Evaluating the efficacy of air pollution control policies is an essential part of the decision-making process to develop new policies and improve existing measures.  In this analysis, we assessed the effects air pollution control policies in the Taiwan Strait Region from 2005 to 2018 using full-coverage, high-resolution PM_2.5generated by a satellite-driven machine learning model. A ten-fold cross-validation for our prediction model showed an R²value of 0.89, demonstrating that these predictions can be used for policy evaluation. During the 14-year period, PM_2.5levels in all areas of Fujian and Taiwan underwent a significant decrease. Separate regression models for policy evaluation in Taiwan and Fujian showed that all considered policies have mitigated PM_2.5pollution to various degrees. The Clean Air Action Plans (CAAP) is the most effective control policy in Taiwan, while the Action Plan of Air Pollution Prevention and Control (APPC-AP) and Three-year Action Plan for Blue Skies (3YAP-BS) as well as their provincial implementation plans are the most successful in Fujian. The effectiveness of control policies, however, varies by land-use types especially for Taiwan.

 

 

In the article, the authors focus on a region where traditional satellite AOD models have not performed as well compared to other areas of the US, in order to determine which region-specific parameters have the highest impact on model accuracy. Using a two-stage linear approach, the authors identified important meteorological and land use parameters including temperature, relative humidity, wind, road distance, distance to coast and other local factors. Of note, a variable representing the ratio of PM_2.5 to PM₁₀ particles was included to account for airborne dust. This parameter is not generally used in other regional analyses and was an important addition to the Southern California model, improving accuracy from R² of 0.70 to 0.80. This study adds significantly to the current body of research in the region of the Southwestern US, suggesting that the influence of PM₁₀ to AOD should be considered in areas with high concentrations of airborne dust.