Chemical and mineralogical variability of sediment in a Quaternary aquifer from Huaihe River Basin, China: Implications for groundwater arsenic source and its mobilization

Xu, Naizheng; Zhang, Fei; Ni, Xiaobo; Li, Liang; Liu, Lin

doi:10.1016/j.scitotenv.2022.160864

Cited by 15 publications

(3 citation statements)

References 30 publications

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“…Although the 14 explanatory variables can explain 55.9% of As variation, some important factors were not included in the MGWR model due to data acquisition limitations. For example, iron minerals, aluminum oxides, and soil sulfur cycling all affect the adsorption/desorption of As [ 65 , 73 , 74 ]. In future research, combining parent materials, high-resolution lithology, and Fe, Al, and S contents may further enhance the R 2 of the regression model.…”

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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“…strength of the linear association between the depth of the water sources and the concentration level was assessed using correlation coe cients. The Pearson correlation coe cient, r (98) = -0.758, p = 0.000 (in Table 3), exhibits a signi cant negative linear relationship between depth and concentration level; hence, going deeper will lower the concentration of arsenic, as Xu et al (2023) discussed that ranges between − 0.6 and − 0.8 represent a strong negative linear relationship, and it is statistically signi cant (Xu et al 2023). Figure 8 shows the association using a scatter diagram.…”

Section: Relationship Between Concentration and Depthmentioning

confidence: 99%

“…Xu et al (2023) discussed that ranges between − 0.6 and − 0.8 represent a strong negative linear relationship, and it is statistically signi cant(Xu et al 2023). …”

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confidence: 98%

Spatial Pattern of Arsenic Concentration and Associated Health Risk Assessment: A Case Study on Gangni Union of Chuadanga District of Bangladesh

Islam

Haque

Hossain

2023

Preprint

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Underground water is one of the world's most important sources of fresh drinking water. However, various contaminants mix with groundwater, such as arsenic. This study aimed to ascertain the present condition of arsenic concentration, its spatial pattern, and its relationship with depth and to assess the associated health risks in the Gangni Union in Alamdanga Thana of Chuadanga district. Systematic sampling was used to gather water samples (í µí± = 100) along with depth information from these tube wells. Water samples were analyzed with the Hach EZ, Dual-Range Arsenic Test Kit (Range: 0.00-0.5 mg/L). This study used both geostatistical (spatial autocorrelation, Hotspot analysis, and IDW) and statistical (descriptive and correlation statistics) methods. The arsenic content of the samples tested ranged from 0.0004 (mg/l) to 0.10 (mg/L). Arsenic levels in almost 42% of the samples were above the WHO standard, 21% were above the Bangladesh standard, and 37% were below both standards. With the same standard, the spatial distribution shows that 63% of the total area is arsenic contaminated. Hotspot’s analysis reveals that the central and southern northern parts of the study area are more arsenic-contaminated than the other parts. Children have a higher average daily dose (ADD) range (8.33E-06-0.00181) than adults (2.78E-06-0.0006). Similarly, the hazard quotient (HQ) value is also higher for children (0.0277-6.033) than for adults (0.0092-2.011). Finally, the Pearson correlation coefficient, r (98) =-.7580, p = 0.000, proved that there is a negative linear relationship between concentration values and depth, meaning that increasing depth will reduce arsenic contamination from tube well water.

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Evaluating the spatiotemporal land ecological changes in the Yangtze-to-Huaihe Water Diversion Project area

Guo,

Li,

Kong

et al. 2024

Environ Sci Pollut Res

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Chemical and mineralogical variability of sediment in a Quaternary aquifer from Huaihe River Basin, China: Implications for groundwater arsenic source and its mobilization

Cited by 15 publications

References 30 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

Spatial Pattern of Arsenic Concentration and Associated Health Risk Assessment: A Case Study on Gangni Union of Chuadanga District of Bangladesh

Evaluating the spatiotemporal land ecological changes in the Yangtze-to-Huaihe Water Diversion Project area

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