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.
To manage and conserve biodiversity, one must know what is being lost, where, and why, as well as which remedies are likely to be most effective. Metabarcoding technology can characterise the species compositions of mass samples of eukaryotes or of environmental DNA. Here, we validate metabarcoding by testing it against three high-quality standard data sets that were collected in Malaysia (tropical), China (subtropical) and the United Kingdom (temperate) and that comprised 55,813 arthropod and bird specimens identified to species level with the expenditure of 2,505 person-hours of taxonomic expertise. The metabarcode and standard data sets exhibit statistically correlated alpha- and beta-diversities, and the two data sets produce similar policy conclusions for two conservation applications: restoration ecology and systematic conservation planning. Compared with standard biodiversity data sets, metabarcoded samples are taxonomically more comprehensive, many times quicker to produce, less reliant on taxonomic expertise and auditable by third parties, which is essential for dispute resolution.
This study aims at developing a novel effluent removal management tool for septic sludge treatment plants (SSTP) using a clonal selection algorithm (CSA). The proposed CSA articulates the idea of utilizing an artificial immune system (AIS) to identify the behaviour of the SSTP, that is, using a sequence batch reactor (SBR) technology for treatment processes. The novelty of this study is the development of a predictive SSTP model for effluent discharge adopting the human immune system. Septic sludge from the individual septic tanks and package plants will be desuldged and treated in SSTP before discharging the wastewater into a waterway. The Borneo Island of Sarawak is selected as the case study. Currently, there are only two SSTPs in Sarawak, namely the Matang SSTP and the Sibu SSTP, and they are both using SBR technology. Monthly effluent discharges from 2007 to 2011 in the Matang SSTP are used in this study. Cross-validation is performed using data from the Sibu SSTP from April 2011 to July 2012. Both chemical oxygen demand (COD) and total suspended solids (TSS) in the effluent were analysed in this study. The model was validated and tested before forecasting the future effluent performance. The CSA-based SSTP model was simulated using MATLAB 7.10. The root mean square error (RMSE), mean absolute percentage error (MAPE), and correction coefficient (R) were used as performance indexes. In this study, it was found that the proposed prediction model was successful up to 84 months for the COD and 109 months for the TSS. In conclusion, the proposed CSA-based SSTP prediction model is indeed beneficial as an engineering tool to forecast the long-run performance of the SSTP and in turn, prevents infringement of future environmental balance in other towns in Sarawak.
Nitrate-nitrogen leaching from agricultural areas is a major cause for groundwater pollution. Polluted groundwater with high levels of nitrate is hazardous and cause adverse health effects. Human consumption of water with elevated levels of NO3-N has been linked to the infant disorder methemoglobinemia and also to non-Hodgkin's disease lymphoma in adults. This research aims to study the temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy soil at Ladang Merdeka Ismail Mulong in Kelantan, Malaysia. The complex data matrix (128 x 16) of nitrate-nitrogen parameters was subjected to multivariate analysis mainly Principal Component Analysis (PCA) and Discriminant Analysis (DA). PCA extracted four principal components from this data set which explained 86.4% of the total variance. The most important contributors were soil physical properties confirmed using Alyuda Forecaster software (R2 = 0.98). Discriminant analysis was used to evaluate the temporal variation in soil nitrate-nitrogen on leaching process. Discriminant analysis gave four parameters (hydraulic head, evapotranspiration, rainfall and temperature) contributing more than 98% correct assignments in temporal analysis. DA allowed reduction in dimensionality of the large data set which defines the four operating parameters most efficient and economical to be monitored for temporal variations. This knowledge is important so as to protect the precious groundwater from contamination with nitrate.
The present study investigates the concentration of Pb, Cd, Ni, Zn, and Cu in the paddy field soils collected from Tumpat, Kelantan. Soil samples were treated with sequential extraction to distinguish the anthropogenic and lithogenic origin of Pb, Cd, Ni, Zn, and Cu. ELFE and oxidizable-organic fractions were detected as the lowest accumulation of Pb, Cd, Ni, Zn, and Cu. Therefore, all the heavy metals examined were concentrated, particularly in resistant fraction, indicating that those heavy metals occurred and accumulated in an unavailable form. The utilization of agrochemical fertilizers and pesticides might not elevate the levels of heavy metals in the paddy field soils. In comparison, the enrichment factor and geoaccumulation index for Pb, Cd, Ni, Zn, and Cu suggest that these heavy metals have the potential to cause environmental risk, although they present abundance in resistant fraction. Therefore, a complete study should be conducted based on the paddy cycle, which in turn could provide a clear picture of heavy metals distribution in the paddy field soils.
This work describes the application of the electrical resistivity (ER) method to delineating subsurface structures and cavities in Kuala Lumpur Limestone within the Batu Cave area of Selangor Darul Ehsan, Malaysia. In all, 17 ER profiles were measured by using a Wenner electrode configuration with 2 m spacing. The field survey was accompanied by laboratory work, which involves taking resistivity measurements of rock, soil, and water samples taken from the field to obtain the formation factor. The relationship between resistivity and the formation factor and porosity for all the samples was established. The porosity values were plotted and contoured. A 2-dimensional and 3-dimensional representation of the subsurface topography of the area was prepared through use of commercial computer software. The results show the presence of cavities and sinkholes in some parts of the study area. This work could help engineers and environmental managers by providing the information necessary to produce a sustainable management plan in order to prevent catastrophic collapses of structures and other related geohazard problems.
Seagrass is a valuable marine ecosystem engineer. However, seagrass population is declining worldwide. The lack of seagrass research in Malaysia raises questions about the status of seagrasses in the country. The seagrasses in Lawas, which is part of the coral-mangrove-seagrass complex, have never been studied in detail. In this study, we examine whether monthly changes of seagrass population in Lawas occurred. Data on estimates of seagrass percentage cover and water physicochemical parameters (pH, turbidity, salinity, temperature, and dissolved oxygen) were measured at 84 sampling stations established within the study area from June 2009 to May 2010. Meteorological data such as total rainfall, air temperature, and Southern Oscillation Index were also investigated. Our results showed that (i) the monthly changes of seagrass percentage cover are significant, (ii) the changes correlated significantly with turbidity measurements, and (iii) weather changes affected the seagrass populations. Our study indicates seagrass percentage increased during the El-Nino period. These results suggest that natural disturbances such as weather changes affect seagrass populations. Evaluation of land usage and measurements of other water physicochemical parameters (such as heavy metal, pesticides, and nutrients) should be considered to assess the health of seagrass ecosystem at the study area.
Heavy metals pollution has become a great threat to the world. Since instrumental methods are expensive and need skilled technician, a simple and fast method is needed to determine the presence of heavy metals in the environment. In this study, an inhibitive enzyme assay for heavy metals has been developed using crude proteases from Coriandrum sativum. In this assay, casein was used as a substrate and Coomassie dye was used to denote the completion of casein hydrolysis. In the absence of inhibitors, casein was hydrolysed and the solution became brown, while in the presence of metal ions such as Hg²⁺ and Zn²⁺, the hydrolysis of casein was inhibited and the solution remained blue. Both Hg²⁺ and Zn²⁺ exhibited one-phase binding curve with IC₅₀ values of 3.217 mg/L and 0.727 mg/L, respectively. The limits of detection (LOD) and limits of quantitation (LOQ) for Hg were 0.241 and 0.802 mg/L, respectively, while the LOD and LOQ for Zn were 0.228 and 0.761 mg/L, respectively. The enzyme exhibited broad pH ranges for activity. The crude proteases extracted from Coriandrum sativum showed good potential for the development of a rapid, sensitive, and economic inhibitive assay for the biomonitoring of Hg²⁺ and Zn²⁺ in the aquatic environments.
An investigative study was carried out in Langat River to determine the heavy metal pollution in the sediment with 22 sampling stations selected for the collection of sediment samples. The sediment samples were digested and analyzed for extractable metal ((48)Cd, (29)Cu, (30)Zn, (33)As, (82)Pb) using the Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). Parameters, such as pH, Eh, electrical conductivity (EC), salinity, cation exchange capacity (CEC) and loss on ignition (LOI) were also determined. The assessment of heavy metal pollution was derived using the enrichment factors (EF) and geoaccumulation index (I(geo)). This study revealed that the sediment is predominantly by As > Cd > Pb > Zn > Cu. As recorded the highest EF value at 187.45 followed by Cd (100.59), Pb (20.32), Zn (12.42) and Cu (3.46). This is similar to the I(geo), which indicates that the highest level goes to As (2.2), exhibits moderately polluted. Meanwhile, Cd recorded 1.8 and Pb (0.23), which illustrates that both of these elements vary from unpolluted to moderately polluted. The Cu and Zn levels are below 0, which demonstrates background concentrations. The findings are expected to update the current status of the heavy metal pollution as well as creating awareness concerning the security of the river water as a drinking water source.
In this paper, we present the effect of varying humidity levels on the electrical parameters and the multi frequency response of the electrical parameters of an organic-inorganic composite (PEPC+NiPc+Cu2O)-based humidity sensor. Silver thin films (thickness ~200 nm) were primarily deposited on plasma cleaned glass substrates by the physical vapor deposition (PVD) technique. A pair of rectangular silver electrodes was formed by patterning silver film through standard optical lithography technique. An active layer of organic-inorganic composite for humidity sensing was later spun coated to cover the separation between the silver electrodes. The electrical characterization of the sensor was performed as a function of relative humidity levels and frequency of the AC input signal. The sensor showed reversible changes in its capacitance with variations in humidity level. The maximum sensitivity ~31.6 pF/%RH at 100 Hz in capacitive mode of operation has been attained. The aim of this study was to increase the sensitivity of the previously reported humidity sensors using PEPC and NiPc, which has been successfully achieved.
Tourism-related activities such as the heavy use of boats for transportation are a significant source of petroleum hydrocarbons that may harm the ecosystem of Langkawi Island. The contamination and toxicity levels of polycyclic aromatic hydrocarbon (PAH) in the sediments of Langkawi were evaluated using sediment quality guidelines (SQGs) and toxic equivalent factors. Ten samples were collected from jetties and fish farms around the island in December 2010. A gas chromatography/flame ionization detector (GC/FID) was used to analyse the 18 PAHs. The concentration of total PAHs was found to range from 869 ± 00 to 1637 ± 20 ng g⁻¹ with a mean concentration of 1167.00 ± 24 ng g⁻¹, lower than the SQG effects range-low (3442 ng g⁻¹). The results indicated that PAHs may not cause acute biological damage. Diagnostic ratios and principal component analysis suggested that the PAHs were likely to originate from pyrogenic and petrogenic sources. The toxic equivalent concentrations of the PAHs ranged from 76.3 to 177 ng TEQ/g d.w., which is lower compared to similar studies. The results of mean effects range-median quotient of the PAHs were lower than 0.1, which indicate an 11% probability of toxicity effect. Hence, the sampling sites were determined to be the low-priority sites.
Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making.
The basic aim of this work is (1) to review and present practically operational requirements for a sustainability assessment of marine environment, such as describing the monitoring process, research approaches, objectives, guidelines, and indicators and (2) to illustrate how physico-chemical and biological indicators can be practically applied, to assess water and sediment quality in marine and coastal environment. These indicators should meet defined criteria for practical usefulness, e.g. they should be simple to understand and apply to managers and scientists with different educational backgrounds. This review aimed to encapsulate that variability, recognizing that meaningful guidance should be flexible enough to accommodate the widely differing characteristics of marine ecosystems.
We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R (2)), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.
Environmental risk assessment of chemicals is reliant on good estimates of product usage information and robust exposure models. Over the past 20 to 30 years, much progress has been made with the development of exposure models that simulate the transport and distribution of chemicals in the environment. However, little progress has been made in our ability to estimate chemical emissions of home and personal care (HPC) products. In this project, we have developed an approach to estimate subnational emission inventory of chemical ingredients used in HPC products for 12 Asian countries including Bangladesh, Cambodia, China, India, Indonesia, Laos, Malaysia, Pakistan, Philippines, Sri Lanka, Thailand, and Vietnam (Asia-12). To develop this inventory, we have coupled a 1 km grid of per capita gross domestic product (GDP) estimates with market research data of HPC product sales. We explore the necessity of accounting for a population's ability to purchase HPC products in determining their subnational distribution in regions where wealth is not uniform. The implications of using high resolution data on inter- and intracountry subnational emission estimates for a range of hypothetical and actual HPC product types were explored. It was demonstrated that for low value products (<500 US$ per capita/annum required to purchase product) the maximum deviation from baseline (emission distributed via population) is less than a factor of 3 and it would not result in significant differences in chemical risk assessments. However, for other product types (>500 US$ per capita/annum required to purchase product) the implications on emissions being assigned to subnational regions can vary by several orders of magnitude. The implications of this on conducting national or regional level risk assessments may be significant. Further work is needed to explore the implications of this variability in HPC emissions to enable the HPC industry and/or governments to advance risk-based chemical management policies in emerging markets.
Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions.
This study aimed to predict monthly columnar ozone (O3) in Peninsular Malaysia by using data on the concentration of environmental pollutants. Data (2003-2008) on five atmospheric pollutant gases (CO2, O3, CH4, NO2, and H2O vapor) retrieved from the satellite Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) were employed to develop a model that predicts columnar ozone through multiple linear regression. In the entire period, the pollutants were highly correlated (R = 0.811 for the southwest monsoon, R = 0.803 for the northeast monsoon) with predicted columnar ozone. The results of the validation of columnar ozone with column ozone from SCIAMACHY showed a high correlation coefficient (R = 0.752-0.802), indicating the model's accuracy and efficiency. Statistical analysis was utilized to determine the effects of each atmospheric pollutant on columnar ozone. A model that can retrieve columnar ozone in Peninsular Malaysia was developed to provide air quality information. These results are encouraging and accurate and can be used in early warning of the population to comply with air quality standards.
This study was conducted to determine the composition of surfactants in the sea-surface microlayer (SML) and atmospheric aerosol around the southern region of the Peninsular Malaysia. Surfactants in samples taken from the SML and atmospheric aerosol were determined using a colorimetric method, as either methylene blue active substances (MBAS) or disulphine blue active substances (DBAS). Principal component analysis with multiple linear regressions (PCA-MLR), using the anion and major element composition of the aerosol samples, was used to determine possible sources of surfactants in atmospheric aerosol. The results showed that the concentrations of surfactants in the SML and atmospheric aerosol were dominated by anionic surfactants and that surfactants in aerosol were not directly correlated (p>0.05) with surfactants in the SML. Further PCA-MLR from anion and major element concentrations showed that combustion of fossil fuel and sea spray were the major contributors to surfactants in aerosol in the study area.
The use of antifouling paints to the boats and ships is one among the threats facing coastal resources including coral reefs in recent decades. This study reports the current contamination status of diuron and its behaviour in the coastal waters of Malaysia. The maximum concentration of diuron was 285 ng/L detected at Johor port. All samples from Redang and Bidong coral reef islands were contaminated with diuron. Temporal variation showed relatively high concentrations but no significant difference (P>0.05) during November and January (North-East monsoon) in Klang ports (North, South and West), while higher levels of diuron were detected during April, 2012 (Inter monsoon) in Kemaman, and Johor port. Although no site has shown concentration above maximum permissible concentration (430 ng/L) as restricted by the Dutch Authorities, however, long term exposure studies for environmental relevance levels of diuron around coastal areas should be given a priority in the future.
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.