Does Embankment Improve Quality of a River? A Case Study in To Lich River Inner City Hanoi, with Special Reference to Heavy Metals

Abstract

To Lich River (TLR) system receives wastewaters from a population of nearly two million people and 100 manufactories of five industrial zones in inner city Hanoi, Vietnam. To improve quality of TLR, the embankment was carried out in 1998 and finished in 2002, resulted in width of 20 -45 m, depth of 2 -4 m, and maximum water flow capacity of 30 m 3 /s. Water and sediment quality indices based on heavy metal concentrations were used to evaluate current river environment compared to that of pre-embankment. Mass balance model was employed to estimate total metal loads for specific river reaches, which corresponds to various types of wastewater discharged along the river. The results indicated that currently there is about 284,000 m 3 sediment accumulated in TLR bed, which is under high contamination of Cr, Mn, Fe, Ni Cu, Zn, As, Cd, and Pb with a total of 7347 tons of all concerned metals. Domestic-discharged river reaches received much lower metal loads, roughly 8% -28% compared to river reaches of both domestic and industrial inputs. Total load of all nine concerned metals at the end of TLR is 161.7 kg/day, which is finally discharged to Nhue River at South Hanoi. Water quality was improved much right after finishing embankment, then it gradually deteriorated. Meanwhile, sediment quality became even much worse after embankment. Relative river quality index as equal weight for both water and sediment quality indices indicated that quality of TLR was not much improved after the embankment. It even became worse due to the urbanization in recent years.

Assessment of spatial and seasonal water quality variation of Oum Er Rbia River (Morocco) using multivariate statistical techniques

“…In the scientific literature, different statistical techniques, including a cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminate analysis (DA), were used for this kind of studies because they are capable to assess temporal and spatial variations in river water quality and to identify potential sources of water contamination (i.e. Phung et al, 2015;Khan et al, 2016;Sharma et al, 2015;Varekar et al, 2015;Kumarasamy et al, 2014;Thuong et al, 2013;Razmkhah et al, 2010b;Kazi et al, 2009;Kumar & Dua, 2009;Varol & Sen, 2009;Zhang et al, 2009). For instance, Phung et al (2015) used CA, PCA, FA and DA to evaluate temporal/spatial variations of surface water quality in Can Tho City, a Mekong Delta area of Vietnam.…”

Reservoir Water Quality Assessment Based on Chemical Parameters and the Chlorophyll Dynamics in Relation to Nutrient Regime

“…Such techniques help interpret the multifaceted data matrices in an easier and more comprehensive manner by allowing the identification of potential sources/factors than can be held responsible for watershed impact as well as present valuable means for trustworthy water resource management and solutions to pollution [28]. Principal component analysis (PCA), cluster analysis (CA), discriminate analysis (DA), and factor analysis (FA) are reportedly used in the scientific literature because of their ability to treat larger datasets of temporal and spatial parameters obtained from various study sites [5,[29][30][31][32][33][34][35]. PCA and CA have proven to be very important statistical tools for determining underlying relationships among various physicochemical parameters, pollutant source identification, and grouping sites or parameters into similar clusters for better understanding [5,28,31].…”