Displaying publications 1 - 20 of 197 in total

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  1. Abdul-Hadi A, Mansor S, Pradhan B, Tan CK
    Environ Monit Assess, 2013 May;185(5):3977-91.
    PMID: 22930185 DOI: 10.1007/s10661-012-2843-2
    A study was conducted to investigate the influence of Asian monsoon on chlorophyll-a (Chl-a) content in Sabah waters and to identify the related oceanographic conditions that caused phytoplankton blooms at the eastern and western coasts of Sabah, Malaysia. A series of remote sensing measurements including surface Chl-a, sea surface temperature, sea surface height anomaly, wind speed, wind stress curl, and Ekman pumping were analyzed to study the oceanographic conditions that lead to large-scale nutrients enrichment in the surface layer. The results showed that the Chl-a content increased at the northwest coast from December to April due to strong northeasterly wind and coastal upwelling in Kota Kinabalu water. The southwest coast (Labuan water) maintained high concentrations throughout the year due to the effect of Padas River discharge during the rainy season and the changing direction of Baram River plume during the northeast monsoon (NEM). However, with the continuous supply of nutrients from the upwelling area, the high Chl-a batches were maintained at the offshore water off Labuan for a longer time during NEM. On the other side, the northeast coast illustrated a high Chl-a in Sandakan water during NEM, whereas the northern tip off Kudat did not show a pronounced change throughout the year. The southeast coast (Tawau water) was highly influenced by the direction of the surface water transport between the Sulu and Sulawesi Seas and the prevailing surface currents. The study demonstrates the presence of seasonal phytoplankton blooms in Sabah waters which will aid in forecasting the possible biological response and could further assist in marine resource managements.
  2. Abdullah L, Khalid ND
    Environ Monit Assess, 2012 Nov;184(11):6957-65.
    PMID: 22160435 DOI: 10.1007/s10661-011-2472-1
    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.
  3. Abdullah MZ, Saat AB, Hamzah ZB
    Environ Monit Assess, 2012 Jun;184(6):3959-69.
    PMID: 21822578 DOI: 10.1007/s10661-011-2236-y
    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.
  4. Abdullah NA, Asri LN, Husin SM, Shukor AM, Darbis NDA, Ismail K, et al.
    Environ Monit Assess, 2021 Sep 07;193(10):634.
    PMID: 34491451 DOI: 10.1007/s10661-021-09426-y
    We studied the water quality of the riparian firefly sanctuary of Sungai Rembau, or Rembau River, in Negeri Sembilan, Malaysia, from January 2018 to November 2018 to determine the possible influence of the physico-chemical characteristics of the water on the firefly populations living within the sanctuary. We set up a total of five water quality sampling stations and 10 firefly sampling stations along the river. Dissolved oxygen (DO), temperature, pH and electrical conductivity (EC) were measured in situ, while chemical oxygen demand (COD), total suspended solids (TSS), biochemical oxygen demand (BOD) and ammonia-nitrogen (NH3-N) were analysed in the laboratory. Firefly samples were collected using a sweep net at both day and night for 1 min. Sungai Rembau was categorized as Class II on the Malaysian water quality index (WQI), which indicates slight pollution. Except for EC and DO, the water quality parameter values were not significantly different (p > 0.05) between the sampling stations. A total of 529 firefly individuals consisting of Pteroptyx tener (n = 525, 99.24%), P. malaccae (n = 3, 0.57%) and P. asymmetria (n = 1, 0.19%) were collected. There was significant correlation between firefly abundance and BOD (r =  - 0.198, p 
  5. Abdullah P, Nainggolan H
    Environ Monit Assess, 1991 Oct;19(1-3):423-31.
    PMID: 24233958 DOI: 10.1007/BF00401330
    Phenolic chemicals with their very low taste and odour thresholds, high persistence and toxicity, are of growing concern as water pollutants. The compounds are known to exist in raw water as well as in treated water. The level of phenolic priority pollutants in water within the catchment area of the Linggi River Treatment Plant in Negeri Sembilan, Malaysia, which includes the Linggi river basin, was monitored. The 4-aminoantipyrin colourimetric method was used to determine total phenols whereas capillary column gas chromatography was used to determine the individual compounds. The results show that at most sampling stations, particularly those within the Seremban municipality, the level of phenols was found to exceed the recommended Malaysian standard of 2.0 μg/L(-1) for raw water. This is seen as the direct impact of industrial and urbanization of the area and clearly indicates the unhealthy state of the Linggi river. The results also indicate the need to improve the water quality if the river is going to be used as a source of raw water.
  6. Abunama T, Othman F, Younes MK
    Environ Monit Assess, 2018 Sep 20;190(10):597.
    PMID: 30238169 DOI: 10.1007/s10661-018-6966-y
    Landfill leachate is one of the sources of surface water pollution in Selangor State (SS), Malaysia. Leachate volume prediction is essential for sustainable waste management and leachate treatment processes. The accurate estimation of leachate generation rates is often considered a challenge, especially in developing countries, due to the lack of reliable data and high measurement costs. Leachate generation is related to several variable factors, including meteorological data, waste generation rates, and landfill design conditions. Large variations in these factors lead to complicated leachate modeling processes. The aims of this study are to determine the key elements contributing to leachate production and then develop an adaptive neural fuzzy inference system (ANFIS) model to predict leachate generation rates. Accuracy of the final model performance was tested and evaluated using the root mean square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (R). The study results defined dumped waste quantity, rainfall level, and emanated gases as the most significant contributing factors in leachate generation. The best model structure consisted of two triangular fuzzy membership functions and a hybrid training algorithm with eight fuzzy rules. The proposed ANFIS model showed a good performance with an overall correlation coefficient of 0.952.
  7. Aburas MM, Ahamad MSS, Omar NQ
    Environ Monit Assess, 2019 Mar 05;191(4):205.
    PMID: 30834982 DOI: 10.1007/s10661-019-7330-6
    Spatio-temporal land-use change modeling, simulation, and prediction have become one of the critical issues in the last three decades due to uncertainty, structure, flexibility, accuracy, the ability for improvement, and the capability for integration of available models. Therefore, many types of models such as dynamic, statistical, and machine learning (ML) models have been used in the geographic information system (GIS) environment to fulfill the high-performance requirements of land-use modeling. This paper provides a literature review on models for modeling, simulating, and predicting land-use change to determine the best approach that can realistically simulate land-use changes. Therefore, the general characteristics of conventional and ML models for land-use change are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various dynamic, statistical, and ML models are determined according to the analysis and discussion of the characteristics of these models. The results of the review confirm that ML models are the most powerful models for simulating land-use change because they can include all driving forces of land-use change in the simulation process and simulate linear and non-linear phenomena, which dynamic models and statistical models are unable to do. However, ML models also have limitations. For instance, some ML models are complex, the simulation rules cannot be changed, and it is difficult to understand how ML models work in a system. However, this can be solved via the use of programming languages such as Python, which in turn improve the simulation capabilities of the ML models.
  8. Aburas MM, Ho YM, Ramli MF, Ash'aari ZH
    Environ Monit Assess, 2018 Feb 20;190(3):156.
    PMID: 29464400 DOI: 10.1007/s10661-018-6522-9
    The identification of spatio-temporal patterns of the urban growth phenomenon has become one of the most significant challenges in monitoring and assessing current and future trends of the urban growth issue. Therefore, spatio-temporal and quantitative techniques should be used hand in hand for a deeper understanding of various aspects of urban growth. The main purpose of this study is to monitor and assess the significant patterns of urban growth in Seremban using a spatio-temporal built-up area analysis. The concentric circles approach was used to measure the compactness and dispersion of built-up area by employing Shannon's Entropy method. The spatial directions approach was also utilised to measure the sustainability and speed of development, while the gradient approach was used to measure urban dynamics by employing landscape matrices. The overall results confirm that urban growth in Seremban is dispersed, unbalanced and unsustainable with a rapid speed of regional development. The main contribution of using existing methods with other methods is to provide several spatial and statistical dimensions that can help researchers, decision makers and local authorities understand the trend of growth and its patterns in order to take the appropriate decisions for future urban planning. For example, Shannon's Entropy findings indicate a high value of dispersion between the years 1990 and 2000 and from 2010 to 2016 with a growth rate of approximately 94 and 14%, respectively. Therefore, these results can help and support decision makers to implement alternative urban forms such as the compactness form to achieve an urban form that is more suitable and sustainable. The results of this study confirm the importance of using spatio-temporal built-up area and quantitative analysis to protect the sustainability of land use, as well as to improve the urban planning system via the effective monitoring and assessment of urban growth trends and patterns.
  9. Abushammala MF, Basri NE, Elfithri R
    Environ Monit Assess, 2013 Dec;185(12):9967-78.
    PMID: 23797636
    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.
  10. Abushammala MF, Basri NE, Basri H, Kadhum AA, El-Shafie AH
    Environ Monit Assess, 2013 Jun;185(6):4919-32.
    PMID: 23054277 DOI: 10.1007/s10661-012-2913-5
    Methane (CH₄) is one of the most relevant greenhouse gases and it has a global warming potential 25 times greater than that of carbon dioxide (CO₂), risking human health and the environment. Microbial CH₄ oxidation in landfill cover soils may constitute a means of controlling CH₄ emissions. The study was intended to quantify CH₄ and CO₂ emissions rates at the Sungai Sedu open dumping landfill during the dry season, characterize their spatial and temporal variations, and measure the CH₄ oxidation associated with the landfill cover soil using a homemade static flux chamber. Concentrations of the gases were analyzed by a Micro-GC CP-4900. Two methods, kriging values and inverse distance weighting (IDW), were found almost identical. The findings of the proposed method show that the ratio of CH₄ to CO₂ emissions was 25.4 %, indicating higher CO₂ emissions than CH₄ emissions. Also, the average CH₄ oxidation in the landfill cover soil was 52.5 %. The CH₄ and CO₂ emissions did not show fixed-pattern temporal variation based on daytime measurements. Statistically, a negative relationship was found between CH₄ emissions and oxidation (R(2) = 0.46). It can be concluded that the variation in the CH₄ oxidation was mainly attributed to the properties of the landfill cover soil.
  11. Adiana G, Shazili NA, Marinah MA, Bidai J
    Environ Monit Assess, 2014 Jan;186(1):421-31.
    PMID: 23974537 DOI: 10.1007/s10661-013-3387-9
    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.
  12. Affum AO, Osae SD, Nyarko BJ, Afful S, Fianko JR, Akiti TT, et al.
    Environ Monit Assess, 2015 Feb;187(2):1.
    PMID: 25600401 DOI: 10.1007/s10661-014-4167-x
    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.
  13. Afroz R, Hanaki K, Tudin R
    Environ Monit Assess, 2011 Aug;179(1-4):509-19.
    PMID: 21046234 DOI: 10.1007/s10661-010-1753-4
    Information on waste generation, socioeconomic characteristics, and willingness of the households to separate waste was obtained from interviews with 402 respondents in Dhaka city. Ordinary least square regression was used to determine the dominant factors that might influence the waste generation of the households. The results showed that the waste generation of the households in Dhaka city was significantly affected by household size, income, concern about the environment, and willingness to separate the waste. These factors are necessary to effectively improve waste management, growth and performance, as well as to reduce the environmental degradation of the household waste.
  14. Ahmed AA, Pradhan B
    Environ Monit Assess, 2019 Feb 26;191(3):190.
    PMID: 30809746 DOI: 10.1007/s10661-019-7333-3
    This study proposes a neural network (NN) model to predict and simulate the propagation of vehicular traffic noise in a dense residential area at the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia. The proposed model comprises of two main simulation steps: that is, the prediction of vehicular traffic noise using NN and the simulation of the propagation of traffic noise emission using a mathematical model. First, the NN model was developed with the following selected noise predictors: the number of motorbikes, the sum of vehicles, car ratio, heavy vehicle ratio (e.g. truck, lorry and bus), highway density and a light detection and ranging (LiDAR)-derived digital surface model (DSM). Subsequently, NN and its hyperparameters were optimised by a systematic optimisation procedure based on a grid search approach. The noise propagation model was then developed in a geographic information system (GIS) using five variables, namely road geometry, barriers, distance, interaction of air particles and weather parameters. The noise measurement was conducted continuously at 15-min intervals and the data were analysed by taking the minimum, maximum and average values recorded during the day. The measurement was performed four times a day (i.e. morning, afternoon, evening, and midnight) over two days of the week (i.e. Sunday and Monday). An optimal radial basis function NN was used with 17 hidden layers. The learning rate and momentum values were 0.05 and 0.9, respectively. Finally, the accuracy of the proposed method achieved 78.4% with less than 4.02 dB (A) error in noise prediction. Overall, the proposed models were found to be promising tools for traffic noise assessment in dense urban areas.
  15. Ajorlo M, Abdullah RB, Yusoff MK, Halim RA, Hanif AH, Willms WD, et al.
    Environ Monit Assess, 2013 Oct;185(10):8649-58.
    PMID: 23604787 DOI: 10.1007/s10661-013-3201-8
    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.
  16. Al-Abadi AM, Pradhan B, Shahid S
    Environ Monit Assess, 2015 Oct;188(10):549.
    PMID: 27600115 DOI: 10.1007/s10661-016-5564-0
    The objective of this study is to delineate groundwater flowing well zone potential in An-Najif Province of Iraq in a data-driven evidential belief function model developed in a geographical information system (GIS) environment. An inventory map of 68 groundwater flowing wells was prepared through field survey. Seventy percent or 43 wells were used for training the evidential belief functions model and the reset 30 % or 19 wells were used for validation of the model. Seven groundwater conditioning factors mostly derived from RS were used, namely elevation, slope angle, curvature, topographic wetness index, stream power index, lithological units, and distance to the Euphrates River in this study. The relationship between training flowing well locations and the conditioning factors were investigated using evidential belief functions technique in a GIS environment. The integrated belief values were classified into five categories using natural break classification scheme to predict spatial zoning of groundwater flowing well, namely very low (0.17-0.34), low (0.34-0.46), moderate (0.46-0.58), high (0.58-0.80), and very high (0.80-0.99). The results show that very low and low zones cover 72 % (19,282 km(2)) of the study area mostly clustered in the central part, the moderate zone concentrated in the west part covers 13 % (3481 km(2)), and the high and very high zones extended over the northern part cover 15 % (3977 km(2)) of the study area. The vast spatial extension of very low and low zones indicates that groundwater flowing wells potential in the study area is low. The performance of the evidential belief functions spatial model was validated using the receiver operating characteristic curve. A success rate of 0.95 and a prediction rate of 0.94 were estimated from the area under relative operating characteristics curves, which indicate that the developed model has excellent capability to predict groundwater flowing well zones. The produced map of groundwater flowing well zones could be used to identify new wells and manage groundwater storage in a sustainable manner.
  17. Al-Asadi ST, Al-Qaim FF, Al-Saedi HFS, Deyab IF, Kamyab H, Chelliapan S
    Environ Monit Assess, 2023 May 16;195(6):676.
    PMID: 37188926 DOI: 10.1007/s10661-023-11334-2
    Fig leaf, an environmentally friendly byproduct of fruit plants, has been used for the first time to treat of methylene blue dye. The fig leaf-activated carbon (FLAC-3) was prepared successfully and used for the adsorption of methylene blue dye (MB). The adsorbent was characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and the Brunauer-Emmett-Teller (BET). In the present study, initial concentrations, contact time, temperatures, pH solution, FLAC-3 dose, volume solution, and activation agent were investigated. However, the initial concentration of MB was investigated at different concentrations of 20, 40, 80, 120, and 200 mg/L. pH solution was examined at these values: pH3, pH7, pH8, and pH11. Moreover, adsorption temperatures of 20, 30, 40, and 50 °C were considered to investigate how the FLAC-3 works on MB dye removal. The adsorption capacity of FLAC-3 was determined to be 24.75 mg/g for 0.08 g and 41 mg/g for 0.02 g. The adsorption process has followed the Langmuir isotherm model (R2 = 0.9841), where the adsorption created a monolayer covering the surface of the adsorbent. Additionally, it was discovered that the maximum adsorption capacity (Qm) was 41.7 mg/g and the Langmuir affinity constant (KL) was 0.37 L/mg. The FLAC-3, as low-cost adsorbents for methylene blue dye, has shown good cationic dye adsorption performance.
  18. Al-Odaini NA, Zakaria MP, Zali MA, Juahir H, Yaziz MI, Surif S
    Environ Monit Assess, 2012 Nov;184(11):6735-48.
    PMID: 22193630 DOI: 10.1007/s10661-011-2454-3
    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.
  19. Al-Shami SA, Salmah MR, Hassan AA, Azizah MN
    Environ Monit Assess, 2011 Jun;177(1-4):233-44.
    PMID: 20697808 DOI: 10.1007/s10661-010-1630-1
    Morphological mentum deformities which represent sublethal effect of exposure to different types of pollutants were evaluated in Chironomus spp. larvae inhabiting three polluted rivers of Juru River Basin in northwestern peninsular Malaysia. Using mentum deformity incidences, the modified toxic score index (MTSI) was developed based on Lenat's toxic score index (TSI). The suggested MTSI was compared with TSI in terms of its effectiveness to identify different pollutants including heavy metals. The MTSI showed stronger relationship to total deformity incidence expressed as percentage. Additionally, the multivariate RDA model showed higher capability of MTSI to explain the variations in heavy metal contents of the river sediments. The MTSI was recommended in bioassessment of water and sediment quality using the mentum deformities of Chironomus spp. larvae from aquatic ecosystems receiving anthropogenic, agricultural, or industrial discharges.
  20. Alagha JS, Said MA, Mogheir Y
    Environ Monit Assess, 2014 Jan;186(1):35-45.
    PMID: 23974533 DOI: 10.1007/s10661-013-3353-6
    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.
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