Abstract: Monitoring air quality is important for pollution evaluation,harm reduction and environmental protection.However,since the number of air quality monitoring stations is extremely limited and air quality varies non-linearly with the change of location,spatial estimation of air quality (i.e.,estimating the air quality of any location without an air quality monitor station) is a challenging task.Currently,the most state-of-the-art spatial estimation methods for air quality take the factors such as traffic flow,human mobility and POI into account,and build estimation models based on machine learning.However,there are still some limitations in these methods.On one hand,the considered factors mainly reflect the characteristics of urban area,so these methods are constrained to be used in urban area.On the other hand,these methods train models based on features directly extracted from the factors without refinement.Aiming at these problems,this paper proposed a spatial estimation method of air quality based on terrain factors.First,terrain database is established and terrain features are extracted.Then,the original terrain features are deeply converted based on an ensemble decision tree model.Finally,a regression model is trained based on factorization machine.The experiments on real datasets suggest that the proposed method has advantage in terms of estimating the air quality over the areas with natural terrain (e.g.,highland,forest,water).

Key words: Air quality index, Ensemble decision tree model, Spatial estimation, Terrain factor