Eutrophication driven by excessive total nitrogen (TN) and total phosphorus (TP) poses a major threat to China's aquatic ecosystems. Precise identification of nutrient hotspots is essential for effective mitigation but remains constrained by hydrologically mediated transport processes across multiple spatial scales. Process-based models such as the Soil and Water Assessment Tool (SWAT) are widely used, yet their coarse subbasin divisions obscure intra-subbasin heterogeneity and limit source apportionment. To address this, we developed a process-informed, multi-parameter downscaling framework integrating SWAT-derived subbasin outputs with high-resolution land use, topographic, and hydrological datasets. This approach downscaled nutrient source tracing from the SWAT subbasin scale (average similar to 485 km(2)) to a 1 km & times; 1 km grid, achieving an approximately 485-fold improvement in spatial resolution, enabling fine-scale spatial patterns of TN and TP sources and revealing intra-subbasin heterogeneity that was not resolved by SWAT alone. The downscaling framework demonstrated robust performance, with calibration R-adj(2) of 0.916 and 0.911 and Nash-Sutcliffe efficiency coefficient (NSE) exceeding 0.90 for log-transformed TN and TP, and leave-one-out cross-validation R-adj(2) of 0.853 and 0.864, confirming strong model generalizability. Variation analysis further revealed substantial heterogeneity within the high-contributing subbasins, with 9 out of 11 for TN and 2 out of 13 for TP exhibiting internal variability over 30%. These results indicate that the downscaling analysis can uncover critical spatial variability in nutrient source distributions. Targeted interventions, such as upgrading sewage treatment and establishing constructed wetlands, should be directed to the precise internal hotspots responsible for disproportionate nutrient export. By bridging the gap between subbasin scale modeling and fine scale source delineation, this approach provides a more precise spatial representation of nutrient distributions and establishes a transferable framework for high-resolution nutrient management in diverse global watersheds.

Nitrogen (N) and phosphorus (P) pollution are key drivers of ecosystem degradation, arising from complex interactions between hydrological processes and human activities. However, existing approaches often fail to capture the coupled dynamics between socio-economic drivers and nutrient transport processes. This study develops an integrated process-based modelling framework that combines System Dynamics (SD) with the Soil and Water Assessment Tool (SWAT) to simulate nutrient dynamics in watershed systems. The framework explicitly links socio-economic feedbacks with spatially distributed hydrological and biogeochemical processes. Using Zhejiang Province, China, as a case study, long-term trajectories of total nitrogen (TNE) and total phosphorus (TPE) emissions from 2005 to 2020 were reconstructed, and future dynamics were simulated under four scenarios (BAU, S1, S2, S3). Results show that nutrient dynamics exhibit strong system coupling and nonlinear responses. Agricultural land use and water-related processes form the dominant control on emissions, while socio-economic factors introduce heterogeneity through feedback mechanisms. The model further reveals distinct system behaviors between nitrogen and phosphorus, reflecting differences in their transport and transformation processes. Scenario simulations indicate substantial reductions under the green development pathway (S2), whereas baseline trajectories show limited improvement. Persistent hotspots are associated with agricultural and peri‑urban systems, where interactions between land use and hydrology are strongest. By integrating socio-economic dynamics with hydrological processes, this study provides a generalizable modelling framework for understanding nutrient dynamics in coupled human–environment systems, with implications for improving process representation and model-based environmental management.

The COVID-19 pandemic provided a unique opportunity to examine how varying levels of social and economic activity influence air quality (AQ). While existing studies mostly focused on short-term AQ improvements during lockdowns, limited attention has been given to the spatiotemporal characteristics and synergistic effects of air pollutants (AP) (PM2.5, CO, NO2, O3, and SO2) and carbon dioxide (CO2) emissions before and after the pandemic. Aiming to evaluate the trend differences, spatiotemporal aggregation characteristics, and co-effects of AP and CO2 before and after the pandemic across 369 Chinese cities, this study develops a methodological framework combining polynomial function fitting to compare inter-city temporal trends, K-means clustering to identify regional spatiotemporal patterns, and a co-effect control coordinate system to evaluate co-effects of AQ and CO2 reduction. Daily AP data and CO2 emissions were analyzed for January to June in 2019–2021. Results show that CO2 and NO2 exhibited broadly consistent trends across cities, while CO and PM2.5 showed similar inter-city patterns. CO2 emissions in eastern, northern, and northeastern China declined substantially in 2020. PM2.5 and CO decreased markedly in the Yellow River downstream and Bohai Rim region and remained lower even after policy relaxation. O3 concentrations increased in the Central Plains and southeastern coastal cities during the pandemic, whereas SO2 declined continuously. Lockdown measures in 2020 achieved significant effects in synergistically reducing AP and CO2 emissions. AP reductions exceeding 10

Metal hyperaccumulators are promising plants for phytoextraction, but the root traits that help them grow toward metal-rich soil remain unclear. Metal-directed root foraging has been proposed as a unique trait of hyperaccumulators, yet inconsistent observations have obscured its functional significance. Here, we combined a meta-analysis of 128 published observations with split-root experiments in Sedum alfredii to evaluate the recurrence, environmental sensitivity, and physiological relevance of metal-directed root foraging. A meta-analysis showed a broad but context-dependent tendency for hyperaccumulators to allocate more roots to metal-enriched patches, and this response was positively associated with shoot metal accumulation and biomass. However, this response was strongly shaped by soil conditions, particularly soil texture and soil source. Split-root experiments further revealed a clear ecotype-specific response in hydroponics. The hyperaccumulating ecotype (HE) allocated more than 60 % of its root biomass to the Cd-enriched compartment, whereas the non-hyperaccumulating ecotype (NHE) showed reduced root allocation to this compartment. This ecotype-specific contrast was not fully retained in the tested soil system: Cd-directed foraging by HE was no longer detectable, whereas Cd avoidance by NHE persisted. These findings identify metal-directed root foraging as a recurrent but soil-dependent trait of hyperaccumulators. By showing that soil conditions can constrain the detectability of this trait, our study reconciles inconsistent observations and points to soil management as a potential strategy to strengthen root foraging and enhance phytoextraction in contaminated soils.

Fluoxetine (FXT), a widely prescribed antidepressant, has been frequently detected in aquatic environments and may pose ecological risks. Photocatalysis with TiO2 is a promising remediation strategy, but its application is often limited by poor selectivity and the possible generation of toxic transformation products. In this study, a core-shell molecularly imprinted TiO2 photocatalyst (TS@MI-TiO2) was fabricated for the selective removal of FXT, and its photocatalytic degradation behavior and toxicity profiles were systematically evaluated. The results demonstrated that TS@MI-TiO2 provided FXT-specific recognition sites, thereby enhancing both the adsorption affinity and photocatalytic degradation efficiency of FXT. For instance, TS@MI-TiO2 achieved almost complete elimination of FXT within 30 min, whereas the non-imprinted TS@NI-TiO2 required 80 min to reach a comparable level. Sixteen transformation products were identified and plausible degradation pathways involving electrophilic hydroxylation, aromatic hydrogenation, ether bond cleavage and CF bond hydrolysis were proposed. EPR and scavenging experiments indicated that •OH, h+ and O2•- jointly contributed to FXT photocatalytic degradation. Microtox® bioassays revealed a transient increase in microtoxicity at the early stage of photocatalysis, followed by a decline to a level considered non-toxic. Besides, TS@MI-TiO2 maintained high FXT removal efficiency, with 90.17% after seven reuse cycles and 93.81% in a real lake water matrix, underscoring its robust operational stability and environmental adaptability to complex aqueous environments. Overall, these results suggested that molecular imprinting is an effective strategy to enhance the preferential recognition and degradation of FXT by TiO2-based photocatalysts under ultraviolet irradiation, with potential application in the treatment of pharmaceutical-contaminated waters.

Extracellular antibiotic resistance genes (eARGs), as critical drivers of antibiotic resistance transmission, exist in diverse structural forms, complicating their environmental persistence and propagation. Goethite (FeOOH), a naturally abundant catalyst, has shown great potential in catalyzing the hydrolysis of phosphate diester bonds. However, the FeOOH-catalyzed hydrolysis of eARGs, particularly the structural-dependent differences in the removal of eARGs, remains poorly understood. This study comprehensively explored the FeOOH-based hydrolysis of eARGs, with a special focus on how the structural forms of eARGs (supercoiled, nicked circular, and linear) influence their hydrolysis kinetics and underlying mechanisms. The results confirmed the effectiveness of FeOOH in catalyzing eARGs removal, with replication activity reduced by over 60% within 11 days. The FeOOH-catalyzed hydrolysis of eARGs exhibited structure-dependent behavior. Specifically, the removal rate of supercoiled eARGs was 1.47 times higher than that of nicked circular eARGs and 3.13 times higher than that of linear eARGs. Agarose gel electrophoresis and atomic force microscopy analyses revealed that supercoiled and nicked circular eARGs underwent both single-point cleavage and multisite damage, whereas linear eARGs primarily experienced multisite hydrolytic damage. qPCR amplification with different primers further verified that hydrolysis preferentially occurred in the nick-adjacent region. Moreover, longer phosphate backbones were more susceptible to hydrolytic cleavage.

Polycyclic aromatic hydrocarbons (PAHs), typical persistent organic pollutants, are ubiquitous around coking plants and pose potential ecological and human health risks. This study used readily available willow leaves collected from areas surrounding a coking plant as a passive sampling medium to investigate PAH accumulation characteristics and evaluate their feasibility as bioindicators. The results showed that PAH concentrations in willow leaves were relatively high, indicating a strong accumulation capacity for atmospheric PAHs. A significant positive correlation was observed between the logarithm of the leaf-air partition coefficient (KLA) and the logarithm of the octanol-air partition coefficient (KOA) (R2 = 0.6170, p < 0.001). Further analysis of concentration correlations and toxic effects revealed that, among individual PAHs, naphthalene exhibited the strongest correlation with total PAH concentration (R2 = 0.6375), although it was insufficient to represent the overall PAH pollution level. Toxic equivalency analysis indicated a strong relationship between the toxic equivalent concentration of benzo[a]pyrene (TECBaP) and the total toxic equivalent concentration (TTEC) (R2 = 0.9387). Leave-one-out cross-validation (LOOCV) further demonstrated good internal robustness of the model (RMSE = 0.157). Overall, under the specific meteorological and source conditions during the sampling period of this study, BaP can serve as a surrogate indicator for the rapid estimation of the total toxic equivalent concentration of PAHs in willow leaves. However, the applicability of this indicator is source-dependent and should be further validated under different plant species, meteorological conditions, and emission source scenarios before broader application.

The environmental dissemination of extracellular antibiotic resistance genes (eARGs) poses a persistent remediation challenge. While biological strategies typically rely on intracellular enzymatic degradation, the potential of microbially driven extracellular chemical oxidation remains largely underexplored. This study investigates a lactic acid bacteria (LAB)-yeast consortium capable of generating extracellular reactive oxygen species (ROS) to degrade eARGs without exogenous chemical addition. Under optimized conditions (dissolved oxygen = 4.5 mg L-1, Mn2+ = 50 mg L-1), the consortium accumulated up to 0.63 mg L-1 of extracellular H2O2. This self-sustained oxidative system achieved a 3.2log removal of the plasmid-borne Chl gene. Mechanistic investigations using scavenger quenching and cell-free filtrates identified extracellular H2O2 as the dominant oxidant. Crucially, EPS created a reactive microenvironment that facilitated microbially driven extracellular chemical oxidation, transforming reversible adsorption into permanent oxidative degradation. Unlike conventional biological methods limited by intracellular uptake, this extracellular oxidation strategy overcomes mass transfer barriers, achieving removal efficiencies comparable to advanced oxidation processes. Despite matrix oxidant demand attenuating efficiency, the system maintained 1-2 log gene reductions in river and aquaculture waters. This work elucidates a novel adsorption-oxidation synergy driven by microbial extracellular ROS, offering a green, sustainable strategy for mitigating eARGs in aquatic environments.

Aquaculture effluent threatens aquatic ecosystem health, yet source tracking remains challenging due to the lack of specific microbial fingerprints and result priority. Here, we developed a microbial fingerprints-based machine learning approach for hierarchically tracking aquaculture effluent sources. Through high-throughput sequencing of 386 source samples (aquaculture effluent, domestic sewage effluent, cropland and orchard runoff), we screened four microbial taxa (g_ML602J-51, g_Silicimonas, g_Lewinella and f_Balneolaceae) as fingerprints for aquaculture effluent, exhibiting high sensitivity (0.512-0.649) and specificity (0.804-0.974). Artificial Neural Network and Support Vector Machine-radial were optimal models using fingerprint relative abundance and presence data, with accuracies of 0.8335 +/- 0.0090 and 0.8221 +/- 0.0047, respectively. The models' ensemble improved accuracy to 0.8706 +/- 0.0175, outperforming individual classifiers by 4.44%-5.90% and fingerprint matching by 17.29%. The predicted uncertainty was stratified into five-credibility tier for tracking primary aquaculture effluent sources. Application across three coastal regions of China demonstrated the generalizability of the approach.

Rapid economic development and population growth within the Qiantang River Basin intensified environmental pressures, leading to deteriorating water pollution and severe eutrophication at the basin’s outlet. To address these challenges, we developed a novel framework to identify the most sensitive subbasins impacting water quality downstream. The framework captured both direct influences from immediate upstream subbasins and indirect influences from more distant ones along nutrient flow paths. Three machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) were employed to simulate nutrient variability at subbasin outlets. RF performed best (R2 = 0.92 for TN and = 0.88 for TP) and was selected for further analysis. Variable importance derived from the RF models suggested that nutrient levels depended strongly on those from the previous month, highlighting legacy effects and retention processes. Node influences analysis revealed significant upstream controls, especially from tributary nodes with higher nutrient loads. Based on these influences, we developed a subbasin-level sensitivity index and combined with stability assessment to identify critical source areas (CSAs) and classify their stability. This approach identified parts of the Jinhua river subbasins as stable CSAs causing sever water quality problems, while those in the Puyang River were classified as unstable CSAs. Different management strategies should be applied to CSAs with different stability levels. This study demonstrates the value of integrating spatial connectivity, temporal nutrient dynamics, and hydrological structure into nutrient management frameworks, providing a scalable and site-specific approach more targeted and effective water management strategies.

The dissemination mechanisms of antibiotic resistance genes (ARGs) under salinity fluctuations remain poorly understood, despite their critical implications for environmental resistance ecology. This study systematically decoupled salinity-driven conjugation dynamics through controlled single-factor experiments and simulated sediment microcosms. Controlled conjugation assays revealed a threshold-dependent response, with RP4 plasmid transfer frequencies peaking at 2.00 % salinity (4.58–13.51-fold increase vs. 0.85 % control, p < 0.01), mechanistically linked to reactive oxygen species (ROS)-mediated SOS pathway activation. In simulated sediment systems, salinity gradients drove host-specific ARGs enrichment, with plasmid-borne tetA and blaTEM abundances increasing 1.49–4.39 fold under brackish conditions (2.00 % salinity). Multidrug resistance genes floR, qacH-01 exhibited synergistic diffusion patterns (r = 0.77–0.94, p < 0.05), while salt-tolerant phyla Campylobacterota and Spirochaetota became dominant ARGs reservoirs at 3.50 % salinity (2.05–3.17 fold enrichment vs. controls). Although exogenous antibiotic resistance bacteria (ARB) introduction marginally reduced α-diversity, phylum-level community structure remained stable. Salinity preferentially suppressed rare taxa, amplifying ARGs co-occurrence networks through niche restructuring. These findings establish salinity as a dual regulator of ARGs dissemination, directly enhancing conjugation via oxidative stress pathways and indirectly reshaping resistance landscapes through microbial host selection. The results underscore the necessity of integrating salinity gradients into ARGs risk assessments, particularly in coastal ecosystems where tidal fluctuations may potentiate resistance propagation.

Machine learning-based non-target analysis (ML-based NTA) faces the critical challenge of linking complex chemical signals to contamination sources. This review proposes a systematic framework of ML-assisted NTA for contaminant source identification, emphasizing the strategies and considerations of key steps in data processing, pattern recognition, and model validation. The framework provides practical guidance for translating raw NTA data to actionable environmental insights that support informed decision-making.

Since the Industrial Revolution, anthropogenic activities have substantially increased the input of nitrogen (N) and phosphorus (P) into river watersheds, exacerbated by uncertainties stemming from climate change. This study provided a detailed analysis of N and P inputs within the Dawen River Watershed in China from 2000 to 2021. The Net Anthropogenic Nitrogen Input (NANI) and Net Anthropogenic Phosphorus Input (NAPI) methods were used in study, which aimed to investigate how they respond to various climate change factors. Our findings reveal a generally decreasing trend in NANI, with an average of 17,882.34 kg/km2/yr. NAPI showed an initially increasing and then decreasing trend, with an average value of 5151.79 kg/km2/yr. Fertilizer usage emerged as the primary sources of nutrient inputs, accounting for approximately 63.42% of N and 61.88% of P inputs. Precipitation positively impacted the NANI and NAPI while temperature exerted more influential but opposing effects. Lag effects were evident as demonstrated by the stronger impacts of temperature in preceding year on NANI and NAPI. Moreover, climate not only influenced the quantity of NANI and NAPI but also impacted their changes, as well as the inputs of their components. Through quantitative analyses, we unveiled key thresholds in the correlation between nutrient inputs and climate variables, with cutoffs of 14.1 degrees C in temperature and 820 mm in precipitation. Our study highlights the complex relationships between anthropogenic nutrient inputs and climate change, and identifies critical climate thresholds that underscore the importance of sustainable management practices in the Dawen River Watershed to mitigate negative environmental impacts.

The accumulation of antibiotic resistance genes (ARGs) in aquatic systems jeopardizes public health and ecological environments. This study investigates ARGs dissemination in freshwater and seawater, focusing on the sources, prevalence and influencing factors. In freshwater, ARGs primarily originate from medical/pharmaceutical wastewaters, industrial operations, agriculture, and livestock sectors. By contrast, in addition to the above sources, seawater is contaminated by mariculture and terrestrial runoff. Comparative analysis indicates that fresh water hosts multidrug resistance, bacitracin resistance, sulfonamides, aminoglycosides, and beta-lactams, whereas seawater exhibits a wider range of ARGs encompassing sulfonamides, tetracyclines, aminoglycosides, beta-lactams, quinolones, macrolides, and chloramphenicol resistance genes. There was a stronger correlation between antibiotics and ARGs in seawater than in freshwater, especially in farmed waters. Human activities significantly contribute to ARGs pollution in both freshwater and seawater. Urbanization influences ARGs pollution in freshwater, while offshore distance and coastal economic development dictate ARGs selection pressure in seawater. This study shed lights on the current ARGs pollutant status in marine and freshwater ecosystems in China, providing a scientific foundation for water health preservation and ecosystem safeguarding measures.

Membrane biofouling, which severely limits the membrane technology application, can be mitigated by quorum sensing inhibitors (QSIs) that suppress quorum sensing (QS) and biofilm formation-related genes. However, antibiotics can potentially interfere QS pathway, thereby altering microbial community structure and biofilm formation. This study investigates the mechanism of antibiotics interference with QSIs of vanillin and methyl anthranilate on microbial gene expression along with bacterial community structure and metabolism in real surface water (SW) and secondary effluent (SE) systems during nanofiltration biofouling. We demonstrated that sulfamethoxazole (SMX) attenuated the quorum quenching (QQ) effect of vanillin by reducing its inhibitory impact on the expression of pseudomonas quinolone signal (PQS) biosynthesis genes (pqsA and pqsC), N-3-oxododecanoyl homoserine lactone (3OC12-HSL) and N-butyryl homoserine lactone (C4-HSL) receptor genes (lasR and rhlR), and rhamnolipids synthesis gene (rhlA) by over 44%. SMX weakened the QQ effect of methyl anthranilate by reducing its inhibition of PQS biosynthesis genes (pqsABCDE) and 3OC12-HSL synthesis gene (lasI) by 12-62%. The coexistence of antibiotics and QSIs led to an overexpression of antibiotic resistance genes (oprM and mexAB) up to 9 times compared to antibiotics alone. Additionally, SMX and tetracycline (TET) also reduced the inhibitory effect of QSIs on dominant genera with high metabolic and secretory performance (Acinetobacter and unclassified_f_Enterobacteriaceae) and carbohydrate/amino acid metabolism genes in SW and SE systems. These findings reveal that antibiotics can interfere with QS regulatory pathways and weaken the effect of QSIs, which provides new insights into applying QSIs for membrane biofouling control in the presence of antibiotics.

High-pollution-load cropland runoff threatens surface water quality globally. Conventional pollution identification using pollutant-specific fingerprints limits applicability to non-target contaminants. Here, we developed microbial fingerprinting coupled with machine learning to identify cropland runoff source. Through high-throughput sequencing of 386 samples (aquaculture wastewater, domestic sewage, cropland and orchard runoff), we screened five obligate anaerobic taxa (f_Desulfuromonadaceae, g_Geobacter, f_AKAU3564_sediment_group, o_Dehalococcoidales, and g_Citrifermentans) as microbial fingerprints for cropland runoff source, exhibiting high sensitivity (0.50-0.62) and specificity (0.81-1.00). Machine learning optimization based on simulated sink datasets identified Artificial Neural Networks (ANN) and eXtreme Gradient Boosting (XGBoost) as optimal models for fingerprint presence and relative abundance data, with accuracies of 0.8133 ± 0.0006 and 0.8261 ± 0.0029, respectively. The ANN-XGBoost model ensemble using logical rule "or" achieved 0.8400 ± 0.0292 accuracy, outperforming fingerprint-detection method by 14.69 % and individual classifiers by 2.05 %-3.36 %. Prediction uncertainty was stratified into five credibility tiers (VHC/HC/MC/LC/NC) based on confidence interval bounds at the 80 %, 90 %, 95 %, and 99 % levels, and their associated coverage properties. This study provides a reliable method for identifying pollution source using microbial fingerprints data, unrestricted by specific pollutants.

Nanofiltration (NF) is an effective process for micro-/nano-plastics (MNPs) interception, but the impact of accumulated MNPs on the microbial community structure and metabolic pathways of biofilms on NF membranes remains unclear. This provides uncertainty with respect to membrane biofouling behavior and the risks to efficient NF operations. In this study, the size-dependent (20 nm-25 μm) and concentration-dependent (0.1-50 mg·L-1) effects of MNPs on the biofouling of a NF membrane treating secondary wastewater effluent were studied. Three MNPs-tolerant, hypermetabolic and polystyrene-degradable genera (i.e., Acinetobacter, Novosphingobium and Asticcacaulis) were detected in biofilms as dominant taxonomic compositions. MNPs led to an increase of 19.3 %-76.7 % in biomass contents and a more rapid decrease in permeate flux, with 0.1 mg·L-1 of 80 nm NPs causing the most severe membrane biofouling. Metagenomic analysis revealed that MNPs upregulated enzymes involved in exopolysaccharide (ExoA/L/M/P/Q/X/Y/Z) and tyrosine (COMT, FeaB and AOC3) biosynthesis and quorum sensing (PhzF and CiaH/R), and suppressed cell motility pathways including flagellar assembly and bacterial chemotaxis. Novel types of perforated column spacer (PCS) enhanced the hydrodynamics of the membrane feed with a lower pressure drop and higher fluid velocity, introduced micro-jets and greater mass transfer inside feed channels, thus eliminating the deposition of MNPs and mitigating membrane biofouling. Overall, a greater understanding of the interaction mechanisms between MNPs and membrane biofouling in secondary effluent filtration will help develop more effective MNPs management strategies and achieve more sustainable NF operations.

The priming effect caused by biodegradable microplastics (MPs) has the potential to change soil organic carbon (SOC) dynamics, but this depends upon the priming intensity and direction (positive, negative) in different soils. In this study, the C-13 natural abundance method was used to determine the priming effect induced by biodegradable poly(lactic acid) (PLA) MP in eight agricultural soils. We quantified the microbial functional genes encoding C degradation and soil C assimilation by phospholipid fatty acid (PLFA)-distinguishable microbial groups, and the weighted response ratio was applied to evaluate the microbial responses to PLA MPs in these soils. In seven alkaline soils, the positive priming effects induced by PLA MP were detected from 0 to 20 d, and their intensities were significantly positively correlated with the response ratios of soil dissolved organic carbon (DOC) and microbial functional genes encoding cellulose degradation (cex) to PLA MP, but negatively related to the response ratios of soil nitrate (NO3-) contents and the ratio of Gram-positive to Gram-negative bacteria (G(+)/G(-)) to PLA MP. Moreover, in the alkaline soils, the increase of DOC was positively correlated (p < 0.05) with positive priming from 0 to 20 d, indicating that the available C from biodegradable MPs is probably stimulates the growth of r-strategists (G(-)), resulting in a positive priming effect by co-metabolism. From 20 to 70 d, negative priming effects were observed in most cases, and the daily priming effect was significantly positively correlated with the degradation rate of PLA MP. Such inhibition of SOC mineralization suggested that soil microorganisms may preferentially metabolize PLA hydrolysis products, rather than SOC. In an acidic soil (pH = 5.5), the positive priming effects caused by PLA MP continually accelerated the SOC decomposition for 70 d. Overall, our results suggest that PLA MP in alkaline soils should not interfere with soil C sequestration in the short-term, but might accelerate SOC turnover in acidic soils.

Phosphorus is one of the key contributors to the eutrophication of aquatic ecosystems. With the rapid growth of the aquaculture industry, aquaculture effluent has gradually become a significant source of phosphorus pollution. However, this important phosphorous source has often been overlooked in previous studies. In this study, we utilized phosphate oxygen isotope (δ18OP) combined with the Bayesian isotope mixing model in R (MixSIAR) to identify and quantify the major phosphorus sources in Xijiang Estuary, China. The results show that the average concentration of total phosphorus (TP) in the receiving waters ranged from 0.03 to 0.34 mg/L, with aquaculture effluent indeed contributing the most to phosphorous loading (30.5 %), generally higher than other phosphorus sources such as livestock feces (29.6 %), Wastewater Treatment Plants (WWTPs) effluent (24.0 %) and agricultural soil (15.8 %). Additionally, significant spatial differences were observed in the contribution of different phosphorous sources. This study highlights the previously underestimated role of aquaculture in phosphorus loading in estuarine regions and provides valuable insights for water quality management strategies.