Atmospheric aerosol contamination has caused widespread concern about human and environmental health. However, research about VOCs as an important precursor of secondary aerosols in ambient air is still limited. In this study, VOCs at sites from three typical functional areas in Hefei, China, were monitored using GC–MS/FID. The VOCs in ambient air from different functional areas showed significantly different characteristics. The highest concentrations and the biggest diurnal difference of VOCs were found in the High-tech Zone (industrial area) with serried emission sources. Additionally, lower VOC concentration was observed in Changjiang West Road, the center area of studied city. The VOC concentration in this area is strongly related to other pollutants. The composition of VOCs at all sampling sites showed certain common characteristics, i.e., alkanes, OVOCs, and halogenated hydrocarbons account for more than 75% of the total VOCs’ quality. The High-tech Zone with the highest concentration of VOCs also has the highest proportion of alkanes. Besides, the positive matrix factorization analysis results revealed that vehicle exhaust, LPG volatilization sources, and chemical solvents were the most important VOC emission sources in Hefei. In terms of the contribution of VOC components to the OFP at the three sites, the olefins and alkynes at the Changjiang West Road site and the Science Island site contribute the most significant proportion. In contrast, the OVOCs at the High-tech Zone site contribute the largest proportion.

Cloud vertical structures over the Tibetan Plateau (TP) and Eastern China Plains (ECP) were analyzed by using data in rainy seasons from 2006 to 2009, in order to clarify the cloud development over adjacent regions but with distinct topographies. Results indicate that the largest occurrences of cloud top height over the TP are at 7-8 km above mean sea level, which is about 4 km lower than that over the ECP. Mixed-phase clouds dominated more than 30% over the TP, while it is lower than 10% over the ECP. The infrequent mixed-phase clouds over the ECP are attributed to the unique dynamic and moisture situations over the downstream areas of the TP. Ice clouds have similar occurrences over the two regions. The prominent distinctions are manifested by the probability density of cloud thickness. The probability density of cloud thickness around 4–8 km is about 2% higher over the TP than the ECP. However, there is almost no ice cloud thicker than 10 km over the TP, while it is about 1% over the ECP. Compared with those over the ECP, every cloud layer within multilayered clouds is generally higher and thinner over the TP, which is closely related to the elevated surface and the resulting thinner troposphere. The significant differences in cloud vertical structures between the TP and the ECP present in this study emphasize that topographical characteristics and the resulting moisture and circulation conditions have strong impacts on the cloud vertical structures.

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To improve the accuracy of atmospheric visibility (V) prediction based on machine learning in different pollution scenarios, a new atmospheric visibility prediction method based on the stacking fusion model (VSFM) is established in this paper. The new method uses the stacking strategy to fuse two base learners—eXtreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM)—to optimize prediction accuracy. Furthermore, seasonal feature importance evaluations and feature selection were utilized to optimize prediction accuracy in different seasons with different pollution sources. The new VSFM was applied to 1-year environmental and meteorological data measured in Qingdao, China. Compared to other traditional non-stacking models, the new VSFM improved precision during different seasons, especially in extremely low-visibility scenarios (V< 2 km). The TS score of the VSFM was significantly better than that of other models. For extremely low-visibility scenarios, the VSFM had a threat score (TS) of 0.5, while the best performance of other models was less than 0.27. The new method is promising for atmospheric visibility prediction under complex urban pollution conditions. The research results can also improve our understanding of the factors that influence urban visibility.

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