Intraparticle sorption and desorption of antibiotics.

Intraparticle domains are the critical locations for storing contaminants and retarding contaminant transport in subsurface environments. While the kinetics and extent of antibiotics sorption and desorption in subsurface materials have been extensively studied, their behaviors in intraparticle domains have not been well understood. This study investigated the sorption and desorption of antibiotics (ATs) in the intraparticle domains using quartz grains and clay, and antibiotic tetracycline (TC) and levofloxacin (LEV) as examples that are commonly present in groundwater systems. Batch experiments coupled with the analyses using various microscopic and spectroscopic techniques were performed to investigate the sorption and desorption kinetics, and to provide insights into the intraparticle sorption and desorption of TC and LEV. Results indicated that both TC and LEV with different physiochemical properties can migrate into intraparticle domains that were consistent with sorptive diffusion. The rate and extent of the sorption are a function of intraparticle surface area and properties, pore volume and connectivity, and ionic properties of the ATs. The sorptive diffusion led to the slow desorption of both TC and LEV after their sorption, apparently showing an irreversible desorption behavior (with desorption percentage about 1.86-20.51%). These results implied that intraparticle domains can be important locations for storing ATs, retarding ATs transport, and may serve as a long-term secondary source for groundwater contamination.

Iterative Approach of Experiment-Machine Learning for Efficient Optimization of Environmental Catalysts: An Example of NOx Selective Reduction Catalysts.

An iterative approach between machine learning (ML) and laboratory experiments was developed to accelerate the design and synthesis of environmental catalysts (ECs) using selective catalytic reduction (SCR) of nitrogen oxides (NOx) as an example. The main steps in the approach include training a ML model using the relevant data collected from the literature, screening candidate catalysts from the trained model, experimentally synthesizing and characterizing the candidates, updating the ML model by incorporating the new experimental results, and screening promising catalysts again with the updated model. This process is iterated with a goal to obtain an optimized catalyst. Using the iterative approach in this study, a novel SCR NOx catalyst with low cost, high activity, and a wide range of application temperatures was found and successfully synthesized after four iterations. The approach is general enough that it can be readily extended for screening and optimizing the design of other environmental catalysts and has strong implications for the discovery of other environmental materials.

A unified parameter model based on machine learning for describing microbial transport in porous media.

The transport and retention of microorganisms are typically described using attachment/detachment and straining/liberation models. However, the parameters in the models varied significantly, posing a significant challenge to describe microbial transport under different environmental conditions. A neural network (ANN) model was developed in this study to link the parameters in the model with the factors influencing microbial transport including the properties of microorganisms such as size and surface potentials, and the properties of porous media such as grain size and porosity, and flow conditions. Exhaustive search of literature renders 420 sets of experimental data of microbial transport, which were fitted using the microbial transport model to obtain model parameters. The model parameters, together with the factors influencing microbial transport, were then used to train an ANN model to search for their relationship. An ANN-based parameter relationship was derived and was then used to simulate microbial transport. The simulated results using the relationship roughly matched with the experimental data under different environmental conditions, indicating that a unified relationship was established between the parameters of the microbial transport model and the factors influencing microbial transport, and that microbial transport can be described using the microbial transport model with the ANN-based unified relationship for model parameters.

River restoration changes distributions of antibiotics, antibiotic resistance genes, and microbial community.

Although river restoration has been increasingly implemented to restore water quality in ecosystems, its effect on the removal of emerging pollutant antibiotics, and their resultant influence on microbial community structure and functions in river water is still unclear. This study investigated the changes of antibiotics, antibiotic resistant genes (ARGs), microbial communities, and their spatial distributions in a megacity river before and after river restoration. Results indicated that although the restoration activities including riverbed dredging, riverbank hardening, sewage and storm water separation and re-pipelining improved water quality such as by decreasing total phosphorus (TP) content from 4.60 ± 6.38 mg/L in 2018 to 0.98 ± 0.44 mg/L in 2020, the antibiotic concentrations in river water increased. Total antibiotic concentrations in the water samples were higher in 2020 (506.89-6952.50 ng/L) than those in 2018 (137.93-1751.51 ng/L), likely caused by increased usage of antibiotics in 2020 for COVID-19 treatment. The spatial distributions of antibiotics were less varied likely as a result of less retardation and fast mixing during antibiotic transport. The result also found that the abundance of Actinobacteria and Proteobacteria, and their correlations with ARGs increased. The spatial distributions of ARGs and microbial communities became less varied in the river water, consistent with the antibiotic variations before and after river restoration. Physicochemical changes such as decreased TP and dissolved organic carbon content may also be a factor. The results indicated that the current river restoration efforts were not effective in removing antibiotics, and implied that further studies are needed to investigate their subsequent transformation and transport, and to assess their risks to the health of ecosystems.

Impact of Physico-Chemical Heterogeneity on Arsenic Sorption and Reactive Transport under Water Extraction.

Heterogeneity in physical and chemical properties is a common characteristic in a subsurface environment. This study investigated the effect of physico-chemical heterogeneity on arsenic (As) sorption and reactive transport under water extraction in a layered system with preferential flow paths. A flume experiment was performed to derive the spatio-temporal data of As reactive transport. The results indicated that the heterogeneous system significantly accelerated downward (vertical direction) As migration as a coupled effect of physical and chemical heterogeneity that led to fast As transport with low As sorption along the preferential flow paths. The results also indicated that such a heterogeneity effect was driven by water extraction that enhanced the downward groundwater flow along the preferential flow paths. Numerical simulations were performed by matching the experimental results to provide insights into the dominant processes controlling the As migration in the heterogeneous systems. The simulation results highlighted the importance of the kinetic oxidation of mineral-bonded Fe(II) to Fe(III) in the clay matrix that dynamically increased As sorption affinity and retarded As reactive transport. A coupled model of reactive transport along the preferential flow paths, sorption-retarded diffusion from the preferential flow paths into the clay matrixes, and reactions that change sorption affinity in the matrix was required to describe the As reactive transport systems with physico-chemical heterogeneities. The results have strong implications for understanding and modeling As downward migration from shallow to deep aquifers under groundwater pumping conditions in field systems with inherent heterogeneity.

Differential responses of stream water and bed sediment microbial communities to watershed degradation.

The importance of microbial communities in the function of lotic ecosystems is unequivocal. However, traditional watershed studies on biodiversity have mostly focused on benthic macroinvertebrates, macroalgae and fish assemblages. Here, we investigated the diversity and interaction patterns of microbial communities in water and bed sediment of streams impacted by intensive watershed activities versus streams with relatively pristine conditions via next-generation sequencing of 16S rRNA amplicons using Illumina HiSeq platform. Both water and sediment microbial communities at forested sites had higher mean alpha-diversity than developed sites. Although microbial alpha-diversity indices were generally higher in bed sediment than water, they were comparable at forested sites. In addition, losses of taxa important in nitrogen cycle were evident particularly in bed sediment of developed sites. Interactions among microorganisms visualized by microbial network were more complex at forested sites versus developed sites, with more keystone taxa predominantly from sediment. Together, these findings suggest stream water and bed sediment microbial communities may be affected by watershed disturbances in distinctive ways, and losses of important functional microbial players and keystone taxa in bed sediment may result in decline of ecosystem functions and services. Therefore, cautions should be taken when implementing remediation strategies such as sediment dredging, and reseeding contaminated sites with key microbial players may catalyze the recovery of ecosystems.