Application of mapping and statistical study for the assessment of surface water quality in the Safsaf River (North-Eastern Algeria)

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Our study focused on the physicochemical analysis of the water of the Safsaf River in Skikda. It is based on monitoring three stations during the months of March and August. The aim of this study was to assess the quality of this water and characterize its suitability for agricultural use. To this end, we determined the values of the following physicochemical parameters: Electrical Conductivity (EC), pH, turbidity, total alkalinity (TA), chlorides (Cl - ), sodium (Na + ), potassium (K + ), nitrite (NO 2 - ), ammonium (NH 4 + ), Biological Oxygen Demand (BOD 5 ), and phosphates (PO 4 -3 ). The results show that electrical conductivity, sodium, potassium, and chlorides increase downstream of the watershed due to seawater intrusion into the plain. As for the origins of phosphate pollution, whose concentrations have significantly increased along the river, we can explain it by pollution generated by the use of phosphate fertilizers and return irrigation water into surface waters, as well as pollution due to direct input of domestic wastewater. Hydrology Skikda pollution Safsaf River irrigation physicochemical Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Water, a vital resource, is constantly threatened by the high concentration of pollutants that are discharged into it, rendering it unusable for humans (Bisimwaa et al. 2022). Water pollution is a widespread issue in most agricultural areas worldwide (Boudibi et al. 2021 ). Recently, aquatic ecosystems have been deteriorating due to human actions, primarily industrial and agricultural activities, generating waste that can disrupt the balance of fauna and flora in these aquatic ecosystems (Belahmadi et al. 2023 ). The water quality of a given geographical area depends on its physical and chemical characteristics, which play a crucial role in evaluating and monitoring its suitability for specific uses. When the values of these parameters exceed the authorized thresholds, it can pose a threat to the environment and human health (Pulak and Patra, 2020 ). Water quality in Algeria has been significantly altered in recent years due to demographic growth from 11 million in 1960 to nearly 40 million inhabitants in 2015 and also due to industrial expansion (Benkaddour et al. 2019 ). In Algeria, numerous studies on the deterioration of water quality in certain regions have shown water enrichment in chemical and organic components as well as nutrients of anthropogenic origin (Saadali et al. 2022 ; Saadali et al. 2019 ; Hallouz et al. 2022 ; Yebdri et al. 2021 ; Reggam et al. 2017 ). This work focuses on the Safsaf River, an essential water resource for irrigation. The Safsaf basin occupies the central part of the Skikda province and extends over an area of 1158 km 2 , with a population of nearly 460 thousand inhabitants (49% of the total population of the province) (Khelfaoui and Zouini, 2010 ). The demand for water for domestic uses is expected to grow considerably in the coming years in developing countries. The Safsaf watershed is no exception; its water needs are continuously increasing. This demand is due to considerable demographic development in the area. Agricultural practice is concentrated mainly in the central part of the Safsaf basin. Downstream, the watershed is heavily influenced by polluting petrochemical industry, resulting in rapid and constant deterioration of surface water quality. Thus, agricultural pollution in the Safsaf plain cannot be ruled out. The aim of this study was therefore to assess the impact of urban, agricultural, and industrial wastewater on the quality of Safsaf River waters. Materials and Methods Geographical Location of the Study Area The Safsaf watershed is located in the center of the Skikda province, in the eastern part of Algeria (Messaoudi, 2007 ). The watershed comprises nine municipalities, and these municipalities are part of an irrigated area with highly valuable agricultural land. The Safsaf watershed is situated between the Guebli River basin to the west and the Kebir River basin to the east, its length is 53.19 Km, (Khelfaoui & Zouini, 2010 ). The demand for water for irrigation is significant in this region, where there is also a notable concentration of various industries, mainly located in the vicinity of Skikda, a city that plays a central role in the petrochemical industry (Titi Benrabah et al., 2013 ). The Safsaf watershed receives discharges from the industrial area of the city of Skikda, where there is a petrochemical complex, as well as inputs from urban domestic water (Kaddeche et al., 2022 ). The industry is mainly located downstream of the Safsaf River basin, where it consumes about 7.1 million cubic meters of water per year (Sakaa et al., 2013 ). Normalized Difference Vegetation Index (NDVI) The Normalized Difference Vegetation Index (NDVI) is a widely used indicator to assess the presence of green vegetation in a given area. NDVI values range from − 1 to 1, where negative values signal the presence of water bodies, while positive values indicate the presence of vegetation (Abdı et al. 2021 ). The higher the NDVI value, the denser the vegetation cover. Figure 2 illustrates that the values of the Normalized Difference Vegetation Index (NDVI) spread in a range from − 0.202 to 0.571 (Fig. 2 ). These values are mainly located in the South, East, and West parts of the watershed, corresponding to areas with variable vegetation. In contrast, in the center of the watershed, the Normalized Difference Vegetation Index (NDVI) is low, indicating the presence of bare soils and aquatic areas. This suggests that the area comprises both water bodies, bare soils, and vegetation cover. Sampling and Analysis Methods Three surface water stations were sampled in 2023. Three stations were chosen due to their different characteristics, particularly the nature of the effluents: Station 1 “El Harrouche” is close to significant anthropic activity and is fed by a network of domestic and agricultural effluents that flow into it. Station 2 “Ramdane Djemel” located in the middle of the study area, it receives several sewers that transport urban and domestic waste. Station 3 “Sonatrach” located near the mouth of the Safaaf River (right next to the Mediterranean Sea). It receives domestic, urban, industrial wastewater from the Sonatrack complex and agricultural water from the city of Skikda (Fig. 1 ). Two sampling campaigns were carried out at these three stations to assess the spatiotemporal variation of physicochemical parameters in the Safsaf watershed during periods of high flow (April) and low flow (August). After filtration, all samples were stored in polyethylene bottles in a cooler at a constant temperature below 4°C and kept in a refrigerator at a temperature below 4°C after acidification by nitric acid HNO 3 (5%). Physical parameters such as pH and electrical conductivity (EC) were determined in situ using a portable conductimeter (HANNA Multiparameter). Turbidity by Turbidity meter. Total alkalinity (TA) by titrimetric method. Chloride ions (Cl - ) were analyzed by volumetric titrations (Mohr’s method). Sodium ions (Na + ) and potassium ions (K + ) were analyzed by flame spectrometer, phosphate ions and ammonium ions were analyzed by colorimetric method, nitrate ions by salicylate method, nitrite ions by Zambelli method and bicarbonate ions by volumetric method. The creation of maps was carried out using ArcGIS 10.8 software. Results and Discussion Descriptive Statistics and mapping of physicochemical parameters The Safsaf river represents the main watercourse in the basin; the quality of its waters varies from upstream to downstream. Monitoring of some chemical elements has allowed us to visualize a spatiotemporal alteration and degradation of the quality of the river’s waters, with an increase in the concentrations of chemical elements from upstream to downstream. We carried out descriptive statistics (maximum, minimum, sum, average, and coefficient of variation) using Origin 2023. We note that the deviation from the mean is significant for certain elements such as electrical conductivity, chlorides, sodium, and potassium. The pH plays a role in regulating the chemical characteristics of water, including its level of alkalinity, acidity, specific chemical composition, and the ability of chemical elements to dissolve (Hossain and Patra, 2020 ). pH is a critical parameter governing freshwater bicarbonate and carbonate processes and is considered integral to biodiversity. A balanced pH range, typically between 6.5 to 8.5, supports aquatic life and indicates water quality (WHO 2017). Monitoring pH levels in the Oued Safsaf basin revealed values ranging from 7.62 to 8.57 in April and 7.86 to 8.2 in August. The higher value of above 8 recorded in the central and lower part of the basin show a slightly alkaline and alkaline nature of the quality of the surface water. Electrical Conductivity (EC) is a parameter that allows assessing the total quantity of ions present in water, thus giving an indication of the degree of mineralization of this water. High EC can affect plant growth by reducing plant transpiration (Jolly et al. 2023 ). Notably, the EC exhibits significant variations, spanning from 1011 to 2340 µS/cm in April and expanding to a range of 4830 to 6840 µS/cm in August. The highest EC values are observed in the upper reaches of the basin, while lower EC values are documented in the central and lower regions in April. The alkalimetric title or Total Alkalinity (TA) is the concentration of carbonate ion CO 3 -2 and strong base OH - in water (Howladar et al. 2021 ). The alkalinity levels experience variations, ranging from 120 to 279.98 mg/l in April and from 133.01 to 369.98 mg/l in August. According to Figs. 4 and 5 , the highest concentration of total alkalinity during April is observed in the second station, whereas during August it is in the third station, which was higher than the permitted limit 100 mg/L (WHO, 2017). We can assume that effluents from oil refinery industry and fertilizers are responsible for increasing TA in river water. The water turbidity is primarily the result of the presence of suspended particles.This increase in turbidity can be caused by various factors, including wastewater discharges and soil erosion, which reduces the transparency of the water and the amount of dissolved oxygen (Zenati et al., 2023 ). According to WHO (2017), median turbidity values were in compliance with the recommended quality of raw water for irrigation (< 1000 NTU). Turbidity, a key parameter, manifests considerable fluctuations, ranging from 1.3702 to 18.299 in April and expanding further to 6.7002 to 20.1 in August. Of particular interest is the distinct spatial pattern observed: the upper reaches of the basin consistently register the highest turbidity values, in stark contrast to the central and lower regions, which consistently exhibit lower turbidity levels. Inorganic nitrogen (nitrite and ammonium) and phosphate are significant factors affecting surface water quality (Diani et al. 2021 ). The analysis of nitrite concentration revealed that the waters had a very low nitrite content according to WHO standards (2017) (0.1 mg/L). The low concentrations of elements such as nitrites and ammonium in the water are due to the reducing conditions of the less developed nitrate form. In the context of NH 4 + , the recorded values depict a distinct seasonal trend. During April, NH 4 + concentrations range from 0.22 to 1.69, while in August, they expand to a range of 1 to 2.3. Notably, the concentration of NH 4 + consistently reveals a higher presence in the central and lower regions of the study area, in stark contrast to the lower levels observed in the upper part, both in April and August. This spatial variation underscores the dynamic nature of NH 4 + distribution, which appears to align with the central and lower sectors of the basin across the two months under examination. Our results correlate with those of Bisimwa et al. ( 2022 ) conducted in Congo. The presence of NH 4 + in surface waters generally indicates an incomplete process of organic matter decomposition and is a relevant indicator of river contamination by urban discharges. Phosphate concentration is an indicator of nutrient levels in the river system and can lead to eutrophication if present in excess (Bushero et al., 2022 ). This release is evident in the concentrations of phosphate ions (PO 4 -3 ), which exhibit a significant range, spanning from 0.737 to 4.46 in April and broadening to 1.1 to 6.32 in August. Interestingly, a recurring pattern emerges, with the highest PO 4 -3 values consistently observed in the uppermost reaches of the basin, while the central and lower regions consistently record lower concentrations. The phosphate content at most monitoring stations is significantly high, indicating significant river pollution and a predisposition to eutrophication. Human activities have exerted considerable influence on the river’s phosphate concentration, with sources such as detergents, fertilizers, industrial and domestic waste frequently contributing to this contamination. Examining the data recorded in April and August within the river area, chloride concentrations reveal a range spanning from 142.01 to 922.98 and 107.52 to 1881.5, respectively, which considers chloride contents above 106 mg/L to pose a serious problem on aquatic ecosystems (Bouaroudj et al., 2019 ). Biochemical Oxygen Demand (BOD) is an indication of the amount of dissolved oxygen used by microorganisms during the oxidation process of reducing substances present in waters and wastes (Hamil et al., 2018 ). The concentrations of Biochemical Oxygen Demand (BOD), exhibiting a significant variance, ranging from 1 to 7 in April and narrowing to 1 to 2 in August. Interestingly, a distinct pattern emerges, with lower BOD values consistently documented in the central and lower regions, while higher concentrations are observed in the upper reaches of the basin during April. Remarkably, this pattern undergoes a reversal in August, where higher BOD values are now prevalent in the central and lower sectors, while the upper region registers lower concentrations. The main sources of Na + and K + are silicate weathering, anthropogenic and agricultural activities, as well as mixing with seawater (Gençer and Basaran, 2023 ). Sodium concentrations ranging from 200.05 to 779.99 in April and expanding to 200.67 to 9099.8 in August. Mapping the distribution of sodium reveals a prevalence in the eastern and lower regions, aligning predominantly with a North-South orientation across the study area. A high concentration due to severe contamination by marine intrusion. The potassium concentrations, showing a notable range from 444.46 to 6314.9 in April and from 375.42 to 5779.9 in August. Intriguingly, the representation of maximum potassium values is consistently discerned in the central and lower regions, while the upper reaches of the basin consistently record lower concentrations. Sample 3 records the highest level of sodium (Na + ) and potassium (K + ), resulting from the influence of industrial discharges from the Sonatrach complex. In addition, high amounts of Na + and K + are observed in samples from agricultural areas. Principal Component Analysis (PCA) The objective of applying Principal Component Analysis (PCA) is to reduce the number of variables into a small number of dimensions (factors), classify variables, and group observations with similar characteristics. A statistical PCA analysis was applied to the set of physicochemical parameters from three stations. This method is widely used for interpreting physicochemical data. For data processing through principal component analysis, we used 11 variables: pH, EC, TA, Turbidity, Na + , K + , Cl - , NH 4 + , NO 2 - , PO 4 -3 , and DBO 5 from two samplings conducted in April and August 2023. The eigenvalue on axes F1 and F2 provides complete information about the studied surface waters (100% information). Therefore, the work is based on axes F1 and F2. In the correlation circle of Fig. 13 (April), the 1st component (PC1), contributing 76.44% of inertia, is defined by the parameters: EC, TA, PO 4 -3 , NO 2 - , Turbidity, and DBO 5 on the positive side and pH, NH 4 + , Cl - , Na + , and K + on the negative side. This factor represents a source of diffuse pollution generally associated with agricultural activities and water mineralization. It also reflects the significant influence of anthropogenic activities in the area, particularly domestic and industrial waste. The strong negative correlation with pH (-0.98) and moderate positive correlation with alkalinity (0.76) explain that alkalinity is related to natural processes of soil constituent dissolution, mainly calcium carbonates. However, pH is linked to leaching of carbonate compounds from the soil. These compounds raise both the pH and water alkalinity. In fact, alkaline conditions promote the growth of algae and other aquatic organisms (Howladar et al., 2021 ). With an inertia of 23.56%, the 2nd component (PC2) is defined by the parameters: EC, Na + , Cl - , K + , and DBO 5 on the positive side and pH and TA on the negative side (Table 1). This factor suggests that positive correlations are associated with anthropogenic contamination through domestic waste water discharge and the origin of water mineralization (industrial discharge and seawater), while negative correlations are related to the alkalinity process. This factor indicates that pH does not show a significant correlation withother parameters, suggesting that chemical parameters come from various sources, and water pH is controlled by carbonate alteration. In the dry period, factor 1 contributes 67.69% to the total inertia and is defined by the parameters pH, Cl - , NH 4 + , and NO 2 - . This factor shows that pollution comes from anthropogenic contamination and organic matter decomposition in the study area. Finally, factor 2 explains 32.31% and is associated with the parameters TA, NH 4 + , PO 4 -3 , Cl - , and K + with positive correlations ranging from 0.59 to 0.96, respectively, and EC with a negative correlation of -0.94. This factor explains that parameters with positive and negative correlations have different origins, originating from both human and natural contamination (return of seawater at the estuary). Therefore, it is summarized that the rapid and un controlled urban growth of Skikda has contributed to the deterioration of the city's environment and its surroundings. The waters of the Safsaf River are polluted by domestic waste water and factories, particularly with the presence of the petrochemical industrial base by the sea. Rapid and uncontrolled urbanization, especially in the area adjacent to the city, and its consumption of a large part of agricultural land, mark water pollution from this agricultural activity in the city. Tableau 1. Principal Component Analysis for surface water samples from the study area April August PC1 76.44% PC2 23.56% PC1 67.69% PC2 32.31% pH -0.98 -0,62 0.93 0,15 EC 0.70 0.97 -0.80 -0,94 TA 0.76 -0.98 -0.95 0,59 NO 2 0.98 0,42 0.94 -0,08 NH 4 -0.99 0.51 0.96 0,57 PO4 0.99 -0.32 -0.56 0,92 DBO 0,83 0.97 -0.93 -0,17 Turbidity 0.97 0.54 -0.95 -0,58 Cl − -0.99 0.51 0.95 0,005 Na + -0.98 0.58 -0.56 0,962 K + -0.98 0.48 0.95 -0,048 Conclusion The Safsaf river represents the main watercourse in the basin, and the quality of its waters varies from upstream to downstream. Monitoring of some chemical elements has revealed spatial and temporal degradation of the river’s water quality, with an increase in chemical element concentrations from upstream to downstream. Downstream, the Safsaf river has experienced degradation in its water quality due to domestic discharges from the El Harrouch, Salah Bouchaour, Ramdane Djamel agglomerations and a part of the discharges from the city of Skikda through the Zeramna river, which flows into the Safsaf. Further downstream, in the industrial zone section, the Safsaf river receives discharges from the Skikda petrochemical units and the thermal power plant without any prior treatment. The chemistry of Safsaf river water shows that these waters have high conductivity values due to the presence of certain chemical elements at high concentrations (K + , Cl − and Na + ). This excess is due to contamination of agricultural and domestic origin (wastewater) and marine invasion especially in the North-West part, and also by pollution due to discharges from the Sonatrach complex rich in chlorine and soda which are directly discharged into the river. Declarations Competing interests The authors declare that they have no financial or non-financial conflicts of interest directly or indirectly related to the work submitted for publication. Authors' contributions Fekrache and Boudeffa wrote and prepared the main text of the original version of the manuscript and maps. Funding The authors declare that this work has received no direct or indirect funding from any public or private funding agency. Availability of data and materials The authors declare that all data presented in this article are real and authentic, and have been collected from reliable scientific sources. The data and materials that support the findings of this study are available on request and we can send them to you. References Abdı A, Bouamrane A, Karech T, Dahri N, Kaouachi A, (2021). Landslide Susceptibility Mapping Using GIS-based Fuzzy Logic and the Analytical Hierarchical Processes Approach: A Case Study in Constantine (North-East Algeria), Geotech Geol Eng 39:5675-5691 https://doi.org/10.1007/s10706-021-01855-3 Belahmadi MSO, Charchar N, Abdessemed A, Gherib A, (2023). Impact of petroleum refinery on aquatic ecosystem of Skikda Bay (Algeria): Diversity and abundance of viable bacterial strains, Marine Pollution Bulletin, 1-11 https://doi.org/10.1016/j.marpolbul.2023.114704 Benkaddour B, Abdelmalek F, Addou A, Noguer T, Aubert D, Vouvé F, (2019). Assessment of Anthropogenic and Natural Factors on Cheliff River Waters (North‑West of Algeria) at Two Contrasted Climatic Seasons, International Journal of Environmental Research 13: 925-941 https://doi.org/10.1007/s41742-019-00223-7 Bisimwa AM, Kisuyab B, Kazadi ZM., Muhayad BB, Kankondae AB, (2022). Monitoring faecal contamination and relationship of physicochemical variables with faecal indicator bacteria numbers in Bukavu surface waters, tributaries of Lake Kivu in Democratic Republic of Congo, Hygiene and Environmental Health Advances, 1-13 https://doi.org/10.1016/j.heha.2022.100012 Bouaroudj S, Menad A, Bounamous A, Ali-Khodja H, Gherib A, Weigel DE, Chenchouni H, (2019). Assessment of water quality at the largest dam in Algeria (Beni Haroun Dam) and effects of irrigation on soil characteristics of agricultural lands, chemosphere, 1-13 https://doi.org/10.1016/j.chemosphere.2018.11.193 Boudibi S, Sakaa B, Benguega Z, (2021). Spatial variability and risk assessment of groundwater pollution in El-Outaya region, Algeria, Journal of African Earth Sciences, 1-12 https://doi.org/10.1016/j.jafrearsci.2021.104135 Bushero DM, Angello ZA, Behailu BM, (2022). Evaluation of hydrochemistry and identification of pollution hotspots of little Akaki river using integrated water quality index and GIS, Environmental Challenges, 1-11 https://doi.org/10.1016/j.envc.2022.100587 Diani K, Amar H, Azmi R, Elbelrhiti H, Nouayti N, Fathi N, (2021). Surface water quality assessment in the semi-arid area by a combination of heavy metal pollution indices and statistical approaches for sustainable management, Environmental Challenges, 1-12 https://doi.org/10.1016/j.envc.2021.100230 Gençer E, Basaran C, (2023). Water quality assessment and pollution of Akarçay River, Türkiye, Kuwait Journal of Science, 1-8 https://doi.org/10.1016/j.kjs.2023.07.003 Hamil S, Arab S, Chaffai A, Baha M, Arab A, (2018). Assessment of surface water quality using multivariate statistical analysis techniques: a case study from Ghrib dam, Algeria, Arabian Journal of Geosciences 11: 754, 1-14 https://doi.org/10.1007/s12517-018-4102-5 Hallouz F, Meddi M, Rahmani SA, (2022). Multivariate analysis to assess the quality of irrigation water in a semi-arid region of north west of Algeria: case of Ghrib reservoir, Environmental Earth Sciences 81:158 1-9 https://doi.org/10.1007/s12665-022-10272-5 Hossain M, Patra PK, (2020). Water pollution index-A new integrated approach to rank water quality, Ecological Indicators, 1-9https://doi.org/10.1016/j.ecolind.2020.106668 Howladar MF., Chakma E., Koley NJ., Islam S., Al Numanbakth MA., Ahmed Z, Chowdhury TR, Akter S, (2021). The water quality and pollution sources assessment of Surma river, Bangladesh using, hydrochemical, multivariate statistical and water quality index methods, 1-9 https://doi.org/10.1016/j.gsd.2020.100523 Howladar MF, Chakma E, Koley NJ, Islam S, Al Numanbakth MA, Ahmed Z, Chowdhury TR, Akter S, (2021). The water quality and pollution sources assessment of Surmariver, Bangladesh using, hydrochemical, multivariate statistical and water quality index methods, Groundwater for Sustainable Development, 1-9 https://doi.org/10.1016/j.gsd.2020.100523 Jolly YN, Jahan Rakib MR, Kumar R, Sultana S, Mizanur Rahman SM, Kabir J, Akter S, Mamun KM, Jannat Fatema K, Mehnaz M, Pal P, Bhat EA, Paray BA, Sharma P, Bhattacharya P, (2023). Evaluation of surface water quality near pollution sources in Buriganga River and deciphering their probable emergence, ecological, and health risk aspects, Regional Studies in Marine Science, 1-13https://doi.org/10.1016/j.rsma.2023.102988 Kaddeche H, Noune F, Dzizi S, Chaib N, Boudjellab ZE, Blanco S, (2022). Determinant factors of diatom assemblage’s distribution along the Coastal Central Constantine (Northeastern Algeria), AquatEcol 56: 1245-1269 https://doi.org/10.1007/s10452-022-09980-8 Khelfaoui F, Zouini D, (2010). Integrated Water Management and Water Quality in the Saf-Saf Watershed (Skikda Province, Northeastern Algeria), Nature & Technology, 50-56 https://www.univ-chlef.dz/revuenatec/art_03_07.pdf Messaoudi K, (2007). The Coffin of the Living.An Algerian Habitat Bordering on the Unbearable, The Geography Journals of Quebec, 51(143), 137-154.https://doi.org/10.7202/016597ar Pulak H, Patra K, (2020). Water pollution index - A new integrated approach to rank water quality Mobarok, , Ecological Indicators, 1-9 https://doi.org/10.1016/j.ecolind.2020.106668 Reggam A, Bouchelaghem EH, Hanane S, Houhamdi M, (2017). Effects of anthropogenic activities on the quality of surface water of Seybouse River (northeast of the Algeria), Arab J Geosci,10: 219, 1-7 DOI 10.1007/s12517-017-3010-4 Sakaa B, Chaffai H, Hani A (2013). The use of Artificial Neural Networks in the modeling of socioeconomic category of Integrated Water Resources Management (Case study: Saf-Saf River Basin, North East of Algeria), Arab J Geosci 6:3969-3978 DOI:10.1007/s12517-012-0654-y Saadali B, Zerrouki H, Hamzi A, Bouhdid C, Khiari A, (2019). Geochemical assessment of water quality and its suitability for agricultural use in the Djedrawadisubwatershed, northeast Algeria, Euro-Mediterranean Journal for Environmental Integration 4:33, 1-12 https://doi.org/10.1007/s41207-019-0121-1 Saadali B, Zerrouki H, Drias T, Khedidja A, Haoues C, Belloula M, (2022). Hydrogeochemical assessment to characterize the water quality for agricultural use in Mexanna and Bougous dams in the province of El Tarf, Algeria, Euro-Mediterranean Journal for Environmental Integration 7: 79-88 https://doi.org/10.1007/s41207-022-00291-w Titi Benrabah S, KhericiBousnoubra H, Kherici N, Cote M, (2013). Assessment and management of water resources in Northeastern Algeria: case of watersheds Kebir West Safsaf and Gueblirivers, Skikda, Appl Water Sci 3:351-357 DOI 10.1007/s13201-013-0085-2 World Health Organization (WHO), (2017). Guidelines for Drinking-Water Quality: Fourth Edition Incorporating the First Addendum. WHO Library Cataloguing-in- Publication Data Guidelines, Switzerland. Yebdri L, Hadji F, Harek Y, Marok A, (2021). Quality assessment of water used for human consumption and irrigation purpose in parts of Tafna watershed (NW Algeria), Environmental Earth Sciences 80:502, 1-15 https://doi.org/10.1007/s12665-021-09805-1 Zenati B, Inal A, Mesbaiah FZ, Kourdali S, Bachouche S, Pinho J, (2023). Pollutant load discharge from a Southwestern Mediterranean river (Mazafran River, Algeria) and its impact on the coastal environment, Arabian Journal of Geosciences, 16:146, 1-16 https://doi.org/10.1007/s12517-023-11260-0 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3440178","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":239822384,"identity":"878e7298-889b-4f78-ab81-8df4f977e2ba","order_by":0,"name":"FEKRACHE 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05:56:04","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-3440178/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3440178/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44712403,"identity":"e2ee6e5d-c056-4ff7-b3cc-6e4067fe2293","added_by":"auto","created_at":"2023-10-16 17:42:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":593085,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical Location of the Study Area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3440178/v1/0d1e7dde8720b4e4c7ede710.png"},{"id":44711484,"identity":"65f05992-4256-49f5-b4cb-6199fda3fd39","added_by":"auto","created_at":"2023-10-16 17:34:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":533872,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized Difference Vegetation Index (NDVI) of the Safsaf River Watershed\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3440178/v1/5cc1bc8128d24413c796903b.png"},{"id":44711478,"identity":"d47912b8-c6c2-4050-94ee-222d76f1a007","added_by":"auto","created_at":"2023-10-16 17:34:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":164631,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive statistics\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3440178/v1/4d40e370087bb46deb1ccaaa.jpg"},{"id":44711479,"identity":"f8cc9ed2-7c9c-49c8-9463-fef420725496","added_by":"auto","created_at":"2023-10-16 17:34:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":550273,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variation of physicochemical parameters during the month of April.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3440178/v1/56f29da9c7b2a0659880a549.jpg"},{"id":44711482,"identity":"2a5fb65f-0693-47e3-ad4f-6147ce7708dd","added_by":"auto","created_at":"2023-10-16 17:34:01","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":585134,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variation of physicochemical parameters during the month of August.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3440178/v1/a694f64980fe33ab4b47efa1.jpg"},{"id":44711481,"identity":"53d4fccd-62f0-4b95-a00e-6d710411b457","added_by":"auto","created_at":"2023-10-16 17:34:01","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":143771,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis for surface water samples from the study area\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3440178/v1/da380af3c295a11d8f068176.jpg"},{"id":44713226,"identity":"c88a435b-416c-490a-ba31-686352c59a5e","added_by":"auto","created_at":"2023-10-16 17:50:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1835528,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3440178/v1/78651f49-0eff-4347-842f-b76981f50797.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cstrong\u003eApplication of mapping and statistical study for the assessment of surface water quality in the Safsaf River (North-Eastern Algeria)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater, a vital resource, is constantly threatened by the high concentration of pollutants that are discharged into it, rendering it unusable for humans (Bisimwaa et al. 2022). Water pollution is a widespread issue in most agricultural areas worldwide (Boudibi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently, aquatic ecosystems have been deteriorating due to human actions, primarily industrial and agricultural activities, generating waste that can disrupt the balance of fauna and flora in these aquatic ecosystems (Belahmadi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The water quality of a given geographical area depends on its physical and chemical characteristics, which play a crucial role in evaluating and monitoring its suitability for specific uses. When the values of these parameters exceed the authorized thresholds, it can pose a threat to the environment and human health (Pulak and Patra, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Water quality in Algeria has been significantly altered in recent years due to demographic growth from 11\u0026nbsp;million in 1960 to nearly 40\u0026nbsp;million inhabitants in 2015 and also due to industrial expansion (Benkaddour et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In Algeria, numerous studies on the deterioration of water quality in certain regions have shown water enrichment in chemical and organic components as well as nutrients of anthropogenic origin (Saadali et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Saadali et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hallouz et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yebdri et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Reggam et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This work focuses on the Safsaf River, an essential water resource for irrigation. The Safsaf basin occupies the central part of the Skikda province and extends over an area of 1158 km\u003csup\u003e2\u003c/sup\u003e, with a population of nearly 460 thousand inhabitants (49% of the total population of the province) (Khelfaoui and Zouini, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The demand for water for domestic uses is expected to grow considerably in the coming years in developing countries. The Safsaf watershed is no exception; its water needs are continuously increasing. This demand is due to considerable demographic development in the area. Agricultural practice is concentrated mainly in the central part of the Safsaf basin. Downstream, the watershed is heavily influenced by polluting petrochemical industry, resulting in rapid and constant deterioration of surface water quality. Thus, agricultural pollution in the Safsaf plain cannot be ruled out. The aim of this study was therefore to assess the impact of urban, agricultural, and industrial wastewater on the quality of Safsaf River waters.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eGeographical Location of the Study Area\u003c/h2\u003e\n\u003cp\u003eThe Safsaf watershed is located in the center of the Skikda province, in the eastern part of Algeria (Messaoudi, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). The watershed comprises nine municipalities, and these municipalities are part of an irrigated area with highly valuable agricultural land. The Safsaf watershed is situated between the Guebli River basin to the west and the Kebir River basin to the east, its length is 53.19 Km, (Khelfaoui \u0026amp; Zouini, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). The demand for water for irrigation is significant in this region, where there is also a notable concentration of various industries, mainly located in the vicinity of Skikda, a city that plays a central role in the petrochemical industry (Titi Benrabah et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The Safsaf watershed receives discharges from the industrial area of the city of Skikda, where there is a petrochemical complex, as well as inputs from urban domestic water (Kaddeche et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The industry is mainly located downstream of the Safsaf River basin, where it consumes about 7.1\u0026nbsp;million cubic meters of water per year (Sakaa et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eNormalized Difference Vegetation Index (NDVI)\u003c/h2\u003e\n\u003cp\u003eThe Normalized Difference Vegetation Index (NDVI) is a widely used indicator to assess the presence of green vegetation in a given area. NDVI values range from \u0026minus;\u0026thinsp;1 to 1, where negative values signal the presence of water bodies, while positive values indicate the presence of vegetation (Abdı et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The higher the NDVI value, the denser the vegetation cover. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates that the values of the Normalized Difference Vegetation Index (NDVI) spread in a range from \u0026minus;\u0026thinsp;0.202 to 0.571 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These values are mainly located in the South, East, and West parts of the watershed, corresponding to areas with variable vegetation. In contrast, in the center of the watershed, the Normalized Difference Vegetation Index (NDVI) is low, indicating the presence of bare soils and aquatic areas. This suggests that the area comprises both water bodies, bare soils, and vegetation cover.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eSampling and Analysis Methods\u003c/h2\u003e\n\u003cp\u003eThree surface water stations were sampled in 2023. Three stations were chosen due to their different characteristics, particularly the nature of the effluents:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eStation 1 \u0026ldquo;El Harrouche\u0026rdquo; is close to significant anthropic activity and is fed by a network of domestic and agricultural effluents that flow into it.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStation 2 \u0026ldquo;Ramdane Djemel\u0026rdquo; located in the middle of the study area, it receives several sewers that transport urban and domestic waste.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStation 3 \u0026ldquo;Sonatrach\u0026rdquo; located near the mouth of the Safaaf River (right next to the Mediterranean Sea). It receives domestic, urban, industrial wastewater from the Sonatrack complex and agricultural water from the city of Skikda (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTwo sampling campaigns were carried out at these three stations to assess the spatiotemporal variation of physicochemical parameters in the Safsaf watershed during periods of high flow (April) and low flow (August). After filtration, all samples were stored in polyethylene bottles in a cooler at a constant temperature below 4\u0026deg;C and kept in a refrigerator at a temperature below 4\u0026deg;C after acidification by nitric acid HNO\u003csub\u003e3\u003c/sub\u003e (5%). Physical parameters such as pH and electrical conductivity (EC) were determined in situ using a portable conductimeter (HANNA Multiparameter). Turbidity by Turbidity meter. Total alkalinity (TA) by titrimetric method. Chloride ions (Cl\u003csup\u003e-\u003c/sup\u003e) were analyzed by volumetric titrations (Mohr\u0026rsquo;s method). Sodium ions (Na\u003csup\u003e+\u003c/sup\u003e) and potassium ions (K\u003csup\u003e+\u003c/sup\u003e) were analyzed by flame spectrometer, phosphate ions and ammonium ions were analyzed by colorimetric method, nitrate ions by salicylate method, nitrite ions by Zambelli method and bicarbonate ions by volumetric method. The creation of maps was carried out using ArcGIS 10.8 software.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eDescriptive Statistics and mapping of physicochemical parameters\u003c/h2\u003e\n\u003cp\u003eThe Safsaf river represents the main watercourse in the basin; the quality of its waters varies from upstream to downstream. Monitoring of some chemical elements has allowed us to visualize a spatiotemporal alteration and degradation of the quality of the river\u0026rsquo;s waters, with an increase in the concentrations of chemical elements from upstream to downstream. We carried out descriptive statistics (maximum, minimum, sum, average, and coefficient of variation) using Origin 2023. We note that the deviation from the mean is significant for certain elements such as electrical conductivity, chlorides, sodium, and potassium.\u003c/p\u003e\n\u003cp\u003eThe pH plays a role in regulating the chemical characteristics of water, including its level of alkalinity, acidity, specific chemical composition, and the ability of chemical elements to dissolve (Hossain and Patra, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). pH is a critical parameter governing freshwater bicarbonate and carbonate processes and is considered integral to biodiversity. A balanced pH range, typically between 6.5 to 8.5, supports aquatic life and indicates water quality (WHO 2017). Monitoring pH levels in the Oued Safsaf basin revealed values ranging from 7.62 to 8.57 in April and 7.86 to 8.2 in August. The higher value of above 8 recorded in the central and lower part of the basin show a slightly alkaline and alkaline nature of the quality of the surface water.\u003c/p\u003e\n\u003cp\u003eElectrical Conductivity (EC) is a parameter that allows assessing the total quantity of ions present in water, thus giving an indication of the degree of mineralization of this water. High EC can affect plant growth by reducing plant transpiration (Jolly et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, the EC exhibits significant variations, spanning from 1011 to 2340 \u0026micro;S/cm in April and expanding to a range of 4830 to 6840 \u0026micro;S/cm in August. The highest EC values are observed in the upper reaches of the basin, while lower EC values are documented in the central and lower regions in April.\u003c/p\u003e\n\u003cp\u003eThe alkalimetric title or Total Alkalinity (TA) is the concentration of carbonate ion CO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-2\u003c/sup\u003e and strong base OH\u003csup\u003e-\u003c/sup\u003e in water (Howladar et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The alkalinity levels experience variations, ranging from 120 to 279.98 mg/l in April and from 133.01 to 369.98 mg/l in August. According to Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the highest concentration of total alkalinity during April is observed in the second station, whereas during August it is in the third station, which was higher than the permitted limit 100 mg/L (WHO, 2017). We can assume that effluents from oil refinery industry and fertilizers are responsible for increasing TA in river water.\u003c/p\u003e\n\u003cp\u003eThe water turbidity is primarily the result of the presence of suspended particles.This increase in turbidity can be caused by various factors, including wastewater discharges and soil erosion, which reduces the transparency of the water and the amount of dissolved oxygen (Zenati et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to WHO (2017), median turbidity values were in compliance with the recommended quality of raw water for irrigation (\u0026lt;\u0026thinsp;1000 NTU). Turbidity, a key parameter, manifests considerable fluctuations, ranging from 1.3702 to 18.299 in April and expanding further to 6.7002 to 20.1 in August. Of particular interest is the distinct spatial pattern observed: the upper reaches of the basin consistently register the highest turbidity values, in stark contrast to the central and lower regions, which consistently exhibit lower turbidity levels.\u003c/p\u003e\n\u003cp\u003eInorganic nitrogen (nitrite and ammonium) and phosphate are significant factors affecting surface water quality (Diani et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The analysis of nitrite concentration revealed that the waters had a very low nitrite content according to WHO standards (2017) (0.1 mg/L). The low concentrations of elements such as nitrites and ammonium in the water are due to the reducing conditions of the less developed nitrate form. In the context of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, the recorded values depict a distinct seasonal trend. During April, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e concentrations range from 0.22 to 1.69, while in August, they expand to a range of 1 to 2.3. Notably, the concentration of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e consistently reveals a higher presence in the central and lower regions of the study area, in stark contrast to the lower levels observed in the upper part, both in April and August. This spatial variation underscores the dynamic nature of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e distribution, which appears to align with the central and lower sectors of the basin across the two months under examination. Our results correlate with those of Bisimwa et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted in Congo. The presence of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e in surface waters generally indicates an incomplete process of organic matter decomposition and is a relevant indicator of river contamination by urban discharges.\u003c/p\u003e\n\u003cp\u003ePhosphate concentration is an indicator of nutrient levels in the river system and can lead to eutrophication if present in excess (Bushero et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). This release is evident in the concentrations of phosphate ions (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e-3\u003c/sup\u003e), which exhibit a significant range, spanning from 0.737 to 4.46 in April and broadening to 1.1 to 6.32 in August. Interestingly, a recurring pattern emerges, with the highest PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e-3\u003c/sup\u003evalues consistently observed in the uppermost reaches of the basin, while the central and lower regions consistently record lower concentrations. The phosphate content at most monitoring stations is significantly high, indicating significant river pollution and a predisposition to eutrophication. Human activities have exerted considerable influence on the river\u0026rsquo;s phosphate concentration, with sources such as detergents, fertilizers, industrial and domestic waste frequently contributing to this contamination.\u003c/p\u003e\n\u003cp\u003eExamining the data recorded in April and August within the river area, chloride concentrations reveal a range spanning from 142.01 to 922.98 and 107.52 to 1881.5, respectively, which considers chloride contents above 106 mg/L to pose a serious problem on aquatic ecosystems (Bouaroudj et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eBiochemical Oxygen Demand (BOD) is an indication of the amount of dissolved oxygen used by microorganisms during the oxidation process of reducing substances present in waters and wastes (Hamil et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The concentrations of Biochemical Oxygen Demand (BOD), exhibiting a significant variance, ranging from 1 to 7 in April and narrowing to 1 to 2 in August. Interestingly, a distinct pattern emerges, with lower BOD values consistently documented in the central and lower regions, while higher concentrations are observed in the upper reaches of the basin during April. Remarkably, this pattern undergoes a reversal in August, where higher BOD values are now prevalent in the central and lower sectors, while the upper region registers lower concentrations.\u003c/p\u003e\n\u003cp\u003eThe main sources of Na\u003csup\u003e+\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e are silicate weathering, anthropogenic and agricultural activities, as well as mixing with seawater (Gen\u0026ccedil;er and Basaran, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Sodium concentrations ranging from 200.05 to 779.99 in April and expanding to 200.67 to 9099.8 in August. Mapping the distribution of sodium reveals a prevalence in the eastern and lower regions, aligning predominantly with a North-South orientation across the study area. A high concentration due to severe contamination by marine intrusion. The potassium concentrations, showing a notable range from 444.46 to 6314.9 in April and from 375.42 to 5779.9 in August. Intriguingly, the representation of maximum potassium values is consistently discerned in the central and lower regions, while the upper reaches of the basin consistently record lower concentrations. Sample 3 records the highest level of sodium (Na\u003csup\u003e+\u003c/sup\u003e) and potassium (K\u003csup\u003e+\u003c/sup\u003e), resulting from the influence of industrial discharges from the Sonatrach complex. In addition, high amounts of Na\u003csup\u003e+\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e are observed in samples from agricultural areas.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003ePrincipal Component Analysis (PCA)\u003c/h2\u003e\n\u003cp\u003eThe objective of applying Principal Component Analysis (PCA) is to reduce the number of variables into a small number of dimensions (factors), classify variables, and group observations with similar characteristics. A statistical PCA analysis was applied to the set of physicochemical parameters from three stations. This method is widely used for interpreting physicochemical data. For data processing through principal component analysis, we used 11 variables: pH, EC, TA, Turbidity, Na\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e-\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e-3\u003c/sup\u003e, and DBO\u003csub\u003e5\u003c/sub\u003e from two samplings conducted in April and August 2023. The eigenvalue on axes F1 and F2 provides complete information about the studied surface waters (100% information). Therefore, the work is based on axes F1 and F2.\u003c/p\u003e\n\u003cp\u003eIn the correlation circle of Fig.\u0026nbsp;13 (April), the 1st component (PC1), contributing 76.44% of inertia, is defined by the parameters: EC, TA, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e-3\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, Turbidity, and DBO\u003csub\u003e5\u003c/sub\u003e on the positive side and pH, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e-\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, and K\u003csup\u003e+\u003c/sup\u003e on the negative side. This factor represents a source of diffuse pollution generally associated with agricultural activities and water mineralization. It also reflects the significant influence of anthropogenic activities in the area, particularly domestic and industrial waste. The strong negative correlation with pH (-0.98) and moderate positive correlation with alkalinity (0.76) explain that alkalinity is related to natural processes of soil constituent dissolution, mainly calcium carbonates. However, pH is linked to leaching of carbonate compounds from the soil. These compounds raise both the pH and water alkalinity. In fact, alkaline conditions promote the growth of algae and other aquatic organisms (Howladar et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWith an inertia of 23.56%, the 2nd component (PC2) is defined by the parameters: EC, Na\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e-\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, and DBO\u003csub\u003e5\u003c/sub\u003e on the positive side and pH and TA on the negative side (Table\u0026nbsp;1). This factor suggests that positive correlations are associated with anthropogenic contamination through domestic waste water discharge and the origin of water mineralization (industrial discharge and seawater), while negative correlations are related to the alkalinity process. This factor indicates that pH does not show a significant correlation withother parameters, suggesting that chemical parameters come from various sources, and water pH is controlled by carbonate alteration.\u003c/p\u003e\n\u003cp\u003eIn the dry period, factor 1 contributes 67.69% to the total inertia and is defined by the parameters pH, Cl\u003csup\u003e-\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e. This factor shows that pollution comes from anthropogenic contamination and organic matter decomposition in the study area. Finally, factor 2 explains 32.31% and is associated with the parameters TA, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e-3\u003c/sup\u003e, Cl\u003csup\u003e-\u003c/sup\u003e, and K\u003csup\u003e+\u003c/sup\u003e with positive correlations ranging from 0.59 to 0.96, respectively, and EC with a negative correlation of -0.94. This factor explains that parameters with positive and negative correlations have different origins, originating from both human and natural contamination (return of seawater at the estuary). Therefore, it is summarized that the rapid and un controlled urban growth of Skikda has contributed to the deterioration of the city's environment and its surroundings. The waters of the Safsaf River are polluted by domestic waste water and factories, particularly with the presence of the petrochemical industrial base by the sea. Rapid and uncontrolled urbanization, especially in the area adjacent to the city, and its consumption of a large part of agricultural land, mark water pollution from this agricultural activity in the city.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTableau 1.\u003c/strong\u003e Principal Component Analysis for surface water samples from the study area\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eApril\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAugust\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePC1\u003c/p\u003e\n\u003cp\u003e76.44%\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePC2\u003c/p\u003e\n\u003cp\u003e23.56%\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePC1\u003c/p\u003e\n\u003cp\u003e67.69%\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePC2\u003c/p\u003e\n\u003cp\u003e32.31%\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0,62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.93\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0,15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.70\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.97\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e-0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0,94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.76\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e-0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0,59\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.98\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0,42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.94\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0,08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.96\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0,57\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePO4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.99\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e-0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0,92\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDBO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0,83\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.97\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e-0.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0,17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTurbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.97\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.54\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e-0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0,58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.51\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.95\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0,005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.58\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e-0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0,962\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.48\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.95\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0,048\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe Safsaf river represents the main watercourse in the basin, and the quality of its waters varies from upstream to downstream. Monitoring of some chemical elements has revealed spatial and temporal degradation of the river\u0026rsquo;s water quality, with an increase in chemical element concentrations from upstream to downstream. Downstream, the Safsaf river has experienced degradation in its water quality due to domestic discharges from the El Harrouch, Salah Bouchaour, Ramdane Djamel agglomerations and a part of the discharges from the city of Skikda through the Zeramna river, which flows into the Safsaf. Further downstream, in the industrial zone section, the Safsaf river receives discharges from the Skikda petrochemical units and the thermal power plant without any prior treatment. The chemistry of Safsaf river water shows that these waters have high conductivity values due to the presence of certain chemical elements at high concentrations (K\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e). This excess is due to contamination of agricultural and domestic origin (wastewater) and marine invasion especially in the North-West part, and also by pollution due to discharges from the Sonatrach complex rich in chlorine and soda which are directly discharged into the river.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no financial or non-financial conflicts of interest directly or indirectly related to the work submitted for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFekrache and Boudeffa wrote and prepared the main text of the original version of the manuscript and maps.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this work has received no direct or indirect funding from any public or private funding agency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that all data presented in this article are real and authentic, and have been collected from reliable scientific sources. The data and materials that support the findings of this study are available on request and we can send them to you.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdı A, Bouamrane A, Karech T, Dahri N, Kaouachi A, (2021). Landslide Susceptibility Mapping Using GIS-based Fuzzy Logic and the Analytical Hierarchical Processes Approach: A Case Study in Constantine (North-East Algeria), Geotech Geol Eng 39:5675-5691 https://doi.org/10.1007/s10706-021-01855-3\u003c/li\u003e\n\u003cli\u003eBelahmadi MSO, Charchar N, Abdessemed A, Gherib A, (2023). Impact of petroleum refinery on aquatic ecosystem of Skikda Bay (Algeria): Diversity and abundance of viable bacterial strains, Marine Pollution Bulletin, 1-11 https://doi.org/10.1016/j.marpolbul.2023.114704\u003c/li\u003e\n\u003cli\u003eBenkaddour B, Abdelmalek F, Addou A, Noguer T, Aubert D, Vouv\u0026eacute; F, (2019). Assessment of Anthropogenic and Natural Factors on Cheliff River Waters (North‑West of Algeria) at Two Contrasted Climatic Seasons, International Journal of Environmental Research 13: 925-941 https://doi.org/10.1007/s41742-019-00223-7\u003c/li\u003e\n\u003cli\u003eBisimwa AM, Kisuyab B, Kazadi ZM., Muhayad BB, Kankondae AB, (2022). Monitoring faecal contamination and relationship of physicochemical variables with faecal indicator bacteria numbers in Bukavu surface waters, tributaries of Lake Kivu in Democratic Republic of Congo, Hygiene and Environmental Health Advances, 1-13 https://doi.org/10.1016/j.heha.2022.100012\u003c/li\u003e\n\u003cli\u003eBouaroudj S, Menad A, Bounamous A, Ali-Khodja H, Gherib A, Weigel DE, Chenchouni H, (2019). Assessment of water quality at the largest dam in Algeria (Beni Haroun Dam) and effects of irrigation on soil characteristics of agricultural lands, chemosphere, 1-13 https://doi.org/10.1016/j.chemosphere.2018.11.193\u003c/li\u003e\n\u003cli\u003eBoudibi S, Sakaa B, Benguega Z, (2021). Spatial variability and risk assessment of groundwater pollution in El-Outaya region, Algeria, Journal of African Earth Sciences, 1-12 https://doi.org/10.1016/j.jafrearsci.2021.104135\u003c/li\u003e\n\u003cli\u003eBushero DM, Angello ZA, Behailu BM, (2022). Evaluation of hydrochemistry and identification of pollution hotspots of little Akaki river using integrated water quality index and GIS, Environmental Challenges, 1-11 https://doi.org/10.1016/j.envc.2022.100587\u003c/li\u003e\n\u003cli\u003eDiani K, Amar H, Azmi R, Elbelrhiti H, Nouayti N, Fathi N, (2021). Surface water quality assessment in the semi-arid area by a combination of heavy metal pollution indices and statistical approaches for sustainable management, Environmental Challenges, 1-12 https://doi.org/10.1016/j.envc.2021.100230\u003c/li\u003e\n\u003cli\u003eGen\u0026ccedil;er E, Basaran C, (2023). Water quality assessment and pollution of Akar\u0026ccedil;ay River, T\u0026uuml;rkiye, Kuwait Journal of Science, 1-8 https://doi.org/10.1016/j.kjs.2023.07.003\u003c/li\u003e\n\u003cli\u003eHamil S, Arab S, Chaffai A, Baha M, Arab A, (2018). Assessment of surface water quality using multivariate statistical analysis techniques: a case study from Ghrib dam, Algeria, Arabian Journal of Geosciences 11: 754, 1-14 https://doi.org/10.1007/s12517-018-4102-5\u003c/li\u003e\n\u003cli\u003eHallouz F, Meddi M, Rahmani SA, (2022). Multivariate analysis to assess the quality of irrigation water in a semi-arid region of north west of Algeria: case of Ghrib reservoir, Environmental Earth Sciences 81:158 1-9 https://doi.org/10.1007/s12665-022-10272-5\u003c/li\u003e\n\u003cli\u003eHossain M, Patra PK, (2020). Water pollution index-A new integrated approach to rank water quality, Ecological Indicators, 1-9https://doi.org/10.1016/j.ecolind.2020.106668\u003c/li\u003e\n\u003cli\u003eHowladar MF., Chakma E., Koley NJ., Islam S., Al Numanbakth MA., Ahmed Z, Chowdhury TR, Akter S, (2021). The water quality and pollution sources assessment of Surma river, Bangladesh using, hydrochemical, multivariate statistical and water quality index methods, 1-9 https://doi.org/10.1016/j.gsd.2020.100523\u003c/li\u003e\n\u003cli\u003eHowladar MF, Chakma E, Koley NJ, Islam S, Al Numanbakth MA, Ahmed Z, Chowdhury TR, Akter S, (2021). The water quality and pollution sources assessment of Surmariver, Bangladesh using, hydrochemical, multivariate statistical and water quality index methods, Groundwater for Sustainable Development, 1-9 https://doi.org/10.1016/j.gsd.2020.100523\u003c/li\u003e\n\u003cli\u003eJolly YN, Jahan Rakib MR, Kumar R, Sultana S, Mizanur Rahman SM, Kabir J, Akter S, Mamun KM, Jannat Fatema K, Mehnaz M, Pal P, Bhat EA, Paray BA, Sharma P, Bhattacharya P, (2023). Evaluation of surface water quality near pollution sources in Buriganga River and deciphering their probable emergence, ecological, and health risk aspects, Regional Studies in Marine Science, 1-13https://doi.org/10.1016/j.rsma.2023.102988\u003c/li\u003e\n\u003cli\u003eKaddeche H, Noune F, Dzizi S, Chaib N, Boudjellab ZE, Blanco S, (2022). Determinant factors of diatom assemblage\u0026rsquo;s distribution along the Coastal Central Constantine (Northeastern Algeria), AquatEcol 56: 1245-1269 https://doi.org/10.1007/s10452-022-09980-8\u003c/li\u003e\n\u003cli\u003eKhelfaoui F, Zouini D, (2010). Integrated Water Management and Water Quality in the Saf-Saf Watershed (Skikda Province, Northeastern Algeria), Nature \u0026amp; Technology, 50-56 https://www.univ-chlef.dz/revuenatec/art_03_07.pdf\u003c/li\u003e\n\u003cli\u003eMessaoudi K, (2007). The Coffin of the Living.An Algerian Habitat Bordering on the Unbearable, The Geography Journals of Quebec, 51(143), 137-154.https://doi.org/10.7202/016597ar\u003c/li\u003e\n\u003cli\u003ePulak H, Patra K, (2020). Water pollution index - A new integrated approach to rank water quality Mobarok, , Ecological Indicators, 1-9 https://doi.org/10.1016/j.ecolind.2020.106668\u003c/li\u003e\n\u003cli\u003eReggam A, Bouchelaghem EH, Hanane S, Houhamdi M, (2017). Effects of anthropogenic activities on the quality of surface water of Seybouse River (northeast of the Algeria), Arab J Geosci,10: 219, 1-7 DOI 10.1007/s12517-017-3010-4\u003c/li\u003e\n\u003cli\u003eSakaa B, Chaffai H, Hani A (2013). The use of Artificial Neural Networks in the modeling of socioeconomic category of Integrated Water Resources Management (Case study: Saf-Saf River Basin, North East of Algeria), Arab J Geosci 6:3969-3978 DOI:10.1007/s12517-012-0654-y\u003c/li\u003e\n\u003cli\u003eSaadali B, Zerrouki H, Hamzi A, Bouhdid C, Khiari A, (2019). Geochemical assessment of water quality and its suitability for agricultural use in the Djedrawadisubwatershed, northeast Algeria, Euro-Mediterranean Journal for Environmental Integration 4:33, 1-12 https://doi.org/10.1007/s41207-019-0121-1\u003c/li\u003e\n\u003cli\u003eSaadali B, Zerrouki H, Drias T, Khedidja A, Haoues C, Belloula M, (2022). Hydrogeochemical assessment to characterize the water quality for agricultural use in Mexanna and Bougous dams in the province of El Tarf, Algeria, Euro-Mediterranean Journal for Environmental Integration 7: 79-88 https://doi.org/10.1007/s41207-022-00291-w\u003c/li\u003e\n\u003cli\u003eTiti Benrabah S, KhericiBousnoubra H, Kherici N, Cote M, (2013). Assessment and management of water resources in Northeastern Algeria: case of watersheds Kebir West Safsaf and Gueblirivers, Skikda, Appl Water Sci 3:351-357 DOI 10.1007/s13201-013-0085-2\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO), (2017). Guidelines for Drinking-Water Quality: Fourth Edition Incorporating the First Addendum. WHO Library Cataloguing-in- Publication Data Guidelines, Switzerland.\u003c/li\u003e\n\u003cli\u003eYebdri L, Hadji F, Harek Y, Marok A, (2021). Quality assessment of water used for human consumption and irrigation purpose in parts of Tafna watershed (NW Algeria), Environmental Earth Sciences 80:502, 1-15 https://doi.org/10.1007/s12665-021-09805-1\u003c/li\u003e\n\u003cli\u003eZenati B, Inal A, Mesbaiah FZ, Kourdali S, Bachouche S, Pinho J, (2023). Pollutant load discharge from a Southwestern Mediterranean river (Mazafran River, Algeria) and its impact on the coastal environment, Arabian Journal of Geosciences, 16:146, 1-16 https://doi.org/10.1007/s12517-023-11260-0\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Skikda, pollution, Safsaf River, irrigation, physicochemical","lastPublishedDoi":"10.21203/rs.3.rs-3440178/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3440178/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Skikda region, primarily industrial and agricultural, has experienced significant accelerated industrial and agricultural development over the past decade, resulting in abundant untreated discharges into the physical environment. Our study focused on the physicochemical analysis of the water of the Safsaf River in Skikda. It is based on monitoring three stations during the months of March and August. The aim of this study was to assess the quality of this water and characterize its suitability for agricultural use. To this end, we determined the values of the following physicochemical parameters: Electrical Conductivity (EC), pH, turbidity, total alkalinity (TA), chlorides (Cl\u003csup\u003e-\u003c/sup\u003e), sodium (Na\u003csup\u003e+\u003c/sup\u003e), potassium (K\u003csup\u003e+\u003c/sup\u003e), nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e), ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e), Biological Oxygen Demand (BOD\u003csub\u003e5\u003c/sub\u003e), and phosphates (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e-3\u003c/sup\u003e). The results show that electrical conductivity, sodium, potassium, and chlorides increase downstream of the watershed due to seawater intrusion into the plain. As for the origins of phosphate pollution, whose concentrations have significantly increased along the river, we can explain it by pollution generated by the use of phosphate fertilizers and return irrigation water into surface waters, as well as pollution due to direct input of domestic wastewater.\u003c/p\u003e","manuscriptTitle":"Application of mapping and statistical study for the assessment of surface water quality in the Safsaf River (North-Eastern Algeria)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-16 17:33:56","doi":"10.21203/rs.3.rs-3440178/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"681d59f0-36d2-4486-a18b-e10944b8bdc1","owner":[],"postedDate":"October 16th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":25354435,"name":"Hydrology"}],"tags":[],"updatedAt":"2023-10-16T17:33:56+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-16 17:33:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3440178","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3440178","identity":"rs-3440178","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}