{ "schema_version": "1.0.0", "status": "reviewed-research-inputs", "official_results_released": false, "reviewed_at": "2026-10-26", "metric": { "id": "pm25", "label": "Population-weighted annual PM2.5", "unit": "micrograms/m3", "protocol_ref": "civometra.pm25.measurement@1.0.0" }, "concentration": { "year": 2024, "model": "SatPM V6.GL.03", "source_url": "https://www.satpm.org/v6-gl-03", "archive_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928", "licence": "CC BY 4.0" }, "population": { "year": 2020, "model": "GHS-POP R2023A", "source_url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "manifest_url": "/data/source-frames/ghs-pop-2020-pm25-input-manifest.json" }, "geography": { "year": 2015, "edition": "GHS-FUA R2019A V1_0", "kind": "estimated-functional-urban-area", "unique_areas": 62, "destination_bindings": 63, "original_source_archive": "/data/source-frames/ghs-fua-2015-r2019a-d54de59b82b8.zip", "adapter_archive": "/data/source-frames/ghs-efua-2015-cohort-validity-adapter-514cc4071511.gpkg" }, "methodology": { "sampling": "Nearest actual native coordinate-vector PM cell at population-cell centre; no neighbour substitution for missing values.", "calculation": "sum(known 2020 population_cell * valid nearest native 0.01-degree annual 2024 PM2.5 at that population-cell centre) / sum(covered known 2020 population_cell). The reviewed 2015 source boundaries align exactly with whole 1 km population cells; no fractional edge allocation is needed. Concentration is represented as constant within each population cell. Missing values are never zero; known zero is retained.", "edge_allocation": "Exact whole 1 km source-cell union; source boundaries/population grid alignment independently verified.", "minimum_population_grid_area_coverage": 0.99, "minimum_pm_population_coverage": 0.99 }, "audit": { "url": "/data/source-frames/pm25-2024-independent-source-audit.json", "sha256": "9cc637cf7b0ef473aa4fe5ab82a09afc6459a113133813f826cae1969ac9e8f6", "geometry_review_url": "/data/source-frames/efua2015-validity-review.json", "geometry_review_sha256": "a9488f023331885056b9a68d5a219319b94bad9291a33f5d280c5101040bc7a6", "prespecified_method_url": "/data/source-frames/pm25-prespecified-method-v0.1.md", "prespecified_method_sha256": "56a0203c398bb734c352014511a21cdc724106e11292fcb1a9142ef86f517aeb" }, "records": [ { "city_slug": "amsterdam", "city_name": "Amsterdam", "analytical_area_id": "ghs-efua-2015-r2019a:1427", "source_area_id": "1427", "source_area_name": "Amsterdam", "value": 9.676952237817188, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 1294, "population_grid_valid_cells": 1294, "population_grid_missing_cells": 0, "population_grid_zero_cells": 34, "total_known_modelled_population": 2017935.3929695138, "pm_covered_population": 2017935.3929695138, "weighted_concentration_sum": 19527464.416766845, "population_in_zero_pm_cells": 0.0 }, "denominator": 2017935.3929695138, "sensitivity": { "bilinear_estimate": 9.681977780889788, "bilinear_population_coverage": 1.0, "coarse_estimate": 9.644974946453443, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1427.npz", "sha256": "d607d817f5ff022dddbf34d959b18fb55c169f6175be4891474f926dfbfc0731" }, "geography_ref": "efua-2015-v1-0:amsterdam", "evidence_id": "pm25-2024-efua2015-amsterdam", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Amsterdam (source ID 1427). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "auckland", "city_name": "Auckland", "analytical_area_id": "ghs-efua-2015-r2019a:869", "source_area_id": "869", "source_area_name": "Auckland", "value": 5.79499486614101, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2159, "population_grid_valid_cells": 2159, "population_grid_missing_cells": 0, "population_grid_zero_cells": 304, "total_known_modelled_population": 1731774.1333824566, "pm_covered_population": 1731774.1333824566, "weighted_concentration_sum": 10035622.212267132, "population_in_zero_pm_cells": 0.0 }, "denominator": 1731774.1333824566, "sensitivity": { "bilinear_estimate": 5.790095736691827, "bilinear_population_coverage": 1.0, "coarse_estimate": 5.870153842061059, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-869.npz", "sha256": "418918b8614315f2eaefcdd7763f67a1d3f19716126ecb696b58c51eb696f338" }, "geography_ref": "efua-2015-v1-0:auckland", "evidence_id": "pm25-2024-efua2015-auckland", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Auckland (source ID 869). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "bangalore", "city_name": "Bangalore", "analytical_area_id": "ghs-efua-2015-r2019a:9099", "source_area_id": "9099", "source_area_name": "Bengaluru", "value": 34.75639316972735, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2054, "population_grid_valid_cells": 2054, "population_grid_missing_cells": 0, "population_grid_zero_cells": 5, "total_known_modelled_population": 14253018.945094977, "pm_covered_population": 14253018.945094977, "weighted_concentration_sum": 495383530.3112936, "population_in_zero_pm_cells": 0.0 }, "denominator": 14253018.945094977, "sensitivity": { "bilinear_estimate": 34.75374594215243, "bilinear_population_coverage": 1.0, "coarse_estimate": 33.91666288023483, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-9099.npz", "sha256": "493f8a5f920083635ac74822e82f7e18dac1a81ce12aa11515806be27074c183" }, "geography_ref": "efua-2015-v1-0:bangalore", "evidence_id": "pm25-2024-efua2015-bangalore", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Bengaluru (source ID 9099). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "bangkok", "city_name": "Bangkok", "analytical_area_id": "ghs-efua-2015-r2019a:1894", "source_area_id": "1894", "source_area_name": "Bangkok", "value": 24.55145547575428, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 5161, "population_grid_valid_cells": 5161, "population_grid_missing_cells": 0, "population_grid_zero_cells": 12, "total_known_modelled_population": 18601400.101104815, "pm_covered_population": 18601400.101104815, "weighted_concentration_sum": 456691446.36896604, "population_in_zero_pm_cells": 0.0 }, "denominator": 18601400.101104815, "sensitivity": { "bilinear_estimate": 24.54920631586285, "bilinear_population_coverage": 1.0, "coarse_estimate": 24.587623381700823, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1894.npz", "sha256": "de4d843bd0719e3a1e35e60bc95472de5fb5601d0c493d3d52f3afeccb30d004" }, "geography_ref": "efua-2015-v1-0:bangkok", "evidence_id": "pm25-2024-efua2015-bangkok", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Bangkok (source ID 1894). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "barcelona", "city_name": "Barcelona", "analytical_area_id": "ghs-efua-2015-r2019a:4523", "source_area_id": "4523", "source_area_name": "Barcelona", "value": 13.642424533498145, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 1967, "population_grid_valid_cells": 1967, "population_grid_missing_cells": 0, "population_grid_zero_cells": 121, "total_known_modelled_population": 5345763.368185047, "pm_covered_population": 5345763.368185047, "weighted_concentration_sum": 72929173.32440336, "population_in_zero_pm_cells": 0.0 }, "denominator": 5345763.368185047, "sensitivity": { "bilinear_estimate": 13.64201355403992, "bilinear_population_coverage": 1.0, "coarse_estimate": 12.73822056366853, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-4523.npz", "sha256": "94319e6898a260b8d07ba2c7500678d5b1efab76b46729e9106d532e3561e75e" }, "geography_ref": "efua-2015-v1-0:barcelona", "evidence_id": "pm25-2024-efua2015-barcelona", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Barcelona (source ID 4523). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "beijing", "city_name": "Beijing", "analytical_area_id": "ghs-efua-2015-r2019a:7704", "source_area_id": "7704", "source_area_name": "Beijing", "value": 43.91105217600718, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 5536, "population_grid_valid_cells": 5536, "population_grid_missing_cells": 0, "population_grid_zero_cells": 93, "total_known_modelled_population": 20738737.867322102, "pm_covered_population": 20738737.867322102, "weighted_concentration_sum": 910659800.5565168, "population_in_zero_pm_cells": 0.0 }, "denominator": 20738737.867322102, "sensitivity": { "bilinear_estimate": 43.89671229745463, "bilinear_population_coverage": 1.0, "coarse_estimate": 43.667838851310194, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-7704.npz", "sha256": "5b76f1a77bd86e6477fab4b3fa367b470b5d309f269c670e52bb41f21b3055c2" }, "geography_ref": "efua-2015-v1-0:beijing", "evidence_id": "pm25-2024-efua2015-beijing", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Beijing (source ID 7704). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "berlin", "city_name": "Berlin", "analytical_area_id": "ghs-efua-2015-r2019a:4946", "source_area_id": "4946", "source_area_name": "Berlin", "value": 10.720344182093204, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 3572, "population_grid_valid_cells": 3572, "population_grid_missing_cells": 0, "population_grid_zero_cells": 443, "total_known_modelled_population": 4558042.61540937, "pm_covered_population": 4558042.61540937, "weighted_concentration_sum": 48863785.633836724, "population_in_zero_pm_cells": 0.0 }, "denominator": 4558042.61540937, "sensitivity": { "bilinear_estimate": 10.720058378403708, "bilinear_population_coverage": 1.0, "coarse_estimate": 10.632282684048253, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-4946.npz", "sha256": "0be031e77f9912f89416f0c162e368e46330dbcbb1d4eefc017968c9e5ec878b" }, "geography_ref": "efua-2015-v1-0:berlin", "evidence_id": "pm25-2024-efua2015-berlin", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Berlin (source ID 4946). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "boston", "city_name": "Boston", "analytical_area_id": "ghs-efua-2015-r2019a:7347", "source_area_id": "7347", "source_area_name": "Boston", "value": 6.125742905814816, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2800, "population_grid_valid_cells": 2800, "population_grid_missing_cells": 0, "population_grid_zero_cells": 171, "total_known_modelled_population": 2814805.310674146, "pm_covered_population": 2814805.310674146, "weighted_concentration_sum": 17242773.66311202, "population_in_zero_pm_cells": 0.0 }, "denominator": 2814805.310674146, "sensitivity": { "bilinear_estimate": 6.120196536070688, "bilinear_population_coverage": 1.0, "coarse_estimate": 6.163159711080538, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-7347.npz", "sha256": "bf5e91e52b8b6814cba75bbbcd8b08544be11097c19c4cc47d6f9f7e06598a15" }, "geography_ref": "efua-2015-v1-0:boston", "evidence_id": "pm25-2024-efua2015-boston", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Boston (source ID 7347). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "brisbane", "city_name": "Brisbane", "analytical_area_id": "ghs-efua-2015-r2019a:2355", "source_area_id": "2355", "source_area_name": "Brisbane", "value": 5.1838401484037515, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 3144, "population_grid_valid_cells": 3144, "population_grid_missing_cells": 0, "population_grid_zero_cells": 228, "total_known_modelled_population": 2349777.369637776, "pm_covered_population": 2349777.369637776, "weighted_concentration_sum": 12180870.268538866, "population_in_zero_pm_cells": 0.0 }, "denominator": 2349777.369637776, "sensitivity": { "bilinear_estimate": 5.1824440994786105, "bilinear_population_coverage": 1.0, "coarse_estimate": 5.161206539441812, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-2355.npz", "sha256": "1b7a9e4ef6c147e04e41878399b2776b1a8eb9bd0d7b3c8b288338b0fdd6f158" }, "geography_ref": "efua-2015-v1-0:brisbane", "evidence_id": "pm25-2024-efua2015-brisbane", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Brisbane (source ID 2355). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "brussels", "city_name": "Brussels", "analytical_area_id": "ghs-efua-2015-r2019a:1269", "source_area_id": "1269", "source_area_name": "Brussels", "value": 9.480405520377232, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 1851, "population_grid_valid_cells": 1851, "population_grid_missing_cells": 0, "population_grid_zero_cells": 22, "total_known_modelled_population": 2338156.6941405023, "pm_covered_population": 2338156.6941405023, "weighted_concentration_sum": 22166673.6306366, "population_in_zero_pm_cells": 0.0 }, "denominator": 2338156.6941405023, "sensitivity": { "bilinear_estimate": 9.472366827018789, "bilinear_population_coverage": 1.0, "coarse_estimate": 9.44240782817612, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1269.npz", "sha256": "8a51c65b07280273da9522696743efa9f4b1554f8c8704d782541e7b042442ee" }, "geography_ref": "efua-2015-v1-0:brussels", "evidence_id": "pm25-2024-efua2015-brussels", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Brussels (source ID 1269). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "buenos-aires", "city_name": "Buenos Aires", "analytical_area_id": "ghs-efua-2015-r2019a:4215", "source_area_id": "4215", "source_area_name": "Buenos Aires", "value": 13.380821409601708, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 5376, "population_grid_valid_cells": 5376, "population_grid_missing_cells": 0, "population_grid_zero_cells": 667, "total_known_modelled_population": 14590525.582297105, "pm_covered_population": 14590525.582297105, "weighted_concentration_sum": 195233217.08894253, "population_in_zero_pm_cells": 0.0 }, "denominator": 14590525.582297105, "sensitivity": { "bilinear_estimate": 13.38413003638135, "bilinear_population_coverage": 1.0, "coarse_estimate": 13.343413338040113, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-4215.npz", "sha256": "5b527b5c64478f59e110a42a1b3b1fb2aa265a8caa188af6ef6726bf4548aca1" }, "geography_ref": "efua-2015-v1-0:buenos-aires", "evidence_id": "pm25-2024-efua2015-buenos-aires", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Buenos Aires (source ID 4215). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "cairo", "city_name": "Cairo", "analytical_area_id": "ghs-efua-2015-r2019a:3981", "source_area_id": "3981", "source_area_name": "Cairo", "value": 45.16148308488991, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 4348, "population_grid_valid_cells": 4348, "population_grid_missing_cells": 0, "population_grid_zero_cells": 102, "total_known_modelled_population": 27925433.20003526, "pm_covered_population": 27925433.20003526, "weighted_concentration_sum": 1261153979.1016154, "population_in_zero_pm_cells": 0.0 }, "denominator": 27925433.20003526, "sensitivity": { "bilinear_estimate": 45.17631859745068, "bilinear_population_coverage": 1.0, "coarse_estimate": 46.11869808034042, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-3981.npz", "sha256": "552b20838f0bc2c692c82f3546cfbd761412fa3d70aecd0e5760f3039c30f2e0" }, "geography_ref": "efua-2015-v1-0:cairo", "evidence_id": "pm25-2024-efua2015-cairo", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Cairo (source ID 3981). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "calgary", "city_name": "Calgary", "analytical_area_id": "ghs-efua-2015-r2019a:1276", "source_area_id": "1276", "source_area_name": "Calgary", "value": 9.126862318413096, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2051, "population_grid_valid_cells": 2051, "population_grid_missing_cells": 0, "population_grid_zero_cells": 346, "total_known_modelled_population": 1598499.7845190063, "pm_covered_population": 1598499.7845190063, "weighted_concentration_sum": 14589287.449317973, "population_in_zero_pm_cells": 0.0 }, "denominator": 1598499.7845190063, "sensitivity": { "bilinear_estimate": 9.127534347445783, "bilinear_population_coverage": 1.0, "coarse_estimate": 8.857442978924364, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1276.npz", "sha256": "f921d111a5498d23f11859c7cbc0527ccd56c1b20ab628a82b630aa11da5537c" }, "geography_ref": "efua-2015-v1-0:calgary", "evidence_id": "pm25-2024-efua2015-calgary", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Calgary (source ID 1276). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "cape-town", "city_name": "Cape Town", "analytical_area_id": "ghs-efua-2015-r2019a:189", "source_area_id": "189", "source_area_name": "Cape Town", "value": 9.04588425695424, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 1729, "population_grid_valid_cells": 1729, "population_grid_missing_cells": 0, "population_grid_zero_cells": 274, "total_known_modelled_population": 4383042.259664604, "pm_covered_population": 4383042.259664604, "weighted_concentration_sum": 39648492.97426518, "population_in_zero_pm_cells": 0.0 }, "denominator": 4383042.259664604, "sensitivity": { "bilinear_estimate": 9.03579545309636, "bilinear_population_coverage": 1.0, "coarse_estimate": 8.948392764118884, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-189.npz", "sha256": "96ce7ee57be72f2f85c875bd30f978dd60d590eea6a6063281e05162a562539e" }, "geography_ref": "efua-2015-v1-0:cape-town", "evidence_id": "pm25-2024-efua2015-cape-town", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Cape Town (source ID 189). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "chicago", "city_name": "Chicago", "analytical_area_id": "ghs-efua-2015-r2019a:6654", "source_area_id": "6654", "source_area_name": "Chicago", "value": 7.983246186547001, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 13185, "population_grid_valid_cells": 13185, "population_grid_missing_cells": 0, "population_grid_zero_cells": 1711, "total_known_modelled_population": 9031457.985048497, "pm_covered_population": 9031457.985048497, "weighted_concentration_sum": 72100352.51809788, "population_in_zero_pm_cells": 0.0 }, "denominator": 9031457.985048497, "sensitivity": { "bilinear_estimate": 7.987727653074161, "bilinear_population_coverage": 1.0, "coarse_estimate": 7.962949167638284, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-6654.npz", "sha256": "6f1856eb0ff07b2e0d7deb4e14aa6fbdc50bec515ebe289a6b5dfcc66f9c902c" }, "geography_ref": "efua-2015-v1-0:chicago", "evidence_id": "pm25-2024-efua2015-chicago", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Chicago (source ID 6654). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "copenhagen", "city_name": "Copenhagen", "analytical_area_id": "ghs-efua-2015-r2019a:537", "source_area_id": "537", "source_area_name": "Copenhagen", "value": 8.984765232455313, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 3406, "population_grid_valid_cells": 3406, "population_grid_missing_cells": 0, "population_grid_zero_cells": 297, "total_known_modelled_population": 2088197.4410470906, "pm_covered_population": 2088197.4410470906, "weighted_concentration_sum": 18761963.76682205, "population_in_zero_pm_cells": 0.0 }, "denominator": 2088197.4410470906, "sensitivity": { "bilinear_estimate": 8.98117296882201, "bilinear_population_coverage": 1.0, "coarse_estimate": 8.856231979409888, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-537.npz", "sha256": "97bf783db4540310a6f68c71e2485e212431cf0f1665ddb9e12cfe508d086878" }, "geography_ref": "efua-2015-v1-0:copenhagen", "evidence_id": "pm25-2024-efua2015-copenhagen", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Copenhagen (source ID 537). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "delhi", "city_name": "Delhi", "analytical_area_id": "ghs-efua-2015-r2019a:7466", "source_area_id": "7466", "source_area_name": "Delhi [New Delhi]", "value": 88.43376029895165, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 5569, "population_grid_valid_cells": 5569, "population_grid_missing_cells": 0, "population_grid_zero_cells": 183, "total_known_modelled_population": 33495554.00720462, "pm_covered_population": 33495554.00720462, "weighted_concentration_sum": 2962137794.153723, "population_in_zero_pm_cells": 0.0 }, "denominator": 33495554.00720462, "sensitivity": { "bilinear_estimate": 88.42759472684423, "bilinear_population_coverage": 1.0, "coarse_estimate": 86.30222576954252, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-7466.npz", "sha256": "d48ceb97c74bf219ef10f583104f71ebe9fb32848073eff8a1429f770dbbba79" }, "geography_ref": "efua-2015-v1-0:delhi", "evidence_id": "pm25-2024-efua2015-delhi", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Delhi [New Delhi] (source ID 7466). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "dubai", "city_name": "Dubai", "analytical_area_id": "ghs-efua-2015-r2019a:357", "source_area_id": "357", "source_area_name": "Dubai", "value": 42.445560193019865, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 0.9967845659163987, "pm_population_coverage": 1.0, "cell_count": 2177, "population_grid_valid_cells": 2170, "population_grid_missing_cells": 7, "population_grid_zero_cells": 204, "total_known_modelled_population": 4912017.148067977, "pm_covered_population": 4912017.148067977, "weighted_concentration_sum": 208493319.5274651, "population_in_zero_pm_cells": 0.0 }, "denominator": 4912017.148067977, "sensitivity": { "bilinear_estimate": 42.42602743631158, "bilinear_population_coverage": 1.0, "coarse_estimate": 39.72857799606897, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-357.npz", "sha256": "12bce80e04d0eb77ebc0e362030651e4a1037da2c87fff653dfb8f29ebc440ab" }, "geography_ref": "efua-2015-v1-0:dubai", "evidence_id": "pm25-2024-efua2015-dubai", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Dubai (source ID 357). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "dublin", "city_name": "Dublin", "analytical_area_id": "ghs-efua-2015-r2019a:415", "source_area_id": "415", "source_area_name": "Dublin", "value": 7.6304374085174285, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2245, "population_grid_valid_cells": 2245, "population_grid_missing_cells": 0, "population_grid_zero_cells": 100, "total_known_modelled_population": 1721811.743779016, "pm_covered_population": 1721811.743779016, "weighted_concentration_sum": 13138176.740156028, "population_in_zero_pm_cells": 0.0 }, "denominator": 1721811.743779016, "sensitivity": { "bilinear_estimate": 7.634034645446627, "bilinear_population_coverage": 1.0, "coarse_estimate": 7.558294053630696, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-415.npz", "sha256": "90bbaba6ea7846f7e7019445a0be955bb2543a2b92fc337d30966d87bd51ba1f" }, "geography_ref": "efua-2015-v1-0:dublin", "evidence_id": "pm25-2024-efua2015-dublin", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Dublin (source ID 415). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "edinburgh", "city_name": "Edinburgh", "analytical_area_id": "ghs-efua-2015-r2019a:2305", "source_area_id": "2305", "source_area_name": "Edinburgh", "value": 7.919955077175897, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 1448, "population_grid_valid_cells": 1448, "population_grid_missing_cells": 0, "population_grid_zero_cells": 337, "total_known_modelled_population": 713622.6716791101, "pm_covered_population": 713622.6716791101, "weighted_concentration_sum": 5651859.501752797, "population_in_zero_pm_cells": 0.0 }, "denominator": 713622.6716791101, "sensitivity": { "bilinear_estimate": 7.922253703123669, "bilinear_population_coverage": 1.0, "coarse_estimate": 7.872561609903901, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-2305.npz", "sha256": "f20969bfc280462db821ef7a9b17d686c3352e6a7ac531d5e6a3c274026ac78e" }, "geography_ref": "efua-2015-v1-0:edinburgh", "evidence_id": "pm25-2024-efua2015-edinburgh", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Edinburgh (source ID 2305). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "helsinki", "city_name": "Helsinki", "analytical_area_id": "ghs-efua-2015-r2019a:687", "source_area_id": "687", "source_area_name": "Helsinki", "value": 6.888214780326909, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 3066, "population_grid_valid_cells": 3066, "population_grid_missing_cells": 0, "population_grid_zero_cells": 229, "total_known_modelled_population": 1439175.176609227, "pm_covered_population": 1439175.176609227, "weighted_concentration_sum": 9913347.722999267, "population_in_zero_pm_cells": 0.0 }, "denominator": 1439175.176609227, "sensitivity": { "bilinear_estimate": 6.876789790719536, "bilinear_population_coverage": 1.0, "coarse_estimate": 6.609792636559865, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-687.npz", "sha256": "a273d4e01ed1a501e800e02f25cdb27596e0fc5f564cf4d96d25c3d8f18bbe13" }, "geography_ref": "efua-2015-v1-0:helsinki", "evidence_id": "pm25-2024-efua2015-helsinki", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Helsinki (source ID 687). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "ho-chi-minh-city", "city_name": "Ho Chi Minh City", "analytical_area_id": "ghs-efua-2015-r2019a:5038", "source_area_id": "5038", "source_area_name": "Ho Chi Minh City", "value": 30.141808302504714, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 3036, "population_grid_valid_cells": 3036, "population_grid_missing_cells": 0, "population_grid_zero_cells": 20, "total_known_modelled_population": 14247592.631594708, "pm_covered_population": 14247592.631594708, "weighted_concentration_sum": 429448205.87370634, "population_in_zero_pm_cells": 0.0 }, "denominator": 14247592.631594708, "sensitivity": { "bilinear_estimate": 30.13822941936339, "bilinear_population_coverage": 1.0, "coarse_estimate": 29.327814030157946, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-5038.npz", "sha256": "cc23dd5089a4f04f51fd9c84e96929a183aa76b387a46cbfa1015997ab1a488b" }, "geography_ref": "efua-2015-v1-0:ho-chi-minh-city", "evidence_id": "pm25-2024-efua2015-ho-chi-minh-city", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Ho Chi Minh City (source ID 5038). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "hong-kong", "city_name": "Hong Kong", "analytical_area_id": "ghs-efua-2015-r2019a:73", "source_area_id": "73", "source_area_name": "Hong Kong", "value": 15.886071114039074, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 684, "population_grid_valid_cells": 684, "population_grid_missing_cells": 0, "population_grid_zero_cells": 124, "total_known_modelled_population": 6026319.458987157, "pm_covered_population": 6026319.458987157, "weighted_concentration_sum": 95734539.48138745, "population_in_zero_pm_cells": 0.0 }, "denominator": 6026319.458987157, "sensitivity": { "bilinear_estimate": 15.911590040257977, "bilinear_population_coverage": 1.0, "coarse_estimate": 15.723462412225757, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-73.npz", "sha256": "1e76d8ec5387b28e34dedbf53763090a1fc40659a885de3f10ac7aaa25e38612" }, "geography_ref": "efua-2015-v1-0:hong-kong", "evidence_id": "pm25-2024-efua2015-hong-kong", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Hong Kong (source ID 73). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "istanbul", "city_name": "Istanbul", "analytical_area_id": "ghs-efua-2015-r2019a:1637", "source_area_id": "1637", "source_area_name": "Istanbul", "value": 20.15829295654356, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2540, "population_grid_valid_cells": 2540, "population_grid_missing_cells": 0, "population_grid_zero_cells": 300, "total_known_modelled_population": 14693269.211055055, "pm_covered_population": 14693269.211055055, "weighted_concentration_sum": 296191225.24580944, "population_in_zero_pm_cells": 0.0 }, "denominator": 14693269.211055055, "sensitivity": { "bilinear_estimate": 20.160790277549236, "bilinear_population_coverage": 1.0, "coarse_estimate": 19.431110796568728, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1637.npz", "sha256": "198d7c12d5ef1b241a459a2d5573ecac0abd36a95a69fcd4c1ebeb39aff3c343" }, "geography_ref": "efua-2015-v1-0:istanbul", "evidence_id": "pm25-2024-efua2015-istanbul", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Istanbul (source ID 1637). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "jakarta", "city_name": "Jakarta", "analytical_area_id": "ghs-efua-2015-r2019a:4897", "source_area_id": "4897", "source_area_name": "Jakarta", "value": 38.104383459833386, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 5292, "population_grid_valid_cells": 5292, "population_grid_missing_cells": 0, "population_grid_zero_cells": 313, "total_known_modelled_population": 32513588.33302874, "pm_covered_population": 32513588.33302874, "weighted_concentration_sum": 1238910237.4968922, "population_in_zero_pm_cells": 0.0 }, "denominator": 32513588.33302874, "sensitivity": { "bilinear_estimate": 38.1126559787534, "bilinear_population_coverage": 1.0, "coarse_estimate": 37.65414769208807, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-4897.npz", "sha256": "8bb5520912b01730561376879346815bd2f36002b44cf36b8cf7fffd3953757a" }, "geography_ref": "efua-2015-v1-0:jakarta", "evidence_id": "pm25-2024-efua2015-jakarta", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Jakarta (source ID 4897). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "kuala-lumpur", "city_name": "Kuala Lumpur", "analytical_area_id": "ghs-efua-2015-r2019a:1322", "source_area_id": "1322", "source_area_name": "Kuala Lumpur", "value": 23.23896414254003, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 3314, "population_grid_valid_cells": 3314, "population_grid_missing_cells": 0, "population_grid_zero_cells": 227, "total_known_modelled_population": 9038690.66690238, "pm_covered_population": 9038690.66690238, "weighted_concentration_sum": 210049808.30365565, "population_in_zero_pm_cells": 0.0 }, "denominator": 9038690.66690238, "sensitivity": { "bilinear_estimate": 23.23218228449294, "bilinear_population_coverage": 1.0, "coarse_estimate": 22.583676343481407, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1322.npz", "sha256": "837fa5519cec4aeba06875503bba4e3ec2a3fce97f1a496e290707bc208ab56b" }, "geography_ref": "efua-2015-v1-0:kuala-lumpur", "evidence_id": "pm25-2024-efua2015-kuala-lumpur", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Kuala Lumpur (source ID 1322). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "kyoto", "city_name": "Kyoto", "analytical_area_id": "ghs-efua-2015-r2019a:3956", "source_area_id": "3956", "source_area_name": "Osaka [Kyoto]", "value": 12.145098721578263, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [ "osaka" ], "coverage": { "population_grid_area_coverage": 0.9994053518334985, "pm_population_coverage": 1.0, "cell_count": 10090, "population_grid_valid_cells": 10084, "population_grid_missing_cells": 6, "population_grid_zero_cells": 1186, "total_known_modelled_population": 16866788.447898425, "pm_covered_population": 16866788.447898425, "weighted_concentration_sum": 204848810.81570217, "population_in_zero_pm_cells": 0.0 }, "denominator": 16866788.447898425, "sensitivity": { "bilinear_estimate": 12.14252967315906, "bilinear_population_coverage": 1.0, "coarse_estimate": 12.133673607837919, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-3956.npz", "sha256": "3c9d076c395a68f6d23f164071a1da8ea2daf97344d2f9fc4f8735e531b8e3d5" }, "geography_ref": "efua-2015-v1-0:kyoto", "evidence_id": "pm25-2024-efua2015-kyoto", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Osaka [Kyoto] (source ID 3956). Historical modelled geography; source country labels and area identities are retained. This physical area is shared with osaka; destination bindings are not independent measurements.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "lima", "city_name": "Lima", "analytical_area_id": "ghs-efua-2015-r2019a:2327", "source_area_id": "2327", "source_area_name": "Lima", "value": 26.78811071950509, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2121, "population_grid_valid_cells": 2121, "population_grid_missing_cells": 0, "population_grid_zero_cells": 602, "total_known_modelled_population": 10496388.841424905, "pm_covered_population": 10496388.841424905, "weighted_concentration_sum": 281178426.43906814, "population_in_zero_pm_cells": 0.0 }, "denominator": 10496388.841424905, "sensitivity": { "bilinear_estimate": 26.791152757841335, "bilinear_population_coverage": 1.0, "coarse_estimate": 26.19982454577103, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-2327.npz", "sha256": "611a87386ef5750e8a8a51e1741cc25f220414ad68186b9364621700e2c991ac" }, "geography_ref": "efua-2015-v1-0:lima", "evidence_id": "pm25-2024-efua2015-lima", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Lima (source ID 2327). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "lisbon", "city_name": "Lisbon", "analytical_area_id": "ghs-efua-2015-r2019a:310", "source_area_id": "310", "source_area_name": "Lisbon", "value": 9.902778817936438, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 1575, "population_grid_valid_cells": 1575, "population_grid_missing_cells": 0, "population_grid_zero_cells": 94, "total_known_modelled_population": 2731340.2467697114, "pm_covered_population": 2731340.2467697114, "weighted_concentration_sum": 27047858.340288382, "population_in_zero_pm_cells": 0.0 }, "denominator": 2731340.2467697114, "sensitivity": { "bilinear_estimate": 9.889288001519388, "bilinear_population_coverage": 1.0, "coarse_estimate": 9.799733409582446, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-310.npz", "sha256": "58c267de332dab81032d97465df570d87e6386d97aec8250b75e7221a7199329" }, "geography_ref": "efua-2015-v1-0:lisbon", "evidence_id": "pm25-2024-efua2015-lisbon", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Lisbon (source ID 310). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "london", "city_name": "London", "analytical_area_id": "ghs-efua-2015-r2019a:5288", "source_area_id": "5288", "source_area_name": "London", "value": 8.606392745028117, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 6605, "population_grid_valid_cells": 6605, "population_grid_missing_cells": 0, "population_grid_zero_cells": 91, "total_known_modelled_population": 13475296.615137912, "pm_covered_population": 13475296.615137912, "weighted_concentration_sum": 115973695.02562487, "population_in_zero_pm_cells": 0.0 }, "denominator": 13475296.615137912, "sensitivity": { "bilinear_estimate": 8.616832919809285, "bilinear_population_coverage": 1.0, "coarse_estimate": 8.619366838823789, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-5288.npz", "sha256": "66493d27972625c16941025797af1116da9189ca46824c9d3bf4da15855062bf" }, "geography_ref": "efua-2015-v1-0:london", "evidence_id": "pm25-2024-efua2015-london", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area London (source ID 5288). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "los-angeles", "city_name": "Los Angeles", "analytical_area_id": "ghs-efua-2015-r2019a:1642", "source_area_id": "1642", "source_area_name": "Los Angeles", "value": 12.523773681869056, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 10407, "population_grid_valid_cells": 10407, "population_grid_missing_cells": 0, "population_grid_zero_cells": 1881, "total_known_modelled_population": 16206528.530156042, "pm_covered_population": 16206528.530156042, "weighted_concentration_sum": 202966895.48042825, "population_in_zero_pm_cells": 0.0 }, "denominator": 16206528.530156042, "sensitivity": { "bilinear_estimate": 12.529604693343646, "bilinear_population_coverage": 1.0, "coarse_estimate": 12.38746809274214, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1642.npz", "sha256": "f51a43aa7366b0fdb3757f9f125dfbfd2c301721efb85bd79f37ffce72449e5c" }, "geography_ref": "efua-2015-v1-0:los-angeles", "evidence_id": "pm25-2024-efua2015-los-angeles", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Los Angeles (source ID 1642). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "madrid", "city_name": "Madrid", "analytical_area_id": "ghs-efua-2015-r2019a:3147", "source_area_id": "3147", "source_area_name": "Madrid", "value": 9.85078713870339, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 4270, "population_grid_valid_cells": 4270, "population_grid_missing_cells": 0, "population_grid_zero_cells": 465, "total_known_modelled_population": 6989714.09332524, "pm_covered_population": 6989714.09332524, "weighted_concentration_sum": 68854185.6937421, "population_in_zero_pm_cells": 0.0 }, "denominator": 6989714.09332524, "sensitivity": { "bilinear_estimate": 9.833481465385791, "bilinear_population_coverage": 1.0, "coarse_estimate": 9.735770394272775, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-3147.npz", "sha256": "c7497cd37d3af22deb5c57f194b999b51d4bd012ac1fbe0b5a62158b56f440e7" }, "geography_ref": "efua-2015-v1-0:madrid", "evidence_id": "pm25-2024-efua2015-madrid", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Madrid (source ID 3147). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "manila", "city_name": "Manila", "analytical_area_id": "ghs-efua-2015-r2019a:2845", "source_area_id": "2845", "source_area_name": "Quezon City [Manila]", "value": 25.413557966101862, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 4873, "population_grid_valid_cells": 4873, "population_grid_missing_cells": 0, "population_grid_zero_cells": 416, "total_known_modelled_population": 27327889.076457687, "pm_covered_population": 27327889.076457687, "weighted_concentration_sum": 694498893.1357594, "population_in_zero_pm_cells": 0.0 }, "denominator": 27327889.076457687, "sensitivity": { "bilinear_estimate": 25.411532262023176, "bilinear_population_coverage": 1.0, "coarse_estimate": 24.16191818368233, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-2845.npz", "sha256": "f08f3da2ef7de5e0624166f24bc0e47db6f8f519ef0ec1e3f41ae666f7b54193" }, "geography_ref": "efua-2015-v1-0:manila", "evidence_id": "pm25-2024-efua2015-manila", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Quezon City [Manila] (source ID 2845). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "melbourne", "city_name": "Melbourne", "analytical_area_id": "ghs-efua-2015-r2019a:1266", "source_area_id": "1266", "source_area_name": "Melbourne", "value": 5.63064447742122, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 8348, "population_grid_valid_cells": 8348, "population_grid_missing_cells": 0, "population_grid_zero_cells": 1073, "total_known_modelled_population": 5177020.587336659, "pm_covered_population": 5177020.587336659, "weighted_concentration_sum": 29149962.379583124, "population_in_zero_pm_cells": 0.0 }, "denominator": 5177020.587336659, "sensitivity": { "bilinear_estimate": 5.625979432127128, "bilinear_population_coverage": 1.0, "coarse_estimate": 5.723289441104155, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1266.npz", "sha256": "4adda9b672c602a4abe660dbe2712777119636d080659b7cbc781e40ebdf1c2c" }, "geography_ref": "efua-2015-v1-0:melbourne", "evidence_id": "pm25-2024-efua2015-melbourne", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Melbourne (source ID 1266). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "montreal", "city_name": "Montreal", "analytical_area_id": "ghs-efua-2015-r2019a:3485", "source_area_id": "3485", "source_area_name": "Montreal", "value": 8.236004807242018, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 5027, "population_grid_valid_cells": 5027, "population_grid_missing_cells": 0, "population_grid_zero_cells": 740, "total_known_modelled_population": 4184064.7379515534, "pm_covered_population": 4184064.7379515534, "weighted_concentration_sum": 34459977.29558081, "population_in_zero_pm_cells": 0.0 }, "denominator": 4184064.7379515534, "sensitivity": { "bilinear_estimate": 8.231804578582484, "bilinear_population_coverage": 1.0, "coarse_estimate": 8.085414587564337, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-3485.npz", "sha256": "0723d4a7a188777ff3399b26c6c37876297ebff0ac3ce597c5532355472d684a" }, "geography_ref": "efua-2015-v1-0:montreal", "evidence_id": "pm25-2024-efua2015-montreal", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Montreal (source ID 3485). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "mumbai", "city_name": "Mumbai", "analytical_area_id": "ghs-efua-2015-r2019a:7163", "source_area_id": "7163", "source_area_name": "Mumbai", "value": 39.02338972609926, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2367, "population_grid_valid_cells": 2367, "population_grid_missing_cells": 0, "population_grid_zero_cells": 348, "total_known_modelled_population": 23435141.114052795, "pm_covered_population": 23435141.114052795, "weighted_concentration_sum": 914518644.9798143, "population_in_zero_pm_cells": 0.0 }, "denominator": 23435141.114052795, "sensitivity": { "bilinear_estimate": 39.01991423079028, "bilinear_population_coverage": 1.0, "coarse_estimate": 38.42426289393302, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-7163.npz", "sha256": "c2f2c9e7a040506801e270064247c044cb971e7e6e57f4a14fe901fc96900ab4" }, "geography_ref": "efua-2015-v1-0:mumbai", "evidence_id": "pm25-2024-efua2015-mumbai", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Mumbai (source ID 7163). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "munich", "city_name": "Munich", "analytical_area_id": "ghs-efua-2015-r2019a:4736", "source_area_id": "4736", "source_area_name": "Munich", "value": 9.34161689853942, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2638, "population_grid_valid_cells": 2638, "population_grid_missing_cells": 0, "population_grid_zero_cells": 299, "total_known_modelled_population": 2618481.6694096336, "pm_covered_population": 2618481.6694096336, "weighted_concentration_sum": 24460852.61147275, "population_in_zero_pm_cells": 0.0 }, "denominator": 2618481.6694096336, "sensitivity": { "bilinear_estimate": 9.330219401543223, "bilinear_population_coverage": 1.0, "coarse_estimate": 9.220838405687873, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-4736.npz", "sha256": "f88f99279225ef35a1f986360fe4c7d336a448b2859347063a07689024ca38ba" }, "geography_ref": "efua-2015-v1-0:munich", "evidence_id": "pm25-2024-efua2015-munich", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Munich (source ID 4736). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "new-york-city", "city_name": "New York City", "analytical_area_id": "ghs-efua-2015-r2019a:7244", "source_area_id": "7244", "source_area_name": "New York", "value": 7.283915422287797, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 0.9998856424037966, "pm_population_coverage": 1.0, "cell_count": 17489, "population_grid_valid_cells": 17487, "population_grid_missing_cells": 2, "population_grid_zero_cells": 1788, "total_known_modelled_population": 20106617.48806346, "pm_covered_population": 20106617.48806346, "weighted_concentration_sum": 146454901.21134695, "population_in_zero_pm_cells": 0.0 }, "denominator": 20106617.48806346, "sensitivity": { "bilinear_estimate": 7.289459431957163, "bilinear_population_coverage": 1.0, "coarse_estimate": 7.405866663426639, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-7244.npz", "sha256": "20c517bf1a02cde82fae6983b27f0ec2ab3eee1485bba06fb4f2dd79b649e32f" }, "geography_ref": "efua-2015-v1-0:new-york-city", "evidence_id": "pm25-2024-efua2015-new-york-city", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area New York (source ID 7244). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "osaka", "city_name": "Osaka", "analytical_area_id": "ghs-efua-2015-r2019a:3956", "source_area_id": "3956", "source_area_name": "Osaka [Kyoto]", "value": 12.145098721578263, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [ "kyoto" ], "coverage": { "population_grid_area_coverage": 0.9994053518334985, "pm_population_coverage": 1.0, "cell_count": 10090, "population_grid_valid_cells": 10084, "population_grid_missing_cells": 6, "population_grid_zero_cells": 1186, "total_known_modelled_population": 16866788.447898425, "pm_covered_population": 16866788.447898425, "weighted_concentration_sum": 204848810.81570217, "population_in_zero_pm_cells": 0.0 }, "denominator": 16866788.447898425, "sensitivity": { "bilinear_estimate": 12.14252967315906, "bilinear_population_coverage": 1.0, "coarse_estimate": 12.133673607837919, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-3956.npz", "sha256": "3c9d076c395a68f6d23f164071a1da8ea2daf97344d2f9fc4f8735e531b8e3d5" }, "geography_ref": "efua-2015-v1-0:osaka", "evidence_id": "pm25-2024-efua2015-osaka", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Osaka [Kyoto] (source ID 3956). Historical modelled geography; source country labels and area identities are retained. This physical area is shared with kyoto; destination bindings are not independent measurements.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "oslo", "city_name": "Oslo", "analytical_area_id": "ghs-efua-2015-r2019a:596", "source_area_id": "596", "source_area_name": "Oslo", "value": 7.4384399251166275, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 4272, "population_grid_valid_cells": 4272, "population_grid_missing_cells": 0, "population_grid_zero_cells": 1190, "total_known_modelled_population": 1422223.145553412, "pm_covered_population": 1422223.145553412, "weighted_concentration_sum": 10579121.428309456, "population_in_zero_pm_cells": 0.0 }, "denominator": 1422223.145553412, "sensitivity": { "bilinear_estimate": 7.431207892044955, "bilinear_population_coverage": 1.0, "coarse_estimate": 7.271696943234316, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-596.npz", "sha256": "eeb273ae82dd8c726c5bcbe7f2e31165e5bb5f3e19aa1e4e1213c01350f90eca" }, "geography_ref": "efua-2015-v1-0:oslo", "evidence_id": "pm25-2024-efua2015-oslo", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Oslo (source ID 596). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "paris", "city_name": "Paris", "analytical_area_id": "ghs-efua-2015-r2019a:2702", "source_area_id": "2702", "source_area_name": "Paris", "value": 9.883460451268263, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 6778, "population_grid_valid_cells": 6778, "population_grid_missing_cells": 0, "population_grid_zero_cells": 794, "total_known_modelled_population": 11249024.858068911, "pm_covered_population": 11249024.858068911, "weighted_concentration_sum": 111179292.30005768, "population_in_zero_pm_cells": 0.0 }, "denominator": 11249024.858068911, "sensitivity": { "bilinear_estimate": 9.87735822098292, "bilinear_population_coverage": 1.0, "coarse_estimate": 9.786180418796457, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-2702.npz", "sha256": "4caff1fbd04395b6d21a5c2bef5a1f0b02339dd3f3667088919f6e01d2460d92" }, "geography_ref": "efua-2015-v1-0:paris", "evidence_id": "pm25-2024-efua2015-paris", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Paris (source ID 2702). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "perth", "city_name": "Perth", "analytical_area_id": "ghs-efua-2015-r2019a:360", "source_area_id": "360", "source_area_name": "Perth", "value": 6.179239933712975, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2972, "population_grid_valid_cells": 2972, "population_grid_missing_cells": 0, "population_grid_zero_cells": 401, "total_known_modelled_population": 1772272.0295397, "pm_covered_population": 1772272.0295397, "weighted_concentration_sum": 10951294.098334255, "population_in_zero_pm_cells": 0.0 }, "denominator": 1772272.0295397, "sensitivity": { "bilinear_estimate": 6.182321265809422, "bilinear_population_coverage": 1.0, "coarse_estimate": 6.1981710538372665, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-360.npz", "sha256": "76c337f5f96c63984f7230c9e0547a143168c963e8f82fc0a6e321c469c65d24" }, "geography_ref": "efua-2015-v1-0:perth", "evidence_id": "pm25-2024-efua2015-perth", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Perth (source ID 360). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "prague", "city_name": "Prague", "analytical_area_id": "ghs-efua-2015-r2019a:535", "source_area_id": "535", "source_area_name": "Prague", "value": 12.825750226807447, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2897, "population_grid_valid_cells": 2897, "population_grid_missing_cells": 0, "population_grid_zero_cells": 268, "total_known_modelled_population": 1977776.2386603754, "pm_covered_population": 1977776.2386603754, "weighted_concentration_sum": 25366464.04157269, "population_in_zero_pm_cells": 0.0 }, "denominator": 1977776.2386603754, "sensitivity": { "bilinear_estimate": 12.828233770525284, "bilinear_population_coverage": 1.0, "coarse_estimate": 12.675873186720473, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-535.npz", "sha256": "0b32c56a2ba883461ab11238ff1a92b306280a37e721546f2fb219587270b91c" }, "geography_ref": "efua-2015-v1-0:prague", "evidence_id": "pm25-2024-efua2015-prague", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Prague (source ID 535). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "rio-de-janeiro", "city_name": "Rio de Janeiro", "analytical_area_id": "ghs-efua-2015-r2019a:6961", "source_area_id": "6961", "source_area_name": "Rio de Janeiro", "value": 16.954066127858244, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 3598, "population_grid_valid_cells": 3598, "population_grid_missing_cells": 0, "population_grid_zero_cells": 601, "total_known_modelled_population": 11068998.79218813, "pm_covered_population": 11068998.79218813, "weighted_concentration_sum": 187664537.4919406, "population_in_zero_pm_cells": 0.0 }, "denominator": 11068998.79218813, "sensitivity": { "bilinear_estimate": 16.950543474577323, "bilinear_population_coverage": 1.0, "coarse_estimate": 16.83281927863571, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-6961.npz", "sha256": "7d20d1577d726e840170a3079cac164e688e185d05288dbeb13af872e1461f1b" }, "geography_ref": "efua-2015-v1-0:rio-de-janeiro", "evidence_id": "pm25-2024-efua2015-rio-de-janeiro", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Rio de Janeiro (source ID 6961). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "rome", "city_name": "Rome", "analytical_area_id": "ghs-efua-2015-r2019a:3881", "source_area_id": "3881", "source_area_name": "Rome", "value": 13.395308567112478, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2696, "population_grid_valid_cells": 2696, "population_grid_missing_cells": 0, "population_grid_zero_cells": 121, "total_known_modelled_population": 3684930.385352138, "pm_covered_population": 3684930.385352138, "weighted_concentration_sum": 49360779.560120575, "population_in_zero_pm_cells": 0.0 }, "denominator": 3684930.385352138, "sensitivity": { "bilinear_estimate": 13.385445406329081, "bilinear_population_coverage": 1.0, "coarse_estimate": 13.055083657543886, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-3881.npz", "sha256": "f685b09e8bc7ed184e9ba3a7a007af37ae46d7a203f1ac5d63188e5eaa1829d5" }, "geography_ref": "efua-2015-v1-0:rome", "evidence_id": "pm25-2024-efua2015-rome", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Rome (source ID 3881). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "san-francisco", "city_name": "San Francisco", "analytical_area_id": "ghs-efua-2015-r2019a:1255", "source_area_id": "1255", "source_area_name": "San Jose", "value": 6.9864378470802, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 4038, "population_grid_valid_cells": 4038, "population_grid_missing_cells": 0, "population_grid_zero_cells": 886, "total_known_modelled_population": 5259987.4063209435, "pm_covered_population": 5259987.4063209435, "weighted_concentration_sum": 36748575.09068586, "population_in_zero_pm_cells": 0.0 }, "denominator": 5259987.4063209435, "sensitivity": { "bilinear_estimate": 6.992108691879406, "bilinear_population_coverage": 1.0, "coarse_estimate": 7.106184380456396, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1255.npz", "sha256": "e9fbc0e3900dbd9a944a1a781f831cb4dee44ee97d0e7753355822ae1f03feac" }, "geography_ref": "efua-2015-v1-0:san-francisco", "evidence_id": "pm25-2024-efua2015-san-francisco", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area San Jose (source ID 1255). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "santiago", "city_name": "Santiago", "analytical_area_id": "ghs-efua-2015-r2019a:2072", "source_area_id": "2072", "source_area_name": "Santiago", "value": 24.37080176119189, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2923, "population_grid_valid_cells": 2923, "population_grid_missing_cells": 0, "population_grid_zero_cells": 553, "total_known_modelled_population": 7394880.052780694, "pm_covered_population": 7394880.052780694, "weighted_concentration_sum": 180219155.81411052, "population_in_zero_pm_cells": 0.0 }, "denominator": 7394880.052780694, "sensitivity": { "bilinear_estimate": 24.353756286696356, "bilinear_population_coverage": 1.0, "coarse_estimate": 23.234180177212103, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-2072.npz", "sha256": "fdfef808feefe93e6a0f8d5e0092bf48c24d6b1b1453cf779f4a34c6f8644e86" }, "geography_ref": "efua-2015-v1-0:santiago", "evidence_id": "pm25-2024-efua2015-santiago", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Santiago (source ID 2072). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "sao-paulo", "city_name": "São Paulo", "analytical_area_id": "ghs-efua-2015-r2019a:6567", "source_area_id": "6567", "source_area_name": "São Paulo", "value": 18.345879486665147, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 6077, "population_grid_valid_cells": 6077, "population_grid_missing_cells": 0, "population_grid_zero_cells": 378, "total_known_modelled_population": 21671856.66410414, "pm_covered_population": 21671856.66410414, "weighted_concentration_sum": 397589270.61193556, "population_in_zero_pm_cells": 0.0 }, "denominator": 21671856.66410414, "sensitivity": { "bilinear_estimate": 18.34478470331252, "bilinear_population_coverage": 1.0, "coarse_estimate": 18.35256109564962, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-6567.npz", "sha256": "8adaee75bf57b0334cb4e6374be59fabadeec3752d599be66f02906df8234e2d" }, "geography_ref": "efua-2015-v1-0:sao-paulo", "evidence_id": "pm25-2024-efua2015-sao-paulo", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area São Paulo (source ID 6567). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "seattle", "city_name": "Seattle", "analytical_area_id": "ghs-efua-2015-r2019a:4510", "source_area_id": "4510", "source_area_name": "Seattle", "value": 4.916366356001811, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 8328, "population_grid_valid_cells": 8328, "population_grid_missing_cells": 0, "population_grid_zero_cells": 1183, "total_known_modelled_population": 4209772.909507131, "pm_covered_population": 4209772.909507131, "weighted_concentration_sum": 20696785.898708716, "population_in_zero_pm_cells": 0.0 }, "denominator": 4209772.909507131, "sensitivity": { "bilinear_estimate": 4.914310171073327, "bilinear_population_coverage": 1.0, "coarse_estimate": 4.94325083883346, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-4510.npz", "sha256": "97ad0dd9a87f8df64a85cea9adc323f29cd1ba5bfbae22b10e8227e8b144faa4" }, "geography_ref": "efua-2015-v1-0:seattle", "evidence_id": "pm25-2024-efua2015-seattle", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Seattle (source ID 4510). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "seoul", "city_name": "Seoul", "analytical_area_id": "ghs-efua-2015-r2019a:94", "source_area_id": "94", "source_area_name": "Seoul", "value": 24.528811714043485, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 0.9997164327236637, "pm_population_coverage": 1.0, "cell_count": 7053, "population_grid_valid_cells": 7051, "population_grid_missing_cells": 2, "population_grid_zero_cells": 357, "total_known_modelled_population": 25199125.371400096, "pm_covered_population": 25199125.371400096, "weighted_concentration_sum": 618104601.593649, "population_in_zero_pm_cells": 0.0 }, "denominator": 25199125.371400096, "sensitivity": { "bilinear_estimate": 24.523095908554474, "bilinear_population_coverage": 1.0, "coarse_estimate": 24.40026725497703, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-94.npz", "sha256": "49da26ac162999fc969b3c006fa048b4b54863999c6344c984088da33cfc3a45" }, "geography_ref": "efua-2015-v1-0:seoul", "evidence_id": "pm25-2024-efua2015-seoul", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Seoul (source ID 94). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "shanghai", "city_name": "Shanghai", "analytical_area_id": "ghs-efua-2015-r2019a:10097", "source_area_id": "10097", "source_area_name": "Shanghai", "value": 34.64893217496383, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 5878, "population_grid_valid_cells": 5878, "population_grid_missing_cells": 0, "population_grid_zero_cells": 154, "total_known_modelled_population": 30504083.43388525, "pm_covered_population": 30504083.43388525, "weighted_concentration_sum": 1056933917.9601278, "population_in_zero_pm_cells": 0.0 }, "denominator": 30504083.43388525, "sensitivity": { "bilinear_estimate": 34.66134385462788, "bilinear_population_coverage": 1.0, "coarse_estimate": 34.49393353636665, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-10097.npz", "sha256": "c51354e470e15a48cd123801eebcc370082a7417a36555305fa4fbc2472d5673" }, "geography_ref": "efua-2015-v1-0:shanghai", "evidence_id": "pm25-2024-efua2015-shanghai", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Shanghai (source ID 10097). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "singapore", "city_name": "Singapore", "analytical_area_id": "ghs-efua-2015-r2019a:154", "source_area_id": "154", "source_area_name": "Singapore", "value": 13.416822778026273, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 637, "population_grid_valid_cells": 637, "population_grid_missing_cells": 0, "population_grid_zero_cells": 178, "total_known_modelled_population": 5908522.0477144765, "pm_covered_population": 5908522.0477144765, "weighted_concentration_sum": 79273593.19424602, "population_in_zero_pm_cells": 0.0 }, "denominator": 5908522.0477144765, "sensitivity": { "bilinear_estimate": 13.431894090910005, "bilinear_population_coverage": 1.0, "coarse_estimate": 13.475740694521546, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-154.npz", "sha256": "7d690aec39ad1b4a8077f33f9c1e57a18900a392ddc7610a5a02a33803550f68" }, "geography_ref": "efua-2015-v1-0:singapore", "evidence_id": "pm25-2024-efua2015-singapore", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Singapore (source ID 154). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "stockholm", "city_name": "Stockholm", "analytical_area_id": "ghs-efua-2015-r2019a:1445", "source_area_id": "1445", "source_area_name": "Stockholm", "value": 5.962643953866693, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2536, "population_grid_valid_cells": 2536, "population_grid_missing_cells": 0, "population_grid_zero_cells": 316, "total_known_modelled_population": 2241651.3560709828, "pm_covered_population": 2241651.3560709828, "weighted_concentration_sum": 13366168.904953718, "population_in_zero_pm_cells": 0.0 }, "denominator": 2241651.3560709828, "sensitivity": { "bilinear_estimate": 5.954755871878652, "bilinear_population_coverage": 1.0, "coarse_estimate": 5.890397786700122, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1445.npz", "sha256": "742d8face701c5beab900d859fc9a0b96ba80225bc59238649aa4603474d7332" }, "geography_ref": "efua-2015-v1-0:stockholm", "evidence_id": "pm25-2024-efua2015-stockholm", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Stockholm (source ID 1445). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "sydney", "city_name": "Sydney", "analytical_area_id": "ghs-efua-2015-r2019a:1830", "source_area_id": "1830", "source_area_name": "Sydney", "value": 5.743625709411895, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 3881, "population_grid_valid_cells": 3881, "population_grid_missing_cells": 0, "population_grid_zero_cells": 693, "total_known_modelled_population": 4784217.805250018, "pm_covered_population": 4784217.805250018, "weighted_concentration_sum": 27478756.385660153, "population_in_zero_pm_cells": 0.0 }, "denominator": 4784217.805250018, "sensitivity": { "bilinear_estimate": 5.747319395548073, "bilinear_population_coverage": 1.0, "coarse_estimate": 5.783263563278759, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1830.npz", "sha256": "47e40a9217d52d2a844c2c69902e71645c4f6643bd73f9a4f12087b86b949cdf" }, "geography_ref": "efua-2015-v1-0:sydney", "evidence_id": "pm25-2024-efua2015-sydney", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Sydney (source ID 1830). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "taipei", "city_name": "Taipei", "analytical_area_id": "ghs-efua-2015-r2019a:1141", "source_area_id": "1141", "source_area_name": "New Taipei [Taipei]", "value": 14.864406405024601, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2463, "population_grid_valid_cells": 2463, "population_grid_missing_cells": 0, "population_grid_zero_cells": 112, "total_known_modelled_population": 10048037.07281363, "pm_covered_population": 10048037.07281363, "weighted_concentration_sum": 149358106.62305558, "population_in_zero_pm_cells": 0.0 }, "denominator": 10048037.07281363, "sensitivity": { "bilinear_estimate": 14.852941764770524, "bilinear_population_coverage": 1.0, "coarse_estimate": 14.609844284860856, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1141.npz", "sha256": "5297d68d0a4c925f39b56f735d1700392f398418c449540c78f0313f95402263" }, "geography_ref": "efua-2015-v1-0:taipei", "evidence_id": "pm25-2024-efua2015-taipei", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area New Taipei [Taipei] (source ID 1141). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "tel-aviv", "city_name": "Tel Aviv", "analytical_area_id": "ghs-efua-2015-r2019a:419", "source_area_id": "419", "source_area_name": "Tel Aviv", "value": 19.672686967683383, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 1102, "population_grid_valid_cells": 1102, "population_grid_missing_cells": 0, "population_grid_zero_cells": 70, "total_known_modelled_population": 3174056.626333899, "pm_covered_population": 3174056.626333899, "weighted_concentration_sum": 62442222.427567974, "population_in_zero_pm_cells": 0.0 }, "denominator": 3174056.626333899, "sensitivity": { "bilinear_estimate": 19.69151323546726, "bilinear_population_coverage": 1.0, "coarse_estimate": 19.737466311190072, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-419.npz", "sha256": "fd36dab1046196d459fd470167a866d49c8b97530140b4f3201fd08dfeac7833" }, "geography_ref": "efua-2015-v1-0:tel-aviv", "evidence_id": "pm25-2024-efua2015-tel-aviv", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Tel Aviv (source ID 419). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "tokyo", "city_name": "Tokyo", "analytical_area_id": "ghs-efua-2015-r2019a:5129", "source_area_id": "5129", "source_area_name": "Tokyo", "value": 12.907995602808032, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 0.9971598028569042, "pm_population_coverage": 1.0, "cell_count": 11971, "population_grid_valid_cells": 11937, "population_grid_missing_cells": 34, "population_grid_zero_cells": 934, "total_known_modelled_population": 36697548.9260917, "pm_covered_population": 36697548.9260917, "weighted_concentration_sum": 473691800.17182434, "population_in_zero_pm_cells": 0.0 }, "denominator": 36697548.9260917, "sensitivity": { "bilinear_estimate": 12.907096731928107, "bilinear_population_coverage": 1.0, "coarse_estimate": 12.889587420219799, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-5129.npz", "sha256": "4f0d6280620ab857695c96370145c4f70bc5241ae13c8c577326097db9f60083" }, "geography_ref": "efua-2015-v1-0:tokyo", "evidence_id": "pm25-2024-efua2015-tokyo", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Tokyo (source ID 5129). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "toronto", "city_name": "Toronto", "analytical_area_id": "ghs-efua-2015-r2019a:2626", "source_area_id": "2626", "source_area_name": "Toronto", "value": 7.436258516437588, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 8223, "population_grid_valid_cells": 8223, "population_grid_missing_cells": 0, "population_grid_zero_cells": 695, "total_known_modelled_population": 7480038.247888051, "pm_covered_population": 7480038.247888051, "weighted_concentration_sum": 55623498.12413642, "population_in_zero_pm_cells": 0.0 }, "denominator": 7480038.247888051, "sensitivity": { "bilinear_estimate": 7.439659413893029, "bilinear_population_coverage": 1.0, "coarse_estimate": 7.3685864451226815, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-2626.npz", "sha256": "01da7808702b23d88e0f9937906e04cb0a5eb4237d374b9b4ea8d5c5cd3cfd86" }, "geography_ref": "efua-2015-v1-0:toronto", "evidence_id": "pm25-2024-efua2015-toronto", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Toronto (source ID 2626). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "vancouver", "city_name": "Vancouver", "analytical_area_id": "ghs-efua-2015-r2019a:375", "source_area_id": "375", "source_area_name": "Vancouver", "value": 5.393363920419621, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 1601, "population_grid_valid_cells": 1601, "population_grid_missing_cells": 0, "population_grid_zero_cells": 212, "total_known_modelled_population": 2507138.354084499, "pm_covered_population": 2507138.354084499, "weighted_concentration_sum": 13521909.542419571, "population_in_zero_pm_cells": 0.0 }, "denominator": 2507138.354084499, "sensitivity": { "bilinear_estimate": 5.395355326842808, "bilinear_population_coverage": 1.0, "coarse_estimate": 5.406431635670564, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-375.npz", "sha256": "d0014739517b793332d827bf92a877613a377fad8070d18099b62c0403273b9f" }, "geography_ref": "efua-2015-v1-0:vancouver", "evidence_id": "pm25-2024-efua2015-vancouver", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Vancouver (source ID 375). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "vienna", "city_name": "Vienna", "analytical_area_id": "ghs-efua-2015-r2019a:790", "source_area_id": "790", "source_area_name": "Vienna", "value": 11.236545106003751, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 3104, "population_grid_valid_cells": 3104, "population_grid_missing_cells": 0, "population_grid_zero_cells": 468, "total_known_modelled_population": 2565196.338830962, "pm_covered_population": 2565196.338830962, "weighted_concentration_sum": 28823944.367029786, "population_in_zero_pm_cells": 0.0 }, "denominator": 2565196.338830962, "sensitivity": { "bilinear_estimate": 11.239491704133446, "bilinear_population_coverage": 1.0, "coarse_estimate": 11.024331610979576, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-790.npz", "sha256": "dc5310d425873252eab0e29692080fb2523ca748527214c11fb78bb086d660cd" }, "geography_ref": "efua-2015-v1-0:vienna", "evidence_id": "pm25-2024-efua2015-vienna", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Vienna (source ID 790). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "warsaw", "city_name": "Warsaw", "analytical_area_id": "ghs-efua-2015-r2019a:3222", "source_area_id": "3222", "source_area_name": "Warsaw", "value": 16.282900767429037, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 4181, "population_grid_valid_cells": 4181, "population_grid_missing_cells": 0, "population_grid_zero_cells": 271, "total_known_modelled_population": 2975931.9294594172, "pm_covered_population": 2975931.9294594172, "weighted_concentration_sum": 48456804.29801132, "population_in_zero_pm_cells": 0.0 }, "denominator": 2975931.9294594172, "sensitivity": { "bilinear_estimate": 16.28271826423839, "bilinear_population_coverage": 1.0, "coarse_estimate": 16.18732896302871, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-3222.npz", "sha256": "104e6af0f513e229aede78ad404f2a0136ece2271391a6060b9ca27c2f3da994" }, "geography_ref": "efua-2015-v1-0:warsaw", "evidence_id": "pm25-2024-efua2015-warsaw", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Warsaw (source ID 3222). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "washington-dc", "city_name": "Washington DC", "analytical_area_id": "ghs-efua-2015-r2019a:7068", "source_area_id": "7068", "source_area_name": "Washington D.C.", "value": 7.024206774902888, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 7446, "population_grid_valid_cells": 7446, "population_grid_missing_cells": 0, "population_grid_zero_cells": 341, "total_known_modelled_population": 5991806.5196682345, "pm_covered_population": 5991806.5196682345, "weighted_concentration_sum": 42087687.94936091, "population_in_zero_pm_cells": 0.0 }, "denominator": 5991806.5196682345, "sensitivity": { "bilinear_estimate": 7.017997089491702, "bilinear_population_coverage": 1.0, "coarse_estimate": 6.974952201989161, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-7068.npz", "sha256": "a8ce05dee873150270db7495faec44def70ecc354a6bcfbc91fcda45a0176579" }, "geography_ref": "efua-2015-v1-0:washington-dc", "evidence_id": "pm25-2024-efua2015-washington-dc", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Washington D.C. (source ID 7068). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] }, { "city_slug": "zurich", "city_name": "Zurich", "analytical_area_id": "ghs-efua-2015-r2019a:1479", "source_area_id": "1479", "source_area_name": "Zurich", "value": 9.384315653447127, "unit": "micrograms/m3", "period_start": "2024-01-01", "period_end": "2024-12-31", "population_reference_year": 2020, "boundary_reference_year": 2015, "concentration_year": 2024, "source_model": { "name": "SatPM CNNPM25", "version": "V6.GL.03", "source_url": "https://satpmdata.s3.us-east-1.amazonaws.com/V6GL03/CNNPM25/Annual/GL/V6GL03.CNNPM25.GL.202401-202412.nc", "sha256": "9f1059f6a7d675a53f987e8b6ccc6981c3c29a867cc3a2324043a4eff2c19928" }, "protocol_ref": "civometra.pm25.measurement@1.0.0", "shared_with": [], "coverage": { "population_grid_area_coverage": 1.0, "pm_population_coverage": 1.0, "cell_count": 2588, "population_grid_valid_cells": 2588, "population_grid_missing_cells": 0, "population_grid_zero_cells": 124, "total_known_modelled_population": 2124245.7611781457, "pm_covered_population": 2124245.7611781457, "weighted_concentration_sum": 19934592.74839278, "population_in_zero_pm_cells": 0.0 }, "denominator": 2124245.7611781457, "sensitivity": { "bilinear_estimate": 9.377556393220683, "bilinear_population_coverage": 1.0, "coarse_estimate": 9.199829018771133, "coarse_population_coverage": 1.0, "notice": "Method-sampling diagnostics with their own coverage; not statistical uncertainty or confidence intervals. Neither fills missing primary values." }, "uncertainty": { "status": "not-reported-for-this-derived-area", "note": "No city-specific statistical interval supplied by this global product; sampling sensitivity is not a confidence interval." }, "frame": { "url": "/data/source-frames/pm25-2024-efua-1479.npz", "sha256": "19934cb164adcc82f52bec57c0e7ee2b7bd1fc4a530d56a6008b4ba099824e3f" }, "geography_ref": "efua-2015-v1-0:zurich", "evidence_id": "pm25-2024-efua2015-zurich", "review_status": "accepted", "scope_notice": "Annual 2024 modelled ambient PM2.5 weighted by 2020 modelled population in the fixed 2015 estimated area Zurich (source ID 1479). Historical modelled geography; source country labels and area identities are retained.", "source_attribution": [ { "publisher": "Atmospheric Composition Analysis Group / Washington University in St. Louis / SatPM", "title": "Shen, S., Li, C., van Donkelaar, A., Jacobs, N., Wang, C., and Martin, R. V. (2024). Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T Air. doi:10.1021/acsestair.3c00054. SatPM V6.GL.03, annual 2024; accessed 26 October 2026.", "url": "https://www.satpm.org/v6-gl-03", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre", "title": "GHS-POP R2023A V1-0, 2020 modelled residential-population counts", "url": "https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe", "licence": "CC BY 4.0" }, { "publisher": "European Commission Joint Research Centre, in collaboration with OECD", "title": "GHS-FUA R2019A V1_0, estimated functional urban areas, 2015", "url": "https://data.jrc.ec.europa.eu/dataset/347f0337-f2da-4592-87b3-e25975ec2c95", "licence": "CC BY 4.0" } ] } ] }