A novel hybrid spatiotemporal land use regression model system at the megacity scale

Abstract

Air pollution has become a global problem and can cause serious damage to human health. Epidemiological studies on the long-term exposure to air pollution can reveal the extent of this damage. Spatiotemporal land use regression (LUR) models can be used to obtain long-term pollutant concentration surfaces with high spatiotemporal resolution. However, previously established spatiotemporal LUR models generally exhibit poor spatial prediction performances in some time panels compared with their average performances. These inaccurate pollutant concentrations lead to misclassification errors in epidemiological studies. To solve this problem, a hybrid spatiotemporal LUR model system is proposed in this study, which consists of support vector regression (SVR), multiple linear regression (MLR), and a special spatiotemporal (ST) algorithm. Three SVR layers were used for the main prediction, whereas MLR and ST were used to supplement time panels with poor spatial prediction performances. In addition, temporal segmentation modeling was adopted for SVR to further improve the performance. We used the megacity Tianjin in China for our case study and six target air pollutants (CO, NO2, O3, PM10, PM2.5, and SO2). The superiority of our model system was tested by cross-validation. The results show that the number of days on which the R2cv of the model is higher than 0.6 for CO, NO2, O3, PM10, PM2.5, and SO2 is 363, 364, 362, 357, 360, and 362, respectively, whereas the mean of the daily R2cv on these days is 0.911, 0.903, 0.891, 0.879, 0.866, and 0.883, respectively. Based on the use of our model system, a relatively high spatial prediction performance was achieved for almost all time panels. This model system can be applied to cohort health studies to obtain the pollutant concentration surfaces of any time panel with high reliability and reduce the exposure measurement errors of misclassifications.