Statistical behavior of carbon monoxide from vehicular exhausts in urban environments

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• 作者：Sharad Gokhale and Mukesh Khare
• 刊名：Environmental Modelling and Software
• 出版年：2007
• 出版时间：April 2007
• 年：2007
• 卷：22
• 期：4
• 页码：526-535
• 全文大小：532 K

文摘

Air pollutant concentrations are essentially random variables and can be well described by statistical distribution models. The statistical distribution models are, therefore, useful tools in predicting the distribution of air pollutant concentrations. The statistical distributional form, fitting to the concentrations data, is based upon several factors, i.e. source types, pollutant types, emission patterns, meteorological conditions, and averaging times [Taylor, J.A., Jakeman, A.J., Simpson, R.W., 1986. Modeling distributions of air pollutant concentrations – I: identification of statistical models. Atmospheric Environment 20 (9), 1781–1789]. The statistical characteristics of dispersion of air pollutants in the atmosphere are represented by successive random dilution process [Ott, W.R., 1995. Environmental Statistics and Data Analysis. Lewis publishers]. This process may, however, differ depending upon the location of pollutant dispersion, i.e. near roadways, at intersections or in street canyons. Further, the distributional form may also differ. Several investigators, in the past, presumed lognormal distribution (LND) for the air quality data. While, a few found other distributional form when carried out the actual data analysis.

The present paper develops the statistical distribution model fitting to carbon monoxide (CO) concentrations for the heterogeneous traffic pattern at the urban hotspots in Delhi, India. Three years of 1-h average CO concentration data (from 1997 to 1999), at the traffic intersection and near a roadway, are examined using goodness-of-fit tests for the suitable statistical distributional form. The results showed that the log logistic distribution model (LLD) best fit the CO concentration data at both the intersection and the roadway. It can therefore be deduced that ‘heterogeneity in traffic’ and ‘emission patterns’ may be affecting the statistical distributional form significantly.