Background: Recently, interest in the utilization of corncob residue (CCR, with high lignin of 45.1%) as a feedstock for bioethanol has been growing. Surfactants have been one of the most popular additives intended to prevent the inhibitory effect of lignin on cellulolytic enzymes, thereby improving hydrolysis. In this study, the effects of biosurfactant tea saponin (TS) on the enzymatic hydrolysis of CCR and the bonding behavior of cellulolytic enzymes to the substrate were investigated. The surface tension in the supernatant was also detected to obtain information about the characteristics and stability of TS. Results: The glucose concentration was 17.15 mg/mL at 120 hours of hydrolysis with the low loading of cellulolytic enzymes (7.0 FPU/g cellulose and 10.5 BGU/g cellulose) and 5% CCR. The optimal dosage of TS was its critical micelle concentration (cmc, 1.80 mg/mL). The glucose yield was enhanced from 34.29 to 46.28 g/100 g dry matter by TS. The results indicate that TS can promote the adsorption of cellulolytic enzymes on the substrate and mediate the release of adsorbed enzymes. Meanwhile, TS improves the recovery of the cellulolytic enzymes after a hydrolysis cycle and prevents deactivation of the enzymes during the intense shaking process. The surface tension in supernatants of digested CCR with TS remained at 50.00 mN/m during the course of hydrolysis. It is interesting to note that biosurfactant TS can maintain the surface tension in supernatants, despite its digestibility by cellulolytic enzymes. Conclusions: Serving as an accelerant of lignocellulose hydrolysis, TS can also be degraded by the cellulolytic enzymes and release glucose while retaining stability, which reduces the cost of both the cellulolytic enzymes and the additive. As the glucose from the TS could be utilized by yeast, further efforts will investigate the mechanism of function and the application of TS in the production of ethanol by simultaneous saccharification and fermentation (SSF).

To explore how climatic factors influence tree growth within the context of global climate changes, we used a dendroclimatological analysis to understand the response of Larix gmelinii to climatic variations along a broad latitudinal gradient from 47.27° to 52.66° N in the Greater Khingan Mountains of Northeastern China. The growth-limiting climate factors and a detailed association between radial growth and climate were identified along the gradient using redundancy analysis (RDA) and standard correlation function analysis over the period 1960–2013. The results showed that temperatures during current June to July represented the most important factor affecting tree radial growth in the study area. Across all studied latitudes, Larix gmelinii growth might be decreasing in radial growth due to higher monthly maximum temperature (Tmax) and monthly mean temperatures (Tm) in the current June, especially for the stands at low and middle latitudes. With continued warming, Larix gmelinii radial growth at high latitudes (e.g., Mangui (MG) and Mohe (MH)) might be reduced by warmer temperatures in July. In addition, Larix gmelinii might be decreasing in radial growth from decreasing precipitation. Our results show that there is a decreasing trend in Larix gmelinii radial growth under the observed general increase of temperatures in the Greater Khingan Mountains in recent years.

Soil is a significant source of atmospheric N₂O, and soil N₂O emissions at a global scale are greatly affected by environment changes that include continuous deposition of atmospheric nitrogen and changing precipitation distribution. However, to date, field simulations of multiple factors that control the interaction between nitrogen deposition and precipitation on forest soil N₂O emissions are scarce. In this study, we conducted a 2-year continuous assessment of N₂O emissions from November 2012 to October 2014 at a nitrogen addition and rainfall reduction manipulation platform in an old broad-leaved Korean pine mixed forest at Changbai Mountain in northeastern China. We found that N₂O emissions from control plots were 1.25 ± 0.22 kg N₂O-N ha⁻¹ a⁻¹. Nitrogen addition significantly increased N₂O emissions, with the emission factor of 1.59%. A 30% reduction in rainfall decreased N₂O emissions by 17–45%. However, in combination, nitrogen addition and rainfall reduction increased N₂O emissions by 58–140%, with the emission factor of 3.19%, and had a larger promotional effect than the addition of nitrogen alone. Our results indicated that drought slightly decreases forest soil N₂O emission; however, with increasing deposition of atmospheric N in temperate forest soils, the effect of drought might become altered to increase N₂O emission.

Chinese fir (Cunninghamia lanceolata) is one of southern China's most important native tree species, which has experienced noticeable climate-induced changes. Published papers (1978–2020) on tree growth of Chinese fir forests in China were collected and critically reviewed. After that, a comprehensive growth data set was developed from 482 sites, which are distributed between 102.19° and 130.07°E in longitude, between 21.87° and 37.24°N in latitude and between 5 and 2260 m in altitude. The dataset consists of 2265 entries, including mean DBH (cm), mean H (m), volume (m³), biomass (dry weight) (kg) (stem (over bark) biomass, branches biomass, leaves biomass, bark biomass, aboveground biomass, roots biomass, total trees biomass) and related information, i.e. geographical location (Country, province, study site, longitude, latitude, altitude, slope, and aspect), climate (mean annual precipitation-MAP and mean annual temperature-MAT), stand description (origin, age, canopy density and stand density), and sample regime (plot size, number and investigation year). Our results showed that (1) the best prediction of height was obtained using nonlinear composite model Height = $$1.3 + 34.23*(1 - {\text{e}}^{{\left( { - 0.01025*{\text{DBH}}^{1.347} } \right)}} )$$1.3 + 34.23 ∗ ( 1 - e - 0.01025 ∗ DBH 1.347 ) , (R² = 0.8715, p < 0.05), (2) the equation Volume = DBH²/(387.8 + 19,190/Height) (R² = 0.9833, p < 0.05) was observed to be the most suitable model for volume estimation. Meanwhile, when the measurements of the variables are difficult to carry out, the volume model Volume = 0.03957 − 0.01215*DBH + 0.00118*DBH² (R² = 0.9573, p < 0.05) is determined from DBH only has a practical advantage, (3) the regression equations of component biomass against DBH explained more significant than 86% variability in almost all biomass data of woody tissues, which were ranked as total trees (97.25%) > aboveground (96.55%) > stems (with bark) (96.17%) > barks (88.95%) > roots (86.71%), and explained greater than 64% variability in branch biomass. The foliage biomass equation was the poorest among biomass components (R² = 0.6122). The estimation equations derived in this study are particularly suitable for the Chinese fir forests in China. This dataset can provide a theoretical basis for predicting and assessing the potential of carbon sequestration and afforestation activities of Chinese fir forests on a national scale.