SUMMARYAir quality monitoring networks are important tools in management and evaluation of air quality. Classifying monitoring stations via homogeneous clusters allows identification of similarities in pollution, of representative sites, and of spatial patterns. Instead of summaries by statistical indicators, we propose to consider the air pollutant concentrations as functional data. We then classify using functional cluster analysis, where Partitioning Around Medoids (PAM) algorithm is embedded. The proposed data analysis approach is applied to the air quality monitoring network in Piemonte (Northern Italy); we consider the three more critical pollutants: NO 2 , PM 10 , and O 3 .
This paper presents a land classification in zones featured by different criticality levels of atmospheric pollution, considering pollutant time series as functional data: we call this proposal "Functional Zoning". We aim to meet a request of European laws that impose to distinguish zones needing further actions from those needing only maintenance according to air quality status. To carry out zoning for Piemonte (northern Italy), we consider the hourly concentration fields of the main pollutants produced by a deterministic air quality model, and we preprocess them by assimilating observations gathered by monitoring networks. In order to consider administrative units which policy makers refer to, we present three different alternatives to upscale data to municipality scale. Then, to aggregate by pollutant, we evaluate two strategies
In this work, we compare the performance of an hydrological model when driven by probabilistic rain forecast derived from two different post-processing techniques. The region of interest is Piemonte, northwestern Italy, a complex orography area close to the Mediterranean Sea where the forecast are often a challenge for weather models. The May 2008 flood is here used as a case study, and the very dense weather station network allows us for a very good description of the event and initialization of the hydrological model. The ensemble probabilistic forecasts of the rainfall fields are obtained with the Bayesian model averaging, with the classical poor man ensemble approach and with a new technique, the Multimodel SuperEnsemble Dressing. In this case study, the meteo-hydrological chain initialized with the Multimodel SuperEnsemble Dressing is able to provide more valuable discharge ranges with respect to the one initialized with Bayesian model averaging multi-model
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