The availability of nitrogen(N)is crucial for both the productivity of terrestrial and aquatic ecosystems globally.However,the overuse of artificial fertilizers and the energy required to fix nitrogen have pushed the ...The availability of nitrogen(N)is crucial for both the productivity of terrestrial and aquatic ecosystems globally.However,the overuse of artificial fertilizers and the energy required to fix nitrogen have pushed the global nitrogen cycle(N-cycle)past its safe operating limits,leading to severe nitrogen pollution and the production of significant amounts of greenhouse gas nitrous oxide(N2O).The anaerobic ammonium oxidation(anammox)mechanism can counteract the release of ammonium and N2O in many oxygenlimited situations,assisting in the restoration of the homeostasis of the Earth’s N biogeochemistry.In this work,we looked into the characteristics of the anammox hotspots’distribution across various types of ecosystems worldwide.Anammox hotspots are present at diverse oxic-anoxic interfaces in terrestrial systems,and they are most prevalent at the oxic-anoxic transition zone in aquatic ecosystems.Based on the discovery of an anammox hotspot capable of oxidizing ammonium anoxically into N2 without N2O by-product,we then designed an innovative concept and technical routes of nature-based anammox hotspot geoengineering for climate change,biodiversity loss,and efficient utilization of water resources.After 15 years of actual use,anammox hotspot geoengineering has proven to be effective in ensuring clean drinking water,regulating the climate,fostering plant and animal diversity,and enhancing longterm environmental quality.The sustainable biogeoengineering of anammox could be a workable natural remedy to resolve the conflicts between environmental pollution and food security connected to N management.展开更多
Background Machine learning is widely used to estimate gross primary productivity(GPP)on large scales.Usually,models are trained at site level using eddy flux observations and remote sensing based vegetation indices.H...Background Machine learning is widely used to estimate gross primary productivity(GPP)on large scales.Usually,models are trained at site level using eddy flux observations and remote sensing based vegetation indices.However,how to more effectively utilize the gradually increasing site observations and select different vegetation indices to improve large-scale estimations remains to be further studied,as there is currently no widely recognized optimal solution.In recent years,flux observations in China have expanded rapidly,and a new batch of publicly shared data has provided opportunities for further research.Results We tested the random forest model at the site scale and found that the model which accounts for vegetation types,using data from FLUXNET2015 and China FLUX sites,was the best for estimating GPP in China(R^(2)=0.73).However,models based on different vegetation indices(leaf area index(LAI)and near-infrared reflectance of vegetation(NIRv))showed no major difference in accuracy.Using these indices separately,we simulated monthly GPP for China from 2001 to 2022 at a 0.05°resolution.The datasets were consistent in annual totals and spatial distribution between 2001 and 2018,reporting totals of 7.52 Pg C yr^(–1).However,significant differences were found in spatiotemporal trends,particularly in southern China,where the linear regression coefficients were 0.04 Pg C yr^(–1) and 0.07 Pg C yr^(–1).Compared to other GPP datasets,our results showed slightly higher totals and trends,but they were still within a reasonable range.Conclusions The study confirms the effectiveness of differentiating between different vegetation types and adding site observations for increasing accuracy of GPP estimates.However,the difference of vegetation index does not affect the accuracy of the model,and more influences are mainly reflected in the regional simulation.These findings will help improve GPP estimates and further highlight sources of uncertainty in regional GPP simulations(input vegetation index datasets).展开更多
基金supported by the National Natural Science Foundation of China(91851204 and 42021005)the Special project of eco-environmental technology for peak carbon dioxide emissions and carbon neutrality(RCEES-TDZ-2021-20).
文摘The availability of nitrogen(N)is crucial for both the productivity of terrestrial and aquatic ecosystems globally.However,the overuse of artificial fertilizers and the energy required to fix nitrogen have pushed the global nitrogen cycle(N-cycle)past its safe operating limits,leading to severe nitrogen pollution and the production of significant amounts of greenhouse gas nitrous oxide(N2O).The anaerobic ammonium oxidation(anammox)mechanism can counteract the release of ammonium and N2O in many oxygenlimited situations,assisting in the restoration of the homeostasis of the Earth’s N biogeochemistry.In this work,we looked into the characteristics of the anammox hotspots’distribution across various types of ecosystems worldwide.Anammox hotspots are present at diverse oxic-anoxic interfaces in terrestrial systems,and they are most prevalent at the oxic-anoxic transition zone in aquatic ecosystems.Based on the discovery of an anammox hotspot capable of oxidizing ammonium anoxically into N2 without N2O by-product,we then designed an innovative concept and technical routes of nature-based anammox hotspot geoengineering for climate change,biodiversity loss,and efficient utilization of water resources.After 15 years of actual use,anammox hotspot geoengineering has proven to be effective in ensuring clean drinking water,regulating the climate,fostering plant and animal diversity,and enhancing longterm environmental quality.The sustainable biogeoengineering of anammox could be a workable natural remedy to resolve the conflicts between environmental pollution and food security connected to N management.
基金Qinghai Provincial Natural Science Foundation(2023-QLGKLYCZX-05,2023-QLGKLYCZX-010)the National Natural Science Foundation of China(42130506,42161144003,and 31570464)
文摘Background Machine learning is widely used to estimate gross primary productivity(GPP)on large scales.Usually,models are trained at site level using eddy flux observations and remote sensing based vegetation indices.However,how to more effectively utilize the gradually increasing site observations and select different vegetation indices to improve large-scale estimations remains to be further studied,as there is currently no widely recognized optimal solution.In recent years,flux observations in China have expanded rapidly,and a new batch of publicly shared data has provided opportunities for further research.Results We tested the random forest model at the site scale and found that the model which accounts for vegetation types,using data from FLUXNET2015 and China FLUX sites,was the best for estimating GPP in China(R^(2)=0.73).However,models based on different vegetation indices(leaf area index(LAI)and near-infrared reflectance of vegetation(NIRv))showed no major difference in accuracy.Using these indices separately,we simulated monthly GPP for China from 2001 to 2022 at a 0.05°resolution.The datasets were consistent in annual totals and spatial distribution between 2001 and 2018,reporting totals of 7.52 Pg C yr^(–1).However,significant differences were found in spatiotemporal trends,particularly in southern China,where the linear regression coefficients were 0.04 Pg C yr^(–1) and 0.07 Pg C yr^(–1).Compared to other GPP datasets,our results showed slightly higher totals and trends,but they were still within a reasonable range.Conclusions The study confirms the effectiveness of differentiating between different vegetation types and adding site observations for increasing accuracy of GPP estimates.However,the difference of vegetation index does not affect the accuracy of the model,and more influences are mainly reflected in the regional simulation.These findings will help improve GPP estimates and further highlight sources of uncertainty in regional GPP simulations(input vegetation index datasets).
