A management framework for sudden water pollution: A systematic review output.

Numerous sudden water pollution (SWP) incidents have occurred frequently in recent years, constituting a potential risk to human, socio-economic, and ecological health. This paper systematically reviews the current literature, with the view to establishing a management framework for SWP incidents. Only 39 of the 327 downloaded articles were selected, and the ROSES protocol was utilized in this review. The results indicated industries, mining sites, and sewage treatment plants as key SWP contributors through accidental leakages, traffic accidents, illegal discharge, natural disasters, and terrorist attacks. These processes also presented five consequences, including the contamination of drinking water sources, disruption of drinking water supply, ecological damage, loss of human life, and agricultural water pollution. Meanwhile, five mitigation strategies included reservoir operation, real-time monitoring, early warning, and chemical and biological treatments. Although an advancement in mitigation strategies against SWP was observed in this review, previous studies reported only a few prevention strategies. Considering that this review provided an SWP-based management framework and a hydrodynamic model selection guideline, which provide a foundation for implementing proactive measures against the SWP. These guidelines and the SWP-based management framework require practical field trials for future studies. PRACTITIONER POINTS: Sudden water pollution increases with industrial growth but decrease with awareness. Human and ecosystem health and social economy are the endpoint receptacles. Mitigation strategies include reservoir dispatch, early warning, and treatments. DPSIR model forms the basis for proving proactive measures against sudden pollution. This review provides a guideline for the selection hydrodynamic models application.

Hydrochemical characteristics and identification of groundwater pollution sources in tropical savanna.

Groundwater pollution of the watershed is mainly influenced by the multifaceted interactions of natural and anthropogenic processes. In this study, classic chemical and multivariate statistical methods were utilized to assess the groundwater quality and ascertain the potential contamination sources affecting the groundwater quality of Galma sub-watershed in a tropical savanna. For this purpose, the data set of 18 groundwater quality variables covering 57 different sampling boreholes (BH) was used. The groundwater samples essentially contained the cations in the following order of dominance: Ca2+ > Na+ > Mg2+ > K+. However, the anions had HCO3- > Cl- > SO4-2 > NO3- respectively. The hydrochemical facies classified the groundwater types of the sub-watershed into mixed Ca-Mg-Cl type of water, which means no cations and anions exceeds 50%. The second dominant water type was Ca-Cl. The Mg-HCO3 water type was found in BH 9, and Na-Cl water type in BH 29 of the studied area. The weathering of the basement rocks was responsible for the concentrations of these ions in the groundwater chemistry of the sub-watershed. Hierarchical cluster analysis (HCA) grouped the groundwater samples (boreholes) into five clusters that are statistically significant regarding the similarities of groundwater quality characteristics. The principal component analysis (PCA) extracted two major principal components explained around 65% of the variance and suggested the natural and anthropogenic processes especially the agricultural pollutants including synthetic fertilizers, and leaching of agricultural waste as the main factors affecting the groundwater quality. The integrated method proved to be efficient and robust for groundwater quality evaluation, as it guaranteed the precise assessment of groundwater chemistry in the sub-watershed of the tropical savanna. The findings of this investigation could be useful to the policy makers for developing effective groundwater management plans for the groundwater resources and protection of the sub-watershed.

Occurrence, environmental implications and risk assessment of Bisphenol A in association with colloidal particles in an urban tropical river in Malaysia.

Phase distribution of emerging organic contaminants is highly influential in their presence, fate and transport in surface water. Therefore, it is crucial to determine their state, partitioning behaviour and tendencies in water environments. In this study, Bisphenol A was investigated in both colloidal and soluble phases in water. BPA concentrations ranged between 1.13 and 5.52 ng L-1 in the soluble phase and n.d-2.06 ng L-1 in the colloidal phase, respectively. BPA was dominant in the soluble phase, however, the colloidal contribution ranged between 0 and 24% which implied that colloids can play a significant role in controlling BPA's transportation in water. Urban and industrial areas were the main sources of BPA while forest areas displayed lower levels outside the populated domains. pH levels were between 6.3 and 7.4 which might have affected BPA's solubility in water to some extent. The particle size distribution showed that the majority of the particles in river samples were smaller than 1.8 µm in diameter with a small presence of nanoparticles. Zeta potential varied between - 25 and - 18 mV, and these negative values suggested instability of particles. Furthermore, BPA was positively correlated with BOD, COD and NH3-N which might indicate that these organic compounds were released concurrently with BPA. RQ assessment showed low levels of risk towards algae and fish in the study area.

Modelling the fate and transport of colloidal particles in association with BPA in river water.

A simplified modelling approach for illustrating the fate of emerging pollutants can improve risk assessment of these chemicals. Once released into aquatic environments, these pollutants will interact with various substances including suspended particles, colloidal or nano particles, which will greatly influence their distribution and ultimate fate. Understanding these interactions in aquatic environments continues to be an important issue because of their possible risk. In this study, bisphenol A (BPA) in the water column of Bentong River, Malaysia, was investigated in both its soluble and colloidal phase. A spatially explicit hydrological model was established to illustrate the associated dispersion processes of colloidal-bound BPA. Modelling results demonstrated the significance of spatial detail in predicting hot spots or peak concentrations of colloidal-bound BPA in the sediment and water columns as well. The magnitude and setting of such spots were system based and depended mainly on flow conditions. The results highlighted the effects of colloidal particles' concentration and density on BPA's removal from the water column. It also demonstrated the tendency of colloidal particles to aggregate and the impact all these processes had on BPA's transport potential and fate in a river water. All scenarios showed that after 7.5-10 km mark BPA's concentration started to reach a steady state with very low concentrations which indicated that a downstream transport of colloidal-bound BPA was less likely due to minute BPA levels.

Economic and efficiency based optimisation of water quality monitoring network for land use impact assessment.

The evaluation of the importance of having accurate and representative stations in a network for river water quality monitoring is always a matter of concern. The minimal budget and time demands of water quality monitoring programme may appear very attractive, especially when dealing with large-scale river watersheds. This article proposes an improved methodology for optimising water quality monitoring network for present and forthcoming monitoring of water quality under a case study of the Selangor River watershed in Malaysia, where different monitoring networks are being used by water management authorities. Knowing that the lack of financial resources in developing countries like Malaysia is one of the reasons for inadequate monitoring network density, to identify an optimised network for cost-efficiency benefits in this study, a geo-statistical technique coupled Kendall's W was first applied to analyse the performance of each monitoring station in the existing networks under the monitored water quality parameters. Second, the present and future changes in non-point pollution sources were simulated using the integrated Cellular Automata and Markov chain model (CA-Markov). Third, Station Potential Pollution Score (SPPS) determined based on Analytic Hierarchy Process (AHP) was used to weight each station under the changes of non-point pollution sources for 2015, 2024, and 2033 prior to prioritisation. Finally, according to the Kendall's W test on kriging results, the weights of non-point sources from the AHP evaluation and fuzzy membership functions, six most efficient sampling stations were identified to build a robust network for the present and future monitoring of water quality status in the Selangor River watershed. This study proposes a useful approach to the pertinent agencies and management authority concerned to establish appropriate methods for developing an efficient water quality monitoring network for tropical rivers.

Spatiotemporal assessment of water quality monitoring network in a tropical river.

Managers of water quality and water monitoring programs are often faced with constraints in terms of budget, time, and laboratory capacity for sample analysis. In such situation, the ideal solution is to reduce the number of sampling sites and/or monitored variables. In this case, selecting appropriate monitoring sites is a challenge. To overcome this problem, this study was conducted to statistically assess and identify the appropriate sampling stations of monitoring network under the monitored parameters. To achieve this goal, two sets of water quality data acquired from two different monitoring networks were used. The hierarchical agglomerative cluster analysis (HACA) were used to group stations with similar characteristics in the networks, the time series analysis was then performed to observe the temporal variation of water quality within the station clusters, and the geo-statistical analysis associated Kendall's coefficient of concordance were finally applied to identify the most appropriate and least appropriate sampling stations. Based on the overall result, five stations were identified in the networks that contribute the most to the knowledge of water quality status of the entire river. In addition, five stations deemed less important were identified and could therefore be considered as redundant in the network. This result demonstrated that geo-statistical technique coupled with Kendall's coefficient of concordance can be a reliable method for water resource managers to identify appropriate sampling sites in a river monitoring network.