Spatio-Temporal Characteristics of PM 2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016–2021

Hongbin Dai, Guangqiu Huang, Jingjing Wang, Huibin Zeng and Fangyu Zhou
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Hongbin Dai: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Guangqiu Huang: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Jingjing Wang: College of Vocational and Technical Education, Guangxi Science & Technology of Normal University, Laibin 546199, China
Huibin Zeng: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Fangyu Zhou: Chengdu Institute, School of Applied English, Sichuan International Studies University, Chengdu 611844, China

IJERPH, 2022, vol. 19, issue 10, 1-20

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 R 2 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: PM 2.5; remote sensing retrieval; land-use regression; LightGBM; spatial and temporal characteristics (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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