Mobile sensors are being widely used to monitor air quality to quantify human exposure to air pollution. These sensors are prone to malfunctions, resulting in many data quality issues, which in turn impacts the reliability of analytical studies. In this work, we address the problem of data quality evaluation in mobile crowd-sensing environments, and we focus on data completeness. We introduce a multi-dimensional model to represent the data coming from the sensors in this context and we discuss different facets of data completeness. We propose quality indicators capturing different facets of completeness along with the corresponding quality metrics. We provide some experiments showing the usefulness of our proposal.