One of the main concerns of smart cities is to improve public health which is mainly threatened by air pollution due to the massively increasing urbanization. The reduction of air pollution starts first with an efficient monitoring of air quality where the main aim is to generate accurate pollution maps in real time. Spatiotemporally fine-grained air pollution maps can be obtained using physical models which simulate the phenomenon of pollution dispersion. However, these simulations are less accurate than measurements that can be obtained using pollution sensors. Combining simulations and measurements, also known as data assimilation, provides better pollution estimations through the correction of the fine-grained simulations of physical models. The quality of data assimilation mainly depends on the number of measurements and their locations. A careful deployment of nodes is therefore necessary in order to get better pollution maps. In this paper, we tackle the deployment problem of pollution sensors and propose a new mixed integer programming model allowing to minimize the overall deployment cost of the network while achieving a required assimilation quality and ensuring the connectivity of the network. We then design a heuristic algorithm to solve efficiently the problem in polynomial time. We perform extensive simulations on a dataset of the Lyon city, France and show that our approach provides better air quality monitoring when compared to existing deployment methods that are designed without taking into account the outputs of physical models. We also show that in terms of connectivity, the communication range of sensor nodes might have a noteworthy impact on the quality of pollution estimation.