Joint Effects of Multipollutant Mixtures on Mortality in Chengdu,China

Significant epidemiological research has revealed that exposure to air pollution is substantially associated with numerous detrimental health consequences^([1-3]).The negative health effects of individual air pollutants(e.g.,fine particulate matter:PM_(2.5);nitrogen dioxide:NO_(2);carbon monoxide,CO;or ozone:O_(3))have been widely explored^([4]).However,humans are constantly exposed to multipollutant mixtures in real life,and biological responses to inhaled pollutants are likely to depend on the interplay of pollutant mixtures.Therefore,it is critical and imperative to explore the joint effects of multipollutant mixtures on human beings.

Optimal preparation of Bose and Fermi atomic gas mixtures of ^(87)Rb and ^(40)K in a crossed optical dipole trap

We report on the optimal production of the Bose and Fermi mixtures with ^(87) Rb and ^(40)K in a crossed optical dipole trap(ODT).We measure the atomic number and lifetime of the mixtures in combination of the spin state |F=9/2,m_(F)=9/2> of^(40)K and |1,1>of ^(87) Rb in the ODT,which is larger and longer compared with the combination of the spin state |9/2,9/2> of^(40)K and 12,2) of ^(87)Rb in the ODT.We observe the atomic numbers of ^(87)Rb and ^(40)K shown in each stage of the sympathetic cooling process while gradually reducing the depth of the optical trap.By optimizing the relative loading time of atomic mixtures in the MOT,we obtain the large atomic number of ^(40)K(~6 ×10^(6)) or the mixtures of atoms with an equal number(~1.6 × 10^(6)) at the end of evaporative cooling in the ODT.We experimentally investigate the evaporative cooling in an enlarged volume of the ODT via adding a third laser beam to the crossed ODT and found that more atoms(8 × 10^(6)) and higher degeneracy(T/T_(F)=0.25) of Fermi gases are obtained.The ultracold atomic gas mixtures pave the way to explore phenomena such as few-body collisions and the Bose-Fermi Hubbard model,as well as for creating ground-state molecules of ^(87)Rb^(40)K.

Long-term performance of recycled asphalt mixtures containing high RAP and RAS

The application of reclaimed asphalt pavement(RAP)and reclaimed asphalt shingles(RAS)on asphalt pavement can reduce the asphalt paving cost,conserve energy and protect the environment.However,the use of high contents of RAP and RAS in asphalt pavement may lead to durability issues,especially the fatigue cracking and thermal cracking.It is necessary to conduct a series of analyses on asphalt mixtures containing high RAP and RAS,and seek methods to enhance their long-term performance.This paper provides a comprehensive over-view of the long-term performance of recycled asphalt mixtures containing high contents of RAP and RAS.The findings in this research show that rutting resistance of high recycled asphalt mixtures is not a concern,whereas their resistance to fatigue and thermal cracking is not conclusive.Recycling agents can be used to improve the thermal cracking resistance of high recycled asphalt mixtures.An optimum decision on recycling agents will improve the durability properties of high recycled asphalt mixtures.It is recommended that to use a balanced mixture design approach with testing of the blended asphalt binders will provide better understanding of long-term performance of recycled asphalt mixtures containing high RAP and RAS.

Predictive modelling of volumetric and Marshall properties of asphalt mixtures modified with waste tire-derived char:A statistical neural network approach

The goals of this study are to assess the viability of waste tire-derived char(WTDC)as a sustainable,low-cost fine aggregate surrogate material for asphalt mixtures and to develop the statistically coupled neural network(SCNN)model for predicting volumetric and Marshall properties of asphalt mixtures modified with WTDC.The study is based on experimental data acquired from laboratory volumetric and Marshall properties testing on WTDCmodified asphalt mixtures(WTDC-MAM).The input variables comprised waste tire char content and asphalt binder content.The output variables comprised mixture unit weight,total voids,voids filled with asphalt,Marshall stability,and flow.Statistical coupled neural networks were utilized to predict the volumetric and Marshall properties of asphalt mixtures.For predictive modeling,the SCNN model is employed,incorporating a three-layer neural network and preprocessing techniques to enhance accuracy and reliability.The optimal network architecture,using the collected dataset,was a 2:6:5 structure,and the neural network was trained with 60%of the data,whereas the other 20%was used for cross-validation and testing respectively.The network employed a hyperbolic tangent(tanh)activation function and a feed-forward backpropagation.According to the results,the network model could accurately predict the volumetric and Marshall properties.The predicted accuracy of SCNN was found to be as high value>98%and low prediction errors for both volumetric and Marshall properties.This study demonstrates WTDC's potential as a low-cost,sustainable aggregate replacement.The SCNN-based predictive model proves its efficiency and versatility and promotes sustainable practices.

Historical tillage promotes grass-legume mixtures establishment and accelerates soil microbial activity and organic carbon decomposition

Perennial grass-legume mixtures have been extensively used to restore degraded grasslands,increasing grassland productivity and forage quality.Tillage is crucial for seedbed preparation and sustainable weed management for the establishment of grass-legume mixtures.However,a common concern is that intensive tillage may alter soil characteristics,leading to losses in soil organic carbon(SOC).We investigated the plant community composition,SOC,soil microbial biomass carbon(MBC),soil enzyme activities,and soil properties in long-term perennial grass-legume mixtures under two different tillage intensities(once and twice)as well as in a fenced grassland(FG).The establishment of grass-legume mixtures increased plant species diversity and plant community coverage,compared with FG.Compared with once tilled grassland(OTG),twice tilled grassland(TTG)enhanced the coverage of high-quality leguminous forage species by 380.3%.Grass-legume mixtures with historical tillage decreased SOC and dissolved organic carbon(DOC)concentrations,whereas soil MBC concentrations in OTG and TTG increased by 16.0%and 16.4%,respectively,compared with FG.TTG significantly decreased the activity of N-acetyl-β-D-glucosaminidase(NAG)by 72.3%,whereas soil enzymeβ-glucosidase(βG)in OTG and TTG increased by 55.9%and 27.3%,respectively,compared with FG.Correlation analysis indicated a close association of the increase in MBC andβG activities with the rapid decline in SOC.This result suggested that MBC was a key driving factor in soil carbon storage dynamics,potentially accelerating soil carbon cycling and facilitating biogeochemical cycling.The establishment of grass-legume mixtures effectively improves forage quality and boosts plant diversity,thereby facilitating the restoration of degraded grasslands.Although tillage assists in establishing legume-grass mixtures by controlling weeds,it accelerates microbial activity and organic carbon decomposition.Our findings provide a foundation for understanding the process and effectiveness of restoration management in degraded grasslands.

Adsorption behavior of CO_(2)/H_(2)S mixtures in calcite slit nanopores for CO_(2) storage:An insight from molecular perspective

It is acknowledged that injecting CO_(2) into oil reservoirs and saline aquifers for storage is a practical and affordable method for CO_(2) sequestration.Most CO_(2) produced from industrial exhaust contains impurity gases such as H_(2)S that might impact CO_(2) sequestration due to competitive adsorption.This study makes a commendable effort to explore the adsorption behavior of CO_(2)/H_(2)S mixtures in calcite slit nanopores.Grand Canonical Monte Carlo(GCMC)simulation is employed to reveal the adsorption of CO_(2),H_(2)S as well as their binary mixtures in calcite nanopores.Results show that the increase in pressure and temperature can promote and inhibit the adsorption capacity of CO_(2) and H_(2)S in calcite nanopores,respectively.CO_(2)exhibits stronger adsorption on calcite surface than H_(2)S.Electrostatic energy plays the dominating role in the adsorption behavior.Electrostatic energy accounts for 97.11%of the CO_(2)-calcite interaction energy and 56.33%of the H_(2)S-calcite interaction energy at 10 MPa and 323.15 K.The presence of H_(2)S inhibits the CO_(2) adsorption in calcite nanopores due to competitive adsorption,and a higher mole fraction of H_(2)S leads to less CO_(2) adsorption.The quantity of CO_(2) adsorbed is lessened by approximately 33%when the mole fraction of H_(2)S reaches 0.25.CO_(2) molecules preferentially occupy the regions near the po re wall and H_(2)S molecules tend to reside at the center of nanopore even when the molar ratio of CO_(2) is low,indicating that CO_(2) has an adsorption priority on the calcite surface over H_(2)S.In addition,moisture can weaken the adsorption of both CO_(2) and H_(2)S,while CO_(2) is more affected.More interestingly,we find that pure CO_(2) is more suitable to be sequestrated in the shallower formations,i.e.,500-1500 m,whereas CO_(2)with H_(2)S impurity should be settled in the deeper reservoirs.

Undrained vane shear strength of sand-foam mixtures subjected to different shear rates

The shear strength of sand-foam mixtures plays a crucial role in ensuring successful earth pressure balance(EPB)shield tunneling.Since the sand-foam mixtures are constantly sheared by the cutterhead and the screw conveyor with varied rotation speeds during tunneling,it is non-trivial to investigate the effect of shear rates on the undrained shear strength of sand-foam mixtures under chamber pressures to extend the understanding on the tunneling process.This study conducted a series of pressurized vane shear tests to investigate the role of shear rates on the peak and residual strengths of sand-foam mixtures at different pore states.Different from the shear-rate characteristics of natural sands or clay,the results showed that the peak strength of sand-foam mixtures under high vertical total stress(σ_(v)≥200 kPa)and low foam injection ratio(FIR30%)decreased with the increase in shear rate.Otherwise,the peak strength was not measurably affected by shear rates.The sand-foam mixtures in the residual state resembled low-viscous fluid with yield stress and the residual strength increased slightly with shear rates.In addition,the peak and residual strengths were approximately linear with vertical effective stress regardless of the total stress and FIR.The peak effective internal friction angle remained almost invariant in a low shear rate(γ′<0.25 s1)but decreased when the shear rate continued increasing.The residual effective internal friction angle was lower than the peak counterpart and insensitive to shear rates.This study unveiled the role of shear rates in the undrained shear strength of sand-foam mixtures with various FIRs and vertical total stresses.The findings can extend the understanding of the rate-dependent shear characteristics of conditioned soils and guide the decision-making of soil conditioning schemes in the EPB shield tunneling practice.

Impact of clay particle reattachment on suffusion of sand-clay mixtures

The detached clay particles directly filtrated through the sand-clay mixture lead to suffusion;however,if the detached clay particles are subjected to reattachment,the degree of suffusion may be less significant.This study investigates the impact of clay particle reattachment on suffusion of sand-clay mixtures through laboratory soil-column experiments.The observed breakthrough curves(BTCs)of kaolinite,illite,and montmorillonite for 5 different column lengths(3 in,6 in,9 in,12 in,and 18 in;1 in=2.54 cm)indicated that a higher breakthrough concentration was observed as the column length(L)decreased for kaolinite and illite,whereas a reverse trend was observed for montmorillonite.In addition,the increase in the fraction of filtrated clay particles(Me)with an increase in L(Me=10.42%for L=3 in and Me=3.59%for L=18 in)for the sand-illite mixture indicated that the reattachment effect became more significant as the travel length of detached clay particles increased.The observed BTCs,retention profiles after injection,and fraction of filtrated clay presented herein suggest the need to incorporate the reattachment effect when assessing the suffusion of clay-containing soils.

NMR diffusion analysis of catalytic conversion mixtures from lignocellulose biomass using PSYCHE-iDOSY

The component analysis and structure characterization of complex mixtures of biomass conversion remain a challenging work.Hence,developing effective and easy to use techniques is necessary.Diffusion-ordered NMR spectroscopy(DOSY)is a non-selective and non-invasive method capable of achieving pseudo-separation and structure assignments of individual compounds from biomass mixtures by providing diffusion coefficients(D)of the components.However,the conventional 1H DOSY NMR is limited by crowded resonances when analyzing complex mixtures containing similar chemical structure resulting in similar coefficient.Herein we describe the application of an advanced diffusion NMR method,Pure Shift Yielded by CHirp Excitation DOSY(PSYCHE-iDOSY),which can record high-resolution signal diffusion spectra efficiently separating compounds in model and genuine mixture samples from cellulose,hemicellulose and lignin.Complicated sets of isomers(D-glucose/D-fructose/D-mannose and 1,2-/1,5-pentadiol),homologous compounds(ethylene glycol and 1,2-propylene glycol),model compounds of lignin,and a genuine reaction system(furfuryl alcohol hydrogenolysis with ring opening)were successfully separated in the diffusion dimension.The results show that the ultrahigh-resolution DOSY technique is capable of detecting and pseudo-separating the mixture components of C_(5)/C_(6) sugar conversion products and its derivative hydrogenation/hydrogenolysis from lignocellulose biomass.

Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and Random Forest Models

Objective Body fluid mixtures are complex biological samples that frequently occur in crime scenes,and can provide important clues for criminal case analysis.DNA methylation assay has been applied in the identification of human body fluids,and has exhibited excellent performance in predicting single-source body fluids.The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification,and accurately predict the mixture samples.In addition,the value of DNA methylation in the prediction of body fluid mixtures was further explored.Methods In the present study,420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system.Each kind of body fluid sample presented the specific methylation profiles of the 10 markers.Results Significant differences in methylation levels were observed between the mixtures and single body fluids.For all kinds of mixtures,the Spearman’s correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions(1:20,1:10,1:5,1:1,5:1,10:1 and 20:1).Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components,based on the methylation levels of 10 markers.For the mixture prediction,Model-1 presented outstanding prediction accuracy,which reached up to 99.3%in 427 training samples,and had a remarkable accuracy of 100%in 243 independent test samples.For the mixture proportion prediction,Model-2 demonstrated an excellent accuracy of 98.8%in 252 training samples,and 98.2%in 168 independent test samples.The total prediction accuracy reached 99.3%for body fluid mixtures and 98.6%for the mixture proportions.Conclusion These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.

Synthesis and Design of Distillation Columns for Wide-Boiling Binary Mixtures with Vapor Recompression Heat Pumps

The vapor recompression heat pump(VRHP) distillation technology offers significant improvements in energy efficiency for distillation systems with small temperature differences between the top and bottom of the column. However, the separation of wide-boiling binary mixtures leads to substantial temperature differences between the top and bottom of the column. This limits the applicability of conventional VRHP due to high capital costs and strict performance requirements of the compressor. To overcome these challenges and to accommodate compressor operating conditions, a novel synthesis and design method is introduced to integrate VRHPs with wide-boiling binary mixture distillation columns(WBMDCs). This method enables quick determination of an initial configuration for the integrated WBMDC-VRHP system and helps identify the optimum configuration with the minimum total annual cost. Two examples, namely the separation of benzene/toluene and isopropanol/chlorobenzene, are employed to derive optimum configurations of the WBMDC-VRHP and compare them with the WBMDC. A systematic comparison between the WBMDC-VRHP and WBMDC demonstrates the superior steady-state performance and economic efficiency of the WBMDC-VRHP.

The current pulses characteristics of the negative corona discharge in SF_(6)/CF_(4)mixtures

This paper presents the results of numerical investigation of the current pulses characteristics in SF_(6)/CF_(4)mixtures for the negative point-plane corona discharge.The pressure and the temperature of gas mixtures are 0.4 MPa and 300 K,respectively.The CF_(4)content varies from20%to 80%.The 2D axisymmetric geometry with point-plane electrodes is investigated,and the three drift-diffusion equations are solved to predict the characteristics of the negative corona discharge.In addition,Poisson’s equation is coupled with the above three continuity equations to calculate the electric field.In order to calculate the electron impact coefficients,including the Townsend ionization and attachment coefficients,as well as the mobilities and diffusion coefficients for electrons,the two-term Boltzmann equation is solved.The characteristics of three ionic species at five stages of the first current pulse in 60%SF_(6)-40%CF_(4)and20%SF_(6)-80%CF_(4)mixtures are selected to discuss the development mechanism of current pulses.Moreover,the reduced electric field strengths at the corresponding time instants are presented to help understand the discharge process.The current waveform and the total number of three species are compared in all the cases to analyze the effects of the CF_(4)content on the discharge.The reduced electric field strength is also helpful in understanding the effects of CF_(4)content.When the CF_(4)content increases to 80%,the discharge is more intensive and the pulse frequency also increases.

Analytical Study on the Effect of Electric Arc Furnace Dust(EAFD)on the Rutting of Asphalt Concrete Mixtures

As a byproduct of the steelmaking process,significant amounts of hazardous electric arc furnace dust(EAFD)are produced.Utilizing the solidification/stabilization technology with asphalt mix is one way to safeguard the environment from its negative effects.Rutting was used as an indicator to assess the asphalt mixture with EAFD since it is an important factor in pavement design.This study’s major goal is to ascertain how EAFD affects the rutting of asphalt-concrete mixtures.To evaluate the ideal asphalt content,the Marshall test method was applied to asphalt-concrete mixtures.EAFD was added to the asphalt cement in four different volume percentages as a binder addition.Then,using the Universal Testing Machine,participants were exposed to a replica of the rutting test(UTM).Experiments were conducted at 25℃,40℃ and 55℃,and at frequencies of 1 Hz,4 Hz and 8 Hz.Rutting was measured for each specimen.Test results showed that rut depth has a negative correlation with EAFD%and a positive correlation with temperature.The use of EAFD has dual advantages,protecting the environment from the adverse impact of EAFD and reducing the cost of asphalt mix without jeopardizing pavement performance.

Hydrogen release of NaBH_(4) below 60 ℃ with binary eutectic mixture of xylitol and erythritol additive

NaBH_(4) was widely regarded as a low-cost hydrogen storage material due to its high-mass hydrogen capacity of approximately 10.8%(mass)and high volumetric hydrogen capacity of around 115 g·L^(–1).However,it exhibits strong stability and requires temperatures above 500℃ for hydrogen release in practical applications.In this study,two polyhydric alcohols,xylitol and erythritol(XE),were prepared as a binary eutectic sugar alcohol through a grinding-melting method.This binary eutectic sugar alcohol was used as a proton-hydrogen carrier to destabilize NaBH_(4).The 19NaBH_(4)-16XE composite material prepared by ball milling could start releasing hydrogen at 57.5℃,and the total hydrogen release can reach over 88.8%(4.45%(mass))of the theoretical capacity.When the 19NaBH_(4)-16XE composite was pressed into solid blocks,the volumetric hydrogen capacity of the block-shaped composite could reach 67.2 g·L^(–1).By controlling the temperature,the hydrogen desorption capacity of the NaBH_(4)-XE composite material was controllable,which has great potential for achieving solid-state hydrogen production from NaBH_(4).

Optimization of Biofuel Formulation by Mixture Design

With the full growth of energy needs in the world, several studies are now focused on finding renewable sources. The aim of this work is to optimise biofuel formulation from a mixture design by studying physical properties, such as specific gravity and kinematic viscosity of various formulated mixtures. Optimization from the mixture plan revealed that in the chosen experimental domain, the optimal conditions are: 40% for used frying oil (UFO), 50% for bioethanol and 10% for diesel. These experimental conditions lead to a biofuel with a density of 0.84 and a kinematic viscosity of 2.97 cSt. These parameters are compliant with the diesel quality certificate in tropical areas. These density and viscosity values were determined according to respective desirability values of 0.68 and 0.75.

Probabilistic seismic inversion based on physics-guided deep mixture density network

Deterministic inversion based on deep learning has been widely utilized in model parameters estimation.Constrained by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model parameters.However,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the solutions.The conventional inversion method based on deep learning can only establish the deterministic relationship between seismic data and parameters,and cannot quantify the uncertainty of inversion.In order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency impedance.In addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion results.To illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion method.Four synthetic data examples of Marmousi II model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed method.In addition,the robustness and generality of the proposed method are verified by the field seismic data.

Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors,site quality,and aridity index

The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.

A Bayesian Mixture Model Approach to Disparity Testing

The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the uncertainty is captured with a given discrete probability distribution over the groups. Such situations arise, for example, in the use of Bayesian imputation methods to assess race and ethnicity disparities with certain insurance, health, and financial data. A widely used method to implement this assessment is the Bayesian Improved Surname Geocoding (BISG) method which assigns a discrete probability over six race/ethnicity groups to an individual given the individual’s surname and address location. Using a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the group means, the probability of a disparity hypothesis is estimated. Four methods are developed and compared with an illustrative data set. Three of these methods are implemented in an R-code and one method in WinBUGS. These methods are programed for any number of groups between two and six inclusive. All the codes are provided in the appendices.

Sensing the heavy water concentration in an H_(2)O-D_(2)O mixture by solid-solid phononic crystals

A new method based on phononic crystals is presented to detect the concentration of heavy water(D_(2)O)in an H_(2)O-D_(2)O mixture.Results have been obtained and analyzed in the concentration range of 0%-10%and 90%-100%D_(2)O.A proposed structure of tungsten scatterers in an aluminum host is studied.In order to detect the target material,a cavity region is considered as a sound wave resonator in which the target material with different concentrations of D_(2)O is embedded.By changing the concentration of D_(2)O in the H_(2)O-D_(2)O mixture,the resonance frequency undergoes a frequency shift.Each 1%change in D_(2)O concentration in the H_(2)O-D_(2)O mixture causes a frequency change of about 120 Hz.The finite element method is used as the numerical method to calculate and analyze the natural frequencies and transmission spectra of the proposed sensor.The performance evaluation index shows a high Q factor up to 1475758 and a high sensitivity up to 13075,which are acceptable values for sensing purposes.The other figures of merit related to the detection performance also indicate high-quality performance of the designed sensor.

Multiscale Study on Low Temperature Crack Resistance Mechanism of Steel Slag Asphalt Mixture

The objective of this paper was to study low temperature crack resistance mechanism of steel slag asphalt mixture(SAM).Thermal stress restrained specimen test(TSRST)and three-point bending test were carried out to evaluate the low-temperature crack resistance of SAM and basalt asphalt mixture(BAM).Based on the digital image correlation technique(DIC),the strain field distribution and crack propagation of SAM were analyzed from the microscopic point of view,and a new index,crack length factor(C),was proposed to evaluate the crack resistance of the asphalt mixture.The crystal phase composition and microstructure of steel slag aggregate(SA)and basalt aggregate(BA)were studied by X-ray diffraction(XRD)and scanning electron microscopy(SEM)to explore the low-temperature crack resistance mechanism of SAM.Results show that the low-temperature crack resistance of SAM is better than that of BAM;SAM has good integrity and persistent elastic deformation,and its bending failure mode is a hysteretic quasi-brittle failure;The SA surface is evenly distributed with pores and has surface roughness.SA has the composition phase of alkaline aggregate-calcite(CaCO3),so it has good adhesion to asphalt,which reveals the mechanism of excellent low-temperature crack resistance of SAM.