Developing allometric equations to estimate forest biomass for tree species categories based on phylogenetic relationships

The development of allometric biomass models is important process in biomass estimation because the reliability of forest biomass and carbon estimations largely depends on the accuracy and precision of such models.National Forest Inventories(NFI)are detailed assessments of forest resources at national and regional levels that provide valuable data for forest biomass estimation.However,the lack of biomass allometric equations for each tree species in the NFI currently hampers the estimation of national-scale forest biomass.The main objective of this study was to develop allometric biomass regression equations for each tree species in the NFI of China based on limited biomass observations.These equations optimally grouped NFI and biomass observation species according to their phylogenetic relationships.Significant phylogenetic signals demonstrated phylogenetic conservation of the crown-to-stem biomass ratio.Based on phylogenetic relationships,we grouped and matched NFI and biomass observation species into 22 categories.Allometric biomass regression models were developed for each of these 22 species categories,and the models performed successfully(R^(2)=0.97,root mean square error(RMSE)=12.9​t·ha^(–1),relative RMSE=11.5%).Furthermore,we found that phylogeny-based models performed more effectively than wood density-based models.The results suggest that grouping species based on their phylogenetic relationships is a reliable approach for the development and selection of accurate allometric equations.

Divergent response of vegetation phenology to extreme temperatures and precipitation of different intensities on the Tibetan Plateau

Quantifying how climate factors affect vegetation phenology is crucial for understanding climate-vegetation interactions and carbon and water cycles under a changing climate.However,the effects of different intensities of extreme climatic events on vegetation phenology remain poorly understood.Using a long-term solar-induced chlorophyll fluorescence dataset,we investigated the response of vegetation phenology to extreme temperatures and precipitation events of different intensities across the Tibetan Plateau(TP)from 2000 to 2018.We found that the effect of maximum temperature exposure days(TxED)and minimum temperature exposure days(TnED)on the start of the growing season(SOS)was initially delayed and shifted to advance along the increasing temperature gradients.However,the response of the end of the growing season(EOS)to TxED and TnED shifted from an advance to a delay with increasing temperature gradients until the temperature thresholds were reached,above which thresholds produced an unfavorable response to vegetation growth and brought the EOS to an early end.The corresponding maximum and minimum temperature thresholds were 10.12 and 2.54℃,respectively.In contrast,cumulative precipitation(CP)was more likely to advance SOS and delay EOS as the precipitation gradient increased,but the advance of SOS is gradually weakening.Four vegetation types(i.e.,forest,shrubland,meadow,and steppe)showed similar trends in response to different climates,but the optimal climatic conditions varied between the vegetation types.Generally,meadow and steppe had lower optimal temperatures and precipitation than forest and shrubland.These findings revealed the divergent responses of vegetation phenology to extreme climate events of different intensities,implying that the SOS will continue to advance with warming,whereas the EOS may undergo a partial transformation from delayed areas to advanced areas with continued warming.

Spatiotemporal analysis of the impact of urban landscape forms on PM_(2.5) in China from 2001 to 2020

Urban landscape forms can be effective in reducing increasing PM_(2.5) concentrations due to urbanization in China,making it crucially important to accurately quantify the spatiotemporal impact of urban landscape forms on PM_(2.5) variations.Three landscape indices and six control variables were selected to assess these impacts in 362 Chinese cities during different time scales from 2001 to 2020,using a spatiotemporal geographically weighted regression model,random forest models and partial dependence plots.The results show that there are spatiotemporal differences in the impacts of landscape indices on PM_(2.5).the proportion of urban green infrastructure(PLAND-UGI)and the fractal dimension of urban green infrastructure(FRACT-UGI)exacerbate PM_(2.5) concentrations in the northwest,the proportion of impervious surfaces(PLAND-Impervious)mitigates air pollution in northwest and southwest China,and shannon’s diversity index(SHDI)has seasonal differences in the northwest.PLAND-UGI is the landscape index with the largest contribution(30%)and interpretable range.The relationship between FRACT and PM_(2.5) was more complex than for other landscape indices.The results of this study contribute to a deeper understanding of the spatial and temporal differences in the impact of urban landscape patterns on PM_(2.5),contributing to clean urban development and sustainable development.

Preparation of ultrahigh-surface-area sludge biopolymers-based carbon using alkali treatment for organic matters recovery coupled to catalytic pyrolysis

In this work,we employed waste activated sludge(WAS)as carbon source to prepare ultrahigh specific surface area(SSA)biopolymers-based carbons(BBCs)through alkali(KOH)treatment coupled to pyrolysis strategy.Before the pyrolysis process,the involvement of KOH made a great recovery of soluble biopolymers from WAS,resulting in highly-efficient catalytic pyrolysis.The Brunner-Emmett-Teller and pore volume of BBCs prepared at 800℃(BBC800)reached the maximum at 2633.89 m2·g-1 and 2.919 m3·g-1,respectively.X-ray photoelectron spectroscopy suggested that aromatic carbon in the form of C=C was the dominant fraction of C element in BBCs.The N element in BBCs were composed of pyrrolic nitrogen and pyridinic nitrogen at 700℃,while a new graphitic nitrogen appeared over 800℃.As a refractory pollutant of wastewater treatment plants,tetracycline(TC)was selected to evaluate adsorption performance of BBCs.The adsorption behavior of BBCs towards TC was conformed to the pseudo-second-order kinetic and the Langmuir models,signifying that chemisorption of monolayers was dominant in TC adsorption.The adsorption capacity of BBC800 reached the maximum at 877.19 mg·g-1 for 90 min at 298 K.Thermodynamic analysis indicated that the adsorption process was endothermic and spontaneous.Hydrogen bonding andπ-πstacking interaction were mainly responsible for TC adsorption,and interfacial diffusion was the main rate-control step in adsorption process.The presence of sol-uble microbial products(SMPs)enhanced TC removal.This work provided a novel strategy to prepare bio-carbon with ultrahigh SSA using WAS for highly-efficient removal of organic pollutants.

The profiles of chiral pesticides in peri-urban areas near Yangtze River: Enantioselective distribution characteristics and correlations with surface sediments

Chiral pesticides account for 30% of pesticides. Pesticides are inevitably leached into the groundwater by runoff. At the watershed level, the distribution characteristics of enantiomers in sediments collected from the river network of an agricultural area near the middle and lower reaches of the Yangtze River were tested, and their potential correlations with the physicochemical properties and microbial communities of the sediments were analyzed. The sediment pollution was serious at sites 8 and 9, with their pollution source possibly being agricultural or industrial sewage. Moreover, there were higher cumulative contents of pesticide residues at sites 4, 8, and 9. Specifically, Cycloxaprid was the most detected chiral pesticide in the study area, followed by Dinotefuran and Diclofop-methyl. Additionally,Ethiprole and Difenoconazole had strong enantioselectivity in the study area. Interestingly,the enantiomers of some chiral pesticides, such as Tebuconazole, had completely different distributions at different sites. Pearson correlation analysis showed that sediment catalase and microbial biomass carbon were important factors for enantioselectivity of chiral pesticides. The effect of sediment physicochemical properties on enantioselective distribution was achieved by influencing the microorganisms in the sediment. Furthermore, the enantioselective distribution of Tebuconazole was closely related to the genus Arenimonas.Overall, the enantioselective distribution of most of the chiral pesticides was positively correlated with the prokaryotic microbial community. This study provides empirical support for agricultural non-point source pollution caused by chiral pesticides, and also lays a research foundation for exploring the factors that affect the fate of chiral pesticides in the environment.