The AOTF-based NO2 camera

Abstract

The abundance of NO₂ in the boundary layer relates to air quality and pollution source monitoring. Observing the spatiotemporal distribution of NO₂ above well-delimited (flue gas stacks, volcanoes, ships) or more extended sources (cities) allows for applications such as monitoring emission fluxes or studying the plume dynamic chemistry and its transport. So far, most attempts to map the NO₂ field from the ground have been made with visible-light scanning grating spectrometers. Benefiting from a high retrieval accuracy, they only achieve a relatively low spatiotemporal resolution that hampers the detection of dynamic features. We present a new type of passive remote sensing instrument aiming at the measurement of the 2-D distributions of NO₂ slant column densities (SCDs) with a high spatiotemporal resolution. The measurement principle has strong similarities with the popular filter-based SO₂ camera as it relies on spectral images taken at wavelengths where the molecule absorption cross section is different. Contrary to the SO₂ camera, the spectral selection is performed by an acousto-optical tunable filter (AOTF) capable of resolving the target molecule's spectral features. The NO₂ camera capabilities are demonstrated by imaging the NO₂ abundance in the plume of a coal-fired power plant. During this experiment, the 2-D distribution of the NO₂ SCD was retrieved with a temporal resolution of 3min and a spatial sampling of 50cm (over a 250 × 250m² area). The detection limit was close to 5 × 10¹⁶moleculescm^-2, with a maximum detected SCD of 4 × 10¹⁷moleculescm^-2. Illustrating the added value of the NO₂ camera measurements, the data reveal the dynamics of the NO to NO₂ conversion in the early plume with an unprecedent resolution: from its release in the air, and for 100m upwards, the observed NO₂ plume concentration increased at a rate of 0.75-1.25gs^-1. In joint campaigns with SO₂ cameras, the NO₂ camera could also help in removing the bias introduced by the NO₂ interference with the SO₂ spectrum.

Atmos. Meas. Tech., 9, 60256034, 2016 www.atmos-meas-tech.net/9/6025/2016/ doi:10.5194/amt-9-6025-2016 Author(s) 2016. CC Attribution 3.0 License.

The AOTF-based NO2 camera

also help in removing the bias introduced by the NO2 interference with the SO2 spectrum.

1 Introduction

Nitrogen oxides (NOx = NO + NO2) play a key role in the

air quality of the boundary layer. While NO is produced in combustion processes (transport, thermal power plants, etc.), NO2 mainly appears through the reaction of NO with O3 or HO2. Eventually, the photolysis of NO2 releases an oxygen atom and a NO molecule. To a good approximation, the balance of NO and NO2 is kept constant through this cycle of photo-chemical reactions, which substantiates the widespread use of the NOx family concept (Seinfeld and Pandis, 2006). Considering the relative ease of measuring NO2 with visible-light spectroscopy, NOx budgets are often inferred based on NO2 measurements and the photochemical equilibrium assumption.

The most common NO2 remote sensing techniques rely on the differential optical absorption spectroscopy (DOAS), which is based on the tting of radiance spectra with the effective absorption cross section of interfering species (e.g., Platt, 1994). If equipped with a 2-D sensor array, these instruments disperse the light spectrum along one dimension and record its spatial variation along the other. Building a complete hyperspectral image requires an incremental depointing of the instantaneous eld of view (FOV) or a translation of the whole instrument. Typical examples of both applications can be found in Heue et al. (2008) or Lohberger et al. (2004). While the DOAS technique is well validated in terms of accuracy and sensitivity, the need for scanning the scene hampers the detection of dynamic processes. As the scene is sampled slice by slice, the nal image does not show a great temporal consistency: different rows (or columns, depending

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

Emmanuel Dekemper, Jurgen Vanhamel, Bert Van Opstal, and Didier Fussen

Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Avenue Circulaire 3, 1180 Brussels, Belgium

Correspondence to: Emmanuel Dekemper ([email protected])

Received: 15 July 2016 Published in Atmos. Meas. Tech. Discuss.: 15 August 2016 Revised: 22 November 2016 Accepted: 24 November 2016 Published: 15 December 2016

Abstract. The abundance of NO2 in the boundary layer relates to air quality and pollution source monitoring. Ob-serving the spatiotemporal distribution of NO2 above well-delimited (ue gas stacks, volcanoes, ships) or more extended sources (cities) allows for applications such as monitoring emission uxes or studying the plume dynamic chemistry and its transport. So far, most attempts to map the NO2 eld from the ground have been made with visible-light scanning grating spectrometers. Beneting from a high retrieval accuracy, they only achieve a relatively low spatiotemporal resolution that hampers the detection of dynamic features.

We present a new type of passive remote sensing instrument aiming at the measurement of the 2-D distributions of NO2 slant column densities (SCDs) with a high spatiotemporal resolution. The measurement principle has strong similarities with the popular lter-based SO2 camera as it relies on spectral images taken at wavelengths where the molecule absorption cross section is different. Contrary to the SO2 camera, the spectral selection is performed by an acousto-optical tunable lter (AOTF) capable of resolving the target molecules spectral features.

The NO2 camera capabilities are demonstrated by imaging the NO2 abundance in the plume of a coal-red power plant. During this experiment, the 2-D distribution of the NO2 SCD was retrieved with a temporal resolution of 3 min and a spatial sampling of 50 cm (over a 250 [notdef] 250 m2 area). The de

tection limit was close to 5 [notdef] 1016 molecules cm2, with a

maximum detected SCD of 4 [notdef] 1017 molecules cm2. Illus

trating the added value of the NO2 camera measurements, the data reveal the dynamics of the NO to NO2 conversion in the early plume with an unprecedent resolution: from its release in the air, and for 100 m upwards, the observed NO2 plume concentration increased at a rate of 0.751.25 g s1.

In joint campaigns with SO2 cameras, the NO2 camera could

6026 E. Dekemper et al.: The AOTF-based NO2 camera

on the scanning direction) are temporally disconnected from each other. The time gap can reach several minutes between both edges of the scene.

There are situations where high spatiotemporal resolution is needed. In volcanology, for instance, the so-called SO2 cameras are now increasingly complementing the measurements performed with classical dispersive techniques like grating spectrometers (Mori and Burton, 2006; Bluth et al., 2007). Their concept is based on taking spectral images of the plume through two interference lters. One lter selects a narrow band of the incident spectrum around 310 nm, where SO2 is still strongly absorbing, while the other one captures the light around 330 nm, where almost no more absorption takes place. The main advantages are a typical temporal resolution of 1 Hz, the capability to capture dynamic features such as puffs in the plume and the possibility to determine the plume speed from the sequence of images. The disadvantages are the interference by the plume aerosols caused by the coarse spectral resolution and the need for regular recalibration with reference cells lled with SO2 to account for changes of illumination conditions (Kern et al., 2010).More recent concepts now use the combined information of a spectrometer with the camera spectral images (Lbcke et al., 2013), which yields a greater measurement accuracy.

We present a new instrument, a spectral imager dedicated to measuring the 2-D NO2 eld above nite sources like thermal power plants, industrial complexes, cities, volcanoes, etc. The measurement principle is close to the SO2 camera: snapshots at two wavelengths emphasize the presence of NO2 by taking advantage of absolute differences in the molecule absorption cross section. Contrary to the SO2 cameras which use interference lters, the new instrument relies on an acousto-optical tunable lter (AOTF) to provide the spectral information. The AOTF can offer sufcient spectral resolution to resolve the structures of the NO2 spectrum. The ability to discriminate between weak and strong absorption within a few nanometers virtually cuts out any sensitivity to aerosol scattering and changes of solar angles. Potential applications include urban and industrial pollution monitoring, emission uxes estimation, satellite-product validation and volcanic plume chemistry.

2 Instrument concept

The AOTF-based NO2 camera springs from the ALTIUS instrument (atmospheric limb tracker for the investigation of the upcoming stratosphere; Fussen et al., 2016). ALTIUS is a space mission project aimed at the retrieval of atmospheric species concentration proles with a global geographical coverage and a high vertical resolution. Its primary scientic objective is to measure ozone, but NO2, aerosols, H2O, CH4, polar stratospheric and noctilucent clouds, and other minor species will be measured as well. Measurements will be performed in two different geometries: limb scattering and oc-

Figure 1. Optical layout of the NO2 camera seen from top. Light propagates from left to right through a pupil and a lens doublet, a polarizer selecting vertically polarized light, the AOTF, a second cross-oriented polarizer, two lens doublets and the detector.

cultations (Sun, Moon, stars, planets). To address the problem of tangent height registration of previous limb scatter instruments, a spectral imager concept based on a tunable lter has been selected. During the feasibility study, a prototype of the visible (VIS) channel (440800 nm) was built from commercially available parts. The detailed description of this prototype is given in Dekemper et al. (2012). We will only point out the key features of the concept.

The instrument images a 6 square FOV onto a Princeton Instrument Pixis 512B peltier-cooled CCD detector (512 [notdef]

512 pixels). The optical layout (Fig. 1) is linear with an intermediate focal plane located close to the AOTF. To preserve the spectral homogeneity across the image, the design is made telecentric by placing an iris at the object focal point of the rst lens. This ensures an identical propagation angle of all light rays through the AOTF.

The most important part of this NO2 camera concept is the AOTF (Chang, 1974). AOTFs have been used in many areas requiring spectral images (agriculture, food industry, uorescence spectroscopy, etc.) but received little attention from the atmospheric remote sensing community. The working principle is based on the interaction of light and sound in a birefringent crystal (see Fig. 2). By the momentum matching of the optical and acoustic waves, a narrow portion of the light spectrum is diffracted into a slightly different direction (a few degrees). If the incident radiation is linearly polarized, the diffracted beam will leave the crystal with the orthogonal polarization. The spatial and polarimetric dissociations can be combined to achieve very efcient extinction of the unwanted spectrum.

The wave vectors matching condition (Fig. 2) creates a monotonic relationship between the light wavelength and the sound frequency. The acoustic wave is launched into the crystal by a piezoelectric transducer bonded to one of its facets. Hence, selecting a particular wavelength simply requires us to drive the transducer to the matching frequency F ( ). The AOTF spectral transmission function (STF) closely follows a sinc2 shape. The amplitude of the STF, which determines the lter diffraction efciency (DE), is controlled by the acoustic power Pa( ), which also exhibits

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E. Dekemper et al.: The AOTF-based NO2 camera 6027

Figure 2. Schematics of the acousto-optic interaction in an AOTF (top view). The gray area depicts the acoustic eld created by the piezoelectric transducer bonded to a lateral face of the TeO2 crystal.

The momentum phase matching of the incident (ki) and diffracted (kd) photons with the acoustic wave (K) is represented in the [110]

crystallographic frame. The phase matching takes advantage of the medium birefringence: incident and diffracted light beams have orthogonal polarizations and different propagation directions, which facilitates their selection.

a smooth wavelength dependence. The transducer length denes the length of the acousto-optic interaction, which directly affects the AOTF bandwidth: a short transducer will induce a larger passband and vice versa.

The parameters of an AOTF are dened by the crystal elastic and optical properties and by the propagation directions of light and sound in the frame of the crystal axes (Voloshinov et al., 2007). The AOTF we used was manufactured out of a TeO2 crystal by the company Gooch & Housego (UK). It offers an aperture of 10 [notdef] 10 mm2 and a tuning range cov

ering the visible spectrum. Laboratory characterization revealed a transparency better than 90 % and a DE better than 95 %. In the relevant spectral range for NO2 measurements,i.e., around 450 nm, the STF showed a bandwidth of 0.6 nm. Typical driving frequencies were around 130 MHz, and less than 100 mW of acoustic power was needed in any circumstances. The theoretical number of resolvable spots at 450 nm is about 350 in the plane of acousto-optic interaction (horizon) and 700 in the vertical direction.

3 Measurement principle

There are strong similarities between the measurement principles of a lter-based SO2 camera and an AOTF-based NO2 camera: the FOV needs to be pointed towards the target region (e.g., a plume) while making sure that the background can still be seen in some areas of the image. Two spectral images of the scene are taken: one at a wavelength s where there is strong absorption by the target species and another at a wavelength w where there is weak absorption. In each image, the signal Sij ( ) (in e) recorded by pixel ij looking at the plume will be normalized by the background signal S0( ) in order to quantify the extinction that took place during the crossing of the plume. The optical thickness ij associated

Figure 3. NO2 absorption cross section measured with a Fourier transform spectrometer (gray line; Vandaele et al., 1998) and with this NO2 camera in the laboratory (red line). At 450 nm, the spectral resolution of both datasets are 0.04 and 0.6 nm respectively.

with the slant column density (SCD) of the target species ob-served in the FOV of pixel ij follows from the comparison of the normalized signals recorded at the two wavelengths.

The major difference comes from the capability of the AOTF-based NO2 camera to resolve the ne structures of the absorption cross section NO2 (Fig. 3). This allows choosing s and w very close to each other (a few nm), minimizing the interference by broadband absorbing and scattering species like aerosols.

3.1 Mathematical model

As AOTFs do not treat different polarizations identically, an AOTF-based NO2 camera exhibits a strong polarization sensitivity. The polarization state of a stream of light is described by the Stokes vector s = (I,Q,U,V )T , where I = Ih + Iv

and Q = Ih Iv, with Ih and Iv being the light intensity

along the horizontal and vertical axes of a scene frame. U and V also refer to the orientation of the polarization ellipse but they will not be discussed further because they do not participate if the AOTF and its surrounding polarizers are well aligned.

When light passes through a polarizing part, its Stokes vector can be changed. A polarizing element is therefore represented by a 4 [notdef] 4 transfer matrix: the Mueller matrix M. A

chain of optical elements is represented by the product of their Mueller matrices. In our design, the light passes rst through a vertical linear polarizer, then the AOTF, and nally a horizontal linear polarizer. The Stokes vector representing the light leaving the second polarizer is therefore given by s[prime] = MPh [notdef] MAOTF [notdef] MPv [notdef] s. The Mueller matrices of the ele-

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6028 E. Dekemper et al.: The AOTF-based NO2 camera

ments are as follows:

MAOTF =

A

2

0

B

B

@

1 1 0 0

1 1 0 0

0 0 0 0 0 0 0 0

1

C

C

A

, (1)

Suppose now that pixel ij is looking through an optically thin plume. NO2 and other species will absorb or scatter photons and decrease the background light intensity Iv0 according to the BeerLambert law of extinction:

Ivij ( ) = Iv0( ) [notdef] exp NO2 ij ( ) ij ( ) [parenrightbig]

2e + 2t 2e 2t 0 0

2e 2t 2e + 2t 0 0

0 0 2 e t 00 0 0 2 e t

1

C

C

A

, (5)

where NO2 ij denotes the plume optical thickness caused by absorption by NO2 along the light path ending on pixel ij, and ij is the effective optical thickness of all other chem

ical species and particles. Over the passband of the AOTF, one can consider ( ) as a constant value ( c) and replace

NO2( ) by its weighted average:

NO2( c) =

[integraltext]

NO2( ) [notdef] G( c)d

[integraltext]

MPv =

1

2

0

B

B

@

, (2)

MPh =

1

2

0

B

B

@

2t + 2e 2t 2e 0 0

2t 2e 2t + 2e 0 0

0 0 2 e t 00 0 0 2 e t

1

C

C

A

, (3)

where A is the amplitude of the AOTF STF (i.e., its DE, 0

A 1), 2t is the attenuation of the light intensity along the

polarizer transmission axis, and 2e is the attenuation along the extinction axis. Assuming that all three elements have their transmission and extinction axes well aligned, the total Mueller matrix of the camera is simply M = 4t [notdef] MAOTF. As

the detector only measures the total light intensity, the rst element of the Stokes vector is the only meaningful quantity: s[prime](1) = A [notdef] 4t [notdef] (I Q)/2 = A [notdef] 4t [notdef] Iv. Hence, in the present

conguration, the NO2 camera is only sensitive to vertically polarized component of the light.

We now have a description of the light intensity which will be measured by the detector, but we still have to account for the transmittance of the lenses (T ) and the quantum efciency (QE) of the detector. These terms exhibit a smooth wavelength dependence. For the AOTF STF, one can use F( ; c) = A( c) [notdef] G( c), where G is essentially a

sinc2 function. Moreover, some parameters are susceptible to vary across the FOV, yielding a pixel-to-pixel variation. This is particularly true when image planes are located close to optical surfaces (mainly the AOTF and the detector). Finally, the electronic current (in e s1) found in pixel ij when the

AOTF is tuned to c is given by

Cij ( c) = [integraldisplay]

. (6)

As the optical thickness is dened by the product of the trace gas SCD k with its absorption cross section , it is clear that NO2( c) = kNO2.NO2( c). Under these assumptions, one

can insert Eq. (5) into Eq. (4) and write for the pixel photoelectric current:

Cij ( c) =rij ( c) [notdef] exp NO2 ij ( c) ij ( c)

[parenrightbig]

[notdef]

[integraldisplay]

G( c)d

Iv0( ) [notdef] G( c)d . (7)

In the meantime, other pixels have been looking at the unattenuated background intensity I0. Suppose that one of them is pixel mn. According to Eq. (4), we have

Cmn( c) = rmn( c) [notdef]

[integraldisplay]

Iv0( ) [notdef] G( c)d . (8)

Averaging all these background-looking pixels yields the reference current associated with the background intensity:

C0( c) = r( c) [notdef]

[integraldisplay]

Iv0( ) [notdef] G( c)d , (9)

with r representing the average instrument response. Dividing Cij by C0 yields the transmittance of the plume alone:

Tij ( c) =

Cij ( c) rij ( c)

C0( c) r( c) =

exp NO2 ij ( c) ij ( c)

[parenrightbig]

Aij ( c) [notdef] 4t( ) [notdef] Ivij ( ) [notdef] G( c)

[notdef] T ( ) [notdef] QEij ( )d ,

[similarequal] Aij ( c) [notdef] 4t( c) [notdef] T ( c) [notdef] QEij ( c)

[integraldisplay]

Ivij ( ) [notdef] G( c)d ,

= rij ( c) [integraldisplay]

. (10)

If the spectral interval between w and s is small enough that the approximation ( w) = ( s) holds, then the ra

tio of the transmittances T ( w)/T ( s) is a quantity which only depends on the NO2 content of the plume. Introducing the relative instrument response at pixel ij, ij ( ) =

rij ( )/r( ), we nd

Tij ( w)

Tij ( s) =

Cij ( w)

C0( w)ij ( w)Cij ( s)C0( s)ij ( s)

= exp NO2 ij ( s) NO2 ij ( w)

[parenrightbig]

Ivij ( ) [notdef] G( c)d . (4)

The decision to leave the smoothly varying parameters out of the integral is supported by the narrow passband of the AOTF (0.6 nm). Their product forms the instrument response at pixel ij and wavelength c: rij ( c). The remaining integral is simply the convolution of the vertically polarized incident light intensity with the AOTF STF.

. (11)

Finally, the NO2 SCD subtended by the area of the plume observed by pixel ij follows by taking the logarithm of the

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E. Dekemper et al.: The AOTF-based NO2 camera 6029

ratio of transmittances:

kNO2 ij =

1

NO2( s) NO2( w) [notdef]

ln

Tij ( w) Tij ( s)

[radicaltp]

[radicalvertex]

[radicalvertex]

[radicalbt]

N

. (12)

Clearly, the best sensitivity is reached by maximizing the differential optical thickness when selecting w and s.

3.2 Ancillary data

Equations (11) and (12) show that the NO2 SCD can be obtained from a combination of measurements (the detector signal), cross-section data and the knowledge of the instrument response. In the results presented below, the cross section is taken from Vandaele et al. (1998). For the ij coefcients, an ad hoc method was set up to build a synthetic at eld. Taking advantage of a cloudy weather (100 % cloudiness), long-exposure frames (10 s) were captured at the required wavelengths looking at zenith. The mean image obtained from tens of such frames constitutes the instrument response to a synthetic, radiometrically at scene. This allows us to remove wavelength-dependent nonuniformities which can be relatively pronounced in, e.g., the AOTF.

Determining the photoelectric current strictly proportional to the signal (i.e., Cij and C0) implies that voltage offset, dark current and stray light have been subtracted from the raw data. In this respect, AOTFs offer a unique feature: one can turn them off. This is simply done by bringing the acoustic wave amplitude to 0. An image acquired in these conditions contains anything but the real signal (i.e., dark current, offset, stray light). Using Dij and Doffij to represent the raw signal of pixel ij (in digital numbers, DN) when the AOTF is turned on or off respectively, the photo-electric signal is given by

Sij =

N

Yk=1Cij ( c,tk)

= rij ( c) [notdef] exp

1 N

[parenrightBigg]

N

Xk=1NO2 ij ( c,tk) + ij ( c,tk)

[notdef]

[integraldisplay]

Iv0( ) [notdef] G( c)d .

= rij ( c).exp NO2 ij ( c,t) ij ( c,t)

[parenrightbig]

Iv0( ).G( c)d . (15)

Another means of increasing the reliability of the measurements is to use different doublets, i.e., pairs of w and s. If the transmittance is known for several doublets, their product strengthens the NO2 SCD retrieval by providing information from multiple spectral regions. If [Delta1]NO2 = NO2( s)

NO2( w), then for two doublets we have for the SCD

kNO2 ij =

1

[Delta1](1)NO2 + [Delta1](2)NO2

[notdef]

[integraldisplay]

[notdef] ln

Tij ( w1) [notdef] Tij ( w2) Tij ( s1) [notdef] Tij ( s2)

. (16)

This approach can potentially attenuate a bias in one of the measurements.

3.4 Error budget and instrument sensitivity

One can work out Eq. (12) with the classical rst-order Taylor expansion approximation to determine the uncertainty on the NO2 SCD. This approach will require estimates of the uncertainty on the photon counts Cij , on the background signal C0, on the relative instrument response ij and on the cross-section data NO2. These estimates are not always easily obtained, and we briey discuss each of them.

The photo-electric counting rates Cij are obtained from Eq. (13): Cij = Sij /t, where t is the sensor exposure time. It

is reasonable to assume that the camera operator selects acquisition settings to ensure that the signal is well into the shot noise regime: Cij =

Dij Doffij

, (13)

where is the sensor gain (in DN/e). The only precaution is to take these dark images regularly because the stray light is a function of the general illumination conditions (e.g., solar angles) and it will vary with local time.

3.3 Data averaging and multiple image doublets

It is often necessary to repeat the measurements in order to average out transient features and increase the signal-to-noise ratio. Assuming that only the plume optical transmission varies, we can write a time-dependent version of Eq. (7):

Cij ( c,t) =rij ( c) [notdef] exp NO2 ij ( c,t) ij ( c,t)

[parenrightbig]

[notdef]

[integraldisplay]

pSij /t. With signals exceeding 104 e in 1 s (the case in the examples below), the relative uncertainty on Cij will be below 1 %.

The background signal C0 is estimated by averaging the pixels looking at the background of the scene. While one would presume that the averaging of a large number of such pixels should yield a very high precision, the accuracy is limited by the difculty of identifying pixels effectively looking at the background. Automated data processing needs a screening of each image to determine if a pixel is looking at the plume, the background, a cloud or even a bird. This screening is based on the interpretation of the raw signals and, for instance, it sometimes fails to recognize pixels which still have in their FOV the residual NO2 molecules left by a past position of the plume. From our experience, the relative uncertainty on C0 determined from a single image is generally larger than 1 % (determined from the sample standard

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Iv0( ) [notdef] G( c)d . (14)

The time-averaged optical thickness ( ,t) can be obtained from the geometric mean of the consecutive images:

6030 E. Dekemper et al.: The AOTF-based NO2 camera

deviation). Using multiple images, as explained in Sect. 3.3, reduces this uncertainty, as C0 is computed for each image and then averaged. A 1 % total relative error is achievable with a few images.

The relative instrument response nonuniformity ij can be

obtained from a homogeneous scene (i.e., a at eld such that Iij ( ) = I ( )8i,j). In this particular case, ij ( ) =

Cij ( )/C( ), where C( ) is the average of Cij over a large number of pixels. If the at eld is built from a number of relatively homogeneous images under the assumption that their average is truly at, then the uncertainty on the atness participates to the error budget of ij ( ) and quickly becomes the driver (signal shot noise is surpassed). This error source is a generic problem of all imaging systems but remains difcult to quantify. The only certainty is that it drops with the sample size.

The NO2 absorption cross-section data are taken from Vandaele et al. (1998), who report a total relative uncertainty of 3 % at a resolution of 2 cm1 (0.04 nm at 450 nm). Taking our coarser resolution into account (about 0.6 nm), the uncertainty drops to about 0.8 % for the convolved spectrum. However, the AOTF tuning curve is temperature dependent, with a typical drift of +0.1 nm per Kelvin (Ohmachi and Uchida,

1970; Uchida, 1971). The driving electronics is currently not enslaved to a temperature sensor. The exact measurement wavelength is computed at the processing stage. Depending on the amount of wavelength drift, the uncertainty on NO2( ) can reach 510 %.

The minimum relative uncertainty on the NO2 SCD will be reached if the uncertainty on the plume transmittance T is driven by C0. Assuming T /T = 1 %, and taking into ac

count a 5 % error on the cross-section term (with a typical value for NO2( s)NO2( w) = 2[notdef]1019), one obtains

k = 5[notdef]1016 molecules cm2. If one assumes less favorable

conditions like a 1 % uncertainty on , yielding T /T = 2 %

and a 10 % error on the cross section, then the SCD error reaches 1017 molecules cm2.

4 Application to the remote sensing of NO2 at a coal-red power plant

The data of a spectral imager such as the NO2 camera are more easily exploited if a number of observational requirements are satised. First, the camera must be placed at a location where both the plume and the background can be captured within the same image. Second, the target plume must remain optically thin in order to preserve the assumption of the BeerLambert extinction along a straight light path. Finally, scattered clouds behind the plume will corrupt the retrieval and should be avoided.

These three requirements were sometimes fullled during the second Airborne ROmanian Measurements of Aerosols and Trace gases (AROMAT-2) campaign in August 2015.The campaign aimed at joining the efforts of several Euro-

Figure 4. Observational geometry during the AROMAT-2 campaign at Turcenis power plant. The NO2 camera was installed on a football pitch looking at the four 280 m tall stacks. The red lines delimit the camera horizontal FOV (6 ). The direction of the Sun at 16:00 local time is approximately indicated, together with two rays illustrating the scattering behind the scene towards the camera. One of the rays passes through the plume, while the other one passes by.Map data from OpenStreetMap.

pean research institutes and universities to spatially and temporally characterize the emissions from two types of sites: a large city (Bucharest) and point sources (large thermal power plants in the Jiu Valley, Romania). Both sites should eventually serve as validation targets for the ESA TROPOMI/S-5P mission.

The NO2 camera was placed at a distance of 2.5 km from a group of four stacks belonging to Turcenis power plant, the largest being in Romania (330 MW per turbine, 2000 GWh year1 total electric power generation of which more than 93 % is generated from coal). Figure 4 depicts the measurement geometry. Our location was 44.6792 N,23.3788 E, the line of sight (LOS) azimuth angle ranged from 113 (left edge of the image) to 119 (right edge) eastward from north, and the LOS zenith angle ranged between75.5 (top edge) and 81.5 (bottom edge). We only report on measurements performed on 24 August between 16:15 and 16:30 LT as the observational conditions were close to ideal and best illustrate the performance of the instrument. In particular, the smokes were optically thin, with the blue sky clearly visible in the background. This ensures that absorption is the dominant process over scattering for the extinction of light rays crossing the plumes (BeerLambert regime).The optical thickness of the smokes was always smaller than0.1 at our measurement wavelengths.

4.1 Exhaust plume NO2 SCD eld

As explained in Sect. 3.1, the 2-D NO2 SCD eld is computed from at least two spectral images recorded at wavelengths showing a signicant difference of absorption cross section. To increase the reliability of the measurements, four doublets of wavelengths were used: w1 = 441.8 and

s1 = 439.3; w2 = 446.7 and s2 = 448.1; w3 = 437.9 and

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E. Dekemper et al.: The AOTF-based NO2 camera 6031

Figure 5. Sample NO2 SCD eld obtained from the averaging of images acquired at w2 = 446.7 nm, s2 = 448.1 nm, w4 = 465.8 nm and

s4 = 463.2 nm (12 of each). The color scale shows the plume NO2 SCD in molecules cm

2. The x and y axes show the image dimensions

in the scene plane, while the title gives the time span (local time).

s3 = 435.1; w4 = 465.8 and s4 = 463.2. The automated

acquisition system was in charge of synchronizing the driving of the AOTF with the image acquisition. A nominal acquisition sequence started by setting the appropriate acoustic signal for the AOTF to lter at w1, opening the CCD shutter for 0.5 s, reading out the image and repeating these operations for the seven other wavelengths. After completion of the nominal sequence, a picture with the AOTF turned off is taken and the nominal sequence is resumed. The dwell time between the closing of the shutter and its reopening was 1.3 s, yielding a total acquisition sequence duration of 13.1 s for the 8 spectral images. In the plane of the stacks, the image footprint spans an area of 250 [notdef] 250 m2 with a 50 cm sampling.

The data analysis revealed that the images from the second and fourth doublets were the less noisy because of a larger natural radiance and sensor sensitivity compared to the wavelengths of doublets 1 and 3. Also, due to the plume displacement over time (wind) and the presence of moving and changing inhomogeneities across the plume (puffs, turbulent eddies), it was necessary to perform time averaging (Sect. 3.3). Indeed, the 1.3 s between two consecutive images is already a long time for features moving at a typical 5 m s1 speed (corresponding to 10 pixels per second).

Figure 5 shows the NO2 SCD eld retrieved from the averaging of images taken at w2, s2, w4 and s4 (12 of each)

using the method described in Sect. 3.3. For each wavelength, the background signal C0 was determined from image areas unaffected by the plume. The relative error on C0 is about 0.5 % (estimated from the standard deviation C0 of the pixels sample yielding C0). Within this precision, no variation of C0 across the FOV could be signicantly detected. The reason is the relatively small FOV of the camera (about 6 ) combined with a high Sun at the time of the measurements (making the scene illumination quite homogeneous). In Fig. 5, the background grayscale image is the mean image at w4, whereas the pixels where the SCD is computed

were selected based on the criterium Cij < C0 2C0. Inves

tigating the random uctuations observed in various areas of the SCD eld, one can estimate the detection limit to about 5 [notdef] 1016 molecules cm2.

4.2 NO2 emission uxes and synergies with SO2 cameras

The capability of resolving the NO2 SCD eld with a high spatial and temporal resolution provides new possibilities for the understanding of the plume chemistry. Coal combustion processes yielding the formation of nitrogen and sulfur species are well known (Flagan and Seinfeld, 1988), and several reactive plume models can simulate the transport, formation and removal of these species over different scales.These models are generally validated by in situ air sampling at distances of several kilometers downwind (see for instance Chowdhury et al., 2015). Very few experiments attempted to characterize the reactive content of the early plume, where the reactions are still governed by the combustion products (Hewitt, 2001). In most cases, a DOAS scanning system was used (Lee et al., 2014, 2009; Lohberger et al., 2004).The same technique was also used for SO2, but to a lesser extent since the introduction of lter-based SO2 cameras (Smekens et al., 2015). Recently, imaging Fourier transform spectroscopy (IFTS) demonstrated capability for the measurement of a number of mid-infrared emitting species such as CO2 and SO2 (Gross et al., 2010). However, NO, but not

NO2, can be retrieved with this technique.

An undisputed advantage of imaging systems with high temporal resolution is their ability to track the displacement of remarkable features from one image to another. We used the complete time series of spectral images (50 sequences of 8 spectral images at a rate of 0.5 Hz) to determine the vertical speed of the plume. This was done by tracking signal features created by local increase or decrease of the NO2 concentration. On average, a vertical speed of 4.8 [notdef] 0.5 m s1 was ob-

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6032 E. Dekemper et al.: The AOTF-based NO2 camera

Figure 6. NO2 ux computed through the plume horizontal cross section as a function of altitude. Stacks height is 280 m. A symmetric Gaussian dispersion is assumed up to the region of apparent intersection of the two plumes.

served. Furthermore, assuming a Gaussian dispersion of the plume, one can infer a circular cross section from the apparent width of the plume at each detector row (i.e., every 50 cm above the stack outlet). As a result, a prole of emission ux (in g s1) can be drawn. Figure 6 shows the NO2 emission ux as a function of altitude up to a height above which the two plumes cannot be discriminated anymore. The uxes were calculated from the two SCD maps of Fig. 5 and both stacks. The increase is the result of the conversion of NO into NO2 mainly by the reactions 2NO + O2 ! 2NO2

and NO + HO2 ! NO2 + OH (Flagan and Seinfeld, 1988;

Miller and Bowman, 1989), even if these processes are balanced by the photodissociation of NO2 as soon as it reaches open air under daylight (NO2+h ! NO+O). Qualitatively,

these results agree well with the increase reported by Lee et al. (2009) in a study of the rate of increase of NO2 above power plant stacks. The analysis of Fig. 6 reveals that within the method approximations, the NO2 concentration in the plume increases at a rate ranging from 0.75 to 1.25 g s1 (9.8[notdef]10211.6[notdef]1022 molecules s1) on average for the rst

20 s.

The knowledge of the spatial distribution of NO2 can also prove useful to correct measurements marked by interference from NO2. A good example is with SO2 cameras where the

SO2 SCD eld is retrieved by comparing the plume transmittance around 310 and 330 nm. In this range, NO2 is also absorbing and its cross section roughly doubles from 310 to 330 nm. Therefore, if both molecules are present in the plume, the SO2 camera alone cannot distinguish their respective signatures. So far, this interference has been over-

looked in SO2 camera validation exercises (Smekens et al., 2015; Kern et al., 2010). In the case of the plumes shown in Fig. 5 for instance, a SO2 camera such as the one used by

Smekens et al. (2015) would observe a [Delta1]NO2 = 0.04 when

the NO2 SCD reaches 3 [notdef] 1017 molecules cm2. This varia

tion of optical thickness corresponds to a SO2 SCD of about 1.6[notdef]1017 molecules cm2, which is twice the detection limit

reported in Smekens et al. (2015). Clearly, the bias would increase with higher concentrations of NO2. Taking advantage of the similar spatial resolution of both instruments, the NO2 camera can provide a complete correction map for the

SO2 data. On the temporal resolution side, however, the NO2 camera is, at the moment, not capable of following the pace of SO2 cameras (1 Hz typical), such that the correction maps would have to be applied to temporally averaged SO2 data.

5 Conclusions

We have described a new passive atmospheric remote sensing instrument for the measurement of NO2 SCDs above strong sources. It is based on an AOTF which offers a sufcient acceptance angle to be placed in an imaging system and the necessary resolution for taking advantage of the ne structures of the NO2 absorption cross section. The AOTF is electrically driven, such that fast synchronized acquisitions of spectral images are possible.

The measurement principle is similar to the lter-based SO2 camera: SCDs are retrieved from at least two spectral images taken at wavelengths where absorption by the target molecule is signicantly different. Wavelengths are picked in the range 440470 nm. Thanks to its higher spectral resolution, the AOTF-based NO2 camera can perform its measurements within a few nanometers. This makes the sensitivity to aerosols negligibly small.

A mathematical framework for data processing has been developed, and the different sources of error have been addressed. In applications focusing on relatively high spatiotemporal resolution, the NO2 SCD detection limit is about 5[notdef]1016 molecules cm2. Different measurement geometries

offering longer integration times or more stable targets would yield a lower limit.

The NO2 camera was successfully tested during the AROMAT-2 campaign where measurements of NO2 SCD elds above the ue gas stacks of a coal-red power plant were performed with a temporal resolution of 3 min and a spatial sampling of 50 cm (for a complete scene of 250 [notdef]

250 m2). Values up to 4 [notdef] 1017 molecules cm2 were ob-

served. The quality of the data allowed us to clearly identify the conversion process from NO to NO2 in the early plume, providing quantitative information on the plume dynamic chemistry. In another example of application, the measurements were used to show how the knowledge of the high-resolution NO2 eld can help to correct SO2 camera data.

If overlooked, the interfering absorption of NO2 can yield

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E. Dekemper et al.: The AOTF-based NO2 camera 6033

a signicant bias in the retrieved SO2 SCDs. Other applications range from emission monitoring to volcanic plume chemistry.

While the concept is mature, a number of improvement directions are still being investigated. The most promising ones are the implementation of a temperature feedback loop to reduce the uncertainty on the ltered wavelength and the replacement of the CCD by a CMOS in order to reduce the cooling needs and increase the temporal resolution of the measurements.

6 Data availability

The data are available upon request to the contact author.

Author contributions. Emmanuel Dekemper developed the NO2 camera measurement principle, led the characterization and the participation in the AROMAT-2 campaign, and processed the data.Bert Van Opstal and Jurgen Vanhamel developed the acquisition software and the AOTF driving electronics and participated in the campaign. Didier Fussen is at the origin of the instrument concept and supported its development in the frame of the ALTIUS project.The authors declare that they have no conict of interest.

Acknowledgements. This work was funded under PRODEX contract 4000110400. Participation to the AROMAT-2 campaign was funded under ESA contract 4000113511. The authors would like to thank Alexis Merlaud for inviting them to participate in the AROMAT-2 campaign. Emmanuel Dekemper would like to thank Kerstin Stebel for the interesting discussions on SO2 cameras and

Vitaly Voloshinov for his support in all AOTF-related matters.

Edited by: F. PrataReviewed by: C. Kern and J.-F. Smekens

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