Deep storage tunnels(DSTs)are used in densely urbanized areas to relieve stormwater collection systems,thereby reducing urban floods and runoff pollution,due to their substantial storage capacity.The computation of th...Deep storage tunnels(DSTs)are used in densely urbanized areas to relieve stormwater collection systems,thereby reducing urban floods and runoff pollution,due to their substantial storage capacity.The computation of the hydraulic characteristics and flow trajectories of DSTs under rapid filling scenarios can help to predict sediment deposition and pollutant accumulation associated with the stored runoff,as well as the likelihood of operational problems,such as excessive surging.However,such assessments are complicated by various inflow scenarios encountered in tunnel systems during their operation.In this study,the Suzhou River DST in China is selected as a study case.Particles were tracked,and hydraulic analysis was conducted with scaled model experiments and numerical models.The flow field,particle movement,air‒water phase,and pressure patterns in the DST were simulated under various one-and two-sided inflow scenarios.The results showed that with regards to the design conditions involving two-sided inflows,flow reversals occurred with stepwise increases in the water surface and pressure.In contrast,this phenomenon was not observed under the one-sided inflow scenario.Under the asymmetric two-sided inflow scenarios,water inflows led to particle accumulation near the shaft,reducing the received inflows.However,under the symmetric inflow conditions,particles were concentrated near the middle of the tunnel.Compared to those under the symmetric inflow scenario,asymmetric inflow caused surface wave and entrapped air reductions.This study could provide support for regulation of the inflow of the Suzhou River DST and for prediction of sediment and pollutant accumulation.展开更多
Arsenic is a widespread contaminant in soils and groundwater. While various iron-based materials have been studied for immobilizing arsenic in contaminated soils, the feasibility of stabilized iron-based nanoparticles...Arsenic is a widespread contaminant in soils and groundwater. While various iron-based materials have been studied for immobilizing arsenic in contaminated soils, the feasibility of stabilized iron-based nanoparticles has not been reported. This study investigates the effectiveness of using three types of starch-stabilized iron-based nanoparticles, including zero-valent iron (ZVI), iron sulfide (FeS), and magnetite (Fe3O4), for immobilization of arsenic in two representative As-contaminated soils (an orchard soil and a fire range soil). To test the effect of the nanoparticles on the arsenic leachability, As-contaminated soils were amended with the nanoparticles at various Fe/As molar ratios (5:1―100:1) and contact time (3 and 7 d). After three days' treatments of a field-contaminated sandy soil, the PBET-based bioaccessibility of As decreased from an initial (71.3±3.1)% (mean±SD) to (30.9±3.2)% with ZVI, (37.6±1.2)% with FeS, and (29.8± 3.1)% with Fe3O4 at an Fe/As molar ratio of 100:1. The TCLP-based leachability of arsenic in a spiked fire range soil decreased from an initial (0.51±0.11)% to (0.24±0.03)%, (0.27±0.04)% and (0.17±0.04)% by ZVI, FeS, and Fe3O4 nanoparticles, respectively. The Fe3O4 nanoparticles appeared to be more effective (5% or more) than other nanoparticles for immobilizing arsenic. When the two soils were compared, the treatment is more effective on the orchard soil that has a lower iron content and higher initial leachability than on the range soil that already has a high iron content. These results suggest that these innocuous iron-based nanoparticles may serve as effective media for immobilization of As in iron-deficient soils, sediments or solid wastes.展开更多
Perrhenate(ReO4-) was used as nonradioactive surrogate for the radionuclide pertechnetate(99TcO-4) to investigate the potential of using starch-stabilized zero valent iron(ZVI) nanoparticles for reductive immobilizati...Perrhenate(ReO4-) was used as nonradioactive surrogate for the radionuclide pertechnetate(99TcO-4) to investigate the potential of using starch-stabilized zero valent iron(ZVI) nanoparticles for reductive immobilization of pertechnetate in soil and groundwater.Batch kinetic tests indicated that the starch-stabilized ZVI nanoparticles were able to reductively remove ～96% of perrhenate(10 mg/L) from water within 8 h.XRD analyses confirmed that ReO 2 was the reduction product.A pseudo-first-order kinetic model was able to interpret the kinetic data,which gave a pseudo first order rate constant(kobs) value of 0.43h-1 at pH 6.9 and room temperature(25℃).Increasing solution pH up to 8 progressively increased the reaction rate.However,highly alkaline pH(10) resulted in much inhibited reaction rate.Consequently,the optimal pH range was identified to be from 7 to 8.Increasing solution temperature from 15 to 45℃ increased k obs from 0.38 to 0.53 h-1.The classical Arrhenius equation was able to interpret the temperature effect,which gave a low activation energy value of 7.61 kJ/mol.When the ReO-4-loaded loess was treated with the stabilized nanoparticles suspension([Fe]=560 mg/L),the water leachable ReO-4 was reduced by 57% and nearly all eluted Re was in the form of ReO2.This finding indicates that starch-stabilized ZVI nanoparticles are promising for facilitating in situ immobilization of ReO-4 in soil and groundwater.展开更多
Wrong-way driving(WWD)has been a long-lasting issue for transportation agencies and law enforcement,since it causes pivotal threats to road users.Notwithstanding being rare,crashes occurring due to WWD are more severe...Wrong-way driving(WWD)has been a long-lasting issue for transportation agencies and law enforcement,since it causes pivotal threats to road users.Notwithstanding being rare,crashes occurring due to WWD are more severe than other types of crashes.In order to analyze WWD crashes,there is a need to obtain WWD incidents or crash data.However,it is time-consuming to identify actual WWD crashes from potential WWD crashes in large crash databases.It often involves large man-hours to review hardcopy of crash narratives in the police reports.Otherwise,it may cause an overestimation or underestimation of WWD crash frequencies.To fill this gap,the present study,as the first-of-its-kind,aims at identifying actual WWD crashes from potential WWD crashes in police reports by using machine learning methods.Recently,Bidirectional Encoder Representations from Transformers(BERT)models have shown promising results in natural language processing.In this study,we implemented the BERT model as well as five conventional classification algorithms,including Naïve Bayes(NB),Support Vector Machine(SVM),Decision Tree(DT),Random Forest(RF),and Single Layer Perceptron(SLP)to classify crash report narratives as actual WWD and non-WWD crashes.Cross-validation and different performance metrics were used to evaluate the performance of each classification algorithm.Results indicated that the BERT model outperformed in identifying actual WWD crashes in comparison with other algorithms with an accuracy of 81.59%.The BERT classification algorithm can be implemented to reduce the time needed to identify actual WWD crashes from crash report narratives.展开更多
基金supported by the National Natural Science Foundation of Jiangsu Province(Grant No.BK20230099)the National Natural Science Foundation of China(Grants No.52379061 and 52179062)the Key Laboratory of Water Grid Project and Regulation of Ministry of Water Resources(Grant No.QTKS0034W23292).
文摘Deep storage tunnels(DSTs)are used in densely urbanized areas to relieve stormwater collection systems,thereby reducing urban floods and runoff pollution,due to their substantial storage capacity.The computation of the hydraulic characteristics and flow trajectories of DSTs under rapid filling scenarios can help to predict sediment deposition and pollutant accumulation associated with the stored runoff,as well as the likelihood of operational problems,such as excessive surging.However,such assessments are complicated by various inflow scenarios encountered in tunnel systems during their operation.In this study,the Suzhou River DST in China is selected as a study case.Particles were tracked,and hydraulic analysis was conducted with scaled model experiments and numerical models.The flow field,particle movement,air‒water phase,and pressure patterns in the DST were simulated under various one-and two-sided inflow scenarios.The results showed that with regards to the design conditions involving two-sided inflows,flow reversals occurred with stepwise increases in the water surface and pressure.In contrast,this phenomenon was not observed under the one-sided inflow scenario.Under the asymmetric two-sided inflow scenarios,water inflows led to particle accumulation near the shaft,reducing the received inflows.However,under the symmetric inflow conditions,particles were concentrated near the middle of the tunnel.Compared to those under the symmetric inflow scenario,asymmetric inflow caused surface wave and entrapped air reductions.This study could provide support for regulation of the inflow of the Suzhou River DST and for prediction of sediment and pollutant accumulation.
基金supported by USEPA STAR (Grant No: R832373)ACES-AAES Ag Initiative Grant, Projects of International Cooperation and Exchanges NSFC (Grant No. 200810224313)a special fund from the State Key Laboratory of Environmental Aquatic Chemistry (Grant No. 08K01ESPCR)
文摘Arsenic is a widespread contaminant in soils and groundwater. While various iron-based materials have been studied for immobilizing arsenic in contaminated soils, the feasibility of stabilized iron-based nanoparticles has not been reported. This study investigates the effectiveness of using three types of starch-stabilized iron-based nanoparticles, including zero-valent iron (ZVI), iron sulfide (FeS), and magnetite (Fe3O4), for immobilization of arsenic in two representative As-contaminated soils (an orchard soil and a fire range soil). To test the effect of the nanoparticles on the arsenic leachability, As-contaminated soils were amended with the nanoparticles at various Fe/As molar ratios (5:1―100:1) and contact time (3 and 7 d). After three days' treatments of a field-contaminated sandy soil, the PBET-based bioaccessibility of As decreased from an initial (71.3±3.1)% (mean±SD) to (30.9±3.2)% with ZVI, (37.6±1.2)% with FeS, and (29.8± 3.1)% with Fe3O4 at an Fe/As molar ratio of 100:1. The TCLP-based leachability of arsenic in a spiked fire range soil decreased from an initial (0.51±0.11)% to (0.24±0.03)%, (0.27±0.04)% and (0.17±0.04)% by ZVI, FeS, and Fe3O4 nanoparticles, respectively. The Fe3O4 nanoparticles appeared to be more effective (5% or more) than other nanoparticles for immobilizing arsenic. When the two soils were compared, the treatment is more effective on the orchard soil that has a lower iron content and higher initial leachability than on the range soil that already has a high iron content. These results suggest that these innocuous iron-based nanoparticles may serve as effective media for immobilization of As in iron-deficient soils, sediments or solid wastes.
基金supported by the National Natural Science Foundation of China (41072265 and 40810152)the Science-Technology Research of Colleges in Shanxi Province (20091022)+1 种基金the Shanxi Provincial 100 Talents Program,Chinaan Auburn University VPR’s IGP
文摘Perrhenate(ReO4-) was used as nonradioactive surrogate for the radionuclide pertechnetate(99TcO-4) to investigate the potential of using starch-stabilized zero valent iron(ZVI) nanoparticles for reductive immobilization of pertechnetate in soil and groundwater.Batch kinetic tests indicated that the starch-stabilized ZVI nanoparticles were able to reductively remove ～96% of perrhenate(10 mg/L) from water within 8 h.XRD analyses confirmed that ReO 2 was the reduction product.A pseudo-first-order kinetic model was able to interpret the kinetic data,which gave a pseudo first order rate constant(kobs) value of 0.43h-1 at pH 6.9 and room temperature(25℃).Increasing solution pH up to 8 progressively increased the reaction rate.However,highly alkaline pH(10) resulted in much inhibited reaction rate.Consequently,the optimal pH range was identified to be from 7 to 8.Increasing solution temperature from 15 to 45℃ increased k obs from 0.38 to 0.53 h-1.The classical Arrhenius equation was able to interpret the temperature effect,which gave a low activation energy value of 7.61 kJ/mol.When the ReO-4-loaded loess was treated with the stabilized nanoparticles suspension([Fe]=560 mg/L),the water leachable ReO-4 was reduced by 57% and nearly all eluted Re was in the form of ReO2.This finding indicates that starch-stabilized ZVI nanoparticles are promising for facilitating in situ immobilization of ReO-4 in soil and groundwater.
文摘Wrong-way driving(WWD)has been a long-lasting issue for transportation agencies and law enforcement,since it causes pivotal threats to road users.Notwithstanding being rare,crashes occurring due to WWD are more severe than other types of crashes.In order to analyze WWD crashes,there is a need to obtain WWD incidents or crash data.However,it is time-consuming to identify actual WWD crashes from potential WWD crashes in large crash databases.It often involves large man-hours to review hardcopy of crash narratives in the police reports.Otherwise,it may cause an overestimation or underestimation of WWD crash frequencies.To fill this gap,the present study,as the first-of-its-kind,aims at identifying actual WWD crashes from potential WWD crashes in police reports by using machine learning methods.Recently,Bidirectional Encoder Representations from Transformers(BERT)models have shown promising results in natural language processing.In this study,we implemented the BERT model as well as five conventional classification algorithms,including Naïve Bayes(NB),Support Vector Machine(SVM),Decision Tree(DT),Random Forest(RF),and Single Layer Perceptron(SLP)to classify crash report narratives as actual WWD and non-WWD crashes.Cross-validation and different performance metrics were used to evaluate the performance of each classification algorithm.Results indicated that the BERT model outperformed in identifying actual WWD crashes in comparison with other algorithms with an accuracy of 81.59%.The BERT classification algorithm can be implemented to reduce the time needed to identify actual WWD crashes from crash report narratives.
