Perna viridis (P. viridis) has been identified as a good biological indicator in identifying environmental pollution, especially when there are various types of Heavy Metals Accumulations (HMA) inside its tissue. Based on the potential of P. viridis to accumulate heavy metals and the data on its physical properties, this study proffers to determine the relationships between both properties. The similarities of the physical properties are used to mathematical model their relationships, which included the size (length, width, height) and weight (wet and dry) of P. viridis, whilst the heavy metals are focused on concentrations of Pb, Cu, Cr, Cd and Zn. The concentrations of metal elements are detected by using Flame Atomic Adsorption Spectrometry. Results show that the mean concentration of Pb, Cu, Cr, Cd, Zn, length, width, height, wet weight and dry weight are: 1.12 +/- 1.00, 2.36 +/- 1.65, 2.12 +/- 2.74, 0.44 +/- 0.41 and 16.52 +/- 10.64 mg kg(-1) (dry weight), 105.08 +/- 14.35, 41.64 +/- 4.64, 28.75 +/- 3.92 mm, 14.56 +/- 3.30 and 2.37 +/- 0.86 g, respectively. It is also found out that the relationships between the Heavy Metals Concentrations (HMA) and the physical properties can be represented using Multiple Linear Regressions (MLR) models, relating that the HMA of Zinc has affected significantly the physical growth properties of P. viridis.
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.
A field investigation was conducted at all water treatment plants throughout 11 states and Federal Territory in Peninsular Malaysia. The sampling points in this study include treatment plant operation, service reservoir outlet and auxiliary outlet point at the water pipelines. Analysis was performed by solid phase micro-extraction technique with a 100 microm polydimethylsiloxane fibre using gas chromatography with mass spectrometry detection to analyse 54 volatile organic compounds (VOCs) of different chemical families in drinking water. The concentration of VOCs ranged from undetectable to 230.2 microg/l. Among all of the VOCs species, chloroform has the highest concentration and was detected in all drinking water samples. Average concentrations of total trihalomethanes (THMs) were almost similar among all states which were in the range of 28.4--33.0 microg/l. Apart from THMs, other abundant compounds detected were cis and trans-1,2-dichloroethylene, trichloroethylene, 1,2-dibromoethane, benzene, toluene, ethylbenzene, chlorobenzene, 1,4-dichlorobenzene and 1,2-dichloro - benzene. Principal component analysis (PCA) with the aid of varimax rotation, and parallel factor analysis (PARAFAC) method were used to statistically verify the correlation between VOCs and the source of pollution. The multivariate analysis pointed out that the maintenance of auxiliary pipelines in the distribution systems is vital as it can become significant point source pollution to Malaysian drinking water.
Heavy metals, particularly cadmium, lead, and arsenic, constitute a significant potential threat to human health. This study was conducted to determine the levels of cadmium, lead, and arsenic in nail samples from farmers at Muda Agricultural Development Authority (MADA), Kedah, Malaysia, and evaluate factors that can contribute to their accumulations. A total of 116 farmers participated in this study. Inductively coupled plasma mass spectrometry (ICP-MS) was used to analyze concentration of heavy metals in the nail samples and questionnaires were given to participants to get demographic, health status, and their agricultural activities data. In this paper, the level of heavy metals was within the normal range and varies according to demographic factors. We found that there were significant correlations between working period with level of lead and arsenic (r=0.315 and r=0.242, resp., P<0.01) and age with lead level (r=0.175, P<0.05). Our findings suggested that agricultural activities could contribute to the accumulation of heavy metals in farmers. Hence, the control of environmental levels of and human exposure to these metals to prevent adverse health effects is still an important public health issue.
This paper presents the pattern and changes of fragmented forest in relation with changes of total forest cover in the state of Selangor in three decades. In this study, inventoried forest cover maps of Selangor in 1971/1972, 1981/1982 and 1991/1992 produced by the Forestry Department of Peninsular Malaysia were digitized to examine the changes in area and number of fragmented forest. Results showed that in 1971/1972, 16 fragmented forests were identified in Selangor. All fragmented forests were identified as dipterocarp forest. A decade later the number of fragmented forests increased by approximately 44% (23). Of the 23 fragmented forests, two were peat swamp forests whereas the remaining were dipterocarp forests. In 1991/1992 the number of fragmented forests (12) was reduced by 47.8%. Two of the fragmented forests were identified as peat swamp forest, seven dipterocarp forest and the other three was mixed of dipterocarp forests and plantation forests. Fragmentation of both dipterocarp and peat swamp forests occurred profoundly during the period between 1971/1972 and 1981/1982, which consequently increased the number of fragmented forests compared with before the period of 1971/1972 where fragmentation happened only at dipterocarp forests. However, many fragmented forests vanished between the 1981/1982 and 1991/1992 periods.
Concentrations of heavy metals in Yellowfin and Skipjack tuna fishes from the Laccadive sea were determined by inductively coupled plasma optical emission spectroscopy (ICP-OES) to evaluate the human health hazards via their consumption. The samples were collected from different atolls of Maldives to ensure a good representation of sample distribution. The metal concentration in tuna fish is found to be below the maximum tolerable limit set by different international organisations. The target hazard quotient values for individual metals were well below the limiting value of 1, indicating an insignificant health risk via the dietary intake of fish. The maximum targeted cancer risk value was 10 -4, indicating low carcinogenic risk from the consumption of tuna fish from the Maldives. Hence, the consumption of tuna from the Laccadive Sea is safe for human health.
In this study, the surface sediments of the Malacca and Prai Rivers were analyzed to identify the distributions, and sources of Polycyclic Aromatic Hydrocarbons (PAHs). The total PAH concentrations varied from 716 to 1210 and 1102 to 7938 ng g(-1)dw in the sediments of the Malacca and Prai Rivers, respectively. The PAH concentrations can be classified as moderate and high level of pollution in the sediments of the Malacca and Prai Rivers, respectively. The comparison of PAHs with the Sediment Quality Guidelines (SQGs) indicates that the PAHs in the sediments of the Malacca and Prai Rivers may have the potential to cause adverse toxicity effects on the sampled ecosystems. The diagnostic ratios of individual PAHs indicate both petrogenic- and pyrogenic-origin PAHs with dominance of pyrogenic source in both rivers. These findings demonstrate that the environmental regulations in Malaysia have effectively reduced the input of petrogenic petroleum hydrocarbons into rivers.
The progress of novel sorbents and their function in preconcentration techniques for determination of trace elements is a topic of great importance. This review discusses numerous analytical approaches including the preparation and practice of unique modification of solid-phase materials. The performance and main features of ion-imprinting polymers, carbon nanotubes, biosorbents, and nanoparticles are described, covering the period 2007-2012. The perspective and future developments in the use of these materials are illustrated.
This study was carried out to evaluate the distribution, enrichment and ecological risk of heavy metals (arsenic (As), zinc (Zn), manganese (Mn), copper (Cu) and lead (Pb)) concentration in Kuala Selangor estuary at the Kuala Selangor Nature Park. The results suggested that As and Pb in sediment were as high as the background value, suggesting the presence of anthropogenic contamination. The risk assessment of sediment Igeo, CD, and PERI, on the other hand, showed low risk of heavy metals in Kuala Selangor estuary. Meanwhile, risk assessment code (RAC) results showed that Mn, As and Zn presented medium to high level of environmental risk. The translocation factor and bioaccumulation factors of heavy metal concentration by mangrove vegetation showed a variety of trends, which indicates the different partitioning and uptake ability of heavy metal in the tissues of different mangrove species. Therefore, underscores the importance of preserving the high diversity of mangroves at securing the health and productivity of the coastal region. These results may play a critical role in facilitating decision makers in managing the sustainability of mangrove forests.
Human health is threatened by significant emissions of heavy metals into the urban environment due to various activities. Various studies describing health risk analyses on soil and dust have been conducted previously. However, there are limited studies that have been carried out regarding the potential health risk assessment of heavy metals in urban road dust of < 63-μm diameter, via incidental ingestion, dermal contact, and inhalation exposure routes by children and adults in developing countries. Therefore, this study evaluated the health risks of heavy metal exposure via ingestion, dermal contact, and inhalation of urban dust particles in Petaling Jaya, Malaysia. Heavy metals such as lead (Pb), chromium (Cr), zinc (Zn), copper (Cu), and manganese (Mn) were measured using dust samples obtained from industrial, high-traffic, commercial, and residential areas by using inductively coupled plasma mass spectrometry (ICP-MS). The principal component and hierarchical cluster analysis showed the dominance of these metal concentrations at sites associated with anthropogenic activities. This was suggestive of industrial, traffic emissions, atmospheric depositions, and wind as the significant contributors towards urban dust contamination in the study sites. Further exploratory analysis underlined Cr, Pb, Cu, and Zn as the most representative metals in the dust samples. In accommodating the uncertainties associated with health risk calculations and simulating the reasonable maximum exposure of these metals, the related health risks were estimated at the 75th and 95th percentiles. Furthermore, assessing the exposure to carcinogenic and non-carcinogenic metals in the dust revealed that ingestion was the primary route of consumption. Children who ingested dust particles in Petaling Jaya could be more vulnerable to carcinogenic and non-carcinogenic risks, but the exposure for both children and adults showed no potential health effects. Therefore, this study serves as an important premise for a review and reformation of the existing environmental quality standards for human health safety.
In oil palm plantations, the fungicide hexaconazole is used to control Ganoderma infection that threatens to destroy or compromisethe palm. The application of hexaconazole is usually through soil drenching, trunk injection, or a combination of these two methods. It is therefore important to have a method to determine the residual amount of hexaconazole in the field such as in samples of water, soil, and leaf to monitor the use and fate of the fungicide in oil palm plantations. This study on the behaviour of hexaconazole in oil palm agro-environment was carried out at the UKM-MPOB Research Station, Bangi Lama, Selangor. Three experimental plots in this estate with 7-year-old Dura x Pisifera (DxP) palms were selected for the field trial. One plot was sprayed with hexaconazole at the manufacturer's recommended dosage, one at double the recommended dosage, and the third plot was untreated control. Hexaconazole residues in the soil, leaf, and water were determined before and after fungicide treatment. Soil samples were randomly collected from three locations at different depths (0-50 cm) and soil collected fromthe same depth were bulked together. Soil, water, and palm leaf were collected at -1 (day before treatment), 0 (day of treatment), 1, 3, 7, 14, 21, 70, 90, and 120 days after treatment. Hexaconazole was detected in soil and oil palm leaf, but was not detected in water from the nearby stream.
Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively.
The devastating health effects of particulate matter (PM10) exposure by susceptible populace has made it necessary to evaluate PM10 pollution. Meteorological parameters and seasonal variation increases PM10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM10 concentration levels. The analyses were carried out using daily average PM10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM10 concentration levels having coefficient of determination (R 2) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.
Sampling of urban runoff was carried out in a small catchment, which represents a residential area (3.34 ha) in Skudai, Johor. One hundred and seventeen runoff samples from ten storm events were analysed. Runoff quality showed large variations in concentrations during storms, especially for SS, BOD5 and COD. Concentrations of NO3-N, NO2-N, NH3-N, and P were also high. Lead (Pb) was also detected but the levels were low (<0.001 mg/L). In general, the river quality is badly polluted and falls in Class V based on the Malaysian Interim National Water Quality Standards. Event mean concentrations for all parameters were found to vary greatly between storms. The values (mg/L) were BOD5 (72), COD (325), SS (386), NO3-N (2.5), NO2-N (0.58), NH3-N (6.8), P (3.4), respectively. First flush phenomena were observed for BOD, COD, SS, NO3-N, NH3-N and P. The first 20-30% of the runoff volume evacuated between 20-59% BOD, 15-69% COD, 15-78% SS, 14-49% NO3-N, 14-19% NO2-N, 23-53% NH3-N and 23-43% P.
Brunei Bay is a unique ecosystem which offers a vast biodiversity. This study was carried out to define the source of metals in the surface sediment of Brunei Bay to ensure the bay's health. The secondary data were analysed using chemometrics analysis to verify the possible factors that influence metals distribution in Brunei Bay sediment. Samples were collected several times during 2013 to 2014 using Ponar grab at 16 stations within the bay. Samples were then dried, pre-treated, digested and analysed using Inductively Coupled Plasma Mass Spectrometry (ICPMS) in the laboratory. Overall, the mean concentration of metal, sediment pH and clay fraction were significantly changed during different sampling periods, as the changes were presumed affected by seasonal changes. The Pearson correlation has pointed that metals were dominantly derived by natural input; however, the total organic carbon was proven to be derived by anthropogenic sources. Moreover, the principal component analysis has verified that the distribution of metals in the bay's sediment was dominantly influenced by natural processes. However, the utilization and manipulation of marine resources are slightly affecting the bay's ecosystem which may deteriorate the ecosystem health soon.
In recent times, surface water resource in the Western Region of Ghana has been found to be inadequate in supply and polluted by various anthropogenic activities. As a result of these problems, the demand for groundwater by the human populations in the peri-urban communities for domestic, municipal and irrigation purposes has increased without prior knowledge of its water quality. Water samples were collected from 14 public hand-dug wells during the rainy season in 2013 and investigated for total coliforms, Escherichia coli, mercury (Hg), arsenic (As), cadmium (Cd) and physicochemical parameters. Multivariate statistical analysis of the dataset and a linear stoichiometric plot of major ions were applied to group the water samples and to identify the main factors and sources of contamination. Hierarchal cluster analysis revealed four clusters from the hydrochemical variables (R-mode) and three clusters in the case of water samples (Q-mode) after z score standardization. Principal component analysis after a varimax rotation of the dataset indicated that the four factors extracted explained 93.3 % of the total variance, which highlighted salinity, toxic elements and hardness pollution as the dominant factors affecting groundwater quality. Cation exchange, mineral dissolution and silicate weathering influenced groundwater quality. The ranking order of major ions was Na(+) > Ca(2+) > K(+) > Mg(2+) and Cl(-) > SO4 (2-) > HCO3 (-). Based on piper plot and the hydrogeology of the study area, sodium chloride (86 %), sodium hydrogen carbonate and sodium carbonate (14 %) water types were identified. Although E. coli were absent in the water samples, 36 % of the wells contained total coliforms (Enterobacter species) which exceeded the WHO guidelines limit of zero colony-forming unit (CFU)/100 mL of drinking water. With the exception of Hg, the concentration of As and Cd in 79 and 43 % of the water samples exceeded the WHO guideline limits of 10 and 3 μg/L for drinking water, respectively. Reported values in some areas in Nigeria, Malaysia and USA indicated that the maximum concentration of Cd was low and As was high in this study. Health risk assessment of Cd, As and Hg based on average daily dose, hazard quotient and cancer risk was determined. In conclusion, multiple natural processes and anthropogenic activities from non-point sources contributed significantly to groundwater salinization, hardness, toxic element and microbiological contamination of the study area. The outcome of this study can be used as a baseline data to prioritize areas for future sustainable development of public wells.
Waste management can be regarded as a cross-cutting environmental 'mega-issue'. Sound waste management practices support the provision of basic needs for general health, such as clean air, clean water and safe supply of food. In addition, climate change mitigation efforts can be achieved through reduction of greenhouse gas emissions from waste management operations, such as landfills. Landfills generate landfill gas, especially methane, as a result of anaerobic degradation of the degradable components of municipal solid waste. Evaluating the mode of generation and collection of landfill gas has posted a challenge over time. Scientifically, landfill gas generation rates are presently estimated using numerical models. In this study the Intergovernmental Panel on Climate Change's Waste Model is used to estimate the methane generated from a Malaysian sanitary landfill. Key parameters of the model, which are the decay rate and degradable organic carbon, are analysed in two different approaches; the bulk waste approach and waste composition approach. The model is later validated using error function analysis and optimum decay rate, and degradable organic carbon for both approaches were also obtained. The best fitting values for the bulk waste approach are a decay rate of 0.08 y(-1) and degradable organic carbon value of 0.12; and for the waste composition approach the decay rate was found to be 0.09 y(-1) and degradable organic carbon value of 0.08. From this validation exercise, the estimated error was reduced by 81% and 69% for the bulk waste and waste composition approach, respectively. In conclusion, this type of modelling could constitute a sensible starting point for landfills to introduce careful planning for efficient gas recovery in individual landfills.
Pollutants put great stress on the environment, especially the aquatic ecosystem; therefore, the ease with which pollutants migrate in water is a subject of global concern. In this study, leachate from landfill that was analyzed with the objective of understanding the potential impact to the environment was tested on Pangasius sutchi. Heavy metals available at various concentrations in raw leachate samples of both closed and active landfills necessitated the determination of their degree of bioaccumulation in this fish species in order to enrich the risk data on toxicity of effluents. Zinc (3.2 µg g(-1)), iron (2.1 µg g(-1)) and chromium (0.24 µg g(-1)) detected in the fish within 96 h of acute exposure is of concern. A histopathology test on excised liver of P. sutchi indicated cellular disruption from normal stain. Heterogeneous effluents like leachate may affect not only groundwater but can endanger aquatic ecosystems, especially in some regions where improper waste disposal and treatment allow the flow of leachate into surface water courses. Though metals might be beneficial to organisms, the extent at which they can accumulate in leachate-exposed fish is a risk and can initiate metal toxicity in aquatic life.