Cold regions are warming faster than the rest of the planet, with the greatest warming occurring during the winter and shoulder seasons. Warmer winters are further predicted to result in more frequent soil freezing and thawing events. Freeze-thaw cycles affect biogeochemical soil processes and alter carbon and nutrient export from soils, hence impacting receiving ground and surface waters. Cold region agricultural management should therefore consider the possible effects on water quality of changing soil freeze-thaw dynamics under future climate conditions. In this study, soil column experiments were conducted to assess the leaching of fertilizer nitrogen (N) from an agricultural soil during the non-growing season. Identical time series temperature and precipitation were imposed to four parallel soil columns, two of which had received fertilizer amendments, the two others not. A 15-30-15 N-P-K fertilizer (5.8% ammonium and 9.2% urea) was used for fertilizer amendments. Leachates from the soil columns were collected and analyzed for major cations and anions. The results show that thawing following freezing caused significant export of chloride (Cl − ), sulfate (SO 4 2− ) and nitrate (NO 3 − ) from the fertilizer-amended soils. Simple plug flow reactor model calculations indicated that the high NO 3 − concentrations produced during the fertilized soil thawing events were due to nitrification of fertilizer N in the upper oxidized portion of the soil. The very low concentrations of NO 3 − and ammonium in the non-fertilized soils leachates implied that the freeze-thaw cycles had little impact on the mineralization of soil organic N. The findings, while preliminary, indicate that unwanted N enrichment of aquifers and rivers in agricultural areas caused by fall application of N fertilizers may be exacerbated by changing freeze-thaw activity.

In many practical geochemical systems that are at the center of providing indispensable energy, resources and service to our society, (bio)geochemical reactions are coupled with other physical processes, such as multiphase flow, fracturing and deformation. Predictive understanding of these processes in hosting and evolving porous media is the key to design reliable and sustainable practices. In this article, we provide a brief review of recent developments and applications of reactive transport modeling to study geochemically driven processes and alteration in porous media. We also provide a perspective on opportunities and challenges for continuously developing and expanding the role of this valuable methodology to advance fundamental understanding and transferable knowledge of various dynamic geochemical systems.

In situ bioremediation is a common remediation strategy for many groundwater contaminants. It was traditionally believed that (in the absence of mixing-limitations) a better in situ bioremediation is obtained in a more homogeneous medium where the even distribution of both substrate and bacteria facilitates the access of a larger portion of the bacterial community to a higher amount of substrate. Such conclusions were driven with the typical assumption of disregarding substrate inhibitory effects on the metabolic activity of enzymes at high concentration levels. To investigate the influence of pore matrix heterogeneities on substrate inhibition, we use a numerical approach to solve reactive transport processes in the presence of pore-scale heterogeneities. To this end, a rigorous reactive pore network model is developed and used to model the reactive transport of a self-inhibiting substrate under both transient and steady-state conditions through media with various, spatially correlated, pore-size distributions. For the first time, we explore on the basis of a pore-scale model approach the link between pore-size heterogeneities and substrate inhibition. Our results show that for a self-inhibiting substrate, (1) pore-scale heterogeneities can consistently promote degradation rates at toxic levels, (2) the effect reverses when the concentrations fall to levels essential for microbial growth, and (3) an engineered combination of homogeneous and heterogeneous media can increase the overall efficiency of bioremediation.

High-latitude cold regions are warming more than twice as fast as the rest of the planet, with the greatest warming occurring during the winter. Warmer winters are associated with shorter periods of snow cover, resulting in more frequent and extensive soil freezing and thawing. Freeze-thaw cycles influence soil chemical, biological, and physical properties and any changes to winter soil processes may impact carbon and nutrients export from affected soils, possibly altering soil health and nearby water quality. These impacts are relevant for agricultural soils and practices in cold regions as they are critical in governing water flows and quality within agroecosystems. In this study, a soil column experiment was conducted to assess the leaching of nutrients from fertilized agricultural soil during the non-growing season. Four soil columns were exposed to a non-growing season temperature and precipitation model and fertilizer amendments were made to two of the columns to determine the efficacy of fall-applied fertilizers and compared to other two unfertilized control columns. Leachates from the soil columns were collected and analyzed for cations and anions. The experiment results showed that a transition from a freeze period to a thaw period resulted in significant loss of chloride (Cl-), sulfate (SO42-) and nitrate (NO3-). Even with low NO3- concentrations in the applied artificial rainwater and fertilizer, high NO3- concentrations (~150 mg l-1) were observed in fertilized column leachates. Simple plug flow reactor model results indicate the high NO3- leachates are found to be due to active nitrification occurring in the upper oxidized portion of the soil columns mimicking overwinter NO3- losses via nitrification in agricultural fields. The low NO3- leachates in unfertilized columns suggest that freeze-thaw cycling had little effect on N mineralization in soil. Findings from this study will ultimately be used to bolster winter soil biogeochemical models by elucidating nutrient fluxes over changing winter conditions to refine best management practices for fertilizer application.

<p>Soil heterotrophic respiration has been considered as a key source of CO<sub>2</sub> flux into the atmosphere and thus plays an important role in global warming. Although the relationship between soil heterotrophic respiration and soil water content has been frequently studied both theoretically and experimentally, model development has thus far been empirically based. Empirical models are often limited to the specific condition of their case studies and cannot be used as a general platform for modeling. Moreover, it is difficult to extend the empirical models by theoretically defined affinities to any desired degree of accuracy. As a result, it is of high priority to develop process-based models that are able to describe the mechanisms behind this phenomenon with more deterministic terms.</p><p>Here we present a mechanistic, mathematically-driven model that is based on the common geometry of a pore in porous media. Assuming that the aerobic respiration of bacteria requires oxygen as an electron acceptor and dissolved organic carbon (DOC) as a substrate, the CO<sub>2</sub> fluxes are considered a function of the bioavailable fraction of both DOC and oxygen. In this modeling approach, the availability of oxygen is controlled by its penetration into the aquatic phase through the interface between air and water. DOC on the other hand is only available to a section of the soil that is in contact with water. As the water saturation in the pore changes, it dynamically and kinematically impacts these interfaces through which the mass transfer of nutrients occurs, and therefore the CO<sub>2</sub> fluxes are directly controlled by water content. We showcased the model applicability on several case studies and illustrated the model capability in simulating the observed microbial respiration rates versus the soil water contents. Furthermore, we showed the model potential to accept additional physically-motivated parameters in order to explain respiration rates in frozen soils or at different temperatures.</p>

Most contaminants of emerging concern are polar and/or ionizable organic compounds, whose removal from engineered and environmental systems is difficult. Carbonaceous sorbents include activated carbon, biochar, fullerenes, and carbon nanotubes, with applications such as drinking water filtration, wastewater treatment, and contaminant remediation. Tools for predicting sorption of many emerging contaminants to these sorbents are lacking because existing models were developed for neutral compounds. A method to select the appropriate sorbent for a given contaminant based on the ability to predict sorption is required by researchers and practitioners alike. Here, we present a widely applicable deep learning neural network approach that excellently predicted the conventionally used Freundlich isotherm fitting parameters log KF and n (R2 > 0.98 for log KF, and R2 > 0.91 for n). The neural network models are based on parameters generally available for carbonaceous sorbents and/or parameters freely available from online databases. A freely accessible graphical user interface is provided.

A potential risk from human uptake of microplastics is the release of plastics-associated xenobiotics, but the key physicochemical properties of microplastics controlling this process are elusive. Here, we show that the gastrointestinal bioaccessibility, assessed using an in vitro digestive model, of two model xenobiotics (pyrene, at 391-624 mg/kg, and 4-nonylphenol, at 3054-8117 mg/kg) bound to 18 microplastics (including pristine polystyrene, polyvinyl chloride, polyethylene terephthalate, polypropylene, thermoplastic polyurethane, and polyethylene, and two artificially aged samples of each polymer) covered wide ranges: 16.1-77.4% and 26.4-83.8%, respectively. Sorption/desorption experiments conducted in simulated gastric fluid indicated that structural rigidity of polymers was an important factor controlling bioaccessibility of the nonpolar, nonionic pyrene, likely by inducing physical entrapment of pyrene in porous domains, whereas polarity of microplastics controlled bioaccessibility of 4-nonylphenol, by regulating polar interactions. The changes of bioaccessibility induced by microplastics aging corroborated the important roles of polymeric structures and surface polarity in dictating sorption affinity and degree of desorption hysteresis, and consequently, gastrointestinal bioaccessibility. Variance-based global sensitivity analysis using a deep learning neural network approach further revealed that micropore volume was the most important microplastics property controlling bioaccessibility of pyrene, whereas the O/C ratio played a key role in dictating the bioaccessibility of 4-nonylphenol in the gastric tract.

Z et al. published a paper on machine learning based predictions of organic contaminant sorption onto carbonaceous materials and resins. The authors provide a novel approach to predict concentration-dependent sorption distribution coefficients (KD) to these materials, without the need to link it to any specific isotherm model. This study is a valuable contribution to the field that can stimulate the scientific discussion in the adsorption-modeling community regarding (i) mechanistic assumptions prior to model building, (ii) the parametrization of the model based on these assumptions, (iii) the grouping of data to train the algorithm, and (iv) data filtering strategies. We recently published a paper on a similar topic and are confident that this discussion is valuable to improve the future applicability of machine learning techniques to sorption phenomena.