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

Interannual variability in meteorological conditions can confound attempts to identify changes in ozone concentrations driven by reduced precursor emi ssions. In this paper, a technique is described that attempts to maximize t he removal of meteorological variability from a daily maximum ozone time se ries, thereby revealing longer term changes in ozone concentrations with in creased confidence. The technique employs artificial neural network (multil ayer perceptron (MLP)] models, and is shown to remove more of the meteorolo gical variability from U.S. ozone data than does a Kolmogorov-Zurbenko (KZ) filter and conventional regression-based technique.