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Investigating the correlation between pesticide bioconcentration and human disease through the integration of remote sensing and physical modeling.

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Title: Investigating the correlation between pesticide bioconcentration and human disease through the integration of remote sensing and physical modeling.
Authors: Xu, Chenyang1,2,3 (AUTHOR), Ye, Pei4 (AUTHOR), Lin, Minghao1,2 (AUTHOR), Liao, Shuangqiao1,2 (AUTHOR), Yue, Qian5 (AUTHOR), Xia, Jizhe4 (AUTHOR) xiajizhe@szu.edu.cn
Source: Geo-Spatial Information Science. Oct2025, Vol. 28 Issue 5, p2230-2243. 14p.
Subjects: Bioconcentration, Public health, Pesticides, Remote sensing, Environmental protection planning, Spatial variation, Disease complications
Geographic Terms: United States
Abstract: Nowadays, the risk of pesticides to the environment and human health arises with its increasing applications. Bioconcentration Factor (BCF) is utilized to evaluate the potential plant contamination by pesticides from the soil. Large-scale BCF mapping is significant to urban planning, yet it remains challenging. To address this issue, this study integrates the plant uptake model with remote sensing techniques to map BCF considering various land surface properties. Two simplified approaches were developed: the first involves relative humidity and air temperature (RA-model), while the second uses plant transpiration (PT-model). To evaluate these models, BCF mappings generated by each approach were compared and found to be consistent in space and time, with R2 at 0.68 over the continental U.S. Both approaches showed that the eastern part of the U.S. had higher BCF values than the west throughout the year, with an annual cycle of BCF where the highest values occurred in summer and the lowest in winter. To further analyze the impact of BCF on human health, spatial heterogeneity detection was used to examine seven diseases at the county scale. The results suggest that BCF may explain the incidence of spatial heterogeneity of most diseases, specifically high blood pressure (q-value >0.3). Overall, this study demonstrates the potential of integrating plant uptake modeling with remote sensing to map BCF and provides insight into the impact of BCF on human health. [ABSTRACT FROM AUTHOR]
Copyright of Geo-Spatial Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 190351899
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: Investigating the correlation between pesticide bioconcentration and human disease through the integration of remote sensing and physical modeling.
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  Data: <searchLink fieldCode="AR" term="%22Xu%2C+Chenyang%22">Xu, Chenyang</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ye%2C+Pei%22">Ye, Pei</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Minghao%22">Lin, Minghao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liao%2C+Shuangqiao%22">Liao, Shuangqiao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yue%2C+Qian%22">Yue, Qian</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xia%2C+Jizhe%22">Xia, Jizhe</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> xiajizhe@szu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Geo-Spatial+Information+Science%22">Geo-Spatial Information Science</searchLink>. Oct2025, Vol. 28 Issue 5, p2230-2243. 14p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Bioconcentration%22">Bioconcentration</searchLink><br /><searchLink fieldCode="DE" term="%22Public+health%22">Public health</searchLink><br /><searchLink fieldCode="DE" term="%22Pesticides%22">Pesticides</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+protection+planning%22">Environmental protection planning</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+variation%22">Spatial variation</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+complications%22">Disease complications</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Nowadays, the risk of pesticides to the environment and human health arises with its increasing applications. Bioconcentration Factor (BCF) is utilized to evaluate the potential plant contamination by pesticides from the soil. Large-scale BCF mapping is significant to urban planning, yet it remains challenging. To address this issue, this study integrates the plant uptake model with remote sensing techniques to map BCF considering various land surface properties. Two simplified approaches were developed: the first involves relative humidity and air temperature (RA-model), while the second uses plant transpiration (PT-model). To evaluate these models, BCF mappings generated by each approach were compared and found to be consistent in space and time, with R2 at 0.68 over the continental U.S. Both approaches showed that the eastern part of the U.S. had higher BCF values than the west throughout the year, with an annual cycle of BCF where the highest values occurred in summer and the lowest in winter. To further analyze the impact of BCF on human health, spatial heterogeneity detection was used to examine seven diseases at the county scale. The results suggest that BCF may explain the incidence of spatial heterogeneity of most diseases, specifically high blood pressure (q-value >0.3). Overall, this study demonstrates the potential of integrating plant uptake modeling with remote sensing to map BCF and provides insight into the impact of BCF on human health. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Geo-Spatial Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=190351899
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/10095020.2024.2313327
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 2230
    Subjects:
      – SubjectFull: Bioconcentration
        Type: general
      – SubjectFull: Public health
        Type: general
      – SubjectFull: Pesticides
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Environmental protection planning
        Type: general
      – SubjectFull: Spatial variation
        Type: general
      – SubjectFull: Disease complications
        Type: general
      – SubjectFull: United States
        Type: general
    Titles:
      – TitleFull: Investigating the correlation between pesticide bioconcentration and human disease through the integration of remote sensing and physical modeling.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Xu, Chenyang
      – PersonEntity:
          Name:
            NameFull: Ye, Pei
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            NameFull: Lin, Minghao
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            NameFull: Liao, Shuangqiao
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            NameFull: Yue, Qian
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            NameFull: Xia, Jizhe
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          Dates:
            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 10095020
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            – Type: volume
              Value: 28
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Geo-Spatial Information Science
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