Global concerns have been observed due to the outbreak and lockdown causal-based COVID-19, and hence, a global pandemic was announced by the World Health Organization (WHO) in January 2020. The Movement Control Order (MCO) in Malaysia acts to moderate the spread of COVID-19 through the enacted measures. Furthermore, massive industrial, agricultural activities and human encroachment were significantly reduced following the MCO guidelines. In this study, first, a reconnaissance survey was carried out on the effects of MCO on the health conditions of two urban rivers (i.e., Rivers of Klang and Penang) in Malaysia. Secondly, the effect of MCO lockdown on the water quality index (WQI) of a lake (Putrajaya Lake) in Malaysia is considered in this study. Finally, four machine learning algorithms have been investigated to predict WQI and the class in Putrajaya Lake. The main observations based on the analysis showed that noticeable enhancements of varying degrees in the WQI had occurred in the two investigated rivers. With regard to Putrajaya Lake, there is a significant increase in the WQI Class I, from 24% in February 2020 to 94% during the MCO month of March 2020. For WQI prediction, Multi-layer Perceptron (MLP) outperformed other models in predicting the changes in the index with a high level of accuracy. For sensitivity analysis results, it is shown that NH3-N and COD play vital rule and contributing significantly to predicting the class of WQI, followed by BOD, while the remaining three parameters (i.e. pH, DO, and TSS) exhibit a low level of importance.
A major programme of dam building is underway in many of the world's tropical countries. This raises the question of whether existing research is sufficient to fully understand the impacts of dams on tropical river systems. This paper provides a systematic review of what is known about the impacts of dams on river flows, sediment dynamics and geomorphic processes in tropical rivers. The review was conducted using the SCOPUS® and Web of Science® databases, with papers analysed to look for temporal and geographic patterns in published work, assess the approaches used to help understand dam impacts, and assess the nature and magnitude of impacts on the flow regimes and geomorphology ('hydromorphology') of tropical rivers. As part of the review, a meta-analysis was used to compare key impacts across different climate regions. Although research on tropical rivers remains scarce, existing work is sufficient to allow us to draw some very broad, general conclusions about the nature of hydromorphic change: tropical dams have resulted in reductions in flow variability, lower flood peaks, reductions in sediment supply and loads, and complex geomorphic adjustments that include both channel incision and aggradation at different times and downstream distances. At this general level, impacts are consistent with those observed in other climate regions. However, studies are too few and variable in their focus to determine whether some of the more specific aspects of change observed in tropical rivers (e.g. time to reach a new, adjusted state, and downstream recovery distance) differ consistently from those in other regions. The review helps stress the need for research that incorporates before-after comparisons of flow and geomorphic conditions, and for the wider application of tools available now for assessing hydromorphic change. Very few studies have considered hydromorphic processes when designing flow operational policies for tropical dams.
Sediment cores and overlying water samples were collected at four sites in Tianjin Coastal Zone, Bohai Bay, to investigate nutrient (N, P and Si) exchanges across the sediment-water interface. The exchange fluxes of each nutrient species were estimated based on the porewater profiles and laboratory incubation experiments. The results showed significant differences between the two methods, which implied that molecular diffusion alone was not the dominant process controlling nutrient exchanges at these sites. The impacts of redox conditions and bioturbation on the nutrient fluxes were confirmed by the laboratory incubation experiments. The results from this study showed that the nutrient fluxes measured directly from the incubation experiment were more reliable than that predicted from the porewater profiles. The possible impacts causing variations in the nutrient fluxes include sewage discharge and land reclamation.
Non-point source (NPS) pollution has become a vital contaminant source affecting the water environment because of its wide distribution, hydrodynamic complexity, and difficulty in prevention and control. In this study, the identification and evaluation of NPS pollution risk based on landscape pattern were carried out in the Hanjiang River basin above Ankang hydrological section, Shaanxi province, China. Landscape distribution information was obtained through land use data, analyzing the contribution of "source-sink" landscape to NPS pollution through the location-weighted landscape contrast index. Using the NPS pollution risk index to identify and evaluate the regional NPS pollution risk considering the slope, cost distance, soil erosion, and precipitation erosion affect migration of pollutants. The results showed that (i) the pollution risk was generally high in the whole watershed, and the sub-watersheds dominated by "source" landscapes account for 74.61% of the whole basin; (ii) the high-risk areas were distributed in the central, eastern, and western regions of the river basin; the extremely high-risk areas accounted for 12.7% of the whole watershed; and the southern and northern regions were dominated by forestland and grassland with little pollution risk; (iii) "source" landscapes were mostly distributed in areas close to the river course, which had a great impact on environment, and the landscape pattern units near the water body needed to be further adjusted to reduce the influence of NPS pollution.
Following the publication of the article it has come to the authors' attention that the first panel of Fig. 11 has been repeated with the second panel of Fig. 11.
Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations.