PM10 matrix</strong><br> file: brs_pm10_exposurewho_scenarios.mat<br> 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 S1, S2, and S3 from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_BRS_Exposure_EU_Scenarios PM2 matrix</strong><br> file: brs_pm2_exposureeu_scenarios.mat<br> 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 S1, S2, and S3 from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_BRS_Exposure_WHO_Scenarios PM2 matrix</strong><br> file: brs_pm2_exposurewho_scenarios.mat<br> 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 S1, S2, and S3 from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>5_BRS_Health_Statistics</strong><br> file: brs_health-analysis.xlsx<br> demographics and population data to calculate the health statistics</li> <li><strong>5_BRS_Health_Scenarios</strong><br> file: summary_results_hia_brs.xlsx<br> 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.</li> <li><strong>6_BRS_Carbon Footprint_Baseline</strong><br> file: ech.ma.15-fr2-wp5-carbon-footprint-ed2.pdf<br> This document reports about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities. The goal of the task is to integrate in the overall model a specific module to compute the carbon footprint of the cities and in particular its scope is to: • Review existing carbon footprinting methodologies • Establish best methodology and relevant emission factors. • Apply footprinting methodologies to six pilot cities based on activity data from previous tasks</li> <li><strong>6_BRS_Carbon Footprint_Baseline</strong><br> file: area_cf_bristol_2015.csv<br> Carbon footprint for Bristol Baseline</li> <li><strong>6_BRS_Carbon Footprint_BAU_Scenarios</strong><br> file: ech.ma.15-fr4-wp5-carbon-footprint-future-ed3-.pdf<br> This document reports about the WP5 Task 5.2.4 Carbon Footprint module activities related to BAU and scenario definition. The module integrates in the overall model the Carbon Footprint evaluation. Another report1 describes methodology and results to integrate in the overall model the specific module to compute the carbon footprint of the cities. In this report methodology and results are reported for: • BAU "business as usual": future situation without any policy interventions beyond what is decided upon at this point with 5-time horizons: 2020, 2025, 2030, 2035 and 2050; • Scenario: added policy interventions to the BAU, same time horizon as results from Stakeholder Dialogue Workshop; • Unified Policy Scenario: final scenario as a results of Policy Workshop.</li> <li><strong>6_BRS_Carbon Footprint_BAU_2025</strong><br> file: 6_brs_carbon-footprint_bau_2025.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2025) footprinting results, in Business As Usual scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_BAU_2035</strong><br> file: 6_brs_carbon-footprint_bau_2035.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2035) footprinting results, in Business As Usual scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_BAU_2050</strong><br> file: 6_brs_carbon-footprint_bau_2050.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2050) footprinting results, in Business As Usual scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_S1_2025</strong><br> file: 6_brs_carbon-footprint_s1_2025.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2025) footprinting results, in S1 scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_S1_2035</strong><br> file: 6_brs_carbon-footprint_s1_2035.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2035) footprinting results, in S1 scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_S1_2050</strong><br> file: 6_brs_carbon-footprint_s1_2050.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2050) footprinting results, in S1 scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_S2_2025</strong><br> file: 6_brs_carbon-footprint_s2_2025.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2025) footprinting results, in S2 scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_S2_2035</strong><br> file: 6_brs_carbon-footprint_s2_2035.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2035) footprinting results, in S2 scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_S2_2050</strong><br> file: 6_brs_carbon-footprint_s2_2050.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2050) footprinting results, in S2 scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_S3_2025</strong><br> file: 6_brs_carbon-footprint_s3_2025.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2025) footprinting results, in S3 scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_S3_2035</strong><br> file: 6_brs_carbon-footprint_s3_2035.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2035) footprinting results, in S3 scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_S3_2050</strong><br> file: 6_brs_carbon-footprint_s3_2050.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2050) footprinting results, in S3 scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_UPS_2025</strong><br> file: 6_brs_carbon-footprint_ups_2025.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2025) footprinting results, in UPS scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_UPS_2035</strong><br> file: 6_brs_carbon-footprint_ups_2035.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2035) footprinting results, in UPS scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> <li><strong>6_BRS_Carbon Footprint_UPS_2050</strong><br> file: 6_brs_carbon-footprint_ups_2050.csv<br> This dataset reports results about the WP5 Task 5.2.8 Carbon footprint methodologies & estimation for the pilot cities module. The module integrates in the overall model a specific module to compute the carbon footprint of the cities. In particular the dataset reports future (2050) footprinting results, in UPS scenario, evaluated during the project as described in "6_BRS_Carbon Footprint_BAU_Scenarios" document.</li> </ul>
Data for The impact of information about tobacco-related reproductive vs. general health risks on South Indian women's tobacco use decisions
<p>Tobacco Intervention Study Mysore India March-April 2016</p> <p>Published version: <a href="https://doi.org/10.1017/ehs.2020.61">https://doi.org/10.1017/ehs.2020.61</a></p>
Data for Measuring the Market Impact of New Auditing Standards in China
<p>Data used in a study of the market impact of new auditing standards in China. The data includes 76 companies listed on the A+H share markets that were subject to the new auditing standards in 2017. It also includes 76 companies listed on the A share market which were matched with the experimental sample using the Propensity-Score Matching method. Files include 2016 & 2017 cumulative abnormal returns (CAR) and volatility (Vol) for the experimental group (A+H shares) and the control group (A shares) and control variables for both groups.</p> <p> </p> <p> </p>
Illuminating the impact of diel vertical migration on visual gene expression in deep-sea shrimp
<p>Diel vertical migration (DVM) of marine animals represents one of the largest migrations on our planet. Migrating fauna are subjected to a variety of light fields and environmental conditions that can have notable impacts on sensory mechanisms, including an organism's visual capabilities. Among deep-sea migrators are oplophorid shrimp, that vertically migrate hundreds of meters to feed in shallow waters at night. These species also have bioluminescent light organs that emit light during migrations to aid in camouflage. The organs have recently been shown to contain visual proteins (opsins) and genes that infer light sensitivity. Knowledge regarding the impacts of vertical migratory behavior, and fluctuating environmental conditions, on sensory system evolution is unknown. In this study, the oplophorid <i>Systellaspis debilis</i> was either collected during the day from deep waters or at night from relatively shallow waters to ensure sampling across the vertical distributional range. <i>De novo </i>transcriptomes of light sensitive tissues (eyes/photophores) from the <i>Day/Night </i>specimens were sequenced and analyzed to characterize opsin diversity and visual/light interaction genes. Gene expression analyses were also conducted to quantify expression differences associated with DVM. Our results revealed an expanded opsin repertoire among the shrimp and differential opsin expression that may be linked to spectral tuning during the migratory process. This study sheds light on the sensory systems of a bioluminescent invertebrate and provides additional evidence for extraocular light sensitivity. Our findings further suggest opsin coexpression and subsequent fluctuations in opsin expression may play an important role in diversifying the visual responses of vertical migrators.</p>
Increasing N content in GaNAsP nanowires suppresses the impact of polytypism on luminescence
<p>Cathodoluminescence (CL) and micro-photoluminescence spectroscopies are employed to investigate effects of structural defects on carrier recombination in GaNAsP nanowires (NWs) grown by molecular beam epitaxy on Si substrates. In the NWs with a low N content of 0.08%, these defects are found to promote non-radiative (NR) recombination, which causes spatial variation of the CL peak position and its intensity. Unexpectedly, these detrimental effects can be suppressed even by a small increase in the nitrogen composition from 0.08% to 0.12%. This is attributed to more efficient trapping of excited carriers/excitons to the localized states promoted by N-induced localization and also the presence of other NR channels. At room temperature, the structural defects no longer dominate in carrier recombination even in the NWs with the lower nitrogen content, likely due to increasing importance of other recombination channels. Our work underlines the need in eliminating important thermally activated NR defects, other than the structural defects, for future optoelectronic applications of these NWs.</p>
DATASET OF "Impact of short-acting vs. standard anaesthetic agents on obstructive sleep apnoea: a randomised, controlled, triple-blind trial"
<p>Sleep apnoea is associated with negative outcomes following general anaesthesia. Current recommendations suggest using short-acting anaesthetic agents in preference to standard agents to reduce this risk, but there is currently no evidence to support this. This randomised controlled triple-blind trial tested the hypothesis that a combination of short-acting agents (desflurane-remifentanil) would reduce the postoperative impact of general anaesthesia on sleep apnoea severity compared with standard agents (sevoflurane-fentanyl). Sixty patients undergoing hip arthroplasty under general anaesthesia were randomised to anaesthesia with desflurane-remifentanil or sevoflurane-fentanyl. Respiratory polygraphy was performed before surgery and on the first and third postoperative nights. The primary outcome was the supine apnoea-hypopnoea index on the first postoperative night. Secondary outcomes were the supine apnoea-hypopnoea index on the third postoperative night, and the oxygen desaturation index on the first and third postoperative nights. Additional outcomes included intravenous morphine equivalent consumption and pain scores on postoperative days 1, 2 and 3. Pre-operative sleep study data were similar between groups. Mean (95%CI) values for the supine apnoea-hypopnoea index on the first postoperative night were 18.9 (12.7–25.0) and 21.4 (14.2–28.7) events.h<sup>-1</sup>, respectively, in the short-acting and standard anaesthesia groups (p=0.64). Corresponding values on the third postoperative night were 28.1 (15.8–40.3) and 38.0 (18.3–57.6) events.h<sup>-1</sup> (p=0.34). Secondary sleep- and pain-related outcomes were generally similar in the two groups. In conclusion, short-acting anaesthetic agents did not reduce the impact of general anaesthesia on sleep apnoea severity compared with standard agents. These data should prompt an update of current recommendations.</p>
Data from: Impacts of nutrient subsidies on salt marsh arthropod food webs: a latitudinal survey
Anthropogenic nutrient inputs into native ecosystems cause fluctuations in resources that normally limit plant growth, which has important consequences for associated foodwebs. Such inputs from agricultural and urban habitats into nearby natural systems are increasing globally and can be highly variable. Despite the global increase in anthropogenically-derived nutrient inputs into native ecosystems, the consequences of variation in subsidy amount on native plants and their associated foodwebs are poorly known. Salt marshes represent an ideal system to address the differential impacts of nutrient inputs on ecosystem and community dynamics because human development and other anthropogenic activities lead to recurrent introductions of nutrients into these natural systems. Previously, we have found in manipulative experiments that arthropod abundance increases in response to nutrient enrichment, with predators being the trophic group most strongly affected. We conducted a survey of Atlantic coastal Spartina marshes to test whether such local responses are indicative of responses at a landscape level. We examined the most abundant arthropod species associated with Spartina coastal marshes that receive variable amounts of anthropogenic nitrogen, and tested how this response varied across different arthropod functional groups (herbivores, epigeic feeders, and predators). Similar to what we found at a local scale, nutrient subsidies alter the trophic structure of the arthropod assemblage by changing the relative abundances of various feeding groups. Variable responses among predators to nitrogen density could be partly explained by diet breadth (e.g. generalists vs. specialists). Herbivores had a negative response to increasing plant nitrogen density; specialist predators tracked their herbivore prey and thus also responded negatively to nitrogen density. However, generalists were not negatively affected by nitrogen density and indeed some generalist predators responded positively to nitrogen density. Thus, the overall predator-to-herbivore ratio was also positively associated with nitrogen density. Our research helps us to understand how long-term nutrient enrichment of native ecosystems by human activities affects arthropod assemblages and foodweb dynamics.
Endocrine disruption: the noise in available data adversely impact the models' performance
<p>This paper is devoted to the analysis of available experimental data and preparation of predictive models for binding affinity of molecules with respect to two nuclear receptors involved in endocrine disruption: the Estrogen (ER) and the Androgen (AR) receptor. The ED-relevant data were retrieved from multiple sources, including the CERAP, CoMPARA, and the Tox21 data challenge projects as well as ChEMBL and PubChem databases. Data analysis performed with the help of Generative Topographic Mapping technique revealed the problem of a low agreement between experimental values issued from different sources.</p> <p>Collected data were used to train both classification models for AR and ER binding activities and regression models for Relative Binding Affinity (RBA) and median Inhibition Concentration (IC50) models. These models displayed relatively poor performance in classification (sensitivities ER = 0.34, AR = 0.49) and in regression (determination coefficient R<sup>2</sup> for the RBA and IC50 models in external validation varied from 0.44 to 0.76). Our analysis demonstrates that low models performances resulted from misinterpreted experimental endpoints or wrongly reported values.</p> <p>Developed models and collected data sets included of 6215 (ER) and 3789 (AR) unique compounds; they are freely available.</p> <p>The repository includes data on estrogen and androgen receptor binding behavior (binder, non-binder), median inhibitory concentration (IC50) and relative binding affinity (RBA). </p> <p><strong>SDF fields:</strong></p> <ul> <li><em>DB</em> = database; where: COMPARA = Collaborative Modelling Project for Androgen Receptor Activity; CERAPP = Collaborative Estrogen Receptor Activity Prediction Project; Tox-DC = data from Tox21 program; PubChem = data from PubChem. </li> <li><em>Set</em> = whether the compound was used in training or test set for the given model</li> <li><em>Receptor</em> = AR stands for Androgen Receptor and ER stands for Estrogen Receptor</li> <li><em>binding_prp</em> = binding behaviour for the classification model (ER and AR). 1 = binder; = non-binder</li> <li><em>IC50 (nM) </em>and <em>logIC50</em> = median inhibitory concentration values in nanoMolar and log.</li> <li><em>RBA(%) </em>and <em>logRBA</em> = relative binding affinity values in % and log.</li> </ul>
Impact of processing and storage conditions on color stability of strawberry puree: the role of PPO reactions revisited
<p>The effect of pre-heating fresh strawberries (hot break) and the use of refrigerated temperatures prior to pasteurization on the stability of anthocyanins, vitamin C, color and polyphenol oxidase (PPO) activity during storage (42 days at 35 ºC) of strawberry puree was studied. Hot break resulted in 20% residual PPO activity and caused 10% anthocyanin degradation, whereas vitamin C was unaffected. After mashing, purees were stored at 4 ºC and 25 ºC for 3 hours. During this period, anthocyanins and PPO activity remained constant independently of the processing history but ascorbic acid was oxidized faster at 25 ºC. Pasteurization caused complete inactivation of PPO, reduction of anthocyanins (25%), of <em>a*</em> value (6%) and of vitamin C (50%). Neither partial inactivation of PPO early in the processing (hot break) nor the use of refrigeration prior to pasteurization had a positive effect on color and anthocyanin stability of strawberry puree during subsequent storage, suggesting that PPO-derived reaction products formed during processing have a very limited impact on color degradation during shelf-life.</p>
A Slice of the Research Cake: The Impact of Open Science in Africa
<p><strong>Episode Summary: </strong></p> <p>In this episode we talk to Joy Owango, Founding Director at Training Centre in Communication. We discussed how Open Science is democratising research and access to data and publishing in Africa, the importance of ownership in research, and the challenges inherent in widespread change. </p> <p>Originally we interviewed another two guests who are involved in the Open Science movement on the African continent: Osman Aldirdiri, founding director of Open Sudan initiative and Jo Havemann, co-founder of the AfricArXiv. Due to technical difficulties we can only publish the audio from Joy Owango. However, we do have a <a href="https://drive.google.com/file/d/1sOhAaiWjAUwQlDMuaqd5YCC3SOhXDFSP/view?usp=sharing">transcript of the full interview</a> with all three guests.</p> <p><strong>Episode Links:</strong></p> <ul> <li><a href="https://info.africarxiv.org/">AfricArXiv</a> <p><a href="https://twitter.com/AfricArxiv">Twitter</a></p> </li> <li>Joy Owango <ul> <li><a href="https://www.tcc-africa.org/">Training Centre in Communication</a></li> <li><a href="https://twitter.com/JoyOwango">Twitter</a></li> </ul> </li> <li>Osman Aldirdiri <ul> <li><a href="https://blog.okfn.org/2017/03/30/open-data-day-sudan-2017-openness-for-advancing-research-and-discovery/">Open Sudan's Open Data Day</a></li> <li><a href="https://twitter.com/aldirdiri">Twitter</a></li> </ul> </li> <li>Jo Havemann <ul> <li><a href="https://access2perspectives.com/team/jo-havemann/">Access2Perspectives </a></li> </ul> </li> </ul>
Very high rate GPS observables from UCOE, UJUR, TNGF and OXUM continously operating stations, 17-19 Feb 2020. Dataset for the study "Assesment of Morelian meteoroid impact on Mexican environment" Part 3
<p>Archived data is Trimble raw T02 format from Trimble NetR9 receivers. Complete metadata for the stations can be obtained at the TLALOCNet archive at http://tlalocnet.udg.mx.</p> <p>This dataset includes high rate (5 and 50hz) versions of the UCOE, UJUR, TNGF and OXUM continous GPS station observables that is otherwise available as a 15 sec recording on the TLALOCNet archive (http://tlalocnet.udg.mx).</p> <p>Any use of this dataset or parts of it must give the proper acknowledgement , including the Zenodo assigned DOI as follows:</p> <p>This material is based on GPS data provided by the Trans-boundary, Land and Atmosphere Long-term Observational and Collaborative Network (TLALOCNet; Cabral-Cano et al., 2018) jointly operated by Servicio de Geodesia Satelital (SGS) at the Instituto de Geofísica-Universidad Nacional Autónoma de México in collaboration with and the GAGE facility operated by UNAVCO Inc. We gratefully acknowledge Armando Carrillo-Vargas and all the personnel from SGS and UNAVCO Inc. for station maintenance, data acquisition, IT support and data curation and distribution. TLALOCNet and related GPS operations at SGS are supported by the Consejo Nacional de Ciencia y Tecnología (CONACyT) projects 253760, 256012 and 2017-01-5955, UNAM-Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (PAPIIT) projects IN104213, IN111509, IN109315-3, IN104818-3, NSF grant 2025104 and supplemental support from UNAM-Instituto de Geofísica. UNAVCO's initial support for TLALOCNet (now part of NOTA) was performed under EAR-1338091 and is currently supported by the National Science Foundation and the National Aeronautics and Space Adminsitration under NSF Cooperative Agreement EAR-1724794. </p>
data set regarding environmental impact of building facades
<p>The file includes a set of reference data and sources used for the determination of the environmental impact (LCA and EPD) for selected materials used for building facades, with a special emphasis on bio-based solutions.</p>
The impact of Opisthorchis felineus infection and praziquantel treatment on the intestinal microbiome in children
<p>The presence of some species of helminths is associated with changes in host microbiota composition and diversity, which varies widely depending on the infecting helminth species and other factors. We conducted a prospective case-control study to evaluate the gut microbiota in children with Opisthorchis felineus infection (n=50) before and after anthelmintic treatment and in uninfected children (n=49) in the endemic region. A total of 99 children and adolescents aged between 7 and 18 years were enrolled to the study. Helminth infection was assessed before and at 3 months after treatment with praziquantel. A complex examination for each participant was performed in the study, including an assessment of the clinical symptoms and an intestinal microbiota survey by 16S rRNA gene sequencing of stool samples. There was no change in alpha diversity between O. felineus-infected and control groups. We found significant changes in the abundances of bacterial taxa at different taxonomic levels between the infected and uninfected individuals. Enterobacteriaceae family was more abundant in infected participants compared to uninfected children. On the genus level, O. felineus-infected participants’ microbiomes showed higher levels of Lachnospira, Escherichia-Shigella, Bacteroides, Eubacterium eligens group, Ruminiclostridium 6, Barnesiella, Oscillibacter, Faecalitalea and Anaerosporobacter and reduction of Blautia, Lachnospiraceae FCS020 and Eubacterium hallii group in comparison with the uninfected individuals. Following praziquantel therapy, there were significant differences in abundances of some microorganisms, including an increase of Faecalibacterium and decrease of Megasphaera, Roseburia. Enterobacteriaceae and Escherichia abundances were decreased up to the control group values. Our results highlight the importance of the host-parasite-microbiota interactions for the community health in the endemic regions.</p>
Can root-associated fungi mediate the impact of abiotic conditions on the growth of a High Arctic herb?
<p>This is a dataset containing fragments of internal transcribed spacer 2 (ITS2) extracted from <em>Bistorta vivipara</em> root-associated fungi, sampled from snow fence experiment in Adventdalen, Svalbard. </p> <p>This dataset was used in Wutkowska et al., 2020, Can root-associated fungi mediate the impact of abiotic conditions on the growth of a High Arctic herb?Can root-associated fungi mediate the impact of abiotic conditions on the growth of a High Arctic herb? [available at biorXiv.org, DOI: 10.1101/2020.06.20.157099]</p> <p>Now the manuscript is available as a peer-reviewed paper:</p> <p>Wutkowska, Magdalena, Dorothee Ehrich, Sunil Mundra, Anna Vader, and Pernille Bronken Eidesen. 2021. ‘Can Root-Associated Fungi Mediate the Impact of Abiotic Conditions on the Growth of a High Arctic Herb?’ <em>Soil Biology and Biochemistry</em> 159:108284. doi: 10.1016/j.soilbio.2021.108284.</p> <p> </p> <p>All other corresponding data are available here: https://github.com/magdawutkowska/bistorta</p>
Dataset from Pardini, E. A., Parsons, L. S., Ştefan, V., & Knight, T. M. (2018). GLMM BACI environmental impact analysis shows coastal dune restoration reduces seed predation on an endangered plant. Restoration Ecology, 26(6), 1190-1194.
<p>Data and its metadata used in the analysis from the publication: Pardini, E. A., Parsons, L. S., Ştefan, V., & Knight, T. M. (2018). GLMM BACI environmental impact analysis shows coastal dune restoration reduces seed predation on an endangered plant. Restoration Ecology, 26(6), 1190-1194. <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/rec.12678">https://onlinelibrary.wiley.com/doi/full/10.1111/rec.12678</a> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.