Elevated tropospheric ozone concentrations induce adverse effects in plants. We reviewed how ozone affects (i) the composition and diversity of plant communities by affecting key physiological traits; (ii) foliar chemistry and the emission of volatiles, thereby affecting plant-plant competition, plant-insect interactions, and the composition of insect communities; and (iii) plant-soil-microbe interactions and the composition of soil communities by disrupting plant litterfall and altering root exudation, soil enzymatic activities, decomposition, and nutrient cycling. The community composition of soil microbes is consequently changed, and alpha diversity is often reduced. The effects depend on the environment and vary across space and time. We suggest that Atlantic islands in the Northern Hemisphere, the Mediterranean Basin, equatorial Africa, Ethiopia, the Indian coastline, the Himalayan region, southern Asia, and Japan have high endemic richness at high ozone risk by 2100.

Urban forests provide important ecosystem services, such as urban air quality improvement by removing pollutants. While robust evidence exists that plant physiology, abundance, and distribution within cities are basic parameters affecting the magnitude and efficiency of air pollution removal, little is known about effects of plant diversity on the stability of this ecosystem service. Here, by means of a spatial analysis integrating system dynamic modeling and geostatistics, we assessed the effects of tree diversity on the removal of tropospheric ozone (O3) in Rome, Italy, in two years (2003 and 2004) that were very different for climatic conditions and ozone levels. Different tree functional groups showed complementary uptake patterns, related to tree physiology and phenology, maintaining a stable community function across different climatic conditions. Our results, although depending on the city-specific conditions of the studied area, suggest a higher function stability at increasing diversity levels in urban ecosystems. In Rome, such ecosystem services, based on published unitary costs of externalities and of mortality associated with O3, can be prudently valued to roughly US$2 and $3 million/year, respectively.

High ground-level ozone concentrations are typical of Mediterranean climates. Plant exposure to this oxidant is known to reduce carbon assimilation. Ozone damage has been traditionally measured through manipulative experiments that do not consider long-term exposure and propagate large uncertainty by up-scaling leaf-level observations to ecosystem-level interpretations. We analyzed long-term continuous measurements (>9 site-years at 30 min resolution) of environmental and eco-physiological parameters at three Mediterranean ecosystems: (i) forest site dominated by Pinus ponderosa in the Sierra Mountains in California, USA; (ii) forest site composed of a mixture of Quercus spp. and P. pinea in the Tyrrhenian sea coast near Rome, Italy; and (iii) orchard site of Citrus sinensis cultivated in the California Central Valley, USA. We hypothesized that higher levels of ozone concentration in the atmosphere result in a decrease in carbon assimilation by trees under field conditions. This hypothesis was tested using time series analysis such as wavelet coherence and spectral Granger causality, and complemented with multivariate linear and nonlinear statistical analyses. We found that reduction in carbon assimilation was more related to stomatal ozone deposition than to ozone concentration. The negative effects of ozone occurred within a day of exposure/uptake. Decoupling between carbon assimilation and stomatal aperture increased with the amount of ozone pollution. Up to 12-19% of the carbon assimilation reduction in P. ponderosa and in the Citrus plantation was explained by higher stomatal ozone deposition. In contrast, the Italian site did not show reductions in gross primary productivity either by ozone concentration or stomatal ozone deposition, mainly due to the lower ozone concentrations in the periurban site over the shorter period of investigation. These results highlight the importance of plant adaptation/sensitivity under field conditions, and the importance of continuous long-term measurements to explain ozone damage to real-world forests and calculate metrics for ozone-risk assessment.

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Tropospheric ozone (O3) produces harmful effects to forests and crops, leading to a reduction of land carbon assimilation that, consequently, influences the land sink and the crop yield production. To assess the potential negative O3 impacts to vegetation, the European Union uses the Accumulated Ozone over Threshold of 40 ppb (AOT40). This index has been chosen for its simplicity and flexibility in handling different ecosystems as well as for its linear relationships with yield or biomass loss. However, AOT40 does not give any information on the physiological O3 uptake into the leaves since it does not include any environmental constraints to O3 uptake through stomata. Therefore, an index based on stomatal O3 uptake (i.e. PODY), which describes the amount of O3 entering into the leaves, would be more appropriate. Specifically, the PODY metric considers the effects of multiple climatic factors, vegetation characteristics and local and phenological inputs rather than the only atmospheric O3 concentration. For this reason, the use of PODY in the O3 risk assessment for vegetation is becoming recommended. We compare different potential O3 risk assessments based on two methodologies (i.e. AOT40 and stomatal O3 uptake) using a framework of mesoscale models that produces hourly meteorological and O3 data at high spatial resolution (12 km) over Europe for the time period 2000-2005. Results indicate a remarkable spatial and temporal inconsistency between the two indices, suggesting that a new definition of European legislative standard is needed in the near future. Besides, our risk assessment based on AOT40 shows a good consistency compared to both in-situ data and other model-based datasets. Conversely, risk assessment based on stomatal O3 uptake shows different spatial patterns compared to other model-based datasets. This strong inconsistency can be likely related to a different vegetation cover and its associated parameterizations.