Metrics of ozone risk assessment for Southern European forests: Canopy moisture content as a potential plant response indicator

Atmospheric Environment 120 (2015) 182e190

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Metrics of ozone risk assessment for Southern European forests: Canopy moisture content as a potential plant response indicator A. De Marco a, P. Sicard b, M. Vitale c, G. Carriero d, C. Renou b, E. Paoletti d, * a

ENEA, Via Anguillarese 301, 00123 Rome, Italy ACRI-ST, 260 route du Pin Montard, BP 234, 06904 Sophia Antipolis Cedex, France c UNIROMA1, Piazzale Aldo Moro, 00196 Rome, Italy d IPSP-CNR, Via Madonna del Piano 10, 50019, Sesto Fiorentino, Florence, Italy b

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


Stomatal ozone flux (POD) is a better metric than AOT40 for the protection of forests.
We recommend POD0 rather than POD1.
We cannot recommend CMC as a plant-response indicator.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 May 2015 Received in revised form 22 August 2015 Accepted 24 August 2015 Available online 2 September 2015

Present standards for protecting ecosystems from ozone (O3), such as AOT40, use atmospheric concentrations. A stomatal flux-based approach (Phytotoxic O3 Dose, PODY) has been suggested. We compared the spatial and temporal distribution of AOT40 and PODY e with and without a hourly threshold of uptake (POD1 and POD0) e for Pinus halepensis and Fagus sylvatica in South-eastern France and North-western Italy. Ozone uptake was simulated by including limitation due to soil water content, as this is an important parameter in water-limited environments. Both AOT40 and POD1 exceeded the critical levels suggested for forests. AOT40 suggested a larger O3 risk relative to PODY. No significant spatial and temporal difference occurred between POD1 and POD0. The use of POD0 in the assessment of ambient O3 risk for vegetation is thus recommended, because it is more biologically-meaningful than AOT40 and easier to be calculated than POD1. Canopy Moisture Content (CMC), a proxy of foliar water content, was modelled and tested as a potential plant O3 response indicator. CMC response to O3 was species-specific, and thus cannot be recommended in the epidemiology of O3 injury to forests. © 2015 Elsevier Ltd. All rights reserved.

Keywords: AOT40 Canopy moisture content Ground-level ozone Phytotoxic ozone dose Random forest analysis Stomatal ozone flux

1. Introduction

* Corresponding author. E-mail addresses: [email protected] (A. De Marco), Pierre.Sicard@ acri-st.fr (P. Sicard), [email protected] (M. Vitale), [email protected] (G. Carriero), [email protected] (E. Paoletti). http://dx.doi.org/10.1016/j.atmosenv.2015.08.071 1352-2310/© 2015 Elsevier Ltd. All rights reserved.

Tropospheric ozone (O3) is a secondary pollutant generated from volatile organic compounds (VOCs), carbon monoxide (CO) and nitrogen oxides (NOx) in photochemical reactions. Ozone is an important air quality issue, causes serious health problems, damages materials and ecosystems, and contributes to climate change

A. De Marco et al. / Atmospheric Environment 120 (2015) 182e190

(Kampa and Castanas, 2008; Screpanti and De Marco, 2009; Sicard et al. 2011a). Background O3 concentrations have doubled since pre-industrial times and have increased by 1e2% per year at northern mid-latitudes since about 1950 (Vingarzan, 2004). Sicard et al. (2013) showed an increase at suburban (0.46% per year) and urban (0.64% per year) background sites around the Western Mediterranean basin over 2000e2010. Compared to 2000, the relative changes in mean tropospheric O3 burden in 2030 (2100) vary from 4% to þ7% (16% to þ18%) (Young et al., 2013). Global surface temperature change for the end of the 21st century is likely to exceed 1.5  C relative to 1850e1900 (IPCC, 2013). Formation of O3 depends on temperature (The Royal Society, 2008). The risk of drought in summer will increase in southern Europe (IPCC, 2013). As O3 exposure is expected to unbalance the water control of vegetation (Paoletti and Grulke, 2010; Hoshika et al., 2012a), such climate changes emphasize the importance of a proper assessment of O3 risk to vegetation, in particular in Mediterranean climates. The Mediterranean region shows the highest O3 concentrations in Europe as atmospheric stability over the summer and high temperature and solar radiation promote O3 formation (Sicard et al., 2013). The increase of background O3 levels may have large negative impacts on vegetation in this region (Paoletti, 2007; Fares et al., 2013). Current European standards use the O3 exposure index AOT40 to protect vegetation, although it does not give information about the environmental constraints to O3 uptake into leaves, such as water stress (Paoletti and Manning, 2007). As a consequence, the AOT40 index may be inadequate for the quantification of O3 impacts on vegetation, particularly in water-limited regions (Paoletti, 2006). For this reason, a stomatal flux-based approach was suggested (Emberson et al., 2000) for estimating the amount of O3 that is absorbed into the leaf through stomata and integrating the effects of climatic factors and vegetation characteristics. The stomatal flux-based model, or DO3SE model, incorporates the seminal Jarvis' (1976) algorithm and describes species-specific effects of soil water availability, vapour pressure deficit, air temperature, irradiation, plant phenology and O3 concentration on stomatal functioning. The flux-based approach uses the maximum stomatal conductance to describe the upper limit of stomatal response to environmental stimuli, as represented by the 95the98th percentile of a cohort of conductance measurements (Hoshika et al., 2012b). Stomatal conductance modelled by DO3SE has been validated against direct measurements carried out by many previous studies (e.g. Altimir et al., 2004; Tuovinen et al., 2004; Nunn et al., 2005; Fares et al., 2014). The flux is then accumulated over a species-specific phenological time window and expressed as PODY (Phytotoxic Ozone Dose), where Y represents a detoxification threshold below which it is assumed that any O3 molecule absorbed by the plant will be detoxified (Mills et al., 2011a). A threshold of 1 is at present recommended for all forest trees (UNECE, 2010), although insufficient evidence is available in order to validate this threshold. The doseeresponse relationships have been derived mostly from fieldbased open-top chamber experiments (Hayes et al., 2007; Mills et al., 2007; Pleijel et al., 2007; UNECE, 2010). Although climatic conditions are naturally fluctuating inside such chambers, there is a constant windspeed which may affect ozone flux and thus a broadscale application in risk assessment is to be verified (Paoletti, 2007). Because of the many factors contributing to effects, epidemiological studies are better suited to validation of standards/thresholds than controlled-condition experiments. The epidemiology of O3 injury may be very helpful when real-world forests are investigated, as large trees require expensive experimental facilities for realistic O3 simulation and usually only a few individuals can be investigated. The epidemiology of injury due to stomatal O3 flux, however, is still in its infancy. Assessing large-scale stomatal O3

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fluxes is challenging as it requires access to data from different sources, complex long-term field measurements or modelling. This is why the majority of previous epidemiological assessments used ambient O3 exposure as a metric of injury (e.g., Braun et al., 2007; McLaughlin et al., 2007; Baumgarten et al., 2009; Sun et al., 2012; Kefauver et al., 2013; De Marco et al., 2013). A few epidemiological studies used stomatal O3 flux. De Marco et al. (2010) used geostatistics for combining the observation networks of O3, meteorology and durum wheat yield in central Italy, and found that stomatal O3 flux was the best O3 predictor for yield decline when compared to the exposure-based metrics used as legislative standards in Europe and North America. Mills et al. (2011b) superimposed visible foliar O3 injury and metrics across Europe and found a better fitting with PODY than with AOT40. Fares et al. (2013) compared long-term eddy-covariance measurements of carbon and O3 fluxes in three Mediterranean-type plant ecosystems and concluded that the observed reduction in carbon assimilation was better related to stomatal O3 flux than to O3 concentration. Similar conclusions were reached in a two-month micrometeorological assessment in a Norway spruce forest (Zapletal et al., 2011). By a sap-flow approach, Braun et al. (2014) estimated radial growth losses in Fagus sylvatica (19.5%) and Picea abies (6.6%) in Switzerland, based on annual O3 stomatal uptake during the period 1991e2011. Canopy Moisture Content (CMC) is a functioning index of forest ecosystems given that biogeochemical processes, such as photosynthesis, evaporation and net primary production, are directly related to foliar water content (Sellers et al., 1992). Quantification of vegetation water content has important implications in agriculture and forestry (Gao and Goetz, 1995), e.g. drought assessment of natural vegetation (Zhang et al., 2013), and prediction of forest susceptibility to fires (Ustin et al., 1998). CMC can be assessed by remote sensing (Goetz, 1990, 1995; Gao, 1996; Dawson et al., 1999) and carefully simulated by modelling (Ceccato et al., 2001; Chen and Dudhia, 2001; Strachan et al., 2002; Colombo et al., 2008). Ozone has been reported to induce both stomatal closure (Wittig et al. 2007) and slower or less efficient stomatal control (Paoletti, 2005; Paoletti and Grulke, 2010; Hoshika et al., 2012a, 2013, 2014). Such O3-induced stomatal sluggishness has the potential to affect leaf water losses and explain why O3-exposed leaves usually show lower water content than control leaves (Nali et al., 2004; Paoletti et al., 2007). We thus tested CMC as a plant-response indicator in the assessment of stomatal O3 flux effects on forest health. The aim of this study was to evaluate the performance of O3 risk indicators, namely POD0, POD1 and AOT40, in South-eastern France and North-western Italy forests in 2010e2011. Data were generated by the WRF-CHIMERE modelling system (9  9 km grid). In addition, modelled CMC was used as potential plant-response indicator for assessing impacts in F. sylvatica and Pinus halepensis. Exposure to simulated O3 enrichment suggests that these species are among the most O3 sensitive forest species (Gerant et al., 1996; Nunn et al., €w et al. 2006). However, they show 2005; Karlsson et al., 2007; Lo very contrasting ecology. F. sylvatica, or European beech, is a deciduous broadleaf tree, whose natural range extends from southern Sweden to Sicily in Italy, and from France and northern Portugal to northwest Turkey. F. sylvatica is an oceanic-climate species, requires a humid atmosphere and well-drained soil, tolerates rigorous winter cold and is sensitive to spring frost. Typically, it extends from 1000 to 1650 m a.s.l. in the investigation area and its growing season is from April to September (Jump et al., 2007). P. halepensis, or Aleppo pine, is a circum-Mediterranean conifer, mostly distributed along the coastline and at low altitudes, from sea level to 200 m a.s.l., except up to 1000 m a.s.l. in Southern Spain. P. halepensis is very drought-resistant and thermophilic, and its

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growing season is year-long (Vicente-Serrano et al., 2010). 2. Materials and methods 2.1. Area of study, data selection, modelling and mapping The study area was divided in 9  9 km grid cells, over northwestern Italy and south-eastern France. Across this area, P. halepensis is distributed in the warm region (along the coastline) and F. sylvatica is mainly distributed at high-altitude sites, where the climate is more humid and cool (Fig. 1S, Supporting information). The species occurrence in each pixel was determined by the EUFORGEN vegetation data (http://www.euforgen. org/distribution_maps.html). Hourly meteorological data (air temperature, relative humidity, soil water content and solar radiation), soil type, CMC and hourly O3 concentrations for 2010 and 2011 were obtained from the WRFCHIMERE modelling system. CHIMERE is an Eulerian offline chemistry-transport model developed to simulate gas-phase chemistry, O3 and aerosol formation, transport and deposition at regional scale (Menut et al., 2013; Schmidt et al., 2001; Bessagnet et al., 2004). CHIMERE runs over a range of spatial scales with resolution from 1 to 100 km. In our configuration CHIMERE has a spatial resolution of 9 km and uses eight vertical levels of increasing thickness away from the ground defined in hybrid sigma-pressure coordinates, with the first level at 0.997 sigma-level (about 20e25 m above the ground) and the top of the last level at 500 hPa. The regional chemical and transport model CHIMERE was coupled with the Weather Research and Forecasting (WRF) model, a limited-area, non-hydrostatic, terrain-following eta-coordinate mesoscale model (Skamarock et al., 2008; Monteiro et al., 2005; De Meij et al., 2009; Sicard et al., 2012). In this study, the version 3.6 with Advanced Research WRF (ARW) dynamic core was implemented with the physical schemes used in Anav et al. (submitted). Seventeen soil categories were translated into the physical parameters of surface characteristics, such as maximum moisture content and saturation soil potential. More details regarding the parameterizations of the above mentioned processes are described in Menut et al. (2013). A comparison of measured and modelled outputs for O3 risk assessment at the European level is in Anav et al. (submitted). Maps of AOT40, POD0 and POD1 were created by a geographic information system (ARC-GIS 9.3, ESRI - Redlands, CA, USA) at the same spatial resolution of 9  9 km. 2.2. Estimation of AOT40 and PODY AOT40 was estimated over the daylight hours of the growing season, i.e. April to September for beech and all year round for pine, according to the methodology for O3 risk assessment (UNECE, 2010):

AOT40 ¼

X

maxððC  40Þ; 0Þ:dt

(1)

where C is hourly O3 concentration (ppb) derived from the coupled WRF-CHIMERE model and dt is time step (1 h). Leaf-level stomatal conductance to water vapour (gsw) was estimated using the multiplicative model (Emberson et al., 2000) and the parameters suggested in UNECE (2010) for Aleppo pine and Mediterranean beech (Table 1S, Supporting information):

o n  gsw ¼ gmax $f phen $f light $max f min ; f temp $f VPD $f SWC

(2)

where gmax is the maximum gsw of a plant species (mmol m2 s1), and fmin is the minimum gsw (fraction of gmax). The other functions

are limiting factors and are scaled from 0 to 1. fphen, flight, ftemp, fVPD, and fSWC are the variation in gmax with leaf age, photosynthetic photon flux density (PPFD, mmol photons m2 s1), temperature (T,  C), vapour pressure deficit (VPD, kPa), and volumetric soil water content (SWC, m3 m3), respectively. The variation in gsw with leaf age (fphen) modifies gmax as a function of time within the leaf duration. In the Mediterranean area, the functions of SWC and phenology are considered redundant, because the availability of water in the soil tracks the phenological development (UNECE, 2010). We thus used fSWC and assumed that fphen was 1 throughout the growing season. The fSWC function was included in the model because it is critical for Medilez-Ferna ndez et al., 2013). terranean environments (Gonza

      SWC  WP þ fmin fSWC ¼ min 1; fmin ; ð1  fmin Þ* FC  WP (3) where WP is SWC at wilting point and FC is SWC at field capacity. These parameters are constant and depend on the soil type (Table 2S, Supplementary information). Soil type was obtained from a module included into the WRF-CHIMERE model. The flight, ftemp and fVPD functions have been expressed by various formulas (e.g., Jarvis, 1976; Sirisampan et al., 2003; Emberson et al., 2000). In this study, the following formulas (UNECE, 2010) were applied:

f light ¼ 1  expðlighta *PPFDÞ  f temp ¼

8 > <

T  Tmin Tmax  T T Topt  Tmin >  Topt max :

(4) 



9 > = Topt Tmin

Tmax Topt

> ;

(5)

     ð1  fmin Þ*ðVPDmin  VPDÞ þ fmin f VPD ¼ min 1; max fmin ; ðVPDmin  VPDmax Þ (6) where lighta is an adimensional constant, PPFD is hourly photosynthetic photon flux density, Topt, Tmin, and Tmax represent the optimum, minimum, and maximum temperature for gsw, respectively, VPDmin and VPDmax are minimum and maximum Vapour Pressure Deficit for gsw, respectively. The parameters used in the model (Table 1S, Supplementary information) were derived from UNECE (2010), although a higher gmax (350 mmol m2 s1) was proposed by Elvira et al. (2007) for Aleppo pine. PODY, i.e. the stomatal O3 uptake above a species-specific threshold Y (nmolO3 m2 s1) accumulated over the growing season, was calculated as:

PODY ¼

X

maxððPOD0  YÞ; 0Þ:dt

(7)

where

POD0 ¼

X ½ðgsw *½O3 *0:663:dt

(8)

[O3] is hourly O3 concentrations, dt is 1 h (UNECE, 2010) and 0.663 is a conversion factor to account for the difference in the molecular diffusivity of water vapour and O3 (Massman, 1998). Two hourly thresholds were tested: 1 nmolO3 m2 PLA s1 as recommended by UNECE (2010), and 0 nmolO3 m2 PLA s1 as suggested by the fact that any O3 molecule entering into the leaf may induce a metabolic response (Musselman et al., 2006).

A. De Marco et al. / Atmospheric Environment 120 (2015) 182e190

2.3. Estimation of canopy moisture content CMC, i.e. the depth of liquid water (m) or the quantity held by the leaves of a plant canopy (g m2), represents the available quantity of water in the vegetation. The advanced Land Surfacehydrology Model (LSM) integrated into the WRF modelling system estimates this parameter with a significant accuracy (105 m) (Chen and Dudhia, 2001). A similar LSM is used in the NCEP (National Centres for Environmental Prediction) operational global and regional models (Chen and Mitchell, 1999). Forest CMC is derived by total precipitation Ptot, wet canopy evaporation Ec, green vegetation fraction f, and precipitation at the ground surface Pun, when CMC is higher than the maximum water retention capacity CMCmax (Jacquemin and Noilhan, 1990; Chen and Dudhia, 2001) that is:

vCMC ¼ f $Ptot  Pun  Ec vt " Ec ¼ f $Ep

CMC CMCmax

(9)

0:5 #

CMCmax ¼ av $LAI

(10)

(11)

where Ep is the potential evaporation, and is calculated by a Penman-based energy balance approach that includes a stabilitydependent aerodynamic resistance (Mahrt and Ek, 1984), and av is a constant depending on vegetation type within a range of 0.15e0.25 (Chen et al., 2007). While the vegetation type is an annually invariant field, some vegetation characteristics vary seasonally such as Leaf Area Index (LAI) and f. Because it is difficult to simultaneously derive LAI and f from a single-product NDVI (Normalized Difference Vegetation Index) and from Advanced Very High Resolution Radiometer (AVHRR), one of them must be pre-set (Gutman and Ignatov, 1998). In the current WRF-LSM, LAI was preset and f was assigned by the monthly 5-year climatology of green vegetation cover data derived from AVHRR of the entire area of study (Gutman and Ignatov, 1998). 2.4. Statistical analyses Ambient O3 impacts on plants are difficult to untangle from cooccurring environmental factors because of multi-colinearity and non-linear effects (De Marco et al., 2010, 2013). Random Forest can be successfully applied to derive information about O3 impacts on plants under real-world conditions (Fares et al., 2013), and is not requested to satisfy the normality pre-requisite, in consideration of the high number of data (Kotz et al., 2000). A Regression Tree Analysis (RTA) was performed in order to determine the importance of each variable in determining CMC (Breiman et al., 1984). The Decision Tree response is an estimate of the dependent variable given the predictors and their responses are combined (averaged) to obtain an estimate of the dependent variable (regression). The final predictor importance values are computed 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. 3. Results Monthly averages of AOT40 and PODY (with Y ¼ 0 and 1) are shown for beech and pine for 2010 and 2011 (Fig. 1). AOT40 peaked in July 2010 and August 2011. Still in July, both species showed a decline in POD0 and POD1, with the exception of pine in 2010. POD0 and POD1 trends were similar. In beech, PODY peaked in May in both years. In Aleppo pine, the peak occurred in July in 2010 and

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in June in 2011. Overall, PODY was high in May to July for beech and April to September for pine. Average values of air temperature, AOT40, POD0 and POD1 were significantly higher in 2011 than in 2010 for both species (Table 1). All O3 metrics were higher in pine than in beech, because they were cumulated over different time-periods (AprileSeptember and all the year round for beech and pine, respectively). Interestingly, the AOT40 value over 12 months for pine was only about 1e2 ppm h greater than the 6 month value for beech (þ4e9%), while POD0 and POD1 were around 145e200% and 220e320% higher in pine than in beech. The AOT40 critical level (CL) of 5 ppm.h was exceeded over the entire beech and pine areas in both years (Fig. 2). The most frequent AOT40 values were between 19 and 23 ppm.h for beech, and exceeding 25 ppm.h for pine. AOT40 peaked in Italy and the lowest value was obtained for France (Table 1, Fig. 2). The spatial distributions of POD0 and POD1 were very similar. In beech, the allarea average PODY values peaked in Italy in 2011, while the lowest values occurred in France in 2011 (Table 1, Fig. 2). The all-area average POD1 of beech was 4.1 mmol/m2 in 2010 and 6.7 mmol/ m2 in 2011. In pine, the highest average POD0 value was still in Italy in 2011 and the lowest value was in France in 2010 (Table 1, Fig. 2). The average POD1 of pine was 17.3 mmol/m2 in 2010 and 21.7 mmol/m2 in 2011. The RTA highlighted that the most important factors affecting CMC in beech were temperature and SWC in both years, with no difference among O3 metrics (Fig. 3a and c). In pine, CMC was affected by several predictors, including all the O3 metrics (Fig. 3b and d). POD0 and POD1, however, were slightly more important than AOT40 (þ46e50% and þ16e0% in 2010e2011, respectively) and annual O3 average (þ34e38% and þ54e26% in 2010e2011, respectively) in affecting CMC. 4. Discussion Both AOT40 and POD1 exceeded the CLs suggested by UNECE (2010) for the protection of forests, i.e. 5 ppm.h (any forest species) and 4 mmol/m2 (deciduous broadleaf species including F. sylvatica), respectively, over the entire area of study (Fig. 2). POD0-based CLs and a POD1-based CL for P. halepensis have not been established in the literature. Previous studies have already shown AOT40 levels well above the threshold for forest protection in France and Italy, with values similar to those found here (26.8 and 29.5 ppm h for Italian deciduous and evergreen forests, respectively, in Paoletti et al. (2007); 25e33 ppm h in Dalstein and Vas (2005); and 31.2e38.2 ppm h in Sicard et al. (2011b) for pine forests in southern France). Also PODY levels are usually higher in Southern Europe than in Nordic countries, although the values cannot be compared with those in the present study because they were calculated for crops (Mills et al., 2011b) and without the soil water content limitation (Tuovinen et al., 2007). Water stress is the main limiting factor to O3 uptake in Mediterranean countries lez-Ferna ndez et al., 2013). As large-scale (Paoletti, 2006; Gonza data of SWC are difficult to obtain, this limitation has often been neglected in calculating PODY. When the effect of SWC on stomata was considered, AOT40 estimated a higher O3 risk relative to PODY, as AOT40 values were even 6 times higher than the CL, while POD1 of beech was <3 times higher. Remarkable differences in the temporal and spatial pattern of O3 risk occurred when comparing AOT40 and PODY (Figs. 1 and 2); for instance, in 2011 the area at highest risk of injury to beech was in Italy when assessed by AOT40, and in France and along the border when assessed by POD0. Previous modelling approaches to estimate O3 risk to vegetation (Tuovinen et al., 2007; Karlsson et al., 2009; Mills et al., 2011b; de Andres et al., 2012) also showed significant differences in the spatial distribution of AOT40 and PODY.

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A. De Marco et al. / Atmospheric Environment 120 (2015) 182e190

Fig. 1. Monthly averages of AOT40 (O3 concentrations accumulated above 40 ppb hourly average), POD0 and POD1 (stomatal O3 uptake without and with a threshold, respectively) for the years 2010 and 2011 in Fagus sylvatica and Pinus halepensis forests of South-eastern France and North-western Italy.

Table 1 Annual averages during the daylight hours of the growing season, as obtained from WRF-CHIMERE simulations: hourly air temperature; hourly O3 concentrations; O3 concentrations accumulated above 40 ppb hourly average (AOT40); and stomatal O3 uptake without (POD0) and with a threshold (POD1) (±SD; min and max values in brackets), calculated for European beech and Aleppo pine in 2010 and 2011. Significant differences between the years are marked by asterisks:*p < 0.05, **p < 0.01 and ***p < 0.001 (Student t test). N is the number of data i.e. of 9  9 km pixels. Plant species

Year

N

Temperature ( C)

Ozone (ppb)

AOT40 (ppm h)

POD0 (mmol/m2)

POD1 (mmol/m2)

Fagus sylvatica

2010

364

2011

364

p 2010

182

2011

178

18.6 ± 1.6 [13.2; 21.3] 21.4 ± 1.5 [15.0; 23.2] *** 14.8 ± 0.7 [12.9; 15.9] 16.3 ± 0.5 [14.2; 17.0] ***

50.99 ± 3.49 [4.9; 138.3] 52.46 ± 2.57 [7.1; 148.8 ] *** 42.76 ± 2.65 [7.9; 69.9] 43.60 ± 2.64 [8.4; 84.1] *

22.4 ± 4.2 [1.2; 32.4] 23.6 ± 6.3 [7.1; 38.8] ** 23.3 ± 4.2 [8.8; 37.7] 25.7 ± 6.0 [9.5; 46.2] ***

9.04 ± 2.02 [5.48; 13.64] 13.13 ± 2.13 [8.60; 18.03] *** 27.52 ± 7.17 [18.03; 53.72] 32.17 ± 8.30 [20.69; 69.17] ***

4.13 ± 1.71 [1.43; 8.19] 6.73 ± 2.01 [2.44; 11.59] *** 17.27 ± 7.1 [7.62; 43.64] 21.74 ± 8.23 [10.66; 58.20] ***

Pinus halepensis

p

For both metrics, higher O3 impacts are expected for southern Europe where O3 concentrations are highest. However, risk maps based on PODY suggested potential effects in central and northern Europe where lower O3 concentrations occur in climatic conditions that are conducive to relatively high stomatal O3 uptake (e.g. Mills et al., 2011b). To the best of our knowledge, a comparison of the spatial and temporal distributions of PODY with different Y thresholds is innovative. The results of this comparison suggest that the patterns of O3 risk did not differ when using POD1 or POD0 (Figs. 1 and 2).

To validate the best indices for summarizing ambient air quality information in biologically meaningful forms, the effects of AOT40 and PODY on plant biological responses have to be investigated. We tested whether CMC can be proposed as an indicator of O3 risk to forests, and obtained the data from the LSM-WRF modelling. Foliar water content is directly affected by the coupling of CO2 assimilation and stomatal conductance, which in turn depends on environmental drivers such as leaf-to-air water vapour difference and SWC (Stokes et al., 2010). As a consequence, CMC is important for understanding the functioning of terrestrial ecosystems, and for

A. De Marco et al. / Atmospheric Environment 120 (2015) 182e190

Fig. 2. Geographical distribution of annual AOT40, POD0 and POD1 values for the years 2010 and 2011 in Fagus sylvatica and Pinus halepensis.

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A. De Marco et al. / Atmospheric Environment 120 (2015) 182e190

Fig. 3. Relative importance of different parameters in affecting Canopy Moisture Content in 2010 and 2011 as assessed by a Regression Tree Analysis performed on monthly averages in the Fagus sylvatica (left panel, a, b. N ¼ 4368) and Pinus halepensis (right panel c, d. N ¼ 4320) distribution areas. Long, longitude; Lat, latitude, Temp, air temperature; RH, relative humidity; Radiation, solar radiation; SWC, soil water content; POD0, stomatal O3 uptake without a threshold; POD1, stomatal O3 uptake with a threshold of 1 nmolO3 m2 PLA s1; and AOT40, O3 concentrations accumulated above 40 ppb hourly average.

~ uelas et al. 1993). drought assessment of natural vegetation (Pen The effects of O3 metrics and climate on CMC depended on the ecological traits of the species (Fig. 3). F. sylvatica is distributed at high-altitude where climate is more humid and cool, and is very sensitive to SWC changes when temperature increases. Beech ecology is consistent with our results, showing that CMC was € w et al. (2006) mostly affected by air temperature and SWC. Lo demonstrated that adult F. sylvatica trees exposed to ambient O3 enrichment in Germany efficiently closed their stomata during the heat wave of 2003. This response may be even more efficient in Southern populations, showing high potential for drought acclimation, e.g. sclerophyll leaves (Grossoni et al., 1998) and low susceptibility to photo-inhibitory damage (Tognetti et al., 1998). These characteristics translate into lower O3 sensitivity compared to Northern populations (Paoletti et al., 2002). Overall, F. sylvatica is classified as O3-sensitive species (Baumgarten et al., 2000; Nunn et al., 2005), and it is thus surprising that CMC was not significantly affected by any O3 metric. Also P. halepensis is considered a very O3-sensitive species (Gerant et al., 1996; Alonso et al., 1999; €€ Kivim€ aenpa a et al., 2010). P. halepensis is distributed in the warm climate region along the coastline. It did not show unequivocal relationships between indices and CMC, and was subject to the simultaneous effects of drought and O3 because responses of similar magnitude to temperature, RH, solar radiation, O3 and SWC were observed (Fig. 3). CMC response to O3 was thus speciesspecific, being negligible in F. sylvatica and significant in P. halepensis. The use of CMC as a plant response indicator in the assessment of O3 risk to forests thus depends on the plant species, and cannot be generally recommended in the epidemiology of O3

injury to different forest types, without first showing evidence of usefulness. This approach, however, could be useful for some species, e.g. P. halepensis. We conclude that the use of the exposure-based AOT40 metric estimated a higher O3 risk for vegetation relative to the flux-based PODY metric. As AOT40 does not consider differences between tree species, site conditions (Matyssek and Innes, 1999; Wieser et al., 2000; VanderHeyden et al., 2001) and the physical, biological, and meteorological processes controlling the transfer of ozone from the atmosphere into the leaf (Musselman et al., 2006), PODY is more biologically-based and its use in assessing O3 risk to forests is to be recommended. To compare POD0 and POD1, we tested the potential of CMC as plant response indicator, but CMC did not result to be well suited for this purpose. When comparing the spatial and temporal differences of using a threshold-based POD1 metric rather than a no-threshold POD0 metric, we did not observe significant differences. Threshold-based indices assume that plants have adapted to low, pre-industrial, naturally occurring O3 concentrations, and postulate that O3 in the mesophyll is detoxified without inducing injury below a defined threshold (Mills et al., 2011a). Disadvantages of such indices are that the thresholds lead to a significant sensitivity to uncertainties in the input flux calculations (Tuovinen et al., 2007) and the calculations are more complex relative to a no-threshold metric. On the basis of our results, we conclude that POD0 and POD1 are equivalent in the assessment of ambient O3 risk for vegetation and recommend the use of POD0, because it has larger biological significance relative to AOT40 and practicality in usage relative to POD1. To define suitable thresholds Y, however, PODY should be correlated to real-world forest impacts

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