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.

Arable soils significantly contribute to atmosphere pollution through N₂O emissions due to the massive use of N-based fertilizers and soil managements. N₂O formation in the soil occurs mainly through nitrification and denitrification processes, which are influenced by soil moisture, temperature, oxygen concentration, pH, and the amount of available organic carbon and nitrogen. This review synthetically presents the mechanisms of N₂O formation and emission in arable land and some of the current strategies to improve crop nutrient use efficiency. Biological nitrification inhibitor-based agronomic strategies are also presented as future prospects for the sustainable management of crops, which is missing in most of the reviews.

Background: The evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms: the linear regression (LR), regression tree (RT), support vector machine (SVM), ensembles of tree (ETs) and Gaussian process regression (GPR) to predict the EF at daily time step. The adopted methodology consisted of three main steps that include: (i) selection of the EF predictors; (ii) comparison of the different classes of ML; (iii) application, cross-validation of the selected ML algorithms and comparison with the observed data.Results: Our results indicate that SVM and GPR were the best classes of ML at predicting the EF, with a total of four different algorithms: cubic SVM, medium Gaussian SVM, the Matern 5/2 GPR, and the rational quadratic GPR. The comparison between observed and predicted EF in all four algorithms, during the training phase, were within the 95% confidence interval: theR²value between observed and predicted EF was 0.76 (RMSE 0.05) for the medium Gaussian SVM, 0.99 (RMSE 0.01) for the rational quadratic GPR, 0.94 (RMSE 0.02) for the Matern 5/2 GPR, and 0.83 (RMSE 0.05) for the cubic SVM algorithms. Similar results were obtained during the testing phase. The results of the cross-validation analysis indicate that theR²values obtained between all iterations for each of the four adopted ML algorithms were basically constant, confirming the ability of ML as a tool to predict EF.Conclusion: ML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable. The application to different geographical areas, or crops, requires further development of the model based on different data sources of soils, climate, and cropping systems.

Mangrove forests are among the most productive ecosystems in the world. These tropical and subtropical coastal forests provide a wide array of ecosystem services, including the ability to sequester...

Mangrove forests are among the most productive ecosystems in the world. These tropical and subtropical coastal forests provide a wide array of ecosystem services, including the ability to sequester and store large amounts of ‘blue carbon’. Given rising concerns over anthropogenic carbon dioxide (CO2) emissions, mangrove forests have been increasingly recognized for their potential in climate change mitigation programs. However, their productivity differs considerably across environments, making it difficult to estimate carbon sequestration potentials at regional scales. Additionally, most research has focused in humid and tropical latitudes, with limited studies in arid and semi-arid regions. A semi-arid mangrove forest in Magdalena Bay, Baja California Sur, Mexico was studied to quantify the average net ecosystem exchange (NEE), determine the annual carbon (C) budget and the environmental controls driving those fluxes. Measurements were taken during 2012-2013 using the eddy covariance technique, with a daily mean NEE of -2.25 ±0.4 g C m-2 d-1 and annual carbon uptake of 894 g C m-2 y-1. Daily variations in NEE were primarily regulated by light, but air temperature and vapor pressure deficit were strong seasonal drivers. Our research demonstrates that despite the harsh and arid climate, the mangroves of Magdalena Bay were nearly as productive as mangroves found in tropical and subtropical climates. These results broaden understanding of the ecosystem services of one of the largest mangrove ecosystems in the Baja California peninsula, and highlight the potential role of arid mangrove ecosystems for C accounting, management and mitigation plans for the region.

The constant growth of population living in urban areas creates new opportunities for urban forest to provides ecosystem services for human wellbeing such as, cooling effect, and carbon neutrality of cities. Nevertheless, experimental observation of carbon and energy exchange in urban forest have been so far fragmented, limited to short period of time, and never spatially distributed. While a considering amount of remote sensing and modelling studies indicates the potential cooling capacity and carbon uptake of urban forest, the impact of climatic extreme events on it is still unclear. Through multiple years of unique Eddy Covariance (EC) observations of a mature urban forest located in southern Europe we highlighted how carbon and water fluxes respond differently, almost as if uncoupled, with evaporative cooling maintained during the climatic drought and net carbon sequestration reversed. A long term EC observation, coupled with modeling simulations, highlight the role of urban forest as potential tool for climate and microclimate mitigation with and without drought limitations. Our results have important policy implications for urban forest management and planning and more generally for strategies, on urban forest, in relation to carbon neutrality and thermal comfort. While the urban forest had an annual net loss of CO2 to the atmosphere, its above- and below- ground biomass and the soil represent a relevant carbon reservoir, and its summer uptake of atmospheric CO2 enabled evaporative cooling of the microclimate. However, the impact of summer drought reduced the levels of cooling benefits compared to non-drought summers. Our results represents the first long term, and continuous experimental observation to demonstrate that the urban forest cooling capacity in warm seasons can decouple from net CO2 uptake and will be limited by the amount of water available, either from precipitation or irrigation sources.