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Chemometric techniques in distribution, characterisation and source apportionment of polycyclic aromatic hydrocarbons (PAHS) in aquaculture sediments in Malaysia

Retnam A, Zakaria MP, Juahir H, Aris AZ, Zali MA, Kasim MF

Mar Pollut Bull, 2013 Apr 15;69(1-2):55-66.
PMID: 23452623 DOI: 10.1016/j.marpolbul.2013.01.009

This study investigated polycyclic aromatic hydrocarbons (PAHs) pollution in surface sediments within aquaculture areas in Peninsular Malaysia using chemometric techniques, forensics and univariate methods. The samples were analysed using soxhlet extraction, silica gel column clean-up and gas chromatography mass spectrometry. The total PAH concentrations ranged from 20 to 1841 ng/g with a mean of 363 ng/g dw. The application of chemometric techniques enabled clustering and discrimination of the aquaculture sediments into four groups according to the contamination levels. A combination of chemometric and molecular indices was used to identify the sources of PAHs, which could be attributed to vehicle emissions, oil combustion and biomass combustion. Source apportionment using absolute principle component scores-multiple linear regression showed that the main sources of PAHs are vehicle emissions 54%, oil 37% and biomass combustion 9%. Land-based pollution from vehicle emissions is the predominant contributor of PAHs in the aquaculture sediments of Peninsular Malaysia.
Spatial assessment and source identification of heavy metals pollution in surface water using several chemometric techniques

Ismail A, Toriman ME, Juahir H, Zain SM, Habir NL, Retnam A, et al.

Mar Pollut Bull, 2016 May 15;106(1-2):292-300.
PMID: 27001716 DOI: 10.1016/j.marpolbul.2015.10.019

This study presents the determination of the spatial variation and source identification of heavy metal pollution in surface water along the Straits of Malacca using several chemometric techniques. Clustering and discrimination of heavy metal compounds in surface water into two groups (northern and southern regions) are observed according to level of concentrations via the application of chemometric techniques. Principal component analysis (PCA) demonstrates that Cu and Cr dominate the source apportionment in northern region with a total variance of 57.62% and is identified with mining and shipping activities. These are the major contamination contributors in the Straits. Land-based pollution originating from vehicular emission with a total variance of 59.43% is attributed to the high level of Pb concentration in the southern region. The results revealed that one state representing each cluster (northern and southern regions) is significant as the main location for investigating heavy metal concentration in the Straits of Malacca which would save monitoring cost and time.
CAPSULE: The monitoring of spatial variation and source of heavy metals pollution at the northern and southern regions of the Straits of Malacca, Malaysia, using chemometric analysis.
Tracing sewage contamination based on sterols and stanols markers within the mainland aquatic ecosystem: a case study of Linggi catchment, Malaysia

Abdul Zali M, Juahir H, Ismail A, Retnam A, Idris AN, Sefie A, et al.

Environ Sci Pollut Res Int, 2021 Apr;28(16):20717-20736.
PMID: 33405159 DOI: 10.1007/s11356-020-11680-5

Sewage contamination is a principal concern in water quality management as pathogens in sewage can cause diseases and lead to detrimental health effects in humans. This study examines the distribution of seven sterol compounds, namely coprostanol, epi-coprostanol, cholesterol, cholestanol, stigmasterol, campesterol, and β-sitosterol in filtered and particulate phases of sewage treatment plants (STPs), groundwater, and river water. For filtered samples, solid-phase extraction (SPE) was employed while for particulate samples were sonicated. Quantification was done by using gas chromatography-mass spectrometer (GC-MS). Faecal stanols (coprostanol and epi-coprostanol) and β-sitosterol were dominant in most STP samples. Groundwater samples were influenced by natural/biogenic sterol, while river water samples were characterized by a mixture of sources. Factor loadings from principal component analysis (PCA) defined fresh input of biogenic sterol and vascular plants (positive varimax factor (VF)1), aged/treated sewage sources (negative VF1), fresh- and less-treated sewage and domestic sources (positive VF2), biological sewage effluents (negative VF2), and fresh-treated sewage sources (VF3) in the samples. Association of VF loadings and factor score values illustrated the correlation of STP effluents and the input of biogenic and plant sterol sources in river and groundwater samples of Linggi. This study focuses on sterol distribution and its potential sources; these findings will aid in sewage assessment in the aquatic environment.
Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment

Ismail A, Juahir H, Mohamed SB, Toriman ME, Kassim AM, Zain SM, et al.

Water Sci Technol, 2021 Mar;83(5):1039-1054.
PMID: 33724935 DOI: 10.2166/wst.2021.038

The main focus of this study is exploring the spatial distribution of polyaromatics hydrocarbon links between oil spills in the environment via Support Vector Machines based on Kernel-Radial Basis Function (RBF) approach for high precision classification of oil spill type from its sample fingerprinting in Peninsular Malaysia. The results show the highest concentrations of Σ Alkylated PAHs and Σ EPA PAHs in ΣTAH concentration in diesel from the oil samples PP3_liquid and GP6_Jetty achieving 100% classification output, corresponding to coherent decision boundary and projective subspace estimation. The high dimensional nature of this approach has led to the existence of a perfect separability of the oil type classification from four clustered oil type components; i.e diesel, bunker C, Mixture Oil (MO), lube oil and Waste Oil (WO) with the slack variables of ξ ≠ 0. Of the four clusters, only the SVs of two are correctly predicted, namely diesel and MO. The kernel-RBF approach provides efficient and reliable oil sample classification, enabling the oil classification to be optimally performed within a relatively short period of execution and a faster dataset classification where the slack variables ξ are non-zero.
Chemometric techniques in oil classification from oil spill fingerprinting

Ismail A, Toriman ME, Juahir H, Kassim AM, Zain SM, Ahmad WKW, et al.

Mar Pollut Bull, 2016 Oct 15;111(1-2):339-346.
PMID: 27397593 DOI: 10.1016/j.marpolbul.2016.06.089

Extended use of GC-FID and GC-MS in oil spill fingerprinting and matching is significantly important for oil classification from the oil spill sources collected from various areas of Peninsular Malaysia and Sabah (East Malaysia). Oil spill fingerprinting from GC-FID and GC-MS coupled with chemometric techniques (discriminant analysis and principal component analysis) is used as a diagnostic tool to classify the types of oil polluting the water. Clustering and discrimination of oil spill compounds in the water from the actual site of oil spill events are divided into four groups viz. diesel, Heavy Fuel Oil (HFO), Mixture Oil containing Light Fuel Oil (MOLFO) and Waste Oil (WO) according to the similarity of their intrinsic chemical properties. Principal component analysis (PCA) demonstrates that diesel, HFO, MOLFO and WO are types of oil or oil products from complex oil mixtures with a total variance of 85.34% and are identified with various anthropogenic activities related to either intentional releasing of oil or accidental discharge of oil into the environment. Our results show that the use of chemometric techniques is significant in providing independent validation for classifying the types of spilled oil in the investigation of oil spill pollution in Malaysia. This, in consequence would result in cost and time saving in identification of the oil spill sources.
Improving oil classification quality from oil spill fingerprint beyond six sigma approach

Juahir H, Ismail A, Mohamed SB, Toriman ME, Kassim AM, Zain SM, et al.

Mar Pollut Bull, 2017 Jul 15;120(1-2):322-332.
PMID: 28535957 DOI: 10.1016/j.marpolbul.2017.04.032

This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC-MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat>Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited.