Aerosol content survey by mini N2-Raman lidar: Application to local and long-range transport aerosols

Atmospheric Environment 45 (2011) 7487e7495

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Aerosol content survey by mini N2-Raman lidar: Application to local and long-range transport aerosols Philippe Royer a, b, *, Patrick Chazette a, Melody Lardier b, Laurent Sauvage b a b

Laboratoire des Sciences du Climat et de l’Environnement, Laboratoire mixte CEA-CNRS-UVSQ, CEA Saclay, 91191 Gif-sur-Yvette, France LEOSPHERE, 76 rue de Monceau, 75008 Paris, France

a r t i c l e i n f o

a b s t r a c t

Article history: Received 6 August 2010 Received in revised form 21 October 2010 Accepted 1 November 2010

This study shows an aerosol content survey in the low and middle troposphere over Paris with a compact and light Nitrogen-Raman lidar which has been recently developed by the Commissariat à l’Energie Atomique (CEA) and LEOSPHERE company. This eye-safe and wide field-of-view system (full overlap between 150 and 200 m) is particularly well-adapted to air pollution survey in the vicinity of Megalopolis. Extinction-to-backscatter coefficient (so-called Lidar Ratio LR) profiles obtained with a Tikhonov regularization scheme are presented for long-range transport events of aerosols (volcanic ash plume LR ¼ 48  10 sr, and desert dust, LR ¼ 45  8 sr) which may contribute to the local load of aerosols emitted by traffic and industries in Megalopolis. Due to an insufficient signal to noise ratio (SNR < 30), a new dichotomous algorithm has been developed to perform daytime inversions every hour which is in accordance with the typical time evolution of aerosols within the planetary boundary layer. This inversion scheme is based on the constraint of the elastic channel with the aerosol optical depth (between typically 0.2 and 0.7 km) determined with the N2-Raman channel and thus only gives access to an equivalent LR between 0.2 and 0.7 km with a relative uncertainty lower than 15%. This approach has been applied to retrieve diurnal cycle of LR for polluted continental aerosols over Paris and is compared with Tikhonov regularization applied during the night. We found a mean value of 85  18 sr for polluted continental aerosols which is in agreement with other studies performed around the Paris urban area. Results for aerosol optical properties are presented and the error sources are discussed for each approach. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: UV N2-Raman lidar Lidar ratio Iterative approach Aerosol Volcanic ash Optical properties

1. Introduction Aerosols come from various sources which can be natural (ocean, desert, volcano) or anthropogenic (traffic, industries and biomass burning). This heterogeneity of sources leads to high spatial and temporal variabilities of aerosol chemical, optical and physical properties which make their study and their characterization difficult. Many studies have also shown that fine particles included in particulate matter with aerodynamic diameter less than 10 mm (PM10) impact on human health especially for pollution and biomass burning aerosols (e.g. Dockery and Pope, 1996). This is a problem to deal with since almost half of the world population lives in urban areas. It is thus important to study, to understand and

* Corresponding author at: Laboratoire des Sciences du Climat et de l’Environnement, Laboratoire mixte CEA-CNRS-UVSQ, CEA Saclay, 91191 Gif-sur-Yvette, France. E-mail addresses: [email protected] (P. Royer), patrick.chazette@lsce. ipsl.fr (P. Chazette), [email protected] (M. Lardier), [email protected] (L. Sauvage). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.11.001

to quantify the impact of anthropogenic aerosols on air quality in high population areas. Several instrumental campaigns have already taken place in different megalopolis such as Mexico (e.g. Raga et al., 2001), Athens (e.g. MEDCAPHOT-TRACE; Ziomas, 1998) and Beijing (e.g. CAREBeijing-2006; Garland et al., 2009). This study is focused on aerosol content survey over Paris area (w12 million inhabitants) which is one of the three megalopolis in Europe (with London and Moscow). The aerosol chemical and optical properties of polluted continental aerosols have already been investigated over Paris in the framework of ESQUIF (Etude et Simulation de la QUalité de l’air en Ile-de-France; Vautard et al., 2003; Chazette et al., 2005) and LISAIR (LIdar pour la Surveillance de l’AIR; Raut and Chazette, 2007). During these campaigns aerosol Extinction-to-Backscatter ratio or so-called Lidar Ratio (LR) values of w90 and w70 sr have been assessed around Paris using lidar/ sun-photometer coupling at the wavelengths of 355 and 532 nm, respectively. LR is a crucial parameter to determine aerosol type and aerosol radiative forcing as it is directly linked to the single scattering albedo. Different techniques have been developed to retrieve this

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parameter from lidar measurements. LR profiles can be obtained with a horizontal pointing Rayleigh-Mie lidar onboard an Ultralight Aircraft (Chazette et al., 2007) or using multi-angular measurements (Sicard et al., 2002). Both approaches need to suppose horizontal homogeneity of atmosphere. A more usual method is the synergy with a passive instrument such as a sun-photometer (e.g. Chazette, 2003) or radiometers (e.g. Berthier et al., 2006; Royer et al., 2010) measuring the total aerosol optical depth (AOD). The main inconvenient of this approach is that it only gives access to a height independent LR which can poorly reflects the reality especially in case of several layers in the troposphere. Furthermore sun-photometer coupling is only possible for daytime and in cloudfree conditions. These limitations are overcame using Raman lidar and High Spectral Resolution Lidar (HSRL). HSRL takes advantage of the Doppler frequency shifts due to the different velocities of molecules (w300 m s1) and aerosols (w1e10 m s1) as it is well described in Shipley et al. (1983). Hereafter we will present two approaches based on N2-Raman lidar measurements which takes advantage of the weak inelastic backscattered signal from nitrogen molecules. This technique has been initially proposed by Leonard (1967) and thereafter improved and applied by several authors for the study of aerosol optical properties (e.g. Ansmann et al., 1992). For many years, Raman lidar observations were only possible during the night. Thanks to the development of highpower transmitters and narrow-band filters daytime measurements are now possible by considerably reducing the daylight background. The next section details the technical requirements and the main characteristics of the nitrogen Raman lidar used for this work. The data processing including lidar equations and the different algorithms (Tikhonov regularization and iterative approach with AOD constraint from Raman channel) used to invert lidar signal are described in Section 3. Their uncertainties assessed thanks to Monte Carlo simulations using a direct-inverse model are presented in Section 4. Finally, different examples of long-range transport (volcanic ashes and desert dust aerosols) and local pollution observations are described and discussed in Section 5. 2. Raman lidar technical characteristics Table 1 summarizes the main technical characteristics of the mini N2-Raman lidar. It is composed of two reception channels: one dedicated to the measurement of the co-polar and cross-polar signals at w355 nm and one to the inelastic nitrogen Raman backscattered signal at w387 nm. All data acquisitions (for elastic and Raman channels) have been performed in analog mode. A dark signal is regularly measured and subtracted to remove electronic fluctuations on analog signals. Analog signals range from 0 to 0.2 V in order to avoid any saturation of photomultiplier tubes. The total elastic signal is computed by a linear combination of the co and cross-polar signals using a normalization at a reference altitude where only molecular scattering occur. This lidar enables the retrieval of aerosol optical properties (extinction, backscatter coefficient and depolarization ratio) and atmospheric structures (boundary layer heights, aerosol layers and clouds) with a resolution of 1.5 m along the line of sight. It has been designed to air quality survey in urban environments and regional climate studies. The design represents a compromise between instrumental performances, a high mobility and a low full-overlap function. Indeed, a wide reception field-of-view of w4 mrad enables to reach a full-overlap between 150 and 200 m. The Raman lidar is based on a Ultra Nd:YAG laser manufactured by QUANTEL delivering pulses with a frequency of 20 Hz and a mean pulse energy of 16 mJ at 354.67 nm (ll). The use of a low-energy laser combined with two small reception channels with diameters of 15 cm give the

Table 1 N2-Raman lidar main technical characteristics. Laser

Nd:Yag Ultra (Quantel)

Energy (mJ) Frequency (Hz)

16 at 355 nm 20

Reception channels and wavelengths

Reception diameters (cm) Field of view (mrad) Full overlap (m)

Elastic // (355 nm) Elastic t (355 nm) N2-Raman (387 nm) 15 4 w150

Detector Detection mode Filter bandwidth (nm) Vertical resolution (m) Acquisition system

Photomultiplier tubes Analog 0.3 1.5 PXi technology at 100 MHz

Lidar head size (cm) Lidar head and electronic weight (kg)

w 70  45  18 <50

opportunity to have a compact (w70  45  18 cm) and light (<50 kg for optical and electronic) system operating onboard the ground-based Mobile Aerosol Station (Chazette et al., 2005). The Raman lidar technique is based on the measurement of the weak inelastic backscattered signal from an atmospheric molecule. We only consider Stokes VibrationeRotation Raman backscattered signal from N2 linear diatomic molecules which only has one vibrational mode. The wavelength number (Ds) associated to this vibrational transition 0 / 1 is 2330.7 cm1. The narrow-band interference filter is centered on the Stokes-Raman Q-branch wavelength (lr) of N2 molecules given by the following equation (e.g. Long, 2002):

lr ¼

1 1

ll

 jDsj

¼ 386:63 nm

(1)

The Full Width at Half Maximum (FWHM) of narrow-band filter has been chosen to select only the Q-branch which is 10 times more intense than O and S-branches of the N2 Raman-Stokes spectrum. The FWHM of the Q-branch is about 0.15 cm1 and stable towards pressure and temperature variations in the atmosphere. The spectral linewidth of the laser (FWHM 3 cm1 (w38 pm)) and the frequency drift due to temperature variations can be neglected because the laser is temperature-regulated. The FWHM of the narrow-band filter has been taken to 0.3 nm which is consistent with the FWHM of the Q-branch line (60 pm). 3. Data processing 3.1. Lidar equations The range-corrected signal Sk (corrected for the sky background, the solid angle and the overlap function) measured at wavelength lk and at altitude z with a vertically pointing lidar can be written under the form (Measures, 1984):

8 <

Sk ðzÞ ¼ Kk bk ðzÞexp

:



Zz  0

with h ¼ ðlk =lj Þ

 a



0 a 0 m 0 a 0 0 am j ðz Þ þ aj ðz Þ þ ak ðz Þ þ haj ðz Þ dz

9 = ;

ð2Þ

Using the nomenclature of equation (1), equation (2) corresponds to either the elastic channel when k ¼ j ¼ l and to the inelastic channel when k ¼ r and j ¼ l. Kk is the instrumental constant which

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includes optical reflection/transmission, quantum efficiency of detectors, amplification gains, laser energy and reception area, and a are the molecular (m) or aerosol (a) extinction coefficients. Aerosol extinction coefficients at lj and lk wavelengths are linked by the relationship with the Ångström exponent å (Ångström, 1964). It is close to 1.5 for pollution aerosols (e.g. Chazette et al., 2005). We have here supposed å ¼ 1.1 which is the mean value observed between 340 and 440 nm by the sunphotometer of the Paris AERONET (http://aeronet.gsfc.nasa.gov/) ground-based station. For the elastic channel, the backscatter coefficient (bl) is the sum of the aerosol (bal ) and molecular (bm l ) backscatter coefficients defined for j ¼ l as:

aa 3am bl ðzÞ ¼ kbw l þ l 8p LRl ðzÞ |fflfflfflfflffl{zfflfflfflfflffl} |fflfflffl{zfflfflffl} m bl

(3)

bal

where kbw is the King factor of air (King, 1923). For N2-Raman channel, br designates the Raman backscatter coefficient which is the product of the Raman differential backscatter cross section (dsr/dU)p and nitrogen density profile dr:

br ðzÞ ¼ dr ðzÞ

ðdsr =dUÞ |fflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflpffl}

(4)

2:81030 cm2 sr1 m

The molecular extinction (am ) and backscatter (b ) coefficients are determined here with a polynomial approximation (Nicolet 1984) using a reference atmospheric density calculated from ancillary measurements (Chazette et al., 2010). The aerosol backscatter (bal ) and extinction (aal ) coefficients are the two unknowns of the lidar equation once instrumental constants of each channels are given. The cumulative aerosol optical depth (AOD) sal between a reference altitude z0 and z can be expressed under the two equivalent forms of Eq. (5). The upper expression can be directly calculated using the Raman channel whereas the lower expression is used to compute LR profile from sal and bal profiles.

sal ðz0 ; zÞ ¼

8 > > > <



ln

 Sr ðzÞ bN2 ðz0 Þ m þ sm l ðz0 ; zÞ þ sr ðz0 ; zÞ Sr ðz0 Þ bN2 ðzÞ ð1þhÞ

Zz > > > a : LRl ðz’Þ bl ðz’Þ dz’

ð5Þ

z0

z0 has to be taken as lowest as possible (ideally just beyond the full overlap) so as to have the highest Signal-to-Noise Ratio (SNR) on the Raman channel. If z0 is selected at the lower (upper) part of the lidar profile sal is a monotonic increasing (decreasing) function against z. bal can be directly derived using sal by



 n h  Sl ðzÞ   exp  2 sal z0 ; zzref  sal ðz0 ; zÞ Sl zref  io m zref ; z  bl ðzÞ ð6Þ  sm l

bal ðzÞ ¼ bm zref l



The instrumental constant Kl is eliminated when the normalizing altitude (zref) is suitably selected. The reference altitude is commonly taken in the Rayleigh zone which is supposed free of aerosols, generally within the upper part of the lidar profile. The retrieval of aal is less direct and need the differentiation of Eq. (5) which is very delicate in presence of noisy signals. Hence, two different approaches have been used to solve this ill-posed problem: a Tikhonov regularization scheme (Section 3.2) and an iterative approach using the AOD from the Raman channel (Section 3.3).

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3.2. Tikhonov regularization method The cumulative aerosol optical depth at ll can be rewritten under matrix expression after discretization as

s ¼ B LR

(7)

where s and LR are the N dimension vectors of components sal (z0, zn)

and LRl(zn), respectively, with n ¼ [1;N]. B is a N  M upper (resp. lower) triangular integral matrix if the cumulated aerosol optical depth increases (resp. decreases). We can use for example the trapezoidal integration rule to approximate the integral (e.g. Press et al., 1992). Matrix B is thus:

0

bal ðz1 Þ

1

0

/

/

0 « « « «

B 2 C B bal ðz1 Þ bal ðz2 Þ C B 2 C 1 2 B C Ba C B bl ðz1 Þ a C bal ðz3 Þ B¼ds B 2 bl ðz2 Þ 2 1 C B C B « C « « B 1 0 C B « C « « @ A a a bl ðz1 Þ a bl ðzN Þ a b b ðz Þ / ðz Þ 2 l l N1 2 2

(8)

The Tikhonov regularization gives the following solution LR to Eq. (7) (Tikhonov and Arsenin, 1977):

h i1 LR ¼ BT B þ gH T H BT s

(9)

where T and 1 exponents denote the matrix transposition the matrix inversion, respectively. H is the nth difference matrix. We have here chosen a tri-diagonal (N  2)  N second-difference matrix. The regularization parameter g (also called the Lagrange multiplier) is a positive scalar which determines the smoothing of the solution and the importance of the constraint s. The lower the value of g, the nearer the inverted and measured aerosol optical depth. Contrarily, for values g / N the solution tends towards LR ¼ cste. This parameter must be appropriately chosen so as the solution retrieved is neither oversmoothed nor undersmoothed to avoid instabilities. The regularization is performed over several orders of magnitude of g values (typically between 1010 to 102). Many methods have been proposed to determine the suitable regularization parameter such as the method of maximum likelihood (e.g. Harville, 1977), the minimum discrepancy (e.g. Engl, 1987), the mean of L-curve (e.g. Shcherbakov, 2007) and the method of generalized cross-validation (GCV; e.g. Golub et al., 1979; Craven and Wahba, 1979). This latter has been used in this study because it requires the fewest assumptions (in particular any a priori estimation of the expected error on the data, unlike the minimum discrepancy or maximum likelihood methods). The suitable regularization parameter is given by the minimum of the following function GCV:

GCVðgÞ ¼

kðI  AðgÞÞsal k2 L

ðtrace½I  AðgÞÞ2

(10)

where I is the identity matrix and A the influence matrix defined by (Golub et al., 1979):

h i1 AðgÞ ¼ B BT B þ gHT H BT

(11)

An interpretation of the GCV method is that it rejects all the eigenvalues of B and thus the eigenvectors which cause high error amplification but do not contribute significantly to reconstruct the solution. GCV shows a smaller tendency towards oversmoothing

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compared with other methods such as the maximum likelihood and the minimum discrepancy (Gustafson, 1991). The retrieval of aerosol optical properties with a Tikhonov regularization scheme requires a good signal-to-noise ratio at the reference altitude zref on s (and thus on the Raman channel Sr) to determine bal and LR profiles. An alternative iterative approach has thus been developed to enable the inversion during both nighttime and daytime periods with reasonable averaging of 1 h. 3.3. Iterative approach using AOD from N2-Raman channel An alternative approach has been developed to retrieve a mean value of LR (LR) within an interval of the low troposphere. This dichotomous algorithm is based on the inversion of the elastic lidar signal with a Klett method (Klett, 1985) using AOD constraint from the Raman channel between an altitude range [z0; z1]. This method is similar than in Chazette (2003) or Raut and Chazette (2007) using the synergy between backscatter lidar and sunphotometer. The error sources are well known and are mainly due to the vertical heterogeneity of the aerosol layers. Hence, the iterative approach is particularly well-adapted to the retrieval of aerosol optical properties during the day when Tikhonov regularization is not applicable due to the low SNR of the reference. It is noteworthy that LR can be poorly representative of the LR profile especially in case of multi-layer observations (e.g. dust or biomass burning layers over the Planteray Boundary Layer (PBL)). Over Paris urban area, Raut and Chazette have shown that hydrophilic aerosol properties do not change the LR value, although bal and aal may significantly evolve (Randriamiarisoa et al., 2006). sal (z0, z1) computed using Eq. (5) is used as a constraint within the Klett (1985) algorithm, which gives the solution to the inverse problem as the solution of a Bernoulli first order differential equation:

0

1

B C B C B C S ðzÞQ ðzÞ B C m l a al ðzÞ ¼ LRB b  ðzÞ C l Zzref B C BSl ðzref Þ C 0 0 0 @ A þ 2LR S ðz ÞQ ðz Þdz l bl ðzref Þ

(12)

z

Q is the correction related to the differential molecular optical thickness calculated from the vertical profile of the molecular scattering coefficient:

    Zzref 3 0 0 am Q ðzÞ ¼ exp 2 kbw LR  1 ðz Þdz l 8p

(13)

z

Accounting for the uncertainties linked to sal (z0, z1), convergence is assumed when the lidar-derived optical thickness (sKlett) calculated in integrating Eq. (12) between z0 and z1 is such as: jsal  sKlettj < 105. The value of LR is chosen in the large interval [10 200] sr that include the most probable values of LR for aerosols trapped inside the Paris PBL. The convergence of the algorithm has been realized with a dichotomous approach by increasing (resp. decreasing) LR if sal (z0,z1) is larger (resp. lower) than sKlett. Uncertainties on LR and aal will be discussed hereafter. 4. Sensitivity study The different sources of uncertainty on aerosol optical properties derived from lidar measurements are mainly related to five sources: (1) the uncertainty on the a priori knowledge of the vertical profile of the molecular backscatter coefficient as determined from ancillary data, (2) the uncertainty of the lidar signal in the altitude range used for the normalization, (3) the statistical

fluctuations in the measured signal, associated with random detection processes, (4) the uncertainty on AOD constraint for iterative approaches and (5) the uncertainty on Ångström exponent value for both Tikhonov regularization and the iterative approach constraint with AOD from N2-Raman lidar measurement. Uncertainty sources have been supposed independent. We have consider a King factor kbw ¼ 1 which causes an overestimate on the molecular volume backscatter coefficient of 1.5% at 355 nm (Collis and Russell, 1976). The uncertainty on the a priori knowledge of the molecular contribution has been assessed to be lower than 2% (Chazette et al., 2010) using a comparison between several vertical sounding (i.e. radiosounding). The calibration of lidar profiles in a region deemed to be free from aerosols can lead to an uncertainty of 6%. This value has been assessed assuming m a m a scattering ratio R ¼ ðbl þ bl Þ=bl of 1.02 at the calibration height for which a significant discrepancy is noticeable between the lidar range-corrected and molecular profiles. The error budget on LR due to random detection processes and Angstrom exponent value have been performed using a Monte Carlo method as described in Chazette et al. (2002) with a direct-inverse model. The mean AOD at 355 nm (AOD) and the mean Ångström exponent (a) between 340 and 440 nm have been determined using AERONET clear days Level 2.0 product from Paris station between 15th March 2006 and 18th October 2009: AOD w 0.30  0.17 and a w 1.1  0.26. Hence, Gaussian aerosol layers with AODs of 0.1, 0.2, 0.3, 0.4 and 0.5 at 355 nm have been simulated extending from 0 to 2 km above the mean sea level (amsl) to be close to the diurnal PBL structure. LR has been supposed to be height-independent with a mean value of 91 sr. These values are consistent with those already observed over Paris area (Raut and Chazette, 2007). To assess the uncertainty on LR due to random detection processes (resp. a) noise has been added to the lidar profiles (resp. Ångström exponent values) assuming p a normal probability density function with a stanffiffiffiffiffi dard deviation of Sl (resp. 0.26) where p is the number of averaged profiles. Fig. 1 shows the evolution of the relative uncertainty on LR for the Tikhonov regularization (Fig. 1a) and iterative approach (Fig. 1b) due to random detection processes as a function of SNR at the reference altitude zref. Results of sensitivity studies are summarized in Table 2 for both nighttime and daytime conditions. For Tikhonov regularization the relative uncertainty on LR (resp. aal ) values due to the Ångström exponent value has been assessed to be 4% (resp. 1.5%). Random detection processes have been found to be the major error source as shown in Fig. 1a ranging from 16% to 100% (resp. 40 to 200%) during the day (SNR z 10 on Raman channel) and 4% to 18% (resp. 8% to 37%) during the night (SNR z 35 on Raman channel). Considering a mean AOD of 0.3 at 355 nm the overall relative uncertainty on LR (resp. aal ) has been assessed to be 25% (resp. 60%) for daytime and 10% (resp. 15%) for nighttime. The uncertainty on LR (resp. aal ) values due to statistical fluctuations for the iterative approach using AOD constraint from Raman lidar measurements between 0.2 and 0.7 km (see Fig. 1b) ranges from 7 to 23% (w3 to 14%) for daytime and 3 to 8% (w1 to 5%) for nighttime considering an AOD of 0.1 and 0.5. The overall uncertainty is thus w15% (resp. w10%) for daytime and w10% (resp. 7%) for nighttime. This approach seems more relevant but needs to be used for homogeneous aerosol layers. Hereafter, it will be only applied on polluted continental aerosol trapped within the PBL. The relative uncertainties linked to the synergy between lidar and sunphotometer applied during daytime are also given, accounting for the absolute error of 0.02 on the sunphotometer AOD (Holben et al., 1998). The overall uncertainty on LR (resp. aal ) is 42%, 29% and 15% (resp. 21%, 13% and 8%) for a mean AOD of 0.1, 0.2 and 0.5, respectively. It is also noteworthy that sunphotometer measurements are not available during the night and in cloudy conditions contrarily to Raman lidar measurements data which can

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Fig. 1. Uncertainty on LR due to random detection processes against the SNR at the reference altitude (zref) on the Raman channel using (a) Tikhonov regularization and (b) iterative approach with AOD from Raman channel between 0.3 and 0.7 km. Each simulation has been realized with a mean AOD of 0.1, 0.2, 0.3, 0.4 and 0.5 corresponding to the AOD value of 0.3  0.17 observed with Paris Aeronet sun-photometer.

be inverted as soon as there is no cloud between the ground and the reference altitude. 5. Application to aerosol content survey in the suburbs of Paris The first two examples deals with long-range transport of dust and volcanic aerosols where LR profiles can be computed by averaging N2-Raman lidar measurements over several hours. Such an approach is more difficult to use for pollution aerosols emitted from local sources. It is more relevant to use the iterative approach to determine LR and then the vertical profile of the aerosol extinction coefficient. The mean LR values retrieved from the mini N2-Raman lidar for the different aerosol types (polluted continental, dust and volcanic aerosol) are summarized in Table 3. Comparisons with other measurements have shown that agreement is largely good. 5.1. Dust aerosols During the night from March 17th to 18th 2010, a desert dust event occurred over Paris. It has been observed with the Raman lidar at the ground-based site of CEA-Saclay (20 km in the south-west suburbs of Paris). The origin of the air masses (not shown here) has been confirmed by backward trajectories with HYbrid Single Particle Lagrangian Integrated Trajectory Model (Hysplit, http://ready.arl.

noaa.gov/HYSPLIT.php) and Dust Regional Atmospheric Model (DREAM, http://www.bsc.es/projects/earthscience/DREAM). Fig. 2 shows the time-height image of the particle depolarization ratio between 19 h local time (LT) on March 17th and 7 h (LT) on March 18th 2010. LR profiles retrieved with the N2-Raman channel using a Tikhonov regularization scheme are superimposed on the figure every 2 hours. Dust layers characterized by high depolarization values (around 10e15%) and LR of 45  8 sr in the residual layer from 0.45 km up to 1.2e1.5 km are in very good agreement with previous results presented in Table 3: 52  6 sr found by Tesche et al. (2009), 50 sr in Balis et al. (2006) and 53  1 sr and 44  2 sr in Papayannis et al. (2005). Near the surface, we can also notice a pronounced increase of LR in the nocturnal layer up to 91  9 sr characteristic of pollution aerosols over Paris (Chazette et al., 2005; Raut and Chazette, 2007). Extinction coefficient values are close to 0.1 km1 in the dust layer and rapidly increases up to 0.35 km1 near the surface. During this night, Airparif ground-based background stations have not observed a significant increase of PM10 concentrations suggesting that dust aerosol layers have not reached the surface. 5.2. Volcanic ash The volcanic ash plume emitted as a result of the Eyjafjöll volcanic eruption that commenced on 14 April, 2010, has been

Table 2 Relative uncertainties on LR and aal due to lidar SNR (for a mean AOD of 0.1, 0.2, 0.3, 0.4 and 0.5) and Ångström exponent for Tikhonov regularization and iterative approaches using AOD constraint from Raman channel and sun-photometer. AOD

Tikhonov regularization

Iterative approaches AOD Raman channel constraint

Shot noise

Daytime (SNR ¼ 10)

Nighttime (SNR ¼ 35)

Ångström exponent (1.27  0.36)

Mean

Std

LR

aal

LR

0.1 0.2 0.3 0.4 0.5 0.1 0.2 0.3 0.4 0.5

40% 25% 17% 14% 12% 16% 8% 6% 5% 4%

100% 37% 24% 20% 16% 18% 9% 7% 5% 4% 4%

200% 90% 60% 50% 40% 37% 20% 13% 10% 8% 1.5%

23% 15% 12% 9% 7% 8% 5% 4% 3% 3% 7%

aal 14% 8% 6% 4% 3% 5% 2.5% 2% 1.5% 1.25% 3%

AOD sun-photometer constraint LR

aal

42% 28% 21% 16% 13% e e e e e e

20% 11% 8% 6% 4%

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Table 3 LR retrieved with the mini N2-Raman lidar and found in the literature for polluted continental, dust and volcanic aerosols. Aerosol types

Site /Campaign

Instrument

Wavelength

LR

References

Polluted continental

Saclay (suburbs of Paris) Paris (France) /ESQUIF and LISAIR

N2-Raman lidar Lidar/sun-photometer coupling

355 nm 355 nm

85  18 sr 90  20 sr

Central Europe EARLINET network

N2-Raman lidar

355 nm

58  12 sr

This study Chazette et al. (2005) Raut and Chazette (2007) Müller et al. (2007)

Dust

Saclay (suburbs of Paris) Southern Marocco /SAMUM Greece /PHOENICS Athens (Greece) Thessaloniki (Greece)

N2-Raman N2-Raman N2-Raman N2-Raman

lidar lidar lidar lidar

355 nm 355 nm 355 nm 355 nm

45  8 sr 52  6 sr w 50 sr 53  1 sr 44  2 sr

This study Tesche et al. (2009) Balis et al. (2006) Papayannis et al. (2005)

Volcanic

Saclay (suburbs of Paris) Naples(Italy) Lecce(Italy) Potenza (Italy) Leipzig (Germany)

N2-Raman lidar N2-Raman lidar

355 nm 351 nm 351 nm 355 nm 355 nm

48  10 sr w50 sr

This study Wang et al. (2008)

55e65 sr

Ansmann et al. (2010)

N2-Raman lidar

monitored over Paris with the N2-Raman lidar from 16 to 22 April, 2010. It was an exceptional event. An example of particle depolarization ratio measurements is given on Fig. 3 for the night of 17 to 18 April. The black curves superimposed on the figure show the LR profiles retrieved every hour with a Tikhonov regularization scheme. The major volcanic ash plume is clearly visible between 1.7 and 2.5 km with high particle depolarization ratio ranging from 15 to 25% due to non-spherical ash particles. The mean LR for volcanic aerosols has been assessed to 48  10 sr, which is in agreement with other values reported in the literature (Table 3): w50 sr in Wang et al. (2008) and 55e65 sr in Ansmann et al. (2010). The mean aerosol extinction coefficient is found to be close to 0.13  0.06 km1 in an ash layer of w300e400 m wide. That lead to a mean AOD of 0.05  0.02 at 355 nm in agreement with the small increase of the AOD derived from sunphotometers

measurements the 16 April, 2010 when the ash plume arrived over the Paris area. 5.3. Urban/periurban aerosols Inversion with Tikhonov regularization is not possible during daytime periods due to the low SNR (<15) at the reference on the N2-Raman channel. The iterative approach has thus been used to retrieve the mean LR between 0.3 and 0.7 km amsl using as a constraint the AOD determined with the N2-Raman channel. This method has been applied to lidar measurement performed from 3 March (17 h LT) to 5 March (11 h LT), 2010. This period is characterized by anti-cyclonic conditions and an average pollution with the particles associated with mean daily PM10 concentrations between 20 and 40 mg m3 and aerosol optical depths between 0.05

Fig. 2. Time-height image of particle depolarization ratio observed at CEA-Saclay ground-based site the night 17e18 March, 2010 during a dust event. The black curves superimposed on the figure show the LR profiles retrieved every 2 h with a Tikhonov regularization scheme.

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Fig. 3. Time-height image of particle depolarization ratio observed at CEA-Saclay ground-based site the night 17e18 April, 2010 after the Eyjafjöll volcanic eruption. The black curves superimposed on the figure show the LR profiles retrieved every hour with a Tikhonov regularization scheme.

and 0.2 at 355 nm. These values are close to those generally observed around Paris area (Chazette et al., 2005). Fig. 4a shows the temporal evolution of column-averaged LR at 355 nm retrieved every hour with the N2-Raman lidar. The error bars have been computed using the results of the sensitivity study and by considering the AOD and the SNR ratio of the N2-Raman channel of each hourly averaged profile. A diurnal cycle is clearly visible with lower LR values between 55 and 80 sr during the day from 12 h to 18 h LT and higher values close to 85e120 sr during the

night. These night-time LR values are consistent with those determined during the ESQUIF and LISAIR campaigns (90 sr). LR values have been used in a Klett algorithm to compute the temporal evolution of the aerosol extinction coefficient profile represented on Fig. 4b. We can detect the diurnal cycle of the PBL Height (PBLH) with values close to 0.4e0.5 km for the night and an increase up to 1 km during the day at 18 h LT. The aerosol extinction coefficient values range between 0.1 and 0.3 km1 in the PBL. The higher values (up to 0.6e0.8 km1) observed between 0 h and 6 h LT on 5

Fig. 4. Temporal evolution at the CEA-Saclay ground-based site of (a) the mean aerosol LR at 355 nm (together with their uncertainties) retrieved using the iterative approach with the AOD from the Raman channel between 0.3 km and 0.7 km (black curve) and using the Tikhonov regularization (red dots) (b) aerosol extinction coefficient profiles at 355 nm between 3 (17 h LT) to 5 (11 h LT) March, 2010. The inversion has been performed using a Klett algorithm with the mean LR values of Fig. 4a.

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Fig. 5. Variability of aerosol extinction retrieved with Tikhonov regularization (dark gray shaded areas) and iterative approach (light gray shaded areas) for (1) the night of the 3th March 2010 and (b) the 4th March 2010. The AOD has been computed for each profile.

March, 2010, are due to hazy conditions as revealed by relative humidity measurements higher than 70%. This observation is correlated with a decrease of LR values from 90 to 70 sr. For nighttime observations of polluted continental aerosols, the inversion has been realized with the Tikhonov regularization scheme every 2 hs. The results have been superimposed on Fig. 4a (red dots) to compare with the iterative approach using a reference AOD between 0.2 and 0.7 km along the line of slight. Both approaches shows very similar LR (w85 sr) with a significant diurnal variation (18 sr) and a mean square difference of 8 sr (8% of uncertainty) and 5 sr (7%) for the night of the 3th and 4th March 2010, respectively. The mean extinction coefficient profiles are compared in Fig. 5. The variability of aerosol extinction profiles is represented by the dark gray shaded area for the Tikhonov regularization and the light gray shaded area for the iterative approach. Both methods of inversion are in good agreement with a mean square difference of 0.015 sr1 and 0.012 sr1 for the night of the 3th and 4th March 2010, respectively. This leads to a relative error of 13% and 11% on AOD values. 6. Conclusion A compact and light ultraviolet N2-Raman lidar dedicated to aerosol content survey has been developed by the Commissariat à l’Energie Atomique (CEA) and LEOSPHERE Company. Two complementary methods of inversion have been implemented for the aerosol content survey over Paris megalopolis depending on aerosol type. For multilayer structures associated with long-ranged aerosol transport (i.e. dust or volcanic ash), the Tikhonov regularization (which requires a high SNR at the reference on the Raman channel) can be applied to retrieve aerosol optical properties with a temporal resolution of typically one hour during the night. The mean LR retrieved is 45  8 sr (resp. 48  10 sr) for dust (resp. volcanic aerosol) layers which is in good agreement with other values reported in the literature. For polluted continental aerosols an iterative approach has been developed to retrieve a heightindependent LR with a temporal resolution of one hour during both nighttime and daytime periods. We found a mean LR of 85  18 sr with a pronounced diurnal cycle. The LR values and extinction coefficient profiles determined with the iterative approach have shown a good agreement when comparing with the retrievals using Tikhonov regularization scheme applied during the night. This iterative approach enables thus daytime inversion when Tikhonov regularization cannot be apply due to low SNR at the reference.

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