Statistical aproach to unravelling heavy metal contamination on sub-soils and roadside dust

Mama, Cordelia Nnennaya; Igwe, Ogbonnaya; Ezugwu, Chimankpam Kenneth; Ozioko, O. H.; Ugwuoke, Ikechukwu John

doi:10.1080/03067319.2021.1958801

Cited by 3 publications

(1 citation statement)

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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

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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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