Global PM2.5 air-pollution exposure panel

Usage commercial OKÀ jourLicence: CC-BY-4.0

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Value-added panel of the World Bank's World Development Indicators (keyless API v2, CC BY 4.0): population-weighted mean annual exposure to ambient PM2.5 fine-particulate air pollution for ~215 economies, 1990-2023, with a WHO 2021 guideline (5 ug/m3) exceedance layer, trailing 1-year and 10-year changes and within-year exposure ranks. Joins on economy_code with the catalog's other World Bank panels.

Les titres et les descriptions proviennent des sources de données, en anglais.

Source: World Bank PM2.5 exposure intelligence
Lignes: 6 800
Colonnes: 12
Cadence de la source: Annuelle
Dernière actualisation: 10 oct. 2026
Thème: environment

Colonne	Type	Description
economy_code	string	ISO 3166-1 alpha-3 economy code (World Bank API field countryiso3code).
economy_name	string	Economy name as published by the World Bank API.
region	string	World Bank region (API field region.value).
income_group	string	World Bank income group (API field incomeLevel.value).
year	integer	Reference year (1990-2023).
pm25_ug_m3	float	Population-weighted mean annual exposure to ambient PM2.5 fine-particulate air pollution (WDI indicator EN.ATM.PM25.MC.M3): the average level of exposure of the nation's population to suspended particulate matter under 2.5 microns in aerodynamic diameter, weighted by population. (unit: micrograms per cubic meter)
pm25_vs_who_guideline_ratio	float	Connector-derived: pm25_ug_m3 divided by the WHO 2021 annual PM2.5 guideline (5 ug/m3). A value of 2.0 means twice the guideline. (unit: ratio)
exceeds_who2021_flag	integer	Connector-derived: 1 where pm25_ug_m3 exceeds the WHO 2021 annual PM2.5 guideline (5 ug/m3), else 0.
pm25_change_1y_pct	float	Trailing 1-year percent change of PM2.5 exposure; null when the base year is absent or zero. (unit: %)
pm25_change_10y_pct	float	Trailing 10-year percent change of PM2.5 exposure (the structural cleanup lens); null when the base year is absent or zero. (unit: %)
pm25_rank	integer	Rank of PM2.5 exposure within the year (1 = highest exposure); null where pm25_ug_m3 is null. (unit: rank)
row_hash	string	Deterministic 12-hex row identity hash (economy_code\|year).

10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.

economy_code	economy_name	region	income_group	year	pm25_ug_m3	pm25_vs_who_guideline_ratio	exceeds_who2021_flag	pm25_change_1y_pct	pm25_change_10y_pct	pm25_rank	row_hash
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 990	48,43	9,686	1	—	—	15	ced4e7b75201
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 991	47,94	9,588	1	-1,01	—	16	8a5cc540a6c6
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 992	47,97	9,594	1	0,06	—	16	8b72a48eb771
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 993	48,01	9,602	1	0,08	—	16	aac610f1a870
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 994	48,04	9,608	1	0,06	—	16	33c8bbfff234
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 995	47,04	9,408	1	-2,08	—	16	ab61a7e0e8e0
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 996	48,11	9,622	1	2,27	—	16	64645b46a4df
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 997	48,14	9,628	1	0,06	—	16	1bdec963d52a
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 998	48,17	9,634	1	0,06	—	16	81b4cb57f871
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 999	48,2	9,64	1	0,06	—	16	61dce9de9a85

Profilé le 10 oct. 2026 à partir de l’instantané 20261010T062003Z-5c785d0d2d50

Mesuré

Complétude: 97,3 %
Lignes: 6 800
Colonnes: 12
Colonnes incomplètes: 2

Lignes par annéeyear · 1990–2023 · 34 ans

19902023

Colonne	Manquant	Distinctes	Plage	Distribution
economy_codevarchar	0 %	257	—	AFG34 AGO34 ALB34
economy_namevarchar	0 %	213	—	Afghanistan34 Albania34 Algeria34
regionvarchar	0 %	6	—	Europe & Central Asia1 768 Sub-Saharan Africa 1 632 Latin America & Caribbean 1 190
income_groupvarchar	0 %	4	—	High income2 380 Upper middle income1 972 Lower middle income1 598
yearbigint	0 %	33	1 990 → 2 023médiane 2 007
pm25_ug_m3double	0 %	3 301	1,39 → 124,66médiane 20,96	136 hors du 1er–99e centile
pm25_vs_who_guideline_ratiodouble	0 %	2 942	0,278 → 24,93médiane 4,19	136 hors du 1er–99e centile
exceeds_who2021_flagbigint	0 %	2	0 → 1médiane 1
pm25_change_1y_pctdouble	2,9 %	2 798	-43,88 → 59,04médiane -0,25	132 hors du 1er–99e centile
pm25_change_10y_pctdouble	29,4 %	3 027	-64,36 → 80,22médiane -6,16	96 hors du 1er–99e centile
pm25_rankbigint	0 %	223	1 → 200médiane 100,5	136 hors du 1er–99e centile
row_hashvarchar	0 %	7 160	—	000df08048851 00190f572f921 001b61ae325d1

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/wb_pm25_exposure_intel/pm25_exposure_panel" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/wb_pm25_exposure_intel/pm25_exposure_panel").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

Point d’accès API : https://datazimuts.com/v1/datasets/wb_pm25_exposure_intel/pm25_exposure_panel

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.

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Épinglé à l’instantané 20261010T062003Z-5c785d0d2d50 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.

APA

World Bank PM2.5 exposure intelligence. (2026). Global PM2.5 air-pollution exposure panel [Data set, snapshot 20261010T062003Z-5c785d0d2d50, sha256 5c785d0d2d50]. Datazimuts. Retrieved 2026-10-10, from https://datazimuts.com/fr/datasets/wb_pm25_exposure_intel/pm25_exposure_panel?snapshot=20261010T062003Z-5c785d0d2d50

BibTeX

@misc{dz_wb_pm25_exposure_intel_pm25_exposure_pan_5c785d0d,
  title        = {{Global PM2.5 air-pollution exposure panel}},
  author       = {{World Bank PM2.5 exposure intelligence}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/fr/datasets/wb_pm25_exposure_intel/pm25_exposure_panel?snapshot=20261010T062003Z-5c785d0d2d50}},
  note         = {Snapshot 20261010T062003Z-5c785d0d2d50, sha256 5c785d0d2d5016f2a3196edc63df5d99f17cd0f199307f4042a4aef705d7ba3e; accessed 2026-10-10}
}

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<iframe src="https://datazimuts.com/embed/chart?dataset=wb_pm25_exposure_intel%2Fpm25_exposure_panel&lang=fr&theme=auto&snapshot=20261010T062003Z-5c785d0d2d50&x=year&y=year&agg=avg" title="Global PM2.5 air-pollution exposure panel" width="100%" height="380" style="border:0" loading="lazy"></iframe>