Short-term streamflow prediction is essential for managing flood early warning and water resources systems. Although numerical models are widely used for this purpose, they require various types of data and experience to operate the model and often tedious calibration processes. Under the digital revolution, the application of data-driven approaches to predict streamflow has increased in recent decades. In this work, multiple linear regression (MLR) and random forest (RF) models with three different input combinations are developed and assessed for multi-step ahead short-term streamflow predictions, using 14 years of hydrological datasets from the Kulim River catchment, Malaysia. Introducing more precedent streamflow events as predictor improves the performance of these data-driven models, especially in predicting peak streamflow during the high-flow event. The RF model (Nash-Sutcliffe efficiency (NSE): 0.599-0.962) outperforms the MLR model (NSE: 0.584-0.963) in terms of overall prediction accuracy. However, with the increasing lead-time length, the models' overall prediction accuracy on the arrival time and magnitude of peak streamflow decrease. These findings demonstrate the potential of decision tree-based models, such as RF, for short-term streamflow prediction and offer insights into enhancing the accuracy of these data-driven models.
This research optimized the adsorption performance of rice husk char (RHC4) for copper (Cu(II)) from an aqueous solution. Various physicochemical analyses such as Fourier transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FESEM), carbon, hydrogen, nitrogen, and sulfur (CHNS) analysis, Brunauer-Emmett-Teller (BET) surface area analysis, bulk density (g/mL), ash content (%), pH, and pHZPC were performed to determine the characteristics of RHC4. The effects of operating variables such as the influences of aqueous pH, contact time, Cu(II) concentration, and doses of RHC4 on adsorption were studied. The maximum adsorption was achieved at 120 min of contact time, pH 6, and at 8 g/L of RHC4 dose. The prediction of percentage Cu(II) adsorption was investigated via an artificial neural network (ANN). The Fletcher-Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R2 of 0.989). The pseudo-second-order kinetic model fitted well with the experimental data, thus indicating chemical adsorption. The intraparticle analysis showed that the adsorption process proceeded by boundary layer adsorption initially and by intraparticle diffusion at the later stage. The Langmuir and Freundlich isotherm models interpreted well the adsorption capacity and intensity. The thermodynamic parameters indicated that the adsorption of Cu(II) by RHC4 was spontaneous. The RHC4 adsorption capacity is comparable to other agricultural material-based adsorbents, making RHC4 competent for Cu(II) removal from wastewater.
Excessive nitrate intake via ingestion pathway and dermal absorption exposures has adverse health impacts on human health. This study evaluated groundwater (GW) nitrate concentrations and health risks which focused on ingestion and dermal exposures to residents in Bachok District, Kelantan, Malaysia. Three hundred (300) samples of private wells were collected and it is found that the nitrate concentrations ranging between 0.11 and 64.01 mg/L NO3-N with a mean value of 10.45 ± 12.67 mg/L NO3-N. The possible health hazards of nitrate by ingestion and dermal contact were assessed using USEPA human health risk assessment model for adult males and females. It is observed that the mean Hazard Quotient (HQ) values of adult males and females were 0.305 ± 0.364 and 0.261 ± 0.330, respectively. About 7.3% (n = 10) and 4.9% (n = 8) of adult males and females had HQ values more than 1, respectively. It was also observed that the mean of HQderm was lesser than HQoral for males and females. The spatial distribution of HQ by interpolation method showed high nitrate concentrations (> 10 mg/L NO3-N) were distributed from the centre to the southern part of the study location, which identified as an agricultural area, indicating the used of nitrogenous fertilizers as the main source of GW nitrate contamination in this area. The findings of this study are valuable for establishing private well water protection measures to stop further deterioration of GW quality caused by nitrate.
Natural product extraction is ingenuity that permits the mass manufacturing of specific products in a cost-effective manner. With the aim of obtaining an alternative chitosan supply, the carapace of dead horseshoe crabs seemed feasible. This sparked an investigation of the structural changes and antioxidant capacity of horseshoe crab chitosan (HCH) by γ-irradiation using 60Co source. Chitosan was extracted from the horseshoe crab (Tachypleus gigas; Müller) carapace using heterogeneous chemical N-deacetylation of chitin, followed by the irradiation of HCH using 60Co at a dose-dependent rate of 10 kGy/hour. The average molecular weight was determined by the viscosimetric method. Regarding the chemical properties, the crystal-like structures obtained from γ-irradiated chitosan powders were determined using Fourier transfer infrared (FTIR) spectroscopy and X-ray diffraction (XRD) analyses. The change in chitosan structure was evident with dose-dependent rates between 10 and 20 kGy/hour. The antioxidant properties of horseshoe crab-derived chitosan were evaluated in vitro. The 20 kGy γ-irradiation applied to chitosan changed the structure and reduced the molecular weight, providing sufficient degradation for an increase in antioxidant activity. Our findings indicate that horseshoe crab chitosan can be employed for both scald-wound healing and long-term food preservation due to its buffer-like and radical ion scavenging ability.
The environmental fate of non-steroidal anti-inflammatory drugs (NSAIDs) in the urban water cycle is still uncertain and their status is mainly assessed based on specific water components and information on human risk assessments. This study (a) explores the environmental fate of NSAIDs (ibuprofen, IBU; naproxen, NAP; ketoprofen, KET; diazepam, DIA; and diclofenac, DIC) in the urban water cycle, including wastewater, river, and treated water via gas chromatography-mass spectrophotometry (GCMS), (b) assesses the efficiency of reducing the targeted NSAIDs in sewage treatment plant (STP) using analysis of variance (ANOVA), and (c) evaluates the ecological risk assessment of these drugs in the urban water cycle via teratogenic index (TI) and risk quotient (RQ). The primary receptor of contaminants comes from urban areas, as a high concentration of NSAIDs is detected (ranging from 5.87 × 103 to 7.18 × 104 ng/L). The percentage of NSAIDs removal in STP ranged from 25.6% to 92.3%. The NAP and KET were still detected at trace levels in treated water, indicating the persistent presence in the water cycle. The TI values for NAP and DIA (influent and effluent) were more than 1, showing a risk of a teratogenic effect. The IBU, KET, and DIC had values of less than 1, indicating the risk of lethal embryo effects. The NAP and DIA can be classified as Human Pregnancy Category C (2.1 > TI ≥ 0.76). This work proved that these drugs exist in the current urban water cycle, which could induce adverse effects on humans and the environment (RQ in high and low-risk categories). Therefore, they should be minimized, if not eliminated, from the primary sources of the pollutant (i.e., STPs). These pollutants should be considered a priority to be monitored, given focus to, and listed in the guideline due to their persistent presence in the urban water cycle.