Comparison of rainwater quality before and during the MCO using chemometric analyses

This study aimed to classify the spatiotemporal analysis of rainwater quality before and during the Movement Control Order (MCO) implementation due to the COVID-19 pandemic. Chemometric analysis was carried out on rainwater samples collected from 24-gauge stations throughout Malaysia to determine the samples' chemical content, pH, and conductivity. Other than that, hierarchical agglomerative cluster analysis (HACA) and discriminant analysis (DA) were used to classify the quality of rainwater at each location into four clusters, namely good, satisfactory, moderate, and bad clusters. Note that DA was carried out on the predefined clusters. The reduction in acidity levels occurred in 11 stations (46% of overall stations) after the MCO was implemented. Chemical content and ion abundance followed a downward trend, indicating that Cl^- and Na⁺ were the most dominant among the anions and cations. Apart from that, NH₄⁺, Ca²⁺, NO₃^-, and SO₄^2- concentrations were evident in areas with significant anthropogenic activity, as there was a difference in the total chemical content in rainwater when compared before and during the MCO. Based on the dataset before the MCO, 75% of gauge stations were in the good cluster, 8.3% in the satisfactory cluster, 12.5% in the moderate cluster, and 4.2% in the bad cluster. Meanwhile, the dataset during the MCO shows that 72.7% of gauge stations were in the good cluster, 9.1% in the satisfactory cluster, 9.1% in the moderate, and 4.5% in the bad cluster. From this study, the chemometric analysis of the year 2020 rainwater chemical composite dataset strongly indicates that reduction of human activities during MCO affected the quality of rainwater.

Water quality modelling using principal component analysis and artificial neural network

The study investigates the latent pollution sources and most significant parameters that cause spatial variation and develops the best input for water quality modelling using principal component analysis (PCA) and artificial neural network (ANN). The dataset, 22 water quality parameters were obtained from Department of Environment Malaysia (DOE). The PCA generated six significant principal component scores (PCs) which explained 65.40 % of the total variance. Parameters for water quality variation are mainlyrelated to mineral components, anthropogenic activities, and natural processes. However, in ANN three input combination models (ANN A, B, and C) were developed to identify the best model that can predict water quality index (WQI) with very high precision. ANN A model appears to have the best prediction capacity with a coefficient of determination (R²) = 0.9999 and root mean square error (RMSE) = 0.0537. These results proved that the PCA and ANN methods can be applied as tools for decision-making and problem-solving for better managing of river quality.

Rhythms Synchronization Effects on Cognition during Listening to Quranic Recitation

Sound has rhythms that can interact with human brain rhythms. The interaction may improve human cognition through neuronal synchronization. However, research on the synchronization effects of listening to the Holy Quran remains elusive. This study aimed to learn the potential synchronous effects of Quranic listening in beta frequency through electroencephalographic oscillatory dataset. Subjects were listening to Fatihah Chapter, Arabic News and Rest in random sequence. Data were pre-processed followed by neuroimaging analysis using BESA Research 6.1. Repeated Measures ANOVA and Agglomerative Hierarchical Clustering (AHC) algorithm were applied to elucidate the significantly different EEG electrode channels compared to Rest and their clusters. Results showed that Beta rhythms synchronization with the Fatihah Chapter is associated with verbal fluency, academic performance, social interaction, inhibitory function, movement planning, self-motivation, self-management and reactivation of sensory features of memory trace as highly activated cluster, followed by working memory, language processing and decision making as medially activated cluster; and tune recognition and visual mental imagery as low activated neural circuits cluster during listening to the Fatihah Chapter.

Clinical manifestations of COVID-19; what have we learned from the global database?

Background: There is a need to analyze a worldwide database of the coronavirus disease of 2019 (COVID-19) pandemic.This may prove valuable to facilitate better strategies and planning on prevention, screening, surveillance, early diagnosis, containment and treatments. Method: We extracted 14,259 case reports of COVID-19 dated 11th November 2019 to 18th March 2020 from Johns Hopkins University Repository Online Databaseof 58 countries. After extensive data preprocessing, a multi-disciplinary expert researcherthen conducted series of vetting to categorizefree-text description of symptoms into discreet standardizedcategories.Continuous variables were presented by using median and inter-quartile range whereas categorical variables were presented by frequency and percentage. Result: A total of 2191 cases (15.4%) were included for demographic analysis. The median age was46 years (IQR26 years) with 787 (35.9%) cases involved patients aged of 60 and above while patients less than18 years of age were reported in 79 (3.6%) cases. Majority of the patients were males (n=1227, 56.7%). There were a total of 20standardized categories ofCOVID-19symptoms.The most prevalent were fever (74.8%), nonproductive cough (42.2%), fatigue (13.1%), sore throat (12.8%) and shortness of breath (11.7%). Other symptoms with frequency of more than 1% were chest discomfort, nasal congestion, muscular pain, chills and rigors, headache, diarrhoea, expectoration and joint pain. Other more uncommon symptoms reported include loss of appetite, conjunctivitis, toothache and abdominal pain. Asymptomatic manisfestations were reported in 8 cases (1.0%).All population are susceptible to COVID-19 especially the older age group. There were 20 standardized categories of symptoms wherefever, non-productive cough, fatigue, sore throat and shortness of breath were the most commonly reported. Conclusion: Findings of this study contribute to a deeper understanding on COVID-19 and may prove useful for researchers to better-design screening and surveillance strategies via more accurate risk-prediction modelling. Bangladesh Journal of Medical Science Vol. 21 No. 03 July’22 Page: 702-709

Estimation of rainwater harvesting by the reflectance of the purity index of rainfall

Rainwater harvesting is an effective alternative practice, particularly within urban regions, during periods of water scarcity and dry weather. The collected water is mostly utilized for non-potable household purposes and irrigation. However, due to the increase in atmospheric pollutants, the quality of rainwater has gradually decreased. This atmospheric pollution can damage the climate, natural resources, biodiversity, and human health. In this study, the characteristics and physicochemical properties of rainfall were assessed using a qualitative approach. The three-year (2017-2019) data on rainfall in Peninsular Malaysia were analysed via multivariate techniques. The physicochemical properties of the rainfall yielded six significant factors, which encompassed 61.39% of the total variance as a result of industrialization, agriculture, transportation, and marine factors. The purity of rainfall index (PRI) was developed based on subjective factor scores of the six factors within three categories: good, moderate, and bad. Of the 23 variables measured, 17 were found to be the most significant, based on the classification matrix of 98.04%. Overall, three different groups of similarities that reflected the physicochemical characteristics were discovered among the rain gauge stations: cluster 1 (good PRI), cluster 2 (moderate PRI), and cluster 3 (bad PRI). These findings indicate that rainwater in Peninsular Malaysia was suitable for non-potable purposes.

Tracing sewage contamination based on sterols and stanols markers within the mainland aquatic ecosystem: a case study of Linggi catchment, Malaysia

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

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.

Occurrence of pesticides in surface water, pesticides removal efficiency in drinking water treatment plant and potential health risk to consumers in Tengi River Basin, Malaysia

Agricultural activities have been arising along with the use of pesticides. The use of pesticides can impact not only on vector or other pest but also able to harm human health. Pesticide may leach from the irrigation of plant into the groundwater and in surface water. These waters could be sources of drinking water in a pesticides polluted area. This study aims to determine the occurrence pesticides in surface water and pesticides removal efficiency in a conventional drinking water treatment plant (DWTP) and the potential health risk to consumers. The study was conducted in Tanjung Karang, Selangor, Malaysia. Thirty river water samples and eighteen water samples from DWTP were collected. The water samples were extracted using solid phase extraction (SPE) before injected to the ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). Five hundreds and ten respondents were interviewed using questionnaires to obtain information for health risk assessments. The results showed that propiconazole had the highest mean concentration (4493.1 ng/L) while pymetrozine had the lowest mean concentration (1.3 ng/L) in river water samples. The pesticides removal efficiencies in the conventional DWTP were 77% (imidacloprid), 86% (propiconazole and buprofezin), 88% (tebuconazole) and 100% (pymetrozine, tricyclazole, chlorantraniliprole, azoxystrobin and trifloxystrobin), respectively. The hazard quotients (HQs) and hazard index (HI) for all target pesticides were <1, indicating there was no significant chronic non-carcinogenic health risk due to consumption of the drinking water. Conventional DWTP was not able to completely remove four pesticide; thus, advanced treatment systems need to be considered to safeguard the health of the community in future.

Psychoacoustic and cognitive effects of brain oscillations during listening to Fatihah Chapter

Abstract not available Bangladesh Journal of Medical Science Vol.18(3) 2019 p.665-667

Development of water quality index of ex-mining ponds in Malaysia

In this paper, a study on the development  of water quality index of ex-mining ponds was carried out for proper assessment  and utilization of the abundant ex-mining ponds in Malaysia. Heavy metals were analyzed using inductively coupled plasma mass spectrophotometer (ICPMS), and physico chemical parameters were analyzed in-situ. Chemometric analysis was successfully applied for the parameter selection process. The water quality index of existing Malaysian river did not accommodate the toxic metal pollutants that dominanted in ex-mining water. The acceptable index should therefore incorporate the metal pollutants in order to evaluate the quality status of ex-mining water for human consumption which is the major point of consideration. Two water quality indices were developed to assess the water quality status of ex-mining ponds with reference to Malaysia’s water quality standard (INWQS). The heavy metal index was comprised of contribution from Pb, As, and Cd as the selected metals, while the physico-chemical parameter index has BOD, pH, DO, and AN. Chemometric analysis revealed the dominance of the selected heavy metals in ex-mining water, and physico-chemical parameters were important in water quality monitoring. Heavy metal water quality index revealed that most ex-mining ponds in Klang Valley were classified to be very poor hence could not be used for human consumption. Due to domestic input in some of the lakes in Klang Valley, the physico-chemical water quality index was in very poor status as well.  In Melaka and Negeri Sembilan, ex-mining ponds and lakes were classified as excellent in both indices.    

Combination of Sensory, Chromatographic, and Chemometrics Analysis of Volatile Organic Compounds for the Discrimination of Authentic and Unauthentic Harumanis Mangoes

This study analyzed the volatile organic compounds (VOCs) of three mango varieties (Harumanis, Tong Dam and Susu) for the discrimination of authentic Harumanis from other mangoes. The VOCs of these mangoes were extracted and analysed nondestructively using Head Space-Solid Phase Micro Extraction (HS-SPME) coupled to Gas Chromatography-Mass Spectrometry (GC-MS). Prior to the analytical method, two simple sensory analyses were carried out to assess the ability of the consumers to differentiate between the Harumanis and Tong Dam mangoes as well as their preferences towards these mangoes. On the other hand, chemometrics techniques, such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and discriminant analysis (DA), were used to visualise grouping tendencies of the volatile compounds detected. These techniques were successful in identifying the grouping tendencies of the mango samples according to the presence of their respective volatile compounds, thus enabling the identification of the groups of substances responsible for the discrimination between the authentic and unauthentic Harumanis mangoes. In addition, three ocimene compounds, namely beta-ocimene, trans beta-ocimene, and allo-ocimene, can be considered as chemical markers of the Harumanis mango, as these compounds exist in all Harumanis mango, regardless the different sources of the mangoes obtained.

Classification of Land Use/Land Cover Changes Using GIS and Remote Sensing Technique in Lake Kenyir Basin, Terengganu, Malaysia

Geographical information system (GIS) techniques and Remote Sensing (RS) data are fundamental in the study of land use (LU) and land cover (LC) changes and classification. The aim of this study is to map and classify the LU and LC change of Lake Kenyir Basin within 40 years’ period (1976 to 2016). Multi-temporal Landsat images used are MSS 1976, 1989, ETM+ 2001 and OLI 8 2016. Supervised Classification on Maximum Likelihood Algorithm method was used in ArcGIS 10.3. The result shows three classes of LU and LC via vegetation, water body and built up area. Vegetation, which is the dominant LC found to be 100%, 88.83%, 86.15%, 81.91% in 1976, 1989, 2001 and 2016 respectively. While water body accounts for 0%, 11.17%, 12.36% and 13.62% in the years 1976, 1989, 2001 and 2016 respectively and built-up area 1.49% and 4.47 in 2001 and 2016 respectively. The predominant LC changes in the study are the water body and vegetation, the earlier increasing rapidly at the expense of the later. Therefore, proper monitoring, policies that integrate conservation of the environment are strongly recommended. 

Impact of Hydrological Study to Water Quality Status in Kuantan River, Pahang, Malaysia

Water is a vital for all aspects of human and ecosystem survival. The evaluations of water quality parameters and hydrological status are necessary to enhance the performance of an assessment operation and develop better water resources management and plan. There are three sampling stations were selected along the Kuantan River. Seven water quality parameters were measured and Malaysian Department of Environment Water Quality Index (DOE-WQI) was calculated and classified based on the National Water Quality Standard, Malaysia (INWQS). The water balance status applied to determine the stability of inflows and outflows into and from the basin. As a result, all water quality parameters (DO, COD, BOD, TSS, NH3-N and pH) which categorized under the ranged of class II until class IV respectively. Kuantan River located in Pahang state, Malaysia which is significantly degrading due to human activities as well as urbanization in and within the area. Overall, the result showed that the estimated average sediment that flows out of Kuantan Rivers is 108.88tonnes/day (24 February 2017) and 44.917 tonnes/day (30 April 2017). The contributors of sedimentation problems in Kuantan River are from unsustainable land use such as urbanization and industrialization which trapping the bed sediments and river band erosion.  

Consumption of water from ex-mining ponds in Klang Valley and Melaka, Malaysia: A health risk study

Evaluation of health risks due to heavy metals exposure via drinking water from ex-mining ponds in Klang Valley and Melaka has been conducted. Measurements of As, Cd, Pb, Mn, Fe, Na, Mg, Ca, and dissolved oxygen, pH, electrical conductivity, total dissolved solid, ammoniacal nitrogen, total suspended solid, biological oxygen demand were collected from 12 ex-mining ponds and 9 non-ex-mining lakes. Exploratory analysis identified As, Cd, and Pb as the most representative water quality parameters in the studied areas. The metal exposures were simulated using Monte Carlo methods and the associated health risks were estimated at 95th and 99th percentile. The results revealed that As was the major risk factor which might have originated from the previous mining activity. For Klang Valley, adults that ingested water from those ponds are at both non-carcinogenic and carcinogenic risks, while children are vulnerable to non-carcinogenic risk; for Melaka, only children are vulnerable to As complications. However, dermal exposure showed no potential health consequences on both adult and children groups.

Tracing the sources of lead (Pb) in Brunei Bay, Borneo by using integrated spectrometry ICP-MS and chemometric techniques

The present study aims to define the possible sources that contribute to the level of Pb into the Brunei Bay, Borneo. The cluster analysis has classified the bay into the northern part with heavy and agriculture-related industries; the southern area with a moderate rural human settlement as well as the southwestern area with a more pristine environment and a low level of human settlement. The score plot of spatial discriminant analysis verified a significant influence of the river system toward the estuary, whereas the temporal discriminant analysis has discriminated the seasonal changes. In comparison to elsewhere, the stable Pb isotopic ratios in Brunei Bay showed a fingerprint similar to coal-related sources and of aerosol input. Briefly, even though Pb in the Brunei Bay ecosystem proved to be at a low level, the stable Pb isotopic ratios showed that human and industrial activities are slowly contributing Pb into the bay ecosystem.

Control limit detection for source apportionment in Perlis River Basin, Malaysia

This study presents the application of selected environmetric in the Perlis River Basin. The results show PCA extracted nine principal components (PCs) with eigenvalues greater than one, which equates to about 77.15% of the total variance in the water-quality data set. The absolute principal component scores (APCS)-MLR model discovered BOD and COD as the main parameters, which indicates the measure of the agricultural pollution in the Perlis River Basin, the hierarchical agglomerative cluster analysis (HACA) shows 11 monitoring stations assembled into two clusters in accordance with similarities in the concentration of BOD and COD, which are grouped in P4. The X ̅ control chart shows that the mean concentration of BOD and COD in P4 is in the control process. The capability ratio (Cp) was applied to measure the risk of the concentration in terms of the river pollution in a subsequent period of time using the limit NWQS.

Improving oil classification quality from oil spill fingerprint beyond six sigma approach

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 F_stat>F_critical 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.

Chemometric techniques in oil classification from oil spill fingerprinting

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.