搜索结果：Environmental science and ecotechnology

Mobile genetic elements (MGEs) such as bacteriophages and plasmids profoundly shape microbial community structure and drive horizontal gene transfer across ecosystems. Wastewater treatment systems, with their high cell densities, steep physicochemical gradients and close cell-to-cell contact, act as hotspots for MGE proliferation and exchange, yet the in situ assembly dynamics and host interaction networks of these elements have remained largely unresolved because conventional methods fail to establish direct MGE-host linkages in complex matrices. Here we show that an integrated framework combining metagenomics, metatranscriptomics, metaviromics, and Hi-C proximity ligation sequencing enables the efficient elucidation of DNA phage and plasmid assembly dynamics alongside their host interaction networks in biofilms. We reconstructed 17,672 viral operational taxonomic units and 11,454 high-confidence non-redundant plasmids, and established 529 phage-host and 5739 plasmid-host associations that link up to 52 % of phages to 56 % of prokaryotes and 70 % of plasmids to 91 % of prokaryotes, respectively. Hi-C substantially expanded and refined these networks, revealing taxon-specific and multi-host patterns. Host community composition and biofilm architecture emerge as primary drivers of MGE occurrence and abundance along the reactor flow path. Expression of auxiliary metabolic genes, antibiotic resistance genes and virulence factors carried by these MGEs demonstrates their active roles in modulating biogeochemical cycles and maintaining ecosystem stability. These findings establish a scalable, cultivation-independent framework for deciphering MGE-host networks in complex microbial ecosystems, and underscore the power of Hi-C sequencing to transform our mechanistic understanding of gene flow, resistome dissemination, and ecological resilience in engineered and natural microbiomes.

Wetlands provide essential ecosystem services, from carbon sequestration and flood mitigation to biodiversity support, yet over 20 % have been lost in recent centuries, prompting global restoration efforts backed by policies like the UN Decade on Ecosystem Restoration. Despite rapid expansion of restoration projects, conventional monitoring remains short-term, expert-driven, and often disconnected from site-specific ecological dynamics, limiting adaptive management and long-term success. Citizen science has revolutionized ecological monitoring in other domains by enabling scalable, participatory data collection, but its application to wetland restoration has been largely overlooked. In this Perspective, we assess 120 restoration project sites worldwide and find that citizen science is currently integrated into fewer than 20 % of projects even in high-activity regions like Europe, leaving significant social and geographic potential untapped. We find that recent advances in affordable remote sensing, miniaturized sensors, and mobile platforms-supported by rigorous data-validation frameworks-are now overcoming historical constraints regarding data reliability and spatial continuity. These technological shifts, when coupled with emerging institutional recognition, allow citizen-generated data to serve as a scalable, cost-effective infrastructure for monitoring ecological change over meaningful timescales. Systematically integrating public participation into restoration practice is therefore essential for closing critical monitoring gaps and ensuring the long-term sustainability of global wetland ecosystems.

Addressing climate change and air pollution exhibits strong synergy, and the Chinese government is actively promoting the integrated management of these two issues. Since 2019, the China Clean Air Policy Partnership has released annual reports on China's progress in climate and air pollution governance. These reports track and analyze the challenges and propose solutions for China's pursuit of carbon neutrality and clean air by developing and monitoring key indicators across five areas. This report is the fourth annual report. Building on previous research, it further refines the collaborative governance monitoring indicator system, including the addition of climate change and extreme weather, atmospheric greenhouse gases, and enhanced efficiency of pollution removal technologies. The report includes the following components: (1) an analysis of the interactions between air pollution and climate change; (2) a discussion of governance systems and practices, with an emphasis on policy implementation and local experiences; (3) coverage of structural changes and emission reduction technologies, including energy and industrial transitions, transportation, low-carbon buildings, carbon capture and storage, and power systems; (4) an overview of atmospheric dynamics and emission pathways, examining emission drivers and offering insights for future coordinated governance; and (5) an evaluation of the health impacts and benefits of joint actions. These efforts underscore China's commitment to integrated control, resulting in slowed carbon emission growth, improved air quality, and enhanced health benefits.

Data centres support artificial intelligence (AI) development but place rapidly increasing demands on electricity and freshwater resources, with cooling representing a significant portion of their total energy consumption. Wastewater treatment plants (WWTPs) discharge large volumes of treated effluent with substantial cooling potential; however, their integration with data centre infrastructure has not been evaluated. Here we construct a global geodatabase of over 4775 data centres and 57,547 municipal WWTPs across 98 countries, integrating spatial analysis, engineering systems modelling, optimisation, and life-cycle assessment to quantify the benefits of combining treated water reuse with bidirectional thermal recovery. The analysis reveals a strong global spatial co-occurrence between data centres and WWTPs, enabling optimized national-scale pairings in which treated effluent is used for data centre cooling and the return heat is recovered to support sludge drying and anaerobic digestion. This symbiotic approach reduces greenhouse gas emissions by approximately 84 million tonnes of CO2 equivalent annually, conserves approximately 1300 million m3 of freshwater, and provides net annual cost savings of approximately US$95.4 billion. The greatest mitigation and water-saving potential lies in the United States, Japan, China, the Netherlands, and the United Kingdom. These findings establish data-water symbiosis as a readily scalable infrastructure solution that decouples AI from its carbon and water footprints. WWTPs are poised to evolve from disposal facilities into critical energy-coupling hubs, enabling efficient thermal and water exchange across urban systems and accelerating progress towards multiple Sustainable Development Goals.

Metal-organic frameworks (MOFs) are widely investigated for water purification, yet conventional materials are often limited by saturated metal nodes that restrict active-site accessibility and by microporous channels that impede mass transport. Defect engineering provides a means to generate unsaturated metal sites and hierarchical porosity while preserving framework integrity. Quasi-MOFs occupy a distinct position within this landscape, retaining partial long-range order and local coordination environments of the parent MOF while incorporating controlled defects that yield high densities of coordinatively unsaturated sites and multimodal pore structures. In this review, we summarize synthetic strategies that enable precise control of defect type, density, and distribution in quasi-MOFs, including thermal activation, post-synthetic ligand exchange, and modulated coordination approaches. We examine advanced characterization techniques that reveal correlations between engineered defects and enhanced pollutant diffusion and catalytic activation. Applications in adsorptive removal and advanced oxidation/reduction processes are analyzed, highlighting performance advantages derived from improved site accessibility and transport kinetics relative to pristine MOFs. Finally, we discuss persisting challenges, including hydrolytic stability, scalable synthesis, and detailed structure-activity relationships, and outline future directions for translating quasi-MOFs into practical water-treatment technologies.

Water depth, as a direct indicator of a river's continuous underwater topography, provides crucial information for studies such as investigating channel morphological evolution, modeling sediment transport, and quantifying material flux. Conventional approaches to acquiring localized water depth, involving in situ measurements or methods based on remote sensing imagery, are mostly employed in clear rivers at small scales. However, these techniques encounter substantial limitations when applied to extensive river reaches with high-sediment-concentration flow, where accurately determining water depth is generally only achievable through in situ field measurements. Here we present an intelligent model named RivDepth, designed to obtain water depth distributions in rivers with relatively high suspended sediment concentrations (SSC) exceeding 1 kg m-3. By integrating satellite-acquired spectral variables and an optically derived SSC proxy as inputs, together with an "AI expert" module capable of inference, decision-making, and prediction, the proposed model captures the coupled depth-reflectance-SSC relationship patterns of sample pixels. This enables pixel-wise retrieval of large-scale water depth distributions in high-SSC rivers. RivDepth was trained and tested on the lower Yellow River, one of the rivers with the highest SSC in the world. The proposed model delivered accurate depth estimates, achieving an R 2 of 0.896, RMSE of 0.456 m, MAE of 0.228 m, and ME of -0.020 m. Shapley additive explanations-based feature-importance analysis indicates that shortwave infrared bands, the red band, a red-edge band, the water vapor band, the aerosol/blue band, and the SSC proxy are among the most influential predictors of water depth in this high-SSC river. Their contributions vary spatially in a complex, non-monotonic manner. This study presents a feasible and robust method for large-scale water-depth retrieval in rivers with high SSC. Furthermore, it provides a valuable reference for large-scale hydrological monitoring and basin-scale integrated management by integrating remote sensing imagery with in situ observations.

Ephedrine is a prevalent sympathomimetic alkaloid and amphetamine-type stimulant precursor that has become a widespread contaminant in global aquatic ecosystems. While the neurotoxic effects of high-dose ephedrine exposure are documented in humans and other mammals, its impact on aquatic vertebrates at environmentally realistic concentrations remains poorly understood. Determining how these persistent residues affect neural development and physiological homeostasis is critical for evaluating ecological risks to aquatic life. Here we show that chronic, low-dose ephedrine exposure impairs neurodevelopment in adult zebrafish by simultaneously disrupting synaptogenesis architecture and neurotransmitter balance. Integrated transcriptomic and histopathological analyses reveal that ephedrine targets the synaptogenesis signaling pathway, resulting in reduced presynaptic vesicles and structural abnormalities in the postsynaptic density. Computational docking and biochemical assays further demonstrate that ephedrine engages the vesicular acetylcholine transporter and tyrosine hydroxylase with high affinity, triggering excitotoxic cascades and biphasic neurochemical dysregulation that manifest as anxiety-like phenotypes and cognitive impairments. These findings indicate that environmentally relevant concentrations of stimulant precursors pose a significant threat to the neural circuit integrity of aquatic species, necessitating urgent regulatory attention to pharmaceutical residues in surface waters.

Current chemical exposures studies characterise chemical risk through mean-based concentrations, treating individual-level variability as statistical noise. However, this variability may carry structured ecological information that mean-based approaches systematically overlook. Here, we propose that individual per- and polyfluoroalkyl substance (PFAS) exposure variability constitutes a structured ecological signal, shaped by habitat use across oceanographic gradients and individual foraging behaviour, one that mean-based approaches are not designed to capture. To test the variability-as-signal hypothesis, we integrated two independent indices of individual stability using two sympatric guillemot species (Uria aalge, n = 67 and Uria lomvia, n = 45) sampled across five Icelandic colonies during the 2018 breeding season. We paired PFAS variability scores, derived from plasma PFAS concentrations, with isotopic consistency scores derived from dual-tissue stable isotopes (δ13C and δ15N in plasma and red blood cells). These consistency scores represent individual foraging stability across the breeding season, enabling a reconstruction of foraging histories and oceanographic habitat use. Our results reveal that PFAS variability is highly structured by compound class, dominated by long-chain perfluoroalkyl carboxylic acids (PFCAs; 79% of variance) and perfluorooctane sulfonate (PFOS; 13%). Cluster analysis identified two main divergent exposure states: constrained PFOS variability versus constrained PFCA variability. Bivariate segmented regression revealed a hierarchical structure to contaminant acquisition: oceanographic regime (proxied by δ13Cconsist) functioned as the primary driver, with PFOS variability intensifying in Atlantic-influenced waters. Within these regimes, trophic sources (proxied by δ15Nconsist) emerged as a secondary, conditional modulator, specifically constraining PFCA variability among high-trophic individuals. At the colony scale, fine-scale niche partitioning, such as vertical foraging strategies and individual specialisation using glacial fjords and ice margins, produced compound-specific patterns that diverged from regional hierarchies. As climate change continues to redistribute Arctic and Atlantic water masses and reshape the food web structures, approaches that treat contaminant variability as ecological signal will be increasingly valuable for anticipating exposure regime shifts.

Microbial protein (MP)-the protein-rich biomass derived from recovered or virgin resources-is attracting interest as a source of food and feed. However, its potential as a feedstock for protein-based bioplastics remains underexplored. Proteins offer desirable properties, including superior oxygen-barrier capabilities and complete biodegradability, making them ideal for applications from food packaging to agricultural mulches. Currently, most protein-based bioplastics derive from crops such as wheat, restricting applications and competing with food production. MP can overcome these limitations by supplying diverse proteins from various inputs, including CO2, biomass, and liquid side-streams. In this review, we evaluate bioprocessing pathways for producing MP from renewable and waste-derived substrates from an interdisciplinary viewpoint. We also examine the technical, regulatory, market, and environmental factors to address, delineating the pathway from substrate to MP-based plastics and highlighting key challenges throughout the production chain. Novel strategies-such as efficient co-recovery of proteins with other cellular products like polyhydroxyalkanoates or direct use of microbial biomass without extraction-are essential to maximize environmental and economic sustainability. Carefully chosen processing methods for recovered proteins, including wet and dry blending or extrusion with other biopolymers, can yield diverse products. Concurrently, policy and market developments are vital for adopting MP-based bioplastics. Addressing these challenges will enable MP-based bioplastics to propel the shift toward a circular economy, diminishing dependence on fossil-derived plastics and alleviating plastic pollution.

Non-point source pollution from agricultural activities poses a significant threat to water quality by introducing excess nutrients like nitrogen into aquatic ecosystems, leading to issues such as eutrophication and groundwater contamination. In agricultural watersheds, nitrate transport involves intricate physical, chemical, and biological processes influenced by meteorological conditions, hydrological features, and spatial topologies, making accurate short-term predictions challenging. Traditional data-driven deep learning models often fail to incorporate physical constraints and complex spatiotemporal dynamics, limiting their interpretability and predictive accuracy. Here we show a hierarchical transformer and graph neural network model that accurately predicts watershed nitrate concentrations by integrating multi-source data and simulating pollutant migration. The model captures nonlinear multivariate temporal patterns through hierarchical transformers, fuses global meteorological and local hydrological features via neural networks, and models runoff topologies with physically constrained graph neural networks. For predicting the concentration changes of pollutants discharged from watersheds, it outperforms baselines like multi-layer perceptrons, recurrent neural networks, and long short-term memory networks, with state-of-the-art performance in root mean square error, mean absolute error, and R 2. Ablation studies confirm the essential roles of multi-source data integration and watershed topological modeling in enhancing performance. This method of directly modeling physical processes by leveraging the characteristics of different neural network architectures opens up a new path for addressing the interpretability problem in neural earth system modeling, apart from the process-guided deep learning and differentiable modelling methods.

Extreme environments such as acid mine drainage (AMD) host highly specialized microbial communities that drive profound biogeochemical cycles. Within these ecosystems, iron- and sulfur-metabolizing taxa catalyze mineral weathering, generating intense acidity and mobilizing heavy metals. However, more than 97% of these microorganisms remain uncultured "microbial dark matter," heavily restricting our understanding of extremophile metabolism and adaptation. Here we present the Microbial Biobank of AMD (mbAMD), a culturomics-derived collection of 652 isolates spanning 42 species-including 21 novel taxa-that achieves 86.7% coverage of the global AMD core microbiome. Functional validation demonstrates that 36 of these taxa possess active iron or sulfur metabolic capacities, including the discovery of the first pure cultures of acid-tolerant sulfate reducers. Comparative genomic analyses across these isolates reveal that extreme environmental adaptation is predominantly driven by pervasive horizontal gene transfer. Specifically, extremophiles preferentially acquire adaptive genes governing acid tolerance and metal resistance from phylogenetically proximal relatives rather than distant donors. These findings elucidate the modular evolutionary strategies of extremophiles and provide critical functional resources for advancing biohydrometallurgy and environmental bioremediation. This mbAMD resource will accelerate biohydrometallurgical process optimization and environmental bioremediation strategies while advancing evolutionary microbial ecology research.

Global climate targets demand a rapid transition to carbon neutrality across all industrial sectors, including wastewater management. Wastewater treatment plants are historically energy-intensive and remain significant sources of potent greenhouse gases, primarily nitrous oxide (N2O) and methane (CH4). Recent biological interventions have targeted quorum sensing (QS)-a microbial communication mechanism regulating gene expression and community behavior-to optimize biological treatment efficiency. However, the highly context-dependent and sometimes paradoxical effects of QS on simultaneous greenhouse gas mitigation and energy recovery remain poorly resolved. Here we synthesize recent advancements to show that QS operates as a master biological regulator of both direct emissions and energy consumption in wastewater ecosystems. Evidence indicates that QS distinctly modulates N2O production through concentration- and signal-dependent pathways, while actively suppressing CH4 escape and enhancing aerobic granulation to cut aeration energy demands. Furthermore, targeted QS deployment in anaerobic digestion accelerates direct interspecies electron transfer, substantially boosting methane recovery to offset operational energy use. These insights reveal that manipulating microbial social networks presents a viable, albeit complex, biological lever for balancing emission reductions with energy optimization. Ultimately, precision control of QS systems offers a transformative technological pathway for achieving carbon-positive wastewater infrastructure.

Many of Earth's most biodiverse and biogeochemically active aquatic ecosystems-including groundwater karst systems, turbid estuaries and the deep ocean-are perpetually dark and hydraulically complex, making long-term, high-resolution monitoring technologically challenging. Conventional optical and acoustic sensors suffer rapid signal attenuation and high energy demand in these conditions. Cavefishes of the genus Sinocyclocheilus, which inhabit lightless subterranean waters, have evolved distinctive cranial morphologies-a duckbilled head, dorsal horn and hump-hypothesized to enhance hydrodynamic perception. Here we show, by combining vital staining of neuromasts with validated computational fluid dynamics simulations across a morphological series of Sinocyclocheilus species, that these structures dramatically amplify differential pressure signals (by up to 429.8%) and near-wall velocity gradients (by up to 69.2%) while extending perceptual range. Regions of maximal hydrodynamic variation predicted by the models closely match the observed distribution of canal and superficial neuromasts, revealing a clear biomimetic design principle: sensors should be positioned where flow-field gradients are strongest. These findings establish a quantitative, evolution-guided framework for optimizing artificial lateral line (ALL) sensor arrays, enabling autonomous underwater vehicles to perform energy-efficient, high-fidelity monitoring in some of the planet's most sensitive and data-scarce aquatic environments.

Nanoplastics and tire-derived chemicals are ubiquitous co-pollutants in aquatic environments, originating from road runoff and posing potential risks to vertebrate development through enhanced bioavailability and synergistic toxicity. Polystyrene nanoplastics (PS) can adsorb hydrophobic organics like the antioxidant N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD), acting as vectors that increase tissue accumulation and exacerbate oxidative stress, while 6PPD alone disrupts mitochondrial function and induces sublethal effects in exposed organisms. The vertebrate eye, with its direct environmental exposure and sensitive neural structures, is particularly vulnerable, yet the combined impact of PS and 6PPD on visual morphogenesis remains underexplored. Here we show that co-exposure to environmentally relevant concentrations of PS (1 mg L-1) and 6PPD (0.1-0.8 mg L-1) markedly potentiates ocular toxicity compared to individual exposures, manifesting as myopia-like malformations, increased cell death, and impaired phototaxis. We integrated phenotypic, histological, and multi-omics analyses using zebrafish embryos as a model. Our results show PS-enhanced bioaccumulation of 6PPD in ocular tissues, leading to severe lens and retinal damage, aberrant vascularization, disrupted myelination, and dysregulated pathways including serine proteolysis, retinoic acid metabolism, and ferroptosis-linked oxidative stress. These findings demonstrate nanoplastic-chemical interactions as an emerging threat to aquatic visual function, with implications for survival behaviors and broader ecosystem health under pervasive pollution.

Microplastics are ubiquitous environmental pollutants that increasingly infiltrate human organs and tissues through multiple exposure pathways. While acute toxicological impacts have been documented, the metabolic fate of these polymers within the enterohepatic circulation remains poorly understood. Bile serves as a critical excretory fluid, and disruptions in its balance can lead to biliary tract diseases such as gallstones. However, the long-term accumulation patterns and chronic toxic effects of microplastics within the human biliary system are largely unknown. Here we show the universal presence of microplastics in human bile. Using a multimodal analytical approach, we identified six polymer types, predominantly polyethylene terephthalate and polyethylene, occurring primarily as 20-50 μm particles. We demonstrate that chronic, low-dose exposure to these microplastics induces mitochondrial dysfunction-associated senescence in cholangiocytes. Notably, targeted antioxidant intervention with melatonin effectively preserves mitochondrial function and mitigates this microplastic-induced cytotoxicity. These findings reveal the biliary system as a major reservoir for microplastic accumulation and excretion. Furthermore, they provide a mechanistic foundation for assessing the health risks of plastic pollution and developing therapeutic interventions for environmentally driven biliary disorders.

Reliable prediction of water supply dynamics in large-scale canal systems is critical for water allocation and operational decision-making in inter-basin water transfer projects. Uncertainty in lateral offtake discharges evolves over time and often exhibits multi-peaked distributions due to real-time hydraulic states and unplanned gate operations. However, reliably quantifying and interpreting the evolving uncertainty remains difficult under such dynamically changing and small-sample conditions. Here we show that a physics-guided mixture density network (PgMDN) can effectively characterize this uncertainty while remaining physically consistent. In the proposed PgMDN, physical knowledge is incorporated into the loss function through local mass balance and a consistency constraint between predictions and their associated uncertainty, while long short-term memory layers are employed to model temporal dependencies and multi-factor influences. In addition, Shapley additive explanation analysis is used to identify the dominant hydraulic inputs contributing to predictive uncertainty. Tested on real-world canal datasets, the proposed PgMDN outperforms the standard mixture density network, achieving over a 25% reduction in both mean absolute error and root mean square error, together with improved reliability, as measured by the R-index (increasing from 0.45 to 0.82), and stronger generalization. The results further reveal that water level fluctuations and boundary inflow are key drivers of predictive uncertainty, supporting the physical interpretability of the proposed model. Overall, this study provides a scalable and interpretable tool for real-time modeling of environmental infrastructure and the operational management of large-scale water diversion systems.

Hyperthermophilic composting (HC) represents a promising approach for converting organic solid waste into valuable resources by leveraging extreme temperatures to enhance microbial degradation and detoxification processes. In this high-temperature environment, microbial communities undergo dynamic succession, where thermophilic bacteria dominate and drive efficient organic matter transformation through adapted metabolic pathways and stress responses. These adaptations include the stabilization of cellular structures and enzymes, often mediated by heat shock proteins (HSPs) that prevent protein misfolding under thermal stress. However, the integrated mechanisms linking community-level functional shifts to molecular-level protein remodeling in thermophiles during HC remain poorly understood. Here we show a coordinated interaction of functional succession and molecular adaptations within thermophilic bacteria in HC, which collectively achieve heat resistance. This interaction encompasses enhanced metabolic and genetic modules, accounting for 97 % of the variance observed in thermophile abundance. Metagenomic analyses revealed upregulation of translation, transcription, amino acid metabolism, and cell wall biosynthesis, coupled with suppression of mobilome functions to maintain genomic stability, as confirmed by partial least squares path modeling and Boruta analyses. Molecular dynamics simulations of key enzymes from the thermophile Truepera further demonstrated intrinsic structural rigidity, reduced hydrophobic exposure, and hierarchical chaperone activity involving DNAJ, DNAK, and GroEL for protein repair. These findings enhance our comprehension of microbial thermotolerance and establish a foundation for optimizing composting efficiency and advancing heat-resistant microbial applications in biotechnology and waste management. Additionally, they offer insights into the evolution of thermophiles, protein engineering, and stress adaptation across various biological and industrial systems, thereby promoting the integration of environmental engineering and systems biology.

Microbial electrorespiration harnesses bacteria to drive reductive dechlorination, offering a sustainable method to remediate environments contaminated with persistent chlorinated organic pollutants (COPs). However, aquifers' complex hydrogeological and hydrochemical conditions, combined with uneven COP distribution, create substantial spatial and temporal variability in biochemical reactions, environmental factors, and microbial communities. Traditional trial-and-error experiments are labor-intensive and slow, impeding the quick identification of conditions that accelerate dechlorination rates. Here we show that a machine learning framework, integrating experimental design with cathodic biofilm data, uncovers key interrelationships among environmental variables, dechlorination kinetics, electrochemical properties, and functional microbes, enabling rapid optimization of bioelectrodechlorination. Trained on literature-derived datasets using models such as extreme gradient boosting, random forest, and multilayer perceptron, this framework identifies temperature and cathode potential as primary drivers in experimental design while highlighting key biofilm genera, including Clostridium, Desulfovibrio, Dehalococcoides, Pseudomonas, Dehalobacter, Arcobacter, Lactococcus, and Geobacter. It supports inverse design to determine optimal parameters-such as cathode potential, temperature, and additives-for dechlorinating representative COPs, including tetrachloroethene, trichloroethene, and 1,2-dichloroethane, achieving reaction rate predictions with errors below 6 %. This approach surpasses conventional methods by increasing efficiency, cutting costs, and accelerating bioremediation without extensive laboratory testing. By incorporating microbial community insights into predictive models, our data-driven strategy advances the scalable application of microbial electrorespiration for COP-contaminated water remediation and paves the way for broader bioelectrochemical uses in environmental engineering.

Water supply systems are critical components of urban infrastructure and significant contributors to global carbon emissions. These systems face an emerging challenge in balancing the increasing demands of water security with international climate mitigation goals. To combat water scarcity, many regions have transitioned toward energy-intensive water sources such as inter-basin water transfer and desalination, which significantly increase electricity-dependent indirect emissions. Concurrently, the global shift toward clean energy in electricity generation has provided a crucial mechanism for mitigating these emissions. However, the complex interactions among shifting water-source mixes, energy transitions, and socioeconomic drivers remain poorly understood, often obscuring the effectiveness of decarbonization strategies. Existing quantification frameworks frequently overlook the spatial spillover effects of economic development and the risk that new water security strategies will offset decarbonization gains. Here we show that China's carbon emissions from water-supply processes rose to 228 Mt CO2 yr-1 by 2022, despite initial declines driven by clean energy expansion. Using a three-stage quantification-decomposition-attribution framework, we find that while economic development generally suppresses emission in neighboring regions via technology diffusion, it exhibits a national U-shaped relationship with carbon output. Crucially, central China displays an inverted U-shaped pattern, suggesting a localized risk of high-carbon lock-in as industries and water demands shift. These findings reveal a critical paradox in the water-energy-carbon nexus where water security measures may inadvertently undermine climate targets. Our results advocate for integrated regional governance and differentiated policy interventions to safeguard both water and climate stability in rapidly developing regions.