JSSSJournal of Sensors and Sensor SystemsJSSSJ. Sens. Sens. Syst.2194-878XCopernicus PublicationsGöttingen, Germany10.5194/jsss-7-373-2018Field evaluation of a low-cost indoor air quality monitor to quantify
exposure to pollutants in residential environmentsField evaluation of a low-cost indoor air quality monitorMoreno-RangelAlejandroa.morenorangel1@student.gsa.ac.ukSharpeTimMusauFilbertMcGillGráinnehttps://orcid.org/0000-0002-8716-9567Mackintosh School of Architecture, The Glasgow School of Art, Glasgow,
G1 6DE, UKMackintosh Environmental Architecture Research Unit, The Glasgow
School of Art, Glasgow, G1 6DE, UKAlejandro Moreno-Rangel (a.morenorangel1@student.gsa.ac.uk)9May20187137338816February201828March201817April2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://jsss.copernicus.org/articles/7/373/2018/jsss-7-373-2018.htmlThe full text article is available as a PDF file from https://jsss.copernicus.org/articles/7/373/2018/jsss-7-373-2018.pdf
Measurements of temporal and spatial changes to indoor contaminant
concentrations are vital to understanding pollution characteristics. Whilst
scientific instruments provide high temporal resolution of indoor pollutants,
their cost and complexity make them unfeasible for large-scale projects.
Low-cost monitors offer an opportunity to collect high-density temporal and
spatial data in a broader range of households.
This paper presents a user study to assess the precision, accuracy, and
usability of a low-cost indoor air quality monitor in a residential
environment to collect data about the indoor pollution. Temperature,
relative humidity, total volatile organic compounds (tVOC), carbon dioxide
(CO2) equivalents, and fine particulate matter (PM2.5) data were
measured with five low-cost (“Foobot”) monitors and were compared with data
from other monitors reported to be scientifically validated.
The study found a significant agreement between the instruments with regard
to temperature, relative humidity, total volatile organic compounds, and
fine particulate matter data. Foobot CO2 equivalent was found to
provide misleading CO2 levels as indicators of ventilation. Calibration
equations were derived for tVOC, CO2, and PM2.5 to improve
sensors' accuracy. The data were analysed based on the percentage of time
pollutant levels that exceeded WHO thresholds.
The performance of low-cost monitors to measure total volatile organic
compounds and particulate matter 2.5 µm has not been properly
addressed. The findings suggest that Foobot is sufficiently accurate for
identifying high pollutant exposures with potential health risks and for
providing data at high granularity and good potential for user or scientific
applications due to remote data retrieval. It may also be well suited to
remote and larger-scale studies in quantifying exposure to
pollutants.
Introduction
Increasingly strict energy efficiency requirements have severe implications
for buildings and indoor air quality (IAQ) (Yu and Kim,
2012). IAQ is crucial for peoples' health as we spend between 80 and 90 %
of our time inside buildings (Jones,
1999; Boyd, 2010) depending on the external weather conditions. Indoor air
pollutants include carbon monoxide (CO), carbon dioxide (CO2), volatile
organic compounds (VOCs), particulate matter (PM2.5 and PM10),
and ozone (O3) among others (Berry et al., 1996; Crump
et al., 2002). Exposure to these can exacerbate existing conditions such as
sensory irritation and other respiratory problems
(WHO, 2000, 2010) and even increase the risk
of developing cancer (Carrer et al., 2008). Residents
are usually unaware of indoor pollution as many pollutants are imperceptible
to humans. For instance, 85 % of tobacco smoke is invisible to the human
eye (Gee et al., 2013). It is necessary to monitor the quality of
the indoor air to detect these pollutants and thus avoid the development of
adverse health effects from inhaling pollutants. Accepted methods for
measurements of indoor pollutants are based on filter-based gravimetric
sensors or similar methods for particulate matter monitoring (Air
Quality Expert Group, 2005), and infrared and photoionisation gas sensors
(Chou, 2000). While accurate and precise, such technologies
are expensive, time-consuming, and often provide little temporal
information. Methods for personal dust, VOC and CO2 scientific monitors
often provide high temporal resolution but are expensive (> GBP 3500.00) and therefore result
in limited spatial information
(White, 2009). Although analytical instruments often
provide high temporal resolution, they are intended for laboratory use and
their requirement for skilled operators, high purchase and maintenance
costs, slow response time, and large size (Chou, 2000) make
them impractical for IAQ studies (Kularatna
and Sudantha, 2008). Moreover, the accuracy of these instruments may be
considered excessive for large-scale IAQ monitoring, where a principal
objective is to investigate the relative concentration of pollutants and
their trends. As the performance of low-cost sensors improves, gas sensors
that are compact, robust, and low-cost, with versatile applications, could be
used as alternatives (Lee, 2001) for certain monitoring
projects and could be used to collect larger datasets.
Technologies such as metal oxide (MOx) semiconductor sensors
(Herberger et al.,
2010; Kadosaki et al., 2010; Liu et al., 2012), light scattering (Tong et al., 2015) and tin oxide sensors
(Watson, 1984; Postolache et al.,
2009), open platforms (Ferdoush
and Li, 2014; Ali et al., 2016), and wireless networks
(Yu et al., 2013) have been adapted into
low-cost monitors, and even allow remote monitoring
(Kahkonen et al., 1997). New low-cost (< GBP 200) monitoring technologies may also help building occupants understand the
quality of air indoors. Low-cost IAQ monitors often implement real-time
monitoring and visualisation for smartphones and tablets to help inform the
users (Hasenfratz et al., 2012). There is, however, limited
information regarding the performance of low-cost monitors in practice.
Nevertheless, many low-cost IAQ monitors such as Speck, Dylos DC1700 Pro
(Manikonda et al., 2016) and Dylos DC1100Pro
(Semple et al., 2013b) have been
tested in laboratory conditions, and the results show a significant
agreement with scientific instruments.
Manufacturer specifications and characteristics for the low-cost
consumer monitors.
Low-cost IAQ monitor manufacturers often include sensors for temperature,
relative humidity, carbon dioxide, particulate matter, and total volatile
organic compounds (tVOC), as evidenced by the Foobot, Speck, Awair, and Air
Mentor Pro devices (see Table 1 for manufacturer specifications). These
low-cost IAQ monitors use microprocessors to collect sensor output, convert
the data, and store or transmit data wirelessly to a remote server. Many of
these devices may use the same or very similar sensors. However,
manufacturers use a variety of algorithms to convert the sensor output to a
concentration of each pollutant. This calibration protocol can have a marked
impact on sensor precision, accuracy, and bias. For instance, the SHARP
GPY1010AU0F, a PM2.5 sensor, was tested in laboratory conditions. It
was found to be accurate; however, the study recommended that an improvement
of the algorithm could enhance its performance
(Wang et al., 2015). Another study evaluated
the same sensor on a monitoring device using a different algorithm; the
results showed better precision and linear response
(Sousan et al., 2017).
The Dylos DC1700 showed a high agreement (R2= 0.90) with SidePak AM510
in controlled chamber experiments (Semple et al., 2013a). It was also tested to quantify second-hand
smoke concentrations in residential settings, where a good agreement
(R2= 0.86) to SidePak AM510 was observed
(Semple et al., 2013b).
Therefore, Dylos DC1700 particulate matter (PM) measurements have exhibited
some agreement between fieldwork and laboratory results. Some limitations of
this device include limited data storage (10 000 data points), lack of
remote access capabilities, and lack of multisensory measurements, such as
temperature or relative humidity. PM2.5 measurements from Speck
SPK18TH, however, showed discrepancies between the
environmental chamber and field measurements. The device demonstrated high
agreement for determination of cigarette smoke (R2= 0.92) and Arizona
test dust (R2= 0.96) under laboratory settings
(Manikonda et al., 2016). However, the performance
of Speck SPK18TH was found to be inadequate when tested at low
concentrations against a scientific instrument in the field, both indoors
(R2= 0.3) and outdoors (R2= 0.1–0.2), showing an overestimation
of 200 % for indoor PM2.5, and 500 % for outdoor compared to the
Grimm 1.109 (Zikova et al., 2017). The accuracy of PM2.5 measurements from the Foobot
(FBT0002100) device has only been evaluated in laboratory measurements,
which showed a strong correlation (r= 0.99 with a variation range of 5
to 8 %) with scientific instruments. Yet site-specific calibration may
help to improve the accuracy of such sensors (Sousan et al.,
2017).
The objective of this study is to evaluate the performance of the Foobot
sensors, especially PM2.5 and tVOC, in typical residential settings.
The linear relationship and bias for temperature, relative humidity,
CO2, tVOC, and PM2.5 concentrations in a residential environment
were assessed and compared to scientifically validated instruments (GrayWolf
TG-502 TVOC, IQ-410, and PC-3016A). To the best of our knowledge, no study
has yet evaluated the Foobot FBT0002100 sensors in field conditions. This
paper compares the specifications of several low-cost IAQ monitors and
explores in detail the components of the Foobot FBT0002100. Following this,
indoor residential measurements from five Foobot FBT0002100 devices are
compared to the GrayWolf instruments, and inter-device variances among the
five Foobot devices are also analysed. Finally, field calibration equations
are proposed to improve the accuracy of the Foobot FBT0002100 relative to
the GrayWolf instruments.
Low-cost IAQ monitors
A web-based search for low-cost, consumer, air pollutant monitors (available
in the US and European markets) was performed in early 2016. The most
popular low-cost IAQ monitors are presented in Table 1. The Foobot
FBT0002100 device was selected for detailed evaluation based on criteria as
suggested by Chou (2000):
availability (in the UK),
capable of being installed in residential locations,
remote connectivity and storage,
dustproof and water-resistant,
easy and minimal maintenance,
easy to operate (no skilled person required),
flexibility in data download,
good responsiveness and quality of technical support,
use of multisensory systems,
long-lasting,
low cost (< UK GBP 200, including equipment and software),
operationally stable,
remote access to data, and
rugged and corrosion resistant.
The Foobot was developed by AirBoxLab (Luxembourg) and measures five
different air quality parameters with reference to maximum recommended
values as defined by Foobot: PM2.5 (25 µg m-3),
tVOC (300 ppb), CO2 (1300 ppm), temperature (40 ∘C), and
relative humidity (RH, 60 %). The device mechanism is simple; a
microprocessor collects the electrical outputs from the sensors and converts
them into data, which are then transmitted wirelessly to a remote server,
where an algorithm is applied to derive the measured concentrations. Data
may be lost if the wireless signal is interrupted, as the Foobot does not
have internal data storage. The manufacturer hosts a website where the data
uploaded can be visualised and downloaded (https://partner.foobot.io/, last access: 14 January 2018),
though a monthly subscription is required for this service. Accessing the
data for free is possible. Nevertheless, the user needs to develop his or her
software with an application programming interface (API) provided by
AirBoxLab, which allows up to 250 daily data requests to the server.
AirBoxLab has developed a calibration algorithm for its sensors, details of
which are not available to the public (personal communication, Inouk Bourgon, 2016). Figure 1
shows the Foobot and the sensors inside of the device.
Foobot FBT0002100 monitor (a) and Foobot Main Board
3.3 (b)
showing the SHARP GP2Y1010AU0F (1), the iAQ-CORE-C (2), and SHT20 (3). (a) from https://foobot.io (last access: 22 November 2017).
Foobot uses the SHARP GP2Y1010AU0F sensor (Sharp Corporation, Japan) to
measure PM2.5 which relies on natural convection to passively move air
to the sensor, measuring particles with an aerodynamic diameter between
0.3 and 2.5 µm. The SHARP GP2Y1010AU0F was laboratory-tested with
two similar low-cost sensors, and showed the highest agreement with the SidePak-measured concentration (R2= 0.9831 to 0.9838 in three different tests)
and a higher sensitivity to smaller particles. The researchers suggested
that the SHARP GP2Y1010AU0F could be enhanced by modifying the flow system
and amending the algorithm for particle concentrations
(Wang et al., 2015).
The Foobot tVOC sensor AMS iAQ-CORE-C (ams AG, Austria) measures a
wide range of VOCs to predict tVOC (ppb). It lacks a CO2 sensor; however,
an algorithm converts tVOC concentration as a CO2 equivalent (ppm). It
has an Inter-Integrated Circuit (I2C) interface allowing the
communication with the main chip. This sensor uses a
micro-electro-mechanical system allowing the metal oxide sensor to measure
VOC concentrations continuously at 1 s intervals (AMS, 2015).
Equations convert the signal output from the sensor to values of tVOC and
CO2 equivalents (equations described at the AMS iAQ-CORE-C manual
(AMS, 2015, pp. 10–11). The AMS iAQ-CORE-C does not report
absolute values for any particular gas, but instead indicates the relative
change in levels of reducing gases such as CO and a wide range of VOCs
(Brown, 2017). This sensor has been used to control
environmental monitoring systems (Kim et al., 2017) and smart health applications (Chan et al.,
2017).
The Foobot temperature and relative humidity sensor is the SENSIRION SHT20
(Sensirion, Switzerland) with an I2C interface (see
SENSIRION, 2014 for more information). This sensor has been on
the market since May 2009 and has been widely accepted as a low-cost sensor
for temperature and humidity. Since then, it has been used for smart home
applications (Hernandez et al., 2014), for weather condition
observation systems (An and Kang, 2014), and to control
mechanical ventilation with heat recovery systems (Matsuoka and
Fisher, 2017).
Manufacturer specifications and characteristics for the GrayWolf
instruments.
1 Require additional software (∼ GBP 1200.00) and a
tablet
(> GBP 500.00). 2 Isobutylene equivalent.
Method
The study was undertaken following the guidelines of the ASTM D72974-14
Standard Practice for Evaluating Residential Indoor Air Quality
(ASTM, 2014). The monitors were located at an approximate height
of 0.90 m over the top of a drawer. Care was taken to ensure the monitors
were placed away from direct pollutant sources, heat sources (such as cookers
or radiators), and ventilation ducts or openings. Given the nature
of the measurements and the desire to ensure that “typical” conditions were
achieved, it was not possible to position the monitors in the centre of the
room (see Fig. 2).
This study tests the accuracy of Foobot FBT0002100 temperature, relative
humidity, particulate matter, and tVOC measurements by comparing the
measurements of five Foobot FBT0002100 devices to measurements from the GrayWolf
TG-502 TVOC, IQ-410, and PC-3016A. Table 2 shows the specification for the
GrayWolf instruments. The monitors were set to measure simultaneously at
5 min intervals for 81 h 25 min (from 28 August 23:50 LT to
1 September 2017 11:25 LT) in an occupied bedroom (floor area 10.5 m2) of a
modern flat in Glasgow, UK. The occupancy levels and activities were
recorded by the occupants in a diary and this was used to contextualise the
data, to ensure that typical conditions were represented, but this
information was not used in the statistical analysis.
Test layout.
Statistical analysis
Data from each monitor were exported into Microsoft Excel for initial data
inspection and to IBM SPSS Statistics for statistical analysis. The 5 min data pairs
(n= 4895 for each measure) across the study were assigned to either a
calibration dataset (n= 2448 for each measure) or a validation dataset
(n= 2449 for each measure). The Kolmogorov–Smirnov test rejected the
hypothesis of normal distribution. Data were measured at intervals and were
found to have a monotonic relationship. Therefore, Spearman's rank-order
correlation (rs) was applied to determine the correlation between the
variables from each of the paired devices. This indicates the association
from one device to another. The closer rs is to unity, the more positive
and direct is the association between devices. Correlations from 0.3 to 0.5
are considered as low positive (weak) correlation, 0.5 to 0.7 are
considerate as a moderate (acceptable) positive correlation, from 0.7 to 0.9
as a high positive (strong) correlation and 0.9 to 1.00 as a very high
positive association (very strong) (Mukaka, 2012).
The uniformity of data from different Foobot FBT0002100 was also determined
by a Spearman's rank-order correlation. Additionally, to compare the
differences between each of the measurements among the five different Foobot
FBT0002100 monitors, the Kruskal–Wallis test, a nonparametric test, was
applied to determine if there were statistically significant differences
between them.
A regression analysis was performed to improve the accuracy of the Foobot
FBT0002100 data relative to the GrayWolf data. Field calibration equations
were then produced from the calibration dataset using the results from the
GrayWolf instruments as dependent variables and the Foobot FBT0002100 as
independent variables and tested on the validation dataset. An analysis in
SPSS of the linear, quadratic, and cubic models was performed individually
for each parameter to find the most accurate equation. A Bland–Altman
analysis was then performed on the validation dataset to examine the
correlation and agreement between data generated by the calibration equation
and data obtained by the GrayWolf instruments. The Bland–Altman method
calculates the mean difference between two methods of measurement (the
“bias”), and 95 % limits of agreement from the mean difference (1.96 SD)
(Myles and Cui, 2007). From this process, a
Bland–Altman plot (or difference plot) can be generated as a graphical
method of comparing two measurements of the same variable.
Measurement of the extent to which data collectors (raters) assign the same
score to the same variable is called interrater reliability. The interrater
reliability of the agreement between the data generated by the calibration
equation and the data from the GrayWolf instruments was tested using the
Cohen's κ test to account for the possibility of agreement happening by
chance; the closer that κ is to 1.00 the better agreement it has.
Temperature levels from 29 August to 1 September 2017 form the
Foobot and GrayWolf instruments. (Activity describes the morning routine:
showering, grooming, and changing.)
ResultsInter-sensor analysis of low-cost and scientific IAQ monitors
The measurements from the five Foobot FBT0002100 monitors were compared to
those from the GrayWolf IQ-410, TG-502 TVOC, and PC-3016A. The results
showed that the temperature measurements were very strongly related
(rs= 0.833 to 0.926, p < .001). Despite this, analysis of the
temperature data showed that the Foobot FBT0002100 underestimated
temperature (mean (M)= 2.59 ∘C, 95 % confidence interval from
2.40 to 2.73 ∘C; Fig. 3). Knowledge of
inter-sensor variability is important for the reliability of sensors in
practice. Analysis of the temperature data from the five Foobot FBT0002100
monitors identified a very significant uniformity (rs= 0.833 to 0.926,
p < .001) and low variability (M= 0.16 ∘C, from
0.16 to 0.33 ∘C) between the different temperature
sensors.
Summary statistics for tVOC calibration dataset divided by
instruments.
Relative humidity levels from 29 August to 1 September 2017 form
the Foobot and GrayWolf instruments. (Activity describes the morning routine:
showering, grooming, and changing.)
A very strong relationship (rs= 0.935 to 0.948, p < .001) was
observed for relative humidity measurements from the five Fooboot FBT0002100
and the GrayWolf monitors. Very low variability was observed between Foobot
and GrayWolf monitors, given that the Foobot FBT0002100 underestimated the
relative humidity levels by 0.01 %RH (from -0.78 to 1.08 %RH,
Fig. 4). Inter-sensor analysis between the five Foobot monitors showed a
very strong uniformity (rs= 0.985 to 0.991, p < .001) and low
variability (M= 0.52 %RH, from -1.86 to 0.75 %RH) of the
relative humidity sensor.
Analysis of the tVOC measurements from the five Foboot monitors and the
GrayWolf TG-502 TVOC showed a significant relationship (rs= 0.827 to
0.869, p < .001). A very low variability between the five Foobot
monitors was observed, but the Foobot underestimated the tVOC levels by
22.12 ppb (from 12.79 to 28.20 ppb, Table 3, Fig. 5). Inter-sensor
analysis between the five Foobot monitors showed a very strong uniformity
(rs= 0.892 to 0.974, p < .001) and low variability (M=-7.05 ppb, from -15.43 to -1.67 ppb)
between the different tVOC sensors.
Total volatile organic compound levels from 29 August to
1 September 2017 form the Foobot and GrayWolf instruments. (Activity describes the
morning routine: showering, grooming, and changing.)
Summary statistics for CO2 calibration dataset divided by
instruments.
Analysis of the CO2 (equivalent from tVOC) data from the Foobot
monitors and the GrayWolf IQ-410 showed that the Foobot CO2 levels
differed from those measured by the GrayWolf instrument. A weak but
significant correlation (rs= 0.397 to 0.525, p < .001) was
observed. The Foobot monitors underestimated the CO2 concentrations
(M= 147.08 ppm, from 99.08 to 155.00 ppm, Fig. 6), a factor which
could lead to problems in assessing ventilation based on CO2 levels.
The percentage of time CO2 > 1000 ppm was considerably
different between the GrayWolf IQ-410 and the five Foobot monitors (Table 4). Inter-sensor analysis of the five Foobot monitors showed a very strong
uniformity (rs= 0.892 to 0.973, p < .001) and a low variance
(M= 25.54 ppm, from 5.99 to 55.92 ppm) between the different CO2
measurements.
Carbon dioxide levels from 29 August to 1 September 2017 form
the Foobot and GrayWolf instruments. (Activity describes the morning routine:
showering, grooming, and changing.)
Summary statistics for PM2.5 calibration dataset divided by
instruments.
PM2.5 levels from 29 August to 1 September 2017
form the Foobot and GrayWolf instruments. (Activity describes the morning
routine: showering, grooming, and changing.)
PM2.5 measurements from the five Foobot monitors and the GrayWolf
PC-3016A were significantly related (rs= 0.787 to 0.866, p < .001) to each other. Despite this, analysis of the data showed that the
Foobot overestimated PM2.5 concentrations (M=-1.4826 µg m-3, from -1.4783 to -1.4870 µg m-3,
Table 5, Fig. 7). A higher degree of agreement between the types of devices is
addressed in the following section. Inter-sensor analysis of the five Foobot
monitors showed that there was an acceptable uniformity
(rs= 0.576–0.843 p < .001) and a low variance
(M=-1.4826 µg m-3 from -0.0068 to 0.0084 µg m-3) between the different PM2.5 sensors.
Relationship between the GrayWolf and Foobot monitorsTotal volatile organic compounds (tVOC)
The results from the tVOC measurements showed that Foobot FBT0002100
underestimated tVOC concentrations. Figure 8 shows the relationship between
the GrayWolf TG-502 TVOC and Foobot FBT0002100 tVOC concentrations from the
calibration dataset used to generate a regression equation. The best fit
produces an R2 value of 0.697 and the equation generated by regression
is
tVOCGrayWolf=-1.56e2+4.5tVOCFoobot-0.02tVOCFoobot2+3.57e-5tVOCFoobot3,
where tVOC is the concentration (ppb). Figure 9 shows the Bland–Altman plot
comparing the GrayWolf tVOC measurements with that estimated from the
Eq. (1) for the dataset from the five Foobot validations. It shows the mean
between the GrayWolf and the Foobot tVOC generated measurements (-0.0148 ppb
with limits of agreement of -36.7935 to 36.7639 ppb at a 95 % confidence
interval). A total of 80 (3.26 %) of the data points were outside of the
limit of agreement (51 above the upper limit and 29 below the lower limit).
This range is significantly lower than 300 ppb (the World Health
Organization, WHO, threshold for tVOC; Koistinen et al., 2008). The plot
shows that Foobot FBT0002100 underestimated the concentrations at high
concentrations (> 300 ppb). A comparison between the tVOC
concentrations from the GrayWolf TG-503 TVOC and the Foobot tVOC generated
showed indoor air quality information that has a very good agreement. The
number of data points on which the tVOC concentration values exceeded the
300 ppb is within ±0.71 % as observed in Table 6. The agreement of
the data points from the calibration and validation datasets were also
corroborated. Both showed a very good agreement on the concentrations above
300 ppb: on the calibration dataset, a κ of 0.75, and on the validation
dataset, a κ of 0.85.
Summary statistics for the generated tVOC from the validation
dataset divided by instruments.
Scatter plot of the 5 min tVOC concentration measured using the
Foobot FBT0002100 and the GrayWolf TG-502 TVOC from the calibration dataset.
Bland–Altman plot of the agreement between the GrayWolf TG-502
TVOC and the Foobot-generated tVOC concentrations.
Carbon dioxide (CO2)
The results from the CO2 measurements showed a weak correlation as the CO2 concentrations
were underestimated. Figure 10 shows the relationship
between the GrayWolf IQ-410 and Foobot FBT0002100 CO2 concentrations
from the calibration dataset used to generate the regression equation. The
best fit produces an R2 value of 0.180 and the equation generated by
regression is
CO2GrayWolf=-1.39e3+7.08CO2Foobot-7.15e-3CO2Foobot2+2.29e-6CO2Foobot3,
where CO2 is the concentration in ppb. Figure 11 shows the Bland–Altman
plot comparing the GrayWolf CO2 measurements with those estimated from
the Eq. (2) to the five Foobot validation datasets. It shows the mean
difference between the GrayWolf and the Foobot CO2 generated
measurements (4.1149 with limits of agreement of -457.453 to 465.683 ppm at
a 95 % confidence interval). A total of 152 (6.21 %) of the data points
were outside of the limits of agreement (152 above the upper limit). This
range is almost equal to the 1000 ppm (the ASHRAE threshold for CO2
ASHRAE, 2007). A comparison between the CO2 concentrations
and the Foobot CO2 generated to produce information about the
ventilation rates showed that there was a poor agreement between them. The
number of data points on which the CO2 concentration values exceed the
1000 ppm was significantly different from the GrayWolf instruments to those
generated by the Eq. (2) as shown in Table 7. The agreement of the data
points from the calibration and validation datasets was also corroborated.
Both showed a complete disagreement on the concentrations above 1000 ppm: on
the calibration dataset, a κ of 0, and on the validation dataset, a
κ of 0.
Summary statistics for the generated CO2 from the validation
dataset divided by instruments.
The results from the PM2.5 measurements showed that Foobot was
overestimating particle matter concentrations. Figure 12 shows the
relationship between the GrayWolf PC-3016A and Foobot FBT0002100 PM2.5
concentrations from the calibration dataset used to generate the regression
equation. The best fit produces an R2 value of 0.887 and the equation
generated by regression is
PM2.5GrayWolf=0.49+0.79PM2.5Foobot+3.76e-3PM2.5Foobot2,
where PM2.5 is the mass concentration (µg m-3). Figure 13
shows the Bland–Altman plot comparing the GrayWolf PM2.5 measurements
with those estimated from Eq. (3) to the five Foobot validation dataset.
It shows the mean difference between the GrayWolf and the Foobot tVOC
generated measurements (-0.0137 with limits of agreement of -2.32 to
2.29 µg m-3 at a 95 % confidence interval). A total
of 100 (4.08 %) of the data points were outside of the limit of agreement
(58 above the upper limit and 42 below the lower limit). This range is
significantly lower than 25 µg m-3 (the WHO threshold for
PM2.5; WHO, 2000). A comparison between the
PM2.5 concentrations and the Foobot PM2.5 generated to produce
indoor air quality information showed that there was a very good agreement
between them. The number of data points on which the PM2.5
concentration values exceeded the 25 µg m-3 was within ±0.21 % as observed in Table 8. The agreement of the data points from the
calibration and validation datasets was also corroborated. Both showed a
very good agreement on the concentrations above 25 µg m-3: on the
calibration dataset, a κ of 0.9, and on the validation dataset, a κ
of 0.85.
Scatter plot of the 5 min PM2.5 concentration measured using
the Foobot FBT0002100 and the GrayWolf PC-3016A from the calibration dataset.
Bland–Altman plot of the agreement between the GrayWolf PC-3016A
and the Foobot-generated PM2.5 concentrations.
Discussion
Measurements of temporal and spatial changes of indoor contaminant
concentrations are vital to gain an in-depth understanding of pollutant
characteristics, particularly in dynamic, spatially variable environments
such as the home. While scientific instruments can provide high temporal
resolution of indoor pollutants such as PM2.5, PM10, and tVOCs, the
cost and complexity of these instruments renders 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
indoor air quality information, which may also enable simultaneous
monitoring of different rooms within the same home. However, it might also
facilitate more extensive indoor air quality 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) of the Foobot FBT0002100 in real-life residential
settings, producing more than 4800 data points.
The graphics compares 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 (a) and kitchen (b). Source: Ulmer and Herberger (2012).
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 the instruments like GrayWolf PC-3016A,
TG-502 TVOC, IQ-410, and Foobot FBT0002100 may vary in real-life homes,
depending on this 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
FBT0002100. This, however, may not be possible in large-scale and remotely
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, use three monitors within the same space and
use the mean from the monitors in each room to provide a more robust
measurement. This alternative provides not only higher accuracy than the
application of a calibration equation, but the redundancy of the acquired
data from several monitors also provides higher confidence 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 and humidity, and to tVOC and PM2.5 when the
regression equations were applied. The CO2 concentration levels were
not accurate as the Foobot FBT0002100 instrument does not possess a real
CO2 sensor, but instead provides a CO2 equivalent from the tVOC
levels as an indication. Differences between CO2 levels from the
GrayWolf IQ-410 and the Foobot are clear in Fig. 6. 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. The 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). Levels of CO2 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 it is the case for tVOC. The
implementation of the algorithm to predict CO2 is relatively new, and the
theory behind it debates that tVOC can be correlated proportionally to
CO2 production 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 to correlate CO2 equivalents to
tVOC have been carried out in schools, offices, meeting rooms, and home
environments. For example, Fig. 14 (Ulmer and Herberger,
2012) compares the CO2 equivalents calculated from tVOC to
CO2;
the left graphic shows a strong correlation in a meeting room, whereas the
right graphic show signals that can be attributed to tVOC but differ from
CO2. Implications of this approach may include misleading CO2
readings that may confuse many new to the IAQ industry; however, it provides the
possibility to add the sensor output to ventilation standards
(Herberger et al., 2010) and implement it
for ventilation systems reducing the energy consumption compared to
time-scheduled ventilation (Ulmer and Herberger, 2012).
However, this approach has only recently been developed and additional development of IAQ
modules is needed (Ulmer and Herberger, 2012), especially in
residential environments. AirBoxLab opted for the iAQ-CORE-C sensor to
provide tVOC concentrations and an idea of CO2 instead of real CO2
measurements for two main reasons. First, they believed that tVOC
measurements are more important to evaluate 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 for the Foobot (personal
communication, Jacques Touillon, 2016).
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 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 to assess 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 as or lower in range
than 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 to
2.2971 µg m-3 (±2.2932 µg m-3 from the mean)
and from -36.7935 to 35.9668 ppb for tVOC (±36.5920 ppb from the
mean). The examination of the instruments to produce indoor air quality
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
determined using data from the Foobot and the GrayWolf monitors was
generally small and was considered to be unlikely to produce major changes
in indoor air quality assessments.
The findings show that the Foobot FBT0002100 provided sufficiently accurate
results for an evaluation of the IAQ in occupied dwellings and that the
information provided could identify trends and exposures above thresholds
within a small margin of error. As the Foobot does not make any noise or
emit light, it could 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 and Ithaca, 1958) and the risk of occupants disconnecting the
monitors. Moreover, the cost, size, mobility, and easy deployment of the
Foobot FBT0002100 combined with its accuracy make it a useful tool to
evaluate occupant pollutant exposure in research and large-scale monitoring
campaigns which could 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 to better
understand temporal and spatial changes and pollutant-activity relationships
in the home.
This study suffers from some identifiable limitations. Firstly, 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 consumer monitors in field conditions, as an experiment in a
controlled environment has been published already. Secondly, 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. Thirdly, 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 it; however, there was no evidence
that the monitors were relocated.
Further work will examine the variability of Foobot devices and explore the
influence of temperature and humidity on their response to air pollutants,
especially to PM2.5. Other research may study the use of low-cost
monitoring devices as IAQ educational tools for home users, looking at the
behavioural changes towards IAQ.
Conclusions
Recently there has been an increase in interest in understanding the effects of
indoor air pollution on human health. Traditional analytical instruments are
impractical, costly, and often their accuracy is much higher than needed to
assess indoor pollution levels. Several low-cost consumer monitors provide
information about the quality of indoor air. Therefore, it is considered
useful to assess their accuracy in environmental chamber and field
experiments to evaluate their utility and accuracy. The Foobot FBT0002100
offers a relatively low-cost and straightforward solution to deliver
households' air quality information that may be used to gather large-scale
household IAQ data and also to motivate occupants to reduce the potential
harm of indoor pollution. It also has the potential to examine the impact
of increased occupant awareness of IAQ on ventilation and pollution-related
behaviours.
The Foobot FBT0002100 was found to have a significant agreement with the
GrayWolf instruments, for temperature (rs= 0.832–0.871), relative
humidity (rs= 0.935–0.948), tVOC (rs= 0.827–0.869), and PM2.5
(rs= 0.787–0.866) data. The temperature was found to be underestimated
by 2.59 ∘C. The calibration equations produced for tVOC
(R2= 0.697) and PM2.5 (R2= 0.887) reduced variability
between the monitors and improved their accuracy when compared to the
GrayWolf instruments. Foobot's lack of a specific CO2 sensor estimated
misleading concentrations. However, results showed that this does not impact
the accuracy of the other sensors. Therefore, Foobot can be used for studies
where ventilation is not an indispensable metric for the research, but it
can be complemented by another CO2 sensor.
The findings suggest that low-cost monitors, such as the Foobot FBT0002100,
have the potential to identify high pollutant exposures and to provide
high-density, reliable, temporal data at high granularity. Its
characteristics, such as remote data retrieval as well as its accuracy, make
Foobot a useful tool to evaluate occupant pollutant exposure at a large-scale
and longer timescales in occupied dwellings, compared to current approaches. However,
as discussed, the use of several units within the same space and with a
calibration equation may improve the overall performance of the monitor.
This study is part of a PhD investigation. Access to the data may be
possible upon written request to the main author.
The manufacturer of the air quality monitor tested in 2016/17 (Foobot)
subsequently offered discounted devices to enable further research.
This offered was accepted only after the review of the device was concluded in order to maintain authorial independence.
Acknowledgements
We would like to thank all those who took part in this study, especially
Janice Foster and Anna Poston for providing technical support for the use of
the monitoring devices. Thanks are given to Adam Hotson and Ian Faller, who offered useful
editing and proofreading of an earlier version of this paper.
CONACyT partially funded this study as part of a PhD scholarship to the main
author.
Edited by: Rosario Morello
Reviewed by: three anonymous referees
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