车慧正¹， 郑宇¹， 赵恒恒¹， 张馨丹¹， 桂柯¹， 李雷¹， 缪育聪¹， 赵胡笳²， 尚楠轩¹，彭诗雅¹， 朱吉彪¹， 张栩滔¹， 刘术辉¹， 张浩灵¹， 居梦瑜¹，杨周¹， 马婧芮¹， 张小曳¹

1.中国气象科学研究院 灾害天气科学与技术全国重点实验室， 北京 100081；2.中国气象局沈阳大气环境研究所， 辽宁 沈阳 110166

引用格式：

车慧正， 郑宇， 赵恒恒， 等. 大气气溶胶光学特性研究进展：从传统观测到智能遥感技术［J］. 中国粉体技术， 2027， 33（2）： 108-126.

Che Huizheng， Zheng Yu， Zhao Hengheng， et al. Research progress on optical properties of atmospheric aerosols： from conventional observations to intelligent remote sensing technologies ［J］. China Powder Science and Technology， 2027， 33（2）： 108-126.

DOI：10.13732/j.issn.1008-5548.2027.02.009

收稿日期： 2026-07-15， 修回日期： 2026-09-26， 上线日期： 2026-10-11。

基金项目： 国家重点研发计划项目，编号：2024YFB3908701； 国家自然科学基金项目，编号：42375188、U2542218、42322504、42575203、42475193； 中国气象科学研究院科技发展基金，编号：2025KJ001； 中国气象科学研究院基本科研业务费项目，编号：2024Z006。

第一作者： 车慧正（1977—），男，研究员，博士生导师，国家杰出青年科学基金获得者、中国气象局科技领军人才，研究方向为气溶胶气候与环境效应。E-mail：chehz@cma.gov.cn。

摘要： 【目的】 大气气溶胶是影响区域空气质量、地气辐射平衡与天气气候系统的重要大气组分，气溶胶光学特性是关联颗粒物理化性质、解析气溶胶环境与气候效应的核心参数。分析大气气溶胶的光学特性，探讨从传统观测到智能遥感技术的进展。 【研究现状】 综述国内气溶胶光学特性的研究进展，从地基观测、卫星遥感、人工智能三大维度展开：总结我国自主地基观测网络、仪器校准体系、激光雷达的发展与典型应用成果；归纳国际卫星本土化应用、国产卫星研发、气溶胶反演算法与多源数据融合技术进展；介绍人工智能、无人机等前沿技术在气溶胶领域的应用现状。 【结论与展望】 经过多年发展，我国已建成较为完善的地基气溶胶观测网络，基本实现从城市群、沙尘源区到青藏高原和背景区的多区域覆盖；已逐步形成以高分、风云、环境系列卫星为代表的气溶胶多平台观测体系；人工智能技术已深度融入气溶胶光学研究的各个关键环节，形成从反演、融合感知到预报和模式增强的系统性推进。认为未来重点发展方向应为观测体系升级、算法与模型优化、新兴技术深化应用、前沿科学研究等。

关键词： 气溶胶光学特性； 气溶胶光学厚度； 地基观测； 卫星遥感； 人工智能

Abstract

Significance Atmospheric aerosols consist of suspended solid and liquid particles ranging from 0.001 μm to 100 μm in aerodynamic diameter， originating from both natural processes including dust emission， sea spray and volcanic eruption， and anthropogenic activities such as industrial exhaust， vehicle emissions， and fossil‑fuel combustion. With typical atmospheric lifetimes ranging from several days to one week， aerosols can undergo long‑range transport driven by atmospheric circulation， exerting profound impacts on near‑surface air quality， regional water cycles， and global climate systems. As emphasized in the IPCC Sixth Assessment Report， anthropogenic aerosols contribute substantial negative radiative forcing with considerable uncertainty， and aerosol–cloud interactions remain one of the dominant error sources in modern climate projections. Aerosols modify the Earth–atmosphere energy budget through direct radiative effects via scattering and absorbing solar and terrestrial radiation， as well as indirect radiative effects by acting as cloud condensation nuclei and ice nuclei to alter cloud microphysical properties and precipitation efficiency. Aerosol optical properties， represented by core parameters including aerosol optical depth （AOD）， Ångström exponent （AE）， single‑scattering albedo （SSA）， phase function， and asymmetry factor， serve as critical bridges linking microphysical‑chemical particle characteristics to environmental and climatic consequences. These optical parameters are predominantly acquired through ground‑based and satellite remote sensing observations. Although global observation networks have been established， aerosol properties exhibit strong spatial heterogeneity and seasonal variability across China. Therefore， a comprehensive understanding of aerosol optical properties is essential for reducing uncertainties in climate models， evaluating air‑pollution hazards， and supporting the implementation of carbon peaking and carbon neutrality strategies.

Progress This review systematically synthesizes recent advances in domestic research on ground‑based observations， satellite remote sensing， and artificial intelligence technologies. For ground‑based observations， China has constructed independent ground networks dominated by the China Aerosol Remote Sensing Network （CARSNET）， alongside CSHNET and SONET， with over 124 CARSNET stations covering urban agglomerations， dust source regions， and the Qinghai-Xizang Plateau. A hierarchical calibration system relying on the high‑altitude Waliguan baseline station has been developed， incorporating improved Langley calibration algorithms and integrating‑sphere sky‑radiation calibration to ensure AOD measurement accuracy better than 0.01. Passive sun‑photometer observations reveal nationwide spatiotemporal patterns of aerosol properties and meteorological modulation mechanisms， while ground‑based lidar enables continuous day‑and-night detection of vertical aerosol structures， supporting investigations of dust transport， haze‑fog evolution， and cross‑border aerosol import. Moreover， ground‑based optical measurements are combined with retrieval algorithms such as GRASP‑Component to constrain aerosol component fractions and quantify direct aerosol radiative forcing. In satellite remote‑sensing research， international satellite products （MODIS， MISR， CALIPSO， etc.） have been validated and applied across diverse land surfaces in China. Domestic satellite payloads from the Gaofen， Fengyun， and environmental‑monitoring satellite series provide multi‑angle polarimetric and geostationary observations. Chinese researchers have optimized dark‑target， deep‑blue and GRASP‑based retrieval algorithms， and evaluated the regional performance of MERRA‑2 and CAMS aerosol reanalysis datasets. Multi‑source fusion approaches combining ground measurements， satellite retrievals， and reanalysis data， particularly machine‑learning‑based fusion frameworks， mitigate data gaps induced by cloud contamination. In terms of artificial intelligence applications， data‑driven methods alleviate the limitations of traditional physical inversion approaches constrained by prior assumptions. Advanced models enable intelligent retrieval of aerosol parameters，seamless spatiotemporal reconstruction of key atmospheric variables such as PM₂.₅ and visibility，and hybrid physical‑data‑driven aerosol forecasting. Deep‑learning surrogate models also accelerate time‑consuming radiation transfer and aerosol chemistry calculations and correct simulation biases within numerical chemical transport models.

Conclusions and Prospects Despite these remarkable achievements， multiple bottlenecks remain. Ground‑based systems still face limitations， including insufficient night‑time observation capacity， uneven spatial distribution of monitoring sites especially over remote complex underlying surfaces， and incomplete parameterization for aerosol-radiation-boundary‑layer coupling processes. Domestic satellite systems are still limited in retrieving high-resolution aerosol microphysical and chemical parameters. Mature multi‑satellite collaborative inversion frameworks have yet to be established， and large retrieval errors remain over bright and complicated land surfaces. For artificial intelligence methodologies， poor model interpretability restricts operational deployment， and interdisciplinary barriers hinder the combination of physical aerosol knowledge and data‑driven algorithms. Looking ahead， future priorities cover four dimensions. First， observation infrastructures should be upgraded to strengthen integrated diurnal monitoring and collaborative multi‑platform observations. Second， inversion and modelling algorithms should be optimized to improve retrievals of fine aerosol parameters and refine aerosol-cloud-radiation parameterization schemes. Third， the operational transformation of artificial intelligence technologies should be promoted by developing physically informed interpretable models and overcoming disciplinary boundaries. Fourth， frontier scientific studies focusing on carbonaceous and dust aerosol evolution under the dual‑carbon background should be advanced to disentangle linkage mechanisms among anthropogenic emission reduction， aerosol variations， and climate feedbacks， thereby providing scientific support for environmental governance and climate change assessment.

Keywords： optical property of atmospheric aerosol； aerosol optical depth； ground-based observation； satellite remote sensing； artificial intelligence

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