The BErkeley Atmospheric CO2 Observation Network: initial evaluation

Abstract

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.

Atmos. Chem. Phys., 16, 1344913463, 2016 www.atmos-chem-phys.net/16/13449/2016/ doi:10.5194/acp-16-13449-2016 Author(s) 2016. CC Attribution 3.0 License.

Alexis A. Shusterman1, Virginia E. Teige1, Alexander J. Turner2, Catherine Newman1, Jinsol Kim3, and Ronald C. Cohen1,3

1Department of Chemistry, University of California Berkeley, Berkeley, CA 94720, USA

2School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA

3Department of Earth and Planetary Science, University of California Berkeley, Berkeley, CA 94720, USA Correspondence to: Alexis A. Shusterman ([email protected]) and Ronald C. Cohen ([email protected])

Received: 17 June 2016 Published in Atmos. Chem. Phys. Discuss.: 23 June 2016 Revised: 9 September 2016 Accepted: 10 October 2016 Published: 31 October 2016

Abstract. 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 specic 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 Oak-land, California. We characterize the network in terms of four performance parameters cost, reliability, precision, and systematic uncertainty and nd the BEACO2N approach to be sufciently 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.

1 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 to-

The BErkeley Atmospheric CO2 Observation Network: initial evaluation

tal 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 efcacy of these individual rules or verifying compliance through direct observations of changes in CO2 at the scale of cities (Duren and Miller, 2012).

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 signicance 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 NASAs Megacities Carbon Project has established sensor networks in the pilot cities of Los Angeles (Kort et al., 2013) and Paris (Bron et al., 2015). These ground-based monitoring efforts are complemented by space-based observations from SCHIAMACY

Published by Copernicus Publications on behalf of the European Geosciences Union.

13450 A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network

ELC

SEV SET

LBL

MAR

PAP

POR

EBM

ROS

NOC

PDS

CPS

MON PHS

HRS

LAU

CHA

EXE

EXB

KAI OHS

SHS

OIN BEL

STL

BOD

LCC FTK

300 km (a)

5 km

(b)

Figure 1. 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.

600m a.s.l.

400

200

0

Figure 2. 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 FranciscoOakland Bay Bridge. Horizontal placement of nodes has been skewed for visual clarity.

(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 specic emission activities at neighborhood scales. To resolve individual emission sources, much ner spatial resolution is needed. Simple Gaussian dispersion models with total reection 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 specicity, 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 ux 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 BEACO2Ns ability to resolve CO2 signals of signicance to the urban environment.

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A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network 13451

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 day1 for the San Fran

cisco 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 verication of regional inter-annual emissions targets, we would therefore require N instruments of sufcient individual sensitivity and spatial representativeness such that their combined signals allow us to detect annual changes of 65 ppb year1 with condence.

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 sufcient temporal resolution to capture these anomalous, often unexpected, events of short duration on top of slowly varying domain-wide uctuations in the background concentration.Because the BEACO2N instrument is unique in its sensitivity to these highly local processes, we will focus on this latter specication of the instrument precision in the characterization that follows.

1.4 Systematic uncertainty

Systematic uncertainties can be incurred somewhat abruptly during the initial eld 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.

2 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

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1.5

1.0

-2-1

0.5

CO 2flux (tC kmh)

0.0

-0.5

Figure 3. A sample high-resolution bottom-up emissions inventory for the Bay Area adapted from Turner et al. (2016).

1.1 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 12 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 recalibration. Thus, we seek to optimize the trade-off between cost and the other considerations.

1.2 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.

1.3 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 specically, 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

13452 A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network

T 298.15K

1

(1)

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 AugustRocheMagnus approximation of the ClausiusClapeyron relation as indicated below.

PH2O = 6.1094hPa exp

[parenleftbigg]

17.625Tdew

243.04 + Tdew

1

PH2O Ptot

[parenrightBig]

[parenrightbigg]

(2)

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/

Web End =https://www.ncdc.noaa. https://www.ncdc.noaa.gov/isd/

Web End =gov/isd/ ).

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-eld Carbo-Cap 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 inuence 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) (3)

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Figure 4. Current BEACO2N node design.

(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 signicant 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 aficted 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.

Airow through the node is maintained by two 30 mm fans, one positioned in the intake orientation and the other in the outow 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 Carbo-Cap sensor upon receipt of the instrument from the supplier and repeated whenever nodes are retrieved from the eld 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 congured to correct for the effects of oxygen, temperature, pressure, and hu-

midity. 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 Daltons law of partial pressures.

[CO2]dry = [CO2]raw

1013.25hPaPtot

A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network 13453

Table 1. List of site names, abbreviated codes, geo-coordinates, and elevations.

Code Full site name Lat. Long. Elev. Elev. (m a.s.l.) (m a.g.l.)

BEL Burckhalter Elementary School 37.775 122.167 97 8

BOD Bishop ODowd High School 37.753 122.155 82 8

CHA Chabot Space & Science Center 37.819 122.181 476 11

CPS College Preparatory School 37.849 122.241 102 4

EBM W. Oakland EBMUD Monitoring Stn. 37.814 122.282 6 2

ELC El Cerrito High School 37.907 122.294 49 13

EXB Exploratorium (Bay) 37.802 122.397 13 9

EXE Exploratorium (Embarcadero) 37.801 122.399 13 5

FTK Fred T. Korematsu Discovery Acad. 37.737 122.173 16 6

HRS Head Royce School 37.809 122.204 114 5

OIN International Community School 37.779 122.231 19 6

KAI Kaiser Center 37.809 122.264 115 111

LAU Laurel Elementary School 37.792 122.196 74 6

LBL Lawrence Berkeley Natl Lab, Bldg. 70 37.876 122.252 246 11

LCC Lighthouse Community Charter School 37.736 122.196 9 5

MAR Berkeley Marina 37.863 122.314 6 2

MON Montclair Elementary School 37.830 122.211 193 4

NOC N. Oakland Community Charter School 37.833 122.277 24 6

OHS Oakland High School 37.805 122.236 49 7

PAP PLACE at Prescott Elementary 37.809 122.298 12 6

PDS Park Day School 37.832 122.257 39 7

PHS Piedmont Middle & High School 37.824 122.233 86 10

POR Port of Oakland Headquarters 37.796 122.279 35 32

ROS Rosa Parks Elementary School 37.865 122.295 22 10

SET Stone Edge Farms (near turbine) 38.289 122.503 54 2

SEV Stone Edge Farms (in vineyard) 38.291 122.506 61 3

SHS Skyline High School 37.798 122.161 359 3

STL St. Elizabeth High School 37.779 122.222 28 11

The effect of this correction is shown in the histogram of CarboCapPicarro 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 manufacturers specications 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.

Calibrated nodes are installed on trailers and buildings 2111 m above ground level (6476 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

Web End =http://beacon.berkeley.edu ). The 2 km spacing is chosen to ensure an approximately 1 km proximity to any signicant 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 nancial obstacles associated with tall tower sampling mechanisms, although it does present additional challenges for the quantication 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

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13454 A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network

Figure 5. 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 ve day time series, (b) 1 h running mean of the differences over the same ve 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).

instead on establishing BEACO2N as a viable platform for investigating such hypotheses.

3 Node performance

3.1 Cost

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 specications, 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 FabryProt interferometer to switch on (4.26 m) and off (3.9 m) 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.

3.2 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 ve 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 2015April 2016 period all exhibit uptimes > 80 %, with ve collecting data and transmitting them within the next 48 h 100 % of the time via either Ethernet

or cellular data communication.

3.3 Precision

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-eld BEACO2N nodes (EXB and

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A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network 13455

Figure 6. 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).

20

40

T (o C)

1030

P (hPa)

35

1020

30

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25

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Wind Dir. (o )

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Sep 30 Oct 1 Oct 2 Oct 3 Oct 4 Oct 5 Oct 6 Oct 7

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

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Figure 7. Representative week-long time series of observations collected at or near two nearby in-eld 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.

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 2060 ppm, the CarboCaps simultaneously

13456 A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network

Table 2. 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 (11:0018:00 LT) statistics; lower row gives the nighttime (22:0004:00 LT). The ELC node is corrected using weekly minimum LI-COR measurements as the regional background.

Site Uptime Mean SD Max Min Code (%) (ppm) (ppm) (ppm) (ppm)

CPS 94.6 416 21.6 589 385

423 24.3 730 384 ELC 90.1 411 18.5 581 387

415 21.3 567 388 FTK 73.4 415 17.7 609 387

418 26.4 567 383 HRS 69.1 410 14.7 506 384

428 18.4 514 398 LAU 82.6 429 22.4 687 392

421 26.4 603 381 KAI 83.1 442 21.8 820 396

418 24.7 604 382 NOC 87.3 411 18.4 560 387

428 50.5 724 384 PAP 55.5 403 9.57 500 387

411 19.1 548 388 STL 59.1 417 17.5 586 389

421 36.9 616 383

(4)

To derive post hoc corrections for Uatemporal and Utemporal

at a given site, we rst 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. BEACO2Ns unique location near the Pacic coast results in a relatively consistent wind direction from largely unpolluted over-ocean origins, such that the weekly minima can be assumed to reect both the seasonal and synoptic variations in network-wide baseline CO2 concentrations while avoiding the inuence 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 Pacic boundary conditions produced by NOAAs 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 NOAAs Pacic Marine Environmental Laboratory and calibrated against compressed gas (400500 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-CORs weekly minima are found to generally follow variations in the Pacic curtain, with an average residual of 2 ppm.

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(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 nd the 1 min mean dry air mole fractions to exhibit 1 precision between 1.2 and 2.0 ppm, roughly in keeping with the 2 ppm precision ob-

served 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 1 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 Picarros 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 manufacturers 2 s specications and a 1 min averaging time (3 ppm/p30 = 0.55 ppm). Nonetheless, the agreement be

tween the time series of the Picarro and CarboCap measurements demonstrates this noise level to be effectively negligible on the scale of ambient urban CO2 uctuations.

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-specied 6 h short-term stability as well as the1.5 ppm maximum short-term drift observed by Rigby et al. (2008), but in many cases longer averaging times can be used to reduce the inuence of short-term drift to well below 1 ppm. Some modeling studies, for example, utilize time steps of 6 h or more (e.g., Bron et al., 2015; Wu et al., 2016), and average diurnal cycles can often be assessed across several days. Although some applications require ner 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.

3.4 Systematic uncertainty

Given the limited access to validation and calibration infrastructure, a major concern for a long-term eld deployment is systematic uncertainty resulting from a combination of gradual temporal drift (Utemporal, in ppm day1) 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 inuences at said site ([CO2]background

and [CO2]local, respectively), as well as these systematic un

certainties.

[CO2]node = [CO2]background + [CO2]local +Uatemporal + Utemporal days

[parenrightbig]

A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network 13457

430

420

410

400

390

380

370

360

Figure 8. 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.

Table 3. 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.

Site code Utemporal Uatemporal

(ppm day1) (ppm)

BEL 0.03 0.02 3 1

CPS 0.014 0.002 3.1 0.5

FTK 0.02 0.01 6.1 0.8

HRS 0.12 0.01 1.2 0.8

LAU 0.10 0.01 26.9 0.8

KAI 0.04 0.01; 0.08 0.05 23 1; 6 4

NOC 0.11 0.02; 0.030 0.006 22 1; 2.7 0.7

PAP 0.092 0.005 8.7 0.7

STL 0.03 0.01 9 1

Once the [CO2]background term is removed, effectively de

seasonalizing the observations, we re-calculate the weekly minima of this new data record and t the result as a (piecewise, if necessary) linear function of time, the slope of which gives the value of Utemporal. This linear t 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 denition, constant in time, so we once again compute the weekly minima of the new data record and dene 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

Before bias correction After bias correction 1:1

CarboCap weekly minimum CO 2(ppm)

350 385 390 395 400 405 410 415 420LI-COR weekly minimum CO2 (ppm)

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 ts employed when Utemporal exhibits discontinuities over the data record. Because we universally dene Day 1 to be 1 January 2013 and Uatemporal is strongly inuenced by the intercept of the linear t 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 sites 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 gure.

To evaluate the efcacy 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 signicantly 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 refer

ence. 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

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ues 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 signicantly.

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 (dened 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 Pacic 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 summerwinter 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, signicant enhancements around the morning rush hour when emissions are increasing faster than boundary layer height, and midday minima reecting 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 sun

set, never achieving the much more pronounced afternoon minimum observed at PAP, 13.5 km away.

Such intra-city heterogeneities are difcult to capture accurately using atmospheric transport models alone. We simulate hourly CO2 concentrations () at each site in the net

work 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 elds 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

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13458 A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network

nonetheless aficted 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.

3.5 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 repeata

bility 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 pre

cision 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 in

dependent verication of these performance specications 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 CarboCaps readings are consistently slightly elevated, as expected given the heat produced by the instrument itself.

The BMP180 and SHT15 are not intended to reect 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).

4 Initial eld results

The BEACO2N eld 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 uctuation 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 (11:0018:00 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 (22:0004:00 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 val-

A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network 13459

Figure 9. 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.

30

25

20

15

10

5

0

-5 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 10. 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 Pacic Ocean empirical boundary curtain values for 38 N.

local emissions.

[Delta1]y = Hx (5)

We then add this local enhancement to a background concentration (yB, from the aforementioned Pacic boundary curtain) to obtain a model estimate of the BEACO2N observations shown as black squares in Fig. 11.

y = [Delta1]y + yB = Hx + yB (6)

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 models treatment of boundary layer dynamics. BEACO2N provides the ground truth necessary to identify such deciencies 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

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NOAA Curtain

CPS

ELC

FTK

PAP

HRS

NOC

CO 2(ppm)

13460 A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network

FTK

440

440 NOC

440 PAP

435

Model error Modeled CO

Observed CO

435

435

430

430

430

425

425

425

420

420

420

CO 2(ppm)

415

415

415

410

410

410

405

405

405

400

400

400

395

395

395

390 0 6 12 18 24

390 0 6 12 18 24

390 0 6 12 18 24Hour (LT)

Figure 11. 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.

470

460

450

440

CO 2(ppm)

430

420

410

400

390 0 6 12 18 24Hour (LT)

Figure 12. 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 ve 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).

collected from a BEACO2N node positioned on top of Oak-land 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 verication of subsequent emissions reductions.

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A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network 13461

5 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 rst 3 years of CO2 ob

servations 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 signicant qualitative and quantitative differences among the diurnal cycles at individual nodes on spatial scales that cannot yet be accurately captured by atmospheric transport models, conrming 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 uctuations as well as slowly varying hemispheric cycles, how best to bootstrap the networks 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 WRFSTILT 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 signicantly 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 signicant improvement over the citywide emissions estimates provided by real and proposed 10 site sensor networks described

by Lauvaux et al. (2016) (25 % uncertainty in ve 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 efcacy of inverse methods based on the BEACO2N approach.

In addition, further characterization of the trace gas and particulate matter sensors will allow for more specic 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 ne-grained verication of space-based observations or even of personal sensors when their inherent mobility brings them within the geographical area well represented by the xed 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 rening facilities. These efforts will be complemented by modeling studies comparing different sampling resolutions(i.e., 2 km vs. 4 km sensor spacing) and spatial congurations, yielding general network optimization principles that will facilitate future implementations of high-resolution CO2 monitoring networks in diverse locations.

6 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

Web End =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 NOAAs Integrated Surface Database, Pacic 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 BEACO2Ns code base.

Edited by: N. HarrisReviewed by: two anonymous referees

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13462 A. A. Shusterman et al.: The BErkeley Atmospheric CO2 Observation Network

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