Broad-leaved evergreen trees create urban forests for mitigation of climate warming and adsorption of particulate matter (PM). This study was performed to identify the species suitable for urban greening by examining the adsorption capacity of the evergreen species in urban areas in Korea, the adsorption points and the elemental composition of PM in the adsorbed tree. Leaf sampling was carried out four times (period of seven months from October 2017 to May 2018) and used after drying (period 28 to 37 days). Particulate matter (PM) was classified and measured according to size PM2.5 (0.2–2.5 μm), PM10 (2.5–10 μm), PM100 (10–100 μm). The total amount of PM adsorbed on the leaf surface was highest in Pinus densiflora (24.6 μg∙cm−2), followed by Quercus salicina (47.4 μg∙cm−2). The composition of PM adsorbed by P. densiflora is 4.0% PM2.5, 39.5% PM10 and 56.5% PM100, while those adsorbed by Q. salicina are evergreen at 25.7% PM2.5, 27.4% PM10 and 46.9% PM100. When the amount of PM adsorbed on the leaf was calculated by LAI, the species that adsorbed PM the most was P. densiflora, followed by Q. salicina, followed by Q. salicina in the wax layer, then P. densiflora. As a result of this study, the amount of PM adsorbed per unit area of leaves, and the amount of PM calculated by LAI, showed a simpler pattern. The hardwoods had a high adsorption rate of PM2.5. The adsorption ratio of ultra-fine PM2.5 by evergreen broad-leaved trees was greater than that of coniferous trees. Therefore, broad-leaved evergreens such as Q. salicina are considered very suitable as species for adsorbing PM in the city. PM2.5 has been shown to be adsorbed through the pores and leaves of trees, indicating that the plant plays an important role in alleviating PM in the atmosphere. As a result of analyzing the elemental components of PM accumulated on leaf leaves by scanning electron microscopy (SEM)/ energy dispersive x-ray spectroscopy (EDXS) analysis, it was composed of O, C, Si, and N, and was found to be mainly generated by human activities around the road. The results of this study provide basic data regarding the selection of evergreen species that can effectively remove aerial PM. It also highlights the importance of evergreen plants for managing PM pollution during the winter and provides insights into planning additional green infrastructure to improve urban air quality.

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The RGB (red, green, and blue) imagery analysis is an important remote sensing tool, which estimates the effect of environmental stress on turfgrass growth and physiology. Therefore, this study investigated the effect of continuous wear stress treatment on Zoysia japonica through RGB imagery analysis. The results of the growth measurement showed that the plant height substantially decreased, after nine hours of treatment with no considerable difference thereafter. Dry weight measurement showed a substantial difference in the morpho-logical growth characteristics of the aerial part of the turfgrass, but none in the stolon and root zone. This could be attributed to the short period of compaction treatment. The ROS (reactive oxygen species) analysis showed that ROS rapidly increased due to wear stress treatment. The MDA content increased during the traffic process, whereas the green pixels increased and decreased repeatedly; however, overall, the trend declined but the overall trend decreased. Thus, this study confirmed that MDA was effective in reflecting the wear stress of turfgrass; however, it could through RGB image analysis.

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