Beaulant A.-L. and L. Wald, 2006. Aerosols detection for urban air pollution monitoring. In Proceedings of SPIE -- Volume 6362 “Remote Sensing of Clouds and the Atmosphere XI”, James R. Slusser, Klaus Schäfer, Adolfo Comerón, Editors, paper #636208 (Oct. 11, 2006), doi: 10.1117/12.689946.

Aerosols detection for urban air pollution monitoring

Anne-Lise Beaulant, Lucien Wald Ecole des Mines de Paris, Center for Energy and Processes, BP. 207, 06904 Sophia Antipolis

Cedex, France

ABSTRACT

In the context of reducing the impact of atmospheric pollution on public health in cities. Previous studies have shown that optical sensors aboard satellites may be sensitive to the level of pollution because of the relation between radiances and aerosol loading and especially particulate matter (PM). The purpose of this paper is to add to this evidence by studying cloud-free satellite images and ground measurements, and then to show that urban aerosols concentrations variations can be detected and quantified by the means of satellite images. We used the radiative transfer model (6S). We simulated the reflectance at pixel level in the Landsat-TM bands. The effects and contributions of parameters (H2O content, O3 content, albedo, aerosols and atmosphere optical thickness) are studied thanks to experimental design approach. Change in aerosol loading is the major contributor to change in reflectance. We thus demonstrate and quantify the sensitivity of reflectance to PM. We find that channel TM4 (~ 815 nm) of Landsat is the appropriate band. However, when taking into account the gain of the sensor, we recommend TM1. On TM1 image, a difference of 71 DN (digital number) represents a variation of 120 µg/m3. The minimal concentration variation detectable is around 2.6 µg/m3. Results from observations (ground measurements and DN on satellites images) are in good agreement with simulations. In particular, an empirical model that converts DN into [PM] confirms simulations. Keywords: urban aerosols, particulate matter (PM), radiative transfer, satellite Landsat TM, 6S model

1. INTRODUCTION To monitor urban pollution in cities, authorities often use a network of ground measuring stations. The measurements are used to constrain dispersion models or for the mapping by interpolation. The result gives an estimation of the pollution concentration in a city with a quite good spatial resolution. Measurements and estimations serv both to inform the population about pollutions episode and to help autorities to take decisions. However, the lack of points measurements yields to non accurate estimations. Indeed, only few tens of stations are installed, whereas Stalker & Dickerson (1962) recommend to dispose of several hundreds monitoring stations to obtain an error of less 20 % on the concentrations estimation when using interpolations methods. A solution to this problem is to install more ground stations. But the cost of such an installation and its maintenance is very high. To overcome this problem, we propose to take benefits of the new sensors aboard satellites in order to estimate the pollutions concentrations in specific points in the city. In this paper, we evaluate the contribution of new sensors to urban pollution detection and concentrations estimation. In particular, we focus our study on particulate matter (PM). Previous studies have shown that optical sensors aboard satellites may be sensitive to the level of pollution because of the relation between radiances and aerosol loading and especially particulate matter (PM). The satellite imagery is already used for the detection of air pollution at local or regional scale (Hashim et al. 2004, Retalis et al. 2003, Sifakis et al. 1992, 1995, 1999). The space-born sensors are NOAA-AVHRR or Landsat. Majority of the authors proceed by comparison of two images whose one is used as reference. Texture is an important element of comparison. Maps of pollution are produced. They bring out the area that the veil of pollution covers, the variations of concentrations but they do not allow to directly estimate the pollutants concentration at local scale. Other studies have been done on the detection of aerosols by the mean of satellite but they have often used directly spectrometer measurements (MODIS, GOME) at larger spatial scales. Our approach completes those already existing. In fact, we want on the one hand, to confirm the detection of PM on images satellite and on the other hand, to estimate the PM concentration in specific points in the city. These points are

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ground stations. This paper aims to demonstrate that it is possible to detect urban aerosols thanks to satellite images at very local scales. Physics of the phenomenon is complex since many parameters are involved. To solve this problem, we will use tools implying radiative transfer principles in the atmosphere and physics of the sensor. The radiative transfer model (6S) combined with an approach by numerical experimental designs will be employed. The 6S model simulates the response of a satellite sensor receiving a signal that has crossed the atmosphere. The numerical experimental designs allow to take into account several atmospheric parameters that vary together, and to reduce the number of useful simulations. Thanks to simulations, we will:

• determine the relative contributions of each parameter in order to know if the aerosols concentration affects significantly the signal received by the satellite and if it is possible to detect this phenomenon on an image,

• determine the optimal channels for the observation, the detection and the quantification of particulate pollution.

The following stage consists in comparing the results issued from simulations with the observations made on the ground and by the satellite. This stage will lead to the establishment of an empirical model to concert satellites data to PM concentration.

2. METHODOLOGY 2.1. Atmospheric principles and hypotheses The atmosphere modifies electromagnetic radiations that cross it (in our study, the radiation source is the sun), by absorption and scattering effects. These effects are due to:

• gas present in the air: nitrogen, oxygen, argon, carbon dioxide, ozone, water vapor and gaseous pollutants, • particles: aerosols, droplets, dust...

Gases and particles, gases essentially, are responsible for the selective absorption of wavelengths. For most gases of the atmosphere, wavelengths for which there is absorption is known (curves of absorption). Therefore, visible and IR radiations are not much absorbed by gases (Vermote et al. 1994). The scattering effects are mainly due to particles and molecules suspended in the atmosphere. It depends on their size compared to the wavelength of the electromagnetic radiation.

• Gases, as N2 and O2, and particles that are definitely smaller than the incident wavelength scatter the light following the Rayleigh law. This scattering effect is in λ-4.

• For particles whose sizes are comparable to or larger than the incident wavelength, the Mie law may apply. For aerosols, the scattering effect is in λ-α with 0 < α < 4 (Wald 2006). For droplets and dust, scattering effects are very important for all the wavelengths in the visible and IR spectrum.

Scattering and absorption due to gazes and aerosols are described by the optical thickness. This parameter quantifies the absorbency and opacity of the atmosphere. This parameter can sometimes be estimated by the visibility. For gases, it is easy to express the optical thickness as a function of the concentration. For a given wavelength, the monochromatic transmittance, in the case of gas molecules, is connected to the concentration as followed (Liou 1980):

µλ

λ

τCk

e−

= (1) where kλ is the absorption coefficient in cm²molecule-1, C is the concentration of the absorbing gas and µ is a function of the local zenith angle of the sun. Equation (2) links the transmittance τλ to the optical thickness δλ:

µδ

λ

λ

τ−

= e (2) Finally, for gases, concentration and optical thickness are dependent according to the equation (3):

Ckλλδ = (3)

However, it is much complex for aerosols. According to Angström, the total transmittance due to scattering effects of aerosols (dry and wet) can be expressed as a function of the incident wavelength, and of the size of the particles, but also of their number and their mass. Any mathematical relation between optical thickness and concentration of aerosols does not exist. Several works (Pancrati 2003, Kacenelenbogen 2004) have studied the bond between these two parameters by using data from the European database AERONET (http://www.aeronet.gsfc.nasa.gov), from ground and

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from embarked spectro-radiometers. These experimental studies lead to a linear approximation often used at first approximation (Retalis et al.. 2003). By using experimental results obtained by Pancrati (2003), we have established the expression (4) that links the PM concentration and the optical thickness at 550 nm.

0022,00082,0550 += PMCδ (4) δ values obtained by this mean when taking realistic CPM values are identical to those measured in experiments (Bellouin et al. 2003, Kacenelenbogen 2004, Thieuleux et al. 2004). To avoid partly atmospheric effects, especially molecular absorption, sensors on board satellites observe the Earth in atmospheric windows where absorption is week and the radiation emitted by the source is sufficiently strong to be detected. The satellite sensor measures a value of luminance. The luminance is defined as the intensity of a radiation source in a given direction, divided by the apparent surface of this source in this direction. The reflectance i.e. the quantity of luminous energy returned by the surface, can be deduced from the luminance. At first approximation, the reflectance measured by the satellite can be expressed as followed:

solatmsat Tρρρ += (5) with, satρ the reflectance measured by the satellite, atmρ the reflectance of the atmosphere, solρ the reflectance of the ground (or albedo), and T, the total transmittance of the atmosphere. T can be written as:

µδ

−= eT (6)

δ is the total optical thickness of the atmosphere. It can be written as the sum of two optical thicknesses: δ of the atmosphere without pollutant (or clear atmosphere) and δ of the pollution (equation 7).

polatm δδδ += (7) Then T is written:

polatmTTT = with

µ0022,00082,0 +

−=

PMC

pol eT (8)

The transmittance T and then the reflectance observed by the satellite are influenced by all the elements that constitute the atmosphere and in particular, by the particulate pollution. According to these theoretical points based on the physics of the atmosphere, a variation of the PM concentration should theoretically involve a variation of the reflectance. The goal of the following stage will consist in confirming the theory using numerical simulations. More precisely, we will use numerical experimental designs combined with a radiative transfer model to show how and how much variations of PM concentrations affect the satellite images radiometry.

2.2. Tools 2.2.1. Point of using a experimental design approach with 6S The PM concentration is not the only factor that affects the signal received by the sensor. Other factors, atmospheric or ground parameters, have an influence on the sensors measurement. Considering all these parameters in a model remains complex. Nevertheless, it is possible to obtain a realistic modeling while varying only the most important parameters. I have chosen the following variables as the most important: the water vapor content (UH2O), the ozone content (UO3), the optical thickness of the urban aerosols (AOTp) and the optical thickness of the clear atmosphere (OTatm) and finally the albedo of the ground target (α). Effects and contributions of the atmospheric and ground parameters are studied for each spectral bands thanks to the utilization of numerical experimental designs. The statistical sampling of the variables values by an experimental design makes it possible to explore more rigorously their own interval of definition, in a limited number of simulations. In other words, the approach by numerical experimental designs allows:

• to take into account the joint variations of several atmospheric parameters, • to define their field of variation, • to reduce the number of simulation the model need to represent the phenomenon.

The experimental designs approach is intended to maximize accessible information from a limited number of experiments (Bacour 2001).

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Variables listed above constitute 6S input variables. For each 5 factor, we have defined a range of realistic variations. For example, variations of AOTp are selected so as they correspond to realistic variations of PM concentrations downtown, following the equation (4). The values taken by the variables are distributed so that the minimal value (maximum) corresponds to the lower limit (higher) incremented of 5 % of the size of the interval. These values are gathered in table 5.3.

Tab. 1 : intervals of variations and levels of the various parameters. Parameter Interval Levels

AOT pollutants 0,1579 - 1,2077 0,1662 ; 0,3302 ; 0,4942 ; 0,6582 ; 0,8222 ; 0,9862 ; 1,1502

OT atm 0,144 - 0,819 0,152 ; 0,257 ; 0,361 ; 0,466 ; 0,570 ; 0,675 ; 0,780

UH2O 0,33 - 3,31 0,35 ; 0,82 ; 1,28 ; 1,75 ; 2,21 ; 2,68 ; 3,15

UO3 0,234 - 0,367 0,246 ; 0,263 ; 0,281 ; 0,298 ; 0,315 ; 0,332 ; 0,350

α 0,057 - 0,630 0,06 ; 0,15 ; 0,24 ; 0,33 ; 0,42 ; 0,51 ; 0,60

Values of albedo are typical values met in urban environment (albedo of concrete, asphalt, vegetation...) (Lachérade et al. 2006). Values of ozone content come from GOME (http://www.wdc.dlr.de) satellite data and values of watervapor content come from measurements taken by the spectro-radiometer MODIS (http://modis-atmos.gsfc.nasa.gov). 2.2.2. Average effects and contributions The average effects of each factors are calculated for each spectral band. The level m of the factor νf appears mf time, then the average of the test results for this modality is written:

f

mfmf m

∑=ρ

ρ (9)

The average effect of each factor, which represents the differences between the average values of the response of the modality m and the general mean , is written:

ρρ −= mfmfe (10)

Let ê f m(λ), the effect of the factor f when it takes the value m. It is expressed as a function of the simulated average reflectance, in a given spectral configuration: )(λρ . The effect is here expressed as a percentage, such as:

100)(

)()()(

λρλρλρ

λ−

= mfmfE

(11) Effects of variables can be classified by considering the relative contribution of each one to the variation of the simulated reflectance. The global variation of the response )(λρ of the model, in a given spectral configuration, is expressed by the sum of squares (SCT):

∑≤≤

−=Ni

iSCT1

)]²()([ λρλρ (12)

with N, the total number of tests, i the tests index. This term is the estimation of the original variance of the results for (N-1) tests. For each spectral band, we determined an index that characterizes the variance explained by each factor:

100)()(

)(λλ

λSCT

SCEC f

f = (13)

with ∑

≤≤

=fnm

mff

f enNSCE

1)]²(ˆ[)( λλ

(14) nf is the number of levels, and m is the levels index.

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2.2.3. The 6S model The 6S model (Second Simulation of the Satellite Signal in the Solar Spectrum) makes it possible to simulate observations carried out from space, to take into account the surface boundary conditions, the transmission by gases and particles in the atmosphere (absorption, Rayleigh or Mie diffusion) (Vermote et al.. 1994) (voir dans le manuel de 6S). With this model, I will simulate the reflectance observed by the sensor TM of Landsat 5 for each test determined by the experimental design and for each spectral band. During each simulation, the joint variations of the atmospheric parameters quoted above are taken into account. Thus a total of 2058 (343 X 6 spectral bands) reflectances were computed for a date.

2.3. Method The goal of this paper is to demonstrate that it is possible to detect and evaluate the particulate pollution by the mean of satellite images. At the very first, we seek the most important atmospheric parameter in terms of effects on satellite images. The atmosphere can be described by many variables. Among these variables, we seek to prove that the PM concentration is the most important factor that affect satellite images. The influence of PM concentration on satellite images has already been shown for other sensors and at other spatial scales, for example for forest fires in South Asia (Hashim et al. 2004). To this aim, we use numerical experimental designs and the 6S model. Then we seek to show that these effects are spectrally dependent. Therefore, we study all the spectral bands of the satellite thanks to simulations. We seek the most sensitive channel to PM concentration. It will constitute the appropriate spectral band for the detection and estimation of PM concentrations. Then we compare results issued from simulations with observations in order to confirm that it is possible to detect pollution from space. A good agreement between simulations and observations yields to the determination of an empirical model that can convert satellite data i.e. image radiometry into PM concentration with a rather good accuracy.

3. STUDY AREA AND DATA The studied area is the city of Strasbourg. Strasbourg is located in Eastern France, separated from Germany by the Rhine river. Geographical coordinates are: 48.33° and 7.38°. ASPA is the local agency in charge of the air quality measuring network in the city of Strasbourg and vicinity. ASPA operates 11 stations; 5 of them measure (PM10, 13, 2.5). Atmospheric pollution in Strasbourg is mostly due to motor vehicles since there is no heavily polluting industries in this area except a refinery located in Reichstett in the North. Wind is blowing mostly from South-West or North-East because it is channeled in the Rhin valley. It can bring significant pollution clouds from the industrial region of Rhur in Germany. Measurements of PM concentrations are provided by ASPA and are issued from the five ground stations. Most of these stations are measuring the background pollution: they are located at about 100 m from a street with little traffic. The values are instantaneous concentrations taken at 10 h. We also dispose of six satellite images of the studied area. These images have a spatial resolution of 30 m and have been acquired by the space-born sensor Landsat-TM at the same hour (10 h) for six different dates. The six days we consider are mainly in spring or summer as the images should be free of clouds.

4. RESULTS AND DISCUSSION 2.4. Simulations 2.4.1. Average effects and contributions Effects of variables UH2O, UO3, Otatm, AOTp, α, on the average reflectance simulated by 6S are presented on figure 1. The results clearly highlight the factors that are spectrally dominant. In visible bands (TM1, 2, 3, 4, 5) and in a less obvious way, in the infra-red band (TM7), we see that the optical thickness of urban aerosols (i.e. PM concentrations) is the factor that the most affects the levels of reflectance. On its interval of definition, the total amplitude of the average effect of AOTp is about 215 % (TM1) then 180 % (TM2) and 145 % (TM3), its effect becoming less important as we go towards the IR wavelengths. The factor which the most affects the reflectance after AOTp is Otatm followed by α, UH2O and UO3. The effects of the two last factor are about similar. The more the wavelength increases, the more the factors tends towards quantitatively close average effects. It is normal to notice that the average effects of UH2O and UO3 are not very influential. Indeed, the spectral bands of the satellite are selected so as to coincide with atmospheric windows in which absorption of H2O and O3 is weak. It is important to

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also note that more AOTp and OTatm increase, more their effect decreases. In fact the more the PM concentration increases, the more the reflectance value decreases. This is in good agreement with the assumption according to which the PM acts like a veil darkening the satellite images (Wald & Baleynaud 1999).

Fig. 1: average effects of factors for Landsat-TM.

channels

1

1

2

2

3

3

4

4

5

5

Fig. 2 : relative contribution of the factors to the reflectance variations for Landsat-TM.

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Tab. 2: contributions (%) computed for each parameter

bands TM1 TM2 TM3 TM4 TM5 TM6

Caotp 37.62 34.42 30.67 24.88 5.49 3.76

Cotatm 11.43 10.77 9.66 7.64 2.52 0.67

CUh2o 0.06 0.07 0.10 0.26 0.35 0.49

CUo3 0.60 0.58 0.61 0.58 0.49 0.35

Cα 2.26 2.21 2.10 1.89 1.06 1.41

Figure 2 represents the relative contribution of each variable plotted for each spectral band. Figure 2 and table 2 reinforce the previous observations (average effects) concerning the parameters that most affect the results. In the visible bands (up to 38 %) but much less in the IR bands (~ 4 %), it brings out that absorption and scattering effects due to PM (AOTp) modify the values of reflectance. The contributions of the atmospheric parameters (UH2O, UO3) and the typical parameter of a urban surface (α) are rather similar and rather weak. These results show that variations of PM concentrations are visible through variations of reflectance on Landsat-TM images. 2.4.2. Sensibility analysis The numerical simulations have shown that the variations of PM concentrations mainly contribute to the variations of reflectance. Then, we seek the spectral band for which there is the greatest variation of reflectance and for which the variation of reflectance is mainly due to the variations of the aerosols optical thickness. This condition eliminates in fact channels TM5 and TM7. Then, on figure 3 (left), we seek the channel for which there is the greatest difference between the minimal reflectances (pink curve) and the maximum reflectances (blue curve). The larger the variation will be, the easier it will be to detect the variations of the reflectances i.e. the variations of pollution, on an image of this channel.

0.0000

0.0500

0.1000

0.1500

0.2000

0.2500

0.3000

TM1 TM2 TM3 TM4 TM5 TM7

channels

Ref

lect

ance

réf maxréf min

0

20

40

60

80

100

120

140

160

TM1 TM2 TM3 TM4 TM5 TM7

channels

Ref

lect

ance

(DN

)

CNmax

CNmin

Fig. 3: (left) reflectances minimal and maximum simulated plotted for each the Landsat-TM channel. (right) minimal and maximum

reflectances expressed in digital number (DN), simulated for each Landsat-TM channel. According to figure 3 and table 3 resulting from simulations, the channel 4 of Landsat-TM is the most sensitive channel to the variations of PM concentrations. This channel corresponds to wavelengths ranging from 730 to 900 nm. The sensor TM of Landsat shows, according to the 6S model, a spectral sensitivity to the aerosols more important in this range wavelengths.

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Tab. 3: differences between minimal and maximal reflectances and the corresponding digital number (DN). TM1 TM2 TM3 TM4 TM5 TM7

reflectance 0.0858 0.1015 0.1169 0.1358 0.0821 -0.0331

DN 79 50 71 54 52 -10

Note: here given reflectances are without unit because the model 6S provides standardized values of reflectance. This sensitivity analysis aims to determine the appropriate channel for the detection of particles by the mean of satellite. On an image of this channel (TM 4), it is possible to distinguish the variations of reflectances i.e. the variations of PM concentrations. However on a satellite image, reflectances are coded; they are expressed with digital numbers that correspond to levels of gray. DN are the values we "read" on a satellite image. Moreover, the digital numbers (DN) do not issue directly from the radiance received by the space-born sensor. The gain of the sensor can modify noticeably the position of the histogram. The gain of the sensor is a function of the spectral band and the acquisition date. The respective positions of the histograms of the various channels are not inevitably those that we could simply deduce from the curves of reflectances. It is essential to study the satellite images directly from the digital number (Girard & Girard 1997). By adopting the same approach as previously but this time with DN, the channel 1 image is the most adapted for the detection of the variations of PM concentrations (figure 3 (right) and table 3). According to table 3, 79 DN are needed to describe the pollutants dynamics in channel TM1 and 54 in TM4. As the range of variations of aerosols optical thicknesses was determined using a range of PM concentrations going from 20 to 140 µg/m3, 79 values describe a variation of 120 µg/m3. On a TM1 image, we are able to discriminate a sufficient number of different concentrations. The channel TM1 sensitivity depends on the acquisition date i.e. when the space-born sensor has acquired the image of the scene. We have determined the sensitivity to PM of channel TM1 for the six dates (figure 4) so that the comparison between simulations and observations are coherent and that the detection of the PM concentrations is precise enough.

0102030405060708090

100

20 40 60 80 100 120 140[PM] (µg/m3)

DN

TM

1

14/08/2001

22/07/2001

10/09/1999

01/04/2001

10/05/2001

08/06/2000

Fig 4: simulated TM1 reflectances (expressed in DN) for each dates as a function of PM concentration.

Figure 4 highlights that channel TM1 is dependent of the acquisition date. It also shows that the PM concentration affects the reflectance in the same way. Indeed, when the PM concentration increases, reflectance decreases as a decreasing exponential. This observation is in conformity with the theory clarified in section 2.1, according to the equations (5) to (8). For each date, we have calculated the channel TM1 sensitivity (table 4) i.e. it is the smallest value of concentration that we are able to distinguish on a TM1 image.

Tab. 4: channel TM1 sensitivity to PM for each date. dates 14/08/2001 22/07/2001 10/09/1999 10/05/2001 08/06/2000 01/04/2001

sensibilité (µg/m3) : 1CN 1.9 1.7 2.2 1.9 1.7 2.3

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The mean value of the TM1 sensitivity is around 1,9 µg/m3. For example, a traditional variation of the levels of PM pollution from 15 to 40 µg/m3 will involve a variation of approximately 13 DN on a TM1 image. Such a change in the images radiometry is easily perceptible. Finally, the simulations have shown that it is possible to detect PM pollution on a Landsat image. Simulations with the 6S model and the experimental design approach have shown that:

• variations of PM concentrations are particularly visible for the wavelengths of Landsat-TM1, • an increase in the PM concentration involves a reduction of the reflectance observed by the sensor following

an exponential law, • and the smallest detectable variation is approximately 2 µg/m3.

2.5. Observations On a TM1 image, we seek a relation between PM concentrations measured by ground stations and the reflectances observed by the satellite. For six acquisition dates, we use measurements of PM concentrations acquired by the five PM- stations of Strasbourg and reflectances deduced from the corresponding TM1 image. We plot, for each date, measurements of TM1 reflectances (in DN) as a function of the PM concentrations. We obtain a distribution of the points (figure 5).

60

65

70

75

80

85

90

95

100

10 20 30 40 50 60

[PM] (µg/m3)

DN

TM

1

14/08/2001

22/07/2001

10/09/1999

10/05/2001

08/06/2000

01/04/2001

Exponentiel(14/08/2001)Exponentiel(22/07/2001)Exponentiel(10/09/1999)Exponentiel(10/05/2001)Exponentiel(08/06/2000)Exponentiel(01/04/2001)

Fig. 5: DN of TM1 image plotted as a function of the in situ PM concentrations, for the six dates. The curves correspond to the exponential regression.

Tab. 5: empirical models, their accuracy and sensibility values.

Simulations

14/08/2001 DN = 96.881e-0.0064[PM] 0.6443 2.63 1.87biais -0.016biais (%) -0.06RMSE 6.033RMSE (%) 23.2

22/07/2001 DN = 147.81e-0.0303[PM] 0.5969 1.53 1.74biais 0.003biais (%) 0.01RMSE 2.289RMSE (%) 11.28

08/06/2000 DN = 125.75e-0.0299[PM] 0.9752 1.78 1.7biais -3.116biais (%) -14.16RMSE 6.182RMSE (%) 28.1

01/04/2001 DN = 121.12e-0.0129[PM] 0.8746 1.63 2.26biais 0.438biais (%) 1.48RMSE 12.789RMSE (%) 43.17

Sensibility 1CN

Observations

dates model Correlation R² Sensibility

1CN

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We observe that the reflectances (in DN) decrease when the PM concentrations increase, which is in agreement with the results of simulations, for four dates out of six. By applying a non linear regression, we find that we can model the relation between DN and [PM] by an exponential function. Equation of this function depends on the acquisition dates (table 5). This decrease can be approximate rather precisely (average coefficient of correlation: 0,8). We deduce empirical models with which the PM concentration can be estimated by using DN values. By using exponential models, we confirm the sensitivity we found with simulations (see the two last columns of table 5), with an error of ± 20 % on average, for four dates. The dates for which DN can not be approximated as a decreasing function of [PM], have not been considered. Indeed they are not in agreement with either simulations or theory. For four dates, we dispose of an empirical model with which we are able to predict the PM concentration by using the DN value of a pixel of a Landsat-TM1 image. The empirical model is more or less precise according to the considered date (table 5). The weakest error on the prediction (~11 %) corresponds to the 22/07/2001.

2.6. Discussion We observe differences in behavior between dates, in particular majority of curves are decreasing but some are increasing. The aerosols optical thickness (AOTp), 6S input parameter stand for [PM], is the integration of the extinction coefficient for all the atmospheric column. In the 6S model, the extinction coefficient follow a well-known vertical profile (Mc Clatchey et al. 1971). By translation, we can say that PM concentrations follow the same vertical profile in 6S. However, observations are made by ground stations at a maximum high of 10 m. In fact, in reality, the pollutants distribution does not follow inevitably this profile. The concentration measured at ground is not equal to the total concentration of this pollutant in the atmosphere. On the other hand, the satellite sensor measures the total concentration. It means that the height of the pollution plume in the atmosphere is important. Consequently, when the DN values decrease when [PM] increase for a given date, it means that all the plume is concentrated at ground level. It also means that the ground stations and the space-born sensor take measurements of the same concentration. Inversely, when the DN values increase when [PM] increase, it means that the plume is distributed in height. The ground station and the space-born sensor do not take measurements of the same concentration. The ground station measures the PM concentration at ground i.e. a little part of the total concentration while the satellite sensor measures the total concentration. Therefore, if [PM] at ground increases, the total PM concentration do not inevitably decreases, it can increase. In fact, in the most frequent case, the variations of the PM concentration measured at the ground are correlated positively with those of the total PM concentration in the atmosphere. In the least frequent case, the variations of the concentration measured at the ground are correlated negatively with the total aerosols concentration in the atmosphere. Differences in the behavior of the curves can have other explanations dependent on the particles size or composition. Indeed, the diameter of particles has an impact on scattering effects and the composition of particles affects absorbency and opacity (section 2.1). However, it seems that the most probable explanation is linked with the importance of the vertical distribution of aerosols (Mallet et al. 2003). The authors are aware of the limitations of the discussion. As already discussed, the physics is very complex with many interactions. Possible cause mechanisms have been investigated but more numerical simulations are necessary as well as more case studies. Moreover, empirical models have quite good accuracy but they are validated for specific points that corresponds to ground measuring stations. A validation process must be carried out in order to prove their validity for other points. A work on that point is recently in process.

5. CONCLUSION

This paper investigates the potentials of the satellite imagery as a tool for detecting PM at local scale meets its aim. We thus confirm that it is possible to detect particulate pollution by satellite. It shows that the sensor Landsat-TM can be used for detecting this pollution and in particular, that it is facilitated if channel TM1 is employed. Simulations with the 6S model combined with an approach by experimental designs have shown that urban aerosols affect the wavelengths around 815 nm. Channel TM4 (730 - 900 nm) is the most sensitive channel to the variations of PM concentrations. Nevertheless, when considering the gain of the sensor, channel TM1 is the appropriate channel to detect the variations of [PM] on an image. Moreover, this channel is sufficiently sensitive so that an average variation around 2 µg/m3 is discernible on an image of this channel. Simulations have also shown that an increase of the PM concentration involves a reduction in the reflectance measured by the sensor following an exponential law. Observations have confirmed simulations. We have shown that, in spite of a a priori complex relation between the PM concentration and reflectance, it is possible to model this relation by a decreasing exponential function with a good

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accuracy (mean R²=0,8) and to confirm the sensitivity value close to 2 µg/m3. This coherence between simulations and observations constitutes already a really encouraging result and confirms the importance to still study more in-depth the possible contributions of the detection from space for pollution monitoring in city. Empirical models to convert DN into [PM] give good results at ground stations level but they have not been validated yet with other points. The validation is in turn. It would be interesting to study and evaluate the benefit of other space-born sensors as MeRIS or SPOT. This could become an outlook of this work. Indeed, it would be certainly interesting to check if the same results are found. Moreover we could study how the different spatial resolutions (Landsat: 30 m, MeRIS: 300 m, SPOT: 10 m) and the different spectral width affect the detection of urban aerosols.

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