This study analyzed the differences in ozone (O₃) sensitivity in four different urban areas in China from February 2019 to January 2020 based on data on various near-surface pollutants from passive multi-axis differential optical absorption spectroscopy (MAX-DOAS) sites and nearby China National Environmental Monitoring Center (CNEMC) sites. Across the four cities, the nitrogen dioxide (NO₂) and formaldehyde (HCHO) concentrations varied seasonally. Xianghe consistently displayed the lowest NO₂ levels, suggesting reduced emissions compared to other cities. Guangzhou, a city with a robust economy and a high level of vehicle ownership, exhibited higher concentrations in spring. Summer brought elevated HCHO levels in Guangzhou, Xianghe, and Shenyang due to intensified photochemical processes. Autumn and winter showed higher HCHO concentrations in Guangzhou and Xianghe compared to Lanzhou and Shenyang. Overall, Guangzhou recorded the highest annual averages, due to its developed economy, while Xianghe’s lower NO₂ levels were offset by the elevated HCHO due to higher O₃ values. The analysis delved into primary and secondary HCHO sources across seasons and used carbon monoxide (CO) and O₃ data. Xianghe showcased the dominance of secondary sources in summer and autumn, while Lanzhou was characterized by primary dominance throughout the year. Shenyang mirrored Xianghe’s evolution due to industrial emissions. In Guangzhou, due to the high levels of vehicular traffic and sunlight conditions, secondary sources predominantly influenced HCHO concentrations. These findings highlight the interplay between primary and secondary emissions in diverse urban settings. This study explored O₃ sensitivity variations across seasons. Xianghe exhibited a balanced distribution among volatile organic compound (VOC)-limited conditions, nitrogen oxide (NO_x)-limited conditions, and transitional influences. Lanzhou was mainly affected by VOC-limited conditions in winter and NO_x-limited conditions in other seasons. Shenyang’s sensitivity varied with the seasons and was primarily influenced by transitions between VOCs and NO_x in autumn and NO_x-limited conditions otherwise. Guangzhou experienced varied influences. During periods of high O₃ pollution, all regions were affected by NO_x-limited conditions, indicating the necessity of NO_x monitoring in these areas, especially during summer in all regions and during autumn in Xianghe and Guangzhou.

The vertical profile of PM_2.5 is important for understanding its secondary formation, transport, and deposition at high altitudes; it also provides important data support for studying the causes and sources of PM_2.5 near the ground. Based on machine learning methods, this study fully utilized simultaneous Multi-Axis Differential Optical Absorption Spectroscopy measurements of multiple air pollutants in the atmosphere and employed the measured vertical profiles of aerosol extinction—as well as the vertical profiles of precursors such as NO₂ and SO₂—to evaluate the vertical distribution of PM_2.5 concentration. Three machine learning models (eXtreme Gradient Boosting, Random Forest, and back-propagation neural network) were evaluated using Multi-Axis Differential Optical Absorption Spectroscopy instruments in four typical cities in China: Beijing, Lanzhou, Guangzhou, and Hefei. According to the comparison between estimated PM_2.5 and in situ measurements on the ground surface in the four cities, the eXtreme Gradient Boosting model has the best estimation performance, with the Pearson correlation coefficient reaching 0.91. In addition, the in situ instrument mounted on the meteorological observation tower in Beijing was used to validate the estimated PM_2.5 profile, and the Pearson correlation coefficient at each height was greater than 0.7. The average PM_2.5 vertical profiles in the four typical cities all show an exponential pattern. In Beijing and Guangzhou, PM_2.5 can diffuse to high altitudes between 500 and 1000 m; in Lanzhou, it can diffuse to around 1500 m, while it is primarily distributed between the near surface and 500 m in Hefei. Based on the vertical distribution of PM_2.5 mass concentration in Beijing, a high-altitude PM_2.5 pollutant transport event was identified from January 19th to 21st, 2021, which was not detected by ground-based in situ instruments. During this process, PM_2.5 was transported from the 200 to 1500 m altitude level and then sank to the near surface, causing the concentration on the ground surface to continuously increase. The sinking process contributes to approximately 7% of the ground surface PM_2.5 every hour.

Profiles of aerosol and trace gases are crucial for assessing air pollution changes, identifying the high‐altitude transport of pollutants, and providing a foundation for tracing pollution sources. This study introduces the USTC Parameterized Retrieval Method (USTC‐PRM), an algorithm for retrieving profiles of aerosol extinction and trace gas concentration from MAX‐DOAS measurements. Using the Radiative Transfer Model (RTM), we evaluate the impact of various observation geometries and profile shapes on the air mass factor (AMF) and establish the look‐up table (LUT). USTC‐PRM overcomes the underestimation of high‐altitude aerosols by optimal estimation method (OEM) since it does not rely on prior profiles. The correlation between the retrieved AOD and AERONET AOD is 0.896, compared to 0.826 for the contrasted OEM. For trace gas retrieval, we propose a real‐time LUT establishment method based on retrieved aerosol profiles, significantly reducing memory requirements by over 90% (7.8 GB) and improving the correlation with in situ measurements from 0.867 to 0.911. Additionally, we introduce the concept of look‐up error table (LET) to quantify the AMF bias by retrieving it from LUT. We establish a quality evaluation system based on fitting results, LUT errors, and parameter statistics. Using synthetic data and long‐term MAX‐DOAS measurements, USTC‐PRM demonstrates high performance in retrieving profiles under various aerosol scenarios across high, medium, and low extinction levels, while also identifying abnormal situations, such as foggy and cloudy conditions. USTC‐PRM provides a robust, accurate and efficient approach for MAX‐DOAS profile retrieval, which can be utilized for studying regional transport and tracing atmospheric pollutants.

Monitoring the horizontal distribution of PM_2.5 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 PM_2.5 within cities. This study proposes a novel approach—the Spectral Analysis-based PM_2.5 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 PM_2.5 concentrations. This approach does not rely on meteorological data and enables multi-directional near-surface PM_2.5 monitoring using only a single independent instrument. SAPML bypasses the intermediate retrieval of aerosol extinction coefficients and directly estimates PM_2.5 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 PM_2.5 spatial distribution at both sites. The results revealed clear intra-urban heterogeneity, with higher PM_2.5 concentrations observed in the western industrial park area. During the same observation period, an east-to-west PM_2.5 pollution transport event was captured: PM_2.5 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 PM_2.5 distributions.

Studying the vertical and horizontal distribution of particulate matter at the hectometer scale in the atmosphere is essential for understanding its sources, transportation, and transmission and its impact on human health. In this study, a method was developed based on hyperspectral instrumentation to obtain both vertical and horizontal distributions of aerosol extinction by employing multiple azimuth angles, selecting optimized elevation angles, and reducing the acquisition time of individual spectra. This method employed observations from different azimuth angles to represent particulate matter concentrations in various directions. The correlation coefficient between the hyperspectral observations and in-situ measurement was 0.627. Observations indicated that the aerosol extinction profile followed an exponential decay, with most aerosols confined below 1 km, implying a likely origin from local near-surface emissions. The horizontal distribution indicated that the northeastern urban areas and the eastern rural areas were the primary regions with high concentrations of particulate matter. The observational evidence suggests the presence of two potential emission sources within the study area. Moreover, health risk results indicated that even within the same town, differences of particulate matter concentration and population density could lead to varying health exposure risks. For instance, in the 200° and 210° directions, which represent adjacent urban areas less than 1 km apart, the number of PM_2.5-related illness cases in the 210° direction was 20.83% higher than that in the 200° direction.