Air quality is one of the major risk factors in human health. Its continued degradation in the recent decades, particularly in urban and industrialized environments, is becoming a central concern for our societies. Emerging connected micro-sensors offer the opportunity to measure each person exposure to air pollution anywhere and anytime, while contributing to the observation network. Mobile Crowd Sensing (MCS) is a trending concept based on this technology. To date, MCS data are under-utilized due to the gap between raw data and usable information. The objective of this paper is to investigate an OLAP approach in filling this gap. In this respect, this paper introduces a methodology and a multidimensional data model designed for processing and querying the different aspects of individual trajectories together with underlying pollution data at different granularity levels. The core data model is generic enough to act as a reference model for further analysis.