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A wide range of studies has been carried out to describe the equilibrium data of adsorption for the surface adsorption process. However, no extensive investigation has been carried out to evaluate the oil sludge based activated carbon surface adsorption. Therefore, the possibility of carbon active production using different oil sludges and consequently the adsorption mechanism of these kind of adsorbents is still unknown. In this study, a novel low-cost approach was introduced to synthesize the activated carbon using oil sludge applying a two-step process including carbonization and chemical activation. In this way, four different types of oil sludges were characterized and then applied to synthesize different carbon actives and their performance were investigated as an adsorbent. The results showed that all synthesized activated carbons, with about 6% ash and pH = 7 and the specific surface area of 110 m2/gr, have the ability to treatment of oily wastewater; which can be referred to the high carbon content (>80%). The iodine number and the efficiency of prepared activated carbon were obtained as 406.8 mg/g and 94%, respectively. The adsorption process was also studied at different process conditions such as temperature (308–338 K), pH value (3–9) and adsorbent amount (50–200 mg/L) to find the optimum condition for wastewater treatment. The results show that the pH value has an optimum in the adsorption rate (the maximum adsorption was measured at pH = 5) and the adsorption capacity can be reduced by increasing the temperature or decreasing the adsorbent amount. Moreover, three different adsorption isotherm models were applied, i.e., Langmuir, Temkin, and Freundlich isotherms; which the Langmuir equation was more suitable than others investigated isotherm models with R2 ≈ 0.999., Validerad;2023;Nivå 2;2023-02-24 (joosat);Funder: King Khalid University (RGP.2/182/43)Licens fulltext: CC BY License

Currently, updating the change detection (CD) of land use/land cover (LU/LC) geospatial information with high accuracy outcomes is important and very confusing with the different classification methods, datasets, satellite images, and ancillary dataset types available. However, using just the low spatial resolution visible bands of the remotely sensed images will not provide good information with high accuracy. Remotely sensed thermal data contains very valuable information to monitor and investigate the CD of the LU/LC. So, it needs to involve the thermal datasets for better outcomes. Fusion plays a big role to map the CD. Therefore, this study aims to find out a refining method for estimating the accurate CD method of the LU/LC patterns by investigating the integration of the effectiveness of the thermal satellite data with visible datasets by (a) adopting a noise removal model, (b) satellite images resampling, (c) image fusion, combining and integrating between the visible and thermal images using the Grim Schmidt spectral (GS) method, (d) applying image classification using Mahalanobis distances (MH), Maximum likelihood (ML) and artificial neural network (ANN) classifiers on datasets captured from the Landsat-8 TIRS and OLI satellite system, these images were captured from operational land imager (OLI) and the thermal infrared (TIRS) sensors of 2015 and 2020 to generate about of twelve LC maps. (e) The comparison was made among all the twelve classifiers' results. The results reveal that adopting the ANN technique on the integrated images of the combined TIRS and OLI datasets has the highest accuracy compared to the rest of the applied image classification approaches. The obtained overall accuracy was 96.31% and 98.40%, and the kappa coefficients were (0.94) and (0.97) for the years 2015 and 2020, respectively. However, the ML classifier obtains better results compared to the MH approach. The image fusion and integration of the thermal images improve the accurac, Validerad;2023;Nivå 1;2023-02-17 (joosat);Licens fulltext: CC BY License

Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface. Scale invariant feature transform (SIFT) has the ability to automatically extract control points (CPs) and is commonly used for remote sensing images. However, its results are mostly inaccurate and sometimes contain incorrect matching caused by generating a small number of false CP pairs. These CP pairs have high false alarm matching. This paper presents a modified method to improve the performance of SIFT CPs matching by applying sum of absolute difference (SAD) in a different manner for the new optical satellite generation called near-equatorial orbit satellite and multi-sensor images. The proposed method, which has a significantly high rate of correct matches, improves CP matching. The data in this study were obtained from the RazakSAT satellite a new near equatorial satellite system. The proposed method involves six steps: 1) data reduction, 2) applying the SIFT to automatically extract CPs, 3) refining CPs matching by using SAD algorithm with empirical threshold, and 4) calculation of true CPs intensity values over all image’ bands, 5) preforming a linear regression model between the intensity values of CPs locate in reverence and sensed image’ bands, 6) Relative radiometric normalization conducting using regression transformation functions. Different thresholds have experimentally tested and used in conducting this study (50 and 70), by followed the proposed method, and it removed the false extracted SIFT CPs to be from 775, 1125, 883, 804, 883 and 681 false pairs to 342, 424, 547, 706, 547, and 469 corrected and matched pairs, respectively., Godkänd;2023;Nivå 0;2023-02-17 (hanlid)

Rapid urbanization, coupled with land use land cover changes (LULC), has caused stress on freshwater resources around the globe. As in the case of Islamabad, the capital of Pakistan, the population has increased significantly, creating a deficit of natural resources and affecting the environment adversely. Therefore, the purpose of this study is to examine the effects of urbanization and LULC on the decline of the static water table in Islamabad. It also seeks to analyze water policy issues in order to achieve sustainable water resource development. The excessive pumping of the existing groundwater has exceeded the safe limit, which is justified by the constantly growing population. However, the changes in the LULC of the study area have turned many green pastures into barren land. Our research data were obtained from the Capital Development Authority (CDA), Pakistan Meteorological Department (PMD), and Landsat Satellite images. After analyzing PMD and CDA data for the last 20 years (2000–2020), the results were interpreted using Arc GIS. It has been observed that the Normalized Difference Vegetation Index (NDVI) value increases as the Land Surface Temperature (LST) decreases. Therefore, the overall observation is a decreasing trend in Islamabad temperatures due to the increased vegetation in the study area during the period of 2000–2020. It was observed that there has been a considerable drop in water levels due to over-pumping in a few areas. It is primarily associated with the increasing population of the capital in the last 2 decades. This study uses a survey to explore the potential locations for check dams to enhance and recharge the groundwater aquifers in the capital, Islamabad. It suggests catchment areas throughout the Margalla Hills along with different localities, such as Rumli Village, Trail 5, and Shahdara., Validerad;2023;Nivå 2;2023-02-15 (hanlid)

The purpose of this research is to delineate the structures of the Lower Goru Formation, investigate fluid properties, and clarify the hydrocarbon-prone areas through seismic attributes analysis. First, the acquired data was matched by the interpretation datum. Structural analysis was done by performing horizon interpretation, fault interpretation, and contour mapping on the C-Interval of the Lower Goru Formation. Hydrocarbon zones were marked with the help of attribute analysis on seismic sections and were justified by petrophysical analysis. An integrated approach such as seismic structural interpretation, seismic attribute, spectral decomposition, and petrophysical analyses was used in current research to better understand geological structure and features. This research showed that normal faults are present in the area showing negative flower structure, horst and graben, and faults oriented north-west to south-east. The contour map shows structural inclination and faults bound closure near well locations. Variance attribute and spectral decomposition attribute were used to verify horizon lineation and fault behavior. Instantaneous amplitude and instantaneous phase attributes justify hydrocarbon bearing zones, and bright spots are present on seismic sections at C–Interval of Lower Goru Formation. Petrophysical analysis of the available wells showed a number of significant hydrocarbon zones having more than 55% of hydrocarbon saturation at the C-Interval of the Lower Goru Formation. The four possible reservoir zones in Sawan-02 well, two zones in Sawan-07 well, and three zones in Sawan-09 well are identified based on well data interpretation. Based on these analyses, the area of interest has a very good reservoir potential, structural closure, and visible bright spots. The current finding of this research will be helpful for future exploration and development of the Sawan area., Validerad;2023;Nivå 2;2023-02-08 (joosat);Licens fulltext: CC BY License

Exact estimation of evaporation rates is very important in a proper planning and efficient operation of water resources projects and agricultural activities. Evaporation is affected by many driving forces characterized by nonlinearity, non-stationary, and stochasticity. Such factors clearly hinder setting up rigorous predictive models. This study evaluates the predictability of coupling the additive regression model (AR) with four ensemble machine-learning algorithms—random Subspace (RSS), M5 pruned (M5P), reduced error pruning tree (REPTree), and bagging for estimating pan evaporation rates. Meteorological data encompass maximum temperature, minimum temperature, mean temperature, relative humidity, and wind speed from three different agroclimatic stations in Iraq (i.e., Baghdad, Mosul, and Basrah) were utilized as predictor parameters. The regression model in addition to the sensitivity analysis was employed to identify the best-input combinations for the evaluated methods. It was demonstrated that the AR-M5P estimated the evaporation with higher accuracy than others when combining wind speed, relative humidity, and the minimum and mean temperatures as input parameters. The AR-M5P model provided the best performance indicators, i.e., MAE = 33.82, RMSE = 45.05, RAE = 24.75, RRSE = 28.50, and r = 0.972 for Baghdad; MAE = 25.82, RMSE = 35.95, RAE = 23.75, RRSE = 29.64, and r = 0.956 for Mosul station, respectively. The outcomes of this study proved the superior performance of the hybridized methods in addressing such intricate hydrological relationships and hence could be employed for other environmental problems., Validerad;2023;Nivå 2;2023-02-02 (sofila)

Water productivity (WP) is one of the most important critical indicators in the essential planning of water consumption in the agricultural sector. For this purpose, the WP and economic water productivity (WPe) were estimated using agronomic technologies. The impact of agronomic technologies on WP and WPe was carried out in two parts of field monitoring and modeling using novel intelligent approaches. Extreme learning machine (ELM), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods were used to model WP and WPe. A dataset including 200 field data was collected from five treatment and control sections in the Malekan region, located in the southeast of Lake Urmia, Iran, for the crop year 2020–2021. Six different input combinations were introduced to estimate WP and WPe. The models used were evaluated using mean squared error (RMSE), relative mean squared error (RRMSE), and efficiency measures (NSE). Field monitoring results showed that in the treatment fields, with the application of agronomic technologies, the crop yield, WP, and WPe increased by 17.9%, 30.1%, and 19.9%, respectively. The results explained that irrigation water in farms W1, W2, W3, W4, and W5 decreased by 23.9%, 21.3%, 29.5%, 16.5%, and 2.7%, respectively. The modeling results indicated that the ANFIS model with values of RMSE = 0.016, RRMSE = 0.018, and NSE = 0.960 performed better in estimating WP and WPe than ANN and ELM models. The results confirmed that the crop variety, fertilizer, and irrigation plot dimensions are the most critical influencing parameters in improving WP and WPe., Validerad;2023;Nivå 2;2023-02-01 (sofila);Funder: The Conservation of Iranian wetlands project (CIWP)

Recent technological advances have made it possible to produce particles with nanometer dimensions that are uniformly and steadily suspended in traditional solar liquids and have enhanced the impact of thermo-physical parameters. In this research, a three-dimensional flat plate solar collector was built using a thin flat plate and a single working fluid pipe. The physical model was solved computationally under conditions of conjugated laminar forced convection in the range 500 ≤ Re ≤ 1900 and a heat flux of 1000 W/m2. Distilled water (DW) and different types of hybrid nanofluids (namely, 0.1%-Al2O3@Cu/DW, 0.1%-MWCNTs@Fe3O4/DW, 0.3%-MWCNTs@Fe3O4/DW, 0.5%-Ag@MgO/DW, 1%-Ag@MgO/DW, 1%-S1 and 1%-S2, where MWCNTs are multi-wall carbon nanotubes, S1 means 2CuO–1Cu and S2 means 1CuO–2Cu nanocomposites) were evaluated via a set of parameters. The numerical results revealed that, by increasing the working fluid velocity (the Reynolds number), the average heat transfer coefficient, pressure loss, heat gain and solar collector efficiency were increased. Meanwhile, outlet fluid temperature and flat plate surface temperature were decreased. At Re = 1900, 1%-S2 and 1%-S1 presented higher thermal performance enhancement by 44.28% and 36.72% relative to DW. Moreover, low thermal performance enhancement of 7.59% and 7.44% were reported by 0.1%-Al2O3@Cu/DW and 0.3%-MWCNTs@Fe3O4/DW, respectively., Validerad;2023;Nivå 2;2023-01-25 (joosat);Licens fulltext: CC BY License

Practicing agricultural drainage strategies is necessary to manage excess water in poorly drained irrigated agricultural lands to protect them from induced waterlogging and salinity problems. This paper provides an overview of subsurface drainage strategies and the modeling of their performance using the DRAINMOD model. Given that the DRAINMOD model considers a fixed value of the surface depression capacity (SDC) for the whole simulation period, which does not suit many agricultural practices, the paper then assesses the model’s performance under time-variable SDC. It was revealed that adopting a fixed value of SDC for the whole simulation period in the DRAINMOD model causes it to produce improper predictions of the water balance in farmlands characterized by time-variable SDC. Such a model drawback will also adversely impact its predictions of the nitrogen and phosphorus fate in farmlands, which represent major inputs when managing both the agricultural process and agricultural water quality. Researchers should pay attention when applying the DRAINMOD model to farmlands characterized by time-variable SDC. Moreover, it is recommended that the DRAINMOD input module be improved by considering changes in SDC during the simulation period to ensure better management of the agricultural process and agricultural water., Validerad;2023;Nivå 2;2023-01-18 (johcin);Full text license: CC BY

The flat-plate solar collector (FPSC) three-dimensional (3D) model was used to numerically evaluate the energy and economic estimates. A laminar flow with 500 ≤ Re ≤ 1900, an inlet temperature of 293 K, and a solar flux of 1000 W/m2 were assumed the operating conditions. Two mono nanofluids, CuO-DW and Cu-DW, were tested with different shapes (Spherical, Cylindrical, Platelets, and Blades) and different volume fractions. Additionally, hybrid nanocomposites from CuO@Cu/DW with different shapes (Spherical, Cylindrical, Platelets and Blades), different mixing ratios (60% + 40%, 50% + 50% and 40% + 60%) and different volume fractions (1 volume%, 2 volume%, 3 volume% and 4 volume%) were compared with mono nanofluids. At 1 volume% and Re = 1900, CuO-Platelets demonstrated the highest pressure drop (33.312 Pa). CuO-Platelets achieved the higher thermal enhancement with (8.761%) at 1 vol.% and Re = 1900. CuO-Platelets reduced the size of the solar collector by 25.60%. Meanwhile, CuO@Cu-Spherical (40:60) needed a larger collector size with 16.69% at 4 vol.% and Re = 1900. CuO-Platelets with 967.61, CuO – Cylindrical with 976.76, Cu Platelets with 983.84, and Cu-Cylindrical with 992.92 presented the lowest total cost. Meanwhile, the total cost of CuO – Cu – Platelets with 60:40, 50:50, and 40:60 was 994.82, 996.18, and 997.70, respectively., Validerad;2023;Nivå 2;2023-01-25 (joosat);Licens fulltext: CC BY License

The carbonate reservoir quality is strongly reliant on the compaction process during sediment burial and other processes such as cementation and dissolution. Porosity and pore pressure are the two main factors directly affected by mechanical and chemical compactions. Porosity reduction in these carbonates is critically dependent on the overburden stress and subsidence rate. A variable sediment influx in younger basins may lead to changes in the reservoir quality in response to increasing lithostatic pressure. Deposition of molasse sediments as a result of the Himalayan orogeny caused variations in the sedimentation influx in the Potwar Basin of Pakistan throughout the Neogene times. The basic idea of this study is to analyze the carbonate reservoir quality variations induced by the compaction and variable sediment influx. The Sakesar Limestone of the Eocene age, one of the proven carbonate reservoirs in the Potwar Basin, shows significant changes in the reservoir quality, specifically in terms of porosity and pressure. A 3D seismic cube (10 km2) and three wells of the Balkassar field are used for this analysis. To determine the vertical and lateral changes of porosity in the Balkassar area, porosity is computed from both the log and seismic data. The results of both the data sets indicate 2–4% porosities in the Sakesar Limestone. The porosity reduction rate with respect to the lithostatic pressure computed with the help of geohistory analysis represents a sharp decrease in porosity values during the Miocene times. Pore pressure predictions in the Balkassar OXY 01 well indicate underpressure conditions in the Sakesar Limestone. The Eocene limestones deposited before the collision of the Indian plate had enough time for fluid expulsion and show underpressure conditions with high porosities., Validerad;2023;Nivå 2;2023-03-09 (joosat);Licens fulltext: CC BY License

Point and nonpoint wastewater sources have a detrimental, negative effect on agriculture, soil, surface, and groundwater supplies. In this research, a wastewater treatment system made up of a sedimentation tank, a horizontal subsurface flow constructed wetland (HSSF-CW), a vertical subsurface flow constructed wetland (VF-CW), and a storage tank was proposed, designed, and cost estimated. Small populations in underdeveloped nations with dry and semi-arid climates can use the treatment system as an affordable construction, maintenance, and operational solution for wastewater treatment. The system will protect agricultural lands and groundwater from pollution. The system can service 6000 capita and has a wastewater discharge of 780 m3/d in the developing arid region in El-Moghra Oasis western desert of Egypt, where the 1.5 million acres used for the land reclamation project based on groundwater irrigation. The relaxed tanks in a series model based on the areal loading rates and background pollutants concentrations (P-K-C*) was utilized to size the HSSF and VF-CWs. The results indicated that the HSSF-CW design treatment surface area was 2375 m2, and the hydraulic surface loading (q) and hydraulic retention time (RT) were 0.33 m/d and 0.55 d, respectively, and utilizing Phragmites australis and Papyrus for the biological treatment. The expected overall cumulative removal efficiencies were 96.7, 70, and 100% for the biological oxygen demand (BOD), total phosphors (TP), and fecal coliforms (FC), respectively. The VF-CW indicates that there was a 2193 m2 design treatment surface area, q = 0.36 m/d, and RT of 0.63 d. The expected BOD, TP, and FC removal efficiencies were 75, 33.3, and 92.7%, respectively. In order to simplify the design stages and the cost estimation, design and investment cost curves were established for a population range from 500 to 9000. The total monthly water loss due to evapotranspiration for the HSSF and VF-CWs indicates a range from 3.7 to 8.5%, res, Validerad;2023;Nivå 2;2023-01-16 (hanlid);Part of special issue: Stormwater/Drainage Systems and Wastewater Management

A hybrid machine learning (ML) model is becoming a common trend in predicting reference evapotranspiration (ETo) research. This study aims to systematically review ML models that are integrated with meta-heuristic algorithms (i.e., parameter optimisation-based hybrid models, OBH) for predicting ETo data. Over five years, from 2018–2022, the articles published in three reliable databases, including Web of Science, ScienceDirect, and IEEE Xplore, were considered. According to the protocol search, 1485 papers were selected. After three filters were applied, the final set contained 33 papers related to the nominated topic. The final set of papers was categorised into five groups. The first group, swarm intelligence-based algorithms, had the highest proportion of papers, (23/33) and was superior to all other algorithms. The second group (evolution computation-based algorithms), third group (physics-based algorithms), fourth group (hybrid-based algorithms), and fifth group (reviews and surveys) had (4/33), (1/33), (2/33), and (3/33), respectively. However, researchers have not treated OBH models in much detail, and there is still room for improvement by investigating both newly single and hybrid meta-heuristic algorithms. Finally, this study hopes to assist researchers in understanding the options and gaps in this line of research., Validerad;2023;Nivå 2;2023-01-02 (joosat)

Accurate forecasting techniques for a stochastic pattern of water demand are essential for any city that faces high variability in climate factors and a shortage of water resources. This study was the first research to assess the impact of climatic factors on urban water demand in Iraq, which is one of the hottest countries in the world. We developed a novel forecasting methodology that includes data preprocessing and an artificial neural network (ANN) model, which we integrated with a recent nature-inspired metaheuristic algorithm [marine predators algorithm (MPA)]. The MPA-ANN algorithm was compared with four nature-inspired metaheuristic algorithms. Nine climatic factors were examined with different scenarios to simulate the monthly stochastic urban water demand over 11 years for Baghdad City, Iraq. The results revealed that (1) precipitation, solar radiation, and dew point temperature are the most relevant factors; (2) the ANN model becomes more accurate when it is used in combination with the MPA; and (3) this methodology can accurately forecast water demand considering the variability in climatic factors. These findings are of considerable significance to water utilities in planning, reviewing, and comparing the availability of freshwater resources and increasing water requests (i.e., adaptation variability of climatic factors)., Validerad;2022;Nivå 2;2022-11-09 (johcin)

The alarming pace of environmental degradation necessitates the treatment of wastewater from the oil industry in order to ensure the long-term sustainability of human civilization. Electrocoagulation has emerged as a promising method for optimizing the removal of chemical oxygen demand (COD) from wastewater obtained from oil refineries. Therefore, in this study, electrocoagulation was experimentally investigated, and a single-factorial approach was employed to identify the optimal conditions, taking into account various parameters such as current density, pH, COD concentration, electrode surface area, and NaCl concentration. The experimental findings revealed that the most favorable conditions for COD removal were determined to be 24 mA/cm2 for current density, pH 8, a COD concentration of 500 mg/l, an electrode surface area of 25.26 cm2, and a NaCl concentration of 0.5 g/l. Correlation equations were proposed to describe the relationship between COD removal and the aforementioned parameters, and double-factorial models were examined to analyze the impact of COD removal over time. The most favorable outcomes were observed after a reaction time of 20 min. Furthermore, an artificial neural network model was developed based on the experimental data to predict COD removal from wastewater generated by the oil industry. The model exhibited a mean absolute error (MAE) of 1.12% and a coefficient of determination (R2) of 0.99, indicating its high accuracy. These findings suggest that machine learning-based models have the potential to effectively predict COD removal and may even serve as viable alternatives to traditional experimental and numerical techniques., Validerad;2023;Nivå 2;2023-09-26 (sofila);Funder: King Khalid UniversityAbha, Saudi Arabia (Grant no. RGP. 2/57/44)

Methane (CH4) is a greenhouse gas resulting from human activities, especially landflls, and it hasmany potential environmental issues, such as its major role in global warming. On the other hand,methane can be converted to liquid fuel or electricity using chemical conversion or gas turbinegenerators. Therefore, reusing such gases could be of great environmental and economic beneft. Inthis context, this study aims to estimate the emissions of methane gas from the landflls in Al-HillahCity, Iraq, from 2023 to 2070 and the producible electric energy from this amount. The estimatingprocess was carried out using the Land GEM model and compared with traditional models. Theobtained results demonstrated that the total estimated landfll methane emissions for 48 years are875,217 tons, and the average annual methane emission is 18,234 tons based on a yearly wasteaccumulation rate of 1,046,413 tons and a total waste amount of 50,227,808 tons. The anticipatedloads of methane gas can be utilized to generate about 287,442 MW/year of electricity from 2023to 2070. In conclusion, the results obtained from this study could be evidence of the potentialenvironmental and economic benefts of harvesting and reusing methane gas from landflls., Validerad;2023;Nivå 2;2023-09-15 (joosat);Funder: Al-Mustaqbal University College, Iraq;CC BY 4.0 License

The design and selection of ideal emitter discharge rates can be aided by accurate information regarding the wetted soil pattern under surface drip irrigation. The current field investigation was conducted in an apple orchard in SKUAST- Kashmir, Jammu and Kashmir, a Union Territory of India, during 2017–2019. The objective of the experiment was to examine the movement of moisture over time and assess the extent of wetting in both horizontal and vertical directions under point source drip irrigation with discharge rates of 2, 4, and 8 L h−1. At 30, 60, and 120 min since the beginning of irrigation, a soil pit was dug across the length of the wetted area on the surface in order to measure the wetting pattern. For measuring the soil moisture movement and wetted soil width and depth, three replicas of soil samples were collected according to the treatment and the average value were considered. As a result, 54 different experiments were conducted, resulting in the digging of pits [3 emitter discharge rates × 3 application times × 3 replications × 2 (after application and 24 after application)]. This study utilized the Drip-Irriwater model to evaluate and validate the accuracy of predictions of wetting fronts and soil moisture dynamics in both orientations. Results showed that the modeled values were very close to the actual field values, with a mean absolute error of 0.018, a mean bias error of 0.0005, a mean absolute percentage error of 7.3, a root mean square error of 0.023, a Pearson coefficient of 0.951, a coefficient of correlation of 0.918, and a Nash–Sutcliffe model efficiency coefficient of 0.887. The wetted width just after irrigation was measured at 14.65, 16.65, and 20.62 cm; 16.20, 20.25, and 23.90 cm; and 20.00, 24.50, and 28.81 cm in 2, 4, and 8 L h−1, at 30, 60, and 120 min, respectively, while the wetted depth was observed 13.10, 16.20, and 20.44 cm; 15.10, 21.50, and 26.00 cm; 19.40, 25.00, and 31.00 cm, respectively. As the flow rate from the emitter in, Validerad;2023;Nivå 2;2023-09-12 (hanlid);Funder: Deanship of Scientifc Research, King Saud University

This study presents the behavior of using many types of reinforcement with slab foundations to satisfy sustainability. This foundation with soil effect and two types of bar reinforcement (steel bars and geogrid) are taken. Nonlinear analysis is used with the theoretical model by finite element program software ABAQUS to represent the many types of reinforced concrete foundations with both unsaturated and saturated soil with the effect of loading. Effect of load-displacementrelationships with many cases which were taken for this study such as the geogrid and steel bar ratio in reinforcement of foundation with unsaturated and saturated soil. From this study, it is shown that reinforcement by geogrid increased the strength of the member or the foundation when taken with different ratios of reinforcement by steel bars and get the best sustainable way or solution by decreasing the reinforcement by steel. The ratio of geogrid is 40% to 20 % from total reinforcement, while the required member or foundation in the case of soil, which is unsaturated, is more strength than it is in the case of saturated soil., Godkänd;2023;Nivå 0;2023-09-13 (joosat);CC BY 4.0 License

The present research work focused on predicting the electrical conductivity (EC) of surface water in the Upper Ganga basin using four machine learning algorithms: multilayer perceptron (MLP), co-adaptive neuro-fuzzy inference system (CANFIS), random forest (RF), and decision tree (DT). The study also utilized the gamma test for selecting appropriate input and output combinations. The results of the gamma test revealed that total hardness (TH), magnesium (Mg), and chloride (Cl) parameters were suitable input variables for EC prediction. The performance of the models was evaluated using statistical indices such as Percent Bias (PBIAS), correlation coefficient (R), Willmott’s index of agreement (WI), Index of Agreement (PI), root mean square error (RMSE) and Legate-McCabe Index (LMI). Comparing the results of the EC models using these statistical indices, it was observed that the RF model outperformed the other algorithms. During the training period, the RF algorithm has a small positive bias (PBIAS = 0.11) and achieves a high correlation with the observed values (R = 0.956). Additionally, it shows a low RMSE value (360.42), a relatively good coefficient of efficiency (CE = 0.932), PI (0.083), WI (0.908) and LMI (0.083). However, during the testing period, the algorithm’s performance shows a small negative bias (PBIAS = − 0.46) and a good correlation (R = 0.929). The RMSE value decreases significantly (26.57), indicating better accuracy, the coefficient of efficiency remains high (CE = 0.915), PI (0.033), WI (0.965) and LMI (− 0.028). Similarly, the performance of the RF algorithm during the training and testing periods in Prayagraj. During the training period, the RF algorithm shows a PBIAS of 0.50, indicating a small positive bias. It achieves an RMSE of 368.3, R of 0.909, CE of 0.872, PI of 0.015, WI of 0.921, and LMI of 0.083. During the testing period, the RF algorithm demonstrates a slight negative bias with a PBIAS of − 0.06. The RMSE reduces significantly to 2, Validerad;2023;Nivå 2;2023-09-12 (hanlid);Funder: Deanship of Scientifc Research, King Saud University

Iraq relies in its water resources on the waters of the Tigris and Euphrates and their tributaries. The country is located in the lower part of the catchment area of these rivers. The long-term average annual flow that enters Iraq from these rivers is about 30 BCM from the Euphrates, 21.2 BCM from the Tigris, 24.78 BCM from tributaries and 7 BCM from side valleys between Iraq and Iran. Now, the flow of these rivers is decreasing due to climate change and hydrological projects established in the upper parts of the catchment. It is indicated that precipitation will decrease by 15% - 25% during this century and that means that the flow of the Tigris and Euphrates rivers will be reduced by 29% - 73%. This will cause a grave depletion of groundwater resources. Türkiye is trying to finish building 22 dams and 19 hydropower stations. Iran built 12 dams and diverted the flow of some tributaries inside Iran and blocked all the valleys that contribute water from its land to Iraq. For these reasons, Iraq is experiencing shortages in its water resources and there is some sort of friction and conflict between riparian countries within the Tigris and Euphrates basins because each country tries to secure its water resources. In this research, the factors affecting the hydro politics within these basins are water scarcity, climate change and hydrological projects, population growth rate, energy issues, water mismanagement, economic changes, expansions of projects and technology, political issues, international water laws and public awareness. In case the situation remains as it is, Iraq will experience many problems in health, environment, economy, and security. To solve the problem of water scarcity in Iraq, two parallel lines of action are to be considered. These are: 1) Reach agreements with Riparian Parties; 2) Develop a long-term strategy that should take the following: a) Rehabilitating of existing dams, barrages & pump stations, b) Improving the efficiency of diversion a, Godkänd;2023;Nivå 0;2023-09-11 (joosat);CC BY 4.0 License

Producing accurate Land-Use and Land-Cover (LU/LC) maps using low-spatial-resolution images is a difficult task. Pan-sharpening is crucial for estimating LU/LC patterns. This study aimed to identify the most precise procedure for estimating LU/LC by adopting two fusion approaches, namely Color Normalized Brovey (BM) and Gram-Schmidt Spectral Sharpening (GS), on high spatial-resolution Multi-sensor and Multi-spectral images, such as (1) the Unmanned Aerial Vehicle (UAV) system, (2) the WorldView-2 satellite system, and (3) low-spatial-resolution images like the Sentinel-2 satellite, to generate six levels of fused images with the three original multi-spectral images. The Maximum Likelihood method (ML) was used for classifying all nine images. A confusion matrix was used to evaluate the accuracy of each single classified image. The obtained results were statistically compared to determine the most reliable, accurate, and appropriate LU/LC map and procedure. It was found that applying GS to the fused image, which integrated WorldView 2 and Sentinel-2 satellite images and was classified by the ML method, produced the most accurate results. This procedure has an overall accuracy of 88.47% and a kappa coefficient of 0.85. However, the overall accuracies of the three classified multispectral images range between 86.84% to 76.49%. Furthermore, the accuracy assessment of the fused images by the Brovey method and the rest of the GS method and classified by the ML method ranges between 85.75% to 76.68%. This proposed procedure shows a lot of promise in the academic sphere for mapping LU/LC. Previous researchers have mostly used satellite images or datasets with similar spatial and spectral resolution, at least for tropical areas like the study area of this research, to detect land surface patterns. However, no one has previously investigated and examined the use and application of different datasets that have different spectral and spatial resolutions and their accuracy for ma, Validerad;2023;Nivå 1;2023-09-05 (hanlid)

Using a safe and easy-to-apply economicfocculant to replace alum is necessary to expandand enhance the water quality in rural regions wheretraditional water treatment is unavailable. The seedof Moringa oleifera is locally available in large volumes and is feasibly economical. A compatibility andapplicability research of the easily accessible localMoringa oleifera seed extract (MOSE) was conductedto examine its potential utilization to lessen variousdegrees of water turbidity at 30±2 °C. The studyconcerns the optimum dosage of MOSE to give highturbidity removal efciency in the water. Experimentswere carried out for nine turbidity samples: 5, 10,15, 30, 50, 70, 100, 200, and 300 NTU. The turbidityremoval efciency of up to 89% was achieved for highinitial turbid water 300 NTU. However, for low turbidwater, its potential efciency declined. The resultsdemonstrate that the de-oiled extract is substantiallymore efcient than the crude extract in agglomerating colloidal particles of low initial turbidity samplesand achieved up to 76% removal rate. The results ofthe ANOVA confrmed that the regression model wassignifcant at (p<0.05) for residual turbidity afterthe treatment process. The study also demonstratesthat a pseudo-frst-order kinetic model matched wellthe nature of the removal of colloidal particles withMOSE in water to form focs. The study also indicatesthat the characteristics of water treated with crudeMOSE produce little change in electrical conductivityand salinity. However, the hardness and alkalinity ofwater decreased signifcantly depending on the initialhardness and the amount of MOSE applied (p<0.05)., Validerad;2023;Nivå 2;2023-04-05 (hanlid);Funder: University of Sulaymaniyah, Iraq

Pollution of a watershed by different land uses and agricultural practices is becoming a major challenging factor that results in deterioration of water quality affecting human health and ecosystems. Sustainable use of available water resources warrants reduction of Non-Point Source (NPS) pollutants from receiving water bodies through best management practices (BMPs). A hydrologic model such as the Soil and Water Assessment Tool (SWAT) can be used for analyzing the impacts of various BMPs and implementing of different management plans for water quality improvement, which will help decision makers to determine the best combination of BMPs to maximize benefits. The objective of this study is to assess the potential reductions of sediments and nutrient loads by utilizing different BMPs on the Yarra River watershed using the SWAT model. The watershed is subdivided into 51 sub-watersheds where seven different BMPs were implemented. A SWAT model was developed and calibrated against a baseline period of 1998–2008. For calibration and validation of the model simulations for both the monthly and annual nutrients and sediments were assessed by using the Nash–Sutcliffe efficiency (NSE) statistical index. The values of the NSE were found more than 0.50 which indicates satisfactory model predictions. By utilizing different BMPs, the highest pollution reduction with minimal costs can be done by 32% targeted mixed-crop area. Furthermore, the combined effect of five BMPs imparts most sediments and nutrient reductions in the watershed. Overall, the selection of a BMP or combinations of BMPs should be set based on the goals set in a BMP application project., Validerad;2023;Nivå 2;2023-03-21 (johcin);Full text license: CC BY

This study investigates the variations of surface Moho inverted from Bouguer gravity data beneath the northwestern Arabian plate. The results were obtained by applying the 3DINVER program developed in a MATLAB environment using the Parker–Oldenburg method. The calculation results show that the maximum Moho depth observed in the western Arabian shield approaches more than 40 km, while the minimum values of 16–20 km are mainly concentrated beneath the Mediterranean Sea. A key innovation of this study is its implementation of exponential density contrast decrease in the sediment–basement interface in the Syrian part of the Arabian plate. The gravity anomaly of the sediment is computed using a density contrast which varies exponentially with depth, leading to a more accurate representation of the subsurface structure and offering valuable insights into the geodynamic processes and seismogenic potential of the area. To validate the inverted results from the 3DINVER program, the study compares these findings with seismic results that show good agreement with our results from the same region. This leads to a better understanding of the crustal structure and provides insights into its implications for the geodynamic and seismogenic nature of the northwestern Arabian plate—critical for assessing the potential for earthquakes and other geological hazards. The density contrast between the Earth’s crust and mantle can be related to variations in the composition, temperature, and pressure of the rocks. These factors influence the mechanical properties of the crust, ultimately affecting the tectonic processes and seismic activity in the region., Validerad;2023;Nivå 2;2023-08-10 (joosat);Licens fulltext: CC BY LicenseFunder: Natural Science Foundation of China (Nos. 42174090 and 41604060); MOST Special Fund, State Key Laboratory of Geological Processes and Mineral Resources (MSFGPMR2022-4)

Membrane filtration exhibit operational limitations such as biofouling, which leads to concentration polarization and reduces permeability and selectivity, despite advantages such as low operating cost, high selectivity, and permeability. In recent years, the antibacterial properties of silver nanoparticles (AgNPs) have been investigated for improving membrane processes; however, the fouling phenomena in presence of AgNPs in the membrane matrix have not been fully discussed. Herein, the antifouling properties of a poly (acrylonitrile-styrene) copolymer incorporated with AgNPs were studied in a microfiltration membrane process. The Creighton method was used to synthesize AgNPs, and the effects of AgNPs on the porosity, morphology, pore size, mechanical strength, permeability, and selectivity of the membranes were investigated. Moreover, to investigate the biofouling of the obtained membranes, microfiltration of industrial oily wastewater was performed at constant pressure over three cycles. Using AgNPs in the membrane matrix resulted in enhanced antifouling properties of the copolymer membrane, which is related to the structure of the AgNPs in the casting solution, as proven by SAXS analysis. The results show that the CFU% for Staphylococcus aureus and E.coli reach 2% and 6%, respectively. Finally, the Derjaguin–Landau–Verwey–Overbeek (DLVO) thermodynamic model was applied to study the antifouling mechanism, correctly predict the separation behavior in the membrane, and design, simulate, and optimize the separation processes in the membrane separation plantsa. The DLVO model could predict the separation behavior in the synthesized membranes, and the poly(acrylonitrile-styrene) copolymer membranes containing AgNPs were proven have promising industrial wastewater treatment applications., Validerad;2023;Nivå 2;2023-08-14 (joosat);Licens fulltext: CC BY License

The present study compares the six crop residue management techniques in main plots (since 2008) and three split nitrogen (N) levels, i.e., 75, 100 and 125 kg N/ha in subplots for rice crops for two years, i.e., 2019 and 2020, in sandy loam soil under field conditions. This experiment evaluated the long-term effect on rice productivity, soil organic carbon content and nutrient requirement in rice-wheat cropping system. The results revealed that different crop residue management practices and N levels significantly influenced rice growth, yield and yield attributes and improved nutrient uptake by grain and straw. Maximum grain yields of 20.8% and 17.8% higher over the conventional (no straw) treatment during 2019 and 2020, respectively, were recorded where the rice and wheat residue was re-tained or incorporated. The rice grain yield without residue responded significantly up to 125 kg N/ha. Whereas, with rice and wheat residue, rice grain yield did not respond to the application of N beyond 75 kg N/ha during both years., Validerad;2023;Nivå 2;2023-08-08 (hanlid);Funder: Deanship of Scientific Research, King Saud University

Knowledge of the stage-discharge rating curve is useful in designing and planning flood warnings; thus, developing a reliable stage-discharge rating curve is a fundamental and crucial component of water resource system engineering. Since the continuous measurement is often impossible, the stage-discharge relationship is generally used in natural streams to estimate discharge. This paper aims to optimize the rating curve using a generalized reduced gradient (GRG) solver and the test the accuracy and applicability of the hybridized linear regression (LR) with other machine learning techniques, namely, linear regression-random subspace (LR-RSS), linear regression-reduced error pruning tree (LR-REPTree), linear regression-support vector machine (LR-SVM) and linear regression-M5 pruned (LR-M5P) models. An application of these hybrid models was performed and test to modeling the Gaula Barrage stage-discharge problem. For this, 12-year historical stage-discharge data were collected and analyzed. The 12-year historical daily flow data (m3/s) and stage (m) from during the monsoon season, i.e., June to October only from 03/06/2007 to 31/10/2018, were used for discharge simulation. The best suitable combination of input variables for LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models was identified and decided using the gamma test. GRG-based rating curve equations were found to be as effective and more accurate as conventional rating curve equations. The outcomes from GRG, LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models were compared to observed values of daily discharge based on Nash Sutcliffe model efficiency coefficient (NSE), Willmott Index of Agreement (d), Kling-Gupta efficiency (KGE), mean absolute error (MAE), mean bias error (MBE), relative bias in percent (RE), root mean square error (RMSE) Pearson correlation coefficient (PCC) and coefficient of determination (R2). The LR-REPTree model (combination 1: NSE = 0.993, d = 0.998, KGE = 0.987, PCC(r) = 0.997, and R2 = 0., Validerad;2023;Nivå 2;2023-05-22 (hanlid)

With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors’ scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts., Validerad;2023;Nivå 2;2023-05-29 (hanlid)

A 3D density structure of the lithosphere and upper mantle beneath the eastern Mediterranean Sea (EMS) and its adjacent region was constructed based on gravity anomaly inversion constrained by a seismic tomography model. Gravity effects of terrain and crust were removed from the observed gravity field (EIGEN-6C4) to obtain the residual mantle gravity anomaly (RMGA). The density distribution of the lithosphere and upper mantle was investigated. The 3D inversion process was constrained by an initial density model projected from the shear-wave velocity model (SL2013sv). The results show some characteristics of the density distribution in the lithosphere and in the upper mantle that could be related to the tectonic importance of the Mediterranean Sea and the surrounding region. A low-density zone dominates the lithosphere beneath the Sea except for the area around the Arabia Shield and North Anatolian Fault belt. A thinner, high-density layer beneath the southwest of the Sea may be related to the older oceanic lithospheric fragments. The high-density anomalies appear at depths below 280 km beneath the Sea and the Turkish Aegean Sea Plate. However, the low-density anomalies appear on the upper mantle under the trenches of the southwestern part of the Mediterranean Sea, the eastern part of the Aegean Sea, the Red Sea, the Black Sea, and the middle of the Arabia shield. The deep structure under the Eratosthenes seamount in the Mediterranean Sea is the source of the intensity and genesis of tectonic activity. Furthermore, the convergence region of two low-density anomaly zones (Africa-Anatolia) may be interpreted as a significant tectonic unit (Eratosthenes seamount) caused by the arrival of the relatively thick and buoyant Eratosthenes block to its present location south of Cyprus in Holocene time based on the density model interpretation beneath the Mediterranean Sea during the Late Cretaceous and early Tertiary period., Validerad;2023;Nivå 2;2023-05-31 (joosat);Funder: Natural Science Foundation of China (Nos. 42174090, 41604060); the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources (MSFGPMR2022-4), China University of Geosciences

Land subsidence is considered a threat to developing cities and is triggered by several natural (geological and seismic) and human (mining, groundwater withdrawal, oil and gas extraction, constructions) factors. This research has gathered datasets consisting of 80 Sentinel-1A ascending and descending SLC images from July 2017 to July 2019. This dataset, concerning InSAR and PS-InSAR, is processed with SARPROZ software to determine the land subsidence in Gwadar City, Balochistan, Pakistan. Later, the maps were created with ArcGIS 10.8. Due to InSAR’s limitations in measuring millimeter-scale surface deformation, Multi-Temporal InSAR techniques, like PS-InSAR, are introduced to provide better accuracy, consistency, and fewer errors of deformation analysis. This remote-based SAR technique is helpful in the Gwadar area; for researchers, city mobility is constrained and has become more restricted post-Covid-19. This technique requires multiple images acquired of the same place at different times for estimating surface deformation per year, along with surface uplifting and subsidence. The InSAR results showed maximum deformation in the Koh-i-Mehdi Mountain from 2017 to 2019. The PS-InSAR results showed subsidence up to −92 mm/year in ascending track and −66 mm/year in descending track in the area of Koh-i-Mehdi Mountain, and up to −48 mm/year in ascending track and −32 mm/year in descending track in the area of the deep seaport. From our experimental results, a high subsidence rate has been found in the newly evolving Gwadar City. This city is very beneficial to the country’s economic development because of its deep-sea port, developed by the China-Pakistan Economic Corridor (CPEC). The research is associated with a detailed analysis of Gwadar City, identifying the areas with significant subsidence, and enlisting the possible causes that are needed to be resolved before further developments. Our findings are helpful to urban development and disaster monitoring as the city, Validerad;2023;Nivå 2;2023-06-29 (sofila)

The Pirmam and Bana Bawi anticlines are two elongated anticlines located in the central northern part of the Kurdistan Region of north Iraq (KRI), in the northeastern part of the Arabian Plate. We have used satellite images and geological maps to interpret the presented data and to calculate the rates of uplift and down wards (subsidence) amounts, besides estimating the rates of deposition and incision of some alluvial fans and stream terraces. Most of the achieved data were checked in the field. In the Pirmam anticline the rate of the uplift and subsidence is 8.613 mm/ 100 year and 7.962 mm/ 100 year, respectively, whereas in the Bana Bawi anticline the rates are 3.036 mm/ 100 year and 4.365 mm/100 year, respectively. The indications of the lateral growth are the presence of water gaps, wind gaps, en-echelon plunging, whale-back shape and different types of valleys. We have calculated the depositional and incision rates in the Kori and Bastoora streams. They are 5.204 mm/ 100 year and 5.555 mm/ 100 year, respectively in the former, whereas in the latter are 3.469 mm/100 year and 8.731 mm/ 100 year, respectively. Moreover, we have calculated the depositional and incision rates in old and recent alluvial fans. In the former, they are 1.665 mm/ 100 year and 0.722 mm/ 100 year, respectively, whereas in the latter are 3.036 and 4.365 mm/ 100 years, respectively., Godkänd;2023;Nivå 0;2023-03-30 (hanlid)

This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps., Validerad;2023;Nivå 2;2023-05-04 (joosat);Funder: National Natural Science Foundation of China (No.61862051); Science and Technology Foundation of Guizhou Province (No.[2019]1299, No.ZK[2022]449); Top-notch Talent Program of Guizhou province (No.KY[2018]080); Natural Science Foundation of Education of Guizhou province (No.[2019]203); Qiannan Normal University for Nationalities (No. qnsy2019rc09); Al-Mustaqbal University College (Grant number: MUC-E-0122)Licens fulltext: CC BY-NC-ND License

Climatic condition is triggering human health emergencies and earth’s surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth’s health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human’s health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50–60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist., Validerad;2023;Nivå 2;2023-05-22 (hanlid)

Many aquatic ecosystems are vulnerable to many pollutants, particularly heavy metals that are now a threat to public health. It may be more effective to manage pollution in waterways if we can better understand the connection between heavy metals in sediments and their accumulation in green mussels. In this study, lead levels in green mussels (Perna viridis) were above the permissible maximum level (WHO, Malaysia Food Regulation, and the FAO). Moreover, zinc levels exceeded the FAO's maximum recommended range. The fractionate of heavy metals in surface sediment from the eastern part of the Johore Straits was investigated using the sequential extraction techniques (SET) method. In the majority of sampling stations, copper's chemical speciation followed the following order: residual > oxidisable-organic > exchangeable > acid reducible, while zinc's and lead's chemical speciation followed the following pattern: residual > oxidisable-organic > acid reducible > exchangeable. The results of Pearson's correlation studies demonstrated a substantial association between Zn concentration in the tissues of green mussels and total Zinc, exchangeable (F1), acid-reducible (F2), and residual (F4) Zn in surface sediment. Green mussel Cu content and surface sediment Cu of exchangeable (F1), residual (F4), and total Cu were found to correlate. As a result, green mussels are being used as a biomonitoring agent for heavy metal contamination on the eastern side of the Straits of Johor in Malaysia., Godkänd;2023;Nivå 0;2023-07-05 (hanlid);Funder: Malaysian Ministry of Higher Education (KPT.P. (S) 400‐7/2/29‐4 (65))

In times of water scarcity, groundwater is a vital resource that provides an alternate source of water for human consumption. In Iraq, the quality of rivers has been greatly affected by climate change and the dwindling availability of surface water. Examining and classifying the groundwater in this region is now vital. The present study sought to incorporate the groundwater property data (drinking purpose) with a geographic information system (GIS). Eleven variables were measured in 25 wells to investigate the physio-chemical properties around the Babylon province of Iraq. Based on the acceptability of groundwater for drinking, the GWQI was categorized into four primary groups in the results. Approximately 28% of the twenty-five wells (1811.04 km2) are of excellent quality, 24% are of good quality (1552.3 km2), 44% are of low quality (2845.9 km2), and 4% are extremely contaminated. (2587.2 km2). The average GWQI for the entire study region was 110.7, making it inappropriate for human consumption. It has been determined that approximately 52% of the groundwater from the examined wells can be deemed safe for consumption, although certain measurements surpass the permissible limits. To guarantee residents in these areas are supplied with water of superior quality and safety, treatment of the tested groundwater is recommended before use., Validerad;2023;Nivå 2;2023-06-29 (joosat);Licens fulltext: CC BY License

Drought is one of the most multifaceted hydrologic phenomena, affecting several factors such as soil moisture, surface runoff, and significant water shortages. Therefore, monitoring and assessing drought occurrences based on a single drought index are inadequate. The current study develops a multiscalar weighted amalgamated drought index (MWADI) to amalgamate multiple drought indices. The MWADI is mainly based on the normalized average dependence posterior probabilities (ADPPs). These ADPPs are obtained from Bayesian networks (BNs)-based Markov Chain Monte Carlo (MCMC) simulations. Results have shown that the MWADI correlates more with the standardized precipitation index (SPI) and the standardized precipitation temperature index (SPTI). As proposed, the MWADI synthesizes drought characteristics of different multiscalar drought indices to reduce the uncertainty of individual drought indices and provide a comprehensive drought assessment., Validerad;2023;Nivå 2;2023-05-04 (hanlid);Funder: Deanship ofScientifc Research at King Khalid University (RGP.2/23/44); Prince Sattam bin Abdulaziz University (PSAU/2023/R/1444);Part of special issue: Technologies-Based Advanced Machine Learning Models: Applications in Civil Engineering 2021

Trash mulches are remarkably effective in preventing soil erosion, reducing runoff-sediment transport-erosion, and increasing infiltration. The study was carried out to observe the sediment outflow from sugar cane leaf (trash) mulch treatments at selected land slopes under simulated rainfall conditions using a rainfall simulator of size 10 m × 1.2 m × 0.5 m with the locally available soil material collected from Pantnagar. In the present study, trash mulches with different quantities were selected to observe the effect of mulching on soil loss reduction. The number of mulches was taken as 6, 8 and 10 t/ha, three rainfall intensities viz. 11, 13 and 14.65 cm/h at 0, 2 and 4% land slopes were selected. The rainfall duration was fixed (10 minutes) for every mulch treatment. The total runoff volume varied with mulch rates for constant rainfall input and land slope. The average sediment concentration (SC) and sediment outflow rate (SOR) increased with the increasing land slope. However, SC and outflow decreased with the increasing mulch rate for a fixed land slope and rainfall intensity. The SOR for no mulch-treated land was higher than trash mulch-treated lands. Mathematical relationships were developed for relating SOR, SC, land slope, and rainfall intensity for a particular mulch treatment. It was observed that SOR and average SC values correlated with rainfall intensity and land slope for each mulch treatment. The developed models' correlation coefficients were more than 90%., Validerad;2023;Nivå 2;2023-05-04 (joosat);Licens fulltext: CC BY License

The information about the subsurface structure, type of fluids present in the reservoir, and physical properties of the rocks is essential for identifying potential leads. The integrated approach of petrophysical analysis, seismic data interpretation, seismic attributes analysis, lithology, mineralogy identification, and Gassmann fluid substitution were used for this purpose. The structural interpretation with the help of seismic data indicated the extensional regime with horst and graben structures in the study area. The two negative flower structures are cutting the entire Cretaceous deposits. The depth contour map also indicate favorable structures for hydrocarbon accumulation. The four possible reservoir zones in the Sawan-01 well and two zones in the Judge-01 well at B sand and C sand levels are identified based on well data interpretation. The main lithology of the Lower Goru Formation is sandstone with thin beds of shale. The clay types confirm the marine depositional environment for Lower Goru Formation. The water substitution in the reservoir at B sand and C sand levels indicated increased P-wave velocity and density. The water substitution affected the shear wave velocity varies slightly due to density changes. The cross plots of P-impedance versus Vp/Vs ratio differentiate the sandstone with low P-impedance and low Vp/Vs ratio from shaly sandstone with high values in the reservoir area. The P-impedance and S-impedance cross plot indicate increasing gas saturation with a decrease in impedance values. The low values of Lambda-Rho and Mu-Rho indicated the gas sandstone in the cross plot., Validerad;2023;Nivå 2;2023-05-04 (sofila)

Urban area expansion and the increase in the built-up area are major threats to agriculture, the natural environment, and ecology throughout the world. The population of the city of Larkana is rapidly increasing due to natural growth and uncontrolled migration from the surrounding areas, which have resulted in a haphazard increase in the built-up area over the fertile agricultural land and affected the city’s thermal environment. This research aims to evaluate the impacts of urban area expansion on agricultural land and the thermal environment of the study area by assessing the change in land use/ land cover (LULC) from 1990 to 2020 and land surface temperature (LST). For this purpose, the Random Forest (RF) algorithm was applied, and LST was calculated in Google Earth Engine (GEE) using Landsat imageries for the years 1990, 2000, 2010, and 2020. For the RF classifier algorithm, 3500 reference random points were generated for each year, which were then randomly divided into two datasets, i.e., a training sample consisting of 70% of the points and a validation dataset (30% of the points). After LULC classification, the results were validated for 1990, 2000, 2010, and 2020, and the accuracy was 88.3%, 89%, 90.01%, and 90.8%, respectively. The study results showed that the built-up area increased from 12.31 to 43.83 km2 while the barren land in the study region decreased from 56.51 km2 to 11.62 km2. The agricultural land was 66.66 km2 in 1990, expanded to 101.38 km2, and then decreased to 79.49 km2 in 2020. The results also revealed that most of the urban expansion in the last decade (2010-2020) took place on agricultural land. The urban thermal environment also showed a gradual increase in surface temperature as recorded by the LST results. The LST results revealed that the maximum LST was 33.4°C in 1990, which increased to 36.1°C in 2020, and similarly, the minimum LST was 25.1°C, which also increased to 26.6°C in 2020. This study provides useful results for stakehol, Validerad;2023;Nivå 2;2023-05-08 (hanlid)

Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions., Validerad;2023;Nivå 2;2023-04-18 (joosat);Licens fulltext: CC BY License

This study investigates the effectiveness of using Iraqi clay as a low-permeability layer to prevent the migration of lead and nickel ions in groundwater-aquifers. Tests of batch operation have been conducted to determine the optimal conditions for removing Pb2+ ions, which were found to be 120 minutes of contact time, a pH of 5, 0.12 g of clay per 100 mL of solution, and an agitation of 250 rpm. These conditions resulted in a 90% removal efficiency for a 50 mg L−1 initial concentration of lead ions. To remove nickel ions with an efficiency of 80%, the optimal conditions were 60 minutes of contact time, a pH of 6, 12 g of clay per 100 mL of solution, and an agitation of 250 rpm. Several sorption models were evaluated, and the Langmuir formula was found to be the most effective. The highest sorption capacities were 1.75 and 137 mg g−1 for nickel and lead ions, respectively. The spread of metal ions was simulated using finite element analysis in the COMSOL multiphysics simulation software, taking into account the presence of a clay barrier. The results showed that the barrier creates low-discharge zones along the down-gradient of the barrier, reducing the rate of pollutant migration to protect the water sources., Validerad;2023;Nivå 2;2023-06-02 (hanlid);Funders: King Saud University, Saudi Arabia (RSP2023R407)

Nowadays, Combine Harvesters are the most commonly used device for harvesting crops; as a result, a large amount of plant material and crop residue is concentrated into a narrow band of plant material that exits the combine, challenging the residue management task. This paper aims to develop a crop residue management machine that can chop paddy residues and mix them with the soil of the combined harvested paddy field. For this purpose, two important units are attached to the developed machine: the chopping and incorporation units. The tractor operates this machine as the main source, with a power range of about 55.95 kW. The four independent parameters selected for the study were rotary speed (R1 = 900 & R2 = 1100 rpm), forward speed (F1 = 2.1 & F2 = 3.0 Kmph), horizontal adjustment (H1 = 550 & H2 = 650 mm), and vertical adjustment (V1 = 100 & V2 = 200 mm) between the straw chopper shaft and rotavator shaft and its effect was found on incorporation efficiency, shredding efficiency, and trash size reduction of chopped paddy residues. The incorporation of residue and shredding efficiency was highest at V1H2F1R2 (95.31%) and V1H2F1R2 (61.92%) arrangements. The trash reduction of chopped paddy residue was recorded maximum at V1H2F2R2 (40.58%). Therefore, this study concludes that the developed residue management machine with some modifications in power transmission can be suggested to the farmers to overcome the paddy residue issue in combined harvested paddy fields., Validerad;2023;Nivå 2;2023-03-30 (hanlid);Finansiär: Vice Deanship of Scientifc Research Chairs, Research Chair of Prince Sultan Bin Abdulaziz International Prize for Water, King Saud University

The Iraqi Western and Southern deserts are part of the Iraqi Stable Shelf (Inner Platform) that belongs to the Arabian Plate. Therefore, both deserts lack tectonic forms like folds and faults; however, very rarely faults and regional lineaments can be seen in both deserts. Although both deserts are tectonically stable, tens of Neotectonic indications can be seen everywhere in both deserts. Among those indications are: Straight valleys, perpendicular valley bendings, sinkholes aligned along straight lines, dislocated valleys, knickpoints within valleys aligned along straight lines, regional lineaments, anomalous valley shapes, trends, and types, dissected alluvial fans. We have used existing geological maps of different scales and Esri World Imagery to recognize those Neotectonic indications. All those recognized features are excellent indications that both the Iraqi Western and Southern deserts are tectonically not stable. Accordingly, new terminology is suggested instead of the Stable Shelf (Inner Platform) which is the “Less Disturbed Shelf”., Godkänd;2023;Nivå 0;2023-05-30 (hanlid)

Sporadic burning of rice straw and the particulate air pollution caused consequently have created a pressing need for identification of practical environmentally sound in situ rice residue management methods. However, the agronomic interventions associated with the agri-inputs particularly the type of nitrogen fertilizer source must be worked out for these interventions. In a two-year field study performed at two different locations representing sandy loam and clay loam soil types, zero tillage with application of nitrophosphate (applied as basal dose through drilling) in combination with urea (applied at 1st irrigation + 3 foliar sprays of urea at weekly interval) significantly enhanced the grain and straw yield of wheat. The soil microbial viable cell counts and dehydrogenase and urease enzyme activities were also recorded to be highest in this treatment indicating the occurrence of higher living microbial population. The treatment × response variable Principle component analysis (PCA) biplot depicted relative variation among the residue management treatments/Nitrogen fertilizer sub-treatments and the enzyme activities as response variables. A variation in the soil organic content components was recognized through Fourier transform infra-red spectroscopy (FT-IRS) studies. Irrespective of the soil types under study, the FT-IR spectra exhibited presence of the aromatic carbon functional groups in residue incorporated treatments as compared to the no residue incorporation treatment., Validerad;2023;Nivå 2;2023-06-07 (hanlid);Funder: Research Chair of Prince Sultan Bin Abdulaziz International Prize for Water, King Saud University

By using microorganisms and the microalgae Chlorella vulgaris in conjunction with sequencing batch reactors (SBRs), the performance of a wastewater treatment facility was studied. For this purpose, the effect of pH, temperature, CODinlet, and air flowrate on COD removal rate and residual was investigated. A single-factorial optimization method is utilized to optimize the amount of COD removal, and the best result is obtained with a pH of 8, CODinlet=600mg/l, and an airflow rate of 55 l/min. Under optimal conditions, the amount of residual COD in the effluent reached 36 mg/l, showing an augmentation in the efficiency of the desired system. Moreover, empirical correlations are proposed for double-factorial optimization of residual COD and COD removal. Also, a multilayer perceptron artificial neural network is proposed to model the process and predict the residual COD concentration. The useful technique of hyperparameter tuning is utilized to obtain the best result for the predictions. All the effective parameters, including the number of hidden layers, neurons, epochs, and batch size, are adjusted. Data from the experiments agreed well with the artificial neural network modeling results. For this modeling, the values of the correlation coefficient (R2) and mean absolute error (MAE) were obtained as 0.98 and 2%, respectively., Validerad;2023;Nivå 2;2023-06-12 (joosat);Licens fulltext: CC BY License

Optimizing nitrogen (N) inputs is crucial for maximizing wheat yield and ensuring environmental sustainability. Wheat’s economic significance in India calls for a comprehensive evaluation of its ecological implications to develop a resilient production system. This study aimed to identify and evaluate ten wheat cultivars for their yield and N-use efficiency under varying nitrogen inputs (control (N0), half of the recommended nitrogen (N75), and the recommended nitrogen (N150)) using the surface application of neem-oil-coated urea. All N inputs were applied in three splits, basal, crown root initiation, and tillering stages, and an experiment was conducted in a split-plot design. The application of N150 gave the highest dry matter accumulation (DMA) at harvesting stage (AHS) (871 g m−2), seed/spike (60), grain yield (GY = 7.4 t ha−1), straw yield (SY = 8.9 t ha−1), harvest index (HI = 45.2%), protein (12.5%), and total uptake of N (TUN) (223 kg ha−1) by the cultivar ‘HD 3249’, being closely followed by the cultivar ‘HD3117’. Six cultivars (‘HD 3298’, ‘HD 3117’, ‘HD 3249’, ‘PBW 550’, ‘HD 3086’, ‘HD 2967’) out of the ten cultivars evaluated responded well to different input treatments with respect to the grain yield efficiency index (GYEI ≥ 1). Regarding N input, N75 and N150 recorded the highest increases in plant height, AHS (16.5%; 21.2%), dry matter accumulation (DMA) at 30 days after sowing (DAS) (37.5%; 64%), DMA-60 DAS (42%; 53%), DMA-90 DAS (39.5%; 52.5%), TILL-30 DAS (19.8%; 26.4%), TILL-60 DAS (33.3%; 44%), TILL-90 DAS (37.2%; 47.2%), seed/spike (8%; 10%), 1000-grain weight (7.8%; 12.2%), and protein content (23.3%; and 33%) when compared with N0. Furthermore, the application of N75 and N150 improved GY (72.1%; 142.6%), SY (61.1%; 110.6%), BY (65.5%; 123%), and HI by 4.4% and 9%, respectively, over N0. Nitrogen addition (N75 and N150) also significantly increased total nitrogen uptake (104.7%; 205.6%), respectively, compared to N0. The correlation analysis re, Validerad;2023;Nivå 2;2023-08-07 (joosat);Funding: Deanship of Scientific Research, King Saud University through the Vice Deanship of Scientific Research Chairs; Research Chair of Prince Sultan Bin Abdulaziz International Prize for WaterLicens fulltext: CC BY License

It is important to determine the limits of flow regimes in the design of stepped weirs because of the hydraulic performance of each regime. The present study investigates the effect of downstream slope and rock fill materials on flow regimes in gabion stepped weirs. Nine physical models of gabion weirs were used in the experiments. The models’ downstream slopes ranged from 1:05 to 1:4 V:H. In addition, two types of rockfill materials: crushed stone of 0.42 porosity and rounded gravel of 0.38 porosity were used to study the effect of rockfill materials on flow regimes. The nominal size of the crushed stone was (37.5 mm - 13.2 mm) D50 = 23 mm and the nominal size of the rounded gravel was (26.5 mm - 13.2 mm) D50 = 16 mm. Each model has been tested with ten runs for discharge per unit width ranging (from 0.006 to 0.105 m3/sec. m) to cover all flow conditions and flow regimes. The onset of each flow regime for all physical models has been observed. The experimental data of the gabion stepped weirs have been used to develop equations to estimate the onset of each flow regime. The coefficient of correlation (R) of the developed equations ranged between 0.95 to 0.97. The results indicated on the steeper downstream slope models (1:0.5, 1:0.83), there is interference between the nappe and transition flow regimes. The nappe flow regime has not appeared on all steps at the same time. Moreover, the shape and size of the rockfill materials have an insignificant effect on flow regimes, especially at a high flow rate., Validerad;2023;Nivå 2;2023-08-16 (joosat);Licens full text: CC BY-NC 4.0.

A well-known tool for assessing the quality of surface water is the water quality index (WQI) model. In this study, the WQI was generated to classify the water flowing in the Euphrates River in Qadisiyah Province. To develop analytical models, a connection between the findings and satellite images was developed. It is possible to determine what category a river’s water quality for domestic use will fall into. The Weighted Arithmetic Water Quality Index (WWQI), Canadian Water Quality Index (CWQI),and Bascarón Water Quality Index (BWQI) were used to evaluate and examine the suitability of the Euphrates River in the city by analysing the water quality of samples taken from the five locations (Muhanawia (L1), Salahia (L2), Shamiyah (L3), Shamiyah (L4), Gammas (L5)). The hydrogen ionspH, temperature T, dissolved oxygen DO, nitrate NO3, calcium Ca, magnesium Mg, total hardness TH, potassium K, sodium Na, sulfate SO4, chlorine Cl, total dissolved solids TDS, and electrical conductivity ECvalues are provided for 2020 and 2021. Results showed the Euphrates River was deemed severely contaminated at location Gammas (L5) but acceptable at location Muhanawia (L1). During the research phase, the water quality for the Euphrates achieved a maximum of 87.43 using the CWQI for Muhanawia (L1) in 2021 and a minimum of 15.6 using the BWQI for Gammas (L5) in 2021. The excessive sulphate, total dissolved solids, calcium, and total hardness concentrations led to the low WQI. The results are analysed using a GIS, and a network database connected to the GIS is required to utilize its analytical capabilities and the geographically scattered data throughout the study region. The Water Quality Index (WQI) is not suitable for drinking, as it is below the average of the World Health Organization (WHO) suggestions., Validerad;2023;Nivå 2;2023-08-16 (joosat);Funder: Al-Mustaqbal University College, Iraq

Groundwater management requires a systematic approach since it is crucial to the long-term viability of livelihoods and regional economies all over the world. There is insufficient groundwater management and difficulties in storage plans as a result of increased population, fast urbanisation, and climate change, as well as unpredictability in rainfall frequency and intensity. Groundwater exploration using remote sensing (RS) data and geographic information system (GIS) has become a breakthrough in groundwater research, assisting in the assessment, monitoring, and conservation of groundwater resources. The study region is the Mand catchment of the Mahanadi basin, covering 5332.07 km2 and is located between 21°42′15.525″N and 23°4′19.746″N latitude and 82°50′54.503″E and 83°36′1.295″E longitude in Chhattisgarh, India. The research comprises the generation of thematic maps, delineation of groundwater potential zones and the recommendation of structures for efficiently and successfully recharging groundwater utilising RS and GIS. Groundwater Potential Zones (GPZs) were identified with nine thematic layers using RS, GIS, and the Multi-Criteria Decision Analysis (MCDA) method. Satty's Analytic Hierarchy Process (AHP) was used to rank the nine parameters that were chosen. The generated GPZs map indicated regions with very low, low to medium, medium to high, and very high groundwater potential encompassing 962.44 km2, 2019.92 km2, 969.19 km2, and 1380.42 km2 of the study region, respectively. The GPZs map was found to be very accurate when compared with the groundwater fluctuation map, and it is used to manage groundwater resources in the Mand catchment. The runoff of the study area can be accommodated by the computing subsurface storage capacity, which will raise groundwater levels in the low and low to medium GPZs. According to the study results, various groundwater recharge structures such as farm ponds, check dams and percolation tanks were suggested in appropriate loca, Validerad;2023;Nivå 2;2023-06-19 (joosat);Licens fulltext: CC BY License

Utilizing the waterworks sludge byproduct in the treatment of wastewater contained methylene blue dye is one approach that has been taken in an effort to lessen the difficulties that are associated with managing such byproduct. The prime aim of this work is manufacturing of novel sorbent from co-precipitation of magnesium oxide nanoparticles on the surfaces of waterworks sludge in the existence of cetyl trimethyl ammonium bromide surfactant. Surfactant 0.04 g/50 mL, dose of sludge 2 g/50 mL, and pH 12 were the most efficient preparation parameters to remove 75.31% of adopted dye. The adsorption studies were conducted under various conditions of contact time (0–240 min), concentration of dye (10–300 mg/L), sorbent mass (0.05–1.5 g), and solution pH (3–12). The best values of batch parameters were identical to the highest percentages of contaminant removal. Results proved that the magnesium oxide nanoparticles are attached to the sludge surfaces. Freundlich and pseudo-second-order models have perfectly described sorption results with 59.92 mg/g maximum sorption capacity. The breakthrough curves can be accurately described by the Bohart-Adams model. The outputs of continuous tests have been paved the way for future usage of the prepared sorbent in the field permeable reactive barrier technology., Validerad;2023;Nivå 2;2023-08-15 (hanlid);Funder: King Saud University, Riyadh, Saudi Arabia (RSP2023R407)