Mobile crowdsensing is a powerful paradigm that takes advantage of low-cost sensors and population density. It allows for large-scale deployments and collection of extensive data, offering a great advantage in multiple fields such as air pollution monitoring, which is a major concern worldwide. Given the mobile nature of the crowd, mobile crowdsensing platforms need to implement adequate route selection/planning solutions to better guide the crowd through the area of interest and maximize the quality of monitoring. In this paper, we propose two route selection algorithms that take into consideration the low accuracy of low-cost sensors in order to find the most informative routes. The similarity-based route selection algorithm aims to maximize spatial coverage by reducing overlaps between participant routes. The cluster-based route selection takes advantage of hierarchical clustering to build groups of similar points of the map according to explanatory variables. We compare the proposed solutions to baseline route selection algorithms, and the results show that our solutions allow for a better estimation while being efficient in terms of travel distance.