Work

Measurement report: Unraveling PM 10 sources and oxidative potential across Chinese regions based on CNN-LSTM data preprocessing and receptor model

Year: 2025

Type: article

Abstract: . The oxidative potential (OP) of particulate matter is a key driver of PM10-induced adverse health effects, triggering oxidative stress and inflammatory responses that increase respiratory and cardiovascular disease risks. To evaluate PM10 and its OP characteristics across China, samples were collected from twelve representative monitoring stations from June 2022 to May 2023. A deep learning model combining Convolutional Neural Networks and Long Short-Term Memory networks (CNN-LSTM) was employed to reconstruct anomalous PM10 data, achieving R2 values of 0.967 and 0.884 for training and test sets, respectively. Significant spatial variations in PM10 were observed, with highest concentrations in the northwestern regions (Xi'an: 98.20 ± 52.92 μg·m-3, Dunhuang: 90.36 ± 54.72 μg·m-3), the lowest in the northeast (Longfengshan: 40.04 ± 24.04 μg·m-3, Dalian: 40.35 ± 15.66 μg·m-3), and elevated levels in suburban areas (average: 85.43 ± 46.69 μg·m-3). Urban sites showed the highest OP values, with significantly higher PM10 concentrations in northern regions compared to southern ones (p<0.05). Most sites exhibited peak PM10 and OP levels in winter and lowest in summer. Source apportionment using Positive Matrix Factorization (PMF) revealed dust (13.2–27.4 %), biomass burning (9.5–39.3 %), traffic (16.6–21.4 %), and agricultural activities (13–22 %) as main contributors to PM10. PMF analysis identified traffic as the primary OP contributor (24–48 %) across sites, with regional variations in biomass burning (57 % in Nanning), agricultural activities (37 % in Zhengzhou), and dust (22–23 % in Gucheng and Longfengshan). These findings highlight the need to control traffic emissions and other major sources to reduce OP and protect public health.

Source: Atmospheric chemistry and physics

DOI: 10.5194/acp-25-14643-2025