Project description

My data story covers the topic of air pollution in Nairobi primary schools. Using sensor data from the sensors.AFRICA project, the story aims to highlight an often ignored health risk for children – harmful air. In a country where issues like famine and poverty are still key issues to be addressed for a large portion of the population, it is hard for the common citizen to cast their focus on the type of air they breathe, and what the long term consequences might be. The goal of the story was, therefore, to highlight this issue and bring attention to the Nairobi County, Government, local NGOs and CSOs and the community at large. The sensors were put in 3 schools by the time of writing. Each cost $5.

What makes this project innovative?

The story was done after months of work in collaboration with the sensors.AFRICA team that involved the use of low-cost sensors that transmit particulate matter data every 2 minutes. The team at Code for Africa, a civic tech organisation that houses the sensors.AFRICA project helped to then analyse this data and visualize it. The innovative part of the project was that the sensors were maintained by the schools' science clubs via their club patron (a teacher in the school). This, therefore, made it a community-led project rather than a case where technologists come in, fix up the equipment and come to check in on it every once in a while, while the people in the school have no idea what it is or how it works. Specifically, the two clubs that were 1) The 4-H Foundation on Kenya - - whose programmes are focused on Youth development including career readiness through Science, Technology, Engineering and Mathematics (STEM), Sustainable Agriculture, Healthy Living, and Citizenship. 2) The GLOBE Programme in Kenya - - an outreach program to promote and sustain interest in the Science, Technology, Engineering and Mathematics (STEM) subjects.

What was the impact of your project? How did you measure it?

The biggest impact of the project was that Nairobi County drafted an air quality action plan for the years 2019-2023: This was due to an increased number of stories on the subject spearheaded by my story that drew attention to the rapid increase in air pollution within the city.

Source and methodology

Most of the core data used was from the air quality sensors. The sensors' data readily available through their open data policy. Other data sources were sourced through desktop research. Most information was retrieved from agencies such as UNICEF who had just released a report on how air pollution affects the brain development of children. UNEP was an especially helpful resource, as they are one of the few other organizations doing air quality research in Kenya. Using data from UN bodies also ensured the validity of the data and also created a good juxtapose for the data collected by the sensors, to ensure the data collected was not erroneous.

Technologies Used

The air quality sensors measure levels of particulate matter and pollutants such as carbon monoxide and nitrous oxides that have been linked to respiratory ailments and health problems linked to lung cancer and other deadly diseases. Particulate matter is one of the deadliest air pollutants, and its width is measured in micrometres. PM10 is particulate matter 10 micrometres or less in diameter, and PM2.5 is particulate matter 2.5 micrometres or less in diameter. For comparison, a human hair is about 100 micrometres, so roughly 40 PM2.5 particles could be placed on one hair’s breadth. The air quality sensor consists of an SDS011 particulate matter sensor, a DHT22 Temperature and Humidity Sensor, and a GSM SIM800L tool that transmits data in real time. All these components are connected to a NodeMCU microcontroller, which reads data from the various sensors and controls their operations. The NodeMCU microcontroller has a wifi module onboard making communication of IoT projects a breeze. The SDS011 particulate matter sensor can measure PM10 and PM2.5 particles for concentration ranges between 0–999 micrograms per cubic meter. It uses laser scattering to measure particle concentration between 0.3 to 10 micrograms in the air. Air passes through an inlet and then through the detection area before being ejected from the sensor through the inbuilt fan. The particles pass a laser, scattering the light and transforming it into electrical signals. These signals show the number and diameter of the particles contained in the air sample. The sensor has a response time of 10 seconds, making data collected near real time. The data was analysed using simple spreadsheets.

Project members

Catherine Gicheru James Chege Tricia Govindasamy Emma Kisa




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