Proposed formulations for error reduction in leachate pollution index (LPI) estimation due to the absence of leachate parameters

The applications of leachate pollution index (LPI) as an environmental index to express the overall leachate-contaminating ability of landfills are increasing. Majority of these applications wrongly quantify LPI due to the absence of various leachate parameters. The traditional linear weighted aggre...

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Main Authors: Abunama, Taher, Othman, Faridah, Seyam, Mohammed, Moodley, Tyrone, Kumari, Sheena, Bux, Faizal
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Published: Elsevier 2021
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Online Access:http://eprints.um.edu.my/28386/
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spelling my.um.eprints.283862022-08-08T06:36:15Z http://eprints.um.edu.my/28386/ Proposed formulations for error reduction in leachate pollution index (LPI) estimation due to the absence of leachate parameters Abunama, Taher Othman, Faridah Seyam, Mohammed Moodley, Tyrone Kumari, Sheena Bux, Faizal QR Microbiology T Technology (General) TA Engineering (General). Civil engineering (General) The applications of leachate pollution index (LPI) as an environmental index to express the overall leachate-contaminating ability of landfills are increasing. Majority of these applications wrongly quantify LPI due to the absence of various leachate parameters. The traditional linear weighted aggregation equation used in LPI estimations, result in larger errors as missing parameters increase. In this article, error reduction equations were established to predict LPI values more accurately. Approximately 1797 unique combinations of leachate weights (Sigma wi) were randomly assigned to cover the missing parameters and augment the accuracy of the proposed error reduction equations. The upper and lower boundaries of LPI were accurately estimated in each case, starting from 1 missing parameter to 12 missing parameters. Various linear equations were generated, when possible, to ease the calculations. Subsequently, the arithmetic, geometric, and harmonic averages were applied between the LPI limits in five different case studies to demonstrate the validity of the developed error reduction equations. These equations were capable of providing reliable LPI values even when up to twelve leachate pollutants were absent. It was found that the harmonic average was the best average to be applied between the LPI boundaries, which mostly resulted in errors around +/- 20% for Sigma wi values up to 0.3. (C) 2021 Elsevier B.V. All rights reserved. Elsevier 2021-08 Article PeerReviewed Abunama, Taher and Othman, Faridah and Seyam, Mohammed and Moodley, Tyrone and Kumari, Sheena and Bux, Faizal (2021) Proposed formulations for error reduction in leachate pollution index (LPI) estimation due to the absence of leachate parameters. Environmental Technology & Innovation, 23. ISSN 2352-1864, DOI https://doi.org/10.1016/j.eti.2021.101623 <https://doi.org/10.1016/j.eti.2021.101623>. 10.1016/j.eti.2021.101623
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QR Microbiology
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle QR Microbiology
T Technology (General)
TA Engineering (General). Civil engineering (General)
Abunama, Taher
Othman, Faridah
Seyam, Mohammed
Moodley, Tyrone
Kumari, Sheena
Bux, Faizal
Proposed formulations for error reduction in leachate pollution index (LPI) estimation due to the absence of leachate parameters
description The applications of leachate pollution index (LPI) as an environmental index to express the overall leachate-contaminating ability of landfills are increasing. Majority of these applications wrongly quantify LPI due to the absence of various leachate parameters. The traditional linear weighted aggregation equation used in LPI estimations, result in larger errors as missing parameters increase. In this article, error reduction equations were established to predict LPI values more accurately. Approximately 1797 unique combinations of leachate weights (Sigma wi) were randomly assigned to cover the missing parameters and augment the accuracy of the proposed error reduction equations. The upper and lower boundaries of LPI were accurately estimated in each case, starting from 1 missing parameter to 12 missing parameters. Various linear equations were generated, when possible, to ease the calculations. Subsequently, the arithmetic, geometric, and harmonic averages were applied between the LPI limits in five different case studies to demonstrate the validity of the developed error reduction equations. These equations were capable of providing reliable LPI values even when up to twelve leachate pollutants were absent. It was found that the harmonic average was the best average to be applied between the LPI boundaries, which mostly resulted in errors around +/- 20% for Sigma wi values up to 0.3. (C) 2021 Elsevier B.V. All rights reserved.
format Article
author Abunama, Taher
Othman, Faridah
Seyam, Mohammed
Moodley, Tyrone
Kumari, Sheena
Bux, Faizal
author_facet Abunama, Taher
Othman, Faridah
Seyam, Mohammed
Moodley, Tyrone
Kumari, Sheena
Bux, Faizal
author_sort Abunama, Taher
title Proposed formulations for error reduction in leachate pollution index (LPI) estimation due to the absence of leachate parameters
title_short Proposed formulations for error reduction in leachate pollution index (LPI) estimation due to the absence of leachate parameters
title_full Proposed formulations for error reduction in leachate pollution index (LPI) estimation due to the absence of leachate parameters
title_fullStr Proposed formulations for error reduction in leachate pollution index (LPI) estimation due to the absence of leachate parameters
title_full_unstemmed Proposed formulations for error reduction in leachate pollution index (LPI) estimation due to the absence of leachate parameters
title_sort proposed formulations for error reduction in leachate pollution index (lpi) estimation due to the absence of leachate parameters
publisher Elsevier
publishDate 2021
url http://eprints.um.edu.my/28386/
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score 13.160551