# Spatial and Temporal Variation of GPP and Its Response to Urban Environmental Changes in Beijing
## 3 Results
### The Influence of Urban Environmental Factors on AGPP  
To analyze the influence of urban environmental factors on AGPP, GWR and GTWR models were tested with the original data sets. Before fitting GWR and GTWR, a multicollinearity test is necessary to eliminate redundant indicators. This study used the Variance Inflation Factor (VIF) as an indicator and screened redundant factors with a threshold of 5 [32]. The results of the multicollinearity test are shown in Table 2. We removed the redundant factor with the highest VIF value step by step until the VIF value of all factors is not greater than 5. The results of the multicollinearity test for the remaining factors are shown in Table 2.  
According to the results in Table 2, we removed PM2.5 and T. Then we fitted GWR and GTWR, and the results are reported in Table 3. The detailed results of GWR model can be seen in the Supplementary Materials. Figure 2: Spatial variation trend of AGPP from 2000 to 2020. (**a**) shows the slope of AGPP interannual variation, (**b**) shows the results of the significance test of AGPP change, (**c**) shows the time series of AGPP from 2000 to 2020, and (**d**) shows the mutation test result of AGPP. Compared to the GTWR fitting results, the GWR fitting results exhibit notable year-to-year variations, which significantly diminishes their representativeness and reliability for in-depth analysis. Although the AICc value associated with GWR is considerably smaller than that of GTWR, this discrepancy is primarily due to the distinct data complexities inherent to each model. GTWR, which incorporates higher data complexity and greater model sophistication, naturally results in a larger AICc value. Therefore, the AICc values produced by GWR and GTWR should not be directly compared as they reflect different levels of model intricacy. A smaller AICc value for GWR does not necessarily imply that its fitting results are superior to those of GTWR. Consequently, the GTWR fitting results are deemed more appropriate and reliable for conducting a thorough analysis. Besides, we evaluated the significance of the GTWR fit using a \(p\)-value threshold of 0.05. The fitting results, along with their significance, are presented in Figure 3.  
Figure 3 presents the GTWR fitting results derived from the original dataset, offering a comprehensive overview of how urban environmental factors influence AGPP in the Beijing area. The analysis reveals that CO and NO\({}_{2}\) are the most critical factors determining AGPP, with O\({}_{3}\) and LST also exerting certain influences, while the impact of precipitation (PRE) is relatively minor. Specifically, CO and O\({}_{3}\) generally have a positive effect on AGPP across most areas of Beijing, with their negative impacts being limited to a few regions in the northeastern and southwestern areas. In the northwest of the city, the positive effects of CO and O\({}_{3}\) are more pronounced. NO\({}_{2}\) and LST generally exhibit negative impacts on AGPP. LST only shows a positive effect on AGPP in a few areas of the southwest and east, with negative effects in the remaining regions and a more obvious negative impact in the southeast. NO\({}_{2}\) shows a positive effect on AGPP in a few areas of the northeast, southwest, and northwest, with negative effects in other regions and a more pronounced negative impact in the northwest. PRE has the least impact on AGPP, and its impact varies in different regions of Beijing. It has a positive effect on AGPP in most central areas of Beijing while having a negative effect in some northern and southwestern areas. Overall, the study results emphasize that CO and NO\({}_{2}\) play the most significant role in shaping the spatial pattern of AGPP in Beijing, with O\({}_{3}\) and LST also having certain influences. The impact of PRE is minimal and generally negligible.