Differences in distributions,assembly mechanisms,and putative interactions of AOB and NOB at a large spatial scale

● Nitrifiers in WWTP were investigated at large spatial scale. ● AOB populations varied greatly but NOB populations were similar among cities. ● Drift dominated both AOB and NOB assembling processes. ● DO did not show a significant effect on NOB. ● NOB tended to cooperate with AOB and non-nitrifying microorganisms. Ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) play crucial roles in removing nitrogen from sewage in wastewater treatment plants (WWTPs) to protect water resources. However, the differences in ecological properties and putative interactions of AOB and NOB in WWTPs at a large spatial scale remain unclear. Hence, 132 activated sludge (AS) samples collected from 11 cities across China were studied by utilizing 16S rRNA gene sequencing technology. Results indicated that Nitrosomonas and Nitrosospira accounted for similar ratios of the AOB community and might play nearly equal roles in ammonia oxidation in AS. However, Nitrospira greatly outnumbered other NOB genera, with proportions varying from 94.7% to 99.9% of the NOB community in all WWTPs. Similar compositions and, hence, a low distance–decay turnover rate of NOB (0.035) across China were observed. This scenario might have partly resulted from the high proportions of homogenizing dispersal (~13%). Additionally, drift presented dominant roles in AOB and NOB assembling mechanisms (85.2% and 81.6% for AOB and NOB, respectively). The partial Mantel test illustrated that sludge retention time and temperature were the primary environmental factors affecting AOB and NOB communities. Network results showed that NOB played a leading role in maintaining module structures and node connections in AS. Moreover, most links between NOB and other microorganisms were positive, indicating that NOB were involved in complex symbioses with bacteria in AS.     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.     

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

● Used a double-stage attention mechanism model to predict ozone. ● The model can autonomously select the appropriate time series for forecasting. ● The model outperforms other machine learning models and WRF-CMAQ. ● We used the model to analyze the driving factors of VOCs that cause ozone pollution. Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.     

China’s carbon neutrality: an extensive and profound systemic reform

● China has pledged ambitious carbon peak and neutrality goals for mitigating global climate change. ● Major challenges to achieve carbon neutrality in China are summarized. ● The new opportunities along the pathway of China’s carbon neutrality are discussed from four aspects. ● Five policy suggestions for China are provided. China is the largest developing economy and carbon dioxide emitter in the world, the carbon neutrality goal of which will have a profound influence on the mitigation pathway of global climate change. The transition towards a carbon-neutral society is integrated into the construction of ecological civilization in China, and brings profound implications for China’s socioeconomic development. Here, we not only summarize the major challenges in achieving carbon neutrality in China, but also identify the four potential new opportunities: namely, the acceleration of technology innovations, narrowing regional disparity by reshaping the value of resources, transforming the industrial structure, and co-benefits of pollution and carbon mitigation. Finally, we provide five policy suggestions and highlight the importance of balancing economic growth and carbon mitigation, and the joint efforts among the government, the enterprises, and the residents.     

Cultivars and oil extraction techniques affect Cd/Pb contents and health risks in oil of rapeseed grown on Cd/Pb-contaminated farmland

● Organic solvent extracted fewer Cd/Pb in rapeseed oil than physical pressing. ● Brassica rapa transferred fewer Cd and Pb from seed to oil than Brassica napus . ● Carcinogenic risk mainly from Cd and worth more concern than noncarcinogenic risk. ● Organic solvent specially SLB pose less heath risk for oil than physical pressing. ● Rapeseed oil posed higher carcinogenic risk for rural residents than urban. Substitute planting with rapeseed offers promise for safely using large areas of Cd/Pb-contaminated farmland. Cd/Pb distributions during rapeseed oil production were investigated and health risks posed by the oil were assessed. Tests were performed using three cultivars (Brassica rapa SYH and ZS100 and Brassica napus QY-1) and four oil extraction techniques (mechanical and low-temperature pressing and n-hexane and subcritical low-temperature butane extraction). The amounts of Cd and Pb in oil were 0.73%–8.44% and 3.14%–11.76%, respectively, of the amounts in rapeseed and were strongly affected by the cultivar and oil extraction technique. The heavy metal (HM) concentrations were lower in solvent-extracted oil (particularly subcritical low-temperature butane extracted oil, in which HMs were not detected) than mechanically pressed oil. The Cd and Pb transfer indices were lower (meaning larger proportions of HMs were retained by the rapeseed meal) for B. rapa than B. napus. This was attributed to a high HM binding protein content of B. rapa seed. Health risks to humans were assessed using a probabilistic risk assessment model. The carcinogenic risk was mainly (97.1%–99.9%) caused by Cd and poses more concern than non-carcinogenic risk. Stronger health risks are posed by mechanically pressed than solvent-extracted oil, and higher carcinogenic risks are posed to people living in rural areas than urban areas. Substitute planting with B. rapa and extracting oil with organic solvent (preferably subcritical low-temperature butane) are optimal for safely utilizing Cd/Pb-contaminated soil. Attention should be paid to the health risks posed by Cd in oil to rural populations.     

A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification

● Established a quantification method of pollutant emission standard. ● Predicted the SO₂ emission intensity of single coking enterprises in China. ● Evaluated the influence of pollutant discharge standard on prediction accuracy. ● Analyzed the SO₂ emissions of Chinese provincial and municipal coking enterprises. Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from single enterprise can determine the exact source of atmospheric pollutants and control atmospheric pollution precisely. Based on China’s coking enterprises in 2020, we proposed a quantitative method for pollutant emission standards and introduced the quantification results of pollutant emission standards (QRPES) into the construction of support vector regression (SVR) and random forest regression (RFR) prediction methods for SO₂ emission of coking enterprises in China. The results show that, affected by the types of coke ovens and regions, China’s current coking enterprises have implemented a total of 21 emission standards, with marked differences. After adding QRPES, it was found that the root mean squared error (RMSE) of SVR and RFR decreased from 0.055 kt/a and 0.059 kt/a to 0.045 kt/a and 0.039 kt/a, and theR² increased from 0.890 and 0.881 to 0.926 and 0.945, respectively. This shows that the QRPES can greatly improve the prediction accuracy, and the SO₂ emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO₂ emissions from Chinese coking enterprises are concentrated in Shanxi, Shaanxi and Hebei provinces in central China. The method created in this paper fills in the blank of forecasting method of air pollutant emission intensity of single enterprise and is of great help to the accurate control of air pollutants.     

Development of machine learning multi-city model for municipal solid waste generation prediction

● A database of municipal solid waste (MSW) generation in China was established. ● An accurate MSW generation prediction model (WGMod) was constructed. ● Key factors affecting MSW generation were identified. ● MSW trends generation in Beijing and Shenzhen in the near future are projected. Integrated management of municipal solid waste (MSW) is a major environmental challenge encountered by many countries. To support waste treatment/management and national macroeconomic policy development, it is essential to develop a prediction model. With this motivation, a database of MSW generation and feature variables covering 130 cities across China is constructed. Based on the database, advanced machine learning (gradient boost regression tree) algorithm is adopted to build the waste generation prediction model, i.e., WGMod. In the model development process, the main influencing factors on MSW generation are identified by weight analysis. The selected key influencing factors are annual precipitation, population density and annual mean temperature with the weights of 13%, 11% and 10%, respectively. The WGMod shows good performance with R² = 0.939. Model prediction on MSW generation in Beijing and Shenzhen indicates that waste generation in Beijing would increase gradually in the next 3–5 years, while that in Shenzhen would grow rapidly in the next 3 years. The difference between the two is predominately driven by the different trends of population growth.     

Salinity exchange between seawater/brackish water and domestic wastewater through electrodialysis for potable water

● Present a general concept called “salinity exchange”. ● Salts transferred from seawater to treated wastewater until completely switch. ● Process demonstrated using a laboratory-scale electrodialysis system. ● High-quality desalinated water obtained at ~1 mL/min consuming < 1 kWh/m ³ energy. Two-thirds of the world’s population has limited access to potable water. As we continue to use up our freshwater resources, new and improved techniques for potable water production are warranted. Here, we present a general concept called “salinity exchange” that transfers salts from seawater or brackish water to treated wastewater until their salinity values approximately switch, thus producing wastewater with an increased salinity for discharge and desalinated seawater as the potable water source. We have demonstrated this process using electrodialysis. Salinity exchange has been successfully achieved between influents of different salinities under various operating conditions. Laboratory-scale salinity exchange electrodialysis (SEE) systems can produce high-quality desalinated water at ~1 mL/min with an energy consumption less than 1 kWh/m³. SEE has also been operated using real water, and the challenges of its implementation at a larger scale are evaluated.     

The impact of different voltage application modes on biodegradation of chloramphenicol and shift of microbial community structure

● Presented coupled system enhanced biodegradation of antibiotic chloramphenicol. ● HRT and electrical stimulation modes were key influencing factors. ● Electrical stimulation had little effect on the chloramphenicol metabolic pathway. ● Microbial community structure varied with the voltage application mode. Exoelectrogenic biofilms have received considerable attention for their ability to enhance electron transfer between contaminants and electrodes in bioelectrochemical systems. In this study, we constructed anaerobic-aerobic-coupled upflow bioelectrochemical reactors (AO-UBERs) with different voltage application modes, voltages and hydraulic retention times (HRTs). In addition, we evaluated their capacity to remove chloramphenicol (CAP). AO-UBER can effectively mineralize CAP and its metabolites through electrical stimulation when an appropriate voltage is applied. The CAP removal efficiencies were ~81.1%±6.1% (intermittent voltage application mode) and 75.2%±4.6% (continuous voltage application mode) under 0.5 V supply voltage, which were ~21.5% and 15.6% greater than those in the control system without voltage applied, respectively. The removal efficiency is mainly attributed to the anaerobic chamber. High-throughput sequencing combined with catabolic pathway analysis indicated that electrical stimulation selectively enriched Megasphaera, Janthinobacterium, Pseudomonas, Emticicia, Zoogloea, Cloacibacterium and Cetobacterium, which are capable of denitrification, dechlorination and benzene ring cleavage, respectively. This study shows that under the intermittent voltage application mode, AO-UBERs are highly promising for treating antibiotic-contaminated wastewater.     

Electrocatalytic biofilm reactor for effective and energy-efficient azo dye degradation: the synergistic effect of MnOx/Ti flow-through anode and biofilm on the cathode

● MnO _x /Ti flow-through anode was coupled with the biofilm-attached cathode in ECBR. ● ECBR was able to enhance the azo dye removal and reduce the energy consumption. ● Mn^IV=O generated on the electrified MnO _x /Ti anode catalyzed the azo dye oxidation. ● Aerobic heterotrophic bacteria on the cathode degraded azo dye intermediate products. ● Biodegradation of intermediate products was stimulated under the electric field. Dyeing wastewater treatment remains a challenge. Although effective, the in-series process using electrochemical oxidation as the pre- or post-treatment of biodegradation is long. This study proposes a compact dual-chamber electrocatalytic biofilm reactor (ECBR) to complete azo dye decolorization and mineralization in a single unit via anodic oxidation on a MnO_x/Ti flow-through anode followed by cathodic biodegradation on carbon felts. Compared with the electrocatalytic reactor with a stainless-steel cathode (ECR-SS) and the biofilm reactor (BR), the ECBR increased the chemical oxygen demand (COD) removal efficiency by 24 % and 31 % (600 mg/L Acid Orange 7 as the feed, current of 6 mA), respectively. The COD removal efficiency of the ECBR was even higher than the sum of those of ECR-SS and BR. The ECBR also reduced the energy consumption (3.07 kWh/kg COD) by approximately half compared with ECR-SS. The advantages of the ECBR in azo dye removal were attributed to the synergistic effect of the MnO_x/Ti flow-through anode and cathodic biofilms. Catalyzed by Mn^IV=O generated on the MnO_x/Ti anode under a low applied current, azo dyes were oxidized and decolored. The intermediate products with improved biodegradability were further mineralized by the cathodic aerobic heterotrophic bacteria (non-electrochemically active) under the stimulation of the applied current. Taking advantage of the mutual interactions among the electricity, anode, and bacteria, this study provides a novel and compact process for the effective and energy-efficient treatment of azo dye wastewater.     

Spatial prediction of soil contamination based on machine learning: a review

● A review of machine learning (ML) for spatial prediction of soil contamination. ● ML have achieved significant breakthroughs for soil contamination prediction. ● A structured guideline for using ML in soil contamination is proposed. ● The guideline includes variable selection, model evaluation, and interpretation. Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.     

Property-performance relationship of core-shell structured black TiO2 photocatalyst for environmental remediation

● Properties and performance relationship of CSBT photocatalyst were investigated. ● Properties of CSBT were controlled by simply manipulating glycerol content. ● Performance was linked to semiconducting and physicochemical properties. ● CSBT (W:G ratio 9:1) had better performance with lower energy consumption. ● Phenols were reduced by 48.30% at a cost of $2.4127 per unit volume of effluent. Understanding the relationship between the properties and performance of black titanium dioxide with core-shell structure (CSBT) for environmental remediation is crucial for improving its prospects in practical applications. In this study, CSBT was synthesized using a glycerol-assisted sol-gel approach. The effect of different water-to-glycerol ratios (W:G = 1:0, 9:1, 2:1, and 1:1) on the semiconducting and physicochemical properties of CSBT was investigated. The effectiveness of CSBT in removing phenolic compounds (PHCs) from real agro-industrial wastewater was studied. The CSBT synthesized with a W:G ratio of 9:1 has optimized properties for enhanced removal of PHCs. It has a distinct core-shell structure and an appropriate amount of Ti³⁺ cations (11.18%), which play a crucial role in enhancing the performance of CSBT. When exposed to visible light, the CSBT performed better: 48.30% of PHCs were removed after 180 min, compared to only 21.95% for TiO₂ without core-shell structure. The CSBT consumed only 45.5235 kWh/m³ of electrical energy per order of magnitude and cost $2.4127 per unit volume of treated agro-industrial wastewater. Under the conditions tested, the CSBT demonstrated exceptional stability and reusability. The CSBT showed promising results in the treatment of phenols-containing agro-industrial wastewater.     

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

● A machine learning model was used to identify lake nutrient pollution sources. ● XGBoost model showed the best performance for lake water quality prediction. ● Model feature size was reduced by screening the key features with the MIC method. ● TN and TP concentrations of Lake Taihu are mainly affected by endogenous sources. ● Next-month lake TN and TP concentrations were predicted accurately. Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources, for which mathematical modeling is commonly adopted. In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling, we employed an ensemble machine learning (ML) model to identify the key nitrogen and phosphorus sources of lakes. Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality, environmental input, and meteorological conditions, among which the XGBoost model stood out as the best model for total nitrogen (TN) and total phosphorus (TP) prediction. The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality, while the lake TP is predominantly from endogenous sources. The prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control. Finally, one-month-ahead prediction of lake TN and TP concentrations (R² of 0.85 and 0.95, respectively) was achieved based on this model with sliding time window lengths of 9 and 6 months, respectively. Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction, which may provide valuable references for early warning and rational control of lake eutrophication.     

Evaluating the impact of sulfamethoxazole on hydrogen production during dark anaerobic sludge fermentation

● SMX promotes hydrogen production from dark anaerobic sludge fermentation. ● SMX significantly enhances the hydrolysis and acidification processes. ● SMX suppresses the methanogenesis process in order to reduce hydrogen consumption. ● SMX enhances the relative abundance of hydrogen-VFAs producers. ● SMX brings possible environmental risks due to the enrichment of ARGs. The impact of antibiotics on the environmental protection and sludge treatment fields has been widely studied. The recovery of hydrogen from waste activated sludge (WAS) has become an issue of great interest. Nevertheless, few studies have focused on the impact of antibiotics present in WAS on hydrogen production during dark anaerobic fermentation. To explore the mechanisms, sulfamethoxazole (SMX) was chosen as a representative antibiotic to evaluate how SMX influenced hydrogen production during dark anaerobic fermentation of WAS. The results demonstrated SMX promoted hydrogen production. With increasing additions of SMX from 0 to 500 mg/kg TSS, the cumulative hydrogen production elevated from 8.07 ± 0.37 to 11.89 ± 0.19 mL/g VSS. A modified Gompertz model further verified that both the maximum potential of hydrogen production (P_m) and the maximum rate of hydrogen production (R_m) were promoted. SMX did not affected sludge solubilization, but promoted hydrolysis and acidification processes to produce more hydrogen. Moreover, the methanogenesis process was inhibited so that hydrogen consumption was reduced. Microbial community analysis further demonstrated that the introduction of SMX improved the abundance of hydrolysis bacteria and hydrogen-volatile fatty acids (VFAs) producers. SMX synergistically influenced hydrolysis, acidification and acetogenesis to facilitate the hydrogen production.     

Control strategies for disinfection byproducts by ion exchange resin,nanofiltration and their sequential combination

● Effects of AER adsorption and NF on DBP precursors, DBPs, and TOX were examined. ● A treatment approach of resin adsorption followed by nanofiltration was developed. ● Both DOC and Br⁻ could be effectively removed by the sequential approach. ● DBPs, TOX, and cytotoxicity were significantly reduced by the sequential approach. Disinfection byproducts (DBPs) are emerging pollutants in drinking water with high health risks. Precursor reduction before disinfection is an effective strategy to control the formation of DBPs. In this study, three types of anion exchange resins (AERs) and two types of nanofiltration (NF) membranes were tested for their control effects on DBP precursors, DBPs, and total organic halogen (TOX). The results showed that, for AER adsorption, the removal efficiencies of DBP precursors, DBPs, and TOX increased with the increase of resin dose, and the strong basic macroporous anion exchange resin (M500MB) had the highest removal efficiencies. For NF, the highest removal efficiencies were achieved at an operating pressure of 4 bar, and the membrane (NF90) with a smaller molecular weight cut-off, had a better control efficiency. However, AER adsorption was inefficient in removing dissolved organic carbon (DOC); NF was inefficient in removing Br⁻ resulting in insufficient control of Br-DBPs. Accordingly, a sequential approach of AER (M500MB) adsorption followed by NF (NF90) was developed to enhance the control efficiency of DBPs. Compared with single AER adsorption and single NF, the sequential approach further increased the removal efficiencies of DOC by 19.4%–101.9%, coupled with the high Br⁻ removal efficiency of 92%, and thus improved the reduction of cyclic DBPs and TOX by 3.5%–4.9%, and 2.4%–8.4%, respectively; the sequential approach also reduced the cytotoxicity of the water sample by 66.4%.     

Characterization and variation of dissolved organic matter in composting: a critical review

● Effect of composting approaches on dissolved organic matter (DOM). ● Effect of composting conditions on the properties of DOM. ● Character indexes of DOM varied in composting. ● The size, hydrophobicity, humification, and electron transfer capacity increased. ● The hydrophilicity, protein-like materials, and aliphatic components reduced. As the most motive organic fraction in composting, dissolved organic matter (DOM) can contribute to the transfer and dispersal of pollutants and facilitate the global carbon cycle in aquatic ecosystems. However, it is still unclear how composting approaches and conditions influence the properties of compost-derived DOM. Further details on the shift of DOM character indexes are required. In this study, the change in properties of compost-derived DOM at different composting approaches and the effect of composting conditions on the DOM characteristics are summarized. Thereafter, the change in DOM character indexes’ in composting was comprehensively reviewed. Along with composting, the elements and spectral properties (chromophoric DOM (CDOM) and fluorescent DOM (FDOM)) were altered, size and hydrophobicity increased, and aromatic-C and electron transfer capacity were promoted. Finally, some prospects to improve this study were put forward. This paper should facilitate the people who have an interest in tracing the fate of DOM in composting.     

Hybrid energy harvesting systems for self-powered sustainable water purification by harnessing ambient energy

● Energy harvesters harness multiple energies for self-powered water purification. ● Hybrid energy harvesters enable continuous output under fluctuating conditions. ● Mechanical, thermal, and solar energies enable synergic harvesting. ● Perspectives of hybrid energy harvester-driven water treatment are proposed. The development of self-powered water purification technologies for decentralized applications is crucial for ensuring the provision of drinking water in resource-limited regions. The elimination of the dependence on external energy inputs and the attainment of self-powered status significantly expands the applicability of the treatment system in real-world scenarios. Hybrid energy harvesters, which convert multiple ambient energies simultaneously, show the potential to drive self-powered water purification facilities under fluctuating actual conditions. Here, we propose recent advancements in hybrid energy systems that simultaneously harvest various ambient energies (e.g., photo irradiation, flow kinetic, thermal, and vibration) to drive water purification processes. The mechanisms of various energy harvesters and point-of-use water purification treatments are first outlined. Then we summarize the hybrid energy harvesters that can drive water purification treatment. These hybrid energy harvesters are based on the mechanisms of mechanical and photovoltaic, mechanical and thermal, and thermal and photovoltaic effects. This review provides a comprehensive understanding of the potential for advancing beyond the current state-of-the-art of hybrid energy harvester-driven water treatment processes. Future endeavors should focus on improving catalyst efficiency and developing sustainable hybrid energy harvesters to drive self-powered treatments under unstable conditions (e.g., fluctuating temperatures and humidity).     

Win-Win: Anthropogenic circularity for metal criticality and carbon neutrality

● Anthropogenic circularity science is an emerging interdisciplinary field. ● Anthropogenic circularity was one effective strategy against metal criticality. ● Carbon neutrality is becoming the new industry paradigm around the world. ● Growing circularity could potentially minimize the CO₂ emission. Resource depletion and environmental degradation have fueled a burgeoning discipline of anthropogenic circularity since the 2010s. It generally consists of waste reuse, remanufacturing, recycling, and recovery. Circular economy and “zero-waste” cities are sweeping the globe in their current practices to address the world’s grand concerns linked to resources, the environment, and industry. Meanwhile, metal criticality and carbon neutrality, which have become increasingly popular in recent years, denote the material's feature and state, respectively. The goal of this article is to determine how circularity, criticality, and neutrality are related. Upscale anthropogenic circularity has the potential to expand the metal supply and, as a result, reduce metal criticality. China barely accomplished 15 % of its potential emission reduction by recycling iron, copper, and aluminum. Anthropogenic circularity has a lot of room to achieve a win-win objective, which is to reduce metal criticality while also achieving carbon neutrality in a near closed-loop cycle. Major barriers or challenges for conducting anthropogenic circularity are deriving from the inadequacy of life-cycle insight governance and the emergence of anthropogenic circularity discipline. Material flow analysis and life cycle assessment are the central methodologies to identify the hidden problems. Mineral processing and smelting, as well as end-of-life management, are indicated as critical priority areas for enhancing anthropogenic circularity.     

The role of lipids in fermentative propionate production from the co-fermentation of lipid and food waste

● Lipid can promote PA production on a target from food waste. ● PA productivity reached 6.23 g/(L∙d) from co-fermentation of lipid and food waste. ● Lipid promoted the hydrolysis and utilization of protein in food waste. ● Prevotella , Veillonella and norank _f _Propioni bacteriaceae were enriched. ● Main pathway of PA production was the succinate pathway. Food waste (FW) is a promising renewable low-cost biomass substrate for enhancing the economic feasibility of fermentative propionate production. Although lipids, a common component of food waste, can be used as a carbon source to enhance the production of volatile fatty acids (VFAs) during co-fermentation, few studies have evaluated the potential for directional propionate production from the co-fermentation of lipids and FW. In this study, co-fermentation experiments were conducted using different combinations of lipids and FW for VFA production. The contributions of lipids and FW to propionate production, hydrolysis of substrates, and microbial composition during co-fermentation were evaluated. The results revealed that lipids shifted the fermentation type of FW from butyric to propionic acid fermentation. Based on the estimated propionate production kinetic parameters, the maximum propionate productivity increased significantly with an increase in lipid content, reaching 6.23 g propionate/(L∙d) at a lipid content of 50%. Propionate-producing bacteria Prevotella, Veillonella, and norank_f_Propionibacteriaceae were enriched in the presence of lipids, and the succinate pathway was identified as a prominent fermentation route for propionate production. Moreover, the Kyoto Encyclopedia of Genes and Genomes functional annotation revealed that the expression of functional genes associated with amino acid metabolism was enhanced by the presence of lipids. Collectively, these findings will contribute to gaining a better understanding of targeted propionate production from FW.     

Effect of ambient polycyclic aromatic hydrocarbons and nicotine on the structure of Aβ42 protein

● B[a]P, nicotine and phenanthrene molecules altered the secondary structure of Aβ₄₂. ● β-content of the peptide was significantly enhanced in the presence of the PAHs. ● Nicotine made stable cluster with Aβ₄₂ peptide via hydrogen bonds. ● Phenanthrene due to its small size, interfered with the Aβ₄₂ monomer more strongly. Recent studies have correlated the chronic impact of ambient environmental pollutants like polycyclic aromatic hydrocarbons (PAHs) with the progression of neurodegenerative disorders, either by using statistical data from various cities, or via tracking biomarkers during in-vivo experiments. Among different neurodegenerative disorders, PAHs are known to cause increased risk for Alzheimer’s disease, related to the development of amyloid beta (Aβ) peptide oligomers. However, the complex molecular interactions between peptide monomers and organic pollutants remains obscured. In this work, we performed an atomistic molecular dynamics study via GROMACS to investigate the structure of Aβ₄₂ peptide monomer in the presence of benzo[a]pyrene, nicotine, and phenanthrene. Interestingly the results revealed strong hydrophobic, and hydrogen-bond based interactions between Aβ peptides and these environmental pollutants that resulted in the formation of stable intermolecular clusters. The strong interactions affected the secondary structure of the Aβ₄₂ peptide in the presence of the organic pollutants, with almost 50 % decrease in the α-helix and 2 %–10 % increase in the β-sheets of the peptide. Overall, the undergoing changes in the secondary structure of the peptide monomer in the presence of the pollutants under the study indicates an enhanced formation of Aβ peptide oligomers, and consequent progression of Alzheimer’s disease.