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Land use and cover change (LUCC) is a key factor influencing global environmental and socioeconomic systems. Many long-term geospatial LUCC datasets have been developed at various scales during the recent decades owing to the availability of long-term satellite data, statistical data and computational techniques. However, most existing LUCC products cannot accurately reflect the spatiotemporal change patterns of LUCC at the regional scale in China. Based on these geospatial LUCC products, normalized difference vegetation index (NDVI), socioeconomic data and statistical data, we developed multiple procedures to represent both the spatial and temporal changes of the major LUC types by applying machine-learning, regular decision-tree and hierarchical assignment methods using northeastern China (NEC) as a case study. In this approach, each individual LUC type was developed in sequence under different schemes and methods. The accuracy evaluation using sampling plots indicated that our approach can accurately reflect the actual spatiotemporal patterns of LUC shares in NEC, with an overall accuracy of 82%, Kappa coefficient of 0.77 and regression coefficient of 0.82. Further comparisons with existing LUCC datasets and statistical data also indicated the accuracy of our approach and datasets. Our approach unfolded the mixed-pixel issue of LUC types and integrated the strengths of existing LUCC products through multiple fusion processes. The analysis based on our developed dataset indicated that forest, cropland and built-up land area increased by 17.11 × 104 km2, 15.19 × 104 km2 and 2.85 × 104 km2, respectively, during 1980–2020, while grassland, wetland, shrubland and bare land decreased by 26.06 × 104 km2, 4.24 × 104 km2, 3.97 × 104 km2 and 0.92 × 104 km2, respectively, in NEC. Our developed approach accurately reconstructed the shares and spatiotemporal patterns of all LUC types during 1980–2020 in NEC. This approach can be further applied to the entirety of China, and worldwide, and our products can provide accurate data supports for studying LUCC consequences and making effective land use policies. [ABSTRACT FROM AUTHOR]

The timing, location, intensity, and drivers of forest disturbance and recovery are crucial for developing effective management strategies and policies for forest conservation and ecosystem resilience. Although many algorithms and improvement methods have been developed, it is still difficult to guarantee the detection accuracy for forest disturbance and recovery patterns in southern China due to the complex climate and topography, faster forest recovery after disturbance, and the low availability of noise-free Landsat images. Here, we improved the LandTrendr parameters for different provinces to detect forest disturbances and recovery trajectories based on the LandTrendr change detection algorithm and time-series Landsat images on the GEE platform, and then applied the secondary random forest classifier to classify the forest disturbance and recovery patterns in southern China during 1990–2020. The accuracy evaluation indicated that our approach and improved parameters of the LandTrendr algorithm can increase the detection accuracy for both the spatiotemporal patterns and multiple events of forest disturbance and recovery, with an overall accuracy greater than 86% and a Kappa coefficient greater than 0.91 for different provinces. The total forest loss area was 1.54 × 105 km2 during 1990–2020 (4931 km2/year); however, most of these disturbed forests were recovered and only 6.39 × 104 km2 was a net loss area (converted to other land cover types). The area with two or more times of disturbance events accounted for 11.50% of the total forest loss area. The total forest gain area (including gain after loss and the afforestation area) was 5.44 × 105 km2, among which, the forest gain area after loss was 8.94 × 104 km2, and the net gain area from afforestation was 4.55 × 105 km2. The timing of the implementation of forestry policies significantly affected the interannual variations in forest disturbance and recovery, with large variations among different provinces. The detected forest loss and gain area was further compared against with inventory and other geospatial products, and proved the effectiveness of our method. Our study suggests that parameter optimization in the LandTrendr algorithm could greatly increase the accuracy for detecting the multiple and lower rate disturbance/recovery events in the fast-regrowing forested areas. Our findings also offer a long-term, moderate spatial resolution, and precise forest dynamic data for achieving sustainable forest management and the carbon neutrality goal in southern China. [ABSTRACT FROM AUTHOR]

Sichuan Province preserves numerous rare and ancient species of plants and animals, making it an important bio-genetic repository in China and even the world. However, this region is also vulnerable to fire disturbance due to the rich forest resources, complex topography, and dry climate, and thus has become one of main regions in China needing wildfire prevention. Analyzing the main driving factors influencing wildfire incidence can provide data and policy guidance for wildfire management in Sichuan Province. Here we analyzed the spatial and temporal distribution characteristics of wildfires in Sichuan Province based on the wildfire spot data during 2010–2019. Based on 14 input variables, including climate, vegetation, human factors, and topography, we applied the Pearson correlation analysis and Random Forest methods to investigate the most important factors in driving wildfire occurrence. Then, the Logistic model was further applied to predict wildfire occurrences. The results showed that: (1) The southwestern Sichuan Province is a high-incidence area for wildfires, and most fires occurred from January to June. (2) The most important factor affecting wildfire occurrence is monthly average temperature, followed by elevation, monthly precipitation, population density, Normalized Difference Vegetation Index (NDVI), NDVI in the previous month, and Road kernel density. (3) The Logistic wildfire prediction model yielded good performance, with the area under curve (AUC) values higher than 0.94, overall accuracy (OA) higher than 86%, true positive rate (TPR) values higher than 0.82, and threat score (TS) values higher than 0.71. The final selected prediction model has an AUC of 0.944, an OA of 87.28%, a TPR of 0.829, and a TS of 0.723. (4) The results of the prediction indicate that extremely high danger of wildfires (probability of fire occurrence higher than 0.8) is concentrated in the southwest, which accounted for about 1% of the area of the study region, specifically in Panzhihua and Liangshan. These findings demonstrated the effectiveness of the Logistic model in predicting forest fires in Sichuan Province, providing valuable insights regarding forest fire management and prevention efforts in this region. [ABSTRACT FROM AUTHOR]

In recent times, airborne and terrestrial laser scanning have been utilized to collect point cloud data for forest resource surveys, aiding in the estimation of tree and stand attributes over hectare-scale plots. In this study, an automated approach was devised to estimate the diameter at breast height (DBH) and tree height across the entire sample area, utilizing information acquired from terrestrial laser scanning (TLS) and airborne laser scanning (ULS). Centered around a meticulously managed artificial forest in Northern China, where Mongolian oak and Chinese Scots pine are the predominant species, both TLS and ULS operations were conducted concurrently on each plot. Subsequent to data collection, a detailed processing of the point cloud data was carried out, introducing an innovative algorithm to facilitate the matching of individual tree point clouds from ULS and TLS sources. To enhance the accuracy of DBH estimation, a weighted regression correction equation based on TLS data was introduced. The estimations obtained for the Chinese Scots pine plots showed a correlation of R2 = 0.789 and a root mean square error (RMSE) of 3.2 cm, while for the Mongolian oak plots, an improved correlation of R2 = 0.761 and a RMSE of 3.1 cm was observed between predicted and measured values. This research significantly augments the potential for non-destructive estimations of tree structural parameters on a hectare scale by integrating TLS and ULS technologies. The advancements hold paramount importance in the domain of large-scale forest surveys, particularly in the calibration and validation of aboveground biomass (AGB) estimations. [ABSTRACT FROM AUTHOR]

Subtropical forests act as a carbon sink and play an important role in mitigating global warming. However, the understanding of the effects of stand characteristics and climatic factors on forest carbon sequestration capacity remains limited. Based on multiple regression analyses using structural equation modeling and plot-level data of coniferous and broadleaf forests from the national forest resource inventories, our study quantified vegetation carbon accumulation rate (VCAR) and the underlying influence pattern in Zhejiang Province, Southeastern China. The mean VCAR of coniferous and broadleaf forests was 2.30 Mg C ha−1 yr−1 and 4.53 Mg C ha−1 yr−1 during 2004–2009, and 1.56 Mg C ha−1 yr−1 and 1.98 Mg C ha−1 yr−1 during 2009–2014, respectively. Stand characteristics (i.e., change in tree density and mean diameter at breast height) and climatic factors (i.e., mean annual precipitation (MAP), temperature, sunshine hours, and monthly precipitation fluctuation) were the major driving factors on the change patterns of VCAR. All factors, except precipitation fluctuation, had positive effects on VCAR. The positive effects of MAP decreased with the increase in MAP, but the negative effects of precipitation fluctuation increased with the increase in precipitation fluctuation. Precipitation increased with increasing elevation, while the VCAR was lower at lower elevations. Additionally, we found that broadleaf forests could be able to cope better with the negative effects of precipitation fluctuation. Overall, this study provides new insights into the sensitivity and potential of vegetation carbon accumulation for structural development and climate change response in Zhejiang Province to better promote sustainable forestry development. [ABSTRACT FROM AUTHOR]

China has implemented a series of forestry law, policies, regulations, and afforestation projects since the 1970s. However, their impacts on the spatial and temporal patterns of forests have not been fully assessed yet. The lack of an accurate, high-resolution, and long-term forest disturbance and recovery dataset has impeded this assessment. Here we improved the forest loss and gain detections by integrating the LandTrendr change detection algorithm with the Random Forest (RF) machine-learning method and applied it to assess forest loss and gain patterns in the Zhejiang, Jiangxi, and Guangxi Provinces of the subtropical vegetation in China. The accuracy evaluation indicated that our approach can adequately detect the spatial and temporal distribution patterns in forest gain and loss, with an overall accuracy of 93% and the Kappa coefficient of 0.89. The forest loss area was 8.30 × 104 km2 in the Zhejiang, Jiangxi, and Guangxi Provinces during 1986–2019, accounting for 43.52% of total forest area in 1986, while the forest gain area was 20.25 × 104 km2, accounting for 106.19% of total forest area in 1986. Although the interannual variation patterns were similar among three provinces, the forest loss and gain area and the magnitude of change trends were significantly different. Guangxi has the largest forest loss and gain area and increasing trends, followed by Jiangxi, and the least in Zhejiang. The variations in annual forest loss and gain area can be mostly explained by the timelines of major forestry policies and regulations. Our study would provide an applicable method and data for assessing the impacts of forest disturbance events and forestry policies and regulations on the spatial and temporal patterns of forest loss and gain in China, and further contributing to regional and national forest carbon and greenhouse gases budget estimations. [ABSTRACT FROM AUTHOR]

The vertical stratification of the stand may lead to a high heterogeneity of microenvironment in the forest, which further influences the understory regeneration and succession of the forest. Most relevant previous studies emphasized the overall effects of the Whole-stand structural characteristics on understory regeneration, while the strata-specific impacts of the overstory should be explored especially for those forests with a complicated combination of overstory species and heights. In this study, a subtropical evergreen broad-leaved forest in Tianmu Mountain of China was intensively investigated within 25 plots of 20 m × 20 m, aiming to find out how significant the stratified overstory (trees with diameter at breast height (DBH) ≥ 5 cm) structure and non-structure characteristics impact the understory (trees with DBH < 5 cm) regeneration. Regardless of species composition, the studied overstory was evenly divided into three strata (i.e. upper, middle and lower strata) according to their heights. Redundancy analysis was applied to explore both overall and strata-specific forest structure on characteristics (height, DBH, species diversity, and density) of tree regeneration. We found that the overall effect of the whole overstory on the forest regeneration depended mostly on diameter at breast height (DBH), tree species richness index and crown width. However, when analyzing with the strata-specific characteristics, the most pronounced impact factors for the regeneration were tree height of the upper and lower forest strata, tree species richness index and crown width of the middle and lower forest strata, and the competition index impact of the lower forest stratum. Among the three strata, the lower forest stratum showed the most significant impact with three characteristics on the understory regeneration, which may be attributed to their direct competition within the overlapping near-ground niches. Among the new generations, seedlings and saplings were more sensitive to the overstory structural characteristics than young trees. Our results suggest that the overstory showed strata-specific effects on the understory regeneration of evergreen broad-leaved forests in subtropical China, which provides theoretical basis for strata-specific forest management in similar forests. [ABSTRACT FROM AUTHOR]

The Loess Plateau of China has the highest soil erosion rate in the world where billion tons of soil is annually washed into Yellow River. In recent decades this region has experienced significant climate change and policy-driven land conversion. However, it has not yet been well investigated how these changes in climate and land use have affected soil organic carbon (SOC) storage on the Loess Plateau. By using the Dynamic Land Ecosystem Model (DLEM), we quantified the effects of climate and land use on SOC storage on the Loess Plateau in the context of multiple environmental factors during the period of 1961–2005. Our results show that SOC storage increased by 0.27 Pg C on the Loess Plateau as a result of multiple environmental factors during the study period. About 55% (0.14 Pg C) of the SOC increase was caused by land conversion from cropland to grassland/forest owing to the government efforts to reduce soil erosion and improve the ecological conditions in the region. Historical climate change reduced SOC by 0.05 Pg C (approximately 19% of the total change) primarily due to a significant climate warming and a slight reduction in precipitation. Our results imply that the implementation of “Grain for Green” policy may effectively enhance regional soil carbon storage and hence starve off further soil erosion on the Loess Plateau. [ABSTRACT FROM AUTHOR]

We investigated how ozone pollution and climate change/variability have interactively affected net primary productivity (NPP) and net carbon exchange (NCE) across China's forest ecosystem in the past half century. Continental China. Using the dynamic land ecosystem model (DLEM) in conjunction with 10-km-resolution gridded historical data sets (tropospheric O concentrations, climate variability/change, and other environmental factors such as land-cover/land-use change (LCLUC), increasing CO and nitrogen deposition), we conducted nine simulation experiments to: (1) investigate the temporo-spatial patterns of NPP and NCE in China's forest ecosystems from 1961-2005; and (2) quantify the effects of tropospheric O pollution alone or in combination with climate variability and other environmental stresses on forests' NPP and NCE. China's forests acted as a carbon sink during 1961-2005 as a result of the combined effects of O, climate, CO, nitrogen deposition and LCLUC. However, simulated results indicated that elevated O caused a 7.7% decrease in national carbon storage, with O-induced reductions in NCE (Pg C year) ranging from 0.4-43.1% among different forest types. Sensitivity experiments showed that climate change was the dominant factor in controlling changes in temporo-spatial patterns of annual NPP. The combined negative effects of O pollution and climate change on NPP and NCE could be largely offset by the positive fertilization effects of nitrogen deposition and CO. In the future, tropospheric O should be taken into account in order to fully understand the variations of carbon sequestration capacity of forests and assess the vulnerability of forest ecosystems to climate change and air pollution. Reducing air pollution in China is likely to increase the resilience of forests to climate change. This paper offers the first estimate of how prevention of air pollution can help to increase forest productivity and carbon sequestration in China's forested ecosystems. [ABSTRACT FROM AUTHOR]

The spatial and temporal patterns of CO and CH fluxes in China's croplands were investigated and attributed to multifactor environmental changes using the agricultural module of the Dynamic Land Ecosystem Model (DLEM), a highly integrated process-based ecosystem model. During 1980-2005 modelled results indicated that China's croplands acted as a carbon sink with an average carbon sequestration rate of 33.4 TgC yr (1 Tg = 10 g). Both the highest net CO uptake rate and the largest CH emission rate were found in southeast region of China's croplands. Of primary influences were land-cover and land-use change, atmospheric CO and nitrogen deposition, which accounted for 76%, 42% and 17% of the total carbon sequestration in China's croplands during the study period, respectively. The total carbon losses due to elevated ozone and climate variability/change were equivalent to 27% and 9% of the total carbon sequestration, respectively. Our further analysis indicated that nitrogen fertilizer application accounted for 60% of total national carbon uptake in cropland, whereas changes in paddy field areas mainly determined the variability of CH emissions. Our results suggest that improving air quality by means such as reducing ozone concentration and optimizing agronomic practices can enhance carbon sequestration capacity of China's croplands. [ABSTRACT FROM AUTHOR]

Our simulations with the Dynamic Land Ecosystem Model (DLEM) indicate that the combined effect of ozone, climate, carbon dioxide and land use have caused China''s grasslands to act as a weak carbon sink during 1961–2000. This combined effect on national grassland net primary productivity (NPP) and carbon storage was small, but changes in annual NPP and total carbon storage across China''s grasslands showed substantial spatial variation, with the maximum total carbon uptake reduction of more than 400gm−2 in some places of northeastern China. The grasslands in the central northeastern China were more sensitive and vulnerable to elevated ozone pollution than other regions. The combined effect excluding ozone could potentially lead to an increase of 14Tg C in annual NPP and 0.11Pg C in total carbon storage for the same time period. This implies that improvement in air quality could significantly increase productivity and carbon storage in China''s grassland ecosystems. [Copyright &y& Elsevier]

• The driest month's precipitation is the most important influencing factor. • The influence of human factors is significant. • SSP3-7.0 emission scenario has the highest suitability. • The combination of geographic detectors and MaxEnt models. Pinus massoniana , an important timber, producing, and silvicultural species in southern China, exhibits high adaptability and wide distribution. This study utilizes the Maximum Entropy Model (MaxEnt), a species distribution model based on the theory of maximum entropy, to forecast the potential suitable distribution areas of P. massoniana in China under four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) for both present and future (2080) conditions. The research integrates and analyzes the effects of various environmental factors, including topography, soil, and population, on the distribution of P. massoniana. Additionally, a geographical detector is employed to assess the interaction between different environmental factors and their contribution to the variation in suitability zones.The findings indicate that the MaxEnt model accurately predicts the potential distribution areas of P. massoniana , with AUC values exceeding 0.94. Precipitation in the driest month (BIO14), population density (POP), and annual precipitation (BIO12) emerge as the main factors influencing the current distribution of P. massoniana. Notably, BIO14 has the greatest impact on the species' distribution (43%), followed by POP (32.7%). Furthermore, lower BIO14 values correspond to higher probabilities of pine distribution, while higher POP values correlate with increased pine distribution probabilities. The potential distribution of P. massoniana is primarily concentrated in southern China under current climatic conditions, encompassing a total suitable survival zone of 25.24 × 105 km2, accounting for 26.29% of China's total area. Among the regions, Guangxi exhibits the largest suitable area for survival, reaching 28.9 × 104 km2, implying that the environmental characteristics of Guangxi are conducive to P. massoniana 's survival. Under future climate scenarios, the overall distribution pattern of the potential range of P. massoniana remains similar to the present one, with an increasing trend in area. Notably, the SSP3-7.0 emissions scenario shows the most significant increase in area, totaling 4.71 × 104 km2, suggesting that this particular scenario is more favorable for the distribution of P. massoniana. This study provides valuable scientific insights for the management, conservation, and rational site selection of P. massoniana. [ABSTRACT FROM AUTHOR]

Summary: Little is known about how the terrestrial hydrological cycle responds to multiple environmental changes at large spatial scale and over long time period. Here, we applied a well calibrated and verified ecosystem model (the Dynamic Land Ecosystem Model, DLEM), in conjunction with newly developed data sets of multiple environmental factors including land use change, climate variability, elevated atmospheric carbon dioxide (CO2), nitrogen deposition, ozone pollution, and nitrogen fertilizer application, to characterize the spatial and temporal patterns of evapotranspiration (ET) and runoff in eastern China during 1961–2005 and further quantified the relative contributions of multiple environmental factors to these patterns at both basin and regional scales. The simulation results indicated that annual ET in the study area had a significantly increasing trend from 1961 to 2005. Yet there were no significant changing trends for estimated runoff and net water balance (precipitation minus ET). Substantial spatial heterogeneities in ET and runoff were observed. Annual ET increased in all basins except Yellow River Basin. Runoff increased in the southern part of the study area (including Pearl River and the Southeast basin), but decreased in northern part of the study area, particularly in Haihe and Huaihe river basins. Factorial analysis showed that climate change was the dominant factor responsible for the interannual variations in ET and runoff in the past 45years. Land use change (including land conversions and land management practices) yielded minor effects on the interannual variations in ET and runoff but caused relatively large effects over long-term period. For the accumulated change in ET at basin scale, climate change was the dominant factor in the basins of Song-Liao, Pearl River, while land use change contributed the most in the basin of Haihe, Huaihe, and Yellow River. Climate change was the dominant factor controlling runoff change in all basins except Huaihe and Yangtze River basins. Our simulated results imply that it is important to quantify relative roles of natural and anthropogenic disturbances on water fluxes for maintaining water sustainability in a changing climate. [Copyright &y& Elsevier]

• Historical disturbances were detected based on time series remote sensing data. • Negative abrupt vegetation change accounted for 78.9% of the disturbed area. • The gradual vegetation change of undisturbed area mainly showed greenness trend. • Spatial clusters of total vegetation change accounted for 40.9% of the study area. • Spatial patterns of vegetation change identified key areas to reduce fragmentation. Measures to characterize the spatial patterns of vegetation change can provide important information for understanding and assessing habitat fragmentation and its causes. This study developed a new framework to assess fragmentation by integrating the spatial patterns of vegetation coverage and its change across the giant panda habitat ecosystem of China. Based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, we detected the historical vegetation disturbances and used "abrupt," "gradual," and "total" to characterize the vegetation change processes from 2000 to 2017. The spatial patterns of vegetation coverage and its change were described by spatial clusters and outliers, and the framework was established to find the potential area for reduction of habitat fragmentation. The results indicated that 1.9% of the study area experienced disturbances during 2000–2017. Most of the disturbed area (78.9%) experienced negative abrupt vegetation change, and the undisturbed area mainly showed an increase in vegetation (85.8%). The spatial clusters of high and low total changes accounted for 22.7% and 18.2% of the study area, respectively. The high change clusters were primarily along the northern and southeastern borders of the giant panda habitat, while the low change clusters spread over the central and southern areas. Considering the spatial patterns of both vegetation coverage and its change, 12.7% of the study area, located mainly in southern Minshan and eastern Qionglaishan, needs careful management to reduce habitat fragmentation. This study provides new insight to understand habitat fragmentation in terms of spatial and temporal characteristics of vegetation status and change, and will benefit future habitat management efforts. [ABSTRACT FROM AUTHOR]

Moso bamboo (Phyllostachys Pubescens) forests exhibit a great potential to sequestrate carbon dioxide from atmosphere and to mitigate global climate change. However, they were increasingly under abandoned (i.e., no fertilization, the low intensity and frequency of felling and bamboo shoot digging) due to decreasing economic values of bamboo-related products and increasing labor cost. So far, the changes in soil carbon (C) and nitrogen (N) pools in bamboo forests following abandonment are poorly addressed. In this study, Moso bamboo stands under intensively management and abandonment for different durations were sampled to explore the C and N pool dynamics at the top 40 cm soil. We classified abandonment durations into three categories: discarded or abandoned management for 1-6 years (DM-I), 7-12 years (DM-II) and 13-18 years (DM-III). Our results indicated that (1) soil organic carbon (SOC) storage was significantly increased with abandonment management compared with intensive management (Control, CK), but the durations of abandonment management had no significant effects on SOC. Microbial biomass carbon (MBC) concentration increased from DM-I to DM-III in the 0-40 cm soil layer (P < 0.01), and water-soluble organic carbon (WSOC) concentration decreased through DM-I (P < 0.01). (2) Abandonment management did not significantly affect soil total nitrogen (TN) storage at depth of 0-40 cm, with 9.54 Mg ha -1 for CK, 9.59 Mg ha -1 for DM-I, 9.89 Mg ha -1 for DM-II and 9.69 Mg ha -1 for DM-III. Water-soluble organic nitrogen (WSON) concentration significantly decreased from CK to DM-III. Ammonium nitrogen (NH 4 + -N) concentration increased from DM-I to DM-III (P < 0.01), and nitrate nitrogen (NO 3 - -N) concentration decreased from CK to DM-III (P < 0.01). The results of the effects of abandonment durations on soil properties in Moso bamboo forests provide valuable information for forest restoration and management., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objective: To evaluate the safety and efficacy of removing blood stasis (RBS) herbal medicine for the treatment of acute intracerebral haemorrhage (AICH) within a 6-hour time window., Study Design: A randomised, multicentre, double-blind, placebo-controlled study performed in 14 hospitals in China., Participants and Interventions: Patients with AICH were randomly assigned to receive a placebo, the ICH-1 (Intracerebral Haemorrhage) formula (eight herbs, including the RBS herbs hirudo and tabanus ) or the ICH-2 formula (six herbs without the RBS herbs hirudo and tabanus ) within 6 hours of ICH onset., Outcomes: The primary safety outcome was the incidence of haematoma enlargement at 24 hours and at 10 days after treatment. The secondary outcome was the incidence of poor prognosis (mortality or modified Rankin Scale score ≥5) assessed at 90 days after symptom onset., Results: A total of 324 subjects were randomised between October 2013 and May 2016: 105 patients received placebo; 108 patients received the ICH-1 formula; and 111 patients received the ICH-2 formula. The incidence of haematoma enlargement at 24 hours was 7.8% in the placebo group, 12.3% in the ICH-1 group and 7.5% in the ICH-2 group; the incidence of haematoma enlargement on day 10 was 1.1% in the placebo group, 1.1% in the ICH-1 group, and 3.1% in the ICH-2 group, with no significant differences among the groups (P>0.05). The mortality rates were 3.8% in the placebo group, 2.8% in the ICH-1 group, and 0.9% in the ICH-2 group; the incidences of poor prognosis were 7.1% in the placebo group, 6.0% in the ICH-1 group and 4.8% in the ICH-2 group at 3 months, with no significant differences among the groups (p>0.05). However, the overall frequency of treatment-emergent adverse events in the ICH-1 group (12.1%) was higher among the three groups (5.8% and 2.8%, respectively, p < 0.05). All three cases of serious adverse events were in the ICH-1 group., Conclusions: Ultra-early administration of ICH-1 formula for AICH patients did not exert significant beneficial effects on clinical outcomes but increased the risk of bleeding, which probably resulted from the inclusion of RBS herbal medicines in ICH-1., Trialregistration Number: NCT01918722., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The amount of atmospheric nitrogen (N) deposited on the land surface has increased globally and by nearly five times in China from 1901 to 2005. Little is known about how elevated reactive N input has affected the carbon (C) sequestration capability of China's terrestrial ecosystems, largely due to the lack of reliable data on N deposition. Here we have used a newly developed data set of historical N deposition at a spatial resolution of 10 km x 10 km in combination with other gridded historical information on climate, atmospheric composition, land use, and land management practices to drive a process-based ecosystem model, the dynamic land ecosystem model (DLEM) for examining how increasing N deposition and its interactions with other environmental changes have affected C fluxes and storage in China's terrestrial ecosystems during 1901-2005. Our model simulations indicate that increased N deposition has resulted in a net C sink of 62 Tg C/yr (1 Tg = 1012 g) in China's terrestrial ecosystems, totaling up to 6.51 Pg C (1 Pg = 10(15) g) in the past 105 years. During the study period, the N-induced C sequestration can compensate for more than 25% of fossil-fuel CO2 emission from China. The largest C sink was found in southeast China, a region that experienced the most significant increase of N deposition in the period 1901-2005. However, the net primary productivity induced by per-unit N deposition (referred to as ecosystem N use efficiency, ENUE, in this paper) has leveled off or declined since the 1980s. This indicates that part of the deposited N may not be invested to stimulate plant growth, but instead leave the ecosystem by various pathways. Except shrubland and northwest/southwest China, signs of N saturation are apparent in the rest major biome types and regions, with ENUE peaking in the 1980s and leveling off or declining thereafter. Therefore, to minimize the excessive N pollution while keeping the N-stimulated C uptake in China's terrestrial ecosystems, optimized management practices should be taken to increase N use efficiency rather than to keep raising N input level in the near future.