The influence of surface-atmosphere exchange
processes on ozone levels
A. D’Allura1,3, R. De Maria2, M. Clemente2, F. Lollobrigida2,
S. Finardi3, C. Silibello3 & G. Brusasca3
1
Dipartimento di Scienze dell'Ambiente e del Territorio, Universita' di
Milano-Bicocca, Italy
2
ARPA Piemonte, Italy
3
ARIANET srl, Italy
Abstract
A comprehensive modelling system taking into account emission, transport,
dispersion, deposition and chemical transformation processes of gaseous
pollutants has been used to simulate an ozone episode over the Piemonte Region
in Northern Italy. A meteorological pre-processor (SURFPRO: SURrfaceatmosphere interFace PROcessor) has been developed and applied in order to
estimate the parameters used to define the status of the lower part of the
atmosphere commonly called the Planetary Boundary Layer (PBL). The vertical
exchanges between surface and lower atmospheric layers are defined estimating
momentum, heat and moisture turbulent fluxes and PBL depth. The capability of
the atmosphere to vertically redistribute the pollutants is represented by vertical
diffusivities. The influence of different land use areas on diffusivities and other
turbulent scaling parameters is shown confirming the importance of adequate
descriptions of the surface cover. The effects of terrain slopes and shadow
projection on incoming solar radiation and consequently on heat exchanges are
also shown. Finally the good agreement between observed and predicted ozone
concentration by the Eulerian photochemical model FARM (Flexible Air quality
Regional Model) confirms the capability of the modelling system and the
meteorological pre-processor to correctly describe chemical and physical
processes that lead to the formation and the accumulation of ozone in Northern
Italy.
Keywords: Land-use, atmospheric turbulence, vertical diffusivity, photochemical
models.
Heat Transfer VIII, B. Sundé n, C. A. Brebbia & A. Mendes (Editors)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-705-1
266 Heat Transfer VIII
1
Introduction
The Earth-atmosphere system receives energy input from solar radiation. The
incoming radiant energy is channelled and stored into different sub-systems,
converted exchanged and transported in different forms and modes. The
atmospheric circulation is a way to transport energy between sites affected by
different energy inputs (e.g. from equatorial regions to polar ones at global scale
and between land and sea areas at local scale). The lower layers of the
atmosphere are mainly influenced by the energy exchanges with the earth
surface, which receives solar radiation and transfer energy back to the
atmosphere both through radiation and heat fluxes. This energy exchanges are
the main causes of local scale circulation (e.g. sea and mountain breezes) and of
the vertical motions of the atmosphere. The direct influence of the surface is
limited to a shallow layer known as the planetary boundary layer (PBL). This
layer is characterized by turbulent motion generated by the bubbling-up of air
parcels from the heated surface and by the surface frictional drag. The PBL
turbulent nature determines the atmospheric power to mix and dilute substances
emitted by the earth surface (Oke [1]).
Air pollutant concentrations, their chemical composition, and their time and
space variability are closely dependent on atmospheric boundary layer motion
(wind and turbulence)and on underlying emissions. In this context a main topic
studying air pollution is the correct understanding and parameterisation of
boundary layer physic. The boundary layer flow is described by a set of nonlinear differential equations that can be solved numerically on a 3D mesh. These
equations have to be supplemented by parameterisations capable to describe the
sub-grid effects related to the energy and momentum exchanges between earth
and atmosphere layers, e.g. according to Monin-Obukhov similarity theory (Stull
[2]).
Prognostic and diagnostic meteorological code have been developed during
the last part of the last century to properly model micrometeorology and local
scale atmospheric dynamics (Fisher et al. [3]). Meteorological models provide
mean and turbulent variables fields, describing the dynamic and thermodynamic
status of the atmosphere, that are used as input information by models describing
chemical formation and dispersion of ozone and other pollutants (Seinfield [4]).
It is well known that central and southern European urban areas are exposed
to high ozone concentrations during summertime. The last European directive
2002/3/EC sets out requirements to assess short term episodes taking into
account local circumstances, and draw up action plans to avoid health risk for
population and vegetation. Nevertheless the approach based only on analysis of
information provided by monitoring networks is limited by the spatial
representativity of measurements and often insufficient to describe the air quality
status on regional basis. This is especially true when complex topography and
atmospheric circulation patterns come in to play, as it happens in most of the
Italian territory. Today computational resources, improvements in input data
availability (meteorological, air quality, emissions), scientific knowledge and a
adequate experience on modelling simulations of photochemical pollution
Heat Transfer VIII, B. Sundé n, C. A. Brebbia & A. Mendes (Editors)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-705-1
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267
episodes (Silibello et al. [5]) permit one to well implement modelling integrated
use of monitoring activities, emission inventories and atmospheric dispersion
modeling as the main approach for air quality assessments.
In this paper an integrated modelling system was applied to simulate a typical
summer ozone episode over Piemonte Region in Northern Italy. A previous
study was conducted in the same area but over a smaller urban scale domain
during winter time (DeMaria et al. [6]). The model simulations showed that the
right description of PBL morning growing trend strongly influenced the results.
The studied area characteristics and the modelling system adopted are described
in section 2, PBL behaviour and final results are disclosed in section 3.
2
Methodology
The simulated episode lasted three days: July 19th – 21st 1999 (Monday to
Wednesday). This period represents a typical summer ozone episode
characterized by exceedances of the Italian limits for hourly average
concentrations of O3 (200 µg/m3). The investigated site is centred over Piemonte
Region (Fig.1). The domain includes the western part of the Po Valley, closed by
the Alpine range on the northern and western side and by the Apennine range on
the southern side. The computational mesh is made by 53x69 cells in horizontal
and 12 vertical levels, horizontal resolution is 4 Km. Owing to its peculiar
topographic features the area is often affected by low winds and air stagnation
events that can cause severe polluting episodes. Due to the limited effect of
synoptic forcing the atmospheric flow features are frequently determined by
meso and local scale phenomenon driven by the steep topographic slopes and
differences in surface cover.
2.1 Input data
Meteorological data available for pollution modelling purpose included wind and
temperature analysed fields from the European Centre for Medium Range
Weather Forecast (ECMWF), two radio-sounding and 69 surface stations.
CORINE land cover database was used to reconstruct surface features. Emission
data were provided by the INEMAR regional emission inventory, based on EU
CORINAIR 1999 methodology and referring to year 1997. The data from the air
quality monitoring network of Piedmont, Valle d’Aosta, Liguria, Lombardia
Regions, the city of Torino and the European Environment Information and
Observation Network (EIONET) have been elaborated to prepare the initial and
boundary conditions on the computational domain.
2.2 Integrated modelling
The integrated modelling system is based on the regional emission inventory, the
data from air quality and meteorological regional monitoring network, and a set
of air pollution transport and dispersion models.
Heat Transfer VIII, B. Sundé n, C. A. Brebbia & A. Mendes (Editors)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-705-1
268 Heat Transfer VIII
2.2.1 The MINERVE wind field model
The meteorological input data (vertical profiles and near-ground wind and
temperature measurements), were employed to reconstruct 3D wind and
temperature fields on an hourly basis, using MINERVE diagnostic model
(Desiato et al. [7]).
2.2.2 The SURFPRO meteorological pre-processor
Turbulence fields needed by the dispersion model (surface layer scaling
parameters boundary layer height and diffusivities) were estimated by
SURFPRO meteorological pre-processor (Finardi et al. [8]). At first the
modelling system analyses land-use data providing season based information on
surface physical features: albedo, soil moisture, surface roughness, leaf area
index. From topography, season, latitude, cloud cover and solar constant, the
incoming solar radiation Q’ (W m-2) is computed as gridded 2D field taking into
account the presence of water bodies, terrain slopes and solar shading effects.
The methods adopted by SURFPRO for the calculation of turbulence scale
parameters over land and water surface are largely adapted from those
implemented in U.S. EPA METPRO (Paine [9]). These methods consider the
following approaches:
Over land boundary layer:
Sensible heat flux: energy balance method (Holtslag and van Ulden [10]);
•
Surface friction velocity and Monin-Obukhov length:
•
o unstable conditions: Holtslag and van Ulden [10] iterative
scheme;
o stable conditions: Venkatram [11], Weil and Brower [12]
iterative schemes;
Mixing height:
•
o daytime convective: modified Carson [13] method;
o daytime mechanical (neutral): Venkatram [11];
o nighttime conditions: Venkatram [11];
Convective velocity scale: from mixing height and sensible heat flux;
•
PGT stability: Turner method.
•
Over water boundary layer:
Micrometeorological parameters:
•
o profile method, using air-sea temperature difference Hanna et
al. [14], and roughness length estimated from wind speed,
using Hosker [15];
Mixing height:
•
o neutral barotropic scaling relationship proposed by Blackadar
and Tennekes [16];
PGT stability: assigned from Monin-Obukhov length Hanna et al. [14].
•
Different formulations are available for the calculation of diffusivities, in detail
the Lange [17] scheme has been applied to estimate vertical diffusivities while
Smagorinsky [18] scheme has been applied to estimate horizontal ones.
Heat Transfer VIII, B. Sundé n, C. A. Brebbia & A. Mendes (Editors)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-705-1
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269
2.2.3 The FARM photochemical Eulerian model
FARM (Flexible Air quality Regional Model) is a three-dimensional Eulerian
model derived from STEM (Carmichael et al. [19]), that accounts for the
transport, chemical conversion and deposition of atmospheric pollutants. FARM
code produces a file containing 2D and 3D gas-phase time-dependent
concentrations of selected species and two files, containing respectively timedependent dry and wet deposition fluxes.
3
Model results
The heterogeneous landscape that characterises the computational domain
allowed to set in evidence the effect of some topographic and land use features
on the earth atmosphere interface fluxes and on the boundary layer turbulence.
5150
5100
5050
MILAN
5000
TURIN
4950
4900
350
400
450
500
Figure 1: Computational domain area. Depicted here are urban areas (in white),
the squared area zoomed in Fig.2 and the dashed line which the cross
sections in Fig.5 are referred to.
The influence of terrain slope and of topography shadow projection on total
radiation field is showed in Fig.2, referred to a mountain region located in the
north-east area of the domain (see Fig.1). The effects of different land use on the
sensible heat flux (H0) field is depicted in Fig. 3/a. The presence of rice-field
areas (see the circled region) decreases H0 values due to the energy absorbed by
water evaporation, that gives rise to large values of latent heat flux. Built-up and
Heat Transfer VIII, B. Sundé n, C. A. Brebbia & A. Mendes (Editors)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-705-1
270 Heat Transfer VIII
urban areas account for opposite behaviour, that can be clearly observed for
Turin and Milan urban areas. The time evolution of boundary layer turbulence is
showed by the convective scale velocity (w*) trends over different terrain types
(Fig. 4). w* is an index of free convection movements due to heated Earth’s
surface during daytime (the magnitude of the vertical velocity fluctuations in
thermals is of the same order of magnitude as w*). The water bodies, not affected
by diurnal heating cycle, give rise to very limited buoyancy effects depending on
water - surface layer air temperature gradient, that show a daily cycle nearly
opposite to that showed by land areas. On the other hand atmospheric turbulence
is generated by surface friction drag, that gives rise to wind shear. The MoninObukhov length is an atmospheric stability parameter and an index of turbulence
intensity. The Monin-Obukhov length can be seen as the height above the surface
at which buoyant (w*) and mechanical (u*) factors are balanced. Above scale
parameters are taken into account to compute the mixed layer height (fig.3/b).
During summer daytime, the PBL can grow even over 2000 m before collapsing
at sunset. During the whole length of the studied period persisting clear sky
whether caused the strong convective forcing whose effects are described by
model simulation results. Fig. 3/a-b show very similar patterns and point out the
clear predominance of convective conditions.
600
760
800
810
820
840
900
5150
5100
5050
350
400
Figure 2: Total radiation (W/m2)field referred to the north-east mountain area
in the domain, 20/07/1999 12:00 (squared area in Fig.1).
The values of vertical diffusivities depend on values of turbulence scaling
parameters, in particular depth of the layer affected by relevant mixing effects is
determined by the value of the mixing height (Fig.5/a). Diffusivities, together
with mean variables fields (wind, temperature and humidity) provide the
photochemical model the necessary information to estimate the own volume in
Heat Transfer VIII, B. Sundé n, C. A. Brebbia & A. Mendes (Editors)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-705-1
Heat Transfer VIII
271
which pollutants are dispersed and the efficiency of turbulent diffusion. The
effect of the reconstructed turbulence fields on the ozone concentrations is
illustrated, in Fig. 5/b by a vertical cross section of ozone concentrations, that
can be compared with the analogous cross section showed in Fig. 5/a for vertical
diffusivity.
a)
-20
0
20
40
60
100
80
120
140
b)
160
0
5150
5150
5100
5100
5050
MILAN
5000
750
4950
4900
4900
450
1000 1250 1500 1750 2000
5050
4950
400
500
MILAN
5000
TURIN
350
250
500
TURIN
350
400
450
500
Figure 3: a) H0 (W/m2)field, b) Mixed layer height (Hmix), 20/07/1999 12:00.
convective scale velocity
3
2.5
2
1.5
1
0.5
0
0.00
12.00
Urban
0.00
Lake
12.00
0.00
Rice-field
12.00
Bare-rock
Figure 4: 19-21/07/1999 hourly convective scale velocity w*(m/s) values for
four different land use sits.
Evaluating similarities and differences it has to be taken into account that
concentration fields are heavily influenced by emission distribution, wind field
and chemical reactions. Higher concentration areas correspond to the position of
Heat Transfer VIII, B. Sundé n, C. A. Brebbia & A. Mendes (Editors)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-705-1
272 Heat Transfer VIII
Milan urban area. Weak wind conditions limited transport effects and allowed to
highlight buoyancy dominated mixing effects. Model simulation results have
been verified comparing computed ozone concentrations with measurements at
different sites (see Fig. 6). Good agreement has been obtained in both urban and
rural stations. Comparing computed and measured values, it has to be taken into
account on one and the limited space representativity of measurements and, on
the other hand, the limited space resolution of modelled concentration fields,
representative of 4x4 km2 grid cells.
a)
Hmix
z+Hmix
b)
Figure 5: Section along x for constant y (5035 Km), see dashed line in Fig.1,
20/07/1999 12:00. a) Vertical diffusivities (m/s) where the thick line
is Hmix above the ground level and the squared one is Hmix a.s.l. b)
Ozone concentration (ppb).
Heat Transfer VIII, B. Sundé n, C. A. Brebbia & A. Mendes (Editors)
© 2004 WIT Press, www.witpress.com, ISBN 1-85312-705-1
Heat Transfer VIII
180
180
160
160
140
140
120
120
100
100
80
80
60
60
40
40
20
20
273
0
0
0.00
12.00
0.00
12.00
0.00
12.00
0.00
12.00
0.00
12.00
0.00
12.00
Figure 6: Comparison between computed (continuous line) and measured
(squares) O3 concentrations (ppb) at different sites .The left one
Torino-Lingotto urban background station; the right Cogne (Valle
D’Aosta) background station in mountain area.
4
Conclusions
An integrated modelling system has been tested in order to verify its capability to
compute boundary layer parameters in the Piemonte Region and neighbouring
areas, characterised by complex topography. The test concerned a period of three
days, characterized by strong convective conditions, during which a large
number of exceedances for O3 occurred. The components of the system that have
been tested are a meteorological pre-processor (SURFPRO), driven by a massconsistent wind model (MINERVE) and the land use data from CORINE Land
Cover database, linked with an Eulerian three-dimensional photochemical
dispersion model (FARM). Turbulent fields computed by SURFPRO highlight
the influence of land use coverage and topographic features on turbulent
parameters and local scale circulation within studied area. Moreover the analysis
of diffusivities and ozone concentration patterns confirm the role of vertical
diffusion on ozone distribution. Finally, the comparison between computed and
observed O3 concentrations at urban and rural sites evidence the capability of the
modelling system to reproduce measured levels and indirectly confirm the
correctness of assumptions used to reproduce surface-atmosphere exchange
processes.
Acknowledgements
The authors wish to acknowledge Drs. S. Bovo, R. Pelosini, M. Muraro and S.
Bande from Regional Weather Service for providing meteorological data and
technical support regarding the meteorological characterisation of the simulated
period, and Drs. G.Pession, G. Agnesod and M.Zublena from ARPA Valle
d’Aosta for providing data from the air quality monitoring network, and Drs. C.
Contardi, A. Benedetti, G. Arduino, F. Sordi. and G. Truffo from Environmental
Protection Regional Department for providing emission inventory.
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