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year
int64
1.96k
2.02k
Algeria
float64
100
100
Angola
float64
100
100
Benin
float64
100
100
Botswana
float64
100
100
Burkina Faso
float64
100
100
Burundi
float64
100
100
Cabo Verde
float64
100
100
Cameroon
float64
100
100
Central African Republic
float64
100
100
Chad
float64
100
100
Comoros
float64
100
100
Congo, Dem. Rep.
float64
100
100
Congo, Rep.
float64
100
100
Cote d'Ivoire
float64
100
100
Djibouti
float64
100
100
Egypt, Arab Rep.
float64
100
100
Equatorial Guinea
float64
100
100
Eritrea
float64
100
100
Eswatini
float64
100
100
Ethiopia
float64
100
100
Gabon
float64
100
100
Gambia, The
float64
100
100
Ghana
float64
100
100
Guinea
float64
100
100
Guinea-Bissau
float64
100
100
Kenya
float64
100
100
Lesotho
float64
100
100
Liberia
float64
100
100
Libya
float64
100
100
Madagascar
float64
100
100
Malawi
float64
100
100
Mali
float64
100
100
Mauritania
float64
100
100
Mauritius
float64
100
100
Morocco
float64
100
100
Mozambique
float64
100
100
Namibia
float64
100
100
Niger
float64
100
100
Nigeria
float64
100
100
Rwanda
float64
100
100
Sao Tome and Principe
float64
100
100
Senegal
float64
100
100
Seychelles
float64
100
100
Sierra Leone
float64
100
100
Somalia
float64
100
100
South Africa
float64
100
100
South Sudan
float64
100
100
Sudan
float64
100
100
Tanzania
float64
100
100
Togo
float64
100
100
Tunisia
float64
100
100
Uganda
float64
100
100
Zambia
float64
100
100
Zimbabwe
float64
100
100
1,960
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,961
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,962
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
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100
100
100
100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,963
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,964
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,965
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,966
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,967
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,968
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,969
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,970
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,971
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,972
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,973
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,974
100
100
100
100
100
100
100
100
100
100
100
100
100
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100
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100
100
100
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100
100
100
100
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100
100
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100
100
100
100
100
100
100
100
100
100
1,975
100
100
100
100
100
100
100
100
100
100
100
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100
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100
100
100
100
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100
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100
100
100
100
100
100
100
100
100
100
1,976
100
100
100
100
100
100
100
100
100
100
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100
100
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100
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100
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100
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100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
1,977
100
100
100
100
100
100
100
100
100
100
100
100
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100
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100
100
100
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100
100
100
100
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100
100
1,978
100
100
100
100
100
100
100
100
100
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100
100
100
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100
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100
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100
100
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100
100
1,979
100
100
100
100
100
100
100
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100
100
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100
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100
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1,980
100
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1,981
100
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1,982
100
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1,983
100
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1,984
100
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1,985
100
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1,986
100
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1,987
100
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1,988
100
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1,989
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1,990
100
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1,991
100
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1,992
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1,993
100
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1,994
100
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1,995
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1,996
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1,997
100
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1,998
100
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1,999
100
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2,000
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2,001
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2,002
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2,003
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Air Pollution Population Exposed to Levels Exceeding WHO Guideline Percentage of Total Africa | Africa (World Health Organization)

Size category: n<1K - Formats: csv - Sector: climate_environment - Engineered by Electric Sheep Africa

size sector downloads license

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: Africa: PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total) Dataset summary This dataset provides values for "PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total)" across African countries, standardized and made ML-ready. Geographic scope: 54 African countries. Temporal coverage: 1960–2024 (annual). Units: As defined by the World Bank indicator. Source & licensing Source: World… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/air-pollution-population-exposed-to-levels-exceeding-who-guideline-percentage-of-total-africa.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/air-pollution-population-exposed-to-levels-exceeding-who-guideline-percentage-of-total-africa
Sector climate_environment
Topic tags biology, climate
Modalities tabular
Formats csv
Size category n<1K
Countries Africa-wide or source-defined African coverage
ISO3 coverage not declared
Last modified on HF 2025-08-16 16:34:22+00:00
Inventory snapshot 2026-07-16T16:00:34Z

How To Read This Dataset

  • Start from the repository files and the dataset viewer when available.
  • Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
  • Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
  • Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/air-pollution-population-exposed-to-levels-exceeding-who-guideline-percentage-of-total-africa")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

  • This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
  • Exact schema, row counts, and source files should be inspected in the repository data files.
  • Metadata gaps from the inventory: country, upstream_publisher.
  • Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Suggested Analyses

  • Inspect schema and missingness before modeling.
  • Profile variables by geography, time, and subgroup columns where present.
  • Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
  • Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_air_pollution_population_exposed_to_levels_exceeding_who_guideline_percentage_of_2026,
  title        = {Air Pollution Population Exposed to Levels Exceeding WHO Guideline Percentage of Total Africa | Africa (World Health Organization)},
  author       = {WHO public data},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/air-pollution-population-exposed-to-levels-exceeding-who-guideline-percentage-of-total-africa},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/air-pollution-population-exposed-to-levels-exceeding-who-guideline-percentage-of-total-africa}}
}

License

Released under gpl.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.


Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

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