This study investigated relationships of a water quality index (WQI) with multiple water quality variables (WQVs), explored variability in water quality over time and space, and established linear and non-linear models predictive of WQI from raw WQVs. Data were processed using Spearman's rank correlation analysis, multiple linear regression, and artificial neural network modeling. Correlation analysis indicated that from a temporal perspective, the WQI, temperature, and zinc, arsenic, chemical oxygen demand, sodium, and dissolved oxygen concentrations increased, whereas turbidity and suspended solids, total solids, nitrate nitrogen (NO3-N), and biochemical oxygen demand concentrations decreased with year. From a spatial perspective, an increase with distance of the sampling station from the headwater was exhibited by 10 WQVs: magnesium, calcium, dissolved solids, electrical conductivity, temperature, NO3-N, arsenic, chloride, potassium, and sodium. At the same time, the WQI; Escherichia coli bacteria counts; and suspended solids, total solids, and dissolved oxygen concentrations decreased with distance from the headwater. Lastly, regression and artificial neural network models with high prediction powers (81.2% and 91.4%, respectively) were developed and are discussed.
This study was designed as the first to assess the trace metal (Cd, Cu, Fe, Mn, Ni and Zn) in coral skeleton in relation to metal availabilities and sampling locations in Sabah. The study also aims to determine the differential abilities of Scleractinian coral species as a bioindicator of environmental conditions. Skeletons of Scleractinian coral (Hydnophora microconos, Favia speciosa and Porites lobata) showed concentrations of Fe, Mn and Ni relatively higher than Cd and Zn in the skeletons. Statistical analyses outputs showed significant relationships between trace metal concentrations in coral species and those in seawater and sediment. The highest bioaccumulation factors among three Scleractinian coral species investigated was for Zn followed by Mn, Ni, Fe, Cd and Cu can provide a sign about pollution levels. However, metal tolerance, coral structure and morphology as well as multispecies monitoring are factors that need to be a focus in future studies.
In many regions around the world, there are issues associated with groundwater resources due to human and natural factors. However, the relation between these factors is difficult to determine due to the large number of parameters and complex processes required. In order to understand the relation between land use allocations, the intrinsic factors of the aquifer, climate change data and groundwater chemistry in the multilayered aquifer system in Malaysia's Northern Kelantan Basin, twenty-two years hydrogeochemical data set was used in this research. The groundwater salinisation in the intermediate aquifer, which mainly extends along the coastal line, was revealed through the hydrogeochemical investigation. Even so, there had been no significant trend detected on groundwater salinity from 1989 to 2011. In contrast to salinity, as seen from the nitrate contaminations there had been significantly increasing trends in the shallow aquifer, particularly in the central part of the study area. Additionally, a strong association between high nitrate values and the areas covered with palm oil cultivations and mixed agricultural have been detected by a multiple correspondence analysis (MCA), which implies that the increasing nitrate concentrations are associated with nitrate loading from the application of N-fertilisers. From the process of groundwater salinisation in the intermediate aquifer, could be seen that it has a strong correlation the aquifer lithology, specifically marine sediments which are influenced by the ancient seawater trapped within the sediments.
The present study examined the concentrations of mercury (Hg), methylmercury (MeHg), and selenium (Se) in the multiple tissues of the Plotosus canius and Periophthalmodon schlosseri collected from the Strait of Malacca. The mean value in mg kg(-1) of Hg (P. canius: 0.34 ± 0.19; P. schlosseri: 0.32 ± 0.18) and MeHg in muscle (P. canius: 0.14 ± 0.11; P. schlosseri: 0.17 ± 0.11) were below the Codex general standard for contaminants and toxins in food and feed (CODEX STAN 193-1995), the Malaysian Food Regulation 1985 and the Japan Food Sanitation Law. For P. canius, the liver contained the highest concentrations of Hg (0.48 ± 0.07 mg kg(-1)) and MeHg (0.21 ± 0.00 mg kg(-1)), whereas for P. schlosseri, the gill contained the highest concentrations of Hg (0.36 ± 0.06 mg kg(-1)) and MeHg (0.21 ± 0.05 mg kg(-1)). The highest concentration of (80)Se (mg kg(-1)) was observed in the liver of P. canius (20.34 ± 5.68) and in the gastrointestinal tract (3.18 ± 0.42) of P. schlosseri. The selenium:mercury (Se:Hg) molar ratios were above 1 and the positive selenium health benefit value (HBVSe) suggesting the possible protective effects of Se against Hg toxicity. The estimate weekly intakes (EWIs) in μg kg(-1) body weight (bw) week(-1) of Hg (P. canius: 0.27; P. schlosseri: 0.15) and MeHg (P. canius: 0.11; P. schlosseri: 0.08) were found to be lower than the provisional tolerable weekly intake established by the Joint FAO/WHO Expert Committee on Food Additives (JECFA). Based on the calculated EWIs, P. canius, and P. schlosseri were found to be unlikely to cause mercury toxicity in human consumption.
This study examined the impacts of the Coronavirus disease 2019 (COVID-19) on the environment in the Southeast Asia region using qualitative content analysis to analyze the textual data of published studies and other online references such as the organizational reports. Besides, the materiality assessment particularly the Global Reporting Initiative was conducted by analyzing short- and long-term impacts from the stakeholders' (local and regional policymakers) perspective. The positive effects of COVID-19 lockdown and movement restriction on the regional environment identified in this study included a reduction in air pollution, improvement of air and water quality, lower noise levels, and reduced land surface temperature. In contrast, the negative effects encompassed a rise in the use of plastics and the generation of medical waste in Indonesia, Malaysia, Thailand, the Philippines, and Vietnam. Materiality assessment findings have offered insights on the need of stakeholders' importance for further to deal with huge amount of waste, inadequate waste management facilities and system, explore the effectiveness of such sustainable work and lifestyle changes, utilize real-time monitoring air quality data and future prediction responses for climate change mitigation and adaptation policies as well as consideration towards new green technologies for clean energy in each Southeast Asian country and at regional level. It is anticipated that this study will contribute towards a better understanding of the impacts of COVID-19 on environmental sustainability in the Southeast Asia region, particularly from the perspective of the stakeholders.
This study was conducted to assess the reliability of Nerita lineata as a bioindicator for metals in sediment and the factors influencing the accumulation of metals and methylmercury in its soft tissue. The two matrices were analyzed for Co, Cr, Cu, THg, MeHg, Mn, Ni, Pb, and Zn. The metal concentrations in N. lineata were comparable to previously reported results with the exception of Ni which was higher. Cu, Mn, and Pb in N. lineata were significantly (p
Robust statistical tools were applied on the water quality datasets with the aim of determining the most significance parameters and their contribution towards temporal water quality variation. Surface water samples were collected from four different sampling points during dry and wet seasons and analyzed for their physicochemical constituents. Discriminant analysis (DA) provided better results with great discriminatory ability by using five parameters with (P < 0.05) for dry season affording more than 96% correct assignation and used five and six parameters for forward and backward stepwise in wet season data with P-value (P < 0.05) affording 68.20% and 82%, respectively. Partial correlation results revealed that there are strong (r(p) = 0.829) and moderate (r(p) = 0.614) relationships between five-day biochemical oxygen demand (BOD(5)) and chemical oxygen demand (COD), total solids (TS) and dissolved solids (DS) controlling for the linear effect of nitrogen in the form of ammonia (NH(3)) and conductivity for dry and wet seasons, respectively. Multiple linear regression identified the contribution of each variable with significant values r = 0.988, R(2) = 0.976 and r = 0.970, R(2) = 0.942 (P < 0.05) for dry and wet seasons, respectively. Repeated measure t-test confirmed that the surface water quality varies significantly between the seasons with significant value P < 0.05.
Revealing the potential of seagrass as a bioindicator for metal pollution is important for assessing marine ecosystem health. Trace metal (111Cd, 63Cu, 60Ni, 208Pb, 66Zn) concentrations in the various parts (root, rhizome, and blade) of tape seagrass (Enhalus acoroides) collected from Merambong shoal of Sungai Pulai estuary, Johor Strait, Malaysia were acid-extracted using a microwave digester and analysed via inductively coupled plasma-mass spectrometry (ICP-MS). The ranges of trace metal concentrations (in μgg-1 dry weight) were as follows: Cd (0.05-0.81), Cu (1.62-27.85), Ni (1.89-9.35), Pb (0.69-4.16), and Zn (3.44-35.98). The translocation factor revealed that E. acoroides is a hyperaccumulator plant, as its blades can accumulate high concentrations of Cd, Cu, Ni, and Zn, but not Pb. The plant limits Pb mobility to minimize Pb's toxic impact. Thus, E. acoroides is a potential bioindicator of metal pollution by Cd, Cu, Ni, and Zn in estuarine environments.
Contamination of endocrine disrupting compounds (EDCs) in tap water is an emerging global issue, and there are abundant influencing factors that have an ambivalent effect on their transportation and fate. Different housing types vary in terms of water distribution system operation and design, water consumption choices, and other hydraulic factors, which potentially affect the dynamics, loadings, and partitioning of pollutants in tap water. Thus, this study analyzed 18 multiclass EDCs in tap water from different housing types (i.e., landed and high-rise) and the associated health risks. Sample analyses revealed the presence of 16 EDCs, namely hormones (5), pharmaceuticals (8), a pesticide (1), and plasticizers (2) in tap water, with the prevalent occurrence of bisphenol A up to 66.40 ng/L in high-rise housing. The presence of caffeine and sulfamethoxazole distribution in tap water was significantly different between landed and high-rise housings (t(152) = -2.298, p = 0.023 and t(109) = 2.135, p = 0.035). Moreover, the salinity and conductivity of tap water in high-rise housings were significantly higher compared to those in landed housings (t(122) = 2.411, p = 0.017 and t(94) = 2.997, p = 0.003, respectively). Furthermore, there were no potential health risks of EDCs (risk quotient
This study aims to determine the status of potentially toxic element concentrations of road dust in a medium-sized city (Rawang, Malaysia). This study adopts source identification via enrichment factor, Pearson correlation analysis, and Fourier spectral analysis to identify sources of potentially toxic element concentrations in road dust in Rawang City, Malaysia. Health risk assessment was conducted to determine potential health risks (carcinogenic and non-carcinogenic risks) among adults and children via multiple pathways (i.e., ingestion, dermal contact, and inhalation). Mean of potentially toxic element concentrations were found in the order of Pb > Zn > Cr(IV) > Cu > Ni > Cd > As > Co. Source identification revealed that Cu, Cd, Pb, Zn, Ni, and Cr(IV) are associated with anthropogenic sources in industrial and highly populated areas in northern and southern Rawang, cement factories in southern Rawang, as well as the rapid development and population growth in northwestern Rawang, which have resulted in high traffic congestion. Cobalt, Fe, and As are related to geological background and lithologies in Rawang. Pathway orders for both carcinogenic and non-carcinogenic risks are ingestion, dermal contact, and inhalation, involving adults and children. Non-carcinogenic health risks in adults were attributed to Cr(IV), Pb, and Cd, whereas Cu, Cd, Cr(IV), Pb, and Zn were found to have non-carcinogenic health risks for children. Cd, Cr(IV), Pb, and As may induce carcinogenic risks in adults and children, and the total lifetime cancer risk values exceeded incremental lifetime.
In recent years, groundwater quality has become a global concern due to its effect on human life and natural ecosystems. To assess the groundwater quality in the Amol-Babol Plain, a total of 308 water samples were collected during wet and dry seasons in 2009. The samples were analysed for their physico-chemical and biological constituents. Multivariate statistical analysis and geostatistical techniques were applied to assess the spatial and temporal variabilities of groundwater quality and to identify the main factors and sources of contamination. Principal component analysis (PCA) revealed that seven factors explained around 75% of the total variance, which highlighted salinity, hardness and biological pollution as the dominant factors affecting the groundwater quality in the Plain. Two-way analysis of variance (ANOVA) was conducted on the dataset to evaluate the spatio-temporal variation. The results showed that there were no significant temporal variations between the two seasons, which explained the similarity between six component factors in dry and wet seasons based on the PCA results. There are also significant spatial differences (p > 0.05) of the parameters under study, including salinity, potassium, sulphate and dissolved oxygen in the plain. The least significant difference (LSD) test revealed that groundwater salinity in the eastern region is significantly different to the central and western side of the study area. Finally, multivariate analysis and geostatistical techniques were combined as an effective method for demonstrating the spatial structure of multivariate spatial data. It was concluded that multiple natural processes and anthropogenic activities were the main sources of groundwater salinization, hardness and microbiological contamination of the study area.
The pollution status of the downstream section of the Jakara River was investigated. Dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), chemical oxygen demand (COD), suspended solids (SS), pH, conductivity, salinity, temperature, nitrogen in the form of ammonia (NH(3)), turbidity, dissolved solids (DS), total solids (TS), nitrates (NO(3)), chloride (Cl) and phosphates (PO(3-)(4)) were evaluated, using both dry and wet season samples, as a measure of variation in surface water quality in the area. The results obtained from the analyses were correlated using Pearson's correlation matrix, principal component analysis (PCA) and paired sample t-tests. Positive correlations were observed for BOD(5), NH(3), COD, and SS, turbidity, conductivity, salinity, DS, TS for dry and wet seasons, respectively. PCA was used to investigate the origin of each water quality parameter, and yielded 5 varimax factors for each of dry and wet seasons, with 70.7 % and 83.1 % total variance, respectively. A paired sample t-test confirmed that the surface water quality varies significantly between dry and wet season samples (P < 0.01). The source of pollution in the area was concluded to be of anthropogenic origin in the dry season and natural origins in the wet season.
This paper determines the controlling factors that influence the metals' behavior water-sediment interaction facies and distribution of elemental content ((75)As, (111)Cd, (59)Co, (52)Cr, (60)Ni, and (208)Pb) in water and sediment samples in order to assess the metal pollution status in the Langat River. A total of 90 water and sediment samples were collected simultaneously in triplicate at 30 sampling stations. Selected metals were analyzed using ICP-MS, and the metals' concentration varied among stations. Metal concentrations of water ranged between 0.08-24.71 μg/L for As, <0.01-0.53 μg/L for Cd, 0.06-6.22 μg/L for Co, 0.32-4.67 μg/L for Cr, 0.80-24.72 μg/L for Ni, and <0.005-6.99 μg/L for Pb. Meanwhile, for sediment, it ranged between 4.47-30.04 mg/kg for As, 0.02-0.18 mg/kg for Cd, 0.87-4.66 mg/kg for Co, 4.31-29.04 mg/kg for Cr, 2.33-8.25 mg/kg for Ni and 5.57-55.71 mg/kg for Pb. The average concentration of studied metals in the water was lower than the Malaysian National Standard for Drinking Water Quality proposed by the Ministry of Health. The average concentration for As in sediment was exceeding ISQG standards as proposed by the Canadian Sediment Quality Guidelines. Statistical analyses revealed that certain metals (As, Co, Ni, and Pb) were generally influenced by pH and conductivity. These results are important when making crucial decisions in determining potential hazardous levels of these metals toward humans.
The present study deals with the assessment of Langat River water quality with some chemometrics approaches such as cluster and discriminant analysis coupled with an artificial neural network (ANN). The data used in this study were collected from seven monitoring stations under the river water quality monitoring program by the Department of Environment (DOE) from 1995 to 2002. Twenty three physico-chemical parameters were involved in this analysis. Cluster analysis successfully clustered the Langat River into three major clusters, namely high, moderate and less pollution regions. Discriminant analysis identified seven of the most significant parameters which contribute to the high variation of Langat River water quality, namely dissolved oxygen, biological oxygen demand, pH, ammoniacal nitrogen, chlorine, E. coli, and coliform. Discriminant analysis also plays an important role as an input selection parameter for an ANN of spatial prediction (pollution regions). The ANN showed better prediction performance in discriminating the regional area with an excellent percentage of correct classification compared to discriminant analysis. Multivariate analysis, coupled with ANN, is proposed, which could help in decision making and problem solving in the local environment.
Multivariate statistical techniques such as hierarchical Agglomerated cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) were applied to identify the spatial variation and pollution sources of Jakara River, Kano, Nigeria. Thirty surface water samples were collected: 23 along Getsi River and 7 along the main channel of River Jakara. Twenty-three water quality parameters, namely pH, temperature, turbidity, electrical conductivity (EC), dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), Faecal coliform, total solids (TS), nitrates (NO(3)(-)), phosphates (PO(4)(3-)), cobalt (Co), iron (Fe), nickel (Ni), manganese (Mn), copper (Cu), sodium (Na), potassium (K), mercury (Hg), chromium (Cr), cadmium (Cd), lead (Pb), magnesium (Mg), and calcium(Ca) were analysed. HACA grouped the sampling points into three clusters based on the similarities of river water quality characteristics: industrial, domestic, and agricultural water pollution sources. Forward and backward DA effectively discriminated 5 and 15 water quality variables, respectively, each assigned with 100% correctness from the original 23 variables. PCA and FA were used to investigate the origin of each water quality parameter due to various land use activities, 7 principal components were obtained with 77.5% total variance, and in addition PCA identified 3 latent pollution sources to support HACA. From this study, one can conclude that the application of multivariate techniques derives meaningful information from water quality data.
The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.
Rapid urbanization and industrial development in the Langat Basin has disturbed the groundwater quality. The populations' reliance on groundwater sources may induce possible risks to human health such as cancer and endocrine dysfunction. This study aims to determine the groundwater quality of an urbanized basin through 24 studied hydrochemical parameters from 45 groundwater samples obtained from 15 different sampling stations by employing integrated multivariate analysis. The abundance of the major ions was in the following order: bicarbonate (HCO3-) > chloride (Cl-) > sodium (Na+) > sulphate (SO42-) > calcium (Ca2+) > potassium (K+) > magnesium (Mg2+). Heavy metal dominance was in the following order: Fe > Mn > Zn > As > Hg > Pb > Ni > Cu > Cd > Se > Sr. Classification of the groundwater facies indicated that the studied groundwater belongs to the Na-Cl with saline water type and Na-HCO3 with mix water type characteristics. The saline water type characteristics are derived from agricultural activities, while the mixed water types occur from water-rock interaction. Multivariate analysis performance suggests that industrial, agricultural, and weathering activities have contributed to groundwater contamination. The study will help in the understanding of the groundwater quality issue and serve as a reference for other basins with similar characteristics.
Rapid increase in industrialization and urbanization in the west coast of Peninsular Malaysia has led to the intense release of petroleum and products of petroleum into the environment. Surface sediment samples were collected from the Selangor River in the west coast of Peninsular Malaysia during four climatic seasons and analyzed for PAHs and biomarkers (hopanes). Sediments were soxhlet extracted and further purified and fractionated through first and second step column chromatography. A gas chromatography-mass spectrometry (GC-MS) was used for analysis of PAHs and hopanes fractions. The average concentrations of total PAHs ranged from 219.7 to 672.3 ng g-1 dw. The highest concentrations of PAHs were detected at 964.7 ng g-1 dw in station S5 in the mouth of the Selangor River during the wet inter-monsoonal season. Both pyrogenic and petrogenic PAHs were detected in the sediments with a predominance of the former. The composition of hopanes was homogeneous showing that petroleum hydrocarbons share an identical source in the study area. Diagnostic ratios of hopanes indicated that some of the sediment samples carry the crankcase oil signature.
Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH4). Partial correlation analysis (r p) results showed that there is a strong relationship between salinity and turbidity (r p=0.930, p=0.001) and BOD5 and COD (r p=0.839, p=0.001) controlling for the linear effects of conductivity and NH4, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD5, COD, SS, NH4, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.
The concentration profile, distribution and risk assessment of pharmaceutically active compounds (PhACs) in the coastal surface water from the Klang River estuary were measured. Surface coastal water samples were extracted using offline solid phase, applying polymeric C18 cartridges as extraction sorbent and measuring with liquid chromatography mass spectrometry-mass spectrometry (LC MS-MS) technique. Extraction method was optimized for its recovery, sensitivity and linearity. Excellent recoveries were obtained from the optimized method with percentage of recoveries ranging from 73 to 126%. The optimized analytical method achieved good sensitivity with limit of detection ranging from 0.05 to 0.15 ng L-1, while linearity of targeted compounds in the LC MS-MS system was more than 0.990. The results showed that amoxicillin has the highest concentration (102.31 ng L-1) followed by diclofenac (10.80 ng L-1) and primidone (7.74 ng L-1). The percentage of contribution (% of total concentration) for the targeted PhACs is in the following order; amoxicillin (92.90%) > diclofenac (3.95%) > primidone (1.23%) > dexamethasone (0.75%) > testosterone (0.70%) > sulfamethoxazole (0.33%) > progesterone (0.14%). Environmental risk assessment calculated based on deterministic approach (the RQ method), showed no present risk from the presence of PhACs in the coastal water of Klang River estuary. Nonetheless, this baseline assessment can be used for better understanding on PhACs pollution profile and distribution in the tropical coastal and estuarine ecosystem as well as for future comparative studies.