One of the most important goals of green logistics is to reduce the destructive side effects of freight transportation which can lead to
several types of health risks. The pollution routing problem (PRP) is an extension of the vehicle routing problem (VRP) which considers
greenhouse gas emission in addition to the travel time, cost, and delivery constraints. Another environmental impact of vehicles,
especially in urban areas is noise emission which is ignored in optimization PRP researches. This form of pollution endangers physical
well-being by causing annoyance, hearing loss, heart disease, mental issues for children, and sleep disorders. In this paper, using noise
emission mathematical equations, we aim to reduce noise and exhaust gas emission in VRP with respect to delivery and time window
constraints. Our model presents a scenario-based robust optimization model for PRP, considering noise and greenhouse emissions as
well as inherent demand uncertainty. In order to model the uncertainty of the problem, we adopted a scenario-based robust
optimization approach which considers uncertain scenarios with a determined occurrence probability. Furthermore, based on the noise
effecting factors such as speed and acceleration of vehicle. This model suggests a range of solutions that can be selected according to
decision maker conservatism level and preferences. To examine the performance of the model, a real-world data sets from PRPLIB
instances which are randomly selected from the cities in United Kingdom. The results approve the possibility of finding a sustainable
solution for VRP which takes into account various parameters including cost, social, and environmental aspects of freight transportation
for a vehicle fleet.