The objectives of this study were to isolate a bensulfuron-methyl (BSM)-degrading strain of Bacillus spp. and to eval-uate its effectiveness in remediation of a BSM-contaminated soil. A BSM-degrading bacterium, strain...The objectives of this study were to isolate a bensulfuron-methyl (BSM)-degrading strain of Bacillus spp. and to eval-uate its effectiveness in remediation of a BSM-contaminated soil. A BSM-degrading bacterium, strain L1, was successfully isolated in this study. Strain L1 was identified as Bacillus megaterium based on its morphological, physiological, and biochemical properties, G+C content, phylogenetic similarity of 16S rDNA, and fatty acid composition. Two experiments were used to examine BSM degradation by strain L1. When BSM was used as a sole carbon source in a mineral salt medium, the average degradation rate of BSM by strain L1 was 12.8%, which suggested that the strain was able to utilize BSM as a sole carbon and energy source. Supplement of yeast extract (200 mg L-1 ) significantly (P ≤ 0.01) accelerated the degradation of BSM by strain L1. Almost complete degradation (97.7%) of BSM could be achieved in 84 h with addition of yeast extract. In addition, in a sterile soil with 50 mg L-1 BSM, BSM degradation rate by strain L1 was 94.3% in 42 d, indicating the potential of using microbes for the remediation of BSM-contaminated soils in fields.展开更多
In recent years, the increasing frequency of debris flow demands enhanced effectiveness and efficiency of warning systems. Effective warning systems are essential not only from an economic point of view but are also c...In recent years, the increasing frequency of debris flow demands enhanced effectiveness and efficiency of warning systems. Effective warning systems are essential not only from an economic point of view but are also considered as a frontline approach to alleviate hazards. Currently, the key issues are the imbalance between the limited lifespan of equipment, the relatively long period between the recurrences of such hazards, and the wide range of critical rainfall that trigger these disasters. This paper attempts to provide a stepwise multi-parameter debris flow warning system after taking into account the shortcomings observed in other warning systems. The whole system is divided into five stages. Differentwarning levels can be issued based on the critical rainfall thresholds. Monitoring starts when early warning is issued and it continues with debris flow near warning, triggering warning, movement warning and hazard warning stages. For early warning, historical archives of earthquake and drought are used to choose a debris flow-susceptible site for further monitoring. Secondly, weather forecasts provide an alert of possible near warning. Hazardous precipitation, model calculation and debris flow initiation tests, pore pressure sensors and water content sensors are combined to check the critical rainfall and to publically announce a triggering warning. In the final two stages, equipment such as rainfall gauges, flow stage sensors, vibration sensors, low sound sensors and infrasound meters are used to assess movement processes and issue hazardwarnings. In addition to these warnings, communitybased knowledge and information is also obtained and discussed in detail. The proposed stepwise, multiparameter debris flow monitoring and warning system has been applied in Aizi valley China which continuously monitors the debris flow activities.展开更多
Accurate prediction on geological hazards can prevent disaster events in advance and greatly reduce property losses and life casualties.Glacial debris flows are the most serious hazards in southeastern Tibet in China ...Accurate prediction on geological hazards can prevent disaster events in advance and greatly reduce property losses and life casualties.Glacial debris flows are the most serious hazards in southeastern Tibet in China due to their complexity in formation mechanism and the difficulty in prediction.Data collected from 102 glacier debris flow events from 31 gullies since 1970 and regional meteorological data from 1970 to 2019 in ParlungZangbo River Basin in southeastern Tibet were used for Artificial Neural Network(ANN)-based prediction of glacial debris flows.The formation mechanism of glacial debris flows in the ParlungZangbo Basin was systematically analyzed,and the calculations involving the meteorological data and disaster events were conducted by using the statistical methods and two layers fully connected neural networks.The occurrence probabilities and scales of glacial debris flows(small,medium,and large)were predicted,and promising results have been achieved.Through the proposed model calculations,a prediction accuracy of 78.33%was achieved for the scale of glacial debris flows in the study area.The prediction accuracy for both large-and medium-scale debris flows are higher than that for small-scale debris flows.The debris flow scale and the probability of occurrence increase with increasing rainfall and temperature.In addition,the K-fold cross-validation method was used to verify the reliability of the model.The average accuracy of the model calculated under this method is about 93.3%,which validates the proposed model.Practices have proved that the combination of ANN and disaster events can provide sound prediction on geological hazards under complex conditions.展开更多
基金Project supported by the National High Technology Research and Development Program of China (863 Program)(No. 2007AA06Z329)the Science and Technology Program of Zhejiang Province (Nos. 2007C23036 and 2008C13014-3)the International Cooperation Program in Science and Technology of Zhejiang Province (No. 2008C14038)
文摘The objectives of this study were to isolate a bensulfuron-methyl (BSM)-degrading strain of Bacillus spp. and to eval-uate its effectiveness in remediation of a BSM-contaminated soil. A BSM-degrading bacterium, strain L1, was successfully isolated in this study. Strain L1 was identified as Bacillus megaterium based on its morphological, physiological, and biochemical properties, G+C content, phylogenetic similarity of 16S rDNA, and fatty acid composition. Two experiments were used to examine BSM degradation by strain L1. When BSM was used as a sole carbon source in a mineral salt medium, the average degradation rate of BSM by strain L1 was 12.8%, which suggested that the strain was able to utilize BSM as a sole carbon and energy source. Supplement of yeast extract (200 mg L-1 ) significantly (P ≤ 0.01) accelerated the degradation of BSM by strain L1. Almost complete degradation (97.7%) of BSM could be achieved in 84 h with addition of yeast extract. In addition, in a sterile soil with 50 mg L-1 BSM, BSM degradation rate by strain L1 was 94.3% in 42 d, indicating the potential of using microbes for the remediation of BSM-contaminated soils in fields.
基金supported by the National Natural Science Foundation of China(Grant Nos.41661134012 and 41501012)Foundation for selected young scientists,Institute of Mountain Hazards and Environment,CAS(Grant Nos.SDSQN-1306,Y3L1340340,sds-135-1202-02)
文摘In recent years, the increasing frequency of debris flow demands enhanced effectiveness and efficiency of warning systems. Effective warning systems are essential not only from an economic point of view but are also considered as a frontline approach to alleviate hazards. Currently, the key issues are the imbalance between the limited lifespan of equipment, the relatively long period between the recurrences of such hazards, and the wide range of critical rainfall that trigger these disasters. This paper attempts to provide a stepwise multi-parameter debris flow warning system after taking into account the shortcomings observed in other warning systems. The whole system is divided into five stages. Differentwarning levels can be issued based on the critical rainfall thresholds. Monitoring starts when early warning is issued and it continues with debris flow near warning, triggering warning, movement warning and hazard warning stages. For early warning, historical archives of earthquake and drought are used to choose a debris flow-susceptible site for further monitoring. Secondly, weather forecasts provide an alert of possible near warning. Hazardous precipitation, model calculation and debris flow initiation tests, pore pressure sensors and water content sensors are combined to check the critical rainfall and to publically announce a triggering warning. In the final two stages, equipment such as rainfall gauges, flow stage sensors, vibration sensors, low sound sensors and infrasound meters are used to assess movement processes and issue hazardwarnings. In addition to these warnings, communitybased knowledge and information is also obtained and discussed in detail. The proposed stepwise, multiparameter debris flow monitoring and warning system has been applied in Aizi valley China which continuously monitors the debris flow activities.
基金supported by the National Natural Science Foundation of China(Grant No.41671112)the Sichuan Province Science and Technology Plan Project Key research and development projects(Grant No.18ZDYF0329)the National Natural Science Foundation of China(Grant No.41861134008)。
文摘Accurate prediction on geological hazards can prevent disaster events in advance and greatly reduce property losses and life casualties.Glacial debris flows are the most serious hazards in southeastern Tibet in China due to their complexity in formation mechanism and the difficulty in prediction.Data collected from 102 glacier debris flow events from 31 gullies since 1970 and regional meteorological data from 1970 to 2019 in ParlungZangbo River Basin in southeastern Tibet were used for Artificial Neural Network(ANN)-based prediction of glacial debris flows.The formation mechanism of glacial debris flows in the ParlungZangbo Basin was systematically analyzed,and the calculations involving the meteorological data and disaster events were conducted by using the statistical methods and two layers fully connected neural networks.The occurrence probabilities and scales of glacial debris flows(small,medium,and large)were predicted,and promising results have been achieved.Through the proposed model calculations,a prediction accuracy of 78.33%was achieved for the scale of glacial debris flows in the study area.The prediction accuracy for both large-and medium-scale debris flows are higher than that for small-scale debris flows.The debris flow scale and the probability of occurrence increase with increasing rainfall and temperature.In addition,the K-fold cross-validation method was used to verify the reliability of the model.The average accuracy of the model calculated under this method is about 93.3%,which validates the proposed model.Practices have proved that the combination of ANN and disaster events can provide sound prediction on geological hazards under complex conditions.
