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With the upcoming growth of the Internet of Things (IoT), which is translated into millions of interconnected devices reporting a high volume of data coming from heterogeneous sources (sensors), it is necessary to assess the confidence of data in order to provide the system with trustable information that can be used to get real insights from the physical world and thus take proper decisions or actions over it. Having in mind that ensuring data quality is key to ease user engagement, acceptance of IoT services and large scale deployments [1], a new critical issue arises which is related to the quality of the data in IoT. Some applications might have a different definitions and indicators for data quality (DQ) and thus different threshold for acceptance of the data. In this work, we explore a smart city application in the field of environmental monitoring and identify the related DQ indicators that apply within this context. Our approach is evaluated over a real dataset retrieved from SIATA's citizen scientist low-cost sensor network, an air quality monitoring system that can be encompassed within the IoT paradigm and that is composed by more than 200 nodes deployed all over the Aburra Valley in Antioquia, Colombia. The results show that feasibility assessing data quality and importance data quality awareness for an IoT application, as a tool for it to take proper actions on the real world. Our approach is evaluated over a real dataset retrieved from SIATA's citizen scientist low-cost sensor network, an air quality monitoring system that can be encompassed within the IoT paradigm and that is composed by more than 200 nodes deployed all over the Aburra Valley in Antioquia, Colombia. The results show that feasibility assessing data quality and importance data quality awareness for an IoT application, as a tool for it to take proper actions on the real world.