Human health risk assessment of lead, cadmium, and mercury co-exposure from agricultural soils in the Tuzla Canton (Bosnia and Herzegovina)

The Tuzla Canton is a resource-rich area located in the north-eastern part of Bosnia and Herzegovina with mining, coal, salt, chemical, and metal processing industry and a thermal power plant, all of which pollute its environment (Figure 1). Most of the industry is located near the rivers Jala and Spreca, which have been receiving large amounts of wastewater. In the rainy seasons, these rivers repeatedly flood adjacent agricultural land, which covers about 49 % of the Canton and often surrounds industrial zones. Our study area has a temperate continental climate with cold winters and hot summers and dominant south-westerly and north-easterly winds.

Figure 1

Soil samples were collected in triplicates from 29 sites (Figure 1) four times a year in all four seasons from 2016 to 2017, totalling 348 samples. In reference to our previous study (17), we significantly increased the number of sampling sites and extended the sampling period to one year. The sites are well known for intensive agricultural production near industrial or mining activities and lie from 500 m to 50 km apart.

The samples were taken and prepared following standard ISO 11464, 10381-5, and 10381-6 procedures (36–38). Topsoils (0–15 cm) were sampled using a plastic tool. Each sample was obtained by collecting 8–10 subsamples using a combination of two sampling designs: simple random and judgmental sampling.

Samples were air-dried in the laboratory at room temperature for seven days, ground into fine powder, sieved through a 0.15 mm polyethylene sieve, and packed until analysis. According to the ISO 11466 (39) standard procedure for extraction of trace elements from soil, 3 g of each sample was digested with concentrated aqua regia (in a 3:1 HCl to HNO₃ ratio) at room temperature for 16 h and then boiled under reflux for 2 h. The extract was then filtered through Whatman filter paper and diluted with deionised water in a volumetric flask. We also prepared a reagent blank for each metal following the same steps. Calibration curves were prepared with analytical standards, and the blank was used to zero the instrument. Each sample was analysed in triplicate, and results expressed in mg/L of filtrate.

Total Pb content was determined with inductively coupled plasma atomic emission spectroscopy (ICP-AES, Optima 2100 DV, Perkin Elmer, Waltham, MA, USA), while total Cd and Hg were determined with atomic absorption spectroscopy (AAS) using a Varian SpectrAA 220 with either GTA-110 or VGA-77, respectively (Varian, Belrose, Australia).

Soil texture was evaluated with particle-size analysis (PSA) as described earlier (17). Air-dried soil samples were manually passed through nine sieves of different mesh sizes in order to calculate the percentage of individual soil types in soil samples.

To assess human exposure to heavy metals we calculated the average daily dose (ADD) for each route of exposure using the following equations: [1] ADDing =C×Ing⁡R×EF×EDBW×AT×CF \mathrm{ADD}_{\text {ing }}=\frac{C \times \operatorname{Ing} \mathrm{R} \times \mathrm{EF} \times \mathrm{ED}}{B W \times \mathrm{AT}} \times \mathrm{CF}

where ADD_ing is the average daily intake of metal through ingestion (mg/kg/day), C is heavy metal mass fraction in soil (mg/kg), IngR is the soil ingestion rate (mg/day), EF exposure frequency (days/year), ED exposure duration (years), BW average body weight (kg), AT average time (days), and CF the conversion factor (10^-6 kg/mg); [2] ADDinh =C×Inh⁡R×EF×EDPEF×BW×AT \mathrm{ADD}_{\text {inh }}=\frac{\mathrm{C} \times \operatorname{Inh} \mathrm{R} \times \mathrm{EF} \times \mathrm{ED}}{\mathrm{PEF} \times \mathrm{BW} \times \mathrm{AT}}

where ADD_inh is the average daily intake of metal through inhalation (mg/kg/day), InhR is inhalation rate (m³/day), and PEF is the particle emission factor (1.36x10^-9 m³/kg), and [3] ADDderm =C×SA×SAF×ABSderm ×EF×EDBW×AT×CF \mathrm{ADD}_{\text {derm }}=\frac{C \times \mathrm{SA} \times \mathrm{SAF} \times \mathrm{ABS}_{\text {derm }} \times \mathrm{EF} \times \mathrm{ED}}{\mathrm{BW} \times \mathrm{AT}} \times \mathrm{CF}

where ADD_derm is the average daily intake of metal through skin (mg/kg/day), SA is the skin surface area in contact with soil (cm²), SAF is the skin adherence factor for soil (mg/cm²), and ABS is the dermal absorption factor for metals (unitless).

Non-cancer risk was calculated for all three heavy metals, while cancer risk was calculated only for Cd, because the International Agency for Research on Cancer (IARC) has classified only Cd and Cd compounds as carcinogens to humans (Group 1) (40).

Human non-cancer risk was calculated using the following equation: [4] HQ=ADDRfD \mathrm{HQ}=\frac{\mathrm{ADD}}{\mathrm{RfD}}

where HQ is the hazard quotient and RfD chronic reference dose for the analysed metal (mg/kg/day). There are three RfDs, one for each exposure route: RfD_O (mg/kg/day) for ingestion, RfD_ABS (mg/kg/day) for skin, and RfD_i (mg/m³) for inhalation (41). The United States Environmental Protection Agency (US EPA) has not yet developed an inhalation reference concentration for Pb and Pb compounds (42). If ADD is lower than the RfD, HQ is ≤1, and the risk is considered acceptable. If ADD is higher than the RfD, HQ is >1, adverse health effects are likely (43, 44). To assess the total non-cancer risk posed by more than one heavy metal, we calculated the hazard index (HI) as follows (45): [5] HI=∑HQi=∑ADDiRfDi \mathrm{HI}=\sum \mathrm{HQ}_{\mathrm{i}}=\sum \frac{\mathrm{ADD}_{\mathrm{i}}}{\mathrm{RfD}_{\mathrm{i}}}

If HI is ≤1, the exposed population is unlikely to experience adverse health effects, and if it is >1, then adverse health effects are likely (46).

The US EPA has developed a method to extrapolate oral toxicity values to assess dermal risk and calculate RfD_ABS (41) as follows: [6] RfDABS=RfDo×ABSGI \mathrm{RfD}_{\mathrm{ABS}}=\mathrm{RfD}_{\mathrm{o}} \times \mathrm{ABS}_{\mathrm{GI}}

where ABS_GI is the gastrointestinal absorption factor (unitless).

Cancer risk for Cd was estimated as incremental probability for an individual to develop cancer over a lifetime as a result of exposure to a potential carcinogen. It was calculated as follows (43): [7] Cancer Risk = ADD x CSF \text { Cancer Risk }=\text { ADD } x \text { CSF }

where, ADD is the chronic daily intake of Cd averaged over 70 years (mg/kg/day) and CSF cancer slope factor. Risks in the range from 10^-6 to 10^-4 have typically been considered acceptable by the US EPA (47–49).

Non-cancer risk was computed using the Monte Carlo simulation, a statistical method which repeatedly calculates random “what-if” scenarios in a single operation to produce the full range of possible outcomes and their likelihoods (50). For uncertainty analysis of estimated risks, we used the @RISK 5.5 software (Palisade, Ithaca, NY, USA). @RISK works with data in Microsoft Office Excel spreadsheets and relies on Monte Carlo simulations to estimate the probability of different outcomes that cannot easily be predicted with the deterministic approach.

Based on behavioural and physiological differences, the population in our study was divided into three groups: adult men (20–70 years old), adult women (20–70 years old), and children (1–6 years old). Health risks associated with exposure to heavy metals in soil for these population groups were calculated using the above equations [1–7]. The statistical distributions of parameters heavy metal concentrations and body weight were assumed to be lognormal. Instead of single-point values, we used different variable values for 348 concentrations of Pb, Cd, and Hg, as well as for body weights of 87 adult men (average age 37.17±1.28 years), 80 adult women (average age 34.72±1.05 years), and 236 children (average age 4.01±0.87 years). Body weight data for the adults were obtained from an independent research project conducted by the students of the University of Tuzla, Faculty of Pharmacy. Body weight data for children were pooled from paediatrics departments of Public Health Institutes in the Tuzla Canton. Soil heavy metal levels and body weights of each population group were considered input parameters to evaluate the probability function for human exposure to heavy metals. The stability of the results was tested at 5,000, 10,000, 25,000, and 50,000 iterations by running randomly selected values of independent variables according to their distribution function. The stability test showed that 10,000 iterations sufficed for a reliable yield. The number of iterations for every equation was then set to 10,000 to derive the certainty level, mean, the 75^th and 95^th percentile, and maximum values of exposure.

Exposure was evaluated through three scenarios with adult men, women, and children from the Tuzla Canton. We assumed exposure to Pb, Cd, and Hg from soil through skin, ingestion, and inhalation. All input parameters are presented in Table 1.

Scenarios 1 and 2 involved adult men and women, respectively, and Scenario 3 children (1–6 years old) of both genders born between 2011 and 2016.

All experiments were done in triplicate and the results expressed as means of 36 measurements. The data were analysed using the IBM SPSS Statistics (version 21) package (Armonk, NY, USA). The normality of variable distribution was tested with the Shapiro-Wilk test. Spearman’s rho correlation analysis of heavy metal concentrations was used to identify the same source of soil contamination if any.

Particle-size analysis showed that all soil samples were sandy. Heavy metal levels were compared to respective national permitted limit values (PLVs) in sandy agricultural soils (51, 52).

Soil levels of Pb, Cd, and Hg are presented in Table 2. Lead levels ranged from 13.33 to 1692.33 mg/kg (median 92.83 mg/kg) and exceeded PLVs in 71.55 % of all soil samples. Cadmium ranged from 0.05 to 3.67 mg/kg (median 0.32 mg/kg) and exceeded PLVs in 35.61 % of all soil samples. Mercury ranged from 0.02 to 2.73 mg/kg (median 0.11 mg/kg) and exceeded PLVs in 8.62 % of samples.

The Shapiro-Wilk test showed that the levels of all three heavy metals were not distributed normally. Spearman’s rho correlation analysis showed that Pb negatively correlated with Cd (r=-0.38; p=0.00), Cd positively correlated with Hg (r=0.50; p=0.00), and Pb negatively correlated with Hg levels (r=-0.14; p=0.01). Judging by these correlations, all three heavy metals could originate from the same source of pollution.

Table 3 shows that the lowest mean soil Pb level remained similar to those reported earlier for the same area, but the highest Pb level measured in agricultural soil near the chloralkali plant in Tuzla significantly exceeds the maxima reported earlier. Additional large sources of Pb soil contamination were located near the highway and the coal-fired power plant, which is the largest power producer in Bosnia and Herzegovina. Its wastewaters contain Pb, Cd, Cr, and Ni in levels above the prescribed limits (29, 30).

N/A – data not available

The lowest mean Cd level is also within national limit, just like the level reported earlier for alkaline coal ash landfills in the County (29), whereas its levels in soil sampled near the salt mine Tetima and urban areas of Lukavac and Kalesija contained much higher Cd levels (31, 32). However, the highest mean Cd level in our study exceeds earlier maxima. Lukavac is an industrial zone with mining, coke, and cement industries, which release large quantities of waste materials and contaminate soil.

Even though the highest Hg level was expected to be measured near the chloralkali plant as reported earlier (30, 33), it was many times lower, and in fact measured in agricultural soil downstream of the river Spreca. The Spreca and its tributary Jala have been industrial and household wastewater recipients for many years, spreading contaminants over agricultural land during floods. This drop in the maximal level may suggest that Hg leakage at the chloralkali plant has been remedied in the meantime.

The non-cancer health risk to adults and children from exposure to Pb, Cd, and Hg in soil through ingestion, skin, and inhalation was calculated based on deterministic approach (Table 4). As expected, Pb contributed to the health risk more than the other two heavy metals. Exposure decreased in the following order: ingestion>skin contact>inhalation. Our results are consistent with similar studies pointing to ingestion as the primary route of exposure to heavy metals from soil (9, 15, 53).

Table 5 shows that hazard indices vary greatly between the exposure scenarios. Children are far more susceptible to heavy metal exposure from soil per body weight than adults due to their physiological characteristics and behaviour, particularly in terms of higher hand-to-mouth ingestion. Their overall health risk is about seven times higher than in adult women and 10 times higher than in adult men. In this respect, our findings are in line with those reported elsewhere, which largely depended on measurement sites (15, 41, 54–56). Some researchers (57, 58) found that high blood Pb levels in children were linked with seasonal trends of suspended soil dust, which, in contrast to hand-to-mouth behaviour, implies that inhalation might be the primary route of exposure to Pb from soil. This calls for closer examination of Pb exposure risk associated with fine airborne soil particles in the summer and near the roads (59).

Even though the hazard indices for all three exposure scenarios and all three heavy metals were lower than 1, indicating exposure below risk thresholds, this estimation does not take into account other sources of exposure, such as food, water, and air (22, 60–63).

In order to obtain a more in-depth view of human health risk, we also took an integrative approach to risk assessment under the assumption that the total effect of the three analysed heavy metals would be additive. In other words, even if the metals are present in soil at levels lower than their No Observed Adverse Effect Level (NOAEL), the sum of their levels might lead to increased health risk. In our study, however, the calculated health risk of exposure to a mixture of all three metals for all three scenarios remained below the risk threshold (HI<1; Table 5).

Still, this additive approach has its own limitations and disadvantages, as it completely neglects different mechanisms of action of the analysed heavy metals and their possible interactions, which might produce weaker or even stronger (synergistic) effects or effects that each metal alone would not exhibit.

As for the cancer risk for Cd exposure through soil, none was found in any of the three scenarios (HI<1; Table 5).

Bearing in mind that deterministic risk assessment, which relies on point estimation, is mainly based on mean or median heavy metal levels and the most likely exposure parameters, it is possible that it could either under-or overestimate health risk (5, 64). To minimise uncertainties and errors, risk levels can also be evaluated with probability distribution functions. Table 6 summarises our Monte Carlo simulations, while Figures 2–4 show simulated probability distribution for the hazard index, calculated as the sum of three hazard quotients (HI=HQ_oral+HQ_dermal+HQ_inhalation).

1 – adult men; 2 – adult women; 3 – children. Figures in bold indicate hazard indices above the no-risk threshold (HI>1)

Figure 2

Figure 3

Figure 4

Considering individual differences and spatial variations, the hazard indices of Scenarios 1 and 2 for Pb showed 0.6 % probability to exceed the threshold value of 1 (Figure 2). For Scenario 3 (with children), this probability rose to 14.2 %. In contrast, the hazard indices for Cd and Hg for all three scenarios showed no probability to exceed the health risk threshold, save for the children scenario for Hg intake (Figures 3 and 4).

Hazard indices higher than 1 were obtained only when exposure assessment was based on the maximal and 95^th percentile levels (Table 6) and all point to higher health risk for children. For Pb exposure, they were several times higher than those reported by studies conducted in Spain and China (HI 3.11E-02 and 4.19E-04, respectively) (16, 65). The child average daily exposure to Cd did not exceed the safe reference dose, and the HIs were within the acceptable limits. Unlike Pb, Cd showed several times lower hazard indices compared to the Spanish and Chinese studies (HI 1.09E-03 and 1.09E-05, respectively) (16, 65). Our findings are also in line with other studies conducted in China and reporting higher health risk in children due to soil heavy metals (54, 55).

As for cancer risk associated with exposure to Cd, our Monte Carlo simulations showed no increased risk. However, as soil is not the main source of human Cd exposure (66–68), our estimate should be taken with reserve, and future modelling should combine soil data with other major sources, such as food, air, cigarette smoking, and water.