TH`ESE Anton Fedarenka The utilization of ground-based polarimetric measurements for ...

175 Pages·2015·3.4 MB·English

Universit´e Lille 1 Sciences et Technologies
`
THESE
Pres´ent´ee pour obtenir le grade de :
Docteur de l’Universit´e de Lille 1
Sp´ecialit´e:
Sciences de la Mati`ere, du Rayonnement
et de l’Environnement
Soutenue publiquement le 9 juillet 2015 par
Anton Fedarenka
Titre :
The utilization of ground-based polarimetric
measurements for improving retrieval of aerosol
microphysics
Jury :
Rapporteurs : Aliaksandr SINYUK
Jean-Claude ROGER
Examinateurs : Alexander KOKHANOVSKY
Carlos TOLEDANO
Directeurs : Oleg DUBOVIK
Philippe GOLOUB
Laboratoire d’Optique Atmosph´erique
U.F.R de Physique Fondamentale
Universit´e Lille 1 Sciences et Technologies
59655 Villeneuve d’Ascq, France
Acknowledgements
I would like to express my deepest appreciations and respects to many people for
their help and support that made this work possible.
First of all, I would like to thank my supervisors Oleg Dubovik and Philippe Goloub
for wise guidance and persistent help in scientific and personal problems.
I am grateful to CIMEL company in the person of its director Didier Crozel as well
as Nord-Pas de Calais Region for financial support of my work.
I sincerely appreciate the significant contribution of Zhengqiang Li to the matter of
my study.
I am thankful to my colleagues Andrei Holdak, Tatiana Lapionak, Pavel Litvinov,
Yevgeni Derimian, Luiz Gonzalez, Luc Blarel and Thierry Podvin for participation in
my work and essential advices. I also thank my friends and fellow students Yana Karol
and Anton Lopatin for diverse help and support.
Special thanks to Marie-Lyse Li´eevin and Ann Priem for all the help with French
bureaucratic runaround.
I express my warm thanks to Alexander Lapionak as well as Konstantin Zabalkansky
for their help and assistance in my life in France.
Finally, I would like to thank all staff of LOA for their hospitality and friendliness
that I was surrounded during my work in Lille.
I am also deeply appreciative to Anatoly Chaikovsky and the staff of Laboratory of
Scattering Media of Institute of Physics of National Academy of Sciences of Belarus for
advices and the possibility to finish my thesis in comfortable scientific environment.
Abstract
The use of polarimetric observations in addition to the observations of total atmo-
spheric radiance is widely expected for aerosol characterization using remote sensing
measurements. However, the polarimetric observations from ground have not yet been
as widely developed as in remote satellite sensing and, at present, there is rather limited
experience in using ground-based measurements of polarization for aerosol characteriza-
tion. In order to explore the potential of ground-based passive polarimetry for aerosol
remote sensing, a new sun-photometer CIMEL CE318-DP enabling multi-wavelength
polarization characteristics of the downward field radiation has been developed. Such
instruments were deployed at several sites of AErosol RObotic NETwork (AERONET).
The thesis presents the efforts on including the polarimetric data to the routine
inversion of the ground-based observations and analysis of the obtained advantages in
retrievalresults. Inordertoprocesslargeamountofpolarimetricdataadatapreparation
tool was developed. It is based on AERONET inversion code adapted for inversion of
both intensity and polarization measurements.
To assess the relevance of the polarization measurements for aerosol retrieval re-
sults, both synthetic simulated data and the real field measurements were processed
and analyzed using developed routine. The sensitivity study has been carried out using
simulated data based on five main aerosol models: desert dust, urban industrial, ur-
ban clean, biomass burning and maritime aerosols. The test investigated the effects of
polarization data applying in presence of random noise, bias in optical thickness mea-
surements and pointing errors. The results demonstrate the advantage of polarization
data utilization in the cases of aerosols with pronounced concentration of fine particles
for all considered errors. The polarimetric data revealed minor sensitivity to the coarse
mode dominated aerosols as desert dust.
Further, the AERONET extended data sets observations were processed. The data
for three observation sites have been investigated: GSFC, USA (clean urban aerosol
dominated by fine particles), Beijing, China (polluted industrial aerosol characterized
by pronounced mixture of both fine and coarse mode) and Dakar, Senegal (desert dust
dominated by coarse particles). The results of the study revealed considerable advan-
tages of polarimetric data utilization in the case of industrial pollution (Beijing). The
use of polarization improves the retrieval of the particle size distribution by decreasing
overestimated fine mode and increasing the coarse mode. Furthermore, it increases un-
derestimated real part of the refractive index and the fraction of spherical particles due
to high sensitivity of polarization to particle shape. The results for the desert dust did
not indicate significant improvements from additional use of polarization measurements.
The inversions of GSFC data take a middle position.
Thus, the analysis of large amount of processed polarimetric observations by ground-
based sun-photometer as well as sensitivity study demonstrates a significant value of
polarimetric data for improving aerosol characterization. This approach is especially
beneficial for aerosol types with pronounced fine fraction or complex aerosol contained
fine and coarse modes both, such as observed over Beijing and likely other regions with
the presence of heavy pollution.
R´esum´e
Plusieurs ´etudes ont montr´e que l’utilisation d’observations polarim´etriques, en
compl´ement, des observations de luminances atmosph´eriques, am´eliorent la car-
act´erisation des a´erosols, par t´el´ed´etection. Paradoxalement, il s’agit principalement
d’observationsr´ealis´eesdepuisl’espace, commelesmissionsPOLDER.Al’inverselesob-
servations polarim´etriques au sol sont encore peu r´epandues, en dehors de celles r´ealis´ees
par le Service d’Observation PHOTONS, composante fran¸caise du r´eseau international
AERONET(AErosolROboticNETwork). Afind’explorerlepotentieldelapolarim´etrie
passive au sol pour la t´el´ed´etection des a´erosols, nous avons utilis´e la version polaris´ee
(CE318-DP) du photom`etre d´evelopp´e par CIMEL Advanced Monitoring, version per-
mettant la mesure de la polarisation du rayonnement de ciel `a plusieurs multi-longueur.
Ces instruments ont ´et´e d´eploy´es sur plusieurs sites de AERONET.
La th`ese pr´esente l’analyse des donn´ees polarim´etriques et param`etres a´erosols
restitu´es par inversion. Afin de pouvoir traiter de mani`ere automatis´ee l’ensemble des
donn´ees polarim´etriques disponible, un outil de pr´eparation de donn´ees a´et´e d´evelopp´e.
Nous avons ensuite adapt´e le code d’inversion utilis´e par le syst`eme de traitement
AERONET pour r´ealiser l’inversion simultan´ee des luminances totales et luminances
polaris´ees mesur´ees par le C318-DP.
Pour ´etablir l’int´erˆet des mesures de polarisation, nous avons d´ebut´e notre travail
par une ´etude de sensibilit´e th´eorique. L’´etude de sensibilit´e a ´et´e r´ealis´ee en utilisant
des donn´ees simul´ees pour une base de cinq principaux mod`eles d’a´erosols: la poussi`ere
d´esertique, industrielle et urbaine, urbains propres, combustion de la biomasse et des
a´erosols maritimes. Nous avons ´evalu´e l’effet d’un bruit al´eatoire, d’un biais sur les
mesures d’´epaisseur optique a´erosols et des erreurs de pointage de l’instrument. Les
r´esultats d´emontrent clairement l’avantage d’utiliser la polarisation dans le cas de con-
centrations importantes en particules fines. A l’inverse, la polarisation s’est r´evel´ee
moins sensible a` la pr´esence d’a´erosols domin´es par les grosses particules (poussi`ere
d´esertique).
Dans un second temps, les donn´ees de trois sites d’observation ont ´et´e trait´ees et
analys´ees: GSFC,USA,(a´erosolurbaindomin´eparlesparticulesfines); Beijing, Chine,
(a´erosols industriels pollu´es caract´eris´es par un m´elange prononc´ee `a la fois en mode fin
et grossier) et a` Dakar, S´en´egal, (poussi`eres d´esertique domin´ees par des particules
grossi`eres). Les r´esultats de l’´etude ont r´ev´el´e des l’int´erˆet d’utiliser la polarisation dans
le cas de la pollution industrielle (Beijing), correction de biais affectant les deux modes
de la distribution de taille des particules.
Notre approche corrige, de plus, la sous-estimation de la partie r´eelle de l’indice
de r´efraction et de la fraction de particules sph´eriques, et cela en raison de la haute
sensibilit´e de la polarisation `a la forme des particules. Pour les a´erosols d´esertiques,
nous ne constatons pas d’am´elioration significative par rapport `a l’inversion standard
AERONET (sans polarisation). Les r´esultats obtenus pour le site de GSFC occupent
une position m´ediane.
Nos r´esultats montrent finalement la valeur ajout´ee apport´ee par les donn´ees po-
larim´etriques pour am´eliorer la caract´erisation des a´erosols. Cette approche est parti-
culi`erement b´en´efique pour les types d’a´erosols avec une fraction fine prononc´ee ou les
a´erosols de m´elange comme ceux pr´esents, en abondance, dans de vastes r´egions d’Asie.
Contents
1 Introduction 4
1.1 Atmospheric aerosols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Aerosol properties and remote sensing 12
2.1 Properties of aerosols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.1 Aerosol physical properties . . . . . . . . . . . . . . . . . . . . . . 12
2.1.2 Aerosol optical properties . . . . . . . . . . . . . . . . . . . . . . 18
2.1.2.1 Refractive index . . . . . . . . . . . . . . . . . . . . . . 18
2.1.2.2 Aerosol optical depth and extinction coefficient . . . . . 19
2.1.2.3 Single Scattering Albedo . . . . . . . . . . . . . . . . . . 20
2.1.2.4 ˚Angstro¨m parameter . . . . . . . . . . . . . . . . . . . . 20
2.1.2.5 Phase matrix . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 Aerosol impact on climate and human health . . . . . . . . . . . . . . . . 22
2.2.1 Radiative impact . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.2 Impact on human health . . . . . . . . . . . . . . . . . . . . . . . 24
2.3 Aerosol observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.1 Ground-based remote sensing . . . . . . . . . . . . . . . . . . . . 25
2.3.2 Satellite observations . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3.3 Airborne instruments . . . . . . . . . . . . . . . . . . . . . . . . . 28
3 Aerosol observations by ground-based Sun/sky-radiometers 30
3.1 AERONET network (overview, aerosol products: direct sun observations
and retrievals) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.1.1 General description of the AERONET basic instrument . . . . . . 31
3.1.2 Measurement concept . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.1.3 Aerosol products . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2 Advanced AERONET instrumentation: DWP Cimel sun-photometer . . 35
3.2.1 Direct-Sun measurements . . . . . . . . . . . . . . . . . . . . . . 36
3.2.2 Sky radiance measurements . . . . . . . . . . . . . . . . . . . . . 36
3.2.3 Polarization measurements . . . . . . . . . . . . . . . . . . . . . . 37
4 Retrieval of the aerosol properties 38
4.1 Modeling of aerosol Polarimetry observations . . . . . . . . . . . . . . . . 38
4.2 Inversion of observations . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
1
2
4.2.1 Forward model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.2.2 Numerical inversion . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5 Development of automated DWP Cimel data processing 54
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.2 Raw data pre-processing and DOLP calculation . . . . . . . . . . . . . . 57
5.2.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.2.1.1 Calibration of direct-Sun measurements . . . . . . . . . 58
5.2.1.2 Calibration of sky-radiance measurements . . . . . . . . 59
5.2.1.3 Calibration of polarization measurements . . . . . . . . 60
5.2.2 Normalization and sorting daily data . . . . . . . . . . . . . . . . 62
5.2.3 DOLP calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.3 Observational geometry settings . . . . . . . . . . . . . . . . . . . . . . . 64
5.4 Setting assumptions for surface reflectance . . . . . . . . . . . . . . . . . 65
5.5 DWP Cimel data quality check routine . . . . . . . . . . . . . . . . . . . 68
6 Data processing, generating and analysis of retrieval results 72
6.1 Sensitivity analysis of accuracy improvements in aerosol retrievals from
polarimetry measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.1.1 Description of the chosen aerosol models . . . . . . . . . . . . . . 73
6.1.2 Methodology of the sensitivity study . . . . . . . . . . . . . . . . 77
6.1.3 Sensitivity to the random noises . . . . . . . . . . . . . . . . . . . 79
6.1.3.1 Urban-industrial aerosol (Mexico City model) . . . . . . 79
6.1.3.2 African savanna aerosol (Mongu model) . . . . . . . . . 81
6.1.3.3 Urban-clean aerosol (GSFC model) . . . . . . . . . . . . 81
6.1.3.4 Desert dust aerosol (Solar Village model) . . . . . . . . . 81
6.1.3.5 Oceanic aerosol (Lanai model) . . . . . . . . . . . . . . . 82
6.1.4 Sensitivity to the AOD bias . . . . . . . . . . . . . . . . . . . . . 82
6.1.4.1 Urban-industrial aerosol (Mexico model) . . . . . . . . . 83
6.1.4.2 African savanna aerosol (Mongu model) . . . . . . . . . 84
6.1.4.3 Urban-clean aerosol (GSFC model) . . . . . . . . . . . . 84
6.1.4.4 Desert dust aerosol (Solar Village model) . . . . . . . . . 85
6.1.4.5 Oceanic aerosol (Lanai model) . . . . . . . . . . . . . . . 85
6.1.5 Sensitivity to the pointing error . . . . . . . . . . . . . . . . . . . 86
6.1.5.1 Urban-industrial aerosol (Mexico City model) . . . . . . 86
6.1.5.2 African savanna aerosol (Mongu model) . . . . . . . . . 87
6.1.5.3 Urban-clean aerosol (GSFC model) . . . . . . . . . . . . 88
6.1.5.4 Desert dust aerosol (Solar Village model) . . . . . . . . . 88
6.1.5.5 Oceanic aerosol (Lanai model) . . . . . . . . . . . . . . . 89
6.2 Illustration and analysis of the retrieval results . . . . . . . . . . . . . . . 90
7 Conclusions 104
A Illustrations of the sensitivity study results 108
A.1 Sensitivity to the random noises . . . . . . . . . . . . . . . . . . . . . . . 108
A.1.1 Urban-industrial aerosol (Mexico model) . . . . . . . . . . . . . . 108
3
A.1.2 African savanna aerosol (Mongu model) . . . . . . . . . . . . . . 112
A.1.3 Urban-industrial aerosol (GSFC model) . . . . . . . . . . . . . . . 115
A.1.4 Desert dust aerosol (Solar Village model) . . . . . . . . . . . . . . 118
A.1.5 Oceanic aerosol aerosol (Lanai model) . . . . . . . . . . . . . . . 121
A.2 Sensitivity to the AOD bias . . . . . . . . . . . . . . . . . . . . . . . . . 124
A.2.1 Urban-industrial aerosol (Mexico model) . . . . . . . . . . . . . . 124
A.2.2 African savanna aerosol (Mongu model) . . . . . . . . . . . . . . 127
A.2.3 Urban-industrial aerosol (GSFC model) . . . . . . . . . . . . . . . 130
A.2.4 Desert dust aerosol (Solar Village model) . . . . . . . . . . . . . . 133
A.2.5 Oceanic aerosol aerosol (Lanai model) . . . . . . . . . . . . . . . 136
A.3 Sensitivity to the pointing error . . . . . . . . . . . . . . . . . . . . . . . 139
A.3.1 Urban-industrial aerosol (Mexico model) . . . . . . . . . . . . . . 139
A.3.2 African savanna aerosol (Mongu model) . . . . . . . . . . . . . . 141
A.3.3 Urban-industrial aerosol (GSFC model) . . . . . . . . . . . . . . . 143
A.3.4 Desert dust aerosol (Solar Village model) . . . . . . . . . . . . . . 145
A.3.5 Oceanic aerosol aerosol (Lanai model) . . . . . . . . . . . . . . . 147
B Inversion examples 149
B.1 GSFC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
B.2 Beijing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
B.3 Dakar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Bibliography 158
Chapter 1
Introduction
1.1 Atmospheric aerosols
Atmospheric aerosol is generally defined as a suspension of liquid and solid particles
in the air, with particle radii varying from a few nm to larger than 100µm. The
most evident examples of aerosols in the atmosphere are clouds, which consist primarily
of condensed water with particle diameters on the order of approximately 10nm. In
atmosphericscience,however,thetermaerosoltraditionallyreferstosuspendedparticles
that contain a large proportion of condensed matter other than water, whereas clouds
are considered as separate phenomena. As another examples can be called haze, smoke,
smog etc.
A human interacts with aerosol all his life: breathes it, creates new particles. More
than50% of technological processesproduce theaerosols. Despite these, a concentration
of the aerosol particles in the atmosphere does not exceed the concentration such rare
inert gas as xenon. Even in very dusty regions an amount of aerosol particles is less
than 10 6 of air mass in which they are contained. And it is three to four orders
−
smaller than the mass fraction of water vapor. Nevertheless, impact of the aerosols on
environment is very important and includes direct health effects, acid deposition and
the Earth radiation balance changing.
4
Chapter 1. Introduction 5
The interaction of the aerosols with water vapor plays an important role in atmo-
spheric processes. Fist of all, the aerosol particles act as condensation nuclei for cloud
formation that has a great influence on climate and radiation balance of the planet
(Boucher, 1999; Lohmann et al., 2003; Lohmann and Feichter, 2005; Lohmann and
Hoose, 2009). In absence of this process, clouds might occur in the atmosphere only
at high altitude due to condensation of water vapor on the ions. Besides, the size and
composition of aerosol particles can be changed due to water absorbing by particles
or conversion of water from a gaseous state into aerosol. The transformation depends
on numerous factors as concentration of aerosol and water vapor, ambient temperature
and relative humidity, intensity and spectrum of incident solar radiation, physical and
chemical properties of aerosol particles. This effect strongly affects on the variability of
aerosol optical properties. It should be mentioned, that the mechanisms of the growth
of aerosol particles is not clear when the humidity changes, especially when it is quite
far from saturation.
The described aerosol impact on cloud formation and their properties is so-called
aerosol indirect effect (Ramanathan et al., 2001; Kaufman et al., 2002). The direct effect
consists in iteration of aerosol particles with solar and thermal radiation. In general,
both of them lead to cool the atmosphere-surface system. Indeed, clouds and aerosols
first cool the surface and atmosphere reflecting sunlight. However, aerosol further cool
the surface but warm the atmosphere absorbing radiation. Moreover, the absorption
leads to modifying the temperature and humidity profiles that is so-called semi-direct
effect.
Fig. 1.1 shows the sign and the intensity of radiative forcing of the main con-
stituents of the atmosphere (on Climate Control , IPCC; IPCC, 2007; Forster et al.,
2007; Hansen et al., 2011). In total, the aerosols have cooling effect (−0.5W/m2 for
direct and 0.7W/m2 for indirect effects in average) but the uncertainty in cooling effect
estimation is high and a level of scientific understanding (”Level of Scientific Under-
standing”, LOSU) is low. It is also shown, that increasing of greenhouse gas emissions
in the troposphere due to human activities leads to a positive radiative forcing esti-
mated at about 2.99W/m2 (from 2.62 to 3.56) (with 1.66±0.17W/m2 of carbon dioxide,
0.48±0.05W/m2 of the methane, 0.16±0.02W/m2 of nitrous oxide, 0.34±0.03W/m2
Topic 2 Causes of change
Chapter 1. Introduction 6
Radiative forcing components
Figure 2.4F. Gilgobual raveera1ge. 1ra:diaGtivel ofobrcainlg (aRvFe) irna 2g00e5 (rbaesdt ieasttimivateesf aonrdc 5i nto g95i%n un2c0er0ta5intyw raintghesr) ewsithp reescptectt oto 1a7l5l0 ifomr CpOo2,r CtHa4n, Nt2O and other
important agents and mechanisms, together with the typical geographical extent (spatial scale) of the forcing and the assessed level of scientific understand-
ing (LOSUa). gAeernotsosls afrnomd emxpleoscivhea vnolicsamnics e,rutpotiognes tchonetrribuwtei tahn atdhditeiontayl pepiicsoadlic gcoeoolinggr ateprmh fiocr aal feewx yteeanrs tfo(lloswpinagt aian lersupctaiolne. )The range for
linear contorafilst dhoees fnoort cinicnlugde aonthedr ptohsseiblae sesffeecstsse odf alveiavtioenl oon fclsoucdiienenstsi. fi{WcGuI Fnigduerer SstPaMn.2d}ing (LOSU); according to
IPCC2007 (Forster et al., 2007)
Most of the observed increase in global average tempera- is very likely due to the combined influences of GHG increases and
tures since the mid-20th century is very likely due to the stratospheric ozone depletion. It is likely that increases in GHG
of the halocarbon and 0.35W/m2 (from 0.25 to 0.65W/m2) of ozone). The radiative
observed increase in anthropogenic GHG concentrations.8 concentrations alone would have caused more warming than ob-
This is an advance since the TAR’s conclusion that “most served because volcanic and anthropogenic aerosols have offset
forcing due to greenhouse gas emissions is estimated with a high LOSU, apart from
of the observed warming over the last 50 years is likely to some warming that would otherwise have taken place. {WGI 2.9, 3.2,
have been due to the increase in GHG concentrations” (Fig- 3.4, 4.8, 5.2, 7.5, 9.4, 9.5, 9.7, TS.4.1, SPM}
an ”average” level for ozone. In total, the radiative forcing caused by the emission of
ure 2.5). {WGI 9.4, SPM}
It is likely that there has been significant anthropogenic
aTnhet hobrsoerpveodg weindeiscpregarde weanrmhiongu osfe thge aatsmoasnphderea aenrdo osceoalns, is cwoanrmsiindge roevedr pthoe spiatsivt e50t yoea1r.s6 aWve/ramge2d (ofvreorm eac0h. 6conti-
together with ice mass loss, support the conclusion that it is ex-
nent (except Antarctica) (Figure 2.5). {WGI 3.2, 9.4, SPM}
tremteoly 2u.n4liWkely/ tmhat2 g)lodbael mcliomnatset crhaantgine ogf tthhe epassti 5g0n yiefiarcsa cnant influence of human activities on warming.
be explained without external forcing and very likely that it is not The observed patterns of warming, including greater warming
due to known natural causes alone. During this period, the sum of over land than over the ocean, and their changes over time, are
solar and volcanic forcings would likely have produced cooling, simulated only by models that include anthropogenic forcing. No
Furthermore, the phenomena of atmospheric electricity are closely related to the
not warming. Warming of the climate system has been detected in coupled global climate model that has used natural forcing only
changes in surface and atmospheric temperatures and in tempera- has reproduced the continental mean warming trends in individual
tureps roef stehen cupepeorf seavtemralo hsupndhreedr imceatreesr oofs othle poaceratni. cTlhees .obJ-. Frceonntikneenltss u(egxcgeepts Atendtarctthicaa)t otvhere thoe rsieeconntde hdalfa odf sthoer 2p0-th cen-
served pattern of tropospheric warming and stratospheric cooling tury. {WGI 3.2, 9.4, TS.4.2, SPM}
tion of water molecules can cause charged particles (Frenkel, 1944). Also, the adhesion
8Coonsfidleirgathiont oifo renmsaintiong aunecreortasinotyl ips abarstedic olne csurlreenat dmsethtoodolaogidese.crease in the conductivity of air. Moreover,
collecting the charge of definite sign on large aerosol particles (R > 0.1 µm) could lead
39
to the formation of a large volume charge in the air.
It should be emphasized that the role of aerosol in atmospheric processes and the