A Review on Analytical Approaches for Ambient Ozone Open Data in Taiwan

Abstract

Purpose of Review

The air quality in Taiwan is significantly impacted by ambient ozone (O₃) pollution, which poses a challenge in terms of control due to the involvement of precursors and influencing factors in its photochemical process. This review investigates the measures that have been implemented in Taiwan over the past two decades to address this issue and evaluate their effectiveness in reducing O₃ concentrations. Furthermore, it highlights relevant studies that have employed advanced methods to examine the O₃ problem.

Recent Findings

Comprehending the complex formation of O₃ and its driving factors is crucial in efficiently managing O₃ pollution. Nevertheless, accurately quantifying the impacts of these factors can be challenging due to their interconnections. To bridge this gap in knowledge, conducting a robust causality analysis becomes imperative to accurately quantify the causal influence of major factors. Furthermore, eliminating seasonal variations can improve the precision and accuracy of trend analyses concerning long-term changes in O₃ concentrations. Deep learning, in particular, holds significant advantages in predicting O₃ concentrations as it can capture non-linear and long-term memory characteristics effectively.

Summary

In summary, attaining acceptable O₃ levels in Taiwan is challenging due to its geographical location, which makes it susceptible to air pollution from both local sources as well as distant upwind areas. The utilization of advanced methods is essential for comprehensively studying the evolution of O₃ and formulating effective mitigation measures. While there is a necessity to develop new analytical methods, implementing existing robust methodologies can also provide valuable insights into the dynamics and impacts of O₃. By leveraging these approaches, we can enhance our comprehension of O₃ pollution in Taiwan and develop effective strategies to mitigate its harmful effects on air quality.