Gardner, Matthew W. and Dorling, Stephen R. (2001) Artificial neural network-derived trends in daily maximum surface ozone concentrations. Journal of the Air and Waste Management Association, 51 (8). pp. 1202-1210. ISSN 2162-2906

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Abstract

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.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA
UEA Research Groups:	Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences Faculty of Social Sciences > Research Centres > Water Security Research Centre
Depositing User:	Rosie Cullington
Date Deposited:	19 May 2011 09:33
Last Modified:	16 Jun 2026 13:53
URI:	https://uea-test-2026.eprints-hosting.org/id/eprint/30971
DOI:	10.1080/10473289.2001.10464338

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