Biochar-Driven Synergistic Adsorption and Catalytic Degradation of Triazine Herbicides in Aquatic Systems: Mechanisms, Pathways, and Sustainable Water Remediation
Abstract
1. Introduction
2. Design and Synthesis of Biochar
2.1. Pyrolysis Technology
2.1.1. Effect of Feedstocks
2.1.2. Effect of Pyrolysis Temperature
2.2. Modification Method
2.2.1. The Effect of Modification Methods on the Adsorption of THs by Biochar
2.2.2. The Effect of Modification Methods on the Degradation of THs by Biochar in AOPs
3. Mechanism of Triazine Herbicides Removal by Biochar
3.1. Adsorption Mechanism
3.1.1. Hydrogen Bonding
3.1.2. Electrostatic Interactions
3.1.3. π–π Interactions
3.1.4. Pore Filling
3.2. Degradation Mechanism
3.2.1. Ozonation
3.2.2. Photocatalytic Oxidation
3.2.3. Advanced Persulfate Oxidation
3.2.4. Challenges for Practical Application
3.3. Microbial Synergistic Degradation
3.4. Degradation Pathway and Degradation Product Analysis
4. Limitations
5. Conclusions and Future Perspectives
5.1. Conclusions
5.2. Future Perspectives
- (1)
- Prioritizing the development of biochar from region-specific agricultural residues (e.g., sugarcane bagasse, rice husk) requires that we refine pyrolysis protocols and functional modifications to enhance TH adsorption while aligning with carbon neutrality goals. Integrating biochar production into circular economy frameworks could reduce costs and improve scalability for large-scale deployment. However, the optimal trade-off between biochar yield and surface functionality remains contested, with some studies advocating lower pyrolysis temperatures for functional group retention [49] and others favoring higher temperatures for porosity development [53].
- (2)
- Investigating the interplay between pyrolysis temperature, feedstock composition, and modification methods (e.g., metal loading, acid activation) is critical to tailor biochar’s physicochemical properties. Implementing life cycle assessments (LCAs) to evaluate risks of secondary pollution (e.g., heavy metal leaching) and promoting green modification strategies (e.g., natural acid treatment, co-pyrolysis) will ensure eco-compatibility. A key unresolved question is whether the enhanced adsorption capacity from metal loading justifies the long-term risk of metal leaching, as current studies rarely report metal release under realistic flow conditions or over extended operational periods.
- (3)
- Combining advanced in situ techniques (e.g., operando EPR, Raman spectroscopy) with computational modeling (e.g., density functional theory) can clarify radical and nonradical mechanisms in TH degradation. Concurrently, employing ecotoxicological assays (e.g., ECOSAR, zebrafish embryo tests) is essential to assess the bioaccumulation potential and genotoxicity of transformation products. Controversy exists regarding whether computational toxicity predictions (e.g., ECOSAR) adequately capture the synergistic effects of intermediate mixtures, given that real degradation produces complex cocktails whose joint toxicity may exceed the sum of individual components.
- (4)
- Expanding research scope to include TH degradation intermediates (e.g., deethylatrazine, hydroxylated metabolites) necessitates that we engineer biochar composites with molecularly imprinted surfaces or defect-rich active sites (e.g., N-doping, metal–organic frameworks). Such designs enhance specificity toward chlorinated or polar intermediates, enabling end-to-end contaminant control. An unresolved issue is whether molecularly imprinted or defect-rich biochars can maintain their selectivity in the presence of competing natural organic matter, which is ubiquitous in real waters but rarely included in laboratory validation.
- (5)
- Leveraging machine learning and multiphysics simulations to correlate biochar structural parameters (e.g., surface area, functional groups), operational conditions (e.g., pH, oxidant dosage), and TH removal kinetics will accelerate material design. Predictive frameworks can optimize treatment protocols for complex matrices containing co-pollutants (e.g., heavy metals, microplastics). A major challenge is the lack of standardized, high-quality datasets across diverse biochar types and water matrices; current machine learning models are often trained on fragmented literature data with inconsistent experimental conditions, limiting their predictive generalizability.
- (6)
- Conducting systematic studies on biochar aging under environmental stressors (e.g., UV exposure, microbial activity) is crucial to identify structural degradation pathways (e.g., pore collapse, functional group loss). Developing regeneration strategies (e.g., thermal reactivation, chemical rinsing) and employing accelerated aging experiments coupled with spectroscopic analyses (e.g., XPS, FTIR) will enhance material durability. It remains unclear whether chemical regeneration (e.g., solvent rinsing) or thermal reactivation is more sustainable, as the former generates secondary liquid waste while the latter consumes energy and may alter surface functionality—a trade-off rarely quantified in existing studies.
- (7)
- Scaling up biochar-based reactors (e.g., packed-bed columns, constructed wetlands) for pilot trials in agricultural drainage or wastewater treatment plants will validate TH removal efficiency. Hybrid systems integrating biochar with microbial consortia could exploit adsorption–biodegradation synergies for sustainable mineralization. Techno-economic analyses and LCAs must concurrently assess scalability, energy efficiency, and cost-effectiveness. A critical controversy is whether biochar-based AOPs can compete with activated carbon systems on a life-cycle cost basis; existing LCAs often ignore the energy penalty of biochar production (pyrolysis at 600–800 °C) and the frequent need for oxidant replenishment in AOPs, which may offset the lower material cost of biochar.
Funding
Data Availability Statement
Conflicts of Interest
References
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| Biochar Feedstock | PT (°C) | SSA (m2⋅g−1) | C (%) | O (%) | O/C | Reference | |
|---|---|---|---|---|---|---|---|
| Plant-based soil waste | Camphor tree fallen leaves | 500 | 2.7 | 69.7 | 22.6 | 0.3 | [53] |
| 600 | 4.4 | 78.0 | 14.0 | 0.2 | |||
| 700 | 5.7 | 83.8 | 9.8 | 0.1 | |||
| Miscanthus | 400 | 5.6 | 70.5 | 25.3 | 0.4 | [45] | |
| 700 | 236.3 | 79.2 | 19.2 | 0.2 | |||
| Apricot shell | 190 | 9.2 | 66.8 | 22.0 | 0.3 | [46] | |
| 230 | 9.4 | 70.7 | 17.7 | 0.3 | |||
| 240 | 9.4 | 72.4 | 16.6 | 0.2 | |||
| Bamboo culm | 350 | 2.7 | 68.5 | 26.7 | 0.4 | [44] | |
| 450 | 4.9 | 70.9 | 24.9 | 0.4 | |||
| 550 | 9.2 | 73.8 | 22.4 | 0.3 | |||
| Australian pine | 500 | 2.6 | 66.7 | 15.7 | 0.2 | [56] | |
| Brazilian pepper | 2.3 | 77.4 | 11.8 | 0.2 | |||
| Coconut husk | 1.9 | 67.0 | 18.3 | 0.3 | |||
| Cypress | 4.2 | 83.6 | 11.1 | 0.1 | |||
| Loblolly pine | 5.2 | 79.5 | 13.0 | 0.2 | |||
| Pecan shell | 2.1 | 79.0 | 12.1 | 0.2 | |||
| Fallen leaves | 500 | 4.1 | 61.9 | 27.1 | 0.4 | [57] | |
| Peanut shell | 450 | 980.2 | 85.5 | 8.8 | 0.1 | [58] | |
| Cedar bark sawdust | 800 | 47.31 | - | - | - | [59] | |
| Oak | 425 | 332.86 | 83.3 | - | - | [60] | |
| Mix of spruce, pine, fir wood chips | 450 | 230 | 88.6 | 5.1 | 0.1 | [61] | |
| Mixed softwood pellets | 700 | 162.3 | 90.2 | 6.0 | 0.1 | [47] | |
| Bamboo chips | 600 | 246.7 | 81.2 | 8.3 | 0.1 | [62] | |
| Eucalyptus bark | 188.2 | 79.1 | 12.2 | 0.2 | |||
| Populus euramericana shavings | 550 | 302 | 76.4 | 13.8 | 0.2 | [48] | |
| Pinus radiate shavings | 800 | 418 | 94.3 | 4.3 | 0.1 | ||
| 600 | 301 | 89.8 | 4.4 | 0.1 | |||
| Animal manure and solid waste | Poultry manure | 450 | 15.4 | 43.8 | 12.8 | 0.3 | [63] |
| Cattle manure | 13.5 | 55.6 | 14.9 | 0.3 | |||
| Pig manure | 13.4 | 41.2 | 11.7 | 0.3 | |||
| Pig manure | 350 | 23.8 | 59.1 | 20.3 | 0.3 | [64] | |
| 700 | 32.6 | 54.8 | 23.0 | 0.3 | |||
| Sludge | 600 | - | 18.6 | 76.8 | 4.1 | [65] | |
| Biogas residue | 750 | 266.2 | 49.5 | 14.4 | 0.3 | [66] | |
| Swine manure | 350 | - | 36.5 | 14.9 | 0.4 | [67] | |
| 450 | - | 33.7 | 10.2 | 0.3 | |||
| 600 | - | 35.6 | 7.9 | 0.2 |
| Feedstock | PT (°C) | Modification Reagents | SSA (m2⋅g−1) | THs | Removal/Recover Efficiency/Capacity | Mechanisms and Remarks | Reference |
|---|---|---|---|---|---|---|---|
| Soybean straw | 450 | - | 17.5 | ATZ | 1.4 mg⋅g−1 | Physical adsorption (better fitting of Freundlich model) | [72] |
| Rice straw | 600 | - | 220.2 | ATZ | 0.3 mg⋅g−1 | Electrostatics, H-bonding, hydrophobic interaction | [62] |
| Corn straw | 600 | - | 297.1 | ATZ | 0.02 mg⋅g−1 | π–π EDA, H-bonding (better fitting of pseudo-second-order kinetic model) | [76] |
| Corn straw | 300 | H3PO4 | 638.1 | ATZ | 79.6 mg⋅g−1 | van der Waals force, H-bonding, electrostatic interaction and pore filling. | [73] |
| Zizyphus jujuba shell | 400 | H3PO4, NaOH, acetic acid, glutaraldehyde | 154.1 | ATZ | 89.3% | Electrostatics, π–π EDA, H-bonding, halogen-bonding, pore filling, hydrophobic interaction and van der Waals force | [98] |
| AMT | 93.4% | ||||||
| TET | 99.5% | ||||||
| TEZ | 107.1% | ||||||
| Coconut shell | 700 | NaOH, H3PO4 | 321.7 | PRT | 80.4% | π–π EDA and van der Waals force (better fitting of Langmuir model) | [97] |
| PRZ | 82.5% | ||||||
| ATZ | 91.1% | ||||||
| Acanthopanax senticosus | 600 | - | 7.7 | ATZ | 70.0% | H-bonding, π–π EDA and pore filling | [99] |
| Biogas residue | 750 | Citric acid | 266.2 | ATZ | 5.9 mg⋅g−1 | Micropore filling, electrostatic adsorption, and interactions with functional groups | [66] |
| - | 96.6 | 3.7 mg⋅g−1 | |||||
| Peanut shell | 450 | H3PO4 | 980.2 | ATZ | - | Hydrophobic partition, π–π EDA, H-bonding, and pore-filling (better fitting of Freundlich model) | [58] |
| - | 13.7 | - | |||||
| Bagasse | 900 | Polystyrene sulfonic acid, aniline monomer and hydrochloric acid | - | ATZ | 92.9% | H-bonding, π–π EDA and in-situ polymerization | [80] |
| Camphor tree leaves | 500 | FeCl3·6H2O, phenol | - | ATZ | 94.0% | H-bonding, hydrophobic interaction | [101] |
| - | - | 10.0% | |||||
| Camphor tree leaves | 500 | - | 2.7 | ATZ | 10.9 mg⋅g−1 | π–π EDA, H-bonding (better fitting of Langmuir model) | [53] |
| 600 | 4.4 | 52.8 mg⋅g−1 | |||||
| 700 | 5.7 | 84.3 mg⋅g−1 | |||||
| Zizyphus jujuba seed | 600 | KOH, HNO3, H2SO4 | 7.4 | AMT | 235.4 mg·g−1 | Hydrophobic, π–π EDA, and H-bonding interactions | [79] |
| 700 | 11.1 | 250.4 mg·g−1 | |||||
| 800 | 17.1 | 260.5 mg·g−1 | |||||
| 900 | 16.9 | 220.7 mg·g−1 | |||||
| Byproducts of sawmills | 350 | - | 1.5 | ATZ | 26.0% | Organic functional groups (better fitting of pseudo-second-order and Freundlich model) | [74] |
| 450 | 2.4 | 9.7% | |||||
| 550 | 3.6 | 7.5% | |||||
| Corn straw | 600 | HCl | 329.0 | SIZ | 3.4 mg⋅g−1 | Hydrophobic effect, charge transfer interaction and pore-filling | [109] |
| Corn stalks | 600 | HCl, FeSO4·7H2O, NaBH4 | - | ATZ | 4.1 mg⋅g−1 | Pseudo-first-order kinetic model | [75] |
| 700 | - | 16.6 mg⋅g−1 | |||||
| 800 | 365.0 | 24.0 mg⋅g−1 | |||||
| Lychee shell powder | 700 | Methanol, NaOH, HClO4 | 254.0 | ATZ | 92.8% | π–π EDA, pore filling | [78] |
| Cedrella fissilis sawdust | 800 | HCl, NaOH | 28.0 | ATZ | 77.0% | Van der Waals interactions or hydrogen bonds | [110] |
| Fallen leaves | 500 | HCl | 4.1 | ATZ | 22.4 mg⋅g−1 | Electrostatics and H-bonding (better fitting of the second-order kinetic and Elovich model) | [57] |
| Rice husk | 700 | Hemin | 30.7 | SIZ | 99.5% | Electrostatics and functional groups (better fitting of Langmuir model) | [85] |
| Rice husk | 700 | Co(NO3)2·6H2O, Fe(NO3)3·9H2O, K3[Fe(CN)6] | 153.0 | ATZ | 100.0% | Electrostatics and pore-filling | [77] |
| Epicarp and mesocarp | 126 | H3PO4 | - | ATZ | 18.05 μg·g−1 | Elovich model | [81] |
| - | AMT | 10.83 μg·g−1 | Better fitting of the second-order kinetic | ||||
| - | MBZ | 10.83 μg·g−1 |
| Degradation Scenarios | Feedstocks | Modification Reagents | PT (°C) | THs | Main Reactive Oxygen Species | Degradation Capacity | Reference |
|---|---|---|---|---|---|---|---|
| Biochar alone | Corn stalks | H3PO4, FeCl3, H2SO4, KOH | 600 | ATZ | - | 83.3% (estimation) | [100] |
| Ozone | Lychee shells | Methanol | 700 | ATZ | •OH, 1O2, O2•− | 92.8% (30 min) | [78] |
| Soybean shells | - | 900 | ATZ | •OH, 1O2, O2•− | >90.0% (1 min) | [114] | |
| Commercial biochar | - | - | ATZ | •OH | 48.0% (30 min) | [113] | |
| KMnO4 | •OH, O2•− | 83.0% (30 min) | |||||
| K2FeO4 | •OH, O2•− | 100.0% (30 min) | |||||
| Corn straws | Urea | 700 | ATZ | •OH, O2•− | 97.1% (15 min) | [115] | |
| SSI | Rice straw | KH2PO4, ZnAc2 | 500 | ATZ | •OH, O2•− | 85.3% (260 min) | [88] |
| SSI/DOM | Olive pomace | Ball-milled | 220 | MBZ | •OH, 1O2 | 91.0% (estimation) | [112] |
| US/PMS | Acanthopanax senticosus | - | 600 | ATZ | •OH, SO4•− | 70.0% (50 min) | [99] |
| PMS | Rice husk | Hemin | 700 | SIZ | •OH, SO4•−, 1O2 | 99.5% (120 min) | [85] |
| Kenaf bar | FeSO4·7H2O | ATZ | SO4•−, •OH, 1O2 | 96.0% (240 min) | [87] | ||
| AMT | 99.0% (240 min) | ||||||
| TEZ | 100.0% (240 min) | ||||||
| PS | Soybean stalks | FeCl3·6H2O, NaBH4, Na2S2O4 | 800 | ATZ | SO4•−, •OH, 1O2 | 100.0% (45 min) | [83] |
| Spinach remnants | - | 500 | ATZ | •OH, SO4•−, 1O2 | 99.8% (120 min) | [116] | |
| Soybean straw | Fe2O3 | 800 | ATZ | •OH, SO4•−, 1O2 | 93.8% (30 min) | [84] | |
| Corn straw | K2C2O4, HCl | 800 | ATZ | •OH, SO4•− | 97.2% (20 min) | [75] | |
| Corn stalks | FeSO4·7H2O, NaBH4 | 450 | ATZ | •OH, SO4•− | 83.8% (30 min) | [117] | |
| Corn stalks | Fe2O3 | 600 | ATZ | SO4•−, •OH, 1O2 | 50.5% (30 min) | [82] | |
| 700 | 80.8% (30 min) | ||||||
| 800 | 100.0% (10 min) | ||||||
| PDS | Capsosiphon fulvescens | - | 800 | SIZ | •OH, SO4•−, 1O2, O2•− | 90.0% (90 min) | [118] |
| Auricularia auricula fungus chaff | Diatomite, urea and H3BO3 | 650 | ATZ | •OH, SO4•− | 98.0% (420 min) | [86] | |
| Sulfite/biochar | Rice straw | Co(NO3)2·6H2O, NiCl2·6H2O, H2SO4 | 600 | ATZ | •OH, SO4•− | 82.0% (10 min) | [119] |
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Sun, H.; Liang, Y. Biochar-Driven Synergistic Adsorption and Catalytic Degradation of Triazine Herbicides in Aquatic Systems: Mechanisms, Pathways, and Sustainable Water Remediation. Processes 2026, 14, 1270. https://doi.org/10.3390/pr14081270
Sun H, Liang Y. Biochar-Driven Synergistic Adsorption and Catalytic Degradation of Triazine Herbicides in Aquatic Systems: Mechanisms, Pathways, and Sustainable Water Remediation. Processes. 2026; 14(8):1270. https://doi.org/10.3390/pr14081270
Chicago/Turabian StyleSun, Haoming, and Yuan Liang. 2026. "Biochar-Driven Synergistic Adsorption and Catalytic Degradation of Triazine Herbicides in Aquatic Systems: Mechanisms, Pathways, and Sustainable Water Remediation" Processes 14, no. 8: 1270. https://doi.org/10.3390/pr14081270
APA StyleSun, H., & Liang, Y. (2026). Biochar-Driven Synergistic Adsorption and Catalytic Degradation of Triazine Herbicides in Aquatic Systems: Mechanisms, Pathways, and Sustainable Water Remediation. Processes, 14(8), 1270. https://doi.org/10.3390/pr14081270

