A study of the water quality changes of Chini Lake was conducted for 12 months, which began in May 2004 and ended in April 2005. Fifteen sampling stations were selected representing the open water body in the lake. A total of 14 water quality parameters were measured and Malaysian Department of Environment Water Quality Index (DOE-WQI) was calculated and classified according to the Interim National Water Quality Standard, Malaysia (INWQS). The physical and chemical variables were temperature, dissolved oxygen (DO), conductivity, pH, total dissolved solid (TDS), turbidity, chlorophyll-a, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solid (TSS), ammonia-N, nitrate, phosphate and sulphate. Results show that base on Malaysian WQI, the water in Chini Lake is classified as class II, which is suitable for recreational activities and allows body contact. With respect to the Interim National Water Quality Standard (INWQS), temperature was within the normal range, conductivity, TSS, nitrate, sulphate and TDS are categorized under class I. Parameters for DO, pH, turbidity, BOD, COD and ammonia-N are categorized under class II. Comparison with eutrophic status indicates that chlorophyll-a concentration in the lake was in mesotrophic condition. In general water quality in Chini Lake varied temporally and spatially, and the most affected water quality parameters were TSS, turbidity, chlorophyll-a, sulphate, DO, ammonia-N, pH and conductivity.
Matched MeSH terms: Environmental Monitoring/statistics & numerical data
Cockles (Anadara granosa) sample obtained from two rivers in the Penang State of Malaysia were analyzed for the content of arsenic (As) and heavy metals (Cr, Cd, Zn, Cu, Pb, and Hg) using a graphite flame atomic absorption spectrometer (GF-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometer (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data. MANOVA showed a strong significant difference between the two rivers in term of As and heavy metals contents in cockles. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used only two parameters (Zn and Cd) affording more than 72% correct assignations. Results indicated that the two rivers were different in terms of As and heavy metal contents in cockle, and the major difference was due to the contribution of Zn and Cd. A positive correlation was found between discriminate functions (DF) and Zn, Cd and Cr, whereas negative correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metals and arsenic content. Taking into account of these results, it can be suggested that a continuous monitoring of As and heavy metals in cockles be performed in these two rivers.
Concentrations of trace elements (V, Cr, Mn, Co, Cu, Zn, Ga, Se, Rb, Sr, Mo, Ag, Cd, Sn, Sb, Cs, Ba, Hg, Tl, Pb and Bi) were determined in muscle and liver of 12 species of marine fish collected from coastal areas in Malaysia. Levels of V, Cr, Mn, Co, Cu, Zn, Ga, Sr, Mo, Ag, Cd, Sn, Ba and Pb in liver were higher than those in muscle, whereas Rb and Cs concentrations showed the opposite trend. Positive correlations between concentrations in liver and muscle were observed for all the trace elements except Cu and Sn. Copper, Zn, Se, Ag, Cd, Cs and Hg concentrations in bigeye scads from the east coast of the Peninsular Malaysia were higher than those from the west, whereas V showed the opposite trend. The high concentration of V in the west coast might indicate oil contamination in the Strait of Malacca. To evaluate the health risk to Malaysian population through consumption of fish, intake rates of trace elements were estimated on the basis of the concentrations of trace elements in muscle of fish and daily fish consumption. Some specimens of the marine fish had Hg levels higher than the guideline value by US Environmental Protection Agency (EPA), indicating that consumption of these fish at the present rate may be hazardous to Malaysian people. To our knowledge, this is the first study on multielemental accumulation in marine fish from the Malaysian coast.
Conversion of tropical peat swamp forest to drainage-based agriculture alters greenhouse gas (GHG) production, but the magnitude of these changes remains highly uncertain. Current emissions factors for oil palm grown on drained peat do not account for temporal variation over the plantation cycle and only consider CO2 emissions. Here, we present direct measurements of GHGs emitted during the conversion from peat swamp forest to oil palm plantation, accounting for CH4 and N2O as well as CO2. Our results demonstrate that emissions factors for converted peat swamp forest is in the range 70-117 t CO2 eq ha-1 yr-1 (95% confidence interval, CI), with CO2 and N2O responsible for ca. 60 and ca. 40% of this value, respectively. These GHG emissions suggest that conversion of Southeast Asian peat swamp forest is contributing between 16.6 and 27.9% (95% CI) of combined total national GHG emissions from Malaysia and Indonesia or 0.44 and 0.74% (95% CI) of annual global emissions.
The objective of the study is to examine the impact of environmental indicators and air pollution on "health" and "wealth" for the low-income countries. The study used a number of promising variables including arable land, fossil fuel energy consumption, population density, and carbon dioxide emissions that simultaneously affect the health (i.e., health expenditures per capita) and wealth (i.e., GDP per capita) of the low-income countries. The general representation for low-income countries has shown by aggregate data that consist of 39 observations from the period of 1975-2013. The study decomposes the data set from different econometric tests for managing robust inferences. The study uses temporal forecasting for the health and wealth model by a vector error correction model (VECM) and an innovation accounting technique. The results show that environment and air pollution is the menace for low-income countries' health and wealth. Among environmental indicators, arable land has the largest variance to affect health and wealth for the next 10-year period, while air pollution exerts the least contribution to change health and wealth of low-income countries. These results indicate the prevalence of war situation, where environment and air pollution become visible like "gun" and "bullet" for low-income countries. There are required sound and effective macroeconomic policies to combat with the environmental evils that affect the health and wealth of the low-income countries.
Matched MeSH terms: Environmental Monitoring/statistics & numerical data
In this study, geophysics, geochemistry, and geostatistical techniques were integrated to assess seawater intrusion in Kapas Island due to its geological complexity and multiple contamination sources. Five resistivity profiles were measured using an electric resistivity technique. The results reveal very low resistivity <1 Ωm, suggesting either marine clay deposit or seawater intrusion or both along the majority of the resistivity images. As a result, geochemistry was further employed to verify the resistivity evidence. The Chadha and Stiff diagrams classify the island groundwater into Ca-HCO3, Ca-Na-HCO3, Na-HCO3, and Na-Cl water types, with Ca-HCO3 as the dominant. The Mg(2+)/Mg(2+)+Ca(2+), HCO3 (-)/anion, Cl(-)/HCO3 (-), Na(+)/Cl(-), and SO4 (2-)/Cl(-) ratios show that some sampling sites are affected by seawater intrusion; these sampling sites fall within the same areas that show low-resistivity values. The resulting ratios and resistivity values were then used in the geographical information system (GIS) environment to create the geostatistical map of individual indicators. These maps were then overlaid to create the final map showing seawater-affected areas. The final map successfully delineates the area that is actually undergoing seawater intrusion. The proposed technique is not area specific, and hence, it can work in any place with similar completed characteristics or under the influence of multiple contaminants so as to distinguish the area that is truly affected by any targeted pollutants from the rest. This information would provide managers and policy makers with the knowledge of the current situation and will serve as a guide and standard in water research for sustainable management plan.
Matched MeSH terms: Environmental Monitoring/statistics & numerical data
In this work, the DRASTIC and GALDIT models were employed to determine the groundwater vulnerability to contamination from anthropogenic activities and seawater intrusion in Kapas Island. In addition, the work also utilized sensitivity analysis to evaluate the influence of each individual parameter used in developing the final models. Based on these effects and variation indices of the said parameters, new effective weights were determined and were used to create modified DRASTIC and GALDIT models. The final DRASTIC model classified the island into five vulnerability classes: no risk (110-140), low (140-160), moderate (160-180), high (180-200), and very high (>200), covering 4, 26, 59, 4, and 7 % of the island, respectively. Likewise, for seawater intrusion, the modified GALDIT model delineates the island into four vulnerability classes: very low (<90), low (90-110), moderate (110-130), and high (>130) covering 39, 33, 18, and 9 % of the island, respectively. Both models show that the areas that are likely to be affected by anthropogenic pollution and seawater intrusion are within the alluvial deposit at the western part of the island. Pearson correlation was used to verify the reliability of the two models in predicting their respective contaminants. The correlation matrix showed a good relationship between DRASTIC model and nitrate (r = 0.58). In a similar development, the correlation also reveals a very strong negative relationship between GALDIT model and seawater contaminant indicator (resistivity Ωm) values (r = -0.86) suggesting that the model predicts more than 86 % of seawater intrusion. In order to facilitate management strategy, suitable areas for artificial recharge were identified through modeling. The result suggested some areas within the alluvial deposit at the western part of the island as suitable for artificial recharge. This work can serve as a guide for a full vulnerability assessment to anthropogenic pollution and seawater intrusion in small islands and will help policy maker and manager with understanding needed to ensure sustainability of the island's aquifer.
Matched MeSH terms: Environmental Monitoring/statistics & numerical data
This study determined the source contribution of PM2.5 (particulate matter <2.5 μm) in air at three locations on the Malaysian Peninsula. PM2.5 samples were collected using a high volume sampler equipped with quartz filters. Ion chromatography was used to determine the ionic composition of the samples and inductively coupled plasma mass spectrometry was used to determine the concentrations of heavy metals. Principal component analysis with multilinear regressions were used to identify the possible sources of PM2.5. The range of PM2.5 was between 10 ± 3 and 30 ± 7 µg m(-3). Sulfate (SO4 (2-)) was the major ionic compound detected and zinc was found to dominate the heavy metals. Source apportionment analysis revealed that motor vehicle and soil dust dominated the composition of PM2.5 in the urban area. Domestic waste combustion dominated in the suburban area, while biomass burning dominated in the rural area.
A variety of trace metals were measured in the egg contents of three clutches of Chelonia mydas collected from Kuala Terengganu state in Peninsular Malaysia. We quantified Mn, Cu, Zn, Se (essential trace metals) and As (anthropogenic pollutant) at several developmental stages obtained by incubating eggs at two different temperatures (27 °C and 31 °C). The incubation temperatures were chosen because they produce predominantly male or predominantly female hatchlings, respectively. The eggs were removed from the sand and washed before being placed in incubators, to ensure that the only possible source of the detected metals was maternal transfer. Other metals: Mo, Co, Ni, Cd, Sn, Sb, Hg, Tl and Pb (all non-essential metals) were detected at concentrations below the lower limit of quantitation (LLOQ). Trace metal concentrations, particularly [Zn], increased during development, other metals (Cu, As, Se and Cr) accumulated to a lesser degree than zinc but no significant differences were observed between the incubation temperatures at any stage of incubation. To date, only a few studies on trace metals in turtle embryos and hatchlings have been reported; this study will provide basic knowledge on the accumulation of trace metals during development at two different incubation temperatures.
Matched MeSH terms: Environmental Monitoring/statistics & numerical data
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.
17α-ethynylestradiol (EE2) is a synthetic hormone, which is a derivative of the natural hormone, estradiol (E2). EE2 is an orally bio-active estrogen, and is one of the most commonly used medications for humans as well as livestock and aquaculture activity. EE2 has become a widespread problem in the environment due to its high resistance to the process of degradation and its tendency to (i) absorb organic matter, (ii) accumulate in sediment and (iii) concentrate in biota. Numerous studies have reported the ability of EE2 to alter sex determination, delay sexual maturity, and decrease the secondary sexual characteristics of exposed organisms even at a low concentration (ng/L) by mimicking its natural analogue, 17β-estradiol (E2). Thus, the aim of this review is to provide an overview of the science regarding EE2, the concentration levels in the environment (water, sediment and biota) and summarize the effects of this compound on exposed biota at various concentrations, stage life, sex, and species. The challenges in respect of EE2 include the extension of the limited database on the EE2 pollution profile in the environment, its fate and transport mechanism, as well as the exposure level of EE2 for better prediction and definition revision of EE2 toxicity end points, notably for the purpose of environmental risk assessment.
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.