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Econ. Environ. Geol. 2022; 55(4): 389-398

Published online August 30, 2022

https://doi.org/10.9719/EEG.2022.55.4.389

© THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY

Study on Accuracy Improvement of Predictive Model of Arsenic Transfer from Contaminated Soil to Polished Rice

Seungha Jo, Hyeop-Jo Han*, Jong-Un Lee*

Department of Energy and Resources Engineering, Chonnamm National University, Gwangju 61186, Korea

Received: August 5, 2022; Revised: August 25, 2022; Accepted: August 25, 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided original work is properly cited.

Abstract

Many studies have been conducted to accurately predict the correlations between As and heavy metals content in contaminated soil and cultivated crops; however, due to the low correlation between the two, few clear results were obtained to date. This study aimed to create statistical models that predict the As content transferred from soil to polished rice, considering the physicochemical properties of the soil, as well as the total content and the single-extracted content of As in the soil. Predictive models were derived through regression analysis while sequentially classifying soil samples according to pH, soluble As content by single extraction, and organic matter content of the soil. The correlation coefficients between the As content in 80 polished rice and total As content and Mehlich soluble As content in the soil were low, 0.533 and 0.493, respectively. However, the models derived after sequential classification of the soil by pH, a ratio of total As content to Mehlich soluble As content, and organic matter content greatly increased the predictive power; ① 0.963 for 13 soils with a pH higher than 6.5, ② 0.849 for 15 soils with pH lower than 6.5 and a high ratio of AsTot/AsMehlich, ③ 0.935 for 30 soils with pH lower than 6.5, a high ratio of AsTot/AsMehlich, and organic matter content lower than 8.5%. The suggested prediction model of As transfer from soil to polished rice derived by soil classification may serve as a statistically significant methodology in establishing a rice cultivation standard for arsenic-contaminated soil.

Keywords arsenic, soil, polished rice, regression analysis, predictive model

오염토양으로부터 백미로 전이되는 비소함량 예측모델의 정확도 향상 연구

조승하 · 한협조* · 이종운*

전남대학교 에너지자원공학과

요 약

비소 및 중금속으로 오염된 토양 내 함량과 농작물로 전이되는 함량 간의 관련성을 도출하기 위한 연구가 지속적으로 수행되고 있으나 두 함량 간의 낮은 상관성으로 인하여 명확한 결과가 도출되지 못하고 있다. 이 연구에서는 토양 내 비소 전함량과 단일용출 가용성 함량뿐만 아니라 토양의 물리·화학적 특성을 함께 고려하여 백미로 전이되는 비소 함량을 예측하는 통계학적 모델을 만들고자 하였다. 토양 특성 중 pH, 단일용출 가용성 함량, 유기물 함량에 따라 순차적으로 토양을 분류하며 회귀분석을 통한 예측 모델을 도출하였다. 80개의 백미 내 비소 함량과 토양 내 비소 전함량 및 Mehlich 가용성 함량 간의 상관계수는 각각 0.533과 0.493으로 낮았다. 그러나 토양을 pH, Mehlich 가용성 함량에 대한 전함량, 유기물 함량으로 순차적으로 분류하여 모델을 도출한 결과, ① pH가 6.5보다 높은 13개의 토양은 0.963, ② pH가 6.5 이하이고 AsTot/AsMehlich 비가 높은 15개의 토양은 0.849, ③ pH가 6.5 이하이고 AsTot/AsMehlich 비가 낮으며 8.5% 이하의 유기물을 함유한 30개의 토양은 0.935로 예측력이 크게 증가하였다. 이 연구에서 도출된 토양 분류에 따른 백미 전이 함량 예측 모델은 비소 오염 토양에 대해 신뢰성 있는 백미 재배 기준을 설정하는데 의미있는 방법론을 제안할 수 있을 것이다.

주요어 비소, 토양, 백미, 회귀분석, 예측모델

Article

Research Paper

Econ. Environ. Geol. 2022; 55(4): 389-398

Published online August 30, 2022 https://doi.org/10.9719/EEG.2022.55.4.389

Copyright © THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY.

Study on Accuracy Improvement of Predictive Model of Arsenic Transfer from Contaminated Soil to Polished Rice

Seungha Jo, Hyeop-Jo Han*, Jong-Un Lee*

Department of Energy and Resources Engineering, Chonnamm National University, Gwangju 61186, Korea

Received: August 5, 2022; Revised: August 25, 2022; Accepted: August 25, 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided original work is properly cited.

Abstract

Many studies have been conducted to accurately predict the correlations between As and heavy metals content in contaminated soil and cultivated crops; however, due to the low correlation between the two, few clear results were obtained to date. This study aimed to create statistical models that predict the As content transferred from soil to polished rice, considering the physicochemical properties of the soil, as well as the total content and the single-extracted content of As in the soil. Predictive models were derived through regression analysis while sequentially classifying soil samples according to pH, soluble As content by single extraction, and organic matter content of the soil. The correlation coefficients between the As content in 80 polished rice and total As content and Mehlich soluble As content in the soil were low, 0.533 and 0.493, respectively. However, the models derived after sequential classification of the soil by pH, a ratio of total As content to Mehlich soluble As content, and organic matter content greatly increased the predictive power; ① 0.963 for 13 soils with a pH higher than 6.5, ② 0.849 for 15 soils with pH lower than 6.5 and a high ratio of AsTot/AsMehlich, ③ 0.935 for 30 soils with pH lower than 6.5, a high ratio of AsTot/AsMehlich, and organic matter content lower than 8.5%. The suggested prediction model of As transfer from soil to polished rice derived by soil classification may serve as a statistically significant methodology in establishing a rice cultivation standard for arsenic-contaminated soil.

Keywords arsenic, soil, polished rice, regression analysis, predictive model

오염토양으로부터 백미로 전이되는 비소함량 예측모델의 정확도 향상 연구

조승하 · 한협조* · 이종운*

전남대학교 에너지자원공학과

Received: August 5, 2022; Revised: August 25, 2022; Accepted: August 25, 2022

요 약

비소 및 중금속으로 오염된 토양 내 함량과 농작물로 전이되는 함량 간의 관련성을 도출하기 위한 연구가 지속적으로 수행되고 있으나 두 함량 간의 낮은 상관성으로 인하여 명확한 결과가 도출되지 못하고 있다. 이 연구에서는 토양 내 비소 전함량과 단일용출 가용성 함량뿐만 아니라 토양의 물리·화학적 특성을 함께 고려하여 백미로 전이되는 비소 함량을 예측하는 통계학적 모델을 만들고자 하였다. 토양 특성 중 pH, 단일용출 가용성 함량, 유기물 함량에 따라 순차적으로 토양을 분류하며 회귀분석을 통한 예측 모델을 도출하였다. 80개의 백미 내 비소 함량과 토양 내 비소 전함량 및 Mehlich 가용성 함량 간의 상관계수는 각각 0.533과 0.493으로 낮았다. 그러나 토양을 pH, Mehlich 가용성 함량에 대한 전함량, 유기물 함량으로 순차적으로 분류하여 모델을 도출한 결과, ① pH가 6.5보다 높은 13개의 토양은 0.963, ② pH가 6.5 이하이고 AsTot/AsMehlich 비가 높은 15개의 토양은 0.849, ③ pH가 6.5 이하이고 AsTot/AsMehlich 비가 낮으며 8.5% 이하의 유기물을 함유한 30개의 토양은 0.935로 예측력이 크게 증가하였다. 이 연구에서 도출된 토양 분류에 따른 백미 전이 함량 예측 모델은 비소 오염 토양에 대해 신뢰성 있는 백미 재배 기준을 설정하는데 의미있는 방법론을 제안할 수 있을 것이다.

주요어 비소, 토양, 백미, 회귀분석, 예측모델

    Fig 1.

    Figure 1.Correlation between measured and predicted arsenic concentrations in rice.
    Economic and Environmental Geology 2022; 55: 389-398https://doi.org/10.9719/EEG.2022.55.4.389

    Fig 2.

    Figure 2.(a) Classification of group B soil by ratio of AsTot/AsMehlich and (b) Correlation between AsTot and AsMehlich for group B soil.
    Economic and Environmental Geology 2022; 55: 389-398https://doi.org/10.9719/EEG.2022.55.4.389

    Fig 3.

    Figure 3.Correlation between predicted and measured arsenic in rice for groups A, B2, and B1-b.
    Economic and Environmental Geology 2022; 55: 389-398https://doi.org/10.9719/EEG.2022.55.4.389

    Fig 4.

    Figure 4.Suggested application for prediction of As concentrations in polished rice from soil.
    Economic and Environmental Geology 2022; 55: 389-398https://doi.org/10.9719/EEG.2022.55.4.389

    Table 1 . Summary of physicochemical properties of soil and polished rice samples. nd = not determined.

    PropertiesMeanMedianStandard deviationRange
    pH6.16.10.63.8 ~ 8.0
    CEC (cmolc/kg)21.920.76.510.8 ~ 38.2
    OM (%)8.58.72.93.5 ~ 16.4
    Fe (%)2.92.90.91.0 ~ 4.5
    As in soil (mg/kg)aqua regia15.714.48.34.5 ~ 38.1
    Mehlich2.31.51.6nd ~ 7.0
    0.1 N HCl2.82.03.9nd ~ 21.5
    1 N HCl2.61.82.8nd ~ 19.0
    As in polished rice (mg/kg)0.150.140.070.05 ~ 0.35

    Table 2 . Correlation coefficients (R) between arsenic concentrations in rice and physicochemical properties of soil. Only R values with p < 0.05 are presented. Numbers in brackets indicate p values..

    ClassificationTotalSoil pHAsTotal/AsMehlichOrganic Matter
    > 6.5< 6.5< 10> 10> 8.5%< 8.5%
    Group nameTotalABB1B2B1-aB1-b
    Sample number80136752153022
    pH-0.330 (0.003)-0.267 (0.029)
    OM0.313 (0.005)0.250 (0.041)
    CEC
    AsTotal0.533 (<0.001)0.608 (<0.001)0.713 (<0.001)0.893 (<0.001)
    FeTotal0.243 (0.030)0.758 (0.003)
    FeTotal/AsTotal-0.386 (<0.001)-0.512 (<0.001-0.567 (<0.001)-0.778 (<0.001)
    AsMehlich0.493 (<0.001)0.555 (<0.001)0.761 (<0.001)0.483 (0.023)0.908 (<0.001)
    As0.1N HCl0.296 (0.008)0.377 (0.002)0.455 (0.001)0.761 (<0.001)
    As1N HCl0.299 (0.007)0.784 (0.002)

    KSEEG
    Apr 30, 2024 Vol.57 No.2, pp. 107~280

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