A study of the temporal dynamics of ambient particulate matter using stochastic and chaotic techniques

Atmospheric Environment

Volumn 69, Issue , 2013, Pages 37-45

  Yu, Hwa Lung ^a   Lin, Yuan Chien ^a   Sivakumar, Bellie ^b,c   Kuo, Yi Ming ^d  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d MINGDAO UNIVERSITY   (Taiwan)

Author keywords

Correlation dimension; Dynamic factor analysis; Particulate matter; System identification; Temporal dynamics

Indexed keywords

AMBIENT PARTICULATE MATTER; CD METHOD; CHAOTIC TECHNIQUES; CHEMICAL INTERACTIONS; CORRELATION DIMENSIONS; DETERMINISTIC PROCESS; DFA METHOD; DYNAMIC FACTOR ANALYSIS; ENVIRONMENTAL SYSTEMS; EXOGENOUS FACTORS; METEOROLOGICAL VARIABLES; PARTICULATE MATTER; PM10 AND PM2.5; STOCHASTIC METHODS; TEMPORAL DYNAMICS;

DYNAMICS; FACTOR ANALYSIS; IDENTIFICATION (CONTROL SYSTEMS); MULTIVARIANT ANALYSIS; PARTICLES (PARTICULATE MATTER); POLLUTION; RANDOM PROCESSES; STOCHASTIC SYSTEMS;

FACE RECOGNITION;

AEROSOL PROPERTY; AMBIENT AIR; ATMOSPHERIC DYNAMICS; CHAOTIC DYNAMICS; CONCENTRATION (COMPOSITION); CORRELATION; PARTICLE SIZE; PARTICULATE MATTER; STOCHASTICITY; TEMPORAL VARIATION; TIME SERIES ANALYSIS;

ARTICLE; CHAOTIC DYNAMICS; CONCENTRATION (PARAMETERS); CORRELATION DIMENSION METHOD; DYNAMIC FACTOR ANALYSIS; PARTICULATE MATTER; POLLUTANT; PRIORITY JOURNAL; PROCEDURES; STOCHASTIC MODEL; TAIWAN;

TAIPEI; TAIWAN;

EID: 84872015867     PISSN: 13522310     EISSN: 18732844     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2012.10.067     Document Type: Article

References (68)

1. Casdagli M. Nonlinear prediction of chaotic time-series. Phys. D 1989, 35:335-356.
2. Chang S.C., Lee C.T. Evaluation of the trend of air quality in Taipei, Taiwan from 1994 to 2003. Environ. Monit. Assess. 2007, 127:87-96.
3. Chang N.B., Weng Y.C., Lee T.Y. Nonlinear time series analysis of ground-level ozone dynamics in Southern Taiwan. J. Environ. Manage. 2008, 87:405-414.
4. Chelani A.B. Nonlinear dynamical analysis of ground level ozone concentrations at different temporal scales. Atmos. Environ. 2010, 44:4318-4324.
5. Chen L.W.A., Watson J.G., Chow J.C., DuBois D.W., Herschberger L. Chemical mass balance source apportionment for combined PM(2) (5) measurements from U S non-urban and urban long-term networks. Atmos. Environ. 2010, 44:4908-4918.
6. Cheng M.D., Hopke P.K. Identification of Markers for chemical mass balance receptor model. Atmos. Environ. 1989, 23:1373-1384.
7. Chow J.C., Watson J.G. Review of PM2.5 and PM10 apportionment for fossil fuel combustion and other sources by the chemical mass balance receptor model. Energy Fuel 2002, 16:222-260.
8. Cleveland R.B., Cleveland W.S., McRae J.E., Terpenning I. STL: a seasonal-trend decomposition procedure based on loess. J. Off. Stat. 1990, 6:3-73.
9. Dockery D.W., Pope C.A., Xu X., Spengler J.D., Ware J.H., Fay M.E., Ferris B.G., Speizer F.E. An association between air pollution and mortality in six U.S. cities. N. Engl. J. Med. 1993, 329:1753-1759.
10. Farmer J.D., Sidorowich J.J. Predicting chaotic time-series. Phys. Rev. Lett. 1987, 59:845-848.
11. Fraser A.M., Swinney H.L. Independent coordinates for strange attractors from mutual information. Phys. Rev. A 1986, 33:1134-1140.
12. Garcia J.H., Li W.W., Cardenas N., Arimoto R., Walton J., Trujillo D. Determination of PM2.5 sources using time-resolved integrated source and receptor models. Chemosphere 2006, 65:2018-2027.
13. Glaser S.M., Ye H., Maunder M., MacCall A., Fogarty M., Sugihara G. Detecting and forecasting complex nonlinear dynamics in spatially structured catch-per-unit-effort time series for North Pacific albacore (Thunnus alalunga). Can. J. Fish. Aquat. Sci. 2011, 68:400-412.
14. Gleick J. Chaos: Making a New Science 1987, Viking, New York, N.Y., U.S.A.
15. Grassberger P., Procaccia I. Characterization of strange attractors. Phys. Rev. Lett. 1983, 50:346-349.
16. Guo H. Source apportionment of volatile organic compounds in Hong Kong homes. Build Environ. 2011, 46:2280-2286.
17. Hammond D.M., Dvonch J.T., Keeler G.J., Parker E.A., Kamal A.S., Barres J.A., Yip F.Y., Brakefield-Caldwell W. Sources of ambient fine particulate matter at two community sites in Detroit, Michigan. Atmos. Environ. 2008, 42:720-732.
18. Hellebust S., Allanic A., O'Connor I.P., Wenger J.C., Sodeau J.R. The use of real-time monitoring data to evaluate major sources of airborne particulate matter. Atmos. Environ. 2010, 44:1116-1125.
19. Hill J., Hossain F., Sivakumar B. Is correlation dimension a reliable proxy for the number of dominant influencing variables for modeling risk of arsenic contamination in groundwater?. Stoch. Environ. Res. Risk Assess. 2008, 22:47-55.
20. Holzfuss J., Mayer-Kress G. Approach to Error-estimation in the Application of Dimension Algorithms 1985, Los Alamos National Lab, NM (USA).
21. Hopke P.K. Recent developments in receptor modeling. J. Chemometr. 2003, 17:255-265.
22. Hopke P.K., Song X.H., Polissar A.V. Sources of fine particle composition in the northeastern US. Atmos. Environ. 2001, 35:5277-5286.
23. Hossain F., Sivakumar B. Spatial pattern of arsenic contamination in shallow wells of Bangladesh: regional geology and nonlinear dynamics. Stoch. Environ. Res. Risk Assess. 2006, 20:66-76.
24. Hsieh C.H., Anderson C., Sugihara G. Extending Nonlinear analysis to short ecological time series. Am. Nat. 2008, 171:71-80.
25. Huang S.L., Rahn K.A., Arimoto R. Testing and optimizing two factor-analysis techniques on aerosol at Narragansett, Rhode Island. Atmos. Environ. 1999, 33:2169-2185.
26. Islam S., Chen J.L., Biswas P. Nonlinear dynamics of hourly ozone concentrations: nonparametric short term prediction. Atmos. Environ. 1998, 32:1839-1848.
27. Kantz H., Schreiber T. Nonlinear Time Series Analysis 2003, Cambridge University Press, Cambridge, p. 1 online resource (388 pp.).
28. Kennel M.B., Brown R., Abarbanel H.D.I. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 1992, 45:3403-3411.
29. Kuo Y.M., Chu H.J., Pan T.Y., Yu H.L. Investigating: common trends of annual maximum rainfalls during heavy rainfall events in southern Taiwan. J. Hydrol. 2011, 409:749-758.
30. Kuo Y.M., Wang S.W., Jang C.S., Yeh N., Yu H.L. Identifying the factors influencing PM2.5 in southern Taiwan using dynamic factor analysis. Atmos. Environ. 2011, 45:7276-7285.
31. Laden F., Neas L.M., Dockery D.W., Schwartz J. Association of fine particulate matter from different sources with daily mortality in six US cities. Environ. Health Perspect. 2000, 108:941-947.
32. Lall R., Thurston G.D. Identifying and quantifying transported vs. local sources of New York City PM2.5 fine particulate matter air pollution. Atmos. Environ. 2006, 40:S333-S346.
33. Lee C.K., Lin S.C. Chaos in air pollutant concentration (APC) time series. Aerosol Air Qual. Res. 2008, 8:381-391.
34. Liebert W., Schuster H.G. Proper choice of the time-delay for the analysis of chaotic time-series. Phys. Lett. A 1989, 142:107-111.
35. Lorenz E.N. Deterministic nonperiodic flow. J. Atmos. Sci. 1963, 20:130-141.
36. Mar T.F., Ito K., Koenig J.Q., Larson T.V., Eatough D.J., Henry R.C., Kim E., Laden F., Lall R., Neas L., Stolzel M., Paatero P., Hopke P.K., Thurston G.D. PM source apportionment and health effects. 3. Investigation of inter-method variations in associations between estimated source contributions of PM2.5 and daily mortality in Phoenix, AZ. J. Expo. Sci. Environ. Epidemiol. 2006, 16:311-320.
37. Molenaar P.C.M. A dynamic factor model for the analysis of multivariate time-series. Psychometrika 1985, 50:181-202.
38. Molenaar P.C.M., Degooijer J.G., Schmitz B. Dynamic factor-analysis of nonstationary multivariate time-series. Psychometrika 1992, 57:333-349.
39. Muñoz-Carpena R., Ritter A., Li Y.C. Dynamic factor analysis of groundwater quality trends in an agricultural area adjacent to Everglades National Park. J. Contamin. Hydrol. 2005, 80:49-70.
40. Ostro B.D., Broadwin R., Lipsett M.J. Coarse and fine particles and daily mortality in the Coachella Valley, California: a follow-up study. J. Expo. Anal. Environ. Epidemiol. 2000, 10:412-419.
41. Ozkaynak H., Xue J., Spengler J., Wallace L., Pellizzari E., Jenkins P. Personal exposure to airborne particles and metals: results from the particle team study in Riverside, California. J. Expo. Anal. Environ. Epidemiol. 1996, 6:57-78.
42. Paatero P., Tapper U. Positive matrix factorization - a nonnegative factor model with optimal utilization of error-estimates of data values. Environmetrics 1994, 5:111-126.
43. Packard N.H., Crutchfield J.P., Farmer J.D., Shaw R.S. Geometry from a time-series. Phys. Rev. Lett. 1980, 45:712-716.
44. Pope C.A.I., Burnett R.T., Thun M.J., Calle E.E., Krewski D., Ito K., Thurston G.D. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J. Am. Med. Assoc. 2002, 287:1132-1141.
45. Raga G.B., LeMoyne L. On the nature of air pollution dynamics in Mexico City. 1. Nonlinear analysis. Atmos. Environ. 1996, 30:3987-3993.
46. Ritter A., Muñoz-Carpena R. Dynamic factor modeling of ground and surface water levels in an agricultural area adjacent to Everglades National Park. J. Hydrol. 2006, 317:340-354.
47. Schwartz J., Neas L.M. Fine particles are more strongly associated than coarse particles with acute respiratory health effects in schoolchildren. Epidemiology 2000, 11:6-10.
48. Schwartz J., Norris G., Larson T., Sheppard L., Claiborn C., Koenig J. Episodes of high coarse particle concentrations are not associated with increased mortality. Environ. Health Perspect. 1999, 107:339-342.
49. Seinfeld J.H. Atmospheric Chemistry and Physics of Air Pollution 1986, Wiley, New York.
50. Shumway R.H., Stoffer D.S. An approach to time series smoothing and forecasting using the EM algorithm. J. Time Ser. Anal. 1982, 3:253-264.
51. Sivakumar B. Chaos theory in hydrology: important issues and interpretations. J. Hydrol. 2000, 227:1-20.
52. Sivakumar B. Chaos theory in geophysics: past, present and future. Chaos Solitons Fractals 2004, 19(2):441-462.
53. Sivakumar B. Correlation dimension estimation of hydrological series and data size requirement: myth and reality. Hydrol. Sci. J. 2005, 50:591-603.
54. Sivakumar B., Sorooshian S., Gupta H.V., Gao X.G. A chaotic approach to rainfall disaggregation. Water Resour. Res. 2001, 37:61-72.
55. Sivakumar B., Jayawardena A.W., Li W.K. Hydrologic complexity and classification: a simple data reconstruction approach. Hydrol. Process 2007, 21:2713-2728.
56. Song X.H., Polissar A.V., Hopke P.K. Sources of fine particle composition in the northeastern US. Atmos. Environ. 2001, 35:5277-5286.
57. Srivastava A., Jain V.K. Seasonal trends in coarse and fine particle sources in Delhi by the chemical mass balance receptor model. J. Hazard. Mater. 2007, 144:283-291.
58. Strogatz S.H. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering 1994, Addison-Wesley Publisher, Reading, Mass.
59. Sugihara G., May R.M. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time-series. Nature 1990, 344:734-741.
60. Takens F. Detecting strange attractors in turbulence. Dynamical Systems and Turbulence, Warwick 1980 1981, vol. 898. pp. 366-381.
61. Tsonis A.A. Chaos: From Theory to Applications 1992, Plenum Press, New York.
62. Viana M., Kuhlbusch T.A.J., Querol X., Alastuey A., Harrison R.M., Hopke P.K., Winiwarter W., Vallius A., Szidat S., Prevot A.S.H., Hueglin C., Bloemen H., Wahlin P., Vecchi R., Miranda A.I., Kasper-Giebl A., Maenhaut W., Hitzenberger R. Source apportionment of particulate matter in Europe: a review of methods and results. J. Aerosol Sci. 2008, 39:827-849.
63. Viana M., Pandolfi M., Minguillon M.C., Querol X., Alastuey A., Monfort E., Celades I. Inter-comparison of receptor models for PM source apportionment: case study in an industrial area. Atmos. Environ. 2008, 42:3820-3832.
64. Watson J.G., Chow J.C., Lu Z.Q., Fujita E.M., Lowenthal D.H., Lawson D.R., Ashbaugh L.L. Chemical mass-balance source apportionment of Pm(10) during the Southern California air-quality study. Aerosol Sci. Technol. 1994, 21:1-36.
65. Wolf A., Swift J.B., Swinney H.L., Vastano J.A. Determining Lyapunov exponents from a time-series. Phys. D 1985, 16:285-317.
66. Yu H.L., Wang C.H. Retrospective prediction of intraurban spatiotemporal distribution of PM2.5 in Taipei. Atmos. Environ. 2010, 44:3053-3065.
67. Zuur A.F., Fryer R.J., Jolliffe I.T., Dekker R., Beukema J.J. Estimating common trends in multivariate time series using dynamic factor analysis. Environmetrics 2003, 14:665-685.
68. Zuur A.F., Ieno E.N., Smith G.M., SpringerLink (Online Service) Analysing Ecological Data, Statistics for Biology and Health 2007, Springer, New York; London, pp. xxvi, 672 p. ill.