[논문]Investigating the underlying structure of particulate matter concentrations: a functional exploratory data analysis study using California monitoring data

Montoya, Eduardo L.

doi:10.29220/csam.2018.25.6.619

[국내논문] Investigating the underlying structure of particulate matter concentrations: a functional exploratory data analysis study using California monitoring data 원문보기

Communications for statistical applications and methods = 한국통계학회논문집, v.25 no.6, 2018년, pp.619 - 631

Montoya, Eduardo L. (Department of Mathematics, California State University)

Abstract ▼ AI-Helper

Functional data analysis continues to attract interest because advances in technology across many fields have increasingly permitted measurements to be made from continuous processes on a discretized scale. Particulate matter is among the most harmful air pollutants affecting public health and the environment, and levels of PM10 (particles less than 10 micrometers in diameter) for regions of California remain among the highest in the United States. The relatively high frequency of particulate matter sampling enables us to regard the data as functional data. In this work, we investigate the dominant modes of variation of PM10 using functional data analysis methodologies. Our analysis provides insight into the underlying data structure of PM10, and it captures the size and temporal variation of this underlying data structure. In addition, our study shows that certain aspects of size and temporal variation of the underlying PM10 structure are associated with changes in large-scale climate indices that quantify variations of sea surface temperature and atmospheric circulation patterns.

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문제 정의

Many studies of PM10 utilize daily and monthly summary measures which are assumed to represent daily individual exposure to PM. In this article, we add to the literature by exploring the underlying data structure of the height or size variation and time variation of PM10 and its association with regimes of Pacific climate using functional data (FD) methodologies. FD methodologies allow us to explore the behavior of PM10 at any time t in the day.
This work presents an avenue for future research. Specifically, it motivates developing a functional data regression model that quantifies the association between the daily PM10 curves (functional response) with changes in several explanatory variables representing spatial location attributes (scalar covariates) and climate processes (functional covariates).

제안 방법

This work proposes an exploratory data analysis of PM10 using California monitoring data by first performing curve alignment on PM10 to provide its height and time variation. Ignoring time variation can lead to distorted or biased estimates when modeling PM10 data, and thus, the time variability should be taken into account when using FD methodologies on PM10.
The scores of the first FPC allowed us to focus on these two aspects of the behavior of PM10. Using a GAMM, we investigated how both the first FPC scores of the PM10 height and time variation varied with large-scale climate indices and spatial location. The analysis showed that the size and timing of PM10 features varied throughout California.
The analysis showed that the size and timing of PM10 features varied throughout California. Furthermore, our analysis gives insight into the complicated relationship of the first FPC scores of the PM10 height and time variation with climate indices that measure variations of sea surface temperature and atmospheric circulation patterns. In particular, certain phase interactions between PNA and ONI are associated with above or below average levels of PM10, as well as its features occurring earlier or later than average, throughout the day.

성능/효과

Ignoring time variation can lead to distorted or biased estimates when modeling PM10 data, and thus, the time variability should be taken into account when using FD methodologies on PM10. The results show substantial height and time variation in the features of PM10, and, for brevity, we focused only on the dominating mode of variability for each process. The dominating mode of the underlying structure of the height and time variation processes of PM10 reflected an overall daily size effect primarily during the rush hours and a time shift effect throughout the day, respectively.

참고문헌 (38)

American Lung Association (2017). American lung association state of the air 2017, Chicago, IL.
Bridgman HA and Oliver JE (2006). The Global Climate System (1st ed), Cambridge University Press, Cambridge.
Burroughs WJ (2007). Climate Change: A Multidisciplinary Approach (2nd ed), Cambridge University Press, Cambridge.
Chow JC, Watson JG, Lowenthal DH, Solomon PA, Magliano KL, Ziman SD, and Richards LW (1992). PM10 source apportionment in California's San Joaquin Valley, Atmospheric Environment. Part A. General Topics, 26, 3335-3354.

상세보기
Csavina J, Field J, Falix O, Corral-Avitia AY, Saez AE, and Betterton EA (2014). Effect of wind speed and relative humidity on atmospheric dust concentrations in semi-arid climates, Science of The Total Environment, 487, 82-90.

상세보기
Dawson JP, Bloomer BJ, Winner DA, and Weaver CP (2014). Understanding the meteorological drivers of US particulate matter concentrations in a changing climate, Bulletin of the American Meteorological Society, 95, 521-532.

상세보기
EPA (2018). Criteria air pollutants, from: https://www.epa.gov/criteria-air-pollutants
Feng J, Liao H, and Li J (2016). The impact of monthly variation of the Pacific-North America (PNA) teleconnection pattern on wintertime surface-layer aerosol concentrations in the United States, Atmospheric Chemistry and Physics, 16, 4927-4943.

상세보기
Ignaccolo R, Mateu J, and Giraldo R (2014). Kriging with external drift for functional data for air quality monitoring, Stochastic Environmental Research and Risk Assessment, 28, 1171-1186.

상세보기
Jerez S, Jimenez-Guerrero P, Montavez JP, and Trigo RM (2013). Impact of the North Atlantic Oscillation on European aerosol ground levels through local processes: a seasonal model-based assessment using fixed anthropogenic emissions, Atmospheric Chemistry and Physics, 13, 11195-11207.

상세보기
Jhun I, Coull BA, Schwartz J, Hubbell B, and Koutrakis P (2015). The impact of weather changes on air quality and health in the United States in 1994-2012, Environmental Research Letters, 10, 084009.

상세보기
Karl TR and Knight RW (1998). Secular trends of precipitation amount, frequency, and intensity in the United States, Bulletin of the American Meteorological Society, 79, 231-241.

상세보기
Klumpp A, Ansel W, and Klumpp G (2004). Urban Air Pollution, Bioindication and Environmental Awareness, Cuvillier Verlag, Gottingen.
Kokoszka P and Reimherr M (2017). Introduction to Functional Data Analysis (1st ed), Chapman and Hall, Florida.
Lin M, Fiore AM, Horowitz LW, Langford AO, Oltmans SJ, Tarasick D, and Rieder HE (2015). Climate variability modulates western US ozone air quality in spring via deep stratospheric intrusions, Nature Communications, 6, 7105.

상세보기
Liu YC, Di P, Chen SH, and DaMassa J (2018). Relationships of rainy season precipitation and temperature to climate indices in California: long-term variability and extreme events, Journal of Climate, 31, 1921-1942.

상세보기
Marron JS, Ramsay JO, Sangalli LM, and Srivastava A (2015). Functional data analysis of amplitude and phase variation, Statistical Science, 30, 468-484.

상세보기
Montoya EL and Meiring W (2016). An F-type test for detecting departure from monotonicity in a functional linear model, Journal of Nonparametric Statistics, 28, 322-337.

상세보기
Montoya EL, Ulloa N, and Miller V (2014). A simulation study comparing knot selection methods with equally spaced knots in a penalized regression spline, International Journal of Statistics and Probability, 3, 96-110.
Morlini I (2007). Searching for structure in measurements of air pollutant concentration, Environmetrics, 18, 823-840.

상세보기
R Development Core Team (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.
Ramsay JO and Silverman BW (2005). Functional Data Analysis (2nd ed), Springer, New York.
Ramsay JO, Wickham H, Graves S, and Hooker G (2017). fda: Functional Data Analysis, R package version 2.4.7.
Ranalli MG, Rocco G, Jona Lasinio G, Moroni B, Castellini S, Crocchianti S, and Cappelletti D (2016). Functional exploratory data analysis for high-resolution measurements of urban particulate matter, Biometrical Journal, 58, 1229-1247.

상세보기
Schnute JT, Boers N, Haigh R, Grandin C, Johnson A,Wessel P, and Antonio F (2017). PBSmapping: Mapping Fisheries Data and Spatial Analysis Tools, R package version 0.8.0.
Shaadan N, Jemain AA, Latif MT, and Deni SM (2015). Anomaly detection and assessment of $PM_{10}$ functional data at several locations in the Klang Valley, Malaysia, Atmospheric Pollution Research, 6, 365-375.

상세보기
Shang HL (2014). A survey of functional principal component analysis, AStA Advances in Statistical Analysis, 98, 121-142.

상세보기
Silverman BW (1996). Smoothed functional principal components analysis by choice of norm, The Annals of Statistics, 24, 1-24.

상세보기
Singh A and Palazoglu A (2012). Climatic variability and its influence on ozone and PM pollution in 6 non-attainment regions in the United States, Atmospheric Environment, 51, 212-224.

상세보기
Srivastava A and Klassen EP (2016). Functional and Shape Data Analysis, Springer, New York.
Srivastava A, Wu W, Kurtek S, Klassen E, and Marron JS (2011). Registration of functional data using Fisher-Rao metric, from: arXiv preprint arXiv:1103.3817v2
Sun Y and Genton MG (2011). Functional boxplots, Journal of Computational and Graphical Statistics, 20, 316-334.

상세보기
Tucker JD (2017). fdasrvf: Elastic Functional Data Analysis, R package version 1.8.2.
Wood SN (2006). Low-rank scale-invariant tensor product smooths for generalized additive mixed models, Biometrics, 62, 1025-1036.

상세보기
Wood SN (2017a). Generalized Additive Models: An Introduction with R, CRC press, New York.
Wood SN (2017b). mgcv: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation, R package version 1.8-23.
Yu JY, Kao HY, Lee T, and Kim ST (2011). Subsurface ocean temperature indices for Central-Pacific and Eastern-Pacific types of El Nino and La Nina events, Theoretical and Applied Climatology, 103, 337-344.

상세보기
Zhang Y andWang Y (2016). Climate-driven ground-level ozone extreme in the fall over the southeast United States. In Proceedings of the National Academy of Sciences, 113, 10025-10030.

상세보기

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[국내논문] Investigating the underlying structure of particulate matter concentrations: a functional exploratory data analysis study using California monitoring data 원문보기

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[국내논문] Investigating the underlying structure of particulate matter concentrations: a functional exploratory data analysis study using California monitoring data 원문보기

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