A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China

Air pollution has received more attention from many countries and scientists due to its high threat to human health. However, air pollution prediction remains a challenging task because of its nonstationarity, randomness, and nonlinearity. In this research, a novel hybrid system is successfully developed for PM2.5 concentration prediction and its application in health effects and economic loss assessment. First, an efficient data mining method is adopted to capture and extract the primary characteristic of PM2.5 dataset and alleviate the noises' adverse effects. Second, Harris hawks optimization algorithm is introduced to tune the extreme learning machine model with high prediction accuracy, then the optimized extreme learning machine can be established to obtain the forecasting values of PM2.5 series. Next, PM2.5-related health effects and economic costs was estimated based on the predicted PM2.5 values, the related health effects, and environmental value assessment methods. Several experiments are designed using three daily PM2.5 datasets from Beijing, Tianjin, and Shijiazhuang. Lastly, the corresponding experimental results showed that this proposed system can not only provide early warning information for environmental management, assist in the formulation of effective measures to reduce air pollutant emissions, and prevent health problems but also help for further research and application in different fields, such as health issues due to PM2.5 pollutant.

Automated summary

This research successfully developed a novel hybrid system for PM2.5 concentration prediction and its application in health effects and economic loss assessment. Through data mining and optimization algorithm, the system can accurately forecast PM2.5 concentration, provide early warning information for environmental management and assist in tackling health issues.