Skip to main content
SpringerLink
Log in

Statistical Methods

  • Chapter
Air Pollution Modeling

Abstract

Statistical methods are frequently used in air pollution studies. Several types of statistical models, methods and analyses will be discussed in this chapter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Alcamo, J., and J. Bartnicki (1987): A framework for error analysis of a long-range transport model with emphasis on parameter uncertainty. Atmos. Environ., 21 (10): 2121–2131.

    Article  Google Scholar 

  • Arkadev, A.G., and E.M. Braverman (1967): Teaching Computers to Recognize Patterns. London: Academic Press.

    Google Scholar 

  • Bacci, P., P. Bolzern, and G. Fronza (1981): A stochastic predictor of air pollution based on short-term meteorological forecasts. J. Appl. Meteor., 20 (2): 121–129.

    Article  Google Scholar 

  • Bankoff, S.G., and E.L. Hanzevack (1975): The adaptive filtering transport model for prediction and control of pollutant concentration in an urban airshed. Atmos. Environ., 18: 701–712.

    Google Scholar 

  • Barnes, M.G. (1980): The use of Kriging for estimating the spatial distribution of radionuclides and other spatial phenomena. Tran-Stat, Battelle Memorial Institute, Pacific Northwest Laboratories., Richland, Washington.

    Google Scholar 

  • Barone, J.B., T.A. Cahill, R.A. Eldred, R.G. Flocchini, D.J. Shadoan, and T.M. Dietz (1978): A multivariate statistical analysis of visibility degradation at four California cities. Atmos. Environ., 12: 2213–2221.

    Article  Google Scholar 

  • Berge, P., Y. Pomeau, and C. Vidal (1984): Order Within Chaos. New York: John Wiley.

    Google Scholar 

  • Bornstein, R.D., and S.F. Anderson (1979): A survey of statistical techniques used in validation studies of air pollution prediction models. Technical Report No. 23, Stanford University, Stanford, California.

    Google Scholar 

  • Box, G.E., and G.M. Jenkins (1976): Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day.

    Google Scholar 

  • Buishand, T.A., G.T. Kempen, A.J. Frantzen, H.F. Reijnders, and A.J. van den Eshof (1988): Trend and seasonal variations of precipitation chemistry data in the Netherlands. Atmos. Environ., 22 (2): 339–348.

    Article  Google Scholar 

  • Cats, G.J., and A.A. Holtslag (1980): Prediction of air pollution frequency distribution. Part I: The lognormal model. Atmos. Environ., 14: 255–258.

    Article  Google Scholar 

  • Chock, D.P., and P.S. Sluchak (1986): Estimating extreme values of air quality data using different fitted distributions. Atmos. Environ., 20 (5): 989–993.

    Article  Google Scholar 

  • Chock, D.P., T.R. Terrel, and S.B. Levitt (1975): Time-series analysis of Riverside, California air quality data. Atmos. Environ., 9: 978–989.

    Article  Google Scholar 

  • Chow, J.C., P.W. Severance, and J.D. Spengler (1985): A composite application of source and receptor models to fine particle concentrations in Portage, Wisconsin. Proceedings, 78th Annual APCA Meeting. Detroit, Michigan, June.

    Google Scholar 

  • Cooper, J.A. (1983): Receptor model approach to source apportionment of acid rain precursors. Proceedings, VIth World Congress on Air Quality, Paris, France, May 16–20, pp. 223–229.

    Google Scholar 

  • Desalu, A.A., L.A. Gould, and F.C. Schweppe (1974): Dynamic estimation of air pollution. IEEE Transactions on Automatic Control AC-19, pp. 904–910.

    Google Scholar 

  • Drufuca, G., and M. Giugliano (1978): Relationship between maximum SO 2 concentration, averaging time and average concentration in an urban area. Atmos. Environ., 12: 1901–1905.

    Article  Google Scholar 

  • Edgerton, S.A., and M.W. Holdren (1987): Use of pattern recognition techniques to characterize local sources of toxic organics in the atmosphere. Environ. Sci. Technol., 21 (11): 1102–1107.

    Article  Google Scholar 

  • Endlich, R.M., F.L. Ludwig, and E.E. Uthe (1979): An automatic method for determining the mixing depth from LIDAR observations. Atmos. Environ., 13: 1051–1056.

    Article  Google Scholar 

  • Eynon, B.P. (1988): Statistical analysis of precipitation chemistry measurements over the eastern United States. Part II: Kriging analysis of regional patterns and trends. J. Appl. Meteor., 27: 1334–1343.

    Article  Google Scholar 

  • Fedorov, V.V. (1989): Kriging and other estimators of spatial field characteristics (with special reference to environmental studies). Atmos. Environ., 23 (1): 175–184.

    Article  Google Scholar 

  • Finzi, G., and G. Tebaldi (1982): A mathematical model for air pollution forecast and alarm in an urban area. Atmos. Environ., 16 (9): 2055–2059.

    Article  Google Scholar 

  • Fox, D.C. (1981): Judging air quality model performance. J. Climate and Appl. Meteor., 62: 599–609.

    Google Scholar 

  • Fox, D.C. (1984): Uncertainty in air quality modeling. J. Climate and Appl. Meteor., 65: 27–36.

    Google Scholar 

  • Fronza, G., A. Spirito, and A. Tonielli (1979): Real-time forecast of air pollution episodes in the Venetian region. Part 2: The Kalman predictor. Appl. Math. Model., 3: 409–415

    Article  Google Scholar 

  • Fu, K.S. (1968): Sequential Methods in Pattern Recognition. New York: Academic Press. Fu, K.S. (1974): Syntactic Methods in Pattern Recognition. New York: Academic Press.

    Google Scholar 

  • Fukunaga, K. (1972): Introduction to Statistical Pattern Recognition. New York: Academic Press.

    Google Scholar 

  • Georgopoulos, P.G., and J.H Seinfeld (1982): Statistical distributions of air pollutant concentrations. Environ. Sci. and Technol., 16: 401A - 415A.

    Article  Google Scholar 

  • Gilbert, R.O. (1987): Statistical Methods for Environmental Pollution Monitoring. New York: Van Nostrand Reinhold.

    Google Scholar 

  • Gomez, M.L., and M.C. Martin (1987): Applications of cluster analysis to identify sources for airborne particles. Atmos. Environ., 21 (7): 1521–1527.

    Article  Google Scholar 

  • Gordon, G.E. (1988): Receptor models. Environ. Sci. and Tech., 22 (10): 1132.

    Article  Google Scholar 

  • Grebogi, C., E. Ott, and J.A. Yorke (1987): Chaos, strange attractors, and fractal basin boundaries in nonlinear dynamics. Science, 238: 632–637.

    Article  Google Scholar 

  • Hanna, S.R., and D.W. Heinold (1985): Development and application of a simple method for evaluating air quality models. American Petroleum Institute Publication 4409, Washington, D.C.

    Google Scholar 

  • Little, R.J., and D.B. Rubin (1987): Statistical Analysis with Missing Data. New York: John Wiley.

    Google Scholar 

  • Hanna, S.R. (1988): Air quality model evaluation and uncertainty. JAPCA, 38: 406–412.

    Article  Google Scholar 

  • Hanna, S.R. (1989): Confidence limits for air quality model evaluations, as estimated by bootstrap and jackknife resampling methods. Atmos. Environ., 23 (6): 1385–1389.

    Article  Google Scholar 

  • Heidam, N.Z. (1987): Bootstrap estimates of factor model variability. Atmos. Environ., 21 (5): 1203–1217.

    Article  Google Scholar 

  • Henry, R.C. (1987): Current factor analysis receptor models are ill-posed. Atmos. Environ., 21 (8): 1815–1820.

    Article  Google Scholar 

  • Henry, R.C., and G.M. Hidy (1979): Multivariate analysis of particulate sulfate and other air quality variables by principal components. Part I: Annual data from Los Angeles and New York. Atmos. Environ., 13: 1581–1596.

    Article  Google Scholar 

  • Henry, R.C., C.W. Lewis, P.K. Hopke, and H.I. Williamson (1984): Review of receptor model fundamentals. Atmos. Environ., 18: 1507–1515.

    Article  Google Scholar 

  • Hino, M. (1974): Prediction of atmospheric pollution by Kalman filtering. Proceedings, Symposium on Modeling for Prediction and Control of Air Pollution.

    Google Scholar 

  • Hopke, P.K. (1988): Target transformation factor analysis as an aerosol mass apportionment method: A review and sensitivity study. Atmos. Environ., 22 (9): 1777–1792.

    Article  Google Scholar 

  • Horowitz, J., and S. Barakat (1979): Statistical analysis of the maximum concentration of an air pollutant: Effects of autocorrelation and non-stationarity. Atmos. Environ., 13: 811–818.

    Article  Google Scholar 

  • Irwin, J.S., and M.E. Smith (1984): Potentially useful additions to the rural model performance evaluation. J. Climate and Appl. Meteor., 65: 559.

    Google Scholar 

  • Irwin, J.S., S.T. Rao, W.B. Peterson, and D.B. Turner (1987): Relating error bounds for maximum concentration estimates to diffusion meteorology uncertainty. Atmos. Environ., 21 (9): 1927–1937.

    Article  Google Scholar 

  • Jazwinski, A.H. (1970): Stochastic Processes and Filtering Theory. New York: Academic Press.

    Google Scholar 

  • Jenkins, G.M., and D.G. Watts (1968): Spectral Analysis and Its Applications. San Francisco: Holden-Day.

    Google Scholar 

  • Kahn, H.D. (1973): Distribution of Air Pollutants. JAPCA, 23: 973.

    Google Scholar 

  • Kalman, R.E. (1960): A new approach to linear filtering and prediction problems. J. Basic Eng., pp. 35–108

    Google Scholar 

  • Langstaff, J.E., C. Seigneur, M.-K. Liu, J.V. Behar, and J.L. McElroy (1987): Design of an optimum air monitoring network for exposure assessments. Atmos. Environ., 21 (6): 1393–1410.

    Google Scholar 

  • Larsen, R.I. (1971): EPA Publication No. AP-89, Research Triangle Park, North Carolina.

    Google Scholar 

  • Lin, G.Y., (1982): Oxidant prediction by discriminant analysis in the south coast air basin of California. Atmos. Environ., 16 (1): 135–143.

    Article  Google Scholar 

  • Longhetto, A., Ed. (1980): Atmospheric Planetary Boundary Layer Physics. New York: Elsevier.

    Google Scholar 

  • Liu, M.-K., J. Arvin, R.I. Pollack, J.V. Behar, and J.L. McElroy (1986): Methodology for designing air quality monitoring networks. I: Theoretical aspects. Environ. Monitoring and Assessment, 6: 1–11.

    Article  Google Scholar 

  • Ludwig, F.L. (1989): Atmospheric fractals — A review. Environ. Software, 4 (1): 9–16.

    Article  Google Scholar 

  • Malm, W., K. Gebhart, D.A. Latimer, T.A. Cahill, R. Eldred, R.A. Pielke, R. Stocker, J.G. Watson (1989): Winter Haze Intensive Tracer Experiment. National Park Service draft final report, December.

    Google Scholar 

  • Mandelbrot, B.B. (1975): On the geometry of homogeneous turbulence with stress on the fractal dimension of the iso-surfaces of scalars. J. Fluid Mech., 72: 401–416.

    Article  Google Scholar 

  • Marani, A., 1. Lavagnini, C. Buttazzoni (1986): Statistical study of air pollutant concentrations via generalized gamma distributions. JAPCA, 36: 1250–1254.

    Google Scholar 

  • Matheron, G. (1971): The theory of regionalized variables and its applications. Ecole des Mines de Paris, Fontainbleau No. 5.

    Google Scholar 

  • Melli, P., P. Bolzern, G. Fronza, and A. Spirito (1981): Real-time control of sulphur dioxide emissions from an industrial area. Atmos. Environ., 15: 653–666.

    Article  Google Scholar 

  • Moore, G.E., M.-K. Liu, and R.J. Londergan (1985): Diagnostic validation of Gaussian and first-order closure plume models at a moderately complex terrain site. Systems Applications, Inc., Final Report EA-3760, San Rafael, California.

    Google Scholar 

  • Murphy, A.H. (1988): Skill scores based on the mean square error and their relationships to the correlation coefficient. J. Climate and Appl. Meteor., 116: 2417–2424.

    Google Scholar 

  • Murray, L.C., and R.J. Farber (1982): Time series analysis of an historical visibility data base. Atmos. Environ., 16: 2299–2308.

    Article  Google Scholar 

  • Nakamori, Y., and Y. Sawaragi (1984): Interactive design of urban level air quality monitoring network. Atmos. Environ., 18 (4): 793–799.

    Article  Google Scholar 

  • Nilsson, N.J. (1965): Learning Machines. McGraw-Hill.

    Google Scholar 

  • Noll, K.E., T.L. Miller, J.E. Norco, and R.K. Raufer (1977): An objective air monitoring site selection methodology for large point sources. Atmos. Environ., 11: 1051–1059.

    Article  Google Scholar 

  • Petersen, J.T. (1970): Distribution of sulfur dioxide over metropolitan St. Louis, as described by empirical eigenvectors, and its relation to meteorological parameters. Atmos. Environ., 4: 501–518.

    Article  Google Scholar 

  • Plackett, R.L. (1950): Biometrika, 37: 149.

    Google Scholar 

  • Reynolds, S.D., C. Seigneur, T.E. Stoeckenius, G.E. Moore, R.G. Johnson, and R.J. Londergan (1984): Operational validation of Gaussian plume models at a plains site. Systems Applications, Inc., Final Report EA-3076, San Rafael, California.

    Google Scholar 

  • Reynolds, S.D., T.C. Myers, J.E. Langstaff, M.—K. Liu, G.E. Moore, and R.E. Morris(1985): Operational validation of Gaussian and first—order closure plume models at a moderately complex terrain site. Systems Applications, Inc., Final Report EA-3759, San Rafael, California.

    Google Scholar 

  • Romesburg, H.C. (1984): Cluster Analysis for Researchers. Belmont, California: Lifetime Learning Publications.

    Google Scholar 

  • Roberts, E.M. (1979): Review of statistics of extreme values with applications to air quality data. Part I: Review. JAPCA, 29 (6): 632–637.

    Google Scholar 

  • Roberts, E.M. (1979): Review of statistics of extreme values with applications to air quality data. Part II: Applications. JAPCA, 29 (7): 733–740.

    Google Scholar 

  • Roy, R., and I. Pellerin (1982): On long term air quality trends and intervention analysis. Atmos. Environ., 16: 161–169.

    Article  Google Scholar 

  • Ruff, R.E., K.C. Nitz, F.L. Ludwig, C.M. Bhumralkar, J.D. Shannon, C.M. Sheih, I.Y. Lee, R. Kumar, and D.J. McNaughton (1984): Regional air quality model assessment and evaluation. SRI International Final Report EA-3671, Menlo Park, California.

    Google Scholar 

  • Sage, A.P., and I.L. Melsa (1971): Estimation Theory with Applications to Communications and Control. New York: McGraw—Hill.

    Google Scholar 

  • Sawaragi, Y., T. Soeda, T. Yoshimura, S. Oh, Y. Chujo, and H. Ishihara (1976): The predictions of air pollution levels by nonphysical models based on Kalman filtering method. J. Dynamic Sys., Measurement and Control, 98 (12): 375–386.

    Article  Google Scholar 

  • Scheff, P.A. R.A. Wadden, and R.I. Allen (1984): Development and validation of a chemical element mass balance for Chicago. Environ. Sci. Technol., 18: 923–931.

    Article  Google Scholar 

  • Seinfeld, J.H. (1972): Optimal location of pollutant monitoring stations in an airshed. Atmos. Environ., 6: 847–858.

    Article  Google Scholar 

  • Seinfeld, J.H. (1986): Atmospheric Chemistry and Physics of Air Pollution. New York: John Wiley.

    Google Scholar 

  • Simpson, R.W., and A.P. Layton (1983): Forecasting peak ozone levels. Atmos. Environ., 17: 1649–1654.

    Article  Google Scholar 

  • Trivikrama, S.R., P.I. Samson, and A.R. Pedadda (1976): Spectral analysis approach to the dynamics of air pollutants. Atmos. Environ., 10: 375–379.

    Article  Google Scholar 

  • Surman, P.G., J. Bodero, and R.W. Simpson (1987): The prediction of the numbers of violations of standards and the frequency of air pollution episodes using extreme value theory. Atmos. Environ., 21 (8): 1843–1848.

    Article  Google Scholar 

  • Tauber, S. (1978): Pattern recognition methods in air pollution control. Atmos. Environ., 12: 2377–2382.

    Article  Google Scholar 

  • Tiao, G.C., G.E. Box, and W.J. Hamming (1975): Analysis of Los Angeles photochemical smog data: A statistical overview. JAPCA, 25: 260–268.

    Google Scholar 

  • Tilley, T., and G.A. McBean (1973): An application of spectrum analysis to synoptic—pollution data. Atmos. Environ., 7: 793–801.

    Article  Google Scholar 

  • Tsukatami, T., and K. Shigemitsu (1980): Atmos. Environ., 14: 245.

    Article  Google Scholar 

  • Borm, W.A., and F.C. Adams (1988): Cluster analysis of electron microprobe analysis data of individual particles for source apportionment of air particulate matter. Atmos. Environ., 22 (10): 2297–2307.

    Article  Google Scholar 

  • Venkatram, A. (1982): A framework for evaluating air quality models. Boundary-Layer Meteor., 24: 371–385.

    Article  Google Scholar 

  • Venkatram, A. (1983): Uncertainty in predictions from air quality models. Boundary-Layer Meteor., 27: 185–196.

    Article  Google Scholar 

  • Venkatram, A. (1988a): Inherent uncertainty in air quality modeling. Atmos. Environ., 22 (6): 1221–1227.

    Article  Google Scholar 

  • Venkatram, A. (1988b): On the use of Kriging in the spatial analysis of acid precipitation data. Atmos. Environ., 22 (9): 1963–1979.

    Article  Google Scholar 

  • Watson, J.G. (1984): Overview of receptor model principles. JAPCA, 34: 619–623.

    Google Scholar 

  • Wiener, N. (1949): The Extrapolation, Interpolation and Smoothing of Stationary Time Series. New York: John Wiley.

    Google Scholar 

  • Williams, P.C. (1984): Data handling, simultaneity, and rare events. JAPCA, 34: 945–951.

    Google Scholar 

  • Young, P. (1974): Recursive approaches to time series analysis. In The Inst. of Mathematics and its Application, pp. 209–224.

    Google Scholar 

  • Zannetti, P. (1978): Short-term real-time control of air pollution episodes in Venice. Proceedings, 71st Annual APCA Meeting, Houston, Texas, June.

    Google Scholar 

  • Zannetti, P., G. Finzi, G. Fronza, and S. Rinaldi (1978): Time series analysis of Venice air quality data. IFAC Symposium on Environmental Systems, Planning, Design, and Control, August 1–5, 1977, Kyoto, Japan.

    Google Scholar 

  • Zannetti, P., and P. Switzer (1979a): The Kalman filtering method and its application to air pollution episode forecasting. Paper presented at the APCA Specialty Conference on Quality Assurance in Air Pollution Measurement, New Orleans, Louisiana, March.

    Google Scholar 

  • Zannetti, P., and P. Switzer (1979b): Some problems of validation and testing of numerical air pollution models. Proceedings, Fourth Amer. Meteor. Soc. Symp. on Turbulence, Diffusion, and Air Pollution, Reno, Nevada. January, pp. 405–410.

    Google Scholar 

  • Zinsmeister, A.R., and T.C. Redman (1980): A time series analysis of aerosol composition measurements. Atmos. Environ., 14: 201–215.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. AeroVironment Inc., Monrovia, California, USA

    Dr Paolo Zannetti

  2. Bergen High Tech Centre, IBM Scientific Centre, Thomohlensgate 55, N-5008, Bergen, Norway

    Dr Paolo Zannetti

Authors
  1. Dr Paolo Zannetti
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 1990 Springer Science+Business Media New York

About this chapter

Cite this chapter

Zannetti, P. (1990). Statistical Methods. In: Air Pollution Modeling. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-4465-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-4465-1_12

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4757-4467-5

  • Online ISBN: 978-1-4757-4465-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation