Date of Award


Document Type


Degree Name

Master of Science (MS)


Environmental Science

First Advisor

Dr. Munjed Maraqa

Second Advisor

Dr. Mwafag Ghamma

Third Advisor

Dr. Salem Khalfan Al Kaabi


Collection and transportation of municipal solid waste (MSW) often account for a significant amount of the total budget allocated for waste management. A major portion of that is attributed to fuel consumption. Meanwhile, vehicles involved with waste collection can emit significant levels of atmospheric pollutants. Hence, optimization of waste collection yields both financial and environmental benefits. No work has been done to optimize fuel consumption during MSW collection in Al Ain city. In this study, several cases were developed using ArcGIS Network Analyst tool in order to establish optimum conditions for MSW collection in Um Gafa district in Al Ain city, with an objective function of minimization of fuel consumption. A geographic information system was created based on data collection and GPS tracking of collection route and bins position. The study revealed that waste collection at Um Gafa at the current time does not strictly follow U-turn and curb approach policies. When route optimization is applied for similar traffic conditions as the current ones, a saving of 14.3% in fuel consumption is gained. In addition, emitted CO2 is reduced by 7.2%. However, by strictly following the U-turn and curb approach policy of the traffic department, the relative saving in fuel consumption was much less (5%) as compared to the current practice of vehicle maneuvering for waste collection. Two new models were proposed for optimal number and location of bins. One model was based on a 40-m service zone while the other was based on population density and landuse. By adopting the first model, the number of bins was reduced by 12%, while in the second model the number of bins was reduced by 20%. In both models, more efficient routes in terms of fuel consumption and reduction in emissions have resulted, with second model showing superiority compared to the first model.