Meteorologically adjusted trends of daily maximum ozone concentrations in Taipei,Taiwan |
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| Affiliation: | 1. Institut de Chimie pour les Procédés, l''Energie, l''Environnement et la Santé (ICPEES, UMR 7515), CNRS – Université de Strasbourg, Strasbourg, France;2. Laboratoire de Gestion des Risques et Environnement (GRE), Université de Haute Alsace, Mulhouse, France;3. In''Air Solutions, 1 rue Blessig, 67000 Strasbourg, France;1. Research Center for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan;2. Department of Public Health, National Taiwan University, Taipei, 10055, Taiwan;3. Graduate Institute of Environmental Engineering, National Central University, Taoyuan, 32001, Taiwan;4. Center for Space and Remote Sensing Research, National Central University, Taoyuan, Taiwan;5. Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, 37996, USA;6. Department of Atmospheric Sciences, National Central University, Taoyuan, 32001, Taiwan;1. Department of Respiratory Medicine, Minami-Nara General Medical Center, 8-1 Fukugami, Oyodo-cho, Yoshino-gun, Nara, 638-8551, Japan;2. Department of Respiratory Medicine, Nara Medical University, 840 Shijo-cho, Kashihara City, Nara, 634-8522, Japan;3. Department of Diagnostic Pathology, Minami-Nara General Medical Center, 8-1 Fukugami, Oyodo-cho, Yoshino-gun, Nara, 638-8551, Japan |
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| Abstract: | The meteorological conditions exert large impacts on ozone concentrations, and may mask the long-term trends in ozone concentrations resulting from precursor emissions. Estimation of long-term trends of ozone concentrations due to the changes in precursor emissions is important for corresponding control strategy. Multiple linear regression (method I), multilayer perceptron (MLP) neural network (method II) and Komogorov-Zurbenko (KZ) filter method plus MLP methodology (method III), are used to estimate the meteorologically adjusted long-term trends of daily maximum ozone concentrations by removing the masking effects of meteorological conditions in this study. The daily maximum ozone concentrations and relative meteorological variables were extracted from six air-monitoring stations in Taipei area from 1994 to 2001. The data collected during 1994–2000 period were used as modeling set and utilized to estimate the meteorologically adjusted trends, and the data of 2001 were used as the validation data. The meteorologically adjusted trends of ozone for these three methods were calculated and compared. The results show that both MLP and KZ filter +MLP models are more suitable than multiple linear regression for estimating the long-term trends of ozone in Taipei, Taiwan. The long-term linear trends of meteorologically adjusted ozone concentrations due to the precursor emissions show an increase trend at all stations, and the percent changes per year range from 1.0% to 2.25% during the modeling period in Taipei area. |
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