Research

23 pages, 5028 KiB

Open AccessArticle

Modification of Polylactide-poly (butylene adipate-co-terephthalate) (PLA/PBAT) Mixed-Matrix Membranes (MMMs) with Green Banana Peel Additives for Oil Wastewater Treatment

by Maryam Y. Ghadhban, Khalid T. Rashid, Adnan A. Abdulrazak, Israa Taha Ibrahim, Qusay F. Alsalhy, Zaidoon M. Shakor and Ihsan Hamawand

Water 2024, 16(7), 1040; https://doi.org/10.3390/w16071040 - 04 Apr 2024

Viewed by 524

Abstract

Ultrafiltration membranes are often considered a highly efficient technique for purifying oily wastewater. The primary objective of this research was to improve the performance and antifouling properties of PLA/PBAT membranes used in oily wastewater treatment by incorporating banana peel (BP) nanoparticles. Various characterization [...] Read more.

Ultrafiltration membranes are often considered a highly efficient technique for purifying oily wastewater. The primary objective of this research was to improve the performance and antifouling properties of PLA/PBAT membranes used in oily wastewater treatment by incorporating banana peel (BP) nanoparticles. Various characterization techniques, including field emission scanning electron microscopy (FESEM), wettability analysis, pure water flux measurement, porosity assessment, tensile analysis, and FTIR analysis, were employed to describe the prepared membranes. The results of the FT-IR test revealed that BP nanoparticles were effectively integrated into the PLA/PBAT membrane matrix. The contact angle decreased from 73.7° for the pristine PLA/PBAT membrane to 38.99° for the membrane incorporating 0.05 wt.% BP-NPs, indicating that the nanoparticles enhanced the hydrophilic characteristics of the membranes. A similar trend was observed for the pure water flux of PLA/PBAT/BP membranes, suggesting that membranes with a BP-NP concentration of 0.05 weight percent exhibited the highest pure water flux. This improvement can be attributed to the synergistic effects of the nanoparticles. Additionally, the presence of BP-NPs enhanced the mechanical properties of the membranes. Finally, an ultrafiltration system using oily wastewater as feed was employed to evaluate the performance of the prepared membranes. The finding demonstrated that PLA/PBAT/BP membranes exhibited a higher flux and a greater oil removal efficiency of 105.3 L/m²h and 95.2% compared to neat PLA/PBAT membranes (62 L/m²h and 88%), respectively. Full article

(This article belongs to the Special Issue Water Quality Assessment and Modelling)

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24 pages, 8031 KiB

Open AccessArticle

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

by Enas E. Hussein, Abdessamed Derdour, Bilel Zerouali, Abdulrazak Almaliki, Yong Jie Wong, Manuel Ballesta-de los Santos, Pham Minh Ngoc, Mofreh A. Hashim and Ahmed Elbeltagi

Water 2024, 16(2), 264; https://doi.org/10.3390/w16020264 - 11 Jan 2024

Cited by 2 | Viewed by 1403

Abstract

The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the irrigation water quality index (IWQI), this research [...] Read more.

The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the irrigation water quality index (IWQI), this research aims to evaluate the groundwater quality in Naama, a region in southwest Algeria. Hydrochemical parameters (cations, anions, pH, and EC), qualitative indices (

S A R, R S C, N a %, M H, a n d P I),

as well as geospatial representations were used to determine the groundwater’s suitability for irrigation in the study area. In addition, efficient machine learning approaches for forecasting IWQI utilizing Extreme Gradient Boosting (XGBoost), Support vector regression (SVR), and K-Nearest Neighbours (KNN) models were implemented. In this research, 166 groundwater samples were used to calculate the irrigation index. The results showed that 42.18% of them were of excellent quality, 34.34% were of very good quality, 6.63% were good quality, 9.64% were satisfactory, and 4.21% were considered unsuitable for irrigation. On the other hand, results indicate that XGBoost excels in accuracy and stability, with a low RMSE (of 2.8272 and a high R of 0.9834. SVR with only four inputs (Ca²⁺, Mg²⁺, Na⁺, and K) demonstrates a notable predictive capability with a low RMSE of 2.6925 and a high R of 0.98738, while KNN showcases robust performance. The distinctions between these models have important implications for making informed decisions in agricultural water management and resource allocation within the region. Full article

(This article belongs to the Special Issue Water Quality Assessment and Modelling)

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14 pages, 6561 KiB

Open AccessArticle

Assessing Heavy Metal Contamination Using Biosensors and a Multi-Branch Integrated Catchment Model in the Awash River Basin, Ethiopia

by Li Jin, Cordelia Rampley, Yosef Abebe, Gianbattista Bussi, Trang Quynh To, Duane Ager and Paul G. Whitehead

Water 2023, 15(23), 4073; https://doi.org/10.3390/w15234073 - 24 Nov 2023

Cited by 1 | Viewed by 903

Abstract

Metal pollution in rivers from untreated industrial and domestic wastewater is a major issue in economically developing countries worldwide. The Awash River Basin in Ethiopia is one of those rivers that faces rising heavy metal concentrations due to poor wastewater management and loose [...] Read more.

Metal pollution in rivers from untreated industrial and domestic wastewater is a major issue in economically developing countries worldwide. The Awash River Basin in Ethiopia is one of those rivers that faces rising heavy metal concentrations due to poor wastewater management and loose law enforcement controlling effluent discharge into rivers. In this study, surface water and wastewater samples were collected within the Awash River Basin, with metals analysis using ICP-MS techniques. Acute toxicity of water was determined using new molecular biosensor technology based on engineered luminescent bacteria. A multi-branch Integrated Catchment Model (INCA) for metals, including Arsenic, Cadmium, Chromium, Copper, Lead, Manganese, and Zinc was applied to the Awash River Basin to simulate the impact of tannery discharge on the river water pollution levels and to evaluate a set of treatment scenarios for pollution control. Results show that all samples from tannery wastewater have high levels of metals, such as Chromium and Manganese with high levels of toxicities. River water samples from upper Awash near Addis Ababa showed elevated concentrations of heavy metals due to the untreated wastewater from the dense population and a large number of industries in that area. The modeling scenarios indicate that improved wastewater management will reduce the metal concentration significantly. With a 50% reduction in effluent concentrations, the mean concentrations of heavy metals (such as Chromium) over two years would be able to reach 20 to 50% reduction in river water samples. Full article

(This article belongs to the Special Issue Water Quality Assessment and Modelling)

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16 pages, 5789 KiB

Open AccessArticle

Assessment of Regression Models for Surface Water Quality Modeling via Remote Sensing of a Water Body in the Mexican Highlands

by Alejandro Cruz-Retana, Rocio Becerril-Piña, Carlos Roberto Fonseca, Miguel A. Gómez-Albores, Sandra Gaytán-Aguilar, Marivel Hernández-Téllez and Carlos Alberto Mastachi-Loza

Water 2023, 15(21), 3828; https://doi.org/10.3390/w15213828 - 02 Nov 2023

Cited by 2 | Viewed by 1201

Abstract

Remote sensing plays a crucial role in modeling surface water quality parameters (WQPs), which aids spatial and temporal variation assessment. However, existing models are often developed independently, leading to uncertainty regarding their applicability. This study focused on two primary objectives. First, it aimed [...] Read more.

Remote sensing plays a crucial role in modeling surface water quality parameters (WQPs), which aids spatial and temporal variation assessment. However, existing models are often developed independently, leading to uncertainty regarding their applicability. This study focused on two primary objectives. First, it aimed to evaluate different models for chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) in a surface water body, the J. A. Alzate dam, in the Mexican highland region (R² ≥ 0.78 and RMSE ≤ 16.1 mg/L). The models were estimated using multivariate regressions, with a focus on identifying dilution and dragging effects in inter-annual flow rate estimations, including runoff from precipitation and municipal discharges. Second, the study sought to analyze the potential scope of application for these models in other water bodies by comparing mean WQP values. Several models exhibited similarities, with minimal differences in mean values (ranging from −9.5 to 0.57 mg/L) for TSS, TN, and TP. These findings suggest that certain water bodies may be compatible enough to warrant the exploration of joint modeling in future research endeavors. By addressing these objectives, this research contributes to a better understanding of the suitability of remote sensing-based models for characterizing surface water quality, both within specific locations and across different water bodies. Full article

(This article belongs to the Special Issue Water Quality Assessment and Modelling)

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18 pages, 4545 KiB

Open AccessArticle

Impact of Riparian Buffer Zone Design on Surface Water Quality at the Watershed Scale, a Case Study in the Jinghe Watershed in China

by Cong Liu, Liqin Qu, John Clausen, Tingwu Lei and Xiusheng Yang

Water 2023, 15(15), 2696; https://doi.org/10.3390/w15152696 - 26 Jul 2023

Viewed by 1222

Abstract

This study was conducted to evaluate the impact of riparian buffer zones on water quality in the Jinghe watershed, China. To evaluate the effectiveness of riparian buffers in reducing sediments and nutrients in surface runoff, we employed two validated models: the agricultural non-point [...] Read more.

This study was conducted to evaluate the impact of riparian buffer zones on water quality in the Jinghe watershed, China. To evaluate the effectiveness of riparian buffers in reducing sediments and nutrients in surface runoff, we employed two validated models: the agricultural non-point source pollution model (AnnAGNPS) and the riparian ecosystem management model (REMM). The AnnAGNPS was used to divide the catchment into homogeneous drainage areas and generate upland loadings for the REMM. The REMM model was then utilized to assess the impact of different riparian buffer designs on sediments and nutrient reduction in surface runoff. We tested five designs, including the recommended standard design by the United States Department of Agriculture (USDA). This design with 20 m herbaceous perennials next to the field (Zone 3), followed by a 20 m wide harvestable deciduous forest in the middle (Zone 2), and a 10 m wide non-harvestable deciduous forest adjacent to the river (Zone 1). We also evaluated alternative designs, such as removing Zone 3, removing Zone 2, and reducing the widths of the buffer zones further. For the entire Jinghe watershed, we calculated, compared, and analyzed the annual totals of water inflow, sediment yields, and dissolved nitrogen in surface runoff into and out of Zone 1, 2, and 3 for all the designs. The analysis indicated that the removal efficiency of sediments ranged from 85.7% to 90.8%, and the removal efficiency of dissolved nitrogen in surface runoff ranged from 85.4% to 91.9% for all the designs. It is also indicated that riparian buffer zones are highly effective in reducing sediments and nutrients in agricultural runoff, even with reduced buffer widths. This finding underscores the importance of implementing riparian buffer zones as a valuable approach in the agricultural intensive watershed with constraints for allocating for the creation of standard riparian buffers. Full article

(This article belongs to the Special Issue Water Quality Assessment and Modelling)

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17 pages, 6009 KiB

Open AccessArticle

Predictive Simulation Study on the Effect of Small and Medium River Basin Outfall Treatment Measures on Water Quality Improvement

by Yong Ye, Jilin Zhang, Huimin Liu and Weikun Zhu

Water 2023, 15(13), 2359; https://doi.org/10.3390/w15132359 - 26 Jun 2023

Cited by 1 | Viewed by 923

Abstract

In recent years, the problem of water pollution in middle and small river basins has become increasingly serious. In order to control the water pollution of small- and medium-sized rivers, based upon the hydrodynamic module and the water quality module in MIKE21, this [...] Read more.

In recent years, the problem of water pollution in middle and small river basins has become increasingly serious. In order to control the water pollution of small- and medium-sized rivers, based upon the hydrodynamic module and the water quality module in MIKE21, this paper established a numerical computing model for middle and small river basins by taking the Xiyong River Basin as a typical representative. The excessive levels of nitrogen in the Xiyong River have significantly impaired the quality of the water in terms of the river status, so seven different scenario hypotheses of treatment measures are proposed, based on which the hydrodynamic simulation on the total nitrogen (TN) concentration’s movement was implemented and the time of the nitrogen concentration to reach the standard was predicted. The results showed that the water quality of the Xiyong River improved significantly after the treatment measure, and the annual mean of the TN concentration will decrease by 0.496 mg/L. The results will help the government to control the pollution sources of small and medium river basins. The research of Xiyong River based on the MIKE21 model can be used as the basis for pollution reduction and water quality improvement, which provides an example for the ecological restoration of small and medium rivers. Full article

(This article belongs to the Special Issue Water Quality Assessment and Modelling)

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17 pages, 2714 KiB

Open AccessArticle

Metal Transport in the Mixing Zone of an Estuarine River to the Northern Gulf of Mexico

by Zhenwei Wu, Songjie He and Yi-Jun Xu

Water 2023, 15(12), 2229; https://doi.org/10.3390/w15122229 - 14 Jun 2023

Viewed by 1047

Abstract

To better understand the pollution potential of metals in estuaries heavily concentrated with petrochemical industries, we measured concentrations of total recoverable metals in the lower Calcasieu River in southwestern Louisiana that flows into the northern Gulf of Mexico. Water samples were collected at [...] Read more.

To better understand the pollution potential of metals in estuaries heavily concentrated with petrochemical industries, we measured concentrations of total recoverable metals in the lower Calcasieu River in southwestern Louisiana that flows into the northern Gulf of Mexico. Water samples were collected at six sites along the last 88 km reach of the river monthly between May 2013 and November 2015, during which salinity ranged from 0.02 to 29.5 ppt from upstream to downstream. The samples were analyzed for a series of total recoverable metals, including aluminum (Al), arsenic (As), boron (B), cadmium (Cd), calcium (Ca), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), lithium (Li), magnesium (Mg), manganese (Mn), nickel (Ni), silicon (Si), titanium (Ti), vanadium (V), and zinc (Zn), of which only Al, As, B, Ca, Fe, Li, Mg, Mn, Si, Ti, and Zn had a detection rate higher than 30% over the 31-month study period. In the freshwater portion of the river, Si, Ca, Al, Fe, and Mg showed the highest concentration (8, 5, 4, 2, and 2 mg/L, respectively), while Li, As, Zn, Ti, and B had the lowest concentration (6, 16, 27, 34 and 50 µg/L, respectively). The concentrations of Al and Si declined by more than 30% from the freshwater to the river mouth, but the concentrations of Li and B increased by 61 and 66 times, respectively. None of these metals’ concentrations were found to exceed US EPA standards, but the rapid increase in Li and B concentrations may indicate a potential anthropogenic influence. On average, the Calcasieu River discharged a total of 35,484 tons of the elements each year (or 8059 kg/km²/yr), of which Si, Al, Ca, Fe, and Mg contributed 98%. Three major components of factors were extracted explaining 34, 20, and 13% (cumulative 67%) of the total variation in the metal concentrations. Salinity and pH were the major (>0.3) parameters in component 1 explaining the variability of B and Li; TSS was the major (>0.3) parameter in component 2 explaining the variation of Al, Fe, and Ti concentration; and temperature and DO% were the major (>0.3) parameters in component 3 explaining the variation of Mn concentration. Further studies on riverbed sediment metals and their effect on metal concentrations in surface water can help understand the metal sources and their potential effects on coastal aquatic ecosystems. Full article

(This article belongs to the Special Issue Water Quality Assessment and Modelling)

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16 pages, 8149 KiB

Open AccessArticle

Total Maximum Daily Load Application Using Biological Oxygen Demand, Chemical Oxygen Demand, and Ammoniacal Nitrogen: A Case Study for Water Quality Assessment in the Perai River Basin, Malaysia

by Siti Multazimah Mohamad Faudzi, Danial Nakhaie Mohd Souhkri, Muhammad Fitri Mohd Akhir, Hamidi Abdul Aziz, Muhammad Zaki Mohd Kasim, Nor Azazi Zakaria and Noor Aida Saad

Water 2023, 15(6), 1227; https://doi.org/10.3390/w15061227 - 21 Mar 2023

Cited by 2 | Viewed by 2318

Abstract

Water shortage has been an issue for urbanized areas. For the Penang state in Malaysia, it is forecast that there will be a significant increase in water demand in the future. Penang authorities in Malaysia are trying to find an alternative water source [...] Read more.

Water shortage has been an issue for urbanized areas. For the Penang state in Malaysia, it is forecast that there will be a significant increase in water demand in the future. Penang authorities in Malaysia are trying to find an alternative water source to overcome the problem, with one of the options being the Perai River catchment. However, the river water quality was found to be polluted and not suitable to be used for water extraction for domestic consumption. This paper aims to study the pollution level variation due to changes in rainfall during the year in the Perai River Basin, and estimate the TMDL of the river in a particular case for BOD, COD, and NH₃N parameters. A water quality model was developed for the Perai River, Jarak River and Kulim River using InfoWorks ICM. The year 2016 was selected as a model event due to data availability. BOD, COD and NH₃N concentrations were used for TMDL calculation, and the load duration curve approach was used to estimate TMDL. The tidal effect at the downstream of the Perai River was found to impact the data analysis in the river stretch. It was found that pollutant load exceedance was the highest during the rainy season and the problematic pollutant was NH₃N. Thus, local authorities need to focus on tidal and seasonal change factors when developing action plans to manage water quality issues in this basin. Full article

(This article belongs to the Special Issue Water Quality Assessment and Modelling)

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18 pages, 7018 KiB

Open AccessArticle

Water Quality Analysis of a Tropical Reservoir Based on Temperature and Dissolved Oxygen Modeling by CE-QUAL-W2

by Humberto Tavera-Quiroz, Mauricio Rosso-Pinto, Gerardo Hernández, Samuel Pinto and Fausto A. Canales

Water 2023, 15(6), 1013; https://doi.org/10.3390/w15061013 - 07 Mar 2023

Viewed by 2033

Abstract

Water quality impacts on water bodies such as reservoirs are strongly influenced by the hydrodynamics of the system. Although multiple models might be applied, they are limited by the simplification of the variables. In this study, a two-dimensional public domain model, CE-QUAL-W2, was [...] Read more.

Water quality impacts on water bodies such as reservoirs are strongly influenced by the hydrodynamics of the system. Although multiple models might be applied, they are limited by the simplification of the variables. In this study, a two-dimensional public domain model, CE-QUAL-W2, was adapted to test whether it would generate an accurate hydrodynamic simulation of the URRÁ Reservoir in Córdoba, Colombia, to understand water quality. The variables to be modeled were temperature and dissolved oxygen due to their importance in ecological terms. Thus, trial and error techniques were used to calibrate and validate the model, varying different parameters such as the wind shelter coefficient (WSC). Although the model accurately predicted the hydrodynamic part by having daily flow information, significant modifications to the eddy diffusivity coefficient were required to simulate acceptable longitudinal currents. This research shows that the CE-QUAL-W2 model fits adequately to tropical lentic systems. However, it is recommended that, for future studies, the modeling be adjusted using hourly data, especially in areas where inflow and boundary conditions are unstable. Full article

(This article belongs to the Special Issue Water Quality Assessment and Modelling)

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23 pages, 7886 KiB

Open AccessArticle

A Machine Learning Approach to Predict Watershed Health Indices for Sediments and Nutrients at Ungauged Basins

by Ganeshchandra Mallya, Mohamed M. Hantush and Rao S. Govindaraju

Water 2023, 15(3), 586; https://doi.org/10.3390/w15030586 - 02 Feb 2023

Cited by 1 | Viewed by 3721

Abstract

Effective water quality management and reliable environmental modeling depend on the availability, size, and quality of water quality (WQ) data. Observed stream water quality data are usually sparse in both time and space. Reconstruction of water quality time series using surrogate variables such [...] Read more.

Effective water quality management and reliable environmental modeling depend on the availability, size, and quality of water quality (WQ) data. Observed stream water quality data are usually sparse in both time and space. Reconstruction of water quality time series using surrogate variables such as streamflow have been used to evaluate risk metrics such as reliability, resilience, vulnerability, and watershed health (WH) but only at gauged locations. Estimating these indices for ungauged watersheds has not been attempted because of the high-dimensional nature of the potential predictor space. In this study, machine learning (ML) models, namely random forest regression, AdaBoost, gradient boosting machines, and Bayesian ridge regression (along with an ensemble model), were evaluated to predict watershed health and other risk metrics at ungauged hydrologic unit code 10 (HUC-10) basins using watershed attributes, long-term climate data, soil data, land use and land cover data, fertilizer sales data, and geographic information as predictor variables. These ML models were tested over the Upper Mississippi River Basin, the Ohio River Basin, and the Maumee River Basin for water quality constituents such as suspended sediment concentration, nitrogen, and phosphorus. Random forest, AdaBoost, and gradient boosting regressors typically showed a coefficient of determination

R^{2} > 0.8

for suspended sediment concentration and nitrogen during the testing stage, while the ensemble model exhibited

R^{2} > 0.95

. Watershed health values with respect to suspended sediments and nitrogen predicted by all ML models including the ensemble model were lower for areas with larger agricultural land use, moderate for areas with predominant urban land use, and higher for forested areas; the trained ML models adequately predicted WH in ungauged basins. However, low WH values (with respect to phosphorus) were predicted at some basins in the Upper Mississippi River Basin that had dominant forest land use. Results suggest that the proposed ML models provide robust estimates at ungauged locations when sufficient training data are available for a WQ constituent. ML models may be used as quick screening tools by decision makers and water quality monitoring agencies for identifying critical source areas or hotspots with respect to different water quality constituents, even for ungauged watersheds. Full article

(This article belongs to the Special Issue Water Quality Assessment and Modelling)

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