Potentially toxic element (PTE) levels in maize, soil, and irrigation water and health risks through maize consumption in northern Ningxia, China

The study area was located in Northern Ningxia, China, where an industrial region called “Shizuishan industrial park” operates. Approximately eight small industrial parks with more than 300 enterprises are located near this area, including metal mining, smelting and processing, petroleum, coal, and other fuel processing, electroplating, and chemical processing facilities. The surrounding soil and groundwater would be polluted by PTEs from the industrial park. The Yellow River passes through this area and water from the Yellow River is used to irrigate the surrounding agricultural fields. The population in this area is approximately 800,000. The climate belongs to the warm temperate zone and maize is the major crop.

As shown in Fig. 1, 45 fresh, raw maize samples and the associated soil were collected from the cultivated area in August 2017. The sampling plot was collected randomly in the vicinity of the industrial park and the area of each sampling plot was 200 m × 200 m. In each plot five replicate samples were taken by five-point sampling method. The same variety of maize (general maize) was selected. Surface soil (0–20 cm) was collected using a wooden spatula through five-point sampling method.

The method of irrigation water sampling was referred to the national standard of NY/T396–2000 [30]. Given that not every site has irrigation water when sampling, water sample was collected when the sampling site was near the tributary of the Yellow river. Five points were suggested by the national standard. Thus, five irrigation water samples were collected from the tributary of the Yellow river near the point of maize sampling in the present study. Each water sample was collected in a polypropylene bottle, and added with 1 mL of concentrated nitric acid (HNO₃) to eliminate microbial activity.

The samples of maize and soil were collected by the permission of the peasant household, while irrigation water was collected without any permission. All the samples were packed, labeled and immediately transported to the laboratory.

Maize was pre-treated according to the national standard of NY/T398–2000 [31]. The samples were dried in an oven (DHG-9030A, China) at 60 °C and powdered using a grinding mill (0.2 mm sieve). The sifted samples (0.5 g) were weighed into digestion tubes, and added with 10 mL of digestion solution (v:v, HNO₃:HClO₄ = 4:1). After cold-digestion overnight, the mixture was further digested with a block digester at 120 °C until the solution was clear. The digested samples were then diluted to a volume of 10 mL with Milli-Q water. The soil samples were prepared referring to the national standard of NY/T 395–2012 in China [32]. The soil was air-dried until reaching a constant weight and powdered using a grinding mill (0.2 mm sieve). Subsequently, 12 mL of nitric acid:hydrofluoric acid (v:v, HNO₃:HF = 5:1) was added to 0.35 g of each soil sample in a Teflon digestion tube. The mixture was heated at 120 °C until the solution volume reached approximately 3 mL. The sample was then added with 5 mL of perchloric acid was added to continue the digestion until the solution was clear. The acid in the sample was evaporated at the same temperature until 1–2 mL of the solution remained. The solution was then transferred to 50 mL colorimetric tube and diluted to a final volume of 50 mL with water. Irrigation water samples were treated according to the national standard of GB 5084–2005 in China [33]. The samples were filtered with 0.45 μm filters, and 20 mL of each filtered solution was digested with 5 mL of nitric acid. The digested samples were diluted to 10 mL with Milli-Q water.

The contents of PTEs (Cd, Pb, Cr, Zn, Cu, and As) were determined by ICP-OES (Varian710-ES, USA). Quality control was performed to ensure the the accuracy and precision of the experiment procedures (Table 1S). Each analysis was performed in triplicate. In each sample batch, a blank reagent, standard reference soil (GBW07419), cabbage (GBW10014), and a multi-element standard solution (GNM-M122877–2013) were used when soil, maize and water were treated. The correlation coefficients of each element were > 0.9990. The recoveries of elements ranged from 85 to 110%, and the relative standard deviation (RSD) values were < 5%.

Some physicochemical properties of soil and irrigation water were evaluated. Soil pH and organic matter (OM) were measured the laboratory according to the standard of NY/T 395–2012 in China [32]. Water pH and electrical conductivity (EC) were detected in situ according to the standard of GB 5084–2005 in China [33].

To determine the maize consumption habits of residents, questionnaires were distributed in villages close to the sampling sites. Food frequency method was used to design the questionnaires in this study [34]. A total of 103 local inhabitants who consumed self-planted maize were selected to complete the questionnaire considering age distribution and gender balance. The questionnaire includes information about age, gender, body weight, frequency, and quantity of maize consumption, and maize source. The maize consumption information of the residents was used for health risk assessment. As experts suggested that the dietary structure of infants (< 3 years old) is different from that of adults, infants were not considered in the current study.

Sensitivity analysis in Crystal Ball software was performed to confirm variables that pose the greatest effect on health risk. First, the rank correlation coefficients between the exposure factors and health risk were determined using probabilistic estimation. Subsequently, the contribution of each variable was calculated by squaring the variance. Finally, the results were normalized to 100%, and the sequence of contributing variables was generated.

Mean, standard deviation (SD), minimum, maximum, and variable coefficient (C.V) were calculated in Microsoft Excel 2010 (Microsoft Ins., USA). ANOVA and correlation analysis were performed in SPSS 17.0 (IBM Ins., USA). The determination of the best-fitting distribution, Monte Carlo simulation, and sensitivity analysis were all carried out in Crystal Ball software (Oracle© Ins., USA). ArcGIS Desktop 10 (ESRI Ins., USA, Authorization number: EFL564098460) was used to map the sampling sites.

Some important physicochemical properties of soil and water were shown as follows: The soil pH was 8.47 ± 0.55 and the OM was 17.92 ± 8.52 g/kg. The water pH was 8.13 ± 0.87 and the EC was 830.27 ± 50.28 μs/cm. The PTE concentrations in maize grains, associated soil, and irrigation water are presented in Table 1. The average concentrations of Cd, Pb, Cr, Zn, Cu, and As in maize grains were 0.037, 0.41, 2.36, 17.02, 1.04, and 0.17 mg/kg, respectively. The maximum allowable concentrations of the six metals in food are 0.1 (Cd), 0.2 (Pb), 1.0 (Cr), 50 (Zn), 10 (Cu) and 0.5 (As) mg/kg [39, 40]. Thus, the average concentrations of Pb and Cr exceeded the maximum allowable concentrations in food. However, the concentrations of the four remaining metals exceeded the standards in some samples. The measured metal level exceeded the limit, and the metal with the highest percentage was Cr (69%) followed by Pb (47%), As (18%), and Cd (2%). The average PTE concentrations in the soil samples were 0.14, 18.16, 37.25, 138.20, 19.61, and 14.18 mg/kg for Cd, Pb, Cr, Zn, Cu, and As, respectively, with large variation among the samples. The average concentrations of Zn and As exceeded the standards (GB15618–2018) for soils in China [41] with over-limit ratio of 18 and 4%, respectively. The background concentrations in Ningxia were 0.11 (Cd), 20.6 (Pb), 60.0 (Cr), 58.8 (Zn), 22.1 (Cu), and 11.90 (As) mg/kg [42]. Thus, the Zn level in the soil measured in this study was remarkably higher than the background value, the Cr level was lower, and the levels of the other metals were similar to the background values. Some sampling sites in this study were close to the industrial parks, which might explain the high metal concentrations found at these sites. However, source identification should be conducted to better understand the results. In the irrigation water samples, the mean concentrations of the six PTEs were 0.11 (Cd), 1.12 (Pb), 41.03 (Cr), 0.91 (Zn), 1.60 (Cu), and 3.60 (As) μg/L, which were below the limits acceptable in China (GB 5084–2005) [33]. The correlation analysis results showed the soil pH was significantly positively correlated with Zn concentration in maize (P < 0.05), and OM in soil was significantly negatively correlated with Pb concentration.

Bioaccumulation factor (BF) is an index used to evaluate the transfer potential of a metal from soil to plant [43]. In the present study, BF was calculated by the concentration of metal in maize relative to that in the corresponding soil. A BF value higher than 1 indicates a high level of metal accumulation in the plant, and a BF value lower than 1 denotes a low level of metal accumulation in the plant [44]. The BF values were far less than 1 for all six PTEs, indicating the low levels of metal accumulation in maize grains. The BF values of the six metals followed the following sequence: Cd > Zn ≈ As > Cr > Cu > Pb. The accumulation ability of Cd was the highest, while that of Pb was the lowest in maize. The results of correlation analysis (Table 2S) showed that the correlation between the BFs of Pb, Zn, and Cr had significant negative (P < 0.01) correlations with the corresponding PTEs in soil, while the BF of Cd had significant positive correlations (P < 0.01) with Cd in soil. Moreover, the relationships of the BFs of all PTEs and corresponding PTEs in maize were significantly positively correlated (p < 0.01).

The results for resident BW, DI, and EF are summarized in Table 2. Of the 103 local inhabitants who participated in this study, 49% were male, and 51% were female. In older to eliminate differences in size of the age population and considering the data of the present study, interval of 20 years was selected to divide the age group. Therefore, the participants were classified into four age groups: below 20 years, 20–40 years, 40–60 years, and more than 60 years old (occupying 25, 34, 26, and 15% of participants, respectively). The average BWs of all participants, male participants, and female participants were 54.60, 58.45, and 50.67 kg, respectively. Based on the reported statistics, the average BW values for adults in China are 62.7 kg for males and 54.4 kg for females [45]. The average BWs was slightly lower than the reported values possibly because children were considered in the current study.

In this study, the maize DI was slightly less for males (160.20 g/day) than for females (162.08 g/day). Among the different age groups, DI was the highest (186.37 g/day) in the 20–40 age group followed by the 40–60 (181.48 g/day) and > 60 (136.36 g/day) groups. The < 20 group had the lowest DI of 118.46 g/day. The maize EF was also lower for males (14.34 day/year) than for females (16.67 day/year). In contrast to DI, EF was the highest in the < 20 age group (17.81 day/year) followed by the 40–60 (17.11 day/year) and 20–40 (14.06 day/year) age groups. The > 60 group had the lowest EF of 10.36 day/year. BW, DI, and EF affected health risk, and their degrees of influence were evaluated by sensitivity analysis.

The sensitivity analyses showed that the contributions of As concentration, maize DI, maize EF, and Pb concentration to non-carcinogenic risk were 35.8, 29.4, 20.7, and 1.8%, respectively. The contributions of As concentration, maize DI, and maize EF to carcinogenic risk were 61.0, 18.5, and 13.1%, respectively. These results imply that controlling the concentration of As would effectively reduce the health risk for local inhabitants.

The PTE concentration in samples of maize irrigated by waste water were significantly higher (P < 0.05) than that irrigated by unpolluted water [48]. The PTE levels of Cd, Pb, Cr, Zn, Cu, and As in maize growing near a petrochemical industry complex were 0.0442, 0.0595, 0.2996, 20.42, 4.49, and 0.0428 mg/kg, respectively [49], and the levels of Pb, Cr, Cu, and As were lower than the present results; hence, maize in the current study was more contaminated. Maize in another industrial area was found to have Pb, Cd, Zn, and Cu levels of were 0.129, 0.021, 3.4, and 2.6 mg/kg, respectively [50], which were below the standard values and our study values. Although their study area was an industrial area, the maize samples were collected from a market and may not have been grown near an industrial zone.

Previous study found that maize grains have lower BF values compared with other parts [51], indicating the low accumulation ability of PTEs in maize grains. Researchers also found that Cd is the easiest transferred metal, and Pb is the hardest transferred metal in maize [51, 52]. Wang et al. [53] collected data from many previous studies and fitted the accumulation equations of PTEs in maize grains. A parameter in the accumulation equation that reflected accumulation ability for Cd was the highest, while that for Pb was the lowest. Their results could prove findings of the present study. The BFs of PTEs in some other plants were consistent with maize. Zhuang et al. [54] found that the BF of Pb in soybeans was less than that of other PTEs. Wang et al. [53] reported Cd in wheat grains was more mobile than other PTEs and the BF of PTEs was also significantly correlated with corresponding PTEs in soil. Moreover, The BFs were related to plant species, heavy metal type, and the surrounding environment. In soil-plant system, PTE accumulations in plant are mainly dependent on metal activity and are greatly influenced by physicochemical properties of soil [55]. For example, high pH makes soil electronegative, thereby increasing the mobility of heavy metals and facilitating the metal adsorption by plants [56]. Organic matter in soil easily combines with metal ions and form organic complexes, which can reduce the availability, mobility, and accumulation of metals [57]. The present results were consistent with the explanation.

The PTE contents in irrigation water were very similar to a previous report, which indicated PTEs concentrations in their filtered water samples of Cd (ND–0.11 μg/L), Cr (74.80–94.70 μg/L), Cu (0.68–2.79 μg/L), Pb (ND–0.82 μg/L), and Zn (0.19–1.82 μg/L) [21]. However, the above samples along with the water samples in the present study were filtered and did not include suspended particles or sediment. According to another study, the PTE concentrations in suspended particulate matter of the Yellow River were 0.428 mg/kg for Cd, 74.9 mg/kg for Cr, 40.1 mg/kg for Cu, 32.6 mg/kg for Pb, and 13.6 mg/kg for As [58], which were below the standards set for soils but higher than the background values in local soil. Moreover, the PTEs concentrations in suspended particles and sediment from the Yellow River [21] were as follows: Cd (0.23–1.09 mg/kg), Cr (64.50–84.90 mg/kg), Cu (25.40–42.20 mg/kg), Pb (20.80–31.70 mg/kg), and Zn (72.50–107.00 mg/kg) for suspended particles and Cd (0.23–1.09 mg/kg), Cr (64.50–84.90 mg/kg), Cu (25.40–42.20 mg/kg), Pb (20.80–31.70 mg/kg), and Zn (72.50–107.00 mg/kg) for sediments. These results imply that PTEs in the Yellow River primarily accumulate in suspended particles and sediment, which should receive more attention.

Non-carcinogenic risk caused by PTEs through maize consumption was not obvious in other reports, similar to the present study. For the contribution of different PTEs to non-carcinogenic risk through maize consumption, the order of this study was As > Pb > Zn > Cd > Cu > Cr. Hu et al. [52] also found As was the major contributor to health risk. El-Hassanin et al. [47] found that Pb showed higher level of health risk index than Cd. However, Yu et al. [59] found Zn and Cu were the most important contributors to health risk (> 80%). Carcinogenic risk caused by As through maize consumption was seldom studied. Most reports have focused on carcinogenic risk resulting from rice consumption [60‐62], Jiang et al. [63] found that among different food sources, rice has the greatest contribution to total As daily intake.

In the present study, the estimates of health risk from PTE exposure are only for maize consumption and do not account for any other sources. In reality, health risk is affected by various sources of PTEs such as different foodstuffs (e.g., rice, wheat, and vegetables) and drinking water. Cai et al. [64] found rice consumption was the major source of PTEs exposure which accounting for more than 70% of the total HQs. The intake of vegetables was a second important source with approximately 10% of the total HQs. Cao et al. [26] also observed rice consumption was the major contributor to HI, and vegetable intake was the next greatest contributor.

Other pathways of PTE exposure including dust inhalation and dermal contact also contribute to health risks. Hu et al. [52] calculated health risk under three pathways (ingestion, dermal contact, and inhalation) of exposure to heavy metals in soil and found that ingestion was the main pathways associated with health risk. Sawut et al. [65] found the ingestion and/or dermal contact of soil carcinogenic risk lead by As was higher than the acceptable risk level. Therefore, exposure from other sources should be considered to estimate the total risk in future works.

Health risk assessment of PTEs through food consumption in previous reports was mainly assessed by deterministic estimation; however, deterministic assessment may overestimate or underestimate risks because of the uncertainty of metal concentration and the variability of exposure parameters. Probabilistic estimation is introduced to provide additional useful information. The proportion of residents exceeding RfDs could be obtained, and correlations between different variables and risk results could be provided. Cao et al. [26] evaluated potential health risks using probabilistic estimation and found that 0.00,4.18, 0.36, 0.03, 0.00 and 0.00% of inhabitants exceeded the corresponding RfD for Cr, Cu, Zn, Cd, Hg and Pb, respectively. Hang et al. [64] found that 3.9, 1.9, and 0.6% of inhabitants exceeded the corresponding RfD for Cu, Pb, and Cd, respectively. Jiang et al. [60] found the proportion of inhabitants exceeding the RfD of As was 27.85% and daily intake of rice, metal concentration in rice and body weight were most relevant variables to health risk results. These previous studies only focused on element exposure and paid less attention to different populations. In the present study, we calculated the proportion exceeding the threshold value for HI and R in different gender and age groups. The corresponding results would provide suggestion to different populations to consume maize.