Evaluating the Human Health Risks of Oryza sativa Contaminated With Heavy Metals in Diversely Irrigated Fields of Sahiwal, Pakistan

Mehwish Aslam1, Zafar Iqbal Khan1, Kafeel Ahmad1,2, Naila Riaz3*, Faiza Zubair3,

Fatima Ghulam Muhammad1, Asma Ashfaq1, Riyadh S. Almalki4, Ijaz Rasool Noorka5

and Hafsa Memona6

1Department of Botany, University of Sargodha, Sargodha, Pakistan

2Department of Botany, Baba Guru Nanak University, Nankana Sahib, Punjab, Pakistan

3Department of Zoology, University of Sargodha, Sargodha, Pakistan

4Department of Pharmacology and Toxicology, Faculty of Pharmacy, Umm-Al-Qura University, Makkah, Saudi Arabia

5Department of Plant Breeding and Genetics, College of Agriculture,University of Sargodha, Sargodha, Pakistan

6Department of Zoology, Queen Mary College, Lahore, Pakistan

ABSTRACT

Heavy metals exposure in Oryza sativa varieties imposes great health risks to human health. The current study was conducted to investigate the heavy metal content in different varieties of rice grown in adversely irrigated fields of Sahiwal, Sargodha. This study evaluates the Cu and Zn content in paddy soil, in edible part of the rice as well as serum of local residents. Various pollution indices were applied to analyze the current status of these metals. Results demonstrated the metal trend in rice varieties as Zn > Cu. Observed Zn (11.43-23.487mg/kg) and Cu (10.524-20.50mg/kg) range in soil was within WHO limits. Analyzed Zn (0.967-2.87 mg/kg) and Cu (0.575-3.066 mg/kg) concentration in all rice grains lie within WHO/FAO limits. Basmati and Irri 6 varieties presented the highest Zn and Cu level in their grains. Positive correlation from soil to grains was observed for both metals. Transfer factor showed the minimum uptake of both metals in Kainat variety while metal accumulation in rice tissues was observed in Irri 6 variety. Zn (0.559-0.932mg/kg) and Cu (1.428 to 1.995mg/kg) range showed the minimal enrichment in soil. Target hazard quotient values for Zn (0.0021-0.0207) and Cu (0.00006-0.00078) were below than 1 for all age groups. In conclusion, there is no significant health risk observed for humans on consuming rice varieties from this area.

Article Information

Received 18 April 2024

Revised 15 May 2024

Accepted 12 June 2024

Available online 13 November 2024

(early access)

Published 13 November 2025

Authors’ Contribution

ZIK and KA supervised the study. MA conducted the experiment. AA, FZ and IRN were responsible for the data analysis. HM and NR were responsible for analyzing and interpreting the data. RSA critically reviewed the manuscript.

Key words

Basmati, Copper, Food chain, Pollution, Public, Zinc

DOI: https://dx.doi.org/10.17582/journal.pjz/20240418044008

* Corresponding author: [email protected]

0030-9923/2025/0006-2855 $ 9.00/00

Copyright 2025 by the authors. Licensee Zoological Society of Pakistan.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Introduction

Agriculture sector has prime importance in Pakistan economy. It supports 21% to total GDP and constitute about 60% of total exports. Pakistan is a country where both Rabi and Kharif crops are cultivated (Syed et al., 2022). Major kahrif crop of Pakistan is rice which is cultivated on 3.034 million hectares of land and is the second most consumed crop after wheat. It is a good source of mineral, vitamins and proteins (Abbas and Dastgeer, 2021).

In Pakistan, rice production is decreased and affected badly because of heavy metal contaminated soil and water being used for its irrigation. Plant’s biological systems are affected by heavy metals accumulation i.e. uptake of oxygen, water and synthesis pathways for chlorophyll, breakdown of mitochondria, damage of cell wall thickness and nuclear envelop are being caused by heavy metals toxicity. Water potential of the plants are decreased by increasing osmolarity and disturbing osmo-regulatory process during development as a result of which transpiration level of leaf area is affected and reactive oxygen species (ROS) are produced due to bursting of lipid membrane DNA, and proteins (Akhtar et al., 2021). Hazardous heavy metals exposure imposes great health risks to human health. A study was carried out in Tamale Aboabo Market in the Northern Region of Ghana. Randomly selected 21 samples from the market containing cereal crop and legumes were taken for analysis of heavy metal contamination using an atomic absorption Spectrophotometer. As concentration was found to be 0.017mg/kg and the concentration of Cu ranged from 0.019 to 0.042mg/kg, and that of Zn concentrations were low. There was no cancer risk by consumption of cereals in Tamale market cereal samples. The overhead concentrations of As in the cereals can be preferable to the usage of arsenic containing pesticides, fertilizer and runoff from other cultivation fields (Adam et al., 2022). The maximum Pb, As, Hg and Cr concentrations were 1.455, 1.1, 2.8, and 1.05 times of their risk thresholds, respectively (Wei et al., 2023). The average Cu and Zn concentrations in commercial rice were 2.31 and 15.429 mg kg−1, respectively, indicating a good nutritional status (Wei et al., 2023).

Zn and Cu are important micro-nutrient for growth, metabolism and protein synthesis in rice plant. Both metals also play their role in photosynthetic and respiratory machinery of plants (Schmidt et al., 2023). Enzymes for nitrogen metabolism uses Zn as co-factor so when there is Zn deficiency the enzymes functions are disturbed and capacity to cope with anaerobic conditions of soil is badly affected in rice seedlings. Flooding of water causing Zn deficiency which is observed shortly after transplanting that results in loss of productivity. Moreover, salinity destroys the physiology of rice plant by decreasing vegetative growth, flowering and vigor of rice seedlings (Nadeem et al., 2020). Cu is used as antifungal agent in agriculture sector. In humans, essential metals are also important like Cu is necessary for haemoglobin formation and metabolism of carbohydrates and some are important as co-factor of enzymes. However, when these essential metals present in excessive amount they cause cell damage. The accumulation of metals in food crops is dangerous as it transfers to animals and humans thus damaging their health badly. It is dire need to control the metals content in food. The aim of this research work is to investigate the heavy metal content in different varieties of rice grown in adversely irrigated fields of Sahiwal, Sargodha. The research objectives were: to evaluate the Cu and Zn content in paddy soil, in edible part of the rice as well as serum of local residents. Various pollution indices were applied to analyze the current status of these metals in diverse fields.

Materials and Methods

Study area

Sahiwal is the one of the Tehsil of Sargodha. It is located at 38 km from Sargodha city which is the 12th major urban city of Pakistan (Fig. 1). Summer temperature observed is 50 °C while winters minimum temperature is 12 °C in this area. The samples were obtained from those areas which were irrigated with tube well water and mixed water (fresh and wastewater) in Tehsil Sahiwal of district Sargodha.

 

Sample collection and preparation

A total of 80 pairs of soil samples were taken from depth of 0-15 cm and their corresponding shoot and grain samples were also taken during harvesting season of rice. Each sampling point was based according to the type of irrigation water. All debris and stones were removed from soil samples and was stored in sampling bags. Soil samples were dried in air for almost 3-5 days and after that in oven for 72 hours at 80℃. At the end these samples were saved for additional digestion methods. All metallic contaminants on rice grains were removed by washing them twice. All rice and soil samples were dried at 65 ºC for 48h in a furnace to maintain constant weight for further analysis. After drying the samples were ground completely into a fine powder and then protected 2 g samples for digestion.

Individuals having age between 5-55 years were selected for blood sampling. All the individuals ages were divided into groups: Group1: 5-15 years (Y), Group 2: 16-25 Y, Group 3: 26-35 Y, Group 4: 36-45 Y and Group 5: 46-55 Y. G All the participants were local residents of study area. Five replicates for each group were taken. About 2g sample was acid-digested with 10 mL of HNO3-H2O2 (2:1) at 140–160°C. The digested solution was filtered by passing through syringe filters (0.45 µm) and the final volume (10 ml) was made by addition of Milli-Q water. The level of heavy metals was measured using CE™ 3400 AAS Atomic Absorption Spectrometer.

Following formulae were used to determine contamination factor (CF), transfer factor (TF), enrichment coefficient (EC) and target hazard quotient (THQ).

EF is defined as retention of metals in soils. Zn and Cu standard concentration was 2.8mg/kg and 9.07 mg/kg in soil (Singh et al. 2010) while 20mg/kg and 50mg/kg in rice grains (Satpathy et al., 2014).

THQ was used for assessing the non-carcinogenic risks for each individual metal through rice consumption. It is an estimated risk level of pollutant exposure.

EF stands for exposure frequency (365 days per year), ED is for exposure duration (70 years in the study for non-cancer risk). FIR stands for food ingestion rate (g/person/day), and CM for rice’s heavy metal content. RfD is for oral reference dose (mg/kg-d) which was 0.300mg/kg for Zn and 0.040mg/kg for Cu (Harmanescu et al., 2011); BW stands for mean body weight of consumers; and AT stands for the average time for non-carcinogenic effects (70 years).

Statistical analysis

ANOVA and correlation analysis was applied on the data by using SPSS (Statistical Package for Social Sciences) software, Version 16.0 and graphs are made through prism software 10.0.

Results and Discussion

Zn and Cu in paddy soil samples

Statistical data showed significant effect of treatments, samples and treatments*samples for Zn and Cu content in the soil samples (Table I). Zinc metal was evaluated in various varieties of rice grown in different treatments like tube well water and mixed water. The mean concentration of Zn metal in paddy soil ranged from 11.43-23.487 mg/kg. The minimum concentration of zinc was detected for kainat (11.43 mg/kg) at tube well water and maximum concentration was detected for super kernel (23.48 mg/kg) at mixed water. The observed Zn order in paddy soil was: kainat<basmati<irri6<super kernel at tubewell water and kainat < irri6< basmati < super kernel at mixed water treatment (Fig. 2). According to Du et al. (2018), the Zn content in the soil was reported as 177 mg kg-1 which is much greater than in the recent study. The level of Zn in soil suggested by Qin et al. (2021) was 16.43 mg/kg which is lower than that of the recent study. Zheng et al. (2020) indicated the Zn level was 46.89 mg kg-1 in the paddy soil which is higher than that of present studies. The results of Jiang et al. (2020) the concentration of Zn was found to be 86.8 mg kg-1 which was higher than recent investigations. Present concentration of Zinc in paddy soil ranged from 15.87-23.4 mg kg-1 that was found to be within WHO/FAO (2007) limits. Deficient Zn concentration was a serious problem in paddy soil because it leads to the Zn deficiency in human body (Hussain et al. 2022). Chen et al. (2022) stated that soil type, dispersal of nitrogen and organic content in soil directly influence the Zn uptake in paddy soil.

 

Table I: ANOVA analysis in soil, rice and serum samples.

Source of Variance	Treatments	Samples	Treatments* Samples
	1	3	3
Soil
Zn	418.267***	19.618***	7.379 ***
Cu	213.953***	14.171***	7.912***
Shoot
Zn	384.101***	8.049***	23.665***
Cu	124.570***	15.607***	6.984***
Grain
Zn	384.101***	8.049***	23.665***
Cu	21.2678***	1.0782***	0.7180***
Serum
Zn	0.005926***	0.010723***	0.006648***
Cu	0.000295***	0.003536***	0.006455***

 

The concentration of copper in soil ranged from 10.524- 20.50 mg/kg. The lowest level of Cu was observed for Kainat (10.52 mg/kg) and its highest level was observed for Basmati (20.50 mg/kg). At Tube well water treatment, the order of concentration was Kainat < Basmati < Irri6< Super kernel. At mixed water treatment, the order of concentration was Kainat < Super kernel < Irri6< Basmati (Figure 3). Concentration of Cu reported by Ahmad et al. (2021) in soil was above the permissible limit that was opposite to present findings. Giri and Singh (2017) indicated the level of Cu in soil was 41.4 mg kg-1 which is much greater than current observation. Present Cu in paddy soil ranged from 10.524- 20.50 mg kg-1 that was found to be within WHO/FAO (2007) limits. According to Kaninga (2022), pH factor negatively affect the Cu availability in soil. As the soil pH increased Cu distribution capacity in soil is decreased. Cu also form bonding with available organic matter in soil which also hinders its distribution on soil medium.

 

 

Zn and Cu in rice samples

The statistical analysis of data showed significant variation at the level of (p<0.001) along treatments, varieties and treatments*varieties for zinc content in the rice samples (Table I). The concentration of zinc ranged from 5.337-18.62 mg/kg in shoot. Zinc level in shoot was found lowest at T1 for kainat (5.33 mg/kg) and highest values were observed for basmati (18.62mg/kg). The order of Zn concentration in shoot was kainat <basmati < super kernal < irri 6 at tube well water. The order of Zn concentration was super kernal< irri 6< kainat< basmati at mixed water treatment. The concentration of Zn in the grains of different rice varieties was recorded as 0.967-2.87 mg/kg. The minimum values were recorded for basmati (0.967 mg/kg) and maximum were observed at T2 for irri 6 (2.87 m/kg). The order of Zn level in grains was basmati < kainat < super kernal< irri 6 at tube well water. The order followed by different varieties for Zn level at T2 was super kernal < kainat < basmati < irri 6 (Figure 2). Kong et al. (2018) recorded the value of Zn was 48 mg/kg which is much higher than recent observations. The previous studies by Zhao et al. (2010) indicated the Zn level in rice grain was 2.24 mg/kg which is in accordance with our study. Mamat et al. (2020) showed the Zn level in rice grain was 0.345 mg/kg which is lower than current observations. According to Tang et al. (2019) the concentration of Zn in rice grain was 1.189 mg/kg which is equal to current results. The previous studies by Abdul Sattar (2021) indicated the level of Zn was 11.23 mg/kg which is much greater than current observations. Present Zn concentration in rice grains was lowered than acceptable Zn limit suggested by WHO/FAO (2007). Nearly 2 billion people were facing Zn deficiency across the world. Hussain et al. (2022) stated that Zn fertilization and irrigation management are the main aspect that influence the Zn concentration in rice grains.

The statistical analysis of data showed significant variation at the level of (p<0.001) along treatments, varieties and treatments*varieties for copper content in the shoot (Table 1). Copper level in shoot ranged from 5.014-12.91 mg/kg. The lowest concentration of Cu was found in shoot of kainat ( 5.014 mg/kg) and highest values were observed for basmati (13.516 mg/kg). The level of Cu in grains samples obtained from various sites was ranged from 0.575 mg/kg to 3.066 mg/kg. At tube well water, the values ranged from 0.575 mg/kg to 0.965 mg/kg in grains of rice. The minimum values were observed for super kernal (0.575mg/kg) and maximum values were observed for variety basmati (0.965 mg/kg). At mixed water, the values ranged from 1.183 to 3.0660 mg/L. The minimum observed in super kernal and maximum value is found in basmati. The order of concentration of copper was super kernal <irri 6 < kainat < basmati at tubewell water the order of concentration observed at mixed water was super kernal < irri 6 < kainat < basmati (Fig. 3). Various shoot samples of different rice varieties using tube well water and mixed water as source of irrigation were determined for Cu level in them. The mean level of Cu varied from 5.014mg/kg- 13.516 mg/kg. Du et al. (2018) suggested the Cu content in Shoot of rice was 3.69 mg kg-1 which is lower than current investigation. This concentration of Cu was according to the results of Mahfooz et al. (2020) whose reported 1.19-2.29 mg/kg of Cu content. Low Cu content in the rice grains was observed by Xiao et al. (2017). Compared with the reports by Sharma et al. (2018) these observations were low. The level of Cu in the rice grain observed by Abualhaija (2023) was 2.49 which are comparable with the recent results. Level of Cu indicated by the Guadie et al. (2021) was found to be in accordance with current results. The level of Cu observed by Adewumi and Lawal (2022) was 0.25 mg/kg which is lower than current observation. Observed Cu range was lower than WHO/FAO (2007) limits that is 40 mg/kg. Results presented that there is low availability of copper metal in soil as well as low uptake by rice varieties.

Heavy metals in serum samples

The statistical analysis of data showed significant variation at the level of (p<0.001) along treatments, serum and treatments*serum for zinc content in serum (Table 1). The concentration of zinc in human serum ranged from 0.015-0.036 mg/L. At tube well water, minimum zinc concentration in serum was found in age group of 16-25 (0.0058 mg/L) and maximum value for age group of 46-55 (0.015 mg/L). At mixed water, the minimum values were observed for 5-15 (0.016 mg/L) and maximum values for age group between 46-55. The order of zinc level was 16-25 < 26-35 < 5-15 < 36-45 <46-55 at tube well water. The order of zinc level was 26-35 < 5-15 < 36-45 < 16-25 <46-55 at mixed water treatment (Fig. 2). Begoña-Zubero et al. (2023) evaluated the Zn content in the serum of Spanish women which were much greater than present observations. Our observed Zn range was much lower than results reported by Mashhadi et al. (2017). Relatively, higher Zn content in serum ((0.43-1.39mg/L) was observed by Markiewicz-Żukowska et al. (2015). Zn deficiency negatively affect the human immune system. Age, sexual category and area location directly influence the Zn level in human body. It also stated that low Zn level damage the mental health (Khan et al. 2023).

The statistical analysis of data showed significant variation at the level (p<0.001) along treatments, serum and treatments*serum for copper content in serum (Table 1). At tube well water treatment, maximum values were observed as 0.08106 mg/L in 46-55 age group and lowest value at the same treatment site was 0.06149 mg/L for age group of 26-35 year. The Cu order in blood was 26-35<36-45 < 5-15 <16-25 <46-55 at this site. At mixed water treatment, the maximum values were observed as 0.1575 mg/L for age group of 46-55 and minimum 0.097 mg/L for age group of 16-25. The order of level of Cu in serum was 16-25 <36-45 < 5-15 <26-35 <46-55 at mixed water site (Fig. 3). Our Cu range in human serum was lower than findings of Al-Ansari et al. (2020) in Iraq. Milde et al. (2001) observed the mean Cu level in human serum as 0.95 that was much higher than present results. Present results show consistency with Toro-Román et al. (2021). To observe the intake of Cu via rice consumption in the study area, the serum of human blood of various age groups was analyzed for Cu level. Cu mainly accumulated in the soft tissues of body, and its deficiency lead to muscle fatigue, stress as well as impaired central nervous system. It also helps in supply of Ca ion to bones (An et al. 2022).

Correlations between soil-shoot, shoot-grain, grain-serum samples

All correlations between soil-shoot, shoot-grain, grain-serum in tube well water and mixed water were non significant. Positive correlation was observed at both sites between soil and shoot. for shoot and grain a positive and non significant correlation was observed at tube well water and positive correlation was found at mixed water. For grain and serum, positive correlation was observed at both sites. all correlations between soil-shoot, shoot-grain, grain-serum in tube well water and mixed water were non significant. Positive correlation was observed between soil and shoot for both sites. For shoot and grain a negative correlation was observed of about at tube well water and positive correlation was found at mixed water. For grain and serum negative correlation for tube well and positive non significant correlation was observed for mixed water treatment (Table II). Khan et al. (2021) resulted the positive correlation between soil, shoot and grain for both Zn and Cu, which showed consistency with this study, but their significant variation was opposite to this study. Similar results to this study were also reported by Xie et al. (2011). In contrast, a weak negative correlation between Cu and Zn was observed by Maares et al. (2023). Noor et al. (2024) also found positive correlation for Zn in soil to grains. It also stated that limited Zn concentration in rhizosphere also produces a thrust for Zn uptake in plants that’s why they modified their root system to efficiently absorb the nutrients during stress conditions.

 

Table II: Zn and Cu correlations among soil-shoot, shoot-grain, grain-serum for different treatments.

Treatment	Soil-shoot	Shoot-grain	Grain- Serum
Zn
Tube well water	0.574ns	0.876ns	0.790ns
Mixed water	0.519ns	0.364ns	0.339ns
Cu
Tube well water	0.942ns	-0.754ns	-0.555ns
Mixed water	0.994**	0.481ns	0.356ns

 

 

 

Contamination factor

Contamination factor (CF) of the Zn ranged from 0.190-0.414 mg/kg. After irrigation with tube well water, minimum zinc contamination was found in kainat and maximum value in super kernal. After mixed water irrigation, the minimum values were observed in basmati and maximum values in kainat. The order of zinc level was: kainat < basmati < irri 6 < super kernal at tube well water. The order of zinc level was basmati < super kernal < irri 6 < kainat (Fig. 4). Mohammadi et al. (2019) reported CF 3.679 which is higher than current observations. Mahfooz et al. (2020) evaluated the CF 0.467 which is in accordance with recent studies. Sukri et al. (2018) evaluated the Zn level 0.19 which suggest the equality with current indications. Zhiyuan et al. (2014) observed the CF 1.23 which is higher than current study results. According to Kaur and Garg (2017) the CF of Zn was 3.45 which is greater than present outcomes. In both water sources, the CF values remained lower than 1 for all rice varieties, suggesting that the rice had lower contaminant concentrations compared to the respective water sources.

CF showed the Cu content between 0.175-0.345 mg/kg for various rice varieties. After tube well water irrigation, the values ranged from 0.175 mg/kg (kainat) to 0.2403 mg/kg (super kernal). The order of C.F values observed was kainat < basmati < irri 6 < super kernal after tube well water irrigation,. At mixed water treatment, highest C.F was observed in Basmati and minimum value was found in kainat. The order of C.F values were; kainat < irri 6 < super kernal < basmati at mixed water treatment (Fig. 5). CF of soil showed the Cu content of 0.175-0.345 for various rice varieties. The results given by Oubane et al. (2021) for Cu metal indicate the higher contamination compared with recent outcomes. Comparatively high CF was observed by Gemeda et al. (2021). Mahmood et al. (2020) noted that CF was recorded as 0.094 which was lower than current observations. Xiao et al. (2017) suggested that CF of current study was 0.321 which is comparable to the present study. Relatively low CF of 0.04 was observed by Ye et al. (2014). The results of present study were comparable to the investigations by Zeng et al. (2021). In both water sources, the Cu CF values remained lower than 1 representing the safer Cu level in soil.

Transfer factor

Zn TF ranged from 0.466-0.777 mg/kg. After tube well water irrigation, minimum zinc contamination was found in kainat and maximum value for irri 6. At mixed water, the minimum values were observed for super kernal and maximum values for kainat. The order of zinc level was: kainat < basmati < super kernal < irri 6 at tube well water (Fig. 4). The order of zinc level was super kernal < basmati < irri 6 < kainat. Kang et al. (2020) recorded the TF of Zn was 1.19 which is higher than results of this study. According to Fan et al. (2017) the TF was observed as 0.067 which is lower than current findings. Li et al. (2012) indicated that the TF was almost equal to the present study results. The transfer factors indicate the extent of contaminant movement from soil medium to rice grains. The results demonstrated that all rice varieties are safe for human consumption.

Cu transfer from soil to grain was observed as 0.476-0.668mg/kg. In tube well water irrigation, the highest TF of copper was found to be in irri 6 and lowest of values were observed in kainat. The order of level of TF was: kainat < basmati < super kernal < irri 6 at tube well water. For mixed water, the highest values were observed for irri 6 and lowest was found in kainat. The order of level of T.F for copper was kainat < super kernal < basmati < irri 6 (Fig. 5). The level of risk reported by Abdulwahid (2023)was much higher than current investigations. According to the study by Liu et al. (2013) TF level was 1.33 for Cu which is higher than current study results. TF observed by Rezapour et al. (2019) was 0.278 which is lower than current observations. All Cu values were lower than 1 indicating that all rice varieties are not Cu accumulator.

Enrichment factor (EF)

Cu enrichment range was observed as 0.559-0.932mg/kg. In tube well water irrigation, the highest EF of copper was found to be in Irri 6 and lowest of values were observed in kainat. The order of level of Cu was; irri 6 < super kernal < basmati < kainat. for mixed water, the highest values were observed for kainat and lowest was found in super kernal. The EF order of level for copper was super kernal < irri 6 < basmati < kainat (Fig. 4). EF values noted by Afonne and Ifediba (2020) was indicated as 0.245 which is lower than current results. According to Wei et al. (2019) the level of EF was observed as 1.34 which is slightly higher than current findings. Previous studies by Karizaki (2016) showed the EF of 0.34 which shows the consistency of results with current observation. In terms of pollutant concentration, these enrichment variables shed light on how different rice cultivars interacted with metal enrichment in soil.

EF was evaluated as 1.428 to 1.995 mg/kg for Cu metal. For tube well water, the highest values were observed for irri 6 and lowest values were observed in kainat. The order of level of EF was observed as kainat< basmati< super kernal < irri 6 at tube well water. For mixed water, the highest values were observed in basmati and lowest values were observed for kainat. The order of level of E.F was observed as kainat< super kernal< basmati< irri 6 at mixed water treatment (Fig. 5). These values showed the variation from the results given by Ahmed at al. (2021). The values of EF recorded by Chandran et al. (2012) were 0.675 which is comparatively less than current findings. Novotná et al. (2015) investigated the EF for Cu was 4.098 which is higher than recent study results. According to Abdul Sattar (2021), if EF values lies between 1-2, then there will be minor metal enrichment. Present results suggested minor enrichment of Cu in studied area.

Target hazard quotient (THQ)

THQ range for Zn was 0.0021-0.0207 at both sites. At tube well water, minimum Zn value was observed in 26-35 age while maximum value found in 36-45. At mixed water treatment, highest THQ observed for 36-45 age group while lowest THQ is in 46-55 age group (Fig. 4). The THQ level of the Zn was recorded as 0.021 by Zheng et al. (2017) which greater than current observations. THQ level investigated by El-Kady and Abdel-Wahhab (2018) is consistent with current observations. Abualhaija (2023) recorded the THQ level of 0.12 for Zn in various age groups which is much higher than recent findings. THQ was calculated to evaluate the potential health risk. The calculated THQ of this study remained consistently below than 1 for all age groups representing the no potential health risks for Zn toxicity in human on rice consumption.

THQ was investigated to evaluate the health risk of rice consumption. THQ range for Cu was 0.00006-0.00078 at both sites. At tube well water, the minimum value was observed for age group 16-25 and maximum values were observed for age group of 46-55. The THQ values observed at mixed water were found to be in maximum at age of 46-55 and minimum at 5-15 age group. The order of level of Copper at T1 was followed as 16-25<46-55<36-45<5-15<26-35. The order followed by T2 was 5-15<16-25<<36-45<26-35<46-55 (Fig. 5). The study by Navaretnam et al. (2023) showed a value of 0.279 which is much greater than present findings. THQ level investigated by Ajah et al. (2022) is consistent with current observations. Hamid et al. (2020) recorded the THQ level for Cu in rice varieties as 0.66-0.89 which are much higher than recent findings. The results indicated the values recorded below 1 which indicates less potential risk for public.

Conclusion

Present results concluded that Zn and Cu concentrations were significantly increased in waste water treatment as compared to tubewell water. Basmati and irri 6 varieties presented the highest Zn and Cu level in their grains. Analyzed Zn (0.967-2.87 mg/kg) and Cu (0.575-3.066 mg/kg) concentration in all rice grains lie within WHO/FAO limits. CF>1 showed higher Zn contamination in the study area as compared to Cu metal. Zn (0.559-0.932) and Cu (1.428 to 1.995) range showed the minimal enrichment in soil. As all THQ values were lower than 1 in all age groups, so there was no potential carcinogenic risk to local consumers. In conclusion, there is no significant health risk observed for humans on consuming rice varieties from this area. Government should organize strategies to regularly monitored the metal level in rice grains to ensure the metal level at harmless level.

DECLARATIONS

Ethical approval

All authors declared that manuscript has not been published previously.

Ethical statement

The institutional Human Ethics Committee of University of Sargodha (Approval No.25-A18 IEC UOS) has allowed all the protocols used in this experiment. All the experimental methods of this study have followed all the appropriate guidance and regulations including NRC standards.

Availability of data and materials

This published article contain all the generated data of this study.

Conflict of interest

All authors declared that they have no conflict of interest.

References

Abbas, S. and Dastgeer, G., 2021. Analysing the impacts of climate variability on the yield of Kharif rice over Punjab, Pakistan. Blackwell Publishing Ltd., Oxford, UK, pp. 329-349. https://doi.org/10.1111/1477-8947.12230

Abdul Sattar, A. 2021. Preparation of novel hybrid (almond shell and pleurotus sajor caju) biosorbent for the removal of heavy metals (nickel and lead) from wastewater. Water Conserv. Manage., 7: 01-07. https://doi.org/10.26480/wcm.01.2021.01.07

Abualhaija, M. 2023. Applying the quality and pollution indices for evaluating the wastewater effluent quality of kufranja wastewater treatment plant, Jordan. Water Conserv. Manage., 7: 06-11. https://doi.org/10.26480/wcm.01.2023.06.11

Abdulwahid, K.D. 2023. Phytoremediation of cadmium pollutants in wastewater by using Ceratophyllum demersum L. as an aquatic macrophytes. Water Conserv. Manage., 7: 83-88. https://doi.org/10.26480/wcm.02.2023.83.88.

Adam, A.A., Sackey, L.N. and Ofori, L.A. 2022. Risk assessment ofheavy metals concentration incereals and legumes sold inthe Tamale Aboabo market, Ghana. Heliyon, 8: e10162. https://doi.org/10.1016/j.heliyon.2022.e10162

Adewumi, A.J. and Lawal, A.E. 2022. Heavy metals in paddy soils and their uptake in rice plants collected along Ogbese River, Southwest Nigeria: Implications for contamination and health risk. Afric. J. Sci., 24: 569-582.

Afonne, O.J. and Ifediba, E.C. 2020. Heavy metals risks in plant foods–need to step up precautionary measures. Curr. Opin. Toxicol., 22: 1-6. https://doi.org/10.1016/j.cotox.2019.12.006

Ahmad, W., Alharthy, R.D., Zubair, M., Ahmed, M., Hameed, A. and Rafique, S. 2021. Toxic and heavy metals contamination assessment in soil and water to evaluate human health risk. Scient. Rep., 11: 17006. https://doi.org/10.1038/s41598-021-94616-4

Ahmed, M., Matsumoto, M. and Kurosawa, K. 2018. Heavy metal contamination of irrigation water, soil, and vegetables in a multi-industry district of Bangladesh. Int. J. environ. Res., 12: 531-542. https://doi.org/10.1007/s41742-018-0113-z

Ajah, D.N., Agboeze, E., Ihedioha, J.N., Chukwudi-Madu, E. and Chime, C.C. 2022. Levels of zinc (Zn), copper (Cu), iron (Fe), and cadmium (Cd) in soil, rice stalk, and Oryza sativa grain in Ishiagu rice field, Ebonyi State, Nigeria; Human Health Risk. J. Nigerian Soc. Phys. Sci., 4: 891-891. https://doi.org/10.46481/jnsps.2022.891

Akhtar, N., Khan S., Rehman, S.U., Rehman, Z.U., Khatoon, A, Rha, E.S. and Jamil, M. 2021. Synergistic effects ofzinc oxide nanoparticles and bacteria reduce heavymetals toxicity in rice (Oryzasativa L.) plant. Toxics, 9:113. https://doi.org/10.3390/toxics9050113

Al-Ansari, R.F., Al-Gebori, A.M. and Sulaiman, G.M. 2020. Serum levels of zinc, copper, selenium and glutathione peroxidase in the different groups of colorectal cancer patients. Casp. J. Intern. Med.,  11: 384–390.

An, Y., Li, S., Huang, X., Chen, X., Shan, H. and Zhang, M. 2022. The role of copper homeostasis in brain disease. Int. J. mol. Sci., 23:13850. https://doi.org/10.3390/ijms232213850

Begoña-Zubero, M., Llop, S., Irizar, A., Murcia, M., Molinuevo, A., Ballester, F., Levi, M., Lozano, M., Ayerdi, M. and Santa-Marina, L. 2023. Serum metal levels in a population of Spanish pregnant women. Gaceta Sanitaria, 36: 468-476. https://doi.org/10.1016/j.gaceta.2021.07.006

Chandran, S., Niranjana, V. and Benny, J. 2012. Accumulation of heavy metals in wastewater irrigated crops in Madurai, India. J. environ. Res. Develop., 6: 432, 438.

Chen, F., Muhammad, F.G., Khan, Z.I., Ahmad, K., Malik, I.S., Ashfaq, A., Naeem, M., Nadeem, M., Ma, J., Awan, M.U.F. and Mahpara, S. 2022. Bioaccumulation and transfer of zinc in soil plant and animal system: a health risk assessment for the grazing animals. Environ. Sci. Pollut. Res., 29: 2718-2727. https://doi.org/10.1007/s11356-021-15808-z

Du, F., Yang, Z., Liu, P. and Wang, L. 2018. Accumulation, translocation, and assessmentof heavy metals in the soil-rice systems near amine-impactedregion. Environ. Sci. Pollut. Res., 25: 32221-32230. https://doi.org/10.1007/s11356-018-3184-7

El-Kady, A.A. and Abdel-Wahhab, M.A. 2018. Occurrence of trace metals in foodstuffs and their health impact. Trends Fd. Sci. Technol., 75: 36-45. https://doi.org/10.1016/j.tifs.2018.03.001

Fan, Y., Zhu, T., Li, M., He, J. and Huang, R. 2017. Heavy metal contamination in soil and brown rice and human health risk assessment near three mining areas in central China. J. Hlthc. Engin., 2017: 1-3 https://doi.org/10.1155/2017/4124302

Gemeda, F.T., Guta, D.D., Wakjira, F.S. and Gebresenbet, G. 2021. Occurrence of heavy metal in water, soil, and plants in fields irrigated with industrial wastewater in Sabata town, Ethiopia. Environ. Sci. Pollut. Res., 28: 12382-12396. https://doi.org/10.1007/s11356-020-10621-6

Giri, S. and Singh, A.K. 2017. Human health risk assessment due to dietary intake of heavy metals through rice in the mining areas of Singhbhum Copper Belt, India. Environ. Sci. Pollut. Res., 24: 14945-14956. https://doi.org/10.1007/s11356-017-9039-9

Guadie, A., Yesigat, A., Gatew, S., Worku, A., Liu, W., Ajibade, F.O. and Wang, A., 2021. Evaluating the health risks of heavy metals from vegetables grown on soil irrigated with untreated and treated wastewater in Arba Minch, Ethiopia. Sci. Total Environ., 761: 143302. https://doi.org/10.1016/j.scitotenv.2020.143302

Hamid, A., Wasim, A., Azfar, A., Amjad, R. and Nazir, R., 2020. Monitoring and health risk assessment of selected trace metals in wheat rice and soil samples. Fd. Sci.Technol., 40: 917-923. https://doi.org/10.1590/fst.23319

Harmanescu, M., Alda, L.M., Bordean, D.M., Gogoasa, I. and Gergen, I., 2011. Heavy metals health risk assessment for population via consumption of vegetables grown in old mining area; a case study: Banat County, Romania. Chem. Centr. J., 5: 1-10. https://doi.org/10.1186/1752-153X-5-64

Hussain, A., Jiang, W., Wang, X., Shahid, S., Saba, N., Ahmad, M., Dar, A., Masood, S.U., Imran, M. and Mustafa, A., 2022. Mechanistic impact of zinc deficiency in human development. Front. Nutri., 9: 717064 https://doi.org/10.3389/fnut.2022.717064

Jiang, H.H., Cai, L.M., Wen, H.H., Hu, G.C., Chen, L.G. and Luo, J., 2020. An integrated approach to quantifying ecological and human health risks from different sources of soil heavy metals. Sci. Total Environ., 701: 134466. https://doi.org/10.1016/j.scitotenv.2019.134466

Kang, Z., Wang, S., Qin, J., Wu, R. and Li, H., 2020. Pollution characteristics and ecological risk assessment of heavy metals in paddy fields of Fujian province, China. Scient. Rep., 10: 12244. https://doi.org/10.1038/s41598-020-69165-x

Kaninga, B., 2022. Bioavailability of heavy metals in soils that are in close proximity to mine tailings in Copperbelt, Zambia. Doctoral dissertation, The University of Zambia.

Karizaki, V.M., 2016. Ethnic and traditional Iranian rice-based foods. J. Ethnic Fd., 3: 124-134. https://doi.org/10.1016/j.jef.2016.05.002

Kaur, H. and Garg, N., 2021. Zinc toxicity in plants: A review. Planta, 253: 129. https://doi.org/10.1007/s00425-021-03642-z

Khan, Z.I., Mansha, A., Saleem, M.H., Tariq, F., Ahmad, K., Ahmad, T., Awan, M.U.F., Abualreesh, M.H., Alatawi, A.A. and Ali, S., 2021. Trace metal accumulation in rice variety kainat irrigated with canal water. Sustainability, 13: 13739. https://doi.org/10.3390/su132413739

Khan, S.T., Malik, A., Alwarthan, A. and Shaik, M.R., 2022. The enormity of the zinc deficiency problem and available solutions; an overview. Arabian J.Chem., 15: 103668. https://doi.org/10.1016/j.arabjc.2021.103668

Kong, X., Liu, T., Yu, Z., Chen, Z., Lei, D., Wang, Z. and Zhang, S. 2018. Heavy metal bioaccumulation in rice from a high geological background area in Guizhou Province, China. Int. J. environ. Res. Publ. Hlth., 15: 2281. https://doi.org/10.3390/ijerph15102281

Li, Q., Chen, Y., Fu, H., Cui, Z., Shi, L., Wang, L. and Liu, Z., 2012. Health risk of heavy metals in food crops grown on reclaimed tidal flat soil in the Pearl River Estuary, China. J. Haz. Mat., 227: 148-154. https://doi.org/10.1016/j.jhazmat.2012.05.023

Liu, J., Ma, X., Wang, M. and Sun, X., 2013. Genotypic differences among rice cultivars in lead accumulation and translocation and the relation with grain Pb levels. Ecotoxicol. environ. Safe., 90: 35-40. https://doi.org/10.1016/j.ecoenv.2012.12.007

Maares, M., Haupt, A., Schüßler, C., Kulike-Koczula, M., Hackler, J., Keil, C., Mohr, I., Schomburg, L., Süssmuth, R.D., Zischka, H. and Merle, U., 2023. A fluorometric assay to determine labile copper (II) ions in serum. Scient. Reps., 13: 12807. https://doi.org/10.1038/s41598-023-39841-9

Mahfooz, Y., Yasar, A., Guijian, L., Islam, Q.U., Akhtar, A.B.T., Rasheed, R., Irshad, S. and Naeem, U., 2020. Critical risk analysis of metals toxicity in waste water irrigated soil and crops: A study of a semi-arid developing region. Scient. Rep.s, 10: 12845. https://doi.org/10.1038/s41598-020-69815-0

Mahmood, A., Mahmoud, A.H., El-Abedein, A.I.Z., Ashraf, A. and Almunqedhi, B.M., 2020. A comparative study of metals concentration in agricultural soil and vegetables irrigated by wastewater and tube well water. J. King Saud Univ. Sci., 32: 1861-1864. https://doi.org/10.1016/j.jksus.2020.01.031

Mamat, A., Zhang, Z., Mamat, Z., Zhang, F. and Yinguang, C., 2020. Pollution assessment and health risk evaluation of eight (metalloid) heavy metals in farmland soil of 146 cities in China. Environ. Geochem. Hlth., 42: 3949-3963. https://doi.org/10.1007/s10653-020-00634-y

Markiewicz-Żukowska, R., Gutowska, A. and Borawska, M.H., 2015. Serum zinc concentrations correlate with mental and physical status of nursing home residents. PLoS One, 10: e0117257. https://doi.org/10.1371/journal.pone.0117257

Mashhadi, M.A., Bakhshipour, A., Zakeri, Z. and Ansari-Moghadam, A., 2017. Reference range for zinc level in young healthy population in southeast of Iran. Hlth. Scope, 6: 1-3. https://doi.org/10.17795/jhealthscope-18181

Milde, D., Novák, O., Stuka, V., Vyslou il, K. and Machá ek, J., 2001. Serum Levels of Selenium, Manganese, Copper, and Iron in Colorectal Cancer Patients. Biol. Trace Elem. Res., 79: 107–114. https://doi.org/10.1385/BTER:79:2:107

Mohammadi, A.A., Zarei, A., Esmaeilzadeh, M., Taghavi, M., Yousefi, M., Yousefi, Z. and Javan, S., 2020. Assessment of heavy metal pollution and human health risks assessment in soils around an industrial zone in Neyshabur, Iran. Biol.Trace Element Res., 195: 343-352. https://doi.org/10.1007/s12011-019-01816-1

Nadeem, F., Azhar, M., Anwar-ul-Haq, M., Sabir, M., Samreen, T., Tufail, A., Awan, H.U.M. and Juan, W., 2020. Comparative response oftwo rice (Oryza sativa L.) cultivars to applied zinc and manganese for mitigation of salt stress. J. Soil Sci. Pl. Nutri., 20: 2059-2072. https://doi.org/10.1007/s42729-020-00275-1

Navaretnam, R., Soong, A.C., Goo, A.Q., Isa, N.M., Aris, A.Z., Haris, H. and Looi, L.J., 2023. Human health risks associated with metals in paddy plant (Oryza sativa) based on target hazard quotient and target cancer risk. Environ. Geochem. Hlth., 45: 2309-2327. https://doi.org/10.1007/s10653-022-01344-3

Noor, M., Kiran, A., Shahbaz, M., Sanaullah, M. and Wakeel, A., 2024. Root system architecture associated zinc variability in wheat (Triticum aestivum L.). Scient. Rep., 14: 1781. https://doi.org/10.1038/s41598-024-52338-3

Novotná, M., Mikeš, O. and Komprdová, K., 2015. Development and comparison of regression models for the uptake of metals into various field crops. Environ. Pollut., 207: 357-364. https://doi.org/10.1016/j.envpol.2015.09.043

Oubane, M., Khadra, A., Ezzariai, A., Kouisni, L. and Hafidi, M., 2021. Heavy metal accumulation and genotoxic effect of long-term wastewater irrigated peri-urban agricultural soils in semiarid climate. Sci. Total Environ., 794: 148611. https://doi.org/10.1016/j.scitotenv.2021.148611

Qin, G., Niu, Z., Yu, J., Li, Z., Ma, J. and Xiang, P., 2021. Soil heavy metal pollution and food safety in China: Effects, sources and removing technology. Chemosphere, 267: 129205. https://doi.org/10.1016/j.chemosphere.2020.129205

Rahman, M.S., Saha, N. and Molla, A.H., 2014. Potential ecological risk assessment of heavy metal contamination in sediment and water body around Dhaka export processing zone, Bangladesh. Environ. Earth Sci.s, 71: 2293-2308. https://doi.org/10.1007/s12665-013-2631-5

Rezapour, S., Atashpaz, B., Moghaddam, S.S. and Damalas, C.A., 2019. Heavy metal bioavailability and accumulation in winter wheat (Triticum aestivum L.) irrigated with treated wastewater in calcareous soils. Sci.Total Environ., 656: 261-269. https://doi.org/10.1016/j.scitotenv.2018.11.288

Satpathy, D., Reddy, M.V. and Dhal, S.P., 2014. Risk assessment of heavy metals contamination in paddy soil, plants, and grains (Oryza sativa L.) at the East Coast of India. Bio-Med. Res. Int., 2014: 1-14 https://doi.org/10.1155/2014/545473

Schmidt, S.B., Vatamaniuk, O. and Schneider, A., 2023. Essential metals for plants: Uptake, transport, regulation of homeostasis and roles in plant development. Front. Pl. Sci., 14: 1156247. https://doi.org/10.3389/fpls.2023.1156247

Sharma, S., Nagpal, A.K. and Kaur, I., 2018. Heavy metal contamination in soil, food crops and associated health risks for residents of Ropar wetland, Punjab, India and its environs. Fd. Chem., 255: 15-22. https://doi.org/10.1016/j.foodchem.2018.02.037

Singh, A., Sharma, R.K., Agrawal, M. and Marshall, F.M., 2010. Health risk assessment of heavy metals via dietary intake of foodstuffs from the wastewater irrigated site of a dry tropical area of India. Fd. chem. Toxicol., 48: 611-619. https://doi.org/10.1016/j.fct.2009.11.041

Sukri, N.S., Aspin, S.A., Kamarulzaman, N.L., Jaafar, N.F., Ghazi, R.M., Ismail, N. and Zakaria, M., 2018. Assessment of metal pollution using enrichment factor (EF) and pollution load index (PLI) in sediments of selected Terengganu rivers, Malaysia. Malaysian J. Fundam. appl. Sci., 14: 235-240. https://doi.org/10.11113/mjfas.v14n2.1065

Syed, A., Raza, T., Bhatti, T.T. and Eash, N.S., 2022. Climate impacts on the agricultural sector of Pakistan: Risks and solutions. Environ. Challen., 6: 100433. https://doi.org/10.1016/j.envc.2021.100433

Tang, L., Deng, S., Tan, D., Long, J. and Lei, M., 2019. Heavy metal distribution, translocation, and human health risk assessment in the soil-rice system around Dongting Lake area, China. Environ. Sci. Pollut. Res., 26: 17655-17665 https://doi.org/10.1007/s11356-019-05134-w

Toro-Román, V., Siquier-Coll, J. and Bartolomé, I., 2021.  Copper concentration in erythrocytes, platelets, plasma, serum and urine: influence of physical training. J. Int. Soc. Sports Nutr., 18: 28. https://doi.org/10.1186/s12970-021-00426-4

Wei, J., Gao, J. and Cen, K., 2019. Levels of eight heavy metals and health risk assessment considering food consumption by China’s residents based on the 5th China total diet study. Sci. Total Environ., 689: 1141-1148. https://doi.org/10.1016/j.scitotenv.2019.06.502

Wei, R., Chen, C. and Kou, M., 2023. Heavy metal concentrations in rice that meet safety standards can still pose a risk to human health. Commun. Earth Environ., 4: 84. https://doi.org/10.1038/s43247-023-00723-7 https://doi.org/10.1038/s43247-023-00723-7

Xiao, L., Guan, D., Peart, M.R., Chen, Y. and Li, Q., 2017. The respective effects of soil heavy metal fractions by sequential extraction procedure and soil properties on the accumulation of heavy metals in rice grains and brassicas.  Environ. Sci. Pollut. Res., 24: 2558-2571. https://doi.org/10.1007/s11356-016-8028-8

Xie, W., Zhou, J., Wang, H., Liu, Q., Xia, J. and Lv, X. 2011. Cu and Pb accumulation in maize (Zea mays L.) and soybean (Glycine max L.) as affected by N, P and K application. Afric. J. agric. Res., 6: 1469-1476.

Ye, X., Li, H., Ma, Y., Wu, L. and Sun, B., 2014. The bioaccumulation of Cd in rice grains in paddy soils as affected and predicted by soil properties. J. Soils Sedim., 14: 1407-1416. https://doi.org/10.1007/s11368-014-0901-9

Zhang, B., Liu, L., Huang, Z., Hou, H., Zhao, L. and Sun, Z., 2021. Application of stochastic model to assessment of heavy metal (loid) s source apportionment and bio-availability in rice fields of karst area. Sci. Total Environ., 793: 148614. https://doi.org/10.1016/j.scitotenv.2021.148614

Zhao, K., Liu, X., Xu, J. and Selim, H.M., 2010. Heavy metal contaminations in a soil–rice system: identification of spatial dependence in relation to soil properties of paddy fields. J. Haz. Mat., 181: 778-787. https://doi.org/10.1016/j.jhazmat.2010.05.081

Zheng, S., Wang, Q., Yuan, Y. and Sun, W., 2020. Human health risk assessment of heavy metals in soil and food crops inthe Pearl River Delta urban agglomeration of China. Fd. Chem., 316: 126213. https://doi.org/10.1016/j.foodchem.2020.126213

Zhiyuan, L., Ma, Z., van der Kuijp, T.J., Yuan, Z. and Huang, L., 2014. A review of soil heavy metal pollution from mines in China: Pollution and health risk assessment. Sci. Total Environ., 468–469: 843–853. https://doi.org/10.1016/j.scitotenv.2013.08.090