Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6.

This research was carried out to predict daily streamflow for the Swat River Basin, Pakistan through four deep learning (DL) models: Feed Forward Artificial Neural Networks (FFANN), Seasonal Artificial Neural Networks (SANN), Time Lag Artificial Neural Networks (TLANN) and Long Short-Term Memory (LSTM) under two Shared Socioeconomic Pathways (SSPs) 585 and 245. Taylor Diagram, Random Forest, and Gradient Boosting techniques were used to select the best combination of General Circulation Models (GCMs) for Multi-Model Ensemble (MME) computation. MME was computed via the Random Forest technique for Maximum Temperature (Tmax), Minimum Temperature (Tmin), and precipitation for the aforementioned three techniques. The best MME for Tmax, Tmin, and precipitation was rendered by Compromise Programming. The DL models were trained and tested using observed precipitation and temperature as independent variables and discharge as dependent variables. The results of deep learning models were evaluated using statistical performance indicators such as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The TLANN demonstrated superior performance compared to the other models based on RMSE, MSE, MAE, and R2 during training (65.25 m3/s, 4256.97 m3/s, 46.793 m3/s and 0.7978) and testing (72.06 m3/s, 5192.95 m3/s, 51.363 m3/s and 0.7443) respectively. Subsequently, TLANN was utilized to make predictions based on MME of SSP245 and SSP585 scenarios for future streamflow until the year 2100. These results can be used for planning, management, and policy-making regarding water resources projects in the study area.

Nanofiltration PVP Membrane with Gradient Cross-Linking for Efficient Polycyclic Aromatic Hydrocarbons Removal.

Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants that may contaminate various water sources and pose serious dangers to human health and the environment. Due to their capacity for size-based separation, nanofiltration membranes have become efficient instruments for PAH removal. However, issues such as membrane fouling and ineffective rejection still exist. To improve PAH rejection while reducing fouling problems, this work created a new gradient cross-linking poly(vinylpyrrolidone) (PVP) nanofiltration membrane. The gradient cross-linking technique enhanced the rejection performance and antifouling characteristics of the membrane. The results demonstrated that the highest membrane flow was achieved at a 0.15% SDS-PVP membrane. There is a trade-off between membrane flux and salt rejection since salt rejection increases with SDS owing to the growth of big pores. The membrane flux was reduced for the 0.25% SDS-PVP membrane owing to poor SDS dispersion. The prepared membrane showed enhanced removal efficiencies for the removal of the PAH compounds. The PVP membrane has the potential to be used in several water treatment applications, improving water quality, and preserving the environment.

Study of Kinetics and the Working Mechanism of Silica-Coated Amino-Functionalized CoFe2O4 Ferrite Nanoparticles to Treat Wastewater for Heavy Metals.

This study used a simple coprecipitation method to produce pristine, silica-coated, and amino-functionalized CoFe2O4 nanoadsorbents. Amino-functionalization was done to increase the active surface area and metal ion removal efficiency. Both pristine and functionalized adsorbents were employed to recover Pb(II), Zn(II), and Cu(II) ions from wastewater. The adsorption tests were performed by varying the initial concentration of metal ions and contact time at a fixed pH of 6.5. Atomic adsorption spectroscopy was utilized to detect the proportion of metals removed from water. Additionally, the pseudo-first-order, pseudo-second-order, Freundlich, and Langmuir models were employed to compute the kinetic and isothermic data from metal ion adsorption onto the adsorbents. The amino-functionalized adsorbent showed adsorption capacities of 277.008, 254.453, and 258.398 mg/g for Cu(II), Pb(II), and Zn(II) ions, respectively. According to the adsorption results, the Langmuir isotherm and the pseudo-second-order model best suit the data. The best fitting of the pseudo-second-order model with the data indicates that coordinative interactions between amino groups and metal ions are responsible for chemisorption. The metal ions bind with -NH2 groups on the adsorbent surface through chelate bonds. Chelate bonds are extremely strong and stable, indicating the effectiveness of the CoFe2O4@SiO2-NH2 adsorbent in adsorbing heavy-metal ions. The tested adsorbent exhibited good performance, batter stability, and good reusable values around 77, 81, and 76% for Cu(II), Pb(II), and Zn(II) ions, respectively, after five adsorption cycles.

Dual S-Scheme Heterojunction CdS/TiO2/g-C3N4 Photocatalyst for Hydrogen Production and Dye Degradation Applications.

This study investigated a ternary CdS/TiO2/g-C3N4 heterojunction for degrading synthetic dyes and hydrogen production from aqueous media through visible light-initiated photocatalytic reactions. CdS, TiO2, and g-C3N4 were combined in different mass ratios through a simple hydrothermal method to create CdS/TiO2/g-C3N4 composite photocatalysts. The prepared heterojunction catalysts were investigated by using FTIR, XRD, EDX, SEM, and UV-visible spectroscopy analysis for their crystal structures, functional groups, elemental composition, microtopography, and optical properties. The rhodamine B dye was then degraded by using fully characterized photocatalysts. The maximum dye degradation efficiency of 99.4% was noted in these experiments. The evolution rate of hydrogen from the aqueous solution with the CdS/TiO2/g-C3N4 photocatalyst remained 2910 µmol·h-1·g-1, which is considerably higher than those of g-C3N4, CdS, CdS/g-C3N4, and g-C3N4/TiO2-catalyzed reactions. This study also proposes a photocatalytic activity mechanism for the tested ternary CdS/TiO2/g-C3N4 heterojunctions.

Green Synthesis of ZnO and Black TiO2 Materials and Their Application in Photodegradation of Organic Pollutants.

ZnO and black TiO2 have been selected as the most efficient materials for organic pollution abatement due to their increased efficiency when compared to other materials. However, the concept of green chemistry makes it desirable to design green synthesis approaches for their production. In this study, black TiO2 was synthesized using an environmentally safe synthetic technique with glycerol as a reductant. ZnO was prepared by using ionic-liquid-based microwave-assisted extracts of Polygonum minus. To investigate the materials' potential to photodegrade organic pollutants, methylene blue (MB) and phenol were chosen as model organic pollutants. Both materials were found to exhibit spherical morphologies and a mesoporous structure and were efficient absorbers of visible light. ZnO exhibited electron-hole pair recombination lower than that of black TiO2. Black TiO2 was discovered to be an anatase phase, whereas ZnO was found to have a hexagonal wurtzite structure. In contrast to black TiO2, which had a surface area of 239.99 m2/g and a particle size of 28 nm, ZnO had a surface area of 353.11 m2/g and a particle size of 32 nm. With a degradation time of 60 min, ZnO was able to eliminate 97.50% of the 40 mg/L MB. Black TiO2, on the other hand, could reduce 90.0% of the same amount of MB in 60 min. When tested for phenol degradation, ZnO and black TiO2 activities were reduced by nearly 15 and 25%, respectively. A detailed examination of both ZnO and black TiO2 materials revealed that ZnO has more potential and versatility for the degradation of organic pollutants under visible light irradiation.

Assessing spatiotemporal trends of total and extreme precipitation in a subtropical highland region: A climate perspective.

This study used a dataset of 30 years (1990-2020) of daily observations from 24 meteorological stations in the northern highlands of Pakistan to assess trends in extreme precipitation indices. The RClimDex model was used to analyze the indices, and the Modified Mann-Kendal test and the Theil-Sen slope estimator were applied to determine trends and slopes, respectively. The results showed a significant decrease in total annual precipitation amount (PRCPTOT) with varying rates of negative trend from -4.44 mm/year to -19.63 mm/year. The total winter and monsoon precipitation amounts were also decreased during the past three decades. The intensity-based precipitation indices (RX1Day, RX5Day, R95p, R99p, and SDII) showed a significant decrease in extreme intensity events over time, while the count of consecutive dry days (CDD) and consecutive wet days (CWD) indicated a significant decrease in duration at multiple stations. The annual counts of days with precipitation more than or equal to 10 mm (R10), 20 mm (R20), and 25 mm (R25) exhibited a significant decrease in frequency of extreme precipitation events, with the decrease more pronounced in the northern parts of the study domain. The findings of this study indicate a significant decline in the intensity, frequency, and extent of precipitation extremes across the northern highlands of Pakistan over the past 30 years.

Determination and risk assessment of pharmaceutical residues in the urban water cycle in Selangor Darul Ehsan, Malaysia.

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.

Analyzing the severity of coronavirus infections in relation to air pollution: evidence-based study from Saudi Arabia.

COVID-19 is one of the major pandemics in history. It has caused various health problems to majority of countries in the world. Several researchers have examined and developed studies regarding concerns on air pollution being considered a major risk factor causing respiratory infections. Such infections are carried out by microorganisms, thus further affecting the immune system. The present study involves the relationship between air pollutants and the total COVID-19 infections along with the estimation of death rates in several regions of Saudi Arabia. The major goal of this study comprises the analysis of the relationship between air pollutants concentration, such as PM10, NO2, CO, SO2, and O3, and the widespread outbreak of COVID-19. This scenario involves the transmission, number of patients, critical cases, and death rates. Results show that the estimation of recorded COVID-19 cases was in the most polluted regions; the mortality rate and critical cases were also more distinct in these regions than in other regions in Saudi Arabia. The finding of this study demonstrates a positive correlation between the mean values of PM10, NO2, CO, and SO2 pollutants. The results represent the significant relationship between air pollution resulting from a high concentration of NO2 and COVID-19 infections and deaths. In addition, a null hypothesis of the relation between other pollutants and COVID-19 infections cannot be rejected. The study also indicates a significant correlation between the means of NO2 and CO and the total number of critical cases. Negative correlations are obtained between the mean of O3 and the total number of cases, total deaths, and critical case per cumulative days.

Dataset of computed N-value and factual N-value traced for Soil Subsurface Profiling.

Soil requires load bearing impact assessment for stability. Therefore, this study aims to utilize the multi-channel analysis surface wave (MASW) for soil subsurface investigation and profiling around Peninsular Malaysia. The standard penetration test (SPT) was conducted for comparison between factual N-value and computed N-value from shear wave velocity (Vs ) obtained from MASW using the Imai and Tonouchi equation. The correlation coefficient (R) and coefficient of determination, (R2 ), showed strong relationship between factual N-value and computed N-value. The model of Vs and factual N-value data distribution is non-normal but the analyzed relationship shows a significant level of p-value < 0.05. The R2 for each location of Vs -N-value relationship are ranging from 0.5 to 0.9.

Modeling of Cu(II) Adsorption from an Aqueous Solution Using an Artificial Neural Network (ANN).

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.

Dataset on leaching properties of coal ashes from Malaysian coal power plant.

Coal combustion by-products (CCPs) (i.e. fly (FA) and bottom (BA) ashes) generated by power plants contain heavy metals. This research presents leaching properties of coal ashes (FA and BA) collected from Jimah coal-fired power station, Port Dickson, Negeri Sembilan using USEPA standard methods namely toxicity characteristic leaching procedure (TCLP), and synthetic precipitation leaching procedure (SPLP). Heavy metals like lead (Pb), zinc (Zn), copper (Cu) and arsenic (As) were quantified using atomic absorption spectrometer (AAS). The leached of heavy metals fluxes were Cu < Zn < Pb < As. As leached the most whilst indicating of possible contamination from As. Overall, the ranges of leached concentration were adhered to permissible limits of hazardous waste criteria for metal (Pb and As) and industrial effluent (Zn and Cu). The presented data has potential reuse as reference for the coal ash concrete mixed design application in construction industries.

Dataset on specific UV absorbances (SUVA254) at stretch components of Perak River basin.

Perak River basin is in Perak state of Peninsular Malaysia. In this research, the river stretch serves as water intake for domestic, agricultural and industrial purposes in Perak Tengah, Hilir Perak and Manjung regions. It is located in mixed use area whilst exposing the river to anthropogenic elements. The sampling locations were conducted at selected points of Perak River namely Tanjung Belanja Bridge (TBB), Water Treatment Plant Parit (WTPP), Parit Town discharge (PTD), Water Treatment Plant Senin (WTPS) and Water Treatment Plant Kepayang (WTPK). The existence of aromatic hydrocarbons in freshwater samples was pre-assessed via qualification analysis; specific ultraviolet absorbance (SUVA254) method at 254 nm of wavelength. The SUVA dataset were 48.38 L/mg-m (TBB), 50.54 L/mg-m (WTPP), 8.05 L/mg-m (PTD), 85.75 L/mg-m (WTPS) and 217.39 L/mg-m (WTPK). The SUVA254 values of fresh water at the river basin have exceeded the water quality standards value equivalent to 2.0 L/mg-m permitted by the Environmental Protection Agency of United States. The exceeding values were an indication of a large portion of aromatic compounds in the water. Qualification analyses evident the existence of water pollutants at treacherous concentrations for public health in freshwater samples of Perak River basin. Thus, this research has presented important findings towards further research and countermeasure for a better alternative of water treatment in Malaysia.

Step by step procedures: Degradation of polycyclic aromatic hydrocarbons in potable water using photo-Fenton oxidation process.

Polycyclic aromatic hydrocarbons (PAHs) are carcinogenic compounds, composed of two or more fused benzene rings and abundantly found in mixed-use areas. Mixed-use areas consist of dense population, urbanization, industrial and agricultural activities. River pollution are common in mixed-use areas and 98% of Malaysia's fresh water supply originates from surface water. The biological degradation, adsorption and advanced oxidation process were documented as the available PAHs treatment for water. To date, the application of the photo-Fenton oxidation process has been reported for the treatment of PAHs from contaminated soil (review paper), landfill leachate, municipal solid waste leachate, sanitary landfill leachate, aniline wastewater, ammunition wastewater and saline aqueous solutions. As for potable water, the application of Fenton reagent was aided with photo treatment or electrolysis not focusing on PAHs removal. •The presented MethodsX was conducted for PAHs degradation analysis in potable water samples using photo-Fenton oxidation process.•The designed reactor for batch experiment is presented.•The batch experiment consists of parameters like concentration of 17 USEPA-PAHs in the prepared aqueous solution (fixed variable), reaction time, pH and molarity ratio of hydrogen peroxide (H2O2): ferrous sulfate (FeSO4).