Browse > Article
http://dx.doi.org/10.7780/kjrs.2022.38.5.3.4

CFD Simulations of the Trees' Effects on the Reduction of Fine Particles (PM2.5): Targeted at the Gammandong Area in Busan  

Han, Sangcheol (Major of Environmental Atmospheric Sciences, Division of Earth Environmental System Science, Pukyong National University)
Park, Soo-Jin (Supercomputer Center, Pukyong National University)
Choi, Wonsik (Major of Environmental Atmospheric Sciences, Division of Earth Environmental System Science, Pukyong National University)
Kim, Jae-Jin (Major of Environmental Atmospheric Sciences, Division of Earth Environmental System Science, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.5_3, 2022 , pp. 851-861 More about this Journal
Abstract
In this study, we analyzed the effects of trees planted in urban areas on PM2.5 reduction using a computational fluid dynamics (CFD) model. For realistic numerical simulations, the meteorological components(e.g., wind velocity components and air temperatures) predicted by the local data assimilation and prediction system (LDAPS), an operational model of the Korea Meteorological Administration, were used as the initial and boundary conditions of the CFD model. The CFD model was validated against, the PM2.5 concentrations measured by the sensor networks. To investigate the effects of trees on the PM2.5 reduction, we conducted the numerical simulations for three configurations of the buildings and trees: i) no tree (NT), ii) trees with only drag effect (TD), and iii) trees with the drag and dry-deposition effects (DD). The results showed that the trees in the target area significantly reduced the PM2.5 concentrations via the dry-deposition process. The PM2.5 concentration averaged over the domain in DD was reduced by 5.7 ㎍ m-3 compared to that in TD.
Keywords
$PM_{2.5}$; CFD model; LDAPS; Trees' drag; Trees' dry deposition;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 He, K., F. Yang, Y. Ma, Q. Zhang, X. Yao, C.K. Chan, S. Cadle, T. Chan, and P. Mulawa, 2001. The characteristics of PM2.5 in Beijing, China, Atmospheric Environment, 35(29): 4959-4970. https://doi.org/10.1016/S1352-2310(01)00301-6   DOI
2 Ji, X., Y. Yao, and X. Long, 2018. What causes PM2.5 pollution? cross-economy empirical analysis from socioeconomic perspective, Energy Policy, 119: 458-472. https://doi.org/10.1016/j.enpol.2018.04.040   DOI
3 Kwon, A-R., S.-J. Park, G. Kang, and J.-J. Kim, 2020. Carbon monoxide dispersion in an urban area simulated by a CFD model coupled to the WRFChem model, Korean Journal of Remote Sensing, 36(5-1): 679-692 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.5.1.3   DOI
4 Mun, D.-S., M.-J. Kim, and J.-J. Kim, 2021. A numerical study on the effects of meteorological conditions on building fires using GIS and a CFD model, Korean Journal of Remote Sensing, 37(3): 395-408 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.3.3   DOI
5 Park, S.-J., W.-S. Choi, and J.-J. Kim, 2020a. A numerical study on the characteristics of flows and fine particulate matter (PM2.5) distributions in an urban area using a multi-scale model: Part I - analysis of detailed flows, Korean Journal of Remote Sensing, 36(6-3): 1643-1652 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.6.3.2   DOI
6 Ruck, B. and F. Schmitt, 1986. Das stromungsfeld der einzelbaumumstromung, Forstwissenschaftliches Centralblatt, 105(1): 178-196. https://doi.org/10.1007/BF02741710   DOI
7 Sun, W.Y., J.C. Kim, J.H. Woo, B.G. Kang, K.S. Kim, et al, 2014. Development emission program for air quality modeling, National Digital Science Library, National Institute of Environmental Research, Incheon, Korea, p. 335.
8 Yakhot, V., S.A. Orszag, S. Thangam, T.B. Gatski, and C.G. Speziale, 1992. Development of turbulence models for shear flows by a double expansion technique, Physics of Fluids, 4(7): 1510-1520. https://doi.org/10.1063/1.858424   DOI
9 Zhang, T., B. Gao, Z. Zhou, and Y. Chang, 2016. The movement and deposition of PM2.5 in the upper respiratory tract for the patients with heart failure: an elementary CFD study, Biomedical Engineering Online, 15(2): 517-530. https://doi.org/10.1186/s12938-016-0281-z   DOI
10 Kim, Y.-H. and J.-B. Joo, 2021. Recent technical trend of removal technologies of air pollutant NO, Journal of Energy & Climate Change, 16(2): 128-148 (in Korean with English abstract). https://data.doi.or.kr/10.22728/jecc.2021.16.2.128   DOI
11 Lim, J.-M. and J.-H. Lee, 2014. Indoor air quality pollution of PM2.5 and associated trace elements affected by environmental tobacco smoke, Journal of Korea Society of Environmental Engineers, 36(5): 317-324 (in Korean with English abstract). http://dx.doi.org/10.4491/KSEE.2014.36.5.317   DOI
12 Miller, L. and X. Xu, 2018. Ambient PM2.5 human health effects-findings in China and research directions, Atmosphere, 9(11): 424. https://doi.org/10.3390/atmos9110424   DOI
13 Park, H.-Y., G.-Y. Oh, H.-S. Park, H.-R. Kim, B.-R. Lee, C.-O. Park, H.-S. Lim, G.-H. Park, J.-S. Park, and M.-S. Bae, 2021. Assessment of regional source contribution of PM2.5 in the Gwangyang bay area, Journal of Environmental Analysis, 24(2): 62-74 (in Korean with English abstract). https://doi.org/10.36278/jeaht.24.2.62   DOI
14 Park, S.-A. and H.-J. Shin, 2017. Analysis of the factors influencing PM2.5 in Korea: focusing on seasonal factors, Environmental Policy, 25(1): 227-248 (in Korean with English abstract). http://dx.doi.org/10.15301/jepa.2017.25.1.227   DOI
15 Park, S.-J., W.-S. Choi, and J.-J. Kim, 2020b. A numerical study on the characteristics of flows and fine particulate matter (PM2.5) distributions in an urban area using a multi-scale model: Part II - effects of road emission, Korean Journal of Remote Sensing, 36(6-3): 1653-1667 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.6.3.3   DOI
16 Giarina, M. and P. Buffa, 2018. A new approach for modeling dry deposition velocity of particles, Atmospheric Environment, 180: 11-22. https://doi.org/10.1016/j.atmosenv.2018.02.038   DOI
17 An, S.-G. and K.-H. Choi, 2021. Effect of forest fire on fine particulate matter concentration in coastal cities of Gangwon-do, South Korea, Association of Korean Geographers, 10(3): 391-400 (in Korean with English abstract). https://doi.org/10.25202/JAKG.10.3.4   DOI
18 Chae, H.-J., 2009, Effect on the PM10 concentration by wind velocity and wind direction, Journal of Environmental and Sanitary Engineers, 24(3): 37-54 (in Korean with English abstract).
19 Choe, J.-I. and Y.-S. Lee, 2015. A study on the impact of PM2.5 emissions on respiratory diseases, Environmental Policy, 23(4): 155-172 (in Korean with English abstract). http://dx.doi.org/10.15301/jepa.2015.23.4.155   DOI
20 Peters, K. and R. Eiden, 1992. Modeling the dry deposition velocity of aerosol particles to a spruce forest, Atmospheric Environment, 26(14): 2555-2564. https://doi.org/10.1016/0960-1686(92)90108-W   DOI
21 Shin, M.-K., C.-D. Lee, H.-S. Ha, C.-S. Choe, and Y.-H. Kim, 2007. The influence of meteorological factors on PM10 concentration in Incheon, Journal of Korean Society for Atmospheric Environment, 23(3): 322-331 (in Korean with English abstract). https://doi.org/10.5572/KOSAE.2007.23.3.322   DOI
22 Kang, J.-E., D.-S. Mun, J.-J. Kim, J.-Y. Choi, J.-B. Lee, and D.-G. Lee, 2021. Geographical characteristics of PM2.5, PM10 and O3 concentrations measured at the air quality monitoring systems in the Seoul metropolitan area, Korean Journal of Remote Sensing, 37(3): 657-664 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.3.24   DOI
23 Freer-Smith, P. H., A. A. El-Khatib, and G. Taylor, 2004. Capture of particulate pollution by trees: a comparison of species typical of semi-arid areas (Ficus nitida and Eucalyptus globulus) with European and north American species, Water, Air, and Soil Pollution, 155(1): 173-187. https://doi.org/10.1023/B:WATE.0000026521.99552.fd   DOI
24 Han, C.-W., S.-T. Kim, Y.-H. Lim, H.-J. Bae, and Y.-C. Hong, 2018. Spatial and temporal trends of number of deaths attributable to ambient PM2.5 in the Korea, Journal of Korean Medical Science, 33(30): 193. https://doi.org/10.3346/jkms.2018.33.e193   DOI
25 Han, M., F. Yang, and H. Sun, 2021. A bibliometric and visualized analysis of research progress and frontiers on health effects caused by PM2.5, Environmental Science and Pollution Research, 28(24): 30595-30612. https://doi.org/10.1007/s11356-021-14086-z   DOI
26 Hwang, I.-J., 2022. Estimation of source apportionment for PM2.5 data of air pollution monitoring site in Pohang using the EPA-PMF model, Journal of Korean Society for Atmospheric Environment, 38(3): 354-374 (in Korean with English abstract). https://doi.org/10.5572/KOSAE.2022.38.3.354   DOI
27 Jeong, S.-B. and H.-W. Lee, 2022. The association between ambient air pollution and outpatient department visits for allergic rhinitis in Busan, South Korea, Korean Public Health Research, 48(3): 79-92 (in Korean with English abstract). https://doi.org/10.22900/kphr.2022.48.3.006   DOI
28 Xing, Y.F., Y.H. Xu, M.H. Shi, and Y.X. Lian, 2016. The impact of PM2.5 on the human respiratory system, Journal of Thoracic Disease, 8(1): 69-74. http://dx.doi.org/10.3978/j.issn.2072-1439.2016.01.19   DOI
29 Smith, J.D., C. Mitsakou, N. Kitwiroon, B.M. Barratt, H.A. Walton, J.G. Taylor, H.R. Anderson, F.J. Kelly, and S.D. Beevers, 2016. London hybrid exposure model: improving human exposure estimates to NO2 and PM2.5 in an urban setting, Environmental Science & Technology, 50(21): 11760-11768. https://doi.org/10.1021/acs.est.6b01817   DOI
30 White, E.J. and F. Turner, 1970. A method of estimating income of nutrients in a catch of airborne particles by a woodland canopy, Journal of Applied Ecology, 7(3): 441-461. https://doi.org/10.2307/2401970   DOI
31 Yang, S., D. Fang, and B. Chen, 2019. Human health impact and economic effect for PM2.5 exposure in typical cities, Applied Energy, 249(1): 316-325. https://doi.org/10.1016/j.apenergy.2019.04.173   DOI
32 Choi, Y.-J., E.-J. Choi, H.-U. Cho, and J.-W. Moon, 2021. Development of an indoor particulate matter (PM2.5) prediction model for improving school indoor air quality environment, The International Journal of The Korea Institute of Ecological Architecture and Environment, 21(1): 35-40 (in Korean with English abstract). http://dx.doi.org/10.12813/kieae.2021.21.1.035   DOI
33 Air Visual, 2021. World Air Quality Report: Region & City PM2.5 Ranking, IQAir, Goldach, Switzerland, pp. 1-43.
34 Chang, J.-C. and S.-R. Hanna, 2004. Air quality model performance evaluation, Meteorology and Atmospheric Physics, 87(1): 167-196. https://doi.org/10.1007/s00703-003-0070-7   DOI