Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks

● Reducting the sampling frequency can enhance the modelling process. ● The pyrolysis of HDPE was investigated at three different heating rates. ● The average E_a and k₀ were calculated by Friedman, KAS, FWO, and CR methods. ● ANN was employed to predict the HDPE weight loss with the optimal MSE and R². Pyrolysis is considered an attractive option and a promising way to dispose waste plastics. The thermogravimetric experiments of high-density polyethylene (HDPE) were conducted from 105 °C to 900 °C at different heating rates (10 °C/min, 20 °C/min, and 30 °C/min) to investigate their thermal pyrolysis behavior. We investigated four methods including three model-free methods and one model-fitting method to estimate dynamic parameters. Additionally, an artificial neural network model was developed by providing the heating rates and temperatures to predict the weight loss (wt.%) of HDPE, and optimized via assessing mean squared error and determination coefficient on the test set. The optimal MSE (2.6297 × 10⁻²) and R² value (R² > 0.999) were obtained. Activation energy and pre-exponential factor obtained from four different models achieves the acceptable value between experimental and predicted results. The relative error of the model increased from 2.4 % to 6.8 % when the sampling frequency changed from 50 s to 60 s, but showed no significant difference when the sampling frequency was below 50 s. This result provides a promising approach to simplify the further modelling work and to reduce the required data storage space. This study revealed the possibility of simulating the HDPE pyrolysis process via machine learning with no significant accuracy loss of the kinetic parameters. It is hoped that this work could potentially benefit to the development of pyrolysis process modelling of HDPE and the other plastics.     

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

● Data acquisition and pre-processing for wastewater treatment were summarized. ● A PSO-SVR model for predicting COD_eff in wastewater was proposed. ● The COD_eff prediction performances of the three models in the paper were compared. ● The COD_eff prediction effects of different models in other studies were discussed. The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination (R²) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R², and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.     

Combination of steam-enhanced extraction and electrical resistance heating for efficient remediation of perchloroethylene-contaminated soil: Coupling merits and energy consumption

● Coupling merits of SEE and ERH were explored by a laboratory-scale device. ● SEE promotes the soil electrical conductivity and ERH process. ● Preheating soil by ERH improves the soil permeability and SEE. ● Combined method is more energy-efficient for perchloroethylene extraction. In situ thermal desorption (ISTD) technology effectively remediates soil contaminated by dense nonaqueous phase liquids (DNAPLs). However, more efforts are required to minimize the energy consumption of ISTD technology. This study developed a laboratory-scale experimental device to explore the coupling merits of two traditional desorption technologies: steam-enhanced extraction (SEE) and electrical resistance heating (ERH). The results showed that injecting high-density steam (> 1 g/min) into loam or clay with relatively high moisture content (> 13.3%) could fracture the soil matrix and lead to the occurrence of the preferential flow of steam. For ERH alone, the electrical resistance and soil moisture loss were critical factors influencing heating power. When ERH and SEE were combined, preheating soil by ERH could increase soil permeability, effectively alleviating the problem of preferential flow of SEE. Meanwhile, steam injection heated the soil and provided moisture for maintaining soil electrical conductivity, thereby ensuring power stability in the ERH process. Compared with ERH alone (8 V/cm) and SEE alone (1 g/min steam), the energy consumption of combined method in remediating perchloroethylene-contaminated soil was reduced by 39.3% and 52.9%, respectively. These findings indicate that the combined method is more favorable than ERH or SEE alone for remediating DNAPL-contaminated subsurfaces when considering ISTD technology.     

Nonpolar cross-stacked super-aligned carbon nanotube membrane for efficient wastewater treatment

● A novel nonpolar super-aligned carbon nanotube (SACNT) membrane was prepared. ● SACNT membranes achieved smoother and more uniform structures. ● SACNT membranes have inert chemistry and unique nonpolar wetting feature. ● SACNT membranes exhibit superior separation and antifouling capabilities. ● SACNT membranes achieved superior oil/water separation efficiency. Membrane separation technology has made great progress in various practical applications, but the unsatisfactory separation performance of prevailing membrane materials hampers its further sustainable growth. This study proposed a novel nonpolar super-aligned carbon nanotube (SACNT) membrane, which was prepared with a layer-by-layer cross-stacking method. Through controlling the number of stacked SACNT layers, three kinds of SACNT membranes (SACNT_200, SACNT_300, and SACNT_400) were prepared. Systematic characterizations and filtration tests were performed to investigate their physico-chemical properties, surface wetting behavior, and filtration performance. Compared with two commercial membranes (Com_0.22 and Com_0.45), all the SACNT membranes achieved smoother and more uniform structures. Due to the hexagonal graphene structure of CNTs, the surface chemistry of the SACNT membranes is simple and inert, thereby potentially eliminating the covalent-bonding-induced membrane fouling. Besides, the SACNT membranes exhibited a typical nonpolar wetting behavior, with high contact angles for polar liquids (water: ~124.9°–126.5°; formamide: ~80.0°–83.9°) but low contact angles for nonpolar diiodomethane (~18.8°–20.9°). This unique nonpolar feature potentially leads to weak interactions with polar substances. Furthermore, compared with the commercial membranes, the SACNT membranes obtained a significantly higher selectivity while achieving a comparable or higher permeability (depending on the number of stacked layers). Moreover, the SACNT membranes exhibited superior separation performance in various application scenarios, including municipal wastewater treatment (> 2.3 times higher cleaning efficiency), electro-assistant fouling inhibition (or even self-cleaning), and oil/water separation (> 99.2 % of separation efficiency), suggesting promising application prospects in various fields.     

Preliminary techno-economic analysis of three typical decentralized composting technologies treating rural kitchen waste: a case study in China

● Decentralized composting (DC) is a profitable KW treating technology. ● SAC and BEC were economically attractive in rural area, while HDC was unprofitable. ● KW handling subsidy plays a vital role in making DC profitable. ● SAC and BEC have great potential in promoting rural KW treatment. This study was designed to evaluate whether the decentralized rural kitchen waste (KW) composting technologies used in China can be widely applied. To this end, we completed a techno-economic analysis of three typical types of KW compositing, namely solar-assisted (SAC), bio-enhanced (BEC), and heat-dewatering composting (HDC). These evaluations revealed that all three technologies produce composting products that meet China’s organic fertilizer standard and that both SAC and BEC are economically self-sustaining and generate net profits (18824.94 and 17791.52 US$/a) and positive net present values (32133.11 and 25035.93 US$). Subsequent sensitivity analysis demonstrated that the KW-handling subsidy plays a critical role in making decentralized composting economically attractive. Based on these analyses, we believe that reducing the coverage area of SAC, reducing the operating cost of BEC and HDC, upgrading composting products, and strengthening secondary pollution control would aid in supporting the technological improvement of these processes. Moreover, providing appropriate subsidies and promulgating specific standards and policies for KW fertilizer are key strategies for decentralized rural KW composting management.     

Modelling the thresholds of nitrogen/phosphorus concentration and hydraulic retention time for bloom control in reclaimed water landscape

● A new model for bloom control in open land scape water was constructed. ● It considers the effects of temperature and light on algae growth. ● It describes threshold curve of nitrogen, phosp horus and hydraulic retention time. ● Light and temperature dependent growth para meters of typical algae were obtained. The risks posed by algal blooms caused by nitrogen and phosphorus in reclaimed water used in urban water landscapes need to be carefully controlled. In this study, the combined effects of the nitrogen and phosphorus concentrations and the light intensity and temperature on the specific growth rates of algae were determined using Monod, Steele, and Arrhenius models, then an integrated algal growth model was developed. The algae biomass, nitrogen concentration, and phosphorus concentration mass balance equations were used to establish a new control model describing the nitrogen and phosphorus concentration and hydraulic retention time thresholds for algal blooms. The model parameters were determined by fitting the models to data acquired experimentally. Finally, the control model and numerical simulations for six typical algae and mixed algae under standard conditions were used to determine nitrogen/phosphorus concentration and hydraulic retention time thresholds for landscape water to which reclaimed water is supplied (i.e., for a reclaimed water landscape).     

Electroconductive RGO-MXene membranes with wettability-regulated channels: improved water permeability and electro-enhanced rejection performance

● Electroconductive RGO-MXene membranes were fabricated. ● Wettable membrane channels were established between RGO and MXene nanosheets. ● Hydrophilic MXene reduces the resistance of water entering the membrane channels. ● Water permeance of RGO-MXene membrane is 16.8 times higher than that of RGO membrane. ● Electro-assistance can enhance the dye rejection performance of RGO-MXene membrane. Reduced graphene oxide (RGO) membranes are theoretically more conducive to the rapid transport of water molecules in their channels compared with graphene oxide (GO) membranes, as they have fewer oxygen-containing functional groups and more non-oxidized regions. However, the weak hydrophilicity of RGO membranes inhibits water entry into their channels, resulting in their low water permeability. In this work, we constructed wettable RGO-MXene channels by intercalating hydrophilic MXene nanosheets into the RGO membrane for improving the water permeance. The RGO-MXene composite membrane exhibits high pure water permeance of 62.1 L/(m²·h·bar), approximately 16.8 times that of the RGO membrane (3.7 L/(m²·h·bar)). Wettability test results and molecular dynamics simulations suggest that the improved water permeance results from the enhanced wettability of RGO-MXene membrane and increased rate of water molecules entering the RGO-MXene channels. Benefiting from good conductivity, the RGO-MXene membrane with electro-assistance exhibits significantly increased rejection rates for negatively charged dyes (from 56.0% at 0 V to 91.4% at 2.0 V for Orange G) without decreasing the permeate flux, which could be attributed to enhanced electrostatic repulsion under electro-assistance.     

Non-toxic,high selectivity process for the extraction of precious metals from waste printed circuit boards

● Au, Ag and Pd were recovered from WPCBs with high efficiencies. ● Au leaching is strictly dependent on WPCB size and reagent concentration. ● High Ag extraction efficiencies are achieved regardless of the WPCB size. ● Pd leaching works better with small and medium WPCB sizes. ● The leaching results suggest the possibility of selective recovery of metals. The work presented here focused on the extraction of gold (Au), silver (Ag) and palladium (Pd) from electronic waste using a solution of ammonium thiosulfate. Thiosulfate was used as a valid alternative to cyanide for precious metal extractions, due to its non-toxicity and high selectivity. The interactions between sodium thiosulfate, total ammonia/ammonium, precious metal concentrations and the particle size of the waste printed circuit boards (WPCBs) were studied by the response surface methodology (RSM) and the principal component analysis (PCA) to maximize precious metal mobilization. Au extraction reached a high efficiency with a granulometry of less than 0.25 mm, but the consumption of reagents was high. On the other hand, Ag extraction depended neither on thiosulfate/ammonia concentration nor granulometry of WPCBs and it showed efficiency of 90% also with the biggest particle size (0.50 < Ø < 1.00 mm). Pd extraction, similarly to Au, showed the best efficiency with the smallest and the medium WPCB sizes, but required less reagents compared to Au. The results showed that precious metal leaching is a complex process (mainly for Au, which requires more severe conditions in order to achieve high extraction efficiencies) correlated with reagent concentrations, precious metal concentrations and WPCB particle sizes. These results have great potentiality, suggesting the possibility of a more selective recovery of precious metals based on the different granulometry of the WPCBs. Furthermore, the high extraction efficiencies obtained for all the metals bode well in the perspective of large-scale applications.     

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality. ● Improved model quickly converges to the global optimal fitness and remains stable. ● The prediction accuracy of water quality parameters is significantly improved. Water quality prediction is vital for solving water pollution and protecting the water environment. In terms of the characteristics of nonlinearity, instability, and randomness of water quality parameters, a short-term water quality prediction model was proposed based on variational mode decomposition (VMD) and improved grasshopper optimization algorithm (IGOA), so as to optimize long short-term memory neural network (LSTM). First, VMD was adopted to decompose the water quality data into a series of relatively stable components, with the aim to reduce the instability of the original data and increase the predictability, then each component was input into the IGOA-LSTM model for prediction. Finally, each component was added to obtain the predicted values. In this study, the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and prediction. The experimental results showed that the prediction accuracy of the VMD-IGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition (EEMD), the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Autoregressive Network with Exogenous Inputs (NARX), Recurrent Neural Network (RNN), as well as other models, showing better performance in short-term prediction. The current study will provide a reliable solution for water quality prediction studies in other areas.     

Biodegradation of waste refrigerator polyurethane by mealworms

● Waste refrigerator polyurethane (WRPU) was ingested and biodegraded by mealworms. ● The carbon in WRPU-based frass was lower than that in WRPU. ● Urethane groups in WRPU were broken down after ingestion by mealworms. ● Thermal stability of WRPU-based frass were deteriorated compared to that of WRPU. ● Gut microbiomes of mealworms fed using WRPU were distinct from that fed using bran. Refrigerator insulation replacement results in discarding a large amount of waste refrigerator polyurethane (WRPU). Insect larvae like mealworms have been used to biodegrade pristine plastics. However, knowledge about mealworms degrading WRPU is scarce. This study presents an in-depth investigation of the degradation of WRPU by mealworms using the micro-morphology, composition, and functional groups of WRPU and the egested frass characteristics. It was found that the WRPU debris in frass was scoured, implying that WRPU was ingested and degraded by mealworms. The carbon content of WRPU-based frass was lower than that of WRPU, indicating that mealworms utilized WRPU as a carbon source. The urethane groups in WRPU were broken, and benzene rings’ C=C and C–H bonds in the isocyanate disappeared after being ingested by mealworms. Thermal gravimetric-differential thermal gravimetry analysis showed that the weight loss temperature of WRPU-based frass was 300 °C lower than that of WRPU, indicating that the thermal stability of WRPU deteriorated after being ingested. The carbon balance analysis confirmed that carbon in the ingested WRPU released as CO₂ increased from 18.84 % to 29.80 %, suggesting that WRPU was partially mineralized. The carbon in the mealworm biomass ingesting WRPU decreased. The possible reason is that WRPU does not supply sufficient nutrients for mealworm growth, and the impurities and odor present in WRPU affect the appetite of the mealworms. The microbial community analysis indicated that WRPU exerts a considerable effect on the gut microorganism of mealworms. These findings confirm that mealworms degrade WRPU.     

Revealing the GHG reduction potential of emerging biomass-based CO2 utilization with an iron cycle system

● Greenhouse gas mitigation by biomass-based CO₂ utilization with a Fe cycle system. ● The system including hydrothermal CO₂ reduction with Fe and Fe recovery by biomass. ● The reduction potential quantified by experiments, simulations, and an ex-ante LCA. ● The greatest GHG reduction potential is −34.03 kg CO₂-eq/kg absorbed CO₂. ● Ex-ante LCA supports process optimization to maximize GHG reduction potential. CO₂ utilization becomes a promising solution for reducing anthropogenic greenhouse gas (GHG) emissions. Biomass-based CO₂ utilization (BCU) even has the potential to generate negative emissions, but the corresponding quantitative evaluation is limited. Herein, the biomass-based CO₂ utilization with an iron cycle (BCU-Fe) system, which converts CO₂ into formate by Fe under hydrothermal conditions and recovers Fe with biomass-derived glycerin, was investigated. The GHG reduction potential under various process designs was quantified by a multidisciplinary method, including experiments, simulations, and an ex-ante life-cycle assessment. The results reveal that the BCU-Fe system could bring considerable GHG emission reduction. Significantly, the lowest value is −34.03 kg CO₂-eq/kg absorbed CO₂ (−2.44 kg CO₂-eq/kg circulated Fe) with the optimal yield of formate (66%) and Fe (80%). The proposed ex-ante evaluation approach not only reveals the benefits of mitigating climate change by applying the BCU-Fe system, but also serves as a generic tool to guide the industrialization of emerging carbon-neutral technologies.     

Resource utilization of typical biomass wastes as biochars in removing plasticizer diethyl phthalate from water: characterization and adsorption mechanisms

● Six largely produced agricultural biomass wastes were pyrolyzed into biochars. ● Feedstock type significantly determined physiochemical properties of biochars. ● The biochars showed powerful adsorption capabilities to Plasticizer DEP. ● Giant reed biochar with higher DEP adsorption was a prominent sorbent. Plastic pollution as a global environmental issue has become a research hotspot, among which the removal of inherent plasticizer (e.g., phthalic acid esters, PAEs) received increasing attention. However, the effects of biochars derived from different feedstocks on the adsorption of PAEs are poorly understood. Thus, the characteristics of biochars derived from six largely produced biomass wastes in China at 400 °C, as well as their performance in adsorption of diethyl phthalate (DEP), one of frequently detected PAEs in aqueous environment, were investigated. The results indicated that the variation in feedstock type showed significant changes in the properties (e.g., porosity, specific surface area, surface functional groups) of biochars, which affected DEP adsorption and desorption. Pseudo-second order and Freundlich models fitted the adsorption data well, and adsorption mechanisms mainly included hydrophobic effect, followed by micropore filling, hydrogen bonding, and π-π EDA interactions. Adsorption thermodynamics revealed that the adsorption was a spontaneous and exothermic the adsorption capacities of DEP on these biochars slightly decreased with the increasing pH but increased with the increasing ionic strength. Among these biochars, the giant reed biochar with relatively higher DEP adsorption and low desorption exhibited the great efficiency for DEP removal as an environment-friendly sorbent. These results highlighted the significant roles of micropore filling and hydrogen bond in determining adsorption capacity of designed biochars prepared from selecting suitable agricultural straws and wetland plant waste to typical plasticizer. The findings are useful for producing designed biochars from different biomass wastes for plasticizer pollution control.     

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov chain Monte Carlo algorithm

● A hydrodynamic-Bayesian inference model was developed for water pollution tracking. ● Model is not stuck in local optimal solutions for high-dimensional problem. ● Model can estimate source parameters accurately with known river water levels. ● Both sudden spill incident and normal sewage inputs into the river can be tracked. ● Model is superior to the traditional approaches based on the test cases. Water quality restoration in rivers requires identification of the locations and discharges of pollution sources, and a reliable mathematical model to accomplish this identification is essential. In this paper, an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge. The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method (MCMC). The methodological framework is tested using a designed case of a sudden wastewater spill incident (i.e., source location, flow rate, and starting and ending times of the discharge) and a real case of multiple sewage inputs into a river (i.e., locations and daily flows of sewage sources). The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites. It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space. In comparison, the inverse models based on the popular random walk Metropolis (RWM) algorithm and microbial genetic algorithm (MGA) do not produce reliable estimates for the two scenarios even after multiple simulation runs, and they fall into locally optimal solutions. Since much more water level data are available than water quality data, the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data, especially for rivers receiving significant sewage discharges.     

Recent advances in electrochemical decontamination of perfluorinated compounds from water: a review

● Recent advances in the electrochemical decontamination of PFAS are reviewed. ● Underlying mechanisms and impacting factors of these processes are discussed. ● Several novel couped systems and electrode materials are emphasized. ● Major knowledge gaps and research prospects on PFAS removal are identified. Per- and polyfluoroalkyl substances (PFAS) pose serious human health and environmental risks due to their persistence and toxicity. Among the available PFAS remediation options, the electrochemical approach is promising with better control. In this review, recent advances in the decontamination of PFAS from water using several state-of-the-art electrochemical strategies, including electro-oxidation, electro-adsorption, and electro-coagulation, were systematically reviewed. We aimed to elucidate their design principles, underlying working mechanisms, and the effects of operation factors (e.g., solution pH, applied voltage, and reactor configuration). The recent developments of innovative electrochemical systems and novel electrode materials were highlighted. In addition, the development of coupled processes that could overcome the shortcomings of low efficiency and high energy consumption of conventional electrochemical systems was also emphasized. This review identified several major knowledge gaps and challenges in the scalability and adaptability of efficient electrochemical systems for PFAS remediation. Materials science and system design developments are forging a path toward sustainable treatment of PFAS-contaminated water through electrochemical technologies.     

Effectiveness of tertiary treatment processes in removing different classes of emerging contaminants from domestic wastewater

● Different advanced treatment processes were tested for ECs removal from wastewater. ● UV radiation showed low to moderate removal for 5 of the 38 micropollutants. ● Among tested membrane processes, nanofiltration showed the better performance. ● The use of PAC achieved high or partially removal for 31 out of the 38 compounds. ● The environmental and economical evaluation of a pilot-scale PAC unit is suggested. In this work, 38 different organic emerging contaminants (ECs), belonging to various chemical classes such as pharmaceuticals (PhCs), endocrine-disrupting chemicals (EDCs), benzotriazoles (BTRs), benzothiazoles (BTHs), and perfluorinated compounds (PFCs), were initially identified and quantified in the biologically treated wastewater collected from Athens’ (Greece) Sewage Treatment Plant (STP). Processes already used in existing STPs such as microfiltration (MF), nanofiltration (NF), ultrafiltration (UF), UV radiation, and powdered activated carbon (PAC) were assessed for ECs’ removal, under the conditions that represent their actual application for disinfection or advanced wastewater treatment. The results indicated that MF removed only one out of the 38 ECs and hence it was selected as pretreatment step for the other processes. UV radiation in the studied conditions showed low to moderate removal for 5 out of the 38 ECs. NF showed better results than UF due to the smaller pore sizes of the filtration system. However, this enhancement was observed mainly for 8 compounds originating from the classes of PhCs and PFCs, while the removal of EDCs was not statistically significant. Among the various studied technologies, PAC stands out due to its capability to sufficiently remove most ECs. In particular, removal rates higher than 70% were observed for 9 compounds, 22 were partially removed, while 7 demonstrated low removal rates. Based on our screening experiments, future research should focus on scaling-up PAC in actual conditions, combining PAC with other processes, and conduct a complete economic and environmental assessment of the treatment.     

Is atmospheric oxidation capacity better in indicating tropospheric O3 formation?

● This study summarizes and evaluates different approaches that indicate O₃ formation. ● Isopleth and sensitivity methods are useful but have many prerequisites. ● AOC is a better indicator of photochemical reactions leading to O₃ formation. Tropospheric ozone (O₃) concentration is increasing in China along with dramatic changes in precursor emissions and meteorological conditions, adversely affecting human health and ecosystems. O₃ is formed from the complex nonlinear photochemical reactions from nitrogen oxides (NO_x = NO + NO₂) and volatile organic compounds (VOCs). Although the mechanism of O₃ formation is rather clear, describing and analyzing its changes and formation potential at fine spatial and temporal resolution is still a challenge today. In this study, we briefly summarized and evaluated different approaches that indicate O₃ formation regimes. We identify that atmospheric oxidation capacity (AOC) is a better indicator of photochemical reactions leading to the formation of O₃ and other secondary pollutants. Results show that AOC has a prominent positive relationship to O₃ in the major city clusters in China, with a goodness of fit (R²) up to 0.6. This outcome provides a novel perspective in characterizing O₃ formation and has significant implications for formulating control strategies of secondary pollutants.     

Photo-induced surface frustrated Lewis pairs for promoted photocatalytic decomposition of perfluorooctanoic acid

● Terminal carboxylate group activation is PFOA degradation’s rate-limiting step. ● Bi₃O(OH)(PO₄)₂ with surface frustrated Lewis pairs (SFLPs) efficiently degrade PFOA. ● Photo-induced Lewis acidic sites and proximal surface hydroxyls constitute SFLPs. ● SFLPs act as collection centers to effectively adsorb PFOA. ● SFLPs endow accessible pathways for photogenerated holes rapid transfer to PFOA. Heterogeneous photocatalysis has gained substantial research interest in treating per- and polyfluoroalkyl substances (PFAS)-contaminated water. However, sluggish degradation kinetics and low defluorination efficiency compromise their practical applications. Here, we report a superior photocatalyst, defected Bi₃O(OH)(PO₄)₂, which could effectively degrade typical PFAS, perfluorooctanoic acid (PFOA), with high defluorination efficiency. The UV light irradiation could in situ generate oxygen vacancies on Bi₃O(OH)(PO₄)₂ through oxidation of the lattice hydroxyls, which further promotes the formation of Lewis acidic coordinately unsaturated bismuth sites. Then, the Lewis acidic sites couple with the proximal surface hydroxyls to constitute the surface frustrated Lewis pairs (SFLPs). With the in-depth spectroscopic analysis, we revealed that the photo-induced SFLPs act as collection centers to effectively adsorb PFOA and endow accessible pathways to transfer photogenerated holes to PFOA rapidly. Consequently, activation of the terminal carboxyl, a rate-limiting step for PFOA decomposition, could be easily achieved over the defected Bi₃O(OH)(PO₄)₂ photocatalyst. These results suggest that SFLPs exhibit great potential in developing highly efficient photocatalysts to degrade persistent organic pollutants.     

Evaluation of activated sludge properties’ changes in industrial-wastewater pre-treatment: role of residual aluminum hydrolyzed species with different polymerization degree

● Medium poly Al salts dominated the PAC residual salts with a rational dosage. ● Settlement flocculation effect under medium poly Al salts showed a better trend. ● Complex of medium poly Al salts and enzymes promoted cell activity. ● Medium poly Al salts were beneficial to the effluent indexes. With the widespread introduction of pre-coagulation prior to the biological unit in various industrial wastewater treatments, it is noteworthy that long-term accumulation of residual coagulants has certains effect on both micro and macro characteristics of activated sludge (AS). In this study, the morphology distributions of residual aluminum salts (RAS) and their effects on the removal efficiency of AS were investigated under different PAC concentrations. The results showed that the dominance of medium polymeric RAS, formed under an appropriate PAC dose of 20 mg/L enhanced the hydrophobicity, flocculation, and sedimentation performances of AS, as well as the enzymatic activity in cells in the sludge system, improving the main pollutants removal efficiency of the treatment system. Comparatively the species composition with monomer and dimer / high polymer RAS as the overwhelming parts under an over-dosed PAC concentration of 55 mg/L resulted in excessive secretion of EPS with loose flocs structure and conspicuous inhibition of cellular activity, leading to the deterioration of physico-chemical and biological properties of AS. Based on these findings, this study can shed light on the role of the RAS hydrolyzed species distributions, closely relevant to Al dosage, in affecting the comprehensive properties of AS and provide a theoretical reference for coagulants dosage precise control in the pretreatment of industrial wastewater.     

V-shaped substrate for surface and volume enhanced Raman spectroscopic analysis of microplastics

● V-shaped substrate was obtained for SERS analysis of microplastics (diameter ≈ 1 μm). ● Enhancement factor of V-shaped substrate can reach 20 in microplastics detection. ● V-shaped nanopore array can bring additional volume enhancement. ● V-shaped substrate was more economic in application compared to Klarite substrate. Research on the microplastics (MPs) is developing towards smaller size, but corresponding methods for the rapid and accurate detection of microplastics, especially nanoplastics still present challenge. In this work, a novel surface and volume enhanced Raman spectroscopy substrate was developed for the rapid detection of microplastic particles below 5 μm. The gold nanoparticles (NPs) were deposited onto the surface and into the V-shaped nanopores of anodized aluminum oxide (AAO) through magnetron sputtering or ion sputtering, and then AuNPs@V-shaped AAO SERS substrate was obtained and studied for microplastic detection. SERS performance of AuNPs@V-shaped AAO SERS substrate was evaluated through the detection of polystyrene and polymethyl methacrylate microspheres. Results indicated that individual polystyrene sphere with a diameter of 1 μm can be well detected on AuNPs@V-shaped AAO SERS substrate, and the maximum enhancement factor (EF) can reach 20. In addition, microplastics in ambient atmospheric samples were collected and tested to verify the effectiveness of the AuNPs@V-shaped AAO SERS substrate in the real environment. This study provides a rapid, economic and simple method for detecting and identifying microplastics with small size.     

Mechanism and characterization of microplastic aging process: A review

● Methods for estimating the aging of environmental micro-plastics were highlighted. ● Aging pathways & characterization methods of microplastics were related and reviewed. ● Possible approaches to reduce the contamination of microplastics were proposed. ● The prospect and deficiency of degradable plastics were analyzed. With the increasing production of petroleum-based plastics, the problem of environmental pollution caused by plastics has aroused widespread concern. Microplastics, which are formed by the fragmentation of macro plastics, are bio-accumulate easily due to their small size and slow degradation under natural conditions. The aging of plastics is an inevitable process for their degradation and enhancement of adsorption performance toward pollutants due to a series of changes in their physiochemical properties, which significantly increase the toxicity and harm of plastics. Therefore, studies should focus on the aging process of microplastics through reasonable characterization methods to promote the aging process and prevent white pollution. This review summarizes the latest progress in natural aging process and characterization methods to determine the natural aging mechanism of microplastics. In addition, recent advances in the artificial aging of microplastic pollutants are reviewed. The degradation status and by-products of biodegradable plastics in the natural environment and whether they can truly solve the plastic pollution problem have been discussed. Findings from the literature pointed out that the aging process of microplastics lacks professional and exclusive characterization methods, which include qualitative and quantitative analyses. To lessen the toxicity of microplastics in the environment, future research directions have been suggested based on existing problems in the current research. This review could provide a systematic reference for in-depth exploration of the aging mechanism and behavior of microplastics in natural and artificial systems.