Real world test - Urban low income - A family of four

A project log for OpenHAP

An opensource DIY tool to help combat "household air pollution"-which kills more people globally than HIV/AIDS and malaria combined.

aloismbuturaaloismbutura 09/01/2019 at 18:270 Comments

A second test was carried out within an urban single-unit household in Kibera informal settlement. Actual names have been altered in order to keep identities private. We thank them for offering us the opportunity and permission to collect and analyse their data and make it public for the sake of public awareness. Asante!

Meet the family :)

John Doe* has lived within Kibera for the past 16 years, he met his wife Jane Doe* within Kibera. They are married with 2 children: A girl aged 15 years in a local day high school and a 6 month bundle of Joy. They live in a single room  made of aged iron sheets roughly measuring 2 meters(~12 feet) by 3 meters(~36 feet) that they share with their 2 children. They also cook within this single room. Amenities such as bathroom are shared. There is electricity but illegal connections causing fires in the neighborhood have made them afraid of connecting their home to the grid! They thus use candle for lighting. The household energy mix includes charcoal briquettes and kerosene. Kerosene is mainly used during the day while briquettes are used in the evening.

A sample kerosene stove 

Image result for briquette stoveA sample briquette stove

Data analysis

Data was captured in  a CSV file as below using an OpenHAP unit and analysed.

The above image shows raw data collected.

Plotting MLX90640 max temperatures, PM2.5 values against time

Tp obtain the plots above of stove temperature and PM2.5 against time, the unix time recorded in column 1 is converted into ISO 8601 format, the day , hour, minute, second is extracted and plotted on a continuous line. The corresponding values of PM2.5  and Max temperature(Obtained by pre-processing the MLX90640 pixel data before saving to the CSV file on the SD card) are plotted against this.

Overlaying activity information on presence information

We obtain 

The above plots are a superposition of two datasets:

The Y axis can take a universe of two values with regard to presence/absence:

As we can only measure the activity when the participant is present, we superpose activity information on the presence information. To do this we scale the dBm information and overlay in on the presence plots above.

The more variation there is, the more active the person. This can be used to infer when one may be asleep or awake.

Analysing presence/activity information

The primary participant here represents an actual mother with a six month old child. She takes care of her child as is with it 24 hours a day. A typical day is split as follows from the data - You do not even need to know the household culture to see what the data is telling you:

Analyzing presence/activity information with pollution information

Calculating pollutant exposure

The indoor pollutant exposure is calculated by getting the mean of the pollutant levels. This is calculated as below:

As can be seen, the husband is negligibly exposed, with his values being 5 times below the WHO limits. The wife and the 6 month child receive majority of the exposure and are around 1.5 times the recommended limit. In addition, this does not consider the carbon monoxide poisoning leading to slow suffocation! We did enquire about this but they did not know the effects of CO poisoning given they have not had the priviledge of education and I would not blame them but it is important that they now know the effects and have changed to cooking in open spaces outside!