Spatio-Temporal Characteristics of PM2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016-2021

Hongbin Dai; Guangqiu Huang; Jingjing Wang; Huibin Zeng; Fangyu Zhou

doi:10.3390/ijerph19106292

Spatio-Temporal Characteristics of PM_2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016-2021

Int J Environ Res Public Health. 2022 May 22;19(10):6292. doi: 10.3390/ijerph19106292.

Authors

Hongbin Dai¹, Guangqiu Huang¹, Jingjing Wang², Huibin Zeng¹, Fangyu Zhou³

Affiliations

¹ School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China.
² College of Vocational and Technical Education, Guangxi Science & Technology of Normal University, Laibin 546199, China.
³ Chengdu Institute, School of Applied English, Sichuan International Studies University, Chengdu 611844, China.

Abstract

Fine particulate matter (PM_2.5) has a continuing impact on the environment, climate change and human health. In order to improve the accuracy of PM_2.5 estimation and obtain a continuous spatial distribution of PM_2.5 concentration, this paper proposes a LUR-GBM model based on land-use regression (LUR), the Kriging method and LightGBM (light gradient boosting machine). Firstly, this study modelled the spatial distribution of PM_2.5 in the Chinese region by obtaining PM_2.5 concentration data from monitoring stations in the Chinese study region and established a PM_2.5 mass concentration estimation method based on the LUR-GBM model by combining data on land use type, meteorology, topography, vegetation index, population density, traffic and pollution sources. Secondly, the performance of the LUR-GBM model was evaluated by a ten-fold cross-validation method based on samples, stations and time. Finally, the results of the model proposed in this paper are compared with those of the back propagation neural network (BPNN), deep neural network (DNN), random forest (RF), XGBoost and LightGBM models. The results show that the prediction accuracy of the LUR-GBM model is better than other models, with the R² of the model reaching 0.964 (spring), 0.91 (summer), 0.967 (autumn), 0.98 (winter) and 0.976 (average for 2016-2021) for each season and annual average, respectively. It can be seen that the LUR-GBM model has good applicability in simulating the spatial distribution of PM_2.5 concentrations in China. The spatial distribution of PM_2.5 concentrations in the Chinese region shows a clear characteristic of high in the east and low in the west, and the spatial distribution is strongly influenced by topographical factors. The seasonal variation in mean concentration values is marked by low summer and high winter values. The results of this study can provide a scientific basis for the prevention and control of regional PM_2.5 pollution in China and can also provide new ideas for the acquisition of data on the spatial distribution of PM_2.5 concentrations within cities.

Keywords: LightGBM; PM2.5; land-use regression; remote sensing retrieval; spatial and temporal characteristics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants* / analysis
China
Cities
Environmental Monitoring / methods
Humans
Particulate Matter / analysis

Substances

Air Pollutants
Particulate Matter

Grants and funding

This paper was supported by Guangqiu Huang’s Natural Science Foundation of China (71874134), and Wang Jingjing’s Guangxi Institute of Science and Technology’s research platform project (GXKSKYPT2021008) and the Laibin Scientific Research and Technology Development Program (211806) support.