Data management in resource-limited settings: Unpacking the role of robust digital solutions for air quality data management in African cities.

Increasing awareness of air pollution requires access to timely and reliable air quality data and information, and yet many African cities lack effective air quality monitoring infrastructure, largely because of the resource constraints of establishing and managing a continuous monitoring network. Low-cost sensor platforms have the potential to close the air quality data gaps in resource-strained settings such as Africa, but the continued lack of accessible and reliable infrastructure for data management is a major hindrance to effective air quality management.

Moreso, managing a large Internet of Things (IoT)-based sensor network can be complex, and the demand for a case-specific and highly customizable platform, coupled with its conceptualization & implementation complexities, renders most existing IoT platforms ineffective. There is a need for a platform infrastructure for continuous support and management of air quality data with a high spatial and temporal resolution to facilitate sophisticated analysis; while taking care of the associated structural challenges of low-cost sensors.  The AirQo platform, a robust could-native software is a novel communityaware digital platform for managing large-scale air quality networks, applicable in resource-strained environments. This customisable and scalable platform attempts to address the data access challenges, with capabilities to become a ‘one-stop centre’ for management of other third party IoT sensor networks. Different interfaces through mobile application, web-based dashbord and platform cater for diverse data needs. The robust approach enables decision makers and other stakeholder communities have access to timely and quality assured air quality data. Using a set of metrics, user-experiece can be computed and compared with existing IoT management platforms. Software design considerations including (1) Multi-tenancy, (2) Data pipeline, (2) Sharded Database Cluster, (3) Microservices architecture, (4) Containerized deployment, and (5) Interoperability are recommended to support replication in other use-cases.