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

Qingbin Wei · Lianjun Zhang · Duan Wenbiao · Zhen Zhen

62 citations0/0 files storedInternational Journal of Environmental Research and Public Health

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Spatial and Panel Data Analysis Air Quality and Health Impacts Air Quality Monitoring and Forecasting

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.

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1970A Computer Movie Simulating Urban Growth in the Detroit Region 2002Geographically Weighted Regression: The Analysis of Spatially Varying Relationships 2010Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices 2015Cause-specific mortality for 240 causes in China during 1990–2013: a systematic subnational analysis for the Global Burden of Disease Study 2013 2015Geographical and Temporal Weighted Regression (GTWR)2015GWmodel : An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models 2018A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership 2002A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity

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