Science of the Total Environment 596–597 (2017) 396–404
Contents lists available at ScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Ozone exposure affects tree defoliation in a continental climate
Alessandra De Marco a,⁎, Marcello Vitale b, Ionel Popa c, Alessandro Anav d,e, Ovidiu Badea c,f,
Diana Silaghi c, Stefan Leca c, Augusto Screpanti a, Elena Paoletti e
a
ENEA, CR Casaccia, SSPT-MET-INAT, Via Anguillarese 301, 00123 Rome, Italy
Department of Environmental Biology, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy
National Institute for Research and Development in Forestry Marin Dracea, Eroilor Blvd. 128, Voluntari, Ilfov, Romania
d
University of Exeter, College of Engineering, Mathematics and Physical Sciences, Exeter, UK
e
CNR, Via Madonna del Piano, Sesto Fiorentino, Florence, Italy
f
Transilvania University of Brasov, Romania
b
c
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• Crown defoliation is an aspecific indicator of tree damage.
• Defoliation of main tree species in Romania decreased since 2000 to 2010.
• Ozone concentration and AOT40 were
the most important predictors of defoliation.
• Ozone uptake was not related to defoliation and was always under the critical
level.
• Air pollution modelling helped to investigate ozone impacts on large-scale defoliation data.
a r t i c l e
i n f o
Article history:
Received 24 January 2017
Received in revised form 7 March 2017
Accepted 15 March 2017
Available online xxxx
Editor: Jay Gan
Keywords:
Air pollution impacts
Crown transparency
Stomatal ozone uptake
Forests
General regression models
a b s t r a c t
Ground-level ozone (O3) affects trees through visible leaf injury, accelerating leaf senescence, declining foliar
chlorophyll content, photosynthetic activity, growth, carbon sequestration, predisposing to pests attack and a variety of other physiological effects. Tree crown defoliation is one of the most important parameters that is representative of forest health and vitality. Effects of air pollution on forests have been investigated through
manipulative experiments that are not representative of the real environmental conditions observed in the
field. In this work we investigated the role of O3 concentration and other metrics (AOT40 and POD0) in affecting
crown defoliation in temperate Romanian forests. The impacts of O3 were estimated in combination with nitrogen pollutants, climatic factors and orographic conditions, by applying a non-linear modelling approach (Random Forest and Generalised Regression Models). Ozone concentration and AOT40 under Romanian conditions
were more important than meteorological parameters in affecting crown defoliation. In these particular conditions, POD0 never exceeded the critical level suggested by previous literature for forest protection, and thus
was not important in affecting crown defoliation.
© 2017 Elsevier B.V. All rights reserved.
1. Introduction
⁎ Corresponding author.
E-mail address: [email protected] (A. De Marco).
http://dx.doi.org/10.1016/j.scitotenv.2017.03.135
0048-9697/© 2017 Elsevier B.V. All rights reserved.
Effects of air pollution on forests have been investigated through
manipulative experiments by means of closed chambers, branch
A. De Marco et al. / Science of the Total Environment 596–597 (2017) 396–404
chambers, open-top chambers (Calatayud et al., 2002), free-air exposure systems (Manning, 2005; Matyssek et al., 2007; Paoletti et al.,
2017). Results obtained by such experimental facilities, however, are
not representative of the real environmental conditions found in the
field (Braun et al., 2017). Another approach to explore causal linkages
between air pollution and forest conditions is given by correlational
studies that explore data coming from forest monitoring activities within the framework of ad hoc programmes. This is the case of many epidemiological studies carried out to evaluate relationships between tree
crown condition and ozone (O3) concentration-based metrics (Innes
and Boswell, 1987, Innes and Whittaker, 1993, Mather et al., 1995,
Dobbertin et al., 1997, Hendricks et al., 1997, Klap et al., 2000a, 2000b,
Stribley and Ashmore, 2002, Zierl, 2002), or nitrogen deposition
(Vitale et al., 2014, De Marco et al., 2015). More recent studies provided
statistical evidence that O3 has a negative impact on crown defoliation,
both in Southern Europe (Sicard and Dalstein-Richier, 2015;
Díaz-de-Quijano et al., 2009) and in a country where the AOT40 values
for forests are usually low, i.e. Lithuania (Augustaitis and Bytnerowicz,
2008; Girgždienė et al., 2009; Augustaitis et al., 2010). Ozone impacts
engender a weakened state in a tree that becomes more sensitive to parasitic attacks and climatic hazards (e.g. drought). To date, limited information about the combined effects of O3 and nitrogen on forest health
status (e.g. crown defoliation) is available for continental temperate climate. Although defoliation can be due to a plethora of causes such as climate (Carnicer et al., 2011), pathogens (Innes and Boswell, 1987), and
air pollution (De Marco et al., 2014), a clear role of O3 was not detected
at the European level. However, Sicard et al. (2016a, 2016b) reported
significant correlations between stomatal O3 uptake and crown defoliation in Mediterranean forests. A constraint to large-scale investigations
on O3 impacts on forests is due to the lack of O3 data for the level I plots
where defoliation is assessed every year within the ICP-Forests network
(see below).
Since O3 effects on vegetation depend not only on the atmospheric
concentrations but also on O3 uptake through the stomata
(Musselman et al., 2006; Matyssek et al., 2007), the stomatal O3 flux approach provides an estimate of the critical amount of O3 entering the
stomata and has the capacity of accounting for environmental conditions that influence stomatal O3 uptake (Emberson et al., 2000), such
as air temperature, soil moisture, vapor pressure deficit (VPD) and
solar radiation. Some evidence of better performance of stomatal O3
flux in protecting forests against negative impacts of O3 were recently
highlighted (De Marco et al., 2015, Sicard et al., 2016a, 2016b; Anav et
al., 2016).
Indeed, forest conditions in Europe received increasing attention in
the early 1980s as a response to growing concern that crown defoliation
could be caused by air pollution (e.g. Schütt, 1982). Afterwards, forest
health has been a subject of scientific, political and public debate,
which is discussed within the wider context of sustainable forest management (Kotwal et al., 2008). The European-wide monitoring of forest
condition started over 30 years ago by the International Cooperative
Programme on Assessment and Monitoring of Air Pollution Effects on
Forests (ICP Forests) under the Convention on Long-range
Transboundary Air Pollution (CLRTAP) of the United Nations Economic
Commission for Europe (UNECE), in close cooperation with the European Union (EU). In order to provide knowledge on spatial and temporal
variations of forest conditions and to contribute to a better understanding of relationships between the health state of forest ecosystems and
stress factors, a systematic large-scale monitoring network (Level I)
has been implemented (United Nations Economic Commission for
Europe, UN/ECE, 2014). Air pollutants may have direct effects on forest
health, biodiversity and ecosystem processes (Bytnerowicz et al., 2002).
They may also have indirect effects by promoting secondary stresses
such as bark beetle infestations or toxicity of heavy metals in soils
(Novotný et al., 2002). Composition and distribution of air pollutants
may vary significantly in time and space due to changes of climate
and human activities, as well as environmental and geomorphological
397
changes with elevation. Ozone caused serious damage to vegetation in
large areas of North America (Krupa and Manning, 1988, Krupa et al.,
2001; Karnosky et al., 2007), Europe (de Vries et al., 2014) and China
(Tang et al., 2013) and may also increase phytotoxic effects of other
air pollutants, especially sulphur (S) and nitrogen (N) oxides
(Ainsworth et al., 2012). High levels of N and S deposition (Schöpp et
al., 2003, De Marco et al., 2014) and increasing O3 concentration may
have undesirable effects on European forest ecosystems (Ashmore,
2005; Matyssek et al., 2007; De Marco et al., 2015; Sicard et al., 2016a,
2016b). Increases in anthropogenic emissions of O3 precursors contributed to the rising levels of tropospheric O3 observed at many long-term
measurement stations over past decades (Oltmans et al., 2006), while
the last decade showed decreasing O3 trends overall over Europe
(Scientific Assessment Report,, SAR, 2016) due to application of air quality control measures. While tropospheric O3 is often considered a regional pollutant that can be addressed with regional-scale precursor
emission controls, it is also a global pollutant that can influence air quality over intercontinental scales, due to the hemispheric transport of O3
and its precursors (Akimoto, 2003; TFHTAP, 2010). Future emission
trends are simulated by the Representative Concentration Pathways
(RCPs) scenarios (IPCC AR5 WG1, 2013). Even if the projections depend
on the scenarios, i.e. decrease for RCP2.6 and RCP4.5 and increase for
RCP8.5 by 2030 or 2100 (Young et al., 2013), annual mean tropospheric
O3 reductions are expected by 2050 over most regions and scenarios,
with the exception of South Asia where increases may be as large as
5 ppb (Wild et al., 2012). However, over the period 1901–2100, global
gross primary productivity (GPP) is projected to decrease by 14–23%
owing to plant O3 damage, with regional reductions above 30% (Sitch
et al., 2007). Large reductions in GPP and land-carbon storage are
projected over North America, Europe, China and India (Imhoff et al.,
2004).
Several monitoring campaigns carried out in Romania, Central Eastern Europe, highlighted elevated O3 concentration at various sites of the
Carpathian Mountains (Bytnerowicz et al., 2005). In the last two decades, an increasing trend of O3 concentrations (around 12%) was observed in Southern Carpathians (Silaghi et al., 2013), with O3 levels
reaching summer 24-h average values around 40 ppb (Silaghi and
Badea, 2012). Furthermore, Carpathian region represents one of the
most representative forest biomes of Europe hosting a unique natural,
cultural and social diversity with over 300,000 ha of primeval forests.
Long-term monitoring of ambient O3 and other pollutants and investigations of biological and ecological changes are thus needed for
predicting future risks to forest ecosystems in the Carpathian Mountains
(Kozak et al., 2007).
This paper aimed to identify the role of O3 concentrations and other
metrics (AOT40 and POD0) in affecting crown defoliation in Romanian
forests, in combination with nitrogen pollutants, climatic drivers and
orographic conditions of the testing sites. Differences in the responses
of three main species (Fagus sylvatica, Picea abies and Quercus sp.)
were investigated. This is the first large scale epidemiological assessment of the impacts of O3 concentration and stomatal uptake on tree
crown defoliation in a continental climate.
2. Materials and methods
2.1. Plot description and defoliation data
Trees health status was estimated using as proxy the mean of tree
crown defoliation at plot level from the existing ICP-Forests level I monitoring network over a grid of 16 × 16 km in Romania (Fig. 1). In each
monitoring plot, a constant number of 24 trees (from one or more species) was assessed during the annual monitoring activities, using the
common methodology adopted by ICP-Forests UN/ECE (ICP FORESTS,
2015). Crowns were evaluated during the summer, by visual assessment of the leaf/needle losses relative to a reference tree with full foliage. The assessment was carried out by 27 experts who underwent
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A. De Marco et al. / Science of the Total Environment 596–597 (2017) 396–404
Fig. 1. Distribution of monitoring sites for defoliation in Romania (ICP-Forests level I plots).
periodic inter-calibration courses. At plot level, mean defoliation was
computed for three main species/groups, i.e. Picea abies, Fagus sylvatica,
and Quercus sp. (including Q. pedunculiflora, Q. petraea, Q. pubescens, Q.
robur, Q. frainetto and Q. cerris). For species-specific statistical analysis,
only plots with minimum 8 trees per species were considered. The analysis covered the time period 2000–2010 with the exception of 2008
when defoliation data were missing. Mean annual number of plots
ranged between 29 for sessile oak (Q. petraea), to 44 for P. abies and
100 plots for beech (Fagus sylvatica). Because the numbers of plot
with individual Quercus species were lower than for Fagus sylvatica
and Picea abies, we pooled these species as Quercus sp. and analysed
all together in order to obtain a significant sample number.
2.2. Modelling air pollution and climate variables
The model used in this work to produce O3 fields was CHIMERE
(Menut et al., 2013), in its 2013b version (http://www.lmd.
polytechnique.fr/chimere/). CHIMERE is a three-dimensional model
that simulates gas-phase chemistry, aerosol formation, transport and
deposition at regional scales (Menut et al., 2013) at a resolution of
12 km grid. Meteorological input needed to force CHIMERE are provided
by Weather Research and Forecasting (WRF) model (Skamarock and
Klemp, 2008), while anthropogenic emissions are provided by EMEP
(Vestreng, 2003). Further information on the WRF and CHIMERE setup used in this study are in Anav et al. (2016). We performed a continuous run from 2000 to 2010. Annual mean temperature, relative humidity, surface short wave radiation, and precipitation simulated at a height
of 2 m a.g.l., and soil moisture simulated at 10 cm depth, were provided
by WRF, while O3 concentrations, nitrogen dioxide (NO2) and ammonia
(NH3) dry depositions at 25 m height were supplied by CHIMERE. Two
metrics proposed for forest protection, i.e., AOT40, based on O3 concentrations, and POD0, based on stomatal O3 uptake, were computed following Anav et al. (2016). AOT40 (in ppb h) is the accumulated
amount of O3 over the threshold value of 40 ppb calculated from 1st January to 31st December over the hours when stomatal conductance
(gsto) is higher than zero, i.e.
8
<
31−Dec
maxð½O3 −40; 0Þdt; g sto N0
∫
: t¼01−Jan
0; g sto ¼ 0
AOT40 ¼
ð1Þ
where [O3] is the hourly O3 concentration (ppb), dt is the time step (1 h)
and gsto (in mmol O3 m−2 s−1) was estimated using the Jarvis' multiplicative model (Jarvis, 1976; Eq. (2)) and the parameters suggested in
UNECE (2015).
g sto ¼ g max f light max f min ; f temp f VPD f SWC
ð2Þ
where gmax is the maximum stomatal conductance of a plant species to
O3, and fmin is the minimum stomatal conductance expressed as a fraction of gmax. The other functions are limiting factors of gmax and are
scaled from 0 to 1. The functions flight, ftemp, fVPD, and fSWC (Eqs.
(3)–(6)) are the variation in gmax with photosynthetic photon flux density (PPFD, μmol photons m− 2 s−1), surface air temperature (T, °C),
vapor pressure deficit (VPD, kPa) estimated through the surface relative
air humidity, and volumetric soil water content (SWC, m3 m−3), respectively. The flight, ftemp, and fVPD functions are expressed by the following
formulations (Emberson et al., 2000; UNECE, 2015):
f light ¼ 1−e−light a PPFD
f temp ¼
T−T min
T opt −T min
ð3Þ
20
6B T max −T
4@
T max −T opt
T max −T opt
T opt −T min
13
C7
A5
ð4Þ
A. De Marco et al. / Science of the Total Environment 596–597 (2017) 396–404
f VPD
1− f min ðVPDmin −VPDÞ
þ f min
¼ min 1; max f min ;
VPDmin −VPDmax
SWC−WP
f SWC ¼ min 1; max f min ;
FC−WP
ð5Þ
ð6Þ
where lighta is a species-specific light response constant, PPFD is hourly
photosynthetic photon flux density, Topt, Tmin, and Tmax represent the
optimum, minimum, and maximum temperature for stomatal conductance, respectively, VPDmin and VPDmax are minimum and maximum Vapour Pressure Deficit for stomatal conductance, respectively, WP and FC
are the soil water content at wilting point and at field capacity, respectively; these two parameters are constant and depend on the soil type.
POD0 (in mmol O3 m− 2) is the Phytotoxic Ozone Dose and depends
on gsto (Eq. (7)).
POD0ðt Þ ¼
31−Dec
∫
t¼01−Jan
½O3 g sto Rc
dt
Rb þ Rc
ð7Þ
where Rc, and Rb are quasi-laminar resistance and leaf surface resistance, respectively. In this work we used the approach indicated by
Anav et al. (2016), where the phenological function fphen was set to 1
and POD0 was calculated all year long, as it is limited by the other functions in the period of no growth. As threshold, we selected 0 assuming
that any O3 molecule entering the leaf is potentially negatively affecting
the leaf. To date a function able to describe the detoxification process is
not yet defined. This approach was used previously by Sicard et al.
(2016a, 2016b) and De Marco et al. (2015). A complete list of the parameters used to estimate POD can be found in UNECE, 2015.
The WRF-CHIMERE model was validated in Anav et al. (2016) for
both O3 concentrations and meteorological parameters. Further information of CHIMERE validation can be found on CHIMERE web site
(http://www.lmd.polytechnique.fr/chimere/).
2.3. Statistical analyses
Data were analysed using descriptive statistics methods. Analysis of
variance (1-way ANOVA) was carried out at a level of significance of α
b0.05, in order to find significant differences among data groups (elevation, slope, exposure, solar radiation, mean, max and min air temperature, relative humidity, precipitation, soil moisture, hot and cold days
number, NO2, NH3, O3 concentration, AOT40, POD0, tree age and defoliation) of the three plant species/groups. The Neumann-Keuls test was
applied as post-hoc test at α ≤0.05.
Furthermore, the presence of monotonous increasing or decreasing
trends in defoliation data and environmental predictors was tested by
using the non-parametric Mann-Kendall trend Z test (Mann, 1945;
Kendall, 1975). If Z is lower that the theoretical one at significance
level α = 0.05 then no trend is in the time series (null hypothesis,
H0). If a linear trend is present in a time series, then the true slope
(change per unit time) can be estimated by using a simple non-parametric procedure developed by Sen (1968). The trend analysis should
discern about similarities (or not) among temporal dynamics of predictors of different tree species. We would like to define how non-similar
trends could reflect different growth environmental conditions. For details concerning calculation of Z statistics and Q and B values see Supplementary Information.
We used the Random Forest (RF) analysis (Breiman, 1996, 2001) to
improve the performance of Decision Tree while retaining most of the
appealing properties considered for determining the most important
predictors significantly affecting a response variable. RF is a machine
learning method that builds an ensemble of classification or regression
trees (Breiman and Cutler, 2003). With this technique, no precise information is required about the form of the relationship between response
and input variables. The final predictor importance values are computed
399
so that the highest average is assigned a value of 1, and the importance
of all other predictors is expressed in terms of relative magnitude of the
average values of the predictor statistics, relative to the most important
predictor (Svetnik et al., 2003). RFA has proven to be a useful tool to discern the most important predictors affecting tree crown conditions (e.g.
Vitale et al., 2014). In this analysis, the most important predictors were
selected until their percentage difference with the most important one
was 30%.
Under conditions of non-linearity of the effect of predictors on the
dependent variable, the dependent variable (crown defoliation) values
were predicted by General Regression Models (GRM). GRM included
the random effect and apply the methods of the general linear model
(McCullagh and Nelder, 1989), allowing it to build models for designs
with multiple-degrees-of-freedom effects for categorical predictor variables, as well as for designs with single-degree-of-freedom effects for
continuous predictor variables. The predictors used in the statistical
modelling were those resulting from the RF Analysis. Statistical models
derived from four runs of the GRM were validated by corresponding
four runs of the cross-validation. The cross-validation involved
partitioning a dataset into a training set (70% of the total defoliation
dataset stratified per year randomly sampled) and a validation set
(30% of dataset). Finally, averages values of parameter coefficients
were reported in the Supplementary Information, in order to define an
average predicting model for canopy defoliation of each tree species.
All statistical analyses were performed by STATISTICA© 12.0 package (StatSoft Inc., Tulsa, OK – USA). All data were given as mean ± standard deviation (or standard error where it is specified). The MannKendall test and the Sen's slope estimation for trend detection were carried out by the trend package (Version: 0.2.0, 2016), running under R©
(Version 3.3.2; The R Foundation for Statistical Computing) and RStudio
(Version 1.0.44 – © 2009–2016 RStudio, Inc.).
3. Results
The three species significantly differed in all environmental and
physiological variables (Table 1), except in the variable Exposure. Average values of defoliation significantly decreased in the order Quercus
sp. N Picea abies N Fagus sylvatica. Air temperature and temperature-related predictors such as the number of Cold and Hot days were also different. It is worth to note that also the mean O3 concentrations were
significantly different among species, thus justifying the different
mean values of AOT40.
When trends were analysed by comparing angular coefficients of
straight lines (z test at p b 0.05, Table S1), defoliation, air temperature,
POD0 and NH3 did not showed significant change among species. Ozone
concentrations showed a significant decreasing trend for the location of
all the species in the decade 2000–2010 (Table S1, Fig. 2). The decreasing
trend of AOT40 was significantly different only for Quercus sp. relative to
the other species and did not follow the O3 trends (Fig. 2). No significat
trends were identified for the other variables shown in Fig. S1.
The random forest analysis highlighted different important variables
affecting tree defoliation (Fig. 3). Picea abies showed much more predictors (11) than the other two species. Ozone concentration and AOT40
were recurrent important predictors (Fig. 3). The flux-based O3 metric,
POD0, was an important predictor only for Fagus sylvatica pointing out
that defoliation was in part due to the O3 taken-up through stomata.
Quercus sp. showed six important predictors only.
GRM were realised by using the predictors identified in the RF analysis, as the variables exceeding a relative predictor importance of 0.7.
For Picea abies, comparisons made among four cross validations
highlighted observed vs. predicted slopes from 0.83 to 0.91 with an average value of 0.90 (Fig. 4A). For Quercus sp., averaged slope gave a good
value of 0.89 (Fig. 4B) between observed and predicted cross-validation
(slopes of the four runs of cross validation lasted from 0.86 to 0.96). For
Fagus sylvatica, the slopes of four observed vs. predicted cross-validations ranged from 0.81 to 0.87, with an average slope for observed vs.
400
A. De Marco et al. / Science of the Total Environment 596–597 (2017) 396–404
Table 1
Crown defoliation and environmental variables (mean and standard deviation values over the period 2000–2010) at Romanian forest plots dominated by Fagus sylvatica, Picea abies or
Quercus species. F is Fisher probability and p is the probability value. Different letters show significant differences among species (Neumann-Keuls test, α ≤0.05). N is number of cases.
Fagus sylvatica (Mean)
N
Defoliation (%)
Slope (degrees)
Exposure (degrees)
Elevation (m a.s.l.)
Age (years)
Tair (°C)
RH (dimensionless)
Soil moisture (m3/m3)
RAD (W/m2)
Rain (mm/day)
O3 (ppb)
Tmax (°C)
Tmin (°C)
POD0 (mmol m−2)
AOT40 (ppb h)
NO2 (molecules/cm2)
NH3 (molecules/cm2)
Hot days (No.)
Cold days (No.)
900
17.5 a
16.0 a
182.1 a
796.1b
86.4 a
9.0 b
0.6 b
0.2 b
187.0 c
0.1 b
42.8 b
12.7 b
5.0 b
16.6 b
31,884 b
7.5 b
45.1 b
108.7 b
113.4 b
Std. Dev.
Picea abies (mean)
10.4
8.8
79.7
314.1
32.4
2.1
0.04
0.02
7.2
0.02
2.4
2.3
1.9
2.8
4887
1.9
28.2
26.8
24.1
389
20.2 b
17.6 b
186.0 a
1216.2a
75.9 b
6.8 a
0.6 c
0.2 c
185.9 b
0.1 c
44.4 c
10.2 a
2.9 a
17.2 a
32,812 c
6.9 a
37.7 a
82.0 a
135.1 c
predicted of 0.82 (Fig. 4C). The ability of the GRM models in estimating
defoliation for the three tree species was confirmed by cross validations
and normality distribution of residuals (standard normal probability
plots). The last one is showed in Fig. S2(a–c) in the Supplementary Information in order to test if residuals (differences between predicted
and observed values coming from validation subset (30% of the data
set)) were normally distributed. N95% of data were tightly following
the normal straight for Picea abies (Fig. 2Sa), Fagus sylvatica (Fig. 2Sb)
and Quercus ssp. (Fig. 2Sc).
Std. Dev.
Quercus spp. (mean)
Std. Dev.
F
p
11.5
7.7
96.8
255.6
33.7
1.7
0.04
0.02
6.6
0.02
1.7
1.8
1.7
2.3
5220
1.3
21.6
22.8
23.1
449
24.6 c
9.5 c
187.9 a
317.2c
70.8 c
12.1 c
0.5 a
0.2 a
191.6 a
0.06 a
39.5 a
16.2 c
7.8 c
15.5 c
30,475 a
9.6 c
62.4 c
145.8 c
85.6 a
11.1
7.5
72.4
182.8
23.2
1.4
0.03
0.01
6.8
0.01
2.1
1.5
1.2
3.3
4174
4.3
36.6
18.6
17.9
68.1
120.3
0.8
1144.8
42.7
945.5
652.2
187.8
127.0
381.9
560.0
954.4
896.7
47.6
34.4
127.0
136.8
893.9
785.2
b0.01
b0.01
0.43
b0.01
b0.01
b0.01
b0.01
b0.01
b0.01
b0.01
b0.01
b0.01
b0.01
b0.01
b0.01
b0.01
b0.01
b0.01
b0.01
The non-linear regressive models were able to capture important
statistical information by using all significant combinations among previously selected predictors. The average regressive models could be
used to predict the defoliation status of plant species analysed here for
future climatic scenarios made for Romania. Table 2 showed the significant parameters obtained by GRM analysis able to predict crown defoliation for P. abies, Quercus sp. and F. sylvatica, respectively, while tables
S2-S4 reported all averaged coefficient values and their variations
around the mean.
Fig. 2. Trends of crown defoliation and ozone concentration and metrics for forest protection (AOT40 and POD0) for Romanian level I plots over the decade 2000–2010. Bars show annual
averages ± sd.
A. De Marco et al. / Science of the Total Environment 596–597 (2017) 396–404
401
Fig. 3. Random Forest analysis carried out on crown defoliation from Romanian ICP-Forests level I plots for Picea abies, Quercus spp., and Fagus sylvatica. Important predictors were selected
in a range of 30% of variation with respect to the most important one, i.e. N0.3 relative importance.
4. Discussion and conclusions
This work shows the importance of O3 concentration and metrics in
affecting crown defoliation in a non-Mediterranean climate. Defoliation
is an important proxy of forest health in Carpathian region, where both
Picea abies and Fagus sylvatica trees with high defoliation showed lower
annual radial increments than trees with low defoliation value
(Bytnerowicz et al., 2005). In this paper, we supplied O3 metrics and
meteorological data by modelling at a resolution of 12 km, i.e. the
highest resolution at present available by the coupled model WRF-CHIMERE (Anav et al., 2016). Furthermore, we focused our study on defoliation at a country level in order to avoid the country-effect as reported
by Klap et al. (2000a, 2000b).
Ozone concentration showed a more stable trend in the time frame
2000–2010 than AOT40 which showed a clear decrease over time. This
apparent contradiction can be due to lower O3 concentration values
(Fig. 2A) with respect to the Mediterranean ones (Anav et al., 2016)
often below the 40 ppb threshold. Such limited O3 pollution in Romania
is in agreement with previous observations by Bytnerowicz et al.
(2005). Furthermore, AOT40 is accumulated only over daylight hours,
suggesting that the decrease in precursors emissions leads to a reduction of daytime O3 maxima and an increase in nighttime concentrations
(Sicard et al., 2016a, 2016b).
A recent report focused on defoliation of the most common tree species in Europe (ICP Forests, 2015) and highlighted unclear trends, as
beech and deciduous temperate oaks showed an increase of defoliation,
while Scots pine showed an evident reduction. However, in Romanian
plots, all three species/groups showed a decreasing defoliation,
confirming the importance of local environmental conditions in affecting the foliage density of trees as demonstrated by De Marco et al.,
2014. Such significant improvement of forest health status in Romania
in the last decade can be linked with the increase of precipitation
combined with relatively low increase of temperature, and low air pollution level (Badea et al., 2004). Despite constantly decreasing trends of
averaged values of O3 concentrations, however, POD0 increased over
time, even if not significantly, because POD0 is not only dependent by
O3 concentrations but also by environmental parameters. The RF analysis emphasised that O3 concentration was the most important factor for
defoliation in F. sylvatica and P. abies, and the second most important
factor in Quercus sp. The high number of predictors important for
Picea abies highlights its great requirements in terms of environmental
resources and its sensitivity to maximum and minimum temperature
for growth (Partanen et al., 1998). An involvement of climate is expected to affect tree health and vitality in Romania (Cuculeanu et al., 2002),
and this is visible by the results of RFA, where the importance of temperature is indicated for Picea and Fagus.
We have an apparent contradiction between the high importance of
O3 concentration in determining defoliation values for all the three tree
species in Romania, while POD0, i.e. the stomatal O3 uptake, showed a
very low importance (Fig. 3). Indeed, crown defoliation is an a-specific
symptom and is not surprising that it is better related to O3 concentration and AOT40 that are dependent on other environmental parameters,
such as temperature and solar radiation, than to POD0. These results are
in agreement with Sicard et al. (2016a, 2016b), where Mediterranean
forests showed a closer relationship between AOT40 and a-specific
crown damage (defoliation and discoloration), than between POD0
and O3– visible foliar injury, which is an O3 specific symptom. Furthermore, the POD0 critical levels of 19–32 mmol/m2 and 25 mmol/m2 suggested by Sicard et al. (2016a, 2016b) for the protection of Southern
European conifers (sensitive - moderately sensitive) and deciduous species, respectively, were never exceeded in the Romanian forest plots.
This can explain the weak relationship between defoliation and POD0
observed in this work. The relatively low levels of POD0 can be explained by the peculiar meteorological conditions of the Carpathian
402
A. De Marco et al. / Science of the Total Environment 596–597 (2017) 396–404
Table 2
GRM significant parameters and coefficients for crown defoliation for the three species,
obtained by averaging four runs of the GRM model.
Picea abies
Intercept
AOT402
Elevation ∗ NH3
Elevation ∗ age
Elevation2
Slope ∗ exposure
Quercus spp
Intercept
O3
Elevation ∗ slope
Exposure
Exposure ∗ O3
Fagus Sylvatica
Intercept
POD02
Age
Age ∗ POD0
Age2
Coeff.
Std.Err.
t
p
−1.06E +04
−8.27E-07
−4.32E-04
−6.73E-04
5.34E-05
−7.18E-03
7.51E+03
3.85E-07
2.13E-04
2.21E-04
2.04E-05
1.55E-03
−1.43
−2.16
−2.03
−3.06
2.64
−4.65
0.201
0.047
0.044
0.008
0.053
0.000
−9.44E+02
4.64E+01
−3.04E-03
8.94E-01
−2.27E-02
3.54E+02
2.13E+01
9.17E-04
2.46E-01
7.43E-03
−2.68
2.19
−3.31
3.63
−3.06
0.021
0.052
0.002
0.001
0.004
1.82E+02
1.16E-01
2.12E+00
−1.46E-02
1.16E-03
1.38E+03
4.01E-02
9.18E-01
5.13E-03
3.75E-04
0.13
2.91
2.31
−2.85
3.09
0.843
0.013
0.036
0.011
0.002
species-specific predictive models may be used in the prediction of future impacts of climate change and air pollution on forest defoliation.
In this frame, they will be useful for local management purposes in
order to select more resilient plant species enduring environmental
changes. Furthermore, predictive defoliation models could be used for
carbon gain assessment at country-level.
In conclusion even if climate is an important driver in affecting forest
health, our observations support the evidence of large impacts of O3 on
forest defoliation. It is thus important to control O3 levels to improve
forest health and vitality.
Acknowledgements
This work was carried out within the bilateral agreements between
National Council of Research of Italy and Romanian Academy (20142016 and 2017-2019, Tropospheric ozone effects on forest growth and
diversity – TROZGRODIV and TROZGRODIV2) and the LIFE15 ENV/IT/
000183 project MOTTLES.
Appendix A. Supplementary data
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.scitotenv.2017.03.135.
References
Fig. 4. Cross validation of General Regression Models (GRM) for Picea abies (A), Quercus
spp. (B) and Fagus sylvatica (C). Predicted defoliation values ranged between 0.82 and
0.90 of the observed ones.
forests where temperature is generally lower than the optimal temperature values necessary for maximizing stomata opening in the forest
species investigated here (Williams et al., 2013). Furthermore
Carpathian forests are characterized by scarce water limitation
(Paltineanu et al., 2007) and the soil water content can be affected by
the terrain slope level.
Both the statistical analyses applied here confirmed that crown defoliation is a-specific indicator of O3, as also suggested by Sicard and
Dalstein-Richier (2015).
Picea abies had a larger predictor number than Fagus sylvatica and
Quercus sp., pointing out different ecological requirements (Pretzsch
et al., 2013; Fotelli et al., 2009). This aspect has important effects on
the building of the GRM model, where a larger number of predictors
was used to define good predictive models for defoliation. These
Ainsworth, E.A., Yendrek, C.R., Sitch, S., Collins, W.J., Emberson, L.D., 2012. The effects of
tropospheric ozone on net primary productivity and implications for climate change.
Annu. Rev. Plant Biol. 63, 637–661.
Akimoto, H., 2003. Global Air Quality and Pollution Science. 302 pp. 1716–1719.
Anav, A., De Marco, A., Proietti, C., Alessandri, A., Dell'Aquila, A., Cionni, I., Friedlingstein, P.,
Khvorostyanov, D., Menut, L., Paoletti, E., Sicard, P., Sitch, S., Vitale, M., 2016. Comparing concentration-based (AOT40) and stomatal uptake (PODY) metrics for ozone risk
assessment to European forests. Glob. Chang. Biol. 22:1608–1627. http://dx.doi.org/
10.1111/gcb.13138.
Ashmore, M.R., 2005. Assessing the future global impacts of ozone on vegetation. Plant
Cell Environ. 28 (8):949–964. http://dx.doi.org/10.1111/j.1365-3040.2005.01341.x.
Augustaitis, A., Bytnerowicz, A., 2008. Contribution of ambient ozone to Scots pine defoliation and reduced growth in the Central European forests: a Lithuanian case
study. Environ. Pollut. 155, 436–445.
Augustaitis, A., Sopauskiene, D., Bauziene, I., 2010. Direct and indirect effects of regional
air pollution on tree crown defoliation. Balt. For. 16, 23–34.
Badea, O., Tanase, M., Georgeta, J., Anisoara, L., Peiov, A., Uhlirova, H., Pajtik, J.,
Wawrzoniak, J., Shparyk, Y., 2004. Forest health status in the Carpathian Mountains
over the period 1997-2001. Environ. Pollut. 130 (1), 93–98.
Braun, S., Achermann, B., De Marco, A., Pleijel, H., Karlsson, P.E., Rihm, B., Schindler, C.,
Paoletti, E., 2017. Epidemiological analysis of ozone and nitrogen impacts on vegetation - critical evaluation and recommendations. Sci. Total Environ. (in press).
Breiman, L., 1996. Bagging predictors. Mach. Learn. 26 (2), 123–140.
A. De Marco et al. / Science of the Total Environment 596–597 (2017) 396–404
Breiman, L., 2001. Random forests. Mach. Learn. 45, 5–32.
Breiman, L., Cutler, A., 2003. Random Forests. Version 4.0. http://www.stat.berkeley.edu/
users/breiman/RandomForests/.
Bytnerowicz, A., Godzik, B., Frączek, W., Grodzińsk, K., Krywult, M., Badea, O., Barančok, P.,
Blum, O., Černy, M., Godzik, S., Mankovska, B., Manning, W., Moravčik, P., Musselman,
R., Oszlanyi, J., Postelnicu, D., Szdźuj, J., Varšavova, M., Zota, M., 2002. Distribution of
ozone and other air pollutants in forests of the Carpathian Mountains in central Europe. Environ. Pollut. 116, 3–25.
Bytnerowicz, A., Badea, O., Popescu, F., Musselman, R., Tanase, M., Barbu, I., Frączek, W.,
Gembasu, N., Surdu, A., Danescu, F., Postelnicu, D., Cenusa, R., Vasile, C., 2005. Air pollution, precipitation chemistry and forest health in the Retezat Mountains, Southern
Carpathians, Romania. Environ. Pollut. 137, 546–567.
Calatayud, A., Alvarado, J., Barreno, E., 2002. Similar Effects of Ozone on Four Cultivars of
Lettuce in Open Top Chambers During Winter Photosynthetica. 40 p. 195.
Carnicer, J., Coll, M., Ninyerola, M., Pons, X., Sánchez, G., Peñuelas, J., 2011. Widespread
crown condition decline, food web disruption, and amplified tree mortality with increased climate change-type drought. PNAS 108, 1474–1478.
Cuculeanu, V., Tuinea, P., Balteanu, D., 2002. Climate change impacts in Romania: vulnerability and adaptation options. GeoJournal 57, 203–209.
De Marco, A., Proietti, C., Cionni, I., Fischer, R., Screpanti, A., Vitale, M., 2014. Future impacts of nitrogen deposition and climate change scenarios on forest crown defoliation. Environ. Pollut. 194, 171–180.
De Marco, A., Sicard, P., Vitale, M., Carriero, G., Renou, C., Paoletti, E., 2015. Metrics of
ozone risks assessment for Southern European forests: canopy moisture content as
a potential plant response indicator. Atmos. Environ. 120, 182–190.
Díaz-de-Quijano, M., Peñuelas, J., Ribas, À., 2009. Increasing interannual and altitudinal
ozone mixing ratios in the Catalan Pyrenees. Atmos. Environ. 43, 6049–6057.
Dobbertin, M., Ghosh, S., Innes, J.L., 1997. National reports: Switzerland. In: MüllerEdzards, C., de Vries, W., Erismam, J.W. (Eds.), Ten Years of Monitoring Forest Condition in Europe. EC-UN/ECE, Brussels, Geneva, pp. 120–124.
Emberson, L.D., Ashmore, M.R., Cambridge, H.M., Tuovinen, J.P., Simpson, D., 2000. Modelling stomatal flux across Europe. Environ. Pollut. 109, 403–413.
Fotelli, M.N., Nahm, M., Radoglou, K., Rennenberg, H., Halyvopoulos, G., Matzarakis, A.,
2009. Seasonal and interannual ecophysiological responses of beech (Fagus sylvatica)
at its south-eastern distribution limit in Europe. For. Ecol. Manag. 257 (3),
1157–1164.
Girgždienė, R., Serafinavičiūtė, B., Stakėnas, V., Byčenkienė, S., 2009. Ambient ozone concentration and its impact on forest vegetation in Lithuania. AMBIO J. Hum. Environ.
38 (8), 432–436.
Hendricks, C.M.A., van der Burg, J., Oude Voshaar, J.H., van Leeuwen, E.P., 1997. Relations
Between Forest Conditions and Stress Factors in The Netherlands in 1995. DLO
Winand Staring Centre for integrated Land, Soil and Water research, Wageningen
(NL Report 148).
ICP FORESTS, 2015. In: Michel, A., Seidling, W. (Eds.), (2015) Forest Condition in Europe:
2015 Technical Report of ICP Forests. Report under the UNECE Convention on LongRange Transboundary Air Pollution (CLRTAP). BFW Austrian Research Centre for Forests, Vienna (BFWDokumentation 21/2015. 182 p).
Imhoff, M.L., et al., 2004. Global patterns in human consumption of net primary production. Nature 429, 870–873.
Innes, J.L., Boswell, R.C., 1987. Forest health survey 1987. Part 2: Analysis and Interpretation. Forestry Commission Bulletin 79HMSO, London, UK.
Innes, J.L., Whittaker, R.J., 1993. Relationships between the crown condition of Sitka and
Norway spruce and the environment in Great Britain: an exploratory analysis.
J. Appl. Ecol. 30, 341–360.
IPCC AR5 WG1, 2013. In: Stocker, T.F., et al. (Eds.), Climate Change 2013: The Physical Science Basis. Working Group 1 (WG1) Contribution to the Intergovernmental Panel on
Climate Change (IPCC) 5th Assessment Report (AR5). Cambridge University Press.
Jarvis, P.G., 1976. The Interpretation of the Variations in Leaf Water Potential and Stomatal
Conductance Found in Canopies in the Field. The Royal Society, pp. 1471–2970.
Karnosky, D.F., Skelly, J.M., Percy, K.E., Chappelka, A.H., 2007. Perspectives regarding
50 years of research on effects of tropospheric ozone air pollution on US forests. Environ. Pollut. 147 (3), 489–506.
Kendall, M.G., 1975. Rank Correlation Methods. fourth ed. Charles Griffin, London.
Klap, J.M., Oude Voshaar, J.H., de Vries, W., Erisman, J.W., 2000a. Effects of environmental
stress on forest crown condition in Europe. Part IV: statistical analysis of relationships. Water Air Soil Pollut. 119, 387–420.
Klap, J.M., Oude Voshaar, J.H., De Vries, W., Erisman, J.W., 2000b. Effects of environmental
stress on forest crown condition in Europe. Part IV: statistical analysis of relationships. Water Air Soil Pollut. 119, 387–420.
Kotwal, P.C., Omprakash, M.D., Gairola, S., Dugaya, D., 2008. Ecological indicators: imperative to sustainable forest. Ecol. Indic. 8, 104–107.
Kozak, J., Estreguil, C., Troll, M., 2007. Forest cover changes in the northern Carpathians in
the 20th century: a slow transition. J. Land Use Sci. 2, 127–146.
Krupa, S.V., Manning, W.J., 1988. Atmospheric ozone: formation and effects on vegetation.
Environ. Pollut. 50, 101e137.
Krupa, S., McGrath, M., Andersen, C.P., Booker, F.L., Burkey, K.O., Chappelka, A.H., Chevone,
B.I., Pell, E.J., Zilinskas, B.A., 2001. Ambient Ozone and Plant Health. 85 pp. 4–12.
Mann, H.B., 1945. Non-parametric tests against trend. Econometrica 13, 163–171.
Manning, J.W., 2005. Establishing a cause and effect relationship for ambient ozone exposure and tree growth in the forest: progress and an experimental approach. Environ.
Pollut. 137, 443–454.
Mather, R., Freer-Smith, P., Savill, P., 1995. Analysis of the Changes in Forest Condition in
Britain 1989 to 1992. Forestry Commission Bulletin 116HMSO, London, UK.
Matyssek, R., Bytnerowicz, A., Karlsson, P.-E., Paoletti, E., Sanz, M., Schaub, M., Wieser, G.,
2007. Promoting the O3 flux concept for European forest trees. Environ. Pollut. 146
(3):587–607. http://dx.doi.org/10.1016/j.envpol.2006.11.011.
403
McCullagh, P., Nelder, J.A., 1989. Generalized Linear Models. second ed. Chapman & Hall,
New York.
Menut, L., Bessagnet, B., Khvorostiyanov, D., et al., 2013. CHIMERE 2013: a model for regional atmospheric composition modeling. Geosci. Model Dev. 6, 981–1028.
Musselman, R.C., Lefohn, A.S., Massman, W.J., Heath, R.L., 2006. A Critical Review and
Analysis of the Use of Exposure- and Flux-Based Ozone Indices for Predicting Vegetation Effects Atmospheric Environment. 40 pp. 1869–1888.
Novotný, J., Grodzki, W., Knízek, M., McManus, M., Turčáni, M., 2002. The impact of spruce
bark beetle populations on mountain spruce forests and ecological approaches to
their management. In effects of air pollution on forest health and biodiversity in forests of the Carpathian Mountains. Proceedings of the NATO Advanced Research
Workshop, Stara Lesna, Slovakia, 22–26 May 2002. IOS Press, pp. 250–258.
Oltmans, S., Lefohn, A.S., Harris, J.M., Galbally, I., Scheel, H.E., Bodeker, G., Brunke, E.,
Claude, H., Tarasick, D., Johnson, B.J., Simmonds, P., Shadwick, D., Anlauf, K.,
Hayden, K., Schmidlin, F., Fujimoto, T., Akagi, K., Meyer, C., Nichol, S., Davies, J.,
Redondas, A., Cuevas, E., 2006. Long-term changes in tropospheric ozone. Atmos. Environ. 40, 3156–3173.
Paltineanu, C., Mihailescu, I.F., Seceleanu, I., Dragota, C., Vasenciuc, F., 2007. Using aridity
indices to describe some climate and soil features in Eastern Europe. Theor. Appl.
Climatol. 90, 263–274.
Paoletti, E., Materassi, A., Fasano, G., Hoshika, Y., Carriero, G., Silaghi, D., Badea, O., 2017. A
new-generation 3D ozone FACE (free air controlled exposure). Sci. Total Environ. 575,
1407–1414.
Partanen, J., Koski, V., Hanninen, H., 1998. Effects of photoperiod and temperature on the
timing of bud burst in Norway spruce (Picea abies). Tree Physiol. 18, 811–816.
Pretzsch, H., Bielak, K., Block, J., Bruchwald, A., Dieler, J., Ehrhart, H.P., ... Zingg, A., 2013.
Productivity of mixed versus pure stands of oak (Quercus petraea (Matt.) Liebl. and
Quercus robur L.) and European beech (Fagus sylvatica L.) along an ecological gradient. Eur. J. For. Res. 132 (2), 263–280.
Schöpp, W., Posch, M., Mylona, S., Johansson, M., 2003. Long-term development of acid
deposition (1880–2030) in sensitive freshwater regions in Europe. Hydrol. Earth
Syst. Sci. 7 (4), 436–446.
Schütt, P., 1982. Aktuelle Schäden am Wald – Versuch einer Bestandesaufnahme. HolzZentralblatt. 108 pp. 369–370.
Scientific Assessment Report, (SAR), 2016. Towards cleaner air. In: Maas, R., Grennfelt, P.
(Eds.), Scientific Assessment Report 2016. EMEP Steering Body and Working Group
on Effects of the Convention on Long-Range Transboundary Air Pollution, Oslo
(xx+50ppEnviron).
Sen, P.K., 1968. Estimates of the regression coefficient based on Kendall's tau. J. Am. Stat.
Assoc. 63, 1379–1389.
Sicard, P., Dalstein-Richier, L., 2015. Health and vitality assessment of two common pine
species in the context of climate change in southern Europe. Environ. Res. 137,
235–245.
Sicard, P., De Marco, A., Dalstein-Richier, L., Tagliaferro, F., Paoletti, E., 2016a. An epidemiological assessment of stomatal ozone flux-based critical levels for visible ozone injury in Southern European forests. Sci. Total Environ. 541, 729–741.
Sicard, P., Serra, R., Rossello, P., 2016b. Spatiotemporal trends of surface ozone concentrations and metrics in France. Environ. Res. 149, 122–144.
Silaghi, D., Badea, O., 2012. Monitoring of ozone in selected Forest ecosystems in Southern
Carpathian and Romanian intensive monitoring network (level II). J. Environ. Monit.
14, 1710–1717.
Silaghi, D., Badea, O., Neagu, S., Iacob, C., Guiman, G., 2013. 3.6. Air quality. In: Badea, O.
(Ed.), Long-Term Ecological Researches on Representative Forest Ecosystems of
Bucegi Natural Park. Ed. Silvica, Bucharest, pp. 119–127.
Sitch, S., Cox, P.M., Collins, W.J., Huntingford, C., 2007. Indirect radiative forcing of
climate change through ozone effects on the land-carbon sink. Nature 448
(7155), 791–794.
Skamarock, W., Klemp, J.B., 2008. A time-split nonhydrostatic atmospheric model for
weather research and forecasting applications. J. Comput. Phys. 227, 3465–3485.
Stribley, G.H., Ashmore, M.R., 2002. Quantitative changes in twig growth pattern of young
woodland beech (Fagus sylvatica L.) in relation to climate and ozone pollution over
10 years. For. Ecol. Manag. 157, 191–204.
Svetnik, V., Liaw, A., Tong, C., Culberson, J., Sheridan, R., Feuston, B., 2003. Random forest:
a classification and regression tool for compound classification and QSAR modeling.
J. Chem. Inf. Comput. Sci. 43, 1947–1958.
Tang, H., Takigawa, M., Liu, G., Zhu, J., Kobayashi, K., 2013. A projection of ozone-induced
wheat production loss in China and India for the years 2000 and 2020 with exposurebased and flux-based approaches. Glob. Chang. Biol. 19, 2739–2752.
TFHTAP, 2010. Hemispheric transport of air pollution 2010. In: Dutchak, S., Zuber, A.
(Eds.), Part C: Persistent Organic Pollutants. Air Pollution Studies No. 19, LRTAP Convention, United Nations (2010).
UNECE, 2015. Mapping Critical Levels for Vegetation. International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops, Bangor, UK.
United Nations Economic Commission for Europe (UN/ECE), 2014. 30 years of monitoring
the effects of long-range transboundary air pollution on forests in Europe and beyond. Anniversary Report, pp. 1–2.
Vestreng, V., 2003. Review and Revision: Emission Data Reported to CLRTAP, MSC-W Status Rep. 2003, Note 1/2003. 134 pp. Oslo Meteorol. Inst., Oslo (ISSN 0804-2446).
Vitale, M., Proietti, C., Cionni, I., Fischer, R., De Marco, A., 2014. Random Forests Analysis: A
Useful Tool for Defining the Relative Importance of Environmental Conditions on
Crown Defoliation Water Air Soil Pollution. 225 p. 1992.
de Vries, W., Dobbertin, M.H., Solberg, S., van Dobben, H.F., Schaub, M., 2014. Impacts of
acid deposition, ozone exposure and weather conditions on forest ecosystems in Europe: an overview. Plant Soil 380, 1–45.
Wild, O., Fiore, A.M., Shindell, D.T., Doherty, R.M., Collins, W.J., Dentener, F.J., Schultz, M.G.,
Gong, S., MacKenzie, I.A., Zeng, G., Hess, P., Duncan, B.N., Bergmann, D.J., Szopa, S.,
404
A. De Marco et al. / Science of the Total Environment 596–597 (2017) 396–404
Jonson, J.E., Keating, T.J., Zuber, A., 2012. Modelling future changes in surface ozone: a
parameterized approach. Atmos. Chem. Phys. 12, 2037–2054.
Williams, A.P., Allen, C.D., Macalady, A.K., Griffin, D., Woodhouse, C.A., Meko, D.M.,
Swetnam, T.W., Rauscher, S., Seager, R., Grissino-Mayer, H.D., Dean, J.S., Cook, E.R.,
Gangodagamage, C., Cai, M., McDowell, N., 2013. Temperature as a potent driver of
regional forest drought stress and tree mortality. Nat. Clim. Chang. 3, 292–297.
Young, P.J., Archibald, A.T., Bowman, K.W., Lamarque, J.F., Naik, V., Stevenson, D.S., Tilmes,
S., Voulgarakis, A., Wild, O., Bergmann, D., Cameron-Smith, P., Cionni, I., Collins, W.J.,
Dalsøren, S.B., Doherty, R.M., Eyring, V., Faluvegi, G., Horowitz, L.W., Josse, B., Lee, Y.H.,
MacKenzie, I.A., Nagashima, T., Plummer, D.A., Righi, M., Rumbold, S.T., Skeie, R.B.,
Shindell, D.T., Strode, S.A., Sudo, K., Szopa, S., Zeng, G., 2013. Pre-industrial to end
21st century projections of tropospheric ozone from the Atmospheric Chemistry
and Climate Model Intercomparison Project (ACCMIP). Atmos. Chem. Phys. 13,
2063–2090.
Zierl, B., 2002. Relations between crown conditions and ozone and its dependence on environmental factors. Environ. Pollut. 119, 55–68.