Media is an important explanatory variable in the research on the social amplification of risk; the public often perceives risks in social life through media consumption such as by relying on, using, and trusting the media dialectically. Currently, researchers have not reached a consistent conclusion about the relationship between media consumption and public risk perception (PRP). This study uses meta-analysis to integrate empirical literature and conducts a more in-depth and systematic analysis of the relationship between media consumption and PRP. The results show that (1) there is no significant correlation between general media use (GMU) and PRP, there is positive relationship between selective media exposure (SMEX) and PRP, and there is no significant correlation between media source credibility (MSC) and PRP. (2) Further meta-regression and subgroup analyses show that country type significantly moderates the relationship between GMU and PRP. Compared with high uncertainty avoidance countries, the relationship between GMU and PRP in low uncertainty avoidance countries is stronger. Moreover, risk type significantly moderates the relationship between SMEX and PRP. Compared with terrorist crime, environmental, accident and other types of risks, the SMEX is more positively correlated with public's food safety and health risk perception. In addition, media type moderates the relationship between MSC and PRP. Compared with traditional media and internet social media, the MSC of mixed media have a stronger positive relationship with PRP.

Practical forecasting of air pollution components is important for monitoring and providing early warning. The accurate prediction of pollutant concentrations remains a challenging issue owing to the inherent complexity and volatility of pollutant series. In this study, a novel hybrid forecasting method for hourly pollutant concentration prediction that comprises a mode decomposition-recombination technique and a deep learning approach was designed. First, a Hampel filter was used to remove outliers from the original data. Subsequently, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) is employed to divide the original pollution data into a finite set of intrinsic mode function (IMF) components. Further, a feature extraction method based on sample-fuzzy entropy and K-means is proposed to reconstruct the main features of IMFs. In conclusion, a deterministic forecasting model based on long short-term memory (LSTM) was established for pollutant prediction. The empirical results of six-hourly pollutant concentrations from Baoding illustrate that the proposed decomposition-recombination technique can effectively handle nonlinear and highly volatile pollution data. The developed hybrid model is significantly better than other comparative models, which is promising for early air quality warning systems.

Practical analysis and forecasting of PM2.5 concentrations is complex and challenging owing to the volatility and non‐stationarity of PM2.5 series. Most previous studies mainly focused on deterministic predictions, whereas the uncertainty in the prediction is not considered. In this study, a novel uncertainty analysis–forecasting system comprising distribution function analysis, intelligent deterministic prediction, and interval prediction is designed. Based on the characteristics of PM2.5 series, 16 hybrid models composed of various distribution functions and swarm optimization algorithms are selected to determine the exact PM2.5 distribution. Subsequently, a hybrid deterministic forecasting model based on a novel decomposition–ensemble framework is established for PM2.5 prediction. Regarding uncertainty analysis, interval prediction is established to provide uncertain information required for decision–making based on the optimal distribution functions and deterministic prediction results. PM2.5 concentration series obtained from three cities in China are used to conduct an empirical study. The empirical results show that the proposed system can achieve better prediction results than other comparable models as well as provide meaningful and practical quantification of future PM trends. Hence, the system can provide more constructive suggestions for government administrators and the public.