Modelling The Multi-year Maximum Daily PM10
Concentration In Edinburgh:
An Application Of The Variability
Decomposition Transfer Function Model
Author(s)
H. Al-Madfai, A. J. Geens & D. G. Snelson
Abstract
Understanding the temporal variability in the concentration of airborne PM10 can
be of benefit as it would lead to more reliable models that can inform the
monitoring and control of air pollution. Established forecasting approaches are
generally data driven and offer little in terms of furthering the understanding of
the dynamics of data. A variability decomposition (VD) based transfer function
model can be used to decompose variability in time series data into inherent and
external, thus concentrating on modelling only the external variability as a
function of the model inputs.
The VD approach was used to model the multi-year maximum daily PM10
concentration recorded in St Leonards, Edinburgh using historic values and PM10
concentrations recorded at the Grangemouth monitoring station situated 19 miles
to the North West using two established approaches as benchmarks. The results
indicate that the transfer function models using the Grangemouth data were
superior to the univariate model in terms of the RMSE and MAPE. The
performance of the VD transfer function model was comparable to the
benchmark in terms of forecast accuracy, but superior in providing improved
physical interpretation of the model components
Keywords: air quality forecasting, variability decomposition, transfer function
modelling, PM10, ARIMA, Box-Jenkins, transfer function models.
Keywords
air quality forecasting, variability decomposition, transfer function
modelling, PM10, ARIMA, Box-Jenkins, transfer function models
Related Book
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