A Novel Framework Integrating Spectrum Analysis and AI for Near-Ground-Surface PM2.5 Concentration Estimation

Monitoring the horizontal distribution of PM2.5 within urban areas is of great significance, not only for environmental management but also for providing essential data to understand the distribution, formation, transport, and transformation of PM2.5 within cities. This study proposes a novel approach—the Spectral Analysis-based PM2.5 Estimation Machine Learning (SAPML) model. This method uses a machine learning model trained with features derived from multi-azimuth and multi-elevation MAX-DOAS observations, specifically the oxygen dimer (O4) differential slant column densities (O4 dSCDs), and labels provided by near-surface ground measurements corresponding to each azimuthal direction, to estimate near-surface PM2.5 concentrations. This approach does not rely on meteorological data and enables multi-directional near-surface PM2.5 monitoring using only a single independent instrument. SAPML bypasses the intermediate retrieval of aerosol extinction coefficients and directly estimates PM2.5 concentrations from spectral analysis results, thereby avoiding the accumulation of errors. Using O4 dSCD data from multiple MAX-DOAS stations for model training eliminates inter-station conversion differences, allowing a single model to be applied across multiple sites. Station-based k-fold cross-validation yielded an average Pearson correlation coefficient (R) of 0.782, demonstrating the robustness and transferability of the method across major regions in China. Among the machine learning algorithms evaluated, Extreme Gradient Boosting (XGBoost) exhibited the best performance. Feature optimization based on importance ranking reduced data collection time by approximately 30%, while the correlation coefficient (R) of the estimation results decreased by only about 1.3%. The trained SAPML model was further applied to two MAX-DOAS stations in Hefei, HF-HD, and HFC, successfully resolving the near-surface PM2.5 spatial distribution at both sites. The results revealed clear intra-urban heterogeneity, with higher PM2.5 concentrations observed in the western industrial park area. During the same observation period, an east-to-west PM2.5 pollution transport event was captured: PM2.5 increases were first detected in the upwind direction at HF-HD, followed by the downwind direction at the same station, and finally at the downwind station HFC. These results indicate that the SAPML model is an effective approach for monitoring intra-urban PM2.5 distributions.