Water from La Pampa, Argentina, was used for washing and cooking rice to examine the in-situ impact of using naturally-contaminated water for food preparation on the elemental dietary intake. Whilst washing with the control tap water (28 μg/L As) reduced the concentration of As in rice by 23%, the use of different well waters (281-1144 μg/L) increased As levels significantly (48-227%) in comparison with the original concentration in the rice (0.056 µg/g). Cooking the rice at a low water-to-rice ratio (2:1) using modern methods increased the levels of As in the cooked samples by 2-3 orders of magnitude for both pre-washed and un-washed rice. Similar trends were observed for vanadium. Although the levels of manganese, iron, copper, zinc and molybdenum in rice were reduced during washing and cooking for most water samples, the molybdenum concentration in the cooked rice doubled (2.2-2.9 µg/g) when using water containing >1 mg/L Mo.
Thirty snakehead fish, Channa micropeltes (Cuvier, 1831) were collected at Lake Kenyir, Malaysia. Muscle, liver, intestine and kidney tissues were removed from each fish and the intestine was opened to reveal cestodes. In order to assess the concentration of heavy metal in the environment, samples of water in the surface layer and sediment were also collected. Tissues were digested and the concentrations of manganese (Mn), zinc (Zn), copper (Cu), cadmium (Cd) and lead (Pb) were analysed by using inductively-coupled plasma mass-spectrometry (ICP-MS) equipment. The results demonstrated that the cestode Senga parva (Fernando and Furtado, 1964) from fish hosts accumulated some heavy metals to a greater extent than the water and some fish tissues, but less than the sediment. In three (Pb, Zn and Mn) of the five elements measured, cestodes accumulated the highest metal concentrations, and in remaining two (Cu and Cd), the second highest metal accumulation was recorded in the cestodes when compared to host tissues. Therefore, the present study indicated that Senga parva accumulated metals and might have potential as a bioindicator of heavy-metal pollution.
Dissolved organic matter (DOM) was characterised in water samples sampled in the Lower Kinabatangan River Catchment, Sabah, Malaysia between October 2009 and May 2010. This study aims at: (i) distinguishing between the quality of DOM in waters draining palm oil plantations (OP), secondary forests (SF) and coastal swamps (CS) and, (ii) identifying the seasonal variability of DOM quantity and quality. Surface waters were sampled during fieldwork campaigns that spanned the wet and dry seasons. DOM was characterised optically by using the fluorescence Excitation Emission Matrix (EEM), the absorption coefficient at 340 nm and the spectral slope coefficient (S). Parallel Factor Analysis (PARAFAC) was undertaken to assess the DOM composition from EEM spectra and five terrestrial derived components were identified: (C1, C2, C3, C4 and C5). Components C1 and C4 contributed the most to DOM fluorescence in all study areas during both the wet and dry seasons. The results suggest that component C4 could be a significant (and common) PARAFAC signal found in similar catchments. Peak M (C2 and C3) was dominant in all samples collected during wet and dry seasons, which could be anthropogenic in origin given the active land use change in the study area. In conclusion, there were significant seasonal and spatial variations in DOM which demonstrated the effects of land use cover and precipitation amounts in the Kinabatangan catchment.
Matched MeSH terms: Water Pollution/statistics & numerical data
Textile wastewater contains methylene blue (MB), a major coloring agent in textile industry. Activated carbon (AC) is the most widely used adsorbent in removing dyes from industrial wastewater. However, high production cost of AC is the major obstacle for its wide application in dye wastewater treatment. In this study, a sustainable approach in synthesizing graphenic adsorbent from palm oil mill effluent (POME), a potential carbonaceous source, has been explored. This new development in adsorption technique is considered as green synthesis as it does not require any binder during the synthesis process, and at the same time, it helps to solve the bottleneck of palm oil industry as POME is the main cause contributed to Malaysia's water pollution problem. The synthesized GSC was characterized through XRD, FESEM, and EDX. The adsorption performance of the synthesized GSC was evaluated by adsorption of MB. The effect of initial concentration of synthetic MB solution (1-20 mg/L) and weight of GSC (5-20 g) were investigated. A remarkable change in color of synthetic MB solution from blue to crystal clear was observed at the end of adsorption study. High efficiency of the synthesized GSC for dye-contaminated wastewater treatment is concluded.
Surface and ground water resources are highly sensitive aquatic systems to contaminants due to their accessibility to multiple-point and non-point sources of pollutions. Determination of water quality variables using mathematical models instead of laboratory experiments can have venerable significance in term of the environmental prospective. In this research, application of a new developed hybrid response surface method (HRSM) which is a modified model of the existing response surface model (RSM) is proposed for the first time to predict biochemical oxygen demand (BOD) and dissolved oxygen (DO) in Euphrates River, Iraq. The model was constructed using various physical and chemical variables including water temperature (T), turbidity, power of hydrogen (pH), electrical conductivity (EC), alkalinity, calcium (Ca), chemical oxygen demand (COD), sulfate (SO4), total dissolved solids (TDS), and total suspended solids (TSS) as input attributes. The monthly water quality sampling data for the period 2004-2013 was considered for structuring the input-output pattern required for the development of the models. An advance analysis was conducted to comprehend the correlation between the predictors and predictand. The prediction performances of HRSM were compared with that of support vector regression (SVR) model which is one of the most predominate applied machine learning approaches of the state-of-the-art for water quality prediction. The results indicated a very optimistic modeling accuracy of the proposed HRSM model to predict BOD and DO. Furthermore, the results showed a robust alternative mathematical model for determining water quality particularly in a data scarce region like Iraq.
Matched MeSH terms: Water Pollution/statistics & numerical data*
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
The conventional treatment process of palm oil mill effluent (POME) produces a highly colored effluent. Colored compounds in POME cause reduction in photosynthetic activities, produce carcinogenic by-products in drinking water, chelate with metal ions, and are toxic to aquatic biota. Thus, failure of conventional treatment methods to decolorize POME has become an important problem to be addressed as color has emerged as a critical water quality parameter for many countries such as Malaysia. Aspergillus fumigatus isolated from POME sludge was successfully grown in POME supplemented with glucose. Statistical optimization studies were conducted to evaluate the effects of the types and concentrations of carbon and nitrogen sources, pH, temperature, and size of the inoculum. Characterization of the fungus was performed using scanning electron microscopy, Fourier transform infrared (FTIR) spectroscopy, and Brunauer, Emmet, and Teller surface area analysis. Optimum conditions using response surface methods at pH 5.7, 35 °C, and 0.57 % w/v glucose with 2.5 % v/v inoculum size resulted in a successful removal of 71 % of the color (initial ADMI of 3,260); chemical oxygen demand, 71 %; ammoniacal nitrogen, 35 %; total polyphenolic compounds, 50 %; and lignin, 54 % after 5 days of treatment. The decolorization process was contributed mainly by biosorption involving pseudo-first-order kinetics. FTIR analysis revealed that the presence of hydroxyl, C-H alkane, amide carbonyl, nitro, and amine groups could combine intensively with the colored compounds in POME. This is the first reported work on the application of A. fumigatus for the decolorization of POME. The present investigation suggested that growing cultures of A. fumigatus has potential applications for the decolorization of POME through the biosorption and biodegradation processes.
Matched MeSH terms: Water Pollution, Chemical/prevention & control*
Global deterioration of water, soil, and atmosphere by the release of toxic chemicals from the ongoing anthropogenic activities is becoming a serious problem throughout the world. This poses numerous issues relevant to ecosystem and human health that intensify the application challenges of conventional treatment technologies. Therefore, this review sheds the light on the recent progresses in nanotechnology and its vital role to encompass the imperative demand to monitor and treat the emerging hazardous wastes with lower cost, less energy, as well as higher efficiency. Essentially, the key aspects of this account are to briefly outline the advantages of nanotechnology over conventional treatment technologies and to relevantly highlight the treatment applications of some nanomaterials (e.g., carbon-based nanoparticles, antibacterial nanoparticles, and metal oxide nanoparticles) in the following environments: (1) air (treatment of greenhouse gases, volatile organic compounds, and bioaerosols via adsorption, photocatalytic degradation, thermal decomposition, and air filtration processes), (2) soil (application of nanomaterials as amendment agents for phytoremediation processes and utilization of stabilizers to enhance their performance), and (3) water (removal of organic pollutants, heavy metals, pathogens through adsorption, membrane processes, photocatalysis, and disinfection processes).
Field and laboratory studies were conducted to estimate concentration of potential contaminants from landfill in the underlying groundwater, leachate, and surface water. Samples collected in the vicinity of the landfill were analyzed for physiochemical parameters, organic contaminants, and toxic heavy metals. Water quality results obtained were compared from published data and reports. The results indicate serious groundwater and surface water contamination in and around the waste disposal site. Analysis of the organic samples revealed that the site contains polychlorinated biphenyls and other organo-chlorine chemicals, principally chloro-benzenes. Although the amount of PCB concentration discovered was not extreme, their presence indicates a potentially serious environmental threat. Elevated concentrations of lead, copper, nickel, manganese, cadmium, and cobalt at the downgradient indicate that the contamination plume migrated further from the site, and the distribution of metals and metals containing wastes in the site is nonhomogeneous. These results clearly indicate that materials are poorly contained and are at risk of entering the environment. Therefore, full characterization of the dump contents and the integrity of the site are necessary to evaluate the scope of the problem and to identify suitable remediation options.
Sungai Puloh mangrove estuary supports a large diversity of macrobenthic organisms and provides social benefits to the local community. Recently, it became a major recipient of heavy metals originating from industries in the hinterland as a result of industrialization and urbanization. This study was conducted to evaluate mobility and pollution status of heavy metals (Cd, Cu, Ni, Pb, Zn, and Fe) in intertidal surface sediments of this area. Surface sediment samples were collected based on four different anthropogenic sources. Metals concentrations were analyzed using an atomic absorption spectrophotometer (AAS). Results revealed that the mean concentrations were Zn (1023.68 ± 762.93 μg/g), Pb (78.8 ± 49.61 μg/g), Cu (46.89 ± 43.79 μg/g), Ni (35.54 ± 10.75 μg/g), Cd (0.94 ± 0.29 μg/g), and Fe (7.14 ± 0.94%). Most of the mean values of analyzed metals were below both the interim sediment quality guidelines (ISQG-low and ISQG-high), except for Pb concentration (above ISQG-low) and Zn concentration (above ISQG-high), thus suggesting that Pb and Zn may pose some environmental concern. Cadmium, Pb, and Zn concentrations were above the threshold effect level (TEL), indicating seldom adverse effect of these metals on macrobenthic organisms. Pollution load index (PLI) indicated deterioration and other indices revealed the intertidal surface sediment is moderately polluted with Cd, Pb, and Zn. Therefore, this mangrove area requires urgent attention to mitigate further contamination. Finally, this study will contribute to data sources for Malaysia in establishing her own ISQG since it is a baseline study with detailed contamination assessment indices for surface sediment of intertidal mangrove area.
We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R (2)), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.
Matched MeSH terms: Water Pollution, Chemical/statistics & numerical data*
The demand for high-quality safe and clean water supply has revolutionized water treatment technologies and become a most focused subject of environmental science. Water contamination generally marks the presence of numerous toxic and harmful substances. These contaminants such as heavy metals, organic and inorganic pollutants, oil wastes, and chemical dyes are discharged from various industrial effluents and domestic wastes. Among several water treatment technologies, the utilization of silica nanostructures has received considerable attention due to their stability, sustainability, and cost-effective properties. As such, this review outlines the latest innovative approaches for synthesis and application of silica nanostructures in water treatment, apart from exploring the gaps that limit their large-scale industrial application. In addition, future challenges for improved water remediation and water quality technologies are keenly discussed.
Most human activities that use water produced sewage. As urbanization grows, the overall demand for water grows. Correspondingly, the amount of produced sewage and pollution-induced water shortage is continuously increasing worldwide. Ensuring there are sufficient and safe water supplies for everyone is becoming increasingly challenging. Sewage treatment is an essential prerequisite for water reclamation and reuse. Sewage treatment plants' (STPs) performance in terms of economic and environmental perspective is known as a critical indicator for this purpose. Here, the window-based data envelopment analysis model was applied to dynamically assess the relative annual efficiency of STPs under different window widths. A total of five STPs across Malaysia were analyzed during 2015-2019. The labor cost, utility cost, operation cost, chemical consumption cost, and removal rate of pollution, as well as greenhouse gases' (GHGs) emissions, all were integrated to interpret the eco-environmental efficiency. Moreover, the ordinary least square as a supplementary method was used to regress the efficiency drivers. The results indicated the particular window width significantly affects the average of overall efficiencies; however, it shows no influence on the ranking of STP efficiency. The labor cost was determined as the most influential parameter, involving almost 40% of the total cost incurred. Hence, higher efficiency was observed with the larger-scale plants. Meanwhile, the statistical regression analysis illustrates the significance of plant scale, inflow cBOD concentrations, and inflow total phosphorus concentrations at [Formula: see text] on the performance. Lastly, some applicable techniques were suggested in terms of GHG emission mitigation.
Bioretention systems have been implemented as stormwater best management practices (BMPs) worldwide to treat non-point sources pollution. Due to insufficient research, the design guidelines for bioretention systems in tropical countries are modeled after those of temperate countries. However, climatic factors and stormwater runoff characteristics are the two key factors affecting the capacity of bioretention system. This paper reviews and compares the stormwater runoff characteristics, bioretention components, pollutant removal requirements, and applications of bioretention systems in temperate and tropical countries. Suggestions are given for bioretention components in the tropics, including elimination of mulch layer and submerged zone. More research is required to identify suitable additives for filter media, study tropical shrubs application while avoiding using grass and sedges, explore function of soil faunas, and adopt final discharged pollutants concentration (mg/L) on top of percentage removal (%) in bioretention design guidelines.
Matched MeSH terms: Water Pollution/prevention & control*
The exponential growth of human population and anthropogenic activities have led to the increase of global surface water contamination especially in river, lakes and ocean. Safe and clean surface water sources are crucial to human health and well-being, aquatic ecosystem, environment and economy. Thus, water monitoring is vital to ensure minimal and controllable contamination in the water sources. The conventional surface water monitoring method involves collecting samples on site and then testing them in the laboratory, which is time-consuming and not able to provide real-time water quality data. In addition, it involves many manpower and resources, costly and lack of integration. These make surface water quality monitoring more challenging. The incorporation of Internet of Things (IoT) and smart technology has contributed to the improvement of monitoring system. There are different approaches in the development and implementation of online surface water quality monitoring system to provide real-time data collection with lower operating cost. This paper reviews the sensors and system developed for the online surface water quality monitoring system in the previous studies. The calibration and validation of the sensors, and challenges in the design and development of online surface water quality monitoring system are also discussed.
The remarkable journey of progression of mankind has created various impacts in the form of polluted environment, amassed heavy metals and depleting resources. This alarming situation demands sustainable energy resources and approaches to deal with these environmental hazards and power deficit. Pyrolysis and co-pyrolysis address both energy and environmental issues caused by civilization and industrialization. The processes use hazardous waste materials including waste tires, plastic and medical waste, and biomass waste such as livestock waste and agricultural waste as feedstock to produce gas, char and pyrolysis oil for energy production. Usage of hazardous materials as pyrolysis and co-pyrolysis feedstock reduces disposal of harmful substances into environment, reducing occurrence of soil and water pollution, and substituting the non-renewable feedstock, fossil fuels. As compared to combustion, pyrolysis and co-pyrolysis have less emission of air pollutants and act as alternative options to landfill disposal and incineration for hazardous materials and biomass waste. Hence, stabilizing heavy metals and solving the energy and waste management problems. This review discusses the pyrolysis and co-pyrolysis of biomass and harmful wastes to strive towards circular economy and eco-friendly, cleaner energy with minimum waste disposal, reducing negative impact on the planet and creating future possibilities.
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
This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.
Matched MeSH terms: Water Pollution, Chemical/statistics & numerical data*
In recent times, surface water resource in the Western Region of Ghana has been found to be inadequate in supply and polluted by various anthropogenic activities. As a result of these problems, the demand for groundwater by the human populations in the peri-urban communities for domestic, municipal and irrigation purposes has increased without prior knowledge of its water quality. Water samples were collected from 14 public hand-dug wells during the rainy season in 2013 and investigated for total coliforms, Escherichia coli, mercury (Hg), arsenic (As), cadmium (Cd) and physicochemical parameters. Multivariate statistical analysis of the dataset and a linear stoichiometric plot of major ions were applied to group the water samples and to identify the main factors and sources of contamination. Hierarchal cluster analysis revealed four clusters from the hydrochemical variables (R-mode) and three clusters in the case of water samples (Q-mode) after z score standardization. Principal component analysis after a varimax rotation of the dataset indicated that the four factors extracted explained 93.3 % of the total variance, which highlighted salinity, toxic elements and hardness pollution as the dominant factors affecting groundwater quality. Cation exchange, mineral dissolution and silicate weathering influenced groundwater quality. The ranking order of major ions was Na(+) > Ca(2+) > K(+) > Mg(2+) and Cl(-) > SO4 (2-) > HCO3 (-). Based on piper plot and the hydrogeology of the study area, sodium chloride (86 %), sodium hydrogen carbonate and sodium carbonate (14 %) water types were identified. Although E. coli were absent in the water samples, 36 % of the wells contained total coliforms (Enterobacter species) which exceeded the WHO guidelines limit of zero colony-forming unit (CFU)/100 mL of drinking water. With the exception of Hg, the concentration of As and Cd in 79 and 43 % of the water samples exceeded the WHO guideline limits of 10 and 3 μg/L for drinking water, respectively. Reported values in some areas in Nigeria, Malaysia and USA indicated that the maximum concentration of Cd was low and As was high in this study. Health risk assessment of Cd, As and Hg based on average daily dose, hazard quotient and cancer risk was determined. In conclusion, multiple natural processes and anthropogenic activities from non-point sources contributed significantly to groundwater salinization, hardness, toxic element and microbiological contamination of the study area. The outcome of this study can be used as a baseline data to prioritize areas for future sustainable development of public wells.
Matched MeSH terms: Water Pollution/statistics & numerical data*