Search

Due to the mass production and daily use of plastic products, the potential toxicity of microplastics to the water environment has attracted worldwide attention. In this work, the effect of typical microplastics (PET) on the performance of activated sludge from membrane bioreactors (MBR) was evaluated. The impacts on biological removal efficiency were unconspicuous with continuous dosing of 60 particles/L. However, further investigations revealed that PET particle accumulation caused adverse impacts on settleability and dewaterability. The SVI value increased from 53.3 ml/g MLSS to 69.9 ml/g MLSS and the CST in the PET reactor increased by 22%. Nevertheless, hydrophobicity was reduced by 49.2%. Mechanism studies exposed that the PET microplastics accumulation improved extracellular polymeric substances (EPS) from 116.96 mg/L to 138.70 mg/L and caused cell membrane damage. The abundance and diversity of microbial community reduced in activated sludge in PET reactor compared with control reactor. These phenomena revealed a possible hypothesis that the microplastic particles increased EPS and cytotoxicity of activated sludge. However, the rate of transmembrane pressure (TMP) build-up was significantly mitigated in PET-MBR compared to that in a control-MBR (1.27 folds), which attributes that physical scour of particles may still alleviate membrane contamination in MBR. • Membrane biofouling was alleviated due to physical scouring of PET particles. • The physicochemical properties of sludge were changed after long-term PET exposure. • Accumulation of PET particles caused EPS secretion and cell membrane damage. • Long-term exposure to PET changed the microbial community of MBR system. [ABSTRACT FROM AUTHOR]

Microplastics (MPs) have recently attracted much attention due to their widespread distribution in the aquatic environment. Microplastics can act as a vector of heavy metals in the aquatic environment, causing a potential threat to aquatic organisms and human health. This review mainly summarized the occurrence of microplastics in the aquatic environment and their interaction with heavy metals. Then, we considered the adsorption mechanisms of MPs and heavy metals, and further critically discussed the effects of microplastics properties and environmental factors (e.g., pH, DOM, and salinity) on the adsorption of heavy metals. Finally, the potential risks of combined exposure of MPs and heavy metals to aquatic biota were briefly evaluated. This work aims to provide a theoretical summary of the interaction between MPs and heavy metals, and is expected to serve as a reference for the accurate assessment of their potential risks in future studies. • Microplastics serve as vectors of heavy metals in the aquatic environment. • The possible mechanisms of heavy metal adsorption by microplastics are complex. • Microplastics properties and environmental factors affect heavy metal adsorption. • Co-exposure of microplastics and heavy metals may have different effects on aquatic biota. [ABSTRACT FROM AUTHOR]

The accurate estimation of coastal water quality parameters (WQPs) is crucial for decision-makers to manage water resources. Although various machine learning (ML) models have been developed for coastal water quality estimation using remote sensing data, the performance of these models has significant uncertainties when applied to regional scales. To address this issue, an ensemble ML-based model was developed in this study. The ensemble ML model was applied to estimate chlorophyll-a (Chla), turbidity, and dissolved oxygen (DO) based on Sentinel-2 satellite images in Shenzhen Bay, China. The optimal input features for each WQP were selected from eight spectral bands and seven spectral indices. A local explanation strategy termed Shapley Additive Explanations (SHAP) was employed to quantify contributions of each feature to model outputs. In addition, the impacts of three climate factors on the variation of each WQP were analyzed. The results suggested that the ensemble ML models have satisfied performance for Chla (errors = 1.7%), turbidity (errors = 1.5%) and DO estimation (errors = 0.02%). Band 3 (B3) has the highest positive contribution to Chla estimation, while Band Ration Index2 (BR2) has the highest negative contribution to turbidity estimation, and Band 7 (B7) has the highest positive contribution to DO estimation. The spatial patterns of the three WQPs revealed that the water quality deterioration in Shenzhen Bay was mainly influenced by input of terrestrial pollutants from the estuary. Correlation analysis demonstrated that air temperature (Temp) and average air pressure (AAP) exhibited the closest relationship with Chla. DO showed the strongest negative correlation with Temp, while turbidity was not sensitive to Temp, average wind speed (AWS), and AAP. Overall, the ensemble ML model proposed in this study provides an accurate and practical method for long-term Chla, turbidity, and DO estimation in coastal waters. • A machine learning (ML) based ensemble model was proposed for the estimation of water quality parameters. • The feature selection was conducted for the ML-based ensemble model. • The ML-based ensemble model improved the estimation accuracy of non-optical active constituent. • SHAP method was employed to quantify the contribution of the input features. • Climate factors driving mechanism was considered on interannunal dynamics of water quality. [ABSTRACT FROM AUTHOR]

To evaluate the long-term climate change impacts on groundwater fluctuations of the Ardabil plain, Iran, a groundwater level (GWL) modeling was proposed in this study. Accordingly, the outputs of Global Climate Models (GCMs) under the sixth report of Coupled Model Intercomparison Project (CMIP6) and future scenario of the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5), were used as climate change forcing to the Machine learning (ML) models. The GCM data were first downscaled and projected for the future via Artificial Neural Networks (ANNs). Based on the results, compared to 2014 (the last year of the base period), the mean annual temperature may increase by 0.8 °C per decade until 2100. On the other hand, the mean precipitation may decrease by about 8% compared to the base period. Then, the centroid wells of clusters were modeled by Feedforward Neural Network (FFNN), examining different input combination sets to simulate both autoregressive and non-autoregressive models. Since each of the ML models can extract different kinds of information from a dataset, after finding the dominant input set via FFNN, GWL time series were modeled via various ML methods. The modeling results indicated that the ensemble of shallow ML models could lead to a 6% more accurate outcome than the individual shallow ML models, and 4% than the deep learning models. Also, the simulation results for future GWLs illustrated that temperature can impact groundwater oscillations directly, whereas precipitation may not have uniform impacts on the GWLs. The uncertainty evolving in the modeling process was quantified and observed to be in acceptable range. Modeling results showed that the main reason for the declining GWL in the Ardabil plain could be primarily linked to the excessive exploitation of the water table, while climate change impact could be also notable. • The average temperature will increase at different rates in different seasons. • The average precipitation will change variously in different months. • Ensemble model is more accurate than other ML models. • Temperature has a direct impact on groundwater fluctuations. • Precipitation impacts the groundwater in complex ways. [ABSTRACT FROM AUTHOR]