ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-13449-2016The BErkeley Atmospheric CO2 Observation Network: initial evaluationShustermanAlexis A.shusterman.alexis@berkeley.eduhttps://orcid.org/0000-0002-4450-5161TeigeVirginia E.TurnerAlexander J.https://orcid.org/0000-0003-1406-7372NewmanCatherineKimJinsolCohenRonald C.rccohen@berkeley.eduhttps://orcid.org/0000-0001-6617-7691Department of Chemistry, University of California Berkeley, Berkeley,
CA 94720, USASchool of Engineering and Applied Sciences, Harvard University,
Cambridge, MA 02138, USADepartment of Earth and Planetary Science, University of California
Berkeley, Berkeley, CA 94720, USAAlexis A. Shusterman (shusterman.alexis@berkeley.edu) and Ronald C. Cohen (rccohen@berkeley.edu)31October20161621134491346317June201623June20169September201610October2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/13449/2016/acp-16-13449-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/13449/2016/acp-16-13449-2016.pdf
With the majority of the world population residing in
urban areas, attempts to monitor and mitigate greenhouse gas emissions must
necessarily center on cities. However, existing carbon dioxide observation
networks are ill-equipped to resolve the specific intra-city emission
phenomena targeted by regulation. Here we describe the design and
implementation of the BErkeley Atmospheric CO2 Observation Network
(BEACO2N), a distributed CO2 monitoring instrument that utilizes
low-cost technology to achieve unprecedented spatial density throughout and
around the city of Oakland, California. We characterize the network in terms
of four performance parameters – cost, reliability, precision, and systematic
uncertainty – and find the BEACO2N approach to be sufficiently
cost-effective and reliable while nonetheless providing high-quality
atmospheric observations. First results from the initial installation
successfully capture hourly, daily, and seasonal CO2 signals relevant
to urban environments on spatial scales that cannot be accurately
represented by atmospheric transport models alone, demonstrating the utility
of high-resolution surface networks in urban greenhouse gas monitoring
efforts.
Introduction
As two-thirds of the human population stand to inhabit cities by 2050
(United Nations, 2014), developing a thorough understanding of urban
greenhouse gas emissions is of ever-growing importance. International and
local law-making bodies around the world are agreeing to caps on total
emissions and enacting multi-faceted regulations aimed at achieving these
caps (e.g., A.B. 32, 2006; United Nations, 2015). As of yet there exists no
mechanism for judging the efficacy of these individual rules or verifying
compliance through direct observations of changes in CO2 at the scale
of cities (Duren and Miller, 2012).
Map of the BEACO2N domain (a) in the context of the western
United States and (b) showing individual node locations. Inset in panel
(b) shows the pair of nodes stationed in Sonoma County.
North-facing schematic of Fig. 1 indicating the vertical distribution of BEACO2N node sites (circles) over the
topography of Oakland, CA. The cloud marks the altitude and thickness of a
typical marine fog layer; the bridge delineates the height of the San
Francisco–Oakland Bay Bridge. Horizontal placement of nodes has been skewed
for visual clarity.
Traditional strategies for assessing greenhouse gas emissions are limited to
a small handful of monitoring instruments scattered sparsely across remote
areas, mostly in developed nations (e.g., Worthy et al., 2003; Thompson et
al., 2009; Andrews et al., 2014). These stations are capable of measuring
regional averages and some integrated urban concentrations with extreme
accuracy and precision, but are purposefully distanced from, and experience
reduced sensitivity to, urban signals, thus giving little to no spatially
resolved information on emissions in the precise areas that the majority of
greenhouse gas rules aim to regulate.
The increasing significance of urban emissions combined with the
proliferation of commercial cavity ring-down spectroscopic instrumentation
has resulted in a recent trend towards network sensing approaches for
constraining greenhouse gas emissions in cities. For example, Ehleringer et al. (2008) maintain a CO2 monitoring network in the Salt Lake City
metropolitan area, the INFLUX network measures CO2, 14CO2,
and total column CO2 across the city of Indianapolis (Turnbull et al.,
2015), and NASA's Megacities Carbon Project has established sensor networks
in the pilot cities of Los Angeles (Kort et al., 2013) and Paris (Bréon
et al., 2015). These ground-based monitoring efforts are complemented by
space-based observations from SCHIAMACY (Burrows et al., 1995), GOSAT
(Yokota et al., 2009), and most recently the Orbiting Carbon Observatory-2
(OCO-2), launched in July 2014, which provides total column CO2
measurements over 1.29 by 2.25 km footprints once every 16 days (Eldering et
al., 2012).
Thus far, the urban surface projects have relied on a relatively small
number of instruments (between 5 and 15) distributed with sensor-to-sensor
distances of 5 to 35 km. Initial efforts suggest this approach may be
effective at characterizing average citywide emissions over monthly to
annual timescales (McKain et al., 2012), however it has yet to be used to
identify and quantify specific emission activities at neighborhood scales.
To resolve individual emission sources, much finer spatial resolution is
needed. Simple Gaussian dispersion models with total reflection at the
surface predict > 95 % of the one-dimensional footprint of a
sensor 10 m above ground level to be within 1.1 km of the sensor under
typical conditions (Seinfeld and Pandis, 2006), and prior studies (e.g., Zhu
et al., 2006; Beckerman et al., 2007; Choi et al., 2014) have observed
e-folding distances of ∼ 100 to 1000 m for urban pollutant
plumes mixing into the local background.
Here we propose an alternative approach that strikes a different balance
between instrument quality and quantity than in previous CO2 monitoring efforts. The BErkeley Atmospheric CO2 Observation Network
(BEACO2N) is a large-scale network instrument that aims to leverage
low-cost sensing techniques in order to enable a spatially dense network of
CO2-sensing “nodes” in and around the city of Oakland, California
(Figs. 1 and 2). Using commercial CO2 instrumentation of moderate
quality and a suite of low-cost trace gas sensors for additional source
attribution specificity, BEACO2N is able to achieve an unprecedented
spatial resolution of approximately 2 km – to our knowledge the only sensor
network to date that monitors CO2 on scale with the heterogeneous
patterns of urban sources and sinks (see Fig. 3 for examples of
intra-city CO2 flux gradients). We present an initial description and
characterization of the instrument, beginning with a description of the
nodes, their locations, and the development of various laboratory and in
situ calibration techniques. We then evaluate the network in terms of four
factors – cost, reliability, precision, and systematic uncertainty, described
below – and conclude by demonstrating BEACO2N's ability to resolve
CO2 signals of significance to the urban environment.
Cost
In order to remain cost-competitive with other, less dense networks
employing higher-grade instrumentation, a high-density network must utilize
sensors with a price 1–2 orders of magnitude lower. However, as sensor
price often scales with quality, low-cost instrumentation may carry
associated penalties in other domains, such as diminished precision,
persistent bias, or the need for frequent maintenance and/or re-calibration.
Thus, we seek to optimize the trade-off between cost and the other
considerations.
A sample high-resolution bottom-up emissions inventory for the Bay
Area adapted from Turner et al. (2016).
Reliability
Network reliability consists of sensor uptime and continuity of the data
stream and is crucial to enabling comparison and averaging across sites as
well as improving the statistics of temporal analyses. Poor reliability also
has an indirect impact on cost via the resources expended on repeat
maintenance visits and/or replacement part purchases.
Precision
The precision requirements at each individual site vs. for a network
instrument as a whole vary depending on the phenomena of interest.
Metropolitan regions produce < 10 ppm CO2 enhancements in the
boundary layer (Pacala et al., 2010), requiring sensitivity to changes that
are orders of magnitude smaller for the characterization of citywide integrated
inter-annual trends, for example. More specifically, according to the First
Update to the Climate Change Scoping Plan, the state of California would
have to reduce its overall CO2 emissions by 4.7 million metric tons per
year to achieve its goal of reaching 1990 emission levels by 2020 (Brown et
al., 2014). Assuming a fraction of that total reduction is attributable to
the San Francisco Bay Area in proportion to its population (∼ 20 % of the California total), this amounts to a change
of -2.6 × 106 kg CO2 day-1 for the San Francisco Bay Area. Given a residence
time of air in the region of 1 day, these emissions reductions spread evenly
over the 22 681 km2 domain and through a 1 km boundary layer would lead
to a 65 ppb annual decrease in the daily CO2 concentrations. If the
goal is verification of regional inter-annual emissions targets, we would
therefore require N instruments of sufficient individual sensitivity and
spatial representativeness such that their combined signals allow us to
detect annual changes of ∼ 65 ppb year-1 with confidence.
However, the true strength of the high-density approach lies in the
individual sensors' (or sub-group of sensors') sensitivity to intra-city
phenomena, which are orders of magnitude larger by virtue of their proximity
to sources not yet diluted by advection. Larger signal sizes forgive poorer
precision in the individual instruments, but demand sufficient temporal
resolution to capture these anomalous, often unexpected, events of short
duration on top of slowly varying domain-wide fluctuations in the background
concentration. Because the BEACO2N instrument is unique in its
sensitivity to these highly local processes, we will focus on this latter
specification of the instrument precision in the characterization that
follows.
Systematic uncertainty
Systematic uncertainties can be incurred somewhat abruptly during the
initial field installation (bias) or accrued more gradually over time
(drift). Systematic uncertainty in the sensor readings is of particular
concern in a large-scale network deployment where on-site calibration
materials such as reference gases are infeasible and frequent maintenance
visits are undesirable. To ensure trustworthy observations, a given network
sensing approach must demonstrate some combination of (a) instrumentation
that is reasonably robust against sudden or gradual introduction of
systematic uncertainty, (b) a post hoc correction for systematic uncertainty
in the data record, and/or (c) a procedure for identifying and replacing
sensors whose systematic uncertainties cannot be remedied via the prior
methods.
Current BEACO2N node design.
Node design, calibration, and deployment
Each BEACO2N node contains a non-dispersive infrared Vaisala CarboCap
GMP343 sensor for CO2 as well as SGX Sensortech MiCS-4514 and MiCS-2614
metal oxide-based micro-sensors used to detect CO/NO2 and O3,
respectively. Following a large-scale node refurbishment and upgrading
effort in mid-2014, these core elements are now supplemented with a
Sensirion SHT15 and Bosch Sensortec BMP180 sensor for measuring humidity
(SHT15), pressure (BMP180), and temperature (both), a Shinyei PPD42NS
nephelometric particulate matter sensor, and a suite of Alphasense B4
electrochemical trace gas sensors for O3, CO, NO, and NO2.
Discussion of these latter, air-quality-related technologies will follow in
a forthcoming paper.
All sensors are assembled into compact, weatherproof enclosures as seen in
Fig. 4. A Raspberry Pi microprocessor automates data collection via a
serial-to-USB converter (for CO2, every ∼ 2 s) and
an Arduino Leonardo microcontroller (for everything else, every
∼ 10 s), then transmits data to a central server using
either (a) a direct on-site Ethernet connection, (b) a Ubiquiti NanoStation
locoM2 Wi-Fi antenna, or (c) an Adafruit FONA MiniGSM cellular module. The
latter has the unintended consequence of introducing a significant amount of
electrical noise into the system. We reduce the impact of this noise by
limiting data transmission to 2 h per day, on a rotating schedule such
that no periods are disproportionately afflicted by elevated noise levels.
Battery-powered real-time clock modules are also included to ensure
timestamp accuracy during planned and unexpected hiatuses in internet
connectivity.
Airflow through the node is maintained by two 30 mm fans, one positioned in
the “intake” orientation and the other in the “outflow” orientation. An
additional, passive air outlet is located adjacent to the AC/DC power supply
converter to prevent excessive heating inside the node. Node enclosures
measure 90 by 160 by 360 mm and are made of corrosion-resistant die-cast aluminium that minimizes meteorological and magnetic complications.
Stainless steel fasteners and a weatherproof seal prevent water intrusion
into the enclosure.
Laboratory calibrations are performed on each CarboCap sensor upon receipt
of the instrument from the supplier and repeated whenever nodes are
retrieved from the field for maintenance, resulting in a re-calibration
every 12–18 months. Reference cylinders of 0, 1000, and either 320 or 370 ppm CO2 (±1 %) are used for
∼ 10 min deliveries of each concentration to a chamber containing the sensor,
which includes a built-in microprocessor that accepts the results of this
multi-point calibration as input and automatically applies the appropriate
corrections to the subsequent observations. The CarboCap microprocessor can
also be configured to correct for the effects of oxygen, temperature,
pressure, and humidity. The built-in oxygen compensation is utilized at a
constant value of 20.95 %, while the latter three compensations are turned
off prior to sensor deployment. Instead, a post hoc correction is derived
from the ideal gas law and Dalton's law of partial pressures.
[CO2]dry=[CO2]raw×1013.25hPaPtot×T298.15K×11-PH2OPtot
Here [CO2]dry is the dry air mole fraction, or the amount of
CO2 that would be measured if the observed air parcel was dried and
brought to standard temperature and pressure. [CO2]raw, T,
Ptot, and PH2O are, respectively, the raw CO2
concentration output by the CarboCap software in ppm, the temperature
measured by the internal thermometer of the CarboCap in K, the atmospheric
pressure in hPa, and the partial pressure of water in hPa, derived from the
dew point temperature (Tdew, in ∘C) using the August–Roche–Magnus
approximation of the Clausius–Clapeyron relation as indicated below.
PH2O=6.1094hPa×exp17.625Tdew243.04+Tdew
For post-2014 observations, we use the pressure and dew point temperature
measured inside each node enclosure by the aforementioned BMP180 and SHT15
sensors, respectively. For data collected prior to 2014, Eqs. (1) and (2) are
calculated from the average sea level pressures (adjusted for altitude) and
dew point temperatures measured within ∼ 50 km of the
BEACO2N domain by weather stations in the NOAA Integrated Surface
Database (https://www.ncdc.noaa.gov/isd/).
List of site names, abbreviated codes, geo-coordinates, and
elevations.
CodeFull site nameLat.Long.Elev.Elev.(m a.s.l.)(m a.g.l.)BELBurckhalter Elementary School37.775-122.167978BODBishop O'Dowd High School37.753-122.155828CHAChabot Space & Science Center37.819-122.18147611CPSCollege Preparatory School37.849-122.2411024EBMW. Oakland EBMUD Monitoring Stn.37.814-122.28262ELCEl Cerrito High School37.907-122.2944913EXBExploratorium (Bay)37.802-122.397139EXEExploratorium (Embarcadero)37.801-122.399135FTKFred T. Korematsu Discovery Acad.37.737-122.173166HRSHead Royce School37.809-122.2041145OINInternational Community School37.779-122.231196KAIKaiser Center37.809-122.264115111LAULaurel Elementary School37.792-122.196746LBLLawrence Berkeley Nat'l Lab, Bldg. 7037.876-122.25224611LCCLighthouse Community Charter School37.736-122.19695MARBerkeley Marina37.863-122.31462MONMontclair Elementary School37.830-122.2111934NOCN. Oakland Community Charter School37.833-122.277246OHSOakland High School37.805-122.236497PAPPLACE at Prescott Elementary37.809-122.298126PDSPark Day School37.832-122.257397PHSPiedmont Middle & High School37.824-122.2338610PORPort of Oakland Headquarters37.796-122.2793532ROSRosa Parks Elementary School37.865-122.2952210SETStone Edge Farms (near turbine)38.289-122.503542SEVStone Edge Farms (in vineyard)38.291-122.506613SHSSkyline High School37.798-122.1613593STLSt. Elizabeth High School37.779-122.2222811
Figure 5 compares 1 min mean CO2 dry air mole fractions calculated
as described above with readings from a custom cavity ring-down reference
instrument based on the Picarro G2301 analyzer system co-located with an
in-field CarboCap over the course of 2 weeks in January 2016. The ratios
between the CarboCap and Picarro observations are then shown in Fig. 6 as a
function of temperature, total pressure, and the partial pressure of water.
Although most of the impact of these environmental variables is removed by
the ideal gas-law-based correction in Eq. (1), slight dependencies on each
variable remain, likely due to their influence on the vibrational spectra of
CO2 via pressure broadening, etc. Performing similar analyses on
observations from in situ co-locations with other reference instruments (see
the LI-COR LI-820 in Sect. 3.4) reveals that the temperature and water dependence
vary in sign and magnitude between individual sensors, while the pressure
dependence is found to be quite robust. We therefore apply the following
empirical correction to all CO2 observations with coincident, on-site
pressure measurements (i.e., post-2014 data sets).
[CO2]corrected=[CO2]dry×(-0.00055Ptot+1.5)
The effect of this correction is shown in the histogram of CarboCap–Picarro
differences in Fig. 5 (gray bars). The offset between the two instruments is
reduced from -1 to ∼ 0 ppm and the standard deviation of
their differences is tightened from ±1.5 to ±1.4 ppm. This
still exceeds the ±1.0 ppm precision one would expect under average
conditions given the form of Eqs. (1) and (2) and the manufacturer's
specifications for the meteorological sensors (see Sect. 3.5), the CarboCap,
and the Picarro (Sect. 3.3), suggesting that the combined effect of the
lingering temperature and water biases with any unknown factors is ±0.4 ppm.
1 min mean results from a two week co-location of a Vaisala
CarboCap GMP343 and a custom cavity ring-down reference instrument based on
the Picarro G2301 system: (a) representative five day time series, (b) 1 h
running mean of the differences over the same five day period, (c) direct
comparison, (d) histogram of the differences. CarboCap observations are dry
air mole fractions calculated using Eq. (1) and subsequently pressure
corrected with Eq. (3).
Calibrated nodes are installed on trailers and buildings 2–111 m above
ground level (6–476 m above sea level), mounted to existing infrastructure
or weighted industrial tripods. Rooftop position and intake orientation are
chosen to optimize wireless connectivity (if applicable), maximize air
exchange with the surrounding area, and minimize sampling of extremely local
emission sources (e.g., rooftop ventilation ducts). BEACO2N nodes are
sited on an approximately 2 km square grid across the Oakland metropolitan
area (see Figs. 1 and 2 and Table 1), often on top of schools and museums,
which possess roughly the desired spatial density and also assist the
service of the educational and outreach goals of the project (see
http://beacon.berkeley.edu). The 2 km spacing is chosen to ensure an
approximately 1 km proximity to any significant CO2 source or sink in
the metropolitan area, maximizing coverage without undue overlap between
neighboring footprints. Additional sites outside the 2 km grid are also
included for sensitivity to potential emission sources of interest, for
co-location with useful reference instruments, or as pilots for network
expansion.
This largely opportunistic siting approach avoids the logistical and
financial obstacles associated with tall tower sampling mechanisms, although
it does present additional challenges for the quantification of network-wide
phenomena in that no low-lying instrument can singlehandedly provide
sensitivity to the entire domain. Installing sensors near the surface and/or
built environment does ensure heightened sensitivity to individual,
ground-level emissions phenomena, but it is currently unknown whether a
well-reasoned combination of these locally sensitive signals from a high
volume of sensors could nonetheless yield reliable information about the
integrated region. A full exploration of this possibility is beyond the
scope of this study; the following analyses focus instead on establishing
BEACO2N as a viable platform for investigating such hypotheses.
Node performanceCost
The Vaisala CarboCap GMP343 CO2 sensor in this study is used in its 0
to 1000 ppm measurement range and “diffusion sampling” mode, such that
representative air samples passively diffuse into the path of the infrared
light beam. With these specifications, each CarboCap costs approximately
USD 2800. Although less expensive technologies are available, the
CarboCap design has a clear advantage in that the unit contains a digitally
controlled Fabry–Pérot interferometer to switch on (4.26 µm) and off
(3.9 µm) of the asymmetric stretching mode of CO2, generating a
baseline intensity measurement for each observation that compensates for
variability in the light source.
Additional sensors, ancillary hardware, and labor then bring the total cost
per node to ∼ USD 5500, or USD 154 000 for the entire
28-node BEACO2N instrument. For comparison, a single commercial cavity
ring-down analyzer is priced around USD 60 000 and the total equipment
cost can exceed USD 85 000 after accounting for pumps, data loggers,
etc.
Ratio of 1 min mean CO2 dry air
mole fractions presented in Fig. 5, shown as a function of temperature (a),
pressure (b), and the partial pressure of water (c).
Representative week-long time series of observations collected at
or near two nearby in-field BEACO2N nodes (EXB and EXE in Fig. 1;
∼ 250 m apart) in October 2015: (a) temperature and pressure
averaged to 1 min, (b) wind speed and direction collected once every
6 min, (c) drift- and bias-corrected CO2 dry air mole fractions
averaged to 1 min.
Reliability
Table 2 gives the percent uptime for nine representative BEACO2N nodes
over the course of 2013, calculated as the fraction of total minutes in the
year during which a given node collected valid data. All nine nodes exhibit
uptimes in excess of 50 % via either hardwired Ethernet connections or
Wi-Fi antennas, with five collecting data > 80 % of the time.
Maintenance visits to these sites beginning in mid-2014 revealed little to
no incidence of hardware failure. Instead, external issues, such as
interruptions in the electricity or Wi-Fi connectivity, are found to be the
limiting factors in determining sensor uptime. Transplanting nodes to sites
with more dependable electricity supplies and increasing implementation of
cellular modules (which are insensitive to interruptions in on-site Wi-Fi
networks) continue to enhance network reliability over time. For example,
the nine most reliable nodes during the January 2015–April 2016 period all
exhibit uptimes > 80 %, with five collecting data and
transmitting them within the next 48 h ∼ 100 % of the time
via either Ethernet or cellular data communication.
Descriptive statistics for the drift- and bias-corrected CO2
dry air mole fractions measured at nine representative sites during 2013.
Upper row for each site gives the daytime (11:00–18:00 LT) statistics; lower
row gives the nighttime (22:00–04:00 LT). The ELC node is corrected using weekly minimum LI-COR measurements as the regional
background.
From a qualitative perspective, the Vaisala CarboCap GMP343 demonstrates
exceptional sensitivity to CO2 enhancements on scales typical of an
urban environment. Figure 7 compares the 1 min mean CO2 dry air mole
fractions measured at two nearby in-field BEACO2N nodes (EXB and EXE in
Fig. 1) during 1 week in early October 2015. As these sensors are not
precisely co-located (one is stationed approximately 5 m above roadside in
downtown San Francisco, while the other sits ∼ 250 m back from
the road, near the bay), an exact correlation is not expected. The two
sensors nonetheless demonstrate remarkable agreement; while typical diurnal
CO2 variations during the same period are on the order of 20–60 ppm,
the CarboCaps simultaneously detect CO2 events as small as 8 ppm,
providing preliminary evidence of the suitability of these sensors for
high-density urban deployment.
More quantitatively, Vaisala advertises the CarboCap as possessing a
response time of 75 s and a precision of ±3 ppm at 2 s
measurement frequency. Here we present our own characterization of the
sensors' precision via comparison to (a) in-laboratory reference gases and
(b) a co-located in situ reference instrument.
After exposing an ensemble of CarboCaps to a constant stream of reference
gas, we find the 1 min mean dry air mole fractions to exhibit 1σ
precision between ±1.2 and ±2.0 ppm, roughly in keeping with
the ±2 ppm precision observed by Rigby et al. (2008). Figure 5 shows
the results from our aforementioned co-location with a Picarro G2301
reference instrument, demonstrating near perfect correlation (R2= 0.9999), slope ≅1, and an offset of approximately 0 ppm after
meteorological corrections. In this case the 1σ precision of the 1 min averages is ±1.4 ppm, given by the standard deviation of the
differences between the minute-averaged CarboCap and Picarro observations
and the Picarro's precision (±0.1 ppm at 5 s measurement
frequency). This presents a slight improvement over the ±2.18 ppm in
situ precision recorded by van Leeuwen (2010), although still greater
variability than would be expected given the manufacturer's 2 s
specifications and a 1 min averaging time (3 ppm/√30=0.55 ppm). Nonetheless, the agreement between the time series of the Picarro and
CarboCap measurements demonstrates this noise level to be effectively
negligible on the scale of ambient urban CO2 fluctuations.
Also presented in Fig. 5 is a time series of the running 1 h means of the
differences between the minute-averaged CarboCap and Picarro observations,
demonstrating a short-term drift incurred on approximately hourly timescales
found to range between 0.01 and 2.9 ppm during any given 6 h period of
the co-location. The upper bound exceeds the ±1 ppm
manufacturer-specified 6 h short-term stability as well as the 1.5 ppm
maximum short-term drift observed by Rigby et al. (2008), but in many cases
longer averaging times can be used to reduce the influence of short-term
drift to well below 1 ppm. Some modeling studies, for example, utilize time
steps of 6 h or more (e.g., Bréon et al., 2015; Wu et al., 2016),
and average diurnal cycles can often be assessed across several days.
Although some applications require finer temporal resolution, these are
typically plume-based analyses that rely on rapidly varying enhancements
above recent background concentrations, essentially eliminating concerns
about short-term drift.
Systematic uncertainty
Given the limited access to validation and calibration infrastructure, a
major concern for a long-term field deployment is systematic uncertainty
resulting from a combination of gradual temporal drift (Utemporal, in
ppm day-1) and constant biases or offsets from the “true” value
(Uatemporal, in ppm), perhaps incurred abruptly upon installation. The
measurement at a given site ([CO2]node, in ppm) is therefore the
sum of the real regional and local influences at said site
([CO2]background and [CO2]local, respectively), as
well as these systematic uncertainties.
[CO2]node=[CO2]background+[CO2]local+Uatemporal+Utemporal×days
To derive post hoc corrections for Uatemporal and Utemporal at a
given site, we first remove the [CO2]background signal from the
data record by subtracting the weekly minimum CO2 concentrations
recorded at a reference site within the network domain. BEACO2N's
unique location near the Pacific coast results in a relatively consistent
wind direction from largely unpolluted over-ocean origins, such that the
weekly minima can be assumed to reflect both the seasonal and synoptic
variations in network-wide baseline CO2 concentrations while avoiding
the influence of shorter-term variability in local sources and sinks. This
assumption is supported by preliminary analyses comparing observations from
a LI-COR LI-820 non-dispersive infrared CO2 gas analyzer with a
smoothed, three-dimensional “curtain” of surface CO2 Pacific boundary
conditions produced by NOAA's Global Greenhouse Gas Reference Network (Jeong
et al., 2013). The LI-COR, positioned at sea level between the EXB and EXE
nodes (see Fig. 1), is maintained by NOAA's Pacific Marine Environmental
Laboratory and calibrated against compressed gas (400–500 ppm CO2)
prior to every hourly measurement and is assumed to have negligible bias.
Despite a proximity to local surface-level emissions and complex boundary
layer dynamics, the LI-COR's weekly minima are found to generally follow
variations in the Pacific curtain, with an average residual of
∼ 2 ppm.
Results from drift- and bias-correction analysis at sites for which
at least 3 months of observations are available for comparison with the ELC
BEACO2N node.
Weekly minimum CO2 concentrations
measured by a LI-COR LI-820 reference instrument compared with weekly minima
calculated from the BEACO2N data record before and
after correction for systematic uncertainties.
Once the [CO2]background term is removed, effectively
de-seasonalizing the observations, we re-calculate the weekly minima of this
new data record and fit the result as a (piecewise, if necessary) linear
function of time, the slope of which gives the value of Utemporal. This
linear fit is then itself subtracted from the de-seasonalized data record,
yielding a remainder comprised of only the [CO2]local and
Uatemporal terms. While the [CO2]local component varies
rapidly, the contribution of
Uatemporal is, by definition, constant in time, so we once again
compute the weekly minima of the new data record and define the mean weekly
minimum as Uatemporal. Having obtained values for Utemporal and
Uatemporal, we simply subtract these components from the original data
record to generate the unbiased observations at each site.
Table 3 gives the results from one iteration of the correction procedure
outlined above, executed using the ELC BEACO2N node (see Fig. 1) as the
reference site needed to calculate [CO2]background. Only sites
that enable at least 3 months of comparison to the ELC node are included;
multiple values at a single site correspond to the piecewise linear fits
employed when Utemporal exhibits discontinuities over the data record.
Because we universally define Day 1 to be 1 January 2013 and Uatemporal
is strongly influenced by the intercept of the linear fit used to
characterize the temporal drift, it should be noted that the magnitude of
Uatemporal does not necessarily represent the actual bias present at a
node on its deployment date (which may be before or after 1 January 2013),
but rather an extrapolation of this initial bias forwards or backwards in
time. Uncertainties in Utemporal and Uatemporal shown in Table 3
are calculated given ±1.4 ppm random error in the 1 min averages,
±2.9 ppm short-term drift, and ±2 ppm agreement with the
reference site's weekly minima, assumed to add in quadrature. Mapped onto
the observations, these uncertainties result in a mean 1 min error of
±4 ppm. This is the assumed cumulative error used in this study,
although longer averaging times could be used to reduce this figure.
To evaluate the efficacy of this procedure, we compare the weekly minima of
both the raw and corrected data records to the weekly minimum CO2
concentrations measured by the aforementioned LI-COR LI-820. The results of
said comparison are shown in Fig. 8, demonstrating significantly improved
agreement (3.7 vs. 9.8 ppm mean residuals) with the LI-COR weekly minima
after correction. This is likely a conservative estimate of the uncertainty
reduction achievable with this method, as the ELC node we use to compute our
[CO2]background value is not itself an uncertainty-free
reference. Although the raw ELC data record demonstrates the least
systematic uncertainty of all the BEACO2N nodes in an initial
comparison with the LI-COR, its observations are nonetheless afflicted by
some unknown nonzero drift and/or atemporal bias. Direct in situ calibration
of the reference instrument would allow us to constrain systematic
uncertainties even further.
Time series of drift- and bias-corrected
CO2 dry air mole fractions collected over the course
of ∼ 2.5 years at 16 BEACO2N sites
(top), 1 month at six representative sites (middle), and 1 week at the
same six sites (bottom). The hiatus around 23 August corresponds to a
large-scale hardware refurbishment effort that began in mid-2014.
Monthly minimum drift- and bias-corrected
CO2 dry air mole fractions observed during 2013 at
the same six BEACO2N sites shown in the bottom panels
of Fig. 9, plotted as the enhancement above the July value. Bold gray curve
shows a similar treatment of the surface level Pacific Ocean empirical
boundary curtain values for 38∘ N.
Performance of ancillary sensor technology
According to manufacturer documentation, the Sensirion SHT15 provides
relative humidity measurements to 0.05 % resolution, with an advertised
accuracy of ±2.0 %, a repeatability of ±0.1 %, an 8 s
response time, and a long-term drift of < 0.5 % per year. Its
temperature measurements are provided to 0.01 ∘C resolution, with an
advertised accuracy of ±0.3 ∘C, a precision of ±0.1 ∘C, a response time of 5 to 30 s, and a long-term drift of
< 0.04 ∘C per year. The Bosch Sensortec BMP180 provides
pressure measurements to 0.01 hPa resolution, with an advertised accuracy of
±0.12 hPa, a precision of ± 0.05 hPa, and a long-term drift of
±1.0 hPa per year. Its temperature measurements are provided to 0.1 ∘C resolution, with an advertised accuracy of ±1.0 ∘C. An
independent verification of these performance specifications is not
attempted here. However, the temperature observations from both sensors
closely follow the structure of that detected by the internal temperature
sensor of the CarboCap, although the CarboCap's readings are consistently
slightly elevated, as expected given the heat produced by the instrument
itself.
The BMP180 and SHT15 are not intended to reflect local meteorological
conditions, but rather to provide a representative picture of conditions
inside the node. These internal conditions are integral to various posterior
corrections applied to the observations (see Sect. 2).
Initial field results
The BEACO2N field campaign is a long-term, ongoing monitoring effort.
Here we provide a time series of data collected from 16 BEACO2N sites
between January 2013 and April 2016 (Fig. 9) and some initial descriptive
statistics of the drift- and bias-corrected dry air CO2 mole fractions at nine
representative sites in 2013 (Table 2).
Figure 9 demonstrates the volume and diversity of urban CO2
concentrations sampled, exhibiting extreme short-term variability
superimposed on a slower, seasonal fluctuation in the minimum values. For
clarity, the bottom panels depicting month- and week-long samples of the
overall time series show data from six representative sites. Network-wide,
daytime (11:00–18:00 LT) means between 403 and 442 ppm are observed, with
maximum values between 500 and 820 ppm and minima between 384 and 396 ppm.
Standard deviations are seen to range from 9.57 to 22.4 ppm, all of which
are lower than the corresponding nighttime (22:00–04:00 LT) standard
deviations due to the reduced convective mixing in the shallow nocturnal
boundary layer. Similarly, the majority of nighttime means and maxima exceed
the daytime values at the same location, with the exception of four sites: ELC, FTK, LAU, and KAI. The dampened or inverted diurnal trends at these sites
may be due to unique boundary layer dynamics at those locales or unusually
large daytime CO2 sources nearby. Daytime and nighttime minima do not
differ as significantly.
Diurnal variation in drift- and bias-corrected
CO2 dry air mole fractions observed and modeled at
three representative BEACO2N sites during September
2013. Error bars indicate the standard error of the mean (instrument error
is negligible at this timescale); thick shaded curves indicate standard
deviation.
Comparison of diurnal variation in drift- and
bias-corrected CO2 dry air mole fractions observed at
Oakland High School (OHS in Fig. 1) during a rain-related school closure on
11 December 2014 vs. the mean variation observed on other Tuesdays,
Wednesdays, and Thursdays during December 2014 when the school was operating
normally. Mean values from five other BEACO2N sites
operational during these time periods are also shown for reference. Error
bars indicate standard error (instrument error is negligible at this
timescale).
Individual BEACO2N nodes are observed to capture a number of patterns
and cycles typical of ambient CO2 monitoring. Figure 10 shows the
monthly minimum CO2 concentrations at six select sites in 2013, as the
difference from their July value (defined as 0 ppm at each site). A distinct
seasonal cycle is observed, with wintertime minima exceeding summertime
values by 7 to 24 ppm. For reference, the gray curve presents a similar
treatment of the aforementioned Pacific boundary curtain. At many sites, the
BEACO2N minima are seen to exhibit a seasonal variation of a magnitude
roughly in keeping with that observed in the curtain, while other sites
demonstrate a more exaggerated summer–winter contrast, as might be expected
within an urban dome.
Figure 11 shows representative diurnal cycles in the drift- and bias-corrected CO2
dry air mole fractions at three different BEACO2N nodes in September
2013. We observe elevated concentrations at night corresponding to a shallow
nocturnal boundary layer, significant enhancements around the morning rush
hour when emissions are increasing faster than boundary layer height, and
midday minima reflecting mixing into the largest volume of air before the
boundary layer collapses again at sunset. However, within this qualitatively
well understood average behavior remains a degree of intra-network
variability that allows us to discover and probe local-scale phenomena of
unknown origin. At FTK, for example, concentrations are seen to decrease
after an initial rush hour peak (∼ 08:00 LT) but remain
somewhat elevated until sunset, never achieving the much more pronounced
afternoon minimum observed at PAP, 13.5 km away.
Such intra-city heterogeneities are difficult to capture accurately using
atmospheric transport models alone. We simulate hourly CO2
concentrations (y^) at each site in the network using the Stochastic
Time-Inverted Lagrangian Transport model (STILT; Lin et al., 2003) coupled
to the Weather Research and Forecasting model (WRF; Skamarock et al., 2008).
The coupled model is known as “WRF-STILT” (Nehrkorn et al., 2010) and the
setup used here follows that of Turner et al. (2016; see their Sect. S1 for
details of the WRF setup). WRF-STILT advects an ensemble of 500 particles 3
days backwards in time, each with a small random perturbation, from the
spatio-temporal locations of the BEACO2N observations using the
meteorological fields from WRF. The trajectories of these 500 particles are
then used to construct footprints for each observation that represent
the sensitivity of the observation to a perturbation in emissions from a
given location. The footprints can be represented in matrix form
(H) and multiplied by a set of gridded emissions (x,
from the high-resolution bottom-up CO2 inventory in Turner et al. 2016)
to compute the CO2 enhancement at each site due to local emissions.
Δy=Hx
We then add this local enhancement to a background concentration
(yB, from the aforementioned Pacific boundary
curtain) to obtain a model estimate of the BEACO2N observations shown
as black squares in Fig. 11.
y^=Δy+yB=Hx+yB
While the model captures midday conditions at NOC and evening levels at PAP,
the presence of both over- and under-estimations at other times suggests a
need to re-examine the bottom-up emissions inventory as well as the model's
treatment of boundary layer dynamics. BEACO2N provides the ground truth
necessary to identify such deficiencies and potentially improve upon them
via inverse modeling, data assimilation, etc.
Comparison of diurnal cycles during noteworthy local scale emission events
with averages such as those seen in Fig. 11 gives further insight into the
potential application of BEACO2N observations to CO2 source
attribution. Figure 12 offers one such comparison using hourly averages
collected from a BEACO2N node positioned on top of Oakland High School
(OHS in Fig. 1 and Table 1) during a weather-related school closure that
occurred on 11 December 2014. Clear reductions in CO2 concentrations
are observed relative to what is typical at this site (and indeed
network-wide, although to a lesser extent), as is expected in the absence of
emissions related to students' commutes and presence on campus. The sensing
technology implemented in the BEACO2N nodes therefore proves adequate
to resolve not only CO2 patterns typical of an urban environment, but
also short-term deviations during anomalous emission events, positioning
BEACO2N as an essential tool for the characterization of current urban
conditions as well as the verification of subsequent emissions reductions.
Discussion and conclusions
We have described the design, implementation, and initial observations from
a novel high-resolution CO2 surface monitoring network instrument. We
demonstrate that low-cost instrumentation enables an unprecedented level of
spatial density, and describe a calibration protocol with post hoc corrections for systematic uncertainties that permits the network to operate precisely and reliably
enough to characterize variations in ambient concentrations with magnitudes
relevant to metropolitan life.
Our preliminary analysis of the first ∼ 3 years of CO2
observations provides evidence of the expected diurnal and seasonal cycles
as well as an encouraging sensitivity to short-term changes in local
emission events. Furthermore, we show significant qualitative and
quantitative differences among the diurnal cycles at individual nodes on
spatial scales that cannot yet be accurately captured by atmospheric
transport models, confirming the necessity of a high-density approach when
attempting to faithfully represent the variability of a complex urban
environment.
Although BEACO2N demonstrates sensitivity to both highly local
fluctuations as well as slowly varying hemispheric cycles, how best to
bootstrap the network's measurements into the analysis of intermediary
mesoscale phenomena remains to be determined. Future work will focus on
constructing inferred emissions patterns and trends at this scale from the
body of observations. In an initial effort in this regard, Turner et al. (2016) constructed and applied a WRF–STILT inverse model to synthetic
observations with density similar to BEACO2N. For an area source the
size of the Oakland metropolitan area, emissions were estimated to within
18 % accuracy; for a freeway-sized line source to within 36 %; and to
within 60 % for the sum of six industrial point sources – consistently
outperforming a smaller hypothetical network (three sites) with
significantly better precision. Using week-long averages, the
BEACO2N-like network was able to further reduce the uncertainty in the
integrated urban area source to < 2 %, a significant improvement
over the citywide emissions estimates provided by real and proposed
∼ 10 site sensor networks described by Lauvaux et al. (2016)
(25 % uncertainty in five day averages), Kort et al. (2013) (> 10 % uncertainty in monthly averages), and Wu et al. (2016) (11 %
uncertainty in monthly totals). These other studies use more conservative
estimates of the combined instrument, model, and representativeness error
(≥ 3 ppm, as opposed to ±1 ppm). These combined error budgets are
typically dominated by transport (model) error, which potentially explains
why models based on BEACO2N-like networks perform comparably to or
better than those based on sparser networks of higher-quality sensors, for
which instrument error may be reduced but accurately representing transport
between observation sites is of greater importance. Further work is needed
to fully assess the efficacy of inverse methods based on the BEACO2N
approach.
In addition, further characterization of the trace gas and particulate
matter sensors will allow for more specific source attribution via the
exploitation of emissions factors unique to various combustion activities
(e.g., Ban-Weiss et al., 2008; Harley et al., 2015), while providing public-health-relevant air quality information as well. There is also potential for
fine-grained verification of space-based observations or even of personal
sensors when their inherent mobility brings them within the geographical
area well represented by the fixed BEACO2N network.
This work constitutes a promising initial infrastructure upon which further
advances in high-density atmospheric monitoring can be built, capable of
providing research, regulatory, and layperson communities with greenhouse
gas and air toxics information on the scale at which emissions and personal
exposure actually occur. We are currently planning to expand this validated
pilot network into the neighboring locales of San Francisco and Richmond,
California, allowing us to characterize other emissions sources, such as oil
refining facilities. These efforts will be complemented by modeling studies
comparing different sampling resolutions (i.e., 2 km vs. 4 km sensor spacing)
and spatial configurations, yielding general network optimization principles
that will facilitate future implementations of high-resolution CO2
monitoring networks in diverse locations.
Data availability
The BEACO2N data used in this study and all subsequently collected data
are available in near real time on the BEACO2N website:
http://beacon.berkeley.edu/Sites.aspx.
Acknowledgements
This work was funded by the National Science Foundation (1035050;
1038191), the National Aeronautics and Aerospace Administration
(NAS2-03144), and the Bay Area Air Quality Management District (2013.145).
Additional support was provided by a NSF Graduate Research Fellowship to
Alexis A. Shusterman, a Department of Energy Computational Science Graduate Fellowship to
Alexander J. Turner, a Kwanjeong Lee Chonghwan Educational Fellowship to Jinsol Kim, and the UC
Berkeley Miller Institute to Ronald C. Cohen. We acknowledge the use of data sets
maintained by NOAA's Integrated Surface Database, Pacific Marine
Environmental Laboratory, and Global Greenhouse Gas Reference Network. We
thank UC Berkeley Research Computing for access to computation resources,
Sebastien C. Biraud for facilitating inter-comparisons with the Picarro
G3201, as well as David M. Holstius and Holly L. Maness for their generous
contributions to BEACO2N's code base.
Edited by: N. Harris
Reviewed by: two anonymous referees
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