The objective of the study was to investigate the air pollution of working place and the fence of aluminum plants. Four sites were selected for conducting the sampling of particulate matter¡]PM¡^to establish the fingerprints of PM in aluminum plants. Moreover, the receptor model was used to apportion the major contributing sources of particulate matter. The results of the study showed that PM concentrations in the working places were high, which might be emitted from manufacturing or mechanical operating process in the melting furnace, the continuous casting, and etc. The total suspended solid concentrations from non-combustion pollution source ¡]such as hot rolling , cold rolling, and plate industry¡^ were usually below 200 µg /m3. Moreover, the size distributions of PM in working places were usually bi-modal type. The particle sizes with the highest concentration in five and coarse fractions were 0.18~0.56 µm respectively. As for the combustion sources, the particle sizes with the highest concentration were 0.56~1.0 µm and 11.5~24.7 µm, respectively. The particle emitted from zinc casting source were mostly in the fine particle fraction¡]1.0~3.2 µm¡^. The fingerprint profiles of PM from the working places showed that the major metal content of the hot rolling were Al, Zn, K, Cr. Among then, Cr might be source of from the using of chromate acid. The most abundant water-soluble ions were sulfate ion and chlorine and chloride. The carbon content accounted for about 22% of the PM mass. The ratio of organic carbon to elemental carbon were 2.54 and 2.80. The major metal content of melting process was Al, the minor metals were Zn, k, Ca, Fe. The ratio of anion to cation¡]A/C¡^ was about 0.80. The most abundant ion sulfate, while the chloride, the fluoride, potassium, and the ammonium ions were minor ones. The carbon content accounted about 19.2% and 27.3% of PM mass respectively. The ratio of organic carbon to elemental carbon were 1.21 and 1.07. Moreover, the major metals were k, Ca, Fe. The major ion was chloride, while the nitrate calcium and potassium ions were minor ones. The A/C was 0.7. The carbon content accounted for 54.8% and 51.0% of PM2.5 and PM10, respectively. The ratio of organic carbon to elemental carbon was about 2.9. The results obtained from receptor modeling showed that the contribution percentage of fugitive PM from the aluminum plant (including hot rolling, melting furnace, and low-wave furnace) was between 5.9 and 13.2%, Another major source was traffic refracted pollution with a contribution percentage between 17.0% and 23.0%, Fur then more, in the surrounding PM, was between 11.2% and 20.7%, while the contribution from the steel plant and Moreover, the municipal incineration was 2.5~6.5% and 2.3~5.0%,respectively. The sea salt contributed 4.3~7.1% of PM mass. The unsolved percentage was 7.0~11.8%.

This research used high-volume air sampling (PS-1) and micro-orifice uniform deposit impactor (MOUDI) to measure concentrations of polycyclic aromatic hydrocarbons (PAHs) in the air of a agricultural residue open burning area in Jhushan and Singang station during the rice straw non-burning and burning periods. And PAHs of different size distributions are analyzed. Finally, absolute principal component analyze (APCA) model confer the probable sources of pollution in open burning area. The average PAHs concentrations were 330.04 and 567.81 ng/m3 during the rice straw non-burning and burning period in Jhushan station, the average PAHs concentrations were 427.16 and 571.80 ng/m3 during the rice straw non-burning and burning period in Singang station, respectively, in the rice straw burning period, which were higher than those on the non-burning days. The results of APCA model analysis showed that the contributions of PAHs from mobile source (gasoline and diesel) were 66.50 ¡Ó 7.99 %, burning incense in temple source were 14.83 ¡Ó 6.68 % and burning coal and wood source were 18.67 ¡Ó 6.17 % during the rice straw non-burning period. PAHs from mobile (gasoline) and rice straw non-burning source were 57.27 ¡Ó 6.90 %, mobile source (diesel) were 42.73 ¡Ó 6.89 % during the rice straw burning period in Jhushan station. The results of APCA model analysis showed that the contributions of PAHs from mobile (diesel) and burning incense in temple source were 45.67 ¡Ó 6.43 %, mobile (gasoline) and plastics incinerator source were 54.33 ¡Ó 6.39 % during the rice straw non-burning period. PAHs from burning incense in temple, rice straw, mobile (gasoline and diesel) source were 50.69 ¡Ó 4.55 %, plastics incinerator source were 36.78 ¡Ó 4.24 % and other source were 12.53 ¡Ó 2.71 %¡C

The high-volume air sampling (PS-1) and micro-orifice uniform deposit impactor (MOUDI) were used to measure the concentrations of polycyclic aromatic hydrocarbons (PAHs) in the atmosphere for four seasons at Zuoying and Siaogang in Kaohsiung city, together with the size distributions. Also, the CMB (Chemical Mass Balance) receptor model was employed to determine the potential sources of PAHs. The results show that the highest concentrations of PAHs occurred in winter, being 143.9 ng/m3 and 182.9 ng/m3 at Zuoying and Siaogang, respectively; while the lowest concentrations of PAHs occurred in summer, being 81.4 ng/m3 and 95.2 ng/m3. The low-weight PAHs in the two sites were abundant in gaseous phase, being 55.89 - 95.89 % and 67.07 - 96.61 % at Zuoying and Siaogang, respectively. Meanwhile, the high-weight PAHs were almost present in particulate phase, being 55.24 - 83.50 % and 46.87 - 77.26 % at Zuoying and Siaogang, respectively. The sizes of 50th percentile of cumulative size distribution, d50 of Zuoying and Siaogang were 0.89 £gm and 0.35 £gm, respectively. Hence, most atmospheric PAHs existed in fine-particle ranges (¡Õ1.0£gm). The results of by CMB receptor modeling indicated that the major sources of pollution was exhaust emission (49 - 62 %) in Zuoying, and was burning source (49 - 64 %) in Siaogang.

In this study, visibility observation, aerosol sampling, statistical analysis and model regression were conducted to investigate the influence of suspended particle characteristics and pollution sources on visibility and extinction coefficient in Koahsiung metropolitan area. The scene monitored by a digital camera was then proceeded by digital image processing and were then compared with observed atmospheric visibility observation. Meteorological parameters of various weather patterns (including relative humidity, wind direction, wind speed and mixing height ) played important roles on the reduction of visibility in metropolitan area. Synoptic charts were collected over the 1992-1999 period to analyze their influence on ambient air quality. This study revealed that high PM10 concentration and unhealthful PSI index occurred at weather patterns of high pressure outflow style I and circus-sluice of high pressure outflow¡C Regular visibility was observed during the period of November 1998- April 2000. The highest visibility was above 20 km while the lowest visibility was loss than 1 km in Koahsiung metropolitan area. The observed visibility was mainly distribution over the 2-6 km. The visibility below 6 km were about 61.88% of total observation days and poor visibility was usually occurred in winter. Besides, intensive visibility observation was conducted in January and March, 2000. Visibility was observed hourly at Kaohsiung Meteorological Station and Fa-Shin Temple, respectively. Suspended particles were continuously sampled for five hours at Chien-Chen, Sen-Min and Chien-Gin ambient air quality stations. These measurements were conducted to investigate the influence of chemical and physical properties of suspended particle and meteorological parameters on visibility and extinction coefficient in Koahsiung metropolitan area. Receptor model was applied to understand the emission sources of fine particles (PM2.5) and investigate the influence of emission sources on visual air quality. In addition, the determination of visibility by imagine processing was discussed. Visibility observation was coincided with scene monitoring in order to clarify the relationship between image processing and observed visibility. A illumination eigenvalue calculated by picture transfer software was used to correlate with observed visibility. This study revealed that, illumination eigenvalue and observed visibility had strong negative correlation (R=-0.9079) at effective visual range of 5-10 km. Results form single-factor analysis indicated that no significant variation of aerosol particle concentration was observed at three ambient air quality stations. A bi-mode size distribution of aerosol particles was observed for most stations in Koahsiung metropolitan area. The peak aerodynamic diameter of fine and coarse particles was observed at 0.56-1.0

Air quality of the Kao-Ping airshed has been the worst of all airsheds which are divided into seven groups by districts in Taiwan. The percentage of annual bad air quality (Pollution Standard Index, PSI > 100) in the Kao-Ping airshed (6.65−13.56%) was twice than it in the Yun-Chia-Nan airshed (2.58−6.98%) during the past decade (1998−2007). Oil refineries, petrol/plastic industries, power plants, and iron/steel/metal plants are the major industries in the Kaohsiung metropolitan area. Due to intensive industrial and traffic activities, the Kao-Ping area has the poorest air quality in Taiwan − either increased ground-level concentrations of particulate matter (PM) or ozone (O3) associated with unfavorable meteorological conditions − particularly between late fall and mid-spring The temporal and spatial characterization of suspended particles in the Kao-Ping area was analyzed by using TAPM (air quality model) and CMB (receptor model) to understand the contributions of the major emission sources. Estimations using the TAPM model suggest that point-source emissions were the predominant contributors (about 49.1%) to PM10 concentrations at Hsiung-Kong industrial site in Kaohsiung City, followed by area sources (approximately 35.0%) and neighboring transport (7.8%). Because Ping-Tung City (urban) and Chao-Chou town (rural) are located downwind of Kaohsiung City when north or northeasterly winds prevail, the two sites also experience severe pollution events despite the lack of industrial sources; neighboring transport contributed roughly 39.1% to PM10 concentrations at Ping-Tung and 48.7% at Chao-Chou. Results of CMB (chemical mass balance) modeling show that the main contributors to PM2.5 mass are vehicle exhaust (gasoline vehicle emission: 43% and diesel vehicle emission: 17% at Hsiung-Kong; gasoline vehicle emission: 45% and diesel vehicle emission: 19% at Ping-Tung; gasoline vehicle emission: 12% and diesel vehicle emission: 29% at Chao-Chou). And the main contribution to PM2.5-10 mass is the paved road emission (Hsiung-Kong: 40%; Ping-Tung: 48%; Chao-Chou: 50%). It is recommended that air quality model is an appropriate tool to large area and receptor model is more suitable to specific point to identify emission sources by the results in this study.

Steel industry is a highly polluted industry and one of the most important stationary sources in Kaohsiung City. The steel manufacturing process could emit a huge amount of particles, such as the sintering process, the blast furnace operation, and the raw material handling process. Suspended particles emitted from steel industry could deteriorate ambient air quality and cause adverse effects on human health. In order to understand the impact of steel industry on ambient air quality in Siaogang District and to identify potential pollution sources, this study selected a integrated steel manufacturing plant located at Siaogang District to conduct a sampling protocol of suspended particulate matter (PM) at ambient sites (A1~A5) and sensitive sites (S1~S5) from July 2011 to March 2012. The size distribution of suspended particles in four seasons was measured with PM10 high-volume samplers, dichotomous samplers, and MOUDI for 3 days (24 hours for single sampling), and dustfall samplers for one month, to investigate the spatial distribution and temporal variation of PM concentration. After sampling, the physicochemical properties of PM, including mass concentration, particle size distribution, dustfall concentration, water-soluble ionic species, metallic elements, and carbonaceous contents, were further analyzed. Field measurement of ambient PM showed that the averaged ambient PM10 concentration (53.54 - 203.56 £gg/m3) were higher than sensitive sites (55.06 - 140.07 £gg/m3) and the averaged ambient PM2.5 concentration of ambient (23.10 - 120.21£gg/m3) were higher than sensitive sites (12.52 - 65.62 £gg/m3). No matter ambient or sensitive sites, it showed a tendency of lower concentration in summer, indicating that concentration variation of PM10 and PM2.5 were highly affected by meteorological factors (such as wind direction, wind speed, and relative humidity) in Siaogang District. Furthermore, a t-test result showed that ambient and sensitive sites have similar pollution sources since the p-values were in significantly different. Chemical analysis of PM results showed that the most abundant water-soluble ionic species of PM at the ambient and sensitive sites were secondary inorganic aerosols (SO42-, NO3-, and NH4+) and [NO3-]/[SO42-] showed that ionic species were mainly emitted from stationary sources. Fe, Al, K and Ca were the major metallic elements of this study, and the major pollution sources contain industries, traffics, and road dusts. Additionally, the raw material handling process was the major pollution source of PM. Correlation analysis of OC and EC showed that PM at ambient and sensitive sites were originated from primary sources, such as vehicles, industries, road dusts, and human activities. Results obtained from PCA and CMB receptor modeling showed that both PM2.5 and PM10 highly correlated with wind direction in different season and the major pollution sources were industry pollution (including petroleum refineries, power plants, waste incinerators, consistent operating steel mills and electric arc furnace steel mills, etc.), followed by local traffics and derivative. Furthermore, marine aerosols were one of the important pollution sources at sensitive sites (S1, S4, and S5) where close to the ocean.

The concentrations of seventy-one hydrocarbons (HC) from C2 to C15 were measured simultaneously at two sites in Kaohsiung city in the morning (07-10), the afternoon (13-16), and the evening (18-21) on 14 days in spring 2003. Results show that the most abundant species of Kaohsiung¡¦s air is toluene (43.36-54.49 £gg m-3), followed by i-pentane, 1,2,4-trimethylbenzene, benzene, n-butane, propane and acetylene, in the range 10.36¡V17.11 £gg m-3. The concentrations of 14 halocarbons are in the range 0.25¡V4.57 £gg m-3. Alkanes (around 44.8%) represent the largest proportion of the total HC, followed by aromatics (35.1%), alkenes (15.5%) and halocarbons (5.4%). The afternoon HC concentrations are much lower than those in the morning and at night, due to relatively intense photochemical reaction and favorable dispersion conditions from noon to afternoon. Notable increases in daily HC concentrations are consistent with high temperature, and low HC concentrations on Sunday coincide with low traffic volume. Photochemical activity is investigated, and HC concentrations are found to decline as the NO2/NOx ratio increases. Correlation analyses imply that vehicle exhaust is the dominant source of atmospheric hydrocarbons in Kaohsiung. The profiles of traffic exhausts were also measured for 25 HC species during the morning and afternoon rush hours on four different days in all three traffic tunnels in Kaohsiung City. Results show that VOC concentrations increase with traffic flow rate, and emission profiles in the three tunnels are mostly in the range C2 ¡V C6. Besides the traffic conditions and vehicle type, the pattern of emissions in each tunnel was also influenced by other factors, such as vehicle age, nearby pollution sources, and the spatial or temporal variation of HC in the urban atmosphere. The ozone formation potential (OFP) in each tunnel was assessed based on the maximum incremental reactivities of the organic species, demonstrating that OFP increases with traffic flow rate. Vehicle distribution influences the contributions of organic group to OFP in a tunnel. Meanwhile, when ranked in descending order of contribution to OFP in all tunnels, the organic groups followed the sequence alkenes, aromatics, and alkanes. The possible source categories affecting the atmospheric HC species were further analyzed using factor analysis. Results showed that the major sources of ambient HC at the Nan-Chie and Hsiung-Kong sites are: vehicle exhaust, petrol/diesel exhaust, industrial processes (for example, plastic/rubber process), combustion exhaust, solvent fugitive or business/consume exhaust. Based on the results of factor analysis, source profiles (or fingerprints) were selected and receptor modeling was conducted based on chemical mass balance (CMB). Results of receptor modeling indicated that, at Nan-Chie site, vehicle exhaust (46.33% and 56.36%) represent the largest proportion of total HC, followed by industrial processes (29.63% and 22.37%) in the morning (07-10) and the evening (18-21), respectively; but were industrial process (40.39%) and solvent fugitive exhaust (30.61%) in the afternoon (13-16). Similarly at Hsiung-Kong site, vehicle exhaust (around 46.19% and 49.29%) represent the largest proportion of total HC, followed by industrial processes (23.19% and 26.11%) in the morning and evening, respectively; but were solvent fugitive exhaust (38.85%), vehicle exhaust (28.95%) and industrial process (25.19%) in the afternoon. It is evident that relatively low traffic volumes in the afternoon at both sites reduce the contribution of traffic exhaust to ambient HC.

In this study, the occurrence and distribution of polychlorinated biphenyls (PCBs) were investigated via sampling studies conducted around a thermal power plant (Seyit&ouml / mer, K&uuml / tahya), a scrap metal yard (Kizilirmak, Kirikkale), transformer repair and maintenance facility (Lake Eymir, Ankara), and two organized industrial districts (izmit and Mersin), and 120 samples composed mainly of sediments were collected from those sites. Total PCBs ranged from not detected to 385 ng/g for all samples. Analysis of samples indicates enrichment of PCBs with special emphasis to sediments collected around the Seyit&ouml / mer thermal power plant. Congener specific results indicate domination of profiles by penta- and hexa-chlorobiphenyls. Overall, the PCB concentrations observed in sampling sites are comparable to the background levels of soil/sediments around the world. To identify relevant pollution sources, congener specific data were further evaluated in the subsequent process of source apportionment using Chemical Mass Balance (CMB) receptor model. A general overview of the source apportionment results indicate that equipments (transformers and capacitors) mainly used in the energy generation/transmission and high energy consuming industries as the major PCB sources. PCBs used in open applications were also predicted as sources depending on site characteristics. Overall, indications of contaminated sites are evident in a number of locations / yet, no major contamination is evident in any media according to the current relevant national regulatory actions. However, findings of this study suggest that, over expanded time exposure, threat to the environment and human health may be of concern.

The high-volume air sampling (PS-1) and micro-orifice uniformdeposit impactor (MOUDI) were used to measure the concentrations ofpolycyclic aromatic hydrocarbons (PAHs) in the atmosphere for fourseasons at Tuzo-Yin and Hsiung-Kong site in Kaohsiung city, in the airof a agricultural residue open burning area in Jhu-Shan and Sin-Gang siteduring the rice straw non-burning and burning periods, together with thesize distributions. Also, the receptor model was employed to determinethe potential sources of PAHs. The results show that the highest concentrations of PAHs occurred inwinter, being 143.9 ng/m3 and 182.9 ng/m3 at Tzuo-Yin and Hsiung-Kongsite, respectively; while the lowest concentrations of PAHs occurred insummer, being 81.4 ng/m3 and 95.2 ng/m3. The low-weight PAHs in thetwo sites were abundant in gaseous phase, being 43.8−96.7% and65.2−97.5% at Tzuo-Yin and Hsiung-Kong site, respectively. Meanwhile,the high-weight PAHs were almost present in particulate phase, being40.5−95.2% and 24.8−94.1 % at Tzuo-Yin and Hsiung-Kong site,respectively. The average PAHs concentrations were 330.04 and 567.81 ng/m3during the rice straw non-burning and burning period in Jhu-Shan site, theaverage PAHs concentrations were 427.16 and 571.80 ng/m3 during therice straw non-burning and burning period in Sin-Gang site, respectively,in the rice straw burning period, which were higher than those on thenon-burning days. The results of by CMB receptor modeling indicated that the major sources of pollution was exhaust emission (49.5−63.3%) in Tzuo-Yin site,and was burning source (49.1−63.7%) in Hsiung-Kong site in Kaohsiungcity. The results of APCA model analysis indicated that the major sourcesof pollution was mobile source (gasoline and diesel) were 66.5¡Ó8.0%during the rice straw non-burning period, and was mobile (gasoline) andrice straw non-burning source were 57.3¡Ó6.9% during the rice strawburning period in Jhu-Shan site in Central Taiwan. The results of APCAmodel analysis indicated that the major sources of pollution was mobile(gasoline) and plastics incinerator source were 54.3¡Ó6.4% during the ricestraw non-burning period, and was burning incense in temple, rice straw,mobile (gasoline and diesel) source were 50.7¡Ó4.6% during the rice strawburning period in Sin-Gang site in Central Taiwan.

In recent years, the air quality of Kinmen-Xiamen region has deteriorated gradually, and PM10 was always the worst air quality indicator. Particularly, high PM10 concentration has been observed in spring and winter. The objective of this study was to characterize the chemical properties of atmospheric aerosol particles sampled at Xiamen Bay located at the west coast of Taiwan Strait by sampling atmospheric particles and using chemical mass balance (CMB) receptor model for source apportionment, which indicated the difference of background and episode periods. Furthermore, this study applied HYSPLIT model to figure out the transportation routes of polluted air mass by backward trajectory. Seven particulate matter (PM) sampling sites at Xiamen Bay, three sites at Kinmen Island and four sites at metro Xiamen, were selected for this particular study. Particulate matter sampling included regular and intensive sampling. Intensive sampling was conducted to collect PM2.5 and PM2.5-10 with dichotomous samplers in the spring and winter of 2008 and 2009, while regular sampling was conducted to collect PM10 with high-volume samplers twice a month since March 2008. Results from PM sampling indicated that atmospheric particles had a tendency to accumulate in Xiamen Bay all year round, particularly in spring and winter. Five sampling sites inside the Xiamen Bay had relatively higher PM concentration than two sampling sites outside the Xiamen Bay. It suggested that local emission at the Xiamen Bay was superior to long-range transportation from the Northeastern Monson. A superimposition phenomenon was regularly observed during the episodes at Xiamen Bay. The most abundant water-soluble ionic species of PM were SO42-, NO3-, and NH4+ at Xiamen Bay, the major chemical species of PM were secondary aerosols (i.e. (NH4)2SO4 and NH4NO3). Crustal elements (e.g. Ca, Mg, Fe, and Al) and anthropogenic elements (e.g. Zn and Pb) dominated the chemical species of particles. Backward trajectory results indicated that polluted air mass originated from Asian continent moved directly to Kinmen-Xiamen region in winter and spring, while air mass originated from the southwestern and southeastern ocean did not pass polluted region in summer, which result in better air quality of Kinmen-Xiamen region in summer than those in winter and spring. Results from CMB receptor modeling showed that the major sources of atmospheric PM10 at Kinmen-Xiamen region were soil dust, secondary aerosol, petroleum industry, motor vehicle exhanst, iron and steel industry, cement industry, Diesel vehicle exhanst marine aersols, and vegetative burning. The stationary sources were the major contributor accounting for approximately 50% of PM10 in Kinmen. It suggested that atmospheric particles were mainly originated from cross-boundary transport rather than local emission sources since there are no such kinds of industrial factories in Kinmen.