The growing interest and usage of green space during the pandemic relates to why greenery increases enjoyment of daily life and alleviates troubles arising from infectious periods. However, it is necessary to understand what reactions to the degree of COVID-19’s spread were expressed in hiking exercise characteristics (hiking frequency, actual movement distance, average speed, total exercise time). To understand the progress of forest usage behavior during the pandemic, we analyzed factors influencing hiking exercise characteristics and relationships between those factors and hiking exercise characteristics. Hiking record data were obtained (2019–2020) from the workout app “Tranggle” pertaining to hikers in their 20s and 30s who visited the 13 mountains in the Seoul Metropolitan Area most frequently. The differences in hiking exercise characteristics (2019 data subtracted from 2020) were linked with factor data that could be related to them, including the degree of COVID-19’s spread ($R_t$). To explore relationships between hiking exercise characteristics and factors, we developed four models with a linear mixed model. We found that $R_t$, week, weekdays/weekends, and ${\mathrm{PM}}_{10}$ contributed to explaining the hiking exercise characteristics’ differences. As a result of this analysis, the degree of COVID-19’s spread ($R_t$) affected hiking frequency; the week affected hiking frequency and actual movement distance; weekdays or weekends affected hiking frequency, actual movement distance, average speed, and total exercise time; and ${\mathrm{PM}}_{10}$ affected hiking frequency. These findings indicate that hiking was an alternative way for those looking for a new strategy to replace lost opportunities for physical activity. Therefore, we conclude that it is necessary to induce the usage of green space so that many people can take advantage of the functions and benefits of greenery, which stood out during the pandemic era.

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 $\mathrm{P}{\mathrm{M}}_{10}$ 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 $\mathrm{P}{\mathrm{M}}_{10}$ concentrations collected during 2021. Considering high $\mathrm{P}{\mathrm{M}}_{10}$ 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 $\mathrm{P}{\mathrm{M}}_{10}$ distribution and reduce outlier influence, the Kruskal–Wallis test was used. The results showed that “good” $\mathrm{P}{\mathrm{M}}_{10}$ levels were lowest in early spring, increasing to a peak in summer before declining. High $\mathrm{P}{\mathrm{M}}_{10}$ events were concentrated in spring, early spring, and early winter. The Kruskal–Wallis test indicated lower median $\mathrm{P}{\mathrm{M}}_{10}$ 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 $\mathrm{P}{\mathrm{M}}_{10}$ periods, all urban forests showed lower $\mathrm{P}{\mathrm{M}}_{10}$ values compared to pollution sources. In conclusion, $\mathrm{P}{\mathrm{M}}_{10}$ 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 $\mathrm{P}{\mathrm{M}}_{2.5}$, to further explore differences between pollution sources.

Urban forests (UFs) are increasingly promoted as a nature-based solution for mitigating particulate matter (PM) pollution, yet their removal performance can vary depending on surrounding emission sources and environmental conditions. Here, we quantified the particulate matter reduction efficiency (PMRE) of UFs located near roads, industrial complexes, and urban areas, together with background forests in South Korea, based on field observations during the late autumn–spring period across two consecutive years (November–May in 2021–2022 and 2022–2023). We applied vector autoregression (VAR) to examine the dynamic relationships between PMRE and meteorological and air pollutant variables across eight representative sites. The results revealed that PM mitigation dynamics were strongly particle-size-dependent and context-specific. Across all sites, ΔPM₁₀ RE was predominantly self-driven, explaining over 90% of its own variance, whereas fine-particle dynamics showed stronger interdependence. In particular, ΔPM_2.5 RE consistently acted as a key mediator, accounting for up to 70%–80% of the variation in ΔPM_1.0 RE depending on source context. Industrial-complex-adjacent UFs exhibited the strongest cross-variable interactions, while urban-core UFs were largely governed by intrinsic mitigation processes. Roadside UFs showed site-specific responses associated with CO and temperature variability. Notably, PMRE responses exhibited damped oscillation patterns across all source contexts, converging toward equilibrium over time, indicating stabilization of mitigation performance following disturbance events. These findings demonstrate that urban forest air-quality benefits are highly context dependent and governed by particle-size-specific dynamics. Our results provide evidence-based guidance for designing and managing urban forests, emphasizing the need for source-specific strategies and prioritization of PM_2.5-oriented mitigation, particularly in industrial and roadside environments where fine-particle interactions are strongest.