Comparative Assessment of Linear Regression and Machine Learning for Analyzing the Spatial Distribution of Ground-level NO2 Concentrations: A Case Study for Seoul, Korea |
Kang, Eunjin
(Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Yoo, Cheolhee (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) Shin, Yeji (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) Cho, Dongjin (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) Im, Jungho (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) |
1 | Hengl, T., G.B. Heuvelink, and D.G. Rossiter, 2007. About regression-kriging: From equations to case studies, Computers and Geosciences, 33(10): 1301-1315. DOI |
2 | Boardman, M. and T. Trappenberg, 2006. A heuristic for free parameter optimization with support vector machines, Proc. of In the 2006 IEEE International Joint Conference on Neural Network, Vancouver, BC, CAN, Jul. 16-21, pp. 610-617. |
3 | Boersma, K.F., H.J. Eskes, J.P. Veefkind, E.J. Brinksma, R.J. van der A, M. Sneep, G.H.J. van den Oord, P.F. Levelt, P. Stammes, J.F. Gleason, and E.J. Bucsela, 2007. Near-real time retrieval of tropospheric NO2 from OMI, Atmospheric Chemistry and Physics, 7(8): 2103-2118. DOI |
4 | Cui, Y., L. Jiang, W. Zhang, H. Bao, B. Geng, Q. He, L. Zhang, and D.G. Streets, 2019. Evaluation of China's environmental pressures based on satellite NO2 observation and the extended STIRPAT model, International Journal of Environmental Research and Public Health, 16(9): 1487. DOI |
5 | Ghahremanloo, M., Y. Lops, Y. Choi, and S. Mousavinezhad, 2021. Impact of the COVID-19 outbreak on air pollution levels in East Asia, Science of the Total Environment, 754: 142226. DOI |
6 | Goldberg, D.L., S.C. Anenberg, D. Griffin, C.A. McLinden, Z. Lu, and D.G. Streets, 2020. Disentangling the impact of the COVID-19 lockdowns on urban NO2 from natural variability, Geophysical Research Letters, 47(17): e2020GL089269. |
7 | Sun, S., J.D. Stewart, M.N. Eliot, J.D. Yanosky, D. Liao, L.F. Tinker, C.B. Eaton, E.A. Whitsel, G.A. Wellenius, 2019. Short-term exposure to air pollution and incidence of stroke in the Women's Health Initiative, Environment International, 132: 105065. DOI |
8 | Wang, J., S. Qin, Q. Zhou, and H. Jiang, 2015. Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China, Renewable Energy, 76: 91-101. DOI |
9 | Wang, L., H. Liu, H. Su, and J. Wang, 2019. Bathymetry retrieval from optical images with spatially distributed support vector machines, GIScience and Remote Sensing, 56(3): 323-337. DOI |
10 | Willmott, C.J., S.M. Robeson, and K. Matsuura, 2012. A refined index of model performance, International Journal of Climatology, 32(13): 2088-2094. DOI |
11 | Choi, H., Y. Kang, and J. Im, 2021. Estimation of TROPOMI-derived Ground-level SO2 Concentrations Using Machine Learning Over East Asia, Korean Journal of Remote Sensing, 37(2): 275-290 (in Korean with English abstract). DOI |
12 | Caballero, S., R. Esclapez, N. Galindo, E. Mantilla, and J. Crespo, 2012. Use of a passive sampling network for the determination of urban NO2 spatiotemporal variations, Atmospheric Environment, 63: 148-155. DOI |
13 | Chao, Z., L. Wang, M. Che, and S. Hou, 2020. Effects of different urbanization levels on land surface temperature change: taking tokyo and shanghai for example, Remote Sensing, 12(12): 2022. DOI |
14 | Cho, D., C. Yoo, J. Im, Y. Lee, and J. Lee, 2020. Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique, GIScience and Remote Sensing, 57(5): 633-649. DOI |
15 | Zhu, Y., Y. Zhan, B. Wang, Z. Li, Y. Qin, and K. Zhang, 2019. Spatiotemporally mapping of the relationship between NO2 pollution and urbanization for a megacity in Southwest China during 2005-2016, Chemosphere, 220: 155-162. DOI |
16 | Oliver, M.A. and R. Webster, 2014. A tutorial guide to geostatistics: Computing and modelling variograms and kriging, Catena, 113: 56-69. DOI |
17 | Ren, X., Z. Mi, and P.G. Georgopoulos, 2020. Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States, Environment International, 142: 105827. DOI |
18 | Ryu, Y.H., J.J. Baik, K.H. Kwak, S. Kim, and N. Moon, 2013. Impacts of urban land-surface forcing on ozone air quality in the Seoul metropolitan area, Atmospheric Chemistry and Physics, 13(4): 2177-2194. DOI |
19 | Wanninkhof, R., 2014. Relationship between wind speed and gas exchange over the ocean revisited, Limnology and Oceanography: Methods, 12(6): 351-362. DOI |
20 | WHO (World Health Organizations), 2005. Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. In Air Quality Guidelines: Global Update, 2005. http://www.euro.who.int/__data/assets/pdf_file/0005/78638/E90038.pdf, Accessed Aug. 15, 2016. |
21 | Kim, S. Y., S.J. Yi, Y.S. Eum, H.J. Choi, H. Shin, H.G. Ryou, and H. Kim, 2014. Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities, Environmental Health and Toxicology, 29: e2014012. DOI |
22 | Chen, J., K. de Hoogh, J. Gulliver, B. Hoffmann, O. Hertel, M. Ketzel, M. Bauwelinck, A. van Donkelaar, U.A. Hvidtfeldt, K. Katsouyanni, N.A.H. Janssen, R.V. Martin, E. Samoli, P.E. Schwartz, M. Stafoggia, T. Bellander, M. Strak, K. Wolf, D. Vienneau, R. Vermeulen, B. Brunekreef, and G. Hoek, 2019. A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide, Environment International, 130: 104934. DOI |
23 | Shukla, K., P. Kumar, G.S. Mann, and M. Khare, 2020. Mapping spatial distribution of particulate matter using Kriging and Inverse Distance Weighting at supersites of megacity Delhi, Sustainable Cities and Society, 54: 101997. DOI |
24 | EPA (United States Environmental Protection Agency), 2013. Integrated Science Assessment (ISA) of Ozone and Related Photochemical Oxidants Final Report, EPA, Washington, DC, USA. |
25 | Vienneau, D., K. de Hoogh, M.J. Bechle, R. Beelen, A. van Donkelaar, R.V. Martin, D.B. Millet, G. Hoek, and J.D. Marshall, 2013. Western European land use regression incorporating satellite-and ground-based measurements of NO2 and PM10, Environmental Science and Technology, 47(23): 13555-13564. DOI |
26 | Wu, C.D., Y.T. Zeng, and S.C.C. Lung, 2018. A hybrid kriging/land-use regression model to assess PM2.5 spatial-temporal variability, Science of the Total Environment, 645: 1456-1464. DOI |
27 | Zuniga, J., M. Tarajia, V. Herrera, W. Urriola, B. Gomez, and J. Motta, 2016. Assessment of the possible association of air pollutants PM10, O3, NO2 with an increase in cardiovascular, respiratory, and diabetes mortality in Panama City: a 2003 to 2013 data analysis, Medicine, 95(2): e2464. DOI |
28 | Kang, Y., H. Choi, J. Im, S. Park, M. Shin, C.K. Song, and S. Kim, 2021. Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia, Environmental Pollution, 288: 117711. DOI |
29 | Kim, M., D. Brunner, and G. Kuhlmann, 2021. Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning, Remote Sensing of Environment, 264: 112573. DOI |
30 | Cui, Y., W. Zhang, H. Bao, C. Wang, W. Cai, J. Yu, and D.G. Streets, 2019. Spatiotemporal dynamics of nitrogen dioxide pollution and urban development: Satellite observations over China, 2005-2016, Resources, Conservation and Recycling, 142: 59-68. DOI |
31 | Harris, P., A.S. Fotheringham, R. Crespo, and M. Charlton, 2010. The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets, Mathematical Geosciences, 42(6): 657-680. DOI |
32 | Christensen, R., 2020. Plane answers to complex questions: the theory of linear models, Springer Science and Business Media, Berlin, GER. |
33 | Draper, N.R. and H. Smith, 1998. Applied regression analysis, Third Edition (Vol. 326), John Wiley and Sons, Hoboken, NJ, USA. |
34 | Graler, B., M. Rehr, L. Gerharz, and E. Pebesma, 2012. Spatio-temporal analysis and interpolation of PM10 measurements in Europe for 2009, ETC/ACM Technical Paper, 8: 1-29. |
35 | Holben, B.N., 1986. Characteristics of maximum-value composite images from temporal AVHRR data, International Journal of Remote Sensing, 7(11): 1417-1434. DOI |
36 | Park, S., J. Im, S. Park, and J. Rhee, 2017. Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula, Agricultural and Forest Meteorology, 237: 257-269. DOI |
37 | Krotkov, N.A., C.A. McLinden, C. Li, L.N. Lamsal, E.A. Celarier, S.V. Marchenko, W.H. Swartz, E.J. Bucsela, J. Joiner, B.N. Duncan, K.F. Boersma, J.P. V, Pieternel F. Levelt, V.E. Fioletov, R.R. Dickerson, H. He, Z. Lu, and D.G. Streets, 2016. Aura OMI observations of regional SO2 and NO2 pollution changes from 2005 to 2015, Atmospheric Chemistry and Physics, 16(7): 4605-4629. DOI |
38 | Li, J. and A.D. Heap, 2014. Spatial interpolation methods applied in the environmental sciences: A review, Environmental Modelling and Software, 53: 173-189. DOI |
39 | LUINTEL, N., W. Ma, Y. Ma, B. Wang, and S. SUBBA, 2019. Spatial and temporal variation of daytime and nighttime MODIS land surface temperature across Nepal, Atmospheric and Oceanic Science Letters, 12(5): 305-312. DOI |
40 | Nguyen, H.T., K.H. Kim, and C. Park, 2015. Long-term trend of NO2 in major urban areas of Korea and possible consequences for health, Atmospheric Environment, 106: 347-357. DOI |
41 | Park, S., M. Kim, and J. Im, 2021. Estimation of Ground-level PM10 and PM2.5 Concentrations Using Boosting-based Machine Learning from Satellite and Numerical Weather Prediction Data, Korean Journal of Remote Sensing, 37(2): 321-335 (in Korean with English Abstract). DOI |
42 | Kuhnlein, M., T. Appelhans, B. Thies, and T. Nauss, 2014. Improving the accuracy of rainfall rates from optical satellite sensors with machine learning-A random forests-based approach applied to MSG SEVIRI, Remote Sensing of Environment, 141: 129-143. DOI |
43 | Guo, Z., S.D. Wang, M.M. Cheng, and Y. Shu, 2012. Assess the effect of different degrees of urbanization on land surface temperature using remote sensing images, Procedia Environmental Sciences, 13: 935-942. DOI |
44 | Wu, S., B. Huang, J. Wang, L. He, Z. Wang, Z. Yan, X. Lao, F. Zhang, R. Liu, and Z. Du 2021. Spatiotemporal mapping and assessment of daily ground NO2 concentrations in China using high-resolution TROPOMI retrievals, Environmental Pollution, 273: 116456. DOI |
45 | Yoo, J.M., Y.-R. Lee, D. Kim, M.-J. Jeong, W.R. Stockwell, P.K. Kundu, S.-M. Oh, D.-B. Shin, and S.-J. Lee, 2014. New indices for wet scavenging of air pollutants (O3, CO, NO2, SO2, and PM10) by summertime rain, Atmospheric Environment, 82: 226-237. DOI |
46 | Zhan, Y., Y. Luo, X. Deng, K. Zhang, M. Zhang, M.L. Grieneisen, and B. Di, 2018. Satellite-based estimates of daily NO2 exposure in China using hybrid random forest and spatiotemporal kriging model, Environmental Science and Technology, 52(7): 4180-4189. DOI |
47 | Gupta, A.K., K. Karar, S. Ayoob, and K. John, 2008. Spatio-temporal characteristics of gaseous and particulate pollutants in an urban region of Kolkata, India, Atmospheric Research, 87(2): 103-115. DOI |
48 | Hengl, T., G.B. Heuvelink, and A. Stein, 2004. A generic framework for spatial prediction of soil variables based on regression-kriging, Geoderma, 120(1-2): 75-93. |
49 | Lin, J.T., Z. Liu, Q. Zhang, H. Liu, J. Mao, and G. Zhuang, 2012. Modeling uncertainties for tropospheric nitrogen dioxide columns affecting satellite-based inverse modeling of nitrogen oxides emissions, Atmospheric Chemistry and Physics, 12(24): 12255-12275. DOI |
50 | Houborg, R. and M.F. McCabe, 2018. A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning, ISPRS Journal of Photogrammetry and Remote Sensing, 135: 173-188. DOI |
51 | Kaminska, J.A., 2019. A random forest partition model for predicting NO2 concentrations from traffic flow and meteorological conditions, Science of the Total Environment, 651: 475-483. DOI |
52 | Li, X., A. Luo, J. Li, and Y. Li, 2019. Air pollutant concentration forecast based on support vector regression and quantum-behaved particle swarm optimization, Environmental Modeling and Assessment, 24(2): 205-222. DOI |
53 | Breiman, L., 2001. Random forests, Machine Learning, 45(1): 5-32. DOI |
54 | Horning, N., 2013. Introduction to decision trees and random forests, American Museum of Natural History, Manhattan, NY, USA. |
55 | Ialongo, I., H. Virta, H. Eskes, J. Hovila, and J. Douros, 2020. Comparison of TROPOMI/Sentinel-5 Precursor NO2 observations with ground-based measurements in Helsinki, Atmospheric Measurement Techniques, 13(1): 205-218. DOI |