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 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.
Gardner
M
M
School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom.
Dorling
S
S
D000397:Air Pollution / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods; D005544:Forecasting; D016571:Neural Networks, Computer; D010083:Oxidants, Photochemical / Q000032:analysis*; D010126:Ozone / Q000032:analysis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D010083:Oxidants, Photochemical; D010126:Ozone