A new methodology development for the regulatory forecasting of PM10. Application in the Greater Athens Area,Greece |
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| Authors: | A Sfetsos D Vlachogiannis |
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| Institution: | 1. School of Environmental Science and Technology/State Key Lab of Engines, Tianjin University, 300072 Tianjin, China;2. Institute of Environmental Technology, Berlin University of Technology,10623 Berlin, Germany;1. Department of Electrical/Computer Engineering, Purdue University, Calumet, 2200 169th St, Hammond, IN 46323, USA;2. Water Institute, Purdue University, Calumet, 2200 169th St, Hammond, IN 46323, USA;3. Department of Mechanical Engineering, Purdue University, Calumet, 2200 169th St, Hammond, IN 46323, USA;4. School of Electrical/Computer Engineering Purdue University, West Lafayette, IN 47907-2035, USA;1. Université Grenoble Alpes, Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA), Laboratoire d’Innovation pour les Technologies des Energies nouvelles et les Nanomatériaux (LITEN), DTCH, LRP, F-38000 Grenoble, France;2. RAGT Energie, F-81000 Albi, France;3. Université Grenoble Alpes, CNRS, Grenoble INP, SIMaP, 38000 Grenoble, France;4. IHE Delft Institute for Water Education, Department of Water Supply Sanitation and Environmental Engineering, Delft, the Netherlands;1. School of Chemical and Process Engineering (SCaPE), University of Leeds, Leeds LS2 9JT, UK;2. School of Chemistry, University of Leeds, Leeds LS2 9JT, UK |
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| Abstract: | The paper introduces a new methodology for the prediction of daily PM10 concentrations, in line with the regulatory framework introduced through the EU Directive 2008/50/EC. The proposed approach is based on the efficient utilisation of the data collected over short time intervals (hourly) rather than the daily values used to derive the daily regulatory threshold. It is sufficiently simple and easily applicable in operational forecasting systems with the ability to accept as inputs both historical data and exogenous paraeters, such as meteorological variables. The application of the proposed methodology is demonstrated using data from five monitoring stations of air pollutants located in Athens, over a five year period (2000–2004) as well as compatible meteorological data from the NCEP (National Centers for Environmental Protection). A set of different models have been tested at the same time to reveal the effectiveness of the proposed approach, both univariate and multivariate, and linear and non-linear models. The analysis of all examined datasets has shown conclusive evidence that the introduction of the newly developed procedure which utilises data collected over a shorter horizon can significantly increase the forecasting ability of any developed model using daily historic PM10 data, under all examined metrics. |
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