## 5 Conclusions  
GPP, Re, and NEP in a subtropical coniferous forest from 2003-2017 were calculated using the empirical models of GPP, Re, and NEP, previously developed using hourly observational data from 2013-2016. Their simulations from the hourly to the daily, monthly, and annual time scales were in reasonable agreement with the observations, and their seasonal variations were well captured. For the annual sums under all skies, the simulations overestimated the observations by 11.38% for GPP and 5.52% for Re, while they underestimated the observations by 34.99% for NEP. The mean annual ratios \(\mathrm{GPP_{call}}/\mathrm{GPP_{obs}}\) and \(\mathrm{GPP_{cal3}}/\mathrm{GPP_{obs}}\) were 1.93 and 1.04 under relatively clean and polluted atmospheric conditions from 2003-2017, indicating that the forest would store more \(\mathrm{CO_{2}}\) (about two times GPP) under a clean atmosphere than a polluted atmosphere, and the objectives of reducing air pollutants and achieving carbon neutrality have win-win effects. Timely maintenance and the calibration of PAR sensors are necessary to obtain accurate PAR observational data, together with better simulations of GPP, Re, and NEP, especially NEP. GPP, Re, and NEE exhibited clear and similar long-term variation trends under all sky conditions from 2003-2017. The calculated and observed annual sums increased by 1.04% and 0.93%, respectively, for GPP, 4.57% and 1.06% for Re, and 0.18% and 0.01% for NEE. The estimated and observed annual sums of NEP decreased by 0.18% and 0.01%, respectively. These results were consistent with the fact that a carbon sink was enhanced in this subtropical coniferous forest from 1985-2017. As for their driving factors, annual mean PAR and air temperature decreased by 0.28% and 0.02%, respectively, and annual mean water vapor pressure increased by 0.87%, demonstrating that the water vapor or water supply played significant roles in and contributed to the increases in GPP, Re, and NEE, i.e., the carbon sink. Strong relationships (linear and non-linear) between the simulated monthly GPP and the satellite monthly SIF (R\({}^{2}\) > 0.90) under polluted and realistic atmospheric conditionswere found, revealing that the atmosphere in the Qianyanzhou coniferous forest was in high GLP loads. The linear and non-linear equations were used to calculate GPP from 2003-2017 with good performances. For annual sums of GPP, the relative biases were 5.20% and 4.88%, respectively, using linear and non-linear equations. It indicates that (1) the non-linear relationship displayed better performance than the linear, implying that a non-linear law existed in PAR transfer (associated with GPP and SIF) in the atmosphere; and (2) better simulations than the EMGPP with a relative bias of 11.38% for the annual sums of GPP were found. Later, similar increasing trends were also found for the calculated and the observed GPP. All these results demonstrated that satellite SIF and the method of GPP/SIF relation provide useful tools to calculate GPP over a large region. Based on the simulations of GPP, Re, and NEP from 2013-2016 and 2003-2017, the PAR energy balance method and its transformations to the empirical models (GPP, Re, NEP, and BVOCs) can be used to study the processes of total carbon exchange (including BVOCs) and understand the multiple and dynamic interactions between atmospheric components (CO\({}_{2}\) and other GLPs through absorption and scattering) and PAR in this subtropical forest, and it is deserved for further development and applications in other forests. Several studies supported that reduced atmospheric substances (i.e., a cleaner atmosphere) are beneficial to more carbon stored in the plants, a clean atmosphere, and slowing down climate warming. Thus, it is recommended to reduce the atmospheric GLPs through the control of direct emissions and secondary formations via CPRs. Conceptualization, J.B.; methodology, J.B.; validation, J.B.; formal analysis, J.B.; investigation, J.B.; resources, F.Y., H.W., M.X. and L.Y.