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Particular Matter (PM) data are the most used for the assessment of air quality, but it is also useful to monitor VOC and CO. The health impact of PM increases with decreasing aerodynamic dimensions, therefore most of the monitoring is aimed at PM10 (fraction of PM with aerodynamic dimensions smaller than 10 µm) and PM2.5 (fraction with aerodynamic dimensions lower than 2.5 µm). Generally, anthropogenic emissions contribute mainly to PM2.5 levels, whereas natural sources can largely affect PM10 concentrations. PM2.5/PM10 ratio can be used as a proxy of the origin (anthropogenic vs natural) of the PM, providing a useful indication about the main sources of PM that characterizes a specific geographical or urban setting. This paper presents the results of the analysis of continuous measurements of PM10 and PM2.5 concentrations at eight stations of the regional air quality monitoring network in Abruzzo (Central Italy), in the period 2017–2018. The application of models based on machine learning technique shows that PM2.5/PM10 ratio can be used to classify PM emissions and to know the nature of the emission source (natural and anthropogenic), under determinate conditions, and properly taking into account the meteorological parameters. [ABSTRACT FROM AUTHOR]

Odor pollution is nowadays recognized as a serious environmental concern. Italy still lacks a national regulation about odors, but several regions issued specific guidelines and regulations regarding odor emissions management, which combine olfactometric measurements with dispersion modeling for assessing odor impacts and verifying compliance with acceptability criteria. However, in cases of variable or diffuse sources, this approach is sometimes hardly applicable, because odor emission rates can hardly be estimated. In such cases, electronic noses, or more generally, Instrumental Odor Monitoring Systems (IOMS), represent a suitable solution for direct odor measurement. Accordingly, IOMS are explicitly mentioned in the most recent regional regulations as advanced tools for odor impact assessment. In Italy, data from instrumental odor monitoring have started to have regulatory value; thus the need arises to have specific quality programs to ensure and verify the reliability of IOMS outcomes. This paper describes the monitoring by a commercial electronic nose (EOS507F) of odors from an area dedicated to tire storage, a diffuse source with variable emissions over time, for which dispersion modeling is not applicable. The paper proposes also a protocol for IOMS performance testing in the field, to provide experimental data to support technical groups working on standardization both on the national and European level. [ABSTRACT FROM AUTHOR]

This paper is devoted to the assessment of urban greening effects on two important ecosystem services, i.e., air quality and CO2 storage, including the corresponding economic impacts in a real urban area, i.e., a district located in the Mediterranean city of Lecce (southern Italy). Two tools were employed, i-Tree Canopy and the computational fluid dynamics (CFD) microclimate model ENVI-met. i-Tree Canopy allowed fully determining the land-cover percentage on the basis of different ground cover classes and obtaining an estimate of annual values of CO2 storage, air pollutant removal, and economic benefits in the presence of urban greening. The estimate in i-Tree Canopy considered only the amount of greening; therefore, air pollutant removal estimates were only potential. As the vegetation was located in street canyons, its interaction with local meteorology and urban geometry strictly affected the dispersion of nitrogen oxides (NOx) (taken here as an example) as obtained from ENVI-met simulations. In ENVI-met, both deposition/absorption and aerodynamic effects were considered, and local increases in concentration were found in the district. The analysis of results obtained from different tools (one complex (CFD model) and the other simple (i-Tree model)) showed the error associated with the simple model in the computation of impacts if the interaction among the vegetation characteristics, the meteorological conditions, and the urban geometry was neglected; however, it also uncovers a novel approach for comprehensively characterizing a given area in terms of its vegetation cover, CO2 storage, and economic benefits, as well as local effects on air quality. This study is set in a broader context aimed at assessing the air quality in urban canopies of Mediterranean areas characterized by the presence of narrow street canyons where pollutants can accumulate due to ineffective air exchange with the above atmosphere. [ABSTRACT FROM AUTHOR]

In this paper, an operational forecasting and daily assessment system of air quality is presented. This new system is thought of as a Copernicus-CAMS downstream national service, able to develop and implement a service for air quality forecasting and monitoring in the Italian domain, running every day on the National territory. The system is being developed on behalf of a cooperation between Agenzia Spaziale Italiana (ASI) and Sistema Nazionale Protezione Ambiente (SNPA). SNPA is the network between Istituto Superiore per la Protezione e Ricerca Ambientale (ISPRA) and the Regional Environmental Agencies (ARPAs). The objective of the cooperation is to provide full operation service in terms of continuity, sustainability, and availability of the air quality forecast and evaluation services at the national level. The system forecasts and analyzes air quality throughout Italy, with a focus on Italian regions, for the principal pollutants: Particulate matter with diameter smaller than 10 μm (PM10), ozone (O3), and nitrogen dioxide (NO2). It includes a Chemical Transport Model (CTM) nested with the Copernicus Atmosphere Monitoring Service (CAMS) global model and data from the air quality monitoring stations in Italy. The system, under public control and based on open software, is now under testing. To date, it is able to deliver free open data, which is available to environmental agencies and citizens. The data are delivered both as maps and graphs, and as numerical data, useful for providing boundary conditions to local–high resolution-air quality models or for developing customized services. In this work, a downscaling application to a regional nested domain highlights how the new air quality forecasting system gains better results than the Copernicus-CAMS system. [ABSTRACT FROM AUTHOR]

Due to some harmful effects on humans and the environment, particulate matter (PM) has recently become among the most studied atmospheric pollutants. Given the growing sensitivity to the problem and, since production and accumulation phenomena involving both primary and secondary P M 10 fractions are complex and non-linear, environmental authorities need tools to assess their plans designed to improve the air quality as requested from environmental laws. Multi-criteria decision analysis (MCDA) can be applied to support decision makers, by processing quantitative opinions provided by pools of experts, especially when different views on social aspects should be considered. The results obtained through this approach, however, can be highly dependent on the subjectivity of experts. To partially overcome these challenges, this paper suggests a two-step methodology in which an MCDA is fed with the solution of a multi-objective analysis (MOA). The methodology has been applied to a test case in northern Italy and the results show that this approach is a viable solution for the inclusion of subjective criteria in decision making, while reducing the impact of uncertain expert opinions for data that can be computed through the MOA. [ABSTRACT FROM AUTHOR]