Global and Geographically and Temporally Weighted Regression Models for Modeling PM2.5 in Heilongjiang, China from 2015 to 2018

Date:2025-06-02 clicks:

Impact Factor:3.39

DOI number:10.3390/ijerph16245107

Journal:International Journal of Environmental Research and Public Health

Key Words:GTWR; GWR; TWR; LMM; PM2.5; air pollutants

Abstract:Objective: This study investigated the relationships between PM2.5 and 5 criteria air pollutants (SO2, NO2, PM10, CO, and O3) in Heilongjiang, China, from 2015 to 2018 using global and geographically and temporally weighted regression models. Methods: Ordinary least squares regression (OLS), linear mixed models (LMM), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) were applied to model the relationships between PM2.5 and 5 air pollutants. Results: The LMM and all GWR-based models (i.e., GWR, TWR, and GTWR) showed great advantages over OLS in terms of higher model R2 and more desirable model residuals, especially TWR and GTWR. The GWR, LMM, TWR, and GTWR improved the model explanation power by 3%, 5%, 12%, and 12%, respectively, from the R2 (0.85) of OLS. TWR yielded slightly better model performance than GTWR and reduced the root mean squared errors (RMSE) and mean absolute error (MAE) of the model residuals by 67% compared with OLS; while GWR only reduced RMSE and MAE by 15% against OLS. LMM performed slightly better than GWR by accounting for both temporal autocorrelation between observations over time and spatial heterogeneity across the 13 cities under study, which provided an alternative for modeling PM2.5. Conclusions: The traditional OLS and GWR are inadequate for describing the non-stationarity of PM2.5. The temporal dependence was more important and significant than spatial heterogeneity in our data. Our study provided evidence of spatial–temporal heterogeneity and possible solutions for modeling the relationships between PM2.5 and 5 criteria air pollutants for Heilongjiang province, China.

Co-author:Wenbiao Duan,Lianjun Zhang

First Author:Q1, Qingbin Wei

Indexed by:Journal paper

Correspondence Author:Zhen Zhen*

Volume:16

Issue:24

Page Number:5107

ISSN No.:1660-4601

Translation or Not:no

Date of Publication:2019-01-01

Included Journals:SCI、SSCI

Pre One:The Effect of Synergistic Approaches of Features and Ensemble Learning Algorithms on Aboveground Biomass Estimation of Natural Secondary Forests Based on ALS and Landsat 8 Next One:Spatial Hurdle Models for Predicting the Number of Children with Lead Poisoning