| 英文摘要 |
This project focuses on the atmospheric fine particle (PM2.5) levels and compositions around Chiayi City. The samples were taken with four seasons, including July to August (summer), October (autumn), December (winter), and February to March (spring). The results of atmospheric PM2.5 concentration and chemical compositions during all sampling season were input to chemical mass balance (CMB) model to simulate the contributions from various emission sources. For determining the contributions of primary particles and secondary gaseous precursors from the various pollution sources, the Model-3/CMAQ was employed to analyze three periodical events suggested by Taiwan EPA. The summery of whole project is as follows:
1.In spring, the PM2.5 concentrations were 33-61 μg/m3. The PM2.5/PM10 ratios were 45.9-58.0%, which is among the levels of normal urban area. The main wind direction were NW and N, leading to the relatively higher PM2.5 levels around the east of Chiayi City. The particulate accumulation in valley or near-mountain location occurred. The mass concentration of PM2.5 were mainly composed of 58.7% water soluble ions 14.4% carbonates, and 5.1% metals. The ion contents were majorly composed of NO3- (22.4%), SO42- (16.8%), and NH4+ (14.3%). The average OC (10.1%) mass content was higher the EC (4.9%).
2.In summer, the PM2.5 concentrations were 9-22 μg/m3. The PM2.5/PM10 ratios were 33.3-42.9%, which is significantly lower than other seasons. This could be resulted from the inhibition of primary particles and secondary gaseous precursors by temporal precipitation. The mass concentration of PM2.5 were mainly composed of 39.1% water soluble ions 18.0% carbonates, and 13.0% metals. The ion contents were majorly composed of SO42- (21.8%), NH4+ (8.8%), and NO3- (7.2%). The average OC (10.6%) mass content was higher the EC (7.4%).
3.In fall, the PM2.5 concentrations were 21-74 μg/m3, when the seasonal highest PM2.5 level occurred (74 μg/m3) at November 11th, 2014. The PM2.5/PM10 ratios were 34.9-59.1%, which is higher than those in summer. This could be resulted from the reducing temperature and the height of atmospheric boundary layer, which further inhibited the diffusion and concentrate the PM2.5 pollution. Also, the immigration of PM2.5 was along with the N wind from other regions. The mass concentration of PM2.5 were mainly composed of 54.2% water soluble ions 10.2% carbonates, and 7.5% metals. The ion contents were majorly composed of SO42- (21.9%), NO3- (16.6%), and NH4+ (11.0%). The average OC (6.4%) mass content was higher the EC (3.8%).
4.In winter, the PM2.5 concentrations were 29-69 μg/m3. There were four forecasting PM alert dates with concentration by 31-50 μg/m3. The PM2.5/PM10 ratios were 52.9-67.9%, reporting the higher fine particle level with more potential harmful effects than other seasons. The mass concentration of PM2.5 were mainly composed of 59.2% water soluble ions 13.7% carbonates, and 4.6% metals. The ion contents were majorly composed of SO42- (20.7%), NO3- (17.5%), and NH4+ (12.6%). The average OC (9.5%) mass content was higher the EC (3.9%).
5.There were two types of higher PM2.5 period: immigrating and local emission.
(1)Immigrating emission (November 1st, 2014). The 24h-average concentration of Chiayi site was 74 μg/m3, which was the highest level in fall in 2014. The peak value of PM2.5 concentration were moved from the mid of Taiwan to Yun-Chia-Nan air quality control region and further to Kao-Ping area. There were no significant accumulation when the atmospheric air stream flow by Chiayi City.
(2)Local emission (January 18th and 21st, 2015). The 24h-average concentration of Chiayi site were 67 and 55μg/m3. The PM2.5 local peak values at all sites in Middle, Yun-Chia-Nan, and Kao-Ping air quality control regions occurred at the same date. This indicates the local emission might dominate the PM2.5 increments in this case, as well as the locally higher concentration were around the Kaohsiung area.
6.Quantifying the effects of precursors and meteorological factors to PM2.5 levels. The “possibility of exceeding standard” were calculated by Logistic multi-component regression. The precursors (NOx, SO2, NMHC, and O3) levels and meteorological factors (temperature, relatively humidity, and wind speed) record by the Chiayi site during 2005-2014 were inputted. The detail seasonal result equation were listed in this report.
7.The main contribution for locally atmospheric PM2.5 by CMB model were as follows (in order of contributions). Spring: secondary nitrate, traffic source, secondary sulfate, re-suspending soil particle, petrochemical industry, and agricultural open burning. Summer: secondary sulfate, secondary nitrate, re-suspending soil particle, petrochemical industry, traffic source, sea salt, cement industry, and metallurgical industry. Fall: secondary sulfate, secondary nitrate, traffic source, petrochemical industry, re-suspending soil particle, agricultural open burning, sea salt, cement industry, and metallurgical industry. Winter: traffic source, secondary nitrate, secondary sulfate, agricultural open burning, petrochemical industry, re-suspending soil particle, sea salt, cement industry, and metallurgical industry.
8.The Taiwan Emission Data System 8.1 (TEDS 8.1) was utilized as the input data to Model-3/CMAQ for modeling seasonal PM2.5 contributions from stationary, mobile, and area sources. Following results show the self-contributions were higher in summer and fall than in winter and spring. Total self-contribution: 0.74% in spring, 7.30% in summer, 4.30% in fall, and 0.19% in winter.
9.The recommendation PM2.5 control strategies for Chiayi City follows the PM2.5 control directions of Taiwan EPA. Please refer to Chapter 6 for detail.
(1)Local pollution control
Mobile source control strategies
Area source control strategies
Stationary source control strategies
Advocacy and protection
(2)The impact of immigrating PM2.5
(3)Air quality alert and emergency management
(4)Enhance the administrative control system
(5)Improve the public communication
(6)Promote the inter-bureau cooperation
(7)Continuous sampling, analyzing, modelling, and setting up the fingerprints of local emission sources
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