A fast-growing area of research is the development of low-cost sensors for
measuring air pollutants. The affordability and size of low-cost particle
sensors makes them an attractive option for use in experiments requiring a
number of instruments such as high-density spatial mapping. However, for
these low-cost sensors to be useful for these types of studies their accuracy
and precision need to be quantified. We evaluated the Alphasense OPC-N2, a
promising low-cost miniature optical particle counter, for monitoring ambient
airborne particles at typical urban background sites in the UK. The precision
of the OPC-N2 was assessed by co-locating 14 instruments at a site to
investigate the variation in measured concentrations. Comparison to two
different reference optical particle counters as well as a TEOM-FDMS enabled
the accuracy of the OPC-N2 to be evaluated. Comparison of the OPC-N2 to the
reference optical instruments shows some limitations for measuring mass
concentrations of PM
Airborne particles are of global concern due to their detrimental health
effects, particularly in the fine fraction (PM
Into this gap a fast-growing area is the development of low-cost sensors for measuring the concentrations of a wide range of species in the atmosphere including gases and particles (Lewis et al., 2016; Rai et al., 2017; Snyder et al., 2013). However, the question remains as to whether the uncertain quality of data from these low-cost sensors can be of value when attempting to determine pollutant concentrations at high spatial resolution (Kumar et al., 2015). Sensors for both gases and particles can suffer from drift and a number of interference artefacts such as relative humidity (RH), temperature and other gas-phase species (Lewis et al., 2016; Mueller et al., 2017; Popoola et al., 2016). Despite these challenges, recent work has shown that low-cost gas sensors can be deployed in large-scale networks provided appropriate corrections for known artefacts are applied (Borrego et al., 2016; Mead et al., 2013; Mueller et al., 2017), with clustering of multiple gas sensors into one unit shown to be an effective methodology (Lewis et al., 2016; Mueller et al., 2017; Smith et al., 2017).
For low-cost particle sensors, their reported performance across the literature is somewhat mixed (Borrego et al., 2016; Castellini et al., 2014; Sousan et al., 2016; Viana et al., 2015) and can depend on the type of particle sensor employed. There are a wide range of low-cost particle sensors available commercially from manufacturers including Dylos, TSI, Airsense and Alphasense. The more widely used and available low-cost particle sensors can be considered as miniaturised versions of optical particle counters (OPCs) and employ a light-scattering technique to measure ambient particle concentrations (see e.g. Gao et al., 2015; Sousan et al., 2016). While these miniature OPCs are not meant to compete with more established instrumentation in terms of their accuracy and precision, their affordability and size makes them attractive for use in experiments requiring a number of such instruments, such as personal monitoring (see e.g. de Nazelle et al., 2017; Steinle et al., 2015). However, to be useful in these types of studies, the precision and accuracy of these instruments needs to be evaluated.
Laboratory assessments of the performance of a number of low-cost miniature OPCs have shown promising results, with adequate precision observed compared to reference instrumentation (Manikonda et al., 2016). Sousan et al. (2016) evaluated the Alphasense OPC-N2 in a laboratory study using reference aerosols (Arizona road dust, NaCl and welding fumes) and found reasonable agreement for size distributions and particle mass between the OPC-N2 and a GRIMM portable aerosol spectrophotometer, provided appropriate and specific calibrations were applied. While these results are encouraging (Manikonda et al., 2016; Sousan et al., 2016), laboratory-based studies using reference aerosols may not be representative of their performance when measuring ambient particles, owing in part to the complex mixture and variable relative humidity and temperature encountered in the real world. Previous field testing of low-cost particle sensors has found that the Dylos (Steinle et al., 2015) and Portable University of Washington Particle (PUWP) monitors (Gao et al., 2015) performed well for ambient sampling of particle mass concentration in both an urban and rural environment when compared to reference instruments; however they were assessed over a short period (4–5 days). In contrast, at a roadside location poor agreement between two different OPC sensors compared to reference instruments was observed by Borrego et al. (2016). Clearly, the results are mixed and longer-term assessment of the stability and longevity of these instruments are needed, as these are critical parameters when considering their worth for use in large-scale networks.
We evaluate here the Alphasense OPC-N2, a promising low-cost miniature
optical particle counter (Sousan et al., 2016), for monitoring
ambient airborne particles at typical urban background sites in the UK. We
assessed the inter-unit precision of the OPC-N2 by co-locating 14 instruments at a single site to investigate the variation in measured
particle mass concentration in the PM
The optical particle sensor (OPC) under evaluation in the current work is the
OPC-N2 manufactured commercially by Alphasense
(
The OPC-N2 is designed to log data via a laptop using software supplied by Alphasense; however, this may not be practical when using multiple OPC-N2s at once or for personal monitoring. Therefore, we developed a custom built system for logging the OPC-N2 during the inter-comparison, utilising either a Raspberry Pi 3 or Arduino system. The Python code to log the outputs from OPC-N2 on a Raspberry Pi 3 is made available in the Supplement. The Python code makes use of the py-opc Python library for operating the OPC-N2 written by Hagan (2017).
The first reference instrument was a TSI 3330 optical particle
spectrophotometer (OPS), which measures particles number concentrations
between 0.3 and 10
For the sake of this inter-comparison, we have taken the TSI 3330 and GRIMM data as an accurate measure of particle mass concentrations. The reference instrument used for the factory calibration of the OPC-N2 by Alphasense is the TSI 3330 (Sousan et al., 2016) and hence included for comparison.
The instruments were housed within the Elms Road Observatory Station (EROS),
located on the University of Birmingham campus. The site is classed as urban
background, with emissions from nearby road and a construction site the major
sources of particles. Fourteen OPC-N2s were deployed at EROS, enabling the
precision of the OPC-N2 to be assessed along with the accuracy relative to
the reference instruments, the TSI 3330 and GRIMM. An intensive
inter-comparison ran for just over 5 weeks, from 26 August until 3 October
2016, during which all 14 OPC-N2s, the TSI 3330 and the GRIMM sampled ambient
air. Minimal lengths (12 cm) of stainless steel tubing (OPC-N2) and
conductive black tubing (TSI 3330 and GRIMM) were used to sample outside air,
with each OPC having its own inlet at a height of 1.5 m. The vertical inlet
for the TSI 3330 necessitated a bend in the tubing; however the calculated
sampling efficiency (using von der Weiden et al., 2009) was 92 % for
particles with a diameter of 10
At the conclusion of the intensive inter-comparison, a subset of the OPC-N2 (5) continued to sample at EROS along with the GRIMM, to test the robustness and suitability of the OPC-N2 for longer-term monitoring. The long-term monitoring concluded on 1 February 2017, meaning that these OPC-N2s sampled ambient air for up to 5 months.
For regulatory purposes, an accepted method for measuring particle mass concentrations is a tapered element oscillating microbalance (TEOM), and therefore we also compared the OPC-N2 to this technique despite the difference in particle measurement approaches. An urban background air monitoring station part of the UK Automatic and Rural Urban Network (AURN) nearby EROS (Tyburn Rd) was chosen for this inter-comparison. At the Tyburn Rd AURN station, the TEOM monitor was fitted with a filter dynamic measurement system (FDMS) (Grover et al., 2006), to correct for semi-volatile particle loss. A subset of OPC-N2s (4) and the GRIMM PAS 1.108 that were deployed at EROS sampled at Tyburn Rd station for 2 weeks during February 2017. The OPC-N2 was housed individually within waterproof boxes on the roof of the cabin near to the TEOM inlet in order to keep the inlet length the same as used at EROS. The GRIMM sampled from a nearby separate inlet.
All OPCs employed in this study count the number of particles and determine
the size based upon particle light scattering of a laser, and to convert to
particle mass concentration they must apply a number of assumptions. To calculate
the particle mass concentration, spherical particles of a uniform density
and shape are assumed, which is not strictly true for airborne particles in
an urban atmosphere but is considered a standard approximation. Therefore to
ensure a fair comparison between the different OPC, the same calculations
and assumptions must be applied to all three OPC measurements. The TSI 3330
data were processed using the TSI AIM software to convert the particle count
concentration to particle mass measurements. The particle counts from the
GRIMM data were converted to particle mass (via particle volume) using the
same calculations, as outlined in the TSI AIM software manual according to
Eqs. (1) to (3):
The OPC-N2 converts, on board via a factory determined calibration, particle
counts to particle mass concentration in PM
All instrument time series were corrected for drift against a reference time. As the sampling intervals varied slightly between the different OPC, a 5 min average of particle concentrations was used for inter-comparison between instruments.
The two light-scattering optical particle counters used as reference
instruments in this study were found to be well correlated
(
The performance of the custom built logging systems varied between
44 and 94 % successful data capture, with the Arduino and Raspberry Pi
systems giving 44–65 and > 92 %, respectively. The Raspberry
Pi data logger system was used for the long-term measurements and for the
inter-comparison with the AURN site due to its better performance. The data
losses were due to hardware issues and not related to performance of the
OPC-N2. Due to the missing data, only a subset of measured PM
As there is a considerable spread in response for the OPC-N2 relative to the
reference instruments, we then quantified whether it was always the same
OPC-N2 reading low and high. Due to the aforementioned data capture issues,
this analysis was only applied to days when all 14 OPC-N2s were running,
21–24 September (Fig. 1). The results are shown as a rank order
plot, where the OPC-N2 observations are ordered from the highest reported
value to the lowest over this period, normalised to the median concentration
at the start of the analysis (
Time series of PM
For the 3-day time period (21–24 September) we applied the rank order analysis. Two subsets of concentrations measured by the OPC-N2 were evident in the time series (Fig. 1), one a period of highly variable mass concentrations (00:00 BST, 21 September, to 12:00 BST, 22 September 2016) of September) followed by more stable mass concentrations (12:00 BST, 22 September 2016 onward). This was reflected in the corresponding rank order plots where relatively consistent OPC rank orders were observed throughout the variable and comparatively stable PM concentrations periods. However, there is a noticeable transition between the two periods in the rank order plot, observed at approximately 12:00 BST on 22 September. This transition in rank orders would reflect the difference in OPC PM sensitivities, random noise and offset values between each OPC. Over the 3-day period the OPCs appeared to hold their response characteristics and hence rank orders well, suggesting that over this timescale quantitative concentrations could be directly compared. Due to the changing response and the incomplete data coverage, for the rest of the analysis in this paper, when comparing to the reference instruments, the median and inter-quartiles concentrations of all 14 OPC-N2s were used.
Sensor ranking analysis for measured PM
One measure of the precision of a group of instruments is the coefficient of
variance (CV) and this was calculated for the measured ambient mass
concentrations of all 14 OPC-N2s to assess the variability between 14 instruments. The average CV was 0.32
Slopes of measured PM mass concentrations of the reference
instruments against the median and inter-quartiles for OPC-N2. The intercepts
were not constrained to zero. Correlation co-efficient,
The median and inter-quartiles of the measured PM concentrations from the
14 OPC-N2s were used to compare the measured particle mass concentrations to
the reference instruments (Fig. 4). From Fig. 4, the notably similar
distributions across all three particle size fractions for the first and
third quartiles indicate good agreement between the 14 OPC-N2s, further
highlighting the reasonable degree of precision between the OPC-N2s as shown
in the previous section. At typical ambient PM
Time series of the hourly average median OPC and CV during the
September intensive inter-comparison at EROS for PM
Histogram of measured PM
When the median and inter-quartile OPC-N2 concentrations were plotted against the TSI and GRIMM concentrations, the slope was greater than unity for all three size fractions (Table 1), indicating that the OPC-N2s were overestimating the ambient particle mass concentrations (approx. 2 to 5 times, Table 1). Overall, the OPC-N2s and GRIMM were in better agreement compared to the TSI for all size fractions (Table 1). The GRIMM was found to record PM concentrations 20–30 % higher compared to the TSI (Fig. S1), and this could in part account for the observed lower slopes between the GRIMM and the OPC-N2s.
The time series of the median OPC-N2 PM
Time series of the measured PM
The factors contributing to this apparent artefact shown by the OPC-N2 were
investigated. In Fig. 6, the agreement between the OPC-N2 and the TSI
instrument appears to vary as a function of ambient RH, with better
agreement observed between the two instruments during periods of relatively
low ambient RH. However, during times when the RH was high (> 90 %), the OPC-N2 recorded concentrations markedly higher than that
measured by the TSI 3330 (Fig. 6). Similar trends were also observed for
PM
Measured concentrations by the TSI 3330 compared to the median concentration measured by the 14 OPC-N2s, coloured by the ambient relative humidity. Also shown are the 1 : 1 (solid) and 0.5 : 1 and 2 : 1 (dashed) lines.
Time series for hourly measured PM mass concentrations by the TEOM, four OPC-N2s and the GRIMM at Tyburn Rd urban background AURN station. The volatile particle mass concentration as measured by the TEOM-FDMS and relative humidity measured at Tyburn Rd are also shown.
Slopes of measured PM mass concentrations of the reference
instruments (TEOM and GRIMM) against the OPC-N2. The correlation
co-efficient,
We deployed a subset of the OPC-N2 devices (4) and the GRIMM at an urban
background AURN station to enable comparison of the measured ambient
particle mass concentrations to a TEOM-FDMS. The time series of the measured
concentrations of PM
Closer inspection of Fig. 7 indicated that the times when the four OPC-N2s
overestimated the particle mass concentrations were during times of high RH
(e.g. 12–14 February), as observed in the previous section. However, there
were periods of high RH when the four OPC-N2s and the TEOM were in better
agreement (e.g. 20 February onwards), indicating that the large positive
artefact observed in the OPC-N2 was not just related to RH. Rather, it
appears that positive artefact was observed during times when the volatile
fraction measured by the TEOM was relatively high, as well as higher RH, as
was observed on 12–14 February (Fig. 7). Thus, it suggests that the ambient
aerosol composition also contributed to the significant positive artefact in
the OPC-N2. A recent laboratory study found that the particle mass
concentrations measured by OPC-N2 for all three size fractions were highly
linear with respect to gravimetrically corrected reference instruments but
that the slope was dependent on the aerosol type (Sousan et al.,
2016). Sousan et al. (2016) observed in the PM
From Fig. 6, the times when there was a large positive artefact in the OPC-N2 occurred when the RH was above 85 %. If we exclude these times when the RH was over this threshold, better agreement between the four OPC-N2s and the TEOM was observed, with slopes between 1.1 and 1.7 for both size fractions (Table 2). One of the OPC-N2s recorded notably higher mass concentrations compared to the reference instruments (OPC11), compared to the other three OPC-N2s (Table 2), and this highlights the need to calibrate each OPC individually before use in field measurements.
Clearly there were times when there was a significant instrument artefact
for the OPC-N2 (Figs. 4 and S4) and the highest overestimations occurred at
high RH at both EROS and Tyburn Rd (e.g. Figs. 5 and 6). Whilst the accuracy
of the instrument was significantly worse at high RH the precision remains
the same within error. The CV analysis conducted in Sect. 3.1.2 is
repeated for the same dataset but put into low (RH < 85 %) and high
RH (RH > 85 %) subsets. For high-RH conditions the CV for
PM
The size of hygroscopic particles is known to be dependent on RH, as the
particle refractive index and size are both a function of RH. Inorganic
aerosols (e.g. sodium chloride, nitrate and sulfate) make up a large
portion of the PM
The ratio of measured mass concentrations by the OPC-N2 relative to the
reference instruments was plotted as a function of RH and appeared to show
an exponential increase above
Figure 8 shows the humidogram plots, for both the PM
Measured and fitted humidograms (
We then applied this fitting constant to model the expected OPC/reference
instrument ratio for a given RH as a result of particle hygroscopic growth
by re-arranging Eq. (4) into Eq. (5):
There were also times when the OPC-N2s were clearly overcorrected (e.g. from
20 February onwards), generally when the ambient RH was low (Fig. 6).
This suggests that when the RH was below a threshold, Eq. (5) overcorrects the
data and this can be observed in the humidograms shown in Fig. 8.
Typically, at RH < 85 % the hygroscopic growth of real atmospheric
aerosols is small and it may be more appropriate to apply a linear
regression correction factor for data recorded under these RH conditions.
Therefore we applied a binary two-model approach to correct the OPC-N2 mass
concentrations, where a linear correction (using the TEOM as reference
concentration) for when RH < 85 %; above this threshold in RH,
Eq. (5) was used. As can be seen Fig. S9 (Supplement), there was
little change in the slope or
Histogram of measured PM
Summary of the comparison between the corrected OPC-N2 (via Eq. 5) against the reference instruments. Intercepts were not constrained to zero.
After the conclusion of the intensive measurements at EROS (Sect. 3.1),
five of the OPC-N2s continued monitoring for a further 4 months to examine
whether
there was any evidence of instrument drift over time, along with the GRIMM
as reference. One of the OPC-N2s failed in December, and so was excluded from
this analysis. The remaining four OPC-N2s were compared to GRIMM and in
January after running for 4 months (Fig. 9a), and while three of the OPC-N2s
had a similar distribution to the GRIMM (OPC12, 13 and 14), OPC9 appeared to
show evidence of instrument drift as the mode has shifted relative to the
GRIMM. However, the increased frequency of higher mass concentrations not
observed by the GRIMM but by all four OPC-N2s (Fig. 9a) suggests that ambient
RH is also a factor, as the average RH in January (91 %) was higher than
September (84 %). Therefore, we calculated the correction for RH as
described in the previous section (Eq. 5), as changes in aerosol composition
would affect the particle hygroscopicity. In addition, the
In the previous sections, the significant positive artefacts observed by the
OPC-N2 relative to the reference instruments were at times when the ambient
RH was high, pointing to particle water content as the cause. This result is
perhaps not surprising, as many studies in the literature have shown that
particle water content can be a major reason for discrepancies between
techniques that measure ambient particle mass (see e.g. Charron et al.,
2004). The use of
The Alphasense OPC-N2 was evaluated for use in ambient monitoring of airborne
particle mass concentration, with TEOM-FDMS and two commercial optical
light-scattering instruments, GRIMM PAS 1.108 and TSI 3330, employed as
reference instruments. Comparison of the OPC-N2 to the reference optical
instruments demonstrated reasonable agreement for a low-cost sensor to the
measured mass concentrations of PM
For PM
Overall, the OPC-N2s have been shown to accurately measure ambient airborne particle mass concentration provided they are correctly calibrated and corrected for RH. The reasonable level of precision demonstrated between multiple OPC-N2 suggests that they would be suitable for applications where a number of instruments are required such as spatial mapping and personal exposure studies.
Original research data are available from the authors on request.
The supplement related to this article is available online at:
The authors declare that they have no conflict of interest.
The authors wish to thank Peter Porter and Birmingham City Council for help in collocating the sensors next to the Tyburn Road AURN site. Francis Pope acknowledges funding form EPSRC (Global Challenges Research Fund IS2016 and IS2017). Alastair C. Lewis and Marvin Shaw acknowledge funding from the NERC National Capability programme ACREW and NE/N007115/1. Edited by: Paolo Laj Reviewed by: two anonymous referees