## 4 Discussion
### Spatio Distribution of NAIs in Zhejiang Province  
With the modeling of NAI distribution, we succeeded in evaluating the spatial and temporal distribution of NAI in the Zhejiang Province of China (Figure 9). NAIs were mostly centered in the hilly southern regions where plants are most abundant, which was consistent with the terrain and SIF-determined NAI dynamics in the variable importance measures (Figure 7). The global Moran's I of NAIs showed a strong positive spatial correlation (0.566, \(p<0.005\)). The H-H accumulation areas of NAI concentrations were mainly distributed in woodlands. By contrast, the L-L accumulation areas were mostly observed in the middle and northern regions with vast basins and plains. The topography was the dominant reason for these spatial patterns of NAIs. The terrain of Zhejiang Province slopes from the southwest toward the northeast [19]. Due to the vast hilly mountains, vegetation is well protected in the southern regions [36]. The luxviant vegetation provides abundant water vapor and strong SIF flux for NAI formation [10]. Furthermore, benefiting from weak human activity [19], the passive effect of air pollution is minimized. On the contrary, vast croplands, cities, and towns are distributed in the middle basins and northern plains, where natural vegetation is severely damaged [19; 36]. Meanwhile, increased impermeable ground and the urban island effect reduce the water vapor concentrations. In addition, the northern regions are close to the cities of Shanghai and Jiangsu, one of the most urbanized regions in China. Consequently, the surrounding pollutants have potentially quenched the presence of NAIs [19]. Furthermore, previous studies have conducted numerous site-based observations to reveal the NAI evolution across the whole year [9; 13; 15]. However, the seasonal dynamics of NAIs on a regional scale had never been achieved. In our study, based on the Fresh Air Network, we proved that the highest NAI concentration was found in summer (Figure 10), when forest productivity also peaked, revealing the regulation of plant photosynthesis on NAIs [10; 20]. The lowest NAI concentration was observed in the winter. These results were consistent with previous site-based studies [10; 13; 37]. It is noted that the seasonal evolution pattern was consistent among different land-use types (Figure 10). Similar seasonal changes were also observed for open spaces and forests in another site-based study [13]. The coordinated variation in the driving factors across the whole year might contribute to this consistent pattern (i.e., highest in the summer and lowest in the winter) under different land types. As shown above (Figure 7), SIF, T, RH, and CO were the most important variables determining NAI variations (except topography, which was constant temporally). In the winter and autumn, cold temperatures, short photoperiods, and weak illumination intensity go against NAI formation and the rate and time of plant photosynthesis, which in turn decreases the NAIs. Meanwhile, higher air pollution in winter also has negative effects on NAI [19; 38]. Meanwhile, in the summer and spring, higher temperature and humidity, stronger photosynthesis capacity, and more fresh air substantially elevate NAI concentrations [10; 15]. The synchronous evolution of the driving factors co-exists in different land types. Among the six land types, forests have the highest NAI concentrations, benefiting from the positive relationship with topography, humidity, and SIF, and the passive relationship with air pollution (Figure 8). NAI concentrations in water bodies followed forest lands. They might benefit from high water vapor and the surrounding forest lands (Figure 1), since NAIs have obvious H-H aggregation (Figure 11). It was noted that barren land had significantly higher NAI concentrations than grasslands, urban and built-up land, and cropland, while many previous studies argued that barren land usually has the lowest NAI concentrations among different land types due to the lack of vegetation [9; 39].