PM10 (suspended particles with diameter below 10 £gm) and O3 (ozone) are the dominant air pollutants in Kao-Ping airshed, in which ozone is a secondary pollutant produced from its precursors of NOx (= NO + NO2) and HC (hydrocarbons) via complex photochemical reactions in sunlight. This study first statistically summarized the trends of ozone concentrations using box plots over recent five to six years from four and three air-quality monitoring stations in Kaohsiung City and Ping-Tung County, respectively. Then, the long-term variations of ozone concentrations were analyzed using trend formula proposed by Holland et al. (1999). Finally, multi-variable factor analysis was applied to study the relationships among the ozone concentrations with other air pollutants and meteorological parameters. Results reveal that the highest peak of ozone concentration appears in October and the second peak appears in March, while the lowest one appears in summer. Except being moderate relationships in Tzyo-Yin station, trend results show strong relationships in all other stations. Results also show that the percentage annual increase in ozone concentration in Kaohsiung City is higher than those in Kaohsiung and Ping-Tung Counties. The factor analyses reveal that the concentration of ozone is positively correlated with air temperature, wind speed and period of sunshine, while negatively correlated with concentrations of NO2, CO, NO, and NOx in the seasons of spring, autumn and winter; but negatively correlated with relative humidity in autumn. Notably, the percentage increases of ozone events in recent years should be also related to the rises of air temperature and period of sunshine, which should be watched continuously.

The long-term trends of PM10, O3 and NOx concentrations were analyzed using Holland model (without meteorological-adjusted) and MM-Regression model (with meteorological-adjusted) based on the data of ten EPA air quality stations from 1997 to 2006 in Kao-Ping area. The aim of this study was to determine the impact of meteorological factors on the trends of these pollutants in Kao-Ping. The annual variations (AV) of O3 was −0.496 % in Kaohsiung county, −0.200 in Pingtung county, and 0.277 % in Kaohsiung city, showing different characteristics in Kao-Pin area. On average, the annual variations (AV) influenced by meteorological factors were: PM10: 0.205 %, O3: −0.127 %, and NOx: 0.338 %. After being adjusted by meteorological factors, the seasonal variations (SV) were about 1, indicating little seasonal change. In Kao-Ping region, the influence by meteorological factors was 9.566 %, 8.026 % and 7.351 % in PM10, O3, NOx, respectively. In total, the average influence was 8.314% in Kao-Ping region, with 7.791% in Kaohsiung city (8.481% at Cianjin, the most influenced area), 9.439% in Kaohsiung County (10.368% at Linyuan, the most influenced area), and 7.110% in Pingtung County (7.516% at Chaojhou, the most influenced area). PM10 was influenced most by meteorological factors (PM10: 9.566 %, O3: 8.026 %, NOx: 7.351 %) in Kao-Ping area.. In Kao-Ping region, the contributions by individual meteorological factors were 70.78% in wind speed, 38.23% in total cloudiness, 36.56% in sunshine hour, 19.86% in temperature, 12.40% in atmospheric pressure, 5.96% in relative humidity and 1.27% in wind direction. The influences by the wind speed were 66.62 %, 72.35 % and 72.31 % on the concentrations of PM10, O3, NOx, respectively. Wind speed was the most important factor controlling concentration trends in Kao-Ping area.

The trends of PM10, O3, NOX and NMHC concentrations were analyzed by the Holland model (without meteorological-adjusted) and the MM-Regression model (with meteorological-adjusted) base on the data of eight EPA air quality stations from 1997 to 2006 in Kaohsiung. The aim of this study was to evaluate the influence of meteorological factors on the pollutants (PM10 and O3) trends. The trends of PM10 concentrations in Kaohsiung city analyzed without meteorological-adjusted were 7.18 % at Tzuo-Yin, 3.20 % at Chien-Chin and 9.72 % at Nan-Chie. After eliminating the meteorological factors, the percent of gradual trends were 1.91 % at Tzuo-Yin, 2.92 % at Chien-Chin and 2.02 % at Nan-Chie. The trends of O3 concentrations without meteorological-adjusted were 11.42 % at Tzuo-Yin, 20.92 % at Hsiung-Kong, 42.08 % at Chien-Chin and 13.69 % at Nan-Chie. The trends of PM10 concentrations in Kaohsiung County analyzed without meteorological-adjusted were 14.96 % at Lin-yuan and 3.24 % at Jen-wu. After meteorological factors eliminating, the trend was 3.15 % at Jen-wu but the trend was -2.53 % at Lin-yuan. Meteorological factor was a primary reason that influences the PM10 concentration in recent years. The trends of O3 in Kaohsiung County without meteorological-adjusted were 18.89 % at Da-liao, 4.40 % at Jen-wu, 35.16 % at Lin-yuan and 29.98 % at Mei-nung. After meteorological factors eliminating, the trends were 1.99 % at Da-liao, 2.23 % at Jen-wu, 1.16 % at Lin-yuan and -1.16 % at Mei-nung. The results show that the influence of meteorological factors for O3 trends was more sensitive in Kaohsiung county than in Kaohsiung city. The concentration of PM10 has no significant difference (64.8 ¡V 92.3 %) in Kaohsiung city. For the concentration of O3, the similarity (78 ¡V 100 %) was extensive in Kaohsiung city because O3 could diffuse easily. O3 episodes has no significant difference as PM10 episodes in Kaohsiung city. As above-mentioned, the results show that the contributions of ambient PM10 were individually but the contributions of ambient O3 were uniform extensively.

PM10 and ozone are the dominant air pollutants in the Urban Kaohsiung. Ozone is a secondary pollutant generated in the troposphere from the precursors nitrogen dioxide and non-methane hydrocarbons. The trends of ozone concentrations first statistically are summarized utilizing the monitoring data during the period 1998¡Ð2007. All data are collected from four fixed-site air quality monitoring stations in Kaohsiung City. The results show that ozone concentration in Kaohsiung has one perennial peak concentration, occurring in October and March. The highest values occur in October and the secondary high value in March. The lowest values occur in the summer. The monitor data possess timeliness of data and the non-linear dynamic tendency. Artificial Neural Network ¡]ANN¡^, a system recognition, self-study function and ability of the solution to non-linearity dynamic system problem, was used as a tool to analyze these monitor data. This work utilizing neural networks develops a model to predict the trend of ozone situations in the Urban Kaohsiung. The network was trained using meteorological factor and air quality data when the ozone concentrations are the highest. The optimum set value of five parameters including date partition, hidden layer neurons, training function, leraning rate , and momentum coefficient were obtained based on trial and error methods. The simulated results of ozone concentration have a correlation coefficient within the range 0.865¡Ð0.899 and IOA within the range 0.927¡Ð0.934. The trend results of ozone concentration reflect strong relationships in all stations. The results of this study indicate that the artificial neural network (ANN) is a promising method for air pollution modeling.

This study separated two parts: PART ¢¹ Meteorologically adjusted trends of ozone Since meteorological changes strongly affect ambient ozone concentrations, trends in concentrations of ozone upon the adjustment of meteorological variations are important of evaluating emission reduction efforts. This work is to study meteorological effects on the long-term trends of ozone concentration using a multi-variable additive model in Kaohsiung. The long-term trends of ozone concentration were analyzed using the Holland model (without meteorological-adjusted) and the robust MM Regression model (with meteorological-adjusted) based on the data of eight EPA air quality stations from 1997 to 2006 in Kaohsiung area. According to the result of the simulation, the simulated value of the robust MM-Regression model present more valid than the Holland model.The simulated results show that the long-term ozone concentration increases at 13.84% (or 13.06%) monthly (or annually) after meteorological adjustments, less than at 26.10% (or 23.80%) without meteorological adjustments in Kaohsiung county. The simulated results show that the long-term ozone concentration increases at 9.01% (or 6.88%) monthly (or annually) after meteorological adjustments, less than at 22.01% (or 19.67%) without meteorological adjustments in Kaohsiung city. Wind speed, duration of sunshine and pressure are the three dominant factors that influence the ground-level ozone levels in Kaohsiung area. PART ¢º Dispersion of air pollutants in the Hsuehshan Tunnel Concentrations of carbon monoxide (CO) and nitrogen oxides (NOx) were measured from November 14 ¡V 17 2008 in a cross-mountain Hsuehshan traffic tunnel stretching 12.9 km and containing eastward and westward channels. Air pollutants of CO (carbon monoxide) and NOx (nitrogen oxides) will be monitored at the inlet, outlet and vertical shafts of the tunnel. Meanwhile, numerical simulation of three-dimensional turbulent flow will be performed using STAR-CD software. Traffic and pollutant concentrations during the weekends exceeded those during the weekdays. Measured concentrations of CO at the two tunnel outlets (14.5 ¡V 22.8 ppm) were approximately three times higher than those at the two tunnel inlets (3.2 ¡V 7.3 ppm), while concentrations of NOx at the two tunnel outlets (1.9 ¡V 2.9 ppm) were approximately four to five times higher than those at the two tunnel inlets (0.3 ¡V 0.8 ppm). The outlet of vertical draft 2 had the highest pollutant concentrations (CO = 12.3 ppm; NOx = 1.9 ppm), followed by vertical drafts 1 and 3. Three-dimensional turbulence modeling results indicate that airflow in the tunnel was primarily driven by the combined effects of axial fans and vehicles. Results of this study demonstrate that simulated pollutant concentrations increase downstream and are vertically stratified, due to tailpipe exhausts close to tunnel floor. Simulations agreed fairly well with measurements.