## Ambient ozone phytotoxic potential over the Czech forests as assessed by AOT40

iForest - Biogeosciences and Forestry, Volume 5, Issue 3, Pages 153-162 (2012)
doi: https://doi.org/10.3832/ifor0617-005

Research Articles

Ambient ozone (O3) represents one of the most prominent air pollution problems in Europe. We present an analysis on O3 with respect to its phytotoxic potential over Czech forests between 1994 and 2008. The phytotoxic potential is estimated based on the exposure index AOT40 for forests calculated from real-time monitoring data at 24 rural sites. Our results indicate high phytotoxic potential for most of the Czech Republic (CR) with considerable inter-annual and spatial variability. The highest AOT40 values were 38-39 ppm·h. The critical level for forest protection (5 ppm·h) was usually exceeded early in the growing season, generally in May. In years with meteorological conditions conducive to ozone formation, the critical level was exceeded by 5-7 folds as compared to years with non-conducive conditions; nevertheless, all sites consistently exceeded the critical level since 1994. In the extremely hot and dry year 2003, the critical level for forests was exceeded over 31 % of the Czech forested area. More research is needed to translate these exceedances into forest injury in the CR.

# Introduction

Ambient ozone (O3) has been a widely studied air pollutant for many years due to its potential toxicity for all living organisms ([16], [6], [31]). It is an important gas playing a key role in atmospheric chemistry ([47]). It contributes to the oxidative power of atmosphere which is essential for scavenging many pollutants from the air. Moreover, due to its absorption-radiation abilities, O3 is an important greenhouse gas ([49], [30]). There are important mutual interactions between O3 and climate change ([33]), which are not fully understood yet. The urgent need to address the knowledge gaps in interactions between air pollution, climate change and forests has been recently stressed ([48], [41]).

Ozone represents one of the most prominent air pollution problems in Europe ([9], [15]). Environmental O3 quality standards are exceeded over vast areas of Europe ([22], [23]). Due to its phytotoxicity, O3 is still considered to be the most important air pollutant for forests ([45]). Due to the fact that O3 is a secondary pollutant formed from precursors during complex photochemical reactions and to the highly non-linear nature of O3 chemistry, it is and will be very difficult to decrease its ambient concentrations ([47]).

Comparison of O3 levels with those measured a century ago indicates that current levels have increased by approximately two times. European measurements between 1850 and 1900 were found to be in the range of 17-23 ppb ([4]). Modern day annual average background O3 concentrations over the mid-latitudes of the northern Hemisphere range between approximately 20-45 ppb, with variability influenced by geographic location, altitude and extent of anthropogenic impacts ([55]). Generally, three types of patterns in ambient O3 have been apparent recently: (1) an increase in the extent of O3 impact and the forest areas at risk; (2) a decrease in maximum 1-h O3 concentrations, at least in the northern hemisphere countries which have introduced O3 precursor control programs; and (3) an increase in background O3 concentrations over much of the world ([46]). It is not an easy task to assess the time trends of O3: the inter-annual variability is fairly high, so that long time series, which mostly are not available ([34]), are needed to detect trends.

In the Czech Republic (CR), ambient air pollution has been perceived as a major environmental problem since the 1950s, particularly due to extremely high emissions of SO2 and particulate matter from large power-generating sources ([42]). Pollution in the form of surface O3 was recognized as an issue as late as in the 1990s. Its levels are regularly measured within the framework of a national ambient air quality network run by the Czech Hydrometeorological Institute (CHMI) since 1993. Ozone levels are relatively high, and the limit values ([13]) over vast regions are frequently exceeded ([25]). Mean O3 concentrations during the growing season at rural sites range between 30 and 45 ppb in years with low O3 levels (as in 2001 and 2008) and between 35 and 60 ppb in years abundant in O3 (as in 2003). Peak 1-h mean O3 concentrations reached 110 ppb in 2003, 90 ppb in other years ([29]).

Based on long term real-time monitoring, we present an analysis of O3 time trends and spatial variability with respect to its phytotoxic potential over Czech forests between 1994 and 2008. Out of the two approaches (concentration-based and flux-based) developed for O3 risk assessment by UN/ECE ([54]), we used the concentration-based approach and applied the exposure index AOT40. We are fully aware of increasing number of studies promoting the flux approach as more scientifically sound (e.g., [1], [40], [53]) and of numerous criticisms of AOT40 concept and robustness (e.g., [50]). Moreover, O3 stomatal flux based indexes are reported to outperform AOT40 for explaining the biological effects such as biomass reduction and leaf visible injury ([36]). From a practical point of view, however, it is obvious that the exposure index has the advantage of relative simplicity, and in regions which are not under stress by drought - which is generally the case of the Czech mountain forests - the areas at risk indicated by exposure index and stomatal flux are not likely to differ substantially. However, even in Japan, where annual precipitation is usually very elevated, the flux approach was recommended when VPD is a limiting factor to stomatal uptake ([24]). The AOT40 has an advantage of relatively simple calculation based on ambient O3 concentration data, while modeling of stomatal flux is much more complicated ([53]). Modeling of stomatal flux needs O3 concentrations and data for stomatal conductance to be measured or modeled. In case the measured data are not available (which is the case for the CR), we are likely to introduce large uncertainties into the calculation. The shortcomings encountered in modeling O3 flux are discussed by Tuovinen et al. ([53]).

# Methods

## Ozone data

For our analysis, we used the data measured within the framework of the nation-wide ambient air quality monitoring network operated by the Czech Hydrometeorological Institute (CHMI). Ambient O3 monitoring over the CR has evolved significantly since its beginning in 1993. The number of sites within this network has largely increased from an original 16 in 1993 to 55 presently covering rural, mountain and urban areas ([43]). The ambient O3 concentrations were measured by real-time analyzers (Thermo Environmental Instruments TEI, M49) using UV-absorbance, a reference method in the EC ([13]). Standard procedures for quality control and quality assurance ([13]) were applied. We only considered sites with relatively large spatial representativeness, i.e., those classified as rural according to the EoI classification ([12]). Overall, we used 24 Czech sites. For mapping purposes, four additional German and four Polish sites were included (Tab. 1, Fig. 1).

Tab. 1 - Sites used for the analysis ranked according to decreasing altitude.

Country Site Name Site Identifier
(ID)
Altitude
(m a. s. l.)
Czech
Republic
Churanov 1 1118
Serlich 2 1011
Krkonose-Rychory 3 1001
Prebuz 4 904
Bily Kriz 5 890
Rudolice v Horach 6 840
Hojna Voda 7 818
Sous 8 771
Cervena 9 749
Primda 10 740
Svratouch 11 735
Jesenik 12 625
Stitna n. Vlari 13 600
Sneznik 14 590
Kostelni Myslova 15 569
Kosetice 16 535
Kocelovice 17 519
Ondrejov 18 514
Valdek 19 438
Kucharovice 20 334
Tusimice 21 322
Lom 22 265
Mikulov-Sedlec 23 245
Studenka 24 231
Poland Sniezne Kotly 25 1490
Czarna Gora 26 1133
Czierniawa 27 645
Jeleniow 28 244
Germany Fichtelberg 29 1213
Carlsfeld 30 896
Zinnwald 31 877
Schwartenberg 32 787

Fig. 1 - Sites with on-line monitoring of ambient ozone used for AOT40 mapping.

## Exposure index AOT40Forest

We analyzed the annual trends for selected sites representing the principal Czech mountain regions (1. Churanov - the Sumava Mts., 3. Krkonose-Rychory - the Krkonose Mts., 5. Bily Kriz - the Beskydy Mts., 6. Rudolice v Horach - the Krusne hory Mts., 8. Sous - the Jizerske hory Mts., 12. Jesenik - the Jeseniky Mts.), a regional site considered to represent the CR background (16. Kosetice - the Czech-Moravian Highlands), and a regional site representing the relatively warm lowlands in Southern Moravia (23. Mikulov-Sedlec). For the assessment of ambient ozone phytotoxic potential for forests, we applied the AOT40 approach ([19], [54]). The exposure index AOT40 was calculated according to eqn. 1 (see below). For practical reasons, we considered the daylight hours between 8 a.m. and 8 p.m. Central European Time ([13]).

$$AOT40 = \sum_{i \in V}\sum_{j=1}^{n} \sum_{k \in D} \left (c_{ijk} - p \right )$$

where cijk is the ground-level O3 concentration measured in the i-th month, j-th day and k-th hour; p is the threshold concentration (40 ppb); V is a set of the months of the growing season (April-September); D is a set of daylight hours, defined as those hours with a mean global radiation of 50 W m-2 or greater; and n is the number of days in the month.

Tab. 2 - Data coverage for calculating AOT40Forest expressed as percentage of 1-h mean O3 concentrations available over the growing season (1.4-30.9). The site ID corresponds to the site name in Tab. 1.

Country Station ID Data coverage (%)
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Czech
Republic
1 - 70 98 99 99 99 99 99 98 96 90 100 100 99 100
2 - 33 94 93 94 92 93 98 96 98 98 98 99 100 100
3 - - 96 87 98 98 92 92 98 99 98 82 89 98 100
4 61 91 99 98 98 98 99 100 97 99 99 98 96 99 99
5 97 97 88 96 93 98 99 99 99 98 99 97 100 99 99
6 - - 98 98 98 90 99 100 98 98 97 97 93 100 100
7 - 99 82 91 88 99 99 92 96 90 98 100 96 99 100
8 88 99 94 97 99 98 99 100 99 97 95 96 99 97 97
9 - - - - - - - - - - 99 99 96 88 98
10 - 81 99 97 98 95 93 98 94 97 96 100 100 100 100
11 90 88 99 85 98 99 98 96 96 81 99 100 99 100 100
12 - 99 94 98 99 99 99 99 97 100 99 100 100 96 100
13 - - - - - - - - - 91 98 99 100 100 99
14 - 95 98 99 96 98 98 98 99 99 32 98 98 100 100
15 - - - 94 98 100 96 97 98 99 78 99 99 99 99
16 92 94 96 99 99 93 100 97 98 100 99 99 96 100 100
17 - - - - - - - - - - 98 98 97 100 100
18 75 98 90 86 94 100 95 97 97 96 91 95 99 100 100
19 - - - - - - - - - 98 81 99 99 99 97
20 - - - - - - - - - - - 95 99 100 99
21 70 94 96 91 100 97 99 98 99 99 99 99 99 100 100
22 - - - - - - - - - - - 99 100 100 99
23 - - 100 100 96 98 99 100 98 99 98 92 97 99 100
24 - - - - 95 98 99 99 99 100 100 96 100 100 100
Poland 25 - - - 80 95 61 76 48 77 63 65 90 93 94 45
26 - - - - 98 99 93 48 96 92 92 94 99 87 96
27 - - - 93 89 75 87 48 98 88 92 69 98 99 95
28 95 93 96 - - - - - 94 87 84 96 93 100 100
Germany 29 - - - 88 90 97 96 98 93 92 91 16 96 98 96
30 - - 75 95 97 95 97 97 93 92 - 16 99 100 100
31 - - 85 94 96 96 91 95 93 95 - 16 99 97 99
32 - - - - 95 94 92 94 90 95 - 16 99 99 99

We carefully checked the data coverage for calculating AOT40. When all possible data were not available for calculation of the AOT40 due to monitoring gaps (Tab. 2), we used the correction factor recommended by EC ([12] - eqn. 2):

$$AOT40_{(estimated)} = AOT40_{(calculated)} \cdot a/b$$

where a is the total possible number of hours, and b is the number of measured hourly values.

## AOT40Forest spatial pattern

The spatial distribution of AOT40Forest was carried out for three years: 2003, 2006 and 2007. We selected 2003 due to its exceptional meteorological conditions (extremely high temperatures spanning long durations), 2006 as an example of a year with high O3 exposures, and 2007, in contrast, as an example of a year with low O3 exposures during the growing season.

For mapping, we used a linear regression model with subsequent IDW (Inverse Distance Weighting) interpolation of the residuals. AOT40 calculated for the monitoring sites was used as a dependent variable for the regression model. Orography (altitude) was used as an independent variable. For the spatial interpolation of the residuals, we used the deterministic method IDW included in ArcGIS Geostatistical Analyst ([35]). The IDW spatial interpolation technique (e.g., [32]) estimates the cell values using a weighted linear combination of values measured at several neighborhood sites, where the weight is an inverse function of the distance, according to the following equation (eqn. 3):

$$Z(s_0) = \frac{\sum_{i=i}^{n} \frac{Z(s_{i})} {h_{0i}^{\beta}}} {\sum_{i=i}^{n} \frac{1}{h_{0i}^{\beta}}}$$

where Z(s0) is the interpolated grid value; Z(si) is the neighboring data point; h0i is the distance between the grid node and the data point; β is the weighting power (the Power parameter); and n is the number of measuring points.

Maps were prepared at resolution 1x1 km. The digital map of Czech forests produced from the European digital land use map (Corine Land Cover 2000 - ⇒ http:/­/­etc-lusi.­eionet.­europa.­eu/­CLC2000) was used. A spatial categorization of AOT40 was carried out only for forested areas, which form about 33 % of the Czech territory. For interpolation in border areas, we used data from four German sites for the region of the Krusne hory Mts. and four Polish sites for the northern part of the CR. The spatial distribution of the sites was surprisingly even in altitude (Tab. 3); in contrast, it was uneven regarding the geographical distribution (Fig. 1). The sites were concentrated mostly in mountain areas; the densest network was in the Krusne hory Mts., i.e., in the north-west.

Tab. 3 - Spatial distribution of the monitoring sites (ranked according to the altitude) used for O3 exposure mapping.

Altitude
(m a.s.l.)
Number
of Czech sites
Total number of sites
(including the sites abroad used for border analysis)
231-400 5 6
401-600 7 7
601-800 5 7
801-1000 4 6
1001-1200 3 4
1201-1490 0 2
Total 24 32

## Ozone precursor data

The data on emission of NOx, VOC, CO were taken from the Register of Emissions and Air Pollution Sources (REZZO), the Czech emission inventory database run by CHMI. REZZO includes information on anthropogenic sources of air pollution, both stationary (categorized as extra large, large, medium and local) and mobile ([43]). The data on emissions of CH4 were taken from the National Greenhouse Gas Inventory Report (NIR) of the CR ([18]) and these include both anthropogenic and natural sources.

## Temperature data

We used the data measured at climatological stations run by the CHMI. Manual climatic measurements were taken by a station thermometer in a standard thermometer screen 2 m above-ground at climatological observation times, i.e., 7 a.m., 2 p.m. and 9 p.m. local mean solar time (LMST). Seasonal mean temperature was calculated based on daily mean temperatures. Daily mean temperature was calculated as an average of the temperatures at the observation times, where the evening observation was used twice. Seasonal mean temperature was calculated from all available stations which had records for the relevant year, i.e., we used about 200 stations per year ([52]).

# Results

Fig. 2 and Fig. 3 summarize the trends of factors which substantially influence ambient O3 levels. Emission of O3 precursors from Czech anthropogenic sources between 1990 and 2008 decreased by 52 % for NOx, by 63 % for non-methane VOCs, by 65 % for CO, and by 42 % for CH4. For the period under consideration, i.e., 1994-2008, the emission of NOx decreased by 30 %, non-methane VOCs by 47 %, CO by 58 %, and CH4 by 23 %. Though somewhat hidden in a seasonal mean, the air temperature variability was fairly high. In 2003, for example, the temperature in the CR was 4 °C and 3.6 °C above the average in July and August, respectively.

Fig. 2 - Annual emission of ozone precursors in the CR (source: CHMI).

Fig. 3 - Average temperature for the CR (source: CHMI).

The distribution of 1-h mean O3 concentrations at selected rural sites (indicated as 1, 3, 5, 6, 8, 12, 16, 23 - see Tab. 1) in the CR for the period under consideration is summarized by a histogram showing that about 70 % of 1-h mean O3 concentrations were above 40 ppb (Fig. 4). The AOT40 Forest trends for different altitudinal layers are presented in Fig. 5. The AOT40 values for Czech rural sites clearly show that the year-to-year variability was considerable. The critical level of 5 ppm·h ([54]) was exceeded every year at all Czech rural sites. In years with abundant O3, the critical level was exceeded 5-7.5 times (2003). The highest AOT40 values were recorded at the Sous site (39 ppm·h in 1994), Prebuz and Krkonose-Rychory sites (38 ppm·h in 1994), and Bily Kriz (37.7 ppm·h in 1995) in the North. Apart from these mountain sites, fairly high AOT40 was recorded also in lower altitude at the Mikulov site (34 ppm·h in 2003).

Fig. 4 - Histogram of 1-h mean O3 concentrations at selected rural sites (indicated as 1, 3, 5, 6, 8, 12, 16, 23) in the CR, 1994-2008.

Fig. 5 - AOT40 at rural sites in the CR, 1994-2008, at several altitudinal layers. Current critical level is 5 ppmh AOT40Forest as set by UN/ECE ([54]).

The annual trend of AOT40 differed depending on the meteorological conditions. The critical level of 5 ppm·h was usually reached in the beginning of the growing season (Tab. 4), with few exceptions. As an example, we present the accumulation of AOT40 over the growing seasons 2003-2008 at two sites: a mountain site (3. Krkonose-Rychory) and a rural site situated at a medium altitude (15. Kostelni Myslova - Fig. 6). Rapid AOT40 rise was recorded in 2003, when the critical level of 5 ppm·h at the Krkonose-Rychory site was reached as early as before the end of April and at the Kostelni Myslova site in the beginning of May. Nevertheless, even in the years with meteorological situations not conducive to O3 formation, the critical level of 5 ppm·h was usually reached during May.

Tab. 4 - Average day of the year (DOY) when the critical level of 5 ppmh was exceeded in the 1994-2008 growing seasons at selected rural sites.

Year Site ID
1 3 5 6 8 12 16 23 Average
1994 - - 5.5 - - - 16.5 - 10.5
1995 - - 23.4 - 12.5 6.5 24.5 - 8.5
1996 21.4 17.4 - 27.4 21.4 26.4 2.5 28.4 26.4
1997 12.5 12.6. 14.5 29.5 17.5 15.5 16.5 19.5 18.5
1998 10.5 25.5 9.5 - 8.5 21.6 12.5 10.5 20.5
1999 30.4 22.5 8.5 18.5 17.5 13.5 19.5 28.5 15.5
2000 7.5 30.4 4.5 6.5 7.5 5.5 7.5 7.5 5.5
2001 20.5 14.5 24.5 23.5 25.5 25.5 9.6 23.5 27.5
2002 9.5 7.5 14.5 14.5 12.5 17.5 17.5 21.5 14.5
2003 29.4 26.4 6.5 6.5 4.5 6.5 5.5 6.5 5.5
2004 26.5 31.5 27.5 31.5 1.6 13.5 7.6 30.5 28.5
2005 12.5 - 8.5 16.5 5.5 3.5 16.5 17.5 9.5
2006 18.4. 6.5 8.5 12.5 7.5 5.5 10.5 10.5 8.5
2007 14.5 14.5 19.5 5.5 13.5 5.5 11.5 13.5 10.5
2008 17.5 13.5 3.6 21.5 14.5 16.5 25.5 30.5 22.5
Average 8.5 13.5 14.5 14.5 11.5 13.5 16.5 16.5 13.5

Fig. 6 - Annual trend of AOT40 at Krkonoše-Rýchory (1001 m a.s.l.) and Kostelní Myslová (569 m a.s.l.), 2003-2008 growing seasons.

Fig. 7 shows the spatial distribution of AOT40 in 2003 when the exceedances of 5 ppm·h were the highest ever recorded. The critical level was exceeded more than 6 times over one third of the Czech forested area. The highest values were recorded over the border mountains - the Krkonose, the western part of the Krusne hory Mts., the Cesky les, the Sumava, and also inland over the Czech-Moravian Upplands (Ceskomoravska Vysocina) and the Brdy. Fig. 8 shows the spatial distribution of AOT40 in 2006 which belonged to the years abundant in O3 (though lower as compared to the extreme year of 2003) as a consequence of a very warm and dry summer in Central Europe. In contrast to 2003, the relative share of forested area with AOT40 above 30 ppm·h was < 3 %. The highest values were recorded in the South, in the Sumava and Novohradske hory Mts. Fig. 9 shows the year 2007 with even lower O3 levels. Still, 78 % of the Czech forested area experienced an exceedance of the critical level by a factor of 3-4.

Fig. 7 - Spatial pattern of the AOT40 for forests, 2003.

Fig. 8 - Spatial pattern of the AOT40 for forests, 2006.

Fig. 9 - Spatial pattern of the AOT40 for forests, 2007.

# Discussion

The crucial factors for ambient O3 concentrations are emission of precursors and prevailing meteorological conditions. Solar intensity is of particular importance. Hot sunny calm weather leads to high O3 concentrations. High temperature, high solar intensity, low wind speed, low atmospheric humidity and absence of precipitation are factors generally considered as favorable for photochemical O3 formation ([47]). While anthropogenic O3 precursor emissions in Europe have decreased ([14]), the air temperature tends to increase. Observational evidence from all continents shows that many natural systems are being affected by regional climate changes, particularly temperature increases ([30]). For CR, Tolasz et al. ([52]) reported a statistically significant temperature increase for the period 1961-2000: annual average temperature increases by 0.028 ºC year-1 (R2 = 0.195) and growing season average temperature increases by 0.025 ºC year-1 (R2 = 0.151). A similar trend is still observable and we can summarize that the average temperature in the CR increases by 0.3 ºC per decade in the last 50 years (⇒ http:/­/­www.­chmi.­cz/­).

Ambient O3 is a regional phenomenon and for its formation the emissions from broader regions are of importance. According to European Environmental Agency (EEA), emissions of the main ambient O3 precursor pollutants have decreased significantly across the EEA region between 1990 and 2009 as follows: NOx by 44 %, non-methane VOCs by 55 %, CO by 62 % and CH4 by 27 % ([15]). These estimates correspond with the trend in the CR, though the O3 precursor emission decrease in the CR was even more pronounced as compared to the EEA region. Though the uncertainties associated with estimated emissions are relatively high, accounting for about 50 % for VOCs and CH4 and 30 % for NOx ([8]), the progress in reducing emissions is obvious. We have to keep in mind, however, that O3 formation changes under differing NOx and VOC regimes ([47]), and tropospheric ozone-forming potential differs for individual precursor gases ([8]).

There is a discrepancy between the substantial cuts in O3 precursor emissions and observed non-decreasing annual average O3 concentrations in Europe. Reasons include increasing inter-continental transport of O3 and its precursors in the northern hemisphere, climate change, biogenic non-methane VOC emission, which are difficult to quantify, and natural fires ([15]).

An inherent property of AOT40 as a cumulative index is that it is very sensitive to the quality of input data. For calculating AOT40Forest, O3 concentrations were measured in real time, thoroughly checked and considered as highly variable; the calculated index, however, is not robust ([50]) and is likely to be burdened by high uncertainty. Consideration of the spatial scale is a crucial issue for air pollution mapping, as stressed by Diem ([11]). A spatial resolution of 1 x 1 km, as used in our mapping, is detailed enough, provides consistent results, and is considered appropriate for a country-scale mapping ([20]). The relative uncertainty of the AOT40Forest maps analyzed by cross-validation and expressed by the root mean square error (RMSE) was about 20 % as shown earlier by Hunová et al. ([26]) in a comparison of 11 different interpolation approaches for ambient O3 mapping. The relative uncertainty of AOT40Forest maps is worse when compared to maps of seasonal mean O3 concentrations, but it is still acceptable.

A similar approach for O3 phytotoxic potential assessment based on O3 concentrations measured in real time and interpolation of calculated AOT40Forest values was used for United Kingdom by Coyle et al. ([7]), and for EU by Horálek et al. ([22], [23]). Different interpolation techniques were used in these studies, but approach and scale were similar. For Italy, where monitoring network does not adequately cover all the territory, De Marco ([10]) applied an integrated assessment model (RAINS-Italy) for developing a map of AOT40Forest.

The highest O3 concentrations in Europe are observed in the Mediterranean countries ([15]). Nevertheless, O3 concentrations measured throughout central Europe ([40]) including the CR are also very high. The AOT40Forest values that we calculated for some Czech sites are comparable to values reported for Italy ([44]).

Our analysis of real-time O3 data recorded during 1994-2008 within the framework of the Czech national ambient air quality monitoring network shows that O3 exposure over the Czech forested areas still remains fairly high and varies considerably in time and space. We assume that in the Czech rural regions, where average hourly O3 concentrations during the growing season are generally significantly higher than 40 ppb ([25], [27]) and, in particular, under optimal nutrient regime and water availability, the AOT40 exposure index provides a reasonable estimation of the risk areas. This assumption would not apply for the year 2003, an extreme year regarding meteorology ([37]), when the high O3 concentrations were coupled with extreme drought affecting stomatal conductance.

As stressed earlier by many authors, it is necessary to observe plant effects to give biological significance and meaning to O3 standards ([38]). Visible ozone injury is assessed regularly at selected plots within the forest condition monitoring in the CR by Forestry and Game Management Research Institute ([3]). Despite the high O3 levels measured over the CR, no serious damage to vegetation attributable to O3 has been reported so far. In a case study carried out in the Jizerske hory Mts. in 2006 and 2007 at five sites situated in the altitudes 900-1000 m a.s.l., the leaves of 22 plant species were assessed for ozone-like visible symptoms according to UN/ECE ([54]). Though injury was found, the extent of visible symptoms was much less than assumed considering the recorded O3 exposure ([39]). Moreover, after verification of symptoms by the Ozone Validation Centre for Central Europe ([21]), visible injury was confirmed as O3-induced on the leaves of only two species, Fagus sylvatica and Rubus idaeus ([28]). This is in agreement with earlier reports from many other authors (e.g., [17], [40], [56], [2], [5]), that the O3 exposure is rather inconsistent with observed ozone injury. Moreover, the assumption that higher ambient O3 exposure results in higher contents of malondialdehyde (MDA) as a product of lipid peroxidation in Picea abies needles was not supported by a study in real forest stands of three Czech mountain areas - the Krkonose, Krusne hory and Jizerske hory Mts. in 1994-2006 ([29]). In contrast, Srámek et al. ([51]) indicated an impact of O3 on beech (Fagus sylvatica) as assessed by the quality and quantity of epicuticular waxes and content of MDA. Furthermore, Zapletal et al. ([57]) reported that ambient O3 reduces net ecosystem production in a Norway spruce (Picea abies) stand in the CR, though spruce as most of the conifers is considered as a relatively ozone-tolerant species ([54]). Thus, so-far reported impacts of O3 on vegetation in the CR are equivocal and further effort is needed to clarify the issue and translate O3 exposure to biological impacts on CR forests.

# Conclusions

Our analysis of real-time ambient O3 measurements at Czech rural sites recorded over the last 15 years shows that O3 exposure over the Czech forested areas still remains fairly high and varies considerably in time and space. All sites consistently exceeded the critical level of 5 ppm·h AOT40Forest since 1994, with peak values reaching 38-39 ppm·h at few sites in different years. Existing studies on ambient O3 biological effects on Czech forests are equivocal and more effort is needed to explore the O3 impacts. Regarding environmental protection the effort should be focused on the most sensitive forest species.

# Acknowledgments

The results of this paper were presented at the COST Action FP0903 conference “Ozone, climate change and forests” held in Prague in June 2011. CHMI provided the ozone data used for the analysis. We are grateful to LfULG and IMGW for providing the data from German and Polish sites. We thank our colleague Linton Corbet, who revised the English for style and commented the final version of the manuscript. The authors highly appreciate detailed comments of an anonymous reviewer on an earlier draft of this manuscript.

# References

(1)
Ashmore M, Emberson LD, Karlsson P, Pleijel H (2004). New directions: A new generation of ozone critical levels for the protection of vegetation in Europe. Atmospheric Environment 38: 2213-2214.
CrossRef | Gscholar
(2)
Baumgarten M, Huber C, Buker P, Emberson L, Dietrich H-P, Nunn AJ, Heerdt C, Beudert B, Matyssek R (2009). Are Bavarian forests (southern Germany) at risk from ground-level ozone? Assessment using exposure and flux based ozone indices. Environmental Pollution 157: 2091-2107.
CrossRef | Gscholar
(3)
Bohacova L, Lomsky B, Sramek V (2010). Forest condition monitoring in the Czech Republic. Forestry and Game Research Institute, Strnady, Czech Republic, pp. 157.
Gscholar
(4)
Bojkov RD (1986). Surface ozone during the second half of the nineteenth century. Journal of Climate and Applied Meteorology 25: 343-352.
CrossRef | Gscholar
(5)
Bussotti F, Ferretti M (2009). Visible injury, crown condition, and growth responses of selected Italian forests in relation to ozone exposure. Environmental Pollution 157: 1427-1437.
CrossRef | Gscholar
(6)
Cape JN (2008). Surface ozone concentrations and ecosystem health: past trends and a guide to future projections. Science of the Total Environment 400: 257-269.
CrossRef | Gscholar
(7)
Coyle M, Smith R, Stedman J, Weston K, Fowler D (2002). Quantifying the spatial distribution of surface ozone concentration in the UK. Atmospheric Environment 36 (6): 1013-1024.
CrossRef | Gscholar
(8)
De Leeuw FAAM (2002). A set of emission indicators for long-range transboundary air pollution. Environmental Science & Policy 5: 135-145.
CrossRef | Gscholar
(9)
De Leeuw FAAM, De Paus TA (2001). Exceedance of EC ozone threshold values in Europe in 1997. Water Air and Soil Pollution 128: 255-281.
CrossRef | Gscholar
(10)
De Marco A (2009). Assessment of present and future risk to Italian forests and human health: modelling and mapping. Environmental Pollution 157: 1407-1412.
CrossRef | Gscholar
(11)
Diem J (2003). A critical examination of ozone mapping from a spatial-scale perspective. Environmental Pollution 125: 369-383.
CrossRef | Gscholar
(12)
EC (1997). Council Decision 97/101/EC of 27 January 1997 establishing a reciprocal exchange of information and data from networks and individual stations measuring ambient air pollution within the Member States. Official Journal of the European Communities, No. L 35/14, Brussels, Belgium.
Gscholar
(13)
EC (2008). 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. OJEC L 152, Brussels, Belgium.
Gscholar
(14)
EEA (2007). Europe´s environment: The Fourth Assessment. EEA, Copenhagen, Denmark, pp. 452
Gscholar
(15)
EEA (2010). Air quality in Europe - 2011 report. EEA, Copenhagen, Denmark, pp. 84.
Gscholar
(16)
Felzer BS, Cronin T, Reilly JM, Melillo JM, Wang X (2007). Impacts of ozone on trees and crops. C. R. Geoscience 339: 784-798.
CrossRef | Gscholar
(17)
Ferretti M, Fagnano M, Amoriello T, Badiani M, Ballarin-Denti A et al. (2007). Measuring, modelling and testing ozone exposure, flux and effects on vegetation in southern European conditions - What does not work? Environmental Pollution 146: 648-658.
CrossRef | Gscholar
(18)
Fott P, Vacha D (2011). National Greenhouse Gas Inventory Report of the CR. CHMI, Prague, Czech Republic.
Online | Gscholar
(19)
Fuhrer J, Skarby L, Ashmore MR (1997). Critical levels for ozone effects on vegetation in Europe. Environmental Pollution 97: 91-106.
CrossRef | Gscholar
(20)
Gottardini E, Cristofori A, Cristofolini F, Ferretti M (2010). Variability of ozone concentration in a montane environment, northern Italy. Atmospheric Environment 44: 147-152.
CrossRef | Gscholar
(21)
Günthardt-Goerg MS, Menard T (2008). Validation of ozone symptoms on leaves from the Jizerske hory Mts. Ozone Validation Centre for Central Europe, WSL, Birmensdorf, Switzerland, pp. 12.
Gscholar
(22)
Horálek J, Denby B, de Smet P, de Leeuw F, Kurfürst P, Swart R, van Noije T (2007). Spatial mapping of air quality for European scale assessment. ETC/ACC Technical paper 2006/6, pp. 184.
Online | Gscholar
(23)
Horálek J, Fiala J, Kurfürst P, Denby B, de Smet P, de Leeuw F (2009). Spatial assessment of PM10 and ozone concentrations in Europe (2005). EEA Technical report No 1/2009, pp. 52.
Online | Gscholar
(24)
Hoshika Y, Shimizu Y, Omasa K (2011). A comparison between stomatal ozone uptake and AOT40 of deciduous trees in Japan. iForest 4: 128-135.
CrossRef | Gscholar
(25)
Hunová I (2003). Ambient air quality of the territory of the Czech Republic in 1996-1999 expressed by three essential factors. The Science of the Total Environment 303: 245-251.
CrossRef | Gscholar
(26)
Hunová I, Horálek J, Schreiberová M, Zapletal M (2012). Ambient ozone exposure in Czech forests - a GIS-based approach to a spatial distribution assessment. The Scientific World Journal, Article ID 123760.
CrossRef | Gscholar
(27)
Hunová I, Livorová H, Ostatnická J (2003). Potential ambient ozone impact on ecosystems in the Czech Republic as indicated by exposure index AOT40. Ecological Indicators 3: 35-47.
CrossRef | Gscholar
(28)
Hunová I, Matousková L, Srnensky R, Kozelková K (2011). Ozone influence on native vegetation in the Jizerske hory Mts. of the Czech Republic: results based on ozone exposure and ozone-induced visible symptoms. Environmental Monitoring and Assessment 183: 501-515.
CrossRef | Gscholar
(29)
Hunová I, Novotny R, Uhlirová H, Vráblik T, Horálek J, Lomsky B, Srámek V (2010). The impact of ambient ozone on mountain spruce forests in the Czech Republic as indicated by malondialdehyde. Environmental Pollution 158: 2393-2401.
CrossRef | Gscholar
(30)
IPCC (2007). Climate Change 2007. Synthesis report. IPCC, Geneva, Switzerland, pp. 104.
Gscholar
(31)
Iriti M, Faoro F (2008). Oxidative stress, the paradigm of ozone toxicity in plants and animals. Water, Air and Soil Pollution 187: 285-301.
CrossRef | Gscholar
(32)
Isaaks EH, Srivastava RM (1989). An Introduction to applied geostatistics. Oxford University Press Inc., Oxford, UK, pp. 561.
Gscholar
(33)
Isaksen ISA (2003). Ozone-climate interactions. Air pollution research report No. 81. EC, Brussels, Belgium, pp. 143.
Gscholar
(34)
Jonson JE, Simpson D, Fagerli H, Solberg S (2006). Can we explain the trends in European ozone levels? Atmospheric Chemistry and Physics 6: 51-66.
CrossRef | Gscholar
(35)
Johnston K, Ver Hoef J, Krivoruchko K, Lucas N (2001). Using ArcGIS geostatistical analyst. Environmental Systems Research Institute, Redlands, USA, pp. 300.
Gscholar
(36)
Karlsson PE, Braun S, Broadmeadow M, Elvira S, Emberson L, Gimeno BS, Le Thiec D, Novak K, Oksanen E, Schaub M, Uddling J, Wilkinson M (2007). Risk assessments for forest trees: the performance of the ozone flux versus the AOT concepts. Environmental Pollution 146: 608-616.
CrossRef | Gscholar
(37)
Luterbacher J, Dietrich D, Xoplaki E, Grosjean M, Wanner H (2004). European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303: 1499-1503.
CrossRef | Gscholar
(38)
Manning WJ (2003). Detecting plant effects is necessary to give biological significance to ambient ozone monitoring standards and predictive ozone standards. Environmental Pollution 126: 375-379.
CrossRef | Gscholar
(39)
Matousková L, Novotný R, Hunová I, Buriánek V (2010). Visible foliar injury as a tool for the assessment of surface ozone impact on native vegetation: a case study from the Jizerské hory Mts. Journal of Forest Science 56: 177-182.
Gscholar
(40)
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.
CrossRef | Gscholar
(41)
Matyssek R, Wieser G, Calfapietra C, de Vries W, Dizengremel P, Ernst D, Jolivet Y, Mikkelsen TN, Mohren GMJ, Le Thiec D, Tuovinen J-P, Weatherall A, Paoletti E (2012). Forests under climate change and air pollution: Gaps in understanding and future directions for research. Environmental Pollution 160: 57-65.
CrossRef | Gscholar
(42)
Moldan B, Schnoor JL (1992). Czechoslovakia: examining a critically ill environment. Environment, Science and Technology 26: 14-21.
CrossRef | Gscholar
(43)
Ostatnická J (2011). Air pollution in the Czech Republic in 2010. CHMI, Prague, Czech Republic, pp. 268.
Gscholar
(44)
Paoletti E (2006). Impact of ozone on Mediterranean forests: a review. Environmental Pollution 144: 463-474.
CrossRef | Gscholar
(45)
Paoletti E, Schaub M, Matyssek R, Wieser G, Augustaitis A, Bastrup-Birk AM, Bytnerowicz A, Gunthardt-Goerg MS, Muller-Starck G, Serengil Y (2010). Advances of air pollution science: From forest decline to multiple-stress effects on forest ecosystem services. Environmental Pollution 158: 1986-1989.
CrossRef | Gscholar
(46)
Percy KE, Legge AH, Krupa SV (2003). Tropospheric ozone: a continuing threat to global forests? In: “Air Pollution, Global Change and Forests in the New Millenium” (Karnosky DF, Percy KE, Chappelka AH, Simpson C, Pikkarainen J eds). Elsevier, Amsterdam, The Netherlands, pp. 85-118.
Gscholar
(47)
Seinfeldt JH, Pandis SN (1998). Atmospheric chemistry and physics. John Wiley, New York, USA, pp. 1326.
Gscholar
(48)
Serengil Y, Augustaitis A, Bytnerowicz A, Grulke N, Kozovitz AR, Matyssek R, Müller-Starck G, Schaub M, Wieser G, Coskun AA, Paoletti E (2011). Adaptation of forest ecosystems to air pollution and climate change: a global assessment on research priorities. iForest 4: 44-48.
CrossRef | Gscholar
(49)
Singh ON, Fabian P (2003). Atmospheric ozone: a millenium issue. EGU Special Publication Series 1, Copernicus, Berlin, Germany, pp. 147.
Gscholar
(50)
Sofiev M, Tuovinen JP (2001). Factors determining the robustness of AOT40 and other ozone exposure indices. Atmospheric Environment 35: 3521-3528.
CrossRef | Gscholar
(51)
Srámek V, Novotný R, Bednárová M, Uhlírová H (2007). Monitoring of ozone risk for forest in the Czech Republic: preliminary results. Scientific World Journal 7: 78-83.
Gscholar
(52)
Tolasz R, Míková T, Valeriánová A, Voženílek V (2007). Climate atlas of Czechia. Ceský hydrometeorologický ústav, Univerzita Palackého, Praha, Czech Republic, pp. 255.
Gscholar
(53)
Tuovinen JP, Emberson L, Simpson D (2009). Modelling ozone fluxes to forests for risk assessment. Annals of Forest Science 66: 401-415.
CrossRef | Gscholar
(54)
UN/ECE (2004). Mapping manual revision. UNECE convention on long-range transboundary air pollution. Manual on the methodologies and criteria for modelling and mapping critical loads and levels and air pollution effects, risks and trends, pp. 254.
Online | Gscholar
(55)
Vingarzan R (2004). A review of surface ozone background levels and trends. Atmospheric Environment 38: 3431-3442.
CrossRef | Gscholar
(56)
Waldner P, Schaub M, Graf Pannatier E, Schmitt M, Thimonier A, Walthert L (2007). Atmospheric deposition and ozone levels in Swiss forests: Are critical values exceeded? Environmental Monitoring and Assessment 128: 5-17.
CrossRef | Gscholar
(57)
Zapletal M, Cudlin P, Chroust P, Urban O, Pokorný R, Edwards-Jonásová M, Czerny R, Janous D, Taufarová K, Vecera Z, Mikuska P, Paoletti E (2011). Ozone flux over a Norway spruce forest and correlation with net ecosystem production. Environmental Pollution 159: 1024-1034.
CrossRef | Gscholar

#### Authors’ Affiliation

(1)
I Hunová
M Schreiberová
Czech Hydrometeorological Institute, Na Šabatce 17, 143 06 Prague 4 - Komorany (Czech Republic)

I Hunová
hunova@chmi.cz

#### Citation

Hunová I, Schreiberová M (2012). Ambient ozone phytotoxic potential over the Czech forests as assessed by AOT40. iForest 5: 153-162. - doi: 10.3832/ifor0617-005

#### Paper history

Accepted: May 11, 2012

First online: Jun 25, 2012
Publication Date: Jun 29, 2012
Publication Time: 1.50 months

© SISEF - The Italian Society of Silviculture and Forest Ecology 2012

#### Breakdown by View Type

(Waiting for server response...)

#### Article Usage

Total Article Views: 26953
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 21451
Abstract Page Views: 1410

Web Metrics
Days since publication: 3400
Overall contacts: 26953
Avg. contacts per week: 55.49

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Jul 2021)

Total number of cites (since 2012): 16
Average cites per year: 1.60

#### iForest Database Search

Search By Author

Search By Keyword

Citing Articles

Search By Author

Search By Keywords

#### PubMed Search

Search By Author

Search By Keyword