Abstract
PM2.5 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 PM2.5 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. 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.
Highlights
Models with point of interests (POIs) Characteristics of the PM2.5 Distribution in Beijing, China
Yang et al [17] developed land use regression (LUR) models to predict ultrafine particle concentrations in London, and the results showed that the LUR models had moderate to good performances within these areas
The results showed that on working days or non-working days in the different seasons, different POIs had a certain influence on the PM2.5 concentration, and their relationships to the pollutants at specific moments had diversity
Summary
Models with POI Characteristics of the PM2.5 Distribution in Beijing, China. Int. Studies that investigate PM2.5 are of great significance for local pollution prevention and targeted health prevention measures. Various modeling methods, such as spatial interpolation [6], the atmospheric dispersion model [7], satellite inversion [8], and deep learning [9,10,11], have been used to simulate the distribution of regional pollutants. The land use regression (LUR) model is a multivariate regression modeling method based on the observation concentration of air pollutants and its surrounding geographical factors. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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