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 PM_2.5 (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% PM_2.5, 39.5% PM₁₀ and 56.5% PM₁₀₀, while those adsorbed by Q. salicina are evergreen at 25.7% PM_2.5, 27.4% PM₁₀ and 46.9% PM_100. 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 PM_2.5. The adsorption ratio of ultra-fine PM_2.5 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. PM_2.5 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.

The skin, recognized as the largest organ in the human body, serves a vital function in safeguarding against external threats. Severe damage to the skin can pose significant risks to human health. There is an urgent requirement for safe and effective therapies for wound healing. While phytotherapy has been widely utilized for various health conditions, the potential of Quercus glauca in promoting wound healing has not been thoroughly explored. Q. glauca is a cultivated crop known for its abundance of bioactive compounds. This study examined the wound-healing properties of Quercus glauca acorn seed coat water extract (QGASE). The findings from the study suggest that QGASE promotes wound closure in HF cells by upregulating essential markers related to the wound-healing process. Additionally, QGASE demonstrates antioxidant effects, mitigating oxidative stress and aiding in recovery from injuries induced by H₂O₂. In vivo experiments provide additional substantiation supporting the efficacy of QGASE in enhancing wound healing. The collective results indicate that QGASE may be a promising candidate for the development of innovative therapeutic strategies aimed at enhancing skin wound repair.

This study conducted a comparative analysis on the effects of smart automatic and semi-automatic irrigation methods on the physiological characteristics and growth of Prunus × yedoensis Matsum. seedlings. The smart automatic irrigation system, which activates irrigation when the soil moisture drops below 15%, demonstrated superior characteristics in sap-wood area and bark ratio, as well as excellent water management efficiency, compared to the semi-automatic irrigation method, which involves watering (2.0 L) for 10 min at 60 min intervals starting at 8 AM every day. The analysis of soil moisture content changes under varying weather conditions and irrigation methods showed that smart automatic irrigation effectively maintained optimal moisture levels. Moreover, sap flow in the smart automatic irrigation treatment was more efficiently regulated in response to seasonal variations, showing a strong correlation with climatic factors such as temperature and solar radiation. In contrast, the semi-automatic irrigation treatment led to excessive sap flow during the summer due to a fixed watering schedule, resulting in unnecessary water supply. Analysis of photosynthesis parameters and chlorophyll fluorescence also revealed that smart automatic irrigation achieved higher values in light compensation and saturation points, maximizing photosynthetic efficiency. These findings suggest that the smart automatic irrigation system can enhance plant growth and water use efficiency, contributing to sustainable water management strategies. This research provides critical foundational data for developing efficient agricultural and horticultural irrigation management strategies in response to future climate change.

Efficient phenotyping is essential for accelerating genetic improvement in turfgrass breeding, where manual measurements are labor-intensive. This study evaluated hyperspectral imaging (HSI) as a high-throughput tool for assessing Zoysia spp. breeding populations consisting of 464 genotypes. HSI data (400–1000 nm) were processed through a user-in-the-loop hybrid segmentation pipeline integrating UMAP dimensionality reduction, DBSCAN clustering, Random Forest classification, and pseudo-RGB refinement. To independently assess vegetation classification performance, 10,000 manually annotated reference points from 50 pseudo-RGB images were compared with the automated module, yielding an overall accuracy of 0.9697, a precision of 0.8830, a recall of 0.9240, a specificity of 0.9779, an F1-score of 0.9030, and Cohen’s kappa of 0.8851. A Combined Ranking Score (CRS) integrating five vegetation indices and vegetation pixel count was significantly associated with aerial shoot count (r = −0.445, p < 0.001) and runner count (r = −0.207, p < 0.001). The highest-ranked genotype showed a 9370.3-pixel increase in vegetation area between 6 and 16 weeks after transplanting, compared with 1417.7 pixels for the lowest-ranked genotype. Classification performance declined under high-coverage conditions, indicating increased mixed-pixel ambiguity in dense canopies. These results suggest that HSI-based CRS can support rapid, objective, and non-destructive relative ranking of density-related vegetative growth in turfgrass breeding. Because the study was conducted at a single location and season and correlations with manual traits were moderate, the framework is best interpreted as a screening and ranking tool rather than a direct predictive model.