High spatial resolution IoT based air PM measurement system

Icoz E., Malik F. M., İÇÖZ K.

ENVIRONMENTAL AND ECOLOGICAL STATISTICS, vol.28, no.4, pp.779-792, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 4
  • Publication Date: 2021
  • Doi Number: 10.1007/s10651-021-00494-4
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, ABI/INFORM, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Environment Index, Geobase, Greenfile, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.779-792
  • Keywords: Air pollution, Cloud computing, High spatial resolution, Internet of things, Optical sensing, Particulate matter, PM2, 5
  • Abdullah Gül University Affiliated: Yes


Air pollution is one of the global problems of the current era. According to World Health Organization more than 80% of the people living in metropolitan areas breathe air which exceeds the guideline limits. Particulate matter, the mixture of liquid and solid particles having diameters less than 10 mu m, is one of the important pollutants in the air. The main source of the Particulate matter is mostly burning reactions associated with industry, vehicles and homes. Several studies have shown the lethal impact of particulate matter to public health and environment. The rise of particulate matter amount in air has been linked to several health problems such as not only respiratory diseases but also mortality in infants and heart attacks. Currently, bulky stations which are high-cost and have limited spatial resolution are used to monitor the air quality. In this study we developed an alternative particulate matter measurement system which is portable and low-cost (less than 200 USD) and also integrated with cloud computing. The system allows real time distant monitoring of PM particles with high spatial resolution (meter range). The developed sensor system is able to provide air quality data in correlation with the existing stations (R-2 = 0.87). The statistical comparison between the developed system and the reference methods revealed that two systems produced statistically equal results in detecting the variations of the particulate matter.