Project 7114 Project 7114 Air pollution monitoringAir pollution monitoring

IFCPAR/CEFIPRA Industrial Research Committee Meeting25th November 2011Udaipur, India

Participating Institution/industryParticipating Institution/industry� Industry:◦ LEOSPHERE (France)� Leosphere products rely on LIDAR technology for atmospheric

monitoring fields

Institutions :� Institutions :◦ Indian Institute of Technology Mumbai (India)� Department of chemical engineering (IITB)

◦ CNRS (France) � Laboratoire d’Aérologie (LA)

◦ Université du Littoral Cote d’Opale (France)� Laboratoire de Physico-chimie de l’atmosphère (LPCA)

Lidar Principle & ALS 450

Plume Tracker

Inversion of LIDAR signal to particle extinctionBoth the volume extinction coefficient (α) and backscattering coefficient (β)are unknown. So, it is necessary to assume some kind of relation between αand β (called as the extinction-to-backscattering ratio).

To eliminate the system constants, the range normalized signal variable is introduced as:

If S0 is the signal at the reference range R0, then we have

2( ) ln( ( ))S R R P R=

Rβ 1 ( )dS d Rβ

0

00

( ) ( ) ln( ) 2 ( )R

R

S R S R r drβ αβ

− = − ∫1 ( )

2 ( )( )

dS d RR

dR R dRβ α

β= −

(in differential form)è

Assuming that the scatterers are homogeneously distributed along the lidar path.

α can be estimated from the slope of the plot between S and R.

The Slope Method:

( )0 2

d R dSdR dRβ

α= ⇒ = −

Aerosol mass extinction cross-section (σ*, m2g-1) relates the mass amount of aerosol particles (PM, μg/m-3) to the optical extinction (α, m-1)

LIDAR signal conversion to particle mass (PM) concentration

– Depends on refractive index and size, shape, coating– Recommended values for mixed fine mode aerosols mass scattering

efficiency [Hand & Malm, 2007]: 3.6 m2/g ± 1.2 (higher in case of

Mass extinction cross-section:

efficiency [Hand & Malm, 2007]: 3.6 m2/g ± 1.2 (higher in case of carbonaceous aerosols) @ 550 nm

– 4.5 m2/g @ 355 nm [Raut & Chazette, 2009]– Depends on ambient relative humidity. A parameterization can be used

with γ ≈ 0.55 for urban aerosols

– The variability of the mass extinction cross-section needs to be assessed and the error associated with its parameterization need to be quantified and clearly understood

Compactness 17kg

Very low overlap(200m)

and 3D scan

Automatic observations and dissemination

Complementarity(Wind and Aerosol)

What EZ Lidars bring to lidar technology

Eye-safety and invisibility

Transportability and robustness

and 3D scan

Very high sensitivity(50nm aerosol)Very high resolution(1.5m/1s)

(Wind and Aerosol)

Upgradability(Water Vapor)

Objective of the project (1/4)Objective of the project (1/4)� Design and scientific validation of an operational protocol allowing

real time and dynamic mapping of particulate pollution using quantitative indicators in the vicinity of intense sources◦ Key component of air quality :

� Adverse effect on human health� Association between exposure to fine particles and mortality and

respiratory and cardiovascular morbidity & reduction in PM2.5 respiratory and cardiovascular morbidity & reduction in PM2.5 increases statistical life expectancy

� National and international regulations on PM10 and PM2.5� WHO recommendations : annual threshold of 10µg/m3 for fine and

20 µg/m3 for coarse particles� The link between exposure to particles and health effect has to be

further investigated� Comprehensive set of in situ observations on chemical and

physical aerosol properties & variability in space and time

Objective of the project (2/4)Objective of the project (2/4)� Design and scientific validation of an operational protocol allowing real time and dynamic mapping of particulate pollution using quantitative indicators in the vicinity of intense sources◦ Complex issue� Large diversity of sources (natural and anthropogenic) and

aerosol typesaerosol types� Different mechanisms of production and removal� Heterogeneous concentrations & properties in space and time

◦ Investigations on new methodologies dedicated to aerosol characterization based on remote sensing techniques

Objective of the project (3/4)Objective of the project (3/4)� Design and scientific validation of an operational protocol allowing

real time and dynamic mapping of particulate pollution using quantitative indicators in the vicinity of intense sources

� LIDAR remote sensing very sensitive to aerosol (Mie scattering)◦ It has a large range of investigation (from m to km) and a High

observation repetition rate (~1 min)� Filling the gap between local observation, satellite data and 3D � Filling the gap between local observation, satellite data and 3D

modeling◦ LEOSPHERE has a leadership in elastic backscattering small lidars

� Compact system, eye-safety, unattended operation, scanning capacity for detecting atmospheric structures (plumes, boundary layer)

� The aim of the project is to develop a methodology to turn LIDAR signal into particulate mass (µg/m3)

� This methodology needs to be validated using state-of-art scientific measurements and modelisation.

Objective of the project (4/4)Objective of the project (4/4)� Design and scientific validation of an operational protocol allowing real time and dynamic mapping of particulate pollution using quantitative indicators in the vicinity of intense sources

� Intense sources means :◦ An identified emission zone emitting a certain kind of particles (e.g. ◦ An identified emission zone emitting a certain kind of particles (e.g.

combustion of biomass or fuel)

◦ A group of sources in a given area contributing to degraded air quality

� Vicinity means less than 1 km between emission and measurements point

Complementary work of each partnerComplementary work of each partner

LPCASpecialist in LIDAR technologyAir pollution (gases & PM) monitoring

Provide expertise for field experiment

LEOSPHEREFrench SME specialized in LIDAR technology for environmental applications

IITBSpecialist in aerosol sourcesreceptor-source modeling

LASpecialist in aerosol optics (measurements & modeling)Remote sensing algorithm

operational software

Aerosol characterization

Design of the protocol

field experiment

EZ-LIDAR systems

Progress madeProgress made� Description of work◦ Field experiments

� March 2010 : Dunkerque pre-experiment� June 2011 : Dunkerque summer period� September 2011: Arcelor site in Spain� January 2012 : Dunkerque winter period dispersion in the winter � January 2012 : Dunkerque winter period dispersion in the winter

shallow boundary layer� 2012 (TBD): Field campaign in India. Visit of French scientist to IITB in

Feb. 2012 for site spotting and planning of the campaign◦ Analysis of the data

� Preliminary results for pre-campaign � Post-doc tenures begin Nov. 2011 at LA and IITB and start of the

analysis of the field data

DunkerqueDunkerque measurement sitemeasurement site

◦ 51°N / 2°E on the shore of the North Sea

◦ 200 000 inhab.

◦ Industrial harbor (oil, ore, tankers, steel)

◦ Major sources of PM, VOC, NOx, SO2

◦ Operational AQ monitoring by Atmo Nord Pas de Calais◦ Operational AQ monitoring by Atmo Nord Pas de Calais

◦ Collaboration with Ecole de Mines de Douai for using Grande-Synthe facility

◦ SW dominant winds with summer NNE sea breeze

urban area

Industries

Step 0: DUNKERQUE February-March 2010

+ TEOM + Nephelometer+ Aethalometer+ Particle counter

Objective:

Assess the feasibility of converting the lidar signal outdoors using a pool of selected instruments

Comparison of LIDAR derived extinction coefficient Comparison of LIDAR derived extinction coefficient to to nephelometernephelometer measurements measurements

• TSI 3-λ integrating nephelometer

• 1 min acquisition• Good correlation R2 = 0.85

(N=2639)• Slope between scattering and • Slope between scattering and

extinction of ≈ 0.8 (@355nm), characteristic of urban aerosol SSA

Comparison of LIDAR extinction Comparison of LIDAR extinction coefficient to PM2.5 coefficient to PM2.5

• Optical particles counter estimated PM2.5 at 1 min

• Dry mass extinction • Dry mass extinction efficiency of 9.1 m2/g best fits the data

• Some discrepancies still not explained

• RMSE ≈ 3 µg/m3 when properly adjusted

Step 1: DUNKERQUE June 2011

+ TEOM + nephelometer + aethalometer + particle counter

Objective:

3D scans over source and exposed area and analysis of aerosol variability

OPC+ AMS+ Nephelometer

LIDAR

Wind

industries Urban area

+ Nephelometer+ Filters+ aethalometer

Data analysis: Receptor modelling approaches

Observations(chemical, meteorological,

LIDAR)

Receptormodels (PMF,others)

Source regions

Emissions from source categories from inventory

Weather / meteorology models

(WRF / NCEP)

models (PMF,others)

Estimated factors/source influenceAir mass

historyTrajectories

Trajectoryensemble models

(PSCF, others)

from inventory

Source-receptor modelling using chemical and LIDAR signals

• Exploration of principal component analysis, PCA, started.

• Goals- identification of factors combining different - identification of factors combining different signals.- quantitative linkage of factor contributions to PM concentrations.

Dust

Sea salt

Biomass burning

Factors influencing fog formation – example 1

Kanpur, winter, 2004

Foggy period Clear periodFactor µgm-3 % µgm-3 %Dust 32 14 57 31Sea salt 34 14 44 24Biomass burning 57 25 47 25Secondary species 107 47 36 20

Mehta et al., 2009 Atmos. Environ.

Secondary species

Source categories and emission magnitude

Potential source regions for secondary species (foggy period) Emission rate of sources

§Kanpur

10.5 0.6 0.7 0.8 0.9 1

Factor identification over Bay of Bengal Factor identification over Bay of Bengal -- example 2example 2

Arabian Sea leg

Integrated Campaign of Aerosols, Trace Gases and Radiation Budget (ICARB), 2006, pre-monsoon.

Measured concentration matrix (X; 10 species x 42 days)

23

Mixed sources from dust and anthropogenic activities dominated over BoB leg, whereas dust and nitrate- and-dust dominated over AS leg.

Bay of Bengal leg

Arabian Sea leg

Cherian et al. 2010, JGR

Source categories and emission magnitude

Source regions & preferred pathways

Satellite detected fires from MODIS

Cherian et al. 2010, JGR

Biofuel28%

Crop residue

28%

Forest7%

Power plant20%

Transport6% Industries

11%

Emission strength : 31 Gg/mon of PM

Biomass and biofuel burning from central Indo‐Gangetic Plain and central India.

24

BudgetBudget

� France◦ Recruitment of Dr. Sumita Kedia (PhD, Physical Research

Laboratory, Ahmedabad) on 23rd November 2011.◦ First year (April 2011-12) Released 10,000 Eur of total

project cost 18,000 Eur + 50,000 Eur (post-doc salary).

� India◦ Recruitment of Dr. B.L. Madhavan (PhD, Andhra University;

Postdoc, CUNY, USA) at IITB on17th October 2011.◦ First year (May 2011-12) Released Rs. 5,09,400 (pending

release of Rs. 1,20,000 for Ist year) of Rs. 11,38,800.

Consortium agreement Consortium agreement & commercialization plan& commercialization plan

� IP plan finalised� Too early to discuss commercialization plan� Request for inclusion of Prof. Mani Bhushan, � Request for inclusion of Prof. Mani Bhushan,

IITB, as joint collaborator, for multivariate modelling

� Acknowledge funding support by IFCPAR/ CEFIPRA.

� Thank you

Extra slidesExtra slides

Tentative PM2.5 cartographyTentative PM2.5 cartography

Source : Sophie Loaëc @ leopshere

AerosolAerosol sourcesource--receptorreceptor modellingmodelling:: PMFPMF

MeasurementsX

PMF

G

F

Linearregression

Source Profiles

Source

30

X = GF + E

Uncertainty

ExplainedVariance (EV)

Total mass(PM or TSP)

Source contributions

Qualitative source apportionment Mass apportionment

2

1 1

( ) /m n

ij iji j

Q E e s= =

= ∑∑

LIDAR: Light Detection And Ranging

§ Operate on the principles similar to RADAR and SODAR.

§ Uses an active system that emits light pulses (i.e., laser) and measures the

intensity of the backscattered light (from air molecules, aerosols, thin

clouds) as a function of time.

§Wavelengths used in lidar depend on application (from 250 nm to 11 μm).

§ Optical interactions relevant to lasers:

Elastic scattering: A process in which wavelength (or frequency) of the

radiation remains unchanged (e.g., Rayleigh-Mie scattering).

Inelastic scattering: when there is change in frequency (e.g., Raman

scattering).

Summary of Rayleigh and Mie Scattering

Rayleigh Mie

Radius/ Wavelength

r<<λ r>>λ

Phasefunction

P11(θ) α (1 + cos2θ) Highly variable depending on α = (2πr/λ)Strong forward peak

Asymmetry

g=0 g>0yparameter

Polarization

Θ = 0, π : LP = 0Θ = + π/2 : LP ~ 1

Generally depolarizing, but variable

Spectral dependence

σR α λ-4 σM α λ-m

m is Angstrom Exponent (-1 < m < 4)

Figure from “An introduction to Atmospheric Radiation” by K.N. Liou

The larger the size parameter, the larger the forward scattering peak

LIDAR: Principle of Operation

§ Short light pulses with lengths of RECEIVER RECEIVER

(Light Collection & Detection)TRANSMITTER(Light Source: LASER)

Radiation Propagating Through Medium

Interaction between radiation and objects

Signal Propagation Through Medium

§ Basic functional blocks:

* Transmitter (i.e., a laser source)

* Receiver

* Data acquisition & Control system

§ Short light pulses with lengths of few to several hundred nanoseconds and specific spectral properties are generated by the laser.

§At the receiver end, a telescope collects the photons backscattered from the atmosphere.

CONTROL SYSTEMDATA ACQUISITION &

CONTROL SYSTEM

DATA ANALYSIS & INTERPRETATION

Basic architecture of a LIDAR

LIDAR Equation

The detected lidar signal can bewritten as:

System constant –

Summarizes performance of lidar systemP0 average power of a single laser pulse τ is the temporal pulse lengthA is the area of the primary receiver optics η the overall system efficiency (includes the optical efficiency

( ) . ( ). ( ). ( )P R K G R R T Rβ=

0 2c

K P Aτ η=

(i.e., the power P received from a distance R is made up of four factors)

Experimentally controlled factors

η the overall system efficiency (includes the optical efficiency of all elements the transmitted and received has to pass & the detection efficiency)

Range Dependent Measurement –

Geometry factor contains the overlap function of the laser beam with the receiver field of view, called O(R), and a R-2dependence.•The area of reception is part of a sphere's surface with radius R, centered in the scattering volume. 2

( )( )

O RG R

R=

Illustration of the LIDAR geometry

effective (spatial) pulse lengthΔR = R1 – R2

= (ct/2) – c(t-τ)/2= cτ/2

Adopted from “LIDAR Range-Resolved optical Remote Sensing of the Atmosphere” by C. Weitkamp

LIDAR Equation

Backscatter Coefficient at distance R –β(R)

Determines the strength of the lidar signal. It describes how much light is scattered in the backward direction.

Transmission term -

Determines how much light gets lost on the way from the lidar to scattering volume and back.* Factor ‘2’ stands for two way transmission path.

Unknown factors (subjects of investigations)

( ) exp 2 ( , )R

T R r drα λ

= − ∫

( , ) ( , ) ( , )m aR R Rβ λ β λ β λ= +

0 20

( )( ) ( , ) exp 2 ( , )

2

Rc O RP R P A R r dr

Rτ η β λ α λ

= − ∫

* Factor ‘2’ stands for two way transmission path.0

( ) exp 2 ( , )T R r drα λ

= − ∫

( , ) ( , ) ( , )m ar r rα λ α λ α λ= +

The lidar equation in more common form is given as:

Ø The background must be subtracted before a lidar signal can be evaluated further.

LIDAR Inversion Methods

Both the volume extinction coefficient (α) and backscattering coefficient (β)are unknown. So, it is necessary to assume some kind of relation between αand β (called as the extinction-to-backscattering ratio).

To eliminate the system constants, the range normalized signal variable is introduced as:

If S0 is the signal at the reference range R0, then we have

2( ) ln( ( ))S R R P R=

Rβ In differential form expressed as0

00

( ) ( ) ln( ) 2 ( )R

R

S R S R r drβ

αβ

− = − ∫

The Slope method of inversion:Assumes that the scatterers are homogeneously distributed along the lidarpath.

α can be estimated from the slope of the plot between S and R.

( )0 2

d R dSdR dRβ α= ⇒ = −

Limitation: Applicable for a homogeneous path only.

1 ( )2 ( )

( )dS d R

RdR R dR

β αβ

= −

In differential form expressed as

LIDAR Inversion Methods

S S−

( )2 ( )

( )dS n d R

RdR R dR

αα

α= −

Techniques based on the extinction-to-backscatter ratio:

- Use a priori relationship between α and β, typically in the form β=bαn

where b and n are specified constants.- Substituting in the previous differential equation

With a general solution at the range R

0

0

0

exp( )

1 2exp( )

R

R

S SnS S

drn n

α

α

−

=−

− ∫

Limitations:- Multiple scattering is ignored.- extinction-to-backscattering ratio assumed.- instable w.r.t α (i.e., some modifications have to be introduced to avoid this problem). Use reference point at the predetermined end range (Rm), so the solution is generated for R < Rm instead of R > R0.

2

1

2

1

2

*

3

( , , ) ( )( , )( )4

( )3

r

extr

r

r

r Q k m n r drR

PM Rr n r dr

π λα λσ

πρ= =

∫

∫

Aerosol mass extinction cross-section (σ*, m2g-1) relates the mass amount of aerosol particles (PM, μg/m-3) to the optical extinction (α, m-1)

LIDAR signal to Particulate Mass (PM) conversion

k = (2πr/λ) is the size parameterparameterQext is the scattering efficiency function for the extinction

Organization of the Approach

Testing the real time and continuousscanning operation of the LIDAR overweeks

Calibration of LIDAR extinction data withcomplementary instruments like aNephelometer and an Aethalometer

Converting the calibrated LIDAR signalinto mass concentrations (using massanalyzers like TEOM)

Evaluate the potential to link LIDARsignal to aerosol emissions withspecific chemical and optical signals

Integrating the calibration and massconversion procedures on a quasi-realtime basis within a unique softwareinterface

Test the integrated process in at least twodifferent source environment overperiods from one week to one month.

Identify emission sources affectingthe measurement site using possiblefactorization techniques

Explore factor analytic techniques toisolate factors containing acombination of chemical, optical andLIDAR signal

E-Z LidarTM

§ Very low overlap (200 m)

§ Eye safe UV-laser

§ Post-processed atmospheric data

§ PBL and troposphere analysis

The Slope Method

§ Best method for the extraction of the mean particulate-extinction coefficient in homogeneous atmospheres

§ Atmospheric horizontal homogeneity is assumed and this can be checked easily by an analysis of the lidar signal shape.

§ A mean value of extinction coefficient over the examined range in a homogeneous atmosphere is obtained.

§ Requires extremely accurate determination of the background component in order to minimize the signal offset remaining after the background order to minimize the signal offset remaining after the background component subtraction.

Limitations:• Applicable for a homogeneous path only.• A precise adjustment of the lidar optics is essential to avoid systematic

distortions of the overlap function O(r) over a range where the slope of the logarithm of P(r)r2 is determined.