DOI: 10.57760/sciencedb.19129
title: Global monthly mean spatiotemporal distribution dataset of seven major aerosol chemical components and their optical and microphysical parameters retrieved from polarization satellites (2008, 2022, 2023).
title: 偏振卫星反演的全球七种主要气溶胶化学成分与光学和微物理参数月均时空分布数据集（2008、2022、2023年）

description: This dataset leverages multi-angle and polarization observations from polarized satellite instruments, with the multiple solution mixing mechanism (MSM2) as its core, enabling rapid retrieval of global aerosol chemical composition and mutually consistent optical and microphysical properties. By constraining solute fractions through the Lorentz-Lorenz relation and Kappa-Köhler theory, the dataset provides inversion results for the spatiotemporal distributions of seven components: black carbon (BC), dust (DU), ammonium nitrate (AN), sea salt (SS), water-soluble organic matter (WSOM), water-insoluble organic matter (WIOM), and aerosol water content (AW). Additionally, advanced artificial intelligence models such as Transformers integrate satellite data with chemical transport models (e.g., MERRA-2) and ground-based observations from ground-observations like AERONET and SONET, along with data from the novel POSP polarization sensor, to achieve high-resolution, multi-parameter rapid retrievals. Compared to traditional statistical methods, the MSM2 approach provides a more comprehensive estimation of fine- and coarse-mode aerosol mass concentrations, absorption properties, and vertical distributions. Its synergistic retrieval of optical and microphysical parameters offers significant advantages for the spatiotemporal monitoring of dust storms and pollution events.The dataset includes monthly global distributions of major aerosol components (black carbon, brown carbon, organic carbon, dust, ammonium sulfate, sea salt, and aerosol water) and key microphysical and optical parameters (e.g., spectral distributions, complex refractive indices, single-scattering albedo) for 2008, 2022, and 2023. Validation against typical scenarios such as dust storms, wildfires, and urban particulate pollution indicates strong agreement. In regions like southern Africa, western Europe, and China, comparisons with MERRA-2 show high correlation coefficients for monthly mean chemical components (R²: BC = 0.94, DU = 0.88), confirming the validity of the MSM2 approach. Further validation using ground-based observations from AERONET and SONET shows consistent results for seven chemical components (R ~ 0.7). For optical and microphysical parameters (e.g., AOD, SSA, coarse- and fine-mode volumetric column concentrations), correlation coefficients exceed 0.8, with most data points falling within expected error ranges, enabling accurate monitoring of dust and haze events.The dataset is provided in NetCDF (NC) format, ensuring easy integration with climate models and geoscientific analysis tools. It offers fundamental data support for various fields, including global and regional climate studies, atmospheric environmental monitoring, and health impact assessments.
description: This dataset leverages multi-angle and polarization observations from polarized satellite instruments, with the multiple solution mixing mechanism (MSM2) as its core, enabling rapid retrieval of global aerosol chemical composition and mutually consistent optical and microphysical properties. By constraining solute fractions through the Lorentz-Lorenz relation and Kappa-Köhler theory, the dataset provides inversion results for the spatiotemporal distributions of seven components: black carbon (BC), dust (DU), ammonium nitrate (AN), sea salt (SS), water-soluble organic matter (WSOM), water-insoluble organic matter (WIOM), and aerosol water content (AW). Additionally, advanced artificial intelligence models such as Transformers integrate satellite data with chemical transport models (e.g., MERRA-2) and ground-based observations from ground-observations like AERONET and SONET, along with data from the novel POSP polarization sensor, to achieve high-resolution, multi-parameter rapid retrievals. Compared to traditional statistical methods, the MSM2 approach provides a more comprehensive estimation of fine- and coarse-mode aerosol mass concentrations, absorption properties, and vertical distributions. Its synergistic retrieval of optical and microphysical parameters offers significant advantages for the spatiotemporal monitoring of dust storms and pollution events.The dataset includes monthly global distributions of major aerosol components (black carbon, brown carbon, organic carbon, dust, ammonium sulfate, sea salt, and aerosol water) and key microphysical and optical parameters (e.g., spectral distributions, complex refractive indices, single-scattering albedo) for 2008, 2022, and 2023. Validation against typical scenarios such as dust storms, wildfires, and urban particulate pollution indicates strong agreement. In regions like southern Africa, western Europe, and China, comparisons with MERRA-2 show high correlation coefficients for monthly mean chemical components (R²: BC = 0.94, DU = 0.88), confirming the validity of the MSM2 approach. Further validation using ground-based observations from AERONET and SONET shows consistent results for seven chemical components (R ~ 0.7). For optical and microphysical parameters (e.g., AOD, SSA, coarse- and fine-mode volumetric column concentrations), correlation coefficients exceed 0.8, with most data points falling within expected error ranges, enabling accurate monitoring of dust and haze events.The dataset is provided in NetCDF (NC) format, ensuring easy integration with climate models and geoscientific analysis tools. It offers fundamental data support for various fields, including global and regional climate studies, atmospheric environmental monitoring, and health impact assessments.