A Study of the Statistical Distribution of Chemical Pollutants in Air

Kalpasanov, Y.; Kurchatova, G

doi:10.1080/00022470.1976.10470349

Cited by 20 publications

(9 citation statements)

References 4 publications

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“…Different studies have 305 observed that several other right-skewed distributions also equally fit or provide a better fit 306 than the lognormal distribution (e.g. Kalpasanov and Kurchatova, 1976;Graedel et al, 1977; 307…”

Section: Model Identification and Parameter Estimation 274mentioning

confidence: 99%

An integrated statistical approach for evaluating the exceedence of criteria pollutants in the ambient air of megacity Delhi

Sharma

Jain

et al. 2013

Atmospheric Environment

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Section: Model Identification and Parameter Estimation 274mentioning

confidence: 99%

An integrated statistical approach for evaluating the exceedence of criteria pollutants in the ambient air of megacity Delhi

Sharma

Jain

et al. 2013

Atmospheric Environment

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“…Therefore, these SDMs were verified using three goodness of fit tests, i.e., Kolmogorov-Smirnov (KS), Anderson -Darling (AD) and Chi-square to identify the best fit (Taylor et al, 1986;Gokhale and Khare, 2007b). The KS test is found more satisfactory to check the fitting of statistical distributional form of the chemical species (Kalpasanov and Kurchatova, 1976). However, AD test is found more sensitive in calculating the extreme values of concentrations towards the tail (Gokhale and Khare, 2007b).…”

Section: Materials and Methodologymentioning

confidence: 99%

Extreme Events of Reactive Ambient Air Pollutants and their Distribution Pattern at Urban Hotspots

Gulia

Nagendra

Khare

2017

Aerosol Air Qual. Res.

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The occurrence of extreme events of air pollutant concentrations at urban hotspots is a routine phenomenon, particularly during the winter season. However, extreme events of reactive air pollutants are more frequent during the summer season. The assessment of air pollution extreme events will provide a platform to formulate an effective and efficient hotspot urban air quality management plan. The statistical distribution model (SDM) is widely used to describe the average as well as extreme air pollutant concentration in a more organized and efficient manner. In the present study, the best fit SDM has been evaluated for hourly average PM 2.5 and NO 2 concentrations at one of the busiest traffic intersections in Delhi city (air pollution hotspot 1: APH-1) and for PM 2.5 at one of the heavily trafficked road corridors in Chennai city (air pollution hotspot 2: APH -2). The SDMs were developed for different seasons to evaluate the impacts of climatic conditions on the air pollution events. Results indicate that NO 2 concentrations were best fitted with lognormal and log logistic distribution models respectively, for winter and summer seasons at APH-1. However, lognormal distribution was best fitted to PM 2.5 concentration of winter and summer seasons at both APHs.

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“…Various approaches, such as computational fluid dynamics model (CFD) (Lu et al 1997;Cheng and Hu 2005) and statistical model (Kalpasanov and Kurchatova 1976;Kottegoda and Rosso 1998), have been applied to study particulate emission phenomena. Among these, the statistical model, particularly the statistical distribution model, has been proven to be a sophisticated tool for representing the useful information of pollutant data (Kumar and Jain 2010; Kim 2010; Romano et al 2004;Cassidy et al 2007;Gokhale and Khare 2007;Maglione and Diblasi 2004;Ocana-Peinado et al 2008;Babak et al 2010;Spöck and Pilz 2010).…”

Section: Introductionmentioning

confidence: 99%

Urban aerosol particulates on Hong Kong roadsides: size distribution and concentration levels with time

2011

Stoch Environ Res Risk Assess

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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.

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A Study of the Statistical Distribution of Chemical Pollutants in Air

Cited by 20 publications

References 4 publications

An integrated statistical approach for evaluating the exceedence of criteria pollutants in the ambient air of megacity Delhi

An integrated statistical approach for evaluating the exceedence of criteria pollutants in the ambient air of megacity Delhi

Extreme Events of Reactive Ambient Air Pollutants and their Distribution Pattern at Urban Hotspots

Urban aerosol particulates on Hong Kong roadsides: size distribution and concentration levels with time

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