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- W1673485646 abstract "The modelling of urban air quality prediction is a difficult task because: i) the processes are controlled by complex chemical and physical mechanisms; ii) its state is ascertained by measuring too few parameters for a sufficient chemical picture; iii) sampling measurements are generally collected at too few points without consideration of scaling (for example CO is a local phenomenon while O 3 is regional and both are often measured by the same monitoring network); iv) balances of chemical species are often forced to work far from local equilibria. In order to overcome these problems, Artificial Neural Networks (ANNs) were used here because they are model free and require very little knowledge about the underlying system structure. CO, NO 2 , and O 3 concentrations at the time (t + Δt) are variables that depend on their previous concentrations and of other external information, such as meteorological data, solar radiation, chemical precursors or vehicle traffic information. ANNs used in this work were able to explain over 90% of the variability of the pollutant concentrations considered at the next hour (CO, NO 2 , and O 3 ) and over 80% of that of the next three-hour O 3 concentration. The forecasting of CO peaks exceeding a given value has been successfully performed by transforming original concentration time series into a probability series and processing the transformed data by an ANN. Sensitivity analysis has provided useful insight into the most important forecasting variables and their relevant links." @default.
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- W1673485646 date "2000-11-01" @default.
- W1673485646 modified "2023-09-28" @default.
- W1673485646 title "Nowcasting of urban air pollutants by neural networks" @default.
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