Data mining modeling on the environmental impact of airport deicing activities

This paper presents a statistical analysis on the environmental impact of airport deicing activities at Dallas-Fort Worth (D/FW) International Airport. The focus of this paper is on aircraft deicing, which typically uses a spray of aircraft deicing and anti-icing fluids (ADAF). ADAF has a high concentration of ethylene/propylene/diethylene glycol, which shears off airfoil surfaces during takeoff and drips to the runways during taxiing. A significant portion of the glycol runs off and mixes with the airport’s receiving waters during heavy deicing periods, resulting in bacterial growth that causes an increase in chemical oxygen demand (COD) and a subsequent reduction in dissolved oxygen (DO) in the receiving waters. In this study, statistical methods for data mining were employed to evaluate the impact of airport deicing activities on COD and DO in the receiving waters immediately surrounding D/FW Airport. In particular, decision tree models were developed to determine important explanatory variables for predicting levels of COD and DO in the airport’s waterways. The decision tree modeling and analysis of COD determined north–south wind, glycol usage at a specific deicing pad, and monitoring site to be significant explanatory variables. The impact of glycol usage on DO was apparent as every decision tree at least one group with a median DO below 4.0 mg/l, and these low-DO groups were associated with high glycol usage. These results are crucial to D/FW Airport in their goal to minimize the potential adverse impact of deicing activities on the water quality in waterways proximate to the airport. The advantages of data-driven modeling and analysis are its cost-effectiveness due to its potential to be implemented without making major changes in physical systems, ease of application, and usefulness in making future management decisions.