This paper addresses the deployment of nodes in wireless sensor networks (WSNs) for monitoring air pollution and hence focusses on optimization of placement of sensors to balance cost and accuracy. Authors employ the Gaussian dispersion model to estimate pollutant concentrations from various sources. The plume model is used to simulate and analyze different scenarios with multiple pollution sources. The study explores various optimization techniques to enhance the efficiency of sensor and sink deployment. By evaluating these techniques through simulations, authors aim to reduce the overall cost of deployment while confirming comprehensive and precise data collection on pollutant levels across the monitored area. The results demonstrate that strategic deployment and optimization can significantly improve the effectiveness and cost-efficiency of air quality monitoring systems using WSNs.