^{Xu Han， Yang Qiyuan， Shi Guoliang， Feng Yinchang}.The State Environment Protection Key Laboratory of Urban Particulate Air Pollution Prevention， College of Environmental Science and Engineering， Nankai University， Tianjin 300350， China； 2.CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research （CLAER）， Tianjin 300074， China

Abstract

Significance Atmospheric particulate matter （PM） is a major air pollutant that exerts substantial impacts on human health， climate， atmospheric visibility， and ecosystems. Accurate identification of emission sources and quantitative estimation of their contributions are fundamental for understanding particulate pollution formation and developing effective emission control strategies. Over the past several decades， particulate matter source apportionment has evolved from conventional emission-based and statistical approaches toward integrated frameworks combining emission inventories， chemical transport models （CTMs）， receptor models， high-time-resolution observations， and artificial intelligence （AI）. In particular， rapid advances in machine learning， high-performance computing， online monitoring， and multi-source environmental data are reshaping the methodological framework of particulate matter source apportionment. This review systematically summarizes the development， characteristics， representative applications， advantages， and limitations of major particulate matter source apportionment methods， with particular emphasis on the transition from traditional approaches to machine learning-driven and mechanism–data-integrated methods. It aims to provide a methodological reference for achieving high-precision and intelligent source apportionment.

Progress Traditional particulate matter source apportionment methods can generally be divided into two categories： source-oriented approaches and receptor-oriented approaches. Emission inventories represent one of the earliest source-oriented approaches and quantitatively characterize emissions from anthropogenic and natural sources based on activity data， emission factors， statistical information， and observational constraints. Their evolution from coarse-resolution statistical inventories toward high-resolution， multi-pollutant， and multi-sector datasets has provided essential inputs for atmospheric modeling and source attribution. With advances in the understanding of atmospheric transport and chemical transformation， CTMs have gradually evolved from simple dispersion models into three-dimensional modeling systems capable of representing emissions， transport， diffusion， chemical reactions， secondary aerosol formation， and deposition. Source-oriented techniques， including brute-force emission perturbation， sensitivity analysis， and source-tagging methods， further enable CTMs to quantify contributions from different regions and emission sectors.

Conclusions and Prospects Overall， particulate matter source apportionment has evolved from emission statistics and linear receptor analysis toward dynamic process simulation， high-time-resolution observations， nonlinear data-driven modeling， and mechanism–data integration. Traditional emission inventories， CTMs， CMB， and PMF remain indispensable due to their clear physical or statistical foundations. However， limitations related to computational cost， parameter uncertainty， source profile dependence， and spatiotemporal resolution remain. Machine learning provides complementary advantages in nonlinear modeling， automatic feature extraction， multi-source data fusion， and computational efficiency， while purely data-driven methods still face challenges in physical interpretability， uncertainty quantification， robustness， and cross-regional generalization. Future research should therefore focus on deeper integration of AI with receptor models， CTMs， and atmospheric physical and chemical mechanisms. Incorporating physical laws， source profile knowledge， chemical transformation mechanisms， and mass conservation relationships into model architectures and training processes， together with the integration of ground observations， online measurements， satellite remote sensing， emission inventories， meteorological data， and socioeconomic information， will facilitate the development of intelligent source apportionment frameworks with high accuracy， efficiency， generalization ability， and interpretability. Such advances are expected to promote particulate matter source apportionment toward an intelligent and mechanism-informed stage， thereby providing stronger scientific support for precision air quality management and coordinated regional pollution control.

Keywords：atmospheric particulate matter； source apportionment； machine learning

Get Citation:  Xu Han， Yang Qiyuan， Shi Guoliang， et al. From traditional source apportionment methods to artificial intelligence： advances in atmospheric particulate matter source apportionment［J］. China Powder Science and Technology， 2027， 33（2）： 24-37.

Received:  2026-07-17， Revised： 2026-08-17， Online： 2026-09-22。

Funding： The research was supported by the National Natural Science Foundation of China (Grant No. 42477105).

DOI：10.13732/j.issn.1008-5548.2027.02.003

CLC No.:  TB4； TQ324.8

Type Code:  A

Serial No.:  1008-5548（2027）02-0024-14