The newest generation of air quality sensors is small,
low cost, and easy to deploy. These sensors are an attractive option for
developing dense observation networks in support of regulatory activities and
scientific research. They are also of interest for use by individuals to
characterize their home environment and for citizen science. However, these
sensors are difficult to interpret. Although some have an approximately
linear response to the target analyte, that response may vary with time,
temperature, and/or humidity, and the cross-sensitivity to non-target
analytes can be large enough to be confounding. Standard approaches to
calibration that are sufficient to account for these variations require a
quantity of equipment and labor that negates the attractiveness of the
sensors' low cost. Here we describe a novel calibration strategy for a set of
sensors, including CO, NO, NO2, and O3, that makes use of (1) multiple
co-located sensors, (2) a priori knowledge about the chemistry of NO, NO2,
and O3, (3) an estimate of mean emission factors for CO, and (4) the global background of CO. The strategy requires one or more well calibrated
anchor points within the network domain, but it does not require direct
calibration of any of the individual low-cost sensors. The procedure
nonetheless accounts for temperature and drift, in both the sensitivity and
zero offset. We demonstrate this calibration on a subset of the sensors
comprising BEACO2N, a distributed network of approximately 50 sensor
“nodes”, each measuring CO2, CO, NO, NO2, O3 and particulate
matter at 10 s time resolution and approximately 2 km spacing within the
San Francisco Bay Area.
Introduction
In urban environments, air quality has complex spatial and temporal
patterns. Diverse emission sources are present with large variations in
emission rate and source type on scales of hundreds of meters. In addition,
dispersion of pollutants into the urban environment is affected by the
topography of the urban landscape and the associated wind flows, which also
vary on length scales of ∼ 100 m
(Vardoulakis et al., 2003; Lateb et al., 2016).
Conventional approaches to air quality monitoring rely on a limited number
of relatively high-cost instruments that lack the spatial resolution needed
to characterize these variations, opting instead to target spatial averages.
This averaging hampers our attempts at source attribution and understanding
of mixing, chemistry, and human exposure in cities where emissions vary on
spatial scales that are small compared to typical observations or models.
One approach to obtaining higher spatial resolution observations is passive
sampling, which has been implemented using inexpensive sampling devices that
can be later analyzed in bulk. Passive samplers do not require electrical
power to function properly and are collected and analyzed 1 to 2 weeks
after deployment. Such protocols provide high spatial resolution but also
have significant drawbacks. Spatial resolution is gained at the expense of
temporal resolution, and analysis after collection of the samplers is time
consuming; thus passive sampling has typically been used only in short-duration experiments (e.g., Krupa and Legge, 2000; Cox,
2003). Furthermore, as a result of boundary layer dynamics, passive sampling
in urban areas is likely dominated by the high concentrations found at night
and relatively insensitive to daytime variability.
Map of San Francisco Bay Area showing current BEACO2N node
sites (red), BAAQMD reference sites with O3 measurements (blue), and
the BAAQMD Bodega Bay regional greenhouse gas background site (orange). The
sites used in this analysis are marked in yellow on the detailed panel.
(a) Current BEACO2N node design and (b) a photo of a node
deployed.
Recent developments in low-cost sensors for trace gases and particulate
matter, as well as advances in software and hardware enabling low-cost data
communication, have made high-density, high-time-resolution air quality
monitoring networks possible. Devices and networks of devices are emerging
that are low cost, report at a time resolution of seconds, and are capable
of long-term deployment, providing potential for improvement over the two
major weaknesses of passive sampling. Examples include metal oxide sensors
used to measure O3, CO, NO2, and total volatile organic compounds
(e.g., Williams et al.,
2013; Bart et al., 2014; Piedrahita et al., 2014; Moltchanov et al., 2015;
Sadighi et al., 2017), and electrochemical sensors used to measure CO, NO,
NO2, O3, and SO2
(e.g.,
Mead et al., 2013; Sun et al., 2015; Jiao et al., 2016; Hagan et al., 2017;
Jerrett et al., 2017; Mueller et al., 2017). These different low-cost sensor
systems have been evaluated and compared
(Borrego et al., 2016; Papapostolou et al.,
2017). While these studies found low-cost trace gas sensors to be successful
at qualitatively characterizing the variability of air quality in an urban
area, challenges related to selectivity and stability remain, hindering more
quantitative interpretation of the data.
The current generation of low-cost sensors is not as easily tied to a
gravimetric calibration standard as many of the passive samplers.
Calibration is known to vary with sensor age, temperature, and in some cases
humidity. In addition, many of the sensors have responses to gases other
than the target analyte
(Mead et
al., 2013; Spinelle et al., 2015, 2017; Cross et al., 2017; Mueller et al., 2017;
Mijling et al., 2018; Zimmerman et al., 2018). One
approach to addressing this challenge is to combine periodic re-calibration
and co-location with regulatory reference instruments in the lab or the
field (Williams et al., 2013;
Moltchanov et al., 2015; Jiao et al., 2016; Mijling et al., 2018). Field
calibration is preferred as in-lab performance is often a poor approximation
of sensor behavior under ambient conditions
(Piedrahita et al., 2014; Masson et al., 2015).
However, either method requires considerable time investment by trained
personnel, especially as the number of sensors increases. The requirement of
time-consuming and labor-intensive calibration then offsets the low-cost advantage of
the sensors.
In this paper, we explore an automated, in situ strategy for the calibration
of individual sensors embedded in an air quality sensor network that
includes both low-cost sensors and anchor points of higher-grade, well-calibrated instrumentation. The BErkeley Atmospheric CO2 Observation
Network (BEACO2N) is a low-cost, high-density greenhouse gas (CO2)
and air quality (CO, NO, NO2, O3, and particulate matter)
monitoring network located in San Francisco Bay Area, California (see Fig. 1
and Shusterman et al., 2016). As of this writing,
BEACO2N consists of approximately 50 sensor “nodes”, deployed with
approximately 2 km horizontal spacing. Most of the nodes are mounted on the
roofs of schools and museums. In previous work, we described an approach to
CO2 sensing and calibration (Shusterman et al., 2016). Here, we focus on
CO, NO, NO2, and O3.
We begin by describing laboratory experiments and in-field comparisons to
co-located reference instruments that give an initial characterization of
the sensors and provide insight into the effects of temperature, humidity,
and cross-sensitivity to non-target analytes. Then we describe an in situ
calibration procedure that accounts for these variables without requiring
co-location with a reference instrument. The calibration procedure is
finally verified against regulatory quality measurements not used in the
procedure itself.
Instrument description
Details of the node design and deployment are described in Shusterman et al. (2016). Briefly, each BEACO2N node contains a Vaisala CarboCap GMP343
non-dispersive infrared sensor for CO2, a Shinyei PPD42NS nephelometric
particulate matter sensor, and a suite of Alphasense electrochemical
sensors: CO-B4, NO-B4, either NO2-B42F or NO2-B43F, and either
Ox-B421 or Ox-B431. All sensors are assembled into compact,
weatherproof enclosures as shown in Fig. 2. Two 30 mm fans are located on
either side of the enclosure to facilitate airflow through the node. A
Raspberry Pi microprocessor collects data via a serial-to-USB converter for
CO2 and an Adafruit Metro Mini microcontroller for all other sensors.
Then, data collected every 5 or 10 s are transmitted to a central
server using a direct on-site Ethernet connection or a local Wi-Fi network.
Representative temperature-dependent sensitivities (a) and zero
offsets (b) of the Alphasense electrochemical sensors calculated by
comparing hourly averaged measurements from Laney College BEACO2N node
to measurements from a co-located reference instrument during February to
April 2016.
The Alphasense B4 electrochemical gas sensing series that we use employs a
four-electrode approach. The electrodes are embedded in an electrolyte
solution separated from the atmosphere by a semi-permeable membrane. The gas
of interest diffuses through the membrane into the electrolyte, where it
contacts a “working” electrode and is either oxidized (in the case of NO
and CO) or reduced (NO2 and O3). The potential at the working
electrode is maintained at a constant value with respect to a “reference”
electrode. Electric charge produced at the working electrode is balanced by
the complementary redox reaction at a “counter” electrode, generating an
electric current. The sensor also contains an “auxiliary” electrode, which
shares the working electrode's catalyst structure, but is isolated from the
ambient environment, accounting for fluctuations in the background current
associated with other processes at the electrode and electrolyte.
Subtracting the auxiliary current from the working current gives a corrected
current dependent on the gas concentration.
The working and auxiliary currents detected by the sensors are converted to
working and auxiliary voltages using amplifiers in the individual sensor
boards (ISBs) provided by Alphasense. Over the mixing ratio range of
interest, the sensors' responses to the gases of interest are approximately
linear. We derive mixing ratios from the observed voltages by subtracting an
offset and then scaling by a constant (Eqs. 1–4):
COambient=(VCO-zeroCO)/kCO,NOambient=(VNO-zeroNO)/kNO,NO2ambient=(VNO2-zeroNO2)/kNO2-(rNO-NO2×NOambient),NO2ambient=(VNO2-zeroNO2)/kNO2+(rCO2-NO2×CO2ambient),O3ambient=(VO3-zeroO3)/kO3-(rNO2-O3×NO2ambient).
Here, CO, NO, NO2, and O3 with the subscript “ambient” refer to
the gas mixing ratios (ppb) in air; VCO, VNO, VNO2 and
VO3 are the signals (mV) measured by each sensor, which is the
voltage of the auxiliary electrode subtracted from the voltage of the
working electrode; zeroCO, zeroNO, zeroNO2 and
zeroO3 indicates the voltage measured in the absence of analyte; and
kCO, kNO, kNO2 and kO3 represent the linear
sensitivity factor that converts mV to ppb. Additional terms corresponding
to the cross-sensitivities of the NO2 and O3 sensors appear in
Eqs. (3a), (3b), and (4), where rNO-NO2 is the cross-sensitivity of the
NO2-B42F sensor to NO gas, rCO2-NO2 is the cross-sensitivity
of the NO2-B43F sensor to CO2 gas, and rNO2-O3 is the
cross-sensitivity of both the O3-B421 and O3-B431 sensors to
NO2 gas.
There are a total of eight sensitivities and zero offsets, as well as two
cross-sensitivity terms. All of these may also vary with time, temperature,
and humidity. Thus we need a calibration strategy that constrains 10
parameters in a single instant as well as the variation of those 10
parameters in response to the environmental variables and time. We begin by
characterizing the sensors in both laboratory and outdoor environments.
We evaluate BEACO2N in terms of four factors: drift, noise,
cross-sensitivity, and temperature dependence. The humidity dependence is
included in the temperature dependence, as there is no evidence for
independent humidity dependence and relative humidity exhibits an
anti-correlation with temperature in the field. In the laboratory, a range
of mixing ratios of target gases were delivered to a chamber containing the
full suite of four Alphasense B4 sensors: CO, NO, NO2, and O3.
Zero air was supplied by a Sabio 1001 compressed zero-air source and blended
with calibration gases using a Thermo Scientific model 146i multi-gas calibrator.
Zero offsets and sensitivities of a representative quartet of
Alphasense B4 electrochemical sensors derived via comparison to delivered
reference gases during two separate laboratory calibration separated by an
approximately 10-week interlude.
Noise. Alphasense reports 2σ noise of ±4, ±15, ±12, and ±15 ppb for CO, NO, NO2, and O3,
respectively over concentrations from 0 to 200 ppb at time resolution of
a second. In our laboratory, noise (±2σ) was measured for
ambient ppb levels with 10 s time resolution and was seen to be ±10 ppb for CO, ±3 ppb for NO, ±6 ppb for NO2
(NO2-B42F and NO2-B43F), and ±12 ppb for O3
(O3-B421 and O3-B431).
Cross-sensitivity. We measured the cross-sensitivity of all four of the trace gas sensors to
the non-target gases. The NO2 sensors and O3 sensors were the only
ones to exhibit sensitivity to other species. The O3 sensor
(O3-B421 and O3-B431) demonstrated 100 % sensitivity to
NO2. This sensor is now being marketed by Alphasense as an odd-oxygen
(Ox≡O3+NO2) sensor. In addition, the NO2-B42F sensor
was found to possess a significant NO sensitivity (130 %) that exceeds the
cross-sensitivity specified in the Alphasense documentation (< 5 %). The NO2-B43F sensor was found to have 0.002 % sensitivity to
CO2 gas, which is in the range of the cross-sensitivity specified in
the Alphasense documentation (< 0.1 %). However, given that
typical ambient CO2 concentrations are 4 orders of magnitude larger
than NO2 concentrations, this relatively small cross-sensitivity to
CO2 gas manifests as a significant interference in the
NO2 sensors. These cross-sensitivities are represented in Eqs. (3) and (4).
Temperature dependence. Electrochemical sensors are known to have temperature-dependent
sensitivities and zero offsets. Alphasense reports sensitivities and zero
offsets for a temperature range between -30 and 50 ∘C. The sensitivities in their data sheets vary with temperature by +0.1 to
+0.3 % K-1 (referenced to sensitivity at 20 ∘C) and the zero
offsets are indicated to vary little except at high temperatures. We
observed similar but slightly larger variations via in situ comparison to
co-located reference instruments. We observed temperature dependence in the
sensitivities of +0.3 to +5 % K-1 and no variation in the zero offset
of the CO, NO2, and O3 sensors from 10 to 24 ∘C (Fig. 3). However, the zero offset of the NO sensor exhibited
a strong temperature dependence of 0.34 mV K-1.
Drift. Two laboratory calibrations were performed roughly 10 weeks apart and
the zero offsets and sensitivities are shown in Table 1. Over the 10-week
interval, zero drift was equivalent to -15.9, -2.3, +15.8, and
-12.7 ppb for CO, NO, NO2, and O3, respectively. Alphasense
reports the stability over time for the zero offset to be < ±100, 0 to 50, 0 to 20, and 0 to 20 ppb yr-1 for these sensors,
respectively; over this 10-week interval, the observed zero drift was within
the range of these specifications. However, it is a large fraction of the
annual drift specification and further experiments would be warranted to
test whether the zero measured is stable over a full year within the
specified tolerances. The drift in the sensitivity (in % of kX) was
-15.9, -17.7, -20.6, and -53.2 %. Alphasense reports < 10,
0 to -20, -20 to -40, and < -20 to -40 % yr-1 for CO, NO,
NO2, and O3 calibration factors, respectively. We find that
drift for the CO and O3 sensitivities exceeded the manufacturer
specifications, but that the NO and NO2 sensitivity drifts were within
the specified tolerances.
Reported emission factors of diesel and gasoline vehicles
(Dallmann et al., 2011, 2012, 2013). Emissions from medium-duty
and heavy-duty diesel trucks, which account for < 1 % of all
vehicles, were removed to give the value for light-duty gasoline vehicles.
Vehicle typeCO emission factorNOX emission factor(g kg fuel-1)(g kg fuel-1)Heavy-duty diesel trucks8.0 ± 1.228.0 ± 1.5Light-duty gasoline vehicles14.3 ± 0.71.90 ± 0.0899 % gasoline vehicles, 1 % diesel trucks14.2 ± 0.72.29 ± 0.12Model for field calibration
Here, we propose a model for field calibration that leverages (1) useful
cross-sensitivities, (2) chemical conservation equations, (3) knowledge of
the global and/or regional background of pollutants, and (4) assumptions
based on well-known characteristics of urban air quality and local
emissions. The result is a calibration procedure for the drift and
temperature dependencies of the 10 calibration parameters that does not
require co-location with a reference instrument or prior laboratory
experiments for each sensor. The first constraint we apply is the O3
sensors' cross-sensitivity to NO2. Laboratory measurements indicate
that this cross-sensitivity is 100 % and we fix it at that value.
Mean absolute error of comparison between regional O3 and
hourly averaged BEACO2N O3 measurements derived from multiple
linear regression models of increasing complexity between February and April
2016.
Regression ModelsMean absolute error(ppb)O3true=VO3kO3- offsetLinearity of observed voltages and gas concentration14.4063O3true=VO3kO3-VNO2kNO2- offsetO3 sensor's cross-sensitivity correction10.6795O3true=VO3kO3-VNO2kNO2+rNO-NO2VNOkNO- offsetNO2 and O3 sensor's cross-sensitivity correction8.8172O3true=VO3kO3-VNO2kNO2+rNO-NO2VNOkNO- offsetAdding temperature correction8.1360
Example of CO plume identification and regression against CO2
to find the CO emission factor using raw, 10 s data. The derived CO
emission ratio (CO / CO2) for this example is 9.7 ppb ppm-1.
Regional ozone uniformity to calibrate the NO2 and O3 sensors'
sensitivities
The NO, NO2, and O3 sensitivity can be derived from observations
with higher-quality instruments at nearby locations. Ozone is a secondary
pollutant with small local-scale variation, except in the very near field of
NO emissions. The Bay Area Air Quality Management District (BAAQMD)
maintains four TECO model 49i ozone analyzers within the BEACO2N study area
(see Fig. 1). We choose the closest site among these four regulatory
monitoring sites to provide O3ambient as a constraint for multiple
linear regression of Eq. (5) (derived from Eqs. 2–4). Different BEACO2N
nodes are thus referenced to different reference instruments.
O3ambient=VO3kO3-VNO2kNO2+rNO-NO2VNOkNO-offset.
Here, offset is a combination of the zero offsets of the NO, NO2, and
O3 sensors, all of which can be constrained as detailed in Sect. 3.2
below. The sensitivity of the O3 and NO2 sensors (kO3 and
kNO2) and relationship between the NO-NO2 cross-sensitivity
and the sensitivity of the NO sensor (rNO-NO2/kNO or rCO2-NO2)
are obtained by multiple linear regression of Eq. (5).
Representative month of 1 min averaged NO and O3
measurements taken between 00:00 and 03:00; plumes excluded.
Time series (top), direct comparison (bottom left), and
histogram (bottom right) of hourly averaged (a) NO, (b) NO2, (c) O3, and (d) CO mixing
ratios from a representative week of calibrated
BEACO2 N and BAAQMD reference data. The black lines in the
bottom left plots indicate the 1:1 line.
Use of co-emitted gases in plumes to calibrate the CO and NO sensors'
sensitivity
The CO and NO sensor cannot be constrained by cross-sensitivity to the other
gases. Instead, we constrain the sensitivity by insisting that the median
emission factor of CO (or NO) per unit CO2 corresponds to median values
reported for the U.S. vehicle fleet. We express the emission factor
(EFX, ppb ppm-1) of gas X, which is CO or NO, as in
Eq. (6):
EFX=ΔXambientΔCO2ambient=1kXΔVXΔCO2ambient.
Our measurements of the concentration of CO2 are described in
Shusterman et al. (2016) and values for EFCO and EFNOx
are reported in (Dallmann
et al., 2013; see Table 2). We constrain the sensitivity of the CO and NO
sensors in the network such that the median ΔX/ΔCO2 of
the plumes is equal to emission factors characteristic of the average
vehicle fleet. The NO sensors' sensitivity is constrained by the emission
factor of NOX, estimating the upper limit of NO concentration.
Figure 4 shows an example of a measured plume and the derived ΔCO /ΔCO2 ratio. We identify plumes as the local maximum found
in a 10 min moving window, starting and ending at the local minima. Each
plume is a few minutes in duration, representing an emission ratio averaged
over several vehicles. Since diesel trucks have an order of magnitude higher
NOX emission factors compared to gasoline vehicles, the percentage of
truck traffic near each site affects the median emission factors. The median
freeway truck ratio varies little across the BEACO2N network; however,
regions with a larger range of median truck ratios will have larger
uncertainties or require a calibration approach that accounts for this
variation.
Use of chemical conservation equations near emissions to calibrate the
NO, NO2, and O3 sensors' zero offsets
We are able to constrain the zero offsets of NO, NO2 and O3
sensors by taking advantage of proximity to local emission sources and the
following chemical conservation equations.
NO+O3→NO2+O2NO2+hv→NO+OO+O2+M→O3+M
These three reactions result in a steady-state relationship among the
nitrogen oxides (NOX≡NO+NO2) and ozone. At nighttime,
Reaction (R2) does not occur due to the absence of sunlight. In the absence of
emissions, the NO concentration goes to zero on nights with sufficient
O3. Conversely, near strong emission sources, NO is found in excess of
ozone and the O3 concentration goes to zero (see Fig. 5). Using this
logic, we identify times between 00:00 to 03:00, when there is zero NO or
O3 to define the zero offsets of the NO and O3 sensors, using
1 min averaged data with plumes excluded (see Sect. 3.3 for details of
the plume identification procedure).
The NO2 offset can be determined using the pseudo-steady-state (PSS)
approximation. We estimate the NO2 concentration through Eq. (7):
jNO2NO2=kNO-O3[NO][O3].
Here, jNO2 (in units of s-1) is the photolysis rate constant
for Reaction (R2) and kNO-O3 (in units of cm3 molecule-1
s-1) is the rate constant for Reaction (R1). X expresses
the concentration of gas X in units of molecules cm-3. We use
sensitivity corrected (see Sect. 3.1 and 3.2), 1 min average NO and
O3 concentrations measured from 12:00 to 15:00, and select data with a
time derivative of O3 near zero to ensure that the measurements reflect
air that has achieved steady state. The NO2 concentration at PSS is
derived using Eq. (7) and the NO2 offset is chosen to ensure the
calculated and observed NO2 are equal. NO2 is also produced
through the reaction of HO2/RO2 with NO, but this is omitted from
the right-hand side of Eq. (7), resulting in a lower bound of the true
NO2 concentration. Estimated NO2 is therefore low by about 5 %
in winter and as much as 30 % in summer. If higher accuracy is needed, the
reaction of HO2/RO2 with NO could be considered to reduce this
bias.
Use of global background to calibrate the CO sensors' zero
offset
To infer the zero offset of the CO sensor, we follow the procedure outlined
in Shusterman et al. (2016) for CO2 sensors. We assume the signal
measured at a given site is decomposed as in Eq. (8):
[CO]ambient=[CO]background+[CO]local+offset.
The measurement of the pollutant CO ([CO]ambient) is the sum of
regional and local signals ([CO]background and [CO]local,
respectively), as well as some offset from the true concentration
(offset). Assuming the monthly minimum concentration measured at a given
site represents [CO]background, this background signal is compared to
that measured at a “supersite” of reference instruments located within the
network domain, allowing the offset to be derived. We also assume that when
[CO]ambient, as well as [CO]local, is minimum in each day, the
concentration measured at a given site has a constant deviation from the
background signal. This is a reasonable assumption for the BEACO2N
domain as the dominant wind pattern frequently brings unpolluted air from
the Pacific Ocean.
Temperature dependence and temporal drift
In order to account for the temperature and time dependence of calibration
parameters, we apply the calibration process described in Sect. 3.1 through 3.4 for temperature increments of 1 ∘C within a 3-month running window.
Then, we are able to define a temperature-dependent sensitivity and zero
offset, which is used to convert the measured voltages to mixing ratios. In
this way, we can also evaluate temporal drift with monthly resolution. The
calibration procedure can be repeated for shorter time intervals if wider
temperature windows are used.
Evaluation with reference observations
We evaluate the efficacy of our calibration method using a BEACO2N node
co-located with reference instruments at the Laney College monitoring site
maintained by the Bay Area Air Quality Management District (BAAQMD). Here we
consider data collected from February to April 2016, calibrate them according
to the procedure described above (following Sect. 3.1 to 3.5), and compare
it against the BAAQMD data. Reference data are collected by a TECO 48i CO
analyzer and a TECO 42i NOx analyzer. Ozone data from the “Oakland
West” location, the closest ozone-monitoring site maintained by BAAQMD, were
used for multiple linear regression of Eq. (5). The zero offset for CO was
calculated using BAAQMD data from the Bodega Bay background site (see Fig. 1; Guha et al., 2016)
as local “supersite” data were unavailable during
this period. A background site closer to the network would likely improve
our ability to constrain the CO zero offset; a reference instrument for that
purpose was installed in summer 2017.
In our calibration procedure, the cross-sensitivities and temperature
dependence are corrected for better accuracy. Table 3 shows the reduction in
mean absolute error (MAE) that results when cross-sensitivity and
temperature dependence issues are considered during multiple linear
regression of Eq. (5). Here, MAE is calculated after conducting the
sensitivity correction explained in Sect. 3.1, but before the offset
correction in Sect. 3.3. Fully calibrated, hourly averaged BEACO2N
sensor data are compared to reference data in Fig. 6. For NO, NO2,
O3, and CO the mixing ratio measured agrees reasonably well with the
reference instrument with correlation coefficients of 0.88, 0.61, 0.69, and
0.74 and MAE of 3.63, 4.12, 5.04, and 54.93 ppb, respectively.
The noise (±2σ) in the differences between the calibrated
hourly BEACO2N data and reference data is 9.74 ppb for NO, 9.97 ppb for
NO2, 13.04 ppb for O3, and 116.23 ppb for CO. These noise values
are dominated by the Alphasense noise except in the case of CO, where noise
is evenly split between the low-cost electrochemical sensors and the
reference instruments.
Time series of fully calibrated 5 min averaged BEACO2N
data from a representative week at four sites deployed in 2017. Observations
from the Hercules, Ohlone, Washington, and Madera sites are plotted in red,
green, orange, and blue, respectively. Particulate matter is converted to
units of mass concentration according to Holstius et al. (2014).
CO vs. NOx measured at Laney College between 08:00 and 10:00.
Examples of network performance
Figure 7 shows a week-long time series of fully calibrated air quality data
from four BEACO2N sites in 2017 (see Fig. 1). BEACO2N nodes
capture the short-term variability associated with local emissions,
superimposed on the diurnal variation caused by mixing and changes in the
height of the boundary layer. Large mixing ratios of NO, NO2, and
O3 are observed at the Hercules and Ohlone sites, likely representing
strong NOx emissions from an oil refinery nearby. The spatial
variability of trace gases observed at these four BEACO2N sites provides a
more diverse perspective on emissions compared to that provided by the one
regulatory monitoring site in the vicinity.
The emission ratios of CO and NOx were also investigated using the
BEACO2N data from sample locations. Figure 8 shows ratios observed at
the Laney College site. The slope of CO / NOx varies from 4.43 to 12.99
across five BEACO2N sites, reflecting spatial variations in local sources.
Sites near roads with more diesel vehicles, such as Laney College, show
lower CO / NOx ratios, as expected given diesel vehicles' higher NOx
emissions. The range of observed CO / NOx emission ratios is similar to
the values reported by McDonald et al. (2013).
Conclusions
Calibration of low-cost sensors is necessary for quantitative analysis. In
this paper, we have described a truly low cost, routine in-field calibration
method and the evaluation of a fully calibrated low-cost, high-density air
quality sensor network. The Alphasense B4 electrochemical gas sensors are
able to detect typical diurnal cycles in gas concentrations as well as
short-term changes corresponding to chemical reactions and local emissions.
These capabilities of the sensors are utilized for a field calibration
protocol that does not require co-location with reference instrumentation,
but does require reference instruments to be sited within the network
domain. The calibrated dataset demonstrates the accuracy required to resolve
information relevant to urban emission sources, such as CO / NOx emission
ratios. Through this work, we can realize the promise of low-cost,
high-density sensor networks as a viable approach for atmospheric
monitoring.
Data availability
The data used in this study can be obtained from the authors upon request.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was funded by the Bay Area Air Quality Management District
(2016.041), the Health Effects Institute (R-82811201), and the Koret
Foundation. Additional support was provided by a Kwanjeong Educational
Fellowship to Jinsol Kim, an NSF Graduate Research Fellowship to Alexis A. Shusterman, and a Hellman Family Graduate Fellowship to Kaitlyn J. Lieschke.
We acknowledge the use of data sets maintained by BAAQMD's Ambient Air
Monitoring Network, as well as David M. Holstius, Holly L. Maness, and
Virginia Teige for their contributions to BEACO2N's code base.
Edited by: Thomas F. Hanisco
Reviewed by: two anonymous referees
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