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Development and Assessment of Neural Network and Multiple Regression Models in Order to Predict PM10 Levels in a Medium-sized Mediterranean City

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Abstract

Suspended particulate matter is significantly related to the degradation of air quality in urban agglomerations, generating adverse health effects. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important in order to improve public awareness and air quality management. This study aims at developing models using multiple regression and neural network (NN) methods that might produce accurate 24-h predictions of daily average (DA) value of PM10 concentration and at comparatively assessing the above mentioned techniques. Pollution and meteorological data were collected in the urban area of Volos, a medium-sized coastal city in central Greece, whose population and industrialization is continuously increasing. Both models utilize five variables as inputs, which incorporate meteorology (difference between daily maximum and minimum hourly value of ground temperature and DA value of wind speed), persistency in PM10 levels and weekly and annual variation of PM10 concentration. The validation of the models revealed that NN model showed slightly better skills in forecasting PM10 concentrations, as the regression and the NN model can forecast 55 and 61% of the variance of the data, respectively. In addition, several statistical indexes were calculated in order to verify the quality and reliability of the developed models. The results showed that their skill scores are satisfying, presenting minor differences. It was also found that both are capable of predicting the exceedances of the limit value of 50 μg/m3 at a satisfactory level.

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References

  • Beale, R., & Jackson, T. (1992). Neural computing: An introduction. Bristol, England: IOP Publishing.

    Google Scholar 

  • Berlyand, M. E. (1991). Prediction and regulation of air pollution. Dordecht, The Netherlands: Atmospheric Sciences Library, Kluwer.

    Google Scholar 

  • Boy, M., & Kulmala, M. (2002). Influence of spectral solar irradiance on the formation of new particles in the continental boundary layer. Atmos. Chem. Phys. Discuss., 2, 1317–1350.

    Article  Google Scholar 

  • Boznar, M., Lesjak, M., & Mlakar, P. (1993). A neural network based method from short term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain. Atmospheric Environment, 27B, 221–230.

    CAS  Google Scholar 

  • Chaloulakou, A., Grivas, G., & Spyrellis, N. (2003a). Neural network and multiple regression models for PM10 prediction in Athens: A comparative assessment. Journal of Air & Waste Management, 53, 1183–1190.

    Google Scholar 

  • Chaloulakou, A., Kassomenos, P., Spyrellis, N., Demokritou, P., & Koutrakis, P. (2003b). Measurements of PM10 and PM2.5 particle concentrations in Athens, Greece. Atmospheric Environment, 37, 649–660.

    Article  CAS  Google Scholar 

  • Chaloulakou, A., Saisana, M., & Spyrellis, N. (2003c). Comparative assessment of neural network and regression models for forecasting summertime ozone in Athens. Science of the Total Environment, 313, 1–13.

    Article  CAS  Google Scholar 

  • Colbeck, I., Chung, M.-C., & Eleftheriadis, K. (2002). Formation and transport of atmospheric aerosol over Athens, Greece. Water, Air, & Soil Pollution: Focus, 2, 223–235.

    Article  CAS  Google Scholar 

  • Comrie, A. (1997). Comparing neural networks and regression models for ozone forecasting. Journal of Air & Waste Management, 47, 653–663.

    CAS  Google Scholar 

  • Dockery, D. W., Schwartz, J., & Spengler, J. D. (1992). Air pollution and daily mortality: Association with particulates and acid aerosols. Environmental Research, 59, 362–373.

    Article  CAS  Google Scholar 

  • Fuller, G. W., Carslaw, D. C., & Lodge, H. W. (2002). An empirical approach for the prediction of daily mean PM10 concentrations. Atmospheric Environment, 36, 1431–1441.

    Article  CAS  Google Scholar 

  • Gardner, M., & Dorling, S. (1998). Artificial neural networks (the multilayer perceptron) – A review of applications in the atmospheric sciences. Atmospheric Environment, 32, 2627–2636.

    Article  CAS  Google Scholar 

  • Hubbard, M. C., & Cobourn, W. G. (1998). Development of a regression model to forecast ground-level ozone concentration in Louisville, KY. Atmospheric Environment, 32, 2637–2647.

    Article  CAS  Google Scholar 

  • Joliffe, I. T., & Stephenson, D. B. (2002). Forecast verification. New York: Wiley.

    Google Scholar 

  • Kassomenos, P. A., Flocas, H. A., Lykoudis, S., & Skouloudis, A. (1998). Spatial and temporal characteristics of the relationship between air quality status and mesoscale circulation over an urban Mediterranean basin. Science of the Total Environment, 217, 37–57.

    Article  CAS  Google Scholar 

  • Kassomenos, P., Kotroni, V., & Kallos, G. (1995). Analysis of climatological and air quality observations from greater Athens area. Atmospheric Environment, 29, 3671–3688.

    Article  CAS  Google Scholar 

  • Katsouyianni, K., Touloumi, G., Spix, C., Scwartz, J., Balducci, F., Medina, S., et al. (1997). Short term effects of ambient SO2 and particulate matter on mortality in 12 European cities: results from time series data from the APHEA project. BMJ, 314, 1658–1663.

    Google Scholar 

  • Massart, B. G. J., Kvalheim, O. M., Stige, L., & Aasheim, R. (1998). Ozone forecasting from meteorological variables. Part II. Daily maximum ground-level O3 concentration from local weather forecasts. Chemometrics and Intelligent Laboratory, 42, 191–197.

    Article  CAS  Google Scholar 

  • Melas, D., Kioutsioukis, I., & Ziomas, I. C. (2000). Neural network model for predicting peak photochemical pollutant levels. Journal of Air & Waste Management, 50, 495–501.

    CAS  Google Scholar 

  • Ostro, B. D., Chestnut, L., Vichit-Vadakan, N., & Laixuthai, A. (1999a). The impact of particulate matter on daily mortality in Bangkok, Thailand. Journal of Air & Waste Management, 49, 100–107.

    CAS  Google Scholar 

  • Ostro, B. D., Eskeland, G. S., Sanchez, J. M., & Feyzioglou, T. (1999b). Air pollution and health effects: A study of medical visits among children in Santiago, Chile. Environmental Health Perspectives, 107, 69–73.

    Article  CAS  Google Scholar 

  • Papanastasiou, D. K., & Melas, D. (2007a). Daily ozone forecasting in an urban area, using meteorological & pollution data. Fresenius Environmental Bulletin, (in press).

  • Papanastasiou, D. K., & Melas, D. (2007b). Statistical characteristics of ozone and PM10 levels in a medium sized Mediterranean city. Special Issue on Air Pollution, International Journal of Environment and Pollution, (in press).

  • Perez, P., & Reyes, J. (2002). Prediction of maximum of 24-h average of PM10 concentrations 30 h in advance in Santiago, Chile. Atmospheric Environment, 36, 4555–4561.

    Article  CAS  Google Scholar 

  • Potukuchi, S., & Wexler, A. S. (1997). Predicting vapor pressures using neural networks. Atmospheric Environment, 31, 741–753.

    Article  CAS  Google Scholar 

  • Prybutok, V. R., Yi, J., & Mitchell, D. (2000). Comparison of neural network models with ARIMA and regression models for prediction of Houston’s daily maximum ozone concentrations. European Journal of Operational Research, 122, 31–40.

    Article  Google Scholar 

  • Schwartz, J., & Dockery, D. W. (1992). Increased mortality in Philadelphia associated with daily air pollution concentrations. American Review of Respiratory Disease, 145, 600–604.

    CAS  Google Scholar 

  • Touloumi, G., Pocock, S. J., Katsouyianni, K., & Trichopoulos, D. (1994). Short term effects of air pollution on daily mortality in Athens. A time series analysis. International Journal of Epidemiology, 23, 957–967.

    Article  CAS  Google Scholar 

  • Van der Wal, J. T., & Janssen, L. H. (2000). Analysis of spatial and temporal variations of PM10 concentrations in The Netherlands using Kalman filtering. Atmospheric Environment, 34, 3675–3687.

    Article  Google Scholar 

  • Wilks, D. (1995). Statistical methods in the atmospheric sciences: An introduction. Academic Press.

  • Yi, J., & Prybutok, V. R. (1996). A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. Environmental Pollution, 92, 349–357.

    Article  CAS  Google Scholar 

  • Zannetti, P. (1990). Air pollution modelling. New York: Computational Mechanics.

    Google Scholar 

  • Ziomas, I. C., Melas, D., Zerefos, C. S., & Bais, A. F. (1995a). On the relationship between peak ozone levels and meteorological variables. Fresenius Environmental Bulletin, 4, 53–58.

    CAS  Google Scholar 

  • Ziomas, I. C., Melas, D., Zerefos, C. S., Paliatsos, A., & Bais, A. F. (1995b). Forecasting peak pollutant levels using meteorological variables and Indexes. Atmospheric Environment, 29, 3703–3711.

    Article  CAS  Google Scholar 

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Acknowledgements

The authors would like to thank Mr. Stefanos Tzavelas who is the head of the pollution and the meteorological station, Department of Environment, Prefecture of Magnessia in Greece.

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Authors and Affiliations

  1. Laboratory of Atmospheric Physics, Department of Applied and Environmental Physics, School of Physics, Faculty of Sciences, Aristotle University of Thessaloniki, Campus Box 149, 54124, Thessaloniki, Greece

    D. K. Papanastasiou, D. Melas & I. Kioutsioukis

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  1. D. K. Papanastasiou
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  2. D. Melas
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  3. I. Kioutsioukis
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Correspondence to D. K. Papanastasiou.

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Papanastasiou, D.K., Melas, D. & Kioutsioukis, I. Development and Assessment of Neural Network and Multiple Regression Models in Order to Predict PM10 Levels in a Medium-sized Mediterranean City. Water Air Soil Pollut 182, 325–334 (2007). https://doi.org/10.1007/s11270-007-9341-0

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  • DOI: https://doi.org/10.1007/s11270-007-9341-0

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