The air pollution index (API) is an important figure used for measuring the quality of air in the environment. The API is determined based on the highest average value of individual indices for all the variables which include sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and suspended particulate matter (PM10) at a particular hour. API values that exceed the limit of 100 units indicate an unhealthy status for the exposed environment. This study investigates the risk of occurrences of API values greater than 100 units for eight urban areas in Peninsular Malaysia for the period of January 2004 to December 2014. An extreme value model, known as the generalized Pareto distribution (GPD), has been fitted to the API values found. Based on the fitted model, return period for describing the occurrences of API exceeding 100 in the different cities has been computed as the indicator of risk. The results obtained indicated that most of the urban areas considered have a very small risk of occurrence of the unhealthy events, except for Kuala Lumpur, Malacca, and Klang. However, among these three cities, it is found that Klang has the highest risk. Based on all the results obtained, the air quality standard in urban areas of Peninsular Malaysia falls within healthy limits to human beings.
Centella asiatica is a commonly used medicinal plant in Malaysia. As heavy metal accumulation in medicinal plants which are highly consumed by human is a serious issue, thus the assessment of heavy metals in C. asiatica is important for the safety of consumers. In this study, the heavy metal accumulation in C. asiatica and the potential health risks were investigated. Samples of C. asiatica and surface soils were collected from nine different sites around Peninsular Malaysia. The concentration of six heavy metals namely Cd, Cu, Ni, Fe, Pb and Zn were determined by air-acetylene flame atomic absorption spectrophotometer (AAS). The degree of anthropogenic influence was assessed by calculating the enrichment factor (EF) and index of geoaccumulation (Igeo). The heavy metal uptake into the plant was estimated through the calculation of translocation factor (TF), bioconcentration factor (BCF) and correlation study. Estimated daily intakes (EDI) and target hazard quotients (THQ) were used to determine the potential health risk of consuming C. asiatica. The results showed that the overall surface soil was polluted by Cd, Cu and Pb, while the uptake of Zn and Ni by the plants was high. The value of EDI and THQ showed that the potential of Pb toxicity in C. asiatica was high as well. As heavy metal accumulation was confirmed in C. asiatica, daily consumption of the plant derived from polluted sites in Malaysia was not recommended.
Wetlands are regarded as one of the most important ecosystems on Earth due to various ecosystem services provided by them such as habitats for biodiversity, water purification, sequestration, and flood attenuation. The Al Hawizeh wetland in the Iran-Iraq border was selected as a study area to evaluate the changes. Maximum likelihood classification was used on the remote sensing data acquired during the period of 1985 to 2013. In this paper, five types of land use/land cover (LULC) were identified and mapped and accuracy assessment was performed. The overall accuracy and kappa coefficient for years 1985, 1998, 2002, and 2013 were 93% and 0.9, 92% and 0.89, 91% and 0.9, and 92% and 0.9, respectively. The classified images were examined with post-classification comparison (PCC) algorithm, and the LULC alterations were assessed. The results of the PCC analysis revealed that there is a drastic change in the area and size of the studied region during the period of investigation. The wetland lost ~73% of its surface area from 1985 to 2002. Meanwhile, post-2002, the wetland underwent a restoration, as a result of which, the area increased slightly and experienced an ~29% growth. Moreover, a large change was noticed at the same period in the wetland that altered ~62% into bare soil in 2002. The areal coverage of wetland of 3386 km(2) in 1985 was reduced to 925 km(2) by 2002 and restored to 1906 km(2) by the year 2013. Human activities particularly engineering projects were identified as the main reason behind the wetland degradation and LULC alterations. And, lastly, in this study, some mitigation measures and recommendations regarding the reclamation of the wetland are discussed. Based on these mitigate measures, the discharge to the wetland must be kept according to the water requirement of the wetland. Moreover, some anthropogenic activities have to be stopped in and around the wetland to protect the ecology of the wetland.
Sediment is a great indicator for assessing coastal mercury contamination. This work profiled the magnitude of mercury pollution in the tropical estuaries and coastal sediments of the Strait of Malacca. Mercury was extracted through the ultrasound-assisted mercury extraction method and analyzed using the flow injection mercury system. The mean concentration of mercury in the sediment samples was 61.43 ± 23.25 μg/kg, ranging from 16.55 ± 0.61 to 114.02 ± 1.54 μg/kg. Geoaccumulation index revealed that a total of 13% of sampling sites were moderately enriched with mercury. The northern part of the Strait of Malacca had the highest mean mercury (Hg) concentration (76.36 ± 27.25 μg/kg), followed by the southern (64.59 ± 16.09 μg/kg) and central (39.33 ± 12.91 μg/kg) parts. Sediment mercury concentration in the current study was lower than other regions like Japan, China, Indian, east Mediterranean, and Taiwan. When compared to the Canadian interim marine and freshwater sediment, China's soil interim environmental guidelines, mercury contamination in the Strait of Malacca was found to be below these permissible limits. Sediment organic matter content was found to have significant correlation with sediment mercury concentration. This study could provide the latest benchmark of mercury pollution and prove beneficial to future pollution studies in relation to monitoring works in tropical estuaries and coastal sediments.
This paper describes the concentration of selected heavy metals (Co, Cu, Ni, Pb, and Zn) in the Mamut river sediments and evaluate the degree of contamination of the river polluted by a disused copper mine. Based on the analytical results, copper showed the highest concentration in most of the river samples. A comparison with Interim Canadian Sediment Quality Guidelines (ICSQG) and Germany Sediment Quality Guidelines (GSQG) indicated that the sediment samples in all the sampling stations, except Mamut river control site (M1), exceeded the limit established for Cu, Ni, and Pb. On the contrary, Zn concentrations were reported well below the guidelines limit (ICSQG and GSQG). Mineralogical analysis indicated that the Mamut river sediments were primarily composed of quartz and accessory minerals such as chalcopyrite, pyrite, edenite, kaolinite, mica, and muscovite, reflected by the geological character of the study area. Enrichment factor (EF) and geoaccumulation index (Igeo) were calculated to evaluate the heavy metal pollution in river sediments. Igeo values indicated that all the sites were strongly polluted with the studied metals in most sampling stations, specifically those located along the Mamut main stream. The enrichment factor with value greater than 1.5 suggested that the source of heavy metals was mainly derived from anthropogenic activity such as mining. The degree of metal changes (δfold) revealed that Cu concentration in the river sediments has increased as much as 20 to 38 folds since the preliminary investigation conducted in year 2004.
This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.
This paper estimates Malaysian farmers' willingness to pay (WTP) for a planned adaptation programme for addressing climate issues in the Malaysian agricultural sector. We used the contingent valuation method (CVM) for a monetary valuation of farmers' preferences for a planned adaptation programme by ascertaining the value attached to address climatic issues in the Malaysian agricultural sector. Structured questionnaires were distributed among the sampled farmers. The study found that 74 % of respondents were willing to pay for a planned adaptation programme and that several socioeconomic and motivation factors have greater influence on their WTP. This paper clearly specifies the steps needed for all institutional bodies to better address issues in climate change. The outcomes of this paper will support policy makers to better design an efficient adaptation framework for adapting to the adverse impacts of climate change.
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.
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.
Thermal structure and water quality in a large and shallow lake in Malaysia were studied between January 2012 and June 2013 in order to understand variations in relation to water level fluctuations and in-stream mining activities. Environmental variables, namely temperature, turbidity, dissolved oxygen, pH, electrical conductivity, chlorophyll-A and transparency, were measured using a multi-parameter probe and a Secchi disk. Measurements of environmental variables were performed at 0.1 m intervals from the surface to the bottom of the lake during the dry and wet seasons. High water level and strong solar radiation increased temperature stratification. River discharges during the wet season, and unsustainable sand mining activities led to an increased turbidity exceeding 100 NTU, and reduced transparency, which changed the temperature variation and subsequently altered the water quality pattern.
This paper deals with the solid waste image detection and classification to detect and classify the solid waste bin level. To do so, Hough transform techniques is used for feature extraction to identify the line detection based on image's gradient field. The feedforward neural network (FFNN) model is used to classify the level content of solid waste based on learning concept. Numbers of training have been performed using FFNN to learn and match the targets of the testing images to compute the sum squared error with the performance goal met. The images for each class are used as input samples for classification. Result from the neural network and the rules decision are used to build the receiver operating characteristic (ROC) graph. Decision graph shows the performance of the system waste system based on area under curve (AUC), WS-class reached 0.9875 for excellent result and WS-grade reached 0.8293 for good result. The system has been successfully designated with the motivation of solid waste bin monitoring system that can applied to a wide variety of local municipal authorities system.
Near-shore surface sediment was collected from five stations off Redang Island located on the eastern coast of Peninsular Malaysia. Freeze-dried sediments were Soxhlet extracted and then fractionated using column chromatography into aliphatic and polar fractions. Determination of these fractions was carried out using gas chromatography mass spectrometry. The concentration of total resolved aliphatic hydrocarbons in sediments ranged from 157 to 308 ng/g. The distribution of aliphatic fraction showed the presence of n-alkanes ranging from nC15 to nC33 with a minor odd-to-even predominance exhibiting carbon maximum, depending on station, at nC17, nC26, nC29 or nC31. Calculation of Carbon Preference Index (CPI) for CPI(15-33) gave values ranging from 1.09 to 1.46. n-Alkanol in all sediment exhibits even-to-odd carbon predominance ranging from nC16 to nC28 and maximising at nC22. n-Fatty acids distribution ranged from nC14 to nC24 with a dominant maximum at nC16 and exhibiting high values of short chain fatty acids (≤nC20) to long chain fatty acids (>nC20) ratios. Unsaturated fatty acids, particularly nC16:1 and nC18:1 is also ubiquitous in all samples. Cholesterol is the most abundant compound amongst the sterol group ranging from 42.8 to 62.6% of the total sterols. β-Sitosterol, brassicasterol and stigmasterol, are also present but of relatively lower amount. These observations suggest that the aliphatic lipids and sterols in the study area originate, mainly, from biogenic sources of marine microbial with minor contribution from epiticular waxes of terrestrial plants.
Concentrations of trace metals in the South China Sea (SCS) were determined off the coast of Terengganu during the months of May and November 2007. The concentrations of dissolved and particulate metals were in the range of 0.019-0.194 μg/L and 50-365 μg/g, respectively, for cadmium (Cd), 0.05-0.45 μg/L and 38-3,570 μg/g for chromium (Cr), 0.05-3.54 μg/L and 21-1,947 μg/g for manganese (Mn), and 0.03-0.49 μg/L and 2-56,982 μg/g for lead (Pb). The order of mean log K D found was Cd > Cr > Pb > Mn. The study suggests that the primary sources of these metals are discharges from the rivers which drain into the SCS, in particular the Dungun River, which flows in close proximity to agricultural areas and petrochemical industries. During the northeast monsoon, levels of particulate metals in the bottom water samples near the shore were found to be much higher than during the dry season, the probable result of re-suspension of the metals from the bottom sediments.
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 option of reusing greywater is proving to be increasingly attractive to address the water shortage issue in many arid and semiarid countries. Greywater represents a constant resource, since an approximately constant amount of greywater is generated from kitchen, laundries, bathroom in every household daily, independent of the weather. However, the use of greywater for irrigation in particular for household gardening may pose major hazards that have not been studied thoroughly. In this study, a 1-year monitoring was conducted in four selected households in Perth, Western Australia. The aim of the monitoring works is to investigate the variability in the greywater flow and quality, and to understand its impact in the surrounding environments. Case studies were selected based on different family structure including number, ages of the occupants, and greywater system they used. Samples of greywater effluent (showers, laundries, bathtub, and sinks), leachate, soil, and plants at each case study were collected between October 2008 and December 2009 which covered the high (spring/summer) and low (autumn/winter) production of greywater. Physical and chemical tests were based on the literature and expected components of laundry and bathroom greywater particularly on greywater components likely to have detrimental impacts on soils, plants, and other water bodies. Monitoring results showed the greywater quality values for BOD, TSS, and pH which sometimes fell outside the range as stipulated in the guidelines. The soil analyses results showed that salinity, SAR, and the organic content of the soil increased as a function of time and affected the plant growth. Nutrient leaching or losses from soil irrigated with greywater shows the movement of nutrients and the sole impact from greywater in uncontrolled plots in case studies is difficult to predicted due to the influence of land dynamics and activities. Investigative and research monitoring was used to understand greywater irrigation in households. Greywater quality is very site specific and difficult to predetermine or control except for the use of some recommended household products when using greywater. Investigative and research monitoring was indicated that greywater quality is very site specific and difficult to predetermine or control except for the use of some recommended household products when using greywater.
This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO3, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH3, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH3, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.
Methane (CH₄) emissions and oxidation were measured at the Air Hitam sanitary landfill in Malaysia and were modeled using the Intergovernmental Panel on Climate Change waste model to estimate the CH₄ generation rate constant, k. The emissions were measured at several locations using a fabricated static flux chamber. A combination of gas concentrations in soil profiles and surface CH₄ and carbon dioxide (CO₂) emissions at four monitoring locations were used to estimate the CH₄ oxidation capacity. The temporal variations in CH₄ and CO₂ emissions were also investigated in this study. Geospatial means using point kriging and inverse distance weight (IDW), as well as arithmetic and geometric means, were used to estimate total CH₄ emissions. The point kriging, IDW, and arithmetic means were almost identical and were two times higher than the geometric mean. The CH₄ emission geospatial means estimated using the kriging and IDW methods were 30.81 and 30.49 gm(−2) day(−1), respectively. The total CH₄ emissions from the studied area were 53.8 kg day(−1). The mean of the CH₄ oxidation capacity was 27.5 %. The estimated value of k is 0.138 year(−1). Special consideration must be given to the CH₄ oxidation in the wet tropical climate for enhancing CH₄ emission reduction.
We sampled extensively (29 stations) at the Klang estuarine system over a 3-day scientific expedition. We measured physical and chemical variables (temperature, salinity, dissolved oxygen, total suspended solids, dissolved inorganic nutrients) and related them to the spatial distribution of phototrophic picoplankton (Ppico). Multivariate analysis of variance of the physicochemical variables showed the heterogeneity of the Klang estuarine system where the stations at each transect were significantly different (Rao's F₁₈, ₃₆ = 8.401, p < 0.001). Correlation analyses also showed that variables related to Ppico abundance and growth were mutually exclusive. Distribution of Ppico was best explained by the physical mixing between freshwater and seawater whereas Ppico growth was correlated with temperature.
Information on the pollution level and the influence of hydrologic regime on the stormwater pollutant loading in tropical urban areas are still scarce. More local data are still required because rainfall and runoff generation processes in tropical environment are very different from the temperate regions. This study investigated the extent of urban runoff pollution in residential, commercial, and industrial catchments in the south of Peninsular Malaysia. Stormwater samples and flow rate data were collected from 51 storm events. Samples were analyzed for total suspended solids, 5-day biochemical oxygen demand, chemical oxygen demand, oil and grease (O&G), nitrate nitrogen (NO3-N), nitrite nitrogen, ammonia nitrogen, soluble reactive phosphorus, total phosphorus (TP), and zinc (Zn). It was found that the event mean concentrations (EMCs) of pollutants varied greatly between storm characteristics and land uses. The results revealed that site EMCs for residential catchment were lower than the published data but higher for the commercial and industrial catchments. All rainfall variables were negatively correlated with EMCs of most pollutants except for antecedent dry days (ADD). This study reinforced the earlier findings on the importance of ADD for causing greater EMC values with exceptions for O&G, NO3-N, TP, and Zn. In contrast, the pollutant loadings are influenced primarily by rainfall depth, mean intensity, and max 5-min intensity in all the three catchments. Overall, ADD is an important variable in multiple linear regression models for predicting the EMC values in the tropical urban catchments.
The first objective of this study was to provide data of arsenic (As) levels in Peninsular Malaysia based on soil samples and accumulation of As in Centella asiatica collected from 12 sampling sites in Peninsular Malaysia. The second objective was to assess the accumulation of As in transplanted C. asiatica between control and semi-polluted or polluted sites. Four sites were selected which were UPM (clean site), Balakong (semi-polluted site), Seri Kembangan (semi-polluted site) and Juru (polluted site). The As concentrations of plant and soil samples were determined by Instrumental Neutron Activation Analysis. The As levels ranged from 9.38 to 57.05 μg/g dw in soils, 0.21 to 4.33 μg/g dw in leaves, 0.18 to 1.83 μg/g dw in stems and 1.32-20.76 μg/g dw in roots. All sampling sites had As levels exceeding the CCME guideline (12 μg/g dw) except for Kelantan, P. Pauh, and Senawang with P. Klang having the highest As in soil (57.05 μg/g dw). In C. asiatica, As accumulation was highest in roots followed by leaves and stems. When the As level in soils were higher, the uptake of As in plants would also be increased. After the transplantation of plants to semi-polluted and polluted sites for 3 weeks, all concentration factors were greater than 50 % of the initial As level. The elimination factor was around 39 % when the plants were transplanted back to the clean sites for 3 weeks. The findings of the present study indicated that the leaves, stems and roots of C. asiatica are ideal biomonitors of As contamination. The present data results the most comprehensive data obtained on As levels in Malaysia.