research paper on water quality assessment

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• Published: 16 December 2020

Water quality assessment based on multivariate statistics and water quality index of a strategic river in the Brazilian Atlantic Forest

• David de Andrade Costa 1 , 2 ,
• José Paulo Soares de Azevedo 1 ,
• Marco Aurélio dos Santos 1 &
• Rafaela dos Santos Facchetti Vinhaes Assumpção 3  

Scientific Reports volume  10 , Article number:  22038 ( 2020 ) Cite this article

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• Environmental sciences

Fifty-four water samples were collected between July and December 2019 at nine monitoring stations and fifteen parameters were analysed to provide an updated diagnosis of the Piabanha River water quality. Further, forty years of monitoring were analysed, including government data and previous research projects. A georeferenced database was also built containing water management data. The Water Quality Index from the National Sanitation Foundation (WQI NSF ) was calculated using two datasets and showed an improvement in overall water quality, despite still presenting systematic violations to Brazilian standards. Principal components analysis (PCA) showed the most contributing parameters to water quality and enabled its association with the main pollution sources identified in the geodatabase. PCA showed that sewage discharge is still the main pollution source. The cluster analysis (CA) made possible to recommend the monitoring network optimization, thereby enabling the expansion of the monitoring to other rivers. Finally, the diagnosis provided by this research establishes the first step towards the Framing of water resources according to their intended uses, as established by the Brazilian National Water Resources Policy.

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Introduction.

Aquatic systems have been significantly affected by human activities causing water quality deterioration, decreasing water availability and reducing the carrying capacity of aquatic life 1 , 2 , 3 , 4 . Water quality deterioration still persists in developed countries, while it is a major problem in developing countries in which a substantial amount of sewage is discharged directly into rivers 5 , 6 , 7 , 8 . Moreover, according to UNEP 9 , water pollution has worsened since the 1990s in the majority of rivers in Latin America. The global concern with water availability and its quality has been growing, and it is estimated that the demand for water will increase between 20 and 30% by 2050 10 , 11 . In addition, spatial and temporal variations in the hydrological cycle and their uncertainties related to climate change may worsen this scenario 12 , 13 , 14 , 15 , 16 .

Monitoring water quality in order to assess its spatial and temporal variations is essential for water management and pollution control 17 . On the other hand, monitoring programs generate large data sets that require interpretation techniques 18 . There are a number of methods for water quality assessment, including single-factor, multi-index, fuzzy mathematics, grey system evaluation, artificial neural network, multi-criteria analysis, geographical interpolation and multivariate statistical approach 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 . Among them, the most used are the Water Quality Indexes (WQI) that transform a complex set of data into a single value indicative of water quality 26 , 27 and reflect its suitability for different uses 28 . Multivariate statistics is another widely used approach 29 , 30 , mainly with Principal Components Analysis (PCA) and Cluster Analysis (CA), helping to achieve a better understanding of the spatial and temporal dynamics of water quality.

A comparison of seven methods for assessing water quality indicated WQI as one of the best 20 . The assessment of Poyang Lake 28 , China and the upper Selenga River 31 , Mongolia showed that WQIs are suitable for the assessment of both interannual trends and seasonal variations 28 . Multivariate statistical techniques associated with WQI have been used for numerous water bodies world-wide, including the Nag River 30 , India, the Paraíba do Sul River 32 , Brazil, and the before mentioned Selenga River 31 . CA grouped the monitoring stations according to their similarities, while the PCA highlighted components that were related to its pollution sources 30 , 31 , 32 .

In order to ensure water quantity and quality, the Brazilian National Water Resources Policy 33 has established a management tool called Framework, according to the main intended uses of water. It has also created participatory management committees, the so-called Basin Committees, which, together with its technical agency, are responsible for the Framework establishment. Unfortunately, even after two decades, Brazil has had very few successful experiences on the subject 34 .

Brazil has a gigantic and complex hydrographic network present in many different ecosystems 34 . The Brazilian Atlantic Forest is one of the most biodiverse biomes on the planet 35 , 36 , extending along the Brazilian coast and currently covering only 11.4% of its original territory 37 under constant threats 38 , 39 , 40 . The hydrographic basin of the Paraíba do Sul river is located in this environment, which is the integration axis of the most industrialized Brazilian states, São Paulo, Rio de Janeiro and Minas Gerais, and home to around 6.2 million people 41 . A water transfer system regularly supplies another 9 million people in the metropolitan region of Rio de Janeiro, through the Guandu system. Another water transfer system connects the Paraíba do Sul river to the Cantareira system, complementing with 5 m 3 /s the water supply to over 9 million people in the metropolitan region of São Paulo 41 . These systems went through an intense water scarcity between 2014 and 2016 with severe impacts on water quality and availability 32 .

Our study is focused on the Piabanha River watershed, a strategic sub-basin of the Paraíba do Sul river, combining urban, industrial, rural characteristics, and large preserved fragments of Atlantic Forest 36 , 42 . The Piabanha Basin has been monitored for over 10 years with the Studies in Experimental and Representative Watersheds (EIBEX) project, a partnership between universities and government agencies 42 , 43 , 44 . The State Environmental Agency of Rio de Janeiro (INEA) has been monitoring the basin since 1980. Other studies in the region include the analysis of contamination by pesticides 45 , energy generation 46 and dispersion of pollutants 47 . The Piabanha Basin received international attention in Nature's article on biodiversity 36 . But in addition to forest preservation, can the Piabanha River support biodiversity? How is its water quality today? In this way, the Piabanha Basin Committee defined the Framework as a priority in its management plan (2018–2020) and to accomplish this goal, established water monitoring as a strategic action 48 .

Our study covers 40 years of monitoring, including government data, our research projects and, currently, a monitoring program that is being conducted with funding from the Piabanha Basin Committee. The main objectives were: (1) to carry out an updated diagnosis of water quality using multivariate techniques and WQI; (2) to examine the parameters that most influence water quality, and (3) to identify river stretches with similar water quality. Our study provides an extensive understanding of the Piabanha River and supports its Steering Committee in the application of public policies. This is a pilot project that can be a reference for other Framework programs for improving water quality in Brazil.

We have requested and received from INEA two water user databases of the Piabanha Basin. The first set corresponds to raw data from the National Water Resources Register (CNARH), with all the registrations until December 2017 and with 1549 registered interferences (water abstraction or effluent discharge). The second one is the registration validated by INEA until August 2018 by the Águas do Rio project comprising a total of 669 validated interferences. With these data, it was possible to build a georeferenced base. By so doing, it was possible to list the main effluent discharges by type for each monitoring station.

In the validated database, from the 669 interferences, 84% are water abstractions and 16% are effluent discharges. Water abstraction account for 425 m 3  day −1 with 75% from wells and 25% from rivers. On the other hand, effluent discharges are 89 m 3  day −1 . The largest volume of effluents comes from the sanitation sector with 57% of the total, whereas industries account for 33%, aquaculture with 4% and mining for 3% of discharges.

When comparing the two databases, it is clear that the universe of registered users is much larger than the universe of validated users; in other words, those whose data were made up by the state environmental agency and, therefore, received a license. For example, the validated database has only six interferences related to agriculture, in contrast to 789 interferences awaiting validation. This is a serious obstacle for water resources management in the region, which threatens the sustainability of water resources.

Short time monitoring and water quality index

In order to assess and compare the water quality of the Piabanha River, we calculated the Water Quality Index from the National Sanitation Foundation (WQI NSF ) using two datasets, the first one from 2012 and the last one from 2019 (Tables 1 and Table 2 ). The 2012 results (Fig.  1 A) oscillated between the bad and medium categories, generally with medium quality (50.5 ± 10.3). In 2019 (Fig.  1 B), the results ranged between the medium and good categories, in general with medium quality (61.6 ± 10.8).

WQI NSF spatial variation over each station from July to December ( A ) 2012 and ( B ) 2019. WQI NSF seasonal variation over the entire length of the river ( C ) 2012 and ( D ) 2019. The entire dataset can be found online as Supplementary Table S1 and S2 , respectively for 2012 and 2019.

Data sets show significant seasonal behavior (p < 0.05) (Fig.  1 C,D) between the end of the dry period (Jul, Aug, Sep) and the beginning of the rainy period (Oct, Nov, Dec) for the parameters DO, WT, pH, nitrate, phosphate and turbidity, while no significant seasonal difference (p > 0.05) was found for the parameters E. coli , BOD and TDS. The parameters that have most impacted the WQI NSF were coliforms and BOD. Ammonia and total phosphorus do not account to WQI NSF , but their concentration has violated Brazilian legislation and their influence can be better understood by PCA.

Principal components and clusters analysis

The 2019 dataset (n = 48), comprising six monitoring campaigns at the eight monitoring stations along the Piabanha River with 15 parameters analysed, was grouped by the average value of each parameter at each station (n = 8). Pearson’s correlation matrix is presented in Table 3 , most parameters showing a strong correlation (r > 0.5) with a confidence interval greater than 95% (α = 0.05). The KMO measures of sampling adequacy (n = 8) were near to 0.5 and the significance level of test of sphericity was less than 0.001, indicating that the data was fit for PCA and the correlation matrix is not an identity matrix and so the variables are significantly related. The Shapiro test confirmed the data normality (p > 0.01) for all parameters, except for E. coli .

ACP was applied to identify groups of parameters that influence water quality. PC 1, PC 2 and PC3 account for 72% (eigenvalue 10.74), 14% (eigenvalue 13.94) and 5% (eigenvalue 0.8), respectively, of the data variance. Components with eigenvalues larger than the unit were selected. That is, the first two components together account for 86% of the total variance. The loadings that compose the first two components are presented in the Table 4 and the stations that most influence the results are represented in Fig.  2 A.

Multivariate techniques. ( A ) PCA plot with station scores and parameters loadings. ( B ) Hierarchical clustering by Ward linkage with Euclidean distance. The entire dataset can be found as Supplementary Table S2 online.

PC1 was substantially correlated with practically all parameters. Stations number 1 to 4 loaded positively (loadings > 0.7) to PC1 with the parameters TDS, Alkalinity, Ammonia, Total Nitrogen, Phosphate, Total Phosphorus, DBO, COD, E. coli , while stations number 5 to 8 loaded negatively (loadings < − 0.7) with Nitrate, Turbidity, SS, pH and WT. PC2 was most influenced by stations in the urban area, notably station 1, and showed a positive correlation (loadings > 0.5) with OD, COD, BOD and less by SS (loading = 0.33), being more influenced by station 1 in the urban area. On the other hand, it was negatively correlated with E. coli (loading = − 0.66) with a large influence of station 3.

The sampling stations were grouped into three statistically significant clusters with 75% of similarity by agglomerative hierarchical clusterization based on the ward linkage by Euclidean distance (Fig.  2 B): cluster 1 (Stations 2 and 3), cluster 2 (Stations 7 and 8) and cluster 3 (Stations 1, 4, 5 and 6).

Longtime monitoring assessment based on Mann–Kendall rank test and Fourier transform

In a complementary way, in order to evaluate a possible trend on water quality and to detect the seasonal behavior of the basin, we used a time series with 40 years of monitoring. Since dissolved oxygen can be used as a surrogate variable for the general health of aquatic ecosystems 49 , 50 , 51 , it was selected to perform the Mann–Kendall rank test of randomness for the station more upstream and further downstream of the Piabanha River, PB002 and PB011 respectively. The upstream station showed a statistically significant increasing trend (n = 166, S = 1507, Z = 2.10, p < 0.03), whereas the downstream station does not show a statistically significant trend (n = 198, S = 1179, Z = 1.27, p = 0.20). The entire dataset can be found as Supplementary Table S3 and S4 .

To detect the seasonal behavior, we have applied a Fourier transform algorithm to the time series from 1980 to 2019 to the station PB011 (Fig.  3 A, which does not display a tendency behavior and can be considered as representative of the entire basin because it is the most downstream station. The data were organized in quarterly averages for the DO parameter. The two most powerful signals correspond to the frequencies of 0.25 and 0.45, nearly (Fig.  3 B) It corresponds to periods of 12 and 6 months, respectively. Taking into account this seasonality, we confirmed that our 2019 field campaigns are representative of seasonality comprising the final half of the dry season and the initial half of the rainy season.

( A ) Temporal distribution of dissolved oxygen from 1980 to 2019 at station PB002 (n = 160). ( B ) Periodogram. The entire dataset can be found in Supplementary Table S5 .

Water quality assessment

The Piabanha River had a better water quality in 2019 than in 2012, according to WQI NSF results (Fig.  1 ). The improvement was substantial over the first 40 km, rated as “bad” in most campaigns in 2012, while rated as medium in most campaigns in 2019 due to sewage collection and treatment system expansion. Since 2012, Petrópolis has built 50 km of sewage collection network and 7 new sewage treatment units 52 . These plants produce secondary level effluents through biological treatment, the plants flow capacity reaches about 800 L s −1 . These stations use different technologies such as: submerged aerated biofilters, anaerobic upflow reactor, moving bed biofilm reactor and upflow anaerobic sludge blanket reactor. Beside this, in some plants are used biosystems 53 . Water quality improved in stretches after 40 km due to self-purification processes and the contribution of clean tributaries. This is in line with findings from other rivers worldwide 31 , 54 , 55 .

Dry seasons, in general, presented better water quality indexes than rainy seasons. Other studies 28 , 56 , 57 have shown similar seasonal behavior, where water quality worsens in the rainy season due to sediments and pollutants input carried by the rain. In addition, most of the sewage network is the same network that collects rainwater. Thus, during rainy events, sewage is no longer treated and is discharged directly into rivers.

Although the WQI NSF had a medium rating in 2019, BOD and Coliforms were substantially above the maximum allowed by Brazilian regulation. In addition, the index is limited to the parameters used in its calculation 58 . This is the case for the ammonium parameter, which presented concentrations up to three times higher than allowed in Brazilian regulation, reminding that only nitrate is used in the WQI NSF . The same occurs with total phosphorus: only phosphate is considered, although it does not have a maximum value established by the Brazilian federal regulation. In what follows, we analyse these parameters in more detail.

Biochemical Oxygen Demand (BOD) is one of the most widely used criteria for water quality assessment. It provides information on the ready biodegradable fraction of the organic load in water 59 . High BOD concentrations reduce oxygen availability, mainly correlated to microbiological activity 60 . Its concentration ranged from 2.00 to 45 mg L −1 (average 7.69 ± 7.52) over the entire data, with its concentrations most of the time substantially above the maximum allowed by Brazilian regulation (5 mg L −1 ). Escherichia coli is naturally present in the intestinal tracts of warm-blooded animals and it is widely used as an indicator of fecal contamination 61 , 62 . Villas-Boas 42 pointed to fecal coliforms as the most relevant water quality parameter in the urban area of Petrópolis, mainly related to pollution caused by untreated domestic sewage.

Phosphorus is an essential nutrient for all forms of life 63 . Its availability can be related to atmospheric deposition 64 , anthropic uses of products such as detergents 65 and due to agricultural activities 66 . Orthophosphates are the most relevant in the aquatic environment as they are the main form of phosphate assimilated by aquatic vegetables 67 . Previous studies 42 , 68 , 69 in the Piabanha Basin found phosphate values in perfect agreement with ours. Alvim 68 points out that the main source of phosphorus for the Piabanha River is the sewage discharge and the higher concentrations are found during the rainy season.

Nitrate is a very common element in surface water since it is the end product of the aerobic decomposition of the organic nitrogenous compound 70 , 71 . Its sources are related to landscape composition, being influenced by both agricultural and urban uses 72 . Villas-Boas 42 found high concentration of nitrate and ammonium in the urban region of Piabanha River in agreement with this study. Alvim 68 reports that domestic sewage discharged into Piabanha River waters account for 43% of the nitrogen load, the atmospheric contribution for 31% and the farming activity for 15%.

The major contributors to water quality and stretches of river with similar water quality

The first two components together account for 86% of the total variance, indicating method high explanatory power of the method. It was far better than other similar studies around the world 29 , 30 , 71 , 73 , 74 , 75 . PC1 predominantly accounts for urban sewage pollution. This is clearly demonstrated by the fact that stations from 1 to 4, located in the urban area of Petrópolis, positively loaded PC1 with organic matter (BOD and COD), TDS and nutrients such as phosphorus and nitrogenous constituents, especially ammonia, indicating recent pollution. Even clearer is the fact that stations from 5 to 8 have negatively loaded with nitrate, showing the nitrogen compounds degradation in the downstream stretches of the urban area. On the other hand, the increase in nitrate concentrations in association with the increase in turbidity in stations outside the urban area may also be associated with land use, especially in agriculture.

PC2 is dominated by the dissolved oxygen parameter and other parameters that indicate the health of the river, as organic load and coliforms. It is explained by water pollution by organic matter and biological activity and reinforces the result of CP1. In the study region, sanitation is still a challenge to be faced by the government, especially in the first urban stretch, after 40 km from the source of the Piabanha River, this region has 26% of untreated sewage 53 .

Cluster analysis was used to group sampling stations into similarity classes indicating the stretches of river with similar water quality. As pointed out by Singh 29 , it implies that only one site in each cluster may serve as good in spatial assessment of the water quality as the whole cluster. So, the number of sampling sites can be reduced; hence, cost without losing any significance of the outcome. On the other hand, this interpretation should be done with caution since trends in different stretches can be very different, making future changes significant. Therefore, great care must be taken to reduce monitoring stations.

It is important to notice that the first cluster (S1, S6 and S4, S5) groups station 1 with station 6, the first one corresponding to the urban area of Petrópolis whose pollution stems from sewage and industrial effluents. Likewise, station 6 is located after the confluence of the Preto-Paquequer River, which crosses Teresópolis, the second largest city in the hydrographic basin, also with the presence of economic and industrial activities. Sand mining is the predominant activity near stations 4 and 5, which together receive the impact of five mining companies. Similarly, station 6, after the Preto River, receives the impact of seven sand mines. In fact, this group brings together economic activities whose impact on water quality is similar. Station 5 could be removed from the network monitoring in order to reduce costs.

The second cluster (S2 and S3) refers to the most urbanized section of the basin. When individually checking the quality parameters between these stations, one can conclude that they differ only by the diluting effect caused by the contribution of the Araras River, on the left bank, and of the Poço do Ferreira River, on the right bank, which receives its waters from the Bonfim River after its source in the Serra dos Órgãos National Park, an important federal conservation unit. Station 3 was introduced precisely to detect this diluting effect, but since the cluster analysis showed that it was not significant it is recommended to remove this station.

The third cluster (S7 and S8) has a very similar behavior: station 8 is just before the Piabanha River mouth and station 7 is located less than 10 km upstream of the mouth. In addition, on this stretch there are only three interferences registered as discharges. Thus, it is recommended to remove station 7, considering the importance of maintaining a station close to the river mouth.

Trend analysis and seasonal variation

Although it still presents systematic violations to Brazilian standards 76 , the water quality, in general, has improved in the Piabanha River over the past 40 years (Fig.  3 A,B). This statement is supported by the Mann–Kendall rank test of randomness, indicating a significant (p = 0.03) tendency to increase the values of the dissolved oxygen parameter at station PB002, located in the urban area of Petrópolis, which is highly impacted by effluent discharges, despite the fact that this region has municipal sewage treatment. PB011 presents high levels of DO, since the beginning of the time series exhibiting an almost monotonic behavior over time, thus it has no tendency. The high DO levels are due to both the river's reoxygenation process and the contribution of clean waters from its tributaries, such as the Fagundes River.

A strong annual and semi-annual seasonality was indicated by the power spectral density, which can be seen in the periodogram (Fig.  3 B) resulting from the Fast Fourier Transform. The results are in accordance with the literature 77 indicating that more than 90% of the total variance of dissolved oxygen is accounted for by the annual periodicity and the next four higher harmonics (semi-annual; tri-annual, etc.). Seasonality follows the rainfall regime with a dry period from April to September, and a wet period from October to March, according to Araújo's 78 study carried out in the Piabanha River basin.

Water quality at point PB002 started to improve in 2000, when the first sewage treatment plant in the city of Petrópolis came into operation. Currently, 95% of the population has access to drinking water, and the coverage of treated urban sewage is 85%. The municipality has 26 sewage treatment units, responsible for the treatment of 56.2 million liters per day. In relation to the other municipalities in the basin, according to the National Sanitation Information System 79 (SNIS), the municipality of Três Rios treats 2.97% of its sewage, while the other municipalities, Teresópolis, Areal, São José do Vale do Rio Preto, Paty do Alferes and Paraíba do Sul did not report their data to SNIS, potentially indicating that they do not perform sewage treatment. In other words, about 50% of the population has no formal access to sewage treatment services.

The diagnosis provided by this research establishes the first step towards the Framing of water resources according to their intended uses, as established by the Brazilian National Water Resources Policy. In addition to the diagnosis which was carried out a georeferenced database was built. There are few cases of Framework in Brazil and none in the studied watershed. This makes this study relevant to Brazilian water resources management. The considerable number of users awaiting regularization from the State Environmental Institute is a limitation to implement the Framework and requires a joint effort of the watershed committee.

Answering our initial question, Piabanha River water quality is medium according to the WQI NSF and certainly is not able to support high levels of biodiversity. Some river stretches have quality compatible with class 4 according to the Brazilian regulation for the coliforms, BOD and TP parameters; hence, they cannot be used for irrigation, human or animal consumption, not even after treatment. On the other hand, the Framework must be carried out according to intended uses. Therefore, we recommend that the Piabanha Committee, in partnership with the State Public Ministry, lead actions to reduce the concentrations of these parameters, mainly in the sanitation sector.

It is recommended that the monitoring program be continued and expanded to stretches where conflicts between water uses occur, in order to implement the Framework to enforce the improvement of water quality. It is also important to point out that this study was financed with public resources from the Piabanha water resources fund and that the present analysis made possible to recommend the exclusion of three of the eight existing stations, thereby enabling the expansion of the monitoring to other tributaries of the Piabanha River under the influence of large population with practically no sanitation, notably the Rio Preto/Paquequer sub-basin.

This work describes a methodological approach that can be useful for other researches in environmental science and management. We have applied an integrated approach using data from different sources combined with data analysis based on WQI, PCA, CA, frequency analysis and trend analysis, which were used in a complementary way to understand a research problem.

Materials and methods

The Piabanha Basin is located in southern Brazil, belonging to the mountainous region of the State of Rio de Janeiro with an area of 2050 km 2 (Fig.  4 ). The Piabanha River source is at 1150 m of altitude and runs down 80 km until it flows into the Paraíba do Sul River at an altitude of 260 m. The upper portion of the basin presents a humid tropical climate. With steep slopes, annual rainfall exceeds 2000 mm. The lower portion of the basin has a sub-humid climate and the average rainfall decreases to 1300 mm. The seasons are well defined throughout the basin and the rainfall regime has symmetry in its distribution between the periods from January to June and from July to December 78 . The territory is home to 535 thousand people in 2018 80 . The two largest cities in the region, Petrópolis and Teresópolis, are located in the headwaters of the basins and give rise to the Piabanha and Preto rivers, respectively. Additionally, because the sewage treatment is limited and the river flows are low, high constituent concentrations are observed (e.g., fecal coliform, nitrate, and BOD), especially in urban areas 42 .

Study area, sample stations and interference points (water abstraction or effluxent discharge). This map was generated in the open source software QGIS version 3.14.15 ( https://qgis.org/ ).

Three sets of monitoring data have been used in this researchh (Fig.  4 ). The first and main one was the result of a monitoring program that is being conducted by the Piabanha watershed Committee, in which data from July to December 2019 have been analysed and are described in more details in the next item. The second were from 6 campaigns carried out in 2012 by HIDROECO project 44 also with financial resources from the Piabanha Committee which is used as a baseline for comparison purposes. The third was comprised of two stations of the basic monitoring network of the Rio de Janeiro Environmental Institute, with data from 1980 to the present, except for periods of data gaps.

A georeferenced database was also built containing water management data. Brazilian National Water Agency (ANA) has developed the National Water Resources Users Register (CNARH) for any bulk water user that changes regime, quantity or quality of a water body. It is a federal platform, but it can be managed by each state. Registration is a prerequisite for the other stages of uses regularization.

Monitoring campaigns and analytical procedures

Physical–chemical parameters were measured in situ using a multiparameter probe (YSI model 556) and a portable turbidimeter (HANNA model HI 98703-0), both previously calibrated and later verified. The samples were placed in specific containers for each analysis, for the necessary parameters the samples were preserved with H 2 SO 4 and kept at a temperature below 4 °C. Laboratory analyses (Table 1 ) were performed according to Standard Methods for the Examination of Water and Wastewater (SMWW) 81 . The laboratory has an accreditation certificate issued by the State Environmental Agency (INEA CCL No. IN044710) and also complies to ISO/IEC 17025 (CRL 1035).

Water Quality Index

A Water Quality Index (WQI) is an empirical expression which integrates significant physical, chemical and microbiological parameters of water quality into a single number 82 . It can be a powerful communication tool to simplify a complex set of parameters, whose individual interpretation can be difficult, into a single index representing the general water quality. A water quality index was initially proposed by Horton 26 and further developed by Brown 27 , 83 resulting in the National (USA) Sanitation Foundation Water Quality Index (WQI NSF ).

The original version of the WQI NSF established an additive expression 27 ; on the other hand, field data analysis suggested that the additive WQI lacked sensitivity in adequately reflecting the effect of a single low value parameter on the overall water quality. As a result, a multiplicative form of WQI was proposed 82 , 83 :

q i is the quality class for the n th variable, a number between 0 and 100, obtained from the respective average quality variation curve 82 , depending on the concentration of each nth variable. W i is the relative weight for the n th variable, number between 0 and 1, assigned according to the importance of the variable for overall quality conformation. WQI NSF is the National Sanitation Foundation Water Quality Index, a number between 0 and 100, rated as "excellent" (100 > WQI ≥ 90), "good," (90 > WQI ≥ 70), "medium" (70 > WQI ≥ 50), "bad" (50 > WQI ≥ 25) or "very bad" (25 > WQI ≥ 0).

The WQI NSF and its many adaptations have been widely used 84 , 85 , however, its use is not uniform, replacing parameters without the necessary adaptation of the respective curve of the indicator. In Brazil, since 1975 the WQI NSF has been used by CETESB (Environmental Company of the State of São Paulo). In the following decades, other Brazilian states adopted, with minor adaptations, this index, which today is the most widely used in the country. In the present study, the weights (w i ) have been used according to the methodology established by INEA (Environmental Institute of the State of Rio de Janeiro): DO (0.17); Fecal coliforms (0.16); pH and BOD (0.11); Nitrates, Phosphate and Temperature (0.10); Turbidity (0.08) and TDS (0.07), rather than total solids.

The replacement of the total solids for dissolved solids parameter may cause an average variation of 0.2% in the final result of WQI NSF , based on our estimates (n = 48, data 2019). In relation to microbiology, E. coli have been used instead of fecal coliforms, applying a correction factor 86 of 1.25 on the result of E. coli .

Principal component analysis and cluster analysis

Principal component analysis (PCA), as defined by Hotelling 87 , is a multivariate technique of covariance modeling that reduces the dimensionality of an originally correlated dataset, with the lowest possible information loss. A new set of variables containing new orthogonal, uncorrelated variables, is formed from a dataset of correlated variables, which are weighed linear combinations of the original variables 30 .

PCA technique extracts the eigenvalues and eigenvectors from the covariance matrix of original variables. The PCs are obtained by multiplying the original correlated variables with the eigenvector, which is a list of coefficients, frequently called “loadings” 29 , 30 , 88 , 89 . A widely accepted and simple qualitative rule proposes that loadings greater than 0.30 or less than − 0.30 are significant; loadings greater than 0.40 or less than − 0.40 are more important, whereas loadings greater than 0.50 or less than − 0.50 are very significant 90 . The suitability of data for PCA was evaluated by Kaiser–Meyer–Olkin 91 , 92 (KMO) measuring of sampling adequacy and Bartlett tests of sphericity 93 . The Shapiro test was evaluated to verify the data normality (α = 0.01).

Cluster analysis reveals the latent behavior of a dataset to categorize the objects into groups or clusters on the basis of similarities 30 , 88 , 89 . Hierarchical agglomerative cluster analysis (CA) classifies objects by first putting each object in a separate cluster, and then joins the clusters together stepwise until a single cluster remains 29 .

Timeseries analysis and trend detection

Mann–Kendall trend test is a nonparametric test used to identify a trend in a series, first proposed by Mann 94 and further improved by Kendall 95 and Hirsch 96 . The null hypothesis (H 0 ) for these tests is that there is no trend in the series. The tests are based on the calculation of Kendall's tau measure of association between two samples, which is itself based on the ranks with the samples. The variables are ranked in pairs, and the difference of each variable to its antecessor is calculated. The total number of pairs that present negative differences is subtracted from the number of pairs with positive differences (S). A positive value of S indicates an upward trend, and a negative value of S a downward trend. For n > 10, a normal approximation is used to calculate Z statistic which is used to calculate p-value 96 .

Fourier decomposition is a technique which allows the separation of frequency components from a data series with seasonal behavior from a complex water quality dataset 97 . Spectral analysis performed using a Fast Fourier Transform (FFT) algorithm is widely used in environmental studies, because it reveals the dominant influences and their scales 50 . Power spectral density (PSD) obtained from FFT and represented by periodograms is a recommended procedure to detect seasonality 98 , 99 .

Brazilian legal regulation

Brazilian fresh waters are divided into four classes, depending on the intended use 76 . The Special Class is intended mainly for the preservation of the natural balance of aquatic communities in fully protected conservation areas. Class 1 is designed for human consumption supply, after simplified treatment, for the protection of aquatic communities and for primary contact recreation. Class 2 requires conventional treatment for human consumption. Class 3 requires conventional or advanced treatment for human consumption and can be used to feed animals and irrigate some crops. Class 4 is intended only for navigation and landscape harmony. It is important to note that the Framework refers to the required water quality target according to water uses. The river basin committees are responsible for implementing the Framework, in accordance with the Brazilian National Water Resources Policy 33 . As long as the Framework is not established by the basin committee, fresh waters will be considered class 2 (Art. 42 CONAMA 357/2005) 76 .

Data availability

All data generated or analysed during this study are included in this published article and its Supplementary Information files.

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Acknowledgements

We thank the Piabanha Committee for financially support our research. We also thank Juliana Pereira Dias for helping with statistical analysis, Renata Demori Costa and Jamie Sweeney for the english review.

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D.A.C. compiled the manuscript, performed the analysis, and generated the figures and tables in the main text. J.P.S.A. contributed to the discussions and carefully reviewed the manuscript. M.A.S., R.S.F.V.A. and J.P.S.A. made substantial contributions to the conception and design of the research. All the authors reviewed the manuscript.

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de Andrade Costa, D., Soares de Azevedo, J.P., dos Santos, M.A. et al. Water quality assessment based on multivariate statistics and water quality index of a strategic river in the Brazilian Atlantic Forest. Sci Rep 10 , 22038 (2020). https://doi.org/10.1038/s41598-020-78563-0

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Drinking water quality assessment and its effects on residents health in Wondo genet campus, Ethiopia

• Yirdaw Meride 1 &
• Bamlaku Ayenew 1  

Environmental Systems Research volume  5 , Article number:  1 ( 2016 ) Cite this article

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Water is a vital resource for human survival. Safe drinking water is a basic need for good health, and it is also a basic right of humans. The aim of this study was to analysis drinking water quality and its effect on communities residents of Wondo Genet.

The mean turbidity value obtained for Wondo Genet Campus is (0.98 NTU), and the average temperature was approximately 28.49 °C. The mean total dissolved solids concentration was found to be 118.19 mg/l, and EC value in Wondo Genet Campus was 192.14 μS/cm. The chloride mean value of this drinking water was 53.7 mg/l, and concentration of sulfate mean value was 0.33 mg/l. In the study areas magnesium ranges from 10.42–17.05 mg/l and the mean value of magnesium in water is 13.67 mg/l. The concentration of calcium ranges from 2.16–7.31 mg/l with an average value of 5.0 mg/l. In study areas, an average value of sodium was 31.23 mg/1and potassium is with an average value of 23.14 mg/1. Water samples collected from Wondo Genet Campus were analyzed for total coliform bacteria and ranged from 1 to 4/100 ml with an average value of 0.78 colony/100 ml.

On the basis of findings, it was concluded that drinking water of the study areas was that all physico–chemical parameters. All the Campus drinking water sampling sites were consistent with World Health Organization standard for drinking water (WHO).

Safe drinking water is a basic need for good health, and it is also a basic right of humans. Fresh water is already a limiting resource in many parts of the world. In the next century, it will become even more limiting due to increased population, urbanization, and climate change (Jackson et al. 2001 ).

Drinking water quality is a relative term that relates the composition of water with effects of natural processes and human activities. Deterioration of drinking water quality arises from introduction of chemical compounds into the water supply system through leaks and cross connection (Napacho and Manyele 2010 ).

Access to safe drinking water and sanitation is a global concern. However, developing countries, like Ethiopia, have suffered from a lack of access to safe drinking water from improved sources and to adequate sanitation services (WHO 2006 ). As a result, people are still dependent on unprotected water sources such as rivers, streams, springs and hand dug wells. Since these sources are open, they are highly susceptible to flood and birds, animals and human contamination (Messeret 2012 ).

The quality of water is affected by an increase in anthropogenic activities and any pollution either physical or chemical causes changes to the quality of the receiving water body (Aremu et al. 2011 ). Chemical contaminants occur in drinking water throughout the world which could possibly threaten human health. In addition, most sources are found near gullies where open field defecation is common and flood-washed wastes affect the quality of water (Messeret 2012 ).

The World Health Organization estimated that up to 80 % of all sicknesses and diseases in the world are caused by inadequate sanitation, polluted water or unavailability of water (WHO 1997 ). A review of 28 studies carried out by the World Bank gives the evidence that incidence of certain water borne, water washed, and water based and water sanitation associated diseases are related to the quality and quantity of water and sanitation available to users (Abebe 1986 ).

In Ethiopia over 60 % of the communicable diseases are due to poor environmental health conditions arising from unsafe and inadequate water supply and poor hygienic and sanitation practices (MOH 2011 ). About 80 % of the rural and 20 % of urban population have no access to safe water. Three-fourth of the health problems of children in the country are communicable diseases arising from the environment, specially water and sanitation. Forty-six percent of less than 5 years mortality is due to diarrhea in which water related diseases occupy a high proportion. The Ministry of Health, Ethiopia estimated 6000 children die each day from diarrhea and dehydration (MOH 2011 ).

There is no study that was conducted to prove the quality water in Wondo Genet Campus. Therefore, this study is conducted at Wondo Genet Campus to check drinking water quality and to suggest appropriate water treated mechanism.

Results and discussions

The turbidity of water depends on the quantity of solid matter present in the suspended state. It is a measure of light emitting properties of water and the test is used to indicate the quality of waste discharge with respect to colloidal matter. The mean turbidity value obtained for Wondo Genet Campus (0.98 NTU) is lower than the WHO recommended value of 5.00 NTU.

Temperature

The average temperature of water samples of the study area was 28.49 °C and in the range of 28–29 °C. Temperature in this study was found within permissible limit of WHO (30 °C). Ezeribe et al. ( 2012 ) reports similar result (29 °C) of well water in Nigeria.

Total dissolved solids (TDS)

Water has the ability to dissolve a wide range of inorganic and some organic minerals or salts such as potassium, calcium, sodium, bicarbonates, chlorides, magnesium, sulfates etc. These minerals produced un-wanted taste and diluted color in appearance of water. This is the important parameter for the use of water. The water with high TDS value indicates that water is highly mineralized. Desirable limit for TDS is 500 mg/l and maximum limit is 1000 mg/l which prescribed for drinking purpose. The concentration of TDS in present study was observed in the range of 114.7 and 121.2 mg/l. The mean total dissolved solids concentration in Wondo Genet campus was found to be 118.19 mg/l, and it is within the limit of WHO standards. Similar value was reported by Soylak et al. ( 2001 ), drinking water of turkey. High values of TDS in ground water are generally not harmful to human beings, but high concentration of these may affect persons who are suffering from kidney and heart diseases. Water containing high solid may cause laxative or constipation effects. According to Sasikaran et al. ( 2012 ).

Electrical conductivity (EC)

Pure water is not a good conductor of electric current rather’s a good insulator. Increase in ions concentration enhances the electrical conductivity of water. Generally, the amount of dissolved solids in water determines the electrical conductivity. Electrical conductivity (EC) actually measures the ionic process of a solution that enables it to transmit current. According to WHO standards, EC value should not exceeded 400 μS/cm. The current investigation indicated that EC value was 179.3–20 μS/cm with an average value of 192.14 μS/cm. Similar value was reported by Soylak et al. ( 2001 ) drinking water of turkey. These results clearly indicate that water in the study area was not considerably ionized and has the lower level of ionic concentration activity due to small dissolve solids (Table 1 ).

PH of water

PH is an important parameter in evaluating the acid–base balance of water. It is also the indicator of acidic or alkaline condition of water status. WHO has recommended maximum permissible limit of pH from 6.5 to 8.5. The current investigation ranges were 6.52–6.83 which are in the range of WHO standards. The overall result indicates that the Wondo Genet College water source is within the desirable and suitable range. Basically, the pH is determined by the amount of dissolved carbon dioxide (CO 2 ), which forms carbonic acid in water. Present investigation was similar with reports made by other researchers’ study (Edimeh et al. 2011 ; Aremu et al. 2011 ).

Chloride (Cl)

Chloride is mainly obtained from the dissolution of salts of hydrochloric acid as table salt (NaCl), NaCO 2 and added through industrial waste, sewage, sea water etc. Surface water bodies often have low concentration of chlorides as compare to ground water. It has key importance for metabolism activity in human body and other main physiological processes. High chloride concentration damages metallic pipes and structure, as well as harms growing plants. According to WHO standards, concentration of chloride should not exceed 250 mg/l. In the study areas, the chloride value ranges from 3–4.4 mg/l in Wondo Genet Campus, and the mean value of this drinking water was 3.7 mg/l. Similar value was reported by Soylak et al. ( 2001 ) drinking water of Turkey.

Sulfate mainly is derived from the dissolution of salts of sulfuric acid and abundantly found in almost all water bodies. High concentration of sulfate may be due to oxidation of pyrite and mine drainage etc. Sulfate concentration in natural water ranges from a few to a several 100 mg/liter, but no major negative impact of sulfate on human health is reported. The WHO has established 250 mg/l as the highest desirable limit of sulfate in drinking water. In study area, concentration of sulfate ranges from 0–3 mg/l in Wondo Genet Campus, and the mean value of SO 4 was 0.33 mg/l. The results exhibit that concentration of sulfate in Wondo Genet campus was lower than the standard limit and it may not be harmful for human health.

Magnesium (Mg)

Magnesium is the 8th most abundant element on earth crust and natural constituent of water. It is an essential for proper functioning of living organisms and found in minerals like dolomite, magnetite etc. Human body contains about 25 g of magnesium (60 % in bones and 40 % in muscles and tissues). According to WHO standards, the permissible range of magnesium in water should be 50 mg/l. In the study areas magnesium was ranges from 10.42 to 17.05 mg/l in Wondo Genet Campus and the mean value of magnesium in water is 13.67 mg/l. Similar value was reported by Soylak et al. ( 2001 ) drinking water of Turkey. The results exhibit that concentration of magnesium in Wondo Genet College was lower than the standard limit of WHO.

Calcium (Ca)

Calcium is 5th most abundant element on the earth crust and is very important for human cell physiology and bones. About 95 % of calcium in human body stored in bones and teeth. The high deficiency of calcium in humans may caused rickets, poor blood clotting, bones fracture etc. and the exceeding limit of calcium produced cardiovascular diseases. According to WHO ( 2011 ) standards, its permissible range in drinking water is 75 mg/l. In the study areas, results show that the concentration of calcium ranges from 2.16 to 7.31 mg/l in Wondo Genet campus with an average value of 5.08 mg/l.

Sodium (Na)

Sodium is a silver white metallic element and found in less quantity in water. Proper quantity of sodium in human body prevents many fatal diseases like kidney damages, hypertension, headache etc. In most of the countries, majority of water supply bears less than 20 mg/l, while in some countries the sodium quantity in water exceeded from 250 mg/l (WHO 1984 ). According to WHO standards, concentration of sodium in drinking water is 200 mg/1. In the study areas, the finding shows that sodium concentration ranges from 28.54 to 34.19 mg/1 at Wondo Genet campus with an average value of 31.23.

Potassium (k)

Potassium is silver white alkali which is highly reactive with water. Potassium is necessary for living organism functioning hence found in all human and animal tissues particularly in plants cells. The total potassium amount in human body lies between 110 and 140 g. It is vital for human body functions like heart protection, regulation of blood pressure, protein dissolution, muscle contraction, nerve stimulus etc. Potassium is deficient in rare but may led to depression, muscle weakness, heart rhythm disorder etc. According to WHO standards the permissible limit of potassium is 12 mg/1. Results show that the concentration of potassium in study areas ranges from 20.83 to 27.51 mg/1. Wondo Genet College with an average value of 23.14 mg/1. Present investigation was similar with reports made by other researchers’ study (Edimeh et al. 2011 ; Aremu et al. 2011 ). These results did not meet the WHO standards and may become diseases associated from potassium extreme surpassed.

Nitrate (NO 3 )

Nitrate one of the most important diseases causing parameters of water quality particularly blue baby syndrome in infants. The sources of nitrate are nitrogen cycle, industrial waste, nitrogenous fertilizers etc. The WHO allows maximum permissible limit of nitrate 5 mg/l in drinking water. In study areas, results more clear that the concentration of nitrate ranges from 1.42 to 4.97 mg/l in Wondo Genet campus with an average value of 2.67 mg/l. These results indicate that the quantity of nitrate in the study site is acceptable in Wondo Genet campus (Table 2 ).

Bacterial contamination

The total coliform group has been selected as the primary indicator bacteria for the presence of disease causing organisms in drinking water. It is a primary indicator of suitability of water for consumption. If large numbers of coliforms are found in water, there is a high probability that other pathogenic bacteria or organisms exist. The WHO and Ethiopian drinking water guidelines require the absence of total coliform in public drinking water supplies.

In this study, all sampling sites were not detected of faecal coliform bacteria. Figure  1 shows the mean values of total coliform bacteria in drinking water collected from the study area. All drinking water samples collected from Wondo Genet Campus were analyzed for total coliform bacteria and ranged from 1 to 4/100 ml with an average value of 0.78 colony/100 ml. In Wondo Genet College, the starting point of drinking water sources (Dam1), the second (Dam2) and Dam3 samples showed the presence of total coliform bacteria (Fig.  1 ). According to WHO ( 2011 ) risk associated in Wondo Genet campus drinking water is low risk (1–10 count/100 ml).

The mean values of total coliform bacteria in drinking water

According to the study all water sampling sites in Wondo Genet campus were meet world health organization standards and Ethiopia drinking water guideline. Figure  2 indicated that mean value of the study sites were under the limit of WHO standards.

Comparison of water quality parameters of drinking water of Wondo Genet campus with WHO and Ethiopia standards

Effect of water quality for residence health’s

Diseases related to contamination of drinking-water constitute a major burden on human health. Interventions to improve the quality of drinking-water provide significant benefits to health. Water is essential to sustain life, and a satisfactory (adequate, safe and accessible) supply must be available to all (Ayenew 2004 ).

Improving access to safe drinking-water can result in tangible benefits to health. Every effort should be made to achieve a drinking-water quality as safe as practicable. The great majority of evident water-related health problems are the result of microbial (bacteriological, viral, protozoan or other biological) contamination (Ayenew 2004 ).

Excessive amount of physical, chemical and biological parameters accumulated in drinking water sources, leads to affect human health. As discussed in the result, all Wondo Genet drinking water sources are under limit of WHO and Ethiopian guideline standards. Therefore, the present study was found the drinking water safe and no residence health impacts.

On the basis of findings, it was concluded that drinking water of the study areas was that all physico–chemical parameters in all the College drinking water sampling sites, and they were consistent with World Health Organization standard for drinking water (WHO). The samples were analyzed for intended water quality parameters following internationally recognized and well established analytical techniques.

It is evident that all the values of sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), chloride (Cl), SO 4 , and NO 3 fall under the permissible limit and there were no toxicity problem. Water samples showed no extreme variations in the concentrations of cations and anions. In addition, bacteriological determination of water from College drinking water sources was carried out to be sure if the water was safe for drinking and other domestic application. The study revealed that all the College water sampling sites were not contained fecal coliforms except the three water sampling sites had total coliforms.

The study was conducted in Wondo Genet College of Forestry and Natural Resources campus, which is located in north eastern direction from the town of Hawassa and about 263 km south of Addis Ababa (Fig.  3 ). It lies between 38°37′ and 38°42′ East longitude and 7°02′ and 7°07′ north latitude. Landscape of the study area varies with an altitude ranging between 1600 and 2580 meters above sea level. Landscape of the study area varies with an altitude ranging between 1600 and 2580 meters above sea level.

Map of study area

The study area is categorized under Dega (cold) agro-ecological zone at the upper part and Woina Dega (temperate) agro-ecological zone at the lower part of the area. The rainfall distribution of the study area is bi-modal, where short rain falls during spring and the major rain comes in summer and stays for the first two months of the autumn season. The annual temperature and rainfall range from 17 to 19 °C and from 700 to 1400 mm, respectively (Wondo Genet office of Agriculture 2011).

Methodology

Water samples were taken at ten locations of Wondo Genet campus drinking water sources. Three water samples were taken at each water caching locations. Ten (10) water samples were collected from different locations of the Wondo Genet campus. Sampling sites for water were selected purposely which represents the entire water bodies.

Instead of this study small dam indicates the starting point of Wondo Genet campus drinking water sources rather than large dams constructed for other purpose. Taps were operated or run for at least 5 min prior to sampling to ensure collection of a representative sample (temperature and electrical conductivity were monitored to verify this). Each sample’s physico–chemical properties of water were measured in the field using portable meters (electrical conductivity, pH and temperature) at the time of sampling. Water samples were placed in clean containers provided by the analytical laboratory (glass and acid-washed polyethylene for heavy metals) and immediately placed on ice. Nitric acid was used to preserve samples for metals analysis.

Analysis of water samples

Determination of ph.

The pH of the water samples was determined using the Hanna microprocessor pH meter. It was standardized with a buffer solution of pH range between 4 and 9.

Measurement of temperature

This was carried out at the site of sample collection using a mobile thermometer. This was done by dipping the thermometer into the sample and recording the stable reading.

Determination of conductivity

This was done using a Jenway conductivity meter. The probe was dipped into the container of the samples until a stable reading will be obtained and recorded.

Determination of total dissolved solids (TDS)

This was measured using Gravimetric Method: A portion of water was filtered out and 10 ml of the filtrate measured into a pre-weighed evaporating dish. Filtrate water samples were dried in an oven at a temperature of 103 to 105 °C for \(2\frac{1}{2}\)  h. The dish was transferred into a desiccators and allowed cool to room temperature and were weighed.

In this formula, A stands for the weight of the evaporating dish + filtrate, and B stands for the weight of the evaporating dish on its own Mahmud et al. ( 2014 ).

Chemical analysis

Chloride concentration was determined using titrimetric methods. The chloride content was determined by argentometric method. The samples were titrated with standard silver nitrate using potassium chromate indicator. Calcium ions concentrations were determined using EDTA titrimetric method. Sulphate ions concentration was determined using colorimetric method.

Microorganism analysis

In the membrane filtration method, a 100 ml water sample was vacuumed through a filter using a small hand pump. After filtration, the bacteria remain on the filter paper was placed in a Petri dish with a nutrient solution (also known as culture media, broth or agar). The Petri dishes were placed in an incubator at a specific temperature and time which can vary according the type of indicator bacteria and culture media (e.g. total coliforms were incubated at 35 °C and fecal coliforms were incubated at 44.5 °C with some types of culture media). After incubation, the bacteria colonies were seen with the naked eye or using a magnifying glass. The size and color of the colonies depends on the type of bacteria and culture media were used.

Statically analysis

All data generated was analyzed statistically by calculating the mean and compare the mean value with the acceptable standards. Data collected was statistically analyzed using Statistical Package for Social Sciences (SPSS 20).

Abbreviations

ethylene dinitrilo tetra acetic acid

Minstor of Health

nephelometric turbidity units

total dissolved solid

World Health Organization

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Authors’ contributions

YM: participated in designing the research idea, field data collection, data analysis, interpretation and report writing; BA: participated in field data collection, interpretation and report writing. Both authors read and approved the final manuscript.

Authors’ information

Yirdaw Meride: Lecturer at Hawassa University, Wondo Genet College of Forestry and Natural Resources. He teaches and undertakes research on solid waste, carbon sequestration and water quality. He has published three articles mainly in international journals. Bamlaku Ayenew: Lecturer at Hawassa University, Wondo Genet College of Forestry and Natural Resources. He teaches and undertakes research on Natural Resource Economics. He has published three article with previous author and other colleagues.

Acknowledgements

Hawassa University, Wondo Genet College of Forestry and Natural Resources provided financial support for field data collection and water laboratory analysis. The authors thank anonymous reviewers for constructive comments.

Competing interests

The authors declare that they have no competing interests.

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Meride, Y., Ayenew, B. Drinking water quality assessment and its effects on residents health in Wondo genet campus, Ethiopia. Environ Syst Res 5 , 1 (2016). https://doi.org/10.1186/s40068-016-0053-6

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Received : 01 September 2015

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Published : 21 January 2016

DOI : https://doi.org/10.1186/s40068-016-0053-6

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Perceived and physical quality of drinking water in pavlodar and akmola rural regions of kazakhstan.

1. Introduction

2. materials and methods, 2.1. research area.

2.2. Chemical Analysis of Drinking Water Samples

2.3. wqi index calculation, 2.4. statistical methods, 3.1. statistical data of residents of pavlodar and akmola regions, 3.2. perceiving drinking water quality, 3.3. hydro–chemical parameters of pavlodar and akmola rural area drinking water and their correlation with perceived quality of water, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Beisenova, R.; Tussupova, K.; Tazitdinova, R.; Tulegenova, S.; Rakhymzhan, Z.; Orkeyeva, A.; Alkhanova, Y.; Myrzagaliyeva, A.; Nugmanov, A.; Zhupysheva, A. Perceived and Physical Quality of Drinking Water in Pavlodar and Akmola Rural Regions of Kazakhstan. Sustainability 2024 , 16 , 7625. https://doi.org/10.3390/su16177625

Beisenova R, Tussupova K, Tazitdinova R, Tulegenova S, Rakhymzhan Z, Orkeyeva A, Alkhanova Y, Myrzagaliyeva A, Nugmanov A, Zhupysheva A. Perceived and Physical Quality of Drinking Water in Pavlodar and Akmola Rural Regions of Kazakhstan. Sustainability . 2024; 16(17):7625. https://doi.org/10.3390/su16177625

Beisenova, Raikhan, Kamshat Tussupova, Rumiya Tazitdinova, Symbat Tulegenova, Zhanar Rakhymzhan, Ainur Orkeyeva, Yerkenaz Alkhanova, Anar Myrzagaliyeva, Askar Nugmanov, and Aktoty Zhupysheva. 2024. "Perceived and Physical Quality of Drinking Water in Pavlodar and Akmola Rural Regions of Kazakhstan" Sustainability 16, no. 17: 7625. https://doi.org/10.3390/su16177625

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Evaluation of Groundwater Quality and Health Risk Assessment in Dawen River Basin, North China

36 Pages Posted: 3 Sep 2024

Wei Shanming

affiliation not provided to SSRN

Zhang Yaxin

Chinese Academy of Geological Sciences - Institute of Hydrogeology and Environmental Geology

Zheng Xuefei

Groundwater is the principal water source of drinking and irrigation in the Dawen River Basin of Shandong Province. Assessing groundwater quality and potential human health risks is crucial for the development and utilization of groundwater resources. Thus, its investigations and evaluations are of significant importances. Based on collected groundwater samples, this study employed a combination of the entropy-weighted water quality index (WQI) and the human health risk assessment model (HHRA) to evaluate groundwater quality and associated health risks. Additionally, geostatistical methods and GIS spatial analysis were used to examine the spatial characteristics of groundwater quality and its relationship with geomorphology. The results indicated that the water quality in the region is generally good, with WQI values ranging from 20.32 to 302.37 and an average of 70.88. Water quality in the middle and lower reaches is worse compared to the upper reaches, and quality tends to be poorer in flat terrains. Key indicators reflecting groundwater quality include Na+, Cl-, SO42-, and NO3-. The HHRA model revealed a high potential non-carcinogenic risk from NO3- and a low potential non-carcinogenic risk from F-; direct ingestion was identified as the main pathway for health risks associated with these indicators, children are more vulnerable than adults under similar conditions. This study provides a scientific basis for the sustainable use and pollution control of groundwater resources in the Dawen River Basin.

Keywords: Groundwater quality, hydrochemistry, Water quality index, Groundwater contamination, groundwater evolution, health risk assessment

Suggested Citation: Suggested Citation

affiliation not provided to SSRN ( email )

No Address Available

Chinese Academy of Geological Sciences - Institute of Hydrogeology and Environmental Geology ( email )

Shijiazhuang, 050061 China

Cai Zizhao (Contact Author)

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Ground water quality evaluation for irrigation purpose: case study al-wafaa area, western iraq.

© 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license ( http://creativecommons.org/licenses/by/4.0/ ).

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This study was conducted during the summer season of 2023 to assess the groundwater quality in the Al-Wafaa region of Anbar Province western Iraq for irrigation purposes.18 water samples were collected from 18 wells Distributed in the study area. pH, EC, Total Dissolved Solid (TDS), main cations, and anions (Na + , K + , Ca 2+ , Mg 2+ , Cl - , HCO 3 - , NO 3 - ) were measured. The main cations were used to calculate the Percent Sodium (%Na) and Sodium Adsorption Ratio (SAR). Additionally, Wilcox and United States Salinity Laboratory (USSL) diagrams were employed to evaluate the suitability of the groundwater for irrigation. The study found that based on the EC values; all groundwater in the research area is classified as having very high salinity and is therefore not suitable for irrigation. Based on the Wilcox diagram, 83% of the well water samples in the Al-Wafaa region are classified as unsuitable for irrigation, and 17% fall within a doubtful to unsuitable category. According to the USSL diagram, 22% of groundwater samples are in the C4S3 category, indicating very high salinity with high sodium. Additionally, 61% of samples fall into the C4S2 category, suggesting very high salinity with medium sodium, and 17% of samples fall into the (C4S1) category, indicating very high salinity with low sodium. Overall, the findings indicate that the samples are not suitable for crop watering.

assessment, irrigation, groundwater quality, hazard, percent sodium, electrical conductivity (EC), Wilcox, United States Salinity Laboratory

In many countries of the world, groundwater is an important source for irrigation of agricultural lands, so groundwater quality evaluation has become a necessary task for managing groundwater quality in the future. In Iraq, the water of the Tigris and Euphrates rivers is considered an important source of drinking water, crop irrigation, and other purposes, but in recent years many problems have appeared that affected the river water quality such as the lack of rainfall and increased pollution. Therefore, it is necessary to search for other sources of water and hydrological evaluation of the well water location. Well water is taken into consideration the high-quality source for irrigating agricultural lands, it is possible to drink, and it is supposed to be dependable and free of contaminants, suspended substances, and sickness-causing microorganisms [1]. Several factors impact the willpower of the suitability charge of water for irrigation, together with water fine, climate, plant capacity to tolerate excessive salinity, soil type, and water drainage [2]. Modern innovations and techniques were utilized to evaluate and observe groundwater for irrigation. Some of these innovations used included irrigation water indicators like sodium adsorption ratio (SAR) and residual sodium carbonate (RSC) [3]. The Water Quality Index (WQI) is a very suitable and powerful approach to evaluate the appropriateness of water best [4]. Many researchers have investigated the valuation of groundwater to irrigate crops and human utilization, specifically in Iraq and comparable arid regions in the world. Allawi et al. [5] presented research to evaluate groundwater quality within the Alnekheeb basin in western Iraq to perceive an extra applicable and sustainable water delivery. In this research, three groundwater water first-rate signs, hardness, SAR, and salinity, are forecast by employing two primarily based on artificial intelligence fashions, the Radial Basis Neural Network (RBF-NN) and the Probabilistic Neural Network (PNN). Furthermore, this study focused on the impact of enters parameters on the overall performance of the advised models. According to the evaluation results, adding greater information variables may once in a while enhance the efficacy of the advised models in forecasting accuracy. The outcomes indicate that the PNN model has an amazing overall performance in forecasting groundwater water exceptional matrices, outperforming the RBF-NN version. Khudair et al. [6] presented a study in 2021 to assess the quality of groundwater in the Al-Qaim metropolis, western Iraq, to irrigate crops within the research area. The research tested seven places in the study location to determine the effectiveness of irrigation. The pH, electric conductivity (EC), important cations, and anions (K, Na, Mg 2 , Ca 2 , HCO 3 , Cl - , SO 4 ), and CO 3 have been determined. The effects revealed that the examined water is suitable for crop watering regarding pH cost and EC. The total hardness values have been modest and did now not represent trouble, and the main cations and anions have been in the acceptable degrees for the indicated classes. The SAR was determined to be in magnificence S1, indicating that the groundwater in the research district is suitable for crop watering. Ghalib [7] conducted research to estimate the quality of groundwater satisfaction in Wasit province, Iraq. The physicochemical traits, consisting of total dissolved strong, important cation and anions, pH, and EC, have been utilized to estimate groundwater high-quality for human use and crop watering by comparing them to World Health Organization and Iraqi standards. TDS, sodium adsorption ratio, residual sodium bicarbonate, permeability index (PI), and magnesium ratio were used to determine irrigation appropriateness. The examined groundwater samples have been oversaturated with carbonate minerals and lacking evaporated minerals. The effects found that almost all of the groundwater samples were hazardous for drinking and irrigation because of salt and salinity risks. The present study has evaluated the quality of groundwater in a 5119 km 2 area in Babylon City, Iraq [8]. This research included well positions, maps, and data about the quality of groundwater provided by way of the special government. The WQI and IWQI were decided for groundwater samples using some characteristics such as EC, Cl-, HCO 3 -, Na, and pH. Furthermore, groundwater suitability for watering is assessed by the use of some Indicators which include Kelly's Ratio (KR), SAR, and PI. Water Quality Indicator graphs were made using the Geographical Information System (GIS) surroundings. The findings show that the groundwater inside the research region needs particular treatments to be appropriate for use. Awad et al. [9] focused on studying the hydrogeochemical properties of groundwater, consisting of ion change, salinization, and hydrochemistry in the Green Belt area in northern Najaf province, Iraq. Also targeted the research on the evaluation of the pleasant of groundwater for crop watering based on the IWQI for thirteen parameters and groundwater quality indices such as TDS, EC, SAR, overall hardness (TH), PI, KR, and magnesium hazard ratio (MHR). The results indicate that groundwater inside the research district is incorrect for crop watering. To ensure the sustainability of groundwater applications, a continuous tracking program and appropriate control techniques. Al-Tameemi et al. [10] assessed the quality of groundwater in Kirkuk province, northern Iraq, for human uses, crop watering, leisure activities, and animal uses from 2017 to 2019, using the Canadian Water Quality Index (CWQI) and GIS. The groundwater quality was tested using Iraqi and World Health Organization (WHO) suggestions as well. The Iraqi standards were utilized for drinking water, whereas WHO standards were applied for watering, leisure activities, and animal purposes. Based on the CWQI, groundwater samples were classed as medium in 2017 and 2018, while there was unsafe drinking water detected in 2019. Al-Kubaisi et al. [11] presented an article to assess the groundwater for irrigation in the Al-Dabdaba aquifer in Karbala - Najaf Plateau in Iraq. The research blanketed mapping of the water quality index and the outcomes labeled the groundwater inside the Al-Dabdaba layer as having moderate. Soren et al. [12] used Wilcox and USSL schemes to evaluate groundwater first-class for irrigation and drinking functions in South 24-Parganas in West Bengal, India. The results confirmed that 46% of the samples had been categorized under the coolest to the permissible category and 37% were categorized below the permissible to questionable class. Sadashivaiah et al. [13] applied the technique of SAR, RSC, salinity hazards, and USSL chart to evaluate water for irrigation purposes in Tukur Taluk. The findings from USSL charts showed that the samples are classified as suitable for irrigation purposes and are classified in the suitable range for irrigation from SAR or RSC values. Hydrochemistry of groundwater in the Ain Azel plain, Algeria was used to evaluate groundwater for irrigation and the results showed that most of the samples are located in the area (C3-S1), meaning the risk of salinity is high and the risk of sodium is low [14]. The groundwater quality for irrigation purposes was evaluated in the city of Acarão Basin in Brazil by developing an IWQI depending on several parameters such as (EC, CL, HCO 3 , Na) [15]. The study showed the risk of soil salinity and water venomousness in the crops. Siswoyo et al. [16] presented a study to evaluate groundwater to irrigate agricultural lands in the Jombang region, East Java, Indonesia. The study relied on IQWI techniques, and the results classified the groundwater quality between moderate restriction and low irrigation restriction. A study was presented to evaluate the groundwater quality for irrigation of agricultural lands in three villages in Iran using a combination of geographic information systems and the irrigation water quality index [17]. Ketata et al. [18] used IWQI as a device to manage groundwater nice within the El Khairat Deep aquifer inside the Tunisian Sahel. Nastos et al. [19] used artificial neural networks to forecast rainfall intensity for four months. The results simulations from the model showed decent forecasting of rainfall intensity values. Using artificial neural networks (ANN) for forecasting the water level of the Euphrates rivers in western Iraq and the result showed the artificial neural networks can valued water level (t+1) with a high grade accuracy [20]. Modeling approaches used in hydrological and hydraulic processes are required to provide accurate and sustainable water resource management [21].

Al-Waffa area is a semi-desert region with no surface water, so groundwater is essential to meet the water needs for irrigation and drinking purposes. This research aims to assess the groundwater quality for irrigation purposes.

Al-Wafaa is an area located in western Iraq, west of Anbar province, 50 km west of Ramadi. The study area is located between latitudes (33°23'51" N) and longitudes (42°51'11" E) The area is about 100 km 2 and has a population of about 8000 people. The Euphrates River flows east of the research region shown in Figure 1. The environment of the region is a very hot desert with and dehydrated summer with a high amount of evaporation and a cold season with a reduction in rainfall. It is characterized by simple slop and presence of the seasonal valleys such as Al-Asal Valley [22]. It is affected by the Abu Al-Jir area fault [23]. The area is also rich in bitumen and sulfates and the area is characterized by the presence of an unconfined aquifer consisting of sandstone with fine gravel and mudstone, covered with a layer of gypsum and sandy soil. Groundwater is extracted in this area by drilling wells [24].

Figure 1. The map of the study area

3.1 Collection of samples

Eighteen wells were selected in the study area shown in Figure 2. The wells' coordinates were determined via (GPS) and documented in Table 1. The samples were collected in August 2023 and kept in 2-liter clean and dry plastic bottles and transferred to the water quality control laboratory at the College of Engineering, Anbar University for the measurement of chemical parameters.

Figure 2. Location of the wells

Table 1. The coordinates of wells in the Al-Wafaa region

3.2 Lab analysis of samples

Water samples were analyzed for chemical parameters: pH, EC, TDS, Calcium (Ca 2+ ), Magnesium (Mg 2+ ), Sodium (Na + ), Potassium (K + ), Sulphate (SO 4 -2 ), Chloride (Cl -1 ) and Bicarbonate (HCO 3 -1 ). pH, EC, and TDS are important parameters for assessing groundwater for several purposes. All parameters were examined depending on the Standard Method for the Examination of water and wastewater following (APHA, 1998) American Public Health Association guidelines [25]. Conductivity and pH were measured by using a portable device pH/EC/ meter (HANNA HI9321). TDS, bicarbonate (HCO 3 − ), chloride (Cl − ), magnesium (Mg 2+ ), and calcium (Ca 2+ ) were analyzed by titration methods; potassium (K + ) and sodium (Na+) were tested using the flame photometric method by flame photometer (Jenway PFP7); and sulfate (SO 4 2− ) were analyzed by spectrophotometer (DR 5000 HACH).

3.3 Calculation of water quality indices for irrigation

There are several key parameters to consider when evaluating the quality of irrigation water. These include pH, salinity levels, bicarbonate concentration (which is related to calcium and magnesium levels), and the presence of components such as sodium and chloride, which can be harmful to plants. To assess the suitability of groundwater for irrigation, water quality indices like the SAR and %Na are commonly used. In addition, graphical methods like the Wilcox diagram and USSL diagram are frequently employed to confirm the suitability of groundwater for irrigation purposes.

The Sodium Adsorption Ratio is considered an important factor to assess the groundwater quality and it was calculated using the equation given by Raghunath [26]. The ion concentration was measured in (meq/l).

$\mathrm{SAR}=\frac{{Na}^{+}}{\sqrt{\left({Ca}^{2+}+{Mg}^{2+}\right) / 2}}$      (1)

%Na was calculated by the equation given by Todd and Mays [27]. The ion concentration was measured in (meq/l).

$\% \mathrm{Na}=\frac{{Na}^{+}+{K}^{+}}{{Ca}^{2+}+{Mg}^{2+}+{Na}^{+}+{K}^{+}} \times 100$        (2)

3.3.3 USSL diagram

Proposed chart for classification of groundwater quality for irrigation purposes. The classification depends on values of SAR and EC [28]. The irrigation water quality is classified as follows (Table 2).

Table 2. Classification of groundwater for irrigation purposes

3.3.4 Wilcox diagram

Proposed chart for classification of groundwater for irrigation purposes. The classification depends on values of %Na and EC [29]. The chart is classified into five categories such as: Excellent to Good, Good to permissible. Permissible to doubtful, Doubtful to unsuitable, and Unsuitable.

4.1 Water quality based on the absolute ions

The concentration of cations in the study region ranges from 179 to 429 mg/l for Ca 2+ , 72 to 283 mg/l for Mg 2+ , 251 to 708 mg/l for Na + , and 4 to 128 mg/l for K + (Table 3). The allowed levels for Ca 2+ , Mg 2+ , Na + , and K + in irrigation water are 80, 35, 200, and 30 mg/l, respectively [26]. Based on these acceptable levels, 0% of groundwater samples were suitable for Ca 2+ , Mg 2+ , and Na + , while 72% were suitable for K + , and 28% were not suitable.

Table 3. Analysis results of water sample

The HCO 3 - and Cl - levels in the groundwater samples ranged from 136 to 543 mg/L and 305 to 811 mg/L, respectively (Table 3). The acceptable limit for HCO 3 - and Cl - in irrigation water is 250 mg/L [26]. Based on these acceptable levels, 0% of groundwater samples were suitable for HCO 3 - , 16% for Cl - , and 84% were not suitable.

4.2 Irrigation water quality assessment depends on pH

The term pH refers to a solution that is either acidic or alkaline. The acidity or basicity of irrigation water is measured by its pH, with a pH below 7.0 being acidic and above 7.0 being basic. The impact of pH on hydraulic conductivity, regardless of SAR, has been proven [30]. Typically, irrigation water has a pH range of 6.5–8.4 [31, 32]. Water with a low pH can be corrosive, while water with a high pH may cause scaling [33]. The pH values of the samples ranged from 7.14 to 7.26, with an average value of 7.19, falling within the typical ranges for irrigation water.

4.3 Irrigation water quality assessment depending on EC values

EC measures the capacity of a material or solution to carry an electric current. The electrical conductivity of groundwater increases as temperature rises and fluctuates with TDS concentration. EC is a valuable indicator of the risk of salinity in agriculture, as it mirrors TDS levels in groundwater. When EC rises, plants have limited access to water [34].

The EC of samples ranges from 2470 µS/cm to 6720 µS/cm, with an average value of 4645 µS/cm as shown in Table 3. According to the results in Table 4, all groundwater samples are classified as very high salinity and cannot be used for watering.

Table 4. EC classification of groundwater [35]

4.4 Irrigation water quality assessment depends on Total Dissolved Solid values

TDS refers to the solids remaining in a filtered water sample after evaporation. These solids include minerals, nutrients, and important ions such as Ca 2+ , Mg 2+ , K + , Na + , HCO 3 -, SO 4 2- , Cl - , etc., found in natural water. TDS levels below 450 mg/l are ideal for irrigation, while levels between 450 and 2000 mg/l are considered moderate. TDS concentrations over 2000 mg/l are not suitable for agricultural purposes [36]. In the study area, groundwater samples had TDS levels ranging from 1600 mg/l to 4370 mg/l, with an average of 3014.72 mg/l. According to Carroll's (1962) classification shown in Table 5, the groundwater in the research area is considered brackish water.

Table 5. Groundwater Classification based on TDS Carroll's (1962) classification

4 .5 Irrigation water quality assessment depends on SAR

Table 6. Water quality indexes

SAR is an important measure of groundwater quality for irrigation. High concentrations of sodium ions can reduce soil permeability, decrease water and air content, and disrupt soil structure by displacing calcium and magnesium ions. The SAR values of groundwater samples ranged from (2.7 to 9.52) meq/l as shown in Table 6. Based on the SAR classification in Table 7, all groundwater samples are classified as excellent and suitable for most crops and soil types, except those sensitive to sodium.

Table 7. Classification of groundwater samples based on sodium adsorption ratio SAR [37]

4.6 Irrigation water quality assessment depends on %Na

Sodium is an essential ion for plant growth at low concentrations, but it can be toxic to crops at high concentrations. The recommended ranges for sodium ion concentration in irrigation water are as follows: below 20% (excellent), 20–40% (good), 40–60% (permissible), 60–80% (doubtful), and greater than 80% (unsuitable). In the present study, the percentage of sodium in the samples as shown in Table 6 ranged from 46.49% to 69.04%. According to Table 8, 28% of the groundwater samples are classified as good, while 61% are permissible, and 11% are doubtful.

Table 8. Classification of groundwater samples based on sodium adsorption ratio %Na [38]

4.7 Irrigation water quality assessment based on the Wilcox diagram

Based on the Wilcox diagram, 83% of water samples were classified as unsuitable for irrigation purposes, and 17% of water samples were classified as doubtful to unsuitable for crop irrigation as Figure 3.

Figure 3. Wilcox diagram to classify ground water quality for irrigation

4.8 Irrigation water quality assessment based on the USSL diagram

Based on Figure 4, the results show that 4 of the samples belong to the (C4S3) class, indicating very high saltiness with high sodium content. Additionally, 11 of the samples from the study region belong to the (C4S2) class, suggesting very high saltiness with medium sodium content. Furthermore, 3 of the samples in the study region are categorized under the (C4S1) class, indicating very high salinity with low sodium content. This implies that the samples are unsuitable for irrigation purposes.

4.9 The potential impact of high salinity and sodium levels on crop yield and soil health

The quality of water is significantly affected by the type and amount of dissolved salts present. Elevated levels of salt in irrigation water can lead to salt deposition in the root region, causing salinity issues and reducing the amount of water available for root absorption [39]. If the soil isn't flushed with low-salt water, the excessive levels of salt in irrigation water can avert plant growth and cause wilting [31]. Salinity damage is a very important aspect in choosing the satisfactory water used for crop watering as a result of its influence on the osmotic strain of the soil [40]. Soil permeability is primarily prompted by the aid of soil salinity and the SAR [41]. High ranges of sodium in water, can impact soil shape and texture. Sodium can disrupt soil aggregates and disperse first-class particles, leading to the clogging of soil pores [41]. The presence of sure ions which include sodium and chloride in high concentrations in irrigation water can result in toxicity issues in vegetation, resulting in reduced boom and output. The quantity of toxicity relies upon the plant range and its rate of absorption.

Figure 4. USSL diagram to classify Groundwater quality for irrigation

4 .10 Comparison with similar studies

The permeability and water filtration in the soil are mainly influenced by salinity and SAR. In the study area, Table 3 shows a high EC value ranging between 2470-6720 µS/cm. These values are higher than those obtained by Hussain et al. [42] in their study of the groundwater of the Dammam aquifer in the western part of Iraq, which ranged between 1531-3460 µS/cm. The increased values EC is most likely owing to the study area's geological formations, which contain evaporated salts, gypsum, and dolomite. This deteriorates the water quality that travels through it. Table 6 reveals that SAR values in the research region were between (2.7-9.52) meq/l, which is consistent with the findings reported by Hussain et al. [42] in their investigation of groundwater in the Dammam aquifer in western Iraq, which ranged between (3.10 - 6.43) meq/l. These comparatively low results are the result of increased calcium and magnesium ion concentrations in the research region.

GIS is a specialized tool to generate spatial distribution maps that indicate acceptable and unsuitable zones based on water quality metrics [43]. This study created spatial distribution maps for EC, pH, TDS, SAR, and %Na.

Figure 5. Spatial distribution map of pH

The spatial distribution map of pH shown in Figure 5 indicates that each study area falls within the permissible limits for irrigation. It also shows that the largest part of the study area has a pH ranging from 7.16-7.20.

Figure 6. Spatial distribution map of EC

The spatial distribution map of EC is shown in Figure 6. This indicates that all study areas have high salinity. It also shows that the largest part of the study area has an EC ranging from 4001-5000 ms/cm.

The spatial distribution map shown in Figure 7 indicates that the TDS in the study area is very high. It also shows that the largest part of the study area has a TDS ranging from 2501-3000 mg/l, and the south part has a TDS ranging from 3501-4000 mg/l.

The spatial distribution map shown in Figure 8 indicates that the SAR in the study area is within the excellent zone. The values SAR ranges between (3.6–9.5) meq/l.

Figure 9 shows the geographical distribution map of %Na, which shows that the eastern half of the research region has very low %Na values when compared to the western sections of the study area.

Figure 7. Spatial distribution map of TDS

Figure 8. Spatial distribution map of SAR

Figure 9. Spatial distribution map of %Na

The use of groundwater is one of the strategic and main solutions in the desert and semi-desert regions such as the Western Desert in Iraq. The surface water quantities decrease significantly, particularly in times of water lack. The current study is a qualitative assessment of groundwater quality in the Al-Wafaa area in western Iraq. In this study, two diagrams were utilized to evaluate the quality of groundwater for irrigation. Below are the summary results of the assessment.

-The research found that most chemical standards exceeded permissible limits for irrigation. Na, Mg, Ca, and HCO 3 ions exceeded acceptable levels for irrigation, while the chloride ions showed low suitability.

-pH values of the groundwater samples are within the normal levels for irrigation water.

-EC of the groundwater is very high salt, ranging from 2470 to 6720 (ms/cm), with an average of 4645. This suggests that samples are improper for watering and pose a health hazard.

-The high salinity levels may be due to the significant dissolution of rock minerals or ion exchange processes, which introduce chloride (Cl), sodium (Na), and bicarbonate (HCO 3 ) ions into the groundwater in those specific areas. Further studies are required to evaluate the groundwater quality for different purposes.

-The water samples were classified as brackish water due to the values of TDS ranging from 1600 to 4370 mg/l, with a mean of 3014.72 mg/l.

-The Wilcox diagram indicates that most water samples are classified as unsuitable for irrigation, while few water samples are classified as doubtful to unsuitable.

-USSL diagram suggested that the groundwater samples belonged to C4S3, C4S2, and C4S1 categories, indicating high saltiness and high to medium to low sodium hazard. The findings show that the samples are not suitable for crop watering.

-This research recommends conducting multiple studies in the study area to assess the groundwater quality for drinking and domestic use.

-This research recommends conducting multiple studies in the research area to analyze heavy and toxic metals. It also suggests using geographic information systems and modeling techniques to rate the groundwater quality for watering.

-This research suggests growing salt-resistant plant species and utilizing modern scientific methods in irrigation operations.

-The findings of this study can assist policymakers in implementing measures to support sustainable agriculture in the research region.

-The proposed practical steps to address groundwater quality problems in the study area, especially high salinity and sodium levels, include the use of ion exchange filters and reverse osmosis filters. Additionally, chemicals such as sodium hydroxide or calcium hydroxide can be used to remove salts by reacting with them.

The authors are thankful to the University of Anbar College of Engineering – Dams and Water Resources Engineering Department and the general commission for Groundwater Department of Geology in Anbar for their support of this research as well as the people of the Al-Wafaa region who guided us to the sites of the wells water and helped us.

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Soil quality assessment and mapping in basaltic terrain of Central India for sustainable soil and crop management using integrated PCA and GIS

• Ingle Sagar Nandulal ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur – 440 033, India https://orcid.org/0000-0002-0674-0101
• MSS Nagaraju ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur – 440 033, India
• Nirmal Kumar ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur – 440 033, India
• Jagdish Prasad ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur – 440 033, India
• Pramod Tiwary ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur – 440 033, India
• Rajeev Srivastava ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur – 440 033, India
• Nisha Sahu ICAR-Indian Institute of Soil Science, Bhopal-462 038, India
• Bharat Lal Bihar Agricultural University Sabour, Bhagalpur-813 210, India
• Sai Parasar Das Bihar Agricultural University Sabour, Bhagalpur-813 210, India
• Amit Kumar Pradhan Bihar Agricultural University Sabour, Bhagalpur-813 210, India https://orcid.org/0000-0002-0389-1297
• Kasturikasen Beura Bihar Agricultural University Sabour, Bhagalpur-813 210, India
• Karad Gaurav Uttamrao Bihar Agricultural University Sabour, Bhagalpur-813 210, India

The cereal-based cropping system plays a vital role in ensuring food security in the Indian subcontinent. However, the productivity of these systems has seen a continuous decline due to the degradation of soil quality. This study aims to develop a Soil Quality Index (SQI) for such cropping systems. A detailed survey was conducted in the Bareli watershed of Seoni district, Madhya Pradesh, at a 1:10000 scale using high-resolution satellite data and Geographic Information System (GIS) technology. The survey identified and mapped 5 soil series: Diwartola, Diwara, Bareli-1, Bareli-2 and Bareli-3. Soil quality was evaluated based on morphological, physical and chemical properties as well as fertility parameters. Key indicators for soil quality assessment included sand, silt, clay content, bulk density, hydraulic conductivity, available water capacity and coefficient of linear extensibility (COLE). Additionally, pH, electrical conductivity, organic carbon, cation exchange capacity and nutrients like N, P, K, Fe, Mn, Cu and Zn were considered. The SQI was calculated using integrated principal component analysis, which involved selecting a minimum data set (MDS), assigning weights and scoring indicators. The results revealed that Diwartola soils had high quality (242.7 ha, 13.5 % TGA), Bareli-1 and Bareli-3 soils were of medium quality (462.8 ha, 25.7 % TGA), while Diwara and Bareli-2 soils were of low quality (966.1 ha, 53.8 % TGA). Agro-interventions such as agri-horticulture, agro-forestry, silvi-pasture, intensive cultivation and soil and water conservation measures were recommended based on the different mapping units.

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Comprehensive approach integrating water quality index and toxic element analysis for environmental and health risk assessment enhanced by simulation techniques

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• Published: 31 August 2024
• Volume 46 , article number  409 , ( 2024 )

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• Mohamed Hamdy Eid   ORCID: orcid.org/0000-0002-3383-1826 1 , 2 ,
• Mahmoud Awad 3 ,
• Essam A. Mohamed 3 ,
• Tamer Nassar 4 ,
• Mostafa R. Abukhadra 2 ,
• Ahmed M. El-Sherbeeny 5 ,
• Attila Kovács 1 &
• Péter Szűcs 1  

Due to water shortages and the potential impact of Ethiopia’s new dam on the Nile River, Egypt is seeking new water resources. This study assesses the drinking water quality and associated risks from potentially toxic elements (PTEs) in the Quaternary aquifer (QA) in Beni-Suef, Egypt. Using a comprehensive approach, including PHREEQC geochemical modeling, ionic ratios, multivariate statistical analyses, and the integrated weight water quality index (WQI), the study evaluated the sources of ion contamination and the mixing of Nile water with QA. Various indices, such as the Heavy Metal Pollution Index (HPI), ecological Risk Index (RI), Hazard Quotient (HQ), and Hazard Index (HI), were used to assess ecological and health risks. Monte Carlo simulations provided probabilistic assessments of non-carcinogenic risks for adults and children. GIS tools were used to map risk indices, identifying the most deteriorated locations for sustainable management. The hydrochemical analysis revealed water facies including Na–Cl, Ca–Mg–HCO 3 , and mixed types, influenced by carbonate dissolution, ion exchange, and silicate weathering. Contamination sources, particularly in the north and south, were linked to agricultural activities, irrigation return flow, municipal waste, and evaporation. The WQI indicated that 10.14% of samples were extremely poor, 21.7% were poor, 26% were medium, and 42% were good to excellent. PTE contamination varied, with HPI values indicating good water quality in the central area in 53.6% of the collected samples (HPI < 30), but contamination in the north and south is high (HPI > 51). Ecological Risk Index values were below the threshold in 100% of samples (RI < 30), confirming water safety regarding PTEs. In comparison, for hazard index (HI) through oral/ingestion, adults exhibited HI values ranging from 0.012 to 2.16, while children showed higher values, ranging from 0.045 to 8.25. However, the hazard index for oral/ingestion exceeded safe limits in the north and south (HI oral > 1), posing non-carcinogenic risks. Monte Carlo simulations revealed significant risks from oral exposure to manganese (HQ oral > 1), particularly in El-Wasta and El-Fashn, necessitating further treatment and management.

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Introduction

In developing countries, water shortage, climate change, quick industrialization, economic expansion, and urbanization are primary drivers of growing environmental pollution, creating national and international concerns (Abukhadra et al., 2022 ; Ata et al., 2024 ; Bin Jumah et al., 2021 ; Salam et al., 2021 ; Szűcs et al., 2024 ). In recent decades, the demand for water has significantly risen due to population growth, increased household consumption, and the expansion of industrial and agricultural activities (Egbueri et al., 2024 ; Karmakar et al., 2023 ; Khan et al., 2023 ). Industries such as petroleum-based chemicals and heavy automotive production produce contaminants such as heavy metals, organics, microplastics, pesticides, and newly developed pollutants, which endanger human health, groundwater resources, sustainable development, and environmental organizations (Bellucci et al., 2022 ; Saeed et al., 2023a , 2023b ). Recent global investigations have discovered groundwater pollution from metals such as manganese, iron, copper, and zinc (Eid et al., 2024a , 2024b ; Gad et al., 2023 ; Saeed et al., 2023b ). Because of the enormous effects on individual issues, the global fear over pollutants in the atmosphere has grown. These hazardous metals are infamous poisons capable of causing organ damage and demonstrating teratogenic and carcinogenic tendencies(Githaiga et al., 2021 ; A. Mohammadpour et al., 2023a , 2023b ; Amin Mohammadpour et al., 2023a , 2023b ).

Heavy metal pollution in groundwater has been linked to major health difficulties such as degenerative neurological illnesses, renal damage, cardiovascular and respiratory ailments, and cancer (Abu et al., 2024 ; Eid, et al., 2024a , 2024b ; Gad et al., 2023 ; Yassin et al., 2024 ). Potential toxic elements (PTEs) are inherently persistent and accumulate in groundwater, making it a major source of exposure for humans. As a result, public agencies frequently check PTE concentrations to reduce any health risks (Eid et al., 2024a , 2024b ). Given the crucial importance of groundwater and the issues it faces, this study explores several forms of dangers to individuals and the environment posed by heavy metal contamination in the Beni-Suef region. Although copper (Cu), zinc (Zn), iron (Fe), and manganese (Mn) are required for metabolic activities, their concentrations in drinking water exceed permissible limits, posing health risks. Heavy metals can enter the body through eating, cutaneous contact, or inhalation (Eid et al., 2024a , 2024b ; Gad et al., 2023 ; Saeed et al., 2023b ). These contaminants are found in potable water sources such as groundwater and surface water, vegetables, and the air (Ata et al., 2024 ; Jafarzadeh et al., 2022 ; Kiani et al., 2021 ). Industrial operations, medicinal, residential, agricultural, and technological events are the primary causes of these metals’ presence in the environment. When heavy metal concentrations in drinkable water exceed the criteria specified by global organizations, it can cause severe health risks (Kazemi Moghaddam et al., 2022 ; Marufi et al., 2022 ). Comprehensive examinations of water quality are required to protect both the environment and human health. This begins with assessing water quality and identifying pollution sources in order to reduce contamination.

Proven approaches for measuring the ecological, environmental, and individual health risks associated with PTEs (Fe, Cu, Mn, and Zn) include many indices (MI, RI, HPI, HQ, and HI) (Egbueri et al., 2024 ), all of which can be improved by incorporating Monte Carlo simulations for increased accuracy and dependability (Githaiga et al., 2021 ; Mohammadpour et al., 2023a , 2023b ). Furthermore, principal component (PC) analysis, as well as cluster analysis, are important approaches for scaling heavy metal pathways and understanding hydrochemical processes in groundwater and surface water (Abu et al., 2024 ; Eid et al., 2023 , 2024a , 2024b ; Flores et al., 2023 ; Gaagai et al., 2023 ; Salem et al., 2023 ; Szabó et al., 2023 ; Szűcs et al., 2024 ). Globally, groundwater supplies are severely contaminated and depleted, a problem that particularly affects the Quaternary flood plain, where Beni-Suef relies heavily on groundwater for consumption and agriculture (Awad et al., 2022 ). According to reports, more than one billion people in developing countries lack access to safe drinking and agricultural water, resulting in approximately 25,000 deaths per year (Onyebuchi Okafor et al., 2024 ).

Determining the sustainability of groundwater, a crucial water supply in arid regions, is complex due to problems like overuse, decreasing water levels, and the deterioration of water quality (Abu et al., 2024 ; Boualem & Egbueri, 2024 ; Egbueri et al., 2024 ; Karmakar et al., 2023 ; Khan et al., 2023 ; Yassin et al., 2024 ). A variety of methodologies have been used for determining groundwater quality, among which are the fuzzy comprehensive evaluation method (Zhang et al., 2020 ; Zhong et al., 2022 ) and the matter-element extension method (Wang et al., 2019 ). The entropy-based weighted technique is used for obtaining the weights of hydrochemical variables in groundwater, which aids in water quality examinations. This method reduces the number of components examined, accurately defines water quality types, and determines if the measured variables meet decision-making requirements for certain functional areas (Ikram et al., 2024 ; Islam et al., 2017 ). The entropy-weighted water quality index (EWQI) accurately evaluates the purity of groundwater, enabling ranking based on groundwater quality parameters (Amiri et al., 2014 ; Islam et al., 2017 ).

Egypt is currently experiencing water scarcity due to an increasing population and the predicted reduction in Nile River flow share due to the buildup of the Grand Ethiopian Renaissance Dam (GERD) (Elsayed et al., 2020 ). To satisfy expanding demand and manage the water scarcity both surface and groundwater have been heavily exploited, resulting in water supply challenges. A systematic hydrogeochemical and multivariate statistical analysis was carried out to better understand the factors that influence groundwater chemistry and pollutant sources in the western part of Beni-Suef region. Employing statistical methods in conjunction with geochemical tools aids in identifying the different mechanisms influencing groundwater ionic compositions, as well as hydrogeochemical change and origins of contamination (Al-Mashreki et al., 2023 ; Eid et al., 2024a , 2024b ; Flores et al., 2023 ; Ibrahim et al., 2023 ). This combination technique is useful in identifying potential ionic sources, which aids in effective water quality management.

This study aims to comprehensively investigate the ecological and individual health issues associated with potentially toxic elements (PTEs) in the groundwater of the Beni-Suef Quaternary aquifer (QA). The research objectives are as follows: (1) To identify potential pollution sources using various statistical approaches, including principal component analysis (PCA), cluster analysis, ionic ratio analysis, and inverse distance weighting (IDW) interpolation. (2) To elucidate the geochemical processes governing water chemistry in the investigated area. (3) Application of integrated weight water quality index (WQI) method to detect suitability of different water resources for drinking. (4) To employ a novel technique combining various water quality criteria and indicators (WQI, HPI, HQ, RI, and HI) with deterministic models or probability-based techniques, notably the Monte Carlo approach, to assess non-carcinogenic health risks from PTEs contamination. The integration of these methodologies represents a significant advancement in the assessment of PTE contamination within the Beni-Suef Quaternary aquifer (QA). Although several studies were performed in the study area to investigate only the hydrochemical evaluation (Awad et al., 2022 ), the current study is the first investigation fill the gap of the health risk regarding toxic elements with simulation technique to decrease the uncertainty and applying advanced approach integrate between hydrochemistry, ion source detection, water quality using integrated weight water quality index.

Materials and methods

Study area and geographic description.

Beni-Suef Governorate within the Nile Valley occupies an area of approximately 10,950 km 2 bounded by the Eastern and Western Deserts (Melegy et al., 2014 ). About 12% of the area is inhabited, while the remaining percentage 88% is desert lands. Agricultural lands represent 85% of the total populated area. Beni-Suef Governorate is located between latitudes 28° 45′ and 29° 25′ N and longitudes 30° 49′ and 31° 18′ E (Fig.  1 ). It includes seven towns namely, Beni-Suef (the capital), El-Wasta, Naser, Beba, Ihnasia, Somosta, and El-Fashn. Northwards, it is bordered by the Giza Governorate and southward by El-Minia Governorate. It lies between El-Fayoum Governorate to the west and the Red Sea Governorate to the east (Awad et al., 2022 ).

Location map of Beni-Suef area

Aquifer system

Within the Nile Valley region, regional and local aquifers are encountered (Awad et al., 2022 ; Melegy et al., 2014 ; Said, 2013 , 2017 ) which are summarized as follows:

Quaternary aquifer system. It consists of Pleistocene graded sands and gravels; it is covered with Holocene silt and clay (semi-confining) with a thickness varying between 0 and 20 m (Fig.  2 a). In the desert fringes, outside the floodplain, the semi confining layer vanishes, and phreatic conditions prevail. The aquifer is underlain by Pliocene clay. The aquifer thickness ranges from 0 (near the fringes), to more than 250 m in the centre (Sohag and El-Minia). The saturated thickness of the aquifer ranges from 0 to more than 200 m. The aquifer transmissivity ranges from 20,000 m 2 /day in the centre of the floodplain, to less than 500 m 2 /day at the edges.

Nubian Sandstone aquifer is the second regional aquifer, which extend from Qena southward, being confined by clays and shales in the north. The body of the aquifer (Cretaceous rocks) slopes from ground level (south of Aswan) to 2,000 m below mean sea level near Cairo. The saturated thickness ranges from a few meters to more than 1,000 m. The Transmissivity ranges from 500  to 6,000 m 3 /day.

Local aquifers constitute Quaternary and Tertiary deposits and represent a moderate source of groundwater in the wadis and along the fringes of the Nile Valley. They are recharged from the occasional rainfall storms, and receive recharge by upward leakage from the underlying deep aquifers. They are generally phreatic, semi confined to confined. Their thicknesses vary from one place to another, ranging from few meters close to the limestone plateau to more than 200 m near the Nile Valley. The surface geology of the study area is illustrated in Fig.  2 b showing the different geological composition and formations. The local ground water flow in the study from south west to north east with cone of depression in the water table exhibited in the north and central part of Beni-Suef area (Fig.  2 c).

Hydrogeological cross-section of Quaternary and Eocene aquifers (Awad et al., 2022 ) ( a ), surface geology ( b ), and ground water flow direction ( c ) at Beni-Suef area

Samples collection and analytical approaches

In 2020, an overall of 69 water samples were methodically gathered from groundwater production well’s locations (Fig.  1 ) To guarantee uniformity, samples were collected after 10 min of production, ensuring comprehensive representation from each investigated point. Before sampling, 500-ml polyethylene containers were extensively cleansed using chemicals, completely rinsed with filtered water, and submerged in a 10% HNO 3 solution overnight. The obtained samples were then taken to a laboratory with a regulated temperature of 4 °C for further analysis. On-site pH measurements, temperature (°C), (TDS), (EC), were conducted using specialized instruments: pH meter, and a digital thermometer (Hannah, Woonsocket, RI, USA). Additionally, TDS and EC were analyzed utilizing digital TDS and EC meters (HM digital, Redondo Beach, CA, USA). To ensure accuracy, all digital meters underwent standardization with deionized water and buffer solutions before the commencement of sample analysis. For cations analysis, the samples underwent filtration through 0.45 µm filters. Subsequently, 10 drops of ultra-pure HNO 3 were added to one set of samples. Calcium (Ca 2+ ) and magnesium (Mg 2+ ) contents were assessed using the EDTA titrimetric method, which employs ethylenediaminetetraacetic acid. Sodium (Na + ) and potassium (K + ) ion contents were measured utilizing a flame photometer (ELEX 6361, Eppendorf AG, Hamburg, Germany). Total hardness (TH) was evaluated using Eriochrome Black-T (C20H12N3O7SNa) and ammonium chloride (NH 4 Cl) indicators in an EDTA solution. To assess chloride (Cl − ) concentrations, a titration method employing silver nitrate (AgNO 3 ) and potassium chromate (K 2 CrO 4 ) indicators was employed. For the detection of bicarbonate (HCO 3 − ) and carbonate (CO 3 2− ) concentrations, a titrimetric technique involving the solution of sulfuric acid (H 2 SO 4 ) and methyl orange indicator was utilized. Additionally, Cl − concentrations were determined through titration with silver nitrate. Concentrations of sulfate (SO 4 2− ) and nitrate (NO 3 − ) were tested using a spectrophotometer based on the visible ultraviolet (UV) spectrum (DR/2040-Loveland, CO, USA). Fe, Cu, Zn and Mn were assessed through flame atomic absorption spectrometry (FAAS).

Quality assurance and control

The water quality analysis followed the standard methodology specified by the American Public Health Association (APHA) in 2012. To ensure the accuracy of on-site testing equipment, we carefully standardized all instruments with deionized water and buffer solutions before starting sample analysis. Various quality assurance procedures were applied during the water sample examination. The analytical processes were validated by instrument calibration, accuracy, and predictability evaluations. Charging balance errors (CBE) were evaluated following field observations and then validated in the laboratory. The samples were examined in triplicate, and the average values were given as well. Equation ( 1 ) was used to analyze anion–cation balance errors based on the neutrality principal, which states that the sum of the number of cations equals the sum of the number of anions in meq/L. The CBE for all examined samples was within the permissible range of ± 5%.

Furthermore, the quality assurance of the analytical procedure was double-checked through a meticulous examination involving Certified Reference Material (CRM) and the blank technique analysis (Tables S1 , S2 ).

The different metal contents in the sample solutions were obtained using a curve for calibration. To calibrate the device, a 50-ml (mL) intermediate standard was employed as an operational standard for Four toxic metals. Table S3 shows the concentrations of intermediate, operating standards, and coefficient of correlation values for each metal. When the correlation coefficient could be more than 0.999, it indicated that the relationship was strong. The measured amount of every metal in the collected sample was determined using interpolation of calibration curves. Every examination was conducted in triplicate.

The abundance of toxic metals in each sample was examined using FAAS. The lamp’s current, slit width, and wavelength of the apparatus have been adjusted to deliver the lowest possible intensity of signal, as described in Table S4 . The principal line sources were hollow cathode lamps operated in accordance with the manufacturer’s specifications. Acetylene along with airflow rates were provided to ensure that the instrument had the proper flame settings. The metallic elements were identified using absorption/concentration manner, and the results were manually recorded. The same approach was used to analyze the metal found in the spiked sample.

The methods employed were validated by performing limit of detection and quantification (LOD and LOQ), accuracy, precision, and recovery testing based on the following equations below;

The variance and % of relative standard deviation (RSD) were used to determine the results’ accuracy and precision. The RSD of triplicate readings for every single sample was applied to calculate precision as described in the equation below;

Hydrochemical evaluation method

In this investigation, the chloralkaline indices (CAI-I and CAI-II) (Eqs. ( 2 – 3 ) were employed to ascertain the mineral composition within the aquifers and the ionic exchanges occurring in groundwater systems.

The hydrochemical evaluation methods included, Gibbs (Gibbs, 1970 ), Piper (Piper, 1944 ), CAI-I, CAI-II (Schoeller, 1977 ), and Ionic ratios (Tlili-Zrelli et al., 2013 )plots to determine the water type of different water resources and the mechanism controlling the water chemistry. The graphs were visualized using Digramme software and an Excel file. Geochemical modeling based on physicochemical characteristics and heavy metals was used to assess the mineral saturation status and which minerals contribute the most to the enrichment of elements in water via water–rock interactions. The model extracted from PHREEQC produced a saturation index (SI) for each mineral (Parkhurst & Appelo, 1999 ; Plummer et al., 1988 ), with a positive value indicating super saturation and a negative value indicating under saturation, while a zero value indicates an equilibrium state in which water is unable to dissolve or precipitate certain minerals. The saturation index was determined based on standard equations 57,58 utilizing log ion activity product divided by solubility product (Eq.  4 ).

IAP stands for “ion activity product,” and \({K}_{sp}\) stands for “solubility product” at a given temperature.

Drinking water quality index (WQI) using integrated weight

The Integrated Weight Water Quality Index (WQI) is a ranking system that uses the Water Quality Index to assess the total impact of physicochemical variables on water quality. In this study, the WQI was used to evaluate water quality (Al-Asad et al., 2023 ; Gao et al., 2020 ). The WQI is calculated in five steps: entropy weighting, CRITIC-based weighting, determining integrated weights, computing the integrated-weight water quality index, and evaluating groundwater quality using WQI values.

Entropy weight calculation

The entropy-weighted water quality index (EWQI) is a method used to estimate water quality by calculating an overall entropy value (Adimalla, 2021 ; Shannon, 1948 ) based on specific hydrochemical variables. The EWQI calculation process consists of three phases, which are detailed below:

In Step 1 , We computed the eigenvalues of the matrix X using Eq. ( 5 ), where m and n denote the total number of investigated samples and hydrochemical parameters to be evaluated, respectively.

In Step 2 , We rely on Eqs. ( 6 ) and ( 7 ) for determining the standard matrix Y. As hydrochemical indicators have considerable dimensional discrepancies, data normalization is essential before calculating the EWQI. Here, ( Xij ) x denotes the maximum value, while ( Xij ) min represents the smallest value for the associated hydrochemical parameters.

In Step 3 , We leverage Eqs. ( 8 – 10 ) to compute information entropy (ej) and entropy weight (wj). Pij reflects sample I index j value.

Objective weight (CRITIC method)

In this study, the criteria’s importance The Inter-criteria Correlation (CRITIC) technique was used to determine the objective weights of variables and overcome the limitations of traditional information entropy methods. The objective weight can be calculated using the following equation (Eqs.  11 – 13 ):

In this context, wj2 represents the objective weight of the jth parameter, with m denoting the total number of variables. Sj represents the quantity of information, while δj represents the standard deviation of the jth parameter.

Integrated-weight estimation

The following equation (Eqs. 14–16)is utilized to determine the integrated-weight Wj:

In this section, p represents a preference coefficient, with pϵ [0,1].

Drinking water quality index (WQI) using integrated Weight

After estimating the entropy weight, wj1, and the objective weight, wj2, the following formulas (Eqs.  17 , 18 ) are used for calculating the Integrated Weight Water Quality Index (WQI):

In the equations, j represents the experimental concentration of each parameter in mg per liter, while Cjp is the variable’s standard value in pure drinking water. All variables are zero, with the exception of pH, which has a standard score of seven. The standard value (Sj) for each physicochemical factor evaluated using WHO standards (WHO 2017 ) is presented in mg/L. Table 1 shows the input parameters and the Integrated Weight.

Water is divided into five categories based on WQI values (Al-Asad et al., 2023 ; Gao et al., 2020 ). When the WQI result falls below 100 (excellent to good), the water is considered safe for oral consumption and other applications. Medium or intermediate quality ranges from 100 to 150, while bad and extremely poor water quality ranges from 150 to more than 200.

Heavy metal pollution index (HPI)

The Pollution Index (HPI) is a valuable metric for assessing the level of heavy metal pollution in water bodies (Al-Hejuje et al., 2017 ). It is particularly effective in determining the suitability of water for consumption in the presence of heavy metals. The HPI is calculated using attribute ratings and weighted mean calculations. Each pollutant characteristic is assigned a weight, and a grading system ranging from 0 to 1 underscores the significance of each quality aspect or its alignment with specified reference standards. The specific computations for determining the HPI are provided in Eqs.  19 and 20 (Shankar, 2019 ).

In the formula, Qi is the sub-index factor, n represents the number of analyzed variables, wi is the weight assigned to each factor, calculated as 1/Si, where Si is the standard value for each variable. Qi is also the sub-index of the boundary, as defined by Eq.  17 .

The HPI indicator determines the amounts of the elements iron (Fe) and manganese (Mn). The metals index is frequently examined using a modified five-category scale. Water quality is categorized as excellent (HPI < 15), good to intermediate (15 < HPI < 30), poor to unsuitable (HPI > 30), very poor (76 < HPI < 100), and unsuitable (HPI > 100) (Edet & Offiong, 2002 ; Qu et al., 2018 ).

Ecological risk index

The ecological risk indicator (RI) for hazardous metals, first developed by Hakanson, is a tool for determining the risk associated with an excess of heavy metals in an ecosystem. As highlighted by Xie, this indicator takes into account heavy metal concentrations, kinds, sensitivity, toxicity, and background levels. While useful to a variety of scientific domains, it was specifically used in this study to assess the ecological risks associated with heavy metals in groundwater. The formula for computing the RI is presented below (Eq.  21 ).

In the equation, \({E}_{r}^{i}\) reflects a substance’s possible ecological indicator portion; \({T}_{r}^{i}\) depicts the specified metal’s toxic reaction variable (Table S5 ); \({c}^{i}\) designates a typical level of PTEs in each sample, and \(c{i}_{bg}\) represents the background score or value of every metal (Table S5 ). The RI represents the whole ecological impact. The risk indicator (RI) is classified into four categories or levels of possible risk: low, moderate, significant, and very high, with RI values < 30, 30–60, 60–120, and > 120, respectively (Yuan et al., 2015 ).

Multivariate statistical methods

Researchers frequently employ multivariate statistical methods to thoroughly understand groundwater condition and their fundamental chemistry (Eid et al., 2024a , 2024b ; Gaagai et al., 2023 ; Saeed et al., 2023b ). Our study combined trace metals, physical, and chemical attributes to investigate the intricate interactions among various factors and components within aquatic ecosystems. This framework used Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) as analytical tools.

Risk of exposure to potential toxic elements (PTEs)

Drinking water contaminated with toxic metals poses risks of both non-carcinogenic and carcinogenic ailments in humans (Bineshpour et al., 2021 ). This study followed procedures established by the U.S. Environmental Protection Agency (USEPA) to measure the non-carcinogenic hazards connected with Cu, Fe, Mn, and Zn (Selvam et al., 2022 ). The USEPA’s framework for health risk assessment, established in 2004, aims to evaluate the non-cancerous health risks posed by heavy metal factors in surface water and groundwater through ingestion, inhalation, and dermal contact. The primary hazard stems from directly ingested water and uptake via the skin (Saeed et al., 2023c ). This approach estimates the effect of PTEs ingested by humans using the CDI method as described in Eqs.  22 and 23 , respectively, (USEPA, 2004 ).

CDI refers to chronic daily intake (mg/kg/day), while C HMs  represent the concentration of each heavy metal (mg/L). The (IR) is the intake rate (children:1.8 L/day; adults: 2.2 L/day). The (ED) indicates the Exposure duration (children: 6 years; adults: 70 years) with an exposure frequency (EF) of 350 days per year for both adults and children. K p denotes the permeability coefficient (cm/h), as indicated in Table S5 ; ET signifies exposure time (0.58 h/day for adults and 1 h/day for children). SA denotes the skin area (18,000 cm 2 for adults and 6600 cm 2 for children). CF represents the unit conversion factor (1 × 10 –3 L/cm 3 ). BW stands for body weight (children: 15 kg; adults: 70 kg). AT refers to the average duration of carcinogenic hazards (Saeed et al., 2023c ; Selvam et al., 2022 ; Xu et al., 2020 ).

Non-carcinogenic risk assessment

This study evaluated the health hazards associated with Fe, Cr, Mn, and Pb (PTEs) in groundwater through risk assessment model created by USEPA. The non-carcinogenic risk index (HI) is a predictive model developed by the USEPA for assessing the health risks of chemical element combination. The HI comprises two components: chronic daily ingestion (CDI) and the hazard quotient (HQ), represented by the following equations (Eqs.  24 – 26 ):

RfD (mg/kg/day) stands for the reference dose of a particular heavy metal. The RfD values for various heavy metals are provided in Table S5 .

Monte Carlo simulation techniques

In this investigation, Monte Carlo was utilized as a simulation technique to estimate the probability distributions of various attributes such as PTEs levels, exposure time and frequency, ingestion rates, absorption coefficients, skin surface area, and individual body weight. This approach or method aims to characterize the probability distributions and uncertainty reduction (Eid et al., 2024a , 2024b ; Qu et al., 2018 ; Saeed et al., 2023a , 2023b ). This technique allows for the prediction of the hazard quotient (HQ) for both oral and dermal exposure in two age groups (children and adults). By integrating this simulation with the USEPA’s health risk assessment framework, we can assess the adverse effects of heavy metal exposure, estimating NCR probability distributions. The analysis includes variables such as heavy metal concentrations and other related factors, as detailed in Eqs. ( 22 – 25 ). To ensure the reliability of the simulation, 10,000 iterations were performed using Python. The consistency between actual and simulated HQ values calibrates the model’s efficiency. The distribution approach for PTEs levels was based on 2020 data, while variables like ingestion rate, exposure duration, skin surface area, and body weight were modeled using normal distributions to accurately represent their real-world distributions.

Results and discussions

Measured parameters statistics.

The physicochemical properties of groundwater in the Beni-Suef Quaternary Aquifer (QA) are critical in the preliminary assessment of its quality and suitability for agricultural and potable use. This is an effective method for identifying environmental concerns, defining trends, and communicating insights into water resources, geochemical dynamics, and water quality. The following factors were used to determine the appropriateness of groundwater for drinking purposes within the selected shallow aquifer.

Meanwhile, the selected parameters like pH, EC, TDS, K + , Na + , Mg 2+ , Ca 2+ , Cl − , SO 4 2˗ , HCO 3 ˗ , and NO 3 ˗ , and heavy metals (HMs) such as Fe, Zn, Cu, and Mn are altering the quality and productivity of the soil and could cause several environmental and health risks. The statistical methods of the analyzes parameters of 69 water samples were presented in Table  2 .

The above-mentioned parameters (Table  2 ) were compared with well-known drinking standard (WHO 2017 ) based on their concentrations in the groundwater of QA (Ben-Suef Quaternary aquifer) and using IDW interpolation method to determine the most deteriorated location from different sources of contaminations for future management and treatment scenarios. The simple statistics including minimum, maximum, mean, and standard deviation could give general differences and ranges of ions and metals concentration and refers to water quality situation.

Total dissolved solids (TDS), indicative of salinity, ranged from 225 to 4930 mg/L, with an average of 982 mg/L, surpassing the permissible drinking limit of 1000 mg/L in 26% of QA water samples. The distribution map of the TDS showed that the salinity of the ground water increase from east to west direction which indicate that there is possibility of mixing fresh water from the Nile river with brackish groundwater from QA decrease the salinity of water through dilution process in the eastern side of the study area. The western side of the study area has low contribution from the Nile river and high residence time of the water rock interaction could increase the groundwater salinity. The groundwater was classified as fresh water in 74% of samples and brackish in 26% of samples in QA. pH levels ranged from 6.5 to 8.3, indicating a neutral to slightly alkaline quality. Calcium concentrations fell within WHO drinking standards, ranging from 2 to 667 mg/L. it was noted that 93% of samples did not exceed the permissible limit (< 200 mg/L) for drinking, while 7% exceed the limits. Notably, five samples (S1, S60, S62, S67, and 69) located in western and northern part of investigated area exhibited the highest Ca 2+ concentration. Magnesium (Mg 2+ ) levels in all samples were generally within acceptable limits (< 150 mg/L) for drinking, showing no restriction regarding magnesium ion concentration. Sodium (Na + ) concentrations ranged from 14 to 1600 mg/L, deemed safe for drinking in 96% of samples but unsuitable for drinking in 4% of samples (S1, S60, and 67), particularly in the south west and north west part of the study area. The predominant anions were sulfate (SO 4 2− ) and chloride (Cl − ), with concentrations ranging from 0.05 to 985 mg/L and 13 to 2799 mg/L, respectively. While these levels met drinking standards in 94% of samples for sulfates and 96% for chloride, they exceeded drinking limits in few samples located in south west and north west part of the study area. Bicarbonate (HCO 3 − ) concentrations were acceptable for drinking in few samples (21.5%) and exceed the limits in 78.5% with value ranges between 65 and 851 mg/L.

Although nitrates availability in groundwater is linked to chemical pollution in the study area, their concentration was lower than 45 mg/L in most samples. It was noted that the maximum concentration of NO 3 − exhibited in S67 with value 158 mg/L.

Potential toxic elements (PTEs) exhibited varied concentrations, with Fe, Mn, Zn, and Cu ranging from 0.0002 to 0.62 mg/L, 0.002 to 1.71 mg/L, 0.003 to 0.88 mg/L, and 0.002 to 0.16 mg/L, respectively. Concerningly, a significant proportion of samples exceeded the permissible drinking limits for Fe (16%), and Mn (56.5%), could cause negative environmental and health risks. On the other hand, Zn and Cu were within the safe limits of drinking in all samples. Distribution maps of measured parameters, depicted in Figure S1 , highlight locations susceptible to water quality deterioration which was mainly in western part of the investigated region. The distripution maps of the measured parameters in the groundwater samples of the Beni-Suef Quaternary aquifer (QA) were ilustrated (Fig.  3 ) to show the spatial variation in the concentration of ions and physical parameters.

Distripution maps of the measured parameters in the groundwater samples of the Beni-Suef Quaternary aquifer (QA)

The distribution maps of measured parameters showed similar trend of increasing the concentration of TDS, Na + , Ca 2+ , Mg 2+ , TH, Cl - , and SO 4 2− from east to west direction. This similar trend indicates that these parameters have similar water rock interaction condition and the dissolution of minerals such as calcite, dolomite, gypsum, halite, and anhydrite could increase the groundwater salinity, while the eastern side of study area is close to the Nile River and could receive recharge of fresh water make dilution. Incontrast the western side of the investigated area receive less fresh water from the Nile river and the groundwater rock interaction is the main mechanism governing the groundwater salinity. The rondum distribution of the NO 3 − and HCO 3 − in the study area compare to the other elements reveal that the anthropogenic activities increase the concentration of HCO 3 − and NO 3 − in the aquifer system.

Hydrochemical characteristics

A Piper plot, first developed by Piper, was used to identify the water types/facies found in water samples (Piper, 1944 ). The Piper scatter plot (Fig.  4 a) divided the gathered samples into five separate facies. It was noted that 23.1% of samples representing the western part of investigated region fell in the Na–Cl facies zone, 34% fell in Ca–Mg–HCO 3 category, 17.4% categorized as mixed Na–Ca–HCO 3 , 21.8% of samples fell into the mixed Ca–Mg–Cl/SO 4 facies zone, and the rest 2.8% of samples represented by Na–HCO 3 . The distribution of samples within five different facies in piper plot reflect the wide range of water chemistry evolution in the Quaternary aquifer from the west to east and from south to north of the study area. These variations could be related to several factors including geological composition, anthropogenic activities, and mixing of different water resources specially the water samples closed to the Nile River. The mechanisms controlling water chemistry and contamination sources could be interpreted through multivariate statistical analyses with support of interpolation of various parameters in the following sections below where piper plot has limited information regarding water type only.

Piper plot a illustrates the water type/facies of all samples from different locations in Beni-Suef Quaternary aquifer, and Gibbs Plot showing the mechanisms controlling water chemistry ( b )

The Gibbs chart or scatter plot (Gibbs, 1970 ) is an effective method for determining the impact of multiple processes on water chemistry, and it divides the diagram into three primary parts (Fig.  4 b). The graphical representation shows that most samples are inside the zone of rock-weathering and the rest fell within evaporation/crystallization zone. The western part of study area is influenced more with evaporation/crystallization mechanism, while the central and eastern part in the direction of the Nile River the groundwater evolutes and the change in water type because of water–rock interaction, ion exchange and/or mixing with different sources. The Sulin graph (Fig.  5 a) is a useful tool for determining groundwater origin and distinguishing between deep meteoric, shallow meteoric, old marine, and recent marine water based on the percentage of (Cl-(Na + K))/Mg and Cl-(Na + K) values in epm% (Eid et al., 2024a , 2024b ; Sulin, 1946 ). In the current study, the water samples were of meteoric origin. The water samples of western part of study region fell within shallow meteoric zone has NaHCO 3 composition and the deep meteoric water samples in eastern part compose of Na 2 SO 4 . To verify that evaporation crystallization/dissolution is the major process influencing water chemistry in the QA, the log ionic ratio of Mg 2+ /Na + versus Ca 2+ /Na + was used (Fig.  5 b) (Tlili-Zrelli et al., 2013 ). The ionic ratio revealed that the majority of samples belonged between the evaporation dissolution and silicate weathering domains, which correspond to the two main mechanisms affecting the chemistry in the QA water. Previous study applied similar log ionic ratio of HCO 3 − /Na + versus Ca 2+/ Na + on 105 groundwater samples in Ghana which revealed that the silicate weathering and dissolution of evaporites were the main significant processes governing the water chemistry (Abu et al., 2024 ).

Sulin scatter plot and ionic ratios Mg 2+ /Na + vs Ca 2+ /Na + showing water origin mechanism control water chemistry in Quaternary aquifer ( a , b ), type of ion exchange using CAI ( c , d )

Ion exchange can be included in the research area because the aquifer composition includes silicate and carbonate minerals. The chloro-alkaline indicators determine whether the water is influenced by direct or reverse ion exchange (CAI-I and CAI-II) by implementing the concentration of Cl − , Na + , CO 3 2− , HCO 3 − , SO 4 2− , and NO 3 − (Fig.  5 c, d). Such indices are a particularly useful tool for demonstrating water–rock interaction by replacing ions like Ca 2+ and Mg 2+ with Na + and K + which was utilized by several global studies recently (Boualem & Egbueri, 2024 ). In the current investigation, the CAI-I and CAI-II results showed that the majority of samples had negative values less than zero, indicating that direct ion exchange is an important mechanism influencing the water chemistry of the Quaternary aquifer. Direct ion exchanges replace calcium and magnesium in water with sodium and potassium in rock as mentioned in the following equations.

Geochemical modeling and ion source detection

The geochemical model was carried out using PHREEQC for identifying the minerals' saturation state, and the association between the ions and the saturation index (SI) may identify the primary impact of minerals in the aquifer system that could increase the amount of the ions calcium, magnesium, chloride, and sodium (Fig.  5 ). The model’s hypotheses are based on the input physical and chemical attributes or parameters, and the output is the saturation index of five minerals. Mineral saturating indicators (SI) were obtained for calcite, gypsum, anhydrite, and dolomite and displayed on a box plot (Fig.  6 ). To ensure that the simulated minerals are accurate, temperature and pH values measured in the field were used to represent the true state of the aquifer condition, where mineral saturation is sensitive to the physical parameters. In the present research, the saturated salts and minerals contained in water have been determined to identify the type of minerals that may develop and precipitate in the soil, reduce its permeability/infiltration rates, cause a water logging problem, and observe the origin of the chemical variables. The model also supplied the partial pressure of CO 2 , which was found as under-saturated with a negative value, indicating a drop in the aquifer’s recharge amount. The QA water samples had a salt combination assemblage, which included NaCl, Na 2 SO 4 , NaHCO 3 , Mg (HCO 3 ) 2 , and Ca (HCO 3 ) 2 , as a result of leaching, terrestrial salt dissolution, and cation exchange. The cation exchange activities raised Na + concentrations while decreasing Ca 2+ and Mg 2+ concentrations in the solution, resulting in a significant increase in water salinity in the western part of study area. Moreover, the loss of Ca 2+ reduced the degree of water saturation concerning anhydrite, and gypsum minerals. The saturation states of key minerals revealed that all of the water samples were under-saturated for anhydrite, halite, and gypsum minerals. This means that water can dissolve more of these minerals, increasing its salinity. Previous study revealed that the undersaturation of minerals such as halite, anhydrite, and gypsum could be due to low residence time of the meteoric water rock interaction specially in the shallow aquifer (Abu et al., 2024 ). Most samples are supersaturated in calcite and dolomite, with minimum values of − 0.99 and − 1.59, and maximum values of 1.32, and 2.46, and mean or average value 0.38 and 0.77 respectively (Fig.  6 and Table S6 ). The amount of precipitation of the aforementioned minerals in the soil’s composition can reduce infiltration, cause waterlogging, and reduce plant productivity. Calcium fertilizers should not be used in the research area to avoid deterioration of the physical and chemical structures.

Box plot showing the saturation state and type of minerals extracted from PHREEQC simulation model

The saturation of calcite and dolomite change from supersaturated in western study area to under saturated in the eastern part close to the Nile River, which indicate that there can be mixing between the Nile River water and Quaternary aquifer water. The over extraction of water from the QA close to the Nile River could create cone of depression in the water table and permit to River water to charge the shallow aquifer and dilute the water and decrease the salinity, hardness and saturation state of calcite and dolomite. These findings confirm previous study was performed on the Nile River in this area showing its undersaturation with calcite and dolomite minerals with water type Ca–Mg–HCO 3 . In the current study showed the water samples close to the Nile River have similar signature to the water from the Nile water in the water type, which indicate that the Nile River in the study area recharge the QA.

Ionic ratio and ion sources

The statistical examination of the associations and ratios of various the primary ions was used to understand the fundamental mechanisms influencing the chemical makeup of groundwater in the research area (Fig.  6 ). The linear graph of Na + versus Cl − (Fig.  7 a) revealed a balanced presence of these ions in most QA samples scattered along 1:1 line, with a strong correlation (0.29) (Fig.  5 s). This link implies that halite dissolution contributes significantly to sodium and chloride ions, indicating a Na–Cl water type in the western part of investigated study area. Significant portion of samples shifted to the right of the 1:1 line (weak correlation), indicating additional Na + sources from silicate minerals weathering, ion exchange, and anthropogenic activities leaching from topsoil due to irrigation water return back to the aquifer with high content of Na + (Fig.  3 s). Fewer measurements shifted to the left, indicating Na+ removal by reverse ion exchange or an additional source of Cl − , including the sylvite minerals, as confirmed by a high correlation (0.7) of Cl − with K + and/or anthropogenic activities (Fig.  3 s).

The ionic ratio between the major ions in the flood plain aquifer (QA)

In the case of dominating gypsum disintegration or dissolution, the amount of calcium and sulfates ratio should be one, as observed in certain investigation area samples. Yet, several samples deviation to the right axis from the 1:1 line owing to excessive Ca 2+ (Fig.  7 b), indicating a greater influence of another source of Ca ion rather than gypsum disolution (carbonate and/or silicate weathering). Samples deviated to the left axis of the 1:1 line indicate that SO 4 2− comes from sources other than gypsum and anhydrite breakdown, which could be from anthropogenic activities. The ions association of calcium and magnesium had no balance (Fig.  7 c) divided samples into two groups: one near to the right axis of the 1:1 line (majority of samples) and few samples deviated to the left axis of the 1:1: line indicating dolomite dissolution is not the primary source of Ca 2+ and Mg 2+ and silicate weathering and ion exchange could have more cotribution to increase Mg ions in water. Similarly, the association between calcium and bicarbonates (Ca 2+ vs HCO 3 − ) is relatively low in the vast majority of QA samples (0.3). This shows that water is unable to dissolve calcite since there is supersaturation of the mineral. In most samples, the Ca 2+ /HCO 3 − ratio was less than one (Fig.  7 d). The increase of HCO 3 − over Ca 2+ in a shallow aquifer can result from calcite precipitation, which removes Ca 2+ but leaves HCO 3 − behind, or from direct cation exchange processes that replace Ca 2+ with other cations like Na + . Additionally, the weathering of silicate minerals, microbial activity producing CO 2 , and agricultural practices can contribute to higher HCO 3 − levels relative to Ca 2+ in groundwater. A linear graph linking sum of Ca 2+ and Mg 2+ vs sum of HCO 3 − and SO 4 2− ions helped identify the source of Ca 2+ and Mg 2+ in samples (Eid et al., 2024a , 2024b ; Gaagai et al., 2023 ; Gad et al., 2023 ). Ion exchange, dissolution of gypsum, anhydrite, and precipitation of dolomite, and calcite decreased the Ca 2+  + Mg 2+ /HCO 3 −  + SO 4 2− ratio over one (Fig.  7 e), whereas carbonate precipitation and ion exchange removes Ca 2+ and Mg 2+ from water.

Anthropogenic activities have significantly impacted the water quality in the study area, primarily due to the discharge of domestic waste water, and agricultural activities. increasing the concentrations of SO 4 2− , Cl − , and NO 3 − . The elevated levels of SO 4 2− and Cl − in water samples are primarily due to evaporite dissolution and agricultural activities, while NO 3 − mainly originates from domestic sewage and agricultural activities.

The ratios of Cl − /Na + versus NO 3 − /Na + (Kou et al., 2019 ) ratios were used to characterize the influence of anthropogenic natural activities on water quality. As shown in Fig.  7 f, the majority of samples shallow aquifer (QA) specially in the east of investigated area were affected by agricultural activities, while evaporite and municipal activities had significant impact on the western part of the study area samples.

Principal component and cluster analysis

The Kaiser–Meyer–Olkin analysis (≥ 76.47) and Bartlett’s Sphericity test ( P  < 0.05) validate the suitability of the water quality dataset for PCA, indicating adequate inter-variable relationships. The eigenvalues were higher than 1 (Table  3 ) which proof the optimum number of components extracted is acceptable for interpretation of the datasets (Fig.  8 a).

The extracted components from PCA based on scree plot ( a ), PCs in 3D plot ( b ), and cluster analysis using Dendrogram circle showing the correlation between ions ( c ), and samples ( d )

PC1, accounting for 58.37% of the variance, shows a correlation between TDS, TH, EC, Na + , K + , Ca 2+ , Cl − , SO 4 2− , and NO 3 − (Table  3 and Fig.  8 b). This suggests that these variables may share a common source or underlying relationship in the context of water quality. PC1 represent the water rock interaction and dissolution process of gypsum and halite minerals are the main reason for increasing the salinity of water in the aquifer system. Similar studies in arid countries such as Algeria and Saudi Arabia applied cluster analysis and PCA for physicochemical parameters and could confirm the high correlation of the above mentioned parameters TDS, TH, EC, Na + , K + , Ca 2+ , Cl − , and SO 4 2− refers to water rock interaction and mineral dissolution (gypsum and halite minerals) are the main reason for increasing the salinity of water in the aquifer system (Abba et al., 2023 ; Boualem & Egbueri, 2024 ).

PC2, explaining 9.93% of the variance, demonstrates a correlation between HCO 3 − (Table  3 and Fig.  8 b) with Mn, implying that these elements may originate from a similar source and/or geological process. PC2 represent the anthropogenic activity source where agricultural practices and irrigation water return back to the aquifer contaminated with fertilizers beside silicate weathering can contribute to higher HCO 3 − levels relative to Ca 2+ in groundwater. Fertilizers often contain compounds that can lead to the formation of bicarbonates in water through various chemical reactions. Additionally, certain agricultural practices might lead to increased mobilization of manganese from the soil into groundwater. The use of certain types of irrigation water or amendments can affect the redox conditions in soils, potentially increasing the dissolution and mobilization of manganese into groundwater (Nadarajan & Sukumaran, 2021 ). In areas with heavy industry or mining, manganese can leach into groundwater. Discharge of industrial or domestic wastewater that contains elevated levels of manganese or organic matter can also contribute to elevated manganese levels in groundwater. The organic matter can change the redox conditions, enhancing the release of manganese from sediments (Islam & Mostafa, 2023 ; Usman et al., 2021 ) which is the case in the current study.

PC3, contributing 8.16% of the variance, shows Fe standing alone with pH (Table  3 and Fig.  8 b), indicating that the concentration of iron in the groundwater is controlled by the acidity/alkalinity of water based on the contribution of geogenic and anthropogenic sources in the different location of investigated study. Increasing the pH value and changing the water from neutral to alkaline condition facilitate the precipitation of iron in the rock matrix and decrease its concentration in water, which is the case in the current study. The PCA tool was effective to analyze the main component and mechanisms could control the water chemistry and confirm the previous statistics and geochemical model. The solubility of iron in water is strongly dependent on pH. In general, iron is more soluble in acidic conditions (low pH) and precipitates out of solution as iron oxides or hydroxides in more alkaline conditions (higher pH). If the groundwater has a low pH, it can dissolve iron-bearing minerals, increasing the concentration of dissolved iron. Conversely, as pH increases, iron may precipitate out of solution, reducing its concentration in the water (Schwertmann, 1991 ). Improper disposal of industrial or domestic wastewater can introduce substances that affect both pH and iron levels in groundwater. Organic matter and certain chemicals can alter the redox conditions and pH, leading to changes in iron solubility. The breakdown of organic matter in soil and water can produce acids, lowering the pH. This process can also consume oxygen, creating reducing conditions that favor the solubilization of iron (Grybos et al., 2009 ). Contaminants like iron and manganese can have health implications if present in drinking water at high concentrations. The study’s results can inform public health strategies to monitor and mitigate the exposure to these elements, thereby contributing to SDG 3 (Abioye & Perera, 2019 ). This study highlights the importance of enhancing water quality to support the goals of SDG 6.1, which aims for universal and fair access to safe drinking water, and SDG 3, which focuses on ensuring good health and well-being, in Egypt.

The Ward’s method and dendrogram analysis revealed the formation of four distinct clusters based on the similarities in the water quality variables. Interpreting a dendrogram from a cluster analysis involves understanding the relationships and similarities between different groundwater quality parameters. A dendrogram is a tree-like diagram that illustrates the arrangement of clusters produced by hierarchical clustering. There are four main clusters (Fig.  8 c) extracted from dendrogram as follows;

Cluster 1 TDS, Ca 2+ , Mg 2+ , Na + , Cl −

These parameters are often related to mineral dissolution and ion exchange processes in fresh groundwater. The presence of TDS, Ca 2+ , Mg 2+ , Na + , and Cl − together suggests that these ions are likely derived from the weathering of carbonate and silicate minerals.

The integration of the cluster1 with piper plot:

Ca–Mg–HCO 3 Facies (34%): This is the dominant facies, indicating that the majority of groundwater is influenced by carbonate weathering, contributing Ca and Mg. Mixed Na–Ca–HCO3 Facies (17.4%): This facies represents areas where there is a mix of sodium and calcium bicarbonate, indicating ion exchange processes where Na replaces Ca in the groundwater matrix. Mixed Ca–Mg–Cl/SO 4 Facies (21.8%): This facies suggests additional sources of Cl − and SO 4 2− , possibly from the dissolution of gypsum (CaSO 4 ) and halite (NaCl), or from agricultural activities.

Cluster 2 SO 4 2− and NO 3 −

The grouping of sulfate and nitrate suggests influence from agricultural activities, such as the use of fertilizers, or natural oxidation of sulfide minerals and nitrification processes. Mixed Ca–Mg–Cl/SO 4 Facies (21.8%): This facies likely reflects areas with higher sulfate concentrations, consistent with the Cluster 2 parameters. Ca–Mg–HCO 3 Facies (34%) and Mixed Na–Ca–HCO 3 Facies (17.4%): These facies might show lower concentrations of SO 4 2− and NO 3 − , indicating less influence from agricultural runoff compared to the mixed Ca–Mg–Cl/SO 4 facies.

Cluster 3 Fe

Iron appearing as a separate cluster indicates unique geochemical conditions, such as redox reactions where iron is mobilized under reducing conditions. Fe is not directly represented on the Piper plot but is crucial for understanding the redox conditions and potential contamination sources in the groundwater. Areas with high Fe might overlap with any of the facies but are more indicative of local reducing conditions or natural iron sources. However, in the study area the Fe concentration is very low in most samples due to oxidizing environment as well as the high concentration comes from anthropogenic activities.

Cluster 4 Mn

Similar to Fe, manganese clustering separately suggests specific redox conditions or geological sources influencing Mn concentrations. Cluster 4 represent the anthropogenic activity source where agricultural practices and irrigation water return back to the aquifer contaminated with fertilizers.

The cluster analyses of the water samples (Fig.  8 d) based on measured parameters divided the samples to 5 clusters. The largest cluster (Cluster 1), containing 61 samples, represents the typical groundwater chemistry of the quaternary aquifer, influenced by common processes like carbonate dissolution and ion exchange, encompassing various hydrochemical facies. Cluster 2, located in the south, likely reflects distinct characteristics with potential agricultural impacts, fitting mainly into the Ca–Mg–HCO 3 facies. Cluster 3, in the northwest, represents a unique sample potentially influenced by localized geological or contamination factors. Clusters 4 and 5, with samples in the central and southwestern parts of the study area, respectively, indicate unique local conditions affecting groundwater chemistry. These clusters highlight the regional variability in groundwater quality, providing insights into specific local influences and broader geochemical processes. These insights can guide targeted investigations and management strategies to address any localized contamination or to understand the regional groundwater dynamics better.

Groundwater suitability for drinking using WQI

The water quality index for consumption (drinking) purposes based on WQI could be classifies into four main quality type (extremely poor, poor, medium or intermediate good, and excellent quality) according to the calculated values. The range of this classification is > 200, 150–200, 100–150, 50–100, and 0–50 for extremely poor, poor, medium or intermediate good, and excellent quality respectively. In the current study, the WQI ranges from 29.57 to 286.3 with an average value 121.45. Figure  9 a classifies water based on WQI values, indicating that 10.14% of water samples fell into the extremely poor category (WQI > 200) including water samples located in north west and south west of study area (S1, S16, S22, S47, S62, and S67), (Fig.  9 b). The results showed that 21.7% of samples had WQI values between 150 and 200 which fell within poor quality range represented by 15 samples. The WQI value in 26% of samples raged from 100 to 150 including 18 samples which indicates medium quality category. The rest of samples (42% of samples) fell within good to excellent quality represented by 29 samples. The distribution of the WQI values (Fig.  9 b) showed the most deteriorated locations in the north east and south of the investigated area which need more attention from the decision makers to make further treatment of water in this location to avoid any health risk could develop from contaminated water. Deterioration of the water quality close to the Nile River could be because of mixing contaminated water of the Nile River with groundwater of QA where all industrial and agricultural drainages discharge water to the Nile River. The current findings demonstrated that the groundwater of the QA aquifer in the central region is the most appropriate for drinking based WQI, while the other parts of the investigated area need further treatment and suitable management to prevent any health risks regarding the water quality.

The calculated WQI and plotting its values for all samples ( a ) and WQI distribution map in Beni-Suef area ( b )

The utilization of the heavy metal pollution model (HPI) constitutes a fundamental approach for comprehensively calculating the pollution levels within surface and groundwater environments. The utilization of this model facilitates the evaluation of PTEs impacts on freshwater quality, thereby enabling effective monitoring and management strategies to mitigate potential risk linked to with PTEs exposure. Water quality is categorized as excellent (HPI < 15), good to intermediate (15 < HPI < 30), poor (76 > HPI > 51), very poor (76 < HPI < 100), and unsuitable (HPI > 100). Notably, the mean HPI value during the period of observation recorded an average of 35, demonstrating a range spanning from 0.37 to 139. These results underscore the substantial presence of heavy metal contaminants in the groundwater samples collected within the study period.

According to above mentioned classification, nearly 4.34% of the samples fell within the unsuitable water quality category (HPI > 100), indicating high levels of heavy metal contamination in three locations (S47, S49, and S62) in the north, south and west of the study area. Approximately 11.6% of the samples fell within very poor category (100 < HPI < 76), indicating relatively high levels of heavy metal contamination in eight locations (S16, S18, S21, S22, S48, S50, S53, and S58) in the north, south and west of the study area. The current results showed about 13% of the samples fell within poor category (76 > HPI > 51), indicating the presence of contamination of water with PTEs. Therefore, there are several important considerations regarding the underlying factors driving heavy metal pollution in the study area. Environmental factors such as industrial activities, urbanization, and agricultural practices may contribute to the influx of heavy metals into groundwater bodies, exacerbating pollution levels.

Additionally, natural processes such as weathering and erosion can also influence the mobilization and transport of heavy metal contaminants, further exacerbating the issue. Comprehensive measures are essential to address these pollution levels effectively and protect the environment for current and future generations. The water quality in QA regarding PTEs changed in the rest of samples to be between Excellent and good category with HPI value below 15 in 46.4% of samples and between 15 and 30 in 7.2% of water samples (Fig.  10 a). The distribution of the HPI values (Fig.  10 b) showed the most deteriorated locations in the north east and south of the investigated area which need more attention from the decision makers to make further treatment of water in this location to avoid any health risk could develop from contaminated water. Deterioration of the water quality close to the Nile River could be because of mixing contaminated water of the Nile River with groundwater of QA where all industrial and agricultural drainages discharge water to the Nile River. The current findings demonstrated that the groundwater of the QA aquifer in the central region is the most appropriate for drinking based HPI, while the other parts of the investigated area need further treatment and suitable management to prevent any health risks regarding the water quality.

Scatter plot of the PTEs indices including HPI ( a ), HPI distribution map ( b ) and ecological risk values calculated from the PTEs with each sample ( c ) in Beni-Suef

Ecological risk index (RI)

The Possible Ecological Risks Indicator, stands as a commonly acknowledged approach utilized to measure the degree of PTEs pollution and its plausible ramifications on both sedimentary and aquatic environments (Hakanson, 1980 ; Saeed et al., 2023a ). This indicator considers various parameters, encompassing heavy metal concentrations, their toxicological and ecological effects, as well as greater environmental effects. This work focused on the ecological risk indicator (RI) of PTEs (Fig.  10 c) in groundwater of Ben-Suef Quaternary aquifer. The computed average RI value for the investigated samples stood at 0.09, ranging from 0.004 to 0.88. These observations indicate that the groundwater of the QA in all location of the investigated area does not have any ecological risk regarding PTEs with very low RI value (RI < 30). However, the investigated area is characterized by considerable agricultural and residential sectors proximate to the basin and its tributaries and could contribute to the accrual of heightened metals amounts in sedimentary deposits, subsequently infiltrating into the study area, there is still no potential risk could be significant.

Human health risk assessment

The evaluation of non-carcinogenic and carcinogenic risk hazard indices involved the computation of hazard quotients (HQ) for dermal (Fig.  11 a) and ingestion (Fig.  11 b) absorption pathways. These results unveil the collective health risks posed to both adults and children due to exposure to various heavy metals.

Box plot illustrate the risk indices including HQ dermal ( a ), HQ oral ( b ), HI ( c ), distribution maps of HI dermal ( d ), HI oral in adult and child

Non-carcinogenic health risk

The hazard quotient (HQ) values for dermal exposure to heavy metals, including Mn, Zn, Fe, and Cu were assessed for both adults and children (Table S7 ).

For adults, the HQ values ranged from 2.0E−07 to 6.3E−04 for Fe, from 3.0E−04 to 2.5E−01 for Mn, from 2.4E−05 to 1.9E−03 for Cu, and from 4.2E−06 to 1.3E−03 for Zn. Meanwhile, children exhibited higher HQ values compared adult across all metals, with ranges from 6.0E−07 to 1.9E−03 for Fe, from 8.8E−04 to 7.5E−01 for Mn, from 7.0E−05 to 5.6E−03 for Cu, and from 1.2E−05 to 3.7E−03 for Zn. The current results showed that the dermal exposure of PTEs (Cu, Zn, Fe, and Mn) in the groundwater of QA does not threaten the health or the skin of the individuals in Beni-Suef area (adult and child).

On the other hand, the hazard quotient (HQ) values for oral exposure to heavy metals Fe, Mn, Cu, Zn were assessed for two age groups (adults and child) (Table S8 ). In adults, HQ values for Fe ranged from 8.6E−6 to 2.7E−02, while for children, they varied from 3.3E−05 to 1.0E−01. For Mn, adults exhibited HQ values ranging from 2.5E−03 to 2.1, whereas children displayed values between 9.6E−03 and 8.2. The Cu showed HQ values ranging from 1.5E−03 to 1.2E−01 in adults and from 5.8E−03 to 4.6E−01 in children. zinc exhibited HQ values ranging from 3.0E−04 to 8.8E−02 in adults and from 1.2E−03 to 3.4E−01 in children. The current findings revealed that the children and adults are more vulnerable to ingestion exposure of PTEs more than dermal exposure especially for Mn metals in the groundwater of QA in significant number of samples. In case of adults group, 21.7% of samples located in the north and south part of study area has high risk regarding Mn metal exposure with high value of HQ (HQ > 1), while the worst case in the children group which showed high risk in more locations (53.6% of samples) with HQ value greater than one. It was confirmed before from the multivariate statistical analysis between ions that main contributor or source of elevated manganese in groundwater is anthropogenic activities. It was noted also that the most toxic metal responsible for water contamination in the QA and could cause health risk specifically in the north and south part of Beni-Suef region is manganese metal. The Hazard quotient value can explain the health risk of every toxic metal separately, while the sum of HQ for all PTEs could give general risk by combining all metals together.

Additionally, HI for dermal exposure showed values in adults ranging from 9.0E−04 to 0.255 and in children from 0.003 to 0.752. These findings highlight the safety of skin contact with the groundwater of QA in the two groups of different age (child and adult) in all the investigated area with very low HI value (HI < 1) (Fig.  11 a). However, the HQ level is higher in the north and south part compared to the central study area (Fig.  11 d). In comparison, for hazard index (HI) through oral/ingestion, adults exhibited HI values ranging from 0.012 to 2.16, while children showed higher values, ranging from 0.045 to 8.25 (Fig.  11 b). In case of adults group, 21.7% of samples located in the north and south part of study area (Fig.  11 e) has high risk regarding PTEs exposure with high value of HI (HI > 1), while the worst case in the children group which showed high risk in more locations (55% of samples) with HI value greater than one.

These results underscore varying degrees of health risks connected with heavy metal exposure, with children consistently showing higher HI values across all metals, highlighting the increased vulnerability of children to heavy metal toxicity. Such findings emphasize the critical need for targeted interventions to reduce exposure and protect public health, particularly among children in the north and south part of study area.

Monte Carlo simulation approach

Monte Carlo simulation was used to estimate the hazard quotient (HQ) values for oral and dermal exposure to of Fe, Mn, Zn, and Cu, and demonstrate reliable risk by decreasing the uncertainty in the datasets. By using python code and 10,000 epochs or iterations in the concentration of metals and reference standards of EPA, the reliable risk regarding every toxic metal was estimated.

Non-carcinogenic risk

The Monte Carlo simulation results indicated that the predicted dermal hazard quotient (HQ) values for adults for all assessed heavy metals (Fe, Mn, Cu, and Zn) remained below the standard safety threshold (HQ < 1) (Fig.  12 a), suggesting a manageable risk level for the adult population. similarly, for children, the dermal HQ values for Fe, Mn, Cu, and Zn did not exceed the threshold limits (HQ < 1) (Fig.  12 b), highlighting a neglected health risk and no need for urgent attention to reduce dermal exposure levels to the measured metals in the two groups (adult and child). In contrast, the oral HQ values Cu, Fe, and Zn in both adults and children did not exceed the standard limits (HQ < 1) (Fig.  12 c, d), confirm the safety of oral exposure to these metals in the groundwater of the quaternary aquifer. The results of Monte Carlo simulation showed that the health risk of groundwater consumption developed only through oral exposure to Mn metal with predicted HQ value greater than one (HQ > 1) in adult and child. These findings indicate a critical risk associated with oral exposure to manganese. However, it’s important to recognize that risk assessments often rely on conservative assumptions and uncertainties in existing data. Therefore, continuously monitoring exposure levels and updating risk assessments with new information as it becomes available is crucial such as measuring Cd, Cr, and Pb metals to evaluate the carcinogenic risk through different exposure routes.

The Hazard quotient simulated from Monte Carlo in all groups with two different ways of exposure

Application of probabilistic method such as Monte Carlo simulation could confirm the traditional calculation and give more realistic interpretation of the health risk of PTEs in the groundwater of the Quaternary aquifer in Beni-Suef region and could be applied globally in different study area.

Limitations, implications, and gaps for future work

The study’s findings on groundwater quality in the Beni-Suef area have significant implications for climate action and several SDGs. By providing a comprehensive understanding of the factors affecting groundwater quality, the research can inform policies and strategies that promote sustainable water management, public health, and environmental protection. Addressing these issues is critical not only for the local population but also as part of broader efforts to achieve sustainable development and resilience in the face of climate change.

Although the current study covers different crucial points integrate between hydrochemistry, ion source and contamination origin with environmental, ecological, and health risk assessment supported with simulation model, there are still some limitations that could be covered in the future work.

Application of stable isotopes and mixing model to detect the reliable pollution source and contribution percentage from the Nile River to the QA or vice versa and the effect of mixing of surface water and groundwater on the water quality. Measuring the carcinogenic elements such as Cd, Cr, and Pb to determine the carcinogenic risk on the human health and provide water management and treatment plan to avoid any health issues. The future work can include also collecting soil samples to detect the accumulation of PTEs in the soil and its effect on the groundwater quality, plant production, and soil fertility.

This study investigates the drinking water quality and health, ecological, and environmental risks associated with potentially toxic elements (PTEs) in the floodplain Quaternary aquifer (QA) located in Beni-Suef, Egypt. A comprehensive approach was used, including the PHREEQC geochemical model, ionic ratios, and multivariate statistical analysis such as principal component (PCA) and cluster (dendrogram) analysis, to estimate the sources of ions, contamination, and the mixing of Nile water with QA. An advanced method called the integrated weight water quality index (WQI), derived from the entropy method, was applied to determine the suitability of water for drinking. Various indices, such as the Heavy Metal Pollution Index (HPI), ecological Risk Index (RI), Hazard Quotient (HQ), and total Hazard Index (HI), were used to assess the ecological, environmental, and human health risks regarding PTEs in QA. Furthermore, the Monte Carlo method was applied for the probabilistic assessment of non-carcinogenic risks through oral and dermal exposure routes in both adults and children. A GIS tool was used to interpolate all indices in the study area to detect the most deteriorated locations for sustainable management.

The hydrochemical characteristics indicated that the water type/facies were Na–Cl, Ca–Mg–HCO 3 , mixed Na–Ca–HCO 3 , mixed Ca–Mg–Cl/SO 4 , and Na–HCO 3 . The mechanisms controlling water quality are carbonate dissolution, direct ion exchange, silicate weathering, and evaporation/crystallization. The sources of contamination with NO 3 - and PTEs in the north and south parts of the study area are agricultural activities, irrigation water returns, municipal waste, and evaporites.

Based on WQI values, 10.14% of water samples fell into the extremely poor category (WQI > 200), 21.7% of samples had WQI values between 150 and 200, falling within the poor-quality range, and 26% ranged from 100 to 150, indicating a medium quality category. The rest of the samples (42%) fell within the good to excellent quality range.

Water quality regarding PTEs fell between excellent and good, with HPI values below 15 in 46.4% of samples and between 15 and 30 in 7.2% of water samples in the central study area, while the north and south are contaminated. The calculated ecological Risk Index (RI) in all samples was below the threshold limit (RI < 30), confirming the water’s safety and lack of ecological risk from PTEs.

The application of Monte Carlo simulation revealed no health risks for children and adults through skin contact, but a high risk from oral exposure (HQ predicted > 1) to manganese (Mn). The results demonstrated that the north (El-Wasta city) and south (El-Fashn city) parts of the study area face different contamination challenges based on PTEs, WQI, HPI, HQ, and HI, necessitating further treatment and management of water before consumption.

Data availability

The datasets utilized and/or analyzed during the current study are available upon request from the corresponding author.

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Acknowledgements

We would like to thank the sustainable development and technologies national program of the Hungarian Academy of Sciences (FFT NP FTA) for funding this research work. This research was supported by Researchers Supporting Project number (RSPD2024R804), King Saud University, Riyadh, Saudi Arabia

Open access funding provided by University of Miskolc. The sustainable development and technologies national program of the Hungarian Academy of Sciences (FFT NP FTA) funded this work. Also, the authors extend their appreciation to King Saud University for funding this work through Researchers Supporting Project number (RSP2024R133), King Saud University, Riyadh, Saudi Arabia.

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Institute of Environmental Management, Faculty of Earth Science, University of Miskolc, 3515, Miskolc- Egyetemváros, Hungary

Mohamed Hamdy Eid, Attila Kovács & Péter Szűcs

Geology Department, Faculty of Science, Beni-Suef University, Beni-Suef, 65211, Egypt

Mohamed Hamdy Eid & Mostafa R. Abukhadra

Faculty of Earth Science, Beni-Suef University, Beni-Suef, 62511, Egypt

Mahmoud Awad & Essam A. Mohamed

Geology Department, Faculty of Science, Cairo University, Cairo, Egypt

Tamer Nassar

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M.H.E., M.A., E.A.M., A.M.E, M.R.A & T.N. designed the study; M.A., E.A.M., M.R.A and T.N. collected and prepared samples, performed field survey; M.A., E.A.M., M.R.A and T.N performed laboratory work; M.H.E., M.A., A.K. & P.S. prepared maps; M.H.E., A.K., P.S., A.M.E, M.R.A and M.A wrote, reviewed, and edited the manuscript. All authors contributed extensively to the discussions about the work and in reviewing and revising the manuscript.

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Eid, M.H., Awad, M., Mohamed, E.A. et al. Comprehensive approach integrating water quality index and toxic element analysis for environmental and health risk assessment enhanced by simulation techniques. Environ Geochem Health 46 , 409 (2024). https://doi.org/10.1007/s10653-024-02182-1

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Received : 13 June 2024

Accepted : 17 August 2024

Published : 31 August 2024

DOI : https://doi.org/10.1007/s10653-024-02182-1

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