Air pollution forecasting with multivariate interval decomposition ensemble approach

As the air pollution particulate matter (PM10) is affected by a variety of factors, especially meteorological factors, resulting in a high degree of complexity and volatility of PM10. However, the existing research does not well deal with the multi-factor PM10 forecasting and is limited to the deterministic point forecast of PM10 concentration and does not consider the interval forecast related to uncertainty. To solve these issues, a novel multivariate interval decomposition ensemble approach is proposed for PM10 concentration forecasting, integrating multifactor selection, data decomposition, intelligent forecasting network and evaluation system. The stability and robustness of this approach have been tested in three cities with different economic characteristics in China, and the results show that our proposed approach is superior other benchmark models in point forecasting and 80% interval forecasting. Our proposed approach is a promising PM10 concentration forecasting approach, which can enable the government to accurately forecast PM10 concentration and make more effective measures to control and manage the adverse effects of air pollution on the current economy and health.