scholarly journals Application of GIS and Multi-Criteria Statistical Techniques in Assessing Water Quality in the Coastal Province of Vietnamese Mekong Delta

2021 ◽  
pp. 17-33
Author(s):  
Nguyen Thanh Giao ◽  
Huynh Thi Hong Nhien

The study was conducted to evaluate the quality and spatial distribution of surface water quality in Soc Trang, a coastal province using Geographic information system (GIS) and multivariate statistical analysis. Water monitoring data was collected from 19 sampling locations with 19 parameters were analyzed from February 2019 to August 2020. The results indicated that water quality was contaminated with organic matters, nutrients, coliforms and salinity. Water quality index (WQI=22–73) indicated that water quality was from poor to medium level. Cluster analysis (CA) classified 19 monitoring sites into 7 groups and 19 months into 3 seasons including rainy season, rainy season-early dry season, dry season-early rainy season. CA results showed that the location and frequency of water quality monitoring could be significantly reduced, saving up to 75% the monitoring costs. The maps of the polluted parameters (TSS, DO, BOD, COD, TOC, NH4+-N, NO2--N, Coliform, Fe, Cl-) illustrated that the areas located in the interior fields and near the sea had poorer water quality compared to the areas adjacent to the Hau River. The combination of multivariable statistics and GIS was very useful for spatial and temporal analysis of water quality monitoring data.

2020 ◽  
pp. 14-27
Author(s):  
Giao Thanh Nguyen ◽  
Huynh Thi Hong Nhien

The study aims to assess spatial and temporal water quality variations in the upper reaches of the Vietnamese Mekong Delta. Thirty-one water monitoring samples of the two main rivers (Tien and Hau Rivers) and six canals flowing through An Giang Province were collected in the dry season (March) and the rainy season (September) from 2009 to 2019. Seven physicochemical parameters were analyzed including temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), total suspended solids (TSS), orthophosphate (P-PO43-), and coliforms. Water quality index (WQI), cluster analysis (CA), and discriminant analysis (DA) were applied to evaluate water quality, spatial and temporal variations, and seasonal discriminant water variables. WQI values (15–71) indicated surface water quality was very bad to medium in which the water quality in larger and in smaller rivers in the dry season was less polluted than that in the rainy season due to erosion and runoff water containing waste materials in the wet season. CA grouped the water quality in the dry and rainy seasons into four clusters mainly due to BOD and coliforms in the dry season; TSS and coliforms in the rainy season. Discriminant analysis revealed that DO, TSS, coliforms, temperature and BOD significantly contributed to seasonal variations in water quality. Therefore, water quality monitoring in the surveyed area could only focus on DO, TSS, coliforms, temperature and BOD to reduce monitoring cost.


2014 ◽  
Vol 10 ◽  
pp. 26-30 ◽  
Author(s):  
Damian Absalon ◽  
Marek Ruman ◽  
Magdalena Matysik ◽  
Krystyna Kozioł ◽  
Żaneta Polkowska

2017 ◽  
Vol 21 (2) ◽  
pp. 949-961 ◽  
Author(s):  
Hang Zheng ◽  
Yang Hong ◽  
Di Long ◽  
Hua Jing

Abstract. Surface water quality monitoring (SWQM) provides essential information for water environmental protection. However, SWQM is costly and limited in terms of equipment and sites. The global popularity of social media and intelligent mobile devices with GPS and photography functions allows citizens to monitor surface water quality. This study aims to propose a method for SWQM using social media platforms. Specifically, a WeChat-based application platform is built to collect water quality reports from volunteers, which have been proven valuable for water quality monitoring. The methods for data screening and volunteer recruitment are discussed based on the collected reports. The proposed methods provide a framework for collecting water quality data from citizens and offer a primary foundation for big data analysis in future research.


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