In this paper we use the Divisia index approach to decompose emission changes of SO2, NOx and CO2 from major economic sectors in Taiwan during 1980 to 1992. The study highlights the interrelationships between energy use and environmental quality, and provides insights for policy making. The emission changes are decomposed into five components-pollution coefficient, fuel mix, energy intensity, economic growth and industrial structure. Of all components analyzed, economic growth had the largest positive effect on emission changes for Taiwan's major economic sectors. Emissions of SO2 in industry and other sectors showed a decreasing trend due to fuel quality improvements and pollution control. However, NOx and CO2 emissions increased sharply in all sectors. Comparisons were also made with Germany, Japan and USA. This study hay shown that improvement in energy efficiency, pollution control and fuel substitution are major options to reduce SO2, NOx and CO2 emissions.

This study investigated the relationships between meteorological data, pollution sources, and receptors over northern Taiwan. During the intensive sampling period in summer 1992, the weather was controlled predominantly by a Pacific subtropical high and by Typhoon Mark. During the other intensive sampling period in winter 1993, while a cold frontal system approached Taiwan, the northeasterly winds prevailed most of the time. The local circulation such as land-sea breeze only developed under weak synoptic environment. Particle concentrations and element composition in winter were higher than in summer. This can be attributed to the high convection of air mass, which leads to the vertical dispersion of pollutants in summer. In addition to the subtropical high pressure, typhoons are frequently accompanied with high-wind speeds and unstable weather conditions that also dilute and eliminate the pollutants. In winter, the prevailing northeasterlies might carry pollutants from Midland China. Furthermore, the anticyclone system develops a stagnant condition that easily leads to pollutant accumulation. In this case, the wind direction affected the source contribution of the receptor and the PM 10 displays a higher correlation with coarse and fine particulate than meteorological parameters in summer. In addition, the mixing height shows a high correlation with PM 10 in winter. INTRODUCTIONCorrelations between air pollution data and meteorological records can provide very useful information for ambient air quality management. Two approaches, that is, dispersion and receptor models, have been used to estimate source contributions to atmospheric aerosols. Dispersion models include the concentration of pollutants from emission rates, meteorological data, physical and chemical transformations, and removal mechanisms. This type of model can be investigated by comparing the predicted spatial and temporal distribution of pollutant concentrations. 1,2 Alternatively, receptor modeling is a best-fit linear combination of emission sources from chemical composition profiles, which can reconstruct the aerosol measured at a receptor location. [3][4][5] Receptor modeling has been used to determine aerosol sources affecting local 6 and regional sites. 7 Source contribution determination from ambient monitoring data, obtained with receptor modeling techniques, relies on the ability to characterize and distinguish differences in the chemical composition of different