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Article

Effects of Different Remediation Treatments and Rice Intercropping on the Integrated Quality of Paddy Soils Mildly Contaminated by Cadmium and Copper

by
Luxiang Cai
1,
Jinlun Lin
2,
Mingtian Huang
2,
Yong Hong
1,
Xuemeng Zhong
1,
Yourui Guo
2,
Wu You
1,
Qingtie Xiao
2 and
Ruiyu Lin
2,*
1
College of JunCao Science and Ecology (College of Carbon Neutrality), Fujian Agriculture and Forestry University, Fuzhou 350002, China
2
College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(24), 11120; https://doi.org/10.3390/su162411120
Submission received: 25 October 2024 / Revised: 30 November 2024 / Accepted: 15 December 2024 / Published: 18 December 2024
(This article belongs to the Special Issue Farmland Soil Pollution Control and Ecological Restoration)
Browse Figures
Review Reports Versions Notes

Abstract

:
The issue of soil contamination by heavy metals (HMs) has attracted extensive attention. In the present study, the effects of four remediation measures combined with rice intercropping on the quality of soils were evaluated in a mildly HM-contaminated paddy field. The results showed that better comprehensive remediation effects were found in the intercropping system with high and low Cd-tolerant rice than in the monoculture system. Both foliar spraying of sodium selenite and inoculation with Pseudomonas TCd-1 significantly reduced the Nemerow comprehensive pollution index (NCPI) of the soils. The application of biochar and lime significantly increased the soil fertility index. Among all the treatments, the application of 30 t∙hm−2 biochar and 3600 kg∙hm−2 lime improved soil fertility the most. The lowest single-factor pollution indices (SFPIs) of Cd, Cu, Zn, Ni and Pb and the NCPI of the soils were observed in the treatment with foliar spraying of sodium selenite at 45 mg∙L−1, showing the greatest comprehensive reduction in soil HMs. The application of 1200 kg∙hm−2 lime and 30 t∙hm−2 biochar and foliar spraying of 45 mg∙L−1 sodium selenite effectively improved the soil quality. Overall, the soil quality of paddy fields dramatically influenced the cleaner production of rice and is of great significance to the maintenance of food security.

1. Introduction

At present, heavy metal (HM) pollution in soil causes a myriad of extreme events and has led to a series of adverse effects [1,2]. The interaction between HM contamination and soil quality may influence the capacity of paddy soil to produce safe food [3,4]. Rice is one of the principal crops in the world, and it is the staple food for 50% of the world population [5,6,7]. Cleaner production of rice plays a pivotal role in food security [8,9]. However, with the intensification of human activities, such as mining and metal smelting [10], sewage irrigation [11] and the application of pesticides and fertilizers [12], HM pollution has become a current concern [13,14]. A previous study demonstrated that overexposure to HMs could lead to a variety of diseases [15,16,17]. It was reported that excessive cadmium (Cd) can lead to bone damage, high blood pressure, kidney disease and lung cancer [18]. Excessive exposure to copper (Cu) can cause liver disease, Alzheimer’s disease and neurasthenia [19]. Long-term dietary intake of arsenic (As) might cause melanosis, bladder cancer and respiratory and nervous system diseases [20]. Taking in excessive zinc (Zn) can damage hematopoiesis and cause iron deficiency anemia in the human body [21]. Exposure to mercury (Hg) might significantly affect one’s mental health and cause heart disease [22]. Excessive intake of chromium (Cr) can cause serious skin allergies, digestive system damage and kidney diseases [23,24]. Ingestion of high concentrations of nickel (Ni) might cause cancer, fatigue, headache and skin diseases [25]. Lead (Pb) can trigger developmental delay, behavioral changes and nervous system damage, especially in children [26]. Obviously, HM pollution in rice has serious adverse consequences for society. To safeguard human health and food security, the issue of HM pollution in paddy fields urgently needs to be solved.
Several studies have shown that the excessive content of HMs in paddy soil is the key factor leading to rice HM pollution, which is closely related to its geographical background value, natural conditions and human activities [12,27,28,29,30]. In recent years, various remediation methods have been developed to address rice HM pollution. In situ remediation technology is commonly used because of its low economic cost, easy operation, ability to produce fast effects and the possibility of soil reuse [31].
It was reported that the application of biochar could effectively decrease the Cd content in brown rice by 45.5–62.5% and increase the rice yield by 2.7–11.8% [32]. The addition of Pseudomonas reduced the soil’s available Cd by 30%, compared with that in the control [33]. With the continuous application of lime, the soil pH significantly increased, and thus the contents of available Cd in paddy soil and the total Cd in rice decreased by 12.9–18.2% and 28.5–56.2%, respectively [34]. Foliar application of sodium selenite reduced the Cd content in brown rice by 61.9%, and the root-to-stem transfer coefficient decreased by 34.5% [35]. In a new rice intercropping system, the Cd concentration in the grains of plants with low Cd accumulation decreased from 0.30 mg·kg−1 to 0.16 mg·kg−1, and the available Cd concentration in the rhizosphere decreased significantly from 0.38 mg·kg−1 to 0.22 mg·kg−1 [36]. In a previous study by Peng et al., it was observed that available soil Cd decreased from 0.50 mg·kg−1 to 0.43 mg·kg−1 with silicon fertilizer applied, and Cd accumulation in rice decreased by 45.5% [37]. Lin et al. indicated that the addition of slag, biochar and slag + biochar increased the soil pH by 0.8–5.7%, 2.1–2.4% and 4.0–6.3%, respectively, and the organic carbon concentration in the soil increased by 4.3–5.0%, 0.5–17.0% and 4.3–7.0%, respectively [38]. Obviously, the application of different in situ remediation measures can mitigate rice HM pollution by immobilizing, separating or extracting contaminants [39,40]. They are also linked to the oxidation, reduction, complexation, adsorption and desorption process of HMs in the crop rhizosphere [41,42,43,44]. Meanwhile, we are also concerned about whether large amounts of soil conditioners and biochemicals applied to the field would or would not change the soil quality and its sustainable productivity, but little of this work has been performed.
Soil quality refers to the ability of soil to support its biological productivity, maintain its environmental quality and promote animal, plant and human health in ecosystems or under specific land use patterns [45]. It is generally evaluated from three aspects: environmental quality, fertility quality and comprehensive quality [46,47]. Soil environmental quality assessment generally focuses on evaluating soil pollution by calculating the single-factor pollution index (SFPI), the Nemerow composite pollution index (NCPI), the geo-accumulation Index (Igeo), the potential ecological risk index (PERI) and the pollution load index (PLI) [48,49]. Soil fertility quality assessment is usually performed via principal component analysis (PCA), the soil integrated fertility index (IFI), the improved Nemerow pollution index (NPI), the nutrient value index (NVI), fuzzy mathematics analysis and cluster analysis [50,51,52,53]. Both environmental and fertility quality can only partly reflect the total quality of the soil. A simple, sensitive, flexible and commonly used comprehensive assessment method is the combination of constructing the minimum data set (MDS) and calculating the soil quality index (SQI) [54]. The MDS is a representative minimum set of soil indicators that can reflect most of the soil property information [55]. The SQI has been widely used because it can fully consider the common influence of the measured value, weight and interaction between the evaluation indices on the evaluation results [56].
In the present study, a high Cd-tolerant hybrid rice variety and a low Cd-tolerant conventional rice variety were used as plant materials. With respect to the monoculture and intercropping of the two rice cultivars at a density of 1:1, four in situ remediation techniques (biochar, Pseudomonas TCd-1, lime and sodium selenite foliar inhibitors) were applied at three concentrations to evaluate the effects of the different treatments on paddy soil quality. We aimed to determine the effects of in situ remediation techniques on (a) the environmental quality of paddy soil, (b) the fertility quality of paddy soil, (c) the comprehensive quality of paddy soil and (d) the best measure to reduce rice HMs and maintain soil quality for sustainable development of rice production in the future.

2. Materials and Methods

2.1. Experimental Site and Field Design

From mid-June to late October of the year 2021, the field experiment was conducted in the Agricultural Science and Technology Demonstration Field of Shanghang County (N24°59.832′, E116°28.481′) in Longyan city, Fujian Province, China. The site has a mid-subtropical monsoon climate, with an average annual temperature of 20 °C and average annual rainfall of 1645.3 mm. The preceding crop in the experimental paddy field was tobacco.
The basic physical and chemical properties of the paddy soil were as follows: pH, 5.31; organic matter, 56.3 g·kg−1; total nitrogen, 2.4 g·kg−1; available nitrogen, 50.7 mg·kg−1; total phosphorus, 2.52 g·kg−1; available phosphorus, 274.4 mg·kg−1; total potassium, 467.6 mg·kg−1; and available potassium, 386.0 mg·kg−1. The total contents of HMs in the soil were 0.59 mg·kg−1 Cd, 102.50 mg·kg−1 Cu, 23.70 mg·kg−1 As, 145.32 mg·kg−1 Zn, 0.11 mg·kg−1 Hg, 42.08 mg·kg−1 Cr, 15.02 mg·kg−1 Ni and 50.63 mg·kg−1 Pb. According to the threshold value of heavy metals (GB15618-2018 [57]) promulgated by the Ministry of Ecology and Environment of the People’s Republic of China, the soil was mildly polluted by Cd and Cu [58].
In this study, two rice cultivars, the high cadmium-tolerant hybrid rice variety Teyou 671 and the low cadmium-tolerant conventional rice variety Baixiang 139, were used as plant materials. The untreated paddy field was used as a blank control (BLK), and the planted soils without additive application were considered the control (CK), where the monoculture of Teyou 671 was CK1, the monoculture of Baixiang 139 was CK2 and the two rice cultivars intercropped at a density of 1:1 were CK3. In addition, four additives, rice straw biochar (BC), the microbial agent Pseudomonas TCd-1 (MA), lime (LM) and sodium selenite (SE), were applied to rice under different planting methods (PMs). Each additive was applied at three levels: ① BC (BC1: 15 t∙hm−2, BC2: 30 t∙hm−2, BC3: 45 t∙hm−2), ② MA (MA1: 1%, MA2: 2%, MA3: 5%), ③ LM (LM1: 1200 kg∙hm−2, LM2: 2400 kg∙hm−2, LM3: 3600 kg∙hm−2) and ④ SE (SE1: 15 mg∙L−1, SE2: 30 mg∙L−1, SE3: 45 mg∙L−1).
Rice straw biochar was purchased from Zhengzhou Jinbang Environmental Protection Technology Co., Ltd., Zhengzhou, China. The product carbonization temperature was 400–600 °C; and the heating rate was 7.5 °C∙min−1. The major characteristics were as follows: particle size, 100 mesh; organic carbon, 75%; pH, 10.5–12; specific surface area, 39.91 m2∙g−1; pore size, 50.2 nm; pore volume, 0.38 cm3∙g−1; available nitrogen, 1.4–1.7%; available phosphorus, 900–1600 mg∙kg−1; available potassium, 14–40 g∙kg−1; and 20–55 μg∙kg−1 As, 2–7 μg∙kg−1 Cd, 40–105 μg∙kg−1 Pb and 600–1300 μg∙kg−1 Cr. Biochar and base fertilizer (Stanley Agriculture Group Co., Ltd., Linyi, China, N-P2O5-K2O 18-18-18) were applied together. Before rice transplanting, the topsoil (0–30 cm) of each plot was manually tilled and mixed. Moreover, the microbial agents of Pseudomonas TCd-1 (the concentration of the bacterial suspension was 3.21 × 107 CFU·mL−1) and lime (Huihui Industrial Co., Ltd., Xinyu, China) were applied in the same way. Foliar spraying of sodium selenite was conducted at the rice tillering stage and full heading stage at concentrations of 15 mg∙L−1, 30 mg∙L−1 and 45 mg∙L−1 at 100 mL∙m−2, and the plants were sprayed at 4 p.m. on a fine day. In total, 16 treatments were employed to evaluate their effects on the available and total HM contents, physicochemical properties, enzyme activity and microbial biomass carbon of the paddy soils. Each treatment was repeated three times, the plants were randomly arranged in the field and the size of each plot was 10 m2 (2.5 m by 4 m).

2.2. Field Management and Sampling

On 3 June 2021, the seeds of the tested rice plants were soaked for germination and seedling raising. On 3 July, each plot was isolated and the ridge was protected by black plastic film. On 7 July, each plot (including BLK) was marked, evenly plowed and fertilized with 0.4 kg of special fertilizer for rice (Stanley Agriculture Group Co., Ltd., Linyi, China, N-P2O5-K2O 18-18-18) as the base fertilizer. On 11 July, rice seedlings of the same size were selected and artificially transplanted into the field at row spacings of 25 cm by 25 cm according to the experimental design. After 10 days of transplanting, 0.15 kg of the above fertilizer was applied to each plot as tillering fertilizer. The local farming practice was adopted as daily water management, usually with shallow water of 2 to 3 cm retained throughout the whole growing period. No additional measures were taken for weed and pest control. At the maturity stage of rice (in mid-October), approximately 1.0 kg of the rhizosphere soil from each treatment was sampled by a 5-point sampling method and taken to a laboratory for chemical analysis. Part of the soil samples were immediately packed in sealed polyethylene bags and stored in a refrigerator at 4 °C for determination of soil enzyme activity and microbial biomass carbon. Another part of the samples were air-dried at room temperature, removing stones, roots and other foreign materials. The soil was crushed into 10 mesh and 100 mesh samples for the determination of soil physical and chemical properties and heavy metal content.

2.3. Chemical Analysis

The collected soil samples were air-dried and ground with an agate mortar, sieved through a 0.149 mm mesh, then stored in sealed polyethylene bags and finally digested with an acid mixture of HClO4 and HNO3. The contents of Cd, Pb, Cr, Ni, Cu and Zn in the soils were determined by inductively coupled plasma mass spectrometry (ICP-MS, NexION 300X, Shanghai, China) [59,60], while the contents of Hg and As were investigated by hydride generation-atomic fluorescence spectrometry (HG-AFS, AFS-9230) [61]. Furthermore, the available Cd, Cu, Cr, Zn, Ni and Pb in the soil samples were determined by ICP-MS (NexION 300X, Shanghai, China), and the available Hg and As were determined by HG-AFS (AFS-9230).
The soil pH, cation exchange capacity (CEC), organic matter (OM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), available phosphorus (AP) and available potassium (AK) were measured as previously described by Zha [62] and Bao [63]. The activities of soil catalase (CAT), peroxidase (POD), polyphenol oxidase (PPO), acid phosphatase (ACP), urease (URE), sucrase (SUC), cellulase (CUE) and protease (PRO) were analyzed following the methods of Guan [64]. Soil microbial biomass carbon (MBC) was determined by the chloroform fumigation–potassium sulfate extraction method [65].

2.4. Assessment of Soil Heavy Metal Pollution

The single-factor pollution index (SFPI) was used to show the pollution degree of HMs, and the Nemerow comprehensive pollution index (NCPI) was used to judge the pollution status of the HMs in the soil [66,67]. Their calculation formulas are as follows [68,69]:
S F P I = C i S i
N C P I = P m a x 2 + P a v e 2 2
where Ci is the total concentration of HMs i; Si is the evaluation standard value of i; and Pave and Pmax are the average and maximum values of the SFPI, respectively. SFPI ≤ 1.0, 1.0 < SFPI ≤ 2.0, 2.0 < SFPI ≤ 3.0, 3.0 < SFPI ≤ 5.0 and SFPI ≥ 5.0 indicate that the HMs are pollution-free (safe), slightly polluted (warning line), mildly polluted, moderately polluted and severely polluted, respectively. The comprehensive pollution risk was divided into five levels: clean (NCPI ≤ 0.7), alert (0.7 < NCPI ≤ 1.0), mild pollution (1.0 < NCPI ≤ 2.0), moderate pollution (2.0 < NCPI ≤ 3.0) and severe pollution (NCPI > 3.0).

2.5. Assessment of Soil Fertility

The soil fertility was evaluated by using the single index and the comprehensive index of soil fertility. Their calculation formulas are as follows [70]:
F i = C i S i
I F I = F i m i n 2 + F i a v e 2 2 · N − 1 N
where F i is the single soil fertility index of the evaluation factors in the soil; C i is the measured value of the evaluation factor; S i is the evaluation criterion for the evaluation factors; I F I is the integrated index of soil fertility; F i m i n is the minimum value of the single fertility index in all the evaluation factors of the soil; and F i a v e is the average value of each individual fertility index in all evaluation factors. With a single index F i   a b o v e   3.0 , when the soil fertility comprehensive index is calculated, the single index F i equals 3.0; N is the number of soil fertility factors in the evaluation; and soil fertility is divided into three grades: Grade I ( I F I ≥ 1.7), Grade II (0.9 ≤ I F I < 1.7) and Grade III ( I F I < 0.9).

2.6. Comprehensive Evaluation of Soil Quality

Soil quality was evaluated by constructing the minimum data set (MDS) and calculating the soil quality index (SQI) [71,72,73,74]. Principal component analysis (PCA) was used to reduce the dimensions of 33 soil indicators. After the grouping was completed, the norm values ( N i k ) of each group were calculated by Formula (5), and the indicators with norm values in the range of 10% of the highest norm value in each group were retained as alternative indicators for the MDS. If there were multiple alternative indicators in a group, they were further screened according to the correlations among the indicators. If the indicators in the group were not correlated or negatively correlated, they were all retained. Otherwise, the indicators with the highest norm values were selected and included in the MDS. The SQI was the integration of the selected indicators in the MDS and was calculated by Formula (6). The larger the SQI value was, the greater the soil quality was.
The calculation formulas are expressed as follows [54,55,56]:
N i k = ∑ 1 k u i k 2 · λ k
SQI = ∑ A i X i ′
A i = C i ∑ i = 1 n C i i = 1 , 2 , 3 , … n
X ′ x = 0.1 ,                     x ≤ x 1   0.9 ( x − x 1 ) / ( x 2 − x 1 ) + 0.1 ,   x 1 < x < x 2 1.0 ,                     x ≥ x 2
X ′ x = 0.1 ,                     x ≥ x 2   0.9 ( x 2 − x ) / ( x 2 − x 1 ) + 0.1 ,   x 1 < x < x 2 1.0 ,                     x ≤ x 1
where N i k is the norm value of the first index on the previous principal component with an eigenvalue ≥ 1; u i k is the load of the first variable on the first principal component, which reflects the relative importance of the first index on the first principal component, is the eigenvalue of the first principal component; and λ k is the weight of the first index. A i in Formula (7) is the membership value of the first index; X i ′ is the common factor variance of the index; C i is the minimum number of data set indicators; and Formula (8) and Formula (9) represent the ascending and the descending membership functions, respectively.

2.7. Data Processing and Statistical Analysis

The descriptive statistics of IBM SPSS Statistics 25.0 software were used to analyze the maximum value, minimum value, average value, standard deviation and coefficient of variation. Factor analysis was used for Pearson correlation analysis and principal component analysis. Correlation analysis of the soil indicators was carried out at a significance level of 0.05 or 0.01. The chart was drawn using Origin 2023 and Microsoft Excel 2019.

3. Results

3.1. Effects of In Situ Remediation Treatments on the Heavy Metal Pollution Indices of the Paddy Soils

As shown in Table 1, after different remediation treatments, the SFPIs of the eight HMs were almost always consistently lower than those of the BLK treatment. The average SFPIs of Cd obviously increased in the BC and MA treatments but decreased in the LM and SE treatments, and the opposite trend was observed for Hg. Slight changes were found in the other HMs. Among all the treatments, the lowest SFPIs of Cd (1.204), Cu (2.112), Zn (0.686), Ni (0.211) and Pb (0.512) were observed in the SE3 treatment, while the lowest SFPI of As (0.742) was observed in the CK1 treatment, the lowest SFPI of Hg (0.079) was observed in the MA2 treatment and the lowest SFPI of Cr (0.147) was observed in the CK3 treatment. The SFPIs of Cu ranged from 2.112 to 2.297, and those of Cd ranged from 1.204 to 1.523. The SFPIs of both Cu and Cd were greater than 1.0, which indicates that the paddy soils were polluted by Cu and Cd.
Compared with those in the BLK treatment, all the treatments without additive application (CK) had a lower SFPI for Cd than did the BLK treatment. The SFPIs for Cd decreased in the order of BLK (1.523) > CK2 (1.505) > CK3 (1.419) > CK1 (1.387), the monoculture of rice Teyou 671 (CK1) exhibited the greatest reduction in soil Cd pollution, and rice intercropping (CK3) had the greatest reduction in soil Cu pollution, with the SFPIs for Cu decreasing in the order of CK1 (2.280) > BLK (2.275) > CK2 (2.231) > CK3 (2.147) (Table 1). Compared with those in the CK treatment (1.437), the average SFPIs of Cd in the biochar (BC), microbial agent (MA), lime (LM) and sodium selenite (SE) treatments were 1.455, 1.424, 1.305 and 1.240, respectively. LM and SE reduced soil Cd pollution, and the average SFPIs of Cd in all the rice planting treatments with or without additive application were lower than that in the BLK treatment (1.523). Moreover, the above four treatments with additives had average Cu SFPIs of 2.217 (BC), 2.187 (MA), 2.244 (LM) and 2.145 (SE). A more obviously lower SFPI of Cu was found in the SE treatment than in the CK treatment (2.219). In addition, for all the treatments with or without additives, the average SFPIS of Cu was lower than that of BLK (2.275).
Furthermore, the NCPIs of the soils ranged from 1.581 to 1.721 (Table 1), which indicated that all the paddy soils were mildly polluted by HMs. Among all the treatments, both LM2 (1.721) and BLK (1.717) had greater NCPIs than did the other treatments, and SE2, SE3 and MA2 exhibited much lower NCPIs than did the other treatments. Compared with that in the BLK treatment, the NCPI in the CK3 treatment was significantly lower than that in the BLK treatment (p < 0.05), which indicated that rice intercropping had important effects on soil HM pollution. Similar results were observed for the average NCPIs in the CK, BC, MA, LM and SE treatments, which decreased in the order of BLK (1.717) > LM (1.682) > BC (1.666) ≈ CK (1.666) > MA (1.644) > SE (1.607), and obvious reductions were found in the MA and SE treatments.

3.2. Effects of In Situ Remediation Treatments on the Soil Fertility Indices of the Paddy Soils

Table 2 shows that the IFIs of the soils ranged from 0.999 to 1.247, which meant that all the paddy soils belonged to the second grade. Compared with those of BLK, the single fertility indices (Fi) of pH, CEC, TN, AN, AP and OM increased overall, and those of TP, TK and AK decreased after the different remediation treatments. The highest single fertility index (Fi) of pH (3.000) was observed in the LM2 treatment; the highest values of CEC (0.734), TN (1.076), TP (3.797), TK (0.507), AP (3.760) and AK (3.786) were observed in the LM3, BC1, BLK, LM3, MA2 and LM2 treatments; and the highest single fertility indices of AN (1.969) and OM (2.164) were both observed in the BC2 treatment.
Among all the treatments (Table 2), several had higher IFIs than did the BLK treatment (1.132). Compared with those in the BLK treatment, obvious improvements in comprehensive soil fertility were found in the LM3 and LM2 treatments (p < 0.05). The LM1 and BC2 treatments also had positive effects on the soil IFI, but the CK1 and CK2 monocultures decreased the soil IFI (p < 0.05); some negative effects were also detected in the MA1 treatment. Greater effects on the IFI were observed in the rice intercropping treatment (CK3) than in the CK1 and CK2 monocultures. In addition, LM had the highest average IFI (1.238) among all the treatments, and the lowest value (1.015) was obtained in the CK. This result suggested that a significantly greater soil fertility supply was consumed by the growing rice treatment than by the BLK treatment in this experiment, and the application of lime effectively enhanced the soil fertility. The IFIs also slightly changed with the amount of BC, MA and SE applied in the experiments (Table 2).

3.3. The Minimum Data Set (MDS) and Soil Quality Indices (SQIs) of Paddy Soils Under Different In Situ Remediation Treatments

As shown in Table 3, 33 soil quality indicators were collected and subjected to principal component analysis (PCA). The results showed that the eigenvalues of the first six principal components were greater than 1, and the cumulative variance contribution rate reached 90.26% (Table 3). These results indicated that the six principal components could explain most of the variability in the selected indicators. According to the factor loading value of each index and the correlation between the indices, as shown in Table 3 and Figure 1, six groups of alternative indices for MDS establishment were derived. The indicators of group 1 included T-Pb, A-Cu, A-As, A-Zn, A-Ni, A-Pb, pH, CEC, DOC, AK, CAT, SUC and PRO; group 2 included T-Cd, T-Cu, T-As, T-Cr, T-Ni and A-Cd; group 3 included T-Hg, TK, URE, POD and MBC; group 4 included OM, TN, AP, CUE and PPO; group 5 included TP and ACP; and group 6 included T-Zn and AN. Then, the norm values of each index in the six groups were further calculated.
Based on the principle of MDS creation, in each group, the indices for which the norm value was within 10% of the highest total score were retained. In group 1, the pH exhibited a maximum norm value of 3.349. The norm values of A-As, A-Zn, A-Ni, A-Pb, CEC, AK, CAT and PRO were all within the range of 10% of the pH, and significant correlations were detected between the two indicators (Figure 1). Therefore, pH was selected as a factor in the MDS. Similarly, in group 2, the index of T-As with the maximum norm value was added to the MDS, and URE in group 3, TN in group 4 and ACP in group 5 were included in the MDS. In group 6, the T-Zn index had a maximum normal value of 1.893, and the norm value of AN was within 10% of that of T-Zn; however, there were no significant relationships between T-Zn and AN, so both the T-Zn and AN indices were included in the MDS. Finally, a total of seven indices of soil T-As, T-Zn, pH, TN, AN, URE and ACP were selected to construct the MDS for paddy soil quality evaluation under different in situ remediation treatments.
The influence of different indices in the MDS on the soil quality index (SQI) is shown in Figure 2. Drastic fluctuations were observed among the different indices. The SQI of ACP had the greatest impact on soil quality, with values ranging from 0.015 to 0.150, T-As ranging from 0.015 to 0.149, T-Zn ranging from 0.012 to 0.122, TN ranging from 0.013 to 0.133, AN ranging from 0.014 to 0.139 and URE ranging from 0.015 to 0.153. Greater fluctuations in the ACP were observed than in most of the other indices, but the smallest effect on the SQI was found in this experiment. In contrast, T-Zn exhibited the minimum fluctuation in this study, but T-Zn had obvious effects on the SQI. The contributions of the seven selected indices to the average SQI were ranked in the order of ACP (0.083) > T-As (0.079) > T-Zn (0.076) > TN (0.065) > pH (0.064) > AN (0.062) > URE (0.055).

3.4. Effects of Different In Situ Remediation Treatments on the Comprehensive Quality of Paddy Soils

Table 4 shows that after the different remediation treatments, the indices of T-As, T-Zn, pH, TN, AN and URE in the soil were universally greater than those in the BLK. The average values of ITN and IACP obviously increased in BC, MA and SE but decreased in the LM treatment. Among all the treatments, the highest values of IT-As (0.133) and IACP (0.108) were observed in the CK1 treatment, and the highest values of IpH (0.138) and IURE (0.100) were observed in the LM3 treatment. Compared with those in the BLK treatment, all the treatments without additive application (CK) had greater values of IT-As, IAN and IACP. The IpH values exhibited the opposite trend, decreasing in the order of BLK (0.042) > CK2 (0.030) > CK1 (0.027) > CK3 (0.018). Rice intercropping (CK3) improved the soil pH, while the rice monoculture Teyou 671 (CK1) increased the soil ACP activity.
The SQIs ranged from 0.384 to 0.532 in the experiment (Table 4). Compared with the BLK treatment, almost all the arranged treatments had significantly greater average SQIs than did the BLK treatment (p < 0.05), except for MA1, MA2 and MA3. Rice growth and in situ remediation measures improved the quality of the paddy soils. The average SQIs of BC, LM and SE were 0.494, 0.518 and 0.501, respectively, all of which were greater than those of CK (0.475) and MA (0.439) (p < 0.05), and both of the latter two SQIs were greater than that of BLK (0.384). Compared with the CK treatment, the combination of rice growth with BC, LM and SE application resulted in a better SQI, and the BLK treatment had the worst effect on maintaining the integrated quality of the soil.

4. Discussion

4.1. Rice Intercropping Reduces Heavy Metal Pollution and Enhances the Quality of Paddy Soils

Intercropping is a viable strategy for diversifying cropping systems to alleviate food insecurity and is gaining popularity in agricultural practice. It can not only inhibit pests and weeds, promote the effective use of soil nutrient uptake by crops and improve crop yield and land use efficiency but also regulate the absorption of HMs, as well as mineral nutrients, from the plant rhizosphere [75]. It is well known that HMs can be effectively absorbed and transported by rice plants, which reduces soil HM concentrations and thus improves soil quality [76]. However, the effects of intercropping systems cocultured with high and low Cd-tolerant rice on soil HM pollution and fertility have not been reported in detail. The present study showed that both the monoculture and intercropping models reduced the SFPI of Cd and Cu, and the rice monoculture Teyou 671 (CK1) had the greatest effect on reducing soil Cd pollution, while the intercropping (CK3) model had the greatest effect on reducing soil Cu pollution (Table 1); but only slight differences in the NCPI were found between the BLK and CK treatments. However, a significantly greater SQI was detected in the CK than in the BLK (p < 0.05). The above results demonstrated that growing rice not only reduced soil HM pollution but also improved the quality of paddy soils [77].
Usually, soil quality can be improved by regulating soil physical and chemical properties, soil microbial diversity and plant mycorrhizae [78]. Growing rice produces a great deal of organic acids secreted by rice roots, which reduces the pH of rhizosphere soil, changes the bioavailability of HMs and promotes the absorption of HMs by intercropping systems [79]. Rice growth also changed the soil enzyme activities, nutrient supplies and nutrient balance in the soils. It has been reported that the soil pH decreases significantly and the activity of ACP increases under growing rice conditions; thus, occluded phosphate is activated, which promotes the absorption of P and K by rice [80]. However, the demand for soil N, P and K differed among planting patterns and rice varieties. In the intercropping model, the utilization of nutrients and HMs was optimized in a system of high/low Cd-tolerant rice varieties, which would be beneficial for enhancing soil fertility and quality and would play a vital role in maintaining the stability of paddy soil under HM-contaminated conditions [36]. Indeed, rice intercropping affects HM absorption, soil fertility and soil quality by altering rhizosphere microorganisms and root secretion [81,82,83,84], and these effects might be positive or negative depending on which cultivar is cocultured. The results illustrated that intercropping with high and low Cd-tolerant rice varieties was an underlying measure for HM-polluted paddy fields to guarantee the safety of agricultural production [83].

4.2. The Application of Different Additives for In Situ Remediation Techniques Has Diverse Effects on Heavy Metal Pollution and the Fertility of Paddy Soils

Recently, biochar, microbial agents, lime, sodium selenite and other kinds of additives have been commonly used to reduce soil and crop HM pollution and have performed well in practice [85,86,87,88,89,90]. In the present study, the application of MA and SE significantly reduced the pollution of HMs in paddy soils, and LM and BC exhibited slight remediation effects on soil HMs (Table 1). In addition, all the applications of BC, MA, LM and SE significantly enhanced the IFI of paddy soils compared to that of CK (Table 2) and enhanced the SQI compared to that of BLK. These findings suggested that growing rice combined with in situ remediation effectively improved the quality of paddy soils mildly contaminated by HMs.
Biochar is mainly composed of aromatic hydrocarbons and single carbon or carbon with graphite structures, with a carbon content of more than 60%, and other elements such as H, O, N, K, Na, Ca, Mg, S, etc., and it has a large amount of carbonate, trace metal oxides and alkaline functional groups on its surface [91,92,93]. The application of biochar increased the soil pH, CEC and OM, affected the occurrence and inhibited the activity and bioavailability of HMs in the soil, improved the soil microbial environment and affected soil enzyme activity [94,95,96,97]. It could reduce the concentrations of dissolved Cu and Cd by reducing colloid-associated HMs [98]. However, we cannot ignore the underlying risk of the enrichment of HMs in biochar originating from a variety of materials [91,93]. Pseudomonas TCd-1 is a strain isolated from the rhizosphere soils of the Cd-tolerant rice cultivar “PI312777”, which has strong Cd tolerance and accumulation ability [99]. Thus, this strain could be used as an effective strain for remediating rice Cd pollution [100]. The metabolites of the strain combined with HM ions in the soil to form insoluble precipitates in the rhizosphere, and they reduced the bioavailability of HMs [101], and this process might also be related to changes in the potential functions of the rhizobacterial microbiome altered by the strain [102], but little extra improvement in the IFI and SQI could be achieved in field applications.
Hydrated lime is a widely used soil HM immobilizing agent [103]. Lime amendments decrease HM solubility by increasing the soil pH and surface charge, triggering the precipitation of metal carbonates, oxides or hydroxides [104,105]. The addition of lime reduced the concentrations of dissolved Cu and Cd by increasing colloid-associated HMs [98,103]. The addition of 0.5% lime could reduce the leaching content of Cd, As, Zn and Pb in the soil around zinc mines by 70%, 77%, 94% and 95%, respectively [106]. Therefore, the application of lime could not only effectively reduce the amounts of available HMs in the soil but also significantly promote the formation of soil aggregates. However, the use of excess alkaline soil amendments could make the soil alkaline and compacted, further decreasing agricultural productivity [107]. The highest average NCPI (1.682) among the combination treatments also indicated the negative effects of lime amendment on soil HMs and soil quality. Although Se is not an essential element for plant growth, foliar application of Se resulted in the greatest increase in the SQI, which could be related to the physiological functions of Se in mitigating HM stress and reconstructing the plant rhizosphere microhabitat through root secretion [108]. In a nutshell, according to the results of the present study, SE3 had the lowest NCPI, LM3 had the highest IFI and LM1 had the highest SQI. Foliar spraying of 45 mg∙L−1 sodium selenite is recommended to reduce HM pollution, 3600 kg∙hm−2 lime is proposed to enhance soil fertility and 1200 kg∙hm−2 lime is advocated to promote soil quality.

4.3. The Potential Mechanism by Which In Situ Remediation Techniques Affect the Quality of Paddy Soils

The soil quality index is a useful tool for assessing the status of soil fertility and its degradation. Several indicators, including the soil HMs, pH, CEC, available nutrients, and microbial and soil enzyme activities, are included in the SQI. Correlation analysis revealed that the NCPI was positively correlated with T-Cu, T-Zn, T-Cr, T-Ni, T-Pb and OM and negatively correlated with POD in the CK treatment, and the IFI was positively correlated with AP. Additionally, the SQI was positively correlated with the CEC and SUC (Figure 3). With the application of different additives, the relationships among the NCPI, IFI, SQI and the 33 indicators collected in the experiment differed for all the in situ remediation techniques. This might be due to differences in the amounts and properties of the input materials, which are unquestionably affected by the biogeochemical process materials in the soils.
After the addition of BC, the NCPI was positively correlated with T-Cd, T-Cu, T-As and POD, which indicated that Cd and As became more important HM pollutants than Cu, Zn, Cr, Ni and Pb in the CK and caused oxidative stress in the soil. The IFI was positively correlated with pH, CEC, OM, TN, TP, AN, AK, URE and PRO but was different from AP in the CK (Figure 3). This meant that the addition of BC effectively stimulated soil nutrient cycling, and the SQI was positively correlated with pH, DOC, TN, TK, AN, URE, PRO and ACP and negatively correlated with T-Zn, T-Pb, A-Zn, A-Pb and PPO, which was also different from the CEC and SUC in the CK (Figure 3). Therefore, the addition of BC obviously changes the quality of paddy soil through complexation, precipitation, adsorption, oxidation and reduction processes after the addition of BC in the field [109,110,111]. This could be verified by the significant degradation of soil-available Cd and Cu under the different planting modes.
Pseudomonas TCd-1 has a strong Cd tolerance and accumulation ability, can promote plant growth-promoting rhizosphere bacterial activity and plays an essential role in protecting plants from HM toxicity [112]. After the inoculation with MA, the NCPI was positively correlated with T-Cu, T-As, T-Zn, T-Pb and OM (Figure 3) and negatively correlated with MBC and PRO. The application of 2% Pseudomonas TCd-1 in the rice intercropping system reduced the contents of soil T-Cd, T-Cu, T-As, T-Cr and T-Pb by 13.63%, 9.93%, 13.05%, 11.18% and 13.38%, respectively, and the contents of soil A-Cd, A-Cu and A-Zn decreased to a certain extent (Table S2). These results indicated that alterations in rhizosphere soil microbes and soil enzyme activities affected soil HM pollution. Moreover, the IFI was positively correlated with pH, CEC, DOC, TN, TP, AN and AP, and the SQI was positively correlated with pH, CEC, TN, AN and URE, which showed that the inoculation with Pseudomonas TCd-1 strongly promoted the activity of soil microbes.
Lime application had the greatest effect on the SQI among all the treatments (Table 4). It can not only affect the adsorption, precipitation and complexation of HMs in soil by changing the soil pH and CEC [113,114] but can also increase rice yield and reduce the content of HMs in various organs of rice [115,116]. With lime treatment, the NCPI was positively correlated with T-Cd, T-Cu, T-As, T-Zn, T-Hg, T-Cr, T-Ni, T-Pb and AK and negatively correlated with AP (Figure 3). The results revealed that the effect of Cu on the NCPI was obviously suppressed by lime. The results showed that the A-Cd concentrations in the soils treated with LM1, LM2 and LM3 were 16.25%, 17.50% and 18.75% lower than that in the BLK, respectively, and the available Cu content decreased by 14.55%, 20.74% and 18.58%, respectively (Table S3). The contribution of T-Hg to the NCPI could not be ignored. The IFI was positively correlated with A-Cu, CEC, OM, TP, AN and AK and negatively correlated with PRO; the SQI was positively correlated with AN and negatively correlated with T-Cd, T-Cu, T-As and A-Cd. Therefore, the increase in soil AN and decrease in soil T-Cd, T-Cu, T-As and A-Cd strongly enhanced the quality of the paddy soils. In addition, the greater average NCPI (1.682) resulting from the application of lime also indicated the negative effects of lime amendment on soil HMs and soil quality.
Although selenium was not an essential element for plant growth, foliar application of selenium resulted in a greater increase in the SQI (Table 4). Under the SE treatment, the NCPI was positively correlated with T-Cd, T-Cu, T-As, T-Zn, T-Cr, T-Ni, T-Pb, OM, DOC and TK and negatively correlated with CUE (Figure 3). Compared with the CK treatment, the SE3 treatment significantly reduced the available contents of Cd, Cu and Zn in the soil (Table S3), and the IFI was positively correlated with TN, TP, AP, AK, CAT, SUC, CUE and MBC (Figure 3), which suggested that foliar spraying of SE regulated plant HM accumulation by decreasing HM bioavailability [117], which could be associated with a variety of HMs that produce antagonistic effects that reduce plant susceptibility to HM absorption [108,118]; however, the low input of nutrients in the additive was a disadvantage of the SE treatment. This could also be observed because the SQI was positively correlated with TN, TP and AN and negatively correlated with T-Cu, T-As, T-Zn, T-Ni, T-Pb, OM, DOC and POD. The remediation of plants by foliar application of Se was mainly based on the physiological regulation of the plants.
HMs in contaminated soils are easily taken up by rice and accumulate in grain, which can lead to food insecurity [119,120,121,122]. The soil-available HMs, soil fertility and other physicochemical properties were strongly affected by the in situ remediation measures. After rice growth, the soil MBC significantly increased, which was consistent with the results of previous studies [123,124]. The aspects of the rhizosphere in a coculture system can be reconstructed by the interaction between the roots of two different kinds of rice [125]. This might be reflected in the N, P and K nutrient levels and enzyme activity of the soil, which constitute crucial factors in soil fertility. In the present study, we focused on the effects of different remediation measures on the HMs, fertility and biological properties of soils, and we also focused on the effects of these measures on rice productivity and grain HM pollution. Additionally, the effects of the measures applied under different rice growing conditions (regions) will be tested in future studies.

5. Conclusions

The paddy soil in the study area was contaminated by Cd and Cu, which posed a threat to food security. The present study showed that the application of different in situ remediation treatments combined with rice intercropping could effectively reduce HM accumulation in paddy soil and enhance soil quality for cleaner rice production. Rice intercropping had greater effects on the soil HMs, IFI and SQI; SE3 and MA2 had greater effects on the HMs, BC2 and LM3 had greater effects on the IFI, and SE3 and LM1 had greater effects on the SQI.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su162411120/s1, Table S1. Soil environmental quality risk control standard for soil contamination of agricultural land, Ministry of Ecology and Environment of the People’s Republic of China (GB15618-2018); Table S2. The contents of total Cd, Cu, As, Zn, Hg, Cr, Ni and Pb in paddy soils under different treatments; Table S3. The contents of available Cd, Cu, As, Zn, Hg, Cr, Ni and Pb in paddy soils under different treatments; Table S4. Soil pH, cation exchange capacity, organic matter content, soluble organic carbon, microbial biomass carbon content in paddy fields under different treatments; Table S5. The contents of total nitrogen, total phosphorus, total potassium, alkali-hydrolyzed nitrogen, available phosphorus and available potassium in paddy soils under different treatments; Table S6. The activities of urease, sucrose, cellulase, protease, acid phosphatase, catalase, peroxidase and polyphenol oxidase in paddy soils under different treatments.

Author Contributions

Conceptualization, L.C., J.L. and Q.X.; Data curation, L.C., J.L., M.H., Y.G. and W.Y.; Formal analysis, L.C., M.H., Y.H. and X.Z.; Funding acquisition, R.L.; Investigation, L.C., J.L., Y.H. and X.Z.; Methodology, J.L., Y.G. and W.Y.; Supervision, R.L.; Visualization, L.C., Y.H., X.Z., Y.G., W.Y. and Q.X.; Writing—original draft, L.C.; Writing—review & editing, Q.X. and R.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Program of China (2017YFD0800900) and Zhejiang Jine Ecological construction Co., Ltd. (KH220242A).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data sets of the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

Authors has received research grants from Zhejiang Jine Ecological construction Co., Ltd. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Correlations among the 33 soil quality indicators. Note: T-Cd: Total cadmium content; T-Cu: Total copper content; T-As: Total arsenic content; T-Zn: Total zinc content; T-Hg: Total mercury content; T-Cr: Total chromium content; T-Ni: Total nickel content; T-Pb: Total lead content; A-Cd: Available cadmium content; A-Cu, Available copper content; A-As: Available arsenic content; A-Zn, Available zinc content; A-Ni: Available nickel content; A-Pb: Available lead content; CEC: Cation exchange capacity; OM: Organic matter; DOC: Dissoluble organic carbon; TN: Total nitrogen; TP: Total phosphorus; TK: Total potassium; AN: Alkali-hydrolyzed nitrogen; AP: Available phosphorus; AK: Available potassium; CAT: Catalase; URE: Urease; SUC: Sucrase; CUE: Cellulase; PRO: Protease; POD: Peroxidase; PPO: Polyphenol oxidase; ACP: Acid phosphatase; MBC: Microbial biomass carbon; *: p < 0.05, **: p < 0.01. Blue shows a negative correlation, red a positive correlation, and the darker the color, the stronger the correlation. The value on the right represents the Pearson correlation index.
Figure 1. Correlations among the 33 soil quality indicators. Note: T-Cd: Total cadmium content; T-Cu: Total copper content; T-As: Total arsenic content; T-Zn: Total zinc content; T-Hg: Total mercury content; T-Cr: Total chromium content; T-Ni: Total nickel content; T-Pb: Total lead content; A-Cd: Available cadmium content; A-Cu, Available copper content; A-As: Available arsenic content; A-Zn, Available zinc content; A-Ni: Available nickel content; A-Pb: Available lead content; CEC: Cation exchange capacity; OM: Organic matter; DOC: Dissoluble organic carbon; TN: Total nitrogen; TP: Total phosphorus; TK: Total potassium; AN: Alkali-hydrolyzed nitrogen; AP: Available phosphorus; AK: Available potassium; CAT: Catalase; URE: Urease; SUC: Sucrase; CUE: Cellulase; PRO: Protease; POD: Peroxidase; PPO: Polyphenol oxidase; ACP: Acid phosphatase; MBC: Microbial biomass carbon; *: p < 0.05, **: p < 0.01. Blue shows a negative correlation, red a positive correlation, and the darker the color, the stronger the correlation. The value on the right represents the Pearson correlation index.
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Figure 2. The influence of soil indicators on the soil quality index (SQI) in minimum data set (MDS). Note: T-As: Total arsenic; T-Zn: Total zinc; TN: Total nitrogen; AN: Alkali-hydrolyzed nitrogen; URE: Urease; ACP: Acid phosphatase.
Figure 2. The influence of soil indicators on the soil quality index (SQI) in minimum data set (MDS). Note: T-As: Total arsenic; T-Zn: Total zinc; TN: Total nitrogen; AN: Alkali-hydrolyzed nitrogen; URE: Urease; ACP: Acid phosphatase.
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Figure 3. Correlation analysis of NCPI, IFI and SQI with 33 soil quality indicators. Note: ** stands for significant correlation at p < 0.01; * stands for significant correlation at p < 0.05.
Figure 3. Correlation analysis of NCPI, IFI and SQI with 33 soil quality indicators. Note: ** stands for significant correlation at p < 0.01; * stands for significant correlation at p < 0.05.
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Table 1. Single-factor pollution index and Nemerow comprehensive pollution index of paddy soils under different treatments.
Table 1. Single-factor pollution index and Nemerow comprehensive pollution index of paddy soils under different treatments.
TreatmentsSFPICdSFPICuSFPIAsSFPIZnSFPIHgSFPICrSFPINiSFPIPbPaveNCPIRank of NCPI
BLK1.5232.2750.9500.7710.2500.1880.2560.5790.8491.717 ± 0.025 a2
CK11.3872.2800.7420.7690.1760.1500.2230.5830.7891.706 ± 0.055 ab3
CK21.5052.2310.7540.7880.1450.1520.2200.5920.7981.675 ± 0.075 abcde7
CK31.4192.1470.8040.7370.2120.1470.2140.5580.7801.615 ± 0.057 bcde13
BC11.4492.2500.8830.7440.0980.1670.2330.5500.7971.688 ± 0.049 abcd5
BC21.4362.1720.8820.7710.1250.1720.2540.5450.7951.636 ± 0.031 abcde11
BC31.4812.2300.9150.7200.1100.1720.2470.5440.8021.676 ± 0.075 abcde6
MA11.4652.1800.8950.7250.1370.1770.2520.5470.7971.641 ± 0.053 abcde10
MA21.4122.1230.8560.7300.0790.1750.2520.5310.7701.597 ± 0.089 de15
MA31.3952.2590.8740.7610.1260.1730.2530.5550.8001.695 ± 0.043 abc4
LM11.2632.2170.8880.7200.2050.1620.2420.5180.7771.661 ± 0.08 abcde9
LM21.3302.2970.9360.7520.1780.1730.2500.5190.8041.721 ± 0.052 a1
LM31.3222.2180.9080.7370.1740.1630.2410.5120.7841.663 ± 0.089 abcde8
SE11.2252.1830.8300.7260.1800.1560.2240.5270.7561.634 ± 0.075 abcde12
SE21.2912.1410.8400.7430.1690.1570.2270.5220.7611.607 ± 0.085 cde14
SE31.2042.1120.7970.6860.2060.1500.2110.5120.7351.581 ± 0.095 e16
Average
BLK1.5232.2750.9500.7710.2500.1880.2560.5790.8491.717 ± 0.025 a1
CK1.4372.2190.7670.7640.1780.1490.2190.5770.7891.666 ± 0.068 abc4
BC1.4552.2170.8930.7450.1110.1710.2440.5460.7981.666 ± 0.058 abc3
MA1.4242.1870.8750.7390.1140.1750.2520.5440.7891.644 ± 0.074 bc5
LM1.3052.2440.9110.7360.1860.1660.2440.5160.7891.682 ± 0.078 ab2
SE1.2402.1450.8220.7180.1850.1550.2210.5210.7511.607 ± 0.085 c6
Total1.3822.2070.8600.7420.1610.1650.2370.5430.787
Note: SFPIi—Single-factor pollution index of HMs; Pave—the average values of SFPI; NCPI—Nemerow comprehensive pollution index. Different letters indicate significant differences among treatments at p < 0.05.
Table 2. Single fertility index and integrated fertility index of paddy soils under different treatments.
Table 2. Single fertility index and integrated fertility index of paddy soils under different treatments.
TreatmentsFpHFCECFTNFTPFTKFANFAPFAKFOMFave I F I Rank of IFI
BLK2.0000.5140.6863.7970.4331.5932.4402.7621.5101.7481.132 ± 0.033 bcd8
CK12.0000.4690.6063.2370.4011.6842.5011.4971.6571.5611.013 ± 0.005 e15
CK22.0000.4470.6893.4690.4241.8222.1461.1511.6491.5330.999 ± 0.025 e16
CK32.0000.4490.9083.0280.3981.6892.8971.4151.5651.5941.033 ± 0.056 de14
BC12.4440.4761.0762.8590.4051.7372.4251.8981.7441.6741.081 ± 0.093 cde10
BC22.8890.6221.0243.2940.4051.9692.1892.4102.1641.8851.212 ± 0.105 ab4
BC32.7780.6150.9853.4140.3901.7111.8952.4952.0201.8111.165 ± 0.060 abc5
MA12.2220.4690.9443.3920.4041.6242.0771.8061.4731.6011.037 ± 0.063 de13
MA22.2220.4800.8493.3620.4021.7313.7602.0141.4401.8071.163 ± 0.085 abc6
MA32.2220.4480.9053.1900.4251.8612.7191.8781.5131.6841.090 ± 0.112 cde9
LM12.9440.6020.5933.1450.4381.9602.4233.3731.6121.8991.223 ± 0.049 ab3
LM23.0000.6440.6443.1920.4651.7212.2413.7861.6021.9221.243 ± 0.096 a2
LM32.6670.7340.6442.6760.5071.7952.8853.7261.6261.9181.247 ± 0.067 a1
SE12.1110.5420.7842.8460.4361.6922.8381.9581.5121.6351.062 ± 0.075 cde12
SE22.3890.5610.9533.0040.4401.6682.0542.3381.4901.6551.077 ± 0.090 cde11
SE32.2780.4911.0143.3330.4321.4702.9552.5301.5041.7781.150 ± 0.076 abc7
Average
BLK2.0000.5140.6863.8000.4331.5932.4402.7621.5101.6601.132 ± 0.033 b3
CK2.0000.4550.7343.2450.4081.7322.5141.3541.6241.5631.015 ± 0.034 c6
BC2.7040.5711.0283.1890.4001.8062.1702.2681.9761.7901.153 ± 0.101 b2
MA2.2220.4660.8993.3150.4101.7392.8521.8991.4751.6971.097 ± 0.100 b4
LM2.8700.6600.6273.0040.4701.8252.5163.6281.6131.9131.238 ± 0.0701 a1
SE2.2590.5310.9173.0610.4361.6102.6152.2751.5021.6901.096 ± 0.087 b5
Total2.3850.5350.8323.2020.4251.7332.5282.3151.6301.732
Note: FpH—pH index; FCEC—Cation exchange capacity index; FTN—Total nitrogen index; FTP—Total phosphorus index; FTK—Total potassium index; FAN—Alkali-hydrolyzed nitrogen index; FAP—Available phosphorus index; FAK—Available potassium index; FOM—Organic matter index; IFI—Integrated fertility index; Fave—the average values of Fi. Different letters indicate significant differences among treatments at p < 0.05.
Table 3. Results of principal component analysis of the soil indicators and their norm value and groups.
Table 3. Results of principal component analysis of the soil indicators and their norm value and groups.
IndicatorsPrincipal ComponentsNorm ValueCommunalitiesGroup
123456
T-Cd−0.1560.8290.346−0.3220.004−0.1192.4410.9502
T-Cu0.3140.662−0.204−0.186−0.0890.1942.1400.6582
T-As0.4490.854−0.049−0.1370.088−0.0262.7480.9602
T-Zn−0.0480.647−0.172−0.099−0.1490.5481.8930.7836
T-Hg0.1000.021−0.8740.1400.304−0.0491.8820.8883
T-Cr−0.0670.9330.208−0.0130.168−0.0652.5170.9512
T-Ni0.1380.8450.3290.0650.1550.2172.4010.9162
T-Pb−0.5020.7620.030−0.3200.0200.0612.7210.9401
A-Cd−0.3010.892−0.1030.135−0.0250.0202.5900.9152
A-Cu−0.5090.7590.0410.287−0.032−0.0062.7180.9211
A-As−0.939−0.0060.1140.1750.174−0.0023.2700.9561
A-Zn−0.8520.235−0.1610.3830.048−0.1123.1090.9681
A-Ni−0.8880.2340.0160.3310.083−0.1343.1910.9781
A-Pb−0.9340.0650.1040.0280.0610.0543.2320.8941
pH0.9670.047−0.047−0.049−0.0740.1983.3490.9871
CEC0.879−0.029−0.246−0.160−0.0960.1323.0950.8871
OM0.3550.0590.293−0.7610.1080.2702.0220.8784
DOC0.783−0.040−0.3730.285−0.363−0.0702.8990.9721
TN−0.486−0.2980.419−0.571−0.072−0.1522.3180.8554
TP−0.3230.4840.013−0.0770.702−0.2651.9540.9075
TK0.609−0.096−0.5790.390−0.2920.0162.5610.9533
AN0.3580.1250.337−0.046−0.0440.7951.7510.8926
AP−0.136−0.1610.1660.738−0.209−0.0791.5980.6664
AK0.8690.069−0.3890.1770.2000.0083.1350.9831
CAT0.9560.131−0.1120.062−0.026−0.0283.3230.9491
URE0.752−0.231−0.5580.207−0.0720.0762.9280.9853
SUC0.830−0.1130.034−0.1100.3730.2432.9410.9141
CUE0.0960.002−0.4470.7240.3600.0261.7450.8634
PRO0.883−0.1160.2550.0450.0560.1583.1110.8881
POD0.0610.4030.725−0.1210.0500.1891.8660.7443
PPO0.0920.5400.265−0.7130.008−0.2302.0800.9314
ACP−0.739−0.2850.063−0.2330.5080.1442.7810.9655
MBC−0.2760.0250.856−0.0460.2730.0402.0280.8883
Eigenvalue11.8836.9434.1853.5961.6661.513
VCR%
Variance contribution rate		36.0121.0412.6810.905.054.58
CVCR%
Cumulative contribution rate		36.0157.0569.7380.6385.6890.26
Note: CEC—Cation exchange capacity; OM—Organic matter; DOC—Dissoluble organic carbon; TN—Total nitrogen; TP—Total phosphorus; TK—Total potassium; AN—Alkali-hydrolyzed nitrogen; AP—Available phosphorus; AK—Available potassium; CAT—Catalase; URE—Urease; SUC—Sucrase; CUE—Cellulase; PRO—Protease; POD—Peroxidase; PPO—Polyphenol oxidase; ACP—Acid phosphatase; MBC—Microbial biomass carbon. VCR: variance contribution rate, CVCR: cumulated variance contribution rate.
Table 4. Single index and comprehensive index of paddy soil quality.
Table 4. Single index and comprehensive index of paddy soil quality.
TreatmentsIT-AsIT-ZnIpHITNIANIUREIACPSQIRank of SQI
BLK0.0450.0640.0420.0490.0490.0510.0830.384 ± 0.055 d16
CK10.1330.0650.0270.0410.0570.0510.1080.482 ± 0.036 abc7
CK20.1280.0590.0300.0490.0690.0540.0870.476 ± 0.041 abc10
CK30.1070.0770.0180.0700.0580.0440.0920.466 ± 0.035 abc12
BC10.0730.0740.0520.0860.0620.0400.0850.473 ± 0.066 abc11
BC20.0740.0650.0900.0810.0820.0470.0900.528 ± 0.099 a2
BC30.0600.0830.0780.0770.0600.0420.0810.481 ± 0.066 abc9
MA10.0680.0810.0370.0730.0520.0240.0850.422 ± 0.043 cd15
MA20.0850.0790.0400.0640.0610.0330.0800.444 ± 0.06 bcd14
MA30.0770.0680.0430.0700.0730.0330.0870.451 ± 0.045 abcd13
LM10.0710.0830.0970.0400.0810.0750.0860.532 ± 0.046 a1
LM20.0510.0710.1090.0450.0610.0900.0710.497 ± 0.047 abc6
LM30.0630.0760.1380.0450.0670.1000.0370.526 ± 0.048 a3
SE10.0960.0800.0400.0580.0580.0580.0910.482 ± 0.069 abc7
SE20.0920.0740.0480.0740.0560.0650.0880.498 ± 0.067 abc5
SE30.1100.0950.0390.0800.0390.0610.0990.523 ± 0.042 ab4
Average
BLK0.0450.0640.0420.0490.0490.0510.0830.384 ± 0.055 c6
CK0.1230.0670.0250.0530.0610.0500.0950.475 ± 0.033 b4
BC0.0690.0740.0730.0810.0680.0430.0850.494 ± 0.080 a3
MA0.0770.0760.0400.0690.0620.0300.0840.439 ± 0.050 b5
LM0.0620.0770.1150.0430.0700.0880.0650.518 ± 0.048 a1
SE0.0990.0830.0430.0710.0510.0610.0930.501 ± 0.061 a2
Total0.0830.0750.0580.0630.0620.0540.084
Note: IT-As—Total arsenic index; IT-Zn—Total zinc index; IpH—pH index; ITN—Total nitrogen index; IAN—Alkali-hydrolyzed nitrogen index; IURE—Urease index; IACP—Acid phosphatase index. Different letters indicate significant differences among treatments at p < 0.05.
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Cai, L.; Lin, J.; Huang, M.; Hong, Y.; Zhong, X.; Guo, Y.; You, W.; Xiao, Q.; Lin, R. Effects of Different Remediation Treatments and Rice Intercropping on the Integrated Quality of Paddy Soils Mildly Contaminated by Cadmium and Copper. Sustainability 2024, 16, 11120. https://doi.org/10.3390/su162411120

AMA Style

Cai L, Lin J, Huang M, Hong Y, Zhong X, Guo Y, You W, Xiao Q, Lin R. Effects of Different Remediation Treatments and Rice Intercropping on the Integrated Quality of Paddy Soils Mildly Contaminated by Cadmium and Copper. Sustainability. 2024; 16(24):11120. https://doi.org/10.3390/su162411120

Chicago/Turabian Style

Cai, Luxiang, Jinlun Lin, Mingtian Huang, Yong Hong, Xuemeng Zhong, Yourui Guo, Wu You, Qingtie Xiao, and Ruiyu Lin. 2024. "Effects of Different Remediation Treatments and Rice Intercropping on the Integrated Quality of Paddy Soils Mildly Contaminated by Cadmium and Copper" Sustainability 16, no. 24: 11120. https://doi.org/10.3390/su162411120

APA Style

Cai, L., Lin, J., Huang, M., Hong, Y., Zhong, X., Guo, Y., You, W., Xiao, Q., & Lin, R. (2024). Effects of Different Remediation Treatments and Rice Intercropping on the Integrated Quality of Paddy Soils Mildly Contaminated by Cadmium and Copper. Sustainability, 16(24), 11120. https://doi.org/10.3390/su162411120

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