In this paper, we focus on the application of air quality monitoring where wireless sensor networks (WSNs) collect pollution measurements to estimate ground-truth pollution maps. We present UrMapSens, a novel WSN design strategy that aims to optimize the deployment locations of sensor nodes and the estimation of urban pollution maps. Unlike prior work, UrMapSens performs the optimization of both sensor deployment locations and pollution maps' estimation in a joint way. We design our UrMapSens approach based on data assimilation by combining physical models' simulations and expected sensor measurements in the estimation process of air pollution maps. We ensure a joint mapping-sensing design by determining the optimal number and locations of sensor nodes at the same time, in addition to finding the best way of assimilating the measurements that will be collected. Moreover, we use enhanced linearization techniques to provide a convex mathematical model based on mixed integer programming, which can be solved efficiently using numerical solvers. To validate UrMapSens in real-life scenarios, we designed an air pollution monitoring platform and used it to collect a dataset of nitrogen dioxide concentrations in Lyon, France. Based on the experimentally collected dataset, we perform extensive evaluations of the proposed approach and show that our joint mapping-sensing design outperforms the prior work in which the optimization of sensor deployment locations and pollution maps' estimation are performed independently.