Assessment of untreated wastewater pollution and heavy metal contamination in the Euphrates river

ABSTRACT

This study investigates the impact of untreated wastewater on a section of the Euphrates River in Al-Diwaniyah, Iraq. The unregulated discharge of sewage and industrial effluents has caused severe pollution in this area. The research measures electrical conductivity, turbidity, total dissolved solids, dissolved oxygen, total hardness, biological oxygen demand and heavy metals (Ni, Cr, Pb, Co, Cd, Cu, Fe) using Inductively Coupled Plasma Atomic Emission Spectrometry. Approximately 40 water sampling stations were selected for analysis and the Heavy Metal Pollution Index (HPI) was used to assess water quality. The study employed Inverse Distance Weighting (IDW) tool, to predict HPI values and their spatial distribution. The accuracy of prediction maps was evaluated using regression analysis by comparing observed values with predicted values from the maps. The study emphasizes the excessive pollution levels in the Euphrates River, specifically exceeding Iraqi standard thresholds for Ni, Fe, and Cd concentrations.

KEYWORDS:

• Wastewater pollution
• heavy metals
• water quality assessment
• predictive mapping
• environmental impact

1. Introduction

Heavy metals are highly toxic substances that can significantly impact the environment and human health [1]. Pollution from these elements is attributed mainly to industrial activities, such as mining and manufacturing processes, which release heavy metals into the air and water [2]. Once released, they can contaminate soil and waterways and eventually enter food chains [3]. In researching toxic contaminants from heavy metals, analyzing the existence of lead in inexpensive plastics, and developing a method for quantifying lead levels in plastic toys using flame atomic absorption spectroscopy [4]. Nickel and chromium are two potentially toxic elements that can be found in various environmental sources, including industrial activities and consumer products. The study in Ankara collected biological samples from 100 healthy mother-newborn pairs to assess the reference levels of nickel and chromium in cord blood, maternal blood, and placenta. GFAAS was used to quantify metal levels with method validation using certified reference materials for accuracy assessment [5]. The dangers of heavy metal poisoning were highlighted in a study on mercury levels in the fetus’ umbilical cord blood, an important marker for predicting low fetal weight and body height. Thus, measuring lead and mercury levels in fetal umbilical cord blood is critical for identifying and providing prenatal care for idiopathic intrauterine growth restriction [6]. The study on health risk assessment concluded that there is no cancer or non-carcinogenic health risk from consuming toxic elements in non-alcoholic beverages. Statistical analyses highlighted three different sources of these elements in the samples, underscoring the importance of ongoing monitoring to ensure long-term food safety [7].

The assessment of trace elements in estuarine sediments involves evaluating ecological risk, toxicity, and potential human health risks. Methods like the geo-accumulation index and enrichment factor are crucial for assessing contamination levels and potential ecological risks. Evaluating potential human health risks via edible tissues of organisms is also important for understanding implications for human consumption [8]. Previous research has emphasized the significance of conducting ecotoxicological risk evaluations that take into account environmental variables and potential leaks from waste disposal sites. Additionally, studies have highlighted the importance of assessing trace metal concentrations in bottom sediments to determine their ecological risk and potential impact on aquatic organisms [6,9,10].

Exposure to heavy metals has been connected to various health problems in humans, including neurological damage, respiratory issues, kidney damage, developmental problems in children, and muscle weakness [11]. Heavy metal can also threatens the environment by disrupting ecosystems and reducing biodiversity [12]. Therefore, industries need to adopt environmentally friendly practices to prevent further accumulation of heavy metals [13,14]. A significant concern about water is growing globally and that can be seen through its rising demand in numerous sectors including household, industrial, hydropower generation, and agriculture. Iraq has been grappling with a shortage of water mainly caused by dams constructed by neighboring countries on the Tigris and Euphrates rivers, as well as other hydrological factors. Meeting the increasing demand for freshwater in Iraq is a daunting task due to the varying availability of water, both geographically and over time [15,16].

Surface water quality evaluation is an essential aspect of managing water resources, and this issue holds utmost significance in developing nations where it directly impacts the health of aquatic ecosystems [17,18]. Moreover, Many countries, including Iraq have experienced a decline in the quality of their surface water as a result of an increase in untreated wastewater being discharged into rivers, rapid population growth, misallocation of water resources, and drought induced by shifts in climate patterns. As it enters the distribution system, it has caused water loss and microbiological contamination due to strained water treatment plant processes (W [19].

Certain metals, such as Mn, Cu, Ni, Zn, and Fe, play a vital role as micronutrients for animals and plants. On the other hand, there is no evidence to suggest that Pb, Co, Cr, and Cd have any physiological consequences. Metal ions exhibited toxicity towards mammalian systems due to their chemical interaction with the membrane system, enzymes, and cellular structural proteins. Humans and other experimental animals have been shown to experience acute and chronic poisoning as a result of metal exposure.  [20,21].

The evaluation of heavy metal concentrations in two river streams, including Mn, Fe, and Ni, was carried out using the Heavy Metal Pollution Index (HPI). In the course of the investigation, it was discovered that the minerals in the stream were sorted in terms of concentration with Sr being the highest followed by Fe, Al, Ni, As, Cu, Mn, Pb, Cr, and Se. Conversely, the ranking of heavy metal concentration in rivers revealed that Sr had the highest concentration followed by Fe, Al, Mn, As, Ni, Cu, Pb, Cr, and Se. Good, poor, and extremely poor were the classifications given to the water samples at most of the sites. The concentrations of Al and Sr in both streams were found to exceed the allowable limit, a result that could be associated with urbanization in those regions or human intervention impacts [22].

Numerous studies have been conducted on the issue of river pollution resulting from city wastewater discharges. These studies have consistently found that heavy metal pollution is the primary cause of river pollution in urban areas. In terms of assessing river water quality, research has shown that water samples from these polluted rivers contain significantly higher concentrations of various heavy metals than the acceptable levels [23].

Using the Heavy Metal Pollution Index, a study examined the Gomti Rivers pollution status by measuring the concentration of heavy metals present. It was discovered that the HMPI had an average value exceeding 100, which indicated a severe pollution condition. At 30% of the sites, the index value was below the high pollution level, while severe pollution condition was signified at 60% of the sites with an index higher than 100 [24,25].

The conclusions derived from these investigations emphasize the concerning extent of heavy metal contamination in diverse aquatic ecosystems. The use of the HPI provides a comprehensive assessment of the pollution status, which can aid in developing effective strategies to mitigate the adverse effects of heavy metal pollution on the environment and human health. Implementing measures to reduce heavy metal pollution and prevent further degradation of natural resources is imperative [26,27].

Practically all rivers in Iraq are experiencing the detrimental effects of low-quality level and pollution [19,28,29]. Due to the agricultural drainage into the river without any treatments, and numerous small village households the river is subjected to various anthropogenic sources. Moreover, while passing through the city, the river accumulated industrial and municipal wastewater [30]. The impact of climate change is manifested through an escalation in the occurrence and intensity of droughts, leading to a subsequent reduction in flow rates and the variability they experience to significant change in water quality [31].

Several scholarly investigations have been carried out to explore how the flow rate of the Euphrates River affects its water quality [32,33]. The investigation into Al-Diwaniyah rivers water quality by past researchers lacked emphasis on identifying a distinct pollution origin for the river. In this study, the link between wastewater discharge and elevated heavy metal levels as well as changes in physicochemical parameters in the Diwaniyah River is examined, highlighting the potential health implications. Additionally, a spatial predictor for the pollutant and a model to validate findings in this particular area are present.

This research aims to: (a) establish a prediction model for the evaluation of wastewater discharge impact for samples from the Euphrates River in the city of Al-Diwaniyah, (b) the heavy metal pollution index was applied to evaluate the impact of pollution and spatial distribution of heavy metal concentration in the water the study region. So, this study supplies the potential spatial prediction with heavy metal pollution in water sources and inform future efforts to mitigate these risks.

2. Materials and Methods

3.1. Study area

Encompassing the branch of the Euphrates River, the study area extends between Babylon’s southern region and Al-Diwaniyah city, located in low sedimentary plain Iraq, is facing severe sewage blockage and overflowing drains. The city’s drainage system, designed and constructed during the 1970s, has deteriorated over the past few decades due to poor maintenance. The city’s rapid population growth has added to the problem of untreated sewage, leading to severe health hazards. Recent efforts to revamp the drainage system have proven inadequate, and the city faces a significant challenge in avoiding ecological and health disasters. The Babylon and Diwaniyah governorates within the Euphrates River’s reach, as depicted in Figure 1. These governorates are between two longitudes and latitudes in the northern hemisphere, specifically (44.4398003–44.8008003) and (32.4698982–32.1805992) [17].

Figure 1. The location of Euphrates river and catchment of study.

In regions where the river acts as the main supplier of potable water and is utilized for industrial and agricultural needs, it also serves as a wastewater discharge estuary. That means the river is not only a vital resource for human consumption and economic activities but also plays a crucial role in maintaining the health of the surrounding environment.

3.2. Framework of the study

To achieve the objectives of the study are divided into 5th steps. In the 1st step: select the sampling locations, collect 40 samples of wastewater discharge along the selected stretch of the Euphrates River in the study area, and the analysis of heavy metal for samples using an Inductively Coupled Plasma Atomic Emission Spectrometry ICP-OES. 2nd step: evaluate the Heavy Metal Pollution Index (HPI) to assess the impact of water quality on various bodies of water. Study the values, analyze the extracted results, and detect water pollution. 3rd step: spatial prediction of heavy metal pollution in water sources by using the ArcGIS-IDW geoprocessing tool, performing a statistical analysis of points to produce a distribution map, filling in values and distances between points, calculating weights and distributing the pollution gradient in the river. 4th step: collect new samples of water. To ensure the accuracy and reliability of the findings, a diverse set of 16 samples were chosen to assess the veracity of results and predictive measurements by comparing the values of the new samples with the prediction values. 5th step: use cross-validation techniques frequently to overcome limitations associated with a limited dataset, providing a comprehensive assessment of overall accuracy.

3.3. Sampling of water

The collection of water samples involved the use of premium-quality polyethylene bottles with a 1 L capacity to guarantee accuracy and reliability. The process of cleaning these bottles involved water first and then a rinse with nitric acid HNO3 in a particular` manner to eliminate potential contaminants. The sampling was conducted during November and December 2022, where 40 water samples were collected from strategic locations of wastewater discharge along the selected Euphrates River stretch.

3.4. Samples analysis

Heavy metal concentrations in water samples were measured through the application of the inductively coupled plasma atomic emission spectrometry (ICP-OES) technique, the model with a silica torch the HORIBA (France) model. The water samples shown in Table 1. were analyzed for the presence of nickel (Ni), chromium (Cr), lead (Pb), cobalt (Co), cadmium (Cd), copper (Cu), and iron (Fe). In analytical chemistry, two important concepts are the Limit of Detection and the Limit of Quantification. The LOD refers to the minimum concentration or quantity that can be accurately detected using a specific method or instrument. Similarly, the LOQ represents the lowest concentration at which quantitation is possible with acceptable levels of accuracy and precision [34]. These values determine the sensitivity and reliability of an analytical technique in determining target substances. During the study, a thorough investigation was conducted to examine the presence of mercury in the collected samples. After careful analysis and testing, it was concluded that no detectable levels of mercury were found in any of the samples. The absence of mercury cannot definitively prove that there is no presence of this toxic heavy metal in the Euphrates River as errors during sample collection or other factors could have influenced these results. Furthermore, it should be noted that nearby sources of pollution like paper mills and electric power plants are not present in close proximity to the river, further supporting its absence.

Table 1. The inclusion of heavy metals as components in the determination of water quality index (WQI).

Heavy metals HMs	Symbol	Concentration (µg/L)	Permissible standard limit Si µg/L	Unit weight value Wi=k/Si	WHO 2022 µg/L	Status Compared to WHO or Iraqi specifications	Iraqi specifications µg/L
Nickel	Ni	13.5	70	0.014	70	−56.5	70
Chromium	Cr	22	50	0.02	50	−28	50
Lead	Pb	0.9	10	0.1	10	−9.1	10
Cobalt	Co	0.9	1.8 ≥	0.909	-	0.9	-
Cadmium	Cd	0.8	3	0.33	3	−2.2	3
Copper	Cu	49.3	2000	0.0005	2000	−950.7	1000
Iron	Fe	671	300	0.003	-	−371	300

To accurately assess heavy metal pollution and calculate the Heavy Metal Pollution Index, it is crucial to consider specific parameters that provide valuable information about contamination. Parameters such as Mercury, Lead, Cadmium, Chromium, and Arsenic are selected for their high toxicity levels and potential harm to ecosystems and human health even at low concentrations. Lead contamination in drinking water poses significant health risks, particularly for children and pregnant women. The issue arises from deteriorating infrastructure that allows lead to leach into the water supply. Ingested lead can accumulate in the body, leading to developmental delays, neurological impairments, decreased intelligence quotient, behavioral problems, and anemia in children. Adults may experience high blood pressure, kidney damage, and reproductive issues due to lead exposure. Stricter regulations enforcement should be prioritized alongside infrastructure improvements and public education efforts as crucial measures to reduce lead exposure risks and protect human health [35]. Monitoring these parameters provides a comprehensive assessment of environmental hazards from heavy metals. The pH of water greatly influences the solubility and mobility of heavy metals in aquatic systems. Changes in pH can affect metal speciation, availability, and environmental impact. Measurement of Total Dissolved Solids or Electrical Conductivity provides insights into water quality undissolved substances present. Monitoring the transport and behavior of heavy metals in aquatic environments is crucial due to its impact on water quality. The Heavy Metal Pollution Index provides a standardized approach for evaluating pollution levels by combining data from various parameters. This index allows for a comprehensive understanding of heavy metal contamination, assessing severity and potential risks more effectively. It also aids in implementing efficient strategies for mitigating adverse environmental impacts associated with heavy metal pollution and preserving ecological systems and human health.

3.5. Heavy metal pollution index HPI

Water quality is evaluated by HPI, a rating system that encompasses the overall effect of dissolved heavy metals. This index is calculated based on the suitability of water for human consumption of metal contamination. Essentially, the HPI comprehensively assesses the level of heavy metal pollution in a given body of water [36]. Measuring the concentrations of heavy metals allows for the determination of the HPI, which can then be compared to the established guidelines for safe drinking water. The resulting rating provides valuable information for policymakers, scientists, and the general public regarding the potential health risks associated with heavy metal pollution.

Understanding the impact of heavy metal pollution on water resources is crucial in developing effective strategies for reducing contamination and ensuring safe, clean water for all [21].

(1) $Q_i=\sum _{i=1}^n\left|\frac{M_i-I_i}{S_i-I_i}\right|\times 100\hspace{0.17em}$(1)

(2) $\frac{{\sum }_{i=1}^n{Q}_i\times W_i}{{\sum }_{i=1}^n{W_i}_i}=HPI\hspace{0.17em}$(2)

Where, $W_i$ is unit weighting of the ith HMs,

$Q_i$ is the sub-index of ith HMs, n is Total number of HMs,

$I_i$ is Ideal value of ith HMs, $S_i$ is standards value of ith HMs, $M_i$ is examined concentration of ith HMs in (μg/L).

Prasad and Bose provided a value of 100 for the critical pollution index of HPI in drinking water [37]. Following Edet and Offiongs’ 2002, study, a three-class modified scale has been implemented in the current research [36]. The HPI allows for the classification of water quality into three categories: low pollution of HMs, threshold risk, and high risk (>100, 100, <100) respectively. If the HPI goes above 100, drinking the water can pose a risk to human health. The maximum concentration limit (MCL) refers to the highest allowable concentration of a specific contaminant in water that is considered safe for human consumption [38; 39].

Heavy metal parameters play a crucial role in determining water quality calculations WHO recommendations were accepted as the standard for determining the maximum concentration limit, and they are based on extensive research and analysis [38] shown in Table 1. in determining the safety and suitability of water for human consumption such as heavy metals, such as lead and cadmium, can have severe health implications if present in high concentrations in drinking water [40].

Measurements are conducted in accordance with the set allowable criteria defined by regulatory bodies, such as the World Health Organization WHO or Iraqi specifications [41,42], Moreover, it is important to consider other designated factors when assessing the quality of water such as pH, temperature, conductivity, and dissolved oxygen. These parameters help to determine the solubility and mobility of heavy metals in water.

3.6. Prediction map

To visualize the spatial distribution of points and predicting the values of the original areas adjacent to the points and along the study area (the river) used the advanced ArcGIS geoprocessing tool. It is one of the most employing an inverse distance weighted (IDW) technique (a descriptive statistical method that shows the gradation of HPI values) to interpolate a surface from the given points [43]. By assigning greater importance to the point nearest to the unknown value and lesser importance to the more distant point, the IDW interpolation approach allows for the estimation of all uncertain values pertinent to distance. This technique is widely employed by researchers to create maps reflecting the spatial distribution of diverse datasets. To accurately depict the stability map, a digital elevation Model layer is seamlessly integrated to serve as the underlying background. This technique is widely employed by researchers to create maps reflecting the spatial distribution of diverse datasets [44,45]. The IDW interpolation method has incorporated the mathematical Eq.(3) listed below [46].

(3) $Y_0=\frac{{\sum }_{i=1}^N{Y}_i\frac{1}{X_i^r}}{{\sum }_{i=1}^n\frac{1}{X_i^r}}$(3)

In this context, $Y_0$stands for the estimated value at Point Zero, whereas $Y_i$represents the Y value at a known Point $i$. Similarly,$X_i$ captures that distance existing between Point and Zero Point; $N$ illustrates how many such Points are being considered during estimation; lastly, there exists an additional parameter represented as $r$which encompasses its stipulated requirement of being greater than 1.

The Heavy Metal Pollution Index calculated with ArcGIS-IDW may provide some insights into the spatial variations of heavy metal pollution. However, it is important to critically evaluate the methodology and consider potential limitations that may affect its accuracy. Factors such as local industrial activities, land use changes, or seasonal variations can introduce uncertainties in the predictions generated by this index. Additionally, reliance on available data points and interpolation techniques in ArcGIS-IDW may limit its ability to accurately capture localized variations in heavy metal pollution levels. Therefore, caution must be exercised when interpreting the results of this study and applying them to water quality assessment and management strategies.

3.7. Evaluation samples

In March 2023, samples of water were collected. To ensure the accuracy and reliability of the findings, a set of 16 samples were chosen to assess the veracity of results and predictive measurements. The concentration of heavy metals in the samples was analyzed in accordance with the approved standards by the inductively coupled plasma atomic emission spectrometry (ICP-OES) technique the HORIBA (France) model, such as the initial sampling phase, and identify HPI within the study area shown in Table 3. Furthermore, this research delves into the chemical and physical characteristics of the samples, encompassing parameters such as total dissolved solids (TDS), electrical conductivity (EC), dissolved oxygen (DO), and pH. Weather changes were moderate during the study period, thus little change in temperature. The values of HPI calculated in the prediction process by ArcGIS were used as a basis for comparison. As a result, the cost and effort involved in conducting periodic river analyses to determine the impact of sewage discharge in the study area and the efficiency of this approach in minimizing distributed data with coordinates are significantly diminished. The inclusion of points positioned in essential locations near wastewater discharge and its vicinity was deemed preferable for comprehensive program training and accurate value prediction within this specific area.

Table 2. Statistical values heavy metals (μg/L).

ID	T cº	pH	EC dS/m	TDS ppa	DO	Ni	Cr	Pb	Co	Cd	Cu	Fe
Max	21	7	7	3297	11.3	13.50	22.00	0.90	0.90	0.80	49.30	671.00
Min	17	6	1.4	2214	8.6	3.90	6.00	0.08	0.19	0.18	14.00	230.00
S. D	17.7	6.5	3.3	2753	9.4	2.83	5.38	0.25	0.19	0.20	11.41	121.77
Median	18.5	6.5	3.2	2235	9.2	8.00	12.50	0.30	0.39	0.39	21.70	356.00
Average	18	6.7	3.8	2725	9.6	8.48	13.33	0.37	0.43	0.44	26.25	379.35

Table 3. Statistical values of heavy metals in the validation (μg/L).

ID	T cº	pH	EC dS/m	TDS ppa	DO	Ni	Cr	Pb	Co	Cd	Cu	Fe	HPI
Max	22	7.3	8	3290	10.56	13.50	22.00	0.90	0.90	0.80	49.30	669.00	150.38
Min	17.5	6.7	1.5	2216	8.3	5.00	7.00	0.09	0.20	0.19	16.00	235.00	33.71
S. D	17.8	6.8	3.6	2755	9.4	2.71	5.60	0.25	0.20	0.21	11.26	132.67	32.74
Median	18.7	6.8	3.4	2241	9.45	7.65	11.70	0.32	0.37	0.39	21.55	324.50	61.28
Average	18.2	6.6	3.9	2732	9.73	8.62	13.41	0.39	0.44	0.44	26.79	370.69	73.38

3.8. Validation

To address the constraints imposed by a limited dataset, various approaches are commonly employed. One such approach is k-fold cross validation, which involves partitioning the dataset into k subsets or folds. In this process, one fold is reserved for testing while the remaining k-1 folds are used for training the model. This procedure allows each subset to be tested k-1 times, thereby mitigating limitations arising from data scarcity [47]. Cross-validation is utilized as a method to quantitatively compare the performance of different interpolation methods, allowing for the reuse of [48]. To validate and document the findings, a validation technique known as cross-validation is utilized. In this research, the predictive HPI values obtained using an inverse distance weighted ArcGIS method are used as a benchmark for comparison. Table 3 presents 16 HPI values for new samples which are considered to be the true values at the study site. These true values act as reference points against which the predicted HPI values can be assessed. The root mean square error statistic is employed to evaluate both bias and variance in the interpolation results [49]. An assessment of overall accuracy is provided by cross-validation, which measures the average magnitude of errors between interpolated values and true values. The RMSE can be used to quantify the difference between anticipated and actual values, where a smaller value indicates greater precision. Additionally, the MAE serves as a loss function in regression models by measuring the sum of absolute differences between predicted and actual values. A lower MAE implies improved prediction quality. Overall, cross-validation is a crucial technique for evaluating the accuracy and performance of interpolation models. To assess the accuracy of interpolated results, cross-validation is an essential technique that allows for the comparison of different interpolation methods by quantitatively evaluating their performance and identifying the most suitable approach for a given dataset [50,51].

(4) $R^2=1-\frac{{\sum }_{i=1}^n\widehat{(X_i}-X_i{)}^2}{{\sum }_{i=1}^n{\hat{X}}_i}$(4)

(5) $RMSE=\sqrt[2]{{\sum }_{i=1}^n\frac{\widehat{(X_i}-X_i{)}^2}{n}}$(5)

R= squared values.

$n$ =number of non-missing data points

${\hat{X}}_i$=actual observations time series

$X_i$=estimated time series

3. Results and Discussion

3.1 Heavy metals concentrations

The findings of the analysis conducted on the physical and chemical properties of water and samples from the Al-Diwaniyah section of the Euphrates River are presented in Tables 2 and Figure 2. The water temperatures observed at different stations along the river showed slight fluctuations. Notably, the lowest recorded water temperature was 17°C.

Figure 2. Heavy metals concentrations µg/L.

The spatial variation of pH values in the water exhibited a distribution pattern similar to that of temperature, appearing uniformly throughout all stations. In particular, the Al-Diwaniyah station in the southern region of the river exhibited marginally elevated pH levels in comparison to the remaining stations. At the sampling stations, the mean pH values spanned 7 towards the end of the river section, suggesting a mildly neutral habitat. In terms of river water velocity, it was found to vary and generally remain low. The mean values of the current velocity ranged between 0.12 to 0.18 m/s. Overall, these findings highlight the consistent pH distribution in the water, with a slight variation in the southern region. Additionally, the river water’s velocity was observed to be relatively low, indicating a steady flow throughout the sampling stations. The total dissolved solids (TDS) and electrical conductivity (EC) exhibit a clear pattern of increase from the northern to the southern regions along the River. The averaged EC levels range from 1.4 to 7 dS/m, as shown in Table 2. Total dissolved solids (TDS) are a measure of water salinity, serving as an indicator of river health and water quality. In this particular section of the river, the salinity can be classified as medium, making it a medium-salinity river. Adequate levels of dissolved oxygen (DO) are crucial for maintaining good water quality. In the case study of the river, the dissolved oxygen levels varied between 6.5 mg/l, which was the minimum recorded at the endpoint station in the south, and 13 mg/l, the maximum observed at point 1 in the north of the river [52].

The river water reveals the levels of dissolved heavy metals. S in Table 2 and Figure 2. This study focuses on seven heavy metals: Ni, Cr, Pb, Co, Cd, Cu, and Fe. Relatively low concentrations of these metals were discovered in the river water, ranging from 0.08 mg/l for Pb to 671 mg/l for Fe, at each station. The river waters low metal concentrations are due to their limited solubility in water and their linkage with suspended colloids through the adsorption process.

The Euphrates River in Al-Diwaniyah has been subjected to heavy metal pollution due to anthropogenic activities, including industrialization and agriculture. Research studies have shown that the river contains elevated concentrations of heavy metals cadmium, copper, lead, cobalt, chromium, iron, and nickel throughout the study period, which harm human health and aquatic ecosystems. The high levels of these pollutants are primarily attributed to upstream industrial activities and wastewater discharge. Figure 2. shows a clear trend in the concentration values, indicating a gradual increase as the river’s length extends towards the south. This phenomenon can be attributed to the influx of irregular wastewater from agricultural, industrial, and residential areas in the region during the study period.

3.2 Heavy metal pollution index HPI

The HPI is a well-established tool used to assess the degree of contamination caused by heavy metal pollutants in environmental systems. HPI considers the concentration of various heavy metals and evaluates the overall contamination level. To find out the effect of pollution of heavy metals in the river, Used HPI. Figure 3. displays the correlation between the HPI (Hazard Potential Index) of the 40 points sampled along the river. This correlation analysis offers valuable insights into the HPI values for all minerals that were analyzed in the study. By examining the relationship between the HPI values at different sampling points, we can gain a better understanding of how these values vary along the river. The observed rise in HPI confirms the presence of pollution in the Euphrates River. As progress southward along the points, pollution values escalate beyond point 28 where the HPI exceeded 100, deemed highly polluted. This trend is a cause for concern as it signals the potential danger of increased polluted water in those areas.

Figure 3. Value HPI of all the river stations.

The distribution of heavy metal points in a river is an essential topic for environmental scientists and regulators to understand. Heavy metals harm aquatic life, potentially disrupting ecosystems [53]. The concentration of these metals varies throughout the river based on factors such as upstream sources of pollution or natural geologic features.

Based on the HPI, water quality can be classified into three categories. The HPI allows for the classification of water quality into three categories: low pollution of HMs, threshold risk, and high risk (>100, 100, <100) respectively. It is important to note that if the HPI is more significant than 100, the water is considered unsafe for human consumption [37].

In this study, ArcGIS software was used to measure data obtained from 40 stations along the Euphrates River during November and December 2022 and generate HPI prediction maps for sewage discharge into Al-Diwaniyah city.The use of the Heavy metal pollution index (HPI) in these maps allows for evaluation of the state of the Euphrates River in the study area, providing researchers with a benchmark for comparing future results from this study.

Figure 4(a) shows the HPI values, ranging from 32 to 150. The HPI values from 32 to 100 are represented in blue and light blue, indicating the least amount of heavy metal contamination. Conversely, the values from 100 to 150 are depicted in light pink and red, indicating high levels of heavy metal contamination. It is essential to note the significant difference in heavy metal pollution between these two ranges.This gradual increase in HIP values along the river as it enters the Al-Diwaniyah Governorate suggests a potential deterioration in water quality. Figure 4(b) visually represents this trend, indicating that the river’s health is compromised as it moves closer to the center of Al-Diwaniyah. Stations 21 to 24 show HIP values ranging from 66 to 75, indicating a moderate level of pollution. This could be attributed to various factors such as agricultural runoff, industrial discharges, or inadequate wastewater treatment facilities in the surrounding areas. As the river approaches the city center, these pollution sources become more concentrated, leading to an increase in HIP values. At stations 26 to 28, the HIP values further escalate to a range of 84 to 93. This significant rise suggests a higher level of contamination and poses potential risks to both aquatic life and human health. The reasons behind this surge could include urbanization effects, improper waste management practices within Al-Diwaniyah, or additional. We observe the HIP (Health Impact Points) values at monitoring stations 29 to 40. The HIP value is a measure of health risks associated with air pollution, and an increase from 93 to 150 indicates a high and dangerous level of pollution. As people approach the city center, they are exposed to these elevated pollution levels. This alarming increase in pollution values before entering the city can be attributed to several factors. Firstly, the progress of the river through unorganized swamps and random housing areas plays a significant role. These areas lack proper infrastructure and waste management systems, leading to untreated sewage and garbage being dumped directly into the river. The treatment plant in the city center is almost stopped, and the city center suffers from polluted water. This discrepancy in pollution values, especially in the center of the city of Al-Diwaniyah, compared to the rest of the regions in the north of the province of Al-Diwaniyah city, which owns more than one treatment plant that works well and is newly established. The HPI values in the Al-Diwaniyah River within the city center in a clear and accelerated manner compared to the previous values and sectors of the city of Babylon. Upon evaluating the data and recognizing the variables influencing the HPI values, it became evident that cobalt holds utmost importance, falling within a range of 95 to 125. On the other hand, the remaining studied elements are comparatively less detrimental. The main source which is used mainly in the production of stainless steel and nickel alloys.

Figure 4. Distribution HPI values in a branch of the Euphrates river using the interpolation method (IDW) by ArcGIS software (a) The Al-Diwaniyah & Babylon section, (b) The Al-Diwaniyah section.

3.3 Evaluation samples and validation process

In March 2023, the water quality parameter concentrations collected at different locations within the study area of the Al-Diwaniya River are presented in Table 3. In every location, the measurement and analysis of seven heavy metals with physicochemical properties of river water samples were carried out. To check the credibility of the projected outcome derived from (ArcGIS_IDW tool), as well as to compare it with the initial examination models, a selection was made for 16 sampling sites in order to examine the influence of wastewater discharge from Al-Diwaniyah city on the overall water quality of the river. Based on the results of the samples, it was noted that the pH values varied between 6.7 and 7.3, suggesting an neutral medium. On average, the TDS concentration measures 2732 mg/L. The TDS concentrations in the southern regions 3290 mg/L differ from those found in the northern regions 2216 mg/L.In the southern direction, there was an effective decrease in DO from 10.56 to 8.3 mg/L, this meant that the river lacked oxygen in certain areas, as a result, the river’s ecosystem.

Concentrations of the seven heavy metals Ni, Cr, Pb, Co, Cd, Cu, and Fe appear to be relatively low in the river water. At each station, the concentrations of these metals in the river water varied between 0.9 mg/l for Pb to 669 mg/l for Fe. Concerning the concentrations of other heavy metals, there is a variation between these two values, as stated in Table 3. Upon examining the impact point of wastewater discharge, it became evident that there was a rise in HPI, reaching 150.38 to 33.71.

This close similarity among the basic HPI value, predicted HPI value, and valid HPI value in Figure 5 indicates that the IDW method is effective in accurately predicting and interpolating river water quality. The IDW (Inverse Distance Weighting) technique has been widely used in various research studies that Previous have consistently shown that the IDW method outperforms other interpolation methods when it comes to providing precise and reliable results in spatial interpolation. The superiority of the IDW technique can be attributed to its ability to capture local variations and patterns in the data accurately. By considering the distances between known and unknown points, it effectively incorporates spatial relationships into the interpolation process. This makes it particularly suitable for mapping river water quality [46,48,54–56].

Figure 5. Comparisons of actual HPI value & predicted HPI value with valid HPI value.

Figure 6 presents the findings of the validity examination conducted on the ARCGIS Geostatistical Wizard. The interpolation methods were compared based on cross-validation and fitted parameters. Calculating the values involved using both measured and interpolated values at each validation sample site. The mean error, mean absolute error, and root mean square error were determined using the measured values. A near-zero ME indicates an unbiased method, while minimizing MAE and RMSE is important for accurate results in evaluating interpolation accuracy. The estimated prediction standard errors closely matched the root-mean-square prediction errors from cross-validation, allowing us to identify optimal models along with associated prediction error values for each parameter.

Figure 6. Regression for prediction vs HIP.

The findings indicated that various parameters, including heavy metals, exhibited a linear relationship with an R^2 value of 98.67%. The achieved root mean square error and mean absolute error values were deemed satisfactory at 0.19 and 0.11 respectively. Ultimately, the integration of cross-validation and optimized parameter fitting provided valuable insights into the robustness of ArcGIS in accurately interpolating data points. The precision measures obtained from this analysis facilitated the identification of suitable models for precise interpolation purposes. These discoveries have significant implications for advancing research in geostatistics within this field [57].

4. Conclusion Remarks

The crucial importance of this study stems from the analysis of a section the branch of the Euphrates River, which acts as a vital drinking water source for Al-Diwaniyah city. Heavy metals pose a significant threat to the health of all living organisms, with the ongoing water crisis being particularly pronounced making it essential to analyze chemical and physical characteristics the water quality of the Babylon and Al-Diwaniyah section for river Euphrates using ICP-OES to measure the concentration of heavy metals such as Cd, Co, Cr, Cu, Fe, Ni, and Pb, also the samples parameters TDS, EC, DO and pH.According to the analysis, the levels of Cd, Ni, Cu, and Fe in all stations were found to surpass the recommended limits established by both the WHO and Iraqi specifications. However, the device did not detect concentrations of other metals, which may be present in insufficient amounts or not in the water samples. Furthermore, the values of each HPI station after point 29 were higher than the pollution level, indicating that the branch of the Euphrates River water to classified in this section as high risk. ArcGIS was employed to implement the IDW approach. The accuracy of the prediction maps was assessed by employing a regression prediction technique, which involved comparing observed values (from 16 new sample in the branch of the Euphrates River) and predicted values (obtained from the maps). The obtained outcomes indicated that the determination coefficient (R2) along with the root mean square error (RMSE) for these particular values were within acceptable limits. In the process of enforcement of standards and pollution control activities, this will aid in identifying the specific sampling locations or areas along the river that exhibit a high level of impairment.

By prioritizing the maintenance of water quality, we can ensure the well-being of both the population and the environment. The presence of certain heavy metals concentrations in the water can have detrimental effects on human health. Moreover, with the scarcity of river water becoming a pressing issue, it is crucial to conserve and protect this valuable resource. Moreover, with the scarcity of river water becoming a pressing issue, it is crucial to conserve and protect this valuable resource. By implementing strict purification processes before distributing water back into the city. Industrial runoff, agricultural practices, and urban development are just a few examples of potential sources of pollution that can affect water quality.

In conclusion, as an efficient means of prediction, the IDW interpolation method offers significant advantages in determining the dissemination of heavy metals without resorting to onerous and costly field or laboratory research. Going forward, there are plans to continue working on it and propose a boosted mechanism for early-stage data prediction and forecasting.

5. Recommendations

Examine the different sources of heavy metals and their impact on various environmental compartments. Explore how these metals exert toxicity on living organisms, including processes such as bioaccumulation and biomagnification in food chains. Investigate the negative health effects associated with chronic exposure to heavy metals, focusing on organ-specific damages like neurotoxicity and nephrotoxicity, as well as implications for overall health. Analyze regulatory standards set by international organizations like WHO and EPA for controlling heavy metal pollution, highlighting the importance of adhering to these standards in developing effective environmental and public health policies. Study the effect of weather conditions on the concentrations of metal ions in the waters, including both point and nonpoint sources.

Acknowledgement

The authors gratefully acknowledge the Earth Observation Centre, Institute of Climate Change Universiti Kebangsaan Malaysia (UKM), UKM YSD Chair of Sustainability (UKMYSD-2021-003) and Civil Engineering Department, College of Engineering, University of Al-Qadisiyah, Al- Qadisiyah, Iraq for the support to conduct this study.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Universiti Kebangsaan Malaysia [UKMYSD-2021-003].