Econ. Environ. Geol. 2021; 54(2): 259-269
Published online April 30, 2021
https://doi.org/10.9719/EEG.2021.54.2.259
© THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY
Correspondence to : jongun@jnu.ac.kr
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.
The effects of the physicochemical properties of soil such as soil pH, cation exchange capacity, and organic matter content on single extraction of Cd, Cr, Cu, Ni, Pb, and Zn using CaCl2, HOAc, HNO3, and DTPA were statistically investigated for 69 agricultural soils in Korea. Correlation analysis and multiple regression analysis were applied for soil samples which were grouped on the basis of average values of the physicochemical properties of the soil. Diluted HNO3 extracted higher concentrations of Cr, Cu, Ni, and Pb when compared with the other extractants, however, similar amounts of Cd and Zn were extracted by HOAc with HNO3. The results of correlation analysis indicated that DTPA extraction showed a high correlation with other single and pseudo-total extraction methods, and the physicochemical properties of soil influenced the concentrations of heavy metals leached by the single extraction methods. In the case of Zn, high correlations between pseudo-total and the studied single extraction methods were observed. As a result of regression analysis, it was found that the physicochemical properties of the soil could explain up to 74% of variances of the single extraction results. These results indicate that the physicochemical properties of the soil can have a direct influence on the concentrations of heavy metals extracted by the single extraction methods.
Keywords soil, heavy metal, single extraction, correlation, regression analysis
한협조 ∙ 송창우 ∙ 이종운*
전남대학교 에너지자원공학과
국내 69개 농경지 토양 내 Cd, Cr, Cu, Ni, Pb, Zn을 대상으로 하여 토양 pH, 양이온교환능력, 유기물 함량 등의 물리화학적 특성이 CaCl2, HOAc, HNO3, DTPA 등을 이용한 중금속의 단일용출 결과에 미치는 영향을 통계학적으로 조사하였다. 통계학적 분석은 토양의 물리화학적 특성의 평균값을 기준으로 높고 낮은 두 표본으로 구분한 후 이에 대해 상관분석과 다중회귀분석을 수행하였다. Cr, Cu, Ni, Pb의 경우 HNO3를 적용하였을 때 다른 추출제에 비하여 높은 함량이 추출되었으나, Cd, Zn의 경우 HOAc를 이용한 추출에서도 유사한 함량이 추출되었다. 상관관계 조사 결과, DTPA 추출이 다른 단일용출 및 전함량과 상관관계가 높았으며, 토양의 물리화학적 특성이 단일용출 결과에 영향을 미치는 것으로 나타났다. Zn의 경우, 전함량과 모든 단일용출법들에 의한 추출 함량이 상호 높은 상관관계를 보였다. 회귀분석 결과, 토양의 물리화학적 특성은 최대 74%에 이르는 단일용출 결과의 분산을 설명할 수 있는 것으로 나타났다. 이러한 결과는 토양의 물리화학적 특성이 단일용출 결과에 직접적인 영향을 미칠 수 있음을 나타낸다.
주요어 토양, 중금속, 단일용출, 상관관계, 회귀분석
Econ. Environ. Geol. 2021; 54(2): 259-269
Published online April 30, 2021 https://doi.org/10.9719/EEG.2021.54.2.259
Copyright © THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY.
Hyeop-Jo Han, Chang-Woo Song, Jong-Un Lee*
Department of Energy and Resources Engineering, Chonnam National University, Gwangju 61186, Korea
Correspondence to:jongun@jnu.ac.kr
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.
The effects of the physicochemical properties of soil such as soil pH, cation exchange capacity, and organic matter content on single extraction of Cd, Cr, Cu, Ni, Pb, and Zn using CaCl2, HOAc, HNO3, and DTPA were statistically investigated for 69 agricultural soils in Korea. Correlation analysis and multiple regression analysis were applied for soil samples which were grouped on the basis of average values of the physicochemical properties of the soil. Diluted HNO3 extracted higher concentrations of Cr, Cu, Ni, and Pb when compared with the other extractants, however, similar amounts of Cd and Zn were extracted by HOAc with HNO3. The results of correlation analysis indicated that DTPA extraction showed a high correlation with other single and pseudo-total extraction methods, and the physicochemical properties of soil influenced the concentrations of heavy metals leached by the single extraction methods. In the case of Zn, high correlations between pseudo-total and the studied single extraction methods were observed. As a result of regression analysis, it was found that the physicochemical properties of the soil could explain up to 74% of variances of the single extraction results. These results indicate that the physicochemical properties of the soil can have a direct influence on the concentrations of heavy metals extracted by the single extraction methods.
Keywords soil, heavy metal, single extraction, correlation, regression analysis
한협조 ∙ 송창우 ∙ 이종운*
전남대학교 에너지자원공학과
국내 69개 농경지 토양 내 Cd, Cr, Cu, Ni, Pb, Zn을 대상으로 하여 토양 pH, 양이온교환능력, 유기물 함량 등의 물리화학적 특성이 CaCl2, HOAc, HNO3, DTPA 등을 이용한 중금속의 단일용출 결과에 미치는 영향을 통계학적으로 조사하였다. 통계학적 분석은 토양의 물리화학적 특성의 평균값을 기준으로 높고 낮은 두 표본으로 구분한 후 이에 대해 상관분석과 다중회귀분석을 수행하였다. Cr, Cu, Ni, Pb의 경우 HNO3를 적용하였을 때 다른 추출제에 비하여 높은 함량이 추출되었으나, Cd, Zn의 경우 HOAc를 이용한 추출에서도 유사한 함량이 추출되었다. 상관관계 조사 결과, DTPA 추출이 다른 단일용출 및 전함량과 상관관계가 높았으며, 토양의 물리화학적 특성이 단일용출 결과에 영향을 미치는 것으로 나타났다. Zn의 경우, 전함량과 모든 단일용출법들에 의한 추출 함량이 상호 높은 상관관계를 보였다. 회귀분석 결과, 토양의 물리화학적 특성은 최대 74%에 이르는 단일용출 결과의 분산을 설명할 수 있는 것으로 나타났다. 이러한 결과는 토양의 물리화학적 특성이 단일용출 결과에 직접적인 영향을 미칠 수 있음을 나타낸다.
주요어 토양, 중금속, 단일용출, 상관관계, 회귀분석
Table 1 . Sample number, mean, median, standard deviation, and range of pH, cation exchange capacity, organic matters, and the studied elements extracted with aqua regia and 0.01 M CaCl2, 0.11 M HOAc, 0.5 M HNO3, and 0.005 M DTPA + 0.01 M CaCl2 + 0.1 M TEA.
Sample number | Mean | Median | Standard deviation | Range | ||
---|---|---|---|---|---|---|
pH | 69 | 6.5 | 6.3 | 0.9 | 4.8~8.6 | |
Cation exchange capacity (meq/100 g) | 69 | 20.0 | 18.8 | 7.4 | 6.0~40.2 | |
Organic matters (%) | 69 | 4.2 | 3.5 | 2.6 | 1.0~15.0 | |
Cd (mg/kg) | Aqua regia | 50 | 1.3 | 1.2 | 0.5 | 0.7~2.9 |
0.01 M CaCl2 | 50 | 0.2 | 0.2 | 0.0 | 0.1~0.3 | |
0.11 M HOAc | 50 | 0.7 | 0.7 | 0.1 | 0.5~1.0 | |
0.5 M HNO3 | 45 | 0.4 | 0.3 | 0.3 | 0.2~1.3 | |
DTPA* | 50 | 0.3 | 0.3 | 0.1 | 0.2~1.0 | |
Cr (mg/kg) | Aqua regia | 69 | 72.2 | 51.7 | 58.3 | 22.3~259.6 |
0.01 M CaCl2 | 0 | n.d. | ||||
0.11 M HOAc | 0 | n.d. | ||||
0.5 M HNO3 | 66 | 1.7 | 1.4 | 1.5 | 0.0~7.3 | |
DTPA | 53 | 0.2 | 0.2 | 0.1 | 0.1~0.5 | |
Cu (mg/kg) | Aqua regia | 69 | 36.0 | 25.7 | 40.8 | 3.6~273.6 |
0.01 M CaCl2 | 39 | 0.2 | 0.1 | 0.0 | 0.1~0.3 | |
0.11 M HOAc | 9 | 1.1 | 0.6 | 1.1 | 0.1~3.2 | |
0.5 M HNO3 | 69 | 7.9 | 7.1 | 6.4 | 1.2~43.9 | |
DTPA | 65 | 3.4 | 2.8 | 2.6 | 0.2~13.8 | |
Ni (mg/kg) | Aqua regia | 69 | 43.0 | 36.4 | 22.3 | 15.1~118.8 |
0.01 M CaCl2 | 8 | 0.1 | 0.1 | 0.1 | 0.0~0.4 | |
0.11 M HOAc | 1 | 0.6 | 0.6 | 0.6 | ||
0.5 M HNO3 | 69 | 2.0 | 1.6 | 1.5 | 0.2~7.8 | |
DTPA | 58 | 0.9 | 0.7 | 0.8 | 0.1~4.2 | |
Pb (mg/kg) | Aqua regia | 69 | 71.1 | 69.7 | 15.1 | 28.1~122.3 |
0.01 M CaCl2 | 16 | 1.4 | 1.4 | 0.2 | 1.0~1.6 | |
0.11 M HOAc | 34 | 3.9 | 3.8 | 1.0 | 2.4~6.0 | |
0.5 M HNO3 | 69 | 12.5 | 11.3 | 5.5 | 4.0~33.8 | |
DTPA | 55 | 4.1 | 3.4 | 2.3 | 1.6~11.3 | |
Zn (mg/kg) | Aqua regia | 68 | 119.1 | 116.9 | 43.6 | 52.7~330.9 |
0.01 M CaCl2 | 56 | 2.7 | 2.6 | 1.2 | 1.7~9.4 | |
0.11 M HOAc | 28 | 21.0 | 14.4 | 13.5 | 11.1~59.8 | |
0.5 M HNO3 | 67 | 18.4 | 11.9 | 17.5 | 2.7~84.8 | |
DTPA | 47 | 12.9 | 8.8 | 10.0 | 5.3~45.6 |
The values lower than detection limits were excluded..
DTPA* : 0.005 M DTPA + 0.01 M CaCl2 + 0.1 M TEA.
Table 2 . Pearson correlation coefficients higher than 0.6 among pseudo-total and each single extraction method.
Cd | Soil condition | DTPA | *pH < 6.6, **CEC > 20.0, ***OM > 4.6 | |||
low pH* | log AR | 0.701 (28) | ||||
high CEC** | log HNO3 | 0.630 (24) | ||||
high OM*** | log HNO3 | 0.651 (22) | ||||
Cr | Soil condition | log HNO3 | *OM < 4.2 | |||
low OM* | log AR | 0.703 (37) | ||||
Cu | Soil condition | log HNO3 | log DTPA | *AR > 36.0, **pH < 6.5, ***CEC > 20.0, ****OM > 4.2 | ||
Total | log CaCl2 | 0.644 (39) | ||||
high AR* | log CaCl2 | 0.669 (22) | ||||
low AR | log AR | 0.637 (36) | ||||
low pH** | log AR | 0.613 (34) | ||||
log CaCl2 | 0.767 (19) | |||||
high CEC*** | log AR | 0.629 (31) | ||||
log CaCl2 | 0.675 (18) | |||||
log HNO3 | 0.657 (28) | |||||
low CEC | log CaCl2 | 0.659 (21) | ||||
high OM**** | log CaCl2 | 0.650 (16) | ||||
low OM | log CaCl2 | 0.637 (23) | ||||
Ni | Soil condition | log HNO3 | log DTPA | *AR < 43.0, **pH < 6.5, ***CEC > 20.0, ****OM < 4.2 | ||
Total | log HNO3 | 0.617 (58) | ||||
low AR* | log HNO3 | 0.658 (29) | ||||
low pH** | log HNO3 | 0.733 (30) | ||||
high CEC*** | log HNO3 | 0.724 (21) | ||||
low OM**** | log AR | 0.641 (39) | ||||
log HNO3 | 0.666 (39) | |||||
Zn | Soil condition | log HOAc | log HNO3 | log DTPA | *AR > 119.1, **pH > 6.5, ***CEC > 19.9, ****OM > 4.3 | |
Total | AR | 0.802 (28) | 0.646 (67) | 0.613 (47) | ||
log HOAc | 0.767 (28) | 0.887 (20) | ||||
log HNO3 | 0.767 (47) | |||||
high AR* | AR | 0.686 (13) | ||||
log HOAc | 0.870 (13) | 0.865 (12) | ||||
log HNO3 | 0.813 (24) | |||||
low AR | AR | 0.689 (23) | ||||
high pH** | AR | 0.808 (12) | 0.634 (31) | |||
log HOAc | 0.885 (12) | 0.847 (9) | ||||
log HNO3 | 0.827 (26) | |||||
low pH | AR | 0.791 (16) | 0.690 (36) | 0.737 (21) | ||
log HOAc | 0.636 (16) | 0.910 (11) | ||||
log HNO3 | 0.713 (21) | |||||
high CEC*** | AR | 0.753 (13) | 0.838 (30) | 0.713 (19) | ||
log HOAc | 0.843 (13) | 0.912 (9) | ||||
log HNO3 | 0.796 (19) | |||||
low CEC | AR | 0.883 (15) | 0.628 (37) | 0.621 (28) | ||
log HOAc | 0.831 (15) | 0.895 (11) | ||||
log HNO3 | 0.738 (28) | |||||
high OM**** | AR | 0.816 (15) | 0.627 (30) | 0.762 (17) | ||
CaCl2 | 0.672 (15) | |||||
log HOAc | 0.706 (15) | 0.892 (10) | ||||
log HNO3 | 0.786 (17) | |||||
low OM | AR | 0.881 (13) | 0.724 (37) | 0.605 (30) | ||
log HOAc | 0.892 (13) | 0.886 (10) | ||||
log HNO3 | 0.768 (30) |
For Pb, no Pearson correlation coefficient higher than 0.6 existed. The numbers in brackets represent the number of samples. CEC: cation exchange capacity (meq/100 g), OM: organic matter content (%), AR: aqua regia extraction (mg/kg).
Table 3 . The results of multiple regression analyses for each heavy metal.
Metals | Soil condition | Variable input | Multiple regression analysis | R2 | |
---|---|---|---|---|---|
Cd | low pH | enter | DTPA = 0.053 pH – 0.001 CEC – 0.130 logOM + 0.858 logAR – 0.003 | 0.561 | 0.001 |
stepwise | DTPA = 0.882 logAR + 0.205 | 0.491 | < 0.001 | ||
Cu | low pH | enter | logDTPA = 0.183 pH + 0.003 CEC – 0.023 logOM + 0.739 logAR – 1.812 | 0.413 | 0.003 |
Ni | low pH | enter | logHNO3 = 0.262 pH + 0.005 CEC – 0.190 logOM + 0.886 logAR – 2.711 | 0.404 | 0.001 |
Zn | all pH | enter | logHOAc = -0.015 pH – 0.001 CEC + 0.003 logOM + 0.003 AR + 0.935 | 0.648 | < 0.001 |
stepwise | logHOAc = 0.003 AR + 0.835 | 0.643 | < 0.001 | ||
enter | logHNO3 = -0.022 pH + 0.012 CEC + 0.317 logOM + 0.005 AR + 0.224 | 0.554 | < 0.001 | ||
stepwise | logHNO3 = 0.005 AR + 0.012 CEC + 0.314 logOM + 0.101 | 0.55 | < 0.001 | ||
enter | logDTPA = -0.015 pH + 0.010 CEC + 0.168 logOM + 0.003 AR + 0.419 | 0.478 | < 0.001 | ||
stepwise | logDTPA = 0.003 AR + 0.010 CEC + 0.398 | 0.459 | < 0.001 | ||
high pH | stepwise | CaCl2 = 0.058 CEC – 0.962 logOM + 2.025 | 0.487 | < 0.001 | |
stepwise | logHOAc = 0.003 AR + 0.868 | 0.652 | 0.001 | ||
enter | logHNO3 = 0.045 pH + 0.018 CEC + 0.206 logOM + 0.005 AR – 0.357 | 0.631 | < 0.001 | ||
stepwise | logHNO3 = 0.005 AR + 0.019 CEC + 0.048 0.593 < 0.001 | 0.593 | < 0.001 | ||
enter | logDTPA = 0.046 pH + 0.016 CEC –0.144 logOM + 0.004 AR – 0.076 | 0.525 | 0.003 | ||
stepwise | logDTPA = 0.004 AR + 0.015 CEC + 0.237 | 0.508 | < 0.001 | ||
low pH | enter | logHOAc = 0.046 pH – 0.014 CEC + 0.034 logOM + 0.005 AR + 0.669 | 0.748 | 0.003 | |
stepwise | logHOAc = 0.004 AR – 0.014 CEC + 0.955 | 0.737 | < 0.001 | ||
enter | logHNO3 = 0.124 pH + 0.007 CEC + 0.367 logOM + 0.006 AR – 0.623 | 0.591 | < 0.001 | ||
stepwise | logHNO3 = 0.006 AR + 0.369 logOM + 0.224 | 0.55 | 0.005 | ||
enter | logDTPA = -0.025 pH – 0.001 CEC + 0.216 logOM + 0.004 AR + 0.550 | 0.584 | 0.005 | ||
stepwise | logDTPA = 0.004 AR + 0.478 0.543 < 0.001 | 0.543 | < 0.001 |
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