AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-12-4643-2019Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environmentsCharacterising low-cost sensors in highly portable platformsChatzidiakouLiaec571@cam.ac.ukhttps://orcid.org/0000-0002-8753-1386KrauseAnikahttps://orcid.org/0000-0002-2212-0252PopoolaOlalekan A. M.https://orcid.org/0000-0003-2390-8436Di AntonioAndreahttps://orcid.org/0000-0001-7012-5552KellawayMikeHanYiqunSquiresFreya A.https://orcid.org/0000-0002-3364-4617WangTengZhangHanbinWangQiFanYunfeiChenShiyiHuMinQuintJennifer K.BarrattBenjaminhttps://orcid.org/0000-0002-5983-0426KellyFrank J.ZhuTonghttps://orcid.org/0000-0002-2752-7924JonesRoderic L.Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UKAtmospheric Sensors Ltd, Bedfordshire, SG19 3SH, UKMRC-PHE Centre for Environment & Health, Imperial College London
and King's College London, London, W2 1PG, UKCollege of Environmental Sciences and Engineering, Peking University,
Beijing, 100871, ChinaDepartment of Analytical, Environmental and Forensic Sciences,
King's College London, London, SE1 9NH, UKDepartment of Chemistry, University of York, York, YO10 5DD, UKThe Beijing Innovation Center for Engineering Science and Advanced
Technology, Peking University, Beijing, 100871, ChinaNIHR Health Protection Research Unit in Health Impact of
Environmental Hazards, King's College London, London, SE1 9NH, UKNational Heart and Lung Institute, Imperial College London, SW3 6LR,
UK
The inaccurate quantification of personal exposure to air
pollution introduces error and bias in health estimations, severely limiting
causal inference in epidemiological research worldwide. Rapid advancements
in affordable, miniaturised air pollution sensor technologies offer the
potential to address this limitation by capturing the high variability of
personal exposure during daily life in large-scale studies with
unprecedented spatial and temporal resolution. However, concerns remain
regarding the suitability of novel sensing technologies for scientific and
policy purposes. In this paper we characterise the performance of a portable
personal air quality monitor (PAM) that integrates multiple miniaturised
sensors for nitrogen oxides (NOx), carbon monoxide (CO), ozone
(O3) and particulate matter (PM) measurements along with temperature,
relative humidity, acceleration, noise and GPS sensors. Overall, the air
pollution sensors showed high reproducibility (mean R‾2=0.93, min–max: 0.80–1.00) and excellent agreement with
standard instrumentation (mean R‾2=0.82, min–max: 0.54–0.99) in outdoor, indoor and commuting
microenvironments across seasons and different geographical settings. An
important outcome of this study is that the error of the PAM is
significantly smaller than the error introduced when estimating personal
exposure based on sparsely distributed outdoor fixed monitoring stations.
Hence, novel sensing technologies such as the ones demonstrated here can
revolutionise health studies by providing highly resolved reliable exposure
metrics at a large scale to investigate the underlying mechanisms of the
effects of air pollution on health.
Introduction
Emerging epidemiological evidence has associated exposure to air pollution
with adverse effects on every major organ system
(Thurston et al., 2017). Most of this evidence
comes from western Europe and North America
(Newell et al., 2017) as
population-scale air pollution health studies have largely relied on
available outdoor air pollution measurements from fixed monitoring stations
(COMEAP, 2018). Due to limitations in the availability of
monitoring networks in low- and middle-income countries (LMICs), the effects
of air pollution on health have been under-researched in these settings. A
clear need exists for more direct epidemiological evidence in diverse
geographical settings with varying air pollution sources considering the
high likelihood that health effects of air pollution are not linear and
cannot be simply transcribed from the western world to LMICs
(Tonne, 2017).
Secondly, the low spatial and temporal resolution of exposure metrics at
postcode level or coarser, which are often employed in large-scale
epidemiological research, cannot separate the individual health effects of
pollutants, which are generally highly correlated at these coarser scales.
Additionally, outdoor measurements cannot capture the total personal exposure
that results from the cumulative effects of an individual moving between
different indoor and outdoor microenvironments. During daily life, peak
exposure events often occur during commuting
(Karanasiou et al., 2014) while the indoor
environment is a significant site for exposure in part because people spend
as much as 90 % of their time indoors (Klepeis et al.,
2001). Indoor air is affected by outdoor pollutants penetrating building
envelopes with additional indoor sinks, sources and emissions from building
materials which cannot be detected by fixed outdoor monitoring networks. The
lack of information on indoor environments at the population scale is a
significant factor in poorly quantified health risks. As a result,
inaccurate personal exposure estimations to air pollution introduce both
bias and error in health estimations, ultimately preventing epidemiological
research from moving from general to specific associations
(Zeger et al., 2000).
Rapid advancements in novel sensing technologies of air pollution sensors
now offer the potential to monitor detailed personal exposure during daily
life at the population scale, thanks to their significantly reduced cost,
smaller size and fast response. Instrument development is accelerating fast
with a growing number of companies utilising combinations of such sensors
(Cross et al., 2017) as well as auxiliary components to build different types of monitors
(Morawska et al., 2018). As a
special case, it is now estimated that there are currently over 30 000
sensors operating in China to monitor concentrations of air pollutants
(Morawska et al., 2018). Several
studies over the last 15 years have attempted to quantify personal exposure
to air pollutants by employing portable sensors, but most of those studies
have been restricted to small-scale surveys
(Steinle et al., 2013). However, large-scale
studies are necessary to assess the health effects of harmful pollutants
because they are often seen in only small subgroups of the population due to
varying individual susceptibility and exposure profiles. Novel sensing
technologies are in fact the only method to expand the personal exposure
coverage at the population level. Yet, concerns remain about the validation
and quality control of those sensors
(Castell et al., 2017) as few
personal exposure studies have evaluated their performance in field
deployment conditions (Rai et al., 2017).
Typically, novel sensing platforms are exclusively evaluated in outdoor
static co-locations with reference instruments and they only target small
numbers of pollutants, most commonly ozone, nitrogen dioxide
(Lin et al., 2015) and/or
particulate matter (Holstius et al., 2014;
Feinberg et al., 2018).
To address these shortcomings, a highly portable personal air pollution
monitor (PAM) that measures a large number of chemical and physical
parameters simultaneously has been developed. This paper aims to evaluate
the performance of the PAM when capturing total personal exposure to air pollution in diverse environmental conditions. To do so, the PAM performance was
assessed in well-characterised outdoor, indoor and commuting
microenvironments across seasons and different geographical settings. The
PAM has already been deployed to participants of two large cardiopulmonary
cohorts in China (Han et al., 2019) (AIRLESS- Theme 3 APHH
project) (Shi et al., 2019) and the UK (COPE)
(Moore et al., 2016),
and in a number of smaller international pilot projects in North America,
Europe, South and East Asia, and Africa. This is the first of a series of
publications that aim to capture total personal exposure to a large number of
pollutants at unprecedented detail, and together with medical outcomes, to
identify underlying mechanisms of specific air pollutants on health. As the
field of novel air pollution sensing technologies expands rapidly, this
paper further aims to provide methodological guidance to researchers from
diverse disciplines on how to comprehensively calibrate and validate
portable monitors suitable for personal exposure quantification.
The personal air quality monitor
The PAM has been developed at the Department of Chemistry, University of
Cambridge in collaboration with Atmospheric Sensors Ltd. It is now
commercially available (independently from the University of Cambridge) from
Atmospheric Sensors Ltd (model AS520, http://www.atmosphericsensors.com, last access: 22 August 2019). The PAM (Fig. 1) is an autonomous
platform that incorporates multiple sensors of physical and chemical
parameters (Table 1). The compact and lightweight design of the PAM (ca.
400 g) makes the unit suitable for personal exposure assessment. The PAM is
almost completely silent and can operate continuously. No other input is
required by the user other than to place it for periodic charging (e.g.
daily) and data upload in a base station. The measurements are also stored
in an SD card inside the monitor and uploaded through a general packet radio
service (GPRS) to a secure access FTP server. Customised system software has
been developed to optimise the performance of the platform. Depending on the
chosen sampling interval of either 20 s or 1 min, the battery life on a
single charge lasts for 10 h or 20 h respectively. The combined cost
of the sensors alone is less than GBP 600 and the total cost of
the PAM is less than GBP 2000, making it a “lower-cost” system
(Cross et al., 2017).
The personal air quality monitor. (a) Design of the PAM platform
internals and (b) PAM charging inside the base station. The external dimensions of the
PAM are 13 cm × 9 cm × 10 cm.
Summary of monitored parameters of the PAM. PM1, PM2.5 and PM10 are the fraction of particles with an aerodynamic diameter
smaller than 1, 2.5 and 10 µm respectively. CO: carbon monoxide; NO: nitric oxide; NO2: nitrogen dioxide; O3: ozone.
User-friendly, bespoke software (Fig. S1) has been
developed to automate the management and post-processing of the large volume
of raw data collected with the PAM network. Data are held in a PostgreSQL
relational database management system, which has an unlimited row-storage
capacity and allows the querying of large quantities of data in a flexible
manner while maintaining performance as the volume of data grows.
Post-processing was performed in R software (R Development Core
Team, 2008) (Fig. S1) following the methodology
outlined in this paper.
Measurements of CO, NO, NO2 and O3
The principle of operation of all commercially available miniaturised
gaseous sensors currently involves measuring changes in specific properties
of a sensing material (e.g. electrical conductivity, capacitance, mass,
optical absorption) when exposed to a gas species
(Morawska et al., 2018). The PAM
integrates small (20 mm diameter) electrochemical (EC) sensors based on an
amperometric principle of operation (Stetter and Li, 2008) for
the quantification of carbon monoxide (CO), nitric oxide (NO), nitrogen
dioxide (NO2) and ozone (O3). These EC sensors are the A4 variant
from Alphasense (NO-A4, Alphasense Ltd, 2016a; CO-A4, Alphasense Ltd, 2017a; NO2-A43F, Alphasense Ltd,
2016b; Ox-A431, Alphasense Ltd, 2017b) and operate on a four-electrode system. The principle of operation of the four-electrode system is
identical to that of the earlier variants of the three-electrode system
(Alphasense, 2013a) where the conventional setup of working
electrode, counter electrode and reference electrode is supplemented with an
additional electrode, the auxiliary (or non-sensing) electrode, to compensate
for the temperature dependence of the cell potential
(Popoola et al., 2016). Earlier variants of EC sensors used in this paper have been extensively characterised in laboratory conditions and in static outdoor dense sensor networks
(Mead et al., 2013). Those studies provided evidence that, after appropriate
post-processing, the sensors had a linear response to the targeted
pollutants and achieved excellent performance with limits of detection (LOD)
< 4 ppb demonstrating their suitability for atmospheric air quality
measurements. The linearity and LOD of the four-electrode sensors (when
integrated in the PAM) have been tested under laboratory conditions
following the same methodology as described in Mead et al. (2013) yielding
very similar results.
Currently, standards for the calibration and performance evaluation of EC
sensors focus on industrial applications (British Standards
Institution, 2017). Following those standards, a widely adopted approach to
calibrate EC sensors is gas chamber experiments to determine offset
(baseline) and sensitivity (gain). To address the lack of standards for
novel sensing technologies, a number of researchers and governmental
organisations are developing protocols and guidelines to evaluate
sensor and monitor performance in the laboratory and in the field, such as the
European Metrology Research Programme of EURAMET
(Spinelle et al., 2013), the European
Standardisation Committee (CEN/TC 264/WG 42, 2018) and US-based
groups (Long et al., 2014; AQ-Spec, 2017).
Building on those protocols, the EC sensors were calibrated by co-location
with certified reference instruments in similar environmental conditions and
the same geographical area where the monitors had been or were to be deployed.
The considerable advantage of this approach over laboratory calibration
includes the exposure of the sensor to the actual air pollution and
temperature–relative humidity conditions under which it is expected to
operate, as well as the assessment of any site-specific potential
cross-interferences. A linear regression model (Eq. 1) was applied to
the co-location data to determine the calibration parameters used to convert
raw sensor signals (mV) to mixing ratios (ppb). Temperature effects were
corrected through the auxiliary electrode (AE), which might have a different
sensitivity to the working electrode (WE, a≠b). The cross-sensitivities
between the NO2 and O3 measurements were corrected via parameter
c (the cross-sensitive gas Y is NO2 for O3 measurements
and vice versa). As the CO and NO sensors were found to be sufficiently
selective, c was set to zero for the calibration of those sensors.
[X]ref=aWEX+bAEX+cWEY+d,
where [X]ref is the reference measurement of pollutant X (ppb),
a is the sensitivity of the working electrode (ppb mV-1),
WEX and AEX are the
raw signal of the working and auxiliary electrodes respectively (mV),
b is the sensitivity of the auxiliary electrode (ppb mV-1) (accounts for temperature), c is the
cross sensitivity with gas Y (ppb mV-1; c=0 for CO and NO),
WEY is the
raw signal of the working electrode of the cross-sensitive gas Y (mV), and
d is the intercept (ppb).
To evaluate the performance of the linear model, the datasets were split
into training (i.e. calibration) and validation periods to first extract the
calibration parameters and then apply them to the validation set and compare
the measurements with those from reference instruments (referred to as the
“calibration–validation” method). The training sets ranged from 1 to 16 d, and the adjusted coefficient of determination (R‾2) remained stable for training periods longer than 3 d.
Therefore, approximately a third of the dataset was selected as a training
set. As relationships in these linear models should ideally not be
extrapolated beyond the range of the observations (including meteorological
conditions), the calibration periods covered the temperature and
concentration ranges in which the sensors were deployed
(Cross et al., 2017). Once the performance of the model was established in diverse environments, we used the full co-location periods to determine the
agreement between PAM sensors and reference instruments.
Particulate mass measurements
The operation of virtually all miniaturised particulate matter (PM) sensors
that are currently commercially available is based on the light-scattering
principle, either volume scattering devices or optical particle counters
(OPCs) (Morawska et al., 2018).
The PAM integrates a commercially available miniaturised OPC (Alphasense
OPC-N2; Alphasense Ltd,
2018), which uses Mie scattering for real-time aerosol characterisation
(Mie, 1908). Particles pass through a sampling volume
illuminated by a light source (in this case a laser) and scatter light into
a photodetector (Bohren and Huffman, 1983). The amplitudes
of the detected scattering signal pulses are then related to particle size.
The OPC counts these pulses and typically sorts them into different particle
size bins (Walser et al., 2017). The
OPC-N2 classifies particles in 16 sizes (bins) in the range 0.38–17 µm. The performance of this OPC in the laboratory (AQ-Spec, 2017; Sousan et al.,
2016) showed a high degree of linearity. Similarly studies evaluating the
OPC performance in outdoor static deployments
(Di Antonio et al., 2018; Crilley et al., 2018) showed
that once site- and season-specific calibrations were applied, the
miniaturised sensor could be used to quantify number and mass concentrations
of particles with a precision similar to other standard commercial reference
optical PM instruments.
The complexity of evaluating PM sensor performance is much greater than that
of gas sensors. Compared with standard instrumentation, optical PM
instruments face four inherent limitations which introduce potential
differences in mass estimations compared with reference gravimetric methods.
Exposure of the particles to relative humidity (RH) results in hygroscopic growth of particles and leads to mass overestimation
(Di Antonio et al., 2018).
Small variations in the sensitivities of the photodetector and the intensity/angle of the laser may result in a systematic error specific to each OPC sensor. Additionally, as particles enter the optical chamber, they may deposit on internal surfaces and optics of the sensor, leading to a reduction in the measured scattered light and thus instrument sensitivity.
A further limitation of all optical methods is their inability to detect particles with diameters below a certain size, typically 200–400 nm (Morawska and Salthammer, 2003).
Finally, optical methods cannot distinguish the physical and chemical parameters of the aerosol (e.g. density, hygroscopicity, volatility), which might vary significantly as people move between different microenvironments with diverse emission sources, further increasing the uncertainty of mass estimation.
(a) To compensate for these limitations, this work first corrected for the
effect of RH by applying an algorithm based on the particle size
distribution which was developed for aerosols in urban environments
(Di Antonio et al., 2018). (b) In the
second step, a scaling factor for each OPC was determined to account for
sensor–sensor variability. This scaling factor was determined from a linear
fit between the RH-corrected mass and the reference measurements for each
season independently and to compensate for instrument sensitivity that may
change over time. (c) As the reference instruments (e.g. TEOM) include
particles below the size range of the OPC in their mass estimations, the
scaling factor partly addresses the under-prediction of mass due to
undetected smaller particles which may vary between seasons. The varying
aerosol composition (d) remains a challenge, and therefore a constant
density of 1.65 g cm-3 was assumed. Although the OPC is able to measure
PM1, PM2.5 and PM10, this paper focusses on the
performance of the PM2.5 measurements because of the availability of
reference instruments.
Performance of the PAM under well-characterised conditions in the field
In the following three sections the performance of the PAM is assessed when
measuring air pollution concentrations in different environments that are
relevant for the quantification of total personal exposure (outdoor, indoor and in movement) in the UK and China. Sensor performance may vary significantly with season (e.g. temperature and RH artefacts) while meteorological conditions may affect the variation in outdoor air pollution levels directly (e.g. stability of the atmosphere) and indirectly by socioeconomic patterns (e.g. increased energy demand for heating). Similarly, indoor air may be directly affected by outdoor air pollution levels and indirectly through occupants' behavioural patterns (e.g. window adjustment to achieve thermal comfort). Taking into account the strong seasonal variation in air pollution levels, the performance of the PAM was evaluated by co-locating one or multiple PAMs with reference instruments during both the “heating” (when the majority of householders heat their home on a regular basis) and “non-heating” seasons. The residential central heating season in Beijing is from 15 November to 15 March (Beijing municipal government), while in the UK the equivalent heating season is 5.6 months (October–March/April) (BRE, 2013).
The description of the sites, principle of operation and models of certified
reference instrumentation used can be found in Table 2. The co-locations in
China involved 60 PAMs which had been previously deployed to 250 participants of a cardiopulmonary cohort for 1 month during the heating season
and 1 month during the non-heating season (Han et al.,
2019). The co-location in the UK involved 60 PAMs that have been previously
deployed to 150 participants of a COPD cohort for 2 years continuously
(Moore et al., 2016).
The reproducibility between co-located sensors was very high even when the
ambient concentrations were close to the LOD (mean R2≥0.80 for EC sensors and R2≥0.91 for the OPC; see
Fig. 2 and Table S1 in the Supplement). Hence, the performance of the
selected PAMs in static deployments as described in this section is
representative and can be extrapolated to the entire sensor network.
Reproducibility of a PAM network (in that case 60 monitors) co-located
outdoors in Beijing during the heating season after 1 month of field
deployment. (a) Scatterplot of the PM2.5 measurements between 10 sensor pairs. The 1 : 1 line is in black, and the linear fit line is in red. (b) Close-up of a scatterplot from (a) of one representative sensor pair.
(c) Histogram of the coefficient of determination (R2)
between all sensor pairs. R2 values during this deployment
were higher than 0.90 for all pollutants indicating the high reproducibility
of the sensors' readings (see Table S1 for all co-locations). O3
sensors R2>0.80 due to very low ambient levels close to
the LOD of the sensors.
Details of the reference instruments used in this study. Time
resolution of all measurements was 1 min.
DeploymentSite descriptionNO, NO2COPMO3Outdoor ChinaUrban background in Peking University (PKU) campus, BeijingChemiluminescence, Thermo Fisher Scientific model 42iNondispersive infrared, Thermo Fisher Scientific model 48iPM2.5* TEOM (tapered element oscillating microbalance)UV absorption Thermo Fisher Scientific model 49iOutdoor UKUrban backgroundat the Departmentof Chemistry, CambridgeChemiluminescence, Thermo Fisher Scientific model 42iNondispersive infrared, Thermo Fisher Scientific model 48iAerosol spectrometer FIDAS PALAS200SUV absorption Thermo Fisher Scientific model 49iIndoor residential ChinaIndoor deploymentin an urban high-rise Beijing flatNO2 cavity attenuated phase shift spectroscopy(CAPS) Teledyne API T500UNAAerosolspectrometer GRIMM 1.108 NACommuting environment UKMonitoring vehicleequipped with commercial instruments driving in centralLondonNO2 CAPS Teledyne API T500UNANephelometer (scattering) Met One ES642UV absorption Teledyne API T400
* Due to malfunctioning of the
TEOM in PKU during the non-heating season, measurements from a TEOM at a nearby
governmental site (Haidianwanliu, time resolution 1 h) were used. NA: not available.
Outdoor performance of sensors in diverse urban environments with
varying pollution profiles and meteorological parameters
In total, four outdoor co-location deployments have been evaluated to
comprehensively characterise the performance of the sensors (in the UK and
China during the heating and non-heating seasons; see Table 3). The PAMs were
placed in protective shelters close to the inlets of the certified air
pollution monitoring stations. The sensor measurements were converted to
physical units following the methodology described in Sect. 2.1 and 2.2.
As an illustrative example, the outdoor co-location in Beijing, China (19 days, December 2016 to January 2017), is presented in Fig. 3 to demonstrate the
previously mentioned calibration–validation method (Sect. 2.1).
The time series of the pollutants measured by the PAM (blue) closely follow
the reference instruments (red) in both the calibration (Fig. 3a) and
validation (Fig. 3b) periods. Similarly, the time series and scatterplots
of the other three co-locations (UK in the heating season, China and UK in
the non-heating season) can be found in the Supplement (Figs. S2–S4).
Outdoor co-location of one representative PAM with calibrated
reference instruments in China (winter 2016/2017) at 1 min time resolution
demonstrating the calibration–validation methodology to evaluate the
performance of the linear model. The first 5 d (a) were used to calibrate the EC sensors. The remaining co-location data (14 d, b) were
used to validate the extracted calibration parameters. The scatterplots on
each side show the correlations between reference and PAM measurements with
the 1 : 1 line in black R‾2
and gradients (m) are shown on each side in the corresponding colour.
Table 3 gives a quantitative overview of the agreement between the PAM
measurements and the reference instruments in outdoor co-locations during
the heating and non-heating seasons. Ambient temperature and RH (median,
range: 5 %–95 %) as well as the mean and maximum pollutant concentration
measured are presented to describe the ambient conditions of each
co-location. Because the PAM internal temperature is on average 7 ∘C higher than the ambient temperature due to heat generated by the internal
battery, the internal conditions the sensors were exposed to are also
presented. The sensor performance was
evaluated against the reference instruments using (1) R‾2 of the linear regression
between PAM and the reference and (2) the root-mean-square error (RMSE) using
both the validation and calibration periods (Table 3). R‾2 may be a misleading indicator of sensor performance when
measurements are taken close to the LOD of the instruments. The RMSE can be
a complementary parameter of R‾2 for the
evaluation of performance, as it summarises the mean difference between
measurements from the sensor and certified instruments. The average values of
R‾2 and RMSE of all N sensors during all
co-locations are given in Table 3.
Overview of sensors' performance during outdoor co-locations in
China and the UK (7 to 19 days). Median values (range: 5th–95th percentiles) of the ambient temperature and relative humidity (RH), internal
temperature and RH of the platform are presented. The 95th percentile
of the concentration measurements of the reference over the entire
co-location period is given as the maximum concentration for each pollutant. The
mean adjusted coefficients (R‾2) and
root-mean-square errors (RMSEs) indicate the agreement between the
measurements of the sensors and reference instruments. The average values of
all N sensors for each variable are given. Co-location in China in June is
shown in italics as sensors were regularly exposed to temperatures higher
than 40 ∘C where sensors do not show linear temperature responses.
The sensor reproducibility for these co-locations is presented in Table S1.
Heating season Non-heating season LocationChinaUKChinaUKStart date–end date 28 Dec 2016–27 Oct–28 Jun–26 Mar–15 Jan 201713 Nov 201716 July 201710 Apr 2018(total hours of co-location deployment) (447 h)(408 h)(432 h)(342 h)Illustrative graphical example Fig. 3Fig. S2Fig. S3Fig. S4AmbientAmbient temp. (∘C)1.1 (-3.6–6.1)9.3 (4.3-14.4)29.9 (22.8–36.3)8.3 (4.7–18.1)conditionsAmbient RH (%)40 (15–79)81 (61–93)68 (43–96)83 (48–93)Internal conditionsInternal temp. (∘C)10.5 (5.3–18.0)15.9 (11.0–20.8)40.2 (32.7–45.8)17.7 (12.2–26.8)of the PAMInternal RH (%)27 (14–44)52 (39–59)38 (23–55)52 (34–60)Number of sensors (N)(–)N=59N=3N=59N=3Maximum (mean)mixing ratio (ppb)6845 (2561)357 (237)916 (575)276 (192)COR‾20.980.740.710.67RMSE in parts per billion(percentage of max)31 (0.5 %)31.6 (8.9 %)212 (23 %)33.3 (12.1 %)Maximum (mean)mixing ratio (ppb)132 (38)19 (5)5 (1)6 (2)NOR‾20.940.890.200.58RMSE in parts per billion(percentage of max)11.7 (8.9 %)3.0 (15.8 %)13.0 (260 %)2.2 (36.6 %)Maximum (mean)mixing ratio (ppb)98 (42)35 (15)42 (22)19 (10)NO2R‾20.840.900.200.84RMSE in parts per billion(percentage of max)11.8 (12.0 %)3.0 (8.6 %)13.3 (31.7 %)2.6 (13.7 %)Maximum (mean)mixing ratio (ppb)33 (13)30 (16)109 (49)44 (28)O3R‾20.870.920.800.89RMSE in parts per billion(percentage of max)3.6 (10.9 %)2.7 (9 %)14.9 (13.7 %)4.2 (9.5 %)Maximum (mean)conc. (µg m-3)432 (114)32 (12)110 (55)37 (3)PM2.5R‾20.930.57a0.65b0.80RMSE in microgrammes per cubic metre (percentage of max)37 (8.6 %)9 (28 %)a25 (22.7 %)b2 (5.4 %)
a Due to unavailable data, PM mass measurements are not corrected for RH
effects. b Comparison with governmental station ∼ 3 km away.
Outdoor performance of the PAM during the heating season co-locations
During the heating season outdoor co-locations of a number of PAMs next to
certified reference instruments, ambient temperatures ranged from
-4 to 6 ∘C in China and between 4 and
14 ∘C in the UK. Air pollution in China was characterised by elevated
levels of CO and PM2.5 (Table 3) for extended time periods
(haze events) partially driven by stagnant winds or a weak southerly wind
circulation (Shi et al., 2019). Compared with pollutant
levels in the UK, the concentrations of CO and PM2.5 were
approximately 10 times higher while the contrast in ambient NO2 levels was less marked with levels in China only approximately 3-fold
higher.
The O3, NO and NO2 sensors exhibited an excellent performance
(R‾2≥0.84) in both geographical settings
(Table 3). The median RMSE values were close to the LOD of the sensors
(< 3 ppb) in the UK and slightly higher in China (< 12 ppb)
(Fig. 3, Table 3). In both deployments, the RMSE values of these gaseous
sensors were negligible compared to the ambient concentration ranges of the
targeted pollutants (less than 16 % of the maximum mixing ratio recorded
by the reference instruments).While the median R‾2 between the CO sensor and the corresponding reference was
reasonably high in both outdoor deployments (≥0.74), the median RMSE
values were also quite large (< 32 ppb). In fact, this is due to the
known high intrinsic noise and LOD of the reference instrumentation
(> 40 ppb, Thermo Fischer Scientific, 2017), which is much higher compared to that of the
electrochemical sensors (LOD < 4 ppb; see Sect. 2.1).
Following the correction of the size-segregated particle measurements for
the effect of RH (Sect. 2.2), the PM mass quantification with the
miniaturised OPC agrees with the TEOM reference instrument with an adjusted
R‾2 of 0.93. The low RMSE values (> 8.6 % of the maximum concentration) demonstrate that the scaling factor
adequately addresses the under-prediction of mass due to undetected smaller
particles when derived from field calibration in the local environment. Due
to unavailable measurements, the PM measurements in the UK could not be
corrected for RH effects, which resulted in only a moderate correlation with
the reference instrument (R‾2=0.57, Fig. S2).
Outdoor performance of the PAM during the non-heating season
co-locations
One outdoor co-location in China (Fig. S3) and one in the UK (Fig. S4)
were performed during the non-heating season, both over periods of 2 weeks
(Table 3). In the UK, seasonal variation in ambient temperatures, RH and
pollution levels was relatively small. In contrast, in China, seasonal
variation was large with ambient temperatures reaching up to 36.3 ∘C
(median: 29.9 ∘C) and generally lower pollution levels compared to
the heating season. However, in both geographical settings, O3 was
significantly elevated. The performance of the O3 sensor remained
reliable in all deployments with median R‾2=
0.80 and RMSE values < 15 ppb, which might provide valuable insights
into the health effects of this pollutant because (a) ozone is a strong
oxidant with a high potential to affect the body (Nuvolone et al., 2018) and (b) has the highest
concentrations during the non-heating season compared to other pollutants
which usually peak during the heating season.
Due to a malfunction of the PM reference (TEOM) instrument during the
non-heating season at PKU, the PAM PM measurements had to be compared with a
TEOM installed at a nearby governmental site (Haidianwanliu). Although not closely
co-located (∼ 3 km), the gradient between the PAMs and
reference measurements was close to unity (average m=0.96, see example
Fig. S3) and there was still a notable correlation (R‾2=0.65) with a median RMSE of 25 µg m-3, indicating that away from direct sources PM concentrations are essentially homogenous over relatively large urban areas. Compared with the heating season, PM concentrations in China were significantly lower, whereas PM levels in the UK varied little with season. After correcting for the effects of RH on PM, the PAM performance in the UK
during the non-heating season significantly improved compared with the
heating season (RMSE = 2 µg m-3 within the
particle size range 0.38–17 µm).
While the performance of the O3 and OPC sensors remained reliable across seasons and geographical settings, the performance of the CO, NO and
NO2 sensors decreased significantly (R‾2≥0.20) during the hottest parts of the non-heating season in China due
to extreme temperatures (internal median temperatures of the PAM:
40.2 ∘C, 5 %–95 %: 32.7–45.8 ∘C, Table 3). It
should be noted that NO levels were close to the LOD of the sensor, which
also affects the R‾2 values. We conclude that the
measurements of the CO, NO and NO2 sensors should be interpreted with
caution when the sensors are exposed to temperatures above 40 ∘C.
However, during the field deployment to participants, the sensors were
exposed to lower temperatures (see Fig. S5) that did not impact on their
performance (see Sect. 3.2).
Indoor performance of the NO2 and PM sensors
Low-cost air pollution sensors have generally been characterised outdoors
next to reference instruments as described in the previous section. However,
little is known about the performance of these sensors in indoor
environments, where people spend most of their time
(Klepeis et al., 2001), and environmental conditions
(e.g. temperature, RH) and emission sources may be significantly different
compared with nearby outdoor environments.
To evaluate the indoor performance of the NO2 and the OPC sensors, an
experiment in an urban flat in central Beijing was performed during the
non-heating season (May 2017). One PAM was deployed in the living area next
to two commercial instruments that were used to provide reference
measurements: (1) a cavity attenuated phase shift spectroscopy instrument
(CAPS Teledyne T500U) for NO2 and (2) a portable commercial
spectrometer (GRIMM 1.108) for particulate matter measurements (Table 2).
During the experiment the occupants relied on natural ventilation, adjusting
the windows freely to achieve thermal comfort. Median indoor temperatures
were 26.0 ∘C (5 %–95 % range: 17.1–28.8 ∘C),
and the median internal PAM temperature was 33.0 ∘C (5 %–95 %
range: 24.3–36.2 ∘C), which is comparable with the
temperature range during the non-heating season field deployment to
participants (internal median temperature: 35.0 ∘C, 5 %–95 %
range: 28.5–39.9 ∘C, Fig. S5).
The conversion of the raw measurements to parts per billion used the sensitivities
extracted using outdoor co-locations during both the heating and non-heating
seasons (Sect. 3.1) with the linear model (Sect. 2.1). The
performance of the low-cost sensors in the indoor environment (Figs. 4 and S6) was comparable to the outdoor performance demonstrated in the
previous section (R‾2=0.91, gradient m=1.1, RMSE = 3 ppb for NO2 (Fig. 4c) and R‾2=0.86, gradient m=0.86, RMSE = 7 µg m-3 for PM2.5 (Fig. 4d)), proving their suitability
to quantify indoor air pollution levels for these species provided they have
been adequately calibrated in the local environment.
Indoor co-location of a PAM with portable commercial
instrumentation (Table 2) in an urban flat in China during the non-heating
season. (a) Time series of NO2 from the PAM (blue) and a
cavity attenuated phase shift spectroscopy (CAPS) instrument (red). Outdoor
NO2 measurements (grey) were collected at a PKU reference site (Table 2),
which was located 5.3 km away. Time resolution of measurements is 1 min.
(b) Time series of PM2.5 mass measured with the PAM
(blue) next to a commercial portable spectrometer (GRIMM 1.108, red). Mass
concentrations were calculated from particle counts within the size range
0.38–17 µm with the same aerosol density for both instruments. Outdoor
PM2.5 mass measurements (grey) were collected at the closest
governmental station (Table 2, 1 h time resolution), which was located 6 km
away. (c, d) Scatterplots show an excellent agreement
between commercial instruments and miniaturised sensors, making them suitable
for the quantification of indoor pollution levels. The 1 : 1 line is in black and gradient m in red. (e, f) Density plots of the
difference between measurements from the PAM and the indoor reference (red)
are compared with the difference between the PAM and the outdoor reference
(black).
Short-term deployment of nine PAMs carried simultaneously by a
pedestrian moving between two indoor environments (laboratory, café) in
Cambridge, UK, in January 2018. (a) Time series of NO
measurements from the PAM sensors (blue lines). (b, c) Scatterplots between two of those PAMs, whereby indoor data were separated
from outdoor data. The 1 : 1 line is in black, and the linear fit line is in red.
Although this short experiment is only a “snapshot” of indoor exposure,
it shows that the measurement error of the PAM relative to established
commercial instruments is negligible compared with the error in indoor
exposure estimates introduced from using inadequate exposure metrics, in
this case outdoor measurements from the closest monitoring reference site.
For example, using outdoor measurements from the closest monitoring station
would have resulted in an over-prediction of indoor PM2.5 concentrations (moderated by attenuation effects of the building envelope)
with an average difference of 30 µg m-3 (standard
deviation: 29 µg m-3), which is significantly
higher than the 7 µg m-3 RMSE value of the PAM
(Fig. 4f). While indoor NO2 levels broadly followed outdoor levels,
the range of the error in under-predicting and over-predicting exposure
events is much broader (min–max range: -18 to 18 ppb; Fig. 4e) compared
with the error introduced from measurement uncertainties (-7 to 5 ppb). Such
peak exposure events might be important triggers for acute health responses.
Performance of the PAM in non-static configurations
The aim of this section is to evaluate the PAM reproducibility and accuracy
while in movement, with pedestrian and in-vehicle deployments.
Reproducibility of the PAM when not static
Multiple (in this case nine) PAMs were carried by a pedestrian while keeping
an activity diary and walking between two indoor environments via a highly
trafficked road in Cambridge, UK (weekday in January). Using NO measurements
(the main traceable component from combustion engines) as an illustrative
example, Fig. 5a shows the simultaneous measurements of all PAMs as a time
series and the scatterplots between the measurements of two of those PAMs
separated into indoor (Fig. 5b) and outdoor data (Fig. 5c).
Significant changes of the pollution levels were observed when moving
between the different environments, illustrating the high granularity of
personal exposure in daily life. Compared with the indoor environments,
walking in traffic resulted in elevated pollution exposure events. As
illustrated in the time series of Fig. 5, the difference in pollution
levels between the three micro-environments was significantly higher than
the variability between PAM measurements.
Table 4 gives an overview of the correlations within the co-located moving
network. In indoor environments an excellent agreement between all sensors
(median R2>0.96) was found, indicating a high sensor
reproducibility. An exception was the O3 sensor, which showed poor
between-sensor reproducibility due to very low indoor and outdoor
concentrations (< 5 ppb) near the LOD of the sensor. The
between-sensor correlations in the road environment were lower than indoors
(median R2>0.85) due to highly heterogeneous air
pollution concentrations driven by complex factors (e.g. canyon air mixing,
moving vehicle sources, topology). This signifies that in such environments
air pollution concentrations might differ on such short spatial and
temporal scales that even sensors that are less than 1 m apart from
each other capture a slightly different exposure profile.
Correlations between PAM sensors. Adjusted R‾2 values of each sensor pair of the simultaneously carried
PAMs were determined. Median R2 values of all combinations are presented
in the table below. Very low O3 levels (< 5 ppb) resulted in
poor between-sensor correlations and are given in italics.
Median R‾2IndoorOutdoorNO0.990.87NO20.960.94O30.160.46CO0.990.95PM2.50.990.85
When moving rapidly between different environments with different
temperatures (i.e. from outdoors to a warmer indoor microenvironment) false
peaks were observed in the EC sensor measurements (Fig. S7)
(Alphasense Ltd, 2013b). The response and recovery
time following rapid temperature transitions was found to vary for different
sensor types. To account for the false sensor responses, first an
algorithm to identify those events was developed and then a 15 min window
for CO and a 5 min window for NO, NO2 and O3 measurements was
removed from the data (Figs. S7 and S8). Though it potentially
excludes peak exposure events as rapid temperature changes often occur when
people leave heated buildings and enter (colder) traffic environments to
commute, this correction method typically removes less than 0.1 % of the
exposure dataset under daily life conditions. The PM measurements are not
affected by these temperature transitions.
Accuracy of the PAM when not static
A PAM was mounted on the roof of a battery-powered vehicle equipped with
multiple commercial instruments (Table 2) mapping air pollution levels in
London at speeds of up to 60 km h-1 for 1 d during the non-heating season (Fig. 6). The PAM was mounted on the roof with the OPC inlet facing
forwards and the EC sensors facing to the sides. The reference instrument
inlets were located on the car roof as well. There was no correlation
between car speed and RMSE values in the gaseous and particulate
measurements. The OPC contains an airflow measurement unit which compensates
for any wind or internal flow dependence.
Considering the high spatial variability of air pollution in traffic
environments (see Sect. 3.3.1), the accuracy of the PAM in a mobile
configuration was high for all targeted pollutants (R‾2≥0.54). To illustrate the large degree of
variability of air pollution concentrations over time, the investigated area
was mapped throughout the day multiple times with the highest concentrations of
PM2.5 and NO2 recorded during the morning rush hour.
Mounting evidence points towards a causal link between exposure to air
pollution and health outcomes. However, due to current limitations in cost,
maintenance and availability of instrumentation, most large-scale health
studies have focused on developed countries and have relied on low-spatial- and low-temporal-resolution (generally outdoor) air quality data as metrics of
exposure, severely limiting causal inferences in epidemiological research
worldwide. Emerging low-cost sensing technologies can offer a potential
paradigm shift in capturing personal exposure of the population during daily
life in addressing this critical shortcoming.
In this paper we demonstrated that, with suitable calibration and
post-processing, the performance of currently available low-cost air quality
sensors, in this case incorporated into a highly portable personal monitor
(the PAM), is comparable with the performance of reference instrumentation
across a wide range of conditions:
in diverse outdoor environments (urban background and traffic);
across seasons (over a wide temperature and RH range);
in two geographical settings with differing air pollution levels and
meteorological profiles (UK and China);
in indoor environments (residential, laboratory, café)
with varying emission sources, and
in static and in non-static deployments.
A critical important outcome of this study is that the performance of the
sensors substantially exceeds that needed to quantify the differences
between indoor and outdoor pollution levels, and thus to quantify exposure
levels in a reliable manner.
There are certain performance caveats with the low-cost sensors used in this
study, which once identified are likely to be addressed in future
generations of sensors.
The performances of the CO, NO and NO2 sensors were found to degrade at temperatures above 40 ∘C. In fact, such extreme environmental conditions were not encountered during the actual personal exposure sample periods for which the PAMs were used, and the performance criteria discussed above were met.
A limitation of all optical PM sensors, low-cost or reference, is that they cannot measure small particles below a critical size threshold (typically 200–400 nm). In this work we show that by appropriate local calibration, this shortcoming can be largely accounted for.
The toxicity of particles is also likely to depend on their chemical
composition (Kelly and Fussell, 2015). Most national networks measure total mass only, and measuring
particle chemical composition is currently largely the domain of the
research community. A major challenge will be to develop techniques to allow
routine PM composition measurements, for both the regulatory networks and applications such as personal monitoring.
The key conclusion is that when suitably operated, highly portable air
pollution personal monitors can deliver traceable high-quality exposure
metrics which can address scientific, health and policy questions for the
indoor and outdoor environment in a way that has not been possible before.
Mobile and static PAM networks have now been deployed in a range of health
studies, and these will be the focus of future papers.
Data availability
Research data supporting this paper are available at 10.17863/CAM.41918 (Chatzidiakou et al., 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/amt-12-4643-2019-supplement.
Author contributions
LC and AK have contributed equally to this paper. It was conceptualised by LC, AK, BB and RLJ. The sensor platform was developed by LC and MK and deployed by LC, AK, YH, TW, HZ, QW and SF. The data curation was performed by LC, AK, OAMP, ADA, YH, FAS and SC. LC, AK, OAMP, ADA and RLJ contributed to the formal data analysis. Resources were provided by MH, JKQ, BB, FJK, TZ and RLJ. The software was developed by LC, AK and MK. Data were visualised by LC and AK. The original draft was written by LC and AK and reviewed and edited by OAMP, YH and RLJ.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
Acknowledgements
The authors would like to thank the following: Qiang Zhang and Kebin He
(Tsinghua University) for PM outdoor data during the non-heating season,
Paul Williams (University of Manchester) for providing the GRIMM instrument,
and Envirotechnology Services for reference mobile measurements.
Financial support
This research has been supported by the NERC (grant no. NE/N007085/1). This research has been jointly supported by the National Natural Science Foundation of China (NSFC grant 81571130100) as well as the Natural Environment Research Council (NERC grant NE/N007018/1) and the Medical Research Council of the UK (AIRLESS project). This research has also been supported by the Medical Research Council of the UK (MR/L019744/1) (COPE project).
Review statement
This paper was edited by Yongjie Li and reviewed by three anonymous referees.
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