# Nonlinear regression models to forecast PM$_{2.5}$ concentration in Wuhan, China

## TL;DR

This paper develops nonlinear regression models for predicting PM$_{2.5}$ levels in Wuhan, achieving precise daily forecasts and effectively capturing high and low pollution days, with integration of climate forecasts to improve accuracy.

## Contribution

It introduces a novel nonlinear regression approach for PM$_{2.5}$ forecasting, including both point and interval models, and combines them with climate system data for enhanced prediction accuracy.

## Key findings

- Single-value forecast bias about 6 μg/m^3
- Interval forecast covers 60%-80% observed samples
- Model demonstrates good independent forecasting capacity

## Abstract

Forecasting PM$_{2.5}$ concentration is important to solving air pollution problems in Wuhan. This paper proposes a PM$_{2.5}$ 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 PM$_{2.5}$ concentration for the next day, with forecast bias about 6 $\mu g/m^3$ 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 PM$_{2.5}$ concentration forecast model with NCEP Climate Forecast System Version 2 to realize its forecast application, then develops NCEP CFS2's PM$_{2.5}$ concentration forecast model to enhance forecast accuracy. The results indicate that the PM$_{2.5}$ concentration forecast model has good capacity for independent forecasting.

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Source: https://tomesphere.com/paper/2302.14505