Development of a short–term ozone prediction tool in Tirana area based on meteorological variables

Developmental Biology

Manjola Banja³  Anastasia Poupkou²  Dimitris K Papanastasiou¹  Dimitris Melas² 

[1] Laboratory of Agricultural Engineering & Environment, Institute of Technology and Management of Agricultural Ecosystems, Centre for Research & Technology – Thessaly, Technology Park of Thessaly, 1st Industrial Area of Volos, P.O. Box 15, PC 38500, Volos, Greece$$;Laboratory of Atmospheric Physics, Department of Physics, Aristotle University of Thessaloniki, PC 54124, Thessaloniki, Greece$$;Institute of Energy, Water and Environment, Polytechnic University of Tirana, Durresi Street 219, Tirana, Albania$$

关键词: Ozone;    Regression model;    Meteorology;    Ozone prediction;    Tirana;   

DOI  :  10.5094/APR.2012.002

学科分类：农业科学（综合）

来源: Dokuz Eylul Universitesi * Department of Environmental Engineering

【 摘 要 】

The short–term prediction of near surface ozone levels is very important due to the negative impacts of ozone on human health, climate and vegetation. The objective of this paper is to develop and test an analytical model that could be applied to predict next day's maximum ozone concentration for the first time in Tirana, Albania, where ozone's monitoring has been recently started. The relationship of the daily maximum hourly ozone values with meteorological variables, including near surface air temperature and relative humidity and with air pollution variables like the persistency of ozone levels and its seasonal variation is examined. The data analysis reveals that the pollution persistency and the near surface air temperature are the factors that mainly affect the peak ozone levels. Multiple linear regression analysis has been performed to establish the relationship between the above mentioned parameters and peak ozone concentration. The agreement between observed and predicted daily maximum hourly ozone values is very good, with a correlation coefficient (R) of 0.87. The model slightly under–predicts the ozone concentration while no significant mispredictions are observed. Additionally, the model’s ability to predict the exceedances of a specific ozone limit value is examined. The model successfully predicts the exceedances of 105 μg m⁻³, a value that corresponds to the 75^th percentile, in the 86% of the cases applied.

【 授权许可】

Unknown   

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