Emissions of atmospheric pollutants such as methane(CH4),hydrogen sulfide(H2S),ammonia(NH3),and volatile organic compounds(VOCs)from wet waste treatment plants pose serious environmental and health risks.Conventional monitoring methods,which rely on fixed stations or manual sampling,often face challenges such as limited spatial coverage,delayed data collection,and operational safety hazards.To address these limitations,this study developed a gas monitoring system utilizing a multi-parameter sensor integrated with an unmanned aerial vehicle(UAV).Field monitoring was conducted at a wet waste treatment plant in Shanghai to assess pollutant distribution,vertical concentration gradients,and correlations with environmental factors across different functional areas.The results revealed that CH4 concentrations were significantly higher than those of other gases,reaching up to 1860 μg/m3 throughout the plant,making CH4 the primary contributor to the total emission load.In contrast,H2S and NH3 exhibited distinct point-source characteristics,with high concentrations closely associated with specific processing stages,including the kitchen waste workshop,the catering waste workshop,the drying workshop,and the unloading hall.Although VOC concentrations were relatively low,their complex composition presented potential environmental risks.Vertical profile monitoring showed that CH4 maintained high concentrations at all heights(1800-1900 μg/m3);NH3 tended to accumulate in the upper sections of the facility,while H2S concentrations gradually increased with height.Conversely,VOCs exhibited a relatively homogeneous vertical distribution across the plant.These diffusion trends suggest that NH3 could intensify odor pollution,while VOCs may enhance ozone formation and the generation of secondary organic aerosols.Correlation analysis indicated that humidity and air pressure were key environmental factors influencing the release and dispersion of these gases.Among these factors,humidity demonstrated the most significant influence on NH3 and VOC levels,suggesting its critical role in determining their atmospheric residence time and transport behavior.This study demonstrates the effectiveness of UAV-based sensing for detecting pollutant gases in complex industrial settings.By enabling precise monitoring and real-time data acquisition,this approach improves environmental risk assessment and supports the creation of targeted pollution control strategies for wet waste treatment plants.Our findings confirm that UAV-mounted systems provide significant advantages over conventional methods,specifically in terms of expanded spatial coverage and enhanced operational safety.Overall,this study highlights the transformative potential of UAV technology in environmental monitoring,offering critical insights for air quality management and evidence-based policymaking in waste treatment sectors.
Biomass sorption-enhanced steam reforming is a promising route for the efficient conversion of biomass into H2-rich syngas by improving H2 selectivity and yield.This study investigates the design and performance of CaO-based hybrid materials for the sorption-enhanced steam reforming(SESR)of pine sawdust for H2 production.The hybrid materials were synthesized by incorporating different proportions of polymorphic Ca2SiO4 into CaO via a hydrothermal method followed by carbon-template removal.A homogeneous precursor solution containing Ni,Ca,and Si species was transferred into a 50 mL autoclave and subjected to hydrothermal treatment at 200 ℃ for 36 h.The obtained samples were then dried and calcined in air by heating to 800 ℃ at a rate of 5 ℃·min-1,followed by a holding period of 2 h.After carbon-template removal,hollow-shell sorbents were obtained.The CO2 sorption capacity and cyclic stability of the undoped sorbents were evaluated,and 10 wt.%Ni was subsequently introduced into the optimized sample.The results indicate that Ca2SiO4 loading significantly affects the balance between CO2 uptake and cyclic stability.Among the tested sorbents,the sample containing 10 wt.%Ca2SiO4 exhibited the best overall performance,achieving the highest cumulative CO2 uptake over 10 cycles while maintaining relatively high CaO utilization.Structural characterization revealed that the stabilization effect of Ca2SiO4 arises both from the dilution of the active phase and from its role as a spatially distributed inert framework between CaO grains.The results from X-ray diffraction(XRD),Brunauer-Emmett-Teller(BET)surface area analysis,and scanning electron microscopy with energy-dispersive X-ray spectroscopy(SEM-EDS)consistently suggest that Ca2SiO4 was uniformly dispersed within the hollow shell.This distribution physically separated adjacent CaO particles,restricted grain growth during repeated carbonation and calcination,and helped preserve pore volume and accessible surface area.Consequently,this microstructural stabilization effectively suppressed sintering and delayed the loss of fast-reaction sites.Furthermore,Ni incorporation reduced the CaO crystallite size and improved the utilization of active CaO while preserving the hollow-shell morphology.Upon further doping with 10 wt.%Ni,both H2 production and purity were significantly enhanced compared with those of the undoped sorbents.After 10 carbonation cycles,the Ni10Ca9Si1-HS sorbent maintained a H2 yield of 1.80 mmol/(gbm·gmat·min),representing only a 4.32%decrease from the initial value of 1.88 mmol/(gbm·gmat·min).Meanwhile,the H2 purity decreased slightly from 71.50%to 67.63%,demonstrating excellent cyclic stability.This study demonstrates that morphological control and the use of polymorphic stabilizers are crucial for improving the cyclic stability of catalyst-sorbent hybrid materials for sustainable H2 production from biomass,providing guidance for the structural design of highly efficient CaO-based hybrid materials.
In addressing the critical challenge of persistently increasing atmospheric concentrations of greenhouse gases (CO2, CH4, N2O), merely measuring their bulk concentrations has proven insufficient for accurately assessing their diverse sources, sinks, and the complex biogeochemical processes that control their global budgets. The stable isotopic compositions of these gases (such as δ13C, δ18O, δ15N) provide powerful "natural fingerprints", offering unique insights that transcend concentration data alone. These isotopic signatures enable researchers to effectively discriminate between biogenic and fossil fuel emission sources, quantitatively apportion contributions from different anthropogenic and natural processes, and reveal the underlying microbial mechanisms governing their production and consumption across various ecosystems. This comprehensive review synthesizes recent methodological advancements in isotopic analysis techniques specifically applied to greenhouse gases. It critically examines the fundamental principles, technical characteristics, and optimal application scenarios of mainstream analytical methods. These include the established benchmark technique of isotope ratio mass spectrometry (IRMS), alongside rapidly developing optical methods such as Fourier transform infrared spectroscopy (FTIR), tunable diode laser absorption spectroscopy (TDLAS), cavity ring-down spectroscopy (CRDS), and off-axis integrated cavity output spectroscopy (OA-ICOS). The comparative advantages and limitations of each technique are discussed in the context of precision, operational requirements, and field deployability. Furthermore, the article systematically elaborates on the transformative applications of these isotopic techniques across a wide spectrum of environmental research. Key areas covered include high-precision tracing of urban emission sources, elucidating production and consumption pathways in aquatic ecosystems, constraining carbon cycling dynamics in soils and wetlands, quantifying greenhouse gas fluxes in sensitive polar regions like the Antarctic tundra, and providing crucial validation for carbon cycle models that incorporate geological sources. Finally, the review critically discusses the persistent challenges confronting the field, particularly the pressing need for advanced capabilities in in-situ, high-frequency, and simultaneous multi-component isotopic measurements. It outlines promising future research directions, strongly emphasizing that the integration of multi-isotope observational networks with sophisticated atmospheric and process-based models will be paramount for precisely deciphering global and regional greenhouse gas budgets. Such integrated approaches are identified as foundational for formulating targeted and effective emission reduction strategies, as well as for advancing our predictive understanding of the biosphere's response to a changing climate.
With the continuous development of industries such as integrated circuits,wind power,and nuclear energy,the accumulation of spent thermosetting resin-based composites has emerged as an increasingly pressing environmental issue.Pyrolysis represents a promising technology for the resource recovery and value-added utilization of these wastes.To elucidate the pyrolysis characteristics of such wastes,this study systematically investigated the thermal decomposition behavior of spent ion-exchange resins based on a styrene-divinylbenzene backbone functionalized with sodium sulfonate groups.In addition,artificial intelligence models were developed to predict key pyrolysis parameters across different types of thermosetting resin-based composite wastes.The mass-loss behavior and heat flow evolution during pyrolysis were analyzed using thermogravimetry-differential scanning calorimetry(TG-DSC).The composition and distribution of gaseous and liquid products were further characterized by thermogravimetry-mass spectrometry(TG-MS)and pyrolysis-gas chromatography/mass spectrometry(Py-GC/MS).The results indicate that the cleavage of the styrene-divinylbenzene crosslinked backbone occurred predominantly between 415 and 505 ℃.During this stage,the major pyrolysis products were styrene,ethylbenzene,and toluene—high-value chemicals that accounted for approximately 77%of the detected products at 455 ℃.At temperatures above 581 ℃,CH4,H2,and CO2 became the dominant gaseous products,forming combustible gases with potential for energy recovery,while a char yield of approximately 45%was observed.An increase in heating rate led to a higher temperature corresponding to the maximum mass-loss rate,a broader temperature range for backbone cleavage,and a higher overall mass-loss rate.These changes collectively influenced the temperature window and yield of volatile products as well as the amount of residual char.Therefore,the heating rate is a key process parameter for the efficient recovery of gas,liquid,and char products from spent ion-exchange resins.Furthermore,regression models and artificial neural network(ANN)models were developed by integrating experimental results from this study with literature data on various thermosetting resin-based wastes.Based on feature importance analysis using the F-test,these models were trained using the proximate and ultimate analyses of spent resins to predict their pyrolysis parameters,including onset temperature,temperature of maximum mass-loss rate,termination temperature,and overall weight loss.Among all modeling approaches,the ANN trained using the Levenberg-Marquardt algorithm exhibited the best predictive performance,achieving a coefficient of determination(R2)of 0.99 and a mean squared error of 0.0007.In future research,emphasis should be placed on improving the purity of gaseous and liquid products,enhancing the performance of char materials,expanding the experimental database,and employing more advanced machine learning techniques.These efforts will further improve the generalization and predictive accuracy of models,thereby providing more reliable guidance for optimizing pyrolysis processes toward the efficient synergistic recovery of gas,liquid,and char products from thermosetting resin-based composite wastes.
In recent years,heated tobacco products(HTPs)have gained increasing popularity worldwide due to their reduced emissions of harmful substances compared to conventional cigarettes.To evaluate their environmental and health impacts,characterizing their physical and chemical properties is essential.This work employs a Scanning Mobility Particle Sizer(SMPS)to measure the puff-resolved concentrations of heated tobacco aerosols in real time.The aerosols were generated by two types of heating devices:an electromagnetic heating device and an infrared heating device.The puff-resolved concentrations of monodisperse particles at multiple selected sizes were measured to derive the overall size distributions.Subsequently,the puff-by-puff total particle number concentrations and mode diameters of the tobacco aerosols were determined.The key findings are as follows:(1)Due to distinct heating mechanisms,the aerosols produced by the two devices exhibit different puff-resolved distribution characteristics.The electromagnetic heating device offers relatively high heating efficiency but results in uneven heat distribution.Consequently,the aerosol concentration fluctuates within a range from 6.3×106 to 8.9×106 particles·cm-3,and the mode diameter varies between 220 and 283 nm.The infrared heating device provides more uniform heating but has lower heating efficiency,leading to a gradually increasing operating temperature during puffing,especially under insufficient preheating.The aerosol concentration increases puff-by-puff from 2.3×106 to 8.7×106 particles·cm-3,with mode diameters increasing from 151 to 319 nm.(2)Heated tobacco aerosol particles in different size ranges exhibit a distinct dependence on heating temperature.Smaller particles(≤80 nm)tend to be produced in large quantities at lower temperatures,while larger particles(≥300 nm)are more likely to be generated at higher temperatures.(3)The total particle concentrations and mode diameters of heated tobacco aerosols vary synchronously.The temperature of the heating device significantly affects both the total particle concentration and the mode diameter.As the heating temperature increases,both the aerosol concentration and the mode diameter rise markedly.These results demonstrate that the total particle number concentrations and mode diameters of heated tobacco aerosols are highly sensitive to the operating temperature and heating mechanism.Thus,the substantial release of small particles under insufficient heating conditions poses challenges for the design of heating control modules,emphasizing the need for adequate preheating and stable operating temperatures.
To meet the increasing demand for low-carbon and high-efficiency nitrogen removal in municipal wastewater treatment plants (WWTPs), this study developed a coupled system integrating partial denitrification and anaerobic ammonium oxidation (PD-anammox). The system's nitrogen removal performance was systematically investigated for the simultaneous treatment of municipal wastewater and secondary effluent. In addition, the characteristics of nitrous oxide (N2O) emissions were evaluated, and reverse transcription quantitative real-time PCR (RT-qPCR) was employed to assess the activity of key functional genes involved in N2O production and reduction pathways. The coupled system was operated under two different volumetric ratios of municipal wastewater to secondary effluent (1:5 and 2:5). Results demonstrated that, under both operating conditions, the total nitrogen concentration in the effluent consistently remained below 8 mg/L, meeting the stringent discharge standards of WWTPs. The system achieved an average nitrogen removal efficiency exceeding 69%. Notably, the contribution of the anammox pathway to overall nitrogen removal ranged from 69.52% to 75.12%, indicating a reduced dependency on external organic carbon and oxygen. Microbial community analysis using high-throughput sequencing revealed that increasing the proportion of municipal wastewater introduced more complex carbon sources, which significantly reduced the relative abundance of the genus Thauera, a key microorganism associated with partial denitrification. In contrast, the genus Denitratisoma, comprising potential functional bacteria capable of metabolizing diverse carbon compounds, maintained or even enhanced its relative abundance. This suggests its crucial role in supplying stable nitrite to anammox bacteria and thereby contributing to the overall resilience and stability of the system. A particularly noteworthy finding was the substantial reduction in N2O emission factors at higher proportions of municipal wastewater. This reduction was primarily attributed to decreased dissolved N2O concentrations rather than increased gas stripping. To further elucidate the underlying mechanisms, RT-qPCR was conducted to quantify the expression of key genes related to N2O production and reduction. The results indicated that a higher municipal wastewater ratio significantly upregulated both the quinol-oxidizing NO reductase gene qnorB (by 2.47-fold) and the clade Ⅱ N2O reductase gene nosZⅡ (by approximately 9-fold). Unlike the conventional nosZⅠ, nosZⅡ is commonly found in atypical denitrifying bacteria and exhibits a higher substrate affinity for N2O, enabling the efficient reduction of dissolved N2O even at low concentrations. This gene expression pattern explains the observed suppression of N2O accumulation, as enhanced nosZⅡ activity reinforces the final step of denitrification, converting N2O to N2. Overall, this study demonstrates the PD-Anammox coupled system as an effective and sustainable approach for the concurrent treatment of municipal wastewater and secondary effluent, offering high nitrogen removal efficiency with minimized greenhouse gas emissions. By leveraging the functional flexibility of Denitratisoma and the high-affinity N2O reduction capacity of nosZⅡ-harboring bacteria, the system achieves a synergistic balance between nitrogen removal and climate impact mitigation. These findings provide a novel technical pathway for simultaneously achieving high-efficiency nitrogen removal and N2O mitigation in biological wastewater treatment.
Food waste(FW)and waste activated sludge(WAS)are major components of municipal organic solid waste and are produced in large quantities annually.Anaerobic co-digestion of FW and WAS is a promising strategy to balance the carbon-to-nitrogen(C/N)ratio and improve the overall operational stability of anaerobic systems.However,the hydrolysis of complex organic components in FW often becomes the rate-limiting step for enhancing system performance.FW contains abundant carbohydrates that can be fermented by yeast to produce ethanol.Compared with the oxidation of other common volatile fatty acids(VFA),ethanol oxidation releases more energy,which favors the reduction of carbon dioxide to methane and thereby promotes direct interspecies electron transfer(DIET)-based anaerobic digestion.Yeast inoculation is a commonly used method for in situ ethanol production.However,yeast can only utilize reducing sugars,whereas carbohydrates in FW primarily exist as polysaccharides,such as starch and cellulose,which limits their effective utilization by yeast.To address the low ethanol yield during fermentation pretreatment of FW,this study employed simultaneous saccharification and fermentation to enhance ethanol production and investigated its effects on subsequent anaerobic co-digestion with WAS.The results showed that under optimal conditions(a saccharifying enzyme dosage of 50 U/mL,a yeast inoculation ratio of 2%,a pretreatment temperature of 30 ℃,and a pretreatment time of 21 h),the ethanol concentration reached 41.1 g/L,accounting for approximately 27.2%of the chemical oxygen demand(COD)of FW.When the pretreated product was used as the substrate for co-digestion,the methane yield increased by 17.2%compared with that of the control(without pretreatment),while the volatile solids(VS)removal rate improved by 3.2%.Electrochemical analysis showed that ethanol-type fermentation pretreatment increased sludge capacitance and decreased internal resistance,indicating enhanced electrochemical activity and improved charge-discharge capability.Moreover,microbial community analysis revealed that DIET-associated microorganisms,including Methanothrix and Methanosarcina,were enriched.Economic analysis estimated that treating 1.0 t of FW and 3.7 t of WAS could generate a net economic benefit of approximately CNY 35.7.This study demonstrates that simultaneous saccharification and fermentation effectively enhances ethanol production from FW and promotes energy recovery and organic waste reduction via the DIET mechanism,providing a theoretical basis for the application of enzymes and functional microorganisms in anaerobic digestion.
Heavy metal ions pose persistent threats to ecological stability and human health due to their high toxicity,poor degradability,and long-term accumulation in the environment.Therefore,the development of analytical techniques capable of rapid,sensitive,and selective detection of these ions in complex aqueous matrices is of significant importance.In this study,the nickel ion(Ni2+),a typical contaminant frequently found in industrial effluents,was selected as the target analyte.The aim was to establish a probe-assisted surface-enhanced Raman scattering(SERS)method that enables efficient trace-level detection of Ni2+even in complex sample matrices.Zincon was employed as a coordination-sensitive molecular probe.However,due to its sulfonic acid functional groups,Zincon cannot effectively adsorb onto the negatively charged surface of the citrate-stabilized silver nanoparticles(NPs).To address this issue,poly(diallyldimethylammonium chloride)(PDDA)was introduced to modify the NP surface,resulting in positively charged and highly stable SERS-active substrates that facilitate effective probe loading.UV-Vis spectroscopy was applied to characterize the Ni2+-Zincon coordination system and to elucidate the associated spectral evolution,thereby confirming the presence of coordination interactions and potential charge-transfer processes.Key experimental parameters,including halide type and concentration,probe dosage,and modification time,were systematically examined.Optimal conditions were identified by evaluating both signal intensity and measurement repeatability.The results showed that upon coordination with Ni2+,the characteristic absorption peak of Zincon at 495 nm red-shifted to 510 nm,accompanied by the emergence of a new absorption band at 665 nm.These spectral variations provide strong evidence of electron redistribution induced by complex formation.Under optimized conditions at pH 9,with 200 μmol/L Zincon,10-min bromide-assisted modification,and 532 nm excitation,the PDDA-Ag NPs substrate generated stable and reproducible SERS signals.The Raman band at 730 cm-1 exhibited a strong linear correlation with Ni2+concentrations ranging from 10 nmol/L to 1 μmol/L(R2=0.9942).The detection limit was as low as 0.0187 μmol/L,with a quantification limit of 0.0625 μmol/L,demonstrating the method's capability for quantitative trace Ni2+detection in electroplating wastewater.Selectivity evaluations confirmed that the proposed sensing platform exhibited excellent resistance to interference from other common metal ions.When applied to real electroplating wastewater samples,the results obtained using this SERS method showed high agreement with those obtained by atomic absorption spectroscopy,exhibiting a relative standard deviation in low-concentration samples(RSD=4.31%),confirming the method's good reproducibility and practical applicability.Further analysis indicated that the effective SERS enhancement primarily originates from the charge-transfer energy level of the Ni2+-Zincon complex,which is well-matched with the excitation wavelength.This mechanism provides theoretical guidance for the design of SERS-based heavy metal detection systems suitable for complex environmental matrices.Overall,the established probe-assisted SERS strategy offers a promising analytical approach for environmental monitoring,pollution source identification,and early risk warning.
In the context of China's dual-carbon goals, plastic restriction policies, and ongoing waste sorting initiatives, aerobic composting serves as a crucial approach for the valorization of organic solid waste. However, this process faces two challenges: a bottleneck in humification efficiency caused by lignocellulose recalcitrance, and an urgent need to accelerate the degradation and transformation of biodegradable plastics under realistic conditions to minimize residues in the final product. This review systematically summarizes the key mechanisms of lignocellulose depolymerization and humus formation, alongside the transformation pathways of biodegradable plastics during physical abrasion, chemical oxidation, enzymatic depolymerization, assimilation, and mineralization. This analysis indicates that while these two substrates differ in origin and composition, they share several common degradation bottlenecks, including the difficulty of depolymerizing recalcitrant components, limited substrate accessibility, and insufficient synergy between hydrolysis and oxidation. In lignocellulose, lignin forms a barrier around cellulose and hemicellulose, restricting subsequent hydrolysis; similarly, in biodegradable plastics, initial activation and chain scission are hindered by high hydrophobicity, stable molecular chains, and limited surface accessibility. Consequently, this review highlights the unique advantages of white-rot fungi in transforming complex polymers via their nonspecific extracellular oxidative enzyme systems (e.g., manganese peroxidase, lignin peroxidase, and laccase). These fungi disrupt the lignin barrier, enhance substrate reactivity, and promote the subsequent hydrolysis of cellulose and hemicellulose. Furthermore, they increase the hydrophilicity of biodegradable plastics and weaken molecular chain stability through surface oxidation, thereby facilitating subsequent hydrolysis and transformation. Therefore, white-rot fungi theoretically possess the potential to simultaneously enhance lignocellulose humification and the degradation and transformation of biodegradable plastics. However, current evidence for white-rot fungi-mediated plastic degradation is derived primarily from controlled culture systems, and the fungi's functional stability and practical applicability under real composting conditions remain insufficiently studied. Finally, this review summarizes the main limitations of their engineering applications, including the mismatch between their optimal temperature window and the thermophilic phase of composting, competitive exclusion by indigenous microbial communities, and localized colonization and enzyme production effects caused by compost heterogeneity. To address these issues, several optimization strategies are proposed: strain screening and acclimation, the development of carrier or catalytic materials to establish stable microenvironments, and phased inoculation combined with refined control of process parameters. Ultimately, this review provides a systematic understanding of white-rot fungus-mediated compost enhancement strategies, offering technical insights to support the coordinated valorization of organic solid waste and the low-risk, end-of-life management of biodegradable plastics.
Currently,the electric vehicle industry is expanding rapidly,leading to challenges in the large-scale retirement and resource recovery of lithium-ion batteries.The black mass from spent LiFePO4 batteries has a complex composition.In this study,an acid-free leaching process based on sodium persulfate was developed to treat the black mass.This method achieves selective lithium recovery and enables cathode material regeneration.Key parameters were optimized via single-factor experiments.At ambient temperature,lithium was selectively leached from the complex black mass by adjusting the leaching time to 40 min,the LiFePO4/Na2S2O8 molar ratio to 2.0:1.2,and the solid-liquid ratio to 50 g/L.A lithium leaching efficiency of 86.4%was achieved,while the dissolution of Fe,Cu,and Al was minimized.Notably,temperatures above 35 ℃ led to a substantial increase in the leaching rates of Cu and Al,and temperatures≥65 ℃ promoted the dissolution of Fe.Therefore,ambient temperature operation saves energy and ensures leaching selectivity.The mechanism was investigated using multiple characterization techniques.X-ray diffraction(XRD)revealed that the majority of LiFePO4 was converted into FePO4,following the reaction:2LiFePO4+Na2S2O8 → 2FePO4+Li2SO4+Na2SO4.Fourier transform infrared spectroscopy(FTIR)showed characteristic peak shifts and a new peak corresponding to the bending vibration of the PO4 3-group.Scanning electron microscopy(SEM)images demonstrated that the graphite and LiFePO4 particle structures were preserved after leaching,confirming the mildness of the process.Based on the shrinking-core model,the incomplete lithium leaching was attributed to in-situ retained FePO4,which obstructed the contact between Na2S2O8 and internal LiFePO4.After impurity removal and leachate concentration,lithium was precipitated using a saturated Na2CO3 solution.High-purity Li2CO3 was obtained through purification.Regenerated LiFePO4(RLFP)was prepared via carbothermal reduction using recycled Li2CO3 and FePO4 as raw materials,with 20%glucose added as the carbon source,followed by roasting at 700 ℃ for 10 h under an N2 atmosphere.RLFP exhibited properties comparable to those of commercial LiFePO4(CLFP):XRD confirmed its standard crystal phase;X-ray photoelectron spectroscopy(XPS)verified that Fe was in the Fe2+valence state;and Raman spectroscopy showed distinct D and G peaks with an ID/IG ratio<1,indicating a high degree of graphitization.Electrochemical tests demonstrated that RLFP delivered a specific discharge capacity exceeding 155 mAh/g at 0.1 C and maintained a capacity retention of 99.2%after 100 charge-discharge cycles at 0.5 C.Our mild,acid-free,short-flow leaching and regeneration strategy enables efficient selective lithium recovery from black mass and the regeneration of high-performance LiFePO4.This work offers a practical pathway for the recycling of spent LiFePO4 batteries and demonstrates promising potential for industrial-scale applications.
Energy conservation and carbon reduction in pumping systems at urban water supply plants are pivotal for achieving carbon peak and carbon neutrality goals.Over 90%of the electricity consumption in such plants is attributed to pump operation.However,current research faces three interconnected problems.First,static models based on theoretical characteristics fail to represent actual dynamic operating conditions accurately,leading to biased optimization baselines.Second,the coupling between intelligent algorithms and increasingly complex pump-optimization problems remains insufficient,often resulting in suboptimal solutions.Third,the evaluation system is fragmented,and assessment results are not effectively fed back into the optimization process to enable iterative improvement.These disconnections among the Model-Algorithm-Assessment components represent a core scientific challenge that hinders precise and effective decarbonization of water-supply systems.This review systematically examines recent advances in the application of intelligent algorithms to pump energy optimization.It first outlines the key elements of pump energy modeling,including operating-point derivation via curve fitting,objective function formulation,and constraint setting,which together provide a foundation for subsequent algorithmic optimization.It then categorizes and analyzes the application scenarios and technical features of traditional heuristics,data-driven methods,and hybrid algorithms.Literature analysis reveals that traditional heuristics remain the most widely applied algorithms but are prone to premature convergence under dynamic conditions,limiting their practical effectiveness.In contrast,emerging hybrid algorithms that integrate mechanistic models with data-driven techniques have demonstrated additional energy-saving potential:specifically,they can reduce energy consumption by 5%to 10%compared to traditional algorithms.A life-cycle perspective indicates that operational-phase carbon emissions account for 70%to 85%of the total footprint,while the manufacturing and disposal stages contribute 15%to 30%.This finding suggests that life-cycle assessment(LCA)could complement existing evaluation systems and underscores the need for a holistic assessment beyond mere operational energy use.Operational-phase metrics are also detailed,as they are essential for quantifying optimization effects and providing feedback to algorithms.The results indicate that the iterative synergy between intelligent algorithms and assessment systems is central to enhancing performance.To address the identified gaps,we propose a Model-Algorithm-Assessment tripartite framework that focuses on three interrelated aspects:(1)a sufficiently accurate and generalizable mathematical model;(2)intelligent algorithms that overcome algorithm-problem mismatch and achieve efficient optimization under complex,time-varying conditions;and(3)a life-cycle assessment system that provides comprehensive validation and broader evaluation dimensions for optimization strategies.This framework promotes the implementation of intelligent energy-saving and carbon-reduction technologies in urban water-supply plants.
The ozone production rate(OPR),a key parameter for characterizing ozone pollution,directly affects the intensity and duration of near-surface ozone accumulation.It plays a crucial role in distinguishing between the contributions of local photochemical ozone formation and regional transport,as well as in understanding the complex non-linear relationships between ozone and its precursors.In recent years,the increasingly severe global ozone pollution problem has posed a serious threat to human health,ecosystems,and climate.Ozone levels are influenced by a combination of photochemical reactions,regional transport,meteorological conditions,and deposition processes,and they exhibit complex non-linear interactions with precursor gases.Accurate measurement and a deep understanding of OPR are essential for developing effective ozone control strategies and advancing the study of atmospheric chemical processes.OPR measurement methods are generally divided into direct and indirect approaches.Direct measurement systems typically consist of a photochemical reaction module,a conversion module,and a detection module,enabling real-time calculation of OPR.The main challenges of direct measurement lie in the accurate acquisition of Ox concentrations and the precise calibration of gas residence time,which require high-performance detection instruments and optimized system design.Nevertheless,due to their simple structure and operating principles,direct OPR measurement systems have attracted widespread attention from researchers worldwide.By minimizing wall losses,improving residence time calibration,and enhancing Ox detection accuracy,current OPR systems can achieve a detection limit as low as 0.2×10-9 h-1,with an overall uncertainty reduced to 10%.Long-term field observations in the United States,Japan,China,and other regions using independently developed direct OPR instruments have demonstrated the feasibility and accuracy of direct measurement techniques.Indirect measurement methods,which rely on modeling or observed radical data,involve the analysis of ozone formation and consumption processes.Model simulations often underestimate radical concentrations,leading to lower OPR values,while observational instruments for radicals are complex and susceptible to measurement errors.However,with the continuous optimization of models and improvements in radical observation technologies,the accuracy of indirect OPR measurement has gradually improved.Researchers in China have conducted long-term indirect measurements in regions such as Beijing-Tianjin-Hebei,the Yangtze River Delta,and the Pearl River Delta,generating a large volume of OPR data that provides valuable support for atmospheric chemical research.This review systematically introduces the principles of OPR measurement technologies,summarizes key challenges,recent research advancements,and performance characteristics of both Chinese and international OPR measurement techniques.It also compares different measurement methods and reviews their applications in urban,suburban,and regional background environments,aiming to provide a scientific basis and future directions for the accurate quantification of OPR.
Accurate and rapid determination of polyester and spandex fiber content in complex waste textile matrices is a critical prerequisite for automated textile sorting and high-value resource recycling.However,existing analytical methods often suffer from limited detection accuracy,strong dependence on sample pretreatment,model overfitting,and insufficient generalization capability when applied to heterogeneous textile blends.To overcome these limitations,this study proposes a rapid,non-destructive,and quantitative detection framework that integrates near-infrared hyperspectral imaging with optimized machine learning and deep learning algorithms for efficient waste textile identification and sorting.A push-broom hyperspectral imaging system operating in the near-infrared spectral range of 1000-1700 nm was employed to acquire high-dimensional spectral data from waste textile samples with various polyester-spandex blending ratios.To improve spectral quality and suppress interference caused by noise,baseline drift,and light scattering,the raw spectral data were preprocessed using white and dark reference correction,Savitzky-Golay(S-G)smoothing,and standard normal variate(SNV)transformation.For polyester fiber content prediction,principal component analysis(PCA)was used to extract representative spectral features and reduce data dimensionality.For spandex fiber detection,Pearson and Spearman correlation analyses were conducted to identify wavelength bands that are highly sensitive to spandex content.Feature distribution characteristics were further explored using t-distributed stochastic neighbor embedding(t-SNE)visualization.Two complementary modeling strategies were developed to address the distinct spectral characteristics of polyester and spandex fibers.First,a multilayer perceptron model optimized by the Snake Optimization algorithm(MLP-SO)was proposed for polyester content prediction.The SO algorithm enabled global optimization of the MLP hyperparameters,effectively reducing the risk of getting trapped in local optima and enhancing model robustness.Second,for spandex content prediction,a random forest(RF)model was constructed using kernel principal component analysis(KPCA)-derived nonlinear features,enabling effective modeling of complex and weak spectral responses associated with low spandex concentrations.Experimental results demonstrated that the proposed models achieved superior predictive performance compared with the conventional approaches.The MLP-SO model attained a coefficient of determination(R2)of 0.96 and a root mean square error(RMSE)of 3.55 on an independent test set,significantly outperforming traditional models such as XGBoost,generalized additive models(GAMs),and partial least squares regression(PLS-R).For spandex fiber content prediction,the RF model achieved an R2 of 0.90 and an RMSE of 1.48,markedly surpassing the performance of the PLS-R model.Correlation analysis revealed that the 1000-1100 nm and 1400-1500 nm wavelength regions were highly sensitive to spandex content.Meanwhile,t-SNE visualization confirmed the clear separability of spectral features corresponding to different spandex concentration levels.Overall,this study validates the feasibility of combining hyperspectral imaging with optimized learning algorithms for the rapid,non-destructive,and precise quantification of polyester and spandex fibers in heterogeneous waste textiles.The proposed framework effectively overcomes the limitations of traditional linear and conventional machine learning models when handling high-dimensional spectral data,providing a robust and scalable technical solution to support automated textile sorting and sustainable resource recovery in the textile recycling industry.
With the rapid increase in motor vehicle numbers,nano-to sub-micron particle pollution in urban street canyons has become an important air-quality concern because of its high spatial heterogeneity and proximity to emission sources.In China,however,high-resolution investigations that simultaneously integrate particle number size distributions,chemical composition,and the source-related formation processes within street-canyon environments remain limited.To address this gap,a 16-day high-time-resolution field campaign was conducted at the Sanli'an section of Changjiang West Road in Hefei,a typical urban street canyon influenced by traffic and commercial activities.Particle number size distributions were measured using a Scanning Mobility Particle Sizer(SMPS),while the non-refractory submicron aerosol chemical composition(NR-PM1)was characterized using an Aerosol Chemical Speciation Monitor(ACSM).These measurements were combined with meteorological observations and source apportionment analysis to investigate the emission characteristics,atmospheric processing,and controlling mechanisms of nano-to sub-micron particles in this microenvironment.The results show a pronounced tri-modal diurnal pattern in particle number concentrations.Three distinct peaks were observed during the morning traffic rush(07:00-09:00),lunchtime cooking activities(11:00-13:00),and the evening traffic rush(18:00-21:00),indicating the combined influence of vehicular exhaust and cooking-related emissions.Number concentrations were dominated by particles in the nucleation and Aitken modes during traffic and cooking periods,while accumulation-mode particles increased under stagnant meteorological conditions.Organic aerosols were the dominant component of NR-PM1,accounting for 55%-75%of the total mass concentration,followed by nitrate(15%-25%).Both organic aerosol and nitrate exhibited higher concentrations during the morning and evening,and lower levels around noon,reflecting the combined effects of emission intensity,photochemical oxidation,and boundary-layer evolution.A positive matrix factorization(PMF)analysis resolved five contributing factors for particle number concentrations:combustion-related emissions,new particle formation,traffic emissions,regional transport,and aged mixed aerosols.For chemical species,three major factors were identified:secondary nitrate,secondary organic aerosol(SOA),and primary organic aerosol(POA).Meteorological conditions played a critical role in regulating particle formation and evolution.Low temperature(<10 ℃)and low relative humidity(<40%)under relatively clean background conditions favored new particle formation in the 7-20 nm size range.In contrast,high temperature(>30 ℃)and high relative humidity(>70%)promoted particle growth and secondary aerosol accumulation.Additionally,low wind speeds(<1.5 m/s),particularly under southwesterly wind conditions,enhanced pollutant accumulation within the street canyon and shifted the particle size distributions toward the accumulation mode.Overall,the results systematically elucidate the emission characteristics,nucleation processes,and aging behavior of nano-to sub-micron particles in an urban street-canyon environment.The findings provide quantitative evidence for refined urban air-quality management and the coordinated control of vehicular exhaust and cooking-related emissions.
In order to effectively control mobile source particulate matter emissions and mitigate the increasingly severe issue of atmospheric pollution, environmental regulations in China have imposed strict control requirements on the particle number (PN) concentration of diesel vehicles since the China V stage. These requirements were further expanded in the China VI stage to include light-duty vehicles. However, the current market features a multitude of PN detection devices from various manufacturers. Although specific regulations for key technical parameters—such as PN counting efficiency and counting linearity—have been promulgated, a unified and traceable calibration system remains absent. This lack of a robust metrological traceability chain compromises data consistency and undermines measurement reliability, posing significant challenges to the implementation of China VI Standards. To address this critical gap, this study designs and develops a calibration system based on a transverse-quenched soot particle generation system. This system is capable of generating soot particles that are stable, controllable, and highly similar in morphology and properties to real vehicle exhaust emissions. From a metrological perspective, the system measures the micro-current induced by charged particles, directly tracing the particle number concentration to the ampere, a base unit of the International System of Units (SI). This approach eliminates reliance on secondary transfer standards, enabling rapid, high-precision, and reliable calibration of PN detectors. Rigorous laboratory tests were conducted to validate the metrological performance of the system. The results indicate that the developed soot particle generation device exhibits exceptional stability, capable of producing standard soot particles in the 10 nm to 100 nm size range. The differential mobility analyzer (DMA), a core component for particle sizing, demonstrates superior performance, with sizing deviations strictly controlled to less than 2.0% across all key particle size nodes from 10 nm to 100 nm. Furthermore, the Faraday cup aerosol electrometer (FCAE), serving as the reference standard, shows excellent linearity in measuring various particle sizes, with a linearity coefficient greater than 99.6%, and its counting efficiency is maintained stably within the range of 100.0% to 110.0%. Application test results demonstrate that the system possesses outstanding versatility in practical engineering applications. In calibration experiments involving both bench-top PN equipment and the Portable Emissions Measurement System Particle Number (PEMS-PN) module, the system exhibited excellent applicability. The counting efficiency obtained from the calibration aligns closely with the manufacturers' nominal values, with errors consistently within ±6.7%. These results verify the precision of the proposed calibration system and provide strong technical support for solving the traceability issues of PN equipment under the China VI Standards.
Amidst growing concerns over global environmental pollution and fossil fuel depletion, developing functional carbon materials from green and renewable biomass is a promising strategy that aligns with carbon peaking and carbon neutrality goals, as well as the principles of sustainable development. Among various biomass resources, algal biomass, regarded as a highly promising third-generation feedstock, offers unique advantages. It does not occupy arable land or compete with food crops, and its short growth cycle enables rapid proliferation, resulting in a yield per unit area significantly higher than that of traditional biomass. Additionally, algae can effectively absorb nutrients such as nitrogen and phosphorus from water during growth, thus contributing to the mitigation of eutrophication. Moreover, algae are inherently enriched with heteroatoms such as nitrogen and oxygen, allowing for in-situ heteroatom doping during carbon material synthesis without the need for additional dopants. This inherent compositional advantage makes algal biomass an excellent precursor for preparing functional carbon materials. This review systematically summarizes the research progress on functional carbon materials derived from algal biomass. First, it provides an in-depth analysis of the raw material characteristics of various algal types, including proximate analysis, elemental composition, and specific component data, thereby highlighting their potential as high-quality carbon sources. Subsequently, the review elaborates on relevant preparation methods, encompassing three fundamental carbonization techniques: pyrolytic carbonization, hydrothermal carbonization, and microwave carbonization. It also compares the effects of key processing parameters on the performance of the resulting carbon materials. Furthermore, it thoroughly discusses three commonly used activation and modification strategies—namely, physical activation, chemical activation, and metal salt/oxide modification—and their roles in optimizing pore structures and tailoring surface functional groups. The review also highlights the applications of algal biochar in three key areas: adsorption, energy storage, and catalysis, demonstrating its diverse application potential. Finally, it summarizes the critical bottlenecks in current research, including high algae collection costs, batch-to-batch compositional inconsistency, difficulties in fine-tuning microstructures, performance gaps with commercial alternatives, and the absence of mature large-scale production technologies. This review provides valuable insights and serves as a comprehensive reference for future theoretical studies and industrial applications.
The rapid development of the electric vehicle and electrochemical energy storage sectors has created an urgent need for sustainable resource management in the lithium-ion battery sector, thereby drawing widespread attention to the recycling of spent lithium iron phosphate (LFP) batteries. Hydrometallurgy has become the mainstream recovery method due to its high metal recovery rate, low energy consumption, and high product purity. However, traditional processes are often associated with multi-step operations, high reagent consumption, and complex wastewater management, posing economic and environmental challenges that hinder large-scale industrial application. Therefore, a systematic review is necessary to integrate recent achievements and critically evaluate the recovery pathways of LFP batteries. This review comprehensively investigates the hydrometallurgical recovery processes of spent LFP batteries. First, pretreatment techniques are compared and analyzed, including discharging, mechanical crushing, and thermal or chemical treatment methods aimed at separating active materials from current collectors. A detailed analysis of various leaching systems follows, including inorganic acids, organic acids, bioleaching, and deep eutectic solvents (DESs), with a comparison of their mechanisms and efficiencies. Subsequently, methods for impurity removal and product purification are evaluated. Finally, material regeneration pathways are discussed, including the solid-state and hydrothermal synthesis of recovered iron phosphate and its upcycling into high-voltage lithium manganese iron phosphate (LMFP). This analysis highlights the trade-offs between different approaches. Specifically, inorganic acid leaching, especially using sulfuric acid, offers high efficiency but raises environmental concerns. Organic acid leaching and bioleaching are more environmentally friendly and exhibit higher lithium selectivity, though they often face challenges such as high reagent costs, slow reaction rates, or sensitivity to pulp density. DESs offer an innovative and tunable platform for selective metal dissolution, though issues of high viscosity and scalability remain. For separation, synergistic solvent extraction systems demonstrate impressive Fe/Li separation factors, while precise pH control is essential to minimize Fe loss during Al removal. The regeneration of cathode materials from purified solutions has proven feasible, with regenerated LFP exhibiting excellent electrochemical performance. Notably, upcycling LFP into materials with higher voltage and energy density enhances the economic viability of the recycling process. While hydrometallurgy effectively recovers valuable metals from spent LFP batteries, its industrialization is constrained by economic and environmental barriers related to process complexity, chemical consumption, and waste management. The future of sustainable LFP recycling lies in integrated innovations: developing short-process, closed-loop flowsheets; designing intelligent, adaptive leaching systems with minimal chemical input; and prioritizing upcycling strategies that directly convert waste into high-performance cathode materials. This transition from simple recovery to high-value regeneration is vital for establishing an economically viable and environmentally friendly circular economy for LFP batteries.
Spaceborne carbon monitoring is a crucial observational approach for achieving accurate global surveillance of anthropogenic carbon emissions and advancing climate change research,thereby playing a pivotal role in supporting greenhouse-gas mitigation and China's"dual-carbon"strategy.Owing to its broad spatial coverage,passive optical remote sensing has become one of the primary technical pathways for spaceborne monitoring of carbon point sources.Identifying carbon point sources and dynamically tracking their emission variations enable quantitative assessment of carbon emissions from cities and industrial sectors,thereby providing a scientific basis for emission-reduction policies.To address the demand for sub-kilometer spatial-resolution monitoring of carbon point sources,this paper first reviews the development status and recent advances of representative international instruments.Key performance characteristics are then systematically summarized in terms of spectral coverage,spectral resolution,spatial resolution,and swath width.Based on this review,we further outline the fundamental principles of several representative instrument architectures,including prism-based dispersion spectrometers,grating spectrometers,Fabry-Pérot interferometers,and spatial heterodyne interferometric imaging spectrometers.For each architecture,we summarize the corresponding scientific objectives,typical application scenarios,and the use of associated data products.The review indicates that sub-kilometer carbon point-source monitoring payloads are generally evolving toward the synergistic optimization of high spectral resolution,high spatial resolution,and wide swath width,thereby simultaneously meeting the requirements of point-source detectability and the accuracy of quantitative emission retrievals.However,concurrent enhancement of these metrics is typically accompanied by trade-offs in optical throughput,system complexity,and engineering feasibility,and different architectures exhibit distinct boundaries in achievable performance and implementation cost.To meet the hotspot greenhouse-gas emission monitoring requirements of China's next-generation carbon satellite,TanSat-2,we designed and developed a laboratory prototype based on a bidirectional heterogeneous-modulation spatial heterodyne spectroscopy(SHS)concept.Preliminary laboratory experiments demonstrate that,within the 1565-1585 nm spectral band,the prototype achieves a spectral resolution better than 0.125 nm.At an orbital altitude of 7443 km,it attains a spatial resolution better than 500 m while maintaining a swath width exceeding 100 km.These results validate the feasibility of this approach in jointly achieving ultra-high spectral resolution,high spatial resolution,and wide-swath coverage,thereby providing key technical support for future deployment on TanSat-2 to enable quantitative greenhouse-gas column retrievals and point-source detection.Based on the proposed scheme and successful prototype validation,future spaceborne high-resolution carbon monitoring systems may further target multi-gas synergistic observations by advancing integrated and miniaturized designs of wide-swath,high-resolution spectrometers.Such systems could also benefit from satellite constellations to increase revisit frequency,and from the integration of cloud screening and aerosol correction methods to extend monitoring capability toward nearly all-weather conditions.These advancements will offer guidance for subsequent engineering development and application system construction.
Escalating global plastic pollution has resulted in the pervasive accumulation of microplastics (MPs) in aquatic environments. Due to their strong pollutant adsorption capacity and difficulties in recovery, MPs pose severe challenges to conventional water treatment technologies. Microalgae, characterized by high environmental adaptability and robust metabolic capabilities, exhibit significant potential for MP bioremediation. This review systematically elucidates the interaction mechanisms between microalgae and MPs, bioremediation strategies, and downstream co-conversion pathways. Specifically, extracellular polymeric substances (EPS) secreted by microalgae act as key drivers for hetero-aggregation, facilitating interfacial adhesion via charge neutralization and hydrogen bonding. The size ratio of MPs to algal cells regulates aggregation behavior: comparable sizes promote co-sedimentation, whereas significantly larger MPs obstruct light, and nanoscale particles induce cytotoxicity. MP toxicity is further modulated by particle concentration and the degree of aging. Conversely, microalgae accelerate MP degradation through physical abrasion and enzymatic hydrolysis. Effective bioremediation requires matching the surface properties of algal strains with MPs, regulating biofilm formation, and balancing hydrodynamic shear forces. Regarding resource recovery, the co-pyrolysis or liquefaction of algal biomass and MPs reduces nitrogen- and oxygen-containing impurities in bio-oil via the hydrogen-donor effect of MPs. This process can also yield porous carbon with a high specific surface area or fluorescent carbon quantum dots. However, current research faces notable limitations. Most studies on toxicity and degradation rely on static, single-species systems that fail to simulate realistic hydrodynamic parameters (e.g., flow velocity and turbulence intensity) and the synergistic effects of co-existing pollutants (e.g., heavy metals and pharmaceutical compounds). Furthermore, co-conversion technologies are limited by discontinuous operation and a lack of robust models correlating feedstock ratios with product quality. Future research should prioritize the development of multi-algal synergistic remediation systems and the introduction of dynamic flow simulations to replicate real aquatic environments. Remediation efficacy must be evaluated under multi-pollutant conditions. Additionally, developing multi-stage continuous-flow reactors with optimized catalysis is crucial. Ultimately, these efforts will bridge the gap between remediation and resource utilization, promoting the transition of bioremediation technology from laboratory research to industrial application.
With the rapid expansion of the electric vehicle industry, the volume of spent lithium-ion batteries (LIBs) has witnessed a sharp increase, underscoring the significance of recycling and reutilizing spent graphite anodes for sustainable development. Although spent graphite retains a relatively stable layered framework after cycling, it exhibits structural defects, residual electrolyte components, and surface contaminants. These issues limit its direct reuse in new batteries but create opportunities for targeted regeneration and functional transformation. This review provides a comprehensive overview of recent advances in the repair, regeneration, and functional utilization of graphite anodes from spent LIBs. Repair and regeneration aim to restore the electrochemical activity of degraded graphite by removing impurities, repairing structural defects, and reconstructing the electrode-electrolyte interface. Low-to-medium-temperature graphitization, enabled by the introduction of transition metal catalysts that reduce the migration energy barrier of carbon atoms, allows the graphitization process to occur at lower temperatures and with reduced energy consumption. Surface treatments focus on constructing protective coatings on damaged graphite to cover defect regions, improve structural integrity, and stabilize the electrode-electrolyte interface, thereby suppressing undesired side reactions. Rapid heating treatments, such as microwave irradiation and Joule heating, generate localized high temperatures within seconds, enabling efficient removal of surface residues and repair of near-surface defects in an energy-saving and environmentally friendly manner. Functional utilization leverages the intrinsic defects, porous structures, and the ability of spent graphite to incorporate heteroatoms or metals. By tailoring surface morphology and introducing functional elements, spent graphite can be converted into advanced functional materials for diverse applications. Specifically, defect sites and residual heteroatoms can serve as catalytic centers for electrocatalysis and pollutant degradation, while the engineered porous structures and surface functional groups enhance the adsorption of heavy metals and organic contaminants in aqueous environments. Furthermore, strategies such as defect engineering, heteroatom doping, and composite formation enhance ionic transport and capacitive performance, facilitating the development of porous carbons, doped graphene, and graphite-based composites for supercapacitors as well as sodium-ion and potassium-ion batteries. From an environmental and economic perspective, graphite regeneration and utilization provide distinct advantages over conventional recycling methods. Repair and regeneration reduce greenhouse gas emissions and minimize secondary pollution, while functional utilization mitigates waste and generates economic value by producing functional materials with ecological benefits. Despite notable progress, large-scale recycling of spent graphite remains challenging due to high energy consumption, complex processing steps, and the limited availability of efficient, scalable technologies. In addition, the diversity of waste sources complicates the establishment of standardized pretreatment and regeneration procedures. Future research should focus on developing intelligent and universal recycling technologies, along with the construction of integrated closed- and open-loop pathways, to achieve resource-efficient, environmentally compatible, and value-added reutilization of spent graphite. The coordinated implementation of these strategies is expected to enhance efficiency, reduce costs, and maximize the resource potential of spent graphite, thereby supporting a sustainable and circular lithium-ion battery industry.
