F
Fedele Pasquale Greco
Researcher at University of Bologna
Publications - 37
Citations - 312
Fedele Pasquale Greco is an academic researcher from University of Bologna. The author has contributed to research in topics: Compositional data & Bayesian probability. The author has an hindex of 8, co-authored 35 publications receiving 282 citations.
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Journal ArticleDOI
Vertically-resolved particle size distribution within and above the mixing layer over the Milan metropolitan area
Luca Ferrero,Maria Grazia Perrone,S. Petraccone,Giuseppe Sangiorgi,B Ferrini,C Lo Porto,Z Lazzati,Daniela Cocchi,Francesca Bruno,Fedele Pasquale Greco,Angelo Riccio,Ezio Bolzacchini +11 more
TL;DR: In this article, the mixing height was determined from the observed vertical aerosol concentration gradient, and from potential temperature and relative humidity profiles, showing that inter-consistent mixing heights can be retrieved highlighting good correlations between particle dispersion in the atmosphere and meteorological parameters.
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Hierarchical space-time modelling of PM10 pollution
TL;DR: The main aims of the proposed model are the identification of the sources of variability characterising the PM 10 process and the estimation of pollution levels at unmonitored spatial locations and a fully Bayesian approach, using Monte Carlo Markov Chain algorithms.
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Spatial reconstruction of rainfall fields from rain gauge and radar data
TL;DR: In this article, Monte Carlo Markov Chain (MCMCMC) algorithms were used to calibrate radar measurements via rain gauge data and make spatial predictions for hourly rainfall, by means of a Bayesian hierarchical framework.
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A multivariate CAR model for improving the estimation of relative risks.
TL;DR: The proposed model is proven to be an effective alternative to existing multivariate models, mainly because it overcome some restrictive hypotheses underlying models previously proposed in this context.
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Some Interpolation Estimators in Environmental Risk Assessment for Spatially Misaligned Health Data
TL;DR: Two fully Bayesian solutions to the relationship between exposure to uranium in drinkable waters and cancer incidence, in South Carolina (USA), based on the kernel-smoothing technique and the tessellation of the study region are proposed.