The influence of regional transport on aerosol pollution has been explored in previous studies based on numerical simulation or surface observation. Nevertheless, owing to inhomogeneous vertical distribution of air pollutants, vertical observations should be conducted for a comprehensive understanding of regional transport. Here we obtained the vertical profiles of aerosol and its precursors using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) at the Nancheng site in suburban Beijing on the southwest transport pathway of the Beijing-Tianjin-Hebei (BTH) region, China, and then estimated the vertical profiles of transport fluxes in the southwest-northeast direction. The maximum net transport fluxes per unit cross-sectional area, calculated as pollutant concentration multiply by wind speed, of aerosol extinction coefficient (AEC), NO₂, SO₂ and HCHO were 0.98 km⁻¹ m s⁻¹, 24, 14 and 8.0 μg m⁻² s⁻¹ from southwest to northeast, which occurred in the 200–300 m, 100–200 m, 500–600 m and 500–600 m layers, respectively, due to much higher pollutant concentrations during southwest transport than during northeast transport in these layers. The average net column transport fluxes were 1200 km⁻¹ m² s⁻¹, 38, 26 and 15 mg m⁻¹ s⁻¹ from southwest to northeast for AEC, NO₂, SO₂ and HCHO, respectively, in which the fluxes in the surface layer (0–100 m) accounted for only 2.3%–4.2%. Evaluation only based on surface observation would underestimate the influence of the transport from southwest cities to Beijing. Northeast or weak southwest transports dominated in clean conditions with PM₂.₅ <75 μg m⁻³ and intense southwest transport dominated in polluted conditions with PM₂.₅ >75 μg m⁻³. Southwest transport through the middle boundary layer was a trigger factor for aerosol pollution events in urban Beijing, because it not only directly bringing air pollutants, but also induced an inverse structure of aerosols, which resulted in stronger atmospheric stability and aggravated air pollution in urban Beijing.

Various precursor emissions and chemical mechanisms for secondary organic aerosol (SOA) formation were incorporated into a regional air quality model system (RAQMS) and applied to investigate the distribution, composition, and source contribution of SOA over east China in summer 2018. Model comparison against a variety of observations at a national scale demonstrated that the model was able to reasonably reproduce meteorological variables, O₃ and PM₂.₅ concentrations, and the model simulated SOA concentration generally agreed with observations, with the overall NMB of 7.0% and R of 0.4 in 10 cities over east China. The simulated period-mean SOA concentrations of 4–15 μg m⁻³ were mainly distributed over the North China Plain (NCP), the middle and lower reaches of the Yangtze River and Chongqing district. SOA dominated organic aerosol (OA) over China in summertime (90%). The percentage contributions to SOA from ASOA (SOA produced from anthropogenic volatile organic compounds (AVOC)), BSOA (SOA produced from biogenic volatile organic compounds (BVOC)), DSOA (SOA produced from aqueous uptake of glyoxal and methylglyoxal) and S/I-SOA (SOA produced from semi-volatile and intermediate volatile organic compounds) were estimated to be 48.3%, 28.6%, 14.3%, and 8.8% respectively, over east China in summertime. In terms of domain and period average, ASOA contributed most to SOA (59%) in north China, while BSOA contributed most to SOA (37.3%) in northeast China. The percentage contribution of DSOA to SOA reached 21.5% in southwest China. S/I-SOA accounted for approximately 10% of SOA in most areas of east China. This study reveals that while AVOC dominates SOA formation on average over east China, the SOA source contributions differ considerably in different regions of China. BVOC makes the same contribution to SOA formation as AVOC in northeast China and southwest China, where forest coverage and BVOC emission are higher and anthropogenic emissions are relatively low, highlighting the significant role of BVOC in summer SOA formation in China.

Organic aerosol (OA) emitted from biomass burning (BB) impacts air quality and global radiation balance. However, the comprehensive characterization of OA remains poorly understood because of the complex evolutionary behavior of OA in atmospheric processes. In this work, smoke particles were generated from rice straw combustion. The effect of OH radicals photooxidation on size distribution, light absorption, and molecular compositions of smoke particles was systematically investigated. The results showed that the median diameters of smoke particles increased by a factor of approximately 1.2 after photooxidation. In the particle compositions, although both non-polar fractions (n-hexane-soluble organic carbon, HSOC) and polar fractions (water-soluble organic carbon, WSOC) underwent photobleaching after aging, the photobleaching properties of HSOC (1.87–2.19) was always higher than that of WSOC (1.52–1.33). Besides, the light-absorbing properties of HSOC were higher than that of WSOC, showing a factor of approximately 1.75 times for mass absorption efficiency at 365 nm (MAE₃₆₅). Consequently, the simple forcing efficiency (SFE) caused by absorption showed that HSOC has higher radiation effects than WSOC. After photooxidation, the concentration of 16 PAHs in HSOC fractions significantly decreased by 15.3%–72.5%. In WSOC fractions, the content of CHO, CHONS, and CHOS compounds decreased slightly, while the content of CHON compounds increased. Meantime, the variations in molecular properties supported the decrease in light absorption of WSOC fractions. These results reveal the aging behavior of smoke particles, then stress the importance of non-polar organic fractions in particles, providing new insights into understanding the atmospheric pollution caused by BB smoke particles.

PM₂.₅ (fine particulate matter with aerodynamics diameter <2.5 μm) is the most important component of air pollutants, and has a significant impact on the atmospheric environment and human health. Using satellite remote sensing aerosol optical depth (AOD) to explore the hourly ground PM₂.₅ distribution is very helpful for PM₂.₅ pollution control. In this study, Himawari-8 AOD, meteorological factors, geographic information, and a new deep forest model were used to construct an AOD-PM₂.₅ estimation model in China. Hourly cross-validation results indicated that estimated PM₂.₅ values were consistent with the site observation values, with an R² range of 0.82–0.91 and root mean square error (RMSE) of 8.79–14.72 μg/m³, among which the model performance reached the optimum value between 13:00 and 15:00 Beijing time (R² > 0.9). Analysis of the correlation coefficient between important features and PM₂.₅ showed that the model performance was related to AOD and affected by meteorological factors, particularly the boundary layer height. Deep forest can detect diurnal variations in pollutant concentrations, which were higher in the morning, peaked at 10:00–11:00, and then began to decline. High-resolution PM₂.₅ concentrations derived from the deep forest model revealed that some cities in China are seriously polluted, such as Xi ‘an, Wuhan, and Chengdu. Our model can also capture the direction of PM₂.₅, which conforms to the wind field. The results indicated that due to the combined effect of wind and mountains, some areas in China experience PM₂.₅ pollution accumulation during spring and winter. We need to be vigilant because these areas with high PM₂.₅ concentrations typically occur near cities.

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM₁₀) and <2.5 μm (PM₂.₅) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM₁₀ and PM₂.₅ were R² = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM₁₀ and PM₂.₅ were R² = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

Intermediate-volatility and semi-volatile organic compounds (I/SVOCs) are key precursors of secondary organic aerosol (SOA). However, the comprehensive characterization of I/SVOCs has long been an analytical challenge. Here, we develop a novel method of speciating and quantifying I/SVOCs using two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-ToF-MS) by constructing class-screening programs based on their characteristic fragments and mass spectrum patterns. Using this new approach, we then present a comprehensive analysis of gaseous I/SVOC emissions from heavy-duty diesel vehicles (HDDVs). Over three-thousand compounds are identified and classified into twenty-one categories. The dominant compound groups of I/SVCOs emitted by HDDVs are alkanes (including normal and branched alkanes, 37–66%), benzylic alcohols (7–20%), alkenes (3–11%), cycloalkanes (3–9%), and benzylic ketones (1–4%). Oxygenated I/SVOCs (O–I/SVOCs, e.g., benzylic alcohols and ketones) are first quantified and account for >20% of the total I/SVOC mass. Advanced aftertreatment devices largely reduce the total I/SVOC emissions but increase the proportion of O–I/SVOCs. With the speciation data, we successfully map the I/SVOCs into the two-dimensional volatility basis set space, which facilitates a better estimation of SOA. As aging time goes by, approximate 45% difference between the two scenarios after seven-day aging is observed, which confirms the significant impact of speciated I/SVOC emission data on SOA prediction.