Effect of light intensity on negative air ion under phytotron control

Negative air ion (NAI) is an important index for measuring air quality and has been widely recognized to be influenced by photosynthesis processes. However, vegetation type and light intensity are also known to impact NAI, contributing to significant uncertainties in the relationship between light and NAI. In this paper, we selected Pinus bungeana, Platycladus orientalis and Buxus sinica as research subjects and obtained their NAI, light intensity, and meteorological data through synchronous observation under the relatively stable condition of the phytotron. We analyzed the change characteristics of NAI and the difference of NAI production ability in needle and broadleaf vegetation under different light intensities. Finally, we determined the relationship and underlying mechanism governing light intensity and NAI using diverse tree species. The results showed that the influence of light on NAI was significant. In the environment without vegetation, the influence of different light intensities on NAI was not significant, and the mean NAI concentration was 310 ions·cm^-3. Conversely, in the presence of vegetation, NAI showed a "single-peak" trend with increasing light intensity. The NAI concentration of the three tree species was significantly higher than under different light intensities when vegetation was not present. The NAI promoting ability of P. bungeana was the highest (675 ions·cm^-3), followed by P. orientalis (478 ions·cm^-3) and B. sinica (430 ions·cm^-3), which increased by 117.5%, 53.9% and 38.6% compared to the environment without vegetation. The NAI growth rate was significantly different between needle and broadleaf vegetation based on the specific tridimensional green biomass. Additionally, the NAI growth rates of P. bungeana and P. orientalis were 647 and 295 ions·cm^-3·m^-3, respectively, which were 3.06 and 1.39 times that of B. sinica (211 ions·cm^-3·m^-3). The piecewise equation fitting effect of NAI and light intensity was better for different tree species, the determination coefficients (R²) of P. bungeana, P. orientalis and B. sinica were 0.926, 0.916 and 0.880, and the root mean square errors (RMSE) were 7.157, 6.008 and 5.389 ion·cm^-3, respectively. Altogether, our study provides a theoretical basis as well as technical support for the construction of healthy vegetation stands, the selection of preferred tree species, and the optimization of vegetation models, and promotes air quality and the provision of ecosystem functions and services.