Methanesulfonic acid particle retrieval method in marine sea-spray aerosol based on box model

Abstract

The present application relates to the field of environmental science and technology, and more particularly to a method for retrieving methane sulfonic acid particles in marine aerosol based on a box model, comprising: step 1: obtaining satellite remote sensing data, ground observation data and laboratory simulation data, and performing data standardization processing; step 2: dividing the target sea area into multiple sub-regions as independent boxes; step 3: calculating the marine aerosol emission flux based on the concentration of dimethyl sulfide and wind speed, and updating the spatial and temporal distribution of the emission source; step 4: embedding the chemical reaction path of dimethyl sulfide oxidation to generate methane sulfonic acid on the surface of aerosol in the box model, and introducing the specific surface area of aerosol to correct the reaction rate; step 5: outputting the spatial and temporal distribution of methane sulfonic acid concentration and verifying it by comparing the measured data. The accuracy of aerosol source term estimation is improved, the fusion efficiency of multi-source data is improved, and the adaptability of the model in complex environment is enhanced.

Description

Technical Field

[0001] This invention relates to the field of environmental science and technology, and in particular to a method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model. Background Technology

[0002] With the increasing severity of global climate change, research on marine aerosols has become an important direction in climate system simulation and atmospheric chemical reaction research. Marine droplet aerosols are formed through the aerosolization process of the ocean surface, and these aerosols have a significant impact on the global climate system and atmospheric chemical cycles. In particular, methanesulfonic acid (MSA) particles are considered an important component of marine aerosols, playing a key role in the formation of atmospheric acidic substances, climate radiative forcing, and cloud physics processes. Therefore, accurately assessing the spatiotemporal distribution and concentration variations of methanesulfonic acid particles in marine droplet aerosols is of great significance for predicting climate change and the evolution of the atmospheric environment.

[0003] Traditional marine aerosol research relies heavily on ground-based observation stations and laboratory simulations. However, these methods are limited by spatial and temporal resolution, making it difficult to provide comprehensive, real-time aerosol distribution data. The advent of satellite remote sensing technology has made large-scale marine aerosol monitoring possible, particularly the application of multispectral imagers and aerosol optical thickness data, providing crucial tools for dynamic monitoring of marine aerosols. However, relying solely on remote sensing data for aerosol inversion still faces challenges related to high accuracy requirements and data uncertainties. Therefore, it is necessary to effectively combine and analyze data from different sources to improve the accuracy and reliability of the models. Summary of the Invention

[0004] To achieve the above objectives, this invention provides a method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model, comprising:

[0005] Step 1: Acquire satellite remote sensing data, ground observation data, and laboratory simulation data, and perform data standardization processing;

[0006] Step 2: Divide the target sea area into multiple sub-regions as independent containers, and input the initial methanesulfonic acid concentration, dimethyl sulfide emission, and meteorological parameters for each container;

[0007] Step 3: Calculate the marine droplet aerosol emission flux based on dimethyl sulfide concentration and wind speed, and use a data fusion algorithm to fuse satellite aerosol optical thickness data and ground observation data to update the spatiotemporal distribution of emission sources;

[0008] Step 4: Embed the chemical reaction pathway of dimethyl sulfide oxidation to methanesulfonic acid on the aerosol surface in the box model, and introduce the aerosol specific surface area to correct the reaction rate.

[0009] Step 5: Solve the partial differential equations of the box model using numerical methods, output the spatiotemporal distribution of methanesulfonic acid concentration, and verify it by comparing it with the measured data.

[0010] Preferably, in step 1:

[0011] The satellite remote sensing data includes aerosol optical thickness data acquired by a multispectral imager, and its spatial resolution meets the requirements for high-precision marine aerosol monitoring.

[0012] The ground observation data includes dimethyl sulfide concentration data, sea surface wind speed data, seawater salinity data, and meteorological parameter data collected in real time by buoys or ship platforms;

[0013] The laboratory simulation data is generated by a controlled environment aerosol generator and includes data on the methane sulfonic acid generation rate and particle size distribution of aerosol samples under different salinity and temperature gradients.

[0014] The preprocessing of the satellite remote sensing data includes correction of ocean surface reflection interference and the use of the multi-band reflectivity ratio method to eliminate the effects of solar flares and specular reflection from the sea surface.

[0015] The preprocessing of the ground observation data includes time synchronization of discrete measurement points and spatial interpolation to generate a continuous gridded dataset that matches the spatial resolution of the satellite data.

[0016] The preprocessing of the laboratory simulation data includes normalization of experimental parameters and establishment of a correlation matrix between salinity, temperature and methanesulfonic acid formation rate.

[0017] Preferably, the data standardization process in step 1 includes:

[0018] Radiometric calibration and atmospheric correction were performed on satellite aerosol optical thickness data to eliminate spectral mixing effects in aerosol type identification.

[0019] Quality control was performed on the dimethyl sulfide concentration data in the ground observation data to remove outliers caused by sensor drift or environmental interference, and the discrete point data was converted into a spatially continuous distribution using the Kriging interpolation algorithm.

[0020] Temperature compensation correction was applied to the methanesulfonic acid formation rate in laboratory simulation data, and a lookup table of reaction kinetic parameters under different temperature and humidity conditions was established.

[0021] The timestamps of satellite data, ground data, and laboratory data are unified to Coordinated Universal Time (UTC), and a sliding time window algorithm is used to align the time bases of different data sources.

[0022] Transform the spatial projection coordinate system of multi-source data to ensure that all data layers are overlaid and analyzed under a unified geographic reference system.

[0023] Preferably, in step 2:

[0024] The division of the sub-regions is based on the spatial gradient distribution characteristics of satellite aerosol optical thickness. An adaptive grid division algorithm is used to dynamically adjust the boundaries of the sub-regions to ensure that the spatial uniformity of aerosol optical properties in each sub-region meets a preset threshold.

[0025] The initial methanesulfonic acid concentration is set based on historical inversion results, laboratory calibration benchmark values, or migration and assignment of observation data from adjacent sea areas.

[0026] The initialization of the meteorological parameters includes extracting sea surface temperature, relative humidity, and boundary layer height data from global or regional reanalysis datasets, and improving the spatial resolution of the data to a level that matches the sub-region division through a dynamic downscaling model;

[0027] The determination of the physical boundary of each independent container needs to take into account the characteristics of the ocean surface flow field, and use the vortex identification algorithm to avoid dividing the strong shear flow region into the same sub-region;

[0028] The vertical height of the enclosure is set based on atmospheric mixing layer height observation data, and a piecewise function is used to describe the diffusion attenuation characteristics of aerosols in the vertical direction.

[0029] Preferably, in step 3:

[0030] The emission flux calculation uses a semi-empirical formula that includes a salinity influence factor. In the formula, the dimethyl sulfide concentration term is provided by spatial interpolation of ground observation data, and the wind speed term is obtained by downscaling the reanalysis dataset.

[0031] The data fusion algorithm adopts a recursive filtering framework, in which satellite aerosol optical thickness data is used as the observation value and ground observation data is used as the prior constraint. The spatiotemporal evolution matrix of emission source terms is constructed through a state-space model.

[0032] The noise covariance matrix of the filtering algorithm is dynamically adjusted according to marine meteorological conditions. Noise weights are added to enhance the robustness of the model when there are sudden changes in wind speed or drastic changes in temperature and humidity.

[0033] The update of emission source terms includes feedback correction of the air-sea exchange rate of dimethyl sulfide and inverse optimization of source intensity distribution using the adjoint matrix method to maximize the spatial correlation between simulated aerosol optical thickness and satellite observations.

[0034] The updated spatiotemporal distribution of emission sources was verified for physical consistency using a particle diffusion model, ensuring that the trend of source intensity variation conforms to the atmospheric boundary layer transport law.

[0035] Preferably, in step 4:

[0036] The embedding of the chemical reaction pathway includes adding a surface reaction source-sink term to the box model mass conservation equation, which is determined by the product of the aerosol specific surface area and the surface reaction rate.

[0037] The calculation of the specific surface area of ​​the aerosol is based on the aerosol particle size distribution data, and a fractal geometric model is used to describe the surface area-to-volume ratio of non-spherical particles.

[0038] The surface reaction rate was determined by calibration using laboratory simulation data, and a lookup table of reaction rates under different particle size ranges and relative humidity conditions was established.

[0039] The spatiotemporal distribution of oxidant concentration is provided by a global chemical transport model, and the concentration field of OH radicals transported outside the region is coupled to the box model using the Lagrange trajectory tracking method.

[0040] The calculation of liquid water content on the aerosol surface adopts the aerosol hygroscopic growth factor model, and the pH parameters of the surface reaction microenvironment are dynamically adjusted in combination with real-time relative humidity data.

[0041] Preferably, in step 5:

[0042] The numerical method for solving the problem includes discretization of the advection-diffusion reaction equations and the use of conformal interpolation algorithms to handle flux exchange at the boundaries of sub-regions.

[0043] The time integration scheme adopts a hybrid implicit-explicit scheme, using an upwind scheme for the advection term and a central difference scheme for the diffusion term.

[0044] The equation system is solved using the preprocessed conjugate gradient method, and a block iteration strategy is designed to improve computational efficiency by taking into account the sparsity of the Jacobian matrix.

[0045] The verification process includes spatial distribution consistency test and temporal change correlation analysis, and the moving window cross-correlation algorithm is used to quantify the time lag effect between the inversion results and the measured data of the buoy.

[0046] The model uncertainty analysis is achieved through the Monte Carlo method, which samples the probability distribution of key input parameters and outputs a spatial distribution map of the confidence interval of methanesulfonic acid concentration.

[0047] Preferably, the method for calibrating the salinity influencing factor includes:

[0048] In a controlled laboratory environment, a series of aerosol samples were generated by adjusting the seawater salinity gradient, while keeping other environmental parameters such as temperature and humidity constant.

[0049] The formation rate of methanesulfonic acid was monitored in real time using an aerosol mass spectrometer, and the dynamic process of aerosol particle size distribution changing with salinity was recorded using a high-speed camera system.

[0050] A nonlinear regression model of salinity and reaction efficiency was constructed, and stepwise regression was used to screen for significant influencing factors and determine their polynomial order.

[0051] The complexity of the regression model was optimized by cross-validation to prevent overfitting, and finally the mapping relationship between the salinity parameter and the proportional coefficient in the emission flux calculation formula was established.

[0052] The portability of the calibration results is verified by comparing the systematic deviations between measured data and model predictions in different sea areas, and regional-specific correction coefficients are introduced when necessary.

[0053] Preferably, the method for inverting the aerosol particle size distribution includes:

[0054] Vertical profiles of aerosol extinction coefficient and backscattering coefficient were obtained using a multi-wavelength lidar system.

[0055] An inversion equation for aerosol particle size distribution is constructed based on Mie scattering theory, and a regularization method is used to deal with the ill-conditioned problem of the equation.

[0056] The introduction of prior constraints includes typical marine aerosol particle size distribution patterns obtained by statistical analysis of historical observation data.

[0057] The optimization of the inversion algorithm includes developing an adaptive weighting function to dynamically adjust the weight distribution of the fitting residuals between coarse-particle and fine-particle modes;

[0058] The reliability of the inversion results is verified by comparing the modal distribution characteristics of the in-situ sampling data and the inversion results under the same air mass path.

[0059] Preferably, the specific steps of the Monte Carlo uncertainty analysis include:

[0060] Step 5.1: Modeling the probability distribution of input parameters:

[0061] A. Determine the probability distribution type and parameter range of the key input parameters of the model:

[0062] a. The concentration data of dimethyl sulfide were obtained using a probability distribution model based on the statistical characteristics of historical observations, and the distribution type was selected according to the results of data skewness and kurtosis tests;

[0063] b. The probability distribution of the salinity correlation coefficient is determined based on the repeatability measurement error of the laboratory calibration experiment, and a truncated probability distribution is used to limit its physically reasonable range of values.

[0064] c. The probability distribution of meteorological parameters is constructed based on the spatiotemporal variability of the reanalysis dataset. Joint probability distributions related to the stability of the ocean boundary layer are established for wind speed and temperature parameters, respectively.

[0065] d. Construct covariance matrices for the non-independent parameters and reduce the dimensionality of the parameter space using principal component analysis (PCA);

[0066] Step 5.2: Random Sample Generation and Model Iteration:

[0067] A stratified sampling strategy is adopted to generate a parameter combination sample set, including Latin hypercube sampling (LHS) and orthogonal array sampling, to ensure high-dimensional uniform coverage of the parameter space;

[0068] For each combination of sampling parameters, perform full-process calculations using a box model, including dynamic source term optimization, surface chemical reaction coupling, and numerical solution steps, and record the output methanesulfonic acid concentration field and intermediate process variables.

[0069] A parallel computing architecture is adopted to accelerate the simulation of large-scale sample sets, and computing node resources are dynamically allocated through a task scheduling algorithm;

[0070] Step 5.3: Calculation of statistics and construction of confidence intervals:

[0071] Based on the sample output results, the concentration statistical characteristics of each spatial grid point are calculated, including mean, variance, skewness and kurtosis. The probability density function of the concentration value is constructed by kernel density estimation.

[0072] The upper and lower limits of concentration at a specified confidence level are calculated using the quantile function, and the stability of the quantile estimation is optimized using an adaptive bandwidth selection algorithm.

[0073] Morphological filtering is applied to the confidence interval of the spatiotemporal continuous field to eliminate local noise caused by the limited sample size.

[0074] Step 5.4: Convergence Verification and Sensitivity Analysis:

[0075] The convergence of the Monte Carlo simulation was evaluated using the asymptotic mean square error index, and the termination condition of the calculation was determined by comparing the rate of change of the statistic under different sample sizes.

[0076] Perform a global sensitivity analysis to calculate the global sensitivity index of each input parameter based on variance decomposition, and quantify its contribution to the uncertainty of the output methanesulfonic acid concentration.

[0077] Local refinement sampling is performed on highly sensitive parameters, and additional samples are added in the key parameter subspace to improve the accuracy of uncertainty assessment;

[0078] Step 5.5: Uncertainty Visualization and Output:

[0079] Spatial distribution maps of the confidence interval width field, sensitivity index field, and probability density function surface are generated. Multidimensional scaling is used to reduce the dimensionality and show the relationship between the high-dimensional parameter space and the output field.

[0080] The uncertainty analysis results are spatiotemporally overlaid with meteorological element fields and ocean current fields, and interactive visualization is achieved through a geographic information system platform.

[0081] Output a standardized uncertainty assessment report, including the original sample set, statistical matrix, and visualization metadata.

[0082] The beneficial effects of this invention are:

[0083] By organically combining data fusion, chemical reaction, and numerical solution steps, high-precision inversion of methanesulfonic acid particles in marine aerosols was achieved. The effective connection of these steps not only improved the model's accurate prediction of spatiotemporal distribution but also enhanced its adaptability and stability. Through refined processing and dynamic updates of different data sources, the changing patterns of marine droplet aerosols can be better captured, which is of great significance for environmental monitoring and climate change research. Attached Figure Description

[0084] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0085] Figure 1 This is a flowchart of the steps of the method of the present invention;

[0086] Figure 2 This is a flowchart of step 5.1 in the method of the present invention;

[0087] Figure 3 This is a flowchart of step 5.2 in the method of the present invention. Detailed Implementation

[0088] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0089] Please see Figures 1-3This invention provides a method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model. In the first step, data from different sources—including satellite remote sensing data, ground observation data, and laboratory simulation data—are collected and standardized. Satellite data provides a high-resolution global view of aerosol optical thickness, while ground data provides real-time local information such as dimethyl sulfide concentration and sea surface wind speed. Laboratory data provides fundamental parameters such as methanesulfonic acid formation rate and particle size distribution. This standardization process ensures that all data can be effectively fused and compared at the same time and spatial scale, resolving gaps and errors from different data sources and providing high-quality input for subsequent modeling.

[0090] After data standardization, the sea area was divided into multiple sub-regions, which were modeled as independent containers. This refined division improves the spatial resolution of the model, enabling it to more accurately simulate the local characteristics of marine aerosols. Simultaneously, each container was input with corresponding initial methanesulfonic acid concentrations, dimethyl sulfide emissions, and meteorological parameters, ensuring that the dynamic changes in each sub-region can be accurately tracked and calculated.

[0091] Furthermore, when calculating the emission flux of marine droplet aerosols, dimethyl sulfide concentration and wind speed are used as key input parameters. Combined with satellite aerosol optical thickness data and ground observation data, a data fusion algorithm is employed to update the spatiotemporal distribution of emission sources. The advantage of data fusion lies in its ability to better compensate for the limitations of a single data source and improve the model's prediction accuracy through the complementarity of satellite remote sensing data and ground observation data.

[0092] Step 4 introduces the chemical reaction pathway of dimethyl sulfide oxidation to methanesulfonic acid on the aerosol surface and corrects the reaction rate using the aerosol specific surface area. This correction ensures that the influence of factors such as aerosol particle size and humidity on the chemical reaction rate is fully considered, thereby improving the model's adaptability to reaction kinetics under different environmental conditions.

[0093] Furthermore, the spatiotemporal distribution of methanesulfonic acid concentration was obtained by numerically solving the partial differential equations of the box model. This numerical solution step involved accurate simulations of diffusion, transport, and reaction processes, employing appropriate time integration methods and spatial discretization algorithms to ensure the model's computational accuracy. Finally, the reliability of the model was verified by comparison with measured data, providing feedback for further model optimization.

[0094] By organically combining data fusion, chemical reaction, and numerical solution steps, high-precision inversion of methanesulfonic acid particles in marine aerosols was achieved. The effective connection of these steps not only improved the model's accurate prediction of spatiotemporal distribution but also enhanced its adaptability and stability. Through refined processing and dynamic updates of different data sources, the changing patterns of marine droplet aerosols can be better captured, which is of great significance for environmental monitoring and climate change research.

[0095] In one possible implementation, satellite remote sensing data provides high-resolution aerosol optical thickness (AOT) data for ocean aerosol monitoring. To ensure this data meets the requirements for high-precision ocean aerosol monitoring, preprocessing of the satellite data is necessary. First, the effects of solar flares and specular reflections from the sea surface are eliminated using a multi-band reflectivity ratio method. This method effectively removes interference from ocean surface reflections on remote sensing images, thereby improving the accuracy of the aerosol optical thickness data. The preprocessed satellite data can provide high-quality input that matches the spatial resolution of other data sources, such as ground-based observations.

[0096] Furthermore, the acquisition of ground-based observation data includes various data collected in real time via buoys or ship platforms, such as dimethyl sulfide concentration, sea surface wind speed, seawater salinity, and other meteorological parameters. To ensure effective integration of this data with satellite remote sensing data, the ground-based observation data needs to undergo time synchronization and spatial interpolation. Time synchronization adjusts real-time data from different measurement points to the same time scale, while spatial interpolation transforms data from discrete measurement points into a continuous gridded dataset with the same spatial resolution as the satellite data. This ensures spatial and temporal consistency between ground-based data and satellite remote sensing data, facilitating subsequent data fusion and model calculations.

[0097] Furthermore, laboratory simulation data were generated using a controlled-environment aerosol generator to obtain data on the methanesulfonic acid formation rate and particle size distribution under different salinity and temperature gradients. To apply this experimental data to actual marine aerosol simulations, preprocessing of the data is necessary, including normalization of the experimental parameters. This preprocessing establishes a correlation matrix between salinity, temperature, and methanesulfonic acid formation rate, ensuring that the experimental data can be effectively converted and applied under different environmental conditions, thereby improving the model's adaptability to real marine environments.

[0098] The preprocessing step eliminates biases and inconsistencies between different data sources, ensuring that various data can be effectively fused within the same coordinate system and time scale. This not only enhances the model's predictive accuracy but also improves its adaptability to complex marine environments. Through the organic combination of satellite remote sensing, ground observation, and laboratory simulation data, the final methanesulfonic acid particle inversion results can more realistically reflect the spatiotemporal distribution of marine droplet aerosols, contributing to further environmental monitoring, climate research, and atmospheric chemical reaction process analysis.

[0099] Preferably, the satellite remote sensing data includes aerosol optical thickness (AOD) data acquired by a multispectral imager, which is calculated based on the Lambertian reflectance model:

[0100] ;

[0101] in, Aerosol optical thickness Reflectance of the top layer of the atmosphere, Contributes to Rayleigh scattering, Sea surface reflectance, For gas absorption optical thickness.

[0102] In one possible implementation, aerosol optical thickness (AOT) data acquired from satellite remote sensing needs to undergo radiometric calibration and atmospheric correction to eliminate errors in aerosol identification caused by atmospheric effects and spectral mixing. Radiometric calibration involves calibrating the sensor using known radiation sources to ensure that the radiation intensity of the remote sensing data matches the actual situation. Atmospheric correction, on the other hand, adjusts the data by taking into account the effects of atmospheric composition, aerosols, and water vapor, thereby more accurately reflecting the aerosol concentration at the ground or sea surface. This step, by eliminating spectral mixing effects, allows satellite data to more clearly distinguish different aerosol types, further improving the inversion accuracy of methanesulfonic acid particles.

[0103] Ground-based observation data, especially dimethyl sulfide (DMS) concentration data, can be affected by sensor drift or environmental interference, leading to data anomalies. To ensure data reliability, the first step is to perform quality control on these data to remove outliers. Common outlier removal methods include setting statistical thresholds or utilizing sensor self-checking data. Secondly, the discrete point data is transformed into a spatially continuous distribution using the Kriging interpolation algorithm. This step ensures the spatial consistency of the ground data through interpolation, matching the spatial resolution of satellite data and further improving the data's usability.

[0104] The methanesulfonic acid formation rate in laboratory simulation data is closely related to temperature and humidity. To eliminate the influence of temperature changes on the reaction rate, temperature compensation correction must be performed. This is typically achieved by establishing a lookup table of reaction kinetic parameters under different temperature and humidity conditions. Using this lookup table, the experimental data can be accurately adjusted to maintain a consistent formation rate under various environmental conditions, thus matching the laboratory simulation data with actual environmental conditions.

[0105] Data from different sources (satellite, ground, and laboratory data) may have different timestamps and need to be unified to Coordinated Universal Time (UTC). Based on this, a sliding time window algorithm is used to align data from different time bases to ensure temporal consistency across different data sources. This step ensures the continuity and accuracy of the time series of different datasets by avoiding temporal misalignments.

[0106] Because satellite data, ground observation data, and laboratory simulation data may use different spatial projection coordinate systems, coordinate system transformation is required for all data to ensure that all data layers are overlaid and analyzed under a unified geographic reference system. This transformation ensures that different data sources are accurately aligned in geospatial space, supporting subsequent comprehensive analysis and model calculations.

[0107] These standardized processing steps ensure data consistency and accuracy by eliminating temporal, spatial, and qualitative differences between different data sources. First, satellite data, after radiometric calibration and atmospheric correction, more accurately reflects the actual conditions of marine aerosols, reducing the impact of atmospheric effects on the inversion results. Second, quality control and Kriging interpolation of ground-based observation data guarantee spatial continuity and accuracy, allowing for comparison and analysis of ground and satellite data within the same geographic coordinate system. Laboratory simulation data, through temperature compensation correction, ensures data consistency under different environmental conditions, providing reliable experimental evidence for the inversion of methanesulfonic acid particles. Finally, timestamp alignment and coordinate system transformation further ensure spatiotemporal consistency across different data sources, resulting in more accurate and reliable final inversion results. The comprehensive application of all these steps significantly improves the accuracy of the inversion results and the effectiveness of the model, providing more comprehensive and precise support for marine aerosol research.

[0108] Preferred satellite AOD data correction formula:

[0109] ;

[0110] in, The corrected aerosol optical thickness. This represents the original aerosol optical thickness. For near-infrared reflectivity, This refers to the reflectivity in the shortwave infrared band.

[0111] The Kriging interpolation formula for ground-level dimethyl sulfide concentration data is: ;

[0112] This is the estimated concentration of dimethyl sulfide at the interpolation point. For the weighting coefficients, satisfying And the semi-mutation function is minimized. The concentration of dimethyl sulfide at a known point.

[0113] In one possible implementation, the sub-regions are divided based on the spatial gradient distribution characteristics of satellite aerosol optical thickness (AOT), employing an adaptive mesh generation algorithm. The spatial gradient of satellite aerosol optical thickness (AOT) represents the variation characteristics of aerosol concentration. Adaptive mesh generation, by dynamically adjusting the boundaries of sub-regions, ensures that the spatial distribution of aerosols is uniform across different regions. Specifically, the boundaries of the sub-regions are dynamically adjusted according to changes in aerosol concentration, ensuring that the uniformity of aerosol optical properties within each sub-region meets a preset threshold. This strategy avoids errors in the inversion results caused by uneven aerosol distribution.

[0114] The initial methanesulfonic acid concentration was set based on historical inversion results, laboratory calibration benchmarks, or observational data from adjacent sea areas. Historical inversion results provided a reference for past methanesulfonic acid concentrations in the sea area, while laboratory calibration benchmarks provided laboratory data support for aerosol generation and reaction rates. Observational data from adjacent sea areas facilitated spatial data transfer and assignment, thereby setting a reasonable initial concentration. This approach ensures high reliability and spatial consistency of the initial methanesulfonic acid concentration during the inversion process, laying the foundation for subsequent inversion calculations.

[0115] The initialization of meteorological parameters involves extracting key meteorological data such as sea surface temperature, relative humidity, and boundary layer height from global or regional reanalysis datasets. A dynamic downscaling model is then used to enhance the spatial resolution of this data to a level commensurate with the sub-regional division. Since meteorological parameters significantly influence the formation and diffusion of methanesulfonic acid particles, accurate meteorological data is crucial for inversion accuracy. The downscaling model effectively improves the spatial resolution of meteorological data, enabling meteorological parameters to reflect marine climate conditions at a more detailed scale.

[0116] The physical boundaries of each independent container must take into account the characteristics of the ocean surface flow field. Features of the ocean surface flow field, such as regions of strong shear flow, can significantly influence aerosol diffusion and transport. Therefore, an eddy recognition algorithm is employed to avoid segmenting strong shear flow regions into the same sub-region, thus avoiding inversion errors caused by significant differences in ocean surface flow field characteristics. By accurately determining the physical boundaries of the containers, the flow process of aerosols in different sea areas can be better simulated, improving the accuracy of the simulation results.

[0117] The vertical height of the chamber is set based on atmospheric mixing layer height observation data, and a piecewise function is used to describe the aerosol diffusion attenuation characteristics in the vertical direction. The atmospheric mixing layer is a crucial region for aerosol diffusion, and setting a reasonable chamber height helps to accurately simulate the aerosol diffusion process at different heights. By describing the vertical diffusion attenuation characteristics of aerosols using a piecewise function, the vertical distribution of aerosols can be effectively simulated, further improving the accuracy of the inversion.

[0118] The combined application of the above steps ensures high accuracy and reliability in the inversion results of methanesulfonic acid particles in marine droplet aerosols. First, dynamic sub-region partitioning using an adaptive grid algorithm avoids the influence of spatially uneven aerosol distribution on the inversion results. A reasonable initial methanesulfonic acid concentration provides accurate starting data for the inversion, ensuring the stability and consistency of the calculations. Second, high-resolution processing of meteorological parameters allows the inversion model to more accurately reflect the impact of meteorological conditions on aerosols, improving the reliability of the inversion. By considering the influence of ocean current characteristics and atmospheric mixing layer height, the physical boundaries and vertical diffusion characteristics of the container can be accurately defined, thus better simulating the diffusion and transport processes of aerosols. The effective connection and optimization of these steps make the application of this inversion method more precise in complex marine environments, providing a powerful tool for marine aerosol research.

[0119] The preferred quadtree partitioning algorithm for subregion division is as follows:

[0120] ;in, The gradient of aerosol optical thickness. This is a preset gradient threshold.

[0121] The initial methanesulfonic acid concentration assignment formula is as follows:

[0122] ;

[0123] in, This represents the initial concentration of methanesulfonic acid. The concentration at the previous moment. For laboratory calibration values, This is the weighting factor.

[0124] In one possible implementation, emission fluxes are calculated using a semi-empirical formula incorporating a salinity factor, which accurately reflects the impact of salinity on aerosol formation. In this formula, the dimethyl sulfide (DMS) concentration term is provided using ground-based observation data after spatial interpolation. Ground-based observation data provides accurate source data for regional aerosol models, ensuring that the models reflect actual aerosol formation. Simultaneously, the wind speed term is obtained through downscaling of the reanalysis dataset. The downscaled data supports high-resolution wind speed data, contributing to accurate simulations of aerosol transport and diffusion. Combining the salinity effect with the interaction between DMS concentration and wind speed allows for a more accurate estimation of marine droplet aerosol emission fluxes.

[0125] Furthermore, to better estimate the spatiotemporal distribution of aerosols, a recursive filtering algorithm is employed for data fusion. Within this framework, satellite aerosol optical thickness data serves as the observed values, while ground-based observation data acts as a priori constraints. A state-space model is used to construct the spatiotemporal evolution matrix of emission source terms. Satellite observation data provides large-scale aerosol distribution information, while ground-based data provides precise observation data for local areas. The recursive filtering algorithm effectively combines these two data points to optimize the spatiotemporal evolution prediction of emission sources. This fusion method leverages the complementarity of satellite and ground-based data to improve the accuracy of emission source term inversion.

[0126] Furthermore, in practical applications, meteorological conditions such as wind speed, temperature, and humidity can change drastically, affecting the model's stability. Therefore, the process noise covariance matrix of the filtering algorithm is dynamically adjusted based on marine meteorological conditions. When wind speed changes abruptly or temperature and humidity fluctuate drastically, the filtering algorithm increases noise weights to enhance the model's robustness. In this way, the filter can better cope with drastic changes in environmental conditions, thereby improving the model's adaptability to complex meteorological conditions.

[0127] The emission source terms are updated through feedback correction of the dimethyl sulfide air-sea exchange rate. The adjoint matrix method is used to inversely optimize the source intensity distribution, maximizing the spatial correlation between the simulated aerosol optical thickness and satellite observations. The adjoint matrix method solves the optimization problem in reverse, minimizing errors while maintaining physical consistency. This method effectively optimizes the spatial distribution of aerosol source intensity and improves the prediction accuracy of emission source terms.

[0128] The updated spatiotemporal distribution of emission sources was validated using a particle diffusion model to ensure its physical consistency. The particle diffusion model simulates the aerosol diffusion process, verifying whether the changes in the source terms conform to the transport laws of the atmospheric boundary layer. By comparing with the physical model, the updated emission source terms not only numerically match satellite observations but also physically guarantee the reasonableness of the aerosol diffusion process.

[0129] By accurately calculating emission fluxes, combining satellite and ground data, and dynamically adjusting the noise weights of the filtering algorithm, the accuracy and robustness of the aerosol inversion process can be effectively improved. Specifically, the combination of data fusion and recursive filtering algorithms makes the spatiotemporal distribution of emission sources more consistent with actual observations, improving the reliability and applicability of the model. Furthermore, feedback correction and optimization of emission source terms ensures a high degree of spatial consistency between the aerosol inversion results and observations, further enhancing the credibility of the simulation results. Finally, validation of the particle diffusion model ensures that the updated emission source strength is consistent with the transport patterns of the atmospheric boundary layer, making the inversion method not only reasonable at the data level but also highly consistent at the physical level. Overall, the design and implementation of this series of steps significantly improves the accuracy and practicality of the inversion method for methanesulfonic acid particles in marine droplet aerosols.

[0130] In one possible implementation, a surface reaction source-sink term is added to the mass conservation equation of the box model. This term is determined by the product of the aerosol specific surface area and the surface reaction rate. The aerosol specific surface area is an important parameter characterizing the particle-gas reaction interface and determining the reaction efficiency. By incorporating this source-sink term into the mass conservation equation, chemical reactions between the aerosol surface and atmospheric gases (such as OH radicals) can be considered, thus more accurately simulating the dynamic changes of aerosols. The added source-sink term can capture the influence of aerosol surface reactions on particle concentration and incorporate it into the inversion model.

[0131] The calculation of aerosol specific surface area is based on aerosol particle size distribution data. Typically, aerosol particle size distribution data provides information on the number distribution of aerosol particles within different size ranges. To accurately calculate specific surface area, a fractal geometric model is used to describe the surface area-to-volume ratio of non-spherical particles. Since most aerosol particles exhibit irregular shapes, traditional spherical models cannot accurately calculate their specific surface area. Using a fractal geometric model, the surface structure of these non-spherical particles can be described more precisely, improving the simulation accuracy of aerosol surface reactions.

[0132] The surface reaction rate was calibrated using laboratory simulation data, and lookup tables for the reaction rate were established under different particle size ranges and relative humidity conditions. These lookup tables, obtained experimentally, allow for dynamic adjustment of the surface reaction rate based on aerosol particle size and ambient humidity conditions. Laboratory data provides key parameters for the reaction rate, which are used in the model in the form of lookup tables, enabling the simulation of aerosol reaction processes under different environmental conditions.

[0133] The spatiotemporal distribution of oxidants (such as OH radicals) is provided by a global chemical transport model. To accurately simulate aerosol surface reactions within the region, the Lagrange trajectory tracking method is used to couple the concentration field of OH radicals transported from outside the region into the box model. The Lagrange trajectory tracking method can accurately track the trajectory of OH radicals in the atmosphere, ensuring that the model can reflect the influence of oxidant concentration from external regions and improving the spatiotemporal consistency of the reaction simulation.

[0134] The calculation of liquid water content on aerosol surfaces employed an aerosol hygroscopic growth factor model. This model, combined with real-time relative humidity data, dynamically adjusts the liquid water content on aerosol surfaces and influences the pH of the aerosol surface reaction microenvironment. This approach allows for the consideration of aerosol variations under different humidity conditions, particularly the impact of humidity on surface chemical reactions. The dynamically adjusted pH parameter helps to accurately describe the role of moistened aerosol particles in aerosol chemical reactions.

[0135] The above-described steps significantly improve the accuracy and reliability of the inversion method for methanesulfonic acid particles in marine droplet aerosols. First, the introduction of surface reaction source and sink terms allows for a more accurate description of the chemical reaction process, especially in aerosol surface reactions, comprehensively considering the relationship between specific surface area and reaction rate. Second, the precise calculation of the specific surface area of ​​non-spherical particles using a fractal geometry model effectively enhances the ability of aerosol particle size distribution to describe the reaction process. Furthermore, the combination of reaction rate lookup tables based on experimental data and the Lagrange trajectory tracing method allows the spatiotemporal distribution of oxidant concentration to accurately reflect changes in the atmospheric boundary layer, improving the simulation accuracy of aerosol surface reactions. Finally, the dynamic adjustment of the liquid water content on the aerosol surface using a hygroscopic growth factor model considers the influence of humidity on surface chemical reactions, enabling the model to more realistically reflect aerosol behavior in actual environments.

[0136] In one possible implementation, the numerical method first discretizes the advection-diffusion reaction equations. These equations describe the advection and diffusion of aerosol particles in the atmosphere with the wind field, a process that needs to be simulated numerically. During discretization, a conformal interpolation algorithm is used to handle flux exchange at sub-region boundaries. This algorithm ensures that the exchange of particle concentration between different sub-regions reflects reality while maintaining the conservation of physical quantities, thus avoiding the accumulation of numerical errors.

[0137] For the time integration of the equations, an implicit-explicit hybrid scheme was employed. This scheme can improve computational efficiency while maintaining computational stability. Specifically, the advection term is discretized using an upwind scheme, which can effectively handle directional advection processes and reduce numerical oscillations; while the diffusion term is discretized using a central difference scheme, which can provide a more accurate approximation of the diffusion process.

[0138] Furthermore, to address the linear system within the equations, a preprocessed conjugate gradient method was employed. This method effectively handles large-scale sparse matrix problems, and is particularly suitable for equations in aerosol models exhibiting Jacobian matrix sparsity. To further improve computational efficiency, a block-based iteration strategy was designed, optimized for the sparsity of matrix blocks, reducing computation time and increasing the solution speed.

[0139] The validation process includes spatial distribution consistency testing and temporal correlation analysis. Spatial distribution consistency testing verifies the consistency between the spatial distribution of the model inversion results and the measured data, ensuring the model's accuracy in retrieving methanesulfonic acid particles across different regions. Temporal correlation analysis quantifies the time lag effect between the inversion results and the buoy's measured data using a moving window cross-correlation algorithm. This step reveals the temporal correlation between the inversion results and actual observation data, further optimizing the model's timeliness and accuracy.

[0140] To evaluate the reliability and stability of the model, uncertainty analysis using the Monte Carlo method was conducted. The Monte Carlo method simulates the model output under different conditions by sampling the probability distribution of key input parameters (such as aerosol concentration, reaction rate, and temperature). This method outputs a spatial distribution map of the confidence interval for methanesulfonic acid concentration, providing the uncertainty range of the model output. This allows users to clearly understand the reliability of the inversion results and its possible error range.

[0141] The design of these numerical methods and validation processes effectively improves the accuracy, stability, and computational efficiency of the box model-based inversion method for methanesulfonic acid particles in marine droplet aerosols, providing strong support for large-scale environmental simulation and aerosol research.

[0142] In one possible implementation, a series of aerosol samples are first generated by adjusting the seawater salinity gradient in a controlled laboratory environment. During the experiment, other environmental parameters such as temperature and humidity are kept constant to ensure that salinity variation is the only variable. This method eliminates interference from other environmental factors, ensuring that the effect of salinity on the methanesulfonic acid formation rate and particle distribution can be accurately observed.

[0143] Furthermore, aerosol mass spectrometry was used for real-time monitoring to record the formation rate of methanesulfonic acid. Mass spectrometry can measure changes in the concentration of methanesulfonic acid in aerosols with high precision, thereby obtaining the effect of salinity changes on methanesulfonic acid formation. Simultaneously, a high-speed imaging system was used to record the dynamic process of aerosol particle size distribution changing with salinity. By combining these two techniques, a comprehensive understanding of the impact of salinity changes on the formation rate and particle size of aerosols can be obtained.

[0144] Furthermore, based on experimental data, a nonlinear regression model between salinity and reaction efficiency was constructed. This step used stepwise regression to screen out significant factors that greatly influence aerosol formation rate and determine their polynomial order. Stepwise regression effectively reduces the influence of redundant factors, making the regression model simpler and more predictive.

[0145] Cross-validation was employed to optimize the complexity of the regression model. Cross-validation effectively evaluates the model's performance on different datasets, thus preventing overfitting. During this process, by adjusting the parameters and order of the regression model, a mapping relationship was established between the salinity parameter and the proportionality coefficient in the emission flux calculation formula. This mapping relationship accurately reflects the influence of salinity in the formation rate of methanesulfonic acid particles during model prediction.

[0146] To ensure the portability of the calibration results, the effectiveness of the model was verified by comparing the systematic bias between measured data and model predictions from different sea areas. Seawater salinity may exhibit regional characteristics in different sea areas; if systematic bias exists, it can be adjusted by introducing a region-specific correction coefficient. This step ensures that the calibration model is not only applicable to laboratory data but also accurately applied to different actual sea areas.

[0147] Through these steps, the calibration process of salinity influencing factors can provide accurate and reliable parameter mapping relationships for the inversion of methanesulfonic acid particles, improve the model's adaptability and predictive ability in different environments, and provide strong support for marine aerosol research.

[0148] In one possible implementation, firstly, a multi-wavelength lidar system is used to acquire vertical profile data of the extinction coefficient and backscattering coefficient of the aerosol. This data provides the necessary foundation for the aerosol's particle size distribution. The extinction coefficient and backscattering coefficient are core parameters for inverting particle size distribution; multi-wavelength lidar allows for the acquisition of optical properties at different wavelengths, thus providing more comprehensive aerosol information.

[0149] Based on Mie scattering theory, an inversion equation for aerosol particle size distribution is constructed. Mie scattering theory provides a mathematical model describing the scattering characteristics of particles under the influence of light waves; these characteristics are closely related to particle size. The inversion equation uses these scattering characteristics to infer the aerosol particle size distribution. To address potential ill-conditioned problems in the inversion equation (i.e., poor solution stability or irreversibility), a regularization method is employed. The regularization method optimizes the stability of the solution through constraints, avoiding errors caused by data noise or model incompleteness during the inversion process.

[0150] Furthermore, the accuracy of the inversion results is further improved by introducing prior constraints. Specifically, typical marine aerosol particle size distribution patterns obtained from historical observation data are used as prior knowledge to constrain possible solutions in the inversion process. This prior knowledge helps limit the possible particle size distribution range, thereby improving the stability and reliability of the inversion process.

[0151] Algorithm optimization during the inversion process is achieved by developing an adaptive weighting function. This function dynamically adjusts the residual weight distribution between coarse and fine particle modes during the fitting process. Coarse and fine particles differ significantly in their scattering characteristics, thus requiring flexible adjustment of their contributions. By dynamically adjusting the weighting function, the accuracy of the inversion algorithm can be improved, ensuring that both particle modes are appropriately fitted.

[0152] Finally, to verify the reliability of the inversion results, the obtained particle size distribution was compared with the in-situ sampling data. These in-situ sampling data, derived from field observations along the same air mass path, provide actual aerosol particle size distribution information. By comparing the modal distribution characteristics of the inversion results with those of the in-situ sampling data, the accuracy of the inversion results can be confirmed. If the inversion results are consistent with the in-situ data, the inversion algorithm is considered effective and reliable.

[0153] In one possible implementation, step 5.1 involves modeling the probability distribution of the input parameters. This step primarily includes modeling the probability distribution of the key input parameters. First, it is necessary to determine the probability distribution type and parameter range of each input parameter:

[0154] The distribution type of dimethyl sulfide concentration data is determined by the skewness and kurtosis test results of historical observation data. Based on the test results, types such as log-normal, Weibull, or gamma distributions can be selected.

[0155] The probability distribution of the salinity correlation coefficient is based on the measurement error of laboratory calibration data, and a truncated probability distribution is used to limit its physically reasonable range of values.

[0156] Meteorological parameters (such as wind speed and temperature) are used to construct probability distributions based on the spatiotemporal variability of the reanalysis dataset. These parameters are usually related to the stability of the ocean boundary layer, so it is necessary to establish a joint probability distribution.

[0157] To handle non-independent parameters, a covariance matrix needs to be constructed, and principal component analysis (PCA) is used to reduce the dimensionality of the parameter space. This reduces computational complexity and improves model efficiency.

[0158] This step, by accurately modeling the probability distribution of the input parameters, can better capture the uncertainty of the input variables, thus providing a realistic parameter distribution basis for subsequent Monte Carlo simulations.

[0159] Furthermore, in step 5.2, a stratified sampling strategy (e.g., Latin hypercube sampling (LHS) or orthogonal array sampling) is used to generate parameter combination sample sets. These sample sets ensure uniform coverage in the high-dimensional parameter space. Each sample combination is used to perform the full-process calculation of the box model, including dynamic source term optimization, surface chemical reaction coupling, and numerical solution steps, recording the output methanesulfonic acid concentration field and related intermediate process variables.

[0160] The use of parallel computing architecture accelerates the simulation of large-scale sample sets. Dynamic allocation of computing node resources through task scheduling algorithms ensures both efficiency and scalability. This step, through efficient sample generation and parallel computing, enables rapid simulation of large-scale parameter spaces, providing a large amount of sample data for subsequent statistical analysis.

[0161] Step 5.3 involves calculating statistics and constructing confidence intervals. This step calculates the concentration statistics (such as mean, variance, skewness, and kurtosis) for each spatial grid point based on the simulation results, and constructs the probability density function of the concentration values ​​using kernel density estimation. Simultaneously, the upper and lower limits of the concentration at a specified confidence level are calculated using the quantile function, and an adaptive bandwidth selection algorithm is employed to optimize the stability of the quantile estimation. Morphological filtering is used to process the confidence intervals of the spatiotemporal continuous field to eliminate local noise.

[0162] This step, by accurately calculating statistics and confidence intervals, can clearly describe the uncertainty of the model output, providing strong statistical basis for decision-making.

[0163] Step 5.4 involves convergence verification and sensitivity analysis. First, the asymptotic mean squared error index is used to assess the convergence of the Monte Carlo simulation. The termination condition is determined by comparing the rate of change of the statistic under different sample sizes. Subsequently, a global sensitivity analysis is performed, calculating the global sensitivity index based on variance decomposition for each input parameter to quantify its contribution to the uncertainty of methanesulfonic acid concentration. Local refinement sampling is then performed on highly sensitive parameters to improve the accuracy of uncertainty assessment. This analysis identifies which input parameters have a significant impact on the model output, thus providing guidance for further research and model improvement. Refinement sampling also improves the model's prediction accuracy.

[0164] In the final step, spatial distribution maps of the confidence interval width field, sensitivity index field, and probability density function surface are generated, and multidimensional scaling is used to reduce dimensionality and demonstrate the correlation between the high-dimensional parameter space and the output field. Using a Geographic Information System (GIS) platform, the uncertainty analysis results are spatiotemporally overlaid with meteorological element fields and ocean current fields for interactive visualization. Finally, a standardized uncertainty assessment report is output, including the original sample set, statistical matrix, and visualization metadata.

[0165] This step uses visualization technology to present the results of complex uncertainty analysis, allowing users to intuitively understand the spatial distribution characteristics and uncertainties of the model output, which facilitates further decision-making and analysis.

[0166] These steps are closely linked and interdependent. By accurately modeling the probability distribution of input parameters, generating efficient sample sets, and conducting detailed statistical and sensitivity analyses, they ultimately output an accurate uncertainty assessment report. The beneficial effects of these steps include improved model accuracy, enhanced reliability of simulation results, and the provision of more intuitive visualization tools, making aerosol inversion and uncertainty analysis more practical and reliable.

[0167] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0168] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model, characterized in that, Includes the following steps: Step 1: Acquire satellite remote sensing data, ground observation data, and laboratory simulation data, and perform data standardization processing; Step 2: Divide the target sea area into multiple sub-regions as independent containers, and input the initial methanesulfonic acid concentration, dimethyl sulfide emission, and meteorological parameters for each container; Step 3: Based on the concentration of dimethyl sulfide and wind speed, the emission flux of marine droplet aerosols is calculated using a semi-empirical formula that includes the salinity influence factor; and the spatiotemporal distribution of emission sources is updated by using satellite aerosol optical thickness data as observations and ground observation data as prior constraints using a data fusion algorithm. Step 4: Add chemical reaction source and sink terms for the oxidation of dimethyl sulfide to methanesulfonic acid on the aerosol surface to the mass conservation equation of the box model, and correct the surface reaction rate according to the specific surface area of ​​the aerosol. Step 5: Solve the advection-diffusion-reaction partial differential equations in the box model using numerical methods, output the spatiotemporal distribution of methanesulfonic acid concentration, and compare and verify it with the measured data.

2. The method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model according to claim 1, characterized in that, In step 1: The satellite remote sensing data includes aerosol optical thickness data acquired by a multispectral imager, and its spatial resolution meets the requirements for high-precision marine aerosol monitoring. The ground observation data includes dimethyl sulfide concentration data, sea surface wind speed data, seawater salinity data, and meteorological parameter data collected in real time by buoys or ship platforms; The laboratory simulation data is generated by a controlled environment aerosol generator and includes data on the methane sulfonic acid generation rate and particle size distribution of aerosol samples under different salinity and temperature gradients. The preprocessing of the satellite remote sensing data includes correction of ocean surface reflection interference and the use of the multi-band reflectivity ratio method to eliminate the effects of solar flares and specular reflection from the sea surface. The preprocessing of the ground observation data includes time synchronization of discrete measurement points and spatial interpolation to generate a continuous gridded dataset that matches the spatial resolution of the satellite data. The preprocessing of the laboratory simulation data includes normalization of experimental parameters and establishment of a correlation matrix between salinity, temperature and methanesulfonic acid formation rate.

3. The method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model according to claim 2, characterized in that, The data standardization process in step 1 includes: Radiometric calibration and atmospheric correction were performed on satellite aerosol optical thickness data to eliminate spectral mixing effects in aerosol type identification. Quality control was performed on the dimethyl sulfide concentration data in the ground observation data to remove outliers caused by sensor drift or environmental interference, and the discrete point data was converted into a spatially continuous distribution using the Kriging interpolation algorithm. Temperature compensation correction was applied to the methanesulfonic acid formation rate in laboratory simulation data, and a lookup table of reaction kinetic parameters under different temperature and humidity conditions was established. The timestamps of satellite data, ground data, and laboratory data are unified to Coordinated Universal Time (UTC), and a sliding time window algorithm is used to align the time bases of different data sources. Transform the spatial projection coordinate system of multi-source data to ensure that all data layers are overlaid and analyzed under a unified geographic reference system.

4. The method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model according to claim 1, characterized in that, In step 2: The division of the sub-regions is based on the spatial gradient distribution characteristics of satellite aerosol optical thickness. An adaptive grid division algorithm is used to dynamically adjust the boundaries of the sub-regions to ensure that the spatial uniformity of aerosol optical properties in each sub-region meets a preset threshold. The initial methanesulfonic acid concentration is set based on historical inversion results, laboratory calibration benchmark values, or migration and assignment of observation data from adjacent sea areas. The initialization of the meteorological parameters includes extracting sea surface temperature, relative humidity, and boundary layer height data from global or regional reanalysis datasets, and improving the spatial resolution of the data to a level that matches the sub-region division through a dynamic downscaling model; The determination of the physical boundary of each independent container needs to take into account the characteristics of the ocean surface flow field, and use the vortex identification algorithm to avoid dividing the strong shear flow region into the same sub-region; The vertical height of the enclosure is set based on atmospheric mixing layer height observation data, and a piecewise function is used to describe the diffusion attenuation characteristics of aerosols in the vertical direction.

5. The method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model according to claim 1, characterized in that, In step 3: The emission flux calculation uses a semi-empirical formula that includes a salinity influence factor. This formula includes a dimethyl sulfide concentration term, a wind speed term, and a salinity correction term. The dimethyl sulfide concentration term was provided by spatial interpolation of ground observation data, and the wind speed term was obtained by downscaling the reanalysis dataset. The data fusion algorithm adopts a recursive filtering algorithm, using satellite aerosol optical thickness data as the observation value and ground observation data as the prior constraint, and constructs the spatiotemporal evolution matrix of emission source terms through a state-space model; The process noise covariance matrix in the recursive filtering algorithm is dynamically adjusted according to marine meteorological conditions. Noise weights are added to enhance the robustness of the model when there are sudden changes in wind speed or drastic changes in temperature and humidity. The update of the emission source terms includes feedback correction of the dimethyl sulfide air-sea exchange rate, and inverse optimization of the emission source intensity distribution using the adjoint matrix method to maximize the spatial correlation between the simulated aerosol optical thickness and satellite observations. The updated spatiotemporal distribution of emission sources is verified for physical consistency through a particle diffusion model to ensure that the emission source intensity variation trend conforms to the atmospheric boundary layer transport law.

6. The method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model according to claim 1, characterized in that, In step 4: The embedding of the chemical reaction source-sink term includes adding a surface reaction source-sink term to the box model mass conservation equation, which is determined by the product of the aerosol specific surface area and the surface reaction rate. The calculation of the specific surface area of ​​the aerosol is based on the aerosol particle size distribution data, and a fractal geometric model is used to describe the surface area-to-volume ratio of non-spherical particles. The surface reaction rate was determined by calibration using laboratory simulation data, and a lookup table of reaction rates under different particle size ranges and relative humidity conditions was established. The spatiotemporal distribution of oxidant concentration is provided by a global chemical transport model, and the concentration field of OH radicals transported outside the region is coupled to the box model using the Lagrange trajectory tracking method. The calculation of liquid water content on the aerosol surface adopts the aerosol hygroscopic growth factor model, and the pH parameters of the surface reaction microenvironment are dynamically adjusted in combination with real-time relative humidity data.

7. The method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model according to claim 1, characterized in that, In step 5: The numerical method for solving the problem includes discretization of the advection-diffusion reaction equations and the use of conformal interpolation algorithms to handle flux exchange at the boundaries of sub-regions. The time integration scheme of the numerical method adopts an implicit-explicit hybrid scheme, in which the advection term is discretized using an implicit upwind scheme and the diffusion term is discretized using an explicit central difference scheme. The equation system is solved using the preprocessed conjugate gradient method, and a block iteration strategy is designed to improve computational efficiency by taking into account the sparsity of the Jacobian matrix. The verification includes spatial distribution consistency test and temporal change correlation analysis, and the moving window cross-correlation algorithm is used to quantify the time lag effect between the inversion results and the measured data of the buoy. The model uncertainty analysis is achieved through the Monte Carlo method, which samples the probability distribution of key input parameters and outputs a spatial distribution map of the confidence interval of methanesulfonic acid concentration.

8. The method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model according to claim 5, characterized in that, The calibration method for the salinity influencing factor includes: In a controlled laboratory environment, a series of aerosol samples were generated by adjusting the seawater salinity gradient, while keeping the temperature and humidity parameters constant. The formation rate of methanesulfonic acid was monitored in real time using an aerosol mass spectrometer, and the dynamic process of aerosol particle size distribution changing with salinity was recorded using a high-speed camera system. A nonlinear regression model of salinity and reaction efficiency was constructed, and stepwise regression was used to screen for significant influencing factors and determine their polynomial order. The complexity of the regression model was optimized by cross-validation to prevent overfitting, and finally the mapping relationship between the salinity parameter and the proportional coefficient in the emission flux calculation formula was established. The portability of the calibration results was verified by comparing the systematic bias between the measured data and the model predictions in different sea areas, and a region-specific correction coefficient was introduced when systematic bias existed.

9. The method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model according to claim 6, characterized in that, The method for inverting the aerosol particle size distribution includes: Vertical profiles of aerosol extinction coefficient and backscattering coefficient were obtained using a multi-wavelength lidar system. An inversion equation for aerosol particle size distribution is constructed based on Mie scattering theory, and a regularization method is used to address the ill-conditioned nature of this inversion equation. The introduction of prior constraints includes typical marine aerosol particle size distribution patterns obtained by statistical analysis of historical observation data. The optimization of the inversion algorithm includes developing an adaptive weighting function to dynamically adjust the weight distribution of the fitting residuals between coarse-particle and fine-particle modes; The reliability of the inversion results is verified by comparing the modal distribution characteristics of the in-situ sampling data and the inversion results under the same air mass path.

10. The method for inverting methanesulfonic acid particles in marine droplet aerosols based on a box model according to claim 7, characterized in that, The uncertainty analysis of the model is performed using the Monte Carlo method, including the following steps: Step 5.1: Modeling the probability distribution of input parameters: A. Determine the probability distribution type and parameter range of the key input parameters of the model: a. The concentration data of dimethyl sulfide were obtained using a probability distribution model based on the statistical characteristics of historical observations, and the distribution type was selected according to the results of data skewness and kurtosis tests; b. The probability distribution of the salinity correlation coefficient is determined based on the repeatability measurement error of the laboratory calibration experiment, and a truncated probability distribution is used to limit its physically reasonable range of values. c. The probability distribution of meteorological parameters is constructed based on the spatiotemporal variability of the reanalysis dataset. Joint probability distributions related to the stability of the ocean boundary layer are established for wind speed and temperature parameters, respectively. d. Construct covariance matrices for the non-independent parameters and reduce the dimensionality of the parameter space using principal component analysis (PCA); Step 5.2: Random Sample Generation and Model Iteration: A stratified sampling strategy is adopted to generate a parameter combination sample set, including Latin hypercube sampling (LHS) and orthogonal array sampling, to ensure high-dimensional uniform coverage of the parameter space; For each combination of sampling parameters, perform full-process calculations using a box model, including dynamic source term optimization, surface chemical reaction coupling, and numerical solution steps, and record the output methanesulfonic acid concentration field and intermediate process variables. A parallel computing architecture is adopted to accelerate the simulation of large-scale sample sets, and computing node resources are dynamically allocated through a task scheduling algorithm; Step 5.3: Calculation of statistics and construction of confidence intervals: Based on the sample output results, the concentration statistical characteristics of each spatial grid point are calculated, including mean, variance, skewness and kurtosis. The probability density function of the concentration value is constructed by kernel density estimation. The upper and lower limits of concentration at a specified confidence level are calculated using the quantile function, and the stability of the quantile estimation is optimized using an adaptive bandwidth selection algorithm. Morphological filtering is applied to the confidence interval of the spatiotemporal continuous field to eliminate local noise caused by the limited sample size. Step 5.4: Convergence Verification and Sensitivity Analysis: The convergence of the Monte Carlo simulation was evaluated using the asymptotic mean square error index, and the termination condition of the calculation was determined by comparing the rate of change of the statistic under different sample sizes. Perform a global sensitivity analysis to calculate the global sensitivity index of each input parameter based on variance decomposition, and quantify its contribution to the uncertainty of the output methanesulfonic acid concentration. Local refinement sampling is performed on highly sensitive parameters, and additional samples are added in the key parameter subspace to improve the accuracy of uncertainty assessment; Step 5.5: Uncertainty Visualization and Output: Spatial distribution maps of the confidence interval width field, sensitivity index field, and probability density function surface are generated. Multidimensional scaling is used to reduce the dimensionality and show the relationship between the high-dimensional parameter space and the output field. The uncertainty analysis results are spatiotemporally overlaid with meteorological element fields and ocean current fields, and interactive visualization is achieved through a geographic information system platform. Output a standardized uncertainty assessment report, including the original sample set, statistical matrix, and visualization metadata.

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