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. 2022 Dec 2;22(23):9403.
doi: 10.3390/s22239403.

Long-Term Assessment of a Set of CO2 Concentration Sensors in an In-Use Office Building

Carmen Serrano Lapuente  1   2 , Héctor Herrada  1   2 , María José Jiménez  1   2 , María Nuria Sánchez  1
Affiliations

Long-Term Assessment of a Set of CO2 Concentration Sensors in an In-Use Office Building

Carmen Serrano Lapuente et al. Sensors (Basel). .

Abstract

The measurement of the CO2 concentration has a wide range of applications. Traditionally, it has been used to assess air quality, with other applications linked to the experimental assessment of occupancy patterns and air renewal rates. More recently, the worldwide dissemination of COVID-19 establishing a relationship between infection risk and the mean CO2 level has abruptly led to the measurement of the CO2 concentration in order to limit the spread of this respiratory disease in the indoor environment. Therefore, the extensive application of this measurement outside of traditional air quality assessment requires an in-depth analysis of the suitability of these sensors for such modern applications. This paper discusses the performance of an array of commercial wall-mounted CO2 sensors, focusing on their application to obtain occupancy patterns and air renovation rates. This study is supported by several long-term test campaigns conducted in an in-use office building located in south-eastern Spain. The results show a spread of 19-101 ppm, with a drift of 28 ppm over 5 years, an offset of 2-301 ppm and fluctuations up to 80 ppm in instantaneous measurements not related to concentration changes. It is proposed that values averaged over 30 min, using a suitable reference value, be used to avoid erroneous results when calibration is not feasible.

Keywords: CO2 concentration; building energy; in situ measurements; long-term measurements; performance gap.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Overview of the building plant indicating the spaces where the CO2 sensors are installed. Sensor nomenclature is showed.
Figure 2
Figure 2
(a) Benchmark set-up 2021. (b) Benchmark set-up 2022.
Figure 3
Figure 3
Difference in daily mean values of the CO2 concentration between Ext and R01 Ref sensors during unoccupied periods of summer (red) and winter (blue), and spread of the measurements (grey) obtained as the standard deviation of the mean daily mean values of the CO2 concentration between R13, R01, R02, R12, R13, R14, SR, CR13, CR19 and R01 Ref CO2 sensors during unoccupied periods in August. The 0 in the x-axis corresponds to the year 2008.
Figure 4
Figure 4
Offset regarding the reference sensor for all the sensors, considering periods when the building was not being used from 2008 to 2021: periods of each year in August and March 2020. From 2008 to 2016, the reference sensor is Ext (purple). Since 2014, the reference sensor is R01 Ref (green). The 0 in the x-axis corresponds to the year 2008.
Figure 5
Figure 5
Summer 2016 as an example. The 14 years show similar behaviour. Raw data (a) and data averaged at intervals of (c) 2 min, (e) 5 min, (g) 10 min and (i) 30 min and calibrated measures with sensor R01 Ref for raw data (b) and the same average intervals (d,f,h,j), respectively.
Figure 6
Figure 6
Benchmark set-up 2021. The r2 value of the lineal regression of the measurements (serial numbers: 78CE, 2823, 814A, 8014, 56E6, 778F, 449E, 25DE, 390D) with sensor R01 Ref. Values obtained for raw data minutely recorded and average intervals of 1 min, 2 min, 5 min, 10 min, 30 min, 1 h and 2 h.
See this image and copyright information in PMC

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