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Abstract This study presents an innovative approach for predicting water and groundwater quality indices (WQI and GWQI) in the Eastern Province of Saudi Arabia, addressing critical challenges of scarcity and pollution in arid regions. Recent literature highlights the increasing attention towards WQI based on water pollution index (WPI) and GWQI as essential tools for simplifying complex hydrogeological data, thereby facilitating effective groundwater management and protection. Unlike previous works, the present research introduces a novel hybrid method that integrates non-parametric kernel Gaussian learning (GPR), adaptive neuro-fuzzy inference system (ANFIS), and decision tree (DT) algorithms. This approach marks the first application of a non-parametric kernel for groundwater quality pollution index prediction in Saudi Arabia, offering a significant advancement in the field. Through laboratory analysis and the combination of various machine learning (ML) techniques, this study enhances prediction capabilities, particularly for unmonitored sites in arid and semi-arid regions. The study’s objectives include feature engineering based on dependency sensitivity analysis to identify the most influential variables affecting WQI and GWQI, and the development of predictive models using ANFIS, GPR, and DT for both indices. Furthermore, it aims to assess the impact of different data portions on WQI and GWQI predictions, exploring data divisions such as (70% / 30%), (60% / 40%), and (80% / 20%) for training and testing phase, respectively. By filling a critical gap in water resource management, this research offers significant implications for the prediction of water quality in regions facing similar environmental challenges. Through its innovative methodology and comprehensive analysis, this study contributes to the broader effort of managing and protecting water resources in arid and semi-arid areas. The result proved that GPR-M1 exhibited exceptional testing phase accuracy with RMSE = 0.0169 for GWQI. Similarly, for WPI, the ANFIS-M1 achieved high testing predictive skills with RMSE = 0.0401. The results emphasize the critical role of data quality and quantity in training for enhancing model robustness and prediction precision in water quality assessment.

ABSTRACTManaging sustainable and digital transformations in the water industry is a challenge. The identification of freshwater resources for green hydrogen production is necessary for the energy transition away from fossil fuels such as oil and gas. This can be achieved through the sustainable flood retention basin (SFRB) concept for identifying optimal locations for the retention of surface water. This unbiased concept helps in conflict resolution between stakeholders with water rights. Moreover, it serves as a simple tool to produce a digital water supply atlas based on dimensionless parameters with the short names’ population density, location advantage, precipitation, groundwater level, water rights, SFRB, excess running water and proximity to networks. While the first five parameters can be determined during a desk study, the last three parameters require site visits for verification purposes. Germany was used as a representative case study country to demonstrate the proposed methodology to obtain a water atlas based on mapped weighted values. The Kaiserstuhl in Germany served as a worked calculation example indicating well above average potential for water storage using existing retention basins. The water atlas can be used to supplement a (green) hydrogen atlas, supporting decision-makers in building hydrogen infrastructure.

Abstract The agricultural sector faces challenges in managing water resources efficiently, particularly in arid regions dealing with water scarcity. To overcome water stress, treated wastewater (TWW) is increasingly utilized for irrigation purpose to conserve available freshwater resources. There are several critical aspects affecting the suitability of TWW for irrigation including salinity which can have detrimental effects on crop yield and soil health. Therefore, this study aimed to develop a novel approach for TWW salinity prediction using artificial intelligent (AI) ensembled machine learning approach. In this regard, several water quality parameters of the TWW samples were collected through field investigation from the irrigation zones in Al-Hassa, Saudi Arabia, which were later assessed in the lab. The assessment involved measuring Temperature (T), pH, Oxidation Reduction Potential (ORP), Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Salinity, through an Internet of Things (IoT) based system integrated with a real-time monitoring and a multiprobe device. Based on the descriptive statistics of the data and correlation obtained through the Pearson matrix, the models were formed for predicting salinity by using the Hammerstein-Wiener Model (HWM) and Support Vector Regression (SVR). The models’ performance was evaluated using several statistical indices including correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), and root mean square error (RMSE). The results revealed that the HWM-M3 model with its superior predictive capabilities achieved the best performance, with R2 values of 82% and 77% in both training and testing stages. This study demonstrates the effectiveness of AI-ensembled machine learning approach for accurate TWW salinity prediction, promoting the safe and efficient utilization of TWW for irrigation in water-stressed regions. The findings contribute to a growing body of research exploring AI applications for sustainable water management.

Abstract One of the direct and unavoidable consequences of global warming-induced rising temperatures is the more recurrent and severe heatwaves. In recent years, even countries like Malaysia seldom had some mild to severe heatwaves. As the Earth's average temperature continues to rise, heatwaves in Malaysia will undoubtedly worsen in the future. It is crucial to characterize and monitor heat events across time to effectively prepare for and implement preventative actions to lessen heatwave's social and economic effects. This study proposes heatwave-related indices that take into account both daily maximum (Tmax) and daily lowest (Tmin) temperatures to evaluate shifts in heatwave features in Peninsular Malaysia (PM). Daily ERA5 temperature dataset with a geographical resolution of 0.25° for the period 1950–2022 was used to analyze the changes in the frequency and severity of heat waves across PM, while the LandScan gridded population data from 2000 to 2020 was used to calculate the affected population to the heatwaves. This study also utilized Sen's slope for trend analysis of heatwave characteristics, which separates multi-decadal oscillatory fluctuations from secular trends. The findings demonstrated that the geographical pattern of heatwaves in PM could be reconstructed if daily Tmax is more than the 95th percentile for 3 or more days. The data indicated that the southwest was more prone to severe heatwaves. The PM experienced more heatwaves after 2000 than before. Overall, the heatwave-affected area in PM has increased by 8.98 km2/decade and its duration by 1.54 days/decade. The highest population affected was located in the central south region of PM. These findings provide valuable insights into the heatwaves pattern and impact.

This study aims to identify the influential parameters and heavy metals in water and assess the water quality classification at the Alpine glacial lakes and rivers in three districts of Pakistan. For this purpose, nine water quality parameters (Cd, Cr, Pb, Ni, Fe, As, and TDS) in mg/L, pH, Ec µS/Cm are used to compute the Water Quality Index (WQI). The Boruta approach was utilized for the identification of influential parameters associated with the water quality classes. Moreover, we employed supervised machine learning models, including a decision tree, the k-nearest neighbor method, a neural network model (multi-layer perception), a support vector machine, and a random forest, to predict and validate the water quality class. The performance of all algorithms is assessed by an accuracy metric. The accuracy rates for the validation set were observed to be 83% for the decision tree model, 75% for the K-nearest neighbor method, 83% for the neural network, 88% for the support vector machine, and 88% for the random forest model. Water quality assessments for observed locations specify significant insights, revealing that 49% of the locations exhibit low water quality. According to the current study, the government should address problems with water quality in Pakistan’s impacted areas by implementing suitable measures designed water monitoring systems and innovative technologies.

In this study, a novel method for the high-performance treatment of oily wastewater was introduced using a tungsten (VI) oxide (WO3)/MXene composite membrane based on poly (arylene ether sulfone) (PAES). Composite membranes were fabricated with superhydrophilic (SH) and superoleophobic (SO) characteristics, which allow for the high-performance treatment of oily wastewater. The fabricated composite membrane can also photodegrade organic types of pollutants with just a short period of UV, enabling self-cleaning and anti-fouling properties. Moreover, the comprehensive characterization of the composite membrane through FTIR, SEM, and XRD analyses yielded valuable insights. The FTIR analysis revealed the characteristic peaks of WO3, MXene, PAES, and the synthesized composite membrane, providing essential information on the chemical composition and properties of the materials. The XRD results demonstrated the crystal structures of WO3, MXene, PAES, and the synthesized composite membrane, further enhancing our understanding of the composite membrane. Additionally, the SEM images illustrated the surface and cross-section of the fabricated membranes, highlighting the differences in pore size and porosity between the PAES membrane and the WO3–MXene composite membrane, which directly impact permeate flux. The study showed that the composite membrane had a remarkable recovery time of only 0.25 h, and the efficiency of the separation process and water flux recovered to 99.98% and 6.4 L/m2.h, respectively. The joint influence of WO3 and MXene on composite membranes degraded contaminants into non-polluting substances after sunlight irradiation. This process effectively solves the treatment performance and decrease in permeate flux caused by contamination. The technology is membrane-based filtration, which is a simple and advanced method for treating polluted water. This innovative work offers promising solutions to address water pollution challenges and holds potential for practical applications from a self-cleaning and anti-fouling point of view.

Selecting appropriate climate change scenarios is crucial, as it influences the outcomes of climate change impact studies. Several storylines could be used to investigate the sensitivity of water resource schemes to weather variability and improve policymakers’ adaptation strategies. This study proposes a comprehensive and generic methodology for assessing the future climate change impact on semi-arid and arid zones at the basin scale by comparing delta perturbation scenarios to the outcomes of seven collections of GCMs (general circulation models). The findings indicate that the two scenarios predicted nearly identical declines in average reservoir discharges over a monthly timescale. Consequently, their maximum values are almost similar. The projected decrease in the streamflow for the period 2080–2099 is approximately 48%—the same as the ratio from the delta perturbation scenario of Future16 (a 30% precipitation decrease and a 30% potential evapotranspiration increase). Furthermore, delta perturbation scenarios allow the impacts of model sensitivity to climate change to be clearly identified in relation to GCM scenarios. Delta perturbation scenarios allow for an extensive collection of possible climate changes at the regional scale. In addition, delta perturbation scenarios are simpler to create and use; therefore, they might complement GCM scenarios.

Contamination in coastal regions attributed to fluoride and nitrate cannot be disregarded, given the substantial environmental and public health issues they present worldwide. For effective decontamination, it is pivotal to identify regional pollution hotspots. This comprehensive study was performed to assess the spatial as well as indexical water quality, identify contamination sources, hotspots, and evaluate associated health risks pertaining to nitrate and fluoride in the Al-Hassa region, KSA. The physicochemical results revealed a pervasive pollution of the overall groundwater. The dominant water type was Na-Cl, indicating saltwater intrusion and reverse ion exchange impact. Spatiotemporal variations in physicochemical properties suggest diverse hydrochemical mechanisms, with geogenic factors primarily influencing groundwater chemistry. The groundwater pollution index varied between 0.8426 and 4.7172, classifying samples as moderately to very highly polluted. Similarly, the synthetic pollution index (in the range of 0.5021–4.0715) revealed that none of the samples had excellent water quality, with various degrees of pollution categories. Nitrate health quotient (HQ) values indicated chronic human health risks ranging from low to severe, with infants being the most vulnerable. Household use of nitrate-rich groundwater for showering and cleaning did not pose significant health risks. Fluoride HQ decreased with age, and children faced the highest risk of fluorosis. The hazard index (HI) yielded moderate- to high-risk values. Nitrate risks were 1.21 times higher than fluoride risks, as per average HI assessment. All samples fell into the vulnerable category based on the total hazard index (THI), with 88.89% classified as very high risk. This research provides valuable insights into groundwater quality, guiding water authorities, inhabitants, and researchers in identifying safe water sources, vulnerable regions, and human populations. The results highlight the need for appropriate treatment techniques and long-term coastal groundwater management plans.

Despite the many articles about activated carbon with different precursors in adsorption process, no in-depth research has been carried out to understand the causes of the difference in surface adsorption characteristics of activated carbon with different precursors and different activation processes. In this work, the ability of two active carbon adsorbents made of walnut shell and peach kernel by two chemical and physical methods (totally 4 different types of activated carbon) in treatment of oily wastewater including diesel, gasoline, used oil or engine lubricant has been compared. The results show that the chemical activated peach carbon active with 97% hardness has provided the highest hardness and physical activated walnut carbon active has obtained the lowest hardness value (87%). It is also found that peach activated carbon has a higher iodine number than walnut activated carbon, and this amount can be increased using chemical methods; Therefore, the highest amount of Iodine Number is related to Peach activated carbon that is made by chemical method (1230 mg/g), and the lowest amount of iodine number is seen in walnut activated carbon that is made by physical method (1020 mg/g). moreover, the pore diameter of physical activated carbon is lower than chemical activated carbon in all cases. So that the pore diameter of chemical activated peach carbon active is equal to 22.08 μm and the measured pore diameter of physical activated peach carbon active is equal to 20.42 μm. These values for walnut are obtained as 22.74 μm and 21.86 μm, respectively. Furthermore, the temperature and pH effects on the adsorption of different synthesized oily wastewater was studied and it was found that a decrease in adsorption can be seen with an increase in temperature or decreasing the pH value, which can be referred to this fact that the process of adsorption is an exothermic process. Finally, to analyze the compatibility of adsorption isotherms with experimental data and to predict the adsorption process, three different isotherms named Langmuir, Temkin, and Freundlich isotherms were applied and their parameters were correlated. The correlation results show that the Langmuir isotherm had the best correlation in all cases compared to the Freundlich and Temkin isotherms, based on the correlation coefficient, and the calculated R2 values which was greater than 0.99 in all the studied cases.

As an alternative wood source for biochar and a cost-effective renewable energy source, sustainable biomass production based on fast-growing willows irrigated with treated wastewater has been explored. Salix alba L. and Salix viminalis L. were selected for assessment of their potentially high woody biomass productivity and phytoremediation efficiency when irrigated with greywater treated by floating treatment wetlands. Both Salix species produced significantly (p < 0.05) high woody biomass in the second harvest, with a significantly higher fresh woody biomass weight with higher water content (53%) for S. viminalis compared to S. alba. The dry biomass weight of S. alba was greater than of S. viminalis at the first harvest. The element accumulations in substrates changed significantly after irrigation, with greywater compared to the raw substrate following this order: Mg > Fe > Al > Cr > Mn > Cd > Cu > B. Element concentrations accumulated in twigs of S. alba following this order: Ca > Mg > Na > Mn > Zn > Fe > Al > Cd > Cu > Cr > Ni > B, but for S. viminalis the order was Ca > Mg > Mn > Zn > Na > Fe > Al > Cd > Cu > Ni > Cr > B. The accumulations of Al, B, Ca, Fe, Mg, Mn, and Ni were significantly greater in S. alba leaves compared to their twigs, which showed significantly high accumulations of Na and Zn. The accumulations of Al, B, Ca, Fe, Mg, Mn, and Na were significantly greater in S. viminalis leaves compared to their twigs.

Abstract Partitioning a water distribution network into several district metered areas is beneficial for its management. Partitioning a network according to its node features and connections remains a challenge. A recent study has realized water network partitioning based on node features or pipe connections individually. This study proposes an unsupervised clustering method for nodes based on a graph neural network, which uses graph attention technology to update node features based on the connections and a neural network to cluster nodes. The similarity between nodes located in each area and the balance of the total water demand between areas are optimized, and the importance of the boundary pipes is calculated to determine the installation position of flowmeters and valves. Three water distribution networks with different structures and sizes are used to verify the proposed model. The results show that the average location differences (LocDiffs) within the areas of the three networks completed by partitioning are 0.12, 0.07, and 0.06, and the total demand differences (DemDiffs) between areas are 0.13, 0.27, and 0.29, respectively. The LocDiff and DemDiff of the proposed method decreased by 6% and 55%, respectively, when compared to the traditional clustering method. Additionally, the proposed method for calculating the importance of boundaries provides an objective basis for boundary closure. When the same number of boundaries are closed, the comprehensive impact of the proposed method on the pipe network decreases by 17.1%. The proposed method can be used in practical applications because it ensures a highly reliable and interpretive water distribution network partitioning method.

Flat plate solar collectors (FPSCs) are the most often used as solar collectors due to their easiness of installation and usage. The current research investigates the energy efficiency of FPSC using different mass concentration with varied base fluids containing Graphene nanofluids (T-Gr). Mass concentration of 0.1%-wt., 0.075%-wt., 0.050%-wt. and 0.025%-wt. were mixed with ethylene glycol (EG) and distilled water (DW) in different rations. The operating conditions were volumetric flowrate (1.5, 1 and 0.5) LPM 50 °C-input fluid temperature and 800 W/m2-global solar irradiation. Scanning electron microscope (SEM) and energy dispersive X-ray (EDX) were used to synthesize the thermally treated nanomaterial. The theoretical investigation indicated that using T-Gr nanosuspensions increased the FPSC efficiency in comparison with the host fluid for all examined mass concentrations and volumetric flowrates. In quantitative terms, the maximum thermal effectiveness improvement for the EG, (DW:70 + EG:30) and DW:EG (DW:50 + EG:50) and using flowrates of (1.5, 1 and 0.5) LPM were 12.54%, 12.46% and 12.48%. In addition, the research results pointed that the essential parameters (i.e., loss energy (FRUL)) and gain energy (FR(τα)) of the T-Gr nanofluids were increased significantly.

The El Nino Southern Oscillation (ENSO) phenomenon is devastating as it negatively impacts global climatic conditions, which can cause extreme events, including floods and droughts, which are harmful to the region’s economy. Pakistan is also considered one of the climate change hotspot regions in the world. Therefore, the present study investigates the effect of the ENSO on extreme precipitation events across the Upper Indus Basin. We examined the connections between 11 extreme precipitation indices (EPIs) and two ENSO indicators, the Southern Oscillation Index (SOI) and the Oceanic Niño Index (ONI). This analysis covers both annual and seasonal scales and spans the period from 1971 to 2019. Statistical tests (i.e., Mann–Kendall (MK) and Innovative Trend Analysis (ITA)) were used to observe the variations in the EPIs. The results revealed that the number of Consecutive Dry Days (CDDs) is increasing more than Consecutive Wet Days (CWDs); overall, the EPIs exhibited increasing trends, except for the Rx1 (max. 1-day precipitation) and Rx5 (max. 5-day precipitation) indices. The ENSO indicator ONI is a temperature-related ENSO index. The results further showed that the CDD value has a significant positive correlation with the SOI for most of the UIB (Upper Indus Basin) region, whereas for the CWD value, high elevated stations gave a positive relationship. A significant negative relationship was observed for the lower portion of the UIB. The Rx1 and Rx5 indices were observed to have a negative relationship with the SOI, indicating that El Nino causes heavy rainfall. The R95p (very wet days) and R99p (extreme wet days) indices were observed to have significant negative trends in most of the UIB. In contrast, high elevated stations depicted a significant positive relationship that indicates they are affected by La Nina conditions. The PRCPTOT index exhibited a negative relationship with the SOI, revealing that the El Nino phase causes wet conditions in the UIB. The ONI gave a significant positive relationship for the UIB region, reinforcing the idea that both indices exhibit more precipitation during El Nino. The above observations imply that while policies are being developed to cope with climate change impacts, the effects of the ENSO should also be considered.

Activated carbon has many potential applications in both the liquid and gas phases. How activated carbon can help practitioners in industry is explained. This practical teaching article introduces the first part of the special issue on Recent Advances in Applied Activated Carbon Research by providing a handbook explaining the basic applications, technologies, processes, methods and material characteristics to readers from different backgrounds. The aim is to improve the knowledge and understanding of the subject of activated carbon for non-adsorption experts such as professionals in industry. Therefore, it is written in a comprehensible manner and dispenses with detailed explanations to complex processes and many background references. This handbook does not claim to be complete and concentrates only on the areas that are of practical relevance for most activated carbon applications. Activated carbon and its activation and reactivation are initially explained. Adsorption and relevant processes are outlined. The mechanical, chemical and adsorption properties of activated carbon are explained. The heart of the handbook outlines key application technologies. Other carbonaceous adsorbents are only introduced briefly. The content of the second part of the special issue is highlighted at the end.

Temperature and coagulant types have an important impact on the quantity and quality of the residue (sludge) in water and wastewater treatment processes. Temperature influences water viscosity and the distribution of the coagulant in water. Coagulants can promote the agglomeration of fine particles into larger flocs so that they can be more easily separated from the water. Experiments have been conducted to explore the relationship between temperature (16-26°C), the type of coagulant, and sludge dewaterability (estimated using the capillary suction time (CST)). Alum, Ferric, and Moringa oleifera Lam were used as coagulants. The influences of different mixer shapes, turbidity values, and flocs sizes on sludge dewaterability have been assessed. The results show that ferric chloride was unaffected by temperature, whereas alum and M. oleifera performances were influenced by temperature. CST results using the coagulant ferric chloride, regardless of mixer shape, turbidity, and floc size, were insensitive to temperature differences.

To accurately manage water resources, a precise prediction of reference evapotranspiration (ETref) is necessary. The best empirical equations to determine ETref are usually the temperature-based Baier and Robertson (BARO), the radiation-based Jensen and Haise (JEHA), and the mass transfer-based Penman (PENM) ones. Two machine learning (ML) models were used: least squares support vector regression (LSSVR) and ANFIS optimized using the particle swarm optimization algorithm (ANFPSO). These models were applied to the daily ETref at 100 synoptic stations for different climates of Iran. Performance of studied models was evaluated by the correlation coefficient (R), coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), scatter index (SI) and the Nash-Sutcliffe efficiency (NSE). The combination-based ML models (LSSVR4 and ANFPSO4) had the lowest error (RMSE = 0.34–2.85 mm d−1) and the best correlation (R = 0.66–0.99). The temperature-based empirical relationships had more precision than the radiation- and mass transfer-based empirical equations.

This paper aims to develop a model for calculating the hydraulic and water quality parameters of wastewater within sewers. Information from the wastewater collection network and the transmission line in Birjand were used to verify the model performance. The parameters used for modelling quality changes include the yield constant for biomass (YH), the maximum specific growth rate (μH), the saturation constant for dissolved oxygen (KOG) and the saturation constant for readily biodegradable substrate within a biofilm (KSF), as well as the Gauckler–Manning–Strickler coefficient (n). They were selected from references and were verified at the calibration stage comparing measurements with the modelling values. Inputs of the created model are the average concentrations of dissolved oxygen and chemical oxygen demand of the incoming wastewater, the flow rate of wastewater at the exit point of the network, physical characteristics of the pipes and the height of drops within the sewer network. The amount of dissolved oxygen at different positions of the sewer network was calculated. The acceptable calculated sum of squares of errors and the correlation coefficient (R2) of the calibrated model for dissolved oxygen were 1.6872 and 0.77, respectively. HIGHLIGHTS A model was developed to calculate the hydraulic and qualitative parameters in sewers.; The model was calibrated using the flow rate, organic strength and the dissolved oxygen.; A sensitivity analysis of the dissolved oxygen change was performed.; Dissolved oxygen was determined using the flow rate at the transmission line.; The coefficient of flow change over time can be used to calculate dissolved oxygen.;

Hydropower is one of the significant renewable energy resources. It is regularly requested at peak time steps to meet the load requirements of power systems resources allocation. Therefore, modeling the optimal operation of hydropower systems to maximize the entire energy production of reservoir systems can be a vital task for energy investment. Deriving optimal unknown decision parameters of these reservoir systems is a nonlinear, nonconvex, and complex optimization problem. Herein, a novel optimization algorithm, called an accelerated version of gradient-based optimization (AGBO), is developed to solve a complex multi-reservoir hydropower system. This advised technique uses an efficient adaptive control parameters mechanism to stabilize the global and local search; utilizing an enhanced local escaping operator (ELEO) to extend the chances of getting away from local optima; expanding the exploitation search by applying the sequential quadratic programming (SQP) technique. At first, the developed AGBO algorithm is employed to solve the optimal operation of a complex 10-reservoir hydropower system. Secondly, the possibility of the AGBO algorithm within the global optimization problems is illustrated by numerical tests of 23 mathematical benchmark functions. Optimal results show that the proposed AGBO can approach to 0.9999% of the optimal global solution. As a result, the advised method is the most superior one compared to the other advanced optimization algorithms for maximizing the load demands in hydropower system. In conclusion, this offers a productive tool to solve the complex hydropower multi-reservoir optimization systems.

Climate change and the continuing increase in human population creates a growing need to tackle urban stormwater problems. One promising mitigation option is by using nature-based solutions (NBS) – especially sustainable urban stormwater management technologies that are key elements of NBS action. We used a synthesis approach to compile available information about urban stormwater retention capacity of the most common sustainable urban drainage systems (SUDS) in different climatic conditions. Those SUDS targeting stormwater management through water retention and removal solutions (mainly by infiltration, overland flow and evapotranspiration), were addressed in this study. Selected SUDS were green roofs, bioretention systems (i.e. rain gardens), buffer and filter strips, vegetated swales, constructed wetlands, and water-pervious pavements. We found that despite a vast amount of data available from real-life applications and research results, there is a lack of decisive information about stormwater retention and removal capacity of selected SUDS. The available data show large variability in performance across different climatic conditions. It is therefore a challenge to set conclusive widely applicable guidelines for SUDS implementation based on available water retention data. Adequate data were available only to evaluate the water retention capacity of green roofs (average 56±20%) and we provide a comprehensive review on this function. However, as with other SUDS, still the same problem of high variability in the performance (min 11% and max 99% of retention) remains. This limits our ability to determine the capacity of green roofs to support better planning and wider implementation across climate zones. The further development of SUDS to support urban stormwater retention should be informed by and developed concurrently with the adaptation strategies to cope with climate change, especially with increasing frequency of extreme precipitation events that lead to high volumes of stormwater runoff.

Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T°, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock–water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training “determination coefficient (R2)” (R2 = 0.99 and 0.97) and testing (R2 = 0.97 and 0.76). The presented models’ promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments.

Non-climatic reasons, such as station replacement and changing the measurement device and calculation method, may make climate data unrepresentative of the actual variation of the regional climate. Data quality control and homogenization tests for climate data are critical. Thus, this study aims to evaluate the quality and homogeneity level of precipitation time series in arid and semi-arid climatic zones and specify the breakpoint in the datasets. The proposed methodology has been used to conduct arid and semi-arid representative case studies comprising 40 annual precipitation series for Iraq between 1979 and 2014. A Box-Cox transformation has been used to adjust the non-normally distributed datasets. Outliers have been censored by truncating extreme values. The results of the outliers indicate that they may be caused more by climate variability than by human-induced reasons. Homogeneity adjustments have been developed by applying these four homogeneity tests: Pettitt’s test, the Standard Normal Homogeneity method, Buishand’s test, and von Neumann’s check. Approximately 40% of the series (i.e., 16 stations out of 40) were homogeneous. Each homogeneity test was evaluated separately, and non-homogeneous stations were identified. Then, the series was classified into three groups that were assigned the labels “useful”, “doubtful”, and “suspect”. The results indicated that twenty-one stations were associated with the class ‘suspect’, three of the stations belonged to the class ‘doubtful’, and sixteen locations were within the class ‘useful’. Furthermore, the data analysis indicated no influence of the outliers on the results of the homogeneity tests. Accordingly, the study recommends further research on homogeneity tests that can be applied without considering outlier tests for similar case studies.

Global warming is by far the most significant issue caused by climate change. Over the past few decades, heat stress has intensified into a serious issue that has a negative impact on crop production. Hence, it is crucial to modify cultivation systems to cope with this kind of stress, particularly in arid dry regions. In comparison to open-field cultivation, tomato production under protected cultivation techniques in walk-in tunnels that are suited for different farmers’ financial abilities was evaluated during the late summer season. The studied tunnels included a shaded net tunnel with natural ventilation, net tunnel with a fogging system and plastic tunnel with evaporative cooling (wet pad and fans). For the operation of fogging and evaporative cooling systems, solar energy was used as a sustainable, eco-friendly energy source. The results indicated that the solar energy system successfully operated the studied cooling systems. All studied protective cultivation techniques mitigated heat stress on tomato plant and improved the microclimate under walk-in tunnels. Moreover, evaporative cooling and fogging systems significantly increased plant leaf area, cell membrane efficiency and the contents of chlorophyll, relative water and proline compared to the net tunnel with natural ventilation. Furthermore, a marked reduction in physiological disorders was noticed. Improved physiological and biochemical parameters and limited physiological diseases led to higher fruit set, marketable fruit yield and total productivity. The percentage of marketable fruit yield increased by around 31.5% with an evaporative cooling system, 28.8% with a fogging system and 17% with a shaded net tunnel with no positive cooling as compared to an open field. However, the plants grown in open-field cultivation without protection significantly deteriorated from heat stress and had a high incidence of physiological disorders. The most incident physiological disorders were blossom-end rot, cracking, internal white tissues, sunscald, puffiness, blotchy ripening, cat face and exserted stigma. It is recommended to use a solar energy system to modify microclimate conditions through fogging or evaporative cooling under walk-in tunnels to ameliorate heat stress on grown tomato in the late summer season for higher fruit yield and fewer physiological disorders.

Businesses in the present era are dealing with a complex and unprecedented brew of social, environmental, and technological trends. This requires sophisticated, sustainability-based management. Yet organizations are often reluctant to place sustainability core to their business strategies with the mistaken belief that the costs associated with environmental investments outweigh the benefits. The Global Climate Risk Index has placed Pakistan on 5th position in the list of nations, most susceptible to climate change in its recent report. Pakistan lost the lives of 9,989 people, incurred economic losses of $ 3.8 billion, and faced 152 shocking climates between 1999 and 2018. Based on this information, it is established that Pakistan’s susceptibility to climate change is growing unprecedentedly and industrial pollution is one of the biggest contributors in this respect. The country needs to take emergency measures to address this issue. With this background, the present study aims to investigate the impact of environmental sustainability on environmental and economic performance (EP) with the mediating effect of environmental innovation (EI) in the manufacturing sector of Pakistan. The results show that environmental sustainability is a significant predictor of environmental performance and EP and EI mediates this relationship. The findings of the present study provide better insights to policymakers to address the environmental degradation, resulting from industrial pollution.

This study aimed to analyze stressors to which medical staff is vulnerable due to the coronavirus disease 2019 (COVID-19) pandemic. It also imposes a hierarchy on complex relations among stressors for excavating underlying structure and builds a model of interrelationships contrasting reality. The design of this study comprises a literature survey, data collection from primary sources, and analysis. Stressors have been explored from within current published/unpublished literature and validated by experts through approval vote. Data were collected from the focus group (panel of experts), and interpretive structural modeling (ISM) was used as the research methodology. Findings of ISM are avowed through “cross-impact matrix multiplication applied to classification” (MICMAC) analysis. As a result of the literature survey, a list of stressors was generated, and a total of 19 stressors qualified as representative of the phenomenon. The results of ISM show that two stressors (i.e., “unavailability of proper personal protective equipment (PPE)” and “lack of proper communication”) emerged as the most critical stressors since they occupy the bottom of the model, whereas, four stressors (i.e., “anxious about isolation/quarantine,” “subject to violent crimes,” “feeling frustrated and powerless,” and “exhausting shifts/hours without clear end”) are relatively less critical since they occupy the top of the model. The rest of the stressors occupy the middle of the model and therefore, have moderate-severe effects on frontline soldiers. The results of MICMAC show that the stressor “subject to violent crimes” is classified in the dependent cluster and the remaining fall in the linkage cluster but no stressor falls in independent and autonomous. Overall results indicate that all stressors are relevant to the phenomenon under this study, but they are currently not settled. This study is invaluable for policymakers, frontline soldiers, researchers, the international community, and society since it provides a lot of new information that is helpful in refining strategies and combating influential stressors.

Ozone is an important air pollutant that causes many challenges for human health, such as lung diseases. The negative impacts of exogenous ozone on cultivated plants have been discussed in several publications, but the positive impacts are less investigated. The current study is an attempt to answer the following question: is there any positive contribution of ozone pretreatment in growing plants under stress? Plants grown in unheated plastic greenhouses suffer from cold stress during the winter when the temperature drops to 5–10 °C. This stress can also be enhanced under saline soil conditions in arid regions. Treatments involving different ozone application methods (seed priming and seedling foliar application) and cold pretreatment (4 °C for 36 h) were compared with untreated controls of two sweet pepper hybrids (Zidenka and Lirica) in an unheated plastic greenhouse. In general, the Lirica hybrid outperformed the Zidenka hybrid in growth and fruit yield and showed good adaptation to cold stress resulting from ozone treatment through foliar application on seedlings. Moreover, cold treatment was associated with higher values of the studied parameters compared to the control (untreated). A concentration of 20 ppm of ozone in the first season and as 30 ppm of ozone in the second season increased plant leaf area, proline content, and APX activity, all of which improved the plant’s defense mechanisms against cold stress. These benefits contributed to high fruit sets; early, total, and marketable fruit yields; and fruit quality under cold stress. The highest yield (16.52 kg m−2) was attained with 30 ppm ozone applied as a seedling spray, compared with 10.07 kg m−2 in the control. Therefore, the foliar application of ozone up to 30 ppm can be recommended for sweet pepper hybrids of Lirica under unheated plastic greenhouse conditions. Further investigations are needed to study the interaction of combined ozone and cold stress, as well as various levels of soil salinity.

The study is aimed to analyze the determinants of the effectiveness of SOPs in mass gatherings for containing COVID-19. The overall design of the study involves a literature review, data collection by field survey, structural modeling, and analysis. The study is built on the experts' opinion of a focus group (representing people who recently participated in and are responsible for mass gatherings). The study uses the discussion of the literature review to identify the determinants, interpretive structural modeling (ISM) for developing and analyzing a structural model, and Matrice d'Impacts Croises Multiplication Appliquée a un Classement (MICMAC) for corroboration of results of the ISM/classification of determinants. From the literature review, a list of determinants is generated and verified by a panel of experts. The results of the ISM revealed that the determinants “legal environment of the country,” “practicability of SOPs,” “perceived benefit of adapting SOPs,” and “possibilities of avoiding gathering” occupied the top of the model, therefore, they are less critical determinants, whereas “nature of gathering” occupied the bottom of the model, and is thus the most critical determinant. The remaining determinants form the middle of the model, and are therefore moderately severe. The results of MICMAC show that the determinant “perceived benefit of adapting SOPs” is dependent, “nature of gathering” is independent, and all others are linked. The results of MICMAC implicitly substantiate the findings of the ISM. The overall results of the study show that “nature of gathering” is the key determinant. This research does not require a priori theory since it is a theory-building study that uses an inductive approach. It is based on real data and it is useful for local authorities, organizers, participants (attendees/visitors) of mass gatherings, health officials/regulators, researchers, and the community at large. This study has fundamental importance for planning and preparing for such events while ensuring the minimum risk of COVID-19 transmission.

Non-steroidal anti-inflammatory drugs (NSAIDs) (concentration

Urban areas are mostly heterogeneous due to settlements and vegetation including forests, water bodies and many other land use and land cover (LULC) classes. Due to the overwhelming population pressure, urbanization, industrial works and transportation systems, urban areas have been suffering from a deficiency of green spaces, which leads to an increase in the variation of temperature in urban areas. This study investigates the conceptual framework design towards urban green space (UGS) and thermal variability over Kolkata and Howrah city using advanced remote sensing (RS) and geospatial methods. The low green space is located in the highly built-up area, which is influenced by thermal variations. Therefore, the heat stress index showed a high area located within the central, north, northwestern and some parts of the southern areas. The vegetated areas decreased by 8.62% during the ten years studied and the other land uses increased by 11.23%. The relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) showed significant changes with R2 values between 0.48 (2010) and 0.23 (2020), respectively. The correlation among the LST and the normalized difference built-up index (NDBI) showed a notable level of change with R2 values between 0.38 (2010) and 0.61 (2020), respectively. The results are expected to contribute significantly towards urban development and planning, policymaking and support for key stakeholders responsible for the sustainable urban planning procedures and processes.

Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced.

The authors conducted a socio-technical cost–benefit analysis (CBA) in Alexandra Township (Alex for short) by combining three water loss control investment strategies: (i) pipeline and infrastructure upgrades, (ii) repair and maintenance, and (iii) socio-domestic retrofitting capital. The researchers performed the CBA using sensitivity analysis methodologies such as marginal cost of capital (MCC), weighted average cost of capital (WACC), coefficient of variance (CV), the net present value (NPV) ratio, and cumulative and total cost methods. The findings for socio-domestic retrofitting capital investment showed that at an average investment cost of USD 5735 per household, consumption was reduced from 1369.4 m3/year to 301.99 m3/year, whereas a projected water savings average of 521.05 m3/household/year was achieved. The results show that the cumulative cost of water losses equaled USD 43.9 million per year, and that, if the water loss trend continues beyond the year 2026, almost 100% of the system input volume (SIV) will be non-revenue water (NRW) in the water balance. Using the MCC method, the integrated results for the socio-technical strategy showed that the economic level of leakage (ELL) was achieved at a WACC of 16.2, a CV of 0.66, and an NPV ratio or net capital risk of 0.246. This study demonstrates that the socio-technical approach is a viable alternative for water loss control and may be adopted in various parts of the world.

Extreme climatic conditions and natural hazard-related phenomenon have been affecting coastal regions and riverine areas. Floods, cyclones, and climate change phenomena have hammered the natural environment and increased the land dynamic, socio-economic vulnerability, and food scarcity. Saltwater intrusion has also triggered cropland vulnerability and, therefore, increased the area of inland brackish water fishery. The cropland area has decreased due to low soil fertility; around 252.06 km2 of cropland area has been lost, and 326.58 km2 of water bodies or inland fishery area has been added in just thirty years in the selected blocks of the North 24 Parganas district, West Bengal, India. After saltwater intrusion, soil fertility appears to have been decreased and crop production has been greatly reduced. The cropland areas were 586.52 km2 (1990), 419.92 km2 (2000), 361.67 km2 (2010) and 334.46 km2 (2020). Gradually the water body areas were increased 156.21 km2 (1990), 328.15 km2 (2000), 397.77 km2 (2010) and 482.78 km2 (2020). The vegetated land area also decreased due to it being converted into inland fishery areas, and around 79.15 km2 were degraded during the last thirty years. The super cyclone Aila, along with other super cyclones and other environmental stresses, like water-logging, soil salinity, and irrigation water scarcity were the reasons for the development of the new fishery areas in the selected blocks. There is a need for proper planning for sustainable development of this area.

For companies, notably in the realms of energy and power supply, the essential requirement for highly efficient thermal transport solutions has become a serious concern. Current research highlighted the use of metallic oxides and carbon-based nanofluids as heat transfer fluids. This work examined two carbon forms (PEG@GNPs & PEG@TGr) and two types of metallic oxides (Al2O3 & SiO2) in a square heated pipe in the mass fraction of 0.1 wt.%. Laboratory conditions were as follows: 6401 ≤ Re ≤ 11,907 and wall heat flux = 11,205 W/m2. The effective thermal–physical and heat transfer properties were assessed for fully developed turbulent fluid flow at 20–60 °C. The thermal and hydraulic performances of nanofluids were rated in terms of pumping power, performance index (PI), and performance evaluation criteria (PEC). The heat transfer coefficients of the nanofluids improved the most: PEG@GNPs = 44.4%, PEG@TGr = 41.2%, Al2O3 = 22.5%, and SiO2 = 24%. Meanwhile, the highest augmentation in the Nu of the nanofluids was as follows: PEG@GNPs = 35%, PEG@TGr = 30.1%, Al2O3 = 20.6%, and SiO2 = 21.9%. The pressure loss and friction factor increased the highest, by 20.8–23.7% and 3.57–3.85%, respectively. In the end, the general performance of nanofluids has shown that they would be a good alternative to the traditional working fluids in heat transfer requests.

Urgent water and food security challenges, particularly in continental and boreal regions, need to be addressed by initiatives such as the Horizon 2020-funded project WATer retention and nutrient recycling in soils and streams for improved AGRIcultural production (WATERAGRI). A new methodological framework for the sustainable management of various solutions resilient to climate change has been developed. The results indicate that the effect of the climate scenario is significantly different for peatlands and constructed wetlands. The findings also highlight that remote-sensing-based yield prediction models developed from vegetation indices have the potential to provide quantitative and timely information on crops for large regions or even at the local farm scale. Verification of remotely sensed data is one of the prerequisites for the proper utilization and understanding of data. Research shows that current serious game applications fall short due to challenges such as not clarifying the decision problem, the lack of use of decision quality indicators and limited use of gaming. Overall, WATERAGRI solutions improve water and food security by adapting agriculture to climate change, recycling nutrients and providing educational tools to the farming community. Farmers in small agricultural catchments benefit directly from WATERAGRI, but over the long-term, the general public does as well.

Water is a finite resource and should be given the attention it deserves to reduce its depletion through leakages in pipe systems. The authors implemented pressure management strategies linked to fixed and variable discharge (FAVAD), the burst and background estimate (BABE), and orifice principle methodologies to analyze a two-phased comparative method for applying optimal pressure management and its efficiency indexes in measuring volumetric cost performance, consumption, leakage flowrate, linear leakage reduction, infrastructural leakage and leakage cost indices. Using time-modulated smart control pressure reducing valve (PRV) simulation processes, the authors selected Alexandra Township in Johannesburg, South Africa as a case study. The results showed a reduction in head pressure, a reduction in the system input volume (SIV) from 26,272,579 m3 to 21,915,943 m3 and a reduction in minimum night flow (MNF) from 14.01% to 12.50%. The annual estimated nodal system output (NSO) was reduced from 14,774.62 m3 to 12,787.85 m3. The monthly average linear system repairs were reduced from 246 to 177, while the efficiency index percentages of leakage frequency/km/pressure were reduced from 8.31% to 5.98%. At a unit cost of $3.18/m3, the cost of leakages declined from $4,009,315.54 to $2,862,053.10 per month, while average household consumption (AMC) reduced from 36.33 m3 to 24.56 m3. Finally, the linear reduction value R2 for the percentage of the total leakage flowrate (TLFR)/SIV declined from 0.58 to 0.5, whereas the infrastructure leakage ratio (ILI) increased from 4 to 4.3. The results fully demonstrated that optimal pressure management is an alternative way to simulate, estimate, quantify and understand where and how water is lost in a distribution system. The authors propose that the implementation of proactive leakage management and domestic background leakage repair could further assist in reducing the frequency and cost of water leakages.

Mathematical models are useful for predicting the reactions of watercourses such as rivers due to the entry of contaminants. Some of these models are able to simulate the effects of present and future loadings as well as aid managers and officials in making decisions even if the data are sparse. In other words, river water quality preservation requires more investment in wastewater treatment and/or the installation of collection and control systems; it may also limit activity expansion in the river basin. The conservation of watersheds such as the Maroon and Jarahi basin, which provide water for drinking and for industrial and agricultural use, is socio-economically vital. Therefore, the first stage of managing the conservation of water resources is understanding their qualitative changes. For this purpose, the QUAL2KW mathematical model was utilized to simulate the river water quality in this example region. According to the reported values of water quality parameters and pollutants at monitoring stations, it was established that the river is at a critical condition in terms of biochemical oxygen demand (BOD) pollution due to the discharge of urban and industrial wastewater, as well as high electrical conductivity (EC) due to the drainage of agricultural lands. Based on the statistics calculated during the validation step, the authors concluded that the QUAL2KW water quality model is reliable in the simulation of qualitative parameters and the pollution data of the study area; namely the Maroon and Jarahi river basin located in the south-west of Iran. This will help stakeholders to better manage watersheds with sparse data. This region has been suffering from climate change which has led to droughts and the construction of several dams to retain water. For the second and third stations, the NASH (named after the mathematician John Forbes Nash) values were 0.96 and 0.92, respectively, indicating a relatively high model accuracy. The evaluation using the root mean square errors (RMSE) and NASH showed that the quality of water at the second station was better than the other two stations based on the coefficient of determination R2. Since there were three drains at station number 3, the wastewater entering the Maroon River had a higher level of contamination.

Although remarkable progress has been achieved in reducing hunger and poverty and improving people’s health in the past couple of decades, humanity still faces considerable socio-economic and sustainability challenges. Ensuring sustainable access to safe and sufficient water, improved sanitation facilities, clean energy sources and healthy food is a necessary requirement for ending hunger and poverty, advancing health and achieving all the goals of the sustainable development agenda. Socio-economic variables are determinant factors of water, energy and food accessibility. Ecological region, income and education are measures used in this study, which aims to examine a pairwise comparison of water and energy sources, sanitation facilities and food accessibilities in six sampling communities and tracking progress towards achieving the sustainable development goals (SDG) at a local scale using nine specific SDG indicators. A study involving questionnaire administration covering 1785 households across the three ecological regions of Katsina state was performed. Two communities representing urban and rural households from each of the ecological regions were selected. An analysis of variance was used to test the equality of resource accessibility across the investigated communities followed up by a post hoc analysis to identify significant mean groups. The results showed that the overall access level to safely manage water and sanitation facilities were 16.5% and 28.1%, respectively. Access indices of 1.83 and 1.24 for electricity and cooking fuel, respectively, were calculated in the study area. The study revealed that location, education and income are the key drivers of water, energy and food access and choice.

Covalent-functionalized graphene nanoplatelets (CF-GNPs) inside a circular heated-pipe and the subsequent pressure decrease loss within a fully developed turbulent flow were discussed in this research. Four samples of nanofluids were prepared and investigated in the ranges of 0.025 wt.%, 0.05 wt.%, 0.075 wt.%, and 0.1 wt.%. Different tools such as field emission scanning electron microscopy (FE-SEM), ultraviolet-visible-spectrophotometer (UV-visible), energy-dispersive X-ray spectroscopy (EDX), zeta potential, and nanoparticle sizing were used for the data preparation. The thermophysical properties of the working fluids were experimentally determined using the testing conditions established via computational fluid dynamic (CFD) simulations that had been designed to solve governing equations involving distilled water (DW) and nanofluidic flows. The average error between the numerical solution and the Blasius formula was ~4.85%. Relative to the DW, the pressure dropped by 27.80% for 0.025 wt.%, 35.69% for 0.05 wt.%, 41.61% for 0.075 wt.%, and 47.04% for 0.1 wt.%. Meanwhile, the pumping power increased by 3.8% for 0.025 wt.%, 5.3% for 0.05 wt.%, 6.6% for 0.075%, and 7.8% for 0.1 wt.%. The research findings on the cost analysis demonstrated that the daily electric costs were USD 214, 350, 416, 482, and 558 for DW of 0.025 wt.%, 0.05 wt.%, 0.075 wt.%, and 0.1 wt.%, respectively.

This paper evaluates six soft computational models along with three statistical data-driven models for the prediction of pan evaporation (EP). Accordingly, improved kriging—as a novel statistical model—is proposed for accurate predictions of EP for two meteorological stations in Turkey. In the standard kriging model, the input data nonlinearity effects are increased by using a nonlinear map and transferring input data from a polynomial to an exponential basic function. The accuracy, precision, and over/under prediction tendencies of the response surface method, kriging, improved kriging, multilayer perceptron neural network using the Levenberg–Marquardt (MLP-LM) as well as a conjugate gradient (MLP-CG), radial basis function neural network (RBFNN), multivariate adaptive regression spline (MARS), M5Tree and support vector regression (SVR) were compared. Overall, all the applied models were highly capable of predicting monthly EP in both stations with a mean absolute error (MAE) < 0.77 mm and a Willmott index (d) > 0.95. Considering periodicity as an input parameter, the MLP-LM provided better results than the other methods among the soft computing models (MAE = 0.492 mm and d = 0.981). However, the improved kriging method surpassed all the other models based on the statistical measures (MAE = 0.471 mm and d = 0.983). Finally, the outcomes of the Mann–Whitney test indicated that the applied soft computational models do not have significant superiority over the statistical ones (p-value > 0.65 at α = 0.01 and α = 0.05).

Accumulation of trace elements, including heavy metals, were evaluated in soil and fruits of chilli plants (Capsicum annuum L.) grown under both laboratory-controlled and semi-controlled greenhouse location conditions. Chilli plant biomass growth in different development stages and fruit productivity were evaluated and compared with each other for the impact of growth boundary conditions and water quality effects. Treated synthetic greywaters by different operational design set-ups of floating treatment wetland systems were recycled for watering chillies in both locations. Effluents of each individual group of treatment set-up systems were labelled to feed sets of three replicates of chilli plants in both locations. Results revealed that the treated synthetic greywater (SGW) complied with thresholds for irrigation water, except for high concentrations (HC) of phosphates, total suspended soils, and some trace elements, such as cadmium. Chilli plants grew in both locations with different growth patterns in each development stage. First blooming and high counts of flowers were observed in the laboratory. Higher fruit production was noted for greenhouse plants: 2266 chilli fruits with a total weight of 16.824 kg with an expected market value of GBP 176.22 compared to 858 chilli fruits from the laboratory with a weight of 3.869 kg and an estimated price of GBP 17.61. However, trace element concentrations were detected in chilli fruits with the ranking order of occurrence as: Mg > Ca > Na > Fe > Zn > Al > Mn > Cu > Cd > Cr > Ni > B. The highest concentrations of accumulated Cd (3.82 mg/kg), Cu (0.56 mg/kg), and Na (0.56 mg/kg) were recorded in chilli fruits from the laboratory, while greater accumulations of Ca, Cd, Cu, Mn, and Ni with concentrations of 4.73, 1.30, 0.20, 0.21, and 0.24 mg/kg, respectively, were linked to fruits from the greenhouse. Trace elements in chilli plant soils followed the trend: Mg > Fe > Al > Cr > Mn > Cd > Cu > B. The accumulated concentrations in either chilli fruits or the soil were above the maximum permissible thresholds, indicating the need for water quality improvements.

Most pyrolysis reactors require small sizes of biomass particles to achieve high-quality products. Moreover, understanding the usefulness of high-pressure systems in pyrolysis is important, given the operational challenges they exhibit specific to various biomass materials. To actualize these aspects, the authors first checked previous reviews involving pyrolysis on different biomass and different conditions/situations with their respective objectives and subsections. From these already existing reviews, the team found that there has not been much emphasis on high-pressure fast pyrolysis and its potential in biomass conversion, showing that it is a novel direction in the pyrolysis technology development. Therefore, this review aims to shed more light on high-pressure fast pyrolysis, drawing from (a) classification of pyrolysis; (b) reactors used in fast pyrolysis; (c) heat transfer in pyrolysis feedstock; (d) fast pyrolysis parameters; (e) properties/yields of fast pyrolysis products; (f) high pressure on pyrolysis process; (g) catalyst types and their application; and (h) problems to overcome in the pyrolysis process. This review increases the understanding regarding high-pressure fast pyrolysis. An attempt has been made to demonstrate how high-pressure fast pyrolysis can bring about high-quality biomass conversion into new products. It has been shown that fluidized bed (bubbling and circulating) reactors are most suitable and profitable in terms of product yield. The high-pressure, especially combined with the fast-heating rate, may be more efficient and beneficial than working under ambient pressure. However, the challenges of pyrolysis on a technical scale appear to be associated with obtaining high product quality and yield. The direction of future work should focus on the design of high-pressure process reactors and material types that might have greater biomass promise, as well understanding the impact of pyrolysis technology on the various output products, especially those with lower energy demands. We propose that the increase of process pressure and biomass particle size decrease should be considered as variables for optimization.

Numerical studies were performed to estimate the heat transfer and hydrodynamic properties of a forced convection turbulent flow using three-dimensional horizontal concentric annuli. This paper applied the standard k–ε turbulence model for the flow range 1 × 104 ≤ Re ≥ 24 × 103. A wide range of parameters like different nanomaterials (Al2O3, CuO, SiO2 and ZnO), different particle nanoshapes (spherical, cylindrical, blades, platelets and bricks), different heat flux ratio (HFR) (0, 0.5, 1 and 2) and different aspect ratios (AR) (1.5, 2, 2.5 and 3) were examined. Also, the effect of inner cylinder rotation was discussed. An experiment was conducted out using a field-emission scanning electron microscope (FE-SEM) to characterize metallic oxides in spherical morphologies. Nano-platelet particles showed the best enhancements in heat transfer properties, followed by nano-cylinders, nano-bricks, nano-blades, and nano-spheres. The maximum heat transfer enhancement was found in SiO2, followed by ZnO, CuO, and Al2O3, in that order. Meanwhile, the effect of the HFR parameter was insignificant. At Re = 24,000, the inner wall rotation enhanced the heat transfer about 47.94%, 43.03%, 42.06% and 39.79% for SiO2, ZnO, CuO and Al2O3, respectively. Moreover, the AR of 2.5 presented the higher heat transfer improvement followed by 3, 2, and 1.5.

This Special Issue is concerned with treatment wetlands, which are artificial (or constructed) wetlands usually treating municipal or industrial wastewater, greywater and storm water runoff [...]

It has become necessary to estimate the quantities of runoff by knowing the amount of rainfall to calculate the required quantities of water storage in reservoirs and to determine the likelihood of flooding. The present study deals with the development of a hydrological model named Hydrologic Engineering Center (HEC-HMS), which uses Digital Elevation Models (DEM). This hydrological model was used by means of the Geospatial Hydrologic Modeling Extension (HEC-GeoHMS) and Geographical Information Systems (GIS) to identify the discharge of the Al-Adhaim River catchment and embankment dam in Iraq by simulated rainfall-runoff processes. The meteorological models were developed within the HEC-HMS from the recorded daily rainfall data for the hydrological years 2015 to 2018. The control specifications were defined for the specified period and one day time step. The Soil Conservation Service-Curve number (SCS-CN), SCS Unit Hydrograph and Muskingum methods were used for loss, transformation and routing calculations, respectively. The model was simulated for two years for calibration and one year for verification of the daily rainfall values. The results showed that both observed and simulated hydrographs were highly correlated. The model’s performance was evaluated by using a coefficient of determination of 90% for calibration and verification. The dam’s discharge for the considered period was successfully simulated but slightly overestimated. The results indicated that the model is suitable for hydrological simulations in the Al-Adhaim river catchment.

Currently, it is important to develop strategic frameworks to support the selection of sustainable resource recovery solutions. This study applies a new framework for planning, implementation, and assessment of resource recovery strategies for a full-scale wastewater treatment plant (WWTP) in São Paulo megacity. The framework comprises several steps based on case study-specific data and information from current literature. Data were collected from various sources: a survey with a wastewater treatment utility, national and regional databases, and review of local regulations and international literature. Treatment configuration, wastewater and by-products composition, potential demand (for water, energy, and phosphorus), stakeholder identification, and local legislation were thoroughly discussed regarding decision-making on resource recovery. Scenario analysis was used to explore suitable nutrient and energy recovery measures based on indicators. Biogas recovery and sewage sludge composting showed more favorable conditions due to similar experiences in the area and robust legislation. The proposed framework is a simplified tool, and its application can support managers to get information on resource recovery and how to plan such initiatives in easier ways to facilitate wiser decision-making, and better operation and management. The results on framework use and refinement can guide potential applications in other contexts and stimulate public policy formulation and further research.

A severe water scarcity challenge is facing Iraq, which is predominantly due to the absence of water management policies, negatively impacting the water quantity and quality provision from the Tigris and Euphrates Rivers. Moreover, these practices have led to the intrusion of the Arabian Gulf salinity wedge into the Shatt Al-Arab River (SAR), which is the main water source for most water treatment plants (WTPs) in Basra city. In addition, the inadequate management and operation for most WTPs is another reason for the deterioration of water quality provided to Basra province. Accordingly, the aim of this study is to evaluate the performance of the main WTP within Basra province and to subsequently make recommendations for decision-makers to come up with new management strategies and policies. The effluents from eight WTPs were selected to study the quality of water supply for Basra city during the period between January 2018 and December 2018. The results showed that all WTPs were inadequate to treat raw water for drinking or irrigation purposes mainly due to the very bad raw water quality provided by the SAR as well as the lack of maintenance for such plants, resulting in very low removal efficiencies for various water contaminants.

Data and literature related to water quality as well as nutrient loads were used to evaluate the Changjiang River (also Yangtze or Yangzi) Basin with respect to its hydrological regime, sediment transport, and eutrophication status. Waterbodies exhibited different eutrophic degrees following the ranking order of river < reservoir < lake. Most of the eutrophic lakes and reservoirs distributed in the upstream Sichuan Basin and Jianghan Plain are located in the middle main stream reaches. During the past decade, the water surface area proportion of moderately eutrophic lakes to total evaluated lakes continually increased from 31.3% in 2009 to 42.7% in 2018, and the trophic level of reservoirs rapidly developed from mesotrophic to slightly eutrophic. Construction and operation of numerous gates and dams changed the natural transportation rhythm of runoff, suspended solids (SS), and nutrients, and reduced flow velocity, resulting in decreased discharge runoff, slow water exchange, and decreased connectivity between rivers and lakes as well as accumulated nutrient and SS, which are the main driving forces of eutrophication. To mitigate eutrophication, jointly controlling and monitoring nutrient concentrations and flux at key sections, strengthening water quality management for irrigation backwater and aquaculture wastewater, and balancing transportation among runoff, SS, and nutrients is recommended.

The ambiguity of diverse functions of sustainable flood retention basins (SFRBs) may lead to conflict and risk in water resources planning and management. How can someone provide an intuitive yet efficient strategy to uncover and distinguish the multiple potential functions of SFRBs under uncertainty? In this study, by exploiting both input and output uncertainties of SFRBs, the authors developed a new data-driven framework to automatically predict the multiple functions of SFRBs by using multi-instance multi-label (MIML) learning. A total of 372 sustainable flood retention basins, characterized by 40 variables associated with confidence levels, were surveyed in Scotland, UK. A Gaussian model with Monte Carlo sampling was used to capture the variability of variables (i.e., input uncertainty), and the MIML-support vector machine (SVM) algorithm was subsequently applied to predict the potential functions of SFRBs that have not yet been assessed, allowing for one basin belonging to different types (i.e., output uncertainty). Experiments demonstrated that the proposed approach enables effective automatic prediction of the potential functions of SFRBs (e.g., accuracy >93%). The findings suggest that the functional uncertainty of SFRBs under investigation can be better assessed in a more comprehensive and cost-effective way, and the proposed data-driven approach provides a promising method of doing so for water resources management.

This study quantifies the sensitivity of surface runoff to drought and climate change in the Diyala watershed shared between Iraq and Iran. This was achieved through a combined use of a wide range of changes in the amount of precipitation (a decline between 0% and −40%) and in the potential evapotranspiration rate (an increase between 0% and +30%). The Medbasin-monthly rainfall-runoff model (Medbasin-M) was used for runoff simulation. The model was calibrated for twelve hydrologic years (1962−1973), and the simulation results were validated with the observed annual runoff for nine water years (1974−1982). For the calibration period, the correlation coefficient (r), the root mean squared error (RMSE), the mean absolute error (MAE) and the index of agreement (IoA) were 0.893, 2.117, 1.733 and 0.852, respectively. The corresponding values for validation were 0.762, 1.250, 1.093 and 0.863, in this order. The Reconnaissance Drought Index (RDI) and the Streamflow Drought Index (SDI) were analysed using DrinC software. Three nomographs were introduced to quantify the projected reductions in the annual runoff and the anticipated RDI and SDI values, respectively. The proposed methodology offers a simple, powerful and generic approach for predicting the rate of change (%) in annual runoff under climate change scenarios.

Environmental and water pollution through heavy metals is a growing concern. The recycling of untreated wastewater, which is often contaminated with heavy metals, for agricultural applications is becoming more popular. However, information on the amount of absorption and accumulation of cadmium (Cd) at variable concentrations by different crops is limited. This study aims to analyze the impact of various Cd concentrations (0, 30, 60 and 120 mg/kg) in the root zone on the quantity of its absorption as well as accumulation in various parts of seven different types of common vegetables. The experiments were carried out under laboratory-like controlled conditions. Four treatments and three replicates were selected. Cadmium accumulation exceeded the permissible limits for human consumption, and its accumulation in different plant parts followed this order: Leaves: broccoli > spinach > basil > garlic > carrot > tarragon > dill. Stems: broccoli > spinach > basil > garlic > tarragon > carrot > dill. Roots: broccoli > garlic > basil > spinach > carrot > dill > tarragon. Therefore, the authors recommend the reuse of treated wastewater, which should be virtually free of contaminants such as heavy metals, to irrigate farm lands in the future.

Smart firewater management and recycling helps reduce water use and protect the environment from pollution. However, contamination of recycled water may pose a health risk to fire fighters. This review assesses international literature to identify best practices, and to recommend new technologies and methods on firewater management and recycling. The literature assessment indicates that this is a new research area where insufficient findings have been published in Web of Science-referenced journals. Therefore, informally published materials (a.k.a. grey literature) were also assessed. Findings indicate the need for practical decision support tools to estimate consumption rates, predict “bottlenecks” and bund capacity, assess water quality and determine pump requirements. This article recommends that cost-efficient and rapid on-site treatment methods, such as compact and mobile filtration units for firewater recycling should be researched in the future. The filters should be based on compartments with different media. The empty pore space should decrease from inflow to outflow. A light plastic media should be positioned near the inflow to retain large particles, such as a grid. Activated carbon media could be placed near the outlet to remove fine suspended solids and dissolved contaminants. This should address concerns by fire fighters dealing with contaminated water, spray and foam.