# Seasonal Characteristics of Particulate Matter by Pollution Source Type and Urban Forest Type
## 4 Discussion
The study found that during winter, coarse and large particle concentrations were higher for urban traffic than in industrial areas, with lower potassium (K) and higher calcium (Ca) levels for roads compared to industrial sources. [PERSON] et al. (2022) [39] reported that the presence of inorganic PM on plant leaves within forest interiors suggests that non-exhaust road emissions can penetrate deep into forest areas. Although this study identified seasonal and plot-specific differences in PM\({}_{10}\) concentrations, further research is necessary to understand the PM\({}_{10}\) reduction effects of urban forests near pollution sources. Such research should involve more detailed classifications of PM concentration levels or methods for distinguishing the PM composition between pollution sources. In summary, during the first half of the year, when PM\({}_{10}\) concentrations were high, it was confirmed through the distribution of data that PM\({}_{10}\) levels in forest interiors and forests were clustered in the lower concentration range. In the second half of the year, lower concentrations in forest interiors and forests were confirmed through median differences. Notably, during early year and early spring--periods when PM\({}_{10}\) median concentrations remained high--the distributions of PM\({}_{10}\) concentrations in pollution sources and forests were distinctly separated. Given the lack of consistent, prominent patterns in meteorological conditions, such as wind speed, wind direction, temperature, and humidity, it is likely that both road and industrial areas are more influenced by physical and environmental factors, such as the distance from pollution sources and the barrier effect of forests. Previous studies conducted at specific sites suggest that the amount of PM accumulated on trees increases as the distance from the pollution source to the forest increases [40; 41]. Vegetative barriers act as physical barriers that spatially separate pollution sources, thereby reducing exposure to air pollutants [12; 39]. The lower PM\({}_{10}\) concentrations in forests, in particular, can be attributed to the physical structure of the vegetation, which restricts airflow through the forest and causes particle deposition on plant surfaces [42]. Additionally, the more extensive the vegetative barrier, the greater the amount of PM removal [43; 44; 45; 46]. In the non-parametric analysis of variance (ANOVA) used to analyze PM\({}_{10}\) concentrations--characterized by a skewed distribution--no significant median differences were observed between groups, whereas differences were evident in the overall distribution. This suggests that it is challenging to identify group differences using means or medians due to the highly asymmetric nature of PM\({}_{10}\) data. In such cases, applying logarithmic or square root transformations can narrow the range between variables, making the distribution more similar to a normal distribution. Alternatively, utilizing homoscedasticity tests rather than relying solely on summary statistics, such as means or medians, could better explain the difference between groups through variance comparison. In this study, we aimed to identify the consistent characteristics of PM\({}_{10}\) concentrations in urban forests near similar pollution sources by focusing on regions with common environmental conditions rather than limiting the analysis to individual locations or specific environmental conditions. However, there are limitations, such as not fully accounting for geographic and environmental differences, including coastal versus inland areas and latitudinal variations. Additionally, PM\({}_{10}\) data are not simple continuous numerical data but are categorized into distinct levels, such as \"good\", \"not bad\", and \"bad\". Moreover, since the distribution is not normally distributed around the mean but instead is right skewed with data clustering in lower value ranges, relying solely on summary statistics, such as means and medians, it was insufficient to fully capture PM characteristics.