Identifying regional service function from PM2.5 mass concentration throughout a city with non-negative tensor factorization approach

Hu, Dongmei; Zhou, Yang; Xu, Ming; Wu, Jianping; Du, Yiman; Song, Bingyue; Hu, K.

Identifying regional service function from PM2.5 mass concentration throughout a city with non-negative tensor factorization approach

2017

This paper examines the holistic viewpoint on pollution pattern from time, day, and region dimensions based on the public data of fine particle concentrations, which cover 35 ambient monitoring stations in Beijing firstly. According to data driven, non-negative tensor factorization (NTF) method is introduced to distinguish pollution patterns which could identify the area service function. Results show that five patterns are obtained and annotated as traffic, industrial, residential, commercial, and steady ones. Each type owns special characteristics on time basis or day basis. Furthermore, calculating the reconstruction correlation of tensors with respect to sites, time, and days approximately approaches to 0.95–0.96, and it can be employed with high evaluation values of the model. Comparing with the original classifications drew by land use, this method corresponds with the reality well for considering the changes of surrounding sources. Some commendations on public travel and controlling measures based on local pollution presented in this study can be provided for further decrease of fine particle and improvement of air quality.

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