electricsheepafrica/africa-synth-climate-indoor-air-pollution-clean-cooking-all

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

Indoor Air Pollution & Clean Cooking in Sub-Saharan Africa

Abstract

This synthetic dataset captures household air pollution (HAP) exposure and clean cooking transitions across three scenarios in sub-Saharan Africa. Each record represents a household-year observation describing cooking fuel, kitchen ventilation, PM2.5 concentrations, exposure indices, and respiratory health risk proxies. Parameterization is informed by WHO household air pollution guidance and empirical PM2.5 measurements in rural Ethiopia.

The WHO reports that around 2.1 billion people still cook with polluting fuels, and household air pollution was responsible for an estimated 2.9 million deaths in 2021, including over 309,000 deaths in children under 5. Exposure to HAP nearly doubles childhood lower respiratory infection (LRI) risk and contributes to 44% of pneumonia deaths in children under 5 (WHO, 2023). A kitchen monitoring study in Northwest Ethiopia reported mean 24-hour kitchen PM2.5 concentrations of 405 µg/m³ (SD 221; range 52–965 µg/m³), underscoring the high exposure levels in biomass-using households (Yohannes et al., 2023). A large Malawian trial of cleaner-burning biomass cookstoves found no significant reduction in childhood pneumonia incidence (IRR 1.01), highlighting the limits of improved biomass stoves alone (Mortimer et al., 2017).

Scenarios

• Traditional Biomass: Predominantly rural households using solid fuels, low ventilation, high PM2.5 exposure.
• Improved Stove Rollout: Expanded improved stove access with partial adoption and compliance; exposure declines but remains elevated.
• Clean Fuel Transition: Urban/peri-urban transition to LPG/electricity/biogas with lower exposures.

Dataset Structure

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

Household Context

• year, setting, household_size, children_u5, primary_cook_female
• cooking_hours_per_day, kitchen_type, ventilation_index

Fuel & Technology

• fuel_type, cookstove_type
• clean_cooking_access, intervention_received, adoption_compliance

Exposure Metrics

• pm25_kitchen_ugm3, co_ppm, exposure_hours, exposure_index, pm25_category

Health Risk Proxies

• pneumonia_incidence_per100, child_lri_risk_ratio
• copd_risk_index, low_birthweight_risk, health_burden_score

Environmental & Time Costs

• black_carbon_kg_per_day, fuel_kg_per_day, fuel_collection_hours

Parameterization Evidence

Parameter	Value Used	Source	Year
2.1B people rely on polluting cooking fuels	Global reliance baseline	WHO HAP factsheet	2023
2.9M deaths annually; 44% of <5 pneumonia deaths attributable to HAP	Health burden & pneumonia linkage	WHO HAP factsheet	2023
Mean kitchen PM2.5 405 µg/m³ (range 52–965) in rural Ethiopia	PM2.5 exposure baseline	Yohannes et al., Frontiers Public Health	2023
No pneumonia reduction with cleaner biomass stoves (IRR 1.01)	Effect size for improved stoves	Mortimer et al., CAPS trial	2017

Validation Summary

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

1. PM2.5 gradient: Traditional biomass has the highest PM2.5, clean fuel the lowest, with improved stoves in between.
2. CO exposure: CO follows fuel and stove efficiency patterns aligned with PM2.5 trends.
3. Exposure–health relationship: Higher exposure indices align with increased pneumonia incidence proxies.
4. Clean access effect: Clean cooking access reduces mean PM2.5 across scenarios.
5. Fuel mix: Scenario-specific fuel distributions mirror expected transition patterns.
6. Ventilation effects: Higher ventilation indices lower PM2.5 concentrations.
7. Burden score: Composite health burden declines with cleaner fuel adoption.
8. Time burden: Biomass scenarios show greater fuel collection time.

Usage

from datasets import load_dataset

# Default: improved stove rollout
subset = load_dataset("electricsheepafrica/indoor-air-pollution-clean-cooking", name="improved_stove_rollout")
df = subset["train"].to_pandas()

# Compare PM2.5 exposure by fuel type
print(df.groupby("fuel_type")["pm25_kitchen_ugm3"].mean().sort_values())

Intended Uses

• Modeling health impacts of household air pollution in SSA
• Scenario analysis of clean cooking transitions and exposure reduction
• Training exposure-response models for respiratory disease risk

Limitations

• Synthetic data: Not derived from direct household surveillance.
• Simplified exposure model: PM2.5 and CO depend on fuel, stove, and ventilation proxies only.
• No spatial coordinates: Scenario encodes geography implicitly.
• Intervention effects: Cookstove efficacy modeled as modest, reflecting mixed evidence.

References

1. World Health Organization. Household air pollution and health. WHO Factsheet, 2023. https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health
2. Yohannes T, et al. Kitchen fine particulate matter (PM2.5) concentrations from biomass fuel use in rural households of Northwest Ethiopia. Frontiers in Public Health, 2023. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1241977/full
3. Mortimer K, et al. A cleaner burning biomass-fuelled cookstove intervention to prevent pneumonia in children under 5 years old in rural Malawi (CAPS trial). Lancet 2017;389:167–75. https://pubmed.ncbi.nlm.nih.gov/27939058/

Citation

@dataset{electricsheepafrica_indoor_air_pollution_clean_cooking_2025,
  title={Indoor Air Pollution and Clean Cooking in Sub-Saharan Africa},
  author={Electric Sheep Africa},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/electricsheepafrica/indoor-air-pollution-clean-cooking}
}

License

CC-BY-4.0