Introduction

The devastating impact of anthropogenic activities has been gaining the global attention of researchers from both governmental and non-governmental organisations. One of the most threatening environmental menace that has taken a central stage in global discussions is the upward trajectory of heavy metal concentrations in the immediate environment of man. Numerous research papers have reported the occurrence of concentrations of heavy metals in soil and water which far exceed stipulated standards. Even though this is a global problem, developing countries are more at risk from exposure to heavy metals especially because of non-enforcement and non-adherence to effluent standards. In a bid to catch up with the rest of the world, developing countries pursuing industrial revolution are prone to environmental dilapidation due to emissions from toxic chemicals derived from industrialisation and urbanisation (Tian et al. 2015; Kobza and Geremek 2017). Specifically, aggressive industrialisation and rapid urbanisation have resulted in a substantial release of heavy metals to the environment (Najmeddin and Keshavarzi 2018; Pateraki et al. 2019; Sun et al. 2019).

Soils in industrialised and urbanised region have been receiving attention in recent times because they can be contaminated with various hazardous substances among which heavy metals play a significant role (Woszczyk et al. 2018). These trace elements can be transferred to the soil through atmospheric deposition of industrial particles emanating from metal smelting, sweltering of fossil fuels and releases from gasoline (Ettler 2015; Alsbou and Al-Khashman 2018; de Carvalho Aguiar et al. 2018). Trace elements are of particular interest to researchers because of their conservative nature, propensity to accumulate in soils and their persistence in the environment (Nazzal et al. 2013). In this regard, various investigators have attributed heavy metals (HM) release into the environment to a “chemical time bomb” caused by environmental changes due to the sudden release of strata-stored elements (Morton-Bermea et al. 2009; Dao et al. 2010).

Environmental concerns have been triggered by the spinoff impacts of urbanisation and industrialisation (Liu et al. 2018). On a global evaluation, 74% of Pb released from anthropogenic sources are derived from gasoline, and 70–73% of Cd, Cu and Zn are from non-ferrous metallurgy. But on a regional perspective, different sources contribute substantial amount of trace elements to the environment. Vehicular emission, industrial waste disposal and municipal waste incineration contribute significant amounts of HM such as Zn, Pb, Cu and Cd to urban soils (Ajmone-Marsan and Biasioli 2010; Martínez and Poleto 2014). Street dust, comprising of particulates emanating from construction materials, vehicular emission, industrial airborne particles deposited over time, soil and soot, has become a major sink for heavy metals (Zhang et al. 2019; Ghanavati et al. 2019). Heavy metals released into the environment also accumulate in the soil, plants, aquatic animals, water sediments and groundwater from which they are transferred to humans by the process of bioaccumulation and biomagnification. Heavy metals released into the environment via anthropogenic activities have specifically been reported to accumulate in tobacco leaves, freshwater fish, rice, tomatoes, leafy vegetables and corn (Wuana and Okieimen 2011; Ali et al. 2019). Street dusts pose particular danger to human beings because these are much more mobile than normal soil particle and other kinds of sinks. In developing countries where massive urban development is a continuous process, thousands of tons of dust particles are generated and dispersed into the environment during road construction, erection of industrial/residential buildings and demolition. These dust particles are distributed in the atmosphere and thus form part of the air that people inhale daily. They are also deposited on human and animal skins, as well as plants which are eventually consumed without much of a precaution. Heavy metals accumulated in urban soils can also be mobilised by a combination of complex chemical processes causing them to be transferred to the food chain and groundwater aquifers, thereby causing environmental hazards to humans and soil biota (Ettler 2015). Humans are exposed to the health hazards resulting from exposure to heavy metals through various routes such as inhalation, ingestion and dermal contact of street dust and urban soils laden with toxic HM.

Urban soils and street dust have attracted the attention of researchers because they constitute the largest sink of heavy metals emanating from diverse forms of anthropogenic activities (Lin et al. 2017). However, in the current investigation, we aimed at investigating the distribution and health risk assessment of nine HM (As, Cr, Cu, Fe, Mn, Ni, Pb, Zn and Cd) in street dust and urban soils of Nsukka. Nsukka is a growing city located in the eastern part of Nigeria characterised by small- and medium-scale food processing industries, metal craft industries, non-ferrous metallurgy, agricultural activities and high vehicular traffic. Local occupants are exposed to toxic emissions originating from direct and indirect anthropogenic sources. With a view to evaluate the variability of trace element origin, composition, as well as their toxicity, investigations related to urban soil, street dust and vehicular traffic are of public interest within the study region. As a result of a shortage of the literature on the subject area, this study investigated the pollution status of roadside soil and dust with the following objectives: (1) to ascertain the metal variability in urban soils and street dust in regions of high commercial activities within Nsukka; (2) to determine the pollution load indices in relation to the chemical constituents of soil and associated potential health risks via inhalation, ingestion and dermal pathways; and (3) to obtain the ecological risk and spatial variation of hazard indices within the study region. This research was conducted between February and August 2019.

Materials and method

Sample collection

The study area, Nsukka, Nigeria, is a rapidly expanding urban settlement characterised by a proliferation of commercial/industrial settings located along the roadsides. Solid waste is improperly dumped by the roadsides, and the local waste management authority is already overwhelmed by the sheer size of waste generated. Twenty-one strategically and adequately staggered points located by the roadsides were selected for investigation. The coordinates of each location were taken using GPS instrument (Infinix S4; model Infinix X626) while digital camera (model Inepix s2980 fujifilm camera semiprofessional) was used in taking pictures of activities that take place at each location. Dust samples were collected from the surface of the roadside while soil samples were collected at a depth of 0.5 m. Suitable samples that served as control were collected at 400 m away from the influence of the road at three different locations. The samples were immediately transported to the National Center for Energy Development, University of Nigeria, Nsukka.

Laboratory analyses

Soil and dust samples were subjected to spectrophotometric analyses using Atomic Absorption Spectrometer model AA-7000 Shimadzu, Japan ROM version 1.01, S/N A30664700709. All glassware used was soaked in 3 M HNO3 overnight and washed with de-ionised water to reduce the chances of interferences. Three grams each of the samples were weighed into digestion flask, and 30 cm3 of aqua regia was added and digested in a fume cupboard until clear solution was obtained; it was cooled, filtered and made up to 50 ml in a standard volumetric flask with de-ionised water. A blank sample was prepared to zero the instrument before running other series of samples. Standards (2 ppm, 4 ppm and 6 ppm) were prepared from 1000 ppm stock solution of the metals and used to obtain the calibration curve. Each metal/mineral was analysed using its respective wavelength (Ni—232 nm, Pb—283.3 nm, Fe—248.3 nm, As—193.7 nm, Mn—279.5 nm, Cr—357.9 nm, Cu—324.8, Zn—213.9 nm) after which its concentration was generated from the standard graph by the instrument.

Data analyses

First, the dataset obtained from laboratory analyses of soil and roadside dust samples was subjected to descriptive statistical analyses to determine the mean, minimum and maximum values of the metals as well as the standard deviation, skewness and coefficient of variation. The data were further subjected to analyses to determine the degree of pollution. The single pollution index (PI) was used to ascertain the degree of contamination of soil and dust by individual metal with reference to background concentrations (Eq. 1). The normalised contribution of all the metals was computed using the pollution load index (PLI) as given in Eq. 2. Related to PLI is the modified degree of contamination (mCD) which allows an assessment of the overall heavy metal contamination (Kowalska et al. 2018) as shown in Eq. 3. Cheng et al. categorised mCD as follows: mCD ≤ 1.5—unpolluted, 1.5 < mCD ≤ 2—slightly polluted, 2 < mCD ≤ 4—moderately polluted, 4 < mCD ≤ 8—considerably polluted, 8 < mCD ≤ 16—highly polluted, 16 < mCD ≤ 32—strongly polluted and mCD > 32—extremely polluted (Cheng et al. 2018). The soil is categorised as highly polluted if PLI > 1 and unpolluted (low pollution) if PLI < 1 (Rabee et al. 2011; Abdelhafez et al. 2015). The multi-element contamination (MEC) factor was computed for all sampling points using Eq. 4. The Nemerow pollution index (NPI) was also calculated using Eq. 5 and applied to the data using the following categorisation: NPI ≤ 0.7 (clean or uncontaminated), 0.7 ≤ NPI< 1 (warning limit), 1 ≤ NPI< 2 (slightly polluted), 2 ≤ NPI< 3 (moderately polluted) and NPI> 3 (heavily polluted) (Cheng et al. 2018; Nazarpour et al. 2019). A general risk assessment was performed using the monomial ecological risk factor \(E_{\text{r}}^{i}\) and potential ecological risk (RI) index (Eqs. 6, 7). This was accomplished using the toxicity factor of the various metals, thus As—10, Cd—30, Cr—2, Cu—5, Ni—5, Pb—5 and Zn—1 (Akoto et al. 2018). Ecological risks are categorised as follows: \(E_{\text{r}}^{i}\) ≤ 40 (low risk), 40 < \(E_{\text{r}}^{i}\) ≤ 80 (moderate risk), 80 < \(E_{\text{r}}^{i}\) ≤160 (considerable risk), 160 < \(E_{\text{r}}^{i}\) ≤ 320 (high risk) and \(E_{\text{r}}^{i}\) > 320 (very high risk) for monomial ecological risk and RI≤ 150 (low risk), 150 < RI≤ 300 (moderate risk), 300 < RI≤ 600 (considerable risk) and RI > 600 (very high risk) (Li 2018).

$${\text{PI}} = \frac{{C^{i} }}{{C_{\text{b}}^{i} }}$$
(1)
$${\text{PLI}} = \sqrt[n]{{\mathop \prod \limits_{i}^{n} {\text{PI}}}}$$
(2)
$${\text{mCD}} = \frac{{\mathop \sum \nolimits_{i}^{n} C^{i} }}{n}$$
(3)
$${\text{MEC}} = \frac{{\mathop \sum \nolimits_{i}^{n} \left( {\frac{{C^{i} }}{T}} \right)}}{n}$$
(4)
$${\text{NPI}} = \sqrt {\frac{{{\text{PI}}_{\text{ave}}^{2} + {\text{PI}}_{\hbox{max} }^{2} }}{2}}$$
(5)
$$E_{\text{r}}^{i} = T_{\text{r}}^{i} \times {\text{PI}}$$
(6)
$${\text{RI}} = \mathop \sum \limits_{i}^{n} E_{\text{r}}^{i}$$
(7)

The human health risks associated with heavy metals are triggered when these metals enter the human body through the three major routes of ingestion, inhalation and dermal contact. The potential of a particular contaminant to induce health problems is directly proportional to the quantity that enters the body through the various pathways and the toxicity of the contaminant. The health risk posed individually and collectively by heavy metals was estimated using the hazard quotient (HQ) as given in Eq. 8, while the hazard index (HI) which represents the total non-carcinogenic risk associated with the intake of contaminants by the three aforementioned pathways was calculated with Eqs. 9–12. A number of heavy metals and metalloids such as As, Cr, Cd, Pb and Ni have been linked to various forms of cancers, with As, Cd, Cr and Ni being categorised as group 1 carcinogens (Mulware 2013; Kim et al. 2015; Alahabadi et al. 2017; Mohammadi et al. 2019). Hence, the probability of inducing carcinogenic health conditions in humans was estimated using the cancer risk (CR) measure as given in Eq. 13. Specific values of the different parameter used for calculating the health risk assessment are documented in Table 1 as obtained from the relevant literature.

$${\text{HQ}} = \sum \frac{{{\text{ADI}}_{i} }}{{{\text{RfD}}_{i} }}$$
(8)
$${\text{ADI}}_{\text{ing}} = \frac{{C^{i} \times I_{\text{ng}} R \times {\text{EF}} \times {\text{ED}}}}{{{\text{BW}} \times {\text{AT}}}} \times 10^{ - 6}$$
(9)
$${\text{ADI}}_{\text{inh}} = \frac{{C^{i} \times I_{\text{nh}} R \times {\text{EF}} \times {\text{ED}}}}{{{\text{PEF}} \times {\text{BW}} \times {\text{AT}}}}$$
(10)
$${\text{ADI}}_{\text{dermal}} = \frac{{C^{i} \times {\text{SA}} \times {\text{ABS}} \times {\text{EF}} \times {\text{ED}}}}{{{\text{BW}} \times {\text{AT}}}} \times 10^{ - 6}$$
(11)
$${\text{HI}} = \sum {\text{HQ}}_{i}$$
(12)
$${\text{CR}} = \sum {\text{ADI}}_{i} \times {\text{SF}}_{i}$$
(13)
Table 1 Factors used to estimate the average daily intake of heavy metals and human health risk indices
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Finally, in order to investigate the spatial distribution of heavy metals in soil and roadside dust, an interpolation scheme was used. The inverse distance weighting interpolation function was implemented on all the heavy metals investigated using the SAGA IDW plug-in in Quantum GIS 2.18.11

$$C_{n} \frac{{\mathop \sum \nolimits_{i}^{n} \frac{{C_{i} }}{{\delta_{i}^{n} }}}}{{\mathop \sum \nolimits_{i}^{n} \frac{1}{{\delta_{i}^{n} }}}}$$
(14)

Results and discussion

Concentration of heavy metals in roadside dust and soil

The average concentration of metals in roadside dust samples was 3.348, 0.572, 0.082, 97.552, 0.250, 0.593, 0.476, 0.406 and 0.027 mg/kg for As, Cr, Cu, Fe, Mn, Ni, Pb, Zn and Cd while the corresponding values for soil were 3.33, 0.381, 0.075, 108.570, 0.278, 0.547, 0.583, 0.399 and 0.050 mg/kg. The order of abundance of these metals in roadside dust varied slightly from their abundance in roadside dust as follows: Fe > As > Ni > Cr > Pb > Zn > Mn > Cu > Cd for roadside dust and Fe > As > Pb > Ni > Zn > Cr > Mn > Cu > Cd for roadside soil (Table 2). Fe and As were the most abundant while Cu and Cd were the least abundant in both roadside dust and soil samples analysed. Of all the trace metals investigated, As had the highest concentration in both roadside dust and soil. However, these values were similar to those obtained by New Castle (UK), Shiraz (Iran), Baoding City (China), Ahvaz City (Iran), Tehran (Iran) and Kirkuk City of Northern Iraq with arsenic concentration ranges of 4.4–8.6, 5.3–8.6, 13.16–67.26, 0.5–18.3, 5.6–9.87 and 8.29–10.4 mg/kg, respectively (Okorie et al. 2012; Keshavarzi et al. 2015; Al-Jumaily 2016; Xie et al. 2017a; Nazarpour et al. 2019; Taghavi et al. 2019). This can be attributed to metal input from the construction works and other anthropogenic activities like vehicular emission, agriculture, aerosols, burning of tire and burning of heavy duty oils which release particulates that are subsequently deposited on to the surface soil (Al-Jumaily 2016). The percentage increase in metal concentrations from background levels was evaluated in order to ascertain the trend of metals build-up in dust and soil. Results revealed that the highest change in concentration from background level was associated with Cd with 2842% and 1861% increase in roadside dust and soil, respectively, followed by Zn with corresponding values of 47% and 600%, respectively, and then As with 456% and 454%, respectively. Though the concentration of these metals in soil and roadside dust was within tolerable limits, current trend of build-up suggests that these metals will eventually attain unacceptable levels in both soil and dust. The average concentration of As, Cr, Cu, Ni and Zn in roadside dust was higher than those in the soil. This can be attributed to vehicle emissions and the interaction between automobile tyres and the road surface leading to wearing and eventual deposition of particulate metals on the road which then mingles with the dust. However, the concentrations of Fe, Mn, Pb and Cd in the soil were higher than those in roadside dust. The concentration of Cd in the soil was about twice its concentration in roadside dust collected from the same spots. The higher concentration of Fe in the soil can be attributed to the lateritic nature of the soil under investigation, and lateritic soils are known to be very rich in Fe. There was also significant spatial variation of metals in both soil and roadside dust (Fig. 1a, b).

Table 2 Descriptive statistics of heavy metals in roadside dust and soil
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Fig. 1
figure 1

Spatial distribution of heavy metals in roadside dust

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Pollution indices as a measure of soil degradation

The values of pollution index (PI) computed for various metals showed varying degrees of soil and dust pollution by metals (Fig. 2). The highest ranges of PI obtained were 34.32–68.59 (Cr), 3.0–15.0 (Pb), 0.75–10.88 (As) and 0.43–6.85 (Mn) for dust and 34.32–68.59 (Cr), 3.0–21.0 (Pb), 0.375–11.63 (As) and 0.429–7.29 (Mn) for roadside dust. Considering all the samples and pollutants analysed, 71% were greater than 1.0 for roadside dust and 77% were greater than 1.0 for soil samples. Following standard classification of pollution index (Kowalska et al. 2018; Salman et al. 2019), the degree of pollution of roadside dust was partitioned as follows: low contamination (29.3%), moderate contamination (39.9%), considerable contamination (18.2%) and high contamination (12.6%). The corresponding values for soil were 23.2%, 41.9%, 24.8% and 10.1%, respectively. Further analysis showed that both roadside dust and soil were moderately contaminated with Cu, Fe, Ni, Zn and Cd; considerably contaminated with As, Mn and Pb; and highly contaminated with Cr. Though it would appear from the preceding that the soil samples were more polluted than roadside dust, the average pollution index (API) aggregated for all sampling points suggests otherwise. The API for roadside dust was 6.17 (highly polluted) while the API for soil was 5.2 (considerably polluted). Soil samples from different locations displayed wide variations in metal concentration. Table 2 shows that the coefficient of variation for all metal concentrations in the soil was higher than those in roadside dust. This is because the soil restricts the mobility of metals while dust particles allow easy distribution of metals on the ground surface due to the mixing action of wind and man. The values of Nemerow pollution index (NPI) show that all dust and soil samples from all locations were heavily polluted with values ranging from 5.23 to 49.59 and 2.47 to 49.22, respectively. This confirms the adverse impact of anthropogenic activities on the quality of the soil samples. These values of NPI are far above the values reported by Nazarpour et al. (2019) for urban soils of Ahvaz City in southwest Iran. The extreme values of NPI obtained in this study were due to the high concentrations of Cr in both soil and roadside dust. However, there was a drastic drop in NPI average values to 6.31 and 6.52 for roadside dust and soil when computed without Cr.

Fig. 2
figure 2

Spatial distribution of heavy metals in the soil

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The soil and dust samples were further assessed using the pollution load index (PLI), which is a measure of the degree of soil deterioration due to anthropogenic activities (Kowalska et al. 2018). The average range of PLI obtained for roadside dust was 1.40–4.67 while that obtained for soil was 1.81–5.38. These values indicate an extensive anthropogenic impact on soil and roadside dust. The modified degree of contamination (mCd) was used to provide a broader classification of the degree of environmental pollution by heavy metals. For roadside dust, 20% of the samples was moderately polluted (2 < mCd < 4), 65% was considerably polluted (4 < mCd < 8) while the remaining 15% was highly polluted (8 < mCd < 16). Soil samples exhibited a different distribution with respect to categorisation of the degree of pollution with 5% being slightly polluted (1.5 < mCd < 2.0), 45% moderately polluted (2 < mCd < 4), 40% considerably polluted (4 < mCd < 8) and 10% highly polluted (8 < mCd < 16). However, on the average, both soil and roadside dust were considerably polluted by metals with average mCd values of 5.2 and 6.17, respectively. Though mCd provides a broader classification of level of pollution, the derived values can be misleading because it simply takes an overall average of the concentrations of pollutants in the soil without due consideration to the background concentration as well as the potential of individual heavy metal to cause environmental and health damage. This drawback is catered for by the ecological risk index (ERI) which integrates both the background concentration of individual metal and their toxicity factor. While all the pollution indices considered so far give an indication of the build-up of pollutants in the soil as a result of anthropogenic activities, ER goes a step further to associate a degree of risk with the concentrations of these pollutants. In this study, the various degrees of risk identified were distributed as follows: low risk (69.7%), moderate risk (24.5%) and considerable risk (5.8%) for roadside dust. The corresponding values for soil were 68%, 22.7% and 9.3%, respectively. At an elemental level, the degree of risk was in this order: Cr (82.4) > Cd (70.9) > As (55.4) > Pb (36.8) > Cu (10.8) > Ni (10) > Mn (3.7) > Zn (1.7) for soil. Hence, Cr posed considerable risk in the soil, Cd and As posed moderate risk while the other elements posed low risk. The level of risk posed by pollutants in roadside dust was of the same order as that of soil, but with a slight difference in ER values except for Cd whose ER value was far less (42.6). Hence, the corresponding ER values for roadside dust were: Cr (77.2) > As (58.4) > Cd (42.7) > Pb (28.6) > Cu (11.0) > Ni (10.8) > Mn (3.3) > Zn (1.7). As, Cu, Ni and Zn posed slightly greater risks in roadside dust than in soil. Table 3 shows that Cr posed considerable risk in 10% of the samples while Cd posed considerable risk in 5% of the roadside dust samples. Cd posed considerable risk in 33.3% of soil samples, As posed considerable risk in 19%, while Cr posed considerable risk in 10% of the soil samples. On a global scale, the risk index (RI) was used to aggregate the collective risk posed by all pollutants identified in the samples. The RI values for soil and roadside dust were 236 and 202, respectively, representing moderate risk. Concerning soil, 60% of the samples exhibited moderate risk while 20% each represented low risk and considerable risk, respectively (Table 1). For roadside dust, 76.2% of the samples exhibited moderate risk while 19% represented low risk and 4.8% represented considerable risk. Hence, it can be seen that soil represented a slightly higher ecological risk than roadside dust. This can be explained by the fact that the concentration of pollutants in dust is constantly being redistributed and leached into the soil by surface runoff and rainwater. Figure 3a shows that there is a nearly uniform distribution of ecological risk due to pollutants in dust, while there is a more sporadic pattern with respect to soil with wider spatial ER range of 120–330 as opposed to roadside dust with a range of 150–250. This again confirms that soil pollutants display a wider range of spatial variation than pollutants in dust considering the results obtained from the hierarchical cluster analysis. It is worth mentioning at this stage that while all the pollution assessment indices considered so far clearly indicate that both soil samples and roadside dust samples were being considerably impacted by anthropogenic activities, the multi-element contamination indices (MEC) obtained for both soil and roadside dust were far below 1.0, with a mean of 0.034 for soil and 0.033 for roadside dust.

Table 3 Distribution of ecological risks according to heavy metals
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Fig. 3
figure 3

Indices of pollution for roadside dust and soil

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Human health risk associated with soil and dust contaminants

The release of toxic pollutants into the environment has been linked to a countless number of human health hazards. Heavy metals cause chronic diseases by disrupting the normal functioning of body cells and organs (Järup 2003; Jaishankar et al. 2014; Jan et al. 2015). Heavy metals can enter the body through three major pathways namely: ingestion, inhalation and dermal contact (Liang et al. 2017; Sun et al. 2017; Xie et al. 2017b). Table 4 shows that the highest concentration of heavy metals enters the body by ingestion with values of 7.49E−04 and 1.56E−04 mg/day of soil for children and adult, respectively, while the daily intake of metals through dust by the same pathway was 6.69E−04 and 1.39E−04 mg/day for children and adults, respectively. The next route of chronic intake of heave metals was by dermal contact with intake values ranging from 2.45E−07 to 2.62E−05, while the least was by inhalation with values ranging from 1.95E−08 to 2.18E−08. Results clearly show that the intake of heavy metals by children through dermal contact was about 6 times more than the intake by adults via the same pathway. But the intake through ingestion is about 5 times higher than that of adults via the same pathway, while intake through inhalation was 1.1 times lower for children than for adults. The daily combined intake of metals through the three routes of exposure was 7.75E−04 and 1.56E−04 for children and adults, respectively, for soil, while the corresponding values for dust was 6.93E−04 and 1.60E−04. These values indicate children take in 5 times more quantities of metals than adults.

Table 4 Summary of hazard quotients for soil and roadside dust
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The hazard quotients (HQs) were used to assess the probability of non-carcinogenic health effects associated with the intake of metals though the major exposure pathways for various pollutants. HQ is a proportion of the probable exposure to an element and the level at which no negative impacts are expected (Kacholi and Sahu 2018). In terms of exposure pathway, the order of non-carcinogenic health risk was ingestion > dermal > inhalation with average HQ of 3.98E−02, 7.12E−03 and 1.17E−06, respectively, for children; and 8.30E−03, 1.24E−03 and 1.28E−06, respectively, for adults considering only pollutants in roadside dusts (Table 5). The corresponding values for soil were 4.27E−02, 9.96E−03 and 1.25E−06 for children and 8.90E−03, 5.93E−04 and 1.37E−06 for adults. The hazard quotients for children were far above those for adults with respect to ingestion and dermal contact, but slightly lower than those of adults with respect to inhalation. The highest HQ of 1.31E−02 was obtained for the ingestion for ingestion of roadside dust for children. In order to identify the pollutants that posed the greatest cumulative non-carcinogenic risk, the pollutants were ranked in decreasing order of HQ as follows: Cr > Cd > As > Pb > Ni > Cu > Zn for roadside dust and Cd > Cr > As > Pb > Ni > Cu > Zn for soil regardless of age category. It can be seen that Cr, Cd and As ranked highest in both soil and roadside dust. This calls for serious concern because all three metals are confirmed carcinogens and also have the potentials to induce other forms of chronic health problems. However, the values of HQ obtained for all cases were less than 1.0 which indicates that the probability of inducing health problems is very low. But considering the trend of pollutants build-up in the soil and roadside dust as depicted by the pollution indices aforementioned, a significant health risk will most likely exist in the future unless drastic measures are taken to retard the rate of metal accumulation in the soil. Further investigation of the hazard quotients showed that Cr was 120 times more likely to cause health problems than Pb and 450 times more than Ni. Cr and Cd exhibited nearly equal likelihood of causing non-carcinogenic health problems. Several studies have reported that Cr and Cd posed the highest potential health in soil (Elnazer et al. 2015; Liang et al. 2017; Ramdani et al. 2018). Many others reported potentially significant health hazards for As and Pb (Huang et al. 2018; Cai et al. 2019; Rinklebe et al. 2019). Cd is particularly dangerous because it provokes cell invasion in human gastric cancer cells due to the overexpression of uPAR via the ERK-1/2, NF-κB, and AP-1 signalling pathways (Yuan et al. 2016).

Table 5 Hazard index
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The hazard index (HI) is a cumulative of the hazard quotient for all the metals. Ingestion had the highest HI with values of 2.79E−01 and 5.82E−02 for children and adults, respectively, for roadside dust and 2.98E−01 and 6.22E−02 for children and adult, respectively, for soil (Table 5). This shows that the hazard index for children with respect to ingestion was 5 times higher than that for adults. Jia et al. (2018) also reported that HI for heavy metals was 5.5 times higher for children than for adults. Ingestion of heavy metals can occur by consumption of plants cultivated in contaminated soil or cultivated with untreated effluents as is the case in many developing countries. Hence the quantity of pollutants that enters the body through direct ingestion is higher than other routes. In this study, the value of hazard index for the ingestion pathway was 6 times higher than that which occurs though dermal contact for adults. However, farmers and other categories of individuals that have constant contact with the soil might be exposed to a significant chance of heavy metals intake by the dermal route. The value of hazard index associated with the ingestion pathway was 5 times higher than that of the dermal pathway for children. It should be noted that in many developing countries, children are usually exposed to higher than average level of heavy metals intake though the dermal pathway because of longer outdoor times and higher degree of exposure to bare soil. This is opposed to many developed countries where paved and grassed surfaces are more prevalent, thus reducing the amount of loose soil particles in the environment. Generally, the additive HI of contaminated soil in this study was 0.368 and 0.074 for children and adults, respectively; and 0.329 and 0.067 for children and adults, respectively, for roadside dust. Moghtaderi et al. (2018) reported that children faced more health risk in their daily lives than adult via unconscious ingestion of soil and dermal contact. HI values are generally less than 1.0, indicating that no lifetime risks are envisaged if the concentrations of these metals in both roadside dust and soil remain the same going forward. But this is most unlikely as anthropogenic activities that release these contaminants into the environment are on the rise. Besides, a spatial characterisation of hazard indices shows that while the risk for adults are very low for adults throughout the study area, values much higher than the average values are possible at some locations for children (Fig. 4). A hazard index of 0.7 was recorded for a spot around the southeast flank of the study area. The hazard indices for soil were generally higher than those for dust at corresponding spots (Fig. 5). Another important issue worth mentioning is that the risk due to inhalation in this study was grossly underestimated. Again the prevalence of bare soils and untarred roads give rise to the release of large amounts of soil particles into the air especially during the dry season.

Fig. 4
figure 4

Ecological risk for soil and dust

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Fig. 5
figure 5

Spatial variation of human hazard indices

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The lifetime risk of developing cancer due to exposure to soil and roadside dust was investigated using cancer risk (CR). It provides an estimate of the probability of a person developing any form of cancer over a lifetime as a result of exposure to potential carcinogens (Enuneku et al. 2018). All values of CR obtained for all cases were within the acceptable range of 10−6 and 10−4. Specifically, children stand a higher chance (1.2 times) of suffering from cancer due to dermal contact. On the other hand, adults are 4.4 times more likely to develop cancer induced by the inhalation of soil and dust particles than children. The aggregate CR values for all exposure pathways obtained for children were about 1.2 times higher than those for adults. Interestingly, though soil posed a higher non-carcinogenic risk than roadside dust as earlier noted, the reverse is the case with respect to carcinogenic risk. CR value for roadside dust is slightly higher than that for soil. This can be traced to the fact that most carcinogenic metals such as As, Cr and Ni had higher average concentrations in roadside dust than in the soil samples. These particular carcinogenic metals (As, Cr, Ni) can evoke posttranslational histone modifications and modulate histone-modifying enzymes including iron- and 2-oxoglutarate-dependent dioxygenase family enzymes, DNA repair enzymes ABH3 and ABH2, and histone methyltransferases, which may affect the epigenome (Chervona et al. 2012). Though the CR values fall within the acceptable range, the cancer risk associated with dust particle might violate acceptable limits when adjusted for excessive inhalation of dust particles prevalent in developing countries.

Conclusion

In the current paper, the concentrations of these metals in soil and roadside dust were within tolerable limits, but current trend of build-up will eventually attain unacceptable levels in both soil and dust. The results also revealed that the highest change in concentration from background level was associated with Cd in roadside dust and soil, respectively, followed by Zn and then As. The pollution index (PI) showed that both roadsides dust and soil were moderately contaminated with Cu, Fe, Ni, Zn and Cd, considerably contaminated with As, Mn and Pb and highly contaminated with Cr. Conversely, the average pollution index (API) aggregated for all sampling points suggests otherwise. The values of Nemerow pollution index (NPI) and PLI showed that all dust and soil samples from all locations were heavily polluted. The values of HQ showed that children take in 5 times more quantities of metals than adults. Meanwhile, the highest value of HQ was obtained for the ingestion of roadside dust for children. Furthermore, the aggregate CR values for all exposure pathways obtained for children were about 1.2 times higher than those for adults. CR value for roadside dust was 1.3 times higher than that of soil. All values of CR obtained for all cases were within the acceptable range. The cancer risk associated with dust particle might violate acceptable limits when adjusted for excessive inhalation of dust particles prevalent in the study area.