a b s t r a c tTreatment of various types of wastewaters is an urgent problem in densely populated areas of many tropical countries. We studied the potential of using Sesbania sesban, an N 2 -fixing shrub, in constructed wetland systems for the treatment of high-strength wastewater. A replicated horizontal subsurface flow system and a saturated vertical downflow system was established with planted and unplanted beds to assess the effects of system design and presence of plants on treatment performance. The systems were loaded with a mixture of domestic and pig farm wastewater at three hydraulic loading rates of 80, 160 and 320 mm d −1 . The S. sesban plants grew very well in the constructed wetland systems and produced 17.2-20.2 kg dry matter m −2 year −1 with a high nitrogen content. Mass removal rates and removal rate constants increased with loading rate, but at 320 mm d −1 the effluent quality was unacceptable and hydraulic problems appeared. Mass removal rates and removal rate constants were much higher than reported in other studies probably because of the high-strength wastewater, the high loading rates and the tropical conditions. Planted systems removed pollutants much more efficiently than the unplanted controls. Direct plant uptake constituted only up to 8% of the total-N removal and 2% of the P removal at the lowest loading rate, and was quantitatively of low importance compared to other removal processes. The significant effects of plants were therefore related more to their indirect effects on the removal processes. This study for the first time documents that S. sesban can be used in constructed wetland systems for the treatment of polluted water while at the same time producing a valuable N rich biomass that can be used for animal fodder or soil amendment.

In this study, spatiotemporal fluctuations in surface water quality in Vinh Long province, Vietnam, were conducted using entropy weighting, water quality index (WQI), and multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), and discriminant analysis (DA). The samples collected at 63 monitoring locations in March, June, and September were measured for 15 parameters. Compared to the Vietnamese standard, surface water was contaminated with organic matters, nutrients, microorganisms, and salinity. DA identified the most typical parameters (pH, turbidity, TSS, EC, DO, Cl−, E. coli, coliform) in distinguishing temporal variations in water quality with greater than 75% of the correction. CA group 63 sampling sites into 22 clusters representing different land use patterns. WQI determined the worst water quality was found in the agricultural areas. Based on the results of entropy weighting, EC, coliform, N-NH4+, BOD, N-NO3−, and Fe had significantly controlled surface water quality. Four principal components obtained from PCA explained 66.45% of the variance, suggesting the influences of geohydrological factors and anthropogenic activities, such as domestic, market area, agriculture, and industry. The findings of this study can provide useful information for authorities to evaluate the effectiveness of monitoring systems and plan for water quality management strategies.

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The use of zoobenthos to evaluate water quality has gained considerable interest due to its low cost and environmental friendliness. This study analyzed water and zoobenthos samples at 13 sites in the water bodies in An Giang province in the rainy season (on March) and dry season (on September) in 2018. The results showed that the surface water was contaminated by organic matters, suspended solids and coliforms. There was occurrence of 28 species of zoobenthos divided into five classes including Oligochaeta, Polychaeta, Insecta, Gastropoda, and Bivalvia in which Bivalvia was the most diverse class accounting for 75%. The densities of zoobenthos in the dry and wet season were from 30 divided into three clusters for the dry season and six clusters for the rainy season indicating seasonal variation of zoobenthos composition possibly due to variance of water and sediment characteristics. The results of Pearson correlation indicated that the composition of zoobenthos was strongly correlated with temperature, pH, N-NH4+ and N-NO3-. Using water quality index (WQI), Shannon-Wiener diversity index (H’) and associated average score per taxon (ASPT) for water quality assessment revealed that water quality ranges from medium (from α to β- mesosaprobe) to heavy pollution (Polysaprobe). There was inconsistency between using physicochemicals and diversity index of zoobenthos for water quality identification leading to the use of zoobenthos for water indication could result in misadjustment of water quality. Further study should investigate the relationship between zoobenthos and water quality in different ecological areas to better indicate role of zoobenthos in quick diagnose water quality.