Fitting Statistical Distributions Functions on Ozone Concentration Data at Coastal Areas

Nasir, Muhammad Yazid; Ghazali, Nurul Adyani; Mokhtar, M. I. Z.; Suhaimi, Norhazlina

doi:10.17576/mjas-2016-2003-13

Cited by 5 publications

(2 citation statements)

References 22 publications

(18 reference statements)

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“…Another study on statistical distribution to predict ozone concentration at the coastal area at Port Klang, Selangor and Port Dickson were used Weibull distribution [25]. The study used a single distribution to fit the concentration.…”

Section: Application Of Statistical Distributions In Air Pollutionmentioning

confidence: 99%

Fitting Statistical Distribution on Air Pollution: an Overview

Jaffar¹,

Hamid²,

Yunus³

et al. 2018

IJET

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High event of air pollution would give adverse effect to human health and cause of instability towards environment. In order to overcome these issues, the statistical air pollution modelling is an important tool to predict the return period of high event on air pollution in future. This tool also will be useful to help the related government agencies for providing a better air quality management and it can provide significantly when air quality

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Section: Application Of Statistical Distributions In Air Pollutionmentioning

confidence: 99%

Fitting Statistical Distribution on Air Pollution: an Overview

Jaffar¹,

Hamid²,

Yunus³

et al. 2018

IJET

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“…However, skewed distribution functions offer a wider field of application, where symmetric distributions are particular types. Concentrations of certain air pollutants, such as ozone, have been described by skewed distributions [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Influence of Wind Speed on CO2 and CH4 Concentrations at a Rural Site

Pérez

García

Sánchez

et al. 2021

IJERPH

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Meteorological variables have a noticeable impact on pollutant concentrations. Among these variables, wind speed is typically measured, although research into how pollutants respond to it can be improved. This study considers nine years of hourly CO2 and CH4 measurements at a rural site, where wind speed values were calculated by the METEX model. Nine wind speed intervals are proposed where concentrations, distribution functions, and daily as well as annual cycles are calculated. Contrasts between local and transported concentrations are around 5 and 0.03 ppm for CO2 and CH4, respectively. Seven skewed distributions are applied, and five efficiency criteria are considered to test the goodness of fit, with the modified Nash–Sutcliffe efficiency proving to be the most sensitive statistic. The Gumbel distribution is seen to be the most suitable for CO2, whereas the Weibull distribution is chosen for CH4, with the exponential function being the worst. Finally, daily and annual cycles are analysed, where a gradual decrease in amplitude is observed, particularly for the daily cycle. Parametric and nonparametric procedures are used to fit both cycles. The latter gave the best fits, with the agreement being higher for the daily cycle, where evolution is smoother than for the annual cycle.

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