The behavioral responses of guppy Poecilia reticulata (Poeciliidae) and prawn Macrobrachium lanchesteri (Palaemonidae) individuals exposed to acid mine drainage (AMD) were monitored online in the laboratory with a Multispecies Freshwater Biomonitor™ (MFB). These responses were compared to those to reference water acidified to the respective pH values (ACID). Test animals in the juvenile stage were used for both species and were exposed to AMD and ACID for 24 hours. The stress behaviors of both test animals consisted mainly of decreased activity in AMD and increased activity in ACID, indicating that the metals in the AMD played a role as a stress factor in addition to pH. The locomotor activity levels of guppies and prawns for the ACID treatment were higher than the locomotor activity levels for the AMD treatment with increasing pH value. For guppies, significant differences were observed when specimens were exposed to AMD and ACID at pH 5.0 and 6.0; the percentage activities were only 16% and 12%, respectively, for AMD treatment, whereas for ACID treatment, the percentage activities were 35% and 40%, respectively, similar to the value of 36% for the controls. Similar trends were also observed for prawns, for which the percentage activities were only 6% and 4%, respectively, for AMD treatment, whereas for ACID treatment, the percentage activities were 31% and 38%, respectively, compared to 44% in the controls. This study showed that both species are suitable for use as indicators for ecotoxicity testing with the MFB.
Rivers play a significant role in providing water resources for human and ecosystem survival and health. Hence, river water quality is an important parameter that must be preserved and monitored. As the state of Selangor and the city of Kuala Lumpur, Malaysia, are undergoing tremendous development, the river is subjected to pollution from point and non-point sources. The water quality of the Klang River basin, one of the most densely populated areas within the region, is significantly degraded due to human activities as well as urbanization. Evaluation of the overall river water quality status is normally represented by a water quality index (WQI), which consists of six parameters, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen and pH. The objectives of this study are to assess the water quality status for this tropical, urban river and to establish the WQI trend. Using monthly WQI data from 1997 to 2007, time series were plotted and trend analysis was performed by employing the first-order autocorrelated trend model on the moving average values for every station. The initial and final values of either the moving average or the trend model were used as the estimates of the initial and final WQI at the stations. It was found that Klang River water quality has shown some improvement between 1997 and 2007. Water quality remains good in the upper stream area, which provides vital water sources for water treatment plants in the Klang valley. Meanwhile, the water quality has also improved in other stations. Results of the current study suggest that the present policy on managing river quality in the Klang River has produced encouraging results; the policy should, however, be further improved alongside more vigorous monitoring of pollution discharge from various point sources such as industrial wastewater, municipal sewers, wet markets, sand mining and landfills, as well as non-point sources such as agricultural or urban runoff and commercial activity.
For decades Malaysia was the world's largest producer of Sn, but now the vast open cast mining operations have left a legacy of some 100,000 ha of what is effectively wasteland, covered with a mosaic of tailings and lagoons. Few plants naturally recolonise these areas. The demand for such land for both urban expansion and agricultural use has presented an urgent need for better characterisation. This study reports on the formation of artificial soils from alluvial Sn mining waste with a focus on the effects of experimental treatments on soil chemistry. Soil organic matter, clay, and pH were manipulated in a controlled environment. Adding both clay tailings and peat enhanced the cation exchange capacity of sand tailings but also reduced the pH. The addition of peat reduced the extractable levels of some elements but increased the availability of Ca and Mg, thus proving beneficial. The use of clay tailings increased the levels of macro and micronutrients but also released Al, As, La, Pb and U. Additionally, the effects of soil mix and mycorrhizal treatments on growth and foliar chemistry were studied. Two plant species were selected: Panicum milicaeum and Pueraria phaseoloides. Different growth patterns were observed with respect to the additions of peat and clay. The results for mycorrhizal treatment (live inoculum or sterile carrier medium) are more complex, but both resulted in improved growth. The use of mycorrhizal fungi could greatly enhance rehabilitation efforts on sand tailings.
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.