F.M. Muvea; G.M. Ogendi; S.O. Omondi
Abstract
The use of constructed wetlands for purifying pre-treated wastewater is a cost effective technology that has been found to be more appropriate for many developing countries. The technology is also environmentally friendly with the wetlands being habitats for many water birds and other aquatic organisms. ...
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The use of constructed wetlands for purifying pre-treated wastewater is a cost effective technology that has been found to be more appropriate for many developing countries. The technology is also environmentally friendly with the wetlands being habitats for many water birds and other aquatic organisms. This study assessed nutrient removal efficiency of two floating macrophytes (Lemna minor and Azolla pinnata). The data generated was analyzed using both descriptive and inferential statistics. The significance level was maintained at 0.05. The results showed that the wastewater physicochemical parameters did not vary during the study period. The concentrations of nitrites and nitrates increased over the experimental period in all the treatments (Azolla pinnata, Lemna minor and control), and the increase between the sampling occasions was statistically significant for the two nutrients (Nitrates: F=24.78, P= 0.00; Nitrates: F=198.26, P= 0.00). To the contrary, in all the treatments the concentrations of ammonia, total phosphorous, soluble reactive phosphorous and total nitrogen, decreased over the experimental period. The decrease in concentration for these nutrients between the sampling occasions was statistically significant (ammonia: F=195.57, p= 0.00; total phosphorous: F= 56.50, p= 0.00; soluble reactive phosphorous: F= 37.11, p= 0.00; total phosphorous: F= 104.025, p= 0.00). Azolla pinnata proved to be better than Lemna minor in the uptake of the nutrients particularly for the soluble reactive phosphorous (F= 35.18, P= 0.044). We conclude that the two macrophytes are good for wastewater treatment. It is recommended introduction and/or multiplication of Azolla pinnata in the constructed wetlands meant for wastewater treatment especially within the tropics.
Environmental Management
U.G. Abhijna
Abstract
Multivariate statistical techniques such as cluster analysis, multidimensional scaling and principal component analysis were applied to evaluate the temporal and spatial variations in water quality data set generated for two years (2008-2010) from six monitoring stations of Veli-Akkulam Lake and compared ...
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Multivariate statistical techniques such as cluster analysis, multidimensional scaling and principal component analysis were applied to evaluate the temporal and spatial variations in water quality data set generated for two years (2008-2010) from six monitoring stations of Veli-Akkulam Lake and compared with a regional reference lake Vellayani of south India. Seasonal variations of 14 different physicochemical parameters analyzed were as follows: pH (6.42-7.48), water temperature (26.0-31.28°C), salinity (0.50-26.81 ppt), electrical conductivity (47-20656.31 µs/cm), dissolved oxygen (0.078-7.65 mg/L), free carbon-dioxide (3.8-51.8 mg/L), total hardness (27.20-2166.6 mg/L), total dissolved solids (84.66-4195 mg/L), biochemical oxygen demand (1.57-25.78 mg/L), chemical oxygen demand (5.35-71.14 mg/L), nitrate (0.012-0.321 µg/ml), nitrite (0.24-0.79 µg/ml), phosphate (0.04-5.88 mg/L), and sulfate (0.27-27.8 mg/L). Cluster analysis showed four clusters based on the similarity of water quality characteristics among sampling stations during three different seasons (pre-monsoon, monsoon and post-monsoon). Multidimensional scaling in conjunction with cluster analysis identified four distinct groups of sites with varied water quality conditions such as upstream, transitional and downstream conditions in Veli-Akkulam Lake and a reference condition at Vellayani Lake. Principal Component Analysis showed that Veli-Akkulam Lake was seriously deteriorated in water quality while acceptable water quality conditions were observed at reference lake Vellayani. Thus the present study could estimate the effectiveness of multivariate statistical approaches for assessing water quality conditions in lakes.