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Industrial Air Quality Visual Sensor Analytics

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Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 721))

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Abstract

Rapid advancements in affordable, miniaturised air pollution sensor technologies and embedded systems are enabling a new wave of reliable air quality sensing devices. Due to their ability to measure air pollution ad hoc and in great spatio-temporal resolution such devices enable advanced processing and analytics.

Our team has been engaged in the development of reliable air quality sensing devices using low-cost sensors, custom sensor boards, embedded software and cloud services. Our devices use pre-calibrated optical Particulate Matter (PM) sensors, measuring concentrations in \(ug/m^3\) of PM1.0, PM2.5 and PM10, NDIR, \( CO_2 \) sensors and electrochemical \(\textit{CO}\) sensors, as well as differential pressure sensors, while all devices monitor also humidity and temperature. The data is sampled at a few seconds interval and it is transferred to a cloud-based platform where is stored and visualised in real-time, raising alerts. A delay tolerant middleware stores data locally, temporarily for up to 12 h.

The devices have good accuracy, response time and sensitivity in indoor pollution levels, however, they suffer from low signal strength of the WiFi receiver as a result of which they often become disconnected for long period of times. A sensor data analytics platform was therefore developed using python. We introduce two new algorithms for auditing the sampling process and detecting and removing outliers specific to air quality data. Furthermore we introduce a new methodology for detecting patterns based on visual analytics.

We have conducted a pilot application in a state-of-the art industrial space that is sensitive to infection caused by particulate matter such as dust. Fifteen PM devices were installed in three different production areas with varying air quality sensitivity. Indicative results from two of the devices from the first production area show that mining sensor timeseries with the above analytics produces useful insights on the level of pollution and industrial activity while confirming the stable performance of our devices.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, 67100, Greece

    Eleftheria Katsiri

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  1. Eleftheria Katsiri
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Correspondence to Eleftheria Katsiri .

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  1. University of Detroit Mercy, Farmington Hills, MI, USA

    Kevin Daimi

  2. Asia Pacific International College, Sydney, NSW, Australia

    Abeer Al Sadoon

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Katsiri, E. (2023). Industrial Air Quality Visual Sensor Analytics. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_31

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