In this work, a data-driven forecasting system based on Convolutional Neural Network is formalized and applied. Convolutional Neural Networks allow the extraction of features from an image or a series of images. In this context, these models are applied to reproduce the concentration level of nitrogen dioxide in advance, starting from the emission maps of nitrogen oxides in the atmosphere. The system is applied to the Milan municipality (Italy), an area often affected by high levels of pollution, with very good performances in terms of both statistical analysis (errors and correlation) and threshold indexes (hit ratio and false alarm ratio). In the near future, the model will be included into a model predictive control system to manage air quality in the area.

In this work, a model predictive approach for climate change is presented. In particular, the problem ob-jective is to maintain temperature anomaly in the years 202S-2100 below a selected threshold, acting on the emissions of carbon dioxide (C02), methane (CH4) and greenhouse gases (GHG). The problem implements the constraints specified by to the last report of the IPCC (Intergovernmental Panel on Climate Change). Accordingly to these reports, three different thresholds have been tested, ranging from I.5oC to 2°C. An evaluation is performed to assess the impact of the different uncertainty sources on the problem results. The application of the methodology on the case study shows the capability of the control to find the solution of the problem only for threshold above I.5oC, as long as the control action will start very early in the next few years.

In this work, an optimization problem to control world temperature anomaly is presented. In particular, the problem is related to the computation of carbon dioxide (CO2), methane (CH4) and greenhouse gases (GHG) emission reduction for the period 2025–2100 in order to maintain temperature anomaly below a selected threshold. In accordance to the last IPCC (Intergovernmental Panel on Climate Change) reports and to the discussion of COP26 meeting (XXVIth COnference of the Parties, UNFCCC - United Nations Framework Convention on Climate Change), two different threshold have been tested, with value 2° and 1.5° respectively. The application of the methodology on the case study shows its capability for the solution of the problem. Nevertheless, while with a threshold of 2° the problem has a solution that respect all the constraints imposed by COP26 discussions, a lightening of the constraints is needed to ensure a temperature anomaly below 1.5° up to 2100.

This article proposes an integrated assessment methodology aimed at supporting decision-makers in design energy production scenarios to power a low emissions traffic fleet. The Multidimensional Air Quality (MAQ) system is used to define and solve a decision problem that selects a set of energy production scenarios minimizing costs, impacts on air quality, and greenhouse gases (GHGs) emissions. This study focuses on the road transport sector, that is responsible for 25% of European GHGs emissions and 39% of NOxemission, a precursor of both NO2and PM10concentrations. The electrification of the light vehicle fleet and the use of biomethane to power heavy vehicles are analyzed, estimating the electricity demand increase, exploring different energy production mixes, and assessing the impacts on air quality, costs, and GHGs according to the fuels/sources used to satisfy the energy demand. A case study over Lombardy region, in Northern Italy, is proposed.Note to Practitioners—The study designs a new decision problem implemented and solved through the Multidimensional Air Quality system (MAQ), an integrated assessment modeling tool. Such system integrates a set of databases, models, optimization, and enumeration algorithms. Composing these elements, specific multiobjective decision problems can be designed defining domain (mesoscale, regional, urban), objectives (air quality index, greenhouse gas emissions, costs, population exposure, health impacts), decision variables (technologies, behavioral measures, energy production, fuel switch), and constraints. MAQ system allows the comprehensive analysis of energy, technological, behavioral policies estimating impacts on air quality, human health, GHGs emissions, and costs.

Secondary pollution derives from complex non-linear reactions involving precursor emissions, namely VOC, NOx, NH3, primary PM and SO2. Due to difficulty to cope with this complexity, Decision Support Systems (DSSs) are key tools to support Environmental Authorities in planning cost-effective air quality policies that fulfill EU Directive 2008/50 requirements. The objective of this work is to formalize and compare the scenario analysis and the multi-objective optimization approach for air quality planning purposes. A case study of Northern Italy is presented.

A geometrical method to determine the vertical distribution of a trace gas in the lower troposphere is presented. The technique is based on quasi-simultaneous measurements of diffuse solar radiation with spectrometric equipment observing the atmosphere from different viewing directions and a measurement of a distant lamp. Differential optical absorption spectroscopy (DOAS) algorithms are applied to retrieve the slant column densities (SCDs) of the analyzed compound. A recursive procedure involving the set of observed SCDs, the geometry of measurement, and several offline radiative transfer simulations provides the vertical profile of the target trace gas. This technique is applied to nitrogen dioxide (NO2) SCDs, and the results are compared to the output of the modeling system Gas Aerosol Modeling Evaluation System (GAMES). The correlation coefficient between the DOAS and GAMES NO2concentration profiles is 0.7 and shows a nonnegligible variability as a function of altitude and sky conditions.