Artificial neural network-derived trends in daily maximum surface ozone concentrations

Matthew W. Gardner, Stephen R. Dorling

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


Interannual variability in meteorological conditions can confound attempts to identify changes in ozone concentrations driven by reduced precursor emissions. In this paper, a technique is described that attempts to maximize the removal of meteorological variability from a daily maximum ozone time series, thereby revealing longer term changes in ozone concentrations with increased confidence. The technique employs artificial neural network [multilayer perceptron (MLP)] models, and is shown to remove more of the meteorological variability from U.S. ozone data than does a Kolmogorov-Zurbenko (KZ) filter and conventional regression-based technique.
Original languageEnglish
Pages (from-to)1202-1210
Number of pages9
JournalJournal of the Air and Waste Management Association
Issue number8
Publication statusPublished - 2001

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