[논문]한국 남부지역 가로수종 잎 미세구조와 미세먼지 흡착량의 계절 변화: 가시나무, 종가시나무, 참가시나무, 동백나무, 왕벚나무 중심으로

진언주; 윤준혁; 최명석; 성창현

doi:10.14578/jkfs.2021.110.2.129

한국 남부지역 가로수종 잎 미세구조와 미세먼지 흡착량의 계절 변화: 가시나무, 종가시나무, 참가시나무, 동백나무, 왕벚나무 중심으로
Seasonal Changes in the Absorption of Particulate Matter and the Fine Structure of Street Trees in the Southern Areas, Korea: With a Reference to Quercus myrsinifolia, Quercus glauca, Quercus salicina, Camellia japonica, and Prunus × yedoensis 원문보기

한국산림과학회지 = Journal of korean society of forest science, v.110 no.2, 2021년, pp.129 - 140

진언주 (국립산림과학원 산림바이오소재연구소) , 윤준혁 (국립산림과학원 산림바이오소재연구소) , 최명석 (경상대학교 환경산림과학부) , 성창현 (국립산림과학원 산림바이오소재연구소)

초록
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본 연구는 한국 남부지역의 주요 조경수 가시나무(Quercus myrsinifolia), 종가시나무(Quercus glauca), 참가시나무(Quercus salicina), 동백나무(Camellia japonica), 왕벚나무(Prunus × yedoensis) 등 5수종을 대상으로 계절별 미세먼지 흡착량 및 수종별 잎 표면 미세구조와의 관계를 연구하였다. 계절별 미세먼지 흡착량 범위는 1월(31.51~110.44 ㎍/cm²), 11월 (23.20~79.30 ㎍/cm²), 5월(22.68~76.90 ㎍/cm²), 8월(9.88~49.91 ㎍/cm²) 순으로, 8월보다 1월에 54.4% 더 높은 미세먼지 흡착량을 보였다. 잎 표면에 홈이 있고 털을 갖고 있으며, 왁스층 함량이 높은 Q. salicina는 미세먼지 입자 크기별 흡착량이 높게 유지되었으며, 광택이 있고 잎 표면이 매끄러우며, 왁스층 함량이 낮은 C. japonica와 Prunus × yedoensis는 계절별 미세먼지 흡착량이 낮았다. 엽면적 크기, 기공밀도 및 기공 길이의 증가는 PM 흡착량의 감소와 관련이 있고 반면, 잎 표피 거칠기, 왁스층 함량, 기공 폭의 증가는 PM 흡착량의 증가와 관련이 있었다. 또한, 잎 표면 왁스층 함량이 증가할수록 잎 표면 PM 흡착량도 증가하였으며, PM₁₀, PM_2.5 보다는 PM_0.2와 관련이 높은 것으로 확인되었다. 또한, 앞으로 개별 수종에 대한 미세먼지 저감 효율을 정량적으로 판단할 수 있는 기준을 통한 저감 수종 선발과 더불어 미세먼지 저감을 위한 숲 조성 가이드라인 또한 제시되어야 할 것으로 판단된다.

Abstract ▼ AI-Helper

The study investigates the correlation between the seasonal changes in the absorption of fine dusts and the fine structure of surface on each type of street tree, such as Quercus myrsinifolia, Quercus glauca, Quercus salicina, Camellia japonica, and Prunus × yedoensis in the southernareas of Korea. The absorption ranges of fine dust were 31.51~110.44 ㎍/cm2 in January, 23.20~79.30 ㎍/cm2 in November, 22.68~76.90 ㎍/cm2 in May, and 9.88~49.91 ㎍/cm2 in August. The absorption value was about 54.4% higher in January than in May. With the grooves and hairs on the leaf surface and lots of wax, Q. salicina seems related to the high absorption rate of fine dust for each fine dust particle size. The one with gloss and smooth leaf surface has a low amount of wax. C. japonica Prunus × yedoensisshowed a low absorption rate of fine dust in each season. Whereas the increase in porosity density, length, and leaf area size can be related to the reduced PM and increasedabsorption rate, the leaf surface roughness, total wax amount, and porosity width can be related to the increase in the PM absorption rate. There was also a high correlation between the total wax amount and absorption rate of the leaf surface at the size of PM0.2 than PM10 and PM2.5. These results imply that the quantitative and qualitative trais of leaf, such as wax amounts and leaf surface,can increase the absorption of fine dusts, and the small-sized particles seem to be highly adsorbed with the high wax amounts.

주제어

표/그림 (13)

그림 Figure 1. The location of sampling points.
그림 Figure 2. Pictures of the selected five plant species at sampling sites in campus (A: Q. myrsinifolia, B: Q. glauca, C: Q. salicina, D: Q. salicina, E: Prunus x yedoensis)
표 Table 1. Atmospheric and meteorological factors at the survey sites during May 2019-January 2020.
표 Table 2. The general characteristics of sample trees in the survey sites (SE±SD, n=3).
표 Table 3. Accumulation of particulate matter on leaves of 5 species in the survey sites during May 2019–January 2020, presented as the sum of surface and wax-deposited particulate matter . Data are mean ± SD, n = 10 (4 months with 3 replicates in each).
$Figure 3. Accumulation of particulate matter (means ± SD) on leaves within three different size fractions in the three clusters found in the survey sites. Treatments with the same letter(a-i) were not significantly different(ANOVA with Duncan test, p<0.05). SE is standard erroe.$ 그림 Figure 3. Accumulation of particulate matter (means ± SD) on leaves within three different size fractions in the three clusters found in the survey sites. Treatments with the same letter(a-i) were not significantly different(ANOVA with Duncan test, p<0.05). SE is standard erroe.
$Table 4. Species analyzed and results from the clustering analysis with surface and wax solved fractions of PM10, PM2.5, PM0.2 and total PM as variables. Cluster 1 had the smallest quantity of deposited PM and Cluster 3 had the largest.$ 표 Table 4. Species analyzed and results from the clustering analysis with surface and wax solved fractions of PM₁₀, PM_2.5, PM_0.2 and total PM as variables. Cluster 1 had the smallest quantity of deposited PM and Cluster 3 had the largest.
표 Table 5. Contribution(%) of surface PM and in-wax PM to total PM deposited on foliage of 5 species in the survey sites. Data are mean ± SD, n = 10 (4 months with 3 replicates in each).
$Table 6. Total amount of surface PM and in-wax PM accumulated on foliage 5 species with respect to three size fractions. Data are mean ± SD, n = 10 (4 months with 3 replicates in each).$ 표 Table 6. Total amount of surface PM and in-wax PM accumulated on foliage 5 species with respect to three size fractions. Data are mean ± SD, n = 10 (4 months with 3 replicates in each).
그림 Figure 4. SEM micrographs showing micro-morphological characteristics on leaves of 5 species in the trees samples sampling sites in the survey sites.
표 Table 7. Leaf surface traits of 5 species in the survey sites.
$Figure 5. Relationship between leaf surface wax content and accumulation of particulate matter in the wax layer. A) Accumulation of total PM in samples collected in the survey sites. B) Accumulation of different PM fractions in relation to wax concent. The linear regression of wax content on PM accumulation is also shown. Significances of linear regression models are all <0.001.$ 그림 Figure 5. Relationship between leaf surface wax content and accumulation of particulate matter in the wax layer. A) Accumulation of total PM in samples collected in the survey sites. B) Accumulation of different PM fractions in relation to wax concent. The linear regression of wax content on PM accumulation is also shown. Significances of linear regression models are all <0.001.
표 Table 8. Partial regression coefficients(B)and standardized regression coefficients(Beta) from a multiple regression analysis of the leaf surface-trait effects on PM accumulation.

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저자의 다른 논문 :

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동의어: Packet Collision Rate

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