ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-4983-2015Characterization of satellite-based proxies for estimating nucleation mode particles over South AfricaSundströmA.-M.anu-maija.sundstrom@helsinki.fiNikandrovaA.AtlaskinaK.https://orcid.org/0000-0001-6068-9450NieminenT.https://orcid.org/0000-0002-2713-715XVakkariV.LaaksoL.BeukesJ. P.ArolaA.https://orcid.org/0000-0002-9220-0194van ZylP. G.https://orcid.org/0000-0003-1470-3359JosipovicM.VenterA. D.JaarsK.PienaarJ. J.PikethS.WiedensohlerA.ChiloaneE. K.de LeeuwG.https://orcid.org/0000-0002-1649-6333KulmalaM.https://orcid.org/0000-0003-3464-7825Department of Physics, University of Helsinki, Helsinki, FinlandFinnish Meteorological Institute, Helsinki, FinlandUnit for Environmental Science and Management, North-West University, Potchefstroom, South AfricaFinnish Meteorological Institute, Kuopio, FinlandLeibniz Institute for Tropospheric Research, Leipzig, GermanyEskom Holdigns SOC Ltd, Sustainability Division, South AfricaA.-M. Sundström (anu-maija.sundstrom@helsinki.fi)4May2015159498349968August201414October20146March20156April2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://www.atmos-chem-phys.net/15/4983/2015/acp-15-4983-2015.htmlThe full text article is available as a PDF file from https://www.atmos-chem-phys.net/15/4983/2015/acp-15-4983-2015.pdf
Proxies for estimating nucleation mode number concentrations and further
simplification for their use with satellite data have been presented in
Kulmala et al. (2011). In this paper we discuss the underlying assumptions
for these simplifications and evaluate the resulting proxies over an area in
South Africa based on a comparison with a suite of ground-based measurements
available from four different stations. The proxies are formulated in terms
of sources (concentrations of precursor gases (NO2 and SO2) and
UVB radiation intensity near the surface) and a sink term related to
removal of the precursor gases due to condensation on pre-existing aerosols.
A-Train satellite data are used as input to compute proxies. Both the input
data and the resulting proxies are compared with those obtained from
ground-based measurements. In particular, a detailed study is presented on
the substitution of the local condensation sink (CS) with satellite aerosol
optical depth (AOD), which is a column-integrated parameter. One of the main
factors affecting the disagreement between CS and AOD is the presence of
elevated aerosol layers. Overall, the correlation between proxies calculated
from the in situ data and observed nucleation mode particle number
concentrations (Nnuc) remained low. At the time of the satellite
overpass (13:00–14:00 LT) the highest correlation is observed for SO2/CS
(R2=0.2). However, when the proxies are calculated using satellite
data, only NO2/AOD showed some correlation with Nnuc
(R2=0.2). This can be explained by the relatively high uncertainties
related especially to the satellite SO2 columns and by the positive
correlation that is observed between the ground-based SO2 and NO2
concentrations. In fact, results show that the satellite NO2 columns
compare better with in situ SO2 concentration than the satellite
SO2 column. Despite the high uncertainties related to the proxies
calculated using satellite data, the proxies calculated from the in situ
data did not better predict Nnuc. Hence, overall
improvements in the formulation of the proxies are needed.
Introduction
Aerosol particles are key constituents in the Earth–atmosphere system that
can alter climate through their direct and indirect effects on the Earth's
radiation budget. Aerosols affect the radiation budget directly by
scattering and absorbing solar radiation and indirectly by acting as cloud
condensation nuclei or ice nuclei and modifying clouds' radiative properties
and lifetimes. However, the quantification of the aerosol effects on climate
is very complex and large uncertainties still exist due to the high spatial
and temporal variability of aerosol mass and particle number concentrations
(e.g. IPCC, 2013). Besides the climatic effects, aerosols affect human life
by reducing the air quality and visibility as well as affecting human health,
especially in urban areas. Particulate air pollution has been associated
with adverse cardiovascular and pulmonary diseases and even with rises in
the numbers of deaths among older people (e.g. Seaton et al., 1995; Utell et
al., 2000; Schnelle-Kreis, 2009).
Primary aerosol particles, e.g. sea
spray aerosol, desert dust, aerosol generated from biomass burning, and
fossil fuel combustion, are emitted directly into the atmosphere. Secondary particles are formed from precursor gases
through gas-to-particle conversion. The formation of new particles is
strongly connected to the presence of sulphuric acid and other vapours of
very low volatility, as well as the magnitude of solar radiation (e.g. Kulmala et al., 2005; Kulmala and Kerminen, 2008). However, pre-existing
aerosol particles act as a sink for the vapours, inhibiting new aerosol
formation (e.g. Kulmala and Kerminen, 2008). These new nanometre-size aerosol
particles grow through condensation and coagulation to sizes where they may
act as cloud condensation nuclei (particle diameter Dp>∼50nm)
or where they are large enough (Dp>∼100nm) to scatter solar radiation and thus affect the
Earth's radiation budget.
Several studies have shown that nucleation occurs frequently in the
continental boundary layer and free troposphere from clean to polluted
environments (Kulmala et al., 2004; Kulmala and Kerminen, 2008, and references
therein). Laakso et al. (2008) and Vakkari et al. (2011) have studied new
particle formation over moderately polluted savannah ecosystems in South
Africa and found that nucleation takes place in the boundary layer almost
every sunny day throughout the year with a frequency of as high as 69 % of
all analyzed days (Vakkari et al., 2011). Hirsikko et al. (2012) extended
the studies in South Africa to a polluted measurement site and found an even
higher frequency for the nucleation event days (86 %), which is among the
highest event frequencies reported in the literature so far. Hirsikko et al. (2013)
also studied the causes for two or three consecutive daytime
nucleation events, followed by subsequent particle growth during the same
day. They concluded that the multiple events were associated with
SO2-rich air from industrial sources.
Satellite instruments have been providing global observations of the Earth's
atmosphere for 3 decades (e.g. Lee et al., 2009; Kokhanovsky and de
Leeuw, 2009; Burrows et al., 2011). Information about the spatial
distribution of aerosols and trace gases can be obtained from multiple
instruments with various temporal and spatial resolution and coverage.
Passive remote sensing instruments such as NASA's Ozone Monitoring
Instrument (OMI) on-board the AURA platform or the Moderate Resolution
Imaging Spectroradiometer (MODIS) on-board the Terra and Aqua platforms use
solar radiation to detect either trace gases or aerosol and cloud
properties. Trace-gas remote sensing techniques using OMI are based on the
trace-gas absorption features in the UV region (wavelength λ∼200–400 nm), whereas the remote sensing of aerosol particles
is mainly based on measurements in the UV/visible and near-infrared regions
(λ∼500–2000 nm). Since the aerosol measurements
utilize only the optically active size range of the solar spectrum, the
detectable aerosol sizes are limited to particles with diameters greater
than about 100 nm. Nucleation mode particles (smaller than about 25–30 nm in
diameter), therefore, cannot be detected directly using satellite
instruments. In 2011, Kulmala et al. introduced proxies, i.e. parameterizations, for estimating the number concentrations of nucleation
mode (Nnuc) simplified for use with satellite data. These
simplifications were made assuming that in situ parameters could be replaced
with satellite-based observations. Their study was the first attempt to
estimate the global nucleation mode aerosol concentrations using data
derived from satellite measurements. The proxies were defined in terms of
sources and sinks. The nucleation source terms consist of precursor gas
column densities (NO2 or SO2) and UV-radiation intensity near the
surface (all from OMI as opposed to in situ data in the initial proxies)
whereas the sink term, i.e. the condensation sink (CS) in the original proxy
formulation related to the aerosol surface area concentration, is assumed to
be proportional to the aerosol optical depth (AOD, from MODIS). More
recently Crippa et al. (2013) formulated a new proxy algorithm for ultrafine
particle number concentrations based on satellite-derived parameters. They
used a multivariate linear regression approach to derive the proxy, which the
source terms consisted of SO2, UV (from OMI), and NH3 (from
Tropospheric Emission Spectrometer). The sink term was formulated using
MODIS (collection 5.0) AOD and the Ångström exponent, which
expresses the spectral dependence of AOD on the wavelength. However, there
are issues with the Ångström coefficient (e.g. Mielonen et al.,
2011), and thus this parameter is no longer included in the most recent
MODIS collection 6.0 land parameters (Levy et al., 2013).
A summary of the measurements used in this study. Listed are
only those measurements from the study period 1 January 2007–31 December 2010.
InstrumentMeasurement area/locationMeasurement periodMeasured parametersOzone Monitoring Instrument (OMI, Aura satellite)25.0–28.0∘ S, 25.5–30.5∘ E (whole study area)Jan 2007–Dec 2010, obs. appr. once/day, only cloud-free obs.NO2 and SO2 column densities, UVB irradianceModerate Imaging Spectroradiometer (MODIS, Aqua satellite)25.0–28.0∘ S, 25.5–30.5∘ E (whole study area)Jan 2007–Dec 2010, obs. appr. once/day, only cloud-free obs.Column-integrated aerosol optical depth at 550 nm wavelengthCloud–Aerosol Lidar with Orthogonal Polarization (CALIOP, satellite-based lidar)Selected locations within the study areaSelected days between Jan 2007 and Dec 2010Vertical profile of aerosol extinction at 532 nm wavelengthAerosol Robotic Network (AERONET) sunphotometer (in situ)Elandsfontein (26.25∘ S, 29.42∘ E)Mar–Dec 2010, only cloud-free obs. during daylight.Column-integrated aerosol optical depth at 500 nm wavelength.Nephelometer (in situ)ElandsfonteinMar–Dec 2010Aerosol scattering coefficientDifferential mobility particle sizer (DMPS, in situ)Marikana (25.70∘ S, 27.48∘ E) Botsalano (25.54∘ S, 25.75∘ E) Welgegund (26.57∘ S, 26.94∘ E)Marikana: Feb 2008–May 2010 Botsalano: Jan 2007–Feb 2008 Welgegund: May–Dec 2010Particle size distribution, condensation sink, event classificationScanning mobility particle sizer (SMPS, in situ)ElandsfonteinMar–Dec 2010Particle size distribution, condensation sinkAll in situ stationsdates/station as aboveNOx and NO, SO2 global radiation, T, RH
In this work we evaluate the simplifications and underlying assumptions of
the method introduced in Kulmala et al. (2011) to estimate the number
concentration of nucleation mode particles from satellite-derived data. The
study area is the north-eastern part of South Africa (25–28∘ S, 25.5–30.5∘ E,
Fig. 1). Even though the area is not very large, it comprises lots of
contrasts from the emission point of view: the cities of Johannesburg and
Pretoria, as well as highly industrialized areas especially east from the
cities, vs. a very clean background in the western part of the study
area. The study period considered is January 2007–December 2010. There are also four
different measurement stations located within the region of interest, where
observations of various in situ parameters were available.
The study area and locations of the in situ measurement stations;
BOT = Botsalano, MAR = Marikana, WEL = Welgegund, and ELA = Elandsfontein.
This work comprises of two parts:
A detailed investigation of replacing the condensation sink (defined below in Eq. 8),
a local parameter evaluated from in situ observations, with the AOD, a column-integrated aerosol property available from satellite.
The estimation of how well satellite data can be used to compute proxies
for nucleation mode particle number concentrations. This comprises
the analysis of both the satellite- and in-situ-based proxy
components and the proxies, as well as the comparison of the proxies
with the measured concentration of nucleation mode particles. The
influence of the uncertainties in the satellite-derived quantities
on the proxy is also evaluated.
Data
In this study, a variety of data was used from satellite instruments and
ground-based stations (see Table 1 for a summary). Satellite data used
originate from NASA's Afternoon-Train (A-Train) constellation. The A-Train
constellation consists of seven satellites that are on a same polar-orbiting
track and follow each other closely, enabling near-simultaneous observations
of a variety of atmospheric parameters. The equatorial overpass for the
A-Train satellites is around 1:30 p.m. local time. In this study we use OMI
Level 2 products, i.e. the NO2 tropospheric column (Bucsela et al.,
2013), the SO2 planetary boundary layer (PBL) product (Krotkov et al.,
2006, 2008), and the 310 nm irradiance (UVB) at the surface at
local noon (Tanskanen et al., 2006). It is noted that the OMI SO2 PBL
product describes the SO2 concentration integrated over the whole
atmospheric column, and PBL refers to the a priori profile assumed in the
retrieval of this product. The OMI L2 products are provided with a nominal
spatial resolution of 13×24km2. For the current study they were
re-gridded onto a 3 km× 3 km geographical grid as in Fioletov et
al. (2011). In this way the effective spatial resolution could be increased
despite the instrument resolution being coarser than the grid. For
NO2 and SO2, only those observations where the
(radiative) cloud fraction was below 20 % were used.
According to Lamsal et al. (2014), and references therein, the uncertainty
in the OMI NO2 tropospheric column concentrations is about 0.75×1015moleccm-2, whereas Krotkov et al. (2008) report
that the SO2 PBL product could be associated with noise as high as 1.5 DU.
However, averaging the SO2 columns over longer a period and/or
over a larger spatial area could reduce the noise to 0.3–0.6 DU. For OMI
UVB irradiance the relative uncertainty is on average 7 % but could be
higher, e.g. due to some episodic aerosol plumes (Tanskanen et al., 2006).
The AOD used in this study is the MODIS Aqua collection 6.0 AOD product at 3 km
spatial resolution (Levy et al., 2013). The relative uncertainty for the
MODIS AOD over land is reported as 0.05+15 %. For selected cases,
vertical aerosol extinction profiles from the Cloud-Aerosol Lidar and
Infrared Pathfinder Satellite Observation (CALIPSO) (Winker et al., 2007)
are also used.
The in situ data used in this study are collected at four different stations
in South Africa: Elandsfontein (ELA), Marikana (MAR), Botsalano (BOT), and
Welgegund (WEL). All of these stations are located in the north-eastern part
of the country shown in Fig. 1. Depending on the station, the measured
parameters included e.g. particle size distribution, extinction coefficient,
and trace-gas concentrations. More detailed description of the in situ
measurements at the Marikana station can be found e.g. in Venter et al. (2012),
at the Welgegund station in Beukes et al. (2013), at the
Elandsfontein station in Laakso et al. (2012), and at the Botsalano station
in Vakkari et al. (2013). Also data from the Aerosol Robotic Network
(AERONET, http://aeronet.gsfc.nasa.gov, Holben et al., 1998) at the
Elandsfontein station are used. AERONET is a global ground-based
sunphotometer network, providing observations of aerosol optical,
microphysical, and radiative properties that are available in a public
domain. The aerosol optical properties in the total atmospheric column are
derived from the direct and diffuse solar radiation measured by the Cimel
sunphotometers.
Proxies
Kulmala et al. (2011) derived the Nnuc proxies for regional-scale
nucleation and nucleation from primary emissions. The proxies were
determined as the ratio of a source and a sink term. Regional-scale
nucleation is associated with photochemistry and typically occurs over a
spatial scale of hundreds of kilometres, whereas nucleation from primary
emissions occur in the vicinity of local sources such as industrial or urban
areas (Kulmala et al., 2011, and references therein). On a regional scale it
was assumed that sulphuric acid acts as the driver of the regional
nucleation process. Sulphuric acid is formed by oxidation of sulphur dioxide
(SO2) with the hydroxyl radical (OH), which, however, is
mainly formed via photolysis of ozone and UV radiation. The main sink for
sulphur acid is collisions with pre-existing aerosols. Petäjä et al. (2009)
derived the proxy for the ambient sulphuric acid as UV⋅[SO2]/CS, which was considered as the source term in the regional-scale
nucleation proxy. Taking into account that in addition to sulphuric acid,
the pre-existing aerosols are also the sink for the newly formed particles
(Nnuc), the regional-scale nucleation proxy is determined as (Kulmala
et al., 2011)
PNnuc,regional=UV⋅[SO2]CS2,
where CS denotes the condensation sink of pre-existing aerosols.
Nucleation from primary emissions can be an extremely rapid process. The source
term of the corresponding proxy is related to the concentration of nitrogen
dioxide (NO2) or sulphur dioxide while the sink term is determined by
the condensation sink. For nucleation from primary emissions, two proxies are
defined as (Kulmala et al., 2011)
PNnuc,prim.=NO2CS,PNnuc,prim.=SO2CS.
In each of the proxies the source terms are estimated from the satellite
measurements by replacing the SO2 and NO2 concentrations at the
surface with the column densities from the satellite. The amount of global
UV radiation is also available from satellite measurements e.g. as a local
noon irradiance at 310 nm wavelength (UVB radiation) at the surface. For
the sink parameter (CS), Kulmala et al. (2011) proposed to use the AOD, which
describes the total aerosol extinction in the atmospheric column. The
relation between the CS and the AOD will be discussed in the following
section. By replacing CS with AOD the simplified proxy for using satellite
data for primary nucleation becomes
PNnucSat.=NO2columnAOD,PNnuc.Sat.=[SO2]columnAOD.
For regional nucleation the proxy expressed in terms of satellite data
becomes
PNnuc.Sat.=UVSO2columnAOD2.
In addition we also considered
PNnuc.Sat.=UVAOD2
as a potential proxy for the number concentration of nucleation mode
particles. This proxy corresponds to the case shown in Kulmala et al. (2011),
where the sulphur dioxide concentration was assumed to be constant.
In this work the proxy defined in Eq. (7) is considered mainly to study how
large effect the satellite-based SO2 has on the performance of the
regional-scale nucleation proxy.
Condensation sink and aerosol extinction
As indicated in the previous section, Kulmala et al. (2011) proposed AOD as
a substitute for CS. Both parameters are also roughly proportional to the
aerosol surface area distribution. According to e.g. Lehtinen et al. (2003)
the condensation sink is defined as
CS=2πρdiff∫0∞DpβM(Dp)n(Dp)dDp,
where Dp is the particle radius, n(Dp) is the particle number
size distribution function, ρdiff is the diffusion coefficient of
the condensing vapour, and βM(Dp) is the transitional
correction factor for mass flux (Fuchs and Sutugin, 1971).
Aerosol optical depth describes quantitatively the column-integrated
extinction of solar light caused by atmospheric aerosols and is one of
the standard aerosol parameters retrieved from the satellite
radiance observations. At a height z and for a wavelength λ the
aerosol extinction is defined as
σext,z,λ=14π∫0∞Qext(λ,Dp,m)Dp2n(Dp)dDp,
where Qext is the extinction efficiency describing aerosols' ability to
scatter and absorb solar light. At a fixed wavelength the extinction
efficiency is a complex function of aerosol size and complex refractive
index m (which in turn depends on the aerosol particle composition). The particle shape also affects somewhat Qext, but this is not
considered in this study. If the particles are assumed to be spherical,
Qext can be calculated using a computer code based on the Lorenz–Mie
theory (Mishchenko et al., 2002). AOD is obtained by integrating σext over the total atmospheric column.
The sensitivity of CS and aerosol extinction coefficient to
different particle sizes. In the left panel, the aerosol size
distribution that is used to calculate CS and σext is
calculated for two wavelengths (0.55 and 0.45 µm) assuming spherical
particles with a refractive index of m=1.48+0.001i. In the right panel
the contribution of each particle size to the total CS and σext is shown. The σext is calculated for two wavelengths (0.55 and
0.45 µm) assuming spherical particles with a refractive index of
m=1.48+0.001i.
The differences between CS and σext (at a certain height) as a
function of particle size are illustrated in Fig. 2. Both parameters are
derived using the same aerosol size distribution (Fig. 2, left panel). The
σext is calculated using a refractive index of
m=1.48+0.003i and wavelengths of 0.55 and 0.45 µm. As Fig. 2 shows,
particles with Dp about 0.05–0.1 µm have the largest contribution
to CS, whereas for σext the largest contribution is coming from
particles with Dp about 0.2–0.8 µm. The notable difference between
the two quantities is that particles Dp<0.1µm can have
a contribution to CS which is several orders of magnitude larger than that
to σext. However, σext is significantly
more sensitive to particles with Dp>1.0µm than CS.
It is clear that a large change in number concentration of the smaller
particle sizes would change the value of total CS when integrated over the
size distribution but would have a minor effect on the value of σext, and vice versa; if e.g. the number concentration of large
particles increased, there would be little effect on CS. It is noted that in
addition to the theoretical differences the possibility of elevated aerosol
layers could affect the column-integrated values of σext, i.e. the
AOD, which must be considered when comparing the satellite-based AOD with in
situ CS.
The response of σext to changes in the particle size
distribution depends to a certain extent on the particle composition and the
measurement wavelength. If the particle absorption is high (i.e. the
imaginary part of m∼0.1i), the contribution of particles Dp<0.1µm to σext would be somewhat higher than in
Fig. 2. Shorter wavelengths increase the sensitivity to smaller particles,
but as Fig. 2 illustrates, a 0.1 µm decrease in wavelength does not
improve the sensitivity significantly. Much shorter wavelengths would be
needed to increase the sensitivity of σext to particles Dp<0.1µm, but such measurements could not be carried out in a
real atmosphere.
Comparison between condensation sinks derived from particle size
distributions, as described in the text, and nephelometer scattering
coefficients measured at Elandsfontein station in 2010 for the warm
(January–April, November–December) and cold (May–October) seasons. CS has been
corrected to the ambient relative humidity but the scattering coefficient
was measured from dry particles. The data are colour-coded according to
ambient relative humidity, of which the strong influence on the
relation between CS and scattering coefficient is evident.
Comparison between AOD at 500 nm available from AERONET (see text)
and in situ scattering coefficients measured at the Elandsfontein station.
The AOD is the column-integrated value of aerosol extinction (scattering +
absorption) obtained from sunphotometer measurements. The in situ scattering
coefficient is measured with a nephelometer.
Comparison between MODIS AOD and in situ CS. The MODIS AOD values
are spatial averages calculated from the observations within 3 km distance
from the measurement station, whereas the CS values are 1 h averages
(13:00–14:00 LT). The black lines represent the slope from least squares
linear fitting (LSQ). The blue lines represent the fitting method where the
uncertainties related to CS and AOD values have been taken into account
(YORK; York et al., 2004). The uncertainty for CS was set to 10 % and for
AOD to 0.05+15 % (Levy et al., 2013).
Results
The proxies as defined in Sect. 3 are formulated in terms of parameters
which are either obtained from ground-based in situ measurements (Eqs. 1–3)
or from satellite data (Eqs. 4–7). In this section the performance of these
proxies is critically evaluated and in particular each of the
satellite-based parameters is critically examined.
Comparison of condensation sink and aerosol optical depth
Replacing CS with AOD is perhaps the most crucial assumption when
determining the proxies using satellite data, as indicated in Kulmala et al. (2011).
Apart from the sensitivity of these parameters for different
particle sizes discussed in Sect. 3.1, other differences play a role, such
as the vertical variation of the aerosol concentrations, the particle size
range considered, and the dependence of aerosol particle size on relative
humidity. CS is determined from measured dry particle size distributions
with a correction for ambient humidity. CS at Botsalano and Marikana has
been estimated from submicron size distribution while at Elandsfontein size
distributions up to 10 µm were used. In contrast, the AOD is an
integrated quantity with contributions from all optically active aerosols
throughout the whole atmospheric column. To assess the effect of these
different factors on the relation between the AOD and CS, the following
comparisons are made:
in situ CS with nephelometer aerosol scattering coefficient
in situ nephelometer aerosol scattering coefficient with AOD from AERONET
in situ CS with AOD from both AERONET and satellite measurements.
Coincident measurements of size distributions to derive the CS and aerosol
scattering coefficients from a nephelometer are only available from the
Elandsfontein measurement station. The comparison between CS and scattering
coefficient serves to eliminate effects of the vertical variation of the
aerosol concentrations on the comparison. The nephelometer measures the dry
particle scattering at 0.525 µm wavelength and the results are
presented at standard temperature and pressure for the atmosphere. The maximum
particle size is limited to Dp∼10µm. It is
noted that the nephelometer considers only aerosol scattering and not the
total extinction, which would also require information on absorption.
However, the contribution of absorption to the total aerosol extinction is
generally much smaller than scattering. Laakso et al. (2012) reported that
at Elandsfontein the absorption was increased during the coldest months
(May–October) due to biomass burning (domestic burning of coal for heating and
cooking) contributing about 15–20 % to the total aerosol extinction,
whereas during the warmer months (November–April) absorption contributed
∼10 % of the total aerosol extinction. To take the seasonal
variation of absorption into account, the CS and the scattering coefficients
were compared separately for the periods May–October and November–April. The results
in Fig. 3 show that, for both periods, scattering coefficients and CS were
well correlated with R2=0.67 for November–April and R2=0.71 for
May–October. The R2 values were somewhat higher than those from
measurements at a clean continental boreal forest measurement site in
Hyytiälä, southern Finland (R2=0.62, Virkkula et al., 2011).
The next step is to compare the nephelometer scattering coefficient to the
AOD to evaluate effects of the possible occurrence of elevated aerosol
layers and/or boundary layer mixing. Also the presence of large dust
particles might have some effect on the comparison due to the limited
particle size in the nephelometer inlet. In this comparison we first compare
with AERONET measurements of AOD at Elandsfontein, which are more accurate
than those retrieved from satellite data. As Fig. 4 shows, the correlations
between the AERONET AOD and the in situ scattering coefficient (warm season
R2=0.46, cold season R2=0.24) are lower than those between the
CS and the scattering coefficient. This indicates that the elevated aerosol
layers and boundary layer mixing might affect more than the theoretical
differences when estimating the sink of pre-existing aerosols by using the
AOD.
For the comparison of CS with the AOD retrieved from MODIS, daily AOD values
were used which are spatial averages of the observations within a 3 km radius
from each measurement station. As Fig. 5 shows, the CS vs. satellite AOD
data are scattered all over the graph and although there is a tendency of
increasing CS with increasing AOD there is no apparent correlation (0.03≤R2≤0.06). As an alternative, a bivariate method (York et al.,
2004) was applied to account for the uncertainties associated with both CSs
and MODIS AODs in the fitting. For CS the uncertainty was assumed to be
10 % (Petäjä et al., 2013) and for MODIS AOD an uncertainty of
0.05+15 % was used (Levy et al., 2013). This means that for low AOD the
relative uncertainty is rather high; e.g. for AOD = 0.1 the relative
uncertainty would be 65 %. As Fig. 5, shows the bivariate method gave very
different results than least squares
linear fitting.
Median CALIPSO extinction profiles for days when MODIS AOD > 0.15 (blue) and AOD ≤ 0.15 (red). The CALIPSO profiles are
collected within 50 km radius from the Marikana station. The horizontal bars
represent the interquartile ranges. The median extinction profile for MODIS
AOD ≤ 0.15 cases extends only up to 2.2 km because the quality of the
data above 2.2 km was too low.
Diurnal variation of (a) NOx-NO, (b)SO2, (c) global
radiation, (d) CS, and (e)Nnuc at Elandsfontein (red) and Marikana
(blue) stations. The grey columns represent the time window for the
satellite overpass. The blue and red shading denote the 75th and
25th percentiles. It is noted that CS at Elandsfontein is defined with
particles Dp<10µm and at Marikana with particles
Dp<1µm. Nnuc at Marikana represents particles
Dp<30nm while at Elandsfontein Nnuc represents
particles Dp10–30 nm.
Diurnal variation of the proxies calculated using in situ data at
Elandsfontein (red) and at Marikana (blue) stations. The red and blue shaded
areas denote the 75th and 25th percentile ranges. The grey column
represents the time of the satellite overpass.
Correlation between nucleation mode number concentration and
SO2/CS proxy calculated using in situ data at Marikana measurement
station at the time of the satellite overpass (13:00–14:00 LT).
MODIS AOD (a), OMI NO2(b), and SO2(c) column density
medians for a 4-year period from January 2007 to December 2010. The locations of
the in situ measurement stations (ELA = Elandsfontein, MAR = Marikana,
BOT = Botsalano, and WEL = Welgegund) are marked with white dots.
Spatial pattern of proxy medians for 2007–2010 calculated using
satellite data. The proxies are (a)NO2/AOD, (b)SO2/AOD,
(c)SO2⋅UVB/AOD2, and (d) UVB/AOD2.
At Marikana and Elandsfontein the largest observed AODs are not related to
largest CS, which could be due to the presence of elevated aerosol layers. In
a recent study by Giannakaki et al. (2015) data from a ground-based lidar
at Elandsfontein are analyzed and the results show that the mean
contribution of elevated aerosol layers to the AOD is 46 %. To estimate
the effect of elevated aerosol layers on the CS–AOD comparison at Marikana,
CALIPSO observations of aerosol vertical extinction profiles are used. All
CALIPSO daytime overpasses between 8 February 2008 and 17 May 2010 within 50 km from
the Marikana station were considered. Due to the small CALIPSO swath width
only 48 days of data are available. At Marikana the median MODIS AOD is 0.15
for the whole measurement period and, as Fig. 5 shows, the CS values are
less scattered when AODs are smaller than the median. Therefore the vertical
aerosol extinction profiles from CALIPSO are studied separately for the
cases where MODIS AOD ≤ 0.15 and AOD > 0.15. As Fig. 6
shows, for higher AODs the median extinction profile indicates an elevated
aerosol layer, which supports the result that high AODs also at Marikana are
likely to be associated with an elevated aerosol layer.
Proxies defined from the in situ data and comparison with Nnuc
The proxies are first computed using in situ measurements from Marikana and
Elandsfontein following Eqs. (1)–(3) to evaluate how well each of them could
predict the nucleation mode number concentration within our study area. It
is noted that due to different instrumentation, Nnuc from Marikana
consists of particles with Dp<30nm, but at Elandsfontein
Nnuc consists of particles with Dp10–30 nm. In addition, CS at
Marikana is defined from submicron particles whereas at Elandfontein CS is
defined from particles with Dp<10µm.
Figure 7 shows the diurnal variation of each of the in situ proxy components
and the number concentration of nucleation mode particles. At Marikana the
Nnuc median peaks about 10 a.m. and at Elandsfontein about 1 h
later. At the time of the satellite overpass the median of Nnuc is
lower than before noon at both locations and about the same order of
magnitude. The diurnal variation of NOx-NO and SO2 concentrations shows somewhat different characteristics at Marikana than at
Elandsfontein. The morning and evening peaks of NOx-NO at Marikana are
most likely associated with household combustion and traffic, whereas the
single SO2 peak in the morning is most likely related to the industrial
emissions and the break-up of the inversion layers that form quite regularly
in the South African Highveld (Venter et al., 2012). At Elandsfontein, where
the major emission source is heavy industry, an increase in the NOx-NO
and the SO2 concentration medians is seen at about 10 a.m. The median
of SO2 concentration decreases in the late afternoon while the median
of NOx-NO concentration does not vary much. At the time of the
satellite overpass the NOx-NO and SO2 medians are much higher at
Elandsfontein than at Marikana. Results show also that at the time of the
satellite overpass NOx-NO and SO2 are positively correlated: at
Elandsfontein R2=0.58 and at Marikana R2=0.32.
At Elandsfontein CS does not show any clear diurnal variation and it is
systematically lower than at Marikana. Also at Marikana the diurnal
variation of the CS is rather weak during the daytime but a peak in the
median is seen in the evening.
Figure 8 shows the diurnal variation of the in situ proxies at Marikana and
Elandsfontein. The comparison of the diurnal variation of the proxies and
Nnuc indicates that the proxy-Nnuc relation depends on the time of
the day. At the time of the satellite overpass (13:00–14:00 LT) the highest
correlation with Nnuc at Marikana is obtained with the
SO2/CS-proxy (R2=0.22, Fig. 9), but at Elandsfontein the
correlation remains below 0.1. At Marikana the correlation of Nnuc with
SO2⋅UV/CS2 proxy (Eq. 1) is not as good at the time of
the satellite overpass, but at 9–10 a.m. R2=0.25. The (NOx-NO)/CS and UV/CS2 proxies do not perform well in
predicting Nnuc. Also, it is noted that at the time of the satellite
overpass all the proxy values show much higher median values at
Elandsfontein than at Marikana while the median for Nnuc is about the
same at both locations. At Elandsfontein somewhat better correlations with
Nnuc are observed when only the source terms of the proxies are
considered. For example, the values of R2 between Nnuc and
SO⋅UV are 0.35 at 10:00–11:00 LT and 0.14 at 13:00–14:00 LT,
but when the sink-term CS2 is included in the proxy there is no
correlation. At Marikana CS does not have as high an influence on the proxy
performance as at Elandsfontein.
This differs from the results reported for southern Finland (Kulmala et
al., 2011) in that SO2 in our study has a strong effect on the
performance of the proxy: without SO2 the UV/CS2 term does not
correlate with Nnuc. Given that the satellite data are associated with
much higher uncertainties than the in situ measurements, these in-situ-based
results can be considered as upper limit for the overall performance of the
proxies computed using satellite data (Eqs. 4–7).
Proxies using satellite data Spatial pattern of the satellite-based proxies
Each of the satellite-based parameters is analyzed from January 2007 to December 2010.
Figure 11 shows the 4-year medians of SO2 and NO2 column
densities obtained from the OMI instrument as well as the AOD at 550 nm
from MODIS Aqua observations. Daily satellite data are used to define the
satellite-based proxies over the study area (Eqs. 4–7). Figure 12 shows the
4-year median spatial patterns for the four satellite-based proxies. The
spatial patterns of these four proxies are quite different and in particular
there is a large difference between the spatial variation of the regional
proxies and that of the proxies for nucleation from primary emissions. As
expected, the latter strongly reflects the spatial distributions of the
precursor gases with high concentrations over the Highveld industrial area,
where the values of NO2 and SO2 columns are high and the sink
(AOD) is low. For the NO2/AOD proxy, elevated values are also observed
over the Johannesburg–Pretoria area while for the other proxies a local
minimum occurs over these cities.
All the four satellite proxies show larger values at Elandsfontein than at
Marikana, which is consistent with the results obtained for the in situ
proxies. Based on the in situ results the SO2-related proxies are
expected to predict Nnuc at the time of the satellite overpass better
than the other proxies. A comparison of the spatial patterns of each proxy
calculated using satellite data in the vicinity of the in situ measurement
stations shows that there is not very much difference between the spatial
pattern of SO2- and NO2-related proxies.
The propagation of relative uncertainty associated with the proxies using
satellite data can be estimated by comparing the uncertainties related to
each satellite parameter (Sect. 3) and the observed median values shown in
Fig. 11. On the one hand, over background areas where both AOD and SO2 are
low the SO2⋅UVB/AOD2 proxy can have an
uncertainty of over 90 %. On the other hand, over source areas where both
NO2 and AOD are slightly elevated the NO2/AOD proxy would have an
uncertainty of about 50 %. Generally over South Africa the uncertainty in
satellite-based proxies is high, especially over areas where low values
of NO2, SO2, and AOD are frequently observed.
Correlations between in-situ- and satellite-based proxies. The
number of coincident observations is denoted with “N”. Scatter plots for
each of the case are provided as a Supplementary Material.
Station(NOx-NO)/CS vs.SO2/CS vs.SO2⋅UVB/CS2 vs.Glob./CS2 vs.NO2/AODSO2/AODSO2⋅UVB/AOD2UVB/AOD2ElandsfonteinR2=0.11, N=46R2=0.20, N=41R2=0.13, N=39R2=0.30, N=52MarikanaR2=0.38, N=93R2=0.005, N=76R2=0.13, N=76R2=0.22, N=117BotsalanoR2=0.004, N=16R2=0.12, N=14R2=0.30, N=14R2=0.11, N=18Comparison of satellite and in situ proxy components
Before evaluating the performance of the proxies using satellite data, first
the quality of the parameters used in these proxies should be examined. The
CS/AOD comparison was discussed in Sect. 4.1. Here we compare satellite data
for NO2, SO2 and UVB with in situ data at each of the measurement
stations. The satellite data for each station are collected within a 12 km
(NO2, SO2, UVB) or a 3 km (AOD) radius from the station and the
results are compared with hourly means of the in situ data extracted between
13:00 and 14:00 LT, i.e. ±30min within the approximate satellite overpass.
The satellite NO2 column densities and the in situ NOx-NO
concentrations are reasonably well correlated as are the satellite UVB
irradiances and the global radiation measured at each station. The highest
correlation for NO2 were obtained at Marikana (R2=0.55) and
the
lowest at Elandsfontein (R2=0.26). For UVB and global radiation the
correlations were 0.61≤R2≤0.77. In Kulmala et al. (2011)
a constant value was assumed for the satellite-based SO2 when
defining the global proxy maps, because the SO2 product they used
(middle tropospheric SO2) did not show a reasonable spatial pattern. In
this study the middle-troposphere SO2 data were replaced by the OMI
boundary layer product (Sect. 3), which improved the characterization of the
SO2 spatial variation (Fig. 10). However, the relative uncertainty in
the satellite-based SO2 remains still high, unless the data are
averaged over a long time period/large spatial area. At all three stations a
lower correlation between the satellite- and in-situ-based SO2
measurements was obtained than for the other source parameters; at Marikana
there is practically no correlation. Similar results were obtained when the
satellite- and in-situ-based proxies were compared (Table 2, figures in the
Supplementary Material). Overall large differences exist between the
satellite proxies and in situ proxies.
Since at Marikana and Elandsfontein the in situ data showed correlation
between the NOx-NO and the SO2 concentrations, the satellite
NO2 column density is also compared with the in situ SO2.
Results show that in fact the OMI NO2 compares better with the in situ
SO2 than the actual OMI SO2 product. At Elandsfontein
R2=0.25 and at Marikana R2=0.31, obtained between the
satellite NO2 column and in situ SO2 concentration.
Comparison of satellite-based proxies with Nnuc
To further evaluate the performance of the satellite-based proxies, they are
compared to the in situ Nnuc. Only data from Elandsfontein and
Marikana are included in the comparison since the number of coincident
Nnuc and satellite proxy observations was too low at the other
stations. As expected, neither of the two satellite-based SO2 proxies
are able to predict Nnuc. Interestingly, the only case where
weak correlation is obtained between a proxy using satellite data and
Nnuc is for NO2/AOD (Fig. 12). This result is very different
than what is expected based on the comparison of the in situ proxies and
Nnuc. In fact, the connection between NO2/AOD and Nnuc is
most probably related to the correlation between the satellite NO2
column density and the in situ SO2 concentration. If the source term
in the SO2⋅UVB/AOD2 proxy was replaced by
NO2⋅UVB, the correlation with Nnuc at Elandsfontein
would be R2=0.23 and at Marikana would be R2=0.06. This implies that
over areas where SO2 and NO2 are affected by some common
factors, e.g. emission sources, the satellite NO2 could be a better
estimate for the source term than SO2.
The comparison between the number concentration of nucleation
mode particles and NO2/AOD calculated from the satellite data at
Marikana and at Elandsfontein stations. The number concentrations are 1 h averages (13:00–14:00 LT) representative of the satellite overpass time. It
is noted that at Elandsfontein Nnuc represents particles with Dp10–30 nm
and at Marikana particles with Dp<30nm. Nobs denotes
the number of coincident observations.
Conclusions
This work explores the use of proxies using satellite data to obtain
information on the concentration of nucleation mode aerosol particles
(Nnuc). These proxies have been formulated using relations derived from
data on ground-based nucleation and precursor gases, which were simplified
for the use of satellite data in Kulmala et al. (2011). The simplifications
and associated assumptions are critically examined. In this study data were
used over part of South Africa where ground-based observations are available
from four experimental sites for comparison with both the satellite-based
parameters used in the proxy formulations and for comparison of the proxies
with ground-based measurements of the nucleation mode aerosol particle
number concentrations. For the computation of the proxies, data from the
A-train satellites are used. The NO2, SO2, and UVB radiation are
obtained from the OMI instrument and AOD from the MODIS instrument. The
NO2 and UVB data are the same as those used in Kulmala et al. (2011),
but the AOD was upgraded to the newest collection 6, 3 km
product. Also, the SO2 product was changed to the planetary boundary
layer product (OMI SO2 PBL) that represents the total column values
with a priori assumption that the emissions are mainly in the boundary
layer. The satellite observations are also extensively compared with in situ
data.
Based on the proxies derived from the in situ data it is expected that the
SO2-related proxies would be the best predictors of Nnuc within
the study area at the time of the satellite overpass (13:00–14:00 LT). It is also
noted that even though the in situ NO2/CS proxy did not do well in
predicting Nnuc, a positive correlation between the SO2 and
NO2 concentrations is found at the measurement stations (at 13:00–14:00 LT).
The R2 between in situ SO2/CS and Nnuc is 0.22 and this value
could be considered as some kind of “upper limit” for the satellite
proxies, for which uncertainties are much higher than for the in situ
proxies. Using ground-based data, Kulmala et al. (2011) reported that
SO2 had only moderate influence on the performance of the SO2⋅UV/CS2 proxy in southern Finland. The overall correlation
between this proxy and Nnuc over South Africa was even lower (R2=0.13) than over southern Finland (R2=0.29), yet our results clearly
indicate a strong influence of SO2 on the performance of the proxy. If
the SO2 was excluded from the proxy, no correlation with in situ
proxies and Nnuc was found.
Kulmala et al. (2011) emphasized that the most crucial assumption in
deriving the satellite-based proxies was the replacement of the CS with AOD.
This assumption is further evaluated in the current study using several
tests. A fundamental reason for differences between CS and AOD is the
intrinsic dependence on different aerosol size ranges, with CS more
sensitive to very small particles (smaller than about 200 nm) and AOD more
sensitive to particles larger than that. Yet, good correlation is obtained
between measured scattering coefficients for dry aerosol and CS evaluated
from collocated particle size distribution measurements. When the in situ
scattering coefficients or CS are compared with collocated AOD measurements,
the correlation decreases. This may be due to several effects. In particular
the presence of elevated aerosol layers and/or large dust particle increases
the AOD but does not affect the CS. However, overall the AOD is rather low
(<0.1) over the major part of the study area; this means that
these values are also associated with substantial relative uncertainty,
which needs to be accounted for when deriving the satellite-based proxies.
Even though the OMI SO2 PBL data product showed a distinct
improvement in describing the spatial patterns of SO2 as compared to
the data set used in Kulmala et al. (2011), the satellite-based SO2 did
not describe well the day-to-day variations at the measurement stations. In
addition, the observed SO2 column values were often close to the noise
level associated with a single column retrieval reported by Krotkov et al. (2008).
The only relation between a satellite-based proxy and Nnuc was
obtained for NO2/AOD (at Elandsfontein R2=0.24 and at Marikana
R2=0.09). The result is different than what was expected based on the
in situ proxies. The most probable explanation is the positive correlation
between the ground-based NO2 and SO2 concentrations within the
study area. It is found that in fact the satellite NO2 column
correlates better with in situ SO2 concentration than the satellite
SO2 column, where no correlation was found.
Overall this study shows that the uncertainties related to the satellite
products remain a major issue in this satellite-based proxy approach,
especially over areas like South Africa, where the AOD and the SO2, and
NO2 concentrations are generally relatively low. Throughout the whole
study the relative uncertainties related to the satellite-based proxies were
well above 50 %. For the NO2/AOD proxy the largest relative
uncertainties were often related to AOD. Otherwise SO2 was clearly the
most uncertain component in the proxies calculated using satellite
data. Despite these uncertainties related to the satellite data, the in situ
data did not do significantly better in predicting Nnuc within our
study area. This indicates that overall improvements in the formulation of
the proxies are needed.
The Supplement related to this article is available online at doi:10.5194/acp-15-4983-2015-supplement.
Acknowledgements
This work is supported by Academy of Finland (1251427, 1139656, Finnish
Centre of Excellence in Atmospheric Science 272041), European Research
Council (ATMNUCLE), the European Aerosol Cloud Climate
and Air Quality Interactions project (EUCAARI), and the ESA projects Aerosol-cci
(ESA-ESRIN project AO/1-6207/09/I-LG) and ALANIS-Aerosols (contract no. 4200023053/10/I-LG, STSE-ALANIS Atmosphere–Land Interactions Study Theme 3:
Aerosols). Eskom and Sasol supplied logistical support for measurements at
Elandsfontein, while the town council of Rustenburg supplied support to the
measurement at Marikana. The OMI NO2, SO2, and UV data were
obtained from the NASA Mirador service maintained by Goddard Earth Sciences
Data and Information Services Center (GES DISC). The OMI surface UV data
were obtained from the NASA Aura Validation Data Center (AVDC). The MODIS
Aqua data were provided by NASA LAADS Web, and the CALIPSO data were obtained
from NASA Atmospheric Science Data Center (ASDC).
Edited by: S. M. Noe
References
Beukes, P., Vakkari, V., van Zyl, P. G., Venter, A., Josipovic, M., Jaars,
K., Tiitta, P., Kulmala, M., Worsnop, D., Pienaar, J., Virkkula, A., and
Laakso L.: Source region plume characterisation of the interior of South
Africa, as measured at Welgegund, Clean Air J., 23, 7–10, 2013.Bucsela, E. J., Krotkov, N. A., Celarier, E. A., Lamsal, L. N., Swartz, W. H.,
Bhartia, P. K., Boersma, K. F., Veefkind, J. P., Gleason, J. F., and Pickering, K. E.:
A new stratospheric and tropospheric NO2 retrieval algorithm for nadir-viewing
satellite instruments: applications to OMI, Atmos. Meas. Tech., 6, 2607–2626, 10.5194/amt-6-2607-2013, 2013.Burrows, J. P., Platt, U., and Borrell, P. (Eds.): The Remote Sensing of
Tropospheric Composition from Space, 536 pp., Springer-Verlag Berlin
Heidelberg, ISBN: 978-3-642-14790-6,
p. 359–313, 10.1007/978-3-642-14791-3, 2011.
Crippa, P., Spracklen, D., and Pryor, S. C.: Satellite-derived estimates of
ultrafine particle concentrations over Eastern North America, J. Geophys.
Res., 118, 9968–9981, 2013.Fioletov, V. E., McLinden, C. A., Krotkov, N., Moran, M. D., and Yang, K.:
Estimation of SO2 emissions using OMI retrievals, Geophys. Res. Lett.,
38, L21811, 10.1029/2011GL049402, 2011.
Fuchs, N. A. and Sutugin, A. G.: Highly dispersed aerosol, in: Topics in current aerosol research
(Part 2), edited by: Hidy, G. M. and Brock, J. R., Pergamon, New York, 1971.Giannakaki, E., Pfüller, A., Korhonen, K., Mielonen, T., Laakso, L.,
Vakkari, V., Baars, H., Engelmann, R., Beukes, J. P., Van Zyl, P. G.,
Josipovic, M., Tiitta, P., Chiloane, K., Piketh, S., Lihavainen, H., Lehtinen, K. E. J.,
and Komppula, M.: One year of Raman lidar observations of free tropospheric
aerosol layers over South Africa, Atmos. Chem. Phys. Discuss., 15, 1343–1384, 10.5194/acpd-15-1343-2015, 2015.Hirsikko, A., Vakkari, V., Tiitta, P., Manninen, H. E., Gagné, S., Laakso, H., Kulmala, M.,
Mirme, A., Mirme, S., Mabaso, D., Beukes, J. P., and Laakso, L.:
Characterisation of sub-micron particle number concentrations and formation events
in the western Bushveld Igneous Complex, South Africa, Atmos. Chem. Phys., 12, 3951–3967, 10.5194/acp-12-3951-2012, 2012.Hirsikko, A., Vakkari, V., Tiitta, P., Hatakka, J., Kerminen, V.-M., Sundström, A.-M.,
Beukes, J. P., Manninen, H. E., Kulmala, M., and Laakso, L.:
Multiple daytime nucleation events in semi-clean savannah and industrial
environments in South Africa: analysis based on observations, Atmos. Chem. Phys., 13, 5523–5532, 10.5194/acp-13-5523-2013, 2013.
Holben, B., Eck, T. F., Slutsker, I., Tanre, D., Buis, J. P., Setzer, A.,
Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F.,
Jankowiak, I., and Smirnov, A.: AERONET – A Federated Instrument Network and
Data Archive for Aerosol Characterization, Rem. Sens. Environ., 66,
1–16, 1998.
IPCC, Intergovernmental Panel on Climate Change: Fifth Assessment Report:
Climate Change, Cambridge University Press, Cambridge, United Kingdom and
New York, NY, USA, 2013.
Kokhanovsky, A. A. and de Leeuw, G. (Eds.): Satellite Aerosol Remote
Sensing over Land, Springer, Berlin, Germany, 2009.Krotkov, N., Carn, S., Krueger A., Bhartia, P., and Yang, K.: Band residual
difference algorithm for retrieval of SO2 from the Aura Ozone
Monitoring Instrument (OMI), IEET T. Geosci. Rem., 44, 1259–1266, 2006.Krotkov, N., McClure, B., Dickerson, R., Carn, S., Li, C., Bhartia, P.,
Yang, K., Krueger, A., Li, Z., Levelt, P., Chen, H., Wang, P., and Lu, D.:
Validation of SO2 retrievals from the Ozone Monitoring Instrument over
NE China, J. Geophys. Res., 113, D16S40,
10.1029/2007JD008818, 2008.
Kulmala, M. and Kerminen, V.-M.: On the formation and growth of atmospheric
nanoparticles, Atmos. Res., 90, 132–150, 2008.
Kulmala, M., Vehkamäki, H., Petäjä, T., Dal Maso, M., Lauri, A.,
Kerminen, V.-M., Birmili, W., and McMurry, P. H.: Formation and growth rates
of ultrafine atmospheric particles: a review of observations, J. Aerosol
Sci., 35, 143–176, 2004.Kulmala, M., Petäjä, T., Mönkkönen, P., Koponen, I. K., Dal Maso, M.,
Aalto, P. P., Lehtinen, K. E. J., and Kerminen, V.-M.:
On the growth of nucleation mode particles: source rates of condensable
vapor in polluted and clean environments, Atmos. Chem. Phys., 5, 409–416, 10.5194/acp-5-409-2005, 2005.Kulmala, M., Arola, A., Nieminen, T., Riuttanen, L., Sogacheva, L., de Leeuw, G.,
Kerminen, V.-M., and Lehtinen, K. E. J.: The first estimates of global
nucleation mode aerosol concentrations based on satellite measurements, Atmos. Chem. Phys., 11, 10791–10801, 10.5194/acp-11-10791-2011, 2011.Laakso, L., Laakso, H., Aalto, P. P., Keronen, P., Petäjä, T., Nieminen, T.,
Pohja, T., Siivola, E., Kulmala, M., Kgabi, N., Molefe, M., Mabaso, D.,
Phalatse, D., Pienaar, K., and Kerminen, V.-M.: Basic characteristics of atmospheric
particles, trace gases and meteorology in a relatively clean Southern African
Savannah environment, Atmos. Chem. Phys., 8, 4823–4839, 10.5194/acp-8-4823-2008, 2008.Laakso, L., Vakkari, V., Virkkula, A., Laakso, H., Backman, J., Kulmala, M., Beukes, J. P.,
van Zyl, P. G., Tiitta, P., Josipovic, M., Pienaar, J. J., Chiloane, K.,
Gilardoni, S., Vignati, E., Wiedensohler, A., Tuch, T., Birmili, W., Piketh, S.,
Collett, K., Fourie, G. D., Komppula, M., Lihavainen, H., de Leeuw, G., and
Kerminen, V.-M.: South African EUCAARI measurements: seasonal variation of
trace gases and aerosol optical properties, Atmos. Chem. Phys., 12, 1847–1864, 10.5194/acp-12-1847-2012, 2012.Lamsal, L. N., Krotkov, N. A., Celarier, E. A., Swartz, W. H., Pickering, K. E.,
Bucsela, E. J., Gleason, J. F., Martin, R. V., Philip, S., Irie, H., Cede, A., Herman, J.,
Weinheimer, A., Szykman, J. J., and Knepp, T. N.: Evaluation of OMI operational
standard NO2 column retrievals using in situ and surface-based
NO2 observations, Atmos. Chem. Phys., 14, 11587–11609, 10.5194/acp-14-11587-2014, 2014.
Lee, K. H., Li, Z., Kim, Y. J., and Kokhanovsky, A.: Atmospheric aerosol
monitoring from satellite observations: a history of three decades, in:
Atmospheric and biological environmental monitoring, Springer, the Netherlands,
2009.
Lehtinen, K. E. J., Korhonen, H., Dal Maso, M., and Kulmala, M.: On the
concept of condensation sink diameter, Boreal Env. Res., 8, 405–411, 2003.Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F.,
and Hsu, N. C.: The Collection 6 MODIS aerosol products over land
and ocean, Atmos. Meas. Tech., 6, 2989–3034, 10.5194/amt-6-2989-2013, 2013.Mielonen, T., Levy, R. C., Aaltonen, V., Komppula, M., de Leeuw, G., Huttunen, J.,
Lihavainen, H., Kolmonen, P., Lehtinen, K. E. J., and Arola, A.:
Evaluating the assumptions of surface reflectance and aerosol
type selection within the MODIS aerosol retrieval over land:
the problem of dust type selection, Atmos. Meas. Tech., 4, 201–214, 10.5194/amt-4-201-2011, 2011.
Mishchenko, M. I., Travis, L. D., and Lacis, A. A.: Scattering, Absorption,
and Emission of Light by Small Particles. Cambridge University Press,
Cambridge, 2002.Petäjä, T., Mauldin, III, R. L., Kosciuch, E., McGrath, J., Nieminen, T.,
Paasonen, P., Boy, M., Adamov, A., Kotiaho, T., and Kulmala, M.:
Sulfuric acid and OH concentrations in a boreal forest site, Atmos. Chem. Phys., 9, 7435–7448, 10.5194/acp-9-7435-2009, 2009.
Petäjä, T., Vakkari, V., Pohja, T., Nieminen, T., Laakso, H., Aalto,
P. P., Keronen, P., Siivola, E., Kerminen, V.-M., Kulmala, M., and Laakso,
L.: Transportable Aerosol Characterization Trailer with Trace Gas Chemistry:
Design, Instruments and Verification, Aerosol Air Qual. Res., 13, 421–435,
2013.
Schnelle-Kreis, J., Küpper, U., Sklorz, M., Cyrys, J., Briedé, J.
J., Peters, A., and Zimmerman, R.: Daily measurements of organic compounds
in ambient particulate matter in Augsburg, Germany: new aspects on aerosol
sources and aerosol related health effects, Biomarkers, 14 (SI), 39–44,
2009.
Seaton, A., MacNee, W., Donaldson, K., and Godden, D.: Particulate air pollution
and acute health effects, The Lancet, 345, 176–178, 1995.Tanskanen, A., Krotkov, N. A., Herman, J. R., and Arola, A.: Surface
Ultraviolet Irradiance From OMI, IEEE Trans. Geosci. Remote Sens., 44,
1267–1271, 2006.
Utell, M. J. and Frampton, M. W.: Acute Health Effects of Ambient Air
Pollution: The Ultrafine Particle Hypothesis, J. Aerosol Med. Pulm. Drug.
Deliv., 13, 355–359, 2000.Vakkari, V., Laakso, H., Kulmala, M., Laaksonen, A., Mabaso, D., Molefe, M.,
Kgabi, N., and Laakso, L.: New particle formation events in
semi-clean South African savannah, Atmos. Chem. Phys., 11, 3333–3346, 10.5194/acp-11-3333-2011, 2011.Vakkari, V., Beukes, J. P., Laakso, H., Mabaso, D., Pienaar, J. J., Kulmala, M.,
and Laakso, L.: Long-term observations of aerosol size distributions
in semi-clean and polluted savannah in South Africa, Atmos. Chem. Phys., 13, 1751–1770, 10.5194/acp-13-1751-2013, 2013.Venter, A. D., Vakkari, V., Beukes, J. P., van Zyl, P. G., Laakso, H., Mabaso,
D., Tiitta, P., Josipovic, M., Kulmala, M., Pienaar, J. J., and Laakso. L.:
An air quality assessment in the industrialised western Bushveld Igneous
Complex, South Afric S. Afr. J. Sci., 108, 1059,
10.4102/sajs.v108i9/10.1059, 2012.Virkkula, A., Backman, J., Aalto, P. P., Hulkkonen, M., Riuttanen, L., Nieminen, T.,
dal Maso, M., Sogacheva, L., de Leeuw, G., and Kulmala, M.: Seasonal cycle, size
dependencies, and source analyses of aerosol optical properties at the SMEAR II
measurement station in Hyytiälä, Finland, Atmos. Chem. Phys., 11, 4445–4468, 10.5194/acp-11-4445-2011, 2011.Winker, D. M., Hunt, W. M., and McGill, M. J.: Initial performance assessment
of CALIOP, Geophys. Res. Lett., 34, L19803, 10.1029/2007GL030135,
2007.
York, D., Evensen, N., Martinez, M., and Delgado, J.: Unified equations for
the slope, intercept, and standard errors of the best straight line, Am. J.
Phys., 72, 367–375, 2004.