Mobile sensors are increasingly used to monitor air quality to accurately quantify human exposure to air pollution. These sensors are subject to various issues (misuse, malfunctions, battery problems, etc) that are likely to cause data quality problems. These quality problems may have a considerable impact on the reliability of analytical studies. In this work, we address the problem of data quality evaluation and improvement in mobile crowd-sensing environments. Our work is focused on the data completeness quality dimension. We introduce a multi-dimensional model to represent the data coming from the sensors in this context, and then present the different facets of data completeness inspired by the model. We propose quality indicators capturing different facets of completeness along with their corresponding quality metrics. We also propose an approach to improve data completeness by extending two existing data imputation techniques, SVDImpute and KNNImpute, with information about the sensor quality. Our experiments show that our quality-aware imputation approach improves the accuracy of the imputation achieved by the original techniques.