CO2 emission estimates from urban areas can
be obtained with a network of in situ instruments measuring atmospheric
CO2 combined with high-resolution (inverse) transport modelling. Because
the distribution of CO2 emissions is highly heterogeneous in space and
variable in time in urban areas, gradients of atmospheric CO2 (here, dry
air mole fractions) need to be measured by numerous instruments placed at
multiple locations around and possibly within these urban areas. This calls
for the development of lower-cost medium-precision sensors to allow a
deployment at required densities. Medium precision is here set to be a random
error (uncertainty) on hourly measurements of ±1 ppm or less, a
precision requirement based on previous studies of network design in urban
areas. Here we present tests of newly developed non-dispersive infrared (NDIR) sensors manufactured by
Senseair AB performed in the laboratory and at actual field stations, the
latter for CO2 dry air mole fractions in the Paris area. The lower-cost
medium-precision sensors are shown to be sensitive to atmospheric pressure
and temperature conditions. The sensors respond linearly to CO2 when
measuring calibration tanks, but the regression slope between measured and
assigned CO2 differs between individual sensors and changes with time.
In addition to pressure and temperature variations, humidity impacts the
measurement of CO2, with all of these factors resulting in systematic errors.
In the field, an empirical calibration strategy is proposed based on parallel
measurements with the lower-cost medium-precision sensors and a
high-precision instrument cavity ring-down instrument for 6 months. The
empirical calibration method consists of using a multivariable regression
approach, based on predictors of air temperature, pressure and humidity. This
error model shows good performances to explain the observed drifts of the
lower-cost medium-precision sensors on timescales of up to 1–2 months when
trained against 1–2 weeks of high-precision instrument time series. Residual
errors are contained within the ±1 ppm target, showing the feasibility
of using networks of HPP3 instruments for urban CO2 networks. Provided
that they could be regularly calibrated against one anchor reference
high-precision instrument these sensors could thus collect the CO2 (dry
air) mole fraction data required as for top-down CO2 flux estimates.
Urban areas cover only a small portion (< 3 %) of the land
surface but account for about 70 % of fossil fuel CO2 emissions
(Liu et al., 2014; Seto et al., 2014). Uncertainties of fossil fuel
CO2 emissions from inventories based on statistics of fuel amounts
and/or energy consumption are on the order of 5 % for OECD countries and
up to 20 % in other countries (Andres et al., 2014) but they are larger
in the case of cities (Bréon et al., 2015; Wu et al., 2016). Further, in many
cities of the world, there are no emission inventories available. The need
for more reliable information on emissions and emission trends has prompted
research projects seeking to provide estimates of greenhouse gas (GHG) budget cities, power
plants and industrial sites. These are often based on in situ measurements
made at surface stations (Staufer et al., 2016; Lauvaux et al., 2016;
Verhulst et al., 2017), aircraft campaigns around emitting locations (Mays et
al., 2009; Cambaliza et al., 2014) and satellite imagery (Broquet et al.,
2018; Nassar et al., 2017). Although sampling strategies and measurement
accuracies differ between these approaches, the commonly used principle is to
measure atmospheric CO2 dry air mole fraction gradients at stations
between the upwind and downwind vicinity of an emitting area and infer the
emissions that are consistent with those CO2 gradients and their
uncertainties, using an atmospheric transport model. This approach is known
as atmospheric CO2 inversion or as a “top-down” estimate.
Inversion studies from Paris, France, attempting to constrain CO2
emissions from measurements of CO2 dry air mole fractions at stations
located around the city along the dominant wind direction have pointed out
that the fast mixing by the atmosphere and the complex structure of urban
CO2 emissions require high-resolution atmospheric transport models
and continuous measurements of the atmosphere to select gradients induced by
emission plumes (Breton et al., 2015; Wu et al., 2016) that can be captured
at the scale of the model.
With the existing three stations, the CO2 emissions from the Paris
megacity could be retrieved with an accuracy of ≈20 % on monthly
budgets (Staufer et al., 2016). A denser network of stations would help to
obtain more information on the spatial details of CO2 emissions. A
network design study by Wu et al. (2016) for the retrieval of CO2
emissions per sector for the Paris megacity has shown that with 10 stations
measuring CO2 with 1 ppm accuracy on hourly time steps, the error of
the annual emission budget could be reduced down to a 10 % uncertainty. Wu
et al. (2016) furthermore found that for a more detailed separation of
emissions into different sectors, more stations were needed, on the order of
70 stations to be able to separate road transport from residential CO2
emissions. This inversion based on pseudo-data allowed the estimation of total
CO2 emissions with an accuracy better than 10 % and emissions of most
major source sectors (building, road energy) with an accuracy better than
20 %. Another urban network design study over the San Francisco Bay Area
reached a similar conclusion, i.e. that in situ CO2 measurements from
34 stations with 1 ppm accuracy at an hourly resolution could estimate
weekly CO2 emissions from the city area with less than 5 % error
(Turner et al., 2016).
In the studies from Wu et al. (2016) and Turner et al. (2016), the additional
number of atmospheric CO2 measurement stations rather than the
individual accuracy of each measurement helped to constrain emissions,
provided that CO2 observation errors have random errors of less than 1 ppm on hourly measurements, uncorrelated in time and in space between
stations. Therefore, we will adopt here a 1 ppm uncertainty on hourly
CO2 data as the target performance for new urban lower-cost medium-precision CO2 sensors.
Today, the continuous CO2 gas analyzers used for continental-scale
observing systems like ICOS (https://www.icos-ri.eu/, last access: 30 April 2019), NOAA
(https://www.esrl.noaa.gov/gmd/, last access: 30 April 2019) or ECCC's
GHG network
(https://www.canada.ca/en/environment-climate-change.html, last access: 30 April 2019) follow the
WMO GAW guidelines and are at least 10 times more precise than our target
of 1 ppm, but are also quite expensive. For urban inversion-based flux
estimates for Paris, Wu et al. (2016) found that the number of instruments is
more important than their individual precision. Furthermore, Turner et al. (2016) reported that weekly urban CO2 fluxes in the San Francisco Bay Area
(California, USA) can be estimated at a precision of 5 % when deploying a
dense network of sensors (ca. every 2 km) with an assumed mismatch error of
1 ppm. This underlines that significant expansion of urban networks is
desirable and could be achieved at an acceptable cost if low-cost sensors
could be produced with the specifications of 1 ppm random error (i.e. bias-free long-term repeatability).
Recently, inexpensive sensors, measuring trace gases, particulate matter and traditional meteorological variables, using various technologies and
accuracy have become commercially available. Evaluation and implementation
of these sensors is quite promising (Eugster and Kling, 2012; Holstius et al., 2014; Piedrahita et al., 2014; Young et al., 2014; Wang et al., 2015). With the
advent of low-cost mid-IR light sources and detectors, different
non-dispersive infrared (NDIR) CO2 sensors have become commercially
available and were tested for their suitability for CO2 monitoring
(e.g. Martin et al., 2017; Kunz et al., 2018) or for CO2 in combination
with air pollutants (e.g. Shusterman et al., 2016; Zimmerman et al., 2018).
In this study, we present the development and stability tests of a low-cost
sensor (HPP3, Senseair AB, Sweden) to measure the mole fraction of CO2
of ambient air (Hummelgård et al., 2015). Throughout the paper we will
use {CO2} to signify the mole fraction and/or dry air mole fraction of
CO2 in air. To improve performance and eventually derive dry air mole
fractions, additional parameters are measured in ambient air and the sensor
is integrated into a platform, which we will refer to as the instrument. Then,
the instrument linearity is evaluated against a suite of CO2 reference
gases with CO2 dry air mole fractions from 330 to 1000 ppm. The
instrument's sensitivities to ambient air temperature, pressure and water
vapour content are assessed in laboratory experiments and climate chamber
tests. The calibrated low-cost medium-precision (LCMP) instruments are then
compared to highly precise cavity ring-down spectroscopy (CRDS) instruments (G2401, Picarro Inc, Santa
Clara, USA).
Lastly, we present the time series of ambient air CO2 measurements in
the Paris region. The time series are compared to measurements by co-located
cavity ring-down spectroscopy (CRDS) analyzers, and an empirical correction
and calibration scheme for the HPP3-based instrument is proposed based on
measured CO2 dry mole air fractions and meteorological variables. These
corrections and calibrations are established during a period of 1 or 2 weeks
and
are used to estimate the drift of the HPP3 instrument on timescales of up
to a month and a half.
Sensor integrationHPP3 sensor
The HPP (high-performance platform) NDIR (non-dispersive infrared) CO2
sensor from Senseair AB (Delsbo, Sweden) is a commercially available
lower-cost system (Hummelgård et al., 2015). The main components of this
sensor are an infrared source (lamp), a sample chamber (ca. 1 m optical path
length), a light filter and an infrared detector. The gas in the sample
chamber causes absorption of specific wavelengths (Hummelgård et al., 2015)
according to the Beer–Lambert law, and the attenuation of light at these
wavelengths is measured by a detector to determine the gas mole fraction.
The detector has an optical filter in front of it that eliminates all light
except the wavelength that the selected gas molecules can absorb. The HPP
has a factory pre-calibrated CO2 measurement range of 0 to 1000 ppm.
The HPP sensor itself uses ca. 0.6 W and requires an operating voltage of 12 V direct current and has a life expectancy superior to 15 years according to
the manufacturer.
Three generations of HPP sensors were built by Senseair AB (Delsbo, Sweden).
In this paper we only report on the tests carried out on the latest
generation (HPP3), being the most performant among the HPP sensor family.
Previous HPP generations were used for more short-term airborne
measurements, for example in the COCAP system (Kunz et al., 2018), and were
found to have an accuracy of 1–1.2 ppm during short-term mobile campaigns. A
number of technical improvements have been made for the new HPP3 generation
described here:
A simple interface through USB connection and the development of a new
software made data transfer easier, quicker and more efficient.
Temperature stability improved due to six independent heaters dispatched
inside the unit.
To reduce long-term drift the sensor is equipped with new electronics and
the IR sources were preconditioned prior to shipment.
The improved second version of HPP3 (HPP3.2) sensors was equipped with a
pressure sensor (LPS331AP, ST Microelectronics, Switzerland) to allow
real-time corrections; the high-resolution mode of the LPS331AP has a
pressure range of 263 to 1277 hPa, and a root mean square (RMS) of 0.02 hPa
can be achieved with a low power consumption (i.e. 30 µA).
The impact of leaks on the measurements is reduced since the third-generation sensor works in a high-pressure mode. A pump is thus needed
upstream of the sensor inlet in order to create high pressure in the
measurement cell.
Different sensors from two versions of HPP were tested and used in this
study, that is, three sensors from a first version (HPP3.1) named S1.1, S1.2
and S1.3, and three others from the second version (HPP3.2) named S2.1, S2.2
and S2.3. For the HPP3.1 sensors, an internal pressure compensation does not
exist, but the HPP3.2 series includes a pressure sensor together with a
compensation algorithm, which normalizes measured CO2 dry air mole
fractions according to ambient pressure (Gaynullin et al., 2016).
Portable integrated instrument
The HPP3 sensors were integrated into a custom-built portable unit, which we
will refer to as the instrument. This instrument should be suitable to perform
in situ CO2 measurements on ambient air. The instrument is composed of
the HPP3 CO2 sensor and temperature (T) and relative humidity (RH)
sensors. To be able to continuously flush the measurement cell a diaphragm
micro-pump with a built-in potentiometer (Gardner Denver Thomas, USA, model
1410VD/1.5/E/BLDC/12V) was added upstream of the HPP3's optical cell.
Temperature and RH were measured at the exterior of the optical cell where
gas is released into the surrounding enclosure. For humidity and
temperature, a DHT22 sensor kit (Adafruit, USA) was added and connected
through an I2C interface. The accuracy of the sensor is ± 2 %–5 % RH
and ± 0.5 ∘C. Its range is 0 % RH–100 % RH and -40 to
+80∘C, respectively. The response time for all sensors was less
than 1 min (which is the time step to which data were integrated).
A Raspberry Pi3 (RPi3) (Raspberry Pi Foundation, 2015) is used to collect
the data of all sensors. The RPi3 is a small (85×56 mm2) single-board
computer running Raspbian OS, an open-source GNU/Linux distribution. The
HPP3 sensors were connected via USB. A 7 in. touch screen monitor is connected
via an adapter board, which handles power and signal conversion. The package
is powered by a switching power supply providing 12 V, but can also be run on
a 12 V battery pack. An image of the components of the portable instrument
package is available in Fig. 1.
Components of the
portable instrument on the top of its box.
Methods
NDIR sensors are sensitive to IR light absorption by CO2 molecules in
the air contained in their optical cell, but the retrieval of CO2 dry
air mole fraction to the desired uncertainty of 1 ppm is made difficult by
sensitivities to temperature, pressure and humidity. Therefore, these
parameters should be controlled as much as possible, and their sensitivities
characterized, to correct and calibrate reported {CO2}. A series of tests were carried out to characterize
the HPP3.1 (S1.1, S1.2, S1.3) and HPP3.2 (S2.1, S2.2, S2.3) performances and
sensitivities to {CO2}, T, p and RH.
Firstly, temperature, pressure and {CO2}
sensitivities were determined in laboratory experiments. Then, field
measurements were conducted with an accurate CRDS instrument (Picarro, USA,
G2401) measuring the same air as the HPP3 sensors. The CRDS short-term
repeatability is estimated to be below 0.02 ppm and the long-term
repeatability to be below 0.03 ppm (Yver-Kwok et al., 2015). Table 1
summarizes all laboratory tests and field test measurements, which are
presented in this section.
Summary of all laboratory tests.
NamePurposeLocationAir measuredParameterRange of T (∘C),Range of [CO2] inRange of [CO2] inDurationSensorsand p (hPa)ambient air (ppm)cal. cylinders (ppm)(days)testedPT1Correlation betweenLaboratoryCalibration cylindersT, p16 to 32 and 968.7not applicable420 to 4503S1.1, S1.2,[CO2] and P, T(Saclay)to 1038.6S1.3PT2Correlation betweenPIT climate chamberCalibration cylindersT, p-2 to 35 and 759.9not applicable420 to 4505S2.1, S2,2,[CO2] and P, T(Guyancourt)to 1013.3S2.3DA1Test calibrationLaboratoryCalibration cylindersT, p, RH, {CO2}24 to 31 and 1009.2417 to 575330 to 100048S1.1, S1.2,frequency(Saclay)and dried ambient airfrom CRDSto 1023.4S1.3WA2-1Test calibrationField stationAmbient airT, p, RH, {CO2}25 to 27 and 1012.2389 to 508not applicable45S2.2, S2.3frequency(Saclay)from CRDSto 1021.4WA2-2Test calibrationField stationAmbient airT, p, RH, {CO2}29 to 31 and 0.993393 to 521not applicable60S2.1frequency(Jussieu)from CRDSto 1034.5Laboratory tests
All laboratory tests used the same fundamental setup shown in Fig. 2 with
only slight modifications. A diaphragm pump (KNF Lab, Germany, model
N86KN.18) was used to pump air from either an ambient air line or
calibration cylinders to a Nafion dryer (Perma Pure, USA, MD-070 series), to eliminate H2O traces in the gas line. A flow controller
(Bronkhorst, France, El-Flow series) was used to regulate the airflow
distributed with a manifold to the HPP3 instruments at 500 mL min-1 to
ensure stable experimental conditions while a CRDS instrument could also be
connected through a gas split to measure the same air.
Test setup.
Sensitivity to temperature and pressure variations
To assess the linearity of the response of each sensor to {CO2} for different pressure and temperature conditions,
two series of temperature and pressure sensitivity tests (PT1, PT2) were
realized in a closed chamber with controlled T and p for the HPP3.1 and
HPP3.2 sensors. No dryer was necessary as dried air from high-pressure
cylinders was used. The CRDS instrument (Picarro, G2401, serial number 2125)
was not connected during these tests.
In test PT1 (Table 1), three HPP3.1 sensors were put in a simple plastic
chamber and exposed to pressure changes ranging from 977.8 to 1038.6 hPa,
and temperature ranges of 16 to 32 ∘C, while measuring gas from a
calibration cylinder. Pressure and temperature were measured by a
high-precision pressure sensor (Keller, Germany, series 33x, 0.2 hPa and
0.05 K precision).
In test PT2, to test wider ranges for pressure and temperature that might
be experienced during field measurements, three HPP3.2 sensors were placed
inside a dedicated temperature and pressure chamber at the Plateforme
d'Intégration et de Tests (PIT) at OVSQ Guyancourt, France, where a much
larger range of T and p variations could be applied. During each T and p test,
four calibration cylinders with dry air CO2 dry air mole fractions from
420 to 450 ppm were measured by all the HPP3.2 sensors for a period of
approximately 120 h for each cylinder. In the PIT chamber, temperature
was varied from -2 to 35 ∘C with a constant rate of change of 1 ∘C h-1, keeping pressure constant at a value of 1013.25 hPa. During
pressure tests the chamber pressure was varied from 1013.25 to 759.94 hPa
with a decrement of 50.66 hPa, regulated with a primary pump, with
temperature fixed at 15 ∘C.
Correction and calibration of
CO2 measurements for dry and wet air
These experiments were performed to evaluate the response of HPP3 sensors to
{CO2} changes in ambient air. Corrections
were established to allow compensation for unintended instrument behaviour
and sensitivities, while calibrations are applied to translate the
instrument readings to the WMO GAW CO2 scale (here, XCO2 2007).
Both steps are combined into one procedure. Two modes of operation for the
HPP3 sensors have been tested, i.e. using a dried or an undried gas stream,
as those are two common modes of operation in greenhouse gas measurements in
different local, regional and global networks (GAW report 242).
Dry air experiments
Water vapour is known to interfere with CO2 measurements, in particular
for NDIR sensors. It is thus important to determine the response of the
sensors to {CO2} under the best possible
conditions, that is, dry air. The experimental setup shown in Fig. 2 was
used. In test DA1 (Table 2) different HPP3 sensors were flushed with the
same dry ambient air, passed through a Nafion dryer. CRDS measurements were
used to monitor and confirm that H2O was reduced to trace amounts, i.e.
0.05±0.05 % H2O. HPP3.1 sensors S1.1, S1.2 and S1.3 were tested
extensively for 45 d, and HPP3.2 sensors S2.1 and S2.2 were tested for
12 d.
Additionally, for a period of 45 d during spring 2016, S1.1, S1.2 and
S1.3 measurements of dry ambient air in parallel with a co-deployed CRDS
instrument (Picarro, USA, G2401) were conducted at the Saclay field site
(see Sect. 3.3.1) There ambient air was pumped from a sampling line fixed
on the roof of the building (ca. 4 m a.g.l.) to flush the setup
described in Fig. 2. Four dry air calibration cylinders (330, 375,
445 and 1000 ppm of {CO2}) were
measured once every 13 h; they were sampled successively each for 30 min (Fig. 3). As the HPP3 responses can be slow and in order to remove
memory effects, only the last 15 min of each measurement period was
used.
Undried (wet) air experiments
As drying is impractical for some applications, we also measured the HPP3's
sensitivities to water vapour in undried ambient air and calibration
cylinders. If these sensitivities were stable over time, they could be used
to correct reported {CO2} for the H2O
interference. For WA2-1 and WA2-2 tests, the Nafion dryer was removed from
the setup. The only modification of the experiment was the removal of the
Nafion dryer.
(a)CO2 dry air mole fractions measured by S1.1
(blue) and the Picarro CRDS analyzer (black). (b) Calibrated dry air
mole fractions of S1.1 (red) compared to the raw values (blue).
(c) Four reference gases (assigned values are 367, 413, 487 and
997 ppm of {CO2}) are used for the calibration. No saturation
effects are observed within our CO2 dry air mole fraction range.
Instrument correction and calibration procedure
In order to correct the reported {CO2} we
have to define a function that allows us to correct for unintended instrument
sensitivities, i.e. to p, T and H2O, as well as to correct {CO2} measurements to an official scale should they show
any offset or non-linear behaviour.
Linearity of instrument response
For dry air measurements in test DA1, a linear calibration curve was found
to be appropriate. Figure 3c shows that the response of the HPP3
instruments to CO2 dry air mole fraction is linear (R2=0.95)
from 330 to 1000 ppm. No saturation effects are observed within this
CO2 dry air mole fraction range since residuals are included in the
±1 ppm range. Therefore, a linear response to {CO2} is assumed further on.
Multivariable correction and calibration
Due to the high correlation of air temperature and water vapour content,
which were both found to be linear (see Sect. 4), we suggest a
multivariable regression method, which includes pressure, temperature and
humidity. Indeed, if variables are corrected one at a time, an
overcorrection of one of the correlated variables may occur.
Multivariable regression is a generalization of linear regression by
considering more than one variable. We used a multivariable linear
regression of the form
{CO2}calibrated, corrected=b+aCO2{CO2}HPP3+app+aTT1+awW+add.
{CO2}calibrated, corrected corresponds to the measured
{CO2} by the reference instrument (CRDS) calibrated on the WMO CO2
X2007 scale. C is the {CO2}HPP3 reported by the HPP3 instrument,
with additional factors to capture the influence of the pressure p, the
temperature T, the water mixing ratio W (as calculated from our T, p and RH measurements), the baseline drift d and a baseline
offset b. All instrument-specific coefficients for the multivariable linear
regression are determined using measurements of the parameters for
several days.
Field tests with urban air measurements
To assess their real-world performance, we conducted additional tests for
the HPP3 sensors measuring ambient air at two field sites under typical
conditions for urban air monitoring. After the sensors were fully integrated
into instruments as described in Sect. 2.2. Three HPP3.1 instruments
(S1.1, S1.2, S1.3) and two HPP3.2 instruments (S2.2, S2.3) were installed at
the
Saclay field site (48.7120∘ N, 2.1462∘ E) to measure ambient air on top of the
building roof. Saclay is located 20 km south of the center of Paris
in a less-urbanized area. In addition, one HPP3.2 instrument (S2.1) was
installed to measure air at the Jussieu field site on the Jussieu University
campus in the center of Paris (48.8464∘ N, 2.3566∘ E).
Saclay field site tests
The sampling line, a 5 m Dekabon tube with an inner tube diameter of 0.6 cm, was fixed on the rooftop of the building at about
4 m a.g.l., which was connected to a setup that was a copy of the laboratory
tests. However, up to five HPP3 instruments were connected and a pump of the
same build as in the previous experiments was used to regulate the air-flow
distributed with a manifold to the HPP3 instruments at 500 mL min-1 to
ensure stable experimental conditions. The field site is equipped with a
cooling and heating unit that was turned off most of the time so that room
temperature varied between 24 and 31 ∘C. During the test of HPP3.2 for 45 d, four reference gas cylinders (330, 375,
445 and 1000 ppm of CO2) were used and each HPP3 was flushed every
12 h for 30 min per cylinder during the dry air experiment. No
calibration cylinders were used during the undried air experiment since the
calibration was based on the co-located high-precision measurement with the
CRDS analyzer. The mean dry air mole fraction of ambient CO2 was 420 ppm and varied between 388 and 575 ppm during dry air experiments, and a
mean of 409 ppm and variations between 389 and 509 ppm were found during
the undried air experiments.
Jussieu field site tests
The measurements were conducted at the OCAPI (Observatoire de la Composition
de l'Air de Paris a l'IPSL) field station. The measurements from the HPP3.2
instrument (S2.1) in Jussieu were compared with those of a co-located CRDS
analyzer (Picarro, USA, G2401). Two independent sampling lines (about 5 m Dekabon tube with an inner tube of 0.6 cm) were used for the CRDS and
the S2.1 instrument. The airflow into S2.1 was regulated by the micro-pump
(see Sect. 2.2) and set to 500 mL min-1 using a potentiometer. At
this location neither a calibration cylinder nor a drying system was
deployed for S2.1, but they were calibrated using the CRDS instrument. The measurement period
was 60 d and the mean ambient CO2 dry air mole fraction was 410 ppm and minute averages varied between 393 and 521 ppm. Room temperature
varied between 28 and 31 ∘C during the observation period.
ResultsSensitivity to temperature and pressure
variations using dried airHPP3.1 instruments tested in the simple chamber
(PT1)
Linear relationships are observed between reported CO2 dry air mole
fractions and p and T (R2=0.99 with p and R2=0.92 with T) in
the simple chamber (see Figs. 4 and 5). Due to the limitation of
experimental conditions in these simple plastic chambers, only a narrow
pressure range of 977.78 to 1033.52 hPa and a temperature range of 16
to 32 ∘C could be tested for these instruments.
Different slopes and intercepts are found for each instrument as reported in
Table 2. This indicates that there is no single universal p and T
calibration curve that could be determined for one instrument and used for
others.
HPP3.2 instruments tested in the PIT chamber (PT2)
The PT2 test results with pressure changes from 1013.25 to 759.94 hPa with
an increment of 50.66 hPa are shown in Fig. 4. Figure 4 shows the variations in CO2 dry air mole fractions due to p changes
(from 0.0049 to 0.0177 ppm hPa-1). Despite the built-in pressure
compensation algorithm developed for HPP3.2, reported {CO2} and p can still co-vary with a positive (S2.1 and
S2.2) or a negative (S2.3) correlation, indicating that an additional
correction is required when aiming to achieve the best possible results (see
also Fig. S1 in the Supplement). Consequently, we applied a linear fit between
Δ{CO2} (differences between the assigned
dry air mole fraction in the cylinder and the dry air mole fraction reported
by HPP3.2 instruments) and pressure (Fig. 6). The slope and intercept
obtained are then used to determine the offset due to p variations that has
to be added to {CO2} reported by the HPP3.2
instruments. The corrected {CO2} values
have a root-mean-square deviation from the assigned dry air mole fraction in
the calibration cylinder (428.6 ppm) of less than 0.02 ppm for all three
HPP3.2 instruments (see also Fig. S2). Figure 5 shows the effect of temperature
variations in the PIT chamber ranging from -2 to 35 ∘C (see Sect. 3.1) on reported CO2 dry air mole fractions of the HPP3.2 instruments.
For the three HPP3.2 instruments, {CO2} is negatively
correlated to T. As for the tests in the simple chamber with the HPP3.1
instruments, different linear T slopes and intercepts are observed for each
HPP3.1 instrument (Fig. 5) in the PIT chamber. After correction for
temperature variations, we obtain corrected {CO2} values with a root-mean-square deviation which does
not exceed 0.01 ppm from the assigned value of the cylinder (444 ppm) for
the three HPP3.2 instruments (see also Fig. S3).
Linear relationship experimentally found between reported
{CO2} and p for S1.1, S1.2 and S1.3 (a) and for the
instruments S2.1, S2.2 and S2.3 (b). Note the different p range,
ranging from 972.7 to 1030 hPa for the HPP3.1 instruments in the simple
plastic chamber and 759.9 to 1013.25 hPa for the HPP3.2 instruments in the
PIT chamber.
Linear relationships between reported {CO2} for S1.1
S1.1, S1.2 and S1.3 (a) at temperature values ranging from 17 to
30 ∘C in the plastic chamber, and for S2.1, S2.2 and S2.3 at temperature
values ranging from 5 to 35 ∘C in the PIT chamber (b).
Table 2 summarizes the results of the pressure and temperature tests for all
instruments. These test results show a sensor-specific response to p and
T. A large difference of reported {CO2} sensitivity to pressure
variations is observed between the two HPP3 versions. A sensitivity of 0.564
to 0.744 ppm hPa-1 is found for the HPP3.1
sensors, whereas this sensitivity ranges from -0.0045 up to
0.0174 ppm hPa-1
for the newer HPP3.2 sensors. The lower sensitivity among HPP3.2 prototypes
is due to the pressure compensation algorithm, which is included in this
model. Since the pressure compensation algorithm still does not fully correct
the reported {CO2} variations due to pressure changes, we found
that it is necessary to apply a correction for pressure, and this
correction should be sensor specific. The {CO2} sensitivity
to temperature variations is found to be in a similar range for both sensor
makes. Sensitivities of -0.3 to 0.1 and -0.2 to
-0.7 ppm ∘C-1 are found for the HPP3.1 and HPP3.2 instruments,
respectively.
Slopes and intercept calculated for CO2
correction due to temperature and pressure. Sensors S1.1 to S1.3 are type
HPP3.1, whereas sensors S2.1 to S2.3 are HPP3.2.
Pressure Temperature Slope (ppm hPa-1)Intercept (ppm)R2Slope (ppm ∘C-1)Intercept (ppm)R2S1.10.664±0.004-297.7±4.30.94-0.124±0.003391.34±0.070.85S1.20.744±0.001-363.3±1.10.95-0.29±0.01408.1±0.20.80S1.30.564±0.001-189.5±1.40.940.107±0.004381.2±0.10.63S2.10.0174±0.0002394.±0.20.95-0.5854±0.0004435.530±0.010.99S2.20.0164±0.0.0001392.4±0.20.97-0.716±0.001427.31±0.020.99S2.3-0.0045±0.0002429.0±0.00.75-0.2453±0.0004442.16±0.010.99
After applying our correction for temperature and pressure, no more
correlations are observed between corrected {CO2} and pressure and temperature. Corrected CO2
mole fractions of HPP3.2 are stable and standard errors do not exceed 0.3
and 0.2 ppm for pressure and temperature corrections respectively,
except for {CO2} after temperature
correction for S2.2, which reaches a standard deviation (SD) of 0.5 ppm.
However, we do not reach the same stability after pressure and temperature
correction for HPP3.1 prototypes. Standard deviations of 0.9, 0.2 and 0.2 ppm are calculated for S1.1, S1.2 and S1.3 respectively after pressure
correction, and standard deviations of 1.3, 2.6 and 1.6 ppm are determined
for S1.1, S1.2 and S1.3 respectively after temperature corrections. These
differences between the results of the two HPP3 versions can be partly
explained by the fact that HPP3.2 prototypes had the opportunity to be
tested in a sophisticated climatic chamber which respects precise
temperature and pressure set points for more longer-term measurements and in
which only one of the two variables is modified at a time.
Instrument calibration and stability during continuous
measurements
Our instrument described in this study is intended for use in field campaign
studies and longer-term monitoring. We assess its performance during
continuous measurements. We also evaluate which calibration frequency is
necessary to track the changes in the sensitivities to p and T found in
Sect. 4.1 and if the instruments can be calibrated when using an undried
gas stream. Given that the instrument response to {CO2} is also affected by atmospheric water vapour, we
present the results from dried and wet ambient air measurements separately.
Measurements of dried ambient air (DA1)
Four calibration cylinders were used in order to calibrate the three HPP3.1
instruments (see Sect. 3.1). To assess the quality of this calibration,
the mean and standard deviation (SD) of Δ{CO2} (i.e. {CO2}HPP3 minus {CO2} CRDS) of 1 min
averaged data were calculated and are shown in Fig. 6. Although calibration
cylinders were measured each 12 h, by ignoring some calibration data, we
processed the time series to recompute calibrated {CO2} assuming a range of different time intervals between
two calibrations. The results shown in Fig. 6 are for calibration
intervals of 0.5, 6, 12, 19, 25, 31, 38 and 45 d. Each point in this
figure corresponds to the values calculated for the instruments S1.1, S1.2
and
S1.3.
We find that the 1 ppm repeatability threshold is nearly met when measuring
dried air for calibration intervals of 6 d. The SD Δ{CO2} of the minute averages slowly
increases with increasing calibration intervals but seems to stabilize
between 3 and 4 ppm. We also see a marked difference between the
performances of each sensor: S1.1 shows the best performance, followed by
S1.3 and S1.2. In addition to an increased SD, we also see that the mean of
Δ{CO2} increases significantly
after not calibrating for 19 d. Surprisingly, one calibration each 45 d does not
seem to deteriorate the mean of Δ{CO2} further. In fact, the mean Δ{CO2} seems to decrease over longer time periods.
SD (a) and mean (b) values of the 1 min average
of mean Δ{CO2}, during a measurement period of 48 d
depending on the calibration frequency.
Saclay ambient air measurements (WA2-1)
During this test (Sect. 3.3.1), {CO2} and
variables affecting the instrument stability, i.e. pressure, temperature and
water vapour content were measured from 20 July until 8 August. The meteorological parameters during the field campaign are given
in Fig. S4. Our previous measurements already indicated that regular
recalibration of the HPP3 instruments is required because of sensitivities to T, p and
water vapour that are instrument specific and time dependent. We call the
period during which the six calibration coefficients of Eq. (1) are
calculated by using the CRDS {CO2} time
series the calibration period. Attempting to determine those calibration
coefficients during a short calibration period, e.g. of 1 week, leads to
high mean Δ{CO2}, as can be seen
in Fig. 7. A calibration period of 2 weeks leads to significantly better
results. We benchmark the instrument performance for both minute averages,
the instruments' typical temporal resolution, and hourly averages, as those
are widely used in modelling studies and data assimilation systems.
We also compared different calibration periods of the same length. As an
example, considering a 45 d experiment, we chose three different
calibration periods of successive 15 d. We also tested the approach of
using the first and last weeks of a 45 d period to create a
non-successive two-week calibration period.
Figure 7a, b show the SD and mean Δ{CO2} values
considering three calibration periods (C1, C2, C3) of 15 d each. The regression
coefficients of the multivariable model of Eq. (1) for C1, C2 and C3 are
calculated using the first, second and third consecutive 15 d of the
experimental period. These coefficients are then used to predict corrected
{CO2}HPP3 for the three
cross-validation periods of 15, 30 and 45 d. Also, calibration coefficients
(W1, W6) were calculated using the first and sixth weeks of the 45 d period
for calibration. Unsurprisingly, using C1 coefficients gives the best results
for the first 15 d used for training, and lead a higher bias for the last
15 d. Using C2 coefficients to correct the 15 d adjacent to the calibration
period gives comparable results. Considering the last calibration period, C3
coefficients show a mean bias of -2.5 ppm when calibration is from the
first 15 d. One reason that can explain this behaviour is the greater
variability of CO2 dry air mole fraction during the last 15 d of
the experiment. The interquartile range of CO2 dry air mole
fraction is 10, 15 and 25 ppm respectively for the first, second and third
periods. The CO2 dry air mole fraction correction is accomplished
mostly by correcting T, P, H2O and the instrument offset. A
small variation in sensitivities may lead to a less appropriate correction
for periods of smaller variability. Another reason for this difference is the
drift component of the correction in Eq. (1). The linear drift of the
instrument also varies with time. One method to better correct for the slow
linear drift of the instrument is to combine the first and last weeks of the
experiment into a calibration period instead of using 2 consecutive weeks.
When using the first week (W1) and the last week (W6) for calibration, the
instrument drift is not properly corrected and a residual linear drift of
0.14 and 0.28 ppm week-1 is visible in the black (W1) and the red (W6)
curves of Fig. S5 respectively. Nearly no drift (0.01 ppm week-1) is
observed when considering both W1 and W6 for the training (blue curve). In
Fig. 7, magenta stars show SD Δ{CO2} and mean Δ{CO2} values of the whole 45 d time series considering both W1
and W6 as calibration periods. With this coefficient determination method,
mean ΔCO2 bias can be reduced to nearly 0 ppm. Finally, we
should note that averaging the 1 min data to hourly averages can further
improve SD Δ{CO2} values up to 28 %. As expected,
mean values do not change for hourly averages.
SD Δ{CO2} (a) and mean Δ{CO2} (b) values considering three calibration periods of
15 consecutive days for calibration each, with C1, C2 and C3 corresponding to
the
first, second and third 15 consecutive days of measurements at the Saclay
field site respectively. W1W6 corresponds to using the first and sixth weeks
as the calibration period. Mean ΔCO2 calculated for the four
calibration periods. SD Δ{CO2} (c) and
mean Δ{CO2} (d) values considering four calibration
periods of 15 consecutive days for calibration each, with C1, C2, C3 and C4
corresponding to the first, second, third and fourth 15 consecutive days of
measurements at the Jussieu field site respectively. W1W6 corresponds to
using the first and eighth weeks as the calibration period. Hourly and minute
values are represented as full and empty symbols respectively.
Furthermore, we can investigate which of the six term multivariable linear
regressions is most important here. The offset correction terms and correction terms depending on dry air mole fraction
(b and aCO2{CO2}HPP3) are the most
significant corrections among all five parameters and allow the reduction of the mean
ΔCO2 from 45 to 0 ppm (see Table 3 and Fig. S3). The
other four parameters (pressure, temperature, water vapour and drift
corrections) further reduce the difference between CRDS and HPP3.2, reducing
the SD Δ{CO2} of minute averages
from 1.03 to 0.67 ppm. Here, the temperature correction (d) and the
water vapour correction (e) provide a correction of similar magnitude,
keeping the same SD and improving mean Δ{CO2} only from 0.16 to 0.13 ppm. This is understandable
since temperature and water vapour are correlated for this type of
measurement.
SD and mean values of 1 min average Δ{CO2} data for each correction step. Note
that corrections are cumulative from left to right.
ReportedOffsetPressureTemperatureRHDrift(raw)correctioncorrectioncorrectioncorrectioncorrectionSD Δ{CO2} (ppm)1.111.031.000.970.970.67Mean Δ{CO2} (ppm)45.331×10-39×10-40.160.13-0.08Jussieu ambient air measurements (WA2-2)
To assess the further performance of the HPP3.2 instruments, additional wet
ambient air measurements at the second field site in Jussieu were carried out
for 60 consecutive days using instrument S2.1 alongside a CRDS instrument. Figure 7c, d show SD Δ{CO2} and mean
Δ{CO2} values calculated with four
calibration periods of 15 consecutive days each and one calibration
considering both the first and last weeks of the experiment. Calibration
coefficients for C1, C2, C3 and C4 are calculated considering calibration
periods of the first, second, third and fourth 15 consecutive days of the
experiment respectively. W1W8 coefficients are calculated considering week
one (W1) and week eight (W8) of the experiment. The results are
qualitatively very similar to the measurements at the Saclay field site, and
combing the first and last weeks as calibration period also results in
achieving our target of SD Δ{CO2} > 1 ppm.
Conclusion and perspective
We integrated HPP3.1 and HPP3.2 NDIR sensors into a portable low-cost
instrument with additional sensors and internal data acquisition. The
laboratory tests reveal a strong sensitivity of reported CO2 dry air
mole fractions to ambient air pressure for the HPP3.1 series and a
significantly decreased, yet noticeable, sensitivity to pressure, for the
upgraded HPP3.2 sensors equipped with the built-in manufacturer
p correction. To achieve the targeted stability (long-term repeatability)
for urban observations of 1 ppm or better, instruments have to be corrected
at regular intervals against data from a reference instrument (here CRDS)
to account for their cross-sensitivities to T,p,W (H2O mixing ratio) changes
and electronic drift, unless those parameters could be controlled externally
in the future. We found that commercially available p, T and RH sensors that
are compatible with the chosen Raspberry Pi3 platform are sufficiently
precise to use these parameters as predictors of the linear equation used to
calibrate each HPP3 instrument against the reference instrument, which was
calibrated to the official WMO CO2 X2007 CO2 scale.
Two common modes of operation have been successfully tested, i.e. using the
HPP3 instruments for either dried or undried ambient air measurements. Our
results indicate that using a dried gas stream does not improve measurement
precision or stability compared to an undried gas stream provided that a
multivariable regression model is used for calibration, which accounts for
all cross-sensitivities including H2O mixing ratio changes.
We furthermore find that sensor-specific corrections are required and they
should be considered time dependent, e.g. by including a linear drift that
only becomes more apparent for longer-term observations. Different
calibration windows were tested for both the Saclay field site and Jussieu
field site ambient air measurements and their results were evaluated against CRDS
data that were not used for calibration. Those sites exhibit the typical
{CO2} levels in urban GHG monitoring
networks where future low-cost medium-precision instruments could be
deployed. Regular (6-weekly) recalibrations are found to be appropriate
to capture sensor drifts and changes in relevant cross-sensitivities, while
not increasing the burden of performing calibration too often. A dedicated
set of calibration gases was not necessary if the low-cost instrument was
calibrated against {CO2} from a CRDS instrument using the
same air. Calibration periods of 1 week with parallel CRDS measurements
before and after a 45 d deployment were sufficient for the SD Δ{CO2} data to be within 1 ppm of the CRDS measurements
during that period (with near-zero bias, i.e. Δ{CO2} ≪1 ppm). This calibration
approach can thus be an alternative to permanently deploying calibration
gases for each individual sensor.
The field tests at the Saclay and Jussieu stations are being continued to see
if the instrument performance deteriorates over its lifetime. Since the
start of the test in 2015 until now, multiple HPP3.1 sensors have been in use without significant performance loss. Other research groups have also
started integrating HPP sensors into their low-cost GHG monitoring strategy
(e.g. Carbosense, http://www.nano-tera.ch/projects/491.php, last access: 11 March 2019).
Future improvements for the LCMP instruments will include the addition of
batteries to allow their transport to the central calibration lab without
power cut as well as using them in field campaigns, e.g. landfills when
connected to solar panels or small wind turbines. During future tests at
sites without reference instruments, small pressurized gas containers (12l,
minican, Linde Gas) will be used to regularly inject target gas to track the
performance during a deployment period.
The overall operational cost of the new calibration scheme using a central
laboratory and rotating the LCMP systems can also only be assessed after
more extensive field deployment has been performed.
Code and data availability
The Python scripts for the data collection from the HPP3 and DHT22 for
Raspberry Pi 3, and the data from the experiments described here are
available from the corresponding authors upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/amt-12-2665-2019-supplement.
Author contributions
The first authors EA and FRV conducted the measurements reported in
this study and planned the work with support of PC. AB and BG supported the
development of the data logging routine and processing scripts. OL and MR
supported measurements by providing access to the ICOS test laboratory
equipment and supplied reference gases. The initial draft of the paper
was developed by EA, FRV and PC. All authors have updated, contributed and
edited the paper.
Competing interests
Bakhram Gaynullin is an employee of Senseair AB, which manufactured the HPP
prototypes. The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue
“The 10th International Carbon Dioxide Conference (ICDC10) and the 19th
WMO/IAEA Meeting on Carbon Dioxide, other Greenhouse Gases and Related
Measurement Techniques (GGMT-2017) (AMT/ACP/BG/CP/ESD inter-journal SI)”. It
is a result of the 19th WMO/IAEA Meeting on Carbon Dioxide, Other Greenhouse
Gases, and Related Measurement Techniques (GGMT-2017), Empa Dübendorf,
Switzerland, 27–31 August 2017.
Acknowledgements
We would like to thank the anonymous reviewers and the editor whose comments
have helped to significantly improve this paper. The work conducted
here was partially funded through the LOCATION project of the Low Carbon
City Laboratory and a SME-VOUCHER from Climate-KIC (EIT) as well as the
Chaire BridGES of UVSQ, CEA, Thales Alenia Space and Veolia S.A.
Review statement
This paper was edited by Martin Steinbacher and reviewed by two anonymous referees.
ReferencesAndres, R. J., Boden, T. A., and Higdon, D.: A new evaluation of the
uncertainty associated with CDIAC estimates of fossil fuel carbon dioxide
emission, Tellus B, 66, 23616, 10.3402/tellusb.v66.23616, 2014.Bréon, F. M., Broquet, G., Puygrenier, V., Chevallier, F., Xueref-Remy,
I., Ramonet, M., Dieudonné, E., Lopez, M., Schmidt, M., Perrussel, O.,
and Ciais, P.: An attempt at estimating Paris area CO2 emissions
from atmospheric concentration measurements, Atmos. Chem. Phys., 15,
1707–1724, 10.5194/acp-15-1707-2015, 2015.Broquet, G., Bréon, F.-M., Renault, E., Buchwitz, M., Reuter, M.,
Bovensmann, H., Chevallier, F., Wu, L., and Ciais, P.: The potential of
satellite spectro-imagery for monitoring CO2 emissions from large
cities, Atmos. Meas. Tech., 11, 681–708,
10.5194/amt-11-681-2018, 2018.Cambaliza, M. O. L., Shepson, P. B., Caulton, D. R., Stirm, B., Samarov, D.,
Gurney, K. R., Turnbull, J., Davis, K. J., Possolo, A., Karion, A., Sweeney,
C., Moser, B., Hendricks, A., Lauvaux, T., Mays, K., Whetstone, J., Huang,
J., Razlivanov, I., Miles, N. L., and Richardson, S. J.: Assessment of
uncertainties of an aircraft-based mass balance approach for quantifying
urban greenhouse gas emissions, Atmos. Chem. Phys., 14, 9029–9050,
10.5194/acp-14-9029-2014, 2014.Eugster, W. and Kling, G. W.: Performance of a low-cost methane sensor for
ambient concentration measurements in preliminary studies, Atmos. Meas.
Tech., 5, 1925–1934, 10.5194/amt-5-1925-2012, 2012.GAW report 242: 19th WMO/IAEA Meeting on Carbon Dioxide, Other Greenhouse
Gases and Related Measurement Techniques (GGMT-2017), World Meteorological
Organization, edited by: Crotwell, A. and Steinbacher, M., available at:
https://library.wmo.int/doc_num.php?explnum_id=5456 (last access:
10 February 2018), 2018.Gaynullin, B., Bryzgalov, M., Hummelgård, C., and Rödjegard, H.: A
practical solution for accurate studies of NDIR gas sensor pressure
dependence, Lab test bench, software and calculation algorithm, IEEE Sensors,
30 October–3 November, Orlando, FL, 1–3, 10.1109/ICSENS.2016.7808828,
2016.Holstius, D. M., Pillarisetti, A., Smith, K. R., and Seto, E.: Field
calibrations of a low-cost aerosol sensor at a regulatory monitoring site in
California, Atmos. Meas. Tech., 7, 1121–1131,
10.5194/amt-7-1121-2014, 2014.Hummelgård, C., Bryntse, I., Bryzgalov, M., Henning, J.-Å, Martin,
H., Norén, M., and Rödjegård, H.: Low-Cost NDIR Based Sensor
Platform for Sub-Ppm Gas Detection, Urban Climate, 14, 342–350,
10.1016/j.uclim.2014.09.001, 2015.Kunz, M., Lavric, J. V., Gerbig, C., Tans, P., Neff, D., Hummelgård, C.,
Martin, H., Rödjegård, H., Wrenger, B., and Heimann, M.: COCAP: a
carbon dioxide analyser for small unmanned aircraft systems, Atmos. Meas.
Tech., 11, 1833–1849, 10.5194/amt-11-1833-2018, 2018.Lauvaux, T., Miles, N. L., Deng, A., Richardson, S. J., Cambal-iza, M. O.,
Davis, K. J., Gaudet, B., Gurney, K. R., Huang, J.,O'Keefe, D., Song, Y.,
Karion, A., Oda, T., Patarasuk, R., Razli-vanov, I., Sarmiento, D., Shepson,
P., Sweeney, C., Turnbull, J., and Wu, K.: High-resolution atmospheric
inversion of urban CO2 emissions during the dormant season of the
Indianapolis Flux Experiment (INFLUX), J. Geophys. Res.-Atmos., 121,
5213–5236, 10.1002/2015JD024473, 2016.Liu, Z., He, C., Zhou, Y., and Wu, J.: How much of the world's land has been
urbanized, really? A hierarchical framework for avoiding confusion, Landscape
Ecol., 29, 763–771, 10.1007/s10980-014-0034-y, 2014.Martin, C. R., Zeng, N., Karion, A., Dickerson, R. R., Ren, X., Turpie, B.
N., and Weber, K. J.: Evaluation and environmental correction of ambient
CO2 measurements from a low-cost NDIR sensor, Atmos. Meas. Tech.,
10, 2383–2395, 10.5194/amt-10-2383-2017, 2017.Mays, K. L., Shepson, P. B., Stirm, B. H., Karion, A., Sweeney, C., and
Gurney, K. R.: Aircraft-based measurements of the carbon footprint of
Indianapolis, Environ. Sci. Technol., 43, 7816–7823, 10.1021/es901326b,
2009.Nassar, R., Hill, T. G., McLinden, C. A., Wunch, D., Jones, D. B. A., and
Crisp, D.: Quantifying CO2 emissions from individual power plants
from space, Geophys. Res. Lett., 44, 10045–10053,
10.1002/2017GL074702, 2017.Piedrahita, R., Xiang, Y., Masson, N., Ortega, J., Collier, A., Jiang, Y.,
Li, K., Dick, R. P., Lv, Q., Hannigan, M., and Shang, L.: The next generation
of low-cost personal air quality sensors for quantitative exposure
monitoring, Atmos. Meas. Tech., 7, 3325–3336,
10.5194/amt-7-3325-2014, 2014.Raspberry Pi Foundation: Raspberry Pi Hardware Documentation, available at:
https://www.raspberrypi.org/documentation/hardware/raspberrypi/, last
access: 30 April 2019.Rella, C. W., Chen, H., Andrews, A. E., Filges, A., Gerbig, C., Hatakka, J.,
Karion, A., Miles, N. L., Richardson, S. J., Steinbacher, M., Sweeney, C.,
Wastine, B., and Zellweger, C.: High accuracy measurements of dry mole
fractions of carbon dioxide and methane in humid air, Atmos. Meas. Tech., 6,
837–860, 10.5194/amt-6-837-2013, 2013.
Seto, K. C., Dhakal, S., Bigio, A., Blanco, H., Delgado, G. C., Dewar, D.,
Huang, L., Inaba, A., Kansal, A., Lwasa, S., McMahon, J. E., Müller, D.
B., Murakami, J., Nagendra, H., and Ramaswami, A.: Human settlements,
infrastructure and spatial planning, in: Climate Change 2014: Mitigation of
Climate Change. Contribution of Working Group III to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change, edited by:
Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S.,
Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B.,
Savolainen, J., Schlömer, S., von Stechow, C., Zwickel, T., and Minx, J.
C., Cambridge, United Kingdom and New York, NY, USA, 2014.Shusterman, A. A., Teige, V. E., Turner, A. J., Newman, C., Kim, J., and
Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network:
initial evaluation, Atmos. Chem. Phys., 16, 13449–13463,
10.5194/acp-16-13449-2016, 2016.Staufer, J., Broquet, G., Bréon, F.-M., Puygrenier, V., Chevallier, F.,
Xueref-Rémy, I., Dieudonné, E., Lopez, M., Schmidt, M., Ramonet, M.,
Perrussel, O., Lac, C., Wu, L., and Ciais, P.: The first 1-year-long estimate
of the Paris region fossil fuel CO2 emissions based on atmospheric
inversion, Atmos. Chem. Phys., 16, 14703–14726,
10.5194/acp-16-14703-2016, 2016.Turner, A. J., Shusterman, A. A., McDonald, B. C., Teige, V., Harley, R. A.,
and Cohen, R. C.: Network design for quantifying urban CO2
emissions: assessing trade-offs between precision and network density, Atmos.
Chem. Phys., 16, 13465–13475, 10.5194/acp-16-13465-2016,
2016.
Verhulst, K. R., Karion, A., Kim, J., Salameh, P. K., Keeling, R. F., Newman,
S., Miller, J., Sloop, C., Pongetti, T., Rao, P., Wong, C., Hopkins, F. M.,
Yadav, V., Weiss, R. F., Duren, R. M., and Miller, C. E.: Carbon dioxide and
methane measurements from the Los Angeles Megacity Carbon Project – Part 1:
calibration, urban enhancements, and uncertainty estimates, Atmos. Chem.
Phys., 17, 8313–8341, 10.5194/acp-17-8313-2017, 2017.Wang, Y., Li, J. Y., Jing, H., Zhang, Q., Jiang, J. K., and Biswas, P.:
Laboratory Evaluation and Calibration of Three Low- Cost Particle Sensors for
Particulate Matter Measurement, Aerosol Sci. Tech., 49, 1063–1077,
10.1080/02786826.2015.1100710, 2015.Wu, L., Broquet, G., Ciais, P., Bellassen, V., Vogel, F., Chevallier, F.,
Xueref-Remy, I., and Wang, Y.: What would dense atmospheric observation
networks bring to the quantification of city CO2 emissions?, Atmos.
Chem. Phys., 16, 7743–7771, 10.5194/acp-16-7743-2016, 2016.Young, D. T., Chapman, L., Muller, C. L., Cai, X. M., and Grimmond, C. S. B.:
A Low-Cost Wireless Temperature Sensor: Evaluation for Use in Environmental
Monitoring Applications, J. Atmos. Ocean. Tech., 31, 938–944,
10.1175/jtech-d-13-00217.1, 2014.Yver Kwok, C., Laurent, O., Guemri, A., Philippon, C., Wastine, B., Rella, C.
W., Vuillemin, C., Truong, F., Delmotte, M., Kazan, V., Darding, M.,
Lebègue, B., Kaiser, C., Xueref-Rémy, I., and Ramonet, M.:
Comprehensive laboratory and field testing of cavity ring-down spectroscopy
analyzers measuring H2O, CO2, CH4 and CO,
Atmos. Meas. Tech., 8, 3867–3892, 10.5194/amt-8-3867-2015,
2015.Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A.,
Robinson, E. S., Robinson, A. L., and R. Subramanian: A machine learning
calibration model using random forests to improve sensor performance for
lower-cost air quality monitoring, Atmos. Meas. Tech., 11, 291–313,
10.5194/amt-11-291-2018, 2018.