This study aims to determine the status of potentially toxic element concentrations of road dust in a medium-sized city (Rawang, Malaysia). This study adopts source identification via enrichment factor, Pearson correlation analysis, and Fourier spectral analysis to identify sources of potentially toxic element concentrations in road dust in Rawang City, Malaysia. Health risk assessment was conducted to determine potential health risks (carcinogenic and non-carcinogenic risks) among adults and children via multiple pathways (i.e., ingestion, dermal contact, and inhalation). Mean of potentially toxic element concentrations were found in the order of Pb > Zn > Cr(IV) > Cu > Ni > Cd > As > Co. Source identification revealed that Cu, Cd, Pb, Zn, Ni, and Cr(IV) are associated with anthropogenic sources in industrial and highly populated areas in northern and southern Rawang, cement factories in southern Rawang, as well as the rapid development and population growth in northwestern Rawang, which have resulted in high traffic congestion. Cobalt, Fe, and As are related to geological background and lithologies in Rawang. Pathway orders for both carcinogenic and non-carcinogenic risks are ingestion, dermal contact, and inhalation, involving adults and children. Non-carcinogenic health risks in adults were attributed to Cr(IV), Pb, and Cd, whereas Cu, Cd, Cr(IV), Pb, and Zn were found to have non-carcinogenic health risks for children. Cd, Cr(IV), Pb, and As may induce carcinogenic risks in adults and children, and the total lifetime cancer risk values exceeded incremental lifetime.
The Mamut Copper Mine (MCM) located in Sabah (Malaysia) on Borneo Island was the only Cu-Au mine that operated in the country. During its operation (1975-1999), the mine produced 2.47 Mt of concentrate containing approximately 600,000 t of Cu, 45 t of Au and 294 t of Ag, and generated about 250 Mt of overburden and waste rocks and over 150 Mt of tailings, which were deposited at the 397 ha Lohan tailings storage facility, 15.8 km from the mine and 980 m lower in altitude. The MCM site presents challenges for environmental rehabilitation due to the presence of large volumes of sulphidic minerals wastes, the very high rainfall and the large volume of polluted mine pit water. This indicates that rehabilitation and treatment is costly, as for example, exceedingly large quantities of lime are needed for neutralisation of the acidic mine pit discharge. The MCM site has several unusual geochemical features on account of the concomitant occurrence of acid-forming sulphide porphyry rocks and alkaline serpentinite minerals, and unique biological features because of the very high plant diversity in its immediate surroundings. The site hence provides a valuable opportunity for researching natural acid neutralisation processes and mine rehabilitation in tropical areas. Today, the MCM site is surrounded by protected nature reserves (Kinabalu Park, a World Heritage Site, and Bukit Hampuan, a Class I Forest Reserve), and the environmental legacy prevents de-gazetting and inclusion in these protected area in the foreseeable future. This article presents a preliminary geochemical investigation of waste rocks, sediments, secondary precipitates, surface water chemistry and foliar elemental uptake in ferns, and discusses these results in light of their environmental significance for rehabilitation.
In order to expound on the present situation and potential risk of road dust heavy metals in major cities, a total of 114 literatures mainly over the past two decades, involving more than 5000 sampling sites in 61 cities of 21 countries, were screened through the collection and analysis of research papers. The concentration, sources, distribution, health risk, sample collection, and analytical methods of heavy metal research on road dust in cities around the world are summarized. The results show that Cd, Zn, and Cu in many urban road dusts in the world are higher than the grade II of the Chinese maximum allowable concentration of potentially toxic elements in the soil. Geo-accumulation index values show that Pb > Cd > Zn > Cu had the highest contamination levels. Hazard index assessment indicates Pb and Cr had the highest potential health risk, especially for children in which ingestion was found as the main exposure pathway. Moreover, through comparative analysis, it is found that some pollutants are higher in developed and industrialized cities and transport (53%) followed by industrial emissions (35%) provide the major contributions to the sources of heavy metals.
Multiple interactions of geogenic and anthropogenic activities can trigger groundwater pollution in the tropical savanna watershed. These interactions and resultant contamination have been studied using applied geochemical modeling, conventional hydrochemical plots, and multivariate geochemometric methods, and the results are presented in this paper. The high alkalinity values recorded for the studied groundwater samples might emanate from the leaching of carbonate soil derived from limestone coupled with low rainfall and high temperature in the area. The principal component analysis (PCA) unveils three components with an eigenvalue > 1 and a total dataset variance of 67.37%; this implies that the temporary hardness of the groundwater and water-rock interaction with evaporite minerals (gypsum, halite, calcite, and trona) is the dominant factor affecting groundwater geochemistry. Likewise, the PCA revealed anthropogenic contamination by discharging [Formula: see text] [Formula: see text][Formula: see text] and [Formula: see text] from agricultural activities and probable sewage leakages. Hierarchical cluster analysis (HCA) also revealed three clusters; cluster I reflects the dissolution of gypsum and halite with a high elevated load of [Formula: see text] released by anthropogenic activities. However, cluster II exhibited high [Formula: see text] and [Formula: see text] loading in the groundwater from weathering of bicarbonate and sylvite minerals. Sulfate ([Formula: see text]) dominated cluster III mineralogy resulting from weathering of anhydrite. The three clusters in the Maiganga watershed indicated anhydrite, gypsum, and halite undersaturation. These results suggest that combined anthropogenic and natural processes in the study area are linked with saturation indexes that regulate the modification of groundwater quality.
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.
Organic pollution continues to be an important worldwide obstacle for tackling health and environmental concerns that require ongoing and prompt response. To identify the LAB content levels as molecular indicators for sewage pollution, surface sediments had obtained from the South region of Malaysia. The origins of the LABs were identified using gas chromatography-mass spectrometry (GC-MS). ANOVA and a Pearson correlation coefficient at p
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.
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.
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.
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.
Surface water is one of the essential resources for supporting sustainable development. The suitability of such water for a given use depends both on the available quantity and tolerable quality. Temporary status for a surface water quality has been identified extensively. Still the suitability of the water for different purposes needs to be verified. This study proposes a water quality evaluation system to assess the aptitude of the Selangor River water for aquatic biota, drinking water production, leisure and aquatic sport, irrigation use, livestock watering, and aquaculture use. Aptitude of the water has been classified in many parts of the river segment as unsuitable for aquatic biota, drinking water production, leisure and aquatic sport as well as aquaculture use. The water quality aptitude classes of the stream water for nine locations along the river are evaluated to contribute to decision support system. The suitability of the water for five different uses and its aquatic ecosystem are verified.
In this paper, an attempt has been made to assess, prognosis and observe dynamism of soil erosion by universal soil loss equation (USLE) method at Penang Island, Malaysia. Multi-source (map-, space- and ground-based) datasets were used to obtain both static and dynamic factors of USLE, and an integrated analysis was carried out in raster format of GIS. A landslide location map was generated on the basis of image elements interpretation from aerial photos, satellite data and field observations and was used to validate soil erosion intensity in the study area. Further, a statistical-based frequency ratio analysis was carried out in the study area for correlation purposes. The results of the statistical correlation showed a satisfactory agreement between the prepared USLE-based soil erosion map and landslide events/locations, and are directly proportional to each other. Prognosis analysis on soil erosion helps the user agencies/decision makers to design proper conservation planning program to reduce soil erosion. Temporal statistics on soil erosion in these dynamic and rapid developments in Penang Island indicate the co-existence and balance of ecosystem.
The growing interest in the environmental occurrence of veterinary and human pharmaceuticals is essentially due to their possible health implications to humans and ecosystem. This study assesses the occurrence of human pharmaceuticals in a Malaysian tropical aquatic environment taking a chemometric approach using cluster analysis, discriminant analysis and principal component analysis. Water samples were collected from seven sampling stations along the heavily populated Langat River basin on the west coast of peninsular Malaysia and its main tributaries. Water samples were extracted using solid-phase extraction and analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for 18 pharmaceuticals and one metabolite, which cover a range of six therapeutic classes widely consumed in Malaysia. Cluster analysis was applied to group both pharmaceutical pollutants and sampling stations. Cluster analysis successfully clustered sampling stations and pollutants into three major clusters. Discriminant analysis was applied to identify those pollutants which had a significant impact in the definition of clusters. Finally, principal component analysis using a three-component model determined the constitution and data variance explained by each of the three main principal components.
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.
The concentrations of arsenic, cadmium, chromium, copper, mercury, nickel, lead and zinc in surface sediments collected from the east coast of peninsular Malaysia, along the South China Sea, were measured by two methods instrumental neutron activation analysis and inductively coupled plasma mass spectroscopy. The obtained results were use to determine the areal distribution of the metals of in the east coast of peninsular Malaysia and potential sources of these metals to this environment. The geochemical data propose that most of the metals found in the east coast of peninsular Malaysia constitute a redistribution of territorial materials within the ecosystem. Then, the metal concentrations can be considered to be present at natural background levels in surface sediments.
Biomonitoring of multi-element atmospheric deposition using terrestrial moss is a well-established technique in Europe. Although the technique is widely known, there were very limited records of using this technique to study atmospheric air pollution in Malaysia. In this present study, the deposition of 11 trace metals surrounding the main petroleum refinery plant in Kerteh Terengganu (eastern part of peninsular Malaysia) has been evaluated using two local moss species, namely Hypnum plumaeforme and Taxithelium instratum as bioindicators. The study was also done by means of observing whether these metals are attributed to work related to oil exploration in this area. The moss samples have been collected at 30 sampling stations in the vicinity of the petrochemical industrial area covering up to 15 km to the south, north, and west in radius. The contents of heavy metal in moss samples were analyzed by energy dispersive x-ray fluorescence technique. Distribution of heavy metal content in all mosses is portrayed using Surfer software. Areas of the highest level of contaminations are highlighted. The results obtained using the principal components analysis revealed that the elements can be grouped into three different components that indirectly reflected three different sources namely anthropogenic factor, vegetation factor, and natural sources (soil dust or substrate) factor. Heavy metals deposited mostly in the distance after 9 km onward to the western part (the average direction of wind blow). V, Cr, Cu, and Hg are believed to have originated from local petrochemical-based industries operated around petroleum industrial area.
Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.
Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.
Strombus canarium Linnaeus, 1758 is an important gastropod species within the study area and was traditionally collected for food by the locals. The objective of the present study is to assess the incidence of imposex and its severity in this species. Adult conchs were sampled during their main reproductive period, from October 2005 to January 2006, at Sungai Pulai estuary, Johor Straits, Malaysia. A total of 32.81% of adult females showed imposex characteristics, with varying degrees of severity though. The relative penis size (RPS) index ranged from 1.74 to 33.29 (mean = 13.40 ± 2.27, n = 21), while the relative penis length (RPL) index ranged from 6.28 to 55.19 (mean = 25.83 ± 3.33, n = 21). The use of vas deferens sequence (VDS) index was however cannot be applied as the presence of egg groove obscured any vas deferens development in affected females. Sequence of imposex (male penis) development in female conch, from merely a small stump to an advance male penis homologous was therefore carefully analyzed and described, and an alternative imposex classification scheme was proposed. S. canarium can be a good indicator for monitoring of organotin pollution within the study area. However, more studies are needed in order to further develop and test its validity and application, such as its correlation with levels of pollutant within the tissues and the environment, as well as its application on other Strombus species.
The integration of communication technologies such as radio frequency identification (RFID), global positioning system (GPS), general packet radio system (GPRS), and geographic information system (GIS) with a camera are constructed for solid waste monitoring system. The aim is to improve the way of responding to customer's inquiry and emergency cases and estimate the solid waste amount without any involvement of the truck driver. The proposed system consists of RFID tag mounted on the bin, RFID reader as in truck, GPRS/GSM as web server, and GIS as map server, database server, and control server. The tracking devices mounted in the trucks collect location information in real time via the GPS. This information is transferred continuously through GPRS to a central database. The users are able to view the current location of each truck in the collection stage via a web-based application and thereby manage the fleet. The trucks positions and trash bin information are displayed on a digital map, which is made available by a map server. Thus, the solid waste of the bin and the truck are being monitored using the developed system.