Frontiers of Environmental Science & Engineering最新文献

Urban roads can be designated as surface flood passages to transport excess runoff during extreme storms, thereby preventing local flooding, which is known as the major drainage system. However, this practice poses significant risks, including human loss and property damage, due to the high flow rate and velocity carried by roads. Moreover, urban roads with low flood-resilience may significantly hamper the transportation function during severe storms, leading to dysfunction of the city. Therefore, there is an urgent need to transform risk-oriented flood passages into resilient urban road-based flood passages. This paper presents a systematic review of existing methodologies in designing a road network-based flood passage system, along with the discussion of new technologies to enhance system resilience. The study also addresses current knowledge gaps and future directions. The results indicate that flood management measures based on the urban road network should integrate accessibility assessment, lifeline and emergency planning to ensure human well-being outcomes. Furthermore, the special needs and features of vulnerable groups must be taken into serious consideration during the planning stage. In addition, a data-driven approach is recommended to facilitate real-time management and evaluate future works.

The biosynthesis of isoprene offers a more sustainable alternative to fossil fuel-based approaches, yet its success has been largely limited to pure organic compounds and the cost remains a challenge. This study proposes a waste-to-wealth strategy for isoprene biosynthesis utilizing genetically engineered E. coli bacteria to convert organic waste from real food wastewater. The impact of organic compounds present in wastewater on E. coli growth and isoprene production was systematically investigated. The results demonstrated that with filtration pretreatment of wastewater, isoprene yield, and production achieved 115 mg/g COD and 7.1 mg/(L·h), respectively. Moreover, even without pretreatment, isoprene yield only decreased by ∼ 24%, indicating promising scalability. Glucose, maltose, glycerol, and lactate are effective substrates for isoprene biosynthesis, whereas starch, protein, and acetate do not support E. coli growth. The optimum C/N ratio for isoprene production was found to be 8:1. Furthermore, augmenting essential nutrients in wastewater elevated the isoprene yield increased to 159 mg/g COD. The wastewater biosynthesis significantly reduced the cost (44%–53% decrease, p-value < 0.01) and CO₂ emission (46%–55% decrease, p-value < 0.01) compared with both sugar fermentation and fossil fuel–based refining. This study introduced a more sustainable and economically viable approach to isoprene synthesis, offering an avenue for resource recovery from wastewater.

The existing automated wastewater treatment control systems encounter challenges such as the utilization of specialized testing instruments, equipment repair complications, high operational costs, substantial operational errors, and low detection accuracy. An effective soft measure model offers a viable approach for real-time monitoring and the development of automated control in the wastewater treatment process. Consequently, a novel hybrid deep learning CNN-BNLSTM-Attention (CBNLSMA) model, which incorporates convolutional neural networks (CNN), bidirectional nested long and short-term memory neural networks (BNLSTM), attention mechanisms (AM), and Tree-structure Parzen Estimators (TPE), has been developed for monitoring effluent water quality during the wastewater treatment process. The CBNLSMA model is divided into four stages: the CNN module for feature extraction and data filtering to expedite operations; the BNLSTM module for temporal data’s temporal information extraction; the AM module for model weight reassignment; and the TPE optimization algorithm for the CBNLSMA model’s hyperparameter search optimization. In comparison with other models (TPE-CNN-BNLSTM, TPE-BNLSTM-AM, TPE-CNN-AM, PSO-CBNLSTMA), the CBNLSMA model reduced the RMSE for effluent COD prediction by 25.4%, decreased the MAPE by 32.9%, and enhanced the R² by 14.9%. For the effluent SS prediction, the CBNLSMA model reduced the RMSE by 26.4%, the MAPE by 21.0%, and improved the R² by 35.7% compared to other models. The simulation results demonstrate that the proposed CBNLSMA model holds significant potential for real-time effluent quality monitoring, indicating its high potential for automated control in wastewater treatment processes.

This study proposed the design, fabrication, and assembly of membrane integrity detection instruments in membrane bioreactors (MBR) based on fluorescence spectroscopy. Based on the PARAFAC model, we found that the peak at 280/335 nm strengthened after membrane breakage. The peak at 340/430 nm reflected the sludge concentration in the MBR and reduced the influence of internal filtration effects on detection. Therefore, we determined that the dual-LED light source excitation detection system can detect tryptophan-like substances at 280 nm (T-peak) and humic acid at 340 nm (C-peak). T-peak was identified as the core index indicating membrane integrity. Moreover, the C-peak is the reference indicator factor for a sensitive response to changes in the sludge concentration. The portable fluorescence instrument exhibited high sensitivity and good feedback accuracy compared to particle counting and turbidity detection, where the log reduction value was greater than 3.5. This overcomes the disadvantage of false alarms in particle counters and is not affected by the position of the pump system. This portable instrument provides a flexible and highly sensitive method for the assessment of industrial membrane integrity.

Chlorine-based disinfection is ubiquitous in conventional drinking water treatment (DWT) and serves to mitigate threats of acute microbial disease caused by pathogens that may be present in source water. An important index of disinfection efficiency is the free chlorine residual (FCR), a regulated disinfection parameter in the US that indirectly measures disinfectant power for prevention of microbial recontamination during DWT and distribution. This work demonstrates how machine learning (ML) can be implemented to improve FCR forecasting when supplied with water quality data from a real, full-scale chlorine disinfection system in Georgia, USA. More precisely, a gradient-boosting ML method (CatBoost) was developed from a full year of DWT plant-generated chlorine disinfection data, including water quality parameters (e.g., temperature, turbidity, pH) and operational process data (e.g., flowrates), to predict FCR. Four gradient-boosting models were implemented, with the highest performance achieving a coefficient of determination, R², of 0.937. Values that provide explanations using Shapley’s additive method were used to interpret the model’s results, uncovering that standard DWT operating parameters, although non-intuitive and theoretically non-causal, vastly improved prediction performance. These results provide a base case for data-driven DWT disinfection supervision and suggest process monitoring methods to provide better information to plant operators for implementation of safe chlorine dosing to maintain optimum FCR.

Secondary water supply systems (SWSSs) are important components of the water supply infrastructure that ensure residents’ drinking water safety. SWSSs are characterized by long detention time, warm temperature, and unreasonable management, which may trigger the deterioration of water quality and increase risks. In this study, drinking water quality index (DWQI) and health risk assessment (HRA) were selected and modified to quantitatively assess the water quality and health risks of SWSSs in residential neighborhoods. In total, 121 seasonal water samples were selected. It was observed that the water quality was excellent with the DWQI of 0.14 ± 0.04, excluding one sample, which was extremely poor owing to its excessive total bacterial count. The HRA results revealed that the health risks were low: negligible non-carcinogenic risk for any population; negligible and acceptable carcinogenic risk for children aged 6–17 and adults. However, samples revealed higher carcinogenic risk (7.63 × 10⁻⁵ ± 3.29 × 10⁻⁶) for children aged 0–5, and arsenic was the major substance. Summer samples had poor water quality and higher health risks, which called for attention. To further investigate the water quality and health risks of SWSSs, monthly sampling was conducted during summer. All 24 water samples were qualified in Chinese standard (Gb 5749-2022) and characterized as excellent quality. Their HRA results were consistent with the seasonal samples’ and the health risks were mainly concentrated in May. Overall, our study provides a suitable framework for water quality security, advice for managers, and references for administrators in other cities.

It has been demonstrated that microplastics (MPs) can accumulate heavy metals from the environment and transfer them into organisms via the food chain. However, adsorption and desorption capacities for biodegradable MPs relative to those for conventional MPs remain poorly understood. In this study, cadmium (Cd(II)) adsorption and desorption characteristics of polylactic acid (PLA), a typical biodegradable MP, were investigated. Two conventional MPs, i.e., polypropylene (PP) and polyamide (PA) were used for comparison. The maximum Cd(II) adsorption capacities of the MPs studied in the adsorption experiments decreased in the order PA (0.96 ± 0.07 mg/g) > PLA (0.64 ± 0.04 mg/g) > PP (0.22 ± 0.03 mg/g). The Pseudo-second-order kinetic model and Freundlich isothermal model described the Cd(II) adsorption behaviors of PLA MPs well. X-ray photoelectron spectroscopy and two-dimensional Fourier transform infrared correlation spectroscopy analysis indicated that oxygen functional groups were the major and preferential binding sites of PLA MPs, which contributed to their high Cd(II) adsorption capacities. Simulated gastric and intestinal fluids both significantly enhanced the desorption capacities of the examined MPs. Notably, degradation of the PLA MPs during in vitro human digestion made the Cd(II) on the PLA MPs more bioaccessible (19% in the gastric phase and 62% in the intestinal phase) than Cd(II) on the PP and PA MPs. These results indicate the remarkable capacities of biodegradable MPs to accumulate Cd(II) and transfer it to the digestive system and show that biodegradable MPs might pose more severe threats to human health than conventional nonbiodegradable MPs.

Polybrominated diphenyl ethers (PBDEs), a class of persistent organic pollutants, have been frequently detected in soil at e-waste recycling sites. However, the key factors controlling the transport of PBDEs from surface soil to the vadose zone and groundwater are unclear. Here, colloid-enhanced leaching of PBDEs from undisturbed soil cores collected at an e-waste recycling site in Tianjin, China, is reported. Spatially heterogeneous release of colloids and PBDEs was observed in all the tested soil cores under chemical and hydrodynamic perturbations, indicating the presence of preferential flow paths. Colloid concentration in the effluent significantly increased as ionic strength decreased (from 10 to 0.01 mmol/L), probably due to the stronger electrostatic repulsion between colloidal particles and the soil matrix at lower ionic strength. In contrast, colloid mobilization was not significantly affected by the changes in pH of the influent (from 4.0 to 10.0) and flow rate (from a Darcy velocity of 1.5 to 6.0 cm/h). The concentrations of 2,2′,3,3′,4,4′,5,5′,6,6′-decabromodiphenyl ether (BDE-209), the predominant PBDE congener at the site, detected in the leachate (ranging from 1.09 to 3.43 ng/L) were much lower than previously reported results from packed column leaching tests, and were positively correlated with colloid concentrations. This indicates that remobilization of colloids at e-waste recycling sites can promote the leaching and downward migration of PBDEs from surface soil. The findings highlight the potential risk of surface soil PBDE contamination to groundwater quality and call for further understanding of colloid-facilitated transport for predicting the fate of PBDEs at e-waste recycling sites.

In the sewage treatment process, facilitating the conversion of pollutants into value-added resources holds great potential for reducing the amount of greenhouse gas emissions and promoting economic circulation. Cyanophycin granule polypeptide (CGP), a recently discovered high value-added biopolymer present in activated sludge, has provided new avenues for the recovery of resources. However, the mechanisms that regulate CGP synthesis and the characteristics of this biopolymer in activated sludge remain unclear thus far. This study investigated the synthesis of CGP, polyhydroxyalkanoates (PHA), and alginate-like exopolysaccharides (ALE) in various microbial aggregates under different carbon sources feeding conditions. Our results showed that the CGP yields was superior that of PHA and ALE when subjected to identical carbon source feeding conditions. Furthermore, biofilm was more conducive to CGP accumulation than floc sludge. Compared with glucose and methanol, sodium acetate significantly enriched the CGP synthetase-encoding gene (cphA_abundance = ~17419), resulting in the highest CGP yield (average 107.1 mg/g MLSS) in both biofilm and floc sludge. This study is the first to reported the characteristic fluorescence of CGP (Ex/Em = ~360/450 nm) caused by the aggregated luminescence of arginine on the side chains. Overall, this study highlights the potential application of CGP as a fluorescent material and offers insights into CGP recovery from activated sludge in wastewater treatment plants.