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Mosul Dam is one of the biggest hydraulic structures in Iraq. It was constructed in 1986 on the Tigris River in the north of Iraq. The initial storage capacity and water surface area of its reservoir reaches 11.11 km3 and 380 km2 respectively at the maximum operation level 330 m a.s.l. The dam was operated in 1986. A total of 56 samples were collected from the bottom of Mosul reservoir covering most of the reservoir area. The results of the analysis of these samples revealed that they were composed of gravel (3.8%), sand (15%), silt (55.5%) and clay (25.7%). The distribution of these sediments indicates that the silt portion represents the highest followed by clay and then sand. However, sand percentages are the highest in the northern zone of the reservoir where the River Tigris enters the reservoir and decreases gradually toward the dam site. In the meantime, silt percentage decreases toward the dam site while the finer fraction (i.e. clay) increases. [ABSTRACT FROM AUTHOR]

Mosul Dam is one of the biggest hydraulic structures in Iraq. Its storage capacity is 11.11 × 109m3at a maximum operation level of 330 m a.s.l. The dam became operational in 1986 and no survey has been conducted to determine its storage capacity and establish new operational curves since this date. A topographic map of scale 1:50 000 dated 1983 was converted into triangulated irregular network (TIN) format using the ArcGIS program to evaluate the operational curves. Then the reservoir was surveyed in 2011 to establish the reduction in its storage capacity and to develop new operational curves. The results indicated that the reduction in the storage capacity of the reservoir was 14.73%. This implies that the rate of sedimentation within the reservoir was 45.72 × 106m3year−1. These results indicate that most of the sediment was deposited within the upper zone of the reservoir where the River Tigris enters the reservoir. EditorD. Koutsoyiannis CitationIssa, E.I., Al-Ansari, N., and Knutsson, S., 2013. Sedimentation and new operational curves for Mosul Dam, Iraq.Hydrological Sciences Journal, 58 (7), 1456–1466. [ABSTRACT FROM AUTHOR]

Modelling drought is vital to water resources management, particularly in arid areas, to reduce its effects. Drought severity and frequency are significantly influenced by climate change. In this study, a novel hybrid methodology was built, data preprocessing and artificial neural network (ANN) combined with the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA), to forecast standard precipitation index (SPI) based on climatic factors. Additionally, the marine predators algorithm (MPA) and the slime mould algorithm (SMA) were used to validate the performance of the CPSOCGSA algorithm. Climatic factors data from 1990 to 2020 were employed to create and evaluate the SPI 1, SPI 3, and SPI 6 models for Al-Kut City, Iraq. The results indicated that data preprocessing methods improve data quality and find the best predictors scenario. The performance of CPSOCGSA-ANN is better than MPA-ANN and SMA-ANN algorithms based on various statistical criteria (i.e., R2, MAE, and RMSE). The proposed methodology yield R2 = 0.93, 0.93, and 0.88 for SPI 1, SPI 3, and SPI 6, respectively. [ABSTRACT FROM AUTHOR]

This work aims to evaluate water scarcity in Thi-Qar governorate, Iraq, based on GIS estimation, environmental data, climate-change effects, and detection of the changes in marshes over the last three decades (1991–2021). The methodology process included collecting and analysing the related data sets such as water quality indicators, surface water quantity, climatic data, and Landsat’s images. GIS-based data and spatial data were acquired from the USGS website. Arc GIS 10.4.1 software was used to create a hydrological analysis. The results showed that generally, in Iraq, the annual volume of water available per person is 1,390.95 m³/cap/year, which is lower than the threshold for water scarcity (1700 m³/cap/year). The average daily potable water per person in Thi-Qar governorate was 284 L/cap/day, lower than the general average daily potable water per person of Iraq (340 L/cap/day). Meanwhile, 6% of the months along 1998–2018 did not meet the water demands. Water quality tests exhibited some high amounts of pollutants in drinking water, e.g., biological pollution was recorded in 55% of the total number of annual samples. Landsat’s images illustrated a high variation in water areas of marshes over the selected period, whereas the highest marshes area was 1548.21 km² in 1991 compared to the lowest area, 65.45 km² found in 1999. To sum up, the research outcomes revealed that the study area faced a serious water scarcity, which had a negative impact on the local people. Also, this research offered a scientific view for the decision-makers to mitigate and manage the water scarcity problem. [ABSTRACT FROM AUTHOR]

Methane (CH4) is a greenhouse gas resulting from human activities, especially landfills, and it has many 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 turbine generators. Therefore, reusing such gases could be of great environmental and economic benefit. In this context, this study aims to estimate the emissions of methane gas from the landfills in Al-Hillah City, Iraq, from 2023 to 2070 and the producible electric energy from this amount. The estimating process was carried out using the Land GEM model and compared with traditional models. The obtained results demonstrated that the total estimated landfill methane emissions for 48 years are 875,217 tons, and the average annual methane emission is 18,234 tons based on a yearly waste accumulation rate of 1,046,413 tons and a total waste amount of 50,227,808 tons. The anticipated loads of methane gas can be utilized to generate about 287,442 MW/year of electricity from 2023 to 2070. In conclusion, the results obtained from this study could be evidence of the potential environmental and economic benefits of harvesting and reusing methane gas from landfills. [ABSTRACT FROM AUTHOR]

Methane (CH4) is a greenhouse gas resulting from human activities, especially landfills, and it has many 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 turbine generators. Therefore, reusing such gases could be of great environmental and economic benefit. In this context, this study aims to estimate the emissions of methane gas from the landfills in Al-Hillah City, Iraq, from 2023 to 2070 and the producible electric energy from this amount. The estimating process was carried out using the Land GEM model and compared with traditional models. The obtained results demonstrated that the total estimated landfill methane emissions for 48 years are 875,217 tons, and the average annual methane emission is 18,234 tons based on a yearly waste accumulation rate of 1,046,413 tons and a total waste amount of 50,227,808 tons. The anticipated loads of methane gas can be utilized to generate about 287,442 MW/year of electricity from 2023 to 2070. In conclusion, the results obtained from this study could be evidence of the potential environmental and economic benefits of harvesting and reusing methane gas from landfills. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

Salinity is a classic problem in water quality management since it is directly associated with low water quality indices. Debate continues about selecting the best model for water quality forecasting, it remains a major challenge and causes much uncertainty. Accordingly, identifying the optimal modelling that can capture the salinity behaviour is becoming a common trend in recent water quality research. This study applies novel combined techniques, including data pre- processing and artificial neural network (ANN) optimised with constriction coeffi- cient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA) to forecast monthly salinity data. Historical monthly total dissolved solids (TDS) and electrical conductivity (EC) data of the Euphrates River at Al- Musayyab, Babylon, and climatic factors from 2010 to 2019 were used to build and validate the methodology. Additionally, for more validation, the CPSOCGSA-ANN was compared with the slime mould algorithm (SMA-ANN), particle swarm optimisation (PSO-ANN) and multi-verse optimiser (MVO-ANN). The results reveal that the pre-processing data approaches improved data quality and selected the best predictors’ scenario. The CPSOCGSA-ANN algorithm is the best based on several statistical criteria. The proposed methodology accurately simulated the TDS and EC time series based on R² = 0.99 and 0.97, respectively, and SI = 0.003 for both parameters. [ABSTRACT FROM AUTHOR]

Most of the hydraulic structures rest on an impervious foundation to reserve water at the upstream side. The water heads difference leads to water movement from the higher to the lower head through the porous soil layer beneath the foundation, generating an uplift pressure under the structure floor. In this study, a new method is presented to estimate the uplift pressures at key points by performing sub-surface flow analysis using the Analysis SYStem (ANSYS) software. Then a statistical analysis to validate the proposed equations is conducted using the SPSS software. The case study for this research is a barrage in Kufa city-Iraq. The used data to implement this study was water levels, soil permeability, and length of imperious foundation. The obtained results show good outcomes from using the proposed method to develop uplift pressure equations. The comparison of the current study results with Khosla's equation showed good agreement where the coefficient of determination (R2) and the standard error of estimation (SEE) for the equations were between (99.9–97.8) and (0.024–0.11), respectively. [ABSTRACT FROM AUTHOR]

This study intends to evaluate university students' awareness regarding water pollution in Iraq to show the environmental education contribution in reducing water pollution. The study was conducted in Wasit university, Wasit province, Iraq by field questionnaire survey with 388 students from the civil engineering department and engineering college. The questionnaires were divided according to the pollution causes into industrial, agricultural and sewage water pollutants. The data were analyzed utilizing the thematic analysis to assess environmental knowledge and awareness of water pollution risks. The results show that most students have high interest in environmental conservation, yet, a lack of education in general environmental education, as the educational curriculum focuses on theoretical aspects more than the practical ones. The results also show that the media role is inactive concerning environmental awareness of water pollution. This study provides a scientific view of policymakers for potential future conditions to find solutions that achieve sustainability goals. [ABSTRACT FROM AUTHOR]

The current study was conducted to determine the quality of the water of Al- Sabil River in Al-Shinafiya district, Province of Al- Diwaniyah, for the period from September 2018 to August 2019. Three sites were selected along the river, a quantitative and qualitative study of the diatoms as well as its indices to assess the quality of water in the river, such as trophic diatom Index (TDI), Trophic State Index (TSI), Diatomic Index (DI), and General Diatoms Index (GDI). The current study was diagnosed about 136 species of diatoms at three sites, where the central diatoms was 12 species while the pannals diatoms reached 124 species, and recorded total numbers of diatoms (35453.8, 29447.2 and 36504.76) cell*310/L, and rates (2954.48, 2453.93 and 3042.06) cells*310/L for the three locations respectively, as shown by the results of the trophic diatom Index(TDI) values ranged from (23.33 to 55.54) and the values of Trophic State Index (TSI) ranged from (0.07 to 0.81) and the Diatomic Index (DI) values ranged from (9.08 to 16.20) and the values of the General Diatoms Index (GDI) ranged from (2.23 to 3.17). It was noted that the water quality was Oligotrophic to Mesotrophic based on TDI and TSI, while its moderate to Good based on DI and GDI, and concluded that the higher the evidence values indicated that the water quality tended to be good, free or low pollution, suitable for living well, and that water was few nutrients, while its low values indicated the deterioration of the water quality in the river. [ABSTRACT FROM AUTHOR]

Growing populations, socio-economic development, the pollution of rivers, and the withdrawal of fresh water are all signs of increasing water scarcity, and with 85% of global use, agriculture is the biggest freshwater user. The water footprint (WF) and virtual water (VW) are concepts used recently for freshwater resources assessment. The WF reflects how much, when and where the water was used whereas VW reveals the volume of water embedded in goods when traded. The first goal of this research is to determine the WF per ton and the WF of production (Mm3/yr) of wheat, barley, rice, and maize in Iraq. The second goal is estimating the quantities of the 4 main cereal crops imported into Iraq and assessing the impact on reducing WF and land savings for 10 years from 2007 to 2016. The results showed that the WF per ton was 1736, 1769, 3694, 2238 m3/ton and the WF of production was 5271, 1475, 997, 820 Mm3/yr for wheat, barley, rice, and maize, respectively. The median total VW imported was 4408 Mm3/yr, the largest volume was 3478 Mm3/yr from wheat, and Iraq saved about 2676 Mm3 of irrigated water and 1,239,539 M ha of land by importing crops every year during 2007–2016. The study revealed the significance of better irrigation management methods to decrease the WF through a selection of crops that need less water and cultivation in rain-fed areas, as well as the use of cereal import to conserve scarce water resources, which is crucial both in terms of water resource management and preservation of the environment. The results of this research could be used as a guideline for better water management practices in Iraq and can provide helpful data for both stakeholders and policymakers. [ABSTRACT FROM AUTHOR]

Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation – the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM) – were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R2 =.92), and with all variables as inputs at Station II (R2 =.97). All the ML models performed well in predicting evaporation at the investigated locations. [ABSTRACT FROM AUTHOR]

[Display omitted] • High-resolution prediction of air fine particular matter concentration is conducted. • New developed machine learning algorithms are adopted for this purpose. • Prediction for temporal/spatial variability of PM 2.5 over Iraq region is studied. • Research provided good forecasting spatial variability of PM 2.5 at high resolution. This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM 2.5) 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 PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM 2.5 forecasting maps. [ABSTRACT FROM AUTHOR]

The evaluation of groundwater quality in the Dammam formation, Faddak farm, Karbala Governorate, Iraq proved that the sulfate (SO42−) concentrations have high values; so, this water is not suitable for livestock, poultry and irrigation purposes. For reclamation of this water, manufacturing of new sorbent for permeable reactive barrier was required through precipitation of Mg and Fe hydroxides nanoparticles on the activated carbon (AC) surface with best Mg/Fe molar ratio of 7.5/2.5. Mixture of 50% coated AC and 50% scrap iron was applied to eliminate SO42− from contaminated water with efficiency of 59% and maximum capacity of adsorption equals to 9.5 mg/g for a time period of 1 h, sorbent dosage 40 g/L, and initial pH = 5 at 50 mg/L initial SO42− concentration and 200 rpm shaking speed. Characterization analyses certified that the plantation of Mg and Fe nanoparticles onto AC was achieved. Continuous tests showed that the longevity of composite sorbent is increased with thicker bed and lower influent concentration and flow rate. Computer solution (COMSOL) software was well simulated for continuous measurements. The reclamation of real contaminated groundwater was achieved in column set-up with efficiency of 70% when flow rate was 5 mL/min, bed depth was 50 cm and inlet SO42− concentration was 2301 mg/L. [ABSTRACT FROM AUTHOR]

The energy sector is integral to the wellbeing of the entire Iraqi economy and will remain so well into the future. In the current study, the Intergovernmental Panel on Climate Change (IPCC) methodology was used to estimate CO2, CH4, and N2O emissions from oil refining and electricity generation in Iraq for a period exceeding 25 years. From 1990, Iraq experienced two wars and an economic siege, then faced political, social, and security instability, which affected its energy production. The results showed that the CO2, CH4, and N2O emissions from the oil refining and electricity generation in Iraq experienced a sharp decline in the years 1991, 2003, and 2007 due to a decrease in the production of oil derivatives in refineries, according to political and security conditions. The total CO2 emissions from the types of fuel used in electricity generation in Iraq was approximately 14,000 Gg and 58,000 Gg in 1990 and 2017, respectively. The increase in CO2 emissions was greater than 300% between 1990 and 2017. The continued use of poor types of fuel, such as fuel oil and crude oil, will lead to an increase in greenhouse gas (GHG) emissions from these sources, and higher levels of environmental pollution. [ABSTRACT FROM AUTHOR]

Agricultural land in the south of Iraq provides habitat for several types of living creatures. This land has a significant impact on the ecosystem. The agricultural land of Al-Hawizeh marsh covers an area of more than 3500 km2 and is considered an enriched resource to produce several harvests. A total of 74% of this area suffers from a high degree of salinity and chemical pollution, which needs to be remedied. Several human-made activities and post-war-related events have caused radical deterioration in soil quality in the agricultural land. The goal of this research was to integrate mathematical models, remote sensing data, and GIS to provide a powerful tool to predict, assess, monitor, manage, and map the salinity and chemical parameters of iron (Fe), lead (Pb), copper (Cu), chromium (Cr), and zinc (Zn) in the soils of agricultural land in Al-Hawizeh marsh in southern Iraq during the four seasons of 2017. The mathematical model consists of four parts. The first depends on the B6 and B11 bands of Landsat-8, to calculate the soil moisture index (SMI). The second is the salinity equation (SE), which depends on the SMI result to retrieve the salinity values from Landsat-8 images. The third part depends on the B6 and B7 bands of Landsat-8, which calculates the clay chemical index (CCIs). The fourth part is the chemical equation (CE), which depends on the CCI to retrieve the chemical values (Fe, Pb, Cu, Cr, and Zn) from Landsat-8 images. The average salinity concentrations during autumn, summer, spring, and winter were 1175, 1010, 1105, and 1789 mg/dm3, respectively. The average Fe concentrations during autumn, summer, spring and winter were 813, 784, 842, and 1106 mg/dm3, respectively. The average Pb concentrations during autumn, summer, spring, and winter were 4.85, 3.79, 4.74, and 7.2 mg/dm3, respectively. The average Cu concentrations during autumn, summer, spring, and winter were 3.9, 3.1, 4.45, and 7.5 mg/dm3, respectively. The average Cr concentrations during autumn, summer, spring, and winter seasons were 1.28, 0.73, 1.03, and 2.91 mg/dm3, respectively. Finally, the average Zn concentrations during autumn, summer, spring, and winter were 8.25, 6, 7.05, and 12 mg/dm3, respectively. The results show that the concentrations of salinity and chemicals decreased in the summer and increased in the winter. The decision tree (DT) classification depended on the output results for salinity and chemicals for both SE and CE equations. This classification refers to all the parameters simultaneously in one stage. The output of DT classification results can display all the soil quality parameters (salinity, Fe, Pb, Cu, Cr, and Zn) in one image. This approach was repeated for each season in this study. In conclusion, the developed systematic and generic approach may constitute a basis for determining soil quality parameters in agricultural land worldwide. [ABSTRACT FROM AUTHOR]

Cost and time performance are considered to be the most important aspects in the construction industry. The exceptional conditions that took place in Iraq since the beginning of the third millennia had a huge vicious impact on the cost and time performance of local construction projects. This may represent the principal motivation for the local authorities to enact some four successive legislations in order to control the performance of the construction industry. In this research, an evaluation is made to the cost and time performance of local construction projects and their variation due to the multiple changes in the internal factors that affect project performance, and changes in the surrounding events include legislative, economic, and security environment during the period that lasted from 2003 to 2014. Data is collected from 30 governmental projects to conduct the evaluation. A comprehensive questionnaire is performed to estimate a quantitative value for the impact of several factors that concern both the owner and the contractor, with special consideration to their variation through the successive legislation periods. These estimates are, in turn, utilized in a system dynamics model, in which the project development process is simulated. The final cost and duration changes in the project are accumulated in the form of stocks to give an indication of the cost and time performance of the project. The developed model returned a progressive reduction of 10.9% for the change in project cost and 135.37% for the change in project schedule throughout the eleven years period. [ABSTRACT FROM AUTHOR]