README.md

metadata

license: cc-by-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - climate-health
  - wildfire
  - smoke
  - pm2-5
  - respiratory
  - synthetic
  - sub-saharan-africa
pretty_name: Wildfire Smoke & Respiratory Outcomes (SSA)
size_categories:
  - 10K<n<100K
configs:
  - config_name: savanna_fire_belt
    data_files: data/wildfire_savanna_fire_belt.csv
  - config_name: forest_clearing_burn
    data_files: data/wildfire_forest_clearing_burn.csv
    default: true
  - config_name: urban_peri_urban_haze
    data_files: data/wildfire_urban_peri_urban_haze.csv

Wildfire Smoke & Respiratory Outcomes in Sub-Saharan Africa

Abstract

A synthetic dataset modelling wildfire smoke exposure and respiratory health outcomes across three fire regime scenarios in sub-Saharan Africa. Each record represents an individual-month observation describing smoke PM2.5 exposure, fire proximity, vulnerability, respiratory symptoms, and health service utilisation. Africa accounts for over 70% of global burned area annually, with savanna fires in West/Central Africa and agricultural clearing burns in the Congo Basin driving massive seasonal smoke plumes.

Scenarios

• Savanna Fire Belt: West/Central African savanna fires (Nov-Mar) with widespread seasonal haze.
• Forest Clearing Burn: Congo Basin agricultural/forest clearing fires (Jun-Sep) with intense localised smoke.
• Urban Peri-Urban Haze: Cities downwind of fire zones with combined ambient + smoke pollution.

Dataset Structure

Each scenario contains 10,000 records (30,000 total). Key columns:

• year, month, in_fire_season, age, sex, setting
• is_child_u5, is_elderly_60plus, pre_existing_asthma, pre_existing_copd, smoker
• pm25_smoke_ugm3, pm25_total_ugm3, aqi_category
• fire_proximity_km, smoke_days_per_season, fire_count
• mask_use, stayed_indoors, vulnerability_index, exposure_risk_score
• cough, wheeze, dyspnoea, eye_irritation
• ari_episode, asthma_exacerbation, copd_exacerbation
• er_visit, hospitalised, mortality, climate_fire_trend

Parameterization Evidence

Parameter	Value Used	Source	Year
Africa >70% of global burned area	Fire frequency baseline	GFEDv4, van der Werf et al.	2017
Savanna fire PM2.5 100-300 µg/m³ during fire season	Smoke exposure	Roberts et al., Atmos Chem Phys	2009
Forest clearing fires produce denser, localised plumes	Clearing scenario	Reddington et al.	2015
PM2.5 exposure increases ARI risk 1.5-3x	Dose-response	WHO AQG	2021

Validation Summary

The 8-panel validation report (validation_report.png) confirms:

1. PM2.5 gradient: Forest clearing has highest smoke PM2.5; urban haze adds ambient baseline.
2. Seasonality: Clear monthly PM2.5 peaks during fire season months per scenario.
3. Symptoms: Cough and eye irritation dominate; rates highest in forest clearing.
4. Exposure-ARI: Higher exposure risk scores correlate with more ARI episodes.
5. Vulnerability: Children <5 and elderly have highest vulnerability indices.
6. Health cascade: ARI > ER visits > hospitalisation > mortality gradient realistic.
7. AQI: Forest clearing has most "hazardous" days; urban haze more "unhealthy" days.
8. Fire proximity: Closer fires produce higher smoke PM2.5 during fire season.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/wildfire-smoke-respiratory", name="forest_clearing_burn")
df = ds["train"].to_pandas()

# Fire season vs non-fire season ARI rates
print(df.groupby("in_fire_season")["ari_episode"].mean())

Intended Uses

• Modelling wildfire smoke health impacts in SSA
• Evaluating respiratory burden during fire seasons
• Training exposure-response models for smoke-related respiratory outcomes

Limitations

• Synthetic data: Not from air quality monitoring networks or clinical records.
• Simplified fire model: Fire proximity and PM2.5 modeled independently.
• No spatial geocoding: Scenarios proxy geography, not precise coordinates.
• Mask efficacy: Modeled as binary; real-world compliance varies.

References

1. van der Werf GR, et al. Global fire emissions estimates during 1997-2016. Earth Syst Sci Data, 2017;9:697-720.
2. Roberts G, et al. African biomass burning emissions. Atmos Chem Phys, 2009.
3. Reddington CL, et al. Air quality and health impacts of vegetation fires. Nat Geosci, 2015.
4. WHO. Global Air Quality Guidelines. WHO, 2021.

Citation

@dataset{electricsheepafrica_wildfire_smoke_respiratory_2025,
  title={Wildfire Smoke and Respiratory Outcomes in Sub-Saharan Africa},
  author={Electric Sheep Africa},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/electricsheepafrica/wildfire-smoke-respiratory}
}

License

CC-BY-4.0