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.
As the division of work within the world economic system becomes increasingly complex, the impact of disturbing events on the economic system is expanding. Recently, Japan proposed to discharge nuclear wastewater into the Pacific Ocean, which will cause damage to marine fisheries, thereby seriously affecting fisheries and other industries in Japan and other countries and regions around the world. Considering different scenarios of final and intermediate demand shifting, this paper uses the Inoperability Input-Output Model (IIM) and Multi-Region Input-Output Model (MRIO) to simulate the economic consequences of nuclear wastewater discharge in Japan and calculate the economic changes of each industry and country (region). The results show that: In the short term, when only the final demand for Japanese fishery products decreases. (1) The ten countries (regions) with significant economic losses are Japan, the United States, Chinese Taipei, Canada, Chile, South Africa, Mexico, Peru, the United Kingdom, and Ireland. (2) The ten countries (regions) with a significant increase in total output due to demand shift are China (People's Republic of), the Rest of the World, India, Indonesia, Viet Nam, the Philippines, Brazil, Myanmar, the Russian Federation, and Malaysia. (3) A ranking of changes in the total output of different industries. In the long term, when both intermediate and final demand for Japanese fishery products decrease. (4) The change in value added in Japan. (5) The change in value added of 67 countries (regions) worldwide. The ten countries (regions) with the most significant increase in value-added are the Russian Federation, China (People's Republic of), the Rest of the World, the United States, Indonesia, Australia, Norway, Korea, Viet Nam, and Myanmar. The ten countries (regions) with the most significant decrease in value-added are Japan, Chinese Taipei, Chile, South Africa, Peru, Thailand, Mexico, Cambodia, Costa Rica, and Morocco. Changes in value added of 45 industrial sectors worldwide.
Contamination of endocrine disrupting compounds (EDCs) in tap water is an emerging global issue, and there are abundant influencing factors that have an ambivalent effect on their transportation and fate. Different housing types vary in terms of water distribution system operation and design, water consumption choices, and other hydraulic factors, which potentially affect the dynamics, loadings, and partitioning of pollutants in tap water. Thus, this study analyzed 18 multiclass EDCs in tap water from different housing types (i.e., landed and high-rise) and the associated health risks. Sample analyses revealed the presence of 16 EDCs, namely hormones (5), pharmaceuticals (8), a pesticide (1), and plasticizers (2) in tap water, with the prevalent occurrence of bisphenol A up to 66.40 ng/L in high-rise housing. The presence of caffeine and sulfamethoxazole distribution in tap water was significantly different between landed and high-rise housings (t(152) = -2.298, p = 0.023 and t(109) = 2.135, p = 0.035). Moreover, the salinity and conductivity of tap water in high-rise housings were significantly higher compared to those in landed housings (t(122) = 2.411, p = 0.017 and t(94) = 2.997, p = 0.003, respectively). Furthermore, there were no potential health risks of EDCs (risk quotient water intake. However, EDC variation in different housing types requires simulation of the occurrence, transport, and fate of EDCs in the distribution system and investigation of the underlying factors for effective mitigation, prevention, and intervention.
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 composite nature of Ennore Creek, receiving polluted waters from Buckingham canal, River Kortalaiyar, and the presence of numerous industries rapidly degrade the coastal environment. In this study, multivariate statistical technique was used to assess the nature of pollution and to identify the factors responsible for the enrichment of trace metals in the creek sediments. Forty samples were collected during pre- and post-monsoon periods to evaluate the seasonal variations on the concentration of trace metals in the sediments. The results indicate that not much seasonal variation exists in the concentration of trace metals in the sediments of Ennore creek. Results of the cluster analysis illustrate that the enrichment of trace metals was mainly from anthropogenic sources. Meanwhile, correlation coefficient among the metals reveals that some of the metals (Fe and Al) derived were of natural origin. The complex data matrix of the sediment were interpreted after reduction to three factors and the results illustrate the extent of the influence of anthropogenic activities. The spatial distribution diagrams demonstrate and demarcate the region of enrichment of metals in the sediments of Ennore creek.
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.
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.
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.
Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.
A study has been conducted to determine the composition of surfactants in runoff water in the semi-urban area of Bandar Baru Bangi, Selangor, Malaysia. Runoff samples were collected from five different locations with contrasting functional activities and the colorimetric method was used to analyze the concentrations of surfactants as methylene blue active substances (MBAS) for anionic surfactants and as disulphine blue active substances (DBAS) for cationic surfactants. The results showed that the highest surfactant concentrations of MBAS and DBAS in runoff water were recorded in the samples collected at the residential area, with the concentrations of 3.192 ± 0.727 and 0.170 ± 0.028 μmol/L, respectively. Anionic surfactants as MBAS were found to dominate the concentration of surfactants in both runoff and rainwater. The concentrations of both anionic and cationic surfactants in runoff water were recorded as being higher than in rainwater.
Matched MeSH terms: Water Pollution, Chemical/statistics & numerical data
In Malaysia, most municipal wastes currently are disposed into poorly managed 'controlled tipping' systems with little or no pollution protection measures. This study was undertaken to assist the relevant governmental bodies and service providers to identify an improved waste disposal management strategy. The study applied the choice experiment technique to estimate the nonmarket values for a number of waste disposal technologies. Implicit prices for environmental attributes such as psychological fear, land use, air pollution, and river water quality were estimated. Compensating surplus estimates incorporating distance from the residences of the respondents to the proposed disposal facility were calculated for a number of generic and technology-specific choice sets. The resulting estimates were higher for technology-specific options, and the distance factor was a significant determinant in setting an equitable solid waste management fee.
Matched MeSH terms: Water Pollution/prevention & control
It has been widely reported that allozyme frequency variation is a potential indicator of heavy metal-induced impacts in aquatic populations. In the present study, wild populations of horseshoe crab (Carcinoscorpius rotundicauda) were collected from contaminated and uncontaminated sites of Peninsular Malaysia. By adopting horizontal starch gel electrophoresis, seven enzyme systems were used to study allozyme polymorphisms. Nine polymorphic loci were observed in C. rotundicauda. The relationships of allozyme variations with the concentrations of Cd, Cu, Ni, and Zn in sediments and in muscle tissues of horseshoe crabs were determined. Based on genetic distance, the lower mean value of Nei's D (0.017) indicated that both of the contaminated populations of Kg. Pasir Puteh and Kuala Juru were very closely related when compared to the relatively uncontaminated Pantai Lido population. Higher heterozygosities were shown by the contaminated populations when compared to the uncontaminated population. Different allelic frequencies could be observed for the aldolase (ALD; E.C. 2.7.5.1) locus between the contaminated and uncontaminated populations of C. rotundicauda. The dendrogram of genetic relationships of the three populations of C. rotundicauda showed the same clustering pattern as the dendrograms are based on heavy metals in the sediments and in the horseshoe crabs' abdominal muscles. From the F statistics, the present study showed that the three populations of horseshoe crabs were considered to have undergone moderate genetic differentiation with a mean F (ST) value of 0.092 .The current results suggest that allozyme polymorphism in horseshoe crabs is a potential biomonitoring tool for metal contamination, although further validation is required.
Palm oil production is one of the major industries in Malaysia and this country ranks one of the largest productions in the world. In Malaysia, the total production of crude palm oil in 2008 was 17,734,441 tonnes. However, the production of this amount of crude palm oil results in even larger amounts of palm oil mill effluent (POME). In the year 2008 alone, at least 44 million tonnes of POME was generated in Malaysia. Currently, the ponding system is the most common treatment method for POME but other processes such as aerobic and anaerobic digestion, physicochemical treatment and membrane filtration may also provide the palm oil industries with possible insights into the improvement of POME treatment processes. Generally, open ponding offers low capital and operating costs but this conventional method is becoming less attractive because the methane produced is wasted to the atmosphere and the system can not be certified for Carbon Emission Reduction trading. On the other hand, anaerobic digestion of POME provides the fastest payback of investment because the treatment enables biogas recovery for heat generation and treated effluent for land application. Lastly, it is proposed herewith that wastewater management based on the promotion of cleaner production and environmentally sound biotechnologies should be prioritized and included as a part of the POME management in Malaysia for attaining sustainable development. This paper thus discusses and compares state-of-the-art POME treatment methods as well as their individual performances.
Matched MeSH terms: Water Pollution, Chemical/prevention & control*
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.