Establishment of physicochemical measurements of water polluting substances via flow perturbation gas chromatography

Spillage of water polluting substances via industrial disaster may cause pollution to our environment. Thus, reversed-flow gas chromatography (RF-GC) technique, which applies flow perturbation gas chromatography, was used to investigate the evaporation and estimate the diffusion coefficients of liquid pollutants. Selected alcohols (99.9% purity) and its mixtures were used as samples. The evaporating liquids (stationary phase) were carried out by carrier gas-nitrogen, 99.9% purity (mobile phase) to the detector. The findings of this work showed the physicochemical measurements may vary depending on the composition of water and alcohol mixtures, temperature of the mixtures, as well as the types of alcohol used. This study implies that there is a variation in the results based on the concentration, types and temperature of the liquids that may contribute in the references for future research in the area of environmental pollution analysis

Spatial assessment of Langat river water quality using chemometrics

The present study deals with the assessment of Langat River water quality with some chemometrics approaches such as cluster and discriminant analysis coupled with an artificial neural network (ANN). The data used in this study were collected from seven monitoring stations under the river water quality monitoring program by the Department of Environment (DOE) from 1995 to 2002. Twenty three physico-chemical parameters were involved in this analysis. Cluster analysis successfully clustered the Langat River into three major clusters, namely high, moderate and less pollution regions. Discriminant analysis identified seven of the most significant parameters which contribute to the high variation of Langat River water quality, namely dissolved oxygen, biological oxygen demand, pH, ammoniacal nitrogen, chlorine, E. coli, and coliform. Discriminant analysis also plays an important role as an input selection parameter for an ANN of spatial prediction (pollution regions). The ANN showed better prediction performance in discriminating the regional area with an excellent percentage of correct classification compared to discriminant analysis. Multivariate analysis, coupled with ANN, is proposed, which could help in decision making and problem solving in the local environment

Using chemometrics in assessing langat river water quality and designing a cost-effective water sampling strategy.

Seasonally dependent water quality data of Langat River was investigated during the period of December 2001 – May 2002, when twenty-four monthly samples were collected from four different plots containing up to 17 stations. For each sample, sixteen physico-chemical parameters were measured in situ. Multivariate treatments using cluster analysis, principal component analysis and factorial design were employed, in which the data were characterised as a function of season and sampling site, thus enabling significant discriminating factors to be discovered. Cluster analysis study based on data which were characterised as a function of sampling sites showed that at a chord distance of 75.25 two clusters are formed. Cluster I consists of 6 samples while Cluster II consists of 18 samples. The sampling plots from which these samples were taken are readily identified and the two clusters are discussed in terms of data variability. In addition, varimax rotations of principal components, which result in varimax factors, were used in interpreting the sources of pollution within the area. The work demonstrates the importance of historical data, if they are available, in planning sampling strategies to achieve desired research objectives, as well as to highlight the possibility of determining the optimum number of sampling stations which in turn would reduce cost and time of sampling

Hydrological trend analysis due to land use changes at Langat River Basin.

This present study was carried out to detect the spatial and temporal change (1974-2000) in hydrological trend and its relationship to land use changes in the Langat River Basin. To obtain a clear picture of the hydrological parameters during the study period, rainfall data were analyzed. With the help of GIS and non-parametric Mann-Kendall (MK) statistical test the significance of trend in hydrological and land use time series was measured. Trend analyses indicated that a relationship between hydrological parameters namely discharge and direct runoff and land use types namely agriculture, forest, urban, waterbody and others was evident. This analysis indicates that rainfall intensity does not play an important role as a pollutant contributor via the rainfall runoff process nor does it directly influence the peak discharges. Land use shows tremendous changes in trend surrounding Dengkil station compared a little changes surrounding Lui station. Mann-Kendall test of trend shows an increasing trend (p-value<0.01) of annual maximum-minimum ratio for Dengkil station, while no significant trend is observed for Lui station. There is evidence that regional variability in discharge behaviour is strongly related to land use or land cover changes along the river basin

Trends in sediment yield of the Kemaman River Estuary, Terengganu- Disember 2002-February 2004

The Kemaman River drains the southern half of Kemaman Chendor coastal system and is the primary source of sediment to Kemaman estuary. In this paper, it is demonstrated that anthropogenic activity within a watershed, such as agriculture and urbanization were affect the sediment yield from the watershed. Over 26 month, the delivery of suspended sediment from the Kemaman River to The Kemaman Estuary has increase by about 25 percent. Using flow and suspended sediment discharge data provided by the Drainage and Irrigation Department (DID) revealed possible increasing trend on suspended sediment discharge and concentration. Temporal analysis indicates that the trend of sediment yield was increase during the monsoon season resulting over sediment supply closed to river mouth. This scenario has implication for nearshore fisherman's navigation due to seabed deposition. In a broader context, this study underscores the need to address the anthropogenic impacts and flood monsoon on sediment yield in the Kemaman-Chendor estuary system

Spatial variations of drinking water quality monitoring in water treatment plant using environmetric techniques

This research investigates the relationship between the physicochemical levels and the drinking water quality in Kuala Kubu Bharu, Selangor, Malaysia based on three different classes of drinking water. The environmetric techniques such as the discriminant analysis (DA), the principal component analysis (PCA) and the factor analysis (FA) were applied to analyze the spatial variation of the most significant physicochemical parameters of the drinking water quality and to determine the source of pollution. Seven physicochemical variables were analyzed. The forward and backward stepwise DA managed to discriminate six and two variables, respectively from the original seven variables. PCA and FA (varimax functionality) were to identify the origin of each water quality variable based on the three different drinking water classes. This study shows that environmetric method is the ideal way into provide meaningful information on the spatial variability of sophisticated drinking water quality data

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data

The prediction of suspended solids of river in forested catchment using artificial neural network

This study presents an artificial neural network (ANN) model that is able to predict suspended solids concentrations in forested catchment namely Berring River, Kelantan, Malaysia.The network was trained using data collected during a period of 13 days in April 2001. The sampling location was established in the middle section of the river for collecting water samples. The study was carried out for a duration of two weeks in April 2001. The water sample was collected at 60% of the total depth from the river bed for every two hours starting from 6:00 am to 12:00 midnight for the whole duration of the study period. In this study five parameters were selected as input parameter for the network which are turbidity, flow velocity, depth, width, and weather condition of during the sampling period, while suspended solids as desire output. The data fed to the neural network were divided into two set: a training set and testing set. 116 of the data were used in training set and 24 remained as testing set. A network of the model was detected automatically by the network to give good predictions for both training and testing data set. A partitioning method of the connection weights of the network was used to study the relative percentage contribution of each of the input variables. It was found that turbidity and river width gives 73.03% and 24.73% each. The performance of the neural network model was measured by computing the correlation coefficient which gives the value of 0.93. It’s shown that the neural network gives superior predictions. Based on the results of this study, ANN modeling appears to be a promising technique for the prediction of suspended solids. Dynamic Metadata(s

Organophosphate exposure: a preliminary assessment on the use of pesticide intensity score to evaluate exposure among fruit growers

This study examines the influence of work hours, personal protective equipment use, and pesticide ingestion on the amount of urinary metabolites among fruit growers applying organophosphate pesticide. Thirty nine urine samples were collected from seven applicators before and after organophosphate applications. All dimethyl metabolites were present in day 1 morning urine samples for all workers. The arithmetic means for day 1 ranged from 21.5-94.17 µg/L DMP, 6.25-81.25 µg/L DMTP, and <LOQ-153.17 µg/L DMDTP. Day 2 urine samples had the highest amount of metabolites. The arithmetic means ranged from 25.8-558 µg/L DMP, 15.75-398 µg/L DMTP, 21.5-568.57 µg/L DMDTP, and <LOQ-17.67 µg/L DEP. The arithmetic means for day 4 ranges from 19.2-182 µg/L DMP, 13.33-138 µg/L DMTP, 22.75-157.83 µg/L DMDTP, and <LOQ-26 µg/L DEP. From the questionnaire, the exposure algorithm based on duration of hours worked, PPE use and pesticide ingestion showed poor relationship with urine concentration (r=0.1847). The linear relationship is not established due to variability within and between applicators