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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    • "To date, DFA is extensively applied in analyzing the complex heartbeat time series, the scaling relations of complex networks, the different phases of traffic flow, and the fluctuations of stock market. In recent years, the long-term correlations of stock market, the impact of climate changes are investigated by using DFA and its modified version (Ruan and Zhou, 2011; Gu et al., 2007, 2008; Mantegna and Stanley, 2000; Mu et al., 2010; Ott and Mage, 1976; Tong and De Pietro, 1997; Romano, 2004; Schmitt et al. 1999, 2000) but less attention was paid to air pollution and the resultant effects. The traditional methods to study fractals and multifractals of targets in time series include the Hurst analysis or the rescaled range analysis (R/S). "
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    ABSTRACT: At urban road intersection, the levels of particulate matters within different size groups present multi-variable relationships and have been attracting increasing attentions. In this study, we attend to apply the recently developed multifractal detrended cross-correlation analysis method to investigate the fractal property and the cross-correlation behavior among the non-stationary time series of particulate matters. Six groups of particulate matters with different sizes are measured at a typical traffic intersection of street canyon under different seasons and weather conditions. Based on the collected database, the statistical analyses are carried out and the results indicate close relationships among these groups. Then the multifractal detrended cross-correlation analyses are performed to explore the interaction patterns among all groups, i.e., the fine particulate matters group of 0.3–0.49 cm with other groups, the coarse particulate matters group of 10 μm above with other groups, and the groups of particulate matters between the sizes of 0.5–9.99 μm respectively. In terms of the results, the multifractal property and long-range cross-correlation behavior are observed among all pairs. Comparing to coarse particulate matters, the distinct multifractal spectrum between fine particulate matters with other size groups are observed, which imply that the relevant cross-correlation behaviors are stronger than that in coarse group. It is also found that the cross-correlation behaviors between fine particulate matters with other size groups are highly dependent on the weather conditions while the cross-correlation behaviors between coarse particulate matters with others tend to more depend on the season variations. Finally, the long-range cross-correlation behaviors between them are also confirmed with randomly shuffled series of the observed particulate matters.
    Stochastic Environmental Research and Risk Assessment 09/2015; DOI:10.1007/s00477-015-1162-x · 2.09 Impact Factor
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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 02/2012; 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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