Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.
Abundance and diversity of benthic macroinvertebrates as well as physico-chemical parameters were investigated in five rivers of the Juru River Basin in northern Peninsula Malaysia: Ceruk Tok Kun River (CTKR), Pasir River (PR), Permatang Rawa River (PRR), Kilang Ubi River (KUR), and Juru River (JR). The physico-chemical parameters and calculated water quality index (WQI) were significantly different among the investigated rivers (ANOVA, P<0.05). The WQI classified CTKR, PR, and JR into class III (slightly polluted). However, PRR and KUR fell into class IV (polluted). High diversity and abundance of macroinvertebrates, especially the intolerant taxa, Ephemeroptera, Plecoptera, and Trichoptera, were observed in the least polluted river, CTKR. Decreasing abundance of macroinvertebrates followed the deterioration of river water quality with the least number of the most tolerant taxa collected from PR. On the basis of composition and sensitivity of macroinvertebrates to pollutants in each river, the highest Biological Monitoring Working Party (BMWP) index score of 93 was reported in CTKR (good water quality). BMWP scores in PRR and JR were 38.7 and 20.1, respectively, classifying both of them into "moderate water quality" category. Poor water quality was reported in PR and KUR. The outcome of the multivariate analysis (CCA) was highly satisfactory, explaining 43.32% of the variance for the assemblages of macroinvertebrates as influenced by 19 physical and chemical variables. According to the CCA model, we assert that there were three levels of stresses on macroinvertebrate communities in the investigated rivers: Level 1, characterized of undisturbed or slightly polluted as in the case of CTKR; Level 2, characterized by a lower habitat quality (the JR) compared to the CTKR; and Level 3 showed severe environmental stresses (PRR, PR, and KUR) primarily contributed by agricultural, industrial, and municipal discharges.
Matched MeSH terms: Water Pollution, Chemical/statistics & numerical data
Southeast (SE) Asia is a highly biodiverse region, yet it is also estimated to cumulatively contribute a third of the total global marine plastic pollution. This threat is known to have adverse impacts on marine megafauna, however, understanding of its impacts has recently been highlighted as a priority for research in the region. To address this knowledge gap, a structured literature review was conducted for species of cartilaginous fishes, marine mammals, marine reptiles, and seabirds present in SE Asia, collating cases on a global scale to allow for comparison, coupled with a regional expert elicitation to gather additional published and grey literature cases which would have been omitted during the structured literature review. Of the 380 marine megafauna species present in SE Asia, but also studied elsewhere, we found that 9.1 % and 4.5 % of all publications documenting plastic entanglement (n = 55) and ingestion (n = 291) were conducted in SE Asian countries. At the species level, published cases of entanglement from SE Asian countries were available for 10 % or less of species within each taxonomic group. Additionally, published ingestion cases were available primarily for marine mammals and were lacking entirely for seabirds in the region. The regional expert elicitation led to entanglement and ingestion cases from SE Asian countries being documented in 10 and 15 additional species respectively, highlighting the utility of a broader approach to data synthesis. While the scale of the plastic pollution in SE Asia is of particular concern for marine ecosystems, knowledge of its interactions and impacts on marine megafauna lags behind other areas of the world, even after the inclusion of a regional expert elicitation. Additional funding to help collate baseline data are critically needed to inform policy and solutions towards limiting the interactions of marine megafauna and plastic pollution in SE Asia.
Boron has been classified as a drinking water pollutant in many countries. It is harmful to many plants, exceptionally sensible plants, and human health. Therefore, boron level needs to be decreased to 0.3 mg/L for drinking water and within 0.5 mg/L to 1 mg/L for irrigation water. In this study, various operational parameters namely pH, contact time and liquid/solid ratio were investigated to determine the potential of using date seed (or date pit or date stone) to remove boron from seawater. This study's main objective was to determine boron adsorption capacities of date seeds prepared by various methods (i.e., powdered, activated, acid-treated and defatted seed) by batch adsorption process using boron contaminated synthetic seawater. The process parameters of the selected biosorbent among the four date seed preparations methods were optimized. The surface characteristics were analyzed by using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM). The results showed that acid-treated date seed was the best biosorbent in terms of removing 89.18% boron from aqueous solution at neutral pH, liquid to solid ratio of 5 within 2 hours of reaction time at room temperature (25°C±2°C).
Water-based cooling towers and their water supply at two hospitals in Johor were surveyed for the presence Legionella pneumophila. L. pneumophila were grown from 19 (76%) out of 25 collected water samples. One hospital cooling tower was contaminated with L. pneumophila serogroup 1.
This article is based on the findings of the Global International Waters Assessment (GIWA) Subregion 53, Bay of Bengal. It introduces the Subregion. The wide disparity in development indicators in the Bay of Bengal Subregion (BOBSR) is presented. The large population of poor people living in South Asia is presented as a factor that needs special attention. The article focuses on the 3 geographic sites selected for detailed analysis: i) the Ganges-Brahmaputra-Meghna river systems; ii) the Merbok Estuary mangroves, Malaysia; and iii) the Sunderbans mangroves, Bangladesh. Integrated water management based upon regional cooperation among Bangladesh, India and Nepal holds opportunities for mutual benefit. Policy options are proposed. For mangrove ecosystems, the impacts of urbanization in Malaysia and the unmanaged expansion of shrimp farming in Bangladesh are analyzed. Improved governance was seen to hold promise for enhancing economic benefits from shrimp farming while safeguarding the natural ecological system. However, these measures need to be a part of national efforts to achieve the UN Millennium Development Goals.
Matched MeSH terms: Water Pollution/prevention & control*
Malacca River water quality is affected due to rapid urbanization development. The present study applied LULC changes towards water quality detection in Malacca River. The method uses LULC, PCA, CCA, HCA, NHCA, and ANOVA. PCA confirmed DS, EC, salinity, turbidity, TSS, DO, BOD, COD, As, Hg, Zn, Fe, E. coli, and total coliform. CCA confirmed 14 variables into two variates; first variate involves residential and industrial activities; and second variate involves agriculture, sewage treatment plant, and animal husbandry. HCA and NHCA emphasize that cluster 1 occurs in urban area with Hg, Fe, total coliform, and DO pollution; cluster 3 occurs in suburban area with salinity, EC, and DS; and cluster 2 occurs in rural area with salinity and EC. ANOVA between LULC and water quality data indicates that built-up area significantly polluted the water quality through E. coli, total coliform, EC, BOD, COD, TSS, Hg, Zn, and Fe, while agriculture activities cause EC, TSS, salinity, E. coli, total coliform, arsenic, and iron pollution; and open space causes contamination of turbidity, salinity, EC, and TSS. Research finding provided useful information in identifying pollution sources and understanding LULC with river water quality as references to policy maker for proper management of Land Use area.
Matched MeSH terms: Water Pollution, Chemical/analysis*
Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model's accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model-which applies two hidden layers-achieved the best performance, then followed by ANN-MLP1 model-which applies one hidden layer and three inputs-while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.
Matched MeSH terms: Water Pollution, Chemical/analysis; Water Pollution, Chemical/prevention & control
Tieshangang Bay in the Beibu Gulf, Guangxi of China, is a strategic location for pearl farming. Although water pollution has been reported in this bay but the general health of the pearl oyster, Pinctada fucata martensii, farmed there has never been assessed. The present study examined the condition of P. fucata martensii farmed in the Tieshangang Bay by analyzing its length-weight relationship (LWR) and relative condition factor (RCF). A total of 111 specimens were sampled for measuring their shell height and total weight for determining the LWR and RCF. The coefficient of correlation of the LWR was high (R2 = 0.93), significant at 0.01 level. Negative allometric growth (b = 2.7048) was observed. However, P. fucata martensii achieved the expected growth in terms of weight, as determined through the RCF (mean 1.13). Negative allometric growth is commonly reported on the wild Pinctada spp. collected from different regions. Apparently, the water pollution in the Tieshangang Bay did not compromise the general health of the pearl oyster cultured there. Nevertheless, further study on the farm’s surrounding water quality and plankton availability is necessary to investigate the interaction between the growth of the oyster and its culture environment. In conclusion, the P. fucata martensii farmed in the Tieshangang Bay was considered healthy and the bay is still suitable for pearl oyster farming.
Clean water is very important for health and well-being of humans and ecosystem. However, over the year, a billion tons of industrial waste, fertilizers and chemical waste were dumped untreated into water bodies, such as rivers, lake and oceans contributing towards water pollution, then threatening human health and ecosystem. Hence, the need for clean water has urged scientists to research and find solutions for improving water quality. Application of nanoparticles in wastewater treatment improves the environmental quality by elimination of harmful pollutants in wastewater. Magnetite is one of the nanoparticles used in wastewater treatment because of its specific large surface area, high reactivity in adsorption and recoverable from treated water via magnetic separation technology. Preparation method of magnetite nanoparticles is the important key to its adsorption efficiency.
Mining activities in the Chini Lake catchment area have been extensive for several years, contributing to acid mine drainage (AMD) events with high concentrations of iron (Fe) and other heavy metals impacting the surface water. However, during the restriction period due to the COVID-19 outbreak, anthropogenic activities have been suspended, which clearly shows a good opportunity for a better environment. Therefore, we aimed to analyze the variation of AMD-associated water pollution in three main zones of the Chini Lake catchment area using Sentinel-2 data for the periods pre-movement control order (MCO), during MCO, and post-MCO from 2019 to 2021. These three zones were chosen due to their proximity to mining areas: zone 1 in the northeastern part, zone 2 in the southeastern part, and zone 3 in the southern part of the Chini Lake area. The acid mine water index (AMWI) was a specific index used to estimate acid mine water. The AMWI values from Sentinel-2 images exhibited that the mean AMWI values in all zones during the MCO period decreased by 14% compared with the pre-MCO period. The spatiotemporal analysis found that the highest polluted zones were recorded in zone 1, followed by zone 3 and zone 2. As compared with during the MCO period, the maximum percentage of increment during post-MCO in all zones was up to 25%. The loosened restriction policy has resulted in more AMD flowing into surface water and increased pollution in Chini Lake. As a whole, our outputs revealed that Sentinel-2 data had a major potential for assessing the AMD-associated pollution of water.
The commonly adopted method of dumping dredge spoil at sea using split-hull barges leads to considerable sediment loss to the water column and a subsequent dispersion of fine material that can pose a risk to sensitive "downstream" habitats such as coral reefs. Containing sediment loads using stitched closed geotextile bags is practiced for minimizing loss of contaminated sediment, but is expensive in terms of operational efficiency. Following promising observations from initial laboratory trials, the plunging of partially shielded sediment loads, released on open sea, was studied. The partial shielding was achieved with rigid, open containers as well as flexible, open bags. The loss of sediment from these modes of shielding was measured, and it was observed that even limited and unstitched shielding can be effective in debilitating the entrainment of water into the descending load. In particular, long-sleeved flexible bags practically self-eliminated the exposure of the load and thus losses.
Matched MeSH terms: Water Pollution/prevention & control*
Nitrate is required to maintain the growth and metabolism of plant and animals. Nevertheless, in excess amount such as polluted water, its concentration can be harmful to living organisms such as microalgae. Recently, studies on microalgae response towards nutrient fluctuation are usually limited to lipid accumulation for the production of biofuels, disregarding the other potential of microalgae to be used in wastewater treatments and as source of important metabolites. Our study therefore captures the need to investigate overall metabolite changes via NMR spectroscopy approach coupled with multivariate data to understand the complex molecular process under high (4X) and low (1/4X) concentrations of nitrate ([Formula: see text]). NMR spectra with the aid of chemometric analysis revealed contrasting metabolites makeup under abundance and limited nitrate treatment. By using NMR technique, 43 types of metabolites and 8 types of fatty acid chains were detected. Nevertheless, only 20 key changes were observed and 16 were down regulated in limited nitrate condition. This paper has demonstrated the feasibility of NMR-based metabolomics approach to study the physiological impact of changing environment such as pollution to the implications for growth and productivity of microalgae population.
Metals are micropollutants that cannot be degraded by microorganisms and are infiltrated into various environmental media, including both freshwater and marine water. Metals from polluted water are absorbed by many aquatic species, especially fish. Fish is a staple food in the diets of many regions in the world; hence, both the type and concentration of metals accumulated and transferred from contaminated water sources to fish must be determined and assessed. In this study, the heavy metal concentration was determined and assessed in fish collected from freshwater sources via published literature and Estimated Daily Intake (EDI), Target hazard quotient (THQ), and Carcinogenic Risk (CR) analyses, aiming to examine the metal pollution in freshwater fish. The fish was used as a bioindicator, and Geographic information system (GIS) was sued to map the polluted regions. The results confirmed that Pb was detected in fish sampled at 28 locations, Cr at 24 locations, Cu and Zn at 30 locations, with values Pb detected ranging from 0.0016 mg kg-1 to 44.3 mg kg-1, Cr detected ranging from 0.07 mg kg-1 to 27 mg kg-1, Cu detected ranging from 0.031 mg kg-1 to 35.54 mg kg-1, and Zn detected ranging from 0.242 mg kg-1 to 103.2 mg kg-1. The strongest positive associations were discovered between Cu-Zn (r = 0.74, p
The microbial composition in coastal water of the Port Dickson beach in Negeri Sembilan, Malaysia was analyzed using several microbial indicators for the purpose of selecting the best indicator for marine water pollution. The indicators studied were total coliform (TC), fecal coliform (FC), fecal streptococci (FS) and coliphage. Five locations were selected along the Port Dickson beaches and samplings were carried out in 1998 and 2001. The results showed an increase in the number of total coliform (TC), fecal coliform (FC) and fecal streptococci (FS) between these two sampling by 98.12%, 86.12% and 99%, respectively. The numbers of TC, FC and FS exceeded the recommended limit for recreational seawater based on U.S. EPA 1986 standard. There was a positive correlation between TC, FC and FS and negative to coliphages.
Water pollution during festival periods is a major problem in all festival cities across the world. Reliable prediction of water pollution is essential in festival cities for sewer and wastewater management in order to ensure public health and a clean environment. This article aims to model the biological oxygen demand (BOD(5)), and total suspended solids (TSS) parameters in wastewater in the sewer networks of Karbala city center during festival and rainy days using structural equation modeling and multiple linear regression analysis methods. For this purpose, 34 years (1980-2014) of rainfall, temperature and sewer flow data during festival periods in the study area were collected, processed, and employed. The results show that the TSS concentration increases by 26-46 mg/l while BOD(5) concentration rises by 9-19 mg/l for an increase of rainfall by 1 mm during festival periods. It was also found that BOD(5) concentration rises by 4-17 mg/l for each increase of 10,000 population.
Morphological deformities in parts of the head capsule of Chironomus spp. larvae inhabiting three polluted rivers (Permatang Rawa [PRR], Pasir [PR], and Kilang Ubi [KUR]) in the Juru River Basin, northeastern peninsular Malaysia, were studied. Samples of the fourth-instar larvae at one location in each river were collected monthly from November 2007 to March 2008 and examined for deformities of the mentum, antenna, mandible, and epipharyngis. At each sample location, in situ measurements of water depth, river width, water pH, dissolved oxygen, and water temperature were made. Samples of river water and benthic sediments were also collected monthly from each larval sample location in each river and taken to the laboratory for appropriate analysis. Total suspended solids (TSSs), ammonium-N, nitrate-N, phosphate-P, chloride, sulfate, and aluminum content in water were analyzed. Total organic matter and nonresidual metals in the sediment samples were also analyzed. Among the three rivers, the highest mean deformity (47.17%) was recorded in larvae collected from KUR that received industrial discharges from surrounding garment and rubber factories, followed by PRR (33.71%) receiving primarily residues of fertilizers and pesticides from adjacent rice fields, and PR (30.34%) contaminated primarily by anthropogenic wastes from the surrounding residential areas. Among the various head capsule structures, deformity of the mentum was strongly reflective of environmental stress and amounted to 27.9, 20.87, and 30.19% in the PRR, PR, and KUR, respectively. Calculated Lenat's toxic score index satisfactorily explained the influence of prevailing environmental variables on the severity of mentum deformities. Redundancy analysis and forward selection selected TSSs, sediment Zn, Mn, Cu, and Ni, and water pH, dissolved oxygen, water temperature, total organic matter, nitrate-N, chloride, phosphate-P, ammonium-N, sulfate, and aluminum as parameters that significantly affected some proportion of deformities. The total deformities correlated closely with deformities of mentum but only weakly with deformities in other parts of head. The total deformity incidence was strongly correlated with high contents of sediment Mn and Ni. The mentum and epipharyngis deformities incidence was highly correlated with an increase of TSSs, total aluminum, and ammonium-N and a decrease in pH and dissolved oxygen.
Matched MeSH terms: Water Pollution/adverse effects*
Rapid socioeconomic development in the Linggi River Basin has contributed to the significant increase of pollution discharge into the Linggi River and its adjacent coastal areas. The toxic element contents and distributions in the sediment samples collected along the Linggi River were determined using neutron activation analysis (NAA) and inductively coupled plasma-mass spectrometry (ICP-MS) techniques. The measured mean concentration of As, Cd, Pb, Sb, U, Th and Zn is relatively higher compared to the continental crust value of the respective element. Most of the elements (As, Cr, Fe, Pb, Sb and Zn) exceeded the freshwater sediment quality guideline-threshold effect concentration (FSQG-TEC) value. Downstream stations of the Linggi River showed that As concentrations in sediment exceeded the freshwater sediment quality guideline-probable effect concentration (FSQG-PEC) value. This indicates that the concentration of As will give an adverse effect to the growth of sediment-dwelling organisms. Generally, the Linggi River sediment can be categorised as unpolluted to strongly polluted and unpolluted to strongly to extremely polluted. The correlation matrix of metal-metal relationship, principle component analysis (PCA) and cluster analysis (CA) indicates that the pollution sources of Cu, Ni, Zn, Cd and Pb in sediments of the Linggi River originated from the industry of electronics and electroplating. Elements of As, Cr, Sb and Fe mainly originated from motor-vehicle workshops and metal work, whilst U and Th originated from natural processes such as terrestrial runoff and land erosion.
Matched MeSH terms: Water Pollution, Chemical/statistics & numerical data
Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data obtained from two rivers in the Penang State of Malaysia for the concentration of heavy metal ions (As, Cr, Cd, Zn, Cu, Pb, and Hg) using a flame atomic absorption spectrometry (F-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometry (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). MANOVA showed a strong significant difference between the two rivers in terms of heavy metal concentrations in water samples. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used four parameters (Zn, Pb, Cd and Cr) affording 100% correct assignations. Results indicated that the two rivers were different in terms of heavy metals concentrations in water, and the major difference was due to the contribution of Zn. A negative correlation was found between discriminate functions (DF) and Cr and As, whereas positive correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metal concentrations. Correlation matrix between the parameters exhibited a strong evidence of mutual dependence of these metals.