A leaf age-dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests.

Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one-third of terrestrial gross primary productivity (GPP). However, the continental-scale leaf phenology-photosynthesis nexus over TEFs is still poorly understood to date. This knowledge gap hinders most light use efficiency (LUE) models from accurately simulating the GPP seasonality in TEFs. Leaf age is the crucial plant trait to link the dynamics of leaf phenology with GPP seasonality. Thus, here we incorporated the seasonal leaf area index of different leaf age cohorts into a widely used LUE model (i.e., EC-LUE) and proposed a novel leaf age-dependent LUE model (denoted as LA-LUE model). At the site level, the LA-LUE model (average R2 = .59, average root-mean-square error [RMSE] = 1.23 gC m-2 day-1) performs better than the EC-LUE model in simulating the GPP seasonality across the nine TEFs sites (average R2 = .18; average RMSE = 1.87 gC m-2 day-1). At the continental scale, the monthly GPP estimates from the LA-LUE model are consistent with FLUXCOM GPP data (R2 = .80; average RMSE = 1.74 gC m-2 day-1), and satellite-based GPP data retrieved from the global Orbiting Carbon Observatory-2 (OCO-2) based solar-induced chlorophyll fluorescence (SIF) product (GOSIF) (R2 = .64; average RMSE = 1.90 gC m-2 day-1) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R2 = .78; average RMSE = 1.88 gC m-2 day-1). Typically, the estimated monthly GPP not only successfully represents the unimodal GPP seasonality near the Tropics of Cancer and Capricorn, but also captures well the bimodal GPP seasonality near the Equator. Overall, this study for the first time integrates the leaf age information into the satellite-based LUE model and provides a feasible implementation for mapping the continental-scale GPP seasonality over the entire TEFs.

The carbon budget of China: 1980-2021.

As one of the world's largest emitters of greenhouse gases, China has set itself the ambitious goal of achieving carbon peaking and carbon neutrality. Therefore, it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide (CO2), and to monitor China's progress toward these goals. Using state-of-the-art datasets and models, this study comprehensively estimated the anthropogenic CO2 emissions from energy, industrial processes and product use, and waste along with natural sources and sinks of CO2 for all of China during 1980-2021. To recognize the differences among various methods of estimating greenhouse emissions, the estimates are compared with China's National Greenhouse Gas Inventories (NGHGIs) for 1994, 2005, 2010, 2012, and 2014. Anthropogenic CO2 emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO2 a-1 in 2021. While benefiting from ecological projects (e.g., Three Norths Shelter Forest System Project), the land carbon sink in China has reached 1.65 Gt CO2 a-1 averaged through 2010-2021, which is almost 15.81 times that of the carbon sink in the 1980s. On average, China's terrestrial ecosystems offset 14.69% ± 2.49% of anthropogenic CO2 emissions through 2010-2021. Two provincial-level administrative regions of China, Xizang and Qinghai, have achieved carbon neutrality according to our estimates, but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10% of anthropogenic CO2 emissions. This study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO2 emissions, but found notable differences for land carbon sinks. Future estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.

[Spatio-temporal Evolution of PM2.5 Concentration During 2000-2019 in China].

An ensemble estimation model of PM2.5 concentration was proposed on the basis of extreme gradient boosting, gradient boosting, random forest model, and stacking model fusion technology. Measured PM2.5 data, MERRA-2 AOD and PM2.5 reanalysis data, meteorological parameters, and night light data sets were used. On this basis, the spatiotemporal evolution features of PM2.5 concentration in China during 2000-2019 were analyzed at monthly, seasonal, and annual temporal scales. The results showed that:① Monthly PM2.5 concentration in China from 2000-2019 can be estimated reliably by the ensemble model. ② PM2.5 annual concentration changed from rapid increase to remaining stable and then changed to significant decline from 2000-2019, with turning points in 2007 and 2014. The monthly variation of PM2.5 concentration showed a U shape that first decreased then increased, with the minimum value in July and the maximum value in December. ③ Natural geographic conditions and human activities laid the foundation for the annual spatial pattern change of PM2.5 concentration in China, and the main trend of monthly spatial pattern change of PM2.5 concentration was determined by meteorological conditions. ④ At an annual scale, the national PM2.5 concentration average center of standard deviation ellipse moved eastward from 2000-2014 and westward from 2014-2018. At a monthly scale, the average center shifted to the west from January to March, moved northward then southward from April to September, and shifted to the east from September to December.

[PM2.5 Concentration Influencing Factors in China Based on the Random Forest Model].

In this paper, aerosol optical depth (AOD), elevation (DEM), annual precipitation (PRE), annual average temperature (TEM), annual average wind speed (WS), population density (POP), gross domestic product density (GDP), and normalized difference vegetation index (NDVI) were selected as factors influencing PM2.5 concentration. The random forest model, order of feature importance, and partial dependency plots were applied to investigate these factors and their regional differences in PM2.5 spatial pattern. The results showed that:① The random forest model was more accurate than multiple regression, generalized additive, and back propagation neural network models in estimating PM2.5 concentration, which can be applied to quantifying PM2.5 influencing factors. ② PM2.5 concentration initially increased and then remained stable with increases in AOD, POP, and GDP, and initially decreased and then stabilized with increases in PRE, WS, and NDVI. The responses of DEM and TEM to PM2.5 concentration changed from decline to ascend and then changed to decline again. ③ AOD had the largest influence on PM2.5 annual concentrations with a spatial influencing magnitude of 37.96%, whereas PRE had the least influence with a merely individual spatial influencing magnitude of 5.75%. ④ The relationships between PM2.5 pollution and influencing variables vary with geography and thus exhibit significant spatial heterogeneity. The same factor had different spatial influencing magnitudes on PM2.5 annual concentrations in seven geographical subareas. AOD had the greatest influence on PM2.5 concentration in the south of China, with the least influence in the northeast.