"""Generate synthetic e-waste recycling & occupational health dataset for SSA. Research-based parameterization: - Agbogbloshie, Ghana = world's largest informal e-waste recycling site; Ghana imports ~215,000 tonnes secondhand electronics/yr (Pure Earth). - E-waste manually dismantled by burning plastic/insulation to extract Cu, Au; releases Pb, Cd, Cr, Hg, PAHs, dioxins, BFRs (PMC10815197). - Biomonitoring: 75 Agbogbloshie workers vs 40 controls showed elevated blood Pb, Cd, Cr, Ni, Hg (PubMed 27858271; PMC8287752). - Children near e-waste sites: elevated BLL, DNA damage (PMC8392572). - Respiratory symptoms & reduced lung function in burning workers (PMC7084368). - No PPE or environmental protection in informal recycling (ScienceDirect 2024). - Most SSA countries lack e-waste legislation; enforcement weak where laws exist (ScienceDirect 2025 review). - E-waste ranked Top 10 toxic threats globally (Pure Earth 2013). """ 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() SCENARIOS = { # Agbogbloshie-type mega-site "mega_site_west_africa": { "setting_probs": {"urban_dumpsite": 0.50, "peri_urban": 0.30, "urban_market": 0.20}, "role_probs": {"burner": 0.25, "dismantler": 0.25, "collector": 0.15, "community_resident": 0.20, "child_worker": 0.15}, # Blood Pb elevated: workers ~15-25 µg/dL (Agbogbloshie studies) "blood_pb_gm_worker": 18.0, "blood_pb_gsd": 2.0, "blood_pb_gm_community": 6.5, "blood_cd_gm_worker": 1.5, # µg/L "blood_cd_gm_community": 0.5, "urine_cr_gm_worker": 3.0, # µg/L "ppe_use_pct": 0.05, "respiratory_symptoms_burner": 0.55, "skin_symptoms_pct": 0.30, "ewaste_volume_tonnes_yr": 215000, }, # Medium informal site (Lagos, Nairobi type) "medium_site_urban": { "setting_probs": {"urban_market": 0.45, "peri_urban": 0.35, "urban_dumpsite": 0.20}, "role_probs": {"burner": 0.20, "dismantler": 0.30, "collector": 0.15, "community_resident": 0.25, "child_worker": 0.10}, "blood_pb_gm_worker": 12.0, "blood_pb_gsd": 1.9, "blood_pb_gm_community": 5.0, "blood_cd_gm_worker": 1.0, "blood_cd_gm_community": 0.4, "urine_cr_gm_worker": 2.0, "ppe_use_pct": 0.10, "respiratory_symptoms_burner": 0.45, "skin_symptoms_pct": 0.22, "ewaste_volume_tonnes_yr": 50000, }, # Small dispersed sites (smaller cities) "small_dispersed_sites": { "setting_probs": {"peri_urban": 0.45, "urban_market": 0.30, "rural": 0.25}, "role_probs": {"burner": 0.15, "dismantler": 0.30, "collector": 0.20, "community_resident": 0.25, "child_worker": 0.10}, "blood_pb_gm_worker": 8.0, "blood_pb_gsd": 1.8, "blood_pb_gm_community": 4.0, "blood_cd_gm_worker": 0.7, "blood_cd_gm_community": 0.3, "urine_cr_gm_worker": 1.5, "ppe_use_pct": 0.08, "respiratory_symptoms_burner": 0.35, "skin_symptoms_pct": 0.18, "ewaste_volume_tonnes_yr": 10000, }, } SCENARIO_FILES = { "mega_site_west_africa": "ewaste_mega_site.csv", "medium_site_urban": "ewaste_medium_urban.csv", "small_dispersed_sites": "ewaste_small_dispersed.csv", } EWASTE_TYPES = {"computers_monitors": 0.25, "mobile_phones": 0.20, "cables_wiring": 0.20, "appliances": 0.15, "batteries": 0.10, "mixed_other": 0.10} PROCESSING_METHODS = {"open_burning": 0.35, "manual_dismantling": 0.30, "acid_bath": 0.10, "hammer_chisel": 0.15, "sorting_only": 0.10} def _choice(rng, prob_map): 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 _simulate_scenario(name, params, seed): rng = np.random.default_rng(seed) records = [] for idx in range(N_PER_SCENARIO): year = int(rng.choice(YEAR_RANGE, p=YEAR_WEIGHTS)) setting = _choice(rng, params["setting_probs"]) age = int(np.clip(rng.normal(28, 12), 8, 60)) sex = rng.choice(["male", "female"], p=[0.72, 0.28]) role = _choice(rng, params["role_probs"]) is_worker = int(role in ("burner", "dismantler", "collector")) is_child = int(age < 18) years_exposure = int(np.clip(rng.normal(5, 3), 0, 25)) if is_worker else int(np.clip(rng.normal(3, 2), 0, 15)) ewaste_type = _choice(rng, EWASTE_TYPES) processing_method = _choice(rng, PROCESSING_METHODS) if is_worker else "none" is_burner = int(role == "burner" or processing_method == "open_burning") ppe_use = int(is_worker and rng.random() < params["ppe_use_pct"]) gloves = int(ppe_use and rng.random() < 0.60) mask = int(ppe_use and rng.random() < 0.30) hours_per_day = float(np.clip(rng.normal(8, 2), 2, 14)) if is_worker else 0 # Biomarkers (PubMed 27858271; PMC8287752) if is_worker: blood_pb = float(np.clip( rng.lognormal(np.log(params["blood_pb_gm_worker"]), np.log(params["blood_pb_gsd"])), 1, 100, )) blood_cd = float(np.clip( rng.lognormal(np.log(params["blood_cd_gm_worker"]), 0.7), 0.1, 20, )) urine_cr = float(np.clip( rng.lognormal(np.log(params["urine_cr_gm_worker"]), 0.6), 0.1, 30, )) else: blood_pb = float(np.clip( rng.lognormal(np.log(params["blood_pb_gm_community"]), np.log(1.7)), 0.5, 50, )) blood_cd = float(np.clip( rng.lognormal(np.log(params["blood_cd_gm_community"]), 0.6), 0.05, 10, )) urine_cr = float(np.clip( rng.lognormal(np.log(max(params["urine_cr_gm_worker"] * 0.3, 0.3)), 0.5), 0.05, 15, )) if ppe_use: blood_pb *= 0.8 blood_cd *= 0.85 elevated_pb = int(blood_pb >= 10) elevated_cd = int(blood_cd >= 1.0) # Health outcomes # Respiratory (PMC7084368: burning workers high risk) resp_risk = params["respiratory_symptoms_burner"] if is_burner else 0.10 cough_chronic = int(rng.random() < resp_risk * 0.8) wheeze = int(rng.random() < resp_risk * 0.5) dyspnoea = int(rng.random() < resp_risk * 0.4) reduced_fev1 = int(is_burner and rng.random() < 0.25) any_respiratory = int(cough_chronic or wheeze or dyspnoea or reduced_fev1) # Skin/dermal (PMC10815197) skin_rash = int(rng.random() < params["skin_symptoms_pct"]) burns_injury = int(is_burner and rng.random() < 0.15) eye_irritation = int(is_burner and rng.random() < 0.30) # Neurological (lead-related) headache = int(rng.random() < np.clip(0.10 + blood_pb * 0.005, 0, 0.5)) fatigue = int(rng.random() < np.clip(0.15 + blood_pb * 0.004, 0, 0.5)) child_developmental = int(is_child and blood_pb > 5 and rng.random() < 0.20) # Kidney (Cd-related) proteinuria = int(blood_cd > 1.0 and rng.random() < 0.15) # DNA damage (PMC8392572) dna_damage_risk = int(rng.random() < np.clip(0.05 + years_exposure * 0.01 + is_burner * 0.10, 0, 0.4)) # Legislation & formalization ewaste_legislation_exists = int(rng.random() < 0.25) formal_recycler = int(is_worker and rng.random() < 0.05) health_screening_access = int(rng.random() < 0.08) record = { "record_id": f"{name[:3].upper()}-{idx:05d}", "scenario": name, "year": year, "setting": setting, "age": age, "sex": sex, "role": role, "is_worker": is_worker, "is_child": is_child, "years_exposure": years_exposure, "ewaste_type": ewaste_type, "processing_method": processing_method, "is_burner": is_burner, "ppe_use": ppe_use, "gloves": gloves, "mask": mask, "hours_per_day": round(hours_per_day, 1), "blood_pb_ugdL": round(blood_pb, 1), "blood_cd_ugL": round(blood_cd, 2), "urine_cr_ugL": round(urine_cr, 2), "elevated_pb": elevated_pb, "elevated_cd": elevated_cd, "cough_chronic": cough_chronic, "wheeze": wheeze, "dyspnoea": dyspnoea, "reduced_fev1": reduced_fev1, "any_respiratory": any_respiratory, "skin_rash": skin_rash, "burns_injury": burns_injury, "eye_irritation": eye_irritation, "headache": headache, "fatigue": fatigue, "child_developmental": child_developmental, "proteinuria": proteinuria, "dna_damage_risk": dna_damage_risk, "ewaste_legislation": ewaste_legislation_exists, "formal_recycler": formal_recycler, "health_screening_access": health_screening_access, } records.append(record) return pd.DataFrame(records) def main(): 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()