"""Generate synthetic indoor air pollution & clean cooking dataset.""" from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd SEED = 42 N_PER_SCENARIO = 10_000 YEAR_RANGE = np.arange(2010, 2025) YEAR_WEIGHTS = np.linspace(0.85, 1.3, len(YEAR_RANGE)) YEAR_WEIGHTS = YEAR_WEIGHTS / YEAR_WEIGHTS.sum() CLEAN_FUELS = {"lpg", "electricity", "biogas", "ethanol"} BIOMASS_FUELS = {"wood", "charcoal", "dung", "crop_residue"} FUEL_PM25_FACTOR = { "wood": 1.0, "charcoal": 0.85, "dung": 1.15, "crop_residue": 1.05, "kerosene": 0.65, "lpg": 0.2, "electricity": 0.05, "biogas": 0.2, "ethanol": 0.2, } FUEL_CO_FACTOR = { "wood": 1.0, "charcoal": 0.8, "dung": 1.1, "crop_residue": 1.0, "kerosene": 0.6, "lpg": 0.25, "electricity": 0.05, "biogas": 0.25, "ethanol": 0.25, } STOVE_PM25_FACTOR = {"traditional": 1.0, "improved": 0.75, "clean": 0.3} FUEL_BC_FACTOR = { "wood": 0.08, "charcoal": 0.05, "dung": 0.1, "crop_residue": 0.07, "kerosene": 0.03, "lpg": 0.01, "electricity": 0.0, "biogas": 0.01, "ethanol": 0.01, } FUEL_CONSUMPTION_FACTOR = { "wood": 1.2, "charcoal": 0.8, "dung": 1.0, "crop_residue": 0.9, "kerosene": 0.4, "lpg": 0.2, "electricity": 0.0, "biogas": 0.1, "ethanol": 0.1, } KITCHEN_VENTILATION = {"enclosed": 0.2, "semi_open": 0.4, "open": 0.65} SCENARIOS = { "traditional_biomass": { "setting_probs": {"rural": 0.7, "peri_urban": 0.2, "urban": 0.1}, "fuel_probs": { "wood": 0.45, "charcoal": 0.2, "dung": 0.2, "crop_residue": 0.1, "kerosene": 0.05, }, "cookstove_probs": {"traditional": 0.85, "improved": 0.1, "clean": 0.05}, "kitchen_probs": {"enclosed": 0.6, "semi_open": 0.3, "open": 0.1}, "pm25_mean": 420, "pm25_sd": 180, "co_mean": 18, "co_sd": 7, "ventilation_mean": 0.25, "cooking_hours_mean": 3.0, "intervention_coverage": 0.1, "adoption_rate": 0.4, "compliance_mean": 0.6, "baseline_pneumonia": 16.5, "household_size_mean": 5.4, "household_size_sd": 1.6, "children_u5_mean": 1.2, }, "improved_stove_rollout": { "setting_probs": {"rural": 0.6, "peri_urban": 0.25, "urban": 0.15}, "fuel_probs": { "wood": 0.35, "charcoal": 0.2, "dung": 0.15, "crop_residue": 0.1, "kerosene": 0.05, "lpg": 0.1, "electricity": 0.05, }, "cookstove_probs": {"traditional": 0.45, "improved": 0.45, "clean": 0.1}, "kitchen_probs": {"enclosed": 0.5, "semi_open": 0.35, "open": 0.15}, "pm25_mean": 260, "pm25_sd": 120, "co_mean": 12, "co_sd": 5, "ventilation_mean": 0.35, "cooking_hours_mean": 2.7, "intervention_coverage": 0.6, "adoption_rate": 0.55, "compliance_mean": 0.65, "baseline_pneumonia": 15.5, "household_size_mean": 5.2, "household_size_sd": 1.5, "children_u5_mean": 1.1, }, "clean_fuel_transition": { "setting_probs": {"urban": 0.45, "peri_urban": 0.35, "rural": 0.2}, "fuel_probs": { "lpg": 0.35, "electricity": 0.25, "biogas": 0.1, "charcoal": 0.15, "wood": 0.1, "ethanol": 0.05, }, "cookstove_probs": {"clean": 0.65, "improved": 0.2, "traditional": 0.15}, "kitchen_probs": {"enclosed": 0.4, "semi_open": 0.35, "open": 0.25}, "pm25_mean": 70, "pm25_sd": 35, "co_mean": 4, "co_sd": 2, "ventilation_mean": 0.5, "cooking_hours_mean": 2.3, "intervention_coverage": 0.8, "adoption_rate": 0.75, "compliance_mean": 0.8, "baseline_pneumonia": 13.0, "household_size_mean": 4.9, "household_size_sd": 1.4, "children_u5_mean": 0.9, }, } SCENARIO_FILES = { "traditional_biomass": "indoor_air_traditional_biomass.csv", "improved_stove_rollout": "indoor_air_improved_stove_rollout.csv", "clean_fuel_transition": "indoor_air_clean_fuel_transition.csv", } def _choice(rng: np.random.Generator, prob_map: dict[str, float]) -> str: keys = list(prob_map.keys()) weights = np.array(list(prob_map.values()), dtype=float) weights = weights / weights.sum() return rng.choice(keys, p=weights) def _sample_year(rng: np.random.Generator) -> int: return int(rng.choice(YEAR_RANGE, p=YEAR_WEIGHTS)) def _simulate_scenario(name: str, params: dict[str, float], seed: int) -> pd.DataFrame: rng = np.random.default_rng(seed) records = [] for idx in range(N_PER_SCENARIO): setting = _choice(rng, params["setting_probs"]) kitchen_type = _choice(rng, params["kitchen_probs"]) fuel_type = _choice(rng, params["fuel_probs"]) cookstove_type = _choice(rng, params["cookstove_probs"]) household_size = int( np.clip( rng.normal(params["household_size_mean"], params["household_size_sd"]), 2, 11 ) ) children_u5 = int(np.clip(rng.poisson(params["children_u5_mean"]), 0, 4)) cooking_hours = float(np.clip(rng.normal(params["cooking_hours_mean"], 0.6), 1.2, 5.5)) ventilation_index = float( np.clip(rng.normal(KITCHEN_VENTILATION[kitchen_type], 0.07), 0.1, 0.9) ) intervention_received = rng.random() < params["intervention_coverage"] adoption = intervention_received and rng.random() < params["adoption_rate"] compliance = float(np.clip(rng.normal(params["compliance_mean"], 0.15), 0.2, 1.0)) if not adoption: compliance = 0.0 clean_access = fuel_type in CLEAN_FUELS or cookstove_type == "clean" base_pm25 = max(20.0, rng.normal(params["pm25_mean"], params["pm25_sd"])) vent_factor = 1.1 - ventilation_index * 0.6 pm25 = ( base_pm25 * FUEL_PM25_FACTOR[fuel_type] * STOVE_PM25_FACTOR[cookstove_type] * vent_factor ) pm25 = float(np.clip(pm25, 15, 1200)) co_ppm = ( rng.normal(params["co_mean"], params["co_sd"]) * FUEL_CO_FACTOR[fuel_type] * STOVE_PM25_FACTOR[cookstove_type] * (1.05 - ventilation_index * 0.5) ) co_ppm = float(np.clip(co_ppm, 0.5, 80)) exposure_hours = float(cooking_hours * rng.uniform(0.5, 0.9)) exposure_index = float(pm25 * exposure_hours / 24) lri_rr = 1 + np.clip((pm25 - 50) / 450, 0, 1) * 0.9 stove_health_factor = 1.0 if cookstove_type == "clean": stove_health_factor = 0.85 elif cookstove_type == "improved": stove_health_factor = 0.98 pneumonia_incidence = params["baseline_pneumonia"] * lri_rr * stove_health_factor if clean_access and compliance > 0: pneumonia_incidence *= 1 - 0.05 * compliance pneumonia_incidence = float(np.clip(pneumonia_incidence, 5, 40)) copd_risk_index = float(np.clip(0.2 + (pm25 / 600) * 0.8, 0.2, 1.0)) low_birthweight_risk = float(np.clip(0.08 + (pm25 / 600) * 0.12, 0.05, 0.35)) health_burden_score = float( np.clip( (pneumonia_incidence / 30) * 0.4 + copd_risk_index * 0.35 + low_birthweight_risk * 0.25, 0, 1, ) ) black_carbon_kg_per_day = float( FUEL_BC_FACTOR[fuel_type] * cooking_hours * (0.3 if cookstove_type == "clean" else 1) ) fuel_kg_per_day = float(FUEL_CONSUMPTION_FACTOR[fuel_type] * cooking_hours) if fuel_type in BIOMASS_FUELS: base_collection = 1.3 if setting == "rural" else 0.8 fuel_collection_hours = float(np.clip(rng.normal(base_collection, 0.4), 0.1, 3.5)) else: fuel_collection_hours = float(np.clip(rng.normal(0.2, 0.1), 0.0, 0.6)) if pm25 >= 500: pm25_category = "extreme" elif pm25 >= 250: pm25_category = "high" elif pm25 >= 100: pm25_category = "moderate" else: pm25_category = "low" record = { "record_id": f"{name[:3].upper()}-{idx:05d}", "scenario": name, "year": _sample_year(rng), "setting": setting, "household_size": household_size, "children_u5": children_u5, "primary_cook_female": int(rng.random() < 0.82), "cooking_hours_per_day": cooking_hours, "kitchen_type": kitchen_type, "ventilation_index": ventilation_index, "fuel_type": fuel_type, "cookstove_type": cookstove_type, "clean_cooking_access": int(clean_access), "intervention_received": int(intervention_received), "adoption_compliance": compliance, "pm25_kitchen_ugm3": pm25, "co_ppm": co_ppm, "exposure_hours": exposure_hours, "exposure_index": exposure_index, "pm25_category": pm25_category, "pneumonia_incidence_per100": pneumonia_incidence, "child_lri_risk_ratio": lri_rr, "copd_risk_index": copd_risk_index, "low_birthweight_risk": low_birthweight_risk, "health_burden_score": health_burden_score, "black_carbon_kg_per_day": black_carbon_kg_per_day, "fuel_kg_per_day": fuel_kg_per_day, "fuel_collection_hours": fuel_collection_hours, } records.append(record) return pd.DataFrame(records) def main() -> None: output_dir = Path("data") output_dir.mkdir(parents=True, exist_ok=True) for idx, (name, params) in enumerate(SCENARIOS.items()): df = _simulate_scenario(name, params, SEED + idx * 211) df.to_csv(output_dir / SCENARIO_FILES[name], index=False) print(f"Saved {name} -> {SCENARIO_FILES[name]}") if __name__ == "__main__": main()