---
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.

![Validation Report](validation_report.png)

## Usage

```python
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

```bibtex
@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