Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

Warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography

High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm.To address these challenges,this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography.Firstly,background difference algorithm is utilized to extract the center and area of each fragment in the image sequence.Subsequently,a multi-object tracking(MOT)algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm.To reconstruct 3D motion trajectories,a global stereo trajectories matching strategy is presented,which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting.Finally,the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×10^(3)fragments in a field of view(FOV)of 3.2 m×2.5 m,and the accuracy of the velocity estimation can achieve 98.6%.

Population trend and spatio-temporal distribution of Greater White-fronted(Anser albifrons)and Bean Geese(Anser fabalis)in Korea

Geese have undergone significant changes in their distribution and population size due to human-induced impacts.To improve our understanding of the two main geese populations in East Asia—Greater White-fronted(Anser albifrons;GWFG)and Bean Geese(Anser fabalis sensu lato;BEAG),which includes the Tundra A.f.serrirostris(TDBG)and Taiga Bean Geese A.f.middendorffii(TGBG),this study estimated their long-and short-term trends and the migration strategies based on their distribution during the migratory and wintering seasons,using nationwide census data collected over 24 years in South Korea.The TRIM(TRends and Indices for Monitoring data)analysis supported a robust long-term population increase since 1999,recently stabilizing around 150,000 individuals in GWFG and 100,000 in TDBG,respectively.But TGBG stabilized at around 7000 with no significant change of population over time.The shorter-term trends within a season suggested that wintering populations of GWFG and TDBG in China might stop over in Korea in their autumn migration before their sea-crossing,evidenced by the large congregation near the Yellow Sea coast followed by a gradual decrease.In contrast to autumn,there was no similar peak in spring migration,suggesting the potential for elliptical migration of some populations around the Yellow Sea.Our findings indicate a growing East Asian population and imply one of the migration strategies of the Greater White-fronted and Tundra Bean Geese.By collaborating with recent tracking data,long-term monitoring efforts could offer more detailed insights into population trends and migration strategies,thereby contributing to the effective management and conservation of goose species in East Asia.

Spatio-temporal distribution of net primary productivity along the northeast China transect and its response to climatic change

An improved Carnegie Ames Stanford Approach model （CASA model） was used to estimate the net primary productivity （NPP） of the Northeast China Transect （NECT） every month from 1982 to 2000. The spatial-temporal distribution of NPP along NECT and its response to climatic change were also analyzed. Results showed that the change tendency of NPP spatial distribution in NECT is quite similar to that of precipitation and their spatial correlation coefficient is up to 0.84 （P 〈 0.01）. The inter-annual variation of NPP in NECT is mainly affected by the change of the aestival NPP every year, which accounts for 67.6% of the inter-annual increase in NPP and their spatial correlation coefficient is 0.95 （P 〈 0.01）. The NPP in NECT is mainly cumulated between May and September, which accounts for 89.8% of the annual NPP. The NPP in summer （June to August） accounts for 65.9% of the annual NPP and is the lowest in winter. Recent climate changes have enhanced plant growth in NECT. The mean NPP increased 14.3% from 1980s to 1990s. The inter-annual linear trend of NPP is 4.6 gC·m^-2·a^-1, and the relative trend is 1.17%, which owns mainly to the increasing temperature.

Analysis on Spatio-temporal Distribution of Lightning in Dalian Area of China between 2007 and 2008

The cloud-to-ground lightning data between 2007 and 2008 were collected by lightning detection and location system,which was composed of four lightning detectors in four different sites of Dalian area.The spatio-temporal distribution of cloud-to-ground lightning in surrounding areas of Dalian was analyzed from several aspects of polarity distribution,diurnal variation,lightning intensity and lightning density.The results showed that the number of negative lightning accounted for 93.9% of the total number of lightning,and its average lightning intensity was 27.99 kA.The number of positive lightning accounted for 6.1% of the total number of lightning,and its average lightning intensity was 35.56 kA.The diurnal variation of lightning frequency showed an obvious structure of two peaks (17:00-18:00 and 04:00-05:00) and two valleys (09:00-10:00 and 00:00-01:00).The number of lightning between May and September was 91.5% of the annual number,and the lightning occurred the most frequently between June and August.Most of positive and negative lightning was at the intensity of 15-35 kA,80.0% lower than 40 kA,and 99.3% lower than 100 kA.The lightning density had obvious regional differences in distribution,high in the Liaodong Bay and the Dalian Bay and low in inland areas.Therefore,coastal areas should attract more attention in lightning disaster defense in the surrounding areas of Dalian.

Implementing geostatistical analysis to study spatio-temporal distribution patterns of swimming crabs(Portunus trituberculatus)

This study attempted to compare the performance of local polynomial interpolation,inverse distance weighted interpolation,and ordinary kriging in studying distribution patterns of swimming crabs.Cross-validation was used to select the optimum method to get distribution results,and kriging was used for making spatial variability analysis.Data were collected from 87 sampling stations in November of 2015(autumn)and February(winter),May(spring)and August(summer)of 2016.Results indicate that swimming crabs widely distributed in autumn and summer:in the summer,they were more spatially independent,and resources in each sampling station varied a lot;in the winter and spring,the abundance of crabs was much lower,but the individual crab size was bigger,and they showed the patchy and more concentrative distribution pattern,which means they were more spatially dependent.Distribution patterns were in accordance with ecological migration features of swimming crabs,which were affected by the changing marine environment.This study could infer that it is applicable to study crab fishery or even other crustacean species using geostatistical analysis.It not only helps practitioners have a better understanding of how swimming crabs migrate from season to season,but also assists researchers in carrying out a more comprehensive assessment of the fishery.Therefore,it may facilitate advancing the implementation in the pilot quota management program of swimming crabs in northern Zhejiang fishing grounds.

Epidemic Characteristics and Spatio-Temporal Patterns of HFRS in Qingdao City,China,2010-2022

Objective This study investigated the epidemic characteristics and spatio-temporal dynamics of hemorrhagic fever with renal syndrome(HFRS)in Qingdao City,China.Methods Information was collected on HFRS cases in Qingdao City from 2010 to 2022.Descriptive epidemiologic,seasonal decomposition,spatial autocorrelation,and spatio-temporal cluster analyses were performed.Results A total of 2,220 patients with HFRS were reported over the study period,with an average annual incidence of 1.89/100,000 and a case fatality rate of 2.52%.The male:female ratio was 2.8:1.75.3%of patients were aged between 16 and 60 years old,75.3%of patients were farmers,and 11.6%had both“three red”and“three pain”symptoms.The HFRS epidemic showed two-peak seasonality:the primary fall-winter peak and the minor spring peak.The HFRS epidemic presented highly spatially heterogeneous,street/township-level hot spots that were mostly distributed in Huangdao,Pingdu,and Jiaozhou.The spatio-temporal cluster analysis revealed three cluster areas in Qingdao City that were located in the south of Huangdao District during the fall-winter peak.Conclusion The distribution of HFRS in Qingdao exhibited periodic,seasonal,and regional characteristics,with high spatial clustering heterogeneity.The typical symptoms of“three red”and“three pain”in patients with HFRS were not obvious.

Spatio-temporal distribution of malaria vectors(Diptera:Culicidae) across different climatic zones of Iran

Malaria is a main vector-borne public health problem in Iran.The last studies on Iranian mosquitoes show 31 Anopheles species including different sibling species and genotypes,eight of them are reported to play role in malaria transmission.The objective of this study is to provide a reference for malaria vectors of Iran and to map their spatial and temporal distribution in different climatic zones.Shape files of administrative boundaries and climates of Iran were provided by National Cartographic Center.Data on distribution and seasonal activity of malaria vectors were obtained from different sources and a databank in district level was created in Excel 2003, inserted to the shape files and analyzed by ArcGIS 9.2 to provide the maps.Anopheles culicifacies Giles s.l.,Anopheles dthali Patton,Anopheles fluviatilis James s.L,Anopheles maculipennis Meigen s.L,Anopheles sacharovi Favre,Anopheles stephensi IJston,and Anopheles superpictus Grassi have been introduced as primary and secondary malaria vectors and Anopheles puicherrimus Theobald as a suspected vector in Iran.Temporal distribution of anopheline mosquitoes is restricted to April_December in northern Iran,however mosquitoes can be found during the year in southern region.Spatial distribution of malaria vectors is different based on species,thus six of them(except for Anopheles maculipennis s.l.and Anopheles sacharovi) are reported from endemic malarious area in southern and southeastern areas of Iran.The climate of this part is usually warm and humid,which makes it favorable for mosquito rearing and malaria transmission.Correlation between climate conditions and vector distribution can help to predict the potential range of activity for each species and preparedness for malaria epidemics.

Improved spatio-temporal alignment measurement method for hull deformation

In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

Utilizing spatio-temporal feature fusion in videos for detecting the fluidity of coal water slurry

The fluidity of coal-water slurry(CWS)is crucial for various industrial applications such as long-distance transportation,gasification,and combustion.However,there is currently a lack of rapid and accurate detection methods for assessing CWS fluidity.This paper proposed a method for analyzing the fluidity using videos of CWS dripping processes.By integrating the temporal and spatial features of each frame in the video,a multi-cascade classifier for CWS fluidity is established.The classifier distinguishes between four levels(A,B,C,and D)based on the quality of fluidity.The preliminary classification of A and D is achieved through feature engineering and the XGBoost algorithm.Subsequently,convolutional neural networks(CNN)and long short-term memory(LSTM)are utilized to further differentiate between the B and C categories which are prone to confusion.Finally,through detailed comparative experiments,the paper demonstrates the step-by-step design process of the proposed method and the superiority of the final solution.The proposed method achieves an accuracy rate of over 90%in determining the fluidity of CWS,serving as a technical reference for future industrial applications.

Recent advances in spatio-temporally controllable systems for management of glioma

Malignant glioma remains one of the most aggressive intracranial tumors with devastating clinical outcomes despite the great advances in conventional treatment approaches,including surgery and chemotherapy.Spatio-temporally controllable approaches to glioma are now being actively investigated due to the preponderance,including spatio-temporal adjustability,minimally invasive,repetitive properties,etc.External stimuli can be readily controlled by adjusting the site and density of stimuli to exert the cytotoxic on glioma tissue and avoid undesired injury to normal tissues.It is worth noting that the removability of external stimuli allows for on-demand treatment,which effectively reduces the occurrence of side effects.In this review,we highlight recent advancements in drug delivery systems for spatio-temporally controllable treatments of glioma,focusing on the mechanisms and design principles of sensitizers utilized in these controllable therapies.Moreover,the potential challenges regarding spatio-temporally controllable therapy for glioma are also described,aiming to provide insights into future advancements in this field and their potential clinical applications.

Root growth and spatio-temporal distribution of three common annual halophytes in a saline desert, northern Xinjiang

Root growth and spatial and temporal distribution in the 0-100 cm soil profiles of three common annual halophytes Salsola subcrassa, Suaeda acuminate and Petrosimonia sibirica distributed in a saline desert in north- ern Xinjiang, China were studied in 2009 and 2010. The results showed that the root systems of the three halo- phytes were of the taproot type, vertically distributed in the 90-cm soil profile, and were deepest in late July. Their taproots reached maximum depth rapidly, early in the growth period, but with rare lateral roots. They were then dug out in an orderly way, from bottom to top, exhibiting vertical development first and then horizontal development. The distribution of specific root length, which reflects the characteristics of the feeder root, was gradually increased from top to bottom, whereas root weight displayed an opposite distribution pattern. The root length distribution of the three halophytes was concentrated （62% to 76%） in the middle soil profile （20-60 cm）, with less distribution in the surface （0-20 cm） and bottom （60-90 cm） soil profiles. The results indicated that the roots of the three annual halophytes grew rapidly into the deeper soil layer after germination, which ensured the plant survival and uptake of water and nutrition, and thus built up a strong tolerance to an arid, high-salt environment.

Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting

Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

Spatio-temporal distribution of vegetation index and its influencing factors——a case study of the Jiaozhou Bay, China

The coastal zone is an area characterized by intense interaction between land and sea, high sensitivity to regional environmental changes, and concentrated human activities. Little research has investigated vegetation cover changes in coastal zones resulting from climate change and land-use change, with a lack of knowledge about the driving mechanism. Normalized diff erence vegetation index(NDVI) can be used as an indicator for change of the coastal environment. In this study, we analyzed the interannual changes and spatial distribution of NDVI in the coastal zone around Jiaozhou Bay in Qingdao, a coastal city undergoing rapid urbanization in northeast China. The underlying causes of NDVI variations were discussed in the context of climate change and land-use change. Results showed that the spatio-temporal distribution of NDVI displayed high spatial variability in the study area and showed a typical trend of gradually increasing from coastal to inland regions. The significant increase area of NDVI was mainly found in newly added construction land, extending along the coastline towards the inland. Land vegetation cover demonstrated a certain response relationship to sea-land climate change and land-based activities. The impact of land-based human activities was slightly greater than that of sea-land climate change for land vegetation cover. The results indicate that promoting ecological policies can build an ecological security framework of vegetation suitable for the resource characteristics of coastal cities. The framework will buf fer the negative ef fects of sea-land climate change and land-based human activities on vegetation cover and thereby achieve the balance of regional development and ecological benefits in the coastal zone.

Self-Attention Spatio-Temporal Deep Collaborative Network for Robust FDIA Detection in Smart Grids

False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.

A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

Vertical distributions of VOCs in the Tibetan Plateau background region

Exploring the vertical variation in volatile organic compounds(VOCs)in background regions can provide infor-mation on the spatial distribution of pollutants,providing a scientific basis for atmospheric pollution prevention and control strategies.From 15 August to 5 September 2023,at the Southeast Tibet Mountain Comprehensive Environmental Observation Station(SETS),a tethered balloon was used to sample VOCs every 100 m from the ground to 1000 m.A total of 403 air bag samples were collected,and 39 vertical profiles of VOCs were obtained.Ninety-two VOC species were detected.The VOC concentration at the SETS did not change significantly verti-cally,and the average VOC concentration was 11.1±2.4 ppbv.The main components were alkanes(51.4%),alkenes(18.7%),and halohydrocarbons(18.1%).There was no obvious diurnal change in VOCs and no signif-icant difference between the different layers.When the surface VOC concentration was less than 10 ppbv,the concentrations,components,and sources of VOCs were evenly distributed vertically,and the main sources of VOCs at different heights were vehicle exhaust and background.When the surface VOC concentration exceeded 10 ppbv,the VOC concentration gradually decreased with height.The proportion of alkanes in surface VOCs in-creased,and the source was mainly vehicle exhaust.This study confirmed that VOCs are vertically homogeneous in the background of the Tibetan Plateau,emphasizing the importance of vehicle emissions as a potential source of VOCs.

Prevalence and Demographic Distributions of Pre-Eclampsia among Pregnant Women at Ho Teaching Hospital

Background: In Sub-Saharan Africa, pre-eclampsia remains a major health problem contributing to high rates of maternal mortality. Despite this condition having adverse effects on maternal and child health, its prevalence and associated risk factors are still significant, especially in developing countries including Ghana. This study aimed to assess the prevalence and demographic distributions associated with pre-eclampsia among pregnant women at the Ho Teaching Hospital. Methods: A facility-based retrospective study was conducted by reviewing available data or hospital records of pregnant mothers admitted to the labor and maternity wards from January 2018 to December 2020. All pregnant women who were diagnosed with pre-eclampsia within this period were included in the study. The data were collected using a structured checklist. Results: 5609 data on pregnant women from 2018 to 2020 were recorded. Out of the 5609 data recorded, 314 pre-eclampsia cases were recorded giving an overall prevalence of 5.6%. The yearly prevalence for 2018, 2019, and 2020 were 4.6%, 5.6%, and 6.6%, respectively. The most recorded pre-eclampsia cases were seen among women within the age group of 18 - 24 years. The data showed that 112 (35.7%) of the pregnant women who had pre-eclampsia were nulliparous. Pre-eclampsia-associated maternal and fetal complications were;preterm delivery 221 (70.4%), intrauterine fetal death 62 (19.7%), eclampsia 9 (2.9%), HELLP syndrome 5 (1.6%) and maternal death 17 (5.4%). Associated factors of pre-eclampsia were parity, level of education, and occupation (p ≤ 0.05). Conclusion: The findings of this study showed a rising trend in the incidence of pre-eclampsia over the years at the Ho Teaching Hospital. Parity, level of education, and occupation were found to be associated with developing pre-eclampsia.

Optical Modeling of Sea Salt Aerosols Using in situ Measured Size Distributions and the Impact of Larger Size Particles

Sea salt aerosols play a critical role in regulating the global climate through their interactions with solar radiation.The size distribution of these particles is crucial in determining their bulk optical properties.In this study,we analyzed in situ measured size distributions of sea salt aerosols from four field campaigns and used multi-mode lognormal size distributions to fit the data.We employed super-spheroids and coated super-spheroids to account for the particles’non-sphericity,inhomogeneity,and hysteresis effect during the deliquescence and crystallization processes.To compute the singlescattering properties of sea salt aerosols,we used the state-of-the-art invariant imbedding T-matrix method,which allows us to obtain accurate optical properties for sea salt aerosols with a maximum volume-equivalent diameter of 12μm at a wavelength of 532 nm.Our results demonstrated that the particle models developed in this study were successful in replicating both the measured depolarization and lidar ratios at various relative humidity(RH)levels.Importantly,we observed that large-size particles with diameters larger than 4μm had a substantial impact on the optical properties of sea salt aerosols,which has not been accounted for in previous studies.Specifically,excluding particles with diameters larger than 4μm led to underestimating the scattering and backscattering coefficients by 27%−38%and 43%−60%,respectively,for the ACE-Asia field campaign.Additionally,the depolarization ratios were underestimated by 0.15 within the 50%−70%RH range.These findings emphasize the necessity of considering large particle sizes for optical modeling of sea salt aerosols.