Agriculture is a vital component of human civilization, providing food, fiber, and fuel for bill... more  Agriculture is a vital component of human civilization, providing food, fiber, and fuel for billions of people worldwide. However, the agricultural sector has also been identified as a significant contributor to air pollution. This study aims to comprehensively investigate and analyze the impact of agrofarming activities on air pollution in a very productive area such as Northern Italy. It explores the various sources and mechanisms through which agriculture affects air quality, and the types of pollutants involved, and quantifies the consequences for human health, ecosystems, and the environment. Furthermore, adopting an integrated assessment modelling approach, it highlights the technologies that can mitigate these negative impacts and promote sustainable agriculture. The paper defines policy recommendations for the area at hand analysing the optimal compromise between air quality improvement and pollution abatement costs. It concludes with an outlook of additional options for addressing the air pollution challenges associated with agrofarming activities.Â
Abstract The problem of defining efficient and environmentally compatible short-term agricultural... more Abstract The problem of defining efficient and environmentally compatible short-term agricultural plans for biodiesel exploitation is dealt with in this paper with a multi-objective modelling framework. To optimally use local resources, the first phase of the plan consists in the analysis of land and climate features in order to evaluate which energy crop can be successfully grown. This phase is performed at local scale using GIS (geographic information system) data and software. The second phase consists in the formulation of a multi-objective mathematical programming problem. Using the land to be cultivated in each parcel with each crop as decision variables, we solve a three objectives problem: the maximization of the net energy produced, of the greenhouse gases avoided with respect to conventional fossil fuels and of the diversity of the energy crop mix. The last is quantitatively measured using a well-known biodiversity index, which allows to study the trade-off between a more varied crop mix and the other two objectives along the frontier of Pareto efficient solutions. The proposed methodology is applied to a region of Mato Grosso, Brazil, where biodiesel is produced from oleaginous crops.
Real-world time series often present missing values due to sensor malfunctions or human errors. T... more Real-world time series often present missing values due to sensor malfunctions or human errors. Traditionally, missing values are simply omitted or reconstructed through imputation or interpolation methods. Omitting missing values may cause temporal discontinuity. Reconstruction methods, on the other hand, alter in some way the original time series. In this paper, we consider an application in the field of meteorological variables that exploits end-to-end machine learning. The idea is to entrust the task of dealing with missing values to a suitably trained recurrent neural network that completely by-passes the phase of reconstruction of missing values. A difficult case of reproduction of a rainfall field from five rain gauges in Northern Italy is used as an example, and the results are compared to those computed by more traditional methods. The proposed methodology is general-purpose and can be easily applied to every kind of spatial time series prediction problem, quite common in many environmental studies.
 Agriculture is a vital component of human civilization, providing food, fiber, and fuel for bill... more  Agriculture is a vital component of human civilization, providing food, fiber, and fuel for billions of people worldwide. However, the agricultural sector has also been identified as a significant contributor to air pollution. This study aims to comprehensively investigate and analyze the impact of agrofarming activities on air pollution in a very productive area such as Northern Italy. It explores the various sources and mechanisms through which agriculture affects air quality, and the types of pollutants involved, and quantifies the consequences for human health, ecosystems, and the environment. Furthermore, adopting an integrated assessment modelling approach, it highlights the technologies that can mitigate these negative impacts and promote sustainable agriculture. The paper defines policy recommendations for the area at hand analysing the optimal compromise between air quality improvement and pollution abatement costs. It concludes with an outlook of additional options for addressing the air pollution challenges associated with agrofarming activities.Â
Abstract The problem of defining efficient and environmentally compatible short-term agricultural... more Abstract The problem of defining efficient and environmentally compatible short-term agricultural plans for biodiesel exploitation is dealt with in this paper with a multi-objective modelling framework. To optimally use local resources, the first phase of the plan consists in the analysis of land and climate features in order to evaluate which energy crop can be successfully grown. This phase is performed at local scale using GIS (geographic information system) data and software. The second phase consists in the formulation of a multi-objective mathematical programming problem. Using the land to be cultivated in each parcel with each crop as decision variables, we solve a three objectives problem: the maximization of the net energy produced, of the greenhouse gases avoided with respect to conventional fossil fuels and of the diversity of the energy crop mix. The last is quantitatively measured using a well-known biodiversity index, which allows to study the trade-off between a more varied crop mix and the other two objectives along the frontier of Pareto efficient solutions. The proposed methodology is applied to a region of Mato Grosso, Brazil, where biodiesel is produced from oleaginous crops.
Real-world time series often present missing values due to sensor malfunctions or human errors. T... more Real-world time series often present missing values due to sensor malfunctions or human errors. Traditionally, missing values are simply omitted or reconstructed through imputation or interpolation methods. Omitting missing values may cause temporal discontinuity. Reconstruction methods, on the other hand, alter in some way the original time series. In this paper, we consider an application in the field of meteorological variables that exploits end-to-end machine learning. The idea is to entrust the task of dealing with missing values to a suitably trained recurrent neural network that completely by-passes the phase of reconstruction of missing values. A difficult case of reproduction of a rainfall field from five rain gauges in Northern Italy is used as an example, and the results are compared to those computed by more traditional methods. The proposed methodology is general-purpose and can be easily applied to every kind of spatial time series prediction problem, quite common in many environmental studies.
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Papers by Giorgio Guariso