Solar-driven evaporation-adsorption for lithium extraction from seawater can improve the adsorption efficiency towards lithium ions, however, the fabrication of conventional solar-driven evaporation-adsorption materials often suffers from secondary pollution. To address these issues, this study developed a biomass-based evaporation-adsorption material PVV@VLJ-LIS by synergistically utilising multiple components of Vaccinium bracteatum Thunb. leaves, enabling the integrated coupling of interfacial evaporation and selective lithium adsorption. A freezing and salting out strategy was employed to crosslink a poly(vinyl alcohol) hydrogel and a VLJ-modified titanium-based lithium-ion sieve on the evaporator surface, thereby achieving in situ self-assembly between the organic components from the leaves and the lithium-ion sieve. VLJ endows PVV@VLJ-LIS with broadband photothermal absorption and antibacterial activity, and simultaneously promotes interfacial Li⁺ diffusion kinetics. Meanwhile, P-VLR serves as a porous supporting framework, facilitating the fixation of the lithium-ion sieve and water transport. Under 1 sun irradiation, the PVV@VLJ-LIS evaporator achieved a photothermal evaporation rate of 1.61 kg/(m²·h) with an evaporation efficiency of 80%. Under 40 °C, an initial Li⁺ concentration of 100 mg/L, and pH 12, PVV@VLJ-LIS achieved an equilibrium Li⁺ uptake of 17.472 mg/g. Relative to dark conditions, the adsorption capacity increased by 104.3%, which was attributed to the photothermally driven interfacial heating and the enhanced lithium-ion migration. In addition, the as-developed multifunctional evaporator exhibited pronounced antibacterial performance, anti-oil fouling, mechanical stability, and effective salt rejection, indicating the broad application prospects of PVV@VLJ-LIS for simultaneous lithium extraction and seawater desalination in complex seawater environments.

Mariculture tailwater, characterized by high nitrate (NO₃⁻-N) and a low carbon to nitrogen (C/N) ratio, presents a significant challenge for coastal environment protection. To address this, we developed a hybrid carrier biofilter combining pyrite and maifanite (PM) to enhance nitrogen removal performance. The PM biofilter achieved 88.98% total nitrogen (TN) removal—34.99% higher than that of a pyrite-only system. No secondary pollutants such as dissolved iron were produced during the treatment process. This enhancement was associated with synergistic effects, such as increased microbial biomass and activity, enhanced hydrophilicity and specific surface area of carriers, and elevated secretion of extracellular polymeric substance (EPS), particularly tryptophan-like proteins and humic acid-like organics. Additionally, the enriched microbial communities and functional genes associated with nitrogen, sulfur, and iron metabolism further supported key biogeochemical pathways in the PM. These findings highlight the PM biofilter as a promising strategy for low C/N ratio mariculture tailwater treatment and coastal environmental management.

Antimony (Sb) is a persistent and highly toxic contaminant. Its environmental risk is dictated by its redox state, as Sb(III) and Sb(V) exhibit vastly different adsorption behaviors. Although previous machine learning (ML) studies have investigated Sb adsorption, this speciation-dependent behavior still hinders the rational design of biochars for effective Sb immobilization. Here, we establish a data-driven and mechanism-informed framework (BiMeSorb) that integrates interpretable ML with density functional theory (DFT) to quantitatively resolve interactions between Sb(III)/Sb(V) and biochars. Built on a curated database of 437 data points, our Gradient Boosting Decision Tree model achieves high predictive accuracy for adsorption capacity (test R² = 0.934). Interpretable ML analyses (SHAP and PDP) reveal that oxygen-containing functional groups, specific surface area, and Sb speciation dominate adsorption, while appropriate initial Sb concentration and adsorbent dosage are critical operational conditions for achieving high adsorption performance. DFT calculations confirm that Sb(III) and Sb(V) interact strongly with carboxyl and hydroxyl groups via hydrogen bonding, exhibiting distinct binding energetics. Targeted adsorption experiments with Fe-modified biochars further validated the ML-identified descriptor-performance relationships. By integrating prediction, mechanism, and validation, the BiMeSorb framework provides a quantitative and transferable strategy for rational design of biochar adsorbents to improve aqueous Sb adsorption performance under tested experimental conditions.

Multidrug-resistant (MDR) pathogens and associated antibiotic resistance genes (ARGs) in tailwater pose a threat to public health and food safety. Bacteriophages have emerged as promising biocontrol agents for MDR pathogens, yet their efficacy in disinfecting tailwater for the elimination of MDR pathogens and ARGs remains unexplored. We developed a bacteriophage-mediated disinfection technique for targeted removal of MDR Vibrio parahaemolyticus and ARGs from aquaculture tailwater. A novel lytic Caudoviricetes phage VBY against MDR V. parahaemolyticus was isolated from aquaculture, while its disinfection performance in aquaculture tailwater outperformed ozone (O₃) and ultraviolet (UV) controls. Genomic and phylogenetic analyses identified VBY as a Caudoviricetes, lacking virulence factors and ARGs. The phage VBY exhibited robust stability under aquaculture-relevant environmental conditions and potential activity against biofilms, accompanied by significant ARGs reduction. In the real tailwater treatment system, the phage VBY achieved 5.5-log reduction in MDR bacterial loads and 4–6 log suppression of key ARGs over 72 h. Phage treatment maintained a remarkably long-term inhibitory effect. The phage VBY could preserve water quality during the removal of MDR V. parahaemolyticus, which overcame the key limitation of conventional chemical disinfection strategies. These findings demonstrated that phage-mediated disinfection, which could effectively remove MDR pathogens and the associated ARGs from recycled tailwater, was an environmentally sustainable water treatment technique.

Nuclear energy plays a crucial role as a clean energy source in modern society. The use of nuclear energy will result in the generation of a large amount of radioactive nuclear wastewater. Separation of nuclides from radioactive wastewater is crucial for the safe disposal of nuclear wastes and the sustainable development of resources. However, it remains a great challenge to achieve precise separation between different radionuclide ions due to their similar properties. Herein, we constructed a radiation-resistant graphene-based membrane via ethylenediaminetetraacetic acid (EDTA) functionalization with highly stable and aligned two-dimensional subnanochannels, which exhibits adjustable ion diffusion energy barrier and ultrahigh radionuclide ion selectivity. The functional groups within the GO-EDTA channel exhibit strong affinitive binding interactions with Sr²⁺ and La³⁺. The mono/multivalent metal-ion selectivity up to 485 and 1300 for Cs⁺/Sr²⁺ and Cs⁺/La³⁺, respectively, outperforms other reported membranes. Besides, the channel can still maintain stable separation performance under irradiation conditions. Furthermore, using quartz crystal microbalance, we break down the contributions of partitioning at the pore mouth and intrapore diffusion to the overall energy barrier for salt transport, indicating that the precise separation of ions is achieved by regulating the diffusion energy barrier. This work provides a mechanism for the design of membranes with high ion-ion selectivity and demonstrates the application potential of nuclear resource recycling.

The integration of anaerobic ammonium oxidation (anammox) with sulfide-dependent autotrophic denitrification (S-SADN) offers a promising low-carbon route for biological nitrogen removal from large volumes of wastewater generated worldwide. However, its application is hindered by sulfide toxicity, nitrite competition, and excessive sulfate production. Here, we developed a stable mixotrophic model system (KAS1–AutoDN2) by integrating an anammox-enriched culture (KAS1) with a sulfide-oxidizing denitrifier, Thauera sp. AutoDN2. At a low carbon-to-nitrogen ratio (C/N) of 0.8, with acetate and sulfide serving as co-electron donors, the KAS1–AutoDN2 system achieved 98.1% ± 2.1% ammonium removal and 99.4% ± 2.2% total nitrogen (TN) removal. Long-term mixotrophic fed-batch operation demonstrated that anammox accounted for 71.2%–77.1% of TN removal, while mixotrophic S-SADN provided a complementary pathway with markedly reduced sulfate yields (63%–68% lower than those of conventional S-SADN systems). Stable transcription of hzsA/hzsB (anammox) and narG/napA (denitrification) confirmed the coexistence and metabolic synergy between the two functional microbial guilds. Notably, no detectable nitrous oxide (N₂O) emission was observed, likely due to the high nitrite affinity of anammox bacteria. This study demonstrates that strategic mixotrophy mitigates sulfide inhibition, balances denitrification stoichiometry, suppresses sulfate overproduction, and stabilizes integrated C-N-S cycling. The anammox-mixotrophic S-SADN platform therefore represents a scalable, energy-efficient biotechnology for treating carbon-limited, sulfide-rich wastewaters in both industrial and municipal sectors.

Automotive electrophoretic coating volatile organic compounds (VOCs) exhibit complex compositions that pose significant atmospheric and human health risks. These emissions are characterized by low concentrations (average 7.67 mg/m³) and a high proportion of oxygenated VOCs (OVOCs, 82.94%), making them poorly amenable to conventional treatment methods, with activated carbon adsorption achieving only 9.0% removal efficiency. This study presents a pilot-scale evaluation of a powdered activated carbon–enhanced wet catalytic ozonation (PAC+WCO) system for treating such exhausts. Over 15 d of continuous operation, the system achieved an average VOC removal efficiency of 85.0%, with removal efficiencies of 97.7% for OVOCs, 85.1% for alkanes, 92.0% for olefins, 75.8% for aromatics, and 31.1% for halocarbons. The absorption solution maintained low chemical oxygen demand (COD, 178.4 mg/L) and total organic carbon (TOC, 107.4 mg/L) levels, with three-dimensional fluorescence spectroscopy confirming negligible pollutant accumulation. Mechanistic analysis revealed that the hydrophilicity of OVOCs, the “particle effect” of PAC, and the adsorption of small oxygen-containing molecules onto PAC surfaces collectively enhanced the mass transfer of hydrophobic VOCs, while efficient catalytic oxidation ensured stable system performance. The system significantly reduced ozone formation potential (OFP) from 25.3 to 0.79 mg/m³ and mitigated associated non-carcinogenic and carcinogenic health risks. An economic assessment indicated competitive operating costs attributable to low PAC consumption and the elimination of hazardous waste disposal. Overall, these findings validate the PAC+WCO system as an efficient, stable, and economically viable technology for controlling challenging VOC emissions from automotive water-based painting processes.

The treatment of air pollutants in iron and steel industry was crucial for environmental protection, but this process generated GHG emissions. This study adopted the emission factor approach to assess the spatiotemporal variation and future trends of GHG emissions from air pollutants treatment in the global steel industry. In 2019, global GHG emission from air pollutants treatment in iron and steel industry reached 5.37×10⁹ kg CO₂e in 2019, comparable in scale to GHG emissions from wastewater and waste treatment. Among these, SO₂ treatment accounted for the largest source of GHG emissions. Spatially, Asia accounted for 91% of global GHG emissions, with China contributing 76% primarily due to its massive crude steel production. Industrial production structure and terminal treatment technology were two important factors influencing GHG emissions. Under baseline scenario, GHG emissions from air pollutants treatment would reach 1.1×10¹⁰ kg CO₂e by 2050. Significant reductions in GHG emissions could be achieved by adjusting production structure. This study quantified GHG emissions in air pollutants treatment and underscored the potential of adjusting industrial production structure to reduce air pollutants generation and mitigate corresponding GHG emissions.

Emerging contaminants (ECs) are becoming increasingly widespread in terrestrial ecosystems, with growing evidence that their presence poses substantial risks to plant health. As EC-induced effects can propagate across molecular, physiological, organismal, and ecological levels, a systematic framework is needed to organize and interpret their biological consequences across scales. In this review, the Adverse Outcome Pathway (AOP) framework is employed to describe the progression of EC-induced effects in plants, from initial molecular interactions to final adverse outcomes (AOs). Major exposure routes in plant environments are first outlined, with particular attention to how uptake, translocation, biotransformation, and subcellular localization shape internal exposure, target-site availability, and potential interactions with biomacromolecular targets. The subsequent key event (KE) modules are then synthesized, linking upstream molecular and cellular perturbations to downstream physiological dysfunction and functional impairment. These mechanistic alterations are further related to plant-relevant AOs, including growth inhibition, deterioration in crop yield and quality, reduced carbon sequestration capacity, and potential broader impairment of ecosystem functioning. Current knowledge gaps are also highlighted, and the potential utility of an EC-plant AOP perspective in risk assessment and management is discussed. By integrating evidence along the AOP continuum, this review provides a mechanistic and multi-scale perspective on EC-induced plant effects and offers a scientific basis for assessing and managing EC risks in ecosystems.

Biological pollution, including pathogenic microorganisms, antibiotic resistance, invasive species, and harmful organisms, poses increasing threats to ecosystems, food security, and public health. In recent years, CRISPR-based technologies have emerged as powerful tools for biological pollution control due to their high precision, programmability, and versatility. In this Review, we summarize recent advances in CRISPR gene-editing applications for mitigating biological pollution, including the control of pathogenic microorganisms, antibiotic-resistant bacteria, and resistance genes, as well as invasive and harmful species through targeted genetic interventions. We also discuss the role of CRISPR diagnostic platforms in environmental surveillance as complementary tools for identifying and tracking biological contaminants. Furthermore, we critically examine key technical, ecological, and governance challenges that constrain the translation of CRISPR-based strategies from laboratory studies to real-world environmental applications. Finally, we highlight emerging directions in high-precision editing, intelligent delivery systems, and responsible governance frameworks that will be essential for the safe and sustainable deployment of CRISPR technologies in environmental pollution control.

In 2024, wind and solar accounted for 96.6% of newly installed global renewable electricity capacity, while biogas accounted for only 0.5%. There are two structural challenges constraining biogas expansion: cost competitiveness and feedstock limitations. Over the past decade, unlike wind and solar energy, whose costs have dropped by 70%–90%, bioenergy costs have remained almost unchanged. Recurring feedstock costs and limited feedstock availability risk turning ambitious biogas expansion into competition with agricultural land. These challenges have created uncertainty for the future development of biogas projects, which are now heavily dependent on government subsidies. Given these economic and resource realities, biogas projects should be strategically reassessed not as a primary energy source but as a niche solution for seasonal energy storage, grid balancing, waste management, and green chemistry. A case-by-case approach is essential to avoid misallocation of limited government subsidies and to ensure biogas projects are operated sustainably both economically and environmentally without becoming a financial burden.

Environmental chemistry, a cornerstone of environmental science, faces critical challenges including research homogenization, the persistent gap between laboratory-scale discoveries and engineering-scale applications, and the need to rationally integrate emerging data-driven tools. Drawing on deliberations from the 6th Youth Forum on Frontiers of Environmental Science and Engineering, this perspective outlines three guiding principles for the discipline’s future. First, fundamental mechanistic research—particularly on interfacial reaction kinetics, radical pathways, and molecular recognition—must be strengthened to provide interpretable physicochemical bases for cross-disciplinary innovations. Second, engineering thinking should be embedded from the outset, incorporating life-cycle assessment and real-water-matrix complexities to bridge the “last mile” between high-performance materials and practical deployment. Third, artificial intelligence (AI) should be positioned as an auxiliary tool rather than a universal solution; its pattern-recognition capabilities can accelerate hypothesis generation and process optimization, but its outputs require mechanistic validation and causality scrutiny. Ultimately, the authors advocate a closed-loop paradigm of “hypothesis–prediction–validation” that integrates empirical research, AI-assisted analytics, and collaborative academia–industry–government platforms, ensuring that environmental chemistry evolves from homogenized competition toward original breakthroughs and tangible environmental benefits.

The rapid integration of artificial intelligence (AI) and data-driven paradigms is profoundly reshaping environmental geoscience research. Based on insights from the “Frontiers of Earth System and Environmental Planning” session at the 6th Youth Forum on Frontiers of Environmental Science and Engineering, this paper synthesizes emerging perspectives on the opportunities, systemic disruptions, and future trajectory of AI in environmental geoscience. While AI offers unprecedented efficiency, shorter research cycles, and enhanced visual readability, it simultaneously creates tension with traditional, labor-intensive empirical approaches such as fieldwork and mechanistic experiments. Furthermore, the sustainability of AI models remains critically dependent on legacy datasets; over-reliance on unvalidated global secondary data threatens scientific reproducibility and risks future data scarcity under changing climate conditions. Ultimately, while AI can automate routine data processing, it cannot substitute for human critical thinking in formulating fundamental scientific questions. We advocate for a balanced, diversified research ecosystem that responsibly integrates AI while safeguarding the foundational role of primary data collection and human-centric scientific inquiry.