The probability distribution model of air pollution index and its dominants in Kuala Lumpur
This paper focuses on the statistical modeling for the distributions of air pollution index (API) and its sub-indexes data observed at Kuala Lumpur in Malaysia. Five pollutants or sub-indexes are measured including, carbon monoxide (CO); sulphur dioxide (SO2); nitrogen dioxide (NO2), and; particulat...
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creator | AL-Dhurafi, Nasr Ahmed Razali, Ahmad Mahir Masseran, Nurulkamal Zamzuri, Zamira Hasanah |
description | This paper focuses on the statistical modeling for the distributions of air pollution index (API) and its sub-indexes data observed at Kuala Lumpur in Malaysia. Five pollutants or sub-indexes are measured including, carbon monoxide (CO); sulphur dioxide (SO2); nitrogen dioxide (NO2), and; particulate matter (PM10). Four probability distributions are considered, namely log-normal, exponential, Gamma and Weibull in search for the best fit distribution to the Malaysian air pollutants data. In order to determine the best distribution for describing the air pollutants data, five goodness-of-fit criteria’s are applied. This will help in minimizing the uncertainty in pollution resource estimates and improving the assessment phase of planning. The conflict in criterion results for selecting the best distribution was overcome by using the weight of ranks method. We found that the Gamma distribution is the best distribution for the majority of air pollutants data in Kuala Lumpur. |
doi_str_mv | 10.1063/1.4966829 |
format | Conference Proceeding |
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Five pollutants or sub-indexes are measured including, carbon monoxide (CO); sulphur dioxide (SO2); nitrogen dioxide (NO2), and; particulate matter (PM10). Four probability distributions are considered, namely log-normal, exponential, Gamma and Weibull in search for the best fit distribution to the Malaysian air pollutants data. In order to determine the best distribution for describing the air pollutants data, five goodness-of-fit criteria’s are applied. This will help in minimizing the uncertainty in pollution resource estimates and improving the assessment phase of planning. The conflict in criterion results for selecting the best distribution was overcome by using the weight of ranks method. We found that the Gamma distribution is the best distribution for the majority of air pollutants data in Kuala Lumpur.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/1.4966829</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Air pollution ; Carbon monoxide ; Goodness of fit ; Nitrogen dioxide ; Particulate emissions ; Pollutants ; Probability distribution functions ; Statistical analysis ; Statistical models ; Sulfur dioxide ; Weight</subject><ispartof>AIP Conference Proceedings, 2016, Vol.1784 (1)</ispartof><rights>Author(s)</rights><rights>2016 Author(s). 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Five pollutants or sub-indexes are measured including, carbon monoxide (CO); sulphur dioxide (SO2); nitrogen dioxide (NO2), and; particulate matter (PM10). Four probability distributions are considered, namely log-normal, exponential, Gamma and Weibull in search for the best fit distribution to the Malaysian air pollutants data. In order to determine the best distribution for describing the air pollutants data, five goodness-of-fit criteria’s are applied. This will help in minimizing the uncertainty in pollution resource estimates and improving the assessment phase of planning. The conflict in criterion results for selecting the best distribution was overcome by using the weight of ranks method. We found that the Gamma distribution is the best distribution for the majority of air pollutants data in Kuala Lumpur.</description><subject>Air pollution</subject><subject>Carbon monoxide</subject><subject>Goodness of fit</subject><subject>Nitrogen dioxide</subject><subject>Particulate emissions</subject><subject>Pollutants</subject><subject>Probability distribution functions</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Sulfur dioxide</subject><subject>Weight</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2016</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNp9kE1LxDAQhoMouK4e_AcBb0LXTPPR5iiLruKClxUEDyFtE8zSJjVpxf33dtkFb55mGB7e4XkRugayACLoHSyYFKLM5QmaAeeQFQLEKZoRIlmWM_p-ji5S2hKSy6IoZ-hj82lwH0OlK9e6YYcbl4boqnFwweMuNKbFwWLtIu5D2x7OzjfmB2vfYDck3ITOee2nzXn8MupW4_XY9WO8RGdWt8lcHeccvT0-bJZP2fp19by8X2d1LumQ8VKX3DBqDa_B5prY2nIJkgKxzHKu69oIDpIZAEurqhSgSwkVL-jkSRido5tD7uTxNZo0qG0Yo59eqhxy4IxKKSfq9kCl2g1676H66DoddwqI2penQB3L-w_-DvEPVH1j6S_elXAz</recordid><startdate>20161117</startdate><enddate>20161117</enddate><creator>AL-Dhurafi, Nasr Ahmed</creator><creator>Razali, Ahmad Mahir</creator><creator>Masseran, Nurulkamal</creator><creator>Zamzuri, Zamira Hasanah</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20161117</creationdate><title>The probability distribution model of air pollution index and its dominants in Kuala Lumpur</title><author>AL-Dhurafi, Nasr Ahmed ; Razali, Ahmad Mahir ; Masseran, Nurulkamal ; Zamzuri, Zamira Hasanah</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-58a85e43fe5c1f2a0fcf5919310f4f55acce65194e11f3bb861a891b573829043</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Air pollution</topic><topic>Carbon monoxide</topic><topic>Goodness of fit</topic><topic>Nitrogen dioxide</topic><topic>Particulate emissions</topic><topic>Pollutants</topic><topic>Probability distribution functions</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Sulfur dioxide</topic><topic>Weight</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>AL-Dhurafi, Nasr Ahmed</creatorcontrib><creatorcontrib>Razali, Ahmad Mahir</creatorcontrib><creatorcontrib>Masseran, Nurulkamal</creatorcontrib><creatorcontrib>Zamzuri, Zamira Hasanah</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>AL-Dhurafi, Nasr Ahmed</au><au>Razali, Ahmad Mahir</au><au>Masseran, Nurulkamal</au><au>Zamzuri, Zamira Hasanah</au><au>Badri, Khairiah Hj</au><au>Yaacob, Wan Zuhairi Wan</au><au>Ibrahim, Nazlina</au><au>Noorani, Mohd Salmi Md</au><au>Jumali, Mohammad Hafizuddin Hj</au><au>Ibrahim, Kamarulzaman</au><au>Rasol, Noor Hayati Ahmad</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>The probability distribution model of air pollution index and its dominants in Kuala Lumpur</atitle><btitle>AIP Conference Proceedings</btitle><date>2016-11-17</date><risdate>2016</risdate><volume>1784</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>This paper focuses on the statistical modeling for the distributions of air pollution index (API) and its sub-indexes data observed at Kuala Lumpur in Malaysia. Five pollutants or sub-indexes are measured including, carbon monoxide (CO); sulphur dioxide (SO2); nitrogen dioxide (NO2), and; particulate matter (PM10). Four probability distributions are considered, namely log-normal, exponential, Gamma and Weibull in search for the best fit distribution to the Malaysian air pollutants data. In order to determine the best distribution for describing the air pollutants data, five goodness-of-fit criteria’s are applied. This will help in minimizing the uncertainty in pollution resource estimates and improving the assessment phase of planning. The conflict in criterion results for selecting the best distribution was overcome by using the weight of ranks method. We found that the Gamma distribution is the best distribution for the majority of air pollutants data in Kuala Lumpur.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/1.4966829</doi><tpages>6</tpages></addata></record> |
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language | eng |
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source | AIP Journals Complete |
subjects | Air pollution Carbon monoxide Goodness of fit Nitrogen dioxide Particulate emissions Pollutants Probability distribution functions Statistical analysis Statistical models Sulfur dioxide Weight |
title | The probability distribution model of air pollution index and its dominants in Kuala Lumpur |
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