F Vidal

J Raffard

E Gendron

S Thijs

V Lapeyrere

T Buey

Y Clénet

D Gratadour

P Jagourel

A Sevin

F Ferreira

F Chemla

P Mahiou

Tests and characterisations of the ALPAO 64×64 deformable mirror, the MICADO-MAORY SCAO AIT facility

Keywords:

MICADO is the ELT near-infrared first light imager. It will provide diffraction limited images using the singleconjugate adaptive optics (SCAO) mode developed inside the MAORY AO module. Although the MICADO-MAORY SCAO mode uses during regular operations the ELT wavefront correction capabilities (M4 & M5 adaptive mirrors), the SCAO system will not be able to work with them until the final instrument commissioning. Since it is crucial to test and validate the SCAO system during various AITs phases in Europe, the need of a high order deformable mirror with comparable number of degrees of freedom is required to test both spatial and temporal behaviour of the SCAO mode.

For that purpose, the SCAO AITs in Europe will use the newly developed ALPAO 64×64 actuators deformable mirror (DM). Before using this deformable mirror in the context of the SCAO mode (i.e controlled by a non-linear pyramid WFS, we built a classical Shack-Hartmann WFS to ensure a proper linear wavefront measurement in the lab and perform the DM characterisation of its 3228 actuators. We present the preliminary results of the tests performed on this DM in a classical closed loop scheme. In particular we study the spatial wavefront correction, actuators additivity and linear response, maximum amplitude range (stroke), hysteresis and temporal stability.

INTRODUCTION

Deformable mirrors (DM) are commonly used in Adaptive Optics (AO) to perform turbulence wavefront correction enabling high angular resolution for ground based telescopes. The need of a high number of actuators is nowadays driven by the increase of telescope size such as the next generation of Extremely Large Telescopes (ELTs) and performance improvement of the current high contrast imaging AO instruments.

MICADO is the near-infrared first light imager for the European ELT. [START_REF] Davies | MICADO: first light imager for the E-ELT[END_REF] It will provide diffraction limited images with a dedicated Single Conjugated AO (SCAO) module using a pyramid wave-front sensor 2 (PWFS) and the telescope built-in deformable mirror: the so-called M4 DM. The latter being only available during the final commissioning of the system in Chile, the various AITs in Europe will therefore need an equivalent wavefront corrector with a similar number of degrees of freedom for the tests and validations.

For that purpose the SCAO system (i.e the WFS path) features a calibration unit (SCU) to emulate the turbulence, the pupil shape, and the M4 deformable mirror during AITs in Europe. It will make it possible to close the loop with similar conditions from those encountered at the ELT and characterize the spatial and temporal behavior of the AO system. To emulate M4 it is decided to use the newly developed deformable mirror 64×64 actuators by ALPAO 3 as a test facility for MICADO SCAO.

Further author information: (Send correspondence to Fabrice Vidal: E-mail: fabrice.vidal@obspm.fr, Telephone: +33 (0)1 45077942

DM SPECIFICATIONS

The ALPAO DM features 3228 actuators across a circular full aperture of 96 mm distributed on a 64 × 64 regular square pattern (inter-actuator pitch of 1.5 mm). Table 1

BENCH SETUP

A dedicated test bench was used to test the deformable mirror. It uses a classical 85×85 Shack-Hartmann (SH) with 8×8 pixels per sub-aperture located in front of the deformable mirror. Table 2 summarises the characteristics of this SH WFS. We did not use any turbulence simulator since measuring the DM characteristics does not necessarily imply to be in close loop scheme (it is actually preferred to be in open loop). It is also important to notice that the Shack-Hartmann was calibrated beforehand to ensure that the linearity of the measurements in the requested dynamic range (i.e stroke of the DM) is always valid. We estimate the wavefront measurement accuracy of the SH at a level smaller than <7 nm rms. The DM electronics are controlled with a classical computer (not real time). The commands can potentially be sent and received as fast as ≈ 2 Khz. However the actual speed limitation is due to the frequency of the camera (25 Hz) and to some synchronisation and software limitations that ensures the freshness of the wave-front measurement. This eventually leads to measurements performed at ≈ 3 Hz. The spatial sampling of the DM surface by the SH is not sufficient enough to characterize the DM influence functions in detail. However we plan for future tests to use a pyramid WFS with a 240×240 measurement points per pupil diameter to characterize the DM influence functions into more details. Furthermore the current capabilities of the bench did not allowed us, at least for this paper, to characterize the temporal behavior of the DM (actuators rising time, overall DM bandwidth, etc.). This activity is planned for the future by using stroboscopic capabilities synchronized with the DM commands. Figure 1 shows the ALPAO DM installed in our test lab.

CALIBRATIONS

Modes computation

The first calibration step is to define a set of modes that will be used to control and characterise the deformable mirror. First the set of coordinates of all the actuators is identified (taking care of ordering them properly) as a set of points A k in the pupil, with k ∈ [0, N act [. The covariance matrix of the phase was computed for the positions of the A k , ignoring any kind of influence function. Then the piston mode is filtered out from this matrix (a pure piston with phase values all equal). The matrix was finally diagonalised, leading to some eigenvectors forming the modes. The Fourier transform of any of these modes shows only a unique spatial frequency. It may have any arbitrary directions, but the modulus of its frequency components is unique per mode.

Why are modes monochromatic ?

This matrix is a discrete convolution matrix, because of the spatial stationarity of the phase. It is known that the eigenmodes of a convolution are sine and cosine modes: indeed any sine wave remains a sine wave after convolution by any kernel, and keeps the same phase when the kernel is symmetric. For that reason, the eigenvalues reflect the attenuation of the spatial frequency after convolution: consequently they are the transfer function, i.e. the Fourier transform of the convolution kernel. This latter is in our case the covariance of the phase and it is known that its Fourier transform is the Kolmogorov spectrum of the turbulence ; that is why the modes are the Karhunen-Loeve modes of the atmosphere, with eigenvalues decreasing as the turbulent spectrum. Diagonalisation also reveals groups of degenerate modes, that form subspaces with identical eigenvalue for any of its vector. A basis of those subspaces is simply the set of sine waves with identical spatial frequency, and arbitrary direction. That last statement is the point we want to emphasize: modes are necessarily single-frequency ones (in norm, not direction). Their Fourier transform is necessarily a circle (because the properties of the phase are spatially isotropic).

Producing the modes with a DM

As expected, one of the eigenvalues is null and corresponds to the filtered piston mode. All the others modes are then orthogonal to piston (using a scalar product in command space). The first modes exhibit tip and tiltlike modes, then defocus and astigmatisms, etc. necessarily sorted by increasing spatial frequency according to Kolmogorov (or Von Karman) spectrum with the piston coming at the very end just after a series of waffle-like modes. Computed as such, those modes are independent from the DM properties, as they have been computed ignoring entirely the actuator influence functions. This ensemble of vectors forms anyway a basis of the DM command space, and has particular properties. First,

B t B = B B t = Id (1)
Second, we assume that the influence function is the same for any actuator. Producing a given mode with the DM amounts to immerse the discrete spatial representation of the DM into the real continuous space and convolve it with the influence function. Ignoring aliasing and edge effects, this operation will not modify the shape of the mode because each of them is spatially monochromatic.

Proceeding that way offers a computationally light way to compute spatially ordered modes of the DM that inherently match the Karhunen-Loeve (KL) modes (the KLs within the DM space, not the atmospheric KL projected onto DM subspace and that might not be orthogonal), featuring quasi-tiptilt as low order modes, then astigmatism, defocus etc. and finishing with waffle-like modes.

Interaction matrix 4.2.1 Sinus method and demodulation

The algorithm used to make the interaction matrix consists in dithering each mode of the basis using a sinusoidal excitation, at a frequency specific to each mode. The frequency and the number of samples n over which the data recording is performed is such that all the modal dithering signals will be orthogonal to each others. Technically, this is obtained first by choosing the dithering signals d k (t) such as:

d k (t) = a k sin(2πf k t/n) (2)
with t, f k , and n all integers. The variable t stands conceptually for the time but is an integer value that represents in the reality the frame number, and is restricted to the range t ∈ [0, n[. The variable f k is the frequency associated to mode number k. Again, the term frequency is just conceptual here. The value of f k shall be an integer one: it is the number of dithering cycles the mode will undergo during the n record samples. The sine waves shall be sufficiently well sampled, which requires to have n > 2f m (m is the number of modes, i.e. f m is the maximum frequency. For safety, we prefer to take some margin on that condition and choose the number of samples n so that:

n = 3 f m (3)
We propose several ways of selecting the frequencies f k . The most natural one that has been chosen for our tests is to set

f k = f 0 + k , k ∈ [0, m[ (4)
with the value of f 0 being an arbitrary integer. The choice of f 0 shall be driven by the real physical value of the frequency F 0 (that one is the real frequency, in Hz) associated to f 0 , that shall keep away from the low-frequency regime of the turbulence of the laboratory or telescope. We typically want to have F 0 > (1 -10)Hz. In order to compute it, one has to take into account the frame period T e (or the frame frequency F e ) in order to compute the slowest sine wave period : T 0 = 3(m + f 0 )T e /f 0 , or its frequency:

F 0 = f 0 3(f 0 + m) F e (5)
which can be solved to get the value of f 0 as a function of the desired F 0 :

f 0 = m Fe 3F0 -1 (6)
with ... that denotes the integer part.

Others ways of selecting the frequencies may be of relevance. Let us notice that harmonics can arise from non-linear behaviour either from the DM or from the sensor. Quadratic, cubic behaviours will induce some doubling or tripling of the frequency so that when a frequency f k has been chosen, one shall skip its multiples 2f k , 3f k , etc. This allows all integers from f 0 to 2f 0 -1 and then the choice rapidly narrows and reduces to primary numbers.

Let us call V the matrix of size (m, n) (an horizontal collection of column-vectors of modal coefficients) with the coefficient v kt defined as:

v kt = a k sin(2πf k t/n) (7)
The matrix V contains the temporal dithering signal of mode k at line number k. Let us call D the interaction matrix of the sensor. Let us call S the set of wave-front slopes measurements provided by our sensor, of size (p, n) (an horizontal collection of n column-vectors of length p of slopes measurements). Those three matrices are linked by the relation:

S = D.V (8)
We assume that we are able to apply on the DM at each frame a new column-vector of V and we place the associated measurement in S. The matrices S and V are known, we shall now find D.

Due to the orthogonality of the dithering signals the matrix V has the remarkable property:

V.V t = n 2 Diag(a 2 k ) (9)
where Diag(a 2 k ) is a diagonal matrix made by the square amplitudes of the sine waves a 2 k . The interaction matrix D can be solved from Eq. 8 by writing:

D = 2 n S.V t .Diag(a -2 k ) (10)
This operation consists in projecting the measurements onto the dithering sine waves for demodulation. It is equivalent to a Fourier transform made by matrix product, and computed uniquely for the particular and exact dithering frequencies and, more importantly, with the proper phase.

Discussion

Eq. 10 can be written for any V that fulfills Eq.9, and our method applies thanks to that equation. Sine waves do the job, but any other orthogonal set of signals would do as well. For instance, the Hadamar matrices 4, 5 are an example of such orthogonal signals, particularly efficient as the poke amplitudes are only ±a k and using a number of samples n equal to the number of actuators. For those who would like to apply normally-distributed signals, it is possible to generate random signals made orthonormal through a Gram-Schmidt orthonormalization for instance. The choice for the orthogonal signals is virtually unlimited and can be adapted to the particular case of each user.

We have chosen sine waves first because they are quite simple to implement, and because we think it is important to master the temporal frequency range where the DM pokes are applied, in order that the demodulation process (i.e. the multiplication by V t ) rejects best the perturbations coming from the lab environment.

Variation using sine and cosine

A refinement of the method consists in using the dithering signal

d 2k = a k cos(2πf k t/N ) d 2k+1 = a k sin(2πf k t/N ) (11)
for the modes number 2k and 2k + 1. Although at the same frequency, those signals are in quadrature and still strictly orthogonal over the n samples of observation. This allows us to get a factor of 2 on the number of frequencies to be employed, i.e. nearly a factor of 2 on the calibration time. Although apparently efficient, we recommend to restrict the use of that method to laboratory experiments only that do not have to deal with latency issues between the DM voltage application and measurement. While a time shift in the measurements has virtually no impact when dealing with individual frequency per mode, some variable latency or jitter would compromise the results in this case.

RESULTS

DM stroke

Hardware limitations

The definition of the DM stroke is more subtile than with many other devices, because the command v = {v i } is hitting against two limitation thresholds, that are

• -1 < v i < 1 • v 2 i < E
; with E = 80 (power limit given by ALPAO).

First the command vector must always be set in the range [-1, 1] (unitless number) because the motion of a single actuator is limited, but also the sum of squares of the actuator commands must be lower than a software factory-defined limit, set to 80 in our case and related to the maximum power that can be delivered by the electronics.

Notion of command energy ; DM transfer function

Let v = {v i } be a unitary command vector, i.e. such that

i v 2 i = 1 . (12)
We will call energy of a command vector the sum of the squares of the actuator values. The rms amplitude of the wavefront produced by the unitary energy command vector v on the DM is a function of the spatial frequency. Due to the spatial extension of the influence function (i.e. due to coupling between actuators), large amplitudes at low spatial frequencies can easily be produced because of the constructive addition of the membrane deflection. At the opposite, high spatial frequencies require more stroke in order to fight against the coupling between actuators. Because of our choice of orthogonal modes (as defined by Eq. 1), they all carry a unitary command energy, and the energy of a linear combination of modes is the sum of the individual energies.

Using the interaction matrix of the modal basis, we were able to reconstruct the wavefront for each mode and measure the wavefront variance. Because our modal basis is also ordered by spatial frequency, we are able to plot the wavefront rms amplitude of the modes against their spatial frequency. This is represented on Fig. 2.

We have chosen to express the spatial frequency in act -1 (as an example to illustrate our purpose, a frequency of 0.1 act -1 is a wave with a spatial period of 10 actuator pitch).

The Fig. 2 can be interpreted as the modulus of a transfer function, since it describes how the unitary spatial frequencies can be produced by the DM. It clearly shows a low-pass filter, which shape is actually the Fourier transform of the influence function. The two first modes are tip and tilt ; they somewhat differ from the rest of the plot for a reason that still to be investigated. Around 0.44 act -1 the plot shows an "accident" that has to see with the aliasing of both the DM and our Shack-Hartmann, and that we leave apart from that article. For Figure 3: Amount of energy required for compensating the atmospheric turbulence with D/r 0 = 1 at 500 nm. The plain line is for an infinite outer scale (D/L 0 = 0). The dashed line is for D/L 0 = 1.56 (representing the ELT case with D = 39 m and L 0 = 25 m). This command energy is plotted against the spatial frequency of the modes. On the right, the values have been cumulated, started at mode number 3 (i.e. excluding tip and tilt). Units of vertical axis is average command energy (sum of squared actuator commands).

the first ≈ 100 modes, up to a frequency of 0.05 act -1 , a roughly constant value of 600 nm rms of OPD (optical path difference) is produced for a unitary command energy. Then the transfer function decreases and ends up at the maximum frequency of 0.5 act -1 , that corresponds to a period of 2 actuators in push-pull: this is the waffle mode. The sensitivity there is 50 nm rms, i.e. a factor of 12 below the first low order modes.

That transfer function allows us to understand how the DM stroke will be used for compensating atmospheric turbulence in the next section.

Compensation of turbulence

In order to know what is the amount of energy that is spent at compensating turbulence per spatial frequency, we can divide the spectrum of turbulence by the square of the previous transfer function. The result is shown on Fig. 3 (left), for a fully developed Kolmogorov turbulence. It shows that most of the energy is required from the low order modes, while the higher orders modes roughly require the same amount after ≈ 0.1 act -1 (i.e. after ≈ 100 th mode). On the right of Fig. 3 the same curve has been plotted, cumulating the energy values over the modes, and starting the integration at defocus mode (i.e. excluding the tip and tilt). The total required energy for (D/r 0 ) = 1 at 500 nm is E 0 = 0.003 when L 0 = ∞, or E 0 = 0.00136 when L 0 = 25 m on a 39 m ELT.

The result is that under fully developed Kolmogorov turbulence, the first 50 modes (tip-tilt excluded) require 70 % of the command energy, the 3175 others being the remaining 30%. It is likely that if potential saturations of the DM occur, they will be induced by the saturation of low-order modes rather than higher order ones.

The peak command energy the electronics can deliver is E = 80. We estimated that the maximum average energy compatible with this peak value and a saturation probability smaller than 0.01 shall be a factor of 3 below the peak, i.e. E average < 27. It follows that the maximum (D/r 0 ) value ensuring "no" saturation is found following that equation:

D r 0 < D r 0 max = E average E 0 3/5 ( 13
)
which gives (D/r 0 ) max = 235 at 500 nm. On a 39m telescope, this would set the limit where the DM occasionally begin to saturate 1% of the time to r 0 = 16.6 cm. This scales to r 0 = 3.4 cm on a 8 m telescope. Table 3 summarize the correctable turbulence depending on the telescope size and the fraction of which the DM will saturate.

D

L 0 < 1% sat. 10% sat. 39 m ∞ 16.6 cm 11.0 cm 39 m 25 m 10.3 cm 6.9 cm 8 m ∞ 3.4 cm 2.3 cm Table 3: The table gives the r 0 values that are compatible with the use of the DM for various telescope sizes D and values of the outer scale L 0 .

Modal interaction matrix

Considering Eq. 5, it is clear that the frequency F 0 is lower when m increases. Not surprisingly, the total time required for calibration increases while the value of F 0 gets smaller. Using m = 3228 actuators with a rather low frame rate, in our case F e = 3 Hz, gives F 0 of the order of 0.01 Hz which is definitely too low even for a laboratory setup. We modify the calibration scheme that consists in splitting the DM calibration in several parts by splitting the modal basis in a number of blocks that will be calibrated one after the other. The previous equations still apply, provided m is replaced by the number of modes in each block.

We decided to use blocks of 150 modes which requires 500 WFS measurement frames per block. Therefore 22 blocks are required for calibrating all the mirror modes I.e 11000 WFS frames. A the current frequency of 3Hz this leads to a total interaction matrix calibration time of 1 hour. We also choose to reduce gradually the amplitude of sine waves with spatial frequencies order. The first block of 150 modes used a 2000 nm rms amplitude while the last 150 high order modes were calibrated with only 200nm rms amplitude.

Unimodal linearity

The linearity of the DM motion with respect to an input modal poke has been tested by ramping up the voltage applied to a given mode from -1 to +1 (command energy units). A linear regression is applied to the measurements, and the ensemble of regression coefficients forms the reference modal measurement (applied iteratively to all modes, this could be another way to build an interaction matrix). What we want to study here is the accuracy of that linear motion for any point of the DM surface. For doing that, the set of measurements are projected (scalar product) onto the regressed modal measurement previously found, and a unique number is derived for each step of the ramp, that correspond to the amplitude of the measured mode.

Subtracting the raw measurements data to the product of the regressed modal measurement and its amplitude would tell us the amount by which the mode produced by the DM distorts with amplitude. But we want to do more, and also want to check how much it deviates from linearity against the input command. So, the amplitude previously found is adjusted by a linear regression with respect to the values of the command ramp, and we finally compute the error between the raw measurements data, and the product between that linear fit and the modal measurements. The rms value (along the ramp) of this error is computed for each measurement (for each slope of the Shack-Hartmann) and the maximum of its value is deduced.

We found for all modes a result below 10 nm rms. The linearity is excellent, and the error is hardly above the capacities of our wave-front sensor. We also display as a bidimensional map the spatial distribution of the error and check whether this is incoherent or if it is actually related to the mode applied. The latter case is true: the signature of the mode appears in the error, which is the evidence that we actually measure a non-linearity error and that the method is valid.

As an example, a set of ramp voltage is applied on low (#30), medium (#300), and high order modes (#3000) which -according to Fig. 2-leads respectively to ±600, ±370, and ±50 nm rms amplitude aberrations. Fig. 5 represents the fitted linear amplitude of the modal measurements and is a straight line with less than 0.1% error, this number not being meaningful as we may be limited here by our sensor. Fig. 6 shows the spatial deviation to this linear model (in pixel rms from SH slopes). The maximal spatial deviation to the linear model is 6 nm rms for mode #30, 10 nm rms for mode #300 and <5 nm rms for the last. The linearity of the DM is thus extremely good and again actually at the limit of the WFS measurements (<5 nm rms). Figure 5: Linearity curves for modes 30 (left), 300 (center), 3000 (right). The amplitude range is ±600 nm rms for mode #30, ±370 nm rms for mode #300 and ±50 nm rms for mode #3000.

Figure 6: Spatial deviation to DM linear model (i.e to the measured interaction matrix) for modes 30 (left), 300 (center) and 3000 (right). Color units are expressed in pixels. The maximum spatial deviation is for mode #300 is: 0.018px×739nm/px = 13nm rms (i.e 3% error)

Additivity

Additivity is the ability of the DM to behave as linear algebra would predict. Mathematically speaking, this should be named linearity but this would be confusing compared to the previous paragraph. Additivity is the property that any linear combination of commands will produce the same linear combination of wave-fronts.

In order to check this, we have recorded interaction matrices made by the sine wave method by varying the frequencies of each mode, and we also performed the interaction matrix by blocks of modes with variable composition. The result is simple: the difference between two interaction matrices achieved in very different conditions of modal composition is the same as the difference between two strictly identical realisations of the same matrix. In a word, the additivity error is smaller than the precision of our measurements, which is of the order of 5 nm rms.

Hysteresis

The principle for measuring the hysteresis is to modally poke the DM back and forth a number of times (usually 10) between two values, and get the associated measurements. The latter are processed exactly as in the case of linearity measurements described in the previous paragraph. The hysteresis cycle is shown as the deviation of the measured value to linearity, as a function of the applied value. Indeed, the level of hysteresis is so low that the tiny cycle is invisible when plotting the whole hysteresis graph: it only becomes observable when the average slope is subtracted.

On low and medium order modes (30, 300) the hysteresis is below <1% (Fig. 7) of the applied value, which is excellent. The performance is even better as the mode order increases, mode number 3000 is measured as low as <0.2% (see Fig. 7). Figure 7: Hysteresis curves for modes #30 (left), #300 (center) and #3000 (right). The overall linearity response has been subtracted in order to magnify and make visible the hysteresis effect. However, to be fair we have to say that the ensemble of results are not fully consistent within each others, and we ended up to the conclusion that the level of hysteresis is dependent from the time spent to measure the cycle. In the end, we are not fully convinced that hysteresis has been measured. Instead this apparent hysteresis could just be a consequence of a creeping effect, which is significant, and described in the next section. If this assumption is exact, then the hysteresis level is even smaller than the number we gave.

Creep

Creep effect is the tendency of a material to move slowly and deform under the influence of a permanent mechanical stress. Creep increases logarithmically with time following the relationship:

z(t) ≈ z(t 0 ) (1 + g. log(t/t 0 )) , ( 14
)
where z is the position/amplitude at time t and g is the creeping factor.

Fig. 8 represents the short term creep when applying the same shape during 3 seconds. It shows multiple (×28) short term cycles of 3 second holding the same shape, and a zoom of one of these cycles, where the creep is visible at the top as a gentle increasing drift of the maximum value. The same effect could be visible for the bottom part of the cycle, right after the descent, the DM keeps some "memory" of the shape previously applied, and take some time to go back to 0. The figure in the middle and bottom of Fig. 8 represents the measured DM position at the end of the rising (blue) and descending (red) time for each of the 28 cycles. It shows that on top of the series of cycles where creep is observable on each of them, an overall creeping effect superimposes due to the fact that the average of the cycles is not null. The overall drift after those 28 cycles (175 seconds) is 0.2%.

Figure 8 (bottom right) also shows the measured creeping effect when applying a constant shape of mode #30 and holding it one hour, and finally resetting the command and observe one hour more. We measured after 3600 s a position of ≈1.17 (from a normalized value at t 0 of 1) leading to a creep factor of ≈0.02 which is an average value (usually in the range of [0.01-0.03]).

Conclusion

The ALPAO 64×64 deformable mirror is currently in our lab for tests since the beginning of April 2019. The DM characteristics are so far promising although we did not started to measure its temporal dynamics yet. The DM static components are very good, in particular, it features a perfect modal additivity and linearity response, a very low hysteresis and low creep/drift effect. We found the stroke slightly limited for being used as a first turbulence correction stage on a 40m telescope class due to the electronics energy saturation management, however this DM is perfectly well suited for being used on a 8-10m telescope class. Next steps will focus on the measurement of the DM temporal response and open loop tests in the framework of the MOSAIC instrument.

Figure 1 :

 1 Figure 1: The ALPAO 64×64 DM installed in the lab, ready for the tests.

Figure 2 :

 2 Figure 2: Value of the rms wavefront amplitude (in nm rms) for each mode or our modal basis with a unitary command energy in log-log (left) or lin-lin (right). The result is plotted against the spatial frequency of the mode expressed in act -1 on the left, or against the mode number of the right. Modes 30, 300 and 3000 are shown with a red dot.

Figure 4 :

 4 Figure 4: Example of generated DM voltages (left), measured by the SH (center) and reconstructed phase (right) for modes 30, 300 and 3000.

Figure 8 :

 8 Figure 8: Top left: representation of 28 cycles of push-release sequences of 3s for mode #30. Top right: zoom on a single cycle of 3 seconds. Bottom left: Drift of the pushed position (blue) and zeros (released in red) across the 28 cycles, that exhibits a creeping tendency due to the fact that the cycle average is not 0. Bottom right: Long-term (1 hour) creep measured over 3 hours, up and down.

Table 1 :

 1 summarises the main relevant DM specifications. Summary of the main ALPAO 64×64 specifications.

	Parameter	Value
	Total number of actuators	3228
	Nb of actuators across the diameter	64
	Full aperture	96 mm
	Useful aperture (pupil)	93.5 mm
	Mechanical stroke	>3.5µm
	Inter-actuator mechanical stroke	>1.2µm
	Actuator hysteresis	<2%
	Electronics protocol	10Gb up to switch then 1Gb
	Command encoding	14 bits

Table 2 :

 2 Optical and physical design parameters of the Shack-Hartmann used for the characterisation of the DM.

	Parameter	Value
	Pixel size	6.45 µm
	Micro-lens focal length	480 µm
	Number of micro-lenses	85 × 85 (5449)
	Micro-lens pitch	55 µm
	Focal length of collimator	75 mm
	Sensitivity	739 nm/pixel/µlens

ACKNOWLEDGMENTS

The deformable mirror presented in this paper has been funded by a grant from DIM ACAV/Région Ile-de-France and thanks to the financial support of INSU. This work was also supported by INSU and Observatoire de Paris through the funding of J. Raffard's position.


Mohamed Anis Fekih

Walid Bechkit

Hervé Rivano

Manoël Dahan

Renard Florent

Lucille Alonso

Florent Pineau

Participatory Air Quality and Urban Heat Islands Monitoring System

Keywords: air quality, urban heat islands, low-cost sensors, participatory sensing, wireless sensor networks, environmental monitoring

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

On the one hand, air pollution is one of the main concerns of many cities around the world. Indeed, millions of people live in cities where the concentration of pollutants exceeds the standard health limit several times a year, which constitutes a serious health problem. According to World Health Organization (WHO) data, indoor and outdoor air pollution caused 7 million deaths in 2016 [START_REF]Burden of disease from the joint effects of household and ambient air pollution for 2016[END_REF]. In fact, long-term exposure to air pollution can result in cancer and damage to the immune, nervous and respiratory systems. Pollution concentration is particularly present in urban cities where the density of population and buildings is higher, augmenting the risks of air pollution exposure [START_REF] Cannistraro | Analysis of air pollution in the urban center of four cities sicilian[END_REF]. On the other hand, urban heat islands (UHI) greatly increase the risk of allergies, respiratory and cardiovascular problems and can lead to excess mortality among vulnerable people during heatwaves [START_REF] Basu | Relation between elevated ambient temperature and mortality: a review of the epidemiologic evidence[END_REF]. Moreover, several studies show that the heat island effect contributes to the degradation of air quality by increasing the concentration of some pollutants (ozone, nitrogen oxides, etc.) [START_REF] Lai | Air quality influenced by urban heat island coupled with synoptic weather patterns[END_REF], [START_REF] Ryu | Effects of anthropogenic heat on ozone air quality in a megacity[END_REF]. Therefore the need to assess air pollution and UHI in our cities is extremely required to establish well adapted public policies.

In order to mitigate the impact of air pollution and UHI, it is essential to set up an effective monitoring system whose main objective is to measure temperature and air pollution concentration and then to produce high resolution maps in real time. Conventional monitoring stations include various sensors, which typically measure a large number of environmental parameters and pollutants, such as temperature, humidity, carbon monoxide (CO), nitrogen oxides (NOx), ozone (O 3 ) and particulate matter (PM) [START_REF] Kumar | The rise of low-cost sensing for managing air pollution in cities[END_REF]. Despite providing accurate concentrations, these monitoring stations are too large and expensive to be deployed anywhere [START_REF] Castell | Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?[END_REF]. In addition to traditional monitoring based on measurements, numerical simulations of physical models can also be used to generate maps of air pollution and the UHI. These models simulate the spread of air pollution as a function of the location of pollution sources, land use, emission rates and meteorological data.

The limitations of large monitoring stations in terms of cost, size, flexibility and spatial granularity have led to the emergence of small and low-cost air quality sensors. These sensors have lower accuracy than traditional monitoring stations, but are more flexible and easier to maintain. This would ultimately help improving the spatial and temporal knowledge of air quality and urban heat islands. Compared to traditional air pollution monitoring solutions, the wireless connectivity of low-cost air quality sensors forms a Wireless Sensor Network (WSN), which has many advantages. In fact, their costeffectiveness enables dense, large-scale deployments, which increase the spatial resolution. In addition, both size and cost factors provide greater deployment flexibility. However, low-cost sensors are less accurate than traditional monitoring stations and their data quality is questionable [START_REF] Gerboles | Measuring air pollution with low-cost sensors[END_REF]. Indeed, electrochemical sensors present high sensitivity to meteorological changes (temperature, humidity, etc.) and have cross-reactivity problems with similar molecular types. Also, measurements of other low-cost sensor categories such as optical particulate counters may depend on the density, shape of particles or even their color [START_REF] Gerboles | Measuring air pollution with low-cost sensors[END_REF].

Mobile crowdsensing is an emerging paradigm that has attracted great attention in the recent years, owing the result of the rapid evolution of Internet of Things (IoT) [START_REF] Ganti | Mobile crowdsensing: current state and future challenges[END_REF]. This paradigm leverages population density to perform large-scale monitoring. One domain of application of mobile crowdsensing among many others is air quality, in which participants use embedded sensors on their smart devices (e.g, smartphones, smartwatches, etc.), or stand-alone sensor nodes to monitor environmental conditions in their surroundings. Therefore, this will result in a better involvement of citizens in the sensing process, measure their individual exposure and giving them access to collaborative maps. In addition, this will help the scientific community to collect more data to improve air quality and UHI knowledge, and the local authorities to implement appropriate policies.

This paper introduces 3M'Air, a project that aims to explore the potential of participatory citizen measures using low-cost sensors in order to improve the local knowledge of air quality and temperature. As part of this project, we present the design, implementation and evaluation of a mobile participatory-based air quality and heat islands monitoring platform based on a network of small sensor nodes carried by people. The remainder of this paper is organized as follows. Section II discusses related research work. Section III presents the context and objectives of this project along with some design guidelines that we have adopted throughout this work. An overview of the proposed system architecture and its main components is presented in Section IV. Subsequently, the obtained experimental results are discussed in section V. Section VI presents briefly some future work and perspectives. Finally, Section VII concludes this paper.

II. RELATED WORK

The studies of urban air quality and heat islands in urban areas, in the context of climate change, form today two complementary issues. In fact, the local air quality is impacted by the urban heat island, since it influences both the characteristics of the airflow and the boundary layer of the atmosphere [START_REF] Agarwal | Modeling of the urban heat island in the form of mesoscale wind and of its effect on air pollution dispersal[END_REF]. Hence, the effectiveness of the actions carried out by public spatial planning policies to deal with these two issues is strongly linked to the fine knowledge of temperature gradients and air quality at local scales. To increase the local knowledge of air quality and urban heat islands, many low-cost sensorbased environmental monitoring projects have emerged over the past decade.

A generic environmental monitoring platform based on fixed indoor sensors is presented in [START_REF] Ferdoush | Wireless sensor network system design using raspberry pi and arduino for environmental monitoring applications[END_REF]. It uses open-source hardware platforms as Arduino and Raspberry Pi, and ZigBee as a communication protocol. The system is composed of sensing nodes, a web interface, and between the two a base station which embeds a gateway node, a database, and a web server. Sampling is performed each 35 min and measurements are sent using ZigBee to the base station that gathers and stores the data for future visualisation on a web browser using the internet or a local network. The solution has been tested indoor by deploying a base station and 3 sensor nodes measuring both temperature and relative humidity. The authors identify the need of an important number of ZigBee routers for large scale deployments.

An implementation of a flexible environmental monitoring system is described in [START_REF] Lombardo | Wireless sensor network for distributed environmental monitoring[END_REF]. The solution is based on a set of small sensor nodes and transceivers communicating in Bluetooth Low Energy (BLE) and a cloud-based back-end. The sensors are equipped with 32-b ARM-M0 micro-controller, a low-power 2.4-GHz transceiver, a 32.768-kHz Real-Time-Clock (RTC), a temperature and relative humidity sensor and a local storage that can store up to 125 000 measurements. These components are soldered along with a lithium battery on a small Printed Circuit Board (PCB). Measurements are logged at intervals of 15 min on the nonvolatile memory and sent to a Raspberry Pi Zero W board that pushes data to the cloud. Two possible ways of visualizing data are offered, on a smartphone by connecting it to the node using BLE or on a web interface using a web browser. This solution has been tested indoor in a heritage building in the north of Italy for more than two months. The results point out the challenge of environmental conditions variation from a position to another even at small distances.

Another cloud-based monitoring system is proposed in [START_REF] Arroyo | Wireless sensor network combined with cloud computing for air quality monitoring[END_REF]. The system is composed of a sensor network, a cloud system and an end-user layer. Sensor nodes incorporate a microcontroller, up to four gas sensors measuring a set of volatile organic compounds (VOCs) (benzene, toluene, ethylbenzene, and xylene), a ZigBee module for communicating with the Gateway, and a power management controller that can switch between a battery and a solar panel to power the node. The "ConnectPort X4" Gateway used in this solution integrates a ZigBee module that receives measurement data at the end of each sampling cycle (10 min) and forwards them to the cloud system where data are stored and processed for delivery to the end-user repsented by an internet application. The solution has been tested in laboratory by generating different compounds at different concentrations using specific tubes containing an analyte that passes through a tube at a maintained speed and temperature. The authors also propose pattern recognition techniques to efficiently detect VOCs and show that they achieve good results in their quantification.

The Citi-Sense-MOB project [START_REF] Castell | Mobile technologies and services for environmental monitoring: The citi-sense-mob approach[END_REF] aims to support green growth and sustainable development through a mobile environmental monitoring system formed by sensor nodes based on electrochemical sensors measuring NO 2 , NO, CO, SO 2 , O 3 , CO 2 , temperature, and relative humidity. Two sensing platforms are proposed, one mounted on buses and the other mounted on electrical bicycles. The first platform performs measurements by pumping the air inside a chamber in contact with the sensors and collects position, speed and use of brakes directly from the bus computer and sends the data to a central database. The other platform is powered by the bicycle battery and has no need for a pump as it is in direct contact with the air. Measurements gathering begins as a person starts riding the bicycle and turns off a short period after the bicycle stops. Collected measurements are sent to the same data base as the first platform. Citizens can visualize nodes' measurements and positions gathered from both platforms on a map using a web interface or a mobile app. Through this work, the authors aim at increasing the awareness of citizens and involving them in the adoption of new habits that contribute in the reduction of air pollution.

The Airsense project [START_REF] Zhuang | Airsense: A portable contextsensing device for personal air quality monitoring[END_REF] provides a portable device for personal air quality monitoring. The sensor node is batterypowered and integrates the Sharp GP 2Y 1010AU 0F dust sensor that measures particulate matter (PM 2.5 ), an SHT15 temperature and relative humidity sensor, an ADXL345 accelerometer sensor to capture users' movements and a GPS module to track users' location. All sensors are incorporated along with a micro-controller on a single PCB. No wireless communication technology is used. Measurements are instead stored on a SD card that is accessible by the user. City Scanner [START_REF] Anjomshoaa | City scanner: Building and scheduling a mobile sensing platform for smart city services[END_REF] is a general-purpose portable sensing platform that offers multiple configurations in terms of sensing probes. The solution does not require a specific power supply and therefore can be mounted on top of any urban vehicle without impacting its operations. Each deployed node consists of a group of sensing modules sending their measurements via a short-range WiFi network to a core unit which ensures power management, data storage and streaming to the cloud using open WiFi hotspots. The proposed solution has been deployed in the city of Cambridge. Environmental probes measuring temperature, humidity and particulate matter have been mounted on trash trucks along with other sensors like thermal cameras and WiFi scanners. The main challenges encountered with the deployment were the data transfer reliability, power consumption and data fidelity.

The UrPolSens project [START_REF] Boubrima | Deployment and scheduling of wireless sensor networks for air pollution monitoring[END_REF] proposes a low-cost energyefficient air quality monitoring platform that uses fixed sensor nodes powered by solar panels and batteries. The nodes measure temperature, relative humidity and NO 2 and can be adapted to measure PM or VOC. Measurements are sampled every second. Every minute an average is recorded on a shortterm EEPROM. The average measurements are then aggregated into 10-minute records. These records are stored on a local SD card and sent using low-power LoRa communication technology to a gateway that forwards the packets via cellular network to a central cloud server for storage, filtering and processing. A web interface has been developed to visualise real-time air pollution concentrations and weather conditions. The solution was deployed and tested for 3 months in Lyon city in an urban street surrounded by two reference stations. The results show that the system is energy-efficient while keeping an acceptable degree of accuracy.

The OpenSense project [START_REF] Hasenfratz | Pushing the spatio-temporal resolution limit of urban air pollution maps[END_REF] studies the performance of low-cost sensing probes by designing and deploying mobile sensor nodes on trams and buses. The nodes measure several environmental parameters such as temperature, humidity, O 3 , CO, NO 2 and PM. In addition, they are equipped with a GPS and an accelerometer to geolocate the measurements. Each node incorporates a Linux-based core component which stores pollution data locally and streams it to the cloud server using either WiFi or cellular. The platform offers a visualization tool that displays pollution concentrations on top of the map of the region. The project was deployed in Zurich and Lausanne in Switzerland, and mounted on top of 10 trams and 10 buses. The researchers point out the challenge of low-cost sensors calibration and propose techniques to reduce the calibration error.

Table I summarizes the characteristics of the aforementioned low-cost environmental monitoring platforms: type of deployment (i.e. fixed or mobile), measured environmental parameters, sampling frequency, capacity to store data locally, communication technology, and energy consumption when available.

Temperature and humidity monitoring is present in most of the platforms, proving the importance and the impact of heat islands on other pollutants and air quality in general. Besides, most of the presented platforms use ZigBee, WiFi or BLE which are quite adapted for indoor monitoring or fixedsensors-based monitoring systems. In the context of crowdsensing, BLE is useful to send measurement data to users' smartphones, while sending the data from the smartphones to the cloud needs a cellular connection or WiFi. However, this operation, whether it requires a human intervention or not, consumes a significant amount of the smartphone's energy and mobile data plans. As an alternative, low-power long-range communication technologies could cover the whole network of sensors with a small number of gateways.

III. 3M'AIR PROJET

3M'Air ("Mobile Citizen Measurements and Modeling: Air Quality and Urban Heat Islands") is a three-year multidisciplinary project that aims to explore the potential of participatory sensing to improve the local knowledge of air quality and Urban Heat Islands. It leverages several scientific expertises going from electrical engineering, computer science and fluid mechanics to geography, urban climatology and sociology.

A. Context and Objectives

The adverse effect of urban heat islands and air pollution on human health and the environment has been widely documented in several studies [START_REF] Pope | Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution[END_REF], [START_REF] Bowatte | The influence of childhood trafficrelated air pollution exposure on asthma, allergy and sensitization: a systematic review and a meta-analysis of birth cohort studies[END_REF], [START_REF] Agency | Air pollution: Current and future challenges[END_REF]. Despite that, pollutant concentrations as well as air temperature continue to increase, mainly due to urbanization, industrialization and other factors [START_REF] Xu | How industrialization and urbanization process impacts on co2 emissions in china: Evidence from nonparametric additive regression models[END_REF] [START_REF] Paranunzio | Evaluating the effects of urbanization evolution on air temperature trends using nightlight satellite data[END_REF] . For instance, in Lyon city, France, an increase of 2.2 °C, representing + 0.4 °C/decade, was recorded in average annual temperatures between 1959 and 2016 [START_REF]Changement climatique en rhône-alpes. températures moyennes annuelles et saisonnières[END_REF] [25]. More precisely, during summer, there is an increase of 2.8 °C between 1959 and 2016 (+ 0.5 °C/decade) [START_REF]Changement climatique en rhône-alpes. températures moyennes annuelles et saisonnières[END_REF]. In order to i) understand the link between microclimates and the potential impact of UHI and urban pollution, and to ii) bridge the gap between individual exposure and regional measurements, a fine characterization of temperature and air quality at local scales is needed. Our project aims to evaluate the added value of moderately accurate pollution data generated by a nonscientific community in the fine-grained characterization of air quality and urban heat islands. Our project also seeks to involve citizens in the scientific process of air quality monitoring and to present adapted and optimized approaches for participatory data analysis to generate fine-grained pollution maps while taking into account the continuity in space and time of measurements and the dynamic nature of the studied phenomena. We believe that the interest of these participatory data will be stronger by combining them with physical models and accurate reference monitoring stations. To this end, the first step of our project is to propose a multi-application participatory monitoring system with a variety of measured parameters (gas, particulate matter, temperature and relative humidity) based on lab-designed modular, autonomous and optimized sensor nodes whose hardware and software can be adapted to the needs of our studies.

B. System Design guidelines

Based on the study and survey results, our first aim was to design a generic network architecture that mainly serves participatory environmental monitoring applications but can be easily adapted to other environmental applications. To achieve this goal a number of recommendations need to be respected. Our main design guidelines can be summarized as follows:

Required parameters: In order to serve the two identified applications (i.e. air quality and urban heat islands), the designed node should measure air temperature, relative humidity, NO 2 , PM 1 , PM 2.5 and PM 10 . Note that an extra calibration is often needed to extract meaningful data from sensors like conversion from voltage to pollutant concentration for gases like NO 2 , or conversion from particle counts to mass for particulate matter.

Data gathering: The network should allow measuring and gathering all the identified air and weather parameters at a remote server in order to allow the remote monitoring via internet. Moreover, to offer a good data availability, it is preferable to store the collected data at multiple levels. This will add robustness against network connection problems.

Autonomy and network lifetime: The sensor nodes are designed to be carried by people, hence they should deliver at least 12 hours of operating time. This does not just mean integrating a bigger battery but also optimizing all the system components to offer better efficiency.

Sampling frequency: The developed network must be able to perform measurements at an acceptable sampling rate to ensure a good spatio-temporal coverage. The system must be easily configurable to be adapted to the mobility of users (e.g. bicycles or vehicles,...). However the main priority is the use on foot, meaning an average speed between and 0.83 and 1.57 m/s [START_REF] Al-Azzawi | Modeling pedestrian walking speeds on sidewalks[END_REF], [START_REF] Rastogi | Pedestrian flow characteristics for different pedestrian facilities and situations[END_REF], [START_REF] Kotkar | Pedestrian flow characteristics in mixed flow conditions[END_REF]. By setting the default sampling frequency to one every 20 sec, consecutive readings are less than 30m apart, which gives a good spatio-temporal resolution.

Modular design: Our objective is to design a platform that will be as flexible and extensible as possible. This can be achieved by opting for a component-based design where the whole system is composed of multiple hardware or software components that can be removed or extended when needed.

Ease of use: Internet of things involves a big number of smart connected devices in our daily lives. This should not complicate our life but on the contrary simplify it. For this reason, the designed nodes need to be light and small. Even more, they should not require technical or scientific knowledge to be used. It should be simple and as intuitive as possible to turn on the node, to use it and to charge it.

Reliable measurements: To guarantee a good data reliability, the sensor nodes should be adapted to real environmental conditions. In fact, solar radiations have a big impact on temperature and humidity measurements but also on other pollutant measurements. To protect the sensor probes from the effect of solar radiations, the nodes should be white and incorporate a solar radiation shield.

IV. SYSTEM ARCHITECTURE

The sensing layer is composed of low-cost, small-size, battery-powered and portable wireless nodes incorporating three environmental sensors: a dust sensor (measuring PM 1 , PM 2.5 and PM 10 ), a NO 2 sensor, and a temperature/humidity sensor. In addition, the nodes include a power manager, a GPS receiver, a microSD card module (to ensure data availability in case the node is outside LoRaWAN network coverage), an analog-to-digital converter (ADC) to enable compatibility with analog sensors, and a LoRa module. All the peripheral components are managed and orchestrated by a micro-controller.

The communication layer relies on the LoRaWAN infrastructure provided by "The Things Network" (TTN) [START_REF]The things network[END_REF]. LoRaWAN [START_REF][END_REF] is a networking protocol defined by the LoRa Alliance which is an organization of more than 500 companies collaborating to promote the LoRaWAN open standard. This standard is designed for sending small data packets over long distance at a low bit rate and is one of the most promising Low-Power Wide-Area Network (LPWAN) technologies for LoRaWAN offers the possibility of using eight configurations (Data Rates) on the EU863-870MHz ISM Band with six possible SF values (SF7 to 12) [START_REF]LoRaWAN® Regional parameters[END_REF]. The six mandatory configurations use a 125 kHz bandwidth and offer a bit rate varying from 250 bps for SF12 to 5.47 Kbps for SF7. Two more configurations use respectively a 250 kHz bandwidth with SF7 achieving a bit rate of 11 Kbps, and FSK modulation offering 50 Kbps [START_REF]LoRaWAN® Regional parameters[END_REF]. We note that the LoRaWAN maximum payload size ranges from 51 to 222 bytes depending on the selected SF value [START_REF]LoRaWAN® Regional parameters[END_REF].

However, LoRaWAN has restrictions due the used frequency band. For instance, in Europe, most of the 868 MHz sub-bands have a duty cycle of 1%, thus, each node must not exceed 1% of spectrum occupancy per channel and per SF.

In addition to the physical configuration, LoRaWAN also defines the network architecture as well as the upper networking layers.

In the proposed architecture by LoRaWAN, the gateways forward the packets sent by sensor nodes to the network server, which does the filtering of the data and makes sure that only one copy of the packet is sent to our application server to avoid data redundancy.

On top of that, LoRaWAN offers two security modes: i) Over The Air Activation (OTTA) mode where a join procedure is performed at the beginning in order to generate dynamic addresses and security keys, and ii) Activation By Personalization (ABP) mode where addresses and security keys are pre-loaded before the deployment. In both modes, a Message Integrity Code (MIC) is added to each message to ensure that data has not been changed and is used by nodes and the network server to ensure data integrity. Thanks to the payload encryption, confidentiality is also ensured between nodes and the application server in both modes. We choose to use OTTA as it is based on dynamic keys, and handles the initial nodes authentication in addition to the confidentiality and data integrity.

TTN is a contributor member of the LoRa Alliance offering a free-to-use LoRaWAN network. They manage the cloud infrastructure while the gateways are mainly deployed by volunteers (including our lab) [START_REF] Blenn | Lorawan in the wild: Measurements from the things network[END_REF]. This layer is responsible for forwarding measurement data received from the nodes to the cloud server which represents the third layer. We have selected this solution for mainly three reasons: 1) the participatory aspect of our platform that meets the collaborative nature of TTN; 2) its robustness and security through a large number of gateways and an end-to-end encryption; 3) its growing adoption in many fields all over the world with an increasing number of deployed gateways [START_REF] Zinas | Proposed open source architecture for long range monitoring. the case study of cattle tracking at pogoniani[END_REF], [START_REF]Placepod[END_REF], [START_REF]Vinduino r3 sensor station[END_REF].

The storage and processing layer is implemented on a multiple functions cloud server. It receives data from the TTN backend and stores them into a NoSQL database for cleaning (i.e. correcting or removing inaccurate or redundant data from the database) and processing. It offers a REST API through which measurement data can be queried using a web browser, a mobile application or a third-party service.

The end-user layer offers the users the possibility to visualise air pollution concentrations and weather parameters using either a mobile application, a third-party service, or a web interface that we designed to display maps and statistics.

A. Sensing and transmission

Our aim in this project is to design small and portable nodes based on low-cost sensors. Given the fact that these nodes are designed to be mobile and carried by people, it is important to have a suitable design to 1) ensure the node's functioning in mobility, 2) protect the node from solar effects while guaranteeing air flow, 3) maintain a reliable communication, and 4) make the nodes the lightest possible for users. We have designed a casing with an integrated solar radiation shield and three separated chambers as depicted in Figure 2. We modeled the casing using Autodesk Fusion 360 software. The casing was 3D-printed using a Selective Laser Sintering process with Nylon PA12 plastic. This material provides good strength and enough flexibility to withstand a fall without damaging the node. The 16 print resolution allows precise placement of the components as well as their attachment directly to the casing.

The first chamber regroups the environmental sensors and is designed to provide natural ventilation while protecting the sensing probes from solar radiations. For the sensors, we employ the AlphaSense series B [START_REF]No2-b43f nitrogen dioxide sensor[END_REF] sensor which measures 2 concentrations. This low-cost electrochemical probe produces a current from the interaction of the target gas with the electrolyte [START_REF] Price | Air analysis | field portable instruments for the measurement of airborne hazards[END_REF]. The Grove HM3301 low-power laser dust detection sensor measures three sizes of particulate matters (PM 1 , PM 2.5 and PM 10 ). This sensor is equipped with a fan driving airflow inside a detection chamber, and is based on laser light scattering technology. It has an effective detection range of 1 to 500 / 3 [START_REF] Co | [END_REF]. The last sensor is the DHT-22, a small low-power sensor based on a polymer capacitor for measuring temperature and relative humidity with an operating range of -40 to 80 °C and 0 to 100% respectively [START_REF] Electronics | Digital-output relative humidity & temperature sensor/module dht22[END_REF].

The second chamber holds a printed circuit board (PCB) that we designed to integrate all necessary components. Our PCB incorporates an Arduino MKR WAN1300 built on the Atmel SAMD21 low-power ARM micro-controller [START_REF]Sam d21 family[END_REF], the Murata CMWX1ZZABZ LoRa module 32 KB SRAM [START_REF] Co | Sub-g module data sheet[END_REF], 8 digital pins, and 7 Analog input pins as depicted in Figure 3. An MKR MEM ASX00008 shield is used to add a microSD card port and 2 MB extra flash memory. For managing analog sensors, a 16bits ADS1115 analog digital converter (ADC) [START_REF]Adafruit 4-channel adc breakouts[END_REF] has been added to have a better precision than the embedded MKR 12-bits ADC. Geolocation is achieved by a low-power GPS receiver based on the MTK3339 chipset. This high-sensitivity module has 66 channels and can track up to 22 simultaneous channels [START_REF]Fgpmmopa6h[END_REF]. In addition, the GPS module has a small battery allowing it to save the current date and time without having to perform a data acquisition from satellites. This is very helpful in situations where no satellite coverage is available (e.g. passing into a building or going inside a metro station). The last component on the PCB is the PowerBoost 1000C, a small power manager [START_REF]Adafruit powerboost 1000c[END_REF] based on a DC/DC boost converter chip that powers the system and is connected to the 3.7V LiPo battery placed in the third chamber. It is possible to charge the node's battery by plugging the node to an external 2A power source using an easily accessible micro USB port located at the bottom of the node. An additional micro USB port is also available for node programming and debugging purposes.

When the node is turned on, the micro-controller initializes the different modules and establishes an Over The Air Activation connection to our application on the TTN network. Data are gathered from all environmental sensors and the GPS at 20 seconds intervals. In order to reduce the energy consumption of data transmission and storage while respecting the maximum payload of LoRaWAN packets, each node accomplishes three measuring cycles before storing the data on the micro-SD card and sending them to TTN gateways. The data record contains the ID of the sensor, the timestamp of the measurement, temperature, relative humidity, NO 2 PM 1 , PM 2.5 , PM 10 values and GPS coordinates. In case there is no GPS signal, the GPS coordinates will have the value zero, but we still can get the exact date and time from the GPS, thanks to the GPS module's battery that allows it to save the current date and time.

We have formatted our packets to contain 51 bytes (3 x 17 bytes) which satisfies the duty cycle constraint using SF7, SF8 and SF9 with a 20-second sensing period and one-minute transmission period. We note that the value (3) is the number of cycles and is also configurable. We also note that higher values of SF can be used by increasing the transmissions period and/or reducing the amount of data to send (e.g. compressing data, reducing the measurement precision, sending averaged data).

In all our tests we send confirmed packets (i.e. the application server has to acknowledge the reception of the packet). However, we do not implement retransmissions since data are stored locally on the nodes and the loss rate was very low. Indeed, thanks to the duty cycle, the collision probability is low when the number of nodes in a close proximity is not large [START_REF] Augustin | A study of lora: Long range & low power networks for the internet of things[END_REF].

When packets are received by TTN, they are forwarded to a router/broker service in the TTN infrastructure that will decode the message. The latter is then published to the right application handler which is our cloud server. For further details on how the transmission layer works, please refer to the TTN documentation [START_REF]The things network[END_REF].

B. Storage and processing cloud server

The large data volume generated by the sensor nodes needs to be stored in a remote server in addition to the local storage. Hence, providing a new layer of storage to ensure the availability of the data and offering the possibility to access data remotely. In order to catch the data sent by the transmission layer we developed a Node.js script based on the integration solution provided by TTN which offers a set of open tools to facilitate the development of IoT applications. Whenever a new measurement is received from the gateway, it is parsed and then stored on a NoSQL database (MongoDB) using Mongoose which is an object modeling tool that provides schema-based solution to model application data.

The cloud server is composed of two main components; the database component and the end-user services component. The latter is responsible of answering user requests through a REST API for mobile apps, third-party services, or a web interface which is accessible via any desktop or mobile web browser as shown in Figure 1. In addition to that, multiple data processing techniques can be added such as detection of outliers and redundant data, measurements calibration, concentrations prediction, missing data reconstruction, etc.

C. End-user application description

In order to visualize the sensor nodes measurements, we have developed a web application that offers a simple and intuitive interface for visualizing pollutant concentrations and weather parameters. The front-end of the web application is built using HTML, CSS and Angular which is an open-source component-based front-end framework for building large-scale single-page applications. It is built on TypeScript which relies For experimentation purposes, we have designed two frontend components: Dashboard and pollution maps as depicted in Fig. 4 and Fig. 5. Through the first component, it is possible to display useful information such as the list of nodes along with their IDs, LoRaWAN addresses and last time seen online, etc. The dashboard also presents statistics about the contribution of each node to the application as well as the total number of measurements or a the number of measurements performed per month. All the information presented in the dashboard is actively updated in real-time to keep the user informed about All requests generated from the web application are sent to the web server asynchronously to improve the user experience with non blocking data loading and waiting times.

V. RESULTS AND DISCUSSION

Following the aforementioned guidelines and architecture, we have built 16 mobile sensor nodes. Although, before using them in measurement campaigns, we have tested the proper functioning of our nodes by: 1) comparing sensor nodes to reference stations; 2) comparing the sensor nodes to each other; 3) evaluating their performance in terms of energy consumption. For this, we performed multiple tests which we summarize in what follows.

A. Measurements validity: comparison with reference stations

To assess the accuracy of our nodes' measurements and the effect of environmental conditions, we have tested them next to two reference devices: the first one is the "Météo France" 1) Temperature and relative humidity: In order to validate the temperature and relative humidity measurements taken by the DHT22 sensor, a metrological test was carried out during summer of 2019, from 27th at 11am to June 28th at 10am (local time). This date was selected for the high temperatures recorded this day, during the heat wave of late June in Lyon, with a temperature that reached 38.10 °C at 5pm. The 3M'Air measurement device was placed on the instrumentation field of "Météo-France" in Lyon, on a 1 meter high platform (see Figure 6). The measurement site meets the standards recommended by the World Meteorological Organization (WMO) for temperature and relative humidity measurements. The reference sensors of the station are the PT100 for temperature and Vaisala HMP110 for relative humidity. For this test, two samples of measurements have been prepared to evaluate the capabilities of the device measurement as a function of direct solar radiation. Thus, the first sample regroups measurements with direct solar radiation taken from 11am to 9:30pm (sunset at 9:34pm) and from 6am to 10am (sunrise at 5:53am). The second sample includes measurements without direct solar radiation performed from 10pm to 5:30am. Measurements from both devices are then compared next to each other. Since the data do not follow a normal distribution, the non-parametric Mann-Whitney test is used here (also called Wilcoxon-Mann-Whitney or Wilcoxon Rank-Sum test) [START_REF] Lehmann | Nonparametrics: statistical methods based on ranks[END_REF], [START_REF] Cheung | The mann whitney wilcoxon distribution using linked lists[END_REF]. The classical linear regression parameters are also used to compare the performance of the two sensors (R, RMSE and bias). Temperature and relative humidity measurements from both sensors are plotted (see Figure 7).

The test indicates a significant difference (p-value = 0.021) in daytime temperatures with 3M'Air recorded values (average temperature of 34.8 °C) higher than the reference sensors measurements (average of 32.7 °C). Significant differences are recorded punctually, with for example a difference of 4.30 °C at 11am and 5 °C at 8am.The correlation coefficient is 0.93, The RMSE is 1.7 °C and the bias is equal to -1.9 °C. On the other hand, no difference is detected for nocturnal temperatures (p-value = 0.451, R = 0.99, RMSE = 0.4, bias = -0.5). No difference was detected in relative humidity measurements either, neither for the day data (pvalue = 0.720, R = 0.97, RMSE = 4.3, bias = 1.4), nor for the night data (p-value = 1, R = 0.99, RMSE = 1.4, bias = 0.1).

The differences obtained for the daytime air temperatures was expected because of the overheating of the shelter with direct exposure to solar radiation. The correlation coefficient for the temperature difference between the two sensors and the global radiation measured at the weather station is equal to 0.53. Indeed, although our node has an anti-radiation shelter, it is only open on 180°for design reasons (to keep the node small, the different chambers of the node were placed one behind the other as depicted in Figure 2). In addition, the small size of the sensor node may play a role in this. The measurements of humidity and night temperature are, for their part, totally satisfactory. It is worth mentioning that the 3M'Air sensor nodes were not designed to perform static but mobile measurements.

To evaluate the node's performance in mobility, six tests in four days during the period of July-September have been performed with the 3M'Air sensor node next to two sensor nodes incorporating the Log32 sensor [START_REF]Log 32 th pdf-data logger for temperature and humidity[END_REF] inside two types of anti solar radiation shelters (TFA [START_REF] Tfa Dostmann | Protective cover for outdoor transmitter[END_REF] and DAVIS [START_REF]Radiation shield(7714) specification sheets[END_REF]). In two tests, all sensors were carried by participants walking on foot using straps, and in the last ones, the sensors were placed in a bicycle basket. All tests took place in the "Presqu'île" peninsula of Lyon city and measurements were taken every The results showed divergence between the tests outputs. Indeed this difference is due to the fact that unlike the 3M'air anti solar radiation shield, the TFA and DAVIS shelters are opened on 360 degrees allowing air flow in all directions. In addition both sensor nodes with the TFA and DAVIS shelters are much bigger than our sensor node and do not include a circuit with an important number of components.

We have also calculated the classical linear regression parameters (R, RMSE, and bias). For temperature measurement, the highest coefficient of determination we have got is equal to 0.95 and the lowest is equal 0.61 with an average of 0.82. The root mean square error is between 0.4193 and 0.1631 degrees Celsius and the measurement bias equals 0.37 °C. For relative humidity, The RMSE belongs to the interval [0.70 and 1.46]% while the coefficient of determination varies between 0.58 and 0.94. The measurement biais is around 0.93%. These results show clearly that we are within the error ranges indicated for the DHT22 sensor [START_REF] Electronics | Digital-output relative humidity & temperature sensor/module dht22[END_REF] which make the use of 3M'Air nodes very satisfactory in mobile measurements.

2) Particulate matters: We have evaluated the performance of the Grove HM3301 PM sensor embedded in our sensor node by testing it next to an approved fine dust measurement device called FIDAS 200 © which is TUV Rheinland certified, and recognized by the LCSQA ("Laboratoire Central de Surveillance de la Qualité de l'Air") for monitoring particulate matter concentrations. The two sensor nodes were placed indoor one next to the other from October 4th,2019 to October 9th,2019. The series of measurement are highly correlated with a low RMSE for PM 1 and PM 2.5 while performances degrades for PM10 as reported in Table II. This may indicate that the composition of the largest PM in our region is different from that assumed when the sensor was calibrated. Another interesting fact is that the 3M'Air sensor slightly overestimates the measurements in comparison to the reference device as shown for PM 2.5 in Figure 8 correlation, the over-estimation can be compensated by an offline correction on the calibration function.

Sensor calibration is needed to cope with the low accuracy and signal drifting of low-cost sensors. In fact, calibration techniques for low-cost sensors have been extensively discussed in the literature. They are generally classified according to the availability of reference stations (reference-based, blind, or partially blind calibration), mobility of the sensors (static, mobile, or hybrid), calibration relationship (univariate or multivariate), and the used calibration model (e.g ordinary Least Squares, multiple Least Squares etc.). [START_REF] Delaine | In situ calibration algorithms for environmental sensor networks: A review[END_REF], [START_REF] Maag | A survey on sensor calibration in air pollution monitoring deployments[END_REF] As a proof of concept, we have implemented a univariate linear regression to calibrate our PM sensor's raw data based on the concentrations observed by the FIDAS sensor. In this process, we vary the learning duration and the temporal resolution to evaluate their impact on the calibration performance. Results show that with 6 hours of training on data with 1minute temporal resolution, the RMSE decreases from 5.785 to 0.835 as can be observed on Figure 9. A training duration of 12 hours with the same temporal resolution allows to achieve a smaller error of 0.734. Moreover, when decreasing the temporal resolution to 1 hour instead of 1 minute, the calibration achieves better performance with a RMSE decreasing from 5.735 to 0.558 using 6 hours of training and 0.465 with 12 hours of training. These observations show that high temporal resolution allows to obtain better results. Interested reader can refer to [START_REF] Delaine | In situ calibration algorithms for environmental sensor networks: A review[END_REF], [START_REF] Maag | A survey on sensor calibration in air pollution monitoring deployments[END_REF] for more details on low cost sensor calibration techniques.

B. Inter-comparison

We have also conducted other tests to compare the designed sensor nodes to each others. For this purpose, six nodes forming three groups (two nodes per group) have been deployed on the rooftop of a three-story building. Each group of sensors has been placed differently.

The reason behind this is to evaluate the impact of nodes' position and orientation. The test has been performed from January 21st,2020 at 12pm to January 22nd,2020 at 8am (local time). While aggregating data into one minute averages, we have calculated the RMSE and the correlation coefficient of Pearson within each group and also between averaged data of the three groups. Results are presented in Table III 1) Temperature and relative humidity: We have compared temperature and relative humidity measurements of every two sensors of each group. Figure 10 presents temperature measurements from all six sensors. The first observation that can be drawn from this figure is that each group measured different temperature and relative humidity values. This was expected since the groups were placed in different positions and orientations, resulting in different sun exposure and wind direction. In addition, there is a good correlation between the nodes of the same group especially with temperature measurements. We also notice that the difference in measurements between the groups still existed despite the absence sunrays. This confirms that measurements can be impacted not only by the sun exposure but also by the wind direction. Another observation that can be noted from Table III is that the RMSE of temperature and relative humidity values inside the same group sometimes exceeds the error margins claimed by the datasheets of the sensor. This is possible as these sensors are low-cost and may present in some cases larger errors. However, these values can be corrected and the sensors can be calibrated regularly. On the other hand, the RMSE between groups stays high due to the fact that each group had a different placement and orientation. All these results show that our nodes perform well and confirm again the impact of wind direction and angle of sun exposure on the sensors. 2) Particulate matter: In the case of particulate matter, Figure 11 presents PM 2.5 concentrations from the six nodes. For readability reasons, measurements have been aggregated into 30 min averages in this plot. However, all calculations were based on 1 min averages. We can observe that PM concentration values from all six nodes present the same trend and a good correlation during the whole period of the test, excepting node 6 which recorded lower concentrations than the others.

Another interesting result presented in Table III is that unlike temperature and relative humidity, the RMSE and the coefficient of correlation for PM 2.5 and PM 10 concentrations have reasonable values regardless of the sun exposure of the nodes or their placement. This indicates that the impact of the sun and wind direction is lower on PM concentrations than temperature and humidity. It has to be noted that the distance separating the sensor nodes was just few meters as they were deployed on the rooftop of the same building.

C. Power consumption

Energy consumption is of great importance in low-cost WSNs. In fact, Energy requirements differ from one application to another. For our case, we have conducted multiple tests with different configurations to evaluate the power consumption and also to determine which configuration or sensor is power consuming. Results are reported in Table IV. As can be expected, the higher the sampling frequency, the higher the energy consumption. The interesting observation is that dividing the sampling frequency by 3, from one sample every 20 sec (Config 1) to one every minute (Config 2), hardly reduces the consumption by 8 mA, around 3.5%. It is worth mentioning that the sensors are not turned off between measuring cycles because the convergence time of some sensors is larger than the chosen sampling period. Indeed the convergence time of the DHT22 is about 2 seconds [START_REF] Electronics | Digital-output relative humidity & temperature sensor/module dht22[END_REF], while the PM sensor needs at least 30 seconds after power-on to start giving reliable results [START_REF] Co | [END_REF]. On the other hand, the required convergence time is more important for the Alphasense NO 2 and may reach around ten minutes (see more details in future work section). Another interesting result is the energy consumption of the PM sensor: turning it off (Config 3) reduces the energy consumption by almost half (from 231 mA to 115 mA). This is due to its integrated fan used to aspire the airflow. In the last test (Config 4), the fan of the PM sensor was turned off. The energy consumption droped to 154 mA, meaning that the fan consumes 77 mA while the electronics of the Grove PM sensor uses 39 mA. Nevertheless, in order to obtain reliable measurement data, it is recommended to use the fan of the sensor.

Based on these tests we choose the first configuration, i.e. maintaining the sampling frequency at 20 seconds and sending data every minute. This will guarantee a good temporal and spatial resolution with an estimated life time of 22 hours using a 5100 mAh battery. We believe that this is a good battery life since our participatory measurement campaigns last about two hours.

VI. FUTURE WORK

Through this project, we were able to design and develop a general IoT platform, and to test it on air quality monitoring application. This could not have been achieved without addressing multiple challenges related to both IoT and air quality. Nevertheless, multiple challenges still need to be addressed in this field. Going in that direction, we are planning to investigate the following issues.

A. Sensors' convergence time and sensing duty cycling

WSN are often limited by their resources, especially when it is about energy. To cope with that, we usually adopt some solutions such as the sensing and the radio duty cycling. The sensing duty cycling consists of putting the sensors in sleep mode unless they are performing measurements. Such duty cycle behavior is limited by the convergence time of the sensors embedded in the node. The convergence time is indeed the duration that a sensor needs in order to reach a steady state in stationary conditions and then to output valid readings. Every sensor has its own stabilization time that depends not only on the type of the probe but may also depend on the duration of the sleep phase. Based on the datasheets, the convergence time of the DHT22 is about 2 seconds [START_REF] Electronics | Digital-output relative humidity & temperature sensor/module dht22[END_REF], while the PM sensor convergence time is about 30 seconds [START_REF] Co | [END_REF]. We have conducted first experiments to estimate the convergence time of Alphasense NO2 sensor in stationary conditions and we found that this time ranges from around 50 seconds to ten minutes when the previous sleep phase goes from one minute to one hour respectively.

We are planning to conduct extensive experiments to assess the impact of sleeping time on the convergence one for the different used sensors. This would help us to propose adequate sensing duty cycling approaches in order to enhance the node lifetime while keeping valid measured data.

B. Sampling scheduling

In the case of battery-powered WSNs, generally, the nodes perform measurements at well-defined time intervals in order to extend the battery life. Though, it is always better to have small time intervals especially in air quality monitoring to improve spatio-temporal resolution and data quality. Currently, our nodes perform their measurements every 20 seconds and then send data after each 1 minute. We want to evaluate the loss in terms of mapping quality when considering higher sensing intervals.

Indeed, we have recently reconsidered and compared some regression approaches to assimilation ones while taking into account the intrinsic characteristics of dense deployment of low-cost WSN for air quality monitoring (high density, numerical model errors and sensing errors) [START_REF] Fekih | On the regression and assimilation for air quality mapping using dense low-cost wsn[END_REF]. We have proposed a general framework that allows the comparison of different strategies based on numerical simulations and adequate estimation of the simulation error covariances as well as the sensing errors covariances. In addition, we have conducted extensive simulations based on a widely used numerical model and the characterization of the simulation errors.

While using the results of this work [START_REF] Fekih | On the regression and assimilation for air quality mapping using dense low-cost wsn[END_REF] and realistic data that we collect in our measurement campaigns, we want to study the impact of the sampling frequency on the overall air quality mapping while using different regression and assimilation approaches. A first simple comparison should be done between 20-second interval data, 40-second interval data, 60-second interval data, etc. This should assess the trade-off between sampling frequency and air quality mapping using mobile low-cost sensors.

In this part, we want also to conduct more analysis in order to propose adaptive sampling scheduling in function of the node's trajectory and speed, the sensing quality, etc. this may lead to irregular sampling intervals in order to enhance the node's lifetime while enhancing the overall air quality estimation. We also believe that it would be interesting to see if we can turn off the GPS receiver at some sensing cycles and then try to predict the position in which a measure was taken based on the time of the measure and its value. This would make it possible to extend the battery life of the node even more.

C. Improve the platform

Our platform is operational and is currently used in several measurement campaigns in Lyon (around ten campaigns until now). However, we believe that there is still room for improvement. To this aim, we are planning to improve our platform by adding the possibility to remotely manage the nodes. This will give us the ability to change the sampling/transmission parameters of the nodes and to put a certain sensor/receiver in sleep mode. Therefore, the web application will not only be used for data visualization but also for nodes administration. Regarding the node, we could add BLE support, this will give us the ability to use, when possible, the GPS of smartphones instead of the integrated GPS module, which could save us some energy. Another possible improvement is the support of downloading sensor data by just connecting the node to a PC or a smartphone via BLE.

VII. CONCLUSIONS

Today, climate change is a global challenge and represents a serious concern. As a result, a fine knowledge of air quality and urban heat islands is strongly recommended to implement appropriate policies. To tackle this challenge, low-cost environmental WSNs are increasingly present and integrated in smart cities solutions. They offer new possibilities of monitoring air quality and urban heat islands, reduce the deployment and maintenance cost, and improve the spatiotemporal resolution.

In this work, we present a mobile participatory-based air quality and urban heat islands monitoring platform. It takes advantage of the low-cost and high accessibility of environmental sensors and long range communication technologies to offer high data availability and spatio-temporal resolution. We have designed small, mobile and modular sensor nodes that can be used not only for air quality applications, but in other environmental applications as well. Furthermore, we have designed an intuitive web interface to visualize sensor data, and have made collaboration between our platform and third party services possible. To validate the platform, we have carried out multiple tests involving reliable reference stations and sensors. The results were promising and it was shown that our nodes can be used in the field and thus contribute to the improvement of the knowledge of air quality and urban heat islands.

Fig. 1 .

 1 Fig. 1. System architecture overview

Fig. 2 .Fig. 3 .

 23 Fig. 2. (a) External side view of the designed sensor node; (b) Internal side view (sensor chamber); (c) Internal top view (second and third chambers)

Fig. 4 .

 4 Fig. 4. The web dashboard of the platform

Fig. 5 .Fig. 6 .

 56 Fig. 5. The web interface showing the measurements of the nodes on a map

Fig. 7 .

 7 Fig. 7. 3M'Air node vs reference station (a) temperature; (b) relative humidity

Fig. 8 .

 8 Fig. 8. 3M'Air node vs reference station: PM 2.5 concentrations

Fig. 10 .

 10 Fig. 10. Temperature measurements from our designed nodes

Fig. 11 .

 11 Fig. 11. PM 2.5 concentrations measured by our designed nodes

TABLE II RMSE

 II . Considering the high AND CORRELATION COEFFICIENT OF PM 1 , PM 2.5 AND PM 10 MEASUREMENTS TAKEN BY THE DESIGNED NODE NEXT TO A REFERENCE DEVICE

	Metric	PM 1	PM 2.5	PM 10
	RMSE	3.86( / 3 )	5.784( / 3 )	7.92( / 3 )
	Pearson Correlation
		0.98	0.92	0.63
	coefficient

ACKNOWLEDGMENT

This work has been supported by the "LABEX IMU" (ANR-10-LABX-0088) of Université de Lyon, within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR).

Manoël Dahan is a Telecommunications Engineer trained at INSA Lyon. He joined the AGORA team of the CITI laboratory following his graduation. Missioned in support of the UrPolSens project, he developed a fleet of battery-powered nodes measuring NO2. Following a deployment in the city of Lyon, he is recruited in the same team for a European project of smart lighting (Interreg eSmartCity) for the design of nodes allowing the detection of activity in the streets. This system provides a database leading to the identification of the benefit of an smart lighting system deployment. As part of the 3M'air project, it also participates in the development of sensors measuring pollution for use by pedestrians.

PLACE PHOTO HERE

Florent Renard is an associate professor in Geography and Spatial Planning at the University of Lyon. He is a researcher at the UMR CNRS Environment City Society, a member of the Labex Intelligence des Mondes Urbains (IMU) and of the Field Observatory for Water Management (OTHU). His main research activities focus on the problems of heat islands, microclimates and thermal comfort in urban areas, the local impacts of climate change, particularly in terms of temperature and rainfall, territorial vulnerabilities and natural risk management. In a complementary way, he is also involved in some aspects of the geography of health, such as the emergency management of stroke and the subsequent territorial organization or the spatial socio-economic determinants of this pathology. The research work carried out combines mixed techniques, both quantitative and qualitative, with the use of spatial analysis and modelling by GIS, satellite imagery or weather radar by remote sensing, semi-directed interviews and participatory science. He is also in charge of the Environmental Management mention and the Master's degree in Environmental Geosystems at the University of Lyon (France).

Lucille Alonso is a PhD student at University of Lyon and a member of the UMR 5600 CNRS "Environment City and Society" research unnit. She works mainly on urban climate monitoring and modelling using satellite data in France, on the Lyon metropolitan area, and the assessing of socio-economic and physiological vulnerabilities of populations to heatwaves. Her research interests include remote sensing, spatial analysis for land use/land cover analysis and modelling, urban environment monitoring and planning, and vulnerabilities to heatwaves.