| 英文摘要 |
(一) Air Quality around Pingtung County
The number of days that Air Quality Index (AQI) was over 100 only represents 23.5% of 2018, which has been 23.9% reduced from those recorded in 2014 (30.9%). This is the best annul air quality record ever. Additionally, the day fractions of red (Unhealthy) status are significantly reduced from 10.6% in 2014 to 4.8% in 2018, while the percentages of green (Good) status day increases from 41.1% in 2014 to 46.3% in 2018, indicating the air quality is being improved by highly concern and undergoing control strategies.
The annual and daily averages of atmospheric PM2.5 are 19.5 and 37.0 µg/m3, respectively, in 2018 by auto-monitoring EPA stations. This is also the best record ever, representing 43.0% and 52.3% reductions, respectively, from 2007. This result again supports the right direction and efficiency of air pollutant control strategies in Pingtung County.
(二) Characteristics of Atmospheric PM2.5 during Air Pollution Events
The air pollution event was predicted by the comprehensively consideration of air quality and wind field forecasts by EPA and CWB, as well as the forecasting simulation of international pollutant transport during the seasons with bad quality in general. The sampling sites were setup in Pingtung, Chaozhou, and Yanjou stations of EPA. The weather styles during pollution events include off-shored high pressure, high pressure reflux, weak northeast monsoon, and frontal passage.
PM2.5 are analyzed for their mass concentrations and chemical compositions, and further using chemical mass balance (CMB) model to evaluate the contributions of potential sources in the current study. The major contributors of atmospheric PM2.5 were secondary sulfate (SO42-, 29.74~32.15%) and nitrate (NO3-, 17.02~21.48%), being followed by traffic sources (16.56~20.98%), soil dust (4.39~8.23%), petrochemical industry (2.33~7.48%), and steel industry (2.82~4.67%). On the other hand, the PM2.5 around sampling sits in Pingtung during pollution event were mainly derived from the precursors in the upwind area according to the analyses of sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR).
(三) Air Quality Modeling
Model-3/CMAQ v5.1 coupled with Weather Research and Forecasting (WRF) model is employed to simulate the air quality during the desired period in the current study. The input emission data is the latest Taiwan Emission Data System (TEDS 9.0) based on the emission inventory collected in 2013 and developed by Taiwan EPA. The emission data includes anthropogenic and natural sources, while the emission database of East Asia is also included. There are four simulated periods, including spring (April), summer (July), autumn (October), and winter (January), to estimate the contributions (%) of international, cross-county, and self-emissions on the atmospheric PM2.5 around Pingtung County. Results show that the averaging contribution of international pollutant transport is 17.4% on total ambient PM2.5 mass concentration in Pingtung, as well as the contributions of self-emission and the other city/county are 37.6% and 45.0%, respectively. In other words, the atmospheric PM2.5 are mainly transported from the upwind counties/cities and followed by self-emissions and international transports. Notably, the contributions on PM2.5 during low-air quality season are much higher than the average level. For example, the inter-county transport dominates 63.5% of atmospheric PM2.5 in Pingtung during January, while the self-contribution reduces to 20.9%, emphasizing the importance of the emissions from upwind area during pollution events. Additionally, the increases of NOx-derived pollutants are the most significant during low-air quality season.
Consequently, the control strategies for reducing atmospheric PM2.5 in Pingtung County should focus not only on primary PM2.5 but NOx emissions. Nevertheless, the cross-county/city cooperation on emission control should be continuously operated for sustainable air quality improvement.
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