The Malaysia PM10 analysis using extreme value

The study of air quality is closely associated to air pollution. Air pollution is of the main concerns of the authority in view of the fact that it can generate damaging effects to human health, crops and environment. This paper assesses the use of Extreme Value Distributions (EVD) of the two-parame...

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Published in:Journal of Engineering Science and Technology
Main Author: 2-s2.0-84948982264
Format: Article
Language:English
Published: Taylor's University 2015
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948982264&partnerID=40&md5=fbd7ea814a65dad59ddf8546d0a5e1a3
id Ahmat H.; Yahaya A.S.; Ramli N.A.
spelling Ahmat H.; Yahaya A.S.; Ramli N.A.
2-s2.0-84948982264
The Malaysia PM10 analysis using extreme value
2015
Journal of Engineering Science and Technology
10
12

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948982264&partnerID=40&md5=fbd7ea814a65dad59ddf8546d0a5e1a3
The study of air quality is closely associated to air pollution. Air pollution is of the main concerns of the authority in view of the fact that it can generate damaging effects to human health, crops and environment. This paper assesses the use of Extreme Value Distributions (EVD) of the two-parameter Gumbel, two and three-parameter Weibull, Generalized Extreme Value (GEV) and two and three-parameter Generalized Pareto Distribution (GPD) on the maximum concentration of daily PM10 data recorded in the year 2010 - 2012 in Pasir Gudang, Johor, Bukit Rambai, Melaka and Nilai, Negeri Sembilan. Parameters for all distributions were estimated using the method of Maximum Likelihood Estimator (MLE). The goodness-of-fit of the distribution was determined using six performance indicators namely; the accuracy measures which include Prediction Accuracy (PA), Coefficient of Determination (R2), Index of Agreement (IA) and error measures that consist of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Normalized Absolute Error (NAE). The best distribution was selected based on the highest accuracy measures which are close to 1 and the smallest error measures. The result showed that the Generalized Extreme Value (GEV) distribution was the best fit for daily maximum concentration for PM10 for all monitoring stations. The GEV gave the smallest errors (NAE, RMSE and MAE) and the highest accuracy measures (PA, R2 and IA) when compared to other distributions. The method gave the accuracy of more than 98% in PA, IA and R2 for all stations. The analysis demonstrated that the estimated numbers of days in which the concentration of PM10 exceeded the Malaysian Ambient Air Quality Guidelines (MAAQG) of 150 μg/m3 were between ½ and 2 days. © School of Engineering, Taylor’s University.
Taylor's University
18234690
English
Article

author 2-s2.0-84948982264
spellingShingle 2-s2.0-84948982264
The Malaysia PM10 analysis using extreme value
author_facet 2-s2.0-84948982264
author_sort 2-s2.0-84948982264
title The Malaysia PM10 analysis using extreme value
title_short The Malaysia PM10 analysis using extreme value
title_full The Malaysia PM10 analysis using extreme value
title_fullStr The Malaysia PM10 analysis using extreme value
title_full_unstemmed The Malaysia PM10 analysis using extreme value
title_sort The Malaysia PM10 analysis using extreme value
publishDate 2015
container_title Journal of Engineering Science and Technology
container_volume 10
container_issue 12
doi_str_mv
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948982264&partnerID=40&md5=fbd7ea814a65dad59ddf8546d0a5e1a3
description The study of air quality is closely associated to air pollution. Air pollution is of the main concerns of the authority in view of the fact that it can generate damaging effects to human health, crops and environment. This paper assesses the use of Extreme Value Distributions (EVD) of the two-parameter Gumbel, two and three-parameter Weibull, Generalized Extreme Value (GEV) and two and three-parameter Generalized Pareto Distribution (GPD) on the maximum concentration of daily PM10 data recorded in the year 2010 - 2012 in Pasir Gudang, Johor, Bukit Rambai, Melaka and Nilai, Negeri Sembilan. Parameters for all distributions were estimated using the method of Maximum Likelihood Estimator (MLE). The goodness-of-fit of the distribution was determined using six performance indicators namely; the accuracy measures which include Prediction Accuracy (PA), Coefficient of Determination (R2), Index of Agreement (IA) and error measures that consist of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Normalized Absolute Error (NAE). The best distribution was selected based on the highest accuracy measures which are close to 1 and the smallest error measures. The result showed that the Generalized Extreme Value (GEV) distribution was the best fit for daily maximum concentration for PM10 for all monitoring stations. The GEV gave the smallest errors (NAE, RMSE and MAE) and the highest accuracy measures (PA, R2 and IA) when compared to other distributions. The method gave the accuracy of more than 98% in PA, IA and R2 for all stations. The analysis demonstrated that the estimated numbers of days in which the concentration of PM10 exceeded the Malaysian Ambient Air Quality Guidelines (MAAQG) of 150 μg/m3 were between ½ and 2 days. © School of Engineering, Taylor’s University.
publisher Taylor's University
issn 18234690
language English
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