Chemometric application in identifying sources of organic contaminants in Langat river basin

Rozita Osman, Norashikin Saim, Hafizan Juahir, Md. Pauzi Abdullah

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

Increasing urbanization and changes in land use in Langat river basin lead to adverse impacts on the environment compartment. One of the major challenges is in identifying sources of organic contaminants. This study presented the application of selected chemometric techniques: cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) to classify the pollution sources in Langat river basin based on the analysis of water and sediment samples collected from 24 stations, monitored for 14 organic contaminants from polycyclic aromatic hydrocarbons (PAHs), sterols, and pesticides groups. The CA and DA enabled to group 24 monitoring sites into three groups of pollution source (industry and urban socioeconomic, agricultural activity, and urban/domestic sewage) with five major discriminating variables: naphthalene, pyrene, benzo[a]pyrene, coprostanol, and cholesterol. PCA analysis, applied to water data sets, resulted in four latent factors explaining 79.0% of the total variance while sediment samples gave five latent factors with 77.6% explained variance. The varifactors (VFs) obtained from PCA indicated that sterols (coprostanol, cholesterol, stigmasterol, β-sitosterol, and stigmastanol) are strongly correlated to domestic and urban sewage, PAHs (naphthalene, acenaphthene, pyrene, benzo[a]anthracene, and benzo[a]pyrene) from industrial and urban activities and chlorpyrifos correlated to samples nearby agricultural sites. The results demonstrated that chemometric techniques can be used for rapid assessment of water and sediment contaminations.

Original languageEnglish
Pages (from-to)1001-1014
Number of pages14
JournalEnvironmental Monitoring and Assessment
Volume184
Issue number2
DOIs
Publication statusPublished - Feb 2012

Fingerprint

Pyrene
pyrene
Catchments
river basin
Rivers
Impurities
Principal component analysis
principal component analysis
Sediments
pollutant
sterol
Cholesterol
Cluster analysis
Discriminant analysis
Naphthalene
Sewage
Polycyclic aromatic hydrocarbons
naphthalene
discriminant analysis
cluster analysis

Keywords

  • Chemometric
  • Cluster analysis
  • Discriminant analysis
  • Organic contaminants
  • Principal component analysis

ASJC Scopus subject areas

  • Environmental Science(all)
  • Management, Monitoring, Policy and Law
  • Pollution

Cite this

Chemometric application in identifying sources of organic contaminants in Langat river basin. / Osman, Rozita; Saim, Norashikin; Juahir, Hafizan; Abdullah, Md. Pauzi.

In: Environmental Monitoring and Assessment, Vol. 184, No. 2, 02.2012, p. 1001-1014.

Research output: Contribution to journalArticle

Osman, Rozita ; Saim, Norashikin ; Juahir, Hafizan ; Abdullah, Md. Pauzi. / Chemometric application in identifying sources of organic contaminants in Langat river basin. In: Environmental Monitoring and Assessment. 2012 ; Vol. 184, No. 2. pp. 1001-1014.
@article{a6238e1532ef4545bdfacda145f650c2,
title = "Chemometric application in identifying sources of organic contaminants in Langat river basin",
abstract = "Increasing urbanization and changes in land use in Langat river basin lead to adverse impacts on the environment compartment. One of the major challenges is in identifying sources of organic contaminants. This study presented the application of selected chemometric techniques: cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) to classify the pollution sources in Langat river basin based on the analysis of water and sediment samples collected from 24 stations, monitored for 14 organic contaminants from polycyclic aromatic hydrocarbons (PAHs), sterols, and pesticides groups. The CA and DA enabled to group 24 monitoring sites into three groups of pollution source (industry and urban socioeconomic, agricultural activity, and urban/domestic sewage) with five major discriminating variables: naphthalene, pyrene, benzo[a]pyrene, coprostanol, and cholesterol. PCA analysis, applied to water data sets, resulted in four latent factors explaining 79.0{\%} of the total variance while sediment samples gave five latent factors with 77.6{\%} explained variance. The varifactors (VFs) obtained from PCA indicated that sterols (coprostanol, cholesterol, stigmasterol, β-sitosterol, and stigmastanol) are strongly correlated to domestic and urban sewage, PAHs (naphthalene, acenaphthene, pyrene, benzo[a]anthracene, and benzo[a]pyrene) from industrial and urban activities and chlorpyrifos correlated to samples nearby agricultural sites. The results demonstrated that chemometric techniques can be used for rapid assessment of water and sediment contaminations.",
keywords = "Chemometric, Cluster analysis, Discriminant analysis, Organic contaminants, Principal component analysis",
author = "Rozita Osman and Norashikin Saim and Hafizan Juahir and Abdullah, {Md. Pauzi}",
year = "2012",
month = "2",
doi = "10.1007/s10661-011-2016-8",
language = "English",
volume = "184",
pages = "1001--1014",
journal = "Environmental Monitoring and Assessment",
issn = "0167-6369",
publisher = "Springer Netherlands",
number = "2",

}

TY - JOUR

T1 - Chemometric application in identifying sources of organic contaminants in Langat river basin

AU - Osman, Rozita

AU - Saim, Norashikin

AU - Juahir, Hafizan

AU - Abdullah, Md. Pauzi

PY - 2012/2

Y1 - 2012/2

N2 - Increasing urbanization and changes in land use in Langat river basin lead to adverse impacts on the environment compartment. One of the major challenges is in identifying sources of organic contaminants. This study presented the application of selected chemometric techniques: cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) to classify the pollution sources in Langat river basin based on the analysis of water and sediment samples collected from 24 stations, monitored for 14 organic contaminants from polycyclic aromatic hydrocarbons (PAHs), sterols, and pesticides groups. The CA and DA enabled to group 24 monitoring sites into three groups of pollution source (industry and urban socioeconomic, agricultural activity, and urban/domestic sewage) with five major discriminating variables: naphthalene, pyrene, benzo[a]pyrene, coprostanol, and cholesterol. PCA analysis, applied to water data sets, resulted in four latent factors explaining 79.0% of the total variance while sediment samples gave five latent factors with 77.6% explained variance. The varifactors (VFs) obtained from PCA indicated that sterols (coprostanol, cholesterol, stigmasterol, β-sitosterol, and stigmastanol) are strongly correlated to domestic and urban sewage, PAHs (naphthalene, acenaphthene, pyrene, benzo[a]anthracene, and benzo[a]pyrene) from industrial and urban activities and chlorpyrifos correlated to samples nearby agricultural sites. The results demonstrated that chemometric techniques can be used for rapid assessment of water and sediment contaminations.

AB - Increasing urbanization and changes in land use in Langat river basin lead to adverse impacts on the environment compartment. One of the major challenges is in identifying sources of organic contaminants. This study presented the application of selected chemometric techniques: cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) to classify the pollution sources in Langat river basin based on the analysis of water and sediment samples collected from 24 stations, monitored for 14 organic contaminants from polycyclic aromatic hydrocarbons (PAHs), sterols, and pesticides groups. The CA and DA enabled to group 24 monitoring sites into three groups of pollution source (industry and urban socioeconomic, agricultural activity, and urban/domestic sewage) with five major discriminating variables: naphthalene, pyrene, benzo[a]pyrene, coprostanol, and cholesterol. PCA analysis, applied to water data sets, resulted in four latent factors explaining 79.0% of the total variance while sediment samples gave five latent factors with 77.6% explained variance. The varifactors (VFs) obtained from PCA indicated that sterols (coprostanol, cholesterol, stigmasterol, β-sitosterol, and stigmastanol) are strongly correlated to domestic and urban sewage, PAHs (naphthalene, acenaphthene, pyrene, benzo[a]anthracene, and benzo[a]pyrene) from industrial and urban activities and chlorpyrifos correlated to samples nearby agricultural sites. The results demonstrated that chemometric techniques can be used for rapid assessment of water and sediment contaminations.

KW - Chemometric

KW - Cluster analysis

KW - Discriminant analysis

KW - Organic contaminants

KW - Principal component analysis

UR - http://www.scopus.com/inward/record.url?scp=84857373252&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84857373252&partnerID=8YFLogxK

U2 - 10.1007/s10661-011-2016-8

DO - 10.1007/s10661-011-2016-8

M3 - Article

C2 - 21494831

AN - SCOPUS:84857373252

VL - 184

SP - 1001

EP - 1014

JO - Environmental Monitoring and Assessment

JF - Environmental Monitoring and Assessment

SN - 0167-6369

IS - 2

ER -