A Multi-Scale Method for PM2.5 Forecasting with Multi-Source Big Data

In the age of big data,the Internet big data can finely reflect public attention to air pollution,which greatly impact ambient PM2.5 concentrations;however,it has not been applied to PM2.5 prediction yet.Therefore,this study introduces such informative Internet big data as an effective predictor for PM2.5,in addition to other big data.To capture the multi-scale relationship between PM2.5 concentrations and multi-source big data,a novel multi-source big data and multi-scale forecasting methodology is proposed for PM2.5.Three major steps are taken:1)Multi-source big data process,to collect big data from different sources(e.g.,devices and Internet)and extract the hidden predictive features;2)Multi-scale analysis,to address the non-uniformity and nonalignment of timescales by withdrawing the scale-aligned modes hidden in multi-source data;3)PM2.5 prediction,entailing individual prediction at each timescale and ensemble prediction for the final results.The empirical study focuses on the top highly-polluted cities and shows that the proposed multi-source big data and multi-scale forecasting method outperforms its original forms(with neither big data nor multi-scale analysis),semi-extended variants(with big data and without multi-scale analysis)and similar counterparts(with big data but from a single source and multi-scale analysis)in accuracy.