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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]

Estimates of light extinction and visibility are routinely performed by the U.S. Interagency Monitoring of Protected Visual Environments (IMPROVE) network using a simple algorithm which assesses light extinction coefficient (b ext ) at remote and rural sites from concentrations of major particulate matter (PM) species, NO 2 , and Rayleigh scattering from clear-air gaseous components. Following the same approach, in this paper an equation with tailored (i.e. site-specific) coefficients was implemented with the aim of reducing uncertainties and assumptions of the IMPROVE algorithm for applications at polluted urban sites. Major differences compared to IMPROVE algorithm are: 1) dry mass extinction efficiencies calculated using a discrete dipole approximation code with aerosol size distributions measured at our monitoring site as input data; 2) site-specific water growth functions computed separately for ammonium sulfate, ammonium nitrate, and organic matter; 3) fine soil evaluated using an equation previously adopted at our urban site; 4) aerosol absorption component assessed directly through filter-based optical measurements. Details about the calculations performed are reported in the paper and the comparison with the IMPROVE revised algorithm is discussed. The tailored approach here proposed to estimate reconstructed light extinction was applied to PM 2.5 test samples collected on purpose in Milan (Italy), where heavy pollution episodes occur during winter periods. In addition, visual range was calculated applying the Koschmieder equation and compared to visibility measured at the nearby Milano-Linate airport obtaining a fairly good correlation. [ABSTRACT FROM AUTHOR]

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

Background: Recent data support the hypothesis that genetic damage occurring early in life during childhood can play an important role in the development of chronic diseases in adulthood, including cancer.Objectives: The objective of this paper, part of the MAPEC_LIFE project, is to describe the frequency of micronuclei and meta-nuclear alterations in exfoliated buccal cells of 6-8year-old Italian children recruited in five Italian towns (i.e., Brescia, Torino, Pisa, Perugia and Lecce) with different air pollution levels.Methods: About 200 children per town were recruited from primary schools. Biological samples were collected twice from the same children, in two different seasons (winter 2014-15 and late spring 2015). Cytogenetic damage was evaluated by the buccal micronucleus cytome assay.Results: Overall,n = 1046 children represent the final cohort of the MAPEC_LIFE study. On the whole, the results showed a higher mean MN frequency in winter (0.42 ± 0.54‰) than late-spring (0.22 ± 0.34‰). MN frequency observed among the five Italian towns showed a trend that follows broadly the levels of air pollution in Italy: the highest MN frequency was observed in Brescia during both seasons, the lowest in Lecce (winter) and Perugia (late-spring).Conclusions: To the best of our knowledge, the number of recruited children included in the analysis (n = 1046) is the highest compared to previous studies evaluating the frequency of MN in exfoliated buccal cells so far. MN frequency was associated with winter season and living in towns at various levels of air pollution, suggesting an important role of this exposure in determining early cytogenetic effects. [ABSTRACT FROM AUTHOR]

Biological monitoring by means of lichens as accumulators of trace elements is a very suitable tool to assess and monitor air pollution and it has been adopted in several surveys in Italy in the last 30 years. In this paper, we try to make a critical analysis of this topic in order to understand the state of research and applications in this field. For this purpose, a database of the main field studies carried out in the last 30 years in Italy was prepared. The paper reports a survey of the characteristics of these studies. The aim is to take stock of the situation at present and to give a boost to the scientific community’s efforts to standardize the method at national and international level. [ABSTRACT FROM PUBLISHER]

Background: Epidemiological evidences support the existence of an effect of airborne particulate on population health. However, few studies evaluated the robustness of the results to different exposure assessment approaches. In this paper, we estimated short term effects and impacts of high levels of particulate matter with aerodynamic diameter ≤10 μm (PM10) and ≤2.5 μm (PM2.5) in the Emilia-Romagna region (Northern Italy), one of the most polluted areas in Europe, in the period 2006-2010, and checked if the results changed when different exposure definitions were used.Methods: Short-term impact of particles on population mortality was assessed, both considering the 9 provincial capitals of the Emilia-Romagna and the region as a whole. We estimated the effects of PM10 and PM2.5 on natural mortality by combining city-specific results in a Bayesian random-effects meta-analysis, and we used these estimates to calculate impacts in terms of attributable deaths. For PM10, we considered different definitions of exposure, based on the use of the air pollutant levels measured by different monitoring stations (background or traffic monitors) or predicted by a dispersion model.Results: Annual average concentrations of PM10 and PM2.5 exceeding the WHO limits of 20 and 10 μg/m3 were respectively responsible for 5.9 and 3.0 deaths per 100 000 inhabitants per year in the provincial capitals, during the period 2006-2010. The total impact in the region in 2010 amounted to 4.4 and 2.8 deaths per 100 000 for PM10 and PM2.5, respectively. The impact estimates for PM10 did not substantially change when the exposure levels were derived from background or traffic monitoring stations, or arose from the dispersion model, in particular when the counterfactual value of 20 μg/m3 was considered. The effect estimates appeared more sensitive to the exposure definition.Conclusions: A reduction in particle concentrations could have produced significant health benefits in the region. This general conclusion did not change when different exposure definitions were used, provided that the same exposure assessment approach was used for both effect and impact estimations. Caution is therefore recommended when using effect estimates from the literature to assess health impacts of air pollution in actual contexts. [ABSTRACT FROM AUTHOR]

Background: The opportunity to assess short term impact of air pollution relies on the causal interpretation of the exposure-response association. However, up to now few studies explicitly faced this issue within a causal inference framework. In this paper, we reformulated the problem of assessing the short term impact of air pollution on health using the potential outcome approach to causal inference. We considered the impact of high daily levels of particulate matter ≤10 μm in diameter (PM10) on mortality within two days from the exposure in the metropolitan area of Milan (Italy), during the period 2003-2006. Our research focus was the causal impact of a hypothetical intervention setting daily air pollution levels under a pre-fixed threshold.Methods: We applied a matching procedure based on propensity score to estimate the total number of attributable deaths (AD) during the study period. After defining the number of attributable deaths in terms of difference between potential outcomes, we used the estimated propensity score to match each high exposure day, namely each day with a level of exposure higher than 40 μg/m3, with a day with similar background characteristics but a level of exposure lower than 40 μg/m3. Then, we estimated the impact by comparing mortality between matched days.Results: During the study period daily exposures larger than 40 μg/m3 were responsible for 1079 deaths (90% CI: 116; 2042). The impact was more evident among the elderly than in the younger age classes. Exposures ≥ 40 μg/m3 were responsible, among the elderly, for 1102 deaths (90% CI: 388, 1816), of which 797 from cardiovascular causes and 243 from respiratory causes. Clear evidence of an impact on respiratory mortality was found also in the age class 65-74, with 87 AD (90% CI: 11, 163).Conclusions: The propensity score matching turned out to be an appealing method to assess historical impacts in this field, which guarantees that the estimated total number of AD can be derived directly as sum of either age-specific or cause-specific AD, unlike the standard model-based procedure. For this reason, it is a promising approach to perform surveillance focusing on very specific causes of death or diseases, or on susceptible subpopulations. Finally, the propensity score matching is free from issues concerning the exposure-confounders-mortality modeling and does not involve extrapolation. On the one hand this enhances the internal validity of our results; on the other, it makes the approach scarcely appropriate for estimating future impacts. [ABSTRACT FROM AUTHOR]

In recent years several European air pollution policies have been based on a cost-effectiveness approach. In the European Union, the European Commission starts using the multi-pollutant, multi-effect GAINS (Greenhouse Gas Air Pollution Interactions and Synergies) model to identify cost-effective National Emission Ceilings and specific emission control measures for each Member State to reach these targets. In this paper, we apply the GAINS methodology to the case of Italy with 20 subnational regions. We present regional results for different approaches to environmental target setting for PM2.5 pollution in the year 2030. We have obtained these results using optimization techniques consistent with those of GAINS-Europe, but at a higher resolution. Our results show that an overall health-impact oriented approach is more cost-effective than setting a nation-wide limit value on ambient air quality, such as the one set for the year 2030 by the European Directive on ambient air quality and cleaner air for Europe. The health-impact oriented approach implies additional emission control costs of 153 million €/yr on top of the baseline costs, compared to 322 million €/yr for attaining the nation-wide air quality limit. We provide insights into the distribution of costs and benefits for regions within Italy and identify the main beneficiaries of a health-impact approach over a limit-value approach. [ABSTRACT FROM AUTHOR]

Northern Italy has one of the highest air pollution levels in the European Union. This paper describes a mobile wireless sensor network system intended to complement the already existing official air quality monitoring systems of the metropolitan town of Torino. The system is characterized by a high portability and low cost, in both acquisition and maintenance. The high portability of the system aims to improve the spatial distribution and resolution of the measurements from the official static monitoring stations. Commercial PM10 and O3 sensors were incorporated into the system and were subsequently tested in a controlled environment and in the field. The test in the field, performed in collaboration with the local environmental agency, revealed that the sensors can provide accurate data if properly calibrated and maintained. Further tests were carried out by mounting the system on bicycles in order to increase their mobility. [ABSTRACT FROM AUTHOR]

This paper provides an empirical analysis on the relationship between income, demographic characteristics and concentrations of air industrial pollutants within the Italian provinces. Two general conclusions can be drawn from the empirical results. First, the estimates obtained are consistent with an inverse U-shaped environmental Kuznets curve: air pollution releases increase with income up to a turning point where the relation reverts. Second, there is evidence that air releases tend to be higher in provinces with high concentration of females as households' head and with high concentration of children. Since our findings do not point to environmental discrimination on the basis of ethnicity, this suggests that environmental justice issues in Italy are not likely to manifest themselves along racial and ethnic terms but instead in terms of social categories and gender composition. We also find that the proxy variables employed to measure the efficiency or inefficiency of law enforcement are associated with higher levels of pollution. In terms of policy implications, this result suggests the need to strengthen, all through the country, the local enforcement of environmental laws in order to possibly reduce the negative effects on ambient air pollution. [ABSTRACT FROM AUTHOR]

Abstract: This paper analyses the efficiency of 33 Italian airports for the period 2005–2008. In addition to conventional outputs, differently from previous contributions, two environmental externalities are considered: noise and local air pollution. We perform a two-stage analysis. First, we implement a directional distance function (DDF) model and get airports’ efficiency scores. Then, we study the factors affecting efficiency using a bootstrapping procedure. We find that the fleet mix significantly affects technical/environmental efficiency. Moreover, we provide evidence that public airports have higher efficiency scores. Last, LCCs have no effect on technical/environmental efficiency. [Copyright &y& Elsevier]

Abstract: Very high levels of ambient particulate matter (PM) are frequently encountered in the north of Italy and air quality limits are regularly exceeded. To obtain quantitative information on the pollution sources and to gain understanding of the dynamics of pollution episodes in this populated area PM10 and/or PM2.5 samples were collected daily at nine urban to regional sites distributed over the central Po Plain and one site in the Valtelline Valley. In total, 23 five-week winter campaigns and one comparative summer/autumn campaign (2007–2009) were organized. The PM was analyzed for 61 chemical constituents and a data-base was built up consisting of approx. 70000 records of the concentrations and their associated uncertainty. In addition 14C/12C ratios were determined in PM10 from four sites. Primary and secondary sources were quantified using macro-tracer methods in combination with chemical mass balance modelling and positive matrix factorization and the combined results were computed by probability- and sensitivity analysis. Monte Carlo simulations yielded probability distributions for seven source categories contributing to the carbonaceous fraction of PM and five major source categories contributing to the PM10 and PM2.5 mass. Despite large uncertainties in the combined source contribution estimates the paper demonstrates that secondary aerosol formed simultaneously over the Po Plain is the main responsible for the typical, rapid build-up of air pollution after clean-air episodes. Next to secondary sources, the most important sources are primary emissions from road transport followed by biomass burning (BB). In the Valtelline Valley, higher contributions from BB and lower contributions from secondary sources were observed. [Copyright &y& Elsevier]

The province of Lodi is located in northern Italy on the Po River plain, where high background levels of air pollutants are prevalent. Lodi province is characterized by intensive agriculture, notably animal husbandry. This paper assesses indoor levels of selected airborne pollutants in 60 homes in the province, with special attention to size-fractionated particulate matter (PM). Indoor PM2.5 concentrations are frequently higher than current guidelines. PM10 and nitrogen dioxide also exceed the respective guideline recommendations in some cases, noting that 24-h nitrogen dioxide levels were compared with an annual limit value. All other studied pollutant levels are below current international guidelines. Among indoor PM size fractions, PM0.5 is predominant in terms of mass concentrations corresponding to 57% of PM10 in summer and 71% in winter. A strong seasonal trend is observed for all studied pollutants, with higher levels in winter corresponding to changes in ambient concentrations. The seasonal variation in PM10 is largely due to PM0.5 increase from summer to winter. Summer indoor PM levels are mainly from indoor-generated particles, while particles of outdoor origin represent the main contribution to winter indoor PM levels. On average, indoor concentrations of coarse PM are mostly constituted by indoor-generated particles. Practical Implications This study presents a comparison between measured indoor concentrations in the study area and indoor air quality guideline criteria. Accordingly, particulate matter (PM) and NO2 are identified as key pollutants that may pose health concerns. It is also found that indoor PM in residential units is mainly constituted by particles with aerodynamic diameters <0.5 μm, especially in winter. Risk mitigation strategies should be focused on the reduction in indoor levels of NO2 and ultrafine and fine particles, both infiltrated from outdoors and generated by indoor sources. [ABSTRACT FROM AUTHOR]

The paper deals with a multidimensional approach demonstrating a direct link between the entity of ongoing dentistry activity (number and kind of interventions) and specific pollution components. Simultaneously indoor/outdoor air concentrations of a set of volatile organic compounds (VOCs) and activity variables, describing the amount and nature of ongoing dentistry activities, were monitored over a year at a dental hospital located in an urban area. Principal Component Analysis (PCA) was used to single out mutually orthogonal pollution components which were then correlated to 'pathology' factors arising from the analysis of dentistry activity indexes. The use of a multidimensional perspective allowed us to obtain a statistically significant model of the link between level of pollution and dentistry activity. In particular, the correlation approach linking pollution results to pathological variables allows us to establish a causative link even in the presence of sub-threshold concentrations of pollutants. [ABSTRACT FROM AUTHOR]

Abstract: Starting from January 2008 Milan implemented a charging scheme to enter an 8km2 area of the city centre. The term used to denote the scheme is Ecopass, conveying the stated political objective of the scheme: a pass to improve the quality of the urban environment (ECO). The charge depends on the Euro emission standard of the vehicle. The paper illustrates the main features and impacts of the Milan Ecopass scheme, and presents a preliminary cost–benefit analysis. The scheme has been effective in curbing not only pollution emissions, but also congestion, and the result has been achieved with low implementation costs and without major political opposition. The cost–benefits analysis presents an overall net benefit. The identification of the winners and losers of the policy is conditioned by penalty payments. Without including the penalties, the surface public transport users and the society at large are the main winners, whereas car and especially freight vehicle users are net losers. [Copyright &y& Elsevier]

Abstract: The paper examines the effects of coordinated traffic lights on CO and C6H6 roadside concentrations in an urban area of Palermo in Southern Italy. Traffic loop detectors and one pollution-monitoring are used to collect data for use in DRACULA traffic microsimulator software. CO and C6H6 roadside concentrations associated with varying cycle and offset times of the coordinated traffic lights are estimated using a neural network. Two functions were set up describing the relations of pollutant concentrations in term of cycle and offset time. [Copyright &y& Elsevier]

Object of this study is the versatility of a remote-sensing measurement system through experimental survey of the levels of primary and secondary pollutants in urban areas. For the first time a long-range measurement of different pollutant for a long period are reported in the urban area of Rome. Over 10 years of data collection, levels of NO2, O3, SO2, benzene and toluene are measured and discussed by means of a differential optical absorption spectroscopy (DOAS) system installed at ground level. The period is interesting because it represents one of the most important life-changes in Europe and in Italy: in fact, during the 1990s some significant decrees introduced the catalytic pots and limited the amount of benzene and sulfur dioxide in the fuel. The paper will describe how the main primary and secondary pollutants are changed during all the period: particularly, benzene and SO2 are not over a big social problem for the human health whereas the secondary pollutants, ozone and nitrogen dioxide, have the same trends during the period, meaning a no contribution to their evolution from the atmospheric processes. The DOAS technique is resulted very useful and versatile: comparisons with other traditional analyzers (i.e., GC-PID for benzene and toluene investigations) demonstrate how it allows to have information over all the territory saving the qualitative trends and giving a similar quantitative result. In this respect the authors show that the difference is due to the different sampling ways (the traditional analyzers are punctual whereas the DOAS is a remote-sensing technique covering a wide area) and does not influence the atmospheric process interpretation. Finally, it should be considered that the pollutant trends have been interpreted using the concentration measurements of natural radioactivity as tracer of the dynamic properties of the atmospheric boundary layer. [Copyright &y& Elsevier]

This paper evaluates the relative impact on air quality of harbour emissions, with respect to other emission sources located in the same area. The impact assessment study was conducted in the city of Taranto, Italy. This area was considered as representative of a typical Mediterranean harbour region, where shipping, industries and urban activities co-exist at a short distance, producing an ideal case to study the interaction among these different sources. Chemical and meteorological field campaigns were carried out to provide data to this study. An emission inventory has been developed taking into account industrial sources, traffic, domestic heating, fugitive and harbour emissions. A 3D Lagrangian particle dispersion model (SPRAY) has then been applied to the study area using reconstructed meteorological fields calculated by the diagnostic meteorological model MINERVE. 3D short term hourly concentrations have been computed for both all and specific sources. Industrial activities are found to be the main contributor to SO2. Industry and traffic emissions are mainly responsible for NO x simulated concentrations. CO concentrations are found to be mainly related to traffic emissions, while primary PM10 simulated concentrations tend to be linked to industrial and fugitive emissions. Contributions of harbour activities to the seasonal average concentrations of SO2 and NO x are predicted to be up to 5 and 30μgm−3, respectively to be compared to a overall peak values of 60μgm−3 for SO2 and 70μgm−3 for NO x . At selected urban monitoring stations, SO2 and NO x average source contributions are predicted to be both of about 9% from harbour activities, while 87% and 41% respectively of total concentrations are predicted to be of industrial origin. [Copyright &y& Elsevier]

Ravenna is one of the main Italian ports and has assumed a leadership position in Italy for some products and markets. The commercial harbour and the adjacent industrial area are very important for the economic system of Ravenna but, at the same time, they are highly critical areas. In particular, on average 8000 ships per year pass through the harbour of Ravenna, influencing air quality in harbour environment. The paper originates from a study about the contribution of different sources of air pollution in Ravenna and its aim is to evaluate the maritime traffic contribution to the air quality in the port area and to determine the suitability of an urban air quality model to support the air quality management in Ravenna. NO x and PM are selected as modelled pollutants. The study is made up of two parts. The first deals with the evaluation of annual emission of PM10 and NO x coming from ships through a standard European methodology while in the second we simulated the diffusion of these pollutants in the whole area. In order to evaluate the capability of the model to treat maritime traffic emissions, we compared hour-by-hour simulated concentrations with data collected by a fixed monitoring station located near the Candiano Canal. NO x concentrations obtained by short- and long-term simulations show a good match with the values measured by the fixed monitoring station, located in the centre of harbour area, and these results are also supported by FA2 performance index. Instead the omission of the secondary particulate and the contribution of other sources of particulate matter in the port area are probably the most important causes of the PM10 underestimation. The worse results obtained according to the performance indexes indicate the need to consider the formation and transport of secondary particulate matter in order to obtain more reliable predictions. [Copyright &y& Elsevier]

Emergency room visits for respiratory complaints, considered an indicator of deterioration of respiratory health, have been positively correlated to traffic-related air pollution. This is an ecological study to evaluate the association between daily levels of urban air pollutants and emergency visits for respiratory complaints, in children and the elderly residing in Pisa, Italy. Daily records of emergency department visits for respiratory complaints were selected from computerized registries, and paper medical reports were identified. Pollutant daily means were computed from hourly values obtained from the public network. Poisson regression, allowing for overdispersion and autocorrelation, was used to evaluate the percent change in daily visits associated with variations of air pollution measures. Among children, an increase in emergency visits of 10% (95% CL: 2.3; 18.2) was associated with a 10-μg/m3 increase in PM10 concentrations of the previous day and an increase of 11.8% (95% CL:1.4; 23.3) was associated with an analogous increment of NO2 during the 2 previous days. Among elderly the same increment of PM10 level during the 2 previous days was correlated with a 8.5% (95% CL: 1.5; 16.1) increase in the risk of recurring emergency department visits for respiratory complaints; this risk increased to 26.5% (95% CL: 3.4; 54.8) if the daily level of CO over the 4 previous days increased by 1 mg/m3. Evidence of morbidity related to short- and long-term air pollution exposures provided in this small study and larger studies conducted in other countries suggests the need for actions to improve air quality in any urban context. [ABSTRACT FROM AUTHOR]

Atmospheric emission inventories are important tools for studying air quality and to set up possible remediation plans in areas characterised by nonattainment of the limit values established by legislation. In industrialised countries a considerable fraction of the emissions is due to road traffic, in particular in urban areas. For this reason emissions from road traffic must be estimated as accurately as possible, a task that can be performed, for the European vehicle fleet, thanks to the availability of the COPERT III methodology. This methodology is powerful and accurate, even if its algorithms can be difficult to apply in a regional emission inventory; moreover the collection of the necessary input data requires a lot of resources and time. This paper describes the road traffic emission inventory estimated for Region Sardinia (Italy) with a bottom-up approach. The estimation has been done by means of a software tool (EMITRA—EMIssions from road TRAnsport) which implements the COPERT III methodology. The resulting emission inventory has been compared against another emission inventory for Sardinia and against emission inventories for other Italian regions, to evaluate its reliability. [Copyright &y& Elsevier]

Abstract: The paper describes the POEM-PM (POllutant Emission Model for gas and Particulate Matter) emission model design. The model, providing actual and alternative emission scenarios, represents a decision support tool to evaluate emission control strategy effectiveness. It estimates emissions at local and mesoscale level and implements a combined top-down and bottom-up approach. The POEM-PM emission fields answer the GAMES (Gas Aerosol Modelling Evaluation System) multiphase modelling system requirements: grid distribution, hourly time modulation, NMVOC split and lumping, PM10 granulometric and chemical description. The emission model has been validated performing episodic and seasonal simulations over Northern Italy by GAMES. POEM-PM is at present the emission model for air quality chemical and transport applications recommended by the Italian National Environmental Agency. [Copyright &y& Elsevier]

Abstract: One of the major thrusts of model application is to assess the relative importance of and VOC controls in reducing ozone levels; in this paper, the long-term assessment of ozone exposure and sensitivity to and VOC emissions, in terms of mean values and seasonal indicators, has been investigated over a complex domain in northern Italy, including Milan metropolitan area. The analysis has been carried out comparing eight seasonal simulations performed by the Gas Aerosol Modelling Evaluation System (GAMES): the summer season 1996, selected as reference case, and seven simulations obtained feeding the modelling system with increasing or decreasing precursor emissions. The study allowed to: (1) recognize the photochemical regimes of the domain, (2) investigate the influence of Milan plume in ozone production and accumulation processes over neighbouring suburban and rural areas, (3) identify parts of the domain where local emission reduction strategies are not effective, (4) provide a support system for local Air Quality Authorities in selecting effective emission reduction strategies. [Copyright &y& Elsevier]

This paper focuses on the top-down approach for estimating road transport emissions at a local level. A bottom up approach is preferable where the data and information required by estimation methodologies are available at regional territorial level. In the absence of this regional data, emissions are adapted from national to smaller levels by means of proxy variables. This study highlights the importance of an improvement of the top down methodology and identifies a corrective index to better characterise road transport emissions at a local level. A set of indicators related to transport activities is selected in order to identify homogeneous areas in the Italian territory. For each area, COPERT (Computer Programme to estimate Emissions from Road Traffic) methodology is applied to estimate the atmospheric emissions of different pollutants; the same methodology is used to calculate road transport emissions at a national level. The results, calculated according to vehicle category and driving mode, are compared with those deriving from a spatial disaggregation of national data by means of simple surrogate variables. [ABSTRACT FROM AUTHOR]

Glass manufacturing, like other process industries, is faced with air pollution compliance problems due to ever stricter emission limits. Several waste gas cleaning equipment options are available for air pollution control (APC) in glass plants, the most common arrangements being based on electrostatic precipitator (ESP) or fabric filter (FF) dust collectors and semi-wet or dry processes for acid gas removal. However, several counteracting aspects affect the choice of gas cleaning technologies, which are confirmed by the discrepancies encountered in actual suppliers' bids. In this paper, the main pollution control options are analyzed by carrying out a critical comparison under the cost-effectiveness point of view to select the lowest cost arrangement considering capital investment, operating expenses, and energy-saving revenues from heat recovery processes. The analysis is carried out with reference to a case study involving actual float glass production lines at Pilkington plants in Italy. [ABSTRACT FROM AUTHOR]

The amelioration of air quality represents one of the most complex challenges that the European Union is facing today. Although a successful strategy to improve air quality should decisively act on pollution sources, Nature-Based Solutions (NBS) may provide a huge contribution to tackle air pollution, while delivering a wide range of Ecosystem Services and enhancing the Natural Capital. In this paper, we analyzed, in both biophysical and monetary terms, the role of the Urban Green Infrastructure (UGI) in improving air quality in the Metropolitan City of Naples (Southern Italy) by implementing an accounting model of PM 10 removal. Our findings indicate that about 1,148 Mg of PM 10 were annually removed by the UGI, for a monetary value of 36 million euros per year. We also assessed the impact of a devastating wildfire that occurred in 2017 in the study area, which resulted in a loss of PM 10 removal of about 97 Mg and led to an estimated monetary loss of 3 million euros per year. The results also showed that PM 10 removal efficiency largely varies seasonally and across different functional groups of vegetation, highlighting the role of functional biodiversity in the provision of Ecosystem Services under Mediterranean climate conditions. In conclusion, we maintain that the biophysical and economic assessment of Ecosystem Services can support local managers and policymakers in charge of developing urban planning strategies aimed at ensuring sustainable development and human well-being in metropolitan cities. [ABSTRACT FROM AUTHOR]

After the outbreak of Corona virus pandemic in Italy, the government has taken extraordinary measures, including a national lockdown, to prevent the spread of the infection. This extraordinary situation has led to a reduction in air pollution levels measured in the whole Po Valley, usually known as one of the most polluted areas in Europe in terms of particulate matter (PM) and nitrogen dioxide (NO 2) concentrations. The main aim of this paper is to evaluate the effectiveness of the lockdown on the air quality improvement. In particular, an interrupted time series modelling approach is employed to test if a significant change in the level and the trend of the pollutant time series has occurred after the lockdown measure. The case study regards the city of Brescia (Northern Italy) and focuses on the comparison of the period before (January 1st–March 7th, 2020) and after (March 8th–March 27th, 2020) the lockdown. By adjusting for meteorology and Sunday effect, the results show that a significant change in air quality occurring in the post intervention period was observed only for a single NO 2 station located in a heavy traffic zone. In particular, the estimate of the time series slope, i.e. the expected change in the concentration associated with a time unit increase, decreases from -0.25 to -1.67 after the lockdown. For the remaining stations, no significant change was found in the concentration time series when comparing the two periods. This confirms the complexity of air pollutant concentration dynamics for the considered area, which is not merely related to emission sources but depends also on other factors as, for example, (micro and macro) meteorological conditions and the chemical and physical processes in the atmosphere, which are all independent of the lockdown measure. • Po Valley in Italy is one of the most polluted areas in Europe. • Italy has taken extraordinary measures to prevent the spread of the Corona virus. • Interrupted time series models are used to analyse PM and NO 2 concentration. • The aim is to evaluate the effectiveness of the lockdown on air quality. [ABSTRACT FROM AUTHOR]

It is well-known that atmospheric pollution, first and foremost the particulate matter (PM), causes serious diseases in humans. China's metropolises and Italy's Po Valley have in fact achieved a concerning degree of notoriety thanks to runaway air pollution problems. The spread of viral respiratory diseases is facilitated in polluted environments, an example of which is the respiratory syncytial virus bronchiolitis. In this opinion paper, we consider the possible relationship between air pollution, primarily airborne PM10–2.5, and the spread of the novel coronavirus in Northern Italy. If it is true that the novel coronavirus remains active from some hours to several days on various surfaces, it is logical to postulate that the same can occur when it is adsorbed or absorbed by the atmospheric particulate matter, which may also help carry the virus into the human respiratory system. As the Earth presents us with a very high bill to pay, governments and other authorities need to take prompt action to counter excessive pollution levels, both in Italy and in other countries. [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]

The article offers information on health impact assessment (HIA) research presented during a workshop organized by the European Observatory on Health Systems and Policies in Brussels, Belgium. One study focused on the methods and results from the HIA in Italy as well as its effects on the decision-making process. Another paper explored an HIA of the effects of fine powder air pollution in Ticino, Switzerland.