Ultrafine particles with a diameter below 1 μm are strongly linked to traffic and industrial emissions, causing a growing global health concern. In order to reveal the characteristics of ultrafine particles in central China, which makes up the sparse research in industrial cities of a developing country, particle number concentrations (PNC) together with meteorological parameters and concentrations of trace gases were measured over one year in Wuhan. The number concentration of ultrafine particles peaked in winter and was the lowest in summer across the entire size range monitored. Further, particles with a diameter smaller than 30 nm increased dramatically in concentration with decreasing diameter. The monthly averaged number concentrations of particles discriminated in three size ranges formed a near- inverse parabolic distribution peaking in January. This trend is supported by a negative correlation between PNC and precipitation, temperature, and mixing layer height, which emphasizes the effect of these meteorological parameters on scouring, convection, and diffusion of particles. However, since wind not only disperses particulate matter but also brings in exogenous particles, wind speed plays an equivocal role in particle number concentrations. The diurnal analysis indicates that hourly measurements of trace gases concentrations could be used as a proxy for dense industrial activities and to reveal some complex chemical reactions. The results of this study offer reasonable estimations of particle impacts and provide references for policymaking of emission control in the industrial cities of developing countries.

Analyzing people’s opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for identifying the psychological characteristics of social network users. However, most studies focus on identifying the sentiments carried in the micro-blogging text and there is no ideal calculation method for users’ real emotional states. In this study, the Profile of Mood State (POMS) is used to characterize users’ real mood states and a regression model is built based on cyber psychometrics and a multitask method. Features of users’ online behavior are selected through structured statistics and unstructured text. Results of the correlation analysis of different features demonstrate that users’ real mood states are not only characterized by the messages expressed through texts, but also correlate with statistical features of online behavior. The sentiment-related features in different timespans indicate different correlations with the real mood state. The comparison among various regression algorithms suggests that the multitask learning method outperforms other algorithms in root-mean-square error and error ratio. Therefore, this cyber psychometrics method based on multitask learning that integrates structural features and temporal emotional information could effectively obtain users’ real mood states and could be applied in further psychological measurements and predictions.