# Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

^{3}[22], such as nickel, mercury, chromium, and copper. Metalloids are substances that are intermediates between a metal and a nonmetal, and have similar properties to metals, such as arsenic [23]. Heavy metal(loid)s have strong capacities to migrate, enrich, and contaminate, and may enter the human body through a number of pathways, such as air, water, and the food chain [24]. Recent studies showed that the excessive human intake of heavy metals could lead to a higher risk of cancer, as well as chronic adverse effects on the respiratory system, circulation, and the nervous system [25]. For example, high levels of Cu in the body can cause anemia, high cholesterol, bone changes, and damage to the capillaries, liver, kidneys, and stomach [26]. One of the most dangerous metalloids found in the Earth’s crust and water is As, and long-term drinking of water contaminated with As can cause various diseases, such as cancer of various organs, cardiovascular problems, diabetes, and neurotoxicity [27,28,29]. Therefore, it is crucial to monitor the heavy metal(loid) contents in farmland soil and in water conservation areas near mining areas.

## 2. Materials and Methods

#### 2.1. Study Area

^{3}. The soil in the study is mainly brown soil, dark brown soil, peat soil, paddy soil, white clay soil, and swamp soil area, accounting for 85% of the total land area, with an average soil thickness of 20–40 cm.

#### 2.2. Data Acquisition

#### 2.2.1. Soil Sample Collection and Chemical Analysis

#### 2.2.2. Spectral Pre-Treatment and Transformations

^{®}3 portable spectrometer (Analytical Spectral Device, Boulder, CO, USA). All the soil samples were placed in wide circular containers 10 cm in diameter and 2 cm in depth. The instrument was preheated for 30 min before the measurement began. To ensure accuracy, each reflection measurement was calibrated using a standard plate with 100% reflectivity [36]. The information obtained ranged from 350 to 2500 nm, and the light source was a built-in halogen lamp, the field angle of the probe was 25°, and the sampling intervals were 1.4 nm (350–1000 nm) and 2.0 nm (1000–2500 nm). When measuring the spectrum of a soil sample, the probe was placed in contact with the surface of the soil sample. From each soil sample, we collected 10 spectral lines, and the average value of 10 spectral lines was taken as the final reflectance (Figure 2). The average spectral reflectance was calculated by ViewSpecPro V5.6 software. Due to instrument noise, spectral bands 350–399 nm and 2451–2500 nm were removed from the initial reflectivity spectra of the soil samples to improve the signal-to-noise ratio (SNR) [37].

#### 2.3. Calculation of Optimal Spectral Indices

#### 2.4. Pearson Correlation Analysis

#### 2.5. Modeling Approaches

#### 2.5.1. Multiple Linear Regression (MLR)

#### 2.5.2. Partial Least-Squares Regression (PLSR)

#### 2.5.3. Random Forest Regression (RFR)

#### 2.5.4. Adaptive Neuro-Fuzzy Inference System (ANFIS)

#### 2.6. Model Validation

## 3. Results

#### 3.1. Descriptive Statistics of Ni, Hg, Cr, Cu, and As Contents in Agricultural Soil

#### 3.2. Soil Spectral Characteristics Analysis

#### 3.3. Correlation Analysis between Soil Spectrum and Ni, Hg, Cr, Cu, and As Contents

#### 3.4. Correlation between Optimal Spectral Indices and Ni, Hg, Cr, Cu, and As Contents

#### 3.5. The Establishment and Verification of the Spectral Prediction Model

## 4. Discussion

^{3+}, goethite, illite, and organic matter [17,59,60], and are strongly correlated with the spectral characteristics of N-H, C-H, O-H$,{\mathrm{CO}}_{3}^{2-}$ group, and -OH stretching vibration of water molecules in agricultural soil [14,61]. The main reason for this is the adsorption of heavy metal(loid)s by organic matter and iron/manganese minerals in soil, which is mainly related to the electronic transition of metal ions [62]. In addition, the chemical bond stretching, the stretching vibration of the water molecular –OH in the soil silicate minerals, and the absorption of water molecules are also main reasons [63,64,65].

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Reflectance spectra of 92 soil samples: (

**A**) the original spectra; (

**B**) those processed by piecewise SG smoothing.

**Figure 3.**Spectral transformation curves of soil: (

**a**) CR; (

**b**) 1/R; (

**c**) FDR; (

**d**) SDR; and (

**e**) $\sqrt{\mathrm{R}}$.

**Figure 4.**Pearson correlation coefficients between the soil Ni, Hg, Cr, Cu, and As contents and spectra for: (

**a**) OR; (

**b**) CR; (

**c**) 1/R; (

**d**) FDR; (

**e**) SDR; and (

**f**) $\sqrt{\mathrm{R}}$ (p = 0.01).

**Figure 5.**Scatterplots illustrating the optimal accuracy of Ni, Hg, Cr, Cu, and As: (

**a**) Ni, SDR partial least-squares regression (MLR) model; (

**b**) Hg, SDR partial least-squares regression (PLSR) model; (

**c**) Cr, SDR partial least-squares regression (PLSR) model; (

**d**) Cu, FDR multiple linear regression (MLR)model; and (

**e**) As, SDR partial least-squares regression (PLSR) model. The solid line is the regression line between the estimated and measured values, and the dashed line is the 1:1 line.

Element | Number | Min (mg/kg) | Max (mg/kg) | Mean (mg/kg) | SD (mg/kg) | CV (%) | Skew-Ness | Kurtosis |
---|---|---|---|---|---|---|---|---|

Ni | 92 | 14.900 | 56.040 | 31.740 | 8.425 | 26.544 | 0.668 | 0.402 |

Hg | 92 | 0.008 | 0.591 | 0.092 | 0.086 | 93.478 | 3.059 | 12.539 |

Cr | 92 | 0.890 | 181.200 | 76.658 | 3.489 | 4.551 | −0.033 | 0.349 |

Cu | 92 | 3.600 | 133.400 | 40.346 | 15.216 | 37.714 | 2.550 | 14.969 |

As | 92 | 3.680 | 21.680 | 8.682 | 3.684 | 42.433 | 1.463 | 2.160 |

Number | Min (km) | Max (km) | Mean (km) | SD (km) | |
---|---|---|---|---|---|

Mining area | 92 | 0.531 | 16.944 | 5.468 | 3.389 |

Road | 92 | 0.005 | 1.323 | 0.165 | 0.214 |

**Table 3.**The maximum absolute value of the correlation coefficient between Ni, Hg, Cr, Cu, and As contents and spectra.

Maximum Correlation | OR | CR | 1/R | FDR | SDR | $\sqrt{{R}}$ |
---|---|---|---|---|---|---|

Ni | 0.145 | 0.617 | 0.148 | 0.670 | 0.445 | 0.147 |

Hg | 0.123 | 0.231 | 0.151 | 0.229 | 0.283 | 0.131 |

Cr | 0.201 | 0.540 | 0.177 | 0.495 | 0.385 | 0.198 |

Cu | 0.104 | 0.330 | 0.093 | 0.328 | 0.348 | 0.102 |

As | 0.305 | 0.422 | 0.295 | 0.434 | 0.448 | 0.131 |

Transform Method | Optimal Spectral Indices | Correlation Coefficient ^{1} |
---|---|---|

OR | DIV_{2272,2271}, NPDI_{408,408}, RI_{2268,2269}, DI_{2269,2271}, NDSI_{2268,2269}, SI_{408,408} | 0.613, 0.138, 0.701, 0.671, 0.701, 0.145 |

CR | DIV_{2271,2269}, NPDI_{2344,2256}, RI_{2088,2272}, DI_{2269,2271}, NDSI_{2269,2271}, SI_{2257,2358} | 0.709, 0.653, 0.711, 0.712, 0.710, 0.648 |

1/R | DIV_{2269,2271}, NPDI_{403,403}, RI_{2269,2268}, DI_{2272,2271}, NDSI_{2269,2268}, SI_{405,405} | 0.671, 0.143, 0.701, 0.613, 0.701, 0.148 |

FDR | DIV_{2265,753}, NPDI_{2268,2384}, RI_{2269,735}, DI_{2102,2268}, NDSI_{734,2268}, SI_{1732,2270} | 0.665, 0.636, 0.718, 0.704, 0.729, 0.714 |

SDR | DIV_{1031,1485}, NPDI_{2298,2293}, RI_{1392,1918}, DI_{2294,2267}, NDSI_{2308,2294}, SI_{2288,2295} | 0.496, 0.605, 0.538, 0.620, 0.527, 0.650 |

$\sqrt{\mathrm{R}}$ | DIV_{2271,2268}, NPDI_{408,408}, RI_{2268,2269}, DI_{2268,2269}, NDSI_{2268,2269}, SI_{406,406} | 0.661, 0.145, 0.701, 0.702, 0.701, 0.147 |

^{1}Correlation coefficient is the largest absolute value of the correlation coefficient.

Transform Method | Optimal Spectral Indices | Correlation Coefficient ^{1} |
---|---|---|

OR | DIV_{2073,2071}, NPDI_{407,2450}, RI_{2065,2086}, DI_{1762,1761}, NDSI_{2065,2086}, SI_{407,1915} | 0.285, 0.109, 0.273, 0.229, 0.273, 0.127 |

CR | DIV_{455,456}, NPDI_{1888,2315}, RI_{456,455}, DI_{456,455}, NDSI_{456,455}, SI_{1038,1427} | 0.370, 0.264, 0.377, 0.380, 0.377, 0.268 |

1/R | DIV_{1762,1761}, NPDI_{1915,1915}, RI_{2086,2065}, DI_{2073,2071}, NDSI_{2086,2065}, SI_{1915,1915} | 0.229, 0.162, 0.273, 0.285, 0.273, 0.151 |

FDR | DIV_{1302,1734}, NPDI_{2291,1762}, RI_{1736,1308}, DI_{1760,1804}, NDSI_{2314,606}, SI_{468,1373} | 0.671, 0.362, 0.661, 0.336, 0.678, 0.327 |

SDR | DIV_{1628,1184}, NPDI_{1042,615}, RI_{700,1629}, DI_{554,1767}, NDSI_{2246,2413}, SI_{680,1767} | 0.683, 0.558, 0.680, 0.405, 0.697, 0.383 |

$\sqrt{\mathrm{R}}$ | DIV_{2078,2068}, NPDI_{407,1516}, RI_{2065,2086}, DI_{1762,1761}, NDSI_{2065,2086}, SI_{407,1915} | 0.293, 0.123, 0.273, 0.199, 0.273, 0.136 |

^{1}Correlation coefficient is the largest absolute value of the correlation coefficient.

Transform Method | Optimal Spectral Indices | Correlation Coefficient ^{1} |
---|---|---|

OR | DIV_{2252,2233}, NPDI_{761,761}, RI_{2275,2229}, DI_{2232,2252}, NDSI_{2229,2266}, SI_{756,756} | 0.511, 0.202, 0.562, 0.501, 0.561, 0.201 |

CR | DIV_{2236,2275}, NPDI_{2232,2443}, RI_{2236,2275}, DI_{2236,2275}, NDSI_{2236,2275}, SI_{2229,2443} | 0.552, 0.576, 0.552, 0.552, 0.552, 0.554 |

1/R | DIV_{2232,2252}, NPDI_{930,955}, RI_{2229,2266}, DI_{2252,2233}, NDSI_{2266,2229}, SI_{767,929} | 0.501, 0.167, 0.561, 0.511, 0.561, 0.177 |

FDR | DIV_{2232,1622}, NPDI_{2146,2251}, RI_{1170,2424}, DI_{1100,2251}, NDSI_{2336,2238}, SI_{1770,2240} | 0.481, 0.597, 0.576, 0.568, 0.535, 0.562 |

SDR | DIV_{735,745}, NPDI_{2241,1122}, RI_{2191,2348}, DI_{1397,2021}, NDSI_{1921,1902}, SI_{1012,1394} | 0.481, 0.553, 0.496, 0.528, 0.498, 0.529 |

$\sqrt{\mathrm{R}}$ | DIV_{2251,2250}, NPDI_{756,756}, RI_{2266,2229}, DI_{2232,2270}, NDSI_{2229,2266}, SI_{756,751} | 0.547, 0.201, 0.561, 0.545, 0.561, 0.198 |

^{1}Correlation coefficient is the largest absolute value of the correlation coefficient.

Transform Method | Optimal Spectral Indices | Correlation Coefficient ^{1} |
---|---|---|

OR | DIV_{2237,2137}, NPDI_{670,670}, RI_{2266,2252}, DI_{2268,2269}, NDSI_{2252,2266}, SI_{670,670} | 0.336, 0.107, 0.394, 0.335, 0.394, 0.104 |

CR | DIV_{2271,2252}, NPDI_{2225,2315}, RI_{2252,2272}, DI_{2252,2272}, NDSI_{2252,2272}, SI_{2224,2325} | 0.422, 0.390, 0.423, 0.424, 0.423, 0.396 |

1/R | DIV_{2268,2269}, NPDI_{711,705}, RI_{2252,2266}, DI_{2237,2227}, NDSI_{2266,2252}, SI_{685,685} | 0.335, 0.087, 0.394, 0.336, 0.394, 0.093 |

FDR | DIV_{2132,1834}, NPDI_{2248,2229}, RI_{2270,1834}, DI_{1150,2229}, NDSI_{468,2191}, SI_{2148,2228} | 0.668, 0.396, 0.664, 0.416, 0.723, 0.416 |

SDR | DIV_{1192,1479}, NPDI_{911,1066}, RI_{1147,1479}, DI_{1217,825}, NDSI_{911,2214}, SI_{634,828} | 0.726, 0.618, 0.716, 0.436, 0.732, 0.428 |

$\sqrt{\mathrm{R}}$ | DIV_{2264,2251}, NPDI_{670,670}, RI_{2252,2266}, DI_{2261,2263}, NDSI_{2252,2266}, SI_{670,670} | 0.371, 0.104, 0.394, 0.375, 0.374, 0.102 |

^{1}Correlation coefficient is the largest absolute value of the correlation coefficient.

Transform Method | Optimal Spectral Indices | Correlation Coefficient ^{1} |
---|---|---|

OR | DIV_{1314,1141}, NPDI_{954,954}, RI_{2330,2346}, DI_{2331,2334}, NDSI_{2229,2266}, SI_{2330,2346} | 0.440, 0.305, 0.445, 0.437, 0.445, 0.305 |

CR | DIV_{2379,2396}, NPDI_{2376,2418}, RI_{2396,2379}, DI_{2396,2379}, NDSI_{2396,2379}, SI_{2376,2419} | 0.572, 0.533, 0.573, 0.572, 0.573, 0.522 |

1/R | DIV_{2331,2334}, NPDI_{988,988}, RI_{2346,2330}, DI_{1413,1411}, NDSI_{2346,2330}, SI_{975,975} | 0.437, 0.285, 0.445, 0.440, 0.445, 0.295 |

FDR | DIV_{1404,2264}, NPDI_{2022,2415}, RI_{2414,1535}, DI_{2388,2414}, NDSI_{2030,2332}, SI_{1477,2414} | 0.413, 0.567, 0.603, 0.623, 0.569, 0.589 |

SDR | DIV_{1941,1736}, NPDI_{470,2354}, RI_{1041,765}, DI_{1451,1397}, NDSI_{634,2182}, SI_{1397,1548} | 0.525, 0.552, 0.584, 0.514, 0.563, 0.554 |

$\sqrt{\mathrm{R}}$ | DIV_{2251,2250}, NPDI_{756,756}, RI_{2266,2229}, DI_{2232,2270}, NDSI_{2229,2266}, SI_{756,751} | 0.445, 0.305, 0.445, 0.439, 0.445, 0.304 |

^{1}Correlation coefficient is the largest absolute value of the correlation coefficient.

Transform Method | MLR | PLSR | RFR | ANFIS | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | |

OR | 0.585 | 6.077 | 1.586 | 0.506 | 6.634 | 1.453 | 0.498 | 6.685 | 1.442 | 0.284 | 7.094 | 1.208 |

CR | 0.595 | 6.005 | 1.605 | 0.522 | 6.524 | 1.478 | 0.502 | 6.658 | 1.448 | 0.315 | 6.936 | 1.236 |

1/R | 0.583 | 6.094 | 1.582 | 0.515 | 6.574 | 1.467 | 0.483 | 6.786 | 1.421 | 0.302 | 7.005 | 1.223 |

FDR | 0.676 | 5.373 | 1.794 | 0.651 | 5.576 | 1.729 | 0.510 | 6.605 | 1.460 | 0.373 | 6.636 | 1.292 |

SDR | 0.713 | 5.053 | 1.908 | 0.595 | 6.003 | 1.606 | 0.201 | 8.434 | 1.143 | - ^{1} | - ^{1} | - ^{1} |

$\sqrt{\mathrm{R}}$ | 0.601 | 5.963 | 1.617 | 0.525 | 6.503 | 1.483 | 0.563 | 6.238 | 1.546 | 0.334 | 6.839 | 1.253 |

^{1}Model prediction failure.

Transform Method | MLR | PLSR | RFR | ANFIS | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | |

OR | 0.050 | 0.123 | 1.048 | 0.031 | 0.124 | 1.038 | 0.010 | 0.125 | 1.027 | - ^{1} | - ^{1} | - ^{1} |

CR | 0.084 | 0.121 | 1.068 | 0.080 | 0.121 | 1.065 | 0.214 | 0.112 | 1.152 | 0.235 | 0.079 | 1.151 |

1/R | 0.005 | 0.126 | 1.024 | 0.019 | 0.125 | 1.031 | 0.059 | 0.130 | 1.012 | - ^{1} | - ^{1} | - ^{1} |

FDR | 0.013 | 0.125 | 1.028 | 0.507 | 0.088 | 1.455 | 0.083 | 0.121 | 1.067 | 0.280 | 0.060 | 1.205 |

SDR | 0.638 | 0.076 | 1.697 | 0.653 | 0.074 | 1.733 | 0.519 | 0.087 | 1.473 | 0.282 | 0.060 | 1.206 |

$\sqrt{\mathrm{R}}$ | 0.073 | 0.121 | 1.061 | 0.042 | 0.123 | 1.044 | 0.154 | 0.115 | 1.111 | - ^{1} | - ^{1} | - ^{1} |

^{1}Model prediction failure.

Transform Method | MLR | PLSR | RFR | ANFIS | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | |

OR | 0.216 | 33.423 | 1.153 | 0.265 | 32.362 | 1.191 | 0.229 | 33.145 | 1.163 | - ^{1} | - ^{1} | - ^{1} |

CR | 0.254 | 32.592 | 1.183 | 0.224 | 33.240 | 1.160 | 0.357 | 30.258 | 1.274 | 0.309 | 26.355 | 1.230 |

1/R | 0.132 | 35.153 | 1.097 | 0.260 | 32.471 | 1.187 | 0.286 | 31.882 | 1.209 | 0.133 | 29.515 | 1.098 |

FDR | 0.488 | 27.000 | 1.428 | 0.491 | 26.917 | 1.432 | 0.353 | 30.358 | 1.270 | 0.411 | 24.318 | 1.333 |

SDR | 0.576 | 24.576 | 1.569 | 0.603 | 23.777 | 1.621 | 0.338 | 30.708 | 1.255 | 0.520 | 21.885 | 1.481 |

$\sqrt{\mathrm{R}}$ | 0.258 | 32.511 | 1.186 | 0.250 | 32.694 | 1.179 | 0.448 | 28.036 | 1.375 | 0.101 | 30.057 | 1.078 |

^{1}Model prediction failure.

Transform Method | MLR | PLSR | RFR | ANFIS | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | |

OR | - ^{1} | - ^{1} | - ^{1} | 0.022 | 21.095 | 1.034 | 0.018 | 21.139 | 1.043 | - ^{1} | - ^{1} | - ^{1} |

CR | 0.019 | 21.125 | 1.032 | 0.032 | 20.981 | 1.039 | - ^{1} | - ^{1} | - ^{1} | - ^{1} | - ^{1} | - ^{1} |

1/R | - ^{1} | - ^{1} | - ^{1} | 0.013 | 21.194 | 0.029 | 0.039 | 20.906 | 1.043 | - ^{1} | - ^{1} | - ^{1} |

FDR | 0.855 | 8.113 | 2.688 | 0.732 | 11.047 | 1.974 | 0.206 | 19.005 | 1.147 | 0.158 | 11.112 | 1.114 |

SDR | 0.417 | 16.291 | 0.339 | 0.571 | 13.977 | 1.560 | 0.488 | 15.263 | 1.429 | - ^{1} | - ^{1} | - ^{1} |

$\sqrt{\mathrm{R}}$ | - ^{1} | - ^{1} | - ^{1} | - ^{1} | - ^{1} | - ^{1} | - ^{1} | - ^{1} | - ^{1} | - ^{1} | - ^{1} | - ^{1} |

^{1}Model prediction failure.

Transform Method | MLR | PLSR | RFR | ANFIS | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | R^{2} | RMSE | RPD | |

OR | 0.317 | 3.584 | 1.156 | 0.201 | 3.621 | 1.144 | 0.172 | 3.686 | 1.124 | - ^{1} | - ^{1} | - ^{1} |

CR | 0.412 | 3.106 | 1.334 | 0.365 | 3.229 | 1.283 | 0.249 | 3.510 | 1.180 | - ^{1} | - ^{1} | - ^{1} |

1/R | 0.211 | 3.599 | 1.151 | 0.213 | 3.594 | 1.153 | 0.116 | 3.809 | 1.088 | - ^{1} | - ^{1} | - ^{1} |

FDR | 0.565 | 2.672 | 1.550 | 0.528 | 2.782 | 1.489 | 0.554 | 2.705 | 1.531 | - ^{1} | - ^{1} | - ^{1} |

SDR | 0.723 | 2.312 | 1.943 | 0.775 | 1.923 | 2.154 | 0.371 | 3.214 | 1.289 | 0.531 | 2.285 | 1.493 |

$\sqrt{\mathrm{R}}$ | 0.240 | 3.523 | 1.173 | 0.223 | 3.571 | 1.160 | 0.295 | 3.402 | 1.218 |

^{1}Model prediction failure.

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## Share and Cite

**MDPI and ACS Style**

Han, C.; Lu, J.; Chen, S.; Xu, X.; Wang, Z.; Pei, Z.; Zhang, Y.; Li, F. Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices. *Sustainability* **2021**, *13*, 12088.
https://doi.org/10.3390/su132112088

**AMA Style**

Han C, Lu J, Chen S, Xu X, Wang Z, Pei Z, Zhang Y, Li F. Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices. *Sustainability*. 2021; 13(21):12088.
https://doi.org/10.3390/su132112088

**Chicago/Turabian Style**

Han, Cheng, Jilong Lu, Shengbo Chen, Xitong Xu, Zibo Wang, Zheng Pei, Yu Zhang, and Fengxuan Li. 2021. "Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices" *Sustainability* 13, no. 21: 12088.
https://doi.org/10.3390/su132112088