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. 2024 Apr 27;14(1):9681.
doi: 10.1038/s41598-024-60044-3.

Spatial variability of heavy metals concentrations in soil of auto-mechanic workshop clusters in Nsukka, Nigeria

Stellamaris Chinenye Duru  1 , Emmanuel Amagu Echiegu  1 , Chinenye C Anyadike  1 , George Uwadiegwu Alaneme  2 , Michael Emeka Okechukwu  1
Affiliations

Spatial variability of heavy metals concentrations in soil of auto-mechanic workshop clusters in Nsukka, Nigeria

Stellamaris Chinenye Duru et al. Sci Rep. .

Abstract

The indiscriminate disposal of spent engine oils and other hazardous waste at auto mechanic workshops clusters in Nsukka, Enugu State, Nigeria is an environmental concern. This study examines the concentration of heavy metals in the soil inside the workshop cluster and in the unpolluted soil outside the workshop cluster at approximately 100 m. Ten sampling points were randomly selected from within the cluster and another ten from outside the cluster. Using a hand-held Global Positioning System, the coordinates of the selected points were established and used to create a digital map. Soil samples at depths of 0-30 cm and 30-60 cm, were analyzed for Cu, Fe, Zn, Pb, As and Cd using Spectrophotometer. Moisture content determination and particle size analysis were also done on the samples. Spatial variability of heavy metals concentrations of the studied site was also mapped with ArcGIS 10.2.2 using interpolation methods. Results showed that the soil ranged from sandy loam to sandy clay loam. Cadmium and Zinc had the lowest and highest concentration, respectively, in the studied area. Comparing the concentrations of heavy metals in soils within and outside the auto mechanic cluster revealed notable differences across various depths (0-30 cm and 30-60 cm). The analysis results for soil samples within the cluster exhibited concentration levels (mg/kg) ranging from 0.716-0.751 (Cu), 2.981-3.327 (Fe), 23.464-30.113 (Zn), 1.115-1.21 (Pb), 2.6-2.912 (As), and 0.133-0.365 (Cd) demonstrating a variation pattern in the order of Zn > Fe > As > Pb > Cu > Cd. Conversely, for soil samples outside the cluster, concentration levels (mg/kg) ranged from 0.611-0.618 (Cu), 2.233-2.516 (Fe), 12.841-15.736 (Zn), 0.887-0.903 (Pb), 1.669-1.911 (As), and 0.091-0.091 (Cd). To assess the disparity in heavy metal concentration levels between samples collected within and outside the clusters, ANOVA test was performed. The test showed significant difference in heavy metal concentrations between samples within and outside the auto mechanic cluster (p < 0.05), implying auto mechanic activities significantly impact heavy metal levels within the cluster compared to outside areas. The assessment of soil pollution utilized indices including the Geo-accumulation Index (Igeo), Contamination factor (Cf), and anthropogenic metal concentration (QoC). Zinc, Cadmium, and Arsenic showed the highest contamination factors, indicating significant soil contamination likely due to anthropogenic activities. The concentrations of the metals analyzed were within WHO permissible limits while the metals concentrations were also observed to decrease as depth was increased. Using ArcGIS 10.2.2, spatial maps showing heavy metal distribution were developed, with the Kriging method proving superior. This study suggests that heavy metal levels in the soil at the area be monitored on a regular basis.

Keywords: Auto mechanics workshop cluster; Heavy metal concentration; Spatial maps; Spent engine oil.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Map of Enugu State showing Nsukka.
Figure 2
Figure 2
Oil spill at the study site.
Figure 3
Figure 3
The sampling points in the study area generated with the aid of ArcGIS Software.
Figure 4
Figure 4
Sieve analyzer (Haver & Boecker 59302 DELDE).
Figure 5
Figure 5
(a) Interpolation distribution of Kriging and Spline for Cu: A = 0–30 cm; B = 30–60 cm. (b) Interpolation distribution of Kriging and Spline for Fe: A = 0–30 cm; B = 30–60 cm. (c) Interpolation distribution of Kriging and Spline for Zn: A = 0–30 cm; B = 30–60 cm. (d) Interpolation distribution of Kriging and Spline for Pb: A = 0–30 cm; B = 30–60 cm. (e) Interpolation distribution of Kriging and Spline for As: A = 0–30 cm; B = 30–60 cm. (f) Interpolation distribution of Kriging and Spline for Cd: A = 0–30 cm; B = 30–60 cm.
Figure 5
Figure 5
(a) Interpolation distribution of Kriging and Spline for Cu: A = 0–30 cm; B = 30–60 cm. (b) Interpolation distribution of Kriging and Spline for Fe: A = 0–30 cm; B = 30–60 cm. (c) Interpolation distribution of Kriging and Spline for Zn: A = 0–30 cm; B = 30–60 cm. (d) Interpolation distribution of Kriging and Spline for Pb: A = 0–30 cm; B = 30–60 cm. (e) Interpolation distribution of Kriging and Spline for As: A = 0–30 cm; B = 30–60 cm. (f) Interpolation distribution of Kriging and Spline for Cd: A = 0–30 cm; B = 30–60 cm.
Figure 5
Figure 5
(a) Interpolation distribution of Kriging and Spline for Cu: A = 0–30 cm; B = 30–60 cm. (b) Interpolation distribution of Kriging and Spline for Fe: A = 0–30 cm; B = 30–60 cm. (c) Interpolation distribution of Kriging and Spline for Zn: A = 0–30 cm; B = 30–60 cm. (d) Interpolation distribution of Kriging and Spline for Pb: A = 0–30 cm; B = 30–60 cm. (e) Interpolation distribution of Kriging and Spline for As: A = 0–30 cm; B = 30–60 cm. (f) Interpolation distribution of Kriging and Spline for Cd: A = 0–30 cm; B = 30–60 cm.
Figure 6
Figure 6
Digital elevation map of mechanic village.
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