Seasonal Characteristics of Particulate Matter by Pollution Source Type and Urban Forest Type - Bobae Lee; Hong-Duck Sou; Poungsik Yeon; Hwayong Lee; Chan-Ryul Park; Sumin Choi; Seoncheol Park (Applied Sciences)
To provide consistent air purification benefits from urban forests, it is crucial to identify common characteristics that allow for similar experimental setups. This study aimed to analyze <semantics>PM10</semantics> concentrations in urban forests near pollution sources and understand their mitigation effects. Data from the Asian Initiative for Clean Air Networks, Korea, were used, focusing on three urban forests adjacent to road and industrial pollution sources in Korea, with <semantics>PM10</semantics> concentrations collected during 2021. Considering high <semantics>PM10</semantics> concentrations during winter and spring, these seasons were divided into two sub-periods, resulting in six seasonal periods for analysis. To address the right-skewed <semantics>PM10</semantics> distribution and reduce outlier influence, the Kruskal–Wallis test was used. The results showed that “good” <semantics>PM10</semantics> levels were lowest in early spring, increasing to a peak in summer before declining. High <semantics>PM10</semantics> events were concentrated in spring, early spring, and early winter. The Kruskal–Wallis test indicated lower median <semantics>PM10</semantics> concentrations in urban forests compared to pollution sources in the latter half of the year, while no significant median differences were found in the first half. Distribution visualizations further confirmed that even during high <semantics>PM10</semantics> periods, all urban forests showed lower <semantics>PM10</semantics> values compared to pollution sources. In conclusion, <semantics>PM10</semantics> concentrations in urban forests were consistently lower than in pollution sources across all seasons, demonstrating their effectiveness in air purification at both road and industrial pollution sources. Future research should consider additional variables, such as <semantics>PM2.5</semantics>, to further explore differences between pollution sources.