Introduction
The understanding of processes governing atmospheric aerosols is primarily
motivated by their adverse effects on health and their contribution to the
radiative budget of the atmosphere. Diseases affecting the respiratory
system have been linked to the inhalation of aerosols, especially their finer
and largest fraction (Beelen et al., 2014; Oberdorster, 2001),
although the mechanisms underlying the health effect associated with the size,
number and composition of particulate matter have only recently begun to be
disclosed (Harrison and Yin, 2000; Daher et al., 2012; Perrone et al.,
2013). Aerosols affect the atmospheric energy balance directly, by
scattering and absorbing radiation (Yu et al., 2006), indirectly, by serving
as cloud condensation nuclei (Lohmann and Feichter, 2005); and
semi-directly, by heating the air through the absorption of radiation and
reducing low cloud cover (Johnson et al., 2004). The assessment of these
effects caused by aerosols is still characterized by large uncertainties,
since our knowledge of the processes determining their abundance, size
distribution and chemical composition, which strongly vary in space and
time, is still limited (Raes et al., 2000; Pöschl, 2005). Here we focus on
the interplay between dynamical and chemical processes in the vertical
direction in order to better understand the budget terms making up the
ground-level particulate matter, a common measure to evaluate air quality.
The study focuses on the urban environment of Milan, situated in the centre
of Italy's Po Valley, a European hot spot for atmospheric pollution.
The correlation between pollutants at the surface and meteorological
variables is well established and the fundamental role played by the
variables associated with the vertical mixing in the planetary boundary layer
(PBL) has been highlighted both for ozone (Di Carlo et al., 2007, and
references therein) and particulate matter (Tai et al., 2010, and references
therein). Moreover, Zhang and Rao (1999) analysed aircraft and tower
measurements over the eastern United States and showed that elevated
nocturnal layers rich in ozone and its precursors aloft, remnants of the
previous day's mixed layer, may strongly affect ground-level ozone levels on
the following morning as vertical motions mix upper and surface air. The
same authors suggested that a reduction of ozone and precursors aloft may be
more effective in reducing pollution than local emission cuts, thus calling
for a region-wide strategy for emissions control. Aerosols are also known to
form layers above or near the top of the mixing layer, especially when the stability and presence of clouds increase (e.g. O'Dowd and Smith, 1996).
Similarly to ozone, an aerosol residual layer aloft is often observed (e.g.
Di Giuseppe et al., 2012), which may influence the aerosol at the surface,
as witnessed by similar size distributions (Maletto et al., 2003). A
significant contribution to surface aerosol from entrainment and vertical
dilution and chemical net production in the boundary layer has also been
pointed out in recent studies using single-column models (van Stratum et
al., 2012; Ouwersloot et al., 2012).
The nontrivial relationship between ground- and upper-level aerosol burdens
is illustrated by comparing surface particulate matter (PM) mass
concentrations to aerosol optical depth (AOD), which is proportional to the
aerosol column load (typically measured by ground-based sun photometers or
retrieved from satellites). In a well-mixed PBL, the AOD may exhibit a high
correlation with surface PM, especially with its fine fraction, and indeed
this assumption is often exploited to infer surface PM2.5 (PM with
diameter < 2.5 µm) from satellite AOD observations (e.g. van
Donkelaar et al., 2010). However, that assumption does not always hold true,
due to the presence of significant aerosol stratification aloft, and
noticeable differences may occur between AOD and surface PM behaviour, such as in the timing of daily peak values or in multiday trends
(Barnaba et al., 2007, 2010; Boselli et al., 2009; Estelles et al., 2012; He
et al., 2012). Indeed, analysing 2-year measurements in the Po Valley (Italy),
Barnaba et al. (2010) pointed out that annual cycles of AOD and
surface PM10 (PM with diameter < 10 µm) display a
remarkable opposite phase. While PM10 peaks in winter, due to the
reduced dilution by a shallower PBL and to the condensation of semi-volatile
species favoured by the lower temperatures, AOD peaks in summer because of
the more persistent presence of an aerosol residual layer aloft, which
contributes up to 30 % of the total AOD.
Aircraft measurements also showed intriguing features of aerosol vertical
gradients in the lower troposphere, in particular when looking at different
chemical components. Several studies reported a generally constant or
slightly decreasing profile in the convective boundary layer of sulfate and
organic matter as opposed to an increasing profile of nitrate (Neuman et
al., 2003; Cook et al., 2007; Crosier et al., 2007; Morgan et al., 2009;
Ferrero et al., 2012). Neuman et al. (2003) attributed the enhanced nitrate
layer near the top of the PBL to the lower temperatures that favour
gas-phase nitric acid (HNO3) and ammonia (NH3) conversion to
particulate ammonium nitrate. The same authors also pointed out that nitrate
and HNO3 display sharp vertical gradients in the PBL, as opposed to
other directly emitted (carbon monoxide) or secondary (ozone) species that
are relatively uniform, and this observation was interpreted as an
indication that thermodynamic equilibrium between gas and particle phases
occurs faster than vertical mixing. However, the issue is still under debate
as subsequent model studies found that an instantaneous thermodynamic
equilibrium between HNO3 and nitrate yields excessively steep and
unrealistic vertical gradients (Morino et al., 2006; Aan de Brugh et al., 2012).
Moreover, the presence of aerosol layers enriched with sulfate and
water-soluble carbonaceous matter was observed above the boundary layer or
in convective clouds during several aircraft campaigns over North America
(Novakov et al., 1997; Heald et al., 2006; Duong et al., 2011; Wonaschuetz
et al., 2012) and attributed to biomass burning plumes or aqueous-chemistry
processes.
A quantitative assessment of the contribution of elevated aerosol layers and
related dynamical and chemical processes to ground-level particulate-matter
level is still lacking. Recent modelling studies that reported budget (or
process) analyses of the simulated aerosol mainly focused on terms of the
continuity equation at the surface or on integrated values over the whole
boundary layer. Surface and PBL total PM2.5 mass is calculated to be
mainly produced by direct emissions and secondary formation by aerosol
processes (e.g. condensation and absorption) and removed by horizontal and
vertical transport and wet deposition (Zhang et al., 2009; Liu et al.,
2011). The controlling processes are different for surface PM number, which
is accumulated mainly by homogeneous nucleation and vertical transport, and
it is lost mainly by dry deposition and coagulation (Zhang et al., 2010).
For primary components such as black carbon (BC), the fate is similar to that
of total PM2.5, while for secondary species it is more intricate.
Sulfate is generally produced in the PBL by aerosol and clouds processes
(the latter being very important) and exported out of the PBL throughout the
year (de Meij et al., 2007; Zhang et al., 2009; Aan de Brugh et al., 2011; Liu
et al., 2011). Averaged over the year, the nitrate budget is similar to that
of sulfate, with the difference that cloud processes (wet deposition) are a
sink (Aan de Brugh et al., 2011; Liu et al., 2011). However, during the summer
there might be competition between PM production (e.g. condensation and
absorption) and destruction (e.g. evaporation and desorption) processes, and
the PBL may become a sink and not a source for nitrate (Zhang et al., 2009). The
same competition between PM production and destruction processes affects secondary organic aerosols (SOAs) throughout the year (Zhang et al., 2009).
Moreover, SOAs are strongly influenced by biogenic volatile organic compound
(BVOC) emissions, through semi-volatile products of isoprene and
terpene oxidation, which also has a marked seasonal cycle (Zhang et al.,
2007; Hodzic et al., 2009).
In the present study, we examined the formation of aerosol near the surface
from the particular perspective of the boundary layer vertical processes
outlined above. We analysed aerosol mass observations, composition, number
and optical properties in the month of July 2007 in Milan (45∘ N,
9∘ E; northern Italy) during the intensive campaigns carried out within the framework of the QUITSAT (Air Quality by the Integration of
Ground- and Satellite-based Observations and Multiphase Chemistry-Transport
Modelling, funded by the Italian Space Agency, ASI) and AeroClouds
(Study of Direct and Indirect Effect of Aerosols and Clouds on Climate,
funded by the Italian Ministry for Higher Education) projects. The
experimental results were then complemented by and interpreted through
WRF/Chem (Weather Research and Forecasting with
Chemistry) model simulations.
Firstly, what is known about the aerosol phenomenology in the investigated
area is briefly reviewed in Sect. 2. We describe the experimental set-up in Sect. 3 and
the model set-up in Sect. 4. In Sect. 5, a preliminary analysis of the observations is
carried out in order to characterize the relevant features of the case study
and pose questions arising from the picture given by the measurements. Then,
these questions are addressed using WRF/Chem model simulations. After a
model validation against available observations, we analyse the budget of
aerosol species as calculated by the model, focusing in particular on the
vertical dimension. The main results are summarized in the conclusion.
The investigated area
Milan is the largest urban area in Italy (ca. 5 million people) and lies in
one of the most polluted areas in Europe, the Po Valley (Putaud et al.,
2010). The topography of the valley (closed off by the Alps to the north and
west and by the Apennines to the south), under high-pressure systems, favours
stagnant atmospheric conditions and the recirculation of air through the typical
mountain-valley breeze (Dosio et al., 2002). The local circulation in
combination with elevated anthropogenic emissions especially from traffic,
residential combustion and agriculture (Lonati et al., 2005; Carnevale et
al., 2008; Perrone et al., 2012; Saarikoski et al., 2012) makes it a
nitrogen dioxide and aerosol hot spot clearly visible from space (e.g. Chu
et al., 2003; Barnaba and Gobbi, 2004; Ordonez et al., 2006; van Donkelaar
et al., 2010).
At the surface, the PM10 annual mean in Milan has been stable between 50
and 60 µg m-3 in the last decade (Carnevale et al., 2008;
Silibello et al., 2008), it has thus been systematically above the European limit for human protection of 40 µg m-3 (EC, 2008). The winter average values
are roughly double those in the summer, and peak values are up to 200 µg m-3 (Marcazzan et al., 2001). The main aerosol components are
sulfate, nitrate and organic matter (OM), which account for roughly 20,
15 and 40 %, respectively, of PM10 mass in summer and 10, 30 and 50 %, respectively, in winter (Marcazzan et al., 2001; Putaud et
al., 2002; Lonati et al., 2005; Carbone et al., 2010; Perrone et al., 2010;
Daher et al., 2012). These values are similar to those in other urban areas
in the Po Valley (Matta et al., 2003; Carbone et al., 2010; Squizzato et
al., 2013). Most of the mass of these species is distributed in the
accumulation mode (particle diameter in the range 0.14–1.2 µm), while
the coarse mode (1.2–10 µm diameter) has a larger fraction of crustal
material and sea salts (Matta et al., 2003; Carbone et al., 2010). In
summer, nitrate can exhibit a broader size distribution as a larger fraction
may also form in the coarse mode. Higher temperatures, lower humidity and a higher load of sulfate competing for the uptake of ammonia are less
favourable to ammonium nitrate accumulation in the fine mode. As a
consequence, more nitric acid is available to react with soil dust or sea
salt, leading to the formation of mineral nitrate on coarse particles. (Matta
et al., 2003; Hodzic et al., 2006; Lee et al., 2008; Carbone et al., 2010).
The total number concentration of aerosol is of the order of 104 cm-3, with ultrafine (diameter d < 100 nm) and submicron
(100 < d < 1000 nm) particles constituting up to 80 and
20 % of the total, respectively (Lonati et al., 2011). The aerosol number
concentration is usually distributed in three modes (Balternsperger et al.,
2002; Lonati et al., 2011). One mode, with diameters in the range of 20–30 nm consists of hydrophobic and highly volatile organic material
originating from combustion (Baltensperger et al., 2002), plus new particles
from nucleation events that occur on about 35 % of the days in the Po Valley
(Hamed et al., 2007). The other two modes are in the submicron range (dry
diameters 50–200 nm): one is almost hydrophobic, related to primary emissions
(e.g. soot), and the other is hydrophilic, related to secondary aerosols
(Balternsperger et al., 2002).
The aerosol vertical profile in Milan and in the wider Po Valley region was
characterized by means of aircraft, lidar and tethered balloon measurements
(Highwood et al., 2007; Barnaba et al., 2007, 2010; Crosier
et al., 2007; Angelini et al., 2009; Ferrero et al., 2010, 2011). Similarly to other polluted valley areas, two layers with distinct
characteristics are often found: one in the PBL, which is humid, rich in
fresh emissions and has a nitrate profile increasing with height, and
another layer, above the PBL, with more aged aerosols enriched in the
sulfate and organic matter fraction (Highwood et al., 2007; Crosier et al.,
2007; Ferrero et al., 2010). This decoupling into two layers is attributed
to the mountain-valley breeze dynamics (Angelini et al., 2009) and to the
sporadic arrival of long-range transported Saharan dust (Barnaba et al.,
2007) or biomass burning plumes (Barnaba et al., 2011). The number
concentration of fine-mode (d < 1.6 µm) particles is found
to be relatively constant with height in the PBL, and it decreases by a
factor of 2–3 above the PBL. In contrast, coarse-particle (d > 1.6 µm) number concentrations display a decrease with height also in
the PBL, due to sedimentation processes (Ferrero et al., 2010).
Experimental set-up
Ground-based and vertical profile measurements used in this study were
conducted at the Torre Sarca site, which is located on the northern side of Milan
(45∘31′19′′ N, 9∘12′46′′ E; within the Milano-Bicocca
University campus), in the midst of an extensive conurbation that is the
most industrialized and heavily populated area in the Po Valley. We report
here a brief description of the experimental set-up and provide relevant
references for further details.
Particulate-matter bulk composition and number size distribution
as well as gas-phase composition
At ground level, PM2.5 and PM1 (EN-14907) samples were
gravimetrically collected using the FAI Hydra dual-channel
low-volume sampler (LVS; 2.3 m3 h-1, 24 h of sampling time,
PTFE filters for PM1 ore-fired quartz fibre filters for PM2.5,
∅= 47 mm), while the aerosol number size distribution was
constantly monitored using an optical particle counter (OPC; Grimm 1.107
“Environcheck”, 31 class sizes ranging from 0.25 to 32 µm). Further details are given in Ferrero et al. (2014).
The aerosol chemistry was assessed on PM2.5 samples for the ionic
fraction, elemental carbon (EC) and organic carbon (OC). For the purpose of ion analysis,
PM2.5 samples were extracted in 3 mL of ultrapure water
(Milli-Q®; 18.2 MΩ× cm) for 20 min using an ultrasonic bath (SONICA, Soltec, Italy). The obtained
solutions were then analysed using a coupled ion chromatography system
consisting of (1) a Dionex ICS-90 (CS12A-5 analytical column) with an
isocratic elution of methanesulfonic acid (20 Mm; 0.5 mL min-1) whose signal
was suppressed by means of tetrabutylammonium hydroxide (0.1 M; CMMS III 4 mm MicroMembrane Suppressor) for cations (Na+, K+, Ca++,
Mg++, NH4+) and (2) a Dionex ICS-2000 (AS14A-5 analytical
columns) with an isocratic solution of Na2CO3 / NaHCO3 (8.0 mM / 1.0 mM; 1 mL min-1) whose signal was suppressed by means of sulfuric acid
(0.05 M; AMMS III 2 mm MicroMembrane Suppressor) for anions (F-,
Cl-, NO3-, SO4=).
EC and OC were determined in PM2.5 using the thermal–optical
transmission method (TOT, Sunset Laboratory Inc.; NIOSH 5040 procedure,
http://www.cdc.gov/niosh/nmam/pdfs/5040f3.pdf). The organic
matter (OM) fraction was then estimated from OC using a coefficient to
account for the presence of heteroatoms (H, O, N, etc.). Following the work
of Turpin and Lim (2001), the factor chosen was 1.6 for the urban Torre
Sarca site.
Finally, meteorological and gas-phase (NOx, O3) observations at
ground level were taken from the weather and monitoring stations operated in
Milan by the local regional environmental protection agency (ARPA
Lombardia).
Size-segregated aerosol composition
From 14 (08:00 local time LT) to 18 (08:00 LT) July 2007, size segregated
daytime (08:00 to 21:00 LT) and night-time (21:00 to 08:00 LT) aerosol samples
were collected by means of a five-stage Berner impactor (LPI 80/0.05) with
a 50 % efficiency size cut at 0.05, 0.14, 0.42, 1.2, 3.5 and 10 µm aerodynamic
diameter. Substrates were analysed off-line for the determination of the
carbonaceous – water-soluble organic (WSOC) and water-insoluble (WINC)
carbon – and soluble inorganic components (NH4+, Na+,
K+, Ca2+, Mg2+, Cl-, NO3-, SO42-).
Mass-to-carbon ratios of 1.8 and 1.2 were used to convert WSOC to the
corresponding mass and to convert WSOM (water-soluble organic matter) and WINC to WINCM
(water-insoluble carbonaceous matter), respectively. A complete description
of the sampling and analytical methods adopted is reported in Carbone et al. (2010) and references therein. In the analysis presented here, we only used
the total mass of aerosol components (sum over size bins).
Lidar-ceilometer profiles
Lidar ceilometers (called lidar for brevity in this paper) operate on
the same physical basis of more complex research-type lidars but are
compact systems, generally with a lower laser energy power, capable of
operating 24 h per day, unattended and in all weather conditions.
Initially developed for cloud-base determination, the technology of these
systems is now mature enough to represent a very convenient and widely used
tool for the operational monitoring of atmospheric aerosol and of
relevant meteorological parameters (e.g. Haeffelin et al., 2012).
A lidar ceilometer (Vaisala LD-40) operating at 855 nm collected aerosol
profiles at the Milan Torre Sarca site in the January 2007–February 2008
period. The system was switched on on selected dates (and mostly when
meteorological conditions allowed the simultaneous launch of balloon-borne
aerosol instruments; Ferrero et al., 2010), collecting a database of more
than 200 days of measurements. On the selected dates, the
lidar ceilometer operated 24 h per day, collecting aerosol profiles
every 15 s; these were afterwards averaged over 15 min to achieve a
better signal-to-noise ratio. Due to the instrumental limitations, the
lowest altitude the system can observe is about 60 m. After the background
noise is subtracted from the collected backscattered signal, the
range-corrected signal (RCS, i.e. the signal S times the square of the
system-to-target distance R) is derived to extract information on the
aerosol vertical distribution. More details on the system and measurement
capabilities can be found in Angelini et al. (2009) and Di Giuseppe et al. (2012).
WRF/Chem model
Description and set-up
The 3.4.1 version of WRF/Chem, with some updates, is used in order to interpret the
observed concentrations of aerosol and its composition at the surface and along
the vertical profile of the PBL. WRF/Chem is a coupled online model, in which meteorological and chemical processes are fully consistent (Grell et al.,
2005).
The model is configured with two one-way nested domains centred on northern
Italy (Po Valley). The mother domain covers western Europe, with 131 × 95 cells at a horizontal resolution of 30 km; the nested domain
covers northern Italy, with 109 × 91 cells at a resolution of 10 km.
The vertical grid is made of 33 eta levels up to 50 hPa, with the first five
levels centred approximately at 12, 36, 64, 100 and 140 m above the ground
and 12 levels below 1 km.
Main physical and chemical parameterizations used in WRF/Chem
simulations. For definition of acronyms please refer to main text.
Process
Scheme
Short-wave radiation
RRTM
Long-wave radiation
RRTM
Surface layer
Monin–Obukhov
Boundary layer
MYNN
Land surface model
Noah LSM
Cumulus convection
Grell scheme G3
Cloud microphysics
Morrison
Gas-phase mechanism
RACM-ESRL
Aerosol mechanism
MADE/SOA-VBS
Photolysis
Fast-J
Cloud chemistry and wet deposition
On
Biogenic emissions
MEGAN
Direct aerosol effect
On
Indirect aerosol effects
Off
The physical and chemical parameterizations used are the same for the two
domains and are listed in Table 1. These include
the rapid radiative transfer model for short- and long-wave radiation (Iacono
et al., 2008), the Mellor–Yamada Nakanishi–Niino boundary layer
parameterization (Nakanishi and Niino, 2006), the Noah Land Surface Model
(Chen and Dudhia, 2001), the Morrison cloud microphysics scheme (Morrison et
al., 2009) and the Grell 3-D ensemble cumulus scheme, which is an updated
version of the Grell–Devenyi scheme (Grell and Devenyi, 2002). Cumulus
cloud feedback with radiation is activated.
The gas-phase chemistry is simulated with an updated version of the Regional
Atmospheric Chemistry Mechanism (RACM), which includes a wide range of
chemical and photolytic reactions for organic and inorganic species
(Stockwell et al., 1997). The aerosol parameterization adopted is the Modal
Aerosol Dynamics for Europe (Ackermann et al., 1998), which uses three
overlapping lognormal modes for Aitken, accumulation and coarse particles.
Thermodynamic equilibrium for inorganic species is calculated using the
Model for an Aerosol Reacting System (MARS) module (Saxena et al., 1986; Binkowski and Roselle, 2003). SOA production is calculated using the
volatility basis set (VBS) scheme implemented in WRF/Chem by Ahmadov et al. (2012), which includes the oxidation of anthropogenic and biogenic
volatile organic compounds (VOC)
currently believed to be important for SOA production (alkanes, alkenes,
xylenes, aromatics, isoprene, monoterpenes and sesquiterpenes). To our
knowledge, this study is the first application over Europe of this new
parameterization for SOA yield by means of WRF/Chem. Photolysis rates are
estimated with the Fast-J scheme (Wild at al., 2000). The dry deposition
flux is simulated with the scheme by Wesely et al. (1989), and the dry
deposition velocity of organic vapours is assumed to be the 25 % that of
nitric acid (HNO3). Cloud chemistry in convective updraft is
parameterized following Walcek and Taylor (1986). Wet deposition due to
convective and large-scale precipitation is also included in our
simulations. The aerosol optical properties are calculated online with the
package by Barnard et al. (2010), using the volume average internal-mixing
assumption. We included the direct effect of aerosol on radiation, but the
indirect aerosol effects on clouds were switched off since this function is
still under testing with the SOA VBS scheme (Tuccella et al., 2015).
In order to enhance our understanding of the influence of the upper-level
processes on the pollutant budget at the surface, we use the diagnostic of the
tendency terms in the continuity equation for chemical species following
Wong et al. (2009). We extended the original module, which included only
some gas-phase compounds, to include aerosol species and processes as well.
Diagnosed terms are emission, horizontal and vertical advection,
photochemistry (gases and aerosols), vertical mixing plus dry deposition
(these cannot be separated in the WRF/Chem implementation), convective
transport, aqueous chemistry, and wet deposition.
We have simulated the period from the 25 June to the 18 July 2007,
discarding the first 10 days as spin-up. Simulation on the mother domain
uses initial and boundary meteorological conditions provided by the National
Centers for Environmental Prediction (NCEP) 6-hourly analyses, having a
horizontal resolution of 1∘ × 1∘. For the
mother domain, chemical boundary conditions are provided with WRF/Chem
default idealized vertical profiles, representative of northern hemispheric,
midlatitude and clean environmental conditions (McKeen et al., 2002; Grell
et al., 2005; Tuccella et al., 2012), while boundary conditions for the
nested domain are provided by the mother domain. The simulations are carried
out in 24 h time segments, starting at 12:00 UTC of each day and then run
for 30 h, with the first 6 h considered as model spin-up. Chemical
fields are restarted from previous runs.
Emissions
Total annual 2007 anthropogenic emissions of nitrogen oxides (NOx),
carbon monoxide (CO), sulfur oxides (SOx), ammonia (NH3),
non-methane volatile organic compounds (NMVOC), unspeciated particulate
matter (PM2.5 and coarse PM), primary organic carbon (OC) and
elemental carbon (EC) are taken from the Netherlands Organization for
Applied Scientific Research (TNO) database (Kuenen et al., 2014). Annual TNO
anthropogenic emissions consist of gridded data from 10 source types (SNAP
sectors), with a horizontal resolution of 1/16∘ latitude by
1/8∘ longitude (about 7 × 7 km2).
TNO emissions are adapted to WRF/Chem following the methodology used by
Tuccella et al. (2012), with minor changes derived from the second phase of
the Air Quality Modelling Evaluation International Initiative (AQMEII)
(Alapaty et al., 2012; Im et al., 2014a, b).
Biogenic emissions are calculated online using the Model of Emissions of
Gases and Aerosols from Nature (MEGAN) (Guenther et al., 2006). Sea salt
flux is calculated online, while dust source is not included.
Results
Preliminary analysis of the observations
In Fig. 1 time series of ground-based meteorological and physicochemical
observations performed in Milan in the 5–20 July 2007 period are shown. The
large-scale circulation is illustrated in Fig. S1 in the Supplement, while the evolution of
cloud cover over northern Italy is illustrated by MODIS-Aqua true-colour
images in Fig. S2. The period starts with a low-pressure system over
Germany, rapidly moving eastward and allowing a pressure increase over
northern Italy from 5 to 8 July, associated with fair weather and sparse
cloud cover. From 9 to 11 July, a North Atlantic low-pressure system induces a
significant increase of cloud cover over Milan with light rain on 10 July.
From 12 July, a wide anticyclonic system forms over the western
Mediterranean, leading to clear-sky and stable conditions until 20 July and
later. Maximum daily temperature is around 30 ∘C before the
Atlantic perturbation; then it increases steadily (from 25 to
35 ∘C) at a rate of ∼ 2∘ day-1 from 11 to
15 July as the high-pressure system settles. Humidity is high at night (above
70 %) on the days following the low-pressure passage; then the atmosphere
gradually dries out under the anticyclone.
During the period preceding the Atlantic perturbation (5–8 July 2007), wind
is prevalently a westerly wind during the daytime, forced by the large-scale circulation and
with a wind speed of around 2.5–3 m s-1. Wind is slowed down to less than 1 m s-1 at
night because the downward transport of momentum toward the surface is
inhibited by the nighttime vertical stratification (Stull, 1988; Whiteman,
1990). Wind speed increases up to 5 m s-1 at the passage of the low-pressure
system (9–11 July 2007), and it stays above 2 m s-1 also at nighttime. From
11 July, when the high-pressure over the Mediterranean begins to settle, the wind
field adjusts to a typical mountain-valley breeze regime (Whiteman, 1990).
Starting from midnight, the slow (∼ 1 m s-1) northerly flow
gradually accelerates and rotates clockwise, reaching peak speeds of
∼ 3 m s-1 in the afternoon in a south-westerly direction, then
gradually slows down and return to a northerly direction. This wind pattern favours
conditions of stagnation and recirculation of air within the valley,
allowing the build-up of pollutants from one day to the next. Figure S3 shows
the simple stagnation and recirculation indices proposed by Allwine and
Whiteman (1994) and confirms that the only ventilated period is that of the
Atlantic perturbation.
Ground-based observations in Milan during 5–20 July 2007. Panel (a) shows hourly measurements of temperature, relative humidity, pressure
and precipitation. Pressure is pressure minus 1000, and precipitation is
multiplied by 10 in order to fit the same y axis. (b) Hourly wind speed and
wind direction (0∘ from the north, 90∘ from the east),
the latter divided by 100 to fit the same y axis. (c) Hourly ozone, nitrogen
dioxide and nitrogen oxide. (d) Particulate-matter mass. Hourly observations
of PM10, PM2.5, and PM1. (e) Particulate-matter
composition. Daily data for sulfate, nitrate, ammonium, elemental carbon and organic
matter collected during QUITSAT campaign. Night-time (21:00 to 08:00 LST) and daytime (08:00 to 21:00 LST) samples of sulfate, nitrate, ammonium,
water-insoluble carbonaceous matter (WINCM) and water-soluble organic matter
(WSOM) collected during AeroClouds campaign (14–17 July). (f) Particulate-matter number size distribution; optical particle counter (OPC) hourly
average measurements; y axis denotes the size bin. (g) Particulate-matter
vertical profile; lidar range-corrected signal; y axis denotes the height
above ground level.
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The passage of the Atlantic low-pressure system on 9–10 July marks a sort of
“restart” for the atmospheric composition at ground level. Indeed,
relatively longer-lived (few days) chemical species, such as ozone and PM,
first accumulate during the days preceding the perturbation; they are then suppressed in perturbed weather and finally re-accumulate afterwards
(Fig. 1c, d). Outside the perturbed period, ozone
and nitrogen oxides (NOx) follow a daily cycle typical of that observed
in many urban areas (Mavroidis and Ilia, 2012, and references therein). The
primary pollutant nitric oxide (NO) displays a sharp peak during morning
rush hours (between 06:00 and 09:00 local solar time), then gradually decreases
during the day. It displays a secondary small peak during evening rush hours
(20:00–22:00 LST), then remains at low values until the following morning.
Nitrogen dioxide (NO2) mainly originates from the oxidation of NO by
ozone and peroxy radicals (Jenkin and Clemitshaw, 2000) and displays peaks
delayed by ∼ 1 h with respect to those of NO. It shows a
plateau between the morning and the evening peak because concentrations are
sustained during the daytime by photochemistry. The photolysis of NO2 is the main
tropospheric source of atomic oxygen (O) that readily reacts with molecular
oxygen (O2) to produce ozone. Indeed, during daylight hours, NO,
NO2 and O3 equilibrate on the so-called “photostationary
equilibrium” on timescales of minutes (Clapp and Jenkin, 2001).
Ozone is depleted during the morning rush hours by reaction with NO; then it
is photochemically formed during the day, peaks during the late afternoon
(14:00–16:00 LST) and thereafter gradually decreases to lower nighttime levels.
In fair weather, the daily cycle of ozone and NOx is regulated by the
solar radiation, the dilution of fresh emissions from the surface in the
growing daytime PBL, the vertical mixing with air entrained from the
residual layer and the free troposphere above the PBL and the dry
deposition at the surface. Past studies pointed out that the entrainment
from ozone-rich residual layer may be as important as the photochemical
production in the PBL during pollution events even in urban atmospheres
(e.g. Zhang and Rao, 1999). In the present case, the build-up of ozone in
the days following the perturbation is evident, but it is difficult to
discern the relative role played by the local photochemical production and
by the vertical mixing regarding the ozone trend observed at the surface.
Accumulation and cleansing of the atmosphere near the surface is even more
evident from aerosol time series (Fig. 1d–g).
PM2.5 and PM1 follow a similar trend, while PM10 often shows a
different behaviour, pointing to the presence of additional sources for the
coarse fraction, most probably the erosion and resuspension of soil material
by vehicles and wind. The aerosol mass is shown to build up before the
Atlantic perturbation (PM10 around 20–30 µg m-3) and to
abruptly decrease (PM10 below 10 µg m-3) during the
low-pressure system passage (probably because of a combination of enhanced
ventilation, wet deposition processes and soil erosion inhibited by
increased soil moisture). Afterwards, PM concentration keeps increasing after
the low-pressure passage (maximum PM10 values of more than 60 µg m-3 reached on 18–19 July). The daily cycle of the fine aerosol mass
(PM2.5 and PM1) displays similarities with that of NO, in
particular a similar morning peak, indicating the important role played by
primary emissions. This is confirmed by the analysis of aerosol speciation
(Fig. 1e), which shows high values of elemental
carbon (EC, 2–4 µg m-3) and insoluble carbonaceous matter (WINCM,
2–10 µg m-3). The latter makes, on average, 40–50 % of the
PM1 mass (Carbone et al., 2010). Major secondary species are inorganic
ions (sulfate, nitrate and ammonium) and part of the organic matter, which
may be associated with its water-soluble fraction (WSOM, Carbone et al.,
2010). Similarly to ozone, secondary aerosol accumulates during the days
preceding and following the perturbation.
The cleansing of the atmosphere after the perturbation and subsequent recovery of
the aerosol load is also clearly visible in the number concentration
time series. At the passage of the perturbation, the aerosol number rapidly
decreases by more than 1 order of magnitude at all observed size ranges,
then returns to the preperturbation levels on a timescale of about 2
days. We note, however, differences in the aerosol regime before and after
the perturbation. Before the cleansing, the aerosol size distribution is
locked into a fixed shape, with no or little daily variability. Conversely, in
the stable conditions of 12–19 July, it displays a clear daily cycle with a
growth towards larger sizes in daytime and a return to narrower
distributions at nighttime.
As mentioned in Sect. 3.3, lidar observations are
only available in the days following the perturbation and give useful
indications of the aerosol vertically resolved infra- and inter-diurnal
variability (e.g. Angelini et al., 2009). During the morning hours, a layer
of aerosol is formed under the growing boundary layer. There, fresh
emissions from the surface are diluted and mixed vertically in the PBL.
Throughout the period, but especially on some days, such as 13 and 15 July in the morning, an enhanced layer of aerosol is visible in the upper
levels near the top of the PBL. Aerosol is subsequently partly removed in
the second half of the day by the mountain breeze, while a residual layer
with relatively high aerosol content may survive above the nocturnal PBL
(e.g. on 13, 15, and 16 July). This layer may potentially be entrained the
following morning into the PBL and contribute to the surface aerosol budget.
On the last days displayed in Fig. 1, a further aerosol layer between 2
and 3 km appears in the lidar signal. As indicated by increased coarse-fraction AOD at the Modena AERONET (AErosol RObotic NETwork) station (Fig. S4) and model
back trajectories (Fig. S5), this is a Saharan dust incursion which is
probably entrained at ground level, as indicated by the enhancement of
PM10 levels on 18–19 July. Since Saharan dust intrusions are
not modelled here, these days are excluded from the analysis.
From the measurements reported here some questions emerged:
What is the composition of the aerosol layer formed during the day in the upper PBL?
How much of the aerosol burden measured on the ground is due to localized processes
and, conversely, how much is due to processes occurring in the upper PBL and to the subsequent
mixing in the lowermost levels? In other words, how important is the interplay between
surface and upper layers in shaping the aerosol mass we measure near the ground?
How much may the residual layer above the PBL contribute to the aerosol budget at ground level the next day?
We attempted to provide answers to these questions using simulations with
the WRF/Chem model and relevant comparisons with the observational data set.
Comparison of observed and simulated hourly meteorological
variables at ground level in Milan on 5–17 July 2007. Simulations are
carried out with WRF/Chem model and results are shown for the nested domain
over northern Italy at a 10 km horizontal resolution. Statistical indices
shown in the inset are defined in Appendix A.
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Model verification against available observations
Before drawing conclusions on the scientific questions outlined at the end of
the previous section, we verified our model simulations against the data set
of observations depicted in Fig. 1 and only displayed results for the nested
domain over northern Italy, using statistical indices defined in Appendix A
as a guidance to quantify model biases.
In Fig. 2 we compared, observed and simulated hourly meteorological variables at
ground level in Milan on 5–17 July 2007. Simulations are carried out with
the WRF/Chem model, and results are shown for the nested domain over northern
Italy at a 10 km horizontal resolution. Statistical indices shown in the inset are
defined in Appendix A. We compared observed and simulated
meteorological variables at ground level in Milan for the period 5–17 July
2007. The temperature is underestimated by about 2.5 ∘C, which is
probably due to poorly resolved dynamics and heat fluxes in the urban
boundary layer. The overestimation of relative humidity by about 10 % is
mostly attributable to the underestimation of temperature. Wind speed at 10 m is overestimated by 0.8 m s-1 and has a relatively low correlation of 0.29
with observations, thus fitting the typical characteristics of current
mesoscale models (e.g. Misenis and Zhang, 2010). The simulated wind speed is
also more variable than that observed as indicated by the root mean square
error (RMSE) of 1.7 m s-1. The wind direction is generally captured well, in
particular the mountain-valley cycles after the passage of the perturbation
of 9 July.
Same as Fig. 2 but for hourly
gas-phase variables at ground level in Milan on 5–17 July 2007. Shown in the inset
are statistical indices defined in Appendix A.
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In Fig. 3 we show a comparison of gas-phase
observations and simulations near the ground. The daily cycle of NO is
reproduced well (r=0.52); the timings of the morning peak and the
subsequent decrease are captured by the model. The magnitude of the morning
peak does not show a tendency to underestimate nor to overestimate, while NO
values for the rest of the day are underestimated, resulting in a bias of
-4.1 ppb (-60 %). The model is also able to capture the basic features of
the NO2 daily cycle, i.e. the morning and evening peaks and the minimum
at night. However, values are generally underestimated (bias of -8.3 ppb or
-34 %), and the trend on weekly timescale displays much less variability
than that observed. Ozone displays a very low systematic bias (-2.3 ppb), but
less variability than observations (RMSE of 11.3 ppb), and a correlation of
0.65. The timing of the daily cycle is captured well, with a maximum in the
afternoon, a secondary peak around midnight and a minimum during the
morning rush hour.
Same as Fig. 2 but for hourly
particulate matter at ground level in Milan on 5–17 July 2007. Shown in the inset
are statistical indices defined in Appendix A.
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In Fig. 4 we compare PM10 and PM2.5
simulated mass to hourly observations at ground level. The PM10 trend is
qualitatively captured by the model, displaying the sharp decrease at the
passage of the perturbation on 10 July and the subsequent gradual
accumulation in the following days. This provides confidence in the simulated
removal and production terms, and the resulting negative bias is low (-4 µg m-3 or -10 %). The model also captures some of the
characteristics of the daily cycle (r=0.57); however, the observed signal
is quite irregular, and the model does not reproduce all the variability.
The negative bias of PM10 could be partly explained by the missing
source for soil dust erosion and resuspension caused by traffic in the
model. For PM2.5 the general features of the comparison are similar to
PM10, but the model has a positive bias (+4 µg m-3 or
+70 %), mostly attributable to a few spurious peaks in the simulation. The
overestimation of PM2.5 partly compensates for and masks the
underestimation of coarse particles (PM2.5-10). The comparison of the
simulated number size distribution against that observed with the OPC (not
shown) suggests that the high bias of PM2.5 is attributable to aerosol
in the size range of 0.5–1 µm.
In Fig. 5 we show the comparison of simulated
PM2.5 composition with daily and twice-daily samplings near the ground. In
the period preceding the perturbation (5–9 July), the model
underestimates the magnitude of the observed peak of sulfate and ammonium,
but it reproduces the subsequent restart and recovery well. Observed nitrate
displays little variability, with a slight decrease at the passage of the
perturbation and almost constant levels during the rest of the period.
Modelled nitrate has a much more variable behaviour, which seems to be
characterized by sudden and irregular pulses. Indeed, the observations recorded twice a
day suggest that the daily average observation masks much of the
underlying variability associated with nitrate. Recently reported hourly
measurements of PM composition in the Po Valley did in fact confirm the same
“pulsed” behaviour of nitrate near the ground, with values near 0
during daytime, and irregular peaks at nighttime (Decesari et al., 2014).
This highlights the inherent difficulties in simulating the nitrate
concentrations at a sub-daily frequency. Elemental carbon, being primary and
almost hydrophobic, is largely unaffected by the perturbation. This feature
is captured by the model, but EC values are underestimated by a factor of
2, probably due to underestimated emissions. Interestingly, the observations of WINCM (EC plus primary insoluble organic material) recorded twice a day display a
large diurnal cycle (maximum at night and minimum during the day) which is
not captured by the model. The organic-carbon trend and magnitude are reproduced
quite well, with the exception of a large spurious peak on 8–9 July not seen
in the observations. The peak is associated with secondary organic aerosol
(not shown). The twice-daily observations of soluble organic material (WSOM) do
not show the strong daily cycle of primary carbonaceous matter and confirm
a tendency of the model to overestimate the SOA fraction.
Same as Fig. 2, for daily (solid lines)
and twice-daily (dashed lines) particulate-matter composition at ground level
in Milan on 5–17 July 2007. Twice-daily observations (dashed lines) are
available only from 14 to 17 July. In panel (d), WINCM is the water-insoluble carbon mass (EC + mostly primary OC); in panel (e) WSOM is
water-soluble organic mass (mostly secondary organic aerosol; Carbone et al.,
2010).
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In Fig. 6 we qualitatively compare the lidar profiles with the simulated PM2.5 profiles. A quantitative comparison
would require the calculation of optical properties of simulated PM2.5
and subsequent solution of the lidar equation (Hodzic et al., 2004).
However, a first-approximation lidar signal may be associated with PM2.5
mass. The model captures some of the basic features of the previously
described aerosol profile cycle observed in this period (Sect. 5.1). Every morning a plume of fresh aerosol
detaches from the ground and traces the growing boundary layer until
its maximum extension in the central part of the day. Then, in the
afternoon, the mountain-valley breeze cleans the lower PBL (note the abrupt
abatement of both the lidar and the model aerosol signals in the second part
of the day), often leaving an upper-air aerosol residual layer above. Model
simulations also reproduce such residual layers (note the afternoon increase
of PM2.5 values in the upper levels, particularly visible on
15–16 July). When such residual layers persist overnight, the lidar shows these
to entrain into the developing PBL the day after (note the merging of the
upper-level aerosol layers with the growing, aerosol-traced PBL in Fig. 6a, particularly evident in the morning of 14 and 15 July). There are also
hints of the same features in model simulations.
Qualitative comparison of (a) lidar range-corrected signal and
(b) simulated PM2.5 vertical profile over Milan on 12–17 July 2007. The
white dashed line in the bottom panel denotes the simulated PBL height.
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Insights into the budget of the aerosol vertical profile over Milan
The “chemical restart” caused by the passage of the perturbation on
9–10 July, and the following settling of an almost periodic circulation pattern,
naturally creates favourable conditions for a study of the processes
leading to aerosol production and accumulation in the area of Milan. Our
analysis shall now focus on the days following the perturbation (12–17 July).
Using model output, we firstly examined the composition of the aerosol
layers noted in the lidar profiles of Fig. 6. In Fig. 7, we show the composition of PM2.5
simulated over Milan. The model predicts a major role played by the primary
fraction (unspeciated anthropogenic, black carbon and primary organic
carbon), which is largely responsible for the two rush hours peaks (morning
and evening) and the bulk of aerosol mass in the PBL. Fresh emissions are
mostly concentrated near the ground and turbulent transport dilutes them in
the PBL during the day. A relatively small fraction (∼ 30 %)
of primary aerosol remains above the PBL overnight and contributes to the
upper aerosol layers seen by the lidar.
The sum of secondary species contributes 40–60 % of the aerosol mass in
the PBL but with remarkable differences in the vertical distribution of
single components. Sulfate and SOA start to form
and dilute under the PBL a few hours after sunrise, contributing in a
relatively homogeneous way to the aerosol column in the PBL. Anthropogenic
SOA (ASOA) contributes more than biogenic SOA (BSOA) to the SOA budget. The
concentration of these secondary species is similar also above the PBL,
thus significantly contributing to the upper aerosol layers. ASOA are
slightly more persistent than BSOA and sulfate in the free troposphere.
Simulated composition of PM2.5 profile shown in
Fig. 6. ASOA and BSOA in panels (e, f) are
anthropogenic and biogenic secondary organic aerosol, respectively.
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Nitrate displays a profile substantially different compared to other
species, with enhanced concentrations in the upper part of the PBL formed
during the central part of the day. These concentrations may largely exceed
those found near the ground (i.e. on 13, 16, 17 July). Moreover, nitrate is
predicted to be the major secondary species contributing to the formation of
the residual aerosol layers above the PBL. Enhanced upper-level
concentrations of nitrate into PM1 were also reported at Monte Cimone
(a mountain peak of 2160 m at the southern border of the Po Valley) by
Carbone et al. (2010, 2014).
In Fig. 8 we show the maps of simulated sulfate
and nitrate over the Po Valley on 13 July 2007 at 16:00 LST at the surface and
at 750 m height. It can be seen that the main features of the composition of
the aerosol profile outlined above are not peculiar to the Milan area but
appear to be representative of the larger area of the Po Valley.
In order to better understand the processes underlying the predicted
characteristics of the aerosol over Milan, we analysed the terms of the
continuity equation for chemical species. Budget terms considered are
emission, horizontal and vertical advection, chemistry, turbulent mixing, and
dry deposition. Terms related to cloud processes (convection, aqueous
chemistry, wet deposition) make a very small contribution in the dry period
under investigation and are not shown to improve the figure's clarity. In
Fig. 9 we show the vertical profile of the budget
terms for sulfate and nitrate at 16:00 LST on 13 July over Milan. For
sulfate, the dominant terms are those related to advection, indicating the
presence of spatially distributed sources and a relatively long lifetime,
making it a regional-scale pollutant. Locally, sulfate is both directly
emitted and produced by secondary pathways throughout the PBL. Turbulent
mixing distributes it vertically in the PBL and dry deposition removes it
from the atmosphere near the ground, determining an almost homogeneous
sulfate profile in the PBL. Conversely, nitrate has relatively low
contribution from advection, while the largest terms are chemistry and
vertical mixing. In the simulation, nitrate is produced only in the upper
half of the PBL and destroyed in the lower half. The vertical transition
between the nitrate destruction and production zone is quite sharp.
Turbulent mixing is nearly in equilibrium with chemical production,
indicating that the model simulates a very rapid adjustment to the thermodynamic
equilibrium for the sulfate–nitrate–ammonium system. This results in nitrate
concentrations higher in the upper part of the PBL compared to the lower
part.
Description of sensitivity tests with WRF/Chem model.
Label
Description
CTRL
Reference run, see Table 1
AERO
Aerosol chemical processes switched off
LPBL
Gas and aerosol chemical processes switched
off in the lower half of the PBL
UPBL
Gas and aerosol chemical processes switched
off in the upper half of the PBL
APBL
Gas and aerosol chemical processes switched
off above the PBL
Similar to nitrate, SOA also displays an enhanced net chemical production in
the upper part of the PBL and destruction in the lower part
(Fig. 10), but since the chemical and vertical
mixing terms are of the same order of the advection terms the resulting
vertical profile is almost constant with height, similar to that of sulfate.
Further insights into the simulated sharp transition to an environment
favourable to nitrate formation in the upper part of the PBL are investigated by means of several model sensitivity tests as outlined in
Table 2. In Fig. 10 we first look at the gas-phase precursor of nitrate, nitric acid (HNO3).
The left panel shows the vertical profile of the budget terms for HNO3
at the same point as in Fig. 9. The chemical and
vertical mixing terms mirror those of particulate nitrate, resulting in a
decreasing concentration profile with height. The right panel of Fig. 9
shows the budget profile from a sensitivity simulation where aerosol
chemistry is switched off (AERO, see Table 2). The
chemistry and vertical mixing terms are greatly reduced and are the same
order of magnitude as advective terms, indicating that the sharp gradients
in net chemical production of HNO3 (and nitrate) are dominated by
aerosol processes and not by gas-phase processes.
Maps of the concentration of PM2.5 sulfate (a, b) and nitrate
(c, d) components simulated at 16:00 LST on 13 July 2007 over the Po Valley. Panels
(a–c) show ground-level concentrations (the first model level is about 24 m
thick); panels (b–d) show concentrations at approximately 750 m.
![]()
Simulated vertical profile of concentration (µg m-3)
and continuity equation terms (µg m-3 h-1) for particulate sulfate
(left) and nitrate (right) at 16:00 LST on 13 July 2007 over Milan. Budget
terms are horizontal advection (ADVH), vertical advection (ADVZ), chemistry
(CHEM), turbulent mixing and dry deposition (VMIX) and emission (EMIT).
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In Fig. 12 we provide further elements to
evaluate the simulated particulate-nitrate thermodynamics. Ambient relative
humidity increases with height in the PBL, from a minimum of ∼ 50 % near the ground to a maximum of ∼ 80 % at an altitude
of 1000 m (∼ 400 m below the PBL top). The nitrate chemical
production term shown in Fig. 9 is reported for
ease of comparison and displays the peak already noted between 500 and 1000 m. The sulfate ratio (ratio of total ammonia and sulfate) is well above the
threshold of 2 along the profiles (not shown) and is thus suitable for particulate-nitrate formation (Seinfeld and Pandis, 2006). The profile of equilibrium
constants for both the aqueous and solid nitrate increases with height, in
response to a decreasing temperature profile (not shown), indicating that the conversion of nitric acid to particulate is favoured with increasing height.
However, no sharp transitions, correlated to the nitrate net chemical term,
can be noticed in the profiles of these equilibrium constants.
Same as Fig. 9 but for secondary
organic aerosol (SOA).
![]()
The profile of ammonium nitrate's deliquescence relative humidity (DRH)
helps disclose the possible reason for such a transition. At ground level,
ambient RH is well below the ammonium nitrate DRH, indicating an environment
thermodynamically favourable only to the solid form of nitrate. However,
since the RH gradient with height is steeper than that of DRH, the two
curves intersect at an altitude of ∼ 500 m, and then again at
∼ 1300 m, because of the RH decrease near the PBL top. Ambient
RH is thus higher than ammonium nitrate DRH in the same altitude range
(∼ 500–1000 m) in which the nitrate net chemical production
peaks. This indicates that, over Milan and in the period under
consideration, the nitrate chemical production is dominated by aqueous
conversion of nitric acid to nitrate ion, a condition that is reached only in
the upper part of the PBL, where RH levels are high enough to sustain the
formation of an aqueous solution containing nitrate. Although the real
multicomponent DRH point will differ from that of pure nitrate, it is known
that the DRH of mixtures is always lower than that of pure salts (Seinfeld
and Pandis, 2006). The thickness of the layer favourable to aqueous-nitrate
formation deducible from Fig. 12 may thus be
regarded as a conservative lower estimate. During daytime, the nitrate
formed in the upper boundary layer re-evaporates back to the gas phase when
brought to the ground by vertical motions, and this is the origin of the
inhomogeneous vertical profile of nitrate. For further discussion on how
much the upper aerosol layer contributes to ground PM, we point the reader to
the next paragraph.
The budget analysis we have presented so far reveals a complex interplay
between chemical processes and vertical mixing taking place at different
altitude ranges. In order to better quantify the impact of chemical
production at upper layers on particulate matter at ground level, we perform
three tests, alternatively switching the chemical process on and off at selected
altitude ranges (namely within the lower half of the PBL; the upper half of
the PBL and above the PBL, see Table 2). Results
are shown in Fig. 13 for PM2.5 and its
components sulfate, nitrate and SOA. In the figure, the contribution to the
ground PM2.5 of the chemical processes in the different altitude ranges
is positive/negative when the associated sensitivity line is below/above the
CTRL. For PM2.5, we have found that chemical process in all regions
positively contribute to the ground-level concentration. During the first
days after the passage of the perturbation, the shutdown of secondary
chemical formation makes very little difference, indicating a dominance of
primary emissions. As time goes by, secondary processes gain importance, but
primary fraction remains the main driver of PM2.5 concentration even
after 1 week. Interestingly, the magnitude of the relative contribution of
the different layers (lower PBL, upper PBL, above PBL) to ground-level
PM2.5 is comparable, and of the order of up to 7–8 µg m-3
each, on an hourly basis. Exceptions are noted on the afternoons of 13 and 16 July,
when a negative contribution from secondary processes in the lower PBL is
simulated (note the blue dashed line above the red line). These peaks are
associated with the nitrate sink in the lower PBL (see panel c). Sulfate has
an identical contribution from lower- and upper-PBL chemical production and
may also have a very important contribution from the region above the PBL,
even higher than processes in the PBL (e.g. on 17 July). SOA budget is
similar to that of sulfate but with an enhanced contribution from PBL
processes vs. those above it. As expected, nitrate displays distinctive
features. Chemical production in the lower PBL contributes positively to
ground-level concentration in the first part of the day and, then, in the
afternoon results in a net destruction. On the other hand, processes in the
upper PBL and above the PBL always contribute positively to ground-level
nitrate concentrations.
Same as Fig. 9 but for nitric acid
(HNO3) and units in ppb: on the left the reference simulation (CTRL);
on the right a sensitivity simulation with aerosol chemistry switched off
(AERO). Please note the different abscissa ranges.
![]()
Simulated vertical profile of relative humidity (blue) and
particulate-nitrate net chemical production term (red) at 16:00 LST
on 13 July 2007 over Milan. Also shown are the vertical profiles of equilibrium
constants of aqueous-phase nitrate (green) and solid ammonium
nitrate (cyan), and ammonium nitrate deliquescence relative
humidity (magenta). The height of PBL is denoted by the horizontal
black dashed line. Please note that equilibrium constants are scaled by the
constant factors shown in the inset legend to fit on the same abscissa range.
![]()
Sensitivity tests on chemical production in different vertical
layers (see Table 2 for explanation of labels) at
ground level over Milan on 10–17 July 2007. Hourly observations (black
line) are only available for PM2.5 (top panel).
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A further quantitative assessment of the impact of upper aerosol layers on
ground concentrations can be estimated combining information in
Figs. 14 and 6. In Fig. 14 we show the time series of the difference
in the simulated PM2.5 profile between APBL and CTRL runs. When a
residual layer is visible, we may roughly estimate from the figure the
related change near the surface on the subsequent morning. We focus our
attention on 17 July, when the presence of a residual layer is clearly
visible. The concentration change (APBL–CTRL) in the residual layer is
about 8–10 µg m-3. The following morning the concentration change
near the surface is 4–5 µg m-3; thus, we may estimate a 50 %
sensitivity of ground PM2.5 to a change in the residual layer. In
Fig. 6b, we see that on 17 July the PM2.5 concentration in the residual
layer is 20–24 µg m-3; thus, the expected impact on hourly
concentrations near the ground is of the order of 10–12 µg m-3,
or about 40 % of the PM2.5 concentration near the ground. This is the
extreme case in the short period analysed here, but it gives a sense of the
potential importance that entrainment of aerosol layers aloft may
occasionally have for PM2.5 observed near the surface.
Conclusions
The object of this study is the analysis of the role played by the
combination of chemical and dynamical processes occurring throughout and
above the PBL in determining the aerosol concentration and composition we
observe near the ground. We analysed the observations of the atmospheric
composition carried out over a period of 2 weeks in Milan (northern
Italy) in July 2007. The period was characterized by the passage of a
perturbation that favoured cleansing of the air in the Po Valley, providing a natural
chemical restart. After the perturbation, stable high-pressure
conditions determined the establishment of a nearly repetitive
meteorological pattern, driven by a mountain-valley breeze system that
allowed for a gradual re-accumulation of pollutants.
Lidar observations after the chemical restart revealed intriguing
features of the aerosol vertical profile over Milan. Every morning, a plume
of fresh emissions from the ground is dispersed in the growing convective
boundary layer. In the afternoon, an enhanced aerosol layer appears in the
upper part of the PBL, while in the evening the bottom part of the PBL is
cleansed by the mountain breeze. A residual aerosol layer may form and
survive the night above the PBL and may be entrained down to the
ground again the day after. We investigated how this “vertical” sequence of
processes affect the aerosol concentrations observed at ground level.
Difference in the simulated PM2.5 profile over Milan
between APBL and CTRL runs (see Table 2). In
combination with Fig. 6b, this is useful for estimating the impact of the aerosol residual layer on ground concentrations.
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With the help of simulations from the state-of-art online
meteorology–chemistry model WRF/Chem we attempted to answer three main
questions resulting from the observations. The questions and the relevant
answers are summarized below:
What is the composition of the aerosol layer formed during the day in the upper PBL?
Model simulations suggest that 40–60 % of the fine aerosol in Milan's
summer PBL is of primary origin, consistent with previous studies (e.g.
Carbone et al., 2010). This primary fraction displays a decreasing
concentration profile with height in the PBL, since the sources are
concentrated near the ground and species are vertically mixed by turbulence.
Sulfate and secondary organic aerosol are produced throughout the PBL and
have a nearly homogeneous profile there. Nitrate and ammonium have a
distinct profile, with enhanced values in the upper PBL, where
concentrations may be much higher than near the ground. The low
temperature and the relative humidity above the ammonium nitrate
deliquescence point in the upper PBL are thought to determine this peculiar
profile. Nitrate is the major component of the upper-PBL aerosol layer,
contributing up to 30 % of the aerosol mass.
How much of the aerosol burden measured on the ground is due to localized
processes and, conversely, how much is due to processes occurring in the upper PBL
and to the subsequent mixing in the lowermost levels? In other words, how important
is the interplay between surface and upper layers in shaping the aerosol mass we measure near the ground?
For PM2.5 mass, our calculations indicate that, in the upper PBL,
secondary aerosols are formed and then mixed in the PBL by turbulence. The
importance of the secondary fraction increases with the aging of air masses,
as shown by the progression of days from the chemical restart. One week
after the perturbation, secondary PM2.5 produced in the upper PBL may
contribute up to 7–8 µg m-3 (or 25 %) to ground-level hourly
concentrations. Sulfate and SOA production is equally shared by the bottom and
upper PBL, while nitrate is mostly produced in the upper PBL, with the bottom PBL
acting as a sink during the afternoon.
How much may the residual layer above the PBL contribute to the aerosol budget at ground level the next day?
It is calculated that the chemical production above the PBL significantly
impacts aerosol levels near the ground, sometimes overtaking the
contribution from the production term in the PBL (especially for sulfate and
SOA). We estimate that the residual layer above the PBL, which is formed by
both primary and secondary species, may occasionally contribute up to 10–12 µg m-3 (or 40 %) to ground-level PM2.5 hourly
concentrations during the following morning.
The peculiar features of the vertical profile of aerosol nitrate have
already emerged in past studies. Neuman et al. (2003) reported aircraft
observations of increasing nitrate profiles with height, attributing them to
the favourable lower temperature in the upper layers, compared to the bottom
PBL, due to the conversion of nitric acid to aerosol nitrate. We confirm
their conclusion and add that a key role in shaping the aerosol nitrate
production profile is played by the relative humidity. In particular,
nitrate production may be enhanced when RH is above the ammonium nitrate
deliquescence point.
This study has focused on some less obvious and recognized aspects of
the aerosol vertical profile budget. Since it is based on the analysis of a
short period of high-pressure conditions in summer over the area of Milan,
further analyses are recommended for winter periods and different
meteorological and geographical conditions. Moreover, it clearly underlines
the fact that the interplay between chemical and dynamical processes must be
considered when interpreting atmospheric chemistry observations near the
ground and that more observational constraints (e.g. profiles of the
aerosol composition in and above the PBL) would certainly be helpful to
achieve a better simulation of these processes.