1,720,995 research outputs found
Association between PM10, PM2.5, NO2, O3 and self-reported diabetes in Italy: A cross-sectional, ecological study
No full textIntroduction Air pollution represents a serious threat to health on a global scale, being responsible for a large portion of the global burden of disease from environmental factors. Current evidence about the association between air pollution exposure and Diabetes Mellitus (DM) is still controversial. We aimed to evaluate the association between area-level ambient air pollution and self-reported DM in a large population sample in Italy. Materials and methods We extracted information about self-reported and physician diagnosed DM, risk factors and socio-economic status from 12 surveys conducted nationwide between 1999 and 2013. We obtained annual averaged air pollution levels for the years 2003, 2005, 2007 and 2010 from the AMS-MINNI national integrated model, which simulates the dispersion and transformation of pollutants. The original maps, with a resolution of 4 x 4 km2, were normalized and aggregated at the municipality class of each Italian region, in order to match the survey data. We fit logistic regression models with a hierarchical structure to estimate the relationship between PM10, PM2.5, NO2 and O3 four-years mean levels and the risk of being affected by DM. Results We included 376,157 individuals aged more than 45 years. There were 39,969 cases of DM, with an average regional prevalence of 9.8% and a positive geographical North-to-South gradient, opposite to that of pollutants’ concentrations. For each 10 μg/m3 increase, the resulting ORs were 1.04 (95% CI 1.01–1.07) for PM10, 1.04 (95% CI 1.02–1.07) for PM2.5, 1.03 (95% CI 1.01–1.05) for NO2 and 1.06 (95% CI 1.01–1.11) for O3, after accounting for relevant individual risk factors. The associations were robust to adjustment for other pollutants in two-pollutant models tested (ozone plus each other pollutant). Conclusions We observed a significant positive association between each examined pollutant and prevalent DM. Risk estimates were consistent with current evidence, and robust to sensitivity analysis. Our study adds evidence about the effects of air pollution on diabetes and suggests a possible role of ozone as an independent factor associated with the development of DM. Such relationship is of great interest for public health and deserves further investigation. © 2018 Orioli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Regulating Ecosystem Services of forests in ten Italian Metropolitan Cities: Air quality improvement by PM10 and O3 removal
No full textUrban and periurban forests, which are integrated within the concept of Green Infrastructure, provide important Ecosystem Services, including air purification. In this study, we quantified the Ecosystem Service of particulate matter (PM10) and Ozone (O3) removal from urban and periurban forests in ten metropolitan cities in Italy, and its total monetary value. In order to gain a better understanding of how Ecosystem Services can be regulated on a wider scale, the vegetation ecosystem types were grouped into Physiognomic-Structural Categories of Vegetation according to morphofunctional criteria. The pollution removal was mapped using a remote sensing and GIS approach, by applying a deposition model and a stomatal flux model. We estimated, for the ten metropolitan cities, an overall pollution abatement of 7150 Mg of PM10 and 30,014 Mg of O3 in the year 2003, which was an extremely hot year. Our findings indicate that structural characteristics (i.e. Leaf Area Index) and functional diversity, linked to stomatal conductance, exert a marked influence on the provision of the regulating Ecosystem Services, whose total monetary value was estimated to be equal to 47 and 297 million USD for PM10 and O3 removal, respectively. This study represent the first national-scale assessment of the Ecosystem Services of air pollution removal in Europe, thus providing information that may be useful to stakeholders to manage Green Infrastructure more efficiently. © 2016 Elsevier Ltd. All rights reserved
Spatial representativeness of air quality monitoring stations: A grid model based approach
No full textA methodology for quantifying areas of spatial representativeness of air quality monitoring station is here proposed, exploiting the wide spatial and temporal coverage of chemical transport models results. The method is based on the analysis of time series of model concentrations, extracted at monitoring sites and around, by means of a Concentration Similarity Function (CSF). The method was tested on AMSMINNI model results, covering Italy and three reference years (2003, 2005, 2007), for assessing the spatial representativeness of PM2.5 and O3 rural background monitoring stations. The CSF methodology shows good performances in describing both the extension and the shape of representativeness areas, taking into account the difference between pollutants and the dependence on averaging time and temporal interval of concentration data. Results show a large variability in the size and shape of the selected stations in Italy, ranging from 220 to 4500 km2. This confirms the importance of carrying out adhoc analyses on monitoring stations, as general a priori classifications and qualitative assessments of spatial representativeness are not able to fully capture the complexity of different territorial contexts. © 2015 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved
Effects of global financial crisis on medium-long term emission scenarios, in the perspective of air pollution and climate change
No full textCost-effective reductions of PM2.5 concentrations and exposure in Italy
No full textIn 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. © 2016 Elsevier Ltd
Rappresentatività spaziale di misure di qualità dell'aria - Valutazione di un metodo di stima basato su fattori oggettivi
Get PDFLa rappresentatività spaziale e temporale dei siti di monitoraggio delle concentrazioni di inquinanti in atmosfera è un
parametro fondamentale nella scelta della dislocazione delle stazioni di misura e nelle valutazioni di esposizione della
popolazione ai livelli di concentrazione misurati. In generale, la rappresentatività spaziale di un sito è definita in
letteratura facendo riferimento alla variabilità dei livelli di concentrazione nell’intorno del sito stesso. In questo studio è
stato applicato un metodo di stima della rappresentatività spaziale di tipo empirico - statistico, basato sull’analisi della
copertura del territorio intorno alle stazioni di monitoraggio con lo sviluppo di uno specifico indicatore statistico (b) che
collega la copertura del territorio con la concentrazione di inquinanti in atmosfera. Questo indicatore viene ricavato
tramite una procedura di ottimizzazione statistica che viene applicata su un set indipendente di stazioni di misura e che
analizza, per ogni sito di monitoraggio, le serie storiche delle misure e la copertura del territorio circostante.
L’indicatore b esprime in modo sintetico la relazione fra la copertura del territorio e la qualità dell’aria. Il metodo,
sperimentato in letteratura all’estero, è stato applicato per la prima sull’intero territorio italiano.
Gli inquinanti considerati sono stati l’O3, il PM2.5, gli IPA e i metalli pesanti. È stata utilizzata la copertura del
territorio del progetto CORINE Land Cover 2006, integrata con tematismi specifici per le reti stradali con strumenti di
geoprocessing in ambiente GIS (Geographic Information Systems). La metodologia è stata applicata all’analisi di
rappresentatività delle Reti Speciali di misura della qualità dell’aria (D.Lgs. 155/2010, art. 6 e 8).
È stata valutata la variabilità dell’indicatore statistico sviluppato, calcolandolo in aree circolari con raggio crescente (5 –
7.5 – 10 km) centrate sulle stazioni. I risultati evidenziano, per quasi tutte le stazioni e inquinanti considerati, variazioni
di b inferiori al 20%, valore assunto come soglia. Il metodo consente un’efficace e speditiva valutazione della
rappresentatività spaziale delle stazioni di monitoraggio qualora non siano a disposizione dati di qualità dell’aria relativi
al territorio circostante ai punti di monitoraggio.Spatial and temporal representativeness of air quality monitoring sites is a critical parameter when choosing location
of sites and assessing effects on population to long term exposure to air pollution. According to literature, the spatial
representativeness of a monitoring site is related to the variability of pollutants concentrations around the site.
In this work a statistical assessment of spatial representativeness is presented, based on the analysis of land cover
around the monitoring sites. A statistical indicator (b) was developed that links land cover pattern to concentration
levels of pollutants.
The b-indicator was optimized for each pollutant individually by means of a statistical procedure which investigates the
relation between historical series of measurements and land cover around the site. This approach, reported in
literature for the Belgian monitoring network, is applied here for the first time on the whole Italian territory.
Pollutants under investigation were ozone, PM2.5, PAH and heavy metals. CORINE Land Cover map of 2006 was used
and integrated with specific layers for road networks, by means of GIS processing. The methodology was applied to
detect the spatial representativeness of official Italian National Network of Special Purpose Monitoring Stations
(according to the law D.Lgs. 155/2010, art. 6 and 8). The variability of the b-indicator was explored within circular
buffers around the site, with increasing radius (5 – 7.5 – 10 km). Results showed that b varies below the established
threshold of 20% for almost all sites of interest.
The methodology allows an useful and quick assessment of spatial representativeness of a monitoring network even if
exhaustive air quality data around a monitoring site are not available
A Hybrid Sampling Strategy for Sparse Magnetic Resonance Imaging
No full textA hybrid acquisition sequence for Sparse 2D Magnetic Resonance Imaging (MRI) is presented.
The method combines random sampling of Cartesian trajectories with an adaptive 2D acquisition of radial projections.
It is based on the evaluation of the information content of a small percentage of the k-space data collected randomly,
to identify radial blades of k-space coefficients having maximum information content. An entropy function is
defined on the power spectrum of the projections for evaluating the information content of each direction. The method
has been tested on MRI images and it was also compared to the weighted Compressed Sensing. Some results are reported
and discussed
Micro-scale dispersion modelling with background correction to simulate air quality in Milan
Get PDFLa qualità dell'aria in ambiente urbano sta sollevando grande interesse negli ultimi anni, per lo studio sia dell'impatto dell'inquinamento sulla salute umana che delle le cause dei superamenti periodici dei limiti di legge nelle zone urbane. Questo è un problema ad alta risoluzione che deve quindi essere affrontato con modelli di dispersione ad alta risoluzione. Purtroppo con gli strumenti di calcolo di cui disponiamo oggi, questi modelli non possono tener conto di aree molto estese (non più di pochi chilometri) o di lunghi periodi di tempo (non più di pochi giorni) e della reale complessità delle reazioni
chimiche in atmosfera a causa del loro approccio lagrangiano. In questo studio abbiamo esaminato la possibilità di introdurre il modello Micro-Swift-Spray (MSS) nella catena modellistica AMS-MINNI, realizzando un esperimento per studiare l'idoneità del modello di dispersione chimica regionale di AMSMINNI (FARM) come fornitore delle concentrazioni di fondo necessarie ad MSS. I risultati di questo esperimento indicano che su una media giornaliera la correzione del fondo fornita da FARM migliora notevolmente le prestazioni del MSS rispetto alle misure di una stazione di traffico. Nonostante questo
le prestazioni orarie di tale correzione non sono altrettanto incoraggianti, e la causa è probabilmente da ricercare nelle ipotesi comunemente fatte in sede di preparazione dei dati di emissione che alimentano il modello MSS.Urban air quality is raising great interest in recent years, for studying both the impact of pollution on human health and the causes of periodic pollution exceedances in urban areas. This is a “high resolution” problem that has to be treated with high resolution dispersion models. Unfortunately with the present day computational facilities, these models cannot take into account large areas (more than few kilometers) or long periods (more than few days) and the real complexity of atmospheric chemical reactions due to their Lagrangian approach. Here we studied the feasibility of introducing the model
Micro-Swift-Spray (MSS) in the AMS-MINNI air quality chain, setting up an experiment to study the suitability of the regional component of AMS-MINNI (FARM) as a background provider for MSS. The results of this experiment indicate that on a daily average the background correction provided by FARM greatly improves the performance of MSS compared to traffic air quality station measurements. On the other hand the hourly performance of this correction is not as encouraging, but it is probably due to some common assumptions made in the preparation of the emission data that feed the MSS
An atmospheric modelling system for Lebanon
Get PDFViene qui descritto lo sviluppo e l’applicazione di un sistema modellistico atmosferico messo a punto per il territorio del Libano (AMS-Libano) nell’ambito dell’assistenza tecnica fornita da ENEA al Ministero dell’Ambiente Libanese tra il 2013 ed il 2014. AMS-Libano rappresenta uno strumento modellistico di riferimento per le politiche di qualità dell’aria in Libano, permettendo di approfondire ipotesi di gestione della qualità dell’aria in maniera efficace ed economica. Il sistema modellistico nasce dall’esperienza di ENEA nel progetto MINNI in supporto al Ministero Italiano dell’Ambiente e della Tutela del Territorio e del Mare sulle politiche nazionali di qualità dell’aria e nell’ambito di esperienze di ricerca su confronti di modelli a livello Europeo. La catena modellistica unisce un modello meteorologico di mesoscala (RAMS), un elaboratore di inventari di emissioni (EMMA) e un modello di trasporto e chimica in atmosfera (FARM). Le concentrazioni tridimensionali di inquinanti atmosferici (NO2, O3, PM10, PM2.5, SO2) sono calcolate considerando le dinamiche dell’atmosfera e le reazioni chimiche fra gas e la speciazione del particolato con una risoluzione spaziale di 5 Km sul Libano. L’inventario atmosferico delle emissioni
di sorgenti antropogeniche per il Libano, compilato dal CEREA (Francia) e dall’Università Saint Joseph (Libano), è stato adattato per AMS-Libano e integrato con nuove informazioni su grandi sorgenti puntuali e simulazioni specifiche di emissioni biogeniche.
La mappe delle concentrazioni medie annuali di NO2, O3, PM10, PM2.5 e SO2 mostrano la distribuzione dell’inquinamento atmosferico su tutto il dominio di studio ed evidenziano situazioni di superamento dei limiti in vigore nell’Unione Europea, considerati come riferimento. Il sistema è stato testato su un caso di base rappresentato dalle emissioni del 2010, e su due scenari emissivi: nuovi limiti ai valori emissivi per i cementifici e nuove configurazioni funzionali per le centrali elettriche situate a Zouk e Jyeh. La messa a punto di un sistema modellistico atmosferico per il Libano ha fornito un quadro completo
delle aree più inquinanti della regione dove le misure di mitigazione appaiono più urgenti, i piani di sviluppo devono essere rivisti o devono essere applicati valori limite delle emissioni più stringenti. I valori di concentrazione forniscono anche un caso base per gli studi di valutazione di impatto ambientale e per la proposta di nuove attività con impatto in atmosfera. Inoltre il sistema modellistico è in grado di valutare l’efficacia di nuovi scenari di emissione, dati per esempio dal cambiamento di valori limite di emissioni delle diverse sorgenti, dalla trasformazioni pianificate di alcune centrali o da future attività estrattive off-shore.The development and application of a dedicated atmospheric modelling system (AMS) on the territory of Lebanon is here described as part of technical assistance provided by ENEA to the Lebanese Ministry of Environment during 2013 and 2014. AMS-Lebanon aims to provide a reference modelling tool for air quality policy in Lebanon, allowing
to investigate hypotheses on air quality management in a quick and cost-effective way.
The modelling system is derived from ENEA’s experience in the MINNI project, supporting to the Italian Ministry of Environment for national air pollution policies, and in research exercises involving model intercomparisons at European scale.
The modelling chain connects a mesoscale meteorological model (RAMS), an emission inventory processor (EMMA) and a Chemical Transport Model (FARM). Three dimensional concentrations of atmospheric pollutants (NO2, O3, PM10, PM2.5, SO2) are calculated keeping into account atmospheric dynamics and chemical reactions among gas and particulate species, on a 5 km horizontal resolution grid covering Lebanon.
The Atmospheric Emission Inventory of Anthropogenic Sources for Lebanon, compiled by CEREA (France) and University of Saint Joseph (Lebanon), was adapted to AMS Lebanon and integrated with new information on large point sources and dedicated simulation of biogenic VOC emissions. The maps of average annual concentrations for NO2, O3, PM10, PM2.5 and SO2 show the distribution of atmospheric pollution all over the study domain and point out the hot-spots of exceedances of EU limit values, taken as reference. The system has been tested on a base case, represented by the 2010 emission inventory of Lebanon, and two scenarios: new emission limit values for cement industries and new functional layouts for the power plants in Zouk and Jyeh.
The setup of the AMS modelling system over Lebanon provided a comprehensive picture of the most polluted areas of the country, where mitigation measures are more urgent, development plans have to be reconsidered or more stringent emission limit values have to be applied. The concentration values can serve as the base case for environmental impact assessments studies and for new activities proposed with atmospheric impact.
Moreover the modelling system is able to provide responses on effectiveness of new emissions scenarios, like changes of Emission Limit Values for the various sources, planned power plants transformation, future offshore drilling activities
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