EventsThe 2nd International Electronic Conference on Applied Sciences
Published
with-doi10.3390/ASEC2021-11171 (registering DOI)
This submission belongs to the session D. Environmental and Sustainable Science and Technology of the event The 2nd International Electronic Conference on Applied Sciences
Published date
15 Oct, 2021
Academic Editor
author-avatarDibyendu Sarkar
Citation
Francis Olawale Abulude, Matthew Ojo Oluwafemi, Kikelomo Mabinuola Arifalo, Indoor Household Air Quality Assessment: The case of the use of low cost sensor, in Proceedings of The 2nd International Electronic Conference on Applied Sciences, 15 October–31 October 2021, MDPI: Basel, Switzerland, doi: 10.3390/ASEC2021-11171
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Indoor Household Air Quality Assessment: The case of the use of low cost sensor

Matthew Ojo Oluwafemi 2
1. Science and Education Development Institute, Akure, Ondo State, Nigeria, Nigeria
2. Department of Horticulture and Landscape Technology, Federal College of Agriculture, Akure, Ondo State, Nigeria
3. Department of Chemistry, University of Education, Science, and Technology, Ikere, Ekiti State, Nigeria
Abstract

According to World Health Organisation (WHO) over 4 million people die world-wide in 2012. This was due to one of the indoor contributors - particulate matter (PM) of a diameter 2.5. The use of low-cost PM measurements is assisting individuals to take actions by providing personalized information on indoor concentrations in real time. The low-cost sensor – SentinAir used in this study was designed and developed by group of researchers from ENEA-Italian National Agency for New Technologies, Energy and Environment. Sustainable Development Department, Research Center of Brindisi, Italy. It measures PM (1, 2.5, 10), NO2, SO2, CO2, O3, temperature, and relative humidity. The aim of this study was to deploy the sensor into the indoor (kitchen) of a household with the view of assessing all the parameters over a period of thirty (30) days as a preliminary investigation measurement. The protocol of the sensor was strictly followed. The results (mean) depicted: PM 1 (17.80 µg/m3), PM 2.5 (25.21 µg/m3), PM 10 (27.61 µg/m3), CO2 (435.3 ppm), O3 (24.75 ppb), NO2 (66.52 ppb), SO2 (48.04 ppb), temperature (34.1 oC), and humidity (64 %). When these results were compared with the WHO and National Environmental Standards and Regulations Enforcement Agency (NESREA) it was observed that the PM2.5 and 10 were within the 24 h guideline values of 25 and 50 µg/m3 respectively. Although that of PM 2.5 may be a risk. There were significant influences of temperature and humidity on the pollutants. Food frying and baking generated the largest increase in PM, in the kitchen activity. Because the data is reproducible, it is recommended that this low-cost PM sensor be integrated into an indoor air-quality measurement network to assist individuals in managing their personal exposure.

Keywords
Indoor air
Particulate matter
Sensor network
Low-cost particulate matter sensor
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