A general purpose univariate probability model for environmental data analysis

Office of Research and Development U.S. Environmental Protection Agency, Washington, D.C. 20460, U.S.A.
Computers & Operations Research (Impact Factor: 1.86). 08/1976; 3(2):209-216. DOI: 10.1016/0305-0548(76)90029-0
Source: DBLP


Analysis of environmental quality data for decision making purposes (evaluation of compliance with standards, examination of environmental trends, determination of confidence intervals) generally requires a suitable univariate probability model. It sometimes is difficult, when many probability models are available, to select the most appropriate one for a given data set. The underlying physical laws which generate pollutant concentrations—diffusion processes—offer insight into which model may be most appropriate for a variety of situations. Treating the diffusion equation as a stochastic differential equation, the time series of pollutant concentration data from diffusion phenomena is shown to have a distribution that is best approximated by the censored, 3-parameter lognormal probability model (LN3C). The model is applied to 10 air quality data sets (SO2, O3, CO, participate, hydrocarbons, and NO2 from the United States, France, West Germany, and Denmark) and 9 water quality data sets (BOD, coliform, chloride, and sulfate from the Ohio River). The authors conclude that the LN3C probability model offers data analysts a superior, general purpose model suitable for a large variety of environmental phenomena.

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Available from: Wayne Ott, Oct 03, 2015
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    • "Generally, modeling of statistical distribution involves two major steps, i.e., identification of statistical distributional form and estimation of associated parameters. In identifying the distributional form, Larsen (1971) and Ott and Mage (1976) proposed to use the 'graphical technique' to treat all types of air pollutants data. However, this technique only suits data of small size. "
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    ABSTRACT: A detailed study on particulate matter from vehicle emission was carried out at roadsides in Hong Kong downtown area. The study aims to explore the variations of particulate matter at roadsides in morning and afternoon. Data of concentrations of different size groups of particulate matter were collected and analyzed. It was found that the particulate levels generally vary periodically with traffic signal changes. During the green traffic light period, concentrations of the particulate matter increase to peak point and then diffuse to a relatively stable level in the red traffic light period. Such stable level is regarded as background level, to which pedestrians are exposed when they walk-by and cross the zebra zones. To analyze and further explore the collected data, a statistical distribution model, i.e., goodness-of-fit test, was employed. It was noticed that the lognormal distribution best fits the particulate matter data in both morning and afternoon. In addition, the non-parametric test was also used to assess the differences between morning and afternoon data. The results show that both data sets statistically differ from each other at 5% significance level. It can be deduced that the change of traffic volume, humidity and wind speed between morning and afternoon may cause this difference. Keywords: Particulate matter; Goodness-of-fit test; Lognormal distribution; Non-parametric test; Vehicle emissions
    Stochastic Environmental Research and Risk Assessment 01/2012; 26(2):177-187. DOI:10.1007/s00477-011-0465-9 · 2.09 Impact Factor
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    • "Curran and Frank (1975) show that exponential or Weibull distributions, in general, yield a better fit to air pollutant data. Ott and Mage (1976) suggested that a censored 3-parameter lognormal distribution is applicable to the air quality data. Bencala and Seinfeld (1976) observed that several common distributions could be used to fit the observed data, one of which may be the lognormal distribution. "
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    ABSTRACT: Vehicles spend more time near junctions and intersections in different driving modes, i.e., queuing, decelerating or accelerating and thus generating more pollutants than at road links [Claggett, M., Shrock, J., Noll, K.E., 1981. Carbon monoxide near an urban intersection. Atmos. Environ. 15, 1633-1642]. As a result, the receptors in these urban corridors are prone to frequent exposures of high pollutant concentrations (episodic conditions). In order to predict such 'episodes', an air quality model, capable of estimating the entire range (middle and extremes) of pollutant concentration distribution is needed. Hybrid models (combining deterministic and statistical distribution models) have demonstrated the ability to predict the entire range of pollutant concentrations in such complex dispersion situations with reasonable accuracy [Jakeman, A., Simpson, R.W., Taylor, J.A., 1988. Modelling distributions of air pollutant concentrations-III: Hybrid modelling deterministic-statistical distributions. Atmos. Environ. 22 (1) 163-174]. The present paper reviews the relevant deterministic and stochastic based vehicular exhaust emission models that may be hybridized and thus generate a hybrid model with improved prediction accuracy. The paper also describes the implications of hybrid models in formulating the Episodic-Urban Air Quality Management Plan (e-UAQMP).
    International Journal of Transport Management 01/2004; 2(2):59-74. DOI:10.1016/j.ijtm.2004.09.001
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    ABSTRACT: Urban air pollutant concentration data often tend to fit a two-parameter averaging-time model having three characteristics: (1) pollutant concentrations are (two-parameter) lognormally distributed for all averaging times; (2) median concentrations are proportional to averaging time raised to an exponent; and (3) maximum concentrations are approximately inversely proportional to averaging time raised to an exponent. Concentration data obtained near many isolated point sources and in some urban areas often do not fit a two-parameter lognormal distribution. An increment (either positive or negative) can be added to each such concentration in order to fit the data instead to a three-parameter lognormal distribution. This increment has been incorporated as the third parameter in a new three-parameter averaging-time model that can be used in both point-source and urban settings. Examples show how this new model can be used to analyze SO/sub 2/ concentration data obtained near a point source to determine the degree of emission reduction needed to achieve the national ambient air quality standards.
    Journal of the Air Pollution Control Association 04/1977; 27:5(5). DOI:10.1080/00022470.1977.10470441
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