Low-cost energy-efficient air quality monitoring system using sensor network

Author(s):  
Mare Srbinovska ◽  
Aleksandra Krkoleva Mateska ◽  
Vesna Andova ◽  
Maja Celeska Krstevska ◽  
Tomislav Kartalov
Author(s):  
Maja Celeska Krstevska ◽  
Mare Srbinovska ◽  
Tomislav Kartalov ◽  
Vesna Andova ◽  
Aleksandra Krkoleva Mateska

2021 ◽  
pp. 597-607
Author(s):  
Thuyet That Vo Nguyen ◽  
Nguyen Duc Thinh ◽  
Hien Vo ◽  
Tran Quang Nhu ◽  
Nguyen Binh Minh

2017 ◽  
Author(s):  
Michael Mueller ◽  
Jonas Meyer ◽  
Christoph Hueglin

Abstract. This study focuses on the investigation and quantification of low-cost sensor performance in application fields such as the extension of traditional air quality monitoring networks or the replacement of diffusion tubes. For this, sensor units consisting of two boxes featuring NO2 and O3 low-cost sensors and wireless data transfer were engineered. The sensor units were initially operated at air quality monitoring sites for three months for performance analysis and initial calibration. Afterwards, they were relocated and operated within a sensor network consisting of six locations for more than one year. Our analyses show that the employed O3 and NO2 sensors can be accurate to 2–5 and 5–7 ppb, respectively, during the first three months of operation. This accuracy, however, could not be maintained during their operation within the sensor network related to changes in sensor behaviour. Hence, the low-cost sensors in our configuration do not reach the accuracy level of NO2 diffusion tubes. Tests in the laboratory revealed that changes in relative humidity can impact the signal of the employed NO2 sensors similarly as changes in ambient NO2 concentration. All the employed low-cost sensors need to be individually calibrated. Best performance of NO2 sensors is achieved when the calibration models include also time dependent parameters accounting for changes in sensor response over time. Accordingly, an effective procedure for continuous data control and correction is essential for obtaining meaningful data. It is demonstrated that linking the measurements from low-cost sensors to the high quality measurements from routine air quality monitoring stations is an effective procedure for both tasks provided that time periods can be identified when pollutant concentrations can be accurately predicted at sensor locations.


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