Using Remote Sensing and Multivariate Statistics in Analyzing the Relationship between Land Use Pattern and Water Quality in Tien Giang Province, Vietnam

Impact of a dam construction on the intertidal environment and free-living nematodes in the Ba Lai, Mekong Estuaries, Vietnam

The impact of high siltation and accumulation of organic and waste material in the intertidal of the dammed Ba Lai River in Vietnam as part of the Mekong estuarine system was investigated by means of marine free-living nematodes. Nutrients content (nitrate, ammonium, total phosphorus, total nitrogen), total suspended solids, total organic carbon, coliform, bacteria E. coli, pH, dissolved oxygen, total dissolved solids, methane and hydrogen sulfide concentration, and the nematode communities were characterized in sediment at selected stations along the river above and below the dam. Our results found elevated methane concentrations at the upstream side of the dam while hydrogen sulfide concentrations found to be highest in the downstream side of the dam. Furthermore, methane and hydrogen sulfide concentrations were correlated to nematode community characteristics such as trophic composition densities and genera composition. There was a clear difference between the communities above and below the dam. The discontinuous nematode community distribution indicated that the Ba Lai River is impacted by dam construction. Potentially the high deposition and eutrophication could turn the area into a methane-rich area related to predicted impact on nematodes.

Entropy-assisted approach to determine priorities in water quality monitoring process

Effective determination of water quality and water pollution assessment is crucial and challenging processes. Evaluating water quality in rivers, researchers have referred to various statistical, probabilistic and stochastic methods to obtain efficient information from the monitoring network. As data are greatly random, the information content can be obtained by utilizing various methods including but not limited to the "entropy." Monitoring is a difficult process due to high measurement costs, while it is also difficult to optimize the network in terms of time, space, and especially the variable to be monitored. In the presented study, it is aimed to create an effective approach to be used in optimizing the monitoring network by determining the "prior" variables by entropy that measures the uncertainty by using all the data without time difference. The presented study proposes an alternative method to define the water quality variables that should be monitored much more frequently. Study is exemplified for demonstrating its potential use in a case study level, Grand River in Canada, by assessing water quality data obtained from 15 water quality monitoring stations. Results showed that BOD, Cl, and NO₂-N among examined 8 different variables are as the "prior" variables should be monitored. It is being proven that the prior variable that should be monitored for optimization of the network can be easily determined with the information obtained from the data statistically evaluated with entropy, and it can be stated as an effective method for managers to use in the decision-making process.

Combination of water quality, pollution indices, and multivariate statistical techniques for evaluating the surface water quality variation in Can Tho City, Vietnam

This study assessed the surface water quality in Can Tho city, Vietnam, using a combination of water quality, pollution indices, and multivariate statistical methods. Surface water samples were collected at 38 locations with a frequency of 4 times in 2020 (March, June, September, and December) and at the time of high and low tides to analyze for 18 indicators. Results showed that surface water in Can Tho city was contaminated with organic matters and microorganisms. Parameters of pH, turbidity, total suspended solids (TSS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), N-NH₄⁺, and N-NO₃^- are significantly increased with low tide. Comprehensive pollution index indicated mild to moderately polluted water in March, June, and September and moderately to severely polluted water in December. Organic pollution index revealed that surface water quality in all locations was polluted with organic matters during the study period especially in March and December. The water quality index also indicated that water quality in December was mostly classified as moderate and bad. The principal component analysis indicated that surface water quality could be affected by five main sources that explain 64.40% of the total variation. This significantly caused the fluctuation of pH, temperature, turbidity, TSS, DO, BOD, COD, N-NH₄⁺, P-PO₄^3-, Fe, and As, which should all be the focus for future monitoring. Surface water management in Can Tho city should also emphasize on the wastewater from urbanization and agriculture, which has been recognized by the analysis to have highest contribution to organic, nutrient, and microbial pollutants in the water bodies.

Spatiotemporal Variations in Physicochemical and Biological Properties of Surface Water Using Statistical Analyses in Vinh Long Province, Vietnam

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.

Analysis of Surface Water Quality in Upstream Province of Vietnamese Mekong Delta Using Multivariate Statistics

The study employed different statistical approaches to assess surface water quality in the upstream region of the Vietnamese Mekong Delta. The dataset included seven parameters (i.e., temperature, pH, total suspended solids (TSS), five-day biological oxygen demand (BOD5), chemical oxygen demand (COD), ammonium nitrogen (NH4+-N) and coliform) at seventy-three locations. Cluster analysis (CA) and principal component analysis (PCA) were applied to analyze spatial variations in surface water quality and recognize the important parameters. The findings revealed that surface water quality was deteriorated by organic matters (high BOD5 and COD), nutrients and microorganisms. Particularly, urban areas were found to be more polluted than the other areas. The PCA results indicated that three potential water pollution sources, including industry, urban and tourism, could explain 87.03% of the total variance. Coliform was identified as the leading latent factor that controls surface water quality in the study area. CA grouped the sampling locations into 11 groups, in which the groups of the baseline monitoring sites and large rivers had better water quality. The results indicated a significant impact of anthropogenic activities (especially, urban and tourism practices) in surface water quality degradation. Moreover, CA suggested that the numbers of the sampling sites could be reduced from 73 to 58 locations, lowering 20.54% of the monitoring cost. Thus, the study recommends scrutinizing the current surface water quality monitoring system to be more economic and urgently implementing appropriate solutions to mitigate coliform pollution in the smaller water bodies.