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Ferrara, Italy

Nguyen T.N.T.,Hanoi University of Science and Technology | Ta V.C.,Hanoi University of Science and Technology | Le T.H.,Hanoi University of Science and Technology | Mantovani S.,MEEO Srl | Mantovani S.,SISTEMA GmbH
Advances in Intelligent Systems and Computing | Year: 2014

Estimation of Particulate Matter concentration (PM1, PM2.5 and PM10) from aerosol product derived from satellite images and meteorological parameters brings a great advantage in air pollution monitoring since observation range is no longer limited around ground stations and estimation accuracy will be increased significantly. In this article, we investigate the application of Multiple Linear Regression (MLR) and Support Vector Regression (SVR) to make empirical data models for PM1/2.5/10 estimation from satellite- and ground-based data. Experiments, which are carried out on data recorded in two year over Hanoi - Vietnam, not only indicate a case study of regional modeling but also present comparison of performance between a widely used technique (MLR) and an advanced method (SVR). © Springer International Publishing Switzerland 2014. Source


Hirtl M.,ZAMG Central Institute for Meteorology and Geodynamics | Mantovani S.,SISTEMA GmbH | Kruger B.C.,University of Natural Resources and Life Sciences, Vienna | Triebnig G.,EOX IT Services GmbH | And 3 more authors.
Atmospheric Environment | Year: 2014

Daily regional scale forecasts of particulate air pollution are simulated for public information and warning. An increasing amount of air pollution measurements is available in real-time from ground stations as well as from satellite observations. In this paper, the Support Vector Regression technique is applied to derive highly-resolved PM10 initial fields for air quality modeling from satellite measurements of the Aerosol Optical Thickness.Additionally, PM10-ground measurements are assimilated using optimum interpolation. The performance of both approaches is shown for a selected PM10 episode. © 2013 Elsevier Ltd. Source


Thanh Nguyen T.N.,University of Ferrara | Mantovani S.,MEEO Srl | Mantovani S.,SISTEMA GmbH | Campalani P.,University of Ferrara | Limone G.P.,University of Ferrara
ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods | Year: 2012

Processing of data recorded by MODIS sensors on board the polar orbiting satellite Terra and Aqua usually provides Aerosol Optical Thickness maps at a coarse spatial resolution. It is appropriate for applications of air pollution monitoring at the global scale but not adequate enough for monitoring at local scales. Different from the traditional approach based on physical algorithms to downscale the spatial resolution, in this article, we propose a methodology to derive AOT maps over land at 1 km 2 of spatial resolution from MODIS data using support vector regression relied on domain knowledge. Experiments carried out on data recorded in three years over Europe areas show promising results on limited areas located around ground measurement sites where data are collected to make empirical data models as well as on large areas over satellite maps. Source


Campalani P.,UNIFE | Campalani P.,MEEO Srl | Nguyen T.N.T.,UNIFE | Nguyen T.N.T.,MEEO Srl | And 3 more authors.
IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2011 | Year: 2011

Daily monitoring of unhealthy particles suspended in the low troposphere is of major concern around the world, and ground-based measuring stations represent a reliable but still inadequate means for a full spatial coverage assessment. Advances in satellite sensors have provided new datasets and though less precise than insitu observations, they can be combined altogether to enhance the prediction of particulate matter. In this article we evaluate a methodology for automatic multi-variate estimation of PM 10 dry mass concentrations along with a comparison of three different cokriging estimators, which integrate ground measurements of PM 10, satellite MODIS-derived retrievals of aerosols optical thickness and further auxiliary data. Results highlight the need for further improvements and studies. The analysis employs the available data in 2007 over the Emilia Romagna region (Padana Plain, Northern Italy), where stagnant meteorological conditions further urge for a comprehensive air quality monitoring. Qualitative PM 10 full maps of Emilia Romagna are then automatically yielded on-line in a dynamic GIS environment for multi-temporal analysis on air quality. © 2011 IEEE. Source


Nguyen T.N.T.,University of Ferrara | Nguyen T.N.T.,MEEO Srl | Mantovani S.,MEEO Srl | Mantovani S.,SISTEMA GmbH | And 2 more authors.
IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2011 | Year: 2011

As a result of great improvements in satellite technologies, satellite-based observations have provided possibilities to monitor air pollution at the global scale with moderate quality in comparison with ground truth measurement. In tradition, the inversion process that derives atmospheric parameters from satellite-based data is replied on simulated physics models of matter interactions. Recently, the usage of machine learning techniques in this field has been investigated and presented competitive results to the physical approach. In this paper, we present validation of Support Vector Regression (SVR) technique in estimating Aerosol Optical Thickness (AOT), one of the most important atmospheric variables, from satellite observations at 1x1 km2 of spatial resolution. Validation by different European countries is carried out on a large amount of datasets collected in three years, which aims at investigating prediction quality of SVR data models built up on discrete and sparse data around ground measurement sites on continuous data domain presented by maps. The validation results obtained from 172 datasets showed good performance of SVR over most of the 31 countries that were considered. © 2011 IEEE. Source

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