The Malacca river runs through the Malacca UNESCO heritage site where a number of historical buildings are located. The river itself runs through several industrial sites that increase the chances of the water being polluted. Water pollution including heavy metals, in the long run, can damage the reputation of the site. Hence monitoring of the water quality needs to be done periodically. As the cost of instrumental monitoring is costly, biomonitoring using enzyme is being intensely developed worldwide. In this study, a rapid inhibitive enzyme assay using the molybdenum-reducing enzyme from the bacterium Serratia sp. strain DRY6 sensitive to the heavy metals mercury, copper, silver, and chromium was developed as a method for a rapid monitoring of heavy metals. The IC¬50 values for mercury, copper, silver and chromium were 0.268, 0.352, 0.393 and 0.499 mg L-1, while the LOD values were 0.166, 0.071, 0.033 and 0.064 mg L-1, respectively. The IC50 values for these heavy metals are comparable and in several cases, more sensitive than established assays. Water samples from various locations in the Melaka river were tested for the presence of heavy metals using the developed assay. Enzyme activity was found to be inhibited in one sampling location, but the concentration of metal ions on the site was found to be below the Maximum Permissible Limit according to Malaysian Environmental Quality standard. The assay for heavy metals can be completed in less than 10 minutes and can be carried out at ambient temperature. The assay is rapid and simple and can be used as a first screening method or even near real-time method for routine monitoring of heavy metals.
Molybdenum is reported to be very toxic to ruminants and shows evidence of spermatogenesis
toxicity in animals and insects. Hence, its removal is important. In this study, we report on the
first isolation of molybdenum-reducing bacterium from agricultural soil. The bacterium reduces
hexavalent molybdenum (sodium molybdate) to molybdenum blue (Mo-blue); a colloidal
product, which can be trapped and removed from solution. Phylogenetic analysis resulted in a
tentative identification of the bacterium as Serratia sp. strain MIE2. The optimum conditions for
Mo-blue production using the normal one-variable-at-a-time (OVAT) approach were 10 mM of
sodium molybdate, pH 6.0, a temperature of 35°C, ammonium sulphate at 10 g/L as the nitrogen
source and sucrose concentrations of between 30-50 g/L as the carbon source and electron donor
for molybdate. Studies on the effects of pesticides and solvents on Mo-blue production showed
that Mo-blue production from whole cells was relatively more affected by these xenobiotics
compared to the crude enzyme. Nevertheless, the strain was resistant to most of the xenobiotics
tested. Based on the strain MIE characteristics, the bacterium will be a suitable candidate for the
remediation of aquatic bodies and agricultural soils contaminated with molybdenum.
The conversion of hexavalent molybdenum (Mo (VI)) to Mo-blue is a bioremediation technique
which reduces the toxicity of molybdenum to a less toxic form by bacteria. The aim of this study
is to determine the optimum conditions of significant parameters or variables that affect the
reduction of Mo (VI) to Mo-blue by the local isolate identified as Serratia sp. strain MIE2.
Response Surface Methodology (RSM) was used in this study to optimize the reduction process
using Central Composite Design (CCD) as an optimization matrix. The optimum conditions
predicted by RSM using the desirability function for the reduction process were 20 mM
molybdate concentration, 3.95 mM phosphate, 6.25 pH and 25 g/L glucose and Mo-blue
production occurred at the absorbance value of 20.5 at 865 nm. The validation of the predicted
optimum points showed the Mo-blue production occurred at the absorbance value of 21.85 with
a deviation around 6.6 % from the RSM predicted value.
Isolate JR1 was isolated from the polluted textile industry activities site in the Juru Penang area.
This bacterium was characterized as a gram-positive Bacillus bacterium and also gave a
positive biochemical test for catalase test and oxidase test. The isolate JR1 gave a maximum
decolourization of Amaranth dye under static conditions with the rate of decolorization of
98.82%. Seven variables which are pH, temperature (°C), ammonium acetate (g/L), glucose
(g/L), sodium chloride (g/L), yeast (g/L) and dye concentration (ppm) was run by using
Plackett-Burman design for the effective parameter of the decolourization of Amaranth. From
the seven variables, three effective variables which were ammonium acetate, glucose, and dye
concentration were further optimized by using a central composite design. The optimum value
of ammonium acetate concentration at 0.74 g/L, glucose concentration at 3.0 g/L and a dye
concentration at 58.1 ppm gave the highest percentage of decolourization. Thus, this isolate
could provide an alternate solution in removing toxic dyes from environments.
Pollution in the environment is deteriorating the ecology due to human activities in a large array
of industrial and agricultural sectors. Bioassay of polluted waters using bioluminescent bacterium
has been touted as one of the most economical, rapid and sensitive tests. The growth of the
bacterium on seawater medium exhibited a typical sigmoidal profile. To extract important growth
parameters useful for further modelling exercise, various primary growth models were utilized in
this study such as Modified Logistic, modified Gompertz, modified Richards, modified Schnute,
Baranyi-Roberts, von Bertalanffy, Huang and the Buchanan three-phase model. The best
performance was Huang model with the lowest value for RMSE, AICc and the highest value for
adjusted R2. The AF and BF values were also excellent for the model with their values were the
closest to 1.0. The Huang parameters, which include A or Y0 (bacterial growth lower asymptote),
μm (maximum specific bacterial growth rate), l (lag time) and Ymax (bacterial growth upper
asymptote) were 7.866 (95% confidence interval of 7.850 to 7.883), 0.329 (95% confidence
interval of 0.299 to 0.359), 1.543 (95% confidence interval of 1.303 to 1.784) and 8.511 (95%
confidence interval of 0.299 to 0.359).