Prediction of Indoor Harmful Gas Based on Information Revision GM(1,1) Model

Abstract

Air quality as an important index of living environment is attended widely. In recent years, harmful gas that is released by new-fashioned building and decorative materials has become the main factor, affecting indoor air quality and threading to human health. How to predict harmful gas concentration will have important effect for setting down preventive methods reasonably and avoiding air pollution effectively. The paper optimizes traditional GM (1,1) model and constitutes information revision model. Practical test shows precision of predicted results is better than traditional model after ameliorating. Predicted results can reflect the trend of harmful gas pollution and provide technical support for pollution prevention of building interior harmful gas.