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Input strategy analysis for an air quality data modelling procedure at a local scale based on neural network

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Abstract

In recent years, a significant part of the studies on air pollutants has been devoted to improve statistical techniques for forecasting the values of their concentrations in the atmosphere. Reliable predictions of pollutant trends are essential not only for setting up preventive measures able to avoid risks for human health but also for helping stakeholders to take decision about traffic limitations. In this paper, we present an operating procedure, including both pollutant concentration measurements (CO, SO2, NO2, O3, PM10) and meteorological parameters (hourly data of atmospheric pressure, relative humidity, wind speed), which improves the simple use of neural network for the prediction of pollutant concentration trends by means of the integration of multivariate statistical analysis. In particular, we used principal component analysis in order to define an unconstrained mix of variables able to improve the performance of the model. The developed procedure is particularly suitable for characterizing the investigated phenomena at a local scale.

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Acknowledgments

We would thank Basilicata ARPA to have kindly made available data of air quality monitoring network. The study was carried out in the framework of the project Smart Basilicata in Smart Cities and Communities and Social Innovation” (MIUR n.84/Ric 2012, PON 2007 – 2013))

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Authors and Affiliations

  1. Engineering School, University of Basilicata, V.le dell’Ateneo Lucano, 85100, Potenza, Italy

    M. Ragosta & G. A. Giorgio

  2. Institute of Methodologies for Environmental Research - National Research Council of Italy, Zona Industriale Tito Scalo, 85050, Tito, Potenza, Italy

    M. D’Emilio

Authors
  1. M. Ragosta
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  2. M. D’Emilio
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  3. G. A. Giorgio
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Correspondence to M. D’Emilio.

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Ragosta, M., D’Emilio, M. & Giorgio, G.A. Input strategy analysis for an air quality data modelling procedure at a local scale based on neural network. Environ Monit Assess 187, 307 (2015). https://doi.org/10.1007/s10661-015-4556-9

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  • DOI: https://doi.org/10.1007/s10661-015-4556-9

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