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2024 Volume 3
Article Contents
Abstract
Introduction
Methods
Plant materials
GC-MS analysis
Transcriptome analysis
Identification and characteristic analysis of metabolosynthetic genes
Real-time PCR analysis and cloning of related genes
Functional characterization in heterologous expression system
In vitro enzymatic reaction
Results
Identification of essential oil components
Transcriptome analysis
Biosynthetic pathway of the volatile oils in P. frutescens
Identification and characterization analysis of the TPS family
Co-expression analysis and verification of functional TPS genes
Functional characterization of PfTPSs from different chemotypes of Perilla
Discussion
Author contributions
Data availability
Acknowledgments
Conflict of interest
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References
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ARTICLE   Open Access    

Effect and model analysis of iron-modified biochar on chloridion and cadmium ion transport in loessial soil

  • 1.

    School of Ecology and Environment, Ningxia University, Yinchuan 750021, China

  • 2.

    Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China, Ningxia University, Yinchuan 750021, China

  • 3.

    School of Geography and Planning, Ningxia University, Yinchuan 750021, China

More Information
  • Iron-modified biochar is an environmently-friendly soil amendment, which plays an important role in regulating heavy metal transport in loessial soil. In this study, the mass ratio of iron-modified biochar to loessial soil was set to 0%, 1%, 2%, 3%, 4%, and 5% as CK, T1, T2, T3, T4, and T5 treatments, respectively. Based on solute transport experiments, the transport process of chloridion (Cl−) and cadmium ion (Cd2+) in soil adding iron-modified biochar was fitted using the convection-dispersion equation (CDE) and two-zone model (TRM). The results showed that the saturated hydraulic conductivity (Ks) gradually decreased to 93.70%, 84.13%, 83.33%, 79.60%, and 66.67% compared with CK, with increased iron-modified biochar addition amount. The total duration time of the equilibrium ion concentration was increased from 1.79 to 40.81 h, indicating that the initial and complete transport time of heavy metals was significantly retarded with the increase of iron-modified biochar addition amount. The mean intervoid water velocity (v) obtained from model fitting was opposite to the trend of the amount of iron-modified biochar. The fitting data of the CDE equation and TRM model were in good agreement with the experimental data, whereas the better simulation accuracy of the TRM model was observed according to the higher correlation coefficient (R2 > 0.99) and lower root mean square error (RMSE) than the CDE equation. These findings indicate that iron-modified biochar effectively retards the transport of heavy metal ions in soil, which could provide a theoretical basis and technical ideas for the remediation of heavy metal-contaminated soil.

  • Introduction

    Perilla frutescens (L.) Britton, belonging to Lamiaceae, is a variety of versatile economic crop, also commonly used in herbal medicine. It has been widely cultivated in China, Japan, South Korea and many other Asian countries in recent years[1]. The leaves of Perilla are used for the preparation of cold granules in Traditional Chinese Medicine (TCM), and as vegetables and spices in Asian countries[1,2]. The leaves of Perilla possess various chemicals, including terpenes, flavonoids, phenolic acids, etc.[3]. The medicinal value of Perilla has been officialized in the Chinese Pharmacopoeia and the catalog of affinal drugs and diet[1,4]. The essential oils of Perilla leaves are the major medicinal flavor components. They are also widely applied in the skin care and aromatization industry[5,6].

    The essential oils of Perilla leaves include an abundant diversity of chemical types, which are classified into monoterpene (MT)-type oils and phenylpropene (PP)-type oils[7,8]. Interestingly, there are multiple kinds of monoterpenes in the leaves of Perilla genus, which can be further classified into the following six chemotypes according to their principal constituents: perillaldehyde (PA), perillaketone (PK), perillene (PL), piperitenone (PT), citral (C) and elsholtziaketone (EK) types[9]. Among these monoterpenes, PA are the major aromatic medical ingredient for prescriptions in China and Japan, while PK were thought to be a potent lung toxin[10]. Besides these main monoterpenes, geraniol (GL) is an acyclic monoterpene also commonly found in a wide range of Perilla plants[11], and linalool (LL) can be found in all Perilla plants and maybe a dead-end compound in general monoterpene biosynthetic pathways[12]. Wherein, PA, PL, GL, and LL are commercially important for perfumery, food and medicine[13,14]. As multiple chemical types of monoterpenes are enriched in Perilla, Perilla hence has been considered to be a model system for the study of monoterpene metabolism.

    The biosynthesis of monoterpene is specially localized to the glandular trichomes and initiated from the mevalonate (MAV)/methylerythritol 4-phosphate (MEP) pathway in plants[15−17]. Then the terpene synthases (TPS) catalyze prenyl pyrophosphates, the products of MVA and MEP pathways, to the formation of terpene compounds, and cytochrome P450s (CYP450s) further modify the backbones of these terpenes[17]. Recently, some TPSs and CYP450s involved in Perilla monoterpene biosynthesis have been reported, including limonene synthase, GL synthase, LL synthase, and mono-oxygenase[18−20]. Enzymes participated in PA biosynthesis, such as CYP71D18 and CYP17A7146, have been characterized[18−20] . Eight double-bond reductases (PfDBRs) that catalyze the conversion of isopyrone and soyone to PK were identified by enzymatic methods in vitro[21]. The recent high-quality and chromosome-scale Perilla genome data establishes a solid foundation for the characterization of Perilla monoterpene biosynthesis[22]. In the present study, transcriptome analysis was carried out on four different Perilla chemotypes some TPSs involved in monoterpene biosynthesis were identified, as well as multiple regulatory factors responsible for this biosynthesis pathway. Furthermore, the function of selected TPSs were characterized by a heterologous expression system and in vitro enzyme assay. These results collectively will help to understand the molecular mechanism of Perilla monoterpene biosynthesis and analyze the biosynthetic pathways of terpenes in Perilla.

    Methods

    Plant materials

    The Perilla cultivars of four chemotypes, including PA-, PK-, PL- and PT-types, were selected and planted in the greenhouse of Guangzhou University of Chinese Medicine (Guangzhou, Guangdong, China, 113°41' E; 23°07' N). The PA-type cultivar, with purple wrinkled leaves, belongs to P. frutescens var. crispa. The PK-, PL- and PT-type cultivars, with green non-wrinkled leaves, belong to P. frutescens var. frutescens (Fig. 1a). The leaves at seedling stages were collected for gas chromatography-mass spectrometry (GC-MS) analysis and RNA extraction. All samples are stored in liquid nitrogen immediately after sampling.

    Figure 1.  The analysis of volatile components in four Perilla cultivars. (a) Phenotype of four chemical types of Perilla; (b) GC-MS analysis of volatile essential components from four Perilla leaves; (c) Heatmap of metabolite contents in four Perilla leaves.
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    GC-MS analysis

    Perilla leaves of four chemotypes were crushed. Then, 0.2 g leaves powder was extracted by petroleum ether and filtered for GC-MS analysis. Analysis conditions include: RXT-5 MS quartz capillary column (30 m × 0.25 μm × 0.25 μm); The front column pressure is 63.9 kPa; The initial temperature of the column was 80 °C and was retained for 1 min. After the heating rate of 15 °C/min, the column was increased to 300 °C and retained for 15 min. MS conditions: ionization mode EI, filament current 0.5 mA; Electron energy 70 eV; Multiplier voltage 0.86 kV; Ion source 230 °C, solvent delay 1 min; Plasma/nucleus ratio m/z: 40~500. The NIST spectrum library was retrieved by Agilent qualitative software, and the chemical structure analysis was combined to identify the species of components. The relative percentage of each chemical component of volatile oil was calculated by the peak area normalization method.

    Transcriptome analysis

    Total RNA from 12 Perilla leaves were extracted using the RNApre Pure plant RNA extraction kit (DP432) (Tiangen, Beijing, China). The mRNA sequencing library was constructed using the NEBNext® Ultra RNA Library Prep Kit (New England Biolabs Inc., Ipswich, MA, USA). Then, the sequencing library was analyzed using the Agilent 2100 Bioanalyzer and was sequenced by an Illumina HiSeq™ 2000 sequencing system (Illumina Inc., San Diego, CA, USA). The original transcriptome data has been uploaded to the NGDC database (National Genomics Data Center) (Number: PRJCA021059).

    The Perilla genome data came from the National Center for Biotechnology Information (NCBI, Accession No.: GCA_019511825.2)[22]. Trimmomatic software is used for quality control of transcriptome data[23]. STAR(v2.7.10a) software was used to build an index and clean data was compared to the Perilla genome[24]. The Python module HTseq is used for P. frutescens transcriptome data quantification[25]. Gene expression levels of fragments per kilobase of transcript per million mapped reads (FPKM) were calculated. Then differentially expressed genes (DEGs) were identified using DESeq2[26]. Genes with |log2 (Fold change) |≥ 1| and p < 0.01 were considered DEGs. The online tool eggnog (http://eggnog-mapper.embl.de) was used to annotate the whole genome protein of Perilla[27]. R package ClusterProfiler was used for GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis of differential genes[28].

    Identification and characteristic analysis of metabolosynthetic genes

    The two hidden Markov models (HMM) of TPS (Terpene_synthase, PF01397 and Terpene_synthase_C, PF03936) were downloaded from Pfam (http://pfam.xfam.org/) and the Perilla genome was searched[29]. The identified PfTPS proteins were further determined by online HMMER (www.ebi.ac.uk/Tools/hmmer) and a phylogenetic tree was constructed using the Neighbor-Joining method in MEGA X (Bootstrap 1000)[30]. Heatmaps of candidate genes were drawn using TBtools (v1.112) (https://github.com/CJ-Chen/TBtools)[31]. Intergenomic collinearity analysis using MCScanX[32]. Chromosomal localization and collinearity results were visualized using TBtools. According to the Annotation information of Metabolic pathway synthase in KEGG and eggnog, the encoded gens were identified in the MVA/MEP pathways in Perilla. The Python script is used to calculate the correlation coefficient between genes expression, using the Pearson correlation coefficient[33].

    Real-time PCR analysis and cloning of related genes

    The full-length transcripts of PfTPSs genes were derived by 5' RACE-PCR and/or 3' RACE-PCR. Then the PCR products were ligated to the PLB vector (Tiangen) and sequenced by Sangon Biotech. Primer3Plus (www.primer3plus.com/index.html) was used to design primers for PfTPS genes. The primers used for genes cloning were listed in Supplemental Table S1. The fluorescence quantitative reaction system was referred to Wu et al., three replicates were used in each group, and PfActin was used as the key gene for analysis[34]. For data analysis, refer to the 2−ΔΔCᴛ method[35].

    Functional characterization in heterologous expression system

    All the cloned PfTPSs genes were introduced into the heterologous expression vector pETDuet-1. Then the expression plasmids were transformed into C41 Escherichia coli (E. coli) strain. The positive colony were firstly cultivated in TB medium at 37 °C to an initial OD600 of 0.4-0.6, and then the cultures were induced by 1 mM IPTG for another incubation of 72 h at 16 °C. After the cultivation, the cultures were extracted for three times by equal volume of n-hexane, and then the extracts were concentrated by rotary evaporation instrument for gas chromatography-mass spectrometry (GC-MS) detection.

    In vitro enzymatic reaction

    The selected PfTPSs genes were introduced into pET28a vector and transformed into E. coli strain BL21 (DE3). The positive colony were incubated in LB medium at 37 °C to the initial OD600 of 0.4−0.6, and then the cultures were induced by 0.5 mM IPTG for another 12 h of cultivation. The cultured cells were harvested and resuspended in lysis buffer (50 mM Tris-HCl, 500 mM NaCl, 20 mM imidazole, 20 mM β-mercaptoethanol, pH 8.0) for 30 min at 4 °C. Then the cells were disrupted by ultrasonication and the lysate was centrifuged at 13,000 g and 4 °C for 30 min. The crude proteins were inside the supernatant. For the soluble PfTPSs proteins, the crude proteins were purified by HIS nickel column.

    For in vitro enzymatic reaction, crude or purified PfTPSs proteins were added to the reaction buffer containing 200 mM Tris-HCl (pH 7.5), 40 mM MgCl2, 10% glycerol and 1 mM geranyl pyrophosphate (GPP) as the precursor. After incubation at 30 °C for 30 min, the reaction system was extracted using an equal volume of n-hexane and then detected by GC-MS.

    Results

    Identification of essential oil components

    The volatile essential oils components of leaves from four Perilla cultivars were analyzed using GC-MS analysis (Fig. 1a, b). A total of 35 terpenes, including 22 monoterpenes and 13 sesquiterpenes were detected in these cultivars. The main monoterpenes are consistent with their chemotypes classification. 68.01% of PA was identified as the main compounds in PA-type cultivars, while 71.65% of PL, 88.76% of PK and, 61.20% of PT are the main compounds in PL-type, PK-type, PT-type varieties, respectively (Fig. 1b). The GL (0.07%−0.68%) and LL (0.03%−1.77%) were the ubiquitous and trace metabolites that existed in these Perilla cultivars. Besides these main chemicals, other monoterpenes and sesquiterpenes were also identified, including limonene, borneol, thujone, verbenol, citral, carvone, trans-shisool, terpine, thymol 2-pinen-4-on, caryophyllene, germacrene, farnesene, trans-nerlidol etc. (Fig. 1c; Supplemental Table S2).

    Transcriptome analysis

    To explore the molecular mechanism involved in different monoterpenes biosynthesis, the transcriptome analysis was performed for the leaves of four chemotype cultivars. After sequence and data filtration, a total of 579 million clean reads, comprising 86.90 Gb nucleotide bases with an average 46.26% GC were obtained (Supplemental Table S3). The average 98% clean reads were assembled to the Perilla genome (GCA_019511825.2) (Supplemental Table S4). Then, the gene annotation and differential expression analysis were carried out among the four cultivars. The PA type are the main medicinal component of Perilla according to Chinese Pharmacopoeia. Hence, more attention is focused on the PA-type. In total, 236 specific up-regulated genes were compared with other three cultivars (Fig. 2a). In the specific up-regulated genes of PA-type, phylpropanoid, and monoterpene biosynthesis were enriched using the KEGG enrichment analysis (Fig. 2b; Supplemental Table S5). Meanwhile, the comparison analysis among the other three different chemotype cultivars, the 79, 92 and 155 specific up-regulated genes were identified in PK-type, PL-type, and PT-type, respectively (Fig. 2c, e & g). Genes involved in terpenes, including monoterpene, sesquiterpene, diterpene and triterpene were enriched in the corresponding chemical type (Fig. 2d−f; Supplemental Tables S6−S8).

    Figure 2.  Different expression genes and KEGG enrichment analysis of four Perilla cultivars. (a), (c), (e), (g) The intersection of PA, PL, PK, PT4 chemotypes with the other three chemotypes are indicated in the Venn diagrams. (b), (d), (f), (h) KEGG enrichment analysis of special up-expressed genes in PA, PL, PK, PT-type, respectively.
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    Biosynthetic pathway of the volatile oils in P. frutescens

    The MEP and MVA pathways are the basic terpene biosynthesis pathways. The 69 genes encoding 17 enzymes in MEP and MVA pathways were identified in the four cultivars (Fig. 3; Supplemental Table S9). The MEP pathway starts with pyruvate, which is catalyzed by DXS to form 1-deoxy-D-xylulose-5-phosphate. Subsequently, it is continuously catalyzed by DXR, MCT, CMK, MDS, HDS, and HDR to form MEcPP. There were two encoded genes of DXR, MCT, CMK, and MDS in Perilla which showed upregulated expression in PA-type. In the MVA pathway, Acetyl-CoA is catalyzed by AACT HMGS, HMGR, MVK, PMK, MPDC IPP, and DMAPP to IPP. Finally, the equilibrium between IPP and DMAPP are controlled by isopentenyl diphosphate isomerase (IPPI)-encoded genes and the further reaction synthesize by geranyl pyrophosphate synthase (GPPS)-encoded genes to produce the GPP in plastids (Fig. 3; Supplemental Table S10).

    Figure 3.  Synthesis pathway and single thread synthesis pathway of P. frutescens isoprene. The MEP pathway: 1-deoxy-D-xylulose 5-phosphate synthase (DXS); 1-deoxy-D-xylulose-5-phosphate reductoisomerase (DXR); 2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase (MCT); 4 diphosphocytidyl-2-C-methyl-D-erythritol kinase (CMK); 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase (MDS/MECPS); (E)-4-hydroxy-3 methylbut-2-enyl-diphosphate synthase (HDS); 4-hydroxy-3-methylbut-2-en-1-yl diphosphate reductase (HDR); The MVA pathway: acetyl-CoA C-acetyltransferase (AACT); hydroxymethylglutaryl-CoA synthase (HMGS); hydroxymethylglutaryl-CoA reductase (HMGR); mevalonate kinase (MVK); phosphomevalonate kinase (PMK); diphosphomevalonate decarboxylase (MPDC); isopentenyl-diphosphate Delta-isomerase (IPPI); geranyl diphosphate synthase (GPPS); Linalool synthase (LLS); Geraniol synthase(GS); limonene synthase (LMS); alcohol dehydrogenase (AD); Cytochrome P450 proteins (CYP); isopiperitenol dehydrogenase (ID); pulegone reductase (PR).
    DownLoad: Full-Size Img PowerPoint

    Moreover, the biosynthesis pathway of PA and PT has been reported in Perilla and Mentha, respectively[18−20,36]. Limonene is the common substrate for the synthesis of PA and PT. Two genes encoded limonene synthase (LMS) were identified in Perilla. For the biosynthesis of PA, limonene is catalyzed by CYP71D18 and CYP17A7146. Two genes encoded CYP71D18 were found and up-expressed in PA-type, while four paralogs encoded CYP17A7146 were identified and one of them showed upregulated expression in PA-type (Fig. 3; Supplemental Table S11). The biosynthesis of PT by isopiperitenol dehydrogenase (ID) and pulegone reductase (PR) were identified in Perilla, which possesses three and four encoded genes and shows different expression in four cultivars, respectively. (Fig. 3; Supplemental Table S11. Moreover, geraniol is catalyzed by GL synthase (GLS). Two genes encoded GLS and showed upregulated expression in PL. GL were further catalyzed by alcohol dehydrogenase (AD) to produce citral, which is the precursor of PL and PK. Besides them, LL synthase (LLS) are also the common monoterpene compounds in Perilla. Two encoded genes encoded LLS and up-expressed in PA-type were identified in Perilla (Fig. 3).

    Identification and characterization analysis of the TPS family

    The TPS use prenyl pyrophosphates as the substrate to synthesize terpenes, which are important for various chemotype formation in Perilla. In Perilla, a total of 109 TPS family members were identified using HMM search. The putative PfTPS proteins ranged from 230 to 817 amino acids in length (Supplemental Table S12), with the exon number from 3 to 15 (Fig. 4b). All members contained N-terminal (PF01397) and C-terminal (PF03936) conserved domains of TPS genes (Fig. 4c), and the RRX8W domain existed in the N-terminal, while the typical DDXXD conserved domain, as well as the typical functional domain RXR, existed in the C-terminal (Fig. 4d). The PfTPS genes displayed different expression trends in the four chemotypes (Fig. 4e).

    Figure 4.  The PfTPS Gene Family Characteristics in P. frutescens. (a)−(e) Phylogenetic evolutionary tree, conservative domain, gene structure, protein motifs and expression heatmap of PfTPS genes. (f) Subfamily classification of the PfTPS Family (LLS: linalool synthase; GS: geraniol synthase; LMS: limonene synthase). (g) Chromosomal localization and collinearity analysis of Perilla. (h) Collinearity analysis of TPS in Perilla and S. baicalensis.
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    PfTPS family members were divided into five major subfamilies, including TPS-a (57 members), TPS-b (24 members), TPS-c (12 members), TPS-e/f (eight members), and TPS-g (eight members) (Fig. 4f). The number of TPS genes in Perilla (109) showed significant expansion compared to that of Arabidopsis thaliana[37], Solanum lycopersicum[38], and other lamiaceae species, including Mentha longifolia[39], Salvia miltiorrhiza[39], Ocimum tenuiflorum[39], and Lavandula angustifolialabiaceae[40]. Among the TPS in Perilla, the TPS-a and TPS-b accounts for the 57.29% and 22.02% proportion as main expanded sub-families in Perilla. (Supplemental Table S13). To explore the evolutionary relationship of TPS, chromosome mapping, and collinear block analysis were carried out. The PfTPS genes were unevenly distributed on the 18 chromosomes. As the tetraploid genome of Perilla, the distribution of allele genes in pairs is a normal phenomenon. There were nine PfTPS genes found on chromosome Chr10/11/12/13, 5/8 PfTPS in Chr5/12, and 4/6 PfTPS in Chr4/6, which showed obvious collinearity in the Perilla genome (Fig. 4g). The collinearity relationship between Perilla and S. baicalensis was further analyzed. Further analysis of the collinear relation between Perilla and S. baicalensis was performed. The collinear block in 11 Perilla chromosomes correspond with seven chromosomes in S. baicalensis. Universally, the TPS in Perilla showed tandem duplication, containing 45 indicating that there is an obvious evolutionary relationship between TPS of Perilla and S. baicalensis. However, it is especially that SbChr09 has obvious chromosome blocks corresponding with multiple chromosomes of Perilla (Fig. 4h).

    Co-expression analysis and verification of functional TPS genes

    To further mine the functional TPS genes in various chemotypes, the gene co-expression analysis was performed. Interestingly, PfTPS18, PfTPS46, PfTPS47, and PfTPS49 as significant core genes were identified. To present the significant relationship between those TPSs, the co-expression network was present in the core TPS and the terpene biosynthesis genes and TFs, respectively. Firstly, PfTPS18 as core genes were significant co-expression with 201 the terpene biosynthesis genes and TFs, including GPPS, HMGS, HDR, AACT, and ERF, MYB, NAC etc. (Fig. 5a). Similarly, PfTPS46 and PfTPS47 were also co-expressed with IPPI, HDR, GPPS, FPPS, HMGS, and MPDC genes, which are important rate-limiting genes in the MVA/MEP biosynthesis pathway (Fig. 5b, c). PfTPS49 was co-expressed with other five PfTPSs, including PfTPS15, PfTPS24, PfTPS38, PfTPS39, and PfTPS63, and associated with The GPPS, HMGS, and other TFs (Fig. 5d).

    Figure 5.  The co-expression analysis and verification of PfTPSs. (a)−(d) Co-expression analysis with core PfTPSs, including (a) PfTPS18, (b) PfTPS46, (c) PfTPS47 , (d) PfTPS49 and five other PfTPSs. (e) qRT-PCR verification of PfTPS genes and MVA pathway genes (qRT-PCR results (left, line) and transcription results (right, bar)).
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    Among them, 12 PfTPS genes and two MVA pathway genes were selected for expression verification using qRT-PCR. The significant coincident gene levels were identified in transcriptome sequencing and qRT-PCR (r > 0.9). Those PfTPSs present general transcription in four cultivars but showed high expression in one chemotype. Such as PfTPS18, PfTPS21, and PfTPS76 showed up-regulated expression in PA types, especially PfTPS49 presents a specific high expression level. PfTPS46, PfTPS47, and PfTPS62 showed up-regulated expression in PL types. The expression levels of PfTPS87, PfTPS93, and PfTPS108 were similar in the four chemical types (Fig. 5e).

    Functional characterization of PfTPSs from different chemotypes of Perilla

    As PfTPS18, PfTPS46, PfTPS47, and PfTPS49 were the significant core genes according to the co-expression analysis, we selected these PfTPSs genes for further functional characterization. Due to the expression levels of the above PfTPSs in different chemotypes (Fig. 5e), genes with predominant expression levels in specific chemotypes were the only successful clones, such as the highest-expressed PfTPS18 and specific-expressed PfTPS49 in PA-type. Thus, the cloned PfTPSs genes were named with their chemotypes as follows: PfTPS46-PL, PfTPS46-PK, PfTPS18-PA, PfTPS47-PA, and PfTPS49-PA, respectively. To predict the possible catalytic functions of these PfTPSs, phylogenic analysis was performed.

    To identify the catalytic functions of the cloned PfTPSs, the CDSs of different TPS to the expression vectors were ligated and transformed into E. coli to characterize the functions of PfTPSs. After being cultivated for 3 d, the cultures were extracted by n-hexane and then the compounds were detected by GC-MS analysis. Strain with PfTPS46-PL produced one peak in GC-MS profile compared to the control group (strain with control vector) (Fig. 6a). The product was determined to be linalool by the comparison of the retention time in total ion chromatograms and the mass spectrum with authentic standard linalool (Fig. 6a; Supplemental Fig. S1a, S1b). As the signal peptide (SP) region in the N terminal of PfTPS46-PL might affect the catalytic activity of the enzyme inside E. coli cells, this region was removed in the CDS of PfTPS46-PL and its function explored using the truncated variant. The strains harboring the truncated PfTPS46-PL brought the same linalool product in GC-MS analysis (Fig. 6a; Supplemental Fig. S1c). Next, to further confirm the catalytic function of PfTPS46-PL, the purification of the PfTPS46-PL protein was attempted and its function characterized using an in vitro enzymatic reaction. The proteins of PfTPS46-PL were not obtained due to its insolubility. Thus, the crude proteins of PfTPS46-PL were used with geranyl pyrophosphate (GPP) as precursor. Consistent with the result in the heterologous expression system, the crude PfTPS46-PL protein also produced the sole product linalool (Fig. 6b). According, PfTPS46-PL is a linalool synthase.

    Figure 6.  Functional characterization of four PfTPSs. (a) Heterologous functional characterization of PfTPS46-PL and (b) the in vitro enzymatic reaction of PfTPS46-PL. (c) Heterologous functional characterization and (d) the in vitro enzymatic reaction of PfTPS46-PK, PfTPS18-PA, and PfTPS49-PA. (e) Heterologous functional characterization and (f) the in vitro enzymatic reaction of PfTPS47-PA. (g) Catalytic model of four PfTPSs.
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    Next, the functions of other candidate monoterpene synthases were characterized using the same strategy. The products of PfTPS46-PK, PfTPS18-PA, and PfTPS49-PA were all found to be linalool in the heterologous expression system, as well as the truncated enzymes (Fig. 6c; Supplemental Figs S2, S3). The crude proteins of PfTPS18-PA were selected as the representative for the in vitro enzymatic analysis. The crude proteins also produced the sole product linalool (Fig. 6d; Supplemental Fig. S4). The results indicated that these PfTPSs are linalool synthases.

    For the function characterization of PfTPS47-PA, two products were detected in PfTPS47-PA and truncated PfTPS47-PA-harboring strains, with the major product citronellol and the minor product geraniol (Fig. 6e; Supplemental Figs S5, S6). However, the purified PfTPS47-PA protein catalyzed GPP to the sole product geraniol (Fig. 6f). Here, we speculated that some certain enzyme inside E. coli cells catalyzed geraniol, the product of PfTPS47-PA, to citronellol. The results showed that PfTPS47-PA was highly similar to geraniol synthase while other PfTPSs were assigned to the linalool synthase category. Collectively, we identified four linalool synthases and one geraniol synthase in different Perilla chemotypes (Fig. 6g).

    Discussion

    The essential oils of Perilla are well-recognized aromatic compounds and possess multiple pharmacological effects. They are also the valuable genetic materials of monoterpene biosynthesis and regulation for multiple kinds of chemical types. In the present study, four monoterpene chemotype cultivars were selected. The important monoterpene biosynthesis pathway and important candidate TPSs were analyzed and verified using transcriptome sequence and heterologous expression verification.

    Terpene biosynthesis initiates from the MVA and MEP pathway in plants[16]. Compared with Arabidopsis, the number of encoded genes in the MVA and MEP pathways increased significantly in Perilla. The gene amplification could induce gene differentiation and affect the biosynthesis of terpenes[38]. The genes encoded HMGS, PMK, and MDPC in the MVA pathway, DXR, MCT, CMK, and MDS in the MEP pathway, and IPPI, and FPPS in the cross-flow pathway were found to have obvious expansion in Perilla. Interestingly, most MEP pathway genes were up-regulated in the PA type, which implied the high-efficiency biosynthesis in the PA type of Perilla.

    Various volatile oil components as chemical type of Perilla have been researched. In the early stage, the genetic basis for the monoterpene chemical type in Perilla was verified using artificial hybrids method. The chemical composition is controlled by a series of multiple alleles (G1, G2, g) and an independent pair of alleles (H, h)[41]. In the present research, 109 TPS members in Perilla genomes were identified. They also showed obvious gene expansion, especially, the expansion of TPS-a and TPS-b, reached 57.29% and 22.02% proportion, more than other majority of Lamiaceae plants. The expression and function of TPSs were also significantly differentiated. In the past few years, more geraniol synthases, linalool synthases, and limonene synthases have been acquired in Perilla[18−20]. The biosynthetic pathway of piperitenon in Mentha longifolia was also reported. Based on genome-wide identified, more GS, LLS, LMS, and the PA and PT biosynthesis encoded genes were also explored. Their expression trends are in accordance with volatile oil components. LMS and GS for example had high expression in PA-types and LLS had high expression in PL-types. Based on co-expression analysis, the four TPSs act as core genes in various chemical types. The high expression in PA types and PL types were selected for function verification.

    The heterologous functional characterization and in vitro enzymatic reaction are two important methods for the functional characterization of TPSs. PfTPS18, PfTPS46, and PfTPS49 were characterized as linalool synthases and PfTPS47 was characterized as geraniol synthases, respectively. Linalool and geraniol are general compounds. The core genes in co-expression analysis were characterized as linalool and geraniol synthases. Further research will be carried out to identify more chemotype-related TPSs. Moreover, the genotype in different cultivars of a certain species is unique and widely used in the recognition of different cultivars in many plants (Supplemental Figs S7 & S8)[42,43]. For example, the polymorphic variant of one sesquiterpene synthase, VvTPS24, in grape conferred the cultivar a different product in the chemotype, which was distinguishable from other grape cultivars (Supplemental Fig. S9)[44]. However, the identified isozymes in different Perilla chemotypes, such as the linalool synthases, including PfTPS46-PL, and PfTPS46-PK, showed no obvious variations in their amino acid sequences. As the constitutions of compounds in different cultivars are determined by the enzymes and their expressions, we guess that the diverse content of linalool in different cultivars is caused by the regulatory elements.

    Author contributions

    The authors confirm contribution to the paper as follows: conceptualization, methodology: Yang SM, An TY, Shen Q; formal analysis: Yang SM, Wang YX, Lin GB; investigation: Yang SM, Wang YX, Lin GB, Chu HY; software: Yang SM, Chu HY, Wang YX; data curation, validation: Wang YX; supervision, writing – review & editing: An TY, Shen Q; resources, writing – original draft: Yang SM, Chu HY; funding acquisition: Shen Q. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    The datasets presented in this study are publicly available. RNA-seq data are available via NGDC with accession No. PRJCA021059.

    Acknowledgments

    This work was funded by the National Natural Science Foundation of China Grant (U22A20446) and the National Natural Science Foundation for Regional Fund (31860391).

  • The authors declare that they have no conflict of interest.

  • Anaman R, Peng C, Jiang Z, Liu X, Zhou Z, et al. 2022. Identifying sources and transport routes of heavy metals in soil with different land uses around a smelting site by GIS based PCA and PMF. The Science of the Total Environment 823:153759

    doi: 10.1016/j.scitotenv.2022.153759

    CrossRef    Google Scholar

    Berkowitz B, Scher H, Silliman SE. 2000. Anomalous transport in laboratory-scale, heterogeneous porous media. Water Resources Research 36:149−58

    doi: 10.1029/1999wr900295

    CrossRef    Google Scholar

    Da Y, Xu M, Ma J, Gao P, Zhang X, et al. 2023. Remediation of cadmium contaminated soil using K2FeO4 modified vinasse biochar. Ecotoxicology and Environmental Safety 262:115171

    doi: 10.1016/j.ecoenv.2023.115171

    CrossRef    Google Scholar

    Dong P, Yin M, Zhang Y, Chen K, Finkel M, et al. 2023. A Fractional-order dual-continuum model to capture non-Fickian solute transport in a regional-scale fractured aquifer. Journal of Contaminant Hydrology 258:104231

    doi: 10.1016/j.jconhyd.2023.104231

    CrossRef    Google Scholar

    Gil C, Boluda R, Rodríguez Martín JA, Guzmán M, del Moral F, et al. 2018. Assessing soil contamination and temporal trends of heavy metal contents in greenhouses on semiarid land. Land Degradation & Development 29:3344−54

    doi: 10.1002/ldr.3094

    CrossRef    Google Scholar

    Gong H, Huang J, Ding Z, Chi J. 2021. A potential method using magnetically modified wheat straw biochars for soil Cd extraction. Ecological Engineering 166:106240

    doi: 10.1016/j.ecoleng.2021.106240

    CrossRef    Google Scholar

    Gotić M, Musić S. 2007. Mössbauer, FT-IR and FE SEM investigation of iron oxides precipitated from FeSO4 solutions. Journal of Molecular Structure 834−836:445−53

    doi: 10.1016/j.molstruc.2006.10.059

    CrossRef    Google Scholar

    He R, Peng Z, Lyu H, Huang H, Nan Q, et al. 2018. Synthesis and characterization of an iron-impregnated biochar for aqueous arsenic removal. The Science of the Total Environment 612:1177−86

    doi: 10.1016/j.scitotenv.2017.09.016

    CrossRef    Google Scholar

    Hervé-Fernández P, Muñoz-Arriagada R, Glucevic-Almonacid C, Bahamonde-Vidal L, Radic-Schilling S. 2023. Influence of rangeland land cover on infiltration rates, field-saturated hydraulic conductivity, and soil water repellency in southern Patagonia. Rangeland Ecology & Management 90:92−100

    doi: 10.1016/j.rama.2023.06.004

    CrossRef    Google Scholar

    Jačka L, Trakal L, Ouředníček P, Pohořelý M, Šípek V. 2018. Biochar presence in soil significantly decreased saturated hydraulic conductivity due to swelling. Soil and Tillage Research 184:181−85

    doi: 10.1016/j.still.2018.07.018

    CrossRef    Google Scholar

    Jiang Y, Yin X, Guan D, Jing T, Sun H, et al. 2019. Co-transport of Pb(II) and oxygen-content-controllable graphene oxide from electron-beam-irradiated graphite in saturated porous media. Journal of Hazardous Materials 375:297−304

    doi: 10.1016/j.jhazmat.2019.05.001

    CrossRef    Google Scholar

    Jiao Y, Wang T, He M, Liu X, Lin C, et al. 2022. Simultaneous stabilization of Sb and As co-contaminated soil by Fe−Mg modified biochar. The Science of the Total Environment 830:154831

    doi: 10.1016/j.scitotenv.2022.154831

    CrossRef    Google Scholar

    Li Q, Liang W, Liu F, Wang G, Wan J, et al. 2022. Simultaneous immobilization of arsenic, lead and cadmium by magnesium-aluminum modified biochar in mining soil. Journal of Environmental Management 310:114792

    doi: 10.1016/j.jenvman.2022.114792

    CrossRef    Google Scholar

    Lim TJ, Spokas K. 2018. Impact of biochar particle shape and size on saturated hydraulic properties of soil. Korean Journal of Environmental Agriculture 37:1−8

    doi: 10.5338/kjea.2018.37.1.09

    CrossRef    Google Scholar

    Liu X, Guo H, Zhang X, Zhang S, Cao X, et al. 2022. Modeling the transport behavior of Pb(II), Ni(II) and Cd(II) in the complex heavy metal pollution site under the influence of coexisting ions. Process Safety and Environmental Protection 162:211−18

    doi: 10.1016/j.psep.2022.04.016

    CrossRef    Google Scholar

    Liu Y, Zhou B, Wang Q, Tan S. 2015. Effects of nano-carbon on water movement and solute migration characteristics of loessial soil. Journal of Soil and Water Conservation 29(1):21−25

    doi: 10.13870/j.cnki.stbcxb.2015.01.005

    CrossRef    Google Scholar

    Mahapatra U, Manna AK, Chatterjee A. 2023. Modeling of breakthrough curves in removal of Cr(VI) and methylene blue using immobilized activated carbon from natural rubber waste. Bioresource Technology Reports 24:101600

    doi: 10.1016/j.biteb.2023.101600

    CrossRef    Google Scholar

    Muhammad H, Wei T, Cao G, Yu S, Ren X, et al. 2021. Study of soil microorganisms modified wheat straw and biochar for reducing cadmium leaching potential and bioavailability. Chemosphere 273:129644

    doi: 10.1016/j.chemosphere.2021.129644

    CrossRef    Google Scholar

    Nakhli SAA, Goy S, Manahiloh KN, Imhoff PT. 2021. Spatial heterogeneity of biochar (segregation) in biochar-amended media: an overlooked phenomenon, and its impact on saturated hydraulic conductivity. Journal of Environmental Management 279:111588

    doi: 10.1016/j.jenvman.2020.111588

    CrossRef    Google Scholar

    Nguyen Ngoc M, Dultz S, Kasbohm J. 2009. Simulation of retention and transport of copper, lead and zinc in a paddy soil of the Red River Delta, Vietnam. Agriculture, Ecosystems & Environment 129:8−16

    doi: 10.1016/j.agee.2008.06.008

    CrossRef    Google Scholar

    Nguyen TB, Sherpa K, Bui XT, Nguyen VT, Vo TDH, et al. 2023. Biochar for soil remediation: a comprehensive review of current research on pollutant removal. Environmental Pollution 337:122571

    doi: 10.1016/j.envpol.2023.122571

    CrossRef    Google Scholar

    Pei Y, Huang L, Li D, Shao M. 2021. Characteristics and controls of solute transport under different conditions of soil texture and vegetation type in the water-wind erosion crisscross region of China’s Loess Plateau. Chemosphere 273:129651

    doi: 10.1016/j.chemosphere.2021.129651

    CrossRef    Google Scholar

    Pietrzak D. 2021. Modeling migration of organic pollutants in groundwater—review of available software. Environmental Modelling & Software 144:105145

    doi: 10.1016/j.envsoft.2021.105145

    CrossRef    Google Scholar

    Qiao J, Sun H, Luo X, Zhang W, Mathews S, et al. 2017. EDTA-assisted leaching of Pb and Cd from contaminated soil. Chemosphere 167:422−28

    doi: 10.1016/j.chemosphere.2016.10.034

    CrossRef    Google Scholar

    Ryu BG, Park GY, Yang JW, Baek K. 2011. Electrolyte conditioning for electrokinetic remediation of As, Cu, and Pb-contaminated soil. Separation and Purification Technology 79:170−76

    doi: 10.1016/j.seppur.2011.02.025

    CrossRef    Google Scholar

    Satyawali Y, Seuntjens P, Van Roy S, Joris I, Vangeel S, et al. 2011. The addition of organic carbon and nitrate affects reactive transport of heavy metals in sandy aquifers. Journal of Contaminant Hydrology 123:83−93

    doi: 10.1016/j.jconhyd.2010.12.009

    CrossRef    Google Scholar

    Schulin R, van Genuchten MT, Flühler H, Ferlin P. 1987. An experimental study of solute transport in a stony field soil. Water Resources Research 23:1785−94

    doi: 10.1029/wr023i009p01785

    CrossRef    Google Scholar

    Shen X, Huang DY, Ren XF, Zhu HH, Wang S, et al. 2016. Phytoavailability of Cd and Pb in crop straw biochar-amended soil is related to the heavy metal content of both biochar and soil. Journal of Environmental Management 168:245−51

    doi: 10.1016/j.jenvman.2015.12.019

    CrossRef    Google Scholar

    Shwetha P, Varija K. 2015. Soil water retention curve from saturated hydraulic conductivity for sandy loam and loamy sand textured soils. Aquatic Procedia 4:1142−49

    doi: 10.1016/j.aqpro.2015.02.145

    CrossRef    Google Scholar

    Sun T, Xu Y, Sun Y, Wang L, Liang X, et al. 2021. Cd immobilization and soil quality under Fe-modified biochar in weakly alkaline soil. Chemosphere 280:130606

    doi: 10.1016/j.chemosphere.2021.130606

    CrossRef    Google Scholar

    Wan X, Li C, Parikh SJ. 2020. Simultaneous removal of arsenic, cadmium, and lead from soil by iron-modified magnetic biochar. Environmental Pollution 261:114157

    doi: 10.1016/j.envpol.2020.114157

    CrossRef    Google Scholar

    Wang H, Liu J, Yao J, He Q, Ma J, et al. 2020. Transport of Tl(I) in water-saturated porous media: role of carbonate, phosphate and macromolecular organic matter. Water Research 186:116325

    doi: 10.1016/j.watres.2020.116325

    CrossRef    Google Scholar

    Xu T, Lou L, Luo L, Cao R, Duan D, et al. 2012. Effect of bamboo biochar on pentachlorophenol leachability and bioavailability in agricultural soil. The Science of the Total Environment 414:727−31

    doi: 10.1016/j.scitotenv.2011.11.005

    CrossRef    Google Scholar

    Yang J, Ge M, Jin Q, Chen Z, Guo Z. 2019. Co-transport of U(VI), humic acid and colloidal gibbsite in water-saturated porous media. Chemosphere 231:405−14

    doi: 10.1016/j.chemosphere.2019.05.091

    CrossRef    Google Scholar

    Yang T, Xu Y, Huang Q, Sun Y, Liang X, et al. 2021. An efficient biochar synthesized by iron-zinc modified corn straw for simultaneously immobilization Cd in acidic and alkaline soils. Environmental Pollution 291:118129

    doi: 10.1016/j.envpol.2021.118129

    CrossRef    Google Scholar

    Yang T, Xu Y, Huang Q, Sun Y, Liang X, et al. 2022. Removal mechanisms of Cd from water and soil using Fe-Mn oxides modified biochar. Environmental Research 212:113406

    doi: 10.1016/j.envres.2022.113406

    CrossRef    Google Scholar

    Yuan Y, Peng X. 2017. Fullerol-facilitated transport of copper ions in water-saturated porous media: influencing factors and mechanism. Journal of Hazardous Materials 340:96−103

    doi: 10.1016/j.jhazmat.2017.07.001

    CrossRef    Google Scholar

    Zhang G, Liu H, Liu R, Qu J. 2009. Adsorption behavior and mechanism of arsenate at Fe-Mn binary oxide/water interface. Journal of Hazardous Materials 168:820−25

    doi: 10.1016/j.jhazmat.2009.02.137

    CrossRef    Google Scholar

    Zhang G, Liu X, Gao M, Song Z. 2020a. Effect of Fe-Mn-Ce modified biochar composite on microbial diversity and properties of arsenic-contaminated paddy soils. Chemosphere 250:126249

    doi: 10.1016/j.chemosphere.2020.126249

    CrossRef    Google Scholar

    Zhang H, Lu T, Shang Z, Li Y, He J, et al. 2020b. Transport of Cd2+ through saturated porous media: insight into the effects of low-molecular-weight organic acids. Water Research 168:115182

    doi: 10.1016/j.watres.2019.115182

    CrossRef    Google Scholar

    Zhang JY, Zhou H, Gu JF, Huang F, Yang WJ, et al. 2020c. Effects of nano-Fe3O4-modified biochar on iron plaque formation and Cd accumulation in rice (Oryza sativa L.). Environmental Pollution 260:113970

    doi: 10.1016/j.envpol.2020.113970

    CrossRef    Google Scholar

    Zhang K, Yi Y, Fang Z. 2023. Remediation of cadmium or arsenic contaminated water and soil by modified biochar: a review. Chemosphere 311:136914

    doi: 10.1016/j.chemosphere.2022.136914

    CrossRef    Google Scholar

    Zhao L, Cao X, Mašek O, Zimmerman A. 2013. Heterogeneity of biochar properties as a function of feedstock sources and production temperatures. Journal of Hazardous Materials 256−257:1−9

    doi: 10.1016/j.jhazmat.2013.04.015

    CrossRef    Google Scholar

    Zhou B, Shao M, Shao H. 2009. Effects of rock fragments on water movement and solute transport in a Loess Plateau soil. Comptes Rendus Géoscience 341:462−72

    doi: 10.1016/j.crte.2009.03.009

    CrossRef    Google Scholar

    Zhou Q, Lin L, Qiu W, Song Z, Liao B. 2018. Supplementation with ferromanganese oxide–impregnated biochar composite reduces cadmium uptake by indica rice (Oryza sativa L.). Journal of Cleaner Production 184:1052−59

    doi: 10.1016/j.jclepro.2018.02.248

    CrossRef    Google Scholar

    Zhou Z, Liu P, Wang S, Finfrock YZ, Ye Z, et al. 2022. Iron-modified biochar-based bilayer permeable reactive barrier for Cr(VI) removal. Journal of Hazardous Materials 439:129636

    doi: 10.1016/j.jhazmat.2022.129636

    CrossRef    Google Scholar

    Zhu K, Bin Q, Shen Y, Huang J, He D, et al. 2020. In-situ formed N-doped bamboo-like carbon nanotubes encapsulated with Fe nanoparticles supported by biochar as highly efficient catalyst for activation of persulfate (PS) toward degradation of organic pollutants. Chemical Engineering Journal 402:126090

    doi: 10.1016/j.cej.2020.126090

    CrossRef    Google Scholar

    Zhu P, Zhang G, Zhang B. 2022. Soil saturated hydraulic conductivity of typical revegetated plants on steep gully slopes of Chinese Loess Plateau. Geoderma 412:115717

    doi: 10.1016/j.geoderma.2022.115717

    CrossRef    Google Scholar

  • Cite this article

    Ma C, Bai Y, Yuan C, Ma Y, Wang Y. 2024. Effect and model analysis of iron-modified biochar on chloridion and cadmium ion transport in loessial soil. Soil Science and Environment 3: e001 doi: 10.48130/sse-0024-0001
    Ma C, Bai Y, Yuan C, Ma Y, Wang Y. 2024. Effect and model analysis of iron-modified biochar on chloridion and cadmium ion transport in loessial soil. Soil Science and Environment 3: e001 doi: 10.48130/sse-0024-0001
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Effect and model analysis of iron-modified biochar on chloridion and cadmium ion transport in loessial soil

  • 1.

    School of Ecology and Environment, Ningxia University, Yinchuan 750021, China

  • 2.

    Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China, Ningxia University, Yinchuan 750021, China

  • 3.

    School of Geography and Planning, Ningxia University, Yinchuan 750021, China

  • Received: 21 May 2024
  • Revised: 26 September 2024
  • Accepted: 14 October 2024
  • Published online: 18 November 2024
Soil Science and Environment  3,  Article number: e001  (2024)  |  Cite this article

Abstract: Iron-modified biochar is an environmently-friendly soil amendment, which plays an important role in regulating heavy metal transport in loessial soil. In this study, the mass ratio of iron-modified biochar to loessial soil was set to 0%, 1%, 2%, 3%, 4%, and 5% as CK, T1, T2, T3, T4, and T5 treatments, respectively. Based on solute transport experiments, the transport process of chloridion (Cl−) and cadmium ion (Cd2+) in soil adding iron-modified biochar was fitted using the convection-dispersion equation (CDE) and two-zone model (TRM). The results showed that the saturated hydraulic conductivity (Ks) gradually decreased to 93.70%, 84.13%, 83.33%, 79.60%, and 66.67% compared with CK, with increased iron-modified biochar addition amount. The total duration time of the equilibrium ion concentration was increased from 1.79 to 40.81 h, indicating that the initial and complete transport time of heavy metals was significantly retarded with the increase of iron-modified biochar addition amount. The mean intervoid water velocity (v) obtained from model fitting was opposite to the trend of the amount of iron-modified biochar. The fitting data of the CDE equation and TRM model were in good agreement with the experimental data, whereas the better simulation accuracy of the TRM model was observed according to the higher correlation coefficient (R2 > 0.99) and lower root mean square error (RMSE) than the CDE equation. These findings indicate that iron-modified biochar effectively retards the transport of heavy metal ions in soil, which could provide a theoretical basis and technical ideas for the remediation of heavy metal-contaminated soil.

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    Introduction

    • As one of the important components of the ecosystem, soil is not only the most basic carrier in agricultural production but also the ultimate destination of environmental pollutants (Zhang et al., 2020a; Gil et al., 2018). In recent years, various heavy metals have entered soil through different processes (e.g., industry, agriculture, transportation, and other ways) with the rapid development of the social economy, which exhibits a significant impact on human health and ecological security (Muhammad et al., 2021). According to the National Soil Pollution Survey Bulletin, cadmium (Cd) was the primary pollutant in cultivated soil in China. The continuous transport and accumulation of Cd in soil not only affects the soil ecological environment but also seriously threatens human health through the food chain, and other ways (Sun et al., 2021; Shen et al., 2016; Gong et al., 2021). Therefore, soil remediation and control of heavy metal pollution have always been the focus of soil environmental research. It is an economical, feasible, and effective method to control heavy metal pollution in soil by using improvers to adsorb and fix heavy metal elements and reduce heavy metal transport (Qiao et al., 2017; Yang T et al., 2022).

      Iron-modified biochar, as an efficient and environmentally-friendly amendment has been widely used in soil remediation and control due to its advantages of low environmental risk, wide range of sources, and low price (Nguyen et al., 2023; Wan et al., 2020; Zhang et al., 2023; Jiao et al., 2022). Iron-modified biochar produced by high-temperature pyrolysis had developed pore structure and stable chemical properties. In addition, due to the complex reduction of Fe3+, the surface functional groups of biochar increased, and the adsorption capacity of biochar for heavy metals was significantly improved (Zhou et al., 2018; Yang et al., 2021; Ryu et al., 2011; He et al., 2018). At present, there is increasing research on the adsorption capacity and mechanism of soil-heavy metals by modified biochar. For example, Zhou et al. (2022) studied the high removal of Cr6+ in iron-modified biochar/double-layer osmotic reaction barriers. Da et al. (2023) reported a decrease of Cd2+ availability in soil using K2FeO4-modified vinasse biochar due to the enrichment of surface functional groups. Li et al. (2022) demonstrated that the simultaneous immobilization mechanism of As3+, Pb2+, and Cd2+ with Mg-Al modified biochar/mining soil composites.

      The solute transport model is an important means by which to study the transport process of heavy metals in soil and estimate the transport parameters accurately (Pei et al., 2021). With the deepening of the research on the transport process of heavy metals in soil, the research on the solute transport models have gradually attracted the attention of experts and scholars (Wang et al., 2020; Yuan et al., 2017; Yang et al., 2019; Jiang et al., 2019). Some researchers used chloridion (Cl−) as a tracer ion and numerical models were used to predict the transport behavior of ions in the soil, such as copper, lead, zinc, and Cd (Pietrzak, 2021; Zhang et al., 2020b; Nguyen Ngoc et al., 2009). Anaman et al. (2022) used GIS and PMF methods found that the transport paths of As3+, Cd2+, Pb2+, Cu2+, and Zn2+ were mainly through surface runoff. Liu et al. (2022) found that the breakthrough curve (BTC) of Cd2+ was roughly of a slow 'S' type through simulation, and the simulated value of Cd2+ by the convection ediffusion equation (CDE) was close to the measured value. Zhou et al. (2009) found that BTC was accurately described by both the CDE equation and the two-zone model (TRM). However, the TRM model fitted the experimental data a little better than the CDE equation, which was possibly more convenient to use. In summary, there are increasingly more studies on the adsorption and transport of heavy metals in soil, but there are few studies on the effect of iron-modified biochar on the transport of heavy metals in soil, and its transport model and parameters were especially unclear.

      The objectives of this study were: (1) to investigate the effect of iron-modified biochar on the transport of Cl− and Cd2+ in loessial soil by column experiments; and (2) to simulate the transport process and obtain relevant parameters of Cl− and Cd2+ in loessial soil added iron-modified biochar using CDE equation and TRM model. The results could clarify the effects of different amounts of iron-modified biochar on heavy metals transport in loessial soil, and provide data reference for the prevention and treatment of heavy metal pollution in soil.

    Materials and methods

      Soil sample collection

    • The geographical location of the sampling point is Wangwa Town of Ningxia Province in China (106°37'20" ~ 106°39'25" E, 36°04'40" ~ 36°08'10" N), with an area of about 9.74 km2. The terrain is complex, with crisscrossed terraces, beams, ridges, valleys, and gullies, and the elevation ranges from 1,698 to 1,903 m. It belongs to the semi-humid and semi-arid climate, with an average annual temperature of 7.1 °C, a daily temperature difference of 27 °C, and an average annual precipitation of about 400 mm. The spatial and temporal distribution of evaporation is uneven, mainly from July to September, and the average annual evaporation is about 1,000 mm. The main soil type is loessial soil, with clay contents of 8.78%, sand content of 17.88%, and silt content of 71.63%. The physical and chemical properties of loessial soil are shown in Table 1. The soil layer is deep and the soil is loose. The sampling depth was 0~10 cm, and five soil samples were obtained by the quince-shaped distribution method during sampling, which were fully mixed and then sampled by the quartering method. The collected soil samples were naturally air-dried and debris removed, and then ground and passed through a 2 mm soil sieve for use.

      Table 1.  The physical and chemical properties of loessial soil.

      Category Organic matter
      (g·kg−1)      		 Total nitrogen
      (g·kg−1)      		 pH Electrical conductivity
      (uS·cm−1)      		 Total potassium
      (g·kg−1)      		 Total phosphorus
      (g·kg−1)      		 Bulk density
      (g·cm−3)
      Loessial soil 11.11 1.02 8.33 112.36 7.51 0.04 1.43

      Preparation of iron-modified biochar

    • The woody biochar was purchased from Yixin Biotechnology Co. Ltd. (Table 1). Firstly, the woody biochar was pre-treated with mixed solution of FeSO4·7H2O and Fe2(SO4)3 under vigorous stirring (800 mL deionized water was added to a 1 L beamer, then 5.0 g FeSO4·7H2O and 4.5 g Fe2(SO4)3 were dissolved to obtain the mixed solution). After that, an even mixture was obtained by ultrasonic treatment for 1 h at 25 °C and was dried at 60 °C in a constant temperature oven. Finally, iron-modified biochar was obtained by pyrolysis in a Muffle furnace at 600 °C for 1 h. The as-prepared iron-modified biochar was ground and sieved for use (Gong et al., 2021). The physical and chemical properties of iron-modified biochar were shown in Table 2.

      Table 2.  The physical and chemical properties of iron-modified biochar.

      Category Total carbon (%) Carbonization time (h) Carbonization temperature (°C) pH Ash content Pore size (> 2 mm) Fe
      Iron-modified biochar 55.63 1 600 7 22.86% 98.55% 23.02%

      Solute transport test

    • The soil samples with different mass ratios of iron-modified biochar (0, 1%, 2%, 3%, 4%, and 5% as CK, T1, T2, T3, T4, and T5, respectively) were obtained by evenly mixing iron-modified biochar and soil (Liu et al., 2015). The solute transport experiments were conducted under plexiglass-columns (diameter of 5 cm and height of 20 cm, Fig. 1). The layer of filter paper was placed at the bottom of the plexiglass column to prevent the loading of uneven soil caused by soil particle leakage and blockage of the outflow hole. Each sample was filled in a plexiglass column with a thickness of 5 cm per layer and a total height of 15 cm. The roughening was trimmed between layers to make the soil of each layer fully contact and fill more evenly (the bulk weight of the soil sample: 1.43 g·cm−3). The upper and lower end of the soil column were a water supply outlet and a solution outlet composed of porous glass, respectively. The filter paper on the top soil surface of the soil column prevented the water supply from damaging the upper soil structure. In the test, a constant water head was maintained at 3 cm using a Markov bottle containing 0.1 mg·L−1 NaCl solution. When the soil column was completely saturated by NaCl solution, the water supply of the soil column was removed, and the surface water of the soil column was immediately sucked up. Then the NaCl solution was replaced with 15 mg·L−1 Cd(NO3)2 to remain water head at 3 cm (Liu et al., 2022). At the same time, a measuring cylinder of 25 mL was used to catch the leaching solution from the bottom end of the soil column, and the leaching solution was dumped once the cylinder was filled, and the time spent was recorded to calculate soil saturated hydraulic conductivity. The concentration of Cd2+ in the leaching solution was determined by ultraviolet spectrophotometry with Xylenol Orange disodium salt as a chromogenic agent and hexamethylenetetramine as buffer solution at 578 nm wavelength. The solute transport test was considered to be over when the differences of three consecutive Cd2+ concentrations were less than 1%. The solute transport parameters were calculated based on the experimental data. Finally, Cl− content was determined by titration and Cd2+ content was determined by ultraviolet spectrophotometry.

      Figure 1. 

      Solute transport device.

      Calculation of Ks

    • As one of the important soil hydraulics parameters, soil-saturated hydraulic conductivity (Ks, cm·min−1) refers to the water flux passing through saturated soil under a unit water potential gradient, which is a comprehensive reflection of soil texture, bulk density, pore distribution characteristics, and solute transport (Zhu et al., 2022). Ks can be calculated as Eqn (1):

      Ks=Q×L/A×H×t (1)

      where, Q (mL) and L (cm) are the flow rate and the height of soil column, respectively; A (cm2), H (cm) and t (min) represent the cross-sectional area of the water flow, the total head difference of the seepage path and the transport time, respectively.

      Solute transport model

    • BTC characterizes the relationship between the relative concentration of liquid flow and the change of pore volume (Mahapatra et al., 2023), which can reflect the characteristics of solute mixed displacement transport in soil (Pei et al., 2021). BTC is one of the important ways to investigate the mechanism of solute transport in soil. In this study, Cl− and Cd2+ were used as the tracer ions to investigate solute transport in saturated soil under one-dimensional steady water flow. The solute transport parameters were input into STANMOD software with the CXTFIT program nested.

      The one-dimensional saturation CDE equation can be expressed as Eqn (2):

      R∂c∂t=D∂2c∂x2−V∂c∂x (2)

      where, c (mg·L−1) and t (h) are solute concentration and time, respectively; D (cm2·h−1) represents the dispersion coefficient, including diffusion and hydrodynamic dispersion; v (cm·h−1), R and x (cm, x ≥ 0) stand for soil pore velocity, retardation coefficient and the distance of solute transport, respectively.

      TRM model under the condition of steady flow can be described as Eqns (3)−(6):

      θm∂Cm∂t+θim∂Cim∂t=θmD∂2Cm∂x2−Vmθm∂Cm∂x (3)
      θm∂Cm∂t=ω(Cm−Cim) (4)
      θ=θm+θim (5)
      β=θm/θ (6)

      where, θ represents soil volumetric water content, cm3·cm−3; θm and θim represent the volumetric water content in the movable and immovable regions, respectively. Cm and Cim (μg·ml−1) represent solute concentrations in mobile and immobile regions, respectively. Vm (cm·h−1), ω (1 h−1) and β represent the average pore velocity in the mobile zone; the mass exchange coefficient between the two zones and the ratio of water content in the mobile area, respectively. The dispersion capacity of the solute in the pore medium can be described by dispersion (λ), which is related to the average particle size and uniformity of the pore medium. Thus, λ can be calculated as Eqn. (7):

      λ=DV (7)

      where, D (cm2·h−1) and v (cm·h−1) represent the hydrodynamic dispersion coefficient and average interstitial water velocity, respectively.

      Data processing and analysis

    • The transport of Cl− and Cd2+ in loessial soil by adding iron-modified biochar was simulated using CDE and TRM models with the aid of the CXTFIT program. Then the parameters (i.e., v, D, determination coefficient R2 and root mean square error RMSE) were obtained. SPSS 22.0 software was used for difference analysis and statistical test.

    Results and analysis

      Characterization

    • The as-prepared iron-modified biochar was characterized by Fourier transform infrared spectroscopy (FT-IR) analysis. FT-IR spectra of biochar (BC), iron-modified biochar (FeBC) before and after reaction are shown in Fig. 2. The characteristic peaks of BC at 3,396 cm−1 corresponded to the stretching vibration of the OH group (Zhao et al., 2013). The presence of the Fe-O/OH group at 500~1,640 cm−1 indicated the successful loading of Fe on the surface of FeBC (Zhu et al., 2020; Gotić et al., 2007; Zhang et al., 2020c). The characteristic peak of FeBC at 1,533 cm−1 (C=O/C=C group) was weaker than that of BC (Zhang et al., 2009), suggesting that the loading of Fe was realized by reacting with the C=O functional groups on the BC surface (Xu et al., 2012).

      Figure 2. 

      FT-IR spectra of BC, FeBC, and FeBC after adsorption. BC represents biochar, FeBC represents iron-modified biochar without test, FeBC after adsorption represents iron-modified biochar adsorbed with heavy metals after test.

      Effect of iron-modified biochar on Ks

    • The Ks is essential for managing soil and groundwater recharge, promoting soil health, and evaluating soil improvement strategies (Hervé-Fernández et al., 2023). The changes of Ks under different addition amounts of iron-modified biochar are shown in Fig. 3. Compared to CK, Ks of T1, T2, T3, T4, and T5 decreased to 93.70%, 84.13%, 83.33%, 79.60%, and 66.67%, respectively, indicating that Ks significantly decreased with the increase of iron-modified biochar amounts from 0 to 50 g·kg−1 (p < 0.05). The results indicated iron-modified biochar was conducive to slowing down the transport of Cd2+ in soil, which significantly enhanced the water holding capacity of soil.

      Figure 3. 

      The effect of iron-modified biochar on soil Ks. Different lowercase letters indicate significant difference between different treatments (p < 0.05).

      Transport process of Cl−

    • Figure 4 shows the transport of Cl− in loessial soil with different iron-modified biochar addition amount. All BTC curves showed the S-like characteristics, which was consistent with the results of Zhou et al. (2009) studied on the BTC curve of Cl−, but the shape and change trend under different iron-modified biochar contents were slightly different. BTC curves of Cl− in loessial soil with iron-modified biochar showed that relative concentration gradually increased with the increasing time. Compared to CK, BTC curves of other treatments shifted to the right with increasing amounts of iron-modified biochar, and showed obvious trailing characteristics due to different amounts of iron-modified biochar added in loessial soil.

      Figure 4. 

      The breakthrough curves of Cl− in loessal soil with different iron-modified biochar contents.

      Initial penetration time (Te), complete penetration time (Ts), and total penetration time (Tt) are important characteristic parameters of solute penetration, which are jointly determined by pore water velocity and hydrodynamic dispersion coefficient of soil. Table 3 shows the the transport time of Cl− in loessial soil with added iron-modified biochar. Te, Ts, and Tt were all positively correlated with the addition amounts of iron-modified biochar, indicating the longer transport process under the greater amount of iron-modified biochar. As listed in Table 3, Te of Cl− increased from 8.57 to 12.76 h with the increasing application amounts of iron-modified biochar from 0 to 50 mg·kg−1. Ts of Cl− (exudate concentration equal to initial solution concentration) was also increased from 84.95 to 122.20 h, revealing that the time of complete migration equilibrium gradually extended with the increasing amounts of iron-modified biochar.

      Table 3.  The transport time of Cl− under different iron-modified biochar addition amounts.

      Treatment Te (h) Ts (h) Tt (h)
      CK 8.57 84.95 76.38
      T1 8.99 87.20 78.21
      T2 10.35 98.82 88.47
      T3 11.38 110.25 98.87
      T4 12.16 115.59 77.75
      T5 12.76 122.20 109.44
      Te represents initial penetration time, Ts represents complete penetration time and Tt represents total penetration time.

      To further investigate the effect of iron-modified biochar on Cl− transport in loessial soil, the transport curve of Cl− in loessial soil by adding different iron-modified biochar was fitted using the CDE equation and TRM model. The main parameters of the CDE equation and TRM model are summarized in Table 4. The fitting effect of the TRM model was better than the CDE equation due to the higher R2 more than 0.985 and lower (RMSE) less than 0.306. The values of the average pore water velocity (v) decreased with the increasing amounts of iron-modified biochar, indicating that adding of iron-modified biochar decreased the pore size of loessial soil and reduced the soil pore water velocity.

      Table 4.  The relevant model parameters obtained by Cl− transport curve fitting.

      Parameters Model name CK T1 T2 T3 T4 T5
      v (cm·h−1) CDE 0.376 0.329 0.264 0.227 0.201 0.135
      TRM 0.308 0.307 0.266 0.241 0.217 0.203
      D (cm2·h−1) CDE 0.100 0.100 0.100 0.100 0.100 0.224
      TRM 0.575 0.100 0.100 0.100 0.100 0.174
      λ CDE 0.266 0.304 0.379 0.441 0.498 0.741
      TRM 0.286 0.326 0.375 0.415 0.406 0.857
      β TRM 0.100 0.100 0.114 0.249 0.974 0.999
      ω TRM 0.100 0.100 0.557 0.596 0.786 0.851
      R2 CDE 0.887 0.944 0.906 0.929 0.938 0.927
      TRM 0.980 0.990 0.985 0.994 0.994 0.999
      RMSE CDE 0.326 0.364 0.272 0.249 0.235 0.201
      TRM 0.306 0.287 0.241 0.230 0.222 0.134
      v represents average pore water velocity, D represents hydrodynamic dispersion coefficient, λ represents the dispersion capacity of the solute in the pore medium, β represents the percentage of solute in the total soil concentration and in the mobile region under equilibrium conditions, ω represents parameter of the degree of solute exchange between movable and immovable regions, R2 represents coefficient of determination, RMSE represents the square root of the mean variance between the simulated value of the model and the measured value.

      λ refers to the dispersion capacity of the solute in the pore medium (Dong et al., 2023), its size is related to the average particle size and uniformity of the pore medium, which is numerically equal to the ratio of hydrodynamic dispersion coefficient (D) and v, the greater the value, the stronger the diffusion capacity of the solute in the pore medium.

      The values of λ for the CDE equation and the TRM model also increased with the increasing amounts of iron-modified biochar, which indicated the diffusion capacity of Cl− was enhanced.

      Transport process of Cd2+

    • Figure 5 shows the change of the BTC curve of Cd2+ in loessial soil under different addition amounts of iron-modified biochar. Unlike the BTC curves of Cl−, BTC curves of Cd2+ showed smooth L-like characteristics except for T4 and T5 treatments, and the concentration of Cd2+ in the effluent changed with time. The BTC curves of Cd2+ with different contents of iron-modified biochar in loessial soil showed that the concentration of Cd2+ increased gradually from low to high with time. Compared with CK, the BTC curves of Cd2+ with different addition amounts of iron-modified biochar all shifted to the right, and with the increase of iron-modified biochar, the BTC curve shifted to the right to a greater extent. The results showed that the addition of iron-modified biochar had a retarding effect on Cd2+ transport in loessial soil.

      Figure 5. 

      Cd2+ breakthrough curves of iron-modified biochar in loessial soil.

      Table 5 showed the transport time of Cd2+ under different addition amounts of iron-modified biochar. It could be seen that Te, Ts, and Tt were all positively correlated with the addition amounts of iron-modified biochar, that was, the greater the application amount of-iron modified biochar, the longer the solute Te and Ts, and the longer the penetration process. The Te of Cd2+ under different addition amounts of iron modified biochar were 6.54, 6.70, 8.03, 9.86, 9.92, and 10.00 h, respectively.

      Table 5.  Transport time of Cd2+ under different iron-modified biochar amounts.

      Treatment Te (h) Ts (h) Tt (h)
      CK 6.54 80.67 74.13
      T1 6.70 82.62 75.92
      T2 8.03 95.16 87.13
      T3 9.86 118.97 109.11
      T4 9.92 119.39 109.47
      T5 10.00 124.94 114.94
      Te represents initial penetration time, Ts represents complete penetration time, and Tt represents total penetration time.

      It could be concluded that Te lengthened with the increase of iron modified biochar addition amounts. The Ts of Cd2+ reaching transport equilibrium (exudate concentration equal to initial solution concentration) in loessial soil with different amounts of iron-modified biochar were 80.67, 82.62, 95.16, 118.97, 119.39, and 124.94 h, respectively. It could be seen that with the increase of iron-modified biochar addition amounts, the time of Cd2+ transport equilibrium was gradually extended, and the extent of the extension was increasing.

      The applicability of different mathematical models to Cd2+ transport was compared and analyzed, and the main parameters of the CDE equation and TRM model were fitted in this paper (Table 6). From the results of parameter fitting, the R2 was close to 1. The values of all v decreased with the increasing iron-modified biochar amounts, indicating that iron-modified biochar could reduce the velocity of soil pore water and effectively slow down the transport of Cd2+ in loessial soil. λ values of the CDE equation and TRM model were larger than CK. RMSE refers to the square root of the mean variance between the simulated value of the model and the measured value. The smaller the value of RMSE is, the closer the simulated value is to the measured value. The simulation results of both the CDE equation and the TRM model showed that the RMSE value of TRM was smaller than that of CDE, indicating that the simulated value of the TRM model was closer to the measured value and the fitting result was better.

      Table 6.  The relevant model parameters obtained by Cd2+ transport curve fitting.

      Parameters Model name CK T1 T2 T3 T4 T5
      v (cm·h−1) CDE 0.882 0.723 0.659 0.489 0.478 0.452
      TRM 0.557 0.476 0.433 0.384 0.354 0.307
      D (cm2·h−1) CDE 0.602 0.212 0.409 0.264 0.281 0.265
      TRM 0.140 0.120 0.108 0.118 0.274 0.220
      λ CDE 0.293 0.540 0.586 0.588 0.621 0.683
      TRM 0.251 0.252 0.249 0.307 0.717 0.774
      β TRM 0.100 0.100 0.100 0.100 0.248 0.845
      ω TRM 0.200 0.214 0.201 0.224 0.278 0.785
      R2 CDE 0.997 0.972 0.979 0.973 0.979 0.976
      TRM 0.997 0.998 0.998 0.996 0.993 0.976
      RMSE CDE 0.770 0.835 0.619 0.828 0.602 0.691
      TRM 0.114 0.781 0.580 0.134 0.246 0.350
      v represents average pore water velocity, D represents hydrodynamic dispersion coefficient, λ represents the dispersion capacity of the solute in the pore medium, β represents the percentage of solute in the total soil concentration and in the mobile region under equilibrium conditions, ω represents parameter of the degree of solute exchange between movable and immovable regions, R2 represents coefficient of determination, RMSE represents the square root of the mean variance between the simulated value of the model and the measured value.

      To more intuitively analyze the difference and connection between measured values and simulated values, the breakthrough curves of Cd2+ under CK, 1%, 2%, 3%, 4%, and 5% treatments were simulated by the CDE equation and TRM model (Fig. 6). The simulation results from CK to T4 were shown, the fitting results from the CDE equation had different degrees of alienation from experimental data, whereas the fitting results from the TRM model were in good agreement with experimental data without the obvious estrangement, indicating that the solute transport process of Cd2+ could be better simulated by the TRM model compared to the CDE equation. The simulation results of the T5 treatment showed that both the CDE equation and the TRM model could fit well.

      Figure 6. 

      Comparison of breakthrough curves of Cd2+ fitted by CDE and the TRM model.

    Discussion

    • Ks represents the ease with which water flows through soil when pore spaces are filled with water, which is an important hydraulic parameter used to investigate the transport characteristics of soil solute (Shwetha et al., 2015). Ks is closely related to soil physical properties such as soil bulk density and pore distribution (Jačka et al., 2018). Similar to the results of this study, Nakhli et al. (2021) observed biochar amendments decreased Ks of soil. Another study by Lim et al. (2018) observed Ks decreased when either larger or smaller-size macadamia nutshell and pine chip biochar particles were added to the soil.

      The transport equilibrium time of Cd2+ in loessial soil was significant delayed with the increasing iron-modified biochar amounts. The addition of iron-modified biochar increased the number of small pores in loessial soil, whereas the flow of water and the interconnected pores were decreased, which would lead to the complexity and uniformity of the pore structure of saturated soil. The soil pores with uneven size and distribution would cause different flow velocity, so an unbalanced solute front was formed in the soil profile, resulting in the extension of the transport time (Dong et al., 2023).

      The BTC curve of Cd2+ in loessial soil was analyzed under different addition amounts of iron-modified biochar. As the BTC curve shifted to the right with the increase of the addition amounts of iron modified biochar, the penetration of Cd2+ in loessial soil was significantly delayed. The penetration time was significantly extended when the relative concentration reached the highest. This was consistent with the penetration results of Cd2+ transport in complex heavy metal contaminated sites by Liu et al. (2022). The results of the CDE equation and TRM model showed that v decreased with the increase of the amount of iron-modified biochar, indicating that the application of iron-modified biochar reduced the velocity of soil pore water and slowed down the transport of Cd2+ in loessial soil. The increase of λ from the CDE equation and TRM model with the increasing amounts of iron-modified biochar could be caused by the addition of iron-modified biochar, which increased the pore complexity of loessial soil. The iron-modified biochar caused blockage of some pores, the curvature of the water channel and unconnected pore area increased, and the pore water flow rate decreased (Berkowitz et al., 2000). Therefore, iron-modified biochar slowed down and prevented the transport of Cd2+ in loessial soil. The adsorption capacity of iron-modified biochar to heavy metals increased significantly due to the increase of surface functional groups (Zhou et al., 2018). The β was the percentage of solute in the total soil concentration and the mobile region under equilibrium conditions (Satyawali et al., 2011; Schulin et al., 1987). the adsorption capacity of iron modified biochar to heavy metals increased significantly due to the increase of surface functional groups. According to fitting of TRM, the increase of β (from 0.100 to 0.845) indicated that the physical process of solute transport was more balanced with the increasing amounts of iron-modified biochar.

      Although the Cd2+ transport process of loessial soil after the application of iron-modified biochar was studied and simulated in this paper, and the characteristics and rules of soil Cd2+ transport were analyzed, this paper mainly focused on the study and analysis of the influence mechanism of iron-modified biochar, which may have certain differences and limitations from the actual application in the field. In the future, this research group will conduct a more in-depth and systematic study on the transport process of heavy metals in soil through field experiments and long-term follow-up studies, so as to provide more reliable data support for the utilization of iron-modified biochar and the prevention and control of soil contaminated by heavy metals in loessial soil.

    Conclusions

    • In the present study, the effect of iron-modified biochar on the transport of Cl- and Cd2+ in loessial soil was investigated and then fitted using the CDE equation and TRM model.

      (1) Compared to CK, Ks gradually decreased to 93.70%, 84.13%, 83.33%, 79.60%, and 66.67% with the increase of iron-modified biochar addition amounts from 1%, 2%, 3%, 4%, and 5%, respectively. The results indicated iron-modified biochar in soil could enhance the water holding capacity of soil.

      (2) The total transport time of Cd2+ increased (from 1.79 to 40.81 h) with the increase of iron-modified biochar addition amounts, because the addition of iron-modified biochar decreased the flow of water and the interconnected pores, and extended the transport time of Cd2+.

      (3) Compared to CDE, the transport of Cd2+ in loessial soil with adding iron-modified biochar could be better simulated by TRM model due to the higher R2 (> 0.97). According to the fitting of TRM model, the increasing amounts of iron-modified biochar slowed down and prevented the transport of Cd2+ in loessial soil. These findings were crucial for the application of iron-modified biochar in remediation of heavy metal-contaminated soil.

      Acknowledgments

      • This study was funded by the Natural Science Foundation of Ningxia Hui Autonomous Region Project (2023AAC03046, 2023AAC02018) and the National Natural Science Foundation of China (NSFC) (32360321) and the Key Research and Development of Ningxia Hui Autonomous Region Project (2021BEG02011).

      Author contributions

      • The authors confirm contribution to the paper as follows: writing - original draft prepration, software: Ma C; investigation: Ma C, Yuan C, Ma Y; supervision, writing - review & editing; visualization; funding acquisition: Bai Y, Wang Y. All authors reviewed the results and approved the final version of the manuscript.

      Data availability

      • All data generated or analyzed during this study are included in this published article.

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Rights and permissions
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
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    Cite this article
    Ma C, Bai Y, Yuan C, Ma Y, Wang Y. 2024. Effect and model analysis of iron-modified biochar on chloridion and cadmium ion transport in loessial soil. Soil Science and Environment 3: e001 doi: 10.48130/sse-0024-0001
    Ma C, Bai Y, Yuan C, Ma Y, Wang Y. 2024. Effect and model analysis of iron-modified biochar on chloridion and cadmium ion transport in loessial soil. Soil Science and Environment 3: e001 doi: 10.48130/sse-0024-0001
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  • Introduction
  • Materials and methods

  • Soil sample collection

  • Preparation of iron-modified biochar

  • Solute transport test

  • Calculation of Ks

  • Solute transport model

  • Data processing and analysis

  • Results and analysis

  • Characterization

  • Effect of iron-modified biochar on Ks

  • Transport process of Cl−

  • Transport process of Cd2+

  • Discussion
  • Conclusions
  • Acknowledgments
  • Author contributions
  • Data availability
  • Conflict of interest
  • Rights and permissions
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