Airport Operations Optimization and Emission Reduction
Module: Sustainable Tech | Difficulty: Premium
Aircraft Emission Index
SAF Blending Formula
Comparison
| Emission Source | Share of Airport CO2 | Reduction Potential | Priority |
|---|---|---|---|
| Aircraft landing/takeoff | 60-70% | 30-50% (SAF) | High |
| Ground vehicles | 10-15% | 80-100% (electric) | High |
| Terminal energy | 15-20% | 50-70% (renewables) | Medium |
| Cargo operations | 5-10% | 40-60% | Medium |
Python Implementation
import numpy as np
class AirportSustainabilityOptimizer:
def __init__(self, annual_operations=200000, avg_fuel_per_op=2500):
self.operations = annual_operations
self.avg_fuel = avg_fuel_per_op
def total_emissions(self, lto_fuel, ground_fuel, terminal_energy, ef=3.16):
return (lto_fuel + ground_fuel) * ef + terminal_energy * 0.5
def saf_blending_optimization(self, base_emissions, saf_blend_ratios, saf_reduction=0.8):
reductions = []
for ratio in saf_blend_ratios:
reduction = base_emissions * ratio * saf_reduction
reductions.append({'blend': ratio, 'reduction': reduction,
'cost_premium': ratio * 2.0})
return reductions
def electric_gse_transition(self, fleet, diesel_emissions, electric_emissions):
n_electric = len([v for v in fleet if v['type'] == 'electric'])
total = len(fleet)
savings = (total - n_electric) * (diesel_emissions - electric_emissions)
return {'electric_pct': n_electric / total * 100, 'annual_savings': savings}
Research Insight: Airport electrification of ground vehicles can reduce ground-side emissions by 80-100%.