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.

In this study, the biodegradation of endocrine-disrupting chemicals (EDCs) (namely the natural and synthetic estrogens 17β-estradiol (E2) and 17α-ethinylestradiol (EE2), respectively) was assessed in an aerobic granular sludge (AGS) sequencing batch reactor (SBR) treating simulated domestic sewage. To better understand the fate of these compounds, their concentrations were determined in both liquid and solid (biomass) samples. Throughout the operation of the reactor, subjected to alternating anaerobic and aerated conditions, the removal of the hormones, both present in the influent at a concentration of 20 μg L⁻¹, amounted to 99% (for E2) and 93% (for EE2), with the latter showing higher resistance to biodegradation. Through yeast estrogen screen assays, an average moderate residual estrogenic activity (0.09 μg L⁻¹ EQ-E2) was found in the samples analysed. E2 and EE2 profiles over the SBR cycle suggest a rapid initial adsorption of these compounds on the granular biomass occurring anaerobically, followed by biodegradation under aeration. A possible sequence of steps for the removal of the micropollutants, including the key microbial players, was proposed. Besides the good capability of the AGS on EDCs removal, the results revealed high removal efficiencies (>90%) of COD, ammonium and phosphate. Most of the incoming organics (>80%) were consumed under anaerobic conditions, when phosphate was released (75.2 mgP L⁻¹). Nitrification and phosphate uptake took place along the aeration phase, with effluent ammonium and phosphate levels around 2 mg L⁻¹. Although nitrite accumulation took place over the cycle, nitrate consisted of the main oxidized nitrogen form in the effluent. The specific ammonium and phosphate uptake rates attained in the SBR were found to be 3.3 mgNH₄⁺-N gVSS⁻¹.h⁻¹ and 6.7 mgPO₄³⁻-P gVSS⁻¹ h⁻¹, respectively, while the specific denitrification rate corresponded to 1.0 mgNOₓ⁻-N gVSS⁻¹ h⁻¹.

Plant uptake is an important process in determining the transfer of pesticides through a food chain. Understanding how crops take up and translocate pesticides is critical in developing powerful models to predict pesticide accumulation in agricultural produce and potential human exposure. Herein, wheat was selected as a model plant species to investigate the uptake and distribution of eleven widely used pesticides in a hydroponic system as a function of time for 144 h. The time-dependent uptake kinetics of these pesticides were fitted with a first-order 1-compartment kinetic model. During 144 h, flusilazole and difenoconazole, with relative high log Kₒw (3.87 and 4.36, respectively), displayed higher root uptake rate constants (k). To clarify the role of root lipid content (fₗᵢₚ) in plant accumulation of pesticides, we conducted a lipid normalization meta-analysis using data from this and previous studies, and found that the fₗᵢₚ value was an important factor in predicting the root concentration factor (RCF) of pesticides. An improved correlation was observed between log RCF and log fₗᵢₚKₒw (R² = 0.748, N = 26, P < 0.001), compared with the correlation between log RCF and log Kₒw (R² = 0.686, N = 26, P < 0.001). Furthermore, the hydrophilic pesticides (e.g. log Kₒw < 2) were found to reach partition equilibrium faster than lipophilic pesticides (e.g. log Kₒw > 3) during the uptake process. The quasi-equilibrium factor (αₚₜ) was inversely related to log Kₒw (R² = 0.773, N = 11, P < 0.001) suggesting a hydrophobicity-regulated uptake equilibrium. Findings from this study could facilitate crop-uptake model optimization.

The unified bioaccessibility method (UBM) was harnessed to assess in vitro oral bioaccessibility pools of dialkyl phthalate congeners (with methyl, –ethyl, –butylbenzyl, –n-butyl, –2-ethylhexyl, and –n-octyl moieties) and bisphenol A at the 17 μg g⁻¹ level in beach sand contaminated with polyethylene microplastics. A variety of sample preparation approaches prior to the analysis of the UBM gastrointestinal extracts, including traditional methods (protein precipitation, liquid-liquid extraction, and solid-phase extraction) and dispersive liquid-liquid microextraction (DLLME) were comprehensively evaluated for clean-up and analyte enrichment. DLLME was chosen among all tested approaches on account of the high extraction efficiency (73–95%, excluding bis(2-ethylhexyl)phthalate and di-n-octyl phthalate), high sample throughput (∼7 min per set of samples), and environmental friendliness as demonstrated by the analytical eco-scale score of 83, and the green analytical procedure index pictogram with green/yellow labeling. The release of the less hydrophobic plastic-laden compounds (dimethyl phthalate, diethyl phthalate and bisphenol A) from the contaminated sample into the body fluids was significant, with bioaccessibility values ranging from 30 to 70%, and from 43 to 74% in gastric and gastrointestinal fluids, respectively, and with relative standard deviation < 17% in all cases. The majority of the compounds were leached during gastric digestion, likely as the combined action of the low pH and the gastric enzymes. The risk exposure analysis revealed that accumulation/concentration in the body fluids is potentially relevant for dimethyl phthalate, diethyl phthalate and bisphenol A, with relative accumulation ratios ranging from 1.1 ± 0.1 to 2.6 ± 0.4. The average daily intake values for the suite of compounds, corrected with the bioaccessibility fraction, ranged from 60 to 430 ng kg of body weight⁻¹·day⁻¹, in all cases, far below the tolerable daily intakes, thus indicating the lack of children health risk by ingestion of microplastic-laden sand with elevated concentrations of plasticizers.

Mangroves are inter-tidal ecosystems with important global ecological roles. Today, mangroves around the world are at risk of fragmentation, especially in areas with rapid urbanization. Mangroves experiencing habitat fragmentation may be more intensely affected by human activities and a scenario that might have been ignored by previous studies on trace metal (TM) environmental geochemistry. Here, we investigated the typically fragmented habitats in a subtropical mangrove estuary (the Danshuei Basin in Taiwan Strait) to evaluate how human activities affect the geochemical behaviors of TMs. Ni, Sb, Zn, Cr, Cu, and Cd were the primary contaminants found in the mangrove patches. Metal sequestration from the riverine (Ni, Cr) and in-patch activity (Sb, Zn, Cu, Cd) are primary sources of TM’s risk. Using the synthesized pollution risk assessment, we showed that most of the mangrove patches are under moderate pollution risk. A significant relationship between the TMs pollution indicators and the absorption coefficient at 254 nm (a254), implying that the a254 could be a potential convenient parameter in the TMs risk assessment, which might be partly explained by the bio-remediation of sulfate-reduction microorganism. This study demonstrates the ecological risks posed by TM pollution on urban mangrove patches and emphasizes the importance of a more comprehensive survey for estuarine mangrove patch environments to achieve Sustainable Development Goals.

The present research is mainly focusing on the characterization of X-ray developer solution and its toxic tolerance studies with Desmodesmus armatus towards the phycoremediation studies for removal of pollutants, silver, and concomitant lipid production. The characterization results suggested the presence of 1.229 ± 0.004 g/l BOD, 27.29 ± 0.230 g/l COD with a silver content of 0.01791 ± 0.000 g/l. The tolerance and toxicity limits of with X-ray developer solution reveals the remarkable growth of microalgae in 3:1.dilution ratio of BBM in the X-ray developer solutions. The phycoremediation with 19 days period shown the noticeable results with a relative BOD (20.86%), COD (13.88%), with 57.10% corresponding total phosphorous removal. The phycoremediation also has proven better relative silver removal potential of 44.06% on the 19th day with concomitant 1.392% lipid production. Overall, the present study shows the potential phycoremediation strategy of hazardous X-ray developer solutions with possible concurrent lipid production through a sustainable approach.