Abstract
Tropospheric ozone (O3) is probably the air pollutant most damaging to vegetation. Understanding how plants respond to O3 pollution under different climate conditions is of central importance for predicting the interactions between climate change, ozone impact and vegetation. This work analyses the effect of O3 fluxes on net ecosystem productivity (NEP), measured directly at the ecosystem level with the eddy covariance (EC) technique. The relationship was explored with artificial neural networks (ANNs), which were used to model NEP using environmental and phenological variables as inputs in addition to stomatal O3 uptake in Spring and Summer, when O3 pollution is expected to be highest. A sensitivity analysis allowed us to isolate the effect of O3, visualize the shape of the O3-NEP functional relationship and explore how climatic variables affect NEP response to O3. This approach has been applied to eleven ecosystems covering a range of climatic areas. The analysis highlighted that O3 effects over NEP are highly non-linear and site-specific. A significant but small NEP reduction was found during Spring in a Scottish shrubland (-0.67%), in two Italian forests (up to -1.37%) and during Summer in a Californian orange orchard (-1.25%). Although the overall seasonal effect of O3 on NEP was not found to be negative for the other sites, with episodic O3 detrimental effect still identified. These episodes were correlated with meteorological variables showing that O3 damage depends on weather conditions. By identifying O3 damage under field conditions and the environmental factors influencing to that damage, this work provides an insight into O3 pollution, climate and weather conditions.
Highlights
Tropospheric ozone (O3) is a harmful air pollutant which affects human health (Ainsworth et al, 2012), damages vegetation, including natural ecosystems and crops (The Royal Society, 2008), and contributes to climate change, being a greenhouse gas with a radiative forcing of 0.35–0.37 W m−2 (Shindell et al, 2009)
The r2 values (Table 3) attested the data mining capability of the artificial neural networks (ANNs): the best performances were obtained for the northern sites, especially Grignon and Hyytiälä (0.93 and 0.94, respectively), while the lowest r2 values were from the Blodgett and Bosco Fontana sites (0.39 and 0.40, respectively)
This work demonstrates that ANN modeling is a useful tool to understand O3 – net ecosystem productivity (NEP) correlation considering other co-varying environmental factors. r2 values produced by ANN were found higher than r2 values produced by Multiple Linear Regression (MLR), indicating that a non-linear statistical data modeling approach as ANN is more appropriate in modeling complex relationships such the dependence of NEP from co-varying environmental factors
Summary
Tropospheric ozone (O3) is a harmful air pollutant which affects human health (Ainsworth et al, 2012), damages vegetation, including natural ecosystems and crops (The Royal Society, 2008), and contributes to climate change, being a greenhouse gas with a radiative forcing of 0.35–0.37 W m−2 (Shindell et al, 2009). It is a secondary pollutant, mainly produced through photochemical reactions of methane, carbon monoxide and volatile organic compounds in the presence of nitrogen oxides (Monks et al, 2015). The main detrimental effect is a reduction in carbon assimilation, which represents the first evidence of O3 impact over vegetation, before the occurrence of visible injuries
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