The presence of organic and inorganic contaminants in drinking water is a global concern. Nitrate and phenol derivatives are examples of pollutants that could be of anthropogenic origin. They are associated with numerous health risks, underscoring the importance of monitoring their presence in drinking water. This study aimed to measure nitrate and phenol derivatives, including 2,4-Dichlorophenol (2,4-DCP), Pentachlorophenol (PCP), 2,4,5-Trichlorophenol (2,4,5-TCP), 2-Chlorophenol (2-CP), 4-Chlorophenol (4-CP), and phenol, in Tehran's water distribution system (WDS). The pollutants in Tehran's WDS were significantly and positively correlated with precipitation. The Hazard Quotient (HQ) and the Excess Lifetime Cancer Risk (ELCR) of the detected pollutants were estimated. The results showed that the regional mean of nitrate and PCP in Tehran's WDS were 35.58±8.71mg L−1 and 76.14±16.93 ng L−1 lower than the guideline values of 50 mg L−1 and 1000 ng L−1, respectively. Some districts exhibited nitrate concentration exceeding the allowable limit by a factor of 1.2 to 2.3. Consequently, the nitrate intake in some districts constituted approximately 50% of the reference dose. While PCP as a phenol derivative with more health concerns was identified in Tehran's WDS, the likelihood of its health effects was determined to be negligible.

  • Regional disparities were evident for nitrate and phenol derivatives.

  • Nitrate concentration exceeded 50 mg L−1 in some regions of Tehran.

  • Nitrate intake in some regions accounted for about 50% of the reference dose.

  • PCP concentration remained below 1,000 ng L−1 across all regions.

  • ELCR for PCP was significantly lower than the threshold of 10−6.

It is well known that water is the essence of life, and every creature's existence depends on it (WHO 2003a). Moreover, water is a public good and a human right, not a commodity (Van Den Berge et al. 2022). Despite the importance of providing safe water for all, the available, limited freshwater resources are under threats of pollution caused by both anthropogenic and natural factors (Lu et al. 2015). The quality of water resources has been threatened by massive population growth, rapid industrialization, irresponsible utilization of natural water bodies, and urbanization (Humbal et al. 2023). Heavy metal ions (Mesdaghinia et al. 2016; Mirzabeygi et al. 2017; Niknejad et al. 2023), organic matter (Lund et al. 2011), radionuclides (Kaliprasad & Narayana 2018), plastics (Belkhiri et al. 2022), dyes (Islam et al. 2023), drugs, pharmaceutical (Ghesti Pivetta & do Carmo Cauduro Gastaldini 2019) and personal care products, etc., are among the most common pollutants threatening the aquatic environments and are of utmost concern.

Phenol (also known as carbolic acid, phenic acid, and phenylic acid), with a highly toxic aromatic smell, is the primary precursor for the production of broad materials and compounds, including paints, explosives, surfactants, textiles, rubbers, fertilizers, rubber proofing, disinfectants, steel industries, paper, etc. (Raza et al. 2019; Brillas & Garcia-Segura 2020). As one of the most common water aromatic pollutants worldwide, phenol's entrance into the water bodies may occur from two routes, either from natural routes or through anthropogenic activities (Mohamad Said et al. 2021). According to the EEC directive 76/464, phenol derivatives are listed as dangerous compounds which can be discharged into aquatic environments (Krastanov et al. 2013; Mallek et al. 2018). USEPA has defined a safe concentration of 1 μg L−1 for phenol and its derivatives in water (Krastanov et al. 2013). Although phenol's structure is similar to alcohol, its pKa is <11 and is considered a weak acid. The high solubility of phenol in water is due to its capability to form strong hydrogen bonds with water molecules (Arfin et al. 2019). Phenolic compounds tend to transform into various moieties, which could be even more dangerous than their original precursors. These transformations mostly occur through physical, chemical, and biological reactions in the water (e.g., disinfection) (Anku et al. 2017). Some of the most common phenol derivatives are chlorophenol, bisphenol A, and nitrophenol, which can be detected in water bodies. Even at low concentrations, phenol and its derivatives can have significant adverse effects on aquatic organisms and humans (Dutta et al. 2019; Raza et al. 2019). Several ramifications have been attributed to exposure to phenolic compounds, such as cancer, damage to red blood cells and the liver, respiratory problems and lung problems, skin blisters, fatigue, weakness (Villegas et al. 2016). Chlorophenols are also capable of suppressing the immune system and are considered carcinogenic; therefore, monitoring the presence of phenol and its derivatives in potable or finished treated water is of great importance (Yahaya et al. 2019).

Nowadays, the presence of nitrate () as an inorganic pollutant in potable water supplies has become a global problem (Fernández-López et al. 2023). During the last few decades, nitrate has been released into the waterways due to the application of various N-rich products for industrial, agricultural, and domestic purposes (Ward et al. 2018; Zhang et al. 2020). Nitrogen compounds can be discharged into the environments in either organic N (e.g., urea, amines, and protein) or inorganic N (e.g., ammonia/ammonium, nitrite, and nitrate), which eventually can enhance the nitrate concentration in both surface and groundwater (Sevda et al. 2018). Improper disposal of animal farming wastes, changes in land use, massive usage of synthetic fertilizers and manures, discharging wastewater effluents, etc., are among the most important reasons for nitrate being released into aquatic environments (Li et al. 2019). Even though several methods including ion exchange, reverse osmosis, biological processes, and catalytic processes can be applied in wastewater treatment plants to reduce the concentration of nitrate, wastewater effluent discharge still is considered a major nitrate contributor to water bodies (Sevda et al. 2018; Kalteh et al. 2022). Although nitrogen (N) is an essential element in energy transfer, building cell structures, and proper functions of living organisms, the consumption of water with contents of nitrate higher than the permissible concentrations can pose several health adverse effects such as stomach cancer (Schullehner et al. 2018), methaemoglobinaemia, and blue baby syndrome in humans (Frisbie et al. 2015). The drinking water standard for nitrate announced by WHO is 50 mg L−1 at exit waterworks, entry to private property, and at the consumer's taps (Schullehner et al. 2017). Based on the classifications of the International Agency for Research on Cancer (IARC), nitrate and nitrite are classified in group 2A and are considered carcinogenic to humans (Schullehner et al. 2017, 2018). Nitrite can produce genotoxic N-nitrosamines, which can react with secondary or tertiary amines (Ward et al. 2018). Recent studies have shown a high concentration of nitrate in both surface and groundwater resources (Rezvani Ghalhari et al. 2021; Zhang et al. 2021). Several studies have also reported the presence of high nitrate concentrations in groundwater resources in Tehran (Imandel et al. 2000; Farshad & Imandel 2003a; Panahi & Alavi Moghaddam 2012; Nejatijahromi et al. 2019; Khorasani et al. 2020). Even though reverse osmosis, chemical reduction with iron nanoparticles, biological denitrification, etc., are some of the most widely used techniques applied to remove nitrate from water supplies (Lazaratou et al. 2020), analyzing the remainder of this pollutant in the finished drinking water and the quantification of the magnitude of exposure is worthwhile.

The primary source of nitrate in Tehran's water supply system is typically attributed to groundwater contamination (Chitsazan et al. 2017; Nejatijahromi et al. 2019). Tehran's drinking water is provided from both surface and groundwater resources. The share of the surface water varies between 30 and 70% of the total water demand in Tehran during dry and wet periods, respectively (Noori et al. 2022). Therefore, Tehran's aquifer provides more than 50% of the water demand in dry seasons for potable purposes and yet almost 50% of the groundwater recharge in Tehran aquifer is achieved from raw wastewater disposal (Khorasani et al. 2020).

Blending groundwater with treated surface water is employed to mitigate nitrate concentration (Noori et al. 2022). However, given that in wet seasons surface water serves as the main source of water provision in Tehran and is often affected by the leaching of agricultural contaminants such as pesticides and nitrate derived by the runoff from the agricultural field and industrial wastewater, further studies are necessary to analyze the occurrence and sources of these pollutants in the water distribution system and the possible health effects.

This study aimed to investigate the nitrate and phenol derivatives including 2,4-dichlorophenol (2,4-DCP), pentachlorophenol (PCP), 2,4,5-trichlorophenol (2,4,5-TCP), 2-chlorophenol (2-CP), and 4-chlorophenol (4-CP) in the water distribution system (WDS) of Tehran city, Iran. Eventually, Hazard Quotient (HQ) and Excess Lifetime Cancer Risk (ELCR) were estimated to evaluate both the non-carcinogenic and carcinogenic effects of the exposure of consumers to the studied compounds.

Sampling points

Tehran's water supply system relies on a combination of both surface and groundwater resources. Six dams including Karaj, Latyan, Mamlo, Taleghan, and Lar are collectively capable of providing approximately 1.95 km3 of water annually (Noori et al. 2022). Figure 1 shows the locations of water treatment plants. To treat the raw surface water, conventional treatment techniques including coagulation and flocculation, sedimentation, and disinfection are applied in several treatment plants in Tehran city. Groundwater sources mainly in the southern part of the city also contribute to meeting water demands. Historically, Tehran's drinking water was sourced separately from nitrate-poor surface water and nitrate-rich groundwater. Nitrate-rich groundwater is stored in 151 reservoir tanks in Tehran and undergoes minimal pretreatment, typically disinfection, before injection into the WDS (Noori et al. 2022). To mitigate nitrate concentration in WDS, the water management system involved the blending of surface and groundwater, which is subsequently distributed through the water network.
Figure 1

Tehran's water supply infrastructure: dams and water treatment plants (Hadi et al. 2016).

Figure 1

Tehran's water supply infrastructure: dams and water treatment plants (Hadi et al. 2016).

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To investigate the presence of nitrate, phenol and its derivatives including 2,4-DCP, 4-CP, 2-CP, 2,4,5-TCP, and PCP in treated distributed water, 123 representative random samples were collected from WDS of 22 districts of Tehran city from the beginning of Shahrivar, a warm month, to the early week of Bahman, which represents a cold month of the Persian calendar year 1395.

All samples were coded on their initial sampling locations and were transferred to the laboratory for further analysis. Figure 2 shows the representative sampling points located in 22 districts of Tehran city. The sampling procedure and pollutants measurement (see Sections 2.2 and 2.3) was done according to the Standard Method for the Examination of Water and Wastewater (AWWA 2017).
Figure 2

Geographical locations of the sampling points.

Figure 2

Geographical locations of the sampling points.

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Phenol and its derivatives measurement

To prevent exposure to light, 1 L of amber glass bottles were used for water sampling for phenol and its derivatives. Before sampling, all bottles were washed and rinsed with appropriate detergent and distilled water. Dechlorination and acidification of samples at the time of collection are of great importance; therefore, a mass of 40–50 mg of sodium sulphite was added to each sample to reduce the residual chlorine contents of the samples. In the acidification step, the added acid plays a major role in both the biological and chemical preservation of samples; in this regard, 6-N hydrochloric acid was used to achieve a pH of less than 2. The water temperature of the samples was measured at the sampling point using a portable thermometer, following which all samples were then transferred to the laboratory at 10°C. The pH of water samples was measured at the laboratory using Kent EIL 7020 pH meter. The EPA Method 528, a chromatographic (GC) technique, was employed to measure the content of phenol and its derivatives including 2,4-DCP, PCP, 2,4,5-TCP, 2-CP, and 4-CP in water samples. The Varian CP-3800 gas chromatograph instrument was used under the following conditions: a Flame Ionization Detector (FID) with a temperature of 300 °C, a CP-Sil 8CB capillary column (50 m in length, 0.32 mm internal diameter, and 0.25 μm film thickness, #CP7454). The initial oven temperature was held at 100 °C for 2 min, then increased at a rate of 20 °C min–1 to reach 270 °C, and maintained at that temperature for 10 min. The final temperature was set at 300 °C for 5 min to ensure column cleanliness. The injection port temperature was 280 °C, and the split ratio was 10%. Helium gas served as the carrier gas with a constant flow rate of 5 mL min−1. Hydrogen gas, air, and makeup flow rates for the FID were 35, 300, and 35 ml min−1, respectively. Two calibration ranges were prepared for mixed standards, one between 10 and 1,000 μg L−1 and another between 1,000 and 5,000 μg L−1 to cover low- and high-concentration samples. For the extraction of phenols from water samples, CHROMABOND HR-X SPE columns (85 μm, 15 mL 1,000 mg−1) were used. Each sample, totaling 1 L, was passed through washed and conditioned SPE columns, and the compounds were subsequently eluted from the solid phase using a small quantity of methylene chloride (final volume of 100 μL). One μL of this eluate was injected into the GC. The recovery values for phenol derivatives ranged from 102.45 to 89.38%, and these values were applied in the final results. To assess repeatability, five samples with known concentrations were analyzed within and between days. The method demonstrated high precision, with a relative standard deviation of less than 9.4%. To determine the LOD, a series of samples with decreasing concentrations of the analyte were prepared. These samples were then analyzed using the same analytical method. The signal obtained from the lowest concentration sample was compared to the background noise level. Then the concentration at which the signal-to-noise ratio reached at least 3:1 was considered as LOD value. The concentration at which the signal-to-noise ratio reached 10:1 was considered the LOQ value. The signal-to-noise ratio was obtained from the data-acquisition software of the measurement instrument. Table 1 summarizes the standard calibration formula and the calculated values for the limit of detection (LOD) and limit of quantification (LOQ) for phenol derivatives.

Table 1

Standard calibration formula and statistic for phenol derivatives

DerivativesFormulaLODLOQR2
2,4-Dicholorophenol (24DCP) y = 3.0355e + 001x–2.9055e + 002 0.64 1.92 0.996 
Pentachlorophenol (PCP) y = 3.2353e + 000x–2.0304e + 002 1.53 4.59 0.948 
2,4,5-Trichlorophenol (245TCP) y = 9.2127e + 000x–1.1139e + 002 0.94 2.82 0.991 
2-Chlorophenol (2CP) y = 2.7803e + 001x–5.4151e + 002 0.47 1.41 0.995 
4-Chlorophenol (4CP) y = 2.3176e + 001x–1.5524e + 002 0.61 1.83 0.997 
Phenol y = 2.1060e + 001x–8.8245e + 001 1.23 3.69 0.999 
DerivativesFormulaLODLOQR2
2,4-Dicholorophenol (24DCP) y = 3.0355e + 001x–2.9055e + 002 0.64 1.92 0.996 
Pentachlorophenol (PCP) y = 3.2353e + 000x–2.0304e + 002 1.53 4.59 0.948 
2,4,5-Trichlorophenol (245TCP) y = 9.2127e + 000x–1.1139e + 002 0.94 2.82 0.991 
2-Chlorophenol (2CP) y = 2.7803e + 001x–5.4151e + 002 0.47 1.41 0.995 
4-Chlorophenol (4CP) y = 2.3176e + 001x–1.5524e + 002 0.61 1.83 0.997 
Phenol y = 2.1060e + 001x–8.8245e + 001 1.23 3.69 0.999 

LOD, limit of detection as ng L−1; LOQ, limit of quantification as ng L−1.

Nitrate measurement

Polyethylene containers with a volume of 250 mL were utilized for nitrate sampling. Before sampling, all containers underwent thorough washing and rinsing. To prevent the conversion of nitrite to nitrate due to acidification, the collected samples were stored at a low temperature (0–4 °C) and kept in the dark until analysis. The samples were dechlorinated using sodium sulphite but not acidified before being transferred to the laboratory.

For the measurement of nitrate, Method 4,500-B (Ultraviolet Spectrophotometric Method) from the 23rd edition of the Standard Methods for the Examination of Water and Wastewater (AWWA 2017) was employed. Nitrate levels were determined using a spectrophotometric instrument (DR 5000, HACH, USA). Calibration standard solutions within the range of 0–7 mg -N L−1 were prepared by diluting 0, 2.00, 5.00, 10.00, 15.00, 25.00, and 35.00 mL of a 10.0 mg -N L−1 solution to a final volume of 50 mL. The wavelength of 220 nm was selected to assess nitrate concentration, while 275 nm was used to account for organic content interference, if present. For samples exceeding the calibration range, dilution was performed as necessary. The coefficient of determination for the calibration curve, LOD and LOQ values for nitrate were determined to be 99.35%, 0.29 mg L−1 and 0.87 mg L−1, respectively.

Reagents

All of the chemicals used in this study were of analytical grade and supplied from Merck company. The analytical grade of EPA 8040A phenol calibration mix (Supleco®) was used as the standard reagent for phenol derivatives measurement. The used distilled water for all washing and rinsing purposes, stock and solution preparation was obtained by the Milli-Q water purification system. A standard stock solution containing nitrate was prepared by dissolving calculated amounts of KNO3 in distilled water. The obtained stock solution was then stored in amber bottles and kept at 4 °C.

Exposure assessment and risk characterization

Risk assessment is a conceptual framework that provides a mechanism for examining information related to the estimation of health and environmental consequences. The risk assessment/risk management paradigm was established in the 1983 NRC report (US National Research Council 1983). The exposure assessment and risk characterization are the third and fourth steps in the risk assessment paradigm that quantify the uptake of the agent and the probability of risk, respectively (Hu et al. 2016). In this study, the excess risk of cancer during the lifetime and the possibility for non-carcinogenic effects were evaluated by estimation of ELCR and the HQ using the information related to pollutant concentration, per capita water consumption and other required parameters, respectively. According to the USEPA (USEPA 2000), the ELCR can be calculated by Equation (1):
formula
(1)
where Cw is the pollutant concentration in water (mg L−1), DI is the daily intake from tap water (L Day−1) which was considered 1.5 L day−1 for adults (Hadi et al. 2019), ED is the exposure duration and in Equation (1) ED was assumed to be 30 years (USEPA 2008) for exposure to phenol derivatives for both carcinogenic and non-carcinogenic effects. EF is the exposure frequency (365 days year−1 in this study), is the body weight (70 kg was assumed for adults), and ATc is the average exposure time to the incidence of carcinogenic effects. For carcinogenic constituents, the ATc has typically been 25,550 days, based on a lifetime exposure of 70 years at 365 days per year (USEPA 2008, 2011). CSF is the cancer slope factor. Based on USEPA/IRIS (IRIS/USEPA 2010), an oral slope factor (SF) of 4 × 10−1 (mg kg−1.day−1)−1 was considered in this study to estimate ELCR for PCP. In this study, the carcinogenic potential was only analyzed for PCP. However, for nitrate and other phenol derivatives, no cancer slope factor is recommended. Therefore, we only evaluated the non-carcinogenic potential of these pollutants by determining the HQ. The HQ, as defined by USEPA (USEPA 2000), can be calculated using Equation (2):
formula
(2)
where ATnc is the average exposure time to the incidence of non-carcinogenic effects (ED × 365 days year−1), and RfD is the reference dose as mg of pollutant per each kg of body weight per day (mg kg−1 day−1). The HQ value less than unity indicates that the adverse effects are impossible, whereas HQ > 1 suggests the possibility of non-carcinogenic effects (USEPA 2011). As presented in Table 2, we aimed to estimate the non-carcinogenic effects of nitrate based on the critical effect of early clinical signs of methaemoglobinaemia in infants aged 0–3 months due to nitrate exposure. We considered a value of the ED parameter for nitrate exposure to be 0.25 years (equivalent to 3 months).
Table 2

RfD values for phenol derivatives and nitrate

CompoundCritical effectExposed populationRfD (mg kg−1 day−1)Reference
2,4-DCP Decreased delayed hypersensitivity response Rat 0.003 IRIS/USEPA (1987a)  
PCP Hepatotoxicity Dog 0.005 IRIS/USEPA (2010)  
2,4,5-TCP Liver and kidney pathology Rat 0.001 IRIS/USEPA (1987a)  
2-CP Reproductive effects Rat 0.005 IRIS/USEPA (1988)  
4-CP Decreased delayed hypersensitivity response Rat 0.003a Moermond & Heugens (2009)  
Phenol Decreased maternal weight gain Rat 0.3 IRIS/USEPA (1987b)  
 Early clinical signs of methaemoglobinaemia Human 7.04b IRIS/USEPA (1987c)  
CompoundCritical effectExposed populationRfD (mg kg−1 day−1)Reference
2,4-DCP Decreased delayed hypersensitivity response Rat 0.003 IRIS/USEPA (1987a)  
PCP Hepatotoxicity Dog 0.005 IRIS/USEPA (2010)  
2,4,5-TCP Liver and kidney pathology Rat 0.001 IRIS/USEPA (1987a)  
2-CP Reproductive effects Rat 0.005 IRIS/USEPA (1988)  
4-CP Decreased delayed hypersensitivity response Rat 0.003a Moermond & Heugens (2009)  
Phenol Decreased maternal weight gain Rat 0.3 IRIS/USEPA (1987b)  
 Early clinical signs of methaemoglobinaemia Human 7.04b IRIS/USEPA (1987c)  

aThe RfD of 0.003 mgkg−1day−1 for 2,4-DCP (IRIS/USEPA 1987a) was deemed applicable to 4-CP (Moermond & Heugens 2009).

b1.6 -N kg−1 day−1 = 7.04 mg kg−1day−1.

Since there was no available weight distribution data for infant groups in Iran, we assumed a default weight value of 4 kg for body weight based on the approach used for estimating the RfD for nitrate (IRIS/USEPA 1987c). Additionally, we assumed a per capita water consumption from tap water to be 0.45 L day−1 for the age category of 0–3 months (Hadi et al. 2019).

An estimated 5–10% of the total nitrate intake is anticipated to undergo conversion to nitrite by bacteria present in the saliva, stomach, and small intestine (ATSDR 2015). Subsequently, nitrite can react with amines and amides in the gastrointestinal tract, forming N-nitroso compounds (NOCs), which are known to be carcinogenic in animals. Therefore, in this study, a worst-case scenario was considered, assuming a conversion factor (CF) of 10% for nitrate conversion to nitrite by multiplying the numerator of Equation (2) by 0.1.

The RfD value for nitrate is based on its non-carcinogenic health effects and relies on the assumption that thresholds exist for certain toxic effects, such as cellular necrosis (IRIS/USEPA 1987c). By considering a NOAEL of 10 mg -N L−1 and a daily drinking water ingestion rate of 0.64 L for a 4 kg infant (0.16 L kg−1day−1) to prepare infants' formula, the estimated reference dose for nitrate is calculated as follows: 10 mg L−1 × 0.64 L day−1 divided by 4 kg, which equals 1.6 mg -N kg−1 day−1 or 7.04 mg kg−1 day−1 (IRIS/USEPA 1987c). Table 2 summarizes the RfD values for each of the studied pollutants, which were derived from studies conducted on animal populations and based on various critical health effects.

Data analysis

The concentrations of pollutants in Tehran's water distribution system were compared to recommended drinking water guideline values, which are consistent with the WHO standard of 50 mg L−1 for nitrate and the EPA-regulated Maximum Contaminant Level (MCL) of 0.001 mg L−1 or 1,000 ng L−1 for PCP. In our research, we conducted this comparison using a t-test, estimating 95% confidence intervals for the mean concentrations of pollutants and graphically comparing them to the guideline threshold values.

To make a spatial comparison, the measured concentration of pollutants in each sampling point was divided by the guideline values. Then the Kriging interpolation method was applied using Arcview GIS version 9.3 to predict unsampled locations using a linear combination of observations at nearby sampled points.

To investigate the relationship between the concentration of pollutants in WDS and precipitation in Tehran, a two-step analysis was conducted. Firstly, pollutant concentrations were aggregated monthly, and the mean concentration, along with confidence intervals, was determined and visually represented for each sampling month. As a second step, precipitation data for Tehran province during the sampling year, sourced from the statistical yearbook of Tehran (SCI 2016), was gathered and subjected to a temporal analysis to discern the trends.

To develop a description model between pollutants and water physicochemical characteristics including temperature and pH in WDS, data for all districts were aggregated and the scatter plot of and PCP versus water temperature and pH were illustrated.

The relationship between pollutants and temperature was analyzed by fitting a Gaussian model with the following equation:
formula
(3)
where P is the pollutant concentration (PCP or ), T is water temperature (C°), A is the amplitude (peak of Gaussian curve), μ is the mean which corresponds to the peak location of the pollutant and σ is the standard deviation controlling the width of the curve.
An exponential relationship was also used to describe the mathematical relationship between pollutant concentration and the pH of water. The relationships were analyzed using the following equation:
formula
(4)
where P is the pollutant concentration (PCP or ), pH is water pH and α is the coefficient associated with the exponential term.

All data analysis and plotting were done using R version 4.1.2.

Concentration of pollutants in WDS

Table 3 summarizes the descriptive statistics of measured nitrate and phenol derivatives within Tehran's WDS. According to the data presented in Table 3, the mean concentrations of 2,4-DCP, PCP, 2,4,5-TCP, 2-CP, and 4-CP, when aggregated for all districts in Tehran, were 1.56 ± 0.39, 76.14 ± 16.93, 15, 3.78 ± 5.84, and 3.90 ± 0.75 ng L−1, respectively. The concentration of phenol compound in all collected samples was below the LOD, making it impossible to calculate the mean concentration of phenol. Based on these results, among the various phenol derivatives detected in the samples, PCP exhibited the highest concentration, while 2,4-DCP had the lowest concentration in Tehran's WDS.

Table 3

The statistical descriptive parameters of nitrate and phenol derivatives in Tehran's WDS

VariableUnitMeanSESDLBUBMaxMin
2,4-DCP ng L−1 1.56 0.39 0.73 1.17 1.95 
PCP ng L−1 76.14 16.93 79.9 59.21 93.07 660 10 
2,4,5-TCP ng L−1 15 n.a. n.a. n.a. n.a. 15 15 
2-CP ng L−1 3.78 5.84 7.6 − 2.06 9.62 24 
4-CP ng L−1 3.9 0.75 1.64 3.15 4.65 
Phenola ng L−1 n.a. n.a. n.a. n.a. n.a. n.a. n.a. 
 mg L−1 35.58 8.71 48.57 26.87 44.29 310 2.66 
pH – 6.78 0.05 0.28 6.73 6.83 
C° 21.39 0.53 2.95 20.86 21.92 27 14 
VariableUnitMeanSESDLBUBMaxMin
2,4-DCP ng L−1 1.56 0.39 0.73 1.17 1.95 
PCP ng L−1 76.14 16.93 79.9 59.21 93.07 660 10 
2,4,5-TCP ng L−1 15 n.a. n.a. n.a. n.a. 15 15 
2-CP ng L−1 3.78 5.84 7.6 − 2.06 9.62 24 
4-CP ng L−1 3.9 0.75 1.64 3.15 4.65 
Phenola ng L−1 n.a. n.a. n.a. n.a. n.a. n.a. n.a. 
 mg L−1 35.58 8.71 48.57 26.87 44.29 310 2.66 
pH – 6.78 0.05 0.28 6.73 6.83 
C° 21.39 0.53 2.95 20.86 21.92 27 14 

n.a., not available; SD, standard deviation; SE, standard error; LB, lower bound 95% CI; UB, upper bound 95% CI.

aPhenol detected in none of the samples.

A total of 19 different chlorinated phenols are known to exist (WHO 2003b), and in this study, we focused on evaluating four of them including 2,4-DCP; 2,4,5-TCP; 2-CP; and 4-CP. These four compounds were chosen because they are more likely to be found in drinking water as potential by-products of disinfection. Chlorophenols, in general, are known for having very low organoleptic thresholds. The taste thresholds in water for 2-CP and 2,4-DCP are incredibly low, at 0.100 and 0.300 μg L−1, respectively. Their odour thresholds are also quite low, at 10 and 40 μg L−1, respectively (Kozak et al. 1979). 2-CP is primarily used as a precursor in the production of higher chlorophenols and dyestuffs, as well as a preservative. On the other hand, 2,4-DCP finds applications as a mothproofing agent, germicide, and antiseptic, and it is also used in the production of the 2,4-Dichlorophenoxyacetic acid (2,4-D) herbicide (Desmurs & Ratton 2000). As indicated in Table 3, the maximum concentrations of 2-CP and 2,4-DCP observed in Tehran's WDS are 0.024 and 0.003 μg L−1, respectively. These values are significantly lower than the thresholds for taste and odour. Due to the insufficient data available on the toxicity of 2-CP and 4-CP, no health-based guideline values have been recommended for these substances (WHO 2003b). The environmental releases of 2,4-DCP occur through the degradation of 2,4-D in contaminated soil and water, leading to its presence in surface water. It's worth noting that 2,4-DCP is not subject to regulation under the Safe Drinking Water Act, and it was not considered a chemical of concern in the EPA's Six-Year Reviews (USEPA 2015). The Drinking Water Standards and Health Advisories document categorizes the carcinogenicity of 2,4-DCP as Category E, signifying evidence of non-carcinogenicity for humans (USEPA 2007).

2,4,5-TCP can be released during its production and usage as a pesticide and pesticide intermediate. It finds applications as a fungicide in paper and pulp mills, as a herbicide, and as an intermediate in the production of other pesticides, such as the herbicides 2,4-D and the insecticide Ronnel. Currently, there is no available information regarding the carcinogenic effects of 2,4,5-TCP in humans. The EPA has categorized 2,4,5-TCP as Group D, indicating that it is not classifiable as a human carcinogen due to the insufficiency of human and animal data (USEPA 2016). As depicted in Figure 3(c), 2,4,5-TCP was exclusively identified in water samples collected from district 16 of Tehran's WDS.
Figure 3

The mean and confidence interval for phenol derivatives (a, b, c, d, e) and nitrate (f) concentration in different districts of Tehran.

Figure 3

The mean and confidence interval for phenol derivatives (a, b, c, d, e) and nitrate (f) concentration in different districts of Tehran.

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Among various derivatives of phenol detected in samples, PCP mean concentration was 76.14 ± 16.93 ng L−1 which was the highest phenol derivative found in Tehran's drinking water. Figure 3(a) compares the PCP concentration in the districts. As can be seen, districts 22 and 19 had the highest PCP concentrations of 200 and 168 ng L−1, respectively. Districts 5, 3, and 1 had the lowest PCP concentrations which were less than 35 ng L−1. The spatial distribution of measured PCP in different districts of Tehran is illustrated in Figure 4.
Figure 4

The distribution of measured PCP concentration in different districts of Tehran.

Figure 4

The distribution of measured PCP concentration in different districts of Tehran.

Close modal

The PCP derivate can be widely used for various industrial purposes such as wood preservatives, pesticides, fungicides, and bactericides and is a highly toxic chemical. PCP has been classified as ‘likely to be carcinogenic to humans’ by the USEPA, and the allowable announced concentration of PCP in drinking water is in the range of 0.01–0.1 μg L−1 (Verbrugge et al. 2018; Adjei et al. 2021). Also, the IARC has classified PCP as a carcinogenic pollutant of class II B and recorded it as an endocrine-disruptor agent (Orton et al. 2009). Based on EPA National Primary Drinking Water Regulations the the MCL value for PCP is 0.001 mg L−1 or 1,000 ng L−1. Notably, as can be seen in Figure 3(a) the average PCP concentration and the upper bound of the 95% confidence limits in all areas are found to be below the MCL of 1,000 ng L−1. The highest average PCP concentration is observed in districts 21 and 19. It's worth highlighting that District 21 stands out due to its extensive industrial presence, featuring several industrial applications, including automobile factories, pharmaceutical and cosmetic industries, along with more than a hundred factories and workshops related to automobile manufacturing. While a direct causal link between land use type and elevated PCP levels in the WDS of this district cannot be established, the possibility of a connection is plausible, particularly if this area primarily relies on underground water sources for its water supply.

PCP is extensively utilized as a fungicide in the wood industry. Notably, the wood and furniture industries are mainly concentrated in districts 18 and 19 of Tehran. On the other hand, at the time of sampling, the primary source of water for this district was groundwater. So it is conceivable that the elevated PCP levels in this district may be linked to groundwater contamination with PCP, possibly stemming from its use in the wood industry as one possible source of pollution. However, it's essential to emphasize that this is merely a hypothesis, and further research is required to substantiate this claim.

It can be assumed that the presence of PCP in the water distribution of Tehran may have mainly originated from agricultural and industrial activities and then probably disinfection processes in WTPs.

Although PCP has been restricted from being produced due to its potential ecological risk, the use of PCP for schistosomiasis control and snail elimination has increased in the east of China (Zheng et al. 2017). Therefore, under the assumption that PCP and other chlorophenol compounds are utilized in agricultural practices within Tehran, their potential leaching from agricultural fields into surface water may lead to an increase in their concentration within the distribution network. This is due to the limited capacity of conventional water treatment processes to completely remove these contaminants. Leachate of municipal landfills also can contain high concentrations of phenol and its derivatives which can contaminate water bodies, especially groundwater (Jamali et al. 2009).

Another plausible origin of phenol derivatives could be associated with the water treatment processes in Tehran, where seven conventional treatment systems are responsible for the water supply. These WTPs use chlorine for the disinfection process, therefore, compounds including 2,4-DCP; 2,4,5-TCP; 2-CP; and 4-CP may be formed as emerging disinfection byproduct (EDBPs) of the disinfection process used in WTPs (Ranjan et al. 2021).

As presented in Table 3 the mean concentration of nitrate in Tehran's water distribution system is 35.58 ± 8.71 mg L−1, which is lower than the recommended guideline values for nitrate in drinking water as it is 50 mg L−1 and 45 mg L−1 by WHO and USEPA, respectively (WHO 2004; Moya et al. 2011).

Figure 3(f) shows the mean and confidence interval for nitrate concentration in different districts of Tehran. Maintaining the nitrate ion concentration below guidelines recommended by USEPA or WHO is essential as it serves as a protective measure against methaemoglobinaemia and thyroid-related effects. In alignment with drinking water guidelines, this protection is especially crucial for at-risk vulnerable groups, such as bottle-fed infants. The confidence interval for the mean of nitrate for each district was estimated and then visually compared to the 50 mg L−1 nitrate threshold. As evident in Figure 3(f), for districts 1, 3, 4, 2, 13, 5, 8, 6, and 19, the 50 mg L−1 threshold surpasses the upper limit of the 95% confidence interval estimates for the mean of nitrate in these districts. Consequently, the average nitrate levels in these districts are significantly lower than the recommended 50 mg L−1 value (p-value <0.05). In contrast, for district 11, the threshold value is lower than the estimated lower bound (LB) of the 95% confidence interval and therefore the average nitrate level in this district is significantly higher than the recommended value of 50 mg L−1 (p-value <0.05). On the other hand, for districts 7, 21, 10, 20, 17, 15, and 12, the mean of samples is lower than 50, but the recommended threshold still falls within the estimated 95% confidence interval bounds (p-value <0.05). This suggests that the average nitrate levels in these districts could be significantly lower than but still equal to 50 mg L−1. For districts including 9, 16, 14, 18, and 22, the sample mean exceeds the threshold, but the 50 mg L−1 threshold remains within the estimated 95% confidence interval bounds. This implies that the population average of nitrate in these districts could be significantly equal to or higher than 50 mg L−1 (p-value < 0.05). The statistical analysis suggests that the nitrate level in most districts may be equal to 50 mg L−1.

Figure 5 shows the Kriging interpolation distribution map of measured nitrate concentration in different districts of Tehran. As can be seen in Figure 5, nitrate concentration in the northern districts of Tehran is below the guideline value but in southern districts especially in district 18 is two times higher than the guideline value.
Figure 5

The distribution of measured nitrate concentration in different districts of Tehran.

Figure 5

The distribution of measured nitrate concentration in different districts of Tehran.

Close modal

In the districts where nitrate levels exceed the guideline value, the groundwater is the primary source of water supply. Groundwater pollution by nitrate may occur from both anthropogenic and geogenic point sources (Huno et al. 2018). Several studies have confirmed the presence of nitrate pollution in the groundwater of these districts. Most studies have mentioned that the application of high amounts of nitrogen fertilizers and manures, agriculture runoffs and drain water, and improper discharge of both domestic and industrial wastewaters and wastes are the main anthropogenic factors responsible for groundwater pollution with nitrate in Tehran (Sajedi-Hosseini et al. 2018; Badihi et al. 2022). Since groundwater has a significant contribution in providing potable water for Tehran, especially in warm seasons, regular monitoring of the water distribution system regarding the concentration of nitrate is of great importance. During the last few decades, many studies in Tehran have reported the contamination of wells and groundwater pollution by nitrate. The rapid population growth and absence of a complete coverage sewage collection system (Khorasani et al. 2020), the long-term discharge of raw wastewater in cesspits (Noori et al. 2022), agricultural activities and irrigation of agricultural lands with partially treated or non-treated sewages (Nejatijahromi et al. 2019) and the disposal of industrial wastes (Farshad & Imandel 2003b) are all among the main reasons for polluting Tehran's aquifer with high concentrations of nitrate especially in southern districts of Tehran. To determine the sources of nitrate pollution in the aquifer located southeast of Tehran an isotopic fingerprinting study of nitrate contaminant was conducted by Nejatijahromi et al. (2019). The study revealed that the main sources of nitrate in groundwater are wastewater treatment plant effluents and chemical fertilizers which are used for irrigation and agriculture purposes. As the population grows, there is a greater demand for food production. This can result in increased agricultural activities, including the use of fertilizers containing nitrate compounds. Excessive application of nitrogen-based fertilizers can lead to nitrate runoff into groundwater. Moreover, with the increasing trend of urban population in Tehran, there may be increased demand for groundwater as a source of drinking water. Over-pumping of groundwater can lead to changes in flow patterns and water movement, potentially drawing nitrate-contaminated water into previously uncontaminated aquifers (Nasrabadi & Abbasi Maedeh 2014).

Pollutant concentration versus precipitation

As detailed in Section 3.1, the occurrence of the studied pollutants in Tehran's WDS is likely associated with agricultural practices involving nitrate fertilizers and the use of pesticides, including PCP. Figure 6 shows the mean and confidence interval for phenol derivatives and nitrate concentration in different months (Persian calendar) of sampling. Figure 7(a) illustrates the monthly precipitation trend in Tehran, with the highest precipitation occurring during the 1st, 9th, 10th, and 11th months, and the lowest precipitation was observed in the 6th and 7th months of the year. As sampling from WDS was conducted during months 6–10, assuming that nitrate and PCP may have originated from surface water sources through leaching from the land due to runoffs, a correlation analysis was done to explore the relationship between the concentration of nitrate and PCP in WDS and the amount of precipitation for the corresponding months (6th–10th). Figure 7(b) and 7(c) depict the correlation between nitrate and PCP concentrations in Tehran's WDS and the magnitude of precipitation during the respective months. As depicted in Figure 7(b), a significant (p-value < 0.05) and positive correlation was observed between the nitrate concentration of Tehran's WDS and the amount of precipitation. The coefficient of determination (R2) for this relationship is 0.90. Based on the slope of the relationship, for each millimetre increase in precipitation in Tehran, the nitrate concentration in the WDS will increase by 1 mg L−1. Figure 7(c) also demonstrates a regression relationship between the PCP concentration in the WDS and the amount of precipitation in Tehran. While this relationship is positively significant (p-value < 0.05), the R2 is slightly lower at 0.68 compared to the coefficient determined for nitrate. Based on the regression analysis, for every one-unit increase in precipitation in Tehran, the concentration of PCP in the WDS increases by 0.9 ng L−1 (ppt). It is worth noting that the background concentrations of nitrate and PCP in Tehran's WDS can be 5.5 mg L−1 and 43 ng L−1, respectively, based on the relationships obtained under the assumption of no precipitation. These concentrations throughout the WDS can be attributed to factors other than precipitation.
Figure 6

The mean and confidence interval for phenol derivatives (a, b, c, d, e) and nitrate (f) concentration in different months (Persian calendar) of sampling.

Figure 6

The mean and confidence interval for phenol derivatives (a, b, c, d, e) and nitrate (f) concentration in different months (Persian calendar) of sampling.

Close modal
Figure 7

Monthly variation of precipitation in Tehran (a), the correlation between and precipitation (b), and the correlation between PCP and precipitation (c) focusing on 6th–10th months of the Persian calendar.

Figure 7

Monthly variation of precipitation in Tehran (a), the correlation between and precipitation (b), and the correlation between PCP and precipitation (c) focusing on 6th–10th months of the Persian calendar.

Close modal

The elevated nitrate levels in Tehran's WDS and the strong correlation between nitrate and precipitation can be attributed to the leaching of nitrate from agricultural lands. This leaching process affects surface water resources, which provide approximately 70% of the potable water supply in Tehran. Nitrogen is a primary component in fertilizers used for crop cultivation. However, when excess nitrogen, which is not utilized by crops, is present, it can transform into nitrate and get carried by runoff into water bodies, thus contributing to the pollution of rivers and streams (Hanson et al. 2016).

Pollutant concentration versus temperature and pH

As can be seen in Figure 8(a) and 8(b) there is a nonlinear relationship between water temperature and pollutants in water samples. In this study, a Gaussian model and an exponential model were applied to provide a mathematical description of the relationship between pollutant concentration versus water temperature and water pH, respectively. Table 4 summarizes the estimated model parameters for PCP and dependency on pH and temperature in Tehran's WDS. The estimated α is a constant that determines the scale or magnitude of the response variable. This parameter is likely related to the initial concentration of the pollutant at a specific pH. β is the coefficient associated with the pH term. It represents the rate at which the concentration of the pollutant changes with respect to changes in pH. As observed for both PCP and , the positive values of β indicate that the pollutant concentration increases as pH increases, while a negative β indicates that it decreases as pH increases. PCP is a weak acid and tends to be more soluble and mobile in its ionic form, which is favoured at higher pH (alkaline conditions). Under lower pH conditions, PCP is hydrophobic and tends to be less soluble and may become adsorbed to solid particles (Shankar et al. 2020) in the water and as can be seen in Figure 8(c) this may affect the measured PCP levels in water samples. Nitrate is a weak base, and its concentration may be influenced by changes in pH. In slightly acidic conditions, nitrate may be converted to nitrous acid (HNO2) and other nitrogen compounds, which can affect the measured nitrate levels. In alkaline conditions, nitrate probably tends to remain in the form of and is relatively stable (Figure 8(d)).
Table 4

Model parameters for PCP and dependency on pH and temperature (T) in Tehran's water distribution system

DependentIndependentModelparameters
αβAμs
PCP pH Exponential 0.8362 0.6565    
PCP Gaussian   88.909 18.964 5.092 
 pH Exponential 0.03804 1.00278    
 Gaussian   53.903 18.658 3.601 
DependentIndependentModelparameters
αβAμs
PCP pH Exponential 0.8362 0.6565    
PCP Gaussian   88.909 18.964 5.092 
 pH Exponential 0.03804 1.00278    
 Gaussian   53.903 18.658 3.601 

SD, standard deviation; SE, standard error; LB, lower bound 95% CI; UB, upper bound 95% CI.

Figure 8

Scatter plots and fitted models for PCP and versus water temperature – Gaussian model (a, b) and pH-exponential model (c, d).

Figure 8

Scatter plots and fitted models for PCP and versus water temperature – Gaussian model (a, b) and pH-exponential model (c, d).

Close modal

As indicated in Table 4 and Figure 8(a) and 8(b), the maximum concentrations of and PCP in the WDS are predicted to occur at a water temperature of approximately 19°C, based on the estimated parameter μ from the Gaussian model. It's important to note that the specific relationship between pollutant concentration and physicochemical water characteristics including pH, and temperature may be affected by several environmental conditions, water chemistry, and the presence of other chemicals or organisms in the water. Therefore proposed models in this study to describe these relationships could be used with caution.

Probability and possibility of health effects

Recent epidemiological investigations have revealed that nitrate exposure through drinking water is linked to a range of adverse outcomes, including congenital anomalies (Blaisdell et al. 2019), low birth weight (Lin et al. 2023), preterm birth (Stayner et al. 2022), neural tube defects, and several types of cancers including bladder cancer (Arafa et al. 2022), stomach cancer (Buller et al. 2021), and most notably colorectal cancer (Elwood & van der Werf 2022). Additionally, through a meta-analysis of eight studies by Temkin et al. (2019) focusing on the relationship between nitrate in drinking water and colorectal cancer, the researchers identified a statistically significant positive connection between nitrate exposure and the risk of colorectal cancer. They computed a one-in-one million cancer risk level, set at a concentration of 0.14 mg -N L−1 (or 0.619 mg L−1) in drinking water. In practical terms, this implies that for every one million individuals exposed to drinking water containing 0.16 mg L−1, an extra case of colorectal cancer might be anticipated. Furthermore, other studies conducted on a cohort of women aged 55–69 in Iowa (Weyer et al. 2001) have demonstrated statistically significant increases in the risk of ovarian, thyroid, kidney, and bladder cancers associated with nitrate exposure.

The well-known attributed health effect of exposure to high contents of nitrate is methaemoglobinaemia disease in infants (Sadler et al. 2016). In this study, we analyzed only the possibility of non-carcinogenic health effects based on the critical effect of early clinical signs of methaemoglobinaemia in 0- to 3-month-old infants.

As mentioned earlier, the HQ and ELCR were calculated to evaluate the possibility of non-carcinogenic and the probability of carcinogenic effects of nitrate and phenol derivatives on water consumers, respectively. HQ is said to be the most commonly used index for non-carcinogenic health effects. It is usually calculated as the ratio of exposure level to the RfD. Even though HQ is considered a simple measure, it provides useful value to judge the possibility of health effects. The higher the HQ value, the higher the possibility of the occurrence of non-carcinogenic health effects (Edokpolo et al. 2015). Table 5 summarizes the descriptive statistics for district-aggregated HQ and ELCR for phenol derivatives and nitrate in Tehran's WDS (see Figure 9).
Table 5

The descriptive statistics for district-aggregated HQ and ELCR for phenol derivatives and nitrate in Tehran's WDS

CompoundMeasureMeanSESDLeftRightMaxMin
2,4-DCP HQ 1.12 × 10−5 2.77 × 10−6 5.20 × 10−6 8.39 × 10−6 1.39 × 10−5 2.14 × 10−5 7.14 × 10−6 
2,4,5-TCP 3.21 × 10−4 NaN NA NaN NaN 3.21 × 10−4 3.21 × 10−4 
2-CP 1.62 × 10−5 2.50 × 10−5 3.26 × 10−5 −8.8 × 10−6 4.12 × 10−5 1.03 × 10−4 4.29 × 10−6 
4-CP 2.79 × 10−5 5.33 × 10−6 1.17 × 10−5 2.26 × 10−5 3.32 × 10−5 5.00 × 10−5 7.14 × 10−6 
 5.68 × 10−2 1.39 × 10−2 7.76 × 10−2 0.042939 7.08 × 10−2 4.95 × 10−1 4.25 × 10−3 
PCP HQ 3.26 × 10−4 7.26 × 10−5 3.42 × 10−4 2.54 × 10−4 3.99 × 10−4 2.83 × 10−3 4.29 × 10−5 
ELCR 2.80 × 10−7 6.22 × 10−8 2.94 × 10−7 2.17 × 10−7 3.42 × 10−7 2.42 × 10−6 3.67 × 10−8 
CompoundMeasureMeanSESDLeftRightMaxMin
2,4-DCP HQ 1.12 × 10−5 2.77 × 10−6 5.20 × 10−6 8.39 × 10−6 1.39 × 10−5 2.14 × 10−5 7.14 × 10−6 
2,4,5-TCP 3.21 × 10−4 NaN NA NaN NaN 3.21 × 10−4 3.21 × 10−4 
2-CP 1.62 × 10−5 2.50 × 10−5 3.26 × 10−5 −8.8 × 10−6 4.12 × 10−5 1.03 × 10−4 4.29 × 10−6 
4-CP 2.79 × 10−5 5.33 × 10−6 1.17 × 10−5 2.26 × 10−5 3.32 × 10−5 5.00 × 10−5 7.14 × 10−6 
 5.68 × 10−2 1.39 × 10−2 7.76 × 10−2 0.042939 7.08 × 10−2 4.95 × 10−1 4.25 × 10−3 
PCP HQ 3.26 × 10−4 7.26 × 10−5 3.42 × 10−4 2.54 × 10−4 3.99 × 10−4 2.83 × 10−3 4.29 × 10−5 
ELCR 2.80 × 10−7 6.22 × 10−8 2.94 × 10−7 2.17 × 10−7 3.42 × 10−7 2.42 × 10−6 3.67 × 10−8 

SD, standard deviation; SE, standard error; LB, lower bound 95% CI; UB, upper bound 95% CI.

NaN, Not a Number; NA, Not Available

Figure 9

Aggregated HQ values for (a) phenol derivatives and (b) nitrates in Tehran's WDS.

Figure 9

Aggregated HQ values for (a) phenol derivatives and (b) nitrates in Tehran's WDS.

Close modal
As summarized in Table 5, the calculated aggregated mean of HQ for all districts for nitrate is 0.056 ± 0.013 with minimum and maximum of 0.004 and 0.49, respectively. An HQ less than 1 indicates that there is no significant possibility for non-carcinogenic adverse effects (clinical signs of methaemoglobinaemia in 0–3 months old infants) from exposure to nitrate via drinking water (Sadler et al. 2016). Figure 10 shows the estimated HQ of each district of Tehran and as can be seen all 22 districts have HQ significantly lower than 1 (p-value < 0.05), indicating no possibility of excess methaemoglobinaemia in infants aged 0–3 months attributed to the nitrate content in potable water in the mentioned districts. Therefore the statistical analysis confirms HQ is lower than unity for all districts.
Figure 10

The calculated HQ for nitrate for each district of Tehran's WDS.

Figure 10

The calculated HQ for nitrate for each district of Tehran's WDS.

Close modal

As mentioned in Section 2.5, the reference dose for nitrate is established at 7.04 mg kg−1 day−1 (IRIS/USEPA 1987c). In simpler terms, if the daily nitrate intake per kilogram of body weight exceeds this threshold, there is a potential risk of methaemoglobinaemia in infants aged 0–3 months. Since nitrate can enter the body through both food and water consumption, it is essential to consider the contribution of nitrate from food when determining daily intake. Typically, the proportion of nitrate intake from drinking water is relatively low, ranging between 2 and 3% (IRIS/USEPA 1987c). The major portion of nitrate intake typically comes from food. Therefore, if the goal is to maintain the daily intake of nitrates in infants from drinking water within the 2% to 3% range, the nitrate concentration in drinking water should ideally be between 10 and 15 mg L−1. According to the findings of this study, the average contribution of nitrate intake from drinking water in Tehran's WDS, compared to the reference dose, is approximately 5.68 ± 1.39% (maximum 49.5% and minimum 0.42%) for all districts. However, in districts 22, 11, 18, and 14, this contribution on average is notably higher than the all districts' mean, at 14.8% (maximum 46.0%), 14.41% (maximum 16.2%), 14.28% (maximum 49.5%), and 12% (maximum 14.1%), respectively. In other districts, the contribution from water is less than 10%. While the estimated HQ value for all districts in Tehran is currently less than 1, indicating no immediate concern for methaemoglobinaemia, but further studies are still needed to estimate the total nitrate intake from both food and water for this particularly vulnerable age group and comparing it to the reference dose to ensure long-term safety.

PCP is associated with carcinogenicity in humans. Additionally, studies have shown that PCP can lead to health issues concerning the liver, thyroid, nervous system, and immune system, as well as reproductive and developmental problems in animals. Prolonged exposure to PCP in humans for a long time could result in adverse health effects, particularly impacting the liver, kidneys, blood, or nervous system (ATSDR 2022).

Because our measurements confirmed the occurrence of PCP in Tehran's WDS, the potential for health effects from PCP and other phenol derivatives in drinking water was further analyzed by estimation of HQ and ELCR measures.

As per the data presented in Table 5 and Figure 9(a), the mean HQ for 2,4-DCP, 2,4,5-TCP, 2-CP, 4-CP, and PCP are calculated to be 1.12 × 10−5, 3.21 × 10−4, 1.62 × 10−5, 1.79 × 10−5, and 3.26 × 10−4, respectively. These values are significantly lower than 1, indicating a very low and negligible likelihood of non-carcinogenic health adverse effects attributed to these compounds. Among all the substances analyzed in this study, only PCP had an available cancer slope factor. Consequently, the estimation of ELCR for assessing carcinogenic effects was exclusively performed for PCP. The aggregated ELCR mean for PCP was 2.80 × 10−7 ± 6.22 × 10−8. The upper bound (UB) and LB for 95% confidence intervals of ELCR were 3.42 × 10−7 and 2.17 × 10−7, respectively. Since the UB falls below 10−6, the carcinogenic risk associated with PCP can be considered negligible. In Figure 11, the calculated ELCR for each district of Tehran's WDS is displayed. The calculated ELCR in districts 19 and 21 is 2.2–2.6 times greater than the estimated mean of ELCR for PCP across all districts. In comparison with the health-based target threshold of 10−6, these values are still significantly lower than or equal to the threshold level. However, it is important to note that the uncertainty in estimating confidence intervals for these two districts is relatively high. Hence, we suggest conducting more specific studies in these two districts to gain a deeper understanding of exposure to PCP.
Figure 11

The calculated ELCR for each district of Tehran's WDS.

Figure 11

The calculated ELCR for each district of Tehran's WDS.

Close modal

This study aimed to investigate the nitrate and phenol derivatives in the water distribution system of Tehran and to evaluate the risk to public health. The findings revealed that on overage the nitrate concentration in Tehran's WDS is lower than the guideline value for nitrate in drinking water, although some districts have a higher concentration than the allowable level. The comparison of nitrate levels with the recommended guideline value emphasizes notable regional disparities in nitrate concentration within Tehran's WDS. Quantitative exposure assessment for nitrate implied that the contribution of the water distribution system in the daily intake of nitrate for some regions is relatively high. These findings carry significant implications for public health administration and underscore the necessity of instituting monitoring initiatives for nitrate in Tehran's WDS, to protect vulnerable consumers and infants, especially in southern districts of Tehran.

The study also revealed that PCP has the highest mean concentration among phenol derivatives in Tehran's WDS. The mean concentration for all phenol derivatives was found to be significantly lower than the MCL concentration in drinking water. It seems based on available knowledge the PCP derivative in Tehran's drinking water doesn't pose a potential health risk to consumers as the ELCR for PCP was significantly lower than the threshold for all regions.

The findings from our study have significant implications for water resource management and public health policies in Tehran. The identification of elevated nitrate levels in specific districts emphasizes the need for targeted interventions to ensure the provision of safe drinking water. We acknowledge the importance of studying the health effects of exposure to nitrate (especially for districts 11 and 22) and PCP (especially for districts 19 and 21) by estimating the overall daily intake from all routes of exposure (water, food, soil, and maybe the air). Addressing this aspect would require a separate and more extensive investigation that goes beyond the scope of our current study. We would encourage future research in this direction, as it can provide critical insights into the potential health effects associated with the observed nitrate and PCP levels.

The authors would like to thank the Institute for Environmental Research (IER) for financial support (Grant number: 36278-46-03-96). They would also like to thank the laboratory staff of TUMS's Faculty of Health for their skilful technical assistance and expertise in the analysis of the water samples.

M.H. was involved in conceptualization, methodology, software, visualization, data collection, data analysis, investigation, curation, writing-original draft, review & editing. P.B. was involved in writing-review & editing. M.S.A. was involved in review & editing; S.N. was involved in laboratory analysis, M.R.Gh. was involved in review & editing. A.M. was involved in conceptualization, S.H.B. was involved in data entry.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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