74 research outputs found
Studio delle relazioni ambientali e relative connessioni con patologie respiratorie per la città di Roma
No full textPAHs urban concentrations maps using support vector machines
No full textAir pollution health effects studies are based on data collected by
monitoring stations. Pollutant exposure maps greatly improve the evaluation of
health effects. The study of exposure to polycyclic aromatic hydrocarbons
(PAHs) in urban areas is one of the goals of the EXPAH LIFE+ Project. An
integrated approach has been applied to simulate PAHs levels in the urban area
of Rome. In particular, support vector machines (SVMs) were applied to
reconstruct PAHs urban concentrations. Starting from PAHs results provided
by a chemical transport model (CTM) FARM and observed data collected in
field campaigns of PM2.5 with PAHs content between June 2011 and May
2012, SVM methods were applied to build a model able to forecast PAHs
exposure. The SVM has shown excellent results in reproduction of
experimental data, improving those achieved by the FARM model. Finally, the
SVM has produced very congruent PAHs exposure maps
PAHs urban concentrations maps using support vector machine
No full textThe studies about health effects are often based on data inferred by monitoring stations. For this purpose, pollutants exposure maps are crucial for evaluating health effects. Studying the Polycyclic Aromatic Hydrocarbons (PAHs) and the Benzo(a)Pyrene (BaP) exposure in urban areas is the major goal of the EXPAH LIFE+ Project. An integrated approach, based on measurements and modeling techniques, was applied to simulate PAHs and BaP levels in the Rome metropolitan area. Field campaigns of PM2.5 with PAHs content were performed for the period June 2011 - May 2012, and a chemical transport model (FARM) was applied to reconstruct PAHs urban concentrations. In this work, Machine Learning methods have been applied to forecast atmospheric pollution, trying also to improve the results achieved by FARM. In particular, Support Vector Machines (SVMs) have been used. They represent one of the most common approaches among Machine Learning methods. Starting from the experimental data, SVM methods have been applied to build models able to forecast PAHs and BaP exposure. The SVM models seem to show excellent results in the reproduction of experimental data and in generalization, improving those achieved by FARM. Finally, the SVM models have produced very congruent PAHs and BaP exposure maps
A microscale hybrid modelling system to assess the air quality over a large portion of a large European city
No full textThe role of atmospheric dispersion models is becoming increasingly relevant to assess air pollution urban population exposure for epidemiological studies. Estimating urban air quality is challenging, because of the intrinsic characteristics of cities atmospheric structure, such as high density of primary emissions and presence of local dispersion processes, that produce strong concentration gradients. Therefore, very high spatial resolution simulations may often be required to improve the accuracy of estimations.
The objective of this study is developing a microscale hybrid modelling system (HMS) to carry out, in a reasonable computational time, long-term simulations providing hourly concentration fields at building-resolving scale in extended urban areas in order to calculate annual indicators to evaluate exposure. The proposed system couples two atmospheric dispersion models suited for different scales: a Eulerian chemical transport model, FARM (Flexible Air quality Regional Model), accounting for dispersion phenomena due to regional and local emission sources, and a Lagrangian particle micro-scale dispersion model, PMSS (Parallel Micro Swift Spray), used to compute concentrations induced by vehicular traffic inside the city. The HMS has been applied on 12 × 12 km2 domain in Rome with a horizontal resolution of 4 m for calculating NO2 and PM10 concentrations for all year 2015. This study has been carried out in the frame of project BEEP (Big data in Environmental and occupational Epidemiology), that is an Italian research project in epidemiological field.
Results show that the combined use of the two models reproduces the spatial and temporal variability of the observed atmospheric pollutants with a good agreement. The statistical analysis performed on daily average concentrations proves that the HMS suits the standard acceptance criteria for urban dispersion model evaluation, with a FAC2 of 0.92 and 0.80 and a Fractional Bias of −0.03 and −0.2 for NO2 and PM10 respectively.
Furthermore, the implementation of an innovative kernel method to calculate concentrations in PMSS has made possible to reduce the computational time by 80%, leading to an average computational time of 3 h per simulated day on an HPC (High Performance Computing) system with 180 cores
Seasonal Abundance of Particle-Phase Organic Pollutants in an Urban/Industrial Atmosphere
No full textPolycyclic aromatic hydrocarbons (PAHs); their derivatives nitro, and methyl-PAHs; n-alkanes; and organic acids were investigated in the aerosol samples collected during two field campaigns conducted at three sampling stations in an industrialized city in southern Italy. The main sources affecting the atmosphere and its toxicity were investigated by means of the diagnostic ratios of: specific particulate-phase PAHs, marker compounds among nitro-PAHs, alkanes, and acids, the dominant wind direction, daily and seasonal abundance of carcinogenic organic substances. The potential importance of the non-regulated pollutants to assess the air quality was confirmed; in fact the carcinogenic organic compounds showed to have scarce correlation with particulate matter (PM) concentration. An exceptionally high variability of toxic compounds at a daily scale was due to meteorological condition causing periods of extremely high pollution levels
Effects of Particulate Matter on the Incidence of Respiratory Diseases in the Pisan Longitudinal Study
No full textThe current study aimed at assessing the effects of exposure to Particulate Matter (PM) on the incidence of respiratory diseases in a sub-sample of participants in the longitudinal analytical epidemiological study in Pisa, Italy. Three hundred and five subjects living at the same address from 1991 to 2011 were included. Individual risk factors recorded during the 1991 survey were considered, and new cases of respiratory diseases were ascertained until 2011. Average PM10 and PM2.5 exposures (μg/m3, year 2011) were estimated at the residential address (1-km2 resolution) through a random forest machine learning approach, using a combination of satellite data and land use variables. Multivariable logistic regression with Firth's correction was applied. The median (25th-75th percentile) exposure levels were 30.1 μg/m3 (29.9-30.7 μg/m3) for PM10 and 19.3 μg/m3 (18.9-19.4 μg/m3) for PM2.5. Incidences of rhinitis and chronic phlegm were associated with increasing PM2.5: OR = 2.25 (95% CI: 1.07, 4.98) per unit increase (p.u.i.) and OR = 4.17 (1.12, 18.71) p.u.i., respectively. Incidence of chronic obstructive pulmonary disease was associated with PM10: OR = 2.96 (1.50, 7.15) p.u.i. These results provide new insights into the long-term respiratory health effects of PM air pollution
SURFACE PARAMETERS EVALUATED FROM SATELLITE REMOTE SENSING IMAGES FOR POLLUTANT ATMOSPHERIC DISPERSION MODELLING
No full textThis contribute deals with the use of surface parameters extracted from satellite remote sensing images for the setup of
the input dataset required by pollutants atmospheric dispersion models (PATM). These models need 2D distributions (grids) of
many surface parameters to model turbulence parameters, as roughness length, albedo, leaf area index and Bowen ratio. Very often
these parameters are set using predefined tables defined as a function of land cover (LC). Usually, this last information is extracted
from public datasets, such as, for European countries, the CORINE Land Cover (CLC). Some of these parameters can be computed
directly from remote sensing. Moreover, land cover classification evaluated from remote sensing can be used to update existing LC
datasets. In this work ASTER images have been used to evaluate, using a supervised classification method, the LC map of the area.
This LC is used to update the CLC. Moreover, albedo was directly calculated from the image. The importance of information
extracted from remote sensing is evaluated using the SPRAY lagrangian PATM. SPRAY has been used to simulate the dispersion of
an inert generic pollutant emitted from two virtual sources on a 30 km x 40 km domain in a study area located at Venice (Northern
Italy), where a big industrial site is found (Porto Marghera). Real (measured) meteorological data have been used
A Game-Theoretic Model of Mutual Benefits in Bilateral Nuclear Security Regimes
No full textStates collaborate to achieve common goals. In the interest of advancing nuclear security globally, states have previously formed bilateral partnerships that allow two states to cooperate in germane areas of the nuclear industry such as safeguarding nuclear material, securing nuclear weapons, and advancing peaceful uses of nuclear technologies. Specifically, some states collaborate in establishing state-level strategies on nuclear security measures in order to protect against possible non-state adversaries (e.g., the Cooperative Threat Reduction and Material Protection, Control, and Accounting Programs between the Russian Federation and the United States). In an attempt to quantify utilities, a methodology has been developed within this work that uses game-theoretic models to measure the value of cooperation. In certain bilateral regimes, the opportunity for influence arises due to asymmetry between the partners. The developed methodology has the potential to identify circumstances under which one state might influence another in securing the latter’s nuclear assets against possible non-state actors by virtue of a potential collective benefit in a bilateral cooperative nuclear security regime.
The methodology employs three different, but related, game-theoretic models – two using non-cooperative approaches and one using a cooperative approach. Determining the existence and magnitude of utilities between uncorrelated and correlated strategies provides the opportunity to study various cooperative strategies between states. The bargaining solutions of the cooperative game that models agreements providing a net
benefit to both parties were then used to evaluate utilities of each such viable cooperative strategy, and the results compared. This process was applied to four case studies exhibiting a temporal progression of cooperation between the Russian Federation (as successor to the Soviet Union) and the United States and a fifth case study assessing possible cooperation between modern-day Pakistan and the United States. A result of applying the methodology to the former bilateral regime illustrated the use of nuclear insecurity as a potentially profitable commodity (a stated concern of nuclear deterrence and nonproliferation scholars). Two notable conclusions include 1) the level of investment for independent action by the states can impact the nature of a collaborative regime and 2) the collective total (investment and consequential) costs of a bilateral regime can be reduced but will require additional investment by at least one state. We conclude that the methodology developed here has the potential to assist future decision makers and analysts in quantifying the value of state-level cooperation for nuclear security
Diagnóstico e intervención en riesgos psicosociales en el trabajo en la Atención Primaria de Salud
No full textLa presente investigación aborda el tema de los Factores y Riesgos Psicosociales en el Trabajo en el sector de la salud pública chilena, específicamente en el ámbito de la Atención Primaria o Municipalizada. Se reporta la experiencia de aplicación del Protocolo de Vigilancia de Riesgos Psicosociales en el Trabajo en el Departamento Comunal de Salud de Curicó, VII región de Chile, desde el inicio de la implementación del Protocolo en septiembre del año 2015 hasta la actualidad. Se analiza críticamente su metodología a la base, el Cuestionario SUSESO- ISTAS 21 con sus respectivas dimensiones, así como su flujograma de acción, y la propuesta de medidas correctivas o preventivas en base al Protocolo. Se busca generar evidencia científica en un sector laboral poco estudiado y con un instrumento de medición relativamente nuevo de Riesgos Psicosociales en el país, con el objetivo de entregar recomendaciones o lineamientos para su uso futuro y con consideraciones específicas para el rubro de la salud pública chilena y el marco regulatorio-legal nacional desde la Psicología de la Salud Ocupacional
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