High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM_2.5 Distribution in Beijing, China.

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 R² of the winter and summer models were 0.951 and 0.628, respectively. Meteorol

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC8201188 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature