acp-16-8479-2016.pdf

Atmos. Chem. Phys., 16, 8479–8498, 2016

www.atmos-chem-phys.net/16/8479/2016/

doi:10.5194/acp-16-8479-2016

Differential column measurements using compact

solar-tracking spectrometers

Jia Chen

1,a

, Camille Viatte

, Jacob K. Hedelius

, Taylor Jones

, Jonathan E. Franklin

, Harrison Parker

, Elaine

W. Gottlieb

, Paul O. Wennberg

, Manvendra K. Dubey

, and Steven C. Wofsy

School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences, Harvard University,

Cambridge, MA 02138, USA

Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA

Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

now at: Electrical and Computer Engineering, Technische Universität München, Munich, 80333, Germany

Correspondence to:

Jia Chen (jia.chen@tum.de)

Received: 29 December 2015 – Published in Atmos. Chem. Phys. Discuss.: 17 February 2016

Revised: 6 June 2016 – Accepted: 8 June 2016 – Published: 12 July 2016

Abstract.

We demonstrate the use of compact solar-tracking

Fourier transform spectrometers (Bruker EM27/SUN) for

differential measurements of the column-averaged dry-air

mole fractions of CH

and CO

within urban areas. Using

Allan variance analysis, we show that the differential column

measurement has a precision of 0.01 % for

and

with an optimum integration time of 10 min, corresponding

to Allan deviations of 0.04 ppm and 0.2 ppb, respectively.

The sensor system is very stable over time and after relo-

cation across the continent. We report tests of the differential

column measurement, and its sensitivity to emission sources,

by measuring the downwind-minus-upwind column differ-

ence

across dairy farms in the Chino area, Califor-

nia, and using the data to verify emissions reported in the lit-

erature. Ratios of spatial column differences

/1X

were observed across Pasadena within the Los Angeles basin,

indicating values consistent with regional emission ratios

from the literature. Our precise, rapid measurements allow us

to determine significant short-term variations (5–10 min) of

and

and to show that they represent atmospheric

phenomena.

Overall, this study helps establish a range of new applica-

tions for compact solar-viewing Fourier transform spectrom-

eters. By accurately measuring the small differences in in-

tegrated column amounts across local and regional sources,

we directly observe the mass loading of the atmosphere due

to the influence of emissions in the intervening locale. The

inference of the source strength is much more direct than in-

version modeling using only surface concentrations and less

subject to errors associated with small-scale transport phe-

nomena.

1 Introduction

Cities and their surrounding urban regions occupy less than

3 % of the global land surface (Grimm et al., 2008) but are

home to 54 % of the world population (WHO, 2014) and ac-

count for more than 70 % of global fossil-fuel CO

emissions

(Gurney et al., 2015). Hence, accurate methods for measur-

ing urban- and regional-scale carbon fluxes are required in

order to design and implement policies for emission reduc-

tion initiatives.

It is challenging to use in situ measurements of CO

and

to derive emission fluxes in urban regions. Surface con-

centrations typically have high variance due to the influ-

ence of nearby sources, and they are strongly modulated by

mesoscale transport phenomena that are difficult to simu-

late in atmospheric models. These include the variation of

the depth of the planetary boundary layer (PBL), sea breeze,

topographic flows, etc. (McKain et al., 2012; Bréon et al.,

2015).

The mass loading of the atmosphere can be directly de-

termined by measuring the column-integrated amount of a

tracer through the whole atmosphere. Column measurements

are insensitive to vertical redistribution of tracer mass, e.g.,

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

8480

J. Chen et al.: Differential column measurements using compact solar-tracking spectrometers

due to growth of the PBL, and are also less influenced by

nearby point sources whose emissions are concentrated in a

thin layer near the surface. Column observations are more

compatible with the scale of atmospheric models and hence

provide stronger constraints for inverse modeling (Linden-

maier et al., 2014).

One potential drawback, however, is that column obser-

vations are sensitive to surface emissions over a very wide

range of spatial scales, spanning nearby emissions and all

those upwind in the urban, continental, and hemispheric do-

mains. In this paper we demonstrate how to use simultaneous

measurements of the column-averaged dry-air mole fractions

(DMFs) of CH

and CO

(denoted by

and

, re-

spectively) at upwind and downwind sites to mitigate this

limitation. The horizontal gradients

within

a region are rel-

atively insensitive to surface fluxes upwind of the domain,

providing favorable input for regional flux inversions.

We use three matched, compact Fourier transform spec-

trometers (FTSs) to measure the small (0.1 %) differences of

and

, and we demonstrate sufficient precision and

speed to determine emission rates at the urban scale. By di-

rectly measuring spatial and temporal gradients of the mass

loading, we reduce the sensitivity of inverse model results to

atmospheric fine structure, such as may arise from vertical

redistribution of trace gases, and that often complicates the

interpretation of surface in situ data (Chang et al., 2014).

Our ground-based network of spectrometers measuring

gradients of column amounts could enable new approaches

to validate the urban–rural gradients of satellite observations

such as OCO-2 (Crisp et al., 2008; Frankenberg et al., 2015)

and TROPOMI (Veefkind et al., 2012). In contrast to the

large, high-spectral-resolution instruments of the Total Car-

bon Column Observing Network (TCCON), which are not

easily relocated, the compact spectrometers can be deployed

directly under satellite tracks that pass near major cities to

assess potential artifacts in satellite-derived tracer gradients

that might arise from urban-rural differences in aerosol bur-

den, land surface properties, etc.

Several recent papers have studied column-averaged con-

centrations of trace gases to derive source fluxes. Wunch

et al. (2009) observed diurnal patterns for

, and

over Los Angeles, similar to the model simulations of

McKain et al. (2012) for Salt Lake City. Kort et al. (2012)

used GOSAT satellite data to measure the difference between

columns inside and outside Los Angeles and to derive a

top-down inventory for CO

. Papers by Stremme et al. (2009,

2013) and Té et al. (2012) used total column measurements

from a ground-based FTS to estimate and monitor CO emis-

sion in Mexico City and Paris, respectively. Mellqvist et al.

(2010) studied plumes from industrial complexes, and Lin-

denmaier et al. (2014) examined plumes from two power

plants and discriminated them. Kort et al. (2014) quantified

large methane sources missing in inventories at Four Corners,

New Mexico. However, these studies did not have simulta-

neous upwind and downwind column data, one of the novel

elements of the present paper.

Frey et al. (2015) and Hase et al. (2015) reported de-

ployments of multiple FTSs of the same type as employed

here, deriving calibration and stability characteristics in a

field setting. We extend this analysis by determining the Al-

lan variances of column concentration differences between

spectrometer pairs deployed side-by-side, providing a rigor-

ous assessment of the precision of the differential column

measurements.

Here we study local-scale gradients in

and

two applications. First, we deployed our spectrometers up-

wind and downwind of the dairy farms in Chino, Califor-

nia (about 50 km

area), and use the data to compare with

emissions reported in the literature. A second application

uses the observed ratio of differences in

and

i.e.,

/1X

, to characterize emission ratios for these

gases within the Los Angeles basin.

In another application of the compact spectrometers, we

co-located spectrometers to demonstrate measurement of

short-term (5–10 min) variations of column-averaged DMFs

in the atmosphere. The high-precision measurements with

rapid scan rates are an advantage of the compact spectrome-

ters compared to larger, higher-spectral-resolution spectrom-

eters that have scan durations in the minute range. We show

that high-frequency observations can be used to quantify the

influence of sporadic events, such as plumes, transient peaks,

or instabilities across the top of the mixed layer (ML), on

measurements in urban areas.

2 Differential column network

2.1 Column measurement and existing FTS network

Solar-tracking FTSs can be used to measure the gas col-

umn number densities, i.e., the number of gas molecules

per unit area in the atmospheric column (column

, unit:

molec. m

−

). The sun is used as light source and the FTS is

located on the ground for measuring the solar radiation trans-

mitted through the atmosphere. The recorded sun radiation

spectrum is broadband and covers the absorption fingerprints

of diverse gas species including CO

, CH

, H

O, and O

The attenuation of the solar intensity at specific frequencies

provides a measure for the column number density of various

gases. For further details of modeling the atmospheric trans-

mittance spectrum, please see Wunch et al. (2011) and Hase

et al. (2004); for the working principles of FTS please refer

to Davis et al. (2001) and Griffiths and De Haseth (2007).

The existing FTS networks include NDACC (Network for

the Detection of Atmospheric Composition Change; Hanni-

gan, 2011) and TCCON (Toon et al., 2009; Wunch et al.,

2010, 2011). NDACC measures at mid-infrared wavelengths

and detects atmospheric O

, HNO

, HCl, HF, CO, N

, HCN, C

, and ClONO

, chosen to help understand

Atmos. Chem. Phys., 16, 8479–8498, 2016

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J. Chen et al.: Differential column measurements using compact solar-tracking spectrometers

8481

the physical and chemical state of the upper troposphere and

the stratosphere. The TCCON network focuses on column

measurements of greenhouse gases, mainly CO

, CH

, N

and CO, at near-infrared wavelengths. It uses the Bruker IFS

125HR spectrometer that is large in dimension (container

size) and heavyweight (

500 kg; Bruker, 2006). The spectra

in the TCCON network are recorded with a spectral resolu-

tion of approx. 0.02 cm

−

and require about 170 s for one

forward/backward scan pair (Hedelius et al., 2016).

2.2 Differential column measurement with compact

FTS

Our differential column network uses at least two spectrom-

eters to make simultaneous measurements of column num-

ber densities of CO

, CH

, and O

. We then compute the

column-averaged DMFs (Wunch et al., 2011) for each gas

G, i.e.,

column

2095

(1)

and differences, i.e.,

−

(2)

where

and

stand for column-averaged DMFs at

downwind and upwind sites.

Our sensors are two EM27/SUN FTS units owned by

Harvard University and one owned by Los Alamos Na-

tional Laboratory: nos. 45, 46, and 34 Bruker Optics (des-

ignated ha, hb, and pl, respectively). They are compact

(62.5 cm

35.6 cm

47.3 cm) and lightweight (22.8 kg in-

cluding the sun tracker), with a spectral resolution of

0.5 cm

−

and a scan time of 5.8 s (forward or backward

scan). The EM27/SUN tracks the sun precisely (1

: 11 arc-

sec) using a camera for fine alignment of the tracking mirrors

(Gisi et al., 2011). It is mechanically very robust, with excel-

lent precision in retrieving

and

(Gisi et al., 2012;

Klappenbach et al., 2015; Hedelius et al., 2016), comparable

to Bruker IFS 125HR used in the TCCON network (Wunch

et al., 2011).

We carried out extensive side-by-side measurements of ha

and hb in Cambridge, Massachusetts and Pasadena, Califor-

nia, over many months, thoroughly examining precision and

robustness, and also compared these spectrometers to the

TCCON spectrometer in Pasadena (Hedelius et al., 2016).

We confirm that these spectrometers are stable (Frey et al.,

2015). We show that comparing pairs of them cancels out

most of the systematic error and bias from diverse sources,

e.g., spectroscopic and retrieval errors, instrument bias, and

errors in pressure and temperature, enabling us to determine

0.1 % differences in column-averaged DMFs across the net-

work.

3 System characterization

3.1 Allan analysis for system precision

Known standards cannot be exchanged for the ambient air

in a total column measurement; hence it is difficult to as-

sess the precision of atmospheric measurements end to end.

Two commonly used literature methods for precision esti-

mates are as follows.

–

The first method is based on measurements of the stan-

dard deviation of the DMF time series, with the trend re-

moved subtracting a moving average (Gisi et al., 2012).

This approach is confounded by real variations in the

atmosphere that occur on short timescales (vide infra).

–

The second method is based on the residual of the

spectral fit. The estimate obtained by this method does

not separate systematic errors, e.g., errors in spectro-

scopic database and modeling of instrument line shape

(ILS), from the measurement noise, and therefore this

approach may overestimate the true random uncertainty

of the measurement (cf. Fu et al., 2014).

In this paper we use the Allan variance method (Allan,

1966; Werle et al., 1993) to estimate the measurement preci-

sion. Figure 1 shows the Allan deviations of the differences

in column-averaged DMFs measured simultaneously by ha

and hb at the same location, i.e,

(t)

−

(t)

The Allan variance of

is denoted by

allan

,1X

, which

is the expectation value

〈〉

of the difference between adjacent

samples averaged over the time period

allan

,1X

(τ)

〈

(

G,n

−

G,n

)

〉

(3)

with

G,n

∫

(t)

Practically,

G,n

is the mean of all

measurements

within the time interval

[

τ)

According to the Allan deviation plots (Fig. 1), we made

the following findings.

–

The optimal integration time, given by the minimum in

the Allan deviation, is 10 to 20 min, for both

and

–

When averaging 10 min, the precision (1 Allan devia-

tion) of the EM27/SUN differential column measure-

ment is 0.04–0.05 ppm (0.01 %) for

and 0.1–

0.2 ppb (0.01 %) for

. Since the two instruments

are statistically uncorrelated, the individual measure-

ment noise is smaller by factor 1

√

2, indicating preci-

sion comparable to near-infrared in situ laser spectrom-

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J. Chen et al.: Differential column measurements using compact solar-tracking spectrometers

-1/2

f

1/2

-2

-1/2

Figure 1.

Allan deviations

allan

,1X

CO2

and

allan

,1X

CH4

as a function of the integrating time

. The black dashed lines represent a slope

−

2 and a slope of 1

2, which correspond to power spectral densities

S(f)

(white noise) and

S(f)

−

(Brownian noise),

respectively. The Allan deviation follows a slope of

−

2 up to an integration time of 10 to 20 min, then stays constant (

S(f)

−

), and

subsequently turns over to a slope of 1

2, which describes a drift.

eters with commensurate optical path length and inte-

gration time (Picarro, 2015a, b). Note that these pre-

cision estimates represent the full end-to-end process-

ing of the observations, including deriving the spectrum

from the interferogram, retrieving the column num-

ber densities in the atmosphere, and normalizing with

the O

column amount to obtain the column-averaged

DMFs.

–

When integrating less than 10 min, the Allan deviation

follows a slope of

−

2 in the double logarithmic scale,

indicating white noise (

−

→

) that has a constant

power spectral density over the frequency

. As the av-

eraging time

increases beyond 10 min, the Allan devi-

ation rises a little, showing a small color noise compo-

nent (

→

−

), which arises from instrument drift,

in part due to temperature differences inside of the spec-

trometers. There is also a small divergence between the

measurements of ha and hb at high solar zenith an-

gles, traceable to their slightly different ILSs. The mea-

sured ILS parameters are given in Appendix A. Mi-

croscale eddies have durations of 10 s to 10 min and

length scales from tens to hundreds of meters (Stull,

1988, Fig. 2.2). Therefore atmospheric turbulence prob-

ably does not play a major role in the Allan plot because

there is little color noise within timescale

≤

10 min for

two spectrometers looking along atmospheric paths sep-

arated by roughly one meter.

We use a shorter integration time (5 min) for measuring

emissions from local- and regional-scale sources (Sects. 4.1

and 4.2), in order to retain high-frequency atmospheric sig-

nals, giving us precision of 0.05–0.06 ppm for

and

0.2–0.3 ppb for

(see Fig. 1). To study the short-term

variations due to pollution plumes or turbulent eddies we use

2 min integration time (Sect. 4.3).

3.2 System stability

Differential column observations by two spectrometers will

inevitably have bias in addition to fluctuations and drift. For

the EM27/SUN, small differences in the alignments of the

interferometers result in minute, but observable and system-

atic, deviations in the retrieval results. We examined the bi-

ases between ha and hb over a long period of time to de-

termine whether these errors can be effectively corrected by

applying a constant calibration factor to the retrieval of one

instrument to match the performance of the other. The cali-

bration factors are determined assuming a linear model, i.e.,

, and for each gas individually.

The value of

was consistent over time for the two

Harvard EM27/SUNs, including shipment across the con-

tiguous United States (Fig. 2, Table 1). We used two re-

trieval software systems, I2S (interferogram-to-spectrum)

combined with GFIT nonlinear least-squares spectral fitting

retrieval software (Wunch et al., 2015; Hedelius et al., 2016),

and PROFFIT (Hase et al., 2004). The calibration factors are

slightly different for GFIT and PROFFIT, traceable to their

specific modeling of the ILS, various a priori volume mix-

ing ratio profiles, and unequal spectral microwindows that

are used. Nevertheless,

is consistent in Cambridge and

Pasadena, before and during the campaign, when the same

retrieval settings are used. Retrievals for ha have been scaled

with

for the Allan analysis (Sect. 3.1) and for the scien-

tific applications (Sect. 4) below. Calibration factors for pl

are shown in Appendix B. The measured ILS parameters of

the two Harvard EM27/SUNs are also consistent over time

and across continent, as given in Appendix A.

Atmos. Chem. Phys., 16, 8479–8498, 2016