Assessment of a Physicochemical Indexing Method for Evaluation of Tropical River Water Quality

Sim, Siong Fong; Tai, Szewei Elaine

doi:10.1155/2018/8385369

Cited by 10 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Effective water quality management is critical for successful aquaculture to maintain cultured species' high quality and productivity. The assessment of water quality in Malaysia involved measuring physical and chemical parameters of water, such as temperature, pH, dissolved oxygen (DO), ammonia (NH3) and ammonium (NH4 + ), following the standard established by the Department of Environment (DOE) Water Quality Index (WQI) (Sim and Tai, 2018). Water temperature plays a vital role in regulating aquaculture organisms' biochemical reactions and physiological activities (León et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Characterization of bacterial communities in prebiotics and probiotics treated shrimp farms from Kuantan

Siew, S. W.,

Kosnin, I. N.,

Zulkifli, N. F.

et al. 2023

MJM

View full text Add to dashboard Cite

Aims: Prebiotics and probiotics profoundly enhance water quality and shrimp development to tackle infectious disease in shrimp farming. This study evaluated the impact of prebiotics and probiotics treatments in water by assessing the physicochemical properties and bacterial communities in local shrimp ponds. Methodology and results: Water was collected from shrimp pond 1 (SP1), treated with prebiotics and probiotics, and shrimp pond 2 (SP2), treated with only prebiotics. The physicochemical parameters of water from two shrimp ponds were measured, including pH, dissolved oxygen (DO), ammonia concentration and temperature. The total environmental DNA (eDNA) was extracted from the water samples and sequenced using amplicon sequencing targeting the full length of the 16S rRNA gene region via the Oxford Nanopore Technology Flongle. The water quality analysis indicated that SP1 had better water quality than SP2 for shrimp aquaculture. The dominant phyla in both shrimp ponds were Proteobacteria and Bacteroidota. SP1 samples had unique microbiota at the phylum level, including Bdellovibrionota, Firmicutes A, Patescibacteria and unclassified Rhizobiales, Saprospiraceae, Vulcanococcus and HIMB114 at the genus level. The alpha-and beta-diversity showed insignificant differences in microbiota composition between SP1 and SP2 (p-value>0.05). Conclusion, significance and impact of study:Research findings demonstrated that the probiotic-treated shrimp pond (SP1) had better water quality and more diverse microbial communities than the shrimp pond that was not treated with probiotics (SP2).

show abstract

Section: Discussionmentioning

confidence: 99%

Characterization of bacterial communities in prebiotics and probiotics treated shrimp farms from Kuantan

Siew, S. W.,

Kosnin, I. N.,

Zulkifli, N. F.

et al. 2023

MJM

View full text Add to dashboard Cite

show abstract

“…The river water quality variances amongst the two clusters revealed variance in types and level of anthropogenic pollution loads. The enhanced quality of water at NH 27 and Lohia Path (downstream) indicated the rivers' self-purification ability and adjustability (Sim and Tai 2018). The worsening of water quality in mid-stretch at KGMC, Hanuman Setu, and Birbal Sahani revealed significant severe pollution due to domestic wastewater, agricultural runoff, municipal solid waste discharge and effluents discharge by industries situated nearby the river bank.…”

Section: Cluster Analysismentioning

confidence: 99%

Pollution mapping in the urban segment of a tropical river: is water quality index (WQI) enough for a nutrient-polluted river?

2019

View full text Add to dashboard Cite

Most of the tropical rivers of the world are being affected by multiple sources of pollution. The intensity of pollution is much bigger in the urban stretches due to discharge of untreated or partially treated sewage. A rapid and cost-effective tool is required for identification of water quality problems and their spatial variation for determining the main pollution sources and to detect relationships between various parameters. For this study, Gomti River, a major tributary of River Ganges, India, was considered which has gained substantial attention because of increasing anthropogenic pollution loads that has badly affected its water quality and ecosystem functions. The urban segment is polluted with organic substances, nutrients and heavy metals. The study provides an overview of the quality of water in the Gomti River through water quality index (WQI) and multivariate statistical techniques to check if WQI is enough for a nutrient-polluted river in the urban stretch. The study suggests that periodic monitoring and the water quality index development are not enough as it does not incorporate all the aspect of a rivers water quality. The separate assessment of nitrogenous biochemical oxygen demand, carbonaceous biochemical oxygen demand, sediment oxygen demand and the nitrification inhibition aspects are required to be integrated when developing a WQI. Present study illustrates that water quality of Gomti River has gradually worsened from upstream and downstream to middle stretch. The middle stretch was found to be most polluted as the major drains are concentrated within this stretch. Principal component analysis/factor analysis (PCA/FA) helped in obtaining and recognizing the factors/ sources accountable for river water quality differences in the study area. The findings are useful for the decisions regarding water quality management and this can also be applied for speedy and low-cost assessment of water quality of the polluted urban stretch of other tropical rivers for better environmental management and planning perspective.

show abstract

“…Typically, WQI consists of four general processes: (1) selecting desired water quality parameters for analysis, (2) reading of water quality data and convert the concentration of each parameter into a single-value dimensionless sub-index, (3) determining the weighting factor for each water quality parameter and (4) calculation of final single value of water quality index using the Fig. 2 The Department of Environment monitoring stations in Johor River basin calculated sub-indices with the weighting factors [4,31,32]. The Malaysian Water Quality Index (MWQI) that was developed by DOE Malaysia is used to assess and classify the surface water quality, then categorized them based on the beneficial uses of water locally.…”

Section: Water Quality Indexmentioning

confidence: 99%

Developing an ensembled machine learning model for predicting water quality index in Johor River Basin

Sidek,

Mohiyaden,

Marufuzzaman

et al. 2024

Environ Sci Eur

View full text Add to dashboard Cite

Currently, the Water Quality Index (WQI) model becomes a widely used tool to evaluate surface water quality for agriculture, domestic and industrial. WQI is one of the simplest mathematical tools that can assist water operator in decision making in assessing the quality of water and it is widely used in the last years. The water quality analysis and prediction is conducted for Johor River Basin incorporating the upstream to downstream water quality monitoring station data of the river. In this research, the numerical method is first used to calculate the WQI and identify the classes for validating the prediction results. Then, two ensemble and optimized machine learning models including gradient boosting regression (GB) and random forest regression (RF) are employed to predict the WQI. The study area selected is the Johor River basin located in Johor, Peninsular Malaysia. The initial phase of this study involves analyzing all available data on parameters concerning the river, aiming to gain a comprehensive understanding of the overall water quality within the river basin. Through temporal analysis, it was determined that Mg, E. coli, SS, and DS emerge as critical factors affecting water quality in this river basin. Then, in terms of WQI calculation, feature importance method is used to identify the most important parameters that can be used to predict the WQI. Finally, an ensemble-based machine learning model is designed to predict the WQI using three parameters. Two ensemble ML approaches are chosen to predict the WQI in the study area and achieved a R2 of 0.86 for RF-based regression and 0.85 for GB-based ML technique. Finally, this research proves that using only the biochemical oxygen demand (BOD), the chemical oxygen demand (COD) and percentage of dissolved oxygen (DO%), the WQI can be predicted accurately and almost 96 times out of 100 sample, the water class can be predicted using GB ensembled ML algorithm. Moving forward, stakeholders may opt to integrate this research into their analyses, potentially yielding economic reliability and time savings.

show abstract

Assessment of a Physicochemical Indexing Method for Evaluation of Tropical River Water Quality

Cited by 10 publications

References 20 publications

Characterization of bacterial communities in prebiotics and probiotics treated shrimp farms from Kuantan

Characterization of bacterial communities in prebiotics and probiotics treated shrimp farms from Kuantan

Pollution mapping in the urban segment of a tropical river: is water quality index (WQI) enough for a nutrient-polluted river?

Developing an ensembled machine learning model for predicting water quality index in Johor River Basin

Contact Info

Product

Resources

About