We investigated the seasonal and diurnal characteristics of volatile organic compound (VOC) concentrations in Shenyang, China, using the whole-year hourly data of 52 types of VOC at three sites over the year 2019. The photochemical reactivities of VOCs were also studied by analyzing the influence of VOCs on ozone and secondary organic aerosol (SOA) formation potential and the hydroxyl radical consumption rate. It is shown that the order of VOC concentrations from high to low is alkanes, alkynes, alkenes, and aromatic hydrocarbons. For various types of VOCs, the maximum appeared in the morning and at night, whereas the minimum appeared in the afternoon. The contributions of VOCs to ozone formation potential are highest for aromatic hydrocarbons with a value of 78%, followed by alkenes and alkanes, among which toluene and isoprene contributed the most. The contributions of VOCs to SOA formation potential are also highest for aromatic hydrocarbons with a value of 94%, followed by alkanes and alkenes, among which the contributions of toluene and benzene add up to over 70%. Being the most active type of VOCs in atmospheric chemical reactions, aromatic hydrocarbons are the dominant contributor to the formation of both ozone and SOA, and therefore being able to control of the use of a large number of solvents and vehicle exhaust emissions would be an effective way to regulate the formation of ozone and SOA in Shenyang.

With repeated changes to local crop residue disposal policies in recent years, the distribution and density of crop residue fire events have been irregular in both space and time. A nonlinear and complex relationship between natural and anthropogenic factors often affects the occurrence of crop residue field fires. To overcome this difficulty, we used the Himawari-8 wildfire data for 2018–2021 to verify the likelihood of crop residue fires against the results of three machine learning methods: logistic regression, backpropagation neural network (BPNN), and decision tree (DT). The results showed the verified accuracies of BPNN and DT methods were 68.59 and 79.59%. Meantime, the sensitivity and specificity of DT performed the best, with the value of area under the curve (AUC) 0.82. Furthermore, among all the influencing factors, open burning prohibition constraints, relative humidity and air pressure showed significant correlations with open burning events. As such, BPNN and DT could accurately forecast the occurrence of agricultural fires. The results presented here may improve the ability to forecast agricultural field fires and provide important advances in understanding fire formation in Northeastern China. They would also provide scientific and technical support for crop fire control and air quality forecasting.

The characteristics of turbulence in the planetary boundary layer (PBL) and the aerosol optical and radiative properties during haze and haze–fog mixed episodes on 22–27 January 2021, in Shenyang, a provincial city in Northeast China, were analyzed using meteorological and aerosol observations. During the haze episode, the hourly mean PM_2.5 concentration reached a maximum of 337 µg m⁻³ and visibility decreased to 1.6 km. The PM_2.5 concentration decreased gradually during the haze–fog mixed episode as a result of the scavenging effects of fog, but visibility mostly remained below 1 km owing to high ambient relative humidity (>90%). During the haze–fog mixed episode, an increasing proportion of PM_2.5 led to a higher ratio of the backward to the total scattering coefficient. As fog occurred, downward shortwave radiation arriving at the surface was significantly reduced, and upward longwave radiation increased and almost equaled the downward longwave radiation, which can be used as a good indicator for distinguishing haze and fog. Mechanical turbulence was weak during both episodes, and latent heat flux varied within a wider range during the haze–fog mixed episode. The PBL dynamic structure affected the vertical distribution of aerosols/fog droplets. Aerosol-rich layers appeared at altitudes below 0.5 km and above 0.6 km during the haze episode. The elevated aerosol layer was related to the aerosol transport from upstream polluted areas caused by strong upper-level turbulence, and it began to mix vertically after sunrise because of convective turbulence. Aerosols and fog droplets were mostly trapped in a shallower PBL with a height of 0.2–0.4 km during the haze–fog mixed episode because of weaker turbulence.

The influence of urbanization on the frequent winter aerosol pollution events in Northeast China is not fully understood. The Weather Research and Forecasting Model with Chemistry (WRF–Chem) coupled with urban canopy (UC) models was used to simulate the impact of urbanization on an aerosol pollution process in the Central Liaoning city cluster (CLCC), China. To investigate the main mechanisms of urban expansion and UC on the winter atmospheric environment and the atmospheric diffusion capacity (ADC) in the CLCC, three simulation cases were designed using land-use datasets from different periods and different UC schemes. A comparative analysis of the simulation results showed that the land-use change (LU) and both LU and UC (LUUC) effects lead to higher surface temperature and lower relative humidity and wind speed in the CLCC by decreasing surface albedo, increasing sensible heat flux, and increasing surface roughness, with a spatial distribution similar to the distribution of LU. The thermal effect leads to an increase in atmospheric instability, an increase in boundary layer height and diffusion coefficient, and an increase in the ADC. The LU and LUUC effects lead to a significant decrease in near-surface PM_2.5 concentrations in the CLCC due to changes in meteorological conditions and ADC within the boundary layer. The reduction in surface PM_2.5 concentrations due to the LU effect is stronger at night than during daytime, while the LUUC effect leads to a greater reduction in surface PM_2.5 concentrations during the day, mainly due to stronger diffusion and dilution caused by the effect of urban turbulence within different levels caused by the more complex UC scheme. In this study, the LU and LUUC effects result in greater thermal than dynamic effects, and both have a negative impact on surface PM_2.5 concentrations, but redistribute pollutants from the lower urban troposphere to higher altitudes.

Dust storms have large impacts on air quality and meteorological elements; however, their relationships with atmospheric greenhouse gases (e.g., CO₂) and radiation components remain uncertain. In this study, the co-variation of dust and CO₂ concentrations and its possible influencing mechanism are examined using observations at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station along with simulations of the Vegetation Photosynthesis and Respiration Model coupled with the Weather Research and Forecasting model (WRF-VPRM), during two dust storm events on 15 and 28 March 2021. During these events, hourly CO₂ concentrations decreased by 40–50 ppm at SDZ while dust concentrations increased to 1240.6 and 712.4 µg m⁻³. The elevated dust increased diffusive shortwave irradiance by 50–60% and decreased direct shortwave irradiance by ~60% along with clouds. The dust events were attributed to the passages of two cold front systems over northern China. At SDZ, during the frontal passages, wind speed increased by 3–6 m s⁻¹, and relative humidity decreased by 50–60%. The CO₂ variations associated with the frontal systems were captured by the WRF-VPRM despite the overestimated surface CO₂ level at SDZ. Biogenic CO₂ flux plays an indistinctive role in the large CO₂ variation at SDZ, as it is weak during the non-growing season. The cold fronts pushed polluted air southeastward over the North China Plain and replaced it with low-CO₂ air from Northwest China, leading to the decline in CO₂. These findings demonstrate that mesoscale synoptic conditions significantly affect the regional transport and dispersion of CO₂, which can influence the prediction of terrestrial carbon balance on a regional scale.