High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM 2.5 Distribution in Beijing, China

Yan Zhang, Hongguang Cheng, Di Huang and Chunbao Fu
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Yan Zhang: School of Environment, Beijing Normal University, Beijing 100875, China
Hongguang Cheng: School of Environment, Beijing Normal University, Beijing 100875, China
Di Huang: School of Environment, Beijing Normal University, Beijing 100875, China
Chunbao Fu: School of Environment, Beijing Normal University, Beijing 100875, China

IJERPH, 2021, vol. 18, issue 11, 1-19

Abstract: PM 2.5 is one of the primary components of air pollutants, and it has wide impacts on human health. Land use regression models have the typical disadvantage of low temporal resolution. In this study, various point of interests (POIs) variables are added to the usual predictive variables of the general land use regression (LUR) model to improve the temporal resolution. Hourly PM 2.5 concentration data from 35 monitoring stations in Beijing, China, were used. Twelve LUR models were developed for working days and non-working days of the heating season and non-heating season, respectively. The results showed that these models achieved good fitness in winter and summer, and the highest R 2 of the winter and summer models were 0.951 and 0.628, respectively. Meteorological factors, POIs, and roads factors were the most critical predictive variables in the models. This study also showed that POIs had time characteristics, and different types of POIs showed different explanations ranging from 5.5% to 41.2% of the models on working days or non-working days, respectively. Therefore, this study confirmed that POIs can greatly improve the temporal resolution of LUR models, which is significant for high precision exposure studies.

Keywords: particular matter; land use regression; point of interest; temporal resolution; exposure (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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