A GAM for Daily Ozone Concentration in Seoul


Article Preview

This study focuses on ozone modeling using meteorological and air monitoring variables. Twenty seven (27) places in Seoul were measured for ozone values from January 1999 to December 1999. Air quality monitoring data consisted of CO, NO2, O3, PM10, TSP while meteorology data consisted of the daily maximum temperature, humidity and wind speed, and solar radiation. The complexity of environmental data dynamics often requires models covering non-linearity. Photochemical ozone pollution is the result of complex non-linear interactions between atmospheric pollutants and meteorology. The generalized additive model is favored because it is the most flexible, has the fewest statistical assumptions, and it can detect and fit potentially complex and nonlinear dependencies. For these reasons we modeled the daily ozone amount using a generalized additive model with smooth loess functions and compared it with a multiple linear regression model.



Key Engineering Materials (Volumes 277-279)

Edited by:

Kwang Hwa Chung, Yong Hyeon Shin, Sue-Nie Park, Hyun Sook Cho, Soon-Ae Yoo, Byung Joo Min, Hyo-Suk Lim and Kyung Hwa Yoo




J. H. Kim and J. Hong, "A GAM for Daily Ozone Concentration in Seoul", Key Engineering Materials, Vols. 277-279, pp. 497-502, 2005

Online since:

January 2005




[1] C. Silva, P. Perez, A.: Trier, Statistical Modeling and Prediction of Atmospheric Pollution by Particulate Material: Two Nonparametric Approach, Environmetrics Vol. 12 (2001), p.147159.

DOI: https://doi.org/10.1002/1099-095x(200103)12:2<147::aid-env451>3.3.co;2-v

[2] D. Chen, M. Nunez: Temporal and Spatial Variability of Total Ozone on Southwest Sweden Revealed by Two Ground-based Instruments, Int. J. Climatal Vol. 18 (1988), pp.1237-1246.

DOI: https://doi.org/10.1002/(sici)1097-0088(199809)18:11<1237::aid-joc285>3.0.co;2-6

[3] G. Bauer, M. Desistler, W. Scherrer: Time Series Models for Short Term Forecasting of Ozone in the Eastern part of Austria, Environmetrics (2001), pp.117-130. Title of Publication (to be inserted by the publisher).

DOI: https://doi.org/10.1002/1099-095x(200103)12:2<117::aid-env448>3.0.co;2-n

[4] G. Lissens, W. Debruyn, C. Mensink, G. Dumont: SMOGSTOP: A Model for Forecasting Maximum Daily Ozone Concentration in Belgium, Environmetrics Vol. 11 (2000), pp.511-521.

DOI: https://doi.org/10.1002/1099-095x(200009/10)11:5<511::aid-env415>3.0.co;2-g

[5] J.M. Davis, B.K. Eder and P. Bloomfield: Modeling Ozone in the Chicago Urban Area, Case Studies in Environmental Statistics (1998), pp.5-26.

DOI: https://doi.org/10.1007/978-1-4612-2226-2_2

[6] K. J. Hsu: Time Series of the Analysis of the Interdependence among Air Pollutants. Atmospheric Environment Vol. 26 (4) (1992), pp.491-503.

DOI: https://doi.org/10.1016/0957-1272(92)90056-x

[7] M. Fasso, I. Negri: Non-linear Statistical Modeling of High Frequency ground Ozone Data, Environmetrics Vol. 13 (2002), pp.225-241.

DOI: https://doi.org/10.1002/env.509

[8] National Institute of Environmental Research, Ministry of Environment: Annual Report of Air Quality in Korea 2001, (2002).

[9] T.J. Hastie, R. J. Tibshirani: Generalized Additive Models, Chapman and Hall (1990).

[10] Y. K. Kim, J. B. Lee: Development of Neural Network Model for Prediction of Daily Maximum Ozone Concentration in Summer, J. KAPRA Vol. 10 (4) (1994), pp.224-232.