Discussions about the validity and accuracy of the Foobot 52

Measurements of temporal and spatial changes in indoor contaminant concentrations are vital to gain an in-depth understanding of pollutant characteristics, particularly in dynamic, spatially variable environments such as the home. International standards and IAQ assessment routines, especially for office buildings, require monitoring of several air pollutants (θr, O3, NOx, CO, Rd,

Formaldehydes, among others) that are not monitored by low-cost sensors (θair,

ɸ, PM2.5, tVOC, CO2). Low-cost IAQ monitor’s manufacturers opt for a limited

number of parameters to lower the costs. While “high-cost” instruments can provide high temporal resolutions of indoor pollutants such as PM2.5, PM10 and

tVOCs, the cost and complexity of these instruments renders the monitoring of spatial and temporal changes on a large scale prohibitively difficult.

This work tries to find a more affordable and suitable instrument to provide IAQ information, which may also enable the simultaneous monitoring of different rooms within the same home. However, it might also facilitate more extensive IAQ monitoring projects looking to characterise pollution and identify potential health risks in indoor building environments with much larger and more statistically significant datasets. A previous experiment in a controlled chamber showed that the monitor could be used to provide mass concentrations of PM2.5 (Sousan et al.,

2017), but this is the first study to evaluate the accuracy of all measurements (temperature, relative humidity, tVOC, CO2 and PM2.5) made by the Foobot

FBT0002100 in real-life residential settings, producing more than 4,800 data points.

Calibration equations for the site were calculated as suggested by Sousan et al., (2017). The equations generated may be influenced by domestic pollution (i.e. pollutants from paint, cleaning and personal care products, household dust, outdoor air and cooking fumes). The density and features of such contaminants will be different depending on the household. Hence, the response of instruments like GrayWolf PC-3016A, TG-502 TVOC, IQ-410 and Foobot FBT0002100 may vary in real-life homes, depending on these and other factors such as monitor location, temperature and humidity. Therefore, to provide the most accurate measurements, an individual calibration equation could be provided for each Foobot and specific contexts, although this may not be possible for large-scale and remote deployable projects. A better alternative for large-scale projects may be to produce a calibration equation for a large set of monitors for each setting (i.e. bedroom, kitchen and living room). Then, in order to reduce the bias of inter- Foobot differences, one could use three monitors within the same space and then employ the mean from the monitors in each room to provide a more robust measurement. This alternative provides not only greater accuracy than the application of a calibration equation, but the redundancy of the acquired data from several monitors also provides higher confidence in and robustness to the dataset.

The validation results showed that there was a very good agreement between the GrayWolf PC-3016A/TG-502 TVOC/IQ-410 and the Foobot FBT0002100 with regard to temperature (Eltek and Tinitag) and humidity. TVOC and PM2.5 had very good

agreement, when the regression equations were applied. CO2 concentration levels

were not accurate, though, as the Foobot FBT0002100 instrument does not possess a real CO2 sensor but instead provides a CO2 equivalent from tVOC levels as an

indication. Differences between CO2 levels from the GrayWolf IQ-410 and the

Foobot were clear, as illustrated in Figure 2.7. While the GrayWolf IQ-410 uses non-dispersive infrared spectroscopy technology to determine CO2 concentrations,

the Foobot uses an algorithm to convert tVOC to CO2 equivalents providing

misleading measurements. Differences in the measurements were expected since CO2 and tVOC are different chemicals and have different sources and

compositions. CO2 concentrations in indoor environments have long been used as

an indicator of ventilation (ASHRAE, 2007), and they correlate to human activities and occupancy (Porteous, 2011) but are not related to sources of pollution such as off-gassing from building materials or furniture (Brown et al., 1994), as is the case for tVOC.

The implementation of the algorithm to predict CO2 is relatively new, and the

theory behind it contends that tVOC can be correlated proportionally to CO2

production, thus providing CO2- and tVOC-related events at the same time

(Herberger et al., 2010). In other words, the algorithm attempts to relate tVOC to CO2 concentrations in indoor spaces where no human activity takes place

(Ulmer and Herberger, 2012). Most of the studies undertaken to correlate CO2

equivalents to tVOC have been carried in schools, offices, meeting rooms and home environments. For example, Figure 2.15 (Ulmer and Herberger, 2012) compares CO2 equivalents calculated from tVOC to CO2. The left graph shows a

strong correlation in a meeting room, whereas the right-hand graph show signals that can be attributed to tVOC but differ from CO2. Implications of this approach

may include misleading CO2 readings that might confuse many new to the IAQ

industry; however, it adds the ability to add the sensor output to ventilation standards (Herberger et al., 2010) and implement it for ventilation systems, thereby reducing energy consumption compared to time-scheduled ventilation (Ulmer and Herberger, 2012). However, this approach is a very recent initiative, and so additional development of IAQ modules is needed (Ulmer and Herberger, 2012), especially in residential environments. Airboxlab opted, for two main reasons, for an iAQ-CORE-C sensor to provide tVOC concentrations and an idea of CO2 instead of real CO2 measurements. First, they believed that tVOC

measurements are more important in evaluating IAQ, as the health impacts of higher levels of tVOC are usually more severe than those from CO2. Second, the

additional cost for the CO2 sensor may increase the price of the Foobot (personal

communication).

Figure 2.15 The graphs compare the real CO2 measurements vs CO2 equivalents from tVOC

of a previous study. Real CO2 (in blue) and CO2 equivalent from tVOC (in black) in a meeting

room (left) and a kitchen (right). Source: (Ulmer and Herberger, 2012).

About 3.2% of the tVOC measurements and 4.1% of PM2.5 were outside of the limits

of agreement when an upper and lower bound of a 1.96 standard deviation (SD) of the difference was applied. There is, however, a concern as to whether or not the 1.96 SD limits are appropriate for assessing the impact of pollution on human health (Bland and Altman, 2010). For this reason, the 1.96 SD was transformed into pollution concentrations to ensure these bounds were either the same or lower in terms of range to those thresholds set by the WHO, which resulted in tighter ranges. The 1.96 SD for PM2.5 resulted in a range from -2.3245 µg/m3 to

2.2971 µg/m3 _{(±2.2932 µg/m}3 _{from the mean), and from -36.7935 ppb to 35.9668}

ppb for tVOC (±36.5920 ppb from the mean). An examination of the instruments employed to produce IAQ information reinforced this conclusion, as the quantitative information provided by the different instruments demonstrated high agreement. Variability between the percentage of time above threshold values as determined using data from the Foobot and the GrayWolf monitors was generally small and was considered to be unlikely to produce major changes in IAQ assessments.

The findings show that the Foobot FBT0002100 provided sufficiently accurate results for evaluation of IAQ in occupied dwellings and that the information provided could identify trends and exposures above thresholds within a small margin of error. The Foobot does not make any noise and the light can be dimmed

or turned off during normal operation, however, if problems with the internet connection arise the Foobot has a light signal. So, the Foobot can be used to perform simultaneous measurements of the indoor environment inside homes, including sensitive spaces such as bedrooms. This should minimise changes in participants’ behaviour in response to their awareness of being observed, minimising the Hawthorne effect (Landsberger, 1958) and the risk of occupants disconnecting the monitors. Moreover, the cost, size, mobility and the easy deployment of the Foobot FBT0002100, combined with its accuracy, make it a useful tool for evaluating occupant pollutant exposure in research and large-scale monitoring campaigns looking to collect high-density temporal and spatial data on indoor pollutant concentrations in a wide range of households at local, regional and national levels. This information could be used to acquire more comprehensive information on indoor pollutant concentrations, in order to understand better temporal and spatial changes and pollutant-activity relationships in the home. There is, however, an important limitation of the Foobot monitor for studies that require a wider consideration of air pollutants and outdoor measurements (that cannot be obtained from outdoor monitoring networks). Due to its characteristics, the Foobot cannot be used outdoors - it still requires a plug and extreme temperatures and humidity may damage the sensors, and such it is not possible to take comparative outdoor measurements.

This study suffers from some identifiable limitations. First, there was no comparison or control group in an environmental chamber. Environmental chamber experiments would include the use of calibration gases and aerosols, allowing comparison with a wider range of highly accurate instruments. However, the purpose of this study was to evaluate the intended purpose of low-cost monitors in field conditions. Additionally, sensor’s drift over the time was not evaluated, as these kind of studies require longer testing periods in addition to controlled environments. Second, it was assumed that GrayWolf PC-3016A/TG-502 TVOC/IQ-410 provided accurate temperature, humidity, CO2, CO, VOCs and PM2.5

concentrations. While the devices were tested and calibrated by the manufacturer a month before this study, this still represents a potential error. Third, we assumed that the monitors were left in place throughout sampling. We asked the participants not to handle the devices, but the light and noise produced by the

GrayWolf instruments might cause occupants to relocate them, though there was no evidence of this.