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Dissolved organic matter (DOM) plays a crucial role in driving biogeochemical processes and determining water quality in shallow groundwater systems, where DOM could be susceptible to dynamic influences of surface water influx. This study employed fluorescence excitation-emission matrix (EEM) spectroscopy combined with principal component coefficients, parallel factor analysis (PARAFAC), co-occurrence network analysis and structural equation modeling (SEM) to examine changes of DOM fractions from surface water to shallow groundwater in a mesoscale lowland river basin. Combining stable isotope and hydrochemical parameters, except for surface water (SW), two groups of groundwater samples were defined, namely, deeply influenced by surface water (IGW) and groundwater nearly non-influenced by surface water (UGW), which were 50.34 % and 19.39 % recharged by surface water, respectively. According to principal component coefficients, reassembled EEM data of these categories highlighted variations of the tyrosine-like peak in DOM. EEMs coupled with PARAFAC extracted five components (C1-C5), i.e. C1, protein-like substances, C2 and C4, humic-like substances, and C3 and C5, microbial-related substances. The abundance of the protein-like was SW > IGW > UGW, while the order of the humic-like was opposite. The bacterial communities exhibited an obvious cluster across three regions, which hinted their sensitivity to variations in environmental conditions. Based on co-occurrence, SW represented the highest connectivity between bacterial OTUs and DOM fractions, followed by IGW and UGW. SEM revealed that microbial activities increased bioavailability of the humic-like in the SW and IGW, whereas microbial compositions promoted the evolution of humic-like substances in the UGW. Generally, these results could be conducive to discern dissimilarity in DOM fractions across surface water and shallow groundwater, and further trace their interactions in the river watershed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Competing Interests: Conflict of interest The authors declare that they have no conflict of interest.

Per‐ and polyfluoroalkyl substances (PFASs), also known as "forever chemicals," are manmade chemicals that have been increasingly detected in various geological settings since the early 2000s. The soil and subsurface environments are the geological media commonly affected by PFAS. We conducted a comprehensive review of peer‐reviewed articles published from 2010 through 2022 concerning the fate and transport of PFAS in subsurface environments. This review is organized into different subsections, covering the basics of PFAS properties and how they affect the occurrence, fate, and transport of PFAS, the fundamental processes affecting subsurface transport and fate of PFAS, and mathematical models for describing and predicting PFAS transport behaviors. Mechanisms governing PFAS transport in the subsurface environment, including the sorption of PFAS at the air‐water interface, solid‐water interface, and nonaqueous phase liquids‐water interface, were explored in detail. Challenges and future research priorities are identified to better mitigate the global challenges of PFAS contamination. Plain Language Summary: Per‐ and polyfluoroalkyl substances (PFASs) are a group of manmade chemicals used in a multitude of applications and commercial products (clothing, cookware, cosmetics, personal care products, fire extinguishers, etc.). Their widespread use has, however, lead to increasing release and distribution in terrestrial and aquatic ecosystems. PFAS, due to their unique properties, are very persistent in the environment as they do not degrade easily. Unfortunately, they are also toxic to organisms, including humans. In this review, we discuss the omnipresence of PFAS in water and soil and explain how PFAS move through subsurface media. There are thousands of different PFAS compounds, and only few of them have been studied in more detail and their transport behavior is known. PFAS tend to accumulate at the air‐water interface in the vadose zone. Generally, PFAS with long carbon chains are retained in soils more strongly than short‐chain compounds. Negatively charged PFAS are more mobile in soils than neutral and positively charged PFAS, and thus can readily pollute groundwater. Nonetheless, due to their persistence and slow desorption, even more strongly sorbed PFAS can be a sustained source for groundwater pollution. We finally identify challenges and future research needs to address the emerging threat of PFAS contamination of the environment. [ABSTRACT FROM AUTHOR]

• Development of a novel device and method for studying multiphase migration and transformation of organic pollutants. • Comparative analysis of DNAPL multiphase migration and transformation in unconfined and confined groundwater systems. • Validation of experimental results through numerical simulation of contaminated site. To explore the migration and transformation process of dense non-aqueous liquid (DNAPL) pollutants' multiphase flow, specifically nitrobenzene (NB), in confined groundwater (CG) versus unconfined groundwater (UG), a two-dimensional sandbox experimental device was designed and constructed. This involved constructing a vadose zone-UG- aquitard-CG structure, which was then subjected to different scenarios. Real-time analysis and numerical simulation methods were established and employed, with a particular focus on the detailed investigation results of actual contaminated site. The study found that when the same amount of NB was injected, the special structure of the CG layer resulted in a more pronounced reverse diffusion of NB in both the dissolved and NAPL phases. This was especially true for the dissolved phase, which was more likely to diffuse reversely. Meanwhile, CG did not directly interact with the vadose zone, and there was no loss of gas phase NB after the leakage in CG. As a result, higher concentrations of dissolved phase NB were generated, leading to the emergence of a larger area of NB contaminant plumes with CG flow. Importantly, the simulation study of the actual site and the laboratory experimental results were found to be validated, further validating the conclusion that direct leakage of NB into CG results in a higher concentration and larger area of dissolved phase contaminant plume, causing more serious pollution to the groundwater environment. [Display omitted] [ABSTRACT FROM AUTHOR]

• Stacked GPR can reduce error propagation and improve model's prediction apparently. • Performing error-corrections for submodels is more feasible than for the coupled model. • Error propagation among submodels can increase the complexity of predictive error. The complex groundwater solute transport model can generally be divided into several coupled submodels, the systematic error is inevitable in each submodel due to the unreasonable simplification or biased representation of the true groundwater environment system. The submodels' systematic errors and their propagation lead to great challenge for reliable model predictions. To improve the predictive performance of groundwater solute transport model, the Stacked Gaussian processes regression (GPR) method was proposed to quantify and reduce the error propagation among the submodels in this study. Stacked GPR corrects the structure errors of groundwater model submodel by submodel gradually. Two case studies are performed to demonstrate the effectiveness of Stacked GPR method, including a synthetical groundwater transport case and a laboratory NaCl solute transport experiment. The results demonstrated that Stacked GPR can reduce the error propagation among submodels and improve model's predictive performance apparently. Moreover, it is more feasible to correct the structure error of each submodel in sequence by Stacked GPR than to correct the lumped structure error by traditional GPR for a component-based groundwater solute transport model. The errors in prior submodels could aggravate the complexity of the error of the posterior submodel though error propagation, which increases the difficulty of constructing an effective error-correcting model. [ABSTRACT FROM AUTHOR]

• Nine GPR kernels are evaluated in improving groundwater model prediction. • GPR kernel has apparent influence on capturing systematic prediction error. • The single kernel RQ has the best performance in improving groundwater prediction. Systematic model error is caused by the unreasonable simplification of real groundwater system, which damages the reliability of groundwater model prediction. Gaussian process regression (GPR) is a popular data-driven method used to build a statistical complementary model to correct systematic prediction error and improve model prediction. Kernel function is a crucial component of GPR, it represents the assumptions on systematic prediction error that we want to learn. In order to evaluate the influence of GPR kernel on capturing systematic prediction error, we compared the performances of GPR with nine kernels in groundwater predictions. These kernels include three commonly used single kernels, i.e., squared exponential (SE), Matérn class (MT), and rational quadratic (RQ) kernels, and six new kernels formed by combining these single kernels as sum or product of pairs (SE_MT_s, SE_RQ_s, MT_RQ_s, SE_MT_p, SE_RQ_p, and MT_RQ_p). Moreover, three groundwater case studies, including two analytical cases and one real case, are used to test these kernels, and four metrics are applied to measure the performances of these kernels. The evaluation results show that kernel functions have apparent influence on the ability of the GPR-based complementary model to capture the systematic prediction error of groundwater model. The single kernel RQ-based GPR yields the best performance in improving groundwater predictions among the nine kernels for all case studies. The combined kernels MT_RQ_s, SE_RQ_s also perform well in groundwater predictions, and the commonly used SE kernel has mediocre performance in predicting groundwater observations. [ABSTRACT FROM AUTHOR]