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Agriculture accounts for a large percentage of nitrous oxide (N2O) emissions, mainly due to the misapplication of nitrogen-based fertilizers, leading to an increase in the greenhouse gas (GHG) footprint. These emissions are of a direct nature, released straight into the atmosphere through nitrification and denitrification, or of an indirect nature, mainly through nitrate leaching, runoff, and N2O volatilization processes. N2O emissions are largely ascribed to the agricultural sector, which represents a threat to sustainability and food production, subsequent to the radical contribution to climate change. In this connection, it is crucial to unveil the relationship between synthetic N fertilizer global use and N2O emissions. To this end, we worked on a dataset drawn from a recent study, which estimates direct and indirect N2O emissions according to each country, by the Intergovernmental Panel on Climate Change (IPCC) guidelines. Machine learning tools are considered great explainable techniques when dealing with air quality problems. Hence, our work focuses on expectile regression (ER) based-approaches to predict N2O emissions based on N fertilizer use. In contrast to classical linear regression (LR), this method allows for heteroscedasticity and omits a parametric specification of the underlying distribution. ER provides a complete picture of the target variable’s distribution, especially when the tails are of interest, or in dealing with heavy-tailed distributions. In this work, we applied expectile regression and the kernel expectile regression estimator (KERE) to predict direct and indirect N2O emissions. The results outline both the flexibility and competitiveness of ER-based techniques in regard to the state-of-the-art regression approaches.

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This paper validates a sinusoidal approach for the proton-exchange membrane fuel cell (PEMFC) model as a supplement to experimental studies. An FC simulation or hardware emulation is necessary for prototype design, testing, and fault diagnosis to reduce the overall cost. For this objective, a sinusoidal model that is capable of accurately estimating the voltage behavior from the operating current value of the DC was developed. The model was tested using experimental data from the Ballard Nexa 1.2 kW fuel cell (FC). This methodology offers a promising approach for static and current-voltage, characteristic of the three regions of operation. A study was carried out to evaluate the effectiveness and superiority of the proposed FC Sinusoidal model compared with the Diffusive Global model and the Evolution Strategy.