Estimating the biogenic emissions of non

Science of the Total Environment 329 (2004) 241–259
Estimating the biogenic emissions of non-methane volatile organic
compounds from the North Western Mediterranean vegetation of
Catalonia, Spain
´ J.M. Baldasano*
R. Parra, S. Gasso,
`
Environmental Modelling Laboratory, Universitat Politecnica
de Catalunya, Diagonal 647, 08028, Barcelona, Spain
Received 12 September 2003; received in revised form 1 March 2004; accepted 7 March 2004
Abstract
An estimation of the magnitude of non-methane volatile organic compounds (NMVOCs) emitted by vegetation in
Catalonia (NE of the Iberian Peninsula, Spain), in addition to their superficial and temporal distribution, is presented
for policy and scientific (photochemical modelling) purposes. It was developed for the year 2000, for different time
resolutions (hourly, daily, monthly and annual) and using a high-resolution land-use map (1-km2 squared cells).
Several meteorological surface stations provided air temperature and solar radiation data. An adjusted mathematical
emission model taking account of Catalonia’s conditions was built into a geographic information system (GIS)
software. This estimation uses the latest information, mainly relating to: (1) emission factors; (2) better knowledge
of the composition of Catalonia’s forest cover; and (3) better knowledge of the particular emission behaviour of
some Mediterranean vegetal species. Results depict an annual cycle with increasing values in the March–April period
with the highest emissions in July–August, followed by a decrease in October–November. Annual biogenic NMVOCs
emissions reach 46.9 kt, with monoterpenes the most abundant species (24.7 kt), followed by other biogenic volatile
organic compounds (e.g. alcohols, aldehydes and acetone) (16.3 kt), and isoprene (5.9 kt). These compounds signify
52%, 35% and 13%, respectively, of total emission estimates. Peak hourly total emission for a winter day could be
less than 10% of the corresponding value for a summer day.
䊚 2004 Elsevier B.V. All rights reserved.
Keywords: Biogenic emissions; Isoprene; Monoterpenes; Geographic information system; Forest; Catalonia
1. Introduction
Vegetation is an important source of biogenic
non-methane
volatile
organic
compounds
(NMVOCs), and therefore biogenic emission
inventories have been developed for different
regions, most recently those by Velasco (2003) for
*Corresponding author. Fax: q34-93-334-02-55.
E-mail address: [email protected] (J.M. Baldasano).
the Valley of Mexico, Wang et al. (2003) for
Beijing (China) and the estimation of biogenic
emissions using satellite observations for the Eastern United States (Xu et al., 2002).
Interactions and reactions of these compounds
in the lower troposphere are important elements
for photochemical pollution episodes. Their impact
on ozone formation for different regions has been
discussed in several research studies, e.g. Tao et
0048-9697/04/$ - see front matter 䊚 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.scitotenv.2004.03.005
242
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
al. (2003) for the continental United States, Derognat et al. (2003) for the Paris area and Thunis
and Cuvilier (2000) for the Mediterranean area.
Catalonia (Spain), located in the NE of the
Iberian Peninsula, presented more than 1390 cases
of exceedances in the European legislative hourly
ozone concentration threshold for people information (180 mg my3) during the period 1991–2002
(ED, 2003). Almost 80% of these incidences
occurred in the June–August period. Akin to other
Mediterranean areas, this region has a complex
vegetal biodiversity and receives high fluxes of
solar radiation in the summertime. Under these
conditions, nitrogen oxides and NMVOCs emissions promote ozone formation and, jointly with
the complex wind configuration of this zone (Barros et al., 2003; Jorba et al., 2003), high tropospheric ozone concentrations could be reached
(Gangoiti et al., 2001).
Mathematical modelling can be used to explain
this sort of pollution (Seinfeld, 1989; Rusell and
Dennis, 2000). When applying a chemical transport model (CTM), it is essential to know the
magnitude of NMVOCs emitted from several
sources, as well as their superficial and temporal
distribution.
´
In Catalonia, Gomez
and Baldasano (1999)
worked with a land-use map for the year 1992 and
used climatological hourly data for air surface
temperature and solar radiation. Emission factors
were defined as the average of the emission factors
of vegetal species for each of the land-use categories. The description of vegetal species was
established by literature revision.
In recent years the quality of this base information has improved in several aspects: (1) availability of quantitative information of vegetal forest
composition; (2) availability of new local emission
factors for some vegetal species; and (3) specific
meteorological data.
Also, researchers have elucidated the particular
emission behaviour for some volatile organic compounds of specific Mediterranean species. This
knowledge and the latest base information are
considered here for an updated estimation of biogenic NMVOCs emissions in Catalonia.
2. Method
The mathematical model for estimating
NMVOCs emissions and databases described in
the following sections was incorporated into geographical information system (GIS) software. We
used the mathematical model developed by
Guenther et al. (1993) with some modification.
2.1. Land-use map
A digital land-use map for the year 1997 (ED,
2003) was used. Originally, it has a resolution of
30-m squared cells and distinguishes 22 land-use
categories. It was integrated into a grid of 1-km
sided cells, assigning one land-use category by
cell. This map covers approximately 32 000 km2
and Table 1 indicates the 11 emitter categories.
Fig. 1 depicts the location of Catalonia and the
geographical distribution of most relevant vegetation land-use categories.
2.2. Assignation of vegetal species by land-use
categories
The ecological and forest inventory of Catalonia,
developed by the Centre for Ecological Research
and Forestry Applications (CREAF), helped assign
vegetal species into forest and shrub land categories (CREAF, 2003). We identified the most important vegetal species in the coniferous, sclerophyllous and deciduous forest, according to both
county coverage and foliar biomass. The weights
for the later assignment of emission factors by
land-use category were established using the foliar
biomass of species.
Nevertheless, the information for shrub lands
contained in the CREAF inventory is short and
just identifies the most significant vegetal species,
without establishing specific weights for them. For
herbaceous crops and fruit tree categories, the
weights were set using statistical production data
for year the 1999 provided by the Statistical
Institute of Catalonia (IDESCAT, 2003). Table 1
also shows the vegetal composition according to
land-use category, foliar type, phenologic calendar
and weights obtained.
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
243
Table 1
Vegetal species by emitter land-use category and weights for emission factors calculation
Code
category
Description
Coverage
(%)
Vegetal species associated
Foliar typey
phenologic calendar
Weight
(%)
15
Shrub lands
27
Arbustus unedo
Buxus sempervirens
Erica arborea
Erica multiflora
Hedera helix
Juniperus communis
Pistacia lentiscus
Q. coccifera
Q. ilex
Rosmanirus officinalis
Rubus ulmifolius
Thymus vulgaris
Perenne
Perenne
Perenne
Perenne
Perenne
Perenne
Perenne
Perenne
Perenne
Perenne
Perenne
Perenne
18
Coniferous
forest
19
Pinus sylvestris
P. halepensis
Pinus nigra
Pinus uncinata
Abies alba
Pinus pinea
Quercus humilis
Pinus pinaster
Perenne
Perenne
Perenne
Perenne
Perenne
Perenne
DeciduousyMay–November
Perenne
9
Non-irrigated
herbaceous
crops
15
Hordeum vulgari (barley)
Triticum aestivum (wheat)
Medicago sativa (alfalfa)
11
Non-irrigated
fruit trees
7.1
Olea europaea (olive)
Prunus dulcis v. dulcis (almond)
Corylus avellana (hazelnut)
10
Irrigated
herbaceous
crops
6.5
Hordeum vulgari (barley)
Triticum aestivum (wheat)
Medicago sativa (alfalfa)
Oriza sativa (rice)
16
Sclerophyllous
forest
6.2
Q. ilex
Quercus suber
Pinus sylvestris
Quercus humilis (pubescens)
Pinus nigra
Perenne
Perenne
Perenne
DeciduousyMay–November
Perenne
79
11
8
1
1
17
Deciduous
forest
5.1
Pinus sylvestris
Q. ilex
Fagus sylvatica
Castanea sativa
Quercus humilis
Quercus petraea
Perenne
Perenne
DeciduousyMay–November
DeciduousyMay–November
DeciduousyMay–November
DeciduousyMay–November
42
41
7
5
4
1
13
Vineyard
2.5
Vitis vinifera (grape)
DeciduousyMay–November
100
12
Irrigated fruit
trees
2.3
Pyrus communis (pear)
Malus domestica (apple)
Prunus persica (peach)
Citrus reticulata (mandarin)
Citrus sinensis (orange)
DeciduousyMarch–November
DeciduousyMarch–November
DeciduousyMarch–November
Perenne
Perenne
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
36
21
19
14
6
3
1
0
51
35
14
Perenne
DeciduousyMarch–November
DeciduousyMarch–November
59
31
10
46
31
13
10
32
29
26
7
6
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
244
Table 1 (Continued)
Code
category
Description
7
Urban areas
19
Wetlands
Coverage
(%)
Vegetal species associated
Foliar typey
phenologic calendar
1.2
Acacia sp
Platanus sp
Pinus pinaster
Pinus pinea
Populus nigra
DeciduousyMay–November
DeciduousyMay–November
Perenne
Perenne
DeciduousyMay–November
20
20
20
20
20
0.1
Typha sp (Typha latifolia)
Juncus sp
Perenne
50
50
2.3. Mathematical model
NMVOCs were grouped in three categories
according to their typical lifetime (Guenther et al.,
1995): (1) isoprene (1–2 h lifetime); (2) monoterpenes (0.5–3 h) (e.g. a-pinene, d-limonene,
D3-carene or b-myrcene); and (3) other biogenic
volatile organic compounds (OBVOCs) (reactive
-1 day, e.g. 2-methyl-3-butene, acetaldehyde, formaldehyde or acetic acid; others )1 day, e.g.
methanol, acetone, formic acid or ethanol). Emission factors are related to the standard conditions:
(1) temperatures30 8C and photosynthetically
active radiation (PAR) 1000 mmol my2 sy1 for
isoprene; and (2) temperatures30 8C for monoterpenes and OBVOCs.
2.3.1. Isoprene
Hourly isoprene emissions were estimated using
the model by Guenther et al. (1993), which is
shown in Eq. (1).
EisoŽk, hourly.sEFjisoØECFŽT,P.ØFBDjØA
(1)
where A is the area of each grid cell (1 km2),
FBDj is the foliar biomass density of the j landuse category (g my2), ECF(T,P) is the environmental correction factor owing to temperature and
PAR (adimensional), EFiso
is the standard isoprene
j
emission factor associated with the j land-use
category (mg gy1 hy1) and Eiso(k, hourly) is the
hourly isoprene emission into the kth cell (g hy1).
The environmental correction factor is calculated using Eq. (2).
ECFŽT,P.sCTØCP
(2)
where CT is the correction factor owing to temper-
Weight
(%)
ature and CP is the correction factor due to PAR.
These factors are defined by Eqs. (3) and (4),
respectively (Guenther et al., 1993):
CLs
aØCL1ØL
(3)
y1qa2ØL2
exp
CT1ØŽTyTs.
CTs
1qexp
RØTSØT
CT2ØŽTyTm.
(4)
RØTsØT
where a (0.0027), CL1 (1.066), CT1 (95000 J
moly1), CT2 (230 000 J moly1) and Tm (314 K),
are empirical coefficients; L is the PAR flux (mmol
my2 sy1), Ts is the standard reference temperature
(303 K), R (8.314 J Ky1 moly1) is the universal
gas constant and T (8C) is the foliar biomass
temperature. We assumed that foliar biomass temperature is similar to the surface air temperature.
For specific days, emissions were estimated
using Eq. (5).
24
Eiso Žk, daily.s 8 EisoŽk,hourly.
(5)
hs1
where Eiso(k, daily) is the daily isoprene emission
into the kth cell (g dayy1).
For monthly values, mean day emissions were
calculated (see Section 2.4) and afterwards Eq.
(6) was applied.
EisoŽk, monthly.s30ØEisoŽk, mdaily.
(6)
where Eiso(k, mdaily) is the isoprene emission (g
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
245
Fig. 1. Catalonia’s location and its main vegetation land-use categories. Shrub lands (cover 27%), coniferous forest (19%), nonirrigated herbaceous crops (15%), non-irrigated fruit trees (7%), sclerophyllous forest crops (7%).
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
246
Fig. 2. Behavior of CT and CP (adimensional).
dayy1) for a mean day of the month and Eiso(k,
monthly) is the monthly isoprene emission (g
monthy1).
Annual emissions were obtained from Eq. (7).
12
EisoŽk, annual.s 8 EisoŽk, monthly.
(7)
ms1
where Eiso (k, annual) is the annual isoprene
emission (g yeary1).
Fig. 2 shows the behaviour of CT and CP. CT is
almost zero for a temperature of 0 8C and increases
to almost 1.9 at 40 8C, decreasing for higher
temperatures. CP is zero for a value of PAR of 0
mmol my2 sy1, hence the model considers no
isoprene emissions during night time. CP increases
with an asymptotic trend to 1.1 over 1000 mmol
my2 sy1. Both CT and CP are 1 in standard
conditions.
2.3.2. Monoterpenes
Hourly monoterpene emissions were estimated
partially using Eq. (8):
EmonŽk, hourly.sEFjmonØMŽT.ØFBDjØA
(8)
where EFmon
is the standard monoterpenes emisj
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
247
Fig. 3. Behaviour of M(T) (adimensional).
sion factor associated with the j land-use category
(mg gy1 hy1) and Emon(k, hourly) is the hourly
monoterpenes emission into the cell kth (g hy1).
M(T) is the environmental correction factor owing
to temperature (adimensional), defined by Eq. (9)
(Guenther et al., 1993):
MŽT.sexpŽbØŽTyTs..
(9)
where b (0.09 Ky1) is an empirical coefficient.
Fig. 3 shows the behaviour of M(T), which is
almost zero for an air temperature of 0 8C, increases until almost 2.5 at 40 8C and keeps rising for
higher temperatures.
Atkinson and Arey (1998) and Kesselmeier et
al. (1998) established that monoterpene emissions
from Quercus ilex (included in shrub land, deciduous and sclerophyllous land-use categories) could
be analogous to those of isoprene, since they also
depend on light-driven processes. Hansen and
Seufert (1996) established that terpenoid emissions
from Quercus coccifera (included in shrub land
category) are quantitatively similar to those of Q.
ilex. Therefore, the isoprene algorithm was used
for monoterpene emissions from these two species.
2.3.3. Other volatile organic compounds
OBVOCs emissions were estimated by a similar
equation to the one used for monoterpenes. Daily,
monthly and annual monoterpenes and OBVOC
emissions were obtained using equivalent equations to those reported for isoprene.
2.4. Meteorological data
The Meteorological Service of Catalonia provided hourly air surface temperature records from 81
meteorological stations for the year 2000. Hourly
global solar radiation records were obtained from
six stations belonging to the Solar Radiation Network of the Catalonian Institute of Energy. PAR
values were set as the 50% of these records. An
average day per month was used to compile
monthly emissions; the average days were defined
by means of calculating the average hourly records
of air surface temperature and global radiation.
Hourly temperature and PAR maps were constructed using the technique of kriging interpolation.
2.5. Emission factors
Emission factors for each individual vegetal
species associated with emitter land-use categories
(Table 1) were collected from a literature review
and existing databases. Specific values were chosen, giving priority to those defined inside Catalonia or the Mediterranean zone. It was possible
to use seasonal emission factors in just a few cases
(for monoterpenes and OBVOCs only). Among
the most important sources were CREAF works
248
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
˜
˜
`
(Llusia` and Penuelas,
2000; Penuelas
and Llusia,
2001a), emission factors from the Biogenic Emissions in the Mediterranean Area (BEMA) project
which are reported in some articles (Owen et al.,
1997; Seufert et al., 1997; Owen et al., 2001), and
databases from Lancaster University (2002a,b).
Table 2 summarizes the values selected for landuse categories that cover, at least, 5% of the
territory. Table 3 classifies emission factors
obtained by emitter land-use categories.
2.6. Foliar biomass density
Using the database of the Ecological and Forest
Inventory of Catalonia, we constructed the histograms of foliar biomass of forest species. Most of
them showed positive skewed distributions, suggesting that median rather than mean is more
suitable as measure of central tendency. Foliar
´
biomass density reported by Gomez
(1998) was
used for other emitter land-use categories.
3. Results
Fig. 4 depicts the geographical distribution and
annual of emissions for the year 2000 grouped by
land-use categories. Total emissions reached 46.9
kt, corresponding to 5.9 kt of isoprene (13%),
24.7 kt of monoterpenes (52%) and 16.3 kt of
OBVOCs (35%). This is similar to our estimation
of the on-road traffic emissions of NMVOCs
(work not yet published), assessed as 49.5 kt.
Also, our first estimation of emission from other
sources (industrial and solvent use sectors) reaches
40 kt yeary1. Hence, about one-third of the annual
NMVOCs emission could be attributed to foliar
biomass vegetation.
Nevertheless, anthropogenic emissions are not
distributed evenly over Catalan territory, since they
are emitted mainly from urbanyindustrial areas and
along the axis of highways and roads, located
mainly following the coastal line.
´
Gomez
(1998) reported an annual emission of
50.9 kt (33% isoprene, 46% monoterpenes, 21%
OBVOCs). The estimation of this work is 10%
lower than the former value, and the composition
in terms of isoprene, monoterpenes and OBVOCs
is different.
The highest annual emissions came from shrub
lands (4.6 kt of isoprene, 7.7 kt of monoterpenes
and 5.2 kt of OBVOCs, which adds up to 17.5 kt
of total NMVOCs), followed by coniferous forest
(0.6 kt of isoprene, 13.0 kt of monoterpenes and
2.4 kt of OBVOCs, totalling 16.0 kt of NMVOCs)
and sclerophyllous forest (0.1 kt of isoprene, 1.8
kt of monoterpenes and 1.9 kt of OBVOCs, adding
up to 3.8 kt of total NMVOCs). In the annual
cycle, isoprene was emitted mainly from shrub
lands (77%) and coniferous forest (10%). Monoterpenes were emitted mainly by coniferous forest
(53%) and shrub lands (32%). OBVOCs came
predominantly from shrub lands (32%), coniferous
forest (15%) and non-irrigated herbaceous crops
(15%).
Fig. 5 shows the monthly evolution of emissions. Isoprene ranged between 40 and 253 t
monthy1 in autumn and winter, but amounted to
598 and 1445 t monthy1 in the period from May
to September. Approximately 60% of annual isoprene emissions were produced during June, July
and August; this percentage is lower for monoterpenes and OBVOC (49% and 50%, respectively).
Fig. 5 also shows the annual cycle of emissions,
with increasing values in March–April and yielding the highest emissions in June–August because
of the higher air temperature and solar radiation.
Emissions decrease in the October–November
period. The NMVOCs emissions for July are
inferior to those of June and August because some
species (Q. ilex, Pinus halepensis and Erica arborea) were assigned lower monoterpenes emission
factors for summer relative to spring values. The
reason was to include the effect of the Mediterranean summer drought stress on the emission
˜
behaviour of these species (Llusia` and Penuelas,
1998, 2000).
For autumn and winter times, the relation of onroad traffic to biogenic emission varies between
1.1 and 2.2, but for the summer months it is only
0.3.
Fig. 6 shows the hourly emissions for a summer
day (15th August 2000). Isoprene emissions are
depicted only during daytime and its highest level
reaches 7.3 t hy1 at midday hours. Monoterpenes
and OBVOCs are emitted throughout the whole
day, presenting lower fluxes in the first hours (2.9
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
249
Table 2
Emission factors selected by land-use category (mg gy1 hy1)
Land-use
category
Vegetal species
associated
Isoprene
Monoterpenes
OBVOCs
Shrub
lands
Arbustus unedo
Buxus sempervirens
E. arborea
Erica multiflora
Hedera helix
Juniperus communis
Pistacia lentiscus
Q. coccifera
Q. ilex
Rosmanirus officinalis
Rubus ulmifolius
Thymus vulgaris
0
11.5
12.7
2
0
0.1
0
0
0
0
0
0
q
q
q
(1)
(2)
(2)
(3)
(1)
q
(3)
(4)
(1)
2.2 w, 2.2 sp, 2.2 sm, 0 a
0
7 w, 17.7 sp, 6.6 sm, 3.2 a
0.01
0.1
0.1
0.6
2.4 w, 1 sp, 4.3 sm, 5.5 a
4.5 w, 11.3 sp, 7.5 sm, 1.7a
2.2
0.35
0.25
(8)
(1)
(8)
(9)
(2)
(2)
(1)
(8)
q
(3)
(4)
(5)
0
0
0 w, 8.13 sp, 0 sm, 0 a
0
0
1.5
0
0 w, 2.6 sp, 0 sm, 0 a
0 w, 5.59 sp, 5.87 sm, 0.35 a
1.26
0
3.37
q
(1)
(11)
q
q
(6)
q
(11)
(11)
(12)
q
(12)
Coniferous
forest
P. halepensis
Pinus sylvestris
Pinus pinea
Pinus nigra
Pinus uncinata
Quercus humilis
Abies Alba
Pinus pinaster
0
0
0
0
0
75
0
0
(1)
(2)
(1)
(2)
q
(1)
(5)
(6)
2.3 w, 9.6 sp, 6.4 sm, 11.4a
7.9
6.5
7.64
0.9
0
9.3
0.2
(8)
q
(1)
(2)
(5)
(1)
q
(6)
0 w, 0.81 sp, 1.36 sm, 4.49 a
1.5
0.22
1.5
0
0
1.5
1.5
(11)
(6)
(12)
q
q
(1)
(6)
(6)
Nonirrigated
herb crops
Hordeum vulgari
Triticum aestivum
Medicago sativa
0
0
0
(2)
(7)
(6)
0
0
0.21
(2)
(7)
(10)
1.5
0.01
0.59
(6)
(7)
(10)
Nonirrigated
fruit trees
Olea europaea
Prunus dulcis v. dulcis
Corylus avellana
0
0
0.1
(1)
(4)
(2)
0.04
0
0.1
(1)
(10)
(2)
1.04
2.3
0
(10)
(10)
q
Irrigated
herb crops
Hordeum vulgari
Triticum aestivum
Medicago sativa
Oriza sativa
0
0
0
0.1
(2)
(7)
(6)
(6)
0
0
0.21
0.24
(2)
(7)
(5)
(6)
1.5
0.01
0.59
1.5
(6)
(7)
(10)
(6)
Sclerophyllous
forest
Q. ilex
Quercus suber
Pinus sylvestris
Quercus humilis
Pinus nigra
0
0
0
75
0
q
(6)
(2)
(1)
(2)
4.5 w, 11.3 sp, 7.5 sm, 1.7a
0.2
7.9
0
7.64
q
(6)
(5)
(1)
(2)
0 w, 5.59 sp, 5.87 sm, 0.35 a
1.5
1.5
0
1.5
(11)
(6)
(6)
(1)
q
Deciduous
forest
Pinus sylvestris
Q. ilex
Quercus humilis
Fagus sylvatica
Castanea sativa
Quercus petraea
0
0
75
0
0
57.4
(2)
q
(1)
(6)
(4)
q
7.9
4.5 w, 11.3 sp, 7.5 sm, 1.7a
0
0.40
11.18
0.35
(5)
q
(1)
q
q
q
1.5
0 w, 5.59 sp, 5.87 sm, 0.35 a
0
0.01
1.5
1.5
(6)
(11)
(1)
(7)
q
(6)
**
Emission factor**
Emission factor
Emission
factor*
Monoterpenes emission factors expressed in standard conditions: temperature 30 8C, PAR 1000 mmol my2 sy1.
Seasonal emission factors: wswinter, spsspring, smssummer, asautumn.
q: This has been established analysing the group of emission factors collected. The others have been chosen directly from the sources indicated
below.
Source: (1) Owen et al. (2001); (2) Lancaster UK database (2002b); (3) Seufert et al. (1997); (4) Lancaster database (2002a); (5) Simon et al.
¨
` and Penuelas
˜
˜
(2001); (6) EEA (2001); (7) Konig
et al. (1995); (8) Llusia
(2000); (9) Owen et al. (1997); (10) Winer et al. (1992); (11) Penuelas
and Llusia` (2001a); (12) Owen et al. (2002).
*
Expressed in standard conditions: temperature 30 8C, PAR 1000 mmol my2 sy1.
**
Expressed in standard conditions: temperature 30 8C.
250
Table 3
Emission factors by land-use category (mg gy1 hy1)
Description
Winter
Spring
Summer
Autumn
February
March
April
May
June
July
August
September
October
November
December
Isoprene
Urban areas
Non-irrigated herbaceous crops
Irrigated herbaceous crops
Non-irrigated fruit trees
Irrigated fruit trees
Vineyards
Shrub lands
Sclerophyllous forest
Deciduous forest
Coniferous forest
Wetlands
0.00
0.00
0.01
0.00
0.00
0.00
2.19
0.00
0.00
0.00
0.05
0.00
0.00
0.01
0.00
0.00
0.00
2.19
0.00
0.00
0.00
0.05
0.00
0.00
0.01
0.01
0.15
0.00
2.19
0.00
0.00
0.00
0.05
0.00
0.00
0.01
0.01
0.15
0.00
2.19
0.00
0.00
0.00
0.05
22.20
0.00
0.01
0.01
0.15
0.05
2.19
0.75
3.57
0.75
0.05
22.20
0.00
0.01
0.01
0.15
0.05
2.19
0.75
3.57
0.75
0.05
22.20
0.00
0.01
0.01
0.15
0.05
2.19
0.75
3.57
0.75
0.05
22.20
0.00
0.01
0.01
0.15
0.05
2.19
0.75
3.57
0.75
0.05
22.20
0.00
0.01
0.01
0.15
0.05
2.19
0.75
3.57
0.75
0.05
22.20
0.00
0.01
0.01
0.15
0.05
2.19
0.75
3.57
0.75
0.05
22.20
0.00
0.01
0.01
0.15
0.05
2.19
0.75
3.57
0.75
0.05
0.00
0.00
0.01
0.00
0.00
0.00
2.19
0.00
0.00
0.00
0.05
Monoterpenes
Urban areas
Non-irrigated herbaceous crops
Irrigated herbaceous crops
Non-irrigated fruit trees
Irrigated fruit trees
Vineyards
Shrub lands (Q. ilexqQ. coccifera)*
Shrub lands (the other species)
Sclerophyllous forest (Q. ilex)*
Sclerophyllous forest (the other species)
Deciduous forest (Q. ilex)*
Deciduous forest (the other species)
Coniferous forest
Wetlands
1.34
0.03
0.05
0.00
0.00
0.00
0.58
1.07
3.56
0.73
1.85
3.32
5.65
0.05
1.34
0.03
0.05
0.00
0.00
0.00
0.58
1.07
3.56
0.73
1.85
3.32
5.65
0.05
1.34
0.03
0.05
0.03
0.49
0.00
0.58
1.07
3.56
0.73
1.85
3.32
5.65
0.05
1.34
0.03
0.05
0.03
0.49
0.00
1.03
1.94
8.93
0.73
4.63
3.32
7.18
0.05
1.95
0.03
0.05
0.03
0.49
0.03
1.03
1.94
8.93
0.73
4.63
3.91
7.18
0.05
1.95
0.03
0.05
0.03
0.49
0.03
1.03
1.94
8.93
0.73
4.63
3.91
7.18
0.05
1.95
0.03
0.05
0.03
0.49
0.03
0.98
1.03
5.93
0.73
3.08
3.91
6.51
0.05
1.95
0.03
0.05
0.03
0.49
0.03
0.98
1.03
5.93
0.73
3.08
3.91
6.51
0.05
1.95
0.03
0.05
0.03
0.49
0.03
0.98
1.03
5.93
0.73
3.08
3.91
6.51
0.05
1.95
0.03
0.05
0.03
0.49
0.03
0.60
0.57
1.34
0.73
0.70
3.91
7.56
0.05
1.95
0.03
0.05
0.03
0.49
0.03
0.60
0.57
1.34
0.73
0.70
3.91
7.56
0.05
1.34
0.03
0.05
0.00
0.00
0.00
0.60
0.57
1.34
0.73
0.70
3.32
7.56
0.05
OBVOCs
Urban areas
Non-irrigated herbaceous crops
Irrigated herbaceous crops
Non-irrigated fruit trees
Irrigated fruit trees
0.34
0.85
0.92
0.00
0.00
0.34
0.85
0.92
0.00
0.00
0.34
0.85
0.92
1.33
1.37
0.34
0.85
0.92
1.33
1.37
0.64
0.85
0.92
1.33
1.37
0.64
0.85
0.92
1.33
1.37
0.64
0.85
0.92
1.33
1.37
0.64
0.85
0.92
1.33
1.37
0.64
0.85
0.92
1.33
1.37
0.64
0.85
0.92
1.33
1.37
0.64
0.85
0.92
1.33
1.37
0.34
0.85
0.92
0.00
0.00
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
January
Table 3 (Continued)
Description
Spring
January
February
March
April
0.00
0.51
0.30
0.63
0.92
0.00
0.00
0.51
0.30
0.63
0.92
0.00
0.00
0.51
0.30
0.63
0.92
0.00
0.00
1.87
4.72
2.92
1.09
0.00
*Monoterpenes emission factors for which isoprene algorithm was used.
Summer
May
0.72
1.87
4.72
3.01
1.09
0.00
June
0.72
1.87
4.72
3.01
1.09
0.00
July
0.72
1.00
4.93
3.13
1.21
0.00
Autumn
August
0.72
1.00
4.93
3.13
1.21
0.00
September
0.72
1.00
4.93
3.13
1.21
0.00
October
0.72
0.54
0.58
0.86
1.87
0.00
November
0.72
0.54
0.58
0.86
1.87
0.00
December
0.00
0.54
0.58
0.77
1.87
0.00
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
Vineyards
Shrub lands
Sclerophyllous forest
Deciduous forest
Coniferous forest
Wetlands
Winter
251
252
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
Fig. 4. Biogenic NMVOCs emission in Catalonia for the 2000 by land-use categories and their geographical distribution. It reached
46.9 kt (5.9 kt (13%) of isopreneq24.7 kt (52%) of monoterpenes and 16.3 kt (35%) of NMVOCs). Main emitters are shrub lands
(37%), coniferous forest (34%) and sclerophyllous forest (8%).
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
253
Fig. 5. Monthly biogenic and on-road traffic emissions of volatile organic compounds in Catalonia for the year 2000. During July,
August and September approximately 60%, 43% and 46% of annual isoprene, monoterpenes and OBVOCs emissions were produced,
respectively.
t hy1 for monoterpenes and 2.5 t hy1 for
OBVOCs), becoming higher at midday (14.9 t
hy1 for monoterpenes and 8.6 t hy1 for OBVOCs).
The peak hourly total emission value is 30.8 t
hy1. Fig. 6 also illustrates the emissions for a
winter day (7th January 2000), for which the
highest hourly total emission is less than 7% of
the corresponding value for August 15th.
4. Sensitivity and uncertainty
To understand the influence of driving variables
(temperature and PAR), we developed a sensitivity
analysis of the model, using summer time emission
factors. For different coupled values of temperature
and PAR, the model gives total hourly emissions
summarized in Table 4. Using the values obtained
with the standard emission factors (temperature 30
8C, PAR 1000 mmol my2 sy1) as a reference,
Table 4 also shows the percentage variations of
emissions. For PAR higher than 250 mmol my2
sy1, emissions at 10 8C of temperature are 83–
94% lower than reference values; at 20 8C they
are 59–76% lower, and at 40 8C emissions are
69–146% higher. Both isoprene and monoterpenes
are influenced by PAR, but percentage variations
are more important for isoprene. Emission of
OBVOCs, being dependent only on temperature,
shows no variations with PAR (similar percentage
variation for each level of temperature). Lower
values of PAR are especially important for isoprene, and for darkness there is no isoprene
emission.
To complement our analysis, and considering
that monoterpene emission factors are best
described, we established a preliminary estimation
of uncertainty of hourly monoterpene emission for
August 15th, using the Monte Carlo approach
under the following assumptions: (1) fixed values
of driving variables; (2) monoterpene emission
factors of the species associated with the most
important emitter land-use categories (shrub lands,
coniferous, sclerophyllous and deciduous forest)
show normal distributions; and (3) emission factors are fixed for the rest of land-use categories
(in the annual cycle we obtained for each of them
less than 2% of the total emission).
254
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
Fig. 6. Hourly biogenic emissions of volatile organic compounds in Catalonia for 7th January and 15th August 2000 (t hy1).
We generated 1200 random numbers for each
coupled value (emission factor and standard deviation) of Table 5, producing the same amount of
lists of emission factors by land-use categories.
Using 50 lists per hour we estimated the emissions shown in Fig. 7. Bars correspond to lower
and upper values of the 95% confidence limits.
Relations upperylower limit, are between 2.3 and
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
255
Table 4
Sensitivity analysis of the biogenic NMVOCs model for Catalonia
Emission (t hy1)
Temperature
8C
PAR
mmol m#2 s#1
10
0
250
500
1000
2000
0.00
0.33
0.38
0.39
0.39
1.30
1.51
1.54
1.55
1.55
20
0
250
500
1000
2000
0.00
1.32
1.49
1.55
1.56
30
0
250
500
1000
2000
0.00
4.61
5.22
5.41*
5.46
40
0
250
500
1000
2000
Isoprene
0.00
9.12
10.33
10.71
10.81
Monoterpenes
Variation (%)
Isoprene
Monoterpenes
OBVOCs
1.06
1.06
1.06
1.06
1.06
y100
y94
y93
y93
y93
y89
y87
y86
y86
y86
y83
y83
y83
y83
y83
3.19
4.04
4.15
4.19
4.20
2.61
2.61
2.61
2.61
2.61
y100
y76
y72
y71
y71
y72
y64
y63
y63
y63
y59
y59
y59
y59
y59
7.85
10.83
11.22
11.35*
11.38
6.41
6.41
6.41
6.41*
6.41
y100
y15
y4
0
1
y31
y5
y1
0
0
0
0
0
0
0
y100
69
91
98
100
70
122
129
131
132
146
146
146
146
146
19.30
25.20
25.98
26.23
26.30
OBVOCs
15.77
15.77
15.77
15.77
15.77
*Reference values.
4.2. The highest upper limit is 60% greater than
its average value and the lowest limit is 40% of
its mean value.
5. Discussion
This contribution describes an updated model
for the estimation of biogenic NMVOCs emitted
in Catalonia for the year 2000, using high temporal
and superficial resolution. It includes the latest
information on vegetal composition, emission factors and knowledge of the particular emitter behaviour of some Mediterranean species. Daily and
annual cycles agreed with the emissions expected
with regard to the influence of air temperature and
solar radiation.
Annual emission is only 10% lower than the
´
estimation reported by Gomez
(1998). The apparent agreement does not imply that both assessments could be considered as similar, or that the
level of uncertainty for this kind of estimate is
low. In fact, the different percentage contribution
obtained for isoprene, monoterpenes and
OBVOCs, means that temporal and superficial
distributions of emissions are different too. The
level of uncertainty of the estimation shown here
´
is less than the one reported by Gomez
(1998).
Because of the complex vegetal biodiversity of
Catalonia, the use of emission factors by land-use
categories is a pragmatic and useful approach. Use
of emission factors by vegetal species could be
feasible for other regions with lower vegetal biodiversity, like those allocated in Northern Europe.
For forest categories, the weights of representative vegetal species were established using their
foliar biomass. The approach employed for shrub
lands was even simpler (no specific weights of
species were established) because of the lower
quality of the information available.
Although this work includes the influences of
air surface temperature and PAR, which are the
two best-understood abiotic factors, emissions of
256
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
Table 5
Averages and standard deviation values of summer monoterpenes emission factors (mg gy1 hy1)
Land-use
Associated species
Average emission factor
S.D.
Shrub lands
Arbustus unedo
E. arborea
Pistacia lentiscus
Q. coccifera
Q. ilex
Rosmanirus officinalis
Rubus ulmifolius
Thymus vulgaris
2.2
6.6
0.6
4.3
7.5
2.2
0.35
0.25
1.46
7.45 (1)
0.13
3.73 (1)
1.16 (1)
2.26
0.19
0.19
Coniferous forest
P. halepensis
Pinus sylvestris
Pinus pinea
Pinus nigra
Pinus uncinata
Abies Alba
Pinus pinaster
6.4
7.9
6.5
7.64
0.9
9.3
0.2
1.6 (1)
7.8 (1)
1.38
4.9
0.6
8.84
0.1
Sclerophyllous forest
Q. ilex
Quercus suber
Pinus sylvestris
Pinus nigra
7.5
0.2
7.9
7.64
1.16 (1)
0.1
7.8 (1)
4.9
Deciduous forest
Pinus sylvestris
Q. ilex
Fagus sylvatica
Castanea sativa
Quercus petraea
7.9
7.5
0.40
11.18
0.35
7.8 (1)
1.16 (1)
0.23
8.70
0.20
(1) Obtained directly from the source indicated in Table 2.
biogenic NMVOCs are the result of a complex net
of interactions that include many factors, both
internal (genetic and biochemical) or external
(abiotic–air temperature, PAR, water availability,
wind, ozone and biotic—animal, plant and micro
˜
`
organisms interactions) (Penuelas
and Llusia,
2001b). Those factors produce large spatial and
temporal variability, especially for local and shortterm scale.
High levels of uncertainty are inherent to biogenic NMVOCs emission estimations. Inventories
provided with detailed analysis of uncertainty are
still scarce. Guenther et al. (2000) estimated the
natural emissions from North America (probably
the region for which inventories are most developed, both in data and modelling, and hence the
best quantified) and mentions a factor of 3 as a
reasonable estimation of the uncertainty associated
with annual total NMVOCs. Also, a factor of 10
is mentioned that could be reached for specific
times, locales and compounds. Simpson et al.
(1995, 1999) indicated similar levels for Europe.
Simon et al. (2001) associated annual uncertainty
factors of 4 for isoprene, 5 for monoterpenes and
7 for OBVOCs from French ecosystems. Our
preliminary uncertainty estimation of hourly monoterpene emission indicates that we could expect
at least variations of "60% of the total emission
over Catalonia. Annual factors provided by Simon
et al. (2001) could be considered as minimum
factors for Catalonia.
Because of the higher atmospheric reactivity of
most biogenic NMVOCs compared to many
anthropogenic NMVOCs (with lifetimes of biogenic being typically a few hours or less compared
to a few days for several chemical classes of
anthropogenic NMVOCs), biogenic NMVOCs
play a dominant role in the chemistry of the lower
troposphere (Atkinson and Arey, 1998). In fact,
due to its rate of reaction, isoprene is one of the
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
257
Fig. 7. Hourly monoterpenes and 95% limits of confidence for emissions on 15th August 2000 (t hy1).
compounds explicitly treated in condensed photo´
chemical mechanisms (Jimenez
et al., 2003).
6. Conclusions
The amount of NMVOCs emitted by vegetation
in Catalonia during the year 2000 was estimated
in 46.9 kt yeary1 (52% monoterpenes, 35%
OBVOCs and 13% isoprene). It is of the same
magnitude as on-road traffic emissions and onethird of the total anthropogenic sources. Approximately 50% of the annual emission was emitted
during summer months, being shrub lands (37%)
and coniferous forest (34%) the most important
sources.
Availability of local emission factors is still very
scarce, especially for isoprene. Therefore, a priority
field of research is their determination for Catalan
and Mediterranean species jointly with a better
knowledge of the composition of shrub lands.
Also, in the future it will be necessary to describe
the seasonal biomass density variations. These are
reasons why the use of more developed emission
models could be premature at present. On the basis
of literature review, there are at present no above
canopy flux measurements (using aircraft or balloon flux systems) made in Catalonia to evaluate
biogenic NMVOCs model emissions.
Nevertheless, the model provides at present the
best estimations of hourly emissions to be used
into a chemical transport model. The preliminary
results of photochemical pollution simulations
show a good agreement between the modelled and
the measured values of tropospheric ozone. This
constitutes, in part, an external assessment of the
fitness of the biogenic NMVOCs emission model
described here.
Acknowledgments
This research was developed under the
IMMPACTE project, funded by the Government
of Catalonia; and the REN2000-1020-C02 project,
funded by the Spanish Government. Authors would
like to thank the information provided by the
Meteorological Service of Catalonia and the Ecological Research and Forestry Applications
(CREAF). Finally, authors would also like to
´
acknowledge Eugeni Lopez
for building the emis-
258
R. Parra et al. / Science of the Total Environment 329 (2004) 241–259
sion model into the GIS tool, and also to Pedro
´
Jimenez
for checking the English manuscript.
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