We present the organization, structure, instrumentation,
and measurements of the Northeast Corridor greenhouse gas observation
network. This network of tower-based in situ carbon dioxide and methane
observation stations was established in 2015 with the goal of quantifying
emissions of these gases in urban areas in the northeastern United States.
A specific focus of the network is the cities of Baltimore, MD, and
Washington, DC, USA, with a high density of observation stations in these
two urban areas. Additional observation stations are scattered throughout
the northeastern US, established to complement other existing urban and
regional networks and to investigate emissions throughout this complex
region with a high population density and multiple metropolitan areas. Data
described in this paper are archived at the National Institute of Standards
and Technology and can be found at 10.18434/M32126
(Karion et al., 2019).
Introduction
As the population of cities grows globally due to trends toward
urbanization, so does their relative contribution to global anthropogenic
greenhouse gas (GHG) budgets (Edenhofer et al., 2014; O'Neill et al., 2010).
City governments are making commitments to reduce their emissions of GHGs
through various sustainability and efficiency measures and coordination with
organizations like the C40 Climate Leadership Group (http://www.c40.org, last access: 23 March 2020), the
Global Covenant of Mayors for Climate and Energy
(https://www.globalcovenantofmayors.org, last access: 23 March 2020), and others. These organizations require
individual cities to conform to certain standardized mechanisms and
practices for reporting their carbon emissions. City governments rely on
inventories compiled using data on fuel use, energy usage, etc., to estimate
their total emissions and changes over time and to determine the efficacy
of various emissions mitigation policies. Analysis of atmospheric
measurements provides additional useful information to such efforts, by
confirming inventory estimates (Sargent et al., 2018; Lauvaux et al.,
2016), detecting trends (Mitchell et al., 2018), or
estimating emissions that are not well quantified using inventory methods,
such as methane emissions (McKain et al., 2015; Ren et al., 2018; Lamb et
al., 2016; Yadav et al., 2019). Several urban top-down measurement efforts
are underway in various cities that include networks of observations, often
in situ CO2 and CH4 measurements from rooftops or towers
(Verhulst et al., 2017; Xueref-Remy et al., 2018; Bares et al., 2019) or
using other long-path and remote sensing methods (Waxman et al., 2019;
Hedelius et al., 2018; Wong et al., 2016; Pillai et al., 2016).
The National Institute of Standards and Technology (NIST) has partnered with
other federal, private, and academic institutions to establish three urban
test beds in the United States: the Indianapolis Flux Experiment (INFLUX,
http://influx.psu.edu, last access: 23 March 2020), the Los Angeles Megacities Carbon Project
(http://megacities.jpl.nasa.gov, last access: 23 March 2020), and the Northeast Corridor (NEC,
http://www.nist.gov/topics/northeast-corridor-urban-test-bed, last access: 23 March 2020). The goals of the
urban test beds are to develop and refine techniques for estimating
greenhouse gas emissions from cities and to understand the uncertainty of
emissions estimates at various spatial and temporal scales (e.g., whole city
annual emissions vs. 1 km weekly emissions). Recent results from the
longest-running test bed, INFLUX, show that whole city emissions can be
estimated using three different methods to within 7 %
(Turnbull et al., 2019).
The Northeast Corridor (NEC) was established in 2015 as the third NIST urban
test bed. The goals for this project are to demonstrate that top-down
atmospheric emissions estimation methods can be used in a domain that is
complicated by many upwind and nearby emissions sources in the form of
surrounding urban areas. The objective is to isolate the anthropogenic GHG
emissions from urban areas along the US East Coast from many confounding
sources upwind (cities, oil and gas development, coal mines, and power
plants) and from the large biological CO2 signal from the highly
productive forests nearby and within the cities. The presence of highly
vegetated areas such as urban parks, local agriculture, and managed lawns is
expected to dominate the CO2 signal in summertime, as has been found in
Boston, MA (Sargent et al., 2018). The NEC project
has a current focus on the urban areas of Washington, DC, and Baltimore,
MD, USA, with existing plans to expand northward to cover the
entire urbanized corridor of the northeastern US, including the cities of
Philadelphia and New York City, and eventually linking up with existing
measurement stations in Boston, MA (McKain et al., 2015;
Sargent et al., 2018).
The NEC project includes multiple measurement and analysis components. The
backbone of the NEC project is a network of in situ CO2 and CH4
observation stations with continuous high-accuracy measurements of these two
greenhouse gases. In addition, periodic flight campaigns of multiple weeks
each year are conducted by the University of Maryland (FLAGG-MD,
http://www.atmos.umd.edu/~flaggmd, last access: 23 March 2020) and Purdue University
(https://www.science.purdue.edu/shepson/research/ALARGreenhouseGas/, last access: 23 March 2020),
focusing on wintertime observations of CO2, CH4, CO, O3,
SO2, and NO2 from instrumented aircraft (Ren et al., 2018; Salmon
et al., 2018; Lopez-Coto et al., 2020a). The use of low-cost CO2 sensors
is also being investigated in Washington, DC, with work focusing on
calibration and determination of long-term stability of inexpensive
nondispersive infrared (NDIR) sensors with potential for use in CO2
data assimilation techniques (Martin et al., 2017). The NEC project also
includes an extensive modeling component. First, high-resolution
meteorological modeling (using the Weather Research and Forecast (WRF)
model) is being conducted (Lopez-Coto et al., 2020b), with output
coupled to Lagrangian dispersion models such as STILT (Lin et al., 2003;
Nehrkorn et al., 2010) and HYSPLIT (Stein et al.,
2015). These transport and dispersion models are used to interpret
observations from both aircraft and tower stations and in atmospheric
inverse analyses to estimate fluxes of CO2 and CH4 from the cities
of Washington, DC, and Baltimore, MD (Lopez-Coto et al., 2020a;
Huang et al., 2019). A high-resolution fossil fuel CO2 inventory,
Hestia, is also being developed for this project (Gurney et al., 2012, 2019).
Here we focus on the high-accuracy tower observation network and associated
data collection and processing methods. Section 2 describes the tower
network design and characterizes the different site locations; Sect. 3
describes the measurement methods, instrumentation, and calibration; Sect. 4 presents the uncertainty derivation for the measurements; and, finally,
Sect. 5 presents some of the observations from the current record.
Network design and site characterization
The NEC project includes 29 observation stations, all managed and operated
by Earth Networks, Inc.
Certain commercial equipment, instruments,
or materials are identified in this paper in order to specify the
experimental procedure adequately. Such identification is not intended to
imply recommendation or endorsement by the National Institute of Standards
and Technology nor is it intended to imply that the materials or equipment
identified are necessarily the best available for the purpose.
(http://www.earthnetworks.com/why-us/networks/greenhouse-gas, last access: 23 March 2020). A total of 10 stations were
existing Earth Networks (EN) measurement sites in the northeastern US that
became part of the NEC project in 2015. A total of 19 stations were established
(or will be established) specifically for the NEC project, with site
locations identified by NIST. A total of 16 of these station locations were chosen
to be used for emissions estimation in a domain around Baltimore and
Washington, DC (red boundary, Fig. 1), using inverse modeling techniques
(Lopez-Coto et al., 2017; Mueller et al., 2018). Three others are in
Mashpee, MA, Philadelphia, PA, and Waterford Works, NJ. As of publication,
14 of these 19 have been established, with delays occurring due to
difficulty finding suitable tower locations to agree to house the systems.
The hardware and software operating at all the sites is identical with few
exceptions as noted in the text.
Locations of Northeast Corridor (NEC) established tower-based
observations, corresponding to Table 1. The red rectangle indicates the
modeling analysis domain. Light grey shading indicates census-designated
urban areas, yellow lines are interstate highways, and black boundaries are
state lines, with a thinner black line showing the City of Baltimore. Green
triangles indicate regional sites, red triangles indicate urban sites, and
blue triangles are more rural or background sites surrounding the
Washington–Baltimore domain. (a) Regional map. (b) Inset focusing on
Washington, DC, and Baltimore, MD. All map data layers were obtained from
either Natural Earth (http://naturalearthdata.com, last access: 23 March 2020) or U.S. Government sources
(http://www.census.gov, last access: 23 March 2020) and are in the public domain.
The initial design of the core urban Baltimore–Washington network was
focused on optimizing tower site locations with the goal of reducing
uncertainty in estimating anthropogenic CO2 emissions from Washington,
DC, and Baltimore using an atmospheric inversion model (Lopez-Coto
et al., 2017). A total of 12 communications towers were identified as part of that
study as ideal locations for measurements. Actual measurement sites were
sometimes established at locations near the ideal study location, usually due
to logistical difficulties obtaining leases at the ideal tower sites. A
second design study determined ideal locations for background stations, i.e.,
observation station locations that would aid in the determination of
background CO2 entering the analysis domain (Mueller et
al., 2018). Four stations were identified as part of that study; an existing
EN site in Bucktown, MD, serves as a fifth background station southeast of
the analysis domain (Fig. 1). Although inlet heights were desired to be 100 m above ground level (a.g.l.), often shorter towers were used due to the lack
of availability of tall towers in ideal locations; the shortest tower in
this network has the uppermost inlet at 38 m a.g.l. (HRD). Table 1 indicates
details and locations of each site.
Northeast Corridor site list. Sites lacking location information
are still in the planning phase, with no assigned site code or exact
location.
* If a station was established prior to the beginning of the
project, its establishment date is listed as 01/2015, the start date of the
project. Data prior to this date are not part of the NIST data release.
The stations in Table 1 are all situated in areas with different land use.
Even among the Washington, DC, and Baltimore area urban stations, the degree
of urban intensity varies, from densely urbanized areas (such as northeastern
Baltimore, NEB) to dense and moderately developed suburbs (such as Capitol
Heights, CPH, and Derwood, DER, both suburbs of Washington, DC, located
in Maryland). Figure 2 indicates the intensity of development from the US
Geological Survey (USGS) 2016 National Land Cover Database
(Yang et al., 2018) surrounding each urban station in the
Washington, DC, and Baltimore network.
Fraction of developed land cover (from the 2016 National Land
Cover Database (NLCD); Yang et al., 2018) within 5 km of each
observation station in the urban regions of Washington, DC, and Baltimore,
MD.
Average fraction of land cover type within 5 km of regional tower
sites in the Northeast Corridor network, in order of decreasingly developed
land. Several NLCD classifications have been grouped for clarity (e.g.,
“developed” includes open spaces and low-, medium-, and high-intensity developed
land). SFD, TMD, and BUC are sites established to help characterize
background conditions for the Washington, DC, and Baltimore urban network.
Similar variability in land cover for the regional stations exists, as
indicated in Fig. 3. The sites established to characterize background
conditions for the urban network in Washington, DC, and Baltimore (SFD,
TMD, BUC) are in areas with little development: SFD and TMD are both in
forested regions, while BUC is near the Chesapeake Bay and large wetland
areas. The other regional sites span a range of land cover types from urban
(MNY in New York City and RIC in Richmond, VA), to mostly rural and forested
(DNH in Durham, NH).
Carbon dioxide, methane, and carbon monoxide measurements,
instrumentation, and calibrationInstrumentation
The instrumentation contained in the Earth Networks (EN) system module has
been described elsewhere (Welp et al., 2013; Verhulst et al., 2017); we
will summarize the system here but refer the reader to those publications
for further details, including additional equipment and part numbers. Figure 4 indicates the plumbing diagram of the typical tower setup. Three inlet
lines reach from the sampling location on the tower into the equipment
housed in a full-size rack inside a shed at the base of the tower.
Typically, two inlet lines sample from the topmost level and one line
samples from a lower level on the tower. Stafford, VA (SFD), is one
exception with inlets at three different levels (50, 100, and 152 m),
and a planned tower in New Jersey (Waterford Works) will have five inlet height
levels, as indicated in Table 1. At some sites there was no space to house
the equipment in existing structures, thus small single- or double-rack sized
enclosures were purchased and installed. Air is pulled through a filter into
the inlet lines (0.953 cm, i.e., 0.375 in., OD Synflex 1300) that are continuously
flushed at ∼10 L min-1 by aquarium pumps (Alita AL-6SA).
The three air lines are connected to a rotary multi-port valve (MPV; eight-port,
VICI, Valco Instruments Co. Inc.) housed within a sample control box
(calibration box). Two or three calibration standards are also connected to
the MPV with 0.156 cm (0.0625 in.) OD stainless steel tubing. The control system
for the MPV directs the air stream to the analyzer cycling every 20 min
through each of the three inlet lines so that each inlet is sampled at
least once an hour and every 22 h through each standard (Sect. 3.2). The common port of the MPV is connected to a pressure controller that
reduces the pressure to 80 kPa (800 mb), after which the sample (either
ambient air or air from a standard gas cylinder) enters a 183 cm long Nafion
dryer (Permapure, Inc., model MD-050-72S-1), where it is dried to a water
vapor mole fraction of ∼0.1 % prior to flowing through the
cavity ring-down spectroscopic (CRDS) analyzer (Picarro, Inc., Model 2301).
The lower-than-ambient inlet pressure of 80 kPa is prescribed in order to
lower the flow rate of the analyzer to ∼70 standard cm3 min-1. At Mashpee, MA (MSH), a CRDS Picarro Model 2401
analyzer is operational, and this is the only site currently also measuring
carbon monoxide (CO) in addition to CO2 and CH4. The CRDS
analyzers report measurements of dry air mole fraction of each gas in air,
also known as the mole fraction, i.e., moles of the trace gas per mole of
dry air. Throughout, we refer to these measurements in units of µmolmol-1 for CO2 and nmol mol-1 for CH4 and CO, following
the SI recommendations (Bureau International des Poids et Mesures,
2019). Software (GCWerks, Inc.) installed on a separate mini-PC at
each site controls the run cycle and the MPV selection valve. The data are
collected on this computer and sent to the central EN data server, also
running GCWerks. All data are processed on the central EN data server but
additional post-processing and uncertainty assignment to hourly observations
is performed at NIST. As recommended by the World Meteorological
Organization (WMO), the software has the capability of reprocessing all the
data from the original raw files and thus can accommodate any changes to the
assigned values of the standards (due to a reference-scale update, for
example) at any time (WMO, 2018).
Plumbing diagram for the Earth Networks sampling system
implemented at the NEC tower stations. Figure replicated from Fig. S1 in
Verhulst et al. (2017), adapted from Welp et al. (2013).
Calibration cylinders
When the Earth Networks GHG monitoring system was established in 2011, each
site hosted two calibration cylinders (standards) with ambient level dry air
mole fractions as part of the original system design. This continues to be
the case at most NEC sites. At the NEC sites, these standards have values
close to 400 µmolmol-1 dry air CO2, 1890 nmol mol-1 dry
air CH4, and 115 nmol mol-1 dry air CO (at MSH only) and are
sampled by the analyzer periodically, in a sequence identical to that
described for the Los Angeles Megacity network by Verhulst et al. (2017).
The standards are purchased from the WMO Central Calibration Laboratory
(CCL), the National Oceanographic and Atmospheric Administration's Earth
System Research Laboratory (NOAA/ESRL) Global Monitoring Division in
Boulder, CO, USA, where they have been calibrated on the WMO scales
(X2007 for CO2, X2004A for CH4, and X2014A for CO, Zhao et al.,
1997; Dlugokencky et al., 2005; Novelli et al., 2003). One of these two
cylinders serves as a standard for calibration and drift-correction, while
the second serves as a target tank or check standard. The target tank is
used for data quality checks and uncertainty calculations (Sect. 4). The
residual of the target tank (the rms difference between its value assignment
when treated as an unknown and its reference value from NOAA) is a critical
indicator of data quality and is monitored in order to alert the operators
of any general problems in the system such as leaks, mistakes in the
assignment of MPV ports, or drift in calibration tank value. In the field,
all gas standards are sampled for 20 min every 22 h. In data
processing, the first 10 min of any tank run are filtered out to allow
for the system equilibration, including flushing of the regulator and
tubing. In some cases, when the standard runs were found not to equilibrate
as quickly as desired, 15 min of data were filtered until the problem
could be fixed (typically either contamination or inadequate regulator
flushing). The first 10 min of the ambient air sample following a
standard run are also filtered for equilibration, and the first 1 min
of each 20 min ambient air run is filtered if it follows another ambient
air run (i.e., an inlet switch). The longer flush time is desired for the
standard runs because of the need to flush stagnant air remaining in the
regulators and tubing when sampling from the cylinder, while the ambient air
lines are continuously flushed.
At a few NEC sites (currently BWD and MSH, with more planned), a third gas
cylinder is installed at the site to serve as a permanent high-concentration
standard (referred to as the high standard), to improve calibration and
reduce uncertainties. This standard typically contains air with a mole
fraction of CO2 close to 500 µmolmol-1, CH4 at
approximately 2300 to 2500 nmol mol-1 and at MSH, CO,
near 320 nmol mol-1. At MSH, this cylinder has been provided directly
by NOAA/ESRL, while at BWD this cylinder was purchased as natural whole air
from Scott-Marrin, Inc. (now Praxair). The Scott-Marrin air is stripped of
its original trace gases (CO2, CH4, CO, hydrocarbons, etc.) with
CO2, CH4, and CO added back in to prescribed values. Several such
standards have been purchased with the intent of placing them at urban
stations to serve as high standards after calibrating them onto the WMO
scales. We note that because they are being used together with NOAA/ESRL
standards in the field, it is essential that these standards also be
assigned values on the same scales. This calibration is transferred in the
NIST laboratory using five standards calibrated and purchased from
NOAA/ESRL. The CO2 in the Scott-Marrin cylinders is isotopically
different (in terms of the 12C/13C ratio in CO2) from the ambient air
tanks that are filled by NOAA/ESRL at Niwot Ridge, CO. However, the
calibration is transferred from the NOAA standards to the Scott-Marrin gases
using the same model (Picarro 2301) analyzer used in the field (i.e.,
measuring only 12CO2) in the NIST laboratory, effectively
canceling out the error that would be caused by this isotopic mismatch
(Chen et al., 2010; Santoni et al., 2014). Thus, the CO2 values
assigned by NIST to these standards are effectively the total dry air mole
fraction of CO2 the cylinders would contain if they were isotopically
similar to the NOAA cylinders.
Additional sites in the network also benefit from the improved two-point
calibration method in cases where measurements of a high standard were
performed prior to analyzer deployment (NWB, NEB, JES, TMD, CPH, and HRD).
Prior to system installation at these sites, tests were conducted at the EN
laboratory in which the designated analyzer was set up measuring the
calibration standard, target standard, and a high-value standard at
∼490µmolmol-1CO2 and ∼2560 nmol mol-1CH4 daily for several days (enough for 3–5 measurements of
20 min each). This single high-standard cylinder was also calibrated by
and purchased from NOAA/ESRL, with assigned values on the WMO scales. These
laboratory tests allow the determination of the secondary correction to the
instrument response or sensitivity, as described in Sect. 3.4.
The high-standard gas measurements are used to perform a secondary
correction (referred to as a two-point calibration) (Sect. 3.4) to the
original one-point calibration described by Verhulst et al. (2017) and in
Sect. 3.3., reducing the uncertainty of the measurements. We note that
while, in principle, a secondary correction is desirable, and the uncertainty
is indeed reduced by its implementation (see Sect. 4.2), it remains quite
small relative to the signals of interest in an urban network. Deployment of
high standards at all sites has not yet occurred due to both costs and
logistical and operational constraints; for example, at many sites the space
available for the equipment is limited and prohibits the installation of a
permanent third tank. Thus, we plan to implement a round-robin procedure
circulating additional standards at various values through the network to
evaluate the calibrations and implement the secondary correction throughout
the network. Although the current state of having two different calibration
methods coexisting in the network is not ideal, we aim to implement the
secondary correction throughout the network as soon as possible.
Drift correction and single-point calibration
Here we describe the calibration and drift correction applied to all the
mole fraction data. This single-point calibration uses only a single
reference value, that of the calibration standard, to correct the raw mole
fractions for each gas. The equations are identical (with a few nomenclature
differences) to those found in Verhulst et al. (2017). In the following
analysis, X′ denotes a raw dry mole fraction measurement (i.e., a
reported value from the CRDS analyzer after internal water vapor
correction), while X denotes a mole fraction after some correction has been
applied (drift and/or calibration, as described in the equations below). A
subscript cal indicates the main calibration standard (usually a single ambient
level standard tank calibrated by NOAA/ESRL), subscript std indicates any other
standard tank, tgt indicates a standard tank that is being used as a target,
and the subscript air indicates the sample measurement. Note that within the
GCWerks software, the meanings of the abbreviations cal and std are reversed from
what is defined here; we choose to use the nomenclature by Verhulst et al. (2017) here for consistency with the literature. We note that we have changed
some nomenclature slightly from Verhulst et al. (2017) for additional
clarity and conciseness. We refer to the drift-corrected mole fraction as
XDC, which is noted as Xcorr by Verhulst et al. (2017); we refer to
the mole fraction after a secondary correction is applied as XSC. We
also refer to the assigned mole fraction of a standard by the calibration
laboratory as C rather than Xassign. We define the sensitivity S to be the
response of the analyzer or the ratio of the measured to the true value. In
the case of the calibration tank, this is the ratio of the raw measured
value, Xcal′, to the assigned value of the standard by the
calibration laboratory on the WMO scale for the given species, Ccal:
S=Xcal′Ccal.
When only a single calibration standard is present (which is the case at
most sites in the NEC network), this sensitivity is assumed to be constant across mole fractions but varying in time. The sensitivity for the calibration
tank is thus interpolated in time and applied as a correction for the dry
air mole fractions of CO2 and CH4 reported by the CRDS analyzer
(Xair′):
XDC,air=Xair′S,
where XDC,air is the drift-corrected air data. An alternative
drift-correction is to use an additive offset, which is also interpolated in
time, rather than a sensitivity for drift correction:
XDC,air=Xair′+Ccal-Xcal′.
Measurements from MSH that include a high-value cylinder suggest that the
single-tank drift correction performs (very slightly) better using the ratio
correction (Eq. 2) than the difference method (Eq. 3) for CO2 and
CH4, while the opposite is true for CO (Fig. 5), thus the difference
method is used only for CO in our network.
The calibration standard mole fractions are interpolated in time between
subsequent runs in order to apply the above corrections to the air data,
thus removing drift in the instrument's response. This drift-corrected
fraction is reported in the hourly data files for sites and time periods
where no range of concentrations is available in the standard tanks.
Time series of standard tank run residuals (i.e., XDC-C) for
CO2(a, b), CH4(c, d), and CO (e, f). XDC is calculated using a
single calibration tank (not shown) and the ratio method (Eq. 2) on the left (a, c, e) and the difference method (Eq. 3) on the right (b, d, f). Assigned
tank values are shown in the legend; one tank was not calibrated for CO so
only the residuals of the high-concentration tank at 315 nmol mol-1 are
shown. The residual magnitude is smaller for CO2 and CH4 using the
ratio method, but the standard deviations (variability) are similar using
both methods. For CO, both the magnitude of the residual and the
standard deviation are smaller using the difference equation; the ratio
equation does not properly account for the drift in the analyzer at the
start of the time series (May–June). Data shown are from MSH; a measurement
gap exists in July.
Multiple-point calibration
At some sites and for some time periods, a higher-mole-fraction standard is
available, and a second-order correction can be made to the instrument
sensitivity, accounting for the sensitivity being a function of mole
fraction. Usually in the field, this correction employs only one additional
standard, the higher-mole-fraction standard so that it is a two-point
calibration; here we describe the general procedure for applying a
correction using multiple standards at a range of concentrations. This is
applied as a second-order correction to the drift-corrected air data. In
general, if a range of standard concentrations is available, the correction
in GCWerks is applied as described below. First, a drift-corrected
sensitivity (SDC) is calculated for each standard when it is measured,
which is the ratio of the drift-corrected mole fraction of that standard
(XDC,std, based on Eq. 2 for CO2 and CH4 or Eq. 3 for CO)
to its assigned value:
SDC,std=XDC,stdCstd.
For the calibration standard, this value is necessarily equal to 1, but
measurements of standard tanks with different assigned values indicate that
the instrument sensitivity is dependent on the composition of the sample gas
(in this case, the mole fraction of the standard tank). In laboratory
calibrations, we find that the drift-corrected sensitivity defined in Eq. (4) is a linear function of the mole fraction ratio to the calibration gas
(X′/Xcal′), thus we use a linear fit to the range of
standards to determine the slope m and intercept b:
SDC=mX′Xcal′+b.
In this fit, we force m+b=1 by fitting a slope m and then setting
b=1-m in order to maintain the proper relationship for the calibration tank
itself, when SDC,cal=1. Applying this fit to the air data, the final
air mole fraction XSC,air is determined from
XSC,air=XDC,airSDC.
In the NEC tower network, there are no sites with multiple standard tanks at
various concentrations. At several sites, there are measurements of a single
high-concentration standard (hstd) in addition to the calibration and target
standards. The high-standard measurements are either performed in the
laboratory before the instrument is deployed to the field, or in the field
if the third standard is permanently installed (Sect. 3.2). The above
secondary correction is applied using only two tanks to perform the fit and
obtain the drift-corrected sensitivity. In this special case, the fit has
zero degrees of freedom with no residuals. The correction parameters (slope
and intercept) are determined based on measurements over time or single
measurements in the laboratory prior to a specific analyzer deployment. The
correction is applied to the data from the site for a time period that is
specified, i.e., it is not automatically applied based on daily measurements
of the high standard. It is determined by the science team and applied for
the time period that is appropriate. This is necessary to avoid applying the
wrong correction if an analyzer is replaced or if there are changes made to
the analyzer that might affect its calibration response. At eight sites
where a high standard has been measured at any point (MSH, BWD, NWB, NEB,
JES, TMD, CPH, and HRD), slopes and intercepts have been determined and the
correction has been applied to the data. At stations with no high-standard
measurements, we rely on the single-tank drift-correction described in
Sect. 3.3.
Example of a laboratory calibration of a CRDS analyzer with five
standards of different assigned CO2 mole fractions. (a) Secondary
correction of drift-corrected sensitivity using either two (red) or all five
(blue) standards. Green line at 1 indicates the assumed sensitivity when
only a single standard is used. (b) Residual of each type of fit; error bars
represent 1σ reproducibility stated by NOAA/ESRL. The simple
single-tank drift correction results in the green circles as residuals;
these residuals were used in the Verhulst et al. (2017) analysis to estimate
the extrapolation uncertainty of the single-point correction. Red x symbols
are the residuals of a fit to two standards, and blue asterisks are the
residuals of the fit to all five standards.
Laboratory tests with multiple standards with the same model instrument used
in the network (Picarro 2301) were performed to assess the relative
improvement of a fit to two standards over a fit to a single standard.
Figure 6a illustrates the fit of the drift-corrected sensitivity
(SDC) to two standards (red line) vs. all five standards (blue line) for
CO2, along with corresponding residuals in Fig. 6b. As was shown by
Verhulst et al. (2017) for multiple analyzers, the fit to a single standard
has a linearly varying residual that is typically 0.1 to 0.2 at 100 µmolmol-1 above the calibration standard value
(green circles, Fig. 6b). The average slope of the one-point residual from
multiple tests is used by Verhulst et al. (2017) to estimate the uncertainty
of the single-point calibrations (called the extrapolation uncertainty,
Uextrap), described in Sect. 4.1. Performing the additional correction
using a high standard shows improvement in the residuals of the fit (Fig. 6b), while using all five standards only improves the residuals
incrementally. The two-point correction (red) in this figure was applied
using the 406 µmolmol-1 tank as the calibration and the 496 µmol mol-1 tank as the high standard; thus, the measurement at
∼711µmolmol-1 is an extrapolation of the
two-point fit. The residuals at values between the calibration and high
standard are very small, equal to or below the uncertainty (reproducibility)
of the scale reported by NOAA; this was confirmed for other analyzers and
other species.
The improvement in calibration from the secondary correction is quite small
compared to the signals and gradients of interest in our network. For
example, when considering the enhancement between the rural site TMD and a
polluted urban site, HRD, the calibration method makes a median difference
of 0.4 % for CO2 and 0.3 % for CH4 (over all hours over 1
calendar year). We intend to implement this calibration throughout the
network through deployment of additional standards and periodic traveling
calibrations when permanent installation is not practical for logistical
reasons.
Data quality and processing
Automated data filtering is performed within the GCWerks software with
parameters set identically to those extensively described by Verhulst et al. (2017) for the Los Angeles Megacities network. For example, individual
measurements that are outside limits for cavity temperature, cavity
pressure, and during transitions between sample streams are filtered. The
data are automatically downloaded from each site's Linux PC to the central EN
Linux server, where they are processed automatically every hour. We note that
all mole fraction assignments can be recalculated by the GCWerks software
from the archived raw files if required due to a change in filtering or
flagging, or in assignment of a standard tank, for example, in the case of a
scale change by the CCL. The data files exported from GCWerks contain
1, 5, and 20 min averaged air data, as well as separate
files with 1, 5, and 20 min averages of all standard runs. Individual
or groups of 1 min data points are flagged manually by EN or NIST
researchers in the GCWerks if there is cause (e.g., a site visit that
disrupted the sample stream or a leak in the line). Some additional
quality checking is performed at this stage, specifically checking for
systematic differences between measurements from two different inlets at the
same height and checking for inconsistencies in the difference between
measurements at different heights. For example, if the lower inlet is
systematically reading lower CO2 than the upper inlet, especially at
night, it would indicate that the inlet lines may be switched (mislabeled)
or there is a leak occurring. These indications would be then verified by a
field technician, and the data are either reprocessed or flagged accordingly.
Filtered and flagged points are excluded from the subsequent averaging
exported by GCWerks. The 1 min air data files and 20 min standard
data files are post-processed at NIST to calculate hourly averages from each
air inlet level and to assign uncertainties to each hourly average (Sect. 4). Data from the two top-level inlets, when they are at the same height, are
combined for inclusion into the hourly average. Thus, because of the
20 min cycling through the three inlets (Sect. 3.1), hourly averages at
the upper inlet include approximately 40 min of measurements, and for
the lower inlet only 20 min (fewer if a calibration occurs). Publicly
released hourly data from this second-level processing are contained in
separate files for each species and each level for each site. The files
contain the hourly average mole fraction (i.e., mole fraction) along with its
uncertainty, standard deviation, and number of 1 min air measurements
included in that particular hourly average. These last two quantities are
provided so users can determine the standard error of the hourly means in
terms of the observed atmospheric variability within the hour. Observations
at higher frequency and standard tank data are available by request.
Comparison with measurements of NOAA whole air samples
Ongoing whole air sampling in flasks at several of the NEC sites by NOAA
Earth System Research Laboratory's Global Monitoring Division (NOAA/GMD)
provides a check on the quality of the in situ measurements. The flasks are
analyzed for CO2, CH4, and CO, among a suite of additional trace
gases and isotopes that are not discussed here. The flask-sampling
equipment draws air from one of the inlet lines at the top of the tower that
is also shared by the continuous in situ measurement equipment (as indicated
by the flask port in Fig. 4). The flask measurements are otherwise
independent from the continuous in situ measurements. Flask samples at LEW
and MSH are collected over a period of 10–30 s (Sweeney et al.,
2015; Andrews et al., 2014), while flask samples integrated over 1 h
are collected at TMD, NEB, NWB, and BWD (Turnbull
et al., 2012) specifically as part of the Northeast Corridor project. All
flask samples are taken in midafternoon local time (usually 19:00 UTC).
Comparisons at all the sites with available data indicate good agreement
with little or no bias in the mean over the time period of the comparison,
with the exception of CO at MSH, which shows a consistent bias with a median
of 8 nmol mol-1, which is larger than the 1σ uncertainty assigned
to either measurement (described in Sect. 4) and the standard deviation of
the offsets themselves (Table 2). Target tank residuals for CO in this
period range from 1 to 7 nmol mol-1, depending on the cylinders
installed, indicating that at least some of this difference is caused by the
calibration standard assigned value (possibly due to cylinder drift in time
between the NOAA calibration and deployment to the site). Similar
differences between NOAA flasks and in situ CO measurements were reported in
Indianapolis (Richardson et al., 2017). This result requires further
investigation, by sending the cylinders for recalibration and/or deploying
different standards to the station. A significant bias in the CH4
offset at NWB is also apparent, at a mean of -5.5 nmol mol-1 but a
median of -1.7 nmol mol-1, the result of a single outlier at -30 nmol mol-1 but with only 17 samples compared. BWD did not have any samples
at the time of writing, thus we compare only LEW, MSH, TMD, NEB, and NWB.
Table 2 also reports the mean uncertainty, intended as a metric for
comparison of the standard deviation of the offsets. For each flask sample,
this uncertainty is the quadrature sum of the continuous data uncertainty
(described in Sect. 4) at that hour, the standard deviation of the
1 min averages in the continuous data during that hour, and the
uncertainty expected in the flask measurement, estimated here as 0.04 µmolmol-1 for CO2, 1.12 nmol mol-1 for CH4, and 0.59 nmol mol-1 for CO. The values for the flask uncertainty are from Table 1 in Sweeney et al. (2015), which reports the average offset between
measurements of surface network and 12-pack flasks (such as those used for
the NEC) filled with identical air after a short-term storage test. For
CO2, flask offsets can be larger than indicated by those dry-air
laboratory tests (Sweeney et al., 2015; Andrews et al., 2014; Karion et
al., 2013), but we use 0.04 µmolmol-1 regardless because the
average uncertainty in Table 2 is dominated by the atmospheric variability
term and increasing the CO2 uncertainty in the flasks to 0.1 µmolmol-1 (for example) does not change the values significantly.
Standard deviations of the offsets (Table 2) show that there is quite a bit
of scatter in the results, especially at the more urban sites that exhibit a
lot of variability in the continuous data. For comparison, Turnbull et al. (2015) report agreement for CO2 between the same flask system and
continuous in situ measurements in Indianapolis as 0.04 µmolmol-1
(mean) with a standard deviation of 0.38 µmolmol-1, somewhat
smaller than what was observed at our sites. The standard deviation of offsets is
usually lower than the average uncertainty, however, with the exception of
CO2 at MSH and LEW, the two sites for which the flask samples are not
integrated over an hour. It is likely that the large variability seen over
an hour is the reason for the large scatter in the offsets. Because the
continuous in situ measurements do not cover the entire hour of sampling (at
the top level, the hourly average is typically the mean of only 40 min),
the variability may not be captured in the mean uncertainty reported here
and has a larger impact on the comparison than it would if the continuous
hourly average was based on the full hour of observations. For example, a
large plume or spike in concentration during a given hour might occur while
the continuous system is sampling from the lower inlet and thus would not
be included in the hourly average from the continuous system, while it would
be included in the full 1 h flask sample.
Offsets (in situ – flask) between continuous in situ and NOAA/GMD
flask measurements. CO2 offsets are reported in µmolmol-1,
CH4 and CO are reported in nmol mol-1. Continuous in situ CO is only measured
at MSH. The average uncertainty column for each gas indicates the 1σ
uncertainty (summed in quadrature over flask uncertainty, in situ
uncertainty, and atmospheric variability over the hour) averaged over the
flask samples. See text for discussion.
SiteNumber ofCO2CO2CO2 standardCO2CH4CH4CH4 standardCH4CO meanCO medianCO standardCO meanflask samplesmean offsetmedian offsetdeviation of offsetsmean unc.mean offsetmedian offsetof offsetsmean unc.offsetoffsetdeviation of offsetsunc.MSH1630.02-0.020.650.43-0.2-0.22.32.1-9.0-8.25.96.0LEW3150.01-0.070.880.681.10.48.47.8––––TMD800.170.150.510.690.00.55.55.2––––NEB32-0.090.080.751.02-0.50.66.813.4––––NWB17-0.0140.020.730.99-5.5-1.79.88.3––––Uncertainty
The data set includes an uncertainty estimate on each hourly average data
point, consistent with recommendations from the WMO (WMO, 2018).
This uncertainty is our estimate of the uncertainty of the measurement
itself and does not include atmospheric variability or assess the
representativeness of the measurement of a true hourly mean.
Uncertainty of hourly mole fraction data
Verhulst et al. (2017) outlined a method for calculating an uncertainty in
mole fraction measurements when using the single-tank calibration correction
(drift correction). Here we present a brief overview but refer the reader to
that paper for further details. All uncertainties are standard
uncertainties, i.e., 1σ or k=1. In the analysis below, we assume
independent uncorrelated error components, given no evidence to the contrary
and no physical reason to believe that they should be correlated; therefore,
we sum the various components of the uncertainty in quadrature.
The uncertainty in the final mole fractions (Uair) is expressed as the
quadrature sum of several uncertainty components:
Uair2=Uextrap2+UH2O2+UM2,
where UH2O is the uncertainty due to the water vapor correction,
UM is a measurement uncertainty, and Uextrap is the uncertainty of
the calibration fit when assigning values relative to a single standard tank
(more detail on this can be found later in this section and in the following
section). UM encompasses errors due to drifting standard tank
measurements (Ub), short-term precision (Up), and error in the
calibration standard's mole fraction assignment by the calibration
laboratory (Uscale):
UM2=Up2+Ub2+Uscale2.
Here we note that Up for CO2 and CH4 is assigned as described
by Verhulst et al. (2017), as the standard deviation of the individual
measurements during each 1 min average during a calibration, but for CO
it is assigned as the standard error (standard deviation divided by the
square root of the number of samples in the mean), based on Allan variance
tests (not shown) indicating that the precision of the CO measurement
increases with the number of points used in the average. If no calibrations
have been performed over an entire calendar year, Up is set to the
10th percentile of the standard deviation of air measurements and
Ub is set to a default value of 0.1 µmolmol-1, 0.5 nmol mol-1, and 4 nmol mol-1 for CO2, CH4, and CO,
respectively. This default value for Ub is based on an upper limit of
values that are observed in the network; typically, Ub is much smaller
than these values (Verhulst et al.,
2017). In the current data set, this has only occurred once: there were no
calibrations run at MNC over the entire 2015 calendar year, but we have no
knowledge of abnormal operations or changes during this period, with
analyzer sensitivity being similar before and after this period.
Because these uncertainty components are also tested through the use of a
target tank, or check standard, the uncertainty UM is assigned as the
root mean square of the target tank errors when those exceed the sum of the
uncertainties above.
UM=UTGT=∑XDC,TGT-CTGT2N
This residual is calculated by GCWerks, and the root-mean-square residual is
interpolated in time as a moving 10 d average. If a target tank has not
been run through the system for 10 d or longer, UTGT is set to a
default value that is currently set to 0.2 µmolmol-1, 1 nmol mol-1, and 6 nmol mol-1 for CO2, CH4, and CO,
respectively, based on typical maximum values for this uncertainty
calculated from many sites over several years. The target tank in the field
generally has a concentration value very similar to the calibration tank, thus
this residual is a good estimate of the uncertainty caused by the precision,
baseline changes, and tank value assignment. However, it is not a good
indicator of uncertainty at mole fractions different from that of the
calibration tank. Therefore, we assign an added uncertainty component,
Uextrap, indicating the uncertainty that increases as the measurement
value moves farther from the value of the calibration tank in the case of a
single calibration standard. This was found to be a linear relationship for
a series of similar model analyzers that were tested in a laboratory, and
the uncertainty was described as follows:
Uextrap=εXDC,air-Ccal.
See Verhulst et al. (2017) for details on determining the unitless slope of
the uncertainty, epsilon (ε), which is currently assigned as
0.0025, 0.0031, and 0.0164 for CO2, CH4, and CO, respectively, for
all data that are only drift corrected (i.e., not using a high standard).
Uncertainty for observations with additional standards available
When a high-standard tank is available and the secondary correction
described in Sect. 3.4 is applied, the uncertainty analysis remains
similar, but the uncertainty Uextrap from Eqs. (7) and (10) is
replaced by an uncertainty in the two-point fit, Ufit. To estimate this
uncertainty for CO2 and CH4, we use the reported uncertainty of
the assigned value of the high-standard and calibration-standard tanks,
Uscale, (typically 0.03 µmolmol-1CO2 and 0.5 nmol mol-1CH4 at 1σ) along with an estimate of the precision of
the analyzer, Up, to estimate an uncertainty in the drift-corrected
sensitivity of the high standard, USDC,hstd, using standard propagation
of errors (black error bar, Fig. 7a). We note that in the case where the
value assigned to the high standard is through a propagation of the WMO
scale at NIST, the assigned value has additional uncertainty; i.e.,
Uscale includes both the uncertainty that NOAA assigned to the cylinders
used for the assignment and the uncertainty from the laboratory fit at NIST.
This second uncertainty is equal to the standard deviation of the residuals
of the fit and is added in quadrature to the NOAA uncertainty.
We note that the analysis described below assumes uncorrelated independent
errors. We express the slope of drift-corrected sensitivity (m) and the
overall drift-corrected sensitivity (SDC) as functions only of the
drift-corrected sensitivity of the high standard, SDC,hstd:
11m=SDC,hstd-1Xhstd′/Xcal′-112SDC=mX′X′cal-1+1.
This second equation uses b=1-m. Here we do not include uncertainty in the
x coordinate, i.e., X′/Xcal′. Uncertainty in the slope is as follows:
Um=USDC,hstdX′hstd/X′cal-1.
We propagate the uncertainty in the drift-corrected sensitivity of the high
standard, USDC,hstd, to the overall drift-corrected sensitivity of all
the air values using Eq. (14) and then to the two-point corrected air data
by propagating through to obtain Eq. (15).
14USDC=UmX′/X′cal-1=USDC,hstdXhstd′/X′cal-1X′/X′cal-115UXSC,air=Ufit=USDCSDCXSC,air
To evaluate the use of standard propagation of errors, we also use a
bootstrap to estimate the uncertainty using the laboratory calibration shown
in Fig. 6 by randomly selecting two tanks of the five tanks from the test to
calculate 1000 versions of the correction (blue shading shows the standard
deviation of the result, Fig. 7). For this test, the calculated 1σ
uncertainty (red shading) was similar to the 1σ bootstrap uncertainty
(slightly larger for CO2 and slightly smaller for CH4, not
shown). This comparison indicates that the estimated uncertainty using the
equations above compares reasonably well with the uncertainty we would
derive from a bootstrap analysis, which gives us confidence in our
methodology.
Uncertainty (1σ) in fit for two-point calibrations. (a) Two-point fit to drift-corrected sensitivity (SDC) (red line) with
uncertainty (red shading) calculated using the uncertainty in the high
standard (black circle with error bar). Blue shading shows uncertainty
calculated using a bootstrap conducted by randomly selecting sets of two
standards from the laboratory test (black circles) to calculate the slope.
There is no uncertainty at 1 because the drift-corrected sensitivity is
defined as equal to one at the value of the calibration standard. (b) Uncertainty in final CO2 as a function of raw CO2; red and blue
shading have the same meaning as in (a).
The uncertainty in SDC leads to the estimate of the fit uncertainty,
Ufit, shown in Fig. 7b. To implement this uncertainty across all times
and towers, we calculate it assuming a fixed nominal value of the high
calibration standard of 490.50 µmolmol-1CO2 and 2560.61 nmol mol-1CH4. This is based on the value of the high standard
that was in residence in the Earth Networks laboratory when several of the
CRDS analyzers were tested and assigned two-point calibration corrections.
We use the site-specific (instrument-specific and period-specific) slope and
intercept that are applied to the data (which are static over the time
period they are applied) and the value of the calibration tank to
calculate the remainder of the values required for the uncertainty analysis.
Only one site so far, MSH, measures continuous CO, and the history of
standard tanks there indicates significant uncertainty in tank value
assignments with large target tank residuals and corresponding UTGT
relative to errors in slope. We have chosen not to implement the two-point
calibration at this site for CO because the range of slopes of SDC
includes one, i.e., the correction is so small that the uncertainty dwarfs
the correction.
Mean absolute residuals of the two-point fit for nine laboratory
calibrations analyzed (seven tested at NOAA/ESRL and described by Verhulst et
al., 2017, Table S2, and two additional units at NIST) average to 0.03 µmolmol-1 for CO2 between the calibration and high-standard data, and
larger for the test that included an even higher-concentration tank, shown
in Fig. 7 at ∼711µmolmol-1 for CO2. The fit
uncertainty encompasses (at 1σ) this residual as well (Fig. 7b). The
residuals at lower values can be explained by the uncertainty in the
measurement (precision) and uncertainty in value assignment of the tanks.
For CO, only eight tests were available, with a mean residual inside the
range of the calibrations of 1.1 nmol mol-1, higher than the reported
reproducibility from NOAA of 0.4 nmol mol-1 (all values are noted here
at 1σ although they are given by NOAA at 2σ). This larger residual
is likely caused by the lower precision of the analyzers for CO but also
could be caused by larger uncertainty in the tank assignments, possibly due
to drift in the mole fraction of the tanks themselves. We intend to conduct
additional tests outside the two-point calibration range with additional
analyzers and tanks to evaluate and possibly update this uncertainty
component, Ufit, as needed, and especially focus on CO if and when
additional CO measurements are added to our network.
Network observations
Here we show some observations and time series of CO2 and CH4 from
the NEC in situ tower network, focusing on data coverage, vertical
gradients, and observed differences between urban and rural or outer
suburban sites.
Data coverage and network expansion
The NEC network is continuously growing, with sites coming online at
different times. Figure 8 shows the availability of hourly observations as
the various sites have come online.
Data (CO2 and CH4, and CO for MSH only) availability
from the various NIST-EN tower sites in the Northeast Corridor network
included in this data release. Gaps represent data outages due to various
failures (analyzer, communications, etc.).
Vertical gradients
Observations in global trace gas measurement networks (e.g., AGAGE, GGRN) are
specifically sited far from local sources or strong sinks to ensure that
air reaching the site is representative of the large spatial scales of
interest to a global study. This allows the observations to be more easily
interpreted by a coarser global model (e.g., Peters et al., 2007). In
urban networks, it is desirable to measure trace gas concentrations closer
to sources so that finer spatial gradients can be used to inform emissions
estimates at urban scales. However, a balance must be struck between the
necessity to observe and distinguish sources that are in close proximity to
each other and the ability of a transport and dispersion model to simulate
the observations. In some instances, novel ways to simulate observations at
low heights above ground level and in very dense networks have been used to
resolve this problem (Berchet et al., 2017). In the NEC urban network in
Washington, DC, and Baltimore, the tower sites were selected to be between 50 and 100 m above the ground given the desire to place a tower in a specific
location (as identified in an initial network design study by
Lopez-Coto et al., 2017). Inlets at two (or three, at SFD) heights
on the tower give some insight as to the proximity of each tower to sources
whose emissions are not always vertically well mixed by the time they reach
the inlets, depending on atmospheric stability conditions. Here we report
average vertical gradients, determined using the observations at different
levels, for the urban and background sites in our network. These gradients
were calculated using hourly average data from each level, but because the
instruments are only sampling from one level at any given time and cycling
between them, there is an assumption of measurements averaged in a given
hour being representative of the entire hour. Because different towers have
different inlet heights and different vertical spacing between the lower and
upper inlet, here we compare three urban sites (ARL, NDC, and JES) with
inlets at similar heights, ∼90 m and ∼50 m a.g.l. We define the gradient as the mole fraction of CO2 or CH4 at
the topmost inlet minus that of the lowermost inlet divided by the distance
between them so that a negative gradient indicates a higher concentration
at the lower inlet (the most common case).
Diurnal cycle of vertical gradients in CO2(a) and CH4(b) for urban towers in the Washington–Baltimore area, averaged over
2015–2017 in winter (blue) and summer (orange), with shading indicating
1σ standard deviation among sites. Some of the spread can be caused by
sampling in different years at the different sites. Sites included are HAL,
ARL, NDC, NEB, NWB, and JES. HRD was excluded due to lack of data in this
period.
Analysis of the diurnal cycle of the vertical gradient at urban sites in the
Washington–Baltimore area (Fig. 9) indicates different characteristics in
summer vs. winter. These differences are most likely caused by different
meteorology and possible seasonal differences in timing of fluxes,
especially for sites influenced by the urban biosphere. Greater turbulent
mixing in summertime boundary layers and different timing in the boundary
layer growth and collapse mostly dominate the seasonal differences. This
analysis shows that at these three sites the wintertime average gradient in
midafternoon hours (defined based on these figures as 11:00–16:00 LST) is
approximately -0.016µmolmol-1 m-1 for CO2 (-0.105 nmol mol-1 m-1 for CH4), which translates to a -0.8µmolmol-1 (-5.2 nmol mol-1 for CH4) difference between levels
spaced 50 m apart; this is not an insignificant gradient. At other urban
sites with shorter towers, they can be even larger. These observations can
help evaluate vertical mixing in transport and dispersion models that might
be used to estimate emissions, or to identify times when modeled and
observed vertical gradients agree. Large vertical gradients overnight into
the early morning at all sites and seasons are indicative of local sources
(likely mostly anthropogenic but also including respiration from the
biosphere) influencing the observations at these times when there is stable
stratification in the boundary layer and concentrations are higher near the
surface. The larger CO2 gradients overnight in summer compared to
winter periods suggest a strong respiration signal at these urban sites,
with a large degree of variability between sites indicated by large spread.
Nighttime CH4 gradients are slightly larger in winter than summer,
possibly reflecting greater wintertime anthropogenic CH4 emissions, or
possibly due to seasonality in mixing layer heights.
Diurnal cycle of vertical gradients in CO2(a) and CH4(b) at the three background towers for the Washington–Baltimore region in
summer (orange shades) and winter (blue shades).
Seasonal cycles from urban and rural sites in the Washington, DC, and Baltimore region with at least 1 year of observations. Midafternoon
(13:00–18:00 LST) daily averages are detrended using a linear fit to the annual
trend at Mauna Loa (for CO2) and the global average (for CH4)
(data from NOAA/ESRL) and then averaged monthly. Rural sites include TMD,
SFD, and BUC; urban sites are ARL, NDC, JES, HAL, NEB, and NWB. Shading
indicates 1 standard deviation of the averages from all the sites.
The diurnal cycle of the vertical gradients from the sites identified as
background stations for the Washington–Baltimore urban network shows large
variability in summertime gradients between the three stations (Fig. 10).
Stafford, VA (SFD), shows that the surrounding biosphere causes relatively
large gradients in nighttime and early morning hours at this low-density
suburban site. These are apparent at Bucktown, MD (BUC), as well but
less so at Thurmont, MD (TMD), a forested site in western Maryland. The
large difference between summertime early morning vertical CO2
gradients at SFD and TMD, despite the similar surrounding land use (mostly
deciduous forest, Fig. 3), might be caused by the elevation difference, as
SFD is close to sea level while TMD is on a ridge at 561 m elevation. BUC
observations show larger CH4 gradients in summer, due to surrounding
wetlands and agriculture (Fig. 3). Wintertime gradients are near zero at all
hours at all three of these sites, indicating that they are far from local
anthropogenic sources of either gas. We note that the top inlet height at
BUC is lower, at 75 m, than at SFD or TMD (100 and 111 m), while the lower
inlet is similar for all three (∼50 m). For SFD (inlets at
152, 100, and 50 m), we use the 100 and 50 m inlets to define the
gradient to be more consistent with the inlet heights of the other towers
(Table 1).
Urban and rural differences in seasonal cycles
Here we continue to describe the network in terms of differences between
rural (background) and urban stations, determining typical enhancements from
urban influences. The seasonal cycles of CO2 and CH4 indicate
enhancements in the urban sites in our network relative to the more rural
stations throughout the year (Fig. 11). Summertime CH4 at urban sites
is not as enhanced compared to the rural sites as it is in winter, possibly
due to wetland sources influencing the background station at BUC or lower
CH4 emissions from natural gas in urban areas. Similarly, for CO2,
some of the rural stations surrounded by active vegetation (Fig. 3) are
likely to show stronger influence from biospheric uptake than urban sites, especially in
the summer months (Fig. 10). We specifically caution against
using any of the in situ data from the NEC rural stations directly as a
background for analysis of the urban enhancement without examining these
issues. Sargent et al. (2018) indicate that for an analysis of
CO2 enhancements in the Boston urban area, CO2 observations from
upwind stations alone did not represent the correct background. Even when
the air that reaches an urban tower originates near an upwind rural site,
back trajectories (from a Lagrangian Particle Dispersion Model such as
STILT, for example) indicate that much of the air may originate from a
higher altitude than the upwind station. Thus, the measurement at an upwind
station is not necessarily representative of the proper background or
incoming concentration, given the large concentration gradients between
measurements within the planetary boundary layer and in the free troposphere
near background stations with local fluxes. Mueller et al. (2018) conducted an analysis of the issues concerning background
determination for this urban network, mostly concerning the large emissions
of both CO2 and CH4 upwind of the region that is difficult to
capture at upwind stations. We will examine the proper background for
investigating urban enhancements in the Washington, DC, and Baltimore, MD, area
further in future work.
Data availability
This data set of hourly averaged observations from the Northeast Corridor
tower-based network is available on the NIST data portal at https://data.nist.gov (last access: 23 March 2020)
under the 10.18434/M32126 (Karion et al., 2019).
Initially, the repository will contain data from 23 sites (Table 1) for
years spanning 2015–2018; not all years are available for all sites. Files
are version-dated, and the current plan is to provide annual updates for
2019 and beyond.
Conclusions
Here we present a data set of hourly average observations of CO2,
CH4, and CO (where applicable) from a network of towers in the
northeastern United States. Measurements are funded by NIST and conducted
in a collaboration with Earth Networks, Inc., with quality control,
assurance, and uncertainty determination conducted by a science team that
includes NIST, Earth Networks, and collaborators from the Los Angeles
Megacities Carbon Project from NASA/JPL and the Scripps Institution of
Oceanography. We present 4 calendar years of data (2015 through 2018),
with different stations coming online through the years, and most
Washington, DC, and Baltimore, MD, urban stations becoming established after
late 2015. We have also presented our methodology for calibrating the
measurements to WMO scales for each gas and determining uncertainties for
these measurements, as recommended by the WMO (WMO, 2018). We show
that analysis of observations at two different inlet heights can be useful
for determining the presence of emissions close to the towers, which may be
necessary for evaluating the efficacy and choice of transport model used to
analyze the data. We also note that the tower stations that were established
to characterize incoming or background air are not necessarily appropriate
for use directly as background for the urban stations, as they are often
affected by local fluxes that do not influence the urban stations. A more
careful treatment of incoming background air is necessary for any given
analysis.
Author contributions
WC, MS, SP, and PS collected data; AK, KRV, JK, and PS performed quality
control and data processing and devised the uncertainty analysis. AK implemented the
uncertainty analysis and wrote the manuscript, with contributions from SP, JK, ILC, and JW.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge the Earth Networks engineering and technical team, including
Uran Veseshta, Clayton Fain, Bryan Biggs, Seth Baldelli, Joe Considine, and
Charlie Draper. We also thank Tamae Wong, Kimberly Mueller, Sharon Gourdji,
Subhomoy Ghosh, and Antonio Possolo (NIST) for helpful discussions.
Financial support
This work was funded by the NIST Greenhouse Gas Measurements Program.
Review statement
This paper was edited by Alexander Kokhanovsky and reviewed by three anonymous referees.
ReferencesAndrews, A. E., Kofler, J. D., Trudeau, M. E., Williams, J. C., Neff, D. H., Masarie, K. A., Chao, D. Y., Kitzis, D. R., Novelli, P. C., Zhao, C. L., Dlugokencky, E. J., Lang, P. M., Crotwell, M. J., Fischer, M. L., Parker, M. J., Lee, J. T., Baumann, D. D., Desai, A. R., Stanier, C. O., De Wekker, S. F. J., Wolfe, D. E., Munger, J. W., and Tans, P. P.: CO2, CO, and CH4 measurements from tall towers in the NOAA Earth System Research Laboratory's Global Greenhouse Gas Reference Network: instrumentation, uncertainty analysis, and recommendations for future high-accuracy greenhouse gas monitoring efforts, Atmos. Meas. Tech., 7, 647–687, 10.5194/amt-7-647-2014, 2014.Bares, R., Mitchell, L., Fasoli, B., Bowling, D. R., Catharine, D., Garcia, M., Eng, B., Ehleringer, J., and Lin, J. C.: The Utah urban carbon dioxide (UUCON) and Uintah Basin greenhouse gas networks: instrumentation, data, and measurement uncertainty, Earth Syst. Sci. Data, 11, 1291–1308, 10.5194/essd-11-1291-2019, 2019.Berchet, A., Zink, K., Muller, C., Oettl, D., Brunner, J., Emmenegger, L.,
and Brunner, D.: A cost-effective method for simulating city-wide air flow
and pollutant dispersion at building resolving scale, Atmos. Environ., 158,
181–196, 10.1016/j.atmosenv.2017.03.030, 2017.Bureau International des Poids et Mesures: The International System of Units
(SI), 9th Edn., edited by: BIPM, BI PM, Pavillon de breteuil, 92312 Sevres
Cedex, France, 2019.Chen, H., Winderlich, J., Gerbig, C., Hoefer, A., Rella, C. W., Crosson, E. R., Van Pelt, A. D., Steinbach, J., Kolle, O., Beck, V., Daube, B. C., Gottlieb, E. W., Chow, V. Y., Santoni, G. W., and Wofsy, S. C.: High-accuracy continuous airborne measurements of greenhouse gases (CO2 and CH4) using the cavity ring-down spectroscopy (CRDS) technique, Atmos. Meas. Tech., 3, 375–386, 10.5194/amt-3-375-2010, 2010.Dlugokencky, E. J., Myers, R. C., Lang, P. M., Masarie, K. A., Crotwell, A.
M., Thoning, K. W., Hall, B. D., Elkins, J. W., and Steele, L. P.:
Conversion of NOAA atmospheric dry air CH4 mole fractions to a
gravimetrically prepared standard scale, J. Geophys. Res.-Atmos., 110, D18306, 10.1029/2005JD006035, 2005.Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Kadner, S., Minx, J. C., Brunner, S., Agrawala, S.,
Baiocchi, G., Bashmakov, I. A., Blanco, G., Broome, J., Bruckner, T., M. Bustamante, Clarke, L.,
Grand, M. C., Creutzig, F., Cruz-Núñez, X., Dhakal, S., Dubash, N. K., Eickemeier, P., Farahani, E.,
Fischedick, M., Fleurbaey, M., Gerlagh, R., Gómez-Echeverri, L., Gupta, S., Harnisch, J., Jiang, K.,
Jotzo, F., Kartha, S., Klasen, S., Kolstad, C., Krey, V., Kunreuther, H., Lucon, O., Masera, O.,
Mulugetta, Y., Norgaard, R. B., Patt, A., Ravindranath, N. H., Riahi, K., Roy, J., Sagar, A.,
Schaeffer, R., Schlömer, S., Seto, K. C., Seyboth, K., Sims, R., Smith, P., Somanathan, E., Stavins,
R., Stechow, C. v., Sterner, T., Sugiyama, T., Suh, S., Ürge-Vorsatz, D., Urama, K., Venables, A.,
Victor, D. G., Weber, E., Zhou, D., Zou, J., and Zwickel, T.: Technical Summary, in: Climate
Change 2014: Mitigation of Climate Change: Contribution of Working Group III Contribution to
the IPCC Fifth Assessment Report, edited by: Intergovernmental Panel on Climate Change,
Cambridge University Press, Cambridge, 2014.Gurney, K. R., Razlivanov, I., Song, Y., Zhou, Y. Y., Benes, B., and
Abdul-Massih, M.: Quantification of Fossil Fuel CO2 Emissions on the
Building/Street Scale for a Large US City, Environ. Sci.
Technol., 46, 12194–12202, 10.1021/es3011282, 2012.Gurney, K. R., Liang, J., O'Keeffe, D., Patarasuk, R., Hutchins, M., Huang,
J., Rao, P., and Song, Y.: Comparison of Global Downscaled Versus Bottom-Up
Fossil Fuel CO2 Emissions at the Urban Scale in Four U.S. Urban Areas,
J. Geophys. Res.-Atmos., 124, 2823–2840, 10.1029/2018jd028859, 2019.Hedelius, J. K., Liu, J., Oda, T., Maksyutov, S., Roehl, C. M., Iraci, L. T., Podolske, J. R., Hillyard, P. W., Liang, J., Gurney, K. R., Wunch, D., and Wennberg, P. O.: Southern California megacity CO2, CH4, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model, Atmos. Chem. Phys., 18, 16271–16291, 10.5194/acp-18-16271-2018, 2018.Huang, Y., Kort, E. A., Gourdji, S., Karion, A., Mueller, K., and Ware, J.:
Seasonally Resolved Excess Urban Methane Emissions from the
Baltimore/Washington, DC Metropolitan Region, Environ. Sci.
Technol., 53, 11285–11293, 10.1021/acs.est.9b02782, 2019.Karion, A., Sweeney, C., Wolter, S., Newberger, T., Chen, H., Andrews, A., Kofler, J., Neff, D., and Tans, P.: Long-term greenhouse gas measurements from aircraft, Atmos. Meas. Tech., 6, 511–526, 10.5194/amt-6-511-2013, 2013.Karion, A., Whetstone, J. R., and Callahan, W.: Observations of CO2, CH4,
and CO mole fractions from the NIST Northeast Corridor urban testbed,
10.18434/M32126, 2019.Lamb, B. K., Cambaliza, M. O. L., Davis, K. J., Edburg, S. L., Ferrara, T.
W., Floerchinger, C., Heimburger, A. M. E., Herndon, S., Lauvaux, T.,
Lavoie, T., Lyon, D. R., Miles, N., Prasad, K. R., Richardson, S., Roscioli,
J. R., Salmon, O. E., Shepson, P. B., Stirm, B. H., and Whetstone, J.:
Direct and Indirect Measurements and Modeling of Methane Emissions in
Indianapolis, Indiana, Environ. Sci. Technol., 50,
8910–8530, 10.1021/acs.est.6b01198, 2016.Lauvaux, T., Miles, N. L., Deng, A. J., Richardson, S. J., Cambaliza, M. O.,
Davis, K. J., Gaudet, B., Gurney, K. R., Huang, J. H., O'Keefe, D., Song,
Y., Karion, A., Oda, T., Patarasuk, R., Razlivanov, I., Sarmiento, D.,
Shepson, P., Sweeney, C., Turnbull, J., and Wu, K.: High-resolution
atmospheric inversion of urban CO2 emissions during the dormant season of
the Indianapolis Flux Experiment (INFLUX), J. Geophys. Res.-Atmos., 121,
5213–5236, 10.1002/2015jd024473, 2016.Lin, J. C., Gerbig, C., Wofsy, S. C., Andrews, A. E., Daube, B. C., Davis,
K. J., and Grainger, C. A.: A near-field tool for simulating the upstream
influence of atmospheric observations: The Stochastic Time-Inverted
Lagrangian Transport (STILT) model, J. Geophys. Res.-Atmos., 108, 4493, 10.1029/2002JD003161, 2003.Lopez-Coto, I., Ghosh, S., Prasad, K., and Whetstone, J.: Tower-based
greenhouse gas measurement network design – The National Institute of
Standards and Technology North East Corridor Testbed, Adv.
Atmos. Sci., 34, 1095–1105, 10.1007/s00376-017-6094-6, 2017.Lopez-Coto, I., Ren, X., Salmon, O. E., Karion, A., Shepson, P. B.,
Dickerson, R. R., Stein, A., Prasad, K. R., and Whetstone, J.: Wintertime
CO2, CH4 and CO emissions estimation for the Washington DC/Baltimore
metropolitan area using an inverse modeling technique, Environ. Sci.
Technol., 54, 2606–2614, 10.1021/acs.est.9b06619, 2020a.Lopez-Coto, I., Hicks, M., Karion, A., Sakai, R. K., Demoz, B., Prasad, K.,
and Whetstone, J.: Assessment of WRF PBL schemes in an urban region and
comparison of the impact of the urban heat island, J. Appl. Meteorol. Clim., in review, 2020b.Martin, C. R., Zeng, N., Karion, A., Dickerson, R. R., Ren, X., Turpie, B. N., and Weber, K. J.: Evaluation and environmental correction of ambient CO2 measurements from a low-cost NDIR sensor, Atmos. Meas. Tech., 10, 2383–2395, 10.5194/amt-10-2383-2017, 2017.McKain, K., Down, A., Raciti, S. M., Budney, J., Hutyra, L. R.,
Floerchinger, C., Herndon, S. C., Nehrkorn, T., Zahniser, M. S., Jackson, R.
B., Phillips, N., and Wofsy, S. C.: Methane emissions from natural gas
infrastructure and use in the urban region of Boston, Massachusetts, P.
Natl. Acad. Sci. USA, 112, 1941–1946, 10.1073/pnas.1416261112, 2015.Mitchell, L. E., Lin, J. C., Bowling, D. R., Pataki, D. E., Strong, C.,
Schauer, A. J., Bares, R., Bush, S. E., Stephens, B. B., Mendoza, D.,
Mallia, D., Holland, L., Gurney, K. R., and Ehleringer, J. R.: Long-term
urban carbon dioxide observations reveal spatial and temporal dynamics
related to urban characteristics and growth, P. Natl.
Acad. Sci. USA, 115, 2912–2917, 10.1073/pnas.1702393115, 2018.Mueller, K., Yadav, V., Lopez-Coto, I., Karion, A., Gourdji, S., Martin, C.,
and Whetstone, J.: Siting Background Towers to Characterize Incoming Air for
Urban Greenhouse Gas Estimation: A Case Study in the Washington,
DC/Baltimore Area, J. Geophys. Res.-Atmos., 123,
2910–2926, 10.1002/2017JD027364, 2018.Nehrkorn, T., Eluszkiewicz, J., Wofsy, S. C., Lin, J. C., Gerbig, C., Longo,
M., and Freitas, S.: Coupled weather research and forecasting-stochastic
time-inverted lagrangian transport (WRF-STILT) model, Meteorol. Atmos.
Phys., 107, 51–64, 10.1007/s00703-010-0068-x, 2010.Novelli, P. C., Masarie, K. A., Lang, P. M., Hall, B. D., Myers, R. C., and
Elkins, J. W.: Reanalysis of tropospheric CO trends: Effects of the
1997–1998 wildfires, J. Geophys. Res.-Atmos., 108, 4464,
10.1029/2002JD003031, 2003.O'Neill, B. C., Dalton, M., Fuchs, R., Jiang, L., Pachauri, S., and Zigova,
K.: Global demographic trends and future carbon emissions, P.
Natl. Acad. Sci. USA, 107, 17521–17526, 10.1073/pnas.1004581107,
2010.Peters, W., Jacobson, A. R., Sweeney, C., Andrews, A. E., Conway, T. J.,
Masarie, K., Miller, J. B., Bruhwiler, L. M. P., Petron, G., Hirsch, A. I.,
Worthy, D. E. J., van der Werf, G. R., Randerson, J. T., Wennberg, P. O.,
Krol, M. C., and Tans, P. P.: An atmospheric perspective on North American
carbon dioxide exchange: CarbonTracker, P. Natl. Acad. Sci. USA,
104, 18925–18930, 10.1073/pnas.0708986104, 2007.Pillai, D., Buchwitz, M., Gerbig, C., Koch, T., Reuter, M., Bovensmann, H., Marshall, J., and Burrows, J. P.: Tracking city CO2 emissions from space using a high-resolution inverse modelling approach: a case study for Berlin, Germany, Atmos. Chem. Phys., 16, 9591–9610, 10.5194/acp-16-9591-2016, 2016.Ren, X. R., Salmon, O. E., Hansford, J. R., Ahn, D., Hall, D., Benish, S.
E., Stratton, P. R., He, H., Sahu, S., Grimes, C., Heimburger, A. M. F.,
Martin, C. R., Cohen, M. D., Stunder, B., Salawitch, R. J., Ehrman, S. H.,
Shepson, P. B., and Dickerson, R. R.: Methane Emissions From the
Baltimore-Washington Area Based on Airborne Observation: Comparison to
Emissions Inventories, J. Geophys. Res.-Atmos., 123, 8869–8882, 10.1029/2018jd028851, 2018.Richardson, S. J., Miles, N. L., Davis, K. J., Lauvaux, T., Martins, D. K.,
Turnbull, J. C., McKain, K., Sweeney, C., and Cambaliza, M. O. L.: Tower
measurement network of in-situ CO2, CH4, and CO in support of the
Indianapolis FLUX (INFLUX) Experiment, Elementa-Sci. Anthrop., 5, p. 59, 10.1525/elementa.140, 2017.Salmon, O. E., Shepson, P. B., Ren, X., He, H., Hall, D. L., Dickerson, R.
R., Stirm, B. H., Brown, S. S., Fibiger, D. L., McDuffie, E. E., Campos, T.
L., Gurney, K. R., and Thornton, J. A.: Top-Down Estimates of NOx and CO
Emissions From Washington, DC-Baltimore During the WINTER Campaign, J.
Geophys. Res.-Atmos., 123, 7705–7724, 10.1029/2018jd028539, 2018.Santoni, G. W., Daube, B. C., Kort, E. A., Jiménez, R., Park, S., Pittman, J. V., Gottlieb, E., Xiang, B., Zahniser, M. S., Nelson, D. D., McManus, J. B., Peischl, J., Ryerson, T. B., Holloway, J. S., Andrews, A. E., Sweeney, C., Hall, B., Hintsa, E. J., Moore, F. L., Elkins, J. W., Hurst, D. F., Stephens, B. B., Bent, J., and Wofsy, S. C.: Evaluation of the airborne quantum cascade laser spectrometer (QCLS) measurements of the carbon and greenhouse gas suite – CO2, CH4, N2O, and CO – during the CalNex and HIPPO campaigns, Atmos. Meas. Tech., 7, 1509–1526, 10.5194/amt-7-1509-2014, 2014.Sargent, M., Barrera, Y., Nehrkorn, T., Hutyra, L. R., Gately, C. K., Jones,
T., McKain, K., Sweeney, C., Hegarty, J., Hardiman, B., Wang, J. A., and
Wofsy, S. C.: Anthropogenic and biogenic CO2 fluxes in the Boston urban region, P.
Natl. Acad. Sci. USA, 115, 7491–7496, 10.1073/pnas.1803715115, 2018.Stein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J. B., Cohen, M. D.,
and Ngan, F.: NOAA's Hysplit Atmospheric Transport and Dispersion Modeling
System, B. Am. Meteorol. Soc., 96, 2059–2077, 10.1175/Bams-D-14-00110.1, 2015.Sweeney, C., Karion, A., Wolter, S., Newberger, T., Guenther, D., Higgs, J.
A., Andrews, A. E., Lang, P. M., Neff, D., and Dlugokencky, E.: Seasonal
climatology of CO2 across North America from aircraft measurements in the
NOAA/ESRL Global Greenhouse Gas Reference Network, J. Geophys.
Res.-Atmos., 120, 5155–5190, 2015.Turnbull, J., Guenther, D., Karion, A., Sweeney, C., Anderson, E., Andrews, A., Kofler, J., Miles, N., Newberger, T., Richardson, S., and Tans, P.: An integrated flask sample collection system for greenhouse gas measurements, Atmos. Meas. Tech., 5, 2321–2327, 10.5194/amt-5-2321-2012, 2012.Turnbull, J. C., Karion, A., Davis, K. J., Lauvaux, T., Miles, N. L.,
Richardson, S. J., Sweeney, C., McKain, K., Lehman, S. J., Gurney, K. R.,
Patarasuk, R., Liang, J., Shepson, P. B., Heimburger, A., Harvey, R., and
Whetstone, J.: Synthesis of Urban CO2 Emission Estimates from Multiple
Methods from the Indianapolis Flux Project (INFLUX), Environ. Sci.
Technol., 53, 287–295, 10.1021/acs.est.8b05552, 2019.Verhulst, K. R., Karion, A., Kim, J., Salameh, P. K., Keeling, R. F.,
Newman, S., Miller, J., Sloop, C., Pongetti, T., Rao, P., Wong, C., Hopkins,
F. M., Yadav, V., Weiss, R. F., Duren, R. M., and Miller, C. E.: Carbon
dioxide and methane measurements from the Los Angeles Megacity Carbon
Project – Part 1: calibration, urban enhancements, and uncertainty
estimates, Atmos. Chem. Phys., 17, 8313-8341, 10.5194/acp-17-8313-2017,
2017.Waxman, E. M., Cossel, K. C., Giorgetta, F., Truong, G.-W., Swann, W. C., Coddington, I., and Newbury, N. R.: Estimating vehicle carbon dioxide emissions from Boulder, Colorado, using horizontal path-integrated column measurements, Atmos. Chem. Phys., 19, 4177–4192, 10.5194/acp-19-4177-2019, 2019.Welp, L. R., Keeling, R. F., Weiss, R. F., Paplawsky, W., and Heckman, S.: Design and performance of a Nafion dryer for continuous operation at CO2 and CH4 air monitoring sites, Atmos. Meas. Tech., 6, 1217–1226, 10.5194/amt-6-1217-2013, 2013.WMO: 19th WMO/IAEA Meeting on Carbon Dioxide, Other Greenhouse Gases and
Related Measurement Techniques (GGMT-2017), WMO/GAW Report No. 242, 2018.Wong, C. K., Pongetti, T. J., Oda, T., Rao, P., Gurney, K. R., Newman, S., Duren, R. M., Miller, C. E., Yung, Y. L., and Sander, S. P.: Monthly trends of methane emissions in Los Angeles from 2011 to 2015 inferred by CLARS-FTS observations, Atmos. Chem. Phys., 16, 13121–13130, 10.5194/acp-16-13121-2016, 2016.Xueref-Remy, I., Dieudonné, E., Vuillemin, C., Lopez, M., Lac, C., Schmidt, M., Delmotte, M., Chevallier, F., Ravetta, F., Perrussel, O., Ciais, P., Bréon, F.-M., Broquet, G., Ramonet, M., Spain, T. G., and Ampe, C.: Diurnal, synoptic and seasonal variability of atmospheric CO2 in the Paris megacity area, Atmos. Chem. Phys., 18, 3335–3362, 10.5194/acp-18-3335-2018, 2018.
Yadav, V., Duren, R., Mueller, K., Verhulst, K. R., Nehrkorn, T., Kim, J.,
Weiss, R. F., Keeling, R., Sander, S., Fischer, M. L., Newman, S., Falk, M.,
Kuwayama, T., Hopkins, F., Rafiq, T., Whetstone, J., and Miller, C.:
Spatio-temporally Resolved Methane Fluxes From the Los Angeles Megacity,
J. Geophys. Res.-Atmos., 124, 5131–5148, 10.1029/2018jd030062, 2019.Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Bender, S. M., Case,
A., Costello, C., Dewitz, J., Fry, J., Funk, M., Granneman, B., Liknes, G.
C., Rigge, M., and Xian, G.: A new generation of the United States National
Land Cover Database: Requirements, research priorities, design, and
implementation strategies, ISPRS J. Photogramm., 146, 108–123,
10.1016/j.isprsjprs.2018.09.006, 2018.Zhao, C. L., Tans, P. P., and Thoning, K. W.: A high precision manometric
system for absolute calibrations of CO2 in dry air, J. Geophys.
Res.-Atmos., 102, 5885–5894, 10.1029/96JD03764, 1997.