Description Of The Meteorological Impacts On The Urban Air Pollution Species By The Fuzzy Logic Approach: A Hong Kong Case Study
The aim of the present paper is to develop an alternative approach to conventional dynamic and photochemical models for operational short-term forecasting of urban air pollutants. It is well known that there are some practical difficulties, which prevent necessary progress in the development of these models as a forecasting tool. A fuzzy logic based method has been developed here to study the impact of meteorological factors on the evolution of air pollutant levels and to describe them quantitatively. This method meets all requirements but requires quite a substantial amount of observational data. The developed model bases on simulation of diurnal cycles of principal meteorological variables (wind speed and direction, solar irradiance, and air temperature) and the corresponding diurnal patterns of various air pollutants (O3, NO2, NO, NOy). In addition, the spatial patterns of these parameters are also studied. Both temporal and spatial parameter distributions have been considered in order to investigate the impacts of meteorological factors, and they are incorporated in the models as state vectors in the multidimensional space. Here we suggest that most of the weather and air pollution phenomena could be simulated by sequences of its conservation inside some fuzzy sets and the transition from one fuzzy set to another. Therefore, an important key point here is the development of the transition rules.
KeywordsUrban air pollutant simulation fuzzy sets short-term forecasting
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