Abstract: Accurately measuring atmospheric pollutants is critical for decision-making and designing policies aiming to improve air quality and reduce human health exposure. The advent of inexpensive sensor-based technologies means that there are isare now a growing number of measuring devices available that could be useful for this purpose. However, not all this range available today will necessarily be adequate forto the problem that the user wants to tackle. It is therefore key to ask whether the information provided by instrument "X" is appropriate for the intended purpose. Clearly focusing on the question to be answered, and defining the required data quality accordingly, is key to identifying those instruments/techniques capable of meeting this requirement. Since the measurement uncertainty ultimately determines the information content, it is therefore critical to estimate this parameter in a robust and transparent way, thus allowing the potential application of the instrument in question to be defined. In this work, we explore the nature of common air pollution measurement sources of errors in the real world and the implications they have for traditional uncertainty metrics and other potentially more insightful approaches to assessing measurement uncertainty. For this, we employed first simulated datasets combining different sources of error/interferences from (i) a non-target chemical, (ii) physical parameters and (iii) electromagnetic fields. Then we study real-world data from the QUANT project which involves a range of LCS technologies and multiple reference instruments in 3 urban sites in the UK. We then use this information to explore the performance of these technologies and develop methods that will enable their integration into the air quality monitoring infrastructure and use in atmospheric chemistry research. This work will ultimately make it possible to optimize the applications of these technologies based on the quality of the LCS data.