Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
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Analysis of Empirical Multiple Linear Regression Models for the Production of PM2.5 Concentrations

PM2.5농도 산출을 위한 경험적 다중선형 모델 분석

  • Choo, Gyo-Hwang (Department of Atmospheric & Environmental Sciences, Gangneung-Wonju National University) ;
  • Lee, Kyu-Tae (Department of Atmospheric & Environmental Sciences, Gangneung-Wonju National University) ;
  • Jeong, Myeong-Jae (Department of Atmospheric & Environmental Sciences, Gangneung-Wonju National University)
  • 추교황 (강릉원주대학교 대기환경과학과) ;
  • 이규태 (강릉원주대학교 대기환경과학과) ;
  • 정명재 (강릉원주대학교 대기환경과학과)
  • Received : 2017.04.06
  • Accepted : 2017.07.21
  • Published : 2017.08.30
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Abstract

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.

본 연구에서는 서울지역의 지상 미세먼지($PM_{2.5}$) 농도를 산출하기 위하여 경험적인 모델들을 개발하였다. 연구에 이용한 자료는 2012년 1월 1일부터 2013년 12월 31일까지이며 Terra와 Aqua위성의 MODIS센서에서 산출되는 에어로졸 광학두께, 옹스트롬 지수, 기상변수들과 행성경계층두께와 관련된 6개의 다중 선형 회귀모델들의 차이를 분석하였다. 그 결과 에어로졸 광학두께와 옹스트롬 지수, 상대습도, 풍속, 풍향, 행성경계층두께, 기온 자료를 입력 자료로 사용한 $M_6$모델이 가장 좋은 결과를 보였다. 통계적인 분석에 따르면 $M_6$ 모델을 사용하여 계산된 $PM_{2.5}$와 관측된 $PM_{2.5}$농도 사이의 결과는 상관계수(R=0.62)와 평균제곱근오차($RMSE=10.70{\mu}gm^{-3}$)이다. 또한 산출된 계절별 지표면 $PM_{2.5}$농도는 여름철(R=0.38)과 겨울철(R=0.56)보다 봄(R=0.66)과 가을철(R=0.75)에 상대적으로 더 좋은 상관 관계를 보였다. 이러한 결과는 에어로졸 광학두께의 계절별 관측 특성으로 인한 것으로써 다른 계절에 비하여 여름과 겨울철 에어로졸 광학두께 관측이 구름과 눈/얼음 표면에 의한 관측 제한과 오차를 가져온 것으로 분석되었다. 따라서 본 연구에서 사용한 경험적 다중선형회귀 모델은 위성에서 산출된 에어로졸 광학두께 자료가 지배적인 변수로 작용하며 $PM_{2.5}$산출 결과들을 향상시키기 위해서는 추가적인 기상 변수를 이용해야 할 것이다. 또한 경험적 다중선형회귀 모델을 이용하여 $PM_{2.5}$를 산출한 결과는 인공위성 자료로부터 대기환경 감시를 가능하게 하는 방법이 될 수 있어 유용할 것이다.

Keywords

References

  1. Benas, N., Beloconi, A., and Chrysoulakis, N., 2013, Estimation of urban PM10 concentration, based on MODIS and MERIS/AATSR synergistic observations, Atmospheric Environment, 79, 448-454. https://doi.org/10.1016/j.atmosenv.2013.07.012
  2. Brook, R.D., Rajagopalan, S., Pope, C.A., Brook, J.R., Bhatnagar, A., Diez-Roux, A.V., Holguin, F., Hong, Y.L., Luepker, R.V., Mittleman, M.A., Peters, A., Siscovick, D., Smith, S.C., Whitsel, L., Kaufman, J.D., Epidemiol, A.H.A.C., Dis, D.K.C., and Metab, C.N.P.A., 2010, Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American heart association, Circulation, 121, 2331-2378. https://doi.org/10.1161/CIR.0b013e3181dbece1
  3. Chen, B.B., Sverdlik, L.G., Imashev, S.A., Solomon, P.A., Lantz, J., Schauer, J.J., Shafer, M.M., and Artamonova, M.S., 2013, Empirical relationship between particulate matter and aerosol optical depth over Northern Tien-Shan, Central Asia, Air Quality, Atmosphere and Health, 6, 385-396. https://doi.org/10.1007/s11869-012-0192-5
  4. Chun, Y. and Lim, J.Y., 2004, The recent characteristics of Asian dust and haze events in Seoul, Korea, Meteorology and Atmospheric Physics, 87, 143-152.
  5. Eck, T.F., Holben, B.N., Reid, J.S., Dubovik, O., Smirnov, A., O'Neill, N.T., Slutsker, I., and Kinne, S., 1999, Wavelength dependence of the optical depth of biomass burning, urban and desert dust aerosols, Journal of Geophysical Research, 104, 31333-31349. https://doi.org/10.1029/1999JD900923
  6. Environmental Protection Agency (EPA), 2015, PM centers, http://www.epa.gov/ncer/science/pm/centers.html, Accessed on 2 October 2015.
  7. Holben, B.N., Tanre, D., Smirnov, A., Eck, T.F., Slutsker, I., Abuhassan, N., Newcomb, W.W., Schafer, J.S., Chatenet, B., Lavenu, F., Kaufman, Y.J., Vande Castle, J., Setzer, A., Markham, B., Clark, D., Frouin, R., Halthore, R., Karneli, A., O'Neill, N.T., Pietras, C., Pinker, R.T., Voss, K., and Zibordi, G., 2001, An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET, Journal of Geophysical Research, 106(D11), 12067-12097, doi:10.1029/2001JD900014.
  8. Hoek, G., Krishnan, R.M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B., and Kaufman, J.D., 2013, Long-term air pollution exposure and cardio-respiratory mortality: a review, Environmental Health, 12, doi:10.1186/1476-069X-12-43.
  9. International Panel on Climate Change (IPCC), 2013, Climate Change 2013, The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  10. Kaufman, Y.J., Tanre, D., and Boucher, O., 2002, A satellite view of aerosols in the climate system, Nature, 419, 215-223. https://doi.org/10.1038/nature01091
  11. Kim, B.K., Lee, D.I., Kim, J.C., and Lee, J.H., 2012, Characteristics of Diurnal Variation of High $PM_{2.5}$ Concentration by Spatio-Temporal Wind System in Busan, Korea, Journal of the Korean Earth Science Society, 33, 469-480. (in Korean) https://doi.org/10.5467/JKESS.2012.33.6.469
  12. Kim, H.S., Kim, J.M., and Sohn, J.J., 2012, An Analysis of MODIS Aerosol Optical Properties and Ground-based Mass Concentrations in Central Korea in 2009, Journal of the Korean Earth Science Society, 33, 269-279. (in Korean) https://doi.org/10.5467/JKESS.2012.33.3.269
  13. Kim, H.S., Yoon, M.B., and Sohn, J.J., 2010, An Analysis on the Episodes of Large-scale Transport of Natural Airborne Particles and Anthropogenically Affected Particles from Different Sources in the East Asian Continent in 2008, Journal of the Korean Earth Science Society, 31, 600-607. (in Korean) https://doi.org/10.5467/JKESS.2010.31.6.600
  14. Lee, K.H., Lee, D.H., and Kim, Y.J., 2006, Application of MODIS satellite observation data for air quality forecast, Journal of Korean Society for Atmospheric Environment, 22(6), 851-862. (in Korean)
  15. Lee, K.H. and Park, S.S., 2012, Relationship between $PM_{2.5}$ mass concentrations and MODIS aerosol optical thickness at Dukjuk and Jeju island, Korean Journal of Remote Sensing, 28(4), 449-458. (in Korean) https://doi.org/10.7780/kjrs.2012.28.4.8
  16. Li, C., Tsa,y S.C., Hsu, N.C., Kim, J.Y., Howell S.G., Huebert B.J., Ji Q., Jeong M.-J., Wang S.-H., Hansell R.A., and Bell S.W., 2012, Characteristics and composition of atmospheric aerosols in Phimai, central Thailand during BASE-ASIA, Atmospheric Environment, doi:10.1016/j.atmosenv.2012.04.003.
  17. Liu, Y., Sarnat, J.A., Kilaru V., Jacob, D.J., and Koutrakis, P., 2005, Estimating ground-level $PM_{2.5}$ in the Eastern United States using satellite remote sensing, Environmental Science and Technology, 39, 3269-3278. https://doi.org/10.1021/es049352m
  18. Liu, Y., Franklin, M., Kahn, R., and Koutrakis, P., 2007, Using aerosol optical thickness to predict ground-level $PM_{2.5}$ concentrations in the St. Louis area: A comparison between MISR and MODIS, Remote Sensing of Environment, 107, 33-44. https://doi.org/10.1016/j.rse.2006.05.022
  19. Liu, X., Cheng, Y., Zhang, Y., Jung, J., Sugimoto, N., Chang, S.Y., Kim, Y.J., Fan, S., and Zeng, L., 2008, Influences of relative humidity and particle chemical composition on aerosol scattering properties during the 2006 PRD campaign, Atmospheric Environment, 42, 1525-1536. https://doi.org/10.1016/j.atmosenv.2007.10.077
  20. Pan, X.L., Yan, P., Tang, J., Ma, J.Z., Wang, Z.F., Gbaguidi, A., and Sun, Y.L., 2009, Observational study of influence of aerosol hygroscopic growth on scattering coefficient over rural area near Beijing mega-city, Atmospheric Chemistry and Physics, 9, 7519-7530. https://doi.org/10.5194/acp-9-7519-2009
  21. Schwartz, J., Dockery, W., and Neas, L.M., 1996, Is daily mortality associated specifically with fine particles, Journal of the Air & Waste Management Association, 46, 927-939. https://doi.org/10.1080/10473289.1996.10467528
  22. Seo, S., Kim J., Jeong, U., Kim, W., Holben, B.N., Kim S.-W., Song C.H., and Lim J.H., 2015, Estimation of $PM_{10}$ concentrations over Seoul using multiple empirical models with AERONET and MODIS data collected during the DRAGON-Asia campaign, Atmospheric Chemistry and Physics, 15, 319-334. https://doi.org/10.5194/acp-15-319-2015
  23. Tiwari, S., Chate, D.M., Srivastava, A.K., Bisht, D.S. and Padmanabhamurty, B., 2012, Assessments of PM1, $PM_{2.5}$ and $PM_{10}$ concentrations in Delhi at different mean cycles. Geofizika, 29, 51-57.
  24. World Health Organization, 2006, Health risks of particulate matter from long-range transboundary air pollution, Eur. Cent. For Environ. And Health, Bonn, Germany.
  25. Xin, J., Zhang, Q., Wang, L., Gong, C., Wang, Y., Liu, Z., and Gao, W., 2014, The empirical relationship between the $PM_{2.5}$ concentration and aerosol optical depth over the background of North China from 2009 to 2011, Atmospheric Research, 138, 179-188. https://doi.org/10.1016/j.atmosres.2013.11.001
  26. Zhao, H., Che, H., Zhang, X., Ma, Y., Wang, Y., Wang, H., and Wang, Y., 2013, Characteristics of visibility and particulate matter (PM) in an urban area of Northeast China., Atmospheric Pollution Research, 4, 427-434. https://doi.org/10.5094/APR.2013.049
  27. Zheng, J., Che, W., Zheng, Z., Chen, L., and Zhong, L., 2013, Analysis of spatial and temporal variability of PM10 concentrations using MODIS aerosol optical thickness in the Pearl River Delta region, China, Aerosol and Air Quality Research, 13, 862-876.