UNIVERSITÀ DEGLI STUDI DI NAPOLI FEDERICO II DIP. INGEGNERIA AGRARIA ED AGRONOMIA

International workshop OZONE RISK ASSESSMENT FOR EUROPEAN VEGETATION 10-11 May 2007 Villa Orlandi, Anacapri, Capri island (Naples, Italy)

PROGRAMME

10 May 15.30-17.00 Welcome and Registration

17.00-17.15 Presentation of the workshop programme (Fagnano M.) 17.15-17.30 Scientific policy developments in the European Commission about ozone effects on vegetation (S.Cieslik)

Session 1. Knowledge gaps in the ozone flux concept Chairman: G.Mills 17.30-17.40 Challenge towards mechanistic O3 risk assessment in forest trees (R. Matyssek, W. Oßwald, G. Wieser) 17.45-17.55 Promoting the O3 flux concept for European crops (H. Pleijel) 18.00-18.10 Long-term ozone enrichment study: how to estimate the flux? (J. Fuhrer, C. Ammann) 18.15-18.25 Flux modeling work of the LRTAP Convention (L.Emberson) 18.30-19.30 Discussion

20.30 Dinner

11 May Session 2. Activity of the Steering Group of the Ozone Risk Assessment Network. Chairman: S.Cieslik 8.30-8.45 Post Ispra activities (S.Cieslik) 8.45-9.00 Forest (R.Matyssek) 9.00-9.15 Seminatural Vegetation (N.Cape) 9.15-9.30 Crops (M.Fagnano)

Session 3. Specific problems of Mediterranean vegetation Chairman: P.Dizengremel 9.30-9.40 Ozone effects on Mediterranean Forests (E.Paoletti) 9.45-9.55 Ozone effects on Mediterranean crops (A.Maggio) 10.00-10.10 Ozone effects on Mediterranean (semi-)natural vegetation (B.Gimeno) 10.15-10.30 Discussion

10.30-11.00 Coffee break

Session 4. Methodological aspects Chairman: A. Maggio 11.00-11.10 Cellular and molecular aspects of ozone flux and impact on higher plants (P.Dizengremel) 11.15-11.25 Upscaling concepts of O3 flux: leaf to landscape (Matyssek, Paoletti, Wieser, Cieslik, Ceulemans) 11.30-11.40 Flux measurements and modeling on fruit trees: methodological approach (G.Rana, F.De Lorenzi) 11.45-11.55 Flux measurement and modeling at canopy level (L. Gruenhage, G. Gerosa, A. Vermeulen) 12.00-13.00 Discussion

13.00-14.00 Lunch

Session 5. Interactions with other European models Chairman: L.Gruenhage 14.00-14.15 Modelling and observation of root water uptaking for agricultural crops (D’Urso G., Palladino M.) 14.15-14.25 APES: an integrated framework for potential implementation of ozone impact models (M.Donatelli)

14.30-16.00 Free parallel sessions

16.00-16.30 Coffee break

Session 6. General discussion and Preparation of common documents Chairman: M.Fagnano & N.Cape 16.30-17.00 The research needs of ozone/vegetation interactions, at scientific and decision-maker levels: crops & seminaturals

Chairman:R.Matyssek 17.00-17.30 The research needs of ozone/vegetation interactions, at scientific and decision-maker levels: forest

Conclusions of the workshop Chairman: S.Cieslik 17.30-18.30 Discussion and approval of common documents toward FP7 applications.

Università degli Studi di Napoli - Federico II Faculty of Agriculture

International workshop OZONE RISK ASSESSMENT FOR EUROPEAN VEGETATION 10-11 May 2007 Villa Orlandi, Anacapri

TYPICAL RURAL LANSCAPES

Orchards: fruit tree cultivation started from the Vesuvian area (i.e. apricot)

Mixed crops: 3 floor farming (1st vegetables, 2nd grape/orange, 3rd walnut/kaki/cherry) 3 crops in 1 year (tomato: Apr-Aug – cauliflower: Aug- Dec – early potato: Dec-Apr)

Terraces: grape, lemons, olive

Roman “Centuriazione”: orthogonal fields bordered by rows of trees

Vite “maritata” (married): grape grown on tall trees (elm, poplar)

11 May 14.30-16.00 Free parallel sessions

1 Legge Integration of research needs 2 Cermak J., Zapletal Sap flow, eddy covariance and related techniques applicable M., Cudlín P. for ozone studies 3 Dieter Ernst Molecular aspects of ozone flux 4 Fagnano Choosing the crops 5 Dizengremel Organization of tasks in physiology and biochemistry 6 Rana Problems in studying the ozone impact on tree crops 7 Mills crops 8 Vandermeiren Quality aspects of crops 9 Saitanis Tropospheric ozone: a menace for crops and natural vegetation in Greece 10 Wieser Flux measurements at the stand level 11 Oksanen Impacts of ozone on birch 12 Matyssek FOREST 13 Oßwald FOREST 14 Braun FOREST 15 Meister Estimating ozone impacts: mapping errors of BGC-model Bolhar-nordenkampf predictions vs. modelled ozone uptake 16 Bolhar-nordenkampf Apoptosis caused by reactive oxygen species, a common Meister stress response? FluxFlux measurementsmeasurements andand modellingmodelling onon fruitfruit trees:trees: methodologicalmethodological approachapproach

GianfrancoGianfranco RanaRana(1) && FrancescaFrancesca DeDe LorenziLorenzi(2)

(1)CRA-Agronomical Research Institute, Bari, Italy (2)CNR-ISAFOM, Naples, Italy MethodsMethods availableavailable forfor fluxflux measurementsmeasurements inin orchardorchard cropscrops

„ Actual evapotranspiration (ET) – Micrometeorological „ Bowen ratio „ Aerodynamic „ EdEddydy covariance Problems: large plot needed, difficult management and maintenance, expensive – Soil water balance Problems: capillary rising and drainage difficult to estimate under Mediterranean conditions, large time scale „ Actual transpiration (T) – Sap flow technique BackgroundBackground andand PrinciplesPrinciples

„ TheThe transpirationtranspiration (water(water lossloss throughthrough thethe stomata)stomata) atat plantplant scalescale isis measuredmeasured byby thethe sapsap flowflow „ ThreeThree techniquestechniques availableavailable 1.1. StemStem heatheat balancebalance 2.2. ThermalThermal dissipationdissipation ““GranierGranier”” 3.3. HeatHeat pulsepulse „ UpUp--scalingscaling neededneeded fromfrom plantplant toto standstand 1.1. StemStem heatheat balancebalance

= + +QQQQ frvi Qi heat supplied (costant) Qv vertical heat conduction Qr radial heat conduction Qf heat convection through sap flow Mass flow rate of the water in the branch Q F = f wΔTc

Qf heat convection – cw heat capacity of sap – ΔT temperature gradient across the heater

BILANCIO TERMICO IN FUSTO DI GIRASOLE 11 agosto 1999 eclissi di sole Heat balance in a vine 11 August 1999 eclipse 0.5 60 800

0.4 ) 600 R g

40 (

0.3 h-1 W m-2

(g 400 0.2 20 ) flow

p 200 flusso di calore (W) calore di flusso 0.1 sa

0 0 0 0 400 800 1200 1600 2000 0 5 10 15 20 ora solare ora solare calore fornito (Qi) conduzione verticale (Qv) conduzione radiale (Qr) flusso di calore nella linfa (Qf) sap flow radiazione solare 2.2. ThermalThermal dissipationdissipation ““GranierGranier”” UP Sap flow system

Heat transport With sap flow Floema

Cambium

Heated gauge Xilema activo

Not heated gauge

231.1 ⎛ max Δ−Δ TT ⎞ Fd = 99.118 ⎜ ⎟ ⎝ ΔT ⎠

ΔTmax minimum (or zero) sap flow during the night) ExampleExample ofof datadata analysisanalysis ((clementineclementine))

22 800,0 cloudy, very hot clear, very hot rain, hot 20 700,0

18

600,0 16

14 500,0 dT 1 dT 2 12 vpd 400,0 Rg 10 Rg (W m-2) dT (°C) vpd (kPa*10) 8 300,0

6 200,0

4

100,0 2

0 0,0 0 0 0 600 600 600 1800 1200 1800 1200 1800 1200 solar time transpirationtranspiration

DAILY COURSE OF SAP FLOW, VAPOUR PRESSURE DEFICIT AND SOLAR RADIATION ON DOY 288 289 290 291

2.0 800

1.8

1.6 600 1.4

1.2

1.0 400

0.8 (MJ m-2 day-1) Global Radiation Sap Flow (gSap s-1) 0.6 200

Vapour Pressure Deficit (kPa) 0.4

0.2

0.0 0 0 600 1200 1800 0 600 1200 1800 0 600 1200 1800 0 600 1200 1800 solar time

flow w est branch flow east branch Vapour Pressure Deficit Global Radiation UpUp--scalingscaling fromfrom treetree toto standstand

Variables used: - Leaf surface - Sapwood area - Diameter trunck - Dimension of the crown

1. direct method: it links the transpiration to the evaporative green surface; it is based on the analysis of spatial variability of plant leaf area 2. indirect method: it supposes a relationship between the stem diameter and the transpiration leaf area of the plant; it is based on the spatial variability of the plant diameter Example:Example: clementineclementine treestrees underunder MediterraneanMediterranean climateclimate 8 )

2 6

y=1081.6x1.79 r2=0.859 anch (m r b r 4 ea pe r a f

total lea 2

0 0.00 0.01 0.02 0.03 0.04 0.05 0.06 branch diameter (m) ComparisonComparison eddyeddy covariance/sapcovariance/sap flowflow atat standstand levellevel ((clementineclementine))

1.00

5 August 2001 0.50 h) / lux (mm f 0.00 E ec T sf

-0.50 0 6 12 18 24 time ConclusionsConclusions onon thethe sapsap flowflow techniquetechnique forfor fruitfruit treestrees

ƒ AdvantagesAdvantages ƒ In situ measurements at plant level ƒ Does not modify the crop microclimate ƒ Good time risolution ƒ Automatic and continuous data collection ƒ DisadvantagesDisadvantages ƒ Accuracy at stand level (variable by species) ƒ Expensive (?): 300€/plant ƒ The gauges can be fragile FluxFlux modelling:modelling: brainstormingbrainstorming aboutabout thethe canopycanopy resistanceresistance

* Δ + γ D r = ρc p = n − GRA Δγ A

* γ r r ⎛ r* ⎞ 1+ c = f ⎜ ⎟ Δ γ Δ+ ra ⎜ ⎟ λ = AE ra ⎝ ra ⎠ +Δ γ γ rc * 1+ rc r γ Δ+ ra a += b ra ra 0.20

rc/ra=0.226r*/ra+0.004 r2=0.60 0.16

0.12 a r /

c r 0.08

0.04

0.00 0.00 0.20 0.40 0.60 r*/ra γ r* 1+ Δ γ Δ+ r λ = AE a +Δ γ γ ⎛ r* ⎞ + ⎜ + 004.0226.01 ⎟ γ Δ+ ⎝ ra ⎠ CanCan thethe OO3 effecteffect onon ETET bebe modelledmodelled byby thisthis kindkind ofof relation?relation?

r r* c a += b ra ra

* rc r O3 a += b O3 O3 ? ra ra FirstFirst attemptattempt onon soybeansoybean

without O3 with O3

20

Without ozone rcO3/ra=1.05r*/ra+1.6 r2=0.77 a=0.96 b=0.4 16 r2=0.90 n=15

With ozone a r / 12 c

r a=1.05 b=1.6 r2=0.77 n=11

8 rc/ra=0.96r*/ra+0.4 r2=0.90

4 4 8 12 16 20 r*/ra Promoting the O3 flux concept for European crops

Håkan Pleijel Plant and Environmental Sciences Göteborg University Challenges - Crops

• Production is the net result of a number of factors and practices including area under cultivation, fertilisation, agrochemicals etc • Productivity is the gain in relation to the investment • Ozone negatively affects productivity! • Ozone will influence productivity also in the absence of ”over-production” and subsidies Science-Policy relations

Modelling Synthesis of Basic science systems Policy scientific data including IAM

Activity of the scientific community

A clear strategy is required Visible injury in tobaco

From Fillella et al. (2005) Consequences of flux vs. AOT40

Maps from D. Simpson Estimated yield loss - wheat Carbon balance

• Harvest index Rel. Harvest index Straw yield influenced by ozone Pleijel et al 91 at higher exposure CF 100 100 NF 100 93 • Straw yield influenced NF+ 96 85 by ozone already at NF++ 90 76 low expsoure Fuhrer et al 92 • Consider in the CF 100 100 function of the NF 100 94 agroecosystem NF+ 96 89 • What about roots? NF++ 86 85 Potato 100 Solanum tuberosum L .cv Bintje HAULM 90 TUBERS >15mm 80 TUBERS <15mm ) 70

60

50

40

30 DRY MASS (g per plant per (g DRY MASS 20

10

0 CF NF NF+ Nitrogen balance 1.4 1.4 a b 1.2 1.2

1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4 Relative Protein yield Relative Protein yield y = -0.01x + 1.0 0.2 H0: Normal distribution y = -0.03x + 1.0 0.2 H0: Normal distribution 2 Residuals:Ns R2 = 0.59; n =29; P: *** Residuals: * R = 0.32; n= 29; P: ** 0.0 0.0 0123456 0 2 4 6 8 1012141618 -2 -1 AFst6 (mmol m ) A OT40 ( µmol mol h)

1.4 1.4 c d 1.2 1.2

1.0 1.0 C C 0.8 0.8

0.6 0.6 Relative GP Relative Relative GPRelative 0.4 0.4

H : Normal distribution 0.2 H0: Normal distribution y = 0.02x + 1.0 0.2 0 y = 0.01x + 1.0 Residuals: Ns R2 = 0.70; n = 31; P: *** Residuals: *** R2 = 0.35; n= 31; P: *** 0.0 0.0 0123456 0 2 4 6 8 1012141618 -2 -1 AFst6 (mmol m ) AOT40 (µmol mol h) Effects on yield quality 1.4 1.4 e f 1.2 1.2

1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4 Relative Specific weight Relative Specific weight H : Normal distribution y = -0.005x + 1.0 0.2 H0: Normal distribution y = - 0.02x + 1.0 0.2 0 2 Residuals: Ns R2 = 0.61; n = 17; P: *** Residuals: Ns R = 0.66; n= 17; P: *** 0.0 0.0 0123456 0 2 4 6 8 10 12 14 16 18 -2 -1 AFst6 ( mmol m ) A OT40 ( µmol mol h)

1.4 1.4 g h 1.2 1.2

1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4 H0: Normal distribution H0: Normal distribution Relative 1000-grain weight 0.2 Index: ** y = -0.04x + 1.0 Relative 1000-grain weight 0.2 Index: *** y = -0.02x + 1.0 Residuals: Ns R2 = 0.75; n=33; P:*** Residuals: ** R2 = 0.56 ;n=33; P:*** 0.0 0.0 0123456 024681012141618 -2 -1 AFst6 (mmol m ) AOT40 (µmol mol h) Comparison of O3 and CO2 effects on grain number and grain size

1.6 1.6 a b 1.4 1.4 1.2 1.2 1.0 1.0 0.8 0.8 0.6 1000-grain weight 0.6 1000-grain weight R2=0.75 (***) R2=0.02 (Ns) RelativeRelative response response 0.4 Relative response response Relative Relative 0.4 No of grains m-2 No of grains m-2 0.2 0.2 R2=0.02 (Ns) R2=0.65 (***) 0.0 0.0 280 0246200 400 600 800 -2 -1 AFst6 (mmol m ) [CO2] (μmol mol ) Important aspects of research • Evidence • Quantification • Consideration of ozone uptake, including non-stomatal (different approaches) • Complete systems – not fragments • Information on several important crops • Mechanistic or biogeographic approach – not countrywise paramerisations etc

• Quality control: gmax, cuvette readings Relation to climate change

1990 1.0 1997 1999 0.8 2000 2004 0.6 temptemp ff

0.4

0.2

0.0 -10% -5% 0% 5% 10% 15% 20% 25%

Relative yield loss AFst6 - Relative yield loss AOT40 Relation between AFst6 andAOT30 for grids with average daytime T above or below 20 ºC

2.5

y = 0.160x + 1.01 2 R2 = 0.61 -2 1.5

6, mmol m mmol 6, 1 st AF

0.5 y = 0.247x + 0.035 R2 = 0.33

0 0123456 AOT30, ppm-hours Considerations for the future

• Not just more of the same – MOS • Relevance and usefulness to IAM and policy including communication • Links to the function of the agroecosystem (carbon, nitrogen) and climate change • Yield quality aspects • Links to health, materials, greenhouse gas • Highlight ancillary benefits Thanks for your attention Flux-response relationships in the MM

1.2 1.2 Wheat Potato

1.0 1.0

0.8 0.8

0.6 0.6 Relative yield Relative 0.4 Relative yield 0.4 BE BE FI FI 0.2 0.2 y = 1.01 – 0.013 * AFST6 y = 1.00 – 0.048 * AFST6 2 r2 = 0.83 IT r = 0.76 GE p < 0.001 0 p < 0.001 SE 0 SE 0612345 024 8 12 16 0

-2 -2 AFst6, mmol m AFst6, mmol m Ozone effects on Mediterranean Forests (90 ppb)

Elena Paoletti Institute of Plant Protection National Council of Research Florence, Italy [email protected]

European Environmental Agency 2007 Air pollution by ozone in Europe in summer 2006 Overview of exceedances of EC ozone threshold values for April–September 2006 Station Type Altitude Year Sampling m a.s.l. efficiency % Donnas S. Osvaldo S.Osvaldo Suburban 93 2001-2004 80 # # Motta Visconti Donnas Rural 371 2002-2004 91 # # # Cason Cason Rural 91 2000-2004 91 Pino Torinese # Gherardi # Motta Rural 100 2000-2004 98 Cengio Settignano Visconti # Chiaravalle # Pino Rural 600 2001-2004 88 Torinese # Cortonese Sacco Gherardi Rural -2 2000- 94 # 2001, 2003-2004 Fontechiari # Cengio Rural 400 2000-2003 94

# Settignano Rural 195 2000-2004 91 Ce54 Chiaravalle Rural 15 2002-2004 75

Cortonese Suburban 300 2000-2004 85

Sacco Suburban 2 2000-2004 93 Bocca di Falco Fontechiari Rural 380 2000-2004 90 # Ce54 Suburban 71 2000, 83 2002-2004

Bocca di Suburban 141 2000-2004 92 Ozone levels in Italy exceed the EuropeanFalco and North American standards

Summary of mean O3 exposure indices in Italian remote stations (2000-2004) Station AOT40 AOT40C AOT40H AOT40 AOT40 AOT40 N. America (North to South) crops potato tomato forests evergreen 2002/03 standard ppm h ppm h ppm h ppm h ppm h ppm h ppb S.Osvaldo 9.53 14.13 11.77 20.40 21.93 13.96 88 Donnas 21.46 23.63 25.40 39.09 42.45 23.92 94 Cason 14.67 22.53 18.34 31.22 31.99 19.98 95 Motta Visconti 17.30 26.29 21.41 38.18 39.92 23.22 97 Pino Torinese 17.84 28.52 22.08 41.40 44.57 21.56 103 Gherardi 18.03 26.57 22.26 39.19 42.15 23.20 94 Cengio 5.77 10.24 7.16 14.91 16.67 7.90 70 Settignano 9.41 15.46 11.74 21.79 23.30 12.90 81 Chiaravalle 6.49 13.03 8.72 16.51 17.40 10.80 78 Cortonese 10.33 13.06 12.92 20.79 22.72 12.63 77 Sacco 9.78 15.96 12.76 23.66 25.16 13.41 72 Fontechiari 11.31 20.13 14.90 30.15 35.21 18.45 84 Ce54 3.70 6.98 4.73 8.91 10.42 5.34 64 Bocca di Falco 17.01 18.47 19.78 33.25 42.39 17.79 79 ITALIAN AVERAGE 12.15 18.04 15.08 26.83 29.50 15.87 84 ±0.79 ±1.07 ±0.94 ±1.60 ±1.78 ±0.93 ±3

Paoletti E, De Marco A, Racalbuto S: 2007, Why Should We Calculate Complex Indices of Ozone Exposure? Results from Mediterranean Background Sites. Environmental Monitoring and Assessment, 128 : 19-30. AOT40 for forests in Italy may be 3 times the values in Fennoscandia

60000 Motta-MI AOT40 50000 Settignano-FI h -1 40000 BoccaFalco-PA 30000 20000 AOT40, nl l nl AOT40, 10000 0 1992 1994 1996 1998 2000 2002 2004 2006

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. Environmental Pollution, 146: 587-607 Inconsistencies between exposure and effects on forests under field conditions

1. Inherent characteristics of response indicators 2. Setting of current exposure-based CLec 3. Environmental limitation to ozone uptake 4. Inherent characteristics of Mediterranean vegetation

Paoletti E.: 2006, Impact of ozone on Mediterranean forests: A review. Environ. Pollut., 144: 463-474. Ferretti, M., Fagnano, M., Amoriello, T., Badiani, M., Ballarin-Denti, A., Buffoni, A., Bussotti, F., Castagna, A., Cieslik, S., Costantini, A., De Marco, A., Gerosa, G., Lorenzini, G., Manes, F., Merola, G., Nali, C., Paoletti, E., Petriccione, B., Racalbuto, S., Rana, G., Ranieri, A., Tagliaferro, A., Vialetto, G. and Vitale, M.: 2007, Measuring, modelling and testing ozone exposure, flux and effects on vegetation in southern European conditions - what does not work. A review from Italy, Environ. Pollut., 146: 648-658. Inconsistencies between exposure and effects on forests under field conditions

Mean defoliation of main European tree species (1990-2005)

Lorenz et al. 2006 Forest Condition in Europe. Technical Report. Hamburg, 57 pp. Inconsistencies between exposure and effects on forests under field conditions

Frequency distribution of trees in 5%-defoliation steps in 2005

Lorenz et al. 2006 Forest Condition in Europe. Technical Report. Hamburg, 57 pp. Ozone-like visible injury has been observed on several species in the Mediterranean area

Pistacia Pinus halepensis P>0.05 lentiscus

Pinus pinaster P.terebinthus Pinus pinea

Percent of symptomatic species versus AOT40

Ferretti, M., Calderisi, M., Bussotti, F. 2007. Ozone exposure, Acer campestre Arbutus unedo defoliation of beech (Fagus sylvatica L.) and visible foliar symptoms on native plants in selected plots of South-Western Europe. Environmental Pollution 145, 644-651 http://www.gva.es/ceam/ICP-forests/ (photo: MJ Sanz) Effects of ozone on winter wheat yield in Central Italy

Winter wheat yield versus AOT40 60 Winter wheat yield versus ozone flux 60 55 y = -1.5127x + 89.398 y = 0,0007x + 33,878 2 50 2 R = 0.20 R = 0,0043 50 p = 0.4732 p < 0.001 45 ns *** 40 40

35 30

30

Yield (q/ha) Yield 20 Yield (q/ha) 25

20 10

15 0 6000 7000 8000 9000 10000 11000 10 28 30 32 34 36 38 40 Ozone flux (mmol/m2) AOT40 (ppbxh)

De Marco A., Paoletti E., Screpanti A., Racalbuto S., Vialetto G. Assessment of ozone impact on winter wheat yield in central Italy. In prep. Reasons to improve the knowledge of O3 effects on Mediterranean forests

•Elevated levels of ozone pollution •Still poor data bases •High variability in vegetation •High variability in environmental conditions

Parameterisation Validation Phytoclimatic map of Italy (Blasi et al., 2001) Towards a Mechanistic Understanding

z Select reference forest tree species z Investigate species-specific and site- specific gmax (common protocols) z Define the best determinants for Open-air Ozone detoxification capacity Enrichment z Define the best indicators for ozone experiments effects on forests z Improve effective ozone flux modelling z Validate ozone flux models Correlative studies z Model scenarios in a changing Modeling climate

Validation of the flux approach OZONE RISK ASSESSMENT FOR EUROPEAN VEGETATION 10-11 May 2007

Villa Orlandi, Anacapri, Capri island (Naples, Italy)

Modelling and observation of root water uptaking for agricultural crops

Guido D’Urso and Mario Palladino

Dipartimento di Ingegneria Agraria e Agronomia del Territorio

Università degli Studi di Napoli “Federico II” ATMOSPHERE

H2O Atmospheric CO2 O2 pollutants CO O R ψ 2 2 S P A Continuum

H2O SOIL SPACSPAC characteristicscharacteristics

•In Soil Plant Atmosphere Continuum plants lie between soil and atmosphere and interact with them with root system and leaves

•We can observe and measure leaves parameters on living plants; it is more difficult to measure and characterize root system

•Molz e Remson (1970): root water uptake flux can be defined as actual transpiration flux

•Ritchie (1972): model to partition evapotranspiration in evaporation and transpiration OUTLINEOUTLINE Mathematical models to simulate water transport in SPAC

Measuring root water uptake at field scale: I. Calibration of reduction functions II. Measuring and simulating root water uptake at field scale III. Increasing of spatial extent MathematicalMathematical modelsmodels toto simulatesimulate waterwater transporttransport inin SPACSPAC

•Policoro (Santini et al., 1978) •SWATR/SWAP (Feddes et al., 1983) •Hydrus 1D e 2D (van Genuchten et al., 1991, 1998) ModelingModeling waterwater transfertransfer intointo thethe soilsoil--plantplant--atmosphereatmosphere systemsystem

∂θ ∂ ⎡ ⎛ ∂h ⎞⎤ = ⎢K θ ⎜ −1)( ⎟⎥ − S ∂ ∂zt ⎣ ⎝ ∂z ⎠⎦

Soil water flow in soil (Richards eq.) Root water uptake Soil hydraulic properties •Soil water retention Î θ(h) •Soil hydraulic conductivity Î K(θ)

0.6 1.E+02 suolo argilloso K (cm/h) θ suolo sabbioso 1.E+00 0.4 θθ− l1/mm 2 1.E-02 sr kh( ) = ks ⋅ SS [1-(1- ) ] θθ=+r n m 1+ αh 1.E-04 ()0.2 suolo argilloso suolo sabbioso 1.E-06

0 1.E-08 10000 1000 100 10 1 10000 1000 100 10 1 -h (cm) -h (cm) MathematicalMathematical modelsmodels toto simulatesimulate waterwater transporttransport inin SPACSPAC

••PolicoroPolicoro (Santini(Santini etet al.,al., 1978)1978) •SWATR/SWAP (Feddes et al., 1983) •Hydrus 1D e 2D (van Genuchten et al., 1991, 1998) PolicoroPolicoro modelmodel (Santini,(Santini, 1978)1978)

Water uptaking

H −ψ c Extraction term w tzS ),( = + rr rs

zr Total flow rate from = (),)( dztzStER root uptake ∫ w 0

Total leaf water potential Ψf = Ψc –rp ER

Allowing for time variation in water stored into plant TR = ER ± qc capacitance: PolicoroPolicoro modelmodel (Santini,(Santini, 1978)1978) Plant water stress

gt = water vapor conductance

gc = cuticular conductance gt = gc + gs gs = stomatal conductance

Actual transpiration is set equal to potential

transpiration (TR = TRp) until the onset of plant water stress. Under stress condition, the 1 stomatal conductance, g , is s kψ = δ reduced by using a multiplication ⎛ψ ⎞ factor that depends on crop type 1+ ⎜ f ⎟ and a threshold value of the ⎜ψ * ⎟ water potential: ⎝ ⎠ PolicoroPolicoro modelmodel (Santini,(Santini, 1978)1978) SoilSoil andand plantplant matricmatric potentialpotential

Model predicted leaf water potential (Ψf) and soil matric potential (Ψ), pepper crop (Santini, 1992) MathematicalMathematical modelsmodels toto simulatesimulate waterwater transporttransport inin SPACSPAC

•Policoro (Santini et al., 1978) ••SWATR/SWAPSWATR/SWAP ((FeddesFeddes etet al.,al., 1983)1983) •Hydrus 1D e 2D (van Genuchten et al., 1991, 1998) SWAPSWAP modelmodel ((FeddesFeddes,, 1983)1983) SWAPSWAP modelmodel ((FeddesFeddes,, 1983)1983) T Maximum root water zS )( = p uptake rate p Droot

Actual root water flux a = α ⋅ p zShzS )()()(

zr Actual transpiration rate ∫ a )( = TdzzS a 0 FeddesFeddes reductionreduction functionfunction

α

1 Tp = 1 mm/d

Tp = 5 mm/d

0 h1 h2 h3h h3l h4 |h| OtherOther reductionreduction functionsfunctions

−hh 4 FeddesFeddes (1978):(1978): rw )h( =α −hh 43 1 VanVan GenuchtenGenuchten (1987):(1987): rw )h( =α p ⎛ h ⎞ 1+ ⎜ ⎟ ⎜h ⎟ 1 ⎝ 50 ⎠ DirksenDirksen (1993):(1993): rw )h( =α p ⎛ * −hh ⎞ 1+⎜ ⎟ ⎜ * ⎟ ⎝ −hh 50 ⎠ 1 HomaeeHomaee (1999):(1999): rw )h( =α p 1 α− ⎛ * −hh ⎞ 1+ 0 ⎜ ⎟ ⎜ * ⎟ α0 ⎝ −hh max⎠ Measuring root water uptake at field scale

Seliano (Sele Plain, SA) Canosa (BA) CROP:CROP: alfalfaalfalfa CROP:CROP: OleaOlea europeaeuropea

¾ meteorological parameters (temperature, humidity, precipitation, solar radiation, wind speed,…) ¾ ET actual(Bowen, Eddy Correlation) ¾ soil water content (TDR) ¾ soil water potential (tensiometers) ¾ groundwater level ¾ crop height ¾ LAI ¾ rooting depth AlfalfaAlfalfa plotplot schemescheme ReductionReduction functionsfunctions calibrationcalibration Experimental values of α(h) can be determined by: T α th ),( = ,ta T ,tp

(h) represents mean soil water potential over the root zone

Coefficients of reductionreduction functionsfunctions can be determined with an optimization procedure ReductionReduction functionsfunctions calibrationcalibration 1.20

1.00 Feddes (1978)

0.80 −hh 4 α0.60 rw )h( =α 0.40 −hh 43

0.20

0 500 1000 1500 2000 2500 3000 1.20 |h| (cm) Van Genuchten (1987) 1.00 0.80 1 rw )h( =α p 0.60 ⎛ ⎞ α h 0.40 1+ ⎜ ⎟ ⎝h50 ⎠ 0.20

0 500 1000 1500 2000 2500 3000 |h| (cm) SimulationSimulation resultsresults:: alfalfaalfalfa HOMAEE 29 0.08 y = 0,7435x - 0,003 ) 2

27 ) R = 0,8511 0.06 25

23 0.04 21

19 VAN GENUCHTEN 0.02 soil water storage (cm water storage soil RMSE=0.5898 ETa simulated (cm/h 17

15 0.00 0 0.02 0.04 0.06 0.08 73 123 173 223 273 323 373 423 473

time (hours) ETa (cm/h)

29 0.08

VAN GENUCHTEN ) 27 ) y = 0,9298x - 0,004 2 0.06 R = 0,8554 25

23 0.04 21 HOMAEE 19 0.02 RMSE=0.9552 soil water storage (cm water storage soil ETa simulated (cm/h simulated ETa 17

0.00 15 0 0.02 0.04 0.06 0.08 73 123 173 223 273 323 373 423 473

ETa (cm/h) time (hours) Implementation of the SWAP model for Olea europea

S I LAI = 4.1 (tree level) LAI = 0.95 1 (stand level)

TOWER

S II S III TREE 2 J ID SAMPLE

TDR probe N DIG RMSE = depth of soil profile:1 m ET0 Priestley-Taylor 0.0096 h1 = 4 cm h2 = 40 cm h3h = 800 cm h3l = 800 cm h4 = 25000 cm

ET0 Makkink kc = 0.6

RMSE = 0.0091

ET0 Penman-Monteith

RMSE = 0.0113 depth of the soil profile: 1 m ET0 Makkink h1 = 4 cm RMSE = h2 = 40 cm 0.0091 h3h = 800 cm h3l = 800 cm h4 = 25000 cm depth of the soil profile: 3 m

kc = 0.6

RMSE = 0.0091

depth of the soil profile: 5 m

RMSE = 0.0091 Map of LAI values, July ’94 Remote Sensing techniques to derived from processing of estimate LAI (upper boundary LANDSAT TM image condition of SWAP) based on Scale Km relationship with vegetation 1 0 index

LAI predicted 5

Artichokes

Forages 4 Maize

Fruit-trees

Vegetables LAI values 3 R2=0.74 <0.2

0.2Ü0.5 2 0.5Ü1.0

1.0Ü1.5 1.5 2.0 1 Ü 2.0Ü2.5

2.5Ü3.0 0 0 1 2 3 4 5 3.0Ü3.5 LAI measured 3.5Ü4.0 Relationship LAI(WDVI) in the Sele river 4.0Ü4.5 plain based on Landsat TM images 4.04.5Ü5.0 >5.0 SpatialSpatial aggregationaggregation ...... CONCLUSIONSCONCLUSIONS

• Modeling soilsoil--waterwater--cropcrop interaction represents a useful tool for irrigation management at fieldfield and districtdistrict scale • Macroscopic approach of root water uptake is effective for simulating realreal fieldfield scalescale processes • All of the tested rootroot waterwater uptakinguptaking functionsfunctions have shown goodgood accuracyaccuracy for crop transpiration, after calibration procedures • Application of soilsoil waterwater balancebalance modelsmodels can be a practical way to quantify actual crop transpiration, in turn strictly linked to atmospheric pollutants penetration in leaves The Challenge of Making Ozone Risk Assessment more Mechanistic

R. Matyssek G. Wieser H. Sandermann W. Oßwald

Freising /D Innsbruck /A Freiburg /D Freising /D Tropospheric O3 player in “Global Change”/Kyoto Fagus sylvatica O3 risk assessment of woody-plant+CO2 systems amb. (C sinkCO /O strength) 2 3 +CO2 requires increased analytical+ O3 precision

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Grams et al. (1999) O3 uptake rather than exposure drives plant response Decoupling between O3 exposure and O3 flux under drought (e.g. Mediterranean climate)

Wieser (pers. comm.) Paoletti (pers. comm.) Quercus ilex: 7 August Pinus canariensis Quercus ilex

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Stomatal conductance conductance Stomatal Gs 20 O3 0 0

A B Daytime O3 uptake 5 6 7 8 9 1011121314151617181920 ..... and under light limitation

Fagus sylvatica Kranzberg Forest

Free-air O3 canopy fumigation experiment

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O3 time course

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decreasing air humidity Nunn et al. (2006) Compensation of „Crown/Trunk“ Time Offset (for one-day resolution)

Matyssek et al. (2004) Combination of Xylem sap flow & eddy covariance approaches: Separation of

stomatal from non-stomatal O3 deposition

Upscaling Concepts of O3 Flux in Forest Systems - Leaf to Landscape

R. Matyssek G. Wieser S. Cieslik E. Paoletti see R. Ceulemans presentation Freising /D tomorrow Innsbruck /A Ispra/I Firence/I Antwerp/B O3 uptake (“physical dose”) ≠ “physiologically effective” O3 dose

Plant responsiveness per unit of O3 uptake

Dependent on defence, phenology/ontogeny, plant life forms, non-linearities in response

O3 flux inductor of stress “self-amplification”: Signal of “alert” Self-amplification of O stress 3 ROS self- amplification

O3 impact

s d e ci s s a te a e a in n lic n programmed k e y o P yl ic m cell death A h al s M et s ja ROS

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2xO3 in proportion of control (1xO3) /extraordinary at each date drought

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Variable sensitivity

Non-linearity 2xO3 in proportion of control (1xO3) /humid year in response at each date

Oct.

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stress stress avoidance tolerance /defence

Stomatal Redox regulation regulation

O impact 3 ROS

Photo: M.S. Günthardt-Goerg & C. Scheidegger Concepts for assessing injury initiation,

i.e. the „effective O3 dose“

Range of ion ? Metabolic injury initiat demands+

Break-down ? inducible constitutive

Wieser & Matyssek (2007) Estimators of O3 defence capacity in view

of O3 risk modelling

Wieser et al. (2002)

SLA = leaf area / leaf mass

SLA = Specific leaf area (SLA) as estimate of defence capacity (SLA = Leaf area / leaf mass)

Î Defence capacity needs to be area-related

Leaf mass behind impact area = estimate of defence

O flux = 3 stomata area-related flux density

lower leaf side O 3 O3 O3 Photo: M.S. Günthardt-Goerg & C. Scheidegger Validation and advancement of

flux-based O3 risk assessment in forests and tree plantations through process-based understanding at the pan-European scale PanEurOz

R. Matyssek (proposed coordinator)

F. Batic/Kraigher (SLO) R. Ceulemans (B) S. Cieslik (I) P. Dizengremel (F)

P.-E. Karlsson (S) E. Oksanen (FIN) W. Oßwald (D) J. Cermak (CZ) E. Paoletti (I) H. Pfanz (D) H. Rennenberg (D) M. Schaub (CH) G. Wieser (A)

GSF: B. Winkler / H. Seidlitz / J.-C. Munch plus up to 7 other European Working Groups

consultants: Prof. Dr. D.F. Karnosky (USA) Prof. Dr. T. Ogawa (JAPAN) based O and a 3 st void ress abce flux Eff + to 3 e l assessment ctive eran O O ce 3 dos risk ic e cause-effect st ni g ha lin ec el M od m plantations, : agroforestry tree forestry esources change anagement r fumigation m 3 O Kyoto Air quality obal Change species Validation l Successions Agroforestry RELEVANCY G methodology C sink strength

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Integration OStomatal3 uptake + “effective”Redox O3 dose regulation regulation

O impact Whole-tree sap flow, stand-level eddy3 covariance,ROS and experimental free-air release approaches promising approaches

Photo: M.S.Will Günthardt-Goerg render & C. OScheidegger3 exposure-based concepts of risk assessment obsolete Free-air O3 fumigation sites Experimental sites covering the major geographical regions across Europe

agreed free-air

O3 fumigation sites

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Land-use changes: Agricultural/wasteland, agroforestry, forestry

Scheyern Examination: Common underlying, unifying principles in cause-effect based

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Micro-scale free-air O3 exposure system (installed on lysimeter system)

SFB 607 Research site

Guiding aims

in promoting O3 flux concepts

(1) validation data for flux-based O3 risk modelling: - adult forest trees & stands, juvenile tree plantations - major geographical regions of Europe Sap flow & eddy approaches

(2) clarify mechanisms of O3 uptake and effective O3 dose

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Albino Maggio and Massimo Fagnano

University of Naples Federico II Department of Agricultural Engineering and Agronomy Ozone stress and abiotic/biotic stresses co-exist. Different types of stress will antagonistically/synergistically interact to eventually affect plant metabolism, growth and yield.

Environmental Specificity

Drought, Salinity and Temperature stress are most critical in Mediterranean environments MONTHLY VALUES 150 6

100 4

50 2 (ppm h (ppm

0 0 ) (mm)

-50 -2 Water Surplus -100 Water Deficit -4 AOT40 -150 -6 JFMAMJnJlASOND

CLIMATE OF NAPLES (40 years):

Year rainfalls = 819 mm Year ETo = 1046 mm Nr. Months with deficit = 6 (April-September) Nr Months with surplus = 5 (October-February) ENVIRONMENTALENVIRONMENTAL STRESSESSTRESSES INTERFERINGINTERFERING WITHWITH OZONEOZONE EFFECTSEFFECTS ONON PLANTSPLANTS 1)1) DuringDuring SpringSpring--SummerSummer:: WaterWater deficitdeficit SalineSaline stressstress TemperatureTemperature stressstress

2)2) DuringDuring AutumnAutumn--WinterWinter (in(in clayclay soilssoils):): WaterWater surplussurplus

rootroot anoxiaanoxia

waterwater uptakeuptake limitationlimitation Katerij et al., 2003. Agric. Water manag., 62, 37-66

In Southern Italy, also irrigated crops (i.e. sugarbeet) may have a reduced stomatal conductance between two waterings Da Katerij et al., 1997. Agric. Water Manag. 34, 57-69. ExperimentalExperimental facilitiesfacilities atat NaplesNaples UniversityUniversity

Torre Lama Portici

OTC OTC

Growth chambers trifolium + lolium

Relative yield loss in response to water shortage

80 + ozone y = 1.0582x - 55.219 60 R 2 = 0.9107 40

20

0

Yield loss (%) -20

perditre di produzione (%) produzione di perditre -40 0 25 50 75 100 dWater isp o navailability ib ilità id (%) rica (%) alfalfa

Ozone dependent yield loss are reduced upon salinization

mag-set 06 y = -4.474x2 - 4.2978x + 452.35 600 R2 = 0.991 500

400

300

200

Biomassa (g/m2) 2 Biomass (g/m2) 100 y = 1.8199x + 1.8795x + 304.57 R2 = 0.9158 0 NF 012345 AF SalinitàWater salinitydell'acqua (g/L) (g/L) Lolium perenne ETc Ryegrass Biomass Replacement (g pt-1) SomeSome speciesspecies areare (%) OTC NF OTC AF sensitive to ozone sensitive to ozone 100 15.3 bc < 20.8 a onlyonly withoutwithout waterwater 66 17.8 ab = 15.0 bc limitationlimitation 33 12.9 b = 14.7 bc

Trifolium repens ETc Clover Biomass Replacement (g pt-1) AA severesevere waterwater stressstress (%) NC-R NC-S can protect also can protect also 100 46.3 a < 22.1 b sensitivesensitive speciesspecies fromfrom 66 20.3 bc < 8.6 d yieldyield losseslosses duedue toto OO3 33 13.9 bd = 8.8 d Salinty induced responses that may counteract ozone effects Stomatal Salinity LAA HAA Plant species conductance (dS/m) (BHT μg/ml ) (AA μg/ml ) (cm/sec) Eggplant 0.5 0.53 - - 8.5 0.36 - - 15.7 0.33 - - Pepper 0.5 0.75 - - 4.4 0.41 - - 8.5 0.40 - - Tomato1 0.5 2.73 12.1 5.5 4.4 2.18 13.0 5.7 15.7 1.61 14.2 6.7 Tomato2 2.5 0.33 - - 6.0 0.22 - - 9.6 0.15 - - 15.0 0.08 - - 1Field; 2Greenhouse. Tomato Overlapping of salinity and ozone stress

Maggio et al., 2007. Can salt stress-induced physiological responses protect tomato crops from ozone damages in Mediterranean environments? Eur. J. Agronomy 26 (2007) 454–461 Using model systems to unravel multiple stress responses

Li et al,, 2006. Effects of chronic ozone exposure on gene expression in Arabidopsis thaliana ecotypes and in Thellungiella halophila. [Plant, Cell and Environment 29, 854–868].

Arabidopsis thaliana (At) ecotypes Columbia-0 (Col- 0), Wassilewskija (WS), Cape Verde Islands (Cvi-0) and a halophytic relative Thellungiella halophila (Th), were exposed to 20–25% over ambient ozone [O3] in a free air concentration enrichment (FACE) experiment (http://www.soyFACE.uiuc.edu). Li et al., 2006 - Results OZONE SENSITIVITY Col-0 and WS accelerated development and developed lesions within 10 d under increased ozone, while Cvi-0 and Th grew slowly.

MICROARRAY ANALYSIS • WS showed the greatest number of changes in gene expression in response to ozone (most susceptible). • Th showed the least changes, suggesting that its expression state at [O3] was sufficient for resistance at increased ozone. • Superior resistance of CVI-0. Gene categories involved in ozone response: • Antioxidant functions • Hormone-related genes • Transcription factors • Arabinogalactans (AGPS), receptor-like kinases (RLKS), mitogen- activated protein kinases (MAPKS) and the cell wall-membrane continuum • Senescence More on model systems: stomatal/non-stomatal contribution to ozone sensitivity/tolerance

OTC experiments at UNINA: Arabidopsis vtc1 - ascorbate deficient mutant nced - constituitive ABA deficient mutant sto1 - induceable ABA deficient mutant

Tomato Feeding compatible solutes to induce stomatal opening by generating an “hypo-osmotic” environment FunctionalFunctional biologybiology ofof plantplant responseresponse toto ozoneozone:: whatwhat dodo wewe needneed ?? 1. Identifying most representative crops for Northern, Central and Southern Europe and addressing specific questions on specific crops, also capitalizing on information acquired through model systems.

2. Assessing stomatal vs. non-stomatal contribution to ozone uptake (and damage).

3. Addressing issues relative to stomatal conductance to ozone vs. other gases and induced detoxification pathways.

4. Defining correction parameters (calibration)calibration to elaborate criteria to develop reliable prediction models. Institut für Pflanzenökologie

Flux measurement and modelling at canopy level

Ludger Grünhage Institute for Plant Ecology, University of Giessen Giacomo Gerosa Department of Mathematics and Physics, University of Brescia Alex Vermeulen Energy Research Centre of the Netherlands, Biomass, Coal & Environmental Research Modelling and Mapping leaf level − canopy level − regional level Enrichment 3 flux measurement O Impact Research 3 O

(physiology, detoxification, growth, yield, …(physiology, detoxification, )

(crops/grassland) Vegetation type Vegetation

Flux measurement and modelling at canopy level

¾ Introduction ¾ Flux measurement • basic assumptions • eddy covariance and gradient techniques • quality control and flux corrections ¾ Calculation of stomatal uptake of ozone ¾ Flux modelling • leaf level (up-scaling) • canopy level • up-scaling to regional scale and mapping ↓ "constant flux layer" postulates, which must be satisfied: • stationarity • horizontal homogeneity of the plant/soil system (fetch problem) • no horizontal advection • no chemical sources or sinks between the reference height and the surface • zero mean vertical wind velocity

↓ eddy covariance technique

w' fluctuation of vertical wind velocity w X' fluctuation of u, T or partial density ρ of trace gas A

flux measurement − basic assumptions eddy covariance technique gradient methods

Braunschweig, Linden, Germany Germany

flux measurement − micrometeorological methods Causes of errors in direct eddy covariance measurements: • e.g. changes in air density due to simultaneous transfer of sensible and latent heat → WPL correction (cf. Webb et al. 1980) • e.g. correction for non-zero mean vertical wind velocity due to not perfectly aligned coordinate system of the sonic anemometer → tilt correction, coordinate rotation (cf. Wilczak et al. 2001) • e.g. Schontanus/Liu correction for sonic temperature and sensible heat flux (cf. Liu et al. 2001)

Tests of fulfillment of theoretical requirements • horizontal homogeneity of the plant/soil system and adequate fetch → footprint analysis (cf. e.g. Haenel & Grünhage 1999, 2001) • stationarity → steady state test (cf. e.g. Thomas & Foken 2002) • well developed turbulent conditions → comparison of measured and modelled integral turbulent characteristics (cf. e.g. Foken & Wichura 1996)

flux measurement − quality control and corrections Fc, zref(O3) = Fstom(O3) + Fnon-stom(O3) + Fair chemistry(O3)

! chemical sinks or sources between the reference height and the surface !

partitioning of total ozone flux ρO3(zref) Fc(O3) = − Rtotal

Rcanopy,O3 = Rtotal − (Rah + Rb,O3)

(d+z ) ρO3 0O3 = Rcanopy,O3 · ρO3(zref) Rtotal

(d+z ) ρO3 0O3 Fc,stom(O3) = − Rc,stom,O3 ρ (d+z ) = − O3 0O3 Rc,stom,H2O · DH2O/DO3

direct calculation of stomatal uptake of ozone Rcanopy, O3 Fc,stom(O3) = −ρO3(zref) · (Rah + Rb,O3 + Rcanopy,O3) · Rc,stom,H2O · DH2O/DO3

assumption: "transpiration is the only source of water vapour from the plant/soil system"

⇒ Rc,stom,H2O = Rcanopy,H2O

100 98 Linden grassland site 96 (June 2004) 94 92 90 88 trans./evapotrans. (%) trans./evapotrans. 86 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 leaf area index (m2/m2)

direct calculation of stomatal uptake of ozone Rcanopy, O3 Fc,stom(O3) = −ρO3(zref) · (Rah + Rb,O3 + Rcanopy,O3) · Rc,stom,H2O · DH2O/DO3

Rc,stom,H2O = Rcanopy,H2O

Penman-Monteith equation

measured parameters needed for calculation of Rc,stom,H2O = Rc,H2O:

• latent heat flux λE, sensible heat flux H, friction velocity u* (by eddy covariance) • net radiation balance Rnet and ground heat flux G • air temp. T, rel. air humidity rH, air pressure p, wind velocity u at two heights

• derived from measurements: MO length L, surface temperature Ts, displace- ment height d, roughness length for momentum z0m direct calculation of stomatal uptake of ozone Rcanopy, O3 Fc,stom(O3) = −ρO3(zref) · (Rah + Rb,O3 + Rcanopy,O3) · Rc,stom,H2O · DH2O/DO3

Rc,stom,H2O = Rcanopy,H2O

"more direct approach" (cf. e.g. Coe et al., 1995)

direct calculation of stomatal uptake of ozone Rcanopy, O3 Fc,stom(O3) = −ρO3(zref) · (Rah + Rb,O3 + Rcanopy,O3) · Rc,stom,H2O · DH2O/DO3

assumption: "transpiration is the only source of water vapour from the plant/soil system"

⇒ Rc,stom,H2O = Rcanopy,H2O

100 98 Linden grassland site (June 2004) 96 = 0.97 94 92 λER =R 90 c,stom canopy = 0.93 88 λE"true" trans./evapotrans. (%) trans./evapotrans. 86 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 leaf area index (m2/m2)

direct calculation of stomatal uptake of ozone Ozone uptake at leaf level − sunlit leaf at the top of the canopy −

ρO3(zlaminarlayer) Fleaf,stom,sunlit(O3) = − Rb-leaf,O3 + Rleaf,stom,sunlit,H2O · DH2O/DO3

• estimation of ρO3(d+z0m) - exposure system (OTC, FATE) -ambient air

• parameterization of Rb,leaf,O3 - species specific?

• parameterization of Rleaf,stom,sunlit,H2O (Jarvis-Stewart or Ball-Berry approach) - Rleaf,stom,sunlit,min or gleaf,stom, sunlit,max : standardized protocol - Jarvis factors f: radiation, temperature, VPD, soil moisture, time, phenology, ozone, carbon dioxide

- formulation of Jarvis algorithm: gleaf,H2O = gmax · f1 · f2 · f3 ·...

or gleaf,H2O = gmax · f1 · max{ fmin, ( f2 ·f3)} ·... • up-scaling from leaf to canopy level flux modelling - leaf level canopy level - SVAT (Soil-Vegetation-Atmosphere-Transfer) models

ρO3(zref) Fc(O3) = − Rah + Rb,O3 + Rcanopy,O3

⇓

Rcanopy, O3 Fc,stom(O3) = − ρO3(zref) · (Rah + Rb,O3 + Rcanopy,O3) · Rc,stom,O3

SVAT schemes with one-dimensional characterization of the canopy: • single layered resolution of vegetation (big leaf): Integrated DEposition Model (ECN) Ozone DEposition Model (UniCAT) • big leaf, single layered sun/shade model: PLant-ATmosphere INteraction model (UniGiessen/FAL-Braunschweig) • multi-layered resolution of vegetation: MultiLayerBioChemical model (ECN)

flux modelling - canopy level ρO3(zref) Fc(O3) = − Rah + Rb,O3 + Rcanopy,O3

big leaf parameterisation of

Ratmosphere, Rquasi-laminar layer, Rcanopy,H2O based on

Rnet = H + λE + G

scaling Rcanopy,H2O according to canopy development stage 1 ⎡ ⎛ 1 1 ⎞ β ⎤ (1 ⎢(1 β∗ ) ⋅−= ⎜ + ⎟ + ⎥ R ⎜ R R ⎟ R H2O c, H2O ⎣⎢ ⎝ c, stom, H2O c, cut, H2O ⎠ soil, H2O ⎦⎥

1 −1 Rc, stom, H2O = · f1(St) · f2(ta) · f3/4(VPD, SM) · f5(time) · f6(Phen) · f7(O3) · f8(CO2) Rc,stom,min,H2O

flux modelling - canopy level PLATIN (PLant-ATmosphere INteraction) Flux estimation for latent and sensible heat, trace gases and aerosols Ludger Grünhage & Hans-Dieter Haenel

canopy flux

stomatal O3 uptake

radiation model

stomatal O3 uptake

of the sunlit leaf fraction http://www.licor.com canopy scale leaf scale

Fleaf,stom,sunlit(O3)

gleaf,stom,sunlit,O3 interface

flux effect flux modelling - canopy level Framework for Atmosphere-Canopy Exchange Modeling

high resolution modelling (Pieterse et al. 2007)

• Multi-layer and leaf angle radiation model: max 20 vert. layers; Canopy rad. (de Pury&Farquhar) • Parameterization for 14 land use types • 14 Vegetation types • Berry regression for photosynthesis and water fluxes • LAI MODIS 1km, aggr. at 0.25° 8-Day / Monthly • LAI, ß-distr., 5 profiles • SWC: bucket model • Soil data 1 km • Parameterization for the USDA soil types • Land use SYNMAP 1km, aggr. at 0.25° IDEM • Soil types SYNMAP 1km, aggr. at 0.25°

flux modelling - up-scaling to regional scale and mapping Thanks for your attention! Ozone effects on Mediterranean pastures

Ben S. Gimeno, Rocío Alonso, Victoria Bermejo, Javier Sanz

OZONE RISK ASSESSMENT FOR EUROPEAN VEGETATION 10-11 May 2007 Villa Orlandi, Anacapri, Capri island (Naples, Italy) What do we know? I.- Exposure-response relationships

Information concentrated in Dehesa pastures (annual, acidic, central Iberian Peninsula)

• Legumes more sensitive than grasses - − Visible injury (Bermejo et al. 2003 Atmos. Env.) − Growth rates (Gimeno et al. 2004a Env. Pollut.)

• Ozone & competition interactions – Seed output and flowering (Gimeno et al. 2004b Atmos. Env.): Trifolium cherleri, T. striatum, T. subterraneum vs. Briza maxima

− T. cherleri (increased when grown in monoculture and under O3 control levels)

− T. striatum, T. subterraneum (depletion by O3 and competition singly, no interactions) What do we know? I.- Exposure-response relationships

ƒ Ozone and N interactions T. subterraneum (Sanz et al., 2005. Atmos. Env.)

- N compensates adverse effects of moderate O3 levels (senescence, flowering and seed weight).

- N enhanced O3-induced adverse effects on nutritive quality Briza maxima – Phenology and seed production (Sanz et al, 2007 – Poster)

Early harvests

- N delayed, O3 accelerated seed production. - N*Above ambient O3 exposure (greatest seed output with the lowest N) Late harvests and overall effects

- N increased seed production, No O3 effects, No N*O3 interactions Constraints

• Research performed in OTC • Plants exposed in pots • Competition restricted to two species in mesocosms

• Effects associated to exposure not to O3 uptake In fact,

Plantperformanceisbest relatedtoO3 uptake rather than to O3 exposure (Response of NCS-NCR clones of T. repens)

Bermejo et al. (2002) New Phytologist, 156:43-55 What do we know? II.- Stomatal conductance assessment Measurements at the leaf level

¾ O3 sensitivity not related to maximum gs values pots ¾ no important intra-specific variation in gs ¾ no significant effects of N enrichment on gas exchange rates field ¾ Gas exchange rates in the field <<< in pots ¾ Intragenus variation, no clear differences on gs between Leguminosae and Poaceae families ¾ gs show very high variability making difficult to model gs behaviour

Alonso et al. 2007. Env. Pollut. 146:692-698 Performance of the gs model new parameterisation of Emberson model

T. subterraneum B. hordeaceus

400 700 350 600 300 500 250 400 200 300 150 calculated

calculated 200 100 100 50 0 0 0 100 200 300 400 0 100 200 300 400 500 600 700 measured measured R2=0.42 R2=0.53

B. sterilis

160

120

80 Same genus, Modelling calculated 40 problems 0 04080120160 measured R2=0.09 What do we know? II.- Stomatal conductance assessment

Measurements at the canopy level

WE ARE ON IT!! 1630m 1647m

2000m 1749m 1506m

1059m

695m 715m

671m N

Air Pollution gradient Passive samplers – Ozone gradient - summer

-3 15 day-periods, July-August 2004 (μg m ) 250

200

150

100

50

0

ín lsa rcuera ñalara o Va El Pardo Viñuelas Guadalix Pe Navafría Miraflores . Canencia .M Pto Pto

A gradation of ozone concentrations is best appreciated in winter. Summer levels doub winter levels. Passive samplers – NO2 - summer

-3 15 day-periods, june-september 2006 (μg m )10 9 8 7 6 5 4 3 2 1 0

ín a frío alara lsa rcuera o Va Rio El Pardo Viñuelas Guadalix Canencia Peñ Navafrí Miraflores .M Pto. Pto

NO2 levels decrease along the gradient Ozone accumulated exposure

Cotos- AOT40 (ppb.h) 2005 2006

3- month OT40 May-July 31.071 36.424

April-June 24.837 35.396

6-month AOT40 May- October 54.224 46.354 9.000 ppb.h

3.000 ppb.h

Buitrago de Lozoya- AOT40 (ppb.h) 2005 2006

3- month OT40 May-July 25.066 21.606

April-June 19.090 15.374

6-month AOT40 May- October 41.705 32.278 What next?

- Need of an ecological approach - Assessment of effects on biodiversity & forage quality - Need of an open-air fumigation facility - Facility at 80 km N from Madrid (Ríosequillo) - Design under discussion (plot size, ring or linear exposure) - External support for measuring fluxes is needed - Combination with air pollution experiments in the field and OTCs Ozone Risk Assessment for European Vegetation Workshop, Capri 10-11 May 2007

Free-Air Ozone Enrichment Experiments: How to estimate the flux?

Jürg Fuhrer and Christof Ammann

Air Pollution/Climate Group Agroscope ART Zurich, Switzerland Le Mouret, 1998-2003

Alp Flix, 2004-2011

agroscope ART – J Fuhrer Ozone exposure system

Turbulent mixing Transparent windscreen zone Turbulent mixing zone

Air flow Air flow O3

Canopy Perforated air duct

Plot border Plot center

agroscope ART – J Fuhrer Ozone gradients

LAI (m2 m-2)

01234 250 Le Mouret

200

Without Top of canopy Air inlet fumigation Height (cm) 50 With fumigation

0 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75

O3 Concentration (rel.) agroscope ART – J Fuhrer Flux measurements

Eddy covariance Dynamic chambers

c c ct() in out Q Fc=⋅[]cc out − in c ⋅= )t(c)t(wF A

Profile method Static chambers

cz()2

V []ct()− ct () F =⋅ 21 ∂ ∂tc cz()1 c A Δt

= − [ 2trc − 1)z(c)z(cvF ]

agroscope ART – J Fuhrer Eddy covariance vs dynamic chambers

15 10 CH 1 CH 2 EC -1 CH 1 s -1 10 -2 CH 2 s -2 5 0 0 -10 -5 -10 flux in µmol m -20 2 flux (CH) in µmol m

-15 2 CO

-20 CO -30 09.07.04 10.07.04 11.07.04 12.07.04 13.07.04 -30 -20 -10 0 10 -2 -1 Local time CO2 flux (EC) in µmol m s

Comparison of CO2 flux by automated dynamic chambers and eddy covariance (EC) Oensingen intensive grassland field (156 x 52 m), 2 chambers (CH 1 & CH 2) in the footprint of EC for 21 days in July 2004 LAI: 2-3 agroscope ART – J Fuhrer Dynamic chambers - field

Volume: 40 l; Flow: 60 l min-1; Measuring cycle: 15 min chamber-1 (4 In, 8 Out, 3 In) Calculations: based on 3-h running means

agroscope ART – J Fuhrer Dynamic chambers - ring

agroscope ART – J Fuhrer H2O Flux

2.5 ambient #1 ambient #2 fumigated #3 2.0 fumigated #4

] 1.5 -1 s -2

1.0 O fluxm [mmol 2

H 0.5

0.0

-0.5 19.06.2006 00:00 21.06.2006 00:00 23.06.2006 00:00 25.06.2006 00:00 27.06.2006 00:00 29.06.2006 00:00 01.07.2006 00:00

agroscope ART – J Fuhrer CO2 Flux

6 ambient #1 ambient #2 4 fumigated #3 fumigated #4

2 ] -1 s -2 0 mol m

-2 flux [ 2 CO -4

-6

-8 19.06.2006 00:00 21.06.2006 00:00 23.06.2006 00:00 25.06.2006 00:00 27.06.2006 00:00 29.06.2006 00:00 01.07.2006 00:00

agroscope ART – J Fuhrer Chamber intercomparison – CO2 Flux

6

4

2

0

-2

CO2 Flux Chamber 2 CO2 Flux -4

-6 R2=0.66

-8 -8 -6 -4 -2 0 2 4 6 CO2 Flux Chamber 1

agroscope ART – J Fuhrer O3 Flux

←No fumigation→←With active fumigation → 10

5

0 ] -1 s -2

-5 flux m [nmol 3

O -10

ambient #1 -15 ambient #2 fumigated #3 fumigated #4 -20 19.06.2006 00:00 21.06.2006 00:00 23.06.2006 00:00 25.06.2006 00:00 27.06.2006 00:00 29.06.2006 00:00 01.07.2006 00:00

agroscope ART – J Fuhrer O3 Flux - Ambient

←No fumigation→←With active fumigation → 10

5

] 0 -1 s -2

-5 flux m [nmol 3

O -10

-15 ambient #2

-20 19.06.2006 00:00 21.06.2006 00:00 23.06.2006 00:00 25.06.2006 00:00 27.06.2006 00:00 29.06.2006 00:00 01.07.2006 00:00

agroscope ART – J Fuhrer Deposition velocity, υd, Ambient

←No fumigation→←With active fumigation → 10

8 ambient #2 ]

6

4

2

0 ozone deposition velocity [mm/s

-2

-4 19.06.2006 00:00 21.06.2006 00:00 23.06.2006 00:00 25.06.2006 00:00 27.06.2006 00:00 29.06.2006 00:00 01.07.2006 00:00

agroscope ART – J Fuhrer υd - Chamber comparison

6 ) -1 4

2

0 Deposition velocity (mm s Deposition velocity -2

1234 Ambient air Fumigated ring

agroscope ART – J Fuhrer Conclusions

Direct measurements of ozone flux are difficult due to high frequency oscillations of concentra- tion and some spill-over Optimization of dynamic chamber system is necessary, and will be realized (2008) Possible combinations with:

estimate of canopy conductance from H2O flux or eddy covariance measurements of latent heat flux outside rings gradient measurements, if homogeneous surface is exposed (i.e., no subplots)

agroscope ART – J Fuhrer EFFECTS OF ATMOSPHERIC POLLUTION ON AGRICULTURE FUNCTIONS: social and economic aspects, landscape and environmental quality.

From: Food production

To: -Conservation of Rural landscape (and cultural inheritance and identity) -Reduction of Greenhouse gas emissions from soil and from fossil fuels. -Reduction of Nitrate pollution. TROPOSPHERIC POLLUTION (mainly OZONE) could worsen some functions of agro-ecosystems:

Social and economical 1. FARM ECONOMICAL PERFORMANCES

Environmental 2. C FLUXES FROM CROPPING SYSTEMS

3. NITRATE LOSSES FROM CROPPING SYSTEMS 1. OZONE can affect FARM ECONOMICAL PERFORMANCES

- Directly (lowering yield)

- Indirectly (i.e. lowering WUE)

Coupling to other factors that are already determining problems

- Economical (decreasing prices and increasing costs)

- Physical (climate change, water availability decrease in quantity and quality) with consequent SOCIAL and ECONOMIC PROBLEMS

Increasing depopulation of marginal areas and increasing urbanization and LANDSCAPE QUALITY WORSENING (and losses in cultural identity) croplands could be considered a structural factor of European landscape, since they cover 30% of total land area on average (from 6% in Sweden to 54% in Denmark). 2. C FLUXES FROM CROPPING SYSTEMS Croplands are considered a source of C Estimates of C losses from soils and emissions from fossil fuels combustion (from Lal et al., 2004. Science, 304, 1623-7) The most effective systems to reduce C fluxes from soils are:

-to increase C input: composted OM, crop residues.

- to decrease SOM mineralization:

reducing its exposure to O2 (minimum tillage, cover crops) increasing soil C/N ratio and Ozone can increase C losses from cropping systems:

2.1 Reducing Net Primary Production (NPP) and Net C Exchange (NCE)

Reducing C return to soil

2.2 Increasing SOM oxidation 2.1 Ozone reduces NPP and NCE

N fertilization increases ozone effects POL no reduction policy POLCAP capping pollutant gases (but no GHG) GSTAB GHG emission control (but not pollutant gases) GSTABCAP GHG and pollutant gases controlled

+F with N fertilization

From Felzer et al., 2005. Climatic change, 73, 345-373. From Felzer et al., 2005. Climatic change, 73, 345-373. Since O3 reduce photosynthesis, NPP and yield, it also reduce crop residues amount (and quality?). 2.2 An increase in air oxidative capacity could also increase aerobic reactions in the soil Recent experimental results obtained in Naples University indicate that humic substances, rather than being macropolymers, are supramolecular self- associations of heterogeneous and relatively small molecules stabilized by weak bonds (H-bonds, van der Waals, π-π, CH-π), which may be disrupted by small amounts of organic acids. However, also oxydants (Oxygen, and OZONE) may have a similar disuptive action in the short or long time course.

CH3COOH

O2, O3

SOM mineralization, nitrification, C emissions 3. NITRATE LOSSES FROM CROPPING SYSTEMS Another environmental priority of Europe is nitrate pollution in watertables (Nitrates Directive, 91/676/EC).

N inputs in the land are increasing (atmospheric deposition, cattle slurry, mineral fertilizers) so to determine - soil C/N ratio lowering - nitrate exceedance in comparison to the ecosytem capacity

- - Since nitrates (NO3 ) are not adsorbed by soil colloids ( ), the only factors that can limit nitrate leaching toward the watertable are: plant uptake N hydrophobic protection in humified SOM. Ozone, since reduces growth of crops, it also reduces: -N uptake from the soil. (??? See Fangmeier et al., 97 - Env.Poll 96, 43-59; Fangmeier et al., 2002 - Eur J.Agron 17, 353-65) -C return to the soil and C/N of crop residues With consequent: C/N further lowering, level of SOM humification decreaing (and ability to protect N in hydrophobic structures).

- C OH = in the external interface with water (CEC) O H S N

N = protected by hydrophobic C chains Therefore, ozone pollution can increase - CO2 (upward) and NO3 (downward) emissions from the soil. Fagnano, Maggio, Piccolo,…. (Univ. Naples), Manes, Vitale,…. (Univ. Rome) Badiani, Nali, Ranieri (Univ. Reggio C./Pisa) Gerosa, Ballarin Denti,… (Univ. Cattolica) Rana,… (ISA-Bari) ------Gruenhage, Jäger (Univ. Giessen) Bender, Haenel, Weigel (FAL Braunschweig) ------Dizemgremel, Jolivet, Le Thiec (INRA UPH Nancy) Noctor (IBP Orsay) Cellier, Béthenod, Loubet, Roche (INRA Grignon) ------Vermeulen, Bleeker, Kraai (ECN) ------Vandermeiren (VAR), Gielen, Horemans (Univ. Anthwerp) ------Saitanis,… (UniAthens) ------Other interested groups: UK (G.Mills), SPA (B.Gimeno), SWE (H.Pleijel) Workpakage List WP Title Coordination 1 Coordination …………..

2 Characterization of ozone uptake at leaf level FRA Pierre

3 Characterization of the ozone uptake at agro- GER Ludger ecosystem level

4 definition of dose/response relationships, and ITA Massimo effects of ozone on European cropping systems (yield quantity and quality)

5 environmental, social and economic impact of atmospheric pollution at European level …………..

WP1 - COORDINATION

WP2 - CHARACTERIZATION OF O3 UPTAKE AT LEAF LEVEL 2.1. Characterization of representative European environmental conditions (climate, soil, air pollution).

2.2. Defining physiological determinants in standardized, not limiting conditions ( gmax, fO3, fPhen, fPPFD)

2.3. Defining biochemical/metabolic determinants: non-stomatal response

2.4. Defining physiological determinants in conditions of site- specific stresses (fT, fVPD, fWQ, fSMD)

2.5. Model development, calibration, comparison at leaf level.

See Pierre coordination activity WP3 – CHARACTERIZATION OF THE OZONE UPTAKE AT AGRO-ECOSYSTEM LEVEL

3.1. Measurement and modelling the ozone fluxes by micrometeorological techniques in actual field conditions at canopy level

3.2. Comparison among different approaches of ozone uptake modeling

3.3. Up-scaling of ozone fluxes from the plot to regional scale and mapping

See Ludger coordination activity WP4 - DEFINITION OF DOSE/RESPONSE RELATIONSHIPS

AND EFFECTS OF O3 ON EUROPEAN CROPPING SYSTEMS (yield quantity and quality)

4.1. Effects of ozone on fodder cropping systems (CS) 4.2. Effects of ozone on small grain CS (cereals & pulses) 4.3. Effects of ozone on industrial CS (sugar/oil) 4.4. Effects of ozone on vegetable CS 4.5. Effects of ozone on fruit tree CS 4.6. Effects of ozone on flower (ornamental ?) CS

See “Choosing the crops” (Massino) during the free session May 11 (h 15.0-16.00) The response to ozone could be evaluated measuring: All the crops Fresh, dry matter, C and N in Total biomass, total yield, marketable yield, crop residues Harvest index (yield/total biomass ratio), WU, WUE Fodder proteins, leafness, NDF, ADF, RFV,..... Cereals average weight of kernels, hectolitre weight (kg/hl), protein, gluten, carotenoids, Pulses average weight of kernels, protein, EFA (essential fatty acid),.... Sugar crops Saccarose, purity,... Oil crops lipids, EFA (essential fatty acid), proteins, .... Vegetables Vitamins, minerals, antioxidants, glucosinolates (Brassica spp.),.... Fruit trees Vitamins, minerals, antioxidants, .....

See Karine during the free sessions WP5. ENVIRONMENTAL, SOCIAL AND ECONOMIC IMPACT OF ATMOSPHERIC POLLUTION AT EUROPEAN LEVEL

5.1. Effects of ozone pollution on environmental impact of European cropping systems. -N and C balance of the cropping systems INTAKE: crop residues, fertilizers, rainfalls, irrigation water;

UPTAKE: yield, leaching, gas emissions (CH4, CO2, NH4, N2O)?? Biomass samples of crop residues can be sent to UniNA group (A.Piccolo) to determine the molecular composition of vegetal tissues: HRMAS-NMR

-From the field-sites, soil samples will be collected and analysed or delivered to the UniNA group (A.Piccolo): SOM, OrgC, OrgN,

MinN (N-NH4+N-NO3), Potential Mineralizable N, SOM changes (CPMAS-NMR). 5.2 Social (Employment) and Economic (Farm Income) Impact of Atmospheric Pollution at European Level (Integration With APES?)

The groups of modelists (INRA Grignon, UHPNancy, UniGiessen, ECN,….) could interact with other European modelling groups with the aim to implement already existing integrated framework and models at European scale (IF-SEAMLESS, APES,...) with the aim to integrate ozone effects among the inputs of these models. Specific algorithms, languages or coding problems will have to be discussed with the Authors of those models. LINEAR SERIES OF EXPERIMENTS LINKED TO EACH OTHER Just an example for tomato

PLANT Growth chambers Standard cultivation in not-limiting conditions, conditionsUniNA-1 (Maggio)

OTC/FIELD OTC split with microlisimeters with stresses UniNA-2 applying multiple site-specific stresses (Fagnano) (i.e. O3, salt, drought) C-N plant and soil UniNA-3 (Piccolo)

actual conditions Open field with micrometeorological station CRA BA (Rana) space scale measurements deliverables

O effect PLANT g , photos., 3 s modelling Standard plant growth conditions f functions for cell level 2.2 UniNA_1 rc model 2.3

WU, WUE, g , photos IBP Orsay model OTC/FIELD s 2.4 UniPI-RC Integration with stresses Yield (quant/qual) 3.2 UniNA_2 4.4⇒ leaf level UniGiessen Plant samples 2.5 INRA-Grignon C-N plant & soil UniGiessen UniNA_3 5.1 ⇒ INRA-Nancy canopy up-scaling level O uptake 3.3 ⇒ 3 3.1 actual conditions Fluxes UniGiessen ECN CRA_BA 3.1 CRA_BA INRA-Grignon O effects on yield 3 O effects on value(quality and Integrated Ozone model 3 C & N balance quantity) ⇒ 3.3. ⇒ 5.1 ⇒ 4.4.

Integration with other EU models

5.2.

Social and economical impacts Environmental impacts NC-S NC-R ThankThank youyou veryvery muchmuch forfor youryour attentionattention Flux modelling work of the LRTAP Convention

Lisa Emberson

[email protected] Ozone Flux-Vegetation centric view of UNECE LRTAP European Air Quality Management

EMEP European emissions data

EMEP photo-oxidant model

DO3SE deposition model

European O3 concentrations EMEP Flux modellingStandard resistance work scheme of the of LRTAP the DO3 SEConvention model

DO3SE deposition model resistance scheme Regional scale photo-oxidant model (EMEP model)

Planetary boundary layer (≈50m)

O3

Vegetated surface Flux modelling work of the LRTAP Convention

The Deposition of Ozone for Stomatal Exchange (DO3SE) Model

•Leaf-levelmodel of stomatal conductance (gs) •gs modelled using modified multiplicative algorithm (Jarvis, 1976) •Scaled to canopy-level by leaf area index (LAI) •“Big leaf” model, sunlit and shaded LAI • Stomatal and non-stomatal deposition to ozone

• Parameterised for 6 crop species, 6 tree species, 1 productive grassland species and 19 cover types

• Evaluated against > 12 data sets (4 crops, 6 forests & 2 semi- natural from 8 countries in Europe)

• Forms basis of UNECE CLRTAP Mapping Manual O3 effects modelling, EMEP chemical transfer deposition module, IIASA IAM modelling Ozone Flux-Vegetation centric view of UNECE LRTAP European Air Quality Management

EMEP European emissions data

EMEP photo-oxidant model

DO3SE deposition model

European O3 concentrations EMEP

Generic parameterisation AFstY: IAM wheat AFstY: Deciduous & Mediterranean evergreen Forests AOT40: semi-natural

EMEP Flux mapping: Identification of areas of higher flux

Integrated Assessment Modelling IIASA Gothernburg protocol

Optimising emission controls for Gothenburg protocol

Revise Gothenburg protocol Ozone Flux-Vegetation centric view of UNECE LRTAP European Air Quality Management

National emissions EMEP European emissions data data

EMEP photo-oxidant model National photo- National O3 oxidant model monitoring DO3SE deposition model

derived O concentrations European O3 concentrations Nationally 3 EMEP

Generic parameterisation “Real” parameterisation AFstY: IAM wheat AFstY: wheat, potato AFstY: Deciduous & Mediterranean evergreen Forests AFstY: Scots pine, Norway spruce, AOT40: semi-natural beech, oak, birch, poplar, holm oak, Aleppo pine AOT40 semi-natural

EMEP Flux mapping: Identification of areas of higher flux EMEP or National Flux mapping: compare Identification of areas of higher flux

Integrated Assessment Modelling IIASA Gothernburg protocol

Optimising emission controls National strategies for for Gothenburg protocol emission control or mitigation

Revise Gothenburg protocol Flux modelling work of the LRTAP Convention

DO3SE risk assessment model resistance scheme e.g Flux-response model

O3 concentration EMEP + DO3SE

rb

rb, gsto, O3 rs

accumulated stomatal O flux Flag leaf 3 (AFstY)

Flux – response relationship Soil e.g. Flag leaf (wheat) Yield loss estimate Flux modelling work of the LRTAP Convention

Agriculture: DO3SE model evaluation

Crops

Species Location Time Period Model Reference performance Spring wheat Sweden June to August Good performance Pleijel et al (2002) 2003 Durum Spain April to June 2003 r2 Bueker et al. 2007 wheat gsto = 0.72 Wheat Italy June 2000 r2 Tuovinen et al Vd = 0.7 (2004) Gsto = 0.72 Grapevine Spain June 2000 r2 Bueker et al. 2007 gsto = 0.77 Grapevine California June 1996 to Aug r2 Emberson et al 1996 (2005) Ftot = 0.76 Flux modelling work of the LRTAP Convention

Forest: DO3SE model evaluation

Species Location Time Period Model Reference performance Scots pine Finland July 1995 r2 Tuovinen et al Vd = 0.79 (2004) Gsto = 0.7 Norway spruce Denmark Jan 1996 to Dec MAE Tuovinen et al 2000 Rsur 40 to 65 s/m (2004) Norway spruce Austria June to Sept 1989 r2 Emberson et al Aug to Sept. 1990 Gsto = 0.66 (2000) Fst = 0.71 Norway spruce Norway July 2000 to March Mean Vd Hole et al (2004) 2003 Observed 1.8 mm/s Modelled 2.1 mm/s Scots pine Finland Dry days in 1998 r2 Altimir et al (2004)

gsto = 0.7 Kermes oak Spain July 1998 to July r2 Elvira et al 2000 (2004) gsto = 0.2

Flux modelling work of the LRTAP Convention Merger CORINE and SEI European Land-cover map

• Classifications condensed from 90 dominant cover types • Scale approximately 2.5 km2 • Includes irrigation and agricultural production statistics

SEI

Harmonisation and merging of European land cover maps

CORINE

Flux modelling work of the LRTAP Convention

Agriculture: Priority DO3SE development

• European variability in LAI and phenology Flux modelling work of the LRTAP Convention Agriculture: Grassland growth model simulated LAI

5

4 ) 2 3 /m 2

2 LAI (m LAI

1

0 1-Jan 12-Mar 21-May 30-Jul 8-Oct 17-Dec Date

NE CCE WM DO3SE

Ashmore et al. 2007 Flux modelling work of the LRTAP Convention

Agriculture: Priority DO3SE development

• European variability in LAI

• Continued development of DO3SE soil water model Flux modelling work of the LRTAP Convention

Agriculture: DO3SE soil moisture model development Based on Penman-Monteith model

P Ei AEt f(Ra, Rb, Rsto) & VPD & Net radiation

Water storage SWP Flux modelling work of the LRTAP Convention

Agriculture: Priority DO3SE development

• European variability in LAI

• Continued development of DO3SE soil water model

• Development of multi-plant functional type grassland model (grass, forbs & legumes) Flux modelling work of the LRTAP Convention

Agriculture: Comparison of gmax for grassland PFTs

1200

1000

800

600

400

gs (mmol O3 *m-2*s-1)PLA 200

0 N = 6 4 5 3 10 3 FORB_SEC LEG_SEC GRA_SEC FORB_PRI LEG_PRI GRA_PRI Flux modelling work of the LRTAP Convention

Agriculture: Priority DO3SE development

• European variability in LAI

• Continued development of DO3SE soil water model

• Development of multi-plant functional type grassland model (grass, forbs & legumes)

• Development of flux-response relationships for additional species

OR

Develop mechanistic detoxification model System for Environmental and Agricultural Modelling; Linking European Science and Society

APES: an integrated framework for potential implementation of ozone impact models

M. Donatelli CRA-ISCI Bologna

Ozone risk assessment workshop, Anacapri, May 2007 From presentations in the workshop: ongoing or desirable

„ Impact of ozone models on systems

„ Process based approaches

„ Sensitivity analysis

„ Competition among species

„ Compare approaches / test with and without model components

„ Agro-management impact

„ Link to climate change scenarios

„ Ozone impact on bio-energy crops

„ Multi-team work

2 M.Donatelli, Ozone risk assessment workshop, Anacapri, Italy, May 2007 The Agricultural Production and Externalities Simulator framework

„ APES is a modular system of biophysical components that allows simulation of biophysical impacts of management and weather variability on agricultural systems

„ APES is composed of model components for the main agricultural activities related to plant production (arable crops, orchards, agroforestry, grasses)

„ To capture climate effects APES comprises a set of deterministic simulation models to be used in a stochastic fashion via various weather scenarios

„ Simulation outputs are used to derive indicators to be used in farm modelling (average values and variability)

3 M.Donatelli, Ozone risk assessment workshop, Anacapri, Italy, May 2007 Why APES?

„ Several tools are currently available to simulate different agricultural production activities, however: z None covers in one package several agricultural activities of relevance z All are closed, “unique solution”, applications z All are proprietary applications z There is a noticeable duplication of implementation for process simulation, but at the same time some processes are not simulated

„ APES is one of the realizations of a modular modelling system, providing: z An open platform for direct, multi-team model development z A transparent source of modelling knowledge and options z A flexible system which allows including and testing new models and modelling approaches

4 M.Donatelli, Ozone risk assessment workshop, Anacapri, Italy, May 2007 Why using a dynamic, process based models?

„ Complex systems with non-linear relationships: need to quantify behaviour of interrelated elements

„ Timing of both actions and system responses is of relevance

„ Estimate the impact of detailed technical management on production and externalities

„ Explore innovative systems

„ Explore climate change scenarios

5 M.Donatelli, Ozone risk assessment workshop, Anacapri, Italy, May 2007 Building models

„ A “model” is the building block of APES simulation capabilities; it basically contains one or more equations either to compute a rate or to estimate a variable of interest

„ Composite models link simple models to estimate a variable or a set of interrelated variables; from the “user” point of view simple and composite models are equivalent

„ More than one option (i.e. more than one model) either to compute a rate or to estimate a variable might be provided within a model “component”

„ Such options are available either at run-time or via a configuration file

6 M.Donatelli, Ozone risk assessment workshop, Anacapri, Italy, May 2007 Components desired features

„ Reusable: the practical possibility of using the component in different software systems; ease of use and solution to a common modelling problem are the keywords.

„ Replaceable: the capability of being replaced by a different component respecting the same contract. “Different” here means either a newer version of the same component, or an implementation from a different party.

„ Extensible: the capability of easily adding alternate processing capabilities to the ones of the component from the side of the component user, without needing to recompile the component, and using the same interface.

7 M.Donatelli, Ozone risk assessment workshop, Anacapri, Italy, May 2007 Current APES models and output types

Crops Trees SoilWater Vineyards SoilErosion AgroManagement Orchards SoilCarbon-Nitrogen Agroforestry AgroChemicals Grasses

Externalities: Production: Resources use: nitrogen leaching, irrigation, nitrogen, grain, fruits, pesticides fate, biomass, ... crop/trees/grasses GHG emissions, operations, tillage, soil erosion, ... agrochemicals, ... Soil sustainability: soil carbon, soil thickness, ...

8 M.Donatelli, Ozone risk assessment workshop, Anacapri, Italy, May 2007 APES model components

„ AgroChemicals – UNICATT (●) „ Agroforestry – INRA (●) „ AgroManagement - CRA (●) „ Crop2 – CRA (●)

„ Crop – WUR (●) „ Diseases – CRA UNICATT(●)

„ SoilWater – UNIMI (●) „ Grasses – INRA (●)

„ SoilC-N2 – UNIMI (●) „ Orchards – INRA (●)

„ SoilErosion – UNIMI (●) „ SoilC-N – CIRAD WUR (●)

„ Vineyards – INRA (●) „ SoilWater2 – IRD CIRAD (●) „ Weather - CRA (●)

9 M.Donatelli,● Prototype Ozone risk assessment 2 wor●kshop,Prototype Anacapri, Italy, 3 May 2007 Technical development (all components)

„ Implementation using C# (.NET platform) „ Design of model components: z Fine granularity of models => easier composition and more effective storage in the knowledge base z Variables attributes: transparent interface z Test of data quality encapsulated in each model z Extensible

10 M.Donatelli, Ozone risk assessment workshop, Anacapri, Italy, May 2007 APES analysis tools / components

„ Applications

z Simulation output evaluator – CRA (●)

z CLIMA weather generator – CRA (●)

z Parameters calibrator – INRA PRI (●)

z Sensitivity analysis – CRA JRC (●)

„ Components

z Indices to evaluate model perfomance - CRA (●)

z Energy budget - CRA (●)

z Pedotransfer functions - CRA UNIMI (●)

11 M.Donatelli,● Prototype Ozone risk assessment 2 wor●kshop,Prototype Anacapri, Italy, 3 May 2007 The teams involved

M. Donatelli, L. Criscuolo, E. Ceotto, A. Di Guardo, M. Botta, A. Serrano – CRA M. Acutis, P. Trevisiol – UNIMI M.Trevisan, A. Sorce –UNICATT C. Gary, C. Dupraz, E. Casellas – INRA F. Ewert, M. Adam, P. Leffelaar, E. Meuter – PPS WUR F. Van Evert – PRI WUR M. Corbels, F. Celette, B. Rapidel – CIRAD M. Duru, P. Cruz – INRA E. Braudeau, P. Martin – IRD CIRAD

12 M.Donatelli, Ozone risk assessment workshop, Anacapri, Italy, May 2007 Conclusions

„ A modelling framework allows integrating ozone impact models in simulation of agriculural systems:

z Use and evaluation of impact of ozone models

z Model development

z Use of the infrastructure (utilities)

„ Using an integrated modelling framework developed / being developed in a EU project would likely facilitate an FP7 application

13 M.Donatelli, Ozone risk assessment workshop, Anacapri, Italy, May 2007 Thanks for your attention!

14 M.Donatelli, Ozone risk assessment workshop, Anacapri, Italy, May 2007 Pierre Dizengremel et al., Capri meeting, May 2007 Forest Ecology and Ecophysiology UMR 1137 INRA / Nancy University flux and impact on higher plants Cellular and molecular aspects of ozone Pierre Dizengremel, Didier Le Thiec, Matthieu Bagard, Yves Jolivet Climate

(T°, RH,…) Plant Metabolism vegetation vegetation cuticle 3 with with

Cuticle Pierre Dizengremel et al., Capri meeting, May 2007 O Canopy exchanges ozone ozone stomata of of Boundary layer Sub-stomatal cavity Exchanges Exchanges Foliar gas exchanges ozone ) ) ppb ppb (2007) . (2007)

et al. of 40 of 40

et al exposure exposure Karlsson Pleijel -40) threshold threshold h ] Pierre Dizengremel et al., Capri meeting, May 2007 3 h ) ozone over a over a ozone 3 ([O i i of j of j ozone) ozone) Σ exposure exposure (FO j i j i uptake uptake Σ Indices Indices accumulated accumulated - Differences exist according to species, climate, etc… - Effective ozone flux - Detoxification/Metabolism CUO = AOT40 = CUO = AOT40 ( CUO (cumulative AOT40 = AOT40 ( CUO (cumulative 2 Rubisco

cycle

CO Krebs Mitochondria metabolism metabolism Catabolism

Calvin cycle Trioses Phosphate cell cell of of ATP NAD(P)H NADPH Chloroplast

Pierre Dizengremel et al., Capri meeting, May 2007 overview overview Photochemistry An An PSII Asc NADPH GSSG NADP GSH DHA NAD(P)H PAR Detoxification

Cytosol

Symplast Asc GSSG DHA GSH NAD(P) Plasmalemma Asc DHA Cell wall Apoplast 2 CO space Extracellular 2 Rubisco

CO cycle Krebs

Catabolism Mitochondria metabolism metabolism HMP

cycle Calvin PEPc PEP Trioses Phosphate cell cell

Glycolysis ATP NADPH Chloroplast NAD(P)H

Pierre Dizengremel et al., Capri meeting, May 2007

Photochemistry PSII NAD(P)H PAR Asc NADPH GSSG NADP GSH DHA How ozone impacts How ozone impacts Detoxification Detoxification Detoxification ROS

Cytosol

Symplast Asc GSSG DHA GSH NAD(P) Plasmalemma Asc DHA Asc DHA ROS Cell wall Apoplast 3 O ROS

2 existing 1st 3

detoxifying barrier CO O space Extracellular t & PEPc lantarum lantarum P P & Environmen Rubisco carboxylation carboxylation ), Plant Physiol. Biochem. 3 1 0 O 0 6.74 2.43 3.82 2 . (1999), Physiologia . (2003), Physiologia ( . (2001), Plant, Cell ratio et al et al ozone on ozone on C et al 47.2 29.3 24.7 of of Rubisco / PEPc ntaine ntaine Pierre Dizengremel et al., Capri meeting, May 2007 Pelloux Fo Dizengremel Fo 3 .35 .55 .96 1 2 1 PEPc Effect CO Effect 0.36 0.45 0.55 3 3 CO O 9.1 6.2 7.5 rbcL Transcripts C Rubisco 17.0 13.2 13.6 3 s ) k -1 e eks e eeks CO we prot Proteins activity LSU 2 5 w pine 2 w spruce , , 3 , 1 3 3 O O O b b b PEPc Specific (nkat.mg Poplar 60 pp Aleppo 200 pp Rubisco Norway 200 pp EM NADP control ozone EM NAD catabolism catabolism to ., unpublished results to , 24 days , 24 Fum 3

et al Poplar 100 ppb O linked linked PEPc PK Dizengremel

5 4 3 2 1 0

3 /C ratio) ratio) /C (O activity Relative

Pierre Dizengremel et al., Capri meeting, May 2007 PEPc G6PDH Fumarase PFK Glycolysis, Krebs cycle and anaplerotic pathway 0 ozone on enzymes ozone on enzymes of of 08 , 12 weeks , 12 3 06 Effect Effect . (1998), Physiologia. (1998), Plantarum 04 Time of (days) fumigation 200 ppb O Norway spruce et al 02 5 0

1

10 Sehmer

3 /C ratio) ratio) /C (O activity Relative ? ? 2 react react

Rubisco cycle Krebs CO Catabolism Mitochondria cell cell HMP

cycle Calvin PEPc PEP

Trioses Phosphate the the Malate

Glycolysis Pyruvate does Malate does Malate ATP

NADP ME NADPH Chloroplast OAA NAD(P)H How How

Pierre Dizengremel et al., Capri meeting, May 2007

Photochemistry PSII NAD(P)H PAR Asc NADPH GSSG NADP GSH DHA Detoxification Detoxification detoxifying response

nd 2 ROS

Cytosol

Symplast Asc GSSG DHA GSH NAD(P) Plasmalemma Asc DHA ROS Cell wall Apoplast 3 O ROS 2 3

1st existing CO O space

Extracellular detoxifying barrier Pierre Dizengremel et al., Capri meeting, May 2007 are relevant to measure? - These nucleotides the are driving force of the detoxifying system. - total Asc But (symplastic apoplastic) does + notindicator seem of to be ozone resistance. good a - Asc pool needs to be regenerated within the symplast. - metabolite This serves to reducereductase activity. Asc through glutathione ’s ? ) SOD Which metabolites/enzymes Ascorbate Glutathione Reducing power NADPH & NADH ( Glutathione Reductase …Andwhatabout …Andwhatabout (3) (2) should 2 CO g . WUE C isotopes 13 C and C and 12 (Ca – Ci) 2 bolism and ozone flux. CO ] 2 ] 2 , b and consequently β assimilation = g assimilation 2 = stomatal conductance to water = 1.6 = discrimination between s WUE = Water Use Efficiency A = CO Δ Ca = atmospheric [CO Ca = atmospheric Ci = internal [CO a = stomatal diffusion b = isotopic discrimination due to carboxylation g Pierre Dizengremel et al., Capri meeting, May 2007 the link between meta (4) (5) (6) (1) = PEPc activity / (PEPc activity + Rubisco activity) = isotopic discrimination due to Rubisco = isotopic = isotopicdue to PEPc discrimination 1 2 β b b in the Rubisco/PEPc ratio, / b – a) Δ . (1982) : 2

et al )b s β : + (1- 1 = A / g = (Ca – Ci) / 1.6 = Ca / 1.6 x ( b - .b β = a + (b – a) ( Ci / Ca) WUE WUE According to Farquhar Δ From (4) and (5) WUE According to Farquhar & Richards (1984) : b = From (1), (2) and (3): - induces a decrease As ozone be modified under ozone treatment. under ozone be modified - indicator of be a good could therefore WUE Which links could exist(stomatal between conductance)? metabolism and ozone fluxes PEPc & WUE Rubisco

Pierre Dizengremel et al., Capri meeting, May 2007 ??? Ozone flux assimilation 2 Gas exchanges Biomass -CO - Stomatal conductance Productivity Cellular and molecular links with ozone flux and on higher impact plants Pierre Dizengremel et al., Capri meeting, May 2007 BUT This line of argument applies a fighting to mature leaf! To what extent can we scale-up whole-plant to level? To what extent can we scale-up field to level? • • Pierre Dizengremel et al., Capri meeting, May 2007 Pierre Dizengremel et al., Capri meeting, May 2007 ozone : 0.13 control: 0.02 Poplar: ß ß b shifts from around 27‰ 23‰ to scaling-up to

BRANCH, CROWN, STAND and LANDSCAPE LEVELS Pavel Cudlín Institute of Systems Biology and Ecology of the Academy of Sciences of the Czech Republic

Physical based approaches in remote sensing: Scaling from leaves to ecosystems; International workshop, Wageningen, October 2006 The Norway spruce canopy represents one of to the most optically heterogeneous forest structure in the temperate zone. Hierarchical morphological structure: needle – annual shoot – multi-annual shoot – branch – crown

0.5 2 25 200 mm 1 m 30m mm mm mm

Physical based approaches in remote sensing: Scaling from leaves to ecosystems; International workshop, Wageningen, October 2006 stanice

íž ř

Experimentální Bílý K

Praha ExperimentalExperimental ecologicalecological stationstation BBíílýlý KKřříížž Open experimental forest stand

• Meteorological tower

• Eddy-covariance system Research of elevated CO2 conc. impacts

A E C

Dome A : concentration CO2 - 350 ppm (ambient) Dome E : concentration CO2 - 700 ppm (elevated) Plot C : control AISA Eagle VNIR imaging • Specifications system – FOV 39.7°, 29,9° – IFOV 0.039°, 0.029° – Spectral Range 400-1000 nm – Spectral Samples/pixels 244 at 2.2 nm intervals – Spectral Resolution 2.9 nm

© Argus Geo System Needle optical properties - measurement Li-Cor integrating sphere Li 1800-12 Needle optical properties - simulation

• PROSPECT 3.01 – leaf radiative transfer model adjusted for Norway spruce • Inputs: – N parameter of mesophyll layers

–Cha+b concentration [μg/cm2 ]

– Water thickness Cw [cm]

– Dry matter content Cm [g/cm2] DART radiative transfer model © CESBIO laboratory, France NADIR

OFF-NADIR

θv = 48° ϕv = 225°

HOTSPOT Genetically based structural parameters

needle angles to the annual shoot axes

shoot angle to the parent branch (the ramification types: comb, brush, plate)

spatial distribution of branches in the vertical crown gradient

Physical based approaches in remote sensing: Scaling from leaves to ecosystems; International workshop, Wageningen, October 2006 Types of branching

comb comb/brush brush secondary brush Physical based approaches in remote sensing: Scaling from leaves to ecosystems; International workshop, Wageningen, October 2006 a Vertical crown gradient

Norway spruce functional crown b parts: a/ juvenile part b/ production part c/ basal, saturation part

c

Norway spruce crown parts - horizontal projection. RGB colour composition in unnatural colours from tower of permanent research plot Načetín at the Ore Mountains (Czech Republic) - April 2000 (R = red, G = NIR, B = empty).

Physical based approaches in remote sensing: Scaling from leaves to ecosystems; International workshop, Wageningen, October 2006 Environmentally conditioned structural parameters (due to the light competition and the other environmental stressors) ¾ increase in a spatially specific defoliation => decrease of the chlorophyll content per m2 and leaf area index (LAI)

¾ formation of the secondary shoots => crown structure transformation types (increasing clumping of the foliage within a crown (Cudlín et al., 2001; Cudlín et al., 2003; Polák et al., 2007)

Physical based approaches in remote sensing: Scaling from leaves to ecosystems; International workshop, Wageningen, October 2006 Ground estimation Selection of representative trees

Branch sampling

Physical based approaches in remote sensing: Scaling from leaves to ecosystems; International workshop, Wageningen, October 2006 Ground observation data

- ground measurements of LAI (PCA Li-2000, hemispherical images of canopy)

-crown status evaluation

- morphological and dendroecological analysis of selected branches

Physical based approaches in remote sensing: Scaling from leaves to ecosystems; International workshop, Wageningen, October 2006 Norway spruce crown transformation stages and description of the tree damage

Physical based approaches in remote sensing: Scaling from leaves to ecosystems; International workshop, Wageningen, October 2006 Types of crown structure transformation

0 1 2 3 4 Non-transformed branch, Modrý důl , Krkonoše Mts., CZ

Transformed branch, Mumlavská hora, Krkonoše Mts., CZ

Physical based approaches in remote sensing: Scaling from leaves to ecosystems; International workshop, Wageningen, October 2006 Morphological and dendroecological branch analysis

1 Time of secondary shoot prevailing

2 4

1 2

1 0

8

6

4 65 70 75 80 85 90 Production of wood per year [mm 95] Time [years] 0 Physical based approaches in remote sensing: Scaling from leaves to ecosystems; International workshop, Wageningen, October 2006 Ozone deposition model

Miloš Zapletal

Centre for Environment and Land Assessment EKOTOXA Description and application of the ozone deposition flux model in the Czech Republic

The deposition flux was estimated from measured concentrations of ozone in air F = Vd(z) C(z) multiplied by the corresponding deposition velocities:

Ozone deposition velocities were calculated according to a multiple 1 resistance model incorporating Vzd ()= aerodynamic resistance (Ra), Rzabc()++ R R laminar layer resistance (Rb) and surfaceresistance(Rc).

Surface resistance (Rc) −1 comprises stomatal resistance ⎛ 111⎞ (Rsto) which includes Rc = ⎜ + + ⎟ dependence upon global ⎝ RRRRsto+ m inc+ soil R ext ⎠ radiation and surface air temperature. Ra and Rb were assessed on basis of known wind velocity and a surface roughness. 73 meteorological monitoring stations for wind speed, global radiation, air temperature and relative humidity measurements) were used.

Annual averages of the surface roughness, z0,wererelatedto the corresponding surface characteristics on the forest territory of the Czech Republic according to the CORINE Land Cover classes (EEA, 2000).

Land Cover classes used were coniferous forest (Picea abies) and deciduous forest (Fagus sylvatica) Total and stomatal deposition flux to a coniferous and deciduous forest in the Czech Republic on a 1 x 1 km grid during growing season (April-September) of the year 2001 were computed by deposition model with the generalized function of Wesely (1989) to estimate the canopy stomatal resistance.

The stomatal resistance (Rsto) includes dependence upon global radiation and surface air temperature. Vegetation – specific parameters used in the ozone deposition model

Parameter Coniferous Coniferous forest Deciduous Deciduous forest forest References forest References LAI 8.6 Erisman and 5 Meyers and Draaijers (1995) Baldocchi (1988) -1 Ri (s cm ) 2.5 Wesely (1989) 1.4 Wesely (1989) -1 Rm (s cm ) 0 Wesely (1989) 0 Wesely (1989); Lagzi et al. (2004) -1 a Rsoil (s cm ) 3 Brook et al. (1999) 3 Brook et al. (1999) -1 a Rext (s cm ) 20 Brook et al. (1999) 20 Brook et al. (1999) aFor mixed forest 30

25 -0.0018 altitude ΔO 3 = 33.627e 2 The effects of boundary layer

) 20 R = 0.7633 -3 stability are quantified using the 15 observed relationship between (µg m 3 the diurnal variability of surface O 10 Δ ozone concentration and 5 altitude. 0 0 200 400 600 800 1000 1200

altitude (m a.s.l) Ozone concentrations The monitoring data from rural areas (21 automated rural monitoring stations for ozone concentrations measurements) were used. Mean ozone concentration (µg m-3) in forest in the Czech Republic on a 1 x 1 km grid in April - September 2001. Ozone concentration values are ranging from 54 to 92 µg m-3. Mean ozone concentration is 76 µg m-3. Mean total and stomatal fluxes of ozone (nmol m-2 s-1) to forest in the Czech Republic on a 1 x 1 km grid in April - September 2001.

The share of stomatal ozone flux on total ozone flux makes 53 % in coniferous forest, while 72 % in deciduous forest. Regression analysis of total ozone flux (April - September) in relation to AOT40 exposure index (May - July) on 1 x 1 km grid on the forest territory of the Czech Republic in 2001. Both correlations are significant at the p<0.01 level.

Zapletal, Chroust (2007) Whole tree approach in studies of forest ecology

Jan Čermák & Nadia Nadezdina

Mendel University of Agriculture and Forestry, Brno Czech Republic Object: continuum soil – tree – atmosphere (tree parameters which can be studied at small and large scales) Meteorological factors

• STRUCTURE . FUNCTION Crown-foliage -Leafamount and distribution - Details of internal structure Sap flow Conducting system -Water - Distribution of elements - consumption - Conducting profile -Radial profile -Water storage Root system - Root amount and distribution ¨ - Changes in - Details of internal structure stem volume (hydration, Soil factors growth) Crown and roots are functionally integrated by sap flow in stems

Dispersed leaf transpiration

Dispersed sap flow in branches Below crown Integrated sap flow in stems = At stem base = Whole tree water Dispersed sap flow consumption in roots Trunk (section) heat balance (THB) method with internal electric heating

* Measures directly sap flow in Electrodes mass or volume units * Sap flow is integrated across whole conducting sapwood Thermo- * Defined and wide measured stem couples sections (40-80 mm) * Suitable even for species with very shallow sapwood and high flow density (e.g. ring-porous) Usually 2-4 * Often applied as a standard for sensors per tree testing other methods of this size` (Čermák et al. 1973, 1982, 2004, Kučera et al. 1977, Tatarinov et al. 2005) THB method with circumferential heating

Thermocouples

Insulation` Flexible heating elements Modified THB sensor based on external heating and sensing is suitable for small shoots or roots (diameter ~ 5-20 mm)

Does not strangulate fast growing shoots Heat field deformation (HFD) method, (Nadezhdina et al. 1998, 2003, 2005, Čermák et al. 2004)

Sap flow sensor: HFD method with a portable datalogger

Each multi-point sensor contains a series of 6-10 single-point sensors

Series of 4-8 sensors Especially suitable for measurement of across stem sapwood installed around stem sap flow patterns of trees this size Visualization of sap flow patterns across stems (= integration from measuring points to whole trees)

Orig.data along stem radii: (48 points per General 3D stem) “Opened tree”

Color pattern Sap flow measurements at the stand level

Uppsalla - Sweden Measurement is performed in a series of sample trees of different size, representing given stands Datalogger

Volga-river source - Russia

Large trees

Sap flow amplitude and dynamics Small trees characterize the situation Up-scaling data from sample-trees to stands (1 ha)

Stocking density Selection of parameter

Regression curves converting data from sample-trees to mean trees

Diameter classes Resulting stand

Cumulation of classes Limits of tree water uptake

Atmospheric evaporation demands Soil water Availability or lack of water (water potential -WP gradient) Soil hydraulic conductivity (especially in heavy soils)

Root respiration - availability or lack of oxygen (hypoxia)

Tree structural balance (root / leaf area ratio) => Whole tree approach Changes of transpiration caused by drought

Sesonal courses (10-day periods)

Diurnal courses Changes of transpiration caused by hypoxia

Excess water Seasonal courses (10-day periods)

Diurnal courses Water balance in the floodplain forest

Mild weather Dry weather Dry weather . Ample underground water No underground water Physical soil properties decisive for tree survival

Retention curve indicates that soil water potential is not a crucial limiting factor

Most important limiting factor of water supply is soil hydraulic conductivity (when water content decreases by 4%, it drops 100x ) Transpiration of upper and lower parts of crown

Upper

Lower Lower

Drought Upper crown transpires daily the same anmount of water as lower crown Tree top Picea abies

Crown radius Interpretation of remote sensing Upper crown images

Lower crown

Radial needle distribution: Whole tree approach is preferable in any field studies Shallow root systems of spruce trees in heavy soil Mean depth 40cm, max 75cm, root length 3-12m Measurements of absorbing root surfaces

Electrodes in stems Background = equation of electric current continuity in a complex electrolyte Modified earth (Staněk 1997, Aubrecht et al. 2006, impedance method Čermák et al. 2006)

Moving electrode

Scheme of measurement

Field work Stable electrodes Electrodes in soil Leaf area and root surface in two spruce stands

Leaf area

Absorbing root surface Impact of root /shoot ratio on tree mortality in a floodplain forest after 2 m decrease of underground water level

OK Verification of root distribution estimates Estimated within a biomass study Derived from sap flow patterns

Janssens et al. 1999.

Long-term work Days-hours

Well developed superficial fine roots

Poorly developed (Gebauer and deep fine roots Martinkova 2005) Water supply dynamics by superficial and sinker roots Example: spruce, age over 100 years Drought caused rapid decrease of sap flow during 20 days (two days were selected for demo)

Water supply by superficial roots decreased dramatically, while the supply by sinker roots partially increased Transfer of information between different levels of biological organization

Tree selection = Quantil of total

Watershed (Landscape) Trees (series of sample trees) Stands (1 ha) Up-scaling from stands to landscape - example:

Volga river source Modeled pixel size area (in Russia) (forest stand) 3 500 km2 4 ha

1:1 Verification of (two models) hydrological models based on stand-level Sap flow ecophysiological studies

Storage estimates based on diurnal courses of flow in stems and foliage and their differences Upper crown

Depletion and refilling of storage Whole tree Sap flow in trunk (different levels above ground) and foliage Cumulated storage and volume changes of stem

Diurnal courses Storage – volume relationships

Upper stem (little time shift)

Whole tree (great time shift) Activities of the Steering Group Ozone Risk Assessment Network

Semi-natural vegetation

J N Cape, S Bassin

Ozone Risk Assessment for European Vegetation Capri, May 2007 Ozone - a rural pollutant

Average ozone - May 2003 50

rural 40 rural 30

urban ppb 20 Eskdalemuir 10 Strath Vaich Central Glasgow 0 00 03 06 09 12 15 18 21 00 Ozone Risk Assessment for Europeantime Vegetationof day Capri, May 2007 Ozone – an upland pollutant 1996 Annual Mean

30

25

20

15

10 0-200 m a.s.l (7 sites) concentration, ppb 3 5 200-600 m a.s.l (6 sites)

O >= 600 m a.s.l (2 sites) 0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 Hour (UTC) Ozone Risk Assessment for European Vegetation Capri, May 2007 What are past trends? Trends in the Annual Mean 40 Strath Vaich ann. mean =0.17y - 298, R2 = 0.31, P = 0.04 30 Dunslair Heights ann. mean =0.31y - 580, R2 = 0.42, P = 0.04 Bush 20 ann. mean =0.19y - 359, ppb R2 = 0.34, P = 0.01

Strath Vaich + 0.4% y-1 Eskdalemuir Dunslair + 0.9% y-1 10 No trend detected. Bush + 0.7% y-1

0 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Ozone Risk Assessment for European Vegetation Capri, May 2007 -1 What are past trends? -2 -1 0% y 1 2 3

USA USA Maritime

USA Continental Alaska

Canada Canada

Canada Surface trends at Canada Canada background sites: Canada Canada 1970-2000 Atlantic Europe Vingarzan (2004) Europe Europe Europe Europe

Europe Japan Japan Larger trends 1970-1990 Japan Japan than 1990-2000 Japan S Africa Australia

Australia Largest trends are in Pacific Pacific N. Hemisphere Antarctic

Ozone Risk Assessment for European Vegetation Capri, May 2007 Long-range transport as contributor to ‘background’

STRATOSPHERE

REACTION FREE EMISSIONS TROPOSPHERE O 3

DEEP

CONVECTION TRANSPORT EMISSIONS VOC REACTION VOC NOx O NOx 3

BOUNDARY LAYER

DEPOSITION

Trans-Pacific 3 -10 ppb in western US Trans-Atlantic 1 -3 ppb in Ireland Trans-Eurasian < 5 ppb on Pacific rim from Europe N. American contribution to mid-troposphere > European Ozone Risk Assessment for European Vegetation Capri, May 2007 Where does ozone come from?

Contribution to annual mean daily maximum ozone concentrations 35

30

25 Strat-trop exchg 20 Europe Ireland Finland N America 15

ppb Ozone Asia

10 (Derwent et al., 2004)

5

0 -20 -10 0 10 20 30 40 longitude

Ozone Risk Assessment for European Vegetation Capri, May 2007 What are future prospects?

Changes in seasonal patterns

Net regional production

from Pochanart et al., 2001

Ozone Risk Assessment for European Vegetation Capri, May 2007 What are future prospects?

Effects of climate change on ozone production

• increased temperatures increase VOC emissions, esp. biogenic • increased temperatures speed up chemical reactions

• increased water vapour concentrations increase OH, but decrease O3 • overall effect of temperature varies spatially • decreased rainfall (increased sunlight) in S. Europe

enhances O3 production • increased cloudiness (decreased sunlight) in N. Europe

reduces O3 production • effect of changes in cloudiness varies spatially

• increased drought inhibits O3 uptake by vegetation • increased CO2 decreases stomatal conductance • higher peak concentrations and mean values overall

Ozone Risk Assessment for European Vegetation Capri, May 2007 What are future prospects?

Implications for plant uptake and effects

• increased CO2 causes larger stomatal resistance, less O3 uptake

but enhanced growth caused by CO2 may be offset by increased O3 (Karnosky et al., FACE experiments)

• increased temperature at mid-latitudes decreases stomatal resistance

predicted positive effects of CO2 may not be realised, especially if O3 concentrations are higher than today

Ozone Risk Assessment for European Vegetation Capri, May 2007 Particular issues with semi-natural vegetation • Great diversity across Europe • Very little currently known • Studies must involve communities, not genotypes (crops) or single species (forests) • Communities have to be studied under natural conditions

Ozone Risk Assessment for European Vegetation Capri, May 2007 Particular issues with semi-natural vegetation

This does not mean that the task is impossible – just difficult

There is a role for chamber studies and other controlled environments with simple systems, but the main focus is on field-based research.

Ozone Risk Assessment for European Vegetation Capri, May 2007 Progress to date

Expressions of interest from 11 groups in 8 countries

• 5 already have, or are planning, field sites with flux measurements or fumigation studies • 4 would like to build fumigation facilities • 2 would be interested in participating

Ozone Risk Assessment for European Vegetation Capri, May 2007 UK – Keenley Fell

Passive samplers & analyser inlets

~2.5 x 10 m

8 m

Wire or electric fence

Line sources, 6 m Power and signal Wire fence cables, O3 tubing

Ducting between the Micro-met mast cabin– release systems– Release micro-met mast, area under ground, to protect (unfenced) wires/tubing from livestock Cabin

Ozone Risk Assessment for European Vegetation Capri, May 2007 Germany - Giessen

Ozone Risk Assessment for European Vegetation Capri, May 2007 Switzerland - Sur

Ozone Risk Assessment for European Vegetation Capri, May 2007 Denmark – addition to FACE

Ozone Risk Assessment for European Vegetation Capri, May 2007 Conclusions

• Upland vegetation is already exposed to potentially damaging ozone concentrations • A major challenge is the interaction of ozone with communities of different plant species, each of which contains a range of genetic variation, allowing adaptation to ozone stress that is not possible with genetically selected crop plants. • The complexity of natural plant communities makes it necessary to conduct experiments under realistic climatic and edaphic conditions.

Ozone Risk Assessment for European Vegetation Capri, May 2007 Conclusions

• Experiments need to consider community and ecosystem function, rather than simply the effects on individual plant species. • Understanding responses in terms of ozone fluxes requires detailed measurement not only of overall ozone fluxes to the canopy, but also the partitioning of the flux within the different canopy and soil components • The proposal to the EU FP7 aims to estimate ozone sensitivity for semi-natural vegetation at a European scale by linking ozone flux measurements and modelling using ozone exposure experiments at the ecosystem level under realistic conditions.

Ozone Risk Assessment for European Vegetation Capri, May 2007 Useof sapfluxdata to validate assumptions for the flux model

Sabine Braun Institute for Applied Plant Biology Schönenbuch, Switzerland Sebastian Leuzinger Botanical Institute, University of Basel, Switzerland Christian Schindler Institute for Social and Preventive Medicine University of Basel, Switzerland Forest plots Tree species: Fagus sylvatica

Rainfall Altitude Stand age Lat. (mm) (1) (m) (years) Soil type

Hofstetten (2) 47.47 1232 540 95 leptosole

Muri 47.27 1127 490 125 cambisole

Sagno 45.86 1569 770 80 leptosole

(1) Rainfall given as average 1980-2000 (2) For Hofstetten see Leuzinger et al. 2005 Canopy conductance according to Köstner

t = (ρ ∗ ∗ kv /*) DETGg

g t : total conductance (mm s-1)

ρ ∗ ∗TG kv : factor derived from density of water, gas constant and temperature (36.25 kPa at 10°C) E : sap flow density (mm h-1) D : vapour pressure deficit (kPa) Raw data plot: total conductance vs. vapour pressure deficit

50

)

1

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s 40

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e 30

c

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u 20

d

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t 0 0 10 20 30 vapour pressure deficit Statistical treatment of the data

• Calculate a daily time lag for sap flow against meteorological parameters • Calculate conductance using the lagged meteorological data • Divide the independent variable into 15-20 classes • Calculate a 90-percentile of conductance for each class, separately for each tree • Apply a nonlinear robust regression to these 90- percentiles Derivation of stomatal functions from sap flow: vapour pressure deficit

1.2 1 gVPD 1−= β*VPD γ

e 1+ e

c

n

a 0.8

t

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u

d

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β: -0.132

e

v γ: 2.532 i 0.4

t

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0.0 0 7 14 21 28 35 vapour pressure deficit (hPa) Derivation of stomatal functions from sap flow: temperature

bt ⎛ − TT − TT ⎞ 1.2 g = ⎜ min × max ⎟ temp ⎜ ⎟ ⎝ opt − TT min max − TT opt ⎠ 1.0

e c red curve:

n

a t 0.8 Tmin: 4.5, Topt: 17.0

c u Tmax: 36.4, bt: 1.55

d

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o 0.6

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a 0.4

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R 0.2

0.0 0 10 20 30 40 Air temperature (°C) Interaction between temperature and VPD Relation between relative conductance and air temperature for different stratifications of VPD 1.2

1.0

e

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i VPD (hPa)

t

a 0.4

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e _5< R >5-10 0.2 >10-15 >15-20 0.0 >20 0 10 20 30 40 Air temperature (°C) Result of the stratified nonlinear regression (VPD classes in hPa)

b1 b2 ⎛ − TT ⎞ ⎛ − TT ⎞ ⎜ min ⎟ ⎜ min ⎟ temp = bg 0 *⎜ ⎟ ⎜1* − ⎟ ⎝ max − TT min ⎠ ⎝ max − TT min ⎠

1.0 <5 0.8 5-10

e

c 10-15

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t 0.6 c 15-20

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e 20-25

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a l 0.2

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0.0 5 15 25 35 temperature (°C) Relation between relative conductance and VPD, stratified according to temperature (°C)

1.0

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a t 15-25 >25

c

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d 0.6

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<15

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i 0.4

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0.0 0 7 14 21 28 35 vapour pressure deficit (hPa) Conclusions

• The analysis of the beech dataset suggests that there is a significant interaction between the stomatal functions for temperature and VPD which may explain the currently suggested lower sensitivity of Mediterranean forest trees against VPD. TroposphericTropospheric ozoneozone:: aa menacemenace forfor cropscrops andand naturalnatural vegetationvegetation inin GreeceGreece

CostasCostas SAITANISSAITANIS

Agricultural University of Athens Labotory of Ecology and Environmental Sciences Map PhotochemicalPhotochemical airair pollutionpollution

ComparisonComparison ofof differentdifferent citiescities

HeavyHeavy metalsmetals biomonitoringbiomonitoring alongalong thethe streetsstreets withwith higherhigher plantsplants,, lichenslichens andand mossesmosses bagsbags

PGEsPGEs onon leafleaf surfacesurface andand onon toptop soilssoils alongalong streetsstreets andand roadsroads

Map Three walk-in chambers Οzone generator Οzone analyzer NOx analyzer Microscope and Stereoscope Atomic Absorbance Spectrophotometer Chlorophyll Fluorescence Gross Photosynthesis Stomatal Conductance Other instruments. 99 LaboratoryLaboratory investigationinvestigation ofof ozoneozone effectseffects onon plantsplants Relative sensitivity of the available cultivated varieties, Ozone effects on physiological parameters Chlorophyll content, Photosynthesis, Chlorophyll fluorescence, Stomatal conductance... Fresh peas Cotton

Sinapis nigra Egg-plants Celery cutting Drama Xanthi

Kozani

Karditsa Pilion mountain region Arta VOLOS Mitilini

Aliartos CORINTHCORINTH MESOGIA PLAIN Pournaria TRIPOLI AthensAthens

Kalamata

Greece Typical ozone symptoms on leaves of Bel-W3 tobacco plants (bioindicator) Ozone Biomonitoring (or Phytodetection)

Zichnomirodata Threshold: 60-80 ppb Bel-W3 Threshold: 40-50 ppb

Station Typical ozone symptoms on leaves of white clover (ozone bioindicator) White clover ozone biomonitoring system incorporates one sensitive to ozone (NC-S) and one resistant to ozone (NC-R) clone. The ration of biomass production is an index of ozone phytotoxicity. Artemis

Markopoulo

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Artemis Ozone concentration (ppb) Ozone concentration Ozone concentration (ppb) Ozone concentration Markopoulo ATHENS Spata

Time MarkopouloAthens, Aug. 2000 Artemis Artemis Artemis Aug. 2000 Aug. 2000 Aug. 2000 Aug. 2000

Injured plants June 20 70 June 26 60

50

40

30 Injury (% ) 20

10

0

Glika Nera Spata Markopoulo Artemis Athens Kiato (5 m)

Astronomical Observatory (950 m)

Bogdani hill (300 m)

OzoneOzone concentrationconcentration (ppb)(ppb) 100 100 40 10 20 30 50 60 70 80 90 10 20 30 50 60 70 80 90 40 0 0 17 17 19 19 21 21 23 23 Days: 45 Days: 45 Bogdani Bogdani 25 25 27 27 29 29 AOT40: 9532 AOT40: 9532 (18 (18 1 1 Ιουν Ιουν 3 3 5 5 . . - - 7 7 1 1 9 9 Αυγ Αυγ Average daily:212ppb*hours Average daily:212ppb*hours 11 11 . . 13 13 2000) 2000) 15 15 17 17 19 19 21 21 23 23 25 25 27 27 29 29 31 31 2 2 100 Days: 23 AOT40: 4406 Average daily: 192 ppb*hours 5000

90 4500

80 4000

70 3500

60 3000

50 2500

40 2000 AOT40 (ppb*hours) O3 concentration (ppb) O3 concentration 30 1500

20 1000

10 500

0 0 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 DATE AstronomicalAstronomical ObservatoryObservatory, Aug. 6 to Aug. 28, 2000 100 Days: 24 AOT40: 1126 Average daily: 47 ppb*hours 1200 90 1000 80

70 800 60

50 600

40 AOT40 (ppb*hours)

O3 concentration (ppb) O3 concentration 400 30

20 200 10

0 0 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 DATE KΚΙΑΤΟiato from Aug. 29 to Sep. 22, 2000 70

60 Astronomical Observatory (950 m)

50

Bogdani, 300 m 40 (ppb) (ppb) 3 3 O O 30 Kiato 5 m

20 ATHENS 80 m Concentration Concentration 10

0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour 0.70 Bel-W3, Athikia

0.60

0.50 (s/cm) (s/cm) 0.40

0.30 Conductance Conductance 0.20

0.10

0.00 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 Hour 0.6 Vitis vinifera (L)

0.5

0.4

0.3

0.2 Conductance (s/cm) Conductance Conductance (s/cm) Conductance

0.1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 Hour 0.25 Pinus, Athikia Current year Second year Third year

0.20

0.15 (s/cm) (s/cm)

0.10 Conductance Conductance

0.05

0.00 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 Hour Aug. 19 Aug. 25 80 70 ) ) . . 60 s.e s.e ± ± 50 40 Mean Mean 30 (%) ( (%) ( 20 10 Injury Injury 0 . . Velo Velo Kiato Kiato Assos Assos Obser Obser Athikia Acient Athikia Acient Solomos Solomos . . Bogdani Bogdani Examilia Examilia Corinth Corinth Mogostos Mogostos Zevgolatio Zevgolatio Astr Astr Astronomical Observatory, Αυγ. 2000 Visible Injury Scale

10 % 25% 50% 75% 100% KIATO Mouggostos (850 m) (5 m)

Zevgolatio Astronomical (20 m) Observatory (920 m) Velo Assos (40 m) (25 m) CORINTH

Acient Corinth (155 m) (125 m) Examilia ATHENS Solomos Bogdani (195 m) (300 m)

Athikia (310 m) Ozone Biomonitoring (or Phytodetection)

TrifoliumTrifolium subterraneumsubterraneum L.L.

Station Trifolium subterraneum L. 39 39 ο ο 01΄ 05΄ Latitude 22 Velestino Velestino 17//19 17//19 ο 48΄ Alikes Alikes 53.9//00 Soros 53.9//00 Soros 27//9 27//9 VOLOS VOLOS Amaliapolis Amaliapolis 14//0 Sourpi 14//0 Sourpi 28//0 Kritharia 28//0 Kritharia 22//9 27//13 22//9 27//13 Makrinitsa Makrinitsa 48//21 48//21 Pteleos Pteleos 8//3 25//0 8//3 25//0 13//6 13//6 22 ο Agria Agria 00΄ 33// 0 33// 0 Chania Chania 51//22 51//22 Kala Kala 65//35 65//35 Agriolefkes Agriolefkes Altitude 33//7 33//7 Trikeri Trikeri 28//29 28//29 Visible InjuryIndex Visible InjuryIndex Afissos Afissos Nera Nera 17//0 17//0 Argalasti Argalasti Milies Milies 25//15 25//15 Milina Milina 34//0 34//0 52//21 52//21 Tsagarada Tsagarada 44/41 Damouxari 44/41 Damouxari 45//23 45//23 Neoxori Neoxori 35//9 35//9 Lafkos Lafkos 29//5 29//5 Platanias Platanias 17//3 17//3 Promiri Promiri Xinovrisi Xinovrisi 36//32 36//32 beach beach 63//36 63//36 Bel Bel Mourtias Mourtias 60//34 60//34 - - W3 W3 // // Zix Zix

Pilio Mountain, (Μουρτιάς) Aug. 2001 Bel-W3

ZicnomirodataZicnomirodata

Pelion Mt (Mourtias), 2001 39 39 39 39 ο ο ο ο 01 0 01 0 5 5 Latitude ΄ ΄ ΄ ΄ 22 22 Velestino ο ο 48 48 ΄ ΄ Alikes Soros Kritharia VOLOS VOLOS Amiapolis Sourpi Makrinitsa Pteleos 22 22 ο ο aaNera Kala 00 00 Chania Agria ΄ ΄ Trikeri Agriolefkes Altitude Afissos Argalasti Milies Milina Tsagarada Damouchari Neoxori Platanias Lafkos Bel-W3 Promiri iors beach Xinovrisi Mourtias Visible InjuryScale 10 25 50 100% 75 10 25 50 100% 75 % % % % % % % % 120 ) 100

80 Con. (ppb Con. 3 60

40

20 Daily average O 0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324 Hour Argos, (Houni village) Aug. 2000 Koroni, (Harokopio village) Aug. 2000

100%

80%

60%

40%

30%

20%

Η ένταση των συμπτωμάτων που παρατηρήθηκαν στα φυτά βιοδείκτες που τοποθετήθηκαν σε 15 σημεία φυτοανίχνευσης του 10% όζοντος στην ευρύτερη περιοχή της Τρίπολης (Ιούνιος, 2005) 100 90 80 (ppb) 70 60 όζοντος 50 40 30 20

Συγκέντρωση 10 0 5/6 20/6 5/7 20/7 4/8 19/8 3/9 18/9 3/10 18/10

Ozone concentration at Tripoli (Peloponnesus). 80

70

60

50

40

30 Concentration (ppb)

3 20 Ο 10

0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324 Hour Diurnal pattern in Tripoli (Peloponnesus). Two Bel-W3 plants of the same age. The first (left) was put at Tripoli for 1 week the second remained in the growth chamber. Summer 2006

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