In air-quality planning, Artificial Neural Networks (ANNs) have emerged as surrogate models capable of capturing complex nonlinear relationships between emissions and pollutant concentrations with reduced computational time required by chemical and transport models. However, the extent to which surrogate model design influences policy optimization outcomes remains poorly investigated. This study analyzes how alternative ANN architectures affect both predictive performance and the identification of cost-effective air-quality policies at the regional scale. A set of ANN architectures differing in their spatial aggregation and weighting strategies is identified and applied within an Integrated Assessment Model aiming at designing efficient Air Quality policies for the Po Valley basin. The ANNs are trained by processing a set of scenarios simulated by a deterministic chemical-transport model and validated against a real scenario. Such ANNs have been implemented within a multi-objective optimization framework to identify cost-effective emission-reduction policies. The results show differences in ANN performance across different configurations. The largest difference appears between the single basin ANN, which achieves a relative mean absolute error of 6.4%, and regionalized ANNs, whose errors range from 2.4–3.3%. This difference propagates in the optimization results. At low implementation costs (50 M€–150 M€ per year), air-quality outcomes are comparable across architectures, with policies targeting three key macrosectors: domestic heating, transport, and agriculture, though with different resource allocation. At higher cost levels, regionalized architectures outperform the basin-wide model, highlighting how surrogate model design can influence both the fidelity of the simulation and the structure of optimal policy portfolios.

Climate mitigation strategies increasingly prioritize interventions that deliver simultaneous benefits for greenhouse-gas reduction, air quality and public health. This study quantifies the co-benefits of scaling biomethane deployment in the Po Valley (Northern Italy), a European air pollution hotspot, comparing two policy-relevant supply levels (2.3 and 6 bcm). An integrated pathway is assessed where biomethane is used for electricity generation to support partial electrification of the vehicle fleet, while digestate, the solid and liquid residue of the anaerobic digestion process, is utilized in agriculture with improved manure management protocols. A bottom-up integrated assessment framework links energy, traffic and agricultural measures to emission changes, ambient concentrations of NO2 and PM2.5, population exposure, and attributable mortality, and provides the analysis of uncertainty propagated across the modelling chain. Both deployment levels reduce CO2 emissions and lower population exposure to NO2 and PM2.5, yielding measurable public health benefits. Direct comparison of 2.3 and 6 bcm scenarios estimates the marginal co-benefits of scaling biomethane deployment and highlights how coordinated actions across the energy, transport, and agricultural sectors can enhance health-relevant decarbonization in highly polluted regions. Scalability indicators, namely ΔCO2, ΔNO2, and ΔPM2.5 per bcm of biomethane, and the incremental benefits of scaling from 2.3 to 6 bcm are estimated, providing a basis for comparing alternative decarbonization pathways in other settings.

This study addresses a multi-pollutant, multi-objective optimization problem to identify efficient air quality control strategies for the cities of the Po Valley, Northern Italy. The objective is to minimize PM2.5 and NO2 concentrations while considering the feasibility of achieving the new European Directive, reducing health impacts. The evaluation is conducted for 2030, assessing air quality projections under current legislation and additional emission reduction measures (including end-of-pipe, energy, and fuel-switch). Results show that current legislation ensures compliance with NO2 standards, but PM2.5 remains above the threshold in several cities due to secondary formation. Additional measures reduce PM2.5-related deaths by 29% and NO2-related mortality drops virtually to zero. The co-benefits of additional air quality policies on greenhouse gas emissions reduction are estimated at 17% of the current legislation in 2030. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license https://creativecommons.org/licenses/by-nc-nd/4.0/)

Electrification of the vehicle fleet is a key strategy for decarbonizing transportation. However, electric vehicles (EVs) typically have higher weight than traditional vehicles, resulting in increased energy consumption, diminished driving range, and raised non-exhaust emissions. This study presents a modelling framework to assess the benefits of EV fleet lightweighting on driving range, greenhouse gas emissions, and air quality. The framework integrates a longitudinal vehicle dynamics simulation tool (TEST) and an integrated assessment model (MAQ). TEST determines the energy consumption, the traditional hydraulic braking system energy, and the maximum driving range as a function of the vehicle's weight. MAQ estimates the reduction of greenhouse gases (including carbon dioxide) and particulate matter emissions, and the impact on PM2.5 and NO2 concentrations of different scenarios defined by assuming a fleet with 10 % and 25 % of electric vehicles. The study focuses on the Po Valley, a vast area in northern Italy characterized by some of the highest levels of air pollution in Europe. Results show that increasing the fleet electrification from 10 % to 25 % without altering the average vehicle's mass reduces the NO2 levels in urban areas by up to 9.3 %, but increases non-exhaust emissions, with possible local negative impact on PM2.5 levels if the energy production is supplied by natural gas. When combined with vehicle lightweighting, electrification yields consistent benefits, including improved driving range, PM2.5 concentration reduction up to 3 % in major and most populated cities, and CO2 emissions decrease by up to 3 %. The framework provides a methodology for evaluating the environmental trade-offs of EV diffusion at the regional scale and the role of EV weight for maximizing the co-benefits on the environment.

This paper addresses a multi-pollutant decision problem to identify effective strategies to improve air quality in densely inhabited and industrialized areas. The problem is formulated as a two-objective one: minimize an air quality indicator and the costs of emission-reduction policies. A comprehensive air quality indicator is defined to include both PM2.5 and NO2 yearly average concentrations. Two different assumptions are used to determine the feasible reduction measures: the sole application of end-of-pipe measures or the combination of energy, fuel switching, and end-of-pipe measures. The case study is the Po Valley in Northern Italy, characterized by one of the worst air quality in Europe. Results show that implementing combined measures results in a 22 % decrease of PM2.5 and 46 % of NO2 mean concentrations. The adopted policies concern energy measures in the transport sector and the application of end-of-pipe measures in domestic heating, agriculture, and paint applications.

Olfactory receptor neurons are in direct contact with the ambient air, making olfactory function particularly susceptible to airborne pollutants. This study investigates the relationship between air pollution and olfactory function among adolescents and young adults residing in the province of Brescia, Italy. It included 169 participants (53.3% female, 15-25 years) of the ongoing longitudinal Public Health Impact of Metals Exposure (PHIME) study. Participants completed assessments of olfactory function using a short version of the "Sniffin' Sticks" identification test at baseline (2008-2014) and the extended test at follow-up (2017-2021). Annual average concentrations of particulate matter (PM10, PM2.5) and nitrogen dioxide (NO2) were estimated on a 4 x 4 km2 spatial resolution grid using the Regional Chemical Transport Model ARIA for the target area between 2016 and 2019 and averaged over time. We applied multivariable linear regression, Bayesian Kernel Machine Regression and Weighted Quantile Sum (WQS) regression to investigate the associations between air pollutants and olfactory function. A significant negative association was observed between the air pollutants (PM2.5: mean 18.5 mu g/m3 Standard Deviation +5.2 mu g/m3; PMCOARSE: 2.7 mu g/m3 +1.2 mu g/m3; NO2: 32.3 mu g/m3 +10.1 mu g/m3), treated as a mixture, and the olfactory functioning measured with the Sniffin' total score (mu -1.44, 95%CI -2.42, -0.34), and the Sniffin' threshold score (mu -1.48, 95%CI -2.91, -0.6) when applying WQS regression. This association was mainly driven by NO2 and PMCOARSE. Findings suggest that air pollution exposure to NO2 and PMCOARSE, can reduce olfactory function among adolescents and young adults residing in a polluted area in northern Italy.

This study demonstrates the applicability of air sampling for the detection of SARS-CoV-2 in a hospital by means of active bioaerosol samplers following a specifically designed air sampling strategy based on digital mapping of the architectural layout of the ward to minimize disruptions of health care activities and reducing operator risks. Prior to the experimental study, some model tests were conducted using the air sampler with a tunable flow rate to determine the most suitable real time polymerase chain reaction (RT-PCR) based detection method. Preliminary results showed the need to perform intensive extraction protocols combined with Real-time reverse transcription PCR (rRT-PCR), rather than conventional, to enhance sensitivity. The experimental study was conducted within the general medicine ward of Spedali Civili Hospital in Brescia during the winter of 2021/2022, a period marked by a high prevalence of COVID-19 cases using three active air sampling devices: Coriolis Compact (R), Coriolis Micro (R), and BioSpot GEM (R). Environmental parameters, such as room size, occupancy, ventilation rates, and activities per-formed during sampling, and patients' conditions were documented to contextualize the findings. The virus was detected in a few rooms with concentrations ranging from 1171 to 2225 copies/m3. These findings support the integration of routinary air sampling as tools for control and assess-ment of transmission risks, not only for SARS-CoV-2 but generalized to all airborne pathogens, supporting patient management and infection control in health care settings.

Fine particulate matter (PM2.5) is a major contributor to air pollution-related mortality in Europe. The European Zero Pollution Action Plan (ZPAP) aims to reduce PM2.5-attributable deaths by 55% by 2030 relative to 2005, while the EU Directive 2024/2881 states the new Ambient Air Quality targets. The open questions are: (1) has the ZPAP and the EU Directive 2024/2881 target for PM2.5 already been reached? If not, (2) will the current policy allow the ZPAP and EU 2024/2881 target for PM2.5 to be met in 2030, or (3) is an additional policy needed? This work proposes a methodology to estimate the attributable deaths caused by PM2.5 exposure in 2005, 2019 (question 1), and 2030 projection, considering the current legislation (CLE2030, question 2) and a policy (OPT2030, question 3), solution of a multi-objective problem that minimizes PM2.5 concentration and measure costs. The methodology was tested on the Po Valley in Northern Italy, one of the most polluted areas in Europe. Results show that premature deaths decreased by 36% on average from 2005 to 2019. By 2030, under current legislation (CLE2030), 18 of the 29 major cities are projected to meet the ZPAP target, while only 2 are expected to comply with the PM2.5 target of the Directive (EU) 2024/2881. With the implementation of additional measures (OPT2030), the number of cities meeting the ZPAP target rises to 26, and 13 are projected to achieve the PM2.5 limit recommended by the Directive (EU) 2024/2881.

This research examined the effects of various GHG reduction policies on climate change via optimization techniques using a top-down approach. The aim was to examine how different aspects of policies to reduce CO2 and CH4 emissions would affect changes in temperature compared to preindustrial levels from 2025 to 2100. The proposed top-down approach allows for the investigation of several factors that may influence the results: (i) the objective function, (ii) the reduction pathway, and (iii) the starting point of the optimization. Two different objective functions were minimized: the overall sum of the temperature between 2025-2100 and the value at 2100. The results were also compared in terms of the reduction trajectories: two different emission trends were assumed: a gradual (gaussian) fall in emissions or a fast (exponential) decline, starting in 2025, in 2030, and in 2035. The mitigation of greenhouse gas (GHG) emissions was limited to a certain range of scenarios outlined by the Intergovernmental Panel on Climate Change (IPCC). These scenarios were determined by analyzing economic, social, and technical developments expected to occur in the next few decades. The analysis also included the interaction in global warming of air pollutant emission variations due to climate policies. The results revealed that exponential trajectories, depending on the initial year, can facilitate the stabilization of global temperatures below 1.5 degrees C. In contrast, gaussian trajectories were more likely to overtake this threshold if implementation is delayed beyond 2025. (c) 2025 Published by Elsevier Ltd.

This work explores methods to address climate change by applying optimization techniques in a top-down approach A decision model is proposed to minimize temperature anomalies compared to pre-industrial levels between 2025 and 2100 by varying greenhouse gases (GHG), namely CO2 and CH4. Two objective functions are minimized The first one considers the overall sum of the temperature anomalies by 2100, while the latter minimizes the temperature anomaly at the end of the century. Two different emission trends are assumed: a gradual (gaussian) fall in emissions or a fast (exponential) decline. The reduction of GHG emissions is constrained to a set of IPCC scenarios identified by assessing economic, social, and technological trends in the next decades. The uncertainty analysis of the decision problem solutions suggests that temperature anomalies can be limited to the range of 0.8-2 degrees C. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

This paper examines the use of integrated assessment modelling to select policies aimed at reducing air pollution in response to growing concerns about the deterioration of air quality, especially in highly populated and industrialized areas. The study focuses on the Po Valley region in northern Italy, renowned for its intricate environmental difficulties arising from industrial operations, extensive farming, and topographic characteristics. The goal of the multi-objective decision problem is to find the best plans for reducing emissions that will minimize the average annual concentrations of PM2.5 while also considering the costs of putting these plans into action and incorporating end-of-pipe, energy, and fuel switch measures. Results highlight the trade-offs between air quality improvement and associated costs, presenting a Pareto curve of optimal solutions. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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 investigates and analyses the impact of agrofarming activities on air pollution in very productive areas such as Northern Italy.It explores the various sources and mechanisms through which agriculture affects air quality compared to all the other emission sectors and the types of pollutants involved, and quantifies the consequences for human health of agricultural emissions.As a further and novel step, it highlights the technologies that can mitigate these negative impacts and promote sustainable agriculture by adopting an integrated assessment modeling approach.This study defines policy recommendations for the area at hand, determining the optimal compromises between air quality improvement and pollution abatement costs.For instance, it shows that it is possible to reduce the average PM2.5 concentration by 17% with an annual expenditure of 300 M€.Four percent of this improvement is due to end-of-pipe abatement measures in the agricultural sector.Such an improvement in air quality would translate into a reduction of tens of thousands of years of life lost by the resident population.This study concludes with an outlook of additional options for addressing the air pollution challenges associated with agro-farming activities that constitute a limit of the current study, but could open new research lines.

Climate Change and Air Quality are the most crucial environmental challenges for population health and our societies. Decision makers at different scales (European, national, and regional) define low carbon and air quality plans to reduce GHG (CO2, CH4, N2O) and air pollution precursors (NOx, NMVOC, NH3, SOx, primary PM2.5) emissions. Integrated Assessment Modelling is a methodology that can support decision makers. In this paper, we formalize a decision problem based on a multi-objective approach. The solution to the problem is the efficient low carbon and air quality emission reduction measure set for the Lombardy region, one of the most polluted areas in Europe, assuming the current energy legislation in 2030.

An integrated modelling approach is used in this work to assess the differences in defining air quality policies in spatial domains of different extensions. The tools used, SHERPA and RIAT+, are public domain and allow to rapidly define the emission scenario of the European area under examination and to solve a multi-objective problem to trade-off air quality improvement versus the costs of implementing the pollutant abatement measures. The territory considered is Northern Italy and the pollutant analysed in PM2.5, which is largely of secondary origin. The study demonstrates the importance of a proper definition of the administrative and physical boundaries of the air pollution problem, which may determine higher costs when the correct scale of decisions is missed.

COVID-19 (Coronavirus disease 2019) hit Europe in January 2020. By March, Europe was the active centre of the pandemic. As a result, widespread "lockdown" measures were enforced across the various European countries, even if to a different extent. Such actions caused a dramatic reduction, especially in road traffic. This event can be considered the most significant experiment ever conducted in Europe to assess the impact of a massive switch-off of atmospheric pollutant sources. In this study, we focus on in situ concentration data of the main atmospheric pollutants measured in twelve European cities, characterized by different climatology, emission sources, and strengths. We propose a methodology for the fair comparison of the impact of lockdown measures considering the non-stationarity of meteorological conditions and emissions, which are progressively declining due to the adoption of stricter air quality measures. The analysis of these unmatched circumstances allowed us to estimate the impact of a nearly zero-emission urban transport scenario on air quality in 12 European cities. The clearest result, common to all the cities, is that a dramatic traffic reduction effectively reduces NO2 concentrations. In contrast, each city's PM and ozone concentrations can respond differently to the same type of emission reduction measure. From the policy point of view, these findings suggest that measures targeting urban traffic alone may not be the only effective option for improving air quality in cities.

Two alternative air quality policies are compared: one is the application of only mandatory abatement measures from 2020 to 2030. The second is the definition of a more active and locally-based policy that will lead to a better air quality at the end of the decade. Using an integrated modelling system, we demonstrate that the active policy is quite more convenient from the economic viewpoint, at least for the specific situation of the Lombardy region, considered in the study. Improving particulate matter concentrations may however produce worse ozone values. A full view of all pollutants is thus necessary when planning for air quality at regional level.

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.

SARS-CoV-2 virus (COVID-19) pandemic has impacted several countries, with also some differences at local levels. When lockdown restrictions were imposed, the concentrations of some air pollutants were reduced, as reported in some other cities in the world. This was often considered a positive by-product of the pandemic. However, often literature reporting the connection of air quality (AQ) and lockdown, suffers of limited and incomplete data analysis, not considering, for example, some confounding factors. This work presents a methodology, and the results of its application, to assess the impact of pandemic restrictions on AQ (in particular nitrogen oxides, NO2 and particulate matter, PM10) in spring 2020 in Brescia, located in one of the most affected areas in terms of virus diffusion and in one of the most polluted areas in Europe (Po Valley, Italy). In particular, the proposed methodology integrates data and AQ modelling simulations to distinguish between the changes in the PM10 and NO2 pollutants concentration that occurred due to the restriction measures and due to other factors, like spatial-temporal characteristics (for example the seasonality), meteorological factors, and governmental actions that were introduced in the past to improve the air quality. Results show that NO2 is strongly dependent to traffic emission. On the contrary, although the expected decrease in PM10 concentrations, the results highlight that the reduction of transport emission would not help to avoid severe air pollution, due to the other pollution sources that contribute to its origin. The results presented for the first time in this work are of particular interest because they may be used as a basis to investigate in more details the sources that can impact on the air quality in Brescia, with the aim to propose effective measures able to reduce it.