Asian Journal of atmospheric environment
Asian Journal of atmospheric environment Asian Journal of atmospheric environment
Asian Journal of atmospheric environment

Asian Journal of Atmospheric Environment - Vol. 12 , No. 1

[ Review Article ]
Asian Journal of Atmospheric Environment - Vol. 12, No. 1, pp.1-16
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 31 Mar 2018
Received 09 Jun 2017 Revised 28 Aug 2017 Accepted 29 Aug 2017
DOI: https://doi.org/10.5572/ajae.2018.12.1.001

A Review Study on Ozone Phytotoxicity Metrics for Setting Critical Levels in Asia
Evgenios Agathokleous1), 2), * ; Mitsutoshi Kitao1) ; Yoshiyuki Kinose3)
1)Hokkaido Research Center, Forestry and Forest Products Research Institute (FFPRI), Forest Research and Management Organization, 7 Hitsujigaoka, Sapporo, Hokkaido 062-8516, Japan
2)Research Faculty of Agriculture, School of Agriculture, Hokkaido University, Kita 9 Nishi 9, Sapporo, Hokkaido 060-8589, Japan
3)College of Agriculture, Ibaraki University, 3-21-1 Chuo, Ami, Inashiki, Ibaraki 300-0393, Japan

Correspondence to : * Tel: +81-11-851-4131, E-mail: globalscience@frontier.hokudai.ac.jp, evgenios@ffpri.affrc.go.jp

Funding Information ▼

Abstract

Ground-level ozone (O3) can be a menace for vegetation, especially in Asia where O3 levels have been dramatically increased over the past decades. To ensure food security and maintain forest ecosystem services, such as nutrient cycling, carbon sequestration and functional diversity of soil biota, in the over-populated Asia, environmental standards are needed. To set proper standards, dose-response relationships should be established from which critical levels are derived. The predictor of the response in the dose-response relationship is an O3 metric that indicates the dose level to which the plant has been exposed. This study aimed to review the relevant scientific literature and summarize the O3 metrics used worldwide to provide insights for Asia. A variety of O3 metrics have been used, for which we discuss their strengths and weaknesses. The most widely used metrics are based only on O3 levels. Such metrics have been adopted by several regulatory agencies in the global. However, they are biologically irrelevant because they ignore the plant physiological capacity. Adopting AOT40 (O3 mixing ratios Accumulated Over the Threshold of 40 nmol mol-1) as the default index for setting critical levels in Asia would be a poor policy with severe consequences at national and Pan-Asian level. Asian studies should focus on flux-based O3 metrics to provide relevant bases for developing proper standards. However, given the technical requirements in calculating flux-based O3 metrics, which can be an important limitation in developing countries, no-threshold cumulative exposure indices like AOT0 should always accompany flux-based indices.


Keywords: AFst6, Air pollution, AOT40, Critical levels, Dose-response, DO3SE, Flux, Metrics, MPOC, Ozone, PODY, SUM06, Threshold, Uptake, W126

1. INTRODUCTION

Ozone (O3) levels at the lower troposphere throughout the Northern Hemisphere have been dramatically increased compared to the pre-industrial ones (Sicard et al., 2016; Saitanis et al., 2015; Kalabokas et al., 2013; Vingarzan, 2004; Akimoto, 2003). Notably, Asia, including China and India, is currently a “hot spot” of air pollution, with O3 levels in some regions exceeding by far the critical levels (CL) of the standards set for protecting vegetation (Kitao et al., 2016; Feng et al., 2015; Kim et al., 2015; Komatsu et al., 2015; Verstraeten et al., 2015; Oksanen et al., 2013; Takigawa et al., 2009). Such increases in O3 levels may detrimentally affect plants and thereby reduce yields, and alter their quality, of major crop plants used for the feeding needs of humanity and other animals (Osborne et al., 2016; Tian et al., 2016; Broberg et al., 2015; Feng et al., 2015, 2008; McGrath et al., 2015; Wilkinson et al., 2012; Morgan et al., 2003). It may thus be a challenge to adequately feed the increasing population of Asia under the scenario of O3-induced losses in yields (Lu et al., 2015). O3-induced losses in yields in Asia (Wang and Mauzerall, 2004) and globally (Avnery et al., 2011) associate with an economic cost of billions of US$. Furthermore, chronic exposure of natural vegetation to such potentially phytotoxic O3 levels may pose a threat for the sustainability of plant communities and ecosystems (Agathokleous et al., 2016, 2015; Chappelka and Grulke, 2016; Koike et al., 2013; Lindroth, 2010; Cape, 2008; Ashmore, 2005; Matyssek and Innes, 1999).

The most critical information when studying O3 effects on plants is the O3 phytotoxicity metric utilized (Matyssek et al., 2007; Paoletti and Manning, 2007; Musselman et al., 2006). The O3 metric is considered a versatile tool that can be utilized for: a) providing information about the exposure of plants to O3; b) establishing dose-response relationships, and thus serving as a predictor of plant response along the full dose-response continuum; c) conducting risk assessment and setting CL for protecting vegetation against adverse effects caused by O3; and d) communicating meaningful information to policy and decision makers for setting environmental standards. O3 metrics should be thus carefully selected and used in a united manner so as to contribute in adopting appropriate and effective standards at national and Pan-Asian level.

In this study we aim to provide the bases for future research on the assessment of O3 impacts on Asian vegetation. In wide regions of Asia, summertime precipitation may be high, a phenomenon which relates to Asian summer monsoons (ASM) (Shi et al., 2017; Ha et al., 2012; Chang, 2004). Despite ASM may contribute to decreasing O3 mixing ratios, such a potential decrease is of a small order, and thus O3 will remain at potentially phytotoxic levels (Surendran et al., 2016). Synchronous summertime precipitation and high O3 mixing ratios may pose a risk for O3 phytotoxicity in Asian vegetation due to a greater stomatal O3 uptake. We thus review the relevant scientific peer-reviewed literature and summarize the current global knowledge on the O3 metrics to provide insights for Asia.


2. O3 PHYTOTOXICITY METRICS

In the present paper, mixing ratios with a unit of nmol mol-1 were used to express the level of O3 in the air. Although the term of “concentration” is often used in many papers, it is inaccurately defined as in most cases. The definition of “concentration” is mass per volume. On the other hand, O3 monitoring systems measure in mixing ratios defined as the abundance (number of O3 moles) of O3 relative to air (per mole of air).

A series of metrics have been proposed and used for assessing the potential O3 injury to vegetation, each of which has its own strengths and weaknesses (Table 1). These metrics are mainly used for setting CL for protecting vegetation based on the O3 exposure (CLee) or the accumulated stomatal O3 flux (CLef). The historical foundations of the basic metrics can be found in earlier articles (Musselman et al., 2006; Pleijel et al., 2004; Wang and Mauzerall, 2004; Danielsson et al., 2003; Grünhage et al., 1999; Fuhrer et al., 1997). Yet, explanations about specialized definitions which have been cited in this article can be found in Musselman et al. (2006).

Table 1. 
Strengths, weaknesses and caution points of the main metrics used for setting critical levels for protecting vegetation based on the O3 exposure (CLee) or the accumulated stomatal O3 flux (CLef).
Strengths Weaknesses Cautions
CLee
AOT40 1) Easy to calculate, only O3 levels data are needed (Fuhrer et al., 1997; Grünhage et al., 1999; Kärenlampi and Skärby, 1996).
2) It has been widely reported in the literature
3) Adopted by several regulatory agencies (without meaning that this is the appropriate policy)
1) O3 levels are not weighed based on the plant ontogenic stage
2) Environmental constrains to O3 uptake into leaf tissue are ignored; it is thus biologically unrealistic (Danielsson et al., 2003; Gerosa et al., 2005; Grünhage and Jäger, 2003; Harmens et al., 2007; Karlsson et al., 2007b; Matyssek et al., 2004; Paoletti and Manning, 2007; Sanz et al., 2016)
3) Ineffective in regions with low O3 pollution
4) Highly overestimated O3 effects in regions with dry climates (Gerosa et al., 2005)
1) O3 levels should be those at the top of the plant canopy (Grünhage et al., 1999; Hicks et al., 1987)
2) Deposition models may be used to estimate the O3 levels at the top of the plant canopy from the O3 levels at a different height (Emberson et al., 2000a, 2000b; Simpson et al., 2012)
3) Improvements may be applied for toxicological effectiveness (Grünhage et al., 1999; Musselman et al., 2006)
4) Higher values may occur along coastlines (Anav et al., 2016)
5) Care should be exercised factors which may cause stomatal limitation, such as soil unavailability and acidification, to be controlled in experiments (Azuchi et al., 2014; Gerosa et al., 2005)
SUM06 1) Easy to calculate, only O3 levels data are needed Same as 1, 2, 3 and 4 in AOT40 Same as 1, 2 and 5 in AOT40
W126 1) Easy to calculate, only O3 levels data are needed (Lefohn et al., 1988; Lefohn and Runeckles, 1987; Musselman et al., 2006; Wang and Mauzerall, 2004). 2) More realistic than AOT40 and SUM06 3) More effective than AOT40 and SUM06 in regions with low pollution 4) It has been adopted by some regulatory agencies Same as 2 in AOT40 Same as 1, 2 and 5 in AOT40
CLef
DO3SE 1) It takes into account environmental factors and vegetation properties (Anav et al., 2016; De Marco et al., 2016)
2) Species-specific responses to O3 in multispecies cultures are meaningful and realistic, in contrast to CLee approaches, because the O3 uptake into the leaf tissue depends on species-specific physiology (Calvete-Sogo et al., 2017)
1) More complex calculation than CLee approaches (Emberson et al., 2000a)
2) Several factors should be monitored and more data should be input in the model than CLee approaches (Anav et al., 2016; De Marco et al., 2016; Emberson et al., 2000a)
3) Efficiency depends on environmental conditions which affect plant physiology (Hu et al., 2015)
AFstY 1) Same as 1 in AOT40 (Emberson et al., 2007; Karlsson et al., 2007b; Pleijel et al., 2007)
2) More effective than the original DO3SE because it implements the delimitation of the exposure period (Emberson et al., 2007; Karlsson et al., 2007b; Pleijel et al., 2007)
3) Biologically more meaningful because plant ontogeny is considered with regards to max stomatal conductance (Pleijel et al., 2007)
4) Same as 2 in DO3SE
Same as 1 and 2 in DO3SE
PODY 1) Same as 1 in AOT40 (CLRTAP, 2015; Matyssek et al., 2004)
2) Same as 2 in DO3SE
Same as 1 and 2 in DO3SE 1) Higher values may occur along coastlines (Anav et al., 2016)
2) Soil water and nitrogen availability should be taken into consideration (Anav et al., 2016; De Marco et al., 2016)
3) Potential site-specificity (Azuchi et al., 2014; De Marco et al., 2016; González-Fernández et al., 2017)

In Asia, parameterization of models of stomatal conductance to water vapor (gs), which can be used for CLef approaches, development of dose-response relationships, and derivation of CLee or CLef have been mainly conducted in China (Shang et al., 2017; Yuan et al., 2017; Zhang et al., 2017; Hu et al., 2015; Feng et al., 2012; Oue et al., 2011, 2009, 2008; Wang and Mauzerall, 2004) and Japan (Kinose et al., 2017, 2014; Kitao et al., 2016, 2014; Watanabe et al., 2016, 2012, 2011, 2010; Hoshika et al., 2015a, b, 2013b, 2012a; Azuchi et al., 2014; Yamaguchi et al., 2014; Watanabe and Yamaguchi, 2011) during the last decade. Amongst these studies, just few deal with the establishment of dose-response relationships and derivation of CL (Kinose et al., 2017; Shang et al., 2017; Yuan et al., 2017; Zhang et al., 2017; Hu et al., 2015; Yamaguchi et al., 2014; Feng et al., 2012; Watanabe et al., 2012, 2011, 2010; Wang and Mauzerall, 2004).


3. CLee

Exposure-based metrics were the first O3 metrics used by the scientific community of air pollution. This category of metrics takes into account only the O3 levels.

3. 1 AOT40

The first and most widely accepted and used metric is the AOTX (O3 mixing ratios Accumulated Over the Threshold of X nmol mol-1), set for long-term O3 exposures. AOTX is practically the sum of exceedances of daytime (solar radiation >50 W m-2) hourly O3 mixing ratios above a threshold of X nmol mol-1, for a given exposure period (Grünhage et al., 1999; Fuhrer et al., 1997; Kärenlampi and Skärby, 1996). The X threshold is commonly set at 40 nmol mol-1 (AOT40), and the AOT40 is thus calculated according to the formula:

AOT40=i=1nO3-40i for O3>40 nmol mol-1

where: i=the running index and n=the number of hours with [O3]>40 nmol mol-1. The units of AOT40 are nmol mol-1 h (classically ppb h). The historical foundations of AOT40 have been previously explained (Fuhrer et al., 1997). AOT40 CL have been adopted by the United States Environmental Protection Agency (USEPA), the United Nations Economic Commission for Europe (UN/ECE), and the World Meteorological Organization (WMO) (World Health Organization (WHO), 2000). The critical AOT40 level (=5 or 10% yield reduction) for agricultural crops and semi-natural vegetation has been set at 3000 nmol mol-1 h. This level should not be exceeded during the running 3-month growing season of plants (e.g. May-July for central Europe), for protection against long-term ambient O3 adverse effects (Kärenlampi and Skärby, 1996). For forests, the UN-ECE set the CL at 10000 nmol mol-1 h over the six months of the active growth of trees. In Europe, 9000 nmol mol-1 h (3-month, averaged over five years) have been proposed as target value, whereas 3000 nmol mol-1 h (3-month) have been proposed as long-term objective for protecting vegetation (“Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe,” n.d.).

For acute O3 toxicity, 5-day AOT40VPD CL, which take into consideration the vapor pressure deficit (VPD), have been proposed (Kärenlampi and Skärby, 1996). These levels are 500 nmol mol-1 h over five days with VPD greater than 1.5 kPa, or, 200 nmol mol-1 h over five days with VPD lower than 1.5 kPa.

A similar VPD correction can be used also for chronic exposures. Hourly O3 mixing ratios [O3] can be multiplied by relevant hourly fVPD factors (Spranger et al., 2004) as follows,

fVPD=1VPD<1.1 kPafVPD=-1.1*VPD+2.21.1 kPa≤VPD≤1.9kPafVPD=0.02VPD>1.9kPa

AOT40 should be calculated using O3 mixing ratios at the upper boundary of the quasi-laminar layer (top of the plant canopy) when the micro-meteorological approach of big-leaf is applied (Grünhage et al., 1999; Hicks et al., 1987); this requirement that has been often overlooked in the relevant literature. It has been demonstrated that failure to properly compute AOT40 with O3 mixing ratios from a reference height may result in dramatic overestimates in potential yield which are unrealistic (Grünhage et al., 1999). However, O3 mixing ratio at measurement height can be effectively converted (approximation) to O3 mixing ratio at the height of canopy top. This can be done using a relevant deposition model which is chosen based on the availability of meteorological data (Simpson et al., 2012; Emberson et al., 2000a, b).

In polluted regions, AOT40 critical values for chronic exposure can be exceeded even in individual months (Agathokleous et al., 2017; Deb Roy et al., 2009). Here we suggest that the AOT40 could be calculated on a monthly base and then a simple weighted correction could be applied for better reflecting the O3 risk. This correction could be done by multiplying the total AOT40 value by the number of months AOT40 critical value was individually exceeded. For a final AOT40 value where the critical value was not exceeded in individual months, the AOT40 would be multiplied by 1; for a final AOT40 value where the critical value was exceeded in one individual month the AOT40 would be multiplied by 2; for a final AOT40 value where the critical value was exceeded in two individual months the AOT40 would be multiplied by 3; etc. This is because exceeding the AOT40 critical values in just a month is likely to exert more pressure on plants than it would happen if the AOT40 critical value is exceeded over three or six months.

AOT40 has been used in several studies because of its simplicity in calculation. However, AOT40 has been questioned because it considers only ambient O3 levels but ignores environmental constraints to O3 uptake through stomata like ambient temperature and water availability (Sanz et al., 2016; Harmens et al., 2007; Karlsson et al., 2007b; Paoletti and Manning, 2007; Gerosa et al., 2005; Matyssek et al., 2004; Danielsson et al., 2003; Grünhage and Jäger, 2003). For instance, a study in Japan suggested that an area with high O3 exposure does not necessarily correspond to a high risk of O3 impact because the impacts depend on the environmental conditions at the habitat, plant sensitivity to O3 and plant physiological capacity (Watanabe et al., 2011). AOT40 may also be effective in highly polluted areas but ineffective in less polluted areas. One important limitation is that O3 levels may exceed the threshold of 40 nmol mol-1 only after noon hours when gs starts decreasing (Agathokleous et al., 2017; Cassimiro et al., 2016), thus less O3 enters plant tissues despite higher O3 levels. Still, as it takes into account only O3 mixing ratios greater than 40 nmol mol-1, it suggests there is no phytotoxicity at lower O3 mixing ratios. However, recent progress in air pollution science suggests that O3 can cause adverse effects to sensitive vegetation at levels lower that 40 nmol mol-1 e.g. (Sugai et al., 2018; Agathokleous et al., 2015; Grünhage et al., 2001). It is also known that vegetation may be “more sensitive to long-term exposure to modest O3 levels (characterized by seasonal means) than frequent exposure to high O3 levels (which are best captured by cumulative indices)” (Wang and Mauzerall, 2004). Hence, AOT40 may be unrealistic and lead to misleading assessment of O3 impacts to sensitive vegetation which is negatively affected by O3 levels below 40 nmol mol-1. Adopting AOT40 as the default index for setting CL in Asia would be a poor policy with severe consequences at national and Pan-Asian level. It should be mentioned that thresholds higher than 40 nmol mol-1 have been considered as well. For instance, in the experiment of Feng et al. (2012), when AOT was calculated with thresholds ranging from 55 to 85 nmol mol-1, it outperformed AOT40 in the exposure-response relationships. However, increasing the threshold does not provide a solution to the limitations explained above.

Instead of AOT40, AOT0 could be more effectively used (Azuchi et al., 2014). There is a preliminary evidence showing that AOT0 may predict the severity of the O3-induced injury (number of injured leaves/total number of leaves on the injured plants×100) better than AOT40 and Phytotoxic Ozone Dose (POD) indices (Cassimiro et al., 2016). In contrast to AOT40, AOT0 does not ignore low O3 levels and could be thus more effective in any regions. Furthermore, since it takes into account low O3 levels, AOT0 can yield more realistic exposure-response relationships when physiological endpoints are the matter of study; it is shown that physiological endpoints display dynamic responses to stressors which vary across the entire exposure-response continuum.

More details on useful improvements for toxicologically effective AOT40 can be found in earlier articles (Musselman et al., 2006; Grünhage et al., 1999).

AOT40 metric dominates the literature with Asian studies. Several experiments with crop plants conducted in Asia utilize AOT40 for deriving CLee. Experimenting with Chinese wheat cultivars (Triticum aestivum L. cvs.) in a Free Air Controlled Exposure (FACE) system, Oue et al. (2011) revealed cultivar-specific exposure responses of photosynthesis and stomatal aperture. In a further study with Chinese wheat cultivars (Feng et al., 2012), the slope between relative yield and AOT40 was significantly larger than European ones (Mills et al., 2007) even at shorter time period. This study also suggested that these Asian wheat cultivars are more sensitive to O3 than North American ones. Two modern Indian wheat cultivars were also assessed as to their sensitivity to O3 using open top chambers (OTCs) in Varanasi, India (Sarkar and Agrawal, 2010): AOT40 CLee for 5% reduction in yield was same to that for temperate wheat in Europe (Mills et al., 2007). In a different OTC experiment in China, four soybean cultivars (Glycine max L. Merr.), typical in Northeast China, were studied (Zhang et al., 2017): AOT40 CLee for 5% reduction in relative seed yield was similar to that for soybean in Europe (Mills et al., 2007). Regarding trees, Yamaguchi et al. (2011) summarized various study examples of O3 impact assessment in tree seedlings using AOT40 in Japan and presented exposure-response relationships with AOT40 as predictor of relative whole-plant dry matter of Japanese larch (Larix kaempferi (Lamb.) Carr.) and Japanese beech (Fagus crenata Blume) seedlings under different soil nitrogen (NH4NO3) availability. A further OTC experiment was conducted with five poplar clones in China (Hu et al., 2015). The AOT40-based CLee derived from exposure-response relationship was 12,000 nmol mol-1 h for 5% reduction in total biomass across clones, whereas the AOT40 value in the experimental site in China was 40,500 nmol mol-1 h, which translates to 16.7% loss of total biomass (Hu et al., 2015). These values exceed by far the values set by worldwide regulatory agencies for protecting vegetation (discussed above). Two poplar clones were also studied in a different experiment employing five O3 exposure levels in OTCs in China (Shang et al., 2017). AOT40 CLee for 5% reduction in total biomass, photosynthetic parameters and leaf mass per area (LMA) for the two clones were 14,800, 4,000 and 5,800 nmol mol-1 h, respectively (Shang et al., 2017).

3. 2 SUM06 and W126

Other than AOTX CLee indices based on cumulative exposure have been also used. Two examples of indices are the sum of all hourly average O3 mixing ratios ≥0.06 μmol mol-1 (SUM06), and the sigmoidally weighed W126 which includes lower O3 levels but assigns greater weight to higher mean hourly O3 levels (Lefohn et al., 1988; Lefohn and Runeckles, 1987). The formulas are:

SUM06=i=1nO3i for O30.06 μmol mol-1 and W126=i=1nwO3i

For SUM06, [O3] is the hourly mean O3 mixing ratio (μmol mol-1), i the index and n the total number of hours in three consecutive months for which the SUM06 value is greatest. For W126, [O3] and i are same as for SUM06 but without a threshold value. W126 rather employs w which is the weighting factor for the ith hour:

wi=1/1+M×exp-A×O3i

where M and A are the arbitrary constants 4403 and 0.123 and [O3]i the O3 mixing ratio i. The units are μmol mol-1 h (classically ppm h). More explanations on the calculations and the historical bases can be found elsewhere (Musselman et al., 2006; Wang and Mauzerall, 2004; Lefohn et al., 1988; Lefohn and Runeckles, 1987).

The SUM06 index has not prevailed in the literature. The W126 index has been adopted by regulatory agencies in North America, where the standard for protection has been set at or below a range of 13,000-17,000 nmol mol-1 h (3-year average). W126 is calculated based on a sigmoidally weighted sum of all hourly mixing ratios observed during a particular daily and seasonal time window, with each ratio having a weight increasing from 0 to 1 with increasing ratio (“National Ambient Air Quality Standards for Ozone; Final Rule,” 2015).

Recently, Zhang et al. (2017) reported that a SUM06 CLee of 7,600 nmol mol-1 h and a W126 CLee of 6,800 nmol mol-1 h relates to 5% reduction in relative seed yield of soybean cultivated in Northeast China (explained above in AOT40).

3. 3 MPOC

An alternative approach is the Maximum Permissible Ozone Concentration (MPOC), which can be used mainly for trees and at national level (Grünhage et al., 2001). This approach requires transforming O3 mixing ratios from a reference height to the canopy top, and thus has been characterized as more realistic (Grünhage et al., 2001). However, MPOC has not been prevailed and AOT40 has hitherto dominated the literature.


4. CLef

Plants have tiny pores, called stomata (from the Greek στóματα, “mouths”, through which exchange gases with the atmosphere (Cieslik et al., 2009). Although plants have stomata on a series of organs, leaf epidermis accounts for the vast majority of the total number of stomata. The functioning of stomata defines the amount of O3 that does enter into the plant tissues (Vaultier and Jolivet, 2015): The greater the number or size of stomata is, or the longer the stomata remain open, a greater amount of O3 enters the plant tissues. This indicates that higher O3 level does not always induce higher stomatal O3 flux when stomata are closed (Kinose et al., 2014; Kitao et al., 2014; Gerosa et al., 2005). Hence, O3 metrics which take into account the O3 dose or flux in plants have been developed.

CLef approaches are considered superior to CLee approaches (Karlsson et al., 2007b). This assumption relies on underlining plant physiological mechanisms according to which plant response is driven by the O3 dose absorbed into the leaf tissue. Therefore plant response has been defined as (Grünhage et al., 1999):

plant response=f1PADO3=f1t1t2fabsorbedO3×dt

where PAD is the pollutant absorbed dose (Fowler and Cape, 1982).

However, CLef approaches are technically more difficult because of the information needed in the information processing system. One important aspect for the CLef approaches is the soil moisture which should be implemented when setting CL (De Marco et al., 2016; Pleijel et al., 2004).

Stomatal conductance is the most critical parameter because it defines the O3 dose entering into the plant tissues. Hence, extensive research has been done on modeling and parameterization of gs at global level. The most common gs model is the empirical multiplicative gs model of Jarvis (Jarvis, 1976), which has been advanced for calculating stomatal O3 flux (Hoshika et al., 2017b; Marzuoli et al., 2017; Kinose et al., 2014; Oue et al., 2011, 2009, 2008; Danielsson et al., 2003; Emberson et al., 2000a); Jarvis model is adopted by UNECE-CLRTAP. An alternative semi-empirical approach is that of Ball-Woodrow-Berry (BWB), which assumes that gs is closely associated with photosynthesis and does not depend on maximum gs (gmax) like Jarvis model (Ball et al., 1987). The BWB model is similarly effective with Jarvis model in O3 risk assessment (Hoshika et al., 2017b; Kinose et al., 2017; Kitao et al., 2016). Improvements in BWB have been later proposed, such as including the leaf-to-air vapor pressure deficit (Leuning, 1995) and gross assimilation rate (Yu et al., 2001) rather than relative humidity and net photosynthetic rate. A potential O3 avoidance mechanism of plants to avoid O3 is by closing stomata, thereby decreasing gs and consequently O3 uptake. O3 itself may induce stomatal closure (Shang et al., 2017; Hoshika et al., 2013b; Kitao et al., 2009; Wittig et al., 2007) or delay stomata response to environmental stimuli, so called stomatal sluggishness (Hoshika et al., 2013a, 2012b; Mills et al., 2009; McAinsh et al., 2002). However, other environmental factors than O3, e.g. soil water unavailability, can affect stomata in a similar way with O3 and at even a considerably greater extent than O3 (Cotrozzi et al., 2017, 2016; Alexou, 2013; Hoshika et al., 2013a). Hence, when developing gs models for estimating O3 uptake, environmental factors inducing stomatal closure, such as N load-induced soil acidification (Azuchi et al., 2014) and soil water limitation (De Marco et al., 2016), should be taken into account. Soil water availability is a very critical driver of O3 uptake by plants as can either increase or decrease gs. Limitation to gs by soil water unavailability may not occur in some Asian regions like Japan (Hiyama et al., 2005) but may commonly occur in other Asian regions like China (Tian et al., 2016; Qiu, 2010). As drought may greatly diminish the uptake of O3 by plants, care should be exercised to take into account stomatal closure due to soil water limitation when conducting O3 risk assessment in Asian regions where drought commonly occurs.

4. 1 DO3SE

The stomatal flux-based model called Deposition of O3 and Stomatal Exchange (DO3SE) has been proposed as an alternative to AOT40 (Emberson et al., 2000a). DO3SE takes into account environmental factors and vegetation properties in addition to ambient O3 levels (Anav et al., 2016; De Marco et al., 2016). Speciesspecific effects of soil water availability, air temperature, vapor pressure deficit, irradiation, plant phenology and stomatal functioning, are factors taken into account. DO3SE, as it has been originally proposed, has not prevailed in the literature.

4. 2 AFstY

The cumulative leaf uptake of O3 per total leaf area over time (CUO) has been proposed as an effective adjustment of the Emberson calibration (Emberson et al., 2000a) of the multiplicative gs for crops and trees, especially for a cumulated period lasted from anthesis to harvest for crops (Karlsson et al., 2004a, 2004b; Danielsson et al., 2003). Based on the CUO concept, the index of accumulated stomatal flux above a flux rate threshold Y (AFstY) has been developed which, in contrast to AOT40, implements the delimitation of the exposure period (Emberson et al., 2007; Karlsson et al., 2007b; Pleijel et al., 2007). AFstY is calculated (Emberson et al., 2007) as:

AFstY=i=1nFsti-Y for FstYY nmol m-2 PLAs-1

where Y is a defined flux rate threshold, n is the number of hours within the accumulation period, Fsti the hourly mean O3 flux (nmol O3 m-2 PLA s-1), and PLA the projected plant leaf area. AFstY is more plant-relevant than AOT and takes into account the influence of plant ontogeny on gmax (Pleijel et al., 2007). Accumulated stomatal O3 flux above a flux rate threshold of 6 nmol O3 m-2 projected sunlit leaf area s-1, based on O3 flux hourly values (AFst6) or above a flux rate threshold of 1.6 nmol O3 m-2 projected sunlit leaf area s-1 (AFst1.6) have been applied to crops (Fuhrer, 2009; Oue et al., 2008; Pleijel et al., 2007) or trees (Emberson et al., 2007; Karlsson et al., 2007b).

4. 3 PODY

The air pollution scientific community and the UN/ECE have put effort in the past decade to redefine O3 CL for protecting vegetation using POD, based on the cumulated stomatal O3 (CLRTAP, 2015; Matyssek et al., 2004). The effort of UN/ECE is being a part of the Convention on Long-Range Transboundary Air Pollution (CLRTAP), which is implemented by the European Monitoring and Evaluation Programme (EMEP) and directed by the UN/ECE. The mission of CLRTAP is to protect the human environment against air pollution and to gradually reduce and prevent air pollution, including long-range trans-boundary air pollution. PODY (POD above a threshold flux of Y nmol m-2 PLA s-1) has been widely used for deriving CL for crop plants, pastures and trees (Calvete-Sogo et al., 2017; Marzuoli et al., 2017; Cassimiro et al., 2016; De Marco et al., 2016; Kitao et al., 2016; Sanz et al., 2016; Bagard et al., 2015; Büker et al., 2015; Danielsson et al., 2013; Grünhage et al., 2012; Mills et al., 2011). It has been shown that POD0 may predict more effectively the incidence of the injury (number of injured plants/total number of plants×100), severity of the injury and leaf abscission than POD1, POD2, POD3, POD4, POD5 and POD6 (Cassimiro et al., 2016). POD approach has been advanced to the point that it has been used to estimate O3 impacts at forest level (Kitao et al., 2016) or setting O3 CL for multi-species canopies of Mediterranean annual pastures (Calvete-Sogo et al., 2017). These efforts are very important because (i) plant response to O3 may vary between monospecific and multispecies cultures and (ii) the target of ecotoxicology is communities and populations and not individuals. The availability of nitrogen (Calvete-Sogo et al., 2017) and water (De Marco et al., 2016) should be taken into account when working with PODY because soil nitrogen and water availabilities influence the flux-response relationships and modify the CLef.

In O3 risk assessments using high spatial resolution over Europe for the years 2000-2005, it was found that the AOT40-based risk assessment showed a good consistency compared to in situ data and other model-based datasets, but stomatal O3-uptake-based assessment showed different spatial patterns compared to other model-based datasets (Anav et al., 2016).

POD CLef for agricultural and horticultural crops, forest trees and (semi-)natural vegetation, based on a series of experiments conducted in Europe, have been previously summarized (Mills et al., 2011). It has been shown that POD6 values of 1, 2, 2, 5 and 2 mmol m-2 were needed to reduce by 5% the grain yield of wheat, 1,000 grain weight of wheat, protein yield of wheat, tuber yield of potato, and fruit yield of tomato, respectively (Mills et al., 2011). Also, POD1 values of 4, 8, and 2 mmol m-2 were needed to reduce by 4% the annual whole tree biomass of beech and birch, 2% the annual whole tree biomass of Norway spruce, and 10% the above-ground biomass of Trifolium spp (Mills et al., 2011). An analysis of published data indicated POD1 CLef (95% CI) of 12.2 (8.9, 15.5), 7.2 (1.1, 13.3) and 4.6 (2.7, 6.5) mmol O3 m-2 for a 10% loss of aboveground biomass, reproductive capacity and consumable food value, respectively, of Mediterranean annual Dehesa-type pastures (Sanz et al., 2016). Recently, a POD6 CLef of 1 mmol O3 m-2 has been also reported for a 15% loss of marketable yield in lettuce (Marzuoli et al., 2017).

There is however a lack of experimental evidence on observation-based CLef in Asia, and, hence, more studies are needed to reach the point of generalizations. Regarding crop plants, POD was estimated in Japanese rice (Oryza sativa L. cv. Koshihikari) using a variety of flux thresholds and phenological integration periods (Yamaguchi et al., 2014). This study suggested that the Koshihikari yields can be assessed using a threshold of 10 nmol O3 m-2 PLA s-1 (POD10) and an integration period of -300 to 100°C days from anthesis. In the experiment with winter wheat in subtropical China discussed in section III, dose-response relationships were tested for POD values with threshold Y ranging from 0 to 24 nmol O3 m-2 PLA s-1 (Feng et al., 2012). Cultivar Yangfumai 2 displayed a greater sensitivity of relative yield to PODY than the other three cultivars tested, but only for Y values≥13 nmol O3 m-2 PLA s-1. Overall, POD11-20 was a better predictor of relative yield than AOT40 (Feng et al., 2012). The authors suggested a threshold of 12 nmol O3 m-2 PLA s-1 for wheat flux-response relationships in subtropical China. Zhang et al. (2017) improved gs model to include the effect of leaf age. They found that the POD6 and POD9.6 CLef for 5% reduction of relative seed yield of soybean in China were 1.8 and 0.9 mmol O3 m-2, and POD9.6 yielded better dose-response relationships than the other tested metrics (explained above in section III). In a different study, simulated O3 was used to evaluate the relative yield loss of rice in a domain of Southern Vietnam using AOT40, mean 7 h daytime O3 level (M7), POD0 and POD10 metrics (Danh et al., 2016): This study revealed a prediction of greater rice production loss by POD10, compared to AOT40, M7 and POD0, in agreement with previous findings in Japan (Yamaguchi et al., 2014). This may be the first attempt for assessing O3-induced crop yield loss with different O3 exposure metrics in Asian developing countries and is encouraging for further studies. Asian studies implementing POD are fewer for trees than crop plants. In the experiment with poplars in China mentioned above (section III), POD1 dose-response relationship indicated a 2% biomass loss at 2.1 mmol m-2 and a 4% loss at 4.8 mmol m-2, whereas POD7 yielded a 5% reduction in total biomass at 3.8 mmol m-2 and was more effective predictor than AOT40 and POD1 (Hu et al., 2015); POD1 is recommended by CLRTAP (2015) for protecting against O3-induced biomass loss of trees. Based on the findings of Hu et al. (2015), Shang et al. (2017) used POD7 for deriving O3 CLef for an experiment with two poplar clones (explained in section III). CLef for 5% reduction in total biomass, photosynthetic parameters and leaf mass per area (LMA) for the two clones were 9.8, 3 and 4 mmol O3 m-2 PLA, respectively; coefficients of determination (R2) were similar between AOT40- and POD7-based dose-response relationships for all the response variables (Shang et al., 2017). Based on these clones, POD7 has been also recommended over AOT40 for large-scale risk assessment of isoprene emission to O3 in poplar (Yuan et al., 2017).


5. CLee OR CLef?

Both CLee and CLef approaches may overestimate O3 effects on vegetation because plant detoxification mechanisms are not taken into account (Musselman et al., 2006). In both cases it is assumed that sensitivity and adaptive capacity are equal across species, which is a challenge in assessing species vulnerability to O3 (Butt et al., 2016; Loibl et al., 2004). While CLef approach is biologically more meaningful and realistic than CLee approach, flux-related assessments taking into account physiological defense capacity could better predict O3 effects because physiological response to O3 can be modified by global change (Tausz et al., 2007; Loibl et al., 2004; Matyssek et al., 2004). In this sense, quantitative understanding of effective flux (Kinose et al., 2017) should be developed before adapting flux-based models by regulatory agencies (Musselman et al., 2006). Nevertheless, while CLef approaches are biologically more meaningful and are considered superior to CLee approaches, their calculation requires plant physiological data which are not always available. This is an important limitation especially in developing Asian countries where there may be no access to instruments for such physiological measurements. Hence, both CLee and CLef approaches are useful for assessing O3 impacts on vegetation with a Pan-Asian perspective. Although biologically more relevant, CLef approaches should be accompanied by a standard CLee approach (e.g. AOT0) in order to provide a scientific base for mutual understanding and benefit among Asian countries.


6. CONCLUSIONS AND PERSPECTIVES

A variety of O3 metrics has been used by the air pollution research community. Considering the strengths and weaknesses of the common indices, it could be suggested that AOT40 may be used for highly polluted regions whereas AOT0, W126 or a CLef approach can be used in regions with low O3 levels. However, although AOT40 has been widely used and adopted by worldwide regulatory agencies, the current scientific literature suggests it is inefficient and should be replaced by other indices, like AOT0 (or SUM00: sum of all hourly average O3 levels), which do not set a threshold.

To facilitate the future developments in science and foster correct policy and regulations at national and Pan-Asian level, it is recommended to set as a standard practice to report more than one indices and never AOT40 alone. It is recommended AOT0, which is easy to calculate, to always accompany AOT40 or flux-based metrics.

O3 effects on plants do not depend only on O3 exposure characteristics but also on plant physiology and biological plasticity, something that should be considered when selecting O3 phytotoxicity metrics. In this context, CLee metrics may totally fail to realistically predict plant response (or O3 effects on plants). For a more realistic assessment of species vulnerability and risk in the future, implementation of plant detoxification capacity should be set as a research priority. This will be a challenging task for the coming decades because of (a) the complexity of detoxification mechanisms; (b) the current incomplete understanding of O3 mode of action in plants, or the plant response to O3; and (c) the limited knowledge about O3 effects on native or local cultivated plants in wide regions of Asia. Nevertheless, earlier trials where photosynthesis (Hoshika et al., 2017a; Kinose et al., 2017; Li et al., 2016; Oue et al., 2011; Kolb and Matyssek, 2001) and LMA (or its inverse specific leaf area; SLA) (Li et al., 2016; Wieser et al., 2002) were used as proxies can serve as a basis for further developments at Pan-Asian level.

More Asian studies are needed to improve the current limited understanding and contribute in setting national and Pan-Asian standards for the protection of crop plants and native vegetation. This is particularly important for Asian tropical regions as relevant experimental studies on plant response to O3 remain very limited.

In contrast to closed exposure systems, FACE systems allow interaction of plants with the natural environment and thus provide experimental conditions close to natural ones. FACE systems (Kobayashi, 2015) can offer an interface for realistic derivation of CLe and a substantial base for adopting regulatory measures in Asia. As FACE systems cannot reduce ambient O3 concentration, they should be established in O3-clean areas or alternative methodologies to exclude potential background O3 stress should be considered (Paoletti et al., 2017).

Environmental conditions, growing medium characteristics and plant competition (inter or intra) are likely to drive dose response relationships (González-Fernández et al., 2017; De Marco et al., 2016; Azuchi et al., 2014). Policy and decision makers and stakeholders of Asian regulatory agencies should consider site-specific differences in dose response relationships before adopting O3 CLef. In this framework, multi-site assessments are required for adoption of representative O3 CLef.


Conflict of interest

The authors declare that their research has no conflict of interest.


Acknowledgments

The authors are inspired by the book “Air Pollution Impacts on Plants in East Asia” (Springer, 2017) edited by Prof. Takeshi Izuta, Tokyo University of Agriculture and Technology, Japan. Evgenios Agathokleous is a JSPS International Research Fellow (ID No: P17102). This work was supported in part by a Grant-in-Aid for Scientific Research (B) (No. JP17H03839). The Japan Society for the Promotion of Science (JSPS) is a nonprofit organization.


References
1. Agathokleous, E., Saitanis, C.J., Burkey, K.O., Ntatsi, G., Vougeleka, V., Mashaheet, A.M., Pallides, A., (2017), Application and further characterization of the snap bean S156/R123 ozone biomonitoring system in relation to ambient air temperature, Science of The Total Environment, 580, p1046-1055.
2. Agathokleous, E., Saitanis, C.J., Koike, T., (2015), Tropospheric O3, the nightmare of wild plants: A review study, Journal of Agricultural Meteorology, 71, p142-152.
3. Agathokleous, E., Saitanis, C.J., Wang, X., Watanabe, M., Koike, T., (2016), A review study on past 40 years of research on effects of tropospheric O3 on belowground structure, functioning, and processes of trees: a linkage with potential ecological implications, Water, Air, & Soil Pollution, 227, p33.
4. Akimoto, H., (2003), Global air quality and pollution, Science, 302.
5. Alexou, M., (2013), Development-specific responses to drought stress in Aleppo pine (Pinus halepensis Mill.) seedlings, Tree Physiology, 33, p1030-1042.
6. Anav, A., De Marco, A., Proietti, C., Alessandri, A., Dell’Aquila, A., Cionni, I., Friedlingstein, P., Khvorostyanov, D., Menut, L., Paoletti, E., Sicard, P., Sitch, S., Vitale, M., (2016), Comparing concentration-based (AOT40) and stomatal uptake (PODY) metrics for ozone risk assessment to European forests, Global Change Biology, 22, p1608-1627.
7. Ashmore, M.R., (2005), Assessing the future global impacts of ozone on vegetation, Plant, Cell and Environment, 28, p949-964.
8. Avnery, S., Mauzerall, D.L., Liu, J., Horowitz, L.W., (2011), Global crop yield reductions due to surface ozone exposure: 1. Year 2000 crop production losses and economic damage, Atmospheric Environment, 45, p2284-2296.
9. Azuchi, F., Kinose, Y., Matsumura, T., Kanomata, T., Uehara, Y., Kobayashi, A., Yamaguchi, M., Izuta, T., (2014), Modeling stomatal conductance and ozone uptake of Fagus crenata grown under different nitrogen loads, Environmental Pollution, 184, p481-487.
10. Bagard, M., Jolivet, Y., Hasenfratz-Sauder, M.-P., Gérard, J., Dizengremel, P., Le Thiec, D., (2015), Ozone exposure and flux-based response functions for photosynthetic traits in wheat, maize and poplar, Environmental Pollution, 206, p411-420.
11. Ball, J.T., Woodrow, I.E., Berry, J.A., (1987), A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions, in: Progress in Photosynthesis Research, Springer Netherlands, Dordrecht, p221-224.
12. Broberg, M.C., Feng, Z., Xin, Y., Pleijel, H., (2015), Ozone effects on wheat grain quality - A summary, Environmental Pollution, 197, p203-213.
13. Büker, P., Feng, Z., Uddling, J., Briolat, A., Alonso, R., Braun, S., Elvira, S., Gerosa, G., Karlsson, P.E., Le Thiec, D., Marzuoli, R., Mills, G., Oksanen, E., Wieser, G., Wilkinson, M., Emberson, L.D., (2015), New flux based dose-response relationships for ozone for European forest tree species, Environmental Pollution, 206, p163-174.
14. Butt, N., Possingham, H.P., De Los Rios, C., Maggini, R., Fuller, R.A., Maxwell, S.L., Watson, J.E.M., (2016), Challenges in assessing the vulnerability of species to climate change to inform conservation actions, Biological Conservation, 199, p10-15.
15. Calvete-Sogo, H., González-Fernández, I., García-Gómez, H., Alonso, R., Elvira, S., Sanz, J., Bermejo-Bermejo, V., (2017), Developing ozone critical levels for multi-species canopies of Mediterranean annual pastures, Environmental Pollution, 220, p186-195.
16. Cape, J.N., (2008), Surface ozone concentrations and ecosystem health: past trends and a guide to future projections, Science of the Total Environment, 400, p257-269.
17. Cassimiro, J.C., Moura, B.B., Alonso, R., Meirelles, S.T., Moraes, R.M., (2016), Ozone stomatal flux and O3 concentration-based metrics for Astronium graveolens Jacq., a Brazilian native forest tree species, Environmental Pollution, 213, p1007-1015.
18. Chappelka, A.H., Grulke, N.E., (2016), Disruption of the “disease triangle” by chemical and physical environmental change, Plant Biology, 18, p5-12.
19. Cieslik, S., Omasa, K., Paoletti, E., (2009), Why and how terrestrial plants exchange gases with air, Plant Biology, 11, p24-34.
20. CLRTAP, (2015), Mapping critical levels for vegetation, Chapter III of manual on methodologies and criteria for modelling and mapping critical loads and levels and air pollution effects, risks and trends, UNECE Convention on Long-range Transboundary Air Pollution.
21. Cotrozzi, L., Remorini, D., Pellegrini, E., Guidi, L., Lorenzini, G., Massai, R., Nali, C., Landi, M., (2017), Cross-talk between physiological and metabolic adjustments adopted by Quercus cerris to mitigate the effects of severe drought and realistic future ozone concentrations, Forests, 8, p148.
22. Cotrozzi, L., Remorini, D., Pellegrini, E., Landi, M., Massai, R., Nali, C., Guidi, L., Lorenzini, G., (2016), Variations in physiological and biochemical traits of oak seedlings grown under drought and ozone stress, Physiologia Plantarum, 157, p69-84.
23. Danh, N.T., Huy, L.N., Oanh, N.T.K., (2016), Assessment of rice yield loss due to exposure to ozone pollution in Southern Vietnam, Science of The Total Environment, 566-567, p1069-1079.
24. Danielsson, H., Karlsson, G.P., Karlsson, P.E., Håkan Pleijel, H., (2003), Ozone uptake modelling and flux-response relationships - an assessment of ozone-induced yield loss in spring wheat, Atmospheric Environment, 37, p475-485.
25. Danielsson, H., Karlsson, P.E., Pleijel, H., (2013), An ozone response relationship for four Phleum pratense genotypes based on modelling of the phytotoxic ozone dose (POD), Environmental and Experimental Botany, 90, p70-77.
26. De Marco, A., Sicard, P., Fares, S., Tuovinen, J.-P., Anav, A., Paoletti, E., (2016), Assessing the role of soil water limitation in determining the Phytotoxic Ozone Dose (PODY) thresholds, Atmospheric Environment, 147, p88-97.
27. Deb Roy, S., Beig, G., Ghude, S.D., (2009), Exposure-plant response of ambient ozone over the tropical Indian region, Atmospheric Chemistry and Physics, 9, p5253-5260.
28. Emberson, L.D., Ashmore, M.R., Cambridge, H.M., Simpson, D., Tuovinen, J.-P., (2000a), Modelling stomatal ozone flux across Europe, Environmental Pollution, 109, p403-413.
29. Emberson, L.D., Simpson, D., Tuovinen, J.P., Ashmore, M., Cambridge, H., (2000b), Towards a model of ozone deposition and stomatal uptake over Europe,in: EMEP MSC-W Note 6/2000, The Norwegian Meteorological Institute, Oslo.
30. Emberson, L.D., Büker, P., Ashmore, M.R., (2007), Assessing the risk caused by ground level ozone to European forest trees: a case study in pine, beech and oak across different climate regions, Environmental pollution, 147, p454-466.
31. Feng, Z., Hu, E., Wang, X., Jiang, L., Liu, X., (2015), Ground-level O3 pollution and its impacts on food crops in China: A review, Environmental Pollution, 199, p42-48.
32. Feng, Z., Kobayashi, K., Ainsworth, E.A., (2008), Impact of elevated ozone concentration on growth, physiology, and yield of wheat (Triticum aestivum L.): a metaanalysis, Global Change Biology, 14, p2696-2708.
33. Feng, Z., Tang, H., Uddling, J., Pleijel, H., Kobayashi, K., Zhu, J., Oue, H., Guo, W., (2012), A stomatal ozone flux-response relationship to assess ozone-induced yield loss of winter wheat in subtropical China, Environmental Pollution, 164, p16-23.
34. Fowler, D., Cape, J.N., (1982), Air pollutants in agriculture and horticulture, in: Unsworth, M.H., Ormrod, D.P. (Eds.), Effects of Gaseous Air Pollution in Agriculture and Horticulture, Butterworth Scientific, London, p3-26.
35. Fuhrer, J., (2009), Ozone risk for crops and pastures in present and future climates, Naturwissenschaften, 96, p173-194.
36. Fuhrer, J., Skärby, L., Ashmore, M.R.R., (1997), Critical levels for ozone effects on vegetation in Europe, Environmental Pollution, 97, p91-106.
37. Gerosa, G., Vitale, M., Finco, A., Manes, F., Denti, A.B., Cieslik, S., (2005), Ozone uptake by an evergreen Mediterranean Forest (Quercus ilex) in Italy. Part I: Micrometeorological flux measurements and flux partitioning, Atmospheric Environment, 39, p3255-3266.
38. González-Fernández, I., Sanz, J., Calvete-Sogo, H., Elvira, S., Alonso, R., Bermejo-Bermejo, V., (2017), Validation of ozone response functions for annual Mediterranean pasture species using close-to-field-conditions experiments, Environmental Science and Pollution Research.
39. Grünhage, L., Jäger, H.-J., (2003), From critical levels to critical loads for ozone: a discussion of a new experimental and modelling approach for establishing flux - response relationships for agricultural crops and native plant species, Environmental Pollution, 125, p99-110.
40. Grünhage, L., Jäger, H.-J., Haenel, H.-D., Löpmeier, F.-J., Hanewald, K., (1999), The European critical levels for ozone: improving their usage, Environmental Pollution, 105, p163-173.
41. Grünhage, L., Krause, G.H., Köllner, B., Bender, J., Weigel, H.-J.J., Jäger, H.-J.J., Guderian, R., (2001), A new flux-orientated concept to derive critical levels for ozone to protect vegetation, Environmental Pollution, 111, p355-362.
42. Grünhage, L., Pleijel, H., Mills, G., Bender, J., Danielsson, H., Lehmann, Y., Castell, J.-F., Bethenod, O., (2012), Updated stomatal flux and flux-effect models for wheat for quantifying effects of ozone on grain yield, grain mass and protein yield, Environmental Pollution, 165, p147-157.
43. Harmens, H., Mills, G., Emberson, L.D., Ashmore, M.R., (2007), Implications of climate change for the stomatal flux of ozone: A case study for winter wheat, Environmental Pollution, 146, p763-770.
44. Hicks, B.B., Baldocchi, D.D., Meyers, T.P., Hosker, R.P., Matt, D.R., (1987), A preliminary multiple resistance routine for deriving dry deposition velocities from measured quantities, Water, Air, and Soil Pollution, 36, p311-330.
45. Hiyama, T., Kochi, K., Kobayashi, N., Sirisampan, S., (2005), Seasonal variation in stomatal conductance and physiological factors observed in a secondary warm-temperate forest, Ecological Research, 20, p333-346.
46. Hoshika, Y., Paoletti, E., Omasa, K., (2012a), Parameterization of Zelkova serrata stomatal conductance model to estimate stomatal ozone uptake in Japan, Atmospheric Environment, 55, p271-278.
47. Hoshika, Y., Watanabe, M., Inada, N., Koike, T., (2012b), Ozone-induced stomatal sluggishness develops progressively in Siebold’s beech (Fagus crenata), Environmental Pollution, 166, p152-156.
48. Hoshika, Y., Omasa, K., Paoletti, E., (2013a), Both ozone exposure and soil water stress are able to induce stomatal sluggishness, Environmental and Experimental Botany, 88, p19-23.
49. Hoshika, Y., Watanabe, M., Inada, N., Koike, T., (2013b), Model-based analysis of avoidance of ozone stress by stomatal closure in Siebold’s beech (Fagus crenata), Annals of Botany, 112, p1149-1158.
50. Hoshika, Y., Katata, G., Deushi, M., Watanabe, M., Koike, T., Paoletti, E., (2015a), Ozone-induced stomatal sluggishness changes carbon and water balance of temperate deciduous forests, Scientific Reports, 5, p9871.
51. Hoshika, Y., Watanabe, M., Inada, N., Koike, T., (2015b), Effects of ozone-induced stomatal closure on ozone uptake and its changes due to leaf age in sun and shade leaves of Siebold’s beech, Journal of Agricultural Meteorology, 71, p218-226.
52. Hoshika, Y., Carrari, E., Zhang, L., Carriero, G., Pignatelli, S., Fasano, G., Materassi, A., Paoletti, E., (2017a), Testing a ratio of photosynthesis to O3 uptake as an index for assessing O3 -induced foliar visible injury in poplar trees, Environmental Science and Pollution Research.
53. Hoshika, Y., Fares, S., Savi, F., Gruening, C., Goded, I., De Marco, A., Sicard, P., Paoletti, E., (2017b), Stomatal conductance models for ozone risk assessment at canopy level in two Mediterranean evergreen forests, Agricultural and Forest Meteorology, 234-235, p212-221.
54. Hu, E., Gao, F., Xin, Y., Jia, H., Li, K., Hu, J., Feng, Z., (2015), Concentration- and flux-based ozone dose-response relationships for five poplar clones grown in North China, Environmental Pollution, 207, p21-30.
55. Jarvis, P.G., (1976), The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field, Philosophical Transactions of the Royal Society B: Biological Sciences, 273, p593-610.
56. Kalabokas, P.D., Cammas, J.-P., Thouret, V., Volz-Thomas, A., Boulanger, D., Repapis, C.C., (2013), Examination of the atmospheric conditions associated with high and low summer ozone levels in the lower troposphere over the Eastern Mediterranean, Atmospheric Chemistry and Physics Discussions, 13, p2457-2491.
57. Kärenlampi, L., Skärby, L., (1996), Critical Levels for Ozone in Europe: Testing and Finalizing the Concepts, in: UN-ECE Workshop Report, p363.
58. Karlsson, P.E., Braun, S., Broadmeadow, M., Elvira, S., Emberson, L.D., Gimeno, B.S., Le Thiec, D., Novak, K., Oksanen, E., Schaub, M., Uddling, J., Wilkinson, M., (2007a), Risk assessments for forest trees: The performance of the ozone flux versus the AOT concepts, Environmental Pollution, 146, p608-616.
59. Karlsson, P.E., Braun, S., Broadmeadow, M., Elvira, S., Emberson, L.D., Gimeno, B.S., Le Thiec, D., Novak, K., Oksanen, E., Schaub, M., Uddling, J., Wilkinson, M., (2007b), Risk assessments for forest trees: the performance of the ozone flux versus the AOT concepts, Environmental Pollution, 146, p608-616.
60. Karlsson, P.E., Medin, E.L., Ottosson, S., Selldén, G., Wallin, G., Pleijel, H., Skärby, L., (2004a), A cumulative ozone uptake-response relationship for the growth of Norway spruce saplings, Environmental Pollution, 128, p405-417.
61. Karlsson, P.E., Uddling, J., Braun, S., Broadmeadow, M., Elvira, S., Gimeno, B.S., Le Thiec, D., Oksanen, E., Vandermeiren, K., Wilkinson, M., Emberson, L.D., (2004b), New critical levels for ozone effects on young trees based on AOT40 and simulated cumulative leaf uptake of ozone, Atmospheric Environment, 38, p2283-2294.
62. Kim, M.J., Park, R.J., Ho, C.-H., Woo, J.-H., Choi, K.-C., Song, C.-K., Lee, J.-B., (2015), Future ozone and oxidants change under the RCP scenarios, Atmospheric Environment, 101, p103-115.
63. Kinose, Y., Azuchi, F., Uehara, Y., Kanomata, T., Kobayashi, A., Yamaguchi, M., Izuta, T., (2014), Modeling of stomatal conductance to estimate stomatal ozone uptake by Fagus crenata , Quercus serrata , Quercus mongolica var. crispula and Betula platyphylla, Environmental Pollution, 194, p235-245.
64. Kinose, Y., Fukamachi, Y., Okabe, S., Hiroshima, H., Watanabe, M., Izuta, T., (2017), Photosynthetic responses to ozone of upper and lower canopy leaves of Fagus crenata Blume seedlings grown under different soil nutrient conditions, Environmental Pollution, 223, p213-222.
65. Kitao, M., Komatsu, M., Hoshika, Y., Yazaki, K., Yoshimura, K., Fujii, S., Miyama, T., Kominami, Y., (2014), Seasonal ozone uptake by a warm-temperate mixed deciduous and evergreen broadleaf forest in western Japan estimated by the Penman-Monteith approach combined with a photosynthesis-dependent stomatal model, Environmental Pollution, 184, p457-463.
66. Kitao, M., Löw, M., Heerdt, C., Grams, T.E.E., Häberle, K.-H., Matyssek, R., (2009), Effects of chronic elevated ozone exposure on gas exchange responses of adult beech trees (Fagus sylvatica) as related to the within-canopy light gradient, Environmental Pollution, 157, p537-544.
67. Kitao, M., Yasuda, Y., Kominami, Y., Yamanoi, K., Komatsu, M., Miyama, T., Mizoguchi, Y., Kitaoka, S., Yazaki, K., Tobita, H., Yoshimura, K., Koike, T., Izuta, T., (2016), Increased phytotoxic O3 dose accelerates autumn senescence in an O3 -sensitive beech forest even under the present-level O3, Scientific Reports, 6, p32549.
68. Kobayashi, K., (2015), FACE-ing the challenges of increasing surface ozone concentration in Asia, Journal of Agricultural Meteorology, 71, p161-166.
69. Koike, T., Watanabe, M., Hoshika, Y., Kitao, M., Matsumura, H., Funada, R., Izuta, T., (2013), Effects of ozone on forest ecosystems in East and Southeast Asia, In Climate Change, Air Pollution and Global Challenges: Understanding and Solutions from Forest Research, A COST action ( Matyssek, R., Clarke, N., Cudlin, P., Mikkelsen, T.N., Tuovinen, J.-P., Wieser, G., and Paoletti, E. Eds), Elsevier, Oxford, p371-390.
70. Kolb, T., Matyssek, R., (2001), Limitations and perspectives about scaling ozone impacts in trees, Environmental Pollution, 115, p373-393.
71. Komatsu, M., Yoshimura, K., Fujii, S., Yazaki, K., Tobita, H., Mizoguchi, Y., Miyama, T., Kominami, Y., Yasuda, Y., Yamanoi, K., Kitao, M., (2015), Estimation of ozone concentrations above forests using atmospheric observations at urban air pollution monitoring stations, Journal of Agricultural Meteorology, 71, p202-210.
72. Lefohn, A.S., Laurence, J.A., Kohut, R.J., (1988), A comparison of indices that describe the relationship between exposure to ozone and reduction in the yield of agricultural crops, Atmospheric Environment (1967), 22, p1229-1240.
73. Lefohn, A.S., Runeckles, V.C., (1987), Establishing standards to protect vegetation-ozone exposure/dose considerations, Atmospheric Environment, 21, p561-568.
74. Leuning, R., (1995), A critical appraisal of a combined stomatal-photosynthesis model for C3 plants, Plant, Cell and Environment, 18, p339-355.
75. Li, P., Calatayud, V., Gao, F., Uddling, J., Feng, Z., (2016), Differences in ozone sensitivity among woody species are related to leaf morphology and antioxidant levels, Tree Physiology, 36, p1105-1116.
76. Lindroth, R.L., (2010), Impacts of elevated atmospheric CO2 and O3 on forests: phytochemistry, trophic interactions, and ecosystem dynamics, Journal of Chemical Ecology, 36, p2-21.
77. Loibl, W., Bolhàr-Nordenkampf, H.R., Herman, F., Smidt, S., (2004), Modelling critical levels of ozone for the forested area of austria modifications of the AOT40 concept, Environmental Science and Pollution Research, 11, p171-180.
78. Lu, Y., Jenkins, A., Ferrier, R.C., Bailey, M., Gordon, I.J., Song, S., Huang, J., Jia, S., Zhang, F., Liu, X., Feng, Z., Zhang, Z., (2015), Addressing China’s grand challenge of achieving food security while ensuring environmental sustainability, Science Advances, 1.
79. Marzuoli, R., Finco, A., Chiesa, M., Gerosa, G., (2017), A dose-response relationship for marketable yield reduction of two lettuce (Lactuca sativa L.) cultivars exposed to tropospheric ozone in Southern Europe, Environmental Science and Pollution Research, p1-10.
80. 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, p587-607.
81. Matyssek, R., Innes, J.L., (1999), Ozone - a risk factor for trees and forests in Europe?, in: Forest Growth Responses to the Pollution Climate of the 21st Century, Springer Netherlands, Dordrecht, p199-226.
82. Matyssek, R., Wieser, G., Nunn, A.J., Kozovits, A.R., Reiter, I.M., Heerdt, C., Winkler, J.B., Baumgarten, M., Häberle, K.-H., Grams, T.E.E., Werner, H., Fabian, P., Havranek, W.M., (2004), Comparison between AOT40 and ozone uptake in forest trees of different species, age and site conditions, Atmospheric Environment, 38, p2271-2281.
83. McAinsh, M.R., Evans, N.H., Montgomery, L.T., North, K.A., (2002), Calcium signalling in stomatal responses to pollutants, New Phytologist, 153, p441-447.
84. McGrath, J.M., Betzelberger, A.M., Wang, S., Shook, E., Zhu, X.-G., Long, S.P., Ainsworth, E.A., (2015), An analysis of ozone damage to historical maize and soybean yields in the United States, Proceedings of the National Academy of Sciences of the United States of America, 112, p14390-14395.
85. Mills, G., Buse, A., Gimeno, B., Bermejo, V., Holland, M., Emberson, L.D., Pleijel, H., (2007), A synthesis of AOT40-based response functions and critical levels of ozone for agricultural and horticultural crops, Atmospheric Environment, 41, p2630-2643.
86. Mills, G., Hayes, F., Wilkinson, S., Davies, W.J., (2009), Chronic exposure to increasing background ozone impairs stomatal functioning in grassland species, Global Change Biology, 15, p1522-1533.
87. Mills, G., Pleijel, H., Braun, S., Büker, P., Bermejo, V., Calvo, E., Danielsson, H., Emberson, L.D., Fernández, I.G., Grünhage, L., Harmens, H., Hayes, F., Karlsson, P.E., Simpson, D., (2011), New stomatal flux-based critical levels for ozone effects on vegetation, Atmospheric Environment, 45, p5064-5068.
88. Morgan, P.B., Ainsworth, E.A., Long, S.P., (2003), How does elevated ozone impact soybean? A meta-analysis of photosynthesis, growth and yield, Plant, Cell and Environment, 26, p1317-1328.
89. Musselman, R.C., Lefohn, A.S., Massman, W.J., Heath, R.L., (2006), A critical review and analysis of the use of exposure- and flux-based ozone indices for predicting vegetation effects, Atmospheric Environment, 40, p1869-1888.
90. National Ambient Air Quality Standards for Ozone; Final Rule [WWW Document], (2015), Federal Register, URL https://www.gpo.gov/fdsys/pkg/FR-2015-10-26/pdf/2015-26594.pdf (accessed 4.9.17).
91. Osborne, S.A., Mills, G., Hayes, F., Ainsworth, E.A., Büker, P., Emberson, L., (2016), Has the sensitivity of soybean cultivars to ozone pollution increased with time? An analysis of published dose-response data, Global Change Biology, 22, p3097-3111.
92. Oue, H., Feng, Z., Pang, J., Miyata, A., Mano, M., Kobayashi, K., Zhu, J., (2009), Modeling the stomatal conductance and photosynthesis of a flag leaf of wheat under elevated O3 concentration, Journal of Agricultural Meteorology, 65, p239-248.
93. Oue, H., Kobayashi, K., Zhu, J., Guo, W., Zhu, X., (2011), Improvements of the ozone dose response functions for predicting the yield loss of wheat due to elevated ozone, Journal of Agricultural Meteorology, 67, p21-32.
94. Oue, H., Motohiro, S., Inada, K., Miyata, A., Mano, M., Kobayashi, K., Zhu, J., (2008), Evaluation of ozone uptake by the rice canopy with the multi-layer model, Journal of Agricultural Meteorology, 64, p223-232.
95. Paoletti, E., Manning, W., (2007), Toward a biologically significant and usable standard for ozone that will also protect plants, Environmental Pollution, 150, p85-95.
96. Paoletti, E., Materassi, A., Fasano, G., Hoshika, Y., Carriero, G., Silaghi, D., Badea, O., (2017), A new-generation 3D ozone FACE (Free Air Controlled Exposure), Science of The Total Environment, 575, p1407-1414.
97. Pleijel, H., Danielsson, H., Emberson, L.D., Ashmore, M.R., Mills, G., (2007), Ozone risk assessment for agricultural crops in Europe: Further development of stomatal flux and flux-response relationships for European wheat and potato, Atmospheric Environment, 41, p3022-3040.
98. Pleijel, H., Danielsson, H., Ojanperä, K., Temmerman, L.D., Högy, P., Badiani, M., Karlsson, P.E., (2004), Relationships between ozone exposure and yield loss in European wheat and potato - a comparison of concentration- and flux-based exposure indices, Atmospheric Environment, 38, p2259-2269.
99. Qiu, J., (2010), China drought highlights future climate threats, Nature, 465, p142-143.
100. Saitanis, C.J., Panagopoulos, G., Dasopoulou, V., Agathokleous, E., Papatheohari, Y., (2015), Integrated assessment of ambient ozone phytotoxicity in Greece’s Tripolis Plateau, Journal of Agricultural Meteorology, 71, p55-64.
101. Sanz, J., González-Fernández, I., Elvira, S., Muntifering, R., Alonso, R., Bermejo-Bermejo, V., (2016), Setting ozone critical levels for annual Mediterranean pasture species: Combined analysis of open-top chamber experiments, Science of The Total Environment, 571, p670-679.
102. Sarkar, A., Agrawal, S.B., (2010), Elevated ozone and two modern wheat cultivars: An assessment of dose dependent sensitivity with respect to growth, reproductive and yield parameters, Environmental and Experimental Botany, 69, p328-337.
103. Shang, B., Feng, Z., Li, P., Yuan, X., Xu, Y., Calatayud, V., (2017), Ozone exposure- and flux-based response relationships with photosynthesis, leaf morphology and biomass in two poplar clones, Science of The Total Environment, 603-604, p185-195.
104. Sicard, P., Serra, R., Rossello, P., (2016), Spatiotemporal trends in ground-level ozone concentrations and metrics in France over the time period 1999-2012, Environmental Research, 149, p122-144.
105. Simpson, D., Benedictow, A., Berge, H., Bergström, R., Emberson, L.D., Fagerli, H., Flechard, C.R., Hayman, G.D., Gauss, M., Jonson, J.E., Jenkin, M.E., Nyíri, A., Richter, C., Semeena, V.S., Tsyro, S., Tuovinen, J.-P., Valdebenito, A., Wind, P., (2012), The EMEP MSC-W chemical transport model - technical description, Atmospheric Chemistry and Physics, 12, p7825-7865.
106. Spranger, T., Lorenz, U., Gregor, H.-D., (2004), Manual on methodologies and criteria for Modelling and Mapping Critical Loads & Levels and Air Pollution Effects, Risks and Trends, Federal Environmental Agency (Umweltbundesamt), Berlin.
107. Sugai, T., Kam, D.-G., Agathokleous, E., Watanabe, M., Kita, K., Koike, T., (2018), Growth and photosynthetic response of two larches exposed to O3 mixing ratios ranging from preindustrial to near future, Photosynthetica, p56, In Press.
108. Takigawa, M., Niwano, M., Akimoto, H., Takahashi, M., Kobayashi, K., (2009), Projection of surface ozone over East Asia in 2020, Journal of Agricultural Meteorology, 65, p161-166.
109. Tang, H., Pang, J., Zhang, G., Takigawa, M., Liu, G., Zhu, J., Kobayashi, K., (2014), Mapping ozone risks for rice in China for years 2000 and 2020 with flux-based and exposure-based doses, Atmospheric Environment, 86, p74-83.
110. Tang, H., Takigawa, M., Liu, G., Zhu, J., Kobayashi, K., (2013), A projection of ozone-induced wheat production loss in China and India for the years 2000 and 2020 with exposure-based and flux-based approaches, Global Change Biology, 19, p2739-2752.
111. Tausz, M., Grulke, N.E., Wieser, G., (2007), Defense and avoidance of ozone under global change, Environmental Pollution, 147, p525-531.
112. Tian, H., Ren, W., Tao, B., Sun, G., Chappelka, A., Wang, X., Pan, S., Yang, J., Liu, J., Felzer, B.S., Melillo, J. M., Reilly, J., (2016), Climate extremes and ozone pollution: a growing threat to China’s food security, Ecosystem Health and Sustainability, 2, e01203.
113. Vaultier, M.-N., Jolivet, Y., (2015), Ozone sensing and early signaling in plants: An outline from the cloud, Environmental and Experimental Botany, 114, p144-152.
114. Verstraeten, W.W., Neu, J.L., Williams, J.E., Bowman, K.W., Worden, J.R., Boersma, K.F., (2015), Rapid increases in tropospheric ozone production and export from China, Nature Geoscience, 8, p690-695.
115. Vingarzan, R., (2004), A review of surface ozone background levels and trends, Atmospheric Environment, 38, p3431-3442.
116. Wang, X., Mauzerall, D.L., (2004), Characterizing distributions of surface ozone and its impact on grain production in China, Japan and South Korea: 1990 and 2020, Atmospheric Environment, 38, p4383-4402.
117. Watanabe, M., Matsuo, N., Yamaguchi, M., Matsumura, H., Kohno, Y., Izuta, T., (2010), Risk assessment of ozone impact on the carbon absorption of Japanese representative conifers, European Journal of Forest Research, 129, p421-430.
118. Watanabe, M., Yamaguchi, M., (2011), Risk assessment of ozone impact on 6 Japanese forest tree species with consideration of nitrogen deposition, Japanese Journal of Ecology, 61, p89-96, (in Japanese).
119. Watanabe, M., Yamaguchi, M., Matsumura, H., Kohno, Y., Izuta, T., (2012), Risk assessment of ozone impact on Fagus crenata in Japan: consideration of atmospheric nitrogen deposition, European Journal of Forest Research, 131, p475-484.
120. Watanabe, M., Yamaguchi, M., Matsumura, H., Kohno, Y., Koike, T., Izuta, T., (2011), A case study of risk assessment of ozone impact on forest tree species in Japan, Asian Journal of Atmospheric Environment, 5, p205-215.
121. Watanabe, T., Izumi, T., Matsuyama, H., (2016), Accumulated phytotoxic ozone dose estimation for deciduous forest in Kanto, Japan in summer, Atmospheric Environment, 129, p176-185.
122. Wieser, G., Tegischer, K., Tausz, M., Häberle, K.-H., Grams, T.E.E., Matyssek, R., (2002), Age effects on Norway spruce (Picea abies) susceptibility to ozone uptake: a novel approach relating stress avoidance to defense, Tree Physiology, 22, p583-590.
123. Wilkinson, S., Mills, G., Illidge, R., Davies, W.J., (2012), How is ozone pollution reducing our food supply?, Journal of Experimental Botany, 63, p527-536.
124. Wittig, V.E., Ainsworth, E.A., Long, S.P., (2007), To what extent do current and projected increases in surface ozone affect photosynthesis and stomatal conductance of trees? A meta-analytic review of the last 3 decades of experiments, Plant, Cell & Environment, 30, p1150-1162.
125. World Health Organization (WHO), (2000), Air Quality Guidelines for Europe, 2nd ed., Reg. Publ. Eur. Ser., WHO Reg. Off. Eur., Copenhagen.
126. Yamaguchi, M., Hoshino, D., Inada, H., Akhtar, N., Sumioka, C., Takeda, K., Izuta, T., (2014), Evaluation of the effects of ozone on yield of Japanese rice (Oryza sativa L.) based on stomatal ozone uptake, Environmental Pollution, 184, p472-480.
127. Yamaguchi, M., Watanabe, M., Matsumura, H., Kohno, Y., Izuta, T., (2011), Experimental studies on the effects of ozone on growth and photosynthetic activity of Japanese forest tree species, Asian Journal of Atmospheric Environment, 5, p65-78.
128. Yu, G.-R., Zhuang, J. i. e., Yu, Z.-L., (2001), An attempt to establish a synthetic model of photosynthesis-transpiration based on stomatal behavior for maize and soybean plants grown in field, Journal of Plant Physiology, 158, p861-874.
129. Yuan, X., Feng, Z., Liu, S., Shang, B., Li, P., Xu, Y., Paoletti, E., (2017), Concentration- and flux-based dose-responses of isoprene emission from poplar leaves and plants exposed to an ozone concentration gradient, Plant, Cell & Environment.
130. Zhang, W., Feng, Z., Wang, X., Liu, X., Hu, E., (2017), Quantification of ozone exposure- and stomatal uptake-yield response relationships for soybean in Northeast China, Science of The Total Environment, 599-600, p710-720.