Environmental pollution and associated ecological risk assessment of the agricultural water in the arid coastal ecosystem

Mohammed Benaafi; Bassam Tawabini; Billel Salhi; Husam Musa Baalousha; Mahfuzur Rahman; Ijaz Hussain; Isam H Aljundi

doi:10.25259/JKSUS_1038_2025

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Research Article

2026

:38;

10382025

doi:

10.25259/JKSUS_1038_2025

Environmental pollution and associated ecological risk assessment of the agricultural water in the arid coastal ecosystem

Mohammed Benaafi^a,b,*, Bassam Tawabini^b, Billel Salhi^a, Husam Musa Baalousha^a,b, Mahfuzur Rahman^a, Ijaz Hussain^c, Isam H Aljundi^a

aInterdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia.

bGeosciences Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, 31261, Saudi Arabia.

cInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China.

*Corresponding author: E-mail address: benaafi@kfupm.edu.sa (M Benaafi)

Received: 2025-06-17, Accepted: 2025-10-27, Epub ahead of print: 2026-02-09, Published: 2026-02-24

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This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

Abstract

Pollution of freshwater in arid regions poses a critical environmental challenge threatening water sustainability and ecosystem integrity. This study presents a comprehensive assessment of combined pollution and ecological risk from agricultural drainage water (ADW) in the Al-Qatif region, eastern Saudi Arabia. Twenty samples were collected from the agricultural drainage system in the study area and from regions with treated wastewater irrigation and groundwater irrigation. The samples were characterized for chemical and pharmaceutical pollutants using standardized laboratory methods. Then, indices of water quality and pollution, and ecological risk were computed. The study revealed that >90% of the analyzed ADW samples were extremely polluted with higher contamination of nutrients and moderate for trace elements. Three categories of pollutants (nutrients, trace and toxic elements, and pharmaceuticals) were recognized, which posed a risk to the aquatic organisms, with a hazard index exceeding the threshold value (HI>1). Moreover, pharmaceuticals showed a higher risk for algae with HI exceeding 229. The study concluded that samples from regions irrigated by treated wastewater display higher combined pollution and ecological risk than samples from fields with groundwater irrigation. This indicates that the potential sources of nutrients, trace metals, and pharmaceuticals are the treated wastewater irrigation and animal manure used to fertilize agricultural soil. Accordingly, mitigation measures, including the advancement of wastewater treatment processes, effective manure management, and constructed wetlands, can be implemented to reduce the pollution load in ADW and safeguard freshwater bodies and aquatic life. This will help enhance long-term water security and ecosystem integrity in arid environments.

Keywords

Agroecosystem pollution

Emerging contaminants

Environmental suitability

Pharmaceuticals

Saudi Arabia

Water resources

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1. Introduction

Agricultural activities are essential for meeting the increasing demand for food; however, they significantly impact freshwater resources, ecological integrity, and environmental sustainability (UN-Water, 2022; X. Wu et al., 2025). Intensifying agricultural practices with improper disposal or reuse of agricultural wastewater, coupled with a significant increase in the use of pharmaceuticals for livestock treatment and municipal wastewater for irrigation, emerged as a major environmental concern in recent years (Ekpeghere et al., 2017; Garduño-Jiménez et al., 2023; Vaishnav et al., 2023). Agricultural drainage water (ADW), rich in nutrients, heavy metals, and pharmaceutical compounds (PCs), may present unexpected ecological risks because of its impact on aquatic systems (Vaishnav et al., 2023). The source of irrigation water and the type and intensity of fertilizer application can make drainage water from agricultural fields harmful to freshwater resources, aquatic life, and ecological systems.

ADW mostly contains significant concentrations of nutrients, primarily nitrogen and phosphorus. These nutrients may be derived from fertilizer, manure application (Mng’ong’o et al., 2022), and treated wastewater irrigation (Chauhan et al., 2025; Yi et al., 2011). These nutrients at elevated concentrations in drainage water can reach freshwater bodies and cause eutrophication issues, resulting in excessive algal growth (Akinnawo, 2023; Khan & Ansari, 2005), oxygen depletion, and degradation of aquatic habitats, posing serious environmental and human health issues (Akinnawo, 2023). Additionally, ADW may contain elevated concentrations of trace elements potentially impacting ecological systems and public health due to their persistence, toxicity, and bioaccumulation in ecosystems (Ahmed et al., 2023; Meng et al., 2022). The presence of these elements (e.g., lead (Pb), chromium (Cr), cadmium (Cd), zinc (Zn), arsenic (As), and copper (Cu)) in ADW may originate from agricultural inputs such as manure, fertilizers, wastewater application for irrigation (Rajendran et al., 2022), soil amendments (Savic et al., 2014), irrigated water, pesticide residues, and industrial and domestic sewage in the surrounding regions (Gola et al., 2016). He et al. (2004) monitored heavy metals in the surface runoff from vegetable and citrus farms in Florida for 2 years. They found that the concentrations of Cu, Fe, and Mn exceeded drinking water standards in a large portion of the analyzed water samples. Additionally, they concluded that the concentration of heavy metals in the surface water from agricultural fields was significantly correlated with the concentration of these metals in the soil.

Pharmaceutical pollution of aquatic environments is an emerging global concern due to its bioaccumulation, persistence in the environment, and potential adverse impacts on aquatic life (Chen et al., 2024; Jurado et al., 2022; Ortúzar et al., 2022). The use of veterinary antibiotics for livestock and the intensified use of pharmaceuticals for human medication may reach the irrigation field through treated wastewater application (Sengar & Vijayanandan, 2022). Conventional treatment of municipal wastewater may not be sufficient to remove pharmaceuticals from wastewater intentionally reaching irrigation fields and is disposed of in aquatic environments through agricultural surface runoff (Ali et al., 2021; Edwards et al., 2009). The application of municipal biosolids in agricultural activities was reported as a source of PC, such as acetaminophen, ibuprofen, and carbamazepine in ADW (Edwards et al., 2009; C. Wu et al., 2009). Additionally, Jaffrézic et al. (2017) detected animal-specific veterinary pharmaceuticals in the surface water of the Haute Rance watershed in northwestern France with a concentration range of 11-350 ng/L. They found that the highest concentration of PCs occurred in February, with a large runoff event from agricultural fields using manure as fertilizer. Fisher & Scott (2008) studied the occurrence of PCs in water bodies receiving surface runoff from dairy farms in the Macalister Irrigation District, Australia. They demonstrated the presence of DEET and some antibiotics, revealing that they reach surface water bodies from dairy farm runoff.

The previous studies mostly addressed single pollutants or isolated pollution assessment that may not reflect the combined effects of multiple pollutants on the environment and ecosystems. These studies usually utilized conventional assessment approaches of water contamination that often neglect the combined risks posed by mixed pollutants in ADW. The cumulative pollution and associated ecological risks of ADW in regions that implemented treated wastewater as an alternative water source for irrigation were not explored. The current study aimed to fill this knowledge gap by assessing the types and levels of pollutants present in ADW in Saudi Arabia’s eastern coastline, where treated wastewater is utilized for irrigation to replace non-renewable groundwater. Additionally, we aimed to evaluate the potential ecological risk posed by multiple contaminants in ADW, considering their cumulative effect on the local aquatic system. The previous studies are largely addressed to single pollutants or isolated pollution assessment that may not reflect the combined effects of multiple pollutants on the environment and ecosystems.

2. Materials and Methods

2.1 Study area and data collection

An integrated approach was adopted to assess the combined pollution level and associated ecological risk of the ADW in Al-Qatif area, Saudi Arabia, with a framework illustrated in Fig. 1. The study area is located in the eastern coastal region of Saudi Arabia within the Al-Qatif Governorate administration boundary, as shown in Fig. 2. Historically, the region has been agricultural, with recent urban development. Groundwater is the main source for irrigation and is mainly extracted from shallow and deep aquifers (Al-Sadeq, 2011; Benaafi et al., 2023). Additionally, treated wastewater was used for irrigation in the central part of the study area (Benaafi, Pradipta, et al., 2024). The eastern region of Saudi Arabia is characterized by an arid climate with limited rainfall (average ∼80 mm/year) and a high evaporation rate (Al-Shaibani, 2013; Benaafi et al., 2024; BinMakhashen & Benaafi, 2024). The region experiences maximum air temperature during the summer season from May to September, which reaches 50 °C. The higher temperature, which lasted for approximately 150 days, accelerated the increased demand for water for irrigation, resulting in surface water runoff from agricultural fields. The increased water demand was associated with a significant population increase of approximately 43.2% from 2010 to 2022, as per the General Authority for Statistics, Saudi Arabia (2022).

Flowchart showing the adopted integrated research framework to assess the ADW pollution and risk.

Map showing the sampling location and study area.

A field survey was conducted during the summer irrigation season (July 2023) under high evapotranspiration and irrigation intensity to collect water samples and in situ data. Twenty samples of ADW were collected from the main and secondary canals to assess the water quality and determine the level of pollution due to agricultural activities. Ten water samples were collected from the region where treated wastewater was used for irrigation, and the remaining samples were collected from regions with groundwater irrigation. Additionally, blank and duplicate samples were collected to ensure the accuracy of the analytical reliability. All field instruments were calibrated daily, and three measurements were obtained in each site to ensure that all collected data is reliable. All blank and duplicate samples were analyzed in the laboratory along with the main 20 water samples, and the results showed very low deviation. The samples were filtered in the field and stored in 4^oC and the same day submitted to the environmental laboratory at King Fahd University of Petroleum and Minerals (KFUPM). The physical and chemical in situ parameters of water quality (T, pH, EC, Eh, Turbidity, and DO) were measured in the field using a multi-parameter meter equipped with a GPS and calibrated before the field survey (Hanna manufacturer Model: HI9829).

Although the number of ADW samples was 20, the samples were selected based on a strategy to cover agricultural areas irrigated with wastewater as well as agricultural areas irrigated with groundwater, enabling comparisons between the impact of different irrigation water sources. Additionally, the collected water samples covered both the main and secondary drainage canals, ensuring representativeness of the local agricultural drainage system. The water samples were collected from sites that represent different irrigation practices (TWW irrigation and groundwater irrigation) and covered both the main and secondary irrigation canals. The distribution of the sampling sites also covered the upstream and downstream canals to capture the spatial variability of pollutant concentration.

2.2 Laboratory analysis

The collected water samples from the agricultural drainage channels were analyzed for ion and elemental compositions in the environmental laboratory at the King Fahd University of Petroleum and Minerals. The ion composition of the drainage water samples was determined using ion chromatography (IC) (Dionex ICS Thermo Fischer 1100, Thermo Fisher Scientific) with a detection limit of 0.05 ppm. Analysis was performed using the EPA 9056A reference method (USEPA, 2007b). The analyzed and detected ions include Calcium (Ca²⁺), Magnesium (Mg²⁺), Sodium (Na⁺), Potassium (K⁺), Chloride (Cl⁻), Bicarbonate (HCO₃⁻), Bromide (Br⁻), Nitrate (NO₃⁻), Sulfate (SO₄²⁻), Nitrite (NO₂⁻), and Phosphate (PO₄³⁻) ions, and Lithium (Li). The elements were analyzed using the inductively coupled plasma mass spectrometry (ICP-MS) with a detection limit of 0.01 μg/L and the EPA Method 200.8 (U.S. EPA, 2014). Seventeen trace elements were analysed: Aluminium (Al), boron (B), vanadium (V), cobalt (Co), manganese (Mn), copper (Cu), nickel (Ni), arsenic (As), Zinc (Zn), Iron (Fe), chromium (Cr), strontium (Sr), mercury (Hg), Lead (Pb), Cadmium (Cd), molybdenum (Mo), and barium (Ba). The accuracy of the analytical results was verified by the ion balance assessment with an accepted error of ±10%. All laboratory analyses were conducted with quality control samples, including duplicates, blanks, and standard samples, to ensure accuracy of the analytical results.

Pharmaceutical composition of 10 selected ADW samples was obtained using liquid chromatography-tandem mass spectrometry (LC-MS/MS) following US EPA Method 1694(USEPA, 2007a). The water samples were subjected to solid-phase extraction (SPE) to concentrate analytes and remove interferences using hydrophilic-lipophilic balanced (HLB) cartridges before injection into the LC-MS/MS. Forty-eight targeted PCs (Anastrozole, Azathioprine, Atenolol, Bezafibrate, Butorphanol, Buprenorphine, Caffeine, Carbamazepine, Capecitabine, Citalopram, Cyclobenzaprine, Clofibric Acid, Cyclophosphamide, Diclofenac, Diazepam, Enalapril, Flutamide, Fluoxetine, Furosemide, Gemfibrozil, Gabapentin, Hydrochlorothiazide, Indomethacin, Ifosfamide, Iohexol, Iopamidol, Iomeprol, Iopromide, Loperamide, Ketoprofen, Metoprolol, Naproxen, Oxazepam, Mycophenolate Mofetil, Paclitaxel, Piroxicam, Paracetamol (Acetaminophen), Propranolol, Sertraline, Salbutamol, Sotalol, Terbutaline, Sulfamethazine, Thebain, Valsartan, Warfarin, Tramadol, Zolpidem, Chloramphenicol, Lincomycin, Ciprofloxacin, Metronidazole, Trimethoprim, and Sulfamethoxazole) were analyzed with detection limit of 10 ng/L.

2.3 Water quality and risk assessment indices

2.3.1 Water quality index

The overall quality of the tested ADW was evaluated using the water quality index (WQI) to determine the quality level and its effect on freshwater resources in the study area. The WQI was calculated using the physical and chemical water quality parameters and ion composition (Ravindra et al., 2022). Fifteen water quality parameters, including pH, EC, Turbidity, TDS, Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl^-, HCO^3-, Br^-, NO₃^-, SO₄^2-, NO₂^-, and PO₄^-, were used to calculate the WQI. Each input parameter was assigned a weight with a value ranging from 1 to 5, revealing its impact on public health and ecological integrity, and is listed in Table S1 (Das et al., 2022). The maximum allowable limit was utilized according to the EPA guidelines and is listed in Table S1(USEPA, 2018). The WQI was calculated using a systematic approach, starting with the calculation of the relative weight (RWi) for each input parameter using Equation 1: The quality rating (Qi) for each parameter was obtained using Equation 2. The sub-index (SIi) for each parameter was obtained using Equation 3. Finally, the WQI was calculated for the water samples using Equation 4.

Table S1

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(1)

R W i = \frac{A W i}{\sum A W i}

(2)

Q i = \frac{C i}{S i} x 100

(3)

S I i = R W i \times Q i

(4)

W Q I = \sum S I i

where AWi, Ci, and Si are the assigned weight for the input parameter, concentration of the water quality parameter, and the maximum allowable level of the EPA for the i^th parameter, respectively (Ravindra et al., 2022). The assigned values for AWi (AW = 1–5) were obtained from previous studies (Das et al., 2022; Masoud & Ali, 2020; Ravindra et al., 2022) and based on their impact on human health and ecosystem integrity. The higher values of AWi were assigned to parameters contributing to salinity and eutrophication issues (EC, NO₃⁻, PO₄³⁻) and lower values for less influential parameters such as pH (see Table S1).

2.3.2 Contamination degree (Cd)

The overall contamination from multiple pollutants in ADW was assessed using the degree of contamination index (Cd). Cd was obtained as nitrate, phosphate, and trace elements using a contamination factor (Cf). The Cf was calculated separately for each pollutant using Equation 5 (Abba et al., 2023). The Cd values representing the total contamination level in each sampling location were then obtained using Equation 6:

(5)

C f i = \frac{C A i}{C N i}

(6)

C d = \sum_{i = 1}^{n} C f i

where the $C A i$ and $C N i$ are the pollutant concentration level and background value, respectively. The maximum contamination and allowable level values of the elements under investigation were used as background values (USEPA, 2012). According to (Hakanson, 1980), the contamination factor Cf, which accounts for the contamination of a single pollutant, has four classes: low, moderate, considerable, and high with Cf values of (Cf<1), (1 ≤ Cf < 3), (3 ≤ Cf < 6), (Cf ≥ 6), respectively. contaminations. The contamination degree is classified into four categories: low (Cd<8), moderate (8 ≤ Cd < 16), considerable (16 ≤ Cd < 32), and high (Cd ≥ 32) contaminations (Hakanson, 1980; Rakib et al., 2022).

2.3.3 Ecological risk assessment

An ecological risk assessment was conducted to evaluate the impacts of the analysed nutrients, trace and toxic elements, and pharmaceutical compounds on aquatic life at three trophic levels: fish, Daphnia, and algae. This assessment addressed the chronic toxicity, reflecting the continuous release of these compounds into aquatic environments. The chronic toxicity value (ChV), which is a toxicity endpoint reflecting the level of pollutants causing adverse impacts on aquatic organisms over their life cycles, was used in this study (Sengar & Vijayanandan, 2022; P. K. Singh & Ranjan, 2024). The ChV data of the analysed PCs were obtained from the ECOSAR model (ECOSAR version 2.2) of EPA, which estimates toxicity based on quantitative structure-activity relationships. The extracted ChV values of the detected nutrients, trace elements, toxic elements, and pharmaceuticals are listed in Table S2. The ECOSAR model data were used to compute the predicted no-effect concentrations (PNEC) for pharmaceutical compounds. For trace elements (Al, Co, Mn, Ni, Cu, As, Fe, and Cr), the PNEC values were obtained from previous work (Alves Miranda et al., 2025). The PNEC values of nitrate were also collected from literature (Camargo et al., 2005; Hickey, 2013).

Table S2

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The PNECs for each compound were obtained using the ChV data by implementing an assessment factor (AF) of 100 for pharmaceuticals, 10 for nitrate and phosphate, and trace and toxic elements, as recommended in earlier studies (Ai et al., 2024; Fan et al., 2022). The assessment factor accounts for the uncertainty in extrapolating laboratory data to real-world conditions (Mangelsdorf et al., 2021). The PNEC was calculated using equation 7; the data have been presented in Table S2. The calculated PNEC values were used to obtain the risk quotients (RQs) according to Equation 8. The RQs were calculated for each tropical level by dividing the measured environmental concentration (MEC) of each analysed PC by the corresponding PNEC values (Mišík et al., 2019). RQ values were obtained separately for each trophic level: fish, Daphnia, and algae. The ecological risk was classified based on the value of the RQ with RQ>= 1, 0.1 <RQ >1, and RQ<0.1indicating high, medium, and low risks, respectively, as documented in earlier studies (Chhipi-Shrestha et al., 2022; P. K. Singh & Ranjan, 2024). The Hazard index (HI), an indicator of the cumulative risk for each aquatic organism, was obtained for each sample site for all detected PCs using Equation 9:

(7)

P N E C = \frac{C h V}{AF}

(8)

R Q = \frac{M E C}{P N E C}

(9)

H I = \sum_{1}^{n} R Q

3. Results and Discussion

3.1 Water quality characteristics and heavy metal concentration

The physical and chemical parameters of the ADW reflecting its quality and impact on freshwater bodies in the study area have been shown in Fig. 3. Based on the observed values of these parameters, the majority of the ADW samples were of low quality with a high degree of pollution, as some of them exceeded the standard limit for driniking water (USEPA, 2012). ADW exceeded the drinking water standards for EC, Ca₂⁺, Mg₂⁺, Na⁺, K⁺, Cl⁻, and SO₄²⁻ by 100%. Additionally, it was above the drinking water standards for turbidity, HCO₃⁻, Br⁻, NO₃⁻, NO₂⁻, and PO₄⁻, with a percentage range of 2-50%. ADW comprised a pH value within the acceptable range of 6.0–8.5. The elevated salinity and ion composition of ADW, combined with the significant concentration of nutrients, may harm the quality of freshwater in the region.

Concentration of physicochemical properties and ions in analysed water.

The tested ADW samples contained trace and toxic elements, including Boron (B), Vanadium (V), Chromium (Cr), Manganese (Mn), Iron (Fe)l, Nickel (Ni), Cobalt (Co), Copper (Cu), Arsenic (As), Selenium (Se), Barium (Ba), Strontium (Sr), Aluminium (Al), and (Mo). The trace element concentrations of the tested ADW samples are shown in Fig. 4. Among these elements, B, Fe, and Sr exhibited the highest concentrations. B has a value range from 740-1220 µg/L. Fe and Sr display values from 417-1730 µg/L and 4600-15990 µg/L. Additionally, Al, V, Ni, and Cr display values ranging from 245-301 µg/L, 23-60 µg/L, 94-247 µg/L, and 58-125 µg/L, respectively. The Al, Ni, Cr, Fe, and Sr concentrations exceeded the drinking water standards (USEPA, 2012; WHO, 2017) in 100% of the tested water samples. However, the B levels exceeded the strict water standards stipulated by the European Union in only 25% of the analysed water samples (EU Parliament, 2008). The remaining elements and trace metals show concentration below the drinking water standards (EU Parliament, 2008; USEPA, 2012; WHO, 2017) with no impact on freshwater resources in the study area.

Box plot showing the distribution of trace element values.

3.2 Pharmaceuticals occurrence

Forty-eight pharmaceutical compounds were analysed using advanced liquid chromatography-tandem mass spectrometry (LC-MS/MS) with a detection limit of 10 ng/L. Among these, only 12 were detected in the ADW samples: Caffeine, Carbamazepine, Iohexol, Sulfamethazine, Valsartan, Atenolol, Diclofenac, Furosemide, Gabapentin, Hydrochlorothiazide, Naproxen, and Paracetamol (acetaminophen). These compounds displayed varied environmental concentrations and detection frequencies, as shown in Fig. 5. Caffeine was detected in 90% of the collected water samples with a maximum concentration (MEC) of 9.19 μg/L. Iohexol and valsartan were detected in 60% and 50% of the water samples, respectively, with MEC levels of 3.37 and 2.41 μg/L. Sulfamethazine was found with a detection frequency of 40% and MEC of 0.069 μg/L. Gabapentin and carbamazepine were also detected within 20% of the analysed samples with MEC of 0.452 μg/L and 0.124 μg/L for Gabapentin and carbamazepine, respectively. The rest of the detected PCs were found with a detection frequency of 10% and MEC values of 0.092 μg/L, 0.266 μg/L, 0.074 μg/L for Atenolol, Diclofenac, Furosemide, and 0.063 μg/L, 0.038 μg/L, 0.064 μg/L for Hydrochlorothiazide, Naproxen, and Paracetamol (Acetaminophen), respectively.

Maximum environmental concentration of the detected PCs and their detection frequency %.

3.3 Water quality assessment

The water quality index (WQI) was used to assess ADW quality using which incorporated 15 physical and chemical water quality parameters. The WQI was calculated to determine the impact of ADW on freshwater resources used for drinking and irrigation in the region. The WQI classified the water quality into five classes: excellent, good, poor, very poor, and extremely polluted water with values of WQI <50, 50 ≤ WQI<100, 100 ≤ WQI < 200, 200 ≤ WQI <300, and WQI >300, respectively (Abba et al., 2023; Benaafi et al., 2025; Das et al., 2022). The WQI values for each sampling site are illustrated in Fig. 6, and the values of the sub-index for each parameter are listed in Table 1. The WQI data range from 191.29-466.53, with an average value of 254.73. According to the WQI results, all water samples fell within the poor, very poor, and extremely polluted water classes. The water samples (50%) showed WQI values within the extremely polluted water category, 45% within the very poor water category, and only 5% comprised WQI values representing the poor water category. Drainage water salinity, Na⁺, and Cl^- are the main water quality parameters showing high values for the water quality sub-index, indicating their impact on lowering the overall quality of drainage water and freshwater resources in the region.

WQI results showing the percentage of each class and quality level in each sampling site.

Table 1. Results of the water quality sub-index (SI) for each parameter for the groundwater samples.

Sample ID	SI (pH)	SI (EC)	SI (Turbi dity)	SI (TDS)	SI (Na+)	SI (K+)	SI (Mg²+)	SI (Ca²⁺)	SI (Cl^-)	SI (HCO^3-)	SI (Br^-)	SI (NO3^-)	SI (SO4^2-)	SI (NO2^-)	SI (PO4^-)
D01	5.34	33.87	1.91	28.31	39.57	15.66	20.52	13.49	37.43	8.27	12.19	16.92	14.96	3.57	11.10
D02	5.43	33.47	1.10	28.31	40.00	15.84	20.57	13.14	37.55	8.13	12.19	3.92	14.99	3.51	10.97
D03	5.60	38.98	8.04	32.49	42.93	19.32	22.88	22.29	40.86	9.76	13.70	8.50	16.08	5.72	11.94
D04	5.60	39.47	7.80	32.49	42.06	19.04	22.49	21.87	40.30	8.32	13.67	7.84	16.02	5.68	11.79
D05	5.58	39.47	4.30	32.86	43.38	19.61	23.14	22.62	41.42	9.89	14.08	8.95	16.13	5.55	11.75
D06	5.48	26.19	1.50	21.78	20.53	10.71	15.64	15.51	27.79	7.68	6.62	13.45	11.14	11.06	18.18
D07	5.44	25.63	5.26	21.33	20.89	10.76	15.55	14.91	27.68	8.29	6.64	14.19	11.30	10.29	16.86
D08	5.44	22.73	6.46	18.93	17.88	8.99	14.00	13.59	24.87	7.09	5.31	12.28	9.83	7.01	16.88
D09	5.41	47.23	1.56	39.42	47.40	24.00	30.37	34.08	51.96	7.41	15.54	7.85	16.35	4.47	13.60
D10	5.41	47.23	1.32	39.42	49.82	24.59	30.44	34.28	52.12	8.16	15.98	7.40	16.36	4.48	13.34
D11	5.57	49.25	16.94	40.99	50.96	28.35	36.27	23.72	66.85	9.82	20.33	0.58	24.63	0.00	0.00
D12	5.59	46.17	3.02	38.30	45.10	26.37	31.83	40.78	56.63	10.12	15.67	1.92	24.66	8.06	32.67
D13	5.71	38.38	0.02	31.89	44.60	21.04	24.50	27.06	45.48	9.94	10.33	2.58	22.27	6.39	29.00
D14	5.64	50.20	1.45	41.74	52.52	23.65	35.52	23.44	67.46	5.64	27.33	0.42	22.67	8.89	28.67
D15	6.01	43.87	2.16	36.51	50.94	22.56	28.18	19.22	54.29	9.09	17.67	0.50	22.62	0.00	38.33
D16	5.71	61.63	2.13	51.29	95.16	31.55	41.20	26.61	76.39	12.01	30.33	0.25	32.26	0.00	0.00
D17	6.33	36.94	0.50	30.77	47.88	19.59	22.61	24.89	43.80	6.10	12.67	5.50	24.13	0.00	0.00
D18	5.99	33.16	0.08	27.62	44.74	17.60	19.76	17.28	38.82	5.48	10.67	8.92	23.07	6.94	0.00
D19	5.53	58.08	6.19	48.43	61.19	28.45	40.98	28.22	75.90	8.61	30.00	0.42	25.64	6.67	0.00
D20	5.55	51.07	0.02	42.59	54.59	25.81	34.75	29.17	65.10	8.22	23.33	2.25	24.48	0.00	0.00

3.4 Contamination factor (Cf) and contamination degree (Cd)

The degree of contamination (Cd) was obtained separately for each pollutant using a contamination factor (Cf), and the results have been presented in Table 2. Additionally, the cumulative contamination by all pollutants, including trace and toxic elements, nitrate, and phosphate was assessed via the degree of contamination (Cd) index and the results are shown in Table 2. Cd was calculated separately for nutrients and trace elements to depict the degree of contamination of each group of pollutants and represent their combined effect on environmental integrity.

Table 2. Statistical summary of the contamination factor (C_f) and contamination degree (C_d).

Parameters	min	max	average	Percentage of contamination level
Parameters	min	max	average	Low	Moderate	Considerable	High
$C_{f}^{P O_{4}^{3 -}}$	0.03	2.03	0. 75	0 65	0 35	0	0
$C_{f}^{N O_{3}^{-}}$	0.00	50.00	17. 51	0 3	0	0	0 7
C_f^B	0.74	1.23	0. 96	0 75	0 25	0	0
c_f^AL	1.23	1.51	1. 36	0	1	0	0
C_f^Co	0.00	0.00	0. 00	1	0	0	0
C_f^V	0. 47	1.21	0. 78	0 85	0 15	0	0
C_f^Mn	0 00	0.41	0. 03	1	0	0	0
C_f^Ni	1. 36	3.54	1. 85	0	0 9	0 1	0
C_f^Cu	0. 02	0.07	0. 05	1	0	0	0
C_f^As	0.02	0.08	0. 04	1	0	0	0
C_f^Fe	1.40	5.79	2.79	0	0 65	0 35	0
C_f^Cr	1. 16	2.50	1. 64	0	1	0	0
C_f^Sr	1.15	4. 00	2. 29	0	0 85	0 15	0
C_f^Mo	0. 15	0.46	0. 26	1	0	0	0
C_f^Ba	0.02	0.06	0. 04	1	0	0	0
C_d^Nutrients	0.03	50.06	18. 26	0	0 25	0 3	0 45
C_d^{Trace elements}	9.11	18.52	12. 09	0	0 85	0 15	0
C_d^{Nutrients+Trace elements}	10.18	60.68	30. 34	0 00	0 25	0 30	0 45

The contamination factor (C_f) results show that the nitrate displays low and moderate contamination degrees in 65% and 35% of the samples, with a value range from 0.03 to 2.03, respectively. Additionally, the C_f value of phosphate ranged from 0.001–50 and revealed low and high degrees of contamination in 30% and 70% of the analyzed samples. The cumulative contamination of nutrients in the analyzed ADW samples was represented by the degree of contamination of nitrate and phosphate (C_d^Nutrients), with Cd values ranging from 0.03-50.06, and moderate, considerable, and high contamination levels in 25%, 30%, and 45% of the tested water samples, respectively. A higher degree of contamination reflects elevated nutrients in the ADW in the study area, which may have an adverse impact on aquatic life via eutrophication and oxygen depletion (Blann et al., 2009; Wurtsbaugh et al., 2019; Yang & Lusk, 2018). The C_f values were also calculated for each nutrient and in each sample location to show the variation in the degree of contamination across the study area and to help in planning effective mitigation strategies and safeguarding aquatic life. The results of C_f^NO₃⁻ and C_f^PO₄³⁻ for each sample location are shown in Table S3. The results indicated moderate contamination of nitrate in samples D1–D10 and low contamination in samples D11–D20, except for D11, which showed moderate contamination. The same was true for phosphate, with a moderate to considerable degree in samples D1–D10, and low contamination in samples D11–D20, except for samples D12–D15. The moderate and considerable contamination of nitrate and phosphate in samples D1–D10 mostly reflects the application of treated wastewater for irrigation and the use of manure fertilizer (Benaafi, Pradipta, et al., 2024; A. Singh, 2021). Samples D11–D20 show low nitrate contamination due to the use of groundwater as the main water irrigation source; however, sample D11 showed exceptional contamination, which may indicate that the application of different fertilizers increases nitrate contamination. For PO₄³⁻, the sample D11–D20, where groundwater used for irrigation displayed a low contamination level; however, samples D12–D15 showed a high degree of contamination that may have originated from phosphate-rich fertilizer application.

Table S3

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The trace and toxic element contamination level was evaluated for each element separately using the contamination factor (Cf) and the cumulative contamination using the degree of contamination index (Cd); the results are shown in Table 2. The results showed that trace and toxic elements, including Co, Mn, Cu, As, Mo, and Ba, showed a low degree of contamination in 100% of the tested water samples, with C_f<1. These elements contributed little to the overall degree of contamination in the tested ADW. Contrastingly, other elements, including Ni, Fe, and Sr, show moderate contamination (1 ≤ C_f < 3) in 90%, 65%, and 85% of the analysed samples and considerable contamination (3 ≤ C_f < 6) in 10%, 35%, 15%, respectively. Furthermore, Al and Cr exhibit moderate contamination (1 ≤ C_f < 3) in 100% of the analysed water samples, and B shows moderate and low contaminations in 25% and 75% of the samples, respectively. B was reported to have an elevated concentration in the groundwater system in the study area (Benaafi, Abba, et al., 2024; Benaafi, Al-Areeq, et al., 2024) due to seawater intrusion. Therefore, the moderate degree of B contamination in the tested ADW may have originated from the groundwater used for irrigation. The moderate and considerable degrees of contamination of Ni, Fe, and Sr resulting from the elevated concentrations of these elements in the tested water may be attributed to various potential sources. Treated wastewater used for irrigation in the study area may contain heavy metals due to domestic waste input, which contributes to trace and toxic element contamination of the tested ADW. These elements can accumulate in soil and crops and then leach into drainage water (Nawaz et al., 2021). Groundwater in carbonate aquifers that contains Sr-bearing minerals is impacted by seawater intrusion, and can also be a potential source of Sr contamination of the tested ADW (Benaafi, Abba, et al., 2024). Furthermore, the leaching of fertilizers and soil amendments rich in Sr could be other sources of elevated Sr concentrations in the tested ADW (Amiri & Nakhaei, 2021).

3.5 Ecological risk assessment

3.5.1 Pharmaceutical compounds

The risk quotients (RQ) values of the 12 detected PCs in the ADW were obtained for three trophic levels, including primary producer (Algae), primary invertebrate consumers (Daphnia), and secondary vertebrate consumers (fish). The results are illustrated in Table 3, which shows that caffeine posed a high risk (RQ>1) for fish and algae and a moderate risk (0.1 <RQ >1) for Daphnia. Carbamazepine, sulfamethazine, and Valsartan showed moderate risk for algae, daphnia, and fish, respectively. The remaining detected PC compounds displayed low ecological risks, with RQ values less than 0.1 for all trophic levels. RQ values were calculated for each sampling location and trophic level, and the results have been presented in Table S4. The total hazard of PCs at each sampling location and for each trophic level was obtained by calculating the hazard index (HI), and the results have been illustrated in Table S4. The HI results showed that PCs displayed high and moderate risks for fish in 10% and 30% of the samples, respectively. For Daphnia, PCs displayed a moderate risk in 30% of the sampling locations. For algae, 90% of the sampling locations showed a high risk of PCs with HI>1 and 10% with a low risk (HI<0.1). The results indicate that the occurrence of PC in ADW significantly impacts algae as sensitive aquatic species for those compounds.

Table S4

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Table 3. RQ and HI for MEC of PCs, nutrients, and trace elements.

Pollutants	(MEC) (μg/L)	Chronic Toxicity Value (ChV)(mg/L)			PNEC (Mg/L)			Risk Quotient (RQ)
Pollutants	(MEC) (μg/L)	Fish	Daphnia	Algae	Fish	Daphnia	Algae	Fish	Daphnia	Algae
Pharmaceuticals
Caffeine	9.19	0.91	2.8	0.004	9.1	28	0.04	1.01	0.33	229.75
Carbamazepine	0.124	1.05	1.17	0.096	10.5	11.7	0.96	0.01	0.01	0.13
lohexol	3.37	2046.44	73,430.89	2076.65	20464.4	734308.9	20766.5	0.00	0.00	0.00
Sulfamethazine	0.069	3.26	0.065	8.8	32.6	0.65	88	0.00	0.11	0.00
Valsartan	2.41	1.69	11.6	18.4	16.9	116	184	0.14	0.02	0.01
Atenolol	0.092	15.22	6.75	38.04	152.2	67.5	380.4	0.00	0.00	0.00
Diclofenac	0.266	4.58	4.22	16.42	45.8	42.2	164.2	0.01	0.01	0.00
Furosemide	0.074	7.86	87.5	52.4	78.6	875	524	0.00	0.00	0.00
Gabapentin	0.452	10400	243	1940	104000	2430	19400	0.00	0.00	0.00
Hydrochlorothiazide	0.063	18.73	368.04	51.43	187.3	3680.4	514.3	0.00	0.00	0.00
Naproxen	0.038	21.31	15.74	45.32	213.1	157.4	453.2	0.00	0.00	0.00
Paracetamol (Acetaminophen)	0.064	0.12	0.19	0.35	1.2	1.9	3.5	0.05	0.03	0.02
Hazard Index (HI)								1.23	0.51	229.91
Nutrients
Nitrate	20.3				160	2130	500	0.13	0.01	0.04
Hazard Index (HI)								0.13	0.01	0.04
Trace and Toxic Elements
Aluminum (Al)	0.30				30.99	0.16	24.45	0.01	1.88	0.01
Cobalt (Co)	0.01				65.48	0.29	1.86	0.00	0.03	0.00
Manganese (Mn)	0.17				38.5	18.59	11.37	0.00	0.01	0.01
Nickel (Ni)	0.25				17.42	0.68	3.46	0.01	0.36	0.07
Copper (Cu)	0.14				12.08	2.62	3.03	0.01	0.05	0.05
Arsenic (As)	0.00				1,200	37.06	7.02	0.00	0.00	0.00
lron (Fe)	1.74				199.8	0.16	0.28	0.01	10.86	6.20
Chromium (Cr)	0.13				23.88	6.39	2.85	0.01	0.02	0.04
Hazard Index (HI)								0.05	13.21	6.40
Total Hazard Index (THI)								1.41	13.73	236.35

3.5.2 Nitrate

Water contaminated with nitrate and phosphate poses a significant ecological risk because of the eutrophication process leading to algal growth and oxygen depletion, which have a detrimental effect on aquatic ecosystems (Bu et al., 2024). Agricultural runoff was reported in the literature as one of the main sources of elevated concentrations of nutrients posing significant risks to humans and aquatic organisms (Cui et al., 2021). Here, the potential ecological risk posed by the occurrence of nitrate in ADW was evaluated using RQ and the HI. The RQ values of nitrate were calculated for three trophic levels (Fish, Daphnia, and Algae), and the results have been shown in Table 3. RQ values were calculated using the maximum environmental contamination (MEC) and predicted no-effect concentration (PNEC) (Camargo et al., 2005; Hickey, 2013). The results indicated that nitrate posed a moderate risk for fish (RQ= 0.13) and a low risk for daphnia and algae. Moderate RQ values of nitrate reflect a higher nitrate contamination level of the ADW and greater impact on the ecological system. The potential ecological risk associated with nitrate was obtained for each sampling location and trophic level, and the results have been shown in Table S5. Nitrate showed a moderate risk (0.1 <RQ >1) for fish in 10% of the analysed water samples. However, low ecological risk values were observed due to nitrate contamination for daphnia and algae. The cumulative risk posed by nitrate pollutants to aquatic life in the study area was calculated using the H1 for each sampling area presented in Table S5. The HI values indicate that the cumulative risk by nitrate is moderate in 20% of the sampling locations, with a range from 0.10 to 0.148. The moderate HI values may reflect the adverse impact of nitrate pollution on aquatic life in the study area.

Table S5

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3.5.3 Trace and toxic elements

Eight trace and toxic elements, Al, Co, Mn, Ni, Cu, As, Fe, and Cr, were evaluated for potential risks to the ecological system in the study area. Individual risk (RQ) and cumulative risk (HI) were computed for the tested elements using the maximum environmental concentration. The results have been presented in Table 3, and the corresponding PNEC values and the results have been illustrated in Table 3. Among the tested elements, Aluminium (Al) posed a high risk for daphnia with an RQ value of 1.88. Additionally. Iron (Fe) also showed high risk for daphnia and algae with RQ values of 10.86 and 6.20, respectively. Nickel (Ni) displays a moderate risk for daphnia with an RQ value of 0.36. In contrast, other elements show low ecological risk for all trophic levels with RQ<0.1. The HI values for the analysed trace elements indicated a high cumulative risk for Daphnia (HI= 13.21), and algae (HI= 6.40), and low risk for fish (HI= 0.05). Accordingly, the occurrence and bioaccumulation of these elements with elevated concentration in the study area may pose a high potential risk to aquatic life that requires monitored to safeguard the ecosystem.

The ecological risk due to the presence of trace and toxic elements in the analysed water was assessed for each sample location, and the results of the risk indices (RQ and HI) have been illustrated in Table S6. The result showed that Iron (Fe) poses a high ecological risk for 20% and moderate risk for 80% of the sampling sites, and for all trophic levels (fish, daphnia, and algae). Aluminium (Al), Ni, and Cu showed moderate risk for 100%,80%, and 50% of the sampling locations for fish. Additionally, Aluminium (Al) showed a moderate risk for daphnia and algae in all tested sites. For Ni and Cu. The result showed moderate risk for daphnia and algae in 90% of the tested sites. For other trace elements (As, Co, Mn, Cr), they showed low risk for all trophic level organisms in the majority of the tested water samples. Moreover, the cumulative risk (HI) posed by all elements was calculated for each sampling site and for each type of trophic level, and the data are illustrated in Table S6. The results showed that 95% of sampling sites were characterized by high ecological risk for fish and daphnia, and 90% for algae. Thus, the occurrence of these elements in tested ADW threatens all three types of trophic level organisms and requires mitigation and effective management.

Table S6

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3.6 Source identification of PCs

The PCs detected in the ADW samples reflect various medication applications for human and veterinary medicine. Human PCs, including Carbamazepine, Iohexol, Valsartan, Atenolol, Diclofenac, Furosemide, Gabapentin, Hydrochlorothiazide, Naproxen, and Paracetamol (acetaminophen), most likely originate from sewage treatment plants. The application of treated wastewater for irrigation in the study area is expected to be the pathway for these contaminants to reach environmental water, as documented in earlier works, Benaafi, Pradipta, et al. (2024). The majority of the detected human PCs were sourced from household wastewater, except for the iohexol compound, which was likely sourced from hospital wastewater. Carbamazepine is an anticonvulsant used to treat neuropathic pain and epilepsy (Carmland et al., 2022). Gabapentin is a prescribed medication used to treat seizures and nerve-related pain (Wiffen et al., 2017). Iohexol is a contrast agent used in medical imaging techniques such as computed tomography (CT) to enhance image clarity (Gönder et al., 2021). Valsartan, Atenolol, Furosemide, and Hydrochlorothiazide are prescribed for cardiovascular disease and blood pressure treatments (Wellington & Faulds, 2002). Diclofenac and Naproxen are prescribed medications used for pain relief and inflammation to treat muscle pain and fever (Stoev et al., 2021). Paracetamol is an over-the-counter drug used to relieve pain and reduce fever (Can et al., 2021). Sulfamethazine is a veterinary antibiotic used to treat bacterial infections in livestock and aquaculture (Ibrahim et al., 2019). The occurrence of veterinary antibiotics reflects the application of livestock manure as the main fertilizer in the farmlands.

The potential sources, pathways, and receptors of pharmaceutical pollution in the study area have been shown in Fig. 7. The pathways of PCs include discharge from households to sewage treatment plants, followed by wastewater reuse for irrigation and infiltration into soil, and potentially groundwater. Additionally, surface runoff may carry these contaminants to nearby surface water and marine environments. However, waste generated from livestock and poultry farms contributes to pharmaceutical pollution via the use of antibiotic-laden manure as a fertilizer in agricultural practices and the release of PC compounds into the environment through surface runoff (Ghirardini et al., 2020; Topi & Spahiu, 2020).

Schematic illustration of source, pathway, and receptor of PCs in the study area.

The profile and occurrence of PCs in the tested ADW in the current study provide insights into the prevalent health issues of the local community, including pain and inflammatory conditions, cardiovascular diseases, and neurological and mental health disorders. Therefore, trends in pharmaceutical pollution in the local environment can be inferred from common public health issues. This will help develop effective management strategies to mitigate their impact on environmental integrity and ecosystem health. Additionally, the identification of PC pathways through wastewater application for irrigation can serve as a proxy for evaluating potential environmental pollution and help design an effective wastewater treatment process to remove these compounds before they are released into the environment.

3.7 Mitigation and management strategies

Surface runoff from agricultural fields containing elevated concentrations of pharmaceuticals requires mitigation measures addressing both point and nonpoint sources. The treated wastewater used for irrigation in the study area was produced from a conventional wastewater treatment plant, which is most likely not designed to remove pharmaceuticals (Benaafi, Pradipta, et al., 2024). Therefore, upgrading the treatment processes of these plants using advanced technologies such as membrane filtration, advanced oxidation processes, and ozonation will reduce the concentration of pharmaceuticals in the effluent and impact the local environment (Patel et al., 2020; Rosman et al., 2018). Additionally, the pharmaceutical concentration in ADW can be attenuated through natural solutions, such as constructed wetlands (Hijosa-Valsero et al., 2010; Ilyas & van Hullebusch, 2020). These techniques are inexpensive and effective in inhibiting the transport of pharmaceuticals from agricultural fields to freshwater bodies in the study area (surface water and groundwater) (Ilyas & van Hullebusch, 2020). Constructed wetlands act as filters, removing pollutants through physical, chemical, and biological processes. These processes include adsorption, sedimentation, plant uptake, phyto-degradation, and microbial degradation. Constructed wetlands can be an effective mitigation approach for both medication originating from households and reaching the environment via treated wastewater for irrigation and veterinary drugs used for medication, growth promotion, and vaccines (Ilyas & van Hullebusch, 2019).

Pharmaceutical compounds (e.g., sulfamethazine), which are most likely sourced from animal manure, especially from cattle and poultry farms, can be mitigated through sustainable manure application management (Ghirardini et al., 2020). Mitigation measures include aerobic composting, anaerobic digestion, and proper timing and application of manure (Larson et al., 2023). The aerobic composting treatment of manure involves enhancing the biological degradation of pharmaceuticals under elevated temperatures (Luo et al., 2020). The anaerobic digestion of the pharmaceuticals in animal manure was approved as an effective treatment to reduce some compounds such as sulfonamides and β-lactams (Larson et al., 2023; Luo et al., 2020). Additionally, proper application of manure in agricultural fields with effective time management can reduce the pharmaceutical load in surface runoff. The application of manure during crop growth can enhance nutrient uptake and microbial degradation of pharmaceuticals in the root zone (Chowdhury et al., 2021). Manure application practices, such as spreading manure at the surface of the crop field, may facilitate the transport of these compounds through surface runoff; therefore, proper mixing of manure with soil can reduce exposure to surface runoff and photodegradation risk (Lacroix et al., 2023; Saha et al., 2023). Photodegradation of animal manure containing antibiotics, such as tetracycline, may produce more toxic metabolites than those of the original compounds (Wang et al., 2022).

The proposed mitigation options that can help reduce the concentration of PCs in environmental waters in the study area are summarised in Fig. 8. These options may include a combination of advanced sewage wastewater treatment processes (e.g., membrane filtration, advanced oxidation processes, and ozonation), effective management of manure fertilizers, and the construction of wetlands (Skalska-Tuomi et al., 2025). The implementation of advanced wastewater treatment is expected to reduce the pharmaceutical type and load of treated wastewater used for irrigation. However, the accumulation of PC residuals in agricultural fields due to the long-term application of wastewater irrigation may increase the load on surface runoff. The subsequent construction of wetlands can mitigate the pollution load of ADW and reduce its impact on freshwater resources and environmental health. Regarding the PCs sourced from livestock manure application can be mitigated through improved fertilizer management, such as a soil and manure mixing approach, and reducing the surface fertilization and application during the crop growth period (Yan et al., 2024). The implementing the optimal mitigation strategy, the PC pollution load in the study area can be significantly decreased, thereby reducing vulnerability to freshwater pollution and safeguarding ecosystem integrity.

Schematic illustration of the proposed mitigation options for pollutants in the study area.

3.8 Study limitations

The current study was based on 20 water samples that were collected from main and secondary drainage channels of the agricultural drainage system in the Qatif region, eastern Saudi Arabia. Although the samples represent the agricultural drainage system in the study area and provide valuable insights into water quality conditions, they may not fully capture the factors that control the temporal and spatial variations in pollutant distribution. These controls include the seasonal variations, irrigation intensity, rainfall, and changes in agricultural practices, which may affect pollutant concentrations and their geographic distribution. In addition, the assessment of pharmaceutical contaminants in the current study was limited to ten samples due to analytical resource constraints, reflecting the need for a future study that focuses on pharmaceutical contaminants comprehensively in terms of the number of samples and the number of compounds analysed, including personal care products and antibiotics. Based on the limitations of the current study, it is recommended that future studies be conducted on a broader scale on pollutants in ADW to cover spatial and temporal variations and consider seasonal variations, and consider irrigation intensity. In addition, future studies may assess a wide range of pollutants that will enhance the understanding of pollutant sources, fate, and transport mechanisms. It is also recommended that future studies include the use of an advanced monitoring system linked to artificial intelligence to predict pollutant occurrences and their concentration. This will help enhance effective water resource management and reduce the risk of environmental pollution.

4. Conclusions

ADW threatens the freshwater resources and the environmental health in the eastern coastal region of Saudi Arabia due to the pollutant load of trace and toxic elements, nutrients, and pharmaceuticals. The current study evaluated the pollution degree of ADW and the potential risk to aquatic life. The study reveals that 90% of analyzed ADW samples were classified as very poor and extremely polluted with WQI > 200. Nutrients (Nitrate and phosphate) display the highest contamination degree, reaching 50.06 and an average of 18.26. Moreover, the ecological risk assessment results showed high cumulative risk for Daphnia and algae with hazard index (HI) of 13.73 and 236.35, respectively. The Pharmaceutical pollutants display a higher risk for algae, while trace and toxic elements showed a higher risk to Daphnia. The study found that ADW drained from treated wastewater-irrigated regions displayed high pollution compared with ADW from groundwater-irrigated regions. The study concluded that the application of wastewater treated for irrigation, along with livestock manure fertilizers, is the most likely source of pollution in ADW. Effective mitigation strategies are required to reduce ADW pollution and its impact on freshwater resources and aquatic life. The proposed mitigation options include upgrading wastewater treatment processes, effective management and application of livestock manure, and wetland construction. Through a combination of these measures, drainage water from agricultural fields in the study area is expected to have less pollution and impact on aquatic life and human health. This will help protect environmental integrity and enhance sustainability and long-term water security in eastern Saudi Arabia, with potential relevance to other arid regions.

Acknowledgment

Authors would like to acknowledge all support provided by the Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals (KFUPM). Article Processing Charge (APC) was provided by the Deanship of Research at KFUPM.

CRediT authorship contribution statement

Mohammed Benaafi: Conceptualization, data curation, formal analysis, investigation, methodology, resources, experimentation, writing-original draft, writing-review & editing. Bassam Tawabini: Data curation, Laboratory analysis, writing-review & editing. Billel Salhi: Methodology, software, writing-original draft, Writing-review & editing. Husam Musa Baalousha: Conceptualization, investigation, methodology, writing-review & editing. Mahfuzur Rahman: software, data curation, investigation, Writing-review & editing. Ijaz Hussain: Methodology, formal analysis, writing-original draft. Isam H. Aljundi: Supervision, writing-review & editing.

Declaration of competing interest

The authors declare that they have no competing financial interests or personal relationships that could have influenced the work presented in this paper.

Data availability

Data will be available on request from the corresponding author.

Declaration of Generative AI and AI-assisted technologies in the writing process

The authors confirm that there was no use of Artificial Intelligence (AI)-Assisted Technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.

Supplementary data

Supplementary material to this article can be found online at https://dx.doi.org/10.25259/JKSUS_1038_2025.

References

Abba S.I., Egbueri J.C., Benaafi M., Usman J., Usman A.G., Aljundi I.H.. Fluoride and nitrate enrichment in coastal aquifers of the Eastern Province, Saudi Arabia: The influencing factors, toxicity, and human health risks. Chemosphere. 2023;336:139083. https://doi.org/10.1016/j.chemosphere.2023.139083
[Google Scholar]
Ahmed R.S., Abuarab M.E., Ibrahim M.M., Baioumy M., Mokhtar A.. Assessment of environmental and toxicity impacts and potential health hazards of heavy metals pollution of agricultural drainage adjacent to industrial zones in Egypt. Chemosphere. 2023;318:137872. https://doi.org/10.1016/j.chemosphere.2023.137872
[Google Scholar]
Ai S., Li J., Wang X., Zhao S., Ge G., Liu Z.. Derivation of aquatic predicted no-effect concentration and ecological risk assessment for triphenyl phosphate and tris(1,3-dichloro-2-propyl) phosphate. Sci Total Environ. 2024;913:169756. https://doi.org/10.1016/j.scitotenv.2023.169756
[Google Scholar]
Akinnawo S.O.. Eutrophication: Causes, consequences, physical, chemical and biological techniques for mitigation strategies. Environmental challenges. 2023;12:100733. https://doi.org/10.1016/j.envc.2023.100733
[Google Scholar]
Ali M., Ahmed T., Abu-Dieyeh M., Al-Ghouti M.. Environmental Impacts of Using Municipal Biosolids on Soil, Plant and Groundwater Qualities. Sustainability. 2021;13:8368. https://doi.org/10.3390/su13158368
[Google Scholar]
Al-Sadeq R.H.. Environmental Assessment of Groundwater Condition in Tarout Island, Eastern Province of Saudi Arabia [Master Thesis]. King Fahd University of Petroleum and Minerals; 2011.
Al-Shaibani A.. Economic potential of brines of Sabkha Jayb Uwayyid, Eastern Saudi Arabia. Arab J Geosci. 2013;6:2607-2618. https://doi.org/10.1007/s12517-012-0521-x
[Google Scholar]
Alves Miranda G., Soares Dos Santos F., Lourenço Pereira Cardoso M., Etterson M., C Amorim C., V M Starling M.C.. Proposal of novel predicted no effect concentrations (PNEC) for metals in freshwater using species sensitivity distribution for different taxonomic groups. Sci Rep. 2025;15:8180. https://doi.org/10.1038/s41598-025-92692-4
[Google Scholar]
Amiri N., Nakhaei M.. An investigation of qualitative variations of groundwater resources under municipal wastewater recharge using numerical and laboratory models, Nazarabad plain, Iran. Environ Sci Pollut Res Int. 2021;28:55771-55785. https://doi.org/10.1007/s11356-021-12638-x
[Google Scholar]
Benaafi M., Abba S.I., Aljundi I.H.. Effects of seawater intrusion on the groundwater quality of multi-layered aquifers in Eastern Saudi Arabia. Molecules. 2023;28:3173. https://doi.org/10.3390/molecules28073173
[Google Scholar]
Benaafi M., Abba S.I., Opeyemi Oyedeji M., Mubarak A.S., Usman J., Aljundi I.H.. Experimental-based groundwater salinization from the carbonate aquifer of eastern Saudi Arabia: Insight into machine learning coupled with meta-heuristic algorithms. Chemom Intell Lab Syst. 2024;249:105135. https://doi.org/10.1016/j.chemolab.2024.105135
[Google Scholar]
Benaafi M., Al-Areeq A.M., Tawabini B., Basaleh A.A., Bafaqeer A., Humphrey J.D., Aljundi I.H.. Combined effects of seawater intrusion and heavy metal pollution on the groundwater resources of Tarout Island. Saudi Arabia Arab J Sci Eng. 2025;50:327-351. https://doi.org/10.1007/s13369-024-09184-2
[Google Scholar]
Benaafi M., Pradipta A., Tawabini B., Al-Areeq A.M., Bafaqeer A., Humphrey J.D., Nazal M.K., Aljundi I.H.. Suitability of treated wastewater for irrigation and its impact on groundwater resources in arid coastal regions: Insights for water resources sustainability. Heliyon. 2024;10:e29320. https://doi.org/10.1016/j.heliyon.2024.e29320
[Google Scholar]
Benaafi M., Tawabini B., Sajid Jillani S.M., Babalghaith A.M., Aljundi I.H.. Tracking the trace element pollution and associated health risk in urban coastal groundwater, eastern Saudi Arabia. Ocean & Coastal Manag. 2025;262:107591. https://doi.org/10.1016/j.ocecoaman.2025.107591
[Google Scholar]
BinMakhashen G.M., Benaafi M.. Predicting seawater intrusion in coastal areas using machine learning: A case study of arid coastal aquifers, Saudi Arabia. Groundwater for Sustainable Development. 2024;26:101300. https://doi.org/10.1016/j.gsd.2024.101300
[Google Scholar]
Blann K.L., Anderson J.L., Sands G.R., Vondracek B.. Effects of agricultural drainage on aquatic ecosystems: A review. Crit Rev Environ Sci Technol. 2009;39:909-1001. https://doi.org/10.1080/10643380801977966
[Google Scholar]
Bu D., Zhu Q., Li J., Huang J., Zhuang Y., Yang W., Qi D.. Mariculture may intensify eutrophication but lower n/P ratios: A case study based on nutrients and dual nitrate isotope measurements in Sansha Bay, southeastern China. Front Mar Sci. 2024;11 https://doi.org/10.3389/fmars.2024.1351657
[Google Scholar]
Camargo J.A., Alonso A., Salamanca A.. Nitrate toxicity to aquatic animals: A review with new data for freshwater invertebrates. Chemosphere. 2005;58:1255-1267. https://doi.org/10.1016/j.chemosphere.2004.10.044
[Google Scholar]
Can Ö, Kıyan G.S., Yalçınlı S.. Comparison of intravenous ibuprofen and paracetamol in the treatment of fever: A randomized double-blind study. Am. J. Emerg. Med.. 2021;46:102-106. https://doi.org/10.1016/j.ajem.2021.02.057
[Google Scholar]
Carmland, M., Jensen, T.S., Finnerup, N.B., 2022. Anticonvulsants in the management of chronic pain. in clinical pain management: A Practical Guide, Second Edition. https://doi.org/10.1002/9781119701170.ch17
Chauhan A., Jain A., Kolton M., Pathak A.. Impacts of long-term irrigation of municipally-treated wastewater to the soil microbial and nutrient properties. Sci Total Environ. 2025;959:178143. https://doi.org/10.1016/j.scitotenv.2024.178143
[Google Scholar]
Chen Y., Li M., Gao W., Guan Y., Hao Z., Liu J.. Occurrence and risks of pharmaceuticals, personal care products, and endocrine-disrupting compounds in Chinese surface waters. J Environ Sci (China). 2024;146:251-263. https://doi.org/10.1016/j.jes.2023.10.011
[Google Scholar]
Chhipi-Shrestha G., Rodriguez M., Behmel S., Pulicharla R., Proulx F., Hewage K., Sadiq R.. Probabilistic framework for assessing ecological risk of Contaminants of emerging concern: Application to a Canadian lake system. Chemosphere. 2022;287:131910. https://doi.org/10.1016/j.chemosphere.2021.131910
[Google Scholar]
Chowdhury T., Chowdhury M.A.H., Qingyue W., Enyoh C.E., Wang W., Khan M.S.I.. Nutrient uptake and pharmaceutical compounds of Aloe vera as influenced by integration of inorganic fertilizer and poultry manure in soil. Heliyon. 2021;7:e07464. https://doi.org/10.1016/j.heliyon.2021.e07464
[Google Scholar]
Cui J., Jin Z., Wang Y., Gao S., Fu Z., Yang Y., Wang Y.. Mechanism of eutrophication process during algal decomposition at the water/sediment interface. J Cleaner Prod. 2021;309:127175. https://doi.org/10.1016/j.jclepro.2021.127175
[Google Scholar]
Das C.R., Das S., Panda S.. Groundwater quality monitoring by correlation, regression and hierarchical clustering analyses using WQI and PAST tools. Groundwater for Sustainable Development. 2022;16:100708. https://doi.org/10.1016/j.gsd.2021.100708
[Google Scholar]
Edwards M., Topp E., Metcalfe C.D., Li H., Gottschall N., Bolton P., Curnoe W., Payne M., Beck A., Kleywegt S., Lapen D.R.. Pharmaceutical and personal care products in tile drainage following surface spreading and injection of dewatered municipal biosolids to an agricultural field. Sci Total Environ. 2009;407:4220-4230. https://doi.org/10.1016/j.scitotenv.2009.02.028
[Google Scholar]
Ekpeghere K.I., Lee J.W., Kim H.Y., Shin S.K., Oh J.E.. Determination and characterization of pharmaceuticals in sludge from municipal and livestock wastewater treatment plants. Chemosphere. 2017;168:1211-1221. https://doi.org/10.1016/j.chemosphere.2016.10.077
[Google Scholar]
Parliament. EU. DIRECTIVE 2008/105/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 16 December 2008 on environmental quality standards in the field of water policy,. Official Journal of the European Union. 2008
[Google Scholar]
Fan H., Wang Y., Liu X., Wang Y.Y.L., Kazmi S.S.U.H., Ohore O.E., Liu W., Wang Z.. Derivation of predicted no-effect concentrations for thirty-five pharmaceuticals and personal care products in freshwater ecosystem. Front Mar Sci. 2022;9 https://doi.org/10.3389/fmars.2022.1043792
[Google Scholar]
Fisher P.M.J., Scott R.. Evaluating and controlling pharmaceutical emissions from dairy farms: A critical first step in developing a preventative management approach. J Cleaner Prod. 2008;16:1437-1446. https://doi.org/10.1016/j.jclepro.2008.04.024
[Google Scholar]
Garduño-Jiménez A.L., Durán-Álvarez J.C., Ortori C.A., Abdelrazig S., Barrett D.A., Gomes R.L.. Delivering on sustainable development goals in wastewater reuse for agriculture: Initial prioritization of emerging pollutants in the Tula Valley, Mexico. Water Res. 2023;238:119903. https://doi.org/10.1016/j.watres.2023.119903
[Google Scholar]
General Authority for Statistics. Saudi Arabia. (2022). Population Summary Report. Retrieved from [https://portal.saudicensus.sa/static-assets/media/content/20230531_GASTAT_Population_Report.pdf?crafterSite=gastat-portal].
Ghirardini A., Grillini V., Verlicchi P.. A review of the occurrence of selected micropollutants and microorganisms in different raw and treated manure – Environmental risk due to antibiotics after application to soil. Sci Total Environ. 2020;707:136118. https://doi.org/10.1016/j.scitotenv.2019.136118
[Google Scholar]
Gola D., Malik A., Shaikh Z.A., Sreekrishnan T.R.. Impact of heavy metal containing wastewater on agricultural soil and produce: Relevance of biological treatment. Environ Process. 2016;3:1063-1080. https://doi.org/10.1007/s40710-016-0176-9
[Google Scholar]
Gönder Z.B., Kara E.M., Celik B.O., Vergili I., Kaya Y., Altinkum S.M., Bagdatli Y., Yilmaz G.. Detailed characterization, antibiotic resistance and seasonal variation of hospital wastewater. Environ Sci Pollut Res Int. 2021;28:16380-16393. https://doi.org/10.1007/s11356-020-12221-w
[Google Scholar]
Hakanson L.. An ecological risk index for aquatic pollution control.a sedimentological approach. Water Res. 1980;14:975-1001. https://doi.org/10.1016/0043-1354(80)90143-8
[Google Scholar]
He Z.L., Zhang M.K., Calvert D.V., Stoffella P.J., Yang X.E., Yu S.. Transport of heavy metals in surface runoff from vegetable and citrus fields. Soil Science Soc of Amer J. 2004;68:1662-1669. https://doi.org/10.2136/sssaj2004.1662
[Google Scholar]
Hickey C.W.. Updating nitrate toxicity effects on freshwater aquatic species. New Zealand: National Institute of Water Atmospheric Research Ltd, Hamilton; 2013.
Hijosa-Valsero M., Matamoros V., Sidrach-Cardona R., Martín-Villacorta J., Bécares E., Bayona J.M.. Comprehensive assessment of the design configuration of constructed wetlands for the removal of pharmaceuticals and personal care products from urban wastewaters. Water Res. 2010;44:3669-3678. https://doi.org/10.1016/j.watres.2010.04.022
[Google Scholar]
Ibrahim M., Ahmad F., Yaqub B., Ramzan A., Imran A., Afzaal M., Mirza S.A., Mazhar I., Younus M., Akram Q., Ali Taseer M.S., Ahmad A., Ahmed S.. Current trends of antimicrobials used in food animals and aquaculture. In: Antibiotics and antimicrobial resistance genes in the environment Antibiotics and antimicrobial resistance genes in the environment. Elsevier; p. :39-69. https://doi.org/10.1016/b978-0-12-818882-8.00004-8
[Google Scholar]
Ilyas H., van Hullebusch E.. Role of design and operational factors in the removal of pharmaceuticals by constructed wetlands. Water. 2019;11:2356. https://doi.org/10.3390/w11112356
[Google Scholar]
Ilyas H., van Hullebusch E.D.. Performance comparison of different types of constructed wetlands for the removal of pharmaceuticals and their transformation products: A review. Environ Sci Pollut Res Int. 2020;27:14342-14364. https://doi.org/10.1007/s11356-020-08165-w
[Google Scholar]
Jaffrézic A., Jardé E., Soulier A., Carrera L., Marengue E., Cailleau A., Le Bot B.. Veterinary pharmaceutical contamination in mixed land use watersheds: From agricultural headwater to water monitoring watershed. Sci Total Environ. 2017;609:992-1000. https://doi.org/10.1016/j.scitotenv.2017.07.206
[Google Scholar]
Jurado A., Labad F., Scheiber L., Criollo R., Nikolenko O., Pérez S., Ginebreda A.. Occurrence of pharmaceuticals and risk assessment in urban groundwater. Adv Geosci. 2022;59:1-7. https://doi.org/10.5194/adgeo-59-1-2022
[Google Scholar]
Khan F.A., Ansari A.A.. Eutrophication: An ecological vision. The Botanical Review. 2005;71:449-482. https://doi.org/10.1663/0006-8101(2005)071[0449:eaev]2.0.co;2
[Google Scholar]
Lacroix M.Z., Ramon-Portugal F., Huesca A., Angastiniotis K., Simitopoulou M., Kefalas G., Ferrari P., Levallois P., Fourichon C., Wolthuis-Fillerup M., De Roest K., Bousquet-Mélou A.. Residues of veterinary antibiotics in manures from pig and chicken farms in a context of antimicrobial use reduction by implementation of health and welfare plans. Environ Res. 2023;238:117242. https://doi.org/10.1016/j.envres.2023.117242
[Google Scholar]
Larson V.J., Rico J.L., Wolfe L.M., Sharvelle S., Prenni J., De Long S.K.. Composting post-anaerobic digestion for emerging contaminant biodegradation: Impacts of operating conditions. J Environ Qual. 2023;52:1152-1165. https://doi.org/10.1002/jeq2.20515
[Google Scholar]
Luo L., Zhang C., Zhang Z., Peng J., Han Y., Wang P., Kong X., Rizwan H.M., Zhang D., Su P., Liu Y.. Differences in tetracycline antibiotic resistance genes and microbial community structure during aerobic composting and anaerobic digestion. Front Microbiol. 2020;11:583995. https://doi.org/10.3389/fmicb.2020.583995
[Google Scholar]
Mangelsdorf I., Schröder K., Escher S.E., Kolossa-Gehring M., Debiak M.. Risk assessment for irritating chemicals – Derivation of extrapolation factors. Int J Hyg Environ Health. 2021;232:113668. https://doi.org/10.1016/j.ijheh.2020.113668
[Google Scholar]
Masoud A.M., Ali M.H.. Coupled multivariate statistical analysis and WQI approaches for groundwater quality assessment in Wadi El-Assiuty downstream area, Eastern Desert, Egypt. J Afr Earth Sci. 2020;172:103982. https://doi.org/10.1016/j.jafrearsci.2020.103982
[Google Scholar]
Meng Z., Bai X., Tang X.. Short−Term assessment of heavy metals in surface water from xiaohe river irrigation area, China: Levels, sources and distribution. Water. 2022;14:1273. https://doi.org/10.3390/w14081273
[Google Scholar]
Mišík M., Filipic M., Nersesyan A., Kundi M., Isidori M., Knasmueller S.. Environmental risk assessment of widely used anticancer drugs (5-fluorouracil, cisplatin, etoposide, imatinib mesylate) Water Res. 2019;164:114953. https://doi.org/10.1016/j.watres.2019.114953
[Google Scholar]
Mng’ong’o M., Munishi L.K., Blake W., Comber S., Hutchinson T.H., Ndakidemi P.A.. Towards sustainability: Threat of water quality degradation and eutrophication in Usangu agro-ecosystem Tanzania. Mar Pollut Bull. 2022;181:113909. https://doi.org/10.1016/j.marpolbul.2022.113909
[Google Scholar]
Nawaz H., Anwar-Ul-Haq M., Akhtar J., Arfan M.. Cadmium, chromium, nickel and nitrate accumulation in wheat (Triticum aestivum l.) using wastewater irrigation and health risks assessment. Ecotoxicol Environ Saf. 2021;208:111685. https://doi.org/10.1016/j.ecoenv.2020.111685
[Google Scholar]
Ortúzar M., Esterhuizen M., Olicón-Hernández D.R., González-López J., Aranda E.. Pharmaceutical pollution in aquatic environments: A concise review of environmental impacts and bioremediation systems. Front Microbiol. 2022;13:869332. https://doi.org/10.3389/fmicb.2022.869332
[Google Scholar]
Patel S., Mondal S., Majumder S.K., Das P., Ghosh P.. Treatment of a pharmaceutical industrial effluent by a hybrid process of advanced oxidation and adsorption. ACS Omega. 2020;5:32305-32317. https://doi.org/10.1021/acsomega.0c04139
[Google Scholar]
Rajendran S., Priya T.A.K., Khoo K.S., Hoang T.K.A., Ng H.S., Munawaroh H.S.H., Karaman C., Orooji Y., Show P.L.. A critical review on various remediation approaches for heavy metal contaminants removal from contaminated soils. Chemosphere. 2022;287:132369. https://doi.org/10.1016/j.chemosphere.2021.132369
[Google Scholar]
Rakib M.A., Quraishi S.B., Newaz M.A., Sultana J., Bodrud-Doza M., Rahman M.A., Patwary M.A., Bhuiyan M.A.H.. Groundwater quality and human health risk assessment in selected coastal and floodplain areas of Bangladesh. J Contam Hydrol. 2022;249:104041. https://doi.org/10.1016/j.jconhyd.2022.104041
[Google Scholar]
Ravindra B., Subba Rao N., Dhanamjaya Rao E.N.. Groundwater quality monitoring for assessment of pollution levels and potability using WPI and WQI methods from a part of Guntur district, Andhra Pradesh, India. Environ Dev Sustain. 2023;25:14785-14815. https://doi.org/10.1007/s10668-022-02689-6
[Google Scholar]
Rosman N., Salleh W.N.W., Mohamed M.A., Jaafar J., Ismail A.F., Harun Z.. Hybrid membrane filtration-advanced oxidation processes for removal of pharmaceutical residue. J Colloid Interface Sci. 2018;532:236-260. https://doi.org/10.1016/j.jcis.2018.07.118
[Google Scholar]
Saha A., Cibin R., Veith T.L., White C., Drohan P.. Water quality benefits of weather-based manure application timing and manure placement strategies. J Environ Manage. 2023;333:117386. https://doi.org/10.1016/j.jenvman.2023.117386
[Google Scholar]
Savic R., Ondrasek G., Josimov-Dundjerski J.. Heavy metals in agricultural landscapes as hazards to human and ecosystem health: A case study on zinc and cadmium in drainage channel sediments. J Sci Food Agric. 2015;95:466-470. https://doi.org/10.1002/jsfa.6515
[Google Scholar]
Sengar A., Vijayanandan A.. Human health and ecological risk assessment of 98 pharmaceuticals and personal care products (PPCPs) detected in Indian surface and wastewaters. Sci Total Environ. 2022;807:150677. https://doi.org/10.1016/j.scitotenv.2021.150677
[Google Scholar]
Singh A.. A review of wastewater irrigation: Environmental implications. Resources, Conservation and Recycling. 2021;168:105454. https://doi.org/10.1016/j.resconrec.2021.105454
[Google Scholar]
Singh P.K., Ranjan N.. Ecological impact of pharmaceutical pollutants and options of river health improvements - A risk analysis-based approach. Sci Total Environ. 2024;928:172358. https://doi.org/10.1016/j.scitotenv.2024.172358
[Google Scholar]
Skalska-Tuomi K., Kaijanen L., Monteagudo J.M., Mänttäri M.. Efficient removal of pharmaceuticals from wastewater: Comparative study of three advanced oxidation processes. J Environ Manage. 2025;375:124276. https://doi.org/10.1016/j.jenvman.2025.124276
[Google Scholar]
Stoev S.N., Gueоrguiev S.R., Madzharov V.G., Lebanova H.V.. Naproxen in pain and inflammation – A Review. Int J Pharm Phytopharm Res. 2021;11:142-148. https://doi.org/10.51847/bqyctoiebj
[Google Scholar]
Topi D., Spahiu J.. Presence of veterinary antibiotics in livestock manure in two Southeastern Europe countries, Albania and Kosovo. Environ Sci Pollut Res Int. 2020;27:44552-44560. https://doi.org/10.1007/s11356-020-10341-x
[Google Scholar]
UN-Water. (2022). United Nations World Water Development Report 2022: groundwater: Making the invisible visible. In Significance, 19.
U.S. EPA. (2014). Method 6020B (SW-846): Inductively coupled plasma-mass spectrometry. Washington, DC.
USEPA. (2007a). Method 1694: Pharmaceuticals and personal care products in water, soil, sediment and biosolids by HPLC/MS/MS. In EPA-821-R-08-002.
USEPA. (2007b). Method 9056A: Determination of inorganic anions by ion chromatography. US EPA Cincinnati.
USEPA. (2012). Drinking water standards and health advisories. U.S. environmental protection agency. (2012th ed.).
USEPA. (2018). edition of the drinking water standards and health advisories Tables. Drinking water standards and health advisories.
Vaishnav S., Saini T., Chauhan A., Gaur G.K., Tiwari R., Dutt T., Tarafdar A.. Livestock and poultry farm wastewater treatment and its valorization for generating value-added products: Recent updates and way forward. Bioresour Technol. 2023;382:129170. https://doi.org/10.1016/j.biortech.2023.129170
[Google Scholar]
Wang J.T., Cai Y.L., Liu X.J., Zhang X.D., Cai F.Y., Cao H.L., Zhong Z., Li Y.F., Lü J.. Unveiling the visible–light–driven photodegradation pathway and products toxicity of tetracycline in the system of Pt/BiVO4 nanosheets. J Hazard Mater. 2022;424:127596. https://doi.org/10.1016/j.jhazmat.2021.127596
[Google Scholar]
Wellington K., Faulds D.M.. Valsartan/hydrochlorothiazide: A review of its pharmacology, therapeutic efficacy and place in the management of hypertension. Drugs. 2002;62:1983-2005. https://doi.org/10.2165/00003495-200262130-00015
[Google Scholar]
WHO. (2017). Guidelines for drinking-water quality: first addendum to the fourth edition.
Wiffen P.J., Derry S., Bell R.F., Rice A.S.C., Tölle T.R., Phillips T., Moore R.A.. Gabapentin for chronic neuropathic pain in adults. Cochrane Database of Systematic Reviews. 2017;6:CD007938. DOI: 10.1002/14651858.CD007938.pub4.
[Google Scholar]
Wu C., Witter J.D., Spongberg A.L., Czajkowski K.P.. Occurrence of selected pharmaceuticals in an agricultural landscape, western Lake Erie basin. Water Res. 2009;43:3407-3416. https://doi.org/10.1016/j.watres.2009.05.014
[Google Scholar]
Wu X., Yang K., Lu J., Li B., Li Y., Zhang Y., Ye L.. Contamination and ecological risk of heavy metals in sediments of urban rivers in a typical economic development zone, southern China. J Environ Sci (China). 2025;153:264-274. https://doi.org/10.1016/j.jes.2024.09.006
[Google Scholar]
Wurtsbaugh W.A., Paerl H.W., Dodds W.K.. Nutrients, eutrophication and harmful algal blooms along the freshwater to marine continuum. Wiley Interdisciplinary Reviews: Water 2019:6. https://doi.org/10.1002/WAT2.1373
[Google Scholar]
Yan M., Wang W., Jin L., Deng G., Han X., Yu X., Tang J., Han X., Ma M., Ji L., Zhao K., Zou L.. Emerging antibiotic and heavy metal resistance in spore-forming bacteria from pig manure, manure slurry and fertilized soil. J Environ Manage. 2024;371:123270. https://doi.org/10.1016/j.jenvman.2024.123270
[Google Scholar]
Yang Y.Y., Lusk M.G.. Nutrients in Urban Stormwater Runoff: Current state of the science and potential mitigation options. Curr Pollution Rep. 2018;4:112-127. https://doi.org/10.1007/s40726-018-0087-7
[Google Scholar]
Yi L., Jiao W., Chen X., Chen W.. An overview of reclaimed water reuse in China. J Environ Sci (China). 2011;23:1585-1593. https://doi.org/10.1016/s1001-0742(10)60627-4
[Google Scholar]

Show Sections

[1] Abba S.I., Egbueri J.C., Benaafi M., Usman J., Usman A.G., Aljundi I.H.. Fluoride and nitrate enrichment in coastal aquifers of the Eastern Province, Saudi Arabia: The influencing factors, toxicity, and human health risks. Chemosphere. 2023;336:139083. https://doi.org/10.1016/j.chemosphere.2023.139083
[Google Scholar]

[2] Ahmed R.S., Abuarab M.E., Ibrahim M.M., Baioumy M., Mokhtar A.. Assessment of environmental and toxicity impacts and potential health hazards of heavy metals pollution of agricultural drainage adjacent to industrial zones in Egypt. Chemosphere. 2023;318:137872. https://doi.org/10.1016/j.chemosphere.2023.137872
[Google Scholar]

[3] Ai S., Li J., Wang X., Zhao S., Ge G., Liu Z.. Derivation of aquatic predicted no-effect concentration and ecological risk assessment for triphenyl phosphate and tris(1,3-dichloro-2-propyl) phosphate. Sci Total Environ. 2024;913:169756. https://doi.org/10.1016/j.scitotenv.2023.169756
[Google Scholar]

[4] Akinnawo S.O.. Eutrophication: Causes, consequences, physical, chemical and biological techniques for mitigation strategies. Environmental challenges. 2023;12:100733. https://doi.org/10.1016/j.envc.2023.100733
[Google Scholar]

[5] Ali M., Ahmed T., Abu-Dieyeh M., Al-Ghouti M.. Environmental Impacts of Using Municipal Biosolids on Soil, Plant and Groundwater Qualities. Sustainability. 2021;13:8368. https://doi.org/10.3390/su13158368
[Google Scholar]

[6] Al-Sadeq R.H.. Environmental Assessment of Groundwater Condition in Tarout Island, Eastern Province of Saudi Arabia [Master Thesis]. King Fahd University of Petroleum and Minerals; 2011.

[7] Al-Shaibani A.. Economic potential of brines of Sabkha Jayb Uwayyid, Eastern Saudi Arabia. Arab J Geosci. 2013;6:2607-2618. https://doi.org/10.1007/s12517-012-0521-x
[Google Scholar]

[8] Alves Miranda G., Soares Dos Santos F., Lourenço Pereira Cardoso M., Etterson M., C Amorim C., V M Starling M.C.. Proposal of novel predicted no effect concentrations (PNEC) for metals in freshwater using species sensitivity distribution for different taxonomic groups. Sci Rep. 2025;15:8180. https://doi.org/10.1038/s41598-025-92692-4
[Google Scholar]

[9] Amiri N., Nakhaei M.. An investigation of qualitative variations of groundwater resources under municipal wastewater recharge using numerical and laboratory models, Nazarabad plain, Iran. Environ Sci Pollut Res Int. 2021;28:55771-55785. https://doi.org/10.1007/s11356-021-12638-x
[Google Scholar]

[10] Benaafi M., Abba S.I., Aljundi I.H.. Effects of seawater intrusion on the groundwater quality of multi-layered aquifers in Eastern Saudi Arabia. Molecules. 2023;28:3173. https://doi.org/10.3390/molecules28073173
[Google Scholar]

[11] Benaafi M., Abba S.I., Opeyemi Oyedeji M., Mubarak A.S., Usman J., Aljundi I.H.. Experimental-based groundwater salinization from the carbonate aquifer of eastern Saudi Arabia: Insight into machine learning coupled with meta-heuristic algorithms. Chemom Intell Lab Syst. 2024;249:105135. https://doi.org/10.1016/j.chemolab.2024.105135
[Google Scholar]

[12] Benaafi M., Al-Areeq A.M., Tawabini B., Basaleh A.A., Bafaqeer A., Humphrey J.D., Aljundi I.H.. Combined effects of seawater intrusion and heavy metal pollution on the groundwater resources of Tarout Island. Saudi Arabia Arab J Sci Eng. 2025;50:327-351. https://doi.org/10.1007/s13369-024-09184-2
[Google Scholar]

[13] Benaafi M., Pradipta A., Tawabini B., Al-Areeq A.M., Bafaqeer A., Humphrey J.D., Nazal M.K., Aljundi I.H.. Suitability of treated wastewater for irrigation and its impact on groundwater resources in arid coastal regions: Insights for water resources sustainability. Heliyon. 2024;10:e29320. https://doi.org/10.1016/j.heliyon.2024.e29320
[Google Scholar]

[14] Benaafi M., Tawabini B., Sajid Jillani S.M., Babalghaith A.M., Aljundi I.H.. Tracking the trace element pollution and associated health risk in urban coastal groundwater, eastern Saudi Arabia. Ocean & Coastal Manag. 2025;262:107591. https://doi.org/10.1016/j.ocecoaman.2025.107591
[Google Scholar]

[15] BinMakhashen G.M., Benaafi M.. Predicting seawater intrusion in coastal areas using machine learning: A case study of arid coastal aquifers, Saudi Arabia. Groundwater for Sustainable Development. 2024;26:101300. https://doi.org/10.1016/j.gsd.2024.101300
[Google Scholar]

[16] Blann K.L., Anderson J.L., Sands G.R., Vondracek B.. Effects of agricultural drainage on aquatic ecosystems: A review. Crit Rev Environ Sci Technol. 2009;39:909-1001. https://doi.org/10.1080/10643380801977966
[Google Scholar]

[17] Bu D., Zhu Q., Li J., Huang J., Zhuang Y., Yang W., Qi D.. Mariculture may intensify eutrophication but lower n/P ratios: A case study based on nutrients and dual nitrate isotope measurements in Sansha Bay, southeastern China. Front Mar Sci. 2024;11 https://doi.org/10.3389/fmars.2024.1351657
[Google Scholar]

[18] Camargo J.A., Alonso A., Salamanca A.. Nitrate toxicity to aquatic animals: A review with new data for freshwater invertebrates. Chemosphere. 2005;58:1255-1267. https://doi.org/10.1016/j.chemosphere.2004.10.044
[Google Scholar]

[19] Can Ö, Kıyan G.S., Yalçınlı S.. Comparison of intravenous ibuprofen and paracetamol in the treatment of fever: A randomized double-blind study. Am. J. Emerg. Med.. 2021;46:102-106. https://doi.org/10.1016/j.ajem.2021.02.057
[Google Scholar]

[20] Carmland, M., Jensen, T.S., Finnerup, N.B., 2022. Anticonvulsants in the management of chronic pain. in clinical pain management: A Practical Guide, Second Edition. https://doi.org/10.1002/9781119701170.ch17

[21] Chauhan A., Jain A., Kolton M., Pathak A.. Impacts of long-term irrigation of municipally-treated wastewater to the soil microbial and nutrient properties. Sci Total Environ. 2025;959:178143. https://doi.org/10.1016/j.scitotenv.2024.178143
[Google Scholar]

[22] Chen Y., Li M., Gao W., Guan Y., Hao Z., Liu J.. Occurrence and risks of pharmaceuticals, personal care products, and endocrine-disrupting compounds in Chinese surface waters. J Environ Sci (China). 2024;146:251-263. https://doi.org/10.1016/j.jes.2023.10.011
[Google Scholar]

[23] Chhipi-Shrestha G., Rodriguez M., Behmel S., Pulicharla R., Proulx F., Hewage K., Sadiq R.. Probabilistic framework for assessing ecological risk of Contaminants of emerging concern: Application to a Canadian lake system. Chemosphere. 2022;287:131910. https://doi.org/10.1016/j.chemosphere.2021.131910
[Google Scholar]

[24] Chowdhury T., Chowdhury M.A.H., Qingyue W., Enyoh C.E., Wang W., Khan M.S.I.. Nutrient uptake and pharmaceutical compounds of Aloe vera as influenced by integration of inorganic fertilizer and poultry manure in soil. Heliyon. 2021;7:e07464. https://doi.org/10.1016/j.heliyon.2021.e07464
[Google Scholar]

[25] Cui J., Jin Z., Wang Y., Gao S., Fu Z., Yang Y., Wang Y.. Mechanism of eutrophication process during algal decomposition at the water/sediment interface. J Cleaner Prod. 2021;309:127175. https://doi.org/10.1016/j.jclepro.2021.127175
[Google Scholar]

[26] Das C.R., Das S., Panda S.. Groundwater quality monitoring by correlation, regression and hierarchical clustering analyses using WQI and PAST tools. Groundwater for Sustainable Development. 2022;16:100708. https://doi.org/10.1016/j.gsd.2021.100708
[Google Scholar]

[27] Edwards M., Topp E., Metcalfe C.D., Li H., Gottschall N., Bolton P., Curnoe W., Payne M., Beck A., Kleywegt S., Lapen D.R.. Pharmaceutical and personal care products in tile drainage following surface spreading and injection of dewatered municipal biosolids to an agricultural field. Sci Total Environ. 2009;407:4220-4230. https://doi.org/10.1016/j.scitotenv.2009.02.028
[Google Scholar]

[28] Ekpeghere K.I., Lee J.W., Kim H.Y., Shin S.K., Oh J.E.. Determination and characterization of pharmaceuticals in sludge from municipal and livestock wastewater treatment plants. Chemosphere. 2017;168:1211-1221. https://doi.org/10.1016/j.chemosphere.2016.10.077
[Google Scholar]

[29] Parliament. EU. DIRECTIVE 2008/105/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 16 December 2008 on environmental quality standards in the field of water policy,. Official Journal of the European Union. 2008
[Google Scholar]

[30] Fan H., Wang Y., Liu X., Wang Y.Y.L., Kazmi S.S.U.H., Ohore O.E., Liu W., Wang Z.. Derivation of predicted no-effect concentrations for thirty-five pharmaceuticals and personal care products in freshwater ecosystem. Front Mar Sci. 2022;9 https://doi.org/10.3389/fmars.2022.1043792
[Google Scholar]

[31] Fisher P.M.J., Scott R.. Evaluating and controlling pharmaceutical emissions from dairy farms: A critical first step in developing a preventative management approach. J Cleaner Prod. 2008;16:1437-1446. https://doi.org/10.1016/j.jclepro.2008.04.024
[Google Scholar]

[32] Garduño-Jiménez A.L., Durán-Álvarez J.C., Ortori C.A., Abdelrazig S., Barrett D.A., Gomes R.L.. Delivering on sustainable development goals in wastewater reuse for agriculture: Initial prioritization of emerging pollutants in the Tula Valley, Mexico. Water Res. 2023;238:119903. https://doi.org/10.1016/j.watres.2023.119903
[Google Scholar]

[33] General Authority for Statistics. Saudi Arabia. (2022). Population Summary Report. Retrieved from [https://portal.saudicensus.sa/static-assets/media/content/20230531_GASTAT_Population_Report.pdf?crafterSite=gastat-portal].

[34] Ghirardini A., Grillini V., Verlicchi P.. A review of the occurrence of selected micropollutants and microorganisms in different raw and treated manure – Environmental risk due to antibiotics after application to soil. Sci Total Environ. 2020;707:136118. https://doi.org/10.1016/j.scitotenv.2019.136118
[Google Scholar]

[35] Gola D., Malik A., Shaikh Z.A., Sreekrishnan T.R.. Impact of heavy metal containing wastewater on agricultural soil and produce: Relevance of biological treatment. Environ Process. 2016;3:1063-1080. https://doi.org/10.1007/s40710-016-0176-9
[Google Scholar]

[36] Gönder Z.B., Kara E.M., Celik B.O., Vergili I., Kaya Y., Altinkum S.M., Bagdatli Y., Yilmaz G.. Detailed characterization, antibiotic resistance and seasonal variation of hospital wastewater. Environ Sci Pollut Res Int. 2021;28:16380-16393. https://doi.org/10.1007/s11356-020-12221-w
[Google Scholar]

[37] Hakanson L.. An ecological risk index for aquatic pollution control.a sedimentological approach. Water Res. 1980;14:975-1001. https://doi.org/10.1016/0043-1354(80)90143-8
[Google Scholar]

[38] He Z.L., Zhang M.K., Calvert D.V., Stoffella P.J., Yang X.E., Yu S.. Transport of heavy metals in surface runoff from vegetable and citrus fields. Soil Science Soc of Amer J. 2004;68:1662-1669. https://doi.org/10.2136/sssaj2004.1662
[Google Scholar]

[39] Hickey C.W.. Updating nitrate toxicity effects on freshwater aquatic species. New Zealand: National Institute of Water Atmospheric Research Ltd, Hamilton; 2013.

[40] Hijosa-Valsero M., Matamoros V., Sidrach-Cardona R., Martín-Villacorta J., Bécares E., Bayona J.M.. Comprehensive assessment of the design configuration of constructed wetlands for the removal of pharmaceuticals and personal care products from urban wastewaters. Water Res. 2010;44:3669-3678. https://doi.org/10.1016/j.watres.2010.04.022
[Google Scholar]

[41] Ibrahim M., Ahmad F., Yaqub B., Ramzan A., Imran A., Afzaal M., Mirza S.A., Mazhar I., Younus M., Akram Q., Ali Taseer M.S., Ahmad A., Ahmed S.. Current trends of antimicrobials used in food animals and aquaculture. In: Antibiotics and antimicrobial resistance genes in the environment Antibiotics and antimicrobial resistance genes in the environment. Elsevier; p. :39-69. https://doi.org/10.1016/b978-0-12-818882-8.00004-8
[Google Scholar]

[42] Ilyas H., van Hullebusch E.. Role of design and operational factors in the removal of pharmaceuticals by constructed wetlands. Water. 2019;11:2356. https://doi.org/10.3390/w11112356
[Google Scholar]

[43] Ilyas H., van Hullebusch E.D.. Performance comparison of different types of constructed wetlands for the removal of pharmaceuticals and their transformation products: A review. Environ Sci Pollut Res Int. 2020;27:14342-14364. https://doi.org/10.1007/s11356-020-08165-w
[Google Scholar]

[44] Jaffrézic A., Jardé E., Soulier A., Carrera L., Marengue E., Cailleau A., Le Bot B.. Veterinary pharmaceutical contamination in mixed land use watersheds: From agricultural headwater to water monitoring watershed. Sci Total Environ. 2017;609:992-1000. https://doi.org/10.1016/j.scitotenv.2017.07.206
[Google Scholar]

[45] Jurado A., Labad F., Scheiber L., Criollo R., Nikolenko O., Pérez S., Ginebreda A.. Occurrence of pharmaceuticals and risk assessment in urban groundwater. Adv Geosci. 2022;59:1-7. https://doi.org/10.5194/adgeo-59-1-2022
[Google Scholar]

[46] Khan F.A., Ansari A.A.. Eutrophication: An ecological vision. The Botanical Review. 2005;71:449-482. https://doi.org/10.1663/0006-8101(2005)071[0449:eaev]2.0.co;2
[Google Scholar]

[47] Lacroix M.Z., Ramon-Portugal F., Huesca A., Angastiniotis K., Simitopoulou M., Kefalas G., Ferrari P., Levallois P., Fourichon C., Wolthuis-Fillerup M., De Roest K., Bousquet-Mélou A.. Residues of veterinary antibiotics in manures from pig and chicken farms in a context of antimicrobial use reduction by implementation of health and welfare plans. Environ Res. 2023;238:117242. https://doi.org/10.1016/j.envres.2023.117242
[Google Scholar]

[48] Larson V.J., Rico J.L., Wolfe L.M., Sharvelle S., Prenni J., De Long S.K.. Composting post-anaerobic digestion for emerging contaminant biodegradation: Impacts of operating conditions. J Environ Qual. 2023;52:1152-1165. https://doi.org/10.1002/jeq2.20515
[Google Scholar]

[49] Luo L., Zhang C., Zhang Z., Peng J., Han Y., Wang P., Kong X., Rizwan H.M., Zhang D., Su P., Liu Y.. Differences in tetracycline antibiotic resistance genes and microbial community structure during aerobic composting and anaerobic digestion. Front Microbiol. 2020;11:583995. https://doi.org/10.3389/fmicb.2020.583995
[Google Scholar]

[50] Mangelsdorf I., Schröder K., Escher S.E., Kolossa-Gehring M., Debiak M.. Risk assessment for irritating chemicals – Derivation of extrapolation factors. Int J Hyg Environ Health. 2021;232:113668. https://doi.org/10.1016/j.ijheh.2020.113668
[Google Scholar]

[51] Masoud A.M., Ali M.H.. Coupled multivariate statistical analysis and WQI approaches for groundwater quality assessment in Wadi El-Assiuty downstream area, Eastern Desert, Egypt. J Afr Earth Sci. 2020;172:103982. https://doi.org/10.1016/j.jafrearsci.2020.103982
[Google Scholar]

[52] Meng Z., Bai X., Tang X.. Short−Term assessment of heavy metals in surface water from xiaohe river irrigation area, China: Levels, sources and distribution. Water. 2022;14:1273. https://doi.org/10.3390/w14081273
[Google Scholar]

[53] Mišík M., Filipic M., Nersesyan A., Kundi M., Isidori M., Knasmueller S.. Environmental risk assessment of widely used anticancer drugs (5-fluorouracil, cisplatin, etoposide, imatinib mesylate) Water Res. 2019;164:114953. https://doi.org/10.1016/j.watres.2019.114953
[Google Scholar]

[54] Mng’ong’o M., Munishi L.K., Blake W., Comber S., Hutchinson T.H., Ndakidemi P.A.. Towards sustainability: Threat of water quality degradation and eutrophication in Usangu agro-ecosystem Tanzania. Mar Pollut Bull. 2022;181:113909. https://doi.org/10.1016/j.marpolbul.2022.113909
[Google Scholar]

[55] Nawaz H., Anwar-Ul-Haq M., Akhtar J., Arfan M.. Cadmium, chromium, nickel and nitrate accumulation in wheat (Triticum aestivum l.) using wastewater irrigation and health risks assessment. Ecotoxicol Environ Saf. 2021;208:111685. https://doi.org/10.1016/j.ecoenv.2020.111685
[Google Scholar]

[56] Ortúzar M., Esterhuizen M., Olicón-Hernández D.R., González-López J., Aranda E.. Pharmaceutical pollution in aquatic environments: A concise review of environmental impacts and bioremediation systems. Front Microbiol. 2022;13:869332. https://doi.org/10.3389/fmicb.2022.869332
[Google Scholar]

[57] Patel S., Mondal S., Majumder S.K., Das P., Ghosh P.. Treatment of a pharmaceutical industrial effluent by a hybrid process of advanced oxidation and adsorption. ACS Omega. 2020;5:32305-32317. https://doi.org/10.1021/acsomega.0c04139
[Google Scholar]

[58] Rajendran S., Priya T.A.K., Khoo K.S., Hoang T.K.A., Ng H.S., Munawaroh H.S.H., Karaman C., Orooji Y., Show P.L.. A critical review on various remediation approaches for heavy metal contaminants removal from contaminated soils. Chemosphere. 2022;287:132369. https://doi.org/10.1016/j.chemosphere.2021.132369
[Google Scholar]

[59] Rakib M.A., Quraishi S.B., Newaz M.A., Sultana J., Bodrud-Doza M., Rahman M.A., Patwary M.A., Bhuiyan M.A.H.. Groundwater quality and human health risk assessment in selected coastal and floodplain areas of Bangladesh. J Contam Hydrol. 2022;249:104041. https://doi.org/10.1016/j.jconhyd.2022.104041
[Google Scholar]

[60] Ravindra B., Subba Rao N., Dhanamjaya Rao E.N.. Groundwater quality monitoring for assessment of pollution levels and potability using WPI and WQI methods from a part of Guntur district, Andhra Pradesh, India. Environ Dev Sustain. 2023;25:14785-14815. https://doi.org/10.1007/s10668-022-02689-6
[Google Scholar]

[61] Rosman N., Salleh W.N.W., Mohamed M.A., Jaafar J., Ismail A.F., Harun Z.. Hybrid membrane filtration-advanced oxidation processes for removal of pharmaceutical residue. J Colloid Interface Sci. 2018;532:236-260. https://doi.org/10.1016/j.jcis.2018.07.118
[Google Scholar]

[62] Saha A., Cibin R., Veith T.L., White C., Drohan P.. Water quality benefits of weather-based manure application timing and manure placement strategies. J Environ Manage. 2023;333:117386. https://doi.org/10.1016/j.jenvman.2023.117386
[Google Scholar]

[63] Savic R., Ondrasek G., Josimov-Dundjerski J.. Heavy metals in agricultural landscapes as hazards to human and ecosystem health: A case study on zinc and cadmium in drainage channel sediments. J Sci Food Agric. 2015;95:466-470. https://doi.org/10.1002/jsfa.6515
[Google Scholar]

[64] Sengar A., Vijayanandan A.. Human health and ecological risk assessment of 98 pharmaceuticals and personal care products (PPCPs) detected in Indian surface and wastewaters. Sci Total Environ. 2022;807:150677. https://doi.org/10.1016/j.scitotenv.2021.150677
[Google Scholar]

[65] Singh A.. A review of wastewater irrigation: Environmental implications. Resources, Conservation and Recycling. 2021;168:105454. https://doi.org/10.1016/j.resconrec.2021.105454
[Google Scholar]

[66] Singh P.K., Ranjan N.. Ecological impact of pharmaceutical pollutants and options of river health improvements - A risk analysis-based approach. Sci Total Environ. 2024;928:172358. https://doi.org/10.1016/j.scitotenv.2024.172358
[Google Scholar]

[67] Skalska-Tuomi K., Kaijanen L., Monteagudo J.M., Mänttäri M.. Efficient removal of pharmaceuticals from wastewater: Comparative study of three advanced oxidation processes. J Environ Manage. 2025;375:124276. https://doi.org/10.1016/j.jenvman.2025.124276
[Google Scholar]

[68] Stoev S.N., Gueоrguiev S.R., Madzharov V.G., Lebanova H.V.. Naproxen in pain and inflammation – A Review. Int J Pharm Phytopharm Res. 2021;11:142-148. https://doi.org/10.51847/bqyctoiebj
[Google Scholar]

[69] Topi D., Spahiu J.. Presence of veterinary antibiotics in livestock manure in two Southeastern Europe countries, Albania and Kosovo. Environ Sci Pollut Res Int. 2020;27:44552-44560. https://doi.org/10.1007/s11356-020-10341-x
[Google Scholar]

[70] UN-Water. (2022). United Nations World Water Development Report 2022: groundwater: Making the invisible visible. In Significance, 19.

[71] U.S. EPA. (2014). Method 6020B (SW-846): Inductively coupled plasma-mass spectrometry. Washington, DC.

[72] USEPA. (2007a). Method 1694: Pharmaceuticals and personal care products in water, soil, sediment and biosolids by HPLC/MS/MS. In EPA-821-R-08-002.

[73] USEPA. (2007b). Method 9056A: Determination of inorganic anions by ion chromatography. US EPA Cincinnati.

[74] USEPA. (2012). Drinking water standards and health advisories. U.S. environmental protection agency. (2012th ed.).

[75] USEPA. (2018). edition of the drinking water standards and health advisories Tables. Drinking water standards and health advisories.

[76] Vaishnav S., Saini T., Chauhan A., Gaur G.K., Tiwari R., Dutt T., Tarafdar A.. Livestock and poultry farm wastewater treatment and its valorization for generating value-added products: Recent updates and way forward. Bioresour Technol. 2023;382:129170. https://doi.org/10.1016/j.biortech.2023.129170
[Google Scholar]

[77] Wang J.T., Cai Y.L., Liu X.J., Zhang X.D., Cai F.Y., Cao H.L., Zhong Z., Li Y.F., Lü J.. Unveiling the visible–light–driven photodegradation pathway and products toxicity of tetracycline in the system of Pt/BiVO4 nanosheets. J Hazard Mater. 2022;424:127596. https://doi.org/10.1016/j.jhazmat.2021.127596
[Google Scholar]

[78] Wellington K., Faulds D.M.. Valsartan/hydrochlorothiazide: A review of its pharmacology, therapeutic efficacy and place in the management of hypertension. Drugs. 2002;62:1983-2005. https://doi.org/10.2165/00003495-200262130-00015
[Google Scholar]

[79] WHO. (2017). Guidelines for drinking-water quality: first addendum to the fourth edition.

[80] Wiffen P.J., Derry S., Bell R.F., Rice A.S.C., Tölle T.R., Phillips T., Moore R.A.. Gabapentin for chronic neuropathic pain in adults. Cochrane Database of Systematic Reviews. 2017;6:CD007938. DOI: 10.1002/14651858.CD007938.pub4.
[Google Scholar]

[81] Wu C., Witter J.D., Spongberg A.L., Czajkowski K.P.. Occurrence of selected pharmaceuticals in an agricultural landscape, western Lake Erie basin. Water Res. 2009;43:3407-3416. https://doi.org/10.1016/j.watres.2009.05.014
[Google Scholar]

[82] Wu X., Yang K., Lu J., Li B., Li Y., Zhang Y., Ye L.. Contamination and ecological risk of heavy metals in sediments of urban rivers in a typical economic development zone, southern China. J Environ Sci (China). 2025;153:264-274. https://doi.org/10.1016/j.jes.2024.09.006
[Google Scholar]

[83] Wurtsbaugh W.A., Paerl H.W., Dodds W.K.. Nutrients, eutrophication and harmful algal blooms along the freshwater to marine continuum. Wiley Interdisciplinary Reviews: Water 2019:6. https://doi.org/10.1002/WAT2.1373
[Google Scholar]

[84] Yan M., Wang W., Jin L., Deng G., Han X., Yu X., Tang J., Han X., Ma M., Ji L., Zhao K., Zou L.. Emerging antibiotic and heavy metal resistance in spore-forming bacteria from pig manure, manure slurry and fertilized soil. J Environ Manage. 2024;371:123270. https://doi.org/10.1016/j.jenvman.2024.123270
[Google Scholar]

[85] Yang Y.Y., Lusk M.G.. Nutrients in Urban Stormwater Runoff: Current state of the science and potential mitigation options. Curr Pollution Rep. 2018;4:112-127. https://doi.org/10.1007/s40726-018-0087-7
[Google Scholar]

[86] Yi L., Jiao W., Chen X., Chen W.. An overview of reclaimed water reuse in China. J Environ Sci (China). 2011;23:1585-1593. https://doi.org/10.1016/s1001-0742(10)60627-4
[Google Scholar]

Environmental pollution and associated ecological risk assessment of the agricultural water in the arid coastal ecosystem

Abstract

Keywords

Agroecosystem pollution

Emerging contaminants

Environmental suitability

Pharmaceuticals

Saudi Arabia

Water resources

1. Introduction

2. Materials and Methods

2.1 Study area and data collection

2.2 Laboratory analysis

2.3 Water quality and risk assessment indices

2.3.1 Water quality index

2.3.2 Contamination degree (Cd)

2.3.3 Ecological risk assessment

3. Results and Discussion

3.1 Water quality characteristics and heavy metal concentration

3.2 Pharmaceuticals occurrence

3.3 Water quality assessment

3.4 Contamination factor (Cf) and contamination degree (Cd)

3.5 Ecological risk assessment

3.5.1 Pharmaceutical compounds

3.5.2 Nitrate

3.5.3 Trace and toxic elements

3.6 Source identification of PCs

3.7 Mitigation and management strategies

3.8 Study limitations

4. Conclusions

Acknowledgment

CRediT authorship contribution statement

Declaration of competing interest

Data availability

Declaration of Generative AI and AI-assisted technologies in the writing process

Supplementary data

References

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