Land use regression modelling of PM2.5 spatial variations in different seasons in urban areas

Tuo Shi; Yuanman Hu; Miao Liu; Chunlin Li; Chuyi Zhang; Chong Liu

doi:10.1016/j.scitotenv.2020.140744

Land use regression modelling of PM_2.5 spatial variations in different seasons in urban areas

Sci Total Environ. 2020 Nov 15:743:140744. doi: 10.1016/j.scitotenv.2020.140744. Epub 2020 Jul 6.

Authors

Tuo Shi¹, Yuanman Hu², Miao Liu³, Chunlin Li⁴, Chuyi Zhang¹, Chong Liu¹

Affiliations

¹ CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang 110016, China; College of Resources and Environment, University of Chinese Academy of Sciences, No. 19, Yuquan Road, Beijing 100049, China.
² CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang 110016, China.
³ CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang 110016, China. Electronic address: lium@iae.ac.cn.
⁴ CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72, Wenhua Road, Shenyang 110016, China. Electronic address: lichunlin@iae.ac.cn.

PMID: 32663682
DOI: 10.1016/j.scitotenv.2020.140744

Abstract

As one of the principal components of haze, fine particulate matter (PM_2.5) has potential negative health effects, causing widespread concern. Identification of the pollutant spatial variation is a prerequisite of understanding ambient air pollution exposure and further improving air quality. Seven urban built-up areas in Liaoning central urban agglomeration (LCUA) were used for land use regression (LUR) modelling of PM_2.5 concentrations using small amounts of spatially aggregated data and to assess the model's seasonal consistency. LUR models explained 52-61% of the variation in the PM_2.5 concentrations at urban scales. The average building floor area was the key predictor in each model, and the percent water area was predictor with a negative coefficient. Good seasonal consistency was observed between the heating-seasonal model and annual average model, showing that the annual average PM_2.5 pollution in the LCUA was mainly influenced by pollution during the heating season. Extending the linear LUR model with regression kriging improved the model's explanatory ability and predictive performance. The predicted PM_2.5 concentrations in Shenyang and Anshan were the highest and that in Yingkou was the lowest. The building three-dimensional variables played important roles in the urban spatial modelling of air pollution.

Keywords: Air pollution; Land use regression model; Liaoning central urban agglomeration; PM(2.5); Seasonal consistency.