We examine and discuss the evolution and current state of chemical migration testing protocols for children’s toys, from early practices to present-day methods. Drawing from a synthesis of literature and comparisons with international standards such as European standard EN 71-10, the key factors influencing chemical migration testing are identified, including temperature, pH, duration of contact, choice of simulant, and agitation methods. We propose enhancements to existing protocols to improve accuracy and realism, such as adjusting the temperature to physiologic levels, incorporating pH adjustments, extending migration testing duration, and diversifying simulants. Moreover, we advocate for a more holistic approach to toy safety, considering interactions among multiple substances and age-specific vulnerabilities of children. Advancements in analytical technologies are also discussed as promising tools for enhancing testing sensitivity and specificity. We stress the urgency and necessity of global harmonization and collaborative solutions to ensure consistent standards and foster a safer environment for children worldwide. By aligning testing protocols, exchanging best practices, and leveraging technological innovations, stakeholders can prioritize children’s well-being and create a world where toys bring joy without compromising safety.
Most hyperaccumulator and economic crops do not grow year-round, leading to limited remediation efficiency. Implementing year-round rotation patterns with known and potential hyperaccumulators or economic crops can improve remediation efficiency. This study evaluated the remediation efficiency and agricultural safety of 10 winter crops and 12 summer crops in field-scale trials. Sedum alfredii Hance (SA) and Cichorium intybus L. (CI) exhibited the highest cadmium (Cd) accumulation among winter crops, reducing soil Cd content by 12.1% and 10.4%, respectively. Helianthus annuus Linn. (HA) was the most effective summer crop, reducing soil Cd content by 3.7%. The vegetable oils of all oil crops were within safe heavy metal limits, whereas the edible parts of other economic crops exceeded Cd limits. A combination of the best winter and summer crops was chosen to comprehensively evaluate the remediation efficiency and economic benefits of three rotation patterns: SA + HA, CI + HA, and Linum usitatissimum L. (LU) + HA. SA + HA and CI + HA were more effective than LU + HA, reducing soil Cd by 12.5%, 8.9%, and 3.7%, respectively. The net profits were −27591.19, 749.50, and 3309.76 US$/ha, respectively. Overall, CI + HA demonstrated the highest combined capacity (comprehensive index = 1.79) for both remediation efficiency and economic benefits, achieving safe production and effective restoration of Cd-contaminated agricultural land. However, the accumulation of heavy metals in oilseed meals warrants further attention.
Combining pulverized coal gasifiers with cement kiln production is promising for application in low-cost and efficient NO reduction. This paper presents a pulverized coal gasifier catalytic denitration technique and investigates the homogeneous reduction (by CO and CH4) and heterogeneous catalytic reduction (by coke, CaO, MgO, and Fe2O3) of NO. A combination of Chemkin simulations and fixed-bed experiments is used to elucidate the reaction pathways and key intermediates of NO reduction by carbon-based gases. In addition, the activation energies for different catalyst combinations were analyzed via reaction kinetics. The results demonstrate that the presence of small amounts of O2 inhibits NO reduction by CO but promotes NO reduction by CH4. The NCO• radical is essential for the NO reduction process, and the generation of this radical depends on the CH4 cleavage intermediate and O• radical. CaO and Fe2O3 exhibit more significant catalytic effects on NO reduction by carbon-based gases than the other catalysts tested. The presence of a small amount of O2 in the reacting gas mixtures facilitates the NO reduction reaction. The activation energy is reduced to 1.02 kJ/mol, and the NO conversion reaches 99.80% when the catalyst is C + CaO + MgO + Fe2O3 and the gas composition is CO + CH4 + O2. This work provides theoretical support and data recommendations for the use of pulverized coal gasifiers for the denitrification of cement kilns.
Chemical stabilization has been widely applied to stabilize various heavy metals in soil, but there have been few systematic investigations of in situ stabilization at practical contaminated sites. In this study, batch experiments were conducted to investigate the stabilization effect of heavy metals in soil from an iron-smelting site using multiple materials. The results showed that FeSO4 simultaneously reduced the bioavailable heavy metal (BHM) concentrations of Pb, Zn, and As by 61.1%, 28.1%, and 68.6%, respectively. Therefore, FeSO4 was further applied at the practical contaminated site. Experimental results indicated that the heterogeneous distribution of stabilization efficiency deviated from that of batch experiments, which were influenced by multiple factors. Compared to the control group, the bioavailable Pb, Zn, and As concentrations decreased by 23.1, 13.6, and 4.73 mg/kg, respectively, when the injected FeSO4 concentration was 0.27 mol/L in the saturated zone. The decreased concentrations of bioavailable Pb, Zn, and As decreased with the distance from the injection well, showing a limited radius of influence at 1.0–2.0 m, larger than the theoretical value (0.92 m). Correlation analysis revealed a significant negative relationship between the change in BHM content and the fraction of BHM content before stabilization (FB), indicating that FB significantly controlled the stabilization efficiency. While excess injected FeSO4 had only a slight influence on the stabilization efficiency of heavy metals, it negatively impacted groundwater. This study provides new perspectives for in situ stabilization and highlights the importance of pilot-scale experiments over batch experiments for guiding engineering activities.
Landfilling remains the primary disposal method for fly ash produced from municipal solid waste incineration (MSWI) following stabilization/solidification. However, the increasing generation of stabilized fly ash (SFA) is accelerating the depletion of landfill capacity. Furthermore, the small particle size and low bulk density of SFA present significant environmental risks during handling and transportation. To mitigate these issues, a cost-effective compaction method was introduced into the SFA disposal process. The results show that SFA from both grate furnaces and fluidized bed incinerators exhibited high porosity, loose structure, and irregular particle morphology, indicating substantial potential for compaction. Key parameters influencing compaction effectiveness included compaction pressure, holding duration, and moisture content, with optimal values identified as 100–200 MPa, 20 s, and 10%–15% moisture, respectively, depending on the incinerator type. After compaction treatment, the density of SFA more than doubled, while its volume was reduced by over 60%, significantly increasing landfill capacity and enhancing the efficiency of SFA disposal. The compaction process was effectively modeled using the Huang Peiyun equation for gerate furnace ash and the Heckel equation for fluidized bed ash. Furthermore, the unconfined compressive strength and three-point bending strength of compacted SFA met the MU10 standard for lime-sand bricks, making the material suitable for transportation and disposal. Finally, the compaction-based disposal method for SFA demonstrated clear techno-economic advantages and significant potential for broader application in waste management strategies.
Membrane fouling poses a significant challenge to the sustainable development of membrane bioreactor (MBR) technologies for wastewater treatment. The accurate prediction of the membrane filtration process is of great importance for identifying and controlling fouling. Machine learning methods address the limitations of traditional statistical approaches, such as low accuracy, poor generalization ability, and slow convergence, particularly in predicting complex filtration and fouling processes within the realm of big data. This article provides an in-depth exposition of machine learning theory. The study then reviews advances in MBRs that utilize machine learning methods, including artificial neural networks (ANN), support vector machines (SVM), decision trees, and ensemble learning. Based on current literature, this study summarizes and compares the model input and output characteristics (including foulant characteristics, solution environments, filtration conditions, operating conditions, and time factors), as well as the selection of models and optimization algorithms. The modeling procedures of SVM, random forest (RF), back propagation neural network (BPNN), long short-term memory (LSTM), and genetic algorithm-back propagation (GA-BP) methods are elucidated through a tutorial example. The simulation results demonstrated that all five methods yielded accurate predictions with R2 > 0.8. Finally, the existing challenges in the implementation of machine learning models in MBRs were analyzed. It is notable that integration of deep learning, automated machine learning (AutoML) and explainable artificial intelligence (XAI) may facilitate the deployment of models in practical engineering applications. The insights presented here are expected to facilitate the establishment of an intelligent control framework for MBR processes in future endeavors.
Antibiotics, endocrine-disrupting compounds (EDCs), per- and polyfluoroalkyl substances (PFAS), and microplastics (MPs) are crucial constituents of the pollutants frequently detected in various aquatic environments. These pollutants can negatively affect human health and aquatic organisms. To eliminate these new pollutants from water economically and environmentally, biochar is considered an efficient environmental functional material because of its excellent performance. Although numerous studies have reported the concentration, distribution, and removal methods of individual new pollutants, a comprehensive and systematic understanding of biochar-based materials for the removal of multiple new pollutants from aquatic environments remains to be comprehensively explored. Therefore, in this mini-review, recent research progress on biochar materials in the decontamination of new pollutants, including antibiotics, EDCs, PFAS, and MPs in different water sources, is summarized, and different mechanisms and influencing factors during the removal process are discussed to have a profound understanding of the application of biochar in the water environment. Future studies on biochar materials for the removal of new pollutants are indicated to enlighten future exploration and alleviate new pollutant burdens in aquatic environments.
Gasoline vapor emissions from service stations significantly affect urban atmospheric. Despite the research on the mechanisms and effectiveness of gasoline vapor removal is limited, this study innovatively investigates the static and dynamic adsorption of xylene—a typical gasoline vapor and one of the most active secondary organic aerosol (SOA) species—by commercial activated carbon (AC). The results showed that the saturation static adsorption capacity (Qe) of 12 ACs varied from 0.9 to 870.7 mg/g, which correlated with the specific surface area (SSA) and pore volume. Among them, 11# and 12# ACs were identified as the most effective adsorbents for typical gasoline vapor removal. The maximum dynamic Qe increased from 301.5 to 414.3 mg/g when the initial xylene concentration rose from 918 to 2008 mg/m3 for 11# AC, and from 201.4 to 406.2 mg/g when the initial xylene concentration increased from 589 to 2120 mg/m3 for 12# AC. These findings implied a direct correlation between higher initial xylene concentrations and greater dynamic Qe values, with static Qe values surpassing dynamic values. The adsorption kinetics simulation were analyzed by the pseudo-first-order (PFO) and pseudo-second-order (PSO) kinetics. The kinetics results demonstrated that the PFO was more effective in characterizing the adsorption of xylene onto ACs (R2 > 0.989), indicating that the adsorption of typical gasoline vapor by ACs primarily involves physical adsorption. The findings of static/dynamic adsorption and kinetics provide valuable guidance for practical applications of gasoline vapor removal in service stations.
Dissolved organic matter (DOM) participates in and affects many biological processes in aquatic ecosystems, altering nutrient cycling, bioavailability and toxicity of pollutants. Lake water contains thousands of DOM molecules, which largely originate from natural sources and are greatly influenced by external inputs through anthropogenic activities. To elucidate the shaping mechanism of the DOM composition under complex habitat conditions and anthropogenic disturbances, the characteristic molecular fingerprint of DOM was determined based on chemodiversity information obtained via ultrahigh-resolution mass spectrometry for the Baiyangdian wetland (a typical large macrophytic shallow lake in North China). In the Baiyangdian wetland, the DOM composition exhibited high complexity and spatial heterogeneity, and more recalcitrant DOM molecules were identified in summer than in spring. Although multiple natural factors (nutrient concentration, depth of water and quality of submerged macrophytes) and anthropogenic factors (different land uses and discharge of pollutants) were associated with the relative abundance of various DOM molecules, only the influences from the anthropogenic factors were statistically significant. The alpha diversity index of DOM could reflect variations in both the natural factors (total dissolved phosphorus) and the anthropogenic factors (discharge of nitrogen and phosphorus). Furthermore, the DOM composition in each group were clustered according to the ecological functions (natural reserves, tourism, breeding, and domestic supply functions), and exhibited molecular fingerprint features. This study provides an effective approach to characterize the molecular fingerprint features of DOM and critical information to better understand the shaping mechanism of the DOM composition under complex habitat conditions and anthropogenic disturbances.
Microorganisms are essential contributors to the forest ecosystems of the Qinghai-Xizang Plateau, encompassing generalists, intermediates, and specialists. Investigating the characteristics and drivers of these microbial sub-communities across varying elevations is essential for understanding their ecological functions in high-altitude forest soils. This study examines the diversity patterns, assembly processes, environmental adaptations, and potential functions of bacterial and fungal sub-communities along an elevation gradient from 3900 m to the timberline on Shergyla Mountain, China. The findings revealed notable differences between low and high elevations in the diversity and composition of microbial sub-communities. According to neutral and null models, generalists, characterized by the widest niche width and highest migration rates, were primarily influenced by stochastic processes (71.9%). In contrast, deterministic factors, including homogeneous and variable selection, exerted a stronger effect on the assembly of specialists (51.0%). Elevation and nutrient availability emerged as key environmental drivers shaping microbial sub-communities, particularly for specialists, while generalists experienced fewer constraints from environmental factors. Network analysis further demonstrated that habitat specialists occupy central positions within microbial networks, playing a pivotal role in maintaining network stability. Additionally, nitrogen cycling genes—specifically nifH, amoA, nirS and nosZ—exhibited a U-shaped distribution across the elevation gradient and showed a substantial correlation (p < 0.05) with the Chao1 and Shannon indices of bacterial specialists. The results enhance our knowledge of microbial community dynamics and underscore the crucial ecological role of microbial specialists in the ecosystems of high-altitude forests.
Reducing pollution and carbon emissions is an important direction for wastewater treatment plants to achieve sustainability. This study established a comprehensive evaluation system based on three indicators, namely, effluent quality index (EQI), operating cost index (OCI), and greenhouse gas emissions (GHG), to systematically compare traditional and emerging wastewater treatment processes. Fourteen kinds of wastewater treatment processes under six inflow and outflow scenarios were simulated using GPS-X software. EQI calculations show that biofilm processes such as biological aerated filter (AO-BAF) achieve the best effluent quality under various scenarios, with a removal value of 2.28 kg EQI/m3. OCI calculations reveal that the average proportions of chemical addition, electricity usage costs, and sludge transportation costs for all processes are 47.69%, 24.62%, and 27.69% respectively, with anammox processes having the lowest OCI (0.047 $/m3)) for the baseline scenario. Carbon emission accounting results showed that direct emissions of CH4 from wastewater treatment, indirect emissions of electricity and chemicals were the main sources of GHG. Finally, through non-dominated sorting considering EQI, OCI and GHG indices, AO-BAF and AAO-VMBR processes were the preferred processes for most scenarios. This study provides valuable insights for the selection and upgrading of wastewater treatment processes from a low-carbon perspective.
UV light absorption by aquatic systems affect the physicochemical characteristics of graphene oxide (GO) nanoparticles which ultimately influence its aggregation behavior in water. Regarding this research, various humic and fulvic acids (HA/FA), extracted from China’s different climate zones, were treated with 2 h UV irradiated large (~500 nm) and (~200 nm) GO in 200 mmol/L NaCl. UV irradiated GO particles displayed aggregation even at low humic acid/fulvic acid (HA/FA) concentrations ranging from 0.2 to 1.0 mgC/L, whereas pristine GO particles did not exhibit such behavior. Reduction of functional groups, containing Oxygen (C=O/C–O), via UV irradiation is responsible for this aggregation phenomenon and conversion of GO to reduced graphene oxide (rGO). Consequently, rGO exhibits lower dispersibility, facilitating its agglomeration. Moreover, both small and large-sized GO particles exhibited less aggregation in HAs compared to FAs due to large molecular weight and high polarity of HAs. Aggregation of GO was more obvious with Makou FA and Maqin HA from Plateau and Mountain climate zone and Subtropical Monsoon climate zone, respectively, owing to DOM’s lower molecular weight and aromaticity that reduced their adsorption. The application of the Derjaguin-landau-verwey-overbeek (DLVO) theory did not reveal any significant interaction energy barrier between the 2 h UV irradiated GO particles even in the presence of DOM, indicating that aggregation prevailed despite the addition of DOM. These findings highlight that UV irradiation poses a significant threat to the GO stability in aquatic environments, particularly in the presence of DOM.
Iron-based nanoparticles (Fe-NPs) exhibit promising potential for soil remediation. However, their toxic effects on plants have also been reported. Typical Fe-NPs have been introduced into soil–plant systems to examine their possible nanotoxicity and other impacts on plants, while Fe-NPs have been added to pollutant–soil–plant systems to evaluate their performance as remediation agents. Mixed opinions and results have been reported regarding interactions between Fe-NPs and soil or plants. Here, meta-analysis was conducted to evaluate the effects of Fe-NPs on plant morphological and physiological characteristics in soil–plant and pollutant–soil–plant systems. Interestingly, morphological characteristics (dry and fresh weight) were significantly improved by Fe-NPs in both soil–plant and pollutant–soil–plant systems. In terms of plant physiological characteristics, Fe-NPs exerted negative effects on plant pigments in soil–plant systems, but positive effects in pollutant–soil–plant systems. In addition, Fe-NPs greatly increased the Fe contents and decreased the pollutant contents of plants. This study also provides a comprehensive review of the positive and negative effects of Fe-NPs on soil–plant systems and summarizes the pollutant remediation mechanisms of Fe-NPs in soil–plant systems. The results underscore the potential of Fe-NPs in agricultural applications and the future development of food safety.
