Air quality is strongly influenced by both local emissions and regional transport. Atmospheric chemical transport models can distinguish between emissions and regional transport sources in air pollutant concentrations. However, quantifying model inventories is challenging due to emission changes caused by the recent strict control measures taken by the Chinese government. In this study, we use NO₂ column observations from the Tropospheric Monitoring Instrument to retrieve top-down nitrogen oxide (NO_X) emissions and quantify the contributions of local emissions and regional transport to NO_x in Beijing (BJ), from 1 November 2018 to 28 February 2019 (W_2018) and 1 November 2019 to 29 February 2020 (W_2019). In W_2018 and W_2019, the BJ bottom-up NO_X emissions from the multi-resolution emission inventory for China in 2017 were overestimated by 11.8% and 40.5%, respectively, and the input of NO_X from other cities to BJ was overestimated by 10.9% and 51.6%, respectively. The simulation using our adjusted inventory exhibited a much higher spatial agreement (slope = 1.0, R² = 0.79) and reduced a mean relative error by 45% compared to those of bottom-up NO_X emissions. The top-down inventory indicated that (1) city boundary transport contributes approximately 40% of the NO_X concentration in BJ; (2) in W_2019, NO_X emissions and transport in BJ decreased by 20.4% and 17.2%, respectively, compared to those of W_2018; (3) in W_2019, NO_X influx substantially decreased (−699 g/s) in BJ compared to that of W_2018 despite negative meteorological conditions that should have increased NOx influx by +503 g/s. Overall, the contribution of intercity input to NOx in BJ has declined with decreasing emissions in the surrounding cities due to regional cooperative control measures, and the role of local emissions in BJ NOx levels was more prominent. Our findings indicate that local emissions may play vital roles in regional center city air quality.

The polarization–Raman Lidar combined sun photometer is a powerful method for separating dust and urban haze backscatter, extinction, and mass concentrations. The observation was performed in Beijing during the 2019 National Day parade, the particle depolarization ratio at 532 nm and Lidar ratio at 355 nm are 0.13 ± 0.05 and 52 ± 9 sr, respectively. It is the typical value of a mixture of dust and urban haze. Here we quantify the contributions of cross-regional transported natural dust and urban haze mass concentrations to Beijing’s air quality. There is a significant correlation between urban haze mass concentrations and surface PM₂.₅ (R = 0.74, p < 0.01). The contributions of local emissions to air pollution during the 2019 National Day parade were insignificant, mainly affected by regional transport, including urban haze in North China plain and Guanzhong Plain (Hebei, Tianjin, Shandong, and Shanxi), and dust aerosol in Mongolia regions and Xinjiang. Moreover, the trans-regional transmission of natural dust dominated the air pollution during the 2019 National Day parade, with a relative contribution to particulate matter mass concentrations exceeding 74% below 4 km. Our results highlight that controlling anthropogenic emissions over regional scales and focusing on the effects of natural dust is crucial and effective to improve Beijing’s air quality.

Limitations in the current capability of monitoring PM_2.5 adversely impact air quality management and health risk assessment of PM_2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM_2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, satellite remote sensing AODs are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM_2.5 cannot be obtained with satellite remote sensing under cloudy/hazy conditions or during nighttime. In this work, we introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We quantified the quantitative impact of input variables on the results using sensitivity and visual analysis of the model. This technique provides ground-level PM_2.5 concentrations with a high spatial resolution (0.01°) and 24-h temporal coverage, hour-by-hour, complete coverage. In central and eastern China, the 10-fold cross-validation results show that R² is between 0.8 and 0.9, and RMSE is between 6 and 26 (µg m⁻³). The relative error varies in different concentration ranges and is generally less than 20%. Better constrained spatiotemporal distributions of PM_2.5 concentrations will contribute to improving health effects studies, atmospheric emission estimates, and air quality predictions.

Urban air pollution has become a regional environmental problem. In order to explore whether island areas were affected by the urban development of surrounding areas, in this paper, we systematically study the vertical distribution characteristics of atmospheric components, meteorological drivers, potential pollution sources, and the population health risks of fine particulate matter in island cities in China. The vertical profiles of three atmospheric pollutants (aerosols, NO₂, and HCHO) in the lower troposphere of Huaniao Island in the East China Sea (ECS) were obtained using ground-based multi-axial differential optical absorption spectroscopy (MAX-DOAS). The results show that the aerosol extinction coefficients, NO₂, and HCHO were primarily distributed at altitudes below 1 km, and the atmospheric pollutants in Zhoushan were obviously affected by high-altitude transfer. The main meteorological driving factors of aerosols, NO₂, and HCHO were different at different altitudes. The key factor contributing to the high column concentrations of NO₂ and HCHO in the upper air (greater than 400 m) was the transport of pollutants brought about by changes in wind speed. By exploring the main potential sources of atmospheric pollutants, it was found that the main sources of aerosols, NO₂, and HCHO are coastal cities in the Yangtze River Delta, including southeast Zhejiang Province, southeast Fujian Province, Shanghai, ECS, and the Yellow Sea. Compared with aerosols and HCHO, local primary emissions are an important source of NO₂, which are mainly related to industrial activities in Zhoushan Port. In addition, using the expose-response function model, the number of attributable cases of PM_2.5 air pollution in Zhoushan City in 2019 accounted for 6.58% of the total population. This study enriches our understanding of the vertical distribution characteristics of atmospheric composition and health risk assessment on Chinese islands.

Due to the difference of industrialization degree and meteorological conditions, there are obvious differences in the composition of air pollution between islands and inland areas. With Zhoushan (ZS) and Nanjing (NJ) representing islands and inland cities in the Yangtze River Delta, the differences in vertical distribution of atmospheric components were investigated. A combination of multi-axial differential optical absorption spectroscopy (MAX-DOAS), weather research and forecasting (WRF), and potential source contribution function (PSCF) models were used to obtain vertical distribution data for aerosols, nitrogen dioxide (NO₂) and formaldehyde (HCHO), meteorological factors, and pollution sources in summer 2019. The findings indicate that, except for the aerosol extinction coefficient (AE), the atmospheric composition at the ZS site was not significantly stratified. However, the AE, NO₂, and HCHO at NJ all displayed a decreasing trend with altitude. Here is the interesting finding that the ZS site has a higher AE value than the NJ site, while NJ displays higher NO₂ and HCHO columns than the ZS site. This discrepancy was primarily attributable to Zhoushan City’s extremely low traffic emissions when compared to inland cities. In addition, HCHO in the YRD region was significantly affected by human activities. Analysis of potential pollution sources found that regional transport contributed to differences in atmospheric composition at different altitudes in different regions. Aerosols, NO₂, and HCHO in Nanjing were significantly affected by transport in inland areas. Aerosols in Zhoushan were easily affected by transport in the Yellow Sea and East China Sea, and NO₂ and HCHO were significantly affected by transport contributions from surrounding areas in inland areas. The study strongly suggests that land and sea breezes play an important role in the vertical distribution of aerosols over island regions.