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 NO _x emission, a precursor of both NO ₂ and PM ₁₀ concentrations. 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.

Air quality plans must be demonstrated to be economically sustainable and environmentally effective. This paper presents a full cost–benefit and environmental analysis of a large regional air quality plan involving several different actions covering a large spectrum of fields, from domestic heating to passenger and freight transport, from electricity generation to agriculture. The impact of each action is analyzed looking at the possible energy savings, greenhouse gases (GHG) emission reductions, the improvement in air quality, and the consequent decrease in external costs, namely the reduced impact on population health. The analysis is performed by applying a flexible and fast computer tool (RIAT+) that allows for a rapid simulation of different pollutant emission scenario, to assess different air quality indices (AQIs) over a regional scale domain. The results show that, in most cases, the economic savings exceed the implementation costs and thus that these actions can be introduced in air quality plans for the domain under study. The reduced health and climate costs, though relevant in absolute terms, are, in general, only a fraction of the economic benefits of energy savings. This is not true for the measures acting on improvements in electricity generation, since a reduction in power plant emissions (generally with high stacks, far from populated areas) does not significantly impact the air quality inside the region. A shift in energy production to renewable sources can instead provide noticeable effects on GHG emissions. This research raises some interesting and general questions about the adequacy of the methodologies applied to attribute costs (and benefits) to actions, improving a variety of sectors that are different from the one in which the measures are applied here.

In recent years, the application of machine learning for virtual sensing has revolutionized the monitoring and management of information. In particular, electrochemical sensors generate large amounts of data, allowing the application of complex machine learning/AI models able to (1) reproduce the measured data and (2) predict and manage faults in the measuring sensor. In this work, data-driven models based on an autoregressive model and an artificial neural network have been identified and used to (i) evaluate sensor redundancy and (ii) predict and manage faults in the context of electrochemical sensors for the measurement of ethanol. The approach shows encouraging results in terms of both performance and sensitivity analyses, allowing for the reconstruction of the values measured by two sensors in a series of six sensors with different dopant levels and to reproduce their values after a fault.

In this article, the problem of pollutant monitoring sensor allocation is formalized as a variant of the well-known set covering problem and solved by means of both a mixed-integer linear programming solver and genetic algorithms. The allocation of the sensors in the monitoring network is one of the most practical and challenging problems that regional authorities have to face, in particular due to the constraints that European Union Directives pose in terms of the number of sensors per area/subarea and in terms of representativeness capability (i.e., the ability that the data coming from the monitoring network have to represent the air quality levels in their surrounding areas). The methodology has been applied to the allocation of sensors for particulate matter (PM10) and nitrogen dioxides (NO $_2$ ) in Lombardy, Italy. The results are promising, showing that the two proposed approaches are able to strongly dominate the current solution.

The Earth’s climate is undergoing unprecedented and rapid changes primarily due to human activities, especially greenhouse gas emissions, leading to a temperature rise of 1.1°C to 1.2°C in the last fifty years. In spite of global efforts like the 2015 Paris Agreement which aims to limit the increase in global temperature to 1.5°C above pre-industrial levels, projections indicate that this threshold will likely be exceeded, causing severe climatic disruptions, that will affect vulnerable regions and populations, altering weather patterns and compromising food security, water resources, and economic stability. This study integrates a data-driven downscaling system within an emission control framework aimed at controlling temperature anomalies by reducing CO2 emissions and evaluating its impact on cereal production in North Africa. The approach employs a simplified autoregressive model with exogenous input (ARX) for downscaling. During the validation phase, the ARX models show strong agreement with data, effectively reproducing global temperatures (correlation close to 1 and a normalized mean absolute error around 0.02) and local temperature (correlation of 0.75 and a normalized mean absolute error of 0.2). Moreover, an ARX model is also used to establish links between cereal production, global and regional temperature anomalies and CO2 emissions. This model demonstrates reasonably good agreement with the data, achieving a correlation of 0.72 and a normalized mean absolute error of 0.18. Once validated, the models are integrated into a decision support system based on model predictive control aimed to balance global and regional climate objectives with economic considerations. North Africa is an ideal case study since it is highly susceptible to the impacts of climate change, despite contributing only 1.76% of global carbon emissions in 2022. The region faces significant risks from increased aridity and heat, threatening agriculture, ecosystems, and socio-economic stability. The results of this research highlight the urgent need for environmental and climate-resilient measures to achieve sustainable development and enhance resilience against climate impacts.