Detection of organic arsenic based on acid-base stable coordination polymer

Ren, Guojian; Tan, Qinyue; Che, Guang; Luo, Jian; Li, Meiling; Zhou, Qi; Guo, Dong-Yu; Pan, Qinhe

doi:10.1016/j.saa.2023.122812

Cited by 8 publications

(3 citation statements)

References 48 publications

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“…Using arsenical animal feed and phosphorus fertilizers in agricultural production is another important source of soil arsenic [ 53 , 54 , 55 , 56 ]. For example, arsenic compounds such as roxarsone are commonly used as feed additives in livestock farming and enter the soil through livestock manure [ 57 , 58 ]. In addition, several studies found As in phosphate and compound fertilizers [ 59 , 60 ].…”

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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“…Recently, Wang et al reported an acid–base stable luminescence coordination polymer, HNU-62, for selective detection of ROX in water. 76 In this work, the hydrophobic organic linker, 4-carboxytriphenylamine (HL, Fig. 9(A)), was reacted with Zn( ii ) ions to generate HNU-62.…”

Section: Metal–organic Framework (Mofs)-based Fluorescence Sensorsmentioning

confidence: 99%

Recent advances in fluorescence-based chemosensing of organoarsenic feed additives using luminescence MOFs, COFs, HOFs, and QDs

Mondal,

Shanmughan,

Murugeswari

et al. 2023

Chem. Commun.

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“…ROX was determined by using fluorescence spectroscopy, 24 electrochemical techniques, 25 photoelectrochemical methods, 26 immunochromatographic assays, 27 and high-performance liquid chromatography. 28 However, these analytic techniques need sample pre-treatment, solvents, and careful operations and are time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Fe₂O₃/NiO nanocomposites: synthesis, characterization and roxarsone sensing by Fourier transform infrared photoacoustic spectroscopy

et al. 2023

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Detection of organic arsenic based on acid-base stable coordination polymer

Cited by 8 publications

References 48 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

Recent advances in fluorescence-based chemosensing of organoarsenic feed additives using luminescence MOFs, COFs, HOFs, and QDs

Fe₂O₃/NiO nanocomposites: synthesis, characterization and roxarsone sensing by Fourier transform infrared photoacoustic spectroscopy

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Detection of organic arsenic based on acid-base stable coordination polymer

Cited by 8 publications

References 48 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

Recent advances in fluorescence-based chemosensing of organoarsenic feed additives using luminescence MOFs, COFs, HOFs, and QDs

Fe2O3/NiO nanocomposites: synthesis, characterization and roxarsone sensing by Fourier transform infrared photoacoustic spectroscopy

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Fe₂O₃/NiO nanocomposites: synthesis, characterization and roxarsone sensing by Fourier transform infrared photoacoustic spectroscopy