Microplastics are emerging contaminants and widely distributed in the environment. They are considered as a vector of numerous organic pollutants including antibiotics in aquatic environments and thereby influence their distribution and transport behaviors. However, the effects of microplastics on the environmental behavior of antibiotics in soils remain largely unclear. In this paper, the influence of polyamide (PA) microplastics on sorption and transport of the selected antibiotic [oxytetracycline (OTC)] in a sandy loamy soil was studied by performing batch and column experiments. Results show that PA microplastics increase the pH of reaction systems, which contributes to OTC sorption onto the tested soils. However, altering pH is not the key influencing mechanism because the overall sorption capacity decreases slightly after adding PA microplastics, which can be attributed to the dilution effect. Reduction of OTC sorption by adding microplastics promotes the migration of OTC in the tested soil, which could be demonstrated by the results of column experiments that the breakthrough of OTC occurs earlier with an increasing content of PA microplastics. According to the fitting parameters of HYDRUS−1D model, PA microplastics can affect the transport of OTC by altering the soil pore structure and dispersion coefficient. These results provide new insight into the interaction between microplastics and organic pollutants in soil environments.

Urban turfgrass ecosystems are expected to increase at unprecedented rates in upcoming decades, due to the increasing population density and urban sprawl worldwide. However, so far urban turfgrasses are among the least understood of all terrestrial ecosystems concerning their impact on biogeochemical N cycling and associated nitrous oxide (N₂O) and nitric oxide (NO) fluxes. In this study, we aimed to characterize and quantify annual N₂O and NO fluxes from urban turfgrasses dominated by either C4, warm-season species or C3, cool-season and shade-enduring species, based on year-round field measurements in Beijing, China. Our results showed that soil N₂O and NO fluxes varied substantially within the studied year, characterizing by higher emissions during the growing season and lower fluxes during the non-growing season. The regression model fitted by soil temperature and soil water content explained approximately 50%–70% and 31%–38% of the variance in N₂O and NO fluxes, respectively. Annual cumulative emissions for all urban turfgrasses ranged from 0.75 to 1.27 kg N ha⁻¹ yr⁻¹ for N₂O and from 0.30 to 0.46 kg N ha⁻¹ yr⁻¹ for NO, both are generally higher than those of Chinese natural grasslands. Non-growing season fluxes contributed 17%–37% and 23%–30% to the annual budgets of N₂O and NO, respectively. Our results also showed that compared to the cool-season turfgrass, annual N₂O and NO emissions were greatly reduced by the warm-season turfgrass, with the high root system limiting the availability of inorganic N substrates to soil microbial processes of nitrification and denitrification. This study indicates the importance of enhanced N retention of urban turfgrasses through the management of effective species for alleviating the potential environmental impacts of these rapidly expanding ecosystems.

First flush or the first pore volume of effluent eluted from biofilters at the start of rainfall contributes to most pollution downstream because it typically contains a high concentration of bacterial pathogens. Thus, it is critical to evaluate designs that could minimize the release of bacteria during a period of high risk. In this study, we test the hypothesis of whether an addition of iron-based media to biofilter could limit the leaching of Escherichia coli (E. coli), a pathogen indicator, during the first flush. We applied E. coli-contaminated stormwater intermittently in columns packed with a mixture of sand and compost (70:30 by volume, respectively) and iron filings at three concentrations: 0% (control), 3%, and 10% by weight. Columns packed with a mixture of sand and iron (3% or 10%) without compost were used to examine the maximum capacity of iron to remove E. coli. In columns with iron, particularly 10% by weight, the leaching of E. coli during the first flush was 32% lower than the leaching from compost columns, indicating that the addition of iron amendments could decrease first-flush leaching of E. coli. We attribute this result to the ability of iron to increase adsorption and decrease growth during antecedent drying periods. Although the addition of iron filings increased E. coli removal, the presence of compost decreased the adsorption capacity: exposure of 1 g of iron filings to 1 mg of DOC reduces E. coli removal by 8%. The result was attributed to the alteration of the surface charge of iron and blocking of adsorption sites shared by E. coli and DOC. Collectively, these results indicate that the addition of sufficient amounts of iron media could decrease pathogen leaching in the first flush effluent and increase the overall biofilter performance and protect downstream water quality.

Insecticidal Cry proteins from Bacillus thuringiensis (Bt) can be transferred from genetically engineered crops to herbivores to natural enemies. For the lady beetle Harmonia axyridis, we investigated potential uptake of Cry proteins from the gut to the body and intergenerational transfer. Third and fourth instar H. axyridis fed with pollen or spider mites from SmartStax maize contained substantial amounts of Cry1A.105, Cry1F, Cry2Ab2, Cry3Bb1, and Cry34Ab1. Cry protein concentrations in lady beetle larvae were typically one order of magnitude lower than in the food. When H. axyridis larvae were fed Bt maize pollen, median amounts of Cry protein in the non-feeding pupae were below the limit of detection except for small amounts of Cry34Ab1. No Cry protein was detected in pupae when spider mites were used as food. Cry protein concentrations decreased quickly after H. axyridis larvae were transferred from pollen or spider mites to Bt-free food. Aphids contained very low or no detectable Cry protein, and no Cry protein was found in H. axyridis larvae fed with aphids, and in pupae. When H. axyridis adults were fed with Bt maize pollen (mixed with Ephestia kuehniella eggs), the median concentrations of Cry proteins in lady beetle eggs were below the limit of detection except for Cry34Ab1 in eggs laid later in adult life. No Bt protein was detected in eggs laid by H. axyridis females fed with aphids from Bt maize. Our results confirm previous observations that Cry proteins are degraded and excreted quickly in the arthropod food web without evidence for bioaccumulation. Despite the fact that small amounts of Cry proteins were detected in some samples of the non-feeding pupal stage of H. axyridis as well as in eggs, we conclude that this route of exposure is unlikely to be significant for predators or parasitoids in a Bt maize field.

Simultaneous stabilization of cadmium (Cd) and arsenic (As) in co-contaminated soil is challenging in environmental remediation because of their opposite properties. In this study, biochar-supported nanoscale zero-valent iron (nZVI-BC) was designed for simultaneously decreasing the soil availability of Cd and As and their bioaccumulation in vegetables. It was found that nZVI-BC exhibited remarkable performance for the stabilization of Cd and As in soil, and their availability decreased by 34.93% and 32.64% compared to the control sample, respectively, under 1.00% nZVI-BC treatment. The increase of soil pH and complexation dominated the Cd remediation process, while the formation of precipitation together and surface complexes transformed labile As into stable forms. Pot experiments showed that nZVI-BC application inhibited the bioaccumulation of Cd and As in vegetables by 23.63–36.48% and 43.09–45.10%, respectively, and hence effectively decreased the cancer risks by 38.19–42.93% related with vegetable consumption (P < 0.05). This study revealed that nZVI-BC is a promising amendment for achieving the simultaneous remediation of Cd and As co-contaminated farmland soil.

Antibiotic resistance in environmental matrices becomes urgently significant for public health and has been considered as an emerging environmental contaminant. In this work, the ampicillin-resistant Escherichia coli (AR E. coli) and corresponding resistance genes (blaTEM₋₁) were effectively eliminated by the electrocatalytic process, and the dissemination risk of antibiotic resistance was also investigated. All the AR E. coli (∼8 log) was inactivated and 8.17 log blaTEM₋₁ was degraded by the carbon nanotubes/agarose/titanium (CNTs/AG/Ti) electrode within 30 min. AR E. coli was inactivated mainly attributing to the damage of cell membrane, which was attacked by reactive oxygen species and subsequent leakage of intracellular cytoplasm. The blaTEM₋₁ was degraded owing to the strand breaking in the process of electrocatalytic degradation. Furthermore, the dissemination risk of antibiotic resistance was effectively controlled after being electrocatalytic treatment. This study provided an effective electrocatalytic technology for the inactivation of antibiotic resistant bacteria and control of antibiotic resistance dissemination risk in the aqueous environment.

Environmental quality data sets are typically imbalanced, because environmental pollution events are rarely observed in daily life. Prediction of imbalanced data sets is a major challenge in machine learning. Our recent work has shown deep cascade forest (DCF), as a base learning model, is promising to be recommended for environmental quality prediction. Although some traditional models were improved by introducing the cost matrix, little is known about whether cost matrix could enhance the prediction performance of DCF. Additionally, feature extraction is also an important way to potentially improve the model's ability to predict the imbalanced data. Here, we developed two novelty learning models based on DCF: cost-sensitive DCF (CS-DCF) and DCF that combines unsupervised learning models and greedy methods (USM-DCF-G). Subsequently, CS-DCF and USM-DCF-G were successfully verified by an imbalanced drinking water quality data set. Our data presented both CS-DCF and USM-DCF-G show better prediction performance than that of DCF alone did. In particular, USM-DCF-G shows the best performance with the highest F1-score (95.12 ± 2.56%), after feature extraction and selection by using unsupervised learning models and greedy methods. Thus, the two learning models, especially USM-DCF-G, were promising learning models to address environmental imbalanced issues and accurately predict environmental quality.

Fine particulate matter (PM₂.₅) is associated with various adverse health outcomes and poses serious concerns for public health. However, ground monitoring stations for PM₂.₅ measurements are mostly installed in population-dense or urban areas. Thus, satellite retrieved aerosol optical depth (AOD) data, which provide spatial and temporal surrogates of exposure, have become an important tool for PM₂.₅ estimates in a study area. In this study, we used AOD estimates of surface PM₂.₅ together with meteorological and land use variables to estimate monthly PM₂.₅ concentrations at a spatial resolution of 3 km² over Taiwan Island from 2015 to 2019. An ensemble two-stage estimation procedure was proposed, with a generalized additive model (GAM) for temporal-trend removal in the first stage and a random forest model used to assess residual spatiotemporal variations in the second stage. We obtained a model-fitting R² of 0.98 with a root mean square error (RMSE) of 1.40 μg/m3. The leave-one-out cross-validation (LOOCV) R² with seasonal stratification was 0.82, and the RMSE was 3.85 μg/m3, whereas the R² and RMSE obtained by using the pure random forest approach produced R² and RMSE values of 0.74 and 4.60 μg/m3, respectively. The results indicated that the ensemble modeling approach had a higher predictive ability than the pure machine learning method and could provide reliable PM₂.₅ estimates over the entire island, which has complex terrain in terms of land use and topography.