Assessment of heavy metal pollution in soils: a case study of Nsukka metropolis

Mama, Cordelia Nnennaya; Nnaji, Chidozie Charles; Igwe, Ogbonnaya; Ozioko, Obinna Hyginus; Ezugwu, Chimankpam Kenneth; Ugwuoke, Ikechukwu John

doi:10.1080/15275922.2020.1850567

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…Zechmeister et al (2005) [ 46 ] found that friction between heavy-duty vehicle tires and the road is particularly conducive to the accumulation of pollutants such as As in roadside soil and moss. Many studies have observed arsenic pollution in the soil on both sides of the road [ 47 , 48 , 49 , 50 ]. In this study, we found that As was positively related to the road density, which is consistent with the research of Seker et al (2022) (Seker, et al [ 51 ]) and Qiao et al (2022) (Qiao, et al [ 52 ]).…”

Section: Discussionmentioning

confidence: 99%

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Zhu,

Liu,

Jin

et al. 2024

Toxics

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Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran’s I of residuals and a higher R2 (0.559). The MGWR model revealed the spatially heterogeneous relationship between As and explanatory variables. Specifically, the road density and total nitrogen, clay, and silt contents were the primary or secondary influencing factors at most points. The distance from an industrial enterprise was the secondary influencing factor at only a few points. The main pollution sources of As were thus inferred as traffic and fertilizer, and industrial emissions were also included in the southern region. These findings highlight the importance of considering adaptative bandwidths for independent variables and demonstrate the effectiveness of MGWR in exploring local sources of soil pollutants.

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Section: Discussionmentioning

confidence: 99%

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Zhu,

Liu,

Jin

et al. 2024

Toxics

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“…where I geo is the index of geoaccumulation; C n is the measured content of heavy metal n; K is the correction factor considering the degree of variability of the geological background value, which is taken here to be 1.5; and B n is the background reference value of heavy metal n. In this paper, we selected the background values of the soils in Gansu Province and the Tianheba cropland, respectively. The grading criteria for evaluating the degree of soil heavy metal accumulation were as follows: I geo < 0 for no pollution; 0 ≤ I geo < 1 for mild pollution; 1 ≤ I geo < 2 for moderate pollution; 2 ≤ I geo < 3 for medium-strength pollution; 3 ≤ I geo < 4 for strong pollution; 4 ≤ I geo < 5 for strong-extremely strong pollution; and I geo ≥ 5 for extremely strong pollution [27].…”

Section: Index Of Geoaccumulationmentioning

confidence: 99%

Characteristics and Sources of Heavy Metal Pollution in Cropland near a Typical Lead–Zinc Processing Plant in Xieping Village, Hui County, China

Ma,

Yao,

Wang

et al. 2023

Land

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Metal beneficiation activities may cause soil pollution in the surrounding cropland, making it crucial to conduct heavy metal pollution assessment and source analysis of the cultivated land in mining areas for the protection of the ecological environment and human health. In this study, a total of 205 surface soil samples (0–20 cm) were collected on site from the Tianheba cropland near the lead–zinc concentrator in Xieping Village, Huixian County, Gansu Province, China; their pH values and their SOM, Zn, As, Cd, Cr, Hg, and Pb contents were determined. Based on the data, we used the Kriging spatial interpolation, the Nemero index, the index of geoaccumulation, and the PMF model to analyze the characteristics of the spatial distribution of soil heavy metals in the region, the degree of contamination, the sources, and the contribution rate. The results indicated that the heavy metals with contamination levels in the Tianheba cropland were Cd, Zn, Pb, Hg, As, and Cr in descending order, with the average concentrations of Cd (0.39 mg/kg), Zn (122 mg/kg), Pb (30.4 mg/kg), and Hg (0.07 mg/kg) being significantly higher than the background values of the Gansu soils. The soil in the region as a whole was heavily polluted; it was heavily polluted with Cd, moderately polluted with Zn, and mildly polluted with Pb. Hg had a larger value in the index of geoaccumulation. By analyzing the spatial distribution and sources of the soil metals, it was found that the cropland contaminated with heavy metals in Tianheba was distributed around the mineral processing plant and the infiltration area of the canal. The PMF model revealed three pollution sources: the industrial source related to mining activities, the fertilizer application source, and the natural source. This study provides a scientific basis for the precise management of heavy metal pollution in the area.

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“…The toxicological repercussions of heavy metal presence in soil have significant environmental implications. These metals have toxic effects, long-term persistence, and can accumulate inside the human body over time (Mama et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…According to Mama et al (2020), the dense urban population exacerbates the direct effects of polluted soils on human health and ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Heavy Metal Contamination and Health Risks in Road Deposited Sediments: A Study in Owerri, Nigeria

Odochi,

Enimakpokpo,

Akaolisa

et al. 2024

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The study aims to conduct an ecological risk assessment and build a pollution model for assessing trace metal concentrations in road dust in Owerri, Nigeria. Key roadways in the urban area were chosen based on traffic volume, population density, and human activity. Data was collected at 500-meter intervals throughout each route, and silt samples were collected by systematic sweeping of a 1 square metre area covering road pavements and curbs. The examination found metallic pollutants such as chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), and lead (Pb) inside the RDS. Nickel (Ni), copper (Cu), cadmium (Cd), and lead (Pb) levels were substantially higher than their respective background values. Carbon monoxide levels along Port Harcourt Road, specifically 670 mg/l, are much higher than the background norm, indicating a considerable influence of human activity. Estimated enrichment values for the metallic elements ranged from insignificant (Mn) to extremely high (Co, Ni, Cu, Zn, Pb). Based on geoaccumulation index data, it can be concluded that the RDS has significant levels of contamination in terms of Ni and Pb, implying a considerable buildup of certain heavy metals, most likely due to anthropogenic acts. The study identified two major sources of heavy metal contamination: natural sources originating in the Earth's crust and transportation-related activities such as air deposition, corrosion, and vehicle degradation.

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Assessment of heavy metal pollution in soils: a case study of Nsukka metropolis

Cited by 10 publications

References 17 publications

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Characteristics and Sources of Heavy Metal Pollution in Cropland near a Typical Lead–Zinc Processing Plant in Xieping Village, Hui County, China

Assessment of Heavy Metal Contamination and Health Risks in Road Deposited Sediments: A Study in Owerri, Nigeria

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