| 英文摘要 |
In order to respond to short-term air pollution caused by weather variations or other factors, both the Environmental Protection Administration(EPA) and local governments took various contingency measures in advance based on air quality forecasts, such as reducing emissions from factories in upwind counties and coordinating coal-fired power plants to reduce emissions and loads. However, after a series of contingency measures were implemented, how effective was the improvement in air quality? Can the qualitative description of improvement from reduction be advanced to quantitatively evaluate the effectiveness of air pollution contingency measures, especially since coal-fired power plants distributed throughout Taiwan had always been an important source of pollution? How to plan different combinations of power plant emissions reduction and load reduction according to different weather conditions to achieve the best improvement in air quality has always been an important issue that the Environmental Protection Administration wants to understand.
In order to clarify the impact of weather conditions on air quality in Taiwan, our team integrated the weather patterns of high pollution events in previous years and investigate their influence on pollution concentrations in different regions of Taiwan. At the same time, we used air quality modeling to simulate high pollution events and assess multiple pollution reduction scenarios for historical air quality events (including PM2.5 and ozone), providing the Environmental Protection Administration with multiple scientific evidence to assist in the formulation of emergency air quality contingency measures. Through air quality modeling and analysis of the differences in emissions in various air quality zones, we will analyze specific pollutant sources that can be improved through better regulation. By integrating the simulation results of the above weather patterns, historical air quality events, and specific pollutant sources, we established a process that combines air quality forecasts with the analysis of the contribution levels of different pollutant sources, which can be used in the execution of central and local air quality emergency contingency measures. Through the analysis of scientific modeling data, this project provide real-time feedback on the contribution levels of different pollutant sources for decision-making reference, and roll out contingency control strategy formulation to enhance the effectiveness of air quality deterioration prevention and response.
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