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The vertical cutoff wall is widely used in the restoration of point source pollutant sites. The evaluation of the long-term service performance of the vertical cutoff wall can provide support for the design and construction of the vertical cutoff wall. In this paper, based on the horizontal spring model, the calculation theory of heterogeneous parameters of vertical cutoff wall under consolidation is proposed. Considering the unsaturated characteristics of each region, a three-dimensional unsteady transport model of pollutants in the inner aquifer-heterogeneous vertical cutoff wall-outer aquifer is established to realize the coupling transport process of convection-diffusion-adsorption-degradation of pollutants. Based on this model, the following conclusions are obtained. The maximum concentration of pollutants at the outlet of the heterogeneous vertical cutoff wall Cout,max decreases with the increase of the horizontal distance Ds from the point source to the wall and the decrease of the buried depth hs of the point source. The peak value of the outlet mass flux MFout,max increases with the increase of Ds and the decrease of hs. The depth of Cout,max gradually moves to the surface with the increase of Ds and the decrease of hs. The depth of MFout,max is located on the surface. In addition, the total mass flux TMFout,steady at the outlet of the wall increases with the increase of Ds and the decrease of hs in the steady state. The larger the Ds and the smaller the hs, the smaller the influence of the wall shear strength parameters (φcw, R) on the breakdown time tb, and vice versa. (φcw, R) has a greater impact on the breakdown time tb. Finally, the design diagram of Zr in the enhanced area of shallow enhanced vertical cutoff wall under different point source positions is given.
This study investigated the role of biofilms formed on different mesoplastics (Polyethylene (PE), Polypropylene (PP), and Polystyrene (PS)) in the accumulation of contaminants in a laboratory model system simulating estuarine conditions. Also, the biofilm penetration of the primary pollutants (pharmaceuticals, total petroleum hydrocarbons (TPH), etc.), their persistence in biofilm, and their impact on the total microbial population were examined. Results showed that biofilm formation occurred on all the mesoplastic samples over six-month period. The biofilm on PP exhibited the highest cell count of aerobic heterotrophic bacteria (AHB) (8.7 × 106 cfu/cm2) by the end of the exposure period. In terms of pollutant concentrations, the biofilm on PE recorded the highest levels of gemfibrozil (GFZ), measuring 2.2 μg/cm2 during the experiment. Moreover, the biofilm formed on PP demonstrated the peak values for TPH accumulation, recorded at 57.1 μg/cm2. These findings indicate that mesoplastics serve as passive samplers, providing surfaces conducive to microbial colonization and thereby enhancing the accumulation and retention of pollutants from the surrounding aquatic environment within biofilms. Therefore, a comprehensive assessment of plastic pollution in marine ecosystems must incorporate considerations of the biological activity and interactions of plastics with environmental contaminants to fully understand their ecological impact.
The injection of granular activated carbon (GAC)-based amendments in artificially-induced fractures provides a promising solution for addressing chlorinated solvent contamination in clay-rich, low-permeability subsurface formations. Achieving high hydraulic conductivity of GAC-filled fractures is critical for long-term treatment efficiency. The evolution of the hydraulic conductivity of GAC-filled fractures in shallow clay formations against various stress and injection conditions has not been fully investigated. To close this knowledge gap, the hydraulic conductivity evolution of GAC-filled fractures created in clay-rich soil was experimentally studied. The hydraulic conductivity and permeability evolutions of sand- and GAC-filled fractures are significantly different. Under low effective stress, the hydraulic conductivity of GAC-filled fractures is more sensitive to variations in effective stress compared to sand-filled fractures, indicating that changes in ground loads might affect the long-term treatment efficiency of GAC-based amendments in shallow formations. The experiments also tested the scenario in which a mixture of sand and GAC particles is placed in the fracture. There exists a critical effective stress at which the benefit of fracture permeability enhancement caused by increased GAC mass fraction is offset by the disadvantage of fracture permeability reduction due to the low mechanical strength of GAC. Our experiments illustrate that this critical effective stress is approximately 500 psi. In practice, the closure stress imposed on an artificially-induced hydraulic fracture is generally lower than 100 psi because in shallow subsurface remediation the formation depth is usually less than 100 ft. Therefore, increasing the GAC mass fraction in particle injections is generally advantageous for enhancing fracture permeability. A double-exponential model was developed to interpret the laboratory data and to predict the minimum hydraulic conductivity of a GAC/sand-filled fracture. This work not only advances the fundamental science in contaminant hydrology, but also supports practical applications of GAC-based amendment injections for subsurface contaminant remediation.
Bioremediating non-aqueous phase liquids (NAPLs) in subsurface environments poses a persistent challenge due to their low solubility and the tendency of microbial biofilms to induce pore clogging, both of which limit contaminant accessibility. This study develops a continuum-scale bioreactive transport model to investigate the competitive dynamics between chemotactic motility - defined as the intrinsic ability of bacteria to migrate in response to chemical gradients - and biofilm formation during toluene biodegradation under diffusion-dominated conditions. The model incorporates NAPL dissolution, solute diffusion, chemotactic migration, microbial growth, and biofilm-induced pore clogging. We tested three microbial strategies: a biofilm-only population, a chemotaxis-only population, and a combined system. Our results reveal that competition for feeding alone, even in the absence of physical pore obstruction, limits bacterial mobility. Simulations show that chemotactic bacteria migrate along solute gradients, forming patterns that refresh the contaminant targeting. However, this directed migration toward the aromatic hydrocarbon is progressively restricted in the presence of growing biofilms by a dynamic feeding competition for dissolved toluene. As bacterial activity suppresses the dissolved toluene gradient, the system shifts into a growth-dominated regime, chemotactic activity is suppressed, and continuous biofilm expansion leads to clogging and more reduced substrate accessibility. Our results also show that chemotactic bacteria can mitigate clogging by suppressing biofilm formation through competitive interactions, but this comes at a cost: reduced overall degradation rates compared to biofilm-only systems. While advective transport and shear-induced biofilm detachment are not considered here, the results isolate key microbial competitive mechanisms relevant to diffusion-controlled environments, with implications for bioremediation and other subsurface applications such as underground hydrogen storage, where suppressing microbial activity and bioclogging are desirable.
In-situ thermal desorption is widely applied to remediate vadose-zone soils impacted by non-aqueous phase liquids (NAPLs), yet the role of adsorbed-phase desorption kinetics in thermal processes has received little attention. Moreover, although several thermal technologies have been studied separately, there is still a lack of quantitative comparison of their remediation performance and energy efficiency within a unified modelling framework. Here, we extend a non-isothermal compositional multiphase-flow model by explicitly coupling a kinetic description of contaminant desorption from the adsorbed phase, and apply it to simulate the removal of n-dodecane (C12) from unsaturated soil using three representative thermal approaches: saturated steam-enhanced extraction (Sat-SEE), superheated steam-enhanced extraction (SSEE), and thermal conductive heating (TCH). The model is used to compare multiphase removal efficiencies and specific energy consumption, and to perform a sensitivity analysis of the kinetic parameters. Simulation results show that, with increasing injection rate, SSEE achieves substantially higher removal of C12 than TCH at comparable or lower specific energy consumption. Both the injection rate and the temperature of the injected steam enhance remediation performance by enlarging the superheated zone that controls adsorbed-phase desorption. The sensitivity analysis identifies activation energy as the dominant kinetic parameter, exerting a stronger control on desorption behavior and required heating temperature than the pre-exponential factor or reaction model. The extended model thus provides a mechanistic basis for designing thermal remediation strategies and selecting appropriate thermal technologies for NAPL-contaminated sites.
Eutrophication of the northern Gulf of America (NGOA) (also known as northern Gulf of Mexico) due to excess nutrients has resulted in harmful algal blooms, the development of hypoxic zones, and negative impacts on seafood production, recreational activities, and marine transportation. With a growing recognition of afforestation to maximize timber production and improve water quality, there is a critical need to investigate impacts of afforestation on nitrogen (N) loads to the NGOA. Using the Pearl River Basin (PRB) located in Mississippi and Louisiana along with the HAWQS (Hydrologic and Water Quality System) model and the Kolmogorov-Smirnov (KS) test, we assessed the impacts of afforestation (by converting all corn and soybean lands in the PRB to mixed-forest lands) on total nitrogen (TN) and nitrate-N loads to the NGOA over a 31-year period from 1990 to 2020. Simulations showed that average annual TN and nitrate-N loads were, respectively, 26% and 28% higher in the base scenario than in the afforestation scenario, with statistically significant differences based on the KS test. The result indicates that afforestation contributed to a very significant reduction in annual N loading from the PRB to the NGOA, which could occur from enhancing N adsorption and immobilization within forest soils, reducing application of synthetic N fertilizers, and decreasing surface runoff after afforestation. Notably, the magnitude of N load reduction was not directly proportional to the area of cropland conversion, suggesting that other factors, including the specific location of afforestation (e.g., riparian zones), land slope, and the types of tree species planted, may also significantly influence N load. Two distinct daily TN loading phases were observed: 1) a slow-loading phase at daily streamflow ≤1200 m3 s-1, and 2) a fast-loading phase at daily streamflow >1200 m3 s-1. These findings have not been reported in the literature and underscore the value of strategically designed afforestation for optimizing N load reduction in the Gulf region. Additionally, very few studies have investigated the impacts of afforestation on daily N load to the NGOA, and this study would help fill the research gap.
This study presents a long-term monitoring strategy for early risk warning of the remobilization of contaminants, mainly attenuated through an ion exchange reaction, induced by abrupt changes in geochemical conditions. The strategy aims to utilize readily in-situ measurable groundwater quality parameters in the prediction of near-future contaminant remobilization caused by cation exchange reactions. The proposed approach was demonstrated using historical monitoring data from the Department of Energy (DOE) Savannah River Site (SRS) F Area, which experienced abrupt geochemical disturbance during the pump-treat-reinjection remedy, and a reactive transport model developed through this study to understand 90Sr migration behavior in the subsurface of the SRS F Area. Both historical monitoring data analysis and reactive transport modeling results revealed a measurable temporal separation (time lag) between the arrival of background electrolyte perturbation and subsequent remobilized contaminant breakthrough. This quantified time lag provides an operational intervention window that can be utilized within an early warning framework. The results suggest that in-situ specific conductance sensors can serve as a practical early warning indicator to detect contaminant remobilization associated with cation exchange species. This strategy is expected to benefit the long-term management of the contaminated site for elements with cation exchange reactions by providing the means to detect the remobilization of contaminants prior to peak contaminant arrival.
Groundwater chemistry in large aquifer systems is reshaped not only by gradual degradation but also by subtle reorganization of contaminant stress. We analyze a decade of federal monitoring data (2012-2021) to assess national-scale hydrochemical change in Mexican aquifers using a distribution-based contaminant hydrology framework that explicitly accounts for non-coincident sampling locations. We track shifts in concentration distributions, exceedance probabilities, hydrochemical facies and multivariate association patterns. We find that salinity extremes have retreated, but without commensurate basin-wide freshening. Maximum electrical conductivity declined from 15,160 to 10,640 μS cm-1, accompanied by strong compression of the upper Na+ and Cl- tails, whereas median salinity indicators remained largely unchanged. This pattern indicates attenuation of localized, high-magnitude geogenic salinity hotspots rather than system-wide dilution. In contrast, NO₃-, PO₄3-, K+ and F- exhibit systematic rightward shifts across broad portions of their distributions, revealing the expansion of diffuse, low- to moderate-level enrichment. Hydrochemical facies remain dominated by Ca2+-Mg2+-HCO₃- waters, but the growing occurrence of mixed Ca2+-Na+-HCO₃- and Na+-Cl- types points to intensified cation exchange and mixing under sustained abstraction. Correlation structures show weakening salinity-controlled coupling and a rising influence of nutrient-related associations, while hierarchical clustering indicates that diffuse anthropogenic indicators now exert stronger control on regional groundwater grouping than salinity metrics. Together, these results point to a fundamental shift in contaminant configuration: from conspicuous, spatially concentrated geogenic salinity toward widespread, moderate anthropogenic pressure that is less visible, more pervasive and harder to reverse. Our findings underscore the need for distribution-sensitive monitoring and analysis capable of detecting subtle, system-wide hydrochemical reorganization in heavily exploited aquifer systems.
Understanding and quantifying the transport of toxic metals and metalloids during flood events downstream of former mining sites is challenging as these events are transient, highly variable and unpredictable, resulting in a scarcity of observational data across watersheds worldwide. In this study, the concentrations and fluxes of dissolved and particulate arsenic (As) were determined to investigate the dynamic of As transfer during flood events in the Orbiel River catchment, in Southern France. This river drains the former Salsigne mining district, where gold-bearing arsenopyrite mineralization was historically exploited. In this Mediterranean catchment, characterized by long dry periods and episodic rainfall, high-frequency automatic sampling of river water was conducted at four stations during five minor to moderate flood events between 2021 and 2023. Arsenic dynamics during each flood were event-specific and varied according to the antecedent hydrological conditions of the basin and the respective contribution of mine-impacted and non-impacted tributaries, which depend on localized rainfall patterns. Dissolved As concentrations ranged from 10 to 35 μg L-1 at the outlet of the catchment, comparable to baseflow levels, while particulate As varied from 20 to 500 mg kg-1, with enrichment factors up to 29 relative to the local geochemical background. Although these recurring, low-intensity floods did not generate strong As concentration peaks, the total As load (i.e., the sum of dissolved and particulate As) transported during the quick-flow phases of individual flood events ranged from 7.5 to 93.2 kg at the catchment outlet, reaching up to 3.9 times that measured during a 50-day low-flow summer period (24 kg). These results demonstrate that recurring minor floods, which occur several times each year, substantially contribute to As export from the Orbiel River catchment despite their moderate magnitude. These findings highlight the need to account for frequent low-intensity floods in contaminant transport assessments and management of legacy mining areas under climate change.
Understanding the mechanisms of anomalous contaminant transport in fracture-matrix systems is crucial for remediating groundwater in fractured porous media. However, the dynamics of such transport induced by asynchronous hydraulic variations between fractures and matrices remain underexplored. This study systematically examined contaminant transport characteristics under asynchronous hydraulic conditions using numerical simulations, revealing the effects of fracture aperture, matrix permeability and porosity on the anomalous transport behavior. The findings indicate that hydraulic asynchrony significantly amplifies anomalous transport characteristics. This manifests as initial preferential flow through fractures, followed by delayed contaminant release from the matrix, leading to lagging and tailing. Furthermore, a higher degree of hydraulic asynchrony increases contaminant transfer rates between fractures and the matrix. A larger fracture aperture accelerates contaminant migration within fractures, causing an 'early arrival' phenomenon. Higher matrix permeability and porosity facilitate contaminant transfer from fractures to the matrix, extending contaminant's residence time in the matrix, thereby intensifying the 'tailing' effect. The breakthrough curves of anomalous transport under hydraulic asynchrony can be effectively characterized using the Logistic function. This offers a novel analytical approach to deepen the understanding of mechanisms underlying this phenomenon.
Using a machine learning framework, this study investigates the spatial distribution and key environmental factors of heavy metal contamination (iron, nickel, lead, copper) in four groundwater aquifers of Isfahan province during the water year 2023-2024. A total of 150 wells were sampled and metal concentrations were determined using ICP-MS, AAS, VGA, and Mercury Analyzer methods in accordance with WHO and Iranian standards. The maximum observed concentrations of iron, nickel, lead and copper were approximately 48, 44.1, 2.9 and 11.2 mg/L respectively, with the peak concentrations of iron and copper in the Damaneh - Daran aquifers, nickel in Bouin and lead in Chadegan. Random Forest (RF) and Support Vector Machine (SVM) models were used, and in RF, 100 trees were used for accurate predictions. Multiple collinearity between environmental predictors, including soil properties, unsaturated and saturated zones, hydraulic parameters, slope, groundwater level, and aquifer depth was assessed through variance inflation factor (VIF), all of which were below 10. Model interpretation showed that soil properties and groundwater level had the greatest influence in RF, while the unsaturated layer was dominant in SVM. Iron decreased with increasing aquifer depth, pore thickness, and water table, while soil permeability and slope increased iron accumulation. Nickel was higher in shallow, shallow, and low-conductivity areas, while lead increased with depth and slope, indicating a nonlinear dependence on hydraulic and soil properties. Copper was positively correlated with soil permeability and negatively correlated with water table. Spatial predictions showed that the Bouin aquifer showed the highest iron and nickel (more than 40 and more than 30 mg/L), lead reached about 44 mg/L in Chadegan, and copper peaked in Bouin from southeast to northwest. RF outperformed SVM by achieving an accuracy of 0.7874, sensitivity of 0.7448, and specificity of 0.8243, while SVM performed poorly. This study innovatively combines machine learning models with the parameters of the DRASTIC analytical model to assess and predict heavy metal contamination in the aquifers of Isfahan province. Overall, the results confirm the nonlinear hydrogeological controls on heavy metal distribution and demonstrate the high capability of RF for reliable prediction of groundwater contamination. This approach provides a transferable method for groundwater quality assessment and supports sustainable aquifer management in arid and semi-arid regions.
As the clean-up of pollutants from large-scale mining progresses in the Clark Fork River (CFR) watershed, mercury (Hg) emerges as an important factor limiting the environmental health and human use of the CFR and its tributaries. Our study is the first to provide a comprehensive set of field data for total Hg concentration in sediment along the upper CFR and its tributaries, with special focus on Flint Creek, the tributary where the majority of Hg in the CFR originates and where targeted remediation activities have recently started. We use spatial modeling to infer the baseline Hg contamination status from a highly variable dataset and to quantify Hg contributions from individual tributaries. Concentrations substantially exceed Severe Effects Levels (2 mg/kg) in most of Flint Creek and in two sections of the CFR. Our stepwise exponential model for Hg concentration integrates mass-balance mixing at tributary confluences and longitudinal decay of Hg concentration with measurement uncertainty through a Gamma error distribution. Longitudinal losses cause Hg concentration to decrease by half every ∼134 km in most of the CFR. We demonstrate the suitability of the model for predicting outcomes of targeted elimination of Hg sources, which may aid in the development of effective remediation programs.
Identifying sources of groundwater contamination and their corresponding release histories is crucial for effective contaminant control and remediation strategies. Although point sources have been extensively studied, non-point sources-characterized by irregular geometries and dynamic release patterns-remain difficult to identify due to significant uncertainties and the high computational demands of high-fidelity models. To address these challenges, we propose an inversion framework (ODRDCN-rEnKF) that integrates an optimized deep residual dense convolutional network (ODRDCN) with a restart ensemble Kalman filter (r-EnKF). ODRDCN serves as a surrogate for the forward model, efficiently capturing the complex relationship between source parameters and concentration distributions. Combined r-EnKF, the framework simultaneously identifies the spatial configuration of non-point sources-represented by rotated ellipsoids-and their time-varying release history-modeled using parameterized exponential functions. Testing in a synthetic aquifer shows that ODRDCN achieves accurate surrogate modeling with minimal training data, and ODRDCN-rEnKF reliably reconstructs both spatial and temporal source features. Compared to r-EnKF alone, the proposed framework significantly lowers computational costs while preserving high inversion accuracy, offering a promising approach for identifying non-point sources in groundwater contamination studies.
The long-term performance of the Canadian deep geologic repository (DGR) relies significantly on bentonite clay, as sealing materials intended for use in the engineered barrier system (EBS). One particular safety concern is microbiologically influenced corrosion of the used fuel containers (UFCs) which may occur if bisulfide (HS-) transports through the bentonite buffer to reach the UFC surface and corrode the copper coating. Understanding HS- sorption onto bentonite is therefore an important aspect of this problem, as HS- sorption can reduce the extent of copper corrosion. However, sorption dynamics onto bentonite are not yet well-understood. As such, this study performed laboratory batch experiments to investigate HS- sorption onto bentonite slurries as a function of temperature (10-40 °C), pH (9-11), and ionic strength (0.01 M-1 M NaCl). These conditions were aimed to reflect the range of possible DGR geochemical conditions. The experimental results showed that HS- sorption onto bentonite increased with increasing temperature but decreased with increasing pH and ionic strength. A 3-way ANOVA (analysis of variance) showed that the variables' individual and 2-way interaction effects are statistically significant, which implies that they should be incorporated into a sorption mechanism. A thermodynamic-based sorption model was also developed in PHREEQC assuming that sorption was driven by three key processes: (i) redox reaction with the structural Fe3+ sites, (ii) surface precipitation as FeS (mackinawite), and (iii) surface complexation reactions with surface hydroxyl group (OH) at the edge sites of montmorillonite. The model successfully described the main experimental trends and provided valuable insights into the relative contribution of these processes to the total HS- sorption mechanism. Altogether, this study provides novel insights from experimental and numerical modelling findings that enhance the understanding of HS- sorption onto bentonite, in the context of Canadian DGR design as well as other nuclear repositories worldwide.
Variations in water quality along the length and depth of a reservoir reveal anisotropic conditions, which pose significant challenges when designing effective monitoring networks. Geostatistical techniques like Bayesian maximum entropy (BME) have proven effective in designing monitoring systems, but they fall short when it comes to planning water quality monitoring in the depth and length of reservoirs. This paper introduces a novel approach for designing long-term, routine water quality monitoring networks specifically tailored for deep reservoirs. Due to the considerable anisotropy in the data and the large length-to-depth ratio of the reservoir, we modeled the anisotropies by scaling the longitudinal distances and rotating the coordinate axes. To examine long-term variations in water quality within reservoirs, a calibrated CE-QUAL-W2 hydrodynamic and water quality simulation model was employed, along with a regular hexagonal grid pattern to determine potential locations for monitoring stations. The proposed methodology outlined the ideal configuration for a reservoir water quality monitoring network, specifying the number of monitoring stations needed and the sampling frequency. The quality monitoring network was designed based on two crucial criteria: the variance of estimation error of the BME method and the sampling cost. The BME method, which can integrate information from various sources, including both hard (deterministic) and soft (stochastic) data, reduces the variance of the estimation error compared to traditional geostatistical methods, leading to more accurate estimates. Using the evidential reasoning (ER) method based on the criteria mentioned earlier, we ranked various alternatives for the locations of monitoring stations and their sampling frequencies. We applied the proposed methodology to the Karkheh Dam reservoir, the largest reservoir in Iran, which faces notable challenges related to thermal stratification and water quality. The results suggest a monitoring network of 10 sampling stations with a 75-day sampling interval for effective water quality management. This approach offers a robust framework for water quality monitoring and resource management in large reservoirs by helping decision-makers balance accuracy, cost, and uncertainty to design resilient and cost-effective monitoring networks.
Total phosphorus (TP) poses a severe threat to the health of fluvial and lacustrine ecosystems in China. Accurate prediction of TP and analysis of its driving mechanisms are thus critical for water quality management, especially in large-scale basins. Due to the strong spatiotemporal heterogeneity of large basins, single machine learning (ML) model prediction and single-scale analysis have considerable limitations. There is an urgent need to develop multi-model ensemble learning and multi-scale analysis to support zonal water quality management. This study takes the Poyang Lake Basin, a representative large-scale basin in the humid region of China, as the research area. It systematically compares 13 single ML models and evaluates three multi-model ensemble methods: Stacking Ensemble (STK), Bayesian Model Averaging (BMA), and TOPSIS-based Ensemble Model (TOPSIS). The SHAP algorithm is used to conduct multi-scale analysis of the relationships between predictive variables and TP. The results show that: (1) Among the single ML models, ensemble tree models achieved the best overall prediction performance. (2) STK achieves better overall prediction performance and a narrower generalization gap than BMA, TOPSIS, and single ML models. The R2, MAE, KGE, and CCC values of STK are 0.7882, 0.0477, 0.8413, and 0.8822 for the training set, and 0.7832, 0.0479, 0.8380, and 0.8843 for the test set, respectively. (3) At the entire-basin scale, precipitation is the most important predictor, while the importance of predictor variables varies among sub-basins. (4) TP concentrations are higher in the rainy season than in the dry season in most sub-basins, but the Raohe River Basin shows the opposite trend. This study not only provides scientific guidance for TP prediction and zonal water quality management in the Poyang Lake Basin, but also highlights the importance of applying multi-model ensemble learning for water quality prediction and implementing zonal water quality management in large-scale basins, which offers a scientific basis for future research on water quality prediction and management in large-scale basins.
This study systematically investigates the transport of carboxyl-modified polystyrene nanoparticles (CPSNPs) in saturated hydroxyapatite (HAP)-quartz sand (QS) porous media through column experiments. The research examines the influence of hydroxyapatite (HAP) mass fraction, ionic types (Na+, Ca2+, H2PO4-), ionic strength, and concentrations of organic acids (oxalic acid, humic acid) and nanocellulose (CNC). Migration mechanisms were elucidated using the extended DLVO theory and a two-site kinetic model. Results demonstrate that increasing the HAP mass fraction from 0.1% to 1% enhances aggregation between HAP and CPSNPs, forming larger aggregates that effectively trap more CPSNPs via increased adsorption sites and pore blocking. This phenomenon leads to a significant reduction in the penetration rate from 97.30% to 1.30%. Monovalent (Na+) and divalent (Ca2+) cations inhibit CPSNPs transport, whereas anions (H2PO4-) promote CPSNPs mobility. Ca2+ exerts stronger inhibition due to more effective charge screening. Both humic acid and oxalic acid reduce CPSNPs mobility, with oxalic acid exhibiting more pronounced inhibition. CNC enhances transport at concentrations below 60 mg·L-1 but inhibits it above this threshold. The chemical non-equilibrium two-site model provides a good fit to the experimental data (R2 > 0.949). This study elucidates the transport rules of CPSNPs in HAP-QS media, offering a scientific basis for risk assessment of nanoplastics in HAP-amended soil-groundwater systems and practical guidance for evaluating the impact of hydrochemical conditions on HAP's nanoplastics capture efficiency. The kinetic parameters obtained also offer critical insights for the in-situ remediation of soils co-contaminated by heavy metals and nanoplastics.
Urban stormwater runoff is a critical pathway for microplastics pollution, yet its detailed transport dynamics remain poorly characterized. This study employed intra-event time-series sampling (at intervals of 0, 5, 15, 30, 60, 120, and 240 min after runoff initiation) during a heavy rainfall event in Shanghai (China) to investigate microplastics concentrations and characteristics across three urban functional areas. Our results revealed that microplastic pollution levels were strongly land-use-dependent: the dining area was a severe hotspot, with a time-weighted average concentration of 689.7 ± 214.1 items/L, which was significantly higher than the residential area (215.6 ± 38.9 items/L) and the parking area (172.8 ± 18.8 items/L), and all concentrations far exceeded local aquatic background values. A pronounced first flush effect was observed, particularly in the dining area, where the peak concentration was reached within just 5 min. The runoff was dominated by small-sized (<1.0 mm) and fibrous microplastics composed of PET and PP. These small fibers were preferentially exported in the early phase of runoff (within the first 30 min), whereas granules and larger-sized microplastics accumulated in the later phase. By elucidating the land-use-dependent transport dynamics and fate of microplastics, this study provides a scientific basis for targeted source control, including prioritizing initial flush interception, and stormwater management in global megacities.
Microplastics (MPs) are emerging contaminants of increasing concern in subsurface environments because of their ability to migrate through porous media and threaten groundwater quality. Although many experimental studies have investigated MP transport, only a limited number of mathematical models exist, and these are mostly restricted to attachment-detachment processes. In reality, MPs exhibit a wide range of sizes and undergo multiple transport and retention mechanisms, including attachment, detachment, straining, blocking, ripening, agglomeration, and size exclusion. To address this gap, this study develops a unified three-dimensional mathematical framework that simultaneously incorporates these key mechanisms to provide a comprehensive description of MP transport in porous media. The governing equations are solved using a semi-implicit Crank-Nicolson finite-difference scheme and validated using four experimentally measured breakthrough curves from sand column studies. The model successfully captures early breakthrough, peak concentration, and long tailing behavior of MPs. Sensitivity analyses demonstrate the strong influence of MP size, collector grain size, attachment kinetics, and straining parameters on transport dynamics. Furthermore, three-dimensional plume simulations over a 4.8-year period reveal that blocking-dominated conditions promote long-range MP migration, whereas ripening enhances retention.