土壤类型变异对XRF定量分析重金属影响的研究。

IF 2.6 3区 化学 Q2 CHEMISTRY, ANALYTICAL
Ying Wang, Tingting Gan, Nanjing Zhao, Gaofang Yin, Tanghu Li, Xingchi Li and Xiaoxuan Tan
{"title":"土壤类型变异对XRF定量分析重金属影响的研究。","authors":"Ying Wang, Tingting Gan, Nanjing Zhao, Gaofang Yin, Tanghu Li, Xingchi Li and Xiaoxuan Tan","doi":"10.1039/D5AY00249D","DOIUrl":null,"url":null,"abstract":"<p >To clarify the impact of soil type variability on the quantitative analysis of heavy metals using X-ray fluorescence (XRF), the feasibility of establishing XRF quantitative analysis curves based on 15 different soil types was investigated. Pearson's correlation coefficient was employed to analyze the relationship between the XRF results and soil matrix constituents. The analysis was focused on four specific soil types: grey fluvo-aquic soil, fluvo-aquic soil, purple soil, and rice soil. The differences in the XRF quantitative analysis curves for heavy metals across these soil types were assessed by examining the overlap of the 95% confidence intervals and the cosine distances between the curves. The accuracy of heavy metal content determinations in grey fluvo-aquic soil was evaluated using the quantitative analysis curves derived from the four soil types. It was found that the linear coefficients of determination for the XRF quantitative analysis curves of heavy metals (Zn, Pb, Ni, Cu, Cr, and Cd) established from the 15 soil types were all below 0.2, indicating a poor fit and rendering them unsuitable for accurate analysis. This highlights that variability in soil types, attributed to differences in soil matrix compositions, significantly affects the accuracy of heavy metal quantification by XRF. When the quantitative analysis curves from fluvo-aquic soil, purple soil, and rice soil were applied to assess heavy metal concentrations (Cr, Ni, Cu, Zn, Pb, Cd, As, and Hg) in grey fluvo-aquic soil, significant increases in the average relative errors were noted. Specifically, these errors rose from 9.89%, 8.56%, 13.51%, 7.10%, 9.86%, 26.19%, 6.71%, and 30.97% to the following ranges: 29.74% to 34.80% (minimum 29.74%, maximum 34.80%), 59.82% to 96.34%, 41.12% to 78.33%, 25.33% to 32.64%, 16.92% to 70.36%, 24.07% to 68.79%, 48.91% to 128.98%, and 130.29% to 238.70%, increasing as much as 0.11 to 18.22 times. Such increases indicate that variability among soil types greatly impacts the accuracy of heavy metal quantitative analysis using XRF. This study establishes an important foundation for the precise quantitative detection of heavy metals <em>via</em> XRF across various soil types.</p>","PeriodicalId":64,"journal":{"name":"Analytical Methods","volume":" 25","pages":" 5186-5202"},"PeriodicalIF":2.6000,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A study of the effect of soil type variability on the quantitative analysis of heavy metals by XRF\",\"authors\":\"Ying Wang, Tingting Gan, Nanjing Zhao, Gaofang Yin, Tanghu Li, Xingchi Li and Xiaoxuan Tan\",\"doi\":\"10.1039/D5AY00249D\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >To clarify the impact of soil type variability on the quantitative analysis of heavy metals using X-ray fluorescence (XRF), the feasibility of establishing XRF quantitative analysis curves based on 15 different soil types was investigated. Pearson's correlation coefficient was employed to analyze the relationship between the XRF results and soil matrix constituents. The analysis was focused on four specific soil types: grey fluvo-aquic soil, fluvo-aquic soil, purple soil, and rice soil. The differences in the XRF quantitative analysis curves for heavy metals across these soil types were assessed by examining the overlap of the 95% confidence intervals and the cosine distances between the curves. The accuracy of heavy metal content determinations in grey fluvo-aquic soil was evaluated using the quantitative analysis curves derived from the four soil types. It was found that the linear coefficients of determination for the XRF quantitative analysis curves of heavy metals (Zn, Pb, Ni, Cu, Cr, and Cd) established from the 15 soil types were all below 0.2, indicating a poor fit and rendering them unsuitable for accurate analysis. This highlights that variability in soil types, attributed to differences in soil matrix compositions, significantly affects the accuracy of heavy metal quantification by XRF. When the quantitative analysis curves from fluvo-aquic soil, purple soil, and rice soil were applied to assess heavy metal concentrations (Cr, Ni, Cu, Zn, Pb, Cd, As, and Hg) in grey fluvo-aquic soil, significant increases in the average relative errors were noted. Specifically, these errors rose from 9.89%, 8.56%, 13.51%, 7.10%, 9.86%, 26.19%, 6.71%, and 30.97% to the following ranges: 29.74% to 34.80% (minimum 29.74%, maximum 34.80%), 59.82% to 96.34%, 41.12% to 78.33%, 25.33% to 32.64%, 16.92% to 70.36%, 24.07% to 68.79%, 48.91% to 128.98%, and 130.29% to 238.70%, increasing as much as 0.11 to 18.22 times. Such increases indicate that variability among soil types greatly impacts the accuracy of heavy metal quantitative analysis using XRF. This study establishes an important foundation for the precise quantitative detection of heavy metals <em>via</em> XRF across various soil types.</p>\",\"PeriodicalId\":64,\"journal\":{\"name\":\"Analytical Methods\",\"volume\":\" 25\",\"pages\":\" 5186-5202\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Analytical Methods\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://pubs.rsc.org/en/content/articlelanding/2025/ay/d5ay00249d\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytical Methods","FirstCategoryId":"92","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2025/ay/d5ay00249d","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

摘要

为了明确土壤类型变异对x射线荧光(XRF)定量分析重金属的影响,研究了基于15种不同土壤类型建立XRF定量分析曲线的可行性。采用Pearson相关系数分析XRF结果与土壤基质成分的关系。分析的重点是四种特定的土壤类型:灰色潮土、潮土、紫色土和水稻土。通过检查95%置信区间的重叠和曲线之间的余弦距离,评估了这些土壤类型中重金属的XRF定量分析曲线的差异。利用四种土壤类型的定量分析曲线,评价了灰色潮土重金属含量测定的准确性。结果表明,15种土壤类型所建立的重金属(Zn、Pb、Ni、Cu、Cr、Cd)定量分析曲线的线性系数均小于0.2,拟合性较差,不宜进行准确分析。这表明,土壤类型的差异,归因于土壤基质组成的差异,显著影响了XRF重金属定量的准确性。将潮土、紫色土和水稻土的定量分析曲线应用于灰色潮土中重金属(Cr、Ni、Cu、Zn、Pb、Cd、As和Hg)浓度评估时,发现平均相对误差显著增加。具体来说,这些误差从9.89%、8.56%、13.51%、7.10%、9.86%、26.19%、6.71%、30.97%上升到29.74% ~ 34.80%(最小29.74%、最大34.80%)、59.82% ~ 96.34%、41.12% ~ 78.33%、25.33% ~ 32.64%、16.92% ~ 70.36%、24.07% ~ 68.79%、48.91% ~ 128.98%、130.29% ~ 238.70%,分别上升了0.11 ~ 18.22倍。这表明土壤类型的差异极大地影响了XRF重金属定量分析的准确性。该研究为利用XRF对不同土壤类型的重金属进行精确定量检测奠定了重要基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A study of the effect of soil type variability on the quantitative analysis of heavy metals by XRF

A study of the effect of soil type variability on the quantitative analysis of heavy metals by XRF

To clarify the impact of soil type variability on the quantitative analysis of heavy metals using X-ray fluorescence (XRF), the feasibility of establishing XRF quantitative analysis curves based on 15 different soil types was investigated. Pearson's correlation coefficient was employed to analyze the relationship between the XRF results and soil matrix constituents. The analysis was focused on four specific soil types: grey fluvo-aquic soil, fluvo-aquic soil, purple soil, and rice soil. The differences in the XRF quantitative analysis curves for heavy metals across these soil types were assessed by examining the overlap of the 95% confidence intervals and the cosine distances between the curves. The accuracy of heavy metal content determinations in grey fluvo-aquic soil was evaluated using the quantitative analysis curves derived from the four soil types. It was found that the linear coefficients of determination for the XRF quantitative analysis curves of heavy metals (Zn, Pb, Ni, Cu, Cr, and Cd) established from the 15 soil types were all below 0.2, indicating a poor fit and rendering them unsuitable for accurate analysis. This highlights that variability in soil types, attributed to differences in soil matrix compositions, significantly affects the accuracy of heavy metal quantification by XRF. When the quantitative analysis curves from fluvo-aquic soil, purple soil, and rice soil were applied to assess heavy metal concentrations (Cr, Ni, Cu, Zn, Pb, Cd, As, and Hg) in grey fluvo-aquic soil, significant increases in the average relative errors were noted. Specifically, these errors rose from 9.89%, 8.56%, 13.51%, 7.10%, 9.86%, 26.19%, 6.71%, and 30.97% to the following ranges: 29.74% to 34.80% (minimum 29.74%, maximum 34.80%), 59.82% to 96.34%, 41.12% to 78.33%, 25.33% to 32.64%, 16.92% to 70.36%, 24.07% to 68.79%, 48.91% to 128.98%, and 130.29% to 238.70%, increasing as much as 0.11 to 18.22 times. Such increases indicate that variability among soil types greatly impacts the accuracy of heavy metal quantitative analysis using XRF. This study establishes an important foundation for the precise quantitative detection of heavy metals via XRF across various soil types.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Analytical Methods
Analytical Methods CHEMISTRY, ANALYTICAL-FOOD SCIENCE & TECHNOLOGY
CiteScore
5.10
自引率
3.20%
发文量
569
审稿时长
1.8 months
期刊介绍: Early applied demonstrations of new analytical methods with clear societal impact
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信