Local design and development of low-cost air sensing and data science methods in sub-Saharan Africa – lessons learnt from Uganda

Abstract

BACKGROUND AND AIM: Sensing and modelling approaches need to be conceived and designed while taking into account unique local contexts in order to be responsive and effective in addressing the environmental data and evidence gaps for the communities and authorities that need them. We report on experiences of designing, developing, deploying and maintaining a local air quality monitoring, modelling and analysis system in Uganda. METHODS: The AirQo system includes a citywide air quality monitoring network of monitors developed locally to reduce maintenance frequency in dusty environments, leverage solar power, have capability for near-real time monitoring and transmission of air quality data over cellular networks, support for complementary static and mobile (motorcycle) deployment approaches, and application of machine learning approaches for calibration to improve data accuracy. RESULTS:The air quality datasets have enabled design and development of data science products that generate evidence to inform awareness, education and policy interventions including: (1) a digital air quality platform targeted at the city governments in Uganda to visualise trends, forecast and spatial variations of air pollution to the local level. (2) public-facing digital channels such as mobile apps and web tools that provide realtime and historical analysis of air pollution in places of interest, for examples, schools. We also report on experiences from community engagement initiatives where citizens can report observed pollution events to the AirQo platform using social media tools such as WhatsApp. CONCLUSIONS:The lessons from this talk are expected to inform ongoing efforts in other African cities and build local capacity in air quality monitoring monitoring and analysis. KEYWORDS: air quality, monitoring, modelling and analysis