Nonlinear regression models to forecast PM2.5 concentration
Abstract
Forecasting PM2.5 concentration is important to solving air pollution problems in Wuhan. This paper proposes a PM2.5 concentration forecast model based on nonlinear regression, including a single-value forecast model and an interval forecast model. The single-value forecast model can precisely forecast PM2.5 concentration for the next day, with forecast bias about 6 μg/m3 in goodness of fit analysis. The interval forecast model can efficiently forecast high-concentration and low-concentration days, which covers 60%-80% observed samples in model validation. Moreover, this paper combines the PM2.5 concentration forecast model with NCEP Climate Forecast System Version 2 to realize its forecast application, then develops NCEP CFS2's PM2.5 concentration forecast model to enhance forecast accuracy. The results indicate that the PM2.5 concentration forecast model has good capacity for independent forecasting.
Turn this paper into a lesson
ArcXiv compiles a structured reading guide from this paper's metadata: plain-English importance, contributions, prerequisite concepts, which sections to read first, flashcards, and a quiz. Grounded in the abstract, never invented.