Marine water quality index trend from eight-year study of Klang Estuary

In the context of marine water quality monitoring, detailed information concerning the marine water quality index is importance. The paper presents the analysis of 8-year period trend (2010-2017) marine water quality index and the other marine water quality parameters fluctuations in the Klang estuary, which is have the famous Port Klang, the one of the largest and busiest ports in peninsular Malaysia. The 2010–2017 data employed in this study entailed 12 marine water quality parameters. In order to investigate the trend analysis, the nonparametric Mann-Kendall statistical test has been used. The result shows the upward trends for MWQI, Salinity, COND, TEMP, DO and O&G and downward trends for pH, TUR, TSS, coliform, PO4, NH3N and NO3. in 8-year period in Klang estuary. The results indicated Klang Estuary has experienced a mild pollution trend due to anthropogenic influence from domestic activities in the vicinity of the estuary

Comparison of prediction model using spatial discriminant analysis for marine water quality index in mangrove estuarine zones

The prediction models of MWQI in mangrove and estuarine zones were constructed. The 2011–2015 data em- ployed in this study entailed 13 parameters from six monitoring stations in West Malaysia. Spatial discriminant analysis (SDA) had recommended seven significant parameters to develop the MWQI which were DO, TSS, O&G, PO4, Cd, Cr and Zn. These selected parameters were then used to develop prediction models for the MWQI using artificial neural network (ANN) and multiple linear regressions (MLR). The SDA-ANN model had higher R2 value for training (0.9044) and validation (0.7113) results than SDA-MLR model and was chosen as the best model in mangrove estuarine zone. The SDA-ANN model had also demonstrated lower RMSE (5.224) than the SDA-MLR (12.7755). In summary, this work suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods

Phytoremediation Process of Water Spinach (Ipomoea aquatica) in Absorbing Heavy Metal Concentration in Wastewater

Heavy metals have become one of the environmental pollutants in water. To overcome this problem, the phytoremediation process was used as the method to cleanse polluted media. The objectives of the study are to determine the heavy metal accumulation by water spinach (Ipomea aquatica) in different types of heavy metal and to determine the level of heavy metal reduction in contaminated water. Ipomea aquatica was placed in containers that had solutions of different heavy metal concentrations. The selected heavy metals are cadmium (Cd), zinc (Zn), and copper (Cu), with a concentration of 5 ppm, 10 ppm, and 15 ppm, respectively. This study lasted about 20 days. Every four days, plant and water samples are collected. The plant samples were dried, digested, and analyzed by using ICP-OES. The two-way ANOVA statistical test was used to measure the differences in the amounts of the heavy metals accumulated in the plant and water. The accumulation of elements in plants shows a gradual increase in the uptake of cadmium, Cu, and Zn. Ipomea aquatica is suitable to take up cadmium, where the highest level of cadmium found was 13.99 mg/kg. On day 8, the level of heavy metals in the water gradually decreases for Cu and Zn. The presence of heavy metals in the water had decreased by 82.20 % on the last day of treatment. Ipomea aquatica accumulated more heavy metals while the number of heavy metals in the water decreased over a period of days. For all heavy metal types, significant differences in heavy metal concentration were obtained at p<0.05, showing that Ipomea aquatica can be used in the phytoremediation approach to remove heavy metals from wastewater

Comparing The Ability to Treat Artificial Cow Wastewater by Constructed Wetland Model Using Sorghastrum nutans and Brachiaria humidicola

In addition to meat production, the cattle industry generates significant waste, including bedding materials, wastewater, animal manure, and losses related to feed. If not managed correctly, these byproducts can have adverse environmental impacts. Constructed wetlands (CWs) offer a cost-effective and eco-friendly solution for sustainable wastewater treatment. By virtue of their extensive root systems and filtration matrices, CWs effectively reduce pollution by eliminating suspended particles, organic matter, heavy metals, and pathogens from wastewater. This research aims to assess pollutants present in cattle wastewater and evaluate the efficacy of Sorghastrum nutans and Brachiaria humidicola in purifying contaminants within constructed wetlands (CWs). CWs planted with B. humidicola exhibited higher removal rates for nutrient pollutants compared to CWs utilizing S. nutans. After a week of treatment, B. humidicola-based CWs demonstrated removal percentages of 94.07% for total nitrogen and 91.58% for phosphate (PO₄³⁻). Constructed wetlands also prove effective in eliminating biological contaminants like Escherichia coli and Shigella sp. This study highlights that the CW model incorporating B. humidicola outperforms the S. nutans model, achieving 100% removal of E. coli and 97.37% removal of Shigella sp. In conclusion, cow wastewater contains nutrient and biological pollutants, both effectively mitigated by CWs using selected plant species. Notably, B. humidicola surpasses S. nutans in its capacity for pollutant removal

Pattern recognition of Kedah River water quality data by implementation of principal component analysis

This study examines Kedah River Basin, Kedah, Malaysia, to achieve the objective of identifying and recognizing pollutant sources contributing to the water quality using a large dataset extending over a period of eight years, from the year 1997 to 2006. Principal Component Analysis was applied to simplify and provide a better understanding for the complex relationships among water quality parameters such as DO, BOD, COD, SS, pH, NH3-NL, temperature, conductivity, turbidity, salinity, dissolved solids, total solids, NO3, Cl, Ca, PO4, As, Hg, Cd, Cr, Pb, Zn, Ca, Fe, K, Mg, Na, Oil and Grease, MBAS, E.coli and Coliform. Graphical presentation of the data also helps a better view of the overall analysis to appoint sources of pollutant in accordance to their effect. Similar pattern of water quality data reveals nine Principal Components responsible for the data structure and explained 73% of the total variance of the data set. PC score model provided apportionment of various sources contributing to the water quality. Consequently the nine causes of pollutants involved are natural causes in terms of strong river current and geological location of this river, industrial and factories effluent discharge, construction, coal and metal mining, agricultural and sewage plant, human waste and illegal oil dumping

River water quality modeling using combined principle component analysis (PCA) and multiple linear regressions (MLR): a case study at Klang River, Malaysia

A collective set of data over five years (2003 to 2007) in Klang River, Selangor were studied in attempt to assess and determine the contributions of sources affecting the water quality. A precise technique of multiple linear regressions (MLR) were prepare as an advance tool for surface water modeling and forecasting. Likewise, principle component analysis (PCA) was used to simplify and understand the complex relationship among water quality parameters. Nine principle components were found responsible for the data structure provisionally named as soil erosion, anthropogenic input, surface runoff, fecal waste, detergent, urban domestic waste, industrial effluent, fertilizer waste and residential waste explains 72% of the total variance for all the data sets. Meanwhile, urban domestic pollution accounted as the highest pollution contributor to the Klang River. Thus, the advancement of receptor model was applied in order to identify the major sources of pollutant at Klang River. Result showed that the use of PCA as inputs improved the MLR model prediction by reducing their complexity and eliminating data collinearity where R2 value in this study is 0.75 and the model indicates that 75% variability of WQI explained by the five independent variables used in the model. This assessment presents the importance and advantages poses by multivariate statistical analysis of large and complex databases in order to get improved information about the water quality and then helps to reduce the sampling time and cost for reagent used prior to analyses

The Population of Airborne Microorganisms and the Presence of Staphylococcus aureus in Cattle Farm at Ladang Pasir Akar

Microorganisms found in bio aerosols from animals’ confinement buildings is not only foster the risk of spreading diseases among livestock itself, but also pose health hazard among farm workers and nearby residents. The objectives of this study are to observe the population of microorganisms and identify the present of Staphylococcus aureus (S. aureus) inside the cattle farm. DRF e-MAS which was owned prepared by Arduino kit had been used to observe the microorganisms’ population. The air from surrounding that contained microorganisms being aspirated into the plate chamber of the DRF e-MAS. The nutrient agar that being placed inside the plate chamber will be incubated along with aspirated microorganisms and the result of microorganisms’ population will be observed. S. aureus is one of the possible bacteria that may be found among the population of the microorganisms in cattle farm. DRF e-MAS along with Baird Parker Agar being used to identify the present of S. aureus. Baird Parker Agar is used to enhance the growth of S. aureus while eliminating the others. After being incubated, a single colony of S. aureus that grew on the Baird Parker Agar had been picked and regrow on another nutrient agar. A single colony that formed on that nutrient agar will be picked and then undergo Coagulase test and IMViC test. Coagulase test involved the citrated plasma and the present of bubbles show positive result. On the other hand, IMViC test divided into four sub-test which are Indole test, Methyl red test, Voges-Proskauer test and Citrate test.  These tests are important in order to confirm the present of S. aureus in this farm. According to the test, there was S. aureus found among the microorganisms’ population inside this barn

A preliminary study of marine water quality status using principal component analysis at three selected mangrove estuaries in east coast Peninsular Malaysia

This research presents marine water quality status in three different mangrove estuaries. The objective of this study is to evaluate the surface water quality of three estuaries in east coast Peninsular Malaysia. The parameters measured were Dissolved Oxygen (DO), pH, Biochemical Oxygen Demand (BOD), total dissolved solid (TDS), ammonium (NH4-N), turbidity (TUR), total suspended solid (TSS) and coliform. Monthly sampling was performed during the dry season, from June 2016 until September 2016. Data were analysed using principal component analysis (PCA). PCA yielded two PCs where VF1 forms strong factor loadings for pH, NH4-N, SAL, and TDS signifying saltwater intrusion in mangrove area. VF2 designed strong factors of BOD, TUR and Coliform and strong negative loading of DO indicating anthropogenic pollutions in the area. This study output will be a baseline setting for future studies in mangrove estuary marine water quality. Mangrove marine water samples of future monitoring studies in mangrove estuary will benefit by enabling understanding of pollution loading and coastal water quality. It is essential to plan a workable water quality modelling as powerful tool to simulate marine water quality and forecast future consequences to facilitate mangrove biodiversity conservation

Development of missing data prediction model for carbon monoxide

Carbon monoxide (CO) is one of the most important pollutants since it is selected for API calculation. Therefore, it is paramount to ensure that there is no missing data of CO during the analysis. There are numbers of occurrences that may contribute to the missing data problems such as inability of the instrument to record certain parameters. In view of this fact, a CO prediction model needs to be developed to address this problem. A dataset of meteorological and air pollutants value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). A total of 113112 datasets were used to develop the model using sensitivity analysis (SA) through artificial neural network (ANN). SA showed particulate matter (PM10) and ozone (O3) were the most significant input variables for missing data prediction model of CO. Three hidden nodes were the optimum number to develop the ANN model with the value of R2 equal to 0.5311. Both models (artificial neural network-carbon monoxide-all parameters (ANN-CO-AP) and artificial neural network-carbon monoxide-leave out (ANN-CO-LO)) showed high value of R2 (0.7639 and 0.5311) and low value of RMSE (0.2482 and 0.3506), respectively. These values indicated that the models might only employ the most significant input variables to represent the CO rather than using all input variables