Fitting statistical distribution on air pollution: an overview

Jaffar, Muhammad Ismail and Hamid, Hazrul Abdul and Yunus, Riduan and Raffee, Ahmad Fauzi (2018) Fitting statistical distribution on air pollution: an overview. International Journal of Engineering and Technology, 7 (3.23). pp. 40-44. ISSN 2227-524X

Full text not available from this repository. (Request a copy)


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 data been analyze appropriately. In fitting air pollutant data, statistical distribution of gamma, lognormal and Weibull distribution is widely used compared to others distributions model. In addition, the aims of this overview study are to identify which distributions is the most used for predicting the air pollution concentration thus, the accuracy for prediction future air quality is the important aspect to give the best prediction. The comprehensive study need to be conducted in statistical distribution of air pollution for fitting pollutant data. By using others statistical distributions model as main suggested in this paper.

Item Type: Article
Uncontrolled Keywords: Air pollution; statistical distribution; pollutant concentration prediction
Subjects: T Technology > T Technology (General)
T Technology > TD Environmental technology. Sanitary engineering
T Technology > TD Environmental technology. Sanitary engineering > TD878-894 Special types of environment, Including soil pollution, air pollution, noise pollution
Divisions: Faculty of Civil Engineering and Built Environment > Department of Architecture
Depositing User: UiTM Student Praktikal
Date Deposited: 22 Nov 2021 03:53
Last Modified: 22 Nov 2021 03:53

Actions (login required)

View Item View Item