NiB2O4 (B = Mn or Co) catalysts for NH3-SCR of NOx at low-temperature in microwave field

● Microwave-assisted catalytic NH₃-SCR reaction over spinel oxides is carried out. ● SCR reaction temperature is tremendously lowered in microwave field. ● NO conversion of NiMn₂O₄ is highly up to 90.6% at 70°C under microwave heating. Microwave-assisted selective catalytic reduction of nitrogen oxides (NO_x) was investigated over Ni-based metal oxides. The NiMn₂O₄ and NiCo₂O₄ catalysts were synthesized by the co-precipitation method and their activities were evaluated as potential candidate catalysts for low-temperature NH₃-SCR in a microwave field. The physicochemical properties and structures of the catalysts were characterized by X-ray diffraction (XRD), Scanning electron microscope (SEM), N₂-physisorption, NO adsorption-desorption in the microwave field, H₂-temperature programmed reduction (H₂-TPR) and NH₃-temperature programmed desorption (NH₃-TPD). The results verified that microwave radiation reduced the reaction temperature required for NH₃-SCR compared to conventional heating, which needed less energy. For the NiMn₂O₄ catalyst, the catalytic efficiency exceeded 90% at 70 °C and reached 96.8% at 110 °C in the microwave field. Meanwhile, the NiMn₂O₄ also exhibited excellent low-temperature NH₃-SCR reaction performance under conventional heating conditions, which is due to the high BET specific surface area, more suitable redox property, good NO adsorption-desorption in the microwave field and rich acidic sites.     

Porous silica synthesis out of coal fly ash with no residue generation and complete silicon separation

● Both amorphous and crystalline silicon are completely separated from coal fly ash. ● Porous silica is synthesized out of coal fly ash. ● No residues is produced during the whole synthesis process. ● The one-step method to synthesize silica don’t need long-time reaction and aging. Ordered mesoporous silica materials exhibit enormous potential in industrial production. Since coal fly ash (CFA) is abundant in Si, it has become a green and promising way to utilize CFA by synthesizing porous silica materials. However, the stable crystalline structure of CFA limits the extraction of Si, and the residue is generated during the process of extracting Si. In this work, we proposed a no-residue method to synthesize ordered mesoporous silica out of CFA. Sodium carbonate (Na₂CO₃) was used to reconstruct the crystals of the CFA, and the calcined mixture then directly reacted with the precipitators. This method combined the process of Si extraction and porous material synthesis. In this method, no residue was generated and the silicon in both amorphous and crystalline phases of CFA was fully utilized. By this method, the extraction efficiency of Si was increased from 31.75% to nearly 100%. The as-synthesized mesoporous silica had a highly-ordered pore structure with a space group of la-3d, a surface area of 663.87 m²/g, a pore volume of 0.41 cm³/g, and an average pore diameter of 2.73 nm. The mechanism of crystalline transformation and material structure formation were systematically studied. This method provides a new idea to dispose of CFA and synthesize porous silica materials.     

Electrocatalytic reduction of nitrate using Pd-Cu modified carbon nanotube membranes

● Pd-Cu modified CNT membranes were prepared successfully by electrodeposition method. ● The deposition voltage and deposition time were optimized for Pd-Cu co-deposition. ● NO₃⁻-N was removed efficiently from water by Pd-Cu modified CNT membranes. ● The presence of dissolved oxygen did not affect the nitrate reduction performance. ● Mass transfer rate was promoted significantly with the increase in membrane flux. Excessive nitrate in water is harmful to the ecological environment and human health. Electrocatalytic reduction is a promising technology for nitrate removal. Herein, a Pd-Cu modified carbon nanotube membrane was fabricated with an electrodeposition method and used to reduce nitrate in a flow-through electrochemical reactor. The optimal potential and duration for codeposition of Pd and Cu were −0.7 V and 5 min, respectively, according to linear scan voltammetry results. The membrane obtained with a Pd:Cu ratio of 1:1 exhibited a relatively high nitrate removal efficiency and N₂ selectivity. Nitrate was almost completely reduced (~99 %) by the membrane at potentials lower than −1.2 V. However, −0.8 V was the optimal potential for nitrate reduction in terms of both nitrate removal efficiency and product selectivity. The nitrate removal efficiency was 56.2 %, and the N₂ selectivity was 23.8 % for the Pd:Cu=1:1 membrane operated at −0.8 V. Nitrate removal was enhanced under acidic conditions, while N₂ selectivity was decreased. The concentrations of Cl⁻ ions and dissolved oxygen showed little effect on nitrate reduction. The mass transfer rate constant was greatly improved by 6.6 times from 1.14 × 10⁻³ m/h at a membrane flux of 1 L/(m²·h) to 8.71 × 10⁻³ m/h at a membrane flux of 15 L/(m²·h), which resulted in a significant increase in the nitrate removal rate from 13.6 to 133.5 mg/(m²·h). These findings show that the Pd-Cu modified CNT membrane is an efficient material for nitrate reduction.     

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.     

Insights into the electron transfer mechanisms of permanganate activation by carbon nanotube membrane for enhanced micropollutants degradation

● A CNT filter enabled effective KMnO₄ activation via facilitated electron transfer. ● Ultra-fast degradation of micropollutants were achieved in KMnO₄/CNT system. ● CNT mediated electron transfer process from electron-rich molecules to KMnO₄. ● Electron transfer dominated organic degradation. Numerous reagents have been proposed as electron sacrificers to induce the decomposition of permanganate (KMnO₄) by producing highly reactive Mn species for micropollutants degradation. However, this strategy can lead to low KMnO₄ utilization efficiency due to limitations associated with poor mass transport and high energy consumption. In the present study, we rationally designed a catalytic carbon nanotube (CNT) membrane for KMnO₄ activation toward enhanced degradation of micropollutants. The proposed flow-through system outperformed conventional batch reactor owing to the improved mass transfer via convection. Under optimal conditionals, a > 70% removal (equivalent to an oxidation flux of 2.43 mmol/(h·m²)) of 80 μmol/L sulfamethoxazole (SMX) solution can be achieved at single-pass mode. The experimental analysis and DFT studies verified that CNT could mediate direct electron transfer from organic molecules to KMnO₄, resulting in a high utilization efficiency of KMnO₄. Furthermore, the KMnO₄/CNT system had outstanding reusability and CNT could maintain a long-lasting reactivity, which served as a green strategy for the remediation of micropollutants in a sustainable manner. This study provides new insights into the electron transfer mechanisms and unveils the advantages of effective KMnO₄ utilization in the KMnO₄/CNT system for environmental remediation.     

Highly effective visible-photocatalytic hydrogen evolution and simultaneous organic pollutant degradation over an urchin-like oxygen-doped MoS2/ZnIn2S4 composite

● An urchin-like OMS/ZIS composite was fabricated by a facile solvothermal method. ● The OMS/ZIS exhibits superior photocatalytic H₂ evolution for organics degradation. ● A probable mechanism of dual-functional photocatalysis was proposed in detail. ● This work provides an inspiration for rational design of dual-functional catalysts. Achieving hydrogen production and simultaneous decomposition of organic pollutants through dual-functional photocatalytic reactions has received increasing attention due to the environmentally friendly and cost-effective characteristics of this approach. In this work, an urchin-like oxygen-doped MoS₂/ZnIn₂S₄ (OMS/ZIS) composite was fabricated for the first time using a simple solvothermal method. The unique microstructure with abundant active sites and fast charge transfer channels further shortened the charge migration distance and compressed carrier recombination. The obtained composite exhibited an efficient H₂ evolution reaction rate of 12.8 mmol/g/h under visible light, which was nearly times higher than pristine ZnIn₂S₄, and the apparent quantum efficiency was 14.9% (420 nm). The results of the simultaneous photocatalytic H₂ evolution and organic pollutant decomposition test were satisfactory, resulting in decomposition efficiencies of resorcinol, tetracycline, and bisphenol A that reached 41.5%, 63.5%, and 53.0% after 4 h, respectively, and the highest H₂ evolution rate was 672.7 μmol/g/h for bisphenol A. Furthermore, natural organic matter (NOM) abundantly found in actual water was adopted as an electron donor for H production under simulated sunlight irradiation, indicating the promising practicability of simultaneous hydrogen evolution and NOM decomposition. Moreover, the mechanisms of the dual-purpose photocatalytic reactions, as well as the synergistic effect between the molecular structures of the organic pollutants and the corresponding adsorption behavior on the photocatalyst surface were illustrated in detail. These obtained results may serve as an inspiration for the rational design of highly efficient, dual-functional photocatalysts in the future.     

Effects of iron oxide nanoparticles on phenotype and metabolite changes in hemp clones (Cannabis sativa L.)

● Fe₃O₄ NPs increased the biomass and chlorophyll content of hemp clones. ● Fe₃O₄ NPs penetrated and were internalized by root cells. ● Fe₃O₄ NPs induced the alteration of metabolite profiles in hemp leaves. ● The psychoactive compound THC in hemp leaves was significantly down-regulated. We investigated the effect of iron oxide nanoparticles (Fe₃O₄ NPs, ~17 nm in size) on the phenotype and metabolite changes in hemp (Cannabis sativa L.), an annual crop distributed worldwide. Hemp clones were grown in hydroponic cultures with Fe₃O₄ NPs (50, 100, 200, or 500 mg/L) for four weeks. TEM and ICP-MS were used to determine Fe₃O₄ NPs uptake and translocation. LC-MS-based metabolomics was employed to explore the deep insight into the effect of Fe₃O₄ NPs on hemp plants. The results revealed that plant growth enhanced gradually with increasing concentrations of given NPs up to 200 mg/L, which improved the fresh weight and dry weight by 36.13% and 74.68%, respectively, compared to the control. Even at a high dose (500 mg/L), Fe₃O₄ NPs promoted plant growth, including increased biomass and tissue length. NPs significantly increased the iron and chlorophyll content in plant tissues Increased catalase activity and reduced hydrogen peroxide content in hemp leaves suggested that the Fe₃O₄ NPs activated the defense system. TEM showed that NPs were abundantly attached to the cell wall and dispersed throughout the root cells. Metabolomics revealed that Fe₃O₄ NPs induced metabolic reprogramming in hemp leaves, including the up-regulation of carbohydrates and organic acids, and down-regulation of antioxidants, especially tetrahydrocannabinol (THC). The significantly up-regulated metabolites, including peonidin and 2-hydroxycinnamic acid, could be involved in photosynthesis in hemp plants. These results demonstrate the potential of Fe₃O₄ NPs for promoting hemp growth and decreasing the THC content at low doses.     

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.     

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.     

Separation/degradation behavior and mechanism for cationic/anionic dyes by Ag-functionalized Fe3O4-PDA core-shell adsorbents

● PDA-Fe₃O₄-Ag was made by hydrothermal and oxidation self-polymerization method. ● PDA-Fe₃O₄-Ag had great magnetic separation performance. ● PDA-Fe₃O₄-Ag had good adsorption and degradation performance for ionic dyes. ● PDA-Fe₃O₄-Ag showed NR and MO degradation potential of 91.2% and 87.5%, respectively. High-performance adsorbents have been well-studied for the removal of organic dye pollutants to promote environment remediation. In this study, an Ag nanoparticle-functionalized Fe₃O₄-PDA nanocomposite adsorbent (PDA-Fe₃O₄-Ag) was synthesized, and the adsorption/separation performance of commonly used cationic and anionic organic dyes by the PDA-Fe₃O₄-Ag adsorbent were assessed. Overall, PDA-Fe₃O₄-Ag exhibited a significantly higher adsorption capacity for cationic dyes compared to anionic dyes, the highest of which was more than 110.0 mg/g (methylene blue (MB)), which was much higher than not only the adsorption capacities of the anionic dyes in this study but also other dye adsorption capacities reported in the literature. The dye adsorption kinetics data fitted well to both the pseudo second-order kinetics model and the Langmuir isotherm model, suggesting a monolayer-chemisorption-dominated adsorption mode. Thermodynamics analysis indicated that the adsorption process was both endothermic and spontaneous. Furthermore, the PDA-Fe₃O₄-Ag adsorbent achieved high photodegradation removal rates of the dyes, especially neutral red (NR) and methyl orange (MO), which were 91.2% and 87.5%, respectively. With the addition of PDA-Fe₃O₄-Ag, the degradation rate constants of NR and MO increased from 0.08 × 10⁻² and 0 min⁻¹ to 2.11 × 10⁻² and 1.73 × 10⁻² min⁻¹, respectively. The high adsorption and photocatalytic degradation performance of the PDA-Fe₃O₄-Ag adsorbent make it an excellent candidate for removing cationic and anionic dyes from the industrial effluents.     

Long-term exposure to air pollution and cerebrovascular disease: findings from Beijing Health Management Cohort study

● This study explored the long-term association by double robust additive models. ● Individual exposure concentrations were assessed by integrating GAM, LUR and BPNN. ● PM_2.5, SO₂ and NO₂ are positively associated with cerebrovascular disease. ● CO could reduce the risk of cerebrovascular disease with the highest robustness. ● The elderly, women and people with normal BMI are at higher risk for air pollution. The relationship between air pollution and cerebrovascular disease has become a popular topic, yet research findings are highly heterogeneous. This study aims to investigate this association based on detailed individual health data and a precise evaluation of their exposure levels. The integrated models of generalized additive model, land use regression model and back propagation neural network were used to evaluate the exposure concentrations. And doubly robust additive model was conducted to explore the association between cerebrovascular disease and air pollution after adjusted for demographic characteristics, physical examination, disease information, geographic and socioeconomic status. A total of 25097 subjects were included in the Beijing Health Management Cohort from 2013 to 2018. With a 1 μg/m³ increase in the concentrations of PM_2.5, SO₂ and NO₂, the incidence risk of cerebrovascular disease increased by 1.02 (95% CI: 1.008–1.034), 1.06 (95% CI: 1.034–1.095) and 1.02 (95% CI: 1.010–1.029) respectively. Whereas CO exposure could decrease the risk, with an odds ratio of 0.38 (95% CI: 0.212–0.626). In the subgroup analysis, individuals under the age of 50 with normal BMI were at higher risk caused by PM_2.5, and SO₂ was considered more hazardous to women. Meanwhile, the protective effect of CO on women and those with normal BMI was stronger. Successful reduction of long-term exposure to PM_2.5, SO₂ and NO₂ would lead to substantial benefits for decrease the risk of cerebrovascular disease especially for the health of the susceptible individuals.     

Influence of H2S and NH3 on biogas dry reforming using Ni catalyst: a study on single and synergetic effect

● NH₃ in biogas had a slight inhibitory effect on dry reforming. ● Coexistence of H₂S and NH₃ led to faster decline of biogas conversion. ● Regeneration was effective for catalysts deactivated under synergetic effect. Biogas is a renewable biomass energy source mainly composed of CH₄ and CO₂. Dry reforming is a promising technology for the high-value utilization of biogas. Some impurity gases in biogas can not be completely removed after pretreatment, which may affect the performance of dry reforming. In this study, the influence of typical impurities H₂S and NH₃ on dry reforming was studied using Ni/MgO catalyst. The results showed that low concentration of H₂S in biogas could cause serious deactivation of catalyst. Characterization results including EDS, XPS and TOF-SIMS confirmed the adsorption of sulfur on the catalyst surface, which was the cause of catalyst poisoning. We used air calcination method to regenerate the sulfur-poisoned catalysts and found that the regeneration temperature higher than 500 °C could help catalyst recover the original activity. NH₃ in the concentration range of 50–10000 ppm showed a slight inhibitory effect on biogas dry reforming. The decline rate of biogas conversion efficiency increased with the increase of NH₃ concentration. This was related to the reduction of oxygen activity on catalyst surface caused by NH₃. The synergetic effect of H₂S and NH₃ in biogas was investigated. The results showed that biogas conversion decreased faster under the coexistence of H₂S and NH₃ than under the effect of H₂S alone, so as the surface oxygen activity of catalyst. Air calcination regeneration could also recover the activity of the deactivated catalyst under the synergetic effect of H₂S and NH₃.     

Same stimuli,different responses: a pilot study assessing air pollution visibility impacts on emotional well-being in a controlled environment

● Emotional responses to visibility-reducing haze was assessed in a controlled lab. ● Valence and arousal have non-linear responses to pollution-caused low visibility. ● Repetitive exposure aggravates negative emotions in severely polluted conditions. ● Emotional bias to pollution relates with gender, decisiveness, attitude to clean air. A growing number of studies have shown that impaired visibility caused by particulate matter pollution influences emotional wellbeing. However, evidence is still scant on how this effect varies across individuals and over repetitive visual exposure in a controlled environment. Herein, we designed a lab-based experiment (41 subjects, 6 blocks) where participants were presented with real-scene images of 12 different PM_2.5 concentrations in each block. Emotional valence (negative to positive) and arousal (calm to excited) were self-rated by participants per image, and the response time for each rating was recorded. We find that as pollution level increases from 10 to 260 µg/m³, valence scores decrease, whereas arousal scores decline first and then bounce back, following a U-shaped trend. When air quality deteriorates, individual variability decreases in hedonic valence but increases in arousal. Over blocks, repetitive visual exposure increases valence at a moderate pollution level but aggravates negative emotions in severely polluted conditions (> 150 µg/m³). Finally, we find females, people who are slow in making responses, and those who are highly aroused by clean air tend to express more negative responses (so-called negativity bias) to ambient pollution than their respective counterparts. These results provide deeper insights into individual-level emotional responses to dirty air in a controlled environment. Although the findings in our pilot study should only be directly applied to the conditions assessed herein, we introduce a framework that can be replicated in different regions to assess the impact of air pollution on local emotional wellbeing.     

Effects of sulfur on variations in the chemical speciation of heavy metals from fly ash glass

● A higher sulfur content reduced the curing rate of Cr in glass. ● Depolymerization increased the amounts of heavy metals in the carbonate bound state. ● Reducing the CaO/SiO₂ ratio increased the proportion of stable heavy metals. This work designed a new CaO-Al₂O₃-SiO₂-SO₃ glass for the immobilization of multiple heavy metals found in dechlorinated fly ash having high amounts of calcium and sulfur. Increasing the (CaO + SO₃)/SiO₂ mass ratio (M(CS/S)) from 0.28 to 0.85 was found to lower the proportions of Mn, Ni and Zn in an unstable state, while an M(CS/S) ratio of 0.51 gave the lowest proportions of unstable Cr and Pb. Decreasing the degree of polymerization of the glassy network increased the proportions of Mn, Cr, Ni, Pb and Zn in the carbonate bound state. The leaching out of metals in this state was the primary cause of degradation of Q³ structural units in the glassy network. The amount of Mn in the iron-manganese oxide bound state was increased by increasing the number of Q² units in the silicate network. Decreasing the CaO/SiO₂ mass ratio (M(C/S)) raised the proportions of Mn, Ni and Zn in the unstable state. An M(C/S) value of 0.43 lowered the proportions of unstable Cr and Pb. A principal components analysis determined that the leaching of toxic heavy metals from the glass was primarily related to the proportions of these metals in the unstable state while there were no evident correlations between leaching and the proportions in stable states.     

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.     

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste. ● Feature selection methods were used to improve models’ prediction accuracy. ● The best model predicted C, H, and N contents with accuracy R ² ≥ 0.93, 0.87, 0.97. ● Some suitable models showed insensitivity to spectral noise. ● Under moisture interference, the models still had good prediction performance. Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R²) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms.     

Performance and mechanism of carbamazepine removal by FeS-S2O82– process: experimental investigation and DFT calculations

● Synergistic removal of carbamazepine (CBZ) was obtained in the FeS-S₂O₈^2– process. ● SO₄^•− and •OH were identified as the main radicals in the FeS-S₂O₈^2– process. ● Heterogeneous oxidation would be dominant first, followed by homogeneous reaction. ● Degradation pathway of CBZ was well elucidated by experiments and DFT calculations. As persulfate (S₂O₈^2–) is being increasingly used as an alternative oxidizing agent, developing low-cost and eco-friendly catalysts for efficient S₂O₈^2– activation is potentially useful for the treatment of wastewater containing refractory organic pollutant. In this study, the degradative features and mechanisms of carbamazepine (CBZ) were systematically investigated in a novel FeS- S₂O₈^2– process under near-neutral conditions. The results exhibited that CBZ can be effectively eliminated by the FeS-S₂O₈^2– process and the optimal conditions were: 250 mg/L FeS, 0.5 mmol/L S₂O₈^2–, and pH = 6.0. The existence of Cl⁻ (1 and 50 mmol/L) has little influence on the CBZ elimination, while both HCO₃⁻ and HPO₄²⁻ (1 and 50 mmol/L) significantly suppressed the CBZ removal in the FeS-S₂O₈^2– process. CBZ could be degraded via a radical mechanism in the FeS-S₂O₈^2– process, the working radical species (i.e., SO₄^•− and •OH) were efficiently formed via the promoted decomposition of S₂O₈^2– by the surface Fe²⁺ on the FeS and the dissolved ferrous ions in solution. Based on the identified oxidized products and Fukui index calculations, a possible degradation pathway of CBZ was speculated. More importantly, a two-stage oxidation mechanism of CBZ elimination was speculated in the FeS-S₂O₈^2– process, the activation of S₂O₈^2– by the surface-active Fe^(II) of FeS dominated in the initial 5 min, while homogeneous oxidation reactions played more essential parts than others in the following reaction stage (5–60 min). Overall, this study demonstrated that the FeS-S₂O₈^2– process is capable of removing CBZ from water efficiently.     

Decoding and quantitative detection of antibiotics by a luminescent mixed-lanthanide-organic framework

● A series of mixed-LOFs and portable LOF-fibers were synthesized. ● LOF-S3 was selected as a luminescent sensor for antibiotics. ● Mixed-LOF was capable of decoding antibiotics by emission intensity ratios. ● Linear relationship between antibiotic concentration and I545nm/I618nm was observed. Due to the potential risk of antibiotics to the environment, the development of inexpensive, simple, and reliable antibiotic detection methods is significant but also faces challenges. In this work, several lanthanide-organic frameworks (LOFs), constructed from lanthanide ions (Eu³⁺ and/or Tb³⁺) and 1,3,5-benzene-tricarboxylic acid (BTC), were synthesized by solvothermal method. LOF-S3 with comparable emission peaks of ⁵D₄ → ⁷F₅ (Tb³⁺, 545 nm) and ⁵D₀ → ⁷F₂ (Eu³⁺, 618 nm) was selected as a luminescent sensor. In this system, the highly efficient energy transferred from the organic linker to lanthanide ions and from Tb³⁺ to Eu³⁺ occurs. LOF-S3 sensor was capable of decoding antibiotics by distinguishable emission intensity ratios. Therefore, a two-dimensional decoded map of antibiotics was established. The linear relationship between antibiotic concentration and emission intensity ratio indicated the quantitative determination of antibiotics was feasible. As a typical analyte, the response mechanism of nalidixic acid (NA) was investigated in detail. The competition of NA and BTC for UV light absorption, as well as the binding propensity of NA and Tb, affected the organic linkers-to-lanthanide ions and Tb-to-Eu energy transfer, resulting in the change of fluorescence intensity ratio. The self-calibrating mixed-LOF sensor overcame the uncontrollable errors of the traditional absolute emission intensity method and generated stable luminescent signals in multiple cycles. Furthermore, the integration of LOF-S3 with polymer fibers enabled the formation of a LOF-polymer fibrous composite with fluorescence detection capability, which is a promising portable sensor for practical applications.     

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.     

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.