Restrictions on human activities remarkably reduced emissions of air pollutants in China during the COVID-19 lockdown periods. However, distinct responses of O₃ concentrations were observed across China. In the Beijing–Tianjin–Hebei (BTH) and Yangtze River Delta (YRD) regions, O₃ concentrations were enhanced by 90.21 and 71.79% from pre-lockdown to lockdown periods in 2020, significantly greater than the equivalent concentrations for the same periods over 2015–2019 (69.99 and 43.62%, p < 0.001). In contrast, a decline was detected (−1.1%) in the Pearl River Delta (PRD) region. To better understand the underlying causes for these inconsistent responses across China, we adopted the least absolute shrinkage and selection operator (Lasso) and ordinary linear squares (OLS) methods in this study. Statistical analysis indicated that a sharp decline in nitrogen dioxide (NO₂) was the major driver of enhanced O₃ in the BTH region as it is a NO_x-saturated region. In the YRD region, season-shift induced changes in the temperature/shortwave radiative flux, while lockdown induced declines in NO₂, attributable to the rise in O₃. In the PRD region, the slight drop in O₃ is attributed to the decreased intensity of radiation. The distinct regimes of the O₃ response to the COVID-19 lockdown in China offer important insights into different O₃ control strategies across China.

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.

The lack of vertical observation of reactive nitrogen oxides in agricultural areas has posed a significant challenge in fully understanding their sources and impacts on atmospheric oxidation. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations were conducted in the agricultural regions of the North China Plain (NCP) during the summer of 2019 to measure the vertical distributions of aerosols, nitrogen dioxide (NO₂), and nitrous acid (HONO). This study aimed at revealing the spatiotemporal distribution, sources, and environmental effects of reactive nitrogen oxides in the NCP agricultural areas. Our findings indicated that the vertical profiles of aerosols and NO₂ exhibited a near-Gaussian distribution, with distinct peak times occurring between 8:00–10:00 and 16:00–18:00. HONO reached its maximum concentration near the surface around 8:00 in the morning and decreased exponentially with altitude. After sunrise, the concentration of HONO rapidly decreased due to photolysis. Additionally, the potential source contribution function (PSCF) was used to evaluate the potential sources of air pollutants. The results indicated that the main potential pollution sources of aerosols were located in the southern part of the Hebei, Shanxi, Shandong, and Jiangsu provinces, while the potential pollution sources of NO₂ were concentrated in the Beijing–Tianjin–Hebei region. At altitudes exceeding 500 m, the heterogeneous reactions of NO₂ on aerosol surfaces were identified as one of the important contributors to the formation of HONO. Furthermore, we discussed the production rate of hydroxyl radicals (OH) from HONO photolysis. It was found that the production rate of OH from HONO photolysis decreased with altitude, with peaks occurring in the morning and late afternoon. This pattern was consistent with the variations in HONO concentration, indicating that HONO was the main contributor to OH production in the agricultural regions of the NCP. This study provides a new perspective on the sources of active nitrogen in agricultural regions and their contribution to atmospheric oxidation capacity from a vertical perspective.

This study examined air quality data collected from 2015 to 2023 across Shenyang, Dalian, Changchun, and Harbin to assess interannual and monthly variations in PM_2.5, PM₁₀, SO₂, NO₂, and O₃, along with their correlations, seasonal meteorological influences, and potential source regions. Annual mean concentrations of PM_2.5, PM₁₀, SO₂, and NO₂ declined substantially (by 39.9–79.3%), whereas O₃ showed a fluctuating pattern, remaining persistently high in the coastal city of Dalian. Seasonally, PM_2.5, PM₁₀, SO₂, and NO₂ concentrations peaked in winter and decreased in summer, while O₃ displayed the opposite trend. Particulate levels in Liaoning rebounded earlier in spring than in Jilin and Heilongjiang. Correlation analysis revealed strong positive relationships among particulate and gaseous pollutants, but O₃ generally exhibited negative correlations with other species. Haze events occurred mainly in winter, whereas complex pollution episodes were more frequent in summer. Meteorological analysis indicated that relative humidity was negatively correlated with PM_2.5, PM₁₀, SO₂, and NO₂ in summer but positively correlated in winter. Elevated temperatures outside the winter months promoted NO₂ dispersion and enhanced O₃ formation. Strong winds in spring and winter markedly reduced PM_2.5 and SO₂ levels, though this effect was less evident in Shenyang. WPSCF results identified significant cross-regional transport from the southwest contributing to PM_2.5, PM₁₀, and NO₂ during spring and winter, while O₃ was primarily affected by long-range transport in spring and only marginally in winter. In Dalian, sea–land breeze circulation further intensified transport processes in summer and autumn. Overall, this work provides an integrated, multi-year, and multi-city assessment of pollution dynamics, meteorological drivers, and transboundary transport in Northeast China, offering new insights into regional air quality improvement and its spatial heterogeneity relative to other regions of China.